diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..6c7113c2bfeceb5b0b33a1a1299e040f3c4ba34f 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +*.hwp7s filter=lfs diff=lfs merge=lfs -text +*.tar.gz filter=lfs diff=lfs merge=lfs -text +*.cms filter=lfs diff=lfs merge=lfs -text +model.safetensors.index.json filter=lfs diff=lfs merge=lfs -text +*.modelenc filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..a1a4b6e09c6eca490e57a43fffe6de41e4870cfd --- /dev/null +++ b/LICENSE @@ -0,0 +1,37 @@ +OpenPangu Model License Agreement Version 2.0 + +This OpenPangu Model License Agreement Version 2.0 (the "Agreement") is a legal agreement between You and Huawei Technologies Co., Ltd. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + 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. + diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cfc2738e6d5685595772f38fa879adbb1ab2f949 --- /dev/null +++ b/README.md @@ -0,0 +1,29 @@ +# openPangu-2.0-Flash-Int8 +中文 | [English](https://ai.gitcode.com/ascend-tribe/openPangu-2.0-Flash-Int8/blob/main/README_EN.md) + +## 1. 简介 +openPangu-2.0-Flash 是基于昇腾 NPU 训练的大规模混合专家（MoE）语言模型，参数规模约 92B，每 token 激活参数规模约 6B，模型支持 512k 上下文长度，训练数据总量约34T tokens。后训练阶段完成快慢合一微调（SFT）、多专项强化学习（RL），并通过在线蒸馏（OPD）完成能力合一。openPangu-2.0-Flash-Int8 是 openPangu-2.0-Flash 的量化版本。 + +## 2. 模型架构 +openPangu-2.0-Flash-Int8 在模型架构上实现了全面的升级： + +- Attention架构：沿用高效 MLA，并采用 DSA+SWA 独立分层混合架构，层配比为 1:2；SWA 层负责局部窗口建模，DSA 层负责稀疏全局聚合，在保持精度的同时显著降低长序列推理的计算、显存与访存开销。 +- 拓扑架构：将传统残差连接升级为 4 支流 mHC 架构，提升表征多样性与泛化能力。 +- 自投机模块：采用 3 头 MTP 架构，一次额外预测 3 个 token，显著提升模型的推理速度。 +- Muon优化器：训练中采用Muon优化器，获得更快的收敛速度。 + +## 3. 部署和使用 +- 使用omni-infer推理框架，部署方式参考[[openPangu-2.0-Flash部署说明](https://gitcode.com/ascend-tribe/openPangu-2.0-Infer/blob/main/README_INT8.md)] +- 源码仓:[[openPangu-2.0-Infer]](https://gitcode.com/ascend-tribe/openPangu-2.0-Infer) + +## 4. 模型许可证 +除文件中对开源许可证另有约定外，openPangu-2.0-Flash-Int8 模型根据 OPENPANGU MODEL LICENSE AGREEMENT VERSION 2.0 授权，旨在允许使用并促进人工智能技术的进一步发展。有关详细信息，请参阅模型存储库根目录中的 [LICENSE](LICENSE) 文件。 + +## 5. 免责声明 +由于 openPangu-2.0-Flash-Int8 （“模型”）所依赖的技术固有的限制，以及人工智能生成的内容是由模型自动生成的，华为无法对以下事项做出任何保证： +- 该模型的输出通过AI算法自动生成，不能排除某些信息可能存在缺陷、不合理或引起不适的可能性，生成的内容不代表华为的态度或立场； +- 无法保证该模型100%准确、可靠、功能齐全、及时、安全、无错误、不间断、持续稳定或无任何故障； +- 该模型的输出内容不构成任何建议或决策，也不保证生成的内容的真实性、完整性、准确性、及时性、合法性、功能性或实用性。生成的内容不能替代医疗、法律等领域的专业人士回答您的问题。生成的内容仅供参考，不代表华为的任何态度、立场或观点。您需要根据实际情况做出独立判断，华为不承担任何责任。 + +## 6. 反馈 +如果有任何意见和建议，请提交issue或联系[openPangu@huawei.com](url)。 \ No newline at end of file diff --git a/README_EN.md b/README_EN.md new file mode 100644 index 0000000000000000000000000000000000000000..9bd7ef138ab0d629af5e30c0f1fe5d5a5c43adef --- /dev/null +++ b/README_EN.md @@ -0,0 +1,33 @@ +# openPangu-2.0-Flash-Int8 +[中文](README.md) | English + +## 1. Introduction +openPangu-2.0-Flash is an MoE model trained on Ascend. The model has 92B total parameters and 6B activated parameters. Its context length is 512k. The total pretraining data contains 34T tokens. During Post-training, openPangu-2.0-Flash is trained through unified SFT with slow and fast thinking capability, multiple specialist RL traning, on-policy distillation combining multiple RL specialists. openPangu-2.0-Flash-Int8 is a quantized version of openPangu-2.0-Flash. + +## 2. Architecture +openPangu-2.0-Flash-Int8 brings several major architectural improvements: + +- Efficient attention: The model retains MLA for efficient inference and combines DSA and SWA in a 1:2 layer ratio. SWA layers handle local-window modeling, while DSA layers capture sparse global context. This design lowers compute, memory footprint, and memory access costs for long-context inference while preserving accuracy. +- Residual topology: The conventional residual path is replaced with a 4-stream mHC design, improving representation diversity and generalization. +- Multi-token