[Linalg] Add *Conv3D* matchers #172141
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[Linalg] Add *Conv3D* matchers #172141
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@llvm/pr-subscribers-mlir-linalg Author: Abhishek Varma (Abhishek-Varma) Changes-- This commit is the sixth in the series of adding matchers Signed-off-by: Abhishek Varma <[email protected]> Full diff: https://github.com/llvm/llvm-project/pull/172141.diff 3 Files Affected:
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp b/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
index bbfbd2e9736a1..397e322a64dea 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
@@ -291,6 +291,9 @@ static FailureOr<LinalgOp> specializeLinalgConvolutions(RewriterBase &rewriter,
CONV_OP_SPECIALIZER(linalg::Conv2DNhwgcGfhwcOp);
CONV_OP_SPECIALIZER(linalg::Conv2DNhwgcGfhwcQOp);
CONV_OP_SPECIALIZER(linalg::Conv3DOp);
+ CONV_OP_SPECIALIZER(linalg::Conv3DNdhwcDhwcfOp);
+ CONV_OP_SPECIALIZER(linalg::Conv3DNdhwcDhwcfQOp);
+ CONV_OP_SPECIALIZER(linalg::Conv3DNcdhwFcdhwOp);
// -----------------------------
// Depthwise Convolution ops.
// -----------------------------
@@ -302,6 +305,8 @@ static FailureOr<LinalgOp> specializeLinalgConvolutions(RewriterBase &rewriter,
CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcQOp);
CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcmOp);
CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcmQOp);
+ CONV_OP_SPECIALIZER(linalg::DepthwiseConv3DNdhwcDhwcOp);
+ CONV_OP_SPECIALIZER(linalg::DepthwiseConv3DNcdhwCdhwOp);
CONV_OP_SPECIALIZER(linalg::DepthwiseConv3DNdhwcDhwcmOp);
// -----------------------------
// Pooling ops.
diff --git a/mlir/lib/Dialect/Linalg/Utils/Utils.cpp b/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
index 1244be90390e2..d2ea2dd32f358 100644
--- a/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
+++ b/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
@@ -569,7 +569,7 @@ class ConvMatcherBuilder {
}
/// Match body pattern. This should be called last.
- bool matchBody(bool zeroPointOffset = false) {
+ bool matchBody(bool containsZeroPointOffset = false) {
if (!matched)
return false;
Block *body = op.getBlock();
@@ -577,7 +577,7 @@ class ConvMatcherBuilder {
switch (poolingType) {
case PoolingType::None:
return bodyMatcherForConvolutionOps(yieldOp.getOperand(0), body,
- zeroPointOffset);
+ containsZeroPointOffset);
case PoolingType::MaxSigned:
return bodyMatcherForMaxSignedPoolOps(yieldOp.getOperand(0), body);
case PoolingType::MaxUnsigned:
@@ -762,7 +762,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNhwcHwcfQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, H, W, F}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
}
// #inputMap = affine_map<(N, H, W, F, h, w, c) -> (N, H + h, W + w, c)>
@@ -825,7 +825,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNhwcFhwcQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, H, W, F}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
}
// #inputMap = affine_map<(N, F, H, W, c, h, w) -> (N, c, H + h, W + w)>
@@ -888,7 +888,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNchwFchwQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, F, H, W}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
}
// #inputMap = affine_map<(N, G, F, H, W, c, h, w) -> (N, G, c, H + h, W + w)>
@@ -987,7 +987,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNgchwGfchwQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, G, F, H, W}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
}
// #inputMap = affine_map<(N, H, W, G, F, h, w, c) -> (N, H + h, W + w, G, c)>
@@ -1054,7 +1054,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNhwgcGfhwcQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, H, W, G, F}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
