1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
|
//===- TosaDecomposeDepthwise.cpp -----------------------------------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// Decompose TOSA Depthwise operation to a series of TOSA Ops specifically
// (1) Convert a 1x1 Depthwise to Reshape -> Mul -> Reshape -> Add
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Tosa/IR/TosaOps.h"
#include "mlir/Dialect/Tosa/Transforms/Passes.h"
#include "mlir/Dialect/Tosa/Utils/ConversionUtils.h"
#include "mlir/IR/BuiltinTypes.h"
using namespace mlir;
using namespace mlir::tosa;
namespace {
struct DepthwiseConv2DIsMul : public OpRewritePattern<tosa::DepthwiseConv2DOp> {
explicit DepthwiseConv2DIsMul(MLIRContext *context)
: OpRewritePattern(context) {}
LogicalResult matchAndRewrite(tosa::DepthwiseConv2DOp op,
PatternRewriter &rewriter) const override {
Value input = op.getInput();
Value weight = op.getWeight();
ShapedType inputType = cast<ShapedType>(input.getType());
ShapedType weightType = cast<ShapedType>(weight.getType());
ShapedType resultType = cast<ShapedType>(op.getOutput().getType());
// Any dimensions other than batchSize cannot be dynamic for input/output
for (unsigned int i = 1; i < 4; ++i) {
if (inputType.isDynamicDim(i) || resultType.isDynamicDim(i))
return failure();
}
if (!weightType.hasStaticShape()) {
return failure();
}
if (!llvm::all_of(op.getStride(), [](int64_t v) { return v == 1; }))
return failure();
// Only works for a 1x1 kernel.
ArrayRef<int64_t> weightShape = weightType.getShape();
if (weightShape[0] != 1 || weightShape[1] != 1) {
return failure();
}
Type inputETy = inputType.getElementType();
Type weightETy = weightType.getElementType();
if (!inputETy.isIntOrFloat() || !weightETy.isIntOrFloat())
return rewriter.notifyMatchFailure(op, "unsupported type");
// Get and verify zero points.
FailureOr<int64_t> maybeIZp = op.getInputZeroPoint();
if (failed(maybeIZp))
return rewriter.notifyMatchFailure(
op, "input zero point cannot be statically determined");
FailureOr<int64_t> maybeWZp = op.getWeightZeroPoint();
if (failed(maybeWZp))
return rewriter.notifyMatchFailure(
op, "weight zero point cannot be statically determined");
int64_t iZp = *maybeIZp;
int64_t wZp = *maybeWZp;
if (op.verifyInputZeroPoint(iZp).failed())
return rewriter.notifyMatchFailure(
op, "input zero point must be zero for non-int8 integer types");
if (op.verifyWeightZeroPoint(wZp).failed())
return rewriter.notifyMatchFailure(
op, "weight zero point must be zero for non-int8 integer types");
// Reshape input to [N, H, W, C] -> [N, H, W, C, 1].
