[Relax][PyTorch] Add support for broadcast_to, narrow ops

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

@@ -966,6 +966,12 @@ def _argsort(self, node: fx.Node) -> relax.Var:
         descending = node.args[2] if len(node.args) > 2 else node.kwargs.get("descending", False)
         return self.block_builder.emit(relax.op.argsort(x, dim, descending))
 
+    def _broadcast_to(self, node: fx.Node) -> relax.Var:
+        args = self.retrieve_args(node)
+        x = args[0]
+        shape = args[1] if len(args) > 1 else args[0]
+        return self.block_builder.emit(relax.op.broadcast_to(x, shape))
+
     def _cat(self, node: fx.Node) -> relax.Var:
         args = self.retrieve_args(node)
         axis = args[1] if len(node.args) > 1 else node.kwargs.get("dim", 0)

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

@@ -422,6 +422,13 @@ def _flatten_module(self, node: fx.Node) -> relax.Var:
         end_dim = module.end_dim
         return self._flatten_impl(x, start_dim, end_dim)
 
+    def _narrow(self, node: fx.Node) -> relax.Var:
+        x = self.env[node.args[0]]
+        dim = node.args[1]
+        start = node.args[2]
+        length = node.args[3]
+        return self.block_builder.emit(relax.op.strided_slice(x, [dim], [start], [length]))
+
     def _numel(self, node: fx.Node) -> relax.Var:
         x = self.env[node.args[0]]
         shape = self.shape_of(x)
@@ -755,6 +762,7 @@ def create_convert_map(
             "where": self._where,
             # tensor manipulation
             "argsort": self._argsort,
+            "broadcast_to": self._broadcast_to,
             "cat": self._cat,
             "chunk": self._chunk,
             "concat": self._cat,
@@ -766,6 +774,7 @@ def create_convert_map(
             "flatten": self._flatten,
             "flip": self._flip,
             "gather": self._gather,
+            "narrow": self._narrow,
             "numel": self._numel,
             "permute": self._permute,
             "repeat": self._repeat,

tests/python/relax/test_frontend_from_fx.py

@@ -4430,5 +4430,48 @@ def main(
     verify_model(Topk(), [([5, 3], "float32")], {}, Expected)
 
 
+def test_broadcast_to():
+    class BroadcastTo(Module):
+        def forward(self, x):
+            return torch.broadcast_to(x, (5, 3))
+
+    @tvm.script.ir_module
+    class Expected:
+        @R.function
+        def main(
+            inp_0: R.Tensor((5, 1), dtype="float32"),
+        ) -> R.Tensor((5, 3), dtype="float32"):
+            with R.dataflow():
+                lv: R.Tensor((5, 3), dtype="float32") = R.broadcast_to(inp_0, (5, 3))
+                gv: R.Tensor((5, 3), dtype="float32") = lv
+                R.output(gv)
+            return gv
+
+    verify_model(BroadcastTo(), [([5, 1], "float32")], {}, Expected)
+
+
+def test_narrow():
+    class Narrow(Module):
+        def forward(self, x):
+            return torch.narrow(x, 1, 0, 2)
+
+    @tvm.script.ir_module
+    class Expected:
+        @R.function
+        def main(
+            inp_0: R.Tensor((5, 3), dtype="float32"),
+        ) -> R.Tensor((5, 2), dtype="float32"):
+            with R.dataflow():
+                lv: R.Tensor((5, 2), dtype="float32") = R.strided_slice(
+                    inp_0, axes=[1], begin=[0], end=[2]
+                )
+                gv: R.Tensor((5, 2), dtype="float32") = lv
+                R.output(gv)
+
+            return gv
+
+    verify_model(Narrow(), [([5, 3], "float32")], {}, Expected)
+
+
 if __name__ == "__main__":
     tvm.testing.main()