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Model: FreedomIntelligence/HuatuoGPT-3-7B-Pangu Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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license: apache-2.0
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pipeline_tag: text-generation
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language:
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- en
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- zh
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base_model:
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- FreedomIntelligence/openPangu-Embedded-7B
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tags:
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- medical
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- reasoning
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---
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<div align="center">
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<h1>🩺 HuatuoGPT-3-7B-Pangu</h1>
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</div>
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<div align="center">
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<a href="https://github.com/FreedomIntelligence/HuatuoGPT-3" target="_blank">🏠 GitHub</a>
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|
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<a href="" target="_blank">📄 Paper</a>
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</div>
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# <span>Introduction</span>
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**HuatuoGPT-3** is an open-source medical LLM trained with **SeedRL**, an RL-only domain adaptation paradigm that transforms a base model into a medical expert in a single RL stage.
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**HuatuoGPT-3-7B-Pangu** is the Pangu-based variant in the HuatuoGPT-3 series. Different from the Qwen-based versions, it is built on **FreedomIntelligence/openPangu-Embedded-7B** and trained with **Ascend NPUs**.
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For more information, visit our GitHub repository:
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[https://github.com/FreedomIntelligence/HuatuoGPT-3](https://github.com/FreedomIntelligence/HuatuoGPT-3)
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> [!IMPORTANT]
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> **HuatuoGPT-3-7B-Pangu is set to thinking mode by default.** Since it is based on openPangu, the generated reasoning content is placed between `[unused16]` and `[unused17]`, and the final response starts after `[unused17]`.
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# <span>Model Info</span>
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| Model | Description | Backbone | Link |
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| --- | --- | --- | --- |
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| **HuatuoGPT-3-32B** | 32B medical LLM trained with SeedRL | Qwen3-32B | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-32B) |
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| **HuatuoGPT-3-8B** | 8B medical LLM trained with SeedRL | Qwen3-8B-Base | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-8B) |
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| **HuatuoGPT-3-7B-Pangu** | 7B medical LLM trained with SeedRL | openPangu-Embedded-7B | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-7B-Pangu) |
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# <span>Usage</span>
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You can use HuatuoGPT-3-7B-Pangu in the same way as `FreedomIntelligence/openPangu-Embedded-7B`.
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When using this model, please note that the Pangu backbone requires `trust_remote_code=True` in Transformers, and `--trust_remote_code` when serving with vLLM.
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- Direct inference:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "FreedomIntelligence/HuatuoGPT-3-7B-Pangu"
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tokenizer = AutoTokenizer.from_pretrained(
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model_name,
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use_fast=False,
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trust_remote_code=True
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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trust_remote_code=True,
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torch_dtype="auto",
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device_map="auto"
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)
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messages = [
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{"role": "user", "content": "What are the common causes of chest pain?"}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = tokenizer([text], return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=4096,
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eos_token_id=45892,
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return_dict_in_generate=True
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)
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input_length = inputs.input_ids.shape[1]
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generated_tokens = outputs.sequences[:, input_length:]
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output_text = tokenizer.decode(generated_tokens[0])
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thinking_content = output_text.split("[unused17]")[0].split("[unused16]")[-1].strip()
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content = output_text.split("[unused17]")[-1].split("[unused10]")[0].strip()
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print("thinking content:", thinking_content)
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print("content:", content)
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```
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- You can also serve the model with vLLM. Make sure to include `--trust_remote_code`:
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```bash
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CUDA_VISIBLE_DEVICES=0 \
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vllm serve FreedomIntelligence/HuatuoGPT-3-7B-Pangu \
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--served-model-name HuatuoGPT-3-7B-Pangu \
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--trust_remote_code \
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--port 8000
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```
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Or:
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```bash
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CUDA_VISIBLE_DEVICES=0 \
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python -m vllm.entrypoints.openai.api_server \
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--model FreedomIntelligence/HuatuoGPT-3-7B-Pangu \
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--served-model-name HuatuoGPT-3-7B-Pangu \
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--trust_remote_code \
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--port 8000
