[MHA] Run black on mha.py

flash_attn/modules/mha.py

-# Copyright (c) 2022, Tri Dao.
+# Copyright (c) 2023, Tri Dao.
 
 import math
 from functools import partial
@@ -6,18 +6,21 @@ from functools import partial
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-
 from einops import rearrange, repeat
 
 try:
-    from flash_attn import flash_attn_varlen_qkvpacked_func, flash_attn_varlen_kvpacked_func
-    from flash_attn import flash_attn_qkvpacked_func, flash_attn_kvpacked_func
+    from flash_attn import (
+        flash_attn_kvpacked_func,
+        flash_attn_qkvpacked_func,
+        flash_attn_varlen_kvpacked_func,
+        flash_attn_varlen_qkvpacked_func,
+    )
 except ImportError:
     flash_attn_varlen_qkvpacked_func, flash_attn_varlen_kvpacked_func = None, None
     flash_attn_qkvpacked_func, flash_attn_kvpacked_func = None, None
 
 try:
-    from flash_attn.ops.fused_dense import FusedDense, ColumnParallelLinear, RowParallelLinear
+    from flash_attn.ops.fused_dense import ColumnParallelLinear, FusedDense, RowParallelLinear
 except ImportError:
     FusedDense, ColumnParallelLinear, RowParallelLinear = None, None, None
 
@@ -42,10 +45,11 @@ class FlashSelfAttention(nn.Module):
         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__()
-        assert flash_attn_varlen_qkvpacked_func is not None, 'FlashAttention is not installed'
-        assert flash_attn_qkvpacked_func is not None, 'FlashAttention is not installed'
+        assert flash_attn_varlen_qkvpacked_func is not None, "FlashAttention is not installed"
+        assert flash_attn_qkvpacked_func is not None, "FlashAttention is not installed"
         self.causal = causal
         self.softmax_scale = softmax_scale
         self.drop = nn.Dropout(attention_dropout)
@@ -76,12 +80,20 @@ class FlashSelfAttention(nn.Module):
             assert max_seqlen is not None
             assert isinstance(max_seqlen, int)
             return flash_attn_varlen_qkvpacked_func(
-                qkv, cu_seqlens, max_seqlen, self.drop.p if self.training else 0.0,
-                softmax_scale=self.softmax_scale, causal=causal
+                qkv,
+                cu_seqlens,
+                max_seqlen,
+                self.drop.p if self.training else 0.0,
+                softmax_scale=self.softmax_scale,
+                causal=causal,
             )
         else:
-            return flash_attn_qkvpacked_func(qkv, self.drop.p if self.training else 0.0,
-                                             softmax_scale=self.softmax_scale, causal=causal)
+            return flash_attn_qkvpacked_func(
+                qkv,
+                self.drop.p if self.training else 0.0,
+                softmax_scale=self.softmax_scale,
+                causal=causal,
+            )
 
 
 class FlashCrossAttention(nn.Module):
@@ -94,16 +106,25 @@ class FlashCrossAttention(nn.Module):
         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__()
-        assert flash_attn_varlen_kvpacked_func is not None, 'FlashAttention is not installed'
-        assert flash_attn_kvpacked_func is not None, 'FlashAttention is not installed'
+        assert flash_attn_varlen_kvpacked_func is not None, "FlashAttention is not installed"
+        assert flash_attn_kvpacked_func is not None, "FlashAttention is not installed"
         self.causal = causal
         self.softmax_scale = softmax_scale
         self.drop = nn.Dropout(attention_dropout)
 
