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Model: pfnet/nekomata-7b-pfn-qfin Source: Original Platform
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------------- LICENSE FOR NVIDIA Megatron-LM code --------------
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------------- LICENSE FOR OpenAI tiktoken code --------------
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Copyright (c) 2022 OpenAI, Shantanu Jain
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SOFTWARE.
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------------- LICENSE FOR PanQiWei AutoGPTQ code --------------
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MIT License
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Copyright (c) 2023 潘其威(William)
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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SOFTWARE.
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114
README.md
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114
README.md
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||||
---
|
||||
license: other
|
||||
license_name: tongyi-qianwen-license
|
||||
license_link: LICENSE
|
||||
language:
|
||||
- en
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||||
- ja
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||||
library_name: transformers
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pipeline_tag: text-generation
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---
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# nekomata-7b-pfn-qfin
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## Model Description
|
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nekomata-7b-pfn-qfin is a fine-tuned model based on [rinna/nekomata-7b](https://huggingface.co/rinna/nekomata-7b/tree/main).
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This is the base model, which is good at generating continuous sentences for finance.
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||||
nekomata-7b-pfn-qfin is fine-tuned on 370M tokens from multiple special datasets generated by Preferred Networks, which is clear to use for commercial usage.
|
||||
The fine-tuned were carried out at a 2048 context length.
|
||||
This model is released under [Tongyi Qianwen LICENSE AGREEMENT](https://github.com/QwenLM/Qwen/blob/e8e15962d897714944773cca57fa2e460a3655e8/Tongyi%20Qianwen%20LICENSE%20AGREEMENT).
|
||||
|
||||
The research article is available on [arXiv](https://arxiv.org/abs/2404.10555).
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|
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# Benchmarking
|
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The benchmark score is obtained using [Japanese Language Model Financial Evaluation Harness](https://github.com/pfnet-research/japanese-lm-fin-harness)
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For the benchmark, 0-shot and default prompts are used.
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```
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| Task |Metric| nekomaba-7b | Ours |
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|----------------|------|------|---|------|------|---|------|
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|chabsa |f1 |0.8134| | |0.8127| | |
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|cma_basics |acc |0.3158|± |0.0764|0.3684|± |0.0793|
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|cpa_audit |acc |0.2085|± |0.0203|0.1809|± |0.0193|
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|fp2 |acc |0.2484|± |0.0198|0.2674|± |0.0203|
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|security_sales_1|acc |0.4912|± |0.0668|0.5088|± |0.0668|
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|----------------|------|------|---|------|------|---|------|
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|OVER ALL | |0.4155 |0.4276 |
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```
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## Usage
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Install the required libraries as follows:
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```sh
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>>> python -m pip install numpy sentencepiece torch transformers accelerate transformers_stream_generator tiktoken einops
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```
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Execute the following python code:
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("pfnet/nekomata-7b-pfn-qfin", trust_remote_code=True)
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# Use GPU with bf16 (recommended for supported devices)
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# model = AutoModelForCausalLM.from_pretrained("pfnet/nekomata-7b-pfn-qfin", device_map="auto", trust_remote_code=True, bf16=True)
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# Use GPU with fp16
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# model = AutoModelForCausalLM.from_pretrained("pfnet/nekomata-7b-pfn-qfin", device_map="auto", trust_remote_code=True, fp16=True)
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# Use GPU with fp32
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# model = AutoModelForCausalLM.from_pretrained("pfnet/nekomata-7b-pfn-qfin", device_map="auto", trust_remote_code=True, fp32=True)
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# Use CPU
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# model = AutoModelForCausalLM.from_pretrained("pfnet/nekomata-7b-pfn-qfin", device_map="cpu", trust_remote_code=True)
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# Automatically select device and precision
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model = AutoModelForCausalLM.from_pretrained("pfnet/nekomata-7b-pfn-qfin", device_map="auto", trust_remote_code=True)
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text = "日本銀行は"
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input_ids = tokenizer(text, return_tensors="pt").input_ids.to(model.device)
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with torch.no_grad():
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generated_tokens = model.generate(
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inputs=input_ids,
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max_new_tokens=32,
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do_sample=True,
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temperature=1.0,
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repetition_penalty=1.1
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)[0]
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generated_text = tokenizer.decode(generated_tokens)
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print(generated_text)
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# 日本銀行は、2016年9月に「長短金利操作付き量的・質的金融緩和」を導入し、長期国
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```
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## Model Details
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- Model size: 7b
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- Fine-tuned tokens: 370M tokens (Japanese: 300M tokens, English: 13M tokens, Digits: 14M tokens)
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- Context length: 2048
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- Developed by: Preferred Networks, Inc
|
||||
- Model type: Causal decoder-only
|
||||
- Language(s): Japanese and English
|
||||
- License: [Tongyi Qianwen LICENSE AGREEMENT](https://github.com/QwenLM/Qwen/blob/e8e15962d897714944773cca57fa2e460a3655e8/Tongyi%20Qianwen%20LICENSE%20AGREEMENT)
|
||||
|
||||
## Bias, Risks, and Limitations
|
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nekomata-7b-pfn-qfin is a new technology that carries risks with use.
|
||||
Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios.
|
||||
For these reasons, as with all LLMs, nekomata-7b-pfn-qfin’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts.
