[Relax][PyTorch] Support binary, statistical and search ops for ExportedProgram importer

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

@@ -185,6 +185,39 @@ def convert(node: fx.Node) -> relax.Var:
 
         return convert
 
+    ########## Binary Ops ##########
+
+    def _binary_op(self, relax_op: Callable, intrinsic_op: Callable) -> Callable:
+        from torch import fx
+
+        def convert(node: fx.Node) -> relax.Var:
+            def promote_binary_op_args(lhs, rhs):
+                if isinstance(lhs, relax.Expr) and isinstance(rhs, relax.Expr):
+                    return lhs, rhs
+                elif isinstance(lhs, relax.Expr):
+                    assert isinstance(lhs.struct_info, relax.TensorStructInfo)
+                    return lhs, relax.const(rhs, lhs.struct_info.dtype)
+                elif isinstance(rhs, relax.Expr):
+                    assert isinstance(rhs.struct_info, relax.TensorStructInfo)
+                    return relax.const(lhs, rhs.struct_info.dtype), rhs
+                else:
+                    assert False
+
+            def call_binary_op(op, lhs, rhs):
+                lhs, rhs = promote_binary_op_args(lhs, rhs)
+                return self.block_builder.emit(op(lhs, rhs))
+
+            lhs, rhs = self.retrieve_args(node)
+            if isinstance(lhs, relax.Var) or isinstance(rhs, relax.Var):
+                return call_binary_op(relax_op, lhs, rhs)
+            elif isinstance(lhs, relax.expr.Constant):
+                return call_binary_op(relax_op, lhs, relax.const(rhs, dtype=lhs.struct_info.dtype))
+            elif isinstance(rhs, relax.expr.Constant):
+                return call_binary_op(relax_op, relax.const(lhs, dtype=rhs.struct_info.dtype), rhs)
+            return intrinsic_op(lhs, rhs)
+
+        return convert
+
     ########## Neural Network ##########
 
     def _adaptive_avg_pool2d(self, node: fx.Node) -> relax.Var:
@@ -283,6 +316,35 @@ def _max_pool2d(self, node: fx.Node) -> relax.Var:
 
         return self._max_pool2d_impl(x, kernel_size, stride, padding, dilation, ceil_mode)
 
+    ########## Statistical ##########
+
+    def _mean(self, node: fx.Node) -> relax.Var:
+        args = self.retrieve_args(node)
+        x = args[0]
+        dim = args[1] if len(node.args) > 1 else node.kwargs.get("dim", None)
+        keepdim = args[2] if len(node.args) > 2 else node.kwargs.get("keepdim", False)
+        return self.block_builder.emit(relax.op.mean(x, dim, keepdims=keepdim))
+
+    def _sum(self, node: fx.Node) -> relax.Var:
+        args = self.retrieve_args(node)
+        keepdim = node.kwargs["keepdim"] if "keepdim" in node.kwargs else False
+        if len(args) == 1:
+            return self.block_builder.emit(relax.op.sum(args[0], keepdims=keepdim))
+        return self.block_builder.emit(relax.op.sum(args[0], args[1]))
+
+    ########## Search ##########
+
+    def _argmax_argmin(self, op: Callable) -> Callable:
+        from torch import fx
+
+        def convert(node: fx.Node):
+            x = self.env[node.args[0]]
+            dim = node.args[1] if len(node.args) > 1 else node.kwargs.get("dim", None)
+            keepdim = node.args[2] if len(node.args) > 2 else node.kwargs.get("keepdim", False)
+            return self.block_builder.emit(op(x, dim, keepdim))
+
+        return convert
+
     ########## Manipulation ##########
 
     def _reshape(self, node: fx.Node) -> relax.Var:

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

@@ -19,6 +19,7 @@
 # pylint: disable=import-outside-toplevel
 """PyTorch ExportedProgram of Relax."""
 from collections import ChainMap, OrderedDict
+from functools import partial
 from typing import Callable, Dict, List, Tuple
 
