mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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324 lines
14 KiB
Python
324 lines
14 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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import torch
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from ...layers import MoeConfig
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from ...logger import logger
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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 GPTConfig(PretrainedConfig):
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def __init__(self,
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*,
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gpt_variant: str = 'gpt2',
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bias: bool = True,
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q_scaling: float = 1.0,
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embedding_scale: Optional[float] = None,
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apply_query_key_layer_scaling: bool = False,
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rotary_pct: float = 1.0,
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rotary_base: float = 10000.0,
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rotary_scaling: Optional[dict] = None,
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inner_layernorm: bool = False,
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norm_before_bmm1: bool = False,
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moe: Optional[Union[MoeConfig, dict]] = None,
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**kwargs):
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self.gpt_variant = gpt_variant
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self.bias = bias
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self.q_scaling = q_scaling
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self.embedding_scale = embedding_scale
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self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
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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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self.inner_layernorm = inner_layernorm
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self.norm_before_bmm1 = norm_before_bmm1
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if moe is None:
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# Legacy MOE config fields
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moe = MoeConfig(
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num_experts=kwargs.pop('moe_num_experts', 0),
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top_k=kwargs.pop('moe_top_k', 0),
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normalization_mode=kwargs.pop(
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'moe_normalization_mode',
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MoeConfig.ExpertScaleNormalizationMode.RENORMALIZE))
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elif isinstance(moe, dict):
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moe = MoeConfig.from_dict(moe)
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assert isinstance(moe, MoeConfig)
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self.moe = moe.validate()
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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 GPTConfig
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output['gpt_variant'] = self.gpt_variant
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output['bias'] = self.bias
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output['q_scaling'] = self.q_scaling
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output['embedding_scale'] = self.embedding_scale
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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['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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output['inner_layernorm'] = self.inner_layernorm
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output['norm_before_bmm1'] = self.norm_before_bmm1
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output['moe'] = self.moe.to_dict()
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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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from .convert import get_needed_padding
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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 = transformers.AutoConfig.from_pretrained(
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hf_config_or_dir, trust_remote_code=trust_remote_code)
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gpt_variant = kwargs.pop('gpt_variant', None)
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if gpt_variant is None:
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logger.info("Inferring gpt variant from path...")
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for v in [
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'starcoder2', 'starcoder', 'santacoder', 'gpt2',
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'persimmon', 'fuyu', 'kosmos-2', 'jais', 'nemotron'
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]:
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if v in hf_config._name_or_path or v == hf_config.model_type:
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gpt_variant = v
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break
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if gpt_variant == 'fuyu':
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gpt_variant = 'persimmon'
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assert gpt_variant in [
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'gpt2', 'santacoder', 'starcoder', 'starcoder2', 'persimmon',
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'kosmos-2', 'jais', 'nemotron'
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]
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logger.info(f"Gpt variant: {gpt_variant}")
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if gpt_variant in ['starcoder2', 'nemotron', 'persimmon']:
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hf_config.n_embd = hf_config.hidden_size
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hf_config.n_inner = hf_config.intermediate_size
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hf_config.n_head = hf_config.num_attention_heads
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hf_config.n_kv_head = hf_config.num_key_value_heads if hasattr(
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hf_config, 'num_key_value_heads') else hf_config.n_head
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hf_config.n_layer = hf_config.num_hidden_layers
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hf_config.n_positions = hf_config.max_position_embeddings
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hf_config.activation_function = 'gelu' if gpt_variant == 'starcoder2' else 'squared-relu'
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if gpt_variant == "nemotron":
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hf_config.layer_norm_eps = hf_config.norm_eps
