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Commit d5878167 authored by mashun1's avatar mashun1
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llava-next

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llava/model/__init__.py 0 → 100644
View file @ d5878167
import os
AVAILABLE_MODELS = {
"llava_llama": "LlavaLlamaForCausalLM, LlavaConfig",
"llava_qwen": "LlavaQwenForCausalLM, LlavaQwenConfig",
"llava_mistral": "LlavaMistralForCausalLM, LlavaMistralConfig",
"llava_mixtral": "LlavaMixtralForCausalLM, LlavaMixtralConfig",
# "llava_qwen_moe": "LlavaQwenMoeForCausalLM, LlavaQwenMoeConfig",
# Add other models as needed
}
for model_name, model_classes in AVAILABLE_MODELS.items():
try:
exec(f"from .language_model.{model_name} import {model_classes}")
except Exception as e:
print(f"Failed to import {model_name} from llava.language_model.{model_name}. Error: {e}")
llava/model/apply_delta.py 0 → 100644
View file @ d5878167
"""
Usage:
python3 -m fastchat.model.apply_delta --base ~/model_weights/llama-7b --target ~/model_weights/vicuna-7b --delta lmsys/vicuna-7b-delta
"""
import argparse
import torch
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
from llava import LlavaLlamaForCausalLM
def apply_delta(base_model_path, target_model_path, delta_path):
print("Loading base model")
base = AutoModelForCausalLM.from_pretrained(base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
print("Loading delta")
delta = LlavaLlamaForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
delta_tokenizer = AutoTokenizer.from_pretrained(delta_path)
print("Applying delta")
for name, param in tqdm(delta.state_dict().items(), desc="Applying delta"):
if name not in base.state_dict():
assert name in ["model.mm_projector.weight", "model.mm_projector.bias"], f"{name} not in base model"
continue
if param.data.shape == base.state_dict()[name].shape:
param.data += base.state_dict()[name]
else:
assert name in ["model.embed_tokens.weight", "lm_head.weight"], f"{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}"
bparam = base.state_dict()[name]
param.data[: bparam.shape[0], : bparam.shape[1]] += bparam
print("Saving target model")
delta.save_pretrained(target_model_path)
delta_tokenizer.save_pretrained(target_model_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--base-model-path", type=str, required=True)
parser.add_argument("--target-model-path", type=str, required=True)
parser.add_argument("--delta-path", type=str, required=True)
args = parser.parse_args()
apply_delta(args.base_model_path, args.target_model_path, args.delta_path)
llava/model/builder.py 0 → 100644
View file @ d5878167
# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import warnings
import shutil
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
import torch
from llava.model import *
from llava.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.utils import rank0_print
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", torch_dtype="float16",attn_implementation="flash_attention_2", customized_config=None, overwrite_config=None, **kwargs):
kwargs["device_map"] = device_map
if load_8bit:
kwargs["load_in_8bit"] = True
elif load_4bit:
kwargs["load_in_4bit"] = True
kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4")
elif torch_dtype == "float16":
kwargs["torch_dtype"] = torch.float16
elif torch_dtype == "bfloat16":
kwargs["torch_dtype"] = torch.bfloat16
else:
import pdb;pdb.set_trace()
if customized_config is not None:
kwargs["config"] = customized_config
if "multimodal" in kwargs:
if kwargs["multimodal"] is True:
is_multimodal = True
kwargs.pop("multimodal")
else:
is_multimodal = False
if "llava" in model_name.lower() or is_multimodal:
# Load LLaVA model
if "lora" in model_name.lower() and model_base is None:
warnings.warn(
"There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged."
)
if "lora" in model_name.lower() and model_base is not None:
lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
rank0_print("Loading LLaVA from base model...")
if "mixtral" in model_name.lower():
from llava.model.language_model.llava_mixtral import LlavaMixtralConfig
lora_cfg_pretrained = LlavaMixtralConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = LlavaMixtralForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
elif "mistral" in model_name.lower():
from llava.model.language_model.llava_mistral import LlavaMistralConfig
lora_cfg_pretrained = LlavaMistralConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = LlavaMistralForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
elif "gemma" in model_name.lower():
from llava.model.language_model.llava_gemma import LlavaGemmaConfig
lora_cfg_pretrained = LlavaGemmaConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = LlavaGemmaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
else:
from llava.model.language_model.llava_llama import LlavaConfig
lora_cfg_pretrained = LlavaConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
if model.lm_head.weight.shape[0] != token_num:
model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
rank0_print("Loading additional LLaVA weights...")
if os.path.exists(os.path.join(model_path, "non_lora_trainables.bin")):
non_lora_trainables = torch.load(os.path.join(model_path, "non_lora_trainables.bin"), map_location="cpu")
else:
# this is probably from HF Hub
from huggingface_hub import hf_hub_download
def load_from_hf(repo_id, filename, subfolder=None):
cache_file = hf_hub_download(repo_id=repo_id, filename=filename, subfolder=subfolder)
return torch.load(cache_file, map_location="cpu")
non_lora_trainables = load_from_hf(model_path, "non_lora_trainables.bin")
non_lora_trainables = {(k[11:] if k.startswith("base_model.") else k): v for k, v in non_lora_trainables.items()}
if any(k.startswith("model.model.") for k in non_lora_trainables):
non_lora_trainables = {(k[6:] if k.startswith("model.") else k): v for k, v in non_lora_trainables.items()}
model.load_state_dict(non_lora_trainables, strict=False)
from peft import PeftModel
rank0_print("Loading LoRA weights...")
model = PeftModel.from_pretrained(model, model_path)
rank0_print("Merging LoRA weights...")
model = model.merge_and_unload()
rank0_print("Model is loaded...")
elif model_base is not None: # this may be mm projector only, loading projector with preset language mdoel
rank0_print(f"Loading LLaVA from base model {model_base}...")
if "mixtral" in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = LlavaMixtralForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
elif "mistral" in model_name.lower() or "zephyr" in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = LlavaMistralForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
elif "gemma" in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = LlavaGemmaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
elif (
"wizardlm-2" in model_name.lower()
and "vicuna" in model_name.lower()
or "llama" in model_name.lower()
or "yi" in model_name.lower()
or "nous-hermes" in model_name.lower()
or "llava-v1.6-34b" in model_name.lower()
or "llava-v1.5" in model_name.lower()
):
from llava.model.language_model.llava_llama import LlavaConfig
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
if customized_config is None:
llava_cfg = LlavaConfig.from_pretrained(model_path)
if "v1.5" in model_name.lower():
llava_cfg.delay_load = True # a workaround for correctly loading v1.5 models
else:
llava_cfg = customized_config
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
llava_cfg = LlavaConfig.from_pretrained(model_path)
model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=llava_cfg, **kwargs)
else:
raise ValueError(f"Model {model_name} not supported")
mm_projector_weights = torch.load(os.path.join(model_path, "mm_projector.bin"), map_location="cpu")
mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
model.load_state_dict(mm_projector_weights, strict=False)
else:
rank0_print(f"Loaded LLaVA model: {model_path}")
if "mixtral" in model_name.lower():
from llava.model.language_model.llava_mixtral import LlavaMixtralConfig
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
if customized_config is None:
llava_cfg = LlavaMixtralConfig.from_pretrained(model_path)
else:
llava_cfg = customized_config
if overwrite_config is not None:
rank0_print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(llava_cfg, k, v)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = LlavaMixtralForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
elif "mistral" in model_name.lower() or "zephyr" in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = LlavaMistralForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, **kwargs)
elif (
"wizardlm-2" in model_name.lower()
and "vicuna" in model_name.lower()
or "llama" in model_name.lower()
or "yi" in model_name.lower()
or "nous-hermes" in model_name.lower()
or "llava-v1.6-34b" in model_name.lower()
or "llava-v1.5" in model_name.lower()
):
from llava.model.language_model.llava_llama import LlavaConfig
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
if customized_config is None:
llava_cfg = LlavaConfig.from_pretrained(model_path)
if "v1.5" in model_name.lower():
llava_cfg.delay_load = True # a workaround for correctly loading v1.5 models
else:
llava_cfg = customized_config
if overwrite_config is not None:
rank0_print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(llava_cfg, k, v)
model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
elif "qwen" in model_name.lower() or "quyen" in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path)
if "moe" in model_name.lower() or "A14B" in model_name.lower():
from llava.model.language_model.llava_qwen_moe import LlavaQwenMoeConfig
if overwrite_config is not None:
llava_cfg = LlavaQwenMoeConfig.from_pretrained(model_path)
rank0_print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(llava_cfg, k, v)
model = LlavaQwenMoeForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
else:
model = LlavaQwenMoeForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, **kwargs)
else:
from llava.model.language_model.llava_qwen import LlavaQwenConfig
if overwrite_config is not None:
llava_cfg = LlavaQwenConfig.from_pretrained(model_path)
rank0_print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(llava_cfg, k, v)
model = LlavaQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
else:
model = LlavaQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, **kwargs)
elif "gemma" in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = LlavaGemmaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, config=cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
else:
try:
from llava.model.language_model.llava_llama import LlavaConfig
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
if customized_config is None:
llava_cfg = LlavaConfig.from_pretrained(model_path)
if "v1.5" in model_path.lower():
llava_cfg.delay_load = True # a workaround for correctly loading v1.5 models
else:
llava_cfg = customized_config
if overwrite_config is not None:
rank0_print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(llava_cfg, k, v)
model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
except:
raise ValueError(f"Model {model_name} not supported")
else:
# Load language model
if model_base is not None:
# PEFT model
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_base, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
print(f"Loading LoRA weights from {model_path}")
model = PeftModel.from_pretrained(model, model_path)
print(f"Merging weights")
model = model.merge_and_unload()
print("Convert to FP16...")
