Fix sink attention accuracy regression, add sink test and cleanup. (#1758)

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## 📌 Description

Update trtllm-gen cubin to fix accuracy regression about sink.

Integrate sink test to attention test as the old sink test didn't test
and catch the fp8 accuracy issue before.

Clean up attention test code.

Attention test case has inflated a lot as param num raise. Reduce by
using pairwise combination instead of Cartesian product.

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needed. -->

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Author: weireweire

flashinfer/artifacts.py

@@ -69,7 +69,7 @@ def get_available_cubin_files(source, retries=3, delay=5, timeout=10):
 
 
 class ArtifactPath:
-    TRTLLM_GEN_FMHA: str = "538f8e38ace07f701f61e26b138b2b8c70ce9e8e/fmha/trtllm-gen/"
+    TRTLLM_GEN_FMHA: str = "7206d64e67f4c8949286246d6e2e07706af5d223/fmha/trtllm-gen/"
     TRTLLM_GEN_BMM: str = (
         "e6f22dcc3fdeb29ff87af2f4a2cb3d30b8d273e0/batched_gemm-45beda1-ee6a802/"
     )
@@ -82,7 +82,7 @@ class ArtifactPath:
 
 class MetaInfoHash:
     TRTLLM_GEN_FMHA: str = (
-        "71f06a8fc03d28cc94ee6fc180fb7e37256a9e1c30ab2a6c0bf20a2d97af3eff"
+        "2f605255e71d673768f5bece66dde9e2e9f4c873347bfe8fefcffbf86a3c847d"
     )
     TRTLLM_GEN_BMM: str = (
         "c98b4ce69a39fd41556d67033c30ea814ef76b0a2fe16e798e55baf0104acc34"

tests/test_attention_sink_blackwell.py

@@ -18,7 +18,6 @@
 import pytest
 import torch
 from sink_attention_reference import sink_attention_unified
-from conftest import assert_close_with_mismatch_tolerance
 
 import flashinfer
 from flashinfer.utils import get_compute_capability
@@ -122,13 +121,7 @@ def test_blackwell_trtllm_gen_decode_attention_sink(
     else:
         raise ValueError(f"Unsupported dtype: {dtype}")
 
-    assert_close_with_mismatch_tolerance(
-        o_ref,
-        output,
-        atol=atol,
-        rtol=rtol,
-        max_mismatched_elements=int(output.numel() * 0.01),
-    )
+    torch.testing.assert_close(o_ref, output, atol=atol, rtol=rtol)
 
 
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])