-    def forward(self, q, kv, causal=None, cu_seqlens=None, max_seqlen=None,
-                cu_seqlens_k=None, max_seqlen_k=None):
+    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
         ---------
@@ -130,16 +151,27 @@ class FlashCrossAttention(nn.Module):
             assert max_seqlen_k is not None
             assert isinstance(max_seqlen, int)
             return flash_attn_varlen_kvpacked_func(
-                q, kv, cu_seqlens, cu_seqlens_k, max_seqlen, max_seqlen_k,
+                q,
+                kv,
+                cu_seqlens,
+                cu_seqlens_k,
+                max_seqlen,
+                max_seqlen_k,
                 self.drop.p if self.training else 0.0,
-                softmax_scale=self.softmax_scale, causal=causal
+                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[4] == q.shape[3]
-            return flash_attn_kvpacked_func(q, kv, self.drop.p if self.training else 0.0,
-                                            causal=causal, softmax_scale=self.softmax_scale)
+            return flash_attn_kvpacked_func(
+                q,
+                kv,
+                self.drop.p if self.training else 0.0,
+                causal=causal,
+                softmax_scale=self.softmax_scale,
+            )
 
 
 class SelfAttention(nn.Module):
@@ -152,6 +184,7 @@ class SelfAttention(nn.Module):
         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
@@ -171,22 +204,25 @@ class SelfAttention(nn.Module):
         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)
+        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 = 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')
+            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)
+            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 = self.drop(attention)
-        output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
+        output = torch.einsum("bhts,bshd->bthd", attention_drop, v)
         return output
 
 
@@ -200,6 +236,7 @@ class CrossAttention(nn.Module):
         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
@@ -224,43 +261,48 @@ class CrossAttention(nn.Module):
             kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.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)
+        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 = 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')
+            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)
+            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 = self.drop(attention)
-        output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
+        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.
-    """
+    """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
 
 
 def _update_kv_cache(kv, inference_params, layer_idx):
-    """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
-    """
+    """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)"""
     # Pre-allocate memory for key-values for inference.
     num_heads, head_dim = kv.shape[-2:]
     if layer_idx not in inference_params.key_value_memory_dict:
         kv_cache = torch.empty(
-            inference_params.max_batch_size, inference_params.max_sequence_len, 2,
-            num_heads, head_dim, dtype=kv.dtype, device=kv.device
+            inference_params.max_batch_size,
+            inference_params.max_sequence_len,
+            2,
+            num_heads,
+            head_dim,
+            dtype=kv.dtype,
+            device=kv.device,
         )
         inference_params.key_value_memory_dict[layer_idx] = kv_cache
     else:
@@ -292,22 +334,30 @@ def _update_kv_cache(kv, inference_params, layer_idx):
         packsize = 4 if kv.dtype == torch.float32 else 8
         if kv_cache is not None:
             kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
-            k_cache = rearrange(kv_cache[:, :, 0], 'b s h (d packsize) -> b h d s packsize',
-                                packsize=packsize).contiguous()
-            v_cache = rearrange(kv_cache[:, :, 1], 'b s h d -> b h s d').contiguous()
+            k_cache = rearrange(
+                kv_cache[:, :, 0], "b s h (d packsize) -> b h d s packsize", packsize=packsize
+            ).contiguous()
+            v_cache = rearrange(kv_cache[:, :, 1], "b s h d -> b h s d").contiguous()
             inference_params.key_value_memory_dict[layer_idx] = (k_cache, v_cache)
         else:
             k_cache[batch_start:batch_end, :, :, :sequence_end, :] = rearrange(
-                kv[:, :, 0], 'b s h (d packsize) -> b h d s packsize', packsize=packsize
+                kv[:, :, 0], "b s h (d packsize) -> b h d s packsize", packsize=packsize
             )
             v_cache[batch_start:batch_end, :, :sequence_end, :] = rearrange(
-                kv[:, :, 1], 'b s h d -> b h s d'
+                kv[:, :, 1], "b s h d -> b h s d"
             )
         return kv
 
