merge

by bigmoyan - opened May 16, 2023

base: refs/heads/main

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from: refs/pr/5

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.gitattributes +0 -1
CHANGES.rst +0 -10
LISENCE +0 -420
README.md +68 -123
demo.py +27 -16
faster_chat_glm/__init__.py +7 -0
faster_chat_glm/__init__.py~ +3 -0
faster_chat_glm/glm.cpython-38-x86_64-linux-gnu.so +0 -0
faster_chat_glm/model.py +131 -0
lyraChatGLM/__init__.py +0 -1
lyraChatGLM/config.py +0 -31
lyraChatGLM/ftlib/libth_transformer_sm70_cu12.so +0 -3
lyraChatGLM/ftlib/libth_transformer_sm80_cu11.so +0 -3
lyraChatGLM/ftlib/libth_transformer_sm80_cu12.so +0 -3
lyraChatGLM/lyra_glm.py +0 -177
lyraChatGLM/model.py +0 -195
models/1-gpu-fp16.bin +0 -3
models/config.ini +0 -13
models/config.json +25 -0
models/configuration_chatglm.py +92 -0
lyraChatGLM/ftlib/libth_transformer_sm70_cu11.so → models/glm6b-bs8.ftm +2 -2
models/ice_text.model +2 -2
models/tokenization_chatglm.py +105 -202
models/tokenizer_config.json +2 -3
requirements.txt +0 -9

.gitattributes CHANGED Viewed

@@ -34,4 +34,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 models/glm6b-kv-cache-dy-bs8.ftm filter=lfs diff=lfs merge=lfs -text
 models/glm6b-bs8.ftm filter=lfs diff=lfs merge=lfs -text
-*.so filter=lfs diff=lfs merge=lfs -text

 *tfevents* filter=lfs diff=lfs merge=lfs -text
 models/glm6b-kv-cache-dy-bs8.ftm filter=lfs diff=lfs merge=lfs -text
 models/glm6b-bs8.ftm filter=lfs diff=lfs merge=lfs -text

CHANGES.rst DELETED Viewed

@@ -1,10 +0,0 @@
-Changelog (lyraChatGLM)
-## 2.0
-- rebuild whole system using modified Fastertransformer
-- add dynamic library & models for Volta architecture.
-- further acceleration, remove token generation limits.
-## 1.0
-- add lyraChatGLM model, from original weights

LISENCE DELETED Viewed

@@ -1,420 +0,0 @@
-MIT License
-Copyright (c) 2023 Tencent Music Entertainment
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
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-Other dependencies and licenses:
-Open Source Software Licensed under The ChatGLM-6B License and the Apache License Version 2.0 :
---------------------------------------------------------------------
-1. chatglm-6b
-File：https://github.com/THUDM/ChatGLM-6B
-License：The ChatGLM-6B License and Apache Licnese Version 2.0
-For details：https://github.com/THUDM/ChatGLM-6B/blob/main/MODEL_LICENSE
-             https://github.com/THUDM/ChatGLM-6B/blob/main/LICENSE
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-Copyright Zhengxiao Du
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-A copy of the Apache License Version 2.0 is included in this file.
-Terms of The ChatGLM-6B License:
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-一、定义
-“许可方”是指分发其软件的 ChatGLM-6B 模型团队。
-“软件”是指根据本许可提供的 ChatGLM-6B 模型参数。
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-请注意，许可证可能会更新到更全面的版本。 有关许可和版权的任何问题，请通过 [email protected] 与我们联系。
-1. Definitions
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-    Release         Derived     Year        Owner       GPL-
-                    from                                compatible? (1)
-    0.9.0 thru 1.2              1991-1995   CWI         yes
-    1.3 thru 1.5.2  1.2         1995-1999   CNRI        yes
-    1.6             1.5.2       2000        CNRI        no
-    2.0             1.6         2000        BeOpen.com  no
-    1.6.1           1.6         2001        CNRI        yes (2)
-    2.1             2.0+1.6.1   2001        PSF         no
-    2.0.1           2.0+1.6.1   2001        PSF         yes
-    2.1.1           2.1+2.0.1   2001        PSF         yes
-    2.1.2           2.1.1       2002        PSF         yes
-    2.1.3           2.1.2       2002        PSF         yes
-    2.2 and above   2.1.1       2001-now    PSF         yes
-Footnotes:
-(1) GPL-compatible doesn't mean that we're distributing Python under
-    the GPL.  All Python licenses, unlike the GPL, let you distribute
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-B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
-===============================================================
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README.md CHANGED Viewed

