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SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/conversation.py 0 → 100644
View file @ 876a36a4
"""
Conversation prompt templates.
We kindly request that you import fastchat instead of copying this file if you wish to use it.
If you have changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
"""
import dataclasses
from enum import IntEnum, auto
from typing import Any, Dict, List, Tuple, Union
class SeparatorStyle(IntEnum):
"""Separator styles."""
ADD_COLON_SINGLE = auto()
ADD_COLON_TWO = auto()
ADD_COLON_SPACE_SINGLE = auto()
NO_COLON_SINGLE = auto()
NO_COLON_TWO = auto()
ADD_NEW_LINE_SINGLE = auto()
LLAMA2 = auto()
CHATGLM = auto()
CHATML = auto()
CHATINTERN = auto()
DOLLY = auto()
RWKV = auto()
PHOENIX = auto()
ROBIN = auto()
FALCON_CHAT = auto()
CHATGLM3 = auto()
INTERNVL_ZH = auto()
MPT = auto()
@dataclasses.dataclass
class Conversation:
"""A class that manages prompt templates and keeps all conversation history."""
# The name of this template
name: str
# The template of the system prompt
system_template: str = "{system_message}"
# The system message
system_message: str = ""
# The names of two roles
roles: Tuple[str] = ("USER", "ASSISTANT")
# All messages. Each item is (role, message).
messages: List[List[str]] = ()
# The number of few shot examples
offset: int = 0
# The separator style and configurations
sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
sep: str = "\n"
sep2: str = None
# Stop criteria (the default one is EOS token)
stop_str: Union[str, List[str]] = None
# Stops generation if meeting any token in this list
stop_token_ids: List[int] = None
def get_prompt(self) -> str:
"""Get the prompt for generation."""
system_prompt = self.system_template.format(system_message=self.system_message)
if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
ret = system_prompt + self.sep
for role, message in self.messages:
if message:
ret += role + ": " + message + self.sep
else:
ret += role + ":"
return ret
elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
seps = [self.sep, self.sep2]
ret = system_prompt + seps[0]
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + ": " + message + seps[i % 2]
else:
ret += role + ":"
return ret
elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
ret = system_prompt + self.sep
for role, message in self.messages:
if message:
ret += role + ": " + message + self.sep
else:
ret += role + ": " # must be end with a space
return ret
elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
ret = "" if system_prompt == "" else system_prompt + self.sep
for role, message in self.messages:
if message:
ret += role + "\n" + message + self.sep
else:
ret += role + "\n"
return ret
elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
ret = system_prompt
for role, message in self.messages:
if message:
ret += role + message + self.sep
else:
ret += role
return ret
elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
seps = [self.sep, self.sep2]
ret = system_prompt
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + message + seps[i % 2]
else:
ret += role
return ret
elif self.sep_style == SeparatorStyle.RWKV:
ret = system_prompt
for i, (role, message) in enumerate(self.messages):
if message:
ret += (
role
+ ": "
+ message.replace("\r\n", "\n").replace("\n\n", "\n")
)
ret += "\n\n"
else:
ret += role + ":"
return ret
elif self.sep_style == SeparatorStyle.LLAMA2:
seps = [self.sep, self.sep2]
if self.system_message:
ret = system_prompt
else:
ret = "[INST] "
for i, (role, message) in enumerate(self.messages):
tag = self.roles[i % 2]
if message:
if i == 0:
ret += message + " "
else:
ret += tag + " " + message + seps[i % 2]
else:
ret += tag
return ret
elif self.sep_style == SeparatorStyle.CHATGLM:
# source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
# source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
round_add_n = 1 if self.name == "chatglm2" else 0
if system_prompt:
ret = system_prompt + self.sep
else:
ret = ""
for i, (role, message) in enumerate(self.messages):
if i % 2 == 0:
ret += f"[Round {i // 2 + round_add_n}]{self.sep}"
if message:
ret += f"{role}：{message}{self.sep}"
else:
ret += f"{role}："
return ret
elif self.sep_style == SeparatorStyle.CHATML:
ret = "" if system_prompt == "" else system_prompt + self.sep + "\n"
for role, message in self.messages:
if message:
ret += role + "\n" + message + self.sep + "\n"
else:
ret += role + "\n"
return ret
elif self.sep_style == SeparatorStyle.CHATGLM3:
ret = ""
if self.system_message:
ret += system_prompt
for role, message in self.messages:
if message:
ret += role + "\n" + " " + message
else:
ret += role
return ret
elif self.sep_style == SeparatorStyle.CHATINTERN:
# source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
seps = [self.sep, self.sep2]
ret = system_prompt
for i, (role, message) in enumerate(self.messages):
# if i % 2 == 0:
# ret += "<s>"
if message:
ret += role + ":" + message + seps[i % 2] + "\n"
else:
ret += role + ":"
return ret
elif self.sep_style == SeparatorStyle.DOLLY:
seps = [self.sep, self.sep2]
ret = system_prompt
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + ":\n" + message + seps[i % 2]
if i % 2 == 1:
ret += "\n\n"
else:
ret += role + ":\n"
return ret
elif self.sep_style == SeparatorStyle.PHOENIX:
ret = system_prompt
for role, message in self.messages:
if message:
ret += role + ": " + "<s>" + message + "</s>"
else:
ret += role + ": " + "<s>"
return ret
elif self.sep_style == SeparatorStyle.ROBIN:
ret = system_prompt + self.sep
for role, message in self.messages:
if message:
ret += role + ":\n" + message + self.sep
else:
ret += role + ":\n"
return ret
elif self.sep_style == SeparatorStyle.FALCON_CHAT:
ret = ""
if self.system_message:
ret += system_prompt + self.sep
for role, message in self.messages:
if message:
ret += role + ": " + message + self.sep
else:
ret += role + ":"
return ret
elif self.sep_style == SeparatorStyle.INTERNVL_ZH:
seps = [self.sep2, self.sep]
ret = self.system_message + seps[0]
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + ": " + message + seps[i % 2]
else:
ret += role + ":"
return ret
elif self.sep_style == SeparatorStyle.MPT:
ret = system_prompt + self.sep
for role, message in self.messages:
if message:
if type(message) is tuple:
message, _, _ = message
ret += role + message + self.sep
else:
ret += role
return ret
else:
raise ValueError(f"Invalid style: {self.sep_style}")
def set_system_message(self, system_message: str):
"""Set the system message."""
self.system_message = system_message
def append_message(self, role: str, message: str):
"""Append a new message."""
self.messages.append([role, message])
def update_last_message(self, message: str):
"""Update the last output.
The last message is typically set to be None when constructing the prompt,
so we need to update it in-place after getting the response from a model.
"""
self.messages[-1][1] = message
def to_gradio_chatbot(self):
"""Convert the conversation to gradio chatbot format."""
ret = []
for i, (role, msg) in enumerate(self.messages[self.offset :]):
if i % 2 == 0:
ret.append([msg, None])
else:
ret[-1][-1] = msg
return ret
def to_openai_api_messages(self):
"""Convert the conversation to OpenAI chat completion format."""
ret = [{"role": "system", "content": self.system_message}]
for i, (_, msg) in enumerate(self.messages[self.offset :]):
if i % 2 == 0:
ret.append({"role": "user", "content": msg})
else:
if msg is not None:
ret.append({"role": "assistant", "content": msg})
return ret
def copy(self):
return Conversation(
name=self.name,
system_template=self.system_template,
system_message=self.system_message,
roles=self.roles,
messages=[[x, y] for x, y in self.messages],
offset=self.offset,
sep_style=self.sep_style,
sep=self.sep,
sep2=self.sep2,
stop_str=self.stop_str,
stop_token_ids=self.stop_token_ids,
)
def dict(self):
return {
"template_name": self.name,
"system_message": self.system_message,
"roles": self.roles,
"messages": self.messages,
"offset": self.offset,
}
# A global registry for all conversation templates
conv_templates: Dict[str, Conversation] = {}
def register_conv_template(template: Conversation, override: bool = False):
"""Register a new conversation template."""
if not override:
assert template.name not in conv_templates, (
f"{template.name} has been registered."
)
conv_templates[template.name] = template
def get_conv_template(name: str) -> Conversation:
"""Get a conversation template."""
return conv_templates[name].copy()
# InternVL-Chat-V1-1 template
register_conv_template(
Conversation(
name="internvl_zh",
system_template="",
roles=("<human>", "<bot>"),
sep_style=SeparatorStyle.INTERNVL_ZH,
sep="</s>",
sep2=" ",
)
)
# Both Hermes-2 and internlm2-chat are chatml-format conversation templates. The difference
# is that during training, the preprocessing function for the Hermes-2 template doesn't add
# <s> at the beginning of the tokenized sequence, while the internlm2-chat template does.
# Therefore, they are completely equivalent during inference.
register_conv_template(
Conversation(
name="Hermes-2",
system_template="<|im_start|>system\n{system_message}",
# note: The new system prompt was not used here to avoid changes in benchmark performance.
# system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
system_message="你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。",
roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
sep_style=SeparatorStyle.MPT,
sep="<|im_end|>",
stop_str="<|endoftext|>",
)
)
register_conv_template(
Conversation(
name="internlm2-chat",
system_template="<|im_start|>system\n{system_message}",
# note: The new system prompt was not used here to avoid changes in benchmark performance.
# system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
system_message="你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。",
roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
sep_style=SeparatorStyle.MPT,
sep="<|im_end|>",
)
)
register_conv_template(
Conversation(
name="phi3-chat",
system_template="<|system|>\n{system_message}",
# note: The new system prompt was not used here to avoid changes in benchmark performance.
# system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
system_message="你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。",
roles=("<|user|>\n", "<|assistant|>\n"),
sep_style=SeparatorStyle.MPT,
sep="<|end|>",
)
)
register_conv_template(
Conversation(
name="internvl2_5",
system_template="<|im_start|>system\n{system_message}",
system_message="你是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。",
roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
sep_style=SeparatorStyle.MPT,
sep="<|im_end|>\n",
)
)
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/dist_utils.py 0 → 100644
View file @ 876a36a4
import os
import socket
import subprocess
from datetime import timedelta
import deepspeed
import torch
import torch.multiprocessing as mp
from torch import distributed as dist
timeout = timedelta(minutes=60)
def _find_free_port():
# Copied from https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/launch.py # noqa: E501
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# Binding to port 0 will cause the OS to find an available port for us
sock.bind(("", 0))
port = sock.getsockname()[1]
sock.close()
# NOTE: there is still a chance the port could be taken by other processes.
return port
def _is_free_port(port):
ips = socket.gethostbyname_ex(socket.gethostname())[-1]
ips.append("localhost")
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
return all(s.connect_ex((ip, port)) != 0 for ip in ips)
def init_dist(launcher, backend="nccl", **kwargs):
if mp.get_start_method(allow_none=True) is None:
mp.set_start_method("spawn")
if launcher == "pytorch":
_init_dist_pytorch(backend, **kwargs)
elif launcher == "mpi":
_init_dist_mpi(backend, **kwargs)
elif launcher == "slurm":
_init_dist_slurm(backend, **kwargs)
else:
raise ValueError(f"Invalid launcher type: {launcher}")
def _init_dist_pytorch(backend, **kwargs):
# TODO: use local_rank instead of rank % num_gpus
rank = int(os.environ["RANK"])
num_gpus = torch.cuda.device_count()
torch.cuda.set_device(rank % num_gpus)
# dist.init_process_group(backend=backend, **kwargs)
deepspeed.init_distributed(dist_backend=backend)
def _init_dist_mpi(backend, **kwargs):
local_rank = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"])
torch.cuda.set_device(local_rank)
if "MASTER_PORT" not in os.environ:
# 29500 is torch.distributed default port
os.environ["MASTER_PORT"] = "29500"
if "MASTER_ADDR" not in os.environ:
raise KeyError("The environment variable MASTER_ADDR is not set")
os.environ["WORLD_SIZE"] = os.environ["OMPI_COMM_WORLD_SIZE"]
os.environ["RANK"] = os.environ["OMPI_COMM_WORLD_RANK"]
dist.init_process_group(backend=backend, **kwargs)
def _init_dist_slurm(backend, port=None):
"""Initialize slurm distributed training environment.
If argument ``port`` is not specified, then the master port will be system
environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system
environment variable, then a default port ``29500`` will be used.
Args:
backend (str): Backend of torch.distributed.
port (int, optional): Master port. Defaults to None.
