Sync with FA v2.6.0 to support soft capping (#13)

csrc/flash_attn/src/flash_fwd_split_hdim32_bf16_sm80.cu

@@ -4,4 +4,4 @@
 
 #include "flash_fwd_launch_template.h"
 
-template void run_mha_fwd_splitkv_dispatch<cutlass::bfloat16_t, 32>(Flash_fwd_params &params, cudaStream_t stream);
+template void run_mha_fwd_splitkv_dispatch<cutlass::bfloat16_t, 32, false>(Flash_fwd_params &params, cudaStream_t stream);

csrc/flash_attn/src/flash_fwd_split_hdim32_fp16_causal_sm80.cu

+// Copyright (c) 2023, Tri Dao.
+// Splitting the different head dimensions to different files to speed up compilation.
+// This file is auto-generated. See "generate_kernels.py"
+
+#include "flash_fwd_launch_template.h"
+
+template void run_mha_fwd_splitkv_dispatch<cutlass::half_t, 32, true>(Flash_fwd_params &params, cudaStream_t stream);

csrc/flash_attn/src/flash_fwd_split_hdim32_fp16_sm80.cu

@@ -4,4 +4,4 @@
 
 #include "flash_fwd_launch_template.h"
 
-template void run_mha_fwd_splitkv_dispatch<cutlass::half_t, 32>(Flash_fwd_params &params, cudaStream_t stream);
+template void run_mha_fwd_splitkv_dispatch<cutlass::half_t, 32, false>(Flash_fwd_params &params, cudaStream_t stream);

csrc/flash_attn/src/flash_fwd_split_hdim64_bf16_causal_sm80.cu

+// Copyright (c) 2023, Tri Dao.
+// Splitting the different head dimensions to different files to speed up compilation.
+// This file is auto-generated. See "generate_kernels.py"
+
+#include "flash_fwd_launch_template.h"
+
+template void run_mha_fwd_splitkv_dispatch<cutlass::bfloat16_t, 64, true>(Flash_fwd_params &params, cudaStream_t stream);

csrc/flash_attn/src/flash_fwd_split_hdim64_bf16_sm80.cu

@@ -4,4 +4,4 @@
 
 #include "flash_fwd_launch_template.h"
 
-template void run_mha_fwd_splitkv_dispatch<cutlass::bfloat16_t, 64>(Flash_fwd_params &params, cudaStream_t stream);
+template void run_mha_fwd_splitkv_dispatch<cutlass::bfloat16_t, 64, false>(Flash_fwd_params &params, cudaStream_t stream);

csrc/flash_attn/src/flash_fwd_split_hdim64_fp16_causal_sm80.cu

+// Copyright (c) 2023, Tri Dao.
+// Splitting the different head dimensions to different files to speed up compilation.
+// This file is auto-generated. See "generate_kernels.py"
+
+#include "flash_fwd_launch_template.h"
+
+template void run_mha_fwd_splitkv_dispatch<cutlass::half_t, 64, true>(Flash_fwd_params &params, cudaStream_t stream);

csrc/flash_attn/src/flash_fwd_split_hdim64_fp16_sm80.cu

@@ -4,4 +4,4 @@
 
 #include "flash_fwd_launch_template.h"
 
-template void run_mha_fwd_splitkv_dispatch<cutlass::half_t, 64>(Flash_fwd_params &params, cudaStream_t stream);
+template void run_mha_fwd_splitkv_dispatch<cutlass::half_t, 64, false>(Flash_fwd_params &params, cudaStream_t stream);

csrc/flash_attn/src/flash_fwd_split_hdim96_bf16_causal_sm80.cu

+// Copyright (c) 2023, Tri Dao.
+// Splitting the different head dimensions to different files to speed up compilation.
+// This file is auto-generated. See "generate_kernels.py"
+
+#include "flash_fwd_launch_template.h"
+
+template void run_mha_fwd_splitkv_dispatch<cutlass::bfloat16_t, 96, true>(Flash_fwd_params &params, cudaStream_t stream);

csrc/flash_attn/src/flash_fwd_split_hdim96_bf16_sm80.cu

@@ -4,4 +4,4 @@
 
 #include "flash_fwd_launch_template.h"
 