}
// #inputMap = affine_map<(D, H, W, d, h, w) -> (D + d, H + h, W + w)>
@@ -1088,6 +1088,114 @@ bool isaConvolutionOpOfType<linalg::Conv3DOp>(LinalgOp op,
.matchBody();
}
+// #inputMap = affine_map<(N, D, H, W, F, d, h, w, c)
+// -> (N, D + d, H + h, W + w, c)>
+// #filterMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (d, h, w, c, F)>
+// #outputMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (N, D, H, W, F)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv3DNdhwcDhwcfOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv3DNdhwcDhwcfOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr D = m.dim(1);
+ AffineExpr H = m.dim(2);
+ AffineExpr W = m.dim(3);
+ AffineExpr F = m.dim(4);
+ AffineExpr d = m.dim(5);
+ AffineExpr h = m.dim(6);
+ AffineExpr w = m.dim(7);
+ AffineExpr c = m.dim(8);
+
+ 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, c, F},
+ /*outputMap=*/{N, D, H, W, F}})
+ .matchBody();
+}
+
+// #inputMap = affine_map<(N, D, H, W, F, d, h, w, c)
+// -> (N, D + d, H + h, W + w, c)>
+// #filterMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (d, h, w, c, F)>
+// #scalarMap = affine_map<(N, D, H, W, F, d, h, w, c) -> ()>
+// #outputMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (N, D, H, W, F)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv3DNdhwcDhwcfQOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv3DNdhwcDhwcfQOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr D = m.dim(1);
+ AffineExpr H = m.dim(2);
+ AffineExpr W = m.dim(3);
+ AffineExpr F = m.dim(4);
+ AffineExpr d = m.dim(5);
+ AffineExpr h = m.dim(6);
+ AffineExpr w = m.dim(7);
+ AffineExpr c = m.dim(8);
+
+ 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, c, F},
+ /*scalarMap=*/{},
+ /*scalarMap=*/{},
+ /*outputMap=*/{N, D, H, W, F}})
+ .matchBody(/*containsZeroPointOffset=*/true);
+}
+
+// #inputMap = affine_map<(N, F, D, H, W, c, d, h, w)
+// -> (N, c, D + d, H + h, W + w)>
+// #filterMap = affine_map<(N, F, D, H, W, c, d, h, w) -> (F, c, d, h, w)>
+// #outputMap = affine_map<(N, F, D, H, W, c, d, h, w) -> (N, F, D, H, W)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv3DNcdhwFcdhwOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv3DNcdhwFcdhwOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr F = m.dim(1);
+ AffineExpr D = m.dim(2);
+ AffineExpr H = m.dim(3);
+ AffineExpr W = m.dim(4);
+ AffineExpr c = m.dim(5);
+ AffineExpr d = m.dim(6);
+ AffineExpr h = m.dim(7);
+ AffineExpr w = m.dim(8);
+
+ return m.matchStride(/*iDim=*/2, /*fDim=*/2, /*oDim=*/2, /*idx=*/0)
+ .matchStride(/*iDim=*/3, /*fDim=*/3, /*oDim=*/3, /*idx=*/1)
+ .matchStride(/*iDim=*/4, /*fDim=*/4, /*oDim=*/4, /*idx=*/2)
+ .matchMaps({/*inputMap=*/{N, c, m.strided(D, d, 0), m.strided(H, h, 1),
+ m.strided(W, w, 2)},
+ /*filterMap=*/{F, c, d, h, w},
+ /*outputMap=*/{N, F, D, H, W}})
+ .matchBody();
+}
+
// #inputMap = affine_map<(N, W, C, w) -> (N, C, W + w)>
// #filterMap = affine_map<(N, W, C, w) -> (C, w)>
// #outputMap = affine_map<(N, W, C, w) -> (N, C, W)>
@@ -1254,7 +1362,7 @@ bool isaConvolutionOpOfType<linalg::DepthwiseConv2DNhwcHwcQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, H, W, C}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
}
// #inputMap = affine_map<(N, H, W, C, CM, h, w) -> (N, H + h, W + w, C)>
@@ -1317,7 +1425,73 @@ bool isaConvolutionOpOfType<linalg::DepthwiseConv2DNhwcHwcmQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, H, W, C, CM}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
+}
+
+// #inputMap = affine_map<(N, D, H, W, d, h, w, C) -> (N, D + d, H + h, W + w, C)>
+// #filterMap = affine_map<(N, D, H, W, d, h, w, C) -> (d, h, w, C)>