"""
proc_id = int(os.environ["SLURM_PROCID"])
ntasks = int(os.environ["SLURM_NTASKS"])
node_list = os.environ["SLURM_NODELIST"]
num_gpus = torch.cuda.device_count()
torch.cuda.set_device(proc_id % num_gpus)
addr = subprocess.getoutput(f"scontrol show hostname {node_list} | head -n1")
# specify master port
if port is not None:
os.environ["MASTER_PORT"] = str(port)
elif "MASTER_PORT" in os.environ:
pass # use MASTER_PORT in the environment variable
else:
# if torch.distributed default port(29500) is available
# then use it, else find a free port
if _is_free_port(29500):
os.environ["MASTER_PORT"] = "29500"
else:
os.environ["MASTER_PORT"] = str(_find_free_port())
# use MASTER_ADDR in the environment variable if it already exists
if "MASTER_ADDR" not in os.environ:
os.environ["MASTER_ADDR"] = addr
os.environ["WORLD_SIZE"] = str(ntasks)
os.environ["LOCAL_RANK"] = str(proc_id % num_gpus)
os.environ["RANK"] = str(proc_id)
# dist.init_process_group(backend=backend, timeout=timeout)
deepspeed.init_distributed(dist_backend=backend)
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/model/__init__.py 0 → 100644
View file @ 876a36a4
# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import math
import torch
from internvl.model.internvl_chat import InternVLChatConfig, InternVLChatModel
from transformers import AutoTokenizer
def split_model(num_layers, vit_alpha=0.5):
device_map = {}
world_size = torch.cuda.device_count()
# Since the first GPU will be used for ViT, treat it as half a GPU.
num_layers_per_gpu = math.ceil(num_layers / (world_size - vit_alpha))
num_layers_per_gpu = [num_layers_per_gpu] * world_size
num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * (1 - vit_alpha))
layer_cnt = 0
for i, num_layer in enumerate(num_layers_per_gpu):
for j in range(num_layer):
device_map[f"language_model.model.layers.{layer_cnt}"] = i
layer_cnt += 1
device_map["vision_model"] = 0
device_map["mlp1"] = 0
device_map["language_model.model.tok_embeddings"] = 0
device_map["language_model.model.embed_tokens"] = 0
device_map["language_model.output"] = 0
device_map["language_model.model.norm"] = 0
device_map["language_model.lm_head"] = 0
device_map[f"language_model.model.layers.{num_layers - 1}"] = 0
device_map["language_model.model.rotary_emb"] = 0
return device_map
def load_model_and_tokenizer(args):
if args.auto:
config = InternVLChatConfig.from_pretrained(args.checkpoint)
num_hidden_layers = config.llm_config.num_hidden_layers
device_map = split_model(num_hidden_layers)
kwargs = {"device_map": device_map} if args.auto else {}
tokenizer = AutoTokenizer.from_pretrained(
args.checkpoint, trust_remote_code=True, use_fast=False
)
model = InternVLChatModel.from_pretrained(
args.checkpoint,
low_cpu_mem_usage=True,
torch_dtype=torch.bfloat16,
load_in_8bit=args.load_in_8bit,
load_in_4bit=args.load_in_4bit,
**kwargs,
).eval()
if not args.load_in_8bit and not args.load_in_4bit and not args.auto:
model = model.cuda()
return model, tokenizer
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/model/internlm2/configuration_internlm2.py 0 → 100644
View file @ 876a36a4
# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
#
# 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.
"""InternLM2 model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
# Modified from transformers.model.llama.configuration_llama.LlamaConfig
class InternLM2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`InternLM2Model`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
Example:
"""
model_type = "internlm2"
_auto_class = "AutoConfig"
def __init__( # pylint: disable=W0102
self,
vocab_size=103168,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
bias=True,
rope_theta=10000,
rope_scaling=None,
attn_implementation="eager",
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.bias = bias
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
self.attn_implementation = attn_implementation
if self.attn_implementation is None:
self.attn_implementation = "eager"
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
)
if (
rope_scaling_factor is None
or not isinstance(rope_scaling_factor, float)
or rope_scaling_factor < 1.0
):
raise ValueError(
f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}"
)
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/model/internlm2/modeling_internlm2.py 0 → 100644
View file @ 876a36a4
# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on transformers/src/transformers/models/llama/modeling_llama.py
#
# 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 InternLM2 model."""
import math
import queue
import threading
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from einops import rearrange
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
SequenceClassifierOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
try:
from transformers.generation.streamers import BaseStreamer
except: # noqa # pylint: disable=bare-except
BaseStreamer = None
from .configuration_internlm2 import InternLM2Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "InternLM2Config"
flash_attn_func, flash_attn_varlen_func = None, None
pad_input, index_first_axis, unpad_input = None, None, None
try:
from flash_attn import flash_attn_func as _flash_attn_func
from flash_attn import flash_attn_varlen_func as _flash_attn_varlen_func
from flash_attn.bert_padding import index_first_axis as _index_first_axis
from flash_attn.bert_padding import pad_input as _pad_input
from flash_attn.bert_padding import unpad_input as _unpad_input
flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
pad_input, index_first_axis, unpad_input = (
_pad_input,
_index_first_axis,
_unpad_input,
)
has_flash_attn = True
except:
has_flash_attn = False
def _import_flash_attn():
global flash_attn_func, flash_attn_varlen_func
global pad_input, index_first_axis, unpad_input
try:
from flash_attn import flash_attn_func as _flash_attn_func
from flash_attn import flash_attn_varlen_func as _flash_attn_varlen_func
from flash_attn.bert_padding import index_first_axis as _index_first_axis
from flash_attn.bert_padding import pad_input as _pad_input
from flash_attn.bert_padding import unpad_input as _unpad_input
flash_attn_func, flash_attn_varlen_func = (
_flash_attn_func,
_flash_attn_varlen_func,
)
pad_input, index_first_axis, unpad_input = (
_pad_input,
_index_first_axis,
_unpad_input,
)
except ImportError:
raise ImportError("flash_attn is not installed.")
# Copied from transformers.models.llama.modeling_llama._get_unpad_data
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.torch.int32), (1, 0)
)
return (
indices,
cu_seqlens,
max_seqlen_in_batch,
)
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
input_ids_shape: torch.Size,
dtype: torch.dtype,
device: torch.device,
past_key_values_length: int = 0,
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full(
(tgt_len, tgt_len),
torch.tensor(torch.finfo(dtype).min, device=device),
device=device,
)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat(
[
torch.zeros(
tgt_len, past_key_values_length, dtype=dtype, device=device
),
mask,
],
dim=-1,
)
return mask[None, None, :, :].expand(
bsz, 1, tgt_len, tgt_len + past_key_values_length
)
# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(dtype).min
)
# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
class InternLM2RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
InternLM2RMSNorm 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)
try:
from functools import partial
from apex.normalization import FusedRMSNorm
InternLM2RMSNorm = partial(FusedRMSNorm, eps=1e-6) # noqa
print(
"Discovered apex.normalization.FusedRMSNorm - will use it instead of InternLM2RMSNorm"
)
except ImportError:
# using the normal LlamaRMSNorm
pass
except Exception:
print("discovered apex but it failed to load, falling back to InternLM2RMSNorm")
pass
# Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
class InternLM2RotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
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).float().to(device) / self.dim)
)
self.register_buffer("inv_freq", inv_freq, persistent=False)
# Build here to make `torch.jit.trace` work.
self._set_cos_sin_cache(
seq_len=max_position_embeddings,
device=self.inv_freq.device,
dtype=torch.get_default_dtype(),
)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device).to(
dtype=self.inv_freq.dtype
)
freqs = torch.einsum("i,j->ij", 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(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(
seq_len=seq_len, device=x.device, dtype=torch.float32
)
return (
self.cos_cached[:seq_len].to(dtype=x.dtype),
self.sin_cached[:seq_len].to(dtype=x.dtype),
)
# Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
"""InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
def __init__(
self,
dim,
max_position_embeddings=2048,
base=10000,
device=None,
scaling_factor=1.0,
):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device).to(
dtype=self.inv_freq.dtype
)
t = t / self.scaling_factor
freqs = torch.einsum("i,j->ij", 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(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
# Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
"""InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
Credits to the Reddit users /u/bloc97 and /u/emozilla.
"""
def __init__(
self,
dim,
max_position_embeddings=2048,
base=10000,
device=None,
scaling_factor=1.0,
):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
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).float().to(device) / self.dim)
)
self.register_buffer("inv_freq", inv_freq, persistent=False)
t = torch.arange(self.max_seq_len_cached, device=device).to(
dtype=self.inv_freq.dtype
)
freqs = torch.einsum("i,j->ij", 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(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
# Copied from transformers.model.llama.modeling_llama.rotate_half
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)
# Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors."""
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
sin = sin[position_ids].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 InternLM2MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
return down_proj
# Copied from transformers.model.llama.modeling_llama.repeat_kv
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)
# Modified from transformers.model.llama.modeling_llama.LlamaAttention
class InternLM2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: InternLM2Config):
super().__init__()
self.config = config
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.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.wqkv = nn.Linear(
self.hidden_size,
(self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
bias=config.bias,
)
self.wo = nn.Linear(
self.num_heads * self.head_dim, self.hidden_size, bias=config.bias
)
self._init_rope()
def _init_rope(self):
if self.config.rope_scaling is None:
self.rotary_emb = InternLM2RotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.config.rope_theta,
)
else:
scaling_type = self.config.rope_scaling["type"]
scaling_factor = self.config.rope_scaling["factor"]
if scaling_type == "dynamic":
self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.config.rope_theta,
scaling_factor=scaling_factor,
)
elif scaling_type == "linear":
self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.config.rope_theta,
scaling_factor=scaling_factor,
)
else:
raise ValueError(
"Currently we only support rotary embedding's type being 'dynamic' or 'linear'."
)
return self.rotary_emb
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return (
tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
.transpose(1, 2)
.contiguous()
)
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: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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.`"
)
bsz, q_len, _ = hidden_states.size()
qkv_states = self.wqkv(hidden_states)
qkv_states = rearrange(
qkv_states,
"b q (h gs d) -> b q h gs d",
gs=2 + self.num_key_value_groups,
d=self.head_dim,
)
query_states = qkv_states[..., : self.num_key_value_groups, :]
query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
key_states = qkv_states[..., -2, :]
value_states = qkv_states[..., -1, :]
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
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 attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(
attn_weights, dim=-1, dtype=torch.float32
).to(query_states.dtype)
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)
attn_output = self.wo(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
# Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
class InternLM2FlashAttention2(InternLM2Attention):
"""
InternLM2 flash attention module. This module inherits from `InternLM2Attention` 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 forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# InternLM2FlashAttention2 attention does not support output_attentions
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.`"
)
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop("padding_mask")
output_attentions = False
bsz, q_len, _ = hidden_states.size()
qkv_states = self.wqkv(hidden_states)
qkv_states = rearrange(
qkv_states,
"b q (h gs d) -> b q h gs d",
gs=2 + self.num_key_value_groups,
d=self.head_dim,
)
query_states = qkv_states[..., : self.num_key_value_groups, :]
query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
key_states = qkv_states[..., -2, :]
value_states = qkv_states[..., -1, :]
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
attn_output = self._flash_attention_forward(
query_states, key_states, value_states, attention_mask, q_len
)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
attn_output = self.wo(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)
"""
# Contains at least one padding token in the sequence
causal = self.is_causal and query_length != 1
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._unpad_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 _unpad_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.to(torch.int64),
(cu_seqlens_q, cu_seqlens_k),
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
)
INTERNLM2_ATTENTION_CLASSES = {
"eager": InternLM2Attention,
"flash_attention_2": InternLM2FlashAttention2,
}
# Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
class InternLM2DecoderLayer(nn.Module):
def __init__(self, config: InternLM2Config):
super().__init__()
self.hidden_size = config.hidden_size
self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](
config=config
)
self.feed_forward = InternLM2MLP(config)
self.attention_norm = InternLM2RMSNorm(
config.hidden_size, eps=config.rms_norm_eps
)
self.ffn_norm = InternLM2RMSNorm(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,
**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.attention_norm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.attention(
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,
**kwargs,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.ffn_norm(hidden_states)
hidden_states = self.feed_forward(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
InternLM2_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 ([`InternLM2Config`]):
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.
"""
# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
@add_start_docstrings(
"The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
InternLM2_START_DOCSTRING,
)
class InternLM2PreTrainedModel(PreTrainedModel):
config_class = InternLM2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["InternLM2DecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = 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_()
InternLM2_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 (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
when `config.use_cache=True`):
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)`) and 2 additional tensors of shape
`(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
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.
"""
# Modified from transformers.model.llama.modeling_llama.LlamaModel
@add_start_docstrings(
"The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
InternLM2_START_DOCSTRING,
)
class InternLM2Model(InternLM2PreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
Args:
config: InternLM2Config
"""
_auto_class = "AutoModel"
def __init__(self, config: InternLM2Config):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.config = config
if not has_flash_attn:
self.config.attn_implementation = "eager"
print(
"Warning: Flash attention is not available, using eager attention instead."
