From 6a27593d08d550735124160fae33126560251bd7 Mon Sep 17 00:00:00 2001 From: xxl <505279206@qq.com> Date: Fri, 1 Nov 2024 13:17:47 +0800 Subject: [PATCH] first commit --- Baichuan2 模型社区许可协议.pdf | Bin 0 -> 257546 bytes Community License for Baichuan2 Model.pdf | Bin 0 -> 202809 bytes README.md | 153 ++++- config.json | 29 + configuration.json | 11 + configuration_baichuan.py | 69 ++ generation_config.json | 14 + generation_utils.py | 83 +++ modeling_baichuan.py | 785 ++++++++++++++++++++++ ms_wrapper.py | 74 ++ pytorch_model.bin | 3 + quantizer.py | 210 ++++++ special_tokens_map.json | 30 + tokenization_baichuan.py | 253 +++++++ tokenizer.model | 3 + tokenizer_config.json | 36 + 16 files changed, 1751 insertions(+), 2 deletions(-) create mode 100644 Baichuan2 模型社区许可协议.pdf create mode 100644 Community License for Baichuan2 Model.pdf create mode 100644 config.json create mode 100644 configuration.json create mode 100644 configuration_baichuan.py create mode 100644 generation_config.json 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zk@~`9GRS@$I6C0iNOCv~)5!h9A)H3GDGq~+BkR&(vLAwQ7#FcKPDf#qE({)6(69Q; z_hy4tNZzvg`T$$T4d4(z@>IRmJRYA4z=*?ev1MLvJOE4lSTLpjMAgTg*%*z+KxwYd xuFf2k332}v7XdFY?e-{}6q5AOI-u&U`3(zwRVaTehFIi + +

+

+ Baichuan 2 +

+
+ +
+
+🚀 百川大模型在线对话平台 已正式向公众开放 🎉 +
+ +# 目录 + +- [📖 模型介绍](#模型介绍) +- [⚙️ 快速开始](#快速开始) +- [📊 Benchmark评估](#评估) +- [📜 声明与协议](#声明与协议) + +# 模型介绍 + +- Baichuan 2 是[百川智能]推出的**新一代开源大语言模型**,采用 **2.6 万亿** Tokens 的高质量语料训练。 +- Baichuan 2 在多个权威的中文、英文和多语言的通用、领域 benchmark 上取得同尺寸**最佳**的效果。 +- 本次发布包含有 **7B**、**13B** 的 **Base** 和 **Chat** 版本,并提供了 Chat 版本的 **4bits 量化**。 +- 所有版本对学术研究完全开放。同时,开发者通过邮件申请并获得官方商用许可后,即可**免费商用**,请参考[协议](#协议)章节。 +- 欢迎阅读我们的技术报告 [Baichuan 2: Open Large-scale Language Models] 获取更多信息。 + +本次发布版本和下载链接见下表: + +| | 基座模型 | 对齐模型 | 对齐模型 4bits 量化 | +|:---:|:--------------------:|:--------------------:|:--------------------------:| +| 7B | [Baichuan2-7B-Base] | [Baichuan2-7B-Chat] | [Baichuan2-7B-Chat-4bits] | +| 13B | [Baichuan2-13B-Base] | [Baichuan2-13B-Chat] | [Baichuan2-13B-Chat-4bits] | + +# 快速开始 + +```python +import torch +from modelscope import snapshot_download, AutoModelForCausalLM, AutoTokenizer,GenerationConfig +model_dir = snapshot_download("baichuan-inc/Baichuan2-7B-Chat", revision='v1.0.4') +tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", + trust_remote_code=True, torch_dtype=torch.float16) +model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", + trust_remote_code=True, torch_dtype=torch.float16) +model.generation_config = GenerationConfig.from_pretrained(model_dir) +messages = [] +messages.append({"role": "user", "content": "讲解一下“温故而知新”"}) +response = model.chat(tokenizer, messages) +print(response) +messages.append({'role': 'assistant', 'content': response}) +messages.append({"role": "user", "content": "背诵一下将进酒"}) +response = model.chat(tokenizer, messages) +print(response) +``` + +# Benchmark 结果 + +我们在[通用]、[法律]、[医疗]、[数学]、[代码]和[多语言翻译]六个领域的中英文权威数据集上对模型进行了广泛测试,更多详细测评结果可查看[GitHub]。 + +### 7B 模型结果 + +| | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** | +|:-----------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:| +| | 5-shot | 5-shot | 5-shot | 5-shot | 5-shot | 3-shot | +| **GPT-4** | 68.40 | 83.93 | 70.33 | 66.15 | 63.27 | 75.12 | +| **GPT-3.5 Turbo** | 51.10 | 68.54 | 54.06 | 47.07 | 46.13 | 61.59 | +| **LLaMA-7B** | 27.10 | 35.10 | 26.75 | 27.81 | 28.17 | 32.38 | +| **LLaMA2-7B** | 28.90 | 45.73 | 31.38 | 25.97 | 26.53 | 39.16 | +| **MPT-7B** | 27.15 | 27.93 | 26.00 | 26.54 | 24.83 | 35.20 | +| **Falcon-7B** | 24.23 | 26.03 | 25.66 | 24.24 | 24.10 | 28.77 | +| **ChatGLM2-6B** | 50.20 | 45.90 | 49.00 | 49.44 | 45.28 | 31.65 | +| **[Baichuan-7B]** | 42.80 | 42.30 | 44.02 | 36.34 | 34.44 | 32.48 | +| **[Baichuan2-7B-Base]** | 54.00 | 54.16 | 57.07 | 47.47 | 42.73 | 41.56 | + +### 13B 模型结果 + +| | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** | +|:---------------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:| +| | 5-shot | 5-shot | 5-shot | 5-shot | 5-shot | 3-shot | +| **GPT-4** | 68.40 | 83.93 | 70.33 | 66.15 | 63.27 | 75.12 | +| **GPT-3.5 Turbo** | 51.10 | 68.54 | 54.06 | 47.07 | 46.13 | 