From 36755be9cc4f5e287e53be4dc0592ee00137a43f Mon Sep 17 00:00:00 2001 From: Jianbing Dong Date: Thu, 23 Oct 2025 19:43:45 -0700 Subject: [PATCH 01/17] add fused_linear_cross_entropy interface Signed-off-by: Jianbing Dong --- .../fusions/fused_linear_cross_entropy.py | 74 +++++++++++++++++++ 1 file changed, 74 insertions(+) create mode 100644 megatron/core/fusions/fused_linear_cross_entropy.py diff --git a/megatron/core/fusions/fused_linear_cross_entropy.py b/megatron/core/fusions/fused_linear_cross_entropy.py new file mode 100644 index 00000000000..6f33bfaf72e --- /dev/null +++ b/megatron/core/fusions/fused_linear_cross_entropy.py @@ -0,0 +1,74 @@ +""" +Linear Cross Entropy API +Fuse cross entropy with linear layer. +""" + +import typing +import torch + +class LinearCrossEntropy(torch.autograd.Function): + """ + This class implements a custom autograd function for linear and cross entropy, whose equivalent logic in PyTorch is: + ```python + def torch_entropy(hidden, weight, labels): + logits = torch.matmul(hidden, weight) + logprobs = torch.nn.functional.cross_entropy(logits, labels) + return logprobs + ``` + """ + + @staticmethod + def forward( + ctx, + hidden: torch.Tensor, + weight: torch.Tensor, + labels: torch.Tensor, + reduction: typing.Optional[str] = "mean", + dist_process_group: typing.Optional[torch.distributed.ProcessGroup] = None, + ignore_index: typing.Optional[int] = -100, + ) -> torch.Tensor: + """ + The forward pass of the Linear Cross Entropy. + If dist_process_group is passed for distributed loss calculation, + the weight tensor to each distributed rank should be (*, vocab_size / world_size). + Note that each of the ranks should get equal shards along the vocab_size dimension. + + Args: + hidden (torch.Tensor): The input tensor of shape (num_tokens, hidden_size). + weight (torch.Tensor): The weight tensor of shape (hidden_size, vocab_size). + labels (torch.Tensor): The labels tensor of shape (num_tokens,). + reduction (str, optional): The reduction method. Defaults to "mean", and can be + one of "none", "sum", "mean". + Returns: + logprobs (torch.Tensor): The cross entropy. + """ + with torch.cuda.nvtx.range("LinearCrossEntropy-forward"): + logprobs = torch.empty( + hidden.view(-1, hidden.shape[-1]).shape[0], + device=hidden.device, + dtype=torch.float32) + + return logprobs + + @staticmethod + def backward(ctx, dlogprobs: torch.Tensor) -> typing.List[torch.Tensor]: + """ + The backward pass of the Linear Cross Entropy. + Args: + dlogprobs (torch.Tensor): The gradient of the cross entropy. + Returns: + dhidden (torch.Tensor): The gradient of the hidden. + dweight (torch.Tensor): The gradient of the weight. + """ + with torch.cuda.nvtx.range("LinearCrossEntropy-backward"): + d_hidden = torch.empty(hidden.shape, device=hidden.device, dtype=hidden.dtype) + d_weight = torch.empty(weight.shape, device=weight.device, dtype=weight.dtype) + return d_hidden, d_weight, None, None, None, None + + +linear_cross_entropy = LinearCrossEntropy.apply + +__all__ = [ + "linear_cross_entropy", + "LinearCrossEntropy", +] From 5781d3dca80c510c3f27cda3e53462f160341567 Mon Sep 17 00:00:00 2001 From: Jianbin Chang Date: Thu, 30 Oct 2025 17:07:47 +0800 Subject: [PATCH 02/17] Merge pull request #1 from shjwudp/jianbinc/fused_linear_ce init fused linear cross-entropy interface --- .../common/language_module/language_module.py | 58 +++++- megatron/core/models/gpt/gpt_model.py | 40 +++- megatron/core/models/mamba/mamba_model.py | 18 +- .../core/transformer/transformer_config.py | 3 + megatron/training/arguments.py | 4 + tests/unit_tests/a2a_overlap/utils.py | 9 +- .../test_fused_linear_cross_entropy.py | 189 ++++++++++++++++++ 7 files changed, 303 insertions(+), 18 deletions(-) create mode 100644 tests/unit_tests/fusions/test_fused_linear_cross_entropy.py diff --git a/megatron/core/models/common/language_module/language_module.py b/megatron/core/models/common/language_module/language_module.py index de2ecfb8011..b8e39693b22 100644 --- a/megatron/core/models/common/language_module/language_module.py +++ b/megatron/core/models/common/language_module/language_module.py @@ -1,7 +1,7 @@ # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import logging import os -from typing import Optional, Tuple +from typing import Any, Dict, Optional, Tuple import torch from torch import Tensor @@ -14,6 +14,7 @@ except: te_parallel_cross_entropy = None from megatron.core.fusions.fused_cross_entropy import fused_vocab_parallel_cross_entropy +from megatron.core.fusions.fused_linear_cross_entropy import linear_cross_entropy from megatron.core.pipeline_parallel.utils import ( is_pp_first_stage, is_pp_last_stage, @@ -125,6 +126,61 @@ def check_and_set_env_variable( check_and_set_env_variable("NVTE_FUSED_ATTN", 1, AttnBackend.auto) check_and_set_env_variable("NVTE_UNFUSED_ATTN", 1, AttnBackend.auto) + def compute_language_model_loss_without_logits( + self, + hidden: Tensor, + labels: Optional[Tensor], + weight: Tensor = None, + column_parallel_linear: torch.nn.Module = None, + col_linear_kwargs: Dict[str, Any] = {}, + reduction: Optional[str] = "mean", + ignore_index: Optional[int] = -100, + ) -> Tuple[Tensor, Optional[Tensor]]: + """Computes the language model logits and loss (Cross entropy across vocabulary) + + Args: + hidden (Tensor): The hidden states from the transformer model + labels (Optional[Tensor]): The labels of dimension [batch size, seq length] + weight (Tensor): The weight tensor of shape [vocab size, hidden size]. + Required if using fused linear cross entropy. + column_parallel_linear (torch.nn.Module): The column parallel linear + layer to use for computing logits when not using fused linear cross entropy. + col_linear_kwargs (Dict[str, Any]): Additional kwargs for column parallel linear layer + reduction (Optional[str]): The reduction method. Defaults to "mean", and can be + one of "none", "sum", "mean". + ignore_index (Optional[int]): The index to ignore in the loss calculation. + Defaults to -100. + + Returns: + Tensor: Loss tensor of dimensions [batch size, sequence_length]. + """ + if self.config.linear_cross_entropy_fusion: + + assert ( + weight is not None + ), "weight cannot be None when using fused linear cross entropy." + # [b s] => [s b] + labels = labels.transpose(0, 1).contiguous() + loss = linear_cross_entropy( + hidden, + weight, + labels, + dist_process_group=self.pg_collection.tp, + reduction=reduction, + ignore_index=ignore_index, + ) + + # [s b] => [b, s] + loss = loss.transpose(0, 1).contiguous() + return loss + else: + assert ( + column_parallel_linear is not None + ), "column_parallel_linear cannot be None when not using fused linear cross entropy." + logits, _ = column_parallel_linear(hidden, **col_linear_kwargs) + + return self.compute_language_model_loss(labels, logits) + def compute_language_model_loss(self, labels: Tensor, logits: Tensor) -> Tensor: """Computes the language model loss (Cross entropy across vocabulary) diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py index a1156012106..b48dcec2078 100644 --- a/megatron/core/models/gpt/gpt_model.py +++ b/megatron/core/models/gpt/gpt_model.py @@ -568,18 +568,24 @@ def _postprocess( # if loss_mask is not provided, use all ones as loss_mask loss_mask = torch.ones_like(mtp_labels) for mtp_layer_number in range(self.config.mtp_num_layers): - # output - mtp_logits, _ = self.output_layer( - hidden_states_list[mtp_layer_number + 1], - weight=output_weight, - runtime_gather_output=runtime_gather_output, - ) # Calc loss for the current Multi-Token Prediction (MTP) layers. mtp_labels, _ = roll_tensor(mtp_labels, shifts=-1, dims=-1, cp_group=self.cp_group) loss_mask, num_tokens = roll_tensor( loss_mask, shifts=-1, dims=-1, cp_group=self.cp_group ) - mtp_loss = self.compute_language_model_loss(mtp_labels, mtp_logits) + + # Compute mtp loss without storing logits to save memory. + mtp_loss = self.compute_language_model_loss_without_logits( + hidden_states_list[mtp_layer_number + 1], + labels=mtp_labels, + weight=output_weight, + column_parallel_linear=self.output_layer, + col_linear_kwargs={ + 'weight': output_weight, + 'runtime_gather_output': runtime_gather_output, + }, + ) + mtp_loss = loss_mask * mtp_loss if self.training: # TODO(shifangx): remove the use of parallel_state here @@ -626,9 +632,12 @@ def _postprocess( hidden_states.squeeze(1).unsqueeze(0) ).unsqueeze(1) - logits, _ = self.output_layer( - hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output - ) + if has_config_logger_enabled(self.config) or labels is not None: + logits, _ = self.output_layer( + hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output + ) + else: + logits = None # Restore sequence parallel execution to the output layer if necessary. if sequence_parallel_override: @@ -655,7 +664,16 @@ def _postprocess( # [s b h] => [b s h] return logits.transpose(0, 1).contiguous() - loss = self.compute_language_model_loss(labels, logits) + loss = self.compute_language_model_loss_without_logits( + hidden_states, + labels=labels, + weight=output_weight, + column_parallel_linear=self.output_layer, + col_linear_kwargs={ + 'weight': output_weight, + 'runtime_gather_output': runtime_gather_output, + }, + ) return loss diff --git a/megatron/core/models/mamba/mamba_model.py b/megatron/core/models/mamba/mamba_model.py index fb3df5e23f2..533f4efc257 100644 --- a/megatron/core/models/mamba/mamba_model.py +++ b/megatron/core/models/mamba/mamba_model.py @@ -247,14 +247,22 @@ def forward( if in_inference_mode and inference_context.materialize_only_last_token_logits: hidden_states = hidden_states[-1, :, :].unsqueeze(0) - logits, _ = self.output_layer( - hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output - ) - if labels is None: + logits, _ = self.output_layer( + hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output + ) # [s b h] => [b s h] return logits.transpose(0, 1).contiguous() - loss = self.compute_language_model_loss(labels, logits) + loss = self.compute_language_model_loss_without_logits( + hidden_states, + labels, + weight=output_weight, + column_parallel_linear=self.output_layer, + col_linear_kwargs={ + "weight": output_weight, + "runtime_gather_output": runtime_gather_output, + }, + ) return loss diff --git a/megatron/core/transformer/transformer_config.py b/megatron/core/transformer/transformer_config.py index aab137b6430..55de1e07181 100644 --- a/megatron/core/transformer/transformer_config.py +++ b/megatron/core/transformer/transformer_config.py @@ -327,6 +327,9 @@ class TransformerConfig(ModelParallelConfig): fused_single_qkv_rope: bool = False """If set, avoid splitting QKV before ROPE forward and avoid concatenating ROPE dgrads.""" + linear_cross_entropy_fusion: bool = False + """If True, fuses the linear layer and cross entropy loss calculation.""" + #################### # activation recomputation #################### diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py index 507c21e6883..439825aaf57 100644 --- a/megatron/training/arguments.py +++ b/megatron/training/arguments.py @@ -2254,6 +2254,10 @@ def _add_training_args(parser): dest='bias_swiglu_fusion') group.add_argument('--use-fused-weighted-squared-relu', action='store_true', help='Use fused weighted squared relu when using MoE.') + group.add_argument('--no-linear-cross-entropy-fusion', action='store_false', + help='Disable fusion of linear layer and cross entropy ' + 'loss calculation.', + dest='linear_cross_entropy_fusion') group.add_argument('--no-bias-dropout-fusion', action='store_false', help='Disable bias and dropout fusion.', dest='bias_dropout_fusion') diff --git a/tests/unit_tests/a2a_overlap/utils.py b/tests/unit_tests/a2a_overlap/utils.py index 7db4256a849..994998337d8 100644 --- a/tests/unit_tests/a2a_overlap/utils.py +++ b/tests/unit_tests/a2a_overlap/utils.py @@ -237,7 +237,14 @@ def get_valid_fp8_flags(): recipes = [] valid_flags = [] if is_te_min_version("2.3.0.dev0"): - recipes.append(Fp8Recipe.blockwise) + props = torch.cuda.get_device_properties(torch.cuda.current_device()) + compute_capability = (props.major, props.minor) + if ( + compute_capability >= (9, 0) + and compute_capability < (10, 0) + and float(torch.version.cuda) >= 12.9 + ): + recipes.append(Fp8Recipe.blockwise) recipes.append(Fp8Recipe.tensorwise) for fp8_type in fp8_types: diff --git a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py new file mode 100644 index 00000000000..4d0ae55b666 --- /dev/null +++ b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py @@ -0,0 +1,189 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +import contextlib +from contextlib import ExitStack + +import numpy as np +import pytest +import torch +from torch.utils.data import DataLoader, Dataset +from torch.utils.data.distributed import DistributedSampler + +import megatron.core.parallel_state as ps +from megatron.core.models.gpt.gpt_layer_specs import ( + get_gpt_decoder_block_spec, + get_gpt_mtp_block_spec, +) +from megatron.core.models.gpt.gpt_model import GPTModel +from tests.unit_tests.a2a_overlap.utils import ( + deterministic_mode, + get_test_config, + get_valid_fp8_flags, + get_valid_token_dispatcher_types, +) +from tests.unit_tests.test_utilities import Utils + + +class MockDataset(Dataset): + """ + Mock dataset for torchtitan GPT training tests + Generates synthetic tokenized sequences on-the-fly + """ + + def __init__( + self, + num_samples=10000, + micro_batch_size=4, + sequence_length=2048, + vocab_size=128256, + seed=42, + ): + """ + Initialize mock dataset + + Args: + num_samples: Total number of samples + sequence_length: Length of each sequence + vocab_size: Size of vocabulary + seed: Random seed for reproducibility + """ + self.num_samples = num_samples + self.micro_batch_size = micro_batch_size + self.sequence_length = sequence_length + self.vocab_size = vocab_size + self.seed = seed + + # Set numpy seed for deterministic generation + np.random.seed(seed) + + def __len__(self): + return self.num_samples + + def __getitem__(self, idx): + """ + Generate a single training sample + + Returns: + dict with 'tokens' and 'labels' + """ + # Use idx as seed for reproducible but varied samples + rng = np.random.RandomState(self.seed + idx) + + # Generate random token sequence + tokens = rng.randint(0, self.vocab_size, size=self.sequence_length, dtype=np.int64) + + # Labels are tokens shifted by 1 (next token prediction) + labels = rng.randint(0, self.vocab_size, size=self.sequence_length, dtype=np.int64) + + return { + 'input_ids': torch.from_numpy(tokens.copy()), + 'labels': torch.from_numpy(labels.copy()), + "attention_mask": torch.ones( + (1, self.sequence_length, self.sequence_length), dtype=bool + ), + } + + +def build_model(config): + max_seq_len = 300 + + # build layer spec + transformer_layer_spec = get_gpt_decoder_block_spec(config=config, use_transformer_engine=True) + mtp_block_spec = get_gpt_mtp_block_spec(config, transformer_layer_spec.layer_specs[-1], True) + + # build model + gpt_model = GPTModel( + config=config, + transformer_layer_spec=transformer_layer_spec, + mtp_block_spec=mtp_block_spec, + vocab_size=100, + pre_process=True, + post_process=True, + max_sequence_length=max_seq_len, + ) + return gpt_model + + +# Define a reusable context manager +@contextlib.contextmanager +def init_model_parallel(tp=1, pp=1, ep=1): + try: + Utils.initialize_model_parallel( + tensor_model_parallel_size=tp, + pipeline_model_parallel_size=pp, + expert_model_parallel_size=ep, + ) + yield + finally: + Utils.destroy_model_parallel() + + +def init_gpt_dataloader( + dp_group, micro_batch_size=1, vocab_size=50257, sequence_length=128, batch_size=8 +): + dataset = MockDataset( + num_samples=1000, + micro_batch_size=micro_batch_size, + sequence_length=sequence_length, + vocab_size=vocab_size, + seed=42, + ) + sampler = DistributedSampler(dataset, num_replicas=dp_group.size(), rank=dp_group.rank()) + dataloader = DataLoader(dataset, batch_size=batch_size, sampler=sampler) + return dataloader + + +class TestFusedLinearCrossEntropy: + + @pytest.mark.parametrize("fp8_flag", get_valid_fp8_flags()) + @pytest.mark.parametrize("mtp_layers", [0, 1]) + @pytest.mark.parametrize("dispatcher_type", get_valid_token_dispatcher_types()) + @pytest.mark.parametrize("layer_num", [2]) + def test_gpt_model(self, mtp_layers, dispatcher_type, fp8_flag, layer_num): + with ExitStack() as stack: + gpu_count = torch.cuda.device_count() + tp = min(2, gpu_count) + ep = gpu_count // tp + stack.enter_context(init_model_parallel(tp=tp, ep=ep)) + stack.enter_context(deterministic_mode()) + + # create TransformerConfig + extra_kwargs = { + "moe_token_dispatcher_type": dispatcher_type, + "sequence_parallel": tp > 1, + "tensor_model_parallel_size": tp, + } + if dispatcher_type == "flex": + extra_kwargs["moe_enable_deepep"] = True + extra_kwargs["moe_router_dtype"] = "fp32" + if fp8_flag is not None: + extra_kwargs["fp8"] = fp8_flag[0] + extra_kwargs["fp8_recipe"] = fp8_flag[1] + if mtp_layers > 0: + extra_kwargs["mtp_num_layers"] = mtp_layers + extra_kwargs["mtp_loss_scaling_factor"] = 1.1 + + # build config + config = get_test_config(num_layers=layer_num, extra_kwargs=extra_kwargs) + config.expert_model_parallel_size = ep + + # build model + gpt_model = build_model(config) + gpt_model.cuda() + + dataloader = init_gpt_dataloader( + ps.get_data_parallel_group(), + vocab_size=gpt_model.vocab_size, + micro_batch_size=1, + sequence_length=gpt_model.max_sequence_length, + batch_size=4, + ) + # for batch in dataloder: + for batch in dataloader: + batch["position_ids"] = torch.arange( + gpt_model.max_sequence_length, dtype=torch.int64 + ) + batch = {k: v.cuda() for k, v in batch.items()} + gpt_model.zero_grad() + output = gpt_model(**batch) + loss = output.sum() + loss.backward() From 289847f3365c9f89096ca40bc8003e58e0602139 Mon Sep 17 00:00:00 2001 From: Jianbing-D <69858819+Jianbing-D@users.noreply.github.com> Date: Thu, 6 Nov 2025 12:37:36 +0800 Subject: [PATCH 03/17] Feat linear cross entropy kernel dev (#2) * add forward-mainloop and bwd_partial_dlogits kernel Signed-off-by: Jianbing Dong * skip TestFusedLinearCrossEntropyOnGptModel for single GPU Signed-off-by: Jianbing Dong * added unit-test for linear_cross_entropy on dp Signed-off-by: Jianbing Dong --------- Signed-off-by: Jianbing Dong --- .../fusions/fused_linear_cross_entropy.py | 218 ++++- .../blackwell/bwd_partial_dlogits.py | 926 ++++++++++++++++++ .../linear_cross_entropy/blackwell/entry.py | 385 ++++++++ .../blackwell/fwd_mainloop.py | 892 +++++++++++++++++ .../linear_cross_entropy/blackwell/triton.py | 303 ++++++ .../fusions/linear_cross_entropy/utils.py | 35 + .../test_fused_linear_cross_entropy.py | 380 ++++++- 7 files changed, 3118 insertions(+), 21 deletions(-) create mode 100644 megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py create mode 100644 megatron/core/fusions/linear_cross_entropy/blackwell/entry.py create mode 100644 megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py create mode 100644 megatron/core/fusions/linear_cross_entropy/blackwell/triton.py create mode 100644 megatron/core/fusions/linear_cross_entropy/utils.py diff --git a/megatron/core/fusions/fused_linear_cross_entropy.py b/megatron/core/fusions/fused_linear_cross_entropy.py index 6f33bfaf72e..e3fccc92a4d 100644 --- a/megatron/core/fusions/fused_linear_cross_entropy.py +++ b/megatron/core/fusions/fused_linear_cross_entropy.py @@ -6,6 +6,24 @@ import typing import torch +def _setup_platform(): + """ + Setup the platform for the Linear Cross Entropy. + """ + assert torch.cuda.is_available(), "CUDA is not available" + device = torch.cuda.current_device() + cc = torch.cuda.get_device_capability(device) + + global forward_func, backward_func + if cc[0] == 10: + # from linear_cross_entropy.blackwell import entry as platform + from .linear_cross_entropy.blackwell import entry as platform + forward_func = platform.forward + backward_func = platform.backward + else: + raise ValueError(f"Unsupported architecture: {cc[0]}") +_setup_platform() + class LinearCrossEntropy(torch.autograd.Function): """ This class implements a custom autograd function for linear and cross entropy, whose equivalent logic in PyTorch is: @@ -16,59 +34,221 @@ def torch_entropy(hidden, weight, labels): return logprobs ``` """ - @staticmethod def forward( ctx, hidden: torch.Tensor, weight: torch.Tensor, labels: torch.Tensor, + tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, reduction: typing.Optional[str] = "mean", - dist_process_group: typing.Optional[torch.distributed.ProcessGroup] = None, ignore_index: typing.Optional[int] = -100, ) -> torch.Tensor: """ The forward pass of the Linear Cross Entropy. - If dist_process_group is passed for distributed loss calculation, - the weight tensor to each distributed rank should be (*, vocab_size / world_size). + If tp_group is not None, the weight tensor to each TP rank should be (vocab_size // world_size, dim). Note that each of the ranks should get equal shards along the vocab_size dimension. Args: - hidden (torch.Tensor): The input tensor of shape (num_tokens, hidden_size). - weight (torch.Tensor): The weight tensor of shape (hidden_size, vocab_size). - labels (torch.Tensor): The labels tensor of shape (num_tokens,). - reduction (str, optional): The reduction method. Defaults to "mean", and can be - one of "none", "sum", "mean". + @param hidden: the input tensor with shape (num_tokens, dim) + @param weight: the lm_head weight tensor with shape (vocab_size, dim) + @param labels: the labels tensor with shape (num_tokens,) + @param tp_group: the distributed process group for TP. + @param reduction: Default to "mean", and can be one of "none", "sum", "mean". + @param ignore_index: The index to ignore. Default to -100. Returns: - logprobs (torch.Tensor): The cross entropy. + @return: logprobs with shape + - either (num_tokens,) when reduction is "none" + - or (1,) when reduction is "mean" or "sum" + """ with torch.cuda.nvtx.range("LinearCrossEntropy-forward"): - logprobs = torch.empty( - hidden.view(-1, hidden.shape[-1]).shape[0], - device=hidden.device, - dtype=torch.float32) + logprobs, _maximum, _acc, _num_valid_tokens, tp_rank, tp_world_size = ( + forward_func( + hidden, weight, labels, + tp_group, + reduction, + ignore_index, + ) + ) + ctx.save_for_backward( + hidden, weight, labels, + _maximum, _acc, _num_valid_tokens, + ) + ctx.tp_group = tp_group + ctx.ignore_index = ignore_index + ctx.reduction = reduction + ctx.tp_rank = tp_rank + ctx.tp_world_size = tp_world_size return logprobs + @staticmethod - def backward(ctx, dlogprobs: torch.Tensor) -> typing.List[torch.Tensor]: + def backward( + ctx, + dlogprobs: torch.Tensor + ) -> typing.List[torch.Tensor]: """ The backward pass of the Linear Cross Entropy. Args: - dlogprobs (torch.Tensor): The gradient of the cross entropy. + dlogprobs (torch.Tensor): The gradient of the cross entropy, with shape + - either (num_tokens,) when reduction is "none" + - or (1,) when reduction is "mean" or "sum" Returns: dhidden (torch.Tensor): The gradient of the hidden. dweight (torch.Tensor): The gradient of the weight. """ with torch.cuda.nvtx.range("LinearCrossEntropy-backward"): - d_hidden = torch.empty(hidden.shape, device=hidden.device, dtype=hidden.dtype) - d_weight = torch.empty(weight.shape, device=weight.device, dtype=weight.dtype) + (hidden, weight, labels, _maximum, _accu, _num_valid_tokens) = ctx.saved_tensors + + tp_group = ctx.tp_group + ignore_index = ctx.ignore_index + reduction = ctx.reduction + tp_rank = ctx.tp_rank + tp_world_size = ctx.tp_world_size + + d_hidden, d_weight = backward_func( + dlogprobs, + hidden, + weight, + labels, + _maximum, + _accu, + _num_valid_tokens, + reduction, + ignore_index, + tp_group, + tp_rank, + tp_world_size + ) + return d_hidden, d_weight, None, None, None, None -linear_cross_entropy = LinearCrossEntropy.apply +def linear_cross_entropy( + hidden: torch.Tensor, + weight: torch.Tensor, + labels: torch.Tensor, + tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, + reduction: typing.Optional[str] = "mean", + ignore_index: typing.Optional[int] = -100, +) -> torch.Tensor: + """ + helper function for linear cross entropy. + """ + _impl = LinearCrossEntropy.apply + return _impl(hidden, weight, labels, tp_group, reduction, ignore_index) __all__ = [ "linear_cross_entropy", "LinearCrossEntropy", ] + + +# FIXME: move this unit-test to other place +if __name__ == "__main__": + def test_dp(): + # batch = 4 + # seqlen = 2035 + # vocab_size = 152063 + # dim = 4096 + batch = 1 + seqlen = 80 + vocab_size = 125 + dim = 64 + dtype = torch.float16 + reduction = "none" + + hidden = ( + torch.empty((batch, seqlen, dim), device="cuda", dtype=dtype) + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocab_size, dim), device="cuda", dtype=dtype) + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + + labels = torch.randint(0, vocab_size, (batch, seqlen), device="cuda", dtype=torch.long) + + logits = hidden @ weight.T + # print(logits) + + _logits = logits.to(torch.float32) + _logits_view = _logits.view(-1, _logits.shape[-1]) + maximum = _logits_view.max(dim=-1, keepdim=False).values + accu = torch.exp(_logits_view - maximum.unsqueeze(-1)).sum(dim=-1) + + logprobs = torch.nn.functional.cross_entropy( + logits.view(-1, logits.shape[-1]), + labels.view(-1), + reduction=reduction, + ) + + custom_logprobs = linear_cross_entropy( + hidden, weight, labels, + reduction=reduction, + ) + + print(custom_logprobs) + print(logprobs) + + # backward + g_logprobs = torch.rand_like(logprobs, dtype=dtype, device="cuda") + + (d_torch_hidden, d_torch_weight) = torch.autograd.grad( + (logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + + # first way to do backward + if reduction == "mean": + _g_logprobs = torch.broadcast_to(g_logprobs / (batch * seqlen), (batch * seqlen,)) + elif reduction == "sum": + _g_logprobs = torch.broadcast_to(g_logprobs, (batch * seqlen,)) + else: + _g_logprobs = g_logprobs + + intermediate = _logits_view - maximum.unsqueeze(-1) + exp_logits = torch.exp(intermediate) + d_logits = exp_logits / accu.unsqueeze(-1) + d_logits *= _g_logprobs.unsqueeze(-1) + # mask = torch.arange(vocab_size, dtype=torch.long, device="cuda") + # mask = torch.broadcast_to(mask, (batch * seqlen, vocab_size)) + # mask = (labels.view(-1).unsqueeze(-1) == mask) + + one_hot = torch.zeros_like(_logits_view) + one_hot.scatter_(1, labels.view(-1).unsqueeze(-1), 1) + + d_logits += one_hot * -_g_logprobs.unsqueeze(-1) + d_logits = d_logits.to(hidden.dtype) + # print(d_logits) + + d_hidden = d_logits @ weight + d_weight = d_logits.T @ hidden.view(-1, dim) + + # print("first way to do backward") + # print(d_hidden.view(hidden.shape)) + # print(d_torch_hidden) + # print(d_weight) + # print(d_torch_weight) + # print(d_logits) + + (d_custom_hidden, d_custom_weight) = torch.autograd.grad( + (custom_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + # print(d_torch_hidden) + # print(d_custom_hidden) + print(d_torch_weight) + print(d_custom_weight) + + torch.manual_seed(42) + + test_dp() \ No newline at end of file diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py b/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py new file mode 100644 index 00000000000..2d5da82ab6a --- /dev/null +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py @@ -0,0 +1,926 @@ +from typing import Optional, Type, Tuple, Union +import cuda.bindings.driver as cuda + +import torch + +import cutlass +import cutlass.cute as cute +import cutlass.utils as utils +import cutlass.pipeline as pipeline +from cutlass.cute.nvgpu import cpasync, tcgen05 +import cutlass.torch as cutlass_torch +import cutlass.utils.blackwell_helpers as sm100_utils +from cutlass.cute.runtime import from_dlpack + + +SM100_TMEM_CAPACITY_COLUMNS: int = 512 + +def make_thread_cooperative_group(size: int, alignment: Optional[int] = None): + return pipeline.CooperativeGroup( + pipeline.Agent.Thread, size, + alignment=alignment if alignment is not None else size) + + +class BwdPartialDlogits: + """ + This class implements the backward kernel for partial d_logits. + """ + def __init__(self, + reduction: int, + acc_dtype: Type[cutlass.Numeric] = cutlass.Float32, + use_2cta_instrs: bool = False, + mma_tiler_mn: Tuple[int, int] = (128, 256), + rank: int = 0, + vocab_per_split: int = 512): + self.REDUCTION: cutlass.Constexpr[cutlass.Int32] = cutlass.const_expr(reduction) + self.acc_dtype = acc_dtype + self.use_2cta_instrs = use_2cta_instrs + self.mma_tiler = (*mma_tiler_mn, 1) + self.rank = rank + self.vocab_per_split = vocab_per_split + + self.cta_group = ( + tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE + ) + self.cluster_shape_mn = (2, 1) if self.use_2cta_instrs else (1, 1) + + self.smem_capacity = utils.get_smem_capacity_in_bytes("sm_100") + + self.threads_per_warp: int = 32 + + self.epi_warp_ids = (0, 1, 2, 3) + self.load_warp_ids = 4 + self.mma_warp_ids = 5 + self.empty_warp_ids = (6, 7) + + self.threads_per_cta: int = self.threads_per_warp * len( + (*self.epi_warp_ids, + self.load_warp_ids, + self.mma_warp_ids, + *self.empty_warp_ids) + ) + self.cta_sync_barrier = pipeline.NamedBarrier( + barrier_id = 1, + num_threads = self.threads_per_cta + ) + + self.buffer_align_bytes: int = 1024 + self.num_regs_other: int = 32 + self.num_regs_epi: int = 192 + + def _compute_grid( + self, + problem_mnk: Tuple[int, int, int], + cluster_shape_mn: Tuple[int, int], + cta_tiler: Tuple[int, int, int], + ) -> Tuple[int, int, int]: + cluster_shape_mnk = (*cluster_shape_mn, 1) + + grid = cute.round_up( + ( + cute.ceil_div(problem_mnk[0], cta_tiler[0]), + cute.ceil_div(self.vocab_per_split, cta_tiler[1]), + 1, + ), + cluster_shape_mnk + ) + return grid + + def _compute_stages( + self, + tiled_mma: cute.TiledMma, + mma_tiler: Tuple[int, int, int], + a_dtype: Type[cutlass.Numeric], + b_dtype: Type[cutlass.Numeric], + ): + num_acc_stage = 1 + num_ab_stage = 4 + num_epi_stage_per_tile = 4 + return num_acc_stage, num_ab_stage, num_epi_stage_per_tile + + def _setup_attributes( + self, + tiled_mma: cute.TiledMma, + a_dtype: Type[cutlass.Numeric], + b_dtype: Type[cutlass.Numeric], + ): + self.cluster_shape_mnk = (*self.cluster_shape_mn, 1) + self.cluster_layout_vmnk = cute.tiled_divide( + cute.make_layout(self.cluster_shape_mnk), + (tiled_mma.thr_id.shape,), + ) + + mma_inst_shape_k = cute.size(tiled_mma.shape_mnk, mode=[2]) + # it requires k-mode to be 128B aligned + mma_inst_tile_k: int = 4 + self.mma_tiler = ( + self.mma_tiler[0], + self.mma_tiler[1], + mma_inst_shape_k * mma_inst_tile_k + ) + + self.num_acc_stage, self.num_ab_stage, self.num_epi_stage_per_tile =\ + self._compute_stages(tiled_mma, self.mma_tiler, a_dtype, b_dtype) + self.tmem_alloc_cols = self.num_acc_stage * self.mma_tiler[1] + assert self.tmem_alloc_cols <= SM100_TMEM_CAPACITY_COLUMNS + + self.cta_tile_shape_mnk = ( + self.mma_tiler[0] // cute.size(tiled_mma.thr_id.shape), + self.mma_tiler[1], + self.mma_tiler[2] + ) + + @cute.kernel + def kernel( + self, + split_idx: cutlass.Int32, + tiled_mma: cute.TiledMma, + tma_atom_a: cute.CopyAtom, + mA: cute.Tensor, + tma_atom_b: cute.CopyAtom, + mB: cute.Tensor, + mLabels: cute.Tensor, + mDlogprobs: cute.Tensor, + mMaximum: cute.Tensor, + mAccu: cute.Tensor, + mDlogits_partial: cute.Tensor, + scalarNumValidTokens: cute.Pointer, + ignore_index: cutlass.Int64, + a_smem_layout_staged: cute.ComposedLayout, + b_smem_layout_staged: cute.ComposedLayout, + cluster_layout_vmnk: cute.Layout, + problem_mnk: Tuple[int, int, int], + ) -> None: + warp_idx = cute.arch.make_warp_uniform( + cute.arch.warp_idx() + ) + tidx, _, _ = cute.arch.thread_idx() + bidx, bidy, _ = cute.arch.block_idx() + # FIXME: block swizzling applied here + pidm, pidn = bidx, bidy + + # FIXME: if 2 CTAs, modify here + cta_rank_in_cluster = 0 + block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord( + cta_rank_in_cluster + ) + + # prefetch tma descriptors + if warp_idx == self.load_warp_ids: + cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_a) + cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_b) + + smem = utils.SmemAllocator() + storage = smem.allocate(self.shared_storage) + + ab_pipeline = pipeline.PipelineTmaUmma.create( + num_stages=self.num_ab_stage, + producer_group=make_thread_cooperative_group(len([self.load_warp_ids])), + consumer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), + tx_count=self.tma_copy_ab_bytes, + barrier_storage=storage.load_ab_mbar_ptr.data_ptr() + ) + ab_producer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, + self.num_ab_stage + ) + ab_consumer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, + self.num_ab_stage + ) + + mma_pipeline = pipeline.PipelineUmmaAsync.create( + num_stages=self.num_acc_stage, + producer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), + consumer_group=make_thread_cooperative_group(self.threads_per_warp * len(self.epi_warp_ids)), + barrier_storage=storage.mma_mbar_ptr.data_ptr() + ) + mma_producer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, + self.num_acc_stage + ) + mma_consumer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, + self.num_acc_stage + ) + + tmem_dealloc_mbar_ptr = storage.tmem_dealloc_mbar_ptr.data_ptr() + if warp_idx == self.empty_warp_ids[0]: + with cute.arch.elect_one(): + cute.arch.mbarrier_init( + tmem_dealloc_mbar_ptr, + self.threads_per_warp * len(self.epi_warp_ids) + ) + cute.arch.mbarrier_init_fence() + + # -------- tensor partition ------------ # + # swizzle o [(tileM, tileK), loopM, loopK, stage] + sA = storage.sA.get_tensor( + a_smem_layout_staged.outer, + swizzle=a_smem_layout_staged.inner + ) + # swizzle o [(tileN, tileK), loopN, loopK, stage] + sB = storage.sB.get_tensor( + b_smem_layout_staged.outer, + swizzle=b_smem_layout_staged.inner + ) + + # FIXME: if 2 CTAs, modify here + thr_mma = tiled_mma.get_slice(0) + # [MMA, loopM, loopK, stage] + tCsA = thr_mma.make_fragment_A(sA) + # [MMA, loopN, loopK, stage] + tCsB = thr_mma.make_fragment_B(sB) + + # [tileM, tileK, loopK] + gA = cute.local_tile( + mA, + (self.cta_tile_shape_mnk[0], self.cta_tile_shape_mnk[2]), + (pidm, None) + ) + # [vocab_per_split, dim] + mB_n = cute.local_tile( + mB, + (self.vocab_per_split, cute.size(mB.layout.shape, mode=[1])), + (split_idx, 0) + ) + # [tileN, tileK, loopK] + gB = cute.local_tile( + mB_n, + (self.cta_tile_shape_mnk[1], self.cta_tile_shape_mnk[2]), + (pidn, None) + ) + + a_cta_layout = cute.make_layout( + cute.slice_( + cluster_layout_vmnk, + (0, 0, None, 0) + ).shape + ) + # just to make sure SMEM and GMEM tensor has the same size in the first rank + tCgA = thr_mma.partition_A(gA) + tCgB = thr_mma.partition_B(gB) + # [CPY, stage] & [CPY, loopK] + tTMAsA, tTMAgA = cpasync.tma_partition( + tma_atom_a, + block_in_cluster_coord_vmnk[2], # cta_coord, + a_cta_layout, + cute.group_modes(sA, 0, 3), + cute.group_modes(tCgA, 0, 3) + ) + b_cta_layout = cute.make_layout( + cute.slice_( + cluster_layout_vmnk, + (0, None, 0, 0) + ).shape + ) + # [CPY, stage] & [CPY, loopK] + tTMAsB, tTMAgB = cpasync.tma_partition( + tma_atom_b, + block_in_cluster_coord_vmnk[1], # cta_coord + b_cta_layout, + cute.group_modes(sB, 0, 3), + cute.group_modes(tCgB, 0, 3) + ) + + # ------ Allocate TMEM ------ # + tmem_holding_buf = storage.tmem_holding_buf + if warp_idx == self.empty_warp_ids[0]: + cute.arch.alloc_tmem( + self.tmem_alloc_cols, + tmem_holding_buf, + is_two_cta=self.use_2cta_instrs + ) + self.cta_sync_barrier.arrive_and_wait() + tmem_ptr = cute.arch.retrieve_tmem_ptr( + self.acc_dtype, + alignment=16, + ptr_to_buffer_holding_addr=tmem_holding_buf + ) + + tmem_shape = (128, self.tmem_alloc_cols) + acc_shape = thr_mma.partition_shape_C(tmem_shape) + tCtC_fake = thr_mma.make_fragment_C(acc_shape) + # [(tileM, tileN), loopM, loopN] + tCtC = cute.make_tensor(tmem_ptr, tCtC_fake.layout) + + # ------ Empty ------ # + if warp_idx in self.empty_warp_ids: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + # ------ Load ------ # + if warp_idx == self.load_warp_ids: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + for k in cutlass.range(cute.size(gA, mode=[2])): + ab_pipeline.producer_acquire(ab_producer_state) + cute.copy( + tma_atom_a, + tTMAgA[(None, k)], + tTMAsA[(None, ab_producer_state.index)], + tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state) + ) + cute.copy( + tma_atom_b, + tTMAgB[(None, k)], + tTMAsB[(None, ab_producer_state.index)], + tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state) + ) + ab_pipeline.producer_commit(ab_producer_state) + ab_producer_state.advance() + + # ------ MMA ------ # + if warp_idx == self.mma_warp_ids: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + tiled_mma.set(tcgen05.Field.ACCUMULATE, False) + mma_pipeline.producer_acquire(mma_producer_state) + + for k in cutlass.range(cute.size(gA, mode=[2])): + ab_pipeline.consumer_wait(ab_consumer_state) + + for kblock_idx in cutlass.range(cute.size(tCsA, mode=[2]), unroll_full=True): + cute.gemm( + tiled_mma, + cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), + tCsA[(None, None, kblock_idx, ab_consumer_state.index)], + tCsB[(None, None, kblock_idx, ab_consumer_state.index)], + cute.append_ones(tCtC[(None, None, mma_producer_state.index)]) + ) + tiled_mma.set(tcgen05.Field.ACCUMULATE, True) + + ab_pipeline.consumer_release(ab_consumer_state) + ab_consumer_state.advance() + + mma_pipeline.producer_commit(mma_producer_state) + mma_producer_state.advance() + + # ------ EPI ------ # + if warp_idx in self.epi_warp_ids: + cute.arch.warpgroup_reg_alloc(self.num_regs_epi) + + copy_atom_t2r = sm100_utils.get_tmem_load_op( + self.cta_tile_shape_mnk, + utils.LayoutEnum.ROW_MAJOR, + self.acc_dtype, + self.acc_dtype, + (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), + self.use_2cta_instrs + ) + # [tileM, subTileN, loopM, CntSubTileN, loopN] + tAcc_epi = cute.flat_divide( + tCtC[((None, None), 0, None)], + (self.epi_tile[0], + self.epi_tile[1] // self.num_epi_stage_per_tile) + ) + tiled_copy_t2r = tcgen05.make_tmem_copy( + copy_atom_t2r, + tAcc_epi[(None, None, 0, 0, 0)] + ) + thr_copy_t2r = tiled_copy_t2r.get_slice(tidx) + tTMEM_load_tAcc = thr_copy_t2r.partition_S(tAcc_epi) + tTMEM_load_tAcc = cute.group_modes( + tTMEM_load_tAcc, 3, cute.rank(tTMEM_load_tAcc) - 1 + ) + + # predicates + cAcc = cute.make_identity_tensor(self.mma_tiler[:2]) + tCcAcc = thr_mma.partition_C(cAcc) + tCcAcc_epi = cute.flat_divide( + tCcAcc[((None, None), 0, None)], + (self.epi_tile[0], + self.epi_tile[1] // self.num_epi_stage_per_tile) + ) + tTMEM_load_cAcc = thr_copy_t2r.partition_D(tCcAcc_epi) + tTMEM_load_cAcc_shape = cute.select( + tTMEM_load_cAcc.shape, + mode=[0, 1, 2] + ) + tTMEM_load_rAcc = cute.make_fragment( + tTMEM_load_cAcc_shape, + self.acc_dtype + ) + + copy_atom_g2r_int64 = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), + mLabels.element_type + ) + copy_atom_g2r_fp32 = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), + mDlogprobs.element_type + ) + epilogue_thread_layout = cute.make_layout( + (128, 1), + stride=(1, 1)) + tiled_copy_g2r_int64 = cute.make_tiled_copy_tv( + copy_atom_g2r_int64, + epilogue_thread_layout, + cute.make_layout((1, 1)) + ) + tiled_copy_g2r_fp32 = cute.make_tiled_copy_tv( + copy_atom_g2r_fp32, + epilogue_thread_layout, + cute.make_layout((1, 1)) + ) + thr_copy_g2r_int64 = tiled_copy_g2r_int64.get_slice(tidx) + thr_copy_g2r_fp32 = tiled_copy_g2r_fp32.get_slice(tidx) + + # [tileM] + gLabels = cute.local_tile( + mLabels, + (self.epi_tile[0],), + (pidm,) + ) + gMaximum = cute.local_tile( + mMaximum, + (self.epi_tile[0],), + (pidm,) + ) + gAccu = cute.local_tile( + mAccu, + (self.epi_tile[0],), + (pidm,) + ) + + # slice along M direction + tMCAcc = thr_copy_g2r_int64.partition_S(cAcc)[(None, None, 0)] + # [(1, 1), 1] + tMCAcc_mask = cute.make_fragment( + tMCAcc.shape, + cutlass.Boolean + ) + # to align shape with gMax and gAccu + tMCAcc_mask = cute.append_ones(tMCAcc_mask) + tMCAcc_mask[0] = cute.elem_less( + pidm * self.epi_tile[0] + tidx, + cute.size(mA, mode=[0]) + ) + # [(1, 1), 1, 1] + tMgLabels = thr_copy_g2r_int64.partition_S( + cute.append_ones(gLabels) + ) + tMrLabels = cute.make_fragment( + tMgLabels.shape, + tMgLabels.element_type + ) + cute.copy( + tiled_copy_g2r_int64, + tMgLabels, + tMrLabels, + pred=tMCAcc_mask + ) + tMgMaximum = thr_copy_g2r_fp32.partition_S( + cute.append_ones(gMaximum) + ) + tMrMaximum = cute.make_fragment( + tMgMaximum.layout, + tMgMaximum.element_type + ) + cute.copy( + tiled_copy_g2r_fp32, + tMgMaximum, + tMrMaximum, + pred=tMCAcc_mask + ) + tMgAccu = thr_copy_g2r_fp32.partition_S( + cute.append_ones(gAccu) + ) + tMrAccu = cute.make_fragment( + tMgAccu.layout, + tMgAccu.element_type + ) + cute.copy( + tiled_copy_g2r_fp32, + tMgAccu, + tMrAccu, + pred=tMCAcc_mask + ) + + tMrDlogprobs = cute.make_fragment( + tMgAccu.layout, + mDlogprobs.element_type + ) + if cutlass.const_expr(self.REDUCTION == 2): + # mean reduction + num_valid_tokens = cute.make_tensor( + scalarNumValidTokens, + layout=(1,), + ) + tMrDlogprobs[0] = mDlogprobs[0] / num_valid_tokens[0].to(cutlass.Float32) + elif cutlass.const_expr(self.REDUCTION == 1): + # sum reduction + tMrDlogprobs[0] = mDlogprobs[0] + else: + # no reduction + gDlogprobs = cute.local_tile( + mDlogprobs, + (self.epi_tile[0],), + (pidm,) + ) + tMgDlogprobs = thr_copy_g2r_fp32.partition_S( + cute.append_ones(gDlogprobs) + ) + cute.copy( + tiled_copy_g2r_fp32, + tMgDlogprobs, + tMrDlogprobs, + pred=tMCAcc_mask + ) + + tMrAccu[0] = cute.arch.rcp_approx(tMrAccu[0]) + tMrDlogprobs[0] *= (tMrLabels[0] != ignore_index) + tMr_d_acc_exp_logits = tMrDlogprobs[0] * tMrAccu[0] + + # ------ Partial output ------ # + # [tileM, tileN] + gDlogits_partial = cute.local_tile( + mDlogits_partial, + (self.epi_tile[0], self.epi_tile[1]), + (pidm, pidn) + ) + # blackwell supports STG.256 + copy_atom_r2g = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), + gDlogits_partial.element_type, + num_bits_per_copy=256 + ) + tiled_copy_r2g = cute.make_tiled_copy_tv( + copy_atom_r2g, + epilogue_thread_layout, + copy_atom_r2g.layout_dst_tv + ) + thr_copy_r2g = tiled_copy_r2g.get_slice(tidx) + + # [CPY, loopM, loopN] + tR2GCAcc = thr_copy_r2g.partition_S(cAcc) + tR2GCAcc_pred = cute.make_fragment( + tR2GCAcc.shape, + cutlass.Boolean + ) + for elem in cutlass.range(cute.size(tR2GCAcc_pred, mode=[0])): + for row in cutlass.range(cute.size(tR2GCAcc_pred, mode=[1])): + for col in cutlass.range(cute.size(tR2GCAcc_pred, mode=[2])): + # tR2GCAcc_pred[elem, row, col] = cute.elem_less( + # pidm * self.epi_tile[0] + tR2GCAcc[elem, row, col][0], + # cute.size(mDlogits_partial, mode=[0]) + # ) and cute.elem_less( + # pidn * self.epi_tile[1] + tR2GCAcc[elem, row, col][1], + # cute.size(mDlogits_partial, mode=[1]) + # ) + tR2GCAcc_pred[elem, row, col] = cute.elem_less( + pidm * self.epi_tile[0] + + tR2GCAcc[elem, row, col][0], + problem_mnk[0] + ) and cute.elem_less( + split_idx * self.vocab_per_split + + pidn * self.epi_tile[1] + + tR2GCAcc[elem, row, col][1], + problem_mnk[1] + ) + + tR2GgDlogits = thr_copy_r2g.partition_D(gDlogits_partial) + + # for type conversion + dLogits_half = cute.make_fragment( + tTMEM_load_rAcc.shape, + tR2GgDlogits.element_type + ) + dLogits_half = cute.tiled_divide( + dLogits_half, + (cute.size(tR2GgDlogits, mode=[0]), 1) + ) + dLogits_half = cute.group_modes(dLogits_half, 2, cute.rank(dLogits_half)) + + mma_pipeline.consumer_wait(mma_consumer_state) + + block_vocab_left_idx: cutlass.Int64 = ( + split_idx * self.vocab_per_split + + pidn * self.epi_tile[1] + ) + block_vocab_right_idx: cutlass.Int64 = ( + min( + split_idx * self.vocab_per_split + + (pidn + 1) * self.epi_tile[1], + min( + (split_idx + 1) * self.vocab_per_split, + problem_mnk[1] + ) + ) + ) + num_n_subtiles: cutlass.Int64 = cute.ceil_div( + (block_vocab_right_idx - block_vocab_left_idx), + cute.size(tTMEM_load_rAcc, mode=[0]) + ) + for n_subtile in cutlass.range(num_n_subtiles): + cute.copy( + tiled_copy_t2r, + tTMEM_load_tAcc[(None, None, None, n_subtile, mma_consumer_state.index)], + tTMEM_load_rAcc + ) + + for idx in cutlass.range(cute.size(tTMEM_load_rAcc, mode=[0]), unroll_full=True): + # exp_logits + tTMEM_load_rAcc[idx] = cute.exp(tTMEM_load_rAcc[idx] - tMrMaximum[0]) + + position: cutlass.Int64 = ( + self.rank * problem_mnk[1] + + split_idx * self.vocab_per_split + + pidn * self.epi_tile[1] + + n_subtile * cute.size(tTMEM_load_rAcc, mode=[0]) + + idx + ) + mask: cutlass.Boolean = ( + position == tMrLabels[0] + and tMrLabels[0] != ignore_index + ) + # d_logits + tTMEM_load_rAcc[idx] *= tMr_d_acc_exp_logits + tTMEM_load_rAcc[idx] += (mask * -tMrDlogprobs[0]) + dLogits_half[idx] = tTMEM_load_rAcc[idx].to(dLogits_half.element_type) + + for idx in cutlass.range(cute.size(dLogits_half, mode=[1]), unroll_full=True): + copy_id = n_subtile * cute.size(dLogits_half, mode=[1]) + idx + cute.copy( + tiled_copy_r2g, + dLogits_half[(None, idx, None)], + tR2GgDlogits[(None, None, copy_id)], + pred=tR2GCAcc_pred[((0, None), None, copy_id)] + ) + + mma_pipeline.consumer_release(mma_consumer_state) + mma_consumer_state.advance() + + + # ------ Deallocate TMEM ------ # + self.cta_sync_barrier.arrive_and_wait() + if warp_idx == self.empty_warp_ids[0]: + cute.arch.relinquish_tmem_alloc_permit() + cute.arch.dealloc_tmem( + tmem_ptr, + self.tmem_alloc_cols, + is_two_cta=self.use_2cta_instrs + ) + + + @cute.jit + def __call__( + self, + split_idx: cutlass.Int32, + hidden: cute.Tensor, + weight: cute.Tensor, + labels: cute.Tensor, + dlogprobs: cute.Tensor, + maximum: cute.Tensor, + accu: cute.Tensor, + dlogits_partial: cute.Tensor, + scalarNumValidTokens: cute.Pointer, + ignore_index: cutlass.Int64, + stream: cuda.CUstream, + ) -> None: + a_dtype: Type[cutlass.Numeric] = hidden.element_type + b_dtype: Type[cutlass.Numeric] = weight.element_type + + if cutlass.const_expr(hidden.element_type != weight.element_type): + raise RuntimeError(f"data type don't match: {hidden.element_type} v.s. {weight.element_type}") + if cutlass.const_expr(hidden.element_type not in [cutlass.Float16, cutlass.BFloat16]): + raise RuntimeError("hidden can only be FP16 or BF16") + if cutlass.const_expr(hidden.layout.shape[1] != weight.layout.shape[1]): + raise RuntimeError("K dimension doesn't match") + + problem_mnk = ( + hidden.layout.shape[0], + weight.layout.shape[0], + hidden.layout.shape[1] + ) + if cutlass.const_expr((problem_mnk[2] * a_dtype.width // 8) % 16 != 0): + raise RuntimeError(f"K dimension is not 16B aligned: {problem_mnk[2]}") + if cutlass.const_expr((problem_mnk[2] * b_dtype.width // 8) % 128 != 0): + raise RuntimeError(f"N dimension is not 128B aligned: {problem_mnk[1]}") + + grid = self._compute_grid( + problem_mnk = problem_mnk, + cluster_shape_mn = self.cluster_shape_mn, + cta_tiler = self.mma_tiler, + ) + + a_major_mode = utils.LayoutEnum.from_tensor(hidden).mma_major_mode() + b_major_mode = utils.LayoutEnum.from_tensor(weight).mma_major_mode() + + tiled_mma = sm100_utils.make_trivial_tiled_mma( + a_dtype, + a_major_mode, + b_major_mode, + self.acc_dtype, + self.cta_group, + self.mma_tiler[:2] + ) + self._setup_attributes(tiled_mma, a_dtype, b_dtype) + + self.epi_tile = self.cta_tile_shape_mnk[:2] + + # Swizzle o [(tileM, tileK), loopM, loopK, stage] + a_smem_layout_staged = sm100_utils.make_smem_layout_a( + tiled_mma, + self.mma_tiler, + a_dtype, + self.num_ab_stage + ) + # Swizzle o [(tileN, tileK), loopN, loopK, stage] + b_smem_layout_staged = sm100_utils.make_smem_layout_b( + tiled_mma, + self.mma_tiler, + b_dtype, + self.num_ab_stage + ) + tma_load_op = cpasync.CopyBulkTensorTileG2SOp(self.cta_group) + tma_store_op = cpasync.CopyBulkTensorTileS2GOp() + + # Swizzle o [(tileM, tileK), loopM, loopK] + a_smem_layout = cute.select( + a_smem_layout_staged, + mode=[0, 1, 2] + ) + tma_atom_a, tma_tensor_a = cute.nvgpu.make_tiled_tma_atom_A( + tma_load_op, + hidden, + a_smem_layout, + self.mma_tiler, + tiled_mma, + self.cluster_layout_vmnk.shape + ) + # Swizzle o [(tileN, tileK), loopN, loopK] + b_smem_layout = cute.select( + b_smem_layout_staged, + mode=[0, 1, 2] + ) + tma_atom_b, tma_tensor_b = cute.nvgpu.make_tiled_tma_atom_B( + tma_load_op, + weight, + b_smem_layout, + self.mma_tiler, + tiled_mma, + self.cluster_layout_vmnk.shape + ) + a_copy_size = cute.size_in_bytes(a_dtype, a_smem_layout) + b_copy_size = cute.size_in_bytes(b_dtype, b_smem_layout) + self.tma_copy_ab_bytes = a_copy_size + b_copy_size + + @cute.struct + class SharedStorage: + load_ab_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage * 2] + mma_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2] + + tmem_dealloc_mbar_ptr: cute.struct.MemRange[cutlass.Int64, 1] + tmem_holding_buf: cutlass.Int32 + + sA: cute.struct.Align[ + cute.struct.MemRange[a_dtype, cute.cosize(a_smem_layout_staged)], + self.buffer_align_bytes, + ] + sB: cute.struct.Align[ + cute.struct.MemRange[b_dtype, cute.cosize(b_smem_layout_staged)], + self.buffer_align_bytes, + ] + self.shared_storage = SharedStorage + + self.kernel( + split_idx, + tiled_mma, + tma_atom_a, + tma_tensor_a, + tma_atom_b, + tma_tensor_b, + labels, + dlogprobs, + maximum, + accu, + dlogits_partial, + scalarNumValidTokens, + ignore_index, + a_smem_layout_staged, + b_smem_layout_staged, + self.cluster_layout_vmnk, + problem_mnk, + ).launch( + grid=grid, + block=[self.threads_per_cta, 1, 1], + cluster=self.cluster_shape_mnk, + stream=stream + ) + + +if __name__ == "__main__": + torch.manual_seed(1113) + + batch = 4 + seqlen = 1023 + dim = 8192 + vocab_size = 152064 + dtype = torch.bfloat16 + split_idx = 0 + vocab_per_split = 512 * 6 + + hidden = torch.randn(batch, seqlen, dim, device="cuda", dtype=dtype) + weight = torch.randn(vocab_size, dim, device="cuda", dtype=dtype) + labels = torch.randint(0, vocab_size, (batch, seqlen), device="cuda", dtype=torch.long) + num_valid_tokens = torch.tensor(batch * seqlen, device="cuda", dtype=torch.int64) + + dlogprobs = torch.randn(batch, seqlen, device="cuda", dtype=torch.float32) + + def get_maximum_and_accu(hidden, weight): + logits = (hidden @ weight.T).to(torch.float32) + maximum, _ = torch.max(logits, dim=-1) + accu = torch.sum(torch.exp(logits - maximum.unsqueeze(-1)), dim=-1) + return maximum, accu + maximum, accu = get_maximum_and_accu(hidden, weight) + + dlogits_partial = torch.empty( + (batch, seqlen, vocab_per_split), + device=hidden.device, + dtype=hidden.dtype + ) + + # compile kernel + bwd_kernel = BwdPartialDlogits( + vocab_per_split=vocab_per_split, + reduction=0 + ) + + hidden_packed = from_dlpack( + hidden.view(-1, dim), + assumed_align=16).mark_compact_shape_dynamic(mode=0) + weight_packed = from_dlpack( + weight, + assumed_align=16 + ) + labels_packed = from_dlpack( + labels.view(-1), + assumed_align=8 + ).mark_compact_shape_dynamic(mode=0) + dlogprobs_packed = from_dlpack( + dlogprobs.view(-1), + assumed_align=16 + ).mark_compact_shape_dynamic(mode=0) + maximum_packed = from_dlpack( + maximum.view(-1), + assumed_align=8 + ).mark_compact_shape_dynamic(mode=0) + accu_packed = from_dlpack( + accu.view(-1), + assumed_align=8 + ).mark_compact_shape_dynamic(mode=0) + dlogits_partial_packed = from_dlpack( + dlogits_partial.view(-1, vocab_per_split), + assumed_align=32, + ).mark_compact_shape_dynamic(mode=0) + scalarNumValidTokens_packed = cute.runtime.make_ptr( + cutlass.Int64, + num_valid_tokens.data_ptr(), + cute.AddressSpace.gmem, + assumed_align=8 + ) + + ignore_index = -100 + + stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) + + compiled = cute.compile( + bwd_kernel, + split_idx, + hidden_packed, + weight_packed, + labels_packed, + dlogprobs_packed, + maximum_packed, + accu_packed, + dlogits_partial_packed, + scalarNumValidTokens_packed, + ignore_index, + stream, + ) + + start, stop = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True) + start.record(stream=torch.cuda.current_stream()) + with torch.cuda.nvtx.range("BwdPartialDlogits"): + compiled( + split_idx, + hidden_packed, + weight_packed, + labels_packed, + dlogprobs_packed, + maximum_packed, + accu_packed, + dlogits_partial_packed, + scalarNumValidTokens_packed, + ignore_index, + stream + ) + stop.record(stream=torch.cuda.current_stream()) + + torch.cuda.synchronize() + + elapsed_time = start.elapsed_time(stop) + + print(dlogits_partial) + + print(f"Success, Elapsed time: {elapsed_time:.4f} ms") \ No newline at end of file diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py new file mode 100644 index 00000000000..c59e7b40d95 --- /dev/null +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py @@ -0,0 +1,385 @@ +import torch +import torch.distributed as dist +import typing +import triton + +import cutlass +import cutlass.cute as cute +from cutlass.cute.runtime import from_dlpack +import cuda.bindings.driver as cuda + +import megatron.core.fusions.linear_cross_entropy.utils as utils +import megatron.core.fusions.linear_cross_entropy.blackwell.fwd_mainloop as fwd_mainloop +import megatron.core.fusions.linear_cross_entropy.blackwell.bwd_partial_dlogits as bwd_partial_dlogits +import megatron.core.fusions.linear_cross_entropy.blackwell.triton as triton_kernels + +# import linear_cross_entropy.utils as utils +# import linear_cross_entropy.blackwell.fwd_mainloop as fwd_mainloop +# import linear_cross_entropy.blackwell.bwd_partial_dlogits as bwd_partial_dlogits +# import linear_cross_entropy.blackwell.triton as triton_kernels + +def forward( + hidden: torch.Tensor, + weight: torch.Tensor, + labels: torch.Tensor, + tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, + reduction: typing.Optional[str] = "mean", + ignore_index: typing.Optional[int] = -100, +) -> typing.Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + forward host function + """ + assert hidden.is_cuda and weight.is_cuda and labels.is_cuda + assert weight.device == hidden.device and labels.device == hidden.device + + # hidden could be [batch, seqlen, dim] or [seqlen, batch, dim] or [tokens, dim] + assert hidden.dim() == 2 or hidden.dim() == 3 + # weight must be [vocab_size, dim] + assert weight.dim() == 2 + # labels could be [batch, seqlen] or [seqlen, batch] or [tokens] + assert ((hidden.dim() == 2 and labels.dim() == 1) + or (hidden.dim() == 3 and labels.dim() == 2)) + assert hidden.is_contiguous() and weight.is_contiguous() and labels.is_contiguous() + + hidden_view = hidden.view(-1, hidden.shape[-1]) + labels_view = labels.view(-1) + + assert hidden_view.shape[0] == labels_view.shape[0] + assert hidden_view.shape[1] == weight.shape[1] + num_tokens, dim = hidden_view.shape + vocab_size, _ = weight.shape + + tp_rank = 0 if tp_group is None else torch.distributed.get_rank(tp_group) + tp_world_size = 1 if tp_group is None else torch.distributed.get_world_size(tp_group) + + if not hasattr(forward, "_initialized"): + global _dedicated_stream, _dedicated_events + _dedicated_stream = torch.cuda.Stream(hidden.device) + _dedicated_events = [torch.cuda.Event() for _ in range(2)] + forward._initialized = True + + REDUCTION = utils.str_to_reduction_enum(reduction) + # declare logprobs + if REDUCTION == utils.EntropyReductionEnum.kNone: + logprobs = torch.empty((num_tokens,), device=hidden.device, dtype=torch.float32) + if tp_group is not None: + logprobs.zero_() + else: + logprobs = torch.zeros((), device=hidden.device, dtype=torch.float32) + # declare auxiliary tensors + maximum = torch.empty((num_tokens,), device=hidden.device, dtype=torch.float32) + accumulate = torch.empty_like(maximum, dtype=torch.float32) + num_valid_tokens = torch.empty((), device=hidden.device, dtype=torch.int64) + assert maximum.is_contiguous() and accumulate.is_contiguous() and num_valid_tokens.is_contiguous() + # declare intermediate tensors + # NOTE: this is a parameter for tuning + vocab_per_split = 512 * 6 + num_splits = (vocab_size + vocab_per_split - 1) // vocab_per_split + _max = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) + _accu = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) + if REDUCTION == utils.EntropyReductionEnum.kNone: + _logprobs = logprobs + else: + _logprobs = torch.empty((num_tokens,), device=hidden.device, dtype=torch.float32) + if tp_group is not None: + _logprobs.zero_() + assert _max.is_contiguous() and _accu.is_contiguous() and _logprobs.is_contiguous() + + triton_kernels.get_num_valid_tokens[(1,)]( + num_tokens, + ignore_index, + labels_view, + labels_view.stride(0), + num_valid_tokens, + ) + + if not hasattr(forward, "_fwd_mainloop_kernels"): + forward._fwd_mainloop_kernels = dict() + + # need to compile the kernel for the first time + hidden_packed = from_dlpack( + hidden_view.detach(), assumed_align=16 + ).mark_compact_shape_dynamic(mode=0) + weight_packed = from_dlpack( + weight.detach(), assumed_align=16 + ) + labels_packed = from_dlpack( + labels_view.detach(), assumed_align=8 + ).mark_compact_shape_dynamic(mode=0) + logprobs_packed = from_dlpack( + _logprobs, assumed_align=16 + ).mark_compact_shape_dynamic(mode=0) + _max_packed = from_dlpack( + _max, assumed_align=8 + ).mark_compact_shape_dynamic(mode=0, stride_order=(0, 1)) + _accu_packed = from_dlpack( + _accu, assumed_align=8 + ).mark_compact_shape_dynamic(mode=0, stride_order=(0, 1)) + cuda_stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) + + # VocabSize and Dim are fixed for a given model, + # only the number of tokens can vary + key = f"vocab_size:{vocab_size}+dim:{dim}+dtype:{hidden.dtype}" + if forward._fwd_mainloop_kernels.get(key) is None: + fwd_mainloop_kernel = fwd_mainloop.FwdMainLoop( + vocab_per_split=vocab_per_split, + ) + fwd_mainloop_compiled_kernel = cute.compile( + fwd_mainloop_kernel, + hidden_packed, + weight_packed, + labels_packed, + logprobs_packed, + _max_packed, + _accu_packed, + ignore_index, + tp_rank, + cuda_stream + ) + forward._fwd_mainloop_kernels[key] = fwd_mainloop_compiled_kernel + else: + fwd_mainloop_compiled_kernel = forward._fwd_mainloop_kernels[key] + fwd_mainloop_compiled_kernel( + hidden_packed, + weight_packed, + labels_packed, + logprobs_packed, + _max_packed, + _accu_packed, + ignore_index, + tp_rank, + cuda_stream + ) + + if tp_group is None: + def grid(meta): + return (triton.cdiv(num_tokens, meta["BLOCK_SIZE_M"]),) + + triton_kernels.forward_dp_epilogue[grid]( + num_tokens, + num_splits, + ignore_index, + labels_view, + labels_view.stride(0), + num_valid_tokens, + _max, + _max.stride(0), + _max.stride(1), + _accu, + _accu.stride(0), + _accu.stride(1), + maximum, + maximum.stride(0), + accumulate, + maximum.stride(0), + _logprobs, + _logprobs.stride(0), + logprobs, + triton.language.constexpr(REDUCTION), + ) + else: + _max_backup = _max.clone() + dist.all_reduce(_max, op=dist.ReduceOp.MAX, group=tp_group) + + torch.cuda.current_stream().record_event(_dedicated_events[0]) + with torch.cuda.stream(_dedicated_stream): + _dedicated_stream.wait_event(_dedicated_events[0]) + dist.all_reduce(_logprobs, op=dist.ReduceOp.SUM, group=tp_group) + _dedicated_stream.record_event(_dedicated_events[1]) + + def grid(meta): + return (triton.cdiv(num_tokens, meta["BLOCK_SIZE_M"]),) + + triton_kernels.forward_tp_epilogue[grid]( + num_tokens, + num_splits, + _max, + _max.stride(0), + _max.stride(1), + _max_backup, + _max_backup.stride(0), + _max_backup.stride(1), + _accu, + _accu.stride(0), + _accu.stride(1), + maximum, + maximum.stride(0), + accumulate, + maximum.stride(0), + ) + # reduce accumulate + dist.all_reduce(accumulate, op=dist.ReduceOp.SUM, group=tp_group) + + # update logprobs + torch.cuda.current_stream().wait_event(_dedicated_events[1]) + triton_kernels.forward_tp_epilogue_update_logprobs[grid]( + num_tokens, + ignore_index, + num_valid_tokens, + labels_view, + labels_view.stride(0), + _logprobs, + _logprobs.stride(0), + maximum, + maximum.stride(0), + accumulate, + accumulate.stride(0), + logprobs, + REDUCTION, + ) + + return logprobs, maximum, accumulate, num_valid_tokens, tp_rank, tp_world_size + +def backward( + dlogprobs: torch.Tensor, + hidden: torch.Tensor, + weight: torch.Tensor, + labels: torch.Tensor, + maximum: torch.Tensor, + accu: torch.Tensor, + num_valid_tokens: torch.Tensor, + reduction: typing.Optional[str] = "mean", + ignore_index: typing.Optional[int] = -100, + tp_group: typing.Optional[dist.ProcessGroup] = None, + tp_rank: typing.Optional[int] = 0, + tp_world_size: typing.Optional[int] = 1, +) -> typing.List[torch.Tensor]: + """ + backward host function + """ + hidden_view = hidden.view(-1, hidden.shape[-1]) + labels_view = labels.view(-1) + + num_tokens, dim = hidden_view.shape + vocab_size, _ = weight.shape + + REDUCTION = utils.str_to_reduction_enum(reduction) + dlogprobs_view = dlogprobs.view(-1) + assert ( + (REDUCTION == utils.EntropyReductionEnum.kNone and dlogprobs.shape == (num_tokens,)) + or (REDUCTION != utils.EntropyReductionEnum.kNone and dlogprobs.dim() == 0) + ) + assert dlogprobs.is_contiguous() and dlogprobs.is_cuda + + assert num_valid_tokens.dim() == 0 and num_valid_tokens.is_cuda and num_valid_tokens.dtype == torch.int64 + + d_hidden = torch.empty_like(hidden) + d_weight = torch.empty_like(weight) + assert d_hidden.is_contiguous() and d_weight.is_contiguous() + + # FIXME: implement different backward methods + _backward = utils.BackwardMethodEnum.kDlogitsSplitN + if _backward == utils.BackwardMethodEnum.kDlogitsSplitN: + vocab_per_split = 512 * 6 + num_splits = (vocab_size + vocab_per_split - 1) // vocab_per_split + + _d_logits = torch.empty( + (num_tokens, vocab_per_split), + device=hidden.device, + dtype=hidden.dtype + ) + + hidden_packed = from_dlpack( + hidden_view.detach(), + assumed_align=16 + ).mark_compact_shape_dynamic(mode=0) + weight_packed = from_dlpack( + weight.detach(), + assumed_align=16 + ) + labels_packed = from_dlpack( + labels_view.detach(), + assumed_align=8 + ).mark_compact_shape_dynamic(mode=0) + dlogprobs_packed = from_dlpack( + dlogprobs_view.detach(), + assumed_align=8 + ).mark_compact_shape_dynamic(mode=0) + maximum_packed = from_dlpack( + maximum.detach(), + assumed_align=8 + ).mark_compact_shape_dynamic(mode=0) + accu_packed = from_dlpack( + accu.detach(), + assumed_align=8 + ).mark_compact_shape_dynamic(mode=0) + dlogits_packed = from_dlpack( + _d_logits, + assumed_align=32 + ).mark_compact_shape_dynamic(mode=0) + scalarNumValidTokens_packed = cute.runtime.make_ptr( + cutlass.Int64, + num_valid_tokens.data_ptr(), + cute.AddressSpace.gmem, + assumed_align=8 + ) + + stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) + + if not hasattr(backward, "_bwd_kernel"): + backward._bwd_kernel = dict() + + key = f"vocab_size:{vocab_size}+dim:{dim}+reduction:{REDUCTION}+dtype:{hidden.dtype}" + if backward._bwd_kernel.get(key) is None: + bwd_kernel = bwd_partial_dlogits.BwdPartialDlogits( + reduction=REDUCTION, + vocab_per_split=vocab_per_split, + ) + bwd_kernel_compiled = cute.compile( + bwd_kernel, + 0, # split_idx + hidden_packed, + weight_packed, + labels_packed, + dlogprobs_packed, + maximum_packed, + accu_packed, + dlogits_packed, + scalarNumValidTokens_packed, + ignore_index, + stream + ) + backward._bwd_kernel[key] = bwd_kernel_compiled + else: + bwd_kernel_compiled = backward._bwd_kernel.get(key) + + for split_idx in range(num_splits): + bwd_kernel_compiled( + split_idx, + hidden_packed, + weight_packed, + labels_packed, + dlogprobs_packed, + maximum_packed, + accu_packed, + dlogits_packed, + scalarNumValidTokens_packed, + ignore_index, + stream + ) + vocab_right_bound = ( + min((split_idx + 1) * vocab_per_split, vocab_size) - split_idx * vocab_per_split + ) + # remove padding areas + _d_logits = _d_logits[:, :vocab_right_bound].contiguous() + + if split_idx == 0: + torch.matmul( + _d_logits, + weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :], + out=d_hidden.view(num_tokens, dim) + ) + else: + d_hidden += torch.matmul( + _d_logits, + weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :], + ).view(d_hidden.shape) + torch.matmul( + _d_logits.T, + hidden_view, + out=d_weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :] + ) + else: + raise NotImplementedError(f"Unsupported backward method: {_backward}") + + return d_hidden, d_weight \ No newline at end of file diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py b/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py new file mode 100644 index 00000000000..81346b0df81 --- /dev/null +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py @@ -0,0 +1,892 @@ +""" +Implementations of the fusion lm_head(Linear) + Cross-Entropy kernel +""" + +from typing import Optional, Type, Tuple, Union +import cuda.bindings.driver as cuda + +import torch + +import cutlass +import cutlass.cute as cute +import cutlass.utils as utils +import cutlass.pipeline as pipeline +from cutlass.cute.nvgpu import cpasync, tcgen05 +import cutlass.torch as cutlass_torch +import cutlass.utils.blackwell_helpers as sm100_utils +from cutlass.cute.runtime import from_dlpack + + +SM100_TMEM_CAPACITY_COLUMNS: int = 512 + +def make_thread_cooperative_group(size: int): + return pipeline.CooperativeGroup(pipeline.Agent.Thread, size, alignment=size) + +class FwdMainLoop: + """ + This class implements the mainloop for forward process. + + Traits stored as attributes. + + :param acc_dtype: + """ + + def __init__(self, + acc_dtype: Type[cutlass.Numeric] = cutlass.Float32, + use_2cta_instrs: bool = False, + mma_tiler_mn: Tuple[int, int] = (128, 256), + vocab_per_split: int = 512): + """ + Configuration including: + - MMA instruction settings + - Cluster Shape + """ + self.acc_dtype: Type[cutlass.Numeric] = acc_dtype + self.use_2cta_instrs = use_2cta_instrs + # This is the shape covered by tiledMMA, not just single MMA instruction + self.mma_tiler = (*mma_tiler_mn, 1) + self.cta_tiler = ( + self.mma_tiler[0], + vocab_per_split, + self.mma_tiler[2] + ) + self.vocab_per_split = vocab_per_split + + self.cta_group = ( + tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE + ) + self.cluster_shape_mn = (2, 1) if self.use_2cta_instrs else (1, 1) + + self.occupancy = 1 + # query SMEM capacity + self.smem_capacity = utils.get_smem_capacity_in_bytes("sm_100") + + # the maximum columns per MMA is 256, and there is only one GEMM, so we can fully + # assign TMEM for that GEMM of different tiles. + # so 512 = 2 * 256 + + self.threads_per_warp: int = 32 + # 1 warp for loading, 1 warp for issuing MMA, 1 WG for storing + self.epi_warp_ids = (0, 1, 2, 3) + self.load_warp_ids = 4 + self.mma_warp_ids = 5 + self.empty_warp_ids = (6, 7) + + self.threads_per_cta: int = self.threads_per_warp * len( + (*self.epi_warp_ids, + self.load_warp_ids, + self.mma_warp_ids, + *self.empty_warp_ids) + ) + + self.cta_sync_barrier = pipeline.NamedBarrier( + barrier_id = 1, + num_threads = self.threads_per_cta + ) + self.tmem_alloc_barrier = pipeline.NamedBarrier( + barrier_id = 2, + num_threads = self.threads_per_cta + ) + + self.buffer_align_bytes: int = 1024 + self.num_regs_other: int = 32 + self.num_regs_epi: int = 192 + + def _compute_stages( + self, + tiled_mma: cute.TiledMma, + mma_tiler: Tuple[int, int, int], + a_dtype: Type[cutlass.Numeric], + b_dtype: Type[cutlass.Numeric] + ): + a_smem_layout_stage_one = sm100_utils.make_smem_layout_a( + tiled_mma, + mma_tiler, + a_dtype, + 1, # only single stage + ) + b_smem_layout_stage_one = sm100_utils.make_smem_layout_b( + tiled_mma, + mma_tiler, + b_dtype, + 1, + ) + a_bytes_per_stage = cute.size_in_bytes( + a_dtype, a_smem_layout_stage_one + ) + b_bytes_per_stage = cute.size_in_bytes( + b_dtype, b_smem_layout_stage_one + ) + num_acc_stage = 2 + num_a_stage = 4 + num_b_stage = 4 + num_epi_stage_per_tile = 4 + + return num_acc_stage, num_a_stage, num_b_stage, num_epi_stage_per_tile + + def _setup_attributes( + self, + tiled_mma: cute.TiledMma, + a_dtype: Type[cutlass.Numeric], + b_dtype: Type[cutlass.Numeric], + ): + self.cluster_shape_mnk = (*self.cluster_shape_mn, 1) + self.cluster_layout_vmnk = cute.tiled_divide( + cute.make_layout(self.cluster_shape_mnk), + (tiled_mma.thr_id.shape,), + ) + + # this is fixed for dense MMA, k=16 + mma_inst_shape_k = cute.size(tiled_mma.shape_mnk, mode=[2]) + # 16*4 = 64; 64 * sizeof(FP16) = 128Bytes + mma_inst_tile_k: int = 4 + self.mma_tiler = ( + self.mma_tiler[0], + self.mma_tiler[1], + mma_inst_shape_k * mma_inst_tile_k + ) + + self.num_acc_stage, self.num_a_stage, self.num_b_stage, self.num_epi_stage_per_tile =\ + self._compute_stages(tiled_mma, self.mma_tiler, a_dtype, b_dtype) + self.tmem_alloc_cols = self.num_acc_stage * self.mma_tiler[1] + assert self.tmem_alloc_cols <= SM100_TMEM_CAPACITY_COLUMNS + + self.cta_tile_shape_mnk = ( + self.mma_tiler[0] // cute.size(tiled_mma.thr_id.shape), + self.mma_tiler[1], + self.mma_tiler[2] + ) + + @cute.kernel + def kernel( + self, + tiled_mma: cute.TiledMma, + tma_atom_a: cute.CopyAtom, + mA: cute.Tensor, + tma_atom_b: cute.CopyAtom, + mB: cute.Tensor, + mLabels: cute.Tensor, + mMax: cute.Tensor, + mAccu: cute.Tensor, + mLogprobs: cute.Tensor, + a_smem_layout_staged: cute.ComposedLayout, + b_smem_layout_staged: cute.ComposedLayout, + cluster_layout_vmnk: cute.Layout, + problem_mnk: Tuple[int, int, int], + ignore_index: cutlass.Int64, + rank: cutlass.Int32 + ): + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) + tidx, _, _ = cute.arch.thread_idx() + bidx, bidy, _ = cute.arch.block_idx() + # FIXME: block swizzling applied here + pidm, pidn = bidx, bidy + + # prefetch tma descriptors + if warp_idx == self.load_warp_ids: + cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_a) + cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_b) + + # declare SMEM + smem = utils.SmemAllocator() + storage = smem.allocate(self.shared_storage) + + ab_pipeline = pipeline.PipelineTmaUmma.create( + num_stages=self.num_a_stage, + producer_group=make_thread_cooperative_group(len([self.load_warp_ids])), + consumer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), + tx_count=self.tma_copy_a_bytes + self.tma_copy_b_bytes, + barrier_storage=storage.load_ab_mbar_ptr.data_ptr() + ) + ab_producer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, self.num_a_stage + ) + ab_consumer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, self.num_a_stage + ) + + mma_pipeline = pipeline.PipelineUmmaAsync.create( + num_stages=self.num_acc_stage, + producer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), + consumer_group=make_thread_cooperative_group( + self.threads_per_warp * len(self.epi_warp_ids) + ), + barrier_storage=storage.mma_mbar_ptr.data_ptr() + ) + mma_producer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, self.num_acc_stage + ) + mma_consumer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, self.num_acc_stage + ) + + tmem_dealloc_mbar_ptr = storage.tmem_dealloc_mbar_ptr.data_ptr() + if warp_idx == self.empty_warp_ids[0]: + with cute.arch.elect_one(): + cute.arch.mbarrier_init( + tmem_dealloc_mbar_ptr, + self.threads_per_warp * len(self.epi_warp_ids) + ) + cute.arch.mbarrier_init_fence() + + # -------- SMEM partition ------------ # + # swizzle o [(tileM, tileK), loopM, loopK, Stage] + sA = storage.sA.get_tensor( + a_smem_layout_staged.outer, + swizzle=a_smem_layout_staged.inner + ) + # swizzle o [(tileN, tileK), loopN, loopK, stage] + sB = storage.sB.get_tensor( + b_smem_layout_staged.outer, + swizzle=b_smem_layout_staged.inner + ) + + # FIXME: if 2 CTAs, modify here + thr_mma = tiled_mma.get_slice(0) + # [MMA, loopM, loopK, stage] + tCsA = thr_mma.make_fragment_A(sA) + # [MMA, loopN, loopK, stage] + tCsB = thr_mma.make_fragment_B(sB) + + # ---------- GMEM partition ----------- # + # [tileM, tileK, loopK] + gA = cute.local_tile( + mA, + (self.mma_tiler[0], self.mma_tiler[2]), + (pidm, None) + ) + + # [vocab_size_per_split, dim] + mB_n = cute.local_tile( + mB, + (self.vocab_per_split, cute.size(mB.layout.shape, mode=[1])), + (pidn, 0) + ) + + # [tileN, tileK, loopN, loopK] + gB = cute.local_tile( + mB_n, + (self.mma_tiler[1], self.mma_tiler[2]), + (None, None) + ) + + # [MMA, tileCntM, tileCntK, loopK] + tCgA = thr_mma.partition_A(gA) + # [MMA, tileCntN, tileCntK, loopN, loopK] + tCgB = thr_mma.partition_B(gB) + + a_cta_layout = cute.make_layout( + cute.slice_( + cluster_layout_vmnk, + (0, 0, None, 0)).shape + ) + # FIXME: if 2 CTAs, modify here + cta_rank_in_cluster = 0 + block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord( + cta_rank_in_cluster + ) + tTMAsA, tTMAgA = cpasync.tma_partition( + tma_atom_a, + block_in_cluster_coord_vmnk[2], # cta_coord, + a_cta_layout, + cute.group_modes(sA, 0, 3), # SMEM tensor + cute.group_modes(tCgA, 0, 3) # GMEM tensor + ) + b_cta_layout = cute.make_layout( + cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape + ) + tTMAsB, tTMAgB = cpasync.tma_partition( + tma_atom_b, + block_in_cluster_coord_vmnk[1], # cta_coord + b_cta_layout, + cute.group_modes(sB, 0, 3), + cute.group_modes(tCgB, 0, 3) + ) + + # Allocate TMEM + tmem_holding_buf = storage.tmem_holding_buf + if warp_idx == self.empty_warp_ids[0]: + cute.arch.alloc_tmem( + self.tmem_alloc_cols, + tmem_holding_buf, + is_two_cta=self.use_2cta_instrs + ) + self.cta_sync_barrier.arrive_and_wait() + tmem_ptr = cute.arch.retrieve_tmem_ptr( + self.acc_dtype, + alignment=16, + ptr_to_buffer_holding_addr=tmem_holding_buf + ) + + # [(tileM, tileN), loopM, loopN] + tmem_shape = (128, self.tmem_alloc_cols) + acc_shape = thr_mma.partition_shape_C(tmem_shape) + tCtC_fake = thr_mma.make_fragment_C(acc_shape) + tCtC = cute.make_tensor(tmem_ptr, tCtC_fake.layout) + + block_vocab_left_idx: cutlass.Int64 = ( + pidn * self.vocab_per_split + ) + block_vocab_right_idx: cutlass.Int64 = ( + min((pidn + 1) * self.vocab_per_split, problem_mnk[1]) + ) + num_n_tiles: cutlass.Int64 = cute.ceil_div( + (block_vocab_right_idx - block_vocab_left_idx), + self.mma_tiler[1]) + + # /////// + # empty + # /////// + if warp_idx in self.empty_warp_ids: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + # /////// + # load + # /////// + if warp_idx == self.load_warp_ids: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + for n in cutlass.range(num_n_tiles): + for k in cutlass.range(cute.size(gA, mode=[2])): + ab_pipeline.producer_acquire(ab_producer_state) + cute.copy( + tma_atom_a, + tTMAgA[(None, k)], + tTMAsA[(None, ab_producer_state.index)], + tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state) + ) + cute.copy( + tma_atom_b, + tTMAgB[(None, n, k)], + tTMAsB[(None, ab_producer_state.index)], + tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state) + ) + ab_pipeline.producer_commit(ab_producer_state) + ab_producer_state.advance() + + # /////// + # mma + # /////// + if warp_idx == self.mma_warp_ids: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + for n in cutlass.range(num_n_tiles): + # disable accumulate for the first tile + tiled_mma.set(tcgen05.Field.ACCUMULATE, False) + mma_pipeline.producer_acquire(mma_producer_state) + + for k in cutlass.range(cute.size(gA, mode=[2])): + ab_pipeline.consumer_wait(ab_consumer_state) + + for kblock_idx in cutlass.range(cute.size(tCsA, mode=[2]), unroll_full=True): + cute.gemm( + tiled_mma, + cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), + tCsA[(None, None, kblock_idx, ab_consumer_state.index)], + tCsB[(None, None, kblock_idx, ab_consumer_state.index)], + cute.append_ones(tCtC[(None, None, mma_producer_state.index)]) + ) + # enable accumulate for the next tile + tiled_mma.set(tcgen05.Field.ACCUMULATE, True) + + ab_pipeline.consumer_release(ab_consumer_state) + ab_consumer_state.advance() + + mma_pipeline.producer_commit(mma_producer_state) + mma_producer_state.advance() + + # ////////// + # epilogue + # ////////// + if warp_idx in self.epi_warp_ids: + cute.arch.warpgroup_reg_alloc(self.num_regs_epi) + + # epilog TMEM copy and partition + copy_atom_t2r = sm100_utils.get_tmem_load_op( + self.cta_tile_shape_mnk, + utils.LayoutEnum.ROW_MAJOR, # This is hard-coded + self.acc_dtype, + self.acc_dtype, + (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), + self.use_2cta_instrs + ) + # [tileM, subTileN, loopM, CntSubTileN, loopN] + tAcc_epi = cute.flat_divide( + tCtC[((None, None), 0, None)], + (self.epi_tile[0], + self.epi_tile[1] // self.num_epi_stage_per_tile) + ) + tiled_copy_t2r = tcgen05.make_tmem_copy( + copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)] + ) + thr_copy_t2r = tiled_copy_t2r.get_slice(tidx) + tTMEM_load_tAcc = thr_copy_t2r.partition_S(tAcc_epi) + # [(pattern), loopM, loopN, CntTileM, CntTileN] + tTMEM_load_tAcc = cute.group_modes(tTMEM_load_tAcc, 3, cute.rank(tTMEM_load_tAcc) - 1) + + cAcc = cute.make_identity_tensor(self.mma_tiler[:2]) + tCcAcc = thr_mma.partition_C(cAcc) + # [tileM, subTileN, loopM, CntSubTileN, CntTileN] + tCcAcc_epi = cute.flat_divide( + tCcAcc[((None, None), 0, None)], + (self.epi_tile[0], + self.epi_tile[1] // self.num_epi_stage_per_tile) + ) + tTMEM_load_cAcc = thr_copy_t2r.partition_D(tCcAcc_epi) + tTMEM_load_cAcc_shape = cute.select( + tTMEM_load_cAcc.shape, + mode=[0, 1, 2] + ) + + # epilogue layouts + epilogue_thread_layout = cute.make_layout((128, 1)) + copy_atom_g2r = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), + mLabels.element_type + ) + tiled_copy_g2r = cute.make_tiled_copy( + copy_atom_g2r, + epilogue_thread_layout, + (128, 1) + ) + thr_copy_g2r = tiled_copy_g2r.get_slice(tidx) + + copy_atom_r2g = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), + cutlass.Float32 + ) + tiled_copy_r2g = cute.make_tiled_copy( + copy_atom_r2g, + epilogue_thread_layout, + (128, 1) + ) + thr_copy_r2g = tiled_copy_r2g.get_slice(tidx) + + + # auxiliary tensors + # [tileM] + gLabels = cute.local_tile( + mLabels, + (self.epi_tile[0],), + (pidm,) + ) + + tLabelsCAcc = thr_copy_g2r.partition_S(cAcc)[(None, None, 0)] + tLabelsCAcc_mask = cute.make_fragment(tLabelsCAcc.shape, cutlass.Boolean) + # [(1, 1), 1] + tLabelsCAcc_mask[0] = cute.elem_less( + pidm * self.epi_tile[0] + tidx, + problem_mnk[0] + ) + # to align shape with gMax and gAccu + tLabelsCAcc_mask = cute.append_ones(tLabelsCAcc_mask) + + # [(1, 1), 1, 1] + tLabelsgLabels = thr_copy_g2r.partition_S(cute.append_ones(gLabels)) + tLabelsrLabels = cute.make_fragment(tLabelsgLabels.shape, tLabelsgLabels.element_type) + cute.copy( + tiled_copy_g2r, + tLabelsgLabels, + tLabelsrLabels, + pred=tLabelsCAcc_mask + ) + valid_mask: cutlass.Boolean =\ + (tLabelsrLabels[0] != ignore_index) and tLabelsCAcc_mask[0] + + # [tileM, 1] + gMax = cute.local_tile( + mMax, + (self.epi_tile[0], 1), + (pidm, pidn) + ) + # [(CPYM, CPYN), loopM, loopN] + tR2GgMax = thr_copy_r2g.partition_D(gMax) + tR2GrMax = cute.make_fragment(tR2GgMax.shape, tR2GgMax.element_type) + tR2GrMax.fill(-1e30) + + # [tileM, 1] + gAccu = cute.local_tile( + mAccu, + (self.epi_tile[0], 1), + (pidm, pidn) + ) + # [(CPYM, CPYN), loopM, loopN] + tR2GgAccu = thr_copy_r2g.partition_D(gAccu) + tR2GrAccu = cute.make_fragment(tR2GgAccu.shape, tR2GgAccu.element_type) + tR2GrAccu.fill(0.0) + + # [tileM, 1] + gLogprobs = cute.append_ones(cute.local_tile( + mLogprobs, + (self.epi_tile[0],), + (pidm,) + )) + # [(CPYM, CPYN), loopM, loopN] + tR2GgLogprobs = thr_copy_r2g.partition_D(gLogprobs) + tR2GrLogprobs = cute.make_fragment(tR2GgLogprobs.shape, tR2GgLogprobs.element_type) + tR2GrLogprobs.fill(0.0) + + # [(tileN // num_epi_stage_per_tile, 1), 1, 1] + tTMEM_load_rAcc = cute.make_fragment( + tTMEM_load_cAcc_shape, + self.acc_dtype + ) + + for n in cutlass.range(num_n_tiles): + mma_pipeline.consumer_wait(mma_consumer_state) + + left: cutlass.Int64 = ( + block_vocab_left_idx + n * self.epi_tile[1] + ) + right: cutlass.Int64 = ( + min((n + 1) * self.epi_tile[1] + block_vocab_left_idx, + block_vocab_right_idx) + ) + num_n_subtiles: cutlass.Int64 = cute.ceil_div( + (right - left), + cute.size(tTMEM_load_rAcc, mode=[0]) + ) + for n_subtile in cutlass.range(num_n_subtiles): + cute.copy( + tiled_copy_t2r, + tTMEM_load_tAcc[(None, None, None, n_subtile, mma_consumer_state.index)], + tTMEM_load_rAcc + ) + + for idx in cutlass.range(cute.size(tTMEM_load_rAcc, mode=[0]), unroll_full=True): + local_position: cutlass.Int64 = ( + n * self.epi_tile[1] + + n_subtile * cute.size(tTMEM_load_rAcc, mode=[0]) + + idx + ) + if (block_vocab_left_idx + local_position) < block_vocab_right_idx: + _max_old = tR2GrMax[0] + tR2GrMax[0] = cute.arch.fmax(tR2GrMax[0], tTMEM_load_rAcc[idx]) + exp_logits = cute.exp(tTMEM_load_rAcc[idx] - tR2GrMax[0]) + coeff = cute.exp(_max_old - tR2GrMax[0]) + tR2GrAccu[0] = coeff * tR2GrAccu[0] + exp_logits + + position: cutlass.Int64 = ( + rank * problem_mnk[1] + + pidn * self.vocab_per_split + + local_position + ) + mask: cutlass.Boolean = valid_mask and (position == tLabelsrLabels[0]) + tR2GrLogprobs[0] += (mask * tTMEM_load_rAcc[idx]) + + mma_pipeline.consumer_release(mma_consumer_state) + mma_consumer_state.advance() + + cute.copy( + tiled_copy_r2g, + tR2GrMax, + tR2GgMax, + pred=tLabelsCAcc_mask + ) + cute.copy( + tiled_copy_r2g, + tR2GrAccu, + tR2GgAccu, + pred=tLabelsCAcc_mask + ) + + vocab_left_idx: cutlass.Int64 = ( + rank * problem_mnk[1] + + pidn * self.vocab_per_split + ) + vocab_right_idx: cutlass.Int64 = ( + rank * problem_mnk[1] + + min((pidn + 1) * self.vocab_per_split, problem_mnk[1]) + ) + valid: cutlass.Boolean = ( + tLabelsrLabels[0] >= vocab_left_idx + and tLabelsrLabels[0] < vocab_right_idx + ) + tLabelsCAcc_mask[0] &= valid + + cute.copy( + tiled_copy_r2g, + tR2GrLogprobs, + tR2GgLogprobs, + pred=tLabelsCAcc_mask + ) + + # Dealloc TMEM + self.cta_sync_barrier.arrive_and_wait() + if warp_idx == self.empty_warp_ids[0]: + cute.arch.relinquish_tmem_alloc_permit() + cute.arch.dealloc_tmem( + tmem_ptr, + self.tmem_alloc_cols, + is_two_cta=self.use_2cta_instrs + ) + + @staticmethod + def _compute_grid( + problem_mnk: Tuple[int, int, int], + cluster_shape_mn: Tuple[int, int], + cta_tiler: Tuple[int, int, int], + num_splits: int + ) -> Tuple[int, int, int]: + + cluster_shape = (*cluster_shape_mn, 1) + + grid = cute.round_up( + ( + cute.ceil_div(problem_mnk[0], cta_tiler[0]), + num_splits, + 1, + ), + cluster_shape + ) + return grid + + @cute.jit + def __call__( + self, + hidden: cute.Tensor, + weight: cute.Tensor, + labels: cute.Tensor, + _logprobs: cute.Tensor, + _max: cute.Tensor, + _accu: cute.Tensor, + ignore_index: cutlass.Int64, + rank: cutlass.Int32, + stream: cuda.CUstream, + ) -> None: + a_dtype: Type[cutlass.Numeric] = hidden.element_type + b_dtype: Type[cutlass.Numeric] = weight.element_type + + if cutlass.const_expr(hidden.element_type != weight.element_type): + raise RuntimeError(f"data type don't match: {hidden.element_type} v.s. {weight.element_type}") + if cutlass.const_expr(hidden.element_type not in [cutlass.Float16, cutlass.BFloat16]): + raise RuntimeError("hidden can only be FP16 or BF16") + if cutlass.const_expr(hidden.layout.shape[1] != weight.layout.shape[1]): + raise RuntimeError("K dimension doesn't match") + + problem_mnk = ( + hidden.layout.shape[0], + weight.layout.shape[0], + hidden.layout.shape[1], + ) + if cutlass.const_expr((problem_mnk[2] * a_dtype.width // 8) % 16 != 0): + raise RuntimeError(f"K dimension is not 16B aligned: {problem_mnk[2]}") + + num_splits = cute.ceil_div(problem_mnk[1], self.vocab_per_split) + # if cutlass.const_expr(_max.layout.shape != (hidden.layout.shape[0], num_splits)): + # raise RuntimeError(f"max shape mismatch: {_max.layout.shape} != ({hidden.layout.shape[0]}, {num_splits})") + # if cutlass.const_expr(_accu.layout.shape != (hidden.layout.shape[0], num_splits)): + # raise RuntimeError(f"accu shape mismatch: {_accu.layout.shape} != ({hidden.layout.shape[0]}, {num_splits})") + + grid = self._compute_grid( + problem_mnk = problem_mnk, + cluster_shape_mn = self.cluster_shape_mn, + cta_tiler = self.cta_tiler, + num_splits = num_splits + ) + a_major_mode = utils.LayoutEnum.from_tensor(hidden).mma_major_mode() + b_major_mode = utils.LayoutEnum.from_tensor(weight).mma_major_mode() + + tiled_mma = sm100_utils.make_trivial_tiled_mma( + a_dtype, + a_major_mode, + b_major_mode, + self.acc_dtype, + self.cta_group, + self.mma_tiler[:2] + ) + + self._setup_attributes(tiled_mma, a_dtype, b_dtype) + if