)
self.tok_embeddings = nn.Embedding(
config.vocab_size, config.hidden_size, self.padding_idx
)
self.layers = nn.ModuleList(
[InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)]
)
self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.tok_embeddings
def set_input_embeddings(self, value):
self.tok_embeddings = value
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
device=inputs_embeds.device,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
).to(inputs_embeds.device)
combined_attention_mask = (
expanded_attn_mask
if combined_attention_mask is None
else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
@add_start_docstrings_to_model_forward(InternLM2_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,
) -> 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 self.config.attn_implementation == "flash_attention_2":
_import_flash_attn()
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time"
)
elif input_ids is not None:
batch_size, seq_length = input_ids.shape[:2]
elif inputs_embeds is not None:
batch_size, seq_length = inputs_embeds.shape[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length,
seq_length + past_key_values_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0)
if inputs_embeds is None:
inputs_embeds = self.tok_embeddings(input_ids)
if self.config.attn_implementation == "flash_attention_2":
# 2d mask is passed through the layers
attention_mask = (
attention_mask
if (attention_mask is not None and 0 in attention_mask)
else None
)
else:
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past),
dtype=torch.bool,
device=inputs_embeds.device,
)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
)
# embed positions
hidden_states = inputs_embeds
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = (
past_key_values[idx] if past_key_values is not None else None
)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, output_attentions, None)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
attention_mask,
position_ids,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
# Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
class InternLM2ForCausalLM(InternLM2PreTrainedModel):
_auto_class = "AutoModelForCausalLM"
_tied_weights_keys = ["output.weight"]
def __init__(self, config):
super().__init__(config)
self.model = InternLM2Model(config)
self.vocab_size = config.vocab_size
self.output = 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.tok_embeddings
def set_input_embeddings(self, value):
self.model.tok_embeddings = value
def get_output_embeddings(self):
return self.output
def set_output_embeddings(self, new_embeddings):
self.output = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@add_start_docstrings_to_model_forward(InternLM2_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,
) -> 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, InternLM2ForCausalLM
>>> model = InternLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> 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,
)
hidden_states = outputs[0]
logits = self.output(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
device = input_ids.device if input_ids is not None else inputs_embeds.device
output = CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
output["logits"] = output["logits"].to(device)
return output
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
**kwargs,
):
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_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 `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:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"position_ids": position_ids,
"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
def build_inputs(
self,
tokenizer,
query: str,
history: List[Tuple[str, str]] = [],
meta_instruction="",
):
if tokenizer.add_bos_token:
prompt = ""
else:
prompt = tokenizer.bos_token
if meta_instruction:
prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
for record in history:
prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
return tokenizer([prompt], return_tensors="pt")
@torch.no_grad()
def chat(
self,
tokenizer,
query: str,
history: List[Tuple[str, str]] = [],
streamer: Optional[BaseStreamer] = None,
max_new_tokens: int = 1024,
do_sample: bool = True,
temperature: float = 0.8,
top_p: float = 0.8,
meta_instruction: str = "You are an AI assistant whose name is InternLM (书生·浦语).\n"
"- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
"- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.",
**kwargs,
):
inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
# also add end-of-assistant token in eos token id to avoid unnecessary generation
eos_token_id = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids(["<|im_end|>"])[0],
]
outputs = self.generate(
**inputs,
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
temperature=temperature,
top_p=top_p,
eos_token_id=eos_token_id,
**kwargs,
)
outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
response = tokenizer.decode(outputs, skip_special_tokens=True)
response = response.split("<|im_end|>")[0]
history = history + [(query, response)]
return response, history
@torch.no_grad()
def stream_chat(
self,
tokenizer,
query: str,
history: List[Tuple[str, str]] = [],
max_new_tokens: int = 1024,
do_sample: bool = True,
temperature: float = 0.8,
top_p: float = 0.8,
**kwargs,
):
"""
Return a generator in format: (response, history)
Eg.
('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
"""
if BaseStreamer is None:
raise ModuleNotFoundError(
"The version of `transformers` is too low. Please make sure "
"that you have installed `transformers>=4.28.0`."
)
response_queue = queue.Queue(maxsize=20)
class ChatStreamer(BaseStreamer):
def __init__(self, tokenizer) -> None:
super().__init__()
self.tokenizer = tokenizer
self.queue = response_queue
self.query = query
self.history = history
self.response = ""
self.cache = []
self.received_inputs = False
self.queue.put((self.response, history + [(self.query, self.response)]))
def put(self, value):
if len(value.shape) > 1 and value.shape[0] > 1:
raise ValueError("ChatStreamer only supports batch size 1")
elif len(value.shape) > 1:
value = value[0]
if not self.received_inputs:
# The first received value is input_ids, ignore here
self.received_inputs = True
return
self.cache.extend(value.tolist())
token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
if token.strip() != "<|im_end|>":
self.response = self.response + token
history = self.history + [(self.query, self.response)]
self.queue.put((self.response, history))
self.cache = []
else:
self.end()
def end(self):
self.queue.put(None)
def stream_producer():
return self.chat(
tokenizer=tokenizer,
query=query,
streamer=ChatStreamer(tokenizer=tokenizer),
history=history,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
temperature=temperature,
top_p=top_p,
**kwargs,
)
def consumer():
producer = threading.Thread(target=stream_producer)
producer.start()
while True:
res = response_queue.get()
if res is None:
return
yield res
return consumer()
# Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
@add_start_docstrings(
"""
The InternLM2 Model transformer with a sequence classification head on top (linear layer).
[`InternLM2ForSequenceClassification`] 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).
""",
InternLM2_START_DOCSTRING,
)
class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = InternLM2Model(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.tok_embeddings
def set_input_embeddings(self, value):
self.model.tok_embeddings = value
@add_start_docstrings_to_model_forward(InternLM2_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:
sequence_lengths = (
torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
).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,
)
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/model/internlm2/tokenization_internlm2.py 0 → 100644
View file @ 876a36a4
# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
#
# 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.
"""Tokenization classes for InternLM."""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
PRETRAINED_VOCAB_FILES_MAP = {}
# Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
class InternLM2Tokenizer(PreTrainedTokenizer):
"""
Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
model_input_names = ["input_ids", "attention_mask"]
_auto_class = "AutoTokenizer"
def __init__(
self,
vocab_file,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token="</s>",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
add_bos_token=True,
add_eos_token=False,
decode_with_prefix_space=False,
clean_up_tokenization_spaces=False,
**kwargs,
):
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
self.vocab_file = vocab_file
self.add_bos_token = add_bos_token
self.add_eos_token = add_eos_token
self.decode_with_prefix_space = decode_with_prefix_space
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
self._no_prefix_space_tokens = None
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
@property
def no_prefix_space_tokens(self):
if self._no_prefix_space_tokens is None:
vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
self._no_prefix_space_tokens = {
i for i, tok in enumerate(vocab) if not tok.startswith("▁")
}
return self._no_prefix_space_tokens
@property
def vocab_size(self):
"""Returns vocab size"""
return self.sp_model.get_piece_size()
@property
def bos_token_id(self) -> Optional[int]:
return self.sp_model.bos_id()
@property
def eos_token_id(self) -> Optional[int]:
return self.sp_model.eos_id()
def get_vocab(self):
"""Returns vocab as a dict"""
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text):
"""Returns a tokenized string."""
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.sp_model.piece_to_id(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
token = self.sp_model.IdToPiece(index)
return token
def _maybe_add_prefix_space(self, tokens, decoded):
if tokens and tokens[0] not in self.no_prefix_space_tokens:
return " " + decoded
else:
return decoded
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
prev_is_special = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(current_sub_tokens) + token
prev_is_special = True
current_sub_tokens = []
else:
current_sub_tokens.append(token)
prev_is_special = False
out_string += self.sp_model.decode(current_sub_tokens)
out_string = self.clean_up_tokenization(out_string)
out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
return out_string[1:]
def save_vocabulary(
self, save_directory, filename_prefix: Optional[str] = None
) -> Tuple[str]:
"""
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (`str`):
The directory in which to save the vocabulary.
Returns:
`Tuple(str)`: Paths to the files saved.
"""
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "")
+ VOCAB_FILES_NAMES["vocab_file"],
)
if os.path.abspath(self.vocab_file) != os.path.abspath(
out_vocab_file
) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (out_vocab_file,)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
if self.add_bos_token:
bos_token_ids = [self.bos_token_id]
else:
bos_token_ids = []
output = bos_token_ids + token_ids_0
if token_ids_1 is not None:
output = output + token_ids_1
if self.add_eos_token:
output = output + [self.eos_token_id]
return output
def get_special_tokens_mask(
self,
token_ids_0: List[int],
token_ids_1: Optional[List[int]] = None,
already_has_special_tokens: bool = False,
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0,
token_ids_1=token_ids_1,
already_has_special_tokens=True,
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
eos = [self.eos_token_id]
if token_ids_1 is None:
return len(token_ids_0 + eos) * [0]
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/model/internlm2/tokenization_internlm2_fast.py 0 → 100644
View file @ 876a36a4
# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on transformers/src/transformers/models/llama/tokenization_llama_fast.py
#
# 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.
"""Tokenization Fast class for InternLM."""
import os
from shutil import copyfile
from typing import Any, Dict, Optional, Tuple
from tokenizers import Tokenizer, decoders, normalizers, processors
from tokenizers.models import BPE
from transformers.convert_slow_tokenizer import (
SLOW_TO_FAST_CONVERTERS,
SentencePieceExtractor,
SpmConverter,
)
from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
from transformers.utils import logging
from .tokenization_internlm2 import InternLM2Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
# Modified from transformers.convert_slow_tokenizer.LlamaConverter
class InternLM2Converter(SpmConverter):
handle_byte_fallback = True
def vocab(self, proto):
vocab = [
("<unk>", 0.0),
("<s>", 0.0),
("</s>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
return vocab
def unk_id(self, proto):
unk_id = 0
return unk_id
def decoder(self, replacement, add_prefix_space):
return decoders.Sequence(
[
decoders.Replace("▁", " "),
decoders.ByteFallback(),
decoders.Fuse(),
decoders.Strip(content=" ", left=1),
]
)
def tokenizer(self, proto):
model_type = proto.trainer_spec.model_type
vocab_scores = self.vocab(proto)
# special tokens
added_tokens = self.original_tokenizer.added_tokens_decoder
for i in range(len(vocab_scores)):
piece, score = vocab_scores[i]
if i in added_tokens:
vocab_scores[i] = (added_tokens[i].content, score)
if model_type == 1:
raise RuntimeError("InternLM2 is supposed to be a BPE model!")
elif model_type == 2:
_, merges = SentencePieceExtractor(
self.original_tokenizer.vocab_file
).extract(vocab_scores)
bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)}
tokenizer = Tokenizer(
BPE(
bpe_vocab,
merges,
unk_token=proto.trainer_spec.unk_piece,
fuse_unk=True,
byte_fallback=True,
)
)
tokenizer.add_special_tokens(
[added_token for index, added_token in added_tokens.items()]
)
else:
raise Exception(
"You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
)
return tokenizer
def normalizer(self, proto):
normalizers_list = []
if proto.normalizer_spec.add_dummy_prefix:
normalizers_list.append(normalizers.Prepend(prepend="▁"))
normalizers_list.append(normalizers.Replace(pattern=" ", content="▁"))
return normalizers.Sequence(normalizers_list)
def pre_tokenizer(self, replacement, add_prefix_space):
return None
SLOW_TO_FAST_CONVERTERS["InternLM2Tokenizer"] = InternLM2Converter
# Modified from transformers.model.llama.tokenization_llama_fast.LlamaTokenizerFast -> InternLM2TokenizerFast
class InternLM2TokenizerFast(PreTrainedTokenizerFast):
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = InternLM2Tokenizer
padding_side = "left"
model_input_names = ["input_ids", "attention_mask"]
_auto_class = "AutoTokenizer"
def __init__(
self,
vocab_file,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token="</s>",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
add_bos_token=True,
add_eos_token=False,
decode_with_prefix_space=False,
clean_up_tokenization_spaces=False,
**kwargs,
):
super().__init__(
vocab_file=vocab_file,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
sp_model_kwargs=sp_model_kwargs,
add_bos_token=add_bos_token,
add_eos_token=add_eos_token,
decode_with_prefix_space=decode_with_prefix_space,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
self._add_bos_token = add_bos_token
self._add_eos_token = add_eos_token
self.update_post_processor()
self.vocab_file = vocab_file
@property
def can_save_slow_tokenizer(self) -> bool:
return os.path.isfile(self.vocab_file) if self.vocab_file else False
def update_post_processor(self):
"""
Updates the underlying post processor with the current `bos_token` and `eos_token`.
"""
bos = self.bos_token
bos_token_id = self.bos_token_id
if bos is None and self.add_bos_token:
raise ValueError("add_bos_token = True but bos_token = None")
eos = self.eos_token
eos_token_id = self.eos_token_id
if eos is None and self.add_eos_token:
raise ValueError("add_eos_token = True but eos_token = None")
single = f"{(bos + ':0 ') if self.add_bos_token else ''}$A:0{(' ' + eos + ':0') if self.add_eos_token else ''}"
pair = f"{single}{(' ' + bos + ':1') if self.add_bos_token else ''} $B:1{(' ' + eos + ':1') if self.add_eos_token else ''}"
special_tokens = []
if self.add_bos_token:
special_tokens.append((bos, bos_token_id))
if self.add_eos_token:
special_tokens.append((eos, eos_token_id))
self._tokenizer.post_processor = processors.TemplateProcessing(
single=single, pair=pair, special_tokens=special_tokens
)
@property
def add_eos_token(self):
return self._add_eos_token
@property
def add_bos_token(self):
return self._add_bos_token
@add_eos_token.setter
def add_eos_token(self, value):
self._add_eos_token = value
self.update_post_processor()
@add_bos_token.setter
def add_bos_token(self, value):
self._add_bos_token = value
self.update_post_processor()
def save_vocabulary(
self, save_directory: str, filename_prefix: Optional[str] = None
) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer."
)
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "")
+ VOCAB_FILES_NAMES["vocab_file"],
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/model/internvl_chat/__init__.py 0 → 100644
View file @ 876a36a4
# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from .configuration_intern_vit import InternVisionConfig
from .configuration_internvl_chat import InternVLChatConfig
from .modeling_intern_vit import InternVisionModel
from .modeling_internvl_chat import InternVLChatModel
__all__ = [
"InternVisionConfig",
"InternVisionModel",
"InternVLChatConfig",
"InternVLChatModel",
]
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/model/internvl_chat/configuration_intern_vit.py 0 → 100644
View file @ 876a36a4
# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import os
from typing import Union
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class InternVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
instantiate a vision encoder according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_channels (`int`, *optional*, defaults to 3):
Number of color channels in the input images (e.g., 3 for RGB).
