megatron_patch/model/qwen2_vl/layer_specs.py

# Copyright (c) 2023 Alibaba PAI and Nvidia Megatron-LM Team.
#
# 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.

from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add

from megatron.core.extensions.transformer_engine import (
    TEDotProductAttention,
    TELayerNormColumnParallelLinear,
    TENorm,
    TERowParallelLinear,
    TEColumnParallelLinear
)

from megatron.core.transformer.enums import AttnMaskType
from megatron.core.transformer.identity_op import IdentityOp

from megatron.core.transformer.spec_utils import ModuleSpec
from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules

from megatron.core.transformer.mlp import MLP, MLPSubmodules
from megatron.core.transformer.attention import SelfAttentionSubmodules

from .attention_vision import SelfAttention
from .attention import SelfAttention as Qwen2VLSelfAttention

# Use this spec to use lower level Transformer Engine modules (required for fp8 training)
def get_gpt_layer_with_transformer_engine_spec(
    qk_layernorm: bool = False
) -> ModuleSpec:
    mlp = get_mlp_module_spec(
        use_te=True, num_experts=None, moe_grouped_gemm=False
    )
    return ModuleSpec(
        module=TransformerLayer,
        submodules=TransformerLayerSubmodules(
            self_attention=ModuleSpec(
                module=Qwen2VLSelfAttention,
                params={"attn_mask_type": AttnMaskType.causal},
                submodules=SelfAttentionSubmodules(
                    linear_qkv=TELayerNormColumnParallelLinear,
                    core_attention=TEDotProductAttention,
                    linear_proj=TERowParallelLinear,
                    q_layernorm=TENorm if qk_layernorm else IdentityOp,
                    k_layernorm=TENorm if qk_layernorm else IdentityOp,
                ),
            ),
            self_attn_bda=get_bias_dropout_add,
            pre_mlp_layernorm=IdentityOp,
            mlp=mlp,
            mlp_bda=get_bias_dropout_add,
        ),
    )

def get_qwen2vl_vision_model_spec(
    is_vit=False     
) -> ModuleSpec:
    attn_mask_type = AttnMaskType.no_mask # THD --> causal_pad

    mlp = ModuleSpec(
        module=MLP,
        submodules=MLPSubmodules(
            linear_fc1=TELayerNormColumnParallelLinear,
            linear_fc2=TERowParallelLinear,
        ),
    )
    return ModuleSpec(
        module=TransformerLayer,
        submodules=TransformerLayerSubmodules(
            self_attention=ModuleSpec(
                module=SelfAttention,
                params={"attn_mask_type": attn_mask_type},
                submodules=SelfAttentionSubmodules(
                    linear_qkv=TELayerNormColumnParallelLinear,
                    core_attention=TEDotProductAttention,
                    linear_proj=TERowParallelLinear,
                    q_layernorm=IdentityOp,
                    k_layernorm=IdentityOp,
                ),
            ),
            self_attn_bda=get_bias_dropout_add,
            pre_mlp_layernorm=IdentityOp,
            mlp=mlp,
            mlp_bda=get_bias_dropout_add,
        ),
    )


# Helper function to get module spec for MLP/MoE
def get_mlp_module_spec(
    use_te: bool = True, num_experts: int = None, moe_grouped_gemm: bool = False, add_norm: bool = True
) -> ModuleSpec:
    if num_experts is None:
        # Dense MLP w/ or w/o TE modules.
        if add_norm:
            return ModuleSpec(
                module=MLP,
                submodules=MLPSubmodules(
                    linear_fc1=TELayerNormColumnParallelLinear if use_te else ColumnParallelLinear,
                    linear_fc2=TERowParallelLinear if use_te else RowParallelLinear,
                ),
            )
        else:
            return ModuleSpec(
                module=MLP,
                submodules=MLPSubmodules(
                    linear_fc1=TEColumnParallelLinear if use_te else ColumnParallelLinear,
                    linear_fc2=TERowParallelLinear if use_te else RowParallelLinear,
                ),
            )            
    else:
        # Mixture of experts with modules in megatron core.
        raise NotImplementedError()