model.to(torch.float16)
else:
use_fast = False
if "mpt" in model_name.lower().replace("prompt", ""):
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs)
else:
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
rank0_print(f"Model Class: {model.__class__.__name__}")
image_processor = None
if "llava" in model_name.lower() or is_multimodal:
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
if mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
if mm_use_im_start_end:
tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
model.resize_token_embeddings(len(tokenizer))
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model(device_map=device_map)
if device_map != "auto":
vision_tower.to(device="cuda", dtype=torch.float16)
image_processor = vision_tower.image_processor
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
elif hasattr(model.config, "max_position_embeddings"):
context_len = model.config.max_position_embeddings
elif hasattr(model.config, "tokenizer_model_max_length"):
context_len = model.config.tokenizer_model_max_length
else:
context_len = 2048
return tokenizer, model, image_processor, context_len
llava/model/consolidate.py 0 → 100644
View file @ d5878167
"""
Usage:
python3 -m llava.model.consolidate --src ~/model_weights/llava-7b --dst ~/model_weights/llava-7b_consolidate
"""
import argparse
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from llava.model import *
from llava.model.utils import auto_upgrade
def consolidate_ckpt(src_path, dst_path):
print("Loading model")
auto_upgrade(src_path)
src_model = AutoModelForCausalLM.from_pretrained(src_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
src_tokenizer = AutoTokenizer.from_pretrained(src_path, use_fast=False)
src_model.save_pretrained(dst_path)
src_tokenizer.save_pretrained(dst_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--src", type=str, required=True)
parser.add_argument("--dst", type=str, required=True)
args = parser.parse_args()
consolidate_ckpt(args.src, args.dst)
llava/model/language_model/llava_gemma.py 0 → 100644
View file @ d5878167
# Copyright 2024 Duc Q. Nguyen, Haotian Liu and Bo Li
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModelForCausalLM, GemmaConfig, GemmaModel, GemmaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
class LlavaGemmaConfig(GemmaConfig):
model_type = "llava_gemma"
class LlavaGemmaModel(LlavaMetaModel, GemmaModel):
config_class = LlavaGemmaConfig
def __init__(self, config: GemmaConfig):
super(LlavaGemmaModel, self).__init__(config)
class LlavaGemmaForCausalLM(GemmaForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaGemmaConfig
def __init__(self, config):
super(GemmaForCausalLM, self).__init__(config)
self.model = LlavaGemmaModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
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,
images: Optional[torch.FloatTensor] = None,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
if inputs_embeds is None:
(input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels) = self.prepare_inputs_labels_for_multimodal(input_ids, position_ids, attention_mask, past_key_values, labels, images, image_sizes)
return super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
image_sizes: Optional[torch.Tensor] = None,
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
position_ids = kwargs.pop("position_ids", None)
attention_mask = kwargs.pop("attention_mask", None)
if "inputs_embeds" in kwargs:
raise NotImplementedError("`inputs_embeds` is not supported")
if images is not None:
(inputs, position_ids, attention_mask, _, inputs_embeds, _) = self.prepare_inputs_labels_for_multimodal(inputs, position_ids, attention_mask, None, None, images, image_sizes=image_sizes)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
return super().generate(position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, **kwargs)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
images = kwargs.pop("images", None)
image_sizes = kwargs.pop("image_sizes", None)
inputs = super().prepare_inputs_for_generation(input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs)
if images is not None:
inputs["images"] = images
if image_sizes is not None:
inputs["image_sizes"] = image_sizes
return inputs
AutoConfig.register("llava_gemma", LlavaGemmaConfig)
AutoModelForCausalLM.register(LlavaGemmaConfig, LlavaGemmaForCausalLM)
llava/model/language_model/llava_llama.py 0 → 100644
View file @ d5878167
# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig
from torch.nn import CrossEntropyLoss
# , LlamaModel, LlamaForCausalLM, GenerationConfig
# from .modeling_llama import LlamaModel, LlamaForCausalLM
from transformers import LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
class LlavaConfig(LlamaConfig):
model_type = "llava_llama"
temperature: float = 0.0 # reset to 0.0, previously 0.9 for Vicuna
max_new_tokens: int = 1024
do_sample: bool = False
top_p: Optional[float] = None
# rope_scaling: Optional[dict] = {}
class LlavaLlamaModel(LlavaMetaModel, LlamaModel):
config_class = LlavaConfig
def __init__(self, config: LlamaConfig):
super(LlavaLlamaModel, self).__init__(config)
class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaConfig
def __init__(self, config):
LlamaForCausalLM.__init__(self, config)
# configure default generation settings
config.model_type = "llava_llama"
# config.rope_scaling = None
self.model = LlavaLlamaModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
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,
images: Optional[torch.FloatTensor] = None,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
modalities: Optional[List[str]] = ["image"],
dpo_forward: Optional[bool] = None,
cache_position=None,
) -> Union[Tuple, CausalLMOutputWithPast]:
if inputs_embeds is None:
(input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels) = self.prepare_inputs_labels_for_multimodal(input_ids, position_ids, attention_mask, past_key_values, labels, images, modalities, image_sizes)
if dpo_forward:
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
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,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
return logits, labels
else:
return super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
image_sizes: Optional[torch.Tensor] = None,
modalities: Optional[List[str]] = ["image"],
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
modalities = kwargs.pop("modalities", None) if "modalities" in kwargs and modalities is None else modalities
position_ids = kwargs.pop("position_ids", None)
attention_mask = kwargs.pop("attention_mask", None)
if "inputs_embeds" in kwargs:
raise NotImplementedError("`inputs_embeds` is not supported")
if images is not None:
(inputs, position_ids, attention_mask, _, inputs_embeds, _) = self.prepare_inputs_labels_for_multimodal(inputs, position_ids, attention_mask, None, None, images, modalities, image_sizes=image_sizes)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
return super().generate(position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, **kwargs)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
images = kwargs.pop("images", None)
image_sizes = kwargs.pop("image_sizes", None)
inputs = super().prepare_inputs_for_generation(input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs)
if images is not None:
inputs["images"] = images
if image_sizes is not None:
inputs["image_sizes"] = image_sizes
return inputs
AutoConfig.register("llava_llama", LlavaConfig)
AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM)
llava/model/language_model/llava_mistral.py 0 → 100644
View file @ d5878167
# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModelForCausalLM, MistralConfig, MistralModel, MistralForCausalLM, GenerationConfig
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
class LlavaMistralConfig(MistralConfig):
model_type = "llava_mistral"
temperature: float = 0.0 # reset to 0.0, previously 0.9 for Vicuna
max_new_tokens: int = 1024
do_sample: bool = False
top_p: Optional[float] = None
class LlavaMistralModel(LlavaMetaModel, MistralModel):
config_class = LlavaMistralConfig
def __init__(self, config: MistralConfig):
super(LlavaMistralModel, self).__init__(config)
class LlavaMistralForCausalLM(MistralForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaMistralConfig
def __init__(self, config):
super(MistralForCausalLM, self).__init__(config)
config.model_type = "llava_mistral"
config.rope_scaling = None
self.model = LlavaMistralModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
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,
images: Optional[torch.FloatTensor] = None,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
cache_position=None,
) -> Union[Tuple, CausalLMOutputWithPast]:
if inputs_embeds is None:
(input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels) = self.prepare_inputs_labels_for_multimodal(input_ids, position_ids, attention_mask, past_key_values, labels, images, image_sizes)
return super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
image_sizes: Optional[torch.Tensor] = None,
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
position_ids = kwargs.pop("position_ids", None)
attention_mask = kwargs.pop("attention_mask", None)
if "inputs_embeds" in kwargs:
raise NotImplementedError("`inputs_embeds` is not supported")
if images is not None:
(inputs, position_ids, attention_mask, _, inputs_embeds, _) = self.prepare_inputs_labels_for_multimodal(inputs, position_ids, attention_mask, None, None, images, image_sizes=image_sizes)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
return super().generate(position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, **kwargs)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
images = kwargs.pop("images", None)
image_sizes = kwargs.pop("image_sizes", None)
inputs = super().prepare_inputs_for_generation(input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs)
if images is not None:
inputs["images"] = images
if image_sizes is not None:
inputs["image_sizes"] = image_sizes
return inputs
AutoConfig.register("llava_mistral", LlavaMistralConfig)
AutoModelForCausalLM.register(LlavaMistralConfig, LlavaMistralForCausalLM)
llava/model/language_model/llava_mixtral.py 0 → 100644
View file @ d5878167
# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModelForCausalLM, MixtralConfig, MixtralModel, MixtralForCausalLM, GenerationConfig
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
class LlavaMixtralConfig(MixtralConfig):
model_type = "llava_mixtral"
class LlavaMixtralModel(LlavaMetaModel, MixtralModel):
config_class = LlavaMixtralConfig
def __init__(self, config: MixtralConfig):
super(LlavaMixtralModel, self).__init__(config)
class LlavaMixtralForCausalLM(MixtralForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaMixtralConfig
def __init__(self, config):
super(MixtralForCausalLM, self).__init__(config)
config.model_type = "llava_mixtral"
config.rope_scaling = None
self.model = LlavaMixtralModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
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,
images: Optional[torch.FloatTensor] = None,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
modalities: Optional[List[str]] = ["image"],
dpo_forward: Optional[bool] = None,
cache_position=None,
) -> Union[Tuple, CausalLMOutputWithPast]:
if inputs_embeds is None:
(input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels) = self.prepare_inputs_labels_for_multimodal(input_ids, position_ids, attention_mask, past_key_values, labels, images, modalities, image_sizes)
if dpo_forward:
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
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,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
return logits, labels
else:
return super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
image_sizes: Optional[torch.Tensor] = None,
modalities: Optional[List[str]] = ["image"],
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
position_ids = kwargs.pop("position_ids", None)
attention_mask = kwargs.pop("attention_mask", None)
if "inputs_embeds" in kwargs:
raise NotImplementedError("`inputs_embeds` is not supported")
if images is not None:
(inputs, position_ids, attention_mask, _, inputs_embeds, _) = self.prepare_inputs_labels_for_multimodal(inputs, position_ids, attention_mask, None, None, images, modalities, image_sizes=image_sizes)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
return super().generate(position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, **kwargs)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
images = kwargs.pop("images", None)
image_sizes = kwargs.pop("image_sizes", None)
inputs = super().prepare_inputs_for_generation(input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs)
if images is not None:
inputs["images"] = images
if image_sizes is not None:
inputs["image_sizes"] = image_sizes
return inputs
AutoConfig.register("llava_mixtral", LlavaMixtralConfig)
AutoModelForCausalLM.register(LlavaMixtralConfig, LlavaMixtralForCausalLM)
llava/model/language_model/llava_mpt.py 0 → 100644
View file @ d5878167
# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple
import torch
from transformers import AutoConfig, AutoModelForCausalLM, MptConfig, MptForCausalLM, MptModel, GenerationConfig
from llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
class LlavaMptConfig(MptConfig):
model_type = "llava_mpt"
class LlavaMptModel(LlavaMetaModel, MptModel):
config_class = LlavaMptConfig
def __init__(self, config: MptConfig):
config.hidden_size = config.d_model
super(LlavaMptModel, self).__init__(config)
def embed_tokens(self, x):
return self.wte(x)
class LlavaMptForCausalLM(MptForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaMptConfig
supports_gradient_checkpointing = True
def __init__(self, config):
super(MptForCausalLM, self).__init__(config)
config.model_type = "llava_mpt"
config.rope_scaling = None
self.generation_config = GenerationConfig(
temperature=0.0,
max_new_tokens=1024,
do_sample=False,
top_p=None,
)
self.transformer = LlavaMptModel(config)
self.lm_head = torch.nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.transformer
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, LlavaMptModel):
module.gradient_checkpointing = value
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position=None,
images=None,
):
input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)
return super().forward(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