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hf_config.layer_norm_epsilon = hf_config.norm_epsilon if gpt_variant == 'starcoder2' else hf_config.layer_norm_eps
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hf_config.bias = hf_config.use_bias if gpt_variant == 'starcoder2' else gpt_variant != 'nemotron'
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hf_config.position_embedding_type = 'rope_gpt_neox'
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hf_config.rotary_base = hf_config.rope_theta
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hf_config.rotary_pct = getattr(
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hf_config, 'partial_rotary_factor',
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getattr(hf_config, 'rope_percent', 1.0))
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try:
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# only for persimmon, not starcoder2
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hf_config.vocab_size = hf_config.text_config.vocab_size
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except AttributeError:
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pass
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elif gpt_variant == "kosmos-2":
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hf_config.n_embd = hf_config.text_config.embed_dim
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hf_config.n_inner = hf_config.text_config.ffn_dim
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hf_config.n_head = hf_config.text_config.attention_heads
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hf_config.n_kv_head = hf_config.n_head
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hf_config.n_layer = hf_config.text_config.layers
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hf_config.n_positions = hf_config.text_config.max_position_embeddings
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hf_config.activation_function = hf_config.text_config.activation_function
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hf_config.layer_norm_epsilon = hf_config.text_config.layer_norm_eps
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hf_config.bias = True
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hf_config.vocab_size = hf_config.text_config.vocab_size
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else:
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if hf_config.n_inner is None:
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hf_config.n_inner = hf_config.n_embd * 4
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if gpt_variant in ['santacoder', 'starcoder']:
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hf_config.n_kv_head = 1
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else:
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hf_config.n_kv_head = hf_config.n_head
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if gpt_variant == 'jais':
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hf_config.q_scaling = (hf_config.n_embd // hf_config.n_head)**0.5
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if hasattr(hf_config, 'width_scale'):
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hf_config.logits_scale = hf_config.width_scale
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else:
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hf_config.logits_scale = hf_config.mup_output_alpha * hf_config.mup_width_scale
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if hasattr(hf_config, 'mup_embeddings_scale'):
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hf_config.embeddings_scale = hf_config.mup_embeddings_scale
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else:
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assert hasattr(hf_config, 'embeddings_scale')
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hf_config.n_inner += get_needed_padding(hf_config.n_inner,
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mapping.tp_size)
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if gpt_variant == 'kosmos-2':
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if hf_config.text_config.scale_embedding:
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hf_config.embeddings_scale = hf_config.n_embd**0.5
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dtype = infer_dtype(dtype, getattr(hf_config, 'torch_dtype', None))
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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.n_layer,
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num_attention_heads=hf_config.n_head,
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num_key_value_heads=hf_config.n_kv_head,
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hidden_size=hf_config.n_embd,
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intermediate_size=hf_config.n_inner,
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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=getattr(hf_config,
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'position_embedding_type',
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'learned_absolute'),
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max_position_embeddings=hf_config.n_positions,
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hidden_act=hf_config.activation_function,
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gpt_variant=gpt_variant,
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bias=getattr(hf_config, 'bias', True),
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apply_query_key_layer_scaling=getattr(
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hf_config, 'apply_query_key_layer_scaling', False),
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rotary_pct=getattr(hf_config, 'rotary_pct', 1.0),
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rotary_base=getattr(hf_config, 'rotary_base', 10000.0),
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rotary_scaling=getattr(hf_config, 'rotary_scaling', None),
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qk_layernorm=gpt_variant == 'persimmon',
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inner_layernorm=gpt_variant == 'kosmos-2',
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norm_before_bmm1=gpt_variant == 'kosmos-2',
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q_scaling=getattr(hf_config, 'q_scaling', 1),
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embedding_scale=getattr(hf_config, 'embeddings_scale', None),
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mapping=mapping,
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quantization=quant_config,
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**kwargs)
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@classmethod
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def from_nemo(cls,
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nemo_ckpt_dir: str,
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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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from .convert import (UnpackedNemoCheckpointDir, cpu_map_location,
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gpu_map_location, rename_keys)
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load_model_on_cpu = kwargs.pop('load_model_on_cpu', False)
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nemo_rename_key = kwargs.pop('nemo_rename_key', [])
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layer_rename_config = {
