fix:eagle3 offline

examples/speculative_decoding/README.md

@@ -312,7 +312,7 @@ trainer.save_model("<path to the output directory>")
 | LLAMA 3, 3.1 | ✅ | ✅ | ✅ |
 | Mistral | ✅ | ✅ | ✅ |
 | Phi 3 | ✅ | ✅ | ✅ |
-| QWen 1.5,2,2.5 | ✅ | ✅ | ✅ |
+| QWen 1.5,2,2.5,3 | ✅ | ✅ | ✅ |
 
 ## Speculation Module Checkpoints
 

examples/speculative_decoding/collect_hidden_states/compute_hidden_states_trtllm.py

@@ -208,13 +208,16 @@ def keep_conversation(entry):
     num_success = 0
     pbar = tqdm(total=len(dataset), desc=f"DP#{args.dp_rank} Processing conversations")
 
-    def _post_process_trtllm_dumped(trtllm_dumped_file: str, conversation_id: int):
-        """Post-process the TRTLLM dumped file to same format as HF dumped:
+    async def _post_process_trtllm_dumped(trtllm_dumped_file: str, conversation_id: int):
+        """
+        Post-process the TRTLLM dumped file to same format as HF dumped:
         1. Remove id field, replace it with conversation_id
         2. Rename hidden_state field to hidden_states
         3. From list of length 1 to dict
         4. Rename file to conversation_id.pt
         """
+        if not trtllm_dumped_file.exists():
+            return False
         with open(trtllm_dumped_file, "rb") as f:
             trtllm_dumped = torch.load(f)
         assert isinstance(trtllm_dumped, list) and len(trtllm_dumped) == 1, (
@@ -232,35 +235,33 @@ def _post_process_trtllm_dumped(trtllm_dumped_file: str, conversation_id: int):
         output_file = args.output_dir / f"{conversation_id}.pt"
         with open(output_file, "wb") as f:
             torch.save(trtllm_dumped, f)
-
-        if trtllm_dumped_file.exists():
-            trtllm_dumped_file.unlink()
+        trtllm_dumped_file.unlink()
+        return True
 
     async def dump_hidden_states(idx: int, conversation_id: int, input_ids: list[int]):
         nonlocal num_success
         await llm_spec.generate_async(input_ids, sampling_params)
         # TRTLLM API name files starts from 1
         # ref:https://github.com/NVIDIA/TensorRT-LLM/pull/7012
         trtllm_dumped_file = args.output_dir / f"{spec_config['file_prefix']}_{idx + 1}.pt"
-        _post_process_trtllm_dumped(trtllm_dumped_file, conversation_id)
-        num_success += 1
+        dump_success = await _post_process_trtllm_dumped(trtllm_dumped_file, conversation_id)
+        num_success += int(dump_success)
         pbar.update(1)
 
     async def submit_generates():
         nonlocal num_skipped_too_long
         nonlocal num_invalid
         tasks = []
-        for idx, entry in enumerate(dataset):
+        idx = 0
+        for entry in dataset:
             conversation_id = entry.get("conversation_id", entry.get("uuid"))
 
             conversations = entry["conversations"]
             if not conversations or not isinstance(conversations, list):
                 num_invalid += 1
                 continue
 
-            input_ids = tokenizer.apply_chat_template(conversations, add_generation_template=False)[
-                :256
-            ]
+            input_ids = tokenizer.apply_chat_template(conversations, add_generation_template=False)
             num_input_tokens = (
                 input_ids.shape[1] if isinstance(input_ids, torch.Tensor) else len(input_ids)
             )
@@ -269,6 +270,8 @@ async def submit_generates():
                 continue
 
             tasks.append(dump_hidden_states(idx, conversation_id, input_ids))
+            # Increment only for valid conversations to match dump file index
+            idx += 1
         await asyncio.gather(*tasks)
 
     asyncio.run(submit_generates())

examples/speculative_decoding/eagle_utils.py

@@ -47,7 +47,6 @@ def preprocess(examples, tokenizer):
         "loss_mask": [],
         "labels": [],
     }
-    roles = ["user", "assistant"]
     for i in range(len(examples)):
         messages = []
         source = examples[i]["conversations"]
@@ -61,13 +60,8 @@ def get_role_content(item):
             else:
                 raise ValueError(f"Unknown conversation format: {item}")
 