|
||||
This model is not designed for legal, tax, investment, financial, or other advice.
|
||||
Therefore, before deploying any applications of nekomata-7b-pfn-qfin, developers should perform safety testing and tuning tailored to their specific applications of the model.
|
||||
|
||||
## How to cite
|
||||
```
|
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@misc{hirano2024,
|
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title={Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training},
|
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author={Masanori Hirano and Kentaro Imajo},
|
||||
year={2024},
|
||||
eprint={2404.10555},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CL}
|
||||
}
|
||||
```
|
||||
|
||||
## Contributors
|
||||
Preferred Networks, Inc.
|
||||
- Masanori Hirano
|
||||
- Kentaro Imajo
|
||||
|
||||
# License
|
||||
[Tongyi Qianwen LICENSE AGREEMENT](https://github.com/QwenLM/Qwen/blob/e8e15962d897714944773cca57fa2e460a3655e8/Tongyi%20Qianwen%20LICENSE%20AGREEMENT)
|
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42
config.json
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config.json
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{
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"_name_or_path": "pfnet/nekomata-7b-pfn-qfin",
|
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"architectures": [
|
||||
"QWenLMHeadModel"
|
||||
],
|
||||
"attn_dropout_prob": 0.0,
|
||||
"auto_map": {
|
||||
"AutoConfig": "configuration_qwen.QWenConfig",
|
||||
"AutoModelForCausalLM": "modeling_qwen.QWenLMHeadModel"
|
||||
},
|
||||
"bf16": false,
|
||||
"emb_dropout_prob": 0.0,
|
||||
"fp16": false,
|
||||
"fp32": false,
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 22016,
|
||||
"kv_channels": 128,
|
||||
"layer_norm_epsilon": 1e-06,
|
||||
"max_position_embeddings": 32768,
|
||||
"model_type": "qwen",
|
||||
"no_bias": true,
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"onnx_safe": null,
|
||||
"rotary_emb_base": 10000,
|
||||
"rotary_pct": 1.0,
|
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"scale_attn_weights": true,
|
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"seq_length": 8192,
|
||||
"softmax_in_fp32": false,
|
||||
"tie_word_embeddings": false,
|
||||
"tokenizer_class": "QWenTokenizer",
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.40.2",
|
||||
"use_cache": true,
|
||||
"use_cache_kernel": false,
|
||||
"use_cache_quantization": false,
|
||||
"use_dynamic_ntk": true,
|
||||
"use_flash_attn": true,
|
||||
"use_logn_attn": true,
|
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"vocab_size": 151936
|
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}
|
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1
configuration.json
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1
configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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71
configuration_qwen.py
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71
configuration_qwen.py
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||||
# Copyright (c) Alibaba Cloud.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
from transformers import PretrainedConfig
|
||||
|
||||
|
||||
class QWenConfig(PretrainedConfig):
|
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model_type = "qwen"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=151936,
|
||||
hidden_size=4096,
|
||||
num_hidden_layers=32,
|
||||
num_attention_heads=32,
|
||||
emb_dropout_prob=0.0,
|
||||
attn_dropout_prob=0.0,
|
||||
layer_norm_epsilon=1e-6,
|
||||
initializer_range=0.02,
|
||||
max_position_embeddings=8192,
|
||||
scale_attn_weights=True,
|
||||
use_cache=True,
|
||||
bf16=False,
|
||||
fp16=False,
|
||||
fp32=False,
|
||||
kv_channels=128,
|
||||
rotary_pct=1.0,
|
||||
rotary_emb_base=10000,
|
||||
use_dynamic_ntk=True,
|
||||
use_logn_attn=True,
|
||||
use_flash_attn="auto",
|
||||
intermediate_size=22016,
|
||||
no_bias=True,
|
||||
tie_word_embeddings=False,
|
||||
use_cache_quantization=False,
|
||||
use_cache_kernel=False,
|
||||
softmax_in_fp32=False,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.emb_dropout_prob = emb_dropout_prob
|
||||
self.attn_dropout_prob = attn_dropout_prob
|
||||
self.layer_norm_epsilon = layer_norm_epsilon
|
||||
self.initializer_range = initializer_range
|
||||
self.scale_attn_weights = scale_attn_weights
|
||||
self.use_cache = use_cache
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.bf16 = bf16
|
||||
self.fp16 = fp16
|
||||
self.fp32 = fp32
|
||||
self.kv_channels = kv_channels
|
||||
self.rotary_pct = rotary_pct
|
||||
self.rotary_emb_base = rotary_emb_base
|
||||
self.use_dynamic_ntk = use_dynamic_ntk
|
||||
self.use_logn_attn = use_logn_attn
|
||||
self.use_flash_attn = use_flash_attn
|
||||
self.no_bias = no_bias
|
||||
self.use_cache_quantization = use_cache_quantization
|
||||
self.use_cache_kernel = use_cache_kernel
|
||||
self.softmax_in_fp32 = softmax_in_fp32
|
||||
super().__init__(
|
||||
tie_word_embeddings=tie_word_embeddings,
|
||||
**kwargs
|
||||
)
|
||||
55
cpp_kernels.py
Normal file
55
cpp_kernels.py
Normal file
@@ -0,0 +1,55 @@
|
||||
from torch.utils import cpp_extension
|
||||
import pathlib
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
def _get_cuda_bare_metal_version(cuda_dir):
|
||||
raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"],
|
||||
universal_newlines=True)
|
||||
output = raw_output.split()
|
||||
release_idx = output.index("release") + 1
|
||||
release = output[release_idx].split(".")
