# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Tuple, Union

import torch
from torch import Tensor

from megatron.core import InferenceParams, parallel_state, tensor_parallel
from megatron.core.models.common.embeddings.rope_utils import (
    apply_rotary_pos_emb,
    apply_rotary_pos_emb_with_cos_sin,
)
from megatron.core.parallel_state import (
    get_data_parallel_group,
    get_data_parallel_rank,
    get_data_parallel_world_size,
    get_tensor_model_parallel_group,
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
)
from megatron.core.transformer.module import MegatronModule
from megatron.core.transformer.spec_utils import ModuleSpec, build_module
from megatron.core.utils import divide

from .enums import AttnMaskType
from .transformer_config import TransformerConfig

try:
    from flash_attn import flash_attn_with_kvcache
except:
    flash_attn_with_kvcache = None


try:
    import transformer_engine  # pylint: disable=unused-import

    HAVE_TE = True
    from megatron.core.extensions.transformer_engine import SplitAlongDim
except ImportError:
    HAVE_TE = False
    SplitAlongDim = None


@dataclass
class SelfAttentionSubmodules:
    """
    Configuration class for specifying the submodules of a self-attention.
    """

    linear_qkv: Union[ModuleSpec, type] = None
    core_attention: Union[ModuleSpec, type] = None
    linear_proj: Union[ModuleSpec, type] = None
    q_layernorm: Union[ModuleSpec, type] = None
    k_layernorm: Union[ModuleSpec, type] = None


@dataclass
class CrossAttentionSubmodules:
    """
    Configuration class for specifying the submodules of a cross-attention.
    """

    linear_q: Union[ModuleSpec, type] = None
    linear_kv: Union[ModuleSpec, type] = None
    core_attention: Union[ModuleSpec, type] = None
    linear_proj: Union[ModuleSpec, type] = None


class Attention(MegatronModule, ABC):
    """Attention layer abstract class.

    This layer only contains common modules required for the "self attn" and
    "cross attn" specializations.
    """

    def __init__(
        self,
        config: TransformerConfig,
        submodules: Union[SelfAttentionSubmodules, CrossAttentionSubmodules],
        layer_number: int,
        attn_mask_type: AttnMaskType,
        attention_type: str,
        cp_comm_type: str = None,
    ):
        super().__init__(config=config)

        self.config = config
        self.layer_number = layer_number
        self.attn_mask_type = attn_mask_type
        self.attention_type = attention_type

        # For normal attention without groups, num_query_groups == num_attention_heads,
        # so these two will be the same
        self.query_projection_size = self.config.kv_channels * self.config.num_attention_heads
        self.kv_projection_size = self.config.kv_channels * self.config.num_query_groups

        # Per attention head and per partition values.
        world_size = parallel_state.get_tensor_model_parallel_world_size()
        self.hidden_size_per_attention_head = divide(
            self.query_projection_size, self.config.num_attention_heads
        )
        self.num_attention_heads_per_partition = divide(self.config.num_attention_heads, world_size)
        self.num_query_groups_per_partition = divide(self.config.num_query_groups, world_size)

        # To support both CUDA Graphs and key value with different hidden size
        self.key_hidden_size = self.hidden_size_per_attention_head
        self.val_hidden_size = self.hidden_size_per_attention_head

        self.core_attention = build_module(
            submodules.core_attention,
            config=self.config,
            layer_number=self.layer_number,
            attn_mask_type=self.attn_mask_type,
            attention_type=self.attention_type,
            cp_comm_type=cp_comm_type,
            softmax_scale=self.config.softmax_scale,
        )

        self.checkpoint_core_attention = self.config.recompute_granularity == 'selective'

        # Output.
        self.linear_proj = build_module(
            submodules.linear_proj,
            self.query_projection_size,
            self.config.hidden_size,
            config=self.config,
            init_method=self.config.output_layer_init_method,
            bias=self.config.add_bias_linear,
            input_is_parallel=True,
            skip_bias_add=True,
            is_expert=False,
            tp_comm_buffer_name='proj',
        )

    def _checkpointed_attention_forward(
        self,
        query,
        key,
        value,
        attention_mask,
        rotary_pos_emb=None,
        attn_mask_type=None,
        attention_bias=None,
        packed_seq_params=None,
    ):
        """Forward method with selective activation checkpointing."""

