Fix TransformerEncoderLayer Full Mask UT Failure on XPU

[yucai-intel]

The Problem Solved
The assertion failed when the nn.TransformerEncoderLayer's test_transformer_encoder_layer test ran on an XPU device.
#2015
Root Cause: The test is designed to check the Transformer's behavior when the Key Padding Mask completely masks the input (Sequence Length=1, Mask=[[True]]).
The MHA module on XPU devices (lacking a specific Fast Path optimization) falls back to the Non Fast Path execution, where the actual calculated result is a non-NaN finite value (this is the mathematically robust result of X + Attention(0)).
However, by default, the test incorrectly entered the Fast Path assertion branch in XPU/Non CrossRef mode, which expected a NaN result. The actual non-NaN result did not match the expected NaN assertion, causing the test to fail.

Why This Solution
To validate the core semantics of TransformerEncoderLayer on XPU, we need to use the numerically most robust Non Fast Path logic as the benchmark.Non Fast Path Semantics: When fully masked, the attention output is 0, and the result is a finite value (Non-NaN) after text LayerNorm(X).
Solution: By forcing the test into the non fast path branch, its assertion is made to match the actual non-NaN result of the XPU device, ensuring the Unit Test (UT) passes.

The Implementation Method
The fix involves manually setting TEST_WITH_CROSSREF=1 within the affected test function in the relevant test file.
This forces the test method to follow the Non Fast Path branch. This switches the test's assertion from the incorrect self.assertTrue(np.isnan(result).all()) to the correct self.assertTrue(not np.isnan(result).any()), thereby fixing the test.

TIPS:
Given that PYTORCH_TEST_WITH_CROSSREF is a variable that can affect global test semantics and performance, this fix was chosen to be effective within the minimal scope to avoid unintended performance degradation or logic changes in other Unit Tests.

[CuiYifeng]

I noticed that CUDA passed these cases with TEST_WITH_CROSSREF = 0. Meanwhile, Fast Paths works with CUDA. We need to check the related XPU kernel.

[yucai-intel]

I ran this test on the A100. CUDA took the non-fast path, and the output of xpu was consistent with CUDA.

[yucai-intel]

On XPU：
res--------- [[[ 2.213 0.1344 -0.721 0.1926]]]
exp--------- False
On CUDA：
test_transformerencoderlayer---------
Non Fast Paths---------
res--------- [[[ 2.213 0.1344 -0.721 0.1926]]]
exp--------- False