[fix] make position_id_per_seconds configurable for Qwen2OmniPlugin (#10281)

Co-authored-by: LincolnBurrows2017 <lincoln@example.com>

README.md

@@ -473,7 +473,7 @@ huggingface-cli login
 
 | Mandatory    | Minimum | Recommend |
 | ------------ | ------- | --------- |
-| python       | 3.9     | 3.10      |
+| python       | 3.11     | >=3.11   |
 | torch        | 2.0.0   | 2.6.0     |
 | torchvision  | 0.15.0  | 0.21.0    |
 | transformers | 4.49.0  | 4.50.0    |

README_zh.md

@@ -475,7 +475,7 @@ huggingface-cli login
 
 | 必需项        | 至少     | 推荐      |
 | ------------ | ------- | --------- |
-| python       | 3.9     | 3.10      |
+| python       | 3.11     | >=3.11   |
 | torch        | 2.0.0   | 2.6.0     |
 | torchvision  | 0.15.0  | 0.21.0    |
 | transformers | 4.49.0  | 4.50.0    |

docker/docker-npu/Dockerfile

@@ -1,6 +1,6 @@
 # https://hub.docker.com/r/ascendai/cann/tags
 
-ARG BASE_IMAGE=quay.io/ascend/cann:8.3.rc2-910b-ubuntu22.04-py3.11
+ARG BASE_IMAGE=quay.io/ascend/cann:8.5.1-910b-ubuntu22.04-py3.11
 FROM ${BASE_IMAGE}
 
 # Installation arguments
@@ -33,9 +33,11 @@ RUN pip config set global.index-url "${PIP_INDEX}" && \
 COPY . /app
 
 # Install torch-npu
-RUN pip uninstall -y torch torchvision torchaudio && \
-    pip install --no-cache-dir "torch==2.7.1" "torch-npu==2.7.1" "torchvision==0.22.1" "torchaudio==2.7.1" --index-url "${PYTORCH_INDEX}" && \
-    pip install --no-cache-dir -e . --no-build-isolation && \
+RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh
+RUN pip uninstall -y torch torchvision torchaudio
+RUN pip install --no-cache-dir -r requirements/npu.txt --index-url "${PYTORCH_INDEX}"
+RUN pip install --no-cache-dir -r requirements/deepspeed.txt
+RUN pip install --no-cache-dir -e . --no-build-isolation && \
     pip install --no-cache-dir -r requirements/metrics.txt --no-build-isolation
 
 # Set up volumes

docker/docker-npu/docker-compose.yml

@@ -33,7 +33,7 @@ services:
       dockerfile: ./docker/docker-npu/Dockerfile
       context: ../..
       args:
-        BASE_IMAGE: quay.io/ascend/cann:8.3.rc2-a3-ubuntu22.04-py3.11
+        BASE_IMAGE: quay.io/ascend/cann:8.5.1-a3-ubuntu22.04-py3.11
         PIP_INDEX: https://pypi.org/simple
     container_name: llamafactory-a3
     image: llamafactory:npu-a3

requirements/npu.txt

@@ -1,4 +1,4 @@
 torch==2.7.1
-torch-npu==2.7.1
+torch-npu==2.7.1.post2
 torchvision==0.22.1
 torchaudio==2.7.1

scripts/megatron_merge.py

@@ -71,6 +71,7 @@ def convert(
     pipeline_model_parallel_size: int = 1,
     expert_model_parallel_size: int = 1,
     virtual_pipeline_model_parallel_size: int | None = None,
+    moe_grouped_gemm: bool | None = None,
 ):
     """Convert checkpoint between MCA and HuggingFace formats.
 
