[Torch] Add copy_ operator

python/tvm/relay/frontend/pytorch.py

@@ -1298,6 +1298,27 @@ def clone(self, inputs, input_types):
         data = inputs[0]
         return _op.tensor.copy(data)
 
+    # Copies the elements of src tensor.
+    # The src tensor must be broadcastable with the dst tensor.
+    # It may be of a different data type
+    def copy(self, inputs, input_types):
+        dst = inputs[0]
+        src = inputs[1]
+        # 3rd bool parameter 'non_blocking' is ignored
+        # get input/output shapes and dtypes
+        dst_shape = self.infer_shape(dst)
+        src_shape = self.infer_shape(src)
+        dst_dtype = input_types[0]
+        src_dtype = input_types[1]
+        # cast src to dst dtype if necessary
+        out = _op.cast(src, dst_dtype) if src_dtype != dst_dtype else src
+        # take output shape prefix and reverse it
+        front_shape_len = len(dst_shape) - len(src_shape)
+        for dim_sz in reversed(dst_shape[:front_shape_len]):
+            # expand dimension and duplicate src data according to dimension size
+            out = _op.stack([out] * dim_sz, axis=0)
+        return out
+
     def log_softmax(self, inputs, input_types):
         data = inputs[0]
         axis = int(inputs[1])
@@ -2246,6 +2267,7 @@ def create_convert_map(self):
             "aten::view": self.view,
             "aten::reshape": self.reshape,
             "aten::clone": self.clone,
+            "aten::copy_": self.copy,
             "aten::log_softmax": self.log_softmax,
             "aten::sigmoid": self.sigmoid,
             "aten::softplus": self.softplus,

tests/python/frontend/pytorch/test_forward.py

@@ -1267,6 +1267,23 @@ def forward(self, *args):
     verify_model(Clone1().float().eval(), input_data=input_data)
 
 
+@tvm.testing.uses_gpu
+def test_forward_copy():
+    def test_fn_copy():
+        return lambda dst, src: dst.copy_(src)
+
+    targets = ["llvm", "cuda"]
+    # Copy src int32 tensor with shape (4) to dst float32 tensor with shape (3,2,4)
+    dst = torch.zeros((3, 2, 4), dtype=torch.float32)
+    src = torch.tensor([0, 1, 2, 6], dtype=torch.int32)
+    verify_trace_model(test_fn_copy(), [dst, src], targets)
+
+    # Copy src float32 tensor with shape (4) to dst float32 tensor with shape (4)
+    dst = torch.zeros((4,), dtype=torch.float32)
+    src = torch.tensor([0, 1, 2, 6], dtype=torch.float32)
+    verify_trace_model(test_fn_copy(), [dst, src], targets)
+
+
 @tvm.testing.uses_gpu
 def test_forward_gather():
     torch.set_grad_enabled(False)
@@ -3718,6 +3735,7 @@ def test_fn(x, mask):
     test_forward_true_divide()
     test_forward_is_floating_point()
     test_forward_clone()
+    test_forward_copy()
     test_forward_softplus()
     test_forward_softsign()
     test_forward_logsoftmax()
@@ -3780,6 +3798,7 @@ def test_fn(x, mask):
     test_forward_unbind()
     test_forward_nonzero()
     test_forward_scatter()
+    test_forward_index_put()
     test_numel()
     test_bincount()
     test_cumsum()