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
qkv_bias (`bool`, *optional*, defaults to `False`):
Whether to add a bias to the queries and values in the self-attention layers.
hidden_size (`int`, *optional*, defaults to 3200):
Dimensionality of the encoder layers and the pooler layer.
num_attention_heads (`int`, *optional*, defaults to 25):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 12800):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
qk_normalization (`bool`, *optional*, defaults to `True`):
Whether to normalize the queries and keys in the self-attention layers.
num_hidden_layers (`int`, *optional*, defaults to 48):
Number of hidden layers in the Transformer encoder.
use_flash_attn (`bool`, *optional*, defaults to `True`):
Whether to use flash attention mechanism.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the layer normalization layers.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
drop_path_rate (`float`, *optional*, defaults to 0.0):
Dropout rate for stochastic depth.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 0.1):
A factor for layer scale.
"""
model_type = "intern_vit_6b"
def __init__(
self,
num_channels=3,
patch_size=14,
image_size=224,
qkv_bias=False,
hidden_size=3200,
num_attention_heads=25,
intermediate_size=12800,
qk_normalization=True,
num_hidden_layers=48,
use_flash_attn=True,
hidden_act="gelu",
norm_type="rms_norm",
layer_norm_eps=1e-6,
dropout=0.0,
drop_path_rate=0.0,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=0.1,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.dropout = dropout
self.drop_path_rate = drop_path_rate
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.norm_type = norm_type
self.qkv_bias = qkv_bias
self.qk_normalization = qk_normalization
self.use_flash_attn = use_flash_attn
@classmethod
def from_pretrained(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
) -> "PretrainedConfig":
config_dict, kwargs = cls.get_config_dict(
pretrained_model_name_or_path, **kwargs
)
if "vision_config" in config_dict:
config_dict = config_dict["vision_config"]
if (
"model_type" in config_dict
and hasattr(cls, "model_type")
and config_dict["model_type"] != cls.model_type
):
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
)
return cls.from_dict(config_dict, **kwargs)
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/model/internvl_chat/configuration_internvl_chat.py 0 → 100644
View file @ 876a36a4
# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import copy
from internvl.model.internlm2.configuration_internlm2 import InternLM2Config
from internvl.model.phi3.configuration_phi3 import Phi3Config
from transformers import AutoConfig, LlamaConfig, Qwen2Config
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from .configuration_intern_vit import InternVisionConfig
logger = logging.get_logger(__name__)
class InternVLChatConfig(PretrainedConfig):
model_type = "internvl_chat"
is_composition = True
def __init__(
self,
vision_config=None,
llm_config=None,
use_backbone_lora=0,
use_llm_lora=0,
pad2square=False,
select_layer=-1,
force_image_size=None,
downsample_ratio=0.5,
template=None,
dynamic_image_size=False,
use_thumbnail=False,
ps_version="v1",
min_dynamic_patch=1,
max_dynamic_patch=6,
**kwargs,
):
super().__init__(**kwargs)
if vision_config is None:
vision_config = {"architectures": ["InternVisionModel"]}
logger.info(
"vision_config is None. Initializing the InternVisionConfig with default values."
)
if llm_config is None:
# TODO: There might still be a bug in transformers version 4.44 and above.
llm_config = {"architectures": [""]}
logger.info(
"llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`)."
)
self.vision_config = InternVisionConfig(**vision_config)
if llm_config["architectures"][0] == "LlamaForCausalLM":
self.llm_config = LlamaConfig(**llm_config)
elif llm_config["architectures"][0] == "InternLM2ForCausalLM":
self.llm_config = InternLM2Config(**llm_config)
elif llm_config["architectures"][0] == "Phi3ForCausalLM":
self.llm_config = Phi3Config(**llm_config)
elif llm_config["architectures"][0] == "Qwen2ForCausalLM":
self.llm_config = Qwen2Config(**llm_config)
else:
raise ValueError(
"Unsupported architecture: {}".format(llm_config["architectures"][0])
)
self.use_backbone_lora = use_backbone_lora
self.use_llm_lora = use_llm_lora
self.pad2square = pad2square
self.select_layer = select_layer
self.force_image_size = force_image_size
self.downsample_ratio = downsample_ratio
self.template = template
self.dynamic_image_size = dynamic_image_size
self.use_thumbnail = use_thumbnail
self.ps_version = ps_version # pixel shuffle version
self.min_dynamic_patch = min_dynamic_patch
self.max_dynamic_patch = max_dynamic_patch
self.hidden_size = self.llm_config.hidden_size
# By default, we use tie_word_embeddings=False for models of all sizes.
self.tie_word_embeddings = False
self.llm_config.tie_word_embeddings = self.tie_word_embeddings
logger.info(f"vision_select_layer: {self.select_layer}")
logger.info(f"ps_version: {self.ps_version}")
logger.info(f"min_dynamic_patch: {self.min_dynamic_patch}")
logger.info(f"max_dynamic_patch: {self.max_dynamic_patch}")
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
output["vision_config"] = self.vision_config.to_dict()
output["llm_config"] = self.llm_config.to_dict()
output["model_type"] = self.__class__.model_type
output["use_backbone_lora"] = self.use_backbone_lora
output["use_llm_lora"] = self.use_llm_lora
output["select_layer"] = self.select_layer
output["force_image_size"] = self.force_image_size
output["downsample_ratio"] = self.downsample_ratio
output["template"] = self.template
output["dynamic_image_size"] = self.dynamic_image_size
output["use_thumbnail"] = self.use_thumbnail
output["ps_version"] = self.ps_version
output["min_dynamic_patch"] = self.min_dynamic_patch
output["max_dynamic_patch"] = self.max_dynamic_patch
return output
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/model/internvl_chat/modeling_intern_vit.py 0 → 100644
View file @ 876a36a4
# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from typing import Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from einops import rearrange
from timm.models.layers import DropPath
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging
from .configuration_intern_vit import InternVisionConfig
try:
from flash_attn.bert_padding import pad_input, unpad_input
from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
has_flash_attn = True
except:
print("FlashAttention2 is not installed.")
has_flash_attn = False
logger = logging.get_logger(__name__)
class FlashAttention(nn.Module):
"""Implement the scaled dot product attention with softmax.
Arguments
---------
softmax_scale: The temperature to use for the softmax attention.
(default: 1/sqrt(d_keys) where d_keys is computed at
runtime)
attention_dropout: The dropout rate to apply to the attention
(default: 0.0)
"""
def __init__(
self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None
):
super().__init__()
self.softmax_scale = softmax_scale
self.dropout_p = attention_dropout
def forward(
self,
qkv,
key_padding_mask=None,
causal=False,
cu_seqlens=None,
max_s=None,
need_weights=False,
):
"""Implements the multihead softmax attention.
Arguments
---------
qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
if unpadded: (nnz, 3, h, d)
key_padding_mask: a bool tensor of shape (B, S)
"""
assert not need_weights
assert qkv.dtype in [torch.float16, torch.bfloat16]
assert qkv.is_cuda
if cu_seqlens is None:
batch_size = qkv.shape[0]
seqlen = qkv.shape[1]
if key_padding_mask is None:
qkv = rearrange(qkv, "b s ... -> (b s) ...")
max_s = seqlen
cu_seqlens = torch.arange(
0,
(batch_size + 1) * seqlen,
step=seqlen,
dtype=torch.int32,
device=qkv.device,
)
output = flash_attn_varlen_qkvpacked_func(
qkv,
cu_seqlens,
max_s,
self.dropout_p if self.training else 0.0,
softmax_scale=self.softmax_scale,
causal=causal,
)
output = rearrange(output, "(b s) ... -> b s ...", b=batch_size)
else:
nheads = qkv.shape[-2]
x = rearrange(qkv, "b s three h d -> b s (three h d)")
x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
x_unpad = rearrange(
x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads
)
output_unpad = flash_attn_varlen_qkvpacked_func(
x_unpad,
cu_seqlens,
max_s,
self.dropout_p if self.training else 0.0,
softmax_scale=self.softmax_scale,
causal=causal,
)
output = rearrange(
pad_input(
rearrange(output_unpad, "nnz h d -> nnz (h d)"),
indices,
batch_size,
seqlen,
),
"b s (h d) -> b s h d",
h=nheads,
)
else:
assert max_s is not None
output = flash_attn_varlen_qkvpacked_func(
qkv,
cu_seqlens,
max_s,
self.dropout_p if self.training else 0.0,
softmax_scale=self.softmax_scale,
causal=causal,
)
return output, None
class InternRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
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)
try:
from apex.normalization import FusedRMSNorm
InternRMSNorm = FusedRMSNorm # noqa
logger.info(
"Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm"
)
except ImportError:
# using the normal InternRMSNorm
pass
except Exception:
logger.warning(
"discovered apex but it failed to load, falling back to InternRMSNorm"
)
pass
NORM2FN = {
"rms_norm": InternRMSNorm,
"layer_norm": nn.LayerNorm,
}
class InternVisionEmbeddings(nn.Module):
def __init__(self, config: InternVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(
torch.randn(1, 1, self.embed_dim),
)
self.patch_embedding = nn.Conv2d(
in_channels=3,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = nn.Parameter(
torch.randn(1, self.num_positions, self.embed_dim)
)
def _get_pos_embed(self, pos_embed, H, W):
target_dtype = pos_embed.dtype
pos_embed = (
pos_embed.float()
.reshape(
1,
self.image_size // self.patch_size,
self.image_size // self.patch_size,
-1,
)
.permute(0, 3, 1, 2)
)
pos_embed = (
F.interpolate(pos_embed, size=(H, W), mode="bicubic", align_corners=False)
.reshape(1, -1, H * W)
.permute(0, 2, 1)
.to(target_dtype)
)
return pos_embed
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(
pixel_values
) # shape = [*, channel, width, height]
batch_size, _, height, width = patch_embeds.shape
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
position_embedding = torch.cat(
[
self.position_embedding[:, :1, :],
self._get_pos_embed(self.position_embedding[:, 1:, :], height, width),
],
dim=1,
)
embeddings = embeddings + position_embedding.to(target_dtype)
return embeddings
class InternAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: InternVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.use_flash_attn = config.use_flash_attn and has_flash_attn
if config.use_flash_attn and not has_flash_attn:
print(
"Warning: Flash Attention is not available, use_flash_attn is set to False."
)
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
self.attn_drop = nn.Dropout(config.attention_dropout)
self.proj_drop = nn.Dropout(config.dropout)
self.qk_normalization = config.qk_normalization
if self.qk_normalization:
self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
if self.use_flash_attn:
self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
self.proj = nn.Linear(self.embed_dim, self.embed_dim)
def _naive_attn(self, x):
B, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B, N, 3, self.num_heads, C // self.num_heads)
.permute(2, 0, 3, 1, 4)
)
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
if self.qk_normalization:
B_, H_, N_, D_ = q.shape
q = (
self.q_norm(q.transpose(1, 2).flatten(-2, -1))
.view(B_, N_, H_, D_)
.transpose(1, 2)
)
k = (
self.k_norm(k.transpose(1, 2).flatten(-2, -1))
.view(B_, N_, H_, D_)
.transpose(1, 2)
)
attn = (q * self.scale) @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
qkv = self.qkv(x)
qkv = rearrange(
qkv, "b s (three h d) -> b s three h d", three=3, h=self.num_heads
)
if self.qk_normalization:
q, k, v = qkv.unbind(2)
q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
qkv = torch.stack([q, k, v], dim=2)
context, _ = self.inner_attn(
qkv,
key_padding_mask=key_padding_mask,
need_weights=need_weights,
causal=False,
)
outs = self.proj(rearrange(context, "b s h d -> b s (h d)"))
outs = self.proj_drop(outs)
return outs
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
x = (
self._naive_attn(hidden_states)
if not self.use_flash_attn
else self._flash_attn(hidden_states)
)
return x
class InternMLP(nn.Module):
def __init__(self, config: InternVisionConfig):
super().__init__()
self.config = config
self.act = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class InternVisionEncoderLayer(nn.Module):
def __init__(self, config: InternVisionConfig, drop_path_rate: float):
super().__init__()
self.embed_dim = config.hidden_size
self.intermediate_size = config.intermediate_size
self.norm_type = config.norm_type
self.attn = InternAttention(config)
self.mlp = InternMLP(config)
self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
self.drop_path1 = (
DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
)
self.drop_path2 = (
DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
)
def forward(
self,
hidden_states: torch.Tensor,
) -> Tuple[
torch.FloatTensor,
Optional[torch.FloatTensor],
Optional[Tuple[torch.FloatTensor]],
]:
"""
Args:
hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
"""
hidden_states = hidden_states + self.drop_path1(
self.attn(self.norm1(hidden_states).to(hidden_states.dtype)) * self.ls1
)
hidden_states = hidden_states + self.drop_path2(
self.mlp(self.norm2(hidden_states).to(hidden_states.dtype)) * self.ls2
)
return hidden_states
class InternVisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`InternEncoderLayer`].
Args:
config (`InternConfig`):
The corresponding vision configuration for the `InternEncoder`.
"""
def __init__(self, config: InternVisionConfig):
super().__init__()
self.config = config
# stochastic depth decay rule
dpr = [
x.item()
for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)
]
self.layers = nn.ModuleList(
[
InternVisionEncoderLayer(config, dpr[idx])
for idx in range(config.num_hidden_layers)
]
)
self.gradient_checkpointing = True
def forward(
self,
inputs_embeds,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Embedded representation of the inputs. Should be float, not int tokens.