@@ -1,153 +1,98 @@
 ---
-license: mit
-language: en
 tags:
 - LLM
-- ChatGLM6B
 ---
-## Breakings!
-**We know what you want, and here you go!**
-- Newly released lyraChatGLM model, suitable for Ampere (A100/A10) as well as Volta (V100)
-- lyraChatGLM has been further optimized, reaching **9000 tokens/s** on A100 and **3900 tokens/s** on V100, about **5.5x** faster than the up-to-date official version (2023/6/1).
-- The memory usage was optimized too, now we can set batch_size up to **256** on A100!
-- INT8 weight only PTQ is supported
-**Note that the code was fully updated too, you need to use the new API, see `Uses` below**
-If you like our work and consider to join us, feel free to drop a line to benbinwu@tencent.com.
-P.S. Recently we have received a lot of inquiries on accelerating customized models. Actually, we **do not have plan** to release the convertion tool at this moment, nor do we think it would be possible to apply your customized models based on our current release.
-****
-## Model Card for lyraChatGLM
-lyraChatGLM is currently the **fastest ChatGLM-6B** available. To the best of our knowledge, it is the **first accelerated version of ChatGLM-6B**.
-The inference speed of lyraChatGLM has achieved **300x** acceleration upon the early original version. We are still working hard to further improve the performance.
-Among its main features are (updated on 2023-06-20):
-- weights: original ChatGLM-6B weights released by THUDM.
-- device: Nvidia GPU with Amperer architecture or Volta architecture (A100, A10, V100...).
-- batch_size: compiled with dynamic batch size, maximum depends on device.
-- We now support cuda version of both 11.X and 12.X
-- lyraChatGLM has been further optimized, with faster model load speed from few minutes to less than 10s for non-int8 mode, and around 1 min for int8 mode!
 ## Speed
-- orginal version(fixed batch infer): commit id 1d240ba
-### test on A100 40G
-1. The maximum batch size and maximum speed table for each version of the model.
-|version|max_batch_size|max_speed|
-|:-:|:-:|:-:|
-|original|1|30 tokens/s|
-|original(fxied batch infer)|192|1638.52 tokens/s|
-|lyraChatGLM(current)|256|9082.60 tokens/s|
-2. The speed table for the same batch size.
-|version|1 batch_size|8 batch_size| 64 batch_size | 128 batch_size |
-|:-:|:-:|:-:|:-:|:-:|
-|original|30 tokens/s| - | - | - |
-|original(fxied batch infer)|34.48 tokens/s|356.29 tokens/s|1638.52 tokens/s|1338.45 tokens/s|
-|lyraChatGLM(current)|110.05 tokens/s|843.60 tokens/s|4926.92 tokens/s|7235.04 tokens/s|
-### test on V100
-1. The maximum batch size and maximum speed table for each version of the model.
-|version|max_batch_size|max_speed|
-|:-:|:-:|:-:|
-|original|1|17.83 tokens/s|
-|original(fxied batch infer)|128|992.20 tokens/s|
-|lyraChatGLM(current)|192|3958.39 tokens/s|
-2. The speed table for the same batch size.
-|version|1 batch_size|8 batch_size| 64 batch_size | 128 batch_size |
-|:-:|:-:|:-:|:-:|:-:|
-|original|17.83 tokens/s| - | - | - |
-|original(fxied batch infer)|17.83 tokens/s|228.95 tokens/s|889.7 tokens/s|922.20 tokens/s|
-|lyraChatGLM(current)|59.33 tokens/s|514.15 tokens/s|2849.88 tokens/s|3958.39 tokens/s|
-## Model Sources
-- **Repository:** https://huggingface.co/THUDM/chatglm-6b
-## Docker Environment Recommendation
-- For Cuda 11.X: we recommend ```nvcr.io/nvidia/pytorch:22.12-py3```
-- For Cuda 12.0: we recommend ```nvcr.io/nvidia/pytorch:23.02-py3```
-```bash
-docker pull nvcr.io/nvidia/pytorch:23.02-py3
-docker run --rm -it --gpus all -v ./:/lyraChatGLM nvcr.io/nvidia/pytorch:23.02-py3
-pip install -r requirements.txt
-python demo.py
-```
-## Uses
-```python
-from lyraChatGLM import LyraChatGLM6B
-model_path = "./models/1-gpu-fp16.bin"
-tokenizer_path = "./models"
-data_type = "fp16"
-int8_mode = 0   # 1 for INT8 WEIGHT ONLY PTQ
-max_output_length = 150
-arch = "Ampere" # Ampere or Volta
-cuda_version = 12
-model = LyraChatGLM6B(model_path, tokenizer_path, data_type, int8_mode, arch, cuda_version)
-prompt = "列出3个不同的机器学习算法，并说明它们的适用范围."
-test_batch_size = 256
-prompts = [prompt, ]
-# If you want to get different output in same batch, you can set do_sample to True
-output_texts = model.generate(prompts, output_length=max_output_length,top_k=30, top_p=0.85, temperature=0.35, repetition_penalty=1.2, do_sample=False)
-print(output_texts)
 ```
-## Demo output
-### input
-列出3个不同的机器学习算法，并说明它们的适用范围.
-### output
-以下是三个常见的机器学习算法及其适用范围:
-1. 决策树(Decision Tree):决策树是一种基于分类和回归问题的朴素贝叶斯模型。它通过构建一系列逐步分裂的分支来预测结果。适用于那些具有简单特征、大量数据且数据集大小在可接受范围内的情况。
-2. 随机森林(Random Forest):随机森林是一种集成学习算法,由多个决策树组成。它的优点是能够处理大规模数据和高维度的特征。适用于需要对多个变量进行建模的场景,例如医疗诊断、金融风险评估等。
-3. 支持向量机(Support Vector Machine):支持向量机是一种监督学习方法,通常用于分类问题。它可以处理高维数据,并且具有较高的准确性。适用于需要对高维数据进行分类或回归的问题,例如图像识别、自然语言处理等。
-## INT8
-**Int8 usage**:
-Our current version supports INT8 weight only PTQ. To enable this mode, simply modify the `int8_mode` to `1` in the demo.py file.
-**In this mode, gpu memory can be further reduced by about half and the speed can be doubled.**
-This solves the issue mentioned in https://github.com/THUDM/ChatGLM-6B/issues/1042.
-However, the speed gain is best achieved with a batch size of no more than 128. If you don't use A100 GPU, you can adjust the
-batch size to reduce it and get the benefits. We recommend a batch size of 64.This mode is very suitable for GPUs with
-limited VRAM or scenarios where it is difficult to use larger batch sizes in real-time services.
-It should be noted that although we have aligned the accuracy in our test cases, there may be slight differences
-in accuracy in some untested scenarios with int8. Please be aware of this.
-## Citation
-``` bibtex
 @Misc{lyraChatGLM2023,
-  author =       {Kangjian Wu, Zhengtao Wang, Yibo Lu, Bin Wu},
-  title =        {lyraChatGLM: Accelerating ChatGLM to 9000+ tokens/s},
-  howpublished = {\url{https://huggingface.co/TMElyralab/lyraChatGLM}},
-  year =         {2023}
 }
 ```
-## Report bug
-- start a discussion to report any bugs!--> https://huggingface.co/TMElyralab/lyraChatGLM/discussions
-- report bug with a `[bug]` mark in the title.

 ---
+license: creativeml-openrail-m
+language:
+- en
 tags:
 - LLM
+- tensorRT
+- ChatGLM
 ---
+## Model Card for lyraChatGLM
+lyraChatGLM is currently the **fastest ChatGLM-6B** available. To the best of our knowledge, it is the **first accelerated version of ChatGLM-6B**.
+The inference speed of lyraChatGLM has achieved **10x** acceleration upon the original version. We are still working hard to further improve the performance.
+Among its main features are:
+- weights: original ChatGLM-6B weights released by THUDM.
+- device: lyraChatGLM is mainly based on TensorRT compiled for SM=80 (A100, for example).
+- batch_size: compiled with dynamic batch size, max batch_size = 8
 ## Speed
+### test environment
+- device: Nvidia A100 40G
+|version|speed|
+|:-:|:-:|
+|original|30 tokens/s|
+|lyraChatGLM|310 tokens/s|
+## Model Sources
+- **Repository:** [https://huggingface.co/THUDM/chatglm-6b]
+## Uses
+```python
+from transformers import AutoTokenizer
+from faster_chat_glm import GLM6B, FasterChatGLM
+MAX_OUT_LEN = 100
+tokenizer = AutoTokenizer.from_pretrained('./models', trust_remote_code=True)
+input_str = ["为什么我们需要对深度学习模型加速？", ]
+inputs = tokenizer(input_str, return_tensors="pt", padding=True)
+input_ids = inputs.input_ids.to('cuda:0')
+plan_path = './models/glm6b-bs8.ftm'
+# kernel for chat model.
+kernel = GLM6B(plan_path=plan_path,
+               batch_size=1,
+               num_beams=1,
+               use_cache=True,
+               num_heads=32,
+               emb_size_per_heads=128,
+               decoder_layers=28,
+               vocab_size=150528,
+               max_seq_len=MAX_OUT_LEN)
+chat = FasterChatGLM(model_dir="./models", kernel=kernel).half().cuda()
+# generate
+sample_output = chat.generate(inputs=input_ids, max_length=MAX_OUT_LEN)
+# de-tokenize model output to text
+res = tokenizer.decode(sample_output[0], skip_special_tokens=True)
+print(res)
 ```
+## Demo output
+### input
+为什么我们需要对深度学习模型加速? 。
+### output
+为什么我们需要对深度学习模型加速? 深度学习模型的训练需要大量计算资源,特别是在训练模型时,需要大量的内存、GPU(图形处理器)和其他计算资源。因此,训练深度学习模型需要一定的时间,并且如果模型不能快速训练,则可能会导致训练进度缓慢或无法训练。
+以下是一些原因我们需要对深度学习模型加速:
+1. 训练深度神经网络需要大量的计算资源,特别是在训练深度神经网络时,需要更多的计算资源,因此需要更快的训练速度。
+## Citation
+``` bibtex
 @Misc{lyraChatGLM2023,
+  author =       {Kangjian Wu, Zhengtao Wang, Bin Wu},
+  title =        {lyraChatGLM: Accelerating ChatGLM by 10x+},
+  howpublished = {\url{https://huggingface.co/TMElyralab/lyraChatGLM}},
+  year =         {2023}
 }
 ```
+## Report bug
+- start a discussion to report any bugs!--> https://huggingface.co/TMElyralab/lyraChatGLM/discussions
+- report bug with a `[bug]` mark in the title.

demo.py CHANGED Viewed

@@ -1,22 +1,33 @@
-from lyraChatGLM import LyraChatGLM6B
-import numpy as np
-model_path = "./models/1-gpu-fp16.bin"
-tokenizer_path = "./models"
-inference_data_type = "fp16"
-int8_mode = 0
-max_output_length = 150
-arch = "Volta" # Ampere or Volta
-cuda_version = 11 # cuda version, we currently support 11 and 12
-model = LyraChatGLM6B(model_path, tokenizer_path, inference_data_type, int8_mode, arch, cuda_version)
-prompt = "今天天气大概 25度，有点小雨，吹着风，我想去户外散步，应该穿什么样的衣服裤子鞋子搭配。"
-# test_batch_size = 256
-prompts = [prompt, ]
-# # If you want to get different output in same batch, you can set do_sample to True
-output_texts = model.generate(prompts, output_length=max_output_length,top_k=30, top_p=0.85, temperature=0.35, repetition_penalty=1.2, do_sample=False)
-print(output_texts)