images = kwargs.pop("images", None)
_inputs = super().prepare_inputs_for_generation(input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs)
_inputs["images"] = images
return _inputs
AutoConfig.register("llava_mpt", LlavaMptConfig)
AutoModelForCausalLM.register(LlavaMptConfig, LlavaMptForCausalLM)
llava/model/language_model/llava_qwen.py 0 → 100644
View file @ d5878167
# Copyright 2024 Hao Zhang
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union, Dict
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
import transformers
from transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig, LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
# from ...constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
from transformers import Qwen2Config, Qwen2Model, Qwen2ForCausalLM
# from .qwen.modeling_qwen import QWenLMHeadModel, QWenModel
# from .qwen.configuration_qwen import QWenConfig
class LlavaQwenConfig(Qwen2Config):
model_type = "llava_qwen"
class LlavaQwenModel(LlavaMetaModel, Qwen2Model):
config_class = LlavaQwenConfig
def __init__(self, config: Qwen2Config):
super(LlavaQwenModel, self).__init__(config)
class LlavaQwenForCausalLM(Qwen2ForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaQwenConfig
def __init__(self, config):
# super(Qwen2ForCausalLM, self).__init__(config)
Qwen2ForCausalLM.__init__(self, config)
config.model_type = "llava_qwen"
config.rope_scaling = None
self.model = LlavaQwenModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
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,
images: Optional[torch.FloatTensor] = None,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
modalities: Optional[List[str]] = ["image"],
dpo_forward: Optional[bool] = False,
cache_position=None,
) -> Union[Tuple, CausalLMOutputWithPast]:
if inputs_embeds is None:
(input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels) = self.prepare_inputs_labels_for_multimodal(input_ids, position_ids, attention_mask, past_key_values, labels, images, modalities, image_sizes)
if dpo_forward:
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
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,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
return logits, labels
else:
return super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
image_sizes: Optional[torch.Tensor] = None,
modalities: Optional[List[str]] = ["image"],
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
position_ids = kwargs.pop("position_ids", None)
attention_mask = kwargs.pop("attention_mask", None)
if "inputs_embeds" in kwargs:
raise NotImplementedError("`inputs_embeds` is not supported")
if images is not None:
(inputs, position_ids, attention_mask, _, inputs_embeds, _) = self.prepare_inputs_labels_for_multimodal(inputs, position_ids, attention_mask, None, None, images, modalities, image_sizes=image_sizes)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
return super().generate(position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, **kwargs)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
images = kwargs.pop("images", None)
image_sizes = kwargs.pop("image_sizes", None)
inputs = super().prepare_inputs_for_generation(input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs)
if images is not None:
inputs["images"] = images
if image_sizes is not None:
inputs["image_sizes"] = image_sizes
return inputs
AutoConfig.register("llava_qwen", LlavaQwenConfig)
AutoModelForCausalLM.register(LlavaQwenConfig, LlavaQwenForCausalLM)
llava/model/language_model/llava_qwen_moe.py 0 → 100644
View file @ d5878167
# Copyright 2024 Hao Zhang
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union, Dict
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
import transformers
from transformers import AutoConfig, AutoModelForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
# from ...constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
from transformers import Qwen2MoeConfig, Qwen2MoeModel, Qwen2MoeForCausalLM
# from .qwen.modeling_qwen import QWenLMHeadModel, QWenModel
# from .qwen.configuration_qwen import QWenConfig
class LlavaQwenMoeConfig(Qwen2MoeConfig):
model_type = "llava_qwen_moe"
class LlavaQwenMoeModel(LlavaMetaModel, Qwen2MoeModel):
config_class = LlavaQwenMoeConfig
def __init__(self, config: Qwen2MoeConfig):
super(LlavaQwenMoeModel, self).__init__(config)
class LlavaQwenMoeForCausalLM(Qwen2MoeForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaQwenMoeConfig
def __init__(self, config):
# super(Qwen2MoeForCausalLM, self).__init__(config)
Qwen2MoeForCausalLM.__init__(self, config)
config.model_type = "llava_qwen_moe"
config.rope_scaling = None
self.model = LlavaQwenMoeModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
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,
images: Optional[torch.FloatTensor] = None,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
modalities: Optional[List[str]] = ["image"],
dpo_forward: Optional[bool] = False,
cache_position=None,
) -> Union[Tuple, CausalLMOutputWithPast]:
if inputs_embeds is None:
(input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels) = self.prepare_inputs_labels_for_multimodal(input_ids, position_ids, attention_mask, past_key_values, labels, images, modalities, image_sizes)
if dpo_forward:
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
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,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
return logits, labels
else:
return super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
image_sizes: Optional[torch.Tensor] = None,
modalities: Optional[List[str]] = ["image"],
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
position_ids = kwargs.pop("position_ids", None)
attention_mask = kwargs.pop("attention_mask", None)
if "inputs_embeds" in kwargs:
raise NotImplementedError("`inputs_embeds` is not supported")
if images is not None:
(inputs, position_ids, attention_mask, _, inputs_embeds, _) = self.prepare_inputs_labels_for_multimodal(inputs, position_ids, attention_mask, None, None, images, modalities, image_sizes=image_sizes)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
return super().generate(position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, **kwargs)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
images = kwargs.pop("images", None)
image_sizes = kwargs.pop("image_sizes", None)
inputs = super().prepare_inputs_for_generation(input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs)
if images is not None:
inputs["images"] = images
if image_sizes is not None:
inputs["image_sizes"] = image_sizes
return inputs
AutoConfig.register("llava_qwen_moe", LlavaQwenMoeConfig)
AutoModelForCausalLM.register(LlavaQwenMoeConfig, LlavaQwenMoeForCausalLM)
llava/model/language_model/modeling_llama.py 0 → 100644
View file @ d5878167
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch LLaMA model."""
import math
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, StaticCache
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
QuestionAnsweringModelOutput,
SequenceClassifierOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
from transformers.utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_flash_attn_2_available,
is_flash_attn_greater_or_equal_2_10,
logging,
replace_return_docstrings,
)
from transformers.models.llama.configuration_llama import LlamaConfig
if is_flash_attn_2_available():
from flash_attn import flash_attn_func, flash_attn_varlen_func
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "LlamaConfig"
def _get_unpad_data(attention_mask):
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
max_seqlen_in_batch = seqlens_in_batch.max().item()
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
return (
indices,
cu_seqlens,
max_seqlen_in_batch,
)
class LlamaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
LlamaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
class LlamaRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
super().__init__()
self.scaling_factor = scaling_factor
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
# For BC we register cos and sin cached
self.max_seq_len_cached = max_position_embeddings
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
t = t / self.scaling_factor
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("_cos_cached", emb.cos().to(torch.get_default_dtype()), persistent=False)
self.register_buffer("_sin_cached", emb.sin().to(torch.get_default_dtype()), persistent=False)
@property
def sin_cached(self):
logger.warning_once("The sin_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use " "the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class")
return self._sin_cached
@property
def cos_cached(self):
logger.warning_once("The cos_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use " "the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class")
return self._cos_cached
@torch.no_grad()
def forward(self, x, position_ids, seq_len=None):
if seq_len is not None:
logger.warning_once("The `seq_len` argument is deprecated and unused. It will be removed in v4.39.")
# x: [bs, num_attention_heads, seq_len, head_size]
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 since bfloat16 loses precision on long contexts
# See https://github.com/huggingface/transformers/pull/29285
device_type = x.device.type
device_type = device_type if isinstance(device_type, str) else "cpu"
with torch.autocast(device_type=device_type, enabled=False):
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos()
sin = emb.sin()
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
"""LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
def forward(self, x, position_ids, seq_len=None):
# difference to the original RoPE: a scaling factor is aplied to the position ids
position_ids = position_ids.float() / self.scaling_factor
cos, sin = super().forward(x, position_ids, seq_len)
return cos, sin
class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
"""LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
def forward(self, x, position_ids, seq_len=None):
# difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
seq_len = torch.max(position_ids) + 1
if seq_len > self.max_position_embeddings:
base = self.base * ((self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)) ** (self.dim / (self.dim - 2))
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: this may break with compilation
cos, sin = super().forward(x, position_ids, seq_len)
return cos, sin
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class LlamaMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
if self.config.pretraining_tp > 1:
slice = self.intermediate_size // self.config.pretraining_tp
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
down_proj = [F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)]
down_proj = sum(down_proj)
else:
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
class LlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: LlamaConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.is_causal = True
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" f" and `num_heads`: {self.num_heads}).")
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
self._init_rope()
def _init_rope(self):
if self.config.rope_scaling is None:
self.rotary_emb = LlamaRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
)
else:
scaling_type = self.config.rope_scaling["type"]
scaling_factor = self.config.rope_scaling["factor"]
if scaling_type == "linear":
self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
elif scaling_type == "dynamic":
self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
else:
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
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,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
if self.config.pretraining_tp > 1:
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0)
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
query_states = torch.cat(query_states, dim=-1)
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
key_states = torch.cat(key_states, dim=-1)
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
value_states = torch.cat(value_states, dim=-1)
else:
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)
past_key_value = getattr(self, "past_key_value", past_key_value)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; position_ids needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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)
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
if cache_position is not None:
causal_mask = attention_mask[:, :, cache_position, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# 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, self.hidden_size)
if self.config.pretraining_tp > 1:
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
else:
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class LlamaRingFlashAttention2(LlamaAttention):
"""
Llama flash attention module. This module inherits from `LlamaAttention` 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.
"""
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.LongTensor] = 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,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
output_attentions = False
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)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
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)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
past_key_value = getattr(self, "past_key_value", past_key_value)
if past_key_value is not None:
# sin and cos are specific to RoPE models; position_ids needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
# to be able to avoid many of these transpose/reshape/view.
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
# 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 the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (LlamaRMSNorm handles it correctly)
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)
attn_output = self._flash_attention_forward(query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate)
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
def _flash_attention_forward(self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None):
"""
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
first unpad the input, then computes the attention scores and pad the final attention scores.