-template void run_mha_fwd_splitkv_dispatch<cutlass::bfloat16_t, 96>(Flash_fwd_params &params, cudaStream_t stream);
+template void run_mha_fwd_splitkv_dispatch<cutlass::bfloat16_t, 96, false>(Flash_fwd_params &params, cudaStream_t stream);

csrc/flash_attn/src/flash_fwd_split_hdim96_fp16_causal_sm80.cu

+// Copyright (c) 2023, Tri Dao.
+// Splitting the different head dimensions to different files to speed up compilation.
+// This file is auto-generated. See "generate_kernels.py"
+
+#include "flash_fwd_launch_template.h"
+
+template void run_mha_fwd_splitkv_dispatch<cutlass::half_t, 96, true>(Flash_fwd_params &params, cudaStream_t stream);

csrc/flash_attn/src/flash_fwd_split_hdim96_fp16_sm80.cu

@@ -4,4 +4,4 @@
 
 #include "flash_fwd_launch_template.h"
 
-template void run_mha_fwd_splitkv_dispatch<cutlass::half_t, 96>(Flash_fwd_params &params, cudaStream_t stream);
+template void run_mha_fwd_splitkv_dispatch<cutlass::half_t, 96, false>(Flash_fwd_params &params, cudaStream_t stream);

csrc/flash_attn/src/generate_kernels.py

@@ -16,17 +16,18 @@ DTYPE_MAP = {
 
 SM = [80]  # Sm80 kernels support up to
 HEAD_DIMENSIONS = [32, 64, 96, 128, 160, 192, 224, 256]
+IS_CAUSAL = ["false", "true"]
 KERNEL_IMPL_TEMPLATE_FWD = """#include "flash_fwd_launch_template.h"
 
 template<>
-void run_mha_fwd_<{DTYPE}, {HEAD_DIM}>(Flash_fwd_params &params, cudaStream_t stream) {{
-    run_mha_fwd_hdim{HEAD_DIM}<{DTYPE}>(params, stream);
+void run_mha_fwd_<{DTYPE}, {HEAD_DIM}, {IS_CAUSAL}>(Flash_fwd_params &params, cudaStream_t stream) {{
+    run_mha_fwd_hdim{HEAD_DIM}<{DTYPE}, {IS_CAUSAL}>(params, stream);
 }}
 """
 
 KERNEL_IMPL_TEMPLATE_FWD_SPLIT = """#include "flash_fwd_launch_template.h"
 
-template void run_mha_fwd_splitkv_dispatch<{DTYPE}, {HEAD_DIM}>(Flash_fwd_params &params, cudaStream_t stream);
+template void run_mha_fwd_splitkv_dispatch<{DTYPE}, {HEAD_DIM}, {IS_CAUSAL}>(Flash_fwd_params &params, cudaStream_t stream);
 """
 
 KERNEL_IMPL_TEMPLATE_BWD = """#include "flash_bwd_launch_template.h"
@@ -43,13 +44,14 @@ class Kernel:
     sm: int
     dtype: str
     head_dim: int
+    is_causal: bool
     direction: str
 
     @property
     def template(self) -> str:
         if self.direction == "fwd":
             return KERNEL_IMPL_TEMPLATE_FWD.format(
-                DTYPE=DTYPE_MAP[self.dtype], HEAD_DIM=self.head_dim
+                DTYPE=DTYPE_MAP[self.dtype], HEAD_DIM=self.head_dim, IS_CAUSAL=self.is_causal
             )
         elif self.direction == "bwd":
             return KERNEL_IMPL_TEMPLATE_BWD.format(
@@ -57,18 +59,21 @@ class Kernel:
             )
         else:
             return KERNEL_IMPL_TEMPLATE_FWD_SPLIT.format(
-                DTYPE=DTYPE_MAP[self.dtype], HEAD_DIM=self.head_dim
+                DTYPE=DTYPE_MAP[self.dtype], HEAD_DIM=self.head_dim, IS_CAUSAL=self.is_causal
             )
 
