Add a decomposition for torch.aten.argmin (#2613)

Adds a lowering for the torch.aten.argmin operator to linalg via decomposition into torch.aten.min.dim.

---------

Co-authored-by: Franz Haniel <franz.haniel@amd.com>

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6846,6 +6846,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0 = call @__torch__.torch.jit._shape_functions.argmax(%arg0, %arg1, %arg2) : (!torch.list<int>, !torch.optional<int>, !torch.bool) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.argmin\"(%arg0: !torch.list<int>, %arg1: !torch.optional<int>, %arg2: !torch.bool) -> !torch.list<int> {\n"
+"    %0 = call @__torch__.torch.jit._shape_functions.argmax(%arg0, %arg1, %arg2) : (!torch.list<int>, !torch.optional<int>, !torch.bool) -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.one_hot\"(%arg0: !torch.list<int>, %arg1: !torch.int) -> !torch.list<int> {\n"
 "    %none = torch.constant.none\n"
 "    %str = torch.constant.str \"AssertionError: getting num_classes from tensor contents is not supported\"\n"
@@ -10659,6 +10663,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %int4 = torch.constant.int 4\n"
 "    return %int4 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.argmin\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.optional<int>, %arg2: !torch.bool) -> !torch.int {\n"
+"    %int4 = torch.constant.int 4\n"
+"    return %int4 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.any.dim\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.int, %arg2: !torch.bool) -> !torch.int {\n"
 "    %int11 = torch.constant.int 11\n"
 "    %int0 = torch.constant.int 0\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -840,12 +840,13 @@ public:
 };
 } // namespace
 
-// Decompose `AtenArgMaxOp` into `AtenMaxDimOp`.
+// Decompose `AtenArgMaxOp` into `AtenMaxDimOp` as well as `AtenArgMinOp` into `AtenMinDimOp`
 namespace {
-class DecomposeAtenArgMaxOp : public OpRewritePattern<AtenArgmaxOp> {
+template <typename OpTy, typename DecompOpTy>
+class DecomposeAtenArgMinMaxOp : public OpRewritePattern<OpTy> {
 public:
-  using OpRewritePattern::OpRewritePattern;
-  LogicalResult matchAndRewrite(AtenArgmaxOp op,
+  using OpRewritePattern<OpTy>::OpRewritePattern;
+  LogicalResult matchAndRewrite(OpTy op,
                                 PatternRewriter &rewriter) const override {
     Location loc = op.getLoc();
     Value input = op.getSelf();
@@ -870,7 +871,7 @@ public:
             .cast<BaseTensorType>();
 
     // If the dim type is `NoneType` i.e. reduce along all the dimensions.
-    // `AtenMaxDimOp` doesn't support dim as `NoneType` so first the input
+    // `AtenMaxDimOp` and `AtenMinDimOp` do not support dim as `NoneType` so first the input
     // tensor is flattened to 1d tensor and then the reduction happens on the
     // 0th dimension.
     if (dim.getType().isa<Torch::NoneType>()) {
@@ -885,13 +886,14 @@ public:
       input = rewriter.create<AtenFlattenUsingIntsOp>(loc, flattenType, input,
                                                       dim, end);
     }
-    Value maxResult =
-        rewriter
-            .create<AtenMaxDimOp>(loc, valueTensorType, indicesTensorType,
-                                  input, dim, keepDim)
-            .getIndices();
 
-    rewriter.replaceOp(op, maxResult);
+    Value resultArg =
+      rewriter
+          .create<DecompOpTy>(loc, valueTensorType, indicesTensorType,
+                                input, dim, keepDim)
+          .getIndices();
+    
+    rewriter.replaceOp(op, resultArg);
     return success();
   }
 };
@@ -5774,7 +5776,8 @@ public:
     addPatternIfTargetOpIsIllegal<DecomposeAtenConvTranspose2dOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenArangeOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenArangeStartOp>(patterns);
-    addPatternIfTargetOpIsIllegal<DecomposeAtenArgMaxOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenArgMinMaxOp<AtenArgmaxOp, AtenMaxDimOp>>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenArgMinMaxOp<AtenArgminOp, AtenMinDimOp>>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenSquareOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenVarOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenStdOp>(patterns);

