mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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256 lines
11 KiB
Python
256 lines
11 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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import json
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from pathlib import Path
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from typing import List, Optional, Union
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from ...functional import LayerNormPositionType, LayerNormType, MLPType
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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 MLLaMAConfig(PretrainedConfig):
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def __init__(self,
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*,
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mlp_bias: bool = False,
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attn_bias: bool = False,
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rotary_base: float = 10000.0,
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rotary_scaling: Optional[dict] = None,
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residual_mlp: bool = False,
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disable_weight_only_quant_plugin: bool = False,
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cross_attention: bool = True,
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cross_attention_layers: List[int] = None,
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vision_output_dim: int = 0,
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has_position_embedding=False,
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type_vocab_size=None,
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rescale_before_lm_head=False,
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layernorm_type=LayerNormType.RmsNorm,
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layernorm_position=LayerNormPositionType.pre_layernorm,
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has_attention_qkvo_bias=False,
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has_mlp_bias=False,
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has_model_final_layernorm=True,
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model_type='MLLaMAForCausalLM',
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skip_cross_kv=False,
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mlp_type=MLPType.GatedMLP,
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has_embedding_scale=False,
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residual_scaling=1.0,
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has_lm_head_bias=False,
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num_buckets=None,
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max_distance=0,
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relative_attention=False,
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**kwargs):
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self.mlp_bias = mlp_bias
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self.attn_bias = attn_bias
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self.rotary_base = rotary_base
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self.rotary_scaling = rotary_scaling
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self.residual_mlp = residual_mlp
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self.disable_weight_only_quant_plugin = disable_weight_only_quant_plugin
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assert cross_attention
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self.cross_attention = cross_attention
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self.cross_attention_layers = cross_attention_layers
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assert vision_output_dim != 0
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self.vision_output_dim = vision_output_dim
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self.has_position_embedding = has_position_embedding
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self.type_vocab_size = type_vocab_size
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self.rescale_before_lm_head = rescale_before_lm_head
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self.layernorm_type = layernorm_type
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self.layernorm_position = layernorm_position
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self.has_attention_qkvo_bias = has_attention_qkvo_bias
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self.has_mlp_bias = has_mlp_bias
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self.has_model_final_layernorm = has_model_final_layernorm
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self.model_type = model_type
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self.skip_cross_kv = skip_cross_kv
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self.mlp_type = mlp_type
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self.has_embedding_scale = has_embedding_scale
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self.residual_scaling = residual_scaling
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self.has_lm_head_bias = has_lm_head_bias
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self.num_buckets = num_buckets
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self.max_distance = max_distance
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self.relative_attention = relative_attention
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self.skip_cross_attn_blocks = True
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kwargs.pop('embed_vocab_size', None)
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kwargs.pop('num_kv_heads_per_layer', None)
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kwargs.pop('num_kv_heads_per_cross_attn_layer', None)
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super().__init__(**kwargs)
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@property
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def embed_vocab_size(self):
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return self.vocab_size + 8 #FIXME The vocab_size of embedding contains the special tokens for image
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@property
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def num_kv_heads_per_layer(self):
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num_kv_heads_per_layer = [
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self.num_key_value_heads for _ in range(self.num_hidden_layers)
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]
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for layer_idx in self.cross_attention_layers:
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num_kv_heads_per_layer[layer_idx] = 0
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return num_kv_heads_per_layer
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@property
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def num_kv_heads_per_cross_attn_layer(self):
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num_kv_heads_per_cross_attn_layer = [
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0 for _ in range(self.num_hidden_layers)
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]
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for layer_idx in self.cross_attention_layers:
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num_kv_heads_per_cross_attn_layer[
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layer_idx] = self.num_key_value_heads
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return num_kv_heads_per_cross_attn_layer
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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 MLLaMAConfig
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output['mlp_bias'] = self.mlp_bias
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output['attn_bias'] = self.attn_bias
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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['residual_mlp'] = self.residual_mlp
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output[
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'disable_weight_only_quant_plugin'] = self.disable_weight_only_quant_plugin
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output['cross_attention'] = self.cross_attention
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output['cross_attention_layers'] = self.cross_attention_layers