prediction (MTP): The model uses three MTP heads to draft 3 additional tokens per step, enabling faster inference through self-speculative decoding. +- Optimizer: Training uses the Muon optimizer for faster convergence. + +## 3. Deployment +- omni-infer：Please refer to [[openPangu-2.0-Flash deploy guide](https://gitcode.com/ascend-tribe/openPangu-2.0-Infer/blob/main/README_INT8_EN.md)] +- source code repo:[[openPangu-2.0-Infer]](https://gitcode.com/ascend-tribe/openPangu-2.0-Infer) + +## 4. Model License +Unless otherwise noted, the openPangu-2.0-Flash-Int8 model is licensed under the terms and conditions of OPENPANGU MODEL LICENSE AGREEMENT VERSION 2.0, which is intended to be used permissively and enable the further development of artificial intelligence technologies. Please refer to the [LICENSE](LICENSE) file located in the root directory of the model repository for details. + +## 5. Disclaimer +Due to the technical limitations inherent in the technology on which the openPangu-2.0-Flash-Int8 model (“Model”) relies and the fact that the artificial intelligence generated content is automatically produced by Model, Huawei cannot make any guarantees regarding the following matters: + +- The output of this Model is automatically generated via AI algorithms, it does not rule out the possibility that some of the information may be flawed, unreasonable, or cause discomfort, and the generated content does not represent Huawei's attitude or standpoint; +- There is no guarantee that this Model is 100% accurate, reliable, functional, timely, secure and safety, error-free, uninterrupted, continuously stable, or free of any faults; +- The output of this Model does not constitute any advices or decisions for you, and it does not guarantee the authenticity, completeness, accuracy, timeliness, legality, functionality, or practicality of the generated content. The generated content cannot replace professionals in medical, legal, and other fields in answering your questions. The generated content is for your reference only and does not represent any attitude, standpoint, or position of Huawei. You need to make independent judgments based on your actual situation, and Huawei does not assume any responsibilities. + +## 6. Contact +If you have any question, please raise an issue or contact us at [openPangu@huawei.com](url). + + + diff --git a/config.json b/config.json new file mode 100644 index 0000000000000000000000000000000000000000..f4f359914aed73e1a20f3249a18b00563788d07e --- /dev/null +++ b/config.json @@ -0,0 +1,585 @@ +{ + "architectures": ["OpenPanguV2ForCausalLM"], + "attention_dropout": 0.0, + "auto_map": { + "AutoConfig": "configuration_openpangu_v2.OpenPanguV2Config" + }, + "_attn_implementation":"eager", + "block_post_layernorm_idx": [0, 4, 9, 14, 19, 24, 29, 34, 39], + "bos_token_id": 148899, + "dsa_layers": [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45], + "dtype": "bfloat16", + "eos_token_id": 148902, + "first_k_dense_replace": 2, + "hidden_act": "silu", + "hidden_size": 2560, + "index_head_dim": 128, + "index_n_heads": 24, + "index_topk": 2048, + "intermediate_size": 9216, + "kv_lora_rank": 512, + "max_position_embeddings": 524288, + "mhc_num_stream": 4, + "mhc_recur_norm": 20, + "mhc_use_gamma": true, + "model_type": "openpangu_v2", + "moe_intermediate_size": 1024, + "n_routed_experts": 256, + "n_shared_experts": 1, + "norm_topk_prob": true, + "num_attention_heads": 48, + "num_experts_per_tok": 8, + "num_hidden_layers": 46, + "num_nextn_predict_layers": 3, + "pad_token_id": 148902, + "param_sink_number": 128, + "q_lora_rank": 1024, + "qk_nope_head_dim": 128, + "qk_rope_head_dim": 64, + "rms_norm_eps": 1e-05, + "rope_theta": 6400000, + "rope_interleave": false, + "router_enable_expert_bias": true, + "routed_scaling_factor": 