Add op support for new_zeros op in Exported Program and fx graph frontend

python/tvm/relax/frontend/torch/base_fx_graph_translator.py

@@ -1549,6 +1549,25 @@ def _new_ones(self, node: fx.Node) -> relax.Var:
             )
         )
 
+    def _new_zeros(self, node: fx.Node) -> relax.Var:
+        args = self.retrieve_args(node)
+        input_tensor = args[0]
+        size = (
+            args[1]
+            if isinstance(args[1], (list, tuple))
+            else (args[1],)
+            if len(args[1:]) == 1
+            else args[1:]
+        )
+        size = relax.ShapeExpr(size)
+        return self.block_builder.emit(
+            relax.op.full(
+                size,
+                relax.const(0, input_tensor.struct_info.dtype),
+                input_tensor.struct_info.dtype,
+            )
+        )
+
     def _ones(self, node: fx.Node) -> relax.Var:
         import torch
 

python/tvm/relax/frontend/torch/exported_program_translator.py

@@ -482,6 +482,7 @@ def create_convert_map(
             "linspace.default": self._linspace,
             "masked_fill.Scalar": self._masked_fill,
             "new_ones.default": self._new_ones,
+            "new_zeros.default": self._new_zeros,
             "one_hot.default": self._one_hot,
             "ones.default": self._ones,
             "ones_like.default": lambda node: self.block_builder.emit(

python/tvm/relax/frontend/torch/fx_translator.py

@@ -829,6 +829,7 @@ def create_convert_map(
             "masked_fill": self._masked_fill,
             "masked_scatter": self._masked_scatter,
             "new_ones": self._new_ones,
+            "new_zeros": self._new_zeros,
             "ones": self._ones,
             "one_hot": self._one_hot,
             "ones_like": lambda node: self.block_builder.emit(

tests/python/relax/test_frontend_from_exported_program.py

@@ -3751,6 +3751,29 @@ def main(
     verify_model(NewOnes(), example_args, {}, expected1)
 
 
+def test_new_zeros():
+    class NewZeros(torch.nn.Module):
+        def forward(self, x):
+            return x.new_zeros(1, 128, 128)
+
+    @tvm.script.ir_module
+    class expected1:
+        @R.function
+        def main(
+            x: R.Tensor((1, 128, 128), dtype="float32")
+        ) -> R.Tuple(R.Tensor((1, 128, 128), dtype="float32")):
+            with R.dataflow():
+                lv: R.Tensor((1, 128, 128), dtype="float32") = R.full(
+                    R.shape([1, 128, 128]), R.const(0, "float32"), dtype="float32"
+                )
+                gv: R.Tuple(R.Tensor((1, 128, 128), dtype="float32")) = (lv,)
+                R.output(gv)
+            return gv
+
+    example_args = (torch.randn(1, 128, 128, dtype=torch.float32),)
+    verify_model(NewZeros(), example_args, {}, expected1)
+
+
 def test_to_copy():
     # float
     class ToFloat(Module):

tests/python/relax/test_frontend_from_fx.py

@@ -3327,6 +3327,31 @@ def main(x: R.Tensor((1, 2, 3), dtype="float32")) -> R.Tensor((1, 2, 3), dtype="
     verify_model(NewOnes(), input_info, {}, expected1)
 
 
+def test_new_zeros():
+    input_info = [([1, 128, 128], "float32")]
+
+    class NewZeros(Module):
+        def forward(self, x):
+            return x.new_zeros(1, 128, 128)
+
+    @tvm.script.ir_module
+    class expected:
+        @R.function
+        def main(
+            x: R.Tensor((1, 128, 128), dtype="float32")
+        ) -> R.Tensor((1, 128, 128), dtype="float32"):
+            # block 0
+            with R.dataflow():
+                lv: R.Tensor((1, 128, 128), dtype="float32") = R.full(
+                    (1, 128, 128), R.const(0.0, "float32"), dtype="float32"
+                )
+                gv: R.Tensor((1, 128, 128), dtype="float32") = lv
+                R.output(gv)
+            return gv
+
+    verify_model(NewZeros(), input_info, {}, expected)
+
+
 def test_expand():
     input_info = [([1, 2, 3, 4], "float32")]