From 90b3181f3f80bab395724395bb719f1821e29fbf Mon Sep 17 00:00:00 2001 From: YunyaoZhou Date: Sat, 11 Jan 2025 03:53:23 +0800 Subject: [PATCH 1/3] =?UTF-8?q?feat=E2=9C=A8:=20=E6=9B=B4=E6=96=B0Python?= =?UTF-8?q?=E7=89=88=E6=9C=AC=E5=92=8C=E4=BE=9D=E8=B5=96=E7=AE=A1=E7=90=86?= =?UTF-8?q?=EF=BC=8C=E4=BC=98=E5=8C=96=E8=AE=AD=E7=BB=83=E8=84=9A=E6=9C=AC?= =?UTF-8?q?=E4=BB=A5=E6=94=AF=E6=8C=81Flash=20Attention=202?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .python-version | 1 + install.sh | 4 +- pyproject.toml | 46 + requirements.txt | 11 - src/model_library/qwen2vl/model.py | 145 ++- src/train.py | 6 +- src/train.sh | 3 +- uv.lock | 1607 ++++++++++++++++++++++++++++ 8 files changed, 1807 insertions(+), 16 deletions(-) create mode 100644 .python-version create mode 100644 pyproject.toml delete mode 100644 requirements.txt create mode 100644 uv.lock diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..d4b278f --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11.7 diff --git a/install.sh b/install.sh index 40bce8e..2af4ec4 100644 --- a/install.sh +++ b/install.sh @@ -1,3 +1,3 @@ #!/bin/bash -uv venv --python 3.11.7 -pip install -U torch==2.5.1+cu124 torchvision==0.20.1+cu124 torchaudio==2.5.1+cu124 --extra-index-url https://download.pytorch.org/whl/cu124 +uv sync +uv sync --extra compile diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..10891b7 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,46 @@ +[project] +dependencies = [ + "accelerate==1.2.1", + "datasets==3.2.0", + "deepspeed==0.16.2", + "evaluate==0.4.3", + "markupsafe==2.1.5", + "peft==0.14.0", + "pip==24.3.1", + "requests==2.32.3", + "setuptools>=70.0.0", + "torch==2.5.1+cu124", + "torchaudio==2.5.1+cu124", + "torchvision==0.20.1+cu124", + "transformers==4.46.1", + "trl==0.13.0", + "wheel>=0.45.1", +] +description = "Add your description here" +name = "cl-lmm" +readme = "README.md" +requires-python = ">=3.11" +version = "0.1.0" + +[project.optional-dependencies] +compile = [ + "flash-attn>=2.7.2.post1", +] + +[tool.uv.sources] +markupsafe = {index = "pytorch"} +requests = {index = "pypi"} +torch = {index = "pytorch"} +torchaudio = {index = "pytorch"} +torchvision = {index = "pytorch"} + +[[tool.uv.index]] +name = "pypi" +url = "https://pypi.org/simple" + +[tool.uv] +no-build-isolation-package = ["flash-attn"] + +[[tool.uv.index]] +name = "pytorch" +url = "https://download.pytorch.org/whl/cu124" diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index f3a9cf3..0000000 --- a/requirements.txt +++ /dev/null @@ -1,11 +0,0 @@ -accelerate==1.2.1 -deepspeed==0.16.2 -evaluate==0.4.3 -peft==0.14.0 -pillow==10.2.0 -torch==2.5.1+cu124 -torchaudio==2.5.1+cu124 -torchvision==0.20.1+cu124 -transformers==4.46.1 -trl==0.13.0 -pillow==9.5.0 diff --git a/src/model_library/qwen2vl/model.py b/src/model_library/qwen2vl/model.py index 10313a4..522355a 100644 --- a/src/model_library/qwen2vl/model.py +++ b/src/model_library/qwen2vl/model.py @@ -1,5 +1,12 @@ # from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLCausalLMOutputWithPast, Qwen2VLModel, Qwen2VLForConditionalGeneration, logger, DynamicCache, Qwen2VLDecoderLayer, Qwen2VLConfig, Qwen2VLAttention, from transformers.models.qwen2_vl.modeling_qwen2_vl import * + +if is_flash_attn_2_available(): + from flash_attn import flash_attn_varlen_func + + from transformers.modeling_flash_attention_utils import _flash_attention_forward +else: + flash_attn_varlen_func = None from transformers.cache_utils import DynamicCache from transformers.modeling_outputs import BaseModelOutputWithPast import torch @@ -246,9 +253,145 @@ class Qwen2VLSdpaAttention_modified(Qwen2VLAttention_modified): return attn_output, None, past_key_value +class Qwen2VLFlashAttention2_modified(Qwen2VLAttention_modified): + """ + Qwen2VL flash attention module, following Qwen2VL attention module. This module inherits from `Qwen2VLAttention` + as the weights of the module stays untouched. The only required change would be on the forward pass + where it needs to correctly call the public API of flash attention and deal with padding tokens + in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom + config.max_window_layers layers. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[ + Tuple[torch.Tensor, torch.Tensor] + ] = None, # will become mandatory in v4.46 + **kwargs, + ): + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states, **kwargs) + key_states = self.k_proj(hidden_states, **kwargs) + value_states = self.v_proj(hidden_states, **kwargs) + + query_states = query_states.view( + bsz, q_len, self.num_heads, self.head_dim + ).transpose(1, 2) + key_states = key_states.view( + bsz, q_len, self.num_key_value_heads, self.head_dim + ).transpose(1, 2) + value_states = value_states.view( + bsz, q_len, self.num_key_value_heads, self.head_dim + ).transpose(1, 2) + + # Because the input can be padded, the absolute sequence length depends on the max position id. + if position_embeddings is None: + logger.warning_once( + "The attention layers in this model are transitioning from computing the RoPE embeddings internally " + "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " + "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " + "removed and `position_embeddings` will be mandatory." + ) + cos, sin = self.rotary_emb(value_states, position_ids) + else: + cos, sin = position_embeddings + + query_states, key_states = apply_multimodal_rotary_pos_emb( + query_states, key_states, cos, sin, self.rope_scaling["mrope_section"] + ) + + if past_key_value is not None: + cache_kwargs = { + "sin": sin, + "cos": cos, + "cache_position": cache_position, + } # Specific to RoPE models + key_states, value_states = past_key_value.update( + key_states, value_states, self.layer_idx, cache_kwargs + ) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + dropout_rate = 0.0 if not self.training else self.attention_dropout + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in float16 just to be sure everything works as expected. + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + # Reashape to the expected shape for Flash Attention + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + if ( + self.config.use_sliding_window + and