61.59 | +| **LLaMA-13B** | 28.50 | 46.30 | 31.15 | 28.23 | 28.22 | 37.89 | +| **LLaMA2-13B** | 35.80 | 55.09 | 37.99 | 30.83 | 32.29 | 46.98 | +| **Vicuna-13B** | 32.80 | 52.00 | 36.28 | 30.11 | 31.55 | 43.04 | +| **Chinese-Alpaca-Plus-13B** | 38.80 | 43.90 | 33.43 | 34.78 | 35.46 | 28.94 | +| **XVERSE-13B** | 53.70 | 55.21 | 58.44 | 44.69 | 42.54 | 38.06 | +| **[Baichuan-13B-Base]** | 52.40 | 51.60 | 55.30 | 49.69 | 43.20 | 43.01 | +| **[Baichuan2-13B-Base]** | 58.10 | 59.17 | 61.97 | 54.33 | 48.17 | 48.78 | + + +## 训练过程模型 + +除了训练了 2.6 万亿 Tokens 的 [Baichuan2-7B-Base] 模型,我们还提供了在此之前的另外 11 个中间过程的模型(分别对应训练了约 0.2 ~ 2.4 万亿 Tokens)供社区研究使用([训练过程checkpoint下载])。下图给出了这些 checkpoints 在 C-Eval、MMLU、CMMLU 三个 benchmark 上的效果变化: + +![checkpoint](https://modelscope.cn/api/v1/models/baichuan-inc/Baichuan2-7B-Base/repo?Revision=master&FilePath=media/checkpoints.jpeg&View=true) + +# 声明与协议 + +## 声明 + +我们在此声明,我们的开发团队并未基于 Baichuan 2 模型开发任何应用,无论是在 iOS、Android、网页或任何其他平台。我们强烈呼吁所有使用者,不要利用 +Baichuan 2 模型进行任何危害国家社会安全或违法的活动。另外,我们也要求使用者不要将 Baichuan 2 +模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则,确保科技的发展能在规范和合法的环境下进行。 + +我们已经尽我们所能,来确保模型训练过程中使用的数据的合规性。然而,尽管我们已经做出了巨大的努力,但由于模型和数据的复杂性,仍有可能存在一些无法预见的问题。因此,如果由于使用 +Baichuan 2 开源模型而导致的任何问题,包括但不限于数据安全问题、公共舆论风险,或模型被误导、滥用、传播或不当利用所带来的任何风险和问题,我们将不承担任何责任。 + +## 协议 + +* Baichuan 2 模型的社区使用需遵循[《Baichuan 2 模型社区许可协议》]。 +* Baichuan 2 支持商用,如果将 Baichuan 2 模型或其衍生品用作商业用途,请您按照如下方式联系许可方,以进行登记并向许可方申请书面授权:联系邮箱 [opensource@baichuan-inc.com]。 + +[GitHub]:https://github.com/baichuan-inc/Baichuan2 +[Baichuan2]:https://github.com/baichuan-inc/Baichuan2 + +[Baichuan-7B]:https://modelscope.cn/models/baichuan-inc/baichuan-7B/summary +[Baichuan2-7B-Base]:https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Base/summary +[Baichuan2-7B-Chat]:https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat/summary +[Baichuan2-7B-Chat-4bits]:https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat-4bits/summary +[Baichuan-13B-Base]:https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Base/summary +[Baichuan2-13B-Base]:https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Base/summary +[Baichuan2-13B-Chat]:https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat/summary +[Baichuan2-13B-Chat-4bits]:https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat-4bits/summary + +[通用]:https://github.com/baichuan-inc/Baichuan2#%E9%80%9A%E7%94%A8%E9%A2%86%E5%9F%9F +[法律]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97 +[医疗]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97 +[数学]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81 +[代码]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81 +[多语言翻译]:https://github.com/baichuan-inc/Baichuan2#%E5%A4%9A%E8%AF%AD%E8%A8%80%E7%BF%BB%E8%AF%91 + +[《Baichuan 2 模型社区许可协议》]:https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Base/file/view/master/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf + +[邮件申请]: mailto:opensource@baichuan-inc.com +[Email]: mailto:opensource@baichuan-inc.com +[opensource@baichuan-inc.com]: mailto:opensource@baichuan-inc.com +[训练过程checkpoint下载]: https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints/summary +[百川智能]: https://www.baichuan-ai.com +[Baichuan 2: Open Large-scale Language Models]:https://cdn.baichuan-ai.com/paper/Baichuan2-technical-report.pdf \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..83027d2 --- /dev/null +++ b/config.json @@ -0,0 +1,29 @@ +{ + "architectures": [ + "BaichuanForCausalLM" + ], + "auto_map": { + "AutoConfig": "configuration_baichuan.BaichuanConfig", + "AutoModelForCausalLM": "modeling_baichuan.BaichuanForCausalLM" + }, + "tokenizer_class": "BaichuanTokenizer", + "bos_token_id": 1, + "eos_token_id": 2, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 11008, + "max_position_embeddings": 4096, + "model_max_length": 4096, + "model_type": "baichuan", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "pad_token_id": 0, + "rms_norm_eps": 1e-06, + "_from_model_config": true, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.29.2", + "use_cache": true, + "vocab_size": 125696 +} diff --git a/configuration.json b/configuration.json new file mode 100644 