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[Linalg] Add *Pooling* matchers #172351

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[Linalg] Add *Pooling* matchers #172351

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12 changes: 12 additions & 0 deletions mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -316,6 +316,18 @@ static FailureOr<LinalgOp> specializeLinalgConvolutions(RewriterBase &rewriter,
CONV_OP_SPECIALIZER(linalg::PoolingNhwcSumOp);
CONV_OP_SPECIALIZER(linalg::PoolingNhwcMaxUnsignedOp);
CONV_OP_SPECIALIZER(linalg::PoolingNhwcMinUnsignedOp);
CONV_OP_SPECIALIZER(linalg::PoolingNchwSumOp);
CONV_OP_SPECIALIZER(linalg::PoolingNchwMaxOp);
CONV_OP_SPECIALIZER(linalg::PoolingNwcSumOp);
CONV_OP_SPECIALIZER(linalg::PoolingNcwSumOp);
CONV_OP_SPECIALIZER(linalg::PoolingNwcMaxOp);
CONV_OP_SPECIALIZER(linalg::PoolingNwcMaxUnsignedOp);
CONV_OP_SPECIALIZER(linalg::PoolingNcwMaxOp);
CONV_OP_SPECIALIZER(linalg::PoolingNwcMinOp);
CONV_OP_SPECIALIZER(linalg::PoolingNwcMinUnsignedOp);
CONV_OP_SPECIALIZER(linalg::PoolingNdhwcSumOp);
CONV_OP_SPECIALIZER(linalg::PoolingNdhwcMaxOp);
CONV_OP_SPECIALIZER(linalg::PoolingNdhwcMinOp);
#undef CONV_OP_SPECIALIZER
return failure();
}
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315 changes: 315 additions & 0 deletions mlir/lib/Dialect/Linalg/Utils/Utils.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1684,6 +1684,321 @@ bool isaConvolutionOpOfType<linalg::PoolingNhwcMinUnsignedOp>(
.matchBody();
}

template <>
bool isaConvolutionOpOfType<linalg::PoolingNchwSumOp>(
LinalgOp op, SmallVector<int64_t> *dilations,
SmallVector<int64_t> *strides) {
if (isa<linalg::PoolingNchwSumOp>(op))
return true;

assert(isaConvolutionOpInterface(op) &&
"expected op to implement ConvolutionOpInterface");

ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides,
PoolingType::Sum);
AffineExpr N = m.dim(0);
AffineExpr C = m.dim(1);
AffineExpr H = m.dim(2);
AffineExpr W = m.dim(3);
AffineExpr h = m.dim(4);
AffineExpr w = m.dim(5);

return m.matchStride(/*iDim=*/2, /*fDim=*/0, /*oDim=*/2, /*idx=*/0)
.matchStride(/*iDim=*/3, /*fDim=*/1, /*oDim=*/3, /*idx=*/1)
.matchMaps({/*inputMap=*/{N, C, m.strided(H, h, 0), m.strided(W, w, 1)},
/*filterMap=*/{h, w},
/*outputMap=*/{N, C, H, W}})
.matchBody();
}

template <>
bool isaConvolutionOpOfType<linalg::PoolingNchwMaxOp>(
LinalgOp op, SmallVector<int64_t> *dilations,
SmallVector<int64_t> *strides) {
if (isa<linalg::PoolingNchwMaxOp>(op))
return true;

assert(isaConvolutionOpInterface(op) &&
"expected op to implement ConvolutionOpInterface");

ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides,
PoolingType::MaxSigned);
AffineExpr N = m.dim(0);
AffineExpr C = m.dim(1);
AffineExpr H = m.dim(2);
AffineExpr W = m.dim(3);
AffineExpr h = m.dim(4);
AffineExpr w = m.dim(5);

return m.matchStride(/*iDim=*/2, /*fDim=*/0, /*oDim=*/2, /*idx=*/0)
.matchStride(/*iDim=*/3, /*fDim=*/1, /*oDim=*/3, /*idx=*/1)
.matchMaps({/*inputMap=*/{N, C, m.strided(H, h, 0), m.strided(W, w, 1)},
/*filterMap=*/{h, w},
/*outputMap=*/{N, C, H, W}})
.matchBody();
}

template <>
bool isaConvolutionOpOfType<linalg::PoolingNwcSumOp>(
LinalgOp op, SmallVector<int64_t> *dilations,
SmallVector<int64_t> *strides) {
if (isa<linalg::PoolingNwcSumOp>(op))
return true;

assert(isaConvolutionOpInterface(op) &&
"expected op to implement ConvolutionOpInterface");