+// #outputMap = affine_map<(N, D, H, W, d, h, w, C) -> (N, D, H, W, C)>
+template <>
+bool isaConvolutionOpOfType<linalg::DepthwiseConv3DNdhwcDhwcOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::DepthwiseConv3DNdhwcDhwcOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr D = m.dim(1);
+ AffineExpr H = m.dim(2);
+ AffineExpr W = m.dim(3);
+ AffineExpr d = m.dim(4);
+ AffineExpr h = m.dim(5);
+ AffineExpr w = m.dim(6);
+ AffineExpr C = 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, C},
+ /*outputMap=*/{N, D, H, W, C}})
+ .matchBody();
+}
+
+// #inputMap = affine_map<(N, D, H, W, d, h, w, C) -> (N, C, D + d, H + h, W + w)>
+// #filterMap = affine_map<(N, D, H, W, d, h, w, C) -> (C, d, h, w)>
+// #outputMap = affine_map<(N, D, H, W, d, h, w, C) -> (N, C, D, H, W)>
+template <>
+bool isaConvolutionOpOfType<linalg::DepthwiseConv3DNcdhwCdhwOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::DepthwiseConv3DNcdhwCdhwOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr D = m.dim(1);
+ AffineExpr H = m.dim(2);
+ AffineExpr W = m.dim(3);
+ AffineExpr d = m.dim(4);
+ AffineExpr h = m.dim(5);
+ AffineExpr w = m.dim(6);
+ AffineExpr C = m.dim(7);
+
+ return m.matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/0)
+ .matchStride(/*iDim=*/3, /*fDim=*/2, /*oDim=*/3, /*idx=*/1)
+ .matchStride(/*iDim=*/4, /*fDim=*/3, /*oDim=*/4, /*idx=*/2)
+ .matchMaps({/*inputMap=*/{N, C, m.strided(D, d, 0), m.strided(H, h, 1),
+ m.strided(W, w, 2)},
+ /*filterMap=*/{C, d, h, w},
+ /*outputMap=*/{N, C, D, H, W}})
+ .matchBody();
}
// #inputMap = affine_map<(N, D, H, W, CM, d, h, w, C)
diff --git a/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir b/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir
index 432fdd12f540d..ac9a33b0528b0 100644
--- a/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir
+++ b/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir
@@ -218,6 +218,45 @@ func.func @conv_3d(%in : tensor<?x?x?xf32>, %filter : tensor<?x?x?xf32>, %out :
// -----
+func.func @conv_3d_ndhwc_dhwcf(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+ %0 = linalg.conv_3d_ndhwc_dhwcf
+ {dilations = dense<2> : tensor<3xi64>, strides = dense<3> : tensor<3xi64>}
+ ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
+ outs (%output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?x?xf32>
+}
+// CHECK: @conv_3d_ndhwc_dhwcf
+// CHECK: linalg.conv_3d_ndhwc_dhwcf
+// CHECK-SAME: dilations = dense<2> : tensor<3xi64>, strides = dense<3> : tensor<3xi64>
+
+// -----
+
+func.func @conv_3d_ndhwc_dhwcf_q(%input: tensor<?x?x?x?x?xi8>, %filter: tensor<?x?x?x?x?xi8>, %output: tensor<?x?x?x?x?xi32>, %zp_input: i32, %zp_filter: i32) -> tensor<?x?x?x?x?xi32> {
+ %0 = linalg.conv_3d_ndhwc_dhwcf_q
+ {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
+ ins (%input, %filter, %zp_input, %zp_filter : tensor<?x?x?x?x?xi8>, tensor<?x?x?x?x?xi8>, i32, i32)
+ outs (%output: tensor<?x?x?x?x?xi32>) -> tensor<?x?x?x?x?xi32>
+ return %0 : tensor<?x?x?x?x?xi32>
+}
+// CHECK: @conv_3d_ndhwc_dhwcf_q
+// CHECK: linalg.conv_3d_ndhwc_dhwcf_q
+// CHECK-SAME: dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>
+
+// -----
+
+func.func @conv_3d_ncdhw_fcdhw(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+ %0 = linalg.conv_3d_ncdhw_fcdhw
+ {dilations = dense<[1, 2, 3]> : tensor<3xi64>, strides = dense<[4, 5, 6]> : tensor<3xi64>}
+ ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
+ outs (%output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?x?xf32>
+}
+// CHECK: @conv_3d_ncdhw_fcdhw
+// CHECK: linalg.conv_3d_ncdhw_fcdhw
+// CHECK-SAME: dilations = dense<[1, 2, 3]> : tensor<3xi64>, strides = dense<[4, 5, 6]> : tensor<3xi64>
+
+// -----
+
// -------------------------------
// Depthwise Convolution ops - 1D.
