MLIR  19.0.0git
TransposeConv2D.cpp
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1 //===- TransposeConv2D.cpp - Convolution transposition -------------------===//
2 //
3 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
4 // See https://llvm.org/LICENSE.txt for license information.
5 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
6 //
7 //===----------------------------------------------------------------------===//
8 
13 #include "mlir/IR/BuiltinTypes.h"
14 #include "mlir/IR/PatternMatch.h"
15 #include "mlir/IR/ValueRange.h"
18 #include "llvm/ADT/SmallVector.h"
19 #include "llvm/Support/ErrorHandling.h"
20 #include "llvm/Support/RWMutex.h"
21 #include <memory>
22 #include <numeric>
23 
24 namespace mlir {
25 namespace linalg {
26 namespace {
27 // clang-format off
28 /// Convolution converter that applies the following rewrite:
29 ///
30 /// Before:
31 ///
32 /// %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
33 /// strides = dense<2> : tensor<2xi64>}
34 /// ins (%input, %filter: tensor<1x4x4x6xf32>, tensor<8x2x2x6xf32>)
35 /// outs (%init: tensor<1x2x2x8xf32>) -> tensor<1x2x2x8xf32>
36 ///
37 /// After:
38 ///
39 /// %cst = arith.constant 0.000000e+00 : f32
40 /// %0 = tensor.empty() : tensor<2x2x6x8xf32>
41 /// %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<2x2x6x8xf32>) -> tensor<2x2x6x8xf32>
42 /// %transposed = linalg.transpose ins(%arg1 : tensor<8x2x2x6xf32>) outs(%1 : tensor<2x2x6x8xf32>)
43 /// permutation = [1, 2, 3, 0]
44 /// %2 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>}
45 /// ins(%arg0, %transposed : tensor<1x4x4x6xf32>, tensor<2x2x6x8xf32>) outs(%arg2 : tensor<1x2x2x8xf32>)
46 /// -> tensor<1x2x2x8xf32>
47 ///
48 /// with an analogous example for the quantized case.
49 // clang-format on
50 template <typename FHWCConvOp, typename HWCFConvOp>
51 FailureOr<Operation *> transposeConv2DHelper(RewriterBase &rewriter,
52  FHWCConvOp op) {
53  // Construct a permutation of the filter tensor dimensions. For a 2D
54  // convolution this will be known statically as [1, 2, 3, 0].
55  SmallVector<int64_t> filterPerm({1, 2, 3, 0});
56 
57  // Create the type for the transposed filter tensor.
58  auto filter = op->getOperand(1);
59  auto filterTy = cast<ShapedType>(filter.getType());
60  SmallVector<int64_t> newFilterShape(filterPerm.size());
61  std::generate(std::begin(newFilterShape), std::end(newFilterShape),
62  [dim = 0, &filterTy, &filterPerm]() mutable {
63  return filterTy.getShape()[filterPerm[dim++]];
64  });
65 
66  // Because linalg.transpose expects an "out" parameter we need to pass it a
67  // tensor of zeros of the result type so here we construct that tensor.
68  auto inputType = op->getOperand(0).getType();
69  auto elementTy = cast<ShapedType>(inputType).getElementType();
70  auto loc = op->getLoc();
71 
72  const auto isTensorOp = isa<TensorType>(inputType);
73  Value input;
74  if (isTensorOp) {
75 
76  input = rewriter.create<tensor::EmptyOp>(loc, newFilterShape, elementTy)
77  .getResult();
78  } else {
79  input = rewriter
80  .create<memref::AllocOp>(
81  loc, MemRefType::get(newFilterShape, elementTy))
82  .getResult();
83  }
84 
85  // We can then construct the transposition on our filter.
86  auto transpose =
87  rewriter.create<linalg::TransposeOp>(loc, filter, input, filterPerm);
88 
89  Value newFilter;
90  if (isTensorOp) {
91  newFilter = transpose.getResult()[0];
92  } else {
93  newFilter = input;
94  }
95 
96  SmallVector<Value> newInputs{op.getInputs()};
97  // The filter is always the second input argument, the other inputs can be
98  // left as they are.
99  newInputs[1] = newFilter;
100  // It is possible the convolution doesn't define any results and its
101  // out argument is just used instead.
102  SmallVector<Type> resultTy;
103  if (op.getNumResults()) {
104  resultTy.push_back(op->getResult(0).getType());
105  }
106  auto newConv =
107  rewriter.create<HWCFConvOp>(loc, resultTy, newInputs, op.getOutputs(),
108  op.getStrides(), op.getDilations());
109  rewriter.replaceOp(op, newConv);
110  return newConv.getOperation();
111 }
112 
113 template <typename FHWCConvOp, typename HWCFConvOp>
114 class ConvConverter : public OpRewritePattern<FHWCConvOp> {
115 public:
117  LogicalResult matchAndRewrite(FHWCConvOp op,
118  PatternRewriter &rewriter) const final {
119  if (failed(transposeConv2DHelper<FHWCConvOp, HWCFConvOp>(rewriter, op))) {
120  return failure();
121  }
122  return success();
123  }
124 };
125 } // namespace
126 
127 FailureOr<Operation *> transposeConv2D(RewriterBase &rewriter,
128  linalg::Conv2DNhwcFhwcOp op) {
129 
130  return transposeConv2DHelper<linalg::Conv2DNhwcFhwcOp,
131  linalg::Conv2DNhwcHwcfOp>(rewriter, op);
132 }
133 
134 FailureOr<Operation *> transposeConv2D(RewriterBase &rewriter,
135  linalg::Conv2DNhwcFhwcQOp op) {
136 
137  return transposeConv2DHelper<linalg::Conv2DNhwcFhwcQOp,
138  linalg::Conv2DNhwcHwcfQOp>(rewriter, op);
139 }
140 
142  MLIRContext *context = patterns.getContext();
143  patterns.insert<
144  ConvConverter<linalg::Conv2DNhwcFhwcOp, linalg::Conv2DNhwcHwcfOp>,
145  ConvConverter<linalg::Conv2DNhwcFhwcQOp, linalg::Conv2DNhwcHwcfQOp>>(
146  context);
147 }
148 } // namespace linalg
149 } // namespace mlir
MLIRContext is the top-level object for a collection of MLIR operations.
Definition: MLIRContext.h:60
RewritePatternSet & insert(ConstructorArg &&arg, ConstructorArgs &&...args)
Add an instance of each of the pattern types 'Ts' to the pattern list with the given arguments.
Definition: PatternMatch.h:931
MLIRContext * getContext() const
Definition: PatternMatch.h:823
This class coordinates the application of a rewrite on a set of IR, providing a way for clients to tr...
Definition: PatternMatch.h:400
void populateTranposeConv2DPatterns(RewritePatternSet &patterns)
FailureOr< Operation * > transposeConv2D(RewriterBase &rewriter, linalg::Conv2DNhwcFhwcOp op)
Convert linalg.conv_2d_nhwc_fhwc(_q) to linalg.conv_2d_nhwc_hwcf(_q) by materializing transpose.
static void transpose(llvm::ArrayRef< int64_t > trans, SmallVector< int64_t > &shape)
Definition: XeGPUOps.cpp:21
Include the generated interface declarations.
auto get(MLIRContext *context, Ts &&...params)
Helper method that injects context only if needed, this helps unify some of the attribute constructio...
OpRewritePattern(MLIRContext *context, PatternBenefit benefit=1, ArrayRef< StringRef > generatedNames={})
Patterns must specify the root operation name they match against, and can also specify the benefit of...
Definition: PatternMatch.h:362