Feat: Add support for cuda 11.x and faster model load speed

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CHANGES.rst

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+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

@@ -0,0 +1,420 @@
+MIT License
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+A. HISTORY OF THE SOFTWARE
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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
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+
+Thanks to the many outside volunteers who have worked under Guido's
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+
+B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
+===============================================================
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+Open Source Software：
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+
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+

README.md

@@ -0,0 +1,157 @@
+---
+license: mit
+language: en
+tags:
+- LLM
+- ChatGLM6B
+---
+## New Features (2023-06-20)
+- 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!
+
+## 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 [email protected].
+
+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:
+- 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. 
+
+## 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.h5"
+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���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.

demo.py

@@ -0,0 +1,22 @@
+from lyraChatGLM import LyraChatGLM6B
+import numpy as np
+
+model_path = "./models/1-gpu-fp16.bin"
+tokenizer_path = "./models"
+data_type = "fp16"
+int8_mode = 0
+max_output_length = 150
+arch = "Ampere" # Ampere or Volta
+cuda_version = 12 # cuda version, we currently support 11 and 12
+
+model = LyraChatGLM6B(model_path, tokenizer_path, 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)

lyraChatGLM/__init__.py

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

lyraChatGLM/config.py

@@ -0,0 +1,31 @@
+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_cu11.so

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+size 114267536

lyraChatGLM/ftlib/libth_transformer_sm70_cu12.so

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+oid sha256:99ac80b2f4c161bbacbf64a7607f323c612c7c5f26b83eaec7f559425f3a818b
+size 114186112

lyraChatGLM/ftlib/libth_transformer_sm80_cu11.so

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+oid sha256:a1d6cd03321b671275fcabb4136562845233875564047ccde20401fca4df45c2
+size 200834616

lyraChatGLM/ftlib/libth_transformer_sm80_cu12.so

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+oid sha256:2da10aad8e92bcdf45b15884cee63e845f582cd28bcc0f7f1c2a4f6a101e9646
+size 200916960

lyraChatGLM/lyra_glm.py

@@ -0,0 +1,174 @@
+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()
+        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

@@ -0,0 +1,194 @@
+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,
+            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/config.ini

@@ -0,0 +1,13 @@
+[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/ice_text.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e974d9a69c242ce014c88c2b26089270f6198f3c0b700a887666cd3e816f17e
+size 2706249

models/tokenization_chatglm.py

@@ -0,0 +1,443 @@
+"""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__)
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "THUDM/chatglm-6b": 2048,
+}
+
+
+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):
+        assert length >= 2
+        return f"<|blank_{length}|>"
+
+    @staticmethod
+    def get_tab_token():
+        return f"<|tab|>"
+
+    @property
+    def num_text_tokens(self):
+        return self.text_tokenizer.num_tokens
+
+    @property
+    def num_tokens(self):
+        return self.num_image_tokens + self.num_text_tokens
+
+    @staticmethod
+    def _encode_whitespaces(text: str, max_len: int = 80):
+        text = text.replace("\t", SPTokenizer.get_tab_token())
+        for i in range(max_len, 1, -1):
+            text = text.replace(" " * i, SPTokenizer.get_blank_token(i))
+        return text
+
+    def _preprocess(self, text: str, linebreak=True, whitespaces=True):
+        if linebreak:
+            text = text.replace("\n", "<n>")
+        if whitespaces:
+            text = self._encode_whitespaces(text, max_len=self.max_blank_length)
+        return text
+
+    def encode(
+            self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True
+    ) -> List[int]:
+        """
+        @param text: Text to encode.
+        @param linebreak: Whether to encode newline (\n) in text.
+        @param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
+        @param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
+        @param add_dummy_prefix: Whether to add dummy blank space in the beginning.
+        """
+        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.
+        @param linebreak: Whether to encode newline (\n) in text.
+        @param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
+        @param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
+        @param add_dummy_prefix: Whether to add dummy blank space in the beginning.
+        """
+        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]):
+        if isinstance(x, int):
+            if x < self.num_image_tokens:
+                return "<image_{}>".format(x)
+            else:
+                return self.text_tokenizer.convert_id_to_token(x - self.num_image_tokens)
+        elif isinstance(x, str):
+            if x.startswith("<image_") and x.endswith(">") and x[7:-1].isdigit():
+                return int(x[7:-1])
+            else:
+                return self.text_tokenizer.convert_token_to_id(x) + self.num_image_tokens
+        else:
+            raise ValueError("The key should be str or int.")
+
+
+class ChatGLMTokenizer(PreTrainedTokenizer):
+    """
+    Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+    """
+
+    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
+        )
+
+        self.do_lower_case = do_lower_case
+        self.remove_space = remove_space
+        self.vocab_file = vocab_file
+
+        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):
+        """ Returns vocab size """
+        return self.sp_tokenizer.num_tokens
+
+    def get_vocab(self):
+        """ Returns vocab as a dict """
+        vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def preprocess_text(self, inputs):
+        if self.remove_space:
+            outputs = " ".join(inputs.strip().split())
+        else:
+            outputs = inputs
+
+        if self.do_lower_case:
+            outputs = outputs.lower()
+
+        return outputs
+
+    def _tokenize(self, text, **kwargs):
+        """ Returns a tokenized string. """
+        text = self.preprocess_text(text)
+
+        seq = self.sp_tokenizer.tokenize(text)
+
+        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. """
+        return self.sp_tokenizer[token]
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.sp_tokenizer[index]
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+            filename_prefix (`str`, *optional*):
+                An optional prefix to add to the named of the saved files.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, self.vocab_files_names["vocab_file"]
+            )
+        else:
+            vocab_file = save_directory
+
+        with open(self.vocab_file, 'rb') as fin:
+            proto_str = fin.read()
+
+        with open(vocab_file, "wb") as writer:
+            writer.write(proto_str)
+
+        return (vocab_file,)
+
+    def build_inputs_with_special_tokens(
+            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A BERT sequence has the following format:
+
+        - single sequence: `[CLS] X [SEP]`
+        - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        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

models/tokenizer_config.json

@@ -0,0 +1,20 @@
+{
+  "name_or_path": "THUDM/chatglm-6b",
+  "bos_token": "<sop>",
+  "eos_token": "<eop>",
+  "end_token": "</s>",
+  "gmask_token": "[gMASK]",
+  "mask_token": "[MASK]",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>",
+  "remove_space": false,
+  "do_lower_case": false,
+  "tokenizer_class": "ChatGLMTokenizer",
+  "num_image_tokens": 0,
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_chatglm.ChatGLMTokenizer",
+      null
+      ]
+  }
+}

requirements.txt

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