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```
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# <span>📖 Citation</span>
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```bibtex
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@article{huatuogpt3,
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title={HuatuoGPT-3: RL-Only Domain Adaptation from Base Models via Off-Policy Seeding},
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author={Coming soon},
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journal={arXiv preprint},
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year={2026}
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}
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```
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chat_template.jinja
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chat_template.jinja
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{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '[unused9]系统：[unused10]' }}{% endif %}{% if message['role'] == 'system' %}{{ '[unused9]系统：' + message['content'] + '[unused10]' }}{% endif %}{% if message['role'] == 'assistant' %}{{'[unused9]助手：' + message['content'] + '[unused10]'}}{% endif %}{% if message['role'] == 'tool' %}{{'[unused9]工具：' + message['content'] + '[unused10]'}}{% endif %}{% if message['role'] == 'function' %}{{'[unused9]方法：' + message['content'] + '[unused10]'}}{% endif %}{% if message['role'] == 'user' %}{{'[unused9]用户：' + message['content'] + '[unused10]'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '[unused9]助手：' }}{% endif %}
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config.json
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config.json
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{
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"architectures": [
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"PanguEmbeddedForCausalLM"
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_openpangu_dense.PanguEmbeddedConfig",
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"AutoModel": "modeling_openpangu_dense.PanguEmbeddedModel",
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"AutoModelForCausalLM": "modeling_openpangu_dense.PanguEmbeddedForCausalLM"
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},
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"bias": true,
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"bos_token_id": 1,
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"dtype": "bfloat16",
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"eos_token_id": 45892,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 12800,
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"max_position_embeddings": 32768,
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"model_type": "PanguEmbedded",
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"num_attention_heads": 32,
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"num_hidden_layers": 34,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-05,
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"rope_theta": 16000000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.3",
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"use_cache": true,
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"vocab_size": 153376
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}
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configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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configuration_openpangu_dense.py
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configuration_openpangu_dense.py
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# coding=utf-8
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All rights reserved.
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from transformers.utils import logging
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from transformers.configuration_utils import PretrainedConfig
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logger = logging.get_logger(__name__)
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class PanguEmbeddedConfig(PretrainedConfig):
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model_type = "PanguEmbedded"
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_auto_class = "AutoConfig"
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def __init__(
|
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self,
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vocab_size=153376,
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hidden_size=4096,
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intermediate_size=12800,
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num_hidden_layers=34,
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num_attention_heads=32,
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num_key_value_heads=8,
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hidden_act="silu",
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max_position_embeddings=32768,
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initializer_range=0.02,
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rms_norm_eps=1e-5,
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use_cache=True,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=45892,
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tie_word_embeddings=False,
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rope_theta=16000000.0,
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bias=True,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.bias = bias
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"do_sample": true,
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"eos_token_id": 45892,
|
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"pad_token_id": 0,
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"top_k": 0,
|
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"top_p": 0.8,
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"transformers_version": "4.57.3"
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}
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"model.layers.8.self_attn.k_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.bias": "model-00003-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
594
modeling_openpangu_dense.py
Normal file
594
modeling_openpangu_dense.py
Normal file
@@ -0,0 +1,594 @@
|
||||
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
||||
# This file was automatically generated from modular_openpangu_dense.py.
|
||||
# Do NOT edit this file manually as any edits will be overwritten by the generation of
|
||||
# the file from the modular. If any change should be done, please apply the change to the
|
||||
# modular_openpangu_dense.py file directly. One of our CI enforces this.
|
||||
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
||||
|
||||
# coding=utf-8
|
||||
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All rights reserved.
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from typing import Callable, Optional, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
try:
|
||||
import torch_npu
|
||||
from torch_npu.contrib import transfer_to_npu
|
||||
if "910" in torch.npu.get_device_name():
|
||||
NPU_ATTN_INFR = True
|
||||
print("[INFO] torch_npu detected. Using NPU fused infer attention.")