        def custom_forward(*inputs):
            query = inputs[0]
            key = inputs[1]
            value = inputs[2]
            attention_mask = inputs[3]
            attn_mask_type = inputs[5]
            attn_mask_type = AttnMaskType(attn_mask_type.item())
            output_ = self.core_attention(
                query,
                key,
                value,
                attention_mask,
                attn_mask_type=attn_mask_type,
                attention_bias=attention_bias,
                packed_seq_params=packed_seq_params,
            )
            return output_

        if attn_mask_type is None:
            attn_mask_type = self.attn_mask_type
        attn_mask_type = torch.tensor([attn_mask_type.value], dtype=torch.int)
        hidden_states = tensor_parallel.checkpoint(
            custom_forward, False, query, key, value, attention_mask, rotary_pos_emb, attn_mask_type
        )

        return hidden_states

    def _allocate_memory(self, inference_max_sequence_length, batch_size, dim, dtype):
        """Allocate memory to store kv cache during inference."""

        return torch.empty(
            inference_max_sequence_length,
            batch_size,
            self.num_query_groups_per_partition,
            dim,
            dtype=dtype,
            device=torch.cuda.current_device(),
        )

    def _adjust_key_value_for_inference(
        self,
        inference_params: InferenceParams,
        query: Tensor,
        key: Tensor,
        value: Tensor,
        rotary_pos_emb: Tensor,
        rotary_pos_cos: Tensor = None,
        rotary_pos_sin: Tensor = None,
        sequence_len_offset=None,
    ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
        """
        Saves the generated key and value tensors to the end of the buffers in inference_params.
        Returns the full size keys and values from the provided inference_params, as well as
        adjusted rotary_pos_emb.

        Returns a tuple: (key, value, rotary_pos_emb)

        """
        attn_mask_type = self.attn_mask_type
        if inference_params is None:
            return query, key, value, rotary_pos_emb, attn_mask_type

        # =================================================
        # Pre-allocate memory for key-values for inference.
        # =================================================
        if self.layer_number not in inference_params.key_value_memory_dict:
            inf_max_seq_length = inference_params.max_sequence_length
            inf_max_batch_size = inference_params.max_batch_size
            inference_key_memory = self._allocate_memory(
                inf_max_seq_length, inf_max_batch_size, self.key_hidden_size, key.dtype
            )
            inference_value_memory = self._allocate_memory(
                inf_max_seq_length, inf_max_batch_size, self.val_hidden_size, value.dtype
            )
            inference_params.key_value_memory_dict[self.layer_number] = (
                inference_key_memory,
                inference_value_memory,
            )
        else:
            # Get the pre-allocated buffers for this layer
            inference_key_memory, inference_value_memory = inference_params.key_value_memory_dict[
                self.layer_number
            ]

        if inference_params.sequence_len_offset > 0:
            # This should mean that we are past the prompt forward_step
            # and so we need to turn off masking
            attn_mask_type = AttnMaskType.no_mask

        batch_start = inference_params.batch_size_offset
        batch_end = batch_start + key.size(1)
        assert batch_end <= inference_key_memory.size(1)
        sequence_start = inference_params.sequence_len_offset
        sequence_end = sequence_start + key.size(0)
        assert sequence_end <= inference_key_memory.size(0)

        if self.config.flash_decode:
            assert (
                rotary_pos_cos is not None and rotary_pos_sin is not None
            ), "Flash decoding requires precomputed cos and sin tensors"
            if inference_params.sequence_len_offset > 0:  # Decode phase, not prefill
                rotary_pos_cos_q = rotary_pos_cos[sequence_end - 1 : sequence_end]
                rotary_pos_sin_q = rotary_pos_sin[sequence_end - 1 : sequence_end]
                rotary_pos_cos_k = rotary_pos_cos[sequence_end - 1 : sequence_end]
                rotary_pos_sin_k = rotary_pos_sin[sequence_end - 1 : sequence_end]
            else:  # Prefill
                rotary_pos_cos_q = rotary_pos_cos[:sequence_end]
                rotary_pos_sin_q = rotary_pos_sin[:sequence_end]
                rotary_pos_cos_k = rotary_pos_cos[:sequence_end]
                rotary_pos_sin_k = rotary_pos_sin[:sequence_end]