index 0000000..dc23a15 --- /dev/null +++ b/configuration.json @@ -0,0 +1,11 @@ +{ + "framework": "pytorch", + "task": "text-generation", + "model": { + "type": "Baichuan2-7B-Chat" + }, + "pipeline": { + "type": "Baichuan2-7B-chatbot-pipe" + }, + "allow_remote": true +} diff --git a/configuration_baichuan.py b/configuration_baichuan.py new file mode 100644 index 0000000..a260931 --- /dev/null +++ b/configuration_baichuan.py @@ -0,0 +1,69 @@ +# 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 transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + + +class BaichuanConfig(PretrainedConfig): + model_type = "baichuan" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=125696, + hidden_size=4096, + intermediate_size=11008, + num_hidden_layers=32, + num_attention_heads=32, + hidden_act="silu", + max_position_embeddings=4096, + 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, + z_loss_weight=0, + **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.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.z_loss_weight = z_loss_weight + 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, + ) diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..b1cd1e5 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,14 @@ +{ + "pad_token_id": 0, + "bos_token_id": 1, + "eos_token_id": 2, + "user_token_id": 195, + "assistant_token_id": 196, + "max_new_tokens": 2048, + "temperature": 0.3, + "top_k": 5, + "top_p": 0.85, + "repetition_penalty": 1.05, + "do_sample": true, + "transformers_version": "4.29.2" +} diff --git a/generation_utils.py b/generation_utils.py new file mode 100644 index 0000000..5771699 --- /dev/null +++ b/generation_utils.py @@ -0,0 +1,83 @@ +from typing import List +from queue import Queue + +import torch + + +def build_chat_input(model, tokenizer, messages: List[dict], max_new_tokens: int=0): + def _parse_messages(messages, split_role="user"): + system, rounds = "", [] + round = [] + for i, message in enumerate(messages): + if message["role"] == "system": + assert i == 0 + system = message["content"] + continue + if message["role"] == split_role and round: + rounds.append(round) + round = [] + round.append(message) + if round: + rounds.append(round) + return system, rounds + + max_new_tokens = max_new_tokens or model.generation_config.max_new_tokens + max_input_tokens = model.config.model_max_length - max_new_tokens + system, rounds = _parse_messages(messages, split_role="user") + system_tokens = tokenizer.encode(system) + max_history_tokens = max_input_tokens - len(system_tokens) + + history_tokens = [] + for round in rounds[::-1]: + round_tokens = [] + for message in round: + if message["role"] == "user": + round_tokens.append(model.generation_config.user_token_id) + else: + round_tokens.append(model.generation_config.assistant_token_id) + round_tokens.extend(tokenizer.encode(message["content"])) + if len(history_tokens) == 0 or len(history_tokens) + len(round_tokens) <= max_history_tokens: + history_tokens = round_tokens + history_tokens # concat left + if len(history_tokens) < max_history_tokens: + continue + break + + input_tokens = system_tokens + history_tokens + if messages[-1]["role"] != "assistant": + input_tokens.append(model.generation_config.assistant_token_id) + input_tokens = input_tokens[-max_input_tokens:] # truncate left + return torch.LongTensor([input_tokens]).to(model.device) + + +class TextIterStreamer: + def __init__(self, tokenizer, skip_prompt=False, skip_special_tokens=False): + self.tokenizer = tokenizer + self.skip_prompt = skip_prompt + self.skip_special_tokens = skip_special_tokens + self.tokens = [] + self.text_queue = Queue() + self.next_tokens_are_prompt = True + + def put(self, value): + if self.skip_prompt and self.next_tokens_are_prompt: + self.next_tokens_are_prompt = False + else: + if len(value.shape) > 1: + value = value[0] + self.tokens.extend(value.tolist()) + self.text_queue.put( + self.tokenizer.decode(self.tokens, skip_special_tokens=self.skip_special_tokens)) + + def end(self): + self.text_queue.put(None) + + def __iter__(self): + return self + + def __next__(self): + value = self.text_queue.get() + if value