From ec907997b561743e9417ad6c14ccaf5c6a70fb92 Mon Sep 17 00:00:00 2001 From: Yunnglin Date: Fri, 9 Jan 2026 15:47:03 +0800 Subject: [PATCH 1/9] update --- .github/copilot-instructions.md | 63 + cookbook/sft/lora.py | 22 +- poetry.lock | 7380 +++++++++++++++++++++++++++++++ pyproject.toml | 82 +- 4 files changed, 7502 insertions(+), 45 deletions(-) create mode 100644 .github/copilot-instructions.md create mode 100644 poetry.lock diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md new file mode 100644 index 00000000..9a7d6783 --- /dev/null +++ b/.github/copilot-instructions.md @@ -0,0 +1,63 @@ +# Twinkle AI Coding Agent Guidelines + +These instructions help AI agents work productively in this repo. Focus on concrete repo patterns and workflows. + +## Big Picture +- **Goal:** Training and serving LLMs with multi-adapter LoRA, efficient data handling, and distributed execution across Ray and Torch. +- **Core Modules:** + - Infrastructure & distributed orchestration: [src/twinkle/infra/__init__.py](src/twinkle/infra/__init__.py) + - Device layout & platform abstraction: [src/twinkle/utils/platform.py](src/twinkle/utils/platform.py), [src/twinkle/utils/framework.py](src/twinkle/utils/framework.py) + - Model stack (Transformers + Multi-LoRA): [src/twinkle/model/multi_lora_transformers.py](src/twinkle/model/multi_lora_transformers.py) + - Sampler (vLLM integration): [src/twinkle/sampler/vllm_sampler.py](src/twinkle/sampler/vllm_sampler.py) + - Losses & metrics: [src/twinkle/loss](src/twinkle/loss), [src/twinkle/metric](src/twinkle/metric) + - Templates & preprocessing: [src/twinkle/template](src/twinkle/template), [src/twinkle/preprocessor](src/twinkle/preprocessor) + - Model/Processor HTTP services via Ray Serve: [src/twinkle/server/twinkle](src/twinkle/server/twinkle) + - Hub integrations (ModelScope/HF): [src/twinkle/hub/hub.py](src/twinkle/hub/hub.py) + +## Architecture & Patterns +- **Lazy import surface:** [src/twinkle/__init__.py](src/twinkle/__init__.py) exposes a small, lazy API (`_LazyModule`), import public symbols from here when possible. +- **Distributed mode selection:** `twinkle.infra.initialize()` toggles between local and Ray modes. Ray mode requires `TWINKLE_MODE=ray` or `initialize(mode='ray', ...)`. +- **Remote execution decorators:** + - `remote_class()` wraps classes for Ray placement; auto-injects `DeviceMesh` if missing. + - `remote_function(dispatch='slice', execute='all', collect='none')` patches methods for distributed dispatch/collect. + - See usage in [src/twinkle/model/multi_lora_transformers.py](src/twinkle/model/multi_lora_transformers.py) and [src/twinkle/sampler/vllm_sampler.py](src/twinkle/sampler/vllm_sampler.py). +- **Device topology:** Represented by `DeviceMesh`/`DeviceGroup`. Visualize with `twinkle.infra.get_device_placement()`; examples in [tests/infra/test_infra_graph.py](tests/infra/test_infra_graph.py). +- **Platform abstractions:** `GPU`/`NPU` selection via env and device discovery. Rank/world size read from env (`RANK`, `WORLD_SIZE`, etc.). See [src/twinkle/utils/platform.py](src/twinkle/utils/platform.py). +- **Hub usage:** `HubOperation` routes to HF or ModelScope by `hf://` or `ms://` prefixes. Dataset/model download/push helpers in [src/twinkle/hub/hub.py](src/twinkle/hub/hub.py). +- **Plugin loading:** Use `Plugin.load_plugin(id, Base)` for remote code from hubs; guarded by `trust_remote_code()` to prevent unsafe execution. See [src/twinkle/utils/plugin.py](src/twinkle/utils/plugin.py). +- **Multi-LoRA conventions:** + - `MultiLoraTransformersModel` wraps a base Transformers model via `MultiAdapter` to manage multiple LoRA adapters. + - FSDP is unsupported for Multi-LoRA (`fsdp_world_size == 1` enforced). Adapter params are strictly controlled to avoid training base weights. + - Adapter ops are routed through remote functions and grouped by DP process groups. + +## Developer Workflows +- **Install:** Python 3.11+. Install with Poetry or pip. + - Poetry: `poetry install --with transformers,ray` + - Pip (editable): `pip install -e .[transformers,ray]` +- **Run tests:** + - Unit tests: `python -m unittest tests/infra/test_infra_graph.py` +- **Local single-process dev:** + - Initialize infra: `twinkle.initialize(mode='local', seed=42)` + - Inspect device placement: call `twinkle.infra.get_device_placement()`. +- **Ray Serve demo (HTTP services):** + - Config and launcher: [cookbook/client/server.py](cookbook/client/server.py), [cookbook/client/server_config.yaml](cookbook/client/server_config.yaml) + - Start: + - `python cookbook/client/server.py` + - Endpoints print on startup (default `localhost:8000`). + - Model app binds `MultiLoraTransformersModel` and exposes routes like `/add_adapter_to_model`, `/forward`, `/calculate_loss`, etc. See [src/twinkle/server/twinkle/model.py](src/twinkle/server/twinkle/model.py). +- **vLLM inference:** Use `VLLMSampler` with engine args; LoRA weight sync via `patch.vllm_lora_weights`. See [src/twinkle/sampler/vllm_sampler.py](src/twinkle/sampler/vllm_sampler.py). + +## Conventions & Gotchas +- **Safety:** Remote plugin code requires `trust_remote_code()` true; avoid loading arbitrary strings into adapter configs (enforced in Multi-LoRA). +- **Env-driven ranks:** Many utilities read ranks/world size from env; set `WORLD_SIZE`, `RANK`, `LOCAL_RANK` when using torchrun. +- **Determinism:** `seed_everything(seed, full_determinism)` controls CUDA/NPU determinism; may set envs like `CUDA_LAUNCH_BLOCKING`. +- **Adapter lifecycle:** Server auto-removes inactive adapters (heartbeat required); per-token adapter limits are enforced. See cleanup in [src/twinkle/server/twinkle/model.py](src/twinkle/server/twinkle/model.py). +- **Templates:** Tokenization/encode via `Template` (e.g., `Qwen3Template`), producing `InputFeature` for model forward. See [src/twinkle/template/base.py](src/twinkle/template/base.py). + +## Examples +- **Visualize a custom mesh:** create `DeviceMesh` and call `get_device_placement()`; example in [tests/infra/test_infra_graph.py](tests/infra/test_infra_graph.py). +- **Add LoRA adapter via HTTP:** POST to `/add_adapter_to_model` with serialized `LoraConfig`; see server routes in [src/twinkle/server/twinkle/model.py](src/twinkle/server/twinkle/model.py). +- **Sample with vLLM:** Configure `VLLMSampler`, set `Template`/`Processor`, then `sample()` on `Trajectory` list; see [src/twinkle/sampler/vllm_sampler.py](src/twinkle/sampler/vllm_sampler.py). + +--- +Questions or gaps? Tell us where guidance is unclear (e.g., missing run scripts, Ray cluster setup), and we’ll refine this document. \ No newline at end of file diff --git a/cookbook/sft/lora.py b/cookbook/sft/lora.py index e5afffce..bcb22194 100644 --- a/cookbook/sft/lora.py +++ b/cookbook/sft/lora.py @@ -1,3 +1,5 @@ +import os +os.environ["TRUST_REMOTE_CODE"] = "1" import numpy as np from peft import LoraConfig from torch.optim import AdamW @@ -23,19 +25,19 @@ ] +# device_mesh = DeviceMesh( +# device_type='cuda', +# mesh=np.array([[0,1], [2,3]]), +# mesh_dim_names=('dp', 'fsdp') +# ) + device_mesh = DeviceMesh( - device_type='cuda', - mesh=np.array([[0,1], [2,3]]), - mesh_dim_names=('dp', 'fsdp') + device_type='cuda', + mesh=np.array([0,1,2,3]), + mesh_dim_names=('dp',) ) -#device_mesh = DeviceMesh( -# device_type='cuda', -# mesh=np.array([0,1,2,3]), -# mesh_dim_names=('dp',) -#) - -twinkle.initialize(mode='ray', groups=device_group, global_device_mesh=device_mesh, lazy_collect=False) 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+] +[[tool.poetry.source]] +name = "aliyun" +url = "https://mirrors.aliyun.com/pypi/simple/" +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" From 723cad7a9231a6e979125f55480521b304beabe9 Mon Sep 17 00:00:00 2001 From: Yunnglin Date: Mon, 12 Jan 2026 10:09:47 +0800 Subject: [PATCH 2/9] update --- cookbook/sft/lora.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/cookbook/sft/lora.py b/cookbook/sft/lora.py index bcb22194..a9601a88 100644 --- a/cookbook/sft/lora.py +++ b/cookbook/sft/lora.py @@ -1,5 +1,3 @@ -import os -os.environ["TRUST_REMOTE_CODE"] = "1" import numpy as np from peft import LoraConfig from torch.optim import AdamW From 85b02868734b9e5e7c613df605be41e93712e407 Mon Sep 17 00:00:00 2001 From: Yunnglin Date: Mon, 12 Jan 2026 19:44:45 +0800 Subject: [PATCH 3/9] update --- cookbook/client/server.py | 8 ++++---- cookbook/sft/multi_lora.py | 2 +- src/twinkle/patch/multi_adapter.py | 4 ++-- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/cookbook/client/server.py b/cookbook/client/server.py index 5d9a2f48..75b1758e 100644 --- a/cookbook/client/server.py +++ b/cookbook/client/server.py @@ -5,10 +5,10 @@ from ray import serve from twinkle.server import build_processor_app, build_sampler_app, build_model_app -ray.init() -serve.shutdown() -import time -time.sleep(5) +# ray.init() +# serve.shutdown() +# import time +# time.sleep(5) file_dir = os.path.abspath(os.path.dirname(__file__)) config = OmegaConf.load(os.path.join(file_dir, 'server_config.yaml')) diff --git a/cookbook/sft/multi_lora.py b/cookbook/sft/multi_lora.py index bf478550..379728b5 100644 --- a/cookbook/sft/multi_lora.py +++ b/cookbook/sft/multi_lora.py @@ -35,7 +35,7 @@ # mesh_dim_names=('dp',) #) -twinkle.initialize(mode='ray', nproc_per_node=4, groups=device_group, global_device_mesh=device_mesh, lazy_collect=False) +twinkle.initialize(mode='local', nproc_per_node=4, groups=device_group, global_device_mesh=device_mesh, lazy_collect=False) def train(): diff --git a/src/twinkle/patch/multi_adapter.py b/src/twinkle/patch/multi_adapter.py index d7b0dc8d..5a635a73 100644 --- a/src/twinkle/patch/multi_adapter.py +++ b/src/twinkle/patch/multi_adapter.py @@ -23,10 +23,10 @@ def get_active_adapters(*args, **kwargs): else: return [] - def set_active_adapters(_, value): + def set_active_adapters(_, value, **kwargs): pass - def set_adapter(self, adapter_names): + def set_adapter(self, adapter_names, **kwargs): pass LoraLayer.active_adapter = property(get_active_adapter, set_active_adapters) From 507581f0d6637cd79aa0d84eeabaeacd96b6e235 Mon Sep 17 00:00:00 2001 From: Yunnglin Date: Wed, 14 Jan 2026 10:16:01 +0800 Subject: [PATCH 4/9] update bug --- src/twinkle/data_format/input_feature.py | 4 +-- src/twinkle/model/multi_lora_transformers.py | 3 +- src/twinkle/server/twinkle/processor.py | 37 +++++++++++--------- 3 files changed, 24 insertions(+), 20 deletions(-) diff --git a/src/twinkle/data_format/input_feature.py b/src/twinkle/data_format/input_feature.py index 7777e460..0f76aefb 100644 --- a/src/twinkle/data_format/input_feature.py +++ b/src/twinkle/data_format/input_feature.py @@ -40,8 +40,8 @@ def to_transformers_dict(feature: InputFeature) -> dict: output = {} _keys = ['input_ids', 'input_embeddings', 'attention_mask', 'position_ids', 'labels', 'completion_mask', 'logits_to_keep', 'num_items_in_batch'] for key in list(feature.keys()): - if key in _keys and not isinstance(output[key], torch.Tensor): - output[key] = np.array(output[key]) + if key in _keys and not isinstance(feature[key], torch.Tensor): # FIXME: bug + output[key] = np.array(feature[key]) return output diff --git a/src/twinkle/model/multi_lora_transformers.py b/src/twinkle/model/multi_lora_transformers.py index c29b14df..cccde98c 100644 --- a/src/twinkle/model/multi_lora_transformers.py +++ b/src/twinkle/model/multi_lora_transformers.py @@ -42,7 +42,8 @@ def __init__(self, # noqa self.add_adapter_to_model('__dummy_adapter__', LoraConfig(r=1, target_modules='all-linear')) def _check_adapter_valid(self, adapter_name: str): - assert adapter_name and adapter_name != '__dummy_adapter__' and adapter_name in self.optimizer_group, f'Use a valid adapter_name first, current is: {adapter_name}' + # FIXME: check on init + assert adapter_name and adapter_name != '__dummy_adapter_' and adapter_name in self.optimizer_group, f'Use a valid adapter_name first, current is: {adapter_name}' def _activate_adapter(self, adapter_name: str): self.multi_adapter.set_current_adapter_name(adapter_name) diff --git a/src/twinkle/server/twinkle/processor.py b/src/twinkle/server/twinkle/processor.py index e6b334ab..590524f6 100644 --- a/src/twinkle/server/twinkle/processor.py +++ b/src/twinkle/server/twinkle/processor.py @@ -16,6 +16,26 @@ logger = get_logger() +# FIXME: move inner class outside +class CreateRequest(BaseModel): + processor_type: str + class_type: str + + class Config: + extra = "allow" + + +class HeartbeatRequest(BaseModel): + processor_id: str + + +class CallRequest(BaseModel): + processor_id: str + function: str + + class Config: + extra = "allow" + def build_processor_app(nproc_per_node: int, device_group: Dict[str, Any], @@ -30,23 +50,6 @@ async def verify_token(request: Request, call_next): processors = ['dataset', 'dataloader', 'preprocessor', 'processor', 'reward', 'template', 'weight_loader'] - class CreateRequest(BaseModel): - processor_type: str - class_type: str - - class Config: - extra = "allow" - - class HeartbeatRequest(BaseModel): - processor_id: str - - class CallRequest(BaseModel): - processor_id: str - function: str - - class Config: - extra = "allow" - @serve.deployment(name="ProcessorManagement") @serve.ingress(app) class ProcessorManagement: From 250fe3c2aba5ec53c1483ed564f5a122cbd1cd0a Mon Sep 17 00:00:00 2001 From: Yunnglin Date: Thu, 15 Jan 2026 15:05:31 +0800 Subject: [PATCH 5/9] update cookbook --- cookbook/client/server_config.yaml | 12 +-- cookbook/sft/lora.py | 6 +- cookbook/sft/multi_lora.py | 10 +-- cookbook/tinker/lora.py | 98 +++++++++++++++++++++++++ src/twinkle/server/twinkle/processor.py | 7 +- 5 files changed, 