mha.py

# Copyright (c) 2022, Tri Dao.

import math
from functools import partial

import torch
import torch.nn as nn
import torch.nn.functional as F

from einops import rearrange

try:
    from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
    from flash_attn.flash_attn_interface import flash_attn_unpadded_kvpacked_func
except ImportError:
    flash_attn_unpadded_qkvpacked_func, flash_attn_unpadded_kvpacked_func = None, None

try:
    from flash_attn.ops.flash_attn_triton import flash_attn_qkvpacked_func, flash_attn_kvpacked_func
except ImportError:
    flash_attn_qkvpacked_func, flash_attn_kvpacked_func = None, None

try:
    from flash_attn.ops.fused_dense import FusedDense, ColumnParallelLinear, RowParallelLinear
except ImportError:
    FusedDense, ColumnParallelLinear, RowParallelLinear = None, None, None

try:
    from flash_attn.layers.rotary import RotaryEmbedding
except ImportError:
    RotaryEmbedding = None


class FlashSelfAttention(nn.Module):
    """Implement the scaled dot product attention with softmax.
    Arguments
    ---------
        softmax_scale: The temperature to use for the softmax attention.
                      (default: 1/sqrt(d_keys) where d_keys is computed at
                      runtime)
        attention_dropout: The dropout rate to apply to the attention
                           (default: 0.0)
    """
    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0,
                 triton=False):
        super().__init__()
        if attention_dropout != 0.0 or not triton:
            assert flash_attn_unpadded_qkvpacked_func is not None, 'FlashAttention is not installed'
        if attention_dropout == 0.0 and triton:
            assert flash_attn_qkvpacked_func is not None, 'FlashAttention Triton is not installed'
        self.causal = causal
        self.softmax_scale = softmax_scale
        self.dropout_p = attention_dropout
        self.triton = triton

    def forward(self, qkv, causal=None, cu_seqlens=None, max_seqlen=None):
        """Implements the multihead softmax attention.
        Arguments
        ---------
            qkv: The tensor containing the query, key, and value.
                If cu_seqlens is None and max_seqlen is None, then qkv has shape (B, S, 3, H, D).
                If cu_seqlens is not None and max_seqlen is not None, then qkv has shape
                (total, 3, H, D), where total is the sum of the sequence lengths in the batch.
            causal: if passed, will override self.causal
            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
                of the sequences in the batch, used to index into qkv.
            max_seqlen: int. Maximum sequence length in the batch.
        Returns:
        --------
            out: (total, H, D) if cu_seqlens is not None and max_seqlen is not None,
                else (B, S, H, D).
        """
        assert qkv.dtype in [torch.float16, torch.bfloat16]
        assert qkv.is_cuda
        causal = self.causal if causal is None else causal
        unpadded = cu_seqlens is not None
        if unpadded:
            assert cu_seqlens.dtype == torch.int32
            assert max_seqlen is not None
            assert isinstance(max_seqlen, int)
            return flash_attn_unpadded_qkvpacked_func(
                qkv, cu_seqlens, max_seqlen, self.dropout_p if self.training else 0.0,
                softmax_scale=self.softmax_scale, causal=causal
            )
        else:
            batch_size, seqlen = qkv.shape[0], qkv.shape[1]
            # Triton version doesn't support dropout
            if self.triton and (self.dropout_p == 0 or not self.training):
                output = flash_attn_qkvpacked_func(qkv, None, causal, self.softmax_scale)
            else:
                qkv = rearrange(qkv, 'b s ... -> (b s) ...')
                max_seqlen = seqlen
                cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
                                        device=qkv.device)
                output = flash_attn_unpadded_qkvpacked_func(
                    qkv, cu_seqlens, max_seqlen, self.dropout_p if self.training else 0.0,
                    softmax_scale=self.softmax_scale, causal=causal
                )
                output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
            return output


class FlashCrossAttention(nn.Module):
    """Implement the scaled dot product attention with softmax.
    Arguments
    ---------
        softmax_scale: The temperature to use for the softmax attention.
                      (default: 1/sqrt(d_keys) where d_keys is computed at
                      runtime)
        attention_dropout: The dropout rate to apply to the attention
                           (default: 0.0)
    """
    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0,
                 triton=False):
        super().__init__()
        if attention_dropout != 0.0 or not triton:
            assert flash_attn_unpadded_kvpacked_func is not None, 'FlashAttention is not installed'
        if attention_dropout == 0.0 and triton:
            assert flash_attn_kvpacked_func is not None, 'FlashAttention Triton is not installed'
        self.causal = causal
        self.softmax_scale = softmax_scale
        self.dropout_p = attention_dropout
        self.triton = triton