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output['vision_output_dim'] = self.vision_output_dim
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output['embed_vocab_size'] = self.embed_vocab_size
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output['num_kv_heads_per_layer'] = self.num_kv_heads_per_layer
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output[
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'num_kv_heads_per_cross_attn_layer'] = self.num_kv_heads_per_cross_attn_layer
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output['skip_cross_attn_blocks'] = self.skip_cross_attn_blocks
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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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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=True)
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hf_text_config = hf_config.text_config
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hf_vision_config = hf_config.vision_config
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num_key_value_heads = getattr(hf_text_config, "num_key_value_heads",
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hf_text_config.num_attention_heads)
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hidden_act = hf_text_config.hidden_act if hasattr(
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hf_text_config, "hidden_act") else hf_text_config.hidden_activation
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norm_epsilon = hf_text_config.rms_norm_eps
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head_dim = getattr(
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hf_text_config, "head_dim",
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hf_text_config.hidden_size // hf_text_config.num_attention_heads)
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head_size = getattr(hf_text_config, "kv_channels", head_dim)
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attn_bias = getattr(hf_text_config, 'bias', False) or getattr(
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hf_text_config, 'attention_bias', False)
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rotary_scaling = getattr(hf_text_config, "rope_scaling", None)
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rotary_base = getattr(hf_text_config, "rope_theta", 10000.0)
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residual_mlp = getattr(hf_text_config, "parallel_attn_mlp_res", False)
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disable_weight_only_quant_plugin = kwargs.pop(
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'disable_weight_only_quant_plugin', False)
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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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num_hidden_layers=hf_text_config.num_hidden_layers,
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num_attention_heads=hf_text_config.num_attention_heads,
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hidden_size=hf_text_config.hidden_size,
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intermediate_size=hf_text_config.intermediate_size,
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num_key_value_heads=num_key_value_heads,
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head_size=head_size,
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vocab_size=hf_text_config.vocab_size,
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position_embedding_type='rope_gpt_neox',
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max_position_embeddings=hf_text_config.max_position_embeddings,
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hidden_act=hidden_act,
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norm_epsilon=norm_epsilon,
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attn_bias=attn_bias,
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rotary_base=rotary_base,
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rotary_scaling=rotary_scaling,
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residual_mlp=residual_mlp,
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disable_weight_only_quant_plugin=disable_weight_only_quant_plugin,
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mapping=mapping,
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quantization=quant_config,
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cross_attention_layers=hf_text_config.cross_attention_layers,
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vision_output_dim=hf_vision_config.vision_output_dim,
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**kwargs)
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@classmethod
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def from_meta_ckpt(cls,
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meta_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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with open(Path(meta_ckpt_dir, "params.json")) as fp:
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meta_config: dict = json.load(fp)
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n_embd = meta_config["dim"]
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n_head = meta_config["n_heads"]
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n_kv_head = meta_config.get("n_kv_heads", n_head)
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vocab_size = meta_config.get("vocab_size", 32000)
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# Reset vocab_size to 32000 for LLama v2 checkpoint.
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if vocab_size == -1:
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vocab_size = 32000
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if "hidden_dim" in meta_config:
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inter_size = meta_config["hidden_dim"]
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else:
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multiple_of = meta_config.get("multiple_of", 1)
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n_embd_ = int(4 * n_embd * 2 / 3)
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ffn_dim_multiplier = meta_config.get("ffn_dim_multiplier", 1)
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inter_size = multiple_of * (
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(int(n_embd_ * ffn_dim_multiplier) + multiple_of - 1) //
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multiple_of)
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dtype = infer_dtype(dtype, 'bfloat16')
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if meta_config.get('use_scaled_rope'):
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rotary_scaling = {"type": "llama3"}
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else:
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rotary_scaling = meta_config.get("rope_scaling")
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# meta checkpoint don't have vocab_size|hidden_act|rotary_base specified, use same default value as HF
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return cls(architecture="MLLaMAForCausalLM",
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dtype=dtype,
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num_hidden_layers=meta_config["n_layers"],
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num_attention_heads=n_head,
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hidden_size=n_embd,
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intermediate_size=inter_size,
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num_key_value_heads=n_kv_head,
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vocab_size=vocab_size,
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position_embedding_type='rope_gpt_neox',
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max_position_embeddings=2048,
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hidden_act='silu',
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rotary_scaling=rotary_scaling,
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rotary_base=meta_config.get('rope_theta', 10000),
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norm_epsilon=meta_config["norm_eps"],
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mapping=mapping,
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quantization=quant_config,
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**kwargs)
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