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fix yi vl vllm infer

This commit is contained in:
hiyouga 2024-05-15 19:25:48 +08:00
parent e1f4e53915
commit 51d61fcc89
16 changed files with 113 additions and 24 deletions
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19
src/llmtuner/chat/vllm_engine.py
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@ -2,9 +2,11 @@ import uuid
from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence
from ..data import get_template_and_fix_tokenizer
from ..extras.logging import get_logger
from ..extras.misc import get_device_count, infer_optim_dtype
from ..extras.packages import is_vllm_available
from ..model import load_config, load_tokenizer
from ..model.utils.visual import LlavaMultiModalProjectorForYiVLForVLLM
from .base_engine import BaseEngine, Response
@ -22,6 +24,9 @@ if TYPE_CHECKING:
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
logger = get_logger(__name__)
class VllmEngine(BaseEngine):
def __init__(
self,
@ -57,13 +62,19 @@ class VllmEngine(BaseEngine):
}
if model_args.visual_inputs:
# TODO: auto derive from config
# https://github.com/vllm-project/vllm/pull/3042#issuecomment-1984893549
self.image_feature_size = 576
image_size = config.vision_config.image_size
patch_size = config.vision_config.patch_size
self.image_feature_size = (image_size // patch_size) ** 2
engine_args["image_input_type"] = "pixel_values"
engine_args["image_token_id"] = self.tokenizer.convert_tokens_to_ids("<image>")
engine_args["image_input_shape"] = "1,3,336,336"
engine_args["image_input_shape"] = "1,3,{},{}".format(image_size, image_size)
engine_args["image_feature_size"] = self.image_feature_size
if getattr(config, "is_yi_vl_derived_model", None):
# bug in vllm 0.4.2, see: https://github.com/vllm-project/vllm/pull/4828
import vllm.model_executor.models.llava
logger.info("Detected Yi-VL model, applying projector patch.")
vllm.model_executor.models.llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVLForVLLM
self.model = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_args))
if model_args.adapter_name_or_path is not None:

2
src/llmtuner/data/template.py
View File

@ -865,7 +865,7 @@ _register_template(
"Assume the role of the AI assistant. Read all the images carefully, "
"and respond to the human's questions with informative, helpful, detailed and polite answers. "
"这是一个好奇的人类和一个人工智能助手之间的对话。假设你扮演这个AI助手的角色。"
"仔细阅读所有的图像，并对人类的问题做出信息丰富、有帮助、详细的和礼貌的回答。\n"
"仔细阅读所有的图像，并对人类的问题做出信息丰富、有帮助、详细的和礼貌的回答。\n\n"
),
stop_words=["###"],
)

2
src/llmtuner/hparams/parser.py
View File

@ -285,7 +285,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
model_args.model_max_length = data_args.cutoff_len
data_args.packing = data_args.packing if data_args.packing is not None else finetuning_args.stage == "pt"
# Log on each process the small summary:
# Log on each process the small summary
logger.info(
"Process rank: {}, device: {}, n_gpu: {}, distributed training: {}, compute dtype: {}".format(
training_args.local_rank,