@@ -84,6 +85,10 @@ def convert(
         pipeline_model_parallel_size: Pipeline model parallel size
         expert_model_parallel_size: Expert model parallel size
         virtual_pipeline_model_parallel_size: Virtual pipeline model parallel size
+        moe_grouped_gemm: Use grouped gemm for MoE experts. When enabled, expert
+            weights are stored in a flattened format (linear_fc1.weight0, weight1, ...)
+            rather than per-expert format (local_experts.0.linear_fc1.weight, ...).
+            Must match the format used when saving the checkpoint.
     """
     if bf16 and fp16:
         raise ValueError("bf16 and fp16 cannot be both True.")
@@ -97,8 +102,9 @@ def convert(
             pipeline_model_parallel_size=pipeline_model_parallel_size,
             expert_model_parallel_size=expert_model_parallel_size,
             virtual_pipeline_model_parallel_size=virtual_pipeline_model_parallel_size,
+            moe_grouped_gemm=moe_grouped_gemm,
+            transformer_impl="transformer_engine",  # hard code here since we default using te for training
         )
-
         convert_checkpoint_to_mca(
             checkpoint_path,
             output_path,

src/llamafactory/data/data_utils.py

@@ -196,7 +196,7 @@ def read_cloud_json(cloud_path: str) -> list[Any]:
 
     # filter out non-JSON files
     files = [x["Key"] for x in fs.listdir(cloud_path)] if fs.isdir(cloud_path) else [cloud_path]
-    files = filter(lambda file: file.endswith(".json") or file.endswith(".jsonl"), files)
+    files = list(filter(lambda file: file.endswith(".json") or file.endswith(".jsonl"), files))
     if not files:
         raise ValueError(f"No JSON/JSONL files found in the specified path: {cloud_path}.")
 

src/llamafactory/data/mm_plugin.py

@@ -161,7 +161,9 @@ class MMPluginMixin:
         video_processor: BaseImageProcessor = getattr(
             processor, "video_processor", getattr(processor, "image_processor", None)
         )
-        feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None)
+        feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None) or getattr(
+            processor, "audio_processor", None
+        )
         if len(images) != 0 and self.image_token is None:
             raise ValueError(
                 "This model does not support image input. Please check whether the correct `template` is used."
@@ -390,7 +392,9 @@ class MMPluginMixin:
                 mm_inputs.update(video_processor(videos, return_tensors="pt"))
 
         if len(audios) != 0:
-            feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None)
+            feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None) or getattr(
+                processor, "audio_processor", None
+            )
             audios = self._regularize_audios(
                 audios,
                 sampling_rate=getattr(processor, "audio_sampling_rate", 16000),
@@ -1876,7 +1880,9 @@ class Qwen2OmniPlugin(Qwen2VLPlugin):
     ) -> dict[str, "torch.Tensor"]:
         image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
         video_processor: BaseVideoProcessor = getattr(processor, "video_processor", None)
-        feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None)
+        feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None) or getattr(
+            processor, "audio_processor", None
+        )
         mm_inputs = {}
         if len(images) != 0:
             images = self._regularize_images(
@@ -1981,6 +1987,7 @@ class Qwen2OmniPlugin(Qwen2VLPlugin):
                             f"Each {VIDEO_PLACEHOLDER} must be followed by an {AUDIO_PLACEHOLDER} when using audio in video."
                         )
 
+                    position_id_per_seconds: int = getattr(processor, "position_id_per_seconds", 25)
                     audio_t_index = torch.arange(audio_lengths[num_audio_tokens])
                     video_t_index = (
                         torch.arange(video_grid_thw[num_video_tokens][0])
@@ -1992,9 +1999,9 @@ class Qwen2OmniPlugin(Qwen2VLPlugin):
                         )
                         .flatten()
                         * mm_inputs["video_second_per_grid"][num_video_tokens]
-                        * 25  # FIXME hardcode of position_id_per_seconds=25
+                        * position_id_per_seconds
                     ).long()
-                    t_ntoken_per_chunk = 50  # FIXME hardcode: [25 * 2]
+                    t_ntoken_per_chunk = position_id_per_seconds * 2
                     video_chunk_indices = processor.get_chunked_index(video_t_index, t_ntoken_per_chunk)
                     audio_chunk_indices = processor.get_chunked_index(audio_t_index, t_ntoken_per_chunk)
                     placeholder_string = ""

