Add aten.maximum op and conversions from aten->tcf.

* Conversions are very simple, suporting mul, maximum and add (alpha=1 only).
* Example added with pass pipeline needed to run.
* Much missing off of the golden path but sufficient for such simple cases.

frontends/pytorch/examples/mul_maximum_e2e.py

@@ -0,0 +1,24 @@
+# -*- Python -*-
+# This file is licensed under a pytorch-style license
+# See frontends/pytorch/LICENSE for license information.
+
+import sys
+import torch
+import torch_mlir
+
+lhs = torch.ones((4, 6, 1))
+rhs = torch.ones((1, 1, 3)) * 0.6
+bias = torch.ones((1, 1, 3)) * 0.2
+threshold = torch.tensor((0.75, 0.25, 0.10))
+
+mb = torch_mlir.ModuleBuilder()
+with mb.capture_function("mul_maximum", [lhs, rhs, threshold, bias]) as f:
+  result = torch.maximum(lhs * rhs, threshold)
+  result = result + bias
+  f.returns([result])
+
+print(f"Result(f{result.size()}) = {result}", file=sys.stderr)
+# TODO: Currently need to route through:
+#   npcomp-opt -aten-recognize-kernels -convert-aten-to-tcf \
+#     -numpy-public-functions-to-tensor -canonicalize
+mb.module.operation.print()

frontends/pytorch/python/torch_mlir_utils/codegen/torch_signature_ods_gen.py

@@ -64,6 +64,9 @@ def generate_ops(g: "OpGenerator"):
   g.ordinary_binary_op("aten::true_divide(Tensor,Tensor)", "TrueDivideOp",
                        "true_divide")
 
+  g.ordinary_binary_op("aten::maximum(Tensor,Tensor)", "MaximumOp", "maximum")
+  g.ordinary_binary_op("aten::minimum(Tensor,Tensor)", "MinimumOp", "minimum")
+
   # Unary-ops. These are all the same so just name munge them.
   g.print_banner("Unary arithmetic ops")
   for uname in [

include/npcomp/Conversion/ATenToTCF/Passes.h

@@ -0,0 +1,21 @@
+//===------------------------------------------------------------*- C++ -*-===//
+//
+// 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
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef NPCOMP_CONVERSION_ATENTOTCF_PASSES_H
+#define NPCOMP_CONVERSION_ATENTOTCF_PASSES_H
+
+#include "mlir/Pass/Pass.h"
+#include <memory>
+
+namespace mlir {
+namespace NPCOMP {
+std::unique_ptr<OperationPass<FuncOp>> createConvertATenToTCFPass();
+}
+} // namespace mlir
+
+#endif // NPCOMP_CONVERSION_ATENTOTCF_PATTERNS_H

include/npcomp/Conversion/ATenToTCF/Patterns.h

@@ -0,0 +1,30 @@
+//===------------------------------------------------------------*- C++ -*-===//
+//
+// 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
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef NPCOMP_CONVERSION_ATENTOTCF_PATTERNS_H
+#define NPCOMP_CONVERSION_ATENTOTCF_PATTERNS_H
+
+#include <memory>
+
+namespace mlir {
+
+class MLIRContext;
+class OwningRewritePatternList;
+
+namespace NPCOMP {
+
+/// Populates patterns for converting core ATen ops to TCF. These patterns
+/// cover core arithmetic ops that are on the order of 1:1 representationally.
+/// More advanced patterns are managed elsewhere.
+void populateCoreATenToTCFPatterns(MLIRContext *context,
+                                   OwningRewritePatternList &patterns);
+
+} // namespace NPCOMP
+} // namespace mlir
+
+#endif // NPCOMP_CONVERSION_ATENTOTCF_PATTERNS_H

include/npcomp/Conversion/Passes.td

@@ -12,12 +12,12 @@
 include "mlir/Pass/PassBase.td"
 
 //===----------------------------------------------------------------------===//
-// TCFToTCP
+// ATen conversions
 //===----------------------------------------------------------------------===//
 
-def ConvertTCFToTCP : Pass<"convert-tcf-to-tcp", "ModuleOp"> {
-  let summary = "Convert TCF to TCP";
-  let constructor = "mlir::NPCOMP::createConvertTCFToTCPPass()";
+def ConvertATenToTCF : Pass<"convert-aten-to-tcf", "FuncOp"> {
+  let summary = "Convert recognized ATen to TCF ops";
+  let constructor = "mlir::NPCOMP::createConvertATenToTCFPass()";
 }
 