 
-def _apply_rotary_single_query_attention(qkv, inference_params, layer_idx, rotary_emb_dim,
-                                         rotary_emb_base, kv=None, rotary_emb_interleaved=False):
+def _apply_rotary_single_query_attention(
+    qkv,
+    inference_params,
+    layer_idx,
+    rotary_emb_dim,
+    rotary_emb_base,
+    kv=None,
+    rotary_emb_interleaved=False,
+):
     """
     qkv: (batch_size, 1, 3, nheads, head_dim) if kv is None else it's just
             q of shape (batch_size, 1, nheads, head_dim)
@@ -316,17 +366,22 @@ def _apply_rotary_single_query_attention(qkv, inference_params, layer_idx, rotar
     assert inference_params.fused_ft_kernel
     assert ft_attention is not None
     if kv is None:
-        q, k, v = rearrange(qkv, 'b 1 three h d -> b three h d').unbind(dim=1)
+        q, k, v = rearrange(qkv, "b 1 three h d -> b three h d").unbind(dim=1)
     else:
-        q = rearrange(qkv, 'b 1 h d -> b h d')
-        k, v = rearrange(kv, 'b 1 two h d -> b two h d').unbind(dim=1)
+        q = rearrange(qkv, "b 1 h d -> b h d")
+        k, v = rearrange(kv, "b 1 two h d -> b two h d").unbind(dim=1)
     batch_start = inference_params.batch_size_offset
     batch_end = batch_start + q.shape[0]
     k_cache, v_cache = inference_params.key_value_memory_dict[layer_idx]
-    lengths_per_sample = (inference_params.lengths_per_sample[batch_start:batch_end]
-                            if inference_params.lengths_per_sample is not None else None)
+    lengths_per_sample = (
+        inference_params.lengths_per_sample[batch_start:batch_end]
+        if inference_params.lengths_per_sample is not None
+        else None
+    )
     context = ft_attention.single_query_attention(
-        q, k, v,
+        q,
+        k,
+        v,
         k_cache[batch_start:batch_end],
         v_cache[batch_start:batch_end],
         lengths_per_sample,
@@ -334,29 +389,47 @@ def _apply_rotary_single_query_attention(qkv, inference_params, layer_idx, rotar
         None,  # rotary_sin_
         None,  # nnz_head_idx
         inference_params.sequence_len_offset,
-        rotary_emb_dim, rotary_emb_base,
-        not rotary_emb_interleaved  # neox_rotary_style
+        rotary_emb_dim,
+        rotary_emb_base,
+        not rotary_emb_interleaved,  # neox_rotary_style
     )
-    return rearrange(context, 'b h d -> b 1 h d')
+    return rearrange(context, "b h d -> b 1 h d")
 
 
 class MHA(nn.Module):
-    """Multi-head self-attention and cross-attention
-    """
-
-    def __init__(self, embed_dim, num_heads, num_heads_kv=None, cross_attn=False,
-                 qkv_proj_bias=True, out_proj_bias=True,
-                 dropout=0.0, softmax_scale=None, causal=False, layer_idx=None, dwconv=False,
-                 rotary_emb_dim=0, rotary_emb_base=10000.0, rotary_emb_scale_base=None,
-                 rotary_emb_interleaved=False, fused_bias_fc=False, use_flash_attn=False,
-                 return_residual=False, checkpointing=False, device=None, dtype=None) -> None:
+    """Multi-head self-attention and cross-attention"""
+
+    def __init__(
+        self,
+        embed_dim,
+        num_heads,
+        num_heads_kv=None,
+        cross_attn=False,
+        qkv_proj_bias=True,
+        out_proj_bias=True,
+        dropout=0.0,
+        softmax_scale=None,
+        causal=False,
+        layer_idx=None,
+        dwconv=False,
+        rotary_emb_dim=0,
+        rotary_emb_base=10000.0,
+        rotary_emb_scale_base=None,
+        rotary_emb_interleaved=False,
+        fused_bias_fc=False,
+        use_flash_attn=False,
+        return_residual=False,
+        checkpointing=False,
+        device=None,
+        dtype=None,
+    ) -> None:
         """
         num_heads_kv: can be used to toggle MQA / GQA. If None, use num_heads.
         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}
+        factory_kwargs = {"device": device, "dtype": dtype}
         super().__init__()
         self.embed_dim = embed_dim
         self.cross_attn = cross_attn
@@ -370,24 +443,31 @@ class MHA(nn.Module):
 