+# coding=utf-8
+from transformers import AutoTokenizer
+from faster_chat_glm import GLM6B, FasterChatGLM
+MAX_OUT_LEN = 100
+chatglm6b_dir = './models'
+tokenizer = AutoTokenizer.from_pretrained(chatglm6b_dir, trust_remote_code=True)
+input_str = ["为什么我们需要对深度学习模型加速？", ]
+inputs = tokenizer(input_str, return_tensors="pt", padding=True)
+input_ids = inputs.input_ids.to('cuda:0')
+plan_path = './models/glm6b-bs8.ftm'
+# kernel for chat model.
+kernel = GLM6B(plan_path=plan_path,
+               batch_size=1,
+               num_beams=1,
+               use_cache=True,
+               num_heads=32,
+               emb_size_per_heads=128,
+               decoder_layers=28,
+               vocab_size=150528,
+               max_seq_len=MAX_OUT_LEN)
+chat = FasterChatGLM(model_dir="./models", kernel=kernel).half().cuda()
+# generate
+sample_output = chat.generate(inputs=input_ids, max_length=MAX_OUT_LEN)
+# de-tokenize model output to text
+res = tokenizer.decode(sample_output[0], skip_special_tokens=True)
+print(res)

faster_chat_glm/__init__.py ADDED Viewed

	@@ -0,0 +1,7 @@

+import os
+os.environ["TORCH_USE_RTLD_GLOBAL"]="YES"
+import torch
+from .glm import GLM6B
+from .model import FasterChatGLM

faster_chat_glm/__init__.py~ ADDED Viewed

	@@ -0,0 +1,3 @@

+import torch
+from .glm import GLM6B
+from .model import FasterChatGLM

faster_chat_glm/glm.cpython-38-x86_64-linux-gnu.so ADDED Viewed

Binary file (188 kB). View file

faster_chat_glm/model.py ADDED Viewed

	@@ -0,0 +1,131 @@

+import torch
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers import AutoConfig
+from typing import Dict, List, Tuple, Union, Optional
+class FasterChatGLM(PreTrainedModel):
+    def __init__(self, model_dir, kernel, *inputs, **kwargs):
+        config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True)
+        config.n_head = config.num_attention_heads
+        config.n_embd = config.hidden_size
+        config.n_layer = config.num_layers
+        super().__init__(config, *inputs, **kwargs)
+        self.kernel = kernel
+        self.fake_reg = torch.nn.Linear(2, 2)
+        self.position_encoding_2d = True
+    def forward(self, input_ids, position_ids, attention_mask, past_key_values, *args, **kwargs):
+        inputs_values = [input_ids, position_ids, attention_mask]
+        if past_key_values is not None:
+            inputs_values = inputs_values + past_key_values
+        computed = self.kernel.infer(inputs_values)
+        logits = computed[0]
+        if len(computed) == 1:
+            present_key_values = None
+        else:
+            present_key_values = computed[1:]
+        return CausalLMOutputWithPast(logits=logits, past_key_values=present_key_values)
+    def get_masks_and_position_ids(self, seq, mask_position, context_length, device, gmask=False):
+        attention_mask = torch.ones((1, context_length, context_length), device=device)
+        attention_mask.tril_()
+        attention_mask[..., :context_length - 1] = 1
+        attention_mask.unsqueeze_(1)
+        attention_mask = (attention_mask < 0.5).bool()
+        if self.position_encoding_2d:
+            seq_length = seq.index(150004)
+            position_ids = torch.arange(context_length, dtype=torch.long, device=device)
+            if not gmask:
+                position_ids[seq_length:] = mask_position
+            block_position_ids = torch.cat((
+                torch.zeros(seq_length, dtype=torch.long, device=device),
+                torch.arange(context_length - seq_length, dtype=torch.long, device=device) + 1
+            ))
+            position_ids = torch.stack((position_ids, block_position_ids), dim=0)
+        else:
+            position_ids = torch.arange(context_length, dtype=torch.long, device=device)
+            if not gmask:
+                position_ids[context_length - 1:] = mask_position
+        position_ids = position_ids.unsqueeze(0)
+        return attention_mask, position_ids
+    def prepare_one_sample(self, input_id, mask_token, past, past_key_values, use_gmask):
+        seq = input_id.tolist()
+        mask_position = seq.index(mask_token)
+        if mask_token not in seq:
+            raise ValueError("You have to add either [MASK] or [gMASK] in your input")
+        # only last token for input_ids if past is not None
+        if past is not None or past_key_values is not None:
+            context_length = seq.index(150004)
+            last_token = input_id[-1].unsqueeze(-1).unsqueeze(0)  # 2 dim
+            proc_input_id = last_token
+            if self.position_encoding_2d:
+                position_ids = torch.tensor([[[mask_position], [len(seq) - context_length]]], dtype=torch.long,
+                                            device=input_id.device)
+            else:
+                position_ids = torch.tensor([[mask_position]], dtype=torch.long, device=input_id.device)
+            attention_mask = torch.zeros(1, 1, 1, 1, device=input_id.device)
+        else:
+            proc_input_id = input_id.unsqueeze(0)
+            attention_mask, position_ids = self.get_masks_and_position_ids(
+                seq=seq,
+                mask_position=mask_position,
+                context_length=len(seq),
+                device=input_id.device,
+                gmask=use_gmask
+            )
+        return (proc_input_id.to(torch.int32), position_ids.to(torch.int32),
+                attention_mask.to(torch.bool))
+    def prepare_inputs_for_generation(
+            self,
+            input_ids: torch.LongTensor,
+            past: Optional[torch.Tensor] = None,
+            past_key_values: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            use_cache: bool = None,
+            **kwargs
+    ) -> dict:
+        MASK, gMASK = 150000, 150001
+        mask_token = MASK if MASK in input_ids else gMASK
+        use_gmask = False if MASK in input_ids else gMASK
+        batch_input_ids, batch_position_ids, batch_attention_mask = [], [], []
+        for input_id in input_ids:
+            proc_input_id, position_id, attention_mask = self.prepare_one_sample(
+                input_id, mask_token, past, past_key_values, use_gmask)
+            batch_input_ids.append(proc_input_id)
+            batch_position_ids.append(position_id)
+            batch_attention_mask.append(attention_mask)
+        batch_input_ids = torch.vstack(batch_input_ids)
+        batch_position_ids = torch.vstack(batch_position_ids)
+        batch_attention_mask = torch.vstack(batch_attention_mask)
+        if past is None:
+            past = past_key_values
+        if past is not None or past_key_values is not None:
+            self.kernel.set_context_mode(False)
+        else:
+            self.kernel.set_context_mode(self.config.use_cache)
+        return {
+            "input_ids": batch_input_ids,
+            "past_key_values": past_key_values,
+            "position_ids": batch_position_ids,
+            "attention_mask": batch_attention_mask
+        }

lyraChatGLM/__init__.py DELETED Viewed

	@@ -1 +0,0 @@
1	- from .lyra_glm import LyraChatGLM6B

lyraChatGLM/config.py DELETED Viewed

@@ -1,31 +0,0 @@
-import dataclasses
-from typing import Optional
-@dataclasses.dataclass
-class ChatGLM6BParam:
-    num_heads: int = 32
-    size_per_head: int = 128
-    inter_size: int = 16384
-    num_layers: int = 28
-    vocab_size: int = 130528
-    start_id: Optional[int] = 130004
-    end_id: Optional[int] = 130005
-    tensor_para_size: int = 1
-    pipeline_para_size: int = 1
-    remove_padding: bool = True
-    shared_contexts_ratio: float = 0.0
-    layernorm_eps: float = 1e-5
-    weights_data_type: str = "fp16"
-    def __post_init__(self):
-        if not 0.0 <= self.shared_contexts_ratio <= 1.0:
-            raise ValueError(
-                f'Got an invalid value of shared_context_ratio '
-                f'{self.shared_contexts_ratio} - range: [0.0, 1.0]')
-    def asdict(self):
-        return dataclasses.asdict(self)
-CHATGLM_6B_PARAM = ChatGLM6BParam()