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.
"""
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
)
encoder_states = () if output_hidden_states else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = torch.utils.checkpoint.checkpoint(
encoder_layer, hidden_states
)
else:
layer_outputs = encoder_layer(
hidden_states,
)
hidden_states = layer_outputs
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states
)
class InternVisionModel(PreTrainedModel):
main_input_name = "pixel_values"
_supports_flash_attn_2 = True
supports_gradient_checkpointing = True
config_class = InternVisionConfig
_no_split_modules = ["InternVisionEncoderLayer"]
def __init__(self, config: InternVisionConfig):
super().__init__(config)
self.config = config
self.embeddings = InternVisionEmbeddings(config)
self.encoder = InternVisionEncoder(config)
def resize_pos_embeddings(self, old_size, new_size, patch_size):
pos_emb = self.embeddings.position_embedding
_, num_positions, embed_dim = pos_emb.shape
cls_emb = pos_emb[:, :1, :]
pos_emb = (
pos_emb[:, 1:, :]
.reshape(1, old_size // patch_size, old_size // patch_size, -1)
.permute(0, 3, 1, 2)
)
pos_emb = F.interpolate(
pos_emb.float(),
size=new_size // patch_size,
mode="bicubic",
align_corners=False,
)
pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
self.embeddings.position_embedding = nn.Parameter(pos_emb)
self.embeddings.image_size = new_size
logger.info(
"Resized position embeddings from {} to {}".format(old_size, new_size)
)
def get_input_embeddings(self):
return self.embeddings
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
pixel_embeds: Optional[torch.FloatTensor] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
if pixel_values is None and pixel_embeds is None:
raise ValueError("You have to specify pixel_values or pixel_embeds")
if pixel_embeds is not None:
hidden_states = pixel_embeds
else:
if len(pixel_values.shape) == 4:
hidden_states = self.embeddings(pixel_values)
else:
raise ValueError(f"wrong pixel_values size: {pixel_values.shape}")
# encoder_outputs = self.encoder(
# inputs_embeds=hidden_states,
# output_hidden_states=output_hidden_states,
# return_dict=return_dict,
# )
# last_hidden_state = encoder_outputs.last_hidden_state
# pooled_output = last_hidden_state[:, 0, :]
bsz = 4
last_hidden_state = []
for splitted_hidden_states in hidden_states.split(bsz, dim=0):
encoder_outputs = self.encoder(
inputs_embeds=splitted_hidden_states,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state.append(encoder_outputs.last_hidden_state)
last_hidden_state = torch.cat(last_hidden_state)
pooled_output = last_hidden_state[:, 0, :]
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/model/internvl_chat/modeling_internvl_chat.py 0 → 100644
View file @ 876a36a4
# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import warnings
from typing import List, Optional, Tuple, Union
import torch.distributed as dist
import torch.utils.checkpoint
import transformers
from internvl.conversation import get_conv_template
from internvl.model.internlm2.modeling_internlm2 import InternLM2ForCausalLM
from internvl.model.phi3.modeling_phi3 import Phi3ForCausalLM
from peft import LoraConfig, get_peft_model
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import (
AutoModel,
GenerationConfig,
LlamaForCausalLM,
LlamaTokenizer,
Qwen2ForCausalLM,
)
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import ModelOutput, logging
from .configuration_internvl_chat import InternVLChatConfig
from .modeling_intern_vit import InternVisionModel, has_flash_attn
logger = logging.get_logger(__name__)
def version_cmp(v1, v2, op="eq"):
import operator
from packaging import version
op_func = getattr(operator, op)
return op_func(version.parse(v1), version.parse(v2))
class InternVLChatModel(PreTrainedModel):
config_class = InternVLChatConfig
main_input_name = "pixel_values"
base_model_prefix = "language_model"
_no_split_modules = [
"InternVisionModel",
"LlamaDecoderLayer",
"InternLM2DecoderLayer",
"Phi3DecoderLayer",
"Qwen2DecoderLayer",
]
_supports_flash_attn_2 = True
supports_gradient_checkpointing = True
def __init__(
self,
config: InternVLChatConfig,
vision_model=None,
language_model=None,
use_flash_attn=True,
):
super().__init__(config)
assert version_cmp(transformers.__version__, "4.37.0", "ge")
image_size = config.force_image_size or config.vision_config.image_size
patch_size = config.vision_config.patch_size
self.patch_size = patch_size
self.select_layer = config.select_layer
self.template = config.template
self.num_image_token = int(
(image_size // patch_size) ** 2 * (config.downsample_ratio**2)
)
self.downsample_ratio = config.downsample_ratio
self.ps_version = config.ps_version
self.llm_arch_name = config.llm_config.architectures[0]
# Enable Flash Attention if supported, otherwise fall back to eager attention.
use_flash_attn = use_flash_attn if has_flash_attn else False
config.vision_config.use_flash_attn = True if use_flash_attn else False
config.llm_config.attn_implementation = (
"flash_attention_2" if use_flash_attn else "eager"
)
logger.info(f"num_image_token: {self.num_image_token}")
logger.info(f"ps_version: {self.ps_version}")
if vision_model is not None:
self.vision_model = vision_model
else:
self.vision_model = InternVisionModel(config.vision_config)
if language_model is not None:
self.language_model = language_model
else:
if config.llm_config.architectures[0] == "LlamaForCausalLM":
self.language_model = LlamaForCausalLM(config.llm_config)
elif config.llm_config.architectures[0] == "InternLM2ForCausalLM":
self.language_model = InternLM2ForCausalLM(config.llm_config)
elif config.llm_config.architectures[0] == "Phi3ForCausalLM":
self.language_model = Phi3ForCausalLM(config.llm_config)
elif config.llm_config.architectures[0] == "Qwen2ForCausalLM":
self.language_model = Qwen2ForCausalLM(config.llm_config)
else:
raise NotImplementedError(
f"{config.llm_config.architectures[0]} is not implemented."
)
vit_hidden_size = config.vision_config.hidden_size
llm_hidden_size = config.llm_config.hidden_size
self.mlp1 = nn.Sequential(
nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
nn.Linear(
vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size
),
nn.GELU(),
nn.Linear(llm_hidden_size, llm_hidden_size),
)
self.img_context_token_id = None
self.conv_template = get_conv_template(self.template)
if hasattr(config, "system_message"):
self.system_message = config.system_message
else:
self.system_message = self.conv_template.system_message
self.num_samples = 0
if config.use_backbone_lora:
self.wrap_backbone_lora(
r=config.use_backbone_lora, lora_alpha=2 * config.use_backbone_lora
)
if config.use_llm_lora:
self.wrap_llm_lora(
r=config.use_llm_lora, lora_alpha=2 * config.use_llm_lora
)
# Initialize weights and apply final processing. See: https://github.com/huggingface/transformers/pull/37708
self.post_init()
def wrap_backbone_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
lora_config = LoraConfig(
r=r,
target_modules=["attn.qkv", "attn.proj", "mlp.fc1", "mlp.fc2"],
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
)
self.vision_model = get_peft_model(self.vision_model, lora_config)
self.vision_model.print_trainable_parameters()
def wrap_llm_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
# Determine the target modules based on the architecture of the language model
if self.llm_arch_name == "InternLM2ForCausalLM":
target_modules = [
"attention.wqkv",
"attention.wo",
"feed_forward.w1",
"feed_forward.w2",
"feed_forward.w3",
]
elif self.llm_arch_name == "Phi3ForCausalLM":
target_modules = [
"mlp.down_proj",
"mlp.gate_up_proj",
"self_attn.o_proj",
"self_attn.qkv_proj",
]
elif self.llm_arch_name in ["Qwen2ForCausalLM", "LlamaForCausalLM"]:
target_modules = [
"self_attn.q_proj",
"self_attn.k_proj",
"self_attn.v_proj",
"self_attn.o_proj",
"mlp.gate_proj",
"mlp.down_proj",
"mlp.up_proj",
]
else:
raise NotImplemented
lora_config = LoraConfig(
r=r,
target_modules=target_modules,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
task_type="CAUSAL_LM",
)
self.language_model = get_peft_model(self.language_model, lora_config)
self.language_model.enable_input_require_grads()
self.language_model.print_trainable_parameters()
def forward(
self,
pixel_values: torch.FloatTensor,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
image_flags: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[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,
statistics: Optional[torch.LongTensor] = None,
loss_weight: Optional[List] = None,
loss_reduction_all_gather: Optional[bool] = False,
) -> Union[Tuple, CausalLMOutputWithPast]:
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
image_flags = image_flags.squeeze(-1)
input_embeds = self.language_model.get_input_embeddings()(input_ids).clone()
vit_embeds = self.extract_feature(pixel_values)
vit_embeds = vit_embeds[image_flags == 1]
vit_batch_size = pixel_values.shape[0]
B, N, C = input_embeds.shape
input_embeds = input_embeds.reshape(B * N, C)
if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
print(
f"dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}"
)
if statistics is not None:
num_samples, num_padding_tokens, num_padding_images = (
statistics.tolist()
)
self.num_samples += num_samples
print(
f"total_samples={self.num_samples}, {num_samples=}, {num_padding_tokens=}, {num_padding_images=}"
)
input_ids = input_ids.reshape(B * N)
selected = input_ids == self.img_context_token_id
try:
input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(
-1, C
)
ignore_flag = False
except Exception as e:
vit_embeds = vit_embeds.reshape(-1, C)
print(
f"warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, "
f"vit_embeds.shape={vit_embeds.shape}"
)
n_token = selected.sum()
input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
ignore_flag = True
input_embeds = input_embeds.reshape(B, N, C)
outputs = self.language_model(
inputs_embeds=input_embeds,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = outputs.logits
loss = None
if labels is not None and loss_weight is not None:
loss_weight = torch.tensor(
loss_weight, dtype=torch.float32, device=labels.device
)
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
shift_weights = loss_weight[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss(reduction="none")
shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
shift_labels = shift_labels.view(-1)
shift_weights = shift_weights.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
shift_weights = shift_weights.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
shift_weights_sum = shift_weights.sum()
if loss_reduction_all_gather:
dist.all_reduce(shift_weights_sum, op=dist.ReduceOp.AVG)
loss = loss * shift_weights
loss = loss.sum() / shift_weights_sum
if ignore_flag:
loss = loss * 0.0
elif 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.language_model.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 ignore_flag:
loss = loss * 0.0
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 pixel_shuffle(self, x, scale_factor=0.5):
n, w, h, c = x.size()
# N, W, H, C --> N, W, H * scale, C // scale
x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
# N, W, H * scale, C // scale --> N, H * scale, W, C // scale
x = x.permute(0, 2, 1, 3).contiguous()
# N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
x = x.view(
n,
int(h * scale_factor),
int(w * scale_factor),
int(c / (scale_factor * scale_factor)),
)
if self.ps_version == "v1":
warnings.warn(
"In ps_version 'v1', the height and width have not been swapped back, "
"which results in a transposed image."
)
else:
x = x.permute(0, 2, 1, 3).contiguous()
return x
def extract_feature(self, pixel_values):
if self.select_layer == -1:
vit_embeds = self.vision_model(
pixel_values=pixel_values, output_hidden_states=False, return_dict=True
).last_hidden_state
else:
vit_embeds = self.vision_model(
pixel_values=pixel_values, output_hidden_states=True, return_dict=True
).hidden_states[self.select_layer]
vit_embeds = vit_embeds[:, 1:, :]
h = w = int(vit_embeds.shape[1] ** 0.5)
vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
vit_embeds = self.mlp1(vit_embeds)
return vit_embeds
def batch_chat(
self,
tokenizer,
pixel_values,
questions,
generation_config,
num_patches_list=None,
history=None,
return_history=False,
IMG_START_TOKEN="<img>",
IMG_END_TOKEN="</img>",
IMG_CONTEXT_TOKEN="<IMG_CONTEXT>",
verbose=False,
image_counts=None,
):
if history is not None or return_history:
print("Now multi-turn chat is not supported in batch_chat.")
raise NotImplementedError
if image_counts is not None:
num_patches_list = image_counts
print(
"Warning: `image_counts` is deprecated. Please use `num_patches_list` instead."