Args:
query_states (`torch.Tensor`):
Input query states to be passed to Flash Attention API
key_states (`torch.Tensor`):
Input key states to be passed to Flash Attention API
value_states (`torch.Tensor`):
Input value states to be passed to Flash Attention API
attention_mask (`torch.Tensor`):
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
position of padding tokens and 1 for the position of non-padding tokens.
dropout (`int`, *optional*):
Attention dropout
softmax_scale (`float`, *optional*):
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
"""
if not self._flash_attn_uses_top_left_mask:
causal = self.is_causal
else:
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
causal = self.is_causal and query_length != 1
# Contains at least one padding token in the sequence
if attention_mask is not None:
batch_size = query_states.shape[0]
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(query_states, key_states, value_states, attention_mask, query_length)
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
attn_output_unpad = zigzag_ring_flash_attn_varlen_func(
query_states,
key_states,
value_states,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
max_seqlen_q=max_seqlen_in_batch_q,
max_seqlen_k=max_seqlen_in_batch_k,
dropout_p=dropout,
softmax_scale=softmax_scale,
causal=causal,
)
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
else:
# pack qkv
# query_states: (batch_size, seqlen, nheads, headdim)
# qkv: (batch_size, seqlen, 3, nheads, headdim)
qkv = torch.stack([query_states, key_states, value_states], dim=2)
attn_output = zigzag_ring_flash_attn_qkvpacked_func(qkv, dropout, softmax_scale, causal=causal)
return attn_output
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k)
value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k)
if query_length == kv_seq_len:
query_layer = index_first_axis(query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k)
cu_seqlens_q = cu_seqlens_k
max_seqlen_in_batch_q = max_seqlen_in_batch_k
indices_q = indices_k
elif query_length == 1:
max_seqlen_in_batch_q = 1
cu_seqlens_q = torch.arange(batch_size + 1, dtype=torch.int32, device=query_layer.device) # There is a memcpy here, that is very bad.
indices_q = cu_seqlens_q[:-1]
query_layer = query_layer.squeeze(1)
else:
# The -q_len: slice assumes left padding.
attention_mask = attention_mask[:, -query_length:]
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
return (
query_layer,
key_layer,
value_layer,
indices_q,
(cu_seqlens_q, cu_seqlens_k),
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
)
class LlamaFlashAttention2(LlamaAttention):
"""
Llama flash attention module. This module inherits from `LlamaAttention` 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.
"""
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.LongTensor] = 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,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
output_attentions = False
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)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
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)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
past_key_value = getattr(self, "past_key_value", past_key_value)
if past_key_value is not None:
# sin and cos are specific to RoPE models; position_ids needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
# to be able to avoid many of these transpose/reshape/view.
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
# 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 the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (LlamaRMSNorm handles it correctly)
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)
attn_output = self._flash_attention_forward(query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate)
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
def _flash_attention_forward(self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None):
"""
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
first unpad the input, then computes the attention scores and pad the final attention scores.
Args:
query_states (`torch.Tensor`):
Input query states to be passed to Flash Attention API
key_states (`torch.Tensor`):
Input key states to be passed to Flash Attention API
value_states (`torch.Tensor`):
Input value states to be passed to Flash Attention API
attention_mask (`torch.Tensor`):
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
position of padding tokens and 1 for the position of non-padding tokens.
dropout (`int`, *optional*):
Attention dropout
softmax_scale (`float`, *optional*):
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
"""
if not self._flash_attn_uses_top_left_mask:
causal = self.is_causal
else:
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
causal = self.is_causal and query_length != 1
# Contains at least one padding token in the sequence
if attention_mask is not None:
batch_size = query_states.shape[0]
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(query_states, key_states, value_states, attention_mask, query_length)
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
attn_output_unpad = flash_attn_varlen_func(
query_states,
key_states,
value_states,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
max_seqlen_q=max_seqlen_in_batch_q,
max_seqlen_k=max_seqlen_in_batch_k,
dropout_p=dropout,
softmax_scale=softmax_scale,
causal=causal,
)
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
else:
attn_output = flash_attn_func(query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal)
return attn_output
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k)
value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k)
if query_length == kv_seq_len:
query_layer = index_first_axis(query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k)
cu_seqlens_q = cu_seqlens_k
max_seqlen_in_batch_q = max_seqlen_in_batch_k
indices_q = indices_k
elif query_length == 1:
max_seqlen_in_batch_q = 1
cu_seqlens_q = torch.arange(batch_size + 1, dtype=torch.int32, device=query_layer.device) # There is a memcpy here, that is very bad.
indices_q = cu_seqlens_q[:-1]
query_layer = query_layer.squeeze(1)
else:
# The -q_len: slice assumes left padding.
attention_mask = attention_mask[:, -query_length:]
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
return (
query_layer,
key_layer,
value_layer,
indices_q,
(cu_seqlens_q, cu_seqlens_k),
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
)
class LlamaSdpaAttention(LlamaAttention):
"""
Llama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`LlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from LlamaAttention.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,
) -> 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(
"LlamaModel is using LlamaSdpaAttention, 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,
)
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)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
# In case static cache is used, it is an instance attribute.
past_key_value = getattr(self, "past_key_value", past_key_value)
if past_key_value is not None:
# sin and cos are specific to RoPE models; position_ids needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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 and cache_position is not None:
causal_mask = causal_mask[:, :, cache_position, : 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 causal_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
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,
)
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
try:
from ring_flash_attn import zigzag_ring_flash_attn_qkvpacked_func, zigzag_ring_flash_attn_varlen_func
except ImportError:
print("Please install the ring-flash-attn package")
LLAMA_ATTENTION_CLASSES = {
"eager": LlamaAttention,
"flash_attention_2": LlamaFlashAttention2,
"ring_flash_attention_2": LlamaRingFlashAttention2,
"sdpa": LlamaSdpaAttention,
}
class LlamaDecoderLayer(nn.Module):
def __init__(self, config: LlamaConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = LlamaMLP(config)
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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,
**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_size, sequence_length)` if flash attention is used or `(batch_size, 1,
query_sequence_length, key_sequence_length)` if default attention is used.
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
"""
if "padding_mask" in kwargs:
warnings.warn("Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`")
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,
**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
LLAMA_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`LlamaConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
LLAMA_START_DOCSTRING,
)
class LlamaPreTrainedModel(PreTrainedModel):
config_class = LlamaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["LlamaDecoderLayer"]
_skip_keys_device_placement = ["past_key_values", "causal_mask"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_cache_class = True
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _setup_cache(self, cache_cls, max_batch_size, max_cache_len: Optional[int] = None):
if self.config._attn_implementation == "flash_attention_2" and cache_cls == StaticCache:
raise ValueError("`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` " "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers")
if max_cache_len > self.model.causal_mask.shape[-1] or self.device != self.model.causal_mask.device:
causal_mask = torch.full((max_cache_len, max_cache_len), fill_value=True, device=self.device, dtype=torch.bool)
self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
for layer in self.model.layers:
device = layer.input_layernorm.weight.device
if hasattr(self.config, "_pre_quantization_dtype"):
dtype = self.config._pre_quantization_dtype
else:
dtype = layer.self_attn.o_proj.weight.dtype
layer.self_attn.past_key_value = cache_cls(self.config, max_batch_size, max_cache_len, device=device, dtype=dtype)
def _reset_cache(self):
for layer in self.model.layers:
layer.self_attn.past_key_value = None
LLAMA_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
Two formats are allowed:
- a [`~cache_utils.Cache`] instance;
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
cache format.
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
legacy cache format will be returned.
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
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`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
LLAMA_START_DOCSTRING,
)
class LlamaModel(LlamaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
Args:
config: LlamaConfig
"""
def __init__(self, config: LlamaConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList([LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Register a causal mask to separate causal and padding mask creation. Merging happens in the attention class.
# NOTE: This is not friendly with TorchScript, ONNX, ExportedProgram serialization for very large `max_position_embeddings`.
causal_mask = torch.full((config.max_position_embeddings, config.max_position_embeddings), fill_value=True, dtype=torch.bool)
self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
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,
) -> 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 cannot specify both input_ids and inputs_embeds at the same time, and must specify either one")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.")
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
past_seen_tokens = 0
if use_cache: # kept for BC (cache positions)
if not isinstance(past_key_values, StaticCache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
past_seen_tokens = past_key_values.get_seq_length()
if cache_position is None:
if isinstance(past_key_values, StaticCache):
raise ValueError("cache_position is a required argument when using StaticCache.")
cache_position = torch.arange(past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds)
# embed positions
hidden_states = inputs_embeds
# 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,
)
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,
)
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 = None
if use_cache:
next_cache = next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache
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,
)
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
def _update_causal_mask(self, attention_mask, input_tensor):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
batch_size, seq_length = input_tensor.shape[:2]
dtype = input_tensor.dtype
device = input_tensor.device
# support going beyond cached `max_position_embedding`
if seq_length > self.causal_mask.shape[-1]:
causal_mask = torch.full((2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]), fill_value=1)
self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
# We use the current dtype to avoid any overflows
min_dtype = torch.finfo(dtype).min
causal_mask = self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype) * min_dtype
causal_mask = causal_mask.to(dtype=dtype, device=device)
if attention_mask is not None and attention_mask.dim() == 2:
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
if self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type == "cuda":
# TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
is_tracing = torch.jit.is_tracing() or isinstance(input_tensor, torch.fx.Proxy) or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
if not is_tracing and torch.any(attention_mask != 1):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
class LlamaForCausalLM(LlamaPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = LlamaModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
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,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
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 transformers import AutoTokenizer, LlamaForCausalLM
>>> model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # 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]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
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
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
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,
)
hidden_states = outputs[0]
if self.config.pretraining_tp > 1:
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
logits = torch.cat(logits, dim=-1)
else:
logits = self.lm_head(hidden_states)
logits = logits.float()
loss = None
if labels is not None:
# 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 CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs):
past_length = 0
if past_key_values is not None:
if isinstance(past_key_values, Cache):
cache_length = past_key_values.get_seq_length()
past_length = past_key_values.seen_tokens
max_cache_length = past_key_values.get_max_length()
else:
cache_length = past_length = past_key_values[0][0].shape[2]
max_cache_length = None
# Keep only the unprocessed tokens:
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
# input)
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
# input_ids based on the past_length.
elif past_length < input_ids.shape[1]:
input_ids = input_ids[:, past_length:]
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
if max_cache_length is not None and attention_mask is not None and cache_length + input_ids.shape[1] > max_cache_length:
attention_mask = attention_mask[:, -max_cache_length:]
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -input_ids.shape[1] :]
if self.generation_config.cache_implementation == "static":
# generation with static cache
cache_position = kwargs.get("cache_position", None)
if cache_position is None:
past_length = 0
else:
past_length = cache_position[-1] + 1
input_ids = input_ids[:, past_length:]
position_ids = position_ids[:, past_length:]
# TODO @gante we should only keep a `cache_position` in generate, and do +=1.
# same goes for position ids. Could also help with continued generation.
input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
position_ids = position_ids.contiguous() if position_ids is not None else None
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
# The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
# recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
# TODO: use `next_tokens` directly instead.
model_inputs = {"input_ids": input_ids.contiguous()}
model_inputs.update(
{
"position_ids": position_ids,
"cache_position": cache_position,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
}
)
return model_inputs
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),)
return reordered_past
@add_start_docstrings(
"""
The LLaMa Model transformer with a sequence classification head on top (linear layer).