|
||||
bare_metal_major = release[0]
|
||||
bare_metal_minor = release[1][0]
|
||||
|
||||
return raw_output, bare_metal_major, bare_metal_minor
|
||||
|
||||
def _create_build_dir(buildpath):
|
||||
try:
|
||||
os.mkdir(buildpath)
|
||||
except OSError:
|
||||
if not os.path.isdir(buildpath):
|
||||
print(f"Creation of the build directory {buildpath} failed")
|
||||
|
||||
# Check if cuda 11 is installed for compute capability 8.0
|
||||
cc_flag = []
|
||||
_, bare_metal_major, bare_metal_minor = _get_cuda_bare_metal_version(cpp_extension.CUDA_HOME)
|
||||
if int(bare_metal_major) >= 11:
|
||||
cc_flag.append('-gencode')
|
||||
cc_flag.append('arch=compute_80,code=sm_80')
|
||||
if int(bare_metal_minor) >= 7:
|
||||
cc_flag.append('-gencode')
|
||||
cc_flag.append('arch=compute_90,code=sm_90')
|
||||
|
||||
# Build path
|
||||
srcpath = pathlib.Path(__file__).parent.absolute()
|
||||
buildpath = srcpath / 'build'
|
||||
_create_build_dir(buildpath)
|
||||
|
||||
def _cpp_extention_load_helper(name, sources, extra_cuda_flags):
|
||||
return cpp_extension.load(
|
||||
name=name,
|
||||
sources=sources,
|
||||
build_directory=buildpath,
|
||||
extra_cflags=['-O3', ],
|
||||
extra_cuda_cflags=['-O3',
|
||||
'-gencode', 'arch=compute_70,code=sm_70',
|
||||
'--use_fast_math'] + extra_cuda_flags + cc_flag,
|
||||
verbose=1
|
||||
)
|
||||
|
||||
extra_flags = []
|
||||
|
||||
cache_autogptq_cuda_256_sources = ["./cache_autogptq_cuda_256.cpp",
|
||||
"./cache_autogptq_cuda_kernel_256.cu"]
|
||||
cache_autogptq_cuda_256 = _cpp_extention_load_helper("cache_autogptq_cuda_256", cache_autogptq_cuda_256_sources, extra_flags)
|
||||
4
generation_config.json
Normal file
4
generation_config.json
Normal file
@@ -0,0 +1,4 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"transformers_version": "4.40.2"
|
||||
}
|
||||
3
model-00001-of-00004.safetensors
Normal file
3
model-00001-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:f806cb077d5fc8780ca2073576d8ca39bcd8bb11c53bb13c175d3164750f2c24
|
||||
size 4988485656
|
||||
3
model-00002-of-00004.safetensors
Normal file
3
model-00002-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:99d4b7ad5238aa4ab2df00b9bb943701d519b9533dea8425adf3a2e97326a8cd
|
||||
size 4981246520
|
||||
3
model-00003-of-00004.safetensors
Normal file
3
model-00003-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:a5bf1156ba496200b11fa19c61bd4ee841caa2dc37808a1d75887434cd3dbb5d
|
||||
size 4228285288
|
||||
3
model-00004-of-00004.safetensors
Normal file
3
model-00004-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:853c927d65a1e982117924b6015a68f1d6bb444d09a027ab31619c93404bf2bd
|
||||
size 1244659840
|
||||
266
model.safetensors.index.json
Normal file
266
model.safetensors.index.json
Normal file
@@ -0,0 +1,266 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 15442649088
|
||||
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|
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|
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"transformer.wte.weight": "model-00001-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
1363
modeling_qwen.py
Normal file
1363
modeling_qwen.py
Normal file
File diff suppressed because it is too large
Load Diff
151643
qwen.tiktoken
Normal file
151643
qwen.tiktoken
Normal file
File diff suppressed because it is too large
Load Diff
416
qwen_generation_utils.py
Normal file
416
qwen_generation_utils.py
Normal file
@@ -0,0 +1,416 @@
|
||||
# Copyright (c) Alibaba Cloud.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
"""Generation support."""
|
||||
|
||||
from typing import Tuple, List, Union, Iterable
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from transformers import PreTrainedTokenizer
|
||||
from transformers import logging
|
||||
from transformers.generation import LogitsProcessor
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
# Types.
|
||||
HistoryType = List[Tuple[str, str]]
|
||||
TokensType = List[int]
|
||||
BatchTokensType = List[List[int]]
|
||||
|
||||
|
||||
def pad_batch(batch: BatchTokensType, pad_id: int, seq_length: int) -> BatchTokensType:
|
||||
for tokens in batch:
|
||||
context_length = len(tokens)
|
||||
if context_length < seq_length:
|
||||
tokens.extend([pad_id] * (seq_length - context_length))
|
||||
return batch
|
||||
|
||||
|
||||
def get_ltor_masks_and_position_ids(
|
||||
data,
|
||||
eod_token,
|
||||
reset_position_ids,
|
||||
reset_attention_mask,
|
||||
eod_mask_loss,
|
||||
):
|
||||
"""Build masks and position id for left to right model."""