115 insertions(+), 18 deletions(-) create mode 100644 cookbook/tinker/lora.py diff --git a/cookbook/client/server_config.yaml b/cookbook/client/server_config.yaml index 9ab4aa16..cb6cd9e6 100644 --- a/cookbook/client/server_config.yaml +++ b/cookbook/client/server_config.yaml @@ -9,14 +9,14 @@ applications: import_path: main:model_qwen25_7B args: model_id: "ms://Qwen/Qwen2.5-7B-Instruct" - nproc_per_node: 4 + nproc_per_node: 2 device_group: name: model - ranks: [ 0,1,2,3 ] + ranks: [0,1] device_type: cuda device_mesh: device_type: cuda - mesh: [0,1,2,3] + mesh: [0,1] mesh_dim_names: ['dp'] deployments: - name: ModelManagement @@ -31,14 +31,14 @@ applications: route_prefix: /processors import_path: main:processor_app args: - nproc_per_node: 4 + nproc_per_node: 2 device_group: name: model - ranks: 4 + ranks: 2 device_type: CPU device_mesh: device_type: CPU - mesh: [0,1,2,3] + mesh: [0,1] mesh_dim_names: ['dp'] deployments: - name: ProcessorManagement diff --git a/cookbook/sft/lora.py b/cookbook/sft/lora.py index 1695a0d3..c53663ce 100644 --- a/cookbook/sft/lora.py +++ b/cookbook/sft/lora.py @@ -17,7 +17,7 @@ device_group = [ DeviceGroup( name='model', - ranks=[0,1,2,3], + ranks=[0,1], device_type=Platform.get_platform().device_prefix(), ) ] @@ -31,11 +31,11 @@ device_mesh = DeviceMesh( device_type='cuda', - mesh=np.array([0,1,2,3]), + mesh=np.array([0,1]), mesh_dim_names=('dp',) ) -twinkle.initialize(mode='ray', groups=device_group, global_device_mesh=device_mesh) +twinkle.initialize(mode='local', groups=device_group, global_device_mesh=device_mesh, lazy_collect=False) def create_dataset(): diff --git a/cookbook/sft/multi_lora.py b/cookbook/sft/multi_lora.py index 4157b30e..8480c2cb 100644 --- a/cookbook/sft/multi_lora.py +++ b/cookbook/sft/multi_lora.py @@ -17,7 +17,7 @@ device_group = [ DeviceGroup( name='model', - ranks=[0,1,2,3], + ranks=[0,1], device_type=Platform.get_platform().device_prefix(), ) ] @@ -25,22 +25,22 @@ device_mesh = DeviceMesh( device_type='cuda', - mesh=np.array([0,1,2,3]), + mesh=np.array([0,1]), mesh_dim_names=('dp',) ) #device_mesh = DeviceMesh( # device_type='cuda', -# mesh=np.array([0,1,2,3]), +# mesh=np.array([0,1]), # mesh_dim_names=('dp',) #) -twinkle.initialize(mode='local', nproc_per_node=4, groups=device_group, global_device_mesh=device_mesh, lazy_collect=False) +twinkle.initialize(mode='ray', nproc_per_node=2, groups=device_group, global_device_mesh=device_mesh, lazy_collect=False) def train(): dataset = Dataset(dataset_meta=DatasetMeta('ms://modelscope/competition_math')) - dataset.set_template('Qwen3Template', model_id='ms://Qwen/Qwen2.5-7B-Instruct') + dataset.set_template('Qwen3Template', model_id='ms://Qwen/Qwen2.5-0.5B-Instruct') dataset.map('CompetitionMathProcessor') dataset.encode(batched=True) dataloader = DataLoader(dataset=dataset, batch_size=8) diff --git a/cookbook/tinker/lora.py b/cookbook/tinker/lora.py new file mode 100644 index 00000000..32fb1486 --- /dev/null +++ b/cookbook/tinker/lora.py @@ -0,0 +1,98 @@ +#%% +import dotenv +dotenv.load_dotenv(".env") +import tinker +service_client = tinker.ServiceClient() +print("Available models:") +for item in service_client.get_server_capabilities().supported_models: + print("- " + item.model_name) + +base_model = "meta-llama/Llama-3.2-1B" +training_client = service_client.create_lora_training_client( + base_model=base_model +) + +#%% +# Create some training examples +examples = [ + {"input": "banana split", "output": "anana-bay plit-say"}, + {"input": "quantum physics", "output": "uantum-qay ysics-phay"}, + {"input": "donut shop", "output": "onut-day op-shay"}, + {"input": "pickle jar", "output": "ickle-pay ar-jay"}, + {"input": "space exploration", "output": "ace-spay exploration-way"}, + {"input": "rubber duck", "output": "ubber-ray uck-day"}, + {"input": "coding wizard", "output": "oding-cay izard-way"}, +] + +# Convert examples into the format expected by the training client +from tinker import types +from modelscope import AutoTokenizer +# Get the tokenizer from the training client +# tokenizer = training_client.get_tokenizer() # NOTE: network call huggingface +tokenizer = AutoTokenizer.from_pretrained('LLM-Research/Llama-3.2-1B') + +def process_example(example: dict, tokenizer) -> types.Datum: + # Format the input with Input/Output template + # For most real use cases, you'll want to use a renderer / chat template, + # (see later docs) but here, we'll keep it simple. + prompt = f"English: {example['input']}\nPig Latin:" + + prompt_tokens = tokenizer.encode(prompt, add_special_tokens=True) + prompt_weights = [0] * len(prompt_tokens) + # Add a space before the output string, and finish with double newline + completion_tokens = tokenizer.encode(f" {example['output']}\n\n", add_special_tokens=False) + completion_weights = [1] * len(completion_tokens) + + tokens = prompt_tokens + completion_tokens + weights = prompt_weights + completion_weights + + input_tokens = tokens[:-1] + target_tokens = tokens[1:] # We're predicting the next token, so targets need to be shifted. + weights = weights[1:] + + # A datum is a single training example for the loss function. + # It has model_input, which is the input sequence that'll be passed into the LLM, + # loss_fn_inputs, which is a dictionary of extra inputs used by the loss function. + return types.Datum( + model_input=types.ModelInput.from_ints(tokens=input_tokens), + loss_fn_inputs=dict(weights=weights, target_tokens=target_tokens) + ) + +processed_examples = [process_example(ex, tokenizer) for ex in examples] + +# Visualize the first example for debugging purposes +datum0 = processed_examples[0] +print(f"{'Input':<20} {'Target':<20} {'Weight':<10}") +print("-" * 50) +for i, (inp, tgt, wgt) in enumerate(zip(datum0.model_input.to_ints(), datum0.loss_fn_inputs['target_tokens'].tolist(), datum0.loss_fn_inputs['weights'].tolist())): + print(f"{repr(tokenizer.decode([inp])):<20} {repr(tokenizer.decode([tgt])):<20} {wgt:<10}") + +#%% +import numpy as np +for _ in range(6): + fwdbwd_future = training_client.forward_backward(processed_examples, "cross_entropy") + optim_future = training_client.optim_step(types.AdamParams(learning_rate=1e-4)) + + # Wait for the results + fwdbwd_result = fwdbwd_future.result() + optim_result = optim_future.result() + + # fwdbwd_result contains the logprobs of all the tokens we put in. Now we can compute the weighted + # average log loss per token. + logprobs = np.concatenate([output['logprobs'].tolist() for output in fwdbwd_result.loss_fn_outputs]) + weights = np.concatenate([example.loss_fn_inputs['weights'].tolist() for example in processed_examples]) + print(f"Loss per token: {-np.dot(logprobs, weights) / weights.sum():.4f}") + +#%% +# First, create a sampling client. We need to transfer weights +sampling_client = training_client.save_weights_and_get_sampling_client(name='pig-latin-model') + +# Now, we can sample from the model. +prompt = types.ModelInput.from_ints(tokenizer.encode("English: coffee break\nPig Latin:")) +params = types.SamplingParams(max_tokens=20, temperature=0.0, stop=["\n"]) # Greedy sampling +future = sampling_client.sample(prompt=prompt, sampling_params=params, num_samples=8) +result = future.result() +print("Responses:") +for i, seq in enumerate(result.sequences): + print(f"{i}: {repr(tokenizer.decode(seq.tokens))}") +# %% diff --git a/src/twinkle/server/twinkle/processor.py b/src/twinkle/server/twinkle/processor.py index 590524f6..1d8124ad 100644 --- a/src/twinkle/server/twinkle/processor.py +++ b/src/twinkle/server/twinkle/processor.py @@ -16,7 +16,6 @@ logger = get_logger() -# FIXME: move inner class outside class CreateRequest(BaseModel): processor_type: str class_type: str @@ -24,11 +23,9 @@ class CreateRequest(BaseModel): class Config: extra = "allow" - class HeartbeatRequest(BaseModel): processor_id: str - class CallRequest(BaseModel): processor_id: str function: str @@ -36,7 +33,6 @@ class CallRequest(BaseModel): class Config: extra = "allow" - def build_processor_app(nproc_per_node: int, device_group: Dict[str, Any], device_mesh: Dict[str, Any], @@ -50,6 +46,8 @@ async def verify_token(request: Request, call_next): processors = ['dataset', 'dataloader', 'preprocessor', 'processor', 'reward', 'template', 'weight_loader'] + + @serve.deployment(name="ProcessorManagement") @serve.ingress(app) class ProcessorManagement: @@ -101,6 +99,7 @@ def handle_processor_count(self, token: str, add: bool): @app.post("/create") def create(self, request: Request, body: CreateRequest): + # breakpoint() processor_type_name = body.processor_type class_type = body.class_type kwargs = body.model_extra or {} From b1b632a1c56814edd63d35a561cba27bd133bbdc Mon Sep 17 00:00:00 2001 From: Yunnglin Date: Fri, 16 Jan 2026 17:46:15 +0800 Subject: [PATCH 6/9] init tinker client --- .gitignore | 1 + cookbook/client/server_config.yaml | 12 +- cookbook/sft/lora.py | 6 +- cookbook/sft/multi_lora.py | 10 +- cookbook/tinker/lora.py | 98 ----------- src/twinkle/model/multi_lora_transformers.py | 4 +- src/twinkle/server/tinker/common/__init__.py | 1 + src/twinkle/server/tinker/model.py | 167 +++++++++++++++++++ src/twinkle/server/tinker/server.py | 135 +++++++++++++++ src/twinkle/server/tinker/state.py | 118 +++++++++++++ src/twinkle/server/twinkle/processor.py | 7 +- src/twinkle_client/__init__.py | 21 ++- src/twinkle_client/http/utils.py | 2 +- 13 files changed, 462 insertions(+), 120 deletions(-) delete mode 100644 cookbook/tinker/lora.py create mode 100644 src/twinkle/server/tinker/common/__init__.py create mode 100644 src/twinkle/server/tinker/model.py create mode 100644 src/twinkle/server/tinker/server.py create mode 100644 src/twinkle/server/tinker/state.py diff --git a/.gitignore b/.gitignore index de8aa916..d967c68a 100644 --- a/.gitignore +++ b/.gitignore @@ -156,3 +156,4 @@ megatron_output/ # ast template ast_index_file.py +test_cookbook/ \ No newline at end of file diff --git a/cookbook/client/server_config.yaml b/cookbook/client/server_config.yaml index cb6cd9e6..9ab4aa16 100644 --- a/cookbook/client/server_config.yaml +++ b/cookbook/client/server_config.yaml @@ -9,14 +9,14 @@ applications: import_path: main:model_qwen25_7B args: model_id: "ms://Qwen/Qwen2.5-7B-Instruct" - nproc_per_node: 2 + nproc_per_node: 4 device_group: name: model - ranks: [0,1] + ranks: [ 0,1,2,3 ] device_type: cuda device_mesh: device_type: cuda - mesh: [0,1] + mesh: [0,1,2,3] mesh_dim_names: ['dp'] deployments: - name: ModelManagement @@ -31,14 +31,14 @@ applications: route_prefix: /processors import_path: main:processor_app args: - nproc_per_node: 2 + nproc_per_node: 4 device_group: name: model - ranks: 2 + ranks: 4 device_type: CPU device_mesh: device_type: CPU - mesh: [0,1] + mesh: [0,1,2,3] mesh_dim_names: ['dp'] deployments: - name: ProcessorManagement diff --git a/cookbook/sft/lora.py b/cookbook/sft/lora.py index c53663ce..1695a0d3 100644 --- a/cookbook/sft/lora.py +++ b/cookbook/sft/lora.py @@ -17,7 +17,7 @@ device_group = [ DeviceGroup( name='model', - ranks=[0,1], + ranks=[0,1,2,3], device_type=Platform.get_platform().device_prefix(), ) ] @@ -31,11 +31,11 @@ device_mesh = DeviceMesh( device_type='cuda', - mesh=np.array([0,1]), + mesh=np.array([0,1,2,3]), mesh_dim_names=('dp',) ) -twinkle.initialize(mode='local', groups=device_group, global_device_mesh=device_mesh, lazy_collect=False) +twinkle.initialize(mode='ray', groups=device_group, global_device_mesh=device_mesh) def create_dataset(): diff --git a/cookbook/sft/multi_lora.py b/cookbook/sft/multi_lora.py index 8480c2cb..4157b30e 100644 --- a/cookbook/sft/multi_lora.py +++ b/cookbook/sft/multi_lora.py @@ -17,7 +17,7 @@ device_group = [ DeviceGroup( name='model', - ranks=[0,1], + ranks=[0,1,2,3], device_type=Platform.get_platform().device_prefix(), ) ] @@ -25,22 +25,22 @@ device_mesh = DeviceMesh( device_type='cuda', - mesh=np.array([0,1]), + mesh=np.array([0,1,2,3]), mesh_dim_names=('dp',) ) #device_mesh = DeviceMesh( # device_type='cuda', -# mesh=np.array([0,1]), +# mesh=np.array([0,1,2,3]), # mesh_dim_names=('dp',) #) -twinkle.initialize(mode='ray', nproc_per_node=2, groups=device_group, global_device_mesh=device_mesh, lazy_collect=False) +twinkle.initialize(mode='local', nproc_per_node=4, groups=device_group, global_device_mesh=device_mesh, lazy_collect=False) def train(): dataset = Dataset(dataset_meta=DatasetMeta('ms://modelscope/competition_math')) - dataset.set_template('Qwen3Template', model_id='ms://Qwen/Qwen2.5-0.5B-Instruct') + dataset.set_template('Qwen3Template', model_id='ms://Qwen/Qwen2.5-7B-Instruct') dataset.map('CompetitionMathProcessor') dataset.encode(batched=True) dataloader = DataLoader(dataset=dataset, batch_size=8) diff --git a/cookbook/tinker/lora.py b/cookbook/tinker/lora.py deleted file mode 100644 index 32fb1486..00000000 --- a/cookbook/tinker/lora.py +++ /dev/null @@ -1,98 +0,0 @@ -#%% -import dotenv -dotenv.load_dotenv(".env") -import tinker -service_client = tinker.ServiceClient() -print("Available models:") -for item in service_client.get_server_capabilities().supported_models: - print("- " + item.model_name) - -base_model = "meta-llama/Llama-3.2-1B" -training_client = service_client.create_lora_training_client( - base_model=base_model -) - -#%% -# Create some training examples -examples = [ - {"input": "banana split", "output": "anana-bay plit-say"}, - {"input": "quantum physics", "output": "uantum-qay ysics-phay"}, - {"input": "donut shop", "output": "onut-day op-shay"}, - {"input": "pickle jar", "output": "ickle-pay ar-jay"}, - {"input": "space exploration", "output": "ace-spay exploration-way"}, - {"input": "rubber