[`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
LLAMA_START_DOCSTRING,
)
class LlamaForSequenceClassification(LlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = LlamaModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
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,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
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,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
sequence_lengths = sequence_lengths % input_ids.shape[-1]
sequence_lengths = sequence_lengths.to(logits.device)
else:
sequence_lengths = -1
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@add_start_docstrings(
"""
The Llama Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
LLAMA_START_DOCSTRING,
)
class LlamaForQuestionAnswering(LlamaPreTrainedModel):
base_model_prefix = "transformer"
# Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Llama
def __init__(self, config):
super().__init__(config)
self.transformer = LlamaModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.transformer.embed_tokens
def set_input_embeddings(self, value):
self.transformer.embed_tokens = value
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1).to(start_logits.device)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1).to(end_logits.device)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
llava/model/llava_arch.py 0 → 100644
View file @ d5878167
# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC, abstractmethod
import math
import re
import time
import torch
import torch.nn as nn
from .multimodal_encoder.builder import build_vision_tower
from .multimodal_resampler.builder import build_vision_resampler
from .multimodal_projector.builder import build_vision_projector
from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.mm_utils import get_anyres_image_grid_shape
from llava.utils import rank0_print, rank_print
import random
class LlavaMetaModel:
def __init__(self, config):
super(LlavaMetaModel, self).__init__(config)
if hasattr(config, "mm_vision_tower"):
delay_load = getattr(config, "delay_load", False)
self.vision_tower = build_vision_tower(config, delay_load=delay_load)
self.vision_resampler = build_vision_resampler(config, vision_tower=self.vision_tower)
self.mm_projector = build_vision_projector(config, vision_cfg=self.vision_tower.config)
if "unpad" in getattr(config, "mm_patch_merge_type", ""):
self.image_newline = nn.Parameter(torch.empty(config.hidden_size, dtype=self.dtype))
def get_vision_tower(self):
vision_tower = getattr(self, "vision_tower", None)
if type(vision_tower) is list:
vision_tower = vision_tower[0]
return vision_tower
def initialize_vision_modules(self, model_args, fsdp=None):
vision_tower = model_args.vision_tower
mm_vision_select_layer = model_args.mm_vision_select_layer
mm_vision_select_feature = model_args.mm_vision_select_feature
pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
mm_patch_merge_type = model_args.mm_patch_merge_type
self.config.mm_vision_tower = vision_tower
self.config.vision_tower_pretrained = getattr(model_args, "vision_tower_pretrained", "")
if self.get_vision_tower() is None:
vision_tower = build_vision_tower(model_args)
vision_resampler = build_vision_resampler(model_args, vision_tower=vision_tower)
for k, v in vision_resampler.config.items():
setattr(self.config, k, v)
if fsdp is not None and len(fsdp) > 0:
self.vision_tower = [vision_tower]
self.vision_resampler = [vision_resampler]
else:
self.vision_tower = vision_tower
self.vision_resampler = vision_resampler
else:
if fsdp is not None and len(fsdp) > 0:
vision_resampler = self.vision_resampler[0]
vision_tower = self.vision_tower[0]
else:
vision_resampler = self.vision_resampler
vision_tower = self.vision_tower
vision_tower.load_model()
# In case it is frozen by LoRA
for p in self.vision_resampler.parameters():
p.requires_grad = True
self.config.use_mm_proj = True
self.config.mm_projector_type = getattr(model_args, "mm_projector_type", "linear")
self.config.mm_hidden_size = getattr(vision_resampler, "hidden_size", vision_tower.hidden_size)
self.config.mm_vision_select_layer = mm_vision_select_layer
self.config.mm_vision_select_feature = mm_vision_select_feature
self.config.mm_patch_merge_type = mm_patch_merge_type
if not hasattr(self.config, 'add_faster_video'):
if model_args.add_faster_video:
embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype))
self.faster_token = nn.Parameter(
torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std
)
if getattr(self, "mm_projector", None) is None:
self.mm_projector = build_vision_projector(self.config, vision_cfg=vision_tower.config)
if "unpad" in mm_patch_merge_type:
embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype))
self.image_newline = nn.Parameter(torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std)
else:
# In case it is frozen by LoRA
for p in self.mm_projector.parameters():
p.requires_grad = True
if pretrain_mm_mlp_adapter is not None:
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location="cpu")
def get_w(weights, keyword):
return {k.split(keyword + ".")[1]: v for k, v in weights.items() if keyword in k}
incompatible_keys = self.mm_projector.load_state_dict(get_w(mm_projector_weights, "mm_projector"))
rank0_print(f"Loaded mm projector weights from {pretrain_mm_mlp_adapter}. Incompatible keys: {incompatible_keys}")
incompatible_keys = self.vision_resampler.load_state_dict(get_w(mm_projector_weights, "vision_resampler"), strict=False)
rank0_print(f"Loaded vision resampler weights from {pretrain_mm_mlp_adapter}. Incompatible keys: {incompatible_keys}")
def unpad_image(tensor, original_size):
"""
Unpads a PyTorch tensor of a padded and resized image.
Args:
tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format.
original_size (tuple): The original size of the image (height, width).
Returns:
torch.Tensor: The unpadded image tensor.
"""
original_width, original_height = original_size
current_height, current_width = tensor.shape[1:]
# Compute aspect ratios
original_aspect_ratio = original_width / original_height
current_aspect_ratio = current_width / current_height
# Determine padding size and direction
if original_aspect_ratio > current_aspect_ratio:
# Padding was added to the height
scale_factor = current_width / original_width
new_height = int(original_height * scale_factor)
padding = (current_height - new_height) // 2
unpadded_tensor = tensor[:, padding : current_height - padding, :]
else:
# Padding was added to the width
scale_factor = current_height / original_height
new_width = int(original_width * scale_factor)
padding = (current_width - new_width) // 2
unpadded_tensor = tensor[:, :, padding : current_width - padding]
return unpadded_tensor
class LlavaMetaForCausalLM(ABC):
@abstractmethod
def get_model(self):
pass
def get_vision_tower(self):
return self.get_model().get_vision_tower()
def get_2dPool(self, image_feature, stride=2):
height = width = self.get_vision_tower().num_patches_per_side
num_frames, num_tokens, num_dim = image_feature.shape
image_feature = image_feature.view(num_frames, height, width, -1)
image_feature = image_feature.permute(0, 3, 1, 2).contiguous()
# image_feature = nn.functional.max_pool2d(image_feature, self.config.mm_spatial_pool_stride)
if self.config.mm_spatial_pool_mode == "average":
image_feature = nn.functional.avg_pool2d(image_feature, stride)
elif self.config.mm_spatial_pool_mode == "max":
image_feature = nn.functional.max_pool2d(image_feature, stride)
elif self.config.mm_spatial_pool_mode == "bilinear":
height, width = image_feature.shape[2:]
scaled_shape = [math.ceil(height / stride), math.ceil(width / stride)]
image_feature = nn.functional.interpolate(image_feature, size=scaled_shape, mode='bilinear')
else:
raise ValueError(f"Unexpected mm_spatial_pool_mode: {self.config.mm_spatial_pool_mode}")
image_feature = image_feature.permute(0, 2, 3, 1)
image_feature = image_feature.view(num_frames, -1, num_dim)
return image_feature
def encode_images(self, images):
image_features = self.get_model().get_vision_tower()(images)
# image_features = self.get_model().vision_resampler(image_features, images=images)
image_features = self.get_model().mm_projector(image_features)
return image_features
def encode_multimodals(self, videos_or_images, video_idx_in_batch, split_sizes=None):
videos_or_images_features = self.get_model().get_vision_tower()(videos_or_images)
per_videos_or_images_features = torch.split(videos_or_images_features, split_sizes, dim=0) # tuple, (dim_1, 576, 4096)
all_videos_or_images_features = []
all_faster_video_features = []
cur_mm_spatial_pool_stride = self.config.mm_spatial_pool_stride
for idx, feat in enumerate(per_videos_or_images_features):
feat = self.get_model().mm_projector(feat)
faster_video_feature = 0
slower_img_feat = 0
if idx in video_idx_in_batch and cur_mm_spatial_pool_stride > 1:
slower_img_feat = self.get_2dPool(feat,cur_mm_spatial_pool_stride)
if self.config.add_faster_video:
cur_mm_spatial_pool_stride = cur_mm_spatial_pool_stride * 2
faster_video_feature = self.get_2dPool(feat,cur_mm_spatial_pool_stride)
if slower_img_feat is not 0:
all_videos_or_images_features.append(slower_img_feat)
else:
all_videos_or_images_features.append(feat)
all_faster_video_features.append(faster_video_feature)
return all_videos_or_images_features,all_faster_video_features
def add_token_per_grid(self, image_feature):
resize_h = int(math.sqrt(image_feature.shape[1]))
num_frames = image_feature.shape[0]
feature_dim = image_feature.shape[-1]
image_feature = image_feature.view(num_frames, 1, resize_h, resize_h, -1)
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
image_feature = torch.cat((image_feature, self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)), dim=-1)
if getattr(self.config, "add_faster_video", False):
# import pdb; pdb.set_trace()
# (3584, 832, 14) -> (3584, 64, 13, 14)
image_feature = image_feature.view(feature_dim, num_frames,resize_h, -1)
# (3584, 64, 13, 14) -> (64, 13, 14, 3584)
image_feature = image_feature.permute(1, 2, 3, 0).contiguous()
# (64, 13, 14, 3584) -> (64, 13*14, 3584)
image_feature = image_feature.flatten(1, 2)
# import pdb; pdb.set_trace()
return image_feature
# import pdb; pdb.set_trace()
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
return image_feature
def add_token_per_frame(self, image_feature):
image_feature = image_feature.permute(2, 0, 1).contiguous()
image_feature = torch.cat((image_feature, self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)), dim=-1)
image_feature = image_feature.permute(1, 2, 0).contiguous()
return image_feature
def prepare_inputs_labels_for_multimodal(self, input_ids, position_ids, attention_mask, past_key_values, labels, images, modalities=["image"], image_sizes=None):
vision_tower = self.get_vision_tower()
# rank_print(modalities)
if vision_tower is None or images is None or input_ids.shape[1] == 1:
return input_ids, position_ids, attention_mask, past_key_values, None, labels
if isinstance(modalities, str):
modalities = [modalities]
# import pdb; pdb.set_trace()
if type(images) is list or images.ndim == 5:
if type(images) is list:
images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images]
video_idx_in_batch = []
for _ in range(len(modalities)):
if modalities[_] == "video":
video_idx_in_batch.append(_)
images_list = []
for image in images:
if image.ndim == 4:
images_list.append(image)
else:
images_list.append(image.unsqueeze(0))
concat_images = torch.cat([image for image in images_list], dim=0)
split_sizes = [image.shape[0] for image in images_list]
encoded_image_features = self.encode_images(concat_images)
# image_features,all_faster_video_features = self.encode_multimodals(concat_images, video_idx_in_batch, split_sizes)
# This is a list, each element is [num_images, patch * patch, dim]
# rank_print(f"Concat images : {concat_images.shape}")
encoded_image_features = torch.split(encoded_image_features, split_sizes)
image_features = []
for idx, image_feat in enumerate(encoded_image_features):
if idx in video_idx_in_batch:
image_features.append(self.get_2dPool(image_feat))
else:
image_features.append(image_feat)
# image_features = self.encode_multimodals(concat_images, video_idx_in_batch, split_sizes)
# rank_print(f"Encoded image feats : {[x.shape for x in image_features]}")
# image_features = torch.split(image_features, split_sizes, dim=0)
mm_patch_merge_type = getattr(self.config, "mm_patch_merge_type", "flat")
image_aspect_ratio = getattr(self.config, "image_aspect_ratio", "square")
mm_newline_position = getattr(self.config, "mm_newline_position", "one_token")
if mm_patch_merge_type == "flat":
image_features = [x.flatten(0, 1) for x in image_features]
elif mm_patch_merge_type.startswith("spatial"):
new_image_features = []
for image_idx, image_feature in enumerate(image_features):
# FIXME: now assume the image is square, and split to 2x2 patches
# num_patches = h * w, where h = w = sqrt(num_patches)
# currently image_feature is a tensor of shape (4, num_patches, hidden_size)
# we want to first unflatten it to (2, 2, h, w, hidden_size)
# rank0_print("At least we are reaching here")
# import pdb; pdb.set_trace()
if image_idx in video_idx_in_batch: # video operations
# rank0_print("Video")
if mm_newline_position == "grid":
# Grid-wise
image_feature = self.add_token_per_grid(image_feature)
if getattr(self.config, "add_faster_video", False):
faster_video_feature = self.add_token_per_grid(all_faster_video_features[image_idx])
# Add a token for each frame
concat_slow_fater_token = []
# import pdb; pdb.set_trace()
for _ in range(image_feature.shape[0]):
if _ % self.config.faster_token_stride == 0:
concat_slow_fater_token.append(torch.cat((image_feature[_], self.model.faster_token[None].to(image_feature.device)), dim=0))
else:
concat_slow_fater_token.append(torch.cat((faster_video_feature[_], self.model.faster_token[None].to(image_feature.device)), dim=0))
# import pdb; pdb.set_trace()
image_feature = torch.cat(concat_slow_fater_token)