-        first_role, _ = get_role_content(source[0])
-        if first_role.lower() != "user":
-            # Skip the first one if it is not from human
-            source = source[1:]
-        for j, sentence in enumerate(source):
+        for sentence in source:
             role, content = get_role_content(sentence)
-            assert role.lower() == roles[j % 2], f"{i}"
             messages.append({"role": role.lower(), "content": content})
         conversation = tokenizer.apply_chat_template(
             messages,
@@ -259,11 +253,20 @@ def make_eagle_supervised_data_module(
         dict: A dictionary containing train and eval datasets.
     """
     # Load the conversations from the source file
-    with open(data_args.data_path) as f:
-        if data_args.data_path.endswith("jsonl"):
-            data_json = [json.loads(line) for line in f]
-        else:
-            data_json = json.load(f)
+    print_rank_0("Loading input conversations...")
+    data_json = []
+    data_path_p = Path(data_args.data_path)
+    if data_path_p.is_dir():
+        # Load all .jsonl files in the directory and combine them
+        for jsonl_file in sorted(data_path_p.glob("*.jsonl")):
+            with open(jsonl_file) as f:
+                data_json.extend(json.loads(line) for line in f)
+    else:
+        with open(data_args.data_path) as f:
+            if data_args.data_path.endswith("jsonl"):
+                data_json = [json.loads(line) for line in f]
+            else:
+                data_json = json.load(f)
 
     if use_offline_training:
         print_rank_0("Loading pre-processed data for offline training...")
@@ -280,12 +283,14 @@ def make_eagle_supervised_data_module(
 
         # Filter to conversations that exist in the offline data and in the provided json
         valid_entries = []
-        for idx, entry in enumerate(data_json):
+        for entry in data_json:
             conv_id = entry.get("conversation_id")
+            if conv_id is None:
+                conv_id = entry.get("uuid")
             if conv_id is None:
                 conv_id = entry.get("id")
             if conv_id is None:
-                conv_id = "{:08d}".format(idx)
+                raise ValueError(f"Conversation ID required but not found for entry {entry}")
             file_path = str(offline_data_path / f"{conv_id}.pt")
             if file_path in all_files:
                 valid_entries.append((entry, file_path))

examples/speculative_decoding/launch_train.sh

@@ -78,6 +78,10 @@ while [ $# -gt 0 ]; do
       if [[ "$1" != *=* ]]; then shift; fi
       NUM_GPU="${1#*=}"
       ;;
+    --disable_tqdm*)
+      if [[ "$1" != *=* ]]; then shift; fi
+      DISABLE_TQDM="${1#*=}"
+      ;;
     *)
       >&2 printf "Error: Invalid argument ${1#*=}\n"
       exit 1
@@ -110,6 +114,7 @@ FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP=${FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP:-"LlamaD
 NUM_GPU=${NUM_GPU:-1}
 TRAINING_SEQ_LEN=${TRAINING_SEQ_LEN:-2048}
 OFFLINE_DATA_PATH=${OFFLINE_DATA_PATH:-""}
+DISABLE_TQDM=${DISABLE_TQDM:-False}
 
 if [[ "$MODE" == "medusa" ]]; then
   SPECULATIVE_ARGS="--medusa_num_heads $MEDUSA_NUM_HEADS --medusa_num_layers $MEDUSA_NUM_LAYERS"
@@ -165,6 +170,7 @@ CMD="accelerate launch $MULTI_GPU --mixed_precision bf16 main.py \
     --logging_steps 100 \
     --tf32 True \
     --data_path $DATA \
+    --disable_tqdm $DISABLE_TQDM \
     $OFFLINE_TRAINING_ARGS \
     $SPECULATIVE_ARGS
 "

examples/speculative_decoding/main.py

@@ -92,6 +92,7 @@ class TrainingArguments(transformers.TrainingArguments):
     bf16: bool = field(default=True)
     mode: Literal["eagle1", "eagle3", "medusa"] = "eagle3"
     ar_validate_steps: int = field(default=1000, metadata={"help": "Steps between AR validation."})
+    disable_tqdm: bool = field(default=False, metadata={"help": "Disable tqdm progress bar."})
 
 
 @dataclass