is None: + raise StopIteration() + else: + return value + diff --git a/modeling_baichuan.py b/modeling_baichuan.py new file mode 100644 index 0000000..a202cb8 --- /dev/null +++ b/modeling_baichuan.py @@ -0,0 +1,785 @@ +# 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." + ) + + +# 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 + 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=True): + 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) + self.first_flag = True + elif self.first_flag: + self.first_flag = False + self.weight.data = 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 isinstance(config.quantization_config, dict) and config.quantization_config.get('load_in_4bit', False): + 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) + + if device_map is not 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) + softmax_normalizer = shift_logits.max(-1).values ** 2 + z_loss = self.config.z_loss_weight * softmax_normalizer.mean() + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + z_loss + + 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 diff --git a/ms_wrapper.py b/ms_wrapper.py new file mode 100644 index 0000000..0fa3d36 --- /dev/null +++ b/ms_wrapper.py @@ -0,0 +1,74 @@ +import os +import torch +from typing import Union, Dict, Any +from modelscope.pipelines.builder import PIPELINES +from modelscope.models.builder import MODELS +from modelscope.utils.constant import Tasks +from modelscope.pipelines.base import Pipeline +from modelscope.outputs import OutputKeys +from modelscope.pipelines.nlp.text_generation_pipeline import TextGenerationPipeline +from modelscope.models.base import Model, TorchModel +from modelscope.utils.logger import get_logger +from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig +from transformers.generation.utils import GenerationConfig + +@PIPELINES.register_module(Tasks.text_generation, module_name='Baichuan2-7B-chatbot-pipe') +class Baichuan7BChatTextGenerationPipeline(TextGenerationPipeline): + def __init__( + self, + model: Union[Model, str], + *args, + **kwargs): + self.model = Baichuan7BChatTextGeneration(model) if isinstance(model, str) else model + super().__init__(model=model, **kwargs) + + def preprocess(self, inputs, **preprocess_params) -> Dict[str, Any]: + return inputs + + def _sanitize_parameters(self, **pipeline_parameters): + return {},pipeline_parameters,{} + + # define the forward pass + def forward(self, inputs: Dict, **forward_params) -> Dict[str, Any]: + output = {} + device = self.model.model.device + input_ids = self.model.tokenizer(inputs, return_tensors="pt").input_ids.to(device) + pred = self.model.model.generate(input_ids,**forward_params) + out = self.model.tokenizer.decode(pred.cpu()[0], skip_special_tokens=True) + output['text'] = out + return output + + # format the outputs from pipeline + def postprocess(self, input, **kwargs) -> Dict[str, Any]: + return input + + +@MODELS.register_module(Tasks.text_generation, module_name='Baichuan2-7B-Chat') +class Baichuan7BChatTextGeneration(TorchModel): + def __init__(self, model_dir=None, *args, **kwargs): + super().__init__(model_dir, *args, **kwargs) + self.logger = get_logger() + # loading tokenizer + self.tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) + self.model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True) + # self.model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto",trust_remote_code=True) + self.model.generation_config = GenerationConfig.from_pretrained(model_dir) + self.model = self.model.eval() + + def forward(self,input: Dict, *args, **kwargs) -> Dict[str, Any]: + output = {} + response = self.model.chat(self.tokenizer, input, *args, **kwargs) + history = input.copy() + history.append({'role': 'assistant', 'content': response}) + return {OutputKeys.RESPONSE:response, OutputKeys.HISTORY: history} + + def quantize(self, bits: int): + self.model = self.model.quantize(bits) + return self + + def infer(self, input, **kwargs): + device = self.model.device + input_ids = self.tokenizer(input, return_tensors="pt").input_ids.to(device) + pred = self.model.generate(input_ids,**kwargs) + out = self.tokenizer.decode(pred.cpu()[0], skip_special_tokens=True) + return out diff --git a/pytorch_model.bin b/pytorch_model.bin new file mode 100644 index 0000000..7a268cc --- /dev/null +++ b/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:594b540d36751fa3b199c609617deafc0329050937c0767fdfb2d38b72d8bec9 +size 15012021529 diff --git a/quantizer.py b/quantizer.py new file mode 100644 index 0000000..239a2fb --- /dev/null +++ b/quantizer.py @@ -0,0 +1,210 @@ +import bitsandbytes as bnb +from bitsandbytes.nn.modules import Params4bit, Int8Params +import torch + +def Params4bitCuda(self, device): + self.data = self.data.cuda(device) + self.quant_state[0] = self.quant_state[0].cuda(device) + self.quant_state[4][0] = self.quant_state[4][0].cuda(device) + self.quant_state[4][1][0] = self.quant_state[4][1][0].cuda(device) + self.quant_state[4][1][1] = self.quant_state[4][1][1].cuda(device) + + self.quant_state[6] = self.quant_state[6].cuda(device) + return self + +class Linear4bitOnline(torch.nn.Module): + def __init__(self, weight, bias, quant_type): + super().__init__() + self.weight = Params4bit( + weight.data, requires_grad=False, compress_statistics=True, quant_type=quant_type + ) + self.compute_dtype = None + #self.weight.cuda(weight.device) + self.bias = bias + + def forward(self, x: torch.Tensor): + # weights are cast automatically as Int8Params, but the bias has to be cast manually + if self.bias is not None and self.bias.dtype != x.dtype: + self.bias.data = self.bias.data.to(x.dtype) + + if getattr(self.weight, "quant_state", None) is None: + print( + "FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first." + ) + inp_dtype = x.dtype + if self.compute_dtype is not None: + x = x.to(self.compute_dtype) + + bias = None if self.bias is None else self.bias.to(self.compute_dtype) + out = bnb.matmul_4bit( + x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state + ) + + out = out.to(inp_dtype) + + return out + +class Linear8bitLtOnline(torch.nn.Module): + def __init__( + self, + weight, + bias, + has_fp16_weights=True, + memory_efficient_backward=False, + threshold=0.0, + index=None, + ): + super().__init__() + assert ( + not memory_efficient_backward + ), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0" + self.state = bnb.MatmulLtState() + self.index = index + + # Necessary for stacked layers + self.state.threshold = threshold + self.state.has_fp16_weights = has_fp16_weights + self.state.memory_efficient_backward = memory_efficient_backward + if threshold > 0.0 and not has_fp16_weights: + self.state.use_pool = True + + self.weight = Int8Params( + weight.data, + has_fp16_weights=has_fp16_weights, + requires_grad=has_fp16_weights, + ) + self.bias = bias + + def init_8bit_state(self): + self.state.CB = self.weight.CB + self.state.SCB = self.weight.SCB + self.weight.CB = None + self.weight.SCB = None + + def forward(self, x: torch.Tensor): + self.state.is_training = self.training + if self.weight.CB is not None: + self.init_8bit_state() + + # weights are cast automatically as Int8Params, but the bias has to be cast manually + if self.bias is not None and self.bias.dtype != x.dtype: + self.bias.data = self.bias.data.to(x.dtype) + + out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state) + + if not self.state.has_fp16_weights: + if self.state.CB is not None and self.state.CxB is not None: + # we converted 8-bit row major to turing/ampere format in the first inference pass + # we no longer need the row-major weight + del self.state.CB + self.weight.data = self.state.CxB + return out + +def quantize_offline(model, bits: int): + assert (bits == 4), f'bits: {bits} is not supported' + + for i, layer in enumerate(model.model.layers): + layer.self_attn.W_pack = bnb.nn.Linear4bit( + layer.self_attn.W_pack.weight.shape[1], + layer.self_attn.W_pack.weight.shape[0], + False, + torch.float16, + compress_statistics=True, + quant_type="nf4", + ) + layer.self_attn.o_proj = bnb.nn.Linear4bit( + layer.self_attn.o_proj.weight.shape[1], + layer.self_attn.o_proj.weight.shape[0], + False, + torch.float16, + compress_statistics=True, + quant_type="nf4", + ) + + layer.mlp.gate_proj = bnb.nn.Linear4bit( + layer.mlp.gate_proj.weight.shape[1], + layer.mlp.gate_proj.weight.shape[0], + False, + torch.float16, + compress_statistics=True, + quant_type="nf4", + ) + layer.mlp.down_proj = bnb.nn.Linear4bit( + layer.mlp.down_proj.weight.shape[1], + layer.mlp.down_proj.weight.shape[0], + False, + torch.float16, + compress_statistics=True, + quant_type="nf4", + ) + layer.mlp.up_proj = bnb.nn.Linear4bit( + layer.mlp.up_proj.weight.shape[1], + layer.mlp.up_proj.weight.shape[0], + False, + torch.float16, + compress_statistics=True, + quant_type="nf4", + ) + return model + +def quantize_online(model, bits: int): + def quant(weight, bias=None): + if bits == 8: + linear = Linear8bitLtOnline( + weight, + bias, + has_fp16_weights=False, + threshold=6.0, + ) + if bias is not None: + linear.bias = torch.nn.Parameter(bias) + elif bits == 4: + linear = Linear4bitOnline( + weight, + bias, + quant_type="nf4", #fp4/nf4 + ) + else: + raise ValueError("quantize only support 4/8 bit") + return linear + + for i, layer in enumerate(model.model.layers): + layer.self_attn.W_pack = quant(layer.self_attn.W_pack.weight) + layer.self_attn.o_proj = quant(layer.self_attn.o_proj.weight) + layer.mlp.gate_proj = quant(layer.mlp.gate_proj.weight) + layer.mlp.down_proj = quant(layer.mlp.down_proj.weight) + layer.mlp.up_proj = quant(layer.mlp.up_proj.weight) + return model + +def init_model_weight_int4(config, model, state_dict): + #replace Params4bit.cuda with Params4bitCuda + Params4bit.cuda = Params4bitCuda + + for i in range(config.num_hidden_layers): + weight_data = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.data'] + weight_quant_state = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.quant_state'] + model.model.layers[i].self_attn.W_pack.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state) + + weight_data = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.data'] + weight_quant_state = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.quant_state'] + model.model.layers[i].self_attn.o_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state) + + weight_data = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.data'] + weight_quant_state = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.quant_state'] + model.model.layers[i].mlp.gate_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state) + + weight_data = state_dict[f'model.layers.{i}.mlp.up_proj.weight.data'] + weight_quant_state = state_dict[f'model.layers.{i}.mlp.up_proj.weight.quant_state'] + model.model.layers[i].mlp.up_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state) + + weight_data = state_dict[f'model.layers.{i}.mlp.down_proj.weight.data'] + weight_quant_state = state_dict[f'model.layers.{i}.mlp.down_proj.weight.quant_state'] + model.model.layers[i].mlp.down_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state) + + model.model.layers[i].input_layernorm.weight = state_dict[f'model.layers.{i}.input_layernorm.weight'] + model.model.layers[i].post_attention_layernorm.weight = state_dict[f'model.layers.{i}.post_attention_layernorm.weight'] + + model.model.embed_tokens.weight = state_dict['model.embed_tokens.weight'] + model.model.norm.weight = state_dict['model.norm.weight'] + model.lm_head.weight = state_dict['lm_head.weight'] + return model \ No newline at end of file diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..5819ea2 --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,30 @@ +{ + "bos_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/tokenization_baichuan.py b/tokenization_baichuan.py new file mode 100644 index 0000000..f8ed2d1 --- /dev/null +++ b/tokenization_baichuan.py @@ -0,0 +1,253 @@ +# 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. + +import os +from shutil import copyfile +from typing import Any, Dict, List, Optional, Tuple + +import sentencepiece as spm + +from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} + +PRETRAINED_VOCAB_FILES_MAP = { + "vocab_file": {}, + "tokenizer_file": {}, +} +PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {} + + +class BaichuanTokenizer(PreTrainedTokenizer): + """ + Construct a Baichuan 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 + max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file, + unk_token="", + bos_token="", + eos_token="", + pad_token=None, + sp_model_kwargs: Optional[Dict[str, Any]] = None, + add_bos_token=True, + add_eos_token=False, + clean_up_tokenization_spaces=False, + **kwargs, + ): + self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs + bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token + eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token + unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token + pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token + + self.vocab_file = vocab_file + self.add_bos_token = add_bos_token + self.add_eos_token = add_eos_token + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(vocab_file) + + super().__init__( + bos_token=bos_token, + eos_token=eos_token, + unk_token=unk_token, + pad_token=pad_token, + add_bos_token=add_bos_token, + add_eos_token=add_eos_token, + sp_model_kwargs=self.sp_model_kwargs, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + def __getstate__(self): + state = self.__dict__.copy() + state["sp_model"] = None + return state + + def __setstate__(self, d): + self.__dict__ = d + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(self.vocab_file) + + @property + def vocab_size(self): + """Returns vocab size""" + return self.sp_model.get_piece_size() + + 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 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 i, token in enumerate(tokens): + # make sure that special tokens are not decoded using sentencepiece model + if token in self.all_special_tokens: + if not prev_is_special and i != 0: + 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) + return out_string + + 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): + bos_token_id = [self.bos_token_id] if self.add_bos_token else [] + eos_token_id = [self.eos_token_id] if self.add_eos_token else [] + + output = bos_token_id + token_ids_0 + eos_token_id + + if token_ids_1 is not None: + output = output + bos_token_id + token_ids_1 + 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 + ) + + bos_token_id = [1] if self.add_bos_token else [] + eos_token_id = [1] if self.add_eos_token else [] + + if token_ids_1 is None: + return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id + return ( + bos_token_id + + ([0] * len(token_ids_0)) + + eos_token_id + + bos_token_id + + ([0] * len(token_ids_1)) + + eos_token_id + ) + + def create_token_type_ids_from_sequences( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT + sequence pair mask has the following format: + + ``` + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + ``` + + if token_ids_1 is None, only returns the first portion of the mask (0s). + + 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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s). + """ + bos_token_id = [self.bos_token_id] if self.add_bos_token else [] + eos_token_id = [self.eos_token_id] if self.add_eos_token else [] + + output = [0] * len(bos_token_id + token_ids_0 + eos_token_id) + + if token_ids_1 is not None: + output += [1] * len(bos_token_id + token_ids_1 + eos_token_id) + + return output diff --git a/tokenizer.model b/tokenizer.model new file mode 100644 index 0000000..b3902c4 --- /dev/null +++ b/tokenizer.model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:79452955be6b419a65984273a9f08af86042e1c2a75ee3ba989cbf620a133cc2 +size 2001107 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..b3e0f76 --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,36 @@ +{ + "auto_map": { + "AutoTokenizer": ["tokenization_baichuan.BaichuanTokenizer", null] + }, + "add_bos_token": false, + "add_eos_token": false, + "use_fast": false, + "clean_up_tokenization_spaces": false, + "eos_token": { + "__type": "AddedToken", + "content": "
", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": true + }, + "model_max_length": 4096, + "sp_model_kwargs": {}, + "tokenizer_class": "BaichuanTokenizer", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": true + }, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": true + } +}