[CI] Add mla ut

[GDzhu01]

What this PR does / why we need it?

add mla_v1.py and mla.py ut

Does this PR introduce any user-facing change?

No

How was this patch tested?

pytest tests/ut/attention/test_mla_v1.py
pytest tests/ut/models/test_mla.py

• vLLM version: v0.11.0
• vLLM main: vllm-project/vllm@2918c1b

[gemini-code-assist]

Code Review

This pull request adds unit tests for the Multi-Head Latent Attention (MLA) implementation. The new tests cover the metadata builder in test_mla_v1.py and the AscendMultiHeadLatentAttention layer in test_mla.py. The changes are a good step towards improving test coverage. I've found one high-severity issue in the new test test_forward within tests/ut/models/test_mla.py, where the mocking of a custom operation is incorrect, leading to a test that doesn't properly validate the intended functionality. I've provided a code suggestion to fix the test and make it more robust.

[gemini-code-assist]

The mock for torch.ops.vllm.mla_forward is not correctly configured, and the assertions are not sufficient to validate the behavior of the forward method.

1. The mla_forward custom op is defined to return None and modify its output argument in-place. However, the test sets a tuple (3, self.hidden_size) as the return_value, which is incorrect and ignored during execution. This means the test doesn't verify that the op correctly modifies the output tensor.
2. The assertions only check the shape of the output tensor and that it's allclose to itself, which will always pass. The test should verify that mla_forward is called correctly and that the output tensor is populated as expected.

To make this test more robust, you should use side_effect to simulate the in-place modification of the output tensor and add assertions to check the op's arguments and the output's content.

Suggested change

	mock_mla_forward.return_value = (3, self.hidden_size)
	output = attn.forward(positions, hidden_states)
	self.assertEqual(output.shape, (3, self.hidden_size))
	self.assertTrue(
	torch.allclose(output, output.view(-1, self.hidden_size)))
	def mla_forward_side_effect(hidden_states, need_gather_q_kv, output, prefix):
	# Simulate the op writing to the output tensor
	output.fill_(1.0)
	mock_mla_forward.side_effect = mla_forward_side_effect
	output = attn.forward(positions, hidden_states)
	mock_mla_forward.assert_called_once_with(hidden_states, False, output, self.prefix)
	self.assertEqual(output.shape, (3, self.hidden_size))
	self.assertTrue(torch.all(output == 1.0))

[github-actions]

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[MengqingCao]

plz update the pr message