|
||||
|
||||
# Extract batch size and sequence length.
|
||||
micro_batch_size, seq_length = data.size()
|
||||
|
||||
# Attention mask (lower triangular).
|
||||
if reset_attention_mask:
|
||||
att_mask_batch = micro_batch_size
|
||||
else:
|
||||
att_mask_batch = 1
|
||||
attention_mask = torch.tril(
|
||||
torch.ones((att_mask_batch, seq_length, seq_length), device=data.device)
|
||||
).view(att_mask_batch, 1, seq_length, seq_length)
|
||||
|
||||
# Loss mask.
|
||||
loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
|
||||
if eod_mask_loss:
|
||||
loss_mask[data == eod_token] = 0.0
|
||||
|
||||
# Position ids.
|
||||
position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device)
|
||||
position_ids = position_ids.unsqueeze(0).expand_as(data)
|
||||
# We need to clone as the ids will be modifed based on batch index.
|
||||
if reset_position_ids:
|
||||
position_ids = position_ids.clone()
|
||||
|
||||
if reset_position_ids or reset_attention_mask:
|
||||
# Loop through the batches:
|
||||
for b in range(micro_batch_size):
|
||||
|
||||
# Find indecies where EOD token is.
|
||||
eod_index = position_ids[b, data[b] == eod_token]
|
||||
# Detach indecies from positions if going to modify positions.
|
||||
if reset_position_ids:
|
||||
eod_index = eod_index.clone()
|
||||
|
||||
# Loop through EOD indecies:
|
||||
prev_index = 0
|
||||
for j in range(eod_index.size()[0]):
|
||||
i = eod_index[j]
|
||||
# Mask attention loss.
|
||||
if reset_attention_mask:
|
||||
attention_mask[b, 0, (i + 1) :, : (i + 1)] = 0
|
||||
# Reset positions.
|
||||
if reset_position_ids:
|
||||
position_ids[b, (i + 1) :] -= i + 1 - prev_index
|
||||
prev_index = i + 1
|
||||
|
||||
# Convert attention mask to binary:
|
||||
attention_mask = attention_mask < 0.5
|
||||
|
||||
return attention_mask, loss_mask, position_ids
|
||||
|
||||
|
||||
def get_batch(context_tokens: torch.LongTensor, eod_id: int):
|
||||
"""Generate batch from context tokens."""
|
||||
# Move to GPU.
|
||||
tokens = context_tokens.contiguous().to(context_tokens.device)
|
||||
# Get the attention mask and postition ids.
|
||||
attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
|
||||
tokens,
|
||||
eod_id,
|
||||
reset_position_ids=False,
|
||||
reset_attention_mask=False,
|
||||
eod_mask_loss=False,
|
||||
)
|
||||
return tokens, attention_mask, position_ids
|
||||
|
||||
|
||||
def get_stop_words_ids(chat_format, tokenizer):
|
||||
if chat_format == "raw":
|
||||
stop_words_ids = [tokenizer.encode("Human:"), [tokenizer.eod_id]]
|
||||
elif chat_format == "chatml":
|
||||
stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
|
||||
else:
|
||||
raise NotImplementedError(f"Unknown chat format {chat_format!r}")
|
||||
return stop_words_ids
|
||||
|
||||
|
||||
def make_context(
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
query: str,
|
||||
history: List[Tuple[str, str]] = None,
|
||||
system: str = "",
|
||||
max_window_size: int = 6144,
|
||||
chat_format: str = "chatml",
|
||||
):
|
||||
if history is None:
|
||||
history = []
|
||||
|
||||
if chat_format == "chatml":
|
||||
im_start, im_end = "<|im_start|>", "<|im_end|>"
|
||||
im_start_tokens = [tokenizer.im_start_id]
|
||||
im_end_tokens = [tokenizer.im_end_id]
|
||||
nl_tokens = tokenizer.encode("\n")
|
||||
|
||||
def _tokenize_str(role, content):
|
||||
return f"{role}\n{content}", tokenizer.encode(
|
||||
role, allowed_special=set()
|
||||
) + nl_tokens + tokenizer.encode(content, allowed_special=set())
|
||||
|
||||
system_text, system_tokens_part = _tokenize_str("system", system)
|
||||
system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
|
||||
|
||||
raw_text = ""
|
||||
context_tokens = []
|
||||
|
||||
for turn_query, turn_response in reversed(history):
|
||||
query_text, query_tokens_part = _tokenize_str("user", turn_query)
|
||||
query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
|
||||
response_text, response_tokens_part = _tokenize_str(
|
||||
"assistant", turn_response
|
||||
)
|
||||
response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
|
||||
|
||||
next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
|
||||
prev_chat = (
|
||||
f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
|
||||
)
|
||||
|
||||
current_context_size = (
|
||||
len(system_tokens) + len(next_context_tokens) + len(context_tokens)
|
||||
)
|
||||
if current_context_size < max_window_size:
|
||||
context_tokens = next_context_tokens + context_tokens
|
||||
raw_text = prev_chat + raw_text
