[v1] use async streamer (#9741)

src/llamafactory/model/loader.py

@@ -230,7 +230,7 @@ def load_model(
         )
         from ..v1.plugins.model_plugins.kernels.interface import apply_default_kernels
 
-        model = apply_default_kernels(model=model, include_kernels=model_args.use_v1_kernels)
+        model = apply_default_kernels(model, include_kernels=model_args.use_v1_kernels)
 
     trainable_params, all_param = count_parameters(model)
     if is_trainable:

src/llamafactory/v1/core/base_sampler.py

@@ -15,11 +15,11 @@
 import asyncio
 import os
 from abc import ABC, abstractmethod
-from collections.abc import AsyncGenerator, Generator
+from collections.abc import AsyncGenerator
 from threading import Thread
 
 import torch
-from transformers import TextIteratorStreamer
+from transformers import AsyncTextIteratorStreamer
 
 from ..accelerator.interface import DistributedInterface
 from ..config import ModelArguments, SampleArguments, SampleBackend
@@ -88,9 +88,10 @@ class HuggingFaceEngine(BaseEngine):
         self.semaphore = asyncio.Semaphore(int(os.getenv("MAX_CONCURRENT", "1")))
 
     @torch.inference_mode()
-    def get_response(self, messages: list[Message], tools: str | None = None) -> Generator[str, None, None]:
+    async def generate(self, messages: list[Message], tools: str | None = None) -> AsyncGenerator[str, None]:
+        async with self.semaphore:
             model_inputs = self.renderer.render_messages(messages, tools, is_generate=True)
-        streamer = TextIteratorStreamer(
+            streamer = AsyncTextIteratorStreamer(
                 tokenizer=get_tokenizer(self.renderer.processor),
                 skip_prompt=True,
                 skip_special_tokens=True,  # TODO: configurable
@@ -105,22 +106,8 @@ class HuggingFaceEngine(BaseEngine):
             thread = Thread(target=self.model.generate, kwargs=kwargs, daemon=True)
             thread.start()
 
-        def stream():
+            async for token in streamer:
-            try:
+                yield token
-                return streamer.__next__()
-            except StopIteration:
-                raise StopAsyncIteration()
-
-        return stream
-
-    async def generate(self, messages: list[Message], tools: str | None = None) -> AsyncGenerator[str, None]:
-        async with self.semaphore:
-            response = self.get_response(messages, tools)
-            while True:
-                try:
-                    yield await asyncio.to_thread(response)
-                except StopAsyncIteration:
-                    break
 
     async def batch_infer(self, dataset: TorchDataset) -> list[Sample]:
         """Batch infer samples.

src/llamafactory/v1/core/base_trainer.py

@@ -28,8 +28,9 @@ Train Phase:
 """
 
 from ..config.training_args import TrainingArguments
-from ..utils.types import HFModel, Processor, TorchDataset
+from ..utils.types import HFModel, TorchDataset
-from .trainer_utils.data_collator import DataCollator
+from .utils.data_collator import DataCollator
+from .utils.rendering import Renderer
 
 
 class BaseTrainer:
@@ -37,21 +38,21 @@ class BaseTrainer:
         self,
         args: TrainingArguments,
         model: HFModel,
-        processor: Processor,
+        renderer: Renderer,
         dataset: TorchDataset,
     ) -> None:
         self.args = args
         self.model = model
-        self.processor = processor
+        self.renderer = renderer
         self.dataset = dataset
         self.data_collator = DataCollator()
         self.optimizer = None
         self.lr_scheduler = None
 
-    def init_model_and_optimizer(self) -> None:
+    def _create_dataloader(self) -> None:
         pass
 
-    def create_dataloader(self) -> None:
+    def _init_model_and_optimizer(self) -> None:
         pass
 
     def fit(self) -> None:

src/llamafactory/v1/core/model_engine.py

@@ -87,7 +87,7 @@ class ModelEngine:
     def _init_model(self) -> HFModel:
         """Init model.
 
-        Let transformers handle the model init context.
+        Transformers can choose the proper model init context.
         https://github.com/huggingface/transformers/blob/v5.0.0rc0/src/transformers/modeling_utils.py#L3538
         """
         if self.args.model_class == ModelClass.LLM:
@@ -141,7 +141,7 @@ class ModelEngine:
             from ..plugins.model_plugins.kernels.interface import KernelPlugin
 
             model = KernelPlugin(self.args.kernel_config.name)(
-                model=model, include_kernels=self.args.kernel_config.get("include_kernels")
+                model, include_kernels=self.args.kernel_config.get("include_kernels")
             )
 
         return model

src/llamafactory/v1/plugins/model_plugins/kernels/interface.py

@@ -24,12 +24,13 @@ Init Phase:
 import importlib
 from pathlib import Path
 
-from ....utils.logging import get_logger
+from ....utils import logging
 from ....utils.plugin import BasePlugin
+from ....utils.types import HFModel
 from .registry import Registry
 
 
-logger = get_logger(__name__)
+logger = logging.get_logger(__name__)
 
 
 def scan_all_kernels():
@@ -110,27 +111,30 @@ class KernelPlugin(BasePlugin):
 
 
 @KernelPlugin("auto").register()
-def apply_default_kernels(**kwargs):
+def apply_default_kernels(model: HFModel, include_kernels: str = None) -> HFModel:
     """Applies all default registered kernels to the model.
 
     Args:
-        **kwargs: Keyword arguments passed to the kernel application function.
+        model (HFModel): The model instance to apply kernels to.
-                  Typically includes the model instance and the include_kernels configuration.
+        include_kernels (str, optional): Comma-separated list of kernel IDs to apply.
+                                         If "auto" or True, applies all default kernels.
+                                         If None or False, no kernels are applied.
+                                         Defaults to None.
 
     Returns:
         HFModel: The model with applied kernels.
     """
-    if not kwargs.get("include_kernels"):  # None/False/empty string
+    if not include_kernels:
-        return kwargs.get("model")
+        return model
-    elif kwargs.get("include_kernels") == "auto" or kwargs.get("include_kernels") is True:  # True/auto
+    elif include_kernels == "auto" or include_kernels is True:
         use_kernels = default_kernels.keys()
     else:
-        use_kernels = kwargs.get("include_kernels").split(",")  # "kernel_id1,kernel_id2,kernel_id3"
+        use_kernels = include_kernels.split(",")  # "kernel_id1,kernel_id2,kernel_id3"
 
     for kernel in use_kernels:
         if kernel not in default_kernels:
             raise ValueError(f"Kernel {kernel} not found")
 
-        apply_kernel(kernel, **kwargs)
+        apply_kernel(kernel, model=model)
 
-    return kwargs.get("model")
+    return model

src/llamafactory/v1/plugins/model_plugins/kernels/registry.py

@@ -20,8 +20,6 @@ Init Phase:
 
 """
 
-from typing import Optional
-
 from ....accelerator.helper import get_current_accelerator
 from .base import BaseKernel
 
@@ -73,14 +71,14 @@ class Registry:
         return kernel_cls
 
     @classmethod
-    def get(cls, kernel_id: str) -> Optional[type[BaseKernel]]:
+    def get(cls, kernel_id: str) -> type[BaseKernel] | None:
         """Retrieves a registered kernel implementation by its ID.
 
         Args:
             kernel_id (str): The ID of the kernel to retrieve.
 
         Returns:
-            Optional[type[BaseKernel]]: The kernel class if found, else ``None``.
+            type[BaseKernel] | None: The kernel class if found, else ``None``.
         """
         return cls._kernels.get(kernel_id)