feat: support argmin aten converter and refactor argmax

Author: bowang007

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -2284,7 +2284,27 @@ def aten_ops_argmax(
     kwargs: Dict[str, Argument],
     name: str,
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    return impl.argmax.argmax(
+    return impl.topk.argmax(
+        ctx,
+        target,
+        SourceIR.ATEN,
+        name,
+        input=args[0],
+        dim=args_bounds_check(args, 1),
+        keep_dim=args_bounds_check(args, 2, False),
+    )
+
+
+@enforce_tensor_types({0: (TRTTensor,)})
+@dynamo_tensorrt_converter(torch.ops.aten.argmin.default)
+def aten_ops_argmin(
+    ctx: ConversionContext,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.topk.argmin(
         ctx,
         target,
         SourceIR.ATEN,

py/torch_tensorrt/dynamo/conversion/impl/__init__.py

@@ -3,7 +3,6 @@
 from . import (
     activation,
     addmm,
-    argmax,
     attention,
     cast,
     cat,
@@ -26,6 +25,7 @@
     slice,
     split,
     squeeze,
+    topk,
     unary,
     unsqueeze,
 )

py/torch_tensorrt/dynamo/conversion/impl/argmax.py renamed to py/torch_tensorrt/dynamo/conversion/impl/topk.py

@@ -15,12 +15,13 @@
 from torch_tensorrt.fx.types import TRTTensor
 
 
-def argmax(
+def argmax_argmin(
     ctx: ConversionContext,
     target: Target,
     source_ir: Optional[SourceIR],
     name: str,
     input: TRTTensor,
+    topk_option: trt.TopKOperation,
     dim: Optional[int],
     keep_dim: bool = False,
 ) -> TRTTensor:
@@ -49,7 +50,7 @@ def argmax(
         get_positive_dim(dim if dim is not None else 0, len(out.shape))
     )
 
-    topk_layer = ctx.net.add_topk(out, trt.TopKOperation.MAX, 1, reduce_mask)
+    topk_layer = ctx.net.add_topk(out, topk_option, 1, reduce_mask)
     set_layer_name(topk_layer, target, name, source_ir)
 
     out = topk_layer.get_output(1)
@@ -72,3 +73,31 @@ def argmax(
         out = impl.squeeze.squeeze(ctx, target, source_ir, f"{name}_squeeze", out, dim)
 
     return out
+
+
+def argmax(
+    ctx: ConversionContext,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input: TRTTensor,
+    dim: Optional[int],
+    keep_dim: bool = False,
+) -> TRTTensor:
+    return argmax_argmin(
+        ctx, target, source_ir, name, input, trt.TopKOperation.MAX, dim, keep_dim
+    )
+
+
+def argmin(
+    ctx: ConversionContext,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input: TRTTensor,
+    dim: Optional[int],
+    keep_dim: bool = False,
+) -> TRTTensor:
+    return argmax_argmin(
+        ctx, target, source_ir, name, input, trt.TopKOperation.MIN, dim, keep_dim
+    )

tests/py/dynamo/conversion/test_argmax_aten.py

@@ -11,11 +11,11 @@ class TestArgmaxConverter(DispatchTestCase):
         [
             # input dimension == 1
             ("dim_1_keep_dim_true", (3,), 0, True),
-            ("dim_1_keep_dim_true", (3,), 0, False),
+            ("dim_1_keep_dim_false", (3,), 0, False),
             # dim == None
-            ("dim_none", (3,), None, True),
-            ("dim_none", (3, 3), None, True),
-            ("dim_none", (3, 3, 3), None, False),
+            ("dim_1_none_true", (3,), None, True),
+            ("dim_2_none_true", (3, 3), None, True),
+            ("dim_3_none_false", (3, 3, 3), None, False),
             # # common cases
             ("dim_1_keep_dim_true", (3, 3), 1, True),
             ("dim_1_keep_dim_false", (3, 3), 1, False),

tests/py/dynamo/conversion/test_argmin_aten.py

@@ -0,0 +1,41 @@
+import torch
+import torch.nn as nn
+from parameterized import parameterized
+from torch.testing._internal.common_utils import run_tests
+
+from .harness import DispatchTestCase
+
+
+class TestArgminConverter(DispatchTestCase):
+    @parameterized.expand(
+        [
+            # input dimension == 1
+            ("dim_1_keep_dim_true", (3,), 0, True),
+            ("dim_1_keep_dim_false", (3,), 0, False),
+            # dim == None
+            ("dim_1_none_true", (3,), None, True),
+            ("dim_2_none_true", (3, 3), None, True),
+            ("dim_3_none_false", (3, 3, 3), None, False),
+            # # common cases
+            ("dim_1_keep_dim_true", (3, 3), 1, True),
+            ("dim_1_keep_dim_false", (3, 3), 1, False),
+            ("dim_0_keep_dim_true", (4, 4, 4), 0, True),
+            ("dim_0_keep_dim_false", (4, 4, 4), 0, False),
+            ("dim_negative_keep_dim_true", (1, 2, 3), -1, True),
+        ]
+    )
+    def test_argmin(self, _, input_shape, dim, keep_dim):
+        class ArgMin(nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, input):
+                return torch.ops.aten.argmin.default(input, dim, keep_dim)
+
+        input = [torch.randn(*input_shape)]
+
+        self.run_test(ArgMin(), input)
+
+
+if __name__ == "__main__":
+    run_tests()