37
src/llmtuner/model/utils/visual.py
View File

@ -16,25 +16,51 @@ if TYPE_CHECKING:
logger = get_logger(__name__)
class LlavaMultiModalProjector(torch.nn.Module):
def __init__(self, config: "LlavaConfig"):
class LlavaMultiModalProjectorForYiVL(torch.nn.Module):
def __init__(self, config: "LlavaConfig") -> None:
super().__init__()
self.config = config
if config is None:
return
self.linear_1 = torch.nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size, bias=True)
self.linear_2 = torch.nn.LayerNorm(config.text_config.hidden_size, bias=True)
self.linear_3 = torch.nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True)
self.linear_4 = torch.nn.LayerNorm(config.text_config.hidden_size, bias=True)
self.act = ACT2FN[config.projector_hidden_act]
def forward(self, image_features):
def forward(self, image_features: "torch.Tensor") -> "torch.Tensor":
hidden_states = self.linear_1(image_features)
hidden_states = self.linear_2(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.linear_3(hidden_states)
hidden_states = self.linear_4(hidden_states)
if hidden_states.dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.linear_1.weight.dtype
logger.warning_once("The hidden states seems to be silently casted in float32.")
hidden_states = hidden_states.to(target_dtype)
return hidden_states
class LlavaMultiModalProjectorForYiVLForVLLM(LlavaMultiModalProjectorForYiVL):
def __init__(self, vision_hidden_size: int, text_hidden_size: int, projector_hidden_act: str) -> None:
super().__init__(config=None)
self.linear_1 = torch.nn.Linear(vision_hidden_size, text_hidden_size, bias=True)
self.linear_2 = torch.nn.LayerNorm(text_hidden_size, bias=True)
self.linear_3 = torch.nn.Linear(text_hidden_size, text_hidden_size, bias=True)
self.linear_4 = torch.nn.LayerNorm(text_hidden_size, bias=True)
self.act = torch.nn.GELU()
def autocast_projector_dtype(
model: "PreTrainedModel", model_args: "ModelArguments", mm_projector_name: str = "multi_modal_projector"
) -> None:
@ -53,5 +79,6 @@ def configure_visual_model(config: "PretrainedConfig") -> None:
if getattr(config, "model_type", None) == "llava":
setattr(config, "hidden_size", getattr(config.text_config, "hidden_size", None))
if getattr(config, "is_yi_vl_derived_model", None):
transformers.models.llava.modeling_llava.LlavaMultiModalProjector = LlavaMultiModalProjector
if getattr(config, "is_yi_vl_derived_model", None):
logger.info("Detected Yi-VL model, applying projector patch.")
transformers.models.llava.modeling_llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVL

10
src/llmtuner/train/dpo/trainer.py
View File

@ -13,7 +13,7 @@ from ..utils import create_custom_optimzer, create_custom_scheduler
if TYPE_CHECKING:
from transformers import PreTrainedModel
from transformers import PreTrainedModel, ProcessorMixin
from ...hparams import FinetuningArguments
@ -24,6 +24,7 @@ class CustomDPOTrainer(DPOTrainer):
model: Union["PreTrainedModel", torch.nn.Module],
ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]],
finetuning_args: "FinetuningArguments",
processor: Optional["ProcessorMixin"],
disable_dropout: bool = True,
**kwargs,
):
@ -33,6 +34,7 @@ class CustomDPOTrainer(DPOTrainer):
disable_dropout_in_model(ref_model)
self.finetuning_args = finetuning_args
self.processor = processor
self.reference_free = False
self.use_dpo_data_collator = True # hack to avoid warning
self.generate_during_eval = False # disable at evaluation
@ -80,6 +82,12 @@ class CustomDPOTrainer(DPOTrainer):
create_custom_scheduler(self.args, num_training_steps, optimizer)
return super().create_scheduler(num_training_steps, optimizer)
def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
super()._save(output_dir, state_dict)
if self.processor is not None:
output_dir = output_dir if output_dir is not None else self.args.output_dir
getattr(self.processor, "image_processor").save_pretrained(output_dir)
def sft_loss(self, chosen_logits: "torch.FloatTensor", chosen_labels: "torch.LongTensor") -> "torch.Tensor":
r"""
Computes supervised cross-entropy loss of given labels under the given logits.

2
src/llmtuner/train/dpo/workflow.py
View File

@ -50,9 +50,9 @@ def run_dpo(
ref_model=ref_model,
args=training_args,
finetuning_args=finetuning_args,
tokenizer=tokenizer,
data_collator=data_collator,
callbacks=callbacks,
**tokenizer_module,
**split_dataset(dataset, data_args, training_args),
)