2.5, + "router_sliding_window": 3, + "sandwich_norm": true, + "sliding_window": 512, + "sliding_window_list": [512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 2048, 2048, 2048], + "swa_layers": [1, 2, 4, 5, 7, 8, 10, 11, 13, 14, 16, 17, 19, 20, 22, 23, 25, 26, 28, 29, 31, 32, 34, 35, 37, 38, 40, 41, 43, 44, 46, 47, 48], + 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All Rights Reserved. +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# 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. +""" OpenPangu_v2 model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +try: + # transformers >= 5.0 exports RopeParameters as a TypedDict. + from transformers.modeling_rope_utils import RopeParameters +except ImportError: + # transformers < 5.0 has no RopeParameters. Provide a TypedDict with the + # same fields so the `rope_parameters` annotation still resolves. It is + # only used for typing -- `rope_parameters` defaults to None and is not + # exercised by config.json, so this fallback never affects behavior. + from typing import TypedDict + + class RopeParameters(TypedDict, total=False): + rope_theta: float + rope_type: str | None + partial_rotary_factor: float | None + factor: float | None + original_max_position_embeddings: int | None + attention_factor: float | None + beta_fast: float | None + beta_slow: float | None + short_factor: list[float] | None + long_factor: list[float] | None + low_freq_factor: float | None + high_freq_factor: float | None + +logger = logging.get_logger(__name__) + + +class OpenPanguV2Config(PretrainedConfig): + model_type = "openpangu_v2" + keys_to_ignore_at_inference = ["past_key_values"] + + # Default tensor parallel plan for base model `OpenPangu_v2` + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + "layers.*.mlp.experts.gate_up_proj": "rowwise", + "layers.*.mlp.experts.down_proj": "rowwise", + "layers.*.mlp.shared_experts.gate_proj": "colwise", + "layers.*.mlp.shared_experts.up_proj": "colwise", + "layers.*.mlp.shared_experts.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + def __init__( + self, + attention_dropout: float | None = 0.0, + block_post_layernorm_idx: list[int] | None = None, + dsa_layers: list[str] | None = None, + first_k_dense_replace: int | None = 0, + head_dim: int | None = None, + hidden_act: str | None = "silu", + hidden_size: int | None = None, + index_head_dim: int | None = None, + index_n_heads: int | None = None, + index_topk: int | None = None, + intermediate_size: int | None = None, + kv_lora_rank: int | None = None, + layer_types: list[str] | None = None, + max_position_embeddings: int | None = None, + mhc_num_stream: int | None = None, + mhc_recur_norm: int | None = None, + mhc_use_gamma: bool | None = None, + moe_intermediate_size: int | None = None, + n_routed_experts: int | None = None, + n_shared_experts: int | None = None, + norm_topk_prob: bool | None = None, + num_attention_heads: int | None = None, + num_experts_per_tok: int | None = None, + num_hidden_layers: int | None = 0, + num_key_value_heads: int | None = None, + param_sink_number: int | None = 0, + q_lora_rank: int | None = None, + qk_nope_head_dim: int | None = None, + qk_rope_head_dim: int | None = None, + rms_norm_eps: float | None = 1e-5, + rope_interleave: bool | None = False, + rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, + routed_scaling_factor: float | None = None, + router_sliding_window: int | None = 0, + sandwich_norm: bool | None = False, + sliding_window: int | list[int] | None = None, + swa_layers: list[str] | None = None, + use_cache: bool | None = True, + use_mhc: bool | None = False, + v_head_dim: int | None = None, + vocab_size: int | None = None, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.moe_intermediate_size = moe_intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + self.num_key_value_heads = num_key_value_heads + self.head_dim = head_dim + self.v_head_dim = v_head_dim + self.hidden_act = hidden_act + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.rope_parameters = rope_parameters + self.attention_dropout = attention_dropout + self.layer_types = layer_types + + self.n_shared_experts = n_shared_experts + self.n_routed_experts = n_routed_experts + self.routed_scaling_factor = routed_scaling_factor + self.kv_lora_rank = kv_lora_rank + self.q_lora_rank = q_lora_rank + self.qk_rope_head_dim = qk_rope_head_dim + self.qk_nope_head_dim = qk_nope_head_dim + if qk_rope_head_dim is not None and qk_nope_head_dim is not None: + self.head_dim = qk_rope_head_dim + self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim + self.num_experts_per_tok = num_experts_per_tok + self.first_k_dense_replace = first_k_dense_replace + self.norm_topk_prob = norm_topk_prob + self.rope_interleave = rope_interleave + + self.sliding_window = sliding_window + self.swa_layers = swa_layers + + self.param_sink_number = param_sink_number + self.router_sliding_window = router_sliding_window + self.sandwich_norm = sandwich_norm + self.block_post_layernorm_idx = block_post_layernorm_idx + self.use_mhc = use_mhc + self.mhc_use_gamma = mhc_use_gamma + self.mhc_recur_norm = mhc_recur_norm + self.mhc_num_stream = mhc_num_stream + + # Indexer (DSA) parameters + self.dsa_layers = dsa_layers + self.index_topk = index_topk + self.index_head_dim = index_head_dim + self.index_n_heads = index_n_heads + + if self.layer_types is None: + if self.swa_layers is not None: + self.layer_types = [ + "sliding_attention" + if i in self.swa_layers + else "full_attention" + for i in range(self.num_hidden_layers) + ] + else: + self.layer_types = ["full_attention" for _ in range(self.num_hidden_layers)] + + if num_hidden_layers is not None and self.layer_types is not None and len(self.layer_types) != num_hidden_layers: + raise ValueError( + f"`num_hidden_layers` ({num_hidden_layers}) must be equal to the number of layer types " + f"({len(layer_types)})" + ) + + super().__init__(**kwargs) + + +__all__ = ["OpenPanguV2Config"] \ No newline at end of file diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 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0000000000000000000000000000000000000000..3b678f7837add3d3c142f3b9c7ac85b82ecf0a6e --- /dev/null +++ b/tokenization_openpangu_v2.py @@ -0,0 +1,249 @@ +# coding=utf-8 +# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved. +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# This code is based on transformers/src/transformers/models/llama/tokenization_llama_fast.py +# +# 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 Dict, List, Optional + +from tokenizers import Regex, Tokenizer, decoders, pre_tokenizers, processors +from tokenizers.models import BPE +from transformers.utils import logging + +try: + from transformers.tokenization_utils_tokenizers import TokenizersBackend +except ImportError: + TokenizersBackend = None + from transformers.tokenization_utils_fast import PreTrainedTokenizerFast + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"tokenizer_file": "tokenizer.json"} + +PRETOKENIZE_REGEX = r"'(?i:[sdmt]|ll|ve|re)| (?=\p{Han}|[＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､　、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·．！？｡。])|[＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､　、〃〈〉《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏﹑﹔·．！？｡。]+[\r\n]*|[^\r\n\p{L}\p{N}]?