    def forward(self, q, kv, causal=None, cu_seqlens=None, max_seqlen=None,
                cu_seqlens_k=None, max_seqlen_k=None):
        """Implements the multihead softmax attention.
        Arguments
        ---------
            q: The tensor containing the query. (B, Sq, H, D)
            kv: The tensor containing the key and value. (B, Sk, 2, H, D)
            causal: if passed, will override self.causal
            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
                of the sequences in the batch, used to index into q.
            max_seqlen: int. Maximum sequence length in the batch of q.
            cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
                of the sequences in the batch, used to index into kv.
            max_seqlen_k: int. Maximum sequence length in the batch of k and v.
        """
        assert q.dtype in [torch.float16, torch.bfloat16]
        assert q.is_cuda and kv.is_cuda
        causal = self.causal if causal is None else causal
        unpadded = cu_seqlens is not None
        if unpadded:
            assert cu_seqlens.dtype == torch.int32
            assert max_seqlen is not None
            assert isinstance(max_seqlen, int)
            assert cu_seqlens_k is not None
            assert cu_seqlens_k.dtype == torch.int32
            assert max_seqlen_k is not None
            assert isinstance(max_seqlen, int)
            return flash_attn_unpadded_kvpacked_func(
                q, kv, cu_seqlens, cu_seqlens_k, max_seqlen, max_seqlen_k,
                self.dropout_p if self.training else 0.0,
                softmax_scale=self.softmax_scale, causal=causal
            )
        else:
            batch_size, seqlen_q = q.shape[0], q.shape[1]
            seqlen_k = kv.shape[1]
            assert kv.shape[0] == batch_size and kv.shape[3] == q.shape[2] and kv.shape[4] == q.shape[3]
            if self.triton and (self.dropout_p == 0.0 or not self.training):  # Triton version doesn't support dropout
                output = flash_attn_kvpacked_func(q, kv, None, causal, self.softmax_scale)
            else:
                q = rearrange(q, 'b s ... -> (b s) ...')
                kv = rearrange(kv, 'b s ... -> (b s) ...')
                cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q,
                                            dtype=torch.int32, device=q.device)
                cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k,
                                            dtype=torch.int32, device=kv.device)
                output = flash_attn_unpadded_kvpacked_func(
                    q, kv, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
                    self.dropout_p if self.training else 0.0,
                    softmax_scale=self.softmax_scale, causal=causal
                )
                output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
            return output


class SelfAttention(nn.Module):
    """Implement the scaled dot product attention with softmax.
    Arguments
    ---------
        softmax_scale: The temperature to use for the softmax attention.
                      (default: 1/sqrt(d_keys) where d_keys is computed at
                      runtime)
        attention_dropout: The dropout rate to apply to the attention
                           (default: 0.0)
    """
    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
        super().__init__()
        self.causal = causal
        self.softmax_scale = softmax_scale
        self.dropout_p = attention_dropout

    def forward(self, qkv, causal=None, key_padding_mask=None):
        """Implements the multihead softmax attention.
        Arguments
        ---------
            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D)
            causal: if passed, will override self.causal
            key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
                False means to mask out. (B, S)
        """
        batch_size, seqlen = qkv.shape[0], qkv.shape[1]
        causal = self.causal if causal is None else causal
        q, k, v = qkv.unbind(dim=2)
        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
        scores = torch.einsum('bthd,bshd->bhts', q, k * softmax_scale)
        if key_padding_mask is not None:
            padding_mask = torch.full((batch_size, seqlen), -10000.0, dtype=scores.dtype,
                                      device=scores.device)
            padding_mask.masked_fill_(key_padding_mask, 0.0)
            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
            scores = scores + rearrange(padding_mask, 'b s -> b 1 1 s')
        if causal:
            # "triu_tril_cuda_template" not implemented for 'BFloat16'
            # So we have to construct the mask in float
            causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
            scores = scores + causal_mask.to(dtype=scores.dtype)
        attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
        attention_drop = F.dropout(attention, self.dropout_p if self.training else 0.0)
        output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
        return output


class CrossAttention(nn.Module):
    """Implement the scaled dot product attention with softmax.
    Arguments
    ---------
        softmax_scale: The temperature to use for the softmax attention.
                      (default: 1/sqrt(d_keys) where d_keys is computed at
                      runtime)
        attention_dropout: The dropout rate to apply to the attention
                           (default: 0.0)
    """
    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
        super().__init__()
        self.causal = causal
        self.softmax_scale = softmax_scale
        self.dropout_p = attention_dropout