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pattern.split(':')[0]: pattern.split(':')[1]
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for pattern in nemo_rename_key
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}
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unpacked_checkpoints_dir = UnpackedNemoCheckpointDir(
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nemo_ckpt_dir, load_checkpoints_to_cpu=load_model_on_cpu)
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nemo_model_config = unpacked_checkpoints_dir.model_config
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training_tp_size = nemo_model_config.get("tensor_model_parallel_size",
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1)
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training_pp_size = nemo_model_config.get("pipeline_model_parallel_size",
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1)
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checkpoints_paths = unpacked_checkpoints_dir.get_checkpoints_paths(
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training_tp_size,
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training_pp_size,
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)
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if unpacked_checkpoints_dir._load_checkpoints_to_cpu:
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map_location_fn = cpu_map_location
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else:
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map_location_fn = gpu_map_location
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model_00 = torch.load(checkpoints_paths[0][0],
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map_location=map_location_fn)
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model_00 = rename_keys(model_00, layer_rename_config)
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vocab_size = model_00[
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"model.language_model.embedding.word_embeddings.weight"].shape[
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0] * training_tp_size
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del model_00
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hf_config = transformers.GPT2Config(
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vocab_size=vocab_size,
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n_positions=nemo_model_config['max_position_embeddings'],
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n_embd=nemo_model_config['hidden_size'],
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n_layer=nemo_model_config['num_layers'],
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n_head=nemo_model_config['num_attention_heads'],
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n_inner=nemo_model_config['ffn_hidden_size'],
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activation_function=nemo_model_config['activation'],
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layer_norm_epsilon=nemo_model_config['layernorm_epsilon'],
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)
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hf_config.n_kv_head = hf_config.n_head
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hf_config.bias = nemo_model_config['bias']
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hf_config.apply_query_key_layer_scaling = False
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hf_config.position_embedding_type = nemo_model_config.get(
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'position_embedding_type', 'learned_absolute')
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if hf_config.position_embedding_type == 'rope':
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hf_config.position_embedding_type = 'rope_gpt_neox'
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hf_config.rotary_base = nemo_model_config.get('rotary_base', 10000.0)
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hf_config.rotary_pct = nemo_model_config.get('rotary_percentage', 1.0)
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assert hf_config.rotary_pct >= 0 and hf_config.rotary_pct <= 1
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rotary_scaling_factor = nemo_model_config.get(
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'seq_len_interpolation_factor', None)
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if rotary_scaling_factor is None:
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hf_config.rotary_scaling = None
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else:
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assert rotary_scaling_factor > 1
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hf_config.rotary_scaling = {
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'type': 'linear',
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'factor': rotary_scaling_factor
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}
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if dtype == 'auto':
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dtype = nemo_model_config.get('precision', None)
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if dtype is None:
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dtype = 'float16'
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elif 'bf16' in dtype or 'bfloat16' in dtype:
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dtype = 'bfloat16'
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else:
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dtype = 'float16'
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logger.info(f"Specified dtype 'auto'; inferred dtype {dtype!r}.")
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return cls(architecture='GPTForCausalLM',
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dtype=dtype,
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num_hidden_layers=hf_config.n_layer,
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num_attention_heads=hf_config.n_head,
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num_key_value_heads=hf_config.n_kv_head,
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hidden_size=hf_config.n_embd,
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intermediate_size=hf_config.n_inner,
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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=hf_config.position_embedding_type,
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max_position_embeddings=hf_config.n_positions,
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hidden_act=hf_config.activation_function,
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bias=hf_config.bias,
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apply_query_key_layer_scaling=hf_config.
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apply_query_key_layer_scaling,
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rotary_pct=hf_config.rotary_pct,
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rotary_base=hf_config.rotary_base,
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rotary_scaling=hf_config.rotary_scaling,
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mapping=mapping,
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quantization=quant_config,
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**kwargs)
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