+[\p{L}\p{M}]+|\p{N}| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+" + + +if TokenizersBackend is not None: + + class OpenPanguV2Tokenizer(TokenizersBackend): + vocab_files_names = VOCAB_FILES_NAMES + padding_side = "left" + model_input_names = ["input_ids", "attention_mask"] + model = BPE + + def __init__( + self, + vocab: Optional[Dict[str, int]] = None, + merges: Optional[List[str]] = None, + tokenizer_file: Optional[str] = None, + bos_token: str = "<|pangu_text_start|>", + eos_token: str = "<|pangu_text_end|>", + unk_token: str = None, + add_bos_token: bool = True, + add_eos_token: bool = False, + add_prefix_space: bool = False, + **kwargs, + ): + self._vocab = vocab or {} + self._merges = merges or [] + self._add_bos_token = add_bos_token + self._add_eos_token = add_eos_token + + if tokenizer_file is not None and vocab is None and merges is None: + self._tokenizer = Tokenizer.from_file(tokenizer_file) + else: + self._tokenizer = Tokenizer( + BPE( + vocab=self._vocab, + merges=self._merges, + dropout=None, + unk_token=unk_token, + byte_fallback=True, + ) + ) + self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence([ + pre_tokenizers.Split( + Regex(PRETOKENIZE_REGEX), + behavior="isolated", + ), + pre_tokenizers.ByteLevel( + add_prefix_space=add_prefix_space, + use_regex=False, + ), + ]) + self._tokenizer.decoder = decoders.ByteLevel() + + super().__init__( + bos_token=bos_token, + eos_token=eos_token, + unk_token=unk_token, + add_bos_token=add_bos_token, + add_eos_token=add_eos_token, + **kwargs, + ) + + self.update_post_processor() + + def update_post_processor(self): + bos = self.bos_token + bos_id = self.bos_token_id + if bos is None and self.add_bos_token: + raise ValueError("add_bos_token = True but bos_token = None") + + eos = self.eos_token + eos_id = self.eos_token_id + if eos is None and self.add_eos_token: + raise ValueError("add_eos_token = True but eos_token = None") + + single = f"{bos}:0 $A:0" if self._add_bos_token else "$A:0" + if self._add_eos_token: + single += f" {eos}:0" + + special_tokens = [] + if self._add_bos_token: + special_tokens.append((bos, bos_id)) + if self._add_eos_token: + special_tokens.append((eos, eos_id)) + + self._tokenizer.post_processor = processors.TemplateProcessing( + single=single, + pair=f"{single} {single.replace('$A', '$B')}", + special_tokens=special_tokens, + ) + + @property + def add_bos_token(self): + return self._add_bos_token + + @add_bos_token.setter + def add_bos_token(self, value): + self._add_bos_token = value + self.update_post_processor() + + @property + def add_eos_token(self): + return self._add_eos_token + + @add_eos_token.setter + def add_eos_token(self, value): + self._add_eos_token = value + self.update_post_processor() + + @property + def vocab_size(self): + return self._tokenizer.get_vocab_size(with_added_tokens=True) + +else: + + class OpenPanguV2Tokenizer(PreTrainedTokenizerFast): + vocab_files_names = VOCAB_FILES_NAMES + padding_side = "left" + model_input_names = ["input_ids", "attention_mask"] + _auto_class = "AutoTokenizer" + + def __init__( + self, + vocab_file=None, + tokenizer_file=None, + bos_token="<|pangu_text_start|>", + eos_token="<|pangu_text_end|>", + add_bos_token=True, + add_eos_token=False, + decode_with_prefix_space=False, + clean_up_tokenization_spaces=False, + **kwargs, + ): + super().__init__( + vocab_file=vocab_file, + tokenizer_file=tokenizer_file, + bos_token=bos_token, + eos_token=eos_token, + add_bos_token=add_bos_token, + add_eos_token=add_eos_token, + decode_with_prefix_space=decode_with_prefix_space, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + self._add_bos_token = add_bos_token + self._add_eos_token = add_eos_token + self.tokenizer_file = tokenizer_file + self.update_post_processor() + + def update_post_processor(self): + """ + Updates the underlying post processor with the current `bos_token` and `eos_token`. + """ + bos = self.bos_token + bos_token_id = self.bos_token_id + if bos is None and self.add_bos_token: + raise