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -414,6 +414,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenArangeOp>();
   target.addIllegalOp<AtenArangeStartOp>();
   target.addIllegalOp<AtenArgmaxOp>();
+  target.addIllegalOp<AtenArgminOp>();
   target.addIllegalOp<AtenSquareOp>();
   target.addIllegalOp<AtenVarOp>();
   target.addIllegalOp<AtenStdOp>();

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/abstract_interp_lib_gen.py

@@ -445,6 +445,10 @@ def aten〇std〇correction〡shape(self: List[int], dim: Optional[List[int]] =
 def aten〇argmax〡shape(self: List[int], dim: Optional[int] = None, keepdim: bool = False) -> List[int]:
     return upstream_shape_functions.argmax(self, dim, keepdim)
 
+def aten〇argmin〡shape(self: List[int], dim: Optional[int] = None, keepdim: bool = False) -> List[int]:
+    # There is no shape function for argmin in pytorch, but the one for argmax does exactly what is needed here.
+    return upstream_shape_functions.argmax(self, dim, keepdim)
+
 # TODO: The result shape when num_classes=-1 depends on the runtime values of the input tensor,
 # making it impossible to add support for it using the current design of the shape library.
 def aten〇one_hot〡shape(self: List[int], num_classes: int = -1) -> List[int]:
@@ -3254,7 +3258,10 @@ def aten〇mean〇dim〡dtype(self_rank_dtype: Tuple[int, int], dim: Optional[Li
 
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
 def aten〇argmax〡dtype(self_rank_dtype: Tuple[int, int], dim: Optional[int] = None, keepdim: bool = False) -> int:
-    self_rank, self_dtype = self_rank_dtype
+    return torch.int64
+
+@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
+def aten〇argmin〡dtype(self_rank_dtype: Tuple[int, int], dim: Optional[int] = None, keepdim: bool = False) -> int:
     return torch.int64
 
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0))

projects/pt1/python/torch_mlir_e2e_test/test_suite/__init__.py

@@ -40,7 +40,6 @@ def register_all_tests():
     from . import type_conversion
     from . import backprop
     from . import reduction
-    from . import argmax
     from . import matmul
     from . import reshape_like
     from . import scalar

projects/pt1/python/torch_mlir_e2e_test/test_suite/argmax.py

@@ -1,65 +0,0 @@
-#  Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
-#  See https://llvm.org/LICENSE.txt for license information.
-#  SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
-
-import torch
-
-from torch_mlir_e2e_test.framework import TestUtils
-from torch_mlir_e2e_test.registry import register_test_case
-from torch_mlir_e2e_test.annotations import annotate_args, export
-
-# ==============================================================================
-
-class ArgmaxModule(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    @export
-    @annotate_args([
-        None,
-        ([-1, -1], torch.float32, True),
-    ])
-
-    def forward(self, a):
-        return torch.argmax(a)
-
-
-@register_test_case(module_factory=lambda: ArgmaxModule())
-def ArgmaxModule_basic(module, tu: TestUtils):
-    module.forward(tu.rand(3, 4))
-
-# ==============================================================================
-
-class ArgmaxWithDimModule(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    @export
-    @annotate_args([
-        None,
-        ([-1, -1, -1], torch.float32, True),
-    ])
-    def forward(self, a):
-        return torch.argmax(a, dim=1)
-
-@register_test_case(module_factory=lambda: ArgmaxWithDimModule())
-def ArgmaxModule_with_dim(module, tu: TestUtils):
-    module.forward(tu.rand(3, 4, 5))
-
-# ==============================================================================
-
-class ArgmaxKeepDimsModule(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    @export
-    @annotate_args([
-        None, 
-        ([-1, -1], torch.float32, True),
-    ])
-    def forward(self, a):
-        return torch.argmax(a, 0, True)
-
-@register_test_case(module_factory=lambda: ArgmaxKeepDimsModule())
-def ArgmaxModule_keepDim(module, tu: TestUtils):
-    module.forward(tu.rand(4, 6))

projects/pt1/python/torch_mlir_e2e_test/test_suite/reduction.py

@@ -755,6 +755,171 @@ def ReduceMinUnsignedIntModule_basic(module, tu: TestUtils):
     module.forward(tu.randint(3, 4, 5, high=100))
 
