megatron_patch/model/mixtral/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 typing import Optional

from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
from megatron.core.transformer.moe.shared_experts import SharedExpertMLP
from megatron.core.transformer.dot_product_attention import DotProductAttention
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 .transformer.mlp import MLP, MLPSubmodules
from .transformer.attention import SelfAttention, SelfAttentionSubmodules
from .moe.moe_layer import MoELayer, MoESubmodules

try:
    from megatron.core.extensions.transformer_engine import (
        TEColumnParallelGroupedLinear,
        TEColumnParallelLinear,
        TEDotProductAttention,
        TELayerNormColumnParallelLinear,
        TENorm,
        TERowParallelGroupedLinear,
        TERowParallelLinear,
    )

    HAVE_TE = True
except ImportError:
    HAVE_TE = False

try:
    import apex  # pylint: disable=unused-import

    from megatron.core.fusions.fused_layer_norm import FusedLayerNorm

    HAVE_APEX = True
    LNImpl = FusedLayerNorm
except ImportError:
    import warnings

    from megatron.core.transformer.torch_layer_norm import WrappedTorchLayerNorm

    warnings.warn('Apex is not installed. Falling back to Torch LayerNorm')
    LNImpl = WrappedTorchLayerNorm

# Use this spec to use lower level Transformer Engine modules (required for fp8 training)
def get_gpt_layer_with_transformer_engine_spec(
    num_experts: int = None, moe_grouped_gemm: bool = False
) -> ModuleSpec:
    """
    Generates a spec for a GPT transformer layer using Transformer Engine modules.

    Args:
        num_experts: Optional; the number of experts to use in a Mixture of Experts (MoE) setup.
                     If `None`, a dense multi-layer perceptron (MLP) is used instead of MoE.
        moe_grouped_gemm: Optional; if `True`, enables grouped GEMM for MoE operations,
                          which can be more efficient for certain configurations.

    Returns:
        A ModuleSpec object that specifies how to construct a GPT transformer layer with
        the appropriate submodules for self-attention and MLP/MoE using Transformer Engine optimizations.
    """
    mlp = _get_mlp_module_spec(
        use_te=True, num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm
    )
    return ModuleSpec(
        module=TransformerLayer,
        submodules=TransformerLayerSubmodules(
            self_attention=ModuleSpec(
                module=SelfAttention,
                params={"attn_mask_type": AttnMaskType.causal},
                submodules=SelfAttentionSubmodules(
                    linear_qkv=TELayerNormColumnParallelLinear,
                    core_attention=TEDotProductAttention,
                    linear_proj=TERowParallelLinear,
                ),
            ),
            self_attn_bda=get_bias_dropout_add,
            pre_mlp_layernorm=TENorm if num_experts else IdentityOp,
            mlp=mlp,
            mlp_bda=get_bias_dropout_add,
        ),
    )


# Use this spec for an implementation using only modules in megatron core
def get_gpt_layer_local_spec(num_experts: int = None, moe_grouped_gemm: bool = False) -> ModuleSpec:
    """
    Generates a specification for a GPT transformer layer using only the core modules from Megatron.

    Args:
        num_experts: Optional; the number of experts to use in a Mixture of Experts (MoE) setup.
                     If `None`, a dense multi-layer perceptron (MLP) is used instead of MoE.
        moe_grouped_gemm: Optional; if `True`, enables grouped GEMM for MoE operations,
                          which can be more efficient for certain configurations.

    Returns:
        A ModuleSpec object that specifies how to construct a GPT transformer layer with
        standard Megatron core modules without the lower-level Transformer Engine optimizations.
    """
    mlp = _get_mlp_module_spec(
        use_te=False, num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm
    )
    return ModuleSpec(
        module=TransformerLayer,
        submodules=TransformerLayerSubmodules(
            input_layernorm=FusedLayerNorm,
            self_attention=ModuleSpec(
                module=SelfAttention,
                params={"attn_mask_type": AttnMaskType.causal},
                submodules=SelfAttentionSubmodules(
                    linear_qkv=ColumnParallelLinear,
                    core_attention=DotProductAttention,
                    linear_proj=RowParallelLinear,
                ),
            ),
            self_attn_bda=get_bias_dropout_add,
            pre_mlp_layernorm=FusedLayerNorm,
            mlp=mlp,
            mlp_bda=get_bias_dropout_add,
            sharded_state_dict_keys_map={
                'input_layernorm.': 'self_attention.linear_qkv.layer_norm_',
                'pre_mlp_layernorm.': 'mlp.linear_fc1.layer_norm_',
            },
        ),
    )


def _get_mlp_module_spec(
    use_te: Optional[bool] = True,
    num_experts: Optional[int] = None,
    moe_grouped_gemm: Optional[bool] = False,
    fp8: Optional[str] = None,
) -> ModuleSpec:
    """Helper function to get module spec for MLP/MoE"""
    if num_experts is None:
        # Dense MLP w/ or w/o TE modules.
        return ModuleSpec(
            module=MLP,
            submodules=MLPSubmodules(
                linear_fc1=TELayerNormColumnParallelLinear if use_te else ColumnParallelLinear,
                linear_fc2=TERowParallelLinear if use_te else RowParallelLinear,
            ),
        )
    else:
        # Mixture of experts with modules in megatron core.
        if use_te and moe_grouped_gemm:
            linear_fc1 = TEColumnParallelGroupedLinear
            linear_fc2 = TERowParallelGroupedLinear
        elif use_te and fp8:
            linear_fc1 = TEColumnParallelLinear
            linear_fc2 = TERowParallelLinear
        else:
            linear_fc1 = ColumnParallelLinear
            linear_fc2 = RowParallelLinear

        use_te_grouped_gemm = use_te and TEColumnParallelGroupedLinear is not None

        return ModuleSpec(
            module=MoELayer,
            submodules=MoESubmodules(
                experts=(
                    MLPSubmodules(linear_fc1=linear_fc1, linear_fc2=linear_fc2)
                    if not moe_grouped_gemm or use_te_grouped_gemm
                    else None
                ),
                shared_experts=ModuleSpec(
                    module=SharedExpertMLP,
                    params={"gate": False},
                    submodules=MLPSubmodules(
                        linear_fc1=TEColumnParallelLinear if use_te else ColumnParallelLinear,
                        linear_fc2=TERowParallelLinear if use_te else RowParallelLinear,
                    ),
                ),
            ),
        )