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[Intel GPU] Extend TestQAT module with xpu testcases #3177

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[Intel GPU] Extend TestQAT module with xpu testcases #3177

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27 changes: 15 additions & 12 deletions test/quantization/test_qat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -653,13 +653,13 @@ def test_qat_4w_primitives(self):

self._assert_close_4w(qat_out, ptq_out)

@unittest.skipIf(not _CUDA_IS_AVAILABLE, "skipping when cuda is not available")
@unittest.skipIf(_DEVICE is None, "skipping when GPU is not available")
def test_qat_4w_linear(self):
from torchao.quantization.GPTQ import WeightOnlyInt4Linear
from torchao.quantization.qat.linear import Int4WeightOnlyQATLinear

group_size = 128
device = torch.device("cuda")
device = torch.device(_DEVICE)
dtype = torch.bfloat16
torch.manual_seed(self.SEED)
qat_linear = Int4WeightOnlyQATLinear(
Expand Down Expand Up @@ -694,7 +694,11 @@ def test_qat_4w_quantizer_gradients(self):
quantizer = Int4WeightOnlyQATQuantizer(groupsize=32, inner_k_tiles=8)
self._test_qat_quantized_gradients(quantizer)

@unittest.skipIf(not _CUDA_IS_AVAILABLE, "skipping when cuda is not available")
@unittest.skipIf(_DEVICE is None, "skipping when GPU is not available")
@unittest.skipIf(
_DEVICE is torch.device("xpu"),
"skipped due to https://github.com/intel/torch-xpu-ops/issues/1770",
)
def test_qat_4w_quantizer(self):
from torchao.quantization.GPTQ import Int4WeightOnlyQuantizer
from torchao.quantization.qat import Int4WeightOnlyQATQuantizer
Expand All @@ -711,8 +715,7 @@ def test_qat_4w_quantizer(self):
inner_k_tiles=inner_k_tiles,
)
ptq_quantizer = Int4WeightOnlyQuantizer(
groupsize=group_size,
inner_k_tiles=inner_k_tiles,
groupsize=group_size, inner_k_tiles=inner_k_tiles, device=device
)
qat_model = qat_quantizer.prepare(m)
ptq_model = ptq_quantizer.quantize(m2)
Expand Down Expand Up @@ -1893,12 +1896,12 @@ def _test_quantize_api_against_ptq(
torch.manual_seed(self.SEED)

if module_type == "linear":
m = M().to(dtype).cuda()
example_inputs = (m.example_inputs()[0].to(dtype).cuda(),)
m = M().to(dtype).to(_DEVICE)
example_inputs = (m.example_inputs()[0].to(dtype).to(_DEVICE),)
filter_fn = lambda m, fqn: isinstance(m, torch.nn.Linear)
elif module_type == "embedding":
m = M3().to(dtype).cuda()
example_inputs = (m.example_inputs()[0].cuda(),)
m = M3().to(dtype).to(_DEVICE)
example_inputs = (m.example_inputs()[0].to(_DEVICE),)
filter_fn = lambda m, fqn: isinstance(m, torch.nn.Embedding)
else:
raise ValueError(f"Unknown module type {module_type}")
Expand Down Expand Up @@ -1973,7 +1976,7 @@ def test_quantize_api_int4(self, version: int, packing_format: Int4PackingFormat
target_convert_sqnr=float("inf"),
)

@unittest.skipIf(not _CUDA_IS_AVAILABLE, "skipping when cuda is not available")
@unittest.skipIf(_DEVICE is None, "skipping when GPU is not available")
def test_quantize_api_int8_int4(self):
"""
Test the following:
Expand All @@ -1986,7 +1989,7 @@ def test_quantize_api_int8_int4(self):
target_convert_sqnr=float("inf"),
)

@unittest.skipIf(not _CUDA_IS_AVAILABLE, "skipping when cuda is not available")
@unittest.skipIf(_DEVICE is None, "skipping when GPU is not available")
@parametrize(
"weight_dtype, weight_granularity, dtype",
[
Expand All @@ -2011,7 +2014,7 @@ def test_quantize_api_int8_intx(self, weight_dtype, weight_granularity, dtype):
dtype=dtype,
)

@unittest.skipIf(not _CUDA_IS_AVAILABLE, "skipping when cuda is not available")
@unittest.skipIf(_DEVICE is None, "skipping when GPU is not available")
@parametrize(
"weight_dtype, granularity, dtype, module_type",
[
Expand Down