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Migrate "cute.core.ThrMma" and "cute.make_fragment" #321

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Merged
Anerudhan merged 2 commits into from
Jun 24, 2026
Merged

Migrate "cute.core.ThrMma" and "cute.make_fragment" #321

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643bfb3
cute.core.ThrMma is deprecated
Vinnie6167 Jun 16, 2026
b019c4a
cute.make_fragment is deprecated
Vinnie6167 Jun 16, 2026
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2 changes: 1 addition & 1 deletion python/cudnn/deepseek_sparse_attention/indexer_forward/indexer_fwd_sm90.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@


def _mma_partition_fragment_AB(
thr_mma: cute.core.ThrMma,
thr_mma: cute.ThrMma,
sA: cute.Tensor,
sB: cute.Tensor,
swap_AB: bool,
Expand Down
4 changes: 2 additions & 2 deletions python/cudnn/deepseek_sparse_attention/indexer_top_k/compactify.py
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Expand Up @@ -78,8 +78,8 @@ def kernel(self, mIn: cute.Tensor, mOut: cute.Tensor, mLen: cute.Tensor, rows: I
row = bidx * ROWS_PER_CTA + warp_id

if row < rows:
local_vals = cute.make_fragment((chunk,), Int32)
local_is_valid = cute.make_fragment((chunk,), cutlass.Boolean)
local_vals = cute.make_rmem_tensor((chunk,), Int32)
local_is_valid = cute.make_rmem_tensor((chunk,), cutlass.Boolean)
cnt_v = Int32(0)
cnt_i = Int32(0)

Expand Down
6 changes: 3 additions & 3 deletions python/cudnn/deepseek_sparse_attention/indexer_top_k/indexer_top_k_varlen_util.py
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Expand Up @@ -962,8 +962,8 @@ def indexer_topk_kernel_per_row(
assert self.top_k % vecsize_out == 0

nvec_per_thread = cutlass.const_expr(cute.ceil_div(self.top_k, vecsize_out * self.num_threads_per_cta))
topk_vals = cute.make_fragment((vecsize_out, nvec_per_thread), self.dtype)
topk_indices = cute.make_fragment((vecsize_out, nvec_per_thread), cutlass.Int32)
topk_vals = cute.make_rmem_tensor((vecsize_out, nvec_per_thread), self.dtype)
topk_indices = cute.make_rmem_tensor((vecsize_out, nvec_per_thread), cutlass.Int32)

stride = self.num_threads_per_cta * vecsize_out
for i in cutlass.range(nvec_per_thread, unroll_full=True):
Expand Down Expand Up @@ -1018,7 +1018,7 @@ def _get_tiled_copy(self):

@cute.jit
def predicate_tile(self, tAcA: cute.Tensor, limit: cutlass.Int32) -> cute.Tensor:
tApA = cute.make_fragment(
tApA = cute.make_rmem_tensor(
cute.make_layout(
(
cute.size(tAcA, mode=[0, 1]),
Expand Down
2 changes: 1 addition & 1 deletion python/cudnn/deepseek_sparse_attention/utils/sm90/mma.py
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Expand Up @@ -91,7 +91,7 @@ def gemm_w_idx(


def mma_partition_fragment_AB(
thr_mma: cute.core.ThrMma,
thr_mma: cute.ThrMma,
sA: Optional[cute.Tensor],
sB: Optional[cute.Tensor],
swap_AB: bool,
Expand Down
12 changes: 6 additions & 6 deletions python/cudnn/native_sparse_attention/compression/fmha.py
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Expand Up @@ -1756,7 +1756,7 @@ def softmax(
cum_seqlen_q: Optional[cute.Tensor],
cum_seqlen_k: Optional[cute.Tensor],
scale_softmax_log2: Float32,
qk_thr_mma: cute.core.ThrMma,
qk_thr_mma: cute.ThrMma,
tStS: cute.Tensor,
tStSi: cute.Tensor,
window_size_left: Optional[Int32],
Expand Down Expand Up @@ -1791,7 +1791,7 @@ def softmax(
:param scale_softmax_log2: Log2 scale factor for softmax operation
:type scale_softmax_log2: Float32
:param qk_thr_mma: Thread MMA operation for QK matrix multiplication
:type qk_thr_mma: cute.core.ThrMma
:type qk_thr_mma: cute.ThrMma
:param tStS: Shared tensor for softmax input/output
:type tStS: cute.Tensor
:param tStSi: Input tensor containing attention scores
Expand Down Expand Up @@ -2056,7 +2056,7 @@ def softmax(
@cute.jit
def correction_rescale(
self,
thr_mma: cute.core.ThrMma,
thr_mma: cute.ThrMma,
tOtO: cute.Tensor,
scale: Float32,
):
Expand All @@ -2073,7 +2073,7 @@ def correction_rescale(
3. Store the rescaled results back to tensor memory

:param thr_mma: Thread MMA operation for the computation
:type thr_mma: cute.core.ThrMma
:type thr_mma: cute.ThrMma
:param tOtO: Tensor representing partial attention output to be rescaled
:type tOtO: cute.Tensor
:param scale: Scaling factor to apply to the partial results
Expand Down Expand Up @@ -2134,7 +2134,7 @@ def correction_rescale(
@cute.jit
def correction_epilog(
self,
thr_mma: cute.core.ThrMma,
thr_mma: cute.ThrMma,
tOtO: cute.Tensor,
mLSE: cute.Tensor | None,
tTMEM_LOAD_VECrS: cute.Tensor,
Expand All @@ -2160,7 +2160,7 @@ def correction_epilog(
5. Preparation for efficient TMA store operations