|
||||
else:
|
||||
NPU_ATTN_INFR = False
|
||||
except ImportError:
|
||||
NPU_ATTN_INFR = False
|
||||
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.cache_utils import Cache, DynamicCache
|
||||
from transformers.generation import GenerationMixin
|
||||
from transformers.masking_utils import create_causal_mask
|
||||
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
|
||||
from transformers.modeling_layers import GradientCheckpointingLayer
|
||||
from transformers.modeling_outputs import (
|
||||
BaseModelOutputWithPast,
|
||||
CausalLMOutputWithPast,
|
||||
SequenceClassifierOutputWithPast,
|
||||
)
|
||||
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
|
||||
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
|
||||
from transformers.processing_utils import Unpack
|
||||
try:
|
||||
from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, logging
|
||||
except ImportError:
|
||||
from transformers.utils import auto_docstring, can_return_tuple, logging
|
||||
from typing import TypedDict
|
||||
class LossKwargs(TypedDict, total=False):
|
||||
pass
|
||||
from .configuration_openpangu_dense import PanguEmbeddedConfig
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class PanguEmbeddedRMSNorm(nn.Module):
|
||||
def __init__(self, hidden_size, eps=1e-6):
|
||||
"""
|
||||
PanguEmbeddedRMSNorm is equivalent to T5LayerNorm
|
||||
"""
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.ones(hidden_size))
|
||||
self.variance_epsilon = eps
|
||||
|
||||
def forward(self, hidden_states):
|
||||
input_dtype = hidden_states.dtype
|
||||
hidden_states = hidden_states.to(torch.float32)
|
||||
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
||||
return self.weight * hidden_states.to(input_dtype)
|
||||
|
||||
def extra_repr(self):
|
||||
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
|
||||
|
||||
|
||||
class PanguEmbeddedRotaryEmbedding(nn.Module):
|
||||
def __init__(self, config: PanguEmbeddedConfig, device=None):
|
||||
super().__init__()
|
||||
# BC: "rope_type" was originally "type"
|
||||
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
|
||||
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
||||
else:
|
||||
self.rope_type = "default"
|
||||
self.max_seq_len_cached = config.max_position_embeddings
|
||||
self.original_max_seq_len = config.max_position_embeddings
|
||||
|
||||
self.config = config
|
||||
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
||||
|
||||
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
||||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||||
self.original_inv_freq = self.inv_freq
|
||||
|
||||
@torch.no_grad()
|
||||
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
|
||||
def forward(self, x, position_ids):
|
||||
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
|
||||
position_ids_expanded = position_ids[:, None, :].float()
|
||||
|
||||
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
|
||||
with torch.autocast(device_type=device_type, enabled=False): # Force float32
|
||||
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
cos = emb.cos() * self.attention_scaling
|
||||
sin = emb.sin() * self.attention_scaling
|
||||
|
||||
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
||||
|
||||
|
||||
def rotate_half(x):
|
||||
"""Rotates half the hidden dims of the input."""
|
||||
x1 = x[..., : x.shape[-1] // 2]
|
||||
x2 = x[..., x.shape[-1] // 2 :]
|
||||
return torch.cat((-x2, x1), dim=-1)
|
||||
|
||||
|
||||
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
||||
"""Applies Rotary Position Embedding to the query and key tensors.
|
||||
|
||||
Args:
|
||||
q (`torch.Tensor`): The query tensor.
|
||||
k (`torch.Tensor`): The key tensor.
|
||||
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
||||
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
||||
position_ids (`torch.Tensor`, *optional*):
|
||||
Deprecated and unused.
|
||||
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
||||
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
||||
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
||||
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
||||
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
||||
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
||||
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
||||
Returns:
|
||||
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
||||
"""
|
||||
cos = cos.unsqueeze(unsqueeze_dim)
|
||||
sin = sin.unsqueeze(unsqueeze_dim)
|
||||
q_embed = (q * cos) + (rotate_half(q) * sin)
|
||||
k_embed = (k * cos) + (rotate_half(k) * sin)
|
||||
return q_embed, k_embed
|
||||
|
||||
|
||||
class PanguEmbeddedMLP(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.hidden_size = config.hidden_size
|
||||
self.intermediate_size = config.intermediate_size
|
||||
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
||||
self.act_fn = ACT2FN[config.hidden_act]
|
||||
|
||||
def forward(self, x):
|
||||
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
||||
return down_proj
|
||||
|
||||
|
||||
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
||||
"""
|
||||
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
||||
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
||||
"""
|
||||