)
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
self.img_context_token_id = img_context_token_id
if verbose and pixel_values is not None:
image_bs = pixel_values.shape[0]
print(f"dynamic ViT batch size: {image_bs}")
queries = []
for idx, num_patches in enumerate(num_patches_list):
question = questions[idx]
if pixel_values is not None and "<image>" not in question:
question = "<image>\n" + question
template = get_conv_template(self.template)
template.system_message = self.system_message
template.append_message(template.roles[0], question)
template.append_message(template.roles[1], None)
query = template.get_prompt()
image_tokens = (
IMG_START_TOKEN
+ IMG_CONTEXT_TOKEN * self.num_image_token * num_patches
+ IMG_END_TOKEN
)
query = query.replace("<image>", image_tokens, 1)
queries.append(query)
tokenizer.padding_side = "left"
model_inputs = tokenizer(queries, return_tensors="pt", padding=True)
device = torch.device(
self.language_model.device if torch.cuda.is_available() else "cpu"
)
input_ids = model_inputs["input_ids"].to(device)
attention_mask = model_inputs["attention_mask"].to(device)
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
generation_config["eos_token_id"] = eos_token_id
generation_output = self.generate(
pixel_values=pixel_values,
input_ids=input_ids,
attention_mask=attention_mask,
**generation_config,
)
responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
responses = [
response.split(template.sep.strip())[0].strip() for response in responses
]
return responses
def chat(
self,
tokenizer,
pixel_values,
question,
generation_config,
history=None,
return_history=False,
num_patches_list=None,
IMG_START_TOKEN="<img>",
IMG_END_TOKEN="</img>",
IMG_CONTEXT_TOKEN="<IMG_CONTEXT>",
verbose=False,
):
if history is None and pixel_values is not None and "<image>" not in question:
question = "<image>\n" + question
if num_patches_list is None:
num_patches_list = (
[pixel_values.shape[0]] if pixel_values is not None else []
)
assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
self.img_context_token_id = img_context_token_id
template = get_conv_template(self.template)
template.system_message = self.system_message
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
history = [] if history is None else history
for old_question, old_answer in history:
template.append_message(template.roles[0], old_question)
template.append_message(template.roles[1], old_answer)
template.append_message(template.roles[0], question)
template.append_message(template.roles[1], None)
query = template.get_prompt()
if verbose and pixel_values is not None:
image_bs = pixel_values.shape[0]
print(f"dynamic ViT batch size: {image_bs}")
for num_patches in num_patches_list:
image_tokens = (
IMG_START_TOKEN
+ IMG_CONTEXT_TOKEN * self.num_image_token * num_patches
+ IMG_END_TOKEN
)
query = query.replace("<image>", image_tokens, 1)
model_inputs = tokenizer(query, return_tensors="pt")
device = torch.device(
self.language_model.device if torch.cuda.is_available() else "cpu"
)
input_ids = model_inputs["input_ids"].to(device)
attention_mask = model_inputs["attention_mask"].to(device)
generation_config["eos_token_id"] = eos_token_id
generation_output = self.generate(
pixel_values=pixel_values,
input_ids=input_ids,
attention_mask=attention_mask,
**generation_config,
)
response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[
0
]
response = response.split(template.sep.strip())[0].strip()
history.append((question, response))
if return_history:
return response, history
else:
query_to_print = query.replace(IMG_CONTEXT_TOKEN, "")
query_to_print = query_to_print.replace(
f"{IMG_START_TOKEN}{IMG_END_TOKEN}", "<image>"
)
if verbose:
print(query_to_print, response)
return response
@torch.no_grad()
def generate(
self,
pixel_values: Optional[torch.FloatTensor] = None,
input_ids: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
visual_features: Optional[torch.FloatTensor] = None,
generation_config: Optional[GenerationConfig] = None,
output_hidden_states: Optional[bool] = None,
**generate_kwargs,
) -> torch.LongTensor:
assert self.img_context_token_id is not None
if pixel_values is not None:
if visual_features is not None:
vit_embeds = visual_features
else:
vit_embeds = self.extract_feature(pixel_values)
input_embeds = self.language_model.get_input_embeddings()(input_ids)
B, N, C = input_embeds.shape
input_embeds = input_embeds.reshape(B * N, C)
input_ids = input_ids.reshape(B * N)
selected = input_ids == self.img_context_token_id
assert selected.sum() != 0
input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
input_embeds = input_embeds.reshape(B, N, C)
else:
input_embeds = self.language_model.get_input_embeddings()(input_ids)
outputs = self.language_model.generate(
inputs_embeds=input_embeds,
attention_mask=attention_mask,
generation_config=generation_config,
output_hidden_states=output_hidden_states,
use_cache=True,
**generate_kwargs,
)
return outputs
@property
def lm_head(self):
return self.language_model.get_output_embeddings()
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def get_output_embeddings(self):
return self.language_model.get_output_embeddings()
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/model/phi3/configuration_phi3.py 0 → 100644
View file @ 876a36a4
# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
#
# 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 atd
#
# 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.
"""Phi-3 model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
"microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
}
class Phi3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the
[microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32064):
Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Phi3Model`].
hidden_size (`int`, *optional*, defaults to 3072):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 8192):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
resid_pdrop (`float`, *optional*, defaults to 0.0):
Dropout probability for mlp outputs.
embd_pdrop (`int`, *optional*, defaults to 0.0):
The dropout ratio for the embeddings.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio after computing the attention scores.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used with.
original_max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model was trained with. This is used to determine the size of the
original RoPE embeddings when using long scaling.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon value used for the RMSNorm.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`dict`, *optional*):
The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be either `su` or `yarn` and
the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
divided by the number of attention heads divided by 2.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 32000):
The id of the "end-of-sequence" token.
pad_token_id (`int`, *optional*, defaults to 32000):
The id of the padding token.
sliding_window (`int`, *optional*):
Sliding window attention window size. If `None`, no sliding window is applied.
Example:
```python
>>> from transformers import Phi3Model, Phi3Config
>>> # Initializing a Phi-3 style configuration
>>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
>>> # Initializing a model from the configuration
>>> model = Phi3Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "phi3"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32064,
hidden_size=3072,
intermediate_size=8192,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
resid_pdrop=0.0,
embd_pdrop=0.0,
attention_dropout=0.0,
hidden_act="silu",
max_position_embeddings=4096,
original_max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
bos_token_id=1,
eos_token_id=32000,
pad_token_id=32000,
sliding_window=None,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attention_dropout = attention_dropout
self.hidden_act = hidden_act
self.max_position_embeddings = max_position_embeddings
self.original_max_position_embeddings = original_max_position_embeddings
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
self.sliding_window = sliding_window
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
raise ValueError(
"`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]:
raise ValueError(
f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}"
)
if not (
isinstance(rope_scaling_short_factor, list)
and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
):
raise ValueError(
f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
)
if (
not len(rope_scaling_short_factor)
== self.hidden_size // self.num_attention_heads // 2
):
raise ValueError(
f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
)
if not (
isinstance(rope_scaling_long_factor, list)
and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
):
raise ValueError(
f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
)
if (
not len(rope_scaling_long_factor)
== self.hidden_size // self.num_attention_heads // 2
):
raise ValueError(
f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
)
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/model/phi3/modeling_phi3.py 0 → 100644
View file @ 876a36a4
# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
#
# 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 Phi-3 model."""
import inspect
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
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
SequenceClassifierOutputWithPast,
TokenClassifierOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
add_code_sample_docstrings,
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 .configuration_phi3 import Phi3Config
logger = logging.get_logger(__name__)
# Transformers scans dependencies in the modeling file, causing issues on conditional loading. The regex only ignores try/catch blocks, but not if statements
# if is_flash_attn_2_available():
_flash_supports_window_size = False
try:
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
_flash_supports_window_size = "window_size" in list(
inspect.signature(flash_attn_func).parameters
)
has_flash_attn = True
except ImportError as error:
logger.warning(
f"`flash-attention` package not found, consider installing for better performance: {error}."
)
if not _flash_supports_window_size:
logger.warning(
"Current `flash-attenton` does not support `window_size`. Either upgrade or use `attn_implementation='eager'`."
)
has_flash_attn = False
_CHECKPOINT_FOR_DOC = "microsoft/Phi-3-mini-4k-instruct"
_CONFIG_FOR_DOC = "Phi3Config"
PHI3_PRETRAINED_MODEL_ARCHIVE_LIST = [
"microsoft/Phi-3-mini-4k-instruct",
"microsoft/Phi-3-mini-128k-instruct",
# See all Phi-3 models at https://huggingface.co/models?filter=Phi-3
]
# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Phi3
class Phi3RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Phi3RMSNorm 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)
# Copied from transformers.models.llama.modeling_llama._get_unpad_data
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,
)
# Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with gemma->phi3, Gemma->Phi3
class Phi3RotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
self.register_buffer("inv_freq", None, persistent=False)
@torch.no_grad()
def forward(self, x, position_ids, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
if self.inv_freq is None:
self.inv_freq = 1.0 / (
self.base
** (
torch.arange(
0, self.dim, 2, dtype=torch.int64, device=x.device
).float()
/ self.dim
)
)
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) and device_type != "mps"
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 Phi3SuScaledRotaryEmbedding(Phi3RotaryEmbedding):
def __init__(self, dim, config, device=None):
super().__init__(dim, config.max_position_embeddings, config.rope_theta, device)
self.short_factor = config.rope_scaling["short_factor"]
self.long_factor = config.rope_scaling["long_factor"]
self.original_max_position_embeddings = config.original_max_position_embeddings
@torch.no_grad()
def forward(self, x, position_ids, seq_len=None):
seq_len = torch.max(position_ids) + 1
if seq_len > self.original_max_position_embeddings:
ext_factors = torch.tensor(
self.long_factor, dtype=torch.float32, device=x.device
)
else:
ext_factors = torch.tensor(
self.short_factor, dtype=torch.float32, device=x.device
)
inv_freq_shape = (
torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float()
/ self.dim
)
self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape)
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) and device_type != "mps"
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)
scale = self.max_position_embeddings / self.original_max_position_embeddings
if scale <= 1.0:
scaling_factor = 1.0
else:
scaling_factor = math.sqrt(
1
+ math.log(scale) / math.log(self.original_max_position_embeddings)
)
cos = emb.cos() * scaling_factor
sin = emb.sin() * scaling_factor
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
class Phi3YarnScaledRotaryEmbedding(Phi3RotaryEmbedding):
def __init__(self, dim, config, device=None):
super().__init__(dim, config.max_position_embeddings, config.rope_theta, device)
self.short_factor = config.rope_scaling["short_factor"]
self.long_factor = config.rope_scaling["long_factor"]
self.original_max_position_embeddings = config.original_max_position_embeddings
@torch.no_grad()
def forward(self, x, position_ids, seq_len=None):
seq_len = torch.max(position_ids) + 1
if seq_len > self.original_max_position_embeddings:
ext_factors = torch.tensor(
self.long_factor, dtype=torch.float32, device=x.device
)
else:
ext_factors = torch.tensor(
self.short_factor, dtype=torch.float32, device=x.device
)
inv_freq_shape = (
torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float()
/ self.dim
)
self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape)
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) and device_type != "mps"
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)
scale = self.max_position_embeddings / self.original_max_position_embeddings
if scale <= 1.0:
scaling_factor = 1.0
else:
scaling_factor = 0.1 * math.log(scale) + 1.0
cos = emb.cos() * scaling_factor
sin = emb.sin() * scaling_factor
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
# Copied from transformers.models.llama.modeling_llama.rotate_half
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)
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
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 Phi3MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.gate_up_proj = nn.Linear(
config.hidden_size, 2 * config.intermediate_size, bias=False
)
self.down_proj = nn.Linear(
config.intermediate_size, config.hidden_size, bias=False
)
self.activation_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
up_states = self.gate_up_proj(hidden_states)
gate, up_states = up_states.chunk(2, dim=-1)
up_states = up_states * self.activation_fn(gate)
return self.down_proj(up_states)
# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
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 Phi3Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Phi3Config, 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.original_max_position_embeddings = config.original_max_position_embeddings
self.rope_theta = config.rope_theta
self.rope_scaling = config.rope_scaling
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})."
)
op_size = self.num_heads * self.head_dim + 2 * (
self.num_key_value_heads * self.head_dim
)
self.o_proj = nn.Linear(
self.num_heads * self.head_dim, self.hidden_size, bias=False
)
self.qkv_proj = nn.Linear(self.hidden_size, op_size, bias=False)
self._init_rope()
def _init_rope(self):
if self.rope_scaling is None:
self.rotary_emb = Phi3RotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
)
else:
scaling_type = self.config.rope_scaling["type"]
if scaling_type == "su":
self.rotary_emb = Phi3SuScaledRotaryEmbedding(
self.head_dim, self.config
)
elif scaling_type == "yarn":
self.rotary_emb = Phi3YarnScaledRotaryEmbedding(
self.head_dim, self.config
)
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,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
logger.warning_once(
"You are not running the flash-attention implementation, expect numerical differences."
)
bsz, q_len, _ = hidden_states.size()
qkv = self.qkv_proj(hidden_states)
query_pos = self.num_heads * self.head_dim
query_states = qkv[..., :query_pos]
key_states = qkv[
..., query_pos : query_pos + self.num_key_value_heads * self.head_dim
]
value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
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)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
if self.layer_idx is None:
raise ValueError(
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
"with a layer index."
)
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(
key_states, value_states, self.layer_idx, cache_kwargs
)
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_weights = torch.matmul(
query_states, key_states.transpose(2, 3)
) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(
attn_weights, dim=-1, dtype=torch.float32
).to(value_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)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class Phi3FlashAttention2(Phi3Attention):
"""
Phi-3 flash attention module. This module inherits from `Phi3Attention` 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.
"""
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
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,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# Phi3FlashAttention2 attention does not support output_attentions
if not _flash_supports_window_size:
logger.warning_once(
"The current flash attention version does not support sliding window attention. Please use `attn_implementation='eager'` or upgrade flash-attn library."
)
raise ValueError(
"The current flash attention version does not support sliding window attention."
)
output_attentions = False
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.`"
)
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop("padding_mask")
bsz, q_len, _ = hidden_states.size()
qkv = self.qkv_proj(hidden_states)
query_pos = self.num_heads * self.head_dim
query_states = qkv[..., :query_pos]
key_states = qkv[
..., query_pos : query_pos + self.num_key_value_heads * self.head_dim
]
value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
# 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)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
if self.layer_idx is None:
raise ValueError(
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
"with a layer index."
