Fix the data format documentation

.github/workflows/package.yml

@@ -10,7 +10,7 @@ on:
       - "docker/finetuning/rocm-silogen-finetuning-worker.Dockerfile"
       - "requirements.txt"
       - ".github/workflows/package.yml"
-      - "tests/"
+      - "tests/**"
 
 jobs:
   common:

src/finetuning/config/data.py

@@ -68,11 +68,14 @@ class ConcatenationDataInput(DataInput):
     The datasets themselves need to be in the finetuning supported JSONL formats.
     For SFT this means lines:
 
-        {"messages": {"content": "string", "role": "string"}}
+        {"messages": [{"content": "string", "role": "string"}]}
 
     For DPO this means lines of:
-
-        {"prompt_messages": {"content": "string", "role": "string"}, "chosen_messages": {"content": "string", "role": "string"}, "rejected_messages": {"content": "string", "role": "string"}}
+        {
+           "prompt_messages": [{"content": "string", "role": "string"}],
+           "chosen_messages": [{"content": "string", "role": "string"}],
+            "rejected_messages": [{"content": "string", "role": "string"}]
+        }
     """
 
     type: Literal[DataInputType.CONCATENATION]
@@ -89,11 +92,14 @@ class WeightedMixDataInput(DataInput):
     The datasets themselves need to be in the finetuning supported JSONL formats.
     For SFT this means lines:
 
-        {"messages": {"content": "string", "role": "string"}}
+        {"messages": [{"content": "string", "role": "string"}]}
 
     For DPO this means lines of:
-
-        {"prompt_messages": {"content": "string", "role": "string"}, "chosen_messages": {"content": "string", "role": "string"}, "rejected_messages": {"content": "string", "role": "string"}}
+        {
+           "prompt_messages": [{"content": "string", "role": "string"}],
+           "chosen_messages": [{"content": "string", "role": "string"}],
+            "rejected_messages": [{"content": "string", "role": "string"}]
+        }
     """
 
     type: Literal[DataInputType.PRECOMPUTE_WEIGHTED_MIX]

tests/test_e2e.py

@@ -218,7 +218,7 @@ def test_sft_gpt(tmpdir):
     assert trainer_state["log_history"][-1]["loss"] < 4.0
 
 
-def test_sft_mistral_lora(tmpdir):
+def test_sft_gemma2_lora(tmpdir):
     # 0. Config definition:
     config = """\
 data_conf:
@@ -267,11 +267,11 @@ def test_sft_mistral_lora(tmpdir):
       - v_proj
       - o_proj
 run_conf:
-  model: hf-internal-testing/tiny-random-MistralForCausalLM
+  model: hf-internal-testing/tiny-random-Gemma2ForCausalLM
   model_args:
     attn_implementation: "eager"
     use_cache: False
-    revision: 7517176934d50d00707900b92ef6a95c782e51a2
+    revision: de7c11b6c25d26ddd1bf4324fcf479b61d18e440
   resume_from_checkpoint: False
 """
     num_steps = 15
@@ -295,10 +295,10 @@ def test_sft_mistral_lora(tmpdir):
     # 3. Inspect results!
     with open(ckpt_dir / f"checkpoint-{num_steps}" / "trainer_state.json") as fi:
         trainer_state = json.loads(fi.read())
-    # At the start, loss should still be around -log(1/32002)~10.3, (Even the tiny Mistral has 32002 units after Chat-ML)
-    assert np.isclose(trainer_state["log_history"][0]["loss"], 10.3, atol=0.5)
-    # In current setup, loss goes at least below 2.5 by 15 steps
-    assert trainer_state["log_history"][-1]["loss"] < 2.5
+    # At the start, loss should still be around -log(1/256000)~12.4529327234617, (Even the tiny Gemma2 has 256000 units after Chat-ML)
+    assert np.isclose(trainer_state["log_history"][0]["loss"], 12.45, atol=0.5)
+    # In current setup, loss goes at least below 5.0 by 15 steps
+    assert trainer_state["log_history"][-1]["loss"] < 5.0
     # LoRA setup should lead to adapter checkpoint
     assert (ckpt_dir / "checkpoint-final" / "adapter_model.safetensors").isfile()
     # And since the basemodel vocab was modified, there is a new basemodel checkpoint too
@@ -310,7 +310,7 @@ def intercept_tokenizer_call(*args, overwrite_chat_template=False, **kwargs):
 
