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Commit 7f03ad53 authored by yinger_z's avatar yinger_z
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updated for dtk24.04.1

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Dockerfile
View file @ 7f03ad53
FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04-py38-latest FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
COPY requirements.txt requirements.txt COPY requirements.txt requirements.txt
RUN source /opt/dtk-23.04/env.sh RUN source /opt/dtk/env.sh
RUN cp /usr/share/zoneinfo/Asia/Shanghai /etc/localtime && echo 'Asia/Shanghai' >/etc/timezone RUN cp /usr/share/zoneinfo/Asia/Shanghai /etc/localtime && echo 'Asia/Shanghai' >/etc/timezone
ENV LANG C.UTF-8 ENV LANG C.UTF-8
RUN pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com RUN pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
README.md
View file @ 7f03ad53
...@@ -30,8 +30,8 @@ Baichuan整体模型基于标准的Transformer结构，采用了和LLaMA一样 ...@@ -30,8 +30,8 @@ Baichuan整体模型基于标准的Transformer结构，采用了和LLaMA一样
### Docker(方式一) ### Docker(方式一)
推荐使用docker方式运行，提供拉取的docker镜像： 推荐使用docker方式运行，提供拉取的docker镜像：
``` ```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04-py38-latest docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
docker run -dit --network=host --name=baichuan2 --privileged --device=/dev/kfd --device=/dev/dri --ipc=host --shm-size=16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root --ulimit stack=-1:-1 --ulimit memlock=-1:-1 image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04-py38-latest docker run -dit --shm-size 80g --network=host --name=baichuan2 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /opt/hyhal/:/opt/hyhal/:ro image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10 /bin/bash
docker exec -it baichuan2 /bin/bash docker exec -it baichuan2 /bin/bash
``` ```
安装docker中没有的依赖: 安装docker中没有的依赖:
...@@ -43,20 +43,20 @@ pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --tru ...@@ -43,20 +43,20 @@ pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --tru
### Dockerfile(方式二) ### Dockerfile(方式二)
``` ```
docker build -t baichuan2:latest . docker build -t baichuan2:latest .
docker run -dit --network=host --name=baichuan2 --privileged --device=/dev/kfd --device=/dev/dri --ipc=host --shm-size=16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root --ulimit stack=-1:-1 --ulimit memlock=-1:-1 baichuan2:latest docker run -dit --shm-size 80g --network=host --name=baichuan2 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /opt/hyhal/:/opt/hyhal/:ro baichuan2:latest /bin/bash
docker exec -it baichuan2 /bin/bash docker exec -it baichuan2 /bin/bash
``` ```
### Conda(方式三) ### Conda(方式三)
1. 创建conda虚拟环境： 1. 创建conda虚拟环境：
``` ```
conda create -n baichuan2 python=3.8 conda create -n baichuan2 python=3.10
``` ```
2. 关于本项目DCU显卡所需的工具包、深度学习库等均可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。 2. 关于本项目DCU显卡所需的工具包、深度学习库等均可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
- [DTK 23.04](https://cancon.hpccube.com:65024/1/main/DTK-23.04.1) - [DTK 24.04](https://cancon.hpccube.com:65024/1/main/DTK-24.04.1)
- [Pytorch 1.13.1](https://cancon.hpccube.com:65024/4/main/pytorch/dtk23.04) - [Pytorch 2.1.0](https://cancon.hpccube.com:65024/4/main/pytorch/DAS1.1.1)
- [Deepspeed 0.9.2](https://cancon.hpccube.com:65024/4/main/deepspeed/dtk23.04) - [Deepspeed 0.12.3](https://cancon.hpccube.com:65024/4/main/deepspeed/DAS1.1)
Tips：以上dtk驱动、python、deepspeed等工具版本需要严格一一对应。 Tips：以上dtk驱动、python、deepspeed等工具版本需要严格一一对应。
...@@ -65,31 +65,10 @@ conda create -n baichuan2 python=3.8 ...@@ -65,31 +65,10 @@ conda create -n baichuan2 python=3.8
pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
``` ```
### 注意 1
```
#到虚拟环境下对应的python/site-packages注释掉一些版本判断
site-packages/accelerate/accelerator.py 文件
287 #if not is_deepspeed_available():
288 # raise ImportError("DeepSpeed is not installed => run `pip install deepspeed` or build it from source.")