            # Flash Decoding assumes that the keys stored in the KV Cache already have RoPE applied.
            # Apply RoPE before we store the keys to make it compatible with flash decoding kernel.
            key = apply_rotary_pos_emb_with_cos_sin(key, rotary_pos_cos_k, rotary_pos_sin_k)
            query = apply_rotary_pos_emb_with_cos_sin(query, rotary_pos_cos_q, rotary_pos_sin_q)
        else:
            rotary_pos_cos_q = None
            rotary_pos_sin_q = None

        # Copy key and values.
        inference_key_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = key
        inference_value_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = value
        key = inference_key_memory[:sequence_end, batch_start:batch_end, ...]
        value = inference_value_memory[:sequence_end, batch_start:batch_end, ...]

        # adjust the key rotary positional embedding
        if rotary_pos_emb is None:
            return query, key, value, rotary_pos_emb, attn_mask_type

        q_pos_emb, k_pos_emb = rotary_pos_emb
        q_pos_emb = q_pos_emb[sequence_start:sequence_end, :, :, :]
        k_pos_emb = k_pos_emb[:sequence_end, :, :, :]
        rotary_pos_emb = (q_pos_emb, k_pos_emb)

        return query, key, value, rotary_pos_emb, attn_mask_type

    @abstractmethod
    def get_query_key_value_tensors(self, hidden_states, key_value_states):
        """
        This method needs to be implemented based on whether the derived class
        is "self-attn" or "cross-attn".
        """

    def flash_decoding(
        self,
        sequence_len_offset: Tensor,
        query_layer: Tensor,
        key_layer: Tensor,
        value_layer: Tensor,
        inference_key_memory: Tensor,
        inference_value_memory: Tensor,
        rotary_cos: Tensor,
        rotary_sin: Tensor,
    ) -> (Tensor, Tensor):
        """
        The flash decoding kernel will do the following in a single execution:
        1. Compute RoPE embedding with precomputed cos & sin tensors
        2. Update the KV Cache
        3. Performs the flash attention operation
        """
        assert flash_attn_with_kvcache is not None, (
            "Flash Decoding requires the flash_attn_with_kvcache kernel, "
            "available in the flash-attn package."
        )
        cache_seqlens = sequence_len_offset - 1
        q = query_layer.permute(1, 0, 2, 3)
        k = key_layer.permute(1, 0, 2, 3)
        v = value_layer.permute(1, 0, 2, 3)
        k_cache = inference_key_memory.permute(1, 0, 2, 3)
        v_cache = inference_value_memory.permute(1, 0, 2, 3)

        if rotary_cos is not None:
            rotary_cos = rotary_cos.to(query_layer.dtype)
        if rotary_sin is not None:
            rotary_sin = rotary_sin.to(query_layer.dtype)

        out = flash_attn_with_kvcache(
            q=q,
            k_cache=k_cache,
            v_cache=v_cache,
            k=k,
            v=v,
            rotary_cos=rotary_cos,
            rotary_sin=rotary_sin,
            cache_seqlens=cache_seqlens,
            rotary_interleaved=False,
        )
        return out

    def forward(
        self,
        hidden_states,
        attention_mask,
        key_value_states=None,
        inference_params=None,
        rotary_pos_emb=None,
        rotary_pos_cos=None,
        rotary_pos_sin=None,
        attention_bias=None,
        packed_seq_params=None,
        sequence_len_offset=None,
    ):
        """
        Perform a forward pass through the attention module.
        """

        # hidden_states: [sq, b, h]
        if self.config.flash_decode:
            rotary_pos_emb = None
        else:
            assert rotary_pos_cos is None and rotary_pos_sin is None

        # For self attention we just duplicate the rotary_pos_emb if it isn't already
        if rotary_pos_emb is not None and not isinstance(rotary_pos_emb, tuple):
            rotary_pos_emb = (rotary_pos_emb,) * 2

        # =====================
        # Query, Key, and Value
        # =====================
        # Get the query, key and value tensors based on the type of attention -
        # self or cross attn.
        query, key, value = self.get_query_key_value_tensors(hidden_states, key_value_states)

        # ===================================================
        # Adjust key, value, and rotary_pos_emb for inference
        # ===================================================