     @property
     def filename(self) -> str:
-        return f"flash_{self.direction}_hdim{self.head_dim}_{self.dtype}_sm{self.sm}.cu"
+        return f"flash_{self.direction}_hdim{self.head_dim}_{self.dtype}_{'causal_' if self.is_causal == 'true' else ''}sm{self.sm}.cu"
 
 
 def get_all_kernels() -> List[Kernel]:
+    for direction in ["fwd", "fwd_split"]:
+        for dtype, head_dim, is_causal, sm in itertools.product(DTYPE_MAP.keys(), HEAD_DIMENSIONS, IS_CAUSAL, SM):
+            yield Kernel(sm=sm, dtype=dtype, head_dim=head_dim, is_causal=is_causal, direction=direction)
+    for direction in ["bwd"]:
         for dtype, head_dim, sm in itertools.product(DTYPE_MAP.keys(), HEAD_DIMENSIONS, SM):
-        for direction in ["fwd", "bwd", "fwd_split"]:
-            yield Kernel(sm=sm, dtype=dtype, head_dim=head_dim, direction=direction)
+            yield Kernel(sm=sm, dtype=dtype, head_dim=head_dim, is_causal="false", direction=direction)
 
 
 def write_kernel(kernel: Kernel, autogen_dir: Path) -> None:

csrc/flash_attn/src/kernel_traits.h

@@ -4,7 +4,7 @@
 
 #pragma once
 
-#include "cute/algorithm/copy.hpp"
+#include "cute/tensor.hpp"
 
 #include "cutlass/cutlass.h"
 #include "cutlass/layout/layout.h"

csrc/flash_attn/src/mask.h

@@ -13,7 +13,7 @@ using namespace cute;
 template <typename Engine, typename Layout>
 __forceinline__ __device__ void apply_mask(Tensor<Engine, Layout> &tensor, const int max_seqlen_k,
                                   const int col_idx_offset_ = 0) {
-    // tensor has shape (ncol=(2, MMA_M), nrow=(2, MMA_N))
+    // tensor has shape (nrow=(2, MMA_M), ncol=(2, MMA_N))
     static_assert(Layout::rank == 2, "Only support 2D Tensor");
     const int lane_id = threadIdx.x % 32;
     const int col_idx_offset = col_idx_offset_ + (lane_id % 4) * 2;
@@ -39,7 +39,7 @@ __forceinline__ __device__ void apply_mask_local(Tensor<Engine, Layout> &tensor,
                                         const int max_seqlen_k, const int row_idx_offset,
                                         const int max_seqlen_q, const int warp_row_stride,
                                         const int window_size_left, const int window_size_right) {
-    // tensor has shape (ncol=(2, MMA_M), nrow=(2, MMA_N))
+    // tensor has shape (nrow=(2, MMA_M), ncol=(2, MMA_N))
     static_assert(Layout::rank == 2, "Only support 2D Tensor");
     const int lane_id = threadIdx.x % 32;
     const int col_idx_offset = col_idx_offset_ + (lane_id % 4) * 2;
@@ -85,7 +85,7 @@ __forceinline__ __device__ void apply_mask_causal_w_idx(
     Tensor<Engine0, Layout0> &tensor, Tensor<Engine1, Layout1> const &idx_rowcol,
     const int col_idx_offset_, const int max_seqlen_k, const int row_idx_offset)
 {
-    // tensor has shape (ncol=(2, MMA_M), nrow=(2, MMA_N))
+    // tensor has shape (nrow=(2, MMA_M), ncol=(2, MMA_N))
     static_assert(Layout0::rank == 2, "Only support 2D Tensor");
     static_assert(Layout1::rank == 2, "Only support 2D Tensor");
     CUTE_STATIC_ASSERT_V(size<0>(tensor) == size<0>(idx_rowcol));