)
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
# Because the input can be padded, the absolute sequence length depends on the max position id.
rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
cos, sin = self.rotary_emb(value_states, position_ids, seq_len=rotary_seq_len)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
use_sliding_windows = (
_flash_supports_window_size
and getattr(self.config, "sliding_window", None) is not None
and kv_seq_len > self.config.sliding_window
)
if past_key_value is not None:
# Activate slicing cache only if the config has a value `sliding_windows` attribute
cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
if (
getattr(self.config, "sliding_window", None) is not None
and kv_seq_len > self.config.sliding_window
and cache_has_contents
):
slicing_tokens = 1 - self.config.sliding_window
past_key = past_key_value[self.layer_idx][0]
past_value = past_key_value[self.layer_idx][1]
past_key = past_key[:, :, slicing_tokens:, :].contiguous()
past_value = past_value[:, :, slicing_tokens:, :].contiguous()
if past_key.shape[-2] != self.config.sliding_window - 1:
raise ValueError(
f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
f" {past_key.shape}"
)
if attention_mask is not None:
attention_mask = attention_mask[:, slicing_tokens:]
attention_mask = torch.cat(
[attention_mask, torch.ones_like(attention_mask[:, -1:])],
dim=-1,
)
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(
key_states, value_states, self.layer_idx, cache_kwargs
)
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_dropout = 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.
if query_states.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.qkv_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
# Reashape to the expected shape for Flash Attention
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
attn_output = self._flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
dropout=attn_dropout,
use_sliding_windows=use_sliding_windows,
)
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
# Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._flash_attention_forward
def _flash_attention_forward(
self,
query_states,
key_states,
value_states,
attention_mask,
query_length,
dropout=0.0,
softmax_scale=None,
use_sliding_windows=False,
):
"""
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 (`float`):
Attention dropout
softmax_scale (`float`, *optional*):
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
use_sliding_windows (`bool`, *optional*):
Whether to activate sliding window attention.
"""
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
if not use_sliding_windows:
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,
)
else:
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,
window_size=(
self.config.sliding_window,
self.config.sliding_window,
),
)
attn_output = pad_input(
attn_output_unpad, indices_q, batch_size, query_length
)
else:
if not use_sliding_windows:
attn_output = flash_attn_func(
query_states,
key_states,
value_states,
dropout,
softmax_scale=softmax_scale,
causal=causal,
)
else:
attn_output = flash_attn_func(
query_states,
key_states,
value_states,
dropout,
softmax_scale=softmax_scale,
causal=causal,
window_size=(
self.config.sliding_window,
self.config.sliding_window,
),
)
return attn_output
# Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
def _upad_input(
self, query_layer, key_layer, value_layer, attention_mask, query_length
):
batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
# On the first iteration we need to properly re-create the padding mask
# by slicing it on the proper place
if kv_seq_len != attention_mask.shape[-1]:
attention_mask_num_tokens = attention_mask.shape[-1]
attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
key_layer = index_first_axis(
key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
)
value_layer = index_first_axis(
value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
)
if query_length == kv_seq_len:
query_layer = index_first_axis(
query_layer.reshape(batch_size * kv_seq_len, 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),
)
# copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Phi3
# TODO @Arthur no longer copied from LLama after static cache
class Phi3SdpaAttention(Phi3Attention):
"""
Phi3 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`Phi3Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from Phi3Attention.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,
) -> 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(
"Phi3Model is using Phi3SdpaAttention, 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,
)
bsz, q_len, _ = hidden_states.size()
qkv = self.qkv_proj(hidden_states)
query_pos = self.num_heads * self.head_dim
query_states = qkv[..., :query_pos]
key_states = qkv[
..., query_pos : query_pos + self.num_key_value_heads * self.head_dim
]
value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
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)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(
key_states, value_states, self.layer_idx, cache_kwargs
)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and attention_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
is_causal=self.is_causal and attention_mask is None and q_len > 1,
)
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
PHI3_ATTENTION_CLASSES = {
"eager": Phi3Attention,
"flash_attention_2": Phi3FlashAttention2,
"sdpa": Phi3SdpaAttention,
}
class Phi3DecoderLayer(nn.Module):
def __init__(self, config: Phi3Config, layer_idx: int):
super().__init__()
self.config = config
self.self_attn = PHI3_ATTENTION_CLASSES[config._attn_implementation](
config, layer_idx=layer_idx
)
self.mlp = Phi3MLP(config)
self.input_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.resid_attn_dropout = nn.Dropout(config.resid_pdrop)
self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop)
self.post_attention_layernorm = Phi3RMSNorm(
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,
**kwargs,
) -> Tuple[
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
]:
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.`"
)
"""
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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
position_ids (`torch.LongTensor` of shape `({0})`, *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)
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
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
attn_outputs, 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,
)
hidden_states = residual + self.resid_attn_dropout(attn_outputs)
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + self.resid_mlp_dropout(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
PHI3_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 ([`Phi3Config`]):
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 Phi-3 model outputting raw hidden-states without any specific head on top.",
PHI3_START_DOCSTRING,
)
class Phi3PreTrainedModel(PreTrainedModel):
config_class = Phi3Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Phi3DecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supports_sdpa = False
_supports_cache_class = True
_version = "0.0.5"
def __init__(self, config: Phi3Config):
if not has_flash_attn:
config._attn_implementation = "eager"
print(
"Warning: Flash attention is not available, using eager attention instead."
)
super().__init__(config)
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_()
PHI3_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 Phi-3 model outputting raw hidden-states without any specific head on top.",
PHI3_START_DOCSTRING,
)
class Phi3Model(Phi3PreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
Args:
config: Phi3Config
"""
def __init__(self, config: Phi3Config):
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.embed_dropout = nn.Dropout(config.embd_pdrop)
self.layers = nn.ModuleList(
[
Phi3DecoderLayer(config, layer_idx)
for layer_idx in range(config.num_hidden_layers)
]
)
self._attn_implementation = config._attn_implementation
self.norm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = 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(PHI3_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,
) -> 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
)
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time"
)
elif input_ids is not None:
batch_size, seq_length = input_ids.shape[:2]
elif inputs_embeds is not None:
batch_size, seq_length = inputs_embeds.shape[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
past_key_values_length = 0
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
if use_cache:
use_legacy_cache = not isinstance(past_key_values, Cache)
if use_legacy_cache:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
past_key_values_length = past_key_values.get_usable_length(seq_length)
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length,
seq_length + past_key_values_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if (
attention_mask is not None
and self._attn_implementation == "flash_attention_2"
and use_cache
):
is_padding_right = attention_mask[:, -1].sum().item() != batch_size
if is_padding_right:
raise ValueError(
"You are attempting to perform batched generation with padding_side='right'"
" this may lead to unexpected behaviour for Flash Attention version of Phi3. Make sure to "
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
)
if self._attn_implementation == "flash_attention_2":
# 2d mask is passed through the layers
attention_mask = (
attention_mask
if (attention_mask is not None and 0 in attention_mask)
else None
)
else:
# 4d mask is passed through the layers
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
sliding_window=self.config.sliding_window,
)
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,
attention_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
)
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 use_legacy_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,
)
class Phi3ForCausalLM(Phi3PreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi3
def __init__(self, config):
super().__init__(config)
self.model = Phi3Model(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()
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
def get_input_embeddings(self):
return self.model.embed_tokens
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
def set_input_embeddings(self, value):
self.model.embed_tokens = value
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
def get_output_embeddings(self):
return self.lm_head
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
def set_decoder(self, decoder):
self.model = decoder
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
def get_decoder(self):
return self.model
# Ignore copy
@add_start_docstrings_to_model_forward(PHI3_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,
) -> 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, Phi3ForCausalLM
>>> model = Phi3ForCausalLM.from_pretrained("microsoft/phi-3-mini-4k-instruct")
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-3-mini-4k-instruct")
>>> prompt = "This is an example script ."
>>> 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]
'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum'
```"""
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,
)
hidden_states = outputs[0]
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,
)
# Copied from transformers.models.persimmon.modeling_persimmon.PersimmonForCausalLM.prepare_inputs_for_generation
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
**kwargs,
):
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 `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) or (
inputs_embeds is not None and len(past_key_values) == 0
):
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
}
)
return model_inputs
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
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 [`Phi3Model`] with a sequence classification head on top (linear layer).
[`Phi3ForSequenceClassification`] 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).
""",
PHI3_START_DOCSTRING,
)
# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Phi3, LLAMA->PHI3, self.transformer->self.model, transformer_outputs->model_outputs
class Phi3ForSequenceClassification(Phi3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Phi3Model(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(PHI3_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
)
model_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 = model_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,) + model_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=model_outputs.past_key_values,
hidden_states=model_outputs.hidden_states,
attentions=model_outputs.attentions,
)
@add_start_docstrings(
"""
[`Phi3Model`] with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
PHI3_START_DOCSTRING,
)
# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with Mpt->Phi3,MPT->PHI3,self.transformer->self.model,transformer_outputs->model_outputs
class Phi3ForTokenClassification(Phi3PreTrainedModel):
def __init__(self, config: Phi3Config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Phi3Model(config)
if (
hasattr(config, "classifier_dropout")
and config.classifier_dropout is not None
):
classifier_dropout = config.classifier_dropout
elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
classifier_dropout = config.hidden_dropout
else:
classifier_dropout = 0.1
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
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,
**deprecated_arguments,
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
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
)
model_outputs = self.model(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = model_outputs[0]
hidden_states = self.dropout(hidden_states)
logits = self.classifier(hidden_states)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
batch_size, seq_length = labels.shape
loss_fct = CrossEntropyLoss()
loss = loss_fct(
logits.view(batch_size * seq_length, self.num_labels),
labels.view(batch_size * seq_length),
)
if not return_dict:
output = (logits,) + model_outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=model_outputs.hidden_states,
attentions=model_outputs.attentions,
)
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/patch/__init__.py 0 → 100644
View file @ 876a36a4
# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from .internlm2_packed_training_patch import replace_internlm2_attention_class
from .internvit_liger_monkey_patch import apply_liger_kernel_to_internvit
from .llama2_flash_attn_monkey_patch import replace_llama2_attn_with_flash_attn
from .llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn
from .llama_packed_training_patch import replace_llama_attention_class
from .llama_rmsnorm_monkey_patch import replace_llama_rmsnorm_with_fused_rmsnorm
from .pad_data_collator import (
concat_pad_data_collator,
dpo_concat_pad_data_collator,
pad_data_collator,
)
from .phi3_packed_training_patch import replace_phi3_attention_class
from .qwen2_packed_training_patch import replace_qwen2_attention_class
from .train_dataloader_patch import replace_train_dataloader
from .train_sampler_patch import replace_train_sampler
__all__ = [
"replace_llama_attn_with_flash_attn",
"replace_llama_rmsnorm_with_fused_rmsnorm",
"replace_llama2_attn_with_flash_attn",
"replace_train_sampler",
"replace_train_dataloader",
"replace_internlm2_attention_class",
"replace_qwen2_attention_class",
"replace_phi3_attention_class",
"replace_llama_attention_class",
"pad_data_collator",
"dpo_concat_pad_data_collator",
"concat_pad_data_collator",
"apply_liger_kernel_to_internvit",
]
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/patch/internlm2_packed_training_patch.py 0 → 100644
View file @ 876a36a4
# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import torch
from flash_attn.flash_attn_interface import flash_attn_varlen_func
from internvl.model.internlm2.modeling_internlm2 import (
INTERNLM2_ATTENTION_CLASSES,
InternLM2FlashAttention2,
apply_rotary_pos_emb,
)
# Modified from internvl.model.internlm2.modeling_internlm2.InternLM2FlashAttention2
class InternLM2FlashAttention2ForPackedTraining(InternLM2FlashAttention2):
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`):
rename from cu_seqlens to keep compatability - (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
of the sequences in the batch.
dropout (`int`, *optional*):
Attention dropout
softmax_scale (`float`, *optional*):
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
"""
assert query_states.size(0) == key_states.size(0) == value_states.size(0) == 1
query_states = query_states.squeeze(0)
key_states = key_states.squeeze(0)
value_states = value_states.squeeze(0)
cu_seqlens = attention_mask.squeeze(0)
with torch.no_grad():
max_seqlen = max(
[
cu_seqlens[idx + 1] - cu_seqlens[idx]
for idx in range(cu_seqlens.size(0) - 1)
]
).item()
# Contains at least one padding token in the sequence
causal = self.is_causal and query_length != 1
attn_output = flash_attn_varlen_func(
q=query_states,
k=key_states,
v=value_states,
cu_seqlens_q=cu_seqlens,
cu_seqlens_k=cu_seqlens,
max_seqlen_q=max_seqlen,
max_seqlen_k=max_seqlen,
dropout_p=dropout,
softmax_scale=softmax_scale,
causal=causal,
)
query_states = query_states.unsqueeze(0)
key_states = key_states.unsqueeze(0)
value_states = value_states.unsqueeze(0)
return attn_output
def replace_internlm2_attention_class():
INTERNLM2_ATTENTION_CLASSES["flash_attention_2"] = (
InternLM2FlashAttention2ForPackedTraining
)
print("Replace INTERNLM2_ATTENTION_CLASSES to support packed training!!")