src/llamafactory/data/template.py

@@ -1113,7 +1113,7 @@ register_template(
 register_template(
     name="gpt_oss",
     format_user=StringFormatter(slots=["<|start|>user<|message|>{{content}}<|end|><|start|>assistant"]),
-    format_assistant=StringFormatter(slots=["{{content}}<|end|>"]),
+    format_assistant=StringFormatter(slots=["{{content}}"]),
     format_system=StringFormatter(slots=["<|start|>system<|message|>{{content}}<|end|>"]),
     default_system="You are ChatGPT, a large language model trained by OpenAI.",
     thought_words=("<|channel|>analysis<|message|>", "<|end|><|start|>assistant<|channel|>final<|message|>"),

src/llamafactory/extras/constants.py

@@ -69,6 +69,8 @@ MCA_SUPPORTED_MODELS = {
     "qwen3",
     "qwen3_moe",
     "qwen3_next",
+    "qwen3_5",
+    "qwen3_5_moe",
 }
 
 METHODS = ["full", "freeze", "lora", "oft"]

src/llamafactory/hparams/parser.py

@@ -470,7 +470,7 @@ def get_train_args(args: dict[str, Any] | list[str] | None = None) -> _TRAIN_CLS
         training_args.resume_from_checkpoint is None
         and training_args.do_train
         and os.path.isdir(training_args.output_dir)
-        and not training_args.overwrite_output_dir
+        and not getattr(training_args, "overwrite_output_dir", False) # for mca training args and transformers >= 5.0
         and can_resume_from_checkpoint
     ):
         last_checkpoint = get_last_checkpoint(training_args.output_dir)

src/llamafactory/train/callbacks.py

@@ -228,7 +228,7 @@ class LogCallback(TrainerCallback):
         if (
             args.should_save
             and os.path.exists(os.path.join(args.output_dir, TRAINER_LOG))
-            and args.overwrite_output_dir
+            and getattr(args, "overwrite_output_dir", False)
         ):
             logger.warning_rank0_once("Previous trainer log in this folder will be deleted.")
             os.remove(os.path.join(args.output_dir, TRAINER_LOG))

src/llamafactory/train/mca/workflow.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 
 import functools
+import json
+import os
 from collections.abc import Sequence
 from copy import deepcopy
 from typing import TYPE_CHECKING, Any, Optional
@@ -77,20 +79,25 @@ def _data_collator_wrapper(data_collator: Any):
 
 def _check_model_support(model_args: "ModelArguments"):
     from transformers import AutoConfig as HfAutoConfig
+    if os.path.exists(os.path.join(model_args.model_name_or_path, "mca_config.json")): # load from mcore ckpt
+        mca_config = json.load(open(os.path.join(model_args.model_name_or_path, "mca_config.json")))
+        model_type = mca_config.get("hf_model_type", None)
+    else:
+        config = HfAutoConfig.from_pretrained(
+            model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
+        )
+        model_type = config.model_type
 
-    config = HfAutoConfig.from_pretrained(
-        model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
-    )
-    if config.model_type not in MCA_SUPPORTED_MODELS:
+    if model_type not in MCA_SUPPORTED_MODELS:
         raise ValueError(
-            f"Model {config.model_type} is not supported by mcore_adapter."
+            f"Model {model_type} is not supported by mcore_adapter."
             "You can try to upgrade mcore_adapter to the latest version for more supported models."
         )
 
 
 def _freeze_model_parameters(model: Any, finetuning_args: "FinetuningArguments"):
     """Freeze model parameters for qwen_vl series models based on finetuning arguments."""
-    if getattr(model.config, "hf_model_type", None) not in ["qwen2_vl", "qwen2_5_vl", "qwen3_vl", "qwen3_vl_moe"]:
+    if getattr(model.config, "hf_model_type", None) not in ["qwen2_vl", "qwen2_5_vl", "qwen3_vl", "qwen3_vl_moe", "qwen3_5", "qwen3_5_moe"]:
         return
 
     params_to_freeze = []