# print("!!!!!!!!!!!!")
new_image_features.append(image_feature)
elif mm_newline_position == "frame":
# Frame-wise
image_feature = self.add_token_per_frame(image_feature)
new_image_features.append(image_feature.flatten(0, 1))
elif mm_newline_position == "one_token":
# one-token
image_feature = image_feature.flatten(0, 1)
if 'unpad' in mm_patch_merge_type:
image_feature = torch.cat((
image_feature,
self.model.image_newline[None].to(image_feature.device)
), dim=0)
new_image_features.append(image_feature)
elif mm_newline_position == "no_token":
new_image_features.append(image_feature.flatten(0, 1))
else:
raise ValueError(f"Unexpected mm_newline_position: {mm_newline_position}")
elif image_feature.shape[0] > 1: # multi patches and multi images operations
# rank0_print("Single-images")
base_image_feature = image_feature[0]
image_feature = image_feature[1:]
height = width = self.get_vision_tower().num_patches_per_side
assert height * width == base_image_feature.shape[0]
if "anyres_max" in image_aspect_ratio:
matched_anyres_max_num_patches = re.match(r"anyres_max_(\d+)", image_aspect_ratio)
if matched_anyres_max_num_patches:
max_num_patches = int(matched_anyres_max_num_patches.group(1))
if image_aspect_ratio == "anyres" or "anyres_max" in image_aspect_ratio:
if hasattr(self.get_vision_tower(), "image_size"):
vision_tower_image_size = self.get_vision_tower().image_size
else:
raise ValueError("vision_tower_image_size is not found in the vision tower.")
try:
num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[image_idx], self.config.image_grid_pinpoints, vision_tower_image_size)
except Exception as e:
rank0_print(f"Error: {e}")
num_patch_width, num_patch_height = 2, 2
image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
else:
image_feature = image_feature.view(2, 2, height, width, -1)
if "maxpool2x2" in mm_patch_merge_type:
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
image_feature = nn.functional.max_pool2d(image_feature, 2)
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
elif "unpad" in mm_patch_merge_type and "anyres_max" in image_aspect_ratio and matched_anyres_max_num_patches:
unit = image_feature.shape[2]
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
image_feature = unpad_image(image_feature, image_sizes[image_idx])
c, h, w = image_feature.shape
times = math.sqrt(h * w / (max_num_patches * unit**2))
if times > 1.1:
image_feature = image_feature[None]
image_feature = nn.functional.interpolate(image_feature, [int(h // times), int(w // times)], mode="bilinear")[0]
image_feature = torch.cat((image_feature, self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)), dim=-1)
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
elif "unpad" in mm_patch_merge_type:
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
image_feature = unpad_image(image_feature, image_sizes[image_idx])
image_feature = torch.cat((image_feature, self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)), dim=-1)
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
else:
image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
image_feature = image_feature.flatten(0, 3)
if "nobase" in mm_patch_merge_type:
pass
else:
image_feature = torch.cat((base_image_feature, image_feature), dim=0)
new_image_features.append(image_feature)
else: # single image operations
image_feature = image_feature[0]
if "unpad" in mm_patch_merge_type:
image_feature = torch.cat((image_feature, self.model.image_newline[None].to(image_feature.device)), dim=0)
new_image_features.append(image_feature)
image_features = new_image_features
else:
raise ValueError(f"Unexpected mm_patch_merge_type: {self.config.mm_patch_merge_type}")
else:
image_features = self.encode_images(images)
# TODO: image start / end is not implemented here to support pretraining.
if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr(self.config, "mm_use_im_start_end", False):
raise NotImplementedError
# rank_print(f"Total images : {len(image_features)}")
# Let's just add dummy tensors if they do not exist,
# it is a headache to deal with None all the time.
# But it is not ideal, and if you have a better idea,
# please open an issue / submit a PR, thanks.
_labels = labels
_position_ids = position_ids
_attention_mask = attention_mask
if attention_mask is None:
attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
else:
attention_mask = attention_mask.bool()
if position_ids is None:
position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
if labels is None:
labels = torch.full_like(input_ids, IGNORE_INDEX)
# remove the padding using attention_mask -- FIXME
_input_ids = input_ids
input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
new_input_embeds = []
new_labels = []
cur_image_idx = 0
# rank_print("Inserting Images embedding")
for batch_idx, cur_input_ids in enumerate(input_ids):
num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
# rank0_print(num_images)
if num_images == 0:
cur_image_features = image_features[cur_image_idx]
cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
new_input_embeds.append(cur_input_embeds)
new_labels.append(labels[batch_idx])
cur_image_idx += 1
continue
image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
cur_input_ids_noim = []
cur_labels = labels[batch_idx]
cur_labels_noim = []
for i in range(len(image_token_indices) - 1):
cur_input_ids_noim.append(cur_input_ids[image_token_indices[i] + 1 : image_token_indices[i + 1]])
cur_labels_noim.append(cur_labels[image_token_indices[i] + 1 : image_token_indices[i + 1]])
split_sizes = [x.shape[0] for x in cur_labels_noim]
cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
cur_new_input_embeds = []
cur_new_labels = []
for i in range(num_images + 1):
cur_new_input_embeds.append(cur_input_embeds_no_im[i])
cur_new_labels.append(cur_labels_noim[i])
if i < num_images:
try:
cur_image_features = image_features[cur_image_idx]
except IndexError:
cur_image_features = image_features[cur_image_idx - 1]
cur_image_idx += 1
cur_new_input_embeds.append(cur_image_features)
cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))
cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds]
# import pdb; pdb.set_trace()
cur_new_input_embeds = torch.cat(cur_new_input_embeds)
cur_new_labels = torch.cat(cur_new_labels)
new_input_embeds.append(cur_new_input_embeds)
new_labels.append(cur_new_labels)
# Truncate sequences to max length as image embeddings can make the sequence longer
tokenizer_model_max_length = getattr(self.config, "tokenizer_model_max_length", None)
# rank_print("Finishing Inserting")
new_input_embeds = [x[:tokenizer_model_max_length] for x, modality in zip(new_input_embeds, modalities)]
new_labels = [x[:tokenizer_model_max_length] for x, modality in zip(new_labels, modalities)]
# TODO: Hard code for control loss spike
# if tokenizer_model_max_length is not None:
# new_input_embeds = [x[:4096] if modality != "video" else x[:tokenizer_model_max_length] for x, modality in zip(new_input_embeds, modalities)]
# new_labels = [x[:4096] if modality != "video" else x[:tokenizer_model_max_length] for x, modality in zip(new_labels, modalities)]
# Combine them
max_len = max(x.shape[0] for x in new_input_embeds)
batch_size = len(new_input_embeds)
new_input_embeds_padded = []
new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
# rank0_print("Prepare pos id")
for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
cur_len = cur_new_embed.shape[0]
if getattr(self.config, "tokenizer_padding_side", "right") == "left":
new_input_embeds_padded.append(torch.cat((torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device), cur_new_embed), dim=0))
if cur_len > 0:
new_labels_padded[i, -cur_len:] = cur_new_labels
attention_mask[i, -cur_len:] = True
position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
else:
new_input_embeds_padded.append(torch.cat((cur_new_embed, torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0))
if cur_len > 0:
new_labels_padded[i, :cur_len] = cur_new_labels
attention_mask[i, :cur_len] = True
position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
# rank0_print("tokenizer padding")
if _labels is None:
new_labels = None
else:
new_labels = new_labels_padded
if _attention_mask is None:
attention_mask = None
else:
attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
if _position_ids is None:
position_ids = None
if getattr(self.config, "use_pos_skipping", False) and self.training:
position_ids = torch.arange(new_input_embeds.size(1), device=new_input_embeds.device).unsqueeze(0).to(new_input_embeds.device)
split_position = random.randint(0, new_input_embeds.size(1))
left_add = random.randint(0, self.config.pos_skipping_range)
right_add = random.randint(left_add, self.config.pos_skipping_range)
position_ids[:, :split_position] += left_add
position_ids[:, split_position:] += right_add
# import pdb; pdb.set_trace()
# rank0_print("Finish preparing")
return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
def initialize_vision_tokenizer(self, model_args, tokenizer):
if model_args.mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
if model_args.mm_use_im_start_end:
num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
input_embeddings = self.get_input_embeddings().weight.data
output_embeddings = self.get_output_embeddings().weight.data
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
input_embeddings[-num_new_tokens:] = input_embeddings_avg
output_embeddings[-num_new_tokens:] = output_embeddings_avg
if model_args.tune_mm_mlp_adapter:
for p in self.get_input_embeddings().parameters():
p.requires_grad = True
for p in self.get_output_embeddings().parameters():
p.requires_grad = False
if model_args.pretrain_mm_mlp_adapter:
mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location="cpu")
embed_tokens_weight = mm_projector_weights["model.embed_tokens.weight"]
assert num_new_tokens == 2
if input_embeddings.shape == embed_tokens_weight.shape:
input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
elif embed_tokens_weight.shape[0] == num_new_tokens:
input_embeddings[-num_new_tokens:] = embed_tokens_weight
else:
raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
elif model_args.mm_use_im_patch_token:
if model_args.tune_mm_mlp_adapter:
for p in self.get_input_embeddings().parameters():
p.requires_grad = False
for p in self.get_output_embeddings().parameters():
p.requires_grad = False
llava/model/make_delta.py 0 → 100644
View file @ d5878167
"""
Usage:
python3 -m llava.model.make_delta --base ~/model_weights/llama-7b --target ~/model_weights/llava-7b --delta ~/model_weights/llava-7b-delta --hub-repo-id liuhaotian/llava-7b-delta
"""
import argparse
import torch
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
from llava.model.utils import auto_upgrade
def make_delta(base_model_path, target_model_path, delta_path, hub_repo_id):
print("Loading base model")
base = AutoModelForCausalLM.from_pretrained(base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
print("Loading target model")
auto_upgrade(target_model_path)
target = AutoModelForCausalLM.from_pretrained(target_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
