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.spec_utils import ModuleSpec
from megatron.core.models.gpt.gpt_layer_specs import _get_mlp_module_spec
from megatron.training import get_args

from mindspeed_llm.core.transformer.custom_layers.transformer_engine import PTNorm
from mindspeed_llm.tasks.models.transformer.transformer_layer import TransformerLayerWithPostNorm, \
    TransformerLayerSubmodulesWithPostNorm

args = get_args()
num_experts, moe_grouped_gemm, qk_layernorm = args.num_experts, args.moe_grouped_gemm, args.qk_layernorm

# Transformer Layer Spec for Grok using post_mlp_layernorm and post_mlp_layernorm.
layer_spec = ModuleSpec(
    module=TransformerLayerWithPostNorm,
    submodules=TransformerLayerSubmodulesWithPostNorm(
        input_layernorm=PTNorm,
        self_attention=ModuleSpec(
            module=SelfAttention,
            params={"attn_mask_type": AttnMaskType.causal},
            submodules=SelfAttentionSubmodules(
                linear_qkv=ColumnParallelLinear,
                core_attention=DotProductAttention,
                linear_proj=RowParallelLinear,
                q_layernorm=PTNorm if qk_layernorm else IdentityOp,
                k_layernorm=PTNorm if qk_layernorm else IdentityOp,
            ),
        ),
        self_attn_bda=get_bias_dropout_add,
        pre_mlp_layernorm=PTNorm,
        post_mlp_layernorm=PTNorm,
        post_attn_norm=PTNorm,
        mlp=_get_mlp_module_spec(
            use_te=False, num_experts=num_experts, moe_grouped_gemm=moe_grouped_gemm
        ),
        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_',
        },
    ),
)