// -------------------------------
@@ -334,6 +373,32 @@ func.func @depthwise_conv_2d_nhwc_hwcm_q(%input: tensor<?x?x?x?xi8>, %filter: te
// Depthwise Convolution ops - 3D.
// -------------------------------
+func.func @depthwise_conv_3d_ndhwc_dhwc(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+ %0 = linalg.depthwise_conv_3d_ndhwc_dhwc
+ {dilations = dense<2> : tensor<3xi64>, strides = dense<3> : tensor<3xi64>}
+ ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?xf32>)
+ outs (%output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?x?xf32>
+}
+// CHECK: @depthwise_conv_3d_ndhwc_dhwc
+// CHECK: linalg.depthwise_conv_3d_ndhwc_dhwc
+// CHECK-SAME: dilations = dense<2> : tensor<3xi64>, strides = dense<3> : tensor<3xi64>
+
+// -----
+
+func.func @depthwise_conv_3d_ncdhw_cdhw(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+ %0 = linalg.depthwise_conv_3d_ncdhw_cdhw
+ {dilations = dense<[1, 2, 3]> : tensor<3xi64>, strides = dense<[4, 5, 6]> : tensor<3xi64>}
+ ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?xf32>)
+ outs (%output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?x?xf32>
+}
+// CHECK: @depthwise_conv_3d_ncdhw_cdhw
+// CHECK: linalg.depthwise_conv_3d_ncdhw_cdhw
+// CHECK-SAME: dilations = dense<[1, 2, 3]> : tensor<3xi64>, strides = dense<[4, 5, 6]> : tensor<3xi64>
+
+// -----
+
func.func @depthwise_conv_3d_ndhwc_dhwcm(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %output: tensor<?x?x?x?x?x?xf32>) -> tensor<?x?x?x?x?x?xf32> {
%0 = linalg.depthwise_conv_3d_ndhwc_dhwcm
{dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
|
Member
|
@llvm/pr-subscribers-mlir Author: Abhishek Varma (Abhishek-Varma) Changes-- This commit is the sixth in the series of adding matchers Signed-off-by: Abhishek Varma <[email protected]> Full diff: https://github.com/llvm/llvm-project/pull/172141.diff 3 Files Affected:
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp b/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
index bbfbd2e9736a1..397e322a64dea 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
@@ -291,6 +291,9 @@ static FailureOr<LinalgOp> specializeLinalgConvolutions(RewriterBase &rewriter,
CONV_OP_SPECIALIZER(linalg::Conv2DNhwgcGfhwcOp);
CONV_OP_SPECIALIZER(linalg::Conv2DNhwgcGfhwcQOp);
CONV_OP_SPECIALIZER(linalg::Conv3DOp);
+ CONV_OP_SPECIALIZER(linalg::Conv3DNdhwcDhwcfOp);
+ CONV_OP_SPECIALIZER(linalg::Conv3DNdhwcDhwcfQOp);
+ CONV_OP_SPECIALIZER(linalg::Conv3DNcdhwFcdhwOp);
// -----------------------------
// Depthwise Convolution ops.
