[mlir][linalg] Propagate attributes when doing named ops conversion.

[hanchung]

Summary

Custom attributes can be set on the operation. It prevents them to be
removed when doing named ops conversion.

[hanchung]

delete unused includes

[mravishankar]

mlir/lib/Dialect/Linalg/Transforms/NamedOpConversions.cpp

93–95

Might be a better way of doing this. All ops have an attrName list. These are names of attributes that are defined in the tablegen op definition. Copy over everything that is not that.

[hanchung]

get attribute list properly

[hanchung]

mlir/lib/Dialect/Linalg/Transforms/NamedOpConversions.cpp

93–95

I finally figure out how to use the method. getAttributeNames only works at op level. It does not work at Operation* level. I think it does look better because we do declare elidedAttrs variable, which has better readability.

[mravishankar]

mlir/lib/Dialect/Linalg/Transforms/NamedOpConversions.cpp

93–95

Thanks!. The kMemoizedIndexingMapAttrName is tricky.... thanks for catching that....

Ill stamp this, but probably something similar is needed in other places. Maybe moving it to a Linalg/Utils/Utils.h is worth it.
Also readability can be slightly better with TypeSwitch.

[hanchung]

Address comments.

• Add the method to Utils.h
• Use TypeSwitch

mlir/lib/Dialect/Linalg/Transforms/NamedOpConversions.cpp

93–95

Moved!

Yes, I like TypeSwitch better. I followed the logic so I did not change it. let's switch to use TypeSwitch.

[hanchung]

delete unneeded includes

mlir/include/mlir/Dialect/Linalg/Utils/Utils.h

 //===- Utils.h - Utilities to support the Linalg dialect --------*- 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 MLIR_DIALECT_LINALG_UTILS_UTILS_H
 #define MLIR_DIALECT_LINALG_UTILS_UTILS_H
 
 #include "mlir/Dialect/Linalg/Analysis/DependenceAnalysis.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/SCF/IR/SCF.h"
 #include "llvm/ADT/MapVector.h"
 #include "llvm/ADT/SetVector.h"
+#include "llvm/ADT/StringSet.h"
 
 namespace mlir {
 class AffineExpr;
 class AffineForOp;
 class AffineMap;
 class PatternRewriter;
 
 namespace tensor {
struct GenerateLoopNest {
   static void doit(OpBuilder &b, Location loc, ArrayRef<Range> loopRanges,
                    LinalgOp linalgOp, ArrayRef<Attribute> iteratorTypes,
                    function_ref<scf::ValueVector(OpBuilder &, Location,
                                                  ValueRange, ValueRange)>
                        bodyBuilderFn,
                    ArrayRef<linalg::ProcInfo> procInfo = {});
 };
 
+/// Returns an attribute list that excludes pre-defined attributes.
+template <typename OpTy>
+SmallVector<NamedAttribute> getPrunedAttributeList(OpTy op) {
+  llvm::StringSet<> elidedAttrs;
+  elidedAttrs.insert(op.getAttributeNames().begin(),
+                     op.getAttributeNames().end());
+  if (isa<linalg::LinalgOp>(op.getOperation()))
+    elidedAttrs.insert(LinalgDialect::kMemoizedIndexingMapsAttrName);
+  SmallVector<NamedAttribute> attrs;
+  for (auto attr : op->getAttrs()) {
+    if (elidedAttrs.count(attr.getName()))
+      continue;
+    attrs.push_back(attr);
+  }
+  return attrs;
+}
+
 } // namespace linalg
 } // namespace mlir
 
 #endif // MLIR_DIALECT_LINALG_UTILS_UTILS_H

mlir/lib/Dialect/Linalg/Transforms/NamedOpConversions.cpp

 
 #include "mlir/Dialect/Linalg/Passes.h"
 
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 #include "llvm/ADT/SmallVector.h"
+#include "llvm/ADT/TypeSwitch.h"
 
 namespace mlir {
 #define GEN_PASS_DEF_LINALGNAMEDOPCONVERSION
 #include "mlir/Dialect/Linalg/Passes.h.inc"
 } // namespace mlir
 
 using namespace mlir;
 using namespace mlir::linalg;
SmallVector<ReassociationIndices, 4> collapsedInitDims = {
       getIndicesVector(3, 5)};
   auto newInitTy =
       RankedTensorType::get({initTy.getDimSize(0), initTy.getDimSize(1),
                              initTy.getDimSize(2), initTy.getDimSize(3)},
                             initTy.getElementType());
   auto collapsedInit = rewriter.create<tensor::CollapseShapeOp>(
       loc, newInitTy, init, collapsedInitDims);
 
-  Value newConv;
-  if (isa<DepthwiseConv2DNhwcHwcmOp>(operation)) {
-    newConv = rewriter
-                  .create<DepthwiseConv2DNhwcHwcOp>(
-                      loc, newInitTy, ValueRange{input, collapsedKernel},
-                      ValueRange{collapsedInit}, stride, dilation)
-                  .getResult(0);
-  } else if (isa<DepthwiseConv2DNhwcHwcmQOp>(operation)) {
-    newConv =
-        rewriter
-            .create<DepthwiseConv2DNhwcHwcQOp>(
+  SmallVector<NamedAttribute> preservedAttrs;
+  Operation *newConv =
+      TypeSwitch<Operation *, Operation *>(operation)
+          .Case<DepthwiseConv2DNhwcHwcmOp>([&](auto op) {
+            preservedAttrs = getPrunedAttributeList(op);
+            return rewriter.create<DepthwiseConv2DNhwcHwcOp>(
+                ValueRange{collapsedInit}, stride, dilation);
+          })
+          .Case<DepthwiseConv2DNhwcHwcmQOp>([&](auto op) {
+            preservedAttrs = getPrunedAttributeList(op);
+            return rewriter.create<DepthwiseConv2DNhwcHwcQOp>(
                 loc, newInitTy, ValueRange{input, collapsedKernel, iZp, kZp},
-                ValueRange{collapsedInit}, stride, dilation)
-            .getResult(0);
-  }
-
+                ValueRange{collapsedInit}, stride, dilation);
+          })
+          .Default([](Operation *op) { return nullptr; });
   if (!newConv)
     return failure();
+  for (auto attr : preservedAttrs)
+    newConv->setAttr(attr.getName(), attr.getValue());
 