cutlass.const_expr((problem_mnk[2] * a_dtype.width // 8) % 128 != 0): + raise RuntimeError(f"K dimension is not 128B aligned: {problem_mnk[2]}") + + self.epi_tile = self.mma_tiler[:2] + + # Swizzle o [(tileM, tileK), loopM, loopK, stage] + a_smem_layout_staged = sm100_utils.make_smem_layout_a( + tiled_mma, + self.mma_tiler, + a_dtype, + self.num_a_stage + ) + # Swizzle o [(tileN, tileK), loopN, loopK, stage] + b_smem_layout_staged = sm100_utils.make_smem_layout_b( + tiled_mma, + self.mma_tiler, + b_dtype, + self.num_b_stage + ) + + # TMA loading + tma_load_op = cpasync.CopyBulkTensorTileG2SOp(self.cta_group) + tma_store_op = cpasync.CopyBulkTensorTileS2GOp() + + # Swizzle o [(tileM, tileK), loopM, loopK] + a_smem_layout = cute.select( + a_smem_layout_staged, + mode=[0, 1, 2] + ) + # create tma copy atom for hidden, + # and the cooresponding tma descriptor tensor + tma_atom_a, tma_desc_a = cute.nvgpu.make_tiled_tma_atom_A( + tma_load_op, + hidden, # gmem_tensor + a_smem_layout, # SMEM layout + self.mma_tiler, # MMA tiler + tiled_mma, # TiledMMA + self.cluster_layout_vmnk.shape # cluster_shape_vmnk + ) + # Swizzle o [(tileN, tileK), loopN, loopK] + b_smem_layout = cute.select( + b_smem_layout_staged, + mode=[0, 1, 2] + ) + tma_atom_b, tma_desc_b = cute.nvgpu.make_tiled_tma_atom_B( + tma_load_op, + weight, # gmem_tensor + b_smem_layout, # SMEM layout + self.mma_tiler, # MMA tiler + tiled_mma, # TiledMMA + self.cluster_layout_vmnk.shape # cluster_shape_vmnk + ) + a_copy_size = cute.size_in_bytes(a_dtype, a_smem_layout) + b_copy_size = cute.size_in_bytes(b_dtype, b_smem_layout) + self.tma_copy_a_bytes = a_copy_size + self.tma_copy_b_bytes = b_copy_size + + assert self.num_a_stage == self.num_b_stage + @cute.struct + class SharedStorage: + # pipeline barriers, 2 = producer + consumer + load_ab_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_a_stage * 2] + mma_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2] + tmem_dealloc_mbar_ptr: cute.struct.MemRange[cutlass.Int64, 1] + # tmem holding buffer + tmem_holding_buf: cutlass.Int32 + # SMEM tensors + sA: cute.struct.Align[ + cute.struct.MemRange[a_dtype, cute.cosize(a_smem_layout_staged)], + self.buffer_align_bytes, + ] + sB: cute.struct.Align[ + cute.struct.MemRange[b_dtype, cute.cosize(b_smem_layout_staged)], + self.buffer_align_bytes, + ] + self.shared_storage = SharedStorage + + # launch kernel + self.kernel( + tiled_mma, + tma_atom_a, + tma_desc_a, + tma_atom_b, + tma_desc_b, + labels, + _max, + _accu, + _logprobs, + a_smem_layout_staged, + b_smem_layout_staged, + self.cluster_layout_vmnk, + problem_mnk, + ignore_index, + rank, + ).launch( + grid=grid, + block=[self.threads_per_cta, 1, 1], + cluster=self.cluster_shape_mnk, + stream=stream, + ) + return None + + +if __name__ == "__main__": + rank = 0 + + vocab_per_split = 512 * 6 + fwd_mainloop = FwdMainLoop( + vocab_per_split=vocab_per_split + ) # use default arguments + + torch.manual_seed(1111) + + num_tokens = 13092 + hidden_size = 4096 + vocab_size = 152064 + # num_tokens = 4 + # hidden_size = 64 + # vocab_size = 512 + dtype = torch.bfloat16 + ignore_index = -100 + + hidden = ( + torch.empty((num_tokens, hidden_size), dtype=dtype, device="cuda") + .uniform_(-0.5, 0.5) + ) + weight = ( + torch.empty((vocab_size, hidden_size), dtype=dtype, device="cuda") + .uniform_(-0.5, 0.5) + ) + # hidden = torch.ones((num_tokens, hidden_size), dtype=dtype, device="cuda") + # weight = torch.ones((vocab_size, hidden_size), dtype=dtype, device="cuda") + labels = torch.randint(0, vocab_size, (num_tokens,), device="cuda") + + # pad 1 ignore_index to the right + padded_labels = torch.nn.functional.pad( + labels, (0, 1), value=ignore_index + ) + # remove first element + labels = padded_labels[..., 1:].contiguous() + + # allocate output tensor + logprobs = torch.empty((num_tokens), dtype=torch.float32, device="cuda") + + # allocate intermediate tensors + num_splits = (vocab_size + vocab_per_split - 1) // vocab_per_split + _max = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) + _accu = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) + + + # compile kernel + _hidden = from_dlpack(hidden, assumed_align=16).mark_compact_shape_dynamic(mode=0, divisibility=1) + _weight = from_dlpack(weight, assumed_align=16) + _labels = from_dlpack(labels, assumed_align=8).mark_compact_shape_dynamic(mode=0) + _logprobs = from_dlpack(logprobs, assumed_align=16).mark_compact_shape_dynamic(mode=0) + _max_ = from_dlpack(_max, assumed_align=8).mark_compact_shape_dynamic(mode=0) + _accu_ = from_dlpack(_accu, assumed_align=8).mark_compact_shape_dynamic(mode=0) + stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) + compiled = cute.compile(fwd_mainloop, + _hidden, _weight, _labels, _logprobs, + _max_, _accu_, + ignore_index, + rank, + stream) + + # launch kernel + start, stop = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True) + + with torch.cuda.nvtx.range("FwdMainLoop"): + start.record(stream=torch.cuda.current_stream()) + compiled(_hidden, _weight, _labels, _logprobs, _max_, _accu_, ignore_index, rank, stream) + stop.record(stream=torch.cuda.current_stream()) + + torch.cuda.synchronize() + + elapsed_time = start.elapsed_time(stop) + + gemm = torch.matmul(hidden.to(torch.float32), weight.T.to(torch.float32)) + # print(gemm) + + # print(_max) + # print(_accu) + # print(logprobs) + + cut_max, _ = torch.max(_max, dim=1) + print(cut_max) + # for i in range(cut_max.shape[0]): + # print(i, cut_max[i]) + + torch_max, _ = torch.max(gemm, dim=1) + print(torch_max) + + print(f"Success, Elapsed time: {elapsed_time:.4f} ms") \ No newline at end of file diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py b/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py new file mode 100644 index 00000000000..fd3f14236fb --- /dev/null +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py @@ -0,0 +1,303 @@ +import triton +import triton.language as tl + +@triton.autotune( + configs=[ + triton.Config({"BLOCK_SIZE_M": 1024}, num_stages=3, num_warps=32), + triton.Config({"BLOCK_SIZE_M": 2048}, num_stages=3, num_warps=32), + ], + key=["num_tokens"], +) +@triton.jit +def get_num_valid_tokens( + num_tokens: tl.int64, + ignore_index: tl.int64, + labels_ptr: tl.pointer_type(tl.int64), + stride_labels: tl.int64, + num_valid_tokens_ptr: tl.pointer_type(tl.int64), + BLOCK_SIZE_M: tl.constexpr, +): + """ + Calculate the number of valid tokens in the labels tensor. + """ + num_pid_m: tl.int64 = tl.cdiv(num_tokens, BLOCK_SIZE_M) + + num_valid_tokens: tl.int64 = tl.zeros((), dtype=tl.int64) + for m in range(0, num_pid_m): + offs_am = m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + + labels = tl.load( + labels_ptr + offs_am * stride_labels, + mask=offs_am < num_tokens, + other=ignore_index + ) + + valid_labels_mask = labels != ignore_index + num_valid_tokens += (tl.sum(valid_labels_mask.to(tl.int32), axis=0)).to(tl.int64) + tl.store(num_valid_tokens_ptr, num_valid_tokens) + + +@triton.autotune( + configs=[ + triton.Config({"BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 64}) + ], + key=["num_tokens", "num_splits"] +) +@triton.jit +def forward_dp_epilogue( + num_tokens: tl.int64, + num_splits: tl.int64, # TODO: maybe this could be a constexpr + ignore_index: tl.int64, + labels_ptr: tl.pointer_type(tl.int64), + stride_labels: tl.int64, + num_valid_tokens_ptr: tl.pointer_type(tl.int64), + max_ptr: tl.pointer_type(tl.float32), + stride_max_m: tl.int64, + stride_max_n: tl.int64, + accu_ptr: tl.pointer_type(tl.float32), + stride_accu_m: tl.int64, + stride_accu_n: tl.int64, + global_max_ptr: tl.pointer_type(tl.float32), + stride_global_max: tl.int64, + global_accu_ptr: tl.pointer_type(tl.float32), + stride_global_accu: tl.int64, + global_logprobs_ptr: tl.pointer_type(tl.float32), + stride_global_logprobs: tl.int64, + global_logprobs_scalar_ptr: tl.pointer_type(tl.float32), + REDUCTION: tl.constexpr, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, +): + """ + forward epilogue in dp + """ + pid_m = tl.program_id(axis=0) + + offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + global_max = tl.zeros((BLOCK_SIZE_M,), dtype=tl.float32) + global_accu = tl.zeros((BLOCK_SIZE_M,), dtype=tl.float32) + + for pid_n in range(0, tl.cdiv(num_splits, BLOCK_SIZE_N)): + offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + + _max = tl.load( + max_ptr + offs_m[:, None] * stride_max_m + offs_n[None, :] * stride_max_n, + mask=(offs_m[:, None] < num_tokens) & (offs_n[None, :] < num_splits), + other=0.0, + ) + _accu = tl.load( + accu_ptr + offs_m[:, None] * stride_accu_m + offs_n[None, :] * stride_accu_n, + mask=(offs_m[:, None] < num_tokens) & (offs_n[None, :] < num_splits), + other=0.0, + ) + + # local reduction + _max_old = global_max + _local_max = tl.max(_max, axis=1, return_indices=False) + global_max = tl.maximum(global_max, _local_max) + + _scale = tl.exp(_max - global_max[:, None]) + _coeff = tl.exp(_max_old - global_max) + global_accu = _coeff * global_accu + tl.sum(_scale * _accu, axis=1) + + # store maximum + tl.store( + global_max_ptr + offs_m * stride_global_max, + global_max, + mask=offs_m < num_tokens, + ) + # store accumulate + tl.store( + global_accu_ptr + offs_m * stride_global_accu, + global_accu, + mask=offs_m < num_tokens, + ) + # update logprobs + labels = tl.load( + labels_ptr + offs_m * stride_labels, + mask=offs_m < num_tokens, + other=ignore_index, + ) + global_logprobs_ptrs = global_logprobs_ptr + offs_m * stride_global_logprobs + global_logprobs = tl.load( + global_logprobs_ptrs, + mask=offs_m < num_tokens, + ) + global_logprobs = global_max + tl.log(global_accu) - global_logprobs + label_mask = labels != ignore_index + global_logprobs = tl.where(label_mask, global_logprobs, 0.0) + + if REDUCTION == 0: # no-reduction + tl.store( + global_logprobs_ptrs, + global_logprobs, + mask=offs_m < num_tokens, + ) + elif REDUCTION == 1: # sum + global_logprobs_scalar = tl.sum(global_logprobs, axis=0) + tl.atomic_add( + global_logprobs_scalar_ptr, + global_logprobs_scalar + ) + elif REDUCTION == 2: # mean + num_valid_tokens = tl.load(num_valid_tokens_ptr) + global_logprobs_scalar = tl.fdiv( + tl.sum(global_logprobs, axis=0), + num_valid_tokens.to(tl.float32), + ) + tl.atomic_add( + global_logprobs_scalar_ptr, + global_logprobs_scalar + ) + + +@triton.autotune( + configs=[ + triton.Config({"BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 64}), + ], + key=["num_tokens", "num_splits"] +) +@triton.jit +def forward_tp_epilogue( + num_tokens: tl.int64, + num_splits: tl.int64, + reduced_max_ptr: tl.pointer_type(tl.float32), + stride_reduced_max_m: tl.int64, + stride_reduced_max_n: tl.int64, + original_max_ptr: tl.pointer_type(tl.float32), + stride_original_max_m: tl.int64, + stride_original_max_n: tl.int64, + accu_ptr: tl.pointer_type(tl.float32), + stride_accu_m: tl.int64, + stride_accu_n: tl.int64, + global_max_ptr: tl.pointer_type(tl.float32), + stride_global_max: tl.int64, + global_accu_ptr: tl.pointer_type(tl.float32), + stride_global_accu: tl.int64, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, +): + """ + forward epilogue in tp + """ + pid_m = tl.program_id(axis=0) + + offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + + global_max = tl.zeros((BLOCK_SIZE_M,), dtype=tl.float32) + global_accu = tl.zeros((BLOCK_SIZE_M,), dtype=tl.float32) + + for pid_n in range(0, tl.cdiv(num_splits, BLOCK_SIZE_N)): + offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + + _reduced_max = tl.load( + reduce_max_ptr + offs_m[:, None] * stride_reduce_max_m + offs_n[None, :] * stride_reduce_max_n, + mask=(offs_m[:, None] < num_tokens) & (offs_n[None, :] < num_splits), + other=0.0, + ) + _original_max = tl.load( + original_max_ptr + offs_m[:, None] * stride_original_max_m + offs_n[None, :] * stride_original_max_n, + mask=(offs_m[:, None] < num_tokens) & (offs_n[None, :] < num_splits), + other=0.0, + ) + _accu = tl.load( + accu_ptr + offs_m[:, None] * stride_accu_m + offs_n[None, :] * stride_accu_n, + mask=(offs_m[:, None] < num_tokens) & (offs_n[None, :] < num_splits), + other=0.0, + ) + + # local reduction + _max_old = global_max + _local_max = tl.max(_reduced_max, axis=1) + global_max = tl.maximum(global_max, _local_max) + + # update accumulate + _coeff = tl.exp(_max_old - global_max) + _scale = tl.exp(_original_max - global_max[:, None]) + global_accu = _coeff * global_accu + tl.sum(_scale * _accu, axis=1) + + # store + tl.store( + global_max_ptr + offs_m * stride_global_max, + global_max, + mask=offs_m < num_tokens, + ) + tl.store( + global_accu_ptr + offs_m * stride_global_accu, + global_accu, + mask=offs_m < num_tokens + ) + + +@triton.autotune( + configs=[ + triton.Config({"BLOCK_SIZE_M": 16}) + ], + key=["num_tokens"] +) +@triton.jit +def forward_tp_epilogue_update_logprobs( + num_tokens: tl.int64, + ignore_index: tl.int64, + num_valid_tokens_ptr: tl.pointer_type(tl.int64), + labels_ptr: tl.pointer_type(tl.int64), + stride_labels: tl.int64, + logprobs_ptr: tl.pointer_type(tl.float32), + stride_logprobs: tl.int64, + maximum_ptr: tl.pointer_type(tl.float32), + stride_maximum: tl.int64, + accumulate_ptr: tl.pointer_type(tl.float32), + stride_accumulate: tl.int64, + logprobs_scalar_ptr: tl.pointer_type(tl.float32), + REDUCTION: tl.constexpr, + BLOCK_SIZE_M: tl.constexpr, +): + """ + update logprobs in tp + """ + pid_m = tl.program_id(axis=0) + + offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + + logprobs = tl.load( + logprobs_ptr + offs_m * stride_logprobs, + mask=offs_m < num_tokens, + ) + maximum = tl.load( + maximum_ptr + offs_m * stride_maximum, + mask=offs_m < num_tokens, + ) + accumulate = tl.load( + accumulate_ptr + offs_m * stride_accumulate, + mask=offs_m < num_tokens, + ) + + labels = tl.load( + labels_ptr + offs_m * stride_labels, + mask=offs_m < num_tokens, + other=ignore_index, + ) + label_mask = labels != ignore_index + + logprobs = maximum + tl.log(accumulate) - logprobs + logprobs = tl.where(label_mask, logprobs, 0.0) + + if REDUCTION == 0: # no-reduction + tl.store( + logprobs_ptr + offs_m * stride_logprobs, + logprobs, + mask=offs_m < num_tokens, + ) + elif REDUCTION == 1: # sum + logprobs_scalar = tl.sum(logprobs, axis=0) + tl.atomic_add( + logprobs_scalar_ptr, + logprobs_scalar + ) + elif REDUCTION == 2: # mean + num_valid_tokens = tl.load(num_valid_tokens_ptr) + logprobs_scalar = tl.fdiv( + tl.sum(logprobs, axis=0), + num_valid_tokens.to(tl.float32), + ) + tl.atomic_add(logprobs_scalar_ptr, logprobs_scalar) \ No newline at end of file diff --git a/megatron/core/fusions/linear_cross_entropy/utils.py b/megatron/core/fusions/linear_cross_entropy/utils.py new file mode 100644 index 00000000000..642a6b3b230 --- /dev/null +++ b/megatron/core/fusions/linear_cross_entropy/utils.py @@ -0,0 +1,35 @@ +import typing +from dataclasses import dataclass + +@dataclass +class EntropyReductionEnum: + """ + Enum for the reduction method of cross entropy. + """ + kNone = 0 + kSum = 1 + kMean = 2 + +def str_to_reduction_enum(reduction: str) -> EntropyReductionEnum: + """ + str -> EntropyReductionEnum + """ + _enum = EntropyReductionEnum.kNone + if reduction == "none": + _enum = EntropyReductionEnum.kNone + elif reduction == "sum": + _enum = EntropyReductionEnum.kSum + elif reduction == "mean": + _enum = EntropyReductionEnum.kMean + else: + raise ValueError(f"Invalid reduction: {reduction}") + return _enum + +@dataclass +class BackwardMethodEnum: + # two separate kernels for d_hidden and d_weight, respectively + kTwoKernels = 0 + # calculate partial d_logits along its N dimension + kDlogitsSplitN = 1 + # fuse d_hidden and d_weight into a single kernel + kFused = 2 diff --git a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py index 4d0ae55b666..a4d759046f9 100644 --- a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py +++ b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py @@ -22,6 +22,9 @@ ) from tests.unit_tests.test_utilities import Utils +from megatron.core.fusions.fused_linear_cross_entropy import linear_cross_entropy + +import os class MockDataset(Dataset): """ @@ -132,8 +135,11 @@ def init_gpt_dataloader( return dataloader -class TestFusedLinearCrossEntropy: - +@pytest.mark.skipif( + "WORLD_SIZE" not in os.environ or os.environ["WORLD_SIZE"] < "2", + reason="Requires torchrun with multiple GPUs" +) +class TestFusedLinearCrossEntropyOnGptModel: @pytest.mark.parametrize("fp8_flag", get_valid_fp8_flags()) @pytest.mark.parametrize("mtp_layers", [0, 1]) @pytest.mark.parametrize("dispatcher_type", get_valid_token_dispatcher_types()) @@ -187,3 +193,373 @@ def test_gpt_model(self, mtp_layers, dispatcher_type, fp8_flag, layer_num): output = gpt_model(**batch) loss = output.sum() loss.backward() + + +@pytest.mark.skipif( + "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "1", + reason="Requires single GPU" +) +class TestFusedLinearCrossEntropyDataParallel: + def cleanup(self): + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + import gc + + gc.collect() + torch.cuda.synchronize() + + @staticmethod + def torch_linear_cross_entropy( + hidden: torch.Tensor, + weight: torch.Tensor, + labels: torch.Tensor, + reduction: str, + ignore_index: int + ): + # NOTE: need to convert to fp32 to fp32 accumulation, + # thus assure accuracy + logits = hidden.to(torch.float32) @ weight.T.to(torch.float32) + logprobs = torch.nn.functional.cross_entropy( + logits.view(-1, logits.shape[-1]), + labels.view(-1), + reduction=reduction, + ignore_index=ignore_index, + ) + return logprobs.to(torch.float32) + + @staticmethod + def get_problems(): + return [ + (80, 125, 64), + (80, 152064, 64), + (1024, 152064, 4096), + (4096, 152063, 8192), + ((1, 4096), 152064, 8192), + ((2, 4096), 152064, 8192), + ] + + @staticmethod + def get_ignore_index(): + return [-100, 4] + + def test_kernel_launch(self): + """ + Check if the compiled kernel can be + launched with different problem sizes + """ + self.cleanup() + + num_tokens = [15, 26, 128, 513, 2048, 8192] + vocab_size = 152064 + dim = 4096 + dtype = torch.bfloat16 + reduction = "mean" + ignore_index = -100 + + weight = torch.randn(vocab_size, dim, dtype=dtype, device="cuda").requires_grad_() + for num_token in num_tokens: + hidden = torch.randn(num_token, dim, dtype=dtype, device="cuda").requires_grad_() + labels = torch.randint(0, vocab_size, (num_token,), dtype=torch.long, device="cuda") + + logprobs = linear_cross_entropy(hidden, weight, labels, reduction=reduction, ignore_index=ignore_index) + assert not torch.isnan(logprobs).any() + + gLogprobs = torch.randn_like(logprobs) + (d_hidden, d_weight) = torch.autograd.grad( + (logprobs,), + (hidden, weight), + (gLogprobs,), + retain_graph=False + ) + assert not torch.isnan(d_hidden).any() + assert not torch.isnan(d_weight).any() + + + @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) + @pytest.mark.parametrize("problem", get_problems()) + @pytest.mark.parametrize("reduction", ["none", "mean", "sum"]) + @pytest.mark.parametrize("ignore_index", get_ignore_index()) + def test_correctness( + self, + dtype, + problem, + reduction, + ignore_index + ): + num_tokens, vocabsize, dim = problem + hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) + labels_shape = (num_tokens,) if isinstance(num_tokens, int) else num_tokens + + hidden = ( + torch.empty(hidden_shape, dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") + if ignore_index >=0 and ignore_index < vocabsize: + pad_labels = torch.nn.functional.pad(labels, (0, 1), value=ignore_index) + labels = pad_labels[..., 1:].contiguous() + + # forward + torch_logprobs = self.torch_linear_cross_entropy(hidden, weight, labels, + reduction=reduction, ignore_index=ignore_index) + + custom_logprobs = linear_cross_entropy(hidden, weight, labels, + reduction=reduction, ignore_index=ignore_index) + + torch.testing.assert_close( + torch_logprobs, + custom_logprobs + ) + + # backward + g_logprobs = ( + torch.empty_like(torch_logprobs) + .uniform_(-0.1, 0.1) + ) + + (d_torch_hidden, d_torch_weight) = torch.autograd.grad( + (torch_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + + (d_custom_hidden, d_custom_weight) = torch.autograd.grad( + (custom_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False) + + torch.testing.assert_close( + d_torch_hidden, + d_custom_hidden, + atol=1e-3, + rtol=1e-3 + ) + torch.testing.assert_close( + d_torch_weight, + d_custom_weight, + atol=1e-3, + rtol=1e-3 + ) + + @pytest.mark.parametrize("problem", [((1, 4096), 129280, 7168)]) + @pytest.mark.parametrize("dtype", [torch.bfloat16]) + @pytest.mark.parametrize("reduction", ["mean"]) + @pytest.mark.parametrize("ignore_index", [-100]) + def test_performance( + self, + problem, + dtype, + reduction, + ignore_index + ): + num_tokens, vocabsize, dim = problem + hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) + labels_shape = (num_tokens,) if isinstance(num_tokens, int) else num_tokens + + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + + torch_fwd_latency = list() + torch_bwd_latency = list() + custom_fwd_latency = list() + custom_bwd_latency = list() + + iterations = 5 + for i in range(iterations): + hidden = ( + torch.empty(hidden_shape, dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") + if ignore_index >=0 and ignore_index < vocabsize: + pad_labels = torch.nn.functional.pad(labels, (0, 1), value=ignore_index) + labels = pad_labels[..., 1:].contiguous() + + # -------- forward -------- # + start_event.record() + torch_logprobs = self.torch_linear_cross_entropy( + hidden, weight, labels, + reduction=reduction, + ignore_index=ignore_index + ) + end_event.record() + torch.cuda.synchronize() + torch_fwd_latency.append( + start_event.elapsed_time(end_event) + ) + + start_event.record() + custom_logprobs = linear_cross_entropy( + hidden, weight, labels, + reduction=reduction, + ignore_index=ignore_index + ) + end_event.record() + torch.cuda.synchronize() + custom_fwd_latency.append( + start_event.elapsed_time(end_event) + ) + + # -------- backward -------- # + g_logprobs = ( + torch.empty_like(torch_logprobs) + .uniform_(-0.1, 0.1) + ) + + start_event.record() + (d_torch_hidden, d_torch_weight) = torch.autograd.grad( + (torch_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + end_event.record() + torch.cuda.synchronize() + torch_bwd_latency.append( + start_event.elapsed_time(end_event) + ) + + start_event.record() + (d_custom_hidden, d_custom_weight) = torch.autograd.grad( + (custom_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + end_event.record() + torch.cuda.synchronize() + custom_bwd_latency.append( + start_event.elapsed_time(end_event) + ) + + # --- remove first latency due to warmup --- # + torch_fwd_latency = torch_fwd_latency[1:] + torch_bwd_latency = torch_bwd_latency[1:] + custom_fwd_latency = custom_fwd_latency[1:] + custom_bwd_latency = custom_bwd_latency[1:] + + print() + print(f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}:") + print(f"[INFO]: Torch forward latency: {sum(torch_fwd_latency) / len(torch_fwd_latency):.2f} ms") + print(f"[INFO]: Custom forward latency: {sum(custom_fwd_latency) / len(custom_fwd_latency):.2f} ms") + print(f"[INFO]: Torch backward latency: {sum(torch_bwd_latency) / len(torch_bwd_latency):.2f} ms") + print(f"[INFO]: Custom backward latency: {sum(custom_bwd_latency) / len(custom_bwd_latency):.2f} ms") + + @pytest.mark.parametrize("problem", [((1, 4096), 129280, 7168)]) + @pytest.mark.parametrize("dtype", [torch.bfloat16]) + @pytest.mark.parametrize("reduction", ["mean"]) + @pytest.mark.parametrize("ignore_index", [-100]) + def test_storage( + self, + problem, + dtype, + reduction, + ignore_index + ): + num_tokens, vocabsize, dim = problem + hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) + labels_shape = (num_tokens,) if isinstance(num_tokens, int) else num_tokens + print() + print(f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}:") + + def torch_storage(): + hidden = ( + torch.empty(hidden_shape, dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") + if ignore_index >=0 and ignore_index < vocabsize: + pad_labels = torch.nn.functional.pad(labels, (0, 1), value=ignore_index) + labels = pad_labels[..., 1:].contiguous() + + torch.cuda.reset_peak_memory_stats() + torch_logprobs = self.torch_linear_cross_entropy( + hidden, weight, labels, + reduction=reduction, + ignore_index=ignore_index + ) + torch.cuda.synchronize() + torch_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 + print(f"[INFO]: Torch Forward pass peak memory: {torch_max_memory:.2f} MB") + + torch.cuda.reset_peak_memory_stats() + g_logprobs = ( + torch.empty_like(torch_logprobs) + .uniform_(-0.1, 0.1) + ) + (d_torch_hidden, d_torch_weight) = torch.autograd.grad( + (torch_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + torch.cuda.synchronize() + torch_backward_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 + print(f"[INFO]: Torch Backward pass peak memory: {torch_backward_max_memory:.2f} MB") + + def custom_storage(): + hidden = ( + torch.empty(hidden_shape, dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") + if ignore_index >=0 and ignore_index < vocabsize: + pad_labels = torch.nn.functional.pad(labels, (0, 1), value=ignore_index) + labels = pad_labels[..., 1:].contiguous() + + torch.cuda.reset_peak_memory_stats() + custom_logprobs = linear_cross_entropy( + hidden, weight, labels, + reduction=reduction, + ignore_index=ignore_index + ) + torch.cuda.synchronize() + custom_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 + print(f"[INFO]: Custom Forward pass peak memory: {custom_max_memory:.2f} MB") + + torch.cuda.reset_peak_memory_stats() + g_logprobs = ( + torch.empty_like(custom_logprobs) + .uniform_(-0.1, 0.1) + ) + (d_custom_hidden, d_custom_weight) = torch.autograd.grad( + (custom_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + torch.cuda.synchronize() + custom_backward_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 + print(f"[INFO]: Custom Backward pass peak memory: {custom_backward_max_memory:.2f} MB") + + + self.cleanup() + torch_storage() + self.cleanup() + custom_storage() \ No newline at end of file From 14e5360008cb22b76edd4293712a575861c956e7 Mon Sep 17 00:00:00 2001 From: Jianbing-D <69858819+Jianbing-D@users.noreply.github.com> Date: Thu, 6 Nov 2025 16:36:49 +0800 Subject: [PATCH 04/17] remove tl.pointer_type() type annotation for triton kernels (#3) Signed-off-by: Jianbing Dong --- .../linear_cross_entropy/blackwell/triton.py | 44 ++++++++++--------- 1 file changed, 23 insertions(+), 21 deletions(-) diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py b/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py index fd3f14236fb..436ede683d0 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py @@ -1,6 +1,8 @@ import triton import triton.language as tl +# NOTE: tl.pointer_type() is not available in Triton 3.3.0 + @triton.autotune( configs=[ triton.Config({"BLOCK_SIZE_M": 1024}, num_stages=3, num_warps=32), @@ -12,9 +14,9 @@ def get_num_valid_tokens( num_tokens: tl.int64, ignore_index: tl.int64, - labels_ptr: tl.pointer_type(tl.int64), + labels_ptr,#: tl.pointer_type(tl.int64), stride_labels: tl.int64, - num_valid_tokens_ptr: tl.pointer_type(tl.int64), + num_valid_tokens_ptr,#: tl.pointer_type(tl.int64), BLOCK_SIZE_M: tl.constexpr, ): """ @@ -48,22 +50,22 @@ def forward_dp_epilogue( num_tokens: tl.int64, num_splits: tl.int64, # TODO: maybe this could be a constexpr ignore_index: tl.int64, - labels_ptr: tl.pointer_type(tl.int64), + labels_ptr,#: tl.pointer_type(tl.int64), stride_labels: tl.int64, - num_valid_tokens_ptr: tl.pointer_type(tl.int64), - max_ptr: tl.pointer_type(tl.float32), + num_valid_tokens_ptr,#: tl.pointer_type(tl.int64), + max_ptr,#: tl.pointer_type(tl.float32), stride_max_m: tl.int64, stride_max_n: tl.int64, - accu_ptr: tl.pointer_type(tl.float32), + accu_ptr,#: tl.pointer_type(tl.float32), stride_accu_m: tl.int64, stride_accu_n: tl.int64, - global_max_ptr: tl.pointer_type(tl.float32), + global_max_ptr,#: tl.pointer_type(tl.float32), stride_global_max: tl.int64, - global_accu_ptr: tl.pointer_type(tl.float32), + global_accu_ptr,#: tl.pointer_type(tl.float32), stride_global_accu: tl.int64, - global_logprobs_ptr: tl.pointer_type(tl.float32), + global_logprobs_ptr,#: tl.pointer_type(tl.float32), stride_global_logprobs: tl.int64, - global_logprobs_scalar_ptr: tl.pointer_type(tl.float32), + global_logprobs_scalar_ptr,#: tl.pointer_type(tl.float32), REDUCTION: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, @@ -161,18 +163,18 @@ def forward_dp_epilogue( def forward_tp_epilogue( num_tokens: tl.int64, num_splits: tl.int64, - reduced_max_ptr: tl.pointer_type(tl.float32), + reduced_max_ptr,#: tl.pointer_type(tl.float32), stride_reduced_max_m: tl.int64, stride_reduced_max_n: tl.int64, - original_max_ptr: tl.pointer_type(tl.float32), + original_max_ptr,#: tl.pointer_type(tl.float32), stride_original_max_m: tl.int64, stride_original_max_n: tl.int64, - accu_ptr: tl.pointer_type(tl.float32), + accu_ptr,#: tl.pointer_type(tl.float32), stride_accu_m: tl.int64, stride_accu_n: tl.int64, - global_max_ptr: tl.pointer_type(tl.float32), + global_max_ptr,#: tl.pointer_type(tl.float32), stride_global_max: tl.int64, - global_accu_ptr: tl.pointer_type(tl.float32), + global_accu_ptr,#: tl.pointer_type(tl.float32), stride_global_accu: tl.int64, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, @@ -239,16 +241,16 @@ def forward_tp_epilogue( def forward_tp_epilogue_update_logprobs( num_tokens: tl.int64, ignore_index: tl.int64, - num_valid_tokens_ptr: tl.pointer_type(tl.int64), - labels_ptr: tl.pointer_type(tl.int64), + num_valid_tokens_ptr,#: tl.pointer_type(tl.int64), + labels_ptr,#: tl.pointer_type(tl.int64), stride_labels: tl.int64, - logprobs_ptr: tl.pointer_type(tl.float32), + logprobs_ptr,#: tl.pointer_type(tl.float32), stride_logprobs: tl.int64, - maximum_ptr: tl.pointer_type(tl.float32), + maximum_ptr,#: tl.pointer_type(tl.float32), stride_maximum: tl.int64, - accumulate_ptr: tl.pointer_type(tl.float32), + accumulate_ptr,#: tl.pointer_type(tl.float32), stride_accumulate: tl.int64, - logprobs_scalar_ptr: tl.pointer_type(tl.float32), + logprobs_scalar_ptr,#: tl.pointer_type(tl.float32), REDUCTION: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, ): From 45dfe42f8f8270d73a4b156d1476ffcb93c02d73 Mon Sep 17 00:00:00 2001 From: Jianbing-D <69858819+Jianbing-D@users.noreply.github.com> Date: Fri, 7 Nov 2025 20:57:27 +0800 Subject: [PATCH 05/17] Support Tensor Parallel and Sequence Parallel (#4) * added unit-test for TP Signed-off-by: Jianbing Dong * add sequence-parallel and its unit-test Signed-off-by: Jianbing Dong --------- Signed-off-by: Jianbing Dong --- .../fusions/fused_linear_cross_entropy.py | 87 +- .../blackwell/bwd_partial_dlogits.py | 11 +- .../linear_cross_entropy/blackwell/entry.py | 70 +- .../linear_cross_entropy/blackwell/triton.py | 2 +- .../test_fused_linear_cross_entropy.py | 1178 ++++++++++++++++- 5 files changed, 1319 insertions(+), 29 deletions(-) diff --git a/megatron/core/fusions/fused_linear_cross_entropy.py b/megatron/core/fusions/fused_linear_cross_entropy.py index e3fccc92a4d..a08735952dc 100644 --- a/megatron/core/fusions/fused_linear_cross_entropy.py +++ b/megatron/core/fusions/fused_linear_cross_entropy.py @@ -43,36 +43,101 @@ def forward( tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, reduction: typing.Optional[str] = "mean", ignore_index: typing.Optional[int] = -100, + sequence_parallel: typing.Optional[bool] = False, ) -> torch.Tensor: """ The forward pass of the Linear Cross Entropy. - If tp_group is not None, the weight tensor to each TP rank should be (vocab_size // world_size, dim). + If tp_group is not None, the weight tensor to each TP rank should be (global_vocab_size // world_size, dim). Note that each of the ranks should get equal shards along the vocab_size dimension. Args: @param hidden: the input tensor with shape (num_tokens, dim) - @param weight: the lm_head weight tensor with shape (vocab_size, dim) + @param weight: the lm_head weight tensor with shape (local_vocab_size, dim) @param labels: the labels tensor with shape (num_tokens,) @param tp_group: the distributed process group for TP. @param reduction: Default to "mean", and can be one of "none", "sum", "mean". @param ignore_index: The index to ignore. Default to -100. + @param sequence_parallel: Whether to use sequence parallel. Default to False. Returns: @return: logprobs with shape - either (num_tokens,) when reduction is "none" - or (1,) when reduction is "mean" or "sum" + tp_group is None ----------------------------------> DP + B + A C + tp_group is not None & sequence_parallel is False -> TP + B0 B1 + A C0 C1 + tp_group is not None & sequence_parallel is True --> SP + B0 B1 + A0 C0 XX + A1 XX C1 + + When tp_group is not None, the weight tensor will be split along the vocab_size dimension, + which means each rank will get equal shards along the global_vocab_size dimension. + Specifically, the weight tensor to each rank will be (local_vocab_size, dim). + And there is an assumption that each rank will get the same local_vocab_size. + + When sequence_parallel is True, the hidden tensor will be split along the sequence length dimension, + which means each rank will get equal shards along the sequence length dimension. + Specifically, the hidden tensor to each rank will be (local_num_tokens, dim). + And there is an assumption that each rank will get the same local_num_tokens. + + In TP forward pass, the hidden tensor and label tensor shall be identical among all TP ranks, + and it's user's responsibility to ensure the hidden tensor is identical among all TP ranks. + Then this operation will produce identical logprobs among all TP ranks. + + In TP backward pass, the gradient of the logprobs shall be identical among all TP ranks, + and it's user's responsibility to ensure the gradient of the logprobs is identical among all TP ranks. + Then this operation will produce distinct gradients for the local weight tensor, + and identical gradients for the hidden tensor. + + ```python + # ------------ forward pass ------------ # + hidden = tp_group.broadcast(hidden, src=0) # handled by framework + labels = tp_group.broadcast(labels, src=0) # handled by framework + logprobs = linear_cross_entropy(...) + # each rank will get the same logprobs + + # ------------ backward pass ------------ # + g_logprobs = tp_group.broadcast(g_logprobs, src=0) # handled by framework + d_hidden, d_weight = torch.autograd.grad(...) + # each rank will get the same d_hidden, + # and distinct d_weight for local weight shard + ``` + + In SP forward pass, the hidden tensor shall be split along the sequence length dimension, + and the label tensor shall be identical among all TP ranks. + Then this operation will produce identical logprobs among all TP ranks. + + In SP backward pass, the gradient of the logprobs shall be identical among all TP ranks, + Then this operation will produce distinct gradients for the local hidden tensor and weight tensor. + ```python + # ------------ forward pass ------------ # + hidden = global_hidden[tp_rank] # handled by framework + labels = tp_group.broadcast(labels, src=0) # handled by framework + logprobs = linear_cross_entropy(...) + # each rank will get the same logprobs + + # ------------ backward pass ------------ # + g_logprobs = tp_group.broadcast(g_logprobs, src=0) # handled by framework + d_hidden, d_weight = torch.autograd.grad(...) + # each rank will get distinct local d_hidden and d_weight + ``` """ with torch.cuda.nvtx.range("LinearCrossEntropy-forward"): - logprobs, _maximum, _acc, _num_valid_tokens, tp_rank, tp_world_size = ( + logprobs, _maximum, _acc, _num_valid_tokens, tp_rank, tp_world_size, global_hidden = ( forward_func( hidden, weight, labels, tp_group, reduction, ignore_index, + sequence_parallel, ) ) ctx.save_for_backward( - hidden, weight, labels, + global_hidden, weight, labels, _maximum, _acc, _num_valid_tokens, ) ctx.tp_group = tp_group @@ -80,6 +145,7 @@ def forward( ctx.reduction = reduction ctx.tp_rank = tp_rank ctx.tp_world_size = tp_world_size + ctx.sequence_parallel = sequence_parallel return logprobs @@ -100,17 +166,18 @@ def backward( dweight (torch.Tensor): The gradient of the weight. """ with torch.cuda.nvtx.range("LinearCrossEntropy-backward"): - (hidden, weight, labels, _maximum, _accu, _num_valid_tokens) = ctx.saved_tensors + (global_hidden, weight, labels, _maximum, _accu, _num_valid_tokens) = ctx.saved_tensors tp_group = ctx.tp_group ignore_index = ctx.ignore_index reduction = ctx.reduction tp_rank = ctx.tp_rank tp_world_size = ctx.tp_world_size + sequence_parallel = ctx.sequence_parallel d_hidden, d_weight = backward_func( dlogprobs, - hidden, + global_hidden, weight, labels, _maximum, @@ -120,10 +187,11 @@ def backward( ignore_index, tp_group, tp_rank, - tp_world_size + tp_world_size, + sequence_parallel, ) - return d_hidden, d_weight, None, None, None, None + return d_hidden, d_weight, None, None, None, None, None def linear_cross_entropy( @@ -133,12 +201,13 @@ def linear_cross_entropy( tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, reduction: typing.Optional[str] = "mean", ignore_index: typing.Optional[int] = -100, + sequence_parallel: typing.Optional[bool] = False, ) -> torch.Tensor: """ helper function for linear cross entropy. """ _impl = LinearCrossEntropy.apply - return _impl(hidden, weight, labels, tp_group, reduction, ignore_index) + return _impl(hidden, weight, labels, tp_group, reduction, ignore_index, sequence_parallel) __all__ = [ "linear_cross_entropy", diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py b/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py index 2d5da82ab6a..97e7c5ab493 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py @@ -30,13 +30,11 @@ def __init__(self, acc_dtype: Type[cutlass.Numeric] = cutlass.Float32, use_2cta_instrs: bool = False, mma_tiler_mn: Tuple[int, int] = (128, 256), - rank: int = 0, vocab_per_split: int = 512): self.REDUCTION: cutlass.Constexpr[cutlass.Int32] = cutlass.const_expr(reduction) self.acc_dtype = acc_dtype self.use_2cta_instrs = use_2cta_instrs self.mma_tiler = (*mma_tiler_mn, 1) - self.rank = rank self.vocab_per_split = vocab_per_split self.cta_group = ( @@ -150,6 +148,7 @@ def kernel( b_smem_layout_staged: cute.ComposedLayout, cluster_layout_vmnk: cute.Layout, problem_mnk: Tuple[int, int, int], + rank: cutlass.Int32, ) -> None: warp_idx = cute.arch.make_warp_uniform( cute.arch.warp_idx() @@ -623,7 +622,7 @@ def kernel( tTMEM_load_rAcc[idx] = cute.exp(tTMEM_load_rAcc[idx] - tMrMaximum[0]) position: cutlass.Int64 = ( - self.rank * problem_mnk[1] + rank * problem_mnk[1] + split_idx * self.vocab_per_split + pidn * self.epi_tile[1] + n_subtile * cute.size(tTMEM_load_rAcc, mode=[0]) @@ -675,6 +674,7 @@ def __call__( dlogits_partial: cute.Tensor, scalarNumValidTokens: cute.Pointer, ignore_index: cutlass.Int64, + rank: cutlass.Int32, stream: cuda.CUstream, ) -> None: a_dtype: Type[cutlass.Numeric] = hidden.element_type @@ -801,6 +801,7 @@ class SharedStorage: b_smem_layout_staged, self.cluster_layout_vmnk, problem_mnk, + rank, ).launch( grid=grid, block=[self.threads_per_cta, 1, 1], @@ -884,6 +885,8 @@ def get_maximum_and_accu(hidden, weight): stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) + rank = 0 + compiled = cute.compile( bwd_kernel, split_idx, @@ -896,6 +899,7 @@ def get_maximum_and_accu(hidden, weight): dlogits_partial_packed, scalarNumValidTokens_packed, ignore_index, + rank, stream, ) @@ -913,6 +917,7 @@ def get_maximum_and_accu(hidden, weight): dlogits_partial_packed, scalarNumValidTokens_packed, ignore_index, + rank, stream ) stop.record(stream=torch.cuda.current_stream()) diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py index c59e7b40d95..e26661ca06a 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py @@ -25,10 +25,15 @@ def forward( tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, reduction: typing.Optional[str] = "mean", ignore_index: typing.Optional[int] = -100, + sequence_parallel: typing.Optional[bool] = False, ) -> typing.Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """ forward host function """ + tp_rank = 0 if tp_group is None else torch.distributed.get_rank(tp_group) + tp_world_size = 1 if tp_group is None else torch.distributed.get_world_size(tp_group) + in_tp_mode = (tp_group is not None) and (tp_world_size > 1) + assert hidden.is_cuda and weight.is_cuda and labels.is_cuda assert weight.device == hidden.device and labels.device == hidden.device @@ -44,14 +49,33 @@ def forward( hidden_view = hidden.view(-1, hidden.shape[-1]) labels_view = labels.view(-1) - assert hidden_view.shape[0] == labels_view.shape[0] + assert ((sequence_parallel and hidden_view.shape[0] * tp_world_size == labels_view.shape[0]) + or (not sequence_parallel and hidden_view.shape[0] == labels_view.shape[0])) assert hidden_view.shape[1] == weight.shape[1] + + global_hidden = hidden + if in_tp_mode and sequence_parallel: + partial_hidden_shape = hidden.shape + global_hidden_shape = ( + partial_hidden_shape[0] * tp_world_size, + *partial_hidden_shape[1:] + ) + global_hidden = torch.empty( + global_hidden_shape, + dtype=hidden.dtype, + device=hidden.device + ) + dist.all_gather_into_tensor( + global_hidden, + hidden, + group=tp_group + ) + assert global_hidden.is_contiguous() + hidden_view = global_hidden.view(-1, global_hidden.shape[-1]) + num_tokens, dim = hidden_view.shape vocab_size, _ = weight.shape - tp_rank = 0 if tp_group is None else torch.distributed.get_rank(tp_group) - tp_world_size = 1 if tp_group is None else torch.distributed.get_world_size(tp_group) - if not hasattr(forward, "_initialized"): global _dedicated_stream, _dedicated_events _dedicated_stream = torch.cuda.Stream(hidden.device) @@ -62,7 +86,7 @@ def forward( # declare logprobs if REDUCTION == utils.EntropyReductionEnum.kNone: logprobs = torch.empty((num_tokens,), device=hidden.device, dtype=torch.float32) - if tp_group is not None: + if in_tp_mode: logprobs.zero_() else: logprobs = torch.zeros((), device=hidden.device, dtype=torch.float32) @@ -81,7 +105,7 @@ def forward( _logprobs = logprobs else: _logprobs = torch.empty((num_tokens,), device=hidden.device, dtype=torch.float32) - if tp_group is not None: + if in_tp_mode: _logprobs.zero_() assert _max.is_contiguous() and _accu.is_contiguous() and _logprobs.is_contiguous() @@ -119,7 +143,7 @@ def forward( # VocabSize and Dim are fixed for a given model, # only the number of tokens can vary - key = f"vocab_size:{vocab_size}+dim:{dim}+dtype:{hidden.dtype}" + key = f"vocab_size:{vocab_size}+dim:{dim}+dtype:{hidden_view.dtype}" if forward._fwd_mainloop_kernels.get(key) is None: fwd_mainloop_kernel = fwd_mainloop.FwdMainLoop( vocab_per_split=vocab_per_split, @@ -151,7 +175,7 @@ def forward( cuda_stream ) - if tp_group is None: + if not in_tp_mode: def grid(meta): return (triton.cdiv(num_tokens, meta["BLOCK_SIZE_M"]),) @@ -228,11 +252,11 @@ def grid(meta): REDUCTION, ) - return logprobs, maximum, accumulate, num_valid_tokens, tp_rank, tp_world_size + return logprobs, maximum, accumulate, num_valid_tokens, tp_rank, tp_world_size, global_hidden def backward( dlogprobs: torch.Tensor, - hidden: torch.Tensor, + global_hidden: torch.Tensor, weight: torch.Tensor, labels: torch.Tensor, maximum: torch.Tensor, @@ -243,11 +267,14 @@ def backward( tp_group: typing.Optional[dist.ProcessGroup] = None, tp_rank: typing.Optional[int] = 0, tp_world_size: typing.Optional[int] = 1, + sequence_parallel: typing.Optional[bool] = False, ) -> typing.List[torch.Tensor]: """ backward host function """ - hidden_view = hidden.view(-1, hidden.shape[-1]) + in_tp_mode = (tp_group is not None) and (tp_world_size > 1) + + hidden_view = global_hidden.view(-1, global_hidden.shape[-1]) labels_view = labels.view(-1) num_tokens, dim = hidden_view.shape @@ -263,7 +290,7 @@ def backward( assert num_valid_tokens.dim() == 0 and num_valid_tokens.is_cuda and num_valid_tokens.dtype == torch.int64 - d_hidden = torch.empty_like(hidden) + d_hidden = torch.empty_like(global_hidden) d_weight = torch.empty_like(weight) assert d_hidden.is_contiguous() and d_weight.is_contiguous() @@ -275,8 +302,8 @@ def backward( _d_logits = torch.empty( (num_tokens, vocab_per_split), - device=hidden.device, - dtype=hidden.dtype + device=global_hidden.device, + dtype=global_hidden.dtype ) hidden_packed = from_dlpack( @@ -319,7 +346,7 @@ def backward( if not hasattr(backward, "_bwd_kernel"): backward._bwd_kernel = dict() - key = f"vocab_size:{vocab_size}+dim:{dim}+reduction:{REDUCTION}+dtype:{hidden.dtype}" + key = f"vocab_size:{vocab_size}+dim:{dim}+reduction:{REDUCTION}+dtype:{hidden_view.dtype}" if backward._bwd_kernel.get(key) is None: bwd_kernel = bwd_partial_dlogits.BwdPartialDlogits( reduction=REDUCTION, @@ -337,6 +364,7 @@ def backward( dlogits_packed, scalarNumValidTokens_packed, ignore_index, + tp_rank, stream ) backward._bwd_kernel[key] = bwd_kernel_compiled @@ -355,6 +383,7 @@ def backward( dlogits_packed, scalarNumValidTokens_packed, ignore_index, + tp_rank, stream ) vocab_right_bound = ( @@ -381,5 +410,16 @@ def backward( ) else: raise NotImplementedError(f"Unsupported backward method: {_backward}") + + if in_tp_mode: + dist.all_reduce(d_hidden, op=dist.ReduceOp.SUM, group=tp_group) + if sequence_parallel: + partial_hidden_shape = ( + global_hidden.shape[0] // tp_world_size, + *global_hidden.shape[1:] + ) + partial_num_tokens = num_tokens // tp_world_size + d_hidden = d_hidden.view(-1, d_hidden.shape[-1])[tp_rank * partial_num_tokens : (tp_rank + 1) * partial_num_tokens, :] + d_hidden = d_hidden.view(partial_hidden_shape).clone() return d_hidden, d_weight \ No newline at end of file diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py b/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py index 436ede683d0..d7f45d152c2 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py @@ -193,7 +193,7 @@ def forward_tp_epilogue( offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) _reduced_max = tl.load( - reduce_max_ptr + offs_m[:, None] * stride_reduce_max_m + offs_n[None, :] * stride_reduce_max_n, + reduced_max_ptr + offs_m[:, None] * stride_reduced_max_m + offs_n[None, :] * stride_reduced_max_n, mask=(offs_m[:, None] < num_tokens) & (offs_n[None, :] < num_splits), other=0.0, ) diff --git a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py index a4d759046f9..130a2bb5a71 100644 --- a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py +++ b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py @@ -7,6 +7,7 @@ import torch from torch.utils.data import DataLoader, Dataset from torch.utils.data.distributed import DistributedSampler +import torch.distributed as dist import megatron.core.parallel_state as ps from megatron.core.models.gpt.gpt_layer_specs import ( @@ -25,6 +26,7 @@ from megatron.core.fusions.fused_linear_cross_entropy import linear_cross_entropy import os +import typing class MockDataset(Dataset): """ @@ -136,7 +138,7 @@ def init_gpt_dataloader( @pytest.mark.skipif( - "WORLD_SIZE" not in os.environ or os.environ["WORLD_SIZE"] < "2", + ("WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2),# or True, reason="Requires torchrun with multiple GPUs" ) class TestFusedLinearCrossEntropyOnGptModel: @@ -559,6 +561,1180 @@ def custom_storage(): print(f"[INFO]: Custom Backward pass peak memory: {custom_backward_max_memory:.2f} MB") + self.cleanup() + torch_storage() + self.cleanup() + custom_storage() + + +@pytest.mark.skipif( + ("WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2),# or True, + reason="Requires torchrun with multiple GPUs" +) +class TestFusedLinearCrossEntropyTensorParallel: + @classmethod + def setup_class(cls): + if dist.is_initialized(): + cls.must_teardown = False + else: + dist.init_process_group( + backend="nccl", + init_method="env://", + world_size=int(os.environ["WORLD_SIZE"]), + rank=int(os.environ["RANK"]) + ) + cls.must_teardown = True + cls.tp_group = dist.group.WORLD + + cls.tp_rank = dist.get_rank(cls.tp_group) + cls.tp_world_size = dist.get_world_size(cls.tp_group) + cls.is_chief = (cls.tp_rank == 0) + device = torch.device(f"cuda:{cls.tp_rank}") + torch.cuda.set_device(device) + print(f"[INFO]: TP rank: {cls.tp_rank}, TP world size: {cls.tp_world_size}") + + @classmethod + def teardown_class(cls): + if cls.must_teardown: + dist.destroy_process_group() + + def cleanup(self): + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + import gc + + gc.collect() + torch.cuda.synchronize() + + @staticmethod + def torch_linear_cross_entropy_single_gpu( + hidden: torch.Tensor, + weight: torch.Tensor, + labels: torch.Tensor, + reduction: typing.Optional[str] = "mean", + ): + logits = hidden.to(torch.float32) @ weight.T.to(torch.float32) + logprobs = torch.nn.functional.cross_entropy( + logits.view(-1, logits.shape[-1]), + labels.view(-1), + reduction=reduction, + ) + return logprobs.to(torch.float32) + + class TorchLinearCrossEntropy(torch.autograd.Function): + @staticmethod + def forward( + ctx, + hidden: torch.Tensor, + weight: torch.Tensor, + labels: torch.Tensor, + tp_group: torch.distributed.ProcessGroup, + reduction: typing.Optional[str] = "mean", + ): + tp_rank = 0 if tp_group is None else torch.distributed.get_rank(tp_group) + tp_world_size = 1 if tp_group is None else torch.distributed.get_world_size(tp_group) + + logits = hidden.to(torch.float32) @ weight.T.to(torch.float32) + + whole_logits = torch.empty( + (logits.shape[0], logits.shape[-1] * tp_world_size), + dtype=logits.dtype, + device=logits.device + ) + whole_logits_ref = [ + whole_logits[..., i * logits.shape[-1] : (i + 1) * logits.shape[-1]] + for i in range(tp_world_size) + ] + dist.all_gather(whole_logits_ref, logits, group=tp_group) + + logprobs = torch.nn.functional.cross_entropy( + whole_logits.view(-1, whole_logits.shape[-1]), + labels.view(-1), + reduction=reduction, + ) + + # If we don't preserve whole_logits, + # we need to re-compute it in the backward pass + ctx.save_for_backward(hidden, weight, labels) + ctx.tp_group = tp_group + ctx.reduction = reduction + ctx.tp_rank = tp_rank + ctx.tp_world_size = tp_world_size + + return logprobs.to(torch.float32) + + @staticmethod + def backward( + ctx, + g_logprobs: torch.Tensor, + ): + hidden, weight, labels = ctx.saved_tensors + tp_group = ctx.tp_group + reduction = ctx.reduction + tp_rank = ctx.tp_rank + tp_world_size = ctx.tp_world_size + + num_tokens, dim = hidden.shape + + if reduction == "mean": + _g_logprobs = torch.broadcast_to( + g_logprobs / num_tokens, + (num_tokens,) + ) + elif reduction == "sum": + _g_logprobs = torch.broadcast_to( + g_logprobs, + (num_tokens,) + ) + else: + _g_logprobs = g_logprobs + + # re-compute whole_logits + logits = hidden.to(torch.float32) @ weight.T.to(torch.float32) + whole_logits = torch.empty( + (logits.shape[0], logits.shape[-1] * tp_world_size), + dtype=logits.dtype, + device=logits.device + ) + whole_logits_ref = [ + whole_logits[..., i * logits.shape[-1] : (i + 1) * logits.shape[-1]] + for i in range(tp_world_size) + ] + dist.all_gather(whole_logits_ref, logits, group=tp_group) + + one_hot = torch.zeros_like(whole_logits) + one_hot.scatter_(1, labels.view(-1).unsqueeze(-1), 1) + + pd = torch.nn.functional.softmax(whole_logits, dim=-1) + d_logits = (pd - one_hot) * _g_logprobs.unsqueeze(-1) + d_logits = d_logits.to(hidden.dtype) + + local_size = weight.size(0) + local_d_logits = d_logits[:, tp_rank * local_size : (tp_rank + 1) * local_size] + + local_d_hidden = local_d_logits @ weight + local_d_weight = local_d_logits.T @ hidden + + dist.all_reduce( + local_d_hidden, + op=dist.ReduceOp.SUM, + group=tp_group + ) + + return local_d_hidden, local_d_weight, None, None, None + + @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) + @pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) + @pytest.mark.parametrize("problem", [(4096, 129280, 8192)]) + def test_torch_tp_vs_single_gpu( + self, + dtype, + reduction, + problem, + ): + num_tokens, vocabsize, dim = problem + + hidden = ( + torch.empty((num_tokens, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, (num_tokens,), dtype=torch.long, device="cuda") + + # ------------ forward pass ------------ # + dist.broadcast(hidden, src=0, group=self.tp_group) + dist.broadcast(labels, src=0, group=self.tp_group) + + # single GPU + whole_weight = torch.empty( + (vocabsize * self.tp_world_size, dim), + dtype=dtype, + device="cuda" + ) + whole_weight_view = [ + whole_weight[i * vocabsize : (i + 1) * vocabsize, :] + for i in range(self.tp_world_size) + ] + dist.all_gather( + whole_weight_view, + weight, + group=self.tp_group + ) + whole_weight = whole_weight.clone().requires_grad_() + logprobs_single_gpu = self.torch_linear_cross_entropy_single_gpu( + hidden, whole_weight, labels, + reduction=reduction, + ) + + # TP + logprobs_tp = self.TorchLinearCrossEntropy.apply( + hidden, weight, labels, + self.tp_group, + reduction, + ) + torch.testing.assert_close( + logprobs_single_gpu, + logprobs_tp, + ) + + # ------------ backward pass ------------ # + g_logprobs = ( + torch.empty_like(logprobs_single_gpu) + .uniform_(-0.1, 0.1) + ) + dist.broadcast(g_logprobs, src=0, group=self.tp_group) + + # single GPU + (d_hidden_single_gpu, d_weight_single_gpu) = torch.autograd.grad( + (logprobs_single_gpu,), + (hidden, whole_weight), + (g_logprobs,), + retain_graph=False + ) + + # TP + (d_hidden_tp, d_weight_tp) = torch.autograd.grad( + (logprobs_tp,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + torch.testing.assert_close( + d_hidden_single_gpu, + d_hidden_tp, + atol=1e-3, + rtol=1e-3, + ) + local_d_weight_single_gpu = d_weight_single_gpu[self.tp_rank * weight.shape[0] : (self.tp_rank + 1) * weight.shape[0], :] + torch.testing.assert_close( + local_d_weight_single_gpu, + d_weight_tp, + atol=1e-3, + rtol=1e-3, + ) + + + @staticmethod + def get_problems(): + return [ + (80, 125, 64), + (80, 152064, 64), + (1024, 152064, 4096), + (4096, 152063, 8192), + ((1, 4096), 152064, 8192), + ((2, 4096), 152064, 8192), + ] + + @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) + @pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) + @pytest.mark.parametrize("problem", get_problems()) + def test_correctness( + self, + dtype, + reduction, + problem, + ): + num_tokens, vocabsize, dim = problem + hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) + labels_shape = (num_tokens,) if isinstance(num_tokens, int) else num_tokens + + hidden = ( + torch.empty(hidden_shape, dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") + + + # ------ forward pass ------ # + dist.broadcast(hidden, src=0, group=self.tp_group) + dist.broadcast(labels, src=0, group=self.tp_group) + + torch_logprobs = self.TorchLinearCrossEntropy.apply( + hidden.view(-1, dim), weight, labels, + self.tp_group, + reduction, + ) + + custom_logprobs = linear_cross_entropy( + hidden, weight, labels, + tp_group=self.tp_group, + reduction=reduction, + ) + + torch.testing.assert_close( + torch_logprobs, + custom_logprobs, + ) + + # ------- backward pass ------- # + g_logprobs = ( + torch.empty_like(torch_logprobs) + .uniform_(-0.1, 0.1) + ) + dist.broadcast(g_logprobs, src=0, group=self.tp_group) + + (d_hidden_torch, d_weight_torch) = torch.autograd.grad( + (torch_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + (d_hidden_custom, d_weight_custom) = torch.autograd.grad( + (custom_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + torch.testing.assert_close( + d_hidden_torch, + d_hidden_custom, + atol=1e-3, + rtol=1e-3, + ) + torch.testing.assert_close( + d_weight_torch, + d_weight_custom, + atol=1e-4, + rtol=1e-4, + ) + + @pytest.mark.parametrize("problem", [((1, 4096), 129280, 7168)]) + @pytest.mark.parametrize("dtype", [torch.bfloat16]) + @pytest.mark.parametrize("reduction", ["mean"]) + def test_performance( + self, + problem, + dtype, + reduction + ): + num_tokens, vocabsize, dim = problem + hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) + labels_shape = (num_tokens,) if isinstance(num_tokens, int) else num_tokens + + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + + torch_fwd_latency = list() + torch_bwd_latency = list() + custom_fwd_latency = list() + custom_bwd_latency = list() + + iterations = 5 + for i in range(iterations): + hidden = ( + torch.empty(hidden_shape, dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") + + # ------ forward pass ------ # + dist.broadcast(hidden, src=0, group=self.tp_group) + dist.broadcast(labels, src=0, group=self.tp_group) + + start_event.record() + torch_logprobs = self.TorchLinearCrossEntropy.apply( + hidden.view(-1, dim), weight, labels, + self.tp_group, + reduction, + ) + end_event.record() + torch.cuda.synchronize() + torch_fwd_latency.append(start_event.elapsed_time(end_event)) + + start_event.record() + custom_logprobs = linear_cross_entropy( + hidden, weight, labels, + tp_group=self.tp_group, + reduction=reduction, + ) + end_event.record() + torch.cuda.synchronize() + custom_fwd_latency.append(start_event.elapsed_time(end_event)) + + # ------- backward pass ------- # + g_logprobs = ( + torch.empty_like(torch_logprobs) + .uniform_(-0.1, 0.1) + ) + dist.broadcast(g_logprobs, src=0, group=self.tp_group) + + start_event.record() + (d_hidden_torch, d_weight_torch) = torch.autograd.grad( + (torch_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + end_event.record() + torch.cuda.synchronize() + torch_bwd_latency.append(start_event.elapsed_time(end_event)) + + start_event.record() + (d_hidden_custom, d_weight_custom) = torch.autograd.grad( + (custom_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + end_event.record() + torch.cuda.synchronize() + custom_bwd_latency.append(start_event.elapsed_time(end_event)) + + # --- remove first latency due to warmup --- # + torch_fwd_latency = torch_fwd_latency[1:] + torch_bwd_latency = torch_bwd_latency[1:] + custom_fwd_latency = custom_fwd_latency[1:] + custom_bwd_latency = custom_bwd_latency[1:] + + if self.is_chief: + print() + print(f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}, TP size {self.tp_world_size}:") + print(f"[INFO]: Torch forward latency: {sum(torch_fwd_latency) / len(torch_fwd_latency):.2f} ms") + print(f"[INFO]: Custom forward latency: {sum(custom_fwd_latency) / len(custom_fwd_latency):.2f} ms") + print(f"[INFO]: Torch backward latency: {sum(torch_bwd_latency) / len(torch_bwd_latency):.2f} ms") + print(f"[INFO]: Custom backward latency: {sum(custom_bwd_latency) / len(custom_bwd_latency):.2f} ms") + + + @pytest.mark.parametrize("problem", [((1, 4096), 129280, 7168)]) + @pytest.mark.parametrize("dtype", [torch.bfloat16]) + @pytest.mark.parametrize("reduction", ["mean"]) + def test_storage( + self, + problem, + dtype, + reduction + ): + num_tokens, vocabsize, dim = problem + hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) + labels_shape = (num_tokens,) if isinstance(num_tokens, int) else num_tokens + + if self.is_chief: + print() + print(f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}, TP size {self.tp_world_size}:") + + def torch_storage(): + hidden = ( + torch.empty(hidden_shape, dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") + + dist.broadcast(hidden, src=0, group=self.tp_group) + dist.broadcast(labels, src=0, group=self.tp_group) + + torch.cuda.reset_peak_memory_stats() + torch_logprobs = self.TorchLinearCrossEntropy.apply( + hidden.view(-1, dim), weight, labels, + self.tp_group, + reduction, + ) + torch.cuda.synchronize() + torch_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 + if self.is_chief: + print(f"[INFO]: On GPU {self.tp_rank}, Torch Forward pass peak memory: {torch_max_memory:.2f} MB") + + g_logprobs = ( + torch.empty_like(torch_logprobs) + .uniform_(-0.1, 0.1) + ) + dist.broadcast(g_logprobs, src=0, group=self.tp_group) + + torch.cuda.reset_peak_memory_stats() + (d_hidden_torch, d_weight_torch) = torch.autograd.grad( + (torch_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + torch.cuda.synchronize() + torch_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 + if self.is_chief: + print(f"[INFO]: On GPU {self.tp_rank}, Torch Backward pass peak memory: {torch_max_memory:.2f} MB") + + def custom_storage(): + hidden = ( + torch.empty(hidden_shape, dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") + + dist.broadcast(hidden, src=0, group=self.tp_group) + dist.broadcast(labels, src=0, group=self.tp_group) + + torch.cuda.reset_peak_memory_stats() + custom_logprobs = linear_cross_entropy( + hidden, weight, labels, + tp_group=self.tp_group, + reduction=reduction, + ) + torch.cuda.synchronize() + custom_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 + if self.is_chief: + print(f"[INFO]: On GPU {self.tp_rank}, Custom Forward pass peak memory: {custom_max_memory:.2f} MB") + + g_logprobs = ( + torch.empty_like(custom_logprobs) + .uniform_(-0.1, 0.1) + ) + dist.broadcast(g_logprobs, src=0, group=self.tp_group) + + torch.cuda.reset_peak_memory_stats() + (d_hidden_custom, d_weight_custom) = torch.autograd.grad( + (custom_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + torch.cuda.synchronize() + custom_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 + if self.is_chief: + print(f"[INFO]: On GPU {self.tp_rank}, Custom Backward pass peak memory: {custom_max_memory:.2f} MB") + + self.cleanup() + torch_storage() + self.cleanup() + custom_storage() + + + +@pytest.mark.skipif( + "WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2, + reason="Requires torchrun with multiple GPUs" +) +class TestFusedLinearCrossEntropySequenceParallel: + @classmethod + def setup_class(cls): + if dist.is_initialized(): + cls.must_teardown = False + else: + dist.init_process_group( + backend="nccl", + init_method="env://", + world_size=int(os.environ["WORLD_SIZE"]), + rank=int(os.environ["RANK"]) + ) + cls.must_teardown = True + cls.tp_group = dist.group.WORLD + + cls.tp_rank = dist.get_rank(cls.tp_group) + cls.tp_world_size = dist.get_world_size(cls.tp_group) + cls.is_chief = (cls.tp_rank == 0) + device = torch.device(f"cuda:{cls.tp_rank}") + torch.cuda.set_device(device) + print(f"[INFO]: TP rank: {cls.tp_rank}, TP world size: {cls.tp_world_size}") + + @classmethod + def teardown_class(cls): + if cls.must_teardown: + dist.destroy_process_group() + + @staticmethod + def timed_barrier(timeout_s=10): + import time + work = torch.distributed.barrier(async_op=True) + t0 = time.time() + while not work.is_completed(): + if time.time() - t0 > timeout_s: + exit(1) + time.sleep(0.05) + work.wait() + + def cleanup(self): + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + import gc + + gc.collect() + torch.cuda.synchronize() + + @staticmethod + def torch_linear_cross_entropy_single_gpu( + hidden: torch.Tensor, + weight: torch.Tensor, + labels: torch.Tensor, + reduction: typing.Optional[str] = "mean", + ): + logits = hidden.to(torch.float32) @ weight.T.to(torch.float32) + logprobs = torch.nn.functional.cross_entropy( + logits.view(-1, logits.shape[-1]), + labels.view(-1), + reduction=reduction, + ) + return logprobs.to(torch.float32) + + class TorchLinearCrossEntropy(torch.autograd.Function): + @staticmethod + def forward( + ctx, + hidden: torch.Tensor, + weight: torch.Tensor, + labels: torch.Tensor, + tp_group: torch.distributed.ProcessGroup, + reduction: typing.Optional[str] = "mean", + ): + tp_rank = 0 if tp_group is None else torch.distributed.get_rank(tp_group) + tp_world_size = 1 if tp_group is None else torch.distributed.get_world_size(tp_group) + + whole_hidden = torch.empty( + (hidden.shape[0] * tp_world_size, hidden.shape[-1]), + dtype=hidden.dtype, + device=hidden.device + ) + dist.all_gather_into_tensor( + whole_hidden, + hidden, + group=tp_group + ) + + logits = whole_hidden.to(torch.float32) @ weight.T.to(torch.float32) + + whole_logits = torch.empty( + (logits.shape[0], logits.shape[-1] * tp_world_size), + dtype=logits.dtype, + device=logits.device + ) + whole_logits_ref = [ + whole_logits[..., i * logits.shape[-1] : (i + 1) * logits.shape[-1]] + for i in range(tp_world_size) + ] + dist.all_gather(whole_logits_ref, logits, group=tp_group) + + logprobs = torch.nn.functional.cross_entropy( + whole_logits.view(-1, whole_logits.shape[-1]), + labels.view(-1), + reduction=reduction, + ) + + # If we don't preserve whole_logits, + # we need to re-compute it in the backward pass + ctx.save_for_backward(whole_hidden, weight, labels) + ctx.tp_group = tp_group + ctx.reduction = reduction + ctx.tp_rank = tp_rank + ctx.tp_world_size = tp_world_size + + return logprobs.to(torch.float32) + + @staticmethod + def backward( + ctx, + g_logprobs: torch.Tensor, + ): + whole_hidden, weight, labels = ctx.saved_tensors + tp_group = ctx.tp_group + reduction = ctx.reduction + tp_rank = ctx.tp_rank + tp_world_size = ctx.tp_world_size + + num_tokens, dim = whole_hidden.shape + + if reduction == "mean": + _g_logprobs = torch.broadcast_to( + g_logprobs / num_tokens, + (num_tokens,) + ) + elif reduction == "sum": + _g_logprobs = torch.broadcast_to( + g_logprobs, + (num_tokens,) + ) + else: + _g_logprobs = g_logprobs + + # re-compute whole_logits + logits = whole_hidden.to(torch.float32) @ weight.T.to(torch.float32) + whole_logits = torch.empty( + (logits.shape[0], logits.shape[-1] * tp_world_size), + dtype=logits.dtype, + device=logits.device + ) + whole_logits_ref = [ + whole_logits[..., i * logits.shape[-1] : (i + 1) * logits.shape[-1]] + for i in range(tp_world_size) + ] + dist.all_gather(whole_logits_ref, logits, group=tp_group) + + one_hot = torch.zeros_like(whole_logits) + one_hot.scatter_(1, labels.view(-1).unsqueeze(-1), 1) + + pd = torch.nn.functional.softmax(whole_logits, dim=-1) + d_logits = (pd - one_hot) * _g_logprobs.unsqueeze(-1) + d_logits = d_logits.to(whole_hidden.dtype) + + local_size = weight.size(0) + local_d_logits = d_logits[:, tp_rank * local_size : (tp_rank + 1) * local_size] + + d_hidden = local_d_logits @ weight + local_d_weight = local_d_logits.T @ whole_hidden + + # dist.all_reduce( + # local_d_hidden, + # op=dist.ReduceOp.SUM, + # group=tp_group + # ) + + # split the local_d_hidden along the sequence length dimension + local_num_tokens = num_tokens // tp_world_size + # local_d_hidden = local_d_hidden[tp_rank * local_num_tokens : (tp_rank + 1) * local_num_tokens, :] + + local_d_hidden = torch.empty( + (local_num_tokens, dim), + dtype=weight.dtype, + device=weight.device + ) + dist.reduce_scatter_tensor( + local_d_hidden, + d_hidden, + op=dist.ReduceOp.SUM, + group=tp_group + ) + return local_d_hidden, local_d_weight, None, None, None + + @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) + @pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) + @pytest.mark.parametrize("problem", [(256, 12928, 8192)]) + def test_torch_tp_vs_single_gpu( + self, + dtype, + reduction, + problem, + ): + num_tokens, vocabsize, dim = problem + + hidden = ( + torch.empty((num_tokens, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, (num_tokens * self.tp_world_size,), + dtype=torch.long, device="cuda") + + # ------------ forward pass ------------ # + dist.broadcast(labels, src=0, group=self.tp_group) + + # single GPU + whole_hidden = torch.empty( + (num_tokens * self.tp_world_size, dim), + dtype=dtype, + device="cuda" + ) + dist.all_gather_into_tensor( + whole_hidden, + hidden, + group=self.tp_group + ) + whole_hidden = whole_hidden.clone().requires_grad_() + + whole_weight = torch.empty( + (vocabsize * self.tp_world_size, dim), + dtype=dtype, + device="cuda" + ) + whole_weight_view = [ + whole_weight[i * vocabsize : (i + 1) * vocabsize, :] + for i in range(self.tp_world_size) + ] + dist.all_gather( + whole_weight_view, + weight, + group=self.tp_group + ) + whole_weight = whole_weight.clone().requires_grad_() + logprobs_single_gpu = self.torch_linear_cross_entropy_single_gpu( + whole_hidden, whole_weight, labels, + reduction=reduction, + ) + + # TP + logprobs_tp = self.TorchLinearCrossEntropy.apply( + hidden, weight, labels, + self.tp_group, + reduction, + ) + torch.testing.assert_close( + logprobs_single_gpu, + logprobs_tp, + ) + + # ------------ backward pass ------------ # + g_logprobs = ( + torch.empty_like(logprobs_single_gpu) + .uniform_(-0.1, 0.1) + ) + dist.broadcast(g_logprobs, src=0, group=self.tp_group) + + # single GPU + (d_hidden_single_gpu, d_weight_single_gpu) = torch.autograd.grad( + (logprobs_single_gpu,), + (whole_hidden, whole_weight), + (g_logprobs,), + retain_graph=False + ) + + # TP + (d_hidden_tp, d_weight_tp) = torch.autograd.grad( + (logprobs_tp,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + + local_d_hidden_single_gpu = d_hidden_single_gpu[self.tp_rank * hidden.shape[0] : (self.tp_rank + 1) * hidden.shape[0], :] + torch.testing.assert_close( + local_d_hidden_single_gpu, + d_hidden_tp, + atol=1e-3, + rtol=1e-3, + ) + local_d_weight_single_gpu = d_weight_single_gpu[self.tp_rank * weight.shape[0] : (self.tp_rank + 1) * weight.shape[0], :] + torch.testing.assert_close( + local_d_weight_single_gpu, + d_weight_tp, + atol=1e-3, + rtol=1e-3, + ) + + self.cleanup() + + @staticmethod + def get_problems(): + return [ + (80, 125, 64), + (80, 152064, 64), + (1024, 152064, 4096), + (4096, 15206, 1024), + ((1, 4096), 15206, 1024), + ((4, 1024), 15206, 1024), + ] + + @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) + @pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) + @pytest.mark.parametrize("problem", get_problems()) + def test_correctness( + self, + dtype, + reduction, + problem, + ): + num_tokens, vocabsize, dim = problem + hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) + labels_shape = (num_tokens * self.tp_world_size,) if isinstance(num_tokens, int) else (num_tokens[0] * self.tp_world_size, *num_tokens[1:]) + + hidden = ( + torch.empty(hidden_shape, dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") + + # ------ forward pass ------ # + dist.broadcast(labels, src=0, group=self.tp_group) + + torch_logprobs = self.TorchLinearCrossEntropy.apply( + hidden.view(-1, dim), weight, labels, + self.tp_group, + reduction, + ) + + custom_logprobs = linear_cross_entropy( + hidden, weight, labels, + tp_group=self.tp_group, + reduction=reduction, + sequence_parallel=True, + ) + + torch.testing.assert_close( + torch_logprobs, + custom_logprobs, + ) + + # ------- backward pass ------- # + g_logprobs = ( + torch.empty_like(torch_logprobs) + .uniform_(-0.1, 0.1) + ) + dist.broadcast(g_logprobs, src=0, group=self.tp_group) + + (d_hidden_torch, d_weight_torch) = torch.autograd.grad( + (torch_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + (d_hidden_custom, d_weight_custom) = torch.autograd.grad( + (custom_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + + # in case one GPU failed, and leading to hang + torch.testing.assert_close( + d_hidden_torch, + d_hidden_custom, + atol=1e-3, + rtol=1e-3, + ) + torch.testing.assert_close( + d_weight_torch, + d_weight_custom, + atol=1e-3, + rtol=1e-3, + ) + self.timed_barrier() + + self.cleanup() + + @pytest.mark.parametrize("problem", [((1, 1024), 129280, 7168)]) + @pytest.mark.parametrize("dtype", [torch.bfloat16]) + @pytest.mark.parametrize("reduction", ["mean"]) + def test_performance( + self, + problem, + dtype, + reduction + ): + num_tokens, vocabsize, dim = problem + hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) + labels_shape = (num_tokens * self.tp_world_size,) if isinstance(num_tokens, int) else (num_tokens[0] * self.tp_world_size, *num_tokens[1:]) + + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + + torch_fwd_latency = list() + torch_bwd_latency = list() + custom_fwd_latency = list() + custom_bwd_latency = list() + + iterations = 5 + for i in range(iterations): + hidden = ( + torch.empty(hidden_shape, dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") + + # ------ forward pass ------ # + dist.broadcast(labels, src=0, group=self.tp_group) + + start_event.record() + torch_logprobs = self.TorchLinearCrossEntropy.apply( + hidden.view(-1, dim), weight, labels, + self.tp_group, + reduction, + ) + end_event.record() + torch.cuda.synchronize() + torch_fwd_latency.append(start_event.elapsed_time(end_event)) + + start_event.record() + custom_logprobs = linear_cross_entropy( + hidden, weight, labels, + tp_group=self.tp_group, + reduction=reduction, + sequence_parallel=True, + ) + end_event.record() + torch.cuda.synchronize() + custom_fwd_latency.append(start_event.elapsed_time(end_event)) + + # ------- backward pass ------- # + g_logprobs = ( + torch.empty_like(torch_logprobs) + .uniform_(-0.1, 0.1) + ) + dist.broadcast(g_logprobs, src=0, group=self.tp_group) + + start_event.record() + (d_hidden_torch, d_weight_torch) = torch.autograd.grad( + (torch_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + end_event.record() + torch.cuda.synchronize() + torch_bwd_latency.append(start_event.elapsed_time(end_event)) + + start_event.record() + (d_hidden_custom, d_weight_custom) = torch.autograd.grad( + (custom_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + end_event.record() + torch.cuda.synchronize() + custom_bwd_latency.append(start_event.elapsed_time(end_event)) + + # --- remove first latency due to warmup --- # + torch_fwd_latency = torch_fwd_latency[1:] + torch_bwd_latency = torch_bwd_latency[1:] + custom_fwd_latency = custom_fwd_latency[1:] + custom_bwd_latency = custom_bwd_latency[1:] + + if self.is_chief: + print() + print(f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}, TP size {self.tp_world_size}, Sequence Parallel: True:") + print(f"[INFO]: Torch forward latency: {sum(torch_fwd_latency) / len(torch_fwd_latency):.2f} ms") + print(f"[INFO]: Custom forward latency: {sum(custom_fwd_latency) / len(custom_fwd_latency):.2f} ms") + print(f"[INFO]: Torch backward latency: {sum(torch_bwd_latency) / len(torch_bwd_latency):.2f} ms") + print(f"[INFO]: Custom backward latency: {sum(custom_bwd_latency) / len(custom_bwd_latency):.2f} ms") + + + @pytest.mark.parametrize("problem", [((1, 1024), 129280, 7168)]) + @pytest.mark.parametrize("dtype", [torch.bfloat16]) + @pytest.mark.parametrize("reduction", ["mean"]) + def test_storage( + self, + problem, + dtype, + reduction + ): + num_tokens, vocabsize, dim = problem + hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) + labels_shape = (num_tokens * self.tp_world_size,) if isinstance(num_tokens, int) else (num_tokens[0] * self.tp_world_size, *num_tokens[1:]) + + if self.is_chief: + print() + print(f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}, TP size {self.tp_world_size}, Sequence Parallel: True:") + + def torch_storage(): + hidden = ( + torch.empty(hidden_shape, dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") + + dist.broadcast(hidden, src=0, group=self.tp_group) + dist.broadcast(labels, src=0, group=self.tp_group) + + torch.cuda.reset_peak_memory_stats() + torch_logprobs = self.TorchLinearCrossEntropy.apply( + hidden.view(-1, dim), weight, labels, + self.tp_group, + reduction, + ) + torch.cuda.synchronize() + torch_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 + if self.is_chief: + print(f"[INFO]: On GPU {self.tp_rank}, Torch Forward pass peak memory: {torch_max_memory:.2f} MB") + + g_logprobs = ( + torch.empty_like(torch_logprobs) + .uniform_(-0.1, 0.1) + ) + dist.broadcast(g_logprobs, src=0, group=self.tp_group) + + torch.cuda.reset_peak_memory_stats() + (d_hidden_torch, d_weight_torch) = torch.autograd.grad( + (torch_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + torch.cuda.synchronize() + torch_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 + if self.is_chief: + print(f"[INFO]: On GPU {self.tp_rank}, Torch Backward pass peak memory: {torch_max_memory:.2f} MB") + + def custom_storage(): + hidden = ( + torch.empty(hidden_shape, dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + weight = ( + torch.empty((vocabsize, dim), dtype=dtype, device="cuda") + .uniform_(-0.1, 0.1) + .requires_grad_() + ) + labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") + + dist.broadcast(hidden, src=0, group=self.tp_group) + dist.broadcast(labels, src=0, group=self.tp_group) + + torch.cuda.reset_peak_memory_stats() + custom_logprobs = linear_cross_entropy( + hidden, weight, labels, + tp_group=self.tp_group, + reduction=reduction, + sequence_parallel=True, + ) + torch.cuda.synchronize() + custom_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 + if self.is_chief: + print(f"[INFO]: On GPU {self.tp_rank}, Custom Forward pass peak memory: {custom_max_memory:.2f} MB") + + g_logprobs = ( + torch.empty_like(custom_logprobs) + .uniform_(-0.1, 0.1) + ) + dist.broadcast(g_logprobs, src=0, group=self.tp_group) + + torch.cuda.reset_peak_memory_stats() + (d_hidden_custom, d_weight_custom) = torch.autograd.grad( + (custom_logprobs,), + (hidden, weight), + (g_logprobs,), + retain_graph=False + ) + torch.cuda.synchronize() + custom_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 + if self.is_chief: + print(f"[INFO]: On GPU {self.tp_rank}, Custom Backward pass peak memory: {custom_max_memory:.2f} MB") + self.cleanup() torch_storage() self.cleanup() From 0d12e006e546b94aa0657603d41cc186b1a53394 Mon Sep 17 00:00:00 2001 From: Jianbin Chang Date: Mon, 10 Nov 2025 16:37:21 +0800 Subject: [PATCH 06/17] Fixed several bugs and added support for sequence-parallel (#5) * 1. fix weight is None issue 2. API compatible fix * 1. fix weight is None issue 2. API compatible fix * fix fused linear-ce fusion loss issue * fix typo in fused_linear_ce triton * 1. fix weight is None issue 2. API compatible fix * fix fused linear-ce fusion loss issue * add sequence_parallel option on compute_language_model_loss_without_logits * Linear cross-entropy fusion is not used by default. --- .../models/common/language_module/language_module.py | 11 ++++++----- megatron/core/models/gpt/gpt_model.py | 6 ++++-- megatron/core/models/mamba/mamba_model.py | 3 ++- megatron/training/arguments.py | 4 ++-- 4 files changed, 14 insertions(+), 10 deletions(-) diff --git a/megatron/core/models/common/language_module/language_module.py b/megatron/core/models/common/language_module/language_module.py index b8e39693b22..15352075661 100644 --- a/megatron/core/models/common/language_module/language_module.py +++ b/megatron/core/models/common/language_module/language_module.py @@ -131,9 +131,10 @@ def compute_language_model_loss_without_logits( hidden: Tensor, labels: Optional[Tensor], weight: Tensor = None, + sequence_parallel_enabled: bool = False, column_parallel_linear: torch.nn.Module = None, col_linear_kwargs: Dict[str, Any] = {}, - reduction: Optional[str] = "mean", + reduction: Optional[str] = "none", ignore_index: Optional[int] = -100, ) -> Tuple[Tensor, Optional[Tensor]]: """Computes the language model logits and loss (Cross entropy across vocabulary) @@ -146,7 +147,7 @@ def compute_language_model_loss_without_logits( column_parallel_linear (torch.nn.Module): The column parallel linear layer to use for computing logits when not using fused linear cross entropy. col_linear_kwargs (Dict[str, Any]): Additional kwargs for column parallel linear layer - reduction (Optional[str]): The reduction method. Defaults to "mean", and can be + reduction (Optional[str]): The reduction method. Defaults to "none", and can be one of "none", "sum", "mean". ignore_index (Optional[int]): The index to ignore in the loss calculation. Defaults to -100. @@ -155,7 +156,6 @@ def compute_language_model_loss_without_logits( Tensor: Loss tensor of dimensions [batch size, sequence_length]. """ if self.config.linear_cross_entropy_fusion: - assert ( weight is not None ), "weight cannot be None when using fused linear cross entropy." @@ -165,13 +165,14 @@ def compute_language_model_loss_without_logits( hidden, weight, labels, - dist_process_group=self.pg_collection.tp, + tp_group=self.pg_collection.tp, + sequence_parallel=sequence_parallel_enabled, reduction=reduction, ignore_index=ignore_index, ) # [s b] => [b, s] - loss = loss.transpose(0, 1).contiguous() + loss = loss.view_as(labels).transpose(0, 1).contiguous() return loss else: assert ( diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py index b48dcec2078..5e3950d0003 100644 --- a/megatron/core/models/gpt/gpt_model.py +++ b/megatron/core/models/gpt/gpt_model.py @@ -578,7 +578,8 @@ def _postprocess( mtp_loss = self.compute_language_model_loss_without_logits( hidden_states_list[mtp_layer_number + 1], labels=mtp_labels, - weight=output_weight, + weight=self.shared_embedding_or_output_weight(), + sequence_parallel_enabled=self.output_layer.sequence_parallel, column_parallel_linear=self.output_layer, col_linear_kwargs={ 'weight': output_weight, @@ -667,7 +668,8 @@ def _postprocess( loss = self.compute_language_model_loss_without_logits( hidden_states, labels=labels, - weight=output_weight, + weight=self.shared_embedding_or_output_weight(), + sequence_parallel_enabled=self.output_layer.sequence_parallel, column_parallel_linear=self.output_layer, col_linear_kwargs={ 'weight': output_weight, diff --git a/megatron/core/models/mamba/mamba_model.py b/megatron/core/models/mamba/mamba_model.py index 533f4efc257..98d918ce448 100644 --- a/megatron/core/models/mamba/mamba_model.py +++ b/megatron/core/models/mamba/mamba_model.py @@ -257,7 +257,8 @@ def forward( loss = self.compute_language_model_loss_without_logits( hidden_states, labels, - weight=output_weight, + weight=self.shared_embedding_or_output_weight(), + sequence_parallel_enabled=self.output_layer.sequence_parallel, column_parallel_linear=self.output_layer, col_linear_kwargs={ "weight": output_weight, diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py index 439825aaf57..ad34c3e5e0a 100644 --- a/megatron/training/arguments.py +++ b/megatron/training/arguments.py @@ -2254,8 +2254,8 @@ def _add_training_args(parser): dest='bias_swiglu_fusion') group.add_argument('--use-fused-weighted-squared-relu', action='store_true', help='Use fused weighted squared relu when using MoE.') - group.add_argument('--no-linear-cross-entropy-fusion', action='store_false', - help='Disable fusion of linear layer and cross entropy ' + group.add_argument('--linear-cross-entropy-fusion', action='store_true', + help='Enable fusion of linear layer and cross entropy ' 'loss calculation.', dest='linear_cross_entropy_fusion') group.add_argument('--no-bias-dropout-fusion', action='store_false', From 24a54659e91da429db9410206a6080f47ca80a5b Mon Sep 17 00:00:00 2001 From: Jianbing-D <69858819+Jianbing-D@users.noreply.github.com> Date: Tue, 11 Nov 2025 15:59:17 +0800 Subject: [PATCH 07/17] formatting and fixing lints (#6) Signed-off-by: Jianbing Dong --- .../fusions/fused_linear_cross_entropy.py | 241 ++---- .../blackwell/bwd_partial_dlogits.py | 576 ++++--------- .../linear_cross_entropy/blackwell/entry.py | 281 +++---- .../blackwell/fwd_mainloop.py | 527 ++++-------- .../linear_cross_entropy/blackwell/triton.py | 183 ++--- .../fusions/linear_cross_entropy/utils.py | 13 +- .../common/language_module/language_module.py | 11 +- megatron/core/models/gpt/gpt_model.py | 14 +- megatron/core/models/mamba/mamba_model.py | 2 +- tests/unit_tests/a2a_overlap/utils.py | 4 +- .../test_fused_linear_cross_entropy.py | 772 ++++++------------ 11 files changed, 837 insertions(+), 1787 deletions(-) diff --git a/megatron/core/fusions/fused_linear_cross_entropy.py b/megatron/core/fusions/fused_linear_cross_entropy.py index a08735952dc..74d38da8243 100644 --- a/megatron/core/fusions/fused_linear_cross_entropy.py +++ b/megatron/core/fusions/fused_linear_cross_entropy.py @@ -1,32 +1,53 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + """ Linear Cross Entropy API Fuse cross entropy with linear layer. """ import typing + import torch -def _setup_platform(): + +class Platform: """ - Setup the platform for the Linear Cross Entropy. + Singleton class for targeted GPU platform. """ - assert torch.cuda.is_available(), "CUDA is not available" - device = torch.cuda.current_device() - cc = torch.cuda.get_device_capability(device) - - global forward_func, backward_func - if cc[0] == 10: - # from linear_cross_entropy.blackwell import entry as platform - from .linear_cross_entropy.blackwell import entry as platform - forward_func = platform.forward - backward_func = platform.backward - else: - raise ValueError(f"Unsupported architecture: {cc[0]}") -_setup_platform() + + _instance: typing.Optional["Platform"] = None + + def __new__(cls) -> "Platform": + if cls._instance is None: + cls._instance = super().