mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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185 lines
8.0 KiB
Python
185 lines
8.0 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Union
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from ...mapping import Mapping
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from ..convert_utils import infer_dtype
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from ..modeling_utils import PretrainedConfig, QuantConfig
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GLM_VERSIONS = ['glm4', 'chatglm3', 'chatglm2', 'chatglm', 'glm']
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GLM_ARCH1_VERSIONS = ['chatglm', 'glm']
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GLM_ARCH2_VERSIONS = ['glm4', 'chatglm3', 'chatglm2']
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class ChatGLMConfig(PretrainedConfig):
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def __init__(self,
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*,
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chatglm_version: str = 'chatglm3',
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add_bias_linear: bool = False,
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add_qkv_bias: bool = True,
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apply_query_key_layer_scaling: bool = False,
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apply_residual_connection_post_layernorm: bool = False,
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rmsnorm: bool = True,
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rotary_pct: float = 0.5,
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rotary_base: float = 10000.0,
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rotary_scaling: Optional[dict] = None,
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**kwargs):
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self.chatglm_version = chatglm_version
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self.add_bias_linear = add_bias_linear
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self.add_qkv_bias = add_qkv_bias
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self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
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self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
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self.rmsnorm = rmsnorm
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self.rotary_pct = rotary_pct
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self.rotary_base = rotary_base
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self.rotary_scaling = rotary_scaling
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super().__init__(**kwargs)
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def to_dict(self):
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output = super().to_dict()
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# Serialize the fields added in ChatGLMConfig
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output['chatglm_version'] = self.chatglm_version
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output['add_bias_linear'] = self.add_bias_linear
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output['add_qkv_bias'] = self.add_qkv_bias
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output[
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'apply_query_key_layer_scaling'] = self.apply_query_key_layer_scaling
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output[
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'apply_residual_connection_post_layernorm'] = self.apply_residual_connection_post_layernorm
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output['rmsnorm'] = self.rmsnorm
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output['rotary_pct'] = self.rotary_pct
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output['rotary_base'] = self.rotary_base
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output['rotary_scaling'] = self.rotary_scaling
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return output
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@classmethod
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def from_hugging_face(
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cls,
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hf_config_or_dir: Union[str, 'transformers.PretrainedConfig'],
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dtype: str = 'auto',
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mapping: Optional[Mapping] = None,
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quant_config: Optional[QuantConfig] = None,
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**kwargs):
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import transformers
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trust_remote_code = kwargs.pop('trust_remote_code', True)
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# load hugging face config
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if isinstance(hf_config_or_dir, transformers.PretrainedConfig):
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hf_config = hf_config_or_dir
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else:
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hf_config_dir = str(hf_config_or_dir)
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hf_config = transformers.AutoConfig.from_pretrained(
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hf_config_dir, trust_remote_code=trust_remote_code)
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logits_dtype = kwargs.pop('logits_dtype', 'float32')
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use_parallel_embedding = kwargs.pop('use_parallel_embedding', False)
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embedding_sharding_dim = kwargs.pop('embedding_sharding_dim', 0)
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share_embedding_table = kwargs.pop('share_embedding_table', False)
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chatglm_version = kwargs.pop('chatglm_version', None)
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# get chatglm version
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if chatglm_version is None:
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print("Inferring chatglm version from path...")
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for v in GLM_VERSIONS:
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if v in hf_config._name_or_path:
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chatglm_version = v
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break
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if 'glm_4' in hf_config._name_or_path.replace("-", "_"):
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chatglm_version = 'glm4'
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assert chatglm_version in GLM_VERSIONS
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print(f"Chatglm version: {chatglm_version}")
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if chatglm_version == 'glm':
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hf_config.num_kv_heads = hf_config.num_attention_heads
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hf_config.ffn_hidden_size = hf_config.hidden_size * 4
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hf_config.hidden_act = 'gelu'
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hf_config.layernorm_epsilon = 1e-5
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hf_config.max_position_embeddings = hf_config.max_sequence_length
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hf_config.add_bias_linear = True
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hf_config.add_qkv_bias = True
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hf_config.apply_query_key_layer_scaling = False
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hf_config.apply_residual_connection_post_layernorm = False
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hf_config.rmsnorm = False
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hf_config.rope_ratio = 1.0
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elif chatglm_version == 'chatglm':
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hf_config.num_kv_heads = hf_config.num_attention_heads
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hf_config.ffn_hidden_size = hf_config.inner_hidden_size
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hf_config.hidden_act = 'gelu'
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hf_config.max_position_embeddings = hf_config.max_sequence_length
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hf_config.add_bias_linear = True
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hf_config.add_qkv_bias = True
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hf_config.apply_query_key_layer_scaling = False
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hf_config.apply_residual_connection_post_layernorm = False
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hf_config.rmsnorm = False
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hf_config.rope_ratio = 1.0
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else:
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hf_config.vocab_size = hf_config.padded_vocab_size
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hf_config.num_kv_heads = hf_config.multi_query_group_num
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hf_config.hidden_act = 'swiglu'
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hf_config.max_position_embeddings = hf_config.seq_length
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hf_config.rmsnorm = getattr(hf_config, 'rmsnorm', 1.0)
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hf_config.rope_ratio = getattr(hf_config, 'rope_ratio', 1.0)
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if chatglm_version == 'glm':
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position_embedding_type = 'learned_absolute'
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elif chatglm_version == 'chatglm':
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position_embedding_type = 'chatglm'
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elif chatglm_version in GLM_ARCH2_VERSIONS:
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position_embedding_type = 'rope_gptj'
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rotary_base = 10000.0
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rotary_embedding_scaling = None
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if chatglm_version == 'chatglm2':
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if hf_config.rope_ratio > 1:
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rotary_embedding_scaling = {
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'type': 'linear',
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'factor': hf_config.rope_ratio
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}
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elif chatglm_version == 'chatglm3' or chatglm_version == 'glm4':
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rotary_base *= hf_config.rope_ratio
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dtype = infer_dtype(dtype, getattr(hf_config, 'torch_dtype', None))
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return cls(
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architecture=hf_config.architectures[0],
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dtype=dtype,
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logits_dtype=logits_dtype,
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num_hidden_layers=hf_config.num_layers,
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num_attention_heads=hf_config.num_attention_heads,
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num_key_value_heads=hf_config.num_kv_heads,
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hidden_size=hf_config.hidden_size,
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intermediate_size=hf_config.ffn_hidden_size,
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norm_epsilon=hf_config.layernorm_epsilon,
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vocab_size=hf_config.vocab_size,
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position_embedding_type=position_embedding_type,
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max_position_embeddings=hf_config.max_position_embeddings,
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rotary_pct=0.5,
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rotary_base=rotary_base,
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rotary_scaling=rotary_embedding_scaling,
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hidden_act=hf_config.hidden_act,
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use_parallel_embedding=use_parallel_embedding,
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embedding_sharding_dim=embedding_sharding_dim,
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share_embedding_table=share_embedding_table,
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quantization=quant_config,
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mapping=mapping,
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chatglm_version=chatglm_version,
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add_bias_linear=hf_config.add_bias_linear,
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add_qkv_bias=hf_config.add_qkv_bias,
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apply_query_key_layer_scaling=False,
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apply_residual_connection_post_layernorm=hf_config.
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apply_residual_connection_post_layernorm,
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rmsnorm=hf_config.rmsnorm,
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)
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