duck", "output": "ubber-ray uck-day"}, - {"input": "coding wizard", "output": "oding-cay izard-way"}, -] - -# Convert examples into the format expected by the training client -from tinker import types -from modelscope import AutoTokenizer -# Get the tokenizer from the training client -# tokenizer = training_client.get_tokenizer() # NOTE: network call huggingface -tokenizer = AutoTokenizer.from_pretrained('LLM-Research/Llama-3.2-1B') - -def process_example(example: dict, tokenizer) -> types.Datum: - # Format the input with Input/Output template - # For most real use cases, you'll want to use a renderer / chat template, - # (see later docs) but here, we'll keep it simple. - prompt = f"English: {example['input']}\nPig Latin:" - - prompt_tokens = tokenizer.encode(prompt, add_special_tokens=True) - prompt_weights = [0] * len(prompt_tokens) - # Add a space before the output string, and finish with double newline - completion_tokens = tokenizer.encode(f" {example['output']}\n\n", add_special_tokens=False) - completion_weights = [1] * len(completion_tokens) - - tokens = prompt_tokens + completion_tokens - weights = prompt_weights + completion_weights - - input_tokens = tokens[:-1] - target_tokens = tokens[1:] # We're predicting the next token, so targets need to be shifted. - weights = weights[1:] - - # A datum is a single training example for the loss function. - # It has model_input, which is the input sequence that'll be passed into the LLM, - # loss_fn_inputs, which is a dictionary of extra inputs used by the loss function. - return types.Datum( - model_input=types.ModelInput.from_ints(tokens=input_tokens), - loss_fn_inputs=dict(weights=weights, target_tokens=target_tokens) - ) - -processed_examples = [process_example(ex, tokenizer) for ex in examples] - -# Visualize the first example for debugging purposes -datum0 = processed_examples[0] -print(f"{'Input':<20} {'Target':<20} {'Weight':<10}") -print("-" * 50) -for i, (inp, tgt, wgt) in enumerate(zip(datum0.model_input.to_ints(), datum0.loss_fn_inputs['target_tokens'].tolist(), datum0.loss_fn_inputs['weights'].tolist())): - print(f"{repr(tokenizer.decode([inp])):<20} {repr(tokenizer.decode([tgt])):<20} {wgt:<10}") - -#%% -import numpy as np -for _ in range(6): - fwdbwd_future = training_client.forward_backward(processed_examples, "cross_entropy") - optim_future = training_client.optim_step(types.AdamParams(learning_rate=1e-4)) - - # Wait for the results - fwdbwd_result = fwdbwd_future.result() - optim_result = optim_future.result() - - # fwdbwd_result contains the logprobs of all the tokens we put in. Now we can compute the weighted - # average log loss per token. - logprobs = np.concatenate([output['logprobs'].tolist() for output in fwdbwd_result.loss_fn_outputs]) - weights = np.concatenate([example.loss_fn_inputs['weights'].tolist() for example in processed_examples]) - print(f"Loss per token: {-np.dot(logprobs, weights) / weights.sum():.4f}") - -#%% -# First, create a sampling client. We need to transfer weights -sampling_client = training_client.save_weights_and_get_sampling_client(name='pig-latin-model') - -# Now, we can sample from the model. -prompt = types.ModelInput.from_ints(tokenizer.encode("English: coffee break\nPig Latin:")) -params = types.SamplingParams(max_tokens=20, temperature=0.0, stop=["\n"]) # Greedy sampling -future = sampling_client.sample(prompt=prompt, sampling_params=params, num_samples=8) -result = future.result() -print("Responses:") -for i, seq in enumerate(result.sequences): - print(f"{i}: {repr(tokenizer.decode(seq.tokens))}") -# %% diff --git a/src/twinkle/model/multi_lora_transformers.py b/src/twinkle/model/multi_lora_transformers.py index cccde98c..1093f995 100644 --- a/src/twinkle/model/multi_lora_transformers.py +++ b/src/twinkle/model/multi_lora_transformers.py @@ -39,11 +39,9 @@ def __init__(self, # noqa grad_scaler_config=grad_scaler_config, **kwargs) self.multi_adapter = MultiAdapter() self.model: PreTrainedModel = self.multi_adapter(self.model) - self.add_adapter_to_model('__dummy_adapter__', LoraConfig(r=1, target_modules='all-linear')) def _check_adapter_valid(self, adapter_name: str): - # FIXME: check on init - assert adapter_name and adapter_name != '__dummy_adapter_' and adapter_name in self.optimizer_group, f'Use a valid adapter_name first, current is: {adapter_name}' + assert adapter_name and adapter_name != '__dummy_adapter__' and adapter_name in self.optimizer_group, f'Use a valid adapter_name first, current is: {adapter_name}' def _activate_adapter(self, adapter_name: str): self.multi_adapter.set_current_adapter_name(adapter_name) diff --git a/src/twinkle/server/tinker/common/__init__.py b/src/twinkle/server/tinker/common/__init__.py new file mode 100644 index 00000000..0829846c --- /dev/null +++ b/src/twinkle/server/tinker/common/__init__.py @@ -0,0 +1 @@ +# Copyright (c) ModelScope Contributors. All rights reserved. \ No newline at end of file diff --git a/src/twinkle/server/tinker/model.py b/src/twinkle/server/tinker/model.py new file mode 100644 index 00000000..ccb110e4 --- /dev/null +++ b/src/twinkle/server/tinker/model.py @@ -0,0 +1,167 @@ +# Copyright (c) ModelScope Contributors. All rights reserved. +from datetime import datetime +import os +import threading +import time +from typing import Dict, Any, Optional +import uuid + +from fastapi import FastAPI, Request +from peft import LoraConfig +from pydantic import BaseModel +from ray import serve +from tinker import types + +import twinkle +from twinkle import DeviceGroup, DeviceMesh +from twinkle.model import TwinkleModel, MultiLoraTransformersModel +from twinkle.model.base import TwinkleModel +from twinkle.data_format import InputFeature, Trajectory +from twinkle.server.twinkle.validation import verify_request_token, init_config_registry, ConfigRegistryProxy +from .state import get_server_state + +def build_model_app(model_id: str, + nproc_per_node: int, + device_group: Dict[str, Any], + device_mesh: Dict[str, Any], + deploy_options: Dict[str, Any], + **kwargs): + app = FastAPI() + + @app.middleware("http") + async def verify_token(request: Request, call_next): + return await verify_request_token(request=request, call_next=call_next) + + @serve.deployment(name="ModelManagement") + @serve.ingress(app) + class ModelManagement(): + + COUNT_DOWN = 60 * 30 + + def __init__(self, model_id: str, nproc_per_node: int, device_group: Dict[str, Any], device_mesh: Dict[str, Any], **kwargs): + self.device_group = DeviceGroup(**device_group) + twinkle.initialize(mode='ray', nproc_per_node=nproc_per_node, groups=[self.device_group], lazy_collect=False) + self.device_mesh = DeviceMesh(**device_mesh) + self.model_id = model_id + self.model: TwinkleModel = None + self.kwargs = kwargs + self.adapter_records: Dict[str, int] = {} + self.hb_thread = threading.Thread(target=self.countdown, daemon=True) + self.hb_thread.start() + self.adapter_lock = threading.Lock() + self.config_registry: ConfigRegistryProxy = init_config_registry() + self.state = get_server_state() + self.per_token_model_limit = int(os.environ.get("TWINKLE_PER_USER_MODEL_LIMIT", 3)) + self.key_token_dict = {} + + def countdown(self): + while True: + time.sleep(1) + for key in list(self.adapter_records.keys()): + self.adapter_records[key] += 1 + if self.adapter_records[key] > self.COUNT_DOWN: + with self.adapter_lock: + self.model.remove_adapter(key) + self.adapter_records.pop(key, None) + token = self.key_token_dict.pop(key, None) + if token: + self.handle_adapter_count(token, False) + + def handle_adapter_count(self, token: str, add: bool): + user_key = token + '_' + 'model_adapter' + cur_count = self.config_registry.get_config(user_key) or 0 + if add: + if cur_count < self.per_token_model_limit: + self.config_registry.add_config(user_key, cur_count + 1) + else: + raise RuntimeError(f'Model adapter count limitation reached: {self.per_token_model_limit}') + else: + if cur_count > 0: + cur_count -= 1 + self.config_registry.add_config(user_key, cur_count) + if cur_count <= 0: + self.config_registry.pop(user_key) + + @staticmethod + def _default_forward_output() -> types.ForwardBackwardOutput: + tensor = types.TensorData(data=[0.0], dtype="float32", shape=[1]) + loss_output: types.LossFnOutput = {"loss": tensor, "logprobs": tensor} + return types.ForwardBackwardOutput( + loss_fn_output_type="TensorData", + loss_fn_outputs=[loss_output], + metrics={"loss:avg": 0.0, "tokens_per_second:avg": 0.0}, + ) + + @staticmethod + def _new_request_id() -> str: + return f"req_{uuid.uuid4().hex}" + + async def _store_future(self, payload: Any, model_id: Optional[str] = None) -> types.UntypedAPIFuture: + request_id = self._new_request_id() + await self.state.store_future(request_id, payload, model_id) + return types.UntypedAPIFuture(request_id=request_id, model_id=model_id) + + @app.post("/create_model") + async def create_model(self, request: types.CreateModelRequest) -> types.UntypedAPIFuture: + model_id = await self.state.register_model(request) + result = types.CreateModelResponse(model_id=model_id) + model = MultiLoraTransformersModel( + model_id=self.model_id, + device_mesh=self.device_mesh, + remote_group=self.device_group.name, + **self.kwargs + ) + return await self._store_future(result, model_id=model_id) + + @app.post("/get_info") + async def get_info(self, request: types.GetInfoRequest) -> types.GetInfoResponse: + metadata = await self.state.get_model_metadata(str(request.model_id)) + model_name = metadata.get("base_model") if metadata else str(request.model_id) + lora_rank = None + is_lora = False + if metadata and metadata.get("lora_config"): + lora_rank = metadata["lora_config"].get("rank") + is_lora = True + return types.GetInfoResponse( + model_data=types.ModelData(model_name=model_name), + model_id=request.model_id, + is_lora=is_lora, + lora_rank=lora_rank, + ) + + @app.post("/unload_model") + async def unload_model(self, request: types.UnloadModelRequest) -> types.UntypedAPIFuture: + await self.state.unload_model(request.model_id) + result = types.UnloadModelResponse(model_id=request.model_id) + return await self._store_future(result, model_id=request.model_id) + + @app.post("/forward") + async def forward(self, request: types.ForwardRequest) -> types.UntypedAPIFuture: + return await self._store_future(self._default_forward_output(), model_id=request.model_id) + + @app.post("/forward_backward") + async def forward_backward(self, request: types.ForwardBackwardRequest) -> types.UntypedAPIFuture: + return await self._store_future(self._default_forward_output(), model_id=request.model_id) + + @app.post("/optim_step") + async def optim_step(self, request: types.OptimStepRequest) -> types.UntypedAPIFuture: + metrics = types.OptimStepResponse(metrics={ + "grad_norm": 0.0, + "weight_norm": 0.0, + "update_norm": 0.0, + }) + return await self._store_future(metrics, model_id=request.model_id) + + @app.post("/save_weights") + async def save_weights(self, request: types.SaveWeightsRequest) -> types.UntypedAPIFuture: + suffix = request.path or f"checkpoint-{datetime.now().strftime('%Y%m%d-%H%M%S')}" + path = f"tinker://{request.model_id}/{suffix}" + result = types.SaveWeightsResponse(path=path) + return await self._store_future(result, model_id=request.model_id) + + @app.post("/load_weights") + async def load_weights(self, request: types.LoadWeightsRequest) -> types.UntypedAPIFuture: + result = types.LoadWeightsResponse(path=request.path) + return await self._store_future(result, model_id=request.model_id) + + return ModelManagement.options(**deploy_options).bind(model_id, nproc_per_node, device_group, device_mesh, **kwargs) \ No newline at end of file diff --git a/src/twinkle/server/tinker/server.py b/src/twinkle/server/tinker/server.py new file mode 100644 index 00000000..055f489f --- /dev/null +++ b/src/twinkle/server/tinker/server.py @@ -0,0 +1,135 @@ +""" +Minimal Ray Serve + FastAPI implementation of the Tinker server API. +The endpoints mirror the Python SDK expectations so the client can talk to +this server without further glue code. This is a lightweight reference +implementation meant for local development, integration tests, or as a +starting point for a production backend. +""" +from __future__ import annotations + +import os +import uuid +from datetime import datetime +from typing import Any, Dict, List, Optional + +import ray +from fastapi import FastAPI, HTTPException +from ray import serve + +from tinker import types +from .state import get_server_state + + +# ----- FastAPI + Serve deployment ------------------------------------------ + + +app = FastAPI(title="Tinker Ray Server", version="0.1.0") + + +@serve.deployment +@serve.ingress(app) +class TinkerCompatServer: + def __init__(self, supported_models: Optional[List[types.SupportedModel]] = None) -> None: + self.state = get_server_state() + self.supported_models = supported_models or [ + types.SupportedModel(model_name="Qwen/Qwen2.5-0.5B-Instruct"), + types.SupportedModel(model_name="Qwen/Qwen2.5-7B-Instruct"), + types.SupportedModel(model_name="Qwen/Qwen2.5-72B-Instruct"), + ] + + # --- Helpers ----------------------------------------------------------- + + @staticmethod + def _new_request_id() -> str: + return f"req_{uuid.uuid4().hex}" + + async def _store_future(self, payload: Any, model_id: Optional[str] = None) -> types.UntypedAPIFuture: + request_id = self._new_request_id() + await self.state.store_future(request_id, payload, model_id) + return types.UntypedAPIFuture(request_id=request_id, model_id=model_id) + + @staticmethod + def _sample_output() -> types.SampleResponse: + sequence = types.SampledSequence(stop_reason="stop", tokens=[1, 2, 3], logprobs=[-0.1, -0.2, -0.3]) + return types.SampleResponse(sequences=[sequence]) + + # --- Endpoints --------------------------------------------------------- + + @app.get("/healthz") + async def healthz(self) -> types.HealthResponse: + return types.HealthResponse(status="ok") + + @app.get("/get_server_capabilities") + async def get_server_capabilities(self) -> types.GetServerCapabilitiesResponse: + return types.GetServerCapabilitiesResponse(supported_models=self.supported_models) + + @app.post("/telemetry") + async def telemetry(self, request: types.TelemetrySendRequest) -> types.TelemetryResponse: + # Telemetry is accepted but not persisted; this endpoint is intentionally lightweight. + return types.TelemetryResponse(status="accepted") + + @app.post("/create_session") + async def create_session(self, request: types.CreateSessionRequest) -> types.CreateSessionResponse: + session_id = await self.state.create_session(request) + return types.CreateSessionResponse(session_id=session_id) + + @app.post("/session_heartbeat") + async def session_heartbeat(self, request: types.SessionHeartbeatRequest) -> types.SessionHeartbeatResponse: + alive = await self.state.touch_session(request.session_id) + if not alive: + raise HTTPException(status_code=404, detail="Unknown session") + return types.SessionHeartbeatResponse() + + + @app.post("/create_sampling_session") + async def create_sampling_session( + self, request: types.CreateSamplingSessionRequest + ) -> types.CreateSamplingSessionResponse: + sampling_session_id = await self.state.create_sampling_session(request) + return types.CreateSamplingSessionResponse(sampling_session_id=sampling_session_id) + + @app.post("/asample") + async def asample(self, request: types.SampleRequest) -> types.UntypedAPIFuture: + return await self._store_future(self._sample_output()) + + + + @app.post("/save_weights_for_sampler") + async def save_weights_for_sampler( + self, request: types.SaveWeightsForSamplerRequest + ) -> types.UntypedAPIFuture: + suffix = request.path or f"sampler-{datetime.now().strftime('%Y%m%d-%H%M%S')}" + path = f"tinker://{request.model_id}/{suffix}" + sampling_session_id = None + if request.sampling_session_seq_id is not None: + sampling_session_id = f"sampling_{request.sampling_session_seq_id}" + result = types.SaveWeightsForSamplerResponseInternal(path=path, sampling_session_id=sampling_session_id) + return await self._store_future(result, model_id=request.model_id) + + @app.post("/retrieve_future") + async def retrieve_future(self, request: types.FutureRetrieveRequest) -> Any: + record = await self.state.get_future(request.request_id) + if record is None: + raise HTTPException(status_code=404, detail="Future not found") + result = record["result"] + if hasattr(result, "model_dump"): + return result.model_dump() + return result + + +def build_graph() -> Any: + """Helper to bind the deployment for serve.run.""" + return TinkerCompatServer.bind() + + +if __name__ == "__main__": + ray.init(ignore_reinit_error=True) + + # Get port from environment variable or default to 8000 + port = int(os.environ.get("TINKER_PORT", "8000")) + + # Start serve with specific HTTP options + serve.start(http_options={"host": "0.0.0.0", "port": port}) + + serve.run(build_graph(), route_prefix="/api/v1") + input("\nPress Enter to stop the server...") \ No newline at end of file diff --git a/src/twinkle/server/tinker/state.py b/src/twinkle/server/tinker/state.py new file mode 100644 index 00000000..e0bbc633 --- /dev/null +++ b/src/twinkle/server/tinker/state.py @@ -0,0 +1,118 @@ +# Copyright (c) ModelScope Contributors. All rights reserved. +from __future__ import annotations + +import time +import uuid +from datetime import datetime +from typing import Any, Dict, Optional + +import ray + + +@ray.remote(name="tinker_server_state", lifetime="detached") +class ServerState: + def __init__(self) -> None: + self.sessions: Dict[str, Dict[str, Any]] = {} + self.models: Dict[str, Dict[str, Any]] = {} + self.sampling_sessions: Dict[str, Dict[str, Any]] = {} + self.futures: Dict[str, Dict[str, Any]] = {} + + def create_session(self, payload: Dict[str, Any]) -> str: + session_id = payload.get("session_id") or f"session_{uuid.uuid4().hex}" + self.sessions[session_id] = { + "tags": list(payload.get("tags") or []), + "user_metadata": payload.get("user_metadata") or {}, + "sdk_version": payload.get("sdk_version"), + "created_at": datetime.now().isoformat(), + } + return session_id + + def touch_session(self, session_id: str) -> bool: + if session_id not in self.sessions: + return False + self.sessions[session_id]["last_heartbeat"] = time.time() + return True + + def register_model(self, payload: Dict[str, Any], model_id: Optional[str] = None) -> str: + model_id = model_id or payload.get("model_id") or f"{payload.get('base_model', 'model')}-{uuid.uuid4().hex[:8]}" + self.models[model_id] = { + "session_id": payload.get("session_id"), + "model_seq_id": payload.get("model_seq_id"), + "base_model": payload.get("base_model"), + "user_metadata": payload.get("user_metadata") or {}, + "lora_config": payload.get("lora_config"), + "created_at": datetime.now().isoformat(), + } + return model_id + + def unload_model(self, model_id: str) -> bool: + return self.models.pop(model_id, None) is not None + + def create_sampling_session(self, payload: Dict[str, Any], sampling_session_id: Optional[str] = None) -> str: + sampling_session_id = sampling_session_id or payload.get("sampling_session_id") or f"sampling_{uuid.uuid4().hex}" + self.sampling_sessions[sampling_session_id] = { + "session_id": payload.get("session_id"), + "seq_id": payload.get("sampling_session_seq_id"), + "base_model": payload.get("base_model"), + "model_path": payload.get("model_path"), + "created_at": datetime.now().isoformat(), + } + return sampling_session_id + + def get_model_metadata(self, model_id: str) -> Optional[Dict[str, Any]]: + return self.models.get(model_id) + + def store_future(self, request_id: str, result: Any, model_id: Optional[str]): + if hasattr(result, "model_dump"): + result = result.model_dump() + self.futures[request_id] = { + "status": "completed", + "result": result, + "model_id": model_id, + "created_at": datetime.now().isoformat(), + } + + def get_future(self, request_id: str) -> Optional[Dict[str, Any]]: + return self.futures.get(request_id) + + +class ServerStateProxy: + def __init__(self, actor_handle): + self._actor = actor_handle + + def create_session(self, payload: Dict[str, Any]) -> str: + return ray.get(self._actor.create_session.remote(payload)) + + def touch_session(self, session_id: str) -> bool: + return ray.get(self._actor.touch_session.remote(session_id)) + + def register_model(self, payload: Dict[str, Any], model_id: Optional[str] = None) -> str: + return ray.get(self._actor.register_model.remote(payload, model_id)) + + def unload_model(self, model_id: str) -> bool: + return ray.get(self._actor.unload_model.remote(model_id)) + + def create_sampling_session(self, payload: Dict[str, Any], sampling_session_id: Optional[str] = None) -> str: + return ray.get(self._actor.create_sampling_session.remote(payload, sampling_session_id)) + + def get_model_metadata(self, model_id: str) -> Optional[Dict[str, Any]]: + return ray.get(self._actor.get_model_metadata.remote(model_id)) + + def store_future(self, request_id: str, result: Any, model_id: Optional[str]): + return ray.get(self._actor.store_future.remote(request_id, result, model_id)) + + def get_future(self, request_id: str) -> Optional[Dict[str, Any]]: + return ray.get(self._actor.get_future.remote(request_id)) + + +def get_server_state(actor_name: str = "tinker_server_state") -> ServerStateProxy: + _ServerState = ServerState + try: + actor = ray.get_actor(actor_name) + except ValueError: + try: + actor = _ServerState.options(name=actor_name, lifetime="detached").remote() + except ValueError: + actor = ray.get_actor(actor_name) + assert actor is not None + return ServerStateProxy(actor) diff --git a/src/twinkle/server/twinkle/processor.py b/src/twinkle/server/twinkle/processor.py index 1d8124ad..919791c5 100644 --- a/src/twinkle/server/twinkle/processor.py +++ b/src/twinkle/server/twinkle/processor.py @@ -34,6 +34,7 @@ class Config: extra = "allow" def build_processor_app(nproc_per_node: int, + ncpu_proc_per_node:int, device_group: Dict[str, Any], device_mesh: Dict[str, Any], deploy_options: Dict[str, Any]): @@ -54,10 +55,10 @@ class ProcessorManagement: COUNT_DOWN = 60 * 30 - def __init__(self, nproc_per_node: int, device_group: Dict[str, Any], device_mesh: Dict[str, Any]): + def __init__(self, nproc_per_node: int, ncpu_proc_per_node:int, device_group: Dict[str, Any], device_mesh: Dict[str, Any]): self.device_group = DeviceGroup(**device_group) twinkle.initialize(mode='ray', nproc_per_node=nproc_per_node, groups=[self.device_group], - lazy_collect=False) + lazy_collect=False, ncpu_proc_per_node=ncpu_proc_per_node) self.device_mesh = DeviceMesh(**device_mesh) self.resource_dict = {} self.resource_records: Dict[str, int] = {} @@ -169,4 +170,4 @@ def call(self, body: CallRequest): else: return {'result': result} - return ProcessorManagement.options(**deploy_options).bind(nproc_per_node, device_group, device_mesh) \ No newline at end of file + return ProcessorManagement.options(**deploy_options).bind(nproc_per_node, ncpu_proc_per_node, device_group, device_mesh) \ No newline at end of file diff --git a/src/twinkle_client/__init__.py b/src/twinkle_client/__init__.py index 0829846c..a53ad5f2 100644 --- a/src/twinkle_client/__init__.py +++ b/src/twinkle_client/__init__.py @@ -1 +1,20 @@ -# Copyright (c) ModelScope Contributors. All rights reserved. \ No newline at end of file +# Copyright (c) ModelScope Contributors. All rights reserved. +from __future__ import annotations + +from typing import TYPE_CHECKING, Optional +from twinkle.utils import requires + +if TYPE_CHECKING: + from tinker import ServiceClient + +def init_tinker_compat_client(base_url: Optional[str] = None, api_key: Optional[str] = None, **kwargs) -> ServiceClient: + requires('tinker') + from tinker import ServiceClient + from twinkle_client.http.utils import TWINKLE_REQUEST_ID, TWINKLE_SERVER_TOKEN + + default_headers = { + "X-Ray-Serve-Request-Id": TWINKLE_REQUEST_ID, + "Authorization": 'Bearer ' + (api_key or TWINKLE_SERVER_TOKEN), + } | kwargs.pop("default_headers", {}) + + return ServiceClient(base_url=base_url, api_key=api_key, default_headers=default_headers, **kwargs) diff --git a/src/twinkle_client/http/utils.py b/src/twinkle_client/http/utils.py index 55d4cb7a..e432e59b 100644 --- a/src/twinkle_client/http/utils.py +++ b/src/twinkle_client/http/utils.py @@ -2,5 +2,5 @@ import uuid TWINKLE_SERVER_URL = os.environ.get("TWINKLE_SERVER_URL") -TWINKLE_SERVER_TOKEN = os.environ.get("TWINKLE_SERVER_TOKEN", "EMPTY_TOKEN") +TWINKLE_SERVER_TOKEN = os.environ.get("TWINKLE_SERVER_TOKEN", "tml-EMPTY_TOKEN") # Must start with tml- TWINKLE_REQUEST_ID = str(uuid.uuid4().hex) From 535afa76a41a50e355335784fc6262e8141c7054 Mon Sep 17 00:00:00 2001 From: Yunnglin Date: Fri, 16 Jan 2026 19:53:49 +0800 Subject: [PATCH 7/9] wip --- src/twinkle/data_format/datum.py | 27 +++++ src/twinkle/server/tinker/model.py | 160 +++++++++++++++++----------- src/twinkle/server/tinker/server.py | 61 +++++------ src/twinkle/server/tinker/state.py | 23 ++++ 4 files changed, 179 insertions(+), 92 deletions(-) create mode 100644 src/twinkle/data_format/datum.py diff --git a/src/twinkle/data_format/datum.py b/src/twinkle/data_format/datum.py new file mode 100644 index 00000000..d0f6b061 --- /dev/null +++ b/src/twinkle/data_format/datum.py @@ -0,0 +1,27 @@ +from __future__ import annotations +from typing import TYPE_CHECKING +import numpy as np +from .input_feature import InputFeature +if TYPE_CHECKING: + from tinker import types + +def datum_to_input_feature(datum: types.Datum) -> InputFeature: + """Convert a Datum to a dictionary of input features for model inference.""" + input_feature: InputFeature = {} + + # 1. Flatten model_input chunks to get input_ids + input_ids = datum.model_input.to_ints() + input_feature['input_ids'] = input_ids + input_feature['attention_mask'] = [1] * len(input_ids) + + # 2. Map loss function inputs + # 'target_tokens' -> 'labels' + if 'target_tokens' in datum.loss_fn_inputs: + input_feature['labels'] = datum.loss_fn_inputs['target_tokens'].to_numpy() + + # 'weights' -> 'completion_mask' + if 'weights' in datum.loss_fn_inputs: + weights = datum.loss_fn_inputs['weights'].to_numpy() + input_feature['completion_mask'] = weights + + return input_feature \ No newline at end of file diff --git a/src/twinkle/server/tinker/model.py b/src/twinkle/server/tinker/model.py index ccb110e4..ceab6504 100644 --- a/src/twinkle/server/tinker/model.py +++ b/src/twinkle/server/tinker/model.py @@ -18,10 +18,9 @@ from twinkle.model.base import TwinkleModel from twinkle.data_format import InputFeature, Trajectory from twinkle.server.twinkle.validation import verify_request_token, init_config_registry, ConfigRegistryProxy -from .state import get_server_state +from .state import get_server_state, schedule_task -def build_model_app(model_id: str, - nproc_per_node: int, +def build_model_app(nproc_per_node: int, device_group: Dict[str, Any], device_mesh: Dict[str, Any], deploy_options: Dict[str, Any], @@ -38,11 +37,10 @@ class ModelManagement(): COUNT_DOWN = 60 * 30 - def __init__(self, model_id: str, nproc_per_node: int, device_group: Dict[str, Any], device_mesh: Dict[str, Any], **kwargs): + def __init__(self, nproc_per_node: int, device_group: Dict[str, Any], device_mesh: Dict[str, Any], **kwargs): self.device_group = DeviceGroup(**device_group) twinkle.initialize(mode='ray', nproc_per_node=nproc_per_node, groups=[self.device_group], lazy_collect=False) self.device_mesh = DeviceMesh(**device_mesh) - self.model_id = model_id self.model: TwinkleModel = None self.kwargs = kwargs self.adapter_records: Dict[str, int] = {} @@ -83,40 +81,48 @@ def handle_adapter_count(self, token: str, add: bool): self.config_registry.pop(user_key) @staticmethod - def _default_forward_output() -> types.ForwardBackwardOutput: - tensor = types.TensorData(data=[0.0], dtype="float32", shape=[1]) - loss_output: types.LossFnOutput = {"loss": tensor, "logprobs": tensor} - return types.ForwardBackwardOutput( - loss_fn_output_type="TensorData", - loss_fn_outputs=[loss_output], - metrics={"loss:avg": 0.0, "tokens_per_second:avg": 0.0}, - ) - - @staticmethod - def _new_request_id() -> str: - return f"req_{uuid.uuid4().hex}" + def get_adapter_name(request: Request, adapter_name: Optional[str]) -> Optional[str]: + if adapter_name is None or adapter_name == '': + return None + return request.state.request_id + '-' + adapter_name + + def assert_adapter_exists(self, adapter_name: str): + assert adapter_name and adapter_name in self.adapter_records, f"Adapter {adapter_name} not found" - async def _store_future(self, payload: Any, model_id: Optional[str] = None) -> types.UntypedAPIFuture: - request_id = self._new_request_id() - await self.state.store_future(request_id, payload, model_id) - return types.UntypedAPIFuture(request_id=request_id, model_id=model_id) + def assert_adapter_valid(self, adapter_name: Optional[str]): + assert adapter_name is None or adapter_name == '' or adapter_name in self.adapter_records, \ + f"Adapter {adapter_name} is invalid" @app.post("/create_model") - async def create_model(self, request: types.CreateModelRequest) -> types.UntypedAPIFuture: - model_id = await self.state.register_model(request) - result = types.CreateModelResponse(model_id=model_id) - model = MultiLoraTransformersModel( - model_id=self.model_id, - device_mesh=self.device_mesh, - remote_group=self.device_group.name, - **self.kwargs - ) - return await self._store_future(result, model_id=model_id) + async def create_model(self, request: Request, body: types.CreateModelRequest) -> types.UntypedAPIFuture: + model_id = await self.state.register_model(body) + + async def _load_model(): + self.model = MultiLoraTransformersModel( + model_id=body.base_model, + device_mesh=self.device_mesh, + remote_group=self.device_group.name, + **body.user_metadata, + ) + if body.lora_config: + # TODO: support more lora config parameters + lora_cfg = LoraConfig( + r=body.lora_config.rank, + ) + with self.adapter_lock: + adapter_name = self.get_adapter_name(request=request, adapter_name=model_id) + self.model.add_adapter_to_model(adapter_name=adapter_name, config_or_dir=lora_cfg) + self.adapter_records[adapter_name] = 0 + self.key_token_dict[adapter_name] = request.state.token + self.handle_adapter_count(request.state.token, True) + return types.CreateModelResponse(model_id=model_id) + + return await schedule_task(self.state, _load_model(), model_id=model_id) @app.post("/get_info") - async def get_info(self, request: types.GetInfoRequest) -> types.GetInfoResponse: - metadata = await self.state.get_model_metadata(str(request.model_id)) - model_name = metadata.get("base_model") if metadata else str(request.model_id) + async def get_info(self, request: Request, body: types.GetInfoRequest) -> types.GetInfoResponse: + metadata = await self.state.get_model_metadata(str(body.model_id)) + model_name = metadata.get("base_model") if metadata else str(body.model_id) lora_rank = None is_lora = False if metadata and metadata.get("lora_config"): @@ -124,44 +130,78 @@ async def get_info(self, request: types.GetInfoRequest) -> types.GetInfoResponse is_lora = True return types.GetInfoResponse( model_data=types.ModelData(model_name=model_name), - model_id=request.model_id, + model_id=body.model_id, is_lora=is_lora, lora_rank=lora_rank, + model_name=model_name, ) @app.post("/unload_model") - async def unload_model(self, request: types.UnloadModelRequest) -> types.UntypedAPIFuture: - await self.state.unload_model(request.model_id) - result = types.UnloadModelResponse(model_id=request.model_id) - return await self._store_future(result, model_id=request.model_id) + async def unload_model(self, request: Request, body: types.UnloadModelRequest) -> types.UntypedAPIFuture: + async def _do_unload(): + await self.state.unload_model(body.model_id) + return types.UnloadModelResponse(model_id=body.model_id) + + return await schedule_task(self.state, _do_unload(), model_id=body.model_id) - @app.post("/forward") - async def forward(self, request: types.ForwardRequest) -> types.UntypedAPIFuture: - return await self._store_future(self._default_forward_output(), model_id=request.model_id) @app.post("/forward_backward") - async def forward_backward(self, request: types.ForwardBackwardRequest) -> types.UntypedAPIFuture: - return await self._store_future(self._default_forward_output(), model_id=request.model_id) + async def forward_backward(self, request: Request, body: types.ForwardBackwardRequest) -> types.UntypedAPIFuture: + async def _do_forward_backward(): + adapter_name = self.get_adapter_name(request, adapter_name=body.model_id) + self.assert_adapter_exists(adapter_name=adapter_name) + + datum_list = body.forward_backward_input.data + loss_fn = body.forward_backward_input.loss_fn + loss_fn_config = body.forward_backward_input.loss_fn_config + + if isinstance(inputs, list): + _input = inputs[0] + if 'input_ids' in _input: + inputs = [InputFeature(**_input) for _input in inputs] + else: + inputs = [Trajectory(**_input) for _input in inputs] + else: + assert isinstance(inputs, dict) + inputs = InputFeature(**inputs) if 'input_ids' in inputs else Trajectory(**inputs) + ret = self.model.forward_backward(inputs=inputs, adapter_name=adapter_name) + return types.F( + loss_fn_output_type="TensorData", + loss_fn_outputs=[loss_output], + metrics={"loss:avg": 0.0, "tokens_per_second:avg": 0.0}, + ) + + return await schedule_task( + self.state, _do_forward_backward(), model_id=body.model_id + ) @app.post("/optim_step") - async def optim_step(self, request: types.OptimStepRequest) -> types.UntypedAPIFuture: - metrics = types.OptimStepResponse(metrics={ - "grad_norm": 0.0, - "weight_norm": 0.0, - "update_norm": 0.0, - }) - return await self._store_future(metrics, model_id=request.model_id) + async def optim_step(self, request: Request, body: types.OptimStepRequest) -> types.UntypedAPIFuture: + async def _do_optim(): + return types.OptimStepResponse( + metrics={ + "grad_norm": 0.0, + "weight_norm": 0.0, + "update_norm": 0.0, + } + ) + + return await schedule_task(self.state, _do_optim(), model_id=body.model_id) @app.post("/save_weights") - async def save_weights(self, request: types.SaveWeightsRequest) -> types.UntypedAPIFuture: - suffix = request.path or f"checkpoint-{datetime.now().strftime('%Y%m%d-%H%M%S')}" - path = f"tinker://{request.model_id}/{suffix}" - result = types.SaveWeightsResponse(path=path) - return await self._store_future(result, model_id=request.model_id) + async def save_weights(self, request: Request, body: types.SaveWeightsRequest) -> types.UntypedAPIFuture: + async def _do_save(): + suffix = body.path or f"checkpoint-{datetime.now().strftime('%Y%m%d-%H%M%S')}" + path = f"tinker://{body.model_id}/{suffix}" + return types.SaveWeightsResponse(path=path) + + return await schedule_task(self.state, _do_save(), model_id=body.model_id) @app.post("/load_weights") - async def load_weights(self, request: types.LoadWeightsRequest) -> types.UntypedAPIFuture: - result = types.LoadWeightsResponse(path=request.path) - return await self._store_future(result, model_id=request.model_id) + async def load_weights(self, request: Request, body: types.LoadWeightsRequest) -> types.UntypedAPIFuture: + async def _do_load(): + return types.LoadWeightsResponse(path=body.path) + + return await schedule_task(self.state, _do_load(), model_id=body.model_id) - return ModelManagement.options(**deploy_options).bind(model_id, nproc_per_node, device_group, device_mesh, **kwargs) \ No newline at end of file + return ModelManagement.options(**deploy_options).bind(nproc_per_node, device_group, device_mesh, **kwargs) \ No newline at end of file diff --git a/src/twinkle/server/tinker/server.py b/src/twinkle/server/tinker/server.py index 055f489f..60881d41 100644 --- a/src/twinkle/server/tinker/server.py +++ b/src/twinkle/server/tinker/server.py @@ -13,11 +13,11 @@ from typing import Any, Dict, List, Optional import ray -from fastapi import FastAPI, HTTPException +from fastapi import FastAPI, HTTPException, Request from ray import serve from tinker import types -from .state import get_server_state +from .state import get_server_state, schedule_task # ----- FastAPI + Serve deployment ------------------------------------------ @@ -39,15 +39,6 @@ def __init__(self, supported_models: Optional[List[types.SupportedModel]] = None # --- Helpers ----------------------------------------------------------- - @staticmethod - def _new_request_id() -> str: - return f"req_{uuid.uuid4().hex}" - - async def _store_future(self, payload: Any, model_id: Optional[str] = None) -> types.UntypedAPIFuture: - request_id = self._new_request_id() - await self.state.store_future(request_id, payload, model_id) - return types.UntypedAPIFuture(request_id=request_id, model_id=model_id) - @staticmethod def _sample_output() -> types.SampleResponse: sequence = types.SampledSequence(stop_reason="stop", tokens=[1, 2, 3], logprobs=[-0.1, -0.2, -0.3]) @@ -56,26 +47,26 @@ def _sample_output() -> types.SampleResponse: # --- Endpoints --------------------------------------------------------- @app.get("/healthz") - async def healthz(self) -> types.HealthResponse: + async def healthz(self, request: Request) -> types.HealthResponse: return types.HealthResponse(status="ok") @app.get("/get_server_capabilities") - async def get_server_capabilities(self) -> types.GetServerCapabilitiesResponse: + async def get_server_capabilities(self, request: Request) -> types.GetServerCapabilitiesResponse: return types.GetServerCapabilitiesResponse(supported_models=self.supported_models) @app.post("/telemetry") - async def telemetry(self, request: types.TelemetrySendRequest) -> types.TelemetryResponse: + async def telemetry(self, request: Request, body: types.TelemetrySendRequest) -> types.TelemetryResponse: # Telemetry is accepted but not persisted; this endpoint is intentionally lightweight. return types.TelemetryResponse(status="accepted") @app.post("/create_session") - async def create_session(self, request: types.CreateSessionRequest) -> types.CreateSessionResponse: - session_id = await self.state.create_session(request) + async def create_session(self, request: Request, body: types.CreateSessionRequest) -> types.CreateSessionResponse: + session_id = await self.state.create_session(body) return types.CreateSessionResponse(session_id=session_id) @app.post("/session_heartbeat") - async def session_heartbeat(self, request: types.SessionHeartbeatRequest) -> types.SessionHeartbeatResponse: - alive = await self.state.touch_session(request.session_id) + async def session_heartbeat(self, request: Request, body: types.SessionHeartbeatRequest) -> types.SessionHeartbeatResponse: + alive = await self.state.touch_session(body.session_id) if not alive: raise HTTPException(status_code=404, detail="Unknown session") return types.SessionHeartbeatResponse() @@ -83,32 +74,38 @@ async def session_heartbeat(self, request: types.SessionHeartbeatRequest) -> typ @app.post("/create_sampling_session") async def create_sampling_session( - self, request: types.CreateSamplingSessionRequest + self, request: Request, body: types.CreateSamplingSessionRequest ) -> types.CreateSamplingSessionResponse: - sampling_session_id = await self.state.create_sampling_session(request) + sampling_session_id = await self.state.create_sampling_session(body) return types.CreateSamplingSessionResponse(sampling_session_id=sampling_session_id) @app.post("/asample") - async def asample(self, request: types.SampleRequest) -> types.UntypedAPIFuture: - return await self._store_future(self._sample_output()) - + async def asample(self, request: Request, body: types.SampleRequest) -> types.UntypedAPIFuture: + async def _do_sample(): + return self._sample_output() + return await schedule_task(self.state, _do_sample()) @app.post("/save_weights_for_sampler") async def save_weights_for_sampler( - self, request: types.SaveWeightsForSamplerRequest + self, request: Request, body: types.SaveWeightsForSamplerRequest ) -> types.UntypedAPIFuture: - suffix = request.path or f"sampler-{datetime.now().strftime('%Y%m%d-%H%M%S')}" - path = f"tinker://{request.model_id}/{suffix}" + suffix = body.path or f"sampler-{datetime.now().strftime('%Y%m%d-%H%M%S')}" + path = f"tinker://{body.model_id}/{suffix}" sampling_session_id = None - if request.sampling_session_seq_id is not None: - sampling_session_id = f"sampling_{request.sampling_session_seq_id}" - result = types.SaveWeightsForSamplerResponseInternal(path=path, sampling_session_id=sampling_session_id) - return await self._store_future(result, model_id=request.model_id) + if body.sampling_session_seq_id is not None: + sampling_session_id = f"sampling_{body.sampling_session_seq_id}" + + async def _do_save(): + return types.SaveWeightsForSamplerResponseInternal( + path=path, sampling_session_id=sampling_session_id + ) + + return await schedule_task(self.state, _do_save(), model_id=body.model_id) @app.post("/retrieve_future") - async def retrieve_future(self, request: types.FutureRetrieveRequest) -> Any: - record = await self.state.get_future(request.request_id) + async def retrieve_future(self, request: Request, body: types.FutureRetrieveRequest) -> Any: + record = await self.state.get_future(body.request_id) if record is None: raise HTTPException(status_code=404, detail="Future not found") result = record["result"] diff --git a/src/twinkle/server/tinker/state.py b/src/twinkle/server/tinker/state.py index e0bbc633..f41f0b9b 100644 --- a/src/twinkle/server/tinker/state.py +++ b/src/twinkle/server/tinker/state.py @@ -3,10 +3,12 @@ import time import uuid +import asyncio from datetime import datetime from typing import Any, Dict, Optional import ray +from tinker import types @ray.remote(name="tinker_server_state", lifetime="detached") @@ -116,3 +118,24 @@ def get_server_state(actor_name: str = "tinker_server_state") -> ServerStateProx actor = ray.get_actor(actor_name) assert actor is not None return ServerStateProxy(actor) + + +async def schedule_task( + state: ServerStateProxy, + coro: Any, + model_id: Optional[str] = None, +) -> types.UntypedAPIFuture: + request_id = f"req_{uuid.uuid4().hex}" + + async def _runner(): + try: + val = await coro + await state.store_future(request_id, val, model_id) + except Exception as e: + # Structure the error so the client SDK can interpret it + err_payload = {"error": str(e), "category": "Internal"} + await state.store_future(request_id, err_payload, model_id) + + # Schedule execution in the background + asyncio.create_task(_runner()) + return types.UntypedAPIFuture(request_id=request_id, model_id=model_id) From 2f9b2252083669a5c83a5e3061754819f4a64c57 Mon Sep 17 00:00:00 2001 From: Yunnglin Date: Mon, 19 Jan 2026 14:56:16 +0800 Subject: [PATCH 8/9] update serve --- src/twinkle/data_format/__init__.py | 3 +- src/twinkle/server/tinker/__init__.py | 4 +- src/twinkle/server/tinker/model.py | 58 +++--- src/twinkle/server/tinker/server.py | 273 +++++++++++++++----------- src/twinkle/server/tinker/state.py | 24 ++- 5 files changed, 211 insertions(+), 151 deletions(-) diff --git a/src/twinkle/data_format/__init__.py b/src/twinkle/data_format/__init__.py index 14ac6123..a447ee3a 100644 --- a/src/twinkle/data_format/__init__.py +++ b/src/twinkle/data_format/__init__.py @@ -1,4 +1,5 @@ # Copyright (c) ModelScope Contributors. All rights reserved. from .message import Tool, ToolCall, Message from .trajectory import Trajectory -from .input_feature import InputFeature, to_transformers_dict, to_megatron_dict \ No newline at end of file +from .input_feature import InputFeature, to_transformers_dict, to_megatron_dict +from .datum import datum_to_input_feature \ No newline at end of file diff --git a/src/twinkle/server/tinker/__init__.py b/src/twinkle/server/tinker/__init__.py index 0829846c..cb3456ba 100644 --- a/src/twinkle/server/tinker/__init__.py +++ b/src/twinkle/server/tinker/__init__.py @@ -1 +1,3 @@ -# Copyright (c) ModelScope Contributors. All rights reserved. \ No newline at end of file +# Copyright (c) ModelScope Contributors. All rights reserved. +from .model import build_model_app +from .server import build_server_app diff --git a/src/twinkle/server/tinker/model.py b/src/twinkle/server/tinker/model.py index ceab6504..9714b195 100644 --- a/src/twinkle/server/tinker/model.py +++ b/src/twinkle/server/tinker/model.py @@ -4,19 +4,16 @@ import threading import time from typing import Dict, Any, Optional -import uuid from fastapi import FastAPI, Request from peft import LoraConfig -from pydantic import BaseModel from ray import serve from tinker import types import twinkle from twinkle import DeviceGroup, DeviceMesh from twinkle.model import TwinkleModel, MultiLoraTransformersModel -from twinkle.model.base import TwinkleModel -from twinkle.data_format import InputFeature, Trajectory +from twinkle.data_format import datum_to_input_feature from twinkle.server.twinkle.validation import verify_request_token, init_config_registry, ConfigRegistryProxy from .state import get_server_state, schedule_task @@ -41,7 +38,7 @@ def __init__(self, nproc_per_node: int, device_group: Dict[str, Any], device_mes self.device_group = DeviceGroup(**device_group) twinkle.initialize(mode='ray', nproc_per_node=nproc_per_node, groups=[self.device_group], lazy_collect=False) self.device_mesh = DeviceMesh(**device_mesh) - self.model: TwinkleModel = None + self.model: Optional[MultiLoraTransformersModel] = None self.kwargs = kwargs self.adapter_records: Dict[str, int] = {} self.hb_thread = threading.Thread(target=self.countdown, daemon=True) @@ -55,6 +52,8 @@ def __init__(self, nproc_per_node: int, device_group: Dict[str, Any], device_mes def countdown(self): while True: time.sleep(1) + if not self.model: + continue for key in list(self.adapter_records.keys()): self.adapter_records[key] += 1 if self.adapter_records[key] > self.COUNT_DOWN: @@ -67,7 +66,7 @@ def countdown(self): def handle_adapter_count(self, token: str, add: bool): user_key = token + '_' + 'model_adapter' - cur_count = self.config_registry.get_config(user_key) or 0 + cur_count: int = self.config_registry.get_config(user_key) or 0 if add: if cur_count < self.per_token_model_limit: self.config_registry.add_config(user_key, cur_count + 1) @@ -81,9 +80,7 @@ def handle_adapter_count(self, token: str, add: bool): self.config_registry.pop(user_key) @staticmethod - def get_adapter_name(request: Request, adapter_name: Optional[str]) -> Optional[str]: - if adapter_name is None or adapter_name == '': - return None + def get_adapter_name(request: Request, adapter_name: str) -> str: return request.state.request_id + '-' + adapter_name def assert_adapter_exists(self, adapter_name: str): @@ -95,14 +92,16 @@ def assert_adapter_valid(self, adapter_name: Optional[str]): @app.post("/create_model") async def create_model(self, request: Request, body: types.CreateModelRequest) -> types.UntypedAPIFuture: - model_id = await self.state.register_model(body) + model_id = self.state.register_model(body.model_dump()) async def _load_model(): + breakpoint() + extra_kwargs = body.user_metadata or {} self.model = MultiLoraTransformersModel( model_id=body.base_model, device_mesh=self.device_mesh, remote_group=self.device_group.name, - **body.user_metadata, + **extra_kwargs, ) if body.lora_config: # TODO: support more lora config parameters @@ -112,16 +111,18 @@ async def _load_model(): with self.adapter_lock: adapter_name = self.get_adapter_name(request=request, adapter_name=model_id) self.model.add_adapter_to_model(adapter_name=adapter_name, config_or_dir=lora_cfg) - self.adapter_records[adapter_name] = 0 - self.key_token_dict[adapter_name] = request.state.token - self.handle_adapter_count(request.state.token, True) + self.adapter_records[adapter_name] = 0 + self.key_token_dict[adapter_name] = request.state.token + self.handle_adapter_count(request.state.token, True) + self.model.set_processor('InputProcessor', adapter_name=adapter_name) + self.model.set_optimizer('AdamW', adapter_name=adapter_name) return types.CreateModelResponse(model_id=model_id) return await schedule_task(self.state, _load_model(), model_id=model_id) @app.post("/get_info") async def get_info(self, request: Request, body: types.GetInfoRequest) -> types.GetInfoResponse: - metadata = await self.state.get_model_metadata(str(body.model_id)) + metadata = self.state.get_model_metadata(str(body.model_id)) model_name = metadata.get("base_model") if metadata else str(body.model_id) lora_rank = None is_lora = False @@ -139,7 +140,13 @@ async def get_info(self, request: Request, body: types.GetInfoRequest) -> types. @app.post("/unload_model") async def unload_model(self, request: Request, body: types.UnloadModelRequest) -> types.UntypedAPIFuture: async def _do_unload(): - await self.state.unload_model(body.model_id) + self.model = None + with self.adapter_lock: + adapter_name = self.get_adapter_name(request=request, adapter_name=body.model_id) + del self.adapter_records[adapter_name] + del self.key_token_dict[adapter_name] + self.handle_adapter_count(request.state.token, add=False) + self.state.unload_model(body.model_id) return types.UnloadModelResponse(model_id=body.model_id) return await schedule_task(self.state, _do_unload(), model_id=body.model_id) @@ -148,6 +155,12 @@ async def _do_unload(): @app.post("/forward_backward") async def forward_backward(self, request: Request, body: types.ForwardBackwardRequest) -> types.UntypedAPIFuture: async def _do_forward_backward(): + if not self.model: + return types.RequestFailedResponse( + error="Model not loaded, please load model first", + category=types.RequestErrorCategory.User, + ) + adapter_name = self.get_adapter_name(request, adapter_name=body.model_id) self.assert_adapter_exists(adapter_name=adapter_name) @@ -155,17 +168,9 @@ async def _do_forward_backward(): loss_fn = body.forward_backward_input.loss_fn loss_fn_config = body.forward_backward_input.loss_fn_config - if isinstance(inputs, list): - _input = inputs[0] - if 'input_ids' in _input: - inputs = [InputFeature(**_input) for _input in inputs] - else: - inputs = [Trajectory(**_input) for _input in inputs] - else: - assert isinstance(inputs, dict) - inputs = InputFeature(**inputs) if 'input_ids' in inputs else Trajectory(**inputs) + inputs = [datum_to_input_feature(datum) for datum in datum_list] ret = self.model.forward_backward(inputs=inputs, adapter_name=adapter_name) - return types.F( + return types.ForwardBackwardOutput( loss_fn_output_type="TensorData", loss_fn_outputs=[loss_output], metrics={"loss:avg": 0.0, "tokens_per_second:avg": 0.0}, @@ -178,6 +183,7 @@ async def _do_forward_backward(): @app.post("/optim_step") async def optim_step(self, request: Request, body: types.OptimStepRequest) -> types.UntypedAPIFuture: async def _do_optim(): + return types.OptimStepResponse( metrics={ "grad_norm": 0.0, diff --git a/src/twinkle/server/tinker/server.py b/src/twinkle/server/tinker/server.py index 60881d41..ef9d31cd 100644 --- a/src/twinkle/server/tinker/server.py +++ b/src/twinkle/server/tinker/server.py @@ -1,132 +1,179 @@ -""" -Minimal Ray Serve + FastAPI implementation of the Tinker server API. -The endpoints mirror the Python SDK expectations so the client can talk to -this server without further glue code. This is a lightweight reference -implementation meant for local development, integration tests, or as a -starting point for a production backend. -""" +# Copyright (c) ModelScope Contributors. All rights reserved. + from __future__ import annotations -import os -import uuid from datetime import datetime from typing import Any, Dict, List, Optional -import ray -from fastapi import FastAPI, HTTPException, Request +import httpx +from fastapi import FastAPI, HTTPException, Request, Response from ray import serve from tinker import types -from .state import get_server_state, schedule_task - - -# ----- FastAPI + Serve deployment ------------------------------------------ - - -app = FastAPI(title="Tinker Ray Server", version="0.1.0") - - -@serve.deployment -@serve.ingress(app) -class TinkerCompatServer: - def __init__(self, supported_models: Optional[List[types.SupportedModel]] = None) -> None: - self.state = get_server_state() - self.supported_models = supported_models or [ - types.SupportedModel(model_name="Qwen/Qwen2.5-0.5B-Instruct"), - types.SupportedModel(model_name="Qwen/Qwen2.5-7B-Instruct"), - types.SupportedModel(model_name="Qwen/Qwen2.5-72B-Instruct"), - ] - - # --- Helpers ----------------------------------------------------------- - - @staticmethod - def _sample_output() -> types.SampleResponse: - sequence = types.SampledSequence(stop_reason="stop", tokens=[1, 2, 3], logprobs=[-0.1, -0.2, -0.3]) - return types.SampleResponse(sequences=[sequence]) - - # --- Endpoints --------------------------------------------------------- - @app.get("/healthz") - async def healthz(self, request: Request) -> types.HealthResponse: - return types.HealthResponse(status="ok") - - @app.get("/get_server_capabilities") - async def get_server_capabilities(self, request: Request) -> types.GetServerCapabilitiesResponse: - return types.GetServerCapabilitiesResponse(supported_models=self.supported_models) - - @app.post("/telemetry") - async def telemetry(self, request: Request, body: types.TelemetrySendRequest) -> types.TelemetryResponse: - # Telemetry is accepted but not persisted; this endpoint is intentionally lightweight. - return types.TelemetryResponse(status="accepted") - - @app.post("/create_session") - async def create_session(self, request: Request, body: types.CreateSessionRequest) -> types.CreateSessionResponse: - session_id = await self.state.create_session(body) - return types.CreateSessionResponse(session_id=session_id) - - @app.post("/session_heartbeat") - async def session_heartbeat(self, request: Request, body: types.SessionHeartbeatRequest) -> types.SessionHeartbeatResponse: - alive = await self.state.touch_session(body.session_id) - if not alive: - raise HTTPException(status_code=404, detail="Unknown session") - return types.SessionHeartbeatResponse() - - - @app.post("/create_sampling_session") - async def create_sampling_session( - self, request: Request, body: types.CreateSamplingSessionRequest - ) -> types.CreateSamplingSessionResponse: - sampling_session_id = await self.state.create_sampling_session(body) - return types.CreateSamplingSessionResponse(sampling_session_id=sampling_session_id) +from twinkle.server.twinkle.validation