mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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139 lines
6.1 KiB
Python
139 lines
6.1 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 ...layers import MoeConfig
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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 Phi3Config(PretrainedConfig):
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def __init__(self,
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*,
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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.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 PhiConfig
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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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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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if hasattr(hf_config, "llm_config"):
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hf_config = hf_config.llm_config
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num_key_value_heads = getattr(hf_config, "num_key_value_heads",
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hf_config.num_attention_heads)
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dtype = infer_dtype(dtype, getattr(hf_config, 'torch_dtype', None))
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small_variant = hf_config.architectures[0] == "Phi3SmallForCausalLM"
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if small_variant:
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kwargs['gegelu_limit'] = getattr(hf_config, "gegelu_limit", None)
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kwargs['rotary_base'] = hf_config.rope_embedding_base
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kwargs['mup_attn_multiplier'] = getattr(hf_config,
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"mup_attn_multiplier", None)
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kwargs['mup_embedding_multiplier'] = getattr(
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hf_config, "mup_embedding_multiplier", None)
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kwargs['mup_use_scaling'] = getattr(hf_config, "mup_use_scaling",
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None)
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kwargs['mup_width_multiplier'] = getattr(hf_config,
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"mup_width_multiplier",
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None)
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kwargs['blocksparse_block_size'] = getattr(
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hf_config, "blocksparse_block_size", None)
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kwargs['blocksparse_homo_head_pattern'] = getattr(
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hf_config, "blocksparse_homo_head_pattern", None)
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kwargs['blocksparse_num_local_blocks'] = getattr(
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hf_config, "blocksparse_num_local_blocks", None)
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kwargs['blocksparse_vertical_stride'] = getattr(
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hf_config, "blocksparse_vert_stride", None)
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kwargs['dense_attention_every_n_layers'] = getattr(
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hf_config, "dense_attention_every_n_layers", None)
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kwargs['norm_epsilon'] = hf_config.layer_norm_epsilon
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else:
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kwargs['rotary_base'] = hf_config.rope_theta
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kwargs['norm_epsilon'] = hf_config.rms_norm_eps
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moe_variant = hf_config.architectures[0] == "PhiMoEForCausalLM"
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if moe_variant:
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kwargs.update({
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'moe': {
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'num_experts': hf_config.num_local_experts,
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'top_k': hf_config.num_experts_per_tok,
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'normalization_mode':
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MoeConfig.ExpertScaleNormalizationMode.SPARSE_MIXER,
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'sparse_mixer_epsilon': hf_config.router_jitter_noise,
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},
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'attention_bias': hf_config.attention_bias
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})
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kwargs['position_embedding_type'] = 'rope_gpt_neox'
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if hf_config.max_position_embeddings >= 128000:
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kwargs[
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'original_max_position_embeddings'] = hf_config.original_max_position_embeddings
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kwargs['position_embedding_type'] = "long_rope"
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kwargs['longrope_scaling_short_factors'] = hf_config.rope_scaling[
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"short_factor"]
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kwargs['longrope_scaling_long_factors'] = hf_config.rope_scaling[
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"long_factor"]
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if small_variant or moe_variant:
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kwargs['longrope_long_mscale'] = hf_config.rope_scaling[
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"long_mscale"]
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kwargs['longrope_short_mscale'] = hf_config.rope_scaling[
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"short_mscale"]
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return cls(architecture=hf_config.architectures[0],
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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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hidden_size=hf_config.hidden_size,
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intermediate_size=hf_config.intermediate_size,
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num_key_value_heads=num_key_value_heads,
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vocab_size=hf_config.vocab_size,
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max_position_embeddings=hf_config.max_position_embeddings,
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hidden_act="swiglu"
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if hf_config.hidden_act == 'silu' else hf_config.hidden_act,
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mapping=mapping,
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quantization=quant_config,
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**kwargs)
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