lyraChatGLM/ftlib/libth_transformer_sm70_cu12.so DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2d9829541f5edccf8d59e275e1259404168750e3419902fc4c88f789baad3f20
-size 114203064

lyraChatGLM/ftlib/libth_transformer_sm80_cu11.so DELETED Viewed

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-size 200894104

lyraChatGLM/ftlib/libth_transformer_sm80_cu12.so DELETED Viewed

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-oid sha256:146841b4ef362048507a576d20cb1e5bb02e0d67f3fcfce351ce25f00989dfbd
-size 200980552

lyraChatGLM/lyra_glm.py DELETED Viewed

@@ -1,177 +0,0 @@
-from __future__ import annotations
-import configparser
-import pathlib
-import typing
-import torch
-import transformers
-from .config import CHATGLM_6B_PARAM
-from .model import ChatGLM6BModel
-class LyraChatGLM6B:
-    def __init__(self, model_path, tokenizer_path=None, dtype='fp16', int8_mode=0, arch="Ampere", cuda_version="11") -> None:
-        self.model_path = model_path
-        self.tokenizer_path = tokenizer_path
-        self.dtype = dtype
-        self.arch=arch
-        # if dtype != 'int8':
-        #     int8_mode = 0
-        self.cuda_version = cuda_version
-        self.int8_mode = int8_mode
-        self.model, self.tokenizer = self.load_model_and_tokenizer()
-        if not (arch in ["Ampere", "Volta"]):
-            raise ValueError("Only support GPU device Ampere(A100,A10) or Volta(V100)")
-        print("Got model and tokenizer")
-    def load_model_and_tokenizer(self):
-        if self.tokenizer_path is None:
-            tokenizer_path = self.model_path
-        else:
-            tokenizer_path = self.tokenizer_path
-        print(f'Loading tokenizer from {pathlib.Path(tokenizer_path).parent}')
-        tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer_path, trust_remote_code=True)
-        checkpoint_path = pathlib.Path(self.model_path)
-        config_path = checkpoint_path.parent / 'config.ini'
-        if config_path.exists():
-            # Read model params from config.
-            cfg = configparser.ConfigParser()
-            cfg.read(config_path)
-            model_name = 'glm6b'
-            inference_data_type = self.dtype
-            if inference_data_type == None:
-                inference_data_type = cfg.get(model_name, "weight_data_type")
-            model_args = dict(
-                head_num=cfg.getint(model_name, 'head_num'),
-                size_per_head=cfg.getint(model_name, "size_per_head"),
-                layer_num=cfg.getint(model_name, "num_layer"),
-                tensor_para_size=cfg.getint(model_name, "tensor_para_size"),
-                vocab_size=cfg.getint(model_name, "vocab_size"),
-                start_id=cfg.getint(model_name, "start_id"),
-                end_id=cfg.getint(model_name, "end_id"),
-                weights_data_type=cfg.get(model_name, "weight_data_type"),
-                layernorm_eps=cfg.getfloat(model_name, 'layernorm_eps'),
-                inference_data_type=inference_data_type)
-        else:
-            inference_data_type = self.dtype
-            if inference_data_type == None:
-                inference_data_type = CHATGLM_6B_PARAM.weights_data_type
-            model_args = dict(head_num=CHATGLM_6B_PARAM.num_heads,
-                              size_per_head=CHATGLM_6B_PARAM.size_per_head,
-                              vocab_size=CHATGLM_6B_PARAM.vocab_size,
-                              start_id=CHATGLM_6B_PARAM.start_id or tokenizer.bos_token_id,
-                              end_id=CHATGLM_6B_PARAM.end_id or tokenizer.eos_token_id,
-                              layer_num=CHATGLM_6B_PARAM.num_layers,
-                              tensor_para_size=CHATGLM_6B_PARAM.tensor_para_size,
-                              weights_data_type=CHATGLM_6B_PARAM.weights_data_type,
-                              layernorm_eps=CHATGLM_6B_PARAM.layernorm_eps,
-                              inference_data_type=inference_data_type,
-                              )
-        # update common parameters
-        model_args.update(dict(
-            rotary_embedding_dim=64,
-            max_seq_len=0,  # for position seq embedding
-            pipeline_para_size=CHATGLM_6B_PARAM.pipeline_para_size,
-            shared_contexts_ratio=CHATGLM_6B_PARAM.shared_contexts_ratio,
-            int8_mode=self.int8_mode,
-            model_path=self.model_path,
-            cuda_version=self.cuda_version,
-        ))
-        print('[INFO] Load Our Highly Optimized LyraChatGLM6B model')
-        for k, v in model_args.items():
-            print(f' - {k.ljust(25, ".")}: {v}')
-        # Check sanity and consistency between the model and tokenizer.
-        checklist = ['head_num', 'size_per_head', 'vocab_size', 'layer_num',
-                     'tensor_para_size', 'tensor_para_size', 'weights_data_type']
-        if None in [model_args[k] for k in checklist]:
-            none_params = [p for p in checklist if model_args[p] is None]
-            print(f'[WARNING] Found None parameters {none_params}. They must '
-                  f'be provided either by config file or CLI arguments.')
-        if model_args['start_id'] != tokenizer.bos_token_id:
-            print('[WARNING] Given start_id is not matched with the bos token '
-                  'id of the pretrained tokenizer.')
-        if model_args['end_id'] not in (tokenizer.pad_token_id, tokenizer.eos_token_id):
-            print('[WARNING] Given end_id is not matched with neither pad '
-                  'token id nor eos token id of the pretrained tokenizer.')
-        print(f'Loading tokenizer from {self.model_path}')
-        model = ChatGLM6BModel(arch=self.arch,**model_args)
-        return model, tokenizer
-    def generate(self, prompts: typing.List[str] | str,
-                 output_length: int = 512,
-                 beam_width: int = 1,
-                 top_k: typing.Optional[torch.IntTensor] = 1,
-                 top_p: typing.Optional[torch.FloatTensor] = 1.0,
-                 beam_search_diversity_rate: typing.Optional[torch.FloatTensor] = 0.0,
-                 temperature: typing.Optional[torch.FloatTensor] = 1.0,
-                 len_penalty: typing.Optional[torch.FloatTensor] = 0.0,
-                 repetition_penalty: typing.Optional[torch.FloatTensor] = 1.0,
-                 presence_penalty: typing.Optional[torch.FloatTensor] = None,
-                 min_length: typing.Optional[torch.IntTensor] = None,
-                 bad_words_list: typing.Optional[torch.IntTensor] = None,
-                 do_sample: bool = False,
-                 return_output_length: bool = False,
-                 return_cum_log_probs: int = 0):
-        #
-        if isinstance(prompts, str):
-            prompts = [prompts, ]
-        inputs = prompts
-        batch_size = len(inputs)
-        ones_int = torch.ones(size=[batch_size], dtype=torch.int32)
-        ones_float = torch.ones(size=[batch_size], dtype=torch.float32)
-        # input_token_ids = self.tokenizer(prompts, return_tensors="pt", padding=True).input_ids.int()
-        raw_input_token_ids = self.tokenizer(prompts, padding=True)
-        input_token_ids = torch.tensor (raw_input_token_ids["input_ids"],dtype=torch.int32)
-        input_lengths = torch.IntTensor([len(ids) for ids in input_token_ids])
-        mask_positions = torch.IntTensor([seq.index(130001) for seq in input_token_ids.tolist()])
-        random_seed = None
-        if do_sample:
-            random_seed = torch.randint(0, 262144, (batch_size,), dtype=torch.long)
-        outputs = self.model(start_ids=input_token_ids,
-                             start_lengths=input_lengths,
-                             mask_positions=mask_positions,
-                             output_len=output_length,
-                             beam_width=beam_width,
-                             top_k=top_k*ones_int,
-                             top_p=top_p*ones_float,
-                             beam_search_diversity_rate=beam_search_diversity_rate*ones_float,
-                             temperature=temperature*ones_float,
-                             len_penalty=len_penalty*ones_float,
-                             repetition_penalty=repetition_penalty*ones_float,
-                             presence_penalty=presence_penalty,
-                             min_length=min_length,
-                             random_seed=random_seed,
-                             bad_words_list=bad_words_list,
-                             return_output_length=return_output_length,
-                             return_cum_log_probs=return_cum_log_probs)
-        if return_cum_log_probs > 0:
-            outputs = outputs[0]  # output_token_ids.
-        # Slice the generated token ids of the 1st beam result.
-        # output = input tokens + generated tokens.
-        output_token_ids = [out[0, length:].cpu()
-                            for out, length in zip(outputs, input_lengths)]
-        output_texts = self.tokenizer.batch_decode(
-            output_token_ids, skip_special_tokens=False)
-        return output_texts