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
||||
if n_rep == 1:
|
||||
return hidden_states
|
||||
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
||||
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
||||
|
||||
|
||||
def eager_attention_forward(
|
||||
module: nn.Module,
|
||||
query: torch.Tensor,
|
||||
key: torch.Tensor,
|
||||
value: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor],
|
||||
scaling: float,
|
||||
dropout: float = 0.0,
|
||||
**kwargs,
|
||||
):
|
||||
key_states = repeat_kv(key, module.num_key_value_groups)
|
||||
value_states = repeat_kv(value, module.num_key_value_groups)
|
||||
|
||||
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
|
||||
if attention_mask is not None:
|
||||
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
||||
attn_weights = attn_weights + causal_mask
|
||||
|
||||
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
|
||||
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
|
||||
return attn_output, attn_weights
|
||||
|
||||
|
||||
class PanguEmbeddedAttention(nn.Module):
|
||||
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
||||
|
||||
def __init__(self, config: PanguEmbeddedConfig, layer_idx: int):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.layer_idx = layer_idx
|
||||
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
||||
self.num_heads = config.num_attention_heads
|
||||
self.num_key_value_heads = config.num_key_value_heads
|
||||
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
|
||||
self.scaling = self.head_dim**-0.5
|
||||
self.attention_dropout = config.attention_dropout
|
||||
self.is_causal = True
|
||||
|
||||
self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.bias)
|
||||
self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias)
|
||||
self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias)
|
||||
self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.bias)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
||||
attention_mask: Optional[torch.Tensor],
|
||||
past_key_value: Optional[Cache] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs: Unpack[FlashAttentionKwargs],
|
||||
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
|
||||
input_shape = hidden_states.shape[:-1]
|
||||
hidden_shape = (*input_shape, -1, self.head_dim)
|
||||
|
||||
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
|
||||
cos, sin = position_embeddings
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
||||
|
||||
if past_key_value is not None:
|
||||
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
||||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
|
||||
attention_interface: Callable = eager_attention_forward
|
||||
if self.config._attn_implementation != "eager":
|
||||
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
||||
|
||||
if not self.training and NPU_ATTN_INFR:
|
||||
q_len = input_shape[1]
|
||||
if attention_mask is not None:
|
||||
attention_mask = ~attention_mask.bool()
|
||||
elif q_len > 1:
|
||||
attention_mask = torch.triu(torch.ones([q_len, q_len]), diagonal=1).bool().unsqueeze(0).unsqueeze(0).to(query_states.device)
|
||||
|
||||
attn_output, _ = torch_npu.npu_fused_infer_attention_score(
|
||||
query_states, key_states, value_states,
|
||||
num_heads=self.num_heads, num_key_value_heads=self.num_key_value_heads,
|
||||
input_layout="BNSD", atten_mask=attention_mask, scale=self.scaling)
|
||||
attn_output = attn_output.transpose(1, 2)
|
||||
attn_weights = None
|
||||
else:
|
||||
attn_output, attn_weights = attention_interface(
|
||||
self,
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attention_mask,
|
||||
dropout=0.0 if not self.training else self.attention_dropout,
|
||||
scaling=self.scaling,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
||||
attn_output = self.o_proj(attn_output)
|
||||
return attn_output, attn_weights
|
||||
|
||||
|
||||
class PanguEmbeddedDecoderLayer(GradientCheckpointingLayer):
|
||||
def __init__(self, config: PanguEmbeddedConfig, layer_idx: int):
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
self.self_attn = PanguEmbeddedAttention(config=config, layer_idx=layer_idx)
|
||||
self.mlp = PanguEmbeddedMLP(config)
|
||||
self.input_layernorm = PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Cache] = None,
|
||||
output_attentions: Optional[bool] = False,
|
||||
use_cache: Optional[bool] = False,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
|
||||
**kwargs: Unpack[FlashAttentionKwargs],
|
||||
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
|
||||
# Self Attention
|
||||
hidden_states, self_attn_weights = self.self_attn(
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
# Fully Connected
|
||||
residual = hidden_states
|
||||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
outputs = (hidden_states,)
|
||||
if output_attentions:
|
||||
outputs += (self_attn_weights,)
|
||||
|
||||
return outputs
|
||||
|
||||
|
||||
@auto_docstring
|
||||
class PanguEmbeddedPreTrainedModel(PreTrainedModel):
|
||||
config_class = PanguEmbeddedConfig
|
||||
base_model_prefix = "model"
|
||||
supports_gradient_checkpointing = True
|
||||
_no_split_modules = ["PanguEmbeddedDecoderLayer"]
|
||||
_skip_keys_device_placement = ["past_key_values"]
|
||||
