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Permute page table in benchmarking #2194

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Merged
yzh119 merged 2 commits into from
Dec 11, 2025
Merged

Permute page table in benchmarking #2194

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a4e1c35
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jhjpark Dec 10, 2025
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jhjpark Dec 10, 2025
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24 changes: 6 additions & 18 deletions benchmarks/routines/attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -396,7 +396,7 @@ def testBatchDecodeWithPagedKVCacheWrapper(args):
# Now initialize the page tables
block_tables = torch.tensor(
[
[k + i * num_pages_per_seq for k in range(num_pages_per_seq)]
[k + i * num_pages_per_seq for k in torch.randperm(num_pages_per_seq)]
for i in range(batch_size)
],
dtype=torch.int,
Expand All @@ -421,11 +421,7 @@ def testBatchDecodeWithPagedKVCacheWrapper(args):
for i in range(len(kv_indptr) - 1):
start_idx = kv_indptr[i]
end_idx = kv_indptr[i + 1]
kv_indices[start_idx:end_idx] = torch.arange(
i * num_pages_per_seq,
i * num_pages_per_seq + (end_idx - start_idx),
device=device,
)
kv_indices[start_idx:end_idx] = block_tables[i, : end_idx - start_idx]

kv_last_page_len = (
torch.where(
Expand Down Expand Up @@ -837,7 +833,7 @@ def testBatchPrefillWithPagedKVCacheWrapper(args):
# Now initialize the page tables
block_tables = torch.tensor(
[
[k + i * num_pages_per_seq for k in range(num_pages_per_seq)]
[k + i * num_pages_per_seq for k in torch.randperm(num_pages_per_seq)]
for i in range(batch_size)
],
dtype=torch.int,
Expand Down Expand Up @@ -887,11 +883,7 @@ def testBatchPrefillWithPagedKVCacheWrapper(args):
for i in range(len(kv_indptr) - 1):
start_idx = kv_indptr[i]
end_idx = kv_indptr[i + 1]
kv_indices[start_idx:end_idx] = torch.arange(
i * num_pages_per_seq,
i * num_pages_per_seq + (end_idx - start_idx),
device=device,
)
kv_indices[start_idx:end_idx] = block_tables[i, : end_idx - start_idx]
kv_last_page_len = (
torch.where(
actual_seq_lens_kv_device.flatten() % page_size == 0,
Expand Down Expand Up @@ -1711,7 +1703,7 @@ def testBatchMLAPagedAttentionWrapper(args):
# Now initialize the page tables
block_tables = torch.tensor(
[
[k + i * num_pages_per_seq for k in range(num_pages_per_seq)]
[k + i * num_pages_per_seq for k in torch.randperm(num_pages_per_seq)]
for i in range(batch_size)
],
dtype=torch.int,
Expand Down Expand Up @@ -1758,11 +1750,7 @@ def testBatchMLAPagedAttentionWrapper(args):
for i in range(len(kv_indptr) - 1):
start_idx = kv_indptr[i]
end_idx = kv_indptr[i + 1]
kv_indices[start_idx:end_idx] = torch.arange(
i * num_pages_per_seq,
i * num_pages_per_seq + (end_idx - start_idx),
device=device,
)
kv_indices[start_idx:end_idx] = block_tables[i, : end_idx - start_idx]

sm_scale = 1.0 / ((128 + 64) ** 0.5) # For DeepSeek-R1
workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8, device=device)
Expand Down