ValueError("add_bos_token = True but bos_token = None") + + eos = self.eos_token + eos_token_id = self.eos_token_id + if eos is None and self.add_eos_token: + raise ValueError("add_eos_token = True but eos_token = None") + + single = f"{(bos + ':0 ') if self.add_bos_token else ''}$A:0{(' ' + eos + ':0') if self.add_eos_token else ''}" + pair = f"{single}{(' ' + bos + ':1') if self.add_bos_token else ''} $B:1{(' ' + eos + ':1') if self.add_eos_token else ''}" + + special_tokens = [] + if self.add_bos_token: + special_tokens.append((bos, bos_token_id)) + if self.add_eos_token: + special_tokens.append((eos, eos_token_id)) + self._tokenizer.post_processor = processors.TemplateProcessing( + single=single, pair=pair, special_tokens=special_tokens + ) + + @property + def add_eos_token(self): + return self._add_eos_token + + @property + def add_bos_token(self): + return self._add_bos_token + + @add_eos_token.setter + def add_eos_token(self, value): + self._add_eos_token = value + self.update_post_processor() + + @add_bos_token.setter + def add_bos_token(self, value): + self._add_bos_token = value + self.update_post_processor() + + @property + def vocab_size(self): + return self.backend_tokenizer.get_vocab_size() + + 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 + + +__all__ = ["OpenPanguV2Tokenizer"] diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000000000000000000000000000000000000..bdb334baaed46f6ae724f4ae68591af7aa5c6a23 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7816b7b6138024e37c508ff82321e11b5ee559289184fa18f7536a3e460d3941 +size 20728220 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..107833cd08728c9f529ca1506bf85bbde8643c00 --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,5625 @@ +{ + "added_tokens_decoder": { + "148899": { + "content": "<|pangu_text_start|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "148900": { + "content": "<|pangu_text_end|>", + "single_word": false, + "lstrip": false, 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false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149548": { + "content": "[unused636]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149549": { + "content": "[unused637]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149550": { + "content": "[unused638]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149551": { + "content": "[unused639]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149552": { + "content": "[unused640]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149553": { + "content": "[unused641]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149554": { + "content": 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+ "special": true + }, + "149561": { + "content": "[unused649]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149562": { + "content": "[unused650]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149563": { + "content": "[unused651]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149564": { + "content": "[unused652]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149565": { + "content": "[unused653]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149566": { + "content": "[unused654]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149567": { + "content": "[unused655]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149568": { + "content": "[unused656]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149569": { + "content": "[unused657]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149570": { + "content": "[unused658]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149571": { + "content": "[unused659]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149572": { + "content": "[unused660]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149573": { + "content": "[unused661]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149574": { + "content": 