// -----------------------------
@@ -302,6 +305,8 @@ static FailureOr<LinalgOp> specializeLinalgConvolutions(RewriterBase &rewriter,
CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcQOp);
CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcmOp);
CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcmQOp);
+ CONV_OP_SPECIALIZER(linalg::DepthwiseConv3DNdhwcDhwcOp);
+ CONV_OP_SPECIALIZER(linalg::DepthwiseConv3DNcdhwCdhwOp);
CONV_OP_SPECIALIZER(linalg::DepthwiseConv3DNdhwcDhwcmOp);
// -----------------------------
// Pooling ops.
diff --git a/mlir/lib/Dialect/Linalg/Utils/Utils.cpp b/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
index 1244be90390e2..d2ea2dd32f358 100644
--- a/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
+++ b/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
@@ -569,7 +569,7 @@ class ConvMatcherBuilder {
}
/// Match body pattern. This should be called last.
- bool matchBody(bool zeroPointOffset = false) {
+ bool matchBody(bool containsZeroPointOffset = false) {
if (!matched)
return false;
Block *body = op.getBlock();
@@ -577,7 +577,7 @@ class ConvMatcherBuilder {
switch (poolingType) {
case PoolingType::None:
return bodyMatcherForConvolutionOps(yieldOp.getOperand(0), body,
- zeroPointOffset);
+ containsZeroPointOffset);
case PoolingType::MaxSigned:
return bodyMatcherForMaxSignedPoolOps(yieldOp.getOperand(0), body);
case PoolingType::MaxUnsigned:
@@ -762,7 +762,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNhwcHwcfQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, H, W, F}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
}
// #inputMap = affine_map<(N, H, W, F, h, w, c) -> (N, H + h, W + w, c)>
@@ -825,7 +825,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNhwcFhwcQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, H, W, F}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
}
// #inputMap = affine_map<(N, F, H, W, c, h, w) -> (N, c, H + h, W + w)>
@@ -888,7 +888,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNchwFchwQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, F, H, W}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
}
// #inputMap = affine_map<(N, G, F, H, W, c, h, w) -> (N, G, c, H + h, W + w)>
@@ -987,7 +987,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNgchwGfchwQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, G, F, H, W}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
}
// #inputMap = affine_map<(N, H, W, G, F, h, w, c) -> (N, H + h, W + w, G, c)>
@@ -1054,7 +1054,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNhwgcGfhwcQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, H, W, G, F}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
}
// #inputMap = affine_map<(D, H, W, d, h, w) -> (D + d, H + h, W + w)>
@@ -1088,6 +1088,114 @@ bool isaConvolutionOpOfType<linalg::Conv3DOp>(LinalgOp op,
.matchBody();
}
+// #inputMap = affine_map<(N, D, H, W, F, d, h, w, c)
+// -> (N, D + d, H + h, W + w, c)>
+// #filterMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (d, h, w, c, F)>
+// #outputMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (N, D, H, W, F)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv3DNdhwcDhwcfOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv3DNdhwcDhwcfOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr D = m.dim(1);
+ AffineExpr H = m.dim(2);
+ AffineExpr W = m.dim(3);