         self.num_heads = num_heads
         self.num_heads_kv = num_heads_kv if num_heads_kv is not None else num_heads
-        assert self.num_heads % self.num_heads_kv == 0, "num_heads must be divisible by num_heads_kv"
+        assert (
+            self.num_heads % self.num_heads_kv == 0
+        ), "num_heads must be divisible by num_heads_kv"
         assert self.embed_dim % num_heads == 0, "embed_dim must be divisible by num_heads"
         self.head_dim = self.embed_dim // num_heads
         qkv_dim = self.head_dim * (self.num_heads + 2 * self.num_heads_kv)
         kv_dim = 2 * self.head_dim * self.num_heads_kv
 
         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, base=rotary_emb_base,
+            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,
+                base=rotary_emb_base,
                 scale_base=rotary_emb_scale_base,
-                                              interleaved=rotary_emb_interleaved, device=device)
+                interleaved=rotary_emb_interleaved,
+                device=device,
+            )
 
         if fused_bias_fc and FusedDense is None:
-            raise ImportError('fused_dense is not installed')
+            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))
+        linear_resid_cls = (
+            LinearResidual if not fused_bias_fc else partial(FusedDense, return_residual=True)
+        )
         wqkv_cls = linear_cls if not self.return_residual else linear_resid_cls
         inner_attn_cls = FlashSelfAttention if use_flash_attn else SelfAttention
         inner_cross_attn_cls = FlashCrossAttention if use_flash_attn else CrossAttention
@@ -398,40 +478,57 @@ class MHA(nn.Module):
             self.Wkv = wqkv_cls(embed_dim, kv_dim, bias=qkv_proj_bias, **factory_kwargs)
         if self.dwconv:
             if self.num_heads_kv == self.num_heads:
-                self.dwconv_qkv = nn.Conv1d(qkv_dim, qkv_dim, kernel_size=3, padding=2,
-                                            groups=qkv_dim)
+                self.dwconv_qkv = nn.Conv1d(
+                    qkv_dim, qkv_dim, kernel_size=3, padding=2, groups=qkv_dim
+                )
             else:
-                self.dwconv_q = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, padding=2,
-                                          groups=embed_dim)
-                self.dwconv_kv = nn.Conv1d(kv_dim, kv_dim, kernel_size=3, padding=2,
-                                           groups=kv_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)
+                self.dwconv_q = nn.Conv1d(
+                    embed_dim, embed_dim, kernel_size=3, padding=2, groups=embed_dim
+                )
+                self.dwconv_kv = nn.Conv1d(kv_dim, kv_dim, kernel_size=3, padding=2, groups=kv_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
+        )
         self.out_proj = linear_cls(embed_dim, embed_dim, bias=out_proj_bias, **factory_kwargs)
 
     def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, fused_ft_kernel=True):
         dtype = self.out_proj.weight.dtype if dtype is None else dtype
         device = self.out_proj.weight.device
         if not fused_ft_kernel:
-            return torch.empty(batch_size, max_seqlen, 2, self.num_heads_kv, self.head_dim,
-                               dtype=dtype, device=device)
+            return torch.empty(
+                batch_size,
+                max_seqlen,
+                2,
+                self.num_heads_kv,
+                self.head_dim,
+                dtype=dtype,
+                device=device,
+            )
         else:
             assert dtype in [torch.float16, torch.bfloat16, torch.float32]
             packsize = 4 if dtype == torch.float32 else 8
             assert self.head_dim % packsize == 0
-            k_cache = torch.empty(batch_size, self.num_heads_kv, self.head_dim // packsize,
-                                  max_seqlen, packsize, dtype=dtype, device=device)
-            v_cache = torch.empty(batch_size, self.num_heads_kv, max_seqlen, self.head_dim,
-                                  dtype=dtype, device=device)
+            k_cache = torch.empty(
+                batch_size,
+                self.num_heads_kv,
+                self.head_dim // packsize,
+                max_seqlen,
+                packsize,
+                dtype=dtype,
+                device=device,
+            )
+            v_cache = torch.empty(
+                batch_size, self.num_heads_kv, max_seqlen, self.head_dim, dtype=dtype, device=device
+            )
             return k_cache, v_cache
 