_supports_flash_attn_3 = True
|
||||
_supports_flash_attn_2 = True
|
||||
_supports_sdpa = True
|
||||
_supports_flex_attn = True
|
||||
_supports_cache_class = True
|
||||
_supports_quantized_cache = True
|
||||
_supports_static_cache = True
|
||||
_supports_attention_backend = True
|
||||
|
||||
def _init_weights(self, module):
|
||||
std = self.config.initializer_range
|
||||
if isinstance(module, nn.Linear):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.Embedding):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.padding_idx is not None:
|
||||
module.weight.data[module.padding_idx].zero_()
|
||||
elif isinstance(module, PanguEmbeddedRMSNorm):
|
||||
module.weight.data.fill_(1.0)
|
||||
|
||||
|
||||
@auto_docstring
|
||||
class PanguEmbeddedModel(PanguEmbeddedPreTrainedModel):
|
||||
def __init__(self, config: PanguEmbeddedConfig):
|
||||
super().__init__(config)
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
||||
self.layers = nn.ModuleList(
|
||||
[PanguEmbeddedDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
||||
)
|
||||
self.norm = PanguEmbeddedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.rotary_emb = PanguEmbeddedRotaryEmbedding(config=config)
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embed_tokens
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embed_tokens = value
|
||||
|
||||
@can_return_tuple
|
||||
@auto_docstring
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
|
||||
) -> BaseModelOutputWithPast:
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
|
||||
if (input_ids is None) ^ (inputs_embeds is not None):
|
||||
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||||
|
||||
if self.gradient_checkpointing and self.training and use_cache:
|
||||
logger.warning_once(
|
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
||||
)
|
||||
use_cache = False
|
||||
|
||||
# TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
|
||||
if not isinstance(past_key_values, (type(None), Cache)):
|
||||
raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
|
||||
if use_cache and past_key_values is None:
|
||||
past_key_values = DynamicCache()
|
||||
|
||||
if cache_position is None:
|
||||
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||||
cache_position = torch.arange(
|
||||
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
||||
)
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = cache_position.unsqueeze(0)
|
||||
|
||||
causal_mask = create_causal_mask(
|
||||
config=self.config,
|
||||
input_embeds=inputs_embeds,
|
||||
attention_mask=attention_mask,
|
||||
cache_position=cache_position,
|
||||
past_key_values=past_key_values,
|
||||
position_ids=position_ids,
|
||||
)
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
# create position embeddings to be shared across the decoder layers
|
||||
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
||||
|
||||
# decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
|
||||
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=causal_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_values,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
**flash_attn_kwargs,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
|
||||
# add hidden states from the last decoder layer
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=past_key_values if use_cache else None,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
)
|
||||
|
||||
|
||||
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
|
||||
|
||||
|
||||
@auto_docstring
|
||||
class PanguEmbeddedForCausalLM(PanguEmbeddedPreTrainedModel, GenerationMixin):
|
||||
_tied_weights_keys = ["lm_head.weight"]
|
||||
_tp_plan = {"lm_head": "colwise_rep"}
|
||||
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.model = PanguEmbeddedModel(config)
|
||||
self.vocab_size = config.vocab_size
|
||||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.model.embed_tokens
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.model.embed_tokens = value
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def set_output_embeddings(self, new_embeddings):
|
||||
self.lm_head = new_embeddings
|
||||
|
||||
def set_decoder(self, decoder):
|
||||
self.model = decoder
|
||||
|
||||
def get_decoder(self):
|
||||
return self.model
|
||||
|
||||
@can_return_tuple
|
||||
@auto_docstring
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
logits_to_keep: Union[int, torch.Tensor] = 0,
|
||||
**kwargs: Unpack[KwargsForCausalLM],
|
||||
) -> CausalLMOutputWithPast:
|
||||
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
|
||||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
||||
outputs: BaseModelOutputWithPast = self.model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
hidden_states = outputs.last_hidden_state
|
||||
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
||||
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
||||
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"PanguEmbeddedForCausalLM",
|
||||
"PanguEmbeddedModel",
|
||||
"PanguEmbeddedPreTrainedModel",
|
||||
]
|
||||
152
modular_openpangu_dense.py
Normal file
152
modular_openpangu_dense.py
Normal file
@@ -0,0 +1,152 @@
|
||||
# coding=utf-8
|
||||
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All rights reserved.