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+ "special": true + }, + "149581": { + "content": "[unused669]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149582": { + "content": "[unused670]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149583": { + "content": "[unused671]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149584": { + "content": "[unused672]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149585": { + "content": "[unused673]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149586": { + "content": "[unused674]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149587": { + "content": "[unused675]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149588": { + "content": "[unused676]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149589": { + "content": "[unused677]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149590": { + "content": "[unused678]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149591": { + "content": "[unused679]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149592": { + "content": "[unused680]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149593": { + "content": "[unused681]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149594": { + "content": "[unused682]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149595": { + "content": "[unused683]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149596": { + "content": "[unused684]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149597": { + "content": "[unused685]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149598": { + "content": "[unused686]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "149599": { + "content": "[unused687]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + } + }, + "auto_map": { + "AutoTokenizer": [ + "tokenization_openpangu_v2.OpenPanguV2Tokenizer", + null + ] + }, + "bos_token": "<|pangu_text_start|>", + "chat_template": "{%- set ns = namespace(is_first_tool=true) %}\n{%- set ns2 = namespace(last_user_index=-1) %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- if not thinking is defined %}\n {%- set thinking = true %}\n{%- endif %}\n{%- if not context_thinking is defined %}\n {%- set context_thinking = \"Interleave\" %}\n{%- endif %}\n{%- if context_thinking != \"Preserve\" and context_thinking != \"DropAll\" %}\n {%- set context_thinking = \"Interleave\" %}\n{%- endif %}\n{%- if not add_generation_prompt is defined %}\n {%- set add_generation_prompt = true %}\n{%- endif %}\n\n{#- content处理 #}\n{%- macro render_content(content) %}\n {%- if content is string %}\n {{- content }}\n {%- elif content is iterable and content is not mapping %}\n {%- for item in content %}\n {%- if 'text' in item %}\n {{- item.text }}\n {%- elif item is string %}\n {{- item }}\n {%- endif %}\n {%- endfor %}\n {%- else %}\n {{- content }}\n {%- endif %}\n{%- endmacro %}\n\n{{- '<|pangu_text_start|><|message_start|>system\\n' -}}\n{#- 提取系统消息 #}\n{%- set system_message = \"\" %}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = render_content(messages[0]['content'] | default('', true)) |trim %}\n {%- set messages = messages[1:] %}\n{%- endif %}\n{#- 调用工具时的默认人设 #}\n{%- if tools %}\n {%- if system_message %}\n {%- set system_message = system_message + \"\\n\" %}\n {%- endif %}\n {%- set system_message = system_message + \"你是一个能够调用外部工具解决问题的专家，你的目标是高效、准确、清晰地完成任务。\\n你需要根据用户的问题，决定是否需要使用工具来完成任务。如果需要，请以明确的格式调用工具；如果不需要，请直接回答。\\n你可以根据上下文决定是否继续调用工具或基于已有结果直接回答用户。如果工具调用已足够，请合理组织语言向用户汇报结论。在没有获得显式的调用结果之前，在调用工具的当轮回复之内严禁虚构或者假设一个工具调用结果来完成任务或者回答问题。也不应在没有返回工具调用信息的情况下，在调用工具的当轮假设或者明确声称工具执行成功。