|
||||
else:
|
||||
break
|
||||
|
||||
context_tokens = system_tokens + context_tokens
|
||||
raw_text = f"{im_start}{system_text}{im_end}" + raw_text
|
||||
context_tokens += (
|
||||
nl_tokens
|
||||
+ im_start_tokens
|
||||
+ _tokenize_str("user", query)[1]
|
||||
+ im_end_tokens
|
||||
+ nl_tokens
|
||||
+ im_start_tokens
|
||||
+ tokenizer.encode("assistant")
|
||||
+ nl_tokens
|
||||
)
|
||||
raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n"
|
||||
|
||||
elif chat_format == "raw":
|
||||
raw_text = query
|
||||
context_tokens = tokenizer.encode(raw_text)
|
||||
else:
|
||||
raise NotImplementedError(f"Unknown chat format {chat_format!r}")
|
||||
|
||||
return raw_text, context_tokens
|
||||
|
||||
|
||||
def _decode_default(
|
||||
tokens: List[int],
|
||||
*,
|
||||
stop_words: List[str],
|
||||
eod_words: List[str],
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
raw_text_len: int,
|
||||
verbose: bool = False,
|
||||
return_end_reason: bool = False,
|
||||
errors: str='replace',
|
||||
):
|
||||
trim_decode_tokens = tokenizer.decode(tokens, errors=errors)[raw_text_len:]
|
||||
if verbose:
|
||||
print("\nRaw Generate: ", trim_decode_tokens)
|
||||
|
||||
end_reason = f"Gen length {len(tokens)}"
|
||||
for stop_word in stop_words:
|
||||
trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
|
||||
for eod_word in eod_words:
|
||||
if eod_word in trim_decode_tokens:
|
||||
end_reason = f"Gen {eod_word!r}"
|
||||
trim_decode_tokens = trim_decode_tokens.split(eod_word)[0]
|
||||
trim_decode_tokens = trim_decode_tokens.strip()
|
||||
if verbose:
|
||||
print("\nEnd Reason:", end_reason)
|
||||
print("\nGenerate: ", trim_decode_tokens)
|
||||
|
||||
if return_end_reason:
|
||||
return trim_decode_tokens, end_reason
|
||||
else:
|
||||
return trim_decode_tokens
|
||||
|
||||
|
||||
def _decode_chatml(
|
||||
tokens: List[int],
|
||||
*,
|
||||
stop_words: List[str],
|
||||
eod_token_ids: List[int],
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
raw_text_len: int,
|
||||
context_length: int,
|
||||
verbose: bool = False,
|
||||
return_end_reason: bool = False,
|
||||
errors: str='replace'
|
||||
):
|
||||
end_reason = f"Gen length {len(tokens)}"
|
||||
eod_token_idx = context_length
|
||||
for eod_token_idx in range(context_length, len(tokens)):
|
||||
if tokens[eod_token_idx] in eod_token_ids:
|
||||
end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
|
||||
break
|
||||
|
||||
trim_decode_tokens = tokenizer.decode(tokens[:eod_token_idx], errors=errors)[raw_text_len:]
|
||||
if verbose:
|
||||
print("\nRaw Generate w/o EOD:", tokenizer.decode(tokens, errors=errors)[raw_text_len:])
|
||||
print("\nRaw Generate:", trim_decode_tokens)
|
||||
print("\nEnd Reason:", end_reason)
|
||||
for stop_word in stop_words:
|
||||
trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
|
||||
trim_decode_tokens = trim_decode_tokens.strip()
|
||||
if verbose:
|
||||
print("\nGenerate:", trim_decode_tokens)
|
||||
|
||||
if return_end_reason:
|
||||
return trim_decode_tokens, end_reason
|
||||
else:
|
||||
return trim_decode_tokens
|
||||
|
||||
|
||||
def decode_tokens(
|
||||
tokens: Union[torch.LongTensor, TokensType],
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
raw_text_len: int,
|
||||
context_length: int,
|
||||
chat_format: str,
|
||||
verbose: bool = False,
|
||||
return_end_reason: bool = False,
|
||||
errors: str="replace",
|
||||
) -> str:
|
||||
if torch.is_tensor(tokens):
|
||||
tokens = tokens.cpu().numpy().tolist()
|
||||
|
||||
if chat_format == "chatml":
|
||||
return _decode_chatml(
|
||||
tokens,
|
||||
stop_words=[],
|
||||
eod_token_ids=[tokenizer.im_start_id, tokenizer.im_end_id],
|
||||
tokenizer=tokenizer,
|
||||
raw_text_len=raw_text_len,
|
||||
context_length=context_length,
|
||||
verbose=verbose,
|
||||
return_end_reason=return_end_reason,
|
||||
errors=errors,
|
||||
)
|
||||
elif chat_format == "raw":
|
||||
return _decode_default(
|
||||
tokens,
|
||||
stop_words=["<|endoftext|>"],
|
||||
eod_words=["<|endoftext|>"],
|
||||
tokenizer=tokenizer,
|
||||
raw_text_len=raw_text_len,
|
||||
verbose=verbose,
|
||||
return_end_reason=return_end_reason,
|
||||
errors=errors,
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError(f"Unknown chat format {chat_format!r}")
|
||||
|
||||
|
||||
class StopWordsLogitsProcessor(LogitsProcessor):
|
||||
"""
|
||||
:class:`transformers.LogitsProcessor` that enforces that when specified sequences appear, stop geration.