getattr(self.config, "sliding_window", None) is not None + and self.layer_idx >= self.config.max_window_layers + ): + sliding_window = self.config.sliding_window + else: + sliding_window = None + + attn_output = _flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + sliding_window=sliding_window, + is_causal=self.is_causal, + use_top_left_mask=self._flash_attn_uses_top_left_mask, + ) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + QWEN2_VL_ATTENTION_CLASSES = { "eager": Qwen2VLAttention, - "flash_attention_2": Qwen2VLFlashAttention2, + "flash_attention_2": Qwen2VLFlashAttention2_modified, "sdpa": Qwen2VLSdpaAttention_modified, } diff --git a/src/train.py b/src/train.py index 062d4de..c5c4368 100644 --- a/src/train.py +++ b/src/train.py @@ -62,6 +62,7 @@ if __name__ == "__main__": trust_remote_code=model_args.trust_remote_code, **model_kwargs, ) + print(model) from model_library.qwen2vl import ( collate_fn_for_train, collate_fn_for_evaluate, @@ -85,13 +86,16 @@ if __name__ == "__main__": peft_config = MMOELoraConfig(target_modules=model_args.lora_target_modules) model = get_peft_model(model, peft_config) - # model = inject_adapter_in_model(peft_config, model) + + if accelerator.is_local_main_process: + model.print_trainable_parameters() elif model_args.peft_type == "LORA": from peft.tuners.lora import LoraConfig peft_config = LoraConfig(target_modules=model_args.lora_target_modules) model = get_peft_model(model, peft_config) + if accelerator.is_local_main_process: model.print_trainable_parameters() diff --git a/src/train.sh b/src/train.sh index 9899457..db3fcf0 100755 --- a/src/train.sh +++ b/src/train.sh @@ -8,7 +8,8 @@ accelerate launch --config_file configs/accelerate_configs/deepspeed_zero2.yaml --lora_target_modules q_proj v_proj \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 2 \ - --gradient_accumulation_steps 8 \ + --attn_implementation flash_attention_2 \ + --gradient_accumulation_steps 16 \ --output_dir checkpoint/sft-llava-1.5-7b-hf \ --bf16 \ --torch_dtype bfloat16 diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..64d3c62 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1607 @@ +version = 1 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version < '3.12'", + "python_full_version >= '3.12'", +] + 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=?UTF-8?q?feat=E2=9C=A8:=20=E6=9B=B4=E6=96=B0?= =?UTF-8?q?=E6=95=B0=E6=8D=AE=E9=9B=86=E5=90=8D=E7=A7=B0=E4=B8=BAOCR-VQA-2?= =?UTF-8?q?00K=EF=BC=8C=E4=BC=98=E5=8C=96=E8=AE=AD=E7=BB=83=E8=84=9A?= =?UTF-8?q?=E6=9C=AC=E5=92=8C=E6=8D=9F=E5=A4=B1=E8=AE=A1=E7=AE=97=E9=80=BB?= =?UTF-8?q?=E8=BE=91?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pyproject.toml | 3 +- src/dataset_library/OCRVQADataset.py | 2 +- src/dataset_library/factory.py | 2 +- src/model_library/qwen2vl/model.py | 19 ++- src/train.py | 19 +++ src/train.sh | 2 +- src/utils/accelerator.py | 7 +- src/utils/trainer.py | 62 +++++++- uv.lock | 221 +++++++++++++++------------ 9 files changed, 224 insertions(+), 113 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 10891b7..cf02630 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ dependencies = [ "torch==2.5.1+cu124", "torchaudio==2.5.1+cu124", "torchvision==0.20.1+cu124", - "transformers==4.46.1", + "transformers==4.48.0", "trl==0.13.0", "wheel>=0.45.1", ] @@ -40,6 +40,7 @@ url = "https://pypi.org/simple" [tool.uv] no-build-isolation-package = ["flash-attn"] +concurrent-builds = 4 [[tool.uv.index]] name = "pytorch" diff --git a/src/dataset_library/OCRVQADataset.py b/src/dataset_library/OCRVQADataset.py index dedb432..5c971dd 100644 --- a/src/dataset_library/OCRVQADataset.py +++ b/src/dataset_library/OCRVQADataset.py @@ -107,7 +107,7 @@ class OCRVQADataset(Dataset): } -class OCRVQADatasetForGeneration(Dataset): +class OCRVQADatasetForGeneration(OCRVQADataset): def __getitem__(self, index): sample = self.data[index] diff --git a/src/dataset_library/factory.py b/src/dataset_library/factory.py index f86cd0f..6a4767a 100644 --- a/src/dataset_library/factory.py +++ b/src/dataset_library/factory.py @@ -6,7 +6,7 @@ def get_dataset( dataset_name, base_path="/home/zyy/research/accelerate/dataset" ) -> dict[Literal["train", "test", "generation"], Dataset]: dataset: dict[Literal["train", "test", "generation"], Dataset] = {} - if dataset_name == "OCR_VQA_200K": + if dataset_name == "OCR-VQA-200K": import os.path as osp from .OCRVQADataset import OCRVQADataset, OCRVQADatasetForGeneration diff --git a/src/model_library/qwen2vl/model.py b/src/model_library/qwen2vl/model.py index 522355a..27d5be8 100644 --- a/src/model_library/qwen2vl/model.py +++ b/src/model_library/qwen2vl/model.py @@ -1,5 +1,22 @@ # from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLCausalLMOutputWithPast, Qwen2VLModel, Qwen2VLForConditionalGeneration, logger, DynamicCache, Qwen2VLDecoderLayer, Qwen2VLConfig, Qwen2VLAttention, -from transformers.models.qwen2_vl.modeling_qwen2_vl import * +from transformers.models.qwen2_vl.modeling_qwen2_vl import ( + is_flash_attn_2_available, + Qwen2VLAttention, + Qwen2VLFlashAttention2, + Qwen2VLDecoderLayer, + Qwen2VLModel, + Qwen2VLForConditionalGeneration, + Qwen2VLConfig, + logger, + apply_rotary_pos_emb_vision, + Cache, + apply_multimodal_rotary_pos_emb, + repeat_kv, + is_flash_attn_greater_or_equal_2_10, + math, + Qwen2VLCausalLMOutputWithPast, + Qwen2VisionTransformerPretrainedModel, +) if is_flash_attn_2_available(): from flash_attn import flash_attn_varlen_func diff --git a/src/train.py b/src/train.py index c5c4368..d40410d 100644 --- a/src/train.py +++ b/src/train.py @@ -72,6 +72,24 @@ if __name__ == "__main__": collate_fn_for_train = partial(collate_fn_for_train, processor=processor) collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor) + if model_args.model_name_or_path == "VITA-MLLM/VITA-1.5": + # from transformers import + # from model_library.qwen2vl import Qwen2VLForConditionalGeneration_modified + + model = Qwen2VLForConditionalGeneration_modified.from_pretrained( + model_args.model_name_or_path, + trust_remote_code=model_args.trust_remote_code, + **model_kwargs, + ) + print(model) + from model_library.qwen2vl import ( + collate_fn_for_train, + collate_fn_for_evaluate, + ) + from functools import partial + + collate_fn_for_train = partial(collate_fn_for_train, processor=processor) + collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor) ################ # Dataset ################ @@ -104,6 +122,7 @@ if __name__ == "__main__": for dataset_name in script_args.dataset_name: dataset = get_dataset(dataset_name) + print(dataset) model.train() trainer = ContinualTrainer( diff --git a/src/train.sh b/src/train.sh index db3fcf0..9169286 100755 --- a/src/train.sh +++ b/src/train.sh @@ -1,7 +1,7 @@ #!