 
 @patch("finetuning.dpo.subsetup_tokenizer", intercept_tokenizer_call)
-def test_dpo_mistral(tmpdir):
+def test_dpo_gemma2(tmpdir):
     # 0. DPO Config definition:
     dpo_config = """\
 data_conf:
@@ -350,11 +350,11 @@ def test_dpo_mistral(tmpdir):
 peft_conf:
   peft_type: "NO_PEFT"
 run_conf:
-  model: hf-internal-testing/tiny-random-MistralForCausalLM
+  model: hf-internal-testing/tiny-random-Gemma2ForCausalLM
   model_args:
     attn_implementation: "eager"
     use_cache: False
-    revision: 7517176934d50d00707900b92ef6a95c782e51a2
+    revision: de7c11b6c25d26ddd1bf4324fcf479b61d18e440
   resume_from_checkpoint: False
 """
     num_steps = 15
@@ -383,12 +383,12 @@ def test_dpo_mistral(tmpdir):
     with open(dpo_ckpt_dir / f"checkpoint-{num_steps}" / "trainer_state.json") as fi:
         trainer_state = json.loads(fi.read())
     # The margins start close to zero
-    assert np.isclose(trainer_state["log_history"][0]["rewards/margins"], 0.0, atol=0.1)
-    # But they rise to >0.10
-    assert trainer_state["log_history"][-1]["rewards/margins"] > 0.10
+    assert np.isclose(trainer_state["log_history"][0]["rewards/margins"], 0.0, atol=0.03)
+    # But they rise to >0.07
+    assert trainer_state["log_history"][-1]["rewards/margins"] > 0.07
 
 
-def test_sft_and_dpo_mistral(tmpdir):
+def test_sft_and_dpo_gemma2(tmpdir):
     # 0. Config definition:
     sft_config = """\
 data_conf:
@@ -427,11 +427,11 @@ def test_sft_and_dpo_mistral(tmpdir):
 peft_conf:
   peft_type: "NO_PEFT"
 run_conf:
-  model: hf-internal-testing/tiny-random-MistralForCausalLM
+  model: hf-internal-testing/tiny-random-Gemma2ForCausalLM
   model_args:
     attn_implementation: "eager"
     use_cache: False
-    revision: 7517176934d50d00707900b92ef6a95c782e51a2
+    revision: de7c11b6c25d26ddd1bf4324fcf479b61d18e440
   resume_from_checkpoint: False
 """
     num_steps = 15
@@ -455,8 +455,8 @@ def test_sft_and_dpo_mistral(tmpdir):
     # # 3. Inspect SFT results!
     with open(sft_ckpt_dir / f"checkpoint-{num_steps}" / "trainer_state.json") as fi:
         trainer_state = json.loads(fi.read())
-    # At the start, loss should still be around -log(1/32002)~10.3, (Even the tiny Mistral has 32002 units after Chat-ML)
-    assert np.isclose(trainer_state["log_history"][0]["loss"], 10.3, atol=0.5)
+    # At the start, loss should still be around -log(1/256000)~12.4529327234617, (Even the tiny Gemma2 has 256000 units after Chat-ML)
+    assert np.isclose(trainer_state["log_history"][0]["loss"], 12.45, atol=0.5)
     # In current setup, loss goes at least below 4.5 by 15 steps
     assert trainer_state["log_history"][-1]["loss"] < 4.5
 
@@ -534,12 +534,12 @@ def test_sft_and_dpo_mistral(tmpdir):
     with open(dpo_ckpt_dir / f"checkpoint-{num_steps}" / "trainer_state.json") as fi:
         trainer_state = json.loads(fi.read())
     # The margins start close to zero
-    assert np.isclose(trainer_state["log_history"][0]["rewards/margins"], 0.0, atol=0.1)
-    # But they rise to >0.15
-    assert trainer_state["log_history"][-1]["rewards/margins"] > 0.15
+    assert np.isclose(trainer_state["log_history"][0]["rewards/margins"], 0.0, atol=0.03)
+    # But they rise to >0.07
+    assert trainer_state["log_history"][-1]["rewards/margins"] > 0.07
 