|
||||
|
||||
Args:
|
||||
stop_words_ids (:obj:`List[List[int]]`):
|
||||
List of list of token ids of stop ids. In order to get the tokens of the words
|
||||
that should not appear in the generated text, use :obj:`tokenizer(bad_word,
|
||||
add_prefix_space=True).input_ids`.
|
||||
eos_token_id (:obj:`int`):
|
||||
The id of the `end-of-sequence` token.
|
||||
"""
|
||||
|
||||
def __init__(self, stop_words_ids: Iterable[Iterable[int]], eos_token_id: int):
|
||||
|
||||
if not isinstance(stop_words_ids, List) or len(stop_words_ids) == 0:
|
||||
raise ValueError(
|
||||
f"`stop_words_ids` has to be a non-emtpy list, but is {stop_words_ids}."
|
||||
)
|
||||
if any(not isinstance(bad_word_ids, list) for bad_word_ids in stop_words_ids):
|
||||
raise ValueError(
|
||||
f"`stop_words_ids` has to be a list of lists, but is {stop_words_ids}."
|
||||
)
|
||||
if any(
|
||||
any(
|
||||
(not isinstance(token_id, (int, np.integer)) or token_id < 0)
|
||||
for token_id in stop_word_ids
|
||||
)
|
||||
for stop_word_ids in stop_words_ids
|
||||
):
|
||||
raise ValueError(
|
||||
f"Each list in `stop_words_ids` has to be a list of positive integers, but is {stop_words_ids}."
|
||||
)
|
||||
|
||||
self.stop_words_ids = list(
|
||||
filter(
|
||||
lambda bad_token_seq: bad_token_seq != [eos_token_id], stop_words_ids
|
||||
)
|
||||
)
|
||||
self.eos_token_id = eos_token_id
|
||||
for stop_token_seq in self.stop_words_ids:
|
||||
assert (
|
||||
len(stop_token_seq) > 0
|
||||
), "Stop words token sequences {} cannot have an empty list".format(
|
||||
stop_words_ids
|
||||
)
|
||||
|
||||
def __call__(
|
||||
self, input_ids: torch.LongTensor, scores: torch.FloatTensor
|
||||
) -> torch.FloatTensor:
|
||||
stopped_samples = self._calc_stopped_samples(input_ids)
|
||||
for i, should_stop in enumerate(stopped_samples):
|
||||
if should_stop:
|
||||
scores[i, self.eos_token_id] = float(2**15)
|
||||
return scores
|
||||
|
||||
def _tokens_match(self, prev_tokens: torch.LongTensor, tokens: List[int]) -> bool:
|
||||
if len(tokens) == 0:
|
||||
# if bad word tokens is just one token always ban it
|
||||
return True
|
||||
elif len(tokens) > len(prev_tokens):
|
||||
# if bad word tokens are longer then prev input_ids they can't be equal
|
||||
return False
|
||||
elif prev_tokens[-len(tokens) :].tolist() == tokens:
|
||||
# if tokens match
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
def _calc_stopped_samples(self, prev_input_ids: Iterable[int]) -> Iterable[int]:
|
||||
stopped_samples = []
|
||||
for prev_input_ids_slice in prev_input_ids:
|
||||
match = False
|
||||
for stop_token_seq in self.stop_words_ids:
|
||||
if self._tokens_match(prev_input_ids_slice, stop_token_seq):
|
||||
# if tokens do not match continue
|
||||
match = True
|
||||
break
|
||||
stopped_samples.append(match)
|
||||
|
||||
return stopped_samples
|
||||
|
||||
|
||||
def top_k_logits(logits, top_k=0, top_p=0.0, filter_value=-float("Inf")):
|
||||
"""This function has been mostly taken from huggingface conversational
|
||||
ai code at
|
||||
https://medium.com/huggingface/how-to-build-a-state-of-the-art-
|
||||
conversational-ai-with-transfer-learning-2d818ac26313"""
|
||||
|
||||
if top_k > 0:
|
||||
# Remove all tokens with a probability less than the
|
||||
# last token of the top-k
|
||||
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
|
||||
logits[indices_to_remove] = filter_value
|
||||
|
||||
if top_p > 0.0:
|
||||
# Cconvert to 1D
|
||||
sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
|
||||
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
|
||||
|
||||
# Remove tokens with cumulative probability above the threshold
|
||||
sorted_indices_to_remove = cumulative_probs > top_p
|
||||
# Shift the indices to the right to keep also the first token
|
||||
# above the threshold
|
||||
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
|
||||
sorted_indices_to_remove[..., 0] = 0
|
||||
for i in range(sorted_indices.size(0)):
|
||||
indices_to_remove = sorted_indices[i][sorted_indices_to_remove[i]]
|
||||
logits[i][indices_to_remove] = filter_value
|
||||
|
||||
return logits
|
||||
|
||||
|
||||
def switch(val1, val2, boolean):
|
||||
boolean = boolean.type_as(val1)
|
||||
return (1 - boolean) * val1 + boolean * val2
|
||||
276
tokenization_qwen.py
Normal file
276
tokenization_qwen.py
Normal file
@@ -0,0 +1,276 @@
|
||||
# Copyright (c) Alibaba Cloud.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
"""Tokenization classes for QWen."""