 # ==============================================================================
+
+class ArgminModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, True),
+    ])
+
+    def forward(self, a):
+        return torch.ops.aten.argmin(a)
+
+
+@register_test_case(module_factory=lambda: ArgminModule())
+def ArgminModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4))
+
+# ==============================================================================
+
+class ArgminIntModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.int64, True),
+    ])
+
+    def forward(self, a):
+        return torch.ops.aten.argmin(a)
+
+
+@register_test_case(module_factory=lambda: ArgminIntModule())
+def ArgminIntModule_basic(module, tu: TestUtils):
+    module.forward(tu.randint(3, 4, low=-100, high=100))
+
+@register_test_case(module_factory=lambda: ArgminIntModule())
+def ArgminIntModule_multiple_mins(module, tu: TestUtils):
+    # To cover the special case that the minimal value occurs more than once.
+    # The pytorch convention is here to consider the first occurence as the argmin.
+    module.forward(torch.full((3,4), tu.randint(1).item(), dtype=torch.int64))
+
+# ==============================================================================
+
+class ArgminWithDimModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.ops.aten.argmin(a, dim=1)
+
+@register_test_case(module_factory=lambda: ArgminWithDimModule())
+def ArgminModule_with_dim(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4, 5))
+
+# ==============================================================================
+
+class ArgminKeepDimsModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None, 
+        ([-1, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.ops.aten.argmin(a, 0, True)
+
+@register_test_case(module_factory=lambda: ArgminKeepDimsModule())
+def ArgminModule_keepDim(module, tu: TestUtils):
+    module.forward(tu.rand(4, 6))
+
+# ==============================================================================
+
+class ArgmaxModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, True),
+    ])
+
+    def forward(self, a):
+        return torch.ops.aten.argmax(a)
+
+
+@register_test_case(module_factory=lambda: ArgmaxModule())
+def ArgmaxModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4))
+
+# ==============================================================================
+
+class ArgmaxIntModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.int64, True),
+    ])
+
+    def forward(self, a):
+        return torch.ops.aten.argmax(a)
+
+
+@register_test_case(module_factory=lambda: ArgmaxIntModule())
+def ArgmaxIntModule_basic(module, tu: TestUtils):
+    module.forward(tu.randint(3, 4, low=-100, high=100))
+
+@register_test_case(module_factory=lambda: ArgmaxIntModule())
+def ArgmaxIntModule_multiple_maxs(module, tu: TestUtils):
+    # To cover the special case that the maximal value occurs more than once.
+    # The pytorch convention is here to consider the first occurence as the argmax.
+    module.forward(torch.full((3,4), tu.randint(1).item(), dtype=torch.int64))
+
+# ==============================================================================
+
+class ArgmaxWithDimModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.ops.aten.argmax(a, dim=1)
+
+@register_test_case(module_factory=lambda: ArgmaxWithDimModule())
+def ArgmaxModule_with_dim(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4, 5))
+
+# ==============================================================================
+
+class ArgmaxKeepDimsModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None, 
+        ([-1, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.ops.aten.argmax(a, 0, True)
+
+@register_test_case(module_factory=lambda: ArgmaxKeepDimsModule())
+def ArgmaxModule_keepDim(module, tu: TestUtils):
+    module.forward(tu.rand(4, 6))
+
+# ==============================================================================
+
 class ReduceL1NormModule(torch.nn.Module):
     def __init__(self):
         super().__init__()