# Copyright (c) 2024, 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.


try:
    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 (
        TEColumnParallelLinear,
        TEDotProductAttention,
        TENorm,
        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

    HAVE_MEGATRON_CORE = True

except (ImportError, ModuleNotFoundError):
    TransformerConfig = ApexGuardDefaults
    HAVE_MEGATRON_CORE = False

from nemo.collections.nlp.models.language_modeling.megatron.bert.bert_model import (
    TransformerLayerSubmodulesWithPostLNSupport,
    TransformerLayerWithPostLNSupport,
)

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

# Use this spec for an implementation using only modules in megatron core
bert_layer_local_spec_postln = ModuleSpec(
    module=TransformerLayerWithPostLNSupport,
    submodules=TransformerLayerSubmodulesWithPostLNSupport(
        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,
        post_att_layernorm=FusedLayerNorm,
        mlp=ModuleSpec(
            module=MLP, submodules=MLPSubmodules(linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear,),
        ),
        mlp_bda=get_bias_dropout_add,
        post_mlp_layernorm=FusedLayerNorm,
    ),
)