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from typing import Callable, Optional, Tuple
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
try:
|
||||
import torch_npu
|
||||
from torch_npu.contrib import transfer_to_npu
|
||||
if "910" in torch.npu.get_device_name():
|
||||
NPU_ATTN_INFR = True
|
||||
print("[INFO] torch_npu detected. Using NPU fused infer attention.")
|
||||
else:
|
||||
NPU_ATTN_INFR = False
|
||||
except ImportError:
|
||||
NPU_ATTN_INFR = False
|
||||
|
||||
from transformers.cache_utils import Cache
|
||||
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
|
||||
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
|
||||
from transformers.processing_utils import Unpack
|
||||
from transformers.utils import logging
|
||||
from transformers.models.llama.modeling_llama import (
|
||||
LlamaAttention,
|
||||
LlamaDecoderLayer,
|
||||
LlamaForCausalLM,
|
||||
LlamaForSequenceClassification,
|
||||
LlamaMLP,
|
||||
LlamaModel,
|
||||
apply_rotary_pos_emb,
|
||||
eager_attention_forward,
|
||||
)
|
||||
from .configuration_openpangu_dense import PanguEmbeddedConfig
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class PanguEmbeddedMLP(LlamaMLP):
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
||||
|
||||
|
||||
class PanguEmbeddedAttention(LlamaAttention):
|
||||
def __init__(self, config: PanguEmbeddedConfig, layer_idx: int):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.layer_idx = layer_idx
|
||||
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
||||
self.num_heads = config.num_attention_heads
|
||||
self.num_key_value_heads = config.num_key_value_heads
|
||||
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
|
||||
self.scaling = self.head_dim**-0.5
|
||||
self.attention_dropout = config.attention_dropout
|
||||
self.is_causal = True
|
||||
|
||||
self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.bias)
|
||||
self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias)
|
||||
self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias)
|
||||
self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.bias)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
||||
attention_mask: Optional[torch.Tensor],
|
||||
past_key_value: Optional[Cache] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs: Unpack[FlashAttentionKwargs],
|
||||
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
|
||||
input_shape = hidden_states.shape[:-1]
|
||||
hidden_shape = (*input_shape, -1, self.head_dim)
|
||||
|
||||
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
|
||||
cos, sin = position_embeddings
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
||||
|
||||
if past_key_value is not None:
|
||||
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
||||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
|
||||
attention_interface: Callable = eager_attention_forward
|
||||
if self.config._attn_implementation != "eager":
|
||||
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
||||
|
||||
if not self.training and NPU_ATTN_INFR:
|
||||
q_len = input_shape[1]
|
||||
if attention_mask is not None:
|
||||
attention_mask = ~attention_mask.bool()
|
||||
elif q_len > 1:
|
||||
attention_mask = torch.triu(torch.ones([q_len, q_len]), diagonal=1).bool().unsqueeze(0).unsqueeze(0).to(query_states.device)
|
||||
|
||||
attn_output, _ = torch_npu.npu_fused_infer_attention_score(
|
||||
query_states, key_states, value_states,
|
||||
num_heads=self.num_heads, num_key_value_heads=self.num_key_value_heads,
|
||||
input_layout="BNSD", atten_mask=attention_mask, scale=self.scaling)
|
||||
attn_output = attn_output.transpose(1, 2)
|
||||
attn_weights = None
|
||||
else:
|
||||
attn_output, attn_weights = attention_interface(
|
||||
self,
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attention_mask,
|
||||
dropout=0.0 if not self.training else self.attention_dropout,
|
||||
scaling=self.scaling,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
||||
attn_output = self.o_proj(attn_output)
|
||||
return attn_output, attn_weights
|
||||
|
||||
|
||||
class PanguEmbeddedDecoderLayer(LlamaDecoderLayer):
|
||||
pass
|
||||
|
||||
|
||||
class PanguEmbeddedModel(LlamaModel):
|
||||
pass
|
||||
|
||||
|
||||
class PanguEmbeddedForCausalLM(LlamaForCausalLM):
|
||||
pass
|
||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "[unused10]",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
273
tokenization_openpangu.py
Normal file
273
tokenization_openpangu.py
Normal file
@@ -0,0 +1,273 @@
|
||||
# coding=utf-8
|
||||
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All rights reserved.
