add aten::pixel_shuffle implementation (#6328) (#6468)

python/tvm/relay/frontend/pytorch.py

@@ -1230,6 +1230,33 @@ def _impl(inputs, input_types):
 
     return _impl
 
+def _pixel_shuffle(prelude):
+    def _impl(inputs, input_types):
+        data = inputs[0]
+        upscale_factor = inputs[1]
+        upscale_squared = upscale_factor * upscale_factor
+        b, c, h, w = _infer_shape(data)
+        assert c % upscale_squared == 0, \
+            "input channel should be divisible by square of upscale_factor"
+
+        ndims = len(_infer_shape(data, prelude.mod))
+        axes = list(range(ndims))
+        num_inputs = len(inputs)
+        oc = c // upscale_squared
+        oh = h * upscale_factor
+        ow = w * upscale_factor
+
+        new_shape = [b, oc, upscale_factor, upscale_factor, h, w]
+        out_shape = [b, oc, oh, ow]
+
+        data = _op.transform.reshape(data, new_shape)
+        # The data will be transposed to
+        # [b, oc, h, upscale_factor, w, upscale_factor]
+        # for further reshape
+        axes = [0, 1, 4, 2, 5, 3]
+        data = _op.transform.transpose(data, axes)
+        return _op.transform.reshape(data, out_shape)
+    return _impl
 
 def _clone():
     def _impl(inputs, input_types):
@@ -2162,6 +2189,7 @@ def _wrap_const(c):
 # Operator mappings
 def _get_convert_map(prelude, default_dtype):
     convert_map = {
+        "aten::pixel_shuffle": _pixel_shuffle(prelude),
         "aten::device": _none(),
         "prim::device": _none(),
         "aten::sub": _elemwise("subtract"),

tests/python/frontend/pytorch/test_forward.py

@@ -182,7 +182,7 @@ def verify_model(model_name, input_data=[], custom_convert_map={}, rtol=1e-5, at
 
     with torch.no_grad():
         baseline_outputs = baseline_model(*baseline_input)
-
+    
     if isinstance(baseline_outputs, tuple):
         baseline_outputs = tuple(out.cpu().numpy() for out in baseline_outputs)
     else:
@@ -223,6 +223,17 @@ def verify_model(model_name, input_data=[], custom_convert_map={}, rtol=1e-5, at
 
 
 # Single operator tests
+@tvm.testing.uses_gpu
+def test_forward_pixel_shuffle():
+    torch.set_grad_enabled(False)
+    input_shape = [1, 144, 16, 16]
+
+    input_data = torch.rand(input_shape).float()
+    verify_model(torch.nn.PixelShuffle(2).float().eval(), input_data=input_data)
+    verify_model(torch.nn.PixelShuffle(3).float().eval(), input_data=input_data)
+    verify_model(torch.nn.PixelShuffle(4).float().eval(), input_data=input_data)
+
+
 @tvm.testing.uses_gpu
 def test_forward_add():
     torch.set_grad_enabled(False)
@@ -3163,6 +3174,7 @@ def test_forward_pretrained_bert_base_uncased():
     test_duplicate_weight_use()
 
     # Single operator tests
+    test_forward_pixel_shuffle()
     test_forward_add()
     test_forward_subtract()
     test_forward_multiply()