# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from typing import Dict, Optional, Tuple

import torch
import torch.nn.functional as F
from fairseq import utils
from torch import Tensor, nn
from fairseq.incremental_decoding_utils import with_incremental_state

try:
    from fairseq.model_parallel.megatron.mpu import (
        get_cuda_rng_tracker,
        get_model_parallel_world_size,
        ColumnParallelLinear,
        RowParallelLinear,
    )
    has_megatron_submodule = True
except (ImportError, ModuleNotFoundError):
    has_megatron_submodule = False


@with_incremental_state
class ModelParallelMultiheadAttention(nn.Module):
    """Model parallel Multi-headed attention.
    This performs the Multi-headed attention over multiple gpus.

    See "Megatron-LM: https://arxiv.org/pdf/1909.08053.pdf" for more details.
    """

    def __init__(
        self,
        embed_dim,
        num_heads,
        kdim=None,
        vdim=None,
        dropout=0.0,
        bias=True,
        self_attention=False,
        encoder_decoder_attention=False,
    ):
        super().__init__()
        if not has_megatron_submodule:
            raise ImportError(
                '\n\nPlease install the megatron submodule:'
                '\n\n  git submodule update --init '
                'fairseq/model_parallel/megatron'
            )
        self.embed_dim = embed_dim
        self.kdim = kdim if kdim is not None else embed_dim
        self.vdim = vdim if vdim is not None else embed_dim
        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim

        self.model_parallel_size = get_model_parallel_world_size()

        self.num_heads_partition = num_heads // self.model_parallel_size
        assert (
            self.num_heads_partition * self.model_parallel_size == num_heads
        ), "Number of heads must be divisble by model parallel size"

        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        assert (
            self.head_dim * num_heads == self.embed_dim
        ), "embed_dim must be divisible by num_heads"
        self.scaling = self.head_dim ** -0.5

        self.self_attention = self_attention
        self.encoder_decoder_attention = encoder_decoder_attention

        assert not self.self_attention or self.qkv_same_dim, (
            "Self-attention requires query, key and value to be of the same size"
        )

        self.k_proj = ColumnParallelLinear(self.kdim, embed_dim, bias=bias, gather_output=False)
        self.v_proj = ColumnParallelLinear(self.vdim, embed_dim, bias=bias, gather_output=False)
        self.q_proj = ColumnParallelLinear(embed_dim, embed_dim, bias=bias, gather_output=False)
        self.out_proj = RowParallelLinear(embed_dim, embed_dim, bias=bias, input_is_parallel=True)

    def forward(
        self,
        query,
        key: Optional[Tensor],
        value: Optional[Tensor],
        key_padding_mask: Optional[Tensor] = None,
        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
        static_kv: bool = False,
        attn_mask: Optional[Tensor] = None,
        **unused_kwargs,
    ) -> Tuple[Tensor, Optional[Tensor]]:
        """Input shape: Time x Batch x Channel

        Args:
            key_padding_mask (ByteTensor, optional): mask to exclude
                keys that are pads, of shape `(batch, src_len)`, where
                padding elements are indicated by 1s.
            attn_mask (ByteTensor, optional): typically used to
                implement causal attention, where the mask prevents the
                attention from looking forward in time (default: None).
        """
        tgt_len, bsz, embed_dim = query.size()
        assert embed_dim == self.embed_dim
        assert list(query.size()) == [tgt_len, bsz, embed_dim]

        if incremental_state is not None:
            saved_state = self._get_input_buffer(incremental_state)
            if saved_state is not None and "prev_key" in saved_state:
                # previous time steps are cached - no need to recompute
                # key and value if they are static
                if static_kv:
                    assert self.encoder_decoder_attention and not self.self_attention
                    key = value = None
        else:
            saved_state = None

        if self.self_attention:
            q = self.q_proj(query)
            k = self.k_proj(query)
            v = self.v_proj(query)
        elif self.encoder_decoder_attention:
            # encoder-decoder attention
            q = self.q_proj(query)
            if key is None:
                assert value is None
                k = v = None
            else:
                k = self.k_proj(key)
                v = self.v_proj(key)

        else:
            assert key is not None and value is not None
            q = self.q_proj(query)
            k = self.k_proj(key)
            v = self.v_proj(value)
        q *= self.scaling


        q = (
            q.contiguous()
            .view(tgt_len, bsz * self.num_heads_partition, self.head_dim)
            .transpose(0, 1)
        )
        if k is not None:
            k = (
                k.contiguous()
                .view(-1, bsz * self.num_heads_partition, self.head_dim)
                .transpose(0, 1)
            )
        if v is not None:
            v = (
                v.contiguous()
                .view(-1, bsz * self.num_heads_partition, self.head_dim)
                .transpose(0, 1)
            )