        # This branch only runs in the decode phase of flash decoding and returns after the linear
        # projection. This conditional is not used in the prefill phase or non-flash-decoding cases.
        if (
            self.config.flash_decode
            and inference_params is not None
            and inference_params.decode_mode
        ):
            assert self.layer_number in inference_params.key_value_memory_dict
            assert inference_params.sequence_len_offset is not None
            inference_key_memory, inference_value_memory = inference_params.key_value_memory_dict[
                self.layer_number
            ]
            output = self.flash_decoding(
                sequence_len_offset=sequence_len_offset,
                query_layer=query,
                key_layer=key,
                value_layer=value,
                inference_key_memory=inference_key_memory,
                inference_value_memory=inference_value_memory,
                rotary_cos=rotary_pos_cos,
                rotary_sin=rotary_pos_sin,
            )
            out = output.transpose(0, 1).contiguous()
            context_layer = out.view(out.size(0), out.size(1), -1)
            output, bias = self.linear_proj(context_layer)
            return output, bias

        query, key, value, rotary_pos_emb, attn_mask_type = self._adjust_key_value_for_inference(
            inference_params,
            query,
            key,
            value,
            rotary_pos_emb,
            rotary_pos_cos,
            rotary_pos_sin,
            sequence_len_offset,
        )

        if packed_seq_params is not None:
            query = query.squeeze(1)
            key = key.squeeze(1)
            value = value.squeeze(1)

        # ================================================
        # relative positional embedding (rotary embedding)
        # ================================================
        if rotary_pos_emb is not None and not self.config.flash_decode:
            q_pos_emb, k_pos_emb = rotary_pos_emb

            if packed_seq_params is not None:
                if packed_seq_params.cu_seqlens_q_padded is not None:
                    cu_seqlens_q = packed_seq_params.cu_seqlens_q_padded
                else:
                    cu_seqlens_q = packed_seq_params.cu_seqlens_q
                if packed_seq_params.cu_seqlens_kv_padded is not None:
                    cu_seqlens_kv = packed_seq_params.cu_seqlens_kv_padded
                else:
                    cu_seqlens_kv = packed_seq_params.cu_seqlens_kv
            else:
                cu_seqlens_q = cu_seqlens_kv = None
            query = apply_rotary_pos_emb(
                query, q_pos_emb, config=self.config, cu_seqlens=cu_seqlens_q
            )
            key = apply_rotary_pos_emb(key, k_pos_emb, config=self.config, cu_seqlens=cu_seqlens_kv)

            # TODO, can apply positional embedding to value_layer so it has
            # absolute positional embedding.
            # otherwise, only relative positional embedding takes effect
            # value_layer = apply_rotary_pos_emb(value_layer, k_pos_emb)

        # ==================================
        # core attention computation
        # ==================================

        if self.checkpoint_core_attention and self.training:
            core_attn_out = self._checkpointed_attention_forward(
                query,
                key,
                value,
                attention_mask,
                attn_mask_type=attn_mask_type,
                attention_bias=attention_bias,
                packed_seq_params=packed_seq_params,
            )
        else:
            core_attn_out = self.core_attention(
                query,
                key,
                value,
                attention_mask,
                attn_mask_type=attn_mask_type,
                attention_bias=attention_bias,
                packed_seq_params=packed_seq_params,
            )

        if packed_seq_params is not None and packed_seq_params.qkv_format == 'thd':
            # reshape to same output shape as unpacked case
            # (t, np, hn) -> (t, b=1, h=np*hn)
            # t is the pack size = sum (sq_i)
            # note that batch is a dummy dimension in the packed case
            core_attn_out = core_attn_out.reshape(core_attn_out.size(0), 1, -1)

