Dynamic Conv1d + W2L (#2976)

Summary:
Pull Request resolved: #2976

Conv1d uses static reshape operator, in order to convert 3d tensor to 4d tensor so xnnpack can operate using conv2d.

For dynamism, reshape only accepts a single dynamic dimension, which is denoted as dynamic with a dim of 0.

Reviewed By: digantdesai

Differential Revision: D55815092

Author: mcr229

backends/xnnpack/operators/op_squeeze.py

@@ -53,7 +53,21 @@ def define_node(
             "val" in input_node.meta,
             "Missing val in tensor metadata for input when serializing XNNStaticReshape node",
         )
-        new_shape = get_shape(input_node)[:-1]
+        dynamic_shape = node.meta["val"].shape
+        new_shape = []
+
+        num_dynamic_dims = 0
+        for dim in dynamic_shape:
+            if isinstance(dim, torch.SymInt):
+                num_dynamic_dims += 1
+                new_shape.append(0)
+            else:
+                new_shape.append(dim)
+
+        check_or_raise(
+            num_dynamic_dims <= 1,
+            "XNNPACK reshape only supports 1 dynamic dimension. This may occur when ",
+        )
 
         ser_node = XNode(
             xnode_union=XNNStaticReshape(
@@ -101,7 +115,21 @@ def define_node(
             "val" in input_node.meta,
             "Missing val in tensor metadata for input when serializing XNNStaticReshape node",
         )
-        new_shape = get_shape(input_node) + [1]
+        dynamic_shape = node.meta["val"].shape
+        new_shape = []
+
+        num_dynamic_dims = 0
+        for dim in dynamic_shape:
+            if isinstance(dim, torch.SymInt):
+                num_dynamic_dims += 1
+                new_shape.append(0)
+            else:
+                new_shape.append(dim)
+
+        check_or_raise(
+            num_dynamic_dims <= 1,
+            "XNNPACK reshape only supports 1 dynamic dimension. This may occur when ",
+        )
 
         ser_node = XNode(
             xnode_union=XNNStaticReshape(

backends/xnnpack/test/models/w2l.py

@@ -15,13 +15,15 @@ class TestW2L(unittest.TestCase):
     batch_size = 10
     input_frames = 700
     vocab_size = 4096
+    num_features = 1
     wav2letter = models.Wav2Letter(num_classes=vocab_size).eval()
 
-    model_inputs = (torch.randn(batch_size, 1, input_frames),)
+    model_inputs = (torch.randn(batch_size, num_features, input_frames),)
+    dynamic_shape = ({0: torch.export.Dim("batch", min=2, max=10)},)
 
     def test_fp32_w2l(self):
         (
-            Tester(self.wav2letter, self.model_inputs)
+            Tester(self.wav2letter, self.model_inputs, self.dynamic_shape)
             .export()
             .to_edge()
             .partition()
@@ -34,12 +36,12 @@ def test_fp32_w2l(self):
             .check(["torch.ops.higher_order.executorch_call_delegate"])
             .to_executorch()
             .serialize()
-            .run_method_and_compare_outputs()
+            .run_method_and_compare_outputs(num_runs=10)
         )
 
     def test_qs8_w2l(self):
         (
-            Tester(self.wav2letter.eval(), self.model_inputs)
+            Tester(self.wav2letter.eval(), self.model_inputs, self.dynamic_shape)
             .quantize()
             .export()
             .to_edge()
@@ -53,5 +55,5 @@ def test_qs8_w2l(self):
             .check(["torch.ops.higher_order.executorch_call_delegate"])
             .to_executorch()
             .serialize()
-            .run_method_and_compare_outputs()
+            .run_method_and_compare_outputs(num_runs=10)
         )

backends/xnnpack/test/ops/conv1d.py

@@ -81,9 +81,15 @@ def forward(self, x):
             z = torch.add(y, z)
             return z
 
-    def _test_conv1d(self, module, inputs, conv_count, quantized=False):
+    def _test_conv1d(
+        self, module, inputs, conv_count, quantized=False, dynamic_shape=None
+    ):
         (
-            (Tester(module, inputs).quantize() if quantized else Tester(module, inputs))
+            (
+                Tester(module, inputs, dynamic_shape).quantize()
+                if quantized
+                else Tester(module, inputs)
+            )
             .export()
             .check_count({"torch.ops.aten.convolution.default": conv_count})
             .to_edge()
@@ -101,21 +107,41 @@ def _test_conv1d(self, module, inputs, conv_count, quantized=False):
         )
 
     def test_fp16_conv1d(self):
-        inputs = (torch.randn(1, 2, 4).to(torch.float16),)
-        self._test_conv1d(self.Conv1d(dtype=torch.float16), inputs, conv_count=1)
+        inputs = (torch.randn(2, 2, 4).to(torch.float16),)
+        dynamic_shapes = ({0: torch.export.Dim("batch", min=2, max=10)},)
+        self._test_conv1d(
+            self.Conv1d(dtype=torch.float16),
+            inputs,
+            conv_count=1,
+            dynamic_shape=dynamic_shapes,
+        )
 
     def test_fp32_conv1d(self):
-        inputs = (torch.randn(1, 2, 4),)
-        self._test_conv1d(self.Conv1d(), inputs, 1)
+        inputs = (torch.randn(2, 2, 4),)
+        dynamic_shapes = ({0: torch.export.Dim("batch", min=2, max=10)},)
+        self._test_conv1d(self.Conv1d(), inputs, 1, dynamic_shape=dynamic_shapes)
 
     def test_fp32_conv1d_batchnorm_seq(self):
-        inputs = (torch.randn(1, 2, 4),)
-        self._test_conv1d(self.Conv1dBatchNormSequential(), inputs, 2)
+        inputs = (torch.randn(2, 2, 4),)
+        dynamic_shapes = ({0: torch.export.Dim("batch", min=2, max=10)},)
+        self._test_conv1d(
+            self.Conv1dBatchNormSequential(), inputs, 2, dynamic_shape=dynamic_shapes
+        )
 
     def test_qs8_conv1d(self):
-        inputs = (torch.randn(1, 2, 4),)
-        self._test_conv1d(self.Conv1d(), inputs, 1, quantized=True)
+        inputs = (torch.randn(2, 2, 4),)
+        dynamic_shapes = ({0: torch.export.Dim("batch", min=2, max=10)},)
+        self._test_conv1d(
+            self.Conv1d(), inputs, 1, quantized=True, dynamic_shape=dynamic_shapes
+        )
 
     def test_qs8_conv1d_batchnorm_seq(self):
-        inputs = (torch.randn(1, 2, 4),)
-        self._test_conv1d(self.Conv1dBatchNormSequential(), inputs, 2, quantized=True)
+        inputs = (torch.randn(2, 2, 4),)
+        dynamic_shapes = ({0: torch.export.Dim("batch", min=2, max=10)},)
+        self._test_conv1d(
+            self.Conv1dBatchNormSequential(),
+            inputs,
+            2,
+            quantized=True,
+            dynamic_shape=dynamic_shapes,
+        )