import verify_request_token +from .state import get_server_state, schedule_task - @app.post("/asample") - async def asample(self, request: Request, body: types.SampleRequest) -> types.UntypedAPIFuture: - async def _do_sample(): - return self._sample_output() - return await schedule_task(self.state, _do_sample()) +def build_server_app( + deploy_options: Dict[str, Any], + supported_models: Optional[List[types.SupportedModel]] = None, + **kwargs +): + app = FastAPI() + + @app.middleware("http") + async def verify_token(request: Request, call_next): + return await verify_request_token(request=request, call_next=call_next) + + + @serve.deployment(name="TinkerCompatServer") + @serve.ingress(app) + class TinkerCompatServer: + def __init__(self, supported_models: Optional[List[types.SupportedModel]] = None, **kwargs) -> None: + self.state = get_server_state() + self.client = httpx.AsyncClient(timeout=None) + self.route_prefix = kwargs.get("route_prefix", "/api/v1") + self.supported_models = supported_models or [ + types.SupportedModel(model_name="Qwen/Qwen2.5-0.5B-Instruct"), + types.SupportedModel(model_name="Qwen/Qwen2.5-7B-Instruct"), + types.SupportedModel(model_name="Qwen/Qwen2.5-72B-Instruct"), + ] + + async def _proxy(self, request: Request, target_path: str) -> Response: + # Construct target URL on the same host + # Ensure we respect the current route prefix (e.g. /api/v1) + # when forwarding to sub-routes like /api/v1/model/... + prefix = self.route_prefix.rstrip("/") if self.route_prefix else "" + base_url = f"{request.url.scheme}://{request.url.netloc}" + target_url = f"{base_url}{prefix}{target_path}" + + # Prepare headers + headers = dict(request.headers) + headers.pop("host", None) + headers.pop("content-length", None) + + try: + rp_ = await self.client.request( + method=request.method, + url=target_url, + content=await request.body(), + headers=headers, + params=request.query_params, + ) + return Response( + content=rp_.content, + status_code=rp_.status_code, + headers=dict(rp_.headers), + media_type=rp_.headers.get("content-type"), + ) + except Exception as e: + return Response(content=f"Proxy Error: {str(e)}", status_code=502) + + @staticmethod + def _sample_output() -> types.SampleResponse: + sequence = types.SampledSequence(stop_reason="stop", tokens=[1, 2, 3], logprobs=[-0.1, -0.2, -0.3]) + return types.SampleResponse(sequences=[sequence]) + + # --- Endpoints --------------------------------------------------------- + + @app.get("/healthz") + async def healthz(self, request: Request) -> types.HealthResponse: + return types.HealthResponse(status="ok") + + @app.get("/get_server_capabilities") + async def get_server_capabilities(self, request: Request) -> types.GetServerCapabilitiesResponse: + return types.GetServerCapabilitiesResponse(supported_models=self.supported_models) + + @app.post("/telemetry") + async def telemetry(self, request: Request, body: types.TelemetrySendRequest) -> types.TelemetryResponse: + # Telemetry is accepted but not persisted; this endpoint is intentionally lightweight. + return types.TelemetryResponse(status="accepted") + + @app.post("/create_session") + async def create_session(self, request: Request, body: types.CreateSessionRequest) -> types.CreateSessionResponse: + session_id = self.state.create_session(body.model_dump()) + return types.CreateSessionResponse(session_id=session_id) + + @app.post("/session_heartbeat") + async def session_heartbeat(self, request: Request, body: types.SessionHeartbeatRequest) -> types.SessionHeartbeatResponse: + alive = self.state.touch_session(body.session_id) + if not alive: + raise HTTPException(status_code=404, detail="Unknown session") + return types.SessionHeartbeatResponse() + + + @app.post("/create_sampling_session") + async def create_sampling_session( + self, request: Request, body: types.CreateSamplingSessionRequest + ) -> types.CreateSamplingSessionResponse: + sampling_session_id = self.state.create_sampling_session(body.model_dump()) + return types.CreateSamplingSessionResponse(sampling_session_id=sampling_session_id) + + @app.post("/asample") + async def asample(self, request: Request, body: types.SampleRequest) -> types.UntypedAPIFuture: + async def _do_sample(): + return self._sample_output() + + return await schedule_task(self.state, _do_sample()) + + @app.post("/save_weights_for_sampler") + async def save_weights_for_sampler( + self, request: Request, body: types.SaveWeightsForSamplerRequest + ) -> types.UntypedAPIFuture: + suffix = body.path or f"sampler-{datetime.now().strftime('%Y%m%d-%H%M%S')}" + path = f"tinker://{body.model_id}/{suffix}" + sampling_session_id = None + if body.sampling_session_seq_id is not None: + sampling_session_id = f"sampling_{body.sampling_session_seq_id}" + + async def _do_save(): + return types.SaveWeightsForSamplerResponseInternal( + path=path, sampling_session_id=sampling_session_id + ) + + return await schedule_task(self.state, _do_save(), model_id=body.model_id) + + @app.post("/retrieve_future") + async def retrieve_future(self, request: Request, body: types.FutureRetrieveRequest) -> Any: + record = self.state.get_future(body.request_id) + if record is None: + raise HTTPException(status_code=404, detail="Future not found") + result = record["result"] + if hasattr(result, "model_dump"): + return result.model_dump() + return result + + # --- Proxy Endpoints --------------------------------------------------------- + + # --- Model Proxy Endpoints ---------------------------------------- - @app.post("/save_weights_for_sampler") - async def save_weights_for_sampler( - self, request: Request, body: types.SaveWeightsForSamplerRequest - ) -> types.UntypedAPIFuture: - suffix = body.path or f"sampler-{datetime.now().strftime('%Y%m%d-%H%M%S')}" - path = f"tinker://{body.model_id}/{suffix}" - sampling_session_id = None - if body.sampling_session_seq_id is not None: - sampling_session_id = f"sampling_{body.sampling_session_seq_id}" + @app.post("/create_model") + async def create_model(self, request: Request) -> Any: + return await self._proxy(request, "/model/create_model") - async def _do_save(): - return types.SaveWeightsForSamplerResponseInternal( - path=path, sampling_session_id=sampling_session_id - ) + @app.post("/get_info") + async def get_info(self, request: Request) -> Any: + return await self._proxy(request, "/model/get_info") - return await schedule_task(self.state, _do_save(), model_id=body.model_id) + @app.post("/unload_model") + async def unload_model(self, request: Request) -> Any: + return await self._proxy(request, "/model/unload_model") - @app.post("/retrieve_future") - async def retrieve_future(self, request: Request, body: types.FutureRetrieveRequest) -> Any: - record = await self.state.get_future(body.request_id) - if record is None: - raise HTTPException(status_code=404, detail="Future not found") - result = record["result"] - if hasattr(result, "model_dump"): - return result.model_dump() - return result + @app.post("/forward_backward") + async def forward_backward(self, request: Request) -> Any: + return await self._proxy(request, "/model/forward_backward") + @app.post("/optim_step") + async def optim_step(self, request: Request) -> Any: + return await self._proxy(request, "/model/optim_step") -def build_graph() -> Any: - """Helper to bind the deployment for serve.run.""" - return TinkerCompatServer.bind() + @app.post("/save_weights") + async def save_weights(self, request: Request) -> Any: + return await self._proxy(request, "/model/save_weights") + @app.post("/load_weights") + async def load_weights(self, request: Request) -> Any: + return await self._proxy(request, "/model/load_weights") -if __name__ == "__main__": - ray.init(ignore_reinit_error=True) - - # Get port from environment variable or default to 8000 - port = int(os.environ.get("TINKER_PORT", "8000")) - - # Start serve with specific HTTP options - serve.start(http_options={"host": "0.0.0.0", "port": port}) - - serve.run(build_graph(), route_prefix="/api/v1") - input("\nPress Enter to stop the server...") \ No newline at end of file + return TinkerCompatServer.options(**deploy_options).bind(supported_models=supported_models, **kwargs) diff --git a/src/twinkle/server/tinker/state.py b/src/twinkle/server/tinker/state.py index f41f0b9b..b447a7e0 100644 --- a/src/twinkle/server/tinker/state.py +++ b/src/twinkle/server/tinker/state.py @@ -1,6 +1,7 @@ # Copyright (c) ModelScope Contributors. All rights reserved. from __future__ import annotations +import re import time import uuid import asyncio @@ -11,7 +12,6 @@ from tinker import types -@ray.remote(name="tinker_server_state", lifetime="detached") class ServerState: def __init__(self) -> None: self.sessions: Dict[str, Dict[str, Any]] = {} @@ -36,8 +36,11 @@ def touch_session(self, session_id: str) -> bool: return True def register_model(self, payload: Dict[str, Any], model_id: Optional[str] = None) -> str: - model_id = model_id or payload.get("model_id") or f"{payload.get('base_model', 'model')}-{uuid.uuid4().hex[:8]}" - self.models[model_id] = { + _session_id = payload.get("session_id") + _model_id: str = model_id or payload.get("model_id") or f"{payload.get('base_model', 'model')}-{_session_id or uuid.uuid4().hex[:8]}" + _model_id = re.sub(r'[^\w\-]', '_', _model_id) + + self.models[_model_id] = { "session_id": payload.get("session_id"), "model_seq_id": payload.get("model_seq_id"), "base_model": payload.get("base_model"), @@ -45,21 +48,21 @@ def register_model(self, payload: Dict[str, Any], model_id: Optional[str] = None "lora_config": payload.get("lora_config"), "created_at": datetime.now().isoformat(), } - return model_id + return _model_id def unload_model(self, model_id: str) -> bool: return self.models.pop(model_id, None) is not None def create_sampling_session(self, payload: Dict[str, Any], sampling_session_id: Optional[str] = None) -> str: - sampling_session_id = sampling_session_id or payload.get("sampling_session_id") or f"sampling_{uuid.uuid4().hex}" - self.sampling_sessions[sampling_session_id] = { + _sampling_session_id: str = sampling_session_id or payload.get("sampling_session_id") or f"sampling_{uuid.uuid4().hex}" + self.sampling_sessions[_sampling_session_id] = { "session_id": payload.get("session_id"), "seq_id": payload.get("sampling_session_seq_id"), "base_model": payload.get("base_model"), "model_path": payload.get("model_path"), "created_at": datetime.now().isoformat(), } - return sampling_session_id + return _sampling_session_id def get_model_metadata(self, model_id: str) -> Optional[Dict[str, Any]]: return self.models.get(model_id) @@ -100,15 +103,16 @@ def create_sampling_session(self, payload: Dict[str, Any], sampling_session_id: def get_model_metadata(self, model_id: str) -> Optional[Dict[str, Any]]: return ray.get(self._actor.get_model_metadata.remote(model_id)) - def store_future(self, request_id: str, result: Any, model_id: Optional[str]): - return ray.get(self._actor.store_future.remote(request_id, result, model_id)) + async def store_future(self, request_id: str, result: Any, model_id: Optional[str]): + # Make the Ray call asynchronously + await self._actor.store_future.remote(request_id, result, model_id) def get_future(self, request_id: str) -> Optional[Dict[str, Any]]: return ray.get(self._actor.get_future.remote(request_id)) def get_server_state(actor_name: str = "tinker_server_state") -> ServerStateProxy: - _ServerState = ServerState + _ServerState = ray.remote(ServerState) try: actor = ray.get_actor(actor_name) except ValueError: From cd7a76bc07b641b29b5603d6fc57b9bc260a0c5f Mon Sep 17 00:00:00 2001 From: Yunnglin Date: Mon, 19 Jan 2026 19:34:55 +0800 Subject: [PATCH 9/9] update tinker serve --- cookbook/client/server.py | 8 +- cookbook/tinker/lora.py | 99 +++++++++++++++++++++++++ cookbook/tinker/server.py | 48 ++++++++++++ cookbook/tinker/server_config.yaml | 42 +++++++++++ src/twinkle/data_format/datum.py | 14 ++-- src/twinkle/infra/_ray/ray_helper.py | 2 + src/twinkle/model/transformers.py | 3 +- src/twinkle/server/tinker/model.py | 28 +++++-- src/twinkle/server/twinkle/processor.py | 2 +- 9 files changed, 226 insertions(+), 20 deletions(-) create mode 100644 cookbook/tinker/lora.py create mode 100644 cookbook/tinker/server.py create mode 100644 cookbook/tinker/server_config.yaml diff --git a/cookbook/client/server.py b/cookbook/client/server.py index 75b1758e..5d9a2f48 100644 --- a/cookbook/client/server.py +++ b/cookbook/client/server.py @@ -5,10 +5,10 @@ from ray import serve from twinkle.server import build_processor_app, build_sampler_app, build_model_app -# ray.init() -# serve.shutdown() -# import time -# time.sleep(5) +ray.init() +serve.shutdown() +import time +time.sleep(5) file_dir = os.path.abspath(os.path.dirname(__file__)) config = OmegaConf.load(os.path.join(file_dir, 'server_config.yaml')) diff --git a/cookbook/tinker/lora.py b/cookbook/tinker/lora.py new file mode 100644 index 00000000..7d1911b4 --- /dev/null +++ b/cookbook/tinker/lora.py @@ -0,0 +1,99 @@ +#%% + +from twinkle_client import init_tinker_compat_client +service_client = init_tinker_compat_client(base_url='http://localhost:8000') + +print("Available models:") +for item in service_client.get_server_capabilities().supported_models: + print("- " + item.model_name) + +#%% +base_model = "ms://Qwen/Qwen2.5-0.5B-Instruct" +training_client = service_client.create_lora_training_client( + base_model=base_model +) + +#%% +# Create some training examples +examples = [ + {"input": "banana split", "output": "anana-bay plit-say"}, + {"input": "quantum physics", "output": "uantum-qay