/bin/bash accelerate launch --config_file configs/accelerate_configs/deepspeed_zero2.yaml train.py \ - --dataset_name CHEM \ + --dataset_name OCR-VQA-200K \ --use_peft \ --peft_type MMOELORA \ --model_name_or_path Qwen/Qwen2-VL-7B-Instruct \ diff --git a/src/utils/accelerator.py b/src/utils/accelerator.py index 44d6c33..858784d 100644 --- a/src/utils/accelerator.py +++ b/src/utils/accelerator.py @@ -62,15 +62,12 @@ def create_accelerator_and_postprocess(args): "when using FSDP." ) - def propagate_args_to_deepspeed(auto_find_batch_size=False): + if is_deepspeed_enabled and getattr(args, "hf_deepspeed_config", None) is None: from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig ds_plugin = accelerator.state.deepspeed_plugin ds_plugin.hf_ds_config = HfTrainerDeepSpeedConfig(ds_plugin.hf_ds_config.config) ds_plugin.deepspeed_config = ds_plugin.hf_ds_config.config - ds_plugin.hf_ds_config.trainer_config_process(args, auto_find_batch_size) - - if is_deepspeed_enabled and getattr(args, "hf_deepspeed_config", None) is None: - propagate_args_to_deepspeed() + ds_plugin.hf_ds_config.trainer_config_process(args, auto_find_batch_size=False) return accelerator diff --git a/src/utils/trainer.py b/src/utils/trainer.py index 0e86bca..b5d1017 100644 --- a/src/utils/trainer.py +++ b/src/utils/trainer.py @@ -1,6 +1,11 @@ # _________________________________________________________ from transformers import Trainer +from transformers.trainer import ( + Trainer, + _is_peft_model, + MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, +) class ContinualTrainer(Trainer): @@ -13,8 +18,7 @@ class ContinualTrainer(Trainer): def create_accelerator_and_postprocess(self): if self.accelerator is not None: self.is_deepspeed_enabled = ( - getattr(self.accelerator.state, "deepspeed_plugin", None) - is not None + getattr(self.accelerator.state, "deepspeed_plugin", None) is not None ) self.is_fsdp_enabled = ( getattr(self.accelerator.state, "fsdp_plugin", None) is not None @@ -22,4 +26,56 @@ class ContinualTrainer(Trainer): self.gather_function = self.accelerator.gather_for_metrics return else: - super().create_accelerator_and_postprocess() \ No newline at end of file + super().create_accelerator_and_postprocess() + + def compute_loss( + self, model, inputs, return_outputs=False, num_items_in_batch=None + ): + """ + How the loss is computed by Trainer. By default, all models return the loss in the first element. + + Subclass and override for custom behavior. + """ + if ( + self.label_smoother is not None or self.compute_loss_func is not None + ) and "labels" in inputs: + labels = inputs.pop("labels") + else: + labels = None + if self.model_accepts_loss_kwargs: + loss_kwargs = {} + if num_items_in_batch is not None: + loss_kwargs["num_items_in_batch"] = num_items_in_batch + inputs = {**inputs, **loss_kwargs} + outputs = model(**inputs) + # Save past state if it exists + # TODO: this needs to be fixed and made cleaner later. + if self.args.past_index >= 0: + self._past = outputs[self.args.past_index] + print(labels) + + if labels is not None: + unwrapped_model = self.accelerator.unwrap_model(model) + if _is_peft_model(unwrapped_model): + model_name = unwrapped_model.base_model.model._get_name() + else: + model_name = unwrapped_model._get_name() + # User-defined compute_loss function + if self.compute_loss_func is not None: + loss = self.compute_loss_func( + outputs, labels, num_items_in_batch=num_items_in_batch + ) + elif model_name in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values(): + loss = self.label_smoother(outputs, labels, shift_labels=True) + else: + loss = self.label_smoother(outputs, labels) + else: + if isinstance(outputs, dict) and "loss" not in outputs: + raise ValueError( + "The model did not return a loss from the inputs, only the following keys: " + f"{','.join(outputs.keys())}. 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| 856 +++++++++++++++++++++ src/train.py | 67 +- src/train.sh | 7 +- src/utils/trainer.py | 1 - uv.lock | 376 +++++++-- 11 files changed, 1501 insertions(+), 111 deletions(-) create mode 100644 src/dataset_library/GigaspeechDataset.py create mode 100644 src/model_library/factory.py create mode 100644 src/model_library/qwen2audio/__init__.py create mode 100644 src/model_library/qwen2audio/collate_fn.py create mode 100644 src/model_library/qwen2audio/model.py diff --git a/pyproject.toml b/pyproject.toml index cf02630..9751420 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,11 +4,14 @@ dependencies = [ "datasets==3.2.0", "deepspeed==0.16.2", "evaluate==0.4.3", + "librosa>=0.10.2.post1", "markupsafe==2.1.5", + "numba>=0.60.0", "peft==0.14.0", "pip==24.3.1", "requests==2.32.3", "setuptools>=70.0.0", + "soundfile>=0.13.0", "torch==2.5.1+cu124", "torchaudio==2.5.1+cu124", "torchvision==0.20.1+cu124", diff --git a/src/dataset_library/GigaspeechDataset.py b/src/dataset_library/GigaspeechDataset.py new file mode 100644 index 0000000..e03be1b --- /dev/null +++ b/src/dataset_library/GigaspeechDataset.py @@ -0,0 +1,97 @@ +from PIL import Image +from torch.utils.data import Dataset +import json +import os +from datasets import load_dataset + + +class GigaspeechDataset(Dataset): + def __init__(self, audio_processor=None, text_processor=None, split="train"): + """ + vis_root (string): Root directory of images (e.g. coco/images/) + ann_root (string): directory to store the annotation file + """ + + self.audio_processor = audio_processor + self.text_processor = text_processor + gs = load_dataset("speechcolab/gigaspeech", "xs") + self.data = gs[split] + + def __len__(self): + return len(self.data) + + def __getitem__(self, index): + sample = self.data[index] + audio = sample["audio"]["array"] + sampling_rate = sample["audio"]["sampling_rate"] + text = sample["text"] + + if self.audio_processor is not None: + audio = self.audio_processor(audio) + if self.text_processor is not None: + text = self.text_processor(text) + + chat = [ + { + "role": "user", + "content": [ + {"type": "audio", "audio_url": ""}, + { + "type": "text", + "text": "Please convert the audio to text", + }, + ], + }, + {"role": "assistant", "content": [{"type": "text", "text": text}]}, + ] + return { + "audio": (audio, sampling_rate), + "chat": chat, + } + + +class GigaspeechDatasetForGeneration(GigaspeechDataset): + + def __getitem__(self, index): + sample = self.data[index] + audio = sample["audio"]["array"] + sampling_rate = sample["audio"]["sampling_rate"] + text = sample["text"] + + if self.audio_processor is not None: + audio = self.audio_processor(audio) + if self.text_processor is not None: + text = self.text_processor(text) + + chat = [ + { + "role": "user", + "content": [ + {"type": "audio", "audio_url": ""}, + { + "type": "text", + "text": "Please convert the audio to text", + }, + ], + }, + ] + + return { + "audio": (audio, sampling_rate), + "chat": chat, + "answer": text, + } + + +if __name__ == "__main__": + dataset = GigaspeechDataset( + split="train", + ) + print(len(dataset)) + print(dataset[0]) + dataset = GigaspeechDatasetForGeneration( + split="train", + ) + print(len(dataset)) + print(dataset[0]) + pass diff --git a/src/dataset_library/factory.py b/src/dataset_library/factory.py index 6a4767a..21b30e2 100644 --- a/src/dataset_library/factory.py +++ b/src/dataset_library/factory.py @@ -48,4 +48,13 @@ def get_dataset( split="test", ), } + + if dataset_name == "gigaspeech": + from .GigaspeechDataset import GigaspeechDataset, GigaspeechDatasetForGeneration + + dataset = { + "train": GigaspeechDataset(split="train"), + "test": GigaspeechDataset(split="test"), + "generation": GigaspeechDatasetForGeneration(split="test"), + } return dataset diff --git a/src/model_library/factory.py b/src/model_library/factory.py new file mode 100644 index 0000000..8aee3df --- /dev/null +++ b/src/model_library/factory.py @@ -0,0 +1,105 @@ +import torch +from trl import ( + get_kbit_device_map, + # get_peft_config, + get_quantization_config, +) + + +def get_model(model_args): + if model_args.model_name_or_path == "Qwen/Qwen2-VL-7B-Instruct": + torch_dtype = ( + model_args.torch_dtype + if model_args.torch_dtype in ["auto", None] + else getattr(torch, model_args.torch_dtype) + ) + quantization_config = get_quantization_config(model_args) + model_kwargs = dict( + revision=model_args.model_revision, + attn_implementation=model_args.attn_implementation, + torch_dtype=torch_dtype, + device_map=( + get_kbit_device_map() if quantization_config is not None else None + ), + quantization_config=quantization_config, + ) + from transformers import Qwen2VLProcessor + from model_library.qwen2vl import Qwen2VLForConditionalGeneration_modified + + model = Qwen2VLForConditionalGeneration_modified.from_pretrained( + model_args.model_name_or_path, + trust_remote_code=model_args.trust_remote_code, + **model_kwargs, + ) + processor = Qwen2VLProcessor.from_pretrained( + model_args.model_name_or_path, + trust_remote_code=model_args.trust_remote_code, + padding_side="left", + ) + print(model) + from model_library.qwen2vl import ( + collate_fn_for_train, + collate_fn_for_evaluate, + ) + from functools import partial + + collate_fn_for_train = partial(collate_fn_for_train, processor=processor) + collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor) + + if model_args.model_name_or_path == "Qwen/Qwen2-Audio-7B-Instruct": + torch_dtype = ( + model_args.torch_dtype + if model_args.torch_dtype in ["auto", None] + else getattr(torch, model_args.torch_dtype) + ) + quantization_config = get_quantization_config(model_args) + model_kwargs = dict( + revision=model_args.model_revision, + attn_implementation=model_args.attn_implementation, + torch_dtype=torch_dtype, + device_map=( + get_kbit_device_map() if quantization_config is not None else None + ), + quantization_config=quantization_config, + ) + from transformers import Qwen2AudioProcessor, Qwen2AudioForConditionalGeneration + + model = Qwen2AudioForConditionalGeneration.from_pretrained( + model_args.model_name_or_path, + trust_remote_code=model_args.trust_remote_code, + **model_kwargs, + ) + processor = Qwen2AudioProcessor.from_pretrained( + model_args.model_name_or_path, + trust_remote_code=model_args.trust_remote_code, + padding_side="left", + ) + print(model) + from model_library.qwen2audio import ( + collate_fn_for_train, + collate_fn_for_evaluate, + ) + from functools import partial + + collate_fn_for_train = partial(collate_fn_for_train, processor=processor) + collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor) + + if model_args.model_name_or_path == "VITA-MLLM/VITA-1.5": + # from transformers import + # from model_library.qwen2vl import Qwen2VLForConditionalGeneration_modified + + model = Qwen2VLForConditionalGeneration_modified.from_pretrained( + model_args.model_name_or_path, + trust_remote_code=model_args.trust_remote_code, + **model_kwargs, + ) + print(model) + from model_library.qwen2vl import ( + collate_fn_for_train, + collate_fn_for_evaluate, + ) + from functools import partial + + collate_fn_for_train = partial(collate_fn_for_train, processor=processor) + collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor) + return model, processor, collate_fn_for_train, collate_fn_for_evaluate diff --git a/src/model_library/qwen2audio/__init__.py b/src/model_library/qwen2audio/__init__.py new file mode 100644 index 0000000..3d2bfcb --- /dev/null +++ b/src/model_library/qwen2audio/__init__.py @@ -0,0 +1,4 @@ +from .collate_fn import collate_fn_for_evaluate, collate_fn_for_train +from .model import Qwen2VLForConditionalGeneration_modified + +__all__ = ["collate_fn_for_train", "collate_fn_for_evaluate", "Qwen2VLForConditionalGeneration_modified"] \ No newline at end of file diff --git a/src/model_library/qwen2audio/collate_fn.py b/src/model_library/qwen2audio/collate_fn.py new file mode 100644 index 0000000..b0cb927 --- /dev/null +++ b/src/model_library/qwen2audio/collate_fn.py @@ -0,0 +1,87 @@ +from transformers import Qwen2AudioProcessor + + +def collate_fn_for_train(examples, processor: Qwen2AudioProcessor): + # Get the texts and images, and apply the chat template + texts = [ + processor.apply_chat_template(example["chat"], tokenize=False) + for example in examples + ] + audios = [example["audio"][0] for example in examples] + # Tokenize the texts and process the images + batch = processor( + text=texts, + audios=audios, + return_tensors="pt", + padding=True, + sampling_rate=examples[0]["audio"][1], + ) + + # The labels are the input_ids, and we mask the padding tokens in the loss computation + labels = batch["input_ids"].clone() + labels[labels == processor.tokenizer.pad_token_id] = -100 # + # Ignore the image token index in the loss computation (model specific) + # 对<|im_start|>system *** <|im_end|>\n加掩码 + im_start_token_id = processor.tokenizer.convert_tokens_to_ids("<|im_start|>") + im_end_token_id = processor.tokenizer.convert_tokens_to_ids("<|im_end|>") + system_token_id = processor.tokenizer.convert_tokens_to_ids("system") + user_token_id = processor.tokenizer.convert_tokens_to_ids("user") + assistant_token_id = processor.tokenizer.convert_tokens_to_ids("assistant") + # enter_token_id = processor.tokenizer.convert_tokens_to_ids("\n") + # print(im_start_token_id, im_end_token_id, system_token_id, user_token_id, assistant_token_id, enter_token_id, processor.tokenizer.pad_token_id) + # 151644 151645 8948 872 77091 None 151643 + + for i, label in enumerate(labels): + now_index = 0 + while now_index < len(label): + if label[now_index] == im_start_token_id: + label[now_index] = -100 + now_index += 1 + if ( + label[now_index] == system_token_id + or label[now_index] == user_token_id + ): + while label[now_index] != im_end_token_id: + label[now_index] = -100 + now_index += 1 + label[now_index] = -100 + elif label[now_index] == assistant_token_id: + label[now_index] = -100 + label[now_index + 1] = -100 + now_index += 2 + while ( + now_index < len(label) and label[now_index] != im_end_token_id + ): + now_index += 1 + now_index += 1 + batch["labels"] = labels + # batch["task_id"] = torch.tensor([0] * len(labels), dtype=torch.long) + + return batch + + +def collate_fn_for_evaluate(examples, processor: Qwen2AudioProcessor): + # Get the texts and images, and apply the chat template + texts = [ + processor.apply_chat_template( + example["chat"], tokenize=False, add_generation_prompt=True + ) + for example in examples + ] + # print(texts) + audios = [example["audio"] for example in examples] + + # Tokenize the texts and process the images + batch = processor(text=texts, audios=audios, return_tensors="pt", padding=True) + + answers = [example["answer"] for example in examples] + answers = processor(text=answers, return_tensors="pt", padding=True) + batch["answers_ids"] = answers["input_ids"] + batch["answers_mask"] = answers["attention_mask"] + # input_ids torch.Size([3, 370]) + # attention_mask torch.Size([3, 370]) + # pixel_values torch.Size([3888, 1176]) + # image_grid_thw torch.Size([3, 3]) + # answers_ids torch.Size([3, 10]) + # answers_mask torch.Size([3, 10]) + return batch diff --git a/src/model_library/qwen2audio/model.py b/src/model_library/qwen2audio/model.py new file mode 100644 index 0000000..27d5be8 --- /dev/null +++ b/src/model_library/qwen2audio/model.py @@ -0,0 +1,856 @@ +# from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLCausalLMOutputWithPast, Qwen2VLModel, Qwen2VLForConditionalGeneration, logger, DynamicCache, Qwen2VLDecoderLayer, Qwen2VLConfig, Qwen2VLAttention, +from transformers.models.qwen2_vl.modeling_qwen2_vl import ( + is_flash_attn_2_available, + Qwen2VLAttention, + Qwen2VLFlashAttention2, + Qwen2VLDecoderLayer, + Qwen2VLModel, + Qwen2VLForConditionalGeneration, + Qwen2VLConfig, + logger, + apply_rotary_pos_emb_vision, + Cache, + apply_multimodal_rotary_pos_emb, + repeat_kv, + is_flash_attn_greater_or_equal_2_10, + math, + Qwen2VLCausalLMOutputWithPast, + Qwen2VisionTransformerPretrainedModel, +) + +if is_flash_attn_2_available(): + from flash_attn import flash_attn_varlen_func + + from transformers.modeling_flash_attention_utils import _flash_attention_forward +else: + flash_attn_varlen_func = None +from transformers.cache_utils import DynamicCache +from transformers.modeling_outputs import BaseModelOutputWithPast +import torch +from typing import Optional, List, Union, Tuple +from torch.nn import CrossEntropyLoss + +from torch import nn +from torch.nn import functional as F +from torch import Tensor + + +class LinearLayer(nn.Linear): + def forward(self, input: Tensor, **kwargs) -> Tensor: + return F.linear(input, self.weight, self.bias) + + +class Qwen2VLAttention_modified(Qwen2VLAttention): + def __init__(self, config: Qwen2VLConfig, layer_idx: Optional[int] = None): + super().__init__(config, layer_idx) + self.q_proj = LinearLayer( + self.hidden_size, self.num_heads * self.head_dim, bias=True + ) + self.k_proj = LinearLayer( + self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True + ) + self.v_proj = LinearLayer( + self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True + ) + self.o_proj = LinearLayer( + self.num_heads * self.head_dim, self.hidden_size, bias=False + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[ + Tuple[torch.Tensor, torch.Tensor] + ] = None, # will become mandatory in v4.46 + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view( + bsz, q_len, self.num_heads, self.head_dim + ).transpose(1, 2) + key_states = key_states.view( + bsz, q_len, self.num_key_value_heads, self.head_dim + ).transpose(1, 2) + value_states = value_states.view( + bsz, q_len, self.num_key_value_heads, self.head_dim + ).transpose(1, 2) + + if position_embeddings is None: + logger.warning_once( + "The attention layers in this model are transitioning from computing the RoPE embeddings internally " + "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " + "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " + "removed and `position_embeddings` will be mandatory." + ) + cos, sin = self.rotary_emb(value_states, position_ids) + else: + cos, sin = position_embeddings + query_states, key_states = apply_multimodal_rotary_pos_emb( + query_states, key_states, cos, sin, self.rope_scaling["mrope_section"] + ) + + if past_key_value is not None: + cache_kwargs = { + "sin": sin, + "cos": cos, + "cache_position": cache_position, + } # Specific to RoPE models + key_states, value_states = past_key_value.update( + key_states, value_states, self.layer_idx, cache_kwargs + ) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul( + query_states, key_states.transpose(2, 3) + ) / math.sqrt(self.head_dim) + + if attention_mask is not None: # no matter the length, we just slice it + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights = attn_weights + causal_mask + + # Fix precision issues in Qwen2-VL float16 inference + # Replace inf values with zeros in attention weights to prevent NaN propagation + if query_states.dtype == torch.float16: + attn_weights = torch.where( + torch.isinf(attn_weights), torch.zeros_like(attn_weights), attn_weights + ) + + # upcast attention to fp32 + attn_weights = nn.functional.softmax( + attn_weights, dim=-1, dtype=torch.float32 + ).to(query_states.dtype) + attn_weights = nn.functional.dropout( + attn_weights, p=self.attention_dropout, training=self.training + ) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, -1) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class Qwen2VLSdpaAttention_modified(Qwen2VLAttention_modified): + """ + Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from Qwen2Attention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[ + Tuple[torch.Tensor, torch.Tensor] + ] = None, # will become mandatory in v4.46 + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "Qwen2VLModel is using Qwen2VLSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + **kwargs, + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states, **kwargs) + key_states = self.k_proj(hidden_states, **kwargs) + value_states = self.v_proj(hidden_states, **kwargs) + + query_states = query_states.view( + bsz, q_len, self.num_heads, self.head_dim + ).transpose(1, 2) + key_states = key_states.view( + bsz, q_len, self.num_key_value_heads, self.head_dim + ).transpose(1, 2) + value_states = value_states.view( + bsz, q_len, self.num_key_value_heads, self.head_dim + ).transpose(1, 2) + + if position_embeddings is None: + logger.warning_once( + "The attention layers in this model are transitioning from computing the RoPE embeddings internally " + "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " + "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " + "removed and `position_embeddings` will be mandatory." + ) + cos, sin = self.rotary_emb(value_states, position_ids) + else: + cos, sin = position_embeddings + query_states, key_states = apply_multimodal_rotary_pos_emb( + query_states, key_states, cos, sin, self.rope_scaling["mrope_section"] + ) + + if past_key_value is not None: + cache_kwargs = { + "sin": sin, + "cos": cos, + "cache_position": cache_position, + } # Specific to RoPE models + key_states, value_states = past_key_value.update( + key_states, value_states, self.layer_idx, cache_kwargs + ) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + causal_mask = attention_mask + if attention_mask is not None: # no matter the length, we just slice it + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and attention_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment + # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. + # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. + is_causal = True if causal_mask is None and q_len > 1 else False + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=causal_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + is_causal=is_causal, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.view(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +class Qwen2VLFlashAttention2_modified(Qwen2VLAttention_modified): + """ + Qwen2VL flash attention module, following Qwen2VL attention module. This module inherits from `Qwen2VLAttention` + as the weights of the module stays untouched. The only required change would be on the forward pass + where it needs to correctly call the public API of flash attention and deal with padding tokens + in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom + config.max_window_layers layers. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[ + Tuple[torch.Tensor, torch.Tensor] + ] = None, # will become mandatory in v4.46 + **kwargs, + ): + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states, **kwargs) + key_states = self.k_proj(hidden_states, **kwargs) + value_states = self.v_proj(hidden_states, **kwargs) + + query_states = query_states.view( + bsz, q_len, self.num_heads, self.head_dim + ).transpose(1, 2) + key_states = key_states.view( + bsz, q_len, self.num_key_value_heads, self.head_dim + ).transpose(1, 2) + value_states = value_states.view( + bsz, q_len, self.num_key_value_heads, self.head_dim + ).transpose(1, 2) + + # Because the input can be padded, the absolute sequence length depends on the max position id. + if position_embeddings is None: + logger.warning_once( + "The attention layers in this model are transitioning from computing the RoPE embeddings internally " + "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " + "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " + "removed and `position_embeddings` will be mandatory." + ) + cos, sin = self.rotary_emb(value_states, position_ids) + else: + cos, sin = position_embeddings + + query_states, key_states = apply_multimodal_rotary_pos_emb( + query_states, key_states, cos, sin, self.rope_scaling["mrope_section"] + ) + + if past_key_value is not None: + cache_kwargs = { + "sin": sin, + "cos": cos, + "cache_position": cache_position, + } # Specific to RoPE models + key_states, value_states = past_key_value.update( + key_states, value_states, self.layer_idx, cache_kwargs + ) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + dropout_rate = 0.0 if not self.training else self.attention_dropout + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in float16 just to be sure everything works as expected. + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + # Reashape to the expected shape for Flash Attention + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + if ( + self.config.use_sliding_window + and getattr(self.config, "sliding_window", None) is not None + and self.layer_idx >= self.config.max_window_layers + ): + sliding_window = self.config.sliding_window + else: + sliding_window = None + + attn_output = _flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + sliding_window=sliding_window, + is_causal=self.is_causal, + use_top_left_mask=self._flash_attn_uses_top_left_mask, + ) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +QWEN2_VL_ATTENTION_CLASSES = { + "eager": Qwen2VLAttention, + "flash_attention_2": Qwen2VLFlashAttention2_modified, + "sdpa": Qwen2VLSdpaAttention_modified, +} + + +class Qwen2VLDecoderLayer_modified(Qwen2VLDecoderLayer): + def __init__(self, config: Qwen2VLConfig, layer_idx: int): + super().