csrc/flash_attn/src/rotary.h

@@ -4,7 +4,7 @@
 
 #pragma once
 
-#include <cute/algorithm/copy.hpp>
+#include <cute/tensor.hpp>
 
 #include "utils.h"
 

csrc/flash_attn/src/static_switch.h

@@ -56,6 +56,16 @@
   #define EVENK_SWITCH BOOL_SWITCH
 #endif
 
+#ifdef FLASHATTENTION_DISABLE_SOFTCAP
+  #define SOFTCAP_SWITCH(COND, CONST_NAME, ...)   \
+  [&] {                                         \
+    constexpr static bool CONST_NAME = false;    \
+    return __VA_ARGS__();                       \
+  }()
+#else
+  #define SOFTCAP_SWITCH BOOL_SWITCH
+#endif
+
 #ifdef FLASHATTENTION_DISABLE_LOCAL
   #define LOCAL_SWITCH(COND, CONST_NAME, ...)   \
   [&] {                                         \

csrc/flash_attn/src/utils.h

@@ -14,8 +14,7 @@
 #include <cuda_bf16.h>
 #endif
 
-#include <cute/algorithm/copy.hpp>
-#include <cute/algorithm/gemm.hpp>
+#include <cute/tensor.hpp>
 
 #include <cutlass/array.h>
 #include <cutlass/cutlass.h>

setup.py

@@ -151,22 +151,22 @@ if not SKIP_CUDA_BUILD:
                 "csrc/flash_attn/src/flash_fwd_hdim224_bf16_sm80.cu",
                 "csrc/flash_attn/src/flash_fwd_hdim256_fp16_sm80.cu",
                 "csrc/flash_attn/src/flash_fwd_hdim256_bf16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim32_fp16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim32_bf16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim64_fp16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim64_bf16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim96_fp16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim96_bf16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim128_fp16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim128_bf16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim160_fp16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim160_bf16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim192_fp16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim192_bf16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim224_fp16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim224_bf16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim256_fp16_sm80.cu",
-                # "csrc/flash_attn/src/flash_bwd_hdim256_bf16_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim32_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim32_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim64_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim64_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim96_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim96_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim128_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim128_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim160_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim160_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim192_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim192_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim224_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim224_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim256_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_hdim256_bf16_causal_sm80.cu",
                 "csrc/flash_attn/src/flash_fwd_split_hdim32_fp16_sm80.cu",
                 "csrc/flash_attn/src/flash_fwd_split_hdim32_bf16_sm80.cu",
                 "csrc/flash_attn/src/flash_fwd_split_hdim64_fp16_sm80.cu",
@@ -183,6 +183,22 @@ if not SKIP_CUDA_BUILD:
                 "csrc/flash_attn/src/flash_fwd_split_hdim224_bf16_sm80.cu",
                 "csrc/flash_attn/src/flash_fwd_split_hdim256_fp16_sm80.cu",
                 "csrc/flash_attn/src/flash_fwd_split_hdim256_bf16_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim32_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim32_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim64_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim64_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim96_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim96_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim128_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim128_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim160_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim160_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim192_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim192_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim224_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim224_bf16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim256_fp16_causal_sm80.cu",
+                "csrc/flash_attn/src/flash_fwd_split_hdim256_bf16_causal_sm80.cu",
             ],
             extra_compile_args={
                 "cxx": ["-O3", "-std=c++17"] + generator_flag,
@@ -203,6 +219,7 @@ if not SKIP_CUDA_BUILD:
                         # "-DFLASHATTENTION_DISABLE_BACKWARD",
                         "-DFLASHATTENTION_DISABLE_DROPOUT",
                         # "-DFLASHATTENTION_DISABLE_ALIBI",
+                        # "-DFLASHATTENTION_DISABLE_SOFTCAP",
                         "-DFLASHATTENTION_DISABLE_UNEVEN_K",
                         # "-DFLASHATTENTION_DISABLE_LOCAL",
                     ]