__new__(cls) + return cls._instance + + def __init__(self) -> None: + if getattr(self, "_initialized", False): + return + + assert torch.cuda.is_available(), "CUDA is not available" + device = torch.cuda.current_device() + cc = torch.cuda.get_device_capability(device) + + if cc[0] == 10: + from .linear_cross_entropy.blackwell import entry as gpu_entry + + self.forward_func: typing.Callable[..., typing.Any] = gpu_entry.forward + self.backward_func: typing.Callable[..., typing.Any] = gpu_entry.backward + else: + raise ValueError(f"Unsupported architecture: {cc[0]}") + + self._initialized = True + + +_platform = Platform() + class LinearCrossEntropy(torch.autograd.Function): """ - This class implements a custom autograd function for linear and cross entropy, whose equivalent logic in PyTorch is: + This class implements a custom autograd function for linear and cross entropy, + whose equivalent logic in PyTorch is: ```python def torch_entropy(hidden, weight, labels): logits = torch.matmul(hidden, weight) @@ -34,6 +55,7 @@ def torch_entropy(hidden, weight, labels): return logprobs ``` """ + @staticmethod def forward( ctx, @@ -41,13 +63,14 @@ def forward( weight: torch.Tensor, labels: torch.Tensor, tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, - reduction: typing.Optional[str] = "mean", - ignore_index: typing.Optional[int] = -100, - sequence_parallel: typing.Optional[bool] = False, + reduction: str = "mean", + ignore_index: int = -100, + sequence_parallel: bool = False, ) -> torch.Tensor: """ The forward pass of the Linear Cross Entropy. - If tp_group is not None, the weight tensor to each TP rank should be (global_vocab_size // world_size, dim). + If tp_group is not None, the weight tensor to each TP rank should be + (global_vocab_size // world_size, dim). Note that each of the ranks should get equal shards along the vocab_size dimension. Args: @@ -74,24 +97,26 @@ def forward( A0 C0 XX A1 XX C1 - When tp_group is not None, the weight tensor will be split along the vocab_size dimension, - which means each rank will get equal shards along the global_vocab_size dimension. - Specifically, the weight tensor to each rank will be (local_vocab_size, dim). + When tp_group is not None, the weight tensor will be split along the vocab_size + dimension, which means each rank will get equal shards along the global_vocab_size + dimension. Specifically, the weight tensor to each rank will be (local_vocab_size, dim). And there is an assumption that each rank will get the same local_vocab_size. - When sequence_parallel is True, the hidden tensor will be split along the sequence length dimension, - which means each rank will get equal shards along the sequence length dimension. - Specifically, the hidden tensor to each rank will be (local_num_tokens, dim). - And there is an assumption that each rank will get the same local_num_tokens. + When sequence_parallel is True, the hidden tensor will be split along the + sequence length dimension, which means each rank will get equal shards along + the sequence length dimension. Specifically, the hidden tensor to each rank + will be (local_num_tokens, dim). And there is an assumption that each rank + will get the same local_num_tokens. - In TP forward pass, the hidden tensor and label tensor shall be identical among all TP ranks, - and it's user's responsibility to ensure the hidden tensor is identical among all TP ranks. - Then this operation will produce identical logprobs among all TP ranks. + In TP forward pass, the hidden tensor and label tensor shall be identical + among all TP ranks, and it's user's responsibility to ensure the hidden tensor + is identical among all TP ranks. Then this operation will produce identical + logprobs among all TP ranks. - In TP backward pass, the gradient of the logprobs shall be identical among all TP ranks, - and it's user's responsibility to ensure the gradient of the logprobs is identical among all TP ranks. - Then this operation will produce distinct gradients for the local weight tensor, - and identical gradients for the hidden tensor. + In TP backward pass, the gradient of the logprobs shall be identical among all + TP ranks, and it's user's responsibility to ensure the gradient of the logprobs + is identical among all TP ranks. Then this operation will produce distinct gradients + for the local weight tensor, and identical gradients for the hidden tensor. ```python # ------------ forward pass ------------ # @@ -103,16 +128,17 @@ def forward( # ------------ backward pass ------------ # g_logprobs = tp_group.broadcast(g_logprobs, src=0) # handled by framework d_hidden, d_weight = torch.autograd.grad(...) - # each rank will get the same d_hidden, + # each rank will get the same d_hidden, # and distinct d_weight for local weight shard ``` - In SP forward pass, the hidden tensor shall be split along the sequence length dimension, + In SP forward pass, the hidden tensor shall be split along the sequence length dimension, and the label tensor shall be identical among all TP ranks. Then this operation will produce identical logprobs among all TP ranks. In SP backward pass, the gradient of the logprobs shall be identical among all TP ranks, - Then this operation will produce distinct gradients for the local hidden tensor and weight tensor. + Then this operation will produce distinct gradients for the local hidden tensor + and local weight tensor. ```python # ------------ forward pass ------------ # hidden = global_hidden[tp_rank] # handled by framework @@ -128,18 +154,11 @@ def forward( """ with torch.cuda.nvtx.range("LinearCrossEntropy-forward"): logprobs, _maximum, _acc, _num_valid_tokens, tp_rank, tp_world_size, global_hidden = ( - forward_func( - hidden, weight, labels, - tp_group, - reduction, - ignore_index, - sequence_parallel, + _platform.forward_func( + hidden, weight, labels, tp_group, reduction, ignore_index, sequence_parallel ) ) - ctx.save_for_backward( - global_hidden, weight, labels, - _maximum, _acc, _num_valid_tokens, - ) + ctx.save_for_backward(global_hidden, weight, labels, _maximum, _acc, _num_valid_tokens) ctx.tp_group = tp_group ctx.ignore_index = ignore_index ctx.reduction = reduction @@ -148,13 +167,11 @@ def forward( ctx.sequence_parallel = sequence_parallel return logprobs - @staticmethod def backward( - ctx, - dlogprobs: torch.Tensor - ) -> typing.List[torch.Tensor]: + ctx, dlogprobs: torch.Tensor + ) -> typing.Tuple[torch.Tensor, torch.Tensor, None, None, None, None, None]: """ The backward pass of the Linear Cross Entropy. Args: @@ -175,7 +192,7 @@ def backward( tp_world_size = ctx.tp_world_size sequence_parallel = ctx.sequence_parallel - d_hidden, d_weight = backward_func( + d_hidden, d_weight = _platform.backward_func( dlogprobs, global_hidden, weight, @@ -199,9 +216,9 @@ def linear_cross_entropy( weight: torch.Tensor, labels: torch.Tensor, tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, - reduction: typing.Optional[str] = "mean", - ignore_index: typing.Optional[int] = -100, - sequence_parallel: typing.Optional[bool] = False, + reduction: str = "mean", + ignore_index: int = -100, + sequence_parallel: bool = False, ) -> torch.Tensor: """ helper function for linear cross entropy. @@ -209,115 +226,5 @@ def linear_cross_entropy( _impl = LinearCrossEntropy.apply return _impl(hidden, weight, labels, tp_group, reduction, ignore_index, sequence_parallel) -__all__ = [ - "linear_cross_entropy", - "LinearCrossEntropy", -] - - -# FIXME: move this unit-test to other place -if __name__ == "__main__": - def test_dp(): - # batch = 4 - # seqlen = 2035 - # vocab_size = 152063 - # dim = 4096 - batch = 1 - seqlen = 80 - vocab_size = 125 - dim = 64 - dtype = torch.float16 - reduction = "none" - - hidden = ( - torch.empty((batch, seqlen, dim), device="cuda", dtype=dtype) - .uniform_(-0.1, 0.1) - .requires_grad_() - ) - weight = ( - torch.empty((vocab_size, dim), device="cuda", dtype=dtype) - .uniform_(-0.1, 0.1) - .requires_grad_() - ) - - labels = torch.randint(0, vocab_size, (batch, seqlen), device="cuda", dtype=torch.long) - - logits = hidden @ weight.T - # print(logits) - - _logits = logits.to(torch.float32) - _logits_view = _logits.view(-1, _logits.shape[-1]) - maximum = _logits_view.max(dim=-1, keepdim=False).values - accu = torch.exp(_logits_view - maximum.unsqueeze(-1)).sum(dim=-1) - - logprobs = torch.nn.functional.cross_entropy( - logits.view(-1, logits.shape[-1]), - labels.view(-1), - reduction=reduction, - ) - - custom_logprobs = linear_cross_entropy( - hidden, weight, labels, - reduction=reduction, - ) - - print(custom_logprobs) - print(logprobs) - - # backward - g_logprobs = torch.rand_like(logprobs, dtype=dtype, device="cuda") - - (d_torch_hidden, d_torch_weight) = torch.autograd.grad( - (logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False - ) - - # first way to do backward - if reduction == "mean": - _g_logprobs = torch.broadcast_to(g_logprobs / (batch * seqlen), (batch * seqlen,)) - elif reduction == "sum": - _g_logprobs = torch.broadcast_to(g_logprobs, (batch * seqlen,)) - else: - _g_logprobs = g_logprobs - - intermediate = _logits_view - maximum.unsqueeze(-1) - exp_logits = torch.exp(intermediate) - d_logits = exp_logits / accu.unsqueeze(-1) - d_logits *= _g_logprobs.unsqueeze(-1) - # mask = torch.arange(vocab_size, dtype=torch.long, device="cuda") - # mask = torch.broadcast_to(mask, (batch * seqlen, vocab_size)) - # mask = (labels.view(-1).unsqueeze(-1) == mask) - - one_hot = torch.zeros_like(_logits_view) - one_hot.scatter_(1, labels.view(-1).unsqueeze(-1), 1) - - d_logits += one_hot * -_g_logprobs.unsqueeze(-1) - d_logits = d_logits.to(hidden.dtype) - # print(d_logits) - - d_hidden = d_logits @ weight - d_weight = d_logits.T @ hidden.view(-1, dim) - - # print("first way to do backward") - # print(d_hidden.view(hidden.shape)) - # print(d_torch_hidden) - # print(d_weight) - # print(d_torch_weight) - # print(d_logits) - - (d_custom_hidden, d_custom_weight) = torch.autograd.grad( - (custom_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False - ) - # print(d_torch_hidden) - # print(d_custom_hidden) - print(d_torch_weight) - print(d_custom_weight) - - torch.manual_seed(42) - - test_dp() \ No newline at end of file + +__all__ = ["linear_cross_entropy", "LinearCrossEntropy"] diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py b/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py index 97e7c5ab493..8a6e03601bf 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py @@ -1,45 +1,47 @@ -from typing import Optional, Type, Tuple, Union -import cuda.bindings.driver as cuda +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. -import torch +from typing import Optional, Tuple, Type +import cuda.bindings.driver as cuda # type: ignore import cutlass import cutlass.cute as cute -import cutlass.utils as utils -import cutlass.pipeline as pipeline +import cutlass.pipeline as pipeline # type: ignore +import cutlass.utils as utils # type: ignore +import cutlass.utils.blackwell_helpers as sm100_utils # type: ignore from cutlass.cute.nvgpu import cpasync, tcgen05 -import cutlass.torch as cutlass_torch -import cutlass.utils.blackwell_helpers as sm100_utils -from cutlass.cute.runtime import from_dlpack - SM100_TMEM_CAPACITY_COLUMNS: int = 512 + def make_thread_cooperative_group(size: int, alignment: Optional[int] = None): + """ + Create a thread cooperative group. + """ return pipeline.CooperativeGroup( - pipeline.Agent.Thread, size, - alignment=alignment if alignment is not None else size) + pipeline.Agent.Thread, size, alignment=alignment if alignment is not None else size + ) class BwdPartialDlogits: """ This class implements the backward kernel for partial d_logits. """ - def __init__(self, - reduction: int, - acc_dtype: Type[cutlass.Numeric] = cutlass.Float32, - use_2cta_instrs: bool = False, - mma_tiler_mn: Tuple[int, int] = (128, 256), - vocab_per_split: int = 512): + + def __init__( + self, + reduction: int, + acc_dtype: Type[cutlass.Numeric] = cutlass.Float32, + use_2cta_instrs: bool = False, + mma_tiler_mn: Tuple[int, int] = (128, 256), + vocab_per_split: int = 512, + ): self.REDUCTION: cutlass.Constexpr[cutlass.Int32] = cutlass.const_expr(reduction) self.acc_dtype = acc_dtype self.use_2cta_instrs = use_2cta_instrs self.mma_tiler = (*mma_tiler_mn, 1) self.vocab_per_split = vocab_per_split - self.cta_group = ( - tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE - ) + self.cta_group = tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE self.cluster_shape_mn = (2, 1) if self.use_2cta_instrs else (1, 1) self.smem_capacity = utils.get_smem_capacity_in_bytes("sm_100") @@ -52,14 +54,10 @@ def __init__(self, self.empty_warp_ids = (6, 7) self.threads_per_cta: int = self.threads_per_warp * len( - (*self.epi_warp_ids, - self.load_warp_ids, - self.mma_warp_ids, - *self.empty_warp_ids) + (*self.epi_warp_ids, self.load_warp_ids, self.mma_warp_ids, *self.empty_warp_ids) ) self.cta_sync_barrier = pipeline.NamedBarrier( - barrier_id = 1, - num_threads = self.threads_per_cta + barrier_id=1, num_threads=self.threads_per_cta ) self.buffer_align_bytes: int = 1024 @@ -80,7 +78,7 @@ def _compute_grid( cute.ceil_div(self.vocab_per_split, cta_tiler[1]), 1, ), - cluster_shape_mnk + cluster_shape_mnk, ) return grid @@ -104,28 +102,24 @@ def _setup_attributes( ): self.cluster_shape_mnk = (*self.cluster_shape_mn, 1) self.cluster_layout_vmnk = cute.tiled_divide( - cute.make_layout(self.cluster_shape_mnk), - (tiled_mma.thr_id.shape,), + cute.make_layout(self.cluster_shape_mnk), (tiled_mma.thr_id.shape,) ) mma_inst_shape_k = cute.size(tiled_mma.shape_mnk, mode=[2]) # it requires k-mode to be 128B aligned mma_inst_tile_k: int = 4 - self.mma_tiler = ( - self.mma_tiler[0], - self.mma_tiler[1], - mma_inst_shape_k * mma_inst_tile_k + self.mma_tiler = (self.mma_tiler[0], self.mma_tiler[1], mma_inst_shape_k * mma_inst_tile_k) + + self.num_acc_stage, self.num_ab_stage, self.num_epi_stage_per_tile = self._compute_stages( + tiled_mma, self.mma_tiler, a_dtype, b_dtype ) - - self.num_acc_stage, self.num_ab_stage, self.num_epi_stage_per_tile =\ - self._compute_stages(tiled_mma, self.mma_tiler, a_dtype, b_dtype) self.tmem_alloc_cols = self.num_acc_stage * self.mma_tiler[1] assert self.tmem_alloc_cols <= SM100_TMEM_CAPACITY_COLUMNS self.cta_tile_shape_mnk = ( self.mma_tiler[0] // cute.size(tiled_mma.thr_id.shape), self.mma_tiler[1], - self.mma_tiler[2] + self.mma_tiler[2], ) @cute.kernel @@ -150,9 +144,10 @@ def kernel( problem_mnk: Tuple[int, int, int], rank: cutlass.Int32, ) -> None: - warp_idx = cute.arch.make_warp_uniform( - cute.arch.warp_idx() - ) + """ + The backward kernel for partial d_logits. + """ + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) tidx, _, _ = cute.arch.thread_idx() bidx, bidy, _ = cute.arch.block_idx() # FIXME: block swizzling applied here @@ -160,9 +155,7 @@ def kernel( # FIXME: if 2 CTAs, modify here cta_rank_in_cluster = 0 - block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord( - cta_rank_in_cluster - ) + block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord(cta_rank_in_cluster) # prefetch tma descriptors if warp_idx == self.load_warp_ids: @@ -177,124 +170,95 @@ def kernel( producer_group=make_thread_cooperative_group(len([self.load_warp_ids])), consumer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), tx_count=self.tma_copy_ab_bytes, - barrier_storage=storage.load_ab_mbar_ptr.data_ptr() + barrier_storage=storage.load_ab_mbar_ptr.data_ptr(), ) ab_producer_state = pipeline.make_pipeline_state( - pipeline.PipelineUserType.Producer, - self.num_ab_stage + pipeline.PipelineUserType.Producer, self.num_ab_stage ) ab_consumer_state = pipeline.make_pipeline_state( - pipeline.PipelineUserType.Consumer, - self.num_ab_stage + pipeline.PipelineUserType.Consumer, self.num_ab_stage ) mma_pipeline = pipeline.PipelineUmmaAsync.create( num_stages=self.num_acc_stage, producer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), - consumer_group=make_thread_cooperative_group(self.threads_per_warp * len(self.epi_warp_ids)), - barrier_storage=storage.mma_mbar_ptr.data_ptr() + consumer_group=make_thread_cooperative_group( + self.threads_per_warp * len(self.epi_warp_ids) + ), + barrier_storage=storage.mma_mbar_ptr.data_ptr(), ) mma_producer_state = pipeline.make_pipeline_state( - pipeline.PipelineUserType.Producer, - self.num_acc_stage + pipeline.PipelineUserType.Producer, self.num_acc_stage ) mma_consumer_state = pipeline.make_pipeline_state( - pipeline.PipelineUserType.Consumer, - self.num_acc_stage + pipeline.PipelineUserType.Consumer, self.num_acc_stage ) tmem_dealloc_mbar_ptr = storage.tmem_dealloc_mbar_ptr.data_ptr() if warp_idx == self.empty_warp_ids[0]: with cute.arch.elect_one(): cute.arch.mbarrier_init( - tmem_dealloc_mbar_ptr, - self.threads_per_warp * len(self.epi_warp_ids) + tmem_dealloc_mbar_ptr, self.threads_per_warp * len(self.epi_warp_ids) ) cute.arch.mbarrier_init_fence() # -------- tensor partition ------------ # # swizzle o [(tileM, tileK), loopM, loopK, stage] - sA = storage.sA.get_tensor( - a_smem_layout_staged.outer, - swizzle=a_smem_layout_staged.inner - ) + sA = storage.sA.get_tensor(a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner) # swizzle o [(tileN, tileK), loopN, loopK, stage] - sB = storage.sB.get_tensor( - b_smem_layout_staged.outer, - swizzle=b_smem_layout_staged.inner - ) - + sB = storage.sB.get_tensor(b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner) + # FIXME: if 2 CTAs, modify here thr_mma = tiled_mma.get_slice(0) # [MMA, loopM, loopK, stage] tCsA = thr_mma.make_fragment_A(sA) # [MMA, loopN, loopK, stage] tCsB = thr_mma.make_fragment_B(sB) - + # [tileM, tileK, loopK] gA = cute.local_tile( - mA, - (self.cta_tile_shape_mnk[0], self.cta_tile_shape_mnk[2]), - (pidm, None) + mA, (self.cta_tile_shape_mnk[0], self.cta_tile_shape_mnk[2]), (pidm, None) ) # [vocab_per_split, dim] mB_n = cute.local_tile( - mB, - (self.vocab_per_split, cute.size(mB.layout.shape, mode=[1])), - (split_idx, 0) + mB, (self.vocab_per_split, cute.size(mB.layout.shape, mode=[1])), (split_idx, 0) ) # [tileN, tileK, loopK] gB = cute.local_tile( - mB_n, - (self.cta_tile_shape_mnk[1], self.cta_tile_shape_mnk[2]), - (pidn, None) + mB_n, (self.cta_tile_shape_mnk[1], self.cta_tile_shape_mnk[2]), (pidn, None) ) - a_cta_layout = cute.make_layout( - cute.slice_( - cluster_layout_vmnk, - (0, 0, None, 0) - ).shape - ) + a_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, 0, None, 0)).shape) # just to make sure SMEM and GMEM tensor has the same size in the first rank tCgA = thr_mma.partition_A(gA) tCgB = thr_mma.partition_B(gB) # [CPY, stage] & [CPY, loopK] tTMAsA, tTMAgA = cpasync.tma_partition( tma_atom_a, - block_in_cluster_coord_vmnk[2], # cta_coord, + block_in_cluster_coord_vmnk[2], # cta_coord, a_cta_layout, cute.group_modes(sA, 0, 3), - cute.group_modes(tCgA, 0, 3) - ) - b_cta_layout = cute.make_layout( - cute.slice_( - cluster_layout_vmnk, - (0, None, 0, 0) - ).shape + cute.group_modes(tCgA, 0, 3), ) + b_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape) # [CPY, stage] & [CPY, loopK] tTMAsB, tTMAgB = cpasync.tma_partition( tma_atom_b, - block_in_cluster_coord_vmnk[1], # cta_coord + block_in_cluster_coord_vmnk[1], # cta_coord b_cta_layout, cute.group_modes(sB, 0, 3), - cute.group_modes(tCgB, 0, 3) + cute.group_modes(tCgB, 0, 3), ) # ------ Allocate TMEM ------ # tmem_holding_buf = storage.tmem_holding_buf if warp_idx == self.empty_warp_ids[0]: cute.arch.alloc_tmem( - self.tmem_alloc_cols, - tmem_holding_buf, - is_two_cta=self.use_2cta_instrs + self.tmem_alloc_cols, tmem_holding_buf, is_two_cta=self.use_2cta_instrs ) self.cta_sync_barrier.arrive_and_wait() tmem_ptr = cute.arch.retrieve_tmem_ptr( - self.acc_dtype, - alignment=16, - ptr_to_buffer_holding_addr=tmem_holding_buf + self.acc_dtype, alignment=16, ptr_to_buffer_holding_addr=tmem_holding_buf ) tmem_shape = (128, self.tmem_alloc_cols) @@ -302,7 +266,7 @@ def kernel( tCtC_fake = thr_mma.make_fragment_C(acc_shape) # [(tileM, tileN), loopM, loopN] tCtC = cute.make_tensor(tmem_ptr, tCtC_fake.layout) - + # ------ Empty ------ # if warp_idx in self.empty_warp_ids: cute.arch.warpgroup_reg_dealloc(self.num_regs_other) @@ -317,13 +281,13 @@ def kernel( tma_atom_a, tTMAgA[(None, k)], tTMAsA[(None, ab_producer_state.index)], - tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state) + tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state), ) cute.copy( tma_atom_b, tTMAgB[(None, k)], tTMAsB[(None, ab_producer_state.index)], - tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state) + tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state), ) ab_pipeline.producer_commit(ab_producer_state) ab_producer_state.advance() @@ -344,7 +308,7 @@ def kernel( cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), tCsA[(None, None, kblock_idx, ab_consumer_state.index)], tCsB[(None, None, kblock_idx, ab_consumer_state.index)], - cute.append_ones(tCtC[(None, None, mma_producer_state.index)]) + cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), ) tiled_mma.set(tcgen05.Field.ACCUMULATE, True) @@ -353,7 +317,7 @@ def kernel( mma_pipeline.producer_commit(mma_producer_state) mma_producer_state.advance() - + # ------ EPI ------ # if warp_idx in self.epi_warp_ids: cute.arch.warpgroup_reg_alloc(self.num_regs_epi) @@ -364,257 +328,139 @@ def kernel( self.acc_dtype, self.acc_dtype, (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), - self.use_2cta_instrs + self.use_2cta_instrs, ) # [tileM, subTileN, loopM, CntSubTileN, loopN] tAcc_epi = cute.flat_divide( tCtC[((None, None), 0, None)], - (self.epi_tile[0], - self.epi_tile[1] // self.num_epi_stage_per_tile) - ) - tiled_copy_t2r = tcgen05.make_tmem_copy( - copy_atom_t2r, - tAcc_epi[(None, None, 0, 0, 0)] + (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), ) + tiled_copy_t2r = tcgen05.make_tmem_copy(copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)]) thr_copy_t2r = tiled_copy_t2r.get_slice(tidx) tTMEM_load_tAcc = thr_copy_t2r.partition_S(tAcc_epi) - tTMEM_load_tAcc = cute.group_modes( - tTMEM_load_tAcc, 3, cute.rank(tTMEM_load_tAcc) - 1 - ) + tTMEM_load_tAcc = cute.group_modes(tTMEM_load_tAcc, 3, cute.rank(tTMEM_load_tAcc) - 1) # predicates cAcc = cute.make_identity_tensor(self.mma_tiler[:2]) tCcAcc = thr_mma.partition_C(cAcc) tCcAcc_epi = cute.flat_divide( tCcAcc[((None, None), 0, None)], - (self.epi_tile[0], - self.epi_tile[1] // self.num_epi_stage_per_tile) + (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), ) tTMEM_load_cAcc = thr_copy_t2r.partition_D(tCcAcc_epi) - tTMEM_load_cAcc_shape = cute.select( - tTMEM_load_cAcc.shape, - mode=[0, 1, 2] - ) - tTMEM_load_rAcc = cute.make_fragment( - tTMEM_load_cAcc_shape, - self.acc_dtype - ) + tTMEM_load_cAcc_shape = cute.select(tTMEM_load_cAcc.shape, mode=[0, 1, 2]) + tTMEM_load_rAcc = cute.make_fragment(tTMEM_load_cAcc_shape, self.acc_dtype) copy_atom_g2r_int64 = cute.make_copy_atom( - cute.nvgpu.CopyUniversalOp(), - mLabels.element_type + cute.nvgpu.CopyUniversalOp(), mLabels.element_type ) copy_atom_g2r_fp32 = cute.make_copy_atom( - cute.nvgpu.CopyUniversalOp(), - mDlogprobs.element_type + cute.nvgpu.CopyUniversalOp(), mDlogprobs.element_type ) - epilogue_thread_layout = cute.make_layout( - (128, 1), - stride=(1, 1)) + epilogue_thread_layout = cute.make_layout((128, 1), stride=(1, 1)) tiled_copy_g2r_int64 = cute.make_tiled_copy_tv( - copy_atom_g2r_int64, - epilogue_thread_layout, - cute.make_layout((1, 1)) + copy_atom_g2r_int64, epilogue_thread_layout, cute.make_layout((1, 1)) ) tiled_copy_g2r_fp32 = cute.make_tiled_copy_tv( - copy_atom_g2r_fp32, - epilogue_thread_layout, - cute.make_layout((1, 1)) + copy_atom_g2r_fp32, epilogue_thread_layout, cute.make_layout((1, 1)) ) thr_copy_g2r_int64 = tiled_copy_g2r_int64.get_slice(tidx) thr_copy_g2r_fp32 = tiled_copy_g2r_fp32.get_slice(tidx) # [tileM] - gLabels = cute.local_tile( - mLabels, - (self.epi_tile[0],), - (pidm,) - ) - gMaximum = cute.local_tile( - mMaximum, - (self.epi_tile[0],), - (pidm,) - ) - gAccu = cute.local_tile( - mAccu, - (self.epi_tile[0],), - (pidm,) - ) - + gLabels = cute.local_tile(mLabels, (self.epi_tile[0],), (pidm,)) + gMaximum = cute.local_tile(mMaximum, (self.epi_tile[0],), (pidm,)) + gAccu = cute.local_tile(mAccu, (self.epi_tile[0],), (pidm,)) + # slice along M direction tMCAcc = thr_copy_g2r_int64.partition_S(cAcc)[(None, None, 0)] # [(1, 1), 1] - tMCAcc_mask = cute.make_fragment( - tMCAcc.shape, - cutlass.Boolean - ) + tMCAcc_mask = cute.make_fragment(tMCAcc.shape, cutlass.Boolean) # to align shape with gMax and gAccu tMCAcc_mask = cute.append_ones(tMCAcc_mask) - tMCAcc_mask[0] = cute.elem_less( - pidm * self.epi_tile[0] + tidx, - cute.size(mA, mode=[0]) - ) + tMCAcc_mask[0] = cute.elem_less(pidm * self.epi_tile[0] + tidx, cute.size(mA, mode=[0])) # [(1, 1), 1, 1] - tMgLabels = thr_copy_g2r_int64.partition_S( - cute.append_ones(gLabels) - ) - tMrLabels = cute.make_fragment( - tMgLabels.shape, - tMgLabels.element_type - ) - cute.copy( - tiled_copy_g2r_int64, - tMgLabels, - tMrLabels, - pred=tMCAcc_mask - ) - tMgMaximum = thr_copy_g2r_fp32.partition_S( - cute.append_ones(gMaximum) - ) - tMrMaximum = cute.make_fragment( - tMgMaximum.layout, - tMgMaximum.element_type - ) - cute.copy( - tiled_copy_g2r_fp32, - tMgMaximum, - tMrMaximum, - pred=tMCAcc_mask - ) - tMgAccu = thr_copy_g2r_fp32.partition_S( - cute.append_ones(gAccu) - ) - tMrAccu = cute.make_fragment( - tMgAccu.layout, - tMgAccu.element_type - ) - cute.copy( - tiled_copy_g2r_fp32, - tMgAccu, - tMrAccu, - pred=tMCAcc_mask - ) - - tMrDlogprobs = cute.make_fragment( - tMgAccu.layout, - mDlogprobs.element_type - ) + tMgLabels = thr_copy_g2r_int64.partition_S(cute.append_ones(gLabels)) + tMrLabels = cute.make_fragment(tMgLabels.shape, tMgLabels.element_type) + cute.copy(tiled_copy_g2r_int64, tMgLabels, tMrLabels, pred=tMCAcc_mask) + tMgMaximum = thr_copy_g2r_fp32.partition_S(cute.append_ones(gMaximum)) + tMrMaximum = cute.make_fragment(tMgMaximum.layout, tMgMaximum.element_type) + cute.copy(tiled_copy_g2r_fp32, tMgMaximum, tMrMaximum, pred=tMCAcc_mask) + tMgAccu = thr_copy_g2r_fp32.partition_S(cute.append_ones(gAccu)) + tMrAccu = cute.make_fragment(tMgAccu.layout, tMgAccu.element_type) + cute.copy(tiled_copy_g2r_fp32, tMgAccu, tMrAccu, pred=tMCAcc_mask) + + tMrDlogprobs = cute.make_fragment(tMgAccu.layout, mDlogprobs.element_type) if cutlass.const_expr(self.REDUCTION == 2): # mean reduction - num_valid_tokens = cute.make_tensor( - scalarNumValidTokens, - layout=(1,), - ) + num_valid_tokens = cute.make_tensor(scalarNumValidTokens, layout=(1,)) tMrDlogprobs[0] = mDlogprobs[0] / num_valid_tokens[0].to(cutlass.Float32) elif cutlass.const_expr(self.REDUCTION == 1): # sum reduction tMrDlogprobs[0] = mDlogprobs[0] else: # no reduction - gDlogprobs = cute.local_tile( - mDlogprobs, - (self.epi_tile[0],), - (pidm,) - ) - tMgDlogprobs = thr_copy_g2r_fp32.partition_S( - cute.append_ones(gDlogprobs) - ) - cute.copy( - tiled_copy_g2r_fp32, - tMgDlogprobs, - tMrDlogprobs, - pred=tMCAcc_mask - ) + gDlogprobs = cute.local_tile(mDlogprobs, (self.epi_tile[0],), (pidm,)) + tMgDlogprobs = thr_copy_g2r_fp32.partition_S(cute.append_ones(gDlogprobs)) + cute.copy(tiled_copy_g2r_fp32, tMgDlogprobs, tMrDlogprobs, pred=tMCAcc_mask) tMrAccu[0] = cute.arch.rcp_approx(tMrAccu[0]) - tMrDlogprobs[0] *= (tMrLabels[0] != ignore_index) + tMrDlogprobs[0] *= tMrLabels[0] != ignore_index tMr_d_acc_exp_logits = tMrDlogprobs[0] * tMrAccu[0] # ------ Partial output ------ # # [tileM, tileN] gDlogits_partial = cute.local_tile( - mDlogits_partial, - (self.epi_tile[0], self.epi_tile[1]), - (pidm, pidn) + mDlogits_partial, (self.epi_tile[0], self.epi_tile[1]), (pidm, pidn) ) # blackwell supports STG.256 copy_atom_r2g = cute.make_copy_atom( - cute.nvgpu.CopyUniversalOp(), - gDlogits_partial.element_type, - num_bits_per_copy=256 + cute.nvgpu.CopyUniversalOp(), gDlogits_partial.element_type, num_bits_per_copy=256 ) tiled_copy_r2g = cute.make_tiled_copy_tv( - copy_atom_r2g, - epilogue_thread_layout, - copy_atom_r2g.layout_dst_tv + copy_atom_r2g, epilogue_thread_layout, copy_atom_r2g.layout_dst_tv ) thr_copy_r2g = tiled_copy_r2g.get_slice(tidx) # [CPY, loopM, loopN] tR2GCAcc = thr_copy_r2g.partition_S(cAcc) - tR2GCAcc_pred = cute.make_fragment( - tR2GCAcc.shape, - cutlass.Boolean - ) + tR2GCAcc_pred = cute.make_fragment(tR2GCAcc.shape, cutlass.Boolean) for elem in cutlass.range(cute.size(tR2GCAcc_pred, mode=[0])): for row in cutlass.range(cute.size(tR2GCAcc_pred, mode=[1])): for col in cutlass.range(cute.size(tR2GCAcc_pred, mode=[2])): - # tR2GCAcc_pred[elem, row, col] = cute.elem_less( - # pidm * self.epi_tile[0] + tR2GCAcc[elem, row, col][0], - # cute.size(mDlogits_partial, mode=[0]) - # ) and cute.elem_less( - # pidn * self.epi_tile[1] + tR2GCAcc[elem, row, col][1], - # cute.size(mDlogits_partial, mode=[1]) - # ) tR2GCAcc_pred[elem, row, col] = cute.elem_less( - pidm * self.epi_tile[0] - + tR2GCAcc[elem, row, col][0], - problem_mnk[0] + pidm * self.epi_tile[0] + tR2GCAcc[elem, row, col][0], problem_mnk[0] ) and cute.elem_less( split_idx * self.vocab_per_split + pidn * self.epi_tile[1] + tR2GCAcc[elem, row, col][1], - problem_mnk[1] + problem_mnk[1], ) tR2GgDlogits = thr_copy_r2g.partition_D(gDlogits_partial) # for type conversion - dLogits_half = cute.make_fragment( - tTMEM_load_rAcc.shape, - tR2GgDlogits.element_type - ) - dLogits_half = cute.tiled_divide( - dLogits_half, - (cute.size(tR2GgDlogits, mode=[0]), 1) - ) + dLogits_half = cute.make_fragment(tTMEM_load_rAcc.shape, tR2GgDlogits.element_type) + dLogits_half = cute.tiled_divide(dLogits_half, (cute.size(tR2GgDlogits, mode=[0]), 1)) dLogits_half = cute.group_modes(dLogits_half, 2, cute.rank(dLogits_half)) mma_pipeline.consumer_wait(mma_consumer_state) block_vocab_left_idx: cutlass.Int64 = ( - split_idx * self.vocab_per_split - + pidn * self.epi_tile[1] + split_idx * self.vocab_per_split + pidn * self.epi_tile[1] ) - block_vocab_right_idx: cutlass.Int64 = ( - min( - split_idx * self.vocab_per_split - + (pidn + 1) * self.epi_tile[1], - min( - (split_idx + 1) * self.vocab_per_split, - problem_mnk[1] - ) - ) + block_vocab_right_idx: cutlass.Int64 = min( + split_idx * self.vocab_per_split + (pidn + 1) * self.epi_tile[1], + min((split_idx + 1) * self.vocab_per_split, problem_mnk[1]), ) num_n_subtiles: cutlass.Int64 = cute.ceil_div( - (block_vocab_right_idx - block_vocab_left_idx), - cute.size(tTMEM_load_rAcc, mode=[0]) + (block_vocab_right_idx - block_vocab_left_idx), cute.size(tTMEM_load_rAcc, mode=[0]) ) for n_subtile in cutlass.range(num_n_subtiles): cute.copy( tiled_copy_t2r, tTMEM_load_tAcc[(None, None, None, n_subtile, mma_consumer_state.index)], - tTMEM_load_rAcc + tTMEM_load_rAcc, ) for idx in cutlass.range(cute.size(tTMEM_load_rAcc, mode=[0]), unroll_full=True): @@ -629,12 +475,11 @@ def kernel( + idx ) mask: cutlass.Boolean = ( - position == tMrLabels[0] - and tMrLabels[0] != ignore_index + position == tMrLabels[0] and tMrLabels[0] != ignore_index ) # d_logits tTMEM_load_rAcc[idx] *= tMr_d_acc_exp_logits - tTMEM_load_rAcc[idx] += (mask * -tMrDlogprobs[0]) + tTMEM_load_rAcc[idx] += mask * -tMrDlogprobs[0] dLogits_half[idx] = tTMEM_load_rAcc[idx].to(dLogits_half.element_type) for idx in cutlass.range(cute.size(dLogits_half, mode=[1]), unroll_full=True): @@ -643,23 +488,17 @@ def kernel( tiled_copy_r2g, dLogits_half[(None, idx, None)], tR2GgDlogits[(None, None, copy_id)], - pred=tR2GCAcc_pred[((0, None), None, copy_id)] + pred=tR2GCAcc_pred[((0, None), None, copy_id)], ) mma_pipeline.consumer_release(mma_consumer_state) mma_consumer_state.advance() - # ------ Deallocate TMEM ------ # self.cta_sync_barrier.arrive_and_wait() if warp_idx == self.empty_warp_ids[0]: cute.arch.relinquish_tmem_alloc_permit() - cute.arch.dealloc_tmem( - tmem_ptr, - self.tmem_alloc_cols, - is_two_cta=self.use_2cta_instrs - ) - + cute.arch.dealloc_tmem(tmem_ptr, self.tmem_alloc_cols, is_two_cta=self.use_2cta_instrs) @cute.jit def __call__( @@ -681,85 +520,66 @@ def __call__( b_dtype: Type[cutlass.Numeric] = weight.element_type if cutlass.const_expr(hidden.element_type != weight.element_type): - raise RuntimeError(f"data type don't match: {hidden.element_type} v.s. {weight.element_type}") + raise RuntimeError( + f"data type don't match: {hidden.element_type} v.s. {weight.element_type}" + ) if cutlass.const_expr(hidden.element_type not in [cutlass.Float16, cutlass.BFloat16]): raise RuntimeError("hidden can only be FP16 or BF16") if cutlass.const_expr(hidden.layout.shape[1] != weight.layout.shape[1]): raise RuntimeError("K dimension doesn't match") - problem_mnk = ( - hidden.layout.shape[0], - weight.layout.shape[0], - hidden.layout.shape[1] - ) + problem_mnk = (hidden.layout.shape[0], weight.layout.shape[0], hidden.layout.shape[1]) if cutlass.const_expr((problem_mnk[2] * a_dtype.width // 8) % 16 != 0): raise RuntimeError(f"K dimension is not 16B aligned: {problem_mnk[2]}") if cutlass.const_expr((problem_mnk[2] * b_dtype.width // 8) % 128 != 0): raise RuntimeError(f"N dimension is not 128B aligned: {problem_mnk[1]}") grid = self._compute_grid( - problem_mnk = problem_mnk, - cluster_shape_mn = self.cluster_shape_mn, - cta_tiler = self.mma_tiler, + problem_mnk=problem_mnk, + cluster_shape_mn=self.cluster_shape_mn, + cta_tiler=self.mma_tiler, ) - + a_major_mode = utils.LayoutEnum.from_tensor(hidden).mma_major_mode() b_major_mode = utils.LayoutEnum.from_tensor(weight).mma_major_mode() tiled_mma = sm100_utils.make_trivial_tiled_mma( - a_dtype, - a_major_mode, - b_major_mode, - self.acc_dtype, - self.cta_group, - self.mma_tiler[:2] + a_dtype, a_major_mode, b_major_mode, self.acc_dtype, self.cta_group, self.mma_tiler[:2] ) self._setup_attributes(tiled_mma, a_dtype, b_dtype) self.epi_tile = self.cta_tile_shape_mnk[:2] - + # Swizzle o [(tileM, tileK), loopM, loopK, stage] a_smem_layout_staged = sm100_utils.make_smem_layout_a( - tiled_mma, - self.mma_tiler, - a_dtype, - self.num_ab_stage + tiled_mma, self.mma_tiler, a_dtype, self.num_ab_stage ) # Swizzle o [(tileN, tileK), loopN, loopK, stage] b_smem_layout_staged = sm100_utils.make_smem_layout_b( - tiled_mma, - self.mma_tiler, - b_dtype, - self.num_ab_stage + tiled_mma, self.mma_tiler, b_dtype, self.num_ab_stage ) tma_load_op = cpasync.CopyBulkTensorTileG2SOp(self.cta_group) tma_store_op = cpasync.CopyBulkTensorTileS2GOp() # Swizzle o [(tileM, tileK), loopM, loopK] - a_smem_layout = cute.select( - a_smem_layout_staged, - mode=[0, 1, 2] - ) + a_smem_layout = cute.select(a_smem_layout_staged, mode=[0, 1, 2]) tma_atom_a, tma_tensor_a = cute.nvgpu.make_tiled_tma_atom_A( tma_load_op, hidden, a_smem_layout, self.mma_tiler, tiled_mma, - self.cluster_layout_vmnk.shape + self.cluster_layout_vmnk.shape, ) # Swizzle o [(tileN, tileK), loopN, loopK] - b_smem_layout = cute.select( - b_smem_layout_staged, - mode=[0, 1, 2] - ) + b_smem_layout = cute.select(b_smem_layout_staged, mode=[0, 1, 2]) tma_atom_b, tma_tensor_b = cute.nvgpu.make_tiled_tma_atom_B( tma_load_op, weight, b_smem_layout, self.mma_tiler, tiled_mma, - self.cluster_layout_vmnk.shape + self.cluster_layout_vmnk.shape, ) a_copy_size = cute.size_in_bytes(a_dtype, a_smem_layout) b_copy_size = cute.size_in_bytes(b_dtype, b_smem_layout) @@ -767,6 +587,10 @@ def __call__( @cute.struct class SharedStorage: + """ + The shared storage for the backward kernel. + """ + load_ab_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage * 2] mma_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2] @@ -781,6 +605,7 @@ class SharedStorage: cute.struct.MemRange[b_dtype, cute.cosize(b_smem_layout_staged)], self.buffer_align_bytes, ] + self.shared_storage = SharedStorage self.kernel( @@ -806,126 +631,5 @@ class SharedStorage: grid=grid, block=[self.threads_per_cta, 1, 1], cluster=self.cluster_shape_mnk, - stream=stream + stream=stream, ) - - -if __name__ == "__main__": - torch.manual_seed(1113) - - batch = 4 - seqlen = 1023 - dim = 8192 - vocab_size = 152064 - dtype = torch.bfloat16 - split_idx = 0 - vocab_per_split = 512 * 6 - - hidden = torch.randn(batch, seqlen, dim, device="cuda", dtype=dtype) - weight = torch.randn(vocab_size, dim, device="cuda", dtype=dtype) - labels = torch.randint(0, vocab_size, (batch, seqlen), device="cuda", dtype=torch.long) - num_valid_tokens = torch.tensor(batch * seqlen, device="cuda", dtype=torch.int64) - - dlogprobs = torch.randn(batch, seqlen, device="cuda", dtype=torch.float32) - - def get_maximum_and_accu(hidden, weight): - logits = (hidden @ weight.T).to(torch.float32) - maximum, _ = torch.max(logits, dim=-1) - accu = torch.sum(torch.exp(logits - maximum.unsqueeze(-1)), dim=-1) - return maximum, accu - maximum, accu = get_maximum_and_accu(hidden, weight) - - dlogits_partial = torch.empty( - (batch, seqlen, vocab_per_split), - device=hidden.device, - dtype=hidden.dtype - ) - - # compile kernel - bwd_kernel = BwdPartialDlogits( - vocab_per_split=vocab_per_split, - reduction=0 - ) - - hidden_packed = from_dlpack( - hidden.view(-1, dim), - assumed_align=16).mark_compact_shape_dynamic(mode=0) - weight_packed = from_dlpack( - weight, - assumed_align=16 - ) - labels_packed = from_dlpack( - labels.view(-1), - assumed_align=8 - ).mark_compact_shape_dynamic(mode=0) - dlogprobs_packed = from_dlpack( - dlogprobs.view(-1), - assumed_align=16 - ).mark_compact_shape_dynamic(mode=0) - maximum_packed = from_dlpack( - maximum.view(-1), - assumed_align=8 - ).mark_compact_shape_dynamic(mode=0) - accu_packed = from_dlpack( - accu.view(-1), - assumed_align=8 - ).mark_compact_shape_dynamic(mode=0) - dlogits_partial_packed = from_dlpack( - dlogits_partial.view(-1, vocab_per_split), - assumed_align=32, - ).mark_compact_shape_dynamic(mode=0) - scalarNumValidTokens_packed = cute.runtime.make_ptr( - cutlass.Int64, - num_valid_tokens.data_ptr(), - cute.AddressSpace.gmem, - assumed_align=8 - ) - - ignore_index = -100 - - stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) - - rank = 0 - - compiled = cute.compile( - bwd_kernel, - split_idx, - hidden_packed, - weight_packed, - labels_packed, - dlogprobs_packed, - maximum_packed, - accu_packed, - dlogits_partial_packed, - scalarNumValidTokens_packed, - ignore_index, - rank, - stream, - ) - - start, stop = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True) - start.record(stream=torch.cuda.current_stream()) - with torch.cuda.nvtx.range("BwdPartialDlogits"): - compiled( - split_idx, - hidden_packed, - weight_packed, - labels_packed, - dlogprobs_packed, - maximum_packed, - accu_packed, - dlogits_partial_packed, - scalarNumValidTokens_packed, - ignore_index, - rank, - stream - ) - stop.record(stream=torch.cuda.current_stream()) - - torch.cuda.synchronize() - - elapsed_time = start.elapsed_time(stop) - - print(dlogits_partial) - - print(f"Success, Elapsed time: {elapsed_time:.4f} ms") \ No newline at end of file diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py index e26661ca06a..786f0fd9b3b 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py @@ -1,32 +1,55 @@ -import torch -import torch.distributed as dist +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + import typing -import triton +import cuda.bindings.driver as cuda # type: ignore import cutlass import cutlass.cute as cute +import torch +import torch.distributed as dist +import triton # type: ignore from cutlass.cute.runtime import from_dlpack -import cuda.bindings.driver as cuda import megatron.core.fusions.linear_cross_entropy.utils as utils -import megatron.core.fusions.linear_cross_entropy.blackwell.fwd_mainloop as fwd_mainloop -import megatron.core.fusions.linear_cross_entropy.blackwell.bwd_partial_dlogits as bwd_partial_dlogits -import megatron.core.fusions.linear_cross_entropy.blackwell.triton as triton_kernels +from megatron.core.fusions.linear_cross_entropy.blackwell import ( + bwd_partial_dlogits as bwd_partial_dlogits, +) +from megatron.core.fusions.linear_cross_entropy.blackwell import fwd_mainloop as fwd_mainloop +from megatron.core.fusions.linear_cross_entropy.blackwell import triton as triton_kernels + + +class FwdConfig: + """ + The configuration for the forward pass. + """ + + _dedicated_stream: torch.cuda.Stream = None + _dedicated_events: typing.List[torch.cuda.Event] = list() + _initialized: bool = False + _fwd_mainloop_kernels: typing.Dict[str, cute.kernel] = dict() + + +class BwdConfig: + """ + The configuration for the backward pass. + """ + + _bwd_kernel: typing.Dict[str, cute.kernel] = dict() + + +_fwd_config = FwdConfig() +_bwd_config = BwdConfig() -# import linear_cross_entropy.utils as utils -# import linear_cross_entropy.blackwell.fwd_mainloop as fwd_mainloop -# import linear_cross_entropy.blackwell.bwd_partial_dlogits as bwd_partial_dlogits -# import linear_cross_entropy.blackwell.triton as triton_kernels def forward( hidden: torch.Tensor, weight: torch.Tensor, labels: torch.Tensor, tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, - reduction: typing.Optional[str] = "mean", - ignore_index: typing.Optional[int] = -100, - sequence_parallel: typing.Optional[bool] = False, -) -> typing.Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + reduction: str = "mean", + ignore_index: int = -100, + sequence_parallel: bool = False, +) -> typing.Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int, int, torch.Tensor]: """ forward host function """ @@ -42,45 +65,34 @@ def forward( # weight must be [vocab_size, dim] assert weight.dim() == 2 # labels could be [batch, seqlen] or [seqlen, batch] or [tokens] - assert ((hidden.dim() == 2 and labels.dim() == 1) - or (hidden.dim() == 3 and labels.dim() == 2)) + assert (hidden.dim() == 2 and labels.dim() == 1) or (hidden.dim() == 3 and labels.dim() == 2) assert hidden.is_contiguous() and weight.is_contiguous() and labels.is_contiguous() hidden_view = hidden.view(-1, hidden.shape[-1]) labels_view = labels.view(-1) - assert ((sequence_parallel and hidden_view.shape[0] * tp_world_size == labels_view.shape[0]) - or (not sequence_parallel and hidden_view.shape[0] == labels_view.shape[0])) + assert (sequence_parallel and hidden_view.shape[0] * tp_world_size == labels_view.shape[0]) or ( + not sequence_parallel and hidden_view.shape[0] == labels_view.shape[0] + ) assert hidden_view.shape[1] == weight.shape[1] global_hidden = hidden if in_tp_mode and sequence_parallel: partial_hidden_shape = hidden.shape - global_hidden_shape = ( - partial_hidden_shape[0] * tp_world_size, - *partial_hidden_shape[1:] - ) - global_hidden = torch.empty( - global_hidden_shape, - dtype=hidden.dtype, - device=hidden.device - ) - dist.all_gather_into_tensor( - global_hidden, - hidden, - group=tp_group - ) + global_hidden_shape = (partial_hidden_shape[0] * tp_world_size, *partial_hidden_shape[1:]) + global_hidden = torch.empty(global_hidden_shape, dtype=hidden.dtype, device=hidden.device) + dist.all_gather_into_tensor(global_hidden, hidden, group=tp_group) assert global_hidden.is_contiguous() hidden_view = global_hidden.view(-1, global_hidden.shape[-1]) num_tokens, dim = hidden_view.shape vocab_size, _ = weight.shape - if not hasattr(forward, "_initialized"): - global _dedicated_stream, _dedicated_events - _dedicated_stream = torch.cuda.Stream(hidden.device) - _dedicated_events = [torch.cuda.Event() for _ in range(2)] - forward._initialized = True + global _fwd_config + if not _fwd_config._initialized: + _fwd_config._dedicated_stream = torch.cuda.Stream(hidden.device) + _fwd_config._dedicated_events = [torch.cuda.Event() for _ in range(2)] + _fwd_config._initialized = True REDUCTION = utils.str_to_reduction_enum(reduction) # declare logprobs @@ -94,7 +106,9 @@ def forward( maximum = torch.empty((num_tokens,), device=hidden.device, dtype=torch.float32) accumulate = torch.empty_like(maximum, dtype=torch.float32) num_valid_tokens = torch.empty((), device=hidden.device, dtype=torch.int64) - assert maximum.is_contiguous() and accumulate.is_contiguous() and num_valid_tokens.is_contiguous() + assert ( + maximum.is_contiguous() and accumulate.is_contiguous() and num_valid_tokens.is_contiguous() + ) # declare intermediate tensors # NOTE: this is a parameter for tuning vocab_per_split = 512 * 6 @@ -110,44 +124,31 @@ def forward( assert _max.is_contiguous() and _accu.is_contiguous() and _logprobs.is_contiguous() triton_kernels.get_num_valid_tokens[(1,)]( - num_tokens, - ignore_index, - labels_view, - labels_view.stride(0), - num_valid_tokens, + num_tokens, ignore_index, labels_view, labels_view.stride(0), num_valid_tokens ) - - if not hasattr(forward, "_fwd_mainloop_kernels"): - forward._fwd_mainloop_kernels = dict() # need to compile the kernel for the first time - hidden_packed = from_dlpack( - hidden_view.detach(), assumed_align=16 - ).mark_compact_shape_dynamic(mode=0) - weight_packed = from_dlpack( - weight.detach(), assumed_align=16 + hidden_packed = from_dlpack(hidden_view.detach(), assumed_align=16).mark_compact_shape_dynamic( + mode=0 + ) + weight_packed = from_dlpack(weight.detach(), assumed_align=16) + labels_packed = from_dlpack(labels_view.detach(), assumed_align=8).mark_compact_shape_dynamic( + mode=0 + ) + logprobs_packed = from_dlpack(_logprobs, assumed_align=16).mark_compact_shape_dynamic(mode=0) + _max_packed = from_dlpack(_max, assumed_align=8).mark_compact_shape_dynamic( + mode=0, stride_order=(0, 1) + ) + _accu_packed = from_dlpack(_accu, assumed_align=8).mark_compact_shape_dynamic( + mode=0, stride_order=(0, 1) ) - labels_packed = from_dlpack( - labels_view.detach(), assumed_align=8 - ).mark_compact_shape_dynamic(mode=0) - logprobs_packed = from_dlpack( - _logprobs, assumed_align=16 - ).mark_compact_shape_dynamic(mode=0) - _max_packed = from_dlpack( - _max, assumed_align=8 - ).mark_compact_shape_dynamic(mode=0, stride_order=(0, 1)) - _accu_packed = from_dlpack( - _accu, assumed_align=8 - ).mark_compact_shape_dynamic(mode=0, stride_order=(0, 1)) cuda_stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) # VocabSize and Dim are fixed for a given model, # only the number of tokens can vary key = f"vocab_size:{vocab_size}+dim:{dim}+dtype:{hidden_view.dtype}" - if forward._fwd_mainloop_kernels.get(key) is None: - fwd_mainloop_kernel = fwd_mainloop.FwdMainLoop( - vocab_per_split=vocab_per_split, - ) + if _fwd_config._fwd_mainloop_kernels.get(key) is None: + fwd_mainloop_kernel = fwd_mainloop.FwdMainLoop(vocab_per_split=vocab_per_split) fwd_mainloop_compiled_kernel = cute.compile( fwd_mainloop_kernel, hidden_packed, @@ -158,11 +159,11 @@ def forward( _accu_packed, ignore_index, tp_rank, - cuda_stream + cuda_stream, ) - forward._fwd_mainloop_kernels[key] = fwd_mainloop_compiled_kernel + _fwd_config._fwd_mainloop_kernels[key] = fwd_mainloop_compiled_kernel else: - fwd_mainloop_compiled_kernel = forward._fwd_mainloop_kernels[key] + fwd_mainloop_compiled_kernel = _fwd_config._fwd_mainloop_kernels[key] fwd_mainloop_compiled_kernel( hidden_packed, weight_packed, @@ -172,10 +173,11 @@ def forward( _accu_packed, ignore_index, tp_rank, - cuda_stream + cuda_stream, ) - + if not in_tp_mode: + def grid(meta): return (triton.cdiv(num_tokens, meta["BLOCK_SIZE_M"]),) @@ -205,11 +207,11 @@ def grid(meta): _max_backup = _max.clone() dist.all_reduce(_max, op=dist.ReduceOp.MAX, group=tp_group) - torch.cuda.current_stream().record_event(_dedicated_events[0]) - with torch.cuda.stream(_dedicated_stream): - _dedicated_stream.wait_event(_dedicated_events[0]) + torch.cuda.current_stream().record_event(_fwd_config._dedicated_events[0]) + with torch.cuda.stream(_fwd_config._dedicated_stream): + _fwd_config._dedicated_stream.wait_event(_fwd_config._dedicated_events[0]) dist.all_reduce(_logprobs, op=dist.ReduceOp.SUM, group=tp_group) - _dedicated_stream.record_event(_dedicated_events[1]) + _fwd_config._dedicated_stream.record_event(_fwd_config._dedicated_events[1]) def grid(meta): return (triton.cdiv(num_tokens, meta["BLOCK_SIZE_M"]),) @@ -235,7 +237,7 @@ def grid(meta): dist.all_reduce(accumulate, op=dist.ReduceOp.SUM, group=tp_group) # update logprobs - torch.cuda.current_stream().wait_event(_dedicated_events[1]) + torch.cuda.current_stream().wait_event(_fwd_config._dedicated_events[1]) triton_kernels.forward_tp_epilogue_update_logprobs[grid]( num_tokens, ignore_index, @@ -254,6 +256,7 @@ def grid(meta): return logprobs, maximum, accumulate, num_valid_tokens, tp_rank, tp_world_size, global_hidden + def backward( dlogprobs: torch.Tensor, global_hidden: torch.Tensor, @@ -262,13 +265,13 @@ def backward( maximum: torch.Tensor, accu: torch.Tensor, num_valid_tokens: torch.Tensor, - reduction: typing.Optional[str] = "mean", - ignore_index: typing.Optional[int] = -100, + reduction: str = "mean", + ignore_index: int = -100, tp_group: typing.Optional[dist.ProcessGroup] = None, - tp_rank: typing.Optional[int] = 0, - tp_world_size: typing.Optional[int] = 1, - sequence_parallel: typing.Optional[bool] = False, -) -> typing.List[torch.Tensor]: + tp_rank: int = 0, + tp_world_size: int = 1, + sequence_parallel: bool = False, +) -> typing.Tuple[torch.Tensor, torch.Tensor]: """ backward host function """ @@ -282,13 +285,16 @@ def backward( REDUCTION = utils.str_to_reduction_enum(reduction) dlogprobs_view = dlogprobs.view(-1) - assert ( - (REDUCTION == utils.EntropyReductionEnum.kNone and dlogprobs.shape == (num_tokens,)) - or (REDUCTION != utils.EntropyReductionEnum.kNone and dlogprobs.dim() == 0) + assert (REDUCTION == utils.EntropyReductionEnum.kNone and dlogprobs.shape == (num_tokens,)) or ( + REDUCTION != utils.EntropyReductionEnum.kNone and dlogprobs.dim() == 0 ) assert dlogprobs.is_contiguous() and dlogprobs.is_cuda - assert num_valid_tokens.dim() == 0 and num_valid_tokens.is_cuda and num_valid_tokens.dtype == torch.int64 + assert ( + num_valid_tokens.dim() == 0 + and num_valid_tokens.is_cuda + and num_valid_tokens.dtype == torch.int64 + ) d_hidden = torch.empty_like(global_hidden) d_weight = torch.empty_like(weight) @@ -301,60 +307,38 @@ def backward( num_splits = (vocab_size + vocab_per_split - 1) // vocab_per_split _d_logits = torch.empty( - (num_tokens, vocab_per_split), - device=global_hidden.device, - dtype=global_hidden.dtype + (num_tokens, vocab_per_split), device=global_hidden.device, dtype=global_hidden.dtype ) hidden_packed = from_dlpack( - hidden_view.detach(), - assumed_align=16 + hidden_view.detach(), assumed_align=16 ).mark_compact_shape_dynamic(mode=0) - weight_packed = from_dlpack( - weight.detach(), - assumed_align=16 - ) + weight_packed = from_dlpack(weight.detach(), assumed_align=16) labels_packed = from_dlpack( - labels_view.detach(), - assumed_align=8 + labels_view.detach(), assumed_align=8 ).mark_compact_shape_dynamic(mode=0) dlogprobs_packed = from_dlpack( - dlogprobs_view.detach(), - assumed_align=8 - ).mark_compact_shape_dynamic(mode=0) - maximum_packed = from_dlpack( - maximum.detach(), - assumed_align=8 - ).mark_compact_shape_dynamic(mode=0) - accu_packed = from_dlpack( - accu.detach(), - assumed_align=8 - ).mark_compact_shape_dynamic(mode=0) - dlogits_packed = from_dlpack( - _d_logits, - assumed_align=32 + dlogprobs_view.detach(), assumed_align=8 ).mark_compact_shape_dynamic(mode=0) + maximum_packed = from_dlpack(maximum.detach(), assumed_align=8).mark_compact_shape_dynamic( + mode=0 + ) + accu_packed = from_dlpack(accu.detach(), assumed_align=8).mark_compact_shape_dynamic(mode=0) + dlogits_packed = from_dlpack(_d_logits, assumed_align=32).mark_compact_shape_dynamic(mode=0) scalarNumValidTokens_packed = cute.runtime.make_ptr( - cutlass.Int64, - num_valid_tokens.data_ptr(), - cute.AddressSpace.gmem, - assumed_align=8 + cutlass.Int64, num_valid_tokens.data_ptr(), cute.AddressSpace.gmem, assumed_align=8 ) stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) - if not hasattr(backward, "_bwd_kernel"): - backward._bwd_kernel = dict() - key = f"vocab_size:{vocab_size}+dim:{dim}+reduction:{REDUCTION}+dtype:{hidden_view.dtype}" - if backward._bwd_kernel.get(key) is None: + if _bwd_config._bwd_kernel.get(key) is None: bwd_kernel = bwd_partial_dlogits.BwdPartialDlogits( - reduction=REDUCTION, - vocab_per_split=vocab_per_split, + reduction=REDUCTION, vocab_per_split=vocab_per_split ) bwd_kernel_compiled = cute.compile( bwd_kernel, - 0, # split_idx + 0, # split_idx hidden_packed, weight_packed, labels_packed, @@ -365,11 +349,11 @@ def backward( scalarNumValidTokens_packed, ignore_index, tp_rank, - stream + stream, ) - backward._bwd_kernel[key] = bwd_kernel_compiled + _bwd_config._bwd_kernel[key] = bwd_kernel_compiled else: - bwd_kernel_compiled = backward._bwd_kernel.get(key) + bwd_kernel_compiled = _bwd_config._bwd_kernel.get(key) for split_idx in range(num_splits): bwd_kernel_compiled( @@ -384,29 +368,28 @@ def backward( scalarNumValidTokens_packed, ignore_index, tp_rank, - stream + stream, ) + # remove padding areas + # cublas can handle non-contiguous tensors + # therefore, we do not need to contiguous the tensor vocab_right_bound = ( min((split_idx + 1) * vocab_per_split, vocab_size) - split_idx * vocab_per_split ) - # remove padding areas - _d_logits = _d_logits[:, :vocab_right_bound].contiguous() - - if split_idx == 0: - torch.matmul( - _d_logits, - weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :], - out=d_hidden.view(num_tokens, dim) - ) - else: - d_hidden += torch.matmul( - _d_logits, - weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :], - ).view(d_hidden.shape) + valid_d_logits = _d_logits[:, :vocab_right_bound] + + torch.addmm( + input=d_hidden.view(-1, dim), + mat1=valid_d_logits, + mat2=weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :], + beta=(split_idx != 0), + alpha=1.0, + out=d_hidden.view(-1, dim), + ) torch.matmul( - _d_logits.T, + valid_d_logits.T, hidden_view, - out=d_weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :] + out=d_weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :], ) else: raise NotImplementedError(f"Unsupported backward method: {_backward}") @@ -416,10 +399,12 @@ def backward( if sequence_parallel: partial_hidden_shape = ( global_hidden.shape[0] // tp_world_size, - *global_hidden.shape[1:] + *global_hidden.shape[1:], ) partial_num_tokens = num_tokens // tp_world_size - d_hidden = d_hidden.view(-1, d_hidden.shape[-1])[tp_rank * partial_num_tokens : (tp_rank + 1) * partial_num_tokens, :] + d_hidden = d_hidden.view(-1, d_hidden.shape[-1])[ + tp_rank * partial_num_tokens : (tp_rank + 1) * partial_num_tokens, : + ] d_hidden = d_hidden.view(partial_hidden_shape).clone() - - return d_hidden, d_weight \ No newline at end of file + + return d_hidden, d_weight diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py b/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py index 81346b0df81..ebb9709822c 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py @@ -1,41 +1,45 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + """ Implementations of the fusion lm_head(Linear) + Cross-Entropy kernel """ -from typing import Optional, Type, Tuple, Union -import cuda.bindings.driver as cuda - -import torch +from typing import Tuple, Type +import cuda.bindings.driver as cuda # type: ignore import cutlass import cutlass.cute as cute -import cutlass.utils as utils -import cutlass.pipeline as pipeline +import cutlass.pipeline as pipeline # type: ignore +import cutlass.utils as utils # type: ignore +import cutlass.utils.blackwell_helpers as sm100_utils # type: ignore from cutlass.cute.nvgpu import cpasync, tcgen05 -import cutlass.torch as cutlass_torch -import cutlass.utils.blackwell_helpers as sm100_utils -from cutlass.cute.runtime import from_dlpack - SM100_TMEM_CAPACITY_COLUMNS: int = 512 + def make_thread_cooperative_group(size: int): + """ + Create a thread cooperative group. + """ return pipeline.CooperativeGroup(pipeline.Agent.Thread, size, alignment=size) + class FwdMainLoop: """ This class implements the mainloop for forward process. Traits stored as attributes. - :param acc_dtype: + :param acc_dtype: """ - def __init__(self, - acc_dtype: Type[cutlass.Numeric] = cutlass.Float32, - use_2cta_instrs: bool = False, - mma_tiler_mn: Tuple[int, int] = (128, 256), - vocab_per_split: int = 512): + def __init__( + self, + acc_dtype: Type[cutlass.Numeric] = cutlass.Float32, + use_2cta_instrs: bool = False, + mma_tiler_mn: Tuple[int, int] = (128, 256), + vocab_per_split: int = 512, + ): """ Configuration including: - MMA instruction settings @@ -45,16 +49,10 @@ def __init__(self, self.use_2cta_instrs = use_2cta_instrs # This is the shape covered by tiledMMA, not just single MMA instruction self.mma_tiler = (*mma_tiler_mn, 1) - self.cta_tiler = ( - self.mma_tiler[0], - vocab_per_split, - self.mma_tiler[2] - ) + self.cta_tiler = (self.mma_tiler[0], vocab_per_split, self.mma_tiler[2]) self.vocab_per_split = vocab_per_split - - self.cta_group = ( - tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE - ) + + self.cta_group = tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE self.cluster_shape_mn = (2, 1) if self.use_2cta_instrs else (1, 1) self.occupancy = 1 @@ -73,19 +71,14 @@ def __init__(self, self.empty_warp_ids = (6, 7) self.threads_per_cta: int = self.threads_per_warp * len( - (*self.epi_warp_ids, - self.load_warp_ids, - self.mma_warp_ids, - *self.empty_warp_ids) + (*self.epi_warp_ids, self.load_warp_ids, self.mma_warp_ids, *self.empty_warp_ids) ) self.cta_sync_barrier = pipeline.NamedBarrier( - barrier_id = 1, - num_threads = self.threads_per_cta + barrier_id=1, num_threads=self.threads_per_cta ) self.tmem_alloc_barrier = pipeline.NamedBarrier( - barrier_id = 2, - num_threads = self.threads_per_cta + barrier_id=2, num_threads=self.threads_per_cta ) self.buffer_align_bytes: int = 1024 @@ -97,26 +90,14 @@ def _compute_stages( tiled_mma: cute.TiledMma, mma_tiler: Tuple[int, int, int], a_dtype: Type[cutlass.Numeric], - b_dtype: Type[cutlass.Numeric] + b_dtype: Type[cutlass.Numeric], ): a_smem_layout_stage_one = sm100_utils.make_smem_layout_a( - tiled_mma, - mma_tiler, - a_dtype, - 1, # only single stage - ) - b_smem_layout_stage_one = sm100_utils.make_smem_layout_b( - tiled_mma, - mma_tiler, - b_dtype, - 1, - ) - a_bytes_per_stage = cute.size_in_bytes( - a_dtype, a_smem_layout_stage_one - ) - b_bytes_per_stage = cute.size_in_bytes( - b_dtype, b_smem_layout_stage_one + tiled_mma, mma_tiler, a_dtype, 1 # only single stage ) + b_smem_layout_stage_one = sm100_utils.make_smem_layout_b(tiled_mma, mma_tiler, b_dtype, 1) + a_bytes_per_stage = cute.size_in_bytes(a_dtype, a_smem_layout_stage_one) + b_bytes_per_stage = cute.size_in_bytes(b_dtype, b_smem_layout_stage_one) num_acc_stage = 2 num_a_stage = 4 num_b_stage = 4 @@ -132,30 +113,26 @@ def _setup_attributes( ): self.cluster_shape_mnk = (*self.cluster_shape_mn, 1) self.cluster_layout_vmnk = cute.tiled_divide( - cute.make_layout(self.cluster_shape_mnk), - (tiled_mma.thr_id.shape,), + cute.make_layout(self.cluster_shape_mnk), (tiled_mma.thr_id.shape,) ) # this is fixed for dense MMA, k=16 mma_inst_shape_k = cute.size(tiled_mma.shape_mnk, mode=[2]) # 16*4 = 64; 64 * sizeof(FP16) = 128Bytes mma_inst_tile_k: int = 4 - self.mma_tiler = ( - self.mma_tiler[0], - self.mma_tiler[1], - mma_inst_shape_k * mma_inst_tile_k - ) + self.mma_tiler = (self.mma_tiler[0], self.mma_tiler[1], mma_inst_shape_k * mma_inst_tile_k) - self.num_acc_stage, self.num_a_stage, self.num_b_stage, self.num_epi_stage_per_tile =\ + self.num_acc_stage, self.num_a_stage, self.num_b_stage, self.num_epi_stage_per_tile = ( self._compute_stages(tiled_mma, self.mma_tiler, a_dtype, b_dtype) + ) self.tmem_alloc_cols = self.num_acc_stage * self.mma_tiler[1] assert self.tmem_alloc_cols <= SM100_TMEM_CAPACITY_COLUMNS self.cta_tile_shape_mnk = ( self.mma_tiler[0] // cute.size(tiled_mma.thr_id.shape), self.mma_tiler[1], - self.mma_tiler[2] - ) + self.mma_tiler[2], + ) @cute.kernel def kernel( @@ -174,8 +151,11 @@ def kernel( cluster_layout_vmnk: cute.Layout, problem_mnk: Tuple[int, int, int], ignore_index: cutlass.Int64, - rank: cutlass.Int32 + rank: cutlass.Int32, ): + """ + The forward kernel for the mainloop. + """ warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) tidx, _, _ = cute.arch.thread_idx() bidx, bidy, _ = cute.arch.block_idx() @@ -196,7 +176,7 @@ def kernel( producer_group=make_thread_cooperative_group(len([self.load_warp_ids])), consumer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), tx_count=self.tma_copy_a_bytes + self.tma_copy_b_bytes, - barrier_storage=storage.load_ab_mbar_ptr.data_ptr() + barrier_storage=storage.load_ab_mbar_ptr.data_ptr(), ) ab_producer_state = pipeline.make_pipeline_state( pipeline.PipelineUserType.Producer, self.num_a_stage @@ -211,7 +191,7 @@ def kernel( consumer_group=make_thread_cooperative_group( self.threads_per_warp * len(self.epi_warp_ids) ), - barrier_storage=storage.mma_mbar_ptr.data_ptr() + barrier_storage=storage.mma_mbar_ptr.data_ptr(), ) mma_producer_state = pipeline.make_pipeline_state( pipeline.PipelineUserType.Producer, self.num_acc_stage @@ -224,23 +204,16 @@ def kernel( if warp_idx == self.empty_warp_ids[0]: with cute.arch.elect_one(): cute.arch.mbarrier_init( - tmem_dealloc_mbar_ptr, - self.threads_per_warp * len(self.epi_warp_ids) + tmem_dealloc_mbar_ptr, self.threads_per_warp * len(self.epi_warp_ids) ) cute.arch.mbarrier_init_fence() # -------- SMEM partition ------------ # # swizzle o [(tileM, tileK), loopM, loopK, Stage] - sA = storage.sA.get_tensor( - a_smem_layout_staged.outer, - swizzle=a_smem_layout_staged.inner - ) + sA = storage.sA.get_tensor(a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner) # swizzle o [(tileN, tileK), loopN, loopK, stage] - sB = storage.sB.get_tensor( - b_smem_layout_staged.outer, - swizzle=b_smem_layout_staged.inner - ) - + sB = storage.sB.get_tensor(b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner) + # FIXME: if 2 CTAs, modify here thr_mma = tiled_mma.get_slice(0) # [MMA, loopM, loopK, stage] @@ -250,72 +223,50 @@ def kernel( # ---------- GMEM partition ----------- # # [tileM, tileK, loopK] - gA = cute.local_tile( - mA, - (self.mma_tiler[0], self.mma_tiler[2]), - (pidm, None) - ) + gA = cute.local_tile(mA, (self.mma_tiler[0], self.mma_tiler[2]), (pidm, None)) # [vocab_size_per_split, dim] mB_n = cute.local_tile( - mB, - (self.vocab_per_split, cute.size(mB.layout.shape, mode=[1])), - (pidn, 0) + mB, (self.vocab_per_split, cute.size(mB.layout.shape, mode=[1])), (pidn, 0) ) # [tileN, tileK, loopN, loopK] - gB = cute.local_tile( - mB_n, - (self.mma_tiler[1], self.mma_tiler[2]), - (None, None) - ) - + gB = cute.local_tile(mB_n, (self.mma_tiler[1], self.mma_tiler[2]), (None, None)) + # [MMA, tileCntM, tileCntK, loopK] tCgA = thr_mma.partition_A(gA) # [MMA, tileCntN, tileCntK, loopN, loopK] tCgB = thr_mma.partition_B(gB) - a_cta_layout = cute.make_layout( - cute.slice_( - cluster_layout_vmnk, - (0, 0, None, 0)).shape - ) + a_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, 0, None, 0)).shape) # FIXME: if 2 CTAs, modify here cta_rank_in_cluster = 0 - block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord( - cta_rank_in_cluster - ) + block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord(cta_rank_in_cluster) tTMAsA, tTMAgA = cpasync.tma_partition( tma_atom_a, - block_in_cluster_coord_vmnk[2], # cta_coord, + block_in_cluster_coord_vmnk[2], # cta_coord, a_cta_layout, - cute.group_modes(sA, 0, 3), # SMEM tensor - cute.group_modes(tCgA, 0, 3) # GMEM tensor - ) - b_cta_layout = cute.make_layout( - cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape + cute.group_modes(sA, 0, 3), # SMEM tensor + cute.group_modes(tCgA, 0, 3), # GMEM tensor ) + b_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape) tTMAsB, tTMAgB = cpasync.tma_partition( tma_atom_b, - block_in_cluster_coord_vmnk[1], # cta_coord + block_in_cluster_coord_vmnk[1], # cta_coord b_cta_layout, cute.group_modes(sB, 0, 3), - cute.group_modes(tCgB, 0, 3) + cute.group_modes(tCgB, 0, 3), ) # Allocate TMEM tmem_holding_buf = storage.tmem_holding_buf if warp_idx == self.empty_warp_ids[0]: cute.arch.alloc_tmem( - self.tmem_alloc_cols, - tmem_holding_buf, - is_two_cta=self.use_2cta_instrs + self.tmem_alloc_cols, tmem_holding_buf, is_two_cta=self.use_2cta_instrs ) self.cta_sync_barrier.arrive_and_wait() tmem_ptr = cute.arch.retrieve_tmem_ptr( - self.acc_dtype, - alignment=16, - ptr_to_buffer_holding_addr=tmem_holding_buf + self.acc_dtype, alignment=16, ptr_to_buffer_holding_addr=tmem_holding_buf ) # [(tileM, tileN), loopM, loopN] @@ -323,16 +274,14 @@ def kernel( acc_shape = thr_mma.partition_shape_C(tmem_shape) tCtC_fake = thr_mma.make_fragment_C(acc_shape) tCtC = cute.make_tensor(tmem_ptr, tCtC_fake.layout) - - block_vocab_left_idx: cutlass.Int64 = ( - pidn * self.vocab_per_split - ) - block_vocab_right_idx: cutlass.Int64 = ( - min((pidn + 1) * self.vocab_per_split, problem_mnk[1]) + + block_vocab_left_idx: cutlass.Int64 = pidn * self.vocab_per_split + block_vocab_right_idx: cutlass.Int64 = min( + (pidn + 1) * self.vocab_per_split, problem_mnk[1] ) num_n_tiles: cutlass.Int64 = cute.ceil_div( - (block_vocab_right_idx - block_vocab_left_idx), - self.mma_tiler[1]) + (block_vocab_right_idx - block_vocab_left_idx), self.mma_tiler[1] + ) # /////// # empty @@ -353,13 +302,13 @@ def kernel( tma_atom_a, tTMAgA[(None, k)], tTMAsA[(None, ab_producer_state.index)], - tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state) + tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state), ) cute.copy( tma_atom_b, tTMAgB[(None, n, k)], tTMAsB[(None, ab_producer_state.index)], - tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state) + tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state), ) ab_pipeline.producer_commit(ab_producer_state) ab_producer_state.advance() @@ -384,7 +333,7 @@ def kernel( cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), tCsA[(None, None, kblock_idx, ab_consumer_state.index)], tCsB[(None, None, kblock_idx, ab_consumer_state.index)], - cute.append_ones(tCtC[(None, None, mma_producer_state.index)]) + cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), ) # enable accumulate for the next tile tiled_mma.set(tcgen05.Field.ACCUMULATE, True) @@ -404,21 +353,18 @@ def kernel( # epilog TMEM copy and partition copy_atom_t2r = sm100_utils.get_tmem_load_op( self.cta_tile_shape_mnk, - utils.LayoutEnum.ROW_MAJOR, # This is hard-coded + utils.LayoutEnum.ROW_MAJOR, # This is hard-coded self.acc_dtype, self.acc_dtype, (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), - self.use_2cta_instrs + self.use_2cta_instrs, ) # [tileM, subTileN, loopM, CntSubTileN, loopN] tAcc_epi = cute.flat_divide( tCtC[((None, None), 0, None)], - (self.epi_tile[0], - self.epi_tile[1] // self.num_epi_stage_per_tile) - ) - tiled_copy_t2r = tcgen05.make_tmem_copy( - copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)] + (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), ) + tiled_copy_t2r = tcgen05.make_tmem_copy(copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)]) thr_copy_t2r = tiled_copy_t2r.get_slice(tidx) tTMEM_load_tAcc = thr_copy_t2r.partition_S(tAcc_epi) # [(pattern), loopM, loopN, CntTileM, CntTileN] @@ -429,131 +375,84 @@ def kernel( # [tileM, subTileN, loopM, CntSubTileN, CntTileN] tCcAcc_epi = cute.flat_divide( tCcAcc[((None, None), 0, None)], - (self.epi_tile[0], - self.epi_tile[1] // self.num_epi_stage_per_tile) + (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), ) tTMEM_load_cAcc = thr_copy_t2r.partition_D(tCcAcc_epi) - tTMEM_load_cAcc_shape = cute.select( - tTMEM_load_cAcc.shape, - mode=[0, 1, 2] - ) + tTMEM_load_cAcc_shape = cute.select(tTMEM_load_cAcc.shape, mode=[0, 1, 2]) # epilogue layouts epilogue_thread_layout = cute.make_layout((128, 1)) - copy_atom_g2r = cute.make_copy_atom( - cute.nvgpu.CopyUniversalOp(), - mLabels.element_type - ) - tiled_copy_g2r = cute.make_tiled_copy( - copy_atom_g2r, - epilogue_thread_layout, - (128, 1) - ) + copy_atom_g2r = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), mLabels.element_type) + tiled_copy_g2r = cute.make_tiled_copy(copy_atom_g2r, epilogue_thread_layout, (128, 1)) thr_copy_g2r = tiled_copy_g2r.get_slice(tidx) - copy_atom_r2g = cute.make_copy_atom( - cute.nvgpu.CopyUniversalOp(), - cutlass.Float32 - ) - tiled_copy_r2g = cute.make_tiled_copy( - copy_atom_r2g, - epilogue_thread_layout, - (128, 1) - ) + copy_atom_r2g = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), cutlass.Float32) + tiled_copy_r2g = cute.make_tiled_copy(copy_atom_r2g, epilogue_thread_layout, (128, 1)) thr_copy_r2g = tiled_copy_r2g.get_slice(tidx) - # auxiliary tensors # [tileM] - gLabels = cute.local_tile( - mLabels, - (self.epi_tile[0],), - (pidm,) - ) + gLabels = cute.local_tile(mLabels, (self.epi_tile[0],), (pidm,)) tLabelsCAcc = thr_copy_g2r.partition_S(cAcc)[(None, None, 0)] tLabelsCAcc_mask = cute.make_fragment(tLabelsCAcc.shape, cutlass.Boolean) # [(1, 1), 1] - tLabelsCAcc_mask[0] = cute.elem_less( - pidm * self.epi_tile[0] + tidx, - problem_mnk[0] - ) + tLabelsCAcc_mask[0] = cute.elem_less(pidm * self.epi_tile[0] + tidx, problem_mnk[0]) # to align shape with gMax and gAccu tLabelsCAcc_mask = cute.append_ones(tLabelsCAcc_mask) # [(1, 1), 1, 1] tLabelsgLabels = thr_copy_g2r.partition_S(cute.append_ones(gLabels)) tLabelsrLabels = cute.make_fragment(tLabelsgLabels.shape, tLabelsgLabels.element_type) - cute.copy( - tiled_copy_g2r, - tLabelsgLabels, - tLabelsrLabels, - pred=tLabelsCAcc_mask - ) - valid_mask: cutlass.Boolean =\ - (tLabelsrLabels[0] != ignore_index) and tLabelsCAcc_mask[0] + cute.copy(tiled_copy_g2r, tLabelsgLabels, tLabelsrLabels, pred=tLabelsCAcc_mask) + valid_mask: cutlass.Boolean = (tLabelsrLabels[0] != ignore_index) and tLabelsCAcc_mask[ + 0 + ] # [tileM, 1] - gMax = cute.local_tile( - mMax, - (self.epi_tile[0], 1), - (pidm, pidn) - ) + gMax = cute.local_tile(mMax, (self.epi_tile[0], 1), (pidm, pidn)) # [(CPYM, CPYN), loopM, loopN] tR2GgMax = thr_copy_r2g.partition_D(gMax) tR2GrMax = cute.make_fragment(tR2GgMax.shape, tR2GgMax.element_type) tR2GrMax.fill(-1e30) # [tileM, 1] - gAccu = cute.local_tile( - mAccu, - (self.epi_tile[0], 1), - (pidm, pidn) - ) + gAccu = cute.local_tile(mAccu, (self.epi_tile[0], 1), (pidm, pidn)) # [(CPYM, CPYN), loopM, loopN] tR2GgAccu = thr_copy_r2g.partition_D(gAccu) tR2GrAccu = cute.make_fragment(tR2GgAccu.shape, tR2GgAccu.element_type) tR2GrAccu.fill(0.0) - + # [tileM, 1] - gLogprobs = cute.append_ones(cute.local_tile( - mLogprobs, - (self.epi_tile[0],), - (pidm,) - )) + gLogprobs = cute.append_ones(cute.local_tile(mLogprobs, (self.epi_tile[0],), (pidm,))) # [(CPYM, CPYN), loopM, loopN] tR2GgLogprobs = thr_copy_r2g.partition_D(gLogprobs) tR2GrLogprobs = cute.make_fragment(tR2GgLogprobs.shape, tR2GgLogprobs.element_type) tR2GrLogprobs.fill(0.0) # [(tileN // num_epi_stage_per_tile, 1), 1, 1] - tTMEM_load_rAcc = cute.make_fragment( - tTMEM_load_cAcc_shape, - self.acc_dtype - ) + tTMEM_load_rAcc = cute.make_fragment(tTMEM_load_cAcc_shape, self.acc_dtype) for n in cutlass.range(num_n_tiles): mma_pipeline.consumer_wait(mma_consumer_state) - left: cutlass.Int64 = ( - block_vocab_left_idx + n * self.epi_tile[1] - ) - right: cutlass.Int64 = ( - min((n + 1) * self.epi_tile[1] + block_vocab_left_idx, - block_vocab_right_idx) + left: cutlass.Int64 = block_vocab_left_idx + n * self.epi_tile[1] + right: cutlass.Int64 = min( + (n + 1) * self.epi_tile[1] + block_vocab_left_idx, block_vocab_right_idx ) num_n_subtiles: cutlass.Int64 = cute.ceil_div( - (right - left), - cute.size(tTMEM_load_rAcc, mode=[0]) + (right - left), cute.size(tTMEM_load_rAcc, mode=[0]) ) for n_subtile in cutlass.range(num_n_subtiles): cute.copy( tiled_copy_t2r, tTMEM_load_tAcc[(None, None, None, n_subtile, mma_consumer_state.index)], - tTMEM_load_rAcc + tTMEM_load_rAcc, ) - for idx in cutlass.range(cute.size(tTMEM_load_rAcc, mode=[0]), unroll_full=True): + for idx in cutlass.range( + cute.size(tTMEM_load_rAcc, mode=[0]), unroll_full=True + ): local_position: cutlass.Int64 = ( n * self.epi_tile[1] + n_subtile * cute.size(tTMEM_load_rAcc, mode=[0]) @@ -567,77 +466,46 @@ def kernel( tR2GrAccu[0] = coeff * tR2GrAccu[0] + exp_logits position: cutlass.Int64 = ( - rank * problem_mnk[1] - + pidn * self.vocab_per_split - + local_position + rank * problem_mnk[1] + pidn * self.vocab_per_split + local_position ) mask: cutlass.Boolean = valid_mask and (position == tLabelsrLabels[0]) - tR2GrLogprobs[0] += (mask * tTMEM_load_rAcc[idx]) + tR2GrLogprobs[0] += mask * tTMEM_load_rAcc[idx] mma_pipeline.consumer_release(mma_consumer_state) mma_consumer_state.advance() - cute.copy( - tiled_copy_r2g, - tR2GrMax, - tR2GgMax, - pred=tLabelsCAcc_mask - ) - cute.copy( - tiled_copy_r2g, - tR2GrAccu, - tR2GgAccu, - pred=tLabelsCAcc_mask - ) + cute.copy(tiled_copy_r2g, tR2GrMax, tR2GgMax, pred=tLabelsCAcc_mask) + cute.copy(tiled_copy_r2g, tR2GrAccu, tR2GgAccu, pred=tLabelsCAcc_mask) - vocab_left_idx: cutlass.Int64 = ( - rank * problem_mnk[1] - + pidn * self.vocab_per_split - ) - vocab_right_idx: cutlass.Int64 = ( - rank * problem_mnk[1] - + min((pidn + 1) * self.vocab_per_split, problem_mnk[1]) + vocab_left_idx: cutlass.Int64 = rank * problem_mnk[1] + pidn * self.vocab_per_split + vocab_right_idx: cutlass.Int64 = rank * problem_mnk[1] + min( + (pidn + 1) * self.vocab_per_split, problem_mnk[1] ) valid: cutlass.Boolean = ( - tLabelsrLabels[0] >= vocab_left_idx - and tLabelsrLabels[0] < vocab_right_idx + tLabelsrLabels[0] >= vocab_left_idx and tLabelsrLabels[0] < vocab_right_idx ) tLabelsCAcc_mask[0] &= valid - cute.copy( - tiled_copy_r2g, - tR2GrLogprobs, - tR2GgLogprobs, - pred=tLabelsCAcc_mask - ) + cute.copy(tiled_copy_r2g, tR2GrLogprobs, tR2GgLogprobs, pred=tLabelsCAcc_mask) # Dealloc TMEM self.cta_sync_barrier.arrive_and_wait() if warp_idx == self.empty_warp_ids[0]: cute.arch.relinquish_tmem_alloc_permit() - cute.arch.dealloc_tmem( - tmem_ptr, - self.tmem_alloc_cols, - is_two_cta=self.use_2cta_instrs - ) + cute.arch.dealloc_tmem(tmem_ptr, self.tmem_alloc_cols, is_two_cta=self.use_2cta_instrs) @staticmethod def _compute_grid( problem_mnk: Tuple[int, int, int], cluster_shape_mn: Tuple[int, int], cta_tiler: Tuple[int, int, int], - num_splits: int + num_splits: int, ) -> Tuple[int, int, int]: cluster_shape = (*cluster_shape_mn, 1) grid = cute.round_up( - ( - cute.ceil_div(problem_mnk[0], cta_tiler[0]), - num_splits, - 1, - ), - cluster_shape + (cute.ceil_div(problem_mnk[0], cta_tiler[0]), num_splits, 1), cluster_shape ) return grid @@ -658,42 +526,31 @@ def __call__( b_dtype: Type[cutlass.Numeric] = weight.element_type if cutlass.const_expr(hidden.element_type != weight.element_type): - raise RuntimeError(f"data type don't match: {hidden.element_type} v.s. {weight.element_type}") + raise RuntimeError( + f"data type don't match: {hidden.element_type} v.s. {weight.element_type}" + ) if cutlass.const_expr(hidden.element_type not in [cutlass.Float16, cutlass.BFloat16]): raise RuntimeError("hidden can only be FP16 or BF16") if cutlass.const_expr(hidden.layout.shape[1] != weight.layout.shape[1]): raise RuntimeError("K dimension doesn't match") - - problem_mnk = ( - hidden.layout.shape[0], - weight.layout.shape[0], - hidden.layout.shape[1], - ) + + problem_mnk = (hidden.layout.shape[0], weight.layout.shape[0], hidden.layout.shape[1]) if cutlass.const_expr((problem_mnk[2] * a_dtype.width // 8) % 16 != 0): raise RuntimeError(f"K dimension is not 16B aligned: {problem_mnk[2]}") num_splits = cute.ceil_div(problem_mnk[1], self.vocab_per_split) - # if cutlass.const_expr(_max.layout.shape != (hidden.layout.shape[0], num_splits)): - # raise RuntimeError(f"max shape mismatch: {_max.layout.shape} != ({hidden.layout.shape[0]}, {num_splits})") - # if cutlass.const_expr(_accu.layout.shape != (hidden.layout.shape[0], num_splits)): - # raise RuntimeError(f"accu shape mismatch: {_accu.layout.shape} != ({hidden.layout.shape[0]}, {num_splits})") grid = self._compute_grid( - problem_mnk = problem_mnk, - cluster_shape_mn = self.cluster_shape_mn, - cta_tiler = self.cta_tiler, - num_splits = num_splits + problem_mnk=problem_mnk, + cluster_shape_mn=self.cluster_shape_mn, + cta_tiler=self.cta_tiler, + num_splits=num_splits, ) a_major_mode = utils.LayoutEnum.from_tensor(hidden).mma_major_mode() b_major_mode = utils.LayoutEnum.from_tensor(weight).mma_major_mode() - + tiled_mma = sm100_utils.make_trivial_tiled_mma( - a_dtype, - a_major_mode, - b_major_mode, - self.acc_dtype, - self.cta_group, - self.mma_tiler[:2] + a_dtype, a_major_mode, b_major_mode, self.acc_dtype, self.cta_group, self.mma_tiler[:2] ) self._setup_attributes(tiled_mma, a_dtype, b_dtype) @@ -701,20 +558,14 @@ def __call__( raise RuntimeError(f"K dimension is not 128B aligned: {problem_mnk[2]}") self.epi_tile = self.mma_tiler[:2] - + # Swizzle o [(tileM, tileK), loopM, loopK, stage] a_smem_layout_staged = sm100_utils.make_smem_layout_a( - tiled_mma, - self.mma_tiler, - a_dtype, - self.num_a_stage + tiled_mma, self.mma_tiler, a_dtype, self.num_a_stage ) # Swizzle o [(tileN, tileK), loopN, loopK, stage] b_smem_layout_staged = sm100_utils.make_smem_layout_b( - tiled_mma, - self.mma_tiler, - b_dtype, - self.num_b_stage + tiled_mma, self.mma_tiler, b_dtype, self.num_b_stage ) # TMA loading @@ -722,32 +573,26 @@ def __call__( tma_store_op = cpasync.CopyBulkTensorTileS2GOp() # Swizzle o [(tileM, tileK), loopM, loopK] - a_smem_layout = cute.select( - a_smem_layout_staged, - mode=[0, 1, 2] - ) + a_smem_layout = cute.select(a_smem_layout_staged, mode=[0, 1, 2]) # create tma copy atom for hidden, # and the cooresponding tma descriptor tensor tma_atom_a, tma_desc_a = cute.nvgpu.make_tiled_tma_atom_A( tma_load_op, - hidden, # gmem_tensor - a_smem_layout, # SMEM layout - self.mma_tiler, # MMA tiler - tiled_mma, # TiledMMA - self.cluster_layout_vmnk.shape # cluster_shape_vmnk + hidden, # gmem_tensor + a_smem_layout, # SMEM layout + self.mma_tiler, # MMA tiler + tiled_mma, # TiledMMA + self.cluster_layout_vmnk.shape, # cluster_shape_vmnk ) # Swizzle o [(tileN, tileK), loopN, loopK] - b_smem_layout = cute.select( - b_smem_layout_staged, - mode=[0, 1, 2] - ) + b_smem_layout = cute.select(b_smem_layout_staged, mode=[0, 1, 2]) tma_atom_b, tma_desc_b = cute.nvgpu.make_tiled_tma_atom_B( tma_load_op, - weight, # gmem_tensor - b_smem_layout, # SMEM layout - self.mma_tiler, # MMA tiler - tiled_mma, # TiledMMA - self.cluster_layout_vmnk.shape # cluster_shape_vmnk + weight, # gmem_tensor + b_smem_layout, # SMEM layout + self.mma_tiler, # MMA tiler + tiled_mma, # TiledMMA + self.cluster_layout_vmnk.shape, # cluster_shape_vmnk ) a_copy_size = cute.size_in_bytes(a_dtype, a_smem_layout) b_copy_size = cute.size_in_bytes(b_dtype, b_smem_layout) @@ -755,8 +600,13 @@ def __call__( self.tma_copy_b_bytes = b_copy_size assert self.num_a_stage == self.num_b_stage + @cute.struct class SharedStorage: + """ + The shared storage for the forward kernel. + """ + # pipeline barriers, 2 = producer + consumer load_ab_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_a_stage * 2] mma_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2] @@ -772,6 +622,7 @@ class SharedStorage: cute.struct.MemRange[b_dtype, cute.cosize(b_smem_layout_staged)], self.buffer_align_bytes, ] + self.shared_storage = SharedStorage # launch kernel @@ -798,95 +649,3 @@ class SharedStorage: stream=stream, ) return None - - -if __name__ == "__main__": - rank = 0 - - vocab_per_split = 512 * 6 - fwd_mainloop = FwdMainLoop( - vocab_per_split=vocab_per_split - ) # use default arguments - - torch.manual_seed(1111) - - num_tokens = 13092 - hidden_size = 4096 - vocab_size = 152064 - # num_tokens = 4 - # hidden_size = 64 - # vocab_size = 512 - dtype = torch.bfloat16 - ignore_index = -100 - - hidden = ( - torch.empty((num_tokens, hidden_size), dtype=dtype, device="cuda") - .uniform_(-0.5, 0.5) - ) - weight = ( - torch.empty((vocab_size, hidden_size), dtype=dtype, device="cuda") - .uniform_(-0.5, 0.5) - ) - # hidden = torch.ones((num_tokens, hidden_size), dtype=dtype, device="cuda") - # weight = torch.ones((vocab_size, hidden_size), dtype=dtype, device="cuda") - labels = torch.randint(0, vocab_size, (num_tokens,), device="cuda") - - # pad 1 ignore_index to the right - padded_labels = torch.nn.functional.pad( - labels, (0, 1), value=ignore_index - ) - # remove first element - labels = padded_labels[..., 1:].contiguous() - - # allocate output tensor - logprobs = torch.empty((num_tokens), dtype=torch.float32, device="cuda") - - # allocate intermediate tensors - num_splits = (vocab_size + vocab_per_split - 1) // vocab_per_split - _max = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) - _accu = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) - - - # compile kernel - _hidden = from_dlpack(hidden, assumed_align=16).mark_compact_shape_dynamic(mode=0, divisibility=1) - _weight = from_dlpack(weight, assumed_align=16) - _labels = from_dlpack(labels, assumed_align=8).mark_compact_shape_dynamic(mode=0) - _logprobs = from_dlpack(logprobs, assumed_align=16).mark_compact_shape_dynamic(mode=0) - _max_ = from_dlpack(_max, assumed_align=8).mark_compact_shape_dynamic(mode=0) - _accu_ = from_dlpack(_accu, assumed_align=8).mark_compact_shape_dynamic(mode=0) - stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) - compiled = cute.compile(fwd_mainloop, - _hidden, _weight, _labels, _logprobs, - _max_, _accu_, - ignore_index, - rank, - stream) - - # launch kernel - start, stop = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True) - - with torch.cuda.nvtx.range("FwdMainLoop"): - start.record(stream=torch.cuda.current_stream()) - compiled(_hidden, _weight, _labels, _logprobs, _max_, _accu_, ignore_index, rank, stream) - stop.record(stream=torch.cuda.current_stream()) - - torch.cuda.synchronize() - - elapsed_time = start.elapsed_time(stop) - - gemm = torch.matmul(hidden.to(torch.float32), weight.T.to(torch.float32)) - # print(gemm) - - # print(_max) - # print(_accu) - # print(logprobs) - - cut_max, _ = torch.max(_max, dim=1) - print(cut_max) - # for i in range(cut_max.shape[0]): - # print(i, cut_max[i]) - - torch_max, _ = torch.max(gemm, dim=1) - print(torch_max) - - print(f"Success, Elapsed time: {elapsed_time:.4f} ms") \ No newline at end of file diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py b/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py index d7f45d152c2..e025cc046f4 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/triton.py @@ -1,8 +1,11 @@ -import triton -import triton.language as tl +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + +import triton # type: ignore +import triton.language as tl # type: ignore # NOTE: tl.pointer_type() is not available in Triton 3.3.0 + @triton.autotune( configs=[ triton.Config({"BLOCK_SIZE_M": 1024}, num_stages=3, num_warps=32), @@ -14,9 +17,9 @@ def get_num_valid_tokens( num_tokens: tl.int64, ignore_index: tl.int64, - labels_ptr,#: tl.pointer_type(tl.int64), + labels_ptr, #: tl.pointer_type(tl.int64), stride_labels: tl.int64, - num_valid_tokens_ptr,#: tl.pointer_type(tl.int64), + num_valid_tokens_ptr, #: tl.pointer_type(tl.int64), BLOCK_SIZE_M: tl.constexpr, ): """ @@ -29,9 +32,7 @@ def get_num_valid_tokens( offs_am = m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) labels = tl.load( - labels_ptr + offs_am * stride_labels, - mask=offs_am < num_tokens, - other=ignore_index + labels_ptr + offs_am * stride_labels, mask=offs_am < num_tokens, other=ignore_index ) valid_labels_mask = labels != ignore_index @@ -40,32 +41,30 @@ def get_num_valid_tokens( @triton.autotune( - configs=[ - triton.Config({"BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 64}) - ], - key=["num_tokens", "num_splits"] + configs=[triton.Config({"BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 64})], + key=["num_tokens", "num_splits"], ) @triton.jit def forward_dp_epilogue( num_tokens: tl.int64, - num_splits: tl.int64, # TODO: maybe this could be a constexpr + num_splits: tl.int64, # TODO: maybe this could be a constexpr ignore_index: tl.int64, - labels_ptr,#: tl.pointer_type(tl.int64), + labels_ptr, #: tl.pointer_type(tl.int64), stride_labels: tl.int64, - num_valid_tokens_ptr,#: tl.pointer_type(tl.int64), - max_ptr,#: tl.pointer_type(tl.float32), + num_valid_tokens_ptr, #: tl.pointer_type(tl.int64), + max_ptr, #: tl.pointer_type(tl.float32), stride_max_m: tl.int64, stride_max_n: tl.int64, - accu_ptr,#: tl.pointer_type(tl.float32), + accu_ptr, #: tl.pointer_type(tl.float32), stride_accu_m: tl.int64, stride_accu_n: tl.int64, - global_max_ptr,#: tl.pointer_type(tl.float32), + global_max_ptr, #: tl.pointer_type(tl.float32), stride_global_max: tl.int64, - global_accu_ptr,#: tl.pointer_type(tl.float32), + global_accu_ptr, #: tl.pointer_type(tl.float32), stride_global_accu: tl.int64, - global_logprobs_ptr,#: tl.pointer_type(tl.float32), + global_logprobs_ptr, #: tl.pointer_type(tl.float32), stride_global_logprobs: tl.int64, - global_logprobs_scalar_ptr,#: tl.pointer_type(tl.float32), + global_logprobs_scalar_ptr, #: tl.pointer_type(tl.float32), REDUCTION: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, @@ -103,78 +102,52 @@ def forward_dp_epilogue( global_accu = _coeff * global_accu + tl.sum(_scale * _accu, axis=1) # store maximum - tl.store( - global_max_ptr + offs_m * stride_global_max, - global_max, - mask=offs_m < num_tokens, - ) + tl.store(global_max_ptr + offs_m * stride_global_max, global_max, mask=offs_m < num_tokens) # store accumulate - tl.store( - global_accu_ptr + offs_m * stride_global_accu, - global_accu, - mask=offs_m < num_tokens, - ) + tl.store(global_accu_ptr + offs_m * stride_global_accu, global_accu, mask=offs_m < num_tokens) # update logprobs labels = tl.load( - labels_ptr + offs_m * stride_labels, - mask=offs_m < num_tokens, - other=ignore_index, + labels_ptr + offs_m * stride_labels, mask=offs_m < num_tokens, other=ignore_index ) global_logprobs_ptrs = global_logprobs_ptr + offs_m * stride_global_logprobs - global_logprobs = tl.load( - global_logprobs_ptrs, - mask=offs_m < num_tokens, - ) + global_logprobs = tl.load(global_logprobs_ptrs, mask=offs_m < num_tokens) global_logprobs = global_max + tl.log(global_accu) - global_logprobs label_mask = labels != ignore_index global_logprobs = tl.where(label_mask, global_logprobs, 0.0) - if REDUCTION == 0: # no-reduction - tl.store( - global_logprobs_ptrs, - global_logprobs, - mask=offs_m < num_tokens, - ) - elif REDUCTION == 1: # sum + if REDUCTION == 0: # no-reduction + tl.store(global_logprobs_ptrs, global_logprobs, mask=offs_m < num_tokens) + elif REDUCTION == 1: # sum global_logprobs_scalar = tl.sum(global_logprobs, axis=0) - tl.atomic_add( - global_logprobs_scalar_ptr, - global_logprobs_scalar - ) - elif REDUCTION == 2: # mean + tl.atomic_add(global_logprobs_scalar_ptr, global_logprobs_scalar) + elif REDUCTION == 2: # mean num_valid_tokens = tl.load(num_valid_tokens_ptr) global_logprobs_scalar = tl.fdiv( - tl.sum(global_logprobs, axis=0), - num_valid_tokens.to(tl.float32), - ) - tl.atomic_add( - global_logprobs_scalar_ptr, - global_logprobs_scalar + tl.sum(global_logprobs, axis=0), num_valid_tokens.to(tl.float32) ) + tl.atomic_add(global_logprobs_scalar_ptr, global_logprobs_scalar) @triton.autotune( - configs=[ - triton.Config({"BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 64}), - ], - key=["num_tokens", "num_splits"] + configs=[triton.Config({"BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 64})], + key=["num_tokens", "num_splits"], ) @triton.jit def forward_tp_epilogue( num_tokens: tl.int64, num_splits: tl.int64, - reduced_max_ptr,#: tl.pointer_type(tl.float32), + reduced_max_ptr, #: tl.pointer_type(tl.float32), stride_reduced_max_m: tl.int64, stride_reduced_max_n: tl.int64, - original_max_ptr,#: tl.pointer_type(tl.float32), + original_max_ptr, #: tl.pointer_type(tl.float32), stride_original_max_m: tl.int64, stride_original_max_n: tl.int64, - accu_ptr,#: tl.pointer_type(tl.float32), + accu_ptr, #: tl.pointer_type(tl.float32), stride_accu_m: tl.int64, stride_accu_n: tl.int64, - global_max_ptr,#: tl.pointer_type(tl.float32), + global_max_ptr, #: tl.pointer_type(tl.float32), stride_global_max: tl.int64, - global_accu_ptr,#: tl.pointer_type(tl.float32), + global_accu_ptr, #: tl.pointer_type(tl.float32), stride_global_accu: tl.int64, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, @@ -193,12 +166,16 @@ def forward_tp_epilogue( offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) _reduced_max = tl.load( - reduced_max_ptr + offs_m[:, None] * stride_reduced_max_m + offs_n[None, :] * stride_reduced_max_n, + reduced_max_ptr + + offs_m[:, None] * stride_reduced_max_m + + offs_n[None, :] * stride_reduced_max_n, mask=(offs_m[:, None] < num_tokens) & (offs_n[None, :] < num_splits), other=0.0, ) _original_max = tl.load( - original_max_ptr + offs_m[:, None] * stride_original_max_m + offs_n[None, :] * stride_original_max_n, + original_max_ptr + + offs_m[:, None] * stride_original_max_m + + offs_n[None, :] * stride_original_max_n, mask=(offs_m[:, None] < num_tokens) & (offs_n[None, :] < num_splits), other=0.0, ) @@ -219,38 +196,25 @@ def forward_tp_epilogue( global_accu = _coeff * global_accu + tl.sum(_scale * _accu, axis=1) # store - tl.store( - global_max_ptr + offs_m * stride_global_max, - global_max, - mask=offs_m < num_tokens, - ) - tl.store( - global_accu_ptr + offs_m * stride_global_accu, - global_accu, - mask=offs_m < num_tokens - ) + tl.store(global_max_ptr + offs_m * stride_global_max, global_max, mask=offs_m < num_tokens) + tl.store(global_accu_ptr + offs_m * stride_global_accu, global_accu, mask=offs_m < num_tokens) -@triton.autotune( - configs=[ - triton.Config({"BLOCK_SIZE_M": 16}) - ], - key=["num_tokens"] -) +@triton.autotune(configs=[triton.Config({"BLOCK_SIZE_M": 16})], key=["num_tokens"]) @triton.jit def forward_tp_epilogue_update_logprobs( num_tokens: tl.int64, ignore_index: tl.int64, - num_valid_tokens_ptr,#: tl.pointer_type(tl.int64), - labels_ptr,#: tl.pointer_type(tl.int64), + num_valid_tokens_ptr, #: tl.pointer_type(tl.int64), + labels_ptr, #: tl.pointer_type(tl.int64), stride_labels: tl.int64, - logprobs_ptr,#: tl.pointer_type(tl.float32), + logprobs_ptr, #: tl.pointer_type(tl.float32), stride_logprobs: tl.int64, - maximum_ptr,#: tl.pointer_type(tl.float32), + maximum_ptr, #: tl.pointer_type(tl.float32), stride_maximum: tl.int64, - accumulate_ptr,#: tl.pointer_type(tl.float32), + accumulate_ptr, #: tl.pointer_type(tl.float32), stride_accumulate: tl.int64, - logprobs_scalar_ptr,#: tl.pointer_type(tl.float32), + logprobs_scalar_ptr, #: tl.pointer_type(tl.float32), REDUCTION: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, ): @@ -261,45 +225,24 @@ def forward_tp_epilogue_update_logprobs( offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) - logprobs = tl.load( - logprobs_ptr + offs_m * stride_logprobs, - mask=offs_m < num_tokens, - ) - maximum = tl.load( - maximum_ptr + offs_m * stride_maximum, - mask=offs_m < num_tokens, - ) - accumulate = tl.load( - accumulate_ptr + offs_m * stride_accumulate, - mask=offs_m < num_tokens, - ) + logprobs = tl.load(logprobs_ptr + offs_m * stride_logprobs, mask=offs_m < num_tokens) + maximum = tl.load(maximum_ptr + offs_m * stride_maximum, mask=offs_m < num_tokens) + accumulate = tl.load(accumulate_ptr + offs_m * stride_accumulate, mask=offs_m < num_tokens) labels = tl.load( - labels_ptr + offs_m * stride_labels, - mask=offs_m < num_tokens, - other=ignore_index, + labels_ptr + offs_m * stride_labels, mask=offs_m < num_tokens, other=ignore_index ) label_mask = labels != ignore_index logprobs = maximum + tl.log(accumulate) - logprobs logprobs = tl.where(label_mask, logprobs, 0.0) - if REDUCTION == 0: # no-reduction - tl.store( - logprobs_ptr + offs_m * stride_logprobs, - logprobs, - mask=offs_m < num_tokens, - ) - elif REDUCTION == 1: # sum + if REDUCTION == 0: # no-reduction + tl.store(logprobs_ptr + offs_m * stride_logprobs, logprobs, mask=offs_m < num_tokens) + elif REDUCTION == 1: # sum logprobs_scalar = tl.sum(logprobs, axis=0) - tl.atomic_add( - logprobs_scalar_ptr, - logprobs_scalar - ) - elif REDUCTION == 2: # mean + tl.atomic_add(logprobs_scalar_ptr, logprobs_scalar) + elif REDUCTION == 2: # mean num_valid_tokens = tl.load(num_valid_tokens_ptr) - logprobs_scalar = tl.fdiv( - tl.sum(logprobs, axis=0), - num_valid_tokens.to(tl.float32), - ) - tl.atomic_add(logprobs_scalar_ptr, logprobs_scalar) \ No newline at end of file + logprobs_scalar = tl.fdiv(tl.sum(logprobs, axis=0), num_valid_tokens.to(tl.float32)) + tl.atomic_add(logprobs_scalar_ptr, logprobs_scalar) diff --git a/megatron/core/fusions/linear_cross_entropy/utils.py b/megatron/core/fusions/linear_cross_entropy/utils.py index 642a6b3b230..9a62b9826cb 100644 --- a/megatron/core/fusions/linear_cross_entropy/utils.py +++ b/megatron/core/fusions/linear_cross_entropy/utils.py @@ -1,16 +1,20 @@ -import typing +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + from dataclasses import dataclass + @dataclass class EntropyReductionEnum: """ Enum for the reduction method of cross entropy. """ + kNone = 0 kSum = 1 kMean = 2 -def str_to_reduction_enum(reduction: str) -> EntropyReductionEnum: + +def str_to_reduction_enum(reduction: str) -> int: """ str -> EntropyReductionEnum """ @@ -25,8 +29,13 @@ def str_to_reduction_enum(reduction: str) -> EntropyReductionEnum: raise ValueError(f"Invalid reduction: {reduction}") return _enum + @dataclass class BackwardMethodEnum: + """ + Enum for the backward method of linear cross entropy. + """ + # two separate kernels for d_hidden and d_weight, respectively kTwoKernels = 0 # calculate partial d_logits along its N dimension diff --git a/megatron/core/models/common/language_module/language_module.py b/megatron/core/models/common/language_module/language_module.py index 15352075661..b7013be89f0 100644 --- a/megatron/core/models/common/language_module/language_module.py +++ b/megatron/core/models/common/language_module/language_module.py @@ -60,7 +60,7 @@ def __init__( "If you don't need embd_group, you need to explicitly set it to None." ) self.embd_group = pg_collection.embd - self.vp_stage = None + self.vp_stage: Optional[int] = None self.vp_size = self.config.virtual_pipeline_model_parallel_size def _is_in_embd_group(self): @@ -134,8 +134,8 @@ def compute_language_model_loss_without_logits( sequence_parallel_enabled: bool = False, column_parallel_linear: torch.nn.Module = None, col_linear_kwargs: Dict[str, Any] = {}, - reduction: Optional[str] = "none", - ignore_index: Optional[int] = -100, + reduction: str = "none", + ignore_index: int = -100, ) -> Tuple[Tensor, Optional[Tensor]]: """Computes the language model logits and loss (Cross entropy across vocabulary) @@ -159,6 +159,9 @@ def compute_language_model_loss_without_logits( assert ( weight is not None ), "weight cannot be None when using fused linear cross entropy." + assert ( + labels is not None + ), "labels cannot be None when using fused linear cross entropy." # [b s] => [s b] labels = labels.transpose(0, 1).contiguous() loss = linear_cross_entropy( @@ -327,7 +330,7 @@ def shared_embedding_or_output_weight(self) -> Tensor: def sharded_state_dict( self, prefix: str = '', - sharded_offsets: Tuple[Tuple[int, int, int]] = (), + sharded_offsets: Tuple[Tuple[int, int, int], ...] = (), metadata: Optional[dict] = None, ) -> ShardedStateDict: """Sharded state dict implementation that handles the output layer weights tying. diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py index 5e3950d0003..0bb144e408d 100644 --- a/megatron/core/models/gpt/gpt_model.py +++ b/megatron/core/models/gpt/gpt_model.py @@ -1,7 +1,7 @@ # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. from collections import OrderedDict -from typing import Dict, Literal, Optional +from typing import Any, Dict, List, Literal, Optional, Tuple import torch from torch import Tensor @@ -118,8 +118,8 @@ def __init__( self.fp16_lm_cross_entropy = fp16_lm_cross_entropy self.parallel_output = parallel_output self.share_embeddings_and_output_weights = share_embeddings_and_output_weights - self.vp_stage = vp_stage self.disable_param_offloading = True + self.vp_stage: Optional[int] = vp_stage if hasattr(self.config, 'position_embedding_type'): self.position_embedding_type = self.config.position_embedding_type @@ -199,7 +199,7 @@ def __init__( ), "mrope require mrope_section setting, but we got None from TransformerConfig" # Cache for RoPE tensors which do not change between iterations. - self.rotary_pos_emb_cache = {} + self.rotary_pos_emb_cache: Dict[int, Tuple[Tensor, Tensor]] = {} # Transformer. self.decoder = TransformerBlock( @@ -219,6 +219,8 @@ def __init__( # Output if self.post_process: + self.embedding_activation_buffer: Optional[List[Tensor]] = None + self.grad_output_buffer: Optional[List[Tensor]] = None if self.config.defer_embedding_wgrad_compute: # The embedding activation buffer preserves a reference to the input activations # of the final embedding projection layer GEMM. It will hold the activations for @@ -395,7 +397,7 @@ def _preprocess( if in_inference_mode and not has_config_logger_enabled(self.config): decoder_input = WrappedTensor(decoder_input) - preproc_output = ( + preproc_output: Tuple[Any, ...] = ( decoder_input, rotary_pos_emb, rotary_pos_cos, @@ -439,7 +441,7 @@ def forward( labels: Tensor = None, inference_context: BaseInferenceContext = None, packed_seq_params: PackedSeqParams = None, - extra_block_kwargs: dict = None, + extra_block_kwargs: Optional[Dict[str, Any]] = None, runtime_gather_output: Optional[bool] = None, *, inference_params: Optional[BaseInferenceContext] = None, @@ -709,7 +711,7 @@ def build_schedule_plan( labels: Tensor = None, inference_context: BaseInferenceContext = None, packed_seq_params: PackedSeqParams = None, - extra_block_kwargs: dict = None, + extra_block_kwargs: Optional[Dict[str, Any]] = None, runtime_gather_output: Optional[bool] = None, inference_params: Optional[BaseInferenceContext] = None, loss_mask: Optional[Tensor] = None, diff --git a/megatron/core/models/mamba/mamba_model.py b/megatron/core/models/mamba/mamba_model.py index 98d918ce448..eab86d6d532 100644 --- a/megatron/core/models/mamba/mamba_model.py +++ b/megatron/core/models/mamba/mamba_model.py @@ -61,7 +61,7 @@ def __init__( pre_process: bool = True, hybrid_attention_ratio: float = 0.0, hybrid_mlp_ratio: float = 0.0, - hybrid_override_pattern: str = None, + hybrid_override_pattern: Optional[str] = None, post_process: bool = True, fp16_lm_cross_entropy: bool = False, parallel_output: bool = True, diff --git a/tests/unit_tests/a2a_overlap/utils.py b/tests/unit_tests/a2a_overlap/utils.py index 994998337d8..d80eaf13f5a 100644 --- a/tests/unit_tests/a2a_overlap/utils.py +++ b/tests/unit_tests/a2a_overlap/utils.py @@ -222,8 +222,8 @@ def get_test_config(num_layers=1, num_moe_experts=8, extra_kwargs={}, moe_groupe def get_valid_token_dispatcher_types(): try: - from deep_ep import Buffer - from deep_ep.utils import EventHandle, EventOverlap + from deep_ep import Buffer # type: ignore + from deep_ep.utils import EventHandle, EventOverlap # type: ignore return ["alltoall", "flex"] except ImportError: diff --git a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py index 130a2bb5a71..a36b8cfb4e0 100644 --- a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py +++ b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py @@ -1,15 +1,19 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + import contextlib +import os +import typing from contextlib import ExitStack import numpy as np import pytest import torch +import torch.distributed as dist from torch.utils.data import DataLoader, Dataset from torch.utils.data.distributed import DistributedSampler -import torch.distributed as dist import megatron.core.parallel_state as ps +from megatron.core.fusions.fused_linear_cross_entropy import linear_cross_entropy from megatron.core.models.gpt.gpt_layer_specs import ( get_gpt_decoder_block_spec, get_gpt_mtp_block_spec, @@ -23,10 +27,6 @@ ) from tests.unit_tests.test_utilities import Utils -from megatron.core.fusions.fused_linear_cross_entropy import linear_cross_entropy - -import os -import typing class MockDataset(Dataset): """ @@ -138,8 +138,8 @@ def init_gpt_dataloader( @pytest.mark.skipif( - ("WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2),# or True, - reason="Requires torchrun with multiple GPUs" + ("WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2), # or True, + reason="Requires torchrun with multiple GPUs", ) class TestFusedLinearCrossEntropyOnGptModel: @pytest.mark.parametrize("fp8_flag", get_valid_fp8_flags()) @@ -198,8 +198,7 @@ def test_gpt_model(self, mtp_layers, dispatcher_type, fp8_flag, layer_num): @pytest.mark.skipif( - "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "1", - reason="Requires single GPU" + "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "1", reason="Requires single GPU" ) class TestFusedLinearCrossEntropyDataParallel: def cleanup(self): @@ -216,7 +215,7 @@ def torch_linear_cross_entropy( weight: torch.Tensor, labels: torch.Tensor, reduction: str, - ignore_index: int + ignore_index: int, ): # NOTE: need to convert to fp32 to fp32 accumulation, # thus assure accuracy @@ -262,36 +261,28 @@ def test_kernel_launch(self): for num_token in num_tokens: hidden = torch.randn(num_token, dim, dtype=dtype, device="cuda").requires_grad_() labels = torch.randint(0, vocab_size, (num_token,), dtype=torch.long, device="cuda") - - logprobs = linear_cross_entropy(hidden, weight, labels, reduction=reduction, ignore_index=ignore_index) + + logprobs = linear_cross_entropy( + hidden, weight, labels, reduction=reduction, ignore_index=ignore_index + ) assert not torch.isnan(logprobs).any() gLogprobs = torch.randn_like(logprobs) (d_hidden, d_weight) = torch.autograd.grad( - (logprobs,), - (hidden, weight), - (gLogprobs,), - retain_graph=False + (logprobs,), (hidden, weight), (gLogprobs,), retain_graph=False ) assert not torch.isnan(d_hidden).any() assert not torch.isnan(d_weight).any() - @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) @pytest.mark.parametrize("problem", get_problems()) @pytest.mark.parametrize("reduction", ["none", "mean", "sum"]) @pytest.mark.parametrize("ignore_index", get_ignore_index()) - def test_correctness( - self, - dtype, - problem, - reduction, - ignore_index - ): + def test_correctness(self, dtype, problem, reduction, ignore_index): num_tokens, vocabsize, dim = problem hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) labels_shape = (num_tokens,) if isinstance(num_tokens, int) else num_tokens - + hidden = ( torch.empty(hidden_shape, dtype=dtype, device="cuda") .uniform_(-0.1, 0.1) @@ -303,65 +294,40 @@ def test_correctness( .requires_grad_() ) labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") - if ignore_index >=0 and ignore_index < vocabsize: + if ignore_index >= 0 and ignore_index < vocabsize: pad_labels = torch.nn.functional.pad(labels, (0, 1), value=ignore_index) labels = pad_labels[..., 1:].contiguous() # forward - torch_logprobs = self.torch_linear_cross_entropy(hidden, weight, labels, - reduction=reduction, ignore_index=ignore_index) - - custom_logprobs = linear_cross_entropy(hidden, weight, labels, - reduction=reduction, ignore_index=ignore_index) + torch_logprobs = self.torch_linear_cross_entropy( + hidden, weight, labels, reduction=reduction, ignore_index=ignore_index + ) - torch.testing.assert_close( - torch_logprobs, - custom_logprobs + custom_logprobs = linear_cross_entropy( + hidden, weight, labels, reduction=reduction, ignore_index=ignore_index ) + torch.testing.assert_close(torch_logprobs, custom_logprobs) + # backward - g_logprobs = ( - torch.empty_like(torch_logprobs) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(torch_logprobs).uniform_(-0.1, 0.1) (d_torch_hidden, d_torch_weight) = torch.autograd.grad( - (torch_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (torch_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) (d_custom_hidden, d_custom_weight) = torch.autograd.grad( - (custom_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False) - - torch.testing.assert_close( - d_torch_hidden, - d_custom_hidden, - atol=1e-3, - rtol=1e-3 - ) - torch.testing.assert_close( - d_torch_weight, - d_custom_weight, - atol=1e-3, - rtol=1e-3 + (custom_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) + torch.testing.assert_close(d_torch_hidden, d_custom_hidden, atol=1e-3, rtol=1e-3) + torch.testing.assert_close(d_torch_weight, d_custom_weight, atol=1e-3, rtol=1e-3) + @pytest.mark.parametrize("problem", [((1, 4096), 129280, 7168)]) @pytest.mark.parametrize("dtype", [torch.bfloat16]) @pytest.mark.parametrize("reduction", ["mean"]) @pytest.mark.parametrize("ignore_index", [-100]) - def test_performance( - self, - problem, - dtype, - reduction, - ignore_index - ): + def test_performance(self, problem, dtype, reduction, ignore_index): num_tokens, vocabsize, dim = problem hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) labels_shape = (num_tokens,) if isinstance(num_tokens, int) else num_tokens @@ -387,66 +353,45 @@ def test_performance( .requires_grad_() ) labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") - if ignore_index >=0 and ignore_index < vocabsize: + if ignore_index >= 0 and ignore_index < vocabsize: pad_labels = torch.nn.functional.pad(labels, (0, 1), value=ignore_index) labels = pad_labels[..., 1:].contiguous() # -------- forward -------- # start_event.record() torch_logprobs = self.torch_linear_cross_entropy( - hidden, weight, labels, - reduction=reduction, - ignore_index=ignore_index + hidden, weight, labels, reduction=reduction, ignore_index=ignore_index ) end_event.record() torch.cuda.synchronize() - torch_fwd_latency.append( - start_event.elapsed_time(end_event) - ) + torch_fwd_latency.append(start_event.elapsed_time(end_event)) start_event.record() custom_logprobs = linear_cross_entropy( - hidden, weight, labels, - reduction=reduction, - ignore_index=ignore_index + hidden, weight, labels, reduction=reduction, ignore_index=ignore_index ) end_event.record() torch.cuda.synchronize() - custom_fwd_latency.append( - start_event.elapsed_time(end_event) - ) + custom_fwd_latency.append(start_event.elapsed_time(end_event)) # -------- backward -------- # - g_logprobs = ( - torch.empty_like(torch_logprobs) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(torch_logprobs).uniform_(-0.1, 0.1) start_event.record() (d_torch_hidden, d_torch_weight) = torch.autograd.grad( - (torch_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (torch_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) end_event.record() torch.cuda.synchronize() - torch_bwd_latency.append( - start_event.elapsed_time(end_event) - ) + torch_bwd_latency.append(start_event.elapsed_time(end_event)) start_event.record() (d_custom_hidden, d_custom_weight) = torch.autograd.grad( - (custom_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (custom_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) end_event.record() torch.cuda.synchronize() - custom_bwd_latency.append( - start_event.elapsed_time(end_event) - ) + custom_bwd_latency.append(start_event.elapsed_time(end_event)) # --- remove first latency due to warmup --- # torch_fwd_latency = torch_fwd_latency[1:] @@ -456,22 +401,24 @@ def test_performance( print() print(f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}:") - print(f"[INFO]: Torch forward latency: {sum(torch_fwd_latency) / len(torch_fwd_latency):.2f} ms") - print(f"[INFO]: Custom forward latency: {sum(custom_fwd_latency) / len(custom_fwd_latency):.2f} ms") - print(f"[INFO]: Torch backward latency: {sum(torch_bwd_latency) / len(torch_bwd_latency):.2f} ms") - print(f"[INFO]: Custom backward latency: {sum(custom_bwd_latency) / len(custom_bwd_latency):.2f} ms") + print( + f"[INFO]: Torch forward latency: {sum(torch_fwd_latency) / len(torch_fwd_latency):.2f} ms" + ) + print( + f"[INFO]: Custom forward latency: {sum(custom_fwd_latency) / len(custom_fwd_latency):.2f} ms" + ) + print( + f"[INFO]: Torch backward latency: {sum(torch_bwd_latency) / len(torch_bwd_latency):.2f} ms" + ) + print( + f"[INFO]: Custom backward latency: {sum(custom_bwd_latency) / len(custom_bwd_latency):.2f} ms" + ) @pytest.mark.parametrize("problem", [((1, 4096), 129280, 7168)]) @pytest.mark.parametrize("dtype", [torch.bfloat16]) @pytest.mark.parametrize("reduction", ["mean"]) @pytest.mark.parametrize("ignore_index", [-100]) - def test_storage( - self, - problem, - dtype, - reduction, - ignore_index - ): + def test_storage(self, problem, dtype, reduction, ignore_index): num_tokens, vocabsize, dim = problem hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) labels_shape = (num_tokens,) if isinstance(num_tokens, int) else num_tokens @@ -490,30 +437,22 @@ def torch_storage(): .requires_grad_() ) labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") - if ignore_index >=0 and ignore_index < vocabsize: + if ignore_index >= 0 and ignore_index < vocabsize: pad_labels = torch.nn.functional.pad(labels, (0, 1), value=ignore_index) labels = pad_labels[..., 1:].contiguous() torch.cuda.reset_peak_memory_stats() torch_logprobs = self.torch_linear_cross_entropy( - hidden, weight, labels, - reduction=reduction, - ignore_index=ignore_index + hidden, weight, labels, reduction=reduction, ignore_index=ignore_index ) torch.cuda.synchronize() torch_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 print(f"[INFO]: Torch Forward pass peak memory: {torch_max_memory:.2f} MB") torch.cuda.reset_peak_memory_stats() - g_logprobs = ( - torch.empty_like(torch_logprobs) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(torch_logprobs).uniform_(-0.1, 0.1) (d_torch_hidden, d_torch_weight) = torch.autograd.grad( - (torch_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (torch_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) torch.cuda.synchronize() torch_backward_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 @@ -531,36 +470,27 @@ def custom_storage(): .requires_grad_() ) labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") - if ignore_index >=0 and ignore_index < vocabsize: + if ignore_index >= 0 and ignore_index < vocabsize: pad_labels = torch.nn.functional.pad(labels, (0, 1), value=ignore_index) labels = pad_labels[..., 1:].contiguous() torch.cuda.reset_peak_memory_stats() custom_logprobs = linear_cross_entropy( - hidden, weight, labels, - reduction=reduction, - ignore_index=ignore_index + hidden, weight, labels, reduction=reduction, ignore_index=ignore_index ) torch.cuda.synchronize() custom_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 print(f"[INFO]: Custom Forward pass peak memory: {custom_max_memory:.2f} MB") torch.cuda.reset_peak_memory_stats() - g_logprobs = ( - torch.empty_like(custom_logprobs) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(custom_logprobs).uniform_(-0.1, 0.1) (d_custom_hidden, d_custom_weight) = torch.autograd.grad( - (custom_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (custom_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) torch.cuda.synchronize() custom_backward_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 print(f"[INFO]: Custom Backward pass peak memory: {custom_backward_max_memory:.2f} MB") - self.cleanup() torch_storage() self.cleanup() @@ -568,8 +498,8 @@ def custom_storage(): @pytest.mark.skipif( - ("WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2),# or True, - reason="Requires torchrun with multiple GPUs" + ("WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2), # or True, + reason="Requires torchrun with multiple GPUs", ) class TestFusedLinearCrossEntropyTensorParallel: @classmethod @@ -581,14 +511,14 @@ def setup_class(cls): backend="nccl", init_method="env://", world_size=int(os.environ["WORLD_SIZE"]), - rank=int(os.environ["RANK"]) + rank=int(os.environ["RANK"]), ) cls.must_teardown = True cls.tp_group = dist.group.WORLD cls.tp_rank = dist.get_rank(cls.tp_group) cls.tp_world_size = dist.get_world_size(cls.tp_group) - cls.is_chief = (cls.tp_rank == 0) + cls.is_chief = cls.tp_rank == 0 device = torch.device(f"cuda:{cls.tp_rank}") torch.cuda.set_device(device) print(f"[INFO]: TP rank: {cls.tp_rank}, TP world size: {cls.tp_world_size}") @@ -615,9 +545,7 @@ def torch_linear_cross_entropy_single_gpu( ): logits = hidden.to(torch.float32) @ weight.T.to(torch.float32) logprobs = torch.nn.functional.cross_entropy( - logits.view(-1, logits.shape[-1]), - labels.view(-1), - reduction=reduction, + logits.view(-1, logits.shape[-1]), labels.view(-1), reduction=reduction ) return logprobs.to(torch.float32) @@ -639,7 +567,7 @@ def forward( whole_logits = torch.empty( (logits.shape[0], logits.shape[-1] * tp_world_size), dtype=logits.dtype, - device=logits.device + device=logits.device, ) whole_logits_ref = [ whole_logits[..., i * logits.shape[-1] : (i + 1) * logits.shape[-1]] @@ -648,9 +576,7 @@ def forward( dist.all_gather(whole_logits_ref, logits, group=tp_group) logprobs = torch.nn.functional.cross_entropy( - whole_logits.view(-1, whole_logits.shape[-1]), - labels.view(-1), - reduction=reduction, + whole_logits.view(-1, whole_logits.shape[-1]), labels.view(-1), reduction=reduction ) # If we don't preserve whole_logits, @@ -664,10 +590,7 @@ def forward( return logprobs.to(torch.float32) @staticmethod - def backward( - ctx, - g_logprobs: torch.Tensor, - ): + def backward(ctx, g_logprobs: torch.Tensor): hidden, weight, labels = ctx.saved_tensors tp_group = ctx.tp_group reduction = ctx.reduction @@ -677,15 +600,9 @@ def backward( num_tokens, dim = hidden.shape if reduction == "mean": - _g_logprobs = torch.broadcast_to( - g_logprobs / num_tokens, - (num_tokens,) - ) + _g_logprobs = torch.broadcast_to(g_logprobs / num_tokens, (num_tokens,)) elif reduction == "sum": - _g_logprobs = torch.broadcast_to( - g_logprobs, - (num_tokens,) - ) + _g_logprobs = torch.broadcast_to(g_logprobs, (num_tokens,)) else: _g_logprobs = g_logprobs @@ -694,7 +611,7 @@ def backward( whole_logits = torch.empty( (logits.shape[0], logits.shape[-1] * tp_world_size), dtype=logits.dtype, - device=logits.device + device=logits.device, ) whole_logits_ref = [ whole_logits[..., i * logits.shape[-1] : (i + 1) * logits.shape[-1]] @@ -715,23 +632,14 @@ def backward( local_d_hidden = local_d_logits @ weight local_d_weight = local_d_logits.T @ hidden - dist.all_reduce( - local_d_hidden, - op=dist.ReduceOp.SUM, - group=tp_group - ) + dist.all_reduce(local_d_hidden, op=dist.ReduceOp.SUM, group=tp_group) return local_d_hidden, local_d_weight, None, None, None @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) @pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) @pytest.mark.parametrize("problem", [(4096, 129280, 8192)]) - def test_torch_tp_vs_single_gpu( - self, - dtype, - reduction, - problem, - ): + def test_torch_tp_vs_single_gpu(self, dtype, reduction, problem): num_tokens, vocabsize, dim = problem hidden = ( @@ -752,72 +660,41 @@ def test_torch_tp_vs_single_gpu( # single GPU whole_weight = torch.empty( - (vocabsize * self.tp_world_size, dim), - dtype=dtype, - device="cuda" + (vocabsize * self.tp_world_size, dim), dtype=dtype, device="cuda" ) whole_weight_view = [ - whole_weight[i * vocabsize : (i + 1) * vocabsize, :] - for i in range(self.tp_world_size) + whole_weight[i * vocabsize : (i + 1) * vocabsize, :] for i in range(self.tp_world_size) ] - dist.all_gather( - whole_weight_view, - weight, - group=self.tp_group - ) + dist.all_gather(whole_weight_view, weight, group=self.tp_group) whole_weight = whole_weight.clone().requires_grad_() logprobs_single_gpu = self.torch_linear_cross_entropy_single_gpu( - hidden, whole_weight, labels, - reduction=reduction, + hidden, whole_weight, labels, reduction=reduction ) # TP logprobs_tp = self.TorchLinearCrossEntropy.apply( - hidden, weight, labels, - self.tp_group, - reduction, - ) - torch.testing.assert_close( - logprobs_single_gpu, - logprobs_tp, + hidden, weight, labels, self.tp_group, reduction ) + torch.testing.assert_close(logprobs_single_gpu, logprobs_tp) # ------------ backward pass ------------ # - g_logprobs = ( - torch.empty_like(logprobs_single_gpu) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(logprobs_single_gpu).uniform_(-0.1, 0.1) dist.broadcast(g_logprobs, src=0, group=self.tp_group) # single GPU (d_hidden_single_gpu, d_weight_single_gpu) = torch.autograd.grad( - (logprobs_single_gpu,), - (hidden, whole_weight), - (g_logprobs,), - retain_graph=False + (logprobs_single_gpu,), (hidden, whole_weight), (g_logprobs,), retain_graph=False ) # TP (d_hidden_tp, d_weight_tp) = torch.autograd.grad( - (logprobs_tp,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (logprobs_tp,), (hidden, weight), (g_logprobs,), retain_graph=False ) - torch.testing.assert_close( - d_hidden_single_gpu, - d_hidden_tp, - atol=1e-3, - rtol=1e-3, - ) - local_d_weight_single_gpu = d_weight_single_gpu[self.tp_rank * weight.shape[0] : (self.tp_rank + 1) * weight.shape[0], :] - torch.testing.assert_close( - local_d_weight_single_gpu, - d_weight_tp, - atol=1e-3, - rtol=1e-3, - ) - + torch.testing.assert_close(d_hidden_single_gpu, d_hidden_tp, atol=1e-3, rtol=1e-3) + local_d_weight_single_gpu = d_weight_single_gpu[ + self.tp_rank * weight.shape[0] : (self.tp_rank + 1) * weight.shape[0], : + ] + torch.testing.assert_close(local_d_weight_single_gpu, d_weight_tp, atol=1e-3, rtol=1e-3) @staticmethod def get_problems(): @@ -833,12 +710,7 @@ def get_problems(): @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) @pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) @pytest.mark.parametrize("problem", get_problems()) - def test_correctness( - self, - dtype, - reduction, - problem, - ): + def test_correctness(self, dtype, reduction, problem): num_tokens, vocabsize, dim = problem hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) labels_shape = (num_tokens,) if isinstance(num_tokens, int) else num_tokens @@ -855,69 +727,37 @@ def test_correctness( ) labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") - # ------ forward pass ------ # dist.broadcast(hidden, src=0, group=self.tp_group) dist.broadcast(labels, src=0, group=self.tp_group) torch_logprobs = self.TorchLinearCrossEntropy.apply( - hidden.view(-1, dim), weight, labels, - self.tp_group, - reduction, + hidden.view(-1, dim), weight, labels, self.tp_group, reduction ) custom_logprobs = linear_cross_entropy( - hidden, weight, labels, - tp_group=self.tp_group, - reduction=reduction, + hidden, weight, labels, tp_group=self.tp_group, reduction=reduction ) - torch.testing.assert_close( - torch_logprobs, - custom_logprobs, - ) + torch.testing.assert_close(torch_logprobs, custom_logprobs) # ------- backward pass ------- # - g_logprobs = ( - torch.empty_like(torch_logprobs) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(torch_logprobs).uniform_(-0.1, 0.1) dist.broadcast(g_logprobs, src=0, group=self.tp_group) (d_hidden_torch, d_weight_torch) = torch.autograd.grad( - (torch_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (torch_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) (d_hidden_custom, d_weight_custom) = torch.autograd.grad( - (custom_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False - ) - torch.testing.assert_close( - d_hidden_torch, - d_hidden_custom, - atol=1e-3, - rtol=1e-3, - ) - torch.testing.assert_close( - d_weight_torch, - d_weight_custom, - atol=1e-4, - rtol=1e-4, + (custom_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) + torch.testing.assert_close(d_hidden_torch, d_hidden_custom, atol=1e-3, rtol=1e-3) + torch.testing.assert_close(d_weight_torch, d_weight_custom, atol=1e-4, rtol=1e-4) @pytest.mark.parametrize("problem", [((1, 4096), 129280, 7168)]) @pytest.mark.parametrize("dtype", [torch.bfloat16]) @pytest.mark.parametrize("reduction", ["mean"]) - def test_performance( - self, - problem, - dtype, - reduction - ): + def test_performance(self, problem, dtype, reduction): num_tokens, vocabsize, dim = problem hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) labels_shape = (num_tokens,) if isinstance(num_tokens, int) else num_tokens @@ -950,9 +790,7 @@ def test_performance( start_event.record() torch_logprobs = self.TorchLinearCrossEntropy.apply( - hidden.view(-1, dim), weight, labels, - self.tp_group, - reduction, + hidden.view(-1, dim), weight, labels, self.tp_group, reduction ) end_event.record() torch.cuda.synchronize() @@ -960,27 +798,19 @@ def test_performance( start_event.record() custom_logprobs = linear_cross_entropy( - hidden, weight, labels, - tp_group=self.tp_group, - reduction=reduction, + hidden, weight, labels, tp_group=self.tp_group, reduction=reduction ) end_event.record() torch.cuda.synchronize() custom_fwd_latency.append(start_event.elapsed_time(end_event)) # ------- backward pass ------- # - g_logprobs = ( - torch.empty_like(torch_logprobs) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(torch_logprobs).uniform_(-0.1, 0.1) dist.broadcast(g_logprobs, src=0, group=self.tp_group) start_event.record() (d_hidden_torch, d_weight_torch) = torch.autograd.grad( - (torch_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (torch_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) end_event.record() torch.cuda.synchronize() @@ -988,10 +818,7 @@ def test_performance( start_event.record() (d_hidden_custom, d_weight_custom) = torch.autograd.grad( - (custom_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (custom_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) end_event.record() torch.cuda.synchronize() @@ -1005,29 +832,35 @@ def test_performance( if self.is_chief: print() - print(f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}, TP size {self.tp_world_size}:") - print(f"[INFO]: Torch forward latency: {sum(torch_fwd_latency) / len(torch_fwd_latency):.2f} ms") - print(f"[INFO]: Custom forward latency: {sum(custom_fwd_latency) / len(custom_fwd_latency):.2f} ms") - print(f"[INFO]: Torch backward latency: {sum(torch_bwd_latency) / len(torch_bwd_latency):.2f} ms") - print(f"[INFO]: Custom backward latency: {sum(custom_bwd_latency) / len(custom_bwd_latency):.2f} ms") - + print( + f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}, TP size {self.tp_world_size}:" + ) + print( + f"[INFO]: Torch forward latency: {sum(torch_fwd_latency) / len(torch_fwd_latency):.2f} ms" + ) + print( + f"[INFO]: Custom forward latency: {sum(custom_fwd_latency) / len(custom_fwd_latency):.2f} ms" + ) + print( + f"[INFO]: Torch backward latency: {sum(torch_bwd_latency) / len(torch_bwd_latency):.2f} ms" + ) + print( + f"[INFO]: Custom backward latency: {sum(custom_bwd_latency) / len(custom_bwd_latency):.2f} ms" + ) @pytest.mark.parametrize("problem", [((1, 4096), 129280, 7168)]) @pytest.mark.parametrize("dtype", [torch.bfloat16]) @pytest.mark.parametrize("reduction", ["mean"]) - def test_storage( - self, - problem, - dtype, - reduction - ): + def test_storage(self, problem, dtype, reduction): num_tokens, vocabsize, dim = problem hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) labels_shape = (num_tokens,) if isinstance(num_tokens, int) else num_tokens if self.is_chief: print() - print(f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}, TP size {self.tp_world_size}:") + print( + f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}, TP size {self.tp_world_size}:" + ) def torch_storage(): hidden = ( @@ -1047,32 +880,28 @@ def torch_storage(): torch.cuda.reset_peak_memory_stats() torch_logprobs = self.TorchLinearCrossEntropy.apply( - hidden.view(-1, dim), weight, labels, - self.tp_group, - reduction, + hidden.view(-1, dim), weight, labels, self.tp_group, reduction ) torch.cuda.synchronize() torch_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 if self.is_chief: - print(f"[INFO]: On GPU {self.tp_rank}, Torch Forward pass peak memory: {torch_max_memory:.2f} MB") + print( + f"[INFO]: On GPU {self.tp_rank}, Torch Forward pass peak memory: {torch_max_memory:.2f} MB" + ) - g_logprobs = ( - torch.empty_like(torch_logprobs) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(torch_logprobs).uniform_(-0.1, 0.1) dist.broadcast(g_logprobs, src=0, group=self.tp_group) torch.cuda.reset_peak_memory_stats() (d_hidden_torch, d_weight_torch) = torch.autograd.grad( - (torch_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (torch_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) torch.cuda.synchronize() torch_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 if self.is_chief: - print(f"[INFO]: On GPU {self.tp_rank}, Torch Backward pass peak memory: {torch_max_memory:.2f} MB") + print( + f"[INFO]: On GPU {self.tp_rank}, Torch Backward pass peak memory: {torch_max_memory:.2f} MB" + ) def custom_storage(): hidden = ( @@ -1086,38 +915,34 @@ def custom_storage(): .requires_grad_() ) labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") - + dist.broadcast(hidden, src=0, group=self.tp_group) dist.broadcast(labels, src=0, group=self.tp_group) torch.cuda.reset_peak_memory_stats() custom_logprobs = linear_cross_entropy( - hidden, weight, labels, - tp_group=self.tp_group, - reduction=reduction, + hidden, weight, labels, tp_group=self.tp_group, reduction=reduction ) torch.cuda.synchronize() custom_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 if self.is_chief: - print(f"[INFO]: On GPU {self.tp_rank}, Custom Forward pass peak memory: {custom_max_memory:.2f} MB") + print( + f"[INFO]: On GPU {self.tp_rank}, Custom Forward pass peak memory: {custom_max_memory:.2f} MB" + ) - g_logprobs = ( - torch.empty_like(custom_logprobs) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(custom_logprobs).uniform_(-0.1, 0.1) dist.broadcast(g_logprobs, src=0, group=self.tp_group) torch.cuda.reset_peak_memory_stats() (d_hidden_custom, d_weight_custom) = torch.autograd.grad( - (custom_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (custom_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) torch.cuda.synchronize() custom_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 if self.is_chief: - print(f"[INFO]: On GPU {self.tp_rank}, Custom Backward pass peak memory: {custom_max_memory:.2f} MB") + print( + f"[INFO]: On GPU {self.tp_rank}, Custom Backward pass peak memory: {custom_max_memory:.2f} MB" + ) self.cleanup() torch_storage() @@ -1125,10 +950,9 @@ def custom_storage(): custom_storage() - @pytest.mark.skipif( "WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2, - reason="Requires torchrun with multiple GPUs" + reason="Requires torchrun with multiple GPUs", ) class TestFusedLinearCrossEntropySequenceParallel: @classmethod @@ -1140,14 +964,14 @@ def setup_class(cls): backend="nccl", init_method="env://", world_size=int(os.environ["WORLD_SIZE"]), - rank=int(os.environ["RANK"]) + rank=int(os.environ["RANK"]), ) cls.must_teardown = True cls.tp_group = dist.group.WORLD cls.tp_rank = dist.get_rank(cls.tp_group) cls.tp_world_size = dist.get_world_size(cls.tp_group) - cls.is_chief = (cls.tp_rank == 0) + cls.is_chief = cls.tp_rank == 0 device = torch.device(f"cuda:{cls.tp_rank}") torch.cuda.set_device(device) print(f"[INFO]: TP rank: {cls.tp_rank}, TP world size: {cls.tp_world_size}") @@ -1160,6 +984,7 @@ def teardown_class(cls): @staticmethod def timed_barrier(timeout_s=10): import time + work = torch.distributed.barrier(async_op=True) t0 = time.time() while not work.is_completed(): @@ -1185,9 +1010,7 @@ def torch_linear_cross_entropy_single_gpu( ): logits = hidden.to(torch.float32) @ weight.T.to(torch.float32) logprobs = torch.nn.functional.cross_entropy( - logits.view(-1, logits.shape[-1]), - labels.view(-1), - reduction=reduction, + logits.view(-1, logits.shape[-1]), labels.view(-1), reduction=reduction ) return logprobs.to(torch.float32) @@ -1207,20 +1030,16 @@ def forward( whole_hidden = torch.empty( (hidden.shape[0] * tp_world_size, hidden.shape[-1]), dtype=hidden.dtype, - device=hidden.device - ) - dist.all_gather_into_tensor( - whole_hidden, - hidden, - group=tp_group + device=hidden.device, ) + dist.all_gather_into_tensor(whole_hidden, hidden, group=tp_group) logits = whole_hidden.to(torch.float32) @ weight.T.to(torch.float32) whole_logits = torch.empty( (logits.shape[0], logits.shape[-1] * tp_world_size), dtype=logits.dtype, - device=logits.device + device=logits.device, ) whole_logits_ref = [ whole_logits[..., i * logits.shape[-1] : (i + 1) * logits.shape[-1]] @@ -1229,9 +1048,7 @@ def forward( dist.all_gather(whole_logits_ref, logits, group=tp_group) logprobs = torch.nn.functional.cross_entropy( - whole_logits.view(-1, whole_logits.shape[-1]), - labels.view(-1), - reduction=reduction, + whole_logits.view(-1, whole_logits.shape[-1]), labels.view(-1), reduction=reduction ) # If we don't preserve whole_logits, @@ -1245,10 +1062,7 @@ def forward( return logprobs.to(torch.float32) @staticmethod - def backward( - ctx, - g_logprobs: torch.Tensor, - ): + def backward(ctx, g_logprobs: torch.Tensor): whole_hidden, weight, labels = ctx.saved_tensors tp_group = ctx.tp_group reduction = ctx.reduction @@ -1258,15 +1072,9 @@ def backward( num_tokens, dim = whole_hidden.shape if reduction == "mean": - _g_logprobs = torch.broadcast_to( - g_logprobs / num_tokens, - (num_tokens,) - ) + _g_logprobs = torch.broadcast_to(g_logprobs / num_tokens, (num_tokens,)) elif reduction == "sum": - _g_logprobs = torch.broadcast_to( - g_logprobs, - (num_tokens,) - ) + _g_logprobs = torch.broadcast_to(g_logprobs, (num_tokens,)) else: _g_logprobs = g_logprobs @@ -1275,7 +1083,7 @@ def backward( whole_logits = torch.empty( (logits.shape[0], logits.shape[-1] * tp_world_size), dtype=logits.dtype, - device=logits.device + device=logits.device, ) whole_logits_ref = [ whole_logits[..., i * logits.shape[-1] : (i + 1) * logits.shape[-1]] @@ -1307,27 +1115,17 @@ def backward( # local_d_hidden = local_d_hidden[tp_rank * local_num_tokens : (tp_rank + 1) * local_num_tokens, :] local_d_hidden = torch.empty( - (local_num_tokens, dim), - dtype=weight.dtype, - device=weight.device + (local_num_tokens, dim), dtype=weight.dtype, device=weight.device ) dist.reduce_scatter_tensor( - local_d_hidden, - d_hidden, - op=dist.ReduceOp.SUM, - group=tp_group + local_d_hidden, d_hidden, op=dist.ReduceOp.SUM, group=tp_group ) return local_d_hidden, local_d_weight, None, None, None @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) @pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) @pytest.mark.parametrize("problem", [(256, 12928, 8192)]) - def test_torch_tp_vs_single_gpu( - self, - dtype, - reduction, - problem, - ): + def test_torch_tp_vs_single_gpu(self, dtype, reduction, problem): num_tokens, vocabsize, dim = problem hidden = ( @@ -1340,93 +1138,60 @@ def test_torch_tp_vs_single_gpu( .uniform_(-0.1, 0.1) .requires_grad_() ) - labels = torch.randint(0, vocabsize, (num_tokens * self.tp_world_size,), - dtype=torch.long, device="cuda") + labels = torch.randint( + 0, vocabsize, (num_tokens * self.tp_world_size,), dtype=torch.long, device="cuda" + ) # ------------ forward pass ------------ # dist.broadcast(labels, src=0, group=self.tp_group) # single GPU whole_hidden = torch.empty( - (num_tokens * self.tp_world_size, dim), - dtype=dtype, - device="cuda" - ) - dist.all_gather_into_tensor( - whole_hidden, - hidden, - group=self.tp_group + (num_tokens * self.tp_world_size, dim), dtype=dtype, device="cuda" ) + dist.all_gather_into_tensor(whole_hidden, hidden, group=self.tp_group) whole_hidden = whole_hidden.clone().requires_grad_() whole_weight = torch.empty( - (vocabsize * self.tp_world_size, dim), - dtype=dtype, - device="cuda" + (vocabsize * self.tp_world_size, dim), dtype=dtype, device="cuda" ) whole_weight_view = [ - whole_weight[i * vocabsize : (i + 1) * vocabsize, :] - for i in range(self.tp_world_size) + whole_weight[i * vocabsize : (i + 1) * vocabsize, :] for i in range(self.tp_world_size) ] - dist.all_gather( - whole_weight_view, - weight, - group=self.tp_group - ) + dist.all_gather(whole_weight_view, weight, group=self.tp_group) whole_weight = whole_weight.clone().requires_grad_() logprobs_single_gpu = self.torch_linear_cross_entropy_single_gpu( - whole_hidden, whole_weight, labels, - reduction=reduction, + whole_hidden, whole_weight, labels, reduction=reduction ) # TP logprobs_tp = self.TorchLinearCrossEntropy.apply( - hidden, weight, labels, - self.tp_group, - reduction, - ) - torch.testing.assert_close( - logprobs_single_gpu, - logprobs_tp, + hidden, weight, labels, self.tp_group, reduction ) + torch.testing.assert_close(logprobs_single_gpu, logprobs_tp) # ------------ backward pass ------------ # - g_logprobs = ( - torch.empty_like(logprobs_single_gpu) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(logprobs_single_gpu).uniform_(-0.1, 0.1) dist.broadcast(g_logprobs, src=0, group=self.tp_group) # single GPU (d_hidden_single_gpu, d_weight_single_gpu) = torch.autograd.grad( - (logprobs_single_gpu,), - (whole_hidden, whole_weight), - (g_logprobs,), - retain_graph=False + (logprobs_single_gpu,), (whole_hidden, whole_weight), (g_logprobs,), retain_graph=False ) # TP (d_hidden_tp, d_weight_tp) = torch.autograd.grad( - (logprobs_tp,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (logprobs_tp,), (hidden, weight), (g_logprobs,), retain_graph=False ) - local_d_hidden_single_gpu = d_hidden_single_gpu[self.tp_rank * hidden.shape[0] : (self.tp_rank + 1) * hidden.shape[0], :] - torch.testing.assert_close( - local_d_hidden_single_gpu, - d_hidden_tp, - atol=1e-3, - rtol=1e-3, - ) - local_d_weight_single_gpu = d_weight_single_gpu[self.tp_rank * weight.shape[0] : (self.tp_rank + 1) * weight.shape[0], :] - torch.testing.assert_close( - local_d_weight_single_gpu, - d_weight_tp, - atol=1e-3, - rtol=1e-3, - ) + local_d_hidden_single_gpu = d_hidden_single_gpu[ + self.tp_rank * hidden.shape[0] : (self.tp_rank + 1) * hidden.shape[0], : + ] + torch.testing.assert_close(local_d_hidden_single_gpu, d_hidden_tp, atol=1e-3, rtol=1e-3) + local_d_weight_single_gpu = d_weight_single_gpu[ + self.tp_rank * weight.shape[0] : (self.tp_rank + 1) * weight.shape[0], : + ] + torch.testing.assert_close(local_d_weight_single_gpu, d_weight_tp, atol=1e-3, rtol=1e-3) self.cleanup() @@ -1444,15 +1209,14 @@ def get_problems(): @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) @pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) @pytest.mark.parametrize("problem", get_problems()) - def test_correctness( - self, - dtype, - reduction, - problem, - ): + def test_correctness(self, dtype, reduction, problem): num_tokens, vocabsize, dim = problem hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) - labels_shape = (num_tokens * self.tp_world_size,) if isinstance(num_tokens, int) else (num_tokens[0] * self.tp_world_size, *num_tokens[1:]) + labels_shape = ( + (num_tokens * self.tp_world_size,) + if isinstance(num_tokens, int) + else (num_tokens[0] * self.tp_world_size, *num_tokens[1:]) + ) hidden = ( torch.empty(hidden_shape, dtype=dtype, device="cuda") @@ -1470,56 +1234,34 @@ def test_correctness( dist.broadcast(labels, src=0, group=self.tp_group) torch_logprobs = self.TorchLinearCrossEntropy.apply( - hidden.view(-1, dim), weight, labels, - self.tp_group, - reduction, + hidden.view(-1, dim), weight, labels, self.tp_group, reduction ) custom_logprobs = linear_cross_entropy( - hidden, weight, labels, + hidden, + weight, + labels, tp_group=self.tp_group, reduction=reduction, sequence_parallel=True, ) - torch.testing.assert_close( - torch_logprobs, - custom_logprobs, - ) + torch.testing.assert_close(torch_logprobs, custom_logprobs) # ------- backward pass ------- # - g_logprobs = ( - torch.empty_like(torch_logprobs) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(torch_logprobs).uniform_(-0.1, 0.1) dist.broadcast(g_logprobs, src=0, group=self.tp_group) (d_hidden_torch, d_weight_torch) = torch.autograd.grad( - (torch_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (torch_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) (d_hidden_custom, d_weight_custom) = torch.autograd.grad( - (custom_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (custom_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) # in case one GPU failed, and leading to hang - torch.testing.assert_close( - d_hidden_torch, - d_hidden_custom, - atol=1e-3, - rtol=1e-3, - ) - torch.testing.assert_close( - d_weight_torch, - d_weight_custom, - atol=1e-3, - rtol=1e-3, - ) + torch.testing.assert_close(d_hidden_torch, d_hidden_custom, atol=1e-3, rtol=1e-3) + torch.testing.assert_close(d_weight_torch, d_weight_custom, atol=1e-3, rtol=1e-3) self.timed_barrier() self.cleanup() @@ -1527,15 +1269,14 @@ def test_correctness( @pytest.mark.parametrize("problem", [((1, 1024), 129280, 7168)]) @pytest.mark.parametrize("dtype", [torch.bfloat16]) @pytest.mark.parametrize("reduction", ["mean"]) - def test_performance( - self, - problem, - dtype, - reduction - ): + def test_performance(self, problem, dtype, reduction): num_tokens, vocabsize, dim = problem hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) - labels_shape = (num_tokens * self.tp_world_size,) if isinstance(num_tokens, int) else (num_tokens[0] * self.tp_world_size, *num_tokens[1:]) + labels_shape = ( + (num_tokens * self.tp_world_size,) + if isinstance(num_tokens, int) + else (num_tokens[0] * self.tp_world_size, *num_tokens[1:]) + ) start_event = torch.cuda.Event(enable_timing=True) end_event = torch.cuda.Event(enable_timing=True) @@ -1564,9 +1305,7 @@ def test_performance( start_event.record() torch_logprobs = self.TorchLinearCrossEntropy.apply( - hidden.view(-1, dim), weight, labels, - self.tp_group, - reduction, + hidden.view(-1, dim), weight, labels, self.tp_group, reduction ) end_event.record() torch.cuda.synchronize() @@ -1574,7 +1313,9 @@ def test_performance( start_event.record() custom_logprobs = linear_cross_entropy( - hidden, weight, labels, + hidden, + weight, + labels, tp_group=self.tp_group, reduction=reduction, sequence_parallel=True, @@ -1584,18 +1325,12 @@ def test_performance( custom_fwd_latency.append(start_event.elapsed_time(end_event)) # ------- backward pass ------- # - g_logprobs = ( - torch.empty_like(torch_logprobs) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(torch_logprobs).uniform_(-0.1, 0.1) dist.broadcast(g_logprobs, src=0, group=self.tp_group) start_event.record() (d_hidden_torch, d_weight_torch) = torch.autograd.grad( - (torch_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (torch_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) end_event.record() torch.cuda.synchronize() @@ -1603,10 +1338,7 @@ def test_performance( start_event.record() (d_hidden_custom, d_weight_custom) = torch.autograd.grad( - (custom_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (custom_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) end_event.record() torch.cuda.synchronize() @@ -1620,29 +1352,39 @@ def test_performance( if self.is_chief: print() - print(f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}, TP size {self.tp_world_size}, Sequence Parallel: True:") - print(f"[INFO]: Torch forward latency: {sum(torch_fwd_latency) / len(torch_fwd_latency):.2f} ms") - print(f"[INFO]: Custom forward latency: {sum(custom_fwd_latency) / len(custom_fwd_latency):.2f} ms") - print(f"[INFO]: Torch backward latency: {sum(torch_bwd_latency) / len(torch_bwd_latency):.2f} ms") - print(f"[INFO]: Custom backward latency: {sum(custom_bwd_latency) / len(custom_bwd_latency):.2f} ms") - + print( + f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}, TP size {self.tp_world_size}, Sequence Parallel: True:" + ) + print( + f"[INFO]: Torch forward latency: {sum(torch_fwd_latency) / len(torch_fwd_latency):.2f} ms" + ) + print( + f"[INFO]: Custom forward latency: {sum(custom_fwd_latency) / len(custom_fwd_latency):.2f} ms" + ) + print( + f"[INFO]: Torch backward latency: {sum(torch_bwd_latency) / len(torch_bwd_latency):.2f} ms" + ) + print( + f"[INFO]: Custom backward latency: {sum(custom_bwd_latency) / len(custom_bwd_latency):.2f} ms" + ) @pytest.mark.parametrize("problem", [((1, 1024), 129280, 7168)]) @pytest.mark.parametrize("dtype", [torch.bfloat16]) @pytest.mark.parametrize("reduction", ["mean"]) - def test_storage( - self, - problem, - dtype, - reduction - ): + def test_storage(self, problem, dtype, reduction): num_tokens, vocabsize, dim = problem hidden_shape = (num_tokens, dim) if isinstance(num_tokens, int) else (*num_tokens, dim) - labels_shape = (num_tokens * self.tp_world_size,) if isinstance(num_tokens, int) else (num_tokens[0] * self.tp_world_size, *num_tokens[1:]) + labels_shape = ( + (num_tokens * self.tp_world_size,) + if isinstance(num_tokens, int) + else (num_tokens[0] * self.tp_world_size, *num_tokens[1:]) + ) if self.is_chief: print() - print(f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}, TP size {self.tp_world_size}, Sequence Parallel: True:") + print( + f"[INFO]: On problem {problem}, dtype {dtype}, reduction {reduction}, TP size {self.tp_world_size}, Sequence Parallel: True:" + ) def torch_storage(): hidden = ( @@ -1662,32 +1404,28 @@ def torch_storage(): torch.cuda.reset_peak_memory_stats() torch_logprobs = self.TorchLinearCrossEntropy.apply( - hidden.view(-1, dim), weight, labels, - self.tp_group, - reduction, + hidden.view(-1, dim), weight, labels, self.tp_group, reduction ) torch.cuda.synchronize() torch_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 if self.is_chief: - print(f"[INFO]: On GPU {self.tp_rank}, Torch Forward pass peak memory: {torch_max_memory:.2f} MB") + print( + f"[INFO]: On GPU {self.tp_rank}, Torch Forward pass peak memory: {torch_max_memory:.2f} MB" + ) - g_logprobs = ( - torch.empty_like(torch_logprobs) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(torch_logprobs).uniform_(-0.1, 0.1) dist.broadcast(g_logprobs, src=0, group=self.tp_group) torch.cuda.reset_peak_memory_stats() (d_hidden_torch, d_weight_torch) = torch.autograd.grad( - (torch_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (torch_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) torch.cuda.synchronize() torch_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 if self.is_chief: - print(f"[INFO]: On GPU {self.tp_rank}, Torch Backward pass peak memory: {torch_max_memory:.2f} MB") + print( + f"[INFO]: On GPU {self.tp_rank}, Torch Backward pass peak memory: {torch_max_memory:.2f} MB" + ) def custom_storage(): hidden = ( @@ -1701,13 +1439,15 @@ def custom_storage(): .requires_grad_() ) labels = torch.randint(0, vocabsize, labels_shape, dtype=torch.long, device="cuda") - + dist.broadcast(hidden, src=0, group=self.tp_group) dist.broadcast(labels, src=0, group=self.tp_group) torch.cuda.reset_peak_memory_stats() custom_logprobs = linear_cross_entropy( - hidden, weight, labels, + hidden, + weight, + labels, tp_group=self.tp_group, reduction=reduction, sequence_parallel=True, @@ -1715,27 +1455,25 @@ def custom_storage(): torch.cuda.synchronize() custom_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 if self.is_chief: - print(f"[INFO]: On GPU {self.tp_rank}, Custom Forward pass peak memory: {custom_max_memory:.2f} MB") + print( + f"[INFO]: On GPU {self.tp_rank}, Custom Forward pass peak memory: {custom_max_memory:.2f} MB" + ) - g_logprobs = ( - torch.empty_like(custom_logprobs) - .uniform_(-0.1, 0.1) - ) + g_logprobs = torch.empty_like(custom_logprobs).uniform_(-0.1, 0.1) dist.broadcast(g_logprobs, src=0, group=self.tp_group) torch.cuda.reset_peak_memory_stats() (d_hidden_custom, d_weight_custom) = torch.autograd.grad( - (custom_logprobs,), - (hidden, weight), - (g_logprobs,), - retain_graph=False + (custom_logprobs,), (hidden, weight), (g_logprobs,), retain_graph=False ) torch.cuda.synchronize() custom_max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 if self.is_chief: - print(f"[INFO]: On GPU {self.tp_rank}, Custom Backward pass peak memory: {custom_max_memory:.2f} MB") + print( + f"[INFO]: On GPU {self.tp_rank}, Custom Backward pass peak memory: {custom_max_memory:.2f} MB" + ) self.cleanup() torch_storage() self.cleanup() - custom_storage() \ No newline at end of file + custom_storage() From f6538389d44d4feca92de73184dafb451df68606 Mon Sep 17 00:00:00 2001 From: Jianbin Chang Date: Wed, 12 Nov 2025 11:43:53 +0800 Subject: [PATCH 08/17] Remove redundant logits calculations in gpt_model (#9) * Remove redundant logits calculations in gpt_model * Merge the linear-cross-entropy-fusion flag and the cross-entropy-fusion flag --- .../core/models/common/language_module/language_module.py | 5 ++++- megatron/core/models/gpt/gpt_model.py | 2 +- megatron/training/arguments.py | 6 +----- 3 files changed, 6 insertions(+), 7 deletions(-) diff --git a/megatron/core/models/common/language_module/language_module.py b/megatron/core/models/common/language_module/language_module.py index b7013be89f0..2144bd8a997 100644 --- a/megatron/core/models/common/language_module/language_module.py +++ b/megatron/core/models/common/language_module/language_module.py @@ -155,7 +155,10 @@ def compute_language_model_loss_without_logits( Returns: Tensor: Loss tensor of dimensions [batch size, sequence_length]. """ - if self.config.linear_cross_entropy_fusion: + if ( + self.config.cross_entropy_loss_fusion + and self.config.cross_entropy_fusion_impl == 'linear' + ): assert ( weight is not None ), "weight cannot be None when using fused linear cross entropy." diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py index 0bb144e408d..a69a2250bce 100644 --- a/megatron/core/models/gpt/gpt_model.py +++ b/megatron/core/models/gpt/gpt_model.py @@ -635,7 +635,7 @@ def _postprocess( hidden_states.squeeze(1).unsqueeze(0) ).unsqueeze(1) - if has_config_logger_enabled(self.config) or labels is not None: + if has_config_logger_enabled(self.config) or labels is None: logits, _ = self.output_layer( hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output ) diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py index ad34c3e5e0a..21849d3dd94 100644 --- a/megatron/training/arguments.py +++ b/megatron/training/arguments.py @@ -2254,10 +2254,6 @@ def _add_training_args(parser): dest='bias_swiglu_fusion') group.add_argument('--use-fused-weighted-squared-relu', action='store_true', help='Use fused weighted squared relu when using MoE.') - group.add_argument('--linear-cross-entropy-fusion', action='store_true', - help='Enable fusion of linear layer and cross entropy ' - 'loss calculation.', - dest='linear_cross_entropy_fusion') group.add_argument('--no-bias-dropout-fusion', action='store_false', help='Disable bias and dropout fusion.', dest='bias_dropout_fusion') @@ -2273,7 +2269,7 @@ def _add_training_args(parser): help='Enabled fusion of cross entropy loss calculation.', dest='cross_entropy_loss_fusion') group.add_argument('--cross-entropy-fusion-impl', type=str, default='native', - choices=['native', 'te'], + choices=['native', 'te', 'linear'], help='Implementation of cross entropy loss calculation.') group.add_argument('--use-flash-attn', action='store_true', help='use FlashAttention implementation of attention. ' From 66d43ff031a964169b2868a83e3e59f5dd7d6231 Mon Sep 17 00:00:00 2001 From: Jianbing Date: Wed, 12 Nov 2025 15:02:12 +0800 Subject: [PATCH 09/17] fixed some styling issue (#10) Signed-off-by: Jianbing Dong --- .../fusions/fused_linear_cross_entropy.py | 4 ++-- .../linear_cross_entropy/blackwell/entry.py | 23 +++++++++++-------- .../fusions/linear_cross_entropy/utils.py | 11 ++++----- .../common/language_module/language_module.py | 4 ++-- 4 files changed, 22 insertions(+), 20 deletions(-) diff --git a/megatron/core/fusions/fused_linear_cross_entropy.py b/megatron/core/fusions/fused_linear_cross_entropy.py index 74d38da8243..720bd1478e7 100644 --- a/megatron/core/fusions/fused_linear_cross_entropy.py +++ b/megatron/core/fusions/fused_linear_cross_entropy.py @@ -63,7 +63,7 @@ def forward( weight: torch.Tensor, labels: torch.Tensor, tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, - reduction: str = "mean", + reduction: typing.Literal["none", "sum", "mean"] = "mean", ignore_index: int = -100, sequence_parallel: bool = False, ) -> torch.Tensor: @@ -216,7 +216,7 @@ def linear_cross_entropy( weight: torch.Tensor, labels: torch.Tensor, tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, - reduction: str = "mean", + reduction: typing.Literal["none", "sum", "mean"] = "mean", ignore_index: int = -100, sequence_parallel: bool = False, ) -> torch.Tensor: diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py index 786f0fd9b3b..e156735ded2 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py @@ -1,6 +1,7 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. import typing +from dataclasses import dataclass, field import cuda.bindings.driver as cuda # type: ignore import cutlass @@ -18,23 +19,25 @@ from megatron.core.fusions.linear_cross_entropy.blackwell import triton as triton_kernels +@dataclass class FwdConfig: """ The configuration for the forward pass. """ - _dedicated_stream: torch.cuda.Stream = None - _dedicated_events: typing.List[torch.cuda.Event] = list() - _initialized: bool = False - _fwd_mainloop_kernels: typing.Dict[str, cute.kernel] = dict() + _dedicated_stream: torch.cuda.Stream = field(default_factory=torch.cuda.Stream) + _dedicated_events: typing.List[torch.cuda.Event] = field(default_factory=list) + _initialized: bool = field(default=False) + _fwd_mainloop_kernels: typing.Dict[str, cute.kernel] = field(default_factory=dict) +@dataclass class BwdConfig: """ The configuration for the backward pass. """ - _bwd_kernel: typing.Dict[str, cute.kernel] = dict() + _bwd_kernel: typing.Dict[str, cute.kernel] = field(default_factory=dict) _fwd_config = FwdConfig() @@ -46,7 +49,7 @@ def forward( weight: torch.Tensor, labels: torch.Tensor, tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, - reduction: str = "mean", + reduction: typing.Literal["none", "sum", "mean"] = "mean", ignore_index: int = -100, sequence_parallel: bool = False, ) -> typing.Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int, int, torch.Tensor]: @@ -201,7 +204,7 @@ def grid(meta): _logprobs, _logprobs.stride(0), logprobs, - triton.language.constexpr(REDUCTION), + triton.language.constexpr(REDUCTION.value), ) else: _max_backup = _max.clone() @@ -251,7 +254,7 @@ def grid(meta): accumulate, accumulate.stride(0), logprobs, - REDUCTION, + REDUCTION.value, ) return logprobs, maximum, accumulate, num_valid_tokens, tp_rank, tp_world_size, global_hidden @@ -265,7 +268,7 @@ def backward( maximum: torch.Tensor, accu: torch.Tensor, num_valid_tokens: torch.Tensor, - reduction: str = "mean", + reduction: typing.Literal["none", "sum", "mean"] = "mean", ignore_index: int = -100, tp_group: typing.Optional[dist.ProcessGroup] = None, tp_rank: int = 0, @@ -334,7 +337,7 @@ def backward( key = f"vocab_size:{vocab_size}+dim:{dim}+reduction:{REDUCTION}+dtype:{hidden_view.dtype}" if _bwd_config._bwd_kernel.get(key) is None: bwd_kernel = bwd_partial_dlogits.BwdPartialDlogits( - reduction=REDUCTION, vocab_per_split=vocab_per_split + reduction=REDUCTION.value, vocab_per_split=vocab_per_split ) bwd_kernel_compiled = cute.compile( bwd_kernel, diff --git a/megatron/core/fusions/linear_cross_entropy/utils.py b/megatron/core/fusions/linear_cross_entropy/utils.py index 9a62b9826cb..d077d64ab17 100644 --- a/megatron/core/fusions/linear_cross_entropy/utils.py +++ b/megatron/core/fusions/linear_cross_entropy/utils.py @@ -1,10 +1,10 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. -from dataclasses import dataclass +import typing +from enum import Enum -@dataclass -class EntropyReductionEnum: +class EntropyReductionEnum(Enum): """ Enum for the reduction method of cross entropy. """ @@ -14,7 +14,7 @@ class EntropyReductionEnum: kMean = 2 -def str_to_reduction_enum(reduction: str) -> int: +def str_to_reduction_enum(reduction: typing.Literal["none", "sum", "mean"]) -> EntropyReductionEnum: """ str -> EntropyReductionEnum """ @@ -30,8 +30,7 @@ def str_to_reduction_enum(reduction: str) -> int: return _enum -@dataclass -class BackwardMethodEnum: +class BackwardMethodEnum(Enum): """ Enum for the backward method of linear cross entropy. """ diff --git a/megatron/core/models/common/language_module/language_module.py b/megatron/core/models/common/language_module/language_module.py index 2144bd8a997..acd81a459bb 100644 --- a/megatron/core/models/common/language_module/language_module.py +++ b/megatron/core/models/common/language_module/language_module.py @@ -1,7 +1,7 @@ # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import logging import os -from typing import Any, Dict, Optional, Tuple +from typing import Any, Dict, Literal, Optional, Tuple import torch from torch import Tensor @@ -134,7 +134,7 @@ def compute_language_model_loss_without_logits( sequence_parallel_enabled: bool = False, column_parallel_linear: torch.nn.Module = None, col_linear_kwargs: Dict[str, Any] = {}, - reduction: str = "none", + reduction: Literal["none", "sum", "mean"] = "none", ignore_index: int = -100, ) -> Tuple[Tensor, Optional[Tensor]]: """Computes the language model logits and loss (Cross entropy across vocabulary) From c1548f883c68392c60267f72906abbaa79d4c750 Mon Sep 17 00:00:00 2001 From: Jianbin Chang Date: Fri, 14 Nov 2025 09:28:02 +0800 Subject: [PATCH 10/17] rename compute_output_layer_and_language_model_loss (#12) * rename compute_output_layer_and_language_model_loss * remove used option fused_linear_cross_entropy in transformer_config --- .../core/models/common/language_module/language_module.py | 2 +- megatron/core/models/gpt/gpt_model.py | 4 ++-- megatron/core/models/mamba/mamba_model.py | 2 +- megatron/core/transformer/transformer_config.py | 3 --- 4 files changed, 4 insertions(+), 7 deletions(-) diff --git a/megatron/core/models/common/language_module/language_module.py b/megatron/core/models/common/language_module/language_module.py index acd81a459bb..c557b3a94e7 100644 --- a/megatron/core/models/common/language_module/language_module.py +++ b/megatron/core/models/common/language_module/language_module.py @@ -126,7 +126,7 @@ def check_and_set_env_variable( check_and_set_env_variable("NVTE_FUSED_ATTN", 1, AttnBackend.auto) check_and_set_env_variable("NVTE_UNFUSED_ATTN", 1, AttnBackend.auto) - def compute_language_model_loss_without_logits( + def compute_output_layer_and_language_model_loss( self, hidden: Tensor, labels: Optional[Tensor], diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py index a69a2250bce..fd1698e3578 100644 --- a/megatron/core/models/gpt/gpt_model.py +++ b/megatron/core/models/gpt/gpt_model.py @@ -577,7 +577,7 @@ def _postprocess( ) # Compute mtp loss without storing logits to save memory. - mtp_loss = self.compute_language_model_loss_without_logits( + mtp_loss = self.compute_output_layer_and_language_model_loss( hidden_states_list[mtp_layer_number + 1], labels=mtp_labels, weight=self.shared_embedding_or_output_weight(), @@ -667,7 +667,7 @@ def _postprocess( # [s b h] => [b s h] return logits.transpose(0, 1).contiguous() - loss = self.compute_language_model_loss_without_logits( + loss = self.compute_output_layer_and_language_model_loss( hidden_states, labels=labels, weight=self.shared_embedding_or_output_weight(), diff --git a/megatron/core/models/mamba/mamba_model.py b/megatron/core/models/mamba/mamba_model.py index eab86d6d532..a10315e8203 100644 --- a/megatron/core/models/mamba/mamba_model.py +++ b/megatron/core/models/mamba/mamba_model.py @@ -254,7 +254,7 @@ def forward( # [s b h] => [b s h] return logits.transpose(0, 1).contiguous() - loss = self.compute_language_model_loss_without_logits( + loss = self.compute_output_layer_and_language_model_loss( hidden_states, labels, weight=self.shared_embedding_or_output_weight(), diff --git a/megatron/core/transformer/transformer_config.py b/megatron/core/transformer/transformer_config.py index 55de1e07181..aab137b6430 100644 --- a/megatron/core/transformer/transformer_config.py +++ b/megatron/core/transformer/transformer_config.py @@ -327,9 +327,6 @@ class TransformerConfig(ModelParallelConfig): fused_single_qkv_rope: bool = False """If set, avoid splitting QKV before ROPE forward and avoid concatenating ROPE dgrads.""" - linear_cross_entropy_fusion: bool = False - """If True, fuses the linear layer and cross entropy loss calculation.""" - #################### # activation recomputation #################### From 500326452fd426390940ca85e523a8697be8771b Mon Sep 17 00:00:00 2001 From: Jianbin Chang Date: Fri, 21 Nov 2025 16:54:49 +0800 Subject: [PATCH 11/17] remove unrelated change (#13) --- megatron/core/models/mamba/mamba_model.py | 9 --------- tests/unit_tests/a2a_overlap/utils.py | 13 +++---------- 2 files changed, 3 insertions(+), 19 deletions(-) diff --git a/megatron/core/models/mamba/mamba_model.py b/megatron/core/models/mamba/mamba_model.py index 4357ef9e9a7..a10315e8203 100644 --- a/megatron/core/models/mamba/mamba_model.py +++ b/megatron/core/models/mamba/mamba_model.py @@ -247,15 +247,6 @@ def forward( if in_inference_mode and inference_context.materialize_only_last_token_logits: hidden_states = hidden_states[-1, :, :].unsqueeze(0) - # Restore sequence parallel execution to the output layer if necessary. - if sequence_parallel_override: - assert ( - in_inference_mode - and inference_context.is_dynamic_batching() - and inference_context.materialize_only_last_token_logits - ) - self.output_layer.sequence_parallel = True - if labels is None: logits, _ = self.output_layer( hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output diff --git a/tests/unit_tests/a2a_overlap/utils.py b/tests/unit_tests/a2a_overlap/utils.py index d80eaf13f5a..7db4256a849 100644 --- a/tests/unit_tests/a2a_overlap/utils.py +++ b/tests/unit_tests/a2a_overlap/utils.py @@ -222,8 +222,8 @@ def get_test_config(num_layers=1, num_moe_experts=8, extra_kwargs={}, moe_groupe def get_valid_token_dispatcher_types(): try: - from deep_ep import Buffer # type: ignore - from deep_ep.utils import EventHandle, EventOverlap # type: ignore + from deep_ep import Buffer + from deep_ep.utils import EventHandle, EventOverlap return ["alltoall", "flex"] except ImportError: @@ -237,14 +237,7 @@ def get_valid_fp8_flags(): recipes = [] valid_flags = [] if is_te_min_version("2.3.0.dev0"): - props = torch.cuda.get_device_properties(torch.cuda.current_device()) - compute_capability = (props.major, props.minor) - if ( - compute_capability >= (9, 0) - and compute_capability < (10, 0) - and float(torch.version.cuda) >= 12.9 - ): - recipes.append(Fp8Recipe.blockwise) + recipes.append(Fp8Recipe.blockwise) recipes.append(Fp8Recipe.tensorwise) for fp8_type in fp8_types: From 78c827e7cf4b65add328a76721580ecf3f0f807d Mon Sep 17 00:00:00 2001 From: Jianbin Chang Date: Thu, 27 Nov 2025 12:23:39 +0800 Subject: [PATCH 12/17] handle non-blackwell arch platform init fail (#14) --- .../core/fusions/fused_linear_cross_entropy.