\" %}\n{%- endif %}\n{{- system_message -}}\n{#- 工具使用描述和规范调用格式 #}\n{%- if tools %}\n {{- '\\n你将在标签对内获得每个工具的描述：\\n\\n' }}\n {{- tools | tojson(ensure_ascii=False, sort_keys=False) }}\n {{- '\\n\\n' }}\n {{- \"对于每个函数调用，返回一个 JSON 对象，放在 <|tool_call_start|><|tool_call_end|> 标签对中，多个调用组成一个列表，其中每个函数包含函数名和对应函数的参数，格式如下：\\n\" }}\n {{- '<|tool_call_start|>\\n[{\"name\": \"<函数名1>\", \"arguments\": }, {\"name\": \"<函数名2>\", \"arguments\": }, ...]\\n<|tool_call_end|>' }}\n {{- '\\n<工具使用原则>\\n1. 只有在所有必填参数(required字段中列出的)都具备有效值时，才能调用该函数\\n2. 如果缺少任何必填参数，必须向用户询问缺失的参数，而不是直接调用函数\\n3. 可选参数如果没有提供可以忽略或使用默认值\\n' }}\n{%- endif %}\n{{- '<|message_end|>' -}}\n\n{#- 找最后一轮user的位置 #}\n{%- for m in messages %}\n {%- if m.role == 'user' %}\n {%- set ns2.last_user_index = loop.index0 -%}\n {%- endif %}\n{%- endfor %}\n\n{#- messages的拼接 #}\n{%- for message in messages %}\n {#- user轮次 #}\n {%- if message['role'] == 'user' %}\n {{- '<|message_start|>user\\n' + render_content(message['content'] | default('', true)) -}}\n {%- if message.get('tool_calls') %}\n {{- '<|tool_call_start|>\\n[' }}\n {%- set function_list = message.tool_calls | selectattr('function') | map(attribute='function') | list %}\n {%- for function_item in function_list %}\n {%- if not ns.is_first_tool %}\n {{- ', ' -}}\n {%- endif %}\n {%- if function_item.arguments is string %}\n {{- '{\"name\": \"' + function_item.name + '\", \"arguments\": ' + function_item.arguments + '}' -}}\n {%- else %}\n {{- '{\"name\": \"' + function_item.name + '\", \"arguments\": ' + function_item.arguments | tojson(ensure_ascii=False, sort_keys=False) + '}' -}}\n {%- endif %}\n {%- set ns.is_first_tool = false %}\n {%- endfor %}\n {%- set ns.is_first_tool = true %}\n {{- ']\\n<|tool_call_end|>' }}\n {%- endif %}\n {{- '<|message_end|>' -}}\n {%- endif %}\n\n {#- tool轮次 #}\n {%- if message['role'] == 'tool' %}\n {{- '<|message_start|>tool\\n' + render_content(message['content'] | default('', true)) -}}\n {{- '<|message_end|>' -}}\n {%- endif %}\n\n {#- assistant轮次 #}\n {%- if message['role'] == 'assistant' %}\n {%- if 'reasoning_content' in message and message['reasoning_content'] %}\n {%- if thinking and context_thinking == \"Preserve\" %}\n {{- '<|message_start|>assistant\\n' + message['reasoning_content'] +'' -}}\n {%- elif thinking and context_thinking == \"Interleave\" and loop.index0 > ns2.last_user_index %}\n {{- '<|message_start|>assistant\\n' + message['reasoning_content'] +'' -}}\n {%- else %}\n {{- '<|message_start|>assistant\\n' -}}\n {%- endif %}\n {%- else %}\n {{- '<|message_start|>assistant\\n' -}}\n {%- endif %}\n {{- render_content(message['content'] | default('', true)) -}}\n {%- if message.get('tool_calls') %}\n {{- '<|tool_call_start|>\\n[' }}\n {%- set function_list = message.tool_calls | selectattr('function') | map(attribute='function') | list %}\n {%- for function_item in function_list %}\n {%- if not ns.is_first_tool %}\n {{- ', ' -}}\n {%- endif %}\n {%- if function_item.arguments is string %}\n {{- '{\"name\": \"' + function_item.name + '\", \"arguments\": ' + function_item.arguments + '}' -}}\n {%- else %}\n {{- '{\"name\": \"' + function_item.name + '\", \"arguments\": ' + function_item.arguments | tojson(ensure_ascii=False, sort_keys=False) + '}' -}}\n {%- endif %}\n {%- set ns.is_first_tool = false %}\n {%- endfor %}\n {%- set ns.is_first_tool = true %}\n {{- ']\\n<|tool_call_end|>' }}\n {%- endif %}\n {{- '<|message_end|>' }}\n {%- endif %}\n{%- endfor %}\n\n{#- 最后的assistant轮 #}\n{%- if add_generation_prompt %}\n {%- if thinking %}\n {{- '<|message_start|>assistant\\n' -}}\n {%- else %}\n {{- '<|message_start|>assistant\\n' -}}\n {%- endif %}\n{%- endif %}\n", + "clean_up_tokenization_spaces": false, + "eos_token": "<|pangu_text_end|>", + "model_max_length": 1000000000000000019884624838656, + "pad_token": "<|pangu_text_end|>", + "tokenizer_class": "OpenPanguV2Tokenizer" +}