10
src/llmtuner/train/orpo/trainer.py
View File

@ -13,7 +13,7 @@ from ..utils import create_custom_optimzer, create_custom_scheduler
if TYPE_CHECKING:
from transformers import PreTrainedModel
from transformers import PreTrainedModel, ProcessorMixin
from ...hparams import FinetuningArguments
@ -23,6 +23,7 @@ class CustomORPOTrainer(DPOTrainer):
self,
model: Union["PreTrainedModel", "torch.nn.Module"],
finetuning_args: "FinetuningArguments",
processor: Optional["ProcessorMixin"],
disable_dropout: bool = True,
**kwargs,
):
@ -30,6 +31,7 @@ class CustomORPOTrainer(DPOTrainer):
disable_dropout_in_model(model)
self.finetuning_args = finetuning_args
self.processor = processor
self.reference_free = False
self.use_dpo_data_collator = True # hack to avoid warning
self.generate_during_eval = False # disable at evaluation
@ -61,6 +63,12 @@ class CustomORPOTrainer(DPOTrainer):
create_custom_scheduler(self.args, num_training_steps, optimizer)
return super().create_scheduler(num_training_steps, optimizer)
def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
super()._save(output_dir, state_dict)
if self.processor is not None:
output_dir = output_dir if output_dir is not None else self.args.output_dir
getattr(self.processor, "image_processor").save_pretrained(output_dir)
def odds_ratio_loss(self, chosen_logps: "torch.Tensor", rejected_logps: "torch.Tensor") -> "torch.Tensor":
r"""
Computes ORPO's odds ratio (OR) loss.

2
src/llmtuner/train/orpo/workflow.py
View File

@ -43,9 +43,9 @@ def run_orpo(
model=model,
args=training_args,
finetuning_args=finetuning_args,
tokenizer=tokenizer,
data_collator=data_collator,
callbacks=callbacks,
**tokenizer_module,
**split_dataset(dataset, data_args, training_args),
)

16
src/llmtuner/train/ppo/trainer.py
View File

@ -23,7 +23,13 @@ from .utils import dump_layernorm, get_rewards_from_server, replace_model, resto
if TYPE_CHECKING:
from datasets import Dataset
from transformers import DataCollatorWithPadding, PreTrainedTokenizer, Seq2SeqTrainingArguments, TrainerCallback
from transformers import (
DataCollatorWithPadding,
PreTrainedTokenizer,
ProcessorMixin,
Seq2SeqTrainingArguments,
TrainerCallback,
)
from trl import AutoModelForCausalLMWithValueHead
from ...hparams import FinetuningArguments, GeneratingArguments, ModelArguments
@ -48,6 +54,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
reward_model: Optional["AutoModelForCausalLMWithValueHead"],
ref_model: Optional["AutoModelForCausalLMWithValueHead"],
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
dataset: "Dataset",
data_collator: "DataCollatorWithPadding",
):
@ -97,6 +104,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
self.finetuning_args = finetuning_args
self.reward_model = reward_model
self.current_device = get_current_device() # patch for deepspeed training
self.processor = processor
self.generation_config = GenerationConfig(
pad_token_id=self.tokenizer.pad_token_id,
@ -295,6 +303,12 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
)
return lr_scheduler
def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
super()._save(output_dir, state_dict)
if self.processor is not None:
output_dir = output_dir if output_dir is not None else self.args.output_dir
getattr(self.processor, "image_processor").save_pretrained(output_dir)
@torch.no_grad()
def get_inputs(self, batch: Dict[str, torch.Tensor]) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
r"""

2
src/llmtuner/train/ppo/workflow.py
View File

@ -49,9 +49,9 @@ def run_ppo(
model=model,
reward_model=reward_model,
ref_model=ref_model,
tokenizer=tokenizer,
dataset=dataset,
data_collator=data_collator,
**tokenizer_module,
)
# Training

14
src/llmtuner/train/pt/trainer.py
View File

@ -1,5 +1,5 @@
from types import MethodType
from typing import TYPE_CHECKING, Optional
from typing import TYPE_CHECKING, Dict, Optional
from transformers import Trainer
@ -9,6 +9,7 @@ from ..utils import create_custom_optimzer, create_custom_scheduler
if TYPE_CHECKING:
import torch
from transformers import ProcessorMixin
from ...hparams import FinetuningArguments
@ -21,9 +22,12 @@ class CustomTrainer(Trainer):
Inherits Trainer for custom optimizer.
"""
def __init__(self, finetuning_args: "FinetuningArguments", **kwargs) -> None:
def __init__(
self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
) -> None:
super().__init__(**kwargs)
self.finetuning_args = finetuning_args
self.processor = processor
if finetuning_args.use_badam:
from badam import clip_grad_norm_for_sparse_tensor
@ -39,3 +43,9 @@ class CustomTrainer(Trainer):
) -> "torch.optim.lr_scheduler.LRScheduler":
create_custom_scheduler(self.args, num_training_steps, optimizer)
return super().create_scheduler(num_training_steps, optimizer)
def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
super()._save(output_dir, state_dict)
if self.processor is not None:
output_dir = output_dir if output_dir is not None else self.args.output_dir
getattr(self.processor, "image_processor").save_pretrained(output_dir)