    def forward(self, q, kv, causal=None, key_padding_mask=None):
        """Implements the multihead softmax attention.
        Arguments
        ---------
            q: The tensor containing the query. (B, Sq, H, D)
            kv: The tensor containing the key and value. (B, Sk, 2, H, D)
            causal: if passed, will override self.causal
            key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
                False means to mask out. (B, Sk)
        """
        batch_size, seqlen_q = q.shape[0], q.shape[1]
        causal = self.causal if causal is None else causal
        seqlen_k = kv.shape[1]
        assert kv.shape[0] == batch_size and kv.shape[3] == q.shape[2] and kv.shape[4] == q.shape[3]
        k, v = kv.unbind(dim=2)
        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
        scores = torch.einsum('bthd,bshd->bhts', q, k * softmax_scale)
        if key_padding_mask is not None:
            padding_mask = torch.full((batch_size, seqlen_k), -10000.0, dtype=scores.dtype,
                                      device=scores.device)
            padding_mask.masked_fill_(key_padding_mask, 0.0)
            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
            scores = scores + rearrange(padding_mask, 'b s -> b 1 1 s')
        if causal:
            # "triu_tril_cuda_template" not implemented for 'BFloat16'
            # So we have to construct the mask in float
            causal_mask = torch.triu(torch.full((seqlen_q, seqlen_k), -10000.0,
                                                device=scores.device), 1)
            # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
            scores = scores + causal_mask.to(dtype=scores.dtype)
        attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
        attention_drop = F.dropout(attention, self.dropout_p if self.training else 0.0)
        output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
        return output


class LinearResidual(nn.Linear):
    """Wrap nn.Linear to return the residual as well. For compatibility with FusedDense.
    """

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return super().forward(input), input


class MHA(nn.Module):
    """Multi-head self-attention and cross-attention
    """

    def __init__(self, embed_dim, num_heads, cross_attn=False, bias=True, dropout=0.0,
                 softmax_scale=None, causal=False, layer_idx=None, dwconv=False, rotary_emb_dim=0,
                 rotary_emb_scale_base=0,
                 fused_bias_fc=False, use_flash_attn=False, return_residual=False,
                 checkpointing=False, device=None, dtype=None) -> None:
        """
            return_residual: whether to return the input x along with the output. This is for
                performance reason: for post-norm architecture, returning the input allows us
                to fuse the backward of nn.Linear with the residual connection.
        """
        factory_kwargs = {'device': device, 'dtype': dtype}
        super().__init__()
        self.embed_dim = embed_dim
        self.cross_attn = cross_attn
        self.causal = causal
        self.layer_idx = layer_idx
        self.dwconv = dwconv
        self.rotary_emb_dim = rotary_emb_dim
        self.use_flash_attn = use_flash_attn
        self.return_residual = return_residual
        self.checkpointing = checkpointing

        self.num_heads = num_heads
        assert self.embed_dim % num_heads == 0, "self.kdim must be divisible by num_heads"
        self.head_dim = self.embed_dim // num_heads

        if self.rotary_emb_dim > 0:
            assert not cross_attn, 'MHA with rotary embedding does not support cross-attention yet'
            assert RotaryEmbedding is not None, 'rotary_emb is not installed'
            self.rotary_emb = RotaryEmbedding(self.rotary_emb_dim, scale_base=rotary_emb_scale_base,
                                              device=device)

        if fused_bias_fc and FusedDense is None:
            raise ImportError('fused_dense is not installed')
        linear_cls = nn.Linear if not fused_bias_fc else FusedDense
        linear_resid_cls = (LinearResidual if not fused_bias_fc
                            else partial(FusedDense, return_residual=True))
        inner_attn_cls = FlashSelfAttention if use_flash_attn else SelfAttention
        inner_cross_attn_cls = FlashCrossAttention if use_flash_attn else CrossAttention
        if not self.cross_attn:
            if not self.return_residual:
                self.Wqkv = linear_cls(embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs)
            else:
                self.Wqkv = linear_resid_cls(embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs)
            if self.dwconv:
                self.dwconv_qkv = nn.Conv1d(3 * embed_dim, 3 * embed_dim, kernel_size=3, padding=2,
                                            groups=3 * embed_dim)
        else:
            self.Wq = linear_cls(embed_dim, embed_dim, bias=bias, **factory_kwargs)
            if not self.return_residual:
                self.Wkv = linear_cls(embed_dim, 2 * embed_dim, bias=bias, **factory_kwargs)
            else:
                self.Wkv = linear_resid_cls(embed_dim, 2 * embed_dim, bias=bias, **factory_kwargs)
            if self.dwconv:
                self.dwconv_q = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, padding=2,
                                        groups=embed_dim)
                self.dwconv_kv = nn.Conv1d(2 * embed_dim, 2 * embed_dim, kernel_size=3, padding=2,
                                        groups=2 * embed_dim)
        self.inner_attn = inner_attn_cls(causal=causal, softmax_scale=softmax_scale,
                                         attention_dropout=dropout)
        self.inner_cross_attn = inner_cross_attn_cls(causal=causal, softmax_scale=softmax_scale,
                                                     attention_dropout=dropout)
        # output projection always have the bias (for now)
        self.out_proj = linear_cls(embed_dim, embed_dim, **factory_kwargs)

