test: use .float() in in F.cosine_similarity() in bmm_fp8 test (flashinfer-ai#2266)

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## 📌 Description

saw some [test
failures](https://gitlab-master.nvidia.com/dl/flashinfer/flashinfer-ci/-/jobs/247866505)
on Blackwell boards after flashinfer-ai#2261, all the failed assertions are related
to the large value 10304.

Use `.float()` to help reduce precision loss during `cosine_similarity`
(`dot(x, y) / (||x|| * ||y||)`) check.

```
FAILED tests/gemm/test_bmm_fp8.py::test_bmm_fp8[True-cutlass-res_dtype1-mat2_dtype0-input_dtype0-256-10304-128-16] - AssertionError: assert tensor(0., device='cuda:0') > 0.99
2025-12-24T07:00:08.299846Z 01O FAILED tests/gemm/test_bmm_fp8.py::test_bmm_fp8[False-cudnn-res_dtype1-mat2_dtype0-input_dtype1-256-10304-128-16] - AssertionError: assert tensor(0., device='cuda:0') > 0.99
... # the failure occurs for all backend (cutlass, cudnn, etc)
```

cc: @zihaoye @bkryu 


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## Summary by CodeRabbit

* **Tests**
* Improved test accuracy by ensuring tensor comparisons use
floating-point precision for cosine similarity calculations.

<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

Author: yongwww

tests/gemm/test_bmm_fp8.py

@@ -51,7 +51,9 @@ def test_bmm_fp8(b, m, n, k, input_dtype, mat2_dtype, res_dtype, backend, auto_t
             backend=backend,
         )
 
-    cos_sim = F.cosine_similarity(reference.reshape(-1), res.reshape(-1), dim=0)
+    cos_sim = F.cosine_similarity(
+        reference.reshape(-1).float(), res.reshape(-1).float(), dim=0
+    )
     assert cos_sim > 0.99