File size: 8,205 Bytes
bcdf9fa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | # Copyright 2025 Bytedance Ltd. and/or its affiliates
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# 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.
# convert huggingface config to mcore transformer config
import torch
import torch.nn.functional as F
from megatron.core.transformer import MLATransformerConfig, TransformerConfig
from transformers import PretrainedConfig
def _get_base_transformer_config(hf_config: PretrainedConfig, dtype: torch.dtype, **kwargs) -> TransformerConfig:
"""
Create a base TransformerConfig with common parameters across different model architectures.
TODO: (ycl) use dataclass or converter config?
Args:
hf_config: HuggingFace model configuration
dtype: Data type for the model
**kwargs: Additional parameters to override defaults
Returns:
TransformerConfig with common parameters
"""
from megatron.core import parallel_state as mpu
# Common parallel state parameters
overlap_p2p_comm = mpu.get_virtual_pipeline_model_parallel_world_size() is not None and mpu.get_virtual_pipeline_model_parallel_world_size() > 1
batch_p2p_comm = False
# Base configuration with common parameters
base_config = {
# Model architecture parameters
"num_layers": hf_config.num_hidden_layers,
"hidden_size": hf_config.hidden_size,
"num_attention_heads": hf_config.num_attention_heads,
"num_query_groups": hf_config.num_key_value_heads,
"ffn_hidden_size": hf_config.intermediate_size,
"attention_dropout": hf_config.attention_dropout,
"hidden_dropout": getattr(hf_config, "hidden_dropout", 0.0),
"kv_channels": getattr(hf_config, "head_dim", None),
"layernorm_epsilon": hf_config.rms_norm_eps,
# Activation and normalization
"activation_func": F.silu,
"normalization": "RMSNorm",
"gated_linear_unit": True,
# Data types
"pipeline_dtype": dtype,
"params_dtype": dtype,
"bf16": dtype is torch.bfloat16,
# Parallel configuration
"tensor_model_parallel_size": mpu.get_tensor_model_parallel_world_size(),
"pipeline_model_parallel_size": mpu.get_pipeline_model_parallel_world_size(),
"virtual_pipeline_model_parallel_size": mpu.get_virtual_pipeline_model_parallel_world_size(),
"context_parallel_size": mpu.get_context_parallel_world_size(),
"overlap_p2p_comm": overlap_p2p_comm,
"batch_p2p_comm": batch_p2p_comm,
"sequence_parallel": mpu.get_tensor_model_parallel_world_size() > 1,
# Common settings
"variable_seq_lengths": True,
"masked_softmax_fusion": True,
"moe_token_dispatcher_type": "alltoall",
}
# Update with any provided overrides
base_config.update(kwargs)
print(f"Overridden TF init config: {base_config}")
return TransformerConfig(**base_config)
def hf_to_mcore_config_dense(hf_config: PretrainedConfig, dtype: torch.dtype) -> TransformerConfig:
# for LlamaForCausalLM or Qwen2ForCausalLM
qkv_bias = True if "Qwen2ForCausalLM" in hf_config.architectures else getattr(hf_config, "attention_bias", False)
qk_layernorm = True if "Qwen3ForCausalLM" in hf_config.architectures else False
return _get_base_transformer_config(
hf_config=hf_config,
dtype=dtype,
use_cpu_initialization=False,
add_bias_linear=False,
add_qkv_bias=qkv_bias,
qk_layernorm=qk_layernorm,
)
def hf_to_mcore_config_qwen2moe(hf_config: PretrainedConfig, dtype: torch.dtype) -> TransformerConfig:
return _get_base_transformer_config(
hf_config=hf_config,
dtype=dtype,
use_cpu_initialization=False,
add_bias_linear=False,
layernorm_epsilon=hf_config.rms_norm_eps,
# MoE specific
moe_ffn_hidden_size=hf_config.moe_intermediate_size,
moe_router_bias_update_rate=0.001,
moe_router_topk=hf_config.num_experts_per_tok,
num_moe_experts=hf_config.num_experts,
moe_shared_expert_intermediate_size=hf_config.shared_expert_intermediate_size,
moe_aux_loss_coeff=hf_config.router_aux_loss_coef,
# moe_aux_loss_coeff=0.0,
moe_router_load_balancing_type="aux_loss",
moe_shared_expert_overlap=True,
moe_grouped_gemm=True,
moe_router_score_function="softmax",
# Other optimizations
persist_layer_norm=True,
bias_activation_fusion=True,
bias_dropout_fusion=True,
# Qwen specific
moe_router_pre_softmax=True,
add_qkv_bias=True,
)
def hf_to_mcore_config_mixtral(hf_config: PretrainedConfig, dtype: torch.dtype) -> TransformerConfig:
return _get_base_transformer_config(
hf_config=hf_config,
dtype=dtype,
use_cpu_initialization=False,
add_bias_linear=False,
layernorm_epsilon=hf_config.rms_norm_eps,
# MoE specific
num_moe_experts=hf_config.num_local_experts,
moe_aux_loss_coeff=hf_config.router_aux_loss_coef,
moe_router_topk=hf_config.num_experts_per_tok,
moe_router_pre_softmax=True,
moe_router_load_balancing_type="aux_loss",
moe_router_score_function="softmax",
moe_shared_expert_intermediate_size=None, # mixtral has no shared expert
moe_shared_expert_overlap=False, # mixtral has no shared expert
moe_ffn_hidden_size=hf_config.intermediate_size,
moe_router_bias_update_rate=0.001,
# moe_permute_fusion=True, # need TE 2.1+
moe_grouped_gemm=True,
# Other optimizations
persist_layer_norm=True,
apply_rope_fusion=True,
bias_activation_fusion=True,
bias_dropout_fusion=True,
)
def hf_to_mcore_config_qwen3moe(hf_config: PretrainedConfig, dtype: torch.dtype) -> TransformerConfig:
return _get_base_transformer_config(
hf_config=hf_config,
dtype=dtype,
use_cpu_initialization=False,
add_bias_linear=False,
layernorm_epsilon=hf_config.rms_norm_eps,
# MoE specific
moe_ffn_hidden_size=hf_config.moe_intermediate_size,
moe_router_bias_update_rate=0.001,
moe_router_topk=hf_config.num_experts_per_tok,
num_moe_experts=hf_config.num_experts,
moe_aux_loss_coeff=hf_config.router_aux_loss_coef,
# moe_aux_loss_coeff=0.0,
moe_router_load_balancing_type="aux_loss",
moe_grouped_gemm=True,
moe_router_score_function="softmax",
# Other optimizations
persist_layer_norm=True,
bias_activation_fusion=True,
bias_dropout_fusion=True,
# Qwen specific
moe_router_pre_softmax=True,
qk_layernorm=True,
)
def hf_to_mcore_config_dpskv3(hf_config: PretrainedConfig, dtype: torch.dtype) -> MLATransformerConfig:
# DeepseekV3ForCausalLM
raise NotImplementedError("DeepseekV3ForCausalLM is not supported yet")
def hf_to_mcore_config_qwen2_5_vl(hf_config: PretrainedConfig, dtype: torch.dtype) -> TransformerConfig:
# Qwen2_5_VLForConditionalGeneration
raise NotImplementedError("Qwen2_5_VLForConditionalGeneration is not supported yet")
def hf_to_mcore_config_llama4(hf_config: PretrainedConfig, dtype: torch.dtype) -> TransformerConfig:
# Llama4ForConditionalGeneration
raise NotImplementedError("Llama4ForConditionalGeneration is not supported yet")
|