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Add support for AQTLayout, PlainAQTLayout and TensorCoreTiledAQTLayout #278

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merged 3 commits into from
May 29, 2024
Merged

Add support for AQTLayout, PlainAQTLayout and TensorCoreTiledAQTLayout #278

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f08f6de
Add support for `AQTStorage` and `PlainAQTStorage`
jerryzh168 May 25, 2024
ec3f79f
Merge branch 'main' into layout
jerryzh168 May 29, 2024
4758982
Merge branch 'main' into layout
jerryzh168 May 29, 2024
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8 changes: 4 additions & 4 deletions test/quantization/test_quant_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,8 +110,8 @@ def __init__(self, m=64, n=32, k=64):
self.linear1 = torch.nn.Linear(m, n, bias=False).to(torch.float)
self.linear2 = torch.nn.Linear(n, k, bias=False).to(torch.float)

def example_inputs(self, batch_size=1):
return (torch.randn(batch_size, self.linear1.in_features).to(torch.float),)
def example_inputs(self, batch_size=1, dtype=torch.float, device="cpu"):
return (torch.randn(batch_size, self.linear1.in_features, dtype=dtype, device=device),)

def forward(self, x):
x = self.linear1(x)
Expand Down Expand Up @@ -450,7 +450,7 @@ def test_quantized_tensor_subclass_int4(self):
# use 1024 so that we don't need padding
m = ToyLinearModel(1024, 1024, 1024).eval().to(torch.bfloat16).to("cuda")
m_copy = copy.deepcopy(m)
example_inputs = tuple(map(lambda x: x.to(torch.bfloat16).to("cuda"), m.example_inputs()))
example_inputs = m.example_inputs(dtype=torch.bfloat16, device="cuda")

groupsize = 32
m = quantize(m, get_apply_int4wo_quant(groupsize=groupsize))
Expand Down Expand Up @@ -496,7 +496,7 @@ def test_quantized_tensor_subclass_int8_dyn_quant(self):
m = ToyLinearModel(1024, 1024, 1024).eval().to(torch.bfloat16).to("cuda")
m_copy = copy.deepcopy(m)
# setting batch_size to 20 to be compatible with the kernel
example_inputs = tuple(map(lambda x: x.to(torch.bfloat16).to("cuda"), m.example_inputs(batch_size=20)))
example_inputs = m.example_inputs(batch_size=20, dtype=torch.bfloat16, device="cuda")
m = quantize(m, get_apply_int8dyn_quant())

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I don't see any tests explicitly testing TensorCoreTiledAQTLayout

assert isinstance(m.linear1.weight, LinearActQuantizedTensor)
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