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[BugFix][310p][Cherry-pick] Handle null quantization config in ShardedStateLoader310&[Feature][310P] Support W8A8 dynamic linear method (#8296)

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### What this PR does / why we need it?
This PR implements the `AscendW8A8DynamicLinearMethod310` quantization
scheme specifically for 310P hardware. It includes the logic for weight
retrieval, per-channel parameter generation, and the application of
dynamic quantization using NPU-specific kernels. Additionally, it
updates `ShardedStateLoader310` to handle quantization configurations
more robustly when generating parameter type maps.

Feedback from the review identified two critical issues in the
implementation:
1. The tensor squeezing logic in the `apply` method incorrectly handles
2D inputs, which may lead to shape mismatches in subsequent layers.
2. The weight tensor in `process_weights_after_loading` is transposed
after being converted to the private NZ format; the transpose operation
should be performed on the ND tensor before conversion to ensure correct
physical layout.

cherry-pick from : #7546 #7725
### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
New unit tests were added in
`tests/ut/_310p/quantization/test_w8a8_dynamic_310.py` to verify the
quantization method, and
`tests/ut/_310p/test_sharded_state_loader_310p.py` was updated to test
the state loader changes.

---------

Signed-off-by: csoulnd <daidaicurry@foxmail.com>
This commit is contained in:
csoulnd
2026-04-16 16:53:39 +08:00
committed by GitHub
parent 52f0f9b5e4
commit 8952fddc7e
6 changed files with 184 additions and 10 deletions
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22
tests/ut/_310p/test_sharded_state_loader_310p.py
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@@ -77,7 +77,7 @@ class TestShardedStateLoader310(TestBase):
model = MockModel(quant_config=quant_config, with_int_weights=False)
with tempfile.TemporaryDirectory() as tmpdir:
ShardedStateLoader310.generate_quant_description(model, tmpdir)
ShardedStateLoader310.generate_quant_description(model, tmpdir, quant_config)
json_path = Path(tmpdir) / "parameters_type_map.json"
self.assertTrue(json_path.exists())
@@ -92,6 +92,24 @@ class TestShardedStateLoader310(TestBase):
self.assertIn("linear.bias", quant_description)
self.assertEqual(quant_description["linear.bias"], "FLOAT")
@patch("vllm.model_executor.model_loader.ShardedStateLoader._filter_subtensors")
def test_generate_quant_description_no_quant_config_310(self, mock_filter):
"""When quant_config is None, treat model as FLOAT."""
mock_filter.side_effect = lambda x: x
model = MockModel(quant_config=None, with_int_weights=False)
with tempfile.TemporaryDirectory() as tmpdir:
ShardedStateLoader310.generate_quant_description(model, tmpdir, None)
json_path = Path(tmpdir) / "parameters_type_map.json"
self.assertTrue(json_path.exists())
with open(json_path, encoding="utf-8") as f:
quant_description = json.load(f)
self.assertEqual(quant_description["model_quant_type"], "FLOAT")
self.assertEqual(quant_description["linear.weight"], "FLOAT")
@patch("vllm.model_executor.model_loader.ShardedStateLoader._filter_subtensors")
def test_generate_quant_description_int_model_310(self, mock_filter):
"""Test generate_quant_description for int8 quantized model."""
@@ -100,7 +118,7 @@ class TestShardedStateLoader310(TestBase):
model = MockModel(quant_config=quant_config, with_int_weights=True)
with tempfile.TemporaryDirectory() as tmpdir:
ShardedStateLoader310.generate_quant_description(model, tmpdir)
ShardedStateLoader310.generate_quant_description(model, tmpdir, quant_config)
json_path = Path(tmpdir) / "parameters_type_map.json"
self.assertTrue(json_path.exists())