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/megatron/core/fusions/fused_linear_cross_entropy.py b/megatron/core/fusions/fused_linear_cross_entropy.py index 720bd1478e7..85308b1c813 100644 --- a/megatron/core/fusions/fused_linear_cross_entropy.py +++ b/megatron/core/fusions/fused_linear_cross_entropy.py @@ -40,9 +40,11 @@ def __init__(self) -> None: self._initialized = True - -_platform = Platform() - +try: + _platform = Platform() +except ValueError as e: + _unsupported_architecture_error = e + _platform = None class LinearCrossEntropy(torch.autograd.Function): """ @@ -152,6 +154,9 @@ def forward( # each rank will get distinct local d_hidden and d_weight ``` """ + if _unsupported_architecture_error: + raise _unsupported_architecture_error + with torch.cuda.nvtx.range("LinearCrossEntropy-forward"): logprobs, _maximum, _acc, _num_valid_tokens, tp_rank, tp_world_size, global_hidden = ( _platform.forward_func( @@ -182,6 +187,9 @@ def backward( dhidden (torch.Tensor): The gradient of the hidden. dweight (torch.Tensor): The gradient of the weight. """ + if _unsupported_architecture_error: + raise _unsupported_architecture_error + with torch.cuda.nvtx.range("LinearCrossEntropy-backward"): (global_hidden, weight, labels, _maximum, _accu, _num_valid_tokens) = ctx.saved_tensors From 011947de8b48c02ad1b508c4f07c398124ef33df Mon Sep 17 00:00:00 2001 From: Jianbin Chang Date: Thu, 27 Nov 2025 15:01:48 +0800 Subject: [PATCH 13/17] Remove the migration code from the main branch to the dev branch (#15) * Remove the code that synchronizes from the main branch to the dev branch. * remove unused typing --- .../common/language_module/language_module.py | 6 +++--- megatron/core/models/gpt/gpt_model.py | 14 ++++++-------- megatron/core/models/mamba/mamba_model.py | 2 +- 3 files changed, 10 insertions(+), 12 deletions(-) diff --git a/megatron/core/models/common/language_module/language_module.py b/megatron/core/models/common/language_module/language_module.py index c557b3a94e7..198b7a06f2f 100644 --- a/megatron/core/models/common/language_module/language_module.py +++ b/megatron/core/models/common/language_module/language_module.py @@ -60,7 +60,7 @@ def __init__( "If you don't need embd_group, you need to explicitly set it to None." ) self.embd_group = pg_collection.embd - self.vp_stage: Optional[int] = None + self.vp_stage = None self.vp_size = self.config.virtual_pipeline_model_parallel_size def _is_in_embd_group(self): @@ -136,7 +136,7 @@ def compute_output_layer_and_language_model_loss( col_linear_kwargs: Dict[str, Any] = {}, reduction: Literal["none", "sum", "mean"] = "none", ignore_index: int = -100, - ) -> Tuple[Tensor, Optional[Tensor]]: + ) -> Tensor: """Computes the language model logits and loss (Cross entropy across vocabulary) Args: @@ -333,7 +333,7 @@ def shared_embedding_or_output_weight(self) -> Tensor: def sharded_state_dict( self, prefix: str = '', - sharded_offsets: Tuple[Tuple[int, int, int], ...] = (), + sharded_offsets: Tuple[Tuple[int, int, int]] = (), metadata: Optional[dict] = None, ) -> ShardedStateDict: """Sharded state dict implementation that handles the output layer weights tying. diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py index bd1b51b6a12..78069e80f71 100644 --- a/megatron/core/models/gpt/gpt_model.py +++ b/megatron/core/models/gpt/gpt_model.py @@ -1,7 +1,7 @@ # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. from collections import OrderedDict -from typing import Any, Dict, List, Literal, Optional, Tuple +from typing import Dict, Literal, Optional import torch from torch import Tensor @@ -119,7 +119,7 @@ def __init__( self.parallel_output = parallel_output self.share_embeddings_and_output_weights = share_embeddings_and_output_weights self.disable_param_offloading = True - self.vp_stage: Optional[int] = vp_stage + self.vp_stage = vp_stage if hasattr(self.config, 'position_embedding_type'): self.position_embedding_type = self.config.position_embedding_type @@ -199,7 +199,7 @@ def __init__( ), "mrope require mrope_section setting, but we got None from TransformerConfig" # Cache for RoPE tensors which do not change between iterations. - self.rotary_pos_emb_cache: Dict[int, Tuple[Tensor, Tensor]] = {} + self.rotary_pos_emb_cache = {} # Transformer. self.decoder = TransformerBlock( @@ -219,8 +219,6 @@ def __init__( # Output if self.post_process: - self.embedding_activation_buffer: Optional[List[Tensor]] = None - self.grad_output_buffer: Optional[List[Tensor]] = None if self.config.defer_embedding_wgrad_compute: # The embedding activation buffer preserves a reference to the input activations # of the final embedding projection layer GEMM. It will hold the activations for @@ -397,7 +395,7 @@ def _preprocess( if in_inference_mode and not has_config_logger_enabled(self.config): decoder_input = WrappedTensor(decoder_input) - preproc_output: Tuple[Any, ...] = ( + preproc_output = ( decoder_input, rotary_pos_emb, rotary_pos_cos, @@ -441,7 +439,7 @@ def forward( labels: Tensor = None, inference_context: BaseInferenceContext = None, packed_seq_params: PackedSeqParams = None, - extra_block_kwargs: Optional[Dict[str, Any]] = None, + extra_block_kwargs: dict = None, runtime_gather_output: Optional[bool] = None, *, inference_params: Optional[BaseInferenceContext] = None, @@ -709,7 +707,7 @@ def build_schedule_plan( labels: Tensor = None, inference_context: BaseInferenceContext = None, packed_seq_params: PackedSeqParams = None, - extra_block_kwargs: Optional[Dict[str, Any]] = None, + extra_block_kwargs: dict = None, runtime_gather_output: Optional[bool] = None, inference_params: Optional[BaseInferenceContext] = None, loss_mask: Optional[Tensor] = None, diff --git a/megatron/core/models/mamba/mamba_model.py b/megatron/core/models/mamba/mamba_model.py index 4f306567565..7138cfad7d6 100644 --- a/megatron/core/models/mamba/mamba_model.py +++ b/megatron/core/models/mamba/mamba_model.py @@ -62,7 +62,7 @@ def __init__( pre_process: bool = True, hybrid_attention_ratio: float = 0.0, hybrid_mlp_ratio: float = 0.0, - hybrid_override_pattern: Optional[str] = None, + hybrid_override_pattern: str = None, post_process: bool = True, fp16_lm_cross_entropy: bool = False, parallel_output: bool = True, From bfee2a383be9eac49d3c100a61118f48180983de Mon Sep 17 00:00:00 2001 From: Jianbing Date: Thu, 27 Nov 2025 18:27:52 +0800 Subject: [PATCH 14/17] Fix review (#16) * lazy init for global objects Signed-off-by: Jianbing Dong * fix distribute init for tp and sp Signed-off-by: Jianbing Dong * add __init__ Signed-off-by: Jianbing Dong --------- Signed-off-by: Jianbing Dong --- .../fusions/fused_linear_cross_entropy.py | 23 ++- .../fusions/linear_cross_entropy/__init__.py | 0 .../blackwell/__init__.py | 0 .../linear_cross_entropy/blackwell/entry.py | 63 ++++---- .../test_fused_linear_cross_entropy.py | 134 +++++++++++------- 5 files changed, 129 insertions(+), 91 deletions(-) create mode 100644 megatron/core/fusions/linear_cross_entropy/__init__.py create mode 100644 megatron/core/fusions/linear_cross_entropy/blackwell/__init__.py diff --git a/megatron/core/fusions/fused_linear_cross_entropy.py b/megatron/core/fusions/fused_linear_cross_entropy.py index 85308b1c813..ca87eb09a8a 100644 --- a/megatron/core/fusions/fused_linear_cross_entropy.py +++ b/megatron/core/fusions/fused_linear_cross_entropy.py @@ -6,6 +6,7 @@ """ import typing +from functools import lru_cache import torch @@ -40,11 +41,13 @@ def __init__(self) -> None: self._initialized = True -try: - _platform = Platform() -except ValueError as e: - _unsupported_architecture_error = e - _platform = None +@lru_cache(maxsize=1) +def _get_platform() -> Platform: + """ + Helper function to lazy initialize the platform. + """ + return Platform() + class LinearCrossEntropy(torch.autograd.Function): """ @@ -154,12 +157,9 @@ def forward( # each rank will get distinct local d_hidden and d_weight ``` """ - if _unsupported_architecture_error: - raise _unsupported_architecture_error - with torch.cuda.nvtx.range("LinearCrossEntropy-forward"): logprobs, _maximum, _acc, _num_valid_tokens, tp_rank, tp_world_size, global_hidden = ( - _platform.forward_func( + _get_platform().forward_func( hidden, weight, labels, tp_group, reduction, ignore_index, sequence_parallel ) ) @@ -187,9 +187,6 @@ def backward( dhidden (torch.Tensor): The gradient of the hidden. dweight (torch.Tensor): The gradient of the weight. """ - if _unsupported_architecture_error: - raise _unsupported_architecture_error - with torch.cuda.nvtx.range("LinearCrossEntropy-backward"): (global_hidden, weight, labels, _maximum, _accu, _num_valid_tokens) = ctx.saved_tensors @@ -200,7 +197,7 @@ def backward( tp_world_size = ctx.tp_world_size sequence_parallel = ctx.sequence_parallel - d_hidden, d_weight = _platform.backward_func( + d_hidden, d_weight = _get_platform().backward_func( dlogprobs, global_hidden, weight, diff --git a/megatron/core/fusions/linear_cross_entropy/__init__.py b/megatron/core/fusions/linear_cross_entropy/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/__init__.py b/megatron/core/fusions/linear_cross_entropy/blackwell/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py index e156735ded2..014c574a635 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py @@ -2,6 +2,8 @@ import typing from dataclasses import dataclass, field +from functools import lru_cache +import os import cuda.bindings.driver as cuda # type: ignore import cutlass @@ -29,6 +31,7 @@ class FwdConfig: _dedicated_events: typing.List[torch.cuda.Event] = field(default_factory=list) _initialized: bool = field(default=False) _fwd_mainloop_kernels: typing.Dict[str, cute.kernel] = field(default_factory=dict) + _vocab_per_split: int = field(default=int(os.environ.get("LCE_FWD_VOCAB_SPLIT_SIZE", 512 * 6))) @dataclass @@ -38,11 +41,23 @@ class BwdConfig: """ _bwd_kernel: typing.Dict[str, cute.kernel] = field(default_factory=dict) + _vocab_per_split: int = field(default=int(os.environ.get("LCE_BWD_VOCAB_SPLIT_SIZE", 512 * 6))) + _backward_method: utils.BackwardMethodEnum = field(default=utils.BackwardMethodEnum.kDlogitsSplitN) -_fwd_config = FwdConfig() -_bwd_config = BwdConfig() +@lru_cache(maxsize=1) +def _get_fwd_config() -> FwdConfig: + """ + Helper function to lazy initialize the forward configuration. + """ + return FwdConfig() +@lru_cache(maxsize=1) +def _get_bwd_config() -> BwdConfig: + """ + Helper function to lazy initialize the backward configuration. + """ + return BwdConfig() def forward( hidden: torch.Tensor, @@ -91,11 +106,10 @@ def forward( num_tokens, dim = hidden_view.shape vocab_size, _ = weight.shape - global _fwd_config - if not _fwd_config._initialized: - _fwd_config._dedicated_stream = torch.cuda.Stream(hidden.device) - _fwd_config._dedicated_events = [torch.cuda.Event() for _ in range(2)] - _fwd_config._initialized = True + if not _get_fwd_config()._initialized: + _get_fwd_config()._dedicated_stream = torch.cuda.Stream(hidden.device) + _get_fwd_config()._dedicated_events = [torch.cuda.Event() for _ in range(2)] + _get_fwd_config()._initialized = True REDUCTION = utils.str_to_reduction_enum(reduction) # declare logprobs @@ -114,8 +128,7 @@ def forward( ) # declare intermediate tensors # NOTE: this is a parameter for tuning - vocab_per_split = 512 * 6 - num_splits = (vocab_size + vocab_per_split - 1) // vocab_per_split + num_splits = (vocab_size + _get_fwd_config()._vocab_per_split - 1) // _get_fwd_config()._vocab_per_split _max = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) _accu = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) if REDUCTION == utils.EntropyReductionEnum.kNone: @@ -150,8 +163,8 @@ def forward( # VocabSize and Dim are fixed for a given model, # only the number of tokens can vary key = f"vocab_size:{vocab_size}+dim:{dim}+dtype:{hidden_view.dtype}" - if _fwd_config._fwd_mainloop_kernels.get(key) is None: - fwd_mainloop_kernel = fwd_mainloop.FwdMainLoop(vocab_per_split=vocab_per_split) + if _get_fwd_config()._fwd_mainloop_kernels.get(key) is None: + fwd_mainloop_kernel = fwd_mainloop.FwdMainLoop(vocab_per_split=_get_fwd_config()._vocab_per_split) fwd_mainloop_compiled_kernel = cute.compile( fwd_mainloop_kernel, hidden_packed, @@ -164,9 +177,9 @@ def forward( tp_rank, cuda_stream, ) - _fwd_config._fwd_mainloop_kernels[key] = fwd_mainloop_compiled_kernel + _get_fwd_config()._fwd_mainloop_kernels[key] = fwd_mainloop_compiled_kernel else: - fwd_mainloop_compiled_kernel = _fwd_config._fwd_mainloop_kernels[key] + fwd_mainloop_compiled_kernel = _get_fwd_config()._fwd_mainloop_kernels[key] fwd_mainloop_compiled_kernel( hidden_packed, weight_packed, @@ -210,11 +223,11 @@ def grid(meta): _max_backup = _max.clone() dist.all_reduce(_max, op=dist.ReduceOp.MAX, group=tp_group) - torch.cuda.current_stream().record_event(_fwd_config._dedicated_events[0]) - with torch.cuda.stream(_fwd_config._dedicated_stream): - _fwd_config._dedicated_stream.wait_event(_fwd_config._dedicated_events[0]) + torch.cuda.current_stream().record_event(_get_fwd_config()._dedicated_events[0]) + with torch.cuda.stream(_get_fwd_config()._dedicated_stream): + _get_fwd_config()._dedicated_stream.wait_event(_get_fwd_config()._dedicated_events[0]) dist.all_reduce(_logprobs, op=dist.ReduceOp.SUM, group=tp_group) - _fwd_config._dedicated_stream.record_event(_fwd_config._dedicated_events[1]) + _get_fwd_config()._dedicated_stream.record_event(_get_fwd_config()._dedicated_events[1]) def grid(meta): return (triton.cdiv(num_tokens, meta["BLOCK_SIZE_M"]),) @@ -240,7 +253,7 @@ def grid(meta): dist.all_reduce(accumulate, op=dist.ReduceOp.SUM, group=tp_group) # update logprobs - torch.cuda.current_stream().wait_event(_fwd_config._dedicated_events[1]) + torch.cuda.current_stream().wait_event(_get_fwd_config()._dedicated_events[1]) triton_kernels.forward_tp_epilogue_update_logprobs[grid]( num_tokens, ignore_index, @@ -304,9 +317,9 @@ def backward( assert d_hidden.is_contiguous() and d_weight.is_contiguous() # FIXME: implement different backward methods - _backward = utils.BackwardMethodEnum.kDlogitsSplitN - if _backward == utils.BackwardMethodEnum.kDlogitsSplitN: - vocab_per_split = 512 * 6 + _backward_method = _get_bwd_config()._backward_method + if _backward_method == utils.BackwardMethodEnum.kDlogitsSplitN: + vocab_per_split = _get_bwd_config()._vocab_per_split num_splits = (vocab_size + vocab_per_split - 1) // vocab_per_split _d_logits = torch.empty( @@ -335,7 +348,7 @@ def backward( stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) key = f"vocab_size:{vocab_size}+dim:{dim}+reduction:{REDUCTION}+dtype:{hidden_view.dtype}" - if _bwd_config._bwd_kernel.get(key) is None: + if _get_bwd_config()._bwd_kernel.get(key) is None: bwd_kernel = bwd_partial_dlogits.BwdPartialDlogits( reduction=REDUCTION.value, vocab_per_split=vocab_per_split ) @@ -354,9 +367,9 @@ def backward( tp_rank, stream, ) - _bwd_config._bwd_kernel[key] = bwd_kernel_compiled + _get_bwd_config()._bwd_kernel[key] = bwd_kernel_compiled else: - bwd_kernel_compiled = _bwd_config._bwd_kernel.get(key) + bwd_kernel_compiled = _get_bwd_config()._bwd_kernel.get(key) for split_idx in range(num_splits): bwd_kernel_compiled( @@ -395,7 +408,7 @@ def backward( out=d_weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :], ) else: - raise NotImplementedError(f"Unsupported backward method: {_backward}") + raise NotImplementedError(f"Unsupported backward method: {_backward_method}") if in_tp_mode: dist.all_reduce(d_hidden, op=dist.ReduceOp.SUM, group=tp_group) diff --git a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py index a36b8cfb4e0..66370271de9 100644 --- a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py +++ b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py @@ -4,6 +4,7 @@ import os import typing from contextlib import ExitStack +from dataclasses import dataclass import numpy as np import pytest @@ -28,6 +29,62 @@ from tests.unit_tests.test_utilities import Utils +# 1. Define a standardized context to hold your distributed info +@dataclass +class DistContext: + rank: int + world_size: int + group: dist.ProcessGroup + is_chief: bool + +# 2. Create a module-scoped fixture +# This runs ONE time per file, no matter how many test classes you have. +@pytest.fixture(scope="module") +def distributed_context(): + # --- PRE-CHECK --- + if "WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2: + pytest.skip("Requires torchrun with multiple GPUs (WORLD_SIZE >= 2)") + + # --- SETUP --- + is_external_init = dist.is_initialized() + + if not is_external_init: + # Initialize only if not already done (e.g., by another test runner) + dist.init_process_group( + backend="nccl", + init_method="env://", + world_size=int(os.environ["WORLD_SIZE"]), + rank=int(os.environ["RANK"]), + ) + + # Set device immediately to avoid cross-device pollution + local_rank = int(os.environ.get("LOCAL_RANK", os.environ["RANK"])) + device = torch.device(f"cuda:{local_rank}") + torch.cuda.set_device(device) + + # Gather context data + rank = dist.get_rank() + world_size = dist.get_world_size() + group = dist.group.WORLD + + print(f"[INFO]: Initialized Rank: {rank} / {world_size}") + + context = DistContext( + rank=rank, + world_size=world_size, + group=group, + is_chief=(rank == 0) + ) + + # Yield control to the tests + yield context + + # --- TEARDOWN --- + # Only destroy if we were the ones who initialized it + if not is_external_init: + dist.destroy_process_group() + + class MockDataset(Dataset): """ Mock dataset for torchtitan GPT training tests @@ -136,9 +193,9 @@ def init_gpt_dataloader( dataloader = DataLoader(dataset, batch_size=batch_size, sampler=sampler) return dataloader - +# skip it for good @pytest.mark.skipif( - ("WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2), # or True, + ("WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2) or True, reason="Requires torchrun with multiple GPUs", ) class TestFusedLinearCrossEntropyOnGptModel: @@ -501,32 +558,18 @@ def custom_storage(): ("WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2), # or True, reason="Requires torchrun with multiple GPUs", ) +@pytest.mark.usefixtures("distributed_context") class TestFusedLinearCrossEntropyTensorParallel: - @classmethod - def setup_class(cls): - if dist.is_initialized(): - cls.must_teardown = False - else: - dist.init_process_group( - backend="nccl", - init_method="env://", - world_size=int(os.environ["WORLD_SIZE"]), - rank=int(os.environ["RANK"]), - ) - cls.must_teardown = True - cls.tp_group = dist.group.WORLD - - cls.tp_rank = dist.get_rank(cls.tp_group) - cls.tp_world_size = dist.get_world_size(cls.tp_group) - cls.is_chief = cls.tp_rank == 0 - device = torch.device(f"cuda:{cls.tp_rank}") - torch.cuda.set_device(device) - print(f"[INFO]: TP rank: {cls.tp_rank}, TP world size: {cls.tp_world_size}") - - @classmethod - def teardown_class(cls): - if cls.must_teardown: - dist.destroy_process_group() + @pytest.fixture(autouse=True) + def setup_attrs(self, distributed_context): + """ + Setup attributes for the test class. + """ + self.tp_group = distributed_context.group + self.tp_rank = distributed_context.rank + self.tp_world_size = distributed_context.world_size + self.is_chief = distributed_context.is_chief + def cleanup(self): torch.cuda.empty_cache() @@ -954,32 +997,17 @@ def custom_storage(): "WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2, reason="Requires torchrun with multiple GPUs", ) +@pytest.mark.usefixtures("distributed_context") class TestFusedLinearCrossEntropySequenceParallel: - @classmethod - def setup_class(cls): - if dist.is_initialized(): - cls.must_teardown = False - else: - dist.init_process_group( - backend="nccl", - init_method="env://", - world_size=int(os.environ["WORLD_SIZE"]), - rank=int(os.environ["RANK"]), - ) - cls.must_teardown = True - cls.tp_group = dist.group.WORLD - - cls.tp_rank = dist.get_rank(cls.tp_group) - cls.tp_world_size = dist.get_world_size(cls.tp_group) - cls.is_chief = cls.tp_rank == 0 - device = torch.device(f"cuda:{cls.tp_rank}") - torch.cuda.set_device(device) - print(f"[INFO]: TP rank: {cls.tp_rank}, TP world size: {cls.tp_world_size}") - - @classmethod - def teardown_class(cls): - if cls.must_teardown: - dist.destroy_process_group() + @pytest.fixture(autouse=True) + def setup_attrs(self, distributed_context): + """ + Setup attributes for the test class. + """ + self.tp_group = distributed_context.group + self.tp_rank = distributed_context.rank + self.tp_world_size = distributed_context.world_size + self.is_chief = distributed_context.is_chief @staticmethod def timed_barrier(timeout_s=10): @@ -1125,7 +1153,7 @@ def backward(ctx, g_logprobs: torch.Tensor): @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) @pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) @pytest.mark.parametrize("problem", [(256, 12928, 8192)]) - def test_torch_tp_vs_single_gpu(self, dtype, reduction, problem): + def test_torch_sp_vs_single_gpu(self, dtype, reduction, problem): num_tokens, vocabsize, dim = problem hidden = ( From 1b603b9a41c731454b7ab7bc9a99318ff41f1e1a Mon Sep 17 00:00:00 2001 From: Jianbin Chang Date: Tue, 2 Dec 2025 10:11:25 +0800 Subject: [PATCH 15/17] Fix for CI (#17) * fix platform fail in test env * fix import error in no CUDA & CUTE test env * Revert "fix import error in no CUDA & CUTE test env" This reverts commit 0b8010b30fcc0795b917b5b177a61ec7e906fe40. * safe_imports check skip blackwell * try clean up * reduce fused_linear_cross_entopy UT problem size for OOM issue * skip UT when device arch not 10 * fix mamba logits compute order --- .gitlab/scripts/check_imports.py | 1 + .../core/fusions/fused_linear_cross_entropy.py | 1 + megatron/core/models/gpt/gpt_model.py | 2 +- megatron/core/models/mamba/mamba_model.py | 8 +++++--- .../fusions/test_fused_linear_cross_entropy.py | 14 +++++++++++++- 5 files changed, 21 insertions(+), 5 deletions(-) diff --git a/.gitlab/scripts/check_imports.py b/.gitlab/scripts/check_imports.py index f46987d8d87..9d82b661681 100644 --- a/.gitlab/scripts/check_imports.py +++ b/.gitlab/scripts/check_imports.py @@ -49,6 +49,7 @@ def __init__(self, package_name: str = "megatron.core", verbose: bool = False): ".git", "test_", "_test", + "blackwell", } # Add current directory to Python path if not already there diff --git a/megatron/core/fusions/fused_linear_cross_entropy.py b/megatron/core/fusions/fused_linear_cross_entropy.py index ca87eb09a8a..3bb3b5c14f1 100644 --- a/megatron/core/fusions/fused_linear_cross_entropy.py +++ b/megatron/core/fusions/fused_linear_cross_entropy.py @@ -41,6 +41,7 @@ def __init__(self) -> None: self._initialized = True + @lru_cache(maxsize=1) def _get_platform() -> Platform: """ diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py index 78069e80f71..b46ea83a4d4 100644 --- a/megatron/core/models/gpt/gpt_model.py +++ b/megatron/core/models/gpt/gpt_model.py @@ -118,8 +118,8 @@ def __init__( self.fp16_lm_cross_entropy = fp16_lm_cross_entropy self.parallel_output = parallel_output self.share_embeddings_and_output_weights = share_embeddings_and_output_weights - self.disable_param_offloading = True self.vp_stage = vp_stage + self.disable_param_offloading = True if hasattr(self.config, 'position_embedding_type'): self.position_embedding_type = self.config.position_embedding_type diff --git a/megatron/core/models/mamba/mamba_model.py b/megatron/core/models/mamba/mamba_model.py index 7138cfad7d6..e4074eda806 100644 --- a/megatron/core/models/mamba/mamba_model.py +++ b/megatron/core/models/mamba/mamba_model.py @@ -267,6 +267,11 @@ def forward( hidden_states.squeeze(1).unsqueeze(0) ).unsqueeze(1) + if labels is None: + logits, _ = self.output_layer( + hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output + ) + # Restore sequence parallel execution to the output layer if necessary. if sequence_parallel_override: assert ( @@ -277,9 +282,6 @@ def forward( self.output_layer.sequence_parallel = True if labels is None: - logits, _ = self.output_layer( - hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output - ) # [s b h] => [b s h] return logits.transpose(0, 1).contiguous() diff --git a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py index 66370271de9..873505fe51c 100644 --- a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py +++ b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py @@ -20,6 +20,7 @@ get_gpt_mtp_block_spec, ) from megatron.core.models.gpt.gpt_model import GPTModel +from megatron.training.utils import get_device_arch_version from tests.unit_tests.a2a_overlap.utils import ( deterministic_mode, get_test_config, @@ -257,6 +258,9 @@ def test_gpt_model(self, mtp_layers, dispatcher_type, fp8_flag, layer_num): @pytest.mark.skipif( "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "1", reason="Requires single GPU" ) +@pytest.mark.skipif( + get_device_arch_version() != 10, reason="Requires GPU architecture = 10" +) class TestFusedLinearCrossEntropyDataParallel: def cleanup(self): torch.cuda.empty_cache() @@ -558,6 +562,9 @@ def custom_storage(): ("WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2), # or True, reason="Requires torchrun with multiple GPUs", ) +@pytest.mark.skipif( + get_device_arch_version() != 10, reason="Requires GPU architecture = 10" +) @pytest.mark.usefixtures("distributed_context") class TestFusedLinearCrossEntropyTensorParallel: @pytest.fixture(autouse=True) @@ -684,6 +691,7 @@ def backward(ctx, g_logprobs: torch.Tensor): @pytest.mark.parametrize("problem", [(4096, 129280, 8192)]) def test_torch_tp_vs_single_gpu(self, dtype, reduction, problem): num_tokens, vocabsize, dim = problem + vocabsize = vocabsize // self.tp_world_size hidden = ( torch.empty((num_tokens, dim), dtype=dtype, device="cuda") @@ -997,6 +1005,9 @@ def custom_storage(): "WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2, reason="Requires torchrun with multiple GPUs", ) +@pytest.mark.skipif( + get_device_arch_version() != 10, reason="Requires GPU architecture = 10" +) @pytest.mark.usefixtures("distributed_context") class TestFusedLinearCrossEntropySequenceParallel: @pytest.fixture(autouse=True) @@ -1152,9 +1163,10 @@ def backward(ctx, g_logprobs: torch.Tensor): @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) @pytest.mark.parametrize("reduction", ["mean", "sum", "none"]) - @pytest.mark.parametrize("problem", [(256, 12928, 8192)]) + @pytest.mark.parametrize("problem", [(256, 129280, 8192)]) def test_torch_sp_vs_single_gpu(self, dtype, reduction, problem): num_tokens, vocabsize, dim = problem + vocabsize = vocabsize // self.tp_world_size hidden = ( torch.empty((num_tokens, dim), dtype=dtype, device="cuda") From fb2ee78a1cddf716acb3545156bb42781a94641f Mon Sep 17 00:00:00 2001 From: Jianbin Chang Date: Tue, 2 Dec 2025 15:48:14 +0800 Subject: [PATCH 16/17] Fix CI (#18) * fix platform fail in test env * fix import error in no CUDA & CUTE test env * Revert "fix import error in no CUDA & CUTE test env" This reverts commit 0b8010b30fcc0795b917b5b177a61ec7e906fe40. * safe_imports check skip blackwell * try clean up * reduce fused_linear_cross_entopy UT problem size for OOM issue * skip UT when device arch not 10 * fix mamba logits compute order * 1. Add Copyright for init.py 2. Allow files under Blackwell to bypass import checks. --- .gitlab/scripts/check_imports.py | 1 - .../fusions/linear_cross_entropy/__init__.py | 1 + .../blackwell/__init__.py | 1 + .../blackwell/bwd_partial_dlogits.py | 1181 ++++++++-------- .../linear_cross_entropy/blackwell/entry.py | 789 +++++------ .../blackwell/fwd_mainloop.py | 1241 +++++++++-------- 6 files changed, 1612 insertions(+), 1602 deletions(-) diff --git a/.gitlab/scripts/check_imports.py b/.gitlab/scripts/check_imports.py index 9d82b661681..f46987d8d87 100644 --- a/.gitlab/scripts/check_imports.py +++ b/.gitlab/scripts/check_imports.py @@ -49,7 +49,6 @@ def __init__(self, package_name: str = "megatron.core", verbose: bool = False): ".git", "test_", "_test", - "blackwell", } # Add current directory to Python path if not already there diff --git a/megatron/core/fusions/linear_cross_entropy/__init__.py b/megatron/core/fusions/linear_cross_entropy/__init__.py index e69de29bb2d..b9a9591fa69 100644 --- a/megatron/core/fusions/linear_cross_entropy/__init__.py +++ b/megatron/core/fusions/linear_cross_entropy/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/__init__.py b/megatron/core/fusions/linear_cross_entropy/blackwell/__init__.py index e69de29bb2d..b9a9591fa69 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/__init__.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py b/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py index 8a6e03601bf..17ad627322e 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py @@ -1,635 +1,638 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. -from typing import Optional, Tuple, Type - -import cuda.bindings.driver as cuda # type: ignore -import cutlass -import cutlass.cute as cute -import cutlass.pipeline as pipeline # type: ignore -import cutlass.utils as utils # type: ignore -import cutlass.utils.blackwell_helpers as sm100_utils # type: ignore -from cutlass.cute.nvgpu import cpasync, tcgen05 - -SM100_TMEM_CAPACITY_COLUMNS: int = 512 - - -def make_thread_cooperative_group(size: int, alignment: Optional[int] = None): - """ - Create a thread cooperative group. - """ - return pipeline.CooperativeGroup( - pipeline.Agent.Thread, size, alignment=alignment if alignment is not None else size - ) - - -class BwdPartialDlogits: - """ - This class implements the backward kernel for partial d_logits. - """ - - def __init__( - self, - reduction: int, - acc_dtype: Type[cutlass.Numeric] = cutlass.Float32, - use_2cta_instrs: bool = False, - mma_tiler_mn: Tuple[int, int] = (128, 256), - vocab_per_split: int = 512, - ): - self.REDUCTION: cutlass.Constexpr[cutlass.Int32] = cutlass.const_expr(reduction) - self.acc_dtype = acc_dtype - self.use_2cta_instrs = use_2cta_instrs - self.mma_tiler = (*mma_tiler_mn, 1) - self.vocab_per_split = vocab_per_split - - self.cta_group = tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE - self.cluster_shape_mn = (2, 1) if self.use_2cta_instrs else (1, 1) - - self.smem_capacity = utils.get_smem_capacity_in_bytes("sm_100") - - self.threads_per_warp: int = 32 - - self.epi_warp_ids = (0, 1, 2, 3) - self.load_warp_ids = 4 - self.mma_warp_ids = 5 - self.empty_warp_ids = (6, 7) - - self.threads_per_cta: int = self.threads_per_warp * len( - (*self.epi_warp_ids, self.load_warp_ids, self.mma_warp_ids, *self.empty_warp_ids) - ) - self.cta_sync_barrier = pipeline.NamedBarrier( - barrier_id=1, num_threads=self.threads_per_cta - ) +try: + from typing import Optional, Tuple, Type - self.buffer_align_bytes: int = 1024 - self.num_regs_other: int = 32 - self.num_regs_epi: int = 192 - - def _compute_grid( - self, - problem_mnk: Tuple[int, int, int], - cluster_shape_mn: Tuple[int, int], - cta_tiler: Tuple[int, int, int], - ) -> Tuple[int, int, int]: - cluster_shape_mnk = (*cluster_shape_mn, 1) - - grid = cute.round_up( - ( - cute.ceil_div(problem_mnk[0], cta_tiler[0]), - cute.ceil_div(self.vocab_per_split, cta_tiler[1]), - 1, - ), - cluster_shape_mnk, - ) - return grid - - def _compute_stages( - self, - tiled_mma: cute.TiledMma, - mma_tiler: Tuple[int, int, int], - a_dtype: Type[cutlass.Numeric], - b_dtype: Type[cutlass.Numeric], - ): - num_acc_stage = 1 - num_ab_stage = 4 - num_epi_stage_per_tile = 4 - return num_acc_stage, num_ab_stage, num_epi_stage_per_tile - - def _setup_attributes( - self, - tiled_mma: cute.TiledMma, - a_dtype: Type[cutlass.Numeric], - b_dtype: Type[cutlass.Numeric], - ): - self.cluster_shape_mnk = (*self.cluster_shape_mn, 1) - self.cluster_layout_vmnk = cute.tiled_divide( - cute.make_layout(self.cluster_shape_mnk), (tiled_mma.thr_id.shape,) - ) + import cuda.bindings.driver as cuda # type: ignore + import cutlass + import cutlass.cute as cute + import cutlass.pipeline as pipeline # type: ignore + import cutlass.utils as utils # type: ignore + import cutlass.utils.blackwell_helpers as sm100_utils # type: ignore + from cutlass.cute.nvgpu import cpasync, tcgen05 - mma_inst_shape_k = cute.size(tiled_mma.shape_mnk, mode=[2]) - # it requires k-mode to be 128B aligned - mma_inst_tile_k: int = 4 - self.mma_tiler = (self.mma_tiler[0], self.mma_tiler[1], mma_inst_shape_k * mma_inst_tile_k) + SM100_TMEM_CAPACITY_COLUMNS: int = 512 - self.num_acc_stage, self.num_ab_stage, self.num_epi_stage_per_tile = self._compute_stages( - tiled_mma, self.mma_tiler, a_dtype, b_dtype - ) - self.tmem_alloc_cols = self.num_acc_stage * self.mma_tiler[1] - assert self.tmem_alloc_cols <= SM100_TMEM_CAPACITY_COLUMNS - self.cta_tile_shape_mnk = ( - self.mma_tiler[0] // cute.size(tiled_mma.thr_id.shape), - self.mma_tiler[1], - self.mma_tiler[2], - ) - - @cute.kernel - def kernel( - self, - split_idx: cutlass.Int32, - tiled_mma: cute.TiledMma, - tma_atom_a: cute.CopyAtom, - mA: cute.Tensor, - tma_atom_b: cute.CopyAtom, - mB: cute.Tensor, - mLabels: cute.Tensor, - mDlogprobs: cute.Tensor, - mMaximum: cute.Tensor, - mAccu: cute.Tensor, - mDlogits_partial: cute.Tensor, - scalarNumValidTokens: cute.Pointer, - ignore_index: cutlass.Int64, - a_smem_layout_staged: cute.ComposedLayout, - b_smem_layout_staged: cute.ComposedLayout, - cluster_layout_vmnk: cute.Layout, - problem_mnk: Tuple[int, int, int], - rank: cutlass.Int32, - ) -> None: + def make_thread_cooperative_group(size: int, alignment: Optional[int] = None): """ - The backward kernel for partial d_logits. + Create a thread cooperative group. """ - warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) - tidx, _, _ = cute.arch.thread_idx() - bidx, bidy, _ = cute.arch.block_idx() - # FIXME: block swizzling applied here - pidm, pidn = bidx, bidy - - # FIXME: if 2 CTAs, modify here - cta_rank_in_cluster = 0 - block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord(cta_rank_in_cluster) - - # prefetch tma descriptors - if warp_idx == self.load_warp_ids: - cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_a) - cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_b) - - smem = utils.SmemAllocator() - storage = smem.allocate(self.shared_storage) - - ab_pipeline = pipeline.PipelineTmaUmma.create( - num_stages=self.num_ab_stage, - producer_group=make_thread_cooperative_group(len([self.load_warp_ids])), - consumer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), - tx_count=self.tma_copy_ab_bytes, - barrier_storage=storage.load_ab_mbar_ptr.data_ptr(), - ) - ab_producer_state = pipeline.make_pipeline_state( - pipeline.PipelineUserType.Producer, self.num_ab_stage - ) - ab_consumer_state = pipeline.make_pipeline_state( - pipeline.PipelineUserType.Consumer, self.num_ab_stage - ) - - mma_pipeline = pipeline.PipelineUmmaAsync.create( - num_stages=self.num_acc_stage, - producer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), - consumer_group=make_thread_cooperative_group( - self.threads_per_warp * len(self.epi_warp_ids) - ), - barrier_storage=storage.mma_mbar_ptr.data_ptr(), - ) - mma_producer_state = pipeline.make_pipeline_state( - pipeline.PipelineUserType.Producer, self.num_acc_stage - ) - mma_consumer_state = pipeline.make_pipeline_state( - pipeline.PipelineUserType.Consumer, self.num_acc_stage + return pipeline.CooperativeGroup( + pipeline.Agent.Thread, size, alignment=alignment if alignment is not None else size ) - tmem_dealloc_mbar_ptr = storage.tmem_dealloc_mbar_ptr.data_ptr() - if warp_idx == self.empty_warp_ids[0]: - with cute.arch.elect_one(): - cute.arch.mbarrier_init( - tmem_dealloc_mbar_ptr, self.threads_per_warp * len(self.epi_warp_ids) - ) - cute.arch.mbarrier_init_fence() - - # -------- tensor partition ------------ # - # swizzle o [(tileM, tileK), loopM, loopK, stage] - sA = storage.sA.get_tensor(a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner) - # swizzle o [(tileN, tileK), loopN, loopK, stage] - sB = storage.sB.get_tensor(b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner) - - # FIXME: if 2 CTAs, modify here - thr_mma = tiled_mma.get_slice(0) - # [MMA, loopM, loopK, stage] - tCsA = thr_mma.make_fragment_A(sA) - # [MMA, loopN, loopK, stage] - tCsB = thr_mma.make_fragment_B(sB) - - # [tileM, tileK, loopK] - gA = cute.local_tile( - mA, (self.cta_tile_shape_mnk[0], self.cta_tile_shape_mnk[2]), (pidm, None) - ) - # [vocab_per_split, dim] - mB_n = cute.local_tile( - mB, (self.vocab_per_split, cute.size(mB.layout.shape, mode=[1])), (split_idx, 0) - ) - # [tileN, tileK, loopK] - gB = cute.local_tile( - mB_n, (self.cta_tile_shape_mnk[1], self.cta_tile_shape_mnk[2]), (pidn, None) - ) - a_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, 0, None, 0)).shape) - # just to make sure SMEM and GMEM tensor has the same size in the first rank - tCgA = thr_mma.partition_A(gA) - tCgB = thr_mma.partition_B(gB) - # [CPY, stage] & [CPY, loopK] - tTMAsA, tTMAgA = cpasync.tma_partition( - tma_atom_a, - block_in_cluster_coord_vmnk[2], # cta_coord, - a_cta_layout, - cute.group_modes(sA, 0, 3), - cute.group_modes(tCgA, 0, 3), - ) - b_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape) - # [CPY, stage] & [CPY, loopK] - tTMAsB, tTMAgB = cpasync.tma_partition( - tma_atom_b, - block_in_cluster_coord_vmnk[1], # cta_coord - b_cta_layout, - cute.group_modes(sB, 0, 3), - cute.group_modes(tCgB, 0, 3), - ) + class BwdPartialDlogits: + """ + This class implements the backward kernel for partial d_logits. + """ - # ------ Allocate TMEM ------ # - tmem_holding_buf = storage.tmem_holding_buf - if warp_idx == self.empty_warp_ids[0]: - cute.arch.alloc_tmem( - self.tmem_alloc_cols, tmem_holding_buf, is_two_cta=self.use_2cta_instrs + def __init__( + self, + reduction: int, + acc_dtype: Type[cutlass.Numeric] = cutlass.Float32, + use_2cta_instrs: bool = False, + mma_tiler_mn: Tuple[int, int] = (128, 256), + vocab_per_split: int = 512, + ): + self.REDUCTION: cutlass.Constexpr[cutlass.Int32] = cutlass.const_expr(reduction) + self.acc_dtype = acc_dtype + self.use_2cta_instrs = use_2cta_instrs + self.mma_tiler = (*mma_tiler_mn, 1) + self.vocab_per_split = vocab_per_split + + self.cta_group = tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE + self.cluster_shape_mn = (2, 1) if self.use_2cta_instrs else (1, 1) + + self.smem_capacity = utils.get_smem_capacity_in_bytes("sm_100") + + self.threads_per_warp: int = 32 + + self.epi_warp_ids = (0, 1, 2, 3) + self.load_warp_ids = 4 + self.mma_warp_ids = 5 + self.empty_warp_ids = (6, 7) + + self.threads_per_cta: int = self.threads_per_warp * len( + (*self.epi_warp_ids, self.load_warp_ids, self.mma_warp_ids, *self.empty_warp_ids) + ) + self.cta_sync_barrier = pipeline.NamedBarrier( + barrier_id=1, num_threads=self.threads_per_cta ) - self.cta_sync_barrier.arrive_and_wait() - tmem_ptr = cute.arch.retrieve_tmem_ptr( - self.acc_dtype, alignment=16, ptr_to_buffer_holding_addr=tmem_holding_buf - ) - tmem_shape = (128, self.tmem_alloc_cols) - acc_shape = thr_mma.partition_shape_C(tmem_shape) - tCtC_fake = thr_mma.make_fragment_C(acc_shape) - # [(tileM, tileN), loopM, loopN] - tCtC = cute.make_tensor(tmem_ptr, tCtC_fake.layout) - - # ------ Empty ------ # - if warp_idx in self.empty_warp_ids: - cute.arch.warpgroup_reg_dealloc(self.num_regs_other) - - # ------ Load ------ # - if warp_idx == self.load_warp_ids: - cute.arch.warpgroup_reg_dealloc(self.num_regs_other) - - for k in cutlass.range(cute.size(gA, mode=[2])): - ab_pipeline.producer_acquire(ab_producer_state) - cute.copy( - tma_atom_a, - tTMAgA[(None, k)], - tTMAsA[(None, ab_producer_state.index)], - tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state), - ) - cute.copy( - tma_atom_b, - tTMAgB[(None, k)], - tTMAsB[(None, ab_producer_state.index)], - tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state), - ) - ab_pipeline.producer_commit(ab_producer_state) - ab_producer_state.advance() - - # ------ MMA ------ # - if warp_idx == self.mma_warp_ids: - cute.arch.warpgroup_reg_dealloc(self.num_regs_other) - - tiled_mma.set(tcgen05.Field.ACCUMULATE, False) - mma_pipeline.producer_acquire(mma_producer_state) - - for k in cutlass.range(cute.size(gA, mode=[2])): - ab_pipeline.consumer_wait(ab_consumer_state) - - for kblock_idx in cutlass.range(cute.size(tCsA, mode=[2]), unroll_full=True): - cute.gemm( - tiled_mma, - cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), - tCsA[(None, None, kblock_idx, ab_consumer_state.index)], - tCsB[(None, None, kblock_idx, ab_consumer_state.index)], - cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), - ) - tiled_mma.set(tcgen05.Field.ACCUMULATE, True) + self.buffer_align_bytes: int = 1024 + self.num_regs_other: int = 32 + self.num_regs_epi: int = 192 + + def _compute_grid( + self, + problem_mnk: Tuple[int, int, int], + cluster_shape_mn: Tuple[int, int], + cta_tiler: Tuple[int, int, int], + ) -> Tuple[int, int, int]: + cluster_shape_mnk = (*cluster_shape_mn, 1) + + grid = cute.round_up( + ( + cute.ceil_div(problem_mnk[0], cta_tiler[0]), + cute.ceil_div(self.vocab_per_split, cta_tiler[1]), + 1, + ), + cluster_shape_mnk, + ) + return grid + + def _compute_stages( + self, + tiled_mma: cute.TiledMma, + mma_tiler: Tuple[int, int, int], + a_dtype: Type[cutlass.Numeric], + b_dtype: Type[cutlass.Numeric], + ): + num_acc_stage = 1 + num_ab_stage = 4 + num_epi_stage_per_tile = 4 + return num_acc_stage, num_ab_stage, num_epi_stage_per_tile + + def _setup_attributes( + self, + tiled_mma: cute.TiledMma, + a_dtype: Type[cutlass.Numeric], + b_dtype: Type[cutlass.Numeric], + ): + self.cluster_shape_mnk = (*self.cluster_shape_mn, 1) + self.cluster_layout_vmnk = cute.tiled_divide( + cute.make_layout(self.cluster_shape_mnk), (tiled_mma.thr_id.shape,) + ) - ab_pipeline.consumer_release(ab_consumer_state) - ab_consumer_state.advance() + mma_inst_shape_k = cute.size(tiled_mma.shape_mnk, mode=[2]) + # it requires k-mode to be 128B aligned + mma_inst_tile_k: int = 4 + self.mma_tiler = (self.mma_tiler[0], self.mma_tiler[1], mma_inst_shape_k * mma_inst_tile_k) - mma_pipeline.producer_commit(mma_producer_state) - mma_producer_state.advance() + self.num_acc_stage, self.num_ab_stage, self.num_epi_stage_per_tile = self._compute_stages( + tiled_mma, self.mma_tiler, a_dtype, b_dtype + ) + self.tmem_alloc_cols = self.num_acc_stage * self.mma_tiler[1] + assert self.tmem_alloc_cols <= SM100_TMEM_CAPACITY_COLUMNS - # ------ EPI ------ # - if warp_idx in self.epi_warp_ids: - cute.arch.warpgroup_reg_alloc(self.num_regs_epi) + self.cta_tile_shape_mnk = ( + self.mma_tiler[0] // cute.size(tiled_mma.thr_id.shape), + self.mma_tiler[1], + self.mma_tiler[2], + ) - copy_atom_t2r = sm100_utils.get_tmem_load_op( - self.cta_tile_shape_mnk, - utils.LayoutEnum.ROW_MAJOR, - self.acc_dtype, - self.acc_dtype, - (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), - self.use_2cta_instrs, + @cute.kernel + def kernel( + self, + split_idx: cutlass.Int32, + tiled_mma: cute.TiledMma, + tma_atom_a: cute.CopyAtom, + mA: cute.Tensor, + tma_atom_b: cute.CopyAtom, + mB: cute.Tensor, + mLabels: cute.Tensor, + mDlogprobs: cute.Tensor, + mMaximum: cute.Tensor, + mAccu: cute.Tensor, + mDlogits_partial: cute.Tensor, + scalarNumValidTokens: cute.Pointer, + ignore_index: cutlass.Int64, + a_smem_layout_staged: cute.ComposedLayout, + b_smem_layout_staged: cute.ComposedLayout, + cluster_layout_vmnk: cute.Layout, + problem_mnk: Tuple[int, int, int], + rank: cutlass.Int32, + ) -> None: + """ + The backward kernel for partial d_logits. + """ + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) + tidx, _, _ = cute.arch.thread_idx() + bidx, bidy, _ = cute.arch.block_idx() + # FIXME: block swizzling applied here + pidm, pidn = bidx, bidy + + # FIXME: if 2 CTAs, modify here + cta_rank_in_cluster = 0 + block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord(cta_rank_in_cluster) + + # prefetch tma descriptors + if warp_idx == self.load_warp_ids: + cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_a) + cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_b) + + smem = utils.SmemAllocator() + storage = smem.allocate(self.shared_storage) + + ab_pipeline = pipeline.PipelineTmaUmma.create( + num_stages=self.num_ab_stage, + producer_group=make_thread_cooperative_group(len([self.load_warp_ids])), + consumer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), + tx_count=self.tma_copy_ab_bytes, + barrier_storage=storage.load_ab_mbar_ptr.data_ptr(), ) - # [tileM, subTileN, loopM, CntSubTileN, loopN] - tAcc_epi = cute.flat_divide( - tCtC[((None, None), 0, None)], - (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), + ab_producer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, self.num_ab_stage ) - tiled_copy_t2r = tcgen05.make_tmem_copy(copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)]) - thr_copy_t2r = tiled_copy_t2r.get_slice(tidx) - tTMEM_load_tAcc = thr_copy_t2r.partition_S(tAcc_epi) - tTMEM_load_tAcc = cute.group_modes(tTMEM_load_tAcc, 3, cute.rank(tTMEM_load_tAcc) - 1) - - # predicates - cAcc = cute.make_identity_tensor(self.mma_tiler[:2]) - tCcAcc = thr_mma.partition_C(cAcc) - tCcAcc_epi = cute.flat_divide( - tCcAcc[((None, None), 0, None)], - (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), + ab_consumer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, self.num_ab_stage ) - tTMEM_load_cAcc = thr_copy_t2r.partition_D(tCcAcc_epi) - tTMEM_load_cAcc_shape = cute.select(tTMEM_load_cAcc.shape, mode=[0, 1, 2]) - tTMEM_load_rAcc = cute.make_fragment(tTMEM_load_cAcc_shape, self.acc_dtype) - copy_atom_g2r_int64 = cute.make_copy_atom( - cute.nvgpu.CopyUniversalOp(), mLabels.element_type - ) - copy_atom_g2r_fp32 = cute.make_copy_atom( - cute.nvgpu.CopyUniversalOp(), mDlogprobs.element_type + mma_pipeline = pipeline.PipelineUmmaAsync.create( + num_stages=self.num_acc_stage, + producer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), + consumer_group=make_thread_cooperative_group( + self.threads_per_warp * len(self.epi_warp_ids) + ), + barrier_storage=storage.mma_mbar_ptr.data_ptr(), ) - epilogue_thread_layout = cute.make_layout((128, 1), stride=(1, 1)) - tiled_copy_g2r_int64 = cute.make_tiled_copy_tv( - copy_atom_g2r_int64, epilogue_thread_layout, cute.make_layout((1, 1)) + mma_producer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, self.num_acc_stage ) - tiled_copy_g2r_fp32 = cute.make_tiled_copy_tv( - copy_atom_g2r_fp32, epilogue_thread_layout, cute.make_layout((1, 1)) + mma_consumer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, self.num_acc_stage ) - thr_copy_g2r_int64 = tiled_copy_g2r_int64.get_slice(tidx) - thr_copy_g2r_fp32 = tiled_copy_g2r_fp32.get_slice(tidx) - - # [tileM] - gLabels = cute.local_tile(mLabels, (self.epi_tile[0],), (pidm,)) - gMaximum = cute.local_tile(mMaximum, (self.epi_tile[0],), (pidm,)) - gAccu = cute.local_tile(mAccu, (self.epi_tile[0],), (pidm,)) - - # slice along M direction - tMCAcc = thr_copy_g2r_int64.partition_S(cAcc)[(None, None, 0)] - # [(1, 1), 1] - tMCAcc_mask = cute.make_fragment(tMCAcc.shape, cutlass.Boolean) - # to align shape with gMax and gAccu - tMCAcc_mask = cute.append_ones(tMCAcc_mask) - tMCAcc_mask[0] = cute.elem_less(pidm * self.epi_tile[0] + tidx, cute.size(mA, mode=[0])) - # [(1, 1), 1, 1] - tMgLabels = thr_copy_g2r_int64.partition_S(cute.append_ones(gLabels)) - tMrLabels = cute.make_fragment(tMgLabels.shape, tMgLabels.element_type) - cute.copy(tiled_copy_g2r_int64, tMgLabels, tMrLabels, pred=tMCAcc_mask) - tMgMaximum = thr_copy_g2r_fp32.partition_S(cute.append_ones(gMaximum)) - tMrMaximum = cute.make_fragment(tMgMaximum.layout, tMgMaximum.element_type) - cute.copy(tiled_copy_g2r_fp32, tMgMaximum, tMrMaximum, pred=tMCAcc_mask) - tMgAccu = thr_copy_g2r_fp32.partition_S(cute.append_ones(gAccu)) - tMrAccu = cute.make_fragment(tMgAccu.layout, tMgAccu.element_type) - cute.copy(tiled_copy_g2r_fp32, tMgAccu, tMrAccu, pred=tMCAcc_mask) - - tMrDlogprobs = cute.make_fragment(tMgAccu.layout, mDlogprobs.element_type) - if cutlass.const_expr(self.REDUCTION == 2): - # mean reduction - num_valid_tokens = cute.make_tensor(scalarNumValidTokens, layout=(1,)) - tMrDlogprobs[0] = mDlogprobs[0] / num_valid_tokens[0].to(cutlass.Float32) - elif cutlass.const_expr(self.REDUCTION == 1): - # sum reduction - tMrDlogprobs[0] = mDlogprobs[0] - else: - # no reduction - gDlogprobs = cute.local_tile(mDlogprobs, (self.epi_tile[0],), (pidm,)) - tMgDlogprobs = thr_copy_g2r_fp32.partition_S(cute.append_ones(gDlogprobs)) - cute.copy(tiled_copy_g2r_fp32, tMgDlogprobs, tMrDlogprobs, pred=tMCAcc_mask) - - tMrAccu[0] = cute.arch.rcp_approx(tMrAccu[0]) - tMrDlogprobs[0] *= tMrLabels[0] != ignore_index - tMr_d_acc_exp_logits = tMrDlogprobs[0] * tMrAccu[0] - - # ------ Partial output ------ # - # [tileM, tileN] - gDlogits_partial = cute.local_tile( - mDlogits_partial, (self.epi_tile[0], self.epi_tile[1]), (pidm, pidn) + + tmem_dealloc_mbar_ptr = storage.tmem_dealloc_mbar_ptr.data_ptr() + if warp_idx == self.empty_warp_ids[0]: + with cute.arch.elect_one(): + cute.arch.mbarrier_init( + tmem_dealloc_mbar_ptr, self.threads_per_warp * len(self.epi_warp_ids) + ) + cute.arch.mbarrier_init_fence() + + # -------- tensor partition ------------ # + # swizzle o [(tileM, tileK), loopM, loopK, stage] + sA = storage.sA.get_tensor(a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner) + # swizzle o [(tileN, tileK), loopN, loopK, stage] + sB = storage.sB.get_tensor(b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner) + + # FIXME: if 2 CTAs, modify here + thr_mma = tiled_mma.get_slice(0) + # [MMA, loopM, loopK, stage] + tCsA = thr_mma.make_fragment_A(sA) + # [MMA, loopN, loopK, stage] + tCsB = thr_mma.make_fragment_B(sB) + + # [tileM, tileK, loopK] + gA = cute.local_tile( + mA, (self.cta_tile_shape_mnk[0], self.cta_tile_shape_mnk[2]), (pidm, None) ) - # blackwell supports STG.256 - copy_atom_r2g = cute.make_copy_atom( - cute.nvgpu.CopyUniversalOp(), gDlogits_partial.element_type, num_bits_per_copy=256 + # [vocab_per_split, dim] + mB_n = cute.local_tile( + mB, (self.vocab_per_split, cute.size(mB.layout.shape, mode=[1])), (split_idx, 0) ) - tiled_copy_r2g = cute.make_tiled_copy_tv( - copy_atom_r2g, epilogue_thread_layout, copy_atom_r2g.layout_dst_tv + # [tileN, tileK, loopK] + gB = cute.local_tile( + mB_n, (self.cta_tile_shape_mnk[1], self.cta_tile_shape_mnk[2]), (pidn, None) ) - thr_copy_r2g = tiled_copy_r2g.get_slice(tidx) - - # [CPY, loopM, loopN] - tR2GCAcc = thr_copy_r2g.partition_S(cAcc) - tR2GCAcc_pred = cute.make_fragment(tR2GCAcc.shape, cutlass.Boolean) - for elem in cutlass.range(cute.size(tR2GCAcc_pred, mode=[0])): - for row in cutlass.range(cute.size(tR2GCAcc_pred, mode=[1])): - for col in cutlass.range(cute.size(tR2GCAcc_pred, mode=[2])): - tR2GCAcc_pred[elem, row, col] = cute.elem_less( - pidm * self.epi_tile[0] + tR2GCAcc[elem, row, col][0], problem_mnk[0] - ) and cute.elem_less( - split_idx * self.vocab_per_split - + pidn * self.epi_tile[1] - + tR2GCAcc[elem, row, col][1], - problem_mnk[1], - ) - - tR2GgDlogits = thr_copy_r2g.partition_D(gDlogits_partial) - - # for type conversion - dLogits_half = cute.make_fragment(tTMEM_load_rAcc.shape, tR2GgDlogits.element_type) - dLogits_half = cute.tiled_divide(dLogits_half, (cute.size(tR2GgDlogits, mode=[0]), 1)) - dLogits_half = cute.group_modes(dLogits_half, 2, cute.rank(dLogits_half)) - mma_pipeline.consumer_wait(mma_consumer_state) - - block_vocab_left_idx: cutlass.Int64 = ( - split_idx * self.vocab_per_split + pidn * self.epi_tile[1] - ) - block_vocab_right_idx: cutlass.Int64 = min( - split_idx * self.vocab_per_split + (pidn + 1) * self.epi_tile[1], - min((split_idx + 1) * self.vocab_per_split, problem_mnk[1]), + a_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, 0, None, 0)).shape) + # just to make sure SMEM and GMEM tensor has the same size in the first rank + tCgA = thr_mma.partition_A(gA) + tCgB = thr_mma.partition_B(gB) + # [CPY, stage] & [CPY, loopK] + tTMAsA, tTMAgA = cpasync.tma_partition( + tma_atom_a, + block_in_cluster_coord_vmnk[2], # cta_coord, + a_cta_layout, + cute.group_modes(sA, 0, 3), + cute.group_modes(tCgA, 0, 3), ) - num_n_subtiles: cutlass.Int64 = cute.ceil_div( - (block_vocab_right_idx - block_vocab_left_idx), cute.size(tTMEM_load_rAcc, mode=[0]) + b_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape) + # [CPY, stage] & [CPY, loopK] + tTMAsB, tTMAgB = cpasync.tma_partition( + tma_atom_b, + block_in_cluster_coord_vmnk[1], # cta_coord + b_cta_layout, + cute.group_modes(sB, 0, 3), + cute.group_modes(tCgB, 0, 3), ) - for n_subtile in cutlass.range(num_n_subtiles): - cute.copy( - tiled_copy_t2r, - tTMEM_load_tAcc[(None, None, None, n_subtile, mma_consumer_state.index)], - tTMEM_load_rAcc, + + # ------ Allocate TMEM ------ # + tmem_holding_buf = storage.tmem_holding_buf + if warp_idx == self.empty_warp_ids[0]: + cute.arch.alloc_tmem( + self.tmem_alloc_cols, tmem_holding_buf, is_two_cta=self.use_2cta_instrs ) + self.cta_sync_barrier.arrive_and_wait() + tmem_ptr = cute.arch.retrieve_tmem_ptr( + self.acc_dtype, alignment=16, ptr_to_buffer_holding_addr=tmem_holding_buf + ) + + tmem_shape = (128, self.tmem_alloc_cols) + acc_shape = thr_mma.partition_shape_C(tmem_shape) + tCtC_fake = thr_mma.make_fragment_C(acc_shape) + # [(tileM, tileN), loopM, loopN] + tCtC = cute.make_tensor(tmem_ptr, tCtC_fake.layout) - for idx in cutlass.range(cute.size(tTMEM_load_rAcc, mode=[0]), unroll_full=True): - # exp_logits - tTMEM_load_rAcc[idx] = cute.exp(tTMEM_load_rAcc[idx] - tMrMaximum[0]) + # ------ Empty ------ # + if warp_idx in self.empty_warp_ids: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) - position: cutlass.Int64 = ( - rank * problem_mnk[1] - + split_idx * self.vocab_per_split - + pidn * self.epi_tile[1] - + n_subtile * cute.size(tTMEM_load_rAcc, mode=[0]) - + idx + # ------ Load ------ # + if warp_idx == self.load_warp_ids: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + for k in cutlass.range(cute.size(gA, mode=[2])): + ab_pipeline.producer_acquire(ab_producer_state) + cute.copy( + tma_atom_a, + tTMAgA[(None, k)], + tTMAsA[(None, ab_producer_state.index)], + tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state), ) - mask: cutlass.Boolean = ( - position == tMrLabels[0] and tMrLabels[0] != ignore_index + cute.copy( + tma_atom_b, + tTMAgB[(None, k)], + tTMAsB[(None, ab_producer_state.index)], + tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state), ) - # d_logits - tTMEM_load_rAcc[idx] *= tMr_d_acc_exp_logits - tTMEM_load_rAcc[idx] += mask * -tMrDlogprobs[0] - dLogits_half[idx] = tTMEM_load_rAcc[idx].to(dLogits_half.element_type) + ab_pipeline.producer_commit(ab_producer_state) + ab_producer_state.advance() + + # ------ MMA ------ # + if warp_idx == self.mma_warp_ids: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + tiled_mma.set(tcgen05.Field.ACCUMULATE, False) + mma_pipeline.producer_acquire(mma_producer_state) + + for k in cutlass.range(cute.size(gA, mode=[2])): + ab_pipeline.consumer_wait(ab_consumer_state) + + for kblock_idx in cutlass.range(cute.size(tCsA, mode=[2]), unroll_full=True): + cute.gemm( + tiled_mma, + cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), + tCsA[(None, None, kblock_idx, ab_consumer_state.index)], + tCsB[(None, None, kblock_idx, ab_consumer_state.index)], + cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), + ) + tiled_mma.set(tcgen05.Field.ACCUMULATE, True) + + ab_pipeline.consumer_release(ab_consumer_state) + ab_consumer_state.advance() + + mma_pipeline.producer_commit(mma_producer_state) + mma_producer_state.advance() + + # ------ EPI ------ # + if warp_idx in self.epi_warp_ids: + cute.arch.warpgroup_reg_alloc(self.num_regs_epi) + + copy_atom_t2r = sm100_utils.get_tmem_load_op( + self.cta_tile_shape_mnk, + utils.LayoutEnum.ROW_MAJOR, + self.acc_dtype, + self.acc_dtype, + (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), + self.use_2cta_instrs, + ) + # [tileM, subTileN, loopM, CntSubTileN, loopN] + tAcc_epi = cute.flat_divide( + tCtC[((None, None), 0, None)], + (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), + ) + tiled_copy_t2r = tcgen05.make_tmem_copy(copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)]) + thr_copy_t2r = tiled_copy_t2r.get_slice(tidx) + tTMEM_load_tAcc = thr_copy_t2r.partition_S(tAcc_epi) + tTMEM_load_tAcc = cute.group_modes(tTMEM_load_tAcc, 3, cute.rank(tTMEM_load_tAcc) - 1) + + # predicates + cAcc = cute.make_identity_tensor(self.mma_tiler[:2]) + tCcAcc = thr_mma.partition_C(cAcc) + tCcAcc_epi = cute.flat_divide( + tCcAcc[((None, None), 0, None)], + (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), + ) + tTMEM_load_cAcc = thr_copy_t2r.partition_D(tCcAcc_epi) + tTMEM_load_cAcc_shape = cute.select(tTMEM_load_cAcc.shape, mode=[0, 1, 2]) + tTMEM_load_rAcc = cute.make_fragment(tTMEM_load_cAcc_shape, self.acc_dtype) - for idx in cutlass.range(cute.size(dLogits_half, mode=[1]), unroll_full=True): - copy_id = n_subtile * cute.size(dLogits_half, mode=[1]) + idx + copy_atom_g2r_int64 = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), mLabels.element_type + ) + copy_atom_g2r_fp32 = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), mDlogprobs.element_type + ) + epilogue_thread_layout = cute.make_layout((128, 1), stride=(1, 1)) + tiled_copy_g2r_int64 = cute.make_tiled_copy_tv( + copy_atom_g2r_int64, epilogue_thread_layout, cute.make_layout((1, 1)) + ) + tiled_copy_g2r_fp32 = cute.make_tiled_copy_tv( + copy_atom_g2r_fp32, epilogue_thread_layout, cute.make_layout((1, 1)) + ) + thr_copy_g2r_int64 = tiled_copy_g2r_int64.get_slice(tidx) + thr_copy_g2r_fp32 = tiled_copy_g2r_fp32.get_slice(tidx) + + # [tileM] + gLabels = cute.local_tile(mLabels, (self.epi_tile[0],), (pidm,)) + gMaximum = cute.local_tile(mMaximum, (self.epi_tile[0],), (pidm,)) + gAccu = cute.local_tile(mAccu, (self.epi_tile[0],), (pidm,)) + + # slice along M direction + tMCAcc = thr_copy_g2r_int64.partition_S(cAcc)[(None, None, 0)] + # [(1, 1), 1] + tMCAcc_mask = cute.make_fragment(tMCAcc.shape, cutlass.Boolean) + # to align shape with gMax and gAccu + tMCAcc_mask = cute.append_ones(tMCAcc_mask) + tMCAcc_mask[0] = cute.elem_less(pidm * self.epi_tile[0] + tidx, cute.size(mA, mode=[0])) + # [(1, 1), 1, 1] + tMgLabels = thr_copy_g2r_int64.partition_S(cute.append_ones(gLabels)) + tMrLabels = cute.make_fragment(tMgLabels.shape, tMgLabels.element_type) + cute.copy(tiled_copy_g2r_int64, tMgLabels, tMrLabels, pred=tMCAcc_mask) + tMgMaximum = thr_copy_g2r_fp32.partition_S(cute.append_ones(gMaximum)) + tMrMaximum = cute.make_fragment(tMgMaximum.layout, tMgMaximum.element_type) + cute.copy(tiled_copy_g2r_fp32, tMgMaximum, tMrMaximum, pred=tMCAcc_mask) + tMgAccu = thr_copy_g2r_fp32.partition_S(cute.append_ones(gAccu)) + tMrAccu = cute.make_fragment(tMgAccu.layout, tMgAccu.element_type) + cute.copy(tiled_copy_g2r_fp32, tMgAccu, tMrAccu, pred=tMCAcc_mask) + + tMrDlogprobs = cute.make_fragment(tMgAccu.layout, mDlogprobs.element_type) + if cutlass.const_expr(self.REDUCTION == 2): + # mean reduction + num_valid_tokens = cute.make_tensor(scalarNumValidTokens, layout=(1,)) + tMrDlogprobs[0] = mDlogprobs[0] / num_valid_tokens[0].to(cutlass.Float32) + elif cutlass.const_expr(self.REDUCTION == 1): + # sum reduction + tMrDlogprobs[0] = mDlogprobs[0] + else: + # no reduction + gDlogprobs = cute.local_tile(mDlogprobs, (self.epi_tile[0],), (pidm,)) + tMgDlogprobs = thr_copy_g2r_fp32.partition_S(cute.append_ones(gDlogprobs)) + cute.copy(tiled_copy_g2r_fp32, tMgDlogprobs, tMrDlogprobs, pred=tMCAcc_mask) + + tMrAccu[0] = cute.arch.rcp_approx(tMrAccu[0]) + tMrDlogprobs[0] *= tMrLabels[0] != ignore_index + tMr_d_acc_exp_logits = tMrDlogprobs[0] * tMrAccu[0] + + # ------ Partial output ------ # + # [tileM, tileN] + gDlogits_partial = cute.local_tile( + mDlogits_partial, (self.epi_tile[0], self.epi_tile[1]), (pidm, pidn) + ) + # blackwell supports STG.256 + copy_atom_r2g = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), gDlogits_partial.element_type, num_bits_per_copy=256 + ) + tiled_copy_r2g = cute.make_tiled_copy_tv( + copy_atom_r2g, epilogue_thread_layout, copy_atom_r2g.layout_dst_tv + ) + thr_copy_r2g = tiled_copy_r2g.get_slice(tidx) + + # [CPY, loopM, loopN] + tR2GCAcc = thr_copy_r2g.partition_S(cAcc) + tR2GCAcc_pred = cute.make_fragment(tR2GCAcc.shape, cutlass.Boolean) + for elem in cutlass.range(cute.size(tR2GCAcc_pred, mode=[0])): + for row in cutlass.range(cute.size(tR2GCAcc_pred, mode=[1])): + for col in cutlass.range(cute.size(tR2GCAcc_pred, mode=[2])): + tR2GCAcc_pred[elem, row, col] = cute.elem_less( + pidm * self.epi_tile[0] + tR2GCAcc[elem, row, col][0], problem_mnk[0] + ) and cute.elem_less( + split_idx * self.vocab_per_split + + pidn * self.epi_tile[1] + + tR2GCAcc[elem, row, col][1], + problem_mnk[1], + ) + + tR2GgDlogits = thr_copy_r2g.partition_D(gDlogits_partial) + + # for type conversion + dLogits_half = cute.make_fragment(tTMEM_load_rAcc.shape, tR2GgDlogits.element_type) + dLogits_half = cute.tiled_divide(dLogits_half, (cute.size(tR2GgDlogits, mode=[0]), 1)) + dLogits_half = cute.group_modes(dLogits_half, 2, cute.rank(dLogits_half)) + + mma_pipeline.consumer_wait(mma_consumer_state) + + block_vocab_left_idx: cutlass.Int64 = ( + split_idx * self.vocab_per_split + pidn * self.epi_tile[1] + ) + block_vocab_right_idx: cutlass.Int64 = min( + split_idx * self.vocab_per_split + (pidn + 1) * self.epi_tile[1], + min((split_idx + 1) * self.vocab_per_split, problem_mnk[1]), + ) + num_n_subtiles: cutlass.Int64 = cute.ceil_div( + (block_vocab_right_idx - block_vocab_left_idx), cute.size(tTMEM_load_rAcc, mode=[0]) + ) + for n_subtile in cutlass.range(num_n_subtiles): cute.copy( - tiled_copy_r2g, - dLogits_half[(None, idx, None)], - tR2GgDlogits[(None, None, copy_id)], - pred=tR2GCAcc_pred[((0, None), None, copy_id)], + tiled_copy_t2r, + tTMEM_load_tAcc[(None, None, None, n_subtile, mma_consumer_state.index)], + tTMEM_load_rAcc, ) - mma_pipeline.consumer_release(mma_consumer_state) - mma_consumer_state.advance() - - # ------ Deallocate TMEM ------ # - self.cta_sync_barrier.arrive_and_wait() - if warp_idx == self.empty_warp_ids[0]: - cute.arch.relinquish_tmem_alloc_permit() - cute.arch.dealloc_tmem(tmem_ptr, self.tmem_alloc_cols, is_two_cta=self.use_2cta_instrs) - - @cute.jit - def __call__( - self, - split_idx: cutlass.Int32, - hidden: cute.Tensor, - weight: cute.Tensor, - labels: cute.Tensor, - dlogprobs: cute.Tensor, - maximum: cute.Tensor, - accu: cute.Tensor, - dlogits_partial: cute.Tensor, - scalarNumValidTokens: cute.Pointer, - ignore_index: cutlass.Int64, - rank: cutlass.Int32, - stream: cuda.CUstream, - ) -> None: - a_dtype: Type[cutlass.Numeric] = hidden.element_type - b_dtype: Type[cutlass.Numeric] = weight.element_type - - if cutlass.const_expr(hidden.element_type != weight.element_type): - raise RuntimeError( - f"data type don't match: {hidden.element_type} v.s. {weight.element_type}" - ) - if cutlass.const_expr(hidden.element_type not in [cutlass.Float16, cutlass.BFloat16]): - raise RuntimeError("hidden can only be FP16 or BF16") - if cutlass.const_expr(hidden.layout.shape[1] != weight.layout.shape[1]): - raise RuntimeError("K dimension doesn't match") - - problem_mnk = (hidden.layout.shape[0], weight.layout.shape[0], hidden.layout.shape[1]) - if cutlass.const_expr((problem_mnk[2] * a_dtype.width // 8) % 16 != 0): - raise RuntimeError(f"K dimension is not 16B aligned: {problem_mnk[2]}") - if cutlass.const_expr((problem_mnk[2] * b_dtype.width // 8) % 128 != 0): - raise RuntimeError(f"N dimension is not 128B aligned: {problem_mnk[1]}") - - grid = self._compute_grid( - problem_mnk=problem_mnk, - cluster_shape_mn=self.cluster_shape_mn, - cta_tiler=self.mma_tiler, - ) + for idx in cutlass.range(cute.size(tTMEM_load_rAcc, mode=[0]), unroll_full=True): + # exp_logits + tTMEM_load_rAcc[idx] = cute.exp(tTMEM_load_rAcc[idx] - tMrMaximum[0]) - a_major_mode = utils.LayoutEnum.from_tensor(hidden).mma_major_mode() - b_major_mode = utils.LayoutEnum.from_tensor(weight).mma_major_mode() + position: cutlass.Int64 = ( + rank * problem_mnk[1] + + split_idx * self.vocab_per_split + + pidn * self.epi_tile[1] + + n_subtile * cute.size(tTMEM_load_rAcc, mode=[0]) + + idx + ) + mask: cutlass.Boolean = ( + position == tMrLabels[0] and tMrLabels[0] != ignore_index + ) + # d_logits + tTMEM_load_rAcc[idx] *= tMr_d_acc_exp_logits + tTMEM_load_rAcc[idx] += mask * -tMrDlogprobs[0] + dLogits_half[idx] = tTMEM_load_rAcc[idx].to(dLogits_half.element_type) + + for idx in cutlass.range(cute.size(dLogits_half, mode=[1]), unroll_full=True): + copy_id = n_subtile * cute.size(dLogits_half, mode=[1]) + idx + cute.copy( + tiled_copy_r2g, + dLogits_half[(None, idx, None)], + tR2GgDlogits[(None, None, copy_id)], + pred=tR2GCAcc_pred[((0, None), None, copy_id)], + ) - tiled_mma = sm100_utils.make_trivial_tiled_mma( - a_dtype, a_major_mode, b_major_mode, self.acc_dtype, self.cta_group, self.mma_tiler[:2] - ) - self._setup_attributes(tiled_mma, a_dtype, b_dtype) + mma_pipeline.consumer_release(mma_consumer_state) + mma_consumer_state.advance() + + # ------ Deallocate TMEM ------ # + self.cta_sync_barrier.arrive_and_wait() + if warp_idx == self.empty_warp_ids[0]: + cute.arch.relinquish_tmem_alloc_permit() + cute.arch.dealloc_tmem(tmem_ptr, self.tmem_alloc_cols, is_two_cta=self.use_2cta_instrs) + + @cute.jit + def __call__( + self, + split_idx: cutlass.Int32, + hidden: cute.Tensor, + weight: cute.Tensor, + labels: cute.Tensor, + dlogprobs: cute.Tensor, + maximum: cute.Tensor, + accu: cute.Tensor, + dlogits_partial: cute.Tensor, + scalarNumValidTokens: cute.Pointer, + ignore_index: cutlass.Int64, + rank: cutlass.Int32, + stream: cuda.CUstream, + ) -> None: + a_dtype: Type[cutlass.Numeric] = hidden.element_type + b_dtype: Type[cutlass.Numeric] = weight.element_type + + if cutlass.const_expr(hidden.element_type != weight.element_type): + raise RuntimeError( + f"data type don't match: {hidden.element_type} v.s. {weight.element_type}" + ) + if cutlass.const_expr(hidden.element_type not in [cutlass.Float16, cutlass.BFloat16]): + raise RuntimeError("hidden can only be FP16 or BF16") + if cutlass.const_expr(hidden.layout.shape[1] != weight.layout.shape[1]): + raise RuntimeError("K dimension doesn't match") + + problem_mnk = (hidden.layout.shape[0], weight.layout.shape[0], hidden.layout.shape[1]) + if cutlass.const_expr((problem_mnk[2] * a_dtype.width // 8) % 16 != 0): + raise RuntimeError(f"K dimension is not 16B aligned: {problem_mnk[2]}") + if cutlass.const_expr((problem_mnk[2] * b_dtype.width // 8) % 128 != 0): + raise RuntimeError(f"N dimension is not 128B aligned: {problem_mnk[1]}") + + grid = self._compute_grid( + problem_mnk=problem_mnk, + cluster_shape_mn=self.cluster_shape_mn, + cta_tiler=self.mma_tiler, + ) - self.epi_tile = self.cta_tile_shape_mnk[:2] + a_major_mode = utils.LayoutEnum.from_tensor(hidden).mma_major_mode() + b_major_mode = utils.LayoutEnum.from_tensor(weight).mma_major_mode() - # Swizzle o [(tileM, tileK), loopM, loopK, stage] - a_smem_layout_staged = sm100_utils.make_smem_layout_a( - tiled_mma, self.mma_tiler, a_dtype, self.num_ab_stage - ) - # Swizzle o [(tileN, tileK), loopN, loopK, stage] - b_smem_layout_staged = sm100_utils.make_smem_layout_b( - tiled_mma, self.mma_tiler, b_dtype, self.num_ab_stage - ) - tma_load_op = cpasync.CopyBulkTensorTileG2SOp(self.cta_group) - tma_store_op = cpasync.CopyBulkTensorTileS2GOp() - - # Swizzle o [(tileM, tileK), loopM, loopK] - a_smem_layout = cute.select(a_smem_layout_staged, mode=[0, 1, 2]) - tma_atom_a, tma_tensor_a = cute.nvgpu.make_tiled_tma_atom_A( - tma_load_op, - hidden, - a_smem_layout, - self.mma_tiler, - tiled_mma, - self.cluster_layout_vmnk.shape, - ) - # Swizzle o [(tileN, tileK), loopN, loopK] - b_smem_layout = cute.select(b_smem_layout_staged, mode=[0, 1, 2]) - tma_atom_b, tma_tensor_b = cute.nvgpu.make_tiled_tma_atom_B( - tma_load_op, - weight, - b_smem_layout, - self.mma_tiler, - tiled_mma, - self.cluster_layout_vmnk.shape, - ) - a_copy_size = cute.size_in_bytes(a_dtype, a_smem_layout) - b_copy_size = cute.size_in_bytes(b_dtype, b_smem_layout) - self.tma_copy_ab_bytes = a_copy_size + b_copy_size + tiled_mma = sm100_utils.make_trivial_tiled_mma( + a_dtype, a_major_mode, b_major_mode, self.acc_dtype, self.cta_group, self.mma_tiler[:2] + ) + self._setup_attributes(tiled_mma, a_dtype, b_dtype) - @cute.struct - class SharedStorage: - """ - The shared storage for the backward kernel. - """ + self.epi_tile = self.cta_tile_shape_mnk[:2] - load_ab_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage * 2] - mma_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2] - - tmem_dealloc_mbar_ptr: cute.struct.MemRange[cutlass.Int64, 1] - tmem_holding_buf: cutlass.Int32 - - sA: cute.struct.Align[ - cute.struct.MemRange[a_dtype, cute.cosize(a_smem_layout_staged)], - self.buffer_align_bytes, - ] - sB: cute.struct.Align[ - cute.struct.MemRange[b_dtype, cute.cosize(b_smem_layout_staged)], - self.buffer_align_bytes, - ] - - self.shared_storage = SharedStorage - - self.kernel( - split_idx, - tiled_mma, - tma_atom_a, - tma_tensor_a, - tma_atom_b, - tma_tensor_b, - labels, - dlogprobs, - maximum, - accu, - dlogits_partial, - scalarNumValidTokens, - ignore_index, - a_smem_layout_staged, - b_smem_layout_staged, - self.cluster_layout_vmnk, - problem_mnk, - rank, - ).launch( - grid=grid, - block=[self.threads_per_cta, 1, 1], - cluster=self.cluster_shape_mnk, - stream=stream, - ) + # Swizzle o [(tileM, tileK), loopM, loopK, stage] + a_smem_layout_staged = sm100_utils.make_smem_layout_a( + tiled_mma, self.mma_tiler, a_dtype, self.num_ab_stage + ) + # Swizzle o [(tileN, tileK), loopN, loopK, stage] + b_smem_layout_staged = sm100_utils.make_smem_layout_b( + tiled_mma, self.mma_tiler, b_dtype, self.num_ab_stage + ) + tma_load_op = cpasync.CopyBulkTensorTileG2SOp(self.cta_group) + tma_store_op = cpasync.CopyBulkTensorTileS2GOp() + + # Swizzle o [(tileM, tileK), loopM, loopK] + a_smem_layout = cute.select(a_smem_layout_staged, mode=[0, 1, 2]) + tma_atom_a, tma_tensor_a = cute.nvgpu.make_tiled_tma_atom_A( + tma_load_op, + hidden, + a_smem_layout, + self.mma_tiler, + tiled_mma, + self.cluster_layout_vmnk.shape, + ) + # Swizzle o [(tileN, tileK), loopN, loopK] + b_smem_layout = cute.select(b_smem_layout_staged, mode=[0, 1, 2]) + tma_atom_b, tma_tensor_b = cute.nvgpu.make_tiled_tma_atom_B( + tma_load_op, + weight, + b_smem_layout, + self.mma_tiler, + tiled_mma, + self.cluster_layout_vmnk.shape, + ) + a_copy_size = cute.size_in_bytes(a_dtype, a_smem_layout) + b_copy_size = cute.size_in_bytes(b_dtype, b_smem_layout) + self.tma_copy_ab_bytes = a_copy_size + b_copy_size + + @cute.struct + class SharedStorage: + """ + The shared storage for the backward kernel. + """ + + load_ab_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage * 2] + mma_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2] + + tmem_dealloc_mbar_ptr: cute.struct.MemRange[cutlass.Int64, 1] + tmem_holding_buf: cutlass.Int32 + + sA: cute.struct.Align[ + cute.struct.MemRange[a_dtype, cute.cosize(a_smem_layout_staged)], + self.buffer_align_bytes, + ] + sB: cute.struct.Align[ + cute.struct.MemRange[b_dtype, cute.cosize(b_smem_layout_staged)], + self.buffer_align_bytes, + ] + + self.shared_storage = SharedStorage + + self.kernel( + split_idx, + tiled_mma, + tma_atom_a, + tma_tensor_a, + tma_atom_b, + tma_tensor_b, + labels, + dlogprobs, + maximum, + accu, + dlogits_partial, + scalarNumValidTokens, + ignore_index, + a_smem_layout_staged, + b_smem_layout_staged, + self.cluster_layout_vmnk, + problem_mnk, + rank, + ).launch( + grid=grid, + block=[self.threads_per_cta, 1, 1], + cluster=self.cluster_shape_mnk, + stream=stream, + ) +except ImportError: + pass diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py index 014c574a635..7ca2e5c91fb 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py @@ -1,172 +1,187 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. -import typing -from dataclasses import dataclass, field -from functools import lru_cache -import os - -import cuda.bindings.driver as cuda # type: ignore -import cutlass -import cutlass.cute as cute -import torch -import torch.distributed as dist -import triton # type: ignore -from cutlass.cute.runtime import from_dlpack - -import megatron.core.fusions.linear_cross_entropy.utils as utils -from megatron.core.fusions.linear_cross_entropy.blackwell import ( - bwd_partial_dlogits as bwd_partial_dlogits, -) -from megatron.core.fusions.linear_cross_entropy.blackwell import fwd_mainloop as fwd_mainloop -from megatron.core.fusions.linear_cross_entropy.blackwell import triton as triton_kernels - - -@dataclass -class FwdConfig: - """ - The configuration for the forward pass. - """ - - _dedicated_stream: torch.cuda.Stream = field(default_factory=torch.cuda.Stream) - _dedicated_events: typing.List[torch.cuda.Event] = field(default_factory=list) - _initialized: bool = field(default=False) - _fwd_mainloop_kernels: typing.Dict[str, cute.kernel] = field(default_factory=dict) - _vocab_per_split: int = field(default=int(os.environ.get("LCE_FWD_VOCAB_SPLIT_SIZE", 512 * 6))) - - -@dataclass -class BwdConfig: - """ - The configuration for the backward pass. - """ - - _bwd_kernel: typing.Dict[str, cute.kernel] = field(default_factory=dict) - _vocab_per_split: int = field(default=int(os.environ.get("LCE_BWD_VOCAB_SPLIT_SIZE", 512 * 6))) - _backward_method: utils.BackwardMethodEnum = field(default=utils.BackwardMethodEnum.kDlogitsSplitN) - - -@lru_cache(maxsize=1) -def _get_fwd_config() -> FwdConfig: - """ - Helper function to lazy initialize the forward configuration. - """ - return FwdConfig() - -@lru_cache(maxsize=1) -def _get_bwd_config() -> BwdConfig: - """ - Helper function to lazy initialize the backward configuration. - """ - return BwdConfig() - -def forward( - hidden: torch.Tensor, - weight: torch.Tensor, - labels: torch.Tensor, - tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, - reduction: typing.Literal["none", "sum", "mean"] = "mean", - ignore_index: int = -100, - sequence_parallel: bool = False, -) -> typing.Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int, int, torch.Tensor]: - """ - forward host function - """ - tp_rank = 0 if tp_group is None else torch.distributed.get_rank(tp_group) - tp_world_size = 1 if tp_group is None else torch.distributed.get_world_size(tp_group) - in_tp_mode = (tp_group is not None) and (tp_world_size > 1) - - assert hidden.is_cuda and weight.is_cuda and labels.is_cuda - assert weight.device == hidden.device and labels.device == hidden.device - - # hidden could be [batch, seqlen, dim] or [seqlen, batch, dim] or [tokens, dim] - assert hidden.dim() == 2 or hidden.dim() == 3 - # weight must be [vocab_size, dim] - assert weight.dim() == 2 - # labels could be [batch, seqlen] or [seqlen, batch] or [tokens] - assert (hidden.dim() == 2 and labels.dim() == 1) or (hidden.dim() == 3 and labels.dim() == 2) - assert hidden.is_contiguous() and weight.is_contiguous() and labels.is_contiguous() - - hidden_view = hidden.view(-1, hidden.shape[-1]) - labels_view = labels.view(-1) - - assert (sequence_parallel and hidden_view.shape[0] * tp_world_size == labels_view.shape[0]) or ( - not sequence_parallel and hidden_view.shape[0] == labels_view.shape[0] +try: + import typing + from dataclasses import dataclass, field + from functools import lru_cache + import os + + import cuda.bindings.driver as cuda # type: ignore + import cutlass + import cutlass.cute as cute + import torch + import torch.distributed as dist + import triton # type: ignore + from cutlass.cute.runtime import from_dlpack + + import megatron.core.fusions.linear_cross_entropy.utils as utils + from megatron.core.fusions.linear_cross_entropy.blackwell import ( + bwd_partial_dlogits as bwd_partial_dlogits, ) - assert hidden_view.shape[1] == weight.shape[1] - - global_hidden = hidden - if in_tp_mode and sequence_parallel: - partial_hidden_shape = hidden.shape - global_hidden_shape = (partial_hidden_shape[0] * tp_world_size, *partial_hidden_shape[1:]) - global_hidden = torch.empty(global_hidden_shape, dtype=hidden.dtype, device=hidden.device) - dist.all_gather_into_tensor(global_hidden, hidden, group=tp_group) - assert global_hidden.is_contiguous() - hidden_view = global_hidden.view(-1, global_hidden.shape[-1]) - - num_tokens, dim = hidden_view.shape - vocab_size, _ = weight.shape - - if not _get_fwd_config()._initialized: - _get_fwd_config()._dedicated_stream = torch.cuda.Stream(hidden.device) - _get_fwd_config()._dedicated_events = [torch.cuda.Event() for _ in range(2)] - _get_fwd_config()._initialized = True - - REDUCTION = utils.str_to_reduction_enum(reduction) - # declare logprobs - if REDUCTION == utils.EntropyReductionEnum.kNone: - logprobs = torch.empty((num_tokens,), device=hidden.device, dtype=torch.float32) - if in_tp_mode: - logprobs.zero_() - else: - logprobs = torch.zeros((), device=hidden.device, dtype=torch.float32) - # declare auxiliary tensors - maximum = torch.empty((num_tokens,), device=hidden.device, dtype=torch.float32) - accumulate = torch.empty_like(maximum, dtype=torch.float32) - num_valid_tokens = torch.empty((), device=hidden.device, dtype=torch.int64) - assert ( - maximum.is_contiguous() and accumulate.is_contiguous() and num_valid_tokens.is_contiguous() - ) - # declare intermediate tensors - # NOTE: this is a parameter for tuning - num_splits = (vocab_size + _get_fwd_config()._vocab_per_split - 1) // _get_fwd_config()._vocab_per_split - _max = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) - _accu = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) - if REDUCTION == utils.EntropyReductionEnum.kNone: - _logprobs = logprobs - else: - _logprobs = torch.empty((num_tokens,), device=hidden.device, dtype=torch.float32) - if in_tp_mode: - _logprobs.zero_() - assert _max.is_contiguous() and _accu.is_contiguous() and _logprobs.is_contiguous() + from megatron.core.fusions.linear_cross_entropy.blackwell import fwd_mainloop as fwd_mainloop + from megatron.core.fusions.linear_cross_entropy.blackwell import triton as triton_kernels + + + @dataclass + class FwdConfig: + """ + The configuration for the forward pass. + """ + + _dedicated_stream: torch.cuda.Stream = field(default_factory=torch.cuda.Stream) + _dedicated_events: typing.List[torch.cuda.Event] = field(default_factory=list) + _initialized: bool = field(default=False) + _fwd_mainloop_kernels: typing.Dict[str, cute.kernel] = field(default_factory=dict) + _vocab_per_split: int = field(default=int(os.environ.get("LCE_FWD_VOCAB_SPLIT_SIZE", 512 * 6))) + + + @dataclass + class BwdConfig: + """ + The configuration for the backward pass. + """ + + _bwd_kernel: typing.Dict[str, cute.kernel] = field(default_factory=dict) + _vocab_per_split: int = field(default=int(os.environ.get("LCE_BWD_VOCAB_SPLIT_SIZE", 512 * 6))) + _backward_method: utils.BackwardMethodEnum = field(default=utils.BackwardMethodEnum.kDlogitsSplitN) + + + @lru_cache(maxsize=1) + def _get_fwd_config() -> FwdConfig: + """ + Helper function to lazy initialize the forward configuration. + """ + return FwdConfig() + + @lru_cache(maxsize=1) + def _get_bwd_config() -> BwdConfig: + """ + Helper function to lazy initialize the backward configuration. + """ + return BwdConfig() + + def forward( + hidden: torch.Tensor, + weight: torch.Tensor, + labels: torch.Tensor, + tp_group: typing.Optional[torch.distributed.ProcessGroup] = None, + reduction: typing.Literal["none", "sum", "mean"] = "mean", + ignore_index: int = -100, + sequence_parallel: bool = False, + ) -> typing.Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int, int, torch.Tensor]: + """ + forward host function + """ + tp_rank = 0 if tp_group is None else torch.distributed.get_rank(tp_group) + tp_world_size = 1 if tp_group is None else torch.distributed.get_world_size(tp_group) + in_tp_mode = (tp_group is not None) and (tp_world_size > 1) + + assert hidden.is_cuda and weight.is_cuda and labels.is_cuda + assert weight.device == hidden.device and labels.device == hidden.device + + # hidden could be [batch, seqlen, dim] or [seqlen, batch, dim] or [tokens, dim] + assert hidden.dim() == 2 or hidden.dim() == 3 + # weight must be [vocab_size, dim] + assert weight.dim() == 2 + # labels could be [batch, seqlen] or [seqlen, batch] or [tokens] + assert (hidden.dim() == 2 and labels.dim() == 1) or (hidden.dim() == 3 and labels.dim() == 2) + assert hidden.is_contiguous() and weight.is_contiguous() and labels.is_contiguous() + + hidden_view = hidden.view(-1, hidden.shape[-1]) + labels_view = labels.view(-1) + + assert (sequence_parallel and hidden_view.shape[0] * tp_world_size == labels_view.shape[0]) or ( + not sequence_parallel and hidden_view.shape[0] == labels_view.shape[0] + ) + assert hidden_view.shape[1] == weight.shape[1] + + global_hidden = hidden + if in_tp_mode and sequence_parallel: + partial_hidden_shape = hidden.shape + global_hidden_shape = (partial_hidden_shape[0] * tp_world_size, *partial_hidden_shape[1:]) + global_hidden = torch.empty(global_hidden_shape, dtype=hidden.dtype, device=hidden.device) + dist.all_gather_into_tensor(global_hidden, hidden, group=tp_group) + assert global_hidden.is_contiguous() + hidden_view = global_hidden.view(-1, global_hidden.shape[-1]) + + num_tokens, dim = hidden_view.shape + vocab_size, _ = weight.shape + + if not _get_fwd_config()._initialized: + _get_fwd_config()._dedicated_stream = torch.cuda.Stream(hidden.device) + _get_fwd_config()._dedicated_events = [torch.cuda.Event() for _ in range(2)] + _get_fwd_config()._initialized = True + + REDUCTION = utils.str_to_reduction_enum(reduction) + # declare logprobs + if REDUCTION == utils.EntropyReductionEnum.kNone: + logprobs = torch.empty((num_tokens,), device=hidden.device, dtype=torch.float32) + if in_tp_mode: + logprobs.zero_() + else: + logprobs = torch.zeros((), device=hidden.device, dtype=torch.float32) + # declare auxiliary tensors + maximum = torch.empty((num_tokens,), device=hidden.device, dtype=torch.float32) + accumulate = torch.empty_like(maximum, dtype=torch.float32) + num_valid_tokens = torch.empty((), device=hidden.device, dtype=torch.int64) + assert ( + maximum.is_contiguous() and accumulate.is_contiguous() and num_valid_tokens.is_contiguous() + ) + # declare intermediate tensors + # NOTE: this is a parameter for tuning + num_splits = (vocab_size + _get_fwd_config()._vocab_per_split - 1) // _get_fwd_config()._vocab_per_split + _max = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) + _accu = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) + if REDUCTION == utils.EntropyReductionEnum.kNone: + _logprobs = logprobs + else: + _logprobs = torch.empty((num_tokens,), device=hidden.device, dtype=torch.float32) + if in_tp_mode: + _logprobs.zero_() + assert _max.is_contiguous() and _accu.is_contiguous() and _logprobs.is_contiguous() - triton_kernels.get_num_valid_tokens[(1,)]( - num_tokens, ignore_index, labels_view, labels_view.stride(0), num_valid_tokens - ) + triton_kernels.get_num_valid_tokens[(1,)]( + num_tokens, ignore_index, labels_view, labels_view.stride(0), num_valid_tokens + ) - # need to compile the kernel for the first time - hidden_packed = from_dlpack(hidden_view.detach(), assumed_align=16).mark_compact_shape_dynamic( - mode=0 - ) - weight_packed = from_dlpack(weight.detach(), assumed_align=16) - labels_packed = from_dlpack(labels_view.detach(), assumed_align=8).mark_compact_shape_dynamic( - mode=0 - ) - logprobs_packed = from_dlpack(_logprobs, assumed_align=16).mark_compact_shape_dynamic(mode=0) - _max_packed = from_dlpack(_max, assumed_align=8).mark_compact_shape_dynamic( - mode=0, stride_order=(0, 1) - ) - _accu_packed = from_dlpack(_accu, assumed_align=8).mark_compact_shape_dynamic( - mode=0, stride_order=(0, 1) - ) - cuda_stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) - - # VocabSize and Dim are fixed for a given model, - # only the number of tokens can vary - key = f"vocab_size:{vocab_size}+dim:{dim}+dtype:{hidden_view.dtype}" - if _get_fwd_config()._fwd_mainloop_kernels.get(key) is None: - fwd_mainloop_kernel = fwd_mainloop.FwdMainLoop(vocab_per_split=_get_fwd_config()._vocab_per_split) - fwd_mainloop_compiled_kernel = cute.compile( - fwd_mainloop_kernel, + # need to compile the kernel for the first time + hidden_packed = from_dlpack(hidden_view.detach(), assumed_align=16).mark_compact_shape_dynamic( + mode=0 + ) + weight_packed = from_dlpack(weight.detach(), assumed_align=16) + labels_packed = from_dlpack(labels_view.detach(), assumed_align=8).mark_compact_shape_dynamic( + mode=0 + ) + logprobs_packed = from_dlpack(_logprobs, assumed_align=16).mark_compact_shape_dynamic(mode=0) + _max_packed = from_dlpack(_max, assumed_align=8).mark_compact_shape_dynamic( + mode=0, stride_order=(0, 1) + ) + _accu_packed = from_dlpack(_accu, assumed_align=8).mark_compact_shape_dynamic( + mode=0, stride_order=(0, 1) + ) + cuda_stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) + + # VocabSize and Dim are fixed for a given model, + # only the number of tokens can vary + key = f"vocab_size:{vocab_size}+dim:{dim}+dtype:{hidden_view.dtype}" + if _get_fwd_config()._fwd_mainloop_kernels.get(key) is None: + fwd_mainloop_kernel = fwd_mainloop.FwdMainLoop(vocab_per_split=_get_fwd_config()._vocab_per_split) + fwd_mainloop_compiled_kernel = cute.compile( + fwd_mainloop_kernel, + hidden_packed, + weight_packed, + labels_packed, + logprobs_packed, + _max_packed, + _accu_packed, + ignore_index, + tp_rank, + cuda_stream, + ) + _get_fwd_config()._fwd_mainloop_kernels[key] = fwd_mainloop_compiled_kernel + else: + fwd_mainloop_compiled_kernel = _get_fwd_config()._fwd_mainloop_kernels[key] + fwd_mainloop_compiled_kernel( hidden_packed, weight_packed, labels_packed, @@ -177,250 +192,238 @@ def forward( tp_rank, cuda_stream, ) - _get_fwd_config()._fwd_mainloop_kernels[key] = fwd_mainloop_compiled_kernel - else: - fwd_mainloop_compiled_kernel = _get_fwd_config()._fwd_mainloop_kernels[key] - fwd_mainloop_compiled_kernel( - hidden_packed, - weight_packed, - labels_packed, - logprobs_packed, - _max_packed, - _accu_packed, - ignore_index, - tp_rank, - cuda_stream, - ) - if not in_tp_mode: + if not in_tp_mode: - def grid(meta): - return (triton.cdiv(num_tokens, meta["BLOCK_SIZE_M"]),) + def grid(meta): + return (triton.cdiv(num_tokens, meta["BLOCK_SIZE_M"]),) - triton_kernels.forward_dp_epilogue[grid]( - num_tokens, - num_splits, - ignore_index, - labels_view, - labels_view.stride(0), - num_valid_tokens, - _max, - _max.stride(0), - _max.stride(1), - _accu, - _accu.stride(0), - _accu.stride(1), - maximum, - maximum.stride(0), - accumulate, - maximum.stride(0), - _logprobs, - _logprobs.stride(0), - logprobs, - triton.language.constexpr(REDUCTION.value), - ) - else: - _max_backup = _max.clone() - dist.all_reduce(_max, op=dist.ReduceOp.MAX, group=tp_group) - - torch.cuda.current_stream().record_event(_get_fwd_config()._dedicated_events[0]) - with torch.cuda.stream(_get_fwd_config()._dedicated_stream): - _get_fwd_config()._dedicated_stream.wait_event(_get_fwd_config()._dedicated_events[0]) - dist.all_reduce(_logprobs, op=dist.ReduceOp.SUM, group=tp_group) - _get_fwd_config()._dedicated_stream.record_event(_get_fwd_config()._dedicated_events[1]) - - def grid(meta): - return (triton.cdiv(num_tokens, meta["BLOCK_SIZE_M"]),) - - triton_kernels.forward_tp_epilogue[grid]( - num_tokens, - num_splits, - _max, - _max.stride(0), - _max.stride(1), - _max_backup, - _max_backup.stride(0), - _max_backup.stride(1), - _accu, - _accu.stride(0), - _accu.stride(1), - maximum, - maximum.stride(0), - accumulate, - maximum.stride(0), - ) - # reduce accumulate - dist.all_reduce(accumulate, op=dist.ReduceOp.SUM, group=tp_group) - - # update logprobs - torch.cuda.current_stream().wait_event(_get_fwd_config()._dedicated_events[1]) - triton_kernels.forward_tp_epilogue_update_logprobs[grid]( - num_tokens, - ignore_index, - num_valid_tokens, - labels_view, - labels_view.stride(0), - _logprobs, - _logprobs.stride(0), - maximum, - maximum.stride(0), - accumulate, - accumulate.stride(0), - logprobs, - REDUCTION.value, - ) + triton_kernels.forward_dp_epilogue[grid]( + num_tokens, + num_splits, + ignore_index, + labels_view, + labels_view.stride(0), + num_valid_tokens, + _max, + _max.stride(0), + _max.stride(1), + _accu, + _accu.stride(0), + _accu.stride(1), + maximum, + maximum.stride(0), + accumulate, + maximum.stride(0), + _logprobs, + _logprobs.stride(0), + logprobs, + triton.language.constexpr(REDUCTION.value), + ) + else: + _max_backup = _max.clone() + dist.all_reduce(_max, op=dist.ReduceOp.MAX, group=tp_group) + + torch.cuda.current_stream().record_event(_get_fwd_config()._dedicated_events[0]) + with torch.cuda.stream(_get_fwd_config()._dedicated_stream): + _get_fwd_config()._dedicated_stream.wait_event(_get_fwd_config()._dedicated_events[0]) + dist.all_reduce(_logprobs, op=dist.ReduceOp.SUM, group=tp_group) + _get_fwd_config()._dedicated_stream.record_event(_get_fwd_config()._dedicated_events[1]) + + def grid(meta): + return (triton.cdiv(num_tokens, meta["BLOCK_SIZE_M"]),) + + triton_kernels.forward_tp_epilogue[grid]( + num_tokens, + num_splits, + _max, + _max.stride(0), + _max.stride(1), + _max_backup, + _max_backup.stride(0), + _max_backup.stride(1), + _accu, + _accu.stride(0), + _accu.stride(1), + maximum, + maximum.stride(0), + accumulate, + maximum.stride(0), + ) + # reduce accumulate + dist.all_reduce(accumulate, op=dist.ReduceOp.SUM, group=tp_group) - return logprobs, maximum, accumulate, num_valid_tokens, tp_rank, tp_world_size, global_hidden - - -def backward( - dlogprobs: torch.Tensor, - global_hidden: torch.Tensor, - weight: torch.Tensor, - labels: torch.Tensor, - maximum: torch.Tensor, - accu: torch.Tensor, - num_valid_tokens: torch.Tensor, - reduction: typing.Literal["none", "sum", "mean"] = "mean", - ignore_index: int = -100, - tp_group: typing.Optional[dist.ProcessGroup] = None, - tp_rank: int = 0, - tp_world_size: int = 1, - sequence_parallel: bool = False, -) -> typing.Tuple[torch.Tensor, torch.Tensor]: - """ - backward host function - """ - in_tp_mode = (tp_group is not None) and (tp_world_size > 1) - - hidden_view = global_hidden.view(-1, global_hidden.shape[-1]) - labels_view = labels.view(-1) - - num_tokens, dim = hidden_view.shape - vocab_size, _ = weight.shape - - REDUCTION = utils.str_to_reduction_enum(reduction) - dlogprobs_view = dlogprobs.view(-1) - assert (REDUCTION == utils.EntropyReductionEnum.kNone and dlogprobs.shape == (num_tokens,)) or ( - REDUCTION != utils.EntropyReductionEnum.kNone and dlogprobs.dim() == 0 - ) - assert dlogprobs.is_contiguous() and dlogprobs.is_cuda + # update logprobs + torch.cuda.current_stream().wait_event(_get_fwd_config()._dedicated_events[1]) + triton_kernels.forward_tp_epilogue_update_logprobs[grid]( + num_tokens, + ignore_index, + num_valid_tokens, + labels_view, + labels_view.stride(0), + _logprobs, + _logprobs.stride(0), + maximum, + maximum.stride(0), + accumulate, + accumulate.stride(0), + logprobs, + REDUCTION.value, + ) - assert ( - num_valid_tokens.dim() == 0 - and num_valid_tokens.is_cuda - and num_valid_tokens.dtype == torch.int64 - ) + return logprobs, maximum, accumulate, num_valid_tokens, tp_rank, tp_world_size, global_hidden + + + def backward( + dlogprobs: torch.Tensor, + global_hidden: torch.Tensor, + weight: torch.Tensor, + labels: torch.Tensor, + maximum: torch.Tensor, + accu: torch.Tensor, + num_valid_tokens: torch.Tensor, + reduction: typing.Literal["none", "sum", "mean"] = "mean", + ignore_index: int = -100, + tp_group: typing.Optional[dist.ProcessGroup] = None, + tp_rank: int = 0, + tp_world_size: int = 1, + sequence_parallel: bool = False, + ) -> typing.Tuple[torch.Tensor, torch.Tensor]: + """ + backward host function + """ + in_tp_mode = (tp_group is not None) and (tp_world_size > 1) - d_hidden = torch.empty_like(global_hidden) - d_weight = torch.empty_like(weight) - assert d_hidden.is_contiguous() and d_weight.is_contiguous() + hidden_view = global_hidden.view(-1, global_hidden.shape[-1]) + labels_view = labels.view(-1) - # FIXME: implement different backward methods - _backward_method = _get_bwd_config()._backward_method - if _backward_method == utils.BackwardMethodEnum.kDlogitsSplitN: - vocab_per_split = _get_bwd_config()._vocab_per_split - num_splits = (vocab_size + vocab_per_split - 1) // vocab_per_split + num_tokens, dim = hidden_view.shape + vocab_size, _ = weight.shape - _d_logits = torch.empty( - (num_tokens, vocab_per_split), device=global_hidden.device, dtype=global_hidden.dtype + REDUCTION = utils.str_to_reduction_enum(reduction) + dlogprobs_view = dlogprobs.view(-1) + assert (REDUCTION == utils.EntropyReductionEnum.kNone and dlogprobs.shape == (num_tokens,)) or ( + REDUCTION != utils.EntropyReductionEnum.kNone and dlogprobs.dim() == 0 ) + assert dlogprobs.is_contiguous() and dlogprobs.is_cuda - hidden_packed = from_dlpack( - hidden_view.detach(), assumed_align=16 - ).mark_compact_shape_dynamic(mode=0) - weight_packed = from_dlpack(weight.detach(), assumed_align=16) - labels_packed = from_dlpack( - labels_view.detach(), assumed_align=8 - ).mark_compact_shape_dynamic(mode=0) - dlogprobs_packed = from_dlpack( - dlogprobs_view.detach(), assumed_align=8 - ).mark_compact_shape_dynamic(mode=0) - maximum_packed = from_dlpack(maximum.detach(), assumed_align=8).mark_compact_shape_dynamic( - mode=0 - ) - accu_packed = from_dlpack(accu.detach(), assumed_align=8).mark_compact_shape_dynamic(mode=0) - dlogits_packed = from_dlpack(_d_logits, assumed_align=32).mark_compact_shape_dynamic(mode=0) - scalarNumValidTokens_packed = cute.runtime.make_ptr( - cutlass.Int64, num_valid_tokens.data_ptr(), cute.AddressSpace.gmem, assumed_align=8 + assert ( + num_valid_tokens.dim() == 0 + and num_valid_tokens.is_cuda + and num_valid_tokens.dtype == torch.int64 ) - stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) + d_hidden = torch.empty_like(global_hidden) + d_weight = torch.empty_like(weight) + assert d_hidden.is_contiguous() and d_weight.is_contiguous() - key = f"vocab_size:{vocab_size}+dim:{dim}+reduction:{REDUCTION}+dtype:{hidden_view.dtype}" - if _get_bwd_config()._bwd_kernel.get(key) is None: - bwd_kernel = bwd_partial_dlogits.BwdPartialDlogits( - reduction=REDUCTION.value, vocab_per_split=vocab_per_split - ) - bwd_kernel_compiled = cute.compile( - bwd_kernel, - 0, # split_idx - hidden_packed, - weight_packed, - labels_packed, - dlogprobs_packed, - maximum_packed, - accu_packed, - dlogits_packed, - scalarNumValidTokens_packed, - ignore_index, - tp_rank, - stream, - ) - _get_bwd_config()._bwd_kernel[key] = bwd_kernel_compiled - else: - bwd_kernel_compiled = _get_bwd_config()._bwd_kernel.get(key) + # FIXME: implement different backward methods + _backward_method = _get_bwd_config()._backward_method + if _backward_method == utils.BackwardMethodEnum.kDlogitsSplitN: + vocab_per_split = _get_bwd_config()._vocab_per_split + num_splits = (vocab_size + vocab_per_split - 1) // vocab_per_split - for split_idx in range(num_splits): - bwd_kernel_compiled( - split_idx, - hidden_packed, - weight_packed, - labels_packed, - dlogprobs_packed, - maximum_packed, - accu_packed, - dlogits_packed, - scalarNumValidTokens_packed, - ignore_index, - tp_rank, - stream, - ) - # remove padding areas - # cublas can handle non-contiguous tensors - # therefore, we do not need to contiguous the tensor - vocab_right_bound = ( - min((split_idx + 1) * vocab_per_split, vocab_size) - split_idx * vocab_per_split - ) - valid_d_logits = _d_logits[:, :vocab_right_bound] - - torch.addmm( - input=d_hidden.view(-1, dim), - mat1=valid_d_logits, - mat2=weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :], - beta=(split_idx != 0), - alpha=1.0, - out=d_hidden.view(-1, dim), + _d_logits = torch.empty( + (num_tokens, vocab_per_split), device=global_hidden.device, dtype=global_hidden.dtype ) - torch.matmul( - valid_d_logits.T, - hidden_view, - out=d_weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :], + + hidden_packed = from_dlpack( + hidden_view.detach(), assumed_align=16 + ).mark_compact_shape_dynamic(mode=0) + weight_packed = from_dlpack(weight.detach(), assumed_align=16) + labels_packed = from_dlpack( + labels_view.detach(), assumed_align=8 + ).mark_compact_shape_dynamic(mode=0) + dlogprobs_packed = from_dlpack( + dlogprobs_view.detach(), assumed_align=8 + ).mark_compact_shape_dynamic(mode=0) + maximum_packed = from_dlpack(maximum.detach(), assumed_align=8).mark_compact_shape_dynamic( + mode=0 ) - else: - raise NotImplementedError(f"Unsupported backward method: {_backward_method}") - - if in_tp_mode: - dist.all_reduce(d_hidden, op=dist.ReduceOp.SUM, group=tp_group) - if sequence_parallel: - partial_hidden_shape = ( - global_hidden.shape[0] // tp_world_size, - *global_hidden.shape[1:], + accu_packed = from_dlpack(accu.detach(), assumed_align=8).mark_compact_shape_dynamic(mode=0) + dlogits_packed = from_dlpack(_d_logits, assumed_align=32).mark_compact_shape_dynamic(mode=0) + scalarNumValidTokens_packed = cute.runtime.make_ptr( + cutlass.Int64, num_valid_tokens.data_ptr(), cute.AddressSpace.gmem, assumed_align=8 ) - partial_num_tokens = num_tokens // tp_world_size - d_hidden = d_hidden.view(-1, d_hidden.shape[-1])[ - tp_rank * partial_num_tokens : (tp_rank + 1) * partial_num_tokens, : - ] - d_hidden = d_hidden.view(partial_hidden_shape).clone() - return d_hidden, d_weight + stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) + + key = f"vocab_size:{vocab_size}+dim:{dim}+reduction:{REDUCTION}+dtype:{hidden_view.dtype}" + if _get_bwd_config()._bwd_kernel.get(key) is None: + bwd_kernel = bwd_partial_dlogits.BwdPartialDlogits( + reduction=REDUCTION.value, vocab_per_split=vocab_per_split + ) + bwd_kernel_compiled = cute.compile( + bwd_kernel, + 0, # split_idx + hidden_packed, + weight_packed, + labels_packed, + dlogprobs_packed, + maximum_packed, + accu_packed, + dlogits_packed, + scalarNumValidTokens_packed, + ignore_index, + tp_rank, + stream, + ) + _get_bwd_config()._bwd_kernel[key] = bwd_kernel_compiled + else: + bwd_kernel_compiled = _get_bwd_config()._bwd_kernel.get(key) + + for split_idx in range(num_splits): + bwd_kernel_compiled( + split_idx, + hidden_packed, + weight_packed, + labels_packed, + dlogprobs_packed, + maximum_packed, + accu_packed, + dlogits_packed, + scalarNumValidTokens_packed, + ignore_index, + tp_rank, + stream, + ) + # remove padding areas + # cublas can handle non-contiguous tensors + # therefore, we do not need to contiguous the tensor + vocab_right_bound = ( + min((split_idx + 1) * vocab_per_split, vocab_size) - split_idx * vocab_per_split + ) + valid_d_logits = _d_logits[:, :vocab_right_bound] + + torch.addmm( + input=d_hidden.view(-1, dim), + mat1=valid_d_logits, + mat2=weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :], + beta=(split_idx != 0), + alpha=1.0, + out=d_hidden.view(-1, dim), + ) + torch.matmul( + valid_d_logits.T, + hidden_view, + out=d_weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :], + ) + else: + raise NotImplementedError(f"Unsupported backward method: {_backward_method}") + + if in_tp_mode: + dist.all_reduce(d_hidden, op=dist.ReduceOp.SUM, group=tp_group) + if sequence_parallel: + partial_hidden_shape = ( + global_hidden.shape[0] // tp_world_size, + *global_hidden.shape[1:], + ) + partial_num_tokens = num_tokens // tp_world_size + d_hidden = d_hidden.view(-1, d_hidden.shape[-1])[ + tp_rank * partial_num_tokens : (tp_rank + 1) * partial_num_tokens, : + ] + d_hidden = d_hidden.view(partial_hidden_shape).clone() + + return d_hidden, d_weight +except ImportError: + pass diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py b/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py index ebb9709822c..da095e3fc64 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py @@ -4,648 +4,651 @@ Implementations of the fusion lm_head(Linear) + Cross-Entropy kernel """ -from typing import Tuple, Type +try: + from typing import Tuple, Type -import cuda.bindings.driver as cuda # type: ignore -import cutlass -import cutlass.cute as cute -import cutlass.pipeline as pipeline # type: ignore -import cutlass.utils as utils # type: ignore -import cutlass.utils.blackwell_helpers as sm100_utils # type: ignore -from cutlass.cute.nvgpu import cpasync, tcgen05 + import cuda.bindings.driver as cuda # type: ignore + import cutlass + import cutlass.cute as cute + import cutlass.pipeline as pipeline # type: ignore + import cutlass.utils as utils # type: ignore + import cutlass.utils.blackwell_helpers as sm100_utils # type: ignore + from cutlass.cute.nvgpu import cpasync, tcgen05 -SM100_TMEM_CAPACITY_COLUMNS: int = 512 + SM100_TMEM_CAPACITY_COLUMNS: int = 512 -def make_thread_cooperative_group(size: int): - """ - Create a thread cooperative group. - """ - return pipeline.CooperativeGroup(pipeline.Agent.Thread, size, alignment=size) - + def make_thread_cooperative_group(size: int): + """ + Create a thread cooperative group. + """ + return pipeline.CooperativeGroup(pipeline.Agent.Thread, size, alignment=size) -class FwdMainLoop: - """ - This class implements the mainloop for forward process. - Traits stored as attributes. + class FwdMainLoop: + """ + This class implements the mainloop for forward process. - :param acc_dtype: - """ + Traits stored as attributes. - def __init__( - self, - acc_dtype: Type[cutlass.Numeric] = cutlass.Float32, - use_2cta_instrs: bool = False, - mma_tiler_mn: Tuple[int, int] = (128, 256), - vocab_per_split: int = 512, - ): - """ - Configuration including: - - MMA instruction settings - - Cluster Shape + :param acc_dtype: """ - self.acc_dtype: Type[cutlass.Numeric] = acc_dtype - self.use_2cta_instrs = use_2cta_instrs - # This is the shape covered by tiledMMA, not just single MMA instruction - self.mma_tiler = (*mma_tiler_mn, 1) - self.cta_tiler = (self.mma_tiler[0], vocab_per_split, self.mma_tiler[2]) - self.vocab_per_split = vocab_per_split - - self.cta_group = tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE - self.cluster_shape_mn = (2, 1) if self.use_2cta_instrs else (1, 1) - - self.occupancy = 1 - # query SMEM capacity - self.smem_capacity = utils.get_smem_capacity_in_bytes("sm_100") - - # the maximum columns per MMA is 256, and there is only one GEMM, so we can fully - # assign TMEM for that GEMM of different tiles. - # so 512 = 2 * 256 - - self.threads_per_warp: int = 32 - # 1 warp for loading, 1 warp for issuing MMA, 1 WG for storing - self.epi_warp_ids = (0, 1, 2, 3) - self.load_warp_ids = 4 - self.mma_warp_ids = 5 - self.empty_warp_ids = (6, 7) - - self.threads_per_cta: int = self.threads_per_warp * len( - (*self.epi_warp_ids, self.load_warp_ids, self.mma_warp_ids, *self.empty_warp_ids) - ) - - self.cta_sync_barrier = pipeline.NamedBarrier( - barrier_id=1, num_threads=self.threads_per_cta - ) - self.tmem_alloc_barrier = pipeline.NamedBarrier( - barrier_id=2, num_threads=self.threads_per_cta - ) - - self.buffer_align_bytes: int = 1024 - self.num_regs_other: int = 32 - self.num_regs_epi: int = 192 - - def _compute_stages( - self, - tiled_mma: cute.TiledMma, - mma_tiler: Tuple[int, int, int], - a_dtype: Type[cutlass.Numeric], - b_dtype: Type[cutlass.Numeric], - ): - a_smem_layout_stage_one = sm100_utils.make_smem_layout_a( - tiled_mma, mma_tiler, a_dtype, 1 # only single stage - ) - b_smem_layout_stage_one = sm100_utils.make_smem_layout_b(tiled_mma, mma_tiler, b_dtype, 1) - a_bytes_per_stage = cute.size_in_bytes(a_dtype, a_smem_layout_stage_one) - b_bytes_per_stage = cute.size_in_bytes(b_dtype, b_smem_layout_stage_one) - num_acc_stage = 2 - num_a_stage = 4 - num_b_stage = 4 - num_epi_stage_per_tile = 4 - - return num_acc_stage, num_a_stage, num_b_stage, num_epi_stage_per_tile - - def _setup_attributes( - self, - tiled_mma: cute.TiledMma, - a_dtype: Type[cutlass.Numeric], - b_dtype: Type[cutlass.Numeric], - ): - self.cluster_shape_mnk = (*self.cluster_shape_mn, 1) - self.cluster_layout_vmnk = cute.tiled_divide( - cute.make_layout(self.cluster_shape_mnk), (tiled_mma.thr_id.shape,) - ) - - # this is fixed for dense MMA, k=16 - mma_inst_shape_k = cute.size(tiled_mma.shape_mnk, mode=[2]) - # 16*4 = 64; 64 * sizeof(FP16) = 128Bytes - mma_inst_tile_k: int = 4 - self.mma_tiler = (self.mma_tiler[0], self.mma_tiler[1], mma_inst_shape_k * mma_inst_tile_k) - - self.num_acc_stage, self.num_a_stage, self.num_b_stage, self.num_epi_stage_per_tile = ( - self._compute_stages(tiled_mma, self.mma_tiler, a_dtype, b_dtype) - ) - self.tmem_alloc_cols = self.num_acc_stage * self.mma_tiler[1] - assert self.tmem_alloc_cols <= SM100_TMEM_CAPACITY_COLUMNS - - self.cta_tile_shape_mnk = ( - self.mma_tiler[0] // cute.size(tiled_mma.thr_id.shape), - self.mma_tiler[1], - self.mma_tiler[2], - ) - - @cute.kernel - def kernel( - self, - tiled_mma: cute.TiledMma, - tma_atom_a: cute.CopyAtom, - mA: cute.Tensor, - tma_atom_b: cute.CopyAtom, - mB: cute.Tensor, - mLabels: cute.Tensor, - mMax: cute.Tensor, - mAccu: cute.Tensor, - mLogprobs: cute.Tensor, - a_smem_layout_staged: cute.ComposedLayout, - b_smem_layout_staged: cute.ComposedLayout, - cluster_layout_vmnk: cute.Layout, - problem_mnk: Tuple[int, int, int], - ignore_index: cutlass.Int64, - rank: cutlass.Int32, - ): - """ - The forward kernel for the mainloop. - """ - warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) - tidx, _, _ = cute.arch.thread_idx() - bidx, bidy, _ = cute.arch.block_idx() - # FIXME: block swizzling applied here - pidm, pidn = bidx, bidy - - # prefetch tma descriptors - if warp_idx == self.load_warp_ids: - cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_a) - cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_b) - - # declare SMEM - smem = utils.SmemAllocator() - storage = smem.allocate(self.shared_storage) - - ab_pipeline = pipeline.PipelineTmaUmma.create( - num_stages=self.num_a_stage, - producer_group=make_thread_cooperative_group(len([self.load_warp_ids])), - consumer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), - tx_count=self.tma_copy_a_bytes + self.tma_copy_b_bytes, - barrier_storage=storage.load_ab_mbar_ptr.data_ptr(), - ) - ab_producer_state = pipeline.make_pipeline_state( - pipeline.PipelineUserType.Producer, self.num_a_stage - ) - ab_consumer_state = pipeline.make_pipeline_state( - pipeline.PipelineUserType.Consumer, self.num_a_stage - ) - - mma_pipeline = pipeline.PipelineUmmaAsync.create( - num_stages=self.num_acc_stage, - producer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), - consumer_group=make_thread_cooperative_group( - self.threads_per_warp * len(self.epi_warp_ids) - ), - barrier_storage=storage.mma_mbar_ptr.data_ptr(), - ) - mma_producer_state = pipeline.make_pipeline_state( - pipeline.PipelineUserType.Producer, self.num_acc_stage - ) - mma_consumer_state = pipeline.make_pipeline_state( - pipeline.PipelineUserType.Consumer, self.num_acc_stage - ) - - tmem_dealloc_mbar_ptr = storage.tmem_dealloc_mbar_ptr.data_ptr() - if warp_idx == self.empty_warp_ids[0]: - with cute.arch.elect_one(): - cute.arch.mbarrier_init( - tmem_dealloc_mbar_ptr, self.threads_per_warp * len(self.epi_warp_ids) - ) - cute.arch.mbarrier_init_fence() - - # -------- SMEM partition ------------ # - # swizzle o [(tileM, tileK), loopM, loopK, Stage] - sA = storage.sA.get_tensor(a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner) - # swizzle o [(tileN, tileK), loopN, loopK, stage] - sB = storage.sB.get_tensor(b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner) - - # FIXME: if 2 CTAs, modify here - thr_mma = tiled_mma.get_slice(0) - # [MMA, loopM, loopK, stage] - tCsA = thr_mma.make_fragment_A(sA) - # [MMA, loopN, loopK, stage] - tCsB = thr_mma.make_fragment_B(sB) - - # ---------- GMEM partition ----------- # - # [tileM, tileK, loopK] - gA = cute.local_tile(mA, (self.mma_tiler[0], self.mma_tiler[2]), (pidm, None)) - - # [vocab_size_per_split, dim] - mB_n = cute.local_tile( - mB, (self.vocab_per_split, cute.size(mB.layout.shape, mode=[1])), (pidn, 0) - ) - - # [tileN, tileK, loopN, loopK] - gB = cute.local_tile(mB_n, (self.mma_tiler[1], self.mma_tiler[2]), (None, None)) - - # [MMA, tileCntM, tileCntK, loopK] - tCgA = thr_mma.partition_A(gA) - # [MMA, tileCntN, tileCntK, loopN, loopK] - tCgB = thr_mma.partition_B(gB) - - a_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, 0, None, 0)).shape) - # FIXME: if 2 CTAs, modify here - cta_rank_in_cluster = 0 - block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord(cta_rank_in_cluster) - tTMAsA, tTMAgA = cpasync.tma_partition( - tma_atom_a, - block_in_cluster_coord_vmnk[2], # cta_coord, - a_cta_layout, - cute.group_modes(sA, 0, 3), # SMEM tensor - cute.group_modes(tCgA, 0, 3), # GMEM tensor - ) - b_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape) - tTMAsB, tTMAgB = cpasync.tma_partition( - tma_atom_b, - block_in_cluster_coord_vmnk[1], # cta_coord - b_cta_layout, - cute.group_modes(sB, 0, 3), - cute.group_modes(tCgB, 0, 3), - ) - - # Allocate TMEM - tmem_holding_buf = storage.tmem_holding_buf - if warp_idx == self.empty_warp_ids[0]: - cute.arch.alloc_tmem( - self.tmem_alloc_cols, tmem_holding_buf, is_two_cta=self.use_2cta_instrs + + def __init__( + self, + acc_dtype: Type[cutlass.Numeric] = cutlass.Float32, + use_2cta_instrs: bool = False, + mma_tiler_mn: Tuple[int, int] = (128, 256), + vocab_per_split: int = 512, + ): + """ + Configuration including: + - MMA instruction settings + - Cluster Shape + """ + self.acc_dtype: Type[cutlass.Numeric] = acc_dtype + self.use_2cta_instrs = use_2cta_instrs + # This is the shape covered by tiledMMA, not just single MMA instruction + self.mma_tiler = (*mma_tiler_mn, 1) + self.cta_tiler = (self.mma_tiler[0], vocab_per_split, self.mma_tiler[2]) + self.vocab_per_split = vocab_per_split + + self.cta_group = tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE + self.cluster_shape_mn = (2, 1) if self.use_2cta_instrs else (1, 1) + + self.occupancy = 1 + # query SMEM capacity + self.smem_capacity = utils.get_smem_capacity_in_bytes("sm_100") + + # the maximum columns per MMA is 256, and there is only one GEMM, so we can fully + # assign TMEM for that GEMM of different tiles. + # so 512 = 2 * 256 + + self.threads_per_warp: int = 32 + # 1 warp for loading, 1 warp for issuing MMA, 1 WG for storing + self.epi_warp_ids = (0, 1, 2, 3) + self.load_warp_ids = 4 + self.mma_warp_ids = 5 + self.empty_warp_ids = (6, 7) + + self.threads_per_cta: int = self.threads_per_warp * len( + (*self.epi_warp_ids, self.load_warp_ids, self.mma_warp_ids, *self.empty_warp_ids) ) - self.cta_sync_barrier.arrive_and_wait() - tmem_ptr = cute.arch.retrieve_tmem_ptr( - self.acc_dtype, alignment=16, ptr_to_buffer_holding_addr=tmem_holding_buf - ) - - # [(tileM, tileN), loopM, loopN] - tmem_shape = (128, self.tmem_alloc_cols) - acc_shape = thr_mma.partition_shape_C(tmem_shape) - tCtC_fake = thr_mma.make_fragment_C(acc_shape) - tCtC = cute.make_tensor(tmem_ptr, tCtC_fake.layout) - - block_vocab_left_idx: cutlass.Int64 = pidn * self.vocab_per_split - block_vocab_right_idx: cutlass.Int64 = min( - (pidn + 1) * self.vocab_per_split, problem_mnk[1] - ) - num_n_tiles: cutlass.Int64 = cute.ceil_div( - (block_vocab_right_idx - block_vocab_left_idx), self.mma_tiler[1] - ) - - # /////// - # empty - # /////// - if warp_idx in self.empty_warp_ids: - cute.arch.warpgroup_reg_dealloc(self.num_regs_other) - - # /////// - # load - # /////// - if warp_idx == self.load_warp_ids: - cute.arch.warpgroup_reg_dealloc(self.num_regs_other) - - for n in cutlass.range(num_n_tiles): - for k in cutlass.range(cute.size(gA, mode=[2])): - ab_pipeline.producer_acquire(ab_producer_state) - cute.copy( - tma_atom_a, - tTMAgA[(None, k)], - tTMAsA[(None, ab_producer_state.index)], - tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state), - ) - cute.copy( - tma_atom_b, - tTMAgB[(None, n, k)], - tTMAsB[(None, ab_producer_state.index)], - tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state), + + self.cta_sync_barrier = pipeline.NamedBarrier( + barrier_id=1, num_threads=self.threads_per_cta + ) + self.tmem_alloc_barrier = pipeline.NamedBarrier( + barrier_id=2, num_threads=self.threads_per_cta + ) + + self.buffer_align_bytes: int = 1024 + self.num_regs_other: int = 32 + self.num_regs_epi: int = 192 + + def _compute_stages( + self, + tiled_mma: cute.TiledMma, + mma_tiler: Tuple[int, int, int], + a_dtype: Type[cutlass.Numeric], + b_dtype: Type[cutlass.Numeric], + ): + a_smem_layout_stage_one = sm100_utils.make_smem_layout_a( + tiled_mma, mma_tiler, a_dtype, 1 # only single stage + ) + b_smem_layout_stage_one = sm100_utils.make_smem_layout_b(tiled_mma, mma_tiler, b_dtype, 1) + a_bytes_per_stage = cute.size_in_bytes(a_dtype, a_smem_layout_stage_one) + b_bytes_per_stage = cute.size_in_bytes(b_dtype, b_smem_layout_stage_one) + num_acc_stage = 2 + num_a_stage = 4 + num_b_stage = 4 + num_epi_stage_per_tile = 4 + + return num_acc_stage, num_a_stage, num_b_stage, num_epi_stage_per_tile + + def _setup_attributes( + self, + tiled_mma: cute.TiledMma, + a_dtype: Type[cutlass.Numeric], + b_dtype: Type[cutlass.Numeric], + ): + self.cluster_shape_mnk = (*self.cluster_shape_mn, 1) + self.cluster_layout_vmnk = cute.tiled_divide( + cute.make_layout(self.cluster_shape_mnk), (tiled_mma.thr_id.shape,) + ) + + # this is fixed for dense MMA, k=16 + mma_inst_shape_k = cute.size(tiled_mma.shape_mnk, mode=[2]) + # 16*4 = 64; 64 * sizeof(FP16) = 128Bytes + mma_inst_tile_k: int = 4 + self.mma_tiler = (self.mma_tiler[0], self.mma_tiler[1], mma_inst_shape_k * mma_inst_tile_k) + + self.num_acc_stage, self.num_a_stage, self.num_b_stage, self.num_epi_stage_per_tile = ( + self._compute_stages(tiled_mma, self.mma_tiler, a_dtype, b_dtype) + ) + self.tmem_alloc_cols = self.num_acc_stage * self.mma_tiler[1] + assert self.tmem_alloc_cols <= SM100_TMEM_CAPACITY_COLUMNS + + self.cta_tile_shape_mnk = ( + self.mma_tiler[0] // cute.size(tiled_mma.thr_id.shape), + self.mma_tiler[1], + self.mma_tiler[2], + ) + + @cute.kernel + def kernel( + self, + tiled_mma: cute.TiledMma, + tma_atom_a: cute.CopyAtom, + mA: cute.Tensor, + tma_atom_b: cute.CopyAtom, + mB: cute.Tensor, + mLabels: cute.Tensor, + mMax: cute.Tensor, + mAccu: cute.Tensor, + mLogprobs: cute.Tensor, + a_smem_layout_staged: cute.ComposedLayout, + b_smem_layout_staged: cute.ComposedLayout, + cluster_layout_vmnk: cute.Layout, + problem_mnk: Tuple[int, int, int], + ignore_index: cutlass.Int64, + rank: cutlass.Int32, + ): + """ + The forward kernel for the mainloop. + """ + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) + tidx, _, _ = cute.arch.thread_idx() + bidx, bidy, _ = cute.arch.block_idx() + # FIXME: block swizzling applied here + pidm, pidn = bidx, bidy + + # prefetch tma descriptors + if warp_idx == self.load_warp_ids: + cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_a) + cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_b) + + # declare SMEM + smem = utils.SmemAllocator() + storage = smem.allocate(self.shared_storage) + + ab_pipeline = pipeline.PipelineTmaUmma.create( + num_stages=self.num_a_stage, + producer_group=make_thread_cooperative_group(len([self.load_warp_ids])), + consumer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), + tx_count=self.tma_copy_a_bytes + self.tma_copy_b_bytes, + barrier_storage=storage.load_ab_mbar_ptr.data_ptr(), + ) + ab_producer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, self.num_a_stage + ) + ab_consumer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, self.num_a_stage + ) + + mma_pipeline = pipeline.PipelineUmmaAsync.create( + num_stages=self.num_acc_stage, + producer_group=make_thread_cooperative_group(len([self.mma_warp_ids])), + consumer_group=make_thread_cooperative_group( + self.threads_per_warp * len(self.epi_warp_ids) + ), + barrier_storage=storage.mma_mbar_ptr.data_ptr(), + ) + mma_producer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, self.num_acc_stage + ) + mma_consumer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, self.num_acc_stage + ) + + tmem_dealloc_mbar_ptr = storage.tmem_dealloc_mbar_ptr.data_ptr() + if warp_idx == self.empty_warp_ids[0]: + with cute.arch.elect_one(): + cute.arch.mbarrier_init( + tmem_dealloc_mbar_ptr, self.threads_per_warp * len(self.epi_warp_ids) ) - ab_pipeline.producer_commit(ab_producer_state) - ab_producer_state.advance() - - # /////// - # mma - # /////// - if warp_idx == self.mma_warp_ids: - cute.arch.warpgroup_reg_dealloc(self.num_regs_other) - - for n in cutlass.range(num_n_tiles): - # disable accumulate for the first tile - tiled_mma.set(tcgen05.Field.ACCUMULATE, False) - mma_pipeline.producer_acquire(mma_producer_state) - - for k in cutlass.range(cute.size(gA, mode=[2])): - ab_pipeline.consumer_wait(ab_consumer_state) - - for kblock_idx in cutlass.range(cute.size(tCsA, mode=[2]), unroll_full=True): - cute.gemm( - tiled_mma, - cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), - tCsA[(None, None, kblock_idx, ab_consumer_state.index)], - tCsB[(None, None, kblock_idx, ab_consumer_state.index)], - cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), - ) - # enable accumulate for the next tile - tiled_mma.set(tcgen05.Field.ACCUMULATE, True) - - ab_pipeline.consumer_release(ab_consumer_state) - ab_consumer_state.advance() - - mma_pipeline.producer_commit(mma_producer_state) - mma_producer_state.advance() - - # ////////// - # epilogue - # ////////// - if warp_idx in self.epi_warp_ids: - cute.arch.warpgroup_reg_alloc(self.num_regs_epi) - - # epilog TMEM copy and partition - copy_atom_t2r = sm100_utils.get_tmem_load_op( - self.cta_tile_shape_mnk, - utils.LayoutEnum.ROW_MAJOR, # This is hard-coded - self.acc_dtype, - self.acc_dtype, - (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), - self.use_2cta_instrs, + cute.arch.mbarrier_init_fence() + + # -------- SMEM partition ------------ # + # swizzle o [(tileM, tileK), loopM, loopK, Stage] + sA = storage.sA.get_tensor(a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner) + # swizzle o [(tileN, tileK), loopN, loopK, stage] + sB = storage.sB.get_tensor(b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner) + + # FIXME: if 2 CTAs, modify here + thr_mma = tiled_mma.get_slice(0) + # [MMA, loopM, loopK, stage] + tCsA = thr_mma.make_fragment_A(sA) + # [MMA, loopN, loopK, stage] + tCsB = thr_mma.make_fragment_B(sB) + + # ---------- GMEM partition ----------- # + # [tileM, tileK, loopK] + gA = cute.local_tile(mA, (self.mma_tiler[0], self.mma_tiler[2]), (pidm, None)) + + # [vocab_size_per_split, dim] + mB_n = cute.local_tile( + mB, (self.vocab_per_split, cute.size(mB.layout.shape, mode=[1])), (pidn, 0) ) - # [tileM, subTileN, loopM, CntSubTileN, loopN] - tAcc_epi = cute.flat_divide( - tCtC[((None, None), 0, None)], - (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), + + # [tileN, tileK, loopN, loopK] + gB = cute.local_tile(mB_n, (self.mma_tiler[1], self.mma_tiler[2]), (None, None)) + + # [MMA, tileCntM, tileCntK, loopK] + tCgA = thr_mma.partition_A(gA) + # [MMA, tileCntN, tileCntK, loopN, loopK] + tCgB = thr_mma.partition_B(gB) + + a_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, 0, None, 0)).shape) + # FIXME: if 2 CTAs, modify here + cta_rank_in_cluster = 0 + block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord(cta_rank_in_cluster) + tTMAsA, tTMAgA = cpasync.tma_partition( + tma_atom_a, + block_in_cluster_coord_vmnk[2], # cta_coord, + a_cta_layout, + cute.group_modes(sA, 0, 3), # SMEM tensor + cute.group_modes(tCgA, 0, 3), # GMEM tensor ) - tiled_copy_t2r = tcgen05.make_tmem_copy(copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)]) - thr_copy_t2r = tiled_copy_t2r.get_slice(tidx) - tTMEM_load_tAcc = thr_copy_t2r.partition_S(tAcc_epi) - # [(pattern), loopM, loopN, CntTileM, CntTileN] - tTMEM_load_tAcc = cute.group_modes(tTMEM_load_tAcc, 3, cute.rank(tTMEM_load_tAcc) - 1) - - cAcc = cute.make_identity_tensor(self.mma_tiler[:2]) - tCcAcc = thr_mma.partition_C(cAcc) - # [tileM, subTileN, loopM, CntSubTileN, CntTileN] - tCcAcc_epi = cute.flat_divide( - tCcAcc[((None, None), 0, None)], - (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), + b_cta_layout = cute.make_layout(cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape) + tTMAsB, tTMAgB = cpasync.tma_partition( + tma_atom_b, + block_in_cluster_coord_vmnk[1], # cta_coord + b_cta_layout, + cute.group_modes(sB, 0, 3), + cute.group_modes(tCgB, 0, 3), ) - tTMEM_load_cAcc = thr_copy_t2r.partition_D(tCcAcc_epi) - tTMEM_load_cAcc_shape = cute.select(tTMEM_load_cAcc.shape, mode=[0, 1, 2]) - - # epilogue layouts - epilogue_thread_layout = cute.make_layout((128, 1)) - copy_atom_g2r = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), mLabels.element_type) - tiled_copy_g2r = cute.make_tiled_copy(copy_atom_g2r, epilogue_thread_layout, (128, 1)) - thr_copy_g2r = tiled_copy_g2r.get_slice(tidx) - - copy_atom_r2g = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), cutlass.Float32) - tiled_copy_r2g = cute.make_tiled_copy(copy_atom_r2g, epilogue_thread_layout, (128, 1)) - thr_copy_r2g = tiled_copy_r2g.get_slice(tidx) - - # auxiliary tensors - # [tileM] - gLabels = cute.local_tile(mLabels, (self.epi_tile[0],), (pidm,)) - - tLabelsCAcc = thr_copy_g2r.partition_S(cAcc)[(None, None, 0)] - tLabelsCAcc_mask = cute.make_fragment(tLabelsCAcc.shape, cutlass.Boolean) - # [(1, 1), 1] - tLabelsCAcc_mask[0] = cute.elem_less(pidm * self.epi_tile[0] + tidx, problem_mnk[0]) - # to align shape with gMax and gAccu - tLabelsCAcc_mask = cute.append_ones(tLabelsCAcc_mask) - - # [(1, 1), 1, 1] - tLabelsgLabels = thr_copy_g2r.partition_S(cute.append_ones(gLabels)) - tLabelsrLabels = cute.make_fragment(tLabelsgLabels.shape, tLabelsgLabels.element_type) - cute.copy(tiled_copy_g2r, tLabelsgLabels, tLabelsrLabels, pred=tLabelsCAcc_mask) - valid_mask: cutlass.Boolean = (tLabelsrLabels[0] != ignore_index) and tLabelsCAcc_mask[ - 0 - ] - - # [tileM, 1] - gMax = cute.local_tile(mMax, (self.epi_tile[0], 1), (pidm, pidn)) - # [(CPYM, CPYN), loopM, loopN] - tR2GgMax = thr_copy_r2g.partition_D(gMax) - tR2GrMax = cute.make_fragment(tR2GgMax.shape, tR2GgMax.element_type) - tR2GrMax.fill(-1e30) - - # [tileM, 1] - gAccu = cute.local_tile(mAccu, (self.epi_tile[0], 1), (pidm, pidn)) - # [(CPYM, CPYN), loopM, loopN] - tR2GgAccu = thr_copy_r2g.partition_D(gAccu) - tR2GrAccu = cute.make_fragment(tR2GgAccu.shape, tR2GgAccu.element_type) - tR2GrAccu.fill(0.0) - - # [tileM, 1] - gLogprobs = cute.append_ones(cute.local_tile(mLogprobs, (self.epi_tile[0],), (pidm,))) - # [(CPYM, CPYN), loopM, loopN] - tR2GgLogprobs = thr_copy_r2g.partition_D(gLogprobs) - tR2GrLogprobs = cute.make_fragment(tR2GgLogprobs.shape, tR2GgLogprobs.element_type) - tR2GrLogprobs.fill(0.0) - - # [(tileN // num_epi_stage_per_tile, 1), 1, 1] - tTMEM_load_rAcc = cute.make_fragment(tTMEM_load_cAcc_shape, self.acc_dtype) - - for n in cutlass.range(num_n_tiles): - mma_pipeline.consumer_wait(mma_consumer_state) - - left: cutlass.Int64 = block_vocab_left_idx + n * self.epi_tile[1] - right: cutlass.Int64 = min( - (n + 1) * self.epi_tile[1] + block_vocab_left_idx, block_vocab_right_idx + + # Allocate TMEM + tmem_holding_buf = storage.tmem_holding_buf + if warp_idx == self.empty_warp_ids[0]: + cute.arch.alloc_tmem( + self.tmem_alloc_cols, tmem_holding_buf, is_two_cta=self.use_2cta_instrs ) - num_n_subtiles: cutlass.Int64 = cute.ceil_div( - (right - left), cute.size(tTMEM_load_rAcc, mode=[0]) + self.cta_sync_barrier.arrive_and_wait() + tmem_ptr = cute.arch.retrieve_tmem_ptr( + self.acc_dtype, alignment=16, ptr_to_buffer_holding_addr=tmem_holding_buf + ) + + # [(tileM, tileN), loopM, loopN] + tmem_shape = (128, self.tmem_alloc_cols) + acc_shape = thr_mma.partition_shape_C(tmem_shape) + tCtC_fake = thr_mma.make_fragment_C(acc_shape) + tCtC = cute.make_tensor(tmem_ptr, tCtC_fake.layout) + + block_vocab_left_idx: cutlass.Int64 = pidn * self.vocab_per_split + block_vocab_right_idx: cutlass.Int64 = min( + (pidn + 1) * self.vocab_per_split, problem_mnk[1] + ) + num_n_tiles: cutlass.Int64 = cute.ceil_div( + (block_vocab_right_idx - block_vocab_left_idx), self.mma_tiler[1] + ) + + # /////// + # empty + # /////// + if warp_idx in self.empty_warp_ids: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + # /////// + # load + # /////// + if warp_idx == self.load_warp_ids: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + for n in cutlass.range(num_n_tiles): + for k in cutlass.range(cute.size(gA, mode=[2])): + ab_pipeline.producer_acquire(ab_producer_state) + cute.copy( + tma_atom_a, + tTMAgA[(None, k)], + tTMAsA[(None, ab_producer_state.index)], + tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state), + ) + cute.copy( + tma_atom_b, + tTMAgB[(None, n, k)], + tTMAsB[(None, ab_producer_state.index)], + tma_bar_ptr=ab_pipeline.producer_get_barrier(ab_producer_state), + ) + ab_pipeline.producer_commit(ab_producer_state) + ab_producer_state.advance() + + # /////// + # mma + # /////// + if warp_idx == self.mma_warp_ids: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + for n in cutlass.range(num_n_tiles): + # disable accumulate for the first tile + tiled_mma.set(tcgen05.Field.ACCUMULATE, False) + mma_pipeline.producer_acquire(mma_producer_state) + + for k in cutlass.range(cute.size(gA, mode=[2])): + ab_pipeline.consumer_wait(ab_consumer_state) + + for kblock_idx in cutlass.range(cute.size(tCsA, mode=[2]), unroll_full=True): + cute.gemm( + tiled_mma, + cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), + tCsA[(None, None, kblock_idx, ab_consumer_state.index)], + tCsB[(None, None, kblock_idx, ab_consumer_state.index)], + cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), + ) + # enable accumulate for the next tile + tiled_mma.set(tcgen05.Field.ACCUMULATE, True) + + ab_pipeline.consumer_release(ab_consumer_state) + ab_consumer_state.advance() + + mma_pipeline.producer_commit(mma_producer_state) + mma_producer_state.advance() + + # ////////// + # epilogue + # ////////// + if warp_idx in self.epi_warp_ids: + cute.arch.warpgroup_reg_alloc(self.num_regs_epi) + + # epilog TMEM copy and partition + copy_atom_t2r = sm100_utils.get_tmem_load_op( + self.cta_tile_shape_mnk, + utils.LayoutEnum.ROW_MAJOR, # This is hard-coded + self.acc_dtype, + self.acc_dtype, + (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), + self.use_2cta_instrs, + ) + # [tileM, subTileN, loopM, CntSubTileN, loopN] + tAcc_epi = cute.flat_divide( + tCtC[((None, None), 0, None)], + (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), ) - for n_subtile in cutlass.range(num_n_subtiles): - cute.copy( - tiled_copy_t2r, - tTMEM_load_tAcc[(None, None, None, n_subtile, mma_consumer_state.index)], - tTMEM_load_rAcc, + tiled_copy_t2r = tcgen05.make_tmem_copy(copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)]) + thr_copy_t2r = tiled_copy_t2r.get_slice(tidx) + tTMEM_load_tAcc = thr_copy_t2r.partition_S(tAcc_epi) + # [(pattern), loopM, loopN, CntTileM, CntTileN] + tTMEM_load_tAcc = cute.group_modes(tTMEM_load_tAcc, 3, cute.rank(tTMEM_load_tAcc) - 1) + + cAcc = cute.make_identity_tensor(self.mma_tiler[:2]) + tCcAcc = thr_mma.partition_C(cAcc) + # [tileM, subTileN, loopM, CntSubTileN, CntTileN] + tCcAcc_epi = cute.flat_divide( + tCcAcc[((None, None), 0, None)], + (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), + ) + tTMEM_load_cAcc = thr_copy_t2r.partition_D(tCcAcc_epi) + tTMEM_load_cAcc_shape = cute.select(tTMEM_load_cAcc.shape, mode=[0, 1, 2]) + + # epilogue layouts + epilogue_thread_layout = cute.make_layout((128, 1)) + copy_atom_g2r = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), mLabels.element_type) + tiled_copy_g2r = cute.make_tiled_copy(copy_atom_g2r, epilogue_thread_layout, (128, 1)) + thr_copy_g2r = tiled_copy_g2r.get_slice(tidx) + + copy_atom_r2g = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), cutlass.Float32) + tiled_copy_r2g = cute.make_tiled_copy(copy_atom_r2g, epilogue_thread_layout, (128, 1)) + thr_copy_r2g = tiled_copy_r2g.get_slice(tidx) + + # auxiliary tensors + # [tileM] + gLabels = cute.local_tile(mLabels, (self.epi_tile[0],), (pidm,)) + + tLabelsCAcc = thr_copy_g2r.partition_S(cAcc)[(None, None, 0)] + tLabelsCAcc_mask = cute.make_fragment(tLabelsCAcc.shape, cutlass.Boolean) + # [(1, 1), 1] + tLabelsCAcc_mask[0] = cute.elem_less(pidm * self.epi_tile[0] + tidx, problem_mnk[0]) + # to align shape with gMax and gAccu + tLabelsCAcc_mask = cute.append_ones(tLabelsCAcc_mask) + + # [(1, 1), 1, 1] + tLabelsgLabels = thr_copy_g2r.partition_S(cute.append_ones(gLabels)) + tLabelsrLabels = cute.make_fragment(tLabelsgLabels.shape, tLabelsgLabels.element_type) + cute.copy(tiled_copy_g2r, tLabelsgLabels, tLabelsrLabels, pred=tLabelsCAcc_mask) + valid_mask: cutlass.Boolean = (tLabelsrLabels[0] != ignore_index) and tLabelsCAcc_mask[ + 0 + ] + + # [tileM, 1] + gMax = cute.local_tile(mMax, (self.epi_tile[0], 1), (pidm, pidn)) + # [(CPYM, CPYN), loopM, loopN] + tR2GgMax = thr_copy_r2g.partition_D(gMax) + tR2GrMax = cute.make_fragment(tR2GgMax.shape, tR2GgMax.element_type) + tR2GrMax.fill(-1e30) + + # [tileM, 1] + gAccu = cute.local_tile(mAccu, (self.epi_tile[0], 1), (pidm, pidn)) + # [(CPYM, CPYN), loopM, loopN] + tR2GgAccu = thr_copy_r2g.partition_D(gAccu) + tR2GrAccu = cute.make_fragment(tR2GgAccu.shape, tR2GgAccu.element_type) + tR2GrAccu.fill(0.0) + + # [tileM, 1] + gLogprobs = cute.append_ones(cute.local_tile(mLogprobs, (self.epi_tile[0],), (pidm,))) + # [(CPYM, CPYN), loopM, loopN] + tR2GgLogprobs = thr_copy_r2g.partition_D(gLogprobs) + tR2GrLogprobs = cute.make_fragment(tR2GgLogprobs.shape, tR2GgLogprobs.element_type) + tR2GrLogprobs.fill(0.0) + + # [(tileN // num_epi_stage_per_tile, 1), 1, 1] + tTMEM_load_rAcc = cute.make_fragment(tTMEM_load_cAcc_shape, self.acc_dtype) + + for n in cutlass.range(num_n_tiles): + mma_pipeline.consumer_wait(mma_consumer_state) + + left: cutlass.Int64 = block_vocab_left_idx + n * self.epi_tile[1] + right: cutlass.Int64 = min( + (n + 1) * self.epi_tile[1] + block_vocab_left_idx, block_vocab_right_idx ) - - for idx in cutlass.range( - cute.size(tTMEM_load_rAcc, mode=[0]), unroll_full=True - ): - local_position: cutlass.Int64 = ( - n * self.epi_tile[1] - + n_subtile * cute.size(tTMEM_load_rAcc, mode=[0]) - + idx + num_n_subtiles: cutlass.Int64 = cute.ceil_div( + (right - left), cute.size(tTMEM_load_rAcc, mode=[0]) + ) + for n_subtile in cutlass.range(num_n_subtiles): + cute.copy( + tiled_copy_t2r, + tTMEM_load_tAcc[(None, None, None, n_subtile, mma_consumer_state.index)], + tTMEM_load_rAcc, ) - if (block_vocab_left_idx + local_position) < block_vocab_right_idx: - _max_old = tR2GrMax[0] - tR2GrMax[0] = cute.arch.fmax(tR2GrMax[0], tTMEM_load_rAcc[idx]) - exp_logits = cute.exp(tTMEM_load_rAcc[idx] - tR2GrMax[0]) - coeff = cute.exp(_max_old - tR2GrMax[0]) - tR2GrAccu[0] = coeff * tR2GrAccu[0] + exp_logits - - position: cutlass.Int64 = ( - rank * problem_mnk[1] + pidn * self.vocab_per_split + local_position + + for idx in cutlass.range( + cute.size(tTMEM_load_rAcc, mode=[0]), unroll_full=True + ): + local_position: cutlass.Int64 = ( + n * self.epi_tile[1] + + n_subtile * cute.size(tTMEM_load_rAcc, mode=[0]) + + idx ) - mask: cutlass.Boolean = valid_mask and (position == tLabelsrLabels[0]) - tR2GrLogprobs[0] += mask * tTMEM_load_rAcc[idx] + if (block_vocab_left_idx + local_position) < block_vocab_right_idx: + _max_old = tR2GrMax[0] + tR2GrMax[0] = cute.arch.fmax(tR2GrMax[0], tTMEM_load_rAcc[idx]) + exp_logits = cute.exp(tTMEM_load_rAcc[idx] - tR2GrMax[0]) + coeff = cute.exp(_max_old - tR2GrMax[0]) + tR2GrAccu[0] = coeff * tR2GrAccu[0] + exp_logits + + position: cutlass.Int64 = ( + rank * problem_mnk[1] + pidn * self.vocab_per_split + local_position + ) + mask: cutlass.Boolean = valid_mask and (position == tLabelsrLabels[0]) + tR2GrLogprobs[0] += mask * tTMEM_load_rAcc[idx] + + mma_pipeline.consumer_release(mma_consumer_state) + mma_consumer_state.advance() + + cute.copy(tiled_copy_r2g, tR2GrMax, tR2GgMax, pred=tLabelsCAcc_mask) + cute.copy(tiled_copy_r2g, tR2GrAccu, tR2GgAccu, pred=tLabelsCAcc_mask) + + vocab_left_idx: cutlass.Int64 = rank * problem_mnk[1] + pidn * self.vocab_per_split + vocab_right_idx: cutlass.Int64 = rank * problem_mnk[1] + min( + (pidn + 1) * self.vocab_per_split, problem_mnk[1] + ) + valid: cutlass.Boolean = ( + tLabelsrLabels[0] >= vocab_left_idx and tLabelsrLabels[0] < vocab_right_idx + ) + tLabelsCAcc_mask[0] &= valid - mma_pipeline.consumer_release(mma_consumer_state) - mma_consumer_state.advance() + cute.copy(tiled_copy_r2g, tR2GrLogprobs, tR2GgLogprobs, pred=tLabelsCAcc_mask) - cute.copy(tiled_copy_r2g, tR2GrMax, tR2GgMax, pred=tLabelsCAcc_mask) - cute.copy(tiled_copy_r2g, tR2GrAccu, tR2GgAccu, pred=tLabelsCAcc_mask) + # Dealloc TMEM + self.cta_sync_barrier.arrive_and_wait() + if warp_idx == self.empty_warp_ids[0]: + cute.arch.relinquish_tmem_alloc_permit() + cute.arch.dealloc_tmem(tmem_ptr, self.tmem_alloc_cols, is_two_cta=self.use_2cta_instrs) - vocab_left_idx: cutlass.Int64 = rank * problem_mnk[1] + pidn * self.vocab_per_split - vocab_right_idx: cutlass.Int64 = rank * problem_mnk[1] + min( - (pidn + 1) * self.vocab_per_split, problem_mnk[1] + @staticmethod + def _compute_grid( + problem_mnk: Tuple[int, int, int], + cluster_shape_mn: Tuple[int, int], + cta_tiler: Tuple[int, int, int], + num_splits: int, + ) -> Tuple[int, int, int]: + + cluster_shape = (*cluster_shape_mn, 1) + + grid = cute.round_up( + (cute.ceil_div(problem_mnk[0], cta_tiler[0]), num_splits, 1), cluster_shape ) - valid: cutlass.Boolean = ( - tLabelsrLabels[0] >= vocab_left_idx and tLabelsrLabels[0] < vocab_right_idx + return grid + + @cute.jit + def __call__( + self, + hidden: cute.Tensor, + weight: cute.Tensor, + labels: cute.Tensor, + _logprobs: cute.Tensor, + _max: cute.Tensor, + _accu: cute.Tensor, + ignore_index: cutlass.Int64, + rank: cutlass.Int32, + stream: cuda.CUstream, + ) -> None: + a_dtype: Type[cutlass.Numeric] = hidden.element_type + b_dtype: Type[cutlass.Numeric] = weight.element_type + + if cutlass.const_expr(hidden.element_type != weight.element_type): + raise RuntimeError( + f"data type don't match: {hidden.element_type} v.s. {weight.element_type}" + ) + if cutlass.const_expr(hidden.element_type not in [cutlass.Float16, cutlass.BFloat16]): + raise RuntimeError("hidden can only be FP16 or BF16") + if cutlass.const_expr(hidden.layout.shape[1] != weight.layout.shape[1]): + raise RuntimeError("K dimension doesn't match") + + problem_mnk = (hidden.layout.shape[0], weight.layout.shape[0], hidden.layout.shape[1]) + if cutlass.const_expr((problem_mnk[2] * a_dtype.width // 8) % 16 != 0): + raise RuntimeError(f"K dimension is not 16B aligned: {problem_mnk[2]}") + + num_splits = cute.ceil_div(problem_mnk[1], self.vocab_per_split) + + grid = self._compute_grid( + problem_mnk=problem_mnk, + cluster_shape_mn=self.cluster_shape_mn, + cta_tiler=self.cta_tiler, + num_splits=num_splits, ) - tLabelsCAcc_mask[0] &= valid - - cute.copy(tiled_copy_r2g, tR2GrLogprobs, tR2GgLogprobs, pred=tLabelsCAcc_mask) - - # Dealloc TMEM - self.cta_sync_barrier.arrive_and_wait() - if warp_idx == self.empty_warp_ids[0]: - cute.arch.relinquish_tmem_alloc_permit() - cute.arch.dealloc_tmem(tmem_ptr, self.tmem_alloc_cols, is_two_cta=self.use_2cta_instrs) - - @staticmethod - def _compute_grid( - problem_mnk: Tuple[int, int, int], - cluster_shape_mn: Tuple[int, int], - cta_tiler: Tuple[int, int, int], - num_splits: int, - ) -> Tuple[int, int, int]: - - cluster_shape = (*cluster_shape_mn, 1) - - grid = cute.round_up( - (cute.ceil_div(problem_mnk[0], cta_tiler[0]), num_splits, 1), cluster_shape - ) - return grid - - @cute.jit - def __call__( - self, - hidden: cute.Tensor, - weight: cute.Tensor, - labels: cute.Tensor, - _logprobs: cute.Tensor, - _max: cute.Tensor, - _accu: cute.Tensor, - ignore_index: cutlass.Int64, - rank: cutlass.Int32, - stream: cuda.CUstream, - ) -> None: - a_dtype: Type[cutlass.Numeric] = hidden.element_type - b_dtype: Type[cutlass.Numeric] = weight.element_type - - if cutlass.const_expr(hidden.element_type != weight.element_type): - raise RuntimeError( - f"data type don't match: {hidden.element_type} v.s. {weight.element_type}" + a_major_mode = utils.LayoutEnum.from_tensor(hidden).mma_major_mode() + b_major_mode = utils.LayoutEnum.from_tensor(weight).mma_major_mode() + + tiled_mma = sm100_utils.make_trivial_tiled_mma( + a_dtype, a_major_mode, b_major_mode, self.acc_dtype, self.cta_group, self.mma_tiler[:2] ) - if cutlass.const_expr(hidden.element_type not in [cutlass.Float16, cutlass.BFloat16]): - raise RuntimeError("hidden can only be FP16 or BF16") - if cutlass.const_expr(hidden.layout.shape[1] != weight.layout.shape[1]): - raise RuntimeError("K dimension doesn't match") - - problem_mnk = (hidden.layout.shape[0], weight.layout.shape[0], hidden.layout.shape[1]) - if cutlass.const_expr((problem_mnk[2] * a_dtype.width // 8) % 16 != 0): - raise RuntimeError(f"K dimension is not 16B aligned: {problem_mnk[2]}") - - num_splits = cute.ceil_div(problem_mnk[1], self.vocab_per_split) - - grid = self._compute_grid( - problem_mnk=problem_mnk, - cluster_shape_mn=self.cluster_shape_mn, - cta_tiler=self.cta_tiler, - num_splits=num_splits, - ) - a_major_mode = utils.LayoutEnum.from_tensor(hidden).mma_major_mode() - b_major_mode = utils.LayoutEnum.from_tensor(weight).mma_major_mode() - - tiled_mma = sm100_utils.make_trivial_tiled_mma( - a_dtype, a_major_mode, b_major_mode, self.acc_dtype, self.cta_group, self.mma_tiler[:2] - ) - - self._setup_attributes(tiled_mma, a_dtype, b_dtype) - if cutlass.const_expr((problem_mnk[2] * a_dtype.width // 8) % 128 != 0): - raise RuntimeError(f"K dimension is not 128B aligned: {problem_mnk[2]}") - - self.epi_tile = self.mma_tiler[:2] - - # Swizzle o [(tileM, tileK), loopM, loopK, stage] - a_smem_layout_staged = sm100_utils.make_smem_layout_a( - tiled_mma, self.mma_tiler, a_dtype, self.num_a_stage - ) - # Swizzle o [(tileN, tileK), loopN, loopK, stage] - b_smem_layout_staged = sm100_utils.make_smem_layout_b( - tiled_mma, self.mma_tiler, b_dtype, self.num_b_stage - ) - - # TMA loading - tma_load_op = cpasync.CopyBulkTensorTileG2SOp(self.cta_group) - tma_store_op = cpasync.CopyBulkTensorTileS2GOp() - - # Swizzle o [(tileM, tileK), loopM, loopK] - a_smem_layout = cute.select(a_smem_layout_staged, mode=[0, 1, 2]) - # create tma copy atom for hidden, - # and the cooresponding tma descriptor tensor - tma_atom_a, tma_desc_a = cute.nvgpu.make_tiled_tma_atom_A( - tma_load_op, - hidden, # gmem_tensor - a_smem_layout, # SMEM layout - self.mma_tiler, # MMA tiler - tiled_mma, # TiledMMA - self.cluster_layout_vmnk.shape, # cluster_shape_vmnk - ) - # Swizzle o [(tileN, tileK), loopN, loopK] - b_smem_layout = cute.select(b_smem_layout_staged, mode=[0, 1, 2]) - tma_atom_b, tma_desc_b = cute.nvgpu.make_tiled_tma_atom_B( - tma_load_op, - weight, # gmem_tensor - b_smem_layout, # SMEM layout - self.mma_tiler, # MMA tiler - tiled_mma, # TiledMMA - self.cluster_layout_vmnk.shape, # cluster_shape_vmnk - ) - a_copy_size = cute.size_in_bytes(a_dtype, a_smem_layout) - b_copy_size = cute.size_in_bytes(b_dtype, b_smem_layout) - self.tma_copy_a_bytes = a_copy_size - self.tma_copy_b_bytes = b_copy_size - - assert self.num_a_stage == self.num_b_stage - - @cute.struct - class SharedStorage: - """ - The shared storage for the forward kernel. - """ - # pipeline barriers, 2 = producer + consumer - load_ab_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_a_stage * 2] - mma_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2] - tmem_dealloc_mbar_ptr: cute.struct.MemRange[cutlass.Int64, 1] - # tmem holding buffer - tmem_holding_buf: cutlass.Int32 - # SMEM tensors - sA: cute.struct.Align[ - cute.struct.MemRange[a_dtype, cute.cosize(a_smem_layout_staged)], - self.buffer_align_bytes, - ] - sB: cute.struct.Align[ - cute.struct.MemRange[b_dtype, cute.cosize(b_smem_layout_staged)], - self.buffer_align_bytes, - ] - - self.shared_storage = SharedStorage - - # launch kernel - self.kernel( - tiled_mma, - tma_atom_a, - tma_desc_a, - tma_atom_b, - tma_desc_b, - labels, - _max, - _accu, - _logprobs, - a_smem_layout_staged, - b_smem_layout_staged, - self.cluster_layout_vmnk, - problem_mnk, - ignore_index, - rank, - ).launch( - grid=grid, - block=[self.threads_per_cta, 1, 1], - cluster=self.cluster_shape_mnk, - stream=stream, - ) - return None + self._setup_attributes(tiled_mma, a_dtype, b_dtype) + if cutlass.const_expr((problem_mnk[2] * a_dtype.width // 8) % 128 != 0): + raise RuntimeError(f"K dimension is not 128B aligned: {problem_mnk[2]}") + + self.epi_tile = self.mma_tiler[:2] + + # Swizzle o [(tileM, tileK), loopM, loopK, stage] + a_smem_layout_staged = sm100_utils.make_smem_layout_a( + tiled_mma, self.mma_tiler, a_dtype, self.num_a_stage + ) + # Swizzle o [(tileN, tileK), loopN, loopK, stage] + b_smem_layout_staged = sm100_utils.make_smem_layout_b( + tiled_mma, self.mma_tiler, b_dtype, self.num_b_stage + ) + + # TMA loading + tma_load_op = cpasync.CopyBulkTensorTileG2SOp(self.cta_group) + tma_store_op = cpasync.CopyBulkTensorTileS2GOp() + + # Swizzle o [(tileM, tileK), loopM, loopK] + a_smem_layout = cute.select(a_smem_layout_staged, mode=[0, 1, 2]) + # create tma copy atom for hidden, + # and the cooresponding tma descriptor tensor + tma_atom_a, tma_desc_a = cute.nvgpu.make_tiled_tma_atom_A( + tma_load_op, + hidden, # gmem_tensor + a_smem_layout, # SMEM layout + self.mma_tiler, # MMA tiler + tiled_mma, # TiledMMA + self.cluster_layout_vmnk.shape, # cluster_shape_vmnk + ) + # Swizzle o [(tileN, tileK), loopN, loopK] + b_smem_layout = cute.select(b_smem_layout_staged, mode=[0, 1, 2]) + tma_atom_b, tma_desc_b = cute.nvgpu.make_tiled_tma_atom_B( + tma_load_op, + weight, # gmem_tensor + b_smem_layout, # SMEM layout + self.mma_tiler, # MMA tiler + tiled_mma, # TiledMMA + self.cluster_layout_vmnk.shape, # cluster_shape_vmnk + ) + a_copy_size = cute.size_in_bytes(a_dtype, a_smem_layout) + b_copy_size = cute.size_in_bytes(b_dtype, b_smem_layout) + self.tma_copy_a_bytes = a_copy_size + self.tma_copy_b_bytes = b_copy_size + + assert self.num_a_stage == self.num_b_stage + + @cute.struct + class SharedStorage: + """ + The shared storage for the forward kernel. + """ + + # pipeline barriers, 2 = producer + consumer + load_ab_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_a_stage * 2] + mma_mbar_ptr: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2] + tmem_dealloc_mbar_ptr: cute.struct.MemRange[cutlass.Int64, 1] + # tmem holding buffer + tmem_holding_buf: cutlass.Int32 + # SMEM tensors + sA: cute.struct.Align[ + cute.struct.MemRange[a_dtype, cute.cosize(a_smem_layout_staged)], + self.buffer_align_bytes, + ] + sB: cute.struct.Align[ + cute.struct.MemRange[b_dtype, cute.cosize(b_smem_layout_staged)], + self.buffer_align_bytes, + ] + + self.shared_storage = SharedStorage + + # launch kernel + self.kernel( + tiled_mma, + tma_atom_a, + tma_desc_a, + tma_atom_b, + tma_desc_b, + labels, + _max, + _accu, + _logprobs, + a_smem_layout_staged, + b_smem_layout_staged, + self.cluster_layout_vmnk, + problem_mnk, + ignore_index, + rank, + ).launch( + grid=grid, + block=[self.threads_per_cta, 1, 1], + cluster=self.cluster_shape_mnk, + stream=stream, + ) + return None +except ImportError: + pass From 48c52289bf229f1ec6dce11e621d6f1851c55f4d Mon Sep 17 00:00:00 2001 From: Jianbin Chang Date: Tue, 2 Dec 2025 17:35:58 +0800 Subject: [PATCH 17/17] Update Dev Branch & Fix CI (#19) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * [DEV] pull main Nov 25 (#2395) Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Signed-off-by: oliver könig Signed-off-by: Ananth Subramaniam Signed-off-by: dimapihtar Signed-off-by: Youngeun Kwon Signed-off-by: Youngeun Signed-off-by: Maanu Grover Signed-off-by: ykarnati Signed-off-by: Deepak Narayanan Signed-off-by: GitHub Actions Signed-off-by: Charlie Truong Signed-off-by: Zhongbo Zhu Signed-off-by: Xiaowei Ren Signed-off-by: Xin Yao Signed-off-by: Keshav Santhanam Signed-off-by: Pablo Garay Signed-off-by: Asha Anoosheh Signed-off-by: Chen Cui Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: Yashaswi Karnati <144376261+yashaswikarnati@users.noreply.github.com> Co-authored-by: Jared Casper <155158+jaredcasper@users.noreply.github.com> Co-authored-by: Antoni-Joan Solergibert Co-authored-by: Jianbin Chang Co-authored-by: oliver könig Co-authored-by: Ananth Subramaniam Co-authored-by: Teodor-Dumitru Ene <34819528+tdene@users.noreply.github.com> Co-authored-by: Siddharth Singh <136645615+sidsingh-nvidia@users.noreply.github.com> Co-authored-by: Mcore Bot Co-authored-by: Dmytro Pykhtar <37850217+dimapihtar@users.noreply.github.com> Co-authored-by: Youngeun Kwon Co-authored-by: Lawrence McAfee <85179052+lmcafee-nvidia@users.noreply.github.com> Co-authored-by: Maanu Grover <109391026+maanug-nv@users.noreply.github.com> Co-authored-by: Lawrence McAfee Co-authored-by: AJ Schmidt Co-authored-by: Deepak Narayanan <2724038+deepakn94@users.noreply.github.com> Co-authored-by: helen ngo Co-authored-by: GitHub Actions Co-authored-by: Aaron Gokaslan Co-authored-by: Robert Kirby Co-authored-by: Teodor-Dumitru Ene Co-authored-by: yeyu-nvidia Co-authored-by: Abhinav Khattar Co-authored-by: Roger Waleffe Co-authored-by: Charlie Truong Co-authored-by: Tong Liu Co-authored-by: Zhongbo Zhu <42691305+zhongbozhu@users.noreply.github.com> Co-authored-by: Xiaowei Ren Co-authored-by: Xin Yao Co-authored-by: Teodor-Dumitru Ene Co-authored-by: Zijie Yan Co-authored-by: root Co-authored-by: Keshav Santhanam Co-authored-by: Pablo Garay Co-authored-by: Asha Anoosheh Co-authored-by: Kan Zhu Co-authored-by: Robert Kirby Co-authored-by: Jorge Albericio Co-authored-by: Jon Barker <19699370+jon-barker@users.noreply.github.com> Co-authored-by: Chen Cui Co-authored-by: Pablo Garay Co-authored-by: Tong Liu * adding action for checking whether PR author is nvidia employee or not for selecting ephemeral ci hosts (#2402) Signed-off-by: oliver könig * fix: exit failure when PR author is external contributor removed (#2410) * fix: adding k8s taints for ephermeral jobs (#2420) * ci: Enable functional tests (#2419) Signed-off-by: oliver könig * Reapply "build: Upgrade deps (NVIDIA#2289)" (#2408) Signed-off-by: oliver könig * fix: use a script to do node tainting in the cicd workflow (#2421) * Revert "[DEV] pull main Nov 25 (#2395)" This reverts commit 56682f80b0db4492afeee013a07187eadfa9dc8f. Signed-off-by: oliver könig * [Dev] Support packed seq in MTP (#2043) Signed-off-by: Li Tao Signed-off-by: lit * Fix runaway Etpt in straggler detector by resetting FLOPs accumulator (#2128) Signed-off-by: Santosh Bhavani Co-authored-by: Li Ruixiao * [Dev] feat(MoE): Refactor cuda_graph_scope - part2 (#2353) Signed-off-by: Robin Zhang * [dev] DeepSeek V3.2 support (#2154) Signed-off-by: kunlunl * Revert "[Dev] feat(MoE): Refactor cuda_graph_scope - part2 (#2353)" This reverts commit 92c8482e6dcd11c3666c61bb8d1f7e8d0730ed13. * Add logs for missing CUDA and Cute. * autoformat --------- Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Signed-off-by: oliver könig Signed-off-by: Ananth Subramaniam Signed-off-by: dimapihtar Signed-off-by: Youngeun Kwon Signed-off-by: Youngeun Signed-off-by: Maanu Grover Signed-off-by: ykarnati Signed-off-by: Deepak Narayanan Signed-off-by: GitHub Actions Signed-off-by: Charlie Truong Signed-off-by: Zhongbo Zhu Signed-off-by: Xiaowei Ren Signed-off-by: Xin Yao Signed-off-by: Keshav Santhanam Signed-off-by: Pablo Garay Signed-off-by: Asha Anoosheh Signed-off-by: Chen Cui Signed-off-by: Li Tao Signed-off-by: lit Signed-off-by: Santosh Bhavani Signed-off-by: Robin Zhang Signed-off-by: kunlunl Co-authored-by: Deyu Fu Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: Yashaswi Karnati <144376261+yashaswikarnati@users.noreply.github.com> Co-authored-by: Jared Casper <155158+jaredcasper@users.noreply.github.com> Co-authored-by: Antoni-Joan Solergibert Co-authored-by: oliver könig Co-authored-by: Ananth Subramaniam Co-authored-by: Teodor-Dumitru Ene <34819528+tdene@users.noreply.github.com> Co-authored-by: Siddharth Singh <136645615+sidsingh-nvidia@users.noreply.github.com> Co-authored-by: Mcore Bot Co-authored-by: Dmytro Pykhtar <37850217+dimapihtar@users.noreply.github.com> Co-authored-by: Youngeun Kwon Co-authored-by: Lawrence McAfee <85179052+lmcafee-nvidia@users.noreply.github.com> Co-authored-by: Maanu Grover <109391026+maanug-nv@users.noreply.github.com> Co-authored-by: Lawrence McAfee Co-authored-by: AJ Schmidt Co-authored-by: Deepak Narayanan <2724038+deepakn94@users.noreply.github.com> Co-authored-by: helen ngo Co-authored-by: GitHub Actions Co-authored-by: Aaron Gokaslan Co-authored-by: Robert Kirby Co-authored-by: Teodor-Dumitru Ene Co-authored-by: yeyu-nvidia Co-authored-by: Abhinav Khattar Co-authored-by: Roger Waleffe Co-authored-by: Charlie Truong Co-authored-by: Tong Liu Co-authored-by: Zhongbo Zhu <42691305+zhongbozhu@users.noreply.github.com> Co-authored-by: Xiaowei Ren Co-authored-by: Xin Yao Co-authored-by: Teodor-Dumitru Ene Co-authored-by: Zijie Yan Co-authored-by: root Co-authored-by: Keshav Santhanam Co-authored-by: Pablo Garay Co-authored-by: Asha Anoosheh Co-authored-by: Kan Zhu Co-authored-by: Robert Kirby Co-authored-by: Jorge Albericio Co-authored-by: Jon Barker <19699370+jon-barker@users.noreply.github.com> Co-authored-by: Chen Cui Co-authored-by: Pablo Garay Co-authored-by: Tong Liu Co-authored-by: Michael Wojcikiewicz Co-authored-by: Li Tao Co-authored-by: Santosh Bhavani Co-authored-by: Li Ruixiao Co-authored-by: Robin Zhang Co-authored-by: Kunlun Li <94586211+kunlunl@users.noreply.github.com> --- .github/actions/action.yml | 57 +- .../check-nvidia-sso-membership/action.yml | 139 + .github/workflows/cicd-main.yml | 113 +- .gitlab/scripts/build.sh | 5 +- docker/Dockerfile.ci.dev | 1 + gpt_builders.py | 7 +- .../core/dist_checkpointing/exchange_utils.py | 2 +- megatron/core/dist_checkpointing/mapping.py | 2 +- .../core/dist_checkpointing/validation.py | 2 +- .../fusions/fused_linear_cross_entropy.py | 14 +- .../blackwell/bwd_partial_dlogits.py | 69 +- .../linear_cross_entropy/blackwell/entry.py | 126 +- .../blackwell/fwd_mainloop.py | 89 +- ...rimental_attention_variant_module_specs.py | 132 + megatron/core/models/gpt/gpt_layer_specs.py | 52 +- megatron/core/models/gpt/gpt_model.py | 14 +- .../gpt/linear_attention_module_specs.py | 27 - megatron/core/transformer/attention.py | 1 + .../experimental_attention_variant/dsa.py | 822 +++++ .../transformer/multi_latent_attention.py | 87 +- .../transformer/multi_token_prediction.py | 118 +- .../core/transformer/transformer_config.py | 42 +- megatron/training/arguments.py | 35 +- megatron/training/training.py | 25 +- pyproject.toml | 35 +- .../download_unit_tests_dataset.py | 205 +- ...pt-dynamic-inference-with-coordinator.yaml | 4 +- .../recipes/gpt-dynamic-inference.yaml | 8 +- .../recipes/gpt-static-inference.yaml | 10 +- tests/test_utils/recipes/gpt.yaml | 122 +- .../recipes/mamba-static-inference.yaml | 6 +- tests/test_utils/recipes/mamba.yaml | 10 +- .../recipes/moe-dynamic-inference.yaml | 6 +- .../recipes/moe-static-inference.yaml | 8 +- tests/test_utils/recipes/moe.yaml | 24 +- .../test_utils/recipes/multimodal-llava.yaml | 6 +- tests/unit_tests/conftest.py | 9 +- .../test_fused_linear_cross_entropy.py | 26 +- tests/unit_tests/ssm/test_gated_delta_net.py | 4 +- .../transformer/test_attention_variant_dsa.py | 1271 ++++++++ .../test_multi_token_prediction.py | 208 +- uv.lock | 2832 ++++++++--------- 42 files changed, 4668 insertions(+), 2107 deletions(-) create mode 100644 .github/actions/check-nvidia-sso-membership/action.yml create mode 100644 megatron/core/models/gpt/experimental_attention_variant_module_specs.py delete mode 100644 megatron/core/models/gpt/linear_attention_module_specs.py create mode 100644 megatron/core/transformer/experimental_attention_variant/dsa.py create mode 100644 tests/unit_tests/transformer/test_attention_variant_dsa.py diff --git a/.github/actions/action.yml b/.github/actions/action.yml index 8c6ca3a6865..5c35385b036 100644 --- a/.github/actions/action.yml +++ b/.github/actions/action.yml @@ -11,28 +11,28 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -name: "Test Template" -description: "Template for running NeMo tests in a containerized environment" +name: 'Test Template' +description: 'Template for running NeMo tests in a containerized environment' inputs: container-image: - description: "Container image to use for test" + description: 'Container image to use for test' required: true timeout: - description: "Max runtime of test in minutes" + description: 'Max runtime of test in minutes' required: false - default: "30" + default: '30' script: - description: "Test script to execute" + description: 'Test script to execute' required: true is-optional: - description: "Pass this job on failure." + description: 'Pass this job on failure.' required: false - default: "false" + default: 'false' is_unit_test: - description: "Upload coverage as unit test" + description: 'Upload coverage as unit test' required: false - default: "false" + default: 'false' tag: description: Latest or legacy test suite required: true @@ -43,11 +43,11 @@ inputs: description: Model to launch required: false PAT: - description: "GitHub Personal Access Token" + description: 'GitHub Personal Access Token' required: true runs: - using: "composite" + using: 'composite' steps: - name: Checkout repository uses: actions/checkout@v2 @@ -114,6 +114,16 @@ runs: HAS_RUN_TESTS_LABEL=$(gh pr view $PR_NUMBER --json labels | jq '[.labels[].name] | any(. == "Run tests")') || echo "false" echo "main=$HAS_RUN_TESTS_LABEL" | tee -a $GITHUB_OUTPUT + - name: Has Run functional tests label + shell: bash -x -e -u -o pipefail {0} + id: has-run-functional-tests-label + env: + GH_TOKEN: ${{ github.token }} + run: | + PR_NUMBER=${{ fromJSON(steps.get-pr-info.outputs.pr-info || '{}').number }} + HAS_RUN_FUNCTIONAL_TESTS_LABEL=$(gh pr view $PR_NUMBER --json labels | jq '[.labels[].name] | any(. == "Run functional tests")') || echo "false" + echo "main=$HAS_RUN_FUNCTIONAL_TESTS_LABEL" | tee -a $GITHUB_OUTPUT + - name: Create run-script (e2e test) shell: bash -x -e -u -o pipefail {0} if: inputs.is_unit_test == 'false' @@ -126,16 +136,19 @@ runs: set -euxo pipefail if [ "${{ steps.has-run-tests-label.outputs.main }}" == "true" ]; then - ARGS=( - --scope mr-github - --enable-lightweight-mode - ) - else - ARGS=( - --scope mr-slim - --enable-lightweight-mode - ) - fi + ARGS=( + --scope mr-github + --enable-lightweight-mode + ) + elif [ "${{ steps.has-run-functional-tests-label.outputs.main }}" == "true" ]; then + ARGS=( + --scope mr-github + ) + else + ARGS=( + --scope mr-github-slim + ) + fi export PYTHONPATH=$(pwd) export NEMORUN_HOME=$(pwd) diff --git a/.github/actions/check-nvidia-sso-membership/action.yml b/.github/actions/check-nvidia-sso-membership/action.yml new file mode 100644 index 00000000000..71926c4547d --- /dev/null +++ b/.github/actions/check-nvidia-sso-membership/action.yml @@ -0,0 +1,139 @@ +name: 'Check NVIDIA SSO Membership' +description: 'Check if a GitHub username exists in the NVIDIA SSO users list from github-audits' +author: 'NVIDIA' + +inputs: + username: + description: 'GitHub username to check' + required: true + github_audits_repo: + description: 'Repository containing SSO users file' + required: false + default: 'NVIDIA-GitHub-Management/github-audits' + github_audits_version: + description: 'Release version tag' + required: false + default: 'v0.1.0' + sso_users_filename: + description: 'Filename of SSO users JSON' + required: false + default: 'users_sso.json' + github_token: + description: 'GitHub token with access to github-audits repo' + required: true + +outputs: + is_member: + description: 'Boolean - true if user is in NVIDIA SSO list, false otherwise' + value: ${{ steps.check-membership.outputs.is_member }} + is_org_member: + description: 'Boolean - true if user has NVIDIA or NVIDIA-NeMo in org_roles' + value: ${{ steps.check-membership.outputs.is_org_member }} + user_orgs: + description: 'Comma-separated list of orgs user is member of' + value: ${{ steps.check-membership.outputs.user_orgs }} + sso_file_available: + description: 'Boolean - true if SSO file was successfully downloaded' + value: ${{ steps.download-sso.outputs.sso_file_available }} + user_count: + description: 'Number of users in the SSO file (0 if download failed)' + value: ${{ steps.download-sso.outputs.user_count }} + +runs: + using: 'composite' + steps: + - name: Download NVIDIA SSO users from github-audits + id: download-sso + shell: bash + env: + GH_TOKEN: ${{ inputs.github_token }} + run: | + echo "Downloading ${{ inputs.sso_users_filename }} from ${{ inputs.github_audits_repo }} ${{ inputs.github_audits_version }} release..." + + # Download the release asset using gh CLI + gh release download ${{ inputs.github_audits_version }} \ + --repo ${{ inputs.github_audits_repo }} \ + --pattern ${{ inputs.sso_users_filename }} \ + --clobber 2>&1 || { + echo "ERROR: Failed to download ${{ inputs.sso_users_filename }} from github-audits release" + echo "sso_file_available=false" >> $GITHUB_OUTPUT + echo "user_count=0" >> $GITHUB_OUTPUT + exit 0 + } + + # Verify file was downloaded and is valid JSON + if [ ! -f ${{ inputs.sso_users_filename }} ]; then + echo "ERROR: ${{ inputs.sso_users_filename }} file not found after download" + echo "sso_file_available=false" >> $GITHUB_OUTPUT + echo "user_count=0" >> $GITHUB_OUTPUT + exit 0 + fi + + # Validate JSON structure + if ! jq -e 'type == "object"' ${{ inputs.sso_users_filename }} > /dev/null 2>&1; then + echo "ERROR: ${{ inputs.sso_users_filename }} is not a valid JSON object" + echo "sso_file_available=false" >> $GITHUB_OUTPUT + echo "user_count=0" >> $GITHUB_OUTPUT + exit 0 + fi + + USER_COUNT=$(jq 'length' ${{ inputs.sso_users_filename }}) + echo "Successfully downloaded ${{ inputs.sso_users_filename }} with $USER_COUNT NVIDIA SSO users" + echo "sso_file_available=true" >> $GITHUB_OUTPUT + echo "user_count=$USER_COUNT" >> $GITHUB_OUTPUT + + - name: Check if user is in SSO list + id: check-membership + shell: bash + run: | + USERNAME="${{ inputs.username }}" + SSO_FILE="${{ inputs.sso_users_filename }}" + + echo "Checking if $USERNAME is in NVIDIA SSO users list..." + + # Check if SSO file is available + if [ "${{ steps.download-sso.outputs.sso_file_available }}" != "true" ] || [ ! -f "$SSO_FILE" ]; then + echo "ERROR: $SSO_FILE not available - cannot check membership" + echo "is_member=false" >> $GITHUB_OUTPUT + echo "is_org_member=false" >> $GITHUB_OUTPUT + echo "user_orgs=" >> $GITHUB_OUTPUT + exit 0 + fi + + # Check if username exists as a key in the JSON object + if jq -e --arg user "$USERNAME" 'has($user)' "$SSO_FILE" > /dev/null 2>&1; then + echo "$USERNAME found in NVIDIA SSO users" + echo "is_member=true" >> $GITHUB_OUTPUT + + # Extract and check org membership + IS_ORG_MEMBER=$(jq -r --arg user "$USERNAME" ' + .[$user].org_roles // [] | + map(select(test("^(NVIDIA|NVIDIA-NeMo):Member$"))) | + length > 0 + ' "$SSO_FILE") + + USER_ORGS=$(jq -r --arg user "$USERNAME" ' + .[$user].org_roles // [] | + map(split(":")[0]) | + unique | + join(",") + ' "$SSO_FILE") + + echo "is_org_member=$IS_ORG_MEMBER" >> $GITHUB_OUTPUT + echo "user_orgs=$USER_ORGS" >> $GITHUB_OUTPUT + + if [ "$IS_ORG_MEMBER" == "true" ]; then + echo "$USERNAME is a member of NVIDIA or NVIDIA-NeMo org" + else + echo "$USERNAME has @nvidia.com email but is not in NVIDIA or NVIDIA-NeMo org (orgs: $USER_ORGS)" + fi + else + echo "$USERNAME NOT found in NVIDIA SSO users" + echo "is_member=false" >> $GITHUB_OUTPUT + echo "is_org_member=false" >> $GITHUB_OUTPUT + echo "user_orgs=" >> $GITHUB_OUTPUT + fi + +branding: + icon: 'shield' + color: 'green' diff --git a/.github/workflows/cicd-main.yml b/.github/workflows/cicd-main.yml index 4a4a1a2cad1..a5a7a82287e 100644 --- a/.github/workflows/cicd-main.yml +++ b/.github/workflows/cicd-main.yml @@ -20,8 +20,8 @@ on: branches: - dev - main - - "pull-request/[0-9]+" - - "deploy-release/*" + - 'pull-request/[0-9]+' + - 'deploy-release/*' merge_group: types: [checks_requested] workflow_dispatch: @@ -44,6 +44,8 @@ jobs: if: github.repository == 'NVIDIA/Megatron-LM' outputs: is_external_contributor: ${{ github.event.pull_request.user.type == 'User' }} + is_maintainer: ${{ steps.check-membership.outputs.is_maintainer }} + selected_runner: ${{ steps.check-membership.outputs.is_maintainer == 'true' && 'nvidia-ci-aws-gpu-x8' || 'nvidia-ci-aws-gpu-x8-ephemeral' }} permissions: issues: write pull-requests: write @@ -61,7 +63,14 @@ jobs: if: startsWith(github.ref, 'refs/heads/pull-request/') uses: nv-gha-runners/get-pr-info@main - - name: Check membership + - name: Check NVIDIA SSO membership + id: check-sso + uses: ./.github/actions/check-nvidia-sso-membership + with: + username: ${{ fromJSON(steps.get-pr-info.outputs.pr-info || '{}').user.login }} + github_token: ${{ secrets.NVIDIA_MANAGEMENT_ORG_PAT }} + + - name: Set maintainer status id: check-membership env: IS_MAIN_BRANCH: ${{ github.ref == 'refs/heads/main' }} @@ -69,38 +78,15 @@ jobs: IS_MERGE_GROUP: ${{ github.event_name == 'merge_group' }} SCHEDULED_JOB: ${{ github.event_name == 'schedule' }} run: | - PR_AUTHOR=${{ fromJSON(steps.get-pr-info.outputs.pr-info || '{}').user.login }} - + # Skip SSO check for scheduled jobs, main branch, dev branch, or merge groups if [ "${{ env.SCHEDULED_JOB }}" == "true" ] || [ "${IS_MAIN_BRANCH}" == "true" ] || [ "${IS_DEV_BRANCH}" == "true" ] || [ "${IS_MERGE_GROUP}" == "true" ]; then echo "is_maintainer=true" | tee -a $GITHUB_OUTPUT exit 0 fi - echo "Checking if $PR_AUTHOR is a repo collaborator..." - API_URL="https://api.github.com/repos/$REPO/collaborators/$PR_AUTHOR" - REPO_MEMBERSHIP_RESPONSE=$(curl -s -o /dev/null -w "%{http_code}" -L \ - -H "Accept: application/vnd.github+json" \ - -H "Authorization: Bearer $GITHUB_TOKEN" \ - -H "X-GitHub-Api-Version: 2022-11-28" \ - $API_URL) - - echo "Checking if $PR_AUTHOR is an org collaborator to NVIDIA-NeMo..." - API_URL="https://api.github.com/orgs/NVIDIA-NeMo/members/$PR_AUTHOR" - ORG_NVIDIA_NEMO_MEMBERSHIP_RESPONSE=$(curl -s -o /dev/null -w "%{http_code}" -L \ - -H "Accept: application/vnd.github+json" \ - -H "Authorization: Bearer $GITHUB_TOKEN" \ - -H "X-GitHub-Api-Version: 2022-11-28" \ - $API_URL) - - echo "Checking if $PR_AUTHOR is an org collaborator to NVIDIA..." - API_URL="https://api.github.com/orgs/NVIDIA/members/$PR_AUTHOR" - ORG_NVIDIA_MEMBERSHIP_RESPONSE=$(curl -s -o /dev/null -w "%{http_code}" -L \ - -H "Accept: application/vnd.github+json" \ - -H "Authorization: Bearer $GITHUB_TOKEN" \ - -H "X-GitHub-Api-Version: 2022-11-28" \ - $API_URL) - - if [ "$REPO_MEMBERSHIP_RESPONSE" -eq 204 ] || [ "$ORG_NVIDIA_NEMO_MEMBERSHIP_RESPONSE" -eq 204 ] || [ "$ORG_NVIDIA_MEMBERSHIP_RESPONSE" -eq 204 ]; then + # Use SSO membership check result + IS_MEMBER="${{ steps.check-sso.outputs.is_member }}" + if [ "$IS_MEMBER" == "true" ]; then echo "is_maintainer=true" | tee -a $GITHUB_OUTPUT else echo "is_maintainer=false" | tee -a $GITHUB_OUTPUT @@ -113,7 +99,7 @@ jobs: with: issue-number: ${{ fromJSON(steps.get-pr-info.outputs.pr-info || '{}').number }} repository: ${{ github.repository }} - body-includes: "" + body-includes: '' - name: Delete comment uses: actions/github-script@v7 @@ -142,14 +128,6 @@ jobs: Thank you for your understanding. - - name: exit - run: | - if [ "${{ steps.check-membership.outputs.is_maintainer }}" == "true" ]; then - exit 0 - else - exit 1 - fi - pre-flight: needs: [is-not-external-contributor] if: github.repository == 'NVIDIA/Megatron-LM' @@ -213,9 +191,8 @@ jobs: echo "is_merge_group: ${{ needs.pre-flight.outputs.is_merge_group }}" cicd-container-build: - needs: [pre-flight, cicd-wait-in-queue] - runs-on: nvidia-ci-aws-gpu-x8 - environment: nemo-ci + needs: [is-not-external-contributor, pre-flight, cicd-wait-in-queue] + runs-on: ${{ needs.is-not-external-contributor.outputs.selected_runner }} if: | ( success() @@ -225,6 +202,11 @@ jobs: && needs.pre-flight.outputs.is_merge_group == 'false' && !cancelled() steps: + - name: Taint node for job isolation + if: contains(needs.is-not-external-contributor.outputs.selected_runner, 'ephemeral') + shell: bash + run: taint-node.sh + - name: Checkout uses: actions/checkout@v4 @@ -255,11 +237,9 @@ jobs: - name: Download test data shell: bash - env: - GH_TOKEN: ${{ secrets.PAT }} run: | echo "::group::Download test data" - pip install --no-cache-dir pygithub click + pip install --no-cache-dir click requests python tests/test_utils/python_scripts/download_unit_tests_dataset.py --assets-dir ./assets echo "::endgroup::" @@ -364,13 +344,13 @@ jobs: matrix: include: ${{ fromJson(needs.cicd-parse-unit-tests.outputs.unit-tests) }} needs: + - is-not-external-contributor - pre-flight - cicd-wait-in-queue - cicd-container-build - cicd-parse-unit-tests - runs-on: nvidia-ci-aws-gpu-x8 - name: "${{ matrix.bucket }} - latest" - environment: nemo-ci + runs-on: ${{ needs.is-not-external-contributor.outputs.selected_runner }} + name: '${{ matrix.bucket }} - latest' if: | ( success() @@ -384,6 +364,11 @@ jobs: PIP_NO_PYTHON_VERSION_WARNING: 1 PIP_ROOT_USER_ACTION: ignore steps: + - name: Taint node for job isolation + if: contains(needs.is-not-external-contributor.outputs.selected_runner, 'ephemeral') + shell: bash + run: taint-node.sh + - name: Checkout uses: actions/checkout@v4 - name: main @@ -392,7 +377,7 @@ jobs: test_case: ${{ matrix.bucket }} tag: latest timeout: ${{ matrix.timeout || 30 }} - is_unit_test: "true" + is_unit_test: 'true' PAT: ${{ secrets.PAT }} container-image: ${{ env.container-registry }}/megatron-lm:${{ github.sha }} @@ -432,10 +417,20 @@ jobs: HAS_RUN_TESTS_LABEL=$(gh pr view $PR_NUMBER --json labels | jq '[.labels[].name] | any(. == "Run tests")') || echo "false" echo "main=$HAS_RUN_TESTS_LABEL" | tee -a $GITHUB_OUTPUT + - name: Has Run functional tests label + id: has-run-functional-tests-label + env: + GH_TOKEN: ${{ secrets.PAT }} + run: | + PR_NUMBER=${{ fromJSON(steps.get-pr-info.outputs.pr-info || '{}').number }} + HAS_RUN_FUNCTIONAL_TESTS_LABEL=$(gh pr view $PR_NUMBER --json labels | jq '[.labels[].name] | any(. == "Run functional tests")') || echo "false" + echo "main=$HAS_RUN_FUNCTIONAL_TESTS_LABEL" | tee -a $GITHUB_OUTPUT + - name: Parse functional tests id: main env: HAS_RUN_TESTS_LABEL: ${{ steps.has-run-tests-label.outputs.main }} + HAS_RUN_FUNCTIONAL_TESTS_LABEL: ${{ steps.has-run-functional-tests-label.outputs.main }} run: | export PYTHONPATH=$(pwd) @@ -444,10 +439,13 @@ jobs: --scope mr-github --enable-lightweight-mode ) + elif [ "$HAS_RUN_FUNCTIONAL_TESTS_LABEL" == "true" ]; then + ARGS=( + --scope mr-github + ) else ARGS=( - --scope mr-slim - --enable-lightweight-mode + --scope mr-github-slim ) fi @@ -478,13 +476,13 @@ jobs: matrix: include: ${{ fromJson(needs.cicd-parse-integration-tests.outputs.integration-tests) }} needs: + - is-not-external-contributor - pre-flight - cicd-wait-in-queue - cicd-parse-integration-tests - cicd-unit-tests-latest - runs-on: nvidia-ci-aws-gpu-x8 - name: "${{ matrix.model }}/${{ matrix.test_case }} - latest" - environment: nemo-ci + runs-on: ${{ needs.is-not-external-contributor.outputs.selected_runner }} + name: '${{ matrix.model }}/${{ matrix.test_case }} - latest' env: PIP_DISABLE_PIP_VERSION_CHECK: 1 PIP_NO_PYTHON_VERSION_WARNING: 1 @@ -498,6 +496,11 @@ jobs: && needs.pre-flight.outputs.is_merge_group == 'false' && !cancelled() steps: + - name: Taint node for job isolation + if: contains(needs.is-not-external-contributor.outputs.selected_runner, 'ephemeral') + shell: bash + run: taint-node.sh + - name: Checkout uses: actions/checkout@v4 - name: main @@ -507,7 +510,7 @@ jobs: model: ${{ matrix.model }} tag: latest timeout: ${{ matrix.timeout || 30 }} - is_unit_test: "false" + is_unit_test: 'false' PAT: ${{ secrets.PAT }} container-image: ${{ env.container-registry }}/megatron-lm:${{ github.sha }} diff --git a/.gitlab/scripts/build.sh b/.gitlab/scripts/build.sh index 960af104628..e64434e834d 100644 --- a/.gitlab/scripts/build.sh +++ b/.gitlab/scripts/build.sh @@ -7,9 +7,9 @@ eval "IMAGE=\$$IMAGE" # Start a named container in detached mode docker run -d --name download_test_data -w /workdir/ python:3.12-slim bash -c 'sleep infinity' docker cp tests/. download_test_data:/workdir/tests -docker exec -e GH_TOKEN=$GH_TOKEN download_test_data bash -c ' +docker exec download_test_data bash -c ' ls -al /workdir/ - pip install --no-cache-dir pygithub click + pip install --no-cache-dir click requests python tests/test_utils/python_scripts/download_unit_tests_dataset.py --assets-dir ./assets ' docker cp download_test_data:/workdir/assets ./ @@ -50,6 +50,7 @@ DOCKER_BUILDKIT=1 docker build \ --builder=container \ --build-arg JET_API_VERSION=$JET_API_VERSION \ --cache-from type=registry,ref=${IMAGE}-buildcache:${CI_MERGE_REQUEST_IID} \ + --cache-from type=registry,ref=${IMAGE}-buildcache:dev \ --cache-from type=registry,ref=${IMAGE}-buildcache:main \ --build-arg FROM_IMAGE_NAME=$BASE_IMAGE \ --push \ diff --git a/docker/Dockerfile.ci.dev b/docker/Dockerfile.ci.dev index 6596fc01aaf..482c6af460c 100644 --- a/docker/Dockerfile.ci.dev +++ b/docker/Dockerfile.ci.dev @@ -36,6 +36,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --no-install-package torch \ --no-install-package torchvision \ --no-install-package triton \ + --no-install-package transformer-engine-cu12 \ --no-install-package nvidia-cublas-cu12 \ --no-install-package nvidia-cuda-cupti-cu12 \ --no-install-package nvidia-cuda-nvrtc-cu12 \ diff --git a/gpt_builders.py b/gpt_builders.py index 9fa1aff72c7..61d159b9967 100644 --- a/gpt_builders.py +++ b/gpt_builders.py @@ -42,7 +42,8 @@ def gpt_builder(args, pre_process, post_process, vp_stage=None, config=None): else: use_te = args.transformer_impl == "transformer_engine" - if args.num_experts or (args.linear_attention_type is not None): + linear_attention_variants = ["gated_delta_net"] + if args.num_experts or args.experimental_attention_variant in linear_attention_variants: # Define the decoder block spec transformer_layer_spec = get_gpt_decoder_block_spec( config, @@ -114,7 +115,7 @@ def _get_transformer_layer_spec(use_te, config): args.moe_grouped_gemm, args.qk_layernorm, args.multi_latent_attention, - args.linear_attention_type, + args.experimental_attention_variant, moe_use_legacy_grouped_gemm=args.moe_use_legacy_grouped_gemm, qk_l2_norm=args.qk_l2_norm, use_kitchen=config.use_kitchen, @@ -126,7 +127,7 @@ def _get_transformer_layer_spec(use_te, config): args.moe_grouped_gemm, args.qk_layernorm, args.multi_latent_attention, - args.linear_attention_type, + args.experimental_attention_variant, moe_use_legacy_grouped_gemm=args.moe_use_legacy_grouped_gemm, normalization=args.normalization, use_kitchen=config.use_kitchen, diff --git a/megatron/core/dist_checkpointing/exchange_utils.py b/megatron/core/dist_checkpointing/exchange_utils.py index def79fb778e..2f791449057 100644 --- a/megatron/core/dist_checkpointing/exchange_utils.py +++ b/megatron/core/dist_checkpointing/exchange_utils.py @@ -63,7 +63,7 @@ class ShardDistribution(NamedTuple): def _shard_size(sh_ten: ShardedTensor): """Returns size in bytes of a given sharded tensor.""" if sh_ten.flattened_range is None: - numel = np.product(sh_ten.local_shape) + numel = np.prod(sh_ten.local_shape) else: numel = sh_ten.flattened_range.stop - sh_ten.flattened_range.start return numel * torch._utils._element_size(sh_ten.dtype) diff --git a/megatron/core/dist_checkpointing/mapping.py b/megatron/core/dist_checkpointing/mapping.py index d38ea57eee0..45a105666ab 100644 --- a/megatron/core/dist_checkpointing/mapping.py +++ b/megatron/core/dist_checkpointing/mapping.py @@ -216,7 +216,7 @@ def local_coordinates(self) -> Tuple[np.ndarray, ...]: ) # TODO: np.unravel_index? - mask = np.zeros(np.product(self.local_shape), dtype=bool) + mask = np.zeros(np.prod(self.local_shape), dtype=bool) mask[self.flattened_range] = True return np.nonzero(mask.reshape(self.local_shape)) diff --git a/megatron/core/dist_checkpointing/validation.py b/megatron/core/dist_checkpointing/validation.py index 96945055319..9bcb59bdbf4 100644 --- a/megatron/core/dist_checkpointing/validation.py +++ b/megatron/core/dist_checkpointing/validation.py @@ -519,7 +519,7 @@ def _validate_sharding_for_key_flattened(tensors_by_shard): all_slices.append((sharding.flattened_range.start, sharding.flattened_range.stop)) starts, stops = map(np.asarray, zip(*sorted(all_slices))) - expected_size = np.product(local_shape) + expected_size = np.prod(local_shape) if starts[0] != 0 or stops[-1] != expected_size or not np.all(starts[1:] == stops[:-1]): raise CheckpointingException( f"Flattened ranges dont cover the whole shard {tensors_by_shard[0]} of size {expected_size}. Ranges: {(starts, stops)}" diff --git a/megatron/core/fusions/fused_linear_cross_entropy.py b/megatron/core/fusions/fused_linear_cross_entropy.py index 3bb3b5c14f1..b533fef7aa3 100644 --- a/megatron/core/fusions/fused_linear_cross_entropy.py +++ b/megatron/core/fusions/fused_linear_cross_entropy.py @@ -159,10 +159,16 @@ def forward( ``` """ with torch.cuda.nvtx.range("LinearCrossEntropy-forward"): - logprobs, _maximum, _acc, _num_valid_tokens, tp_rank, tp_world_size, global_hidden = ( - _get_platform().forward_func( - hidden, weight, labels, tp_group, reduction, ignore_index, sequence_parallel - ) + ( + logprobs, + _maximum, + _acc, + _num_valid_tokens, + tp_rank, + tp_world_size, + global_hidden, + ) = _get_platform().forward_func( + hidden, weight, labels, tp_group, reduction, ignore_index, sequence_parallel ) ctx.save_for_backward(global_hidden, weight, labels, _maximum, _acc, _num_valid_tokens) ctx.tp_group = tp_group diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py b/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py index 17ad627322e..3178e8c6909 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/bwd_partial_dlogits.py @@ -1,8 +1,9 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. -try: - from typing import Optional, Tuple, Type +import logging +from typing import Optional, Tuple, Type +try: import cuda.bindings.driver as cuda # type: ignore import cutlass import cutlass.cute as cute @@ -13,7 +14,6 @@ SM100_TMEM_CAPACITY_COLUMNS: int = 512 - def make_thread_cooperative_group(size: int, alignment: Optional[int] = None): """ Create a thread cooperative group. @@ -22,7 +22,6 @@ def make_thread_cooperative_group(size: int, alignment: Optional[int] = None): pipeline.Agent.Thread, size, alignment=alignment if alignment is not None else size ) - class BwdPartialDlogits: """ This class implements the backward kernel for partial d_logits. @@ -109,10 +108,14 @@ def _setup_attributes( mma_inst_shape_k = cute.size(tiled_mma.shape_mnk, mode=[2]) # it requires k-mode to be 128B aligned mma_inst_tile_k: int = 4 - self.mma_tiler = (self.mma_tiler[0], self.mma_tiler[1], mma_inst_shape_k * mma_inst_tile_k) + self.mma_tiler = ( + self.mma_tiler[0], + self.mma_tiler[1], + mma_inst_shape_k * mma_inst_tile_k, + ) - self.num_acc_stage, self.num_ab_stage, self.num_epi_stage_per_tile = self._compute_stages( - tiled_mma, self.mma_tiler, a_dtype, b_dtype + self.num_acc_stage, self.num_ab_stage, self.num_epi_stage_per_tile = ( + self._compute_stages(tiled_mma, self.mma_tiler, a_dtype, b_dtype) ) self.tmem_alloc_cols = self.num_acc_stage * self.mma_tiler[1] assert self.tmem_alloc_cols <= SM100_TMEM_CAPACITY_COLUMNS @@ -205,9 +208,13 @@ def kernel( # -------- tensor partition ------------ # # swizzle o [(tileM, tileK), loopM, loopK, stage] - sA = storage.sA.get_tensor(a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner) + sA = storage.sA.get_tensor( + a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner + ) # swizzle o [(tileN, tileK), loopN, loopK, stage] - sB = storage.sB.get_tensor(b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner) + sB = storage.sB.get_tensor( + b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner + ) # FIXME: if 2 CTAs, modify here thr_mma = tiled_mma.get_slice(0) @@ -336,10 +343,14 @@ def kernel( tCtC[((None, None), 0, None)], (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), ) - tiled_copy_t2r = tcgen05.make_tmem_copy(copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)]) + tiled_copy_t2r = tcgen05.make_tmem_copy( + copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)] + ) thr_copy_t2r = tiled_copy_t2r.get_slice(tidx) tTMEM_load_tAcc = thr_copy_t2r.partition_S(tAcc_epi) - tTMEM_load_tAcc = cute.group_modes(tTMEM_load_tAcc, 3, cute.rank(tTMEM_load_tAcc) - 1) + tTMEM_load_tAcc = cute.group_modes( + tTMEM_load_tAcc, 3, cute.rank(tTMEM_load_tAcc) - 1 + ) # predicates cAcc = cute.make_identity_tensor(self.mma_tiler[:2]) @@ -379,7 +390,9 @@ def kernel( tMCAcc_mask = cute.make_fragment(tMCAcc.shape, cutlass.Boolean) # to align shape with gMax and gAccu tMCAcc_mask = cute.append_ones(tMCAcc_mask) - tMCAcc_mask[0] = cute.elem_less(pidm * self.epi_tile[0] + tidx, cute.size(mA, mode=[0])) + tMCAcc_mask[0] = cute.elem_less( + pidm * self.epi_tile[0] + tidx, cute.size(mA, mode=[0]) + ) # [(1, 1), 1, 1] tMgLabels = thr_copy_g2r_int64.partition_S(cute.append_ones(gLabels)) tMrLabels = cute.make_fragment(tMgLabels.shape, tMgLabels.element_type) @@ -416,7 +429,9 @@ def kernel( ) # blackwell supports STG.256 copy_atom_r2g = cute.make_copy_atom( - cute.nvgpu.CopyUniversalOp(), gDlogits_partial.element_type, num_bits_per_copy=256 + cute.nvgpu.CopyUniversalOp(), + gDlogits_partial.element_type, + num_bits_per_copy=256, ) tiled_copy_r2g = cute.make_tiled_copy_tv( copy_atom_r2g, epilogue_thread_layout, copy_atom_r2g.layout_dst_tv @@ -430,7 +445,8 @@ def kernel( for row in cutlass.range(cute.size(tR2GCAcc_pred, mode=[1])): for col in cutlass.range(cute.size(tR2GCAcc_pred, mode=[2])): tR2GCAcc_pred[elem, row, col] = cute.elem_less( - pidm * self.epi_tile[0] + tR2GCAcc[elem, row, col][0], problem_mnk[0] + pidm * self.epi_tile[0] + tR2GCAcc[elem, row, col][0], + problem_mnk[0], ) and cute.elem_less( split_idx * self.vocab_per_split + pidn * self.epi_tile[1] @@ -442,7 +458,9 @@ def kernel( # for type conversion dLogits_half = cute.make_fragment(tTMEM_load_rAcc.shape, tR2GgDlogits.element_type) - dLogits_half = cute.tiled_divide(dLogits_half, (cute.size(tR2GgDlogits, mode=[0]), 1)) + dLogits_half = cute.tiled_divide( + dLogits_half, (cute.size(tR2GgDlogits, mode=[0]), 1) + ) dLogits_half = cute.group_modes(dLogits_half, 2, cute.rank(dLogits_half)) mma_pipeline.consumer_wait(mma_consumer_state) @@ -455,7 +473,8 @@ def kernel( min((split_idx + 1) * self.vocab_per_split, problem_mnk[1]), ) num_n_subtiles: cutlass.Int64 = cute.ceil_div( - (block_vocab_right_idx - block_vocab_left_idx), cute.size(tTMEM_load_rAcc, mode=[0]) + (block_vocab_right_idx - block_vocab_left_idx), + cute.size(tTMEM_load_rAcc, mode=[0]), ) for n_subtile in cutlass.range(num_n_subtiles): cute.copy( @@ -464,7 +483,9 @@ def kernel( tTMEM_load_rAcc, ) - for idx in cutlass.range(cute.size(tTMEM_load_rAcc, mode=[0]), unroll_full=True): + for idx in cutlass.range( + cute.size(tTMEM_load_rAcc, mode=[0]), unroll_full=True + ): # exp_logits tTMEM_load_rAcc[idx] = cute.exp(tTMEM_load_rAcc[idx] - tMrMaximum[0]) @@ -499,7 +520,9 @@ def kernel( self.cta_sync_barrier.arrive_and_wait() if warp_idx == self.empty_warp_ids[0]: cute.arch.relinquish_tmem_alloc_permit() - cute.arch.dealloc_tmem(tmem_ptr, self.tmem_alloc_cols, is_two_cta=self.use_2cta_instrs) + cute.arch.dealloc_tmem( + tmem_ptr, self.tmem_alloc_cols, is_two_cta=self.use_2cta_instrs + ) @cute.jit def __call__( @@ -545,7 +568,12 @@ def __call__( b_major_mode = utils.LayoutEnum.from_tensor(weight).mma_major_mode() tiled_mma = sm100_utils.make_trivial_tiled_mma( - a_dtype, a_major_mode, b_major_mode, self.acc_dtype, self.cta_group, self.mma_tiler[:2] + a_dtype, + a_major_mode, + b_major_mode, + self.acc_dtype, + self.cta_group, + self.mma_tiler[:2], ) self._setup_attributes(tiled_mma, a_dtype, b_dtype) @@ -634,5 +662,6 @@ class SharedStorage: cluster=self.cluster_shape_mnk, stream=stream, ) + except ImportError: - pass + logging.warning("Cutlass or CUDA bindings not found. BwdPartialDlogits will not be available.") diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py index 7ca2e5c91fb..dc369a7c558 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/entry.py @@ -1,11 +1,12 @@ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. -try: - import typing - from dataclasses import dataclass, field - from functools import lru_cache - import os +import logging +import os +import typing +from dataclasses import dataclass, field +from functools import lru_cache +try: import cuda.bindings.driver as cuda # type: ignore import cutlass import cutlass.cute as cute @@ -21,7 +22,6 @@ from megatron.core.fusions.linear_cross_entropy.blackwell import fwd_mainloop as fwd_mainloop from megatron.core.fusions.linear_cross_entropy.blackwell import triton as triton_kernels - @dataclass class FwdConfig: """ @@ -32,8 +32,9 @@ class FwdConfig: _dedicated_events: typing.List[torch.cuda.Event] = field(default_factory=list) _initialized: bool = field(default=False) _fwd_mainloop_kernels: typing.Dict[str, cute.kernel] = field(default_factory=dict) - _vocab_per_split: int = field(default=int(os.environ.get("LCE_FWD_VOCAB_SPLIT_SIZE", 512 * 6))) - + _vocab_per_split: int = field( + default=int(os.environ.get("LCE_FWD_VOCAB_SPLIT_SIZE", 512 * 6)) + ) @dataclass class BwdConfig: @@ -42,9 +43,12 @@ class BwdConfig: """ _bwd_kernel: typing.Dict[str, cute.kernel] = field(default_factory=dict) - _vocab_per_split: int = field(default=int(os.environ.get("LCE_BWD_VOCAB_SPLIT_SIZE", 512 * 6))) - _backward_method: utils.BackwardMethodEnum = field(default=utils.BackwardMethodEnum.kDlogitsSplitN) - + _vocab_per_split: int = field( + default=int(os.environ.get("LCE_BWD_VOCAB_SPLIT_SIZE", 512 * 6)) + ) + _backward_method: utils.BackwardMethodEnum = field( + default=utils.BackwardMethodEnum.kDlogitsSplitN + ) @lru_cache(maxsize=1) def _get_fwd_config() -> FwdConfig: @@ -68,7 +72,9 @@ def forward( reduction: typing.Literal["none", "sum", "mean"] = "mean", ignore_index: int = -100, sequence_parallel: bool = False, - ) -> typing.Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int, int, torch.Tensor]: + ) -> typing.Tuple[ + torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int, int, torch.Tensor + ]: """ forward host function """ @@ -84,22 +90,29 @@ def forward( # weight must be [vocab_size, dim] assert weight.dim() == 2 # labels could be [batch, seqlen] or [seqlen, batch] or [tokens] - assert (hidden.dim() == 2 and labels.dim() == 1) or (hidden.dim() == 3 and labels.dim() == 2) + assert (hidden.dim() == 2 and labels.dim() == 1) or ( + hidden.dim() == 3 and labels.dim() == 2 + ) assert hidden.is_contiguous() and weight.is_contiguous() and labels.is_contiguous() hidden_view = hidden.view(-1, hidden.shape[-1]) labels_view = labels.view(-1) - assert (sequence_parallel and hidden_view.shape[0] * tp_world_size == labels_view.shape[0]) or ( - not sequence_parallel and hidden_view.shape[0] == labels_view.shape[0] - ) + assert ( + sequence_parallel and hidden_view.shape[0] * tp_world_size == labels_view.shape[0] + ) or (not sequence_parallel and hidden_view.shape[0] == labels_view.shape[0]) assert hidden_view.shape[1] == weight.shape[1] global_hidden = hidden if in_tp_mode and sequence_parallel: partial_hidden_shape = hidden.shape - global_hidden_shape = (partial_hidden_shape[0] * tp_world_size, *partial_hidden_shape[1:]) - global_hidden = torch.empty(global_hidden_shape, dtype=hidden.dtype, device=hidden.device) + global_hidden_shape = ( + partial_hidden_shape[0] * tp_world_size, + *partial_hidden_shape[1:], + ) + global_hidden = torch.empty( + global_hidden_shape, dtype=hidden.dtype, device=hidden.device + ) dist.all_gather_into_tensor(global_hidden, hidden, group=tp_group) assert global_hidden.is_contiguous() hidden_view = global_hidden.view(-1, global_hidden.shape[-1]) @@ -125,11 +138,15 @@ def forward( accumulate = torch.empty_like(maximum, dtype=torch.float32) num_valid_tokens = torch.empty((), device=hidden.device, dtype=torch.int64) assert ( - maximum.is_contiguous() and accumulate.is_contiguous() and num_valid_tokens.is_contiguous() + maximum.is_contiguous() + and accumulate.is_contiguous() + and num_valid_tokens.is_contiguous() ) # declare intermediate tensors # NOTE: this is a parameter for tuning - num_splits = (vocab_size + _get_fwd_config()._vocab_per_split - 1) // _get_fwd_config()._vocab_per_split + num_splits = ( + vocab_size + _get_fwd_config()._vocab_per_split - 1 + ) // _get_fwd_config()._vocab_per_split _max = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) _accu = torch.empty((num_tokens, num_splits), device=hidden.device, dtype=torch.float32) if REDUCTION == utils.EntropyReductionEnum.kNone: @@ -145,14 +162,16 @@ def forward( ) # need to compile the kernel for the first time - hidden_packed = from_dlpack(hidden_view.detach(), assumed_align=16).mark_compact_shape_dynamic( - mode=0 - ) + hidden_packed = from_dlpack( + hidden_view.detach(), assumed_align=16 + ).mark_compact_shape_dynamic(mode=0) weight_packed = from_dlpack(weight.detach(), assumed_align=16) - labels_packed = from_dlpack(labels_view.detach(), assumed_align=8).mark_compact_shape_dynamic( + labels_packed = from_dlpack( + labels_view.detach(), assumed_align=8 + ).mark_compact_shape_dynamic(mode=0) + logprobs_packed = from_dlpack(_logprobs, assumed_align=16).mark_compact_shape_dynamic( mode=0 ) - logprobs_packed = from_dlpack(_logprobs, assumed_align=16).mark_compact_shape_dynamic(mode=0) _max_packed = from_dlpack(_max, assumed_align=8).mark_compact_shape_dynamic( mode=0, stride_order=(0, 1) ) @@ -165,7 +184,9 @@ def forward( # only the number of tokens can vary key = f"vocab_size:{vocab_size}+dim:{dim}+dtype:{hidden_view.dtype}" if _get_fwd_config()._fwd_mainloop_kernels.get(key) is None: - fwd_mainloop_kernel = fwd_mainloop.FwdMainLoop(vocab_per_split=_get_fwd_config()._vocab_per_split) + fwd_mainloop_kernel = fwd_mainloop.FwdMainLoop( + vocab_per_split=_get_fwd_config()._vocab_per_split + ) fwd_mainloop_compiled_kernel = cute.compile( fwd_mainloop_kernel, hidden_packed, @@ -226,9 +247,13 @@ def grid(meta): torch.cuda.current_stream().record_event(_get_fwd_config()._dedicated_events[0]) with torch.cuda.stream(_get_fwd_config()._dedicated_stream): - _get_fwd_config()._dedicated_stream.wait_event(_get_fwd_config()._dedicated_events[0]) + _get_fwd_config()._dedicated_stream.wait_event( + _get_fwd_config()._dedicated_events[0] + ) dist.all_reduce(_logprobs, op=dist.ReduceOp.SUM, group=tp_group) - _get_fwd_config()._dedicated_stream.record_event(_get_fwd_config()._dedicated_events[1]) + _get_fwd_config()._dedicated_stream.record_event( + _get_fwd_config()._dedicated_events[1] + ) def grid(meta): return (triton.cdiv(num_tokens, meta["BLOCK_SIZE_M"]),) @@ -271,8 +296,15 @@ def grid(meta): REDUCTION.value, ) - return logprobs, maximum, accumulate, num_valid_tokens, tp_rank, tp_world_size, global_hidden - + return ( + logprobs, + maximum, + accumulate, + num_valid_tokens, + tp_rank, + tp_world_size, + global_hidden, + ) def backward( dlogprobs: torch.Tensor, @@ -302,9 +334,9 @@ def backward( REDUCTION = utils.str_to_reduction_enum(reduction) dlogprobs_view = dlogprobs.view(-1) - assert (REDUCTION == utils.EntropyReductionEnum.kNone and dlogprobs.shape == (num_tokens,)) or ( - REDUCTION != utils.EntropyReductionEnum.kNone and dlogprobs.dim() == 0 - ) + assert ( + REDUCTION == utils.EntropyReductionEnum.kNone and dlogprobs.shape == (num_tokens,) + ) or (REDUCTION != utils.EntropyReductionEnum.kNone and dlogprobs.dim() == 0) assert dlogprobs.is_contiguous() and dlogprobs.is_cuda assert ( @@ -324,7 +356,9 @@ def backward( num_splits = (vocab_size + vocab_per_split - 1) // vocab_per_split _d_logits = torch.empty( - (num_tokens, vocab_per_split), device=global_hidden.device, dtype=global_hidden.dtype + (num_tokens, vocab_per_split), + device=global_hidden.device, + dtype=global_hidden.dtype, ) hidden_packed = from_dlpack( @@ -337,18 +371,24 @@ def backward( dlogprobs_packed = from_dlpack( dlogprobs_view.detach(), assumed_align=8 ).mark_compact_shape_dynamic(mode=0) - maximum_packed = from_dlpack(maximum.detach(), assumed_align=8).mark_compact_shape_dynamic( + maximum_packed = from_dlpack( + maximum.detach(), assumed_align=8 + ).mark_compact_shape_dynamic(mode=0) + accu_packed = from_dlpack(accu.detach(), assumed_align=8).mark_compact_shape_dynamic( + mode=0 + ) + dlogits_packed = from_dlpack(_d_logits, assumed_align=32).mark_compact_shape_dynamic( mode=0 ) - accu_packed = from_dlpack(accu.detach(), assumed_align=8).mark_compact_shape_dynamic(mode=0) - dlogits_packed = from_dlpack(_d_logits, assumed_align=32).mark_compact_shape_dynamic(mode=0) scalarNumValidTokens_packed = cute.runtime.make_ptr( cutlass.Int64, num_valid_tokens.data_ptr(), cute.AddressSpace.gmem, assumed_align=8 ) stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) - key = f"vocab_size:{vocab_size}+dim:{dim}+reduction:{REDUCTION}+dtype:{hidden_view.dtype}" + key = ( + f"vocab_size:{vocab_size}+dim:{dim}+reduction:{REDUCTION}+dtype:{hidden_view.dtype}" + ) if _get_bwd_config()._bwd_kernel.get(key) is None: bwd_kernel = bwd_partial_dlogits.BwdPartialDlogits( reduction=REDUCTION.value, vocab_per_split=vocab_per_split @@ -406,7 +446,9 @@ def backward( torch.matmul( valid_d_logits.T, hidden_view, - out=d_weight[split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, :], + out=d_weight[ + split_idx * vocab_per_split : (split_idx + 1) * vocab_per_split, : + ], ) else: raise NotImplementedError(f"Unsupported backward method: {_backward_method}") @@ -425,5 +467,9 @@ def backward( d_hidden = d_hidden.view(partial_hidden_shape).clone() return d_hidden, d_weight + except ImportError: - pass + logging.warning( + "Cutlass or CUDA bindings not found. LinearCrossEntropy Blackwell entry " + "points will not be available." + ) diff --git a/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py b/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py index da095e3fc64..93f5b9523e7 100644 --- a/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py +++ b/megatron/core/fusions/linear_cross_entropy/blackwell/fwd_mainloop.py @@ -4,9 +4,10 @@ Implementations of the fusion lm_head(Linear) + Cross-Entropy kernel """ -try: - from typing import Tuple, Type +import logging +from typing import Tuple, Type +try: import cuda.bindings.driver as cuda # type: ignore import cutlass import cutlass.cute as cute @@ -17,14 +18,12 @@ SM100_TMEM_CAPACITY_COLUMNS: int = 512 - def make_thread_cooperative_group(size: int): """ Create a thread cooperative group. """ return pipeline.CooperativeGroup(pipeline.Agent.Thread, size, alignment=size) - class FwdMainLoop: """ This class implements the mainloop for forward process. @@ -96,7 +95,9 @@ def _compute_stages( a_smem_layout_stage_one = sm100_utils.make_smem_layout_a( tiled_mma, mma_tiler, a_dtype, 1 # only single stage ) - b_smem_layout_stage_one = sm100_utils.make_smem_layout_b(tiled_mma, mma_tiler, b_dtype, 1) + b_smem_layout_stage_one = sm100_utils.make_smem_layout_b( + tiled_mma, mma_tiler, b_dtype, 1 + ) a_bytes_per_stage = cute.size_in_bytes(a_dtype, a_smem_layout_stage_one) b_bytes_per_stage = cute.size_in_bytes(b_dtype, b_smem_layout_stage_one) num_acc_stage = 2 @@ -121,7 +122,11 @@ def _setup_attributes( mma_inst_shape_k = cute.size(tiled_mma.shape_mnk, mode=[2]) # 16*4 = 64; 64 * sizeof(FP16) = 128Bytes mma_inst_tile_k: int = 4 - self.mma_tiler = (self.mma_tiler[0], self.mma_tiler[1], mma_inst_shape_k * mma_inst_tile_k) + self.mma_tiler = ( + self.mma_tiler[0], + self.mma_tiler[1], + mma_inst_shape_k * mma_inst_tile_k, + ) self.num_acc_stage, self.num_a_stage, self.num_b_stage, self.num_epi_stage_per_tile = ( self._compute_stages(tiled_mma, self.mma_tiler, a_dtype, b_dtype) @@ -211,9 +216,13 @@ def kernel( # -------- SMEM partition ------------ # # swizzle o [(tileM, tileK), loopM, loopK, Stage] - sA = storage.sA.get_tensor(a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner) + sA = storage.sA.get_tensor( + a_smem_layout_staged.outer, swizzle=a_smem_layout_staged.inner + ) # swizzle o [(tileN, tileK), loopN, loopK, stage] - sB = storage.sB.get_tensor(b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner) + sB = storage.sB.get_tensor( + b_smem_layout_staged.outer, swizzle=b_smem_layout_staged.inner + ) # FIXME: if 2 CTAs, modify here thr_mma = tiled_mma.get_slice(0) @@ -328,7 +337,9 @@ def kernel( for k in cutlass.range(cute.size(gA, mode=[2])): ab_pipeline.consumer_wait(ab_consumer_state) - for kblock_idx in cutlass.range(cute.size(tCsA, mode=[2]), unroll_full=True): + for kblock_idx in cutlass.range( + cute.size(tCsA, mode=[2]), unroll_full=True + ): cute.gemm( tiled_mma, cute.append_ones(tCtC[(None, None, mma_producer_state.index)]), @@ -365,11 +376,15 @@ def kernel( tCtC[((None, None), 0, None)], (self.epi_tile[0], self.epi_tile[1] // self.num_epi_stage_per_tile), ) - tiled_copy_t2r = tcgen05.make_tmem_copy(copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)]) + tiled_copy_t2r = tcgen05.make_tmem_copy( + copy_atom_t2r, tAcc_epi[(None, None, 0, 0, 0)] + ) thr_copy_t2r = tiled_copy_t2r.get_slice(tidx) tTMEM_load_tAcc = thr_copy_t2r.partition_S(tAcc_epi) # [(pattern), loopM, loopN, CntTileM, CntTileN] - tTMEM_load_tAcc = cute.group_modes(tTMEM_load_tAcc, 3, cute.rank(tTMEM_load_tAcc) - 1) + tTMEM_load_tAcc = cute.group_modes( + tTMEM_load_tAcc, 3, cute.rank(tTMEM_load_tAcc) - 1 + ) cAcc = cute.make_identity_tensor(self.mma_tiler[:2]) tCcAcc = thr_mma.partition_C(cAcc) @@ -383,12 +398,18 @@ def kernel( # epilogue layouts epilogue_thread_layout = cute.make_layout((128, 1)) - copy_atom_g2r = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), mLabels.element_type) - tiled_copy_g2r = cute.make_tiled_copy(copy_atom_g2r, epilogue_thread_layout, (128, 1)) + copy_atom_g2r = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), mLabels.element_type + ) + tiled_copy_g2r = cute.make_tiled_copy( + copy_atom_g2r, epilogue_thread_layout, (128, 1) + ) thr_copy_g2r = tiled_copy_g2r.get_slice(tidx) copy_atom_r2g = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), cutlass.Float32) - tiled_copy_r2g = cute.make_tiled_copy(copy_atom_r2g, epilogue_thread_layout, (128, 1)) + tiled_copy_r2g = cute.make_tiled_copy( + copy_atom_r2g, epilogue_thread_layout, (128, 1) + ) thr_copy_r2g = tiled_copy_r2g.get_slice(tidx) # auxiliary tensors @@ -404,11 +425,13 @@ def kernel( # [(1, 1), 1, 1] tLabelsgLabels = thr_copy_g2r.partition_S(cute.append_ones(gLabels)) - tLabelsrLabels = cute.make_fragment(tLabelsgLabels.shape, tLabelsgLabels.element_type) + tLabelsrLabels = cute.make_fragment( + tLabelsgLabels.shape, tLabelsgLabels.element_type + ) cute.copy(tiled_copy_g2r, tLabelsgLabels, tLabelsrLabels, pred=tLabelsCAcc_mask) - valid_mask: cutlass.Boolean = (tLabelsrLabels[0] != ignore_index) and tLabelsCAcc_mask[ - 0 - ] + valid_mask: cutlass.Boolean = ( + tLabelsrLabels[0] != ignore_index + ) and tLabelsCAcc_mask[0] # [tileM, 1] gMax = cute.local_tile(mMax, (self.epi_tile[0], 1), (pidm, pidn)) @@ -425,7 +448,9 @@ def kernel( tR2GrAccu.fill(0.0) # [tileM, 1] - gLogprobs = cute.append_ones(cute.local_tile(mLogprobs, (self.epi_tile[0],), (pidm,))) + gLogprobs = cute.append_ones( + cute.local_tile(mLogprobs, (self.epi_tile[0],), (pidm,)) + ) # [(CPYM, CPYN), loopM, loopN] tR2GgLogprobs = thr_copy_r2g.partition_D(gLogprobs) tR2GrLogprobs = cute.make_fragment(tR2GgLogprobs.shape, tR2GgLogprobs.element_type) @@ -447,7 +472,9 @@ def kernel( for n_subtile in cutlass.range(num_n_subtiles): cute.copy( tiled_copy_t2r, - tTMEM_load_tAcc[(None, None, None, n_subtile, mma_consumer_state.index)], + tTMEM_load_tAcc[ + (None, None, None, n_subtile, mma_consumer_state.index) + ], tTMEM_load_rAcc, ) @@ -467,9 +494,13 @@ def kernel( tR2GrAccu[0] = coeff * tR2GrAccu[0] + exp_logits position: cutlass.Int64 = ( - rank * problem_mnk[1] + pidn * self.vocab_per_split + local_position + rank * problem_mnk[1] + + pidn * self.vocab_per_split + + local_position + ) + mask: cutlass.Boolean = valid_mask and ( + position == tLabelsrLabels[0] ) - mask: cutlass.Boolean = valid_mask and (position == tLabelsrLabels[0]) tR2GrLogprobs[0] += mask * tTMEM_load_rAcc[idx] mma_pipeline.consumer_release(mma_consumer_state) @@ -493,7 +524,9 @@ def kernel( self.cta_sync_barrier.arrive_and_wait() if warp_idx == self.empty_warp_ids[0]: cute.arch.relinquish_tmem_alloc_permit() - cute.arch.dealloc_tmem(tmem_ptr, self.tmem_alloc_cols, is_two_cta=self.use_2cta_instrs) + cute.arch.dealloc_tmem( + tmem_ptr, self.tmem_alloc_cols, is_two_cta=self.use_2cta_instrs + ) @staticmethod def _compute_grid( @@ -551,7 +584,12 @@ def __call__( b_major_mode = utils.LayoutEnum.from_tensor(weight).mma_major_mode() tiled_mma = sm100_utils.make_trivial_tiled_mma( - a_dtype, a_major_mode, b_major_mode, self.acc_dtype, self.cta_group, self.mma_tiler[:2] + a_dtype, + a_major_mode, + b_major_mode, + self.acc_dtype, + self.cta_group, + self.mma_tiler[:2], ) self._setup_attributes(tiled_mma, a_dtype, b_dtype) @@ -650,5 +688,6 @@ class SharedStorage: stream=stream, ) return None + except ImportError: - pass + logging.warning("Cutlass or CUDA Python bindings not found. FwdMainLoop will not be available.") diff --git a/megatron/core/models/gpt/experimental_attention_variant_module_specs.py b/megatron/core/models/gpt/experimental_attention_variant_module_specs.py new file mode 100644 index 00000000000..cbe59618baf --- /dev/null +++ b/megatron/core/models/gpt/experimental_attention_variant_module_specs.py @@ -0,0 +1,132 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + +from typing import Optional + +from megatron.core.models.backends import BackendSpecProvider +from megatron.core.ssm.gated_delta_net import GatedDeltaNet, GatedDeltaNetSubmodules +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.experimental_attention_variant.dsa import ( + DSAIndexer, + DSAIndexerSubmodules, + DSAttention, + DSAttentionSubmodules, +) +from megatron.core.transformer.identity_op import IdentityOp +from megatron.core.transformer.multi_latent_attention import ( + MLASelfAttention, + MLASelfAttentionSubmodules, +) +from megatron.core.transformer.spec_utils import ModuleSpec + + +def get_gated_delta_net_module_spec_for_backend( + backend: BackendSpecProvider, normalization: Optional[str] = None +) -> ModuleSpec: + """Helper function to get module spec for Linear Attention""" + rms_norm = normalization == "RMSNorm" + attention = ModuleSpec( + module=GatedDeltaNet, + submodules=GatedDeltaNetSubmodules( + in_proj=backend.column_parallel_layer_norm_linear(), + out_norm=backend.layer_norm(rms_norm=rms_norm, for_qk=False), + out_proj=backend.row_parallel_linear(), + ), + metainfo={"fuse_input_layernorm": True}, + ) + return attention + + +def get_dsa_module_spec_for_backend( + backend: BackendSpecProvider, + qk_layernorm: Optional[bool] = False, + qk_l2_norm: Optional[bool] = False, + multi_latent_attention: Optional[bool] = False, + mla_down_proj_use_column_parallel: Optional[bool] = False, + normalization: Optional[str] = None, + fallback_to_eager_attn: Optional[bool] = False, +) -> ModuleSpec: + """Helper function to get module spec for Sparse Attention.""" + assert multi_latent_attention, "Currently only MLA supports sparse attention." + assert qk_l2_norm is False, "qk_l2_norm is not supported with MLA." + assert fallback_to_eager_attn is False, "Fallback to eager attention is not supported with DSA." + + linear_q_down_proj = ( + backend.column_parallel_linear() if mla_down_proj_use_column_parallel else backend.linear() + ) + linear_kv_down_proj = ( + backend.column_parallel_linear() if mla_down_proj_use_column_parallel else backend.linear() + ) + linear_q_up_proj = backend.column_parallel_linear() + linear_kv_up_proj = backend.column_parallel_linear() + + # Because TransformerEngine does not support sparse attention yet, we use local + # implementation whether the backend is TransformerEngine or not. + core_attention = ModuleSpec( + module=DSAttention, + submodules=DSAttentionSubmodules( + indexer=ModuleSpec( + module=DSAIndexer, + submodules=DSAIndexerSubmodules( + linear_wq_b=backend.linear(), + linear_wk=backend.linear(), + k_norm=backend.layer_norm(rms_norm=False, for_qk=True), + linear_weights_proj=backend.linear(), + ), + ) + ), + ) + + # Adjust for RMS norm. + rms_norm = normalization == "RMSNorm" + qk_norm = backend.layer_norm(rms_norm=rms_norm, for_qk=True) if qk_layernorm else IdentityOp + + attention = ModuleSpec( + module=MLASelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=MLASelfAttentionSubmodules( + linear_q_proj=backend.column_parallel_linear(), + linear_q_down_proj=linear_q_down_proj, + linear_q_up_proj=linear_q_up_proj, + linear_kv_down_proj=linear_kv_down_proj, + linear_kv_up_proj=linear_kv_up_proj, + core_attention=core_attention, + linear_proj=backend.row_parallel_linear(), + q_layernorm=qk_norm, + kv_layernorm=qk_norm, + ), + metainfo={"fuse_input_layernorm": False}, + ) + + return attention + + +def get_experimental_attention_variant_module_spec_for_backend( + backend: BackendSpecProvider, + sharded_state_dict_keys_map: dict, + experimental_attention_variant: Optional[str] = None, + qk_layernorm: Optional[bool] = False, + qk_l2_norm: Optional[bool] = False, + multi_latent_attention: Optional[bool] = False, + mla_down_proj_use_column_parallel: Optional[bool] = False, + normalization: Optional[str] = None, + fallback_to_eager_attn: Optional[bool] = False, +) -> ModuleSpec: + """Helper function to get module spec for Attention""" + if experimental_attention_variant == "gated_delta_net": + return get_gated_delta_net_module_spec_for_backend( + backend=backend, normalization=normalization + ) + elif experimental_attention_variant == "dsa": + return get_dsa_module_spec_for_backend( + backend=backend, + qk_layernorm=qk_layernorm, + qk_l2_norm=qk_l2_norm, + multi_latent_attention=multi_latent_attention, + mla_down_proj_use_column_parallel=mla_down_proj_use_column_parallel, + normalization=normalization, + fallback_to_eager_attn=fallback_to_eager_attn, + ) + else: + raise ValueError( + f"Invalid experimental attention variant: {experimental_attention_variant}" + ) diff --git a/megatron/core/models/gpt/gpt_layer_specs.py b/megatron/core/models/gpt/gpt_layer_specs.py index c5c9caa3d67..5395b158749 100755 --- a/megatron/core/models/gpt/gpt_layer_specs.py +++ b/megatron/core/models/gpt/gpt_layer_specs.py @@ -5,8 +5,8 @@ from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add from megatron.core.models.backends import BackendSpecProvider, LocalSpecProvider -from megatron.core.models.gpt.linear_attention_module_specs import ( - get_linear_attention_module_spec_for_backend, +from megatron.core.models.gpt.experimental_attention_variant_module_specs import ( + get_experimental_attention_variant_module_spec_for_backend, ) from megatron.core.models.gpt.moe_module_specs import get_moe_module_spec_for_backend from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules @@ -78,7 +78,7 @@ def get_gpt_layer_with_transformer_engine_spec( moe_grouped_gemm: Optional[bool] = False, qk_layernorm: Optional[bool] = False, multi_latent_attention: Optional[bool] = False, - linear_attention_type: Optional[str] = None, + experimental_attention_variant: Optional[str] = None, fp8: Optional[str] = None, # pylint: disable=unused-argument moe_use_legacy_grouped_gemm: Optional[bool] = False, normalization: Optional[str] = None, @@ -96,7 +96,8 @@ def get_gpt_layer_with_transformer_engine_spec( moe_grouped_gemm (bool, optional): To use Grouped GEMM. Defaults to False. qk_layernorm (bool, optional): To use layernorm for queries/keys. Defaults to False. multi_latent_attention (bool, optional): To use multi-latent attention. Defaults to False. - linear_attention_type (str, optional): The type of linear attention. Defaults to None. + experimental_attention_variant (str, optional): The type of experimental attention variant. + Defaults to None. fp8 (str, optional): Deprecated. For temporary Nemo compatibility. moe_use_legacy_grouped_gemm (bool, optional): Force use the legacy GroupedMLP. Defaults to False. @@ -133,7 +134,7 @@ def get_gpt_layer_with_transformer_engine_spec( attention = get_attention_module_spec_for_backend( backend=backend, sharded_state_dict_keys_map=sharded_state_dict_keys_map, - linear_attention_type=linear_attention_type, + experimental_attention_variant=experimental_attention_variant, qk_layernorm=qk_layernorm, qk_l2_norm=qk_l2_norm, multi_latent_attention=multi_latent_attention, @@ -166,7 +167,7 @@ def get_gpt_layer_local_spec( moe_grouped_gemm: Optional[bool] = False, qk_layernorm: Optional[bool] = False, multi_latent_attention: Optional[bool] = False, - linear_attention_type: Optional[str] = None, + experimental_attention_variant: Optional[str] = None, fp8: Optional[str] = None, # pylint: disable=unused-argument moe_use_legacy_grouped_gemm: Optional[bool] = False, normalization: Optional[str] = None, @@ -181,7 +182,8 @@ def get_gpt_layer_local_spec( moe_grouped_gemm (bool, optional): To use Grouped GEMM. Defaults to False. qk_layernorm (bool, optional): To use layernorm for queries/keys. Defaults to False. multi_latent_attention (bool, optional): To use multi-latent attention. Defaults to False. - linear_attention_type (str, optional): The type of linear attention. Defaults to None. + experimental_attention_variant (str, optional): The type of experimental attention variant. + Defaults to None. fp8 (str, optional): Deprecated. For temporary Nemo compatibility. moe_use_legacy_grouped_gemm (bool, optional): Force use the legacy GroupedMLP. Defaults to False. @@ -205,15 +207,17 @@ def get_gpt_layer_local_spec( " and will be removed soon. Please update your code accordingly." ) - if linear_attention_type is not None: - raise NotImplementedError("Linear attention is not supported with local spec yet.") + if experimental_attention_variant is not None: + raise NotImplementedError( + "Experimental attention variant is not supported with local spec yet." + ) sharded_state_dict_keys_map = {} attention = get_attention_module_spec_for_backend( backend=backend, sharded_state_dict_keys_map=sharded_state_dict_keys_map, - linear_attention_type=linear_attention_type, + experimental_attention_variant=experimental_attention_variant, qk_layernorm=qk_layernorm, qk_l2_norm=qk_l2_norm, multi_latent_attention=multi_latent_attention, @@ -278,7 +282,7 @@ def get_transformer_layer_spec_for_backend( def get_attention_module_spec_for_backend( backend: BackendSpecProvider, sharded_state_dict_keys_map: dict, - linear_attention_type: Optional[str] = None, + experimental_attention_variant: Optional[str] = None, qk_layernorm: Optional[bool] = False, qk_l2_norm: Optional[bool] = False, multi_latent_attention: Optional[bool] = False, @@ -288,11 +292,17 @@ def get_attention_module_spec_for_backend( ) -> ModuleSpec: """Helper function to get module spec for Attention""" - if linear_attention_type is not None: - return get_linear_attention_module_spec_for_backend( - backend=backend, - linear_attention_type=linear_attention_type, - normalization=normalization, + if experimental_attention_variant is not None: + return get_experimental_attention_variant_module_spec_for_backend( + backend, + sharded_state_dict_keys_map, + experimental_attention_variant, + qk_layernorm, + qk_l2_norm, + multi_latent_attention, + mla_down_proj_use_column_parallel, + normalization, + fallback_to_eager_attn, ) # Adjust for RMS norm. @@ -526,13 +536,12 @@ def get_gpt_decoder_layer_specs( num_experts = None moe_grouped_gemm = None if attention_type == "linear_attention": - if config.linear_attention_type is None: + linear_attention_variants = ["gated_delta_net"] + if config.experimental_attention_variant not in linear_attention_variants: # Skip if there is no linear attention layer in the model. continue - linear_attention_type = config.linear_attention_type multi_latent_attention = None else: - linear_attention_type = None multi_latent_attention = config.multi_latent_attention layer_spec_key = f"{mlp_type}_{attention_type}" @@ -540,7 +549,7 @@ def get_gpt_decoder_layer_specs( num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm, multi_latent_attention=multi_latent_attention, - linear_attention_type=linear_attention_type, + experimental_attention_variant=config.experimental_attention_variant, **get_layer_spec_kwargs, ) @@ -583,7 +592,8 @@ def get_gpt_decoder_layer_specs( f"current linear attention pattern: {config.linear_attention_freq}" ) elif config.linear_attention_freq is None: - if config.linear_attention_type is None: + linear_attention_variants = ["gated_delta_net"] + if config.experimental_attention_variant not in linear_attention_variants: linear_attention_pattern = [0] * config.num_layers else: linear_attention_pattern = [1] * config.num_layers diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py index b46ea83a4d4..b3f6bdcc728 100644 --- a/megatron/core/models/gpt/gpt_model.py +++ b/megatron/core/models/gpt/gpt_model.py @@ -569,9 +569,19 @@ def _postprocess( loss_mask = torch.ones_like(mtp_labels) for mtp_layer_number in range(self.config.mtp_num_layers): # Calc loss for the current Multi-Token Prediction (MTP) layers. - mtp_labels, _ = roll_tensor(mtp_labels, shifts=-1, dims=-1, cp_group=self.cp_group) + mtp_labels, _ = roll_tensor( + mtp_labels, + shifts=-1, + dims=-1, + cp_group=self.cp_group, + packed_seq_params=packed_seq_params, + ) loss_mask, num_tokens = roll_tensor( - loss_mask, shifts=-1, dims=-1, cp_group=self.cp_group + loss_mask, + shifts=-1, + dims=-1, + cp_group=self.cp_group, + packed_seq_params=packed_seq_params, ) # Compute mtp loss without storing logits to save memory. diff --git a/megatron/core/models/gpt/linear_attention_module_specs.py b/megatron/core/models/gpt/linear_attention_module_specs.py deleted file mode 100644 index 7e76d845cff..00000000000 --- a/megatron/core/models/gpt/linear_attention_module_specs.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. - -from typing import Optional - -from megatron.core.models.backends import BackendSpecProvider -from megatron.core.ssm.gated_delta_net import GatedDeltaNet, GatedDeltaNetSubmodules -from megatron.core.transformer.spec_utils import ModuleSpec - - -def get_linear_attention_module_spec_for_backend( - backend: BackendSpecProvider, linear_attention_type: str, normalization: Optional[str] = None -) -> ModuleSpec: - """Helper function to get module spec for Linear Attention""" - rms_norm = normalization == "RMSNorm" - if linear_attention_type == "gated_delta_net": - attention = ModuleSpec( - module=GatedDeltaNet, - submodules=GatedDeltaNetSubmodules( - in_proj=backend.column_parallel_layer_norm_linear(), - out_norm=backend.layer_norm(rms_norm=rms_norm, for_qk=False), - out_proj=backend.row_parallel_linear(), - ), - metainfo={"fuse_input_layernorm": True}, - ) - else: - raise ValueError(f"Invalid linear attention type: {linear_attention_type}") - return attention diff --git a/megatron/core/transformer/attention.py b/megatron/core/transformer/attention.py index 74031f38219..f6f40027789 100644 --- a/megatron/core/transformer/attention.py +++ b/megatron/core/transformer/attention.py @@ -190,6 +190,7 @@ def __init__( self.key_hidden_size = self.hidden_size_per_attention_head self.val_hidden_size = self.hidden_size_per_attention_head + # TODO: This is built twice when using MLA, should be refactored. self.core_attention = build_module( submodules.core_attention, config=self.config, diff --git a/megatron/core/transformer/experimental_attention_variant/dsa.py b/megatron/core/transformer/experimental_attention_variant/dsa.py new file mode 100644 index 00000000000..fc994490b1b --- /dev/null +++ b/megatron/core/transformer/experimental_attention_variant/dsa.py @@ -0,0 +1,822 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + +import copy +import math +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch + +from megatron.core import parallel_state +from megatron.core.models.common.embeddings import ( + RotaryEmbedding, + YarnRotaryEmbedding, + apply_rotary_pos_emb, +) +from megatron.core.packed_seq_params import PackedSeqParams +from megatron.core.process_groups_config import ProcessGroupCollection +from megatron.core.tensor_parallel.mappings import gather_from_sequence_parallel_region +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.module import MegatronModule +from megatron.core.transformer.spec_utils import ModuleSpec, build_module +from megatron.core.transformer.transformer_config import TransformerConfig + +try: + from fast_hadamard_transform import hadamard_transform +except ImportError: + hadamard_transform = None + + +def rotate_activation(x: torch.Tensor) -> torch.Tensor: + """Apply Hadamard rotation activation. + Reference: + https://github.com/deepseek-ai/DeepSeek-V3.2-Exp/blob/main/inference/model.py#L424-L428 + + Args: + x: Input tensor (must be bfloat16). + + Returns: + Rotated tensor. + """ + assert ( + x.dtype == torch.bfloat16 + ), f"rotate_activation only support bf16 input, but got {x.dtype}" + assert hadamard_transform is not None, "fast_hadamard_transform is not installed." + hidden_size = x.size(-1) + return hadamard_transform(x, scale=hidden_size**-0.5) + + +class DSAIndexerLossLoggingHelper: + """Helper class for logging sparse attention indexer losses.""" + + tracker = {} + + @staticmethod + def save_loss_to_tracker( + loss: torch.Tensor, + layer_number: int, + num_layers: int, + reduce_group: torch.distributed.ProcessGroup = None, + avg_group: torch.distributed.ProcessGroup = None, + ): + """Save the indexer loss for logging. + + Args: + loss: The loss tensor. + layer_number: Layer index of the loss, 1-indexed. + num_layers: The number of total layers. + reduce_group: The group for reducing the loss. + avg_group: The group for averaging the loss. + """ + # Skip indexer loss logging if layer_number is None. + if layer_number is None: + return + + tracker = DSAIndexerLossLoggingHelper.tracker + if "values" not in tracker: + tracker["values"] = torch.zeros(num_layers, device=torch.cuda.current_device()) + tracker["values"][layer_number - 1] += loss.detach() + tracker["reduce_group"] = reduce_group + tracker["avg_group"] = avg_group + + @staticmethod + def clean_loss_in_tracker(): + """Clear the indexer losses.""" + tracker = DSAIndexerLossLoggingHelper.tracker + if "values" in tracker: + tracker["values"].zero_() + tracker["reduce_group"] = None + tracker["avg_group"] = None + + @staticmethod + def reduce_loss_in_tracker(): + """Collect and reduce the indexer losses across ranks.""" + tracker = DSAIndexerLossLoggingHelper.tracker + if "values" not in tracker: + return + values = tracker["values"] + + torch.distributed.all_reduce( + values, group=parallel_state.get_pipeline_model_parallel_group() + ) + # Reduce indexer losses across ranks. + if tracker.get('reduce_group') is not None: + torch.distributed.all_reduce(values, group=tracker.get('reduce_group')) + if tracker.get('avg_group') is not None: + torch.distributed.all_reduce( + values, group=tracker['avg_group'], op=torch.distributed.ReduceOp.AVG + ) + torch.distributed.all_reduce( + values, + group=parallel_state.get_data_parallel_group(with_context_parallel=False), + op=torch.distributed.ReduceOp.AVG, + ) + + @staticmethod + def track_indexer_metrics( + loss_scale: float, + iteration: int, + writer, + wandb_writer=None, + total_loss_dict=None, + per_layer_logging: bool = False, + ): + """Track the sparse attention indexer metrics for logging. + + Args: + loss_scale: Scale factor for the loss. + iteration: Current training iteration. + writer: TensorBoard writer. + wandb_writer: Weights & Biases writer. + total_loss_dict: Dictionary to accumulate total losses. + per_layer_logging: Whether to log per-layer losses. + """ + DSAIndexerLossLoggingHelper.reduce_loss_in_tracker() + tracker = DSAIndexerLossLoggingHelper.tracker + if "values" not in tracker: + return + + indexer_loss_values = tracker["values"] * loss_scale + num_layers = indexer_loss_values.shape[0] + + # Average across all layers (assuming all layers have sparse attention) + avg_indexer_loss = indexer_loss_values.sum() / num_layers + + # Log average loss + if total_loss_dict is not None: + if "indexer loss" in total_loss_dict: + total_loss_dict["indexer loss"] += avg_indexer_loss + else: + total_loss_dict["indexer loss"] = avg_indexer_loss + + if writer is not None: + writer.add_scalar("indexer loss", avg_indexer_loss, iteration) + + if wandb_writer is not None: + wandb_writer.log({"indexer loss": avg_indexer_loss}, iteration) + + DSAIndexerLossLoggingHelper.clean_loss_in_tracker() + + +def compute_dsa_indexer_loss( + index_scores: torch.Tensor, + topk_indices: torch.Tensor, + query: torch.Tensor, + key: torch.Tensor, + softmax_scale: float, + loss_coeff: float, + sparse_loss: bool, + pg_collection: ProcessGroupCollection, +) -> torch.Tensor: + """ + Compute KL divergence loss between index_scores and true attention_scores. + + This loss trains the indexer to predict which tokens are important by matching the distribution + of true attention scores. + + Reference: Section 2.1 of + https://github.com/deepseek-ai/DeepSeek-V3.2-Exp/blob/main/DeepSeek_V3_2.pdf + + Args: + index_scores: Scores predicted by indexer [batch, seqlen_q, seqlen_k]. + topk_indices: Top-k indices [batch, seqlen_q, index_topk]. + query: Query tensor [seqlen_q, batch, heads, dim]. + key: Key tensor [seqlen_k, batch, heads, dim]. + softmax_scale: Scale coefficient after q @ k^T. + loss_coeff: Coefficient for the indexer KL divergence loss. + sparse_loss: bool, whether to use sparse indexer loss. If True, only the topk + indices will be used to compute the loss. + pg_collection: Process group collection, must have TP process group. + + Returns: + index_loss: KL divergence loss (scalar). + """ + sq, b, np, hn = query.size() + sk = key.size(0) + + # [sq, b, np, hn] -> [b, np, sq, hn] -> [b * np, sq, hn] + query = query.permute(1, 2, 0, 3).reshape(b * np, sq, hn) + # [sk, b, np, hn] -> [b, np, hn, sk] -> [b * np, hn, sk] + key = key.permute(1, 2, 3, 0).reshape(b * np, hn, sk) + # Compute attention scores [b * np, sq, sk] + attention_scores = torch.bmm(query.float(), key.float()) * softmax_scale + # Reshape to [b, np, sq, sk] + attention_scores = attention_scores.reshape(b, np, sq, sk) + + # causal_mask [sq, sk] + causal_mask = torch.triu( + torch.full((sq, sk), float('-inf'), dtype=torch.float32, device=attention_scores.device), + diagonal=1, + ) + # index_mask [b, sq, sk] + index_mask = torch.full( + (b, sq, sk), float("-inf"), dtype=torch.float32, device=causal_mask.device + ).scatter_(-1, topk_indices, 0) + + # [b, np, sq, skv] + [1, 1, sq, skv] -> [b, np, sq, skv] + attention_scores += causal_mask.view(1, 1, sq, sk) + if sparse_loss: + # [b, np, sq, sk] + [b, 1, sq, sk] -> [b, np, sq, sk] + attention_scores += index_mask.view(b, 1, sq, sk) + # [b, sq, sk] + [b, sq, sk] -> [b, sq, sk] + index_scores += index_mask + + # [b, np, sq, sk] -> [b, np, sq, sk] + attention_scores = torch.nn.functional.softmax(attention_scores, dim=-1, dtype=torch.float32) + # [b, sq, sk] -> [b, sq, sk] + index_scores = torch.nn.functional.softmax(index_scores, dim=-1, dtype=torch.float32) + + # Sum attention scores across heads. + # [batch, heads, seqlen_q, seqlen_k] -> [batch, seqlen_q, seqlen_k] + attention_scores = attention_scores.sum(dim=1) + if pg_collection.tp.size() > 1: + # attention scores are scattered to TP ranks in head dimension. + torch.distributed.all_reduce(attention_scores.contiguous(), group=pg_collection.tp) + # L1 normalize target on the last dimension. Doesn't use abs() because attention_scores are + # obtained from softmax so they are already non-negative. + attention_scores = attention_scores / attention_scores.sum(dim=-1, keepdim=True) + + # Compute KL divergence: KL(target || index) = target(x) * log(target(x) / index(x)) + # kl_per_element [b, sq, sk] + kl_per_element = attention_scores * ( + torch.log(attention_scores + 1e-10) - torch.log(index_scores + 1e-10) + ) + + # [b, sq, sk] -> [b, sq] -> [1] + # Each token has same weight in the loss. + kl_div = kl_per_element.sum(dim=-1).mean() + + # Scale by coefficient. + indexer_loss = kl_div * loss_coeff + + return indexer_loss + + +class DSAIndexerLossAutoScaler(torch.autograd.Function): + """An AutoScaler that triggers the backward pass and scales the grad for indexer loss. + + This custom autograd function attaches a KL divergence loss to the activation + to train the indexer to predict attention scores without affecting the forward pass. + """ + + main_loss_backward_scale: torch.Tensor = None + + @staticmethod + def forward(ctx, output: torch.Tensor, indexer_loss: torch.Tensor): + """Preserve the indexer_loss by storing it in the context to avoid garbage collection. + + Args: + output: The output tensor (activation). + indexer_loss: The indexer KL divergence loss tensor. + + Returns: + torch.Tensor: The output tensor unchanged. + """ + ctx.save_for_backward(indexer_loss) + return output + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + """Compute and scale the gradient for indexer loss. + + Args: + grad_output: The gradient of the output. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: The gradient of the output, scaled indexer loss + gradient. + """ + (indexer_loss,) = ctx.saved_tensors + if DSAIndexerLossAutoScaler.main_loss_backward_scale is None: + DSAIndexerLossAutoScaler.main_loss_backward_scale = torch.tensor( + 1.0, device=indexer_loss.device + ) + indexer_loss_backward_scale = DSAIndexerLossAutoScaler.main_loss_backward_scale + scaled_indexer_loss_grad = torch.ones_like(indexer_loss) * indexer_loss_backward_scale + return grad_output, scaled_indexer_loss_grad + + @staticmethod + def set_loss_scale(scale: torch.Tensor): + """Set the scale of the indexer loss. + + Args: + scale: The scale value to set. + """ + if DSAIndexerLossAutoScaler.main_loss_backward_scale is None: + DSAIndexerLossAutoScaler.main_loss_backward_scale = scale + else: + DSAIndexerLossAutoScaler.main_loss_backward_scale.copy_(scale) + + +@dataclass +class DSAIndexerSubmodules: + """ + Configuration class for specifying the submodules of an DSA Indexer. + + Args: + linear_wq_b: Linear projection for query bottleneck expansion. + linear_wk: Linear projection for key. + k_norm: Layer normalization for key. + linear_weights_proj: Linear projection for attention weights. + """ + + linear_wq_b: Union[ModuleSpec, type] = None + linear_wk: Union[ModuleSpec, type] = None + k_norm: Union[ModuleSpec, type] = None + linear_weights_proj: Union[ModuleSpec, type] = None + + +@dataclass +class DSAttentionSubmodules: + """ + Configuration class for specifying the submodules of DSAttention. + + Args: + indexer: DSA Indexer module for computing sparse attention indices. + """ + + indexer: Union[ModuleSpec, type] = None + + +class DSAIndexer(MegatronModule): + """ + DSA Lightning Indexer for DeepSeek Sparse Attention. + + Computes index scores to identify the top-k most relevant key-value pairs for each query in + sparse attention. + + Reference: + https://github.com/deepseek-ai/DeepSeek-V3.2-Exp/blob/main/inference/model.py#L431-L480 + """ + + def __init__( + self, + config: TransformerConfig, + submodules: DSAIndexerSubmodules, + pg_collection: Optional[ProcessGroupCollection] = None, + ) -> None: + """Initialize the indexer. + + Args: + config (TransformerConfig): The configuration for the transformer model. + submodules (DSAIndexerSubmodules): Indexer submodules specification. + pg_collection (ProcessGroupCollection, optional): Process groups for the indexer. + """ + super().__init__(config=config) + self.hidden_size = self.config.hidden_size + self.qk_pos_emb_head_dim = self.config.qk_pos_emb_head_dim + self.q_lora_rank = ( + self.config.q_lora_rank + if self.config.q_lora_rank is not None + else self.config.hidden_size + ) + + self.index_n_heads = self.config.dsa_indexer_n_heads + self.index_head_dim = self.config.dsa_indexer_head_dim + self.index_topk = self.config.dsa_indexer_topk + + self.softmax_scale: float = self.index_head_dim**-0.5 + + if pg_collection is None: + pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) + self.pg_collection = pg_collection + + # Initialize Position Embedding. + if self.config.rope_type == 'rope': + self.rotary_pos_emb = RotaryEmbedding( + self.qk_pos_emb_head_dim, + rotary_percent=self.config.rotary_percent, + rotary_base=self.config.rotary_base, + cp_group=self.pg_collection.cp, + ) + elif self.config.rope_type == 'yarn': + self.rotary_pos_emb = YarnRotaryEmbedding( + self.qk_pos_emb_head_dim, + rotary_base=self.config.rotary_base, + scaling_factor=self.config.rotary_scaling_factor, + original_max_position_embeddings=self.config.original_max_position_embeddings, + beta_fast=self.config.beta_fast, + beta_slow=self.config.beta_slow, + mscale=self.config.mscale, + mscale_all_dim=self.config.mscale_all_dim, + cp_group=self.pg_collection.cp, + ) + else: + raise ValueError( + f'Unsupported RoPE type: {self.config.rope_type}, supported types are "rope" and ' + f'"yarn"' + ) + + self.linear_wq_b = build_module( + submodules.linear_wq_b, + self.q_lora_rank, + self.index_n_heads * self.index_head_dim, + config=self.config, + init_method=self.config.init_method, + bias=False, + skip_bias_add=False, + skip_weight_param_allocation=False, + parallel_mode="duplicated", + ) + + self.linear_wk = build_module( + submodules.linear_wk, + self.hidden_size, + self.index_head_dim, + config=self.config, + init_method=self.config.init_method, + bias=False, + skip_bias_add=False, + skip_weight_param_allocation=False, + parallel_mode="duplicated", + ) + + k_norm_config = copy.copy(self.config) + k_norm_config.normalization = "LayerNorm" + self.k_norm = build_module( + submodules.k_norm, + config=k_norm_config, + hidden_size=self.index_head_dim, + eps=self.config.layernorm_epsilon, + ) + + self.linear_weights_proj = build_module( + submodules.linear_weights_proj, + self.hidden_size, + self.index_n_heads, + config=self.config, + init_method=self.config.init_method, + bias=False, + skip_bias_add=False, + skip_weight_param_allocation=False, + parallel_mode="duplicated", + ) + + def _apply_rope(self, x: torch.Tensor, rotary_pos_emb: torch.Tensor, mscale: float): + """Apply RoPE to the input tensor.""" + # x_nope [seqlen, batch, *, index_head_dim - qk_pos_emb_head_dim] + # x_pe [seqlen, batch, *, qk_pos_emb_head_dim] + x_nope, x_pe = torch.split( + x, [self.index_head_dim - self.qk_pos_emb_head_dim, self.qk_pos_emb_head_dim], dim=-1 + ) + x_pe = apply_rotary_pos_emb( + x_pe, + rotary_pos_emb, + config=self.config, + cu_seqlens=None, + mscale=mscale, + cp_group=self.pg_collection.cp, + ) + # [seqlen, batch, *, index_head_dim] + x = torch.cat([x_nope, x_pe], dim=-1) + return x + + def _compute_index_scores( + self, q: torch.Tensor, weights: torch.Tensor, k: torch.Tensor + ) -> torch.Tensor: + """ + Perform index score using BF16 precision. + + Reference: + https://github.com/deepseek-ai/DeepSeek-V3.2-Exp/blob/main/inference/kernel.py#L254-L274 + This is a BF16 implementation of the `fp8_index` logic: + 1. Compute attention scores: q @ k^T; + 2. Apply ReLU activation; + 3. Weight by attention weights; + 4. Sum across attention heads. + + Args: + q: BF16 [seqlen_q, batch, index_n_heads, index_head_dim], the query tensor. + weights: BF16 [seqlen_q, batch, index_n_heads], the attention weights. + k: BF16 [seqlen_k, batch, index_head_dim], the key tensor. + + Returns: + index_scores: FP32 [batch, seqlen_q, seqlen_k], the index scores. + """ + # Compute attention scores: q @ k^T + # [seqlen_q, batch, index_n_heads, index_head_dim] @ [seqlen_k, batch, index_head_dim]^T + # -> [seqlen_q, batch, index_n_heads, seqlen_k] + index_scores = torch.einsum('sbhd,tbd->sbht', q.float(), k.float()) + + # Apply ReLU activation. + index_scores = torch.relu(index_scores) + + # Weight each head by attention weights. + # [seqlen_q, batch, index_n_heads, seqlen_k] * [seqlen_q, batch, index_n_heads, 1] + # -> [seqlen_q, batch, index_n_heads, seqlen_k] + index_scores = index_scores * weights.unsqueeze(-1) + + # Sum across attention heads. + # [seqlen_q, batch, index_n_heads, seqlen_k] -> [seqlen_q, batch, seqlen_k] + index_scores = index_scores.sum(dim=2) + + # Transpose to [batch, seqlen_q, seqlen_k]. + index_scores = index_scores.transpose(0, 1) + + return index_scores + + def forward_with_scores( + self, + x: torch.Tensor, + qr: torch.Tensor, + mask: Optional[torch.Tensor] = None, + packed_seq_params: Optional[PackedSeqParams] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Forward pass for DSA Indexer that returns both index scores and top-k indices. + + This is used when KL loss is enabled to compare indexer scores with true attention scores. + + Args: + x: hidden states [seqlen, batch, hidden_size]. + qr: Low-rank query tensor [seqlen, batch, q_lora_rank]. + mask: Attention mask [batch, seqlen, seqlen]. + packed_seq_params: Packed sequence parameters for variable length sequences. + + Returns: + index_scores: Index scores [batch, seqlen, seqlen]. + topk_indices: Top-k indices [batch, seqlen, index_topk]. + """ + assert packed_seq_params is None, "Packed sequence is not supported for DSAttention" + + # ========================================= + # Prepare RoPE params + # ========================================= + rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len( + None, None, x, self.config, packed_seq_params + ) + if self.config.rope_type == "rope": + rotary_pos_emb = self.rotary_pos_emb(rotary_seq_len, packed_seq=False) + mscale = 1.0 + else: + rotary_pos_emb, mscale = self.rotary_pos_emb(rotary_seq_len, packed_seq=False) + + # ========================================= + # Gather inputs if sp is enabled + # ========================================= + if self.config.sequence_parallel and self.pg_collection.tp.size() > 1: + x = gather_from_sequence_parallel_region(x, group=self.pg_collection.tp) + qr = gather_from_sequence_parallel_region(qr, group=self.pg_collection.tp) + + # ========================================= + # Get sequence length and batch size + # ========================================= + seqlen, bsz, _ = x.size() + + # ========================================= + # q linear and apply rope to q + # ========================================= + # [seqlen, batch, q_lora_rank] -> [seqlen, batch, index_n_heads * index_head_dim] + q, _ = self.linear_wq_b(qr) + # [seqlen, batch, index_n_heads * index_head_dim] + # -> [seqlen, batch, index_n_heads, index_head_dim] + q = q.reshape(seqlen, bsz, self.index_n_heads, self.index_head_dim) + q = self._apply_rope(q, rotary_pos_emb, mscale) + + # ========================================= + # k linear and apply rope to k + # ========================================= + # [seqlen, batch, hidden_size] -> [seqlen, batch, index_head_dim] + k, _ = self.linear_wk(x) + k = self.k_norm(k) + # [seqlen, batch, index_head_dim] -> [seqlen, batch, 1, index_head_dim] + k = k.reshape(seqlen, bsz, 1, self.index_head_dim) + k = self._apply_rope(k, rotary_pos_emb, mscale) + # [seqlen, batch, 1, index_head_dim] -> [seqlen, batch, index_head_dim] + k = k.reshape(seqlen, bsz, self.index_head_dim) + + # ========================================= + # Rotate activation + # ========================================= + q = rotate_activation(q) + k = rotate_activation(k) + + # ========================================= + # Compute index scores + # ========================================= + # [seqlen, batch, hidden_size] -> [seqlen, batch, index_n_heads] + weights, _ = self.linear_weights_proj(x) + weights = weights * (self.index_n_heads**-0.5) * self.softmax_scale + # [batch, seqlen, seqlen] + index_scores = self._compute_index_scores(q, weights, k) + if mask is not None: + assert mask.dtype == index_scores.dtype, "Mask dtype must match index scores dtype" + index_scores = index_scores + mask + + # ========================================= + # Select top-k indices + # ========================================= + topk_k = min(self.index_topk, seqlen) + # [batch, seqlen, index_topk] + topk_indices = index_scores.topk(topk_k, dim=-1)[1] + + return index_scores, topk_indices + + def forward( + self, + x: torch.Tensor, + qr: torch.Tensor, + mask: Optional[torch.Tensor] = None, + packed_seq_params: Optional[PackedSeqParams] = None, + ): + """ + Forward pass for DSA Indexer. + + Args: + x: hidden states [seqlen, batch, hidden_size]. + qr: Low-rank query tensor [seqlen, batch, q_lora_rank]. + mask: Attention mask [batch, seqlen, seqlen]. + packed_seq_params: Packed sequence parameters for variable length sequences. + + Returns: + topk_indices: Top-k indices for sparse attention [batch, seqlen, index_topk]. + """ + _, topk_indices = self.forward_with_scores(x, qr, mask, packed_seq_params) + return topk_indices + + +def unfused_dsa_fn(query, key, value, topk_indices, softmax_scale): + """ + Unfused sparse attention implementation. + """ + sq, b, np, hn = query.size() + skv = key.size(0) + hnv = value.size(3) + + # =================================== + # Raw attention scores [b, np, sq, skv] + # =================================== + # [sq, b, np, hn] -> [b, np, sq, hn] -> [b * np, sq, hn] + query = query.permute(1, 2, 0, 3).reshape(b * np, sq, hn) + # [skv, b, np, hn] -> [b, np, hn, skv] -> [b * np, hn, skv] + key = key.permute(1, 2, 3, 0).reshape(b * np, hn, skv) + # Compute attention scores [b * np, sq, skv] + attention_scores = torch.bmm(query.float(), key.float()) * softmax_scale + # Reshape to [b, np, sq, skv] + attention_scores = attention_scores.reshape(b, np, sq, skv) + + # =================================== + # Apply sparse mask from indexer + # =================================== + # index_mask [b, sq, skv] + index_mask = torch.full((b, sq, skv), float("-inf"), device=attention_scores.device) + index_mask.scatter_(-1, topk_indices, 0) + # causal_mask [sq, skv] + causal_mask = torch.triu( + torch.full((sq, skv), float('-inf'), dtype=torch.float32, device=index_mask.device), + diagonal=1, + ) + # [b, sq, skv] + [1, sq, skv] -> [b, sq, skv] + index_mask += causal_mask.view(1, sq, skv) + # [b, np, sq, skv] + [b, 1, sq, skv] -> [b, np, sq, skv] + attention_scores += index_mask.unsqueeze(1) + attention_scores = torch.nn.functional.softmax(attention_scores, dim=-1, dtype=torch.float32) + + # =================================== + # Output + # =================================== + # [skv, b, np, hnv] -> [b, np, skv, hnv] -> [b * np, skv, hnv] + value = value.permute(1, 2, 0, 3).reshape(b * np, skv, hnv) + # Reshape attention_scores: [b, np, sq, skv] -> [b * np, sq, skv] + attention_scores = attention_scores.reshape(b * np, sq, skv) + # Compute output: [b * np, sq, hnv] + output = torch.bmm(attention_scores.to(value.dtype), value) + # Reshape output: [b * np, sq, hnv] -> [b, np, sq, hnv] -> [sq, b, np, hnv] + output = output.reshape(b, np, sq, hnv).permute(2, 0, 1, 3).contiguous() + # Flatten: [sq, b, np, hnv] -> [sq, b, np * hnv] + output = output.reshape(sq, b, np * hnv) + return output + + +class DSAttention(MegatronModule): + """ + This module implements sparse attention mechanism using an DSA Indexer to compute top-k + attention indices for reducing computational complexity. + + Reference: + https://github.com/deepseek-ai/DeepSeek-V3.2-Exp/blob/main/inference/model.py#L491-L597 + """ + + def __init__( + self, + config: TransformerConfig, + submodules: DSAttentionSubmodules, + layer_number: int, + attn_mask_type: AttnMaskType, + attention_type: str, + attention_dropout: Optional[float] = None, + softmax_scale: Optional[float] = None, + k_channels: Optional[int] = None, + v_channels: Optional[int] = None, + cp_comm_type: str = "p2p", + pg_collection: ProcessGroupCollection = None, + ): + super().__init__(config=config) + + self.layer_number = layer_number + + self.indexer = build_module( + submodules.indexer, config=self.config, pg_collection=pg_collection + ) + + if softmax_scale is None: + softmax_scale = 1.0 / math.sqrt( + k_channels if k_channels is not None else config.kv_channels + ) + self.softmax_scale = softmax_scale + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + x: torch.Tensor, + qr: torch.Tensor, + attention_mask: torch.Tensor, + attn_mask_type: AttnMaskType = None, + attention_bias: torch.Tensor = None, + packed_seq_params: PackedSeqParams = None, + ): + """ + Forward pass for Sparse Attention. + + Args: + query: Query tensor [sq, b, np, hn]. + key: Key tensor [skv, b, np, hn]. + value: Value tensor [skv, b, np, hnv]. + x: Original hidden states [sq, b, hidden_size]. + qr: Low-rank query representation [sq, b, q_lora_rank]. + attention_mask: Attention mask tensor [b, 1, sq, sk]. + attn_mask_type: Type of attention mask. + attention_bias: Optional attention bias. + packed_seq_params: Packed sequence parameters. + + Returns: + output: Output tensor [sq, b, hidden_size] + """ + sq, b, np, hn = query.size() + skv = key.size(0) + hnv = value.size(3) + + # Detach x and qr to prevent gradients of indexer from flowing back to the main model. + x = x.detach() + qr = qr.detach() + + # Get a FP32 mask with -inf for masked positions. + if attn_mask_type is not None: + assert attn_mask_type == AttnMaskType.causal, 'Only causal mask is supported for now' + # Generate upper triangular mask with -inf above diagonal, 0 elsewhere + # torch.triu with diagonal=1 creates upper triangular matrix (excluding main diagonal) + # float_mask [sq, skv] + float_mask = torch.triu( + torch.full((sq, skv), float('-inf'), dtype=torch.float32, device=x.device), + diagonal=1, + ) + else: + assert attention_mask.shape == (b, 1, sq, skv), 'attention_mask shape mismatch' + # [b, 1, sq, skv] -> [b, sq, skv] + mask = attention_mask.squeeze() + # float_mask [b, sq, skv] + float_mask = torch.zeros_like(mask, dtype=torch.float32).masked_fill( + mask, float('-inf') + ) + + # =================================== + # Get index scores and top-k indices + # =================================== + index_scores, topk_indices = self.indexer.forward_with_scores( + x, qr, mask=float_mask, packed_seq_params=packed_seq_params + ) + + # =================================== + # Run sparse attention kernel + # =================================== + output = unfused_dsa_fn(query, key, value, topk_indices, self.softmax_scale) + + # =================================== + # Attach indexer loss + # =================================== + if self.training and torch.is_grad_enabled(): + # Compute KL divergence loss between indexer scores and true attention scores + indexer_loss_coeff = getattr(self.config, 'dsa_indexer_loss_coeff', 0.0) + indexer_loss = compute_dsa_indexer_loss( + index_scores, + topk_indices, + query.detach(), + key.detach(), + self.softmax_scale, + indexer_loss_coeff, + getattr(self.config, "dsa_indexer_use_sparse_loss", False), + self.indexer.pg_collection, + ) + # Save indexer loss for logging + if indexer_loss_coeff > 0: + DSAIndexerLossLoggingHelper.save_loss_to_tracker( + loss=indexer_loss, + layer_number=self.layer_number, + num_layers=self.config.num_layers, + ) + # Attach loss to output + output = DSAIndexerLossAutoScaler.apply(output, indexer_loss) + + return output diff --git a/megatron/core/transformer/multi_latent_attention.py b/megatron/core/transformer/multi_latent_attention.py index 074523afd7b..3953d933b45 100644 --- a/megatron/core/transformer/multi_latent_attention.py +++ b/megatron/core/transformer/multi_latent_attention.py @@ -243,13 +243,28 @@ def forward( # Get the query, key and value tensors based on the type of attention - # self or cross attn. # query: [96, 1, 16, 128], key:[96, 1, 16, 128], value:[96, 1, 16, 128] - query, key, value = self.get_query_key_value_tensors( - hidden_states, - key_value_states, - position_ids, - packed_seq_params, - inference_context=inference_context, - ) + if self.config.experimental_attention_variant is None: + query, key, value = self.get_query_key_value_tensors( + hidden_states, + key_value_states, + position_ids, + packed_seq_params, + inference_context=inference_context, + ) + elif self.config.experimental_attention_variant == "dsa": + query, key, value, q_compressed, _ = self.get_query_key_value_tensors( + hidden_states, + key_value_states, + position_ids, + packed_seq_params, + inference_context=inference_context, + return_compressed_tensors=True, + ) + else: + raise ValueError( + f"Unsupported experimental attention variant: " + f"{self.config.experimental_attention_variant}" + ) # =================================================== # Adjust key, value for inference @@ -281,14 +296,34 @@ def forward( if inference_context is None or inference_context.is_static_batching(): with get_fine_grained_offloading_context(self.offload_core_attention): - core_attn_out = self.core_attention( - query, - key, - value, - attention_mask, - packed_seq_params=packed_seq_params, - attn_mask_type=attn_mask_type, - ) + if self.config.experimental_attention_variant is None: + core_attn_out = self.core_attention( + query, + key, + value, + attention_mask, + packed_seq_params=packed_seq_params, + attn_mask_type=attn_mask_type, + ) + elif self.config.experimental_attention_variant == "dsa": + # For dsa we need to pass in the original hidden states and the compressed + # query representation. + core_attn_out = self.core_attention( + query, + key, + value, + x=hidden_states, + qr=q_compressed, + attention_mask=attention_mask, + attn_mask_type=attn_mask_type, + attention_bias=None, + packed_seq_params=packed_seq_params, + ) + else: + raise ValueError( + f"Unsupported attention variant: " + f"{self.config.experimental_attention_variant}" + ) elif self.cache_mla_latents: # Dynamic batching attention kernel. q, k, v = (query, key, value) @@ -494,6 +529,7 @@ def get_query_key_value_tensors( inference_context=None, *, inference_params=None, + return_compressed_tensors=False, ): """ Derives `query`, `key` and `value` tensors from `hidden_states`. @@ -603,6 +639,16 @@ def get_query_key_value_tensors( kv_compressed = kv_compressed.squeeze(1) k_pos_emb = k_pos_emb.squeeze(1) + # ========================================= + # Apply norm + # ========================================= + + if self.config.q_lora_rank is not None: + # q_compressed: [num_tokens, q_lora_rank] + q_compressed = self.q_layernorm(q_compressed) + + kv_compressed = self.kv_layernorm(kv_compressed) + # ========================================= # QKV up projection and RoPE apply # ========================================= @@ -613,7 +659,6 @@ def qkv_up_proj_and_rope_apply_for_cached_latent_kv( if self.config.q_lora_rank is not None: # q_compressed: [num_tokens, q_lora_rank] # q: [num_tokens, n * (qk_head_dim + qk_pos_emb_head_dim)] - q_compressed = self.q_layernorm(q_compressed) q, _ = self.linear_q_up_proj(q_compressed) else: # q_compressed: [num_tokens, hidden_size] @@ -623,8 +668,6 @@ def qkv_up_proj_and_rope_apply_for_cached_latent_kv( # q: [num_tokens, n, q_head_dim] q = q.view(*q.size()[:-1], self.num_attention_heads_per_partition, self.q_head_dim) - kv_compressed = self.kv_layernorm(kv_compressed) - # [num_tokens, qk_pos_emb_head_dim] -> [num_tokens, 1, qk_pos_emb_head_dim] k_pos_emb = torch.unsqueeze(k_pos_emb, -2) @@ -688,7 +731,6 @@ def qkv_up_proj_and_rope_apply(q_compressed, kv_compressed, k_pos_emb, rotary_po if self.config.q_lora_rank is not None: # q_compressed: [num_tokens, q_lora_rank] # q: [num_tokens, n * (qk_head_dim + qk_pos_emb_head_dim)] - q_compressed = self.q_layernorm(q_compressed) q, _ = self.linear_q_up_proj(q_compressed) else: # q_compressed: [num_tokens, hidden_size] @@ -698,8 +740,6 @@ def qkv_up_proj_and_rope_apply(q_compressed, kv_compressed, k_pos_emb, rotary_po # q: [num_tokens, n, q_head_dim] q = q.view(*q.size()[:-1], self.num_attention_heads_per_partition, self.q_head_dim) - kv_compressed = self.kv_layernorm(kv_compressed) - # kv: [num_tokens, n * (qk_head_dim + v_head_dim)] kv, _ = self.linear_kv_up_proj(kv_compressed) @@ -824,7 +864,10 @@ def qkv_up_proj_and_rope_apply(q_compressed, kv_compressed, k_pos_emb, rotary_po q_compressed, kv_compressed, k_pos_emb, rotary_pos_emb ) - return query, key, value + if return_compressed_tensors: + return query, key, value, q_compressed, kv_compressed + else: + return query, key, value def uncompress_kv_from_cache(self, kv_cached): """ diff --git a/megatron/core/transformer/multi_token_prediction.py b/megatron/core/transformer/multi_token_prediction.py index e79af23ef04..a8f4abfcdd3 100755 --- a/megatron/core/transformer/multi_token_prediction.py +++ b/megatron/core/transformer/multi_token_prediction.py @@ -126,7 +126,7 @@ def tie_output_layer_state_dict( ) -def roll_tensor(tensor, shifts=-1, dims=-1, cp_group=None): +def roll_tensor(tensor, shifts=-1, dims=-1, cp_group=None, packed_seq_params=None): """Roll the tensor input along the sequence dimension with Context Parallelism (CP) support. This function extends the original roll_tensor to support Context Parallelism, which allows @@ -138,15 +138,24 @@ def roll_tensor(tensor, shifts=-1, dims=-1, cp_group=None): For CP>1: Splits tensor into chunks, performs rolling within each chunk, then exchanges boundary elements between adjacent CP ranks to maintain sequence continuity. + For packed sequences: Respects sequence boundaries when rolling to avoid mixing tokens + from different sequences. + Args: tensor (Tensor): The input tensor to roll. shifts (int): The shift of the tensor (typically -1 for MTP). dims (int): The dimension to roll (typically -1 for sequence dimension). cp_group (ProcessGroup): The context parallelism process group. If None or size=1, falls back to standard rolling behavior. + packed_seq_params (PackedSeqParams): Parameters for packed sequence processing. + If provided, respects sequence boundaries. Returns: tuple: (rolled_tensor, sum_of_rolled_tensor) """ + # Handle packed sequences cases + if packed_seq_params is not None: + return _roll_tensor_packed_seq(tensor, shifts, dims, packed_seq_params, cp_group) + # Standard rolling behavior when CP is not enabled (cp_group is None or size=1) if cp_group is None or cp_group.size() == 1: rolled_tensor = torch.roll(tensor, shifts=shifts, dims=dims) @@ -215,6 +224,91 @@ def roll_tensor(tensor, shifts=-1, dims=-1, cp_group=None): return rolled_tensor, rolled_tensor.sum() +def _roll_tensor_packed_seq(tensor, shifts, dims, packed_seq_params, cp_group=None): + """Roll tensor with packed sequence support. + This function handles rolling for packed sequences by respecting sequence boundaries + """ + + # Notice: This is a naive implementation to test the correctness, + # a better solution will only sync the boundary tokens once. + assert ( + dims == -1 or dims == tensor.dim() - 1 + ), "Packed sequence roll only supports the last dimension." + assert shifts == -1, "Packed sequence roll only supports a single-token left shift." + cu_seqlens = packed_seq_params.cu_seqlens_q + assert cu_seqlens is not None, "Packed sequence parameters must provide cu_seqlens_q." + + rolled_tensor = tensor.clone() + + cp_size = cp_group.size() if cp_group is not None else 1 + if cp_size == 1: + # CP disabled: roll each packed sequence independently within its boundaries + for i in range(len(cu_seqlens) - 1): + start_idx = cu_seqlens[i] + end_idx = cu_seqlens[i + 1] + seq_slice = tensor[..., start_idx:end_idx] + rolled_seq = torch.roll(seq_slice, shifts=shifts, dims=dims) + # Zero out the last position(s) that would cross sequence boundaries + rolled_seq[..., shifts:] = 0 + rolled_tensor[..., start_idx:end_idx] = rolled_seq + return rolled_tensor, rolled_tensor.sum() + + # CP enabled: each rank owns two chunks per sequence (front and mirrored tail). + local_rank = torch.distributed.get_rank(group=cp_group) + global_ranks = torch.distributed.get_process_group_ranks(group=cp_group) + next_rank = global_ranks[(local_rank + 1) % cp_size] + prev_rank = global_ranks[(local_rank - 1) % cp_size] + + # Iterate over each sequence individually + for i in range(len(cu_seqlens) - 1): + start_idx = cu_seqlens[i] + end_idx = cu_seqlens[i + 1] + + # the idx has been multiplied by cp_size, need to divide it by cp_size to get the local idx + local_start_idx = start_idx // cp_size + local_end_idx = end_idx // cp_size + tensor_slice = rolled_tensor[..., local_start_idx:local_end_idx].clone() + + # The following code is very similar as the code in roll_tensor function + local_chunks = tensor_slice.chunk(2, dim=dims) + rolled_chunks = [torch.roll(chunk, shifts=shifts, dims=dims) for chunk in local_chunks] + + tensor_send_list = [] + tensor_recv_list = [] + for chunk in rolled_chunks: + boundary = chunk.select(dims, shifts).contiguous().clone() + tensor_send_list.append(boundary) + tensor_recv_list.append(torch.empty_like(boundary)) + + ops = [] + if local_rank != 0: + ops.append(torch.distributed.isend(tensor=tensor_send_list[0], dst=prev_rank)) + ops.append(torch.distributed.irecv(tensor=tensor_recv_list[1], src=prev_rank)) + else: + tensor_recv_list[1].zero_() + + if local_rank != cp_size - 1: + ops.append(torch.distributed.irecv(tensor=tensor_recv_list[0], src=next_rank)) + ops.append(torch.distributed.isend(tensor=tensor_send_list[1], dst=next_rank)) + else: + tensor_recv_list[0].copy_(tensor_send_list[1]) + + for op in ops: + op.wait() + + index = [slice(None)] * rolled_chunks[0].dim() + index[dims] = shifts + for chunk, recv in zip(rolled_chunks, tensor_recv_list): + chunk[tuple(index)] = recv + + seq_result = torch.cat(rolled_chunks, dim=dims) + + # update the rolled tensor + rolled_tensor[..., local_start_idx:local_end_idx] = seq_result + + return rolled_tensor, rolled_tensor.sum() + + class MTPLossLoggingHelper: """Helper class for logging MTP losses.""" @@ -595,6 +689,7 @@ def _get_embeddings( position_ids: torch.Tensor, embedding: Callable, hidden_states: torch.Tensor, + packed_seq_params: Optional[PackedSeqParams] = None, ): """ Preprocesses input data for the Multi-Token Prediction (MTP) layers. @@ -609,10 +704,23 @@ def _get_embeddings( from gpt model to compute the decoder input. hidden_states (torch.Tensor): hidden states tensor of shape [s, b, h] where s is the sequence length, b is the batch size, and h is the hidden size. + packed_seq_params (PackedSeqParams): Parameters for packed sequence processing. """ # Calc logits for the current Multi-Token Prediction (MTP) layers. - input_ids, _ = roll_tensor(input_ids, shifts=-1, dims=-1, cp_group=self.cp_group) - position_ids, _ = roll_tensor(position_ids, shifts=-1, dims=-1, cp_group=self.cp_group) + input_ids, _ = roll_tensor( + input_ids, + shifts=-1, + dims=-1, + cp_group=self.cp_group, + packed_seq_params=packed_seq_params, + ) + position_ids, _ = roll_tensor( + position_ids, + shifts=-1, + dims=-1, + cp_group=self.cp_group, + packed_seq_params=packed_seq_params, + ) # embedding decoder_input = embedding(input_ids=input_ids, position_ids=position_ids) @@ -795,15 +903,13 @@ def forward( [s, b, h], and optionally the updated context tensor if cross-attention is used. """ assert context is None, f"multi token prediction + cross attention is not yet supported." - assert ( - packed_seq_params is None - ), f"multi token prediction + sequence packing is not yet supported." input_ids, position_ids, decoder_input, hidden_states = self._get_embeddings( input_ids=input_ids, position_ids=position_ids, embedding=embedding, hidden_states=hidden_states, + packed_seq_params=packed_seq_params, ) if self.config.recompute_granularity == 'full' and self.training: diff --git a/megatron/core/transformer/transformer_config.py b/megatron/core/transformer/transformer_config.py index fae2e2f5d4d..656699ea2a2 100644 --- a/megatron/core/transformer/transformer_config.py +++ b/megatron/core/transformer/transformer_config.py @@ -233,11 +233,14 @@ class TransformerConfig(ModelParallelConfig): 16 SMs can generally achieve good bandwidth.""" #################### - # linear attention + # attention variant #################### - linear_attention_type: Optional[str] = None - """Type of linear attention to use. Currently support gated_delta_net.""" + experimental_attention_variant: Optional[str] = None + """Type of attention variant to use. Currently support gated_delta_net and dsa.""" + #################### + # attention variant: gated_delta_net + #################### linear_attention_freq: Optional[Union[int, List[int]]] = None """Frequency between LA (linear attention) layers and SDPA (scaled dot-product attention) layers. @@ -260,6 +263,25 @@ class TransformerConfig(ModelParallelConfig): linear_num_value_heads: Optional[int] = None """Number of value and gate heads for the gated delta net.""" + #################### + # attention variant: dsa + #################### + dsa_indexer_n_heads: Optional[int] = None + """Number of DSA indexer heads.""" + + dsa_indexer_head_dim: Optional[int] = None + """Dimension per DSA indexer head.""" + + dsa_indexer_topk: Optional[int] = None + """Number of top-k tokens to select in DSA indexer.""" + + dsa_indexer_loss_coeff: Optional[float] = None + """Coefficient for the DSA indexer KL divergence loss. Set to 0 to disable indexer loss.""" + + dsa_indexer_use_sparse_loss: Optional[bool] = None + """Whether to use sparse DSA indexer loss. If True, the indexer loss will be computed using the + top-k indices.""" + #################### # initialization #################### @@ -855,17 +877,12 @@ def __post_init__(self): f"tensor_model_parallel_size ({self.tensor_model_parallel_size})." ) - if self.linear_attention_type is not None: - supported_la_types = ["gated_delta_net"] - assert self.linear_attention_type in supported_la_types, ( - f"linear_attention_type ({self.linear_attention_type}) only support" - f" one of {supported_la_types}." - ) + if self.experimental_attention_variant in ["gated_delta_net"]: assert ( self.linear_attention_freq is not None ), f"linear_attention_freq must be set for linear attention." - if self.linear_attention_type == "gated_delta_net": + if self.experimental_attention_variant == "gated_delta_net": # Check required parameters assert ( self.linear_conv_kernel_dim is not None @@ -900,6 +917,11 @@ def __post_init__(self): f"Gated delta net does not support context parallel for now," f" but got {self.context_parallel_size=}." ) + elif self.experimental_attention_variant == "dsa": + assert ( + self.context_parallel_size == 1 + ), "Currently context parallelism is not supported by DSAttention!" + assert not self.apply_rope_fusion, "RoPE fusion is not supported for DSAttention" if self.fp8: # cannot support first last layer bf16 with delayed scaling diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py index 7fca6a20f40..48ba9c8bd5f 100644 --- a/megatron/training/arguments.py +++ b/megatron/training/arguments.py @@ -69,7 +69,7 @@ def add_megatron_arguments(parser: argparse.ArgumentParser): parser = _add_vision_args(parser) parser = _add_moe_args(parser) parser = _add_mla_args(parser) - parser = _add_linear_attention_args(parser) + parser = _add_experimental_attention_variant_args(parser) parser = _add_heterogeneous_args(parser) parser = _add_logging_args(parser) parser = _add_straggler_detector_args(parser) @@ -1194,13 +1194,21 @@ def validate_args(args, defaults={}): args.no_load_rng = True print('Warning: disabling --no-load-rng for upcycling.') + if args.linear_attention_type is not None: + print_rank_0( + '--linear-attention-type is deprecated, use --experimental-attention-variant instead.', + args.rank, + ) + args.experimental_attention_variant = args.linear_attention_type + del args.linear_attention_type + # Muon optimizercheck if 'muon' in args.optimizer: assert not args.use_distributed_optimizer, "Muon optimizer does not support distributed optimizer for now." assert not args.use_torch_fsdp2, "Muon optimizer does not support Torch-FSDP2 for now." assert not args.use_megatron_fsdp, "Muon optimizer does not support Megatron-FSDP for now." assert args.ckpt_format in ["torch", "torch_dist"], "Muon optimizer supports torch and torch_dist checkpoint format." - assert args.linear_attention_type is None, "Muon optimizer does not support linear attention type for now." + assert args.experimental_attention_variant is None, "Muon optimizer does not support attention variant for now." assert not args.attention_output_gate, "Muon optimizer does not support attention output gate for now." # Optimizer CPU offload check @@ -3351,10 +3359,14 @@ def _add_mla_args(parser): return parser -def _add_linear_attention_args(parser): - group = parser.add_argument_group(title="la") +def _add_experimental_attention_variant_args(parser): + group = parser.add_argument_group(title="experimental_attention_variant") + group.add_argument('--experimental-attention-variant', default=None, choices=['gated_delta_net', 'dsa'], type=str, + help='Type of attention variant to use. Currently support gated_delta_net and dsa.') + + # Linear attention group.add_argument('--linear-attention-type', default=None, choices=['gated_delta_net'], type=str, - help='Type of linear attention to use. Currently support gated_delta_net.') + help='(Deprecated, use --experimental-attention-variant instead) Type of linear attention to use. Currently support gated_delta_net.') group.add_argument('--linear-attention-freq', type=la_freq_type, default=None, help='Frequency between LA (linear attention) layers and' ' SDPA (scaled dot-product attention) layers. Accepts either: ' @@ -3374,6 +3386,19 @@ def _add_linear_attention_args(parser): help='Number of query and key heads for the gated delta net.') group.add_argument('--linear-num-value-heads', default=32, type=int, help='Number of value and gate heads for the gated delta net.') + + # DSA + group.add_argument('--dsa-indexer-n-heads', default=None, type=int, + help='Number of indexer heads for sparse attention. If not set, defaults to num-attention-heads.') + group.add_argument('--dsa-indexer-head-dim', default=None, type=int, + help='Dimension per indexer head for sparse attention. If not set, defaults to kv-channels.') + group.add_argument('--dsa-indexer-topk', default=None, type=int, + help='Number of top-k tokens to select in sparse attention indexer.') + group.add_argument('--dsa-indexer-loss-coeff', default=0.0, type=float, + help='Coefficient for the indexer KL divergence loss. Set to 0 to disable indexer loss.') + group.add_argument('--dsa-indexer-use-sparse-loss', action='store_true', + help='Use sparse indexer loss. If set, the indexer loss will be computed using the top-k indices.') + return parser def _add_heterogeneous_args(parser): diff --git a/megatron/training/training.py b/megatron/training/training.py index 9986f931641..5c9de623ce5 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -91,6 +91,7 @@ from megatron.core.optimizer_param_scheduler import OptimizerParamScheduler from megatron.core.transformer.moe import upcycling_utils from megatron.core.transformer.moe.moe_utils import track_moe_metrics +from megatron.core.transformer.experimental_attention_variant.dsa import DSAIndexerLossLoggingHelper from megatron.core.transformer.multi_token_prediction import MTPLossLoggingHelper from megatron.core.parallel_state import ( destroy_global_memory_buffer, @@ -375,7 +376,8 @@ def transformer_flops(): ) ) - if args.linear_attention_type is not None: + linear_attention_variants = ["gated_delta_net"] + if args.experimental_attention_variant in linear_attention_variants: # Calculate number of dense and MoE Transformer MLPs. if isinstance(args.linear_attention_freq, int): linear_attention_pattern = [ @@ -400,7 +402,7 @@ def transformer_flops(): num_linear_attention_layers = sum(linear_attention_pattern) num_standard_attention_layers = num_layers - num_linear_attention_layers - if args.linear_attention_type == "gated_delta_net": + if args.experimental_attention_variant == "gated_delta_net": # Calculate the FLOPs for the gated delta net attention. qk_head_dim = args.linear_key_head_dim v_head_dim = args.linear_value_head_dim @@ -1698,6 +1700,16 @@ def training_log( MTPLossLoggingHelper.track_mtp_metrics( mtp_loss_scale, iteration, writer, wandb_writer, total_loss_dict ) + # Track sparse attention indexer loss + if args.dsa_indexer_loss_coeff is not None and args.dsa_indexer_loss_coeff > 0: + indexer_loss_scale = 1 / get_num_microbatches() + DSAIndexerLossLoggingHelper.track_indexer_metrics( + loss_scale=indexer_loss_scale, + iteration=iteration, + writer=writer, + wandb_writer=wandb_writer, + total_loss_dict=total_loss_dict, + ) if iteration % args.log_interval == 0: if args.record_memory_history and (is_last_rank() or torch.distributed.get_backend() == 'fake'): snapshot = torch.cuda.memory._snapshot() @@ -1929,6 +1941,7 @@ def post_training_step_callbacks( # Straggler detector. if iteration % args.log_interval == 0 and args.log_straggler: + # Use FLOPs accumulated since last log event and then reset the counter stimer.report(num_floating_point_operations_since_last_log_event, args.log_interval) num_floating_point_operations_since_last_log_event = 0.0 @@ -1970,6 +1983,9 @@ def post_training_step_callbacks( if args.manual_gc_interval != 0 and iteration % args.manual_gc_interval == 0: gc.collect() + # Return updated FLOPs accumulator so caller can persist the reset + return num_floating_point_operations_since_last_log_event + def checkpoint_and_decide_exit( model, @@ -2585,8 +2601,9 @@ def get_e2e_base_metrics(): energy_monitor.resume() # Miscellaneous post-training-step functions (e.g., FT heartbeats, GC). - # Some of these only happen at specific iterations. - post_training_step_callbacks( + # Some of these only happen at specific iterations. Capture updated FLOPs accumulator + # (it is reset inside the callback after logging). + num_floating_point_operations_since_last_log_event = post_training_step_callbacks( model, optimizer, opt_param_scheduler, diff --git a/pyproject.toml b/pyproject.toml index 7f734927c1a..553f898ae6f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,7 @@ dynamic = ["version", "readme"] description = "Megatron Core - a library for efficient and scalable training of transformer based models" requires-python = ">=3.10" license = { text = "Apache 2.0" } -dependencies = ["torch", "numpy<2.0.0", "packaging>=24.2"] +dependencies = ["torch", "numpy", "packaging>=24.2"] authors = [{ name = "NVIDIA", email = "nemo-toolkit@nvidia.com" }] maintainers = [{ name = "NVIDIA", email = "nemo-toolkit@nvidia.com" }] keywords = [ @@ -67,37 +67,44 @@ Homepage = "https://github.com/NVIDIA/Megatron-LM/megatron/core" mlm = ["flask-restful", "sentencepiece", "tiktoken", "wandb", "transformers"] dev = [ - "nvidia-modelopt[torch]>=0.33.0a0,<0.34.0; sys_platform != 'darwin'", - "transformer-engine[pytorch]>=2.9.0a0,<2.10.0", - "nvidia-resiliency-ext>=0.4.0a0,<0.5.0", + "nvidia-modelopt[torch]; sys_platform != 'darwin'", + "transformer-engine[pytorch,core_cu13]>=2.9.0a0,<2.10.0", + "nvidia-resiliency-ext", "tqdm", "einops~=0.8", "tensorstore~=0.1,!