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[FIX] test_kernel_output_torch_fused #1908

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Merged
merged 2 commits into from
Sep 25, 2025
+9 −9
Merged

[FIX] test_kernel_output_torch_fused #1908

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7427018
fix test_kernel_output_torch_fused
ZX-ModelCloud Sep 25, 2025
a2f1f12
TorchFusedQuantLinear also supports CPU
ZX-ModelCloud Sep 25, 2025
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4 changes: 2 additions & 2 deletions gptqmodel/nn_modules/qlinear/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -415,8 +415,8 @@ def post_init(self, **kwargs):
dtype=t.int32,
).reshape(1, 3, 12).to(device=self.g_idx.device)

self.wf_unsqueeze_zero = wf.unsqueeze(0).to(device=self.g_idx.device)
self.wf_unsqueeze_neg_one = wf.unsqueeze(-1).to(device=self.g_idx.device)
self.register_buffer("wf_unsqueeze_zero", wf.unsqueeze(0).to(device=self.g_idx.device), persistent=False)
self.register_buffer("wf_unsqueeze_neg_one", wf.unsqueeze(-1).to(device=self.g_idx.device), persistent=False)

super().post_init(**kwargs)

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4 changes: 2 additions & 2 deletions gptqmodel/nn_modules/qlinear/torch_fused.py
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Expand Up @@ -46,7 +46,7 @@ class TorchFusedQuantLinear(PackableQuantLinear):
SUPPORTS_OUT_FEATURES_DIVISIBLE_BY = [1]

# optimized for XPU but should run on all
SUPPORTS_DEVICES = [DEVICE.XPU] # change this to XPU to limit to Intel XPU
SUPPORTS_DEVICES = [DEVICE.CPU, DEVICE.XPU] # change this to XPU to limit to Intel XPU
SUPPORTS_PLATFORM = [PLATFORM.ALL]
SUPPORTS_PACK_DTYPES = [torch.int32]
SUPPORTS_ADAPTERS = [Lora]
Expand Down Expand Up @@ -174,7 +174,7 @@ def forward(self, x: torch.Tensor):
def _forward(self, x, out_shape):
num_itr = self.g_idx.shape[0] // x.shape[-1]

if not self.training and not self.transformed and TORCH_HAS_XPU_FUSED_OPS:
if not self.training and not self.transformed and TORCH_HAS_XPU_FUSED_OPS and "xpu" == x.device.type:
# one-time transform per module for xpu aten fused ops
self.transform(x.dtype)
self.transformed = True
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10 changes: 5 additions & 5 deletions tests/test_kernel_output_torch_fused.py
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Expand Up @@ -19,7 +19,7 @@ class TestKernelOutput(unittest.TestCase):
BACKEND.TORCH_FUSED: TorchFusedQuantLinear,
}
target = 'model.layers.6.self_attn.v_proj'
device_map = "cpu"
device = "cpu"
m = [1, 16, 64, 256, 1024]
k = 2048
dtype = torch.float16
Expand All @@ -29,7 +29,7 @@ class TestKernelOutput(unittest.TestCase):

@classmethod
def setUp(self):
self.torch_model = GPTQModel.load(self.model_path, backend=BACKEND.TORCH, device_map=self.device_map, dtype=self.dtype)
self.torch_model = GPTQModel.load(self.model_path, backend=BACKEND.TORCH, device=self.device, dtype=self.dtype)
self.x = []
self.torch_kernel_outs = []
for dim_0 in self.m:
Expand Down Expand Up @@ -61,8 +61,8 @@ def assert_on_mismatch(self, a: Tensor, b: Tensor, rtol=0.00005, atol=0.00005):
(BACKEND.TORCH_FUSED, r_tolerance, a_tolerance),
])
def test_kernel_output(self, backend: BACKEND, r_tolerance: float, a_tolerance: float):
model = GPTQModel.load(self.model_path, backend=backend, device_map=self.device_map, dtype=self.dtype)
log.info(f"device_map: {self.device_map} ")
model = GPTQModel.load(self.model_path, backend=backend, device=self.device, dtype=self.dtype)
log.info(f"device: {self.device} ")
log.info(f"backend: {backend} ")
for i in range(len(self.x)):
out = self.forward(model, self.x[i], backend=backend)
Expand All @@ -75,7 +75,7 @@ class TestKernelOutputBFloat16(TestKernelOutput):

@unittest.skipUnless(hasattr(torch, "xpu") and torch.xpu.is_available(), reason="Test requires XPU")
class TestKernelOutputXPU(TestKernelOutput):
device_map = "xpu:0"
device = "xpu:0"
a_tolerance = 0.0005


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