Swap seqlen_q, nheads for MQA when seqlen_q=1 for fwd (h/t Daniel H)

csrc/flash_attn/flash_api.cpp

@@ -235,13 +235,13 @@ inline int num_splits_heuristic(int batch_nheads_mblocks, int num_SMs, int num_n
 }
 
 std::vector<at::Tensor>
-mha_fwd(const at::Tensor &q,         // batch_size x seqlen_q x num_heads x head_size
+mha_fwd(at::Tensor &q,         // batch_size x seqlen_q x num_heads x head_size
         const at::Tensor &k,         // batch_size x seqlen_k x num_heads_k x head_size
         const at::Tensor &v,         // batch_size x seqlen_k x num_heads_k x head_size
         c10::optional<at::Tensor> &out_,             // batch_size x seqlen_q x num_heads x head_size
         const float p_dropout,
         const float softmax_scale,
-        const bool is_causal,
+        bool is_causal,
         const bool return_softmax,
         c10::optional<at::Generator> gen_) {
 
@@ -271,8 +271,8 @@ mha_fwd(const at::Tensor &q,         // batch_size x seqlen_q x num_heads x head
     const auto sizes = q.sizes();
 
     const int batch_size = sizes[0];
-    const int seqlen_q = sizes[1];
-    const int num_heads = sizes[2];
+    int seqlen_q = sizes[1];
+    int num_heads = sizes[2];
     const int head_size_og = sizes[3];
     const int seqlen_k = k.size(1);
     const int num_heads_k = k.size(2);
@@ -280,6 +280,15 @@ mha_fwd(const at::Tensor &q,         // batch_size x seqlen_q x num_heads x head
     TORCH_CHECK(head_size_og <= 256, "FlashAttention forward only supports head dimension at most 256");
     TORCH_CHECK(num_heads % num_heads_k == 0, "Number of heads in key/value must divide number of heads in query");
 
+    if (seqlen_q == 1) { is_causal = false; }  // causal=true is the same as causal=false in this case
+
+    // Faster to transpose q from (b, 1, h, d) to (b, h, 1, d) in this case
+    const int seqlenq_nheads_swapped = seqlen_q == 1 && num_heads_k == 1 && num_heads > 1 and p_dropout == 0.f and head_size_og % 8 == 0;
+    if (seqlenq_nheads_swapped) {
+        q = q.transpose(1, 2);
+        std::swap(seqlen_q, num_heads);
+    }
+
     CHECK_SHAPE(q, batch_size, seqlen_q, num_heads, head_size_og);
     CHECK_SHAPE(k, batch_size, seqlen_k, num_heads_k, head_size_og);
     CHECK_SHAPE(v, batch_size, seqlen_k, num_heads_k, head_size_og);
@@ -388,6 +397,12 @@ mha_fwd(const at::Tensor &q,         // batch_size x seqlen_q x num_heads x head
         if (out_.has_value()) { out_.value().copy_(out); }
     }
 
+    if (seqlenq_nheads_swapped) {
+        out = out.transpose(1, 2);
+        out_padded = out_padded.transpose(1, 2);
+        q_padded = q_padded.transpose(1, 2);
+        softmax_lse = softmax_lse.transpose(1, 2);
+    }
     return {out, q_padded, k_padded, v_padded, out_padded, softmax_lse, p, rng_state};
 }
 

tests/test_flash_attn.py

@@ -908,6 +908,7 @@ def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, d
 @pytest.mark.parametrize(
     "seqlen_q,seqlen_k",
     [
+        (1, 147),
         (113, 203),
         (128, 217),
         (113, 211),