ArrayRef<int64_t> inputShape = inputType.getShape();
llvm::SmallVector<int64_t, 2> revisedInputShape{
inputShape[0], inputShape[1], inputShape[2], inputShape[3], 1};
inputType = RankedTensorType::get(
revisedInputShape,
dyn_cast<RankedTensorType>(input.getType()).getElementType());
auto revisedInputShapeValue =
getTosaConstShape(rewriter, op.getLoc(), revisedInputShape);
input = tosa::ReshapeOp::create(rewriter, op.getLoc(), inputType, input,
revisedInputShapeValue)
.getResult();
Type resultETy = resultType.getElementType();
if (inputETy != resultETy) {
inputType = inputType.clone(resultETy);
input = tosa::CastOp::create(rewriter, op.getLoc(), inputType, input);
}
if (weightETy != resultETy) {
weightType = weightType.clone(resultETy);
weight = tosa::CastOp::create(rewriter, op.getLoc(), weightType, weight);
}
if (iZp != 0 || wZp != 0) {
auto applyZp = [&](Value val, int64_t zp) -> Value {
if (zp == 0)
return val;
auto ety = cast<ShapedType>(val.getType()).getElementType();
std::vector<int64_t> shape(cast<ShapedType>(val.getType()).getRank(),
1);
auto zpTy = RankedTensorType::get(shape, ety);
auto zpAttr =
DenseElementsAttr::get(zpTy, rewriter.getIntegerAttr(ety, zp));
auto zpVal = tosa::ConstOp::create(rewriter, op.getLoc(), zpTy, zpAttr);
return tosa::SubOp::create(rewriter, op.getLoc(), val.getType(), val,
zpVal);
};
input = applyZp(input, iZp);
weight = applyZp(weight, wZp);
}
ArrayRef<int64_t> padAttr = op.getPad();
llvm::SmallVector<int64_t> pad(10, 0);
for (const auto &it : llvm::enumerate(padAttr))
pad[it.index() + 2] = it.value();
if (llvm::any_of(pad, [](int64_t p) { return p != 0; })) {
Attribute zeroAttr = rewriter.getZeroAttr(inputETy);
llvm::SmallVector<int64_t> newShape(inputType.getShape());
for (int i = 0, s = pad.size(); i < s; ++i) {
if (newShape[i / 2] != ShapedType::kDynamic) {
newShape[i / 2] += pad[i];
}
}
Value padSizeVal = getTosaConstShape(rewriter, op->getLoc(), pad);
auto padTy = RankedTensorType::get({1}, inputETy);
auto padAttr = DenseElementsAttr::get(padTy, zeroAttr);
Value padVal =
tosa::ConstOp::create(rewriter, op->getLoc(), padTy, padAttr);
inputType = RankedTensorType::get(newShape, inputETy);
input = tosa::PadOp::create(rewriter, op->getLoc(), inputType, input,
padSizeVal, padVal);
}
// Perform an elementwise mul over the reshaped input and weight.
llvm::SmallVector<int64_t, 2> mulShape{
inputType.getDimSize(0), inputType.getDimSize(1),
inputType.getDimSize(2), inputType.getDimSize(3), weightShape[3]};
auto mulShapeType = RankedTensorType::get(
mulShape,
dyn_cast<RankedTensorType>(weight.getType()).getElementType());
if (EqualizeRanks(rewriter, op.getLoc(), input, weight).failed()) {
return failure();
}
auto shiftElementType = IntegerType::get(rewriter.getContext(), 8);
auto shiftType = RankedTensorType::get({1}, shiftElementType);
auto shiftZeroAttr = DenseElementsAttr::get(
shiftType, rewriter.getIntegerAttr(shiftElementType, 0));
Value constZero =
tosa::ConstOp::create(rewriter, op.getLoc(), shiftType, shiftZeroAttr);
Value mulValue = tosa::MulOp::create(rewriter, op.getLoc(), mulShapeType,
input, weight, constZero)
.getResult();
// Reshape output to [N, H, W, C * M].
auto outputShape = cast<ShapedType>(op.getOutput().getType()).getShape();
auto outputShapeType = RankedTensorType::get(
outputShape,
dyn_cast<RankedTensorType>(input.getType()).getElementType());
auto outputShapeValue =
getTosaConstShape(rewriter, op->getLoc(), outputShape);
Value outputValue = tosa::ReshapeOp::create(
rewriter, op.getLoc(), outputShapeType, mulValue, outputShapeValue);
Value bias = op.getBias();
if (EqualizeRanks(rewriter, op.getLoc(), outputValue, bias).failed()) {
return failure();
}
// Add in the bias.
rewriter
.replaceOpWithNewOp<tosa::AddOp>(op, outputShapeType, outputValue, bias)
.getResult();
return success();
}
};
} // namespace
void mlir::tosa::populateTosaDecomposeDepthwise(MLIRContext *ctx,
RewritePatternSet &patterns) {
patterns.add<DepthwiseConv2DIsMul>(ctx);
}
|