289 #if compare_versions("deepspeed", "<", "0.9.3"):
290 # raise ImportError("DeepSpeed version must be >= 0.9.3. Please update DeepSpeed.")
site-packages/transformers/utils/versions.py 文件
43 #if not ops[op](version.parse(got_ver), version.parse(want_ver)):
44 # raise ImportError(
45 # f"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"
46 # )
```
### 注意 2
训练前请参考[modeling_baichuan.py](./modeling_baichuan.py)修改模型文件夹中modeling_baichuan.py的`Attention`类的代码，主要(暂时)去除去torch2.X的依赖。
### 注意3 ### 注意3
若不支持xformers，在多节点训练中可能会出现xformers相关报错:"ImportError: This modeling file reguires the following packages that were not found in your environment: xformers." ，您可通过直接将[modeling_baichuan.py](./modeling_baichuan.py)中xpos设置为None来解决，即注释import xformers相关代码，并设置`xops=None`。 若不支持xformers，在训练中可能会出现xformers相关报错:"ImportError: This modeling file reguires the following packages that were not found in your environment: xformers." ，您可通过直接将[modeling_baichuan.py](./modeling_baichuan.py)中xpos设置为None来解决，即注释import xformers相关代码，并设置`xops=None`。
## 数据集 ## 数据集
......
fine-tune/ft_train.sh
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hostfile="" hostfile=""
HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 deepspeed --hostfile=$hostfile fine-tune.py \ HIP_VISIBLE_DEVICES=0,1,2,3,4,5 deepspeed --hostfile=$hostfile fine-tune.py \
--report_to "none" \ --report_to "none" \
--data_path "data/belle_chat_ramdon_10k.json" \ --data_path "data/belle_chat_ramdon_10k.json" \
--model_name_or_path "../../baichuan2-13b-chat-hf" \ --model_name_or_path "./baichuan2-13b-chat" \
--output_dir "output" \ --output_dir "output" \
--model_max_length 512 \ --model_max_length 512 \
--num_train_epochs 4 \ --num_train_epochs 4 \
......
fine-tune/lora_train.sh
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hostfile="" hostfile=""
HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 deepspeed --hostfile=$hostfile fine-tune.py \ HIP_VISIBLE_DEVICES=0,1,2,3 deepspeed --hostfile=$hostfile fine-tune.py \
--report_to "none" \ --report_to "none" \
--data_path "data/belle_chat_ramdon_10k.json" \ --data_path "data/belle_chat_ramdon_10k.json" \
--model_name_or_path "../../baichuan2-13b-chat-hf" \ --model_name_or_path "./baichuan2-13b-chat" \
--output_dir "output" \ --output_dir "output" \
--model_max_length 64 \ --model_max_length 64 \
--num_train_epochs 4 \ --num_train_epochs 4 \
......
modeling_baichuan.py deleted 100644 → 0
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# Copyright 2023 Baichuan Inc. All Rights Reserved.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .configuration_baichuan import BaichuanConfig
from .generation_utils import build_chat_input, TextIterStreamer
import math
from typing import List, Optional, Tuple, Union
from threading import Thread
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torch.nn import functional as F
from transformers import PreTrainedModel, PretrainedConfig
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.generation.utils import GenerationConfig
from transformers.utils import logging, ContextManagers
import os
from contextlib import contextmanager
logger = logging.get_logger(__name__)
# try:
# from xformers import ops as xops
# except ImportError:
# xops = None
# logger.warning(
# "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers."
# )
xops = None
# 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)
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]`.
"""
if len(mask.size()) == 3:
bsz, src_len, _ = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:,None,:,:].expand(bsz, 1, tgt_len, src_len).to(dtype)
else:
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)
class RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
RMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states
class RotaryEmbedding(torch.nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
self.max_seq_len_cached = max_position_embeddings
t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
freqs = torch.outer(t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32)
self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32)
def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
if seq_len > self.max_seq_len_cached:
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
freqs = torch.outer(t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32).to(x.device)
self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32).to(x.device)
elif self.cos_cached.device != x.device:
self.cos_cached = self.cos_cached.to(x.device)
self.sin_cached = self.sin_cached.to(x.device)
return (
self.cos_cached[:, :, :seq_len, ...],
self.sin_cached[:, :, :seq_len, ...],
)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2:]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos_, sin_, position_ids):
cos = cos_.squeeze(1).squeeze(0) # [seq_len, dim]
sin = sin_.squeeze(1).squeeze(0) # [seq_len, dim]
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
q_embed = (q.float() * cos) + (rotate_half(q.float()) * sin)
k_embed = (k.float() * cos) + (rotate_half(k.float()) * sin)
return q_embed.to(q.dtype), k_embed.to(k.dtype)
class MLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
):
super().__init__()
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.act_fn = ACT2FN[hidden_act]
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
class Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: BaichuanConfig):
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.max_position_embeddings = config.max_position_embeddings
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.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.rotary_emb = RotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
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,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
proj = self.W_pack(hidden_states)
proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = proj[2].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)
# [bsz, nh, t, hd]
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
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)
# if xops is not None and self.training:
# attn_weights = None
# query_states = query_states.transpose(1, 2)
# key_states = key_states.transpose(1, 2)
# value_states = value_states.transpose(1, 2)
# attn_output = xops.memory_efficient_attention(
# query_states, key_states, value_states, attn_bias=xops.LowerTriangularMask()
# )
# else:
# with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
# attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask = attention_mask)
# 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
class DecoderLayer(nn.Module):
def __init__(self, config: BaichuanConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Attention(config=config)
self.mlp = MLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
)
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(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,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
class BaichuanPreTrainedModel(PreTrainedModel):
config_class = BaichuanConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["DecoderLayer"]
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, BaichuanModel):
module.gradient_checkpointing = value
class BaichuanModel(BaichuanPreTrainedModel):
def __init__(self, config: BaichuanConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
self.norm = RMSNorm(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
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
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
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 decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_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).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)
# embed positions
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
)
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,
)
class NormHead(nn.Module):
def __init__(self, hidden_size, vocab_size, bias=False):
super().__init__()
self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size)))
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
self.first_flag = True
def forward(self, hidden_states):
if self.training:
norm_weight = nn.functional.normalize(self.weight)
elif self.first_flag:
self.first_flag = False
self.weight = nn.Parameter(nn.functional.normalize(self.weight))
norm_weight = self.weight
else:
norm_weight = self.weight
return nn.functional.linear(hidden_states, norm_weight)
_init_weights = True
@contextmanager
def no_init_weights(_enable=True):
global _init_weights
old_init_weights = _init_weights
if _enable:
_init_weights = False
try:
yield
finally:
_init_weights = old_init_weights
class BaichuanForCausalLM(BaichuanPreTrainedModel):
def __init__(self, config, *model_args, **model_kwargs):
super().__init__(config, *model_args, **model_kwargs)
self.model = BaichuanModel(config)
self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False)
if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
try:
from .quantizer import quantize_offline, init_model_weight_int4
except ImportError:
raise ImportError(f"Needs QLinear to run quantize.")