    def _update_kv_cache(self, kv, inference_params):
        """kv: (batch_size, 1, nheads, head_dim)
        """
        assert not self.dwconv, 'Generation does not support dwconv yet'
        assert self.layer_idx is not None, 'Generation requires layer_idx in the constructor'
        # Pre-allocate memory for key-values for inference.
        if self.layer_idx not in inference_params.key_value_memory_dict:
            inference_kv_cache = torch.empty(
                inference_params.max_batch_size, inference_params.max_sequence_len, 2,
                self.num_heads, self.head_dim, dtype=kv.dtype, device=kv.device
            )
            inference_params.key_value_memory_dict[self.layer_idx] = inference_kv_cache
        else:
            inference_kv_cache = inference_params.key_value_memory_dict[self.layer_idx]
        # Adjust key and value for inference
        batch_start = inference_params.batch_size_offset
        batch_end = batch_start + kv.shape[0]
        assert batch_end <= inference_kv_cache.shape[0]
        sequence_start = inference_params.sequence_len_offset
        sequence_end = sequence_start + kv.shape[1]
        assert sequence_end <= inference_kv_cache.shape[1]
        # Copy key and values.
        inference_kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
        kv = inference_kv_cache[batch_start:batch_end, :sequence_end, ...]
        return kv

    def forward(self, x, x_kv=None, key_padding_mask=None, cu_seqlens=None, max_seqlen=None,
                inference_params=None, **kwargs):
        """
        Arguments:
            x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if
                cu_seqlens is None and max_seqlen is None, else (total, hidden_dim) where total
                is the is the sum of the sequence lengths in the batch.
            x_kv: (batch, seqlen, hidden_dim), only applicable for cross-attention. If None, use x.
            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
                of the sequences in the batch, used to index into x. Only applicable when using
                FlashAttention.
            max_seqlen: int. Maximum sequence length in the batch.
            key_padding_mask: boolean mask, True means to keep, False means to mask out.
                (batch, seqlen). Only applicable when not using FlashAttention.
            inference_params: for generation. Adapted from Megatron-LM (and Apex)
            https://github.com/NVIDIA/apex/blob/3ff1a10f72ec07067c4e44759442329804ac5162/apex/transformer/testing/standalone_transformer_lm.py#L470
        """
        if cu_seqlens is not None:
            assert max_seqlen is not None
            assert key_padding_mask is None
            assert self.use_flash_attn
            assert not self.dwconv
            assert self.rotary_emb_dim == 0
        if key_padding_mask is not None:
            assert cu_seqlens is None
            assert max_seqlen is None
            assert not self.use_flash_attn
        if inference_params is not None:
            assert key_padding_mask is None
            assert cu_seqlens is None and max_seqlen is None
            assert not self.dwconv

