Improve SmoothQuant test cases

Author: Xia-Weiwen

test/prototype/test_smoothquant.py

@@ -15,9 +15,15 @@
 )
 from torchao.prototype.smoothquant.core import SmoothQuantStep
 from torchao.quantization import quantize_
+from torchao.quantization.linear_activation_scale import (
+    WeightTensorWithLinearActivationScaleMetadata,
+)
 from torchao.quantization.quant_api import (
     Int8DynamicActivationInt8WeightConfig,
 )
+from torchao.quantization.utils import (
+    compute_error as SQNR,
+)
 
 
 class ToyLinearModel(torch.nn.Module):
@@ -34,16 +40,19 @@ def example_inputs(
         dtype=torch.bfloat16,
         device="cuda",
     ):
-        return [
-            torch.randn(
-                1,
-                sequence_length,
-                self.linear1.in_features,
-                dtype=dtype,
-                device=device,
-            )
-            for j in range(batch_size)
-        ]
+        # For SmoothQuant tests, we intentionally insert some outliers to input features
+        x = torch.randn(
+            batch_size,
+            sequence_length,
+            self.linear1.in_features,
+            dtype=dtype,
+            device=device,
+        )
+        n_outliers = max(1, int(x.size(-1) * 0.1))
+        # Randomly select outlier features
+        outlier_indices = torch.randperm(x.size(-1))[:n_outliers]
+        x[:, :, outlier_indices] *= 10.0
+        return (x,)
 
     def forward(self, x):
         x = self.linear1(x)
@@ -52,7 +61,9 @@ def forward(self, x):
         return x
 
 
-@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
+device_list = ["cpu", "cuda"] if torch.cuda.is_available() else ["cpu"]
+
+
 @unittest.skipIf(torch.version.hip is not None, "Skipping tests in ROCm")
 class TestSmoothQuant(unittest.TestCase):
     """SmoothQuant tests using only supported quantization configs."""
@@ -72,37 +83,25 @@ def setUpClass(cls):
             # TODO(#1639): Fix for supporting more API in torchao/quantization/quant_api.py
         ],
     )
-    @common_utils.parametrize("device", ["cpu", "cuda"])
+    @common_utils.parametrize("device", device_list)
     @common_utils.parametrize("input_dtype", [torch.bfloat16])
     def test_smoothquant_accuracy(self, alpha, base_config, device, input_dtype):
         """Test if SmoothQuant achieves lower loss than basic quantization."""
-        in_features = 64
-        out_features = 128
-
-        # Note: This is sanity check. For real run, consider Transformer model to reproduce.
-        X = torch.randn(16, in_features, dtype=input_dtype, device=device)
-        W = torch.randn(out_features, in_features, dtype=input_dtype, device=device)
-
         # Create linear layer
-        linear = (
-            torch.nn.Linear(in_features, out_features, bias=False)
-            .to(device)
-            .to(input_dtype)
-        )
-        with torch.no_grad():
-            linear.weight.copy_(W)
+        m = ToyLinearModel().eval().to(device).to(input_dtype)
+        x = m.example_inputs(batch_size=16, dtype=input_dtype, device=device)
 
         # Reference output
-        out_ref = linear(X)
+        out_ref = m(*x)
 
         # Step 1. Basic quantization
-        basic_model = deepcopy(linear)
+        basic_model = deepcopy(m)
         quantize_(basic_model, base_config)
-        out_basic = basic_model(X)
+        out_basic = basic_model(*x)
         loss_base = torch.nn.functional.mse_loss(out_basic, out_ref).item()
 
-        # SmoothQuant quantization
-        model = deepcopy(linear)
+        # Step 2. SmoothQuant
+        model = deepcopy(m)
         config = SmoothQuantConfig(
             base_config=base_config,
             step=SmoothQuantStep.PREPARE,
@@ -111,18 +110,25 @@ def test_smoothquant_accuracy(self, alpha, base_config, device, input_dtype):
         quantize_(model, config)
 
         # Perform calibration with test data
-        model(X)
+        model(*x)
 
-        # Step 2. SmoothQuant
         config.step = SmoothQuantStep.CONVERT
         quantize_(model, config)
+        assert isinstance(
+            model.linear1.weight, WeightTensorWithLinearActivationScaleMetadata
+        )
+        assert isinstance(
+            model.linear2.weight, WeightTensorWithLinearActivationScaleMetadata
+        )
 
-        out_smoothquant = model(X)
+        out_smoothquant = model(*x)
         loss_smoothquant = torch.nn.functional.mse_loss(out_smoothquant, out_ref).item()
 
         assert loss_smoothquant < loss_base, (
             f"SmoothQuant loss ({loss_smoothquant:.6f}) should not be higher than basic loss ({loss_base:.6f})"
         )
+        # Make sure the result is reasonable
+        self.assertGreater(SQNR(out_ref, out_smoothquant), 20.0)
 
     @common_utils.parametrize(
         "base_config",