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import os
|
||||
from shutil import copyfile
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import sentencepiece as spm
|
||||
|
||||
from transformers.tokenization_utils import PreTrainedTokenizer
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
|
||||
|
||||
PRETRAINED_VOCAB_FILES_MAP = {}
|
||||
|
||||
|
||||
def convert_bool(string):
|
||||
if isinstance(string, str):
|
||||
if string.lower() == "true":
|
||||
return True
|
||||
elif string.lower() == "false":
|
||||
return False
|
||||
else:
|
||||
return string
|
||||
else:
|
||||
return string
|
||||
|
||||
|
||||
class PanguTokenizer(PreTrainedTokenizer):
|
||||
"""
|
||||
Construct a tokenizer. Based on byte-level Byte-Pair-Encoding.
|
||||
|
||||
Args:
|
||||
vocab_file (`str`):
|
||||
Path to the vocabulary file.
|
||||
"""
|
||||
|
||||
vocab_files_names = VOCAB_FILES_NAMES
|
||||
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
||||
model_input_names = ["input_ids", "attention_mask"]
|
||||
_auto_class = "AutoTokenizer"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_file,
|
||||
unk_token="<unk>",
|
||||
bos_token="<s>",
|
||||
eos_token="</s>",
|
||||
pad_token="</s>",
|
||||
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
||||
add_bos_token=True,
|
||||
add_eos_token=False,
|
||||
decode_with_prefix_space=False,
|
||||
clean_up_tokenization_spaces=False,
|
||||
**kwargs,
|
||||
):
|
||||
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
||||
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
||||
self.sp_model.Load(vocab_file)
|
||||
super().__init__(
|
||||
bos_token=bos_token,
|
||||
eos_token=eos_token,
|
||||
unk_token=unk_token,
|
||||
pad_token=pad_token,
|
||||
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
||||
**kwargs,
|
||||
)
|
||||
self.vocab_file = vocab_file
|
||||
self.add_bos_token = convert_bool(add_bos_token)
|
||||
self.add_eos_token = add_eos_token
|
||||
self.decode_with_prefix_space = decode_with_prefix_space
|
||||
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
||||
self.sp_model.Load(vocab_file)
|
||||
self._no_prefix_space_tokens = None
|
||||
|
||||
""" Initialisation"""
|
||||
|
||||
@property
|
||||
def no_prefix_space_tokens(self):
|
||||
if self._no_prefix_space_tokens is None:
|
||||
vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
|
||||
self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
|
||||
return self._no_prefix_space_tokens
|
||||
|
||||
@property
|
||||
def vocab_size(self):
|
||||
"""Returns vocab size"""
|
||||
return self.sp_model.get_piece_size()
|
||||
|
||||
@property
|
||||
def bos_token_id(self) -> Optional[int]:
|
||||
return self.sp_model.bos_id()
|
||||
|
||||
@property
|
||||
def eos_token_id(self) -> Optional[int]:
|
||||
return super().eos_token_id
|
||||
|
||||
def get_vocab(self):
|
||||
"""Returns vocab as a dict"""
|
||||
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
||||
vocab.update(self.added_tokens_encoder)
|
||||
return vocab
|
||||
|
||||
def _tokenize(self, text):
|
||||
"""Returns a tokenized string."""
|
||||
return self.sp_model.encode(text, out_type=str)
|
||||
|
||||
def _convert_token_to_id(self, token):
|
||||
"""Converts a token (str) in an id using the vocab."""
|
||||
return self.sp_model.piece_to_id(token)
|
||||
|
||||
def _convert_id_to_token(self, index):
|
||||
"""Converts an index (integer) in a token (str) using the vocab."""
|
||||
token = self.sp_model.IdToPiece(index)
|
||||
return token
|
||||
|
||||
def _maybe_add_prefix_space(self, tokens, decoded):
|
||||
if tokens and tokens[0] not in self.no_prefix_space_tokens:
|
||||
return " " + decoded
|
||||
else:
|
||||
return decoded
|
||||
|
||||
def convert_tokens_to_string(self, tokens):
|
||||
"""Converts a sequence of tokens (string) in a single string."""
