# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
import torch

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.spec_utils import ModuleSpec
from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules
try:
    from megatron.core.extensions.transformer_engine import (
        TEColumnParallelLinear,
        TEDotProductAttention,
        TELayerNormColumnParallelLinear,
        TENorm,
        TERowParallelLinear,
    )

    HAVE_TE = True
except ImportError:
    HAVE_TE = False

try:
    import apex

    from megatron.core.fusions.fused_layer_norm import FusedLayerNorm
    from megatron.core.transformer.torch_norm import WrappedTorchNorm

    HAVE_APEX = True
    LNImpl = FusedLayerNorm
except ImportError:
    import warnings

    from megatron.core.transformer.torch_norm import WrappedTorchNorm

    warnings.warn(f'Apex is not installed. Falling back to Torch Norm')
    LNImpl = WrappedTorchNorm


def get_layer_spec(is_vit, normalization) -> ModuleSpec:
    attn_mask_type = AttnMaskType.no_mask if is_vit else AttnMaskType.causal
    if normalization == "LayerNorm":
        norm = LNImpl
    elif normalization == "RMSNorm":
        if HAVE_TE:
            norm = TENorm
        else:
            version = torch.__version__.split('.')
            version_geq_2_4 = (
                int(TORCH_VERSION[0]) > 2
                or (
                    int(TORCH_VERSION[0]) == 2
                    and int(TORCH_VERSION[1]) >= 4
                )
            )
            assert version_geq_2_4, "Torch version >= 2.4.0 is required for RMSNorm"
            if HAVE_APEX:
                warnings.warn(f'Apex does not support RMSNorm. Falling back to Torch Norm')
            norm = WrappedTorchNorm
    else:
        raise RuntimeError("unknown normalization", normalization)

    mlp = get_mlp_module_spec(use_te=False)  # doesn't include norm.

    return ModuleSpec(
        module=TransformerLayer,
        submodules=TransformerLayerSubmodules(
            input_layernorm=norm,
            self_attention=ModuleSpec(
                module=SelfAttention,
                params={"attn_mask_type": attn_mask_type},
                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=norm,
            mlp=mlp,
            mlp_bda=get_bias_dropout_add,
        ),
    )


def get_layer_spec_te(is_vit=False, padding=False) -> ModuleSpec:
    attn_mask_type = AttnMaskType.no_mask if is_vit else AttnMaskType.causal
    # Padding mask is needed for e.g. Context Parallel.
    if padding:
        assert not is_vit, "padding_causal mask not used with ViT"
        attn_mask_type = AttnMaskType.padding_causal

    mlp = get_norm_mlp_module_spec_te()
    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,
        ),
    )


def get_mlp_module_spec(use_te: bool = True, num_experts: int = None, moe_grouped_gemm: bool = False, add_norm: bool = True) -> ModuleSpec:
    # Dense MLP w/ or w/o TE modules.
    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()


def get_norm_mlp_module_spec_te() -> ModuleSpec:
    return ModuleSpec(
        module=MLP,
        submodules=MLPSubmodules(
            linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear
        ),
    )


#nhb
def get_qwen2vl_vision_layer_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,
        ),
    )