__init__(config, layer_idx) + self.self_attn = QWEN2_VL_ATTENTION_CLASSES[config._attn_implementation]( + config, layer_idx + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[ + Tuple[torch.Tensor, torch.Tensor] + ] = None, # will become mandatory in v4.46 + **kwargs, + ) -> Tuple[ + torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] + ]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, sequence_length)` where padding elements are indicated by 0. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. + position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*): + Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`, + with `head_dim` being the embedding dimension of each attention head. + kwargs (`dict`, *optional*): + Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code + into the model + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +class Qwen2VLModel_modified(Qwen2VLModel): + def __init__(self, config): + super().__init__(config) + self.layers = nn.ModuleList( + [ + Qwen2VLDecoderLayer_modified(config, layer_idx) + for layer_idx in range(config.num_hidden_layers) + ] + ) + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError( + "You must specify exactly one of input_ids or inputs_embeds" + ) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # torch.jit.trace() doesn't support cache objects in the output + if use_cache and past_key_values is None and not torch.jit.is_tracing(): + past_key_values = DynamicCache() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if cache_position is None: + past_seen_tokens = ( + past_key_values.get_seq_length() if past_key_values is not None else 0 + ) + cache_position = torch.arange( + past_seen_tokens, + past_seen_tokens + inputs_embeds.shape[1], + device=inputs_embeds.device, + ) + + # the hard coded `3` is for temporal, height and width. + if position_ids is None: + position_ids = cache_position.view(1, 1, -1).expand( + 3, inputs_embeds.shape[0], -1 + ) + elif position_ids.dim() == 2: + position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1) + + causal_mask = self._update_causal_mask( + attention_mask, + inputs_embeds, + cache_position, + past_key_values, + output_attentions, + ) + + hidden_states = inputs_embeds + + # create position embeddings to be shared across the decoder layers + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + causal_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + cache_position, + position_embeddings, + **kwargs, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + + if not return_dict: + return tuple( + v + for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] + if v is not None + ) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class Qwen2VLForConditionalGeneration_modified(Qwen2VLForConditionalGeneration): + def __init__(self, config): + super().__init__(config) + self.visual = Qwen2VisionTransformerPretrainedModel._from_config( + config.vision_config + ) + self.model = Qwen2VLModel_modified(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.rope_deltas = None # cache rope_deltas here + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + pixel_values: Optional[torch.Tensor] = None, + pixel_values_videos: Optional[torch.FloatTensor] = None, + image_grid_thw: Optional[torch.LongTensor] = None, + video_grid_thw: Optional[torch.LongTensor] = None, + rope_deltas: Optional[torch.LongTensor] = None, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> Union[Tuple, Qwen2VLCausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import AutoProcessor, Qwen2VLForConditionalGeneration + + >>> model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct") + >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct") + + >>> messages = [ + { + "role": "user", + "content": [ + {"type": "image"}, + {"type": "text", "text": "What is shown in this image?"}, + ], + }, + ] + >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos]) + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..." + ```""" + + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if inputs_embeds is None: + inputs_embeds = self.model.embed_tokens(input_ids) + if pixel_values is not None: + pixel_values = pixel_values.type(self.visual.get_dtype()) + image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw) + n_image_tokens = (input_ids == self.config.image_token_id).sum().item() + n_image_features = image_embeds.shape[0] + if n_image_tokens != n_image_features: + raise ValueError( + f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}" + ) + image_mask = ( + (input_ids == self.config.image_token_id) + .unsqueeze(-1) + .expand_as(inputs_embeds) + .to(inputs_embeds.device) + ) + image_embeds = image_embeds.to( + inputs_embeds.device, inputs_embeds.dtype + ) + inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) + + if pixel_values_videos is not None: + pixel_values_videos = pixel_values_videos.type(self.visual.get_dtype()) + video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw) + n_video_tokens = (input_ids == self.config.video_token_id).sum().item() + n_video_features = video_embeds.shape[0] + if n_video_tokens != n_video_features: + raise ValueError( + f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}" + ) + video_mask = ( + (input_ids == self.config.video_token_id) + .unsqueeze(-1) + .expand_as(inputs_embeds) + .to(inputs_embeds.device) + ) + video_embeds = video_embeds.to( + inputs_embeds.device, inputs_embeds.dtype + ) + inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) + + if attention_mask is not None: + attention_mask = attention_mask.to(inputs_embeds.device) + + # if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme + if ( + position_ids is None + and input_ids is not None + and (attention_mask is None or attention_mask.ndim == 2) + ): + # calculate RoPE index once per generation in the pre-fill stage only + if ( + cache_position is not None and cache_position[0] == 0 + ) or self.rope_deltas is None: + position_ids, rope_deltas = self.get_rope_index( + input_ids, image_grid_thw, video_grid_thw, attention_mask + ) + self.rope_deltas = rope_deltas + # then use the prev pre-calculated rope-deltas to get the correct position ids + else: + batch_size, seq_length, _ = inputs_embeds.shape + delta = ( + cache_position[0] + self.rope_deltas + if cache_position is not None + else 0 + ) + position_ids = torch.arange(seq_length, device=inputs_embeds.device) + position_ids = position_ids.view(1, -1).expand(batch_size, -1) + if cache_position is not None: # otherwise `deltas` is an int `0` + delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0) + position_ids = position_ids.add(delta) + position_ids = position_ids.unsqueeze(0).expand(3, -1, -1) + + outputs = self.model( + input_ids=None, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + cache_position=cache_position, + **kwargs, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # Upcast to float if we need to compute the loss to avoid potential precision issues + logits = logits.float() + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return Qwen2VLCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=self.rope_deltas, + ) diff --git a/src/train.py b/src/train.py index d40410d..da5d265 100644 --- a/src/train.py +++ b/src/train.py @@ -1,16 +1,10 @@ -import torch from dataset_library.factory import get_dataset from transformers import ( - AutoModelForVision2Seq, - AutoProcessor, TrainingArguments, ) from trl import ( TrlParser, - get_kbit_device_map, - # get_peft_config, - get_quantization_config, ) from peft_library import get_peft_model @@ -32,64 +26,12 @@ if __name__ == "__main__": training_args.remove_unused_columns = False training_args.dataset_kwargs = {"skip_prepare_dataset": True} - # peft_config = get_peft_config(dict(**vars(model_args))) + from model_library.factory import get_model - torch_dtype = ( - model_args.torch_dtype - if model_args.torch_dtype in ["auto", None] - else getattr(torch, model_args.torch_dtype) - ) - quantization_config = get_quantization_config(model_args) - model_kwargs = dict( - revision=model_args.model_revision, - attn_implementation=model_args.attn_implementation, - torch_dtype=torch_dtype, - device_map=get_kbit_device_map() if quantization_config is not None else None, - quantization_config=quantization_config, - ) - processor = AutoProcessor.from_pretrained( - model_args.model_name_or_path, - trust_remote_code=model_args.trust_remote_code, - padding_side="left", + model, processor, collate_fn_for_train, collate_fn_for_evaluate = get_model( + model_args ) - if model_args.model_name_or_path == "Qwen/Qwen2-VL-7B-Instruct": - # from transformers import Qwen2VLForConditionalGeneration - from model_library.qwen2vl import Qwen2VLForConditionalGeneration_modified - - model = Qwen2VLForConditionalGeneration_modified.from_pretrained( - model_args.model_name_or_path, - trust_remote_code=model_args.trust_remote_code, - **model_kwargs, - ) - print(model) - from model_library.qwen2vl import ( - collate_fn_for_train, - collate_fn_for_evaluate, - ) - from functools import partial - - collate_fn_for_train = partial(collate_fn_for_train, processor=processor) - collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor) - - if model_args.model_name_or_path == "VITA-MLLM/VITA-1.5": - # from transformers import - # from model_library.qwen2vl import Qwen2VLForConditionalGeneration_modified - - model = Qwen2VLForConditionalGeneration_modified.from_pretrained( - model_args.model_name_or_path, - trust_remote_code=model_args.trust_remote_code, - **model_kwargs, - ) - print(model) - from model_library.qwen2vl import ( - collate_fn_for_train, - collate_fn_for_evaluate, - ) - from functools import partial - - collate_fn_for_train = partial(collate_fn_for_train, processor=processor) - collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor) ################ # Dataset ################ @@ -110,7 +52,7 @@ if __name__ == "__main__": elif model_args.peft_type == "LORA": from peft.tuners.lora import LoraConfig - peft_config = LoraConfig(target_modules=model_args.lora_target_modules) + peft_config = LoraConfig(target_modules=model_args.lora_target_modules, r=2) model = get_peft_model(model, peft_config) @@ -122,7 +64,6 @@ if __name__ == "__main__": for dataset_name in script_args.dataset_name: dataset = get_dataset(dataset_name) - print(dataset) model.train() trainer = ContinualTrainer( diff --git a/src/train.sh b/src/train.sh index 9169286..7374f79 100755 --- a/src/train.sh +++ b/src/train.sh @@ -1,14 +1,13 @@ #!/bin/bash accelerate launch --config_file configs/accelerate_configs/deepspeed_zero2.yaml train.py \ - --dataset_name OCR-VQA-200K \ + --dataset_name gigaspeech \ --use_peft \ - --peft_type MMOELORA \ - --model_name_or_path Qwen/Qwen2-VL-7B-Instruct \ + --peft_type LORA \ + --model_name_or_path Qwen/Qwen2-Audio-7B-Instruct \ --lora_target_modules q_proj v_proj \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 2 \ - --attn_implementation flash_attention_2 \ --gradient_accumulation_steps 16 \ --output_dir checkpoint/sft-llava-1.5-7b-hf \ --bf16 \ diff --git a/src/utils/trainer.py b/src/utils/trainer.py index b5d1017..0152005 100644 --- a/src/utils/trainer.py +++ b/src/utils/trainer.py @@ -52,7 +52,6 @@ class ContinualTrainer(Trainer): # TODO: this needs to be fixed and made cleaner later. if self.args.past_index >= 0: self._past = outputs[self.args.past_index] - print(labels) if labels is not None: unwrapped_model = self.accelerator.unwrap_model(model) diff --git a/uv.lock b/uv.lock index 94a35dd..7a78591 100644 --- a/uv.lock +++ b/uv.lock @@ -124,6 +124,15 @@ wheels = [ { url = 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