 
-def test_sft_and_dpo_lora_mistral(tmpdir):
+def test_sft_and_dpo_lora_gemma2(tmpdir):
     # 0. Config definition:
     sft_config = """\
 data_conf:
@@ -561,6 +561,7 @@ def test_sft_and_dpo_lora_mistral(tmpdir):
   gradient_checkpointing_kwargs:
     use_reentrant: false
   learning_rate: 0.5
+  logging_first_step: true
   logging_steps: 1
   logging_strategy: "steps"
   lr_scheduler_type: "constant"
@@ -578,11 +579,11 @@ def test_sft_and_dpo_lora_mistral(tmpdir):
 peft_conf:
   peft_type: "NO_PEFT"
 run_conf:
-  model: hf-internal-testing/tiny-random-MistralForCausalLM
+  model: hf-internal-testing/tiny-random-Gemma2ForCausalLM
   model_args:
     attn_implementation: "eager"
     use_cache: False
-    revision: 7517176934d50d00707900b92ef6a95c782e51a2
+    revision: de7c11b6c25d26ddd1bf4324fcf479b61d18e440
   resume_from_checkpoint: False
 """
     num_steps = 15
@@ -606,8 +607,8 @@ def test_sft_and_dpo_lora_mistral(tmpdir):
     # 3. Inspect SFT results!
     with open(sft_ckpt_dir / f"checkpoint-{num_steps}" / "trainer_state.json") as fi:
         trainer_state = json.loads(fi.read())
-    # At the start, loss should still be around -log(1/32002)~10.3, (Even the tiny Mistral has 32002 units after Chat-ML)
-    assert np.isclose(trainer_state["log_history"][0]["loss"], 10.3, atol=0.5)
+    # At the start, loss should still be around -log(1/256000)~12.4529327234617, (Even the tiny Gemma2 has 256000 units after Chat-ML)
+    assert np.isclose(trainer_state["log_history"][0]["loss"], 12.45, atol=0.5)
     # In current setup, loss goes at least below 4.5 by 15 steps
     assert trainer_state["log_history"][-1]["loss"] < 4.5
 
@@ -633,6 +634,7 @@ def test_sft_and_dpo_lora_mistral(tmpdir):
   gradient_checkpointing_kwargs:
     use_reentrant: false
   learning_rate: 0.5
+  logging_first_step: true
   logging_steps: 1
   logging_strategy: "steps"
   lr_scheduler_type: "constant"
@@ -668,7 +670,7 @@ def test_sft_and_dpo_lora_mistral(tmpdir):
   resume_from_checkpoint: False
 """
     # note: potential to redefine
-    num_steps = 15
+    num_steps = 20
     # 5. setup
     # Set paths
     dpotmpdir = tmpdir / "dpo"
@@ -695,9 +697,9 @@ def test_sft_and_dpo_lora_mistral(tmpdir):
     with open(dpo_ckpt_dir / f"checkpoint-{num_steps}" / "trainer_state.json") as fi:
         trainer_state = json.loads(fi.read())
     # The margins start close to zero
-    assert np.isclose(trainer_state["log_history"][0]["rewards/margins"], 0.0, atol=0.1)
-    # But they rise to >0.15
-    assert trainer_state["log_history"][-1]["rewards/margins"] > 0.15
+    assert np.isclose(trainer_state["log_history"][0]["rewards/margins"], 0.0, atol=0.02)
+    # But they rise to >0.03
+    assert trainer_state["log_history"][-1]["rewards/margins"] > 0.03
 
     # LoRA setup should lead to adapter checkpoint
     assert (dpo_ckpt_dir / "checkpoint-final" / "adapter_model.safetensors").isfile()
@@ -707,12 +709,12 @@ def test_generate(tmpdir):
     # 0. Config definition:
     generate_config = """\
 model_conf:
-  model: hf-internal-testing/tiny-random-MistralForCausalLM
+  model: hf-internal-testing/tiny-random-Gemma2ForCausalLM
   model_args:
     attn_implementation: "eager"
     use_cache: False
     device_map: cpu
-    revision: 7517176934d50d00707900b92ef6a95c782e51a2
+    revision: de7c11b6c25d26ddd1bf4324fcf479b61d18e440
 prompt_conf:
   type: "open-input"
   input: "Hi my name is "
@@ -725,4 +727,4 @@ def test_generate(tmpdir):
     finetuning.cli.generation_cli([str(tmpdir / "gen_conf.yaml"), str(tmpdir / "generated_output.yaml")])
     with open(tmpdir / "generated_output.yaml") as fin:
         list_of_outputs = yaml.safe_load(fin)
-    assert list_of_outputs[0]["answer"] == "Bod OFF soon FT"  # This is what tiny-random-MistralForCausalLM replies.
+    assert list_of_outputs[0]["answer"] == "amientosamientosérômeérôme"  # This is what tiny-random-Gemma2 replies.