        # =================
        # Output. [sq, b, h]
        # =================

        output, bias = self.linear_proj(core_attn_out)

        return output, bias


class SelfAttention(Attention):
    """Self-attention layer class

    Self-attention layer takes input with size [s, b, h]
    and returns output of the same size.
    """

    def __init__(
        self,
        config: TransformerConfig,
        submodules: SelfAttentionSubmodules,
        layer_number: int,
        attn_mask_type=AttnMaskType.padding,
        cp_comm_type: str = None,
    ):
        super().__init__(
            config=config,
            submodules=submodules,
            layer_number=layer_number,
            attn_mask_type=attn_mask_type,
            attention_type="self",
            cp_comm_type=cp_comm_type,
        )

        self.linear_qkv = build_module(
            submodules.linear_qkv,
            self.config.hidden_size,
            self.query_projection_size + 2 * self.kv_projection_size,
            config=self.config,
            init_method=self.config.init_method,
            gather_output=False,
            bias=self.config.add_bias_linear or self.config.add_qkv_bias,
            skip_bias_add=False,
            is_expert=False,
            tp_comm_buffer_name='qkv',
        )

        if submodules.q_layernorm is not None:
            self.q_layernorm = build_module(
                submodules.q_layernorm,
                hidden_size=self.hidden_size_per_attention_head,
                config=self.config,
                eps=self.config.layernorm_epsilon,
            )
        else:
            self.q_layernorm = None

        if submodules.k_layernorm is not None:
            self.k_layernorm = build_module(
                submodules.k_layernorm,
                hidden_size=self.hidden_size_per_attention_head,
                config=self.config,
                eps=self.config.layernorm_epsilon,
            )
        else:
            self.k_layernorm = None

    def run_realtime_tests(self):
        """Performs a consistency check.

        This function makes sure that tensors across devices are the same during an experiment.
        This is often not guaranteed to be so because of silent hardware failures (eg, memory
        corruption loading a checkpoint, network traffic corruption encountered during
        data transmission).

        (TODO) In the future, more tensors should be checked across the training run and
        checked every X iterations. This is left for future work. Equality of tensors is probably
        not required; transmitting hashes is sufficient."""

        if not self.config.qk_layernorm:
            return

        # check that all tensor parallel and data parallel ranks have the same
        # Q & K layernorm parameters.
        rank = get_data_parallel_rank()
        inputs = torch.stack(
            [
                self.q_layernorm.weight.data,
                self.q_layernorm.bias.data,
                self.k_layernorm.weight.data,
                self.k_layernorm.bias.data,
            ]
        )
        dp_list = [torch.empty_like(inputs) for _ in range(get_data_parallel_world_size())]
        dp_list[rank] = inputs
        torch.distributed.all_gather(dp_list, inputs, group=get_data_parallel_group())

        def _compare(srcs, tgts, names, parallelism):
            assert len(srcs) == len(tgts) == len(names)
            for src, tgt, name in zip(srcs, tgts, names):
                assert torch.all(src == tgt), (
                    f"Discrepancy between {name} in {parallelism} ranks {i} and {rank}. "
                    f"Diff: {torch.norm(src - tgt)}"
                )

        for i, dp in enumerate(dp_list):
            q_w, q_b, k_w, k_b = torch.unbind(dp)
            _compare(
                [q_w, q_b, k_w, k_b],
                [
                    self.q_layernorm.weight.data,
                    self.q_layernorm.bias.data,
                    self.k_layernorm.weight.data,
                    self.k_layernorm.bias.data,
                ],
                ["q_w", "q_b", "k_w", "k_b"],
                "DP",
            )

        rank = get_tensor_model_parallel_rank()
        tp_list = [torch.empty_like(inputs) for _ in range(get_tensor_model_parallel_world_size())]
        tp_list[rank] = inputs
        torch.distributed.all_gather(tp_list, inputs, group=get_tensor_model_parallel_group())

        for i, tp in enumerate(tp_list):
            q_w, q_b, k_w, k_b = torch.unbind(tp)
            _compare(
                [q_w, q_b, k_w, k_b],
                [
                    self.q_layernorm.weight.data,
                    self.q_layernorm.bias.data,
                    self.k_layernorm.weight.data,
                    self.k_layernorm.bias.data,
                ],
                ["q_w", "q_b", "k_w", "k_b"],
                "TP",
            )

    def get_query_key_value_tensors(self, hidden_states, key_value_states=None):
        """
        Derives `query`, `key` and `value` tensors from `hidden_states`.
        """
        # Attention heads [sq, b, h] --> [sq, b, ng * (np/ng + 2) * hn)]
        mixed_qkv, _ = self.linear_qkv(hidden_states)

