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Fix IMA caused by OOB lanes in varlen indexer top-k #410

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Fix IMA caused by OOB lanes in varlen indexer top-k #410

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94 changes: 52 additions & 42 deletions python/cudnn/deepseek_sparse_attention/indexer_top_k/indexer_top_k_varlen_util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -525,12 +525,18 @@ def indexer_topk_kernel_per_row(
-tXrX.element_type.inf,
)

cur_tXcX = tXcX[None, None, None, tile_idx]
for i in cutlass.range(cute.size(tXrX), unroll_full=True):
bin_val = self.to_coarse_key(tXrX[i])
atomicAdd(
s_histogram.iterator + cutlass.Int32(bin_val),
val_one,
)
relative_idx = cutlass.Int32(cur_tXcX[i // vec_size][1] + i % vec_size)
# The predicated copy fills lanes outside aligned_size with
# -inf, but those lanes are not part of the input row and
# must not contribute to the radix histogram.
if relative_idx < aligned_size:
bin_val = self.to_coarse_key(tXrX[i])
atomicAdd(
s_histogram.iterator + cutlass.Int32(bin_val),
val_one,
)

# for initial scalar load part.
for j in range(tidx, prologue_elems, self.num_threads_per_cta):
Expand Down Expand Up @@ -594,11 +600,13 @@ def indexer_topk_kernel_per_row(
)
for i in cutlass.range(cute.size(tXrX), unroll_full=True):
cur_tXcX = tXcX[None, None, None, tile_idx]
bin_val = self.to_coarse_key(tXrX[i])
if bin_val < threshold_bin:
pos = atomicAdd(s_counter.iterator, val_one)
idx = self.index_type(cur_tXcX[i // vec_size][1] + i % vec_size + vec_start)
s_indices[pos] = idx
relative_idx = cutlass.Int32(cur_tXcX[i // vec_size][1] + i % vec_size)
if relative_idx < aligned_size:
bin_val = self.to_coarse_key(tXrX[i])
if bin_val < threshold_bin:
pos = atomicAdd(s_counter.iterator, val_one)
idx = self.index_type(relative_idx + vec_start)
s_indices[pos] = idx

# for initial scalar load part.
for j in range(tidx, prologue_elems, self.num_threads_per_cta):
Expand Down Expand Up @@ -648,42 +656,44 @@ def indexer_topk_kernel_per_row(
)

for i in cutlass.range(cute.size(tXrX), unroll_full=True):
raw_input = tXrX[i]
bin_val = self.to_coarse_key(raw_input)
cur_tXcX = tXcX[None, None, None, tile_idx]
idx = self.index_type(cur_tXcX[i // vec_size][1] + i % vec_size + vec_start)
if bin_val < threshold_bin:
pos = atomicAdd(s_counter.iterator, val_one)
s_indices[pos] = idx
elif bin_val == threshold_bin:
# pos = atomicAdd(s_num_input[0], 1)
pos = atomicAdd(s_num_input.iterator, val_one)
if cutlass.const_expr(self.enable_gmem_store):
if pos < self.indexer_topk_smem_input_size:
s_input_idx[0, pos] = idx
relative_idx = cutlass.Int32(cur_tXcX[i // vec_size][1] + i % vec_size)
if relative_idx < aligned_size:
raw_input = tXrX[i]
bin_val = self.to_coarse_key(raw_input)
idx = self.index_type(relative_idx + vec_start)
if bin_val < threshold_bin:
pos = atomicAdd(s_counter.iterator, val_one)
s_indices[pos] = idx
elif bin_val == threshold_bin:
# pos = atomicAdd(s_num_input[0], 1)
pos = atomicAdd(s_num_input.iterator, val_one)
if cutlass.const_expr(self.enable_gmem_store):
if pos < self.indexer_topk_smem_input_size:
s_input_idx[0, pos] = idx
else:
buffer_pos = atomicAdd(
g_num_input.iterator,
val_one,
)
buffer[0, buffer_pos] = cutlass.Int32(cutlass.Uint32(idx))
ordered = self.to_ordered(raw_input)
sub_bin = (ordered >> self.first_refine_shift) & 0xFF
# atomicAdd(s_histogram[sub_bin], 1)
atomicAdd(
s_histogram.iterator + cutlass.Int32(sub_bin),
val_one,
)
else:
buffer_pos = atomicAdd(
g_num_input.iterator,
if pos < self.indexer_topk_smem_input_size:
s_input_idx[0, pos] = idx
ordered = self.to_ordered(raw_input)
sub_bin = (ordered >> self.first_refine_shift) & 0xFF
# atomicAdd(s_histogram[sub_bin], 1)
atomicAdd(
s_histogram.iterator + cutlass.Int32(sub_bin),
val_one,
)
buffer[0, buffer_pos] = cutlass.Int32(cutlass.Uint32(idx))
ordered = self.to_ordered(raw_input)
sub_bin = (ordered >> self.first_refine_shift) & 0xFF
# atomicAdd(s_histogram[sub_bin], 1)
atomicAdd(
s_histogram.iterator + cutlass.Int32(sub_bin),
val_one,
)
else:
if pos < self.indexer_topk_smem_input_size:
s_input_idx[0, pos] = idx
ordered = self.to_ordered(raw_input)
sub_bin = (ordered >> self.first_refine_shift) & 0xFF
# atomicAdd(s_histogram[sub_bin], 1)
atomicAdd(
s_histogram.iterator + cutlass.Int32(sub_bin),
val_one,
)

# for initial scalar load part.
for j in range(tidx, prologue_elems, self.num_threads_per_cta):
Expand Down
54 changes: 54 additions & 0 deletions test/python/fe_api/dsa/test_DSA_indexer_top_k.py
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Original file line number Diff line number Diff line change
Expand Up @@ -143,3 +143,57 @@ def test_DSA_indexer_top_k_wrapper(
values,
return_val,
)


@pytest.mark.L0
@torch_fork_set_rng(seed=0)
def test_DSA_indexer_top_k_wrapper_ignores_vector_padding_with_negative_infinity():
"""OOB vector lanes must not join a real -inf threshold bin."""
try:
from cudnn import DSA
except ImportError:
pytest.skip("Environment not supported: cudnn[cutedsl] not installed")

if torch.cuda.get_device_capability()[0] < 9:
pytest.skip("Indexer top-k requires compute capability 9.0 or newer")

num_rows = 633
num_cols = 768
seq_len = 633
finite_values = 475
top_k = 512

input_values = torch.full(
(num_rows, num_cols),
float("-inf"),
dtype=torch.float32,
device="cuda",
)
input_values[:, :finite_values] = torch.randn(
num_rows,
finite_values,
dtype=torch.float32,
device="cuda",
)
seq_lens = torch.full((num_rows,), seq_len, dtype=torch.int32, device="cuda")

result = DSA.indexer_top_k_wrapper(
input_values,
seq_lens,
top_k=top_k,
next_n=1,
return_val=False,
)
torch.cuda.synchronize()

indices = result["indices"]
assert torch.all((indices >= 0) & (indices < seq_len)).item()

selected_values = torch.gather(input_values, 1, indices.to(torch.int64))
expected_values = torch.topk(input_values[:, :seq_len], top_k, dim=1).values
torch.testing.assert_close(
torch.sort(selected_values, dim=1).values,
torch.sort(expected_values, dim=1).values,
atol=0.0,
rtol=0.0,
)