lyraChatGLM/model.py DELETED Viewed

@@ -1,195 +0,0 @@
-import os
-import h5py
-import pathlib
-import typing
-import numpy as np
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-class ChatGLM6BModel(nn.Module):
-    def __init__(self,
-                 head_num, size_per_head,
-                 vocab_size,
-                 rotary_embedding_dim,
-                 start_id, end_id, layer_num,
-                 arch,
-                 max_seq_len: int,
-                 tensor_para_size: int,
-                 pipeline_para_size: int,
-                 inference_data_type: str,
-                 model_path,
-                 cuda_version,
-                 inter_size: int = 0,
-                 # glm_variant_params
-                 layernorm_eps: float = 1e-5,
-                 layernorm_type: typing.Literal['pre_layernorm', 'post_layernorm'] = "pre_layernorm",
-                 activation_type: str = "Gelu",
-                 gpt_with_moe: bool = False,
-                 expert_num: int = 0,
-                 moe_k: int = 0,
-                 moe_layer_index: typing.List = [],
-                 has_positional_encoding: bool = False,
-                 has_pre_decoder_layernorm: bool = False,
-                 has_post_decoder_layernorm: bool = True,
-                 has_adapters: bool = False,
-                 adapter_inter_size: int = 0,
-                 use_attention_linear_bias: bool = False,
-                 int8_mode: int = 0,
-                 weights_data_type: typing.Union[str, np.dtype] = np.float32,
-                 shared_contexts_ratio: float = 1.0):
-        super().__init__()
-        self.head_num = head_num
-        self.size_per_head = size_per_head
-        self.vocab_size = vocab_size
-        self.rotary_embedding_dim = rotary_embedding_dim
-        self.start_id = start_id
-        self.end_id = end_id
-        self.layer_num = layer_num
-        self.inter_size = inter_size if inter_size != 0 else 4 * self.head_num * self.size_per_head
-        self.arch = arch
-        self.model_path = model_path
-        # gpt_variant_params
-        self.layernorm_eps = layernorm_eps
-        self.layernorm_type = layernorm_type
-        self.activation_type = activation_type
-        self.gpt_with_moe = gpt_with_moe
-        self.expert_num = expert_num
-        self.moe_k = moe_k
-        self.moe_layer_index = moe_layer_index
-        self.has_positional_encoding = has_positional_encoding
-        self.has_pre_decoder_layernorm = has_pre_decoder_layernorm
-        self.has_post_decoder_layernorm = has_post_decoder_layernorm
-        self.has_adapters = has_adapters
-        self.adapter_inter_size = adapter_inter_size
-        self.use_attention_linear_bias = use_attention_linear_bias
-        # multi-gpu params
-        self.tensor_para_size = tensor_para_size
-        self.pipeline_para_size = pipeline_para_size
-        self.use_sparse_gemm = False
-        self.build_model = False
-        self.int8_mode = int8_mode
-        self.weights_data_type = weights_data_type
-        self.shared_contexts_ratio = shared_contexts_ratio
-        assert torch.cuda.is_available(), "CUDA is required for this model."
-        assert head_num % tensor_para_size == 0, "head_num must be a multiple of tensor_para_size."
-        assert layer_num % pipeline_para_size == 0, "layer_num must be a multiple of pipeline_para_size."
-        self.device = 0
-        # Load the C++ model into Pytorch model.
-        sm = "sm80"
-        if arch == "Ampere":
-            sm = "sm80"
-        elif arch == "Volta":
-            sm = "sm70"
-        else:
-            raise Exception(f"unsupported arch: {arch}")
-        cu = 'cu11'
-        if cuda_version == 11:
-            cu = 'cu11'
-        elif cuda_version == 12:
-            cu = 'cu12'
-        else:
-            raise Exception(f"unsupported cuda version: {cuda_version}")
-        lib_path = pathlib.Path(__file__).parent / "ftlib" / f"libth_transformer_{sm}_{cu}.so"
-        torch.classes.load_library(os.path.abspath(lib_path))
-        self.model = torch.classes.FasterTransformer.GlmOp(
-            self.head_num, self.size_per_head, self.inter_size,
-            self.layer_num,
-            self.expert_num,
-            self.moe_k,
-            self.moe_layer_index,
-            self.vocab_size,
-            self.rotary_embedding_dim,
-            self.start_id, self.end_id,
-            self.tensor_para_size, self.pipeline_para_size, self.int8_mode,
-            # GLM variant parameters
-            self.layernorm_eps,
-            self.layernorm_type,
-            self.activation_type,
-            self.has_positional_encoding,
-            self.has_pre_decoder_layernorm,
-            self.has_post_decoder_layernorm,
-            self.has_adapters,
-            self.adapter_inter_size,
-            self.use_attention_linear_bias,
-            self.model_path,
-            self.weights_data_type,
-            inference_data_type,
-            self.shared_contexts_ratio)
-        self.build_model = True
-    def forward(self,
-                start_ids: torch.IntTensor,
-                start_lengths: torch.IntTensor,
-                mask_positions: torch.IntTensor,
-                output_len: int,
-                beam_width: int = 1,
-                top_k: typing.Optional[torch.IntTensor] = None,
-                top_p: typing.Optional[torch.FloatTensor] = None,
-                beam_search_diversity_rate: typing.Optional[torch.FloatTensor] = None,
-                temperature: typing.Optional[torch.FloatTensor] = None,
-                len_penalty: typing.Optional[torch.FloatTensor] = None,
-                repetition_penalty: typing.Optional[torch.FloatTensor] = None,
-                presence_penalty: typing.Optional[torch.FloatTensor] = None,
-                min_length: typing.Optional[torch.IntTensor] = None,
-                random_seed: typing.Optional[torch.LongTensor] = None,
-                bad_words_list: typing.Optional[torch.IntTensor] = None,
-                return_output_length: bool = False,
-                return_cum_log_probs: int = 0):
-        input_len = start_ids.size(1)
-        assert input_len > 0, "input len must be larger than zero. For an unconditional case, use start_id as the first token."
-        # Inputs to device
-        start_ids = start_ids.cuda(self.device)
-        start_lengths = start_lengths.cuda(self.device)
-        mask_positions = mask_positions.cuda(self.device)
-        # outputs: output_ids, output_lengths, output_cum_log_probs (optional)
-        outputs = self.model.forward(start_ids,
-                                     start_lengths,
-                                     mask_positions,
-                                     output_len,
-                                     beam_width,  # optional, can be None
-                                     top_k,  # optional, can be None
-                                     top_p,  # optional, can be None
-                                     beam_search_diversity_rate,  # optional, can be None
-                                     temperature,  # optional, can be None
-                                     len_penalty,  # optional, can be None
-                                     repetition_penalty,  # optional, can be None
-                                     presence_penalty,  # optional, can be None
-                                     min_length,  # optional, can be None
-                                     random_seed,  # optional, can be None
-                                     bad_words_list,  # optional, can be None
-                                     return_cum_log_probs)  # optional, can be None
-        if return_cum_log_probs == 0:
-            output_ids, output_lengths = outputs
-        else:
-            output_ids, output_lengths, output_cum_log_probs = outputs
-        if return_output_length:
-            if return_cum_log_probs > 0:
-                return output_ids, output_lengths, output_cum_log_probs
-            else:
-                return output_ids, output_lengths
-        else:
-            return output_ids
-    def set_input_tensor(self, input_tensor):
-        """Set input tensor to be used instead of forward()'s input.
-        When doing pipeline parallelism the input from the previous
-        stage comes from communication, not from the input, so the
-        model's forward_step_func won't have it. This function is thus
-        used by internal code to bypass the input provided by the
-        forward_step_func"""
-        self.input_tensor = input_tensor

