from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
from megatron.core.fusions.fused_layer_norm import FusedLayerNorm
from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules
from megatron.core.transformer.custom_layers.transformer_engine import (
    TEDotProductAttention,
    TELayerNormColumnParallelLinear,
    TERowParallelLinear,
)
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.spec_utils import ModuleSpec
from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules

# Use this spec to use lower level Transformer Engine modules (required for fp8 training)
bert_layer_with_transformer_engine_spec = ModuleSpec(
    module=TransformerLayer,
    submodules=TransformerLayerSubmodules(
        self_attention=ModuleSpec(
            module=SelfAttention,
            params={"attn_mask_type": AttnMaskType.padding},
            submodules=SelfAttentionSubmodules(
                linear_qkv=TELayerNormColumnParallelLinear,
                core_attention=TEDotProductAttention,
                linear_proj=TERowParallelLinear,
                q_layernorm=IdentityOp,
                k_layernorm=IdentityOp,
            ),
        ),
        self_attn_bda=get_bias_dropout_add,
        mlp=ModuleSpec(
            module=MLP,
            submodules=MLPSubmodules(
                linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear,
            ),
        ),
        mlp_bda=get_bias_dropout_add,
    ),
)

# Use this spec for an implementation using only modules in megatron core
bert_layer_local_spec = ModuleSpec(
    module=TransformerLayer,
    submodules=TransformerLayerSubmodules(
        input_layernorm=FusedLayerNorm,
        self_attention=ModuleSpec(
            module=SelfAttention,
            params={"attn_mask_type": AttnMaskType.padding},
            submodules=SelfAttentionSubmodules(
                linear_qkv=ColumnParallelLinear,
                core_attention=DotProductAttention,
                linear_proj=RowParallelLinear,
                q_layernorm=IdentityOp,
                k_layernorm=IdentityOp,
            ),
        ),
        self_attn_bda=get_bias_dropout_add,
        pre_mlp_layernorm=FusedLayerNorm,
        mlp=ModuleSpec(
            module=MLP,
            submodules=MLPSubmodules(
                linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear,
            ),
        ),
        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_',
        },
    ),
)