ConvMatcherBuilder m(op, /*spatialRank=*/1, dilations, strides,
PoolingType::Sum);
AffineExpr N = m.dim(0);
AffineExpr W = m.dim(1);
AffineExpr C = m.dim(2);
AffineExpr w = m.dim(3);

return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
.matchMaps({/*inputMap=*/{N, m.strided(W, w, 0), C},
/*filterMap=*/{w},
/*outputMap=*/{N, W, C}})
.matchBody();
}

template <>
bool isaConvolutionOpOfType<linalg::PoolingNcwSumOp>(
LinalgOp op, SmallVector<int64_t> *dilations,
SmallVector<int64_t> *strides) {
if (isa<linalg::PoolingNcwSumOp>(op))
return true;

assert(isaConvolutionOpInterface(op) &&
"expected op to implement ConvolutionOpInterface");

ConvMatcherBuilder m(op, /*spatialRank=*/1, dilations, strides,
PoolingType::Sum);
AffineExpr N = m.dim(0);
AffineExpr C = m.dim(1);
AffineExpr W = m.dim(2);
AffineExpr w = m.dim(3);

return m.matchStride(/*iDim=*/2, /*fDim=*/0, /*oDim=*/2, /*idx=*/0)
.matchMaps({/*inputMap=*/{N, C, m.strided(W, w, 0)},
/*filterMap=*/{w},
/*outputMap=*/{N, C, W}})
.matchBody();
}

template <>
bool isaConvolutionOpOfType<linalg::PoolingNwcMaxOp>(
LinalgOp op, SmallVector<int64_t> *dilations,
SmallVector<int64_t> *strides) {
if (isa<linalg::PoolingNwcMaxOp>(op))
return true;

assert(isaConvolutionOpInterface(op) &&
"expected op to implement ConvolutionOpInterface");

ConvMatcherBuilder m(op, /*spatialRank=*/1, dilations, strides,
PoolingType::MaxSigned);
AffineExpr N = m.dim(0);
AffineExpr W = m.dim(1);
AffineExpr C = m.dim(2);
AffineExpr w = m.dim(3);

return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
.matchMaps({/*inputMap=*/{N, m.strided(W, w, 0), C},
/*filterMap=*/{w},
/*outputMap=*/{N, W, C}})
.matchBody();
}

template <>
bool isaConvolutionOpOfType<linalg::PoolingNwcMaxUnsignedOp>(
LinalgOp op, SmallVector<int64_t> *dilations,
SmallVector<int64_t> *strides) {
if (isa<linalg::PoolingNwcMaxUnsignedOp>(op))
return true;

assert(isaConvolutionOpInterface(op) &&
"expected op to implement ConvolutionOpInterface");

ConvMatcherBuilder m(op, /*spatialRank=*/1, dilations, strides,
PoolingType::MaxUnsigned);
AffineExpr N = m.dim(0);
AffineExpr W = m.dim(1);
AffineExpr C = m.dim(2);
AffineExpr w = m.dim(3);

return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
.matchMaps({/*inputMap=*/{N, m.strided(W, w, 0), C},
/*filterMap=*/{w},
/*outputMap=*/{N, W, C}})
.matchBody();
}

template <>
bool isaConvolutionOpOfType<linalg::PoolingNcwMaxOp>(
LinalgOp op, SmallVector<int64_t> *dilations,
SmallVector<int64_t> *strides) {
if (isa<linalg::PoolingNcwMaxOp>(op))
return true;

assert(isaConvolutionOpInterface(op) &&
"expected op to implement ConvolutionOpInterface");

ConvMatcherBuilder m(op, /*spatialRank=*/1, dilations, strides,
PoolingType::MaxSigned);
AffineExpr N = m.dim(0);
AffineExpr C = m.dim(1);
AffineExpr W = m.dim(2);
AffineExpr w = m.dim(3);

return m.matchStride(/*iDim=*/2, /*fDim=*/0, /*oDim=*/2, /*idx=*/0)
.matchMaps({/*inputMap=*/{N, C, m.strided(W, w, 0)},
/*filterMap=*/{w},
/*outputMap=*/{N, C, W}})
.matchBody();
}

template <>
bool isaConvolutionOpOfType<linalg::PoolingNwcMinOp>(
LinalgOp op, SmallVector<int64_t> *dilations,
SmallVector<int64_t> *strides) {
if (isa<linalg::PoolingNwcMinOp>(op))
return true;

assert(isaConvolutionOpInterface(op) &&
"expected op to implement ConvolutionOpInterface");