     def _update_kv_cache(self, kv, inference_params):
-        """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, 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'
+        """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, 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"
         return _update_kv_cache(kv, inference_params, self.layer_idx)
 
     def _apply_rotary_single_query_attention(self, qkv, inference_params, kv=None):
@@ -442,12 +539,28 @@ class MHA(nn.Module):
         """
         rotary_emb_base = self.rotary_emb.base if self.rotary_emb_dim > 0 else 0
         return _apply_rotary_single_query_attention(
-            qkv, inference_params, self.layer_idx, self.rotary_emb_dim, rotary_emb_base, kv=kv,
-            rotary_emb_interleaved=self.rotary_emb.interleaved if self.rotary_emb_dim > 0 else False,
+            qkv,
+            inference_params,
+            self.layer_idx,
+            self.rotary_emb_dim,
+            rotary_emb_base,
+            kv=kv,
+            rotary_emb_interleaved=self.rotary_emb.interleaved
+            if self.rotary_emb_dim > 0
+            else False,
         )
 
-    def forward(self, x, x_kv=None, key_padding_mask=None, cu_seqlens=None, max_seqlen=None,
-                mixer_subset=None, inference_params=None, **kwargs):
+    def forward(
+        self,
+        x,
+        x_kv=None,
+        key_padding_mask=None,
+        cu_seqlens=None,
+        max_seqlen=None,
+        mixer_subset=None,
+        inference_params=None,
+        **kwargs,
+    ):
         """
         Arguments:
             x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if
@@ -481,8 +594,11 @@ class MHA(nn.Module):
             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})
+        kwargs = (
+            {"cu_seqlens": cu_seqlens, "max_seqlen": max_seqlen, **kwargs}
+            if self.use_flash_attn
+            else {"key_padding_mask": key_padding_mask, **kwargs}
+        )
         seqlen_offset = 0 if inference_params is None else inference_params.sequence_len_offset
         if not self.cross_attn and self.num_heads_kv == self.num_heads:
             assert x_kv is None and mixer_subset is None
@@ -491,19 +607,22 @@ class MHA(nn.Module):
             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 or inference_params.sequence_len_offset == 0
-                or not inference_params.fused_ft_kernel):
+                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
+                or inference_params.sequence_len_offset == 0
+                or not inference_params.fused_ft_kernel
+            ):
                 if self.rotary_emb_dim > 0:
                     qkv = self.rotary_emb(qkv, seqlen_offset=seqlen_offset)
                 if inference_params is None:
                     if not self.checkpointing:
                         context = self.inner_attn(qkv, **kwargs)
                     else:
-                        context = torch.utils.checkpoint.checkpoint(self.inner_attn, qkv,
-                                                                    **kwargs)
+                        context = torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, **kwargs)
                 else:
                     q = qkv[:, :, 0]
                     kv = self._update_kv_cache(qkv[:, :, 1:], inference_params)
@@ -530,25 +649,31 @@ class MHA(nn.Module):
                     qkv = self.Wqkv(x)
                 else:
                     qkv, x = self.Wqkv(x)
-                q = qkv[..., :self.num_heads * self.head_dim]
-                kv = qkv[..., self.num_heads * self.head_dim:]
-            q = rearrange(q, '... (h d) -> ... h d', d=self.head_dim)
-            kv = rearrange(kv, '... (two hkv d) -> ... two hkv d', two=2, d=self.head_dim)
+                q = qkv[..., : self.num_heads * self.head_dim]
+                kv = qkv[..., self.num_heads * self.head_dim :]
