feat: support many elementwise dynamo converters

add output_dtypes in test

Author: zewenli98

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -843,3 +843,256 @@ def aten_ops_isinf(
         name,
         args[0],
     )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.add.Tensor)
+def aten_ops_add(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.add(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.mul.Tensor)
+def aten_ops_mul(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.mul(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.maximum.default)
+def aten_ops_max(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.max(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.minimum.default)
+def aten_ops_min(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.min(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.sub.Tensor)
+def aten_ops_sub(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.sub(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+# TODO: keep this or line 54...?
+# @dynamo_tensorrt_converter(torch.ops.aten.div.Tensor)
+# def aten_ops_div(
+#     network: TRTNetwork,
+#     target: Target,
+#     args: Tuple[Argument, ...],
+#     kwargs: Dict[str, Argument],
+#     name: str,
+# ) -> Union[TRTTensor, Sequence[TRTTensor]]:
+#     return impl.elementwise.div(
+#         network,
+#         target,
+#         SourceIR.ATEN,
+#         name,
+#         args[0],
+#         args[1],
+#     )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.pow.Tensor_Tensor)
+def aten_ops_pow(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.pow(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.floor_divide.default)
+def aten_ops_floor_div(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.floor_divide(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.logical_and.default)
+def aten_ops_logical_and(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.logical_and(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.logical_or.default)
+def aten_ops_logical_or(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.logical_or(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.logical_xor.default)
+def aten_ops_logical_xor(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.logical_xor(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.eq.Tensor)
+def aten_ops_equal(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.eq(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.gt.Tensor)
+def aten_ops_greater(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.gt(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.lt.Tensor)
+def aten_ops_less(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.elementwise.lt(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+    )