|
||||
current_sub_tokens = []
|
||||
out_string = ""
|
||||
prev_is_special = False
|
||||
for token in tokens:
|
||||
# make sure that special tokens are not decoded using sentencepiece model
|
||||
if token in self.all_special_tokens:
|
||||
# Decode the current sub-tokens first
|
||||
if current_sub_tokens:
|
||||
out_string += self.sp_model.decode(current_sub_tokens)
|
||||
current_sub_tokens = []
|
||||
# Append the special token without adding extra spaces
|
||||
out_string += token
|
||||
prev_is_special = True
|
||||
else:
|
||||
current_sub_tokens.append(token)
|
||||
prev_is_special = False
|
||||
# Decode any remaining sub-tokens
|
||||
if current_sub_tokens:
|
||||
out_string += self.sp_model.decode(current_sub_tokens)
|
||||
# Clean up leading and trailing spaces
|
||||
if self.clean_up_tokenization_spaces:
|
||||
out_string = self.clean_up_tokenization(out_string)
|
||||
out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
|
||||
return out_string[1:]
|
||||
|
||||
# Override decode to set spaces_between_special_tokens to True as default
|
||||
def decode(self,
|
||||
token_ids,
|
||||
spaces_between_special_tokens: bool = False,
|
||||
**kwargs):
|
||||
return super().decode(
|
||||
token_ids=token_ids,
|
||||
spaces_between_special_tokens=spaces_between_special_tokens,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||||
"""
|
||||
Save the vocabulary and special tokens file to a directory.
|
||||
|
||||
Args:
|
||||
save_directory (`str`):
|
||||
The directory in which to save the vocabulary.
|
||||
|
||||
Returns:
|
||||
`Tuple(str)`: Paths to the files saved.
|
||||
"""
|
||||
if not os.path.isdir(save_directory):
|
||||
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
||||
return ("",)
|
||||
out_vocab_file = os.path.join(
|
||||
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||||
)
|
||||
|
||||
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
||||
copyfile(self.vocab_file, out_vocab_file)
|
||||
elif not os.path.isfile(self.vocab_file):
|
||||
with open(out_vocab_file, "wb") as fi:
|
||||
content_spiece_model = self.sp_model.serialized_model_proto()
|
||||
fi.write(content_spiece_model)
|
||||
|
||||
return (out_vocab_file,)
|
||||
|
||||
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
||||
if self.add_bos_token:
|
||||
bos_token_ids = [self.bos_token_id]
|
||||
else:
|
||||
bos_token_ids = []
|
||||
|
||||
output = bos_token_ids + token_ids_0
|
||||
|
||||
if token_ids_1 is not None:
|
||||
output = output + token_ids_1
|
||||
|
||||
if self.add_eos_token:
|
||||
output = output + [self.eos_token_id]
|
||||
|
||||
return output
|
||||
|
||||
def get_special_tokens_mask(
|
||||
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
||||
) -> List[int]:
|
||||
"""
|
||||
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
||||
special tokens using the tokenizer `prepare_for_model` method.
|
||||
|
||||
Args:
|
||||
token_ids_0 (`List[int]`):
|
||||
List of IDs.
|
||||
token_ids_1 (`List[int]`, *optional*):
|
||||
Optional second list of IDs for sequence pairs.
|
||||
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
||||
Whether or not the token list is already formatted with special tokens for the model.
|
||||
|
||||
Returns:
|
||||
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
||||
"""
|
||||
if already_has_special_tokens:
|
||||
return super().get_special_tokens_mask(
|
||||
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
||||
)
|
||||
|
||||
if token_ids_1 is None:
|
||||
return [1] + ([0] * len(token_ids_0)) + [1]
|
||||
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
|
||||
|
||||
def create_token_type_ids_from_sequences(
|
||||
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||||
) -> List[int]:
|
||||
"""
|
||||
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
|
||||
use of token type ids, therefore a list of zeros is returned.
|
||||
|
||||
Args:
|
||||
token_ids_0 (`List[int]`):
|
||||
List of IDs.
|
||||
token_ids_1 (`List[int]`, *optional*):
|
||||
Optional second list of IDs for sequence pairs.
|
||||
|
||||
Returns:
|
||||
`List[int]`: List of zeros.
|
||||
"""
|
||||
eos = [self.eos_token_id]
|
||||
|
||||
if token_ids_1 is None:
|
||||
return len(token_ids_0 + eos) * [0]
|
||||
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6b16f1558c0cd4ae6ef1a2c605713be0a514f50e1ce2d2c878979ce988c148ec
|
||||
size 2477809
|
||||
1
tokenizer_config.json
Normal file
1
tokenizer_config.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user