ysics-phay"}, + {"input": "donut shop", "output": "onut-day op-shay"}, + {"input": "pickle jar", "output": "ickle-pay ar-jay"}, + {"input": "space exploration", "output": "ace-spay exploration-way"}, + {"input": "rubber duck", "output": "ubber-ray uck-day"}, + {"input": "coding wizard", "output": "oding-cay izard-way"}, +] + +# Convert examples into the format expected by the training client +from tinker import types +from modelscope import AutoTokenizer +# Get the tokenizer from the training client +# tokenizer = training_client.get_tokenizer() # NOTE: network call huggingface +tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", trust_remote_code=True) + +def process_example(example: dict, tokenizer) -> types.Datum: + # Format the input with Input/Output template + # For most real use cases, you'll want to use a renderer / chat template, + # (see later docs) but here, we'll keep it simple. + prompt = f"English: {example['input']}\nPig Latin:" + + prompt_tokens = tokenizer.encode(prompt, add_special_tokens=True) + prompt_weights = [0] * len(prompt_tokens) + # Add a space before the output string, and finish with double newline + completion_tokens = tokenizer.encode(f" {example['output']}\n\n", add_special_tokens=False) + completion_weights = [1] * len(completion_tokens) + + tokens = prompt_tokens + completion_tokens + weights = prompt_weights + completion_weights + + input_tokens = tokens[:-1] + target_tokens = tokens[1:] # We're predicting the next token, so targets need to be shifted. + weights = weights[1:] + + # A datum is a single training example for the loss function. + # It has model_input, which is the input sequence that'll be passed into the LLM, + # loss_fn_inputs, which is a dictionary of extra inputs used by the loss function. + return types.Datum( + model_input=types.ModelInput.from_ints(tokens=input_tokens), + loss_fn_inputs=dict(weights=weights, target_tokens=target_tokens) + ) + +processed_examples = [process_example(ex, tokenizer) for ex in examples] + +# Visualize the first example for debugging purposes +datum0 = processed_examples[0] +print(f"{'Input':<20} {'Target':<20} {'Weight':<10}") +print("-" * 50) +for i, (inp, tgt, wgt) in enumerate(zip(datum0.model_input.to_ints(), datum0.loss_fn_inputs['target_tokens'].tolist(), datum0.loss_fn_inputs['weights'].tolist())): + print(f"{repr(tokenizer.decode([inp])):<20} {repr(tokenizer.decode([tgt])):<20} {wgt:<10}") + +#%% +import numpy as np +for _ in range(6): + fwdbwd_future = training_client.forward_backward(processed_examples, "cross_entropy") + optim_future = training_client.optim_step(types.AdamParams(learning_rate=1e-4)) + + # Wait for the results + fwdbwd_result = fwdbwd_future.result() + optim_result = optim_future.result() + + # fwdbwd_result contains the logprobs of all the tokens we put in. Now we can compute the weighted + # average log loss per token. + logprobs = np.concatenate([output['logprobs'].tolist() for output in fwdbwd_result.loss_fn_outputs]) + weights = np.concatenate([example.loss_fn_inputs['weights'].tolist() for example in processed_examples]) + print(f"Loss per token: {-np.dot(logprobs, weights) / weights.sum():.4f}") + +#%% +# First, create a sampling client. We need to transfer weights +sampling_client = training_client.save_weights_and_get_sampling_client(name='pig-latin-model') + +# Now, we can sample from the model. +prompt = types.ModelInput.from_ints(tokenizer.encode("English: coffee break\nPig Latin:")) +params = types.SamplingParams(max_tokens=20, temperature=0.0, stop=["\n"]) # Greedy sampling +future = sampling_client.sample(prompt=prompt, sampling_params=params, num_samples=8) +result = future.result() +print("Responses:") +for i, seq in enumerate(result.sequences): + print(f"{i}: {repr(tokenizer.decode(seq.tokens))}") +# %% diff --git a/cookbook/tinker/server.py b/cookbook/tinker/server.py new file mode 100644 index 00000000..15cbb39c --- /dev/null +++ b/cookbook/tinker/server.py @@ -0,0 +1,48 @@ +import os +os.environ['RAY_DEBUG'] = '1' +import ray +from omegaconf import OmegaConf +from ray import serve +from twinkle.server.tinker import build_model_app, build_server_app + +ray.init(namespace="twinkle_cluster") +serve.shutdown() +import time +time.sleep(5) + +file_dir = os.path.abspath(os.path.dirname(__file__)) +config = OmegaConf.load(os.path.join(file_dir, 'server_config.yaml')) + +APP_BUILDERS = { + 'main:build_server_app': build_server_app, + 'main:build_model_app': build_model_app, + # 'main:build_sampler_app': build_sampler_app, +} + +for app_config in config.applications: + print(f"Starting {app_config.name} at {app_config.route_prefix}...") + + builder = APP_BUILDERS[app_config.import_path] + args = OmegaConf.to_container(app_config.args, resolve=True) if app_config.args else {} + + deploy_options = {} + deploy_config = app_config.deployments[0] + if 'autoscaling_config' in deploy_config: + deploy_options['autoscaling_config'] = OmegaConf.to_container(deploy_config.autoscaling_config) + if 'ray_actor_options' in deploy_config: + deploy_options['ray_actor_options'] = OmegaConf.to_container(deploy_config.ray_actor_options) + + app = builder( + deploy_options=deploy_options, + route_prefix=app_config.route_prefix, + **{k: v for k, v in args.items()} + ) + + serve.run(app, name=app_config.name, route_prefix=app_config.route_prefix) + +print("\nAll applications started!") +print("Endpoints:") +for app_config in config.applications: + print(f" - http://localhost:8000{app_config.route_prefix}") + +input("\nPress Enter to stop the server...") \ No newline at end of file diff --git a/cookbook/tinker/server_config.yaml b/cookbook/tinker/server_config.yaml new file mode 100644 index 00000000..2ab358f0 --- /dev/null +++ b/cookbook/tinker/server_config.yaml @@ -0,0 +1,42 @@ +proxy_location: EveryNode +http_options: + host: 0.0.0.0 + port: 8000 + +applications: + - name: server + route_prefix: /api/v1 + import_path: main:build_server_app + args: + + deployments: + - name: TinkerCompatServer + autoscaling_config: + min_replicas: 1 + max_replicas: 1 + target_ongoing_requests: 128 + ray_actor_options: + num_cpus: 0.1 + + - name: models + route_prefix: /api/v1/model + import_path: main:build_model_app + args: + nproc_per_node: 2 + device_group: + name: model + ranks: [ 0,1 ] + device_type: cuda + device_mesh: + device_type: cuda + mesh: [0,1 ] + mesh_dim_names: ['dp'] + deployments: + - name: ModelManagement + autoscaling_config: + min_replicas: 1 + max_replicas: 1 + target_ongoing_requests: 16 + ray_actor_options: + num_cpus: 0.1 + diff --git a/src/twinkle/data_format/datum.py b/src/twinkle/data_format/datum.py index d0f6b061..16566b20 100644 --- a/src/twinkle/data_format/datum.py +++ b/src/twinkle/data_format/datum.py @@ -1,7 +1,9 @@ from __future__ import annotations + from typing import TYPE_CHECKING import numpy as np from .input_feature import InputFeature + if TYPE_CHECKING: from tinker import types @@ -13,15 +15,15 @@ def datum_to_input_feature(datum: types.Datum) -> InputFeature: input_ids = datum.model_input.to_ints() input_feature['input_ids'] = input_ids input_feature['attention_mask'] = [1] * len(input_ids) + input_feature['length'] = len(input_ids) + input_feature['position_ids'] = list(range(len(input_ids))) # 2. Map loss function inputs # 'target_tokens' -> 'labels' - if 'target_tokens' in datum.loss_fn_inputs: - input_feature['labels'] = datum.loss_fn_inputs['target_tokens'].to_numpy() - - # 'weights' -> 'completion_mask' - if 'weights' in datum.loss_fn_inputs: + if 'target_tokens' in datum.loss_fn_inputs and 'weights' in datum.loss_fn_inputs: weights = datum.loss_fn_inputs['weights'].to_numpy() - input_feature['completion_mask'] = weights + labels = datum.loss_fn_inputs['target_tokens'].to_numpy() + + input_feature['labels'] = np.where(weights > 0, labels, -100) return input_feature \ No newline at end of file diff --git a/src/twinkle/infra/_ray/ray_helper.py b/src/twinkle/infra/_ray/ray_helper.py index 57cde76e..dfafb527 100644 --- a/src/twinkle/infra/_ray/ray_helper.py +++ b/src/twinkle/infra/_ray/ray_helper.py @@ -209,6 +209,8 @@ def create_workers(worker_cls: Type[T], group: str, execute: Literal['all', 'pee # Whether the input data of each remote is independent, or they are a part of the whole device mesh? from ray.runtime_env import RuntimeEnv from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy + + # breakpoint() workers = [] device_config = RayHelper.resource_manager.get_config(group) placement_groups = RayHelper.resource_manager.get_group(group) diff --git a/src/twinkle/model/transformers.py b/src/twinkle/model/transformers.py index a12a6570..2a2c81e8 100644 --- a/src/twinkle/model/transformers.py +++ b/src/twinkle/model/transformers.py @@ -160,6 +160,7 @@ def forward(self, *, inputs: Union[InputFeature, List[InputFeature], Trajectory, Returns: The output of the model forward. """ + # breakpoint() adapter_name = kwargs.pop('adapter_name', _default_adapter_name) optimizer_config = self.optimizer_group[adapter_name] self._lazy_wrap_model() @@ -327,7 +328,7 @@ def clip_grad_and_step(self, max_grad_norm: float=1.0, norm_type=2, **kwargs): self.zero_grad(**kwargs) self.lr_step(**kwargs) - def _create_param_group(self, adapter_name: str, lr: float, weight_decay:float, **kwargs): + def _create_param_group(self, adapter_name: str, lr: float=1e-5, weight_decay:float=0.01, **kwargs): # Some code borrowed from transformers def get_parameter_names(model, forbidden_layer_types, forbidden_layer_names=None): diff --git a/src/twinkle/server/tinker/model.py b/src/twinkle/server/tinker/model.py index 9714b195..54f06bfb 100644 --- a/src/twinkle/server/tinker/model.py +++ b/src/twinkle/server/tinker/model.py @@ -39,6 +39,7 @@ def __init__(self, nproc_per_node: int, device_group: Dict[str, Any], device_mes twinkle.initialize(mode='ray', nproc_per_node=nproc_per_node, groups=[self.device_group], lazy_collect=False) self.device_mesh = DeviceMesh(**device_mesh) self.model: Optional[MultiLoraTransformersModel] = None + self.model_id: Optional[str] = None self.kwargs = kwargs self.adapter_records: Dict[str, int] = {} self.hb_thread = threading.Thread(target=self.countdown, daemon=True) @@ -81,7 +82,7 @@ def handle_adapter_count(self, token: str, add: bool): @staticmethod def get_adapter_name(request: Request, adapter_name: str) -> str: - return request.state.request_id + '-' + adapter_name + return adapter_name def assert_adapter_exists(self, adapter_name: str): assert adapter_name and adapter_name in self.adapter_records, f"Adapter {adapter_name} not found" @@ -92,10 +93,14 @@ def assert_adapter_valid(self, adapter_name: Optional[str]): @app.post("/create_model") async def create_model(self, request: Request, body: types.CreateModelRequest) -> types.UntypedAPIFuture: - model_id = self.state.register_model(body.model_dump()) + # In case create_model called multiple times, we reuse the same model_id + model_id = self.model_id or self.state.register_model(body.model_dump()) + self.model_id = model_id async def _load_model(): - breakpoint() + if self.model is not None and self.model_id is not None: + return types.CreateModelResponse(model_id=self.model_id) + extra_kwargs = body.user_metadata or {} self.model = MultiLoraTransformersModel( model_id=body.base_model, @@ -115,6 +120,7 @@ async def _load_model(): self.key_token_dict[adapter_name] = request.state.token self.handle_adapter_count(request.state.token, True) self.model.set_processor('InputProcessor', adapter_name=adapter_name) + self.model.set_loss('CrossEntropyLoss', adapter_name=adapter_name) self.model.set_optimizer('AdamW', adapter_name=adapter_name) return types.CreateModelResponse(model_id=model_id) @@ -141,6 +147,7 @@ async def get_info(self, request: Request, body: types.GetInfoRequest) -> types. async def unload_model(self, request: Request, body: types.UnloadModelRequest) -> types.UntypedAPIFuture: async def _do_unload(): self.model = None + self.model_id = None with self.adapter_lock: adapter_name = self.get_adapter_name(request=request, adapter_name=body.model_id) del self.adapter_records[adapter_name] @@ -166,14 +173,19 @@ async def _do_forward_backward(): datum_list = body.forward_backward_input.data loss_fn = body.forward_backward_input.loss_fn - loss_fn_config = body.forward_backward_input.loss_fn_config + loss_fn_config = body.forward_backward_input.loss_fn_config or {} + # convert datum to input feature inputs = [datum_to_input_feature(datum) for datum in datum_list] - ret = self.model.forward_backward(inputs=inputs, adapter_name=adapter_name) + + output = self.model.forward(inputs=inputs, adapter_name=adapter_name) + loss = self.model.calculate_loss(adapter_name=adapter_name, **loss_fn_config) + self.model.backward(adapter_name=adapter_name) + return types.ForwardBackwardOutput( - loss_fn_output_type="TensorData", - loss_fn_outputs=[loss_output], - metrics={"loss:avg": 0.0, "tokens_per_second:avg": 0.0}, + loss_fn_output_type="TorchLossReturn", + loss_fn_outputs=[{'logprobs': types.TensorData.from_torch(output['logits'])}], + metrics={"loss:avg": loss.tolist()}, ) return await schedule_task( diff --git a/src/twinkle/server/twinkle/processor.py b/src/twinkle/server/twinkle/processor.py index 919791c5..cc22a066 100644 --- a/src/twinkle/server/twinkle/processor.py +++ b/src/twinkle/server/twinkle/processor.py @@ -100,7 +100,7 @@ def handle_processor_count(self, token: str, add: bool): @app.post("/create") def create(self, request: Request, body: CreateRequest): - # breakpoint() + processor_type_name = body.processor_type class_type = body.class_type kwargs = body.model_extra or {}