# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import sys
from pathlib import Path
from typing import Optional, Union
import torch
from ..._utils import torch_dtype_to_str
from ...layers import MoeConfig
from ...mapping import Mapping
from ..modeling_utils import PretrainedConfig, QuantConfig
[docs]
class LLaMAConfig(PretrainedConfig):
def __init__(self,
*,
mlp_bias: bool = False,
attn_bias: bool = False,
rotary_base: float = 10000.0,
rotary_scaling: Optional[dict] = None,
residual_mlp: bool = False,
disable_weight_only_quant_plugin: bool = False,
moe: Optional[Union[MoeConfig, dict]] = None,
remove_duplicated_kv_heads: bool = False,
**kwargs):
self.mlp_bias = mlp_bias
self.attn_bias = attn_bias
self.rotary_base = rotary_base
self.rotary_scaling = rotary_scaling
self.residual_mlp = residual_mlp
self.disable_weight_only_quant_plugin = disable_weight_only_quant_plugin
if moe is None:
# Legacy MOE config fields
moe = MoeConfig(
num_experts=kwargs.pop('moe_num_experts', 0),
top_k=kwargs.pop('moe_top_k', 0),
normalization_mode=kwargs.pop(
'moe_normalization_mode',
MoeConfig.ExpertScaleNormalizationMode.RENORMALIZE))
elif isinstance(moe, dict):
moe = MoeConfig.from_dict(moe)
assert isinstance(moe, MoeConfig)
self.moe = moe.validate()
self.remove_duplicated_kv_heads = remove_duplicated_kv_heads
super().__init__(**kwargs)
[docs]
def to_dict(self):
output = super().to_dict()
# Serialize the fields added in LLaMAConfig
output['mlp_bias'] = self.mlp_bias
output['attn_bias'] = self.attn_bias
output['rotary_base'] = self.rotary_base
output['rotary_scaling'] = self.rotary_scaling
output['residual_mlp'] = self.residual_mlp
output[
'disable_weight_only_quant_plugin'] = self.disable_weight_only_quant_plugin
output['moe'] = self.moe.to_dict()
return output
[docs]
@classmethod
def from_hugging_face(
cls,
hf_config_or_dir: Union[str, 'transformers.PretrainedConfig'],
dtype: str = 'auto',
mapping: Optional[Mapping] = None,
quant_config: Optional[QuantConfig] = None,
**kwargs):
import transformers
if isinstance(hf_config_or_dir, transformers.PretrainedConfig):
hf_config = hf_config_or_dir
else:
hf_config_dir = str(hf_config_or_dir)
if "vila" in hf_config_dir:
sys.path.append(hf_config_dir + "/../VILA")
from llava.model import LlavaLlamaConfig # noqa
from llava.model import LlavaLlamaModel
transformers.AutoConfig.register("llava_llama",
LlavaLlamaConfig)
transformers.AutoModelForCausalLM.register(
LlavaLlamaConfig, LlavaLlamaModel)
hf_config = transformers.AutoConfig.from_pretrained(
hf_config_dir, trust_remote_code=True)
if hf_config.model_type == "llava":
# LLaVA = Vision model + Llama LLM
# We load a llava config and use its' text config as llama config
from transformers import LlavaConfig
hf_config = LlavaConfig.from_pretrained(
hf_config_dir).text_config
if hf_config.model_type == "llava_next":
from transformers import LlavaNextConfig
hf_config = LlavaNextConfig.from_pretrained(
hf_config_dir).text_config
if hf_config.model_type == "llava_llama":
hf_config.llm_cfg["architecture"] = hf_config.llm_cfg[
"architectures"][0]
hf_config.llm_cfg["dtype"] = hf_config.llm_cfg["torch_dtype"]
hf_config = PretrainedConfig.from_dict(hf_config.llm_cfg)
num_key_value_heads = getattr(hf_config, "num_key_value_heads",
hf_config.num_attention_heads)
if hf_config.model_type == "exaone":
hidden_act = hf_config.activation_function
# NOTE
# EXAONE also uses RMS norm but they represent as layer_norm_epsilon.
norm_epsilon = getattr(hf_config, "layer_norm_epsilon", 1e-5)
else:
hidden_act = hf_config.hidden_act
norm_epsilon = hf_config.rms_norm_eps
head_dim = getattr(
hf_config, "head_dim",
hf_config.hidden_size // hf_config.num_attention_heads)
head_size = getattr(hf_config, "kv_channels", head_dim)
attn_bias = getattr(hf_config, 'bias', False) or getattr(
hf_config, 'attention_bias', False)
rotary_scaling = getattr(hf_config, "rope_scaling", None)
rotary_base = getattr(hf_config, "rope_theta", 10000.0)
residual_mlp = getattr(hf_config, "parallel_attn_mlp_res", False)
disable_weight_only_quant_plugin = kwargs.pop(
'disable_weight_only_quant_plugin', False)
remove_duplicated_kv_heads = kwargs.pop('remove_duplicated_kv_heads',
False)
if hf_config.model_type == "mixtral" or hf_config.model_type == "arctic":
# HF LLaMA-type models are implicitly using gated activation.
# With our MoE implementation, we must make it explicit
hidden_act = "swiglu"
moe_normalization_mode = MoeConfig.ExpertScaleNormalizationMode.RENORMALIZE
else:
moe_normalization_mode = None
moe_num_experts = getattr(hf_config, "num_local_experts", 0)
moe_top_k = getattr(hf_config, "num_experts_per_tok", 0)
moe_config = MoeConfig(num_experts=moe_num_experts,
top_k=moe_top_k,
normalization_mode=moe_normalization_mode)
moe_config.validate()
if dtype == 'auto':
dtype = getattr(hf_config, 'torch_dtype', None)
if dtype is None:
dtype = 'float16'
if isinstance(dtype, torch.dtype):
dtype = torch_dtype_to_str(dtype)
if dtype == 'float32':
dtype = 'float16'
return cls(
architecture=hf_config.architectures[0],
dtype=dtype,
num_hidden_layers=hf_config.num_hidden_layers,
num_attention_heads=hf_config.num_attention_heads,
hidden_size=hf_config.hidden_size,
intermediate_size=hf_config.intermediate_size,
num_key_value_heads=num_key_value_heads,
head_size=head_size,
vocab_size=hf_config.vocab_size,
position_embedding_type='rope_gpt_neox',
max_position_embeddings=hf_config.max_position_embeddings,
hidden_act=hidden_act,
norm_epsilon=norm_epsilon,
attn_bias=attn_bias,
rotary_base=rotary_base,
rotary_scaling=rotary_scaling,
residual_mlp=residual_mlp,
disable_weight_only_quant_plugin=disable_weight_only_quant_plugin,
moe=moe_config,
mapping=mapping,
quantization=quant_config,
remove_duplicated_kv_heads=remove_duplicated_kv_heads,
**kwargs)