 //===----------------------------------------------------------------------===//
@@ -38,6 +38,15 @@ def ConvertNumpyToTCF : Pass<"convert-numpy-to-tcf", "FuncOp"> {
   let constructor = "mlir::NPCOMP::createConvertNumpyToTCFPass()";
 }
 
+//===----------------------------------------------------------------------===//
+// TCFToTCP
+//===----------------------------------------------------------------------===//
+
+def ConvertTCFToTCP : Pass<"convert-tcf-to-tcp", "ModuleOp"> {
+  let summary = "Convert TCF to TCP";
+  let constructor = "mlir::NPCOMP::createConvertTCFToTCPPass()";
+}
+
 //===----------------------------------------------------------------------===//
 // Conditionally compiled IREE backend passes
 //===----------------------------------------------------------------------===//

include/npcomp/Dialect/ATen/IR/GeneratedATenOps.cpp.inc

@@ -152,6 +152,44 @@ const Torch::BuildKernelMetadata &TrueDivideOp::getTorchBuildKernelMetadata() {
   return metadata;
 }
 
+Torch::KernelMetadata MaximumOp::getTorchKernelMetadata() {
+  return getTorchBuildKernelMetadata();
+}
+
+const Torch::BuildKernelMetadata &MaximumOp::getTorchBuildKernelMetadata() {
+  using KVC = Torch::KernelValueConversion::BitMask;
+  static Torch::BuildKernelMetadata metadata = ([]() {
+    Torch::BuildKernelMetadata m;
+    m.kernelName = "aten::maximum";
+    m.promoteTrailingOutTensor = true;
+    m.addArgTypes({"Tensor", "Tensor"});
+    m.addArgConversions({KVC::kImmutableTensor, KVC::kImmutableTensor|KVC::kPromoteScalar});
+    m.addReturnTypes({"Tensor"});
+    m.addReturnConversions({KVC::kImmutableTensor});
+    return m;
+  })();
+  return metadata;
+}
+
+Torch::KernelMetadata MinimumOp::getTorchKernelMetadata() {
+  return getTorchBuildKernelMetadata();
+}
+
+const Torch::BuildKernelMetadata &MinimumOp::getTorchBuildKernelMetadata() {
+  using KVC = Torch::KernelValueConversion::BitMask;
+  static Torch::BuildKernelMetadata metadata = ([]() {
+    Torch::BuildKernelMetadata m;
+    m.kernelName = "aten::minimum";
+    m.promoteTrailingOutTensor = true;
+    m.addArgTypes({"Tensor", "Tensor"});
+    m.addArgConversions({KVC::kImmutableTensor, KVC::kImmutableTensor|KVC::kPromoteScalar});
+    m.addReturnTypes({"Tensor"});
+    m.addReturnConversions({KVC::kImmutableTensor});
+    return m;
+  })();
+  return metadata;
+}
+
 // -----------------------------------------------------------------------------
 // Unary arithmetic ops
 // -----------------------------------------------------------------------------

include/npcomp/Dialect/ATen/IR/GeneratedATenOps.td

@@ -96,6 +96,28 @@ def aten_TrueDivideOp: aten_Op<"true_divide", [NoSideEffect, DeclareOpInterfaceM
   );
 }
 
+def aten_MaximumOp: aten_Op<"maximum", [NoSideEffect, DeclareOpInterfaceMethods<TorchBuildableKernelOpInterface>, DeclareOpInterfaceMethods<TorchKernelOpInterface>]> {
+  let summary = "Recognized op for kernel aten::maximum";
+  let arguments = (ins
+    AnyTorchImmutableTensor:$self,
+    AnyTorchImmutableTensor:$other
+  );
+  let results = (outs
+    AnyTorchImmutableTensor
+  );
+}
+
+def aten_MinimumOp: aten_Op<"minimum", [NoSideEffect, DeclareOpInterfaceMethods<TorchBuildableKernelOpInterface>, DeclareOpInterfaceMethods<TorchKernelOpInterface>]> {
+  let summary = "Recognized op for kernel aten::minimum";
+  let arguments = (ins
+    AnyTorchImmutableTensor:$self,
+    AnyTorchImmutableTensor:$other
+  );
+  let results = (outs
+    AnyTorchImmutableTensor
+  );
+}
+
 // -----------------------------------------------------------------------------
 // Unary arithmetic ops
 // -----------------------------------------------------------------------------

