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adaptive attention op #4

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Merged
BingooYang merged 5 commits into from
Jan 6, 2026
Merged

adaptive attention op #4

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6fa8eb4
support trtllm_batch_context_with_kv_cache and trtllm_fp8_block_scale…
BingooYang Dec 24, 2025
adf9606
adaptive attention and moe test
BingooYang Dec 26, 2025
9a8f8c4
support test
BingooYang Dec 26, 2025
f8d129f
fix conflict issues
BingooYang Jan 6, 2026
9fb038e
fix pytest issues
BingooYang Jan 6, 2026
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4 changes: 2 additions & 2 deletions tests/attention/test_attention_sink_blackwell.py
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Original file line number Diff line number Diff line change
Expand Up @@ -250,6 +250,6 @@ def test_blackwell_trtllm_gen_context_attention_sink(
atol, rtol = 1e-2, 1e-2
else:
raise ValueError(f"Unsupported dtype: {dtype}")

ref_o = o_ref.float().numpy()
np.testing.assert_allclose(ref_o, paddle_o, atol=atol, rtol=rtol)
output_o = output.float().numpy()
np.testing.assert_allclose(ref_o, output_o, atol=atol, rtol=rtol)
2 changes: 1 addition & 1 deletion tests/conftest.py
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Expand Up @@ -145,7 +145,7 @@ def pytest_runtest_call(item):
try:
item.runtest()
except:
# assert(False)
assert(False)
# try:
# item.runtest()
# except (torch.cuda.OutOfMemoryError, RuntimeError) as e:
Expand Down
7 changes: 2 additions & 5 deletions tests/test_helpers/sink_attention_reference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -312,8 +312,7 @@ def sink_attention_unified(
# 1
# ) >= torch.arange(0, kv_len, device=q.device).unsqueeze(0)
mask = torch.arange(kv_len - qo_len, kv_len).unsqueeze(
1
) >= torch.arange(0, kv_len).unsqueeze(0)
1) >= torch.arange(0, kv_len).unsqueeze(0)
if window_left >= 0:
row_idx = torch.arange(qo_len, dtype=torch.int32, device=q.device)[
:, None
Expand Down Expand Up @@ -343,10 +342,8 @@ def sink_attention_unified(
mask &= abs_row_positions - window_left <= col_idx
else:
# Non-causal mask
q_device = q.place
print("+++",q_device)
if mode == "incremental":
mask = torch.ones(1, kv_len, device=q_device, dtype=torch.bool)
mask = torch.ones(1, kv_len, dtype=torch.bool)
if window_left >= 0:
col_idx = torch.arange(kv_len, dtype=torch.int32, device=q.device)
mask = (kv_len - 1 - window_left) <= col_idx
Expand Down
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