tests/test_flash_attn.py

@@ -216,6 +216,7 @@ def attention_ref(
     dropout_mask=None,
     causal=False,
     window_size=(-1, -1),  # -1 means infinite window size
+    softcap=0.0,
     upcast=True,
     reorder_ops=False,
 ):
@@ -233,7 +234,7 @@ def attention_ref(
         window_size: (int, int), left and right window size
         upcast: whether to cast all inputs to fp32, do all computation in fp32, then cast
             output back to fp16/bf16.
-        reorder_ops: whether to change the order of operations (scaling k instead of scaling k, etc.)
+        reorder_ops: whether to change the order of operations (scaling k instead of scaling q, etc.)
             without changing the math. This is to estimate the numerical error from operation
             reordering.
     Output:
@@ -253,6 +254,10 @@ def attention_ref(
         scores = torch.einsum("bthd,bshd->bhts", q / math.sqrt(d), k)
     else:
         scores = torch.einsum("bthd,bshd->bhts", q, k / math.sqrt(d))
+    if softcap > 0:
+        scores /= softcap
+        scores = scores.tanh()
+        scores *= softcap
     if key_padding_mask is not None:
         scores.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf"))
     if window_size[0] >= 0 or window_size[1] >= 0:
@@ -298,6 +303,7 @@ def attention_kvpacked_ref(
     dropout_mask=None,
     causal=False,
     window_size=(-1, -1),  # -1 means infinite window size
+    softcap=0.0,
     upcast=True,
     reorder_ops=False,
 ):
@@ -313,6 +319,7 @@ def attention_kvpacked_ref(
         upcast=upcast,
         causal=causal,
         window_size=window_size,
+        softcap=softcap,
         reorder_ops=reorder_ops,
     )
 
@@ -325,6 +332,7 @@ def attention_qkvpacked_ref(
     dropout_mask=None,
     causal=False,
     window_size=(-1, -1),  # -1 means infinite window size
+    softcap=0.0,
     upcast=True,
     reorder_ops=False,
 ):
@@ -340,6 +348,7 @@ def attention_qkvpacked_ref(
         upcast=upcast,
         causal=causal,
         window_size=window_size,
+        softcap=softcap,
         reorder_ops=reorder_ops,
     )
 