   // Expand dimensions back out to
   rewriter.replaceOpWithNewOp<tensor::ExpandShapeOp>(
-      operation, resultTy, newConv, collapsedInitDims);
+      operation, resultTy, newConv->getResult(0), collapsedInitDims);
   return success();
 }
 
 namespace {
 struct SimplifyDepthwiseConvOp
     : public OpRewritePattern<DepthwiseConv2DNhwcHwcmOp> {
   using OpRewritePattern<DepthwiseConv2DNhwcHwcmOp>::OpRewritePattern;
 

mlir/test/Dialect/Linalg/namedop_conversion.mlir

 // RUN: mlir-opt %s -linalg-named-op-conversion -split-input-file | FileCheck %s
 
 // CHECK-LABEL: @depthwise_conv
 func.func @depthwise_conv(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<?x?x?x1xf32>, %arg2: tensor<?x?x?x?x1xf32>) -> tensor<?x?x?x?x1xf32> {
   // CHECK-DAG: %[[KERNEL:.+]] = tensor.collapse_shape %arg1 {{\[\[}}0], [1], [2, 3]]
   // CHECK-DAG: %[[INIT:.+]] = tensor.collapse_shape %arg2 {{\[\[}}0], [1], [2], [3, 4]]
-  // CHECK-DAG: %[[CONV:.+]] = linalg.depthwise_conv_2d_nhwc_hwc {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %[[KERNEL]] : tensor<?x?x?x?xf32>, tensor<?x?x?xf32>) outs(%[[INIT]] : tensor<?x?x?x?xf32>)
+  // CHECK-DAG: %[[CONV:.+]] = linalg.depthwise_conv_2d_nhwc_hwc {_someattr, dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %[[KERNEL]] : tensor<?x?x?x?xf32>, tensor<?x?x?xf32>) outs(%[[INIT]] : tensor<?x?x?x?xf32>)
   // CHECK: %[[OUT:.+]] = tensor.expand_shape %[[CONV]] {{\[\[}}0], [1], [2], [3, 4]]
-  %0 = linalg.depthwise_conv_2d_nhwc_hwcm {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<?x?x?x?xf32>, tensor<?x?x?x1xf32>) outs(%arg2 : tensor<?x?x?x?x1xf32>) -> tensor<?x?x?x?x1xf32>
+  %0 = linalg.depthwise_conv_2d_nhwc_hwcm {_someattr, dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<?x?x?x?xf32>, tensor<?x?x?x1xf32>) outs(%arg2 : tensor<?x?x?x?x1xf32>) -> tensor<?x?x?x?x1xf32>
   return %0 : tensor<?x?x?x?x1xf32>
 }
 
 
 // -----
 
 // CHECK-LABEL: @depthwise_conv_q
 func.func @depthwise_conv_q(%arg0: tensor<?x?x?x?xi8>, %arg1: tensor<?x?x?x1xi8>, %arg2: tensor<?x?x?x?x1xi32>, %arg3 : i32, %arg4 : i32) -> tensor<?x?x?x?x1xi32> {
   // CHECK-DAG: %[[KERNEL:.+]] = tensor.collapse_shape %arg1 {{\[\[}}0], [1], [2, 3]]
   // CHECK-DAG: %[[INIT:.+]] = tensor.collapse_shape %arg2 {{\[\[}}0], [1], [2], [3, 4]]
-  // CHECK-DAG: %[[CONV:.+]] = linalg.depthwise_conv_2d_nhwc_hwc_q {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %[[KERNEL]], %arg3, %arg4 : tensor<?x?x?x?xi8>, tensor<?x?x?xi8>, i32, i32) outs(%[[INIT]] : tensor<?x?x?x?xi32>)
+  // CHECK-DAG: %[[CONV:.+]] = linalg.depthwise_conv_2d_nhwc_hwc_q {_someattr, dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %[[KERNEL]], %arg3, %arg4 : tensor<?x?x?x?xi8>, tensor<?x?x?xi8>, i32, i32) outs(%[[INIT]] : tensor<?x?x?x?xi32>)
   // CHECK: %[[OUT:.+]] = tensor.expand_shape %[[CONV]] {{\[\[}}0], [1], [2], [3, 4]]
-  %0 = linalg.depthwise_conv_2d_nhwc_hwcm_q {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1, %arg3, %arg4 : tensor<?x?x?x?xi8>, tensor<?x?x?x1xi8>, i32, i32) outs(%arg2 : tensor<?x?x?x?x1xi32>) -> tensor<?x?x?x?x1xi32>
+  %0 = linalg.depthwise_conv_2d_nhwc_hwcm_q {_someattr, dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1, %arg3, %arg4 : tensor<?x?x?x?xi8>, tensor<?x?x?x1xi8>, i32, i32) outs(%arg2 : tensor<?x?x?x?x1xi32>) -> tensor<?x?x?x?x1xi32>
   return %0 : tensor<?x?x?x?x1xi32>
 }