models/1-gpu-fp16.bin DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9bab22c98c57766bc31410c819858fa704490ca76dc04df7331d188c56fba1b1
-size 12346572800

models/config.ini DELETED Viewed

@@ -1,13 +0,0 @@
-[glm6b]
-model_name = chatglm-6b
-head_num = 32
-size_per_head = 128
-inter_size = 16384
-max_pos_seq_len = 2048
-num_layer = 28
-vocab_size = 130528
-start_id = 130004
-end_id = 130005
-weight_data_type = fp16
-tensor_para_size = 1
-layernorm_eps = 1e-5

models/config.json ADDED Viewed

	@@ -0,0 +1,25 @@

+{
+  "_name_or_path": "THUDM/chatglm-6b",
+  "architectures": [
+    "ChatGLMModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_chatglm.ChatGLMConfig",
+    "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
+    "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration"
+  },
+  "bos_token_id": 150004,
+  "eos_token_id": 150005,
+  "hidden_size": 4096,
+  "inner_hidden_size": 16384,
+  "layernorm_epsilon": 1e-05,
+  "max_sequence_length": 2048,
+  "model_type": "chatglm",
+  "num_attention_heads": 32,
+  "num_layers": 28,
+  "position_encoding_2d": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.23.1",
+  "use_cache": true,
+  "vocab_size": 150528
+}