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/patch/internvit_liger_monkey_patch.py 0 → 100644
View file @ 876a36a4
# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
def apply_liger_kernel_to_internvit() -> None:
from internvl.model.internvl_chat import modeling_intern_vit
from liger_kernel.transformers.layer_norm import LigerLayerNorm
from liger_kernel.transformers.rms_norm import LigerRMSNorm
modeling_intern_vit.NORM2FN["rms_norm"] = LigerRMSNorm
modeling_intern_vit.NORM2FN["layer_norm"] = LigerLayerNorm
print("Liger kernel applied to InternViT")
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/patch/llama2_flash_attn_monkey_patch.py 0 → 100644
View file @ 876a36a4
"""
This file is copied from: https://github.com/lm-sys/FastChat
"""
import warnings
from typing import Optional, Tuple
import torch
from flash_attn import __version__ as flash_attn_version
from flash_attn.bert_padding import pad_input, unpad_input
from flash_attn.flash_attn_interface import (
flash_attn_func,
flash_attn_varlen_kvpacked_func,
)
from transformers.models.llama.modeling_llama import (
LlamaAttention,
LlamaModel,
rotate_half,
)
def apply_rotary_pos_emb(q, k, cos_sin, position_ids):
gather_indices = position_ids[:, :, None, None] # [bsz, seq_len, 1, 1]
gather_indices = gather_indices.repeat(
1, 1, cos_sin[0].shape[1], cos_sin[0].shape[3]
)
bsz = gather_indices.shape[0]
cos, sin = (
torch.gather(x.transpose(1, 2).repeat(bsz, 1, 1, 1), 1, gather_indices)
for x in cos_sin
)
q, k = ((x * cos) + (rotate_half(x) * sin) for x in (q, k))
return q, k
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
padding_mask: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if output_attentions:
warnings.warn(
"Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
)
bsz, q_len, _ = hidden_states.size()
kv_heads = getattr(self, "num_key_value_heads", self.num_heads)
q, k, v = (
op(hidden_states).view(bsz, q_len, nh, self.head_dim)
for op, nh in (
(self.q_proj, self.num_heads),
(self.k_proj, kv_heads),
(self.v_proj, kv_heads),
)
)
# shape: (b, s, num_heads, head_dim)
kv_seq_len = k.shape[1]
past_kv_len = 0
if past_key_value is not None:
past_kv_len = past_key_value[0].shape[2]
kv_seq_len += past_kv_len
cos_sin = self.rotary_emb(v, seq_len=kv_seq_len)
q, k = apply_rotary_pos_emb(q, k, cos_sin, position_ids)
if past_key_value is not None:
assert flash_attn_version >= "2.1.0", (
"past_key_value support requires flash-attn >= 2.1.0"
)
# reuse k, v
k = torch.cat([past_key_value[0].transpose(1, 2), k], dim=1)
v = torch.cat([past_key_value[1].transpose(1, 2), v], dim=1)
past_key_value = (k.transpose(1, 2), v.transpose(1, 2)) if use_cache else None
if attention_mask is None:
output = flash_attn_func(q, k, v, 0.0, softmax_scale=None, causal=True).view(
bsz, q_len, -1
)
else:
q, indices, cu_q_lens, max_s = unpad_input(q, attention_mask[:, -q_len:])
# We can skip concat and call unpad twice but seems better to call unpad only once.
kv, _, cu_k_lens, max_k = unpad_input(
torch.stack((k, v), dim=2), attention_mask
)
output_unpad = flash_attn_varlen_kvpacked_func(
q,
kv,
cu_q_lens,
cu_k_lens,
max_s,
max_k,
0.0,
softmax_scale=None,
causal=True,
)
output_unpad = output_unpad.reshape(-1, self.num_heads * self.head_dim)
output = pad_input(output_unpad, indices, bsz, q_len)
return self.o_proj(output), None, past_key_value
# Disable the transformation of the attention mask in LlamaModel as flash attention
# takes a boolean key_padding_mask. Fills in the past kv length for use in forward.
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
# [bsz, seq_len]
if past_key_values_length > 0 and attention_mask is not None:
attention_mask = torch.cat(
(
torch.full(
(input_shape[0], past_key_values_length),
True,
dtype=attention_mask.dtype,
device=attention_mask.device,
),
attention_mask,
),
dim=-1,
)
if attention_mask is not None and torch.all(attention_mask):
return None # This uses the faster call when training with full samples
return attention_mask
def replace_llama2_attn_with_flash_attn():
cuda_major, cuda_minor = torch.cuda.get_device_capability()
if cuda_major < 8:
warnings.warn(
"Flash attention is only supported on A100 or H100 GPU during training due to head dim > 64 backward."
"ref: https://github.com/HazyResearch/flash-attention/issues/190#issuecomment-1523359593"
)
LlamaModel._prepare_decoder_attention_mask = _prepare_decoder_attention_mask
LlamaAttention.forward = forward
def test():
from fastchat.train.llama_flash_attn_monkey_patch import forward as fastchat_forward
from transformers.models.llama.configuration_llama import LlamaConfig
config = LlamaConfig(
hidden_size=1024,
intermediate_size=128,
num_hidden_layers=1,
num_attention_heads=8,
max_position_embeddings=16,
)
device = torch.device("cuda")
model = LlamaModel(config)
attn = LlamaAttention(config).to(device).half()
bsz, hs, seqlen = 2, config.hidden_size, config.max_position_embeddings
position_ids = torch.arange(seqlen, dtype=torch.long, device=device).view(
-1, seqlen
)
mask = torch.full((bsz, seqlen), True, dtype=torch.bool, device=device)
for i in range(4):
hidden = torch.rand((bsz, seqlen, hs), dtype=torch.float16, device=device)
if i:
mask[0, -i:] = False
mask[1, :i] = False
lmask = model._prepare_decoder_attention_mask(mask, hidden.shape[:2], hidden, 0)
ref, _, _ = attn.forward(
hidden, attention_mask=lmask, position_ids=position_ids
)
fast, _, _ = fastchat_forward(
attn, hidden, attention_mask=mask, position_ids=position_ids
)
lmask = _prepare_decoder_attention_mask(
model, mask, hidden.shape[:2], hidden, 0
)
test, _, _ = forward(
attn, hidden, attention_mask=lmask, position_ids=position_ids
)
print(f"Mean(abs(ref)) = {torch.mean(torch.abs(ref))}")
print(f"Mean(abs(ref - fast)) = {torch.mean(torch.abs(ref - fast))}")
print(f"Mean(abs(ref - test)) = {torch.mean(torch.abs(ref - test))}")
print(f"Mean(abs(fast - test)) = {torch.mean(torch.abs(fast - test))}")
print(f"allclose(fast, test) = {torch.allclose(fast, test)}")
with torch.no_grad():
# Also check that past_kv is handled properly
hidden = torch.rand((bsz, seqlen, hs), dtype=torch.float16, device=device)
part_len = seqlen // 4
assert part_len * 4 == seqlen
mask = torch.full((bsz, seqlen), True, dtype=torch.bool, device=device)
mask[0, -2:] = False
lmask = _prepare_decoder_attention_mask(
model, mask, hidden.shape[:2], hidden, 0
)
oneshot, _, _ = forward(
attn, hidden, attention_mask=lmask, position_ids=position_ids
)
parts = []
past_kv, past_kv_len = None, 0
for i in range(4):
start = part_len * i
end = start + part_len
hidden_part = hidden[:, start:end, ...]
lmask = _prepare_decoder_attention_mask(
model,
mask[:, start:end],
hidden_part.shape[:2],
hidden_part,
past_kv_len,
)
part, _, past_kv = forward(
attn,
hidden_part.clone(),
attention_mask=lmask,
position_ids=position_ids[:, start:end],
past_key_value=past_kv,
use_cache=True,
)
parts.append(part)
past_kv_len = past_kv[0].shape[2]
print(
f"allclose(oneshot[:, 0], parts[0]) = {torch.allclose(oneshot[:, :part_len], parts[0])}"
)
print(
f"allclose(oneshot, parts) = {torch.allclose(oneshot, torch.cat(parts, dim=1))}"
)
if __name__ == "__main__":
test()
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/patch/llama_flash_attn_monkey_patch.py 0 → 100644
View file @ 876a36a4
# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import math
from typing import Optional, Tuple
import torch
import torch.nn.functional as F
import transformers
from torch import nn
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel
attention_mask: [bsz, q_len]
"""
from einops import rearrange
try: # v1
from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
except: # v2
from flash_attn.flash_attn_interface import (
flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func,
)
from flash_attn.bert_padding import pad_input, unpad_input
bsz, q_len, _ = hidden_states.size()
query_states = (
self.q_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
key_states = (
self.k_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
value_states = (
self.v_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
# [bsz, q_len, nh, hd]
# [bsz, nh, q_len, hd]
kv_seq_len = key_states.shape[-2]
assert past_key_value is None, "past_key_value is not supported"
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
# [bsz, nh, t, hd]
assert not output_attentions, "output_attentions is not supported"
assert not use_cache, "use_cache is not supported"
# Flash attention codes from
# https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py
# transform the data into the format required by flash attention
qkv = torch.stack(
[query_states, key_states, value_states], dim=2
) # [bsz, nh, 3, q_len, hd]
qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd]
# We have disabled _prepare_decoder_attention_mask in LlamaModel
# the attention_mask should be the same as the key_padding_mask
key_padding_mask = attention_mask
if key_padding_mask is None:
qkv = rearrange(qkv, "b s ... -> (b s) ...")
max_s = q_len
cu_q_lens = torch.arange(
0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device
)
output = flash_attn_unpadded_qkvpacked_func(
qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
)
output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
else:
nheads = qkv.shape[-2]
x = rearrange(qkv, "b s three h d -> b s (three h d)")
x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
x_unpad = rearrange(
x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads
)
output_unpad = flash_attn_unpadded_qkvpacked_func(
x_unpad, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
)
output = rearrange(
pad_input(
rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, bsz, q_len
),
"b s (h d) -> b s h d",
h=nheads,
)
return self.o_proj(rearrange(output, "b s h d -> b s (h d)")), None, None
# Disable the transformation of the attention mask in LlamaModel as the flash attention
# requires the attention mask to be the same as the key_padding_mask
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
# [bsz, seq_len]
return attention_mask
def forward_2(
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: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
query_states = (
self.q_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
key_states = (
self.k_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
value_states = (
self.v_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
assert not output_attentions, "output_attentions is not supported"
assert not use_cache, "use_cache is not supported"
assert past_key_value is None, "past_key_value is not supported"
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
if self.training:
attn_output = F.scaled_dot_product_attention(
query_states, key_states, value_states, dropout_p=0.0, is_causal=True
)
attn_weights = None
else:
attn_weights = torch.matmul(
query_states, key_states.transpose(2, 3)
) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
attn_weights = torch.max(
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
)
# upcast attention to fp32
attn_weights = nn.functional.softmax(
attn_weights, dim=-1, dtype=torch.float32
).to(query_states.dtype)
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)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
def replace_llama_attn_with_flash_attn():
if hasattr(F, "scaled_dot_product_attention"):
transformers.models.llama.modeling_llama.LlamaAttention.forward = forward_2
else:
transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = _prepare_decoder_attention_mask
transformers.models.llama.modeling_llama.LlamaAttention.forward = forward
SenseNova-SI-main/training/intern_vl/internvl_chat/internvl/patch/llama_packed_training_patch.py 0 → 100644
View file @ 876a36a4
# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import torch
from flash_attn.flash_attn_interface import flash_attn_varlen_func
from transformers.models.llama.modeling_llama import (
LLAMA_ATTENTION_CLASSES,
LlamaFlashAttention2,
)
# Modified from transformers.models.llama.modeling_llama.LlamaFlashAttention2
class LlamaFlashAttention2ForPackedTraining(LlamaFlashAttention2):
def _flash_attention_forward(
self,
query_states,
key_states,
value_states,
attention_mask,
query_length,
dropout=0.0,
softmax_scale=None,
use_sliding_windows=False,
):
"""
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)
use_sliding_windows (`bool`, *optional*):
Whether to activate sliding window attention.
"""
assert query_states.size(0) == key_states.size(0) == value_states.size(0) == 1
query_states = query_states.squeeze(0)
key_states = key_states.squeeze(0)
value_states = value_states.squeeze(0)
cu_seqlens = attention_mask.squeeze(0)
with torch.no_grad():
max_seqlen = max(
[
cu_seqlens[idx + 1] - cu_seqlens[idx]
for idx in range(cu_seqlens.size(0) - 1)
]
).item()
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
# Decide whether to use SWA or not by layer index.
if use_sliding_windows and self.layer_idx >= self.config.max_window_layers:
use_sliding_windows = False
if not use_sliding_windows:
attn_output = flash_attn_varlen_func(
q=query_states,
k=key_states,
v=value_states,
cu_seqlens_q=cu_seqlens,
cu_seqlens_k=cu_seqlens,
max_seqlen_q=max_seqlen,
max_seqlen_k=max_seqlen,
dropout_p=dropout,
softmax_scale=softmax_scale,
causal=causal,
)
else:
attn_output = flash_attn_varlen_func(
q=query_states,
k=key_states,
v=value_states,
cu_seqlens_q=cu_seqlens,
cu_seqlens_k=cu_seqlens,
max_seqlen_q=max_seqlen,
max_seqlen_k=max_seqlen,
dropout_p=dropout,
softmax_scale=softmax_scale,
causal=causal,
window_size=(self.config.sliding_window, self.config.sliding_window),
)
query_states = query_states.unsqueeze(0)
key_states = key_states.unsqueeze(0)
value_states = value_states.unsqueeze(0)
return attn_output
def replace_llama_attention_class():
LLAMA_ATTENTION_CLASSES["flash_attention_2"] = LlamaFlashAttention2ForPackedTraining
print("Replace LLAMA_ATTENTION_CLASSES to support packed training!!")
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