|
||||
|
||||
import base64
|
||||
import logging
|
||||
import os
|
||||
import unicodedata
|
||||
from typing import Collection, Dict, List, Set, Tuple, Union
|
||||
|
||||
import tiktoken
|
||||
from transformers import PreTrainedTokenizer, AddedToken
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
|
||||
|
||||
PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
|
||||
ENDOFTEXT = "<|endoftext|>"
|
||||
IMSTART = "<|im_start|>"
|
||||
IMEND = "<|im_end|>"
|
||||
# as the default behavior is changed to allow special tokens in
|
||||
# regular texts, the surface forms of special tokens need to be
|
||||
# as different as possible to minimize the impact
|
||||
EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
|
||||
# changed to use actual index to avoid misconfiguration with vocabulary expansion
|
||||
SPECIAL_START_ID = 151643
|
||||
SPECIAL_TOKENS = tuple(
|
||||
enumerate(
|
||||
(
|
||||
(
|
||||
ENDOFTEXT,
|
||||
IMSTART,
|
||||
IMEND,
|
||||
)
|
||||
+ EXTRAS
|
||||
),
|
||||
start=SPECIAL_START_ID,
|
||||
)
|
||||
)
|
||||
SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
|
||||
|
||||
|
||||
def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
|
||||
with open(tiktoken_bpe_file, "rb") as f:
|
||||
contents = f.read()
|
||||
return {
|
||||
base64.b64decode(token): int(rank)
|
||||
for token, rank in (line.split() for line in contents.splitlines() if line)
|
||||
}
|
||||
|
||||
|
||||
class QWenTokenizer(PreTrainedTokenizer):
|
||||
"""QWen tokenizer."""
|
||||
|
||||
vocab_files_names = VOCAB_FILES_NAMES
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_file,
|
||||
errors="replace",
|
||||
extra_vocab_file=None,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
# how to handle errors in decoding UTF-8 byte sequences
|
||||
# use ignore if you are in streaming inference
|
||||
self.errors = errors
|
||||
|
||||
self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
|
||||
self.special_tokens = {
|
||||
token: index
|
||||
for index, token in SPECIAL_TOKENS
|
||||
}
|
||||
|
||||
# try load extra vocab from file
|
||||
if extra_vocab_file is not None:
|
||||
used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
|
||||
extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
|
||||
for token, index in extra_mergeable_ranks.items():
|
||||
if token in self.mergeable_ranks:
|
||||
logger.info(f"extra token {token} exists, skipping")
|
||||
continue
|
||||
if index in used_ids:
|
||||
logger.info(f'the index {index} for extra token {token} exists, skipping')
|
||||
continue
|
||||
self.mergeable_ranks[token] = index
|
||||
# the index may be sparse after this, but don't worry tiktoken.Encoding will handle this
|
||||
|
||||
enc = tiktoken.Encoding(
|
||||
"Qwen",
|
||||
pat_str=PAT_STR,
|
||||
mergeable_ranks=self.mergeable_ranks,
|
||||
special_tokens=self.special_tokens,
|
||||
)
|
||||
assert (
|
||||
len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
|
||||
), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
|
||||
|
||||
self.decoder = {
|
||||
v: k for k, v in self.mergeable_ranks.items()
|
||||
} # type: dict[int, bytes|str]
|
||||
self.decoder.update({v: k for k, v in self.special_tokens.items()})
|
||||
|
||||
self.tokenizer = enc # type: tiktoken.Encoding
|
||||
|
||||
self.eod_id = self.tokenizer.eot_token
|
||||
self.im_start_id = self.special_tokens[IMSTART]
|
||||
self.im_end_id = self.special_tokens[IMEND]
|
||||
|
||||
def __getstate__(self):
|
||||
# for pickle lovers
|
||||
state = self.__dict__.copy()
|
||||
del state["tokenizer"]
|
||||
return state
|
||||
|
||||
def __setstate__(self, state):
|
||||
# tokenizer is not python native; don't pass it; rebuild it
|
||||
self.__dict__.update(state)
|
||||
enc = tiktoken.Encoding(
|
||||
"Qwen",
|
||||
pat_str=PAT_STR,
|
||||
mergeable_ranks=self.mergeable_ranks,
|
||||
special_tokens=self.special_tokens,
|
||||
)
|
||||
self.tokenizer = enc
|
||||
|
||||
def __len__(self) -> int:
|
||||
return self.tokenizer.n_vocab
|
||||
|
||||
def get_vocab(self) -> Dict[bytes, int]:
|
||||
return self.mergeable_ranks
|
||||
|
||||
def convert_tokens_to_ids(
|
||||
self, tokens: Union[bytes, str, List[Union[bytes, str]]]
|
||||
) -> List[int]:
|
||||
ids = []
|
||||
if isinstance(tokens, (str, bytes)):
|
||||
if tokens in self.special_tokens:
|
||||
return self.special_tokens[tokens]
|
||||
else:
|
||||
return self.mergeable_ranks.get(tokens)
|
||||
for token in tokens:
|
||||
if token in self.special_tokens:
|
||||
ids.append(self.special_tokens[token])
|
||||
else:
|
||||
ids.append(self.mergeable_ranks.get(token))
|
||||
return ids
|
||||
|
||||
def _add_tokens(
|
||||
self,
|
||||
new_tokens: Union[List[str], List[AddedToken]],
|
||||
special_tokens: bool = False,
|
||||
) -> int:
|
||||
if not special_tokens and new_tokens:
|
||||
raise ValueError("Adding regular tokens is not supported")
|
||||
for token in new_tokens:
|
||||
surface_form = token.content if isinstance(token, AddedToken) else token
|
||||
if surface_form not in SPECIAL_TOKENS_SET:
|
||||
raise ValueError("Adding unknown special tokens is not supported")
|
||||
return 0
|
||||
|
||||
def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
|
||||
"""
|
||||
Save only the vocabulary of the tokenizer (vocabulary).