lib/Conversion/ATenToTCF/CMakeLists.txt

@@ -0,0 +1,17 @@
+add_npcomp_conversion_library(NPCOMPATenToTCF
+  ConvertATenToTCFPass.cpp
+  CoreOpConversionPatterns.cpp
+
+  DEPENDS
+  NPCOMPConversionPassIncGen
+
+  LINK_COMPONENTS
+  Core
+
+  LINK_LIBS PUBLIC
+  MLIRIR
+  MLIRPass
+  MLIRTransforms
+  NPCOMPATenDialect
+  NPCOMPTCFDialect
+)

lib/Conversion/ATenToTCF/ConvertATenToTCFPass.cpp

@@ -0,0 +1,41 @@
+//===----------------------------------------------------------------------===//
+//
+// 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
+//
+//===----------------------------------------------------------------------===//
+
+#include "npcomp/Conversion/ATenToTCF/Passes.h"
+
+#include "../PassDetail.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
+#include "npcomp/Conversion/ATenToTCF/Patterns.h"
+#include "npcomp/Dialect/TCF/IR/TCFDialect.h"
+
+using namespace mlir;
+using namespace mlir::NPCOMP;
+
+namespace {
+
+class ConvertATenToTCF : public ConvertATenToTCFBase<ConvertATenToTCF> {
+public:
+  void getDependentDialects(DialectRegistry &registry) const override {
+    registry.insert<tcf::TCFDialect>();
+  }
+
+  void runOnOperation() override {
+    FuncOp funcOp = getOperation();
+    MLIRContext *context = &getContext();
+    OwningRewritePatternList patterns;
+    populateCoreATenToTCFPatterns(context, patterns);
+    (void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
+  }
+};
+
+} // namespace
+
+std::unique_ptr<OperationPass<FuncOp>>
+mlir::NPCOMP::createConvertATenToTCFPass() {
+  return std::make_unique<ConvertATenToTCF>();
+}

lib/Conversion/ATenToTCF/CoreOpConversionPatterns.cpp

@@ -0,0 +1,77 @@
+//===----------------------------------------------------------------------===//
+//
+// 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
+//
+//===----------------------------------------------------------------------===//
+
+#include "npcomp/Conversion/ATenToTCF/Patterns.h"
+
+#include "mlir/IR/MLIRContext.h"
+#include "mlir/IR/Matchers.h"
+#include "mlir/IR/PatternMatch.h"
+#include "npcomp/Dialect/ATen/IR/ATenDialect.h"
+#include "npcomp/Dialect/TCF/IR/TCFOps.h"
+
+using namespace mlir;
+using namespace mlir::NPCOMP;
+
+namespace {
+
+/// The ATen AddOp actually has three arguments:
+///   self, other, alpha
+/// Alpha is an integer that is multiplied by 'other' prior to adding.
+class ConvertATenAdd : public OpRewritePattern<aten::AddOp> {
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(aten::AddOp srcAddOp,
+                                PatternRewriter &rewriter) const override {
+    // Special case: Match when alpha is constant 1, which is the default,
+    // quite common and maps directly to a TCF add. Note that regardless of
+    // the type of self/other (i.e. if they are float), alpha emits as an
+    // integer with value 1 when defaulted. It is this specific case that we
+    // are detecting (default value) and will leave all others to the fully
+    // generic conversion.
+    APInt alphaValue;
+    if (matchPattern(srcAddOp.alpha(), m_ConstantInt(&alphaValue)) &&
+        alphaValue.getZExtValue() == 1) {
+      rewriter.replaceOpWithNewOp<tcf::AddOp>(
+          srcAddOp, srcAddOp.getResult().getType(), srcAddOp.self(),
+          srcAddOp.other());
+      return success();
+    }
+
+    return rewriter.notifyMatchFailure(
+        srcAddOp, "aten.add to tcf.add currently only supports alpha == 1");
+  }
+};
+
+/// Common conversion template for true binary elementwise ops.
+/// This does not apply to the handful of not-actually-binary PyTorch ops that
+/// have broadcastable self/other operands but may have additional parameters.
+template <typename SourceOp, typename TargetOp>
+class ConvertBinaryElementwise : public OpRewritePattern<SourceOp> {
+public:
+  using OpRewritePattern<SourceOp>::OpRewritePattern;
+  LogicalResult matchAndRewrite(SourceOp srcOp,
+                                PatternRewriter &rewriter) const override {
+    auto operands = srcOp.getOperation()->getOperands();
+    auto results = srcOp.getOperation()->getResults();
+    assert(operands.size() == 2 && "expected true binary op");
+    assert(results.size() == 1 && "expected single result op");
+    Type resultType = results[0].getType();
+    rewriter.replaceOpWithNewOp<TargetOp>(
+        srcOp, resultType, srcOp.getOperand(0), srcOp.getOperand(1));
+    return success();
+  }
+};
+
+} // namespace
+
+void mlir::NPCOMP::populateCoreATenToTCFPatterns(
+    MLIRContext *context, OwningRewritePatternList &patterns) {
+  patterns.insert<ConvertATenAdd>(context);
+  patterns.insert<ConvertBinaryElementwise<aten::MulOp, tcf::MulOp>>(context);
+  patterns.insert<ConvertBinaryElementwise<aten::MaximumOp, tcf::MaxOp>>(
+      context);
+}