        # [sq, b, hp] --> [sq, b, ng, (np/ng + 2) * hn]
        new_tensor_shape = mixed_qkv.size()[:-1] + (
            self.num_query_groups_per_partition,
            (
                (self.num_attention_heads_per_partition // self.num_query_groups_per_partition + 2)
                * self.hidden_size_per_attention_head
            ),
        )
        mixed_qkv = mixed_qkv.view(*new_tensor_shape)

        split_arg_list = [
            (
                self.num_attention_heads_per_partition
                // self.num_query_groups_per_partition
                * self.hidden_size_per_attention_head
            ),
            self.hidden_size_per_attention_head,
            self.hidden_size_per_attention_head,
        ]

        if SplitAlongDim is not None:

            # [sq, b, ng, (np/ng + 2) * hn]
            # --> [sq, b, ng, np/ng * hn], [sq, b, ng, hn], [sq, b, ng, hn]
            (query, key, value) = SplitAlongDim(mixed_qkv, 3, split_arg_list)
        else:

            # [sq, b, ng, (np/ng + 2) * hn]
            # --> [sq, b, ng, np/ng * hn], [sq, b, ng, hn], [sq, b, ng, hn]
            (query, key, value) = torch.split(mixed_qkv, split_arg_list, dim=3)

        # [sq, b, ng, np/ng * hn] -> [sq, b, np, hn]
        query = query.reshape(query.size(0), query.size(1), -1, self.hidden_size_per_attention_head)

        if self.q_layernorm is not None:
            query = self.q_layernorm(query)

        if self.k_layernorm is not None:
            key = self.k_layernorm(key)

        if self.config.test_mode:
            self.run_realtime_tests()

        return query, key, value


class CrossAttention(Attention):
    """Cross-attention layer class

    Cross-attention layer takes input with size [s, b, h] and context with size
    [s, b, h] and returns output of the same size.
    """

    def __init__(
        self,
        config: TransformerConfig,
        submodules: CrossAttentionSubmodules,
        layer_number: int,
        attn_mask_type=AttnMaskType.padding,
        cp_comm_type: str = None,
    ):
        super().__init__(
            config=config,
            submodules=submodules,
            layer_number=layer_number,
            attn_mask_type=attn_mask_type,
            attention_type="cross",
            cp_comm_type=cp_comm_type,
        )

        if self.config.num_query_groups != self.config.num_attention_heads:
            raise ValueError("Group query attention is not currently supported in cross attention.")
        assert self.query_projection_size == self.kv_projection_size

        self.linear_q = build_module(
            submodules.linear_q,
            self.config.hidden_size,
            self.query_projection_size,
            config=self.config,
            init_method=self.config.init_method,
            gather_output=False,
            bias=self.config.add_bias_linear,
            skip_bias_add=False,
            is_expert=False,
        )

        self.linear_kv = build_module(
            submodules.linear_kv,
            self.config.hidden_size,
            2 * self.kv_projection_size,
            config=self.config,
            init_method=self.config.init_method,
            gather_output=False,
            bias=self.config.add_bias_linear,
            skip_bias_add=False,
            is_expert=False,
        )

    def get_query_key_value_tensors(self, hidden_states, key_value_states):
        """
        Derives `query` tensor from `hidden_states`, and `key`/`value` tensors
        from `key_value_states`.
        """
        # Attention heads [sk, b, h] --> [sk, b, (np * 2 * hn)]
        mixed_kv, _ = self.linear_kv(key_value_states)

        # [sk, b, (np * 2 * hn)] --> [sk, b, np, 2 * hn]
        new_tensor_shape = mixed_kv.size()[:-1] + (
            self.num_attention_heads_per_partition,
            2 * self.hidden_size_per_attention_head,
        )
        mixed_kv = mixed_kv.view(*new_tensor_shape)

        # [sk, b, np, 2 * hn] --> 2 [sk, b, np, hn]
        (key, value) = tensor_parallel.split_tensor_along_last_dim(mixed_kv, 2)

        # Attention head [sq, b, h] --> [sq, b, hp]
        query, _ = self.linear_q(hidden_states)

        # [sq, b, hp] --> [sq, b, np, hn]
        new_tensor_shape = query.size()[:-1] + (
            self.num_attention_heads_per_partition,
            self.hidden_size_per_attention_head,
        )
        query = query.view(*new_tensor_shape)

        return query, key, value
