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[CPU] enable int8_dynamic_activation_int4_weight with Int4CPULayout #2128

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[CPU] enable int8_dynamic_activation_int4_weight with Int4CPULayout #2128

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0581451
[CPU] enable int8_dynamic_activation_int4_weight with Int4CPULayout
Xia-Weiwen Apr 25, 2025
dffbbab
Merge branch 'main' into da8w4_with_int4_cpu_layout
Xia-Weiwen Apr 25, 2025
9fb7f77
Fix format issue
Xia-Weiwen Apr 25, 2025
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25 changes: 25 additions & 0 deletions test/quantization/test_quant_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -875,6 +875,31 @@ def test_int4wo_cpu(self, dtype, x_dim, use_hqq):
assert "_weight_int4pack_mm_for_cpu" in code[0]
assert "aten.mm.default" not in code[0]

@unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_6, "Test only enabled for 2.6+")
@common_utils.parametrize("dtype", [torch.float, torch.bfloat16, torch.half])
@common_utils.parametrize("x_dim", [2, 3])
def test_8da4w_cpu(self, dtype, x_dim):
device = "cpu"
m = ToyLinearModel().eval().to(dtype).to(device)
example_inputs = m.example_inputs(dtype=dtype, device=device)
if x_dim == 3:
example_inputs = (example_inputs[0].unsqueeze(0),)

with torch.no_grad():
quantize_(
m,
int8_dynamic_activation_int4_weight(
group_size=32, layout=Int4CPULayout()
),
)
# ensure the expected op is in the code
_, code = torch._inductor.utils.run_and_get_code(
torch.compile(m, fullgraph=True, dynamic=True),
*example_inputs,
)
assert "_weight_int4pack_mm_for_cpu" in code[0]
assert "aten.mm.default" not in code[0]

# TODO(#1690): move to new config names
@unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+")
@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
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5 changes: 3 additions & 2 deletions torchao/dtypes/uintx/int4_cpu_layout.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,9 +112,10 @@ def from_plain(
assert isinstance(_layout, Int4CPULayout)

if TORCH_VERSION_AT_LEAST_2_6:
assert int_data.dtype == torch.int32, (
"torch.ops.aten._convert_weight_to_int4pack_for_cpu expects `int32` dtype"
assert int_data.dtype in [torch.int32, torch.int8], (
"torch.ops.aten._convert_weight_to_int4pack_for_cpu expects `int32` or `int8` dtype"
)
int_data = int_data.to(torch.int32)
packed_weight = torch.ops.aten._convert_weight_to_int4pack_for_cpu(
int_data,
1, # TODO:remove
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