lib/Conversion/CMakeLists.txt

@@ -1,3 +1,4 @@
+add_subdirectory(ATenToTCF)
 add_subdirectory(BasicpyToStd)
 add_subdirectory(NumpyToTCF)
 add_subdirectory(TCFToTCP)

lib/Conversion/Passes.cpp

@@ -8,6 +8,7 @@
 
 #include "npcomp/Conversion/Passes.h"
 
+#include "npcomp/Conversion/ATenToTCF/Passes.h"
 #include "npcomp/Conversion/BasicpyToStd/Passes.h"
 #include "npcomp/Conversion/NumpyToTCF/Passes.h"
 #include "npcomp/Conversion/TCFToTCP/TCFToTCP.h"

test/Conversion/ATenToTCF/core_op_conversions.mlir

@@ -0,0 +1,26 @@
+// RUN: npcomp-opt <%s -convert-aten-to-tcf | FileCheck %s --dump-input=fail
+
+// CHECK-LABEL: @binary_elementwise_ops
+// NOTE: These are all template expanded, so just testing an examplar op and
+// special cases.
+func @binary_elementwise_ops(%arg0: tensor<4x6x1xf32>, %arg1: tensor<1x1x3xf32>) -> tensor<4x6x3xf32> {
+  // CHECK: tcf.mul %arg0, %arg1 : (tensor<4x6x1xf32>, tensor<1x1x3xf32>) -> tensor<4x6x3xf32>
+  %0 = "aten.mul"(%arg0, %arg1) : (tensor<4x6x1xf32>, tensor<1x1x3xf32>) -> tensor<4x6x3xf32>
+  return %0 : tensor<4x6x3xf32>
+}
+
+// CHECK-LABEL: @add_alpha_constant1
+func @add_alpha_constant1(%arg0: tensor<4x6x3xf32>, %arg1: tensor<1x1x3xf32>) -> tensor<4x6x3xf32> {
+  %c1_i64 = constant 1 : i64
+  // CHECK: tcf.add %arg0, %arg1 : (tensor<4x6x3xf32>, tensor<1x1x3xf32>) -> tensor<4x6x3xf32>
+  %0 = "aten.add"(%arg0, %arg1, %c1_i64) : (tensor<4x6x3xf32>, tensor<1x1x3xf32>, i64) -> tensor<4x6x3xf32>
+  return %0 : tensor<4x6x3xf32>
+}
+
+// CHECK-LABEL: @add_alpha_non_constant1
+func @add_alpha_non_constant1(%arg0: tensor<4x6x3xf32>, %arg1: tensor<1x1x3xf32>) -> tensor<4x6x3xf32> {
+  %c1_i64 = constant 2 : i64
+  // CHECK: "aten.add"
+  %0 = "aten.add"(%arg0, %arg1, %c1_i64) : (tensor<4x6x3xf32>, tensor<1x1x3xf32>, i64) -> tensor<4x6x3xf32>
+  return %0 : tensor<4x6x3xf32>
+}