        if saved_state is not None:
            # saved states are stored with shape (bsz, num_heads_partition, seq_len, head_dim)
            if "prev_key" in saved_state:
                _prev_key = saved_state["prev_key"]
                assert _prev_key is not None
                prev_key = _prev_key.view(bsz * self.num_heads_partition, -1, self.head_dim)
                if static_kv:
                    k = prev_key
                else:
                    assert k is not None
                    k = torch.cat([prev_key, k], dim=1)
            if "prev_value" in saved_state:
                _prev_value = saved_state["prev_value"]
                assert _prev_value is not None
                prev_value = _prev_value.view(bsz * self.num_heads_partition, -1, self.head_dim)
                if static_kv:
                    v = prev_value
                else:
                    assert v is not None
                    v = torch.cat([prev_value, v], dim=1)
            prev_key_padding_mask: Optional[Tensor] = None
            if "prev_key_padding_mask" in saved_state:
                prev_key_padding_mask = saved_state["prev_key_padding_mask"]
            assert k is not None and v is not None
            key_padding_mask = ModelParallelMultiheadAttention._append_prev_key_padding_mask(
                key_padding_mask=key_padding_mask,
                prev_key_padding_mask=prev_key_padding_mask,
                batch_size=bsz,
                src_len=k.size(1),
                static_kv=static_kv,
            )

            saved_state["prev_key"] = k.view(bsz, self.num_heads_partition, -1, self.head_dim)
            saved_state["prev_value"] = v.view(bsz, self.num_heads_partition, -1, self.head_dim)
            saved_state["prev_key_padding_mask"] = key_padding_mask
            # In this branch incremental_state is never None
            assert incremental_state is not None
            incremental_state = self._set_input_buffer(incremental_state, saved_state)
        assert k is not None
        src_len = k.size(1)

        # This is part of a workaround to get around fork/join parallelism
        # not supporting Optional types.
        if key_padding_mask is not None and key_padding_mask.dim() == 0:
            key_padding_mask = None

        if key_padding_mask is not None:
            assert key_padding_mask.size(0) == bsz
            assert key_padding_mask.size(1) == src_len

        attn_weights = torch.bmm(q, k.transpose(1, 2))

        assert list(attn_weights.size()) == [bsz * self.num_heads_partition, tgt_len, src_len]

        if attn_mask is not None:
            attn_mask = attn_mask.unsqueeze(0)
            attn_weights += attn_mask

        if key_padding_mask is not None:
            # don't attend to padding symbols
            attn_weights = attn_weights.view(bsz, self.num_heads_partition, tgt_len, src_len)
            attn_weights = attn_weights.masked_fill(
                key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf")
            )
            attn_weights = attn_weights.view(bsz * self.num_heads_partition, tgt_len, src_len)

        attn_weights_float = utils.softmax(
            attn_weights, dim=-1
        )
        attn_weights = attn_weights_float.type_as(attn_weights)

        with get_cuda_rng_tracker().fork():
            attn_probs = F.dropout(
                attn_weights_float.type_as(attn_weights),
                p=self.dropout,
                training=self.training,
            )

        assert v is not None
        attn = torch.bmm(attn_probs, v)
        assert list(attn.size()) == [bsz * self.num_heads_partition, tgt_len, self.head_dim]
        embed_dim_partition = embed_dim // self.model_parallel_size
        attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim_partition)
        attn = self.out_proj(attn)
        # return attn_weights None to keep the return type same as single gpu multihead attention
        # This will be deprecated.
        attn_weights: Optional[Tensor] = None

        return attn, attn_weights

    @staticmethod
    def _append_prev_key_padding_mask(
        key_padding_mask: Optional[Tensor],
        prev_key_padding_mask: Optional[Tensor],
        batch_size: int,
        src_len: int,
        static_kv: bool,
    ) -> Optional[Tensor]:
        # saved key padding masks have shape (bsz, seq_len)
        if prev_key_padding_mask is not None and static_kv:
            new_key_padding_mask = prev_key_padding_mask
        elif prev_key_padding_mask is not None and key_padding_mask is not None:
            new_key_padding_mask = torch.cat(
                [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1
            )
        # During incremental decoding, as the padding token enters and
        # leaves the frame, there will be a time when prev or current
        # is None
        elif prev_key_padding_mask is not None:

            filler = torch.zeros(batch_size, src_len - prev_key_padding_mask.size(1))
            if prev_key_padding_mask.is_cuda:
                filler = filler.cuda()
            new_key_padding_mask = torch.cat(
                [prev_key_padding_mask.float(), filler.float()], dim=1
            )
        elif key_padding_mask is not None:
            filler = torch.zeros(batch_size, src_len - key_padding_mask.size(1))
            if key_padding_mask.is_cuda:
                filler = filler.cuda()
            new_key_padding_mask = torch.cat(
                [filler.float(), key_padding_mask.float()], dim=1
            )
        else:
            new_key_padding_mask = prev_key_padding_mask
        return new_key_padding_mask

    def reorder_incremental_state(
        self, incremental_state: Dict[str, Dict[str, Optional[Tensor]]], new_order
    ):
        """Reorder buffered internal state (for incremental generation)."""
        input_buffer = self._get_input_buffer(incremental_state)
        if input_buffer is not None:
            for k in input_buffer.keys():
                if input_buffer[k] is not None:
                    input_buffer[k] = input_buffer[k].index_select(0, new_order)
            incremental_state = self._set_input_buffer(incremental_state, input_buffer)
        return incremental_state

    def _get_input_buffer(
        self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
    ) -> Dict[str, Optional[Tensor]]:
        result = self.get_incremental_state(incremental_state, "attn_state")
        if result is not None:
            return result
        else:
            empty_result: Dict[str, Optional[Tensor]] = {}
            return empty_result

    def _set_input_buffer(
        self,
        incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
        buffer: Dict[str, Optional[Tensor]],
    ):
        return self.set_incremental_state(incremental_state, "attn_state", buffer)