+            q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim)
+            kv = rearrange(kv, "... (two hkv d) -> ... two hkv 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 or inference_params.sequence_len_offset == 0
-                or not inference_params.fused_ft_kernel):
+                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
+                or inference_params.sequence_len_offset == 0
+                or not inference_params.fused_ft_kernel
+            ):
                 if self.rotary_emb_dim > 0:
                     q, kv = self.rotary_emb(q, kv, seqlen_offset=seqlen_offset)
                 if inference_params is None:
                     if not self.checkpointing:
                         context = self.inner_cross_attn(q, kv, **kwargs)
                     else:
-                        context = torch.utils.checkpoint.checkpoint(self.inner_cross_attn, q, kv,
-                                                                    **kwargs)
+                        context = torch.utils.checkpoint.checkpoint(
+                            self.inner_cross_attn, q, kv, **kwargs
+                        )
                 else:
                     kv = self._update_kv_cache(kv, inference_params)
                     # If we're processing the prompt, causal=None (use self.causal).
@@ -557,21 +682,36 @@ class MHA(nn.Module):
                     context = self.inner_cross_attn(q, kv, causal=causal)
             else:
                 context = self._apply_rotary_single_query_attention(q, inference_params, kv=kv)
-        out = self.out_proj(rearrange(context, '... h d -> ... (h d)'))
+        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, num_heads_kv=None,
-                 qkv_proj_bias=True, out_proj_bias=True,
-                 dropout=0.0, softmax_scale=None, causal=False, layer_idx=None,
-                 rotary_emb_dim=0, rotary_emb_base=10000.0, rotary_emb_scale_base=None,
-                 rotary_emb_interleaved=False, use_flash_attn=False, checkpointing=False,
-                 sequence_parallel=True, device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
+    """Multi-head self-attention and cross-attention"""
+
+    def __init__(
+        self,
+        embed_dim,
+        num_heads,
+        process_group,
+        num_heads_kv=None,
+        qkv_proj_bias=True,
+        out_proj_bias=True,
+        dropout=0.0,
+        softmax_scale=None,
+        causal=False,
+        layer_idx=None,
+        rotary_emb_dim=0,
+        rotary_emb_base=10000.0,
+        rotary_emb_scale_base=None,
+        rotary_emb_interleaved=False,
+        use_flash_attn=False,
+        checkpointing=False,
+        sequence_parallel=True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
         super().__init__()
         self.embed_dim = embed_dim
         self.causal = causal
@@ -586,55 +726,93 @@ class ParallelMHA(nn.Module):
         self.num_heads_kv = num_heads_kv if num_heads_kv is not None else num_heads
         self.num_heads_per_rank = num_heads // self.world_size
         self.num_heads_kv_per_rank = self.num_heads_kv // self.world_size
-        assert self.num_heads % self.num_heads_kv == 0, "num_heads must be divisible by num_heads_kv"
+        assert (
+            self.num_heads % self.num_heads_kv == 0
+        ), "num_heads must be divisible by num_heads_kv"
         assert self.embed_dim % num_heads == 0, "embed_dim must be divisible by num_heads"
-        assert self.num_heads_kv % self.world_size == 0, "num_heads_kv must be divisible by world_size"
+        assert (
+            self.num_heads_kv % self.world_size == 0
+        ), "num_heads_kv must be divisible by world_size"
         self.head_dim = self.embed_dim // num_heads
         qkv_dim = self.head_dim * (self.num_heads + 2 * self.num_heads_kv)
         kv_dim = 2 * self.head_dim * self.num_heads_kv
 