+ AffineExpr F = m.dim(4);
+ AffineExpr d = m.dim(5);
+ AffineExpr h = m.dim(6);
+ AffineExpr w = m.dim(7);
+ AffineExpr c = m.dim(8);
+
+ 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, c, F},
+ /*outputMap=*/{N, D, H, W, F}})
+ .matchBody();
+}
+
+// #inputMap = affine_map<(N, D, H, W, F, d, h, w, c)
+// -> (N, D + d, H + h, W + w, c)>
+// #filterMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (d, h, w, c, F)>
+// #scalarMap = affine_map<(N, D, H, W, F, d, h, w, c) -> ()>
+// #outputMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (N, D, H, W, F)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv3DNdhwcDhwcfQOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv3DNdhwcDhwcfQOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr D = m.dim(1);
+ AffineExpr H = m.dim(2);
+ AffineExpr W = m.dim(3);
+ AffineExpr F = m.dim(4);
+ AffineExpr d = m.dim(5);
+ AffineExpr h = m.dim(6);
+ AffineExpr w = m.dim(7);
+ AffineExpr c = m.dim(8);
+
+ 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, c, F},
+ /*scalarMap=*/{},
+ /*scalarMap=*/{},
+ /*outputMap=*/{N, D, H, W, F}})
+ .matchBody(/*containsZeroPointOffset=*/true);
+}
+
+// #inputMap = affine_map<(N, F, D, H, W, c, d, h, w)
+// -> (N, c, D + d, H + h, W + w)>
+// #filterMap = affine_map<(N, F, D, H, W, c, d, h, w) -> (F, c, d, h, w)>
+// #outputMap = affine_map<(N, F, D, H, W, c, d, h, w) -> (N, F, D, H, W)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv3DNcdhwFcdhwOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv3DNcdhwFcdhwOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr F = m.dim(1);
+ AffineExpr D = m.dim(2);
+ AffineExpr H = m.dim(3);
+ AffineExpr W = m.dim(4);
+ AffineExpr c = m.dim(5);
+ AffineExpr d = m.dim(6);
+ AffineExpr h = m.dim(7);
+ AffineExpr w = m.dim(8);
+
+ return m.matchStride(/*iDim=*/2, /*fDim=*/2, /*oDim=*/2, /*idx=*/0)
+ .matchStride(/*iDim=*/3, /*fDim=*/3, /*oDim=*/3, /*idx=*/1)
+ .matchStride(/*iDim=*/4, /*fDim=*/4, /*oDim=*/4, /*idx=*/2)
+ .matchMaps({/*inputMap=*/{N, c, m.strided(D, d, 0), m.strided(H, h, 1),
+ m.strided(W, w, 2)},
+ /*filterMap=*/{F, c, d, h, w},
+ /*outputMap=*/{N, F, D, H, W}})
+ .matchBody();
+}
+
// #inputMap = affine_map<(N, W, C, w) -> (N, C, W + w)>
// #filterMap = affine_map<(N, W, C, w) -> (C, w)>
// #outputMap = affine_map<(N, W, C, w) -> (N, C, W)>
@@ -1254,7 +1362,7 @@ bool isaConvolutionOpOfType<linalg::DepthwiseConv2DNhwcHwcQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, H, W, C}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
}
// #inputMap = affine_map<(N, H, W, C, CM, h, w) -> (N, H + h, W + w, C)>
@@ -1317,7 +1425,73 @@ bool isaConvolutionOpOfType<linalg::DepthwiseConv2DNhwcHwcmQOp>(
/*scalarMap=*/{},
/*scalarMap=*/{},
/*outputMap=*/{N, H, W, C, CM}})
- .matchBody(/*zeroPointOffset=*/true);
+ .matchBody(/*containsZeroPointOffset=*/true);
+}
+
+// #inputMap = affine_map<(N, D, H, W, d, h, w, C) -> (N, D + d, H + h, W + w, C)>
+// #filterMap = affine_map<(N, D, H, W, d, h, w, C) -> (d, h, w, C)>
+// #outputMap = affine_map<(N, D, H, W, d, h, w, C) -> (N, D, H, W, C)>
+template <>
+bool isaConvolutionOpOfType<linalg::DepthwiseConv3DNdhwcDhwcOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::DepthwiseConv3DNdhwcDhwcOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr D = m.dim(1);
+ AffineExpr H = m.dim(2);
+ AffineExpr W = m.dim(3);
+ AffineExpr d = m.dim(4);