2
src/llmtuner/train/pt/workflow.py
View File

@ -36,9 +36,9 @@ def run_pt(
model=model,
args=training_args,
finetuning_args=finetuning_args,
tokenizer=tokenizer,
data_collator=data_collator,
callbacks=callbacks,
**tokenizer_module,
**split_dataset(dataset, data_args, training_args),
)

13
src/llmtuner/train/rm/trainer.py
View File

@ -11,7 +11,7 @@ from ..utils import create_custom_optimzer, create_custom_scheduler
if TYPE_CHECKING:
from transformers.modeling_utils import PreTrainedModel
from transformers import PreTrainedModel, ProcessorMixin
from transformers.trainer import PredictionOutput
from ...hparams import FinetuningArguments
@ -25,9 +25,12 @@ class PairwiseTrainer(Trainer):
Inherits Trainer to compute pairwise loss.
"""
def __init__(self, finetuning_args: "FinetuningArguments", **kwargs) -> None:
def __init__(
self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
) -> None:
super().__init__(**kwargs)
self.finetuning_args = finetuning_args
self.processor = processor
self.can_return_loss = True # override property to return eval_loss
if finetuning_args.use_badam:
from badam import clip_grad_norm_for_sparse_tensor
@ -45,6 +48,12 @@ class PairwiseTrainer(Trainer):
create_custom_scheduler(self.args, num_training_steps, optimizer)
return super().create_scheduler(num_training_steps, optimizer)
def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
super()._save(output_dir, state_dict)
if self.processor is not None:
output_dir = output_dir if output_dir is not None else self.args.output_dir
getattr(self.processor, "image_processor").save_pretrained(output_dir)
def compute_loss(
self, model: "PreTrainedModel", inputs: Dict[str, torch.Tensor], return_outputs: bool = False
) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]:

2
src/llmtuner/train/rm/workflow.py
View File

@ -39,10 +39,10 @@ def run_rm(
model=model,
args=training_args,
finetuning_args=finetuning_args,
tokenizer=tokenizer,
data_collator=data_collator,
callbacks=callbacks + [FixValueHeadModelCallback()],
compute_metrics=compute_accuracy,
**tokenizer_module,
**split_dataset(dataset, data_args, training_args),
)

3
src/llmtuner/webui/runner.py
View File

@ -107,6 +107,7 @@ class Runner:
model_name_or_path=get("top.model_path"),
adapter_name_or_path=adapter_name_or_path,
cache_dir=user_config.get("cache_dir", None),
preprocessing_num_workers=16,
finetuning_type=get("top.finetuning_type"),
quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None,
template=get("top.template"),
@ -141,6 +142,7 @@ class Runner:
fp16=(get("train.compute_type") == "fp16"),
bf16=(get("train.compute_type") == "bf16"),
pure_bf16=(get("train.compute_type") == "pure_bf16"),
plot_loss=True,
)
if args["finetuning_type"] == "freeze":
@ -214,6 +216,7 @@ class Runner:
model_name_or_path=get("top.model_path"),
adapter_name_or_path=adapter_name_or_path,
cache_dir=user_config.get("cache_dir", None),
preprocessing_num_workers=16,
finetuning_type=get("top.finetuning_type"),
quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None,
template=get("top.template"),

1
src/llmtuner/webui/utils.py
View File

@ -42,7 +42,6 @@ def clean_cmd(args: Dict[str, Any]) -> Dict[str, Any]:
def gen_cmd(args: Dict[str, Any]) -> str:
args["plot_loss"] = args.get("do_train", None)
current_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
cmd_lines = ["CUDA_VISIBLE_DEVICES={} llamafactory-cli train ".format(current_devices)]
for k, v in clean_cmd(args).items():