        kwargs = ({'cu_seqlens': cu_seqlens, 'max_seqlen': max_seqlen, **kwargs}
                  if self.use_flash_attn else {'key_padding_mask': key_padding_mask, **kwargs})
        if not self.cross_attn:
            if not self.return_residual:
                qkv = self.Wqkv(x)
            else:
                qkv, x = self.Wqkv(x)
            if self.dwconv:
                qkv = rearrange(self.dwconv_qkv(rearrange(qkv, 'b s d -> b d s'))[..., :-2],
                                'b d s -> b s d').contiguous()
            qkv = rearrange(qkv, '... (three h d) -> ... three h d', three=3, d=self.head_dim)
            if inference_params is None:
                if self.rotary_emb_dim > 0:
                    qkv = self.rotary_emb(qkv)
                if not self.checkpointing:
                    context = self.inner_attn(qkv, **kwargs)
                else:
                    context = torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, **kwargs)
            else:
                if self.rotary_emb_dim > 0:
                    qkv = self.rotary_emb(qkv, seqlen_offset=inference_params.sequence_len_offset)
                q = qkv[:, :, 0]
                kv = self._update_kv_cache(qkv[:, :, 1:], inference_params)
                # If we're processing the prompt, causal=None (use self.causal).
                # If we're decoding, then causal=False.
                causal = None if inference_params.sequence_len_offset == 0 else False
                context = self.inner_cross_attn(q, kv, causal=causal)
        else:
            if not self.return_residual:
                q = self.Wq(x)
                kv = self.Wkv(x_kv if x_kv is not None else x)
            else:
                if x_kv is not None:
                    kv, x_kv = self.Wkv(x_kv)
                else:
                    kv, x = self.Wkv(x)
                q = self.Wq(x)
            q = rearrange(q, '... (h d) -> ... h d', d=self.head_dim)
            kv = rearrange(kv, '... (two h d) -> ... two h d', two=2, d=self.head_dim)
            if self.dwconv:
                q = rearrange(self.dwconv_q(rearrange(q, 'b s d -> b d s'))[..., :-2],
                              'b d s -> b s d').contiguous()
                kv = rearrange(self.dwconv_kv(rearrange(kv, 'b s d -> b d s'))[..., :-2],
                               'b d s -> b s d').contiguous()
            if inference_params is None:
                if not self.checkpointing:
                    context = self.inner_attn(q, kv, **kwargs)
                else:
                    context = torch.utils.checkpoint.checkpoint(self.inner_attn, q, kv, **kwargs)
            else:
                kv = self._update_kv_cache(kv)
                context = self.inner_cross_attn(q, kv, causal=False)
        out = self.out_proj(rearrange(context, '... h d -> ... (h d)'))
        return out if not self.return_residual else (out, x)


class ParallelMHA(nn.Module):
    """Multi-head self-attention and cross-attention
    """

    def __init__(self, embed_dim, num_heads, process_group, bias=True, dropout=0.0,
                 softmax_scale=None, causal=False, rotary_emb_dim=0, rotary_emb_scale_base=0,
                 use_flash_attn=False, checkpointing=False, device=None, dtype=None) -> None:
        factory_kwargs = {'device': device, 'dtype': dtype}
        super().__init__()
        self.process_group = process_group
        self.embed_dim = embed_dim
        self.causal = causal
        self.rotary_emb_dim = rotary_emb_dim
        self.use_flash_attn = use_flash_attn
        self.checkpointing = checkpointing

        self.num_heads = num_heads
        assert self.embed_dim % num_heads == 0, "self.kdim must be divisible by num_heads"
        self.head_dim = self.embed_dim // num_heads

        if self.rotary_emb_dim > 0:
            assert RotaryEmbedding is not None, 'rotary_emb is not installed'
            self.rotary_emb = RotaryEmbedding(self.rotary_emb_dim, scale_base=rotary_emb_scale_base,
                                              device=device)

        if ColumnParallelLinear is None or RowParallelLinear is None:
            raise ImportError('fused_dense is not installed')
        self.Wqkv = ColumnParallelLinear(embed_dim, 3 * embed_dim, process_group, bias=bias,
                                         **factory_kwargs)
        inner_attn_cls = FlashSelfAttention if use_flash_attn else SelfAttention
        self.inner_attn = inner_attn_cls(causal=causal, softmax_scale=softmax_scale,
                                         attention_dropout=dropout)
        # output projection always have the bias (for now)
        self.out_proj = RowParallelLinear(embed_dim, embed_dim, process_group, **factory_kwargs)

    def forward(self, x, seqlen=None, **kwargs):
        """
        Arguments:
            x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if seqlen=None.
                If seqlen is not None, x is (batch * seqlen, hidden_dim). This is so that when we
                split x during sequence parallel, we split the batch * seqlen dimension
                (in case batch is small).
        """
        qkv = self.Wqkv(x)
        if seqlen is None:
            qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, d=self.head_dim)
        else:
            qkv = rearrange(qkv, '(b s) (three h d) -> b s three h d', s=seqlen, three=3,
                            d=self.head_dim)
        if self.rotary_emb_dim > 0:
            qkv = self.rotary_emb(qkv)
        if not self.checkpointing:
            context = self.inner_attn(qkv, **kwargs)
        else:
            context = torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, **kwargs)
        if seqlen is None:
            context = rearrange(context, 'b s h d -> b s (h d)')
        else:
            context = rearrange(context, 'b s h d -> (b s) (h d)')
        out = self.out_proj(context)
        return out