:param thr_mma: Thread MMA operation for the computation
:type thr_mma: cute.core.ThrMma
:type thr_mma: cute.ThrMma
:param tOtO: Tensor containing accumulated attention output
:type tOtO: cute.Tensor
:param mLSE: Tensor containing log-sum-exp values for LSE calculation
Expand Down
18 changes: 9 additions & 9 deletions python/cudnn/sdpa/utils.py
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Expand Up @@ -165,7 +165,7 @@ def make_tiled_copy_B( # noqa: N802

def mma_make_fragment_A( # noqa: N802
smem: cute.Tensor,
thr_mma: cute.core.ThrMma,
thr_mma: cute.ThrMma,
Comment thread
Vinnie6167 marked this conversation as resolved.
swapAB: cutlass.Constexpr[bool] = False, # noqa: N803
) -> cute.Tensor:
"""Wrapper for cute.mma_make_fragment."""
Expand All @@ -176,7 +176,7 @@ def mma_make_fragment_A( # noqa: N802

def mma_make_fragment_B( # noqa: N802
smem: cute.Tensor,
thr_mma: cute.core.ThrMma,
thr_mma: cute.ThrMma,
swapAB: cutlass.Constexpr[bool] = False, # noqa: N803
) -> cute.Tensor:
"""Wrapper for cute.mma_make_fragment."""
Expand Down Expand Up @@ -207,7 +207,7 @@ def warp_reduce(
) -> cute.TensorSSA | cute.Numeric:
"""Reduction in warp."""
if cutlass.const_expr(isinstance(val, cute.TensorSSA)):
res = cute.make_fragment(val.shape, val.dtype)
res = cute.make_rmem_tensor(val.shape, val.dtype)
res.store(val)
for i in cutlass.range_constexpr(cute.size(val.shape)):
res[i] = warp_reduce(res[i], op, width)
Expand Down Expand Up @@ -350,7 +350,7 @@ def exp2f(x: cute.TensorSSA | Float32) -> cute.TensorSSA | Float32:
:rtype: cute.TensorSSA or Float32.
"""
if cutlass.const_expr(isinstance(x, cute.TensorSSA)):
res = cute.make_fragment(x.shape, Float32)
res = cute.make_rmem_tensor(x.shape, Float32)
res.store(x)
for i in cutlass.range_constexpr(cute.size(x.shape)):
res[i] = cute.math.exp2(res[i], fastmath=True)
Expand Down Expand Up @@ -399,7 +399,7 @@ def fmax(a: float | Float32, b: float | Float32, c: float | Float32 | None = Non
def fmax_reduce(x: cute.TensorSSA, init_val: float | Float32 | None = None, arch: cutlass.Constexpr[int] = 80) -> Float32:
"""Fmax reduce wrapper."""
if cutlass.const_expr(arch < ARCH_SM100 or cute.size(x.shape) % 8 != 0):
res = cute.make_fragment(x.shape, Float32)
res = cute.make_rmem_tensor(x.shape, Float32)
res.store(x)
local_max = [res[0], res[1], res[2], res[3]]
for i in cutlass.range_constexpr(4, cute.size(x.shape), 4):
Expand All @@ -413,7 +413,7 @@ def fmax_reduce(x: cute.TensorSSA, init_val: float | Float32 | None = None, arch
return local_max[0] if cutlass.const_expr(init_val is None) else fmax(local_max[0], init_val)
# [2025-06-15] x.reduce only seems to use 50% 3-input max and 50% 2-input max
# We instead force the 3-input max.
res = cute.make_fragment(x.shape, Float32)
res = cute.make_rmem_tensor(x.shape, Float32)
res.store(x)
local_max = [
fmax(init_val, res[0], res[1]) if cutlass.const_expr(init_val is not None) else fmax(res[0], res[1]),
Expand All @@ -437,7 +437,7 @@ def fadd_reduce(x: cute.TensorSSA, init_val: float | Float32 | None = None, arch
if cutlass.const_expr(init_val is None):
init_val = Float32.zero
return x.reduce(cute.ReductionOp.ADD, init_val, 0)
res = cute.make_fragment(x.shape, Float32)
res = cute.make_rmem_tensor(x.shape, Float32)
res.store(x)
local_sum_0 = cute.arch.add_packed_f32x2((init_val, 0.0), (res[0], res[1])) if cutlass.const_expr(init_val is not None) else (res[0], res[1])
local_sum = [local_sum_0, (res[2], res[3]), (res[4], res[5]), (res[6], res[7])]
Expand Down Expand Up @@ -503,7 +503,7 @@ def predicate_k(
) -> cute.Tensor:
"""Wrapper for predicate."""
# Only compute predicates for the "k" dimension. For the mn dimension, we will use "if"
tApA = cute.make_fragment( # noqa: N806
tApA = cute.make_rmem_tensor( # noqa: N806
cute.make_layout(
(cute.size(tAcA, mode=[0, 1]), cute.size(tAcA, mode=[1]), cute.size(tAcA, mode=[2])),
stride=(cute.size(tAcA, mode=[2]), 0, 1),
Expand Down Expand Up @@ -547,7 +547,7 @@ def shuffle_sync(
mask = cute.arch.WARP_SIZE - width
clamp = cute.arch.WARP_SIZE - 1
mask_and_clamp = mask << 8 | clamp
val = cute.make_fragment(1, type(value))
val = cute.make_rmem_tensor(1, type(value))
val[0] = value
val_i32 = cute.recast_tensor(val, cutlass.Int32)
for i in cutlass.range_constexpr(cute.size(val_i32)):
Expand Down