=0.1.46,!=0.1.72", "nvtx~=0.2", "multi-storage-client~=0.27", "opentelemetry-api~=1.33.1", - "setuptools<80.0.0", "mamba-ssm~=2.2", "causal-conv1d~=1.5", "nv-grouped-gemm~=1.1", "megatron-energon[av_decode]~=6.0", - "av<16.0.0", # At the time, av 16.0.0 is not compatible with Python 3.12 + "av", "flashinfer-python", "wget", "onnxscript", "flash-linear-attention~=0.3.2", "emerging_optimizers", + "fastapi~=0.50", # Forcing a little bit more recent version of fastapi to be compatible with pydantic 2.0 ] lts = [ "tqdm", - "einops", - "tensorstore!=0.1.46,!=0.1.72", - "nvtx", - "transformers", - "zarr", - "setuptools<80.0.0", + "einops~=0.8", + "tensorstore~=0.1,!=0.1.46,!=0.1.72", + "nvtx~=0.2", + "multi-storage-client~=0.27", + "opentelemetry-api~=1.33.1", + "mamba-ssm~=2.2", + "causal-conv1d~=1.5", + "nv-grouped-gemm~=1.1", + "megatron-energon[av_decode]~=6.0", + "av", + "flashinfer-python", "wget", + "onnxscript", + "fastapi~=0.50", # Forcing a little bit more recent version of fastapi to be compatible with pydantic 2.0 ] [dependency-groups] @@ -141,7 +148,7 @@ linting = [ "pylint==3.2.6", ] ci = ["python-gitlab", "slack-sdk", "pandas"] -flash_mla = ["flash_mla"] +no_pypi_wheels = ["flash_mla", "emerging_optimizers"] [tool.uv] default-groups = ["linting", "build", "test"] @@ -168,7 +175,7 @@ override-dependencies = [ flash_mla = [ { git = "https://github.com/deepseek-ai/FlashMLA", rev = "9edee0c022cd0938148a18e334203b0aab43aa19" }, ] -transformer-engine = { git = "https://github.com/NVIDIA/TransformerEngine.git", rev = "release_v2.9" } # on `release_v2.9` +# transformer-engine = { git = "https://github.com/NVIDIA/TransformerEngine.git", rev = "release_v2.9" } # on `release_v2.9` nemo-run = { git = "https://github.com/NVIDIA-NeMo/Run.git", rev = "01a9a8ba360f7b2908728ad0516e0ad9d936966d" } emerging_optimizers = { git = "https://github.com/NVIDIA-NeMo/Emerging-Optimizers.git", rev = "v0.1.0" } diff --git a/tests/test_utils/python_scripts/download_unit_tests_dataset.py b/tests/test_utils/python_scripts/download_unit_tests_dataset.py index 04470c2f820..a29394c29de 100644 --- a/tests/test_utils/python_scripts/download_unit_tests_dataset.py +++ b/tests/test_utils/python_scripts/download_unit_tests_dataset.py @@ -1,21 +1,35 @@ +# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + #!/usr/bin/env python3 """ Script to fetch the oldest release of NVIDIA/Megatron-LM on GitHub and list its assets. Uses the PyGithub SDK to interact with the GitHub API. """ -import os -import sys +import logging import tarfile import zipfile from pathlib import Path import click import requests -from github import Github +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +ASSETS = [ + { + "name": "datasets.zip", + "url": "https://github.com/NVIDIA/Megatron-LM/releases/download/v2.5/datasets.zip", + }, + { + "name": "tokenizers.zip", + "url": "https://github.com/NVIDIA/Megatron-LM/releases/download/v2.5/tokenizers.zip", + }, +] -def download_and_extract_asset(asset_url: str, asset_name: str, assets_dir: Path) -> bool: + +def download_and_extract_asset(assets_dir: Path) -> bool: """ Download and extract an asset to the assets directory. @@ -27,144 +41,43 @@ def download_and_extract_asset(asset_url: str, asset_name: str, assets_dir: Path Returns: bool: True if successful, False otherwise """ - try: - # Download the asset - print(f" Downloading {asset_name}...") - response = requests.get(asset_url, stream=True) - response.raise_for_status() - - # Save to temporary file - temp_file = assets_dir / asset_name - with open(temp_file, 'wb') as f: - for chunk in response.iter_content(chunk_size=8192): - f.write(chunk) - - print(f" Extracting {asset_name} to {assets_dir}...") - - # Extract based on file type - if asset_name.endswith('.zip'): - with zipfile.ZipFile(temp_file, 'r') as zip_ref: - zip_ref.extractall(assets_dir) - elif asset_name.endswith(('.tar.gz', '.tgz')): - with tarfile.open(temp_file, 'r:gz') as tar_ref: - tar_ref.extractall(assets_dir) - elif asset_name.endswith('.tar'): - with tarfile.open(temp_file, 'r') as tar_ref: - tar_ref.extractall(assets_dir) - else: - print(f" Warning: Unknown file type for {asset_name}, skipping extraction") - return False - - # Clean up temporary file - temp_file.unlink() - print(f" Successfully extracted to {assets_dir}") - return True - - except Exception as e: - print(f" Error downloading/extracting {asset_name}: {e}") - return False - - -def get_oldest_release_and_assets( - repo_name: str = "NVIDIA/Megatron-LM", assets_dir: str = "assets" -) -> None: - """ - Fetch the oldest release of a GitHub repository and list its assets. - - Args: - repo_name: The repository name in format "owner/repo" - assets_dir: Directory to extract assets to - """ - try: - # Initialize GitHub client - g = Github(login_or_token=os.getenv('GH_TOKEN', None)) - - # Get the repository - repo = g.get_repo(repo_name) - print(f"Repository: {repo.full_name}") - print(f"Description: {repo.description}") - print(f"URL: {repo.html_url}") - print("-" * 80) - - # Get all releases - releases = list(repo.get_releases()) - - if not releases: - print("No releases found for this repository.") - return - - # Sort releases by creation date to find the oldest - releases.sort(key=lambda x: x.created_at) - oldest_release = releases[0] - - print(f"Oldest Release:") - print(f" Tag: {oldest_release.tag_name}") - print(f" Title: {oldest_release.title}") - print(f" Created: {oldest_release.created_at}") - print(f" Published: {oldest_release.published_at}") - print(f" Draft: {oldest_release.draft}") - print(f" Prerelease: {oldest_release.prerelease}") - print(f" URL: {oldest_release.html_url}") - - if oldest_release.body: - print(f" Description: {oldest_release.body[:200]}...") - - print("-" * 80) - - # List assets - assets = list(oldest_release.get_assets()) - - if not assets: - print("No assets found for this release.") - return - - print(f"Assets ({len(assets)} total):") - print("-" * 80) - - for i, asset in enumerate(assets, 1): - print(f"{i}. {asset.name}") - print(f" Size: {asset.size} bytes ({asset.size / 1024 / 1024:.2f} MB)") - print(f" Downloads: {asset.download_count}") - print(f" Content Type: {asset.content_type}") - print(f" URL: {asset.browser_download_url}") - print(f" Created: {asset.created_at}") - print(f" Updated: {asset.updated_at}") - print() - - # Summary - total_size = sum(asset.size for asset in assets) - total_downloads = sum(asset.download_count for asset in assets) - - print(f"Summary:") - print(f" Total assets: {len(assets)}") - print(f" Total size: {total_size} bytes ({total_size / 1024 / 1024:.2f} MB)") - print(f" Total downloads: {total_downloads}") - - # Download and extract assets if requested - if assets: - print("-" * 80) - print("Downloading and extracting assets...") - - # Create assets directory - assets_path = Path(assets_dir) - assets_path.mkdir(parents=True, exist_ok=True) - print(f"Created assets directory: {assets_path.absolute()}") - - successful_downloads = 0 - for asset in assets: - print(f"\nProcessing asset: {asset.name}") - if download_and_extract_asset(asset.browser_download_url, asset.name, assets_path): - successful_downloads += 1 - - print(f"\nDownload Summary:") - print( - f" Successfully downloaded and extracted: {successful_downloads}/{len(assets)} assets" - ) - print(f" Assets directory: {assets_path.absolute()}") - - except Exception as e: - print(f"Error: {e}") - sys.exit(1) + for asset in ASSETS: + asset_name, asset_url = asset.values() + try: + # Download the asset + logger.info(f" Downloading {asset_name}...") + response = requests.get(asset_url, stream=True) + response.raise_for_status() + + # Save to temporary file + temp_file = assets_dir / asset_name + with open(temp_file, 'wb') as f: + for chunk in response.iter_content(chunk_size=8192): + f.write(chunk) + + logger.info(f" Extracting {asset_name} to {assets_dir}...") + + # Extract based on file type + if asset_name.endswith('.zip'): + with zipfile.ZipFile(temp_file, 'r') as zip_ref: + zip_ref.extractall(assets_dir) + elif asset_name.endswith(('.tar.gz', '.tgz')): + with tarfile.open(temp_file, 'r:gz') as tar_ref: + tar_ref.extractall(assets_dir) + elif asset_name.endswith('.tar'): + with tarfile.open(temp_file, 'r') as tar_ref: + tar_ref.extractall(assets_dir) + else: + logger.warning( + f" Warning: Unknown file type for {asset_name}, skipping extraction" + ) + + # Clean up temporary file + temp_file.unlink() + logger.info(f" Successfully extracted to {assets_dir}") + + except Exception as e: + logger.error(f" Error downloading/extracting {asset_name}: {e}") @click.command() @@ -174,10 +87,12 @@ def get_oldest_release_and_assets( @click.option('--assets-dir', default='assets', help='Directory to extract assets to') def main(repo, assets_dir): """Fetch the oldest release of a GitHub repository and download its assets.""" - print(f"Fetching oldest release of {repo}...") - print("=" * 80) + logger.info(f"Fetching oldest release of {repo}...") + logger.info("=" * 80) + + Path(assets_dir).mkdir(parents=True, exist_ok=True) - get_oldest_release_and_assets(repo_name=repo, assets_dir=assets_dir) + download_and_extract_asset(Path(assets_dir)) if __name__ == "__main__": diff --git a/tests/test_utils/recipes/gpt-dynamic-inference-with-coordinator.yaml b/tests/test_utils/recipes/gpt-dynamic-inference-with-coordinator.yaml index c61128aaca2..1b4786e8230 100644 --- a/tests/test_utils/recipes/gpt-dynamic-inference-with-coordinator.yaml +++ b/tests/test_utils/recipes/gpt-dynamic-inference-with-coordinator.yaml @@ -3,7 +3,7 @@ format_version: 1 maintainers: [mcore] loggers: [stdout] spec: - name: "{test_case}_{environment}_{platforms}" + name: '{test_case}_{environment}_{platforms}' model: gpt build: mcore-pyt-{environment} nodes: 1 @@ -62,5 +62,5 @@ products: - test_case: [gpt_dynamic_inference_tp1_pp1_dp8_583m_logitsmatch_zmq] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] diff --git a/tests/test_utils/recipes/gpt-dynamic-inference.yaml b/tests/test_utils/recipes/gpt-dynamic-inference.yaml index 66fa6887de8..a3853c3d9e1 100644 --- a/tests/test_utils/recipes/gpt-dynamic-inference.yaml +++ b/tests/test_utils/recipes/gpt-dynamic-inference.yaml @@ -3,7 +3,7 @@ format_version: 1 maintainers: [mcore] loggers: [stdout] spec: - name: "{test_case}_{environment}_{platforms}" + name: '{test_case}_{environment}_{platforms}' model: gpt build: mcore-pyt-{environment} nodes: 1 @@ -62,15 +62,15 @@ products: - test_case: [gpt_dynamic_inference_tp8_pp1_583m_logitsmatch] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt_dynamic_inference_tp1_pp1_583m_cuda_graphs_fp8_logitsmatch] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt_dynamic_inference_tp1_pp1_583m_cuda_graphs_logitsmatch_decode_graphs_only] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] diff --git a/tests/test_utils/recipes/gpt-static-inference.yaml b/tests/test_utils/recipes/gpt-static-inference.yaml index 033c6c35116..39c2c3c934e 100644 --- a/tests/test_utils/recipes/gpt-static-inference.yaml +++ b/tests/test_utils/recipes/gpt-static-inference.yaml @@ -3,7 +3,7 @@ format_version: 1 maintainers: [mcore] loggers: [stdout] spec: - name: "{test_case}_{environment}_{platforms}" + name: '{test_case}_{environment}_{platforms}' model: gpt build: mcore-pyt-{environment} nodes: 1 @@ -57,20 +57,20 @@ products: - test_case: [gpt_static_inference_tp1_pp1_583m_logitsmatch] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt_static_inference_tp1_pp1_583m_cudagraphs] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt_static_inference_tp1_pp1_583m_fp8_cudagraphs] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt_static_inference_tp1_pp1_16b_multiprompt_tokensmatch] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] diff --git a/tests/test_utils/recipes/gpt.yaml b/tests/test_utils/recipes/gpt.yaml index 0b3606fd702..0b068c55220 100644 --- a/tests/test_utils/recipes/gpt.yaml +++ b/tests/test_utils/recipes/gpt.yaml @@ -110,7 +110,7 @@ products: - test_case: [gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] @@ -124,201 +124,201 @@ products: - test_case: [gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] # - test_case: [gpt3_mcore_te_tp1_pp2_resume_torch_dist_cp4_a2a_p2p_nondeterministic] # products: # - environment: [dev] - # scope: [mr, mr-github] + # scope: [mr] # - environment: [lts] # scope: [nightly] # Non-deterministic: #487 - test_case: [gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] # - environment: [lts] # scope: [nightly] # outdated TE: #501 - test_case: [gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_interleaved_no_fusion] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] # - environment: [lts] # scope: [nightly] # non-determinism: #436 - test_case: [gpt3_mcore_te_tp1_pp4_resume_torch_dist_persistent_disable_bias_linear] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_resume_torch_dist_swiglu] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] # - environment: [lts] # scope: [nightly] # non-determinism: #437 - test_case: [gpt3_mcore_te_tp1_pp4_resume_torch_dist_untie_embeddings_and_outputs] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_vp1] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] # - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist] # products: # - environment: [dev] - # scope: [mr, mr-github] + # scope: [mr] # platforms: [dgx_h100] # Hangs: #513 # - environment: [lts] # scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather] products: # - environment: [dev] - # scope: [mr, mr-github] + # scope: [mr] # platforms: [dgx_h100] # Hangs: #513 - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_untied] products: # - environment: [dev] - # scope: [mr, mr-github] # Hangs: #513 + # scope: [mr] # Hangs: #513 # platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_tunable_overlap] products: # - environment: [dev] - # scope: [mr, mr-github] # Hangs: #513 + # scope: [mr] # Hangs: #513 # platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_vp2_account_for_embedding_loss_in_pipeline_split] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp2_cp2] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp2_pp2_cp2_etp4_dp_last] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp2_pp2_cp2_nondeterministic] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp2_cp2_etp4_nondeterministic_dp_last] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_nondeterministic] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_nondeterministic_dp_last] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp2_cross_entropy_loss_fusion] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] @@ -326,14 +326,14 @@ products: - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_cross_entropy_loss_fusion] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] @@ -345,96 +345,96 @@ products: - test_case: [gpt3_mcore_te_tp2_pp2_mla] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_defer_embedding_wgrad_compute] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_no_create_attention_mask_in_dataloader] products: # - environment: [dev] - # scope: [mr, mr-github] # Hangs: #513 + # scope: [mr] # Hangs: #513 # platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_te_tp4_pp1_resume_torch_dist_qk_layernorm_test_mode] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_mcore_tp2_pp2_uninstall_te] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] scope: [nightly] - test_case: [gpt3_7b_tp1_pp4_memory_speed] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] # - environment: [lts] # scope: [nightly] # OOM: #434 - test_case: [gpt3_7b_tp4_pp1_memory_speed] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] # - environment: [lts] # scope: [nightly] # OOM: #434 - test_case: [gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] # - environment: [lts] # scope: [nightly] - test_case: [gpt3_mcore_te_tp2_pp1_modelopt_distill_resume] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] # - environment: [lts] # scope: [nightly] # Outdated: #502 # - test_case: [gpt3_mcore_te_tp2_pp1_fsdp2_resume_torch_dist] # products: # - environment: [dev] - # scope: [mr, mr-github] # Broken: #484 + # scope: [mr] # Broken: #484 # - environment: [lts] # scope: [nightly] # Requires PyT 2.4: #481 ####################################################################### @@ -450,57 +450,57 @@ products: # - test_case: [gpt3_mcore_reruns_persistent_2] # products: # - environment: [dev] - # scope: [mr, mr-github] + # scope: [mr] # platforms: [dgx_h100] # - environment: [lts] # scope: [nightly] - test_case: [gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer] products: - environment: [lts] - scope: [mr, mr-github] + scope: [mr] - environment: [dev] - scope: [mr, mr-github, mr-slim] + scope: [mr, mr-github, mr-github-slim] platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] - scope: [mr, mr-github] + scope: [mr] - test_case: [gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather] products: - environment: [lts] - scope: [mr, mr-github] + scope: [mr] - environment: [dev] - scope: [mr, mr-github, mr-slim] + scope: [mr, mr-github, mr-github-slim] platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp4_pp2_resume_torch_dist_reshard_8x1xNone] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - environment: [lts] - scope: [mr, mr-github] + scope: [mr] # - test_case: [gpt3_mcore_te_tp4_pp2_frozen_resume_torch_dist_reshard_8x1xNone] # products: # - environment: [dev] - # scope: [mr, mr-github] + # scope: [mr] # platforms: [dgx_h100] # - test_case: [gpt3_mcore_te_tp1_pp1_frozen_resume_torch_dist_dist_optimizer] # products: # - environment: [dev] - # scope: [mr, mr-github] + # scope: [mr] # platforms: [dgx_h100] # - test_case: [gpt3_mcore_te_tp1_pp4_frozen_resume_torch_dist_swiglu] # products: # - environment: [dev] - # scope: [mr, mr-github] + # scope: [mr] # platforms: [dgx_h100] # - test_case: [gpt3_mcore_te_tp2_pp1_frozen_resume_torch_dist_cp2_nondeterministic] # products: # - environment: [dev] - # scope: [mr, mr-github] + # scope: [mr] # platforms: [dgx_a100, dgx_h100] # - test_case: [gpt3_weekly_dgx_b200_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap] # products: @@ -550,4 +550,4 @@ products: # - test_case: [gpt3_mcore_tp2_pp2_resume_torch_dist_uninstall_te] # products: # - environment: [dev, lts] - # scope: [mr, mr-github] # Non-deterministic: #483 + # scope: [mr] # Non-deterministic: #483 diff --git a/tests/test_utils/recipes/mamba-static-inference.yaml b/tests/test_utils/recipes/mamba-static-inference.yaml index 06107618916..9645b1b0b8a 100644 --- a/tests/test_utils/recipes/mamba-static-inference.yaml +++ b/tests/test_utils/recipes/mamba-static-inference.yaml @@ -3,7 +3,7 @@ format_version: 1 maintainers: [mcore] loggers: [stdout] spec: - name: "{test_case}_{environment}_{platforms}" + name: '{test_case}_{environment}_{platforms}' model: hybrid build: mcore-pyt-{environment} nodes: 1 @@ -57,10 +57,10 @@ products: - test_case: [hybrid_static_inference_tp1_pp1_2B_logitsmatch] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [hybrid_static_inference_tp1_pp1_2B_cudagraphs] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] diff --git a/tests/test_utils/recipes/mamba.yaml b/tests/test_utils/recipes/mamba.yaml index bb742200d26..92b799d3d1c 100644 --- a/tests/test_utils/recipes/mamba.yaml +++ b/tests/test_utils/recipes/mamba.yaml @@ -3,7 +3,7 @@ format_version: 1 maintainers: [mcore] loggers: [stdout] spec: - name: "{test_case}_{environment}_{platforms}" + name: '{test_case}_{environment}_{platforms}' model: hybrid build: mcore-pyt-{environment} nodes: 1 @@ -58,7 +58,7 @@ products: - test_case: [hybrid_mr_mcore_te_tp1_pp1_cp1_dgx_a100_1N8G] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] # - environment: [lts] # disabled until triton is bumped # scope: [nightly] @@ -67,14 +67,14 @@ products: # - test_case: [hybrid_mr_mcore_te_tp1_pp4_cp1_dgx_a100_1N8G] # products: # - environment: [dev] - # scope: [mr, mr-github] + # scope: [mr] # - environment: [lts] # disabled until triton is bumped # scope: [nightly] - test_case: [hybrid_mr_mcore_te_tp2_pp1_cp1_dgx_a100_1N8G] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] # - environment: [lts] # disabled until triton is bumped # scope: [nightly] @@ -82,7 +82,7 @@ products: - test_case: [hybrid_mr_mcore_te_tp2_pp1_cp4_dgx_a100_1N8G] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] # - environment: [lts] # disabled until triton is bumped # scope: [nightly] diff --git a/tests/test_utils/recipes/moe-dynamic-inference.yaml b/tests/test_utils/recipes/moe-dynamic-inference.yaml index 9bb23f8a322..6d8fdc533e1 100644 --- a/tests/test_utils/recipes/moe-dynamic-inference.yaml +++ b/tests/test_utils/recipes/moe-dynamic-inference.yaml @@ -3,7 +3,7 @@ format_version: 1 maintainers: [mcore] loggers: [stdout] spec: - name: "{test_case}_{environment}_{platforms}" + name: '{test_case}_{environment}_{platforms}' model: moe build: mcore-pyt-{environment} nodes: 1 @@ -57,10 +57,10 @@ products: - test_case: [gpt_dynamic_inference_tp4_pp1_ep4_16B_logitsmatch] products: - environment: [dev] - scope: [mr-broken, mr-github] + scope: [mr-broken] platforms: [dgx_h100] - test_case: [gpt_dynamic_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] diff --git a/tests/test_utils/recipes/moe-static-inference.yaml b/tests/test_utils/recipes/moe-static-inference.yaml index 136606d0955..9cebb66f2e2 100644 --- a/tests/test_utils/recipes/moe-static-inference.yaml +++ b/tests/test_utils/recipes/moe-static-inference.yaml @@ -3,7 +3,7 @@ format_version: 1 maintainers: [mcore] loggers: [stdout] spec: - name: "{test_case}_{environment}_{platforms}" + name: '{test_case}_{environment}_{platforms}' model: moe build: mcore-pyt-{environment} nodes: 1 @@ -57,15 +57,15 @@ products: - test_case: [gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] diff --git a/tests/test_utils/recipes/moe.yaml b/tests/test_utils/recipes/moe.yaml index 2d4e8c4c94c..285d16c99f3 100644 --- a/tests/test_utils/recipes/moe.yaml +++ b/tests/test_utils/recipes/moe.yaml @@ -3,7 +3,7 @@ format_version: 1 maintainers: [mcore] loggers: [stdout] spec: - name: "{test_case}_{environment}_{platforms}" + name: '{test_case}_{environment}_{platforms}' model: moe build: mcore-pyt-{environment} nodes: 1 @@ -84,27 +84,27 @@ products: - test_case: [gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp2_pp2_ep4_etp1_resume_torch_dist_attn_cudagraph] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] # hang: #513 # - test_case: [gpt3_mcore_te_tp2_pp2_ep4_etp1_selective_recompute_experimental] # products: # - environment: [dev] - # scope: [mr, mr-github] + # scope: [mr] # platforms: [dgx_h100] # hang: #513 - test_case: [gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp2_zp_z3_resume_torch_dist_te_8experts2parallel_top2router] products: @@ -114,12 +114,12 @@ products: - test_case: [gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] # - test_case: [gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon] # products: @@ -152,12 +152,12 @@ products: # - test_case: [gpt3_mcore_te_tp2_pp1_frozen_resume_torch_dist_te_8experts2parallel_dist_optimizer] # products: # - environment: [dev] - # scope: [mr, mr-github] + # scope: [mr] # platforms: [dgx_h100] # - test_case: [gpt3_mcore_te_tp2_pp1_frozen_resume_torch_dist_te_8experts2parallel_groupedGEMM] # products: # - environment: [dev] - # scope: [mr, mr-github] + # scope: [mr] # platforms: [dgx_h100] ########################### # Merge train tests # @@ -165,12 +165,12 @@ products: - test_case: [gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer] products: - environment: [dev] - scope: [mr, mr-github, mr-slim] + scope: [mr, mr-github, mr-github-slim] platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp2_pp2_ep4_etp1_memory_speed] products: - environment: [dev] - scope: [mr, mr-github, mr-slim] + scope: [mr] platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp2_pp2_ep4_etp1_mtp_resume_torch_dist_fp8] products: diff --git a/tests/test_utils/recipes/multimodal-llava.yaml b/tests/test_utils/recipes/multimodal-llava.yaml index 0e199764c09..72702de33c5 100644 --- a/tests/test_utils/recipes/multimodal-llava.yaml +++ b/tests/test_utils/recipes/multimodal-llava.yaml @@ -3,7 +3,7 @@ format_version: 1 maintainers: [mcore] loggers: [stdout] spec: - name: "{test_case}_{environment}_{platforms}" + name: '{test_case}_{environment}_{platforms}' model: multimodal-llava build: mcore-pyt-{environment} nodes: 1 @@ -61,10 +61,10 @@ products: - test_case: [multimodal_llava_mcore_te_tp1_pp1] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] - test_case: [multimodal_llava_mcore_te_tp4_sp_cp2] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr] platforms: [dgx_h100] diff --git a/tests/unit_tests/conftest.py b/tests/unit_tests/conftest.py index 611f9ae6098..e251a3c1e7e 100644 --- a/tests/unit_tests/conftest.py +++ b/tests/unit_tests/conftest.py @@ -1,5 +1,6 @@ +# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + import os -import sys from pathlib import Path import pytest @@ -8,9 +9,7 @@ from megatron.core import config from megatron.core.utils import is_te_min_version -from tests.test_utils.python_scripts.download_unit_tests_dataset import ( - get_oldest_release_and_assets, -) +from tests.test_utils.python_scripts.download_unit_tests_dataset import download_and_extract_asset from tests.unit_tests.dist_checkpointing import TempNamedDir from tests.unit_tests.test_utilities import Utils @@ -83,7 +82,7 @@ def ensure_test_data(): try: # Download assets to /opt/data - get_oldest_release_and_assets(assets_dir=str(data_path)) + download_and_extract_asset(assets_dir=str(data_path)) print("Test data downloaded successfully.") diff --git a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py index 873505fe51c..3ac8e7f6200 100644 --- a/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py +++ b/tests/unit_tests/fusions/test_fused_linear_cross_entropy.py @@ -38,6 +38,7 @@ class DistContext: group: dist.ProcessGroup is_chief: bool + # 2. Create a module-scoped fixture # This runs ONE time per file, no matter how many test classes you have. @pytest.fixture(scope="module") @@ -48,7 +49,7 @@ def distributed_context(): # --- SETUP --- is_external_init = dist.is_initialized() - + if not is_external_init: # Initialize only if not already done (e.g., by another test runner) dist.init_process_group( @@ -67,15 +68,10 @@ def distributed_context(): rank = dist.get_rank() world_size = dist.get_world_size() group = dist.group.WORLD - + print(f"[INFO]: Initialized Rank: {rank} / {world_size}") - context = DistContext( - rank=rank, - world_size=world_size, - group=group, - is_chief=(rank == 0) - ) + context = DistContext(rank=rank, world_size=world_size, group=group, is_chief=(rank == 0)) # Yield control to the tests yield context @@ -194,6 +190,7 @@ def init_gpt_dataloader( dataloader = DataLoader(dataset, batch_size=batch_size, sampler=sampler) return dataloader + # skip it for good @pytest.mark.skipif( ("WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2) or True, @@ -258,9 +255,7 @@ def test_gpt_model(self, mtp_layers, dispatcher_type, fp8_flag, layer_num): @pytest.mark.skipif( "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "1", reason="Requires single GPU" ) -@pytest.mark.skipif( - get_device_arch_version() != 10, reason="Requires GPU architecture = 10" -) +@pytest.mark.skipif(get_device_arch_version() != 10, reason="Requires GPU architecture = 10") class TestFusedLinearCrossEntropyDataParallel: def cleanup(self): torch.cuda.empty_cache() @@ -562,9 +557,7 @@ def custom_storage(): ("WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2), # or True, reason="Requires torchrun with multiple GPUs", ) -@pytest.mark.skipif( - get_device_arch_version() != 10, reason="Requires GPU architecture = 10" -) +@pytest.mark.skipif(get_device_arch_version() != 10, reason="Requires GPU architecture = 10") @pytest.mark.usefixtures("distributed_context") class TestFusedLinearCrossEntropyTensorParallel: @pytest.fixture(autouse=True) @@ -576,7 +569,6 @@ def setup_attrs(self, distributed_context): self.tp_rank = distributed_context.rank self.tp_world_size = distributed_context.world_size self.is_chief = distributed_context.is_chief - def cleanup(self): torch.cuda.empty_cache() @@ -1005,9 +997,7 @@ def custom_storage(): "WORLD_SIZE" not in os.environ or int(os.environ["WORLD_SIZE"]) < 2, reason="Requires torchrun with multiple GPUs", ) -@pytest.mark.skipif( - get_device_arch_version() != 10, reason="Requires GPU architecture = 10" -) +@pytest.mark.skipif(get_device_arch_version() != 10, reason="Requires GPU architecture = 10") @pytest.mark.usefixtures("distributed_context") class TestFusedLinearCrossEntropySequenceParallel: @pytest.fixture(autouse=True) diff --git a/tests/unit_tests/ssm/test_gated_delta_net.py b/tests/unit_tests/ssm/test_gated_delta_net.py index dbf8d203634..89a185e3755 100644 --- a/tests/unit_tests/ssm/test_gated_delta_net.py +++ b/tests/unit_tests/ssm/test_gated_delta_net.py @@ -88,7 +88,7 @@ def setup_method(self, tp_size, sp, cp_size): context_parallel_size=cp_size, ) gdn_submodules = get_gpt_layer_with_transformer_engine_spec( - linear_attention_type="gated_delta_net", normalization="RMSNorm" + experimental_attention_variant="gated_delta_net", normalization="RMSNorm" ).submodules.self_attention.submodules self.gdn = GatedDeltaNet( @@ -157,7 +157,7 @@ def test_parallel_gated_delta_net_correctness(tmp_path_dist_ckpt, tp, sp, cp): # Model initialization function def initialize_gpt_model(config, pre_process=True, post_process=True, vp_stage=None): layer_spec = get_gpt_layer_with_transformer_engine_spec( - linear_attention_type="gated_delta_net", normalization=normalization + experimental_attention_variant="gated_delta_net", normalization=normalization ) gpt_model = GPTModel( config=config, diff --git a/tests/unit_tests/transformer/test_attention_variant_dsa.py b/tests/unit_tests/transformer/test_attention_variant_dsa.py new file mode 100644 index 00000000000..bd106aa6f0e --- /dev/null +++ b/tests/unit_tests/transformer/test_attention_variant_dsa.py @@ -0,0 +1,1271 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + +from unittest.mock import patch + +import pytest +import torch + +import megatron.core.parallel_state as parallel_state +from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec +from megatron.core.process_groups_config import ProcessGroupCollection +from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed +from megatron.core.transformer import TransformerConfig +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.experimental_attention_variant.dsa import ( + DSAIndexer, + DSAIndexerLossAutoScaler, + DSAIndexerSubmodules, + DSAttention, + DSAttentionSubmodules, + compute_dsa_indexer_loss, + rotate_activation, +) +from megatron.core.transformer.transformer_config import MLATransformerConfig +from tests.unit_tests.test_utilities import Utils + +try: + from fast_hadamard_transform import hadamard_transform as _hadamard_transform + + HAVE_HADAMARD = True +except ImportError: + HAVE_HADAMARD = False + _hadamard_transform = None + + +def mock_hadamard_transform(x: torch.Tensor, scale: float = 1.0) -> torch.Tensor: + """Mock implementation of hadamard_transform for testing without the library installed. + + This is a simple identity-like transformation that preserves shape and applies scaling. + """ + return x * scale + + +@pytest.fixture(autouse=True) +def patch_hadamard_if_needed(): + """Automatically patch hadamard_transform in dsa module if not installed.""" + if not HAVE_HADAMARD: + with patch( + 'megatron.core.transformer.experimental_attention_variant.dsa.hadamard_transform', + mock_hadamard_transform, + ): + yield + else: + yield + + +class TestRotateActivation: + """Test rotate_activation function.""" + + @pytest.fixture(scope='function', autouse=True) + def setup_method(self): + Utils.initialize_model_parallel( + tensor_model_parallel_size=1, pipeline_model_parallel_size=1 + ) + yield + Utils.destroy_model_parallel() + + def test_rotate_activation_shape(self): + """Test that rotate_activation preserves shape.""" + batch_size = 2 + seq_len = 16 + hidden_size = 128 + + x = torch.randn(seq_len, batch_size, hidden_size, dtype=torch.bfloat16).cuda() + output = rotate_activation(x) + + assert output.shape == x.shape + assert output.dtype == torch.bfloat16 + + def test_rotate_activation_dtype_check(self): + """Test that rotate_activation only accepts bfloat16.""" + x = torch.randn(16, 2, 128, dtype=torch.float32).cuda() + + with pytest.raises(AssertionError, match="only support bf16"): + rotate_activation(x) + + +@pytest.mark.parametrize("seqlen_and_topk", [[16, 32], [64, 32]]) +class TestComputeDSAIndexerLoss: + """Test compute_dsa_indexer_loss function.""" + + @pytest.fixture(scope='function', autouse=True) + def setup_method(self): + Utils.initialize_model_parallel( + tensor_model_parallel_size=1, pipeline_model_parallel_size=1 + ) + self.pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp']) + yield + Utils.destroy_model_parallel() + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_indexer_loss_shape(self, seqlen_and_topk): + """Test that indexer loss returns a scalar.""" + batch_size = 2 + seqlen = seqlen_and_topk[0] + num_heads = 4 + head_dim = 128 + index_topk = seqlen_and_topk[1] + + # Create dummy index scores + index_scores = torch.randn(batch_size, seqlen, seqlen, dtype=torch.float32).cuda() + + # Apply causal mask to index_scores before computing topk + causal_mask = torch.triu( + torch.full( + (seqlen, seqlen), float('-inf'), dtype=torch.float32, device=index_scores.device + ), + diagonal=1, + ) + # [batch_size, seqlen, seqlen] + [seqlen, seqlen] -> [batch_size, seqlen, seqlen] + masked_index_scores = index_scores + causal_mask + + # Get topk indices from masked index_scores + topk_k = min(index_topk, seqlen) + topk_indices = masked_index_scores.topk(topk_k, dim=-1)[1] + + query = torch.randn(seqlen, batch_size, num_heads, head_dim, dtype=torch.bfloat16).cuda() + key = torch.randn(seqlen, batch_size, num_heads, head_dim, dtype=torch.bfloat16).cuda() + softmax_scale = head_dim**-0.5 + + loss = compute_dsa_indexer_loss( + index_scores=index_scores, + topk_indices=topk_indices, + query=query, + key=key, + softmax_scale=softmax_scale, + loss_coeff=1.0, + sparse_loss=False, + pg_collection=self.pg_collection, + ) + + assert loss.shape == torch.Size([]) + assert loss.dtype == torch.float32 + assert loss >= 0 # KL divergence should be non-negative + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_indexer_loss_sparse(self, seqlen_and_topk): + """Test sparse indexer loss computation.""" + batch_size = 2 + seqlen = seqlen_and_topk[0] + num_heads = 4 + head_dim = 128 + index_topk = seqlen_and_topk[1] + + # Create dummy index scores + index_scores = torch.randn(batch_size, seqlen, seqlen, dtype=torch.float32).cuda() + + # Apply causal mask to index_scores before computing topk + causal_mask = torch.triu( + torch.full( + (seqlen, seqlen), float('-inf'), dtype=torch.float32, device=index_scores.device + ), + diagonal=1, + ) + # [batch_size, seqlen, seqlen] + [seqlen, seqlen] -> [batch_size, seqlen, seqlen] + masked_index_scores = index_scores + causal_mask + + # Get topk indices from masked index_scores + topk_k = min(index_topk, seqlen) + topk_indices = masked_index_scores.topk(topk_k, dim=-1)[1] + + query = torch.randn(seqlen, batch_size, num_heads, head_dim, dtype=torch.bfloat16).cuda() + key = torch.randn(seqlen, batch_size, num_heads, head_dim, dtype=torch.bfloat16).cuda() + softmax_scale = head_dim**-0.5 + + loss_sparse = compute_dsa_indexer_loss( + index_scores=index_scores, + topk_indices=topk_indices, + query=query, + key=key, + softmax_scale=softmax_scale, + loss_coeff=1.0, + sparse_loss=True, + pg_collection=self.pg_collection, + ) + + loss_dense = compute_dsa_indexer_loss( + index_scores=index_scores, + topk_indices=topk_indices, + query=query, + key=key, + softmax_scale=softmax_scale, + loss_coeff=1.0, + sparse_loss=False, + pg_collection=self.pg_collection, + ) + + # Sparse loss should be different from dense loss + if seqlen > index_topk: + assert loss_sparse != loss_dense + else: + assert loss_sparse == loss_dense + assert loss_sparse >= 0 + assert loss_dense >= 0 + + +class TestDSAIndexerLossAutoScaler: + """Test DSAIndexerLossAutoScaler autograd function.""" + + @pytest.fixture(scope='function', autouse=True) + def setup_method(self): + Utils.initialize_model_parallel( + tensor_model_parallel_size=1, pipeline_model_parallel_size=1 + ) + yield + Utils.destroy_model_parallel() + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_forward_pass(self): + """Test that forward pass preserves output.""" + output = torch.randn(16, 2, 128).cuda() + output.requires_grad_(True) + indexer_loss = torch.tensor(0.5).cuda() + indexer_loss.requires_grad_(True) + + result = DSAIndexerLossAutoScaler.apply(output, indexer_loss) + + assert torch.allclose(result, output, atol=0, rtol=0) + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_backward_pass(self): + """Test that backward pass triggers indexer loss backward and scales gradient correctly.""" + output = torch.randn(16, 2, 128).cuda() + output.requires_grad_(True) + + # Create indexer_loss with computation graph + # This simulates compute_dsa_indexer_loss which computes KL divergence + dummy_input = torch.randn(10).cuda() + dummy_input.requires_grad_(True) + indexer_loss = dummy_input.mean() + + # Set loss scale + scale = torch.tensor(2.0).cuda() + DSAIndexerLossAutoScaler.set_loss_scale(scale) + + # Apply the autograd function + result = DSAIndexerLossAutoScaler.apply(output, indexer_loss) + + # Trigger backward + main_loss = result.sum() + main_loss.backward() + + # Check that gradients flow back to output + assert output.grad is not None, "Gradient should flow back to parameters" + + # Check that indexer_loss backward was triggered + assert dummy_input.grad is not None, "Indexer loss backward should be triggered" + + # Verify the gradient is scaled correctly + expected_grad_per_element = scale.item() / len(dummy_input) + assert torch.allclose( + dummy_input.grad, + torch.full_like(dummy_input, expected_grad_per_element), + rtol=0, + atol=0, + ), f"Gradient should be scaled by loss scale, expected {expected_grad_per_element}, got {dummy_input.grad[0].item()}" + + +@pytest.mark.parametrize("seqlen", [16, 64]) +class TestDSAIndexer: + """Test DSA Indexer module basic functionality with TP=1.""" + + @pytest.fixture(scope='function', autouse=True) + def setup_method(self): + Utils.initialize_model_parallel( + tensor_model_parallel_size=1, pipeline_model_parallel_size=1 + ) + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + # Create MLA config with sparse attention parameters + self.index_topk = 32 + self.config = MLATransformerConfig( + num_layers=2, + hidden_size=256, + num_attention_heads=16, + use_cpu_initialization=True, + bf16=True, + params_dtype=torch.bfloat16, + # MLA specific configs + q_lora_rank=64, + kv_lora_rank=64, + qk_head_dim=64, + qk_pos_emb_head_dim=32, + v_head_dim=64, + rope_type='rope', + rotary_base=10000, + rotary_percent=1.0, + # Sparse attention specific configs + dsa_indexer_n_heads=8, + dsa_indexer_head_dim=64, + dsa_indexer_topk=self.index_topk, + ) + + # Create indexer submodules spec + from megatron.core.extensions.transformer_engine import TELinear, TENorm + from megatron.core.transformer.spec_utils import ModuleSpec + + indexer_submodules = DSAIndexerSubmodules( + linear_wq_b=ModuleSpec(module=TELinear), + linear_wk=ModuleSpec(module=TELinear), + k_norm=ModuleSpec(module=TENorm), + linear_weights_proj=ModuleSpec(module=TELinear), + ) + + self.pg_collection = ProcessGroupCollection.use_mpu_process_groups( + required_pgs=['tp', 'cp'] + ) + self.indexer = DSAIndexer(self.config, indexer_submodules, self.pg_collection) + + yield + Utils.destroy_model_parallel() + + def test_dsa_indexer_constructor(self, seqlen): + """Test indexer initialization.""" + assert isinstance(self.indexer, DSAIndexer) + assert self.indexer.hidden_size == 256 + assert self.indexer.index_n_heads == 8 + assert self.indexer.index_head_dim == 64 + assert self.indexer.index_topk == 32 + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_indexer_forward(self, seqlen): + """Test indexer forward pass.""" + batch_size = 2 + + self.indexer.cuda() + + # Create input tensors + x = torch.randn(seqlen, batch_size, self.config.hidden_size, dtype=torch.bfloat16).cuda() + qr = torch.randn(seqlen, batch_size, self.config.q_lora_rank, dtype=torch.bfloat16).cuda() + + # Forward pass + topk_indices = self.indexer(x, qr) + + # Check output shape + assert topk_indices.shape == (batch_size, seqlen, min(self.config.dsa_indexer_topk, seqlen)) + assert topk_indices.dtype == torch.long + assert torch.all((topk_indices >= 0) & (topk_indices < seqlen)) + # Make sure no duplicate indices are selected + assert torch.all( + torch.sort(topk_indices, dim=-1).values[:, :, 1:] + != torch.sort(topk_indices, dim=-1).values[:, :, :-1] + ) + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_indexer_forward_with_scores(self, seqlen): + """Test indexer forward pass with scores.""" + batch_size = 2 + + self.indexer.cuda() + + # Create input tensors + x = torch.randn(seqlen, batch_size, self.config.hidden_size, dtype=torch.bfloat16).cuda() + qr = torch.randn(seqlen, batch_size, self.config.q_lora_rank, dtype=torch.bfloat16).cuda() + + # Forward pass with scores + index_scores, topk_indices = self.indexer.forward_with_scores(x, qr) + + # Check output shapes + assert index_scores.shape == (batch_size, seqlen, seqlen) + assert topk_indices.shape == (batch_size, seqlen, min(self.config.dsa_indexer_topk, seqlen)) + assert index_scores.dtype == torch.float32 + assert topk_indices.dtype == torch.long + assert torch.all((topk_indices >= 0) & (topk_indices < seqlen)) + # Make sure no duplicate indices are selected + assert torch.all( + torch.sort(topk_indices, dim=-1).values[:, :, 1:] + != torch.sort(topk_indices, dim=-1).values[:, :, :-1] + ) + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_indexer_with_mask(self, seqlen): + """Test indexer with attention mask.""" + batch_size = 2 + + self.indexer.cuda() + + # Create input tensors + x = torch.randn(seqlen, batch_size, self.config.hidden_size, dtype=torch.bfloat16).cuda() + qr = torch.randn(seqlen, batch_size, self.config.q_lora_rank, dtype=torch.bfloat16).cuda() + mask = torch.triu( + torch.full((batch_size, seqlen, seqlen), float('-inf'), dtype=torch.float32).cuda(), + diagonal=1, + ) + + # Forward pass with mask + index_scores, topk_indices = self.indexer.forward_with_scores(x, qr, mask=mask) + + # Check that masked positions are not selected + # For causal mask, topk_indices[b, i, :] should all be <= i (except for the case that + # i < index_topk). + for b in range(batch_size): + for i in range(seqlen): + assert torch.all(topk_indices[b, i] <= max(self.index_topk, i)) + + +class TestDSAttention: + """Test DSAttention module basic functionality with TP=1.""" + + @pytest.fixture(scope='function', autouse=True) + def setup_method(self): + Utils.initialize_model_parallel( + tensor_model_parallel_size=1, pipeline_model_parallel_size=1 + ) + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + # Create MLA config with sparse attention parameters + self.config = MLATransformerConfig( + num_layers=2, + hidden_size=256, + num_attention_heads=16, + use_cpu_initialization=True, + bf16=True, + params_dtype=torch.bfloat16, + # MLA specific configs + q_lora_rank=64, + kv_lora_rank=64, + qk_head_dim=64, + qk_pos_emb_head_dim=32, + v_head_dim=64, + rope_type='rope', + rotary_base=10000, + rotary_percent=1.0, + # Sparse attention specific configs + dsa_indexer_n_heads=8, + dsa_indexer_head_dim=64, + dsa_indexer_topk=32, + dsa_indexer_loss_coeff=1.0, + dsa_indexer_use_sparse_loss=False, + ) + + # Create sparse attention submodules spec + from megatron.core.extensions.transformer_engine import TELinear, TENorm + from megatron.core.transformer.spec_utils import ModuleSpec + + indexer_submodules = DSAIndexerSubmodules( + linear_wq_b=ModuleSpec(module=TELinear), + linear_wk=ModuleSpec(module=TELinear), + k_norm=ModuleSpec(module=TENorm), + linear_weights_proj=ModuleSpec(module=TELinear), + ) + indexer_spec = ModuleSpec(module=DSAIndexer, submodules=indexer_submodules) + sparse_attention_submodules = DSAttentionSubmodules(indexer=indexer_spec) + + self.pg_collection = ProcessGroupCollection.use_mpu_process_groups( + required_pgs=['tp', 'cp'] + ) + + self.sparse_attention = DSAttention( + config=self.config, + submodules=sparse_attention_submodules, + layer_number=1, + attn_mask_type=AttnMaskType.causal, + attention_type='self', + pg_collection=self.pg_collection, + ) + + yield + Utils.destroy_model_parallel() + + def test_dsa_constructor(self): + """Test sparse attention initialization.""" + assert isinstance(self.sparse_attention, DSAttention) + assert hasattr(self.sparse_attention, 'indexer') + assert isinstance(self.sparse_attention.indexer, DSAIndexer) + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_forward(self): + """Test sparse attention forward pass.""" + seq_len = 16 + batch_size = 2 + num_heads = self.config.num_attention_heads + head_dim = self.config.hidden_size // num_heads + + self.sparse_attention.cuda() + + # Create input tensors [seq_len, batch, num_heads, head_dim] + query = ( + torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) + .cuda() + .requires_grad_(True) + ) + key = ( + torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) + .cuda() + .requires_grad_(True) + ) + value = ( + torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) + .cuda() + .requires_grad_(True) + ) + + # Original hidden states and low-rank query + x = torch.randn(seq_len, batch_size, self.config.hidden_size, dtype=torch.bfloat16).cuda() + qr = torch.randn(seq_len, batch_size, self.config.q_lora_rank, dtype=torch.bfloat16).cuda() + + # Create causal attention mask + attention_mask = torch.ones(batch_size, 1, seq_len, seq_len, dtype=torch.bool).cuda() + attention_mask = torch.tril(attention_mask) + + # Forward pass + output = self.sparse_attention( + query=query, + key=key, + value=value, + x=x, + qr=qr, + attention_mask=attention_mask, + attn_mask_type=AttnMaskType.causal, + ) + + # Check output shape + assert output.shape == (seq_len, batch_size, self.config.hidden_size) + assert output.dtype == torch.bfloat16 + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_backward(self): + """Test sparse attention backward pass with indexer loss.""" + seq_len = 16 + batch_size = 2 + num_heads = self.config.num_attention_heads + head_dim = self.config.hidden_size // num_heads + + self.sparse_attention.train() + self.sparse_attention.cuda() + + # Create input tensors + query = ( + torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) + .cuda() + .requires_grad_(True) + ) + key = ( + torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) + .cuda() + .requires_grad_(True) + ) + value = ( + torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) + .cuda() + .requires_grad_(True) + ) + + # Original hidden states and low-rank query + x = torch.randn(seq_len, batch_size, self.config.hidden_size, dtype=torch.bfloat16).cuda() + qr = torch.randn(seq_len, batch_size, self.config.q_lora_rank, dtype=torch.bfloat16).cuda() + + # Create causal attention mask + attention_mask = torch.ones(batch_size, 1, seq_len, seq_len, dtype=torch.bool).cuda() + attention_mask = torch.tril(attention_mask) + + # Forward pass + output = self.sparse_attention( + query=query, + key=key, + value=value, + x=x, + qr=qr, + attention_mask=attention_mask, + attn_mask_type=AttnMaskType.causal, + ) + + # Backward pass + loss = output.sum() + loss.backward() + + # Check that gradients are computed for inputs + assert query.grad is not None + assert key.grad is not None + assert value.grad is not None + + # Check that indexer parameters have gradients + for name, param in self.sparse_attention.indexer.named_parameters(): + if param.requires_grad: + assert param.grad is not None, f"Indexer parameter {name} has no gradient" + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_topk_selection(self): + """Test that sparse attention correctly selects top-k indices.""" + seq_len = 16 + batch_size = 2 + num_heads = self.config.num_attention_heads + head_dim = self.config.hidden_size // num_heads + + self.sparse_attention.eval() + self.sparse_attention.cuda() + + # Create input tensors + query = torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16).cuda() + key = torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16).cuda() + value = torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16).cuda() + + # Original hidden states and low-rank query + x = torch.randn(seq_len, batch_size, self.config.hidden_size, dtype=torch.bfloat16).cuda() + qr = torch.randn(seq_len, batch_size, self.config.q_lora_rank, dtype=torch.bfloat16).cuda() + + # Create causal attention mask + attention_mask = torch.ones(batch_size, 1, seq_len, seq_len, dtype=torch.bool).cuda() + attention_mask = torch.tril(attention_mask) + + with torch.no_grad(): + # Get topk indices from indexer + _, topk_indices = self.sparse_attention.indexer.forward_with_scores(x, qr) + + # Forward pass + output = self.sparse_attention( + query=query, + key=key, + value=value, + x=x, + qr=qr, + attention_mask=attention_mask, + attn_mask_type=AttnMaskType.causal, + ) + + # Check that topk_indices are valid + assert torch.all(topk_indices >= 0) + assert torch.all(topk_indices < seq_len) + assert topk_indices.shape[2] == min(self.config.dsa_indexer_topk, seq_len) + + +# ====================================================================================== +# Tensor Parallel Consistency Tests +# ====================================================================================== + + +@pytest.mark.parametrize("tensor_model_parallel_size", [2, 4, 8]) +@pytest.mark.parametrize("sequence_parallel", [False, True]) +class TestIndexerTensorParallel: + """Test DSA Indexer with different TP sizes and SP settings, compare with TP=1 baseline.""" + + def _create_config(self, sequence_parallel=False): + """Helper to create MLA config.""" + # Get TP size from parallel_state + tensor_model_parallel_size = parallel_state.get_tensor_model_parallel_world_size() + + return MLATransformerConfig( + num_layers=2, + hidden_size=256, + num_attention_heads=16, + use_cpu_initialization=True, + bf16=True, + params_dtype=torch.bfloat16, + tensor_model_parallel_size=tensor_model_parallel_size, + sequence_parallel=sequence_parallel, + # MLA specific configs + q_lora_rank=64, + kv_lora_rank=64, + qk_head_dim=64, + qk_pos_emb_head_dim=32, + v_head_dim=64, + rope_type='rope', + rotary_base=10000, + rotary_percent=1.0, + # Sparse attention specific configs + dsa_indexer_n_heads=8, + dsa_indexer_head_dim=64, + dsa_indexer_topk=32, + ) + + def _create_indexer(self, config, pg_collection): + """Helper to create indexer.""" + from megatron.core.extensions.transformer_engine import TELinear, TENorm + from megatron.core.transformer.spec_utils import ModuleSpec + + indexer_submodules = DSAIndexerSubmodules( + linear_wq_b=ModuleSpec(module=TELinear), + linear_wk=ModuleSpec(module=TELinear), + k_norm=ModuleSpec(module=TENorm), + linear_weights_proj=ModuleSpec(module=TELinear), + ) + + return DSAIndexer(config, indexer_submodules, pg_collection) + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_indexer_weight_consistency(self, tensor_model_parallel_size, sequence_parallel): + """Test that indexer weights are identical across ALL GPUs.""" + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 + ) + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + config = self._create_config(sequence_parallel=sequence_parallel) + pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) + indexer = self._create_indexer(config, pg_collection).cuda() + + # Check that all weights are identical across ALL ranks (not just TP group) + world_size = torch.distributed.get_world_size() + world_rank = torch.distributed.get_rank() + + if world_size > 1: + for name, param in indexer.named_parameters(): + # Gather weights from ALL ranks in WORLD group + param_list = [torch.zeros_like(param.data) for _ in range(world_size)] + torch.distributed.all_gather(param_list, param.data) + + # All weights should be identical across all GPUs + for i in range(1, world_size): + assert torch.allclose( + param_list[0], param_list[i], rtol=0, atol=0 + ), f"Parameter {name} differs between rank 0 and rank {i} (world)" + + Utils.destroy_model_parallel() + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_indexer_forward_consistency(self, tensor_model_parallel_size, sequence_parallel): + """Test that indexer gives consistent results across different TP sizes and SP settings.""" + # First run with TP=1 to get baseline + Utils.initialize_model_parallel( + tensor_model_parallel_size=1, pipeline_model_parallel_size=1 + ) + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + config_tp1 = self._create_config(sequence_parallel=False) # TP=1 doesn't use SP + pg_collection_tp1 = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) + indexer_tp1 = self._create_indexer(config_tp1, pg_collection_tp1).cuda() + + seq_len = 64 + batch_size = 2 + + # Create one common input (all ranks create same input with same seed) + x_input = torch.randn( + seq_len, batch_size, config_tp1.hidden_size, dtype=torch.bfloat16 + ).cuda() + qr_input = torch.randn( + seq_len, batch_size, config_tp1.q_lora_rank, dtype=torch.bfloat16 + ).cuda() + + # Forward pass with gradients enabled + index_scores_tp1, topk_indices_tp1 = indexer_tp1.forward_with_scores(x_input, qr_input) + + # Backward pass + loss_tp1 = index_scores_tp1.sum() + loss_tp1.backward() + + # Save gradients from TP=1 + indexer_tp1_grads = { + name: param.grad.clone().cpu() + for name, param in indexer_tp1.named_parameters() + if param.grad is not None + } + + Utils.destroy_model_parallel() + + # Now run with target TP size + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 + ) + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + config_tpn = self._create_config(sequence_parallel=sequence_parallel) + pg_collection_tpn = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) + indexer_tpn = self._create_indexer(config_tpn, pg_collection_tpn).cuda() + + # Prepare input: split along seqlen if SP is enabled + if sequence_parallel: + tp_rank = parallel_state.get_tensor_model_parallel_rank() + seq_per_rank = seq_len // tensor_model_parallel_size + start_idx = tp_rank * seq_per_rank + end_idx = (tp_rank + 1) * seq_per_rank + x_tpn = x_input[start_idx:end_idx] + qr_tpn = qr_input[start_idx:end_idx] + else: + # No SP: all TP ranks see full input + x_tpn = x_input + qr_tpn = qr_input + + # Forward pass with gradients enabled + index_scores_tpn, topk_indices_tpn = indexer_tpn.forward_with_scores(x_tpn, qr_tpn) + + # Backward pass + loss_tpn = index_scores_tpn.sum() + loss_tpn.backward() + + # Compare forward outputs + assert index_scores_tpn.shape == index_scores_tp1.shape + assert topk_indices_tpn.shape == topk_indices_tp1.shape + + # Check that index scores are close (allow for floating point accumulation errors) + assert torch.allclose( + index_scores_tpn, index_scores_tp1, rtol=0, atol=0 + ), f"Index scores mismatch between TP=1 and TP={tensor_model_parallel_size}, SP={sequence_parallel}" + + # Check that topk indices are exactly the same + assert torch.equal( + topk_indices_tpn, topk_indices_tp1 + ), f"Top-k indices mismatch between TP=1 and TP={tensor_model_parallel_size}, SP={sequence_parallel}" + + # Compare gradients - indexer grads should be identical (duplicated weights) + for name, param in indexer_tpn.named_parameters(): + if param.grad is not None and name in indexer_tp1_grads: + assert torch.allclose( + param.grad.cpu(), indexer_tp1_grads[name], rtol=0, atol=0 + ), f"Indexer gradient {name} mismatch between TP=1 and TP={tensor_model_parallel_size}, SP={sequence_parallel}" + + Utils.destroy_model_parallel() + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_indexer_gradient_sync(self, tensor_model_parallel_size, sequence_parallel): + """Test that gradients are properly synchronized within TP group.""" + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 + ) + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + config = self._create_config(sequence_parallel=sequence_parallel) + pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) + indexer = self._create_indexer(config, pg_collection).cuda() + + seq_len = 64 + batch_size = 2 + + # Create one common input (all ranks create same input with same seed) + x_input = torch.randn(seq_len, batch_size, config.hidden_size, dtype=torch.bfloat16).cuda() + qr_input = torch.randn(seq_len, batch_size, config.q_lora_rank, dtype=torch.bfloat16).cuda() + + # Prepare input: split along seqlen if SP is enabled + if sequence_parallel: + tp_rank = parallel_state.get_tensor_model_parallel_rank() + tp_size = parallel_state.get_tensor_model_parallel_world_size() + seq_per_rank = seq_len // tp_size + start_idx = tp_rank * seq_per_rank + end_idx = (tp_rank + 1) * seq_per_rank + x = x_input[start_idx:end_idx] + qr = qr_input[start_idx:end_idx] + else: + # No SP: all TP ranks see full input + x = x_input + qr = qr_input + + # Forward and backward + index_scores, topk_indices = indexer.forward_with_scores(x, qr) + loss = index_scores.sum() + loss.backward() + + # Check that all parameters have gradients + for name, param in indexer.named_parameters(): + if param.requires_grad: + assert param.grad is not None, f"Parameter {name} has no gradient" + + # After TP sync, check that gradients are identical within TP group + # Note: We only check TP group because DDP sync happens separately + tp_size = parallel_state.get_tensor_model_parallel_world_size() + if tp_size > 1: + for name, param in indexer.named_parameters(): + if param.requires_grad and param.grad is not None: + # Gather gradients from all ranks in TP group only + grad_list = [torch.zeros_like(param.grad) for _ in range(tp_size)] + torch.distributed.all_gather(grad_list, param.grad, group=pg_collection.tp) + + # All gradients should be identical within TP group after sync + for i in range(1, tp_size): + assert torch.allclose( + grad_list[0], grad_list[i], rtol=0, atol=0 + ), f"Gradient for {name} differs between TP rank 0 and rank {i} after TP sync" + + Utils.destroy_model_parallel() + + +@pytest.mark.parametrize("tensor_model_parallel_size", [2, 4]) +@pytest.mark.parametrize("sequence_parallel", [False, True]) +@pytest.mark.parametrize("use_sparse_indexer_loss", [False, True]) +class TestDSAttentionTensorParallel: + """Test DSAttention with different TP sizes, SP settings, and sparse indexer loss.""" + + def _create_config(self, sequence_parallel=False, use_sparse_indexer_loss=False): + """Helper to create MLA config.""" + # Get TP size from parallel_state + tensor_model_parallel_size = parallel_state.get_tensor_model_parallel_world_size() + + return MLATransformerConfig( + num_layers=2, + hidden_size=256, + num_attention_heads=16, + use_cpu_initialization=True, + bf16=True, + params_dtype=torch.bfloat16, + tensor_model_parallel_size=tensor_model_parallel_size, + sequence_parallel=sequence_parallel, + # MLA specific configs + q_lora_rank=64, + kv_lora_rank=64, + qk_head_dim=64, + qk_pos_emb_head_dim=32, + v_head_dim=64, + rope_type='rope', + rotary_base=10000, + rotary_percent=1.0, + # Sparse attention specific configs + dsa_indexer_n_heads=8, + dsa_indexer_head_dim=64, + dsa_indexer_topk=32, + dsa_indexer_loss_coeff=1.0, + dsa_indexer_use_sparse_loss=use_sparse_indexer_loss, + ) + + def _create_sparse_attention(self, config, pg_collection): + """Helper to create sparse attention.""" + from megatron.core.extensions.transformer_engine import TELinear, TENorm + from megatron.core.transformer.spec_utils import ModuleSpec + + indexer_submodules = DSAIndexerSubmodules( + linear_wq_b=ModuleSpec(module=TELinear), + linear_wk=ModuleSpec(module=TELinear), + k_norm=ModuleSpec(module=TENorm), + linear_weights_proj=ModuleSpec(module=TELinear), + ) + indexer_spec = ModuleSpec(module=DSAIndexer, submodules=indexer_submodules) + sparse_attention_submodules = DSAttentionSubmodules(indexer=indexer_spec) + + return DSAttention( + config=config, + submodules=sparse_attention_submodules, + layer_number=1, + attn_mask_type=AttnMaskType.causal, + attention_type='self', + pg_collection=pg_collection, + ) + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_weight_consistency( + self, tensor_model_parallel_size, sequence_parallel, use_sparse_indexer_loss + ): + """Test that sparse attention indexer weights are identical across ALL GPUs.""" + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 + ) + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + config = self._create_config( + sequence_parallel=sequence_parallel, use_sparse_indexer_loss=use_sparse_indexer_loss + ) + pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) + sparse_attention = self._create_sparse_attention(config, pg_collection).cuda() + + # Check that all indexer weights are identical across ALL ranks + world_size = torch.distributed.get_world_size() + world_rank = torch.distributed.get_rank() + + if world_size > 1: + for name, param in sparse_attention.indexer.named_parameters(): + # Gather weights from ALL ranks in WORLD group + param_list = [torch.zeros_like(param.data) for _ in range(world_size)] + torch.distributed.all_gather(param_list, param.data) + + # All weights should be identical across all GPUs + for i in range(1, world_size): + torch.testing.assert_close(param_list[0], param_list[i], rtol=0, atol=0) + + Utils.destroy_model_parallel() + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_forward_consistency( + self, tensor_model_parallel_size, sequence_parallel, use_sparse_indexer_loss + ): + """Test that sparse attention gives consistent results across different TP, SP, and sparse loss settings.""" + # First run with TP=1 to get baseline + Utils.initialize_model_parallel( + tensor_model_parallel_size=1, pipeline_model_parallel_size=1 + ) + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + config_tp1 = self._create_config( + sequence_parallel=False, use_sparse_indexer_loss=use_sparse_indexer_loss + ) # TP=1 doesn't use SP + pg_collection_tp1 = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) + sparse_attention_tp1 = self._create_sparse_attention(config_tp1, pg_collection_tp1).cuda() + + seq_len = 64 + batch_size = 2 + num_heads = config_tp1.num_attention_heads + head_dim = config_tp1.hidden_size // num_heads + + # Create one common input (all ranks create same input with same seed) + query_input = ( + torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) + .cuda() + .requires_grad_(True) + ) + key_input = ( + torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) + .cuda() + .requires_grad_(True) + ) + value_input = ( + torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) + .cuda() + .requires_grad_(True) + ) + x_input = torch.randn( + seq_len, batch_size, config_tp1.hidden_size, dtype=torch.bfloat16 + ).cuda() + qr_input = torch.randn( + seq_len, batch_size, config_tp1.q_lora_rank, dtype=torch.bfloat16 + ).cuda() + attention_mask = torch.ones(batch_size, 1, seq_len, seq_len, dtype=torch.bool).cuda() + attention_mask = torch.tril(attention_mask) + + # Forward pass with gradients enabled + sparse_attention_tp1.train() + output_tp1 = sparse_attention_tp1( + query=query_input, + key=key_input, + value=value_input, + x=x_input, + qr=qr_input, + attention_mask=attention_mask, + attn_mask_type=AttnMaskType.causal, + ) + + # Backward pass + loss_tp1 = output_tp1.sum() + loss_tp1.backward() + + # Save gradients from TP=1 + indexer_tp1_grads = { + name: param.grad.clone() + for name, param in sparse_attention_tp1.indexer.named_parameters() + if param.grad is not None + } + query_tp1_grad = query_input.grad.clone().cpu() + key_tp1_grad = key_input.grad.clone().cpu() + value_tp1_grad = value_input.grad.clone().cpu() + + Utils.destroy_model_parallel() + + # Now run with target TP size + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 + ) + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + config_tpn = self._create_config( + sequence_parallel=sequence_parallel, use_sparse_indexer_loss=use_sparse_indexer_loss + ) + pg_collection_tpn = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) + sparse_attention_tpn = self._create_sparse_attention(config_tpn, pg_collection_tpn).cuda() + + # Create one common input (all ranks create same input with same seed) + query_input = torch.randn( + seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + key_input = torch.randn( + seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + value_input = torch.randn( + seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + x_input = torch.randn( + seq_len, batch_size, config_tp1.hidden_size, dtype=torch.bfloat16 + ).cuda() + qr_input = torch.randn( + seq_len, batch_size, config_tp1.q_lora_rank, dtype=torch.bfloat16 + ).cuda() + attention_mask = torch.ones(batch_size, 1, seq_len, seq_len, dtype=torch.bool).cuda() + attention_mask = torch.tril(attention_mask) + + # Prepare input: split along seqlen if SP is enabled + tp_rank = parallel_state.get_tensor_model_parallel_rank() + if sequence_parallel: + seq_per_rank = seq_len // tensor_model_parallel_size + start_idx = tp_rank * seq_per_rank + end_idx = (tp_rank + 1) * seq_per_rank + x_tpn = x_input[start_idx:end_idx] + qr_tpn = qr_input[start_idx:end_idx] + else: + x_tpn = x_input + qr_tpn = qr_input + + query_input = query_input.detach() + key_input = key_input.detach() + value_input = value_input.detach() + head_per_rank = num_heads // tensor_model_parallel_size + start_head = tp_rank * head_per_rank + end_head = (tp_rank + 1) * head_per_rank + query_tpn = query_input[:, :, start_head:end_head, :].clone().requires_grad_(True) + key_tpn = key_input[:, :, start_head:end_head, :].clone().requires_grad_(True) + value_tpn = value_input[:, :, start_head:end_head, :].clone().requires_grad_(True) + attention_mask_tpn = attention_mask + + # Forward pass with gradients enabled + sparse_attention_tpn.train() + output_tpn = sparse_attention_tpn( + query=query_tpn, + key=key_tpn, + value=value_tpn, + x=x_tpn, + qr=qr_tpn, + attention_mask=attention_mask_tpn, + attn_mask_type=AttnMaskType.causal, + ) + + # Backward pass + loss_tpn = output_tpn.sum() + loss_tpn.backward() + + from megatron.core.tensor_parallel.mappings import gather_from_tensor_model_parallel_region + + output_tpn_gathered = gather_from_tensor_model_parallel_region( + output_tpn, group=pg_collection_tpn.tp + ) + assert output_tpn_gathered.shape == output_tp1.shape + assert torch.allclose( + output_tpn_gathered.detach(), output_tp1.detach(), rtol=0, atol=0 + ), f"Sparse attention outputs mismatch between TP=1 and TP={tensor_model_parallel_size}, SP={sequence_parallel}, sparse_loss={use_sparse_indexer_loss}" + + # 1. Check indexer gradients. + for name, param in sparse_attention_tpn.indexer.named_parameters(): + if param.grad is not None and name in indexer_tp1_grads: + torch.testing.assert_close( + param.grad, indexer_tp1_grads[name], rtol=1e-5, atol=1e-5 + ) + + # 2. Query/Key/Value gradients need to be gathered along num_heads dim (dim 2) if SP is enabled + # Flatten last two dims: [seq_len, batch, num_heads, head_dim] -> [seq_len, batch, num_heads * head_dim] + sq, b, nh, hd = query_tpn.grad.shape + query_grad_flat = query_tpn.grad.reshape(sq, b, nh * hd) + key_grad_flat = key_tpn.grad.reshape(sq, b, nh * hd) + value_grad_flat = value_tpn.grad.reshape(sq, b, nh * hd) + + # Gather along last dim + query_grad_gathered_flat = gather_from_tensor_model_parallel_region( + query_grad_flat, group=pg_collection_tpn.tp + ) + key_grad_gathered_flat = gather_from_tensor_model_parallel_region( + key_grad_flat, group=pg_collection_tpn.tp + ) + value_grad_gathered_flat = gather_from_tensor_model_parallel_region( + value_grad_flat, group=pg_collection_tpn.tp + ) + + # Reshape back: [seq_len, batch, num_heads * head_dim] -> [seq_len, batch, num_heads, head_dim] + query_tpn_grad_gathered = query_grad_gathered_flat.reshape(sq, b, num_heads, hd) + key_tpn_grad_gathered = key_grad_gathered_flat.reshape(sq, b, num_heads, hd) + value_tpn_grad_gathered = value_grad_gathered_flat.reshape(sq, b, num_heads, hd) + + assert torch.allclose( + query_tpn_grad_gathered.cpu(), query_tp1_grad, rtol=0, atol=0 + ), f"Query gradient mismatch between TP=1 and TP={tensor_model_parallel_size}" + assert torch.allclose( + key_tpn_grad_gathered.cpu(), key_tp1_grad, rtol=0, atol=0 + ), f"Key gradient mismatch between TP=1 and TP={tensor_model_parallel_size}" + assert torch.allclose( + value_tpn_grad_gathered.cpu(), value_tp1_grad, rtol=0, atol=0 + ), f"Value gradient mismatch between TP=1 and TP={tensor_model_parallel_size}" + + Utils.destroy_model_parallel() + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + def test_dsa_gradient_sync( + self, tensor_model_parallel_size, sequence_parallel, use_sparse_indexer_loss + ): + """Test that indexer gradients are properly synchronized within TP group.""" + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 + ) + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + config = self._create_config( + sequence_parallel=sequence_parallel, use_sparse_indexer_loss=use_sparse_indexer_loss + ) + pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) + sparse_attention = self._create_sparse_attention(config, pg_collection).cuda() + sparse_attention.train() + + seq_len = 64 + batch_size = 2 + num_heads = config.num_attention_heads + head_dim = config.hidden_size // num_heads + + # Create one common input (all ranks create same input with same seed) + query_input = torch.randn( + seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + key_input = torch.randn( + seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + value_input = torch.randn( + seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + x_input = torch.randn(seq_len, batch_size, config.hidden_size, dtype=torch.bfloat16).cuda() + qr_input = torch.randn(seq_len, batch_size, config.q_lora_rank, dtype=torch.bfloat16).cuda() + + # Prepare input: split along seqlen if SP is enabled + tp_rank = parallel_state.get_tensor_model_parallel_rank() + if sequence_parallel: + tp_size = parallel_state.get_tensor_model_parallel_world_size() + seq_per_rank = seq_len // tp_size + start_idx = tp_rank * seq_per_rank + end_idx = (tp_rank + 1) * seq_per_rank + x = x_input[start_idx:end_idx] + qr = qr_input[start_idx:end_idx] + else: + x = x_input + qr = qr_input + + # query, key, value should be split along num_heads dim + head_per_rank = num_heads // tensor_model_parallel_size + start_head = tp_rank * head_per_rank + end_head = (tp_rank + 1) * head_per_rank + query = query_input[:, :, start_head:end_head, :] + key = key_input[:, :, start_head:end_head, :] + value = value_input[:, :, start_head:end_head, :] + + attention_mask = torch.ones(batch_size, 1, seq_len, seq_len, dtype=torch.bool).cuda() + attention_mask = torch.tril(attention_mask) + + query.requires_grad_(True) + key.requires_grad_(True) + value.requires_grad_(True) + + # Forward and backward + output = sparse_attention( + query=query, + key=key, + value=value, + x=x, + qr=qr, + attention_mask=attention_mask, + attn_mask_type=AttnMaskType.causal, + ) + + loss = output.sum() + loss.backward() + + # Check that gradients exist before sync + assert query.grad is not None + assert key.grad is not None + assert value.grad is not None + + # Check that indexer parameters have gradients + for name, param in sparse_attention.indexer.named_parameters(): + if param.requires_grad: + assert param.grad is not None, f"Indexer parameter {name} has no gradient" + + # Check that indexer gradients are identical within TP group + tp_size = parallel_state.get_tensor_model_parallel_world_size() + if tp_size > 1: + for name, param in sparse_attention.indexer.named_parameters(): + if param.requires_grad and param.grad is not None: + # Gather gradients from all ranks in TP group only + grad_list = [torch.zeros_like(param.grad) for _ in range(tp_size)] + torch.distributed.all_gather(grad_list, param.grad, group=pg_collection.tp) + + # All gradients should be identical within TP group after sync + for i in range(1, tp_size): + assert torch.allclose( + grad_list[0], grad_list[i], rtol=0, atol=0 + ), f"Indexer gradient for {name} differs between TP rank 0 and rank {i} after TP sync" + + Utils.destroy_model_parallel() diff --git a/tests/unit_tests/transformer/test_multi_token_prediction.py b/tests/unit_tests/transformer/test_multi_token_prediction.py index 9b9d2c67881..ddfa9bfba16 100644 --- a/tests/unit_tests/transformer/test_multi_token_prediction.py +++ b/tests/unit_tests/transformer/test_multi_token_prediction.py @@ -1,4 +1,4 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import os import sys @@ -14,11 +14,14 @@ ) from megatron.core.models.gpt.gpt_model import GPTModel from megatron.core.num_microbatches_calculator import destroy_num_microbatches_calculator +from megatron.core.packed_seq_params import PackedSeqParams +from megatron.core.parallel_state import get_context_parallel_group from megatron.core.process_groups_config import ProcessGroupCollection from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer.multi_token_prediction import ( MTPLossLoggingHelper, MultiTokenPredictionBlock, + roll_tensor, ) from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.utils import is_te_min_version @@ -245,6 +248,66 @@ def get_batch(self, seq_length, micro_batch_size): } return batch + def get_packed_batch(self, seq_lengths, micro_batch_size): + """ + Create a packed sequence batch with multiple sequences of varying lengths. + + Args: + seq_lengths: List of sequence lengths (e.g., [10, 15, 8] for 3 sequences) + micro_batch_size: Batch size (typically 1 for packed sequences) + + Returns: + batch: Dictionary containing packed sequences and PackedSeqParams + """ + total_seq_length = sum(seq_lengths) + + # Create packed input_ids, labels, and position_ids + input_ids_list = [] + labels_list = [] + position_ids_list = [] + + for seq_len in seq_lengths: + data = list(range(seq_len)) + input_ids_list.extend(data) + labels_list.extend([x + 1 for x in data]) + position_ids_list.extend(data) + + # Convert to tensors with shape [batch, total_seq_length] + input_ids = torch.tensor(input_ids_list, dtype=torch.int64).unsqueeze(0).cuda() + labels = torch.tensor(labels_list, dtype=torch.int64).unsqueeze(0).cuda() + position_ids = torch.tensor(position_ids_list, dtype=torch.int64).unsqueeze(0).cuda() + + # Create attention mask for packed sequences (all ones for simplicity) + attention_mask = torch.ones( + (micro_batch_size, 1, total_seq_length, total_seq_length), dtype=bool + ).cuda() + + # Create loss mask with shape [batch, total_seq_length] + loss_mask = torch.ones(micro_batch_size, total_seq_length).cuda() + + # Create cumulative sequence lengths for PackedSeqParams + cu_seqlens = torch.tensor( + [0] + [sum(seq_lengths[: i + 1]) for i in range(len(seq_lengths))], dtype=torch.int32 + ).cuda() + + packed_seq_params = PackedSeqParams( + cu_seqlens_q=cu_seqlens, + cu_seqlens_kv=cu_seqlens, + max_seqlen_q=max(seq_lengths), + max_seqlen_kv=max(seq_lengths), + qkv_format='thd', + ) + + batch = { + 'tokens': input_ids, + 'labels': labels, + 'loss_mask': loss_mask, + 'attention_mask': attention_mask, + 'position_ids': position_ids, + 'packed_seq_params': packed_seq_params, + } + return batch + @pytest.mark.skipif( not HAVE_TE or not is_te_min_version("2.1.0"), reason="grouped_gemm requires TransformerEngine >= 2.1.0", @@ -404,6 +467,149 @@ def test_fp8_support(self, full_recompute): loss = output.mean() loss.backward() + @pytest.mark.skipif( + not HAVE_TE or not is_te_min_version("2.1.0"), + reason="grouped_gemm requires TransformerEngine >= 2.1.0", + ) + @pytest.mark.parametrize(("tp", "cp"), [(1, 1), (2, 1), (2, 2)]) + def test_packed_sequences(self, tp, cp): + """Test MTP with packed sequences.""" + # Create args with packed sequences support + seq_lengths = [16, 24, 12] # Three sequences of different lengths + total_seq_length = sum(seq_lengths) + + args = self.create_test_args(tp, cp, total_seq_length, micro_batch_size=1) + set_args(args) + + torch.manual_seed(_SEED) + Utils.initialize_model_parallel(tensor_model_parallel_size=tp, context_parallel_size=cp) + + # Get packed batch + batch = self.get_packed_batch(seq_lengths, micro_batch_size=1) + tokens = batch['tokens'] + labels = batch['labels'] + loss_mask = batch['loss_mask'] + attention_mask = batch['attention_mask'] + position_ids = batch['position_ids'] + packed_seq_params = batch['packed_seq_params'] + + # Create model + gpt_model, optimizer, opt_param_scheduler = setup_model_and_optimizer( + self.model_provider, ModelType.encoder_or_decoder + ) + + # Forward pass with packed sequences + output = gpt_model[0].forward( + input_ids=tokens, + position_ids=position_ids, + attention_mask=attention_mask, + labels=labels, + loss_mask=loss_mask, + packed_seq_params=packed_seq_params, + ) + + # Verify output shape + assert output.shape[0] == 1 # batch size + assert output.shape[1] == total_seq_length + + # Verify MTP loss was computed + tracker = MTPLossLoggingHelper.tracker + assert "values" in tracker + mtp_loss = tracker['values'].clone() + assert mtp_loss.shape[0] == args.mtp_num_layers + MTPLossLoggingHelper.clean_loss_in_tracker() + + # Backward pass + loss = output.mean() + loss.backward() + + # Verify gradients exist + for name, param in gpt_model[0].named_parameters(): + assert param.main_grad is not None, f"Gradient missing for {name}" + + @pytest.mark.parametrize("cp", [1, 2]) + def test_roll_tensor_with_packed_sequences(self, cp): + """Test roll_tensor function with packed sequences, with and without CP. + + For CP=1: Tests standard packed sequence rolling with verified expected values + For CP=2: Tests CP-enabled rolling executes without errors + """ + Utils.initialize_model_parallel(tensor_model_parallel_size=1, context_parallel_size=cp) + cp_group = get_context_parallel_group() if cp > 1 else None + cp_rank = torch.distributed.get_rank(group=cp_group) if cp_group is not None else 0 + + if cp == 1: + # Test case: Simple packed sequences (CP disabled) + tensor = torch.tensor([1, 2, 3, 4, 5], dtype=torch.float32).cuda() + cu_seqlens = torch.tensor([0, 3, 5], dtype=torch.int32).cuda() + + packed_seq_params = PackedSeqParams( + cu_seqlens_q=cu_seqlens, + cu_seqlens_kv=cu_seqlens, + max_seqlen_q=3, + max_seqlen_kv=3, + qkv_format='thd', + ) + + # Roll by -1 (shift left) + rolled, sum_val = roll_tensor( + tensor, shifts=-1, dims=0, cp_group=cp_group, packed_seq_params=packed_seq_params + ) + + # Expected: [2, 3, 0, 5, 0] - boundaries at indices 2 and 4 are zeroed + expected = torch.tensor([2, 3, 0, 5, 0], dtype=torch.float32).cuda() + assert torch.equal(rolled, expected), f"Expected {expected}, got {rolled}" + else: + # Test case: Packed sequences with CP=2 + # Two sequences: + # seq1 = [1, 2, 3, 4, 5, 6, 7, 8] + # seq2 = [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22] + + if cp_rank == 0: + # CP Rank 0: first half of each sequence + tensor = torch.tensor( + [1, 2, 7, 8, 11, 12, 13, 20, 21, 22], dtype=torch.float32 + ).cuda() + expected = torch.tensor( + [2, 3, 8, 0, 12, 13, 14, 21, 22, 0], dtype=torch.float32 + ).cuda() + else: + # CP Rank 1: second half of each sequence + tensor = torch.tensor( + [3, 4, 5, 6, 14, 15, 16, 17, 18, 19], dtype=torch.float32 + ).cuda() + expected = torch.tensor( + [4, 5, 6, 7, 15, 16, 17, 18, 19, 20], dtype=torch.float32 + ).cuda() + + cu_seqlens = torch.tensor([0, 8, 20], dtype=torch.int32).cuda() + + packed_seq_params = PackedSeqParams( + cu_seqlens_q=cu_seqlens, + cu_seqlens_kv=cu_seqlens, + max_seqlen_q=6, # max(4, 6) - max local seq length per sequence + max_seqlen_kv=6, + qkv_format='thd', + ) + + # Roll by -1 (shift left) with CP communication + rolled, sum_val = roll_tensor( + tensor, shifts=-1, dims=0, cp_group=cp_group, packed_seq_params=packed_seq_params + ) + + # Verify the rolled tensor matches expected values + assert ( + rolled.shape == expected.shape + ), f"Shape mismatch: expected {expected.shape}, got {rolled.shape}" + assert torch.equal( + rolled, expected + ), f"CP Rank {cp_rank}: Expected\n{expected}\nbut got\n{rolled}\nDiff:\n{rolled - expected}" + + # Verify sum is correct + assert sum_val.numel() == 1, "Sum should be a scalar" + + Utils.destroy_model_parallel() + class TestMTPLossLoggingHelper: def setup_method(self, method): diff --git a/uv.lock b/uv.lock index f636a791f12..af8e548b625 100644 --- a/uv.lock +++ b/uv.lock @@ -2,50 +2,16 @@ version = 1 revision = 2 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'linux' and extra != 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts'", - "python_full_version == '3.13.*' and platform_python_implementation != 'PyPy' and sys_platform == 'linux' and extra != 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts'", - "python_full_version >= '3.14' and platform_python_implementation == 'PyPy' and sys_platform == 'linux' and extra != 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts'", - "python_full_version == '3.13.*' and platform_python_implementation == 'PyPy' and sys_platform == 'linux' and extra != 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts'", - "python_full_version == '3.12.*' and sys_platform == 'linux' and extra != 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts'", - "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform != 'linux' and extra != 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts'", - "python_full_version == '3.13.*' and platform_python_implementation != 'PyPy' and sys_platform != 'linux' and extra != 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts'", - "python_full_version >= '3.14' and platform_python_implementation == 'PyPy' and sys_platform != 'linux' and extra != 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts'", - "python_full_version == '3.13.*' and platform_python_implementation == 'PyPy' and sys_platform != 'linux' and extra != 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts'", - "python_full_version == '3.12.*' and sys_platform != 'linux' and extra != 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts'", - "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 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