Enable a test for loading state_dict with tensor subclasses (pytorch#389)

Summary:
Enabling the use case ofsaving a quantized state dict from our new quantization API and then load that to a non-quantized model, see test for more details

Test Plan:
python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_save_load

Reviewers:

Subscribers:

Tasks:

Tags:

Author: jerryzh168

test/quantization/test_quant_api.py

@@ -617,5 +617,25 @@ def apply_my_dtype(weight):
         quantize(m, "my_dtype")
         m(*example_inputs)
 
+    @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "Test only enabled for 2.4+")
+    @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
+    def test_quantized_tensor_subclass_save_load(self):
+        m = ToyLinearModel().eval().to(torch.bfloat16)
+        m_copy = copy.deepcopy(m)
+        example_inputs = m.example_inputs(dtype=torch.bfloat16)
+
+        m = quantize(m, "int8_weight_only")
+        ref = m(*example_inputs)
+        with tempfile.NamedTemporaryFile() as f:
+            torch.save(m.state_dict(), f)
+            f.seek(0)
+            state_dict = torch.load(f)
+
+        m_copy.load_state_dict(state_dict, assign=True)
+
+        res = m_copy(*example_inputs)
+        self.assertEqual(res, ref)
+
+
 if __name__ == "__main__":
     unittest.main()

torchao/dtypes/aqt.py

@@ -229,7 +229,7 @@ def from_float(
         )
 
     @property
-    def layout(self) -> str:
+    def extended_layout(self) -> str:
         return self.layout_tensor.extended_layout
 
     @classmethod
@@ -555,8 +555,8 @@ def _quantized_linear_op(input_tensor, weight_qtensor, bias):
                 is_cuda and
                 input_is_int8 and
                 input_tensor.dtype == weight_qtensor.dtype and
-                input_tensor.layout == "plain" and
-                weight_qtensor.layout == "plain"
+                input_tensor.extended_layout == "plain" and
+                weight_qtensor.extended_layout == "plain"
             ):
                 #
                 # 1. do the matrix form of dot(X_i, W_j)
@@ -598,7 +598,7 @@ def _quantized_linear_op(input_tensor, weight_qtensor, bias):
             weight_qtensor.dtype == torch.bfloat16 and
             len(weight_qtensor.shape) == 2 and
             weight_qtensor.zero_point_domain == ZeroPointDomain.FLOAT and
-            weight_qtensor.layout == "tensor_core_tiled"
+            weight_qtensor.extended_layout == "tensor_core_tiled"
         ):
             assert weight_qtensor.block_size[0] == 1, f"Requires groupwise quantization, got block_size: {block_size}"
             assert input_tensor.shape[-1] == weight_qtensor.shape[1], (
@@ -641,7 +641,7 @@ def _quantized_linear_op(input_tensor, weight_qtensor, bias):
             weight_qtensor.block_size[0] == 1 and
             weight_qtensor.block_size[1] == weight_qtensor.shape[1] and
             weight_qtensor.zero_point_domain == ZeroPointDomain.INT and
-            weight_qtensor.layout == "plain"
+            weight_qtensor.extended_layout == "plain"
         ):
             # TODO: enable cpu and mps efficient path
             # per channel int8 weight only quantizated mm

torchao/quantization/README.md

@@ -171,7 +171,7 @@ from torchao.quantization import quant_api
 
 # for torch 2.4+
 from torchao.quantization.quant_api import quantize
-quantize(model, "int8_dynamic_quant")
+quantize(model, "int8_dynamic")
 
 # for torch 2.2.2 and 2.3
 from torchao.quantization.quant_api import change_linear_weights_to_int8_dqtensors