W4A8 based on CUTLASS

docs/source/api_ref_quantization.rst

@@ -18,6 +18,7 @@ torchao.quantization
     Int4WeightOnlyQuantizer
     quantize_
     int8_dynamic_activation_int4_weight
+    int8_dynamic_activation_int4_weight_cutlass
     int8_dynamic_activation_int8_weight
     int4_weight_only
     int8_weight_only

setup.py

@@ -65,6 +65,12 @@ def get_extensions():
     extension = CUDAExtension if use_cuda else CppExtension
 
     if not IS_WINDOWS:
+        import cutlass_library
+        cutlass_library_dir = os.path.dirname(cutlass_library.__file__)
+        cutlass_include_dir = os.path.join(cutlass_library_dir, "source", "include")
+        # FIXME: remove this once CUTLASS package updated to include int4/int8 MM
+        cutlass_include_dir = "/data/quansight/scratch/cutlass/include"
+
         extra_link_args = []
         extra_compile_args = {
             "cxx": [
@@ -74,6 +80,7 @@ def get_extensions():
             "nvcc": [
                 "-O3" if not debug_mode else "-O0",
                 "-t=0",
+                "-I" + cutlass_include_dir,
             ]
         }
 

test/dtypes/test_affine_quantized.py

@@ -6,6 +6,7 @@
     int4_weight_only,
     int8_weight_only,
     int8_dynamic_activation_int4_weight,
+    int8_dynamic_activation_int4_weight_cutlass,
     int8_dynamic_activation_int8_weight,
     int8_dynamic_activation_int8_semi_sparse_weight,
     float8_weight_only,
@@ -25,6 +26,7 @@ def get_quantization_functions(do_sparse: bool, do_int4: bool):
     base_functions = [
         int8_weight_only(),
         int8_dynamic_activation_int4_weight(),
+        int8_dynamic_activation_int4_weight_cutlass(),
         int8_dynamic_activation_int8_weight(),
     ]
     if do_int4:

test/quantization/test_quant_api.py

@@ -39,6 +39,7 @@
     Quantizer,
     TwoStepQuantizer,
     int8_dynamic_activation_int4_weight,
+    int8_dynamic_activation_int4_weight_cutlass,
     int4_weight_only,
     int8_weight_only,
     int8_dynamic_activation_int8_weight,

test/test_s8s4_linear_cutlass.py

@@ -0,0 +1,49 @@
+# FIXME: move this test to the appropriate test file!!!
+
+import copy
+
+from torchao.quantization import quantize_
+from torchao.quantization.quant_api import int8_dynamic_activation_int4_weight_cutlass
+
+import torch
+from torch.testing._internal.common_utils import (
+    TestCase,
+    run_tests,
+)
+
+import pytest
+
+
+class ToyModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear1 = torch.nn.Linear(128, 256)
+        self.linear2 = torch.nn.Linear(256, 128, bias=False)
+
+    def forward(self, x):
+        x = self.linear1(x)
+        x = torch.nn.functional.relu(x)
+        x = self.linear2(x)
+        return x
+
+
+class TestS8S4LinearCUTLASS(TestCase):
+    @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+    def test_s8s4_linear_cutlass_(self):
+        # FIXME: remove this!
+        torch.manual_seed(0)
+
+        input = torch.rand((64, 128)).half().cuda()
+        model = ToyModel().half().cuda()
+
+        output_ref = model(input)
+
+        modelq = copy.deepcopy(model)
+        quantize_(modelq, int8_dynamic_activation_int4_weight_cutlass())
+        output = modelq(input)
+
+        assert torch.allclose(output, output_ref, rtol=1e-1, atol=0)
+
+
+if __name__ == "__main__":
+    run_tests()