+ AffineExpr h = m.dim(5);
+ AffineExpr w = m.dim(6);
+ AffineExpr C = 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, C},
+ /*outputMap=*/{N, D, H, W, C}})
+ .matchBody();
+}
+
+// #inputMap = affine_map<(N, D, H, W, d, h, w, C) -> (N, C, D + d, H + h, W + w)>
+// #filterMap = affine_map<(N, D, H, W, d, h, w, C) -> (C, d, h, w)>
+// #outputMap = affine_map<(N, D, H, W, d, h, w, C) -> (N, C, D, H, W)>
+template <>
+bool isaConvolutionOpOfType<linalg::DepthwiseConv3DNcdhwCdhwOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::DepthwiseConv3DNcdhwCdhwOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr D = m.dim(1);
+ AffineExpr H = m.dim(2);
+ AffineExpr W = m.dim(3);
+ AffineExpr d = m.dim(4);
+ AffineExpr h = m.dim(5);
+ AffineExpr w = m.dim(6);
+ AffineExpr C = m.dim(7);
+
+ return m.matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/0)
+ .matchStride(/*iDim=*/3, /*fDim=*/2, /*oDim=*/3, /*idx=*/1)
+ .matchStride(/*iDim=*/4, /*fDim=*/3, /*oDim=*/4, /*idx=*/2)
+ .matchMaps({/*inputMap=*/{N, C, m.strided(D, d, 0), m.strided(H, h, 1),
+ m.strided(W, w, 2)},
+ /*filterMap=*/{C, d, h, w},
+ /*outputMap=*/{N, C, D, H, W}})
+ .matchBody();
}
// #inputMap = affine_map<(N, D, H, W, CM, d, h, w, C)
diff --git a/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir b/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir
index 432fdd12f540d..ac9a33b0528b0 100644
--- a/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir
+++ b/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir
@@ -218,6 +218,45 @@ func.func @conv_3d(%in : tensor<?x?x?xf32>, %filter : tensor<?x?x?xf32>, %out :
// -----
+func.func @conv_3d_ndhwc_dhwcf(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+ %0 = linalg.conv_3d_ndhwc_dhwcf
+ {dilations = dense<2> : tensor<3xi64>, strides = dense<3> : tensor<3xi64>}
+ ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
+ outs (%output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?x?xf32>
+}
+// CHECK: @conv_3d_ndhwc_dhwcf
+// CHECK: linalg.conv_3d_ndhwc_dhwcf
+// CHECK-SAME: dilations = dense<2> : tensor<3xi64>, strides = dense<3> : tensor<3xi64>
+
+// -----
+
+func.func @conv_3d_ndhwc_dhwcf_q(%input: tensor<?x?x?x?x?xi8>, %filter: tensor<?x?x?x?x?xi8>, %output: tensor<?x?x?x?x?xi32>, %zp_input: i32, %zp_filter: i32) -> tensor<?x?x?x?x?xi32> {
+ %0 = linalg.conv_3d_ndhwc_dhwcf_q
+ {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
+ ins (%input, %filter, %zp_input, %zp_filter : tensor<?x?x?x?x?xi8>, tensor<?x?x?x?x?xi8>, i32, i32)
+ outs (%output: tensor<?x?x?x?x?xi32>) -> tensor<?x?x?x?x?xi32>
+ return %0 : tensor<?x?x?x?x?xi32>
+}
+// CHECK: @conv_3d_ndhwc_dhwcf_q
+// CHECK: linalg.conv_3d_ndhwc_dhwcf_q
+// CHECK-SAME: dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>
+
+// -----
+
+func.func @conv_3d_ncdhw_fcdhw(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+ %0 = linalg.conv_3d_ncdhw_fcdhw
+ {dilations = dense<[1, 2, 3]> : tensor<3xi64>, strides = dense<[4, 5, 6]> : tensor<3xi64>}
+ ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
+ outs (%output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?x?xf32>
+}
+// CHECK: @conv_3d_ncdhw_fcdhw
+// CHECK: linalg.conv_3d_ncdhw_fcdhw
+// CHECK-SAME: dilations = dense<[1, 2, 3]> : tensor<3xi64>, strides = dense<[4, 5, 6]> : tensor<3xi64>
+
+// -----
+
// -------------------------------
// Depthwise Convolution ops - 1D.