|
||||
|
||||
Returns:
|
||||
`Tuple(str)`: Paths to the files saved.
|
||||
"""
|
||||
file_path = os.path.join(save_directory, "qwen.tiktoken")
|
||||
with open(file_path, "w", encoding="utf8") as w:
|
||||
for k, v in self.mergeable_ranks.items():
|
||||
line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
|
||||
w.write(line)
|
||||
return (file_path,)
|
||||
|
||||
def tokenize(
|
||||
self,
|
||||
text: str,
|
||||
allowed_special: Union[Set, str] = "all",
|
||||
disallowed_special: Union[Collection, str] = (),
|
||||
**kwargs,
|
||||
) -> List[Union[bytes, str]]:
|
||||
"""
|
||||
Converts a string in a sequence of tokens.
|
||||
|
||||
Args:
|
||||
text (`str`):
|
||||
The sequence to be encoded.
|
||||
allowed_special (`Literal["all"]` or `set`):
|
||||
The surface forms of the tokens to be encoded as special tokens in regular texts.
|
||||
Default to "all".
|
||||
disallowed_special (`Literal["all"]` or `Collection`):
|
||||
The surface forms of the tokens that should not be in regular texts and trigger errors.
|
||||
Default to an empty tuple.
|
||||
|
||||
kwargs (additional keyword arguments, *optional*):
|
||||
Will be passed to the underlying model specific encode method.
|
||||
|
||||
Returns:
|
||||
`List[bytes|str]`: The list of tokens.
|
||||
"""
|
||||
tokens = []
|
||||
text = unicodedata.normalize("NFC", text)
|
||||
|
||||
# this implementation takes a detour: text -> token id -> token surface forms
|
||||
for t in self.tokenizer.encode(
|
||||
text, allowed_special=allowed_special, disallowed_special=disallowed_special
|
||||
):
|
||||
tokens.append(self.decoder[t])
|
||||
return tokens
|
||||
|
||||
def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
|
||||
"""
|
||||
Converts a sequence of tokens in a single string.
|
||||
"""
|
||||
text = ""
|
||||
temp = b""
|
||||
for t in tokens:
|
||||
if isinstance(t, str):
|
||||
if temp:
|
||||
text += temp.decode("utf-8", errors=self.errors)
|
||||
temp = b""
|
||||
text += t
|
||||
elif isinstance(t, bytes):
|
||||
temp += t
|
||||
else:
|
||||
raise TypeError("token should only be of type types or str")
|
||||
if temp:
|
||||
text += temp.decode("utf-8", errors=self.errors)
|
||||
return text
|
||||
|
||||
@property
|
||||
def vocab_size(self):
|
||||
return self.tokenizer.n_vocab
|
||||
|
||||
def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
|
||||
"""Converts an id to a token, special tokens included"""
|
||||
if index in self.decoder:
|
||||
return self.decoder[index]
|
||||
raise ValueError("unknown ids")
|
||||
|
||||
def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
|
||||
"""Converts a token to an id using the vocab, special tokens included"""
|
||||
if token in self.special_tokens:
|
||||
return self.special_tokens[token]
|
||||
if token in self.mergeable_ranks:
|
||||
return self.mergeable_ranks[token]
|
||||
raise ValueError("unknown token")
|
||||
|
||||
def _tokenize(self, text: str, **kwargs):
|
||||
"""
|
||||
Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
|
||||
vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
|
||||
|
||||
Do NOT take care of added tokens.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
def _decode(
|
||||
self,
|
||||
token_ids: Union[int, List[int]],
|
||||
skip_special_tokens: bool = False,
|
||||
errors: str = None,
|
||||
**kwargs,
|
||||
) -> str:
|
||||
if isinstance(token_ids, int):
|
||||
token_ids = [token_ids]
|
||||
if skip_special_tokens:
|
||||
token_ids = [i for i in token_ids if i < self.eod_id]
|
||||
return self.tokenizer.decode(token_ids, errors=errors or self.errors)
|
||||
13
tokenizer_config.json
Normal file
13
tokenizer_config.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"model_max_length": 32768,
|
||||
"tokenizer_class": "QWenTokenizer",
|
||||
"auto_map": {
|
||||
"AutoTokenizer": [
|
||||
"tokenization_qwen.QWenTokenizer",
|
||||
null
|
||||
]
|
||||
},
|
||||
"bos_token": "<|endoftext|>",
|
||||
"eos_token": "<|endoftext|>",
|
||||
"pad_token": "<|extra_204|>"
|
||||
}
|
||||
Reference in New Issue
Block a user