mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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118 lines
5.3 KiB
Python
118 lines
5.3 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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class FalconConfig(PretrainedConfig):
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def __init__(self,
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*,
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bias: bool = False,
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parallel_attention: bool = False,
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num_ln_in_parallel_attn: int | None = None,
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new_decoder_architecture: bool = False,
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rotary_base: float = 10000.0,
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**kwargs):
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self.bias = bias
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self.parallel_attention = parallel_attention
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self.num_ln_in_parallel_attn = num_ln_in_parallel_attn
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self.new_decoder_architecture = new_decoder_architecture
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self.rotary_base = rotary_base
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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 LLaMAConfig
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output['bias'] = self.bias
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output['parallel_attention'] = self.parallel_attention
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output['new_decoder_architecture'] = self.new_decoder_architecture
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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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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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# Falcon-7B config may not have num_kv_heads or n_head_kv.
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# Although Falcon-180B uses GQA (num_kv_heads=8), its config
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# has multi_query=True.
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if getattr(hf_config, 'multi_query', False) and not getattr(
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hf_config, 'new_decoder_architecture', False):
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hf_config.num_kv_heads = 1
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if hf_config.model_type == 'RefinedWeb':
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# Case 1. Falcon-40B / Falcon-40B-instruct
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# https://huggingface.co/tiiuae/falcon-40b/blob/main/config.json
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hf_config.num_hidden_layers = hf_config.n_layer
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hf_config.num_attention_heads = hf_config.n_head
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hf_config.num_kv_heads = hf_config.n_head_kv
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hf_config.new_decoder_architecture = True
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elif hf_config.model_type == 'RefinedWebModel':
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# Case 2. Falcon-7B / Falcon-7B-instruct
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# https://huggingface.co/tiiuae/falcon-7b/blob/main/config.json
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hf_config.num_hidden_layers = hf_config.n_layer
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hf_config.num_attention_heads = hf_config.n_head
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hf_config.num_kv_heads = 1 if hf_config.multi_query else hf_config.n_head
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hf_config.new_decoder_architecture = False
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elif hf_config.model_type != 'falcon':
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raise ValueError("Shouldn't reach here.")
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hf_config.model_type = 'falcon'
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dtype = infer_dtype(dtype, getattr(hf_config, 'torch_dtype', None))
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return cls(architecture='FalconForCausalLM',
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dtype=dtype,
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num_hidden_layers=hf_config.num_hidden_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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norm_epsilon=hf_config.layer_norm_epsilon,
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vocab_size=hf_config.vocab_size,
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position_embedding_type='alibi_with_scale'
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if hf_config.alibi else 'rope_gpt_neox',
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hidden_act='gelu',
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bias=hf_config.bias,
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parallel_attention=hf_config.parallel_attn,
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num_ln_in_parallel_attn=getattr(hf_config,
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'num_ln_in_parallel_attn',
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None),
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new_decoder_architecture=hf_config.new_decoder_architecture,
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max_position_embeddings=getattr(hf_config,
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'max_position_embeddings',
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2048),
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rotary_base=getattr(hf_config, 'rope_theta', 10000.0),
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intermediate_size=getattr(hf_config, 'ffn_hidden_size',
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None),
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mapping=mapping,
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quantization=quant_config,
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**kwargs)
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