@@ -877,23 +886,29 @@ def test_flash_attn_varlen_qkvpacked(
 # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)])
 @pytest.mark.parametrize("dropout_p", [0.0, 0.17])
 # @pytest.mark.parametrize("dropout_p", [0.17])
+@pytest.mark.parametrize("softcap", [0.0, 50.0])
 def test_flash_attn_output(
-    seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked
+    seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked, softcap
 ):
     if (
         max(seqlen_q, seqlen_k) >= 2048
         and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
     ):
         pytest.skip()  # Reference implementation OOM
+    if softcap > 0.0 and dropout_p > 0.0:
+        pytest.skip("Softcap and dropout not supported together")
     device = "cuda"
     # set seed
     torch.random.manual_seed(0)
     batch_size = 4
-    nheads = 9
-    nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
+    nheads = 6 if softcap == 0.0 else 4  # softcap reference impl takes more memory
+    nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 2)
     assert nheads % nheads_k == 0
     window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
     q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
+    if softcap > 0:
+        # Ensure the values of qk are at least within softcap range.
+        q = q * softcap
     if kvpacked:
         kv = torch.randn(
             batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype, requires_grad=True
@@ -918,6 +933,7 @@ def test_flash_attn_output(
             dropout_p,
             causal=causal,
             window_size=window_size,
+            softcap=softcap,
             alibi_slopes=alibi_slopes,
             deterministic=deterministic,
             return_attn_probs=True,
@@ -930,6 +946,7 @@ def test_flash_attn_output(
             dropout_p,
             causal=causal,
             window_size=window_size,
+            softcap=softcap,
             alibi_slopes=alibi_slopes,
             deterministic=deterministic,
             return_attn_probs=True,
@@ -984,6 +1001,7 @@ def test_flash_attn_output(
             dropout_mask,
             causal=causal,
             window_size=window_size,
+            softcap=softcap,
         )
         out_pt, attn_pt = attention_kvpacked_ref(
             q,
@@ -995,6 +1013,7 @@ def test_flash_attn_output(
             dropout_mask,
             causal=causal,
             window_size=window_size,
+            softcap=softcap,
             upcast=False,
             reorder_ops=True,
         )
@@ -1010,6 +1029,7 @@ def test_flash_attn_output(
             dropout_mask,
             causal=causal,
             window_size=window_size,
+            softcap=softcap,
         )
         out_pt, attn_pt = attention_ref(
             q,
@@ -1022,6 +1042,7 @@ def test_flash_attn_output(
             dropout_mask,
             causal=causal,
             window_size=window_size,
+            softcap=softcap,
             upcast=False,
             reorder_ops=True,
         )
@@ -1036,7 +1057,7 @@ def test_flash_attn_output(
 
     g = torch.randn_like(out)
     do_o = (g.float() * out.float()).sum(-1)
-    if (d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90):
+    if ((d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90)) and softcap == 0.0:
         if kvpacked:
             (
                 dq,
@@ -1092,7 +1113,7 @@ def test_flash_attn_output(
         if not alibi:
             assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.025)
 
-    if (d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90):
+    if ((d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90)) and softcap == 0.0:
         assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item()
         assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item()
         assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item()
@@ -1133,24 +1154,31 @@ def test_flash_attn_output(
 )
 # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)])
 @pytest.mark.parametrize("dropout_p", [0.0, 0.17])
+@pytest.mark.parametrize("softcap", [0.0, 50.0])
 # @pytest.mark.parametrize('dropout_p', [0.0])
 def test_flash_attn_varlen_output(
-    seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked
+    seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked, softcap
 ):
     if (
         max(seqlen_q, seqlen_k) >= 2048
         and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
     ):
         pytest.skip()  # Reference implementation OOM
+    if softcap > 0.0 and dropout_p > 0.0:
+        pytest.skip("Softcap and dropout not supported together")
     device = "cuda"
     # set seed
     torch.random.manual_seed(0)
     batch_size = 4
-    nheads = 9
-    nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
+    nheads = 6 if softcap == 0.0 else 4  # softcap reference impl takes more memory
+    nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 2)
     assert nheads % nheads_k == 0
     window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
     q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
+    if softcap > 0:
+        # Ensure the values of qk are at least within softcap range.
+        q = q * softcap
+
     if kvpacked:
         kv = torch.randn(
             batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype, requires_grad=True
@@ -1198,6 +1226,7 @@ def test_flash_attn_varlen_output(
             dropout_p,
             causal=causal,
             window_size=window_size,
+            softcap=softcap,
             alibi_slopes=alibi_slopes,
             deterministic=deterministic,
             return_attn_probs=True,
@@ -1229,6 +1258,7 @@ def test_flash_attn_varlen_output(
             dropout_p,
             causal=causal,
             window_size=window_size,
+            softcap=softcap,
             alibi_slopes=alibi_slopes,
             deterministic=deterministic,
             return_attn_probs=True,
@@ -1288,6 +1318,7 @@ def test_flash_attn_varlen_output(
             dropout_mask,
             causal=causal,
             window_size=window_size,
+            softcap=softcap,
         )
         out_pt, attn_pt = attention_kvpacked_ref(
             q,
@@ -1299,6 +1330,7 @@ def test_flash_attn_varlen_output(
             dropout_mask,
             causal=causal,
             window_size=window_size,
+            softcap=softcap,
             upcast=False,
             reorder_ops=True,
         )
@@ -1314,6 +1346,7 @@ def test_flash_attn_varlen_output(
             dropout_mask,
             causal=causal,
             window_size=window_size,
+            softcap=softcap,
         )
         out_pt, attn_pt = attention_ref(
             q,
@@ -1326,6 +1359,7 @@ def test_flash_attn_varlen_output(
             dropout_mask,
             causal=causal,
             window_size=window_size,
+            softcap=softcap,
             upcast=False,
             reorder_ops=True,
         )
@@ -1339,7 +1373,7 @@ def test_flash_attn_varlen_output(
         print(f"Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}")
 
