# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.

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.attention import SelfAttention, SelfAttentionSubmodules
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.mlp import MLP, MLPSubmodules
from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules
from megatron.core.transformer.moe.shared_experts import SharedExpertMLP
from megatron.core.transformer.multi_latent_attention import (
    MLASelfAttention,
    MLASelfAttentionSubmodules,
)
from megatron.core.transformer.spec_utils import ModuleSpec
from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules

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


def get_gpt_layer_with_transformer_engine_spec(
    num_experts: Optional[int] = None,
    moe_grouped_gemm: Optional[bool] = False,
    qk_layernorm: Optional[bool] = False,
    multi_latent_attention: Optional[bool] = False,
    fp8: Optional[str] = None,
) -> ModuleSpec:
    """Use this spec to use lower-level Transformer Engine modules (required for fp8 training).


    Args:
        num_experts (int, optional): Number of experts. Defaults to None.
        moe_grouped_gemm (bool, optional): To use Grouped GEMM. Defaults to False.
        qk_layernorm (bool, optional): To use layernorm for queries/keys. Defaults to False.
        fp8 (str, optional): Flag to decide the linear layer spec for MoE. Defaults to None.

    Returns:
        ModuleSpec: Module specification with TE modules
    """
    mlp = _get_mlp_module_spec(
        use_te=True, num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm, fp8=fp8
    )

    if multi_latent_attention:
        return ModuleSpec(
            module=TransformerLayer,
            submodules=TransformerLayerSubmodules(
                self_attention=ModuleSpec(
                    module=MLASelfAttention,
                    params={"attn_mask_type": AttnMaskType.causal},
                    submodules=MLASelfAttentionSubmodules(
                        linear_q_proj=TEColumnParallelLinear,
                        linear_q_down_proj=TEColumnParallelLinear,
                        linear_q_up_proj=TEColumnParallelLinear,
                        linear_kv_down_proj=TEColumnParallelLinear,
                        linear_kv_up_proj=TEColumnParallelLinear,
                        core_attention=TEDotProductAttention,
                        linear_proj=TERowParallelLinear,
                        q_layernorm=TENorm if qk_layernorm else IdentityOp,
                        kv_layernorm=TENorm if qk_layernorm else IdentityOp,
                    ),
                ),
                self_attn_bda=get_bias_dropout_add,
                pre_mlp_layernorm=TENorm if num_experts else IdentityOp,
                input_layernorm=TENorm if num_experts else IdentityOp,
                mlp=mlp,
                mlp_bda=get_bias_dropout_add,
            ),
        )
    else:
        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,
                        # TENorm significantly harms convergence when used
                        # for QKLayerNorm; we instead use the Apex implementation.
                        q_layernorm=FusedLayerNorm if qk_layernorm else IdentityOp,
                        k_layernorm=FusedLayerNorm if qk_layernorm else IdentityOp,
                    ),
                ),
                self_attn_bda=get_bias_dropout_add,
                pre_mlp_layernorm=TENorm if num_experts else IdentityOp,
                mlp=mlp,
                mlp_bda=get_bias_dropout_add,
            ),
        )


def get_gpt_layer_local_spec(
    num_experts: Optional[int] = None,
    moe_grouped_gemm: Optional[bool] = False,
    qk_layernorm: Optional[bool] = False,
    multi_latent_attention: Optional[bool] = False,
) -> ModuleSpec:
    """Use this spec for an implementation using only modules in Megatron-Core.


    Args:
        num_experts (int, optional): Number of experts. Defaults to None.
        moe_grouped_gemm (bool, optional): To use Grouped GEMM. Defaults to False.
        qk_layernorm (bool, optional): To use layernorm for queries/keys. Defaults to False.

    Returns:
        ModuleSpec: Module specification with Megatron-Core modules
    """
    mlp = _get_mlp_module_spec(
        use_te=False, num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm
    )
    if multi_latent_attention:
        return ModuleSpec(
            module=TransformerLayer,
            submodules=TransformerLayerSubmodules(
                self_attention=ModuleSpec(
                    module=MLASelfAttention,
                    params={"attn_mask_type": AttnMaskType.causal},
                    submodules=MLASelfAttentionSubmodules(
                        linear_q_proj=ColumnParallelLinear,
                        linear_q_down_proj=ColumnParallelLinear,
                        linear_q_up_proj=ColumnParallelLinear,
                        linear_kv_down_proj=ColumnParallelLinear,
                        linear_kv_up_proj=ColumnParallelLinear,
                        core_attention=DotProductAttention,
                        linear_proj=RowParallelLinear,
                        q_layernorm=LNImpl if qk_layernorm else IdentityOp,
                        kv_layernorm=LNImpl if qk_layernorm else IdentityOp,
                    ),
                ),
                self_attn_bda=get_bias_dropout_add,
                pre_mlp_layernorm=LNImpl if num_experts else IdentityOp,
                input_layernorm=LNImpl if num_experts else IdentityOp,
                mlp=mlp,
                mlp_bda=get_bias_dropout_add,
            ),
        )
    else:
        return ModuleSpec(
            module=TransformerLayer,
            submodules=TransformerLayerSubmodules(
                input_layernorm=LNImpl,
                self_attention=ModuleSpec(
                    module=SelfAttention,
                    params={"attn_mask_type": AttnMaskType.causal},
                    submodules=SelfAttentionSubmodules(
                        linear_qkv=ColumnParallelLinear,
                        core_attention=DotProductAttention,
                        linear_proj=RowParallelLinear,
                        q_layernorm=LNImpl if qk_layernorm else IdentityOp,
                        k_layernorm=LNImpl if qk_layernorm else IdentityOp,
                    ),
                ),
                self_attn_bda=get_bias_dropout_add,
                pre_mlp_layernorm=LNImpl,
                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,
                    ),
                ),
            ),
        )