quantize_offline(self, 4)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
*model_args,
config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
cache_dir: Optional[Union[str, os.PathLike]] = None,
ignore_mismatched_sizes: bool = False,
force_download: bool = False,
local_files_only: bool = False,
token: Optional[Union[str, bool]] = None,
revision: str = "main",
use_safetensors: bool = None,
**kwargs,
):
# Load config if we don't provide a configuration
if not isinstance(config, PretrainedConfig):
config_path = config if config is not None else pretrained_model_name_or_path
config, model_kwargs = cls.config_class.from_pretrained(
config_path,
cache_dir=cache_dir,
return_unused_kwargs=True,
force_download=force_download,
resume_download=False,
proxies=None,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder="",
_from_auto=False,
_from_pipeline=None,
**kwargs,
)
else:
model_kwargs = kwargs
if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
try:
from .quantizer import init_model_weight_int4
from accelerate import init_empty_weights, dispatch_model, infer_auto_device_map
from accelerate.utils import CustomDtype
from accelerate.utils import get_balanced_memory
except ImportError:
raise ImportError(f"Needs import model weight init func to run quantize.")
# Instantiate model.
init_contexts = [no_init_weights(_enable=True)]
init_contexts.append(init_empty_weights())
with ContextManagers(init_contexts):
model = cls(config)
model_file = os.path.join(pretrained_model_name_or_path, 'pytorch_model.bin')
state_dict = torch.load(model_file, map_location="cpu")
model.is_quantized = True
device_map = kwargs.pop("device_map", None)
torch_dtype = kwargs.pop("torch_dtype", None)
kwargs = {"no_split_module_classes": model._no_split_modules}
target_dtype = CustomDtype.INT4
max_memory = get_balanced_memory(
model,
dtype=target_dtype,
low_zero=(device_map == "balanced_low_0"),
max_memory=None,
**kwargs,
)
kwargs["max_memory"] = max_memory
device_map = infer_auto_device_map(model, dtype=target_dtype, **kwargs)
model = init_model_weight_int4(config, model, state_dict)
# Set model in evaluation mode to deactivate DropOut modules by default
model.eval()
# If it is a model with generation capabilities, attempt to load the generation config
if model.can_generate():
try:
model.generation_config = GenerationConfig.from_pretrained(
pretrained_model_name_or_path,
cache_dir=cache_dir,
force_download=force_download,
resume_download=False,
proxies=None,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder="",
_from_auto=False,
_from_pipeline=None,
**kwargs,
)
except (OSError, TypeError):
logger.info(
"Generation config file not found, using a generation config created from the model config."
)
pass
if device_map is not None:
dispatch_model(model, device_map=device_map)
return model
return super(BaichuanForCausalLM, cls).from_pretrained(pretrained_model_name_or_path, *model_args,
config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes,
force_download=force_download, local_files_only=local_files_only, token=token, revision=revision,
use_safetensors=use_safetensors, **kwargs)
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]:
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)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
if past_key_values:
input_ids = input_ids[:, -1:]
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[:, -1].unsqueeze(-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) for past_state in layer_past),)
return reordered_past
def quantize(self, bits: int):
try:
from .quantizer import quantize_online
except ImportError:
raise ImportError(f"Needs QLinear to run quantize.")
return quantize_online(self, bits)
def chat(self, tokenizer, messages: List[dict], stream=False,
generation_config: Optional[GenerationConfig]=None):
generation_config = generation_config or self.generation_config
input_ids = build_chat_input(self, tokenizer, messages, generation_config.max_new_tokens)
if stream:
streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
Thread(target=self.generate, kwargs=dict(
inputs=input_ids, streamer=streamer,
generation_config=generation_config,
)).start()
return streamer
else:
outputs = self.generate(input_ids, generation_config=generation_config)
response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
return response
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