     g = torch.randn_like(out)
-    if (d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90):
+    if ((d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90)) and softcap == 0.0:
         if kvpacked:
             (
                 dq_unpad,
@@ -1396,9 +1430,9 @@ def test_flash_attn_varlen_output(
         assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
         # With alibi, many of the prob values are 0.0 & -0.0 so dropout_fraction isn't accurate
         if not alibi:
-            assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.025)
+            assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.04)
 
-    if (d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90):
+    if ((d <= MAX_HEADDIM_SM8x or (d > 224 and dropout_p == 0)) or (is_sm80 or is_sm90)) and softcap == 0.0:
         assert (dq - dq_ref).abs().max().item() <= 3 * (dq_pt - dq_ref).abs().max().item()
         assert (dk - dk_ref).abs().max().item() <= 3 * (dk_pt - dk_ref).abs().max().item()
         assert (dv - dv_ref).abs().max().item() <= 3 * (dv_pt - dv_ref).abs().max().item()
@@ -1917,9 +1951,11 @@ def test_flash_attn_kvcache(
     cache_seqlens = torch.randint(
         0 if new_kv else 1,
         # If we don't use seqlen_q in the case of causal and rotary, cos/sin won't be long enough
+        (
             (seqlen_k - (seqlen_q if (causal or local) and rotary_dim > 1 else seqlen_new) + 1)
             if new_kv
-        else (seqlen_k + 1),
+            else (seqlen_k + 1)
+        ),
         (batch_size,),
         dtype=torch.int32,
         device=device,
@@ -2455,12 +2491,12 @@ def test_flash_attn_varlen_deterministic(seqlen_q, seqlen_k, swap_sq_sk, d, caus
 
     g = torch.randn_like(out)
     if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90):
-        dq, dk, dv = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True)
+        dq0, dk0, dv0 = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True)
         for _ in range(50):
             dq, dk, dv = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True)
-            assert torch.equal(dv, dv)
-            assert torch.equal(dk, dk)
-            assert torch.equal(dq, dq)
+            assert torch.equal(dv, dv0)
+            assert torch.equal(dk, dk0)
+            assert torch.equal(dq, dq0)
 
 
 @pytest.mark.parametrize("dtype", [torch.float16])

training/Dockerfile

@@ -85,7 +85,7 @@ RUN pip install transformers==4.25.1 datasets==2.8.0 pytorch-lightning==1.8.6 tr
 RUN pip install git+https://github.com/mlcommons/logging.git@2.1.0
 
 # Install FlashAttention
-RUN pip install flash-attn==2.5.7
+RUN pip install flash-attn==2.6.0
 
 # Install CUDA extensions for fused dense
-RUN pip install git+https://github.com/HazyResearch/flash-attention@v2.5.7#subdirectory=csrc/fused_dense_lib
+RUN pip install git+https://github.com/HazyResearch/flash-attention@v2.6.0#subdirectory=csrc/fused_dense_lib