ConvMatcherBuilder m(op, /*spatialRank=*/1, dilations, strides,
PoolingType::MinSigned);
AffineExpr N = m.dim(0);
AffineExpr W = m.dim(1);
AffineExpr C = m.dim(2);
AffineExpr w = m.dim(3);

return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
.matchMaps({/*inputMap=*/{N, m.strided(W, w, 0), C},
/*filterMap=*/{w},
/*outputMap=*/{N, W, C}})
.matchBody();
}

template <>
bool isaConvolutionOpOfType<linalg::PoolingNwcMinUnsignedOp>(
LinalgOp op, SmallVector<int64_t> *dilations,
SmallVector<int64_t> *strides) {
if (isa<linalg::PoolingNwcMinUnsignedOp>(op))
return true;

assert(isaConvolutionOpInterface(op) &&
"expected op to implement ConvolutionOpInterface");

ConvMatcherBuilder m(op, /*spatialRank=*/1, dilations, strides,
PoolingType::MinUnsigned);
AffineExpr N = m.dim(0);
AffineExpr W = m.dim(1);
AffineExpr C = m.dim(2);
AffineExpr w = m.dim(3);

return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
.matchMaps({/*inputMap=*/{N, m.strided(W, w, 0), C},
/*filterMap=*/{w},
/*outputMap=*/{N, W, C}})
.matchBody();
}

template <>
bool isaConvolutionOpOfType<linalg::PoolingNdhwcSumOp>(
LinalgOp op, SmallVector<int64_t> *dilations,
SmallVector<int64_t> *strides) {
if (isa<linalg::PoolingNdhwcSumOp>(op))
return true;

assert(isaConvolutionOpInterface(op) &&
"expected op to implement ConvolutionOpInterface");

ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides,
PoolingType::Sum);
AffineExpr N = m.dim(0);
AffineExpr D = m.dim(1);
AffineExpr H = m.dim(2);
AffineExpr W = m.dim(3);
AffineExpr C = m.dim(4);
AffineExpr d = m.dim(5);
AffineExpr h = m.dim(6);
AffineExpr w = m.dim(7);

return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
.matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/1)
.matchStride(/*iDim=*/3, /*fDim=*/2, /*oDim=*/3, /*idx=*/2)
.matchMaps({/*inputMap=*/{N, m.strided(D, d, 0), m.strided(H, h, 1),
m.strided(W, w, 2), C},
/*filterMap=*/{d, h, w},
/*outputMap=*/{N, D, H, W, C}})
.matchBody();
}

template <>
bool isaConvolutionOpOfType<linalg::PoolingNdhwcMaxOp>(
LinalgOp op, SmallVector<int64_t> *dilations,
SmallVector<int64_t> *strides) {
if (isa<linalg::PoolingNdhwcMaxOp>(op))
return true;

assert(isaConvolutionOpInterface(op) &&
"expected op to implement ConvolutionOpInterface");

ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides,
PoolingType::MaxSigned);
AffineExpr N = m.dim(0);
AffineExpr D = m.dim(1);
AffineExpr H = m.dim(2);
AffineExpr W = m.dim(3);
AffineExpr C = m.dim(4);
AffineExpr d = m.dim(5);
AffineExpr h = m.dim(6);
AffineExpr w = m.dim(7);

return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
.matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/1)
.matchStride(/*iDim=*/3, /*fDim=*/2, /*oDim=*/3, /*idx=*/2)
.matchMaps({/*inputMap=*/{N, m.strided(D, d, 0), m.strided(H, h, 1),
m.strided(W, w, 2), C},
/*filterMap=*/{d, h, w},
/*outputMap=*/{N, D, H, W, C}})
.matchBody();
}

template <>
bool isaConvolutionOpOfType<linalg::PoolingNdhwcMinOp>(
LinalgOp op, SmallVector<int64_t> *dilations,
SmallVector<int64_t> *strides) {
if (isa<linalg::PoolingNdhwcMinOp>(op))
return true;

assert(isaConvolutionOpInterface(op) &&
"expected op to implement ConvolutionOpInterface");

ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides,
PoolingType::MinSigned);
AffineExpr N = m.dim(0);
AffineExpr D = m.dim(1);
AffineExpr H = m.dim(2);
AffineExpr W = m.dim(3);
AffineExpr C = m.dim(4);
AffineExpr d = m.dim(5);
AffineExpr h = m.dim(6);
AffineExpr w = m.dim(7);

return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
.matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/1)
.matchStride(/*iDim=*/3, /*fDim=*/2, /*oDim=*/3, /*idx=*/2)
.matchMaps({/*inputMap=*/{N, m.strided(D, d, 0), m.strided(H, h, 1),
m.strided(W, w, 2), C},
/*filterMap=*/{d, h, w},
/*outputMap=*/{N, D, H, W, C}})
.matchBody();
}

Value makeComposedPadHighOp(OpBuilder &b, Location loc, RankedTensorType type,
Value source, Value pad, bool nofold,
ValueRange typeDynDims) {
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