models/configuration_chatglm.py ADDED Viewed

	@@ -0,0 +1,92 @@

+""" ChatGLM model configuration """
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+logger = logging.get_logger(__name__)
+class ChatGLMConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`~ChatGLMModel`].
+    It is used to instantiate an ChatGLM model according to the specified arguments, defining the model
+    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
+    the ChatGLM-6B [THUDM/ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b) architecture.
+    Configuration objects inherit from  [`PretrainedConfig`] and can be used
+    to control the model outputs. Read the documentation from  [`PretrainedConfig`]
+    for more information.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 150528):
+            Vocabulary size of the ChatGLM-6B model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`~ChatGLMModel`] or
+            [`~TFChatGLMModel`].
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 28):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        inner_hidden_size (`int`, *optional*, defaults to 16384):
+            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        max_sequence_length (`int`, *optional*, defaults to 512):
+            The maximum sequence length that this model might ever be used with.
+            Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
+        layernorm_epsilon (`float`, *optional*, defaults to 1e-5):
+            The epsilon used by the layer normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether the model should return the last key/values attentions (not used by all models).
+        Example:
+    ```python
+    >>> from configuration_chatglm import ChatGLMConfig
+    >>> from modeling_chatglm import ChatGLMModel
+    >>> # Initializing a ChatGLM-6B THUDM/ChatGLM-6B style configuration
+    >>> configuration = ChatGLMConfig()
+    >>> # Initializing a model from the THUDM/ChatGLM-6B style configuration
+    >>> model = ChatGLMModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
+"""
+    model_type = "chatglm"
+    def __init__(
+            self,
+            vocab_size=150528,
+            hidden_size=4096,
+            num_layers=28,
+            num_attention_heads=32,
+            layernorm_epsilon=1e-5,
+            use_cache=False,
+            bos_token_id=150004,
+            eos_token_id=150005,
+            pad_token_id=0,
+            max_sequence_length=2048,
+            inner_hidden_size=16384,
+            position_encoding_2d=True,
+            **kwargs
+    ):
+        self.num_layers = num_layers
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_attention_heads = num_attention_heads
+        self.max_sequence_length = max_sequence_length
+        self.layernorm_epsilon = layernorm_epsilon
+        self.inner_hidden_size = inner_hidden_size
+        self.use_cache = use_cache
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.pad_token_id = pad_token_id
+        self.position_encoding_2d = position_encoding_2d
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs
+        )

lyraChatGLM/ftlib/libth_transformer_sm70_cu11.so → models/glm6b-bs8.ftm RENAMED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0826346c748380e8e9fdd7e1f7130bad0f2485a65a8ecd4beb33d19e85c4d79e
-size 114280392

 version https://git-lfs.github.com/spec/v1
+oid sha256:54e97fb542110a3a226058eb76b6019bbaf91d3165da6ac95aa3976ee75b0421
+size 14706031108

models/ice_text.model CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5e974d9a69c242ce014c88c2b26089270f6198f3c0b700a887666cd3e816f17e
-size 2706249

 version https://git-lfs.github.com/spec/v1
+oid sha256:99871e0c85db81ad7af1028854fd091cd5778c8414ae9d94bbbc10d02c831c21
+size 2699926

models/tokenization_chatglm.py CHANGED Viewed

@@ -1,13 +1,17 @@
 """Tokenization classes for ChatGLM."""
 from typing import List, Optional, Union
 import os
 from transformers.tokenization_utils import PreTrainedTokenizer
-from transformers.utils import logging, PaddingStrategy
-from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
-from typing import Dict
-import sentencepiece as spm
-import numpy as np
 logger = logging.get_logger(__name__)
@@ -16,55 +20,61 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
 }
-class TextTokenizer:
-    def __init__(self, model_path):
-        self.sp = spm.SentencePieceProcessor()
-        self.sp.Load(model_path)
-        self.num_tokens = self.sp.vocab_size()
-    def encode(self, text):
-        return self.sp.EncodeAsIds(text)
-    def decode(self, ids: List[int]):
-        return self.sp.DecodeIds(ids)
-    def tokenize(self, text):
-        return self.sp.EncodeAsPieces(text)
-    def convert_tokens_to_string(self, tokens):
-        return self.sp.DecodePieces(tokens)
-    def convert_tokens_to_ids(self, tokens):
-        return [self.sp.PieceToId(token) for token in tokens]
-    def convert_token_to_id(self, token):
-        return self.sp.PieceToId(token)
-    def convert_id_to_token(self, idx):
-        return self.sp.IdToPiece(idx)
-    def __len__(self):
-        return self.num_tokens
 class SPTokenizer:
     def __init__(
-            self,
-            vocab_file,
-            num_image_tokens=20000,
-            max_blank_length=80,
-            byte_fallback=True,
     ):
         assert vocab_file is not None
         self.vocab_file = vocab_file
-        self.num_image_tokens = num_image_tokens
         self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"]
         self.max_blank_length = max_blank_length
         self.byte_fallback = byte_fallback
-        self.text_tokenizer = TextTokenizer(vocab_file)
-    def _get_text_tokenizer(self):
-        return self.text_tokenizer
     @staticmethod
     def get_blank_token(length: int):
@@ -75,6 +85,10 @@ class SPTokenizer:
     def get_tab_token():
         return f"<|tab|>"
     @property
     def num_text_tokens(self):
         return self.text_tokenizer.num_tokens
@@ -98,7 +112,7 @@ class SPTokenizer:
         return text
     def encode(
-            self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True
     ) -> List[int]:
         """
         @param text: Text to encode.
@@ -110,31 +124,22 @@ class SPTokenizer:
         text = self._preprocess(text, linebreak, whitespaces)
         if not add_dummy_prefix:
             text = "<n>" + text
-        tmp = self._get_text_tokenizer().encode(text)
         tokens = [x + self.num_image_tokens for x in tmp]
         return tokens if add_dummy_prefix else tokens[2:]
-    def postprocess(self, text):
         text = text.replace("<n>", "\n")
         text = text.replace(SPTokenizer.get_tab_token(), "\t")
         for i in range(2, self.max_blank_length + 1):
             text = text.replace(self.get_blank_token(i), " " * i)
         return text
-    def decode(self, text_ids: List[int]) -> str:
-        ids = [int(_id) - self.num_image_tokens for _id in text_ids]
-        ids = [_id for _id in ids if _id >= 0]
-        text = self._get_text_tokenizer().decode(ids)
-        text = self.postprocess(text)
-        return text
-    def decode_tokens(self, tokens: List[str]) -> str:
-        text = self._get_text_tokenizer().convert_tokens_to_string(tokens)
-        text = self.postprocess(text)
-        return text
     def tokenize(
-            self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True
     ) -> List[str]:
         """
         @param text: Text to encode.
@@ -146,7 +151,7 @@ class SPTokenizer:
         text = self._preprocess(text, linebreak, whitespaces)
         if not add_dummy_prefix:
             text = "<n>" + text
-        tokens = self._get_text_tokenizer().tokenize(text)
         return tokens if add_dummy_prefix else tokens[2:]
     def __getitem__(self, x: Union[int, str]):
@@ -175,36 +180,25 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
     vocab_files_names = {"vocab_file": "ice_text.model"}
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["input_ids", "attention_mask", "position_ids"]
     def __init__(
             self,
             vocab_file,
             do_lower_case=False,
             remove_space=False,
-            bos_token='<sop>',
-            eos_token='<eop>',
-            end_token='</s>',
             mask_token='[MASK]',
             gmask_token='[gMASK]',
             padding_side="left",
-            pad_token="<pad>",
-            unk_token="<unk>",
-            num_image_tokens=20000,
             **kwargs
     ) -> None:
         super().__init__(
             do_lower_case=do_lower_case,
             remove_space=remove_space,
             padding_side=padding_side,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            end_token=end_token,
-            mask_token=mask_token,
-            gmask_token=gmask_token,
-            pad_token=pad_token,
-            unk_token=unk_token,
-            num_image_tokens=num_image_tokens,
             **kwargs
         )
@@ -214,29 +208,23 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
         self.bos_token = bos_token
         self.eos_token = eos_token
-        self.end_token = end_token
         self.mask_token = mask_token
-        self.gmask_token = gmask_token
-        self.sp_tokenizer = SPTokenizer(vocab_file, num_image_tokens=num_image_tokens)
         """ Initialisation """
     @property
-    def gmask_token_id(self) -> Optional[int]:
-        if self.gmask_token is None:
-            return None
-        return self.convert_tokens_to_ids(self.gmask_token)
-    @property
-    def end_token_id(self) -> Optional[int]:
         """
-        `Optional[int]`: Id of the end of context token in the vocabulary. Returns `None` if the token has not been
         set.
         """
-        if self.end_token is None:
             return None
-        return self.convert_tokens_to_ids(self.end_token)
     @property
     def vocab_size(self):
@@ -268,21 +256,25 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
         return seq
-    def convert_tokens_to_string(self, tokens: List[str]) -> str:
-        return self.sp_tokenizer.decode_tokens(tokens)
-    def _decode(
             self,
-            token_ids: Union[int, List[int]],
             **kwargs
     ) -> str:
-        if isinstance(token_ids, int):
-            token_ids = [token_ids]
-        if len(token_ids) == 0:
-            return ""
-        if self.pad_token_id in token_ids:  # remove pad
-            token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
-        return super()._decode(token_ids, **kwargs)
     def _convert_token_to_id(self, token):
         """ Converts a token (str) in an id using the vocab. """
@@ -307,7 +299,7 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
         """
         if os.path.isdir(save_directory):
             vocab_file = os.path.join(
-                save_directory, self.vocab_files_names["vocab_file"]
             )
         else:
             vocab_file = save_directory
@@ -339,105 +331,16 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
         Returns:
             `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
         """
-        gmask_id = self.sp_tokenizer[self.gmask_token]
-        eos_id = self.sp_tokenizer[self.eos_token]
-        token_ids_0 = token_ids_0 + [gmask_id, self.sp_tokenizer[self.bos_token]]
         if token_ids_1 is not None:
-            token_ids_0 = token_ids_0 + token_ids_1 + [eos_id]
-        return token_ids_0
-    def _pad(
-            self,
-            encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
-            max_length: Optional[int] = None,
-            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-            pad_to_multiple_of: Optional[int] = None,
-            return_attention_mask: Optional[bool] = None,
-    ) -> dict:
-        """
-        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
-        Args:
-            encoded_inputs:
-                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
-            max_length: maximum length of the returned list and optionally padding length (see below).
-                Will truncate by taking into account the special tokens.
-            padding_strategy: PaddingStrategy to use for padding.
-                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
-                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
-                - PaddingStrategy.DO_NOT_PAD: Do not pad
-                The tokenizer padding sides are defined in self.padding_side:
-                    - 'left': pads on the left of the sequences
-                    - 'right': pads on the right of the sequences
-            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
-                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
-                `>= 7.5` (Volta).
-            return_attention_mask:
-                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
-        """
-        # Load from model defaults
-        bos_token_id = self.sp_tokenizer[self.bos_token]
-        mask_token_id = self.sp_tokenizer[self.mask_token]
-        gmask_token_id = self.sp_tokenizer[self.gmask_token]
-        assert self.padding_side == "left"
-        required_input = encoded_inputs[self.model_input_names[0]]
-        seq_length = len(required_input)
-        if padding_strategy == PaddingStrategy.LONGEST:
-            max_length = len(required_input)
-        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
-            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
-        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
-        # Initialize attention mask if not present.
-        if max_length is not None:
-            if "attention_mask" not in encoded_inputs:
-                if bos_token_id in required_input:
-                    context_length = required_input.index(bos_token_id)
-                else:
-                    context_length = seq_length
-                attention_mask = np.ones((1, seq_length, seq_length))
-                attention_mask = np.tril(attention_mask)
-                attention_mask[:, :, :context_length] = 1
-                attention_mask = np.bool_(attention_mask < 0.5)
-                encoded_inputs["attention_mask"] = attention_mask
-            if "position_ids" not in encoded_inputs:
-                if bos_token_id in required_input:
-                    context_length = required_input.index(bos_token_id)
-                else:
-                    context_length = seq_length
-                position_ids = np.arange(seq_length, dtype=np.int64)
-                mask_token = mask_token_id if mask_token_id in required_input else gmask_token_id
-                if mask_token in required_input:
-                    mask_position = required_input.index(mask_token)
-                    position_ids[context_length:] = mask_position
-                block_position_ids = np.concatenate(
-                    [np.zeros(context_length, dtype=np.int64),
-                     np.arange(1, seq_length - context_length + 1, dtype=np.int64)])
-                encoded_inputs["position_ids"] = np.stack([position_ids, block_position_ids], axis=0)
-        if needs_to_be_padded:
-            difference = max_length - len(required_input)
-            if "attention_mask" in encoded_inputs:
-                encoded_inputs["attention_mask"] = np.pad(encoded_inputs["attention_mask"],
-                                                          pad_width=[(0, 0), (difference, 0), (difference, 0)],
-                                                          mode='constant', constant_values=True)
-            if "token_type_ids" in encoded_inputs:
-                encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
-                    "token_type_ids"
-                ]
-            if "special_tokens_mask" in encoded_inputs:
-                encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
-            if "position_ids" in encoded_inputs:
-                encoded_inputs["position_ids"] = np.pad(encoded_inputs["position_ids"],
-                                                        pad_width=[(0, 0), (difference, 0)])
-            encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
-        return encoded_inputs

 """Tokenization classes for ChatGLM."""
+import sys
+import unicodedata
 from typing import List, Optional, Union
+from functools import lru_cache
 import os
+import collections
+import re
 from transformers.tokenization_utils import PreTrainedTokenizer
+from icetk.text_tokenizer import TextTokenizer
+from icetk.utils import auto_create
+import icetk.sentencepiece_model_pb2 as sp_model
+from transformers.utils import logging
 logger = logging.get_logger(__name__)
 }
 class SPTokenizer:
     def __init__(
+        self,
+        vocab_file,
+        max_blank_length=80,
+        byte_fallback=True,
     ):
         assert vocab_file is not None
         self.vocab_file = vocab_file
         self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"]
         self.max_blank_length = max_blank_length
         self.byte_fallback = byte_fallback
+        self.text_tokenizer = self._build_text_tokenizer(encode_special_tokens=False)
+        self.special_text_tokenizer = self._build_text_tokenizer(encode_special_tokens=True)
+    @staticmethod
+    def _configure_tokenizer(
+        text_tokenizer: TextTokenizer,
+        special_tokens: List[str],
+        max_blank_length: int,
+        byte_fallback: bool,
+        encode_special_tokens=False,
+    ):
+        # special token
+        special_token_type = 4 if encode_special_tokens else 3  # 3 - CONTROL, 4 - USER_DEFINE
+        for token in special_tokens:
+            text_tokenizer.proto.pieces.append(
+                sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=special_token_type)
+            )
+        # whitespaces
+        for token in [SPTokenizer.get_tab_token()] + [
+            SPTokenizer.get_blank_token(i) for i in range(2, max_blank_length + 1)
+        ]:
+            text_tokenizer.proto.pieces.append(sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=4))
+        # byte fallback
+        if byte_fallback:
+            text_tokenizer.proto.trainer_spec.byte_fallback = True
+            for i in range(256):
+                text_tokenizer.proto.pieces.append(
+                    sp_model.ModelProto.SentencePiece(piece="<0x{:02X}>".format(i), score=0.0, type=6)
+                )
+        text_tokenizer.refresh()
+    def _build_text_tokenizer(self, encode_special_tokens=False):
+        tokenizer = TextTokenizer(self.vocab_file)
+        self._configure_tokenizer(
+            tokenizer, self.special_tokens, self.max_blank_length, self.byte_fallback, encode_special_tokens
+        )
+        return tokenizer
+    def _get_text_tokenizer(self, encode_special_tokens=False):
+        if encode_special_tokens:
+            return self.special_text_tokenizer
+        else:
+            return self.text_tokenizer
     @staticmethod
     def get_blank_token(length: int):
     def get_tab_token():
         return f"<|tab|>"
+    @property
+    def num_image_tokens(self):
+        return 20000
     @property
     def num_text_tokens(self):
         return self.text_tokenizer.num_tokens
         return text
     def encode(
+        self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True
     ) -> List[int]:
         """
         @param text: Text to encode.
         text = self._preprocess(text, linebreak, whitespaces)
         if not add_dummy_prefix:
             text = "<n>" + text
+        tmp = self._get_text_tokenizer(encode_special_tokens=special_tokens).encode(text)
         tokens = [x + self.num_image_tokens for x in tmp]
         return tokens if add_dummy_prefix else tokens[2:]
+    def decode(self, text_ids: List[int], special_tokens=False) -> str:
+        ids = [int(_id) - self.num_image_tokens for _id in text_ids]
+        ids = [_id for _id in ids if _id >= 0]
+        text = self._get_text_tokenizer(encode_special_tokens=special_tokens).decode(ids)
         text = text.replace("<n>", "\n")
         text = text.replace(SPTokenizer.get_tab_token(), "\t")
         for i in range(2, self.max_blank_length + 1):
             text = text.replace(self.get_blank_token(i), " " * i)
         return text
     def tokenize(
+        self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True
     ) -> List[str]:
         """
         @param text: Text to encode.
         text = self._preprocess(text, linebreak, whitespaces)
         if not add_dummy_prefix:
             text = "<n>" + text
+        tokens = self._get_text_tokenizer(encode_special_tokens=special_tokens).tokenize(text)
         return tokens if add_dummy_prefix else tokens[2:]
     def __getitem__(self, x: Union[int, str]):
     vocab_files_names = {"vocab_file": "ice_text.model"}
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    model_input_names = ["input_ids"]
     def __init__(
             self,
             vocab_file,
             do_lower_case=False,
             remove_space=False,
+            bos_token='sop',
+            eos_token='eos',
+            eop_token='eop',
             mask_token='[MASK]',
             gmask_token='[gMASK]',
             padding_side="left",
             **kwargs
     ) -> None:
         super().__init__(
             do_lower_case=do_lower_case,
             remove_space=remove_space,
             padding_side=padding_side,
             **kwargs
         )
         self.bos_token = bos_token
         self.eos_token = eos_token
+        self.eop_token = eop_token
         self.mask_token = mask_token
+        self.gMASK_token = gmask_token
+        self.sp_tokenizer = SPTokenizer(vocab_file)
         """ Initialisation """
     @property
+    def eop_token_id(self) -> Optional[int]:
         """
+        `Optional[int]`: Id of the end of sentence token in the vocabulary. Returns `None` if the token has not been
         set.
         """
+        if self.eop_token is None:
             return None
+        return self.convert_tokens_to_ids(self.eop_token)
     @property
     def vocab_size(self):
         return seq
+    def decode(
             self,
+            token_ids: Union[List[int], List[List[int]]],
+            skip_special_tokens: bool = False,
+            clean_up_tokenization_spaces: bool = True,
+            spaces_between_special_tokens: bool = True,
             **kwargs
     ) -> str:
+        if isinstance(token_ids[0], list):
+            tokens = []
+            for single_token_ids in token_ids:
+                if self.pad_token_id in single_token_ids:  # remove pad
+                    single_token_ids = list(filter((self.pad_token_id).__ne__, single_token_ids))
+                tokens.append(self.sp_tokenizer.decode(single_token_ids))
+            return (tokens)
+        else:
+            if self.pad_token_id in token_ids:  # remove pad
+                token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
+            return self.sp_tokenizer.decode(token_ids)
     def _convert_token_to_id(self, token):
         """ Converts a token (str) in an id using the vocab. """
         """
         if os.path.isdir(save_directory):
             vocab_file = os.path.join(
+                save_directory, VOCAB_FILES_NAMES["vocab_file"]
             )
         else:
             vocab_file = save_directory
         Returns:
             `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
         """
         if token_ids_1 is not None:
+            token_ids_0 += token_ids_1
+        mask_ids = self.sp_tokenizer[self.mask_token]
+        gmask_ids = self.sp_tokenizer[self.gMASK_token]
+        if mask_ids not in token_ids_0 and gmask_ids not in token_ids_0:
+            token_ids_0 += [gmask_ids]
+        if token_ids_0[-1] != mask_ids and token_ids_0[-1] != gmask_ids:
+            token_ids_0 += [self.sp_tokenizer[self.eos_token]]
+        token_ids_0 += [self.sp_tokenizer[self.bos_token]]
+        return token_ids_0

models/tokenizer_config.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
   "name_or_path": "THUDM/chatglm-6b",
   "bos_token": "<sop>",
-  "eos_token": "<eop>",
-  "end_token": "</s>",
   "gmask_token": "[gMASK]",
   "mask_token": "[MASK]",
   "pad_token": "<pad>",
@@ -10,7 +10,6 @@
   "remove_space": false,
   "do_lower_case": false,
   "tokenizer_class": "ChatGLMTokenizer",
-  "num_image_tokens": 0,
   "auto_map": {
     "AutoTokenizer": [
       "tokenization_chatglm.ChatGLMTokenizer",

 {
   "name_or_path": "THUDM/chatglm-6b",
   "bos_token": "<sop>",
+  "eop_token": "<eop>",
+  "eos_token": "</s>",
   "gmask_token": "[gMASK]",
   "mask_token": "[MASK]",
   "pad_token": "<pad>",
   "remove_space": false,
   "do_lower_case": false,
   "tokenizer_class": "ChatGLMTokenizer",
   "auto_map": {
     "AutoTokenizer": [
       "tokenization_chatglm.ChatGLMTokenizer",

requirements.txt DELETED Viewed

@@ -1,9 +0,0 @@
-icetk
-cpm_kernels
-transformers
-huggingface_hub
-numpy
-setuptools
-torch
-h5py
-protobuf==3.20.3