         if self.rotary_emb_dim > 0:
-            assert RotaryEmbedding is not None, 'rotary_emb is not installed'
-            self.rotary_emb = RotaryEmbedding(self.rotary_emb_dim, base=rotary_emb_base,
+            assert RotaryEmbedding is not None, "rotary_emb is not installed"
+            self.rotary_emb = RotaryEmbedding(
+                self.rotary_emb_dim,
+                base=rotary_emb_base,
                 scale_base=rotary_emb_scale_base,
-                                              interleaved=rotary_emb_interleaved, device=device)
+                interleaved=rotary_emb_interleaved,
+                device=device,
+            )
 
         if ColumnParallelLinear is None or RowParallelLinear is None:
-            raise ImportError('fused_dense is not installed')
-        self.Wqkv = ColumnParallelLinear(embed_dim, qkv_dim, process_group,
+            raise ImportError("fused_dense is not installed")
+        self.Wqkv = ColumnParallelLinear(
+            embed_dim,
+            qkv_dim,
+            process_group,
             bias=qkv_proj_bias,
-                                         sequence_parallel=sequence_parallel, **factory_kwargs)
+            sequence_parallel=sequence_parallel,
+            **factory_kwargs,
+        )
         inner_attn_cls = FlashSelfAttention if use_flash_attn else SelfAttention
         inner_cross_attn_cls = FlashCrossAttention if use_flash_attn else CrossAttention
-        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)
-        self.out_proj = RowParallelLinear(embed_dim, embed_dim, process_group,
+        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
+        )
+        self.out_proj = RowParallelLinear(
+            embed_dim,
+            embed_dim,
+            process_group,
             bias=out_proj_bias,
-                                          sequence_parallel=sequence_parallel, **factory_kwargs)
+            sequence_parallel=sequence_parallel,
+            **factory_kwargs,
+        )
 
     def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, fused_ft_kernel=True):
         dtype = self.out_proj.weight.dtype if dtype is None else dtype
         device = self.out_proj.weight.device
         if not fused_ft_kernel:
-            return torch.empty(batch_size, max_seqlen, 2, self.num_heads_kv_per_rank,
-                               self.head_dim, dtype=dtype, device=device)
+            return torch.empty(
+                batch_size,
+                max_seqlen,
+                2,
+                self.num_heads_kv_per_rank,
+                self.head_dim,
+                dtype=dtype,
+                device=device,
+            )
         else:
             assert dtype in [torch.float16, torch.bfloat16, torch.float32]
             packsize = 4 if dtype == torch.float32 else 8
             assert self.head_dim % packsize == 0
-            k_cache = torch.empty(batch_size, self.num_heads_kv_per_rank,
+            k_cache = torch.empty(
+                batch_size,
+                self.num_heads_kv_per_rank,
                 self.head_dim // packsize,
-                                  max_seqlen, packsize, dtype=dtype, device=device)
-            v_cache = torch.empty(batch_size, self.num_heads_kv_per_rank, max_seqlen,
-                                  self.head_dim, dtype=dtype, device=device)
+                max_seqlen,
+                packsize,
+                dtype=dtype,
+                device=device,
+            )
+            v_cache = torch.empty(
+                batch_size,
+                self.num_heads_kv_per_rank,
+                max_seqlen,
+                self.head_dim,
+                dtype=dtype,
+                device=device,
+            )
             return k_cache, v_cache
 
     def _update_kv_cache(self, kv, inference_params):
-        """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
-        """
-        assert self.layer_idx is not None, 'Generation requires layer_idx in the constructor'
+        """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)"""
+        assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"
         return _update_kv_cache(kv, inference_params, self.layer_idx)
 