 import torch
@@ -76,6 +77,8 @@ def _hardtanh(self, node: fx.Node) -> relax.Expr:
     def create_convert_map(
         self,
     ) -> Dict[str, Callable[[fx.Node], relax.Var]]:
+        import operator
+
         return {
             # unary
             "acos.default": self._unary_op(relax.op.acos),
@@ -109,11 +112,33 @@ def create_convert_map(
             "tanh.default": self._unary_op(relax.op.tanh),
             "tril.default": self._tril_triu(relax.op.tril),
             "triu.default": self._tril_triu(relax.op.triu),
+            # binary
+            "add.Tensor": self._binary_op(relax.op.add, operator.add),
+            "div.Tensor": self._binary_op(relax.op.divide, operator.truediv),
+            "eq.Scalar": self._binary_op(relax.op.equal, operator.eq),
+            "eq.Tensor": self._binary_op(relax.op.equal, operator.eq),
+            "floor_divide.default": self._binary_op(relax.op.floor_divide, operator.floordiv),
+            "lt.Scalar": self._binary_op(relax.op.less, operator.lt),
+            "lt.Tensor": self._binary_op(relax.op.less, operator.lt),
+            "matmul.default": self._binary_op(
+                partial(relax.op.linear_algebra.matmul, out_dtype="float32"), operator.matmul
+            ),
+            "max.other": self._binary_op(relax.op.maximum, max),
+            "mul.Tensor": self._binary_op(relax.op.multiply, operator.mul),
+            "pow.Tensor_Scalar": self._binary_op(relax.op.power, operator.pow),
+            "pow.Tensor_Tensor": self._binary_op(relax.op.power, operator.pow),
+            "sub.Tensor": self._binary_op(relax.op.subtract, operator.sub),
             # neural network
             "adaptive_avg_pool2d.default": self._adaptive_avg_pool2d,
             "conv2d.default": self._conv2d,
             "linear.default": self._linear,
             "max_pool2d.default": self._max_pool2d,
+            # statistical
+            "mean.dim": self._mean,
+            "sum.dim_IntList": self._sum,
+            # search
+            "argmax.default": self._argmax_argmin(relax.op.argmax),
+            "argmin.default": self._argmax_argmin(relax.op.argmin),
             # tensor manipulation
             "view.default": self._reshape,
         }

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

@@ -96,39 +96,6 @@ def convert(node: fx.Node) -> relax.Var:
 
         return convert
 
-    ########## Binary Ops ##########
-
-    def _binary_op(self, relax_op: Callable, intrinsic_op: Callable) -> Callable:
-        from torch import fx
-
-        def convert(node: fx.Node) -> relax.Var:
-            def promote_binary_op_args(lhs, rhs):
-                if isinstance(lhs, relax.Expr) and isinstance(rhs, relax.Expr):
-                    return lhs, rhs
-                elif isinstance(lhs, relax.Expr):
-                    assert isinstance(lhs.struct_info, relax.TensorStructInfo)
-                    return lhs, relax.const(rhs, lhs.struct_info.dtype)
-                elif isinstance(rhs, relax.Expr):
-                    assert isinstance(rhs.struct_info, relax.TensorStructInfo)
-                    return relax.const(lhs, rhs.struct_info.dtype), rhs
-                else:
-                    assert False
-
-            def call_binary_op(op, lhs, rhs):
-                lhs, rhs = promote_binary_op_args(lhs, rhs)
-                return self.block_builder.emit(op(lhs, rhs))
-
-            lhs, rhs = self.retrieve_args(node)
-            if isinstance(lhs, relax.Var) or isinstance(rhs, relax.Var):
-                return call_binary_op(relax_op, lhs, rhs)
-            elif isinstance(lhs, relax.expr.Constant):
-                return call_binary_op(relax_op, lhs, relax.const(rhs, dtype=lhs.struct_info.dtype))
-            elif isinstance(rhs, relax.expr.Constant):
-                return call_binary_op(relax_op, relax.const(lhs, dtype=rhs.struct_info.dtype), rhs)
-            return intrinsic_op(lhs, rhs)
-
-        return convert
-
     ########## Neural Network ##########
 
     def _adaptive_avg_pool2d_module(self, node: fx.Node) -> relax.Var:
@@ -794,35 +761,6 @@ def _unbind(self, node: fx.Node) -> relax.Var:
             ret.append(self.block_builder.emit(relax.op.squeeze(split[i], axis=dim)))
         return self.block_builder.emit(relax.Tuple(ret))
 
-    ########## Statistical ##########
-
-    def _mean(self, node: fx.Node) -> relax.Var:
-        args = self.retrieve_args(node)
-        x = args[0]
-        dim = args[1] if len(node.args) > 1 else node.kwargs.get("dim", None)
-        keepdim = args[2] if len(node.args) > 2 else node.kwargs.get("keepdim", False)
-        return self.block_builder.emit(relax.op.mean(x, dim, keepdims=keepdim))
-
-    def _sum(self, node: fx.Node) -> relax.Var:
-        args = self.retrieve_args(node)
-        keepdim = node.kwargs["keepdim"] if "keepdim" in node.kwargs else False
-        if len(args) == 1:
-            return self.block_builder.emit(relax.op.sum(args[0], keepdims=keepdim))
-        return self.block_builder.emit(relax.op.sum(args[0], args[1]))
-
-    ########## Search ##########
-
-    def _argmax_argmin(self, op: Callable) -> Callable:
-        from torch import fx
-
-        def convert(node: fx.Node):
-            x = self.env[node.args[0]]
-            dim = node.args[1] if len(node.args) > 1 else node.kwargs.get("dim", None)
-            keepdim = node.args[2] if len(node.args) > 2 else node.kwargs.get("keepdim", False)
-            return self.block_builder.emit(op(x, dim, keepdim))
-
-        return convert
-
     ########## Manipulation ##########
 
     def _cat(self, node: fx.Node) -> relax.Var: