# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
#
# 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 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:
    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(f'Apex is not installed. Falling back to Torch LayerNorm')
    LNImpl = WrappedTorchLayerNorm


def get_layer_spec(is_vit, normalization) -> ModuleSpec:
    """Transformer Layer Spec"""
    attn_mask_type = AttnMaskType.no_mask if is_vit else AttnMaskType.causal
    if normalization == "LayerNorm":
        norm = LNImpl
    elif normalization == "RMSNorm":
        norm = TENorm
    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) -> ModuleSpec:
    """Transformer Layer Spec w/ TE Modules"""
    attn_mask_type = AttnMaskType.no_mask if is_vit else AttnMaskType.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) -> ModuleSpec:
    """MLP Submodule Spec"""
    # Dense MLP w/ or w/o TE modules.
    return ModuleSpec(
        module=MLP,
        submodules=MLPSubmodules(
            linear_fc1=TEColumnParallelLinear if use_te else ColumnParallelLinear,
            linear_fc2=TERowParallelLinear if use_te else RowParallelLinear,
        ),
    )


def get_norm_mlp_module_spec_te() -> ModuleSpec:
    """Norm + MLP Submodule Spec"""
    return ModuleSpec(
        module=MLP,
        submodules=MLPSubmodules(linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear),
    )