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[PyTorch][CP] Make THD CP helper kernels to be shared memory size aware #3698

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[PyTorch][CP] Make THD CP helper kernels to be shared memory size aware #3698

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7249766
[CUDA] Handle THD boundary capacity across CP utility kernels
sudhakarsingh27 Oct 16, 2026
468c99e
[PyTorch] Guard THD boundary tests on available device limits
sudhakarsingh27 Oct 16, 2026
a3a3cec
Merge branch 'main' into sudhakars/thd-all-kernels-scale-helper
sudhakarsingh27 Oct 19, 2026
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121 changes: 121 additions & 0 deletions tests/pytorch/attention/test_cp_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1017,6 +1017,127 @@ def test_thd_get_partitioned_indices_matches_dual_chunk_expected_indices(self):
self.assertTrue(torch.equal(rank0, expected_rank0))
self.assertTrue(torch.equal(rank1, expected_rank1))

def test_thd_get_partitioned_indices_shared_memory_boundary(self):
props = torch.cuda.get_device_properties(torch.cuda.current_device())
if not hasattr(props, "shared_memory_per_block") or not hasattr(
props, "shared_memory_per_block_optin"
):
self.skipTest(f"PyTorch {torch.__version__} does not expose shared-memory limits")
max_shared_docs = props.shared_memory_per_block // 4 - 1
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for documents in (
max_shared_docs,
max_shared_docs + 1,
props.shared_memory_per_block_optin // 4 - 1,
props.shared_memory_per_block_optin // 4,
):
for cp_size in (2, 4):
cu_seqlens = torch.arange(documents + 1, device="cuda", dtype=torch.int32)
cu_seqlens *= 2 * cp_size
tokens = torch.arange(
documents * 2 * cp_size, device="cuda", dtype=torch.int32
).view(documents, 2 * cp_size)
for rank in range(cp_size):
with self.subTest(documents=documents, cp_size=cp_size, rank=rank):
indices = tex.thd_get_partitioned_indices(
cu_seqlens, tokens.numel(), cp_size, rank
)
expected = tokens[:, [rank, 2 * cp_size - 1 - rank]].reshape(-1)
torch.cuda.synchronize()
self.assertTrue(torch.equal(indices, expected))

def test_thd_kernels_shared_memory_boundary(self):
props = torch.cuda.get_device_properties(torch.cuda.current_device())
if not hasattr(props, "shared_memory_per_block") or not hasattr(
props, "shared_memory_per_block_optin"
):
self.skipTest(f"PyTorch {torch.__version__} does not expose shared-memory limits")
for tables in (1, 2):
bytes_per_document = 4 * tables
for documents in (
props.shared_memory_per_block // bytes_per_document - 1,
props.shared_memory_per_block // bytes_per_document,
props.shared_memory_per_block_optin // bytes_per_document - 1,
props.shared_memory_per_block_optin // bytes_per_document,
):
with self.subTest(tables=tables, documents=documents):
cu = torch.arange(documents + 1, dtype=torch.int32, device="cuda") * 4
total = documents * 4
x = torch.arange(total * 4, dtype=torch.float32, device="cuda").view(
total, 1, 4
)
docs = x.view(documents, 4, 1, 4)
if tables == 2:
out = torch.full_like(x, -1)
expected = out.clone().view_as(docs)
expected[:, :2] = docs[:, :2]
tex.thd_copy_valid_tokens_from_per_split_to_rank_local(out, x, cu, cu // 2)
self.assertTrue(torch.equal(out, expected.reshape_as(out)))
continue

reordered = tex.thd_sequence_order_to_cp_rank_order(x, cu, 2, total)
expected = torch.cat(
[docs[:, [rank, 3 - rank]].reshape(-1, 1, 4) for rank in range(2)]
)
self.assertTrue(torch.equal(reordered, expected))
restored = tex.thd_cp_rank_order_to_sequence_order(reordered, cu, 2, total)
self.assertTrue(torch.equal(restored, x))
for half in (0, 1):
actual = tex.thd_read_half_tensor(x, cu, half)
expected = docs[:, 2 * half : 2 * half + 2].reshape_as(actual)
self.assertTrue(torch.equal(actual, expected))

for packed in (False, True):
shape = (1, total) if packed else (documents, 1, 4)
half_shape = (1, total // 2) if packed else (documents, 1, 2)
lse = torch.arange(total, dtype=torch.float32, device="cuda").view(shape)
lse /= total
half_lse = tex.thd_read_second_half_lse(
lse, cu, packed, total // 2 if packed else 2
)
expected_half = lse.reshape(documents, 4)[:, 2:].reshape(half_shape)
self.assertTrue(torch.equal(half_lse, expected_half))
expected = lse.clone()
expected.reshape(documents, 4)[:, 2:] = torch.logaddexp(
expected_half.reshape(documents, 2),
torch.zeros_like(expected_half).reshape(documents, 2),
)
tex.thd_second_half_lse_correction(
lse, torch.zeros_like(half_lse), cu, packed
)
torch.testing.assert_close(lse, expected)

for second_half in (False, True):
out = torch.full_like(x, 3)
step = torch.ones_like(x[: total // (2 if second_half else 1)])
old_lse = torch.full(shape, 0.5, device="cuda").log()
expected = out.clone().view_as(docs)
expected[:, 2 if second_half else 0 :] *= 0.5
expected[:, 2 if second_half else 0 :] += 1
tex.thd_out_correction(
out,
step,
old_lse,
torch.zeros(shape, device="cuda"),
torch.zeros(half_shape if second_half else shape, device="cuda"),
cu,
second_half,
packed,
)
torch.testing.assert_close(out, expected.reshape_as(out))

grad = x.clone()
tex.thd_grad_correction(grad, torch.ones_like(x), cu, "add", "copy")
expected = docs.clone()
expected[:, :2] += 1
expected[:, 2:] = 1
self.assertTrue(torch.equal(grad, expected.reshape_as(grad)))
grad.fill_(-1)
tex.thd_grad_correction(grad, x[: total // 2], cu, "copy", "zero")
expected.zero_()
expected[:, :2] = x[: total // 2].view(documents, 2, 1, 4)
torch.cuda.synchronize()
self.assertTrue(torch.equal(grad, expected.reshape_as(grad)))

def test_thd_copy_valid_tokens_from_per_split_matches_legacy_slice_copy_loop(self):
cu_seqlens_padded = torch.tensor([2, 6, 12], dtype=torch.int32, device="cuda")
cu_seqlens = torch.tensor([0, 3, 7], dtype=torch.int32, device="cuda")
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