     def _apply_rotary_single_query_attention(self, qkv, inference_params, kv=None):
@@ -645,8 +823,15 @@ class ParallelMHA(nn.Module):
         """
         rotary_emb_base = self.rotary_emb.base if self.rotary_emb_dim > 0 else 0
         return _apply_rotary_single_query_attention(
-            qkv, inference_params, self.layer_idx, self.rotary_emb_dim, rotary_emb_base, kv=kv,
-            rotary_emb_interleaved=self.rotary_emb.interleaved if self.rotary_emb_dim > 0 else False,
+            qkv,
+            inference_params,
+            self.layer_idx,
+            self.rotary_emb_dim,
+            rotary_emb_base,
+            kv=kv,
+            rotary_emb_interleaved=self.rotary_emb.interleaved
+            if self.rotary_emb_dim > 0
+            else False,
         )
 
     def forward(self, x, seqlen=None, inference_params=None, **kwargs):
@@ -662,9 +847,12 @@ class ParallelMHA(nn.Module):
             qkv = rearrange(qkv, "(b s) ... -> b s ...", s=seqlen)
         seqlen_offset = 0 if inference_params is None else inference_params.sequence_len_offset
         if self.num_heads_kv == self.num_heads:
-            qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, d=self.head_dim)
-            if (inference_params is None or inference_params.sequence_len_offset == 0
-                or not inference_params.fused_ft_kernel):
+            qkv = rearrange(qkv, "b s (three h d) -> b s three h d", three=3, d=self.head_dim)
+            if (
+                inference_params is None
+                or inference_params.sequence_len_offset == 0
+                or not inference_params.fused_ft_kernel
+            ):
                 if self.rotary_emb_dim > 0:
                     qkv = self.rotary_emb(qkv, seqlen_offset=seqlen_offset)
                 if inference_params is None:
@@ -682,20 +870,31 @@ class ParallelMHA(nn.Module):
             else:
                 context = self._apply_rotary_single_query_attention(qkv, inference_params)
         else:
-            q = rearrange(qkv[..., :self.num_heads_per_rank * self.head_dim],
-                          "... (h d) -> ... h d", d=self.head_dim)
-            kv = rearrange(qkv[..., self.num_heads_per_rank * self.head_dim:],
-                           "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim)
-            if (inference_params is None or inference_params.sequence_len_offset == 0
-                or not inference_params.fused_ft_kernel):
+            q = rearrange(
+                qkv[..., : self.num_heads_per_rank * self.head_dim],
+                "... (h d) -> ... h d",
+                d=self.head_dim,
+            )
+            kv = rearrange(
+                qkv[..., self.num_heads_per_rank * self.head_dim :],
+                "... (two hkv d) -> ... two hkv d",
+                two=2,
+                d=self.head_dim,
+            )
+            if (
+                inference_params is None
+                or inference_params.sequence_len_offset == 0
+                or not inference_params.fused_ft_kernel
+            ):
                 if self.rotary_emb_dim > 0:
                     q, kv = self.rotary_emb(q, kv, seqlen_offset=seqlen_offset)
                 if inference_params is None:
                     if not self.checkpointing:
                         context = self.inner_cross_attn(q, kv, **kwargs)
                     else:
-                        context = torch.utils.checkpoint.checkpoint(self.inner_cross_attn, q, kv,
-                                                                    **kwargs)
+                        context = torch.utils.checkpoint.checkpoint(
+                            self.inner_cross_attn, q, kv, **kwargs
+                        )
                 else:
                     kv = self._update_kv_cache(kv, inference_params)
                     # If we're processing the prompt, causal=None (use self.causal).
@@ -704,8 +903,8 @@ class ParallelMHA(nn.Module):
                     context = self.inner_cross_attn(q, kv, causal=causal)
             else:
                 context = self._apply_rotary_single_query_attention(q, inference_params, kv=kv)
-        context = rearrange(context, 'b s h d -> b s (h d)')
+        context = rearrange(context, "b s h d -> b s (h d)")
         if seqlen is not None:
-            context = rearrange(context, 'b s d -> (b s) d')
+            context = rearrange(context, "b s d -> (b s) d")
         out = self.out_proj(context)
         return out