print("Calculating delta")
for name, param in tqdm(target.state_dict().items(), desc="Calculating delta"):
if name not in base.state_dict():
assert name in ["model.mm_projector.weight", "model.mm_projector.bias"], f"{name} not in base model"
continue
if param.data.shape == base.state_dict()[name].shape:
param.data -= base.state_dict()[name]
else:
assert name in ["model.embed_tokens.weight", "lm_head.weight"], f"{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}"
bparam = base.state_dict()[name]
param.data[: bparam.shape[0], : bparam.shape[1]] -= bparam
print("Saving delta")
if hub_repo_id:
kwargs = {"push_to_hub": True, "repo_id": hub_repo_id}
else:
kwargs = {}
target.save_pretrained(delta_path, **kwargs)
target_tokenizer = AutoTokenizer.from_pretrained(target_model_path)
target_tokenizer.save_pretrained(delta_path, **kwargs)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--base-model-path", type=str, required=True)
parser.add_argument("--target-model-path", type=str, required=True)
parser.add_argument("--delta-path", type=str, required=True)
parser.add_argument("--hub-repo-id", type=str, default=None)
args = parser.parse_args()
make_delta(args.base_model_path, args.target_model_path, args.delta_path, args.hub_repo_id)
llava/model/multimodal_encoder/builder.py 0 → 100644
View file @ d5878167
import os
from .clip_encoder import CLIPVisionTower
from .imagebind import ImageBindWrapper
from .open_clip_encoder import OpenCLIPVisionTower
from .hf_vision import HFVisionTower
from .siglip_encoder import SigLipVisionTower
from .clip_encoder import CLIPVisionTower, CLIPVisionTowerS2
from .mlcd_encoder import MLCDVisionTower, MLCDVisionTowerS2
# from .eva_clip.eva_clip_encoder import EvaClipVisionTower
# from .dev_eva_clip.eva_vit import EvaViTWrapper
def build_vision_tower(vision_tower_cfg, **kwargs):
vision_tower = getattr(vision_tower_cfg, "mm_vision_tower", getattr(vision_tower_cfg, "vision_tower", None))
is_absolute_path_exists = os.path.exists(vision_tower)
use_s2 = getattr(vision_tower_cfg, "s2", False)
if is_absolute_path_exists or vision_tower.startswith("openai") or vision_tower.startswith("laion") or "ShareGPT4V" in vision_tower:
if use_s2:
return CLIPVisionTowerS2(vision_tower, args=vision_tower_cfg, **kwargs)
else:
return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
elif "siglip" in vision_tower:
return SigLipVisionTower(vision_tower, vision_tower_cfg=vision_tower_cfg, **kwargs)
elif vision_tower.startswith("hf:"):
return HFVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
elif vision_tower in ["imagebind_huge"]:
return ImageBindWrapper(vision_tower, args=vision_tower_cfg, **kwargs)
elif vision_tower.startswith("open_clip_hub"):
return OpenCLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
elif "mlcd-vit-bigG-patch14" in vision_tower:
if use_s2:
return MLCDVisionTowerS2(vision_tower, args=vision_tower_cfg, **kwargs)
else:
return MLCDVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
# elif "internal-eva" in vision_tower.lower() or "eva02" in vision_tower.lower():
# return EvaClipVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
# elif vision_tower in ["EVA-CLIP-8B", "EVA-CLIP-8B-plus"]:
# return EvaViTWrapper(vision_tower, args=vision_tower_cfg, **kwargs)
raise ValueError(f"Unknown vision tower: {vision_tower}")
llava/model/multimodal_encoder/clip_encoder.py 0 → 100644
View file @ d5878167
import torch
import torch.nn as nn
from llava.utils import rank0_print
from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
try:
from s2wrapper import forward as multiscale_forward
except:
pass
class CLIPVisionTower(nn.Module):
def __init__(self, vision_tower, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_tower_name = vision_tower
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, "mm_vision_select_feature", "patch")
if not delay_load:
rank0_print(f"Loading vision tower: {vision_tower}")
self.load_model()
elif getattr(args, "unfreeze_mm_vision_tower", False):
# TODO: better detector is needed.
rank0_print(f"The checkpoint seems to contain `vision_tower` weights: `unfreeze_mm_vision_tower`: True.")
self.load_model()
elif hasattr(args, "mm_tunable_parts") and "mm_vision_tower" in args.mm_tunable_parts:
rank0_print(f"The checkpoint seems to contain `vision_tower` weights: `mm_tunable_parts` contains `mm_vision_tower`.")
self.load_model()
else:
self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
def load_model(self, device_map=None):
if self.is_loaded:
rank0_print("{} is already loaded, `load_model` called again, skipping.".format(self.vision_tower_name))
return
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
self.vision_tower.requires_grad_(False)
self.is_loaded = True
def feature_select(self, image_forward_outs):
select_feature_type = self.select_feature
if self.select_feature in ["slicefour_patch", "slicefour_cls_patch"]:
select_every_k_layer = len(image_forward_outs.hidden_states) // 4
image_features = torch.cat([image_forward_outs.hidden_states[i] for i in range(select_every_k_layer + self.select_layer, len(image_forward_outs.hidden_states), select_every_k_layer)], dim=-1)
select_feature_type = select_feature_type.replace("slicefour_", "")
elif self.select_feature in ["slice_m25811_f6_patch", "slice_m25811_f6_cls_patch"]:
select_layers = [-2, -5, -8, -11, 6]
image_features = torch.cat([image_forward_outs.hidden_states[i] for i in select_layers], dim=-1)
select_feature_type = select_feature_type.replace("slice_m25811_f6_", "")
else:
image_features = image_forward_outs.hidden_states[self.select_layer]
if select_feature_type == "patch":
image_features = image_features[:, 1:]
elif select_feature_type == "cls_patch":
image_features = image_features
else:
raise ValueError(f"Unexpected select feature: {select_feature_type}")
return image_features
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
image_feature = self.feature_select(image_forward_out).to(image.dtype)
image_features.append(image_feature)
else:
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
@property
def dummy_feature(self):
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
@property
def dtype(self):
return self.vision_tower.dtype
@property
def device(self):
return self.vision_tower.device
@property
def config(self):
if self.is_loaded:
return self.vision_tower.config
else:
return self.cfg_only
@property
def hidden_size(self):
_hidden_size = self.config.hidden_size
if "slicefour" in self.select_feature:
_hidden_size *= 4
if "slice_m25811_f6" in self.select_feature:
_hidden_size *= 5
return _hidden_size
@property
def num_patches_per_side(self):
return self.config.image_size // self.config.patch_size
@property
def num_patches(self):
_num_patches = (self.config.image_size // self.config.patch_size) ** 2
if "cls_patch" in self.select_feature:
_num_patches += 1
return _num_patches
@property
def image_size(self):
return self.config.image_size
class CLIPVisionTowerS2(CLIPVisionTower):
def __init__(self, vision_tower, args, delay_load=False):
self.s2_scales = getattr(args, "s2_scales", "336,672,1008")
self.s2_scales = list(map(int, self.s2_scales.split(",")))
self.s2_scales.sort()
self.s2_split_size = self.s2_scales[0]
self.s2_image_size = self.s2_scales[-1]
super().__init__(vision_tower, args, delay_load)
# change resize/crop size in preprocessing to the largest image size in s2_scale
if not delay_load or getattr(args, "unfreeze_mm_vision_tower", False):
self.image_processor.size["shortest_edge"] = self.s2_image_size
self.image_processor.crop_size["height"] = self.image_processor.crop_size["width"] = self.s2_image_size
def load_model(self, device_map=None):
if self.is_loaded:
rank0_print("{} is already loaded, `load_model` called again, skipping.".format(self.vision_tower_name))
return
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
self.vision_tower.requires_grad_(False)
self.image_processor.size["shortest_edge"] = self.s2_image_size
self.image_processor.crop_size["height"] = self.image_processor.crop_size["width"] = self.s2_image_size
self.is_loaded = True
def forward_feature(self, images):
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_feature = multiscale_forward(self.forward_feature, image.unsqueeze(0), img_sizes=self.s2_scales, max_split_size=self.s2_split_size, split_forward=True)
image_features.append(image_feature)
else:
image_features = multiscale_forward(self.forward_feature, images, img_sizes=self.s2_scales, max_split_size=self.s2_split_size, split_forward=True)
return image_features
@property
def hidden_size(self):
return self.config.hidden_size * len(self.s2_scales)
llava/model/multimodal_encoder/dev_eva_clip/eva_clip/__init__.py 0 → 100644
View file @ d5878167
from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer
from .factory import list_models, add_model_config, get_model_config, load_checkpoint
from .loss import ClipLoss
from .model import CLIP, CustomCLIP, CLIPTextCfg, CLIPVisionCfg, convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype
from .openai import load_openai_model, list_openai_models
from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model, get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained
from .tokenizer import SimpleTokenizer, tokenize
from .transform import image_transform
llava/model/multimodal_encoder/dev_eva_clip/eva_clip/constants.py 0 → 100644
View file @ d5878167
OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
llava/model/multimodal_encoder/dev_eva_clip/eva_clip/eva_vit_model.py 0 → 100644
View file @ d5878167
# --------------------------------------------------------
# Adapted from https://github.com/microsoft/unilm/tree/master/beit
# --------------------------------------------------------
import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
except:
from timm.layers import drop_path, to_2tuple, trunc_normal_
from .transformer import PatchDropout
from .rope import VisionRotaryEmbedding, VisionRotaryEmbeddingFast
if os.getenv("ENV_TYPE") == "deepspeed":
try:
from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint
except:
from torch.utils.checkpoint import checkpoint
else:
from torch.utils.checkpoint import checkpoint
try:
import xformers.ops as xops
except ImportError:
xops = None
# print("Please 'pip install xformers'")
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
def extra_repr(self) -> str:
return "p={}".format(self.drop_prob)
class Mlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
drop=0.0,
subln=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
# x = self.drop(x)
# commit this for the orignal BERT implement
x = self.ffn_ln(x)
x = self.fc2(x)
x = self.drop(x)
return x
class SwiGLU(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.0, norm_layer=nn.LayerNorm, subln=False):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.w1 = nn.Linear(in_features, hidden_features)
self.w2 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()
self.w3 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x1 = self.w1(x)
x2 = self.w2(x)
hidden = self.act(x1) * x2
x = self.ffn_ln(hidden)
x = self.w3(x)
x = self.drop(x)
return x
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, window_size=None, attn_head_dim=None, xattn=False, rope=None, subln=False, norm_layer=nn.LayerNorm):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
if attn_head_dim is not None:
head_dim = attn_head_dim
all_head_dim = head_dim * self.num_heads
self.scale = qk_scale or head_dim**-0.5
self.subln = subln
if self.subln:
self.q_proj = nn.Linear(dim, all_head_dim, bias=False)
self.k_proj = nn.Linear(dim, all_head_dim, bias=False)
self.v_proj = nn.Linear(dim, all_head_dim, bias=False)
else:
self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
if qkv_bias:
self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
else:
self.q_bias = None
self.v_bias = None
if window_size:
self.window_size = window_size
self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
self.relative_position_bias_table = nn.Parameter(torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
# cls to token & token 2 cls & cls to cls
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(window_size[0])
coords_w = torch.arange(window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += window_size[1] - 1
relative_coords[:, :, 0] *= 2 * window_size[1] - 1
relative_position_index = torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
relative_position_index[0, 0:] = self.num_relative_distance - 3
relative_position_index[0:, 0] = self.num_relative_distance - 2
relative_position_index[0, 0] = self.num_relative_distance - 1
self.register_buffer("relative_position_index", relative_position_index)
else:
self.window_size = None
self.relative_position_bias_table = None
self.relative_position_index = None
self.attn_drop = nn.Dropout(attn_drop)
self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity()
# self.proj = nn.Linear(all_head_dim, all_head_dim)
self.proj = nn.Linear(all_head_dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.xattn = xattn
self.xattn_drop = attn_drop
self.rope = rope
def forward(self, x, rel_pos_bias=None, attn_mask=None):
B, N, C = x.shape
if self.subln:
q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias)
k = F.linear(input=x, weight=self.k_proj.weight, bias=None)
v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias)
q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) # B, num_heads, N, C
k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
else:
qkv_bias = None
if self.q_bias is not None:
qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # 3, B, num_heads, N, C
q, k, v = qkv[0], qkv[1], qkv[2]
if self.rope:
# slightly fast impl
q_t = q[:, :, 1:, :]
ro_q_t = self.rope(q_t)
q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v)
k_t = k[:, :, 1:, :]
ro_k_t = self.rope(k_t)
k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v)
if self.xattn:
q = q.permute(0, 2, 1, 3) # B, num_heads, N, C -> B, N, num_heads, C
k = k.permute(0, 2, 1, 3)
v = v.permute(0, 2, 1, 3)
x = xops.memory_efficient_attention(
q,
k,
v,
p=self.xattn_drop,
scale=self.scale,
)
x = x.reshape(B, N, -1)
x = self.inner_attn_ln(x)
x = self.proj(x)
x = self.proj_drop(x)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
if self.relative_position_bias_table is not None:
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(self.window_size[0] * self.window_size[1] + 1, self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0).type_as(attn)
if rel_pos_bias is not None:
attn = attn + rel_pos_bias.type_as(attn)
if attn_mask is not None:
attn_mask = attn_mask.bool()
attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf"))
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
x = self.inner_attn_ln(x)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.0,
qkv_bias=False,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
init_values=None,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
window_size=None,
attn_head_dim=None,
xattn=False,
rope=None,
postnorm=False,
subln=False,
naiveswiglu=False,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim, xattn=xattn, rope=rope, subln=subln, norm_layer=norm_layer
)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
if naiveswiglu:
self.mlp = SwiGLU(
in_features=dim,
hidden_features=mlp_hidden_dim,
subln=subln,
norm_layer=norm_layer,
)
else:
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, subln=subln, drop=drop)
if init_values is not None and init_values > 0:
self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
else:
self.gamma_1, self.gamma_2 = None, None
self.postnorm = postnorm
def forward(self, x, rel_pos_bias=None, attn_mask=None):
if self.gamma_1 is None:
if self.postnorm:
x = x + self.drop_path(self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)))
x = x + self.drop_path(self.norm2(self.mlp(x)))
else:
x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))
x = x + self.drop_path(self.mlp(self.norm2(x)))
else:
if self.postnorm:
x = x + self.drop_path(self.gamma_1 * self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)))
x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x)))
else:
x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
return x
class PatchEmbed(nn.Module):
"""Image to Patch Embedding"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x, **kwargs):
B, C, H, W = x.shape
# FIXME look at relaxing size constraints
assert H == self.img_size[0] and W == self.img_size[1], f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class RelativePositionBias(nn.Module):
def __init__(self, window_size, num_heads):
super().__init__()
self.window_size = window_size
self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
self.relative_position_bias_table = nn.Parameter(torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
# cls to token & token 2 cls & cls to cls
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(window_size[0])
coords_w = torch.arange(window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += window_size[1] - 1
relative_coords[:, :, 0] *= 2 * window_size[1] - 1
relative_position_index = torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
relative_position_index[0, 0:] = self.num_relative_distance - 3
relative_position_index[0:, 0] = self.num_relative_distance - 2
relative_position_index[0, 0] = self.num_relative_distance - 1
self.register_buffer("relative_position_index", relative_position_index)
def forward(self):
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(self.window_size[0] * self.window_size[1] + 1, self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
class EVAVisionTransformer(nn.Module):
"""Vision Transformer with support for patch or hybrid CNN input stage"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.0,
qkv_bias=False,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.0,
norm_layer=nn.LayerNorm,
init_values=None,
patch_dropout=0.0,
use_abs_pos_emb=True,
use_rel_pos_bias=False,
use_shared_rel_pos_bias=False,
rope=False,
use_mean_pooling=True,
init_scale=0.001,
grad_checkpointing=False,
xattn=False,
postnorm=False,
pt_hw_seq_len=16,
intp_freq=False,
naiveswiglu=False,
subln=False,
):
super().__init__()
self.image_size = img_size
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
# self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
if use_abs_pos_emb:
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
else:
self.pos_embed = None
self.pos_drop = nn.Dropout(p=drop_rate)
if use_shared_rel_pos_bias:
self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads)
else:
self.rel_pos_bias = None
if rope:
half_head_dim = embed_dim // num_heads // 2
hw_seq_len = img_size // patch_size
self.rope = VisionRotaryEmbeddingFast(
dim=half_head_dim,
pt_seq_len=pt_hw_seq_len,
ft_seq_len=hw_seq_len if intp_freq else None,
# patch_dropout=patch_dropout
)
else:
self.rope = None
self.naiveswiglu = naiveswiglu
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.use_rel_pos_bias = use_rel_pos_bias
self.blocks = nn.ModuleList(
[
Block(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
init_values=init_values,
window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None,
xattn=xattn,
rope=self.rope,
postnorm=postnorm,
subln=subln,
naiveswiglu=naiveswiglu,
)
for i in range(depth)
]
)
self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
self.head = nn.Linear(embed_dim, num_classes, bias=qkv_bias) if num_classes > 0 else nn.Identity()
if self.pos_embed is not None:
trunc_normal_(self.pos_embed, std=0.02)
trunc_normal_(self.cls_token, std=0.02)
self.apply(self._init_weights)
self.fix_init_weight()
if isinstance(self.head, nn.Linear):
trunc_normal_(self.head.weight, std=0.02)
self.head.weight.data.mul_(init_scale)
if self.head.bias is not None:
self.head.bias.data.mul_(init_scale)
# setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn
self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0.0 else nn.Identity()
self.grad_checkpointing = grad_checkpointing
def fix_init_weight(self):
def rescale(param, layer_id):
param.div_(math.sqrt(2.0 * layer_id))
for layer_id, layer in enumerate(self.blocks):
rescale(layer.attn.proj.weight.data, layer_id + 1)
if self.naiveswiglu:
rescale(layer.mlp.w3.weight.data, layer_id + 1)
else:
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
def get_cast_dtype(self) -> torch.dtype:
return self.blocks[0].mlp.fc2.weight.dtype
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def get_num_layers(self):
return len(self.blocks)
def lock(self, unlocked_groups=0, freeze_bn_stats=False):
assert unlocked_groups == 0, "partial locking not currently supported for this model"
for param in self.parameters():
param.requires_grad = False
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def no_weight_decay(self):
return {"pos_embed", "cls_token"}
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=""):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x, return_all_features=False):
x = self.patch_embed(x)
batch_size, seq_len, _ = x.size()
cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
x = torch.cat((cls_tokens, x), dim=1)
if self.pos_embed is not None:
x = x + self.pos_embed
x = self.pos_drop(x)
# a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
# if os.getenv("RoPE") == "1":
# if self.training and not isinstance(self.patch_dropout, nn.Identity):
# x, patch_indices_keep = self.patch_dropout(x)
# self.rope.forward = partial(self.rope.forward, patch_indices_keep=patch_indices_keep)
# else:
# self.rope.forward = partial(self.rope.forward, patch_indices_keep=None)
# x = self.patch_dropout(x)
# else:
x = self.patch_dropout(x)
rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
for blk in self.blocks:
if self.grad_checkpointing:
x = checkpoint(blk, x, (rel_pos_bias,))
else:
x = blk(x, rel_pos_bias=rel_pos_bias)
if not return_all_features:
x = self.norm(x)
if self.fc_norm is not None:
return self.fc_norm(x.mean(1))
else:
return x[:, 0]
return x
def forward(self, x, return_all_features=False):
if return_all_features:
return self.forward_features(x, return_all_features)
x = self.forward_features(x)
x = self.head(x)
return x
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