// -------------------------------
@@ -334,6 +373,32 @@ func.func @depthwise_conv_2d_nhwc_hwcm_q(%input: tensor<?x?x?x?xi8>, %filter: te
// Depthwise Convolution ops - 3D.
// -------------------------------
+func.func @depthwise_conv_3d_ndhwc_dhwc(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+ %0 = linalg.depthwise_conv_3d_ndhwc_dhwc
+ {dilations = dense<2> : tensor<3xi64>, strides = dense<3> : tensor<3xi64>}
+ ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?xf32>)
+ outs (%output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?x?xf32>
+}
+// CHECK: @depthwise_conv_3d_ndhwc_dhwc
+// CHECK: linalg.depthwise_conv_3d_ndhwc_dhwc
+// CHECK-SAME: dilations = dense<2> : tensor<3xi64>, strides = dense<3> : tensor<3xi64>
+
+// -----
+
+func.func @depthwise_conv_3d_ncdhw_cdhw(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+ %0 = linalg.depthwise_conv_3d_ncdhw_cdhw
+ {dilations = dense<[1, 2, 3]> : tensor<3xi64>, strides = dense<[4, 5, 6]> : tensor<3xi64>}
+ ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?xf32>)
+ outs (%output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?x?xf32>
+}
+// CHECK: @depthwise_conv_3d_ncdhw_cdhw
+// CHECK: linalg.depthwise_conv_3d_ncdhw_cdhw
+// CHECK-SAME: dilations = dense<[1, 2, 3]> : tensor<3xi64>, strides = dense<[4, 5, 6]> : tensor<3xi64>
+
+// -----
+
func.func @depthwise_conv_3d_ndhwc_dhwcm(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %output: tensor<?x?x?x?x?x?xf32>) -> tensor<?x?x?x?x?x?xf32> {
%0 = linalg.depthwise_conv_3d_ndhwc_dhwcm
{dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
|
|
✅ With the latest revision this PR passed the C/C++ code formatter. |
-- This commit is the sixth in the series of adding matchers for linalg.*conv*/*pool*. Refer: llvm#163724 -- In this commit all variants of Conv3D convolution ops have been added. Signed-off-by: Abhishek Varma <[email protected]>
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hanhanW
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Dec 15, 2025
| // #inputMap = affine_map<(N, D, H, W, F, d, h, w, c) | ||
| // -> (N, D + d, H + h, W + w, c)> | ||
| // #filterMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (d, h, w, c, F)> | ||
| // #outputMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (N, D, H, W, F)> |
Contributor
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I did not point this out in previous reviews. It is optional. I think we can drop the comments because the code self-documents this. We can keep them for consistency.
.matchMaps({/*inputMap=*/{N, m.strided(D, d, 0), m.strided(H, h, 1),
m.strided(W, w, 2), c},
/*filterMap=*/{d, h, w, c, F},
/*outputMap=*/{N, D, H, W, F}})
Contributor
Author
There was a problem hiding this comment.
Sure. Makes sense. I'll drop this in a follow-up PR from other matchers as well.
Contributor
Author
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Raised follow-up PR addressing this : #172344
Contributor
Author
|
Merging this in. Will raise a complete NFC PR removing the doc comments. |
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that referenced
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Dec 15, 2025
-- This commit removes the redundant doc comments pertaining
to indexing maps of convolution ops from their respective matchers.
-- This is being done because the code self-documents the same and is
a follow-up based on
#172141 (comment)
Signed-off-by: Abhishek Varma <[email protected]>
llvm-sync bot
pushed a commit
to arm/arm-toolchain
that referenced
this pull request
Dec 15, 2025
…hers (#172344)
-- This commit removes the redundant doc comments pertaining
to indexing maps of convolution ops from their respective matchers.
-- This is being done because the code self-documents the same and is
a follow-up based on
llvm/llvm-project#172141 (comment)
Signed-off-by: Abhishek Varma <[email protected]>
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-- This commit is the sixth in the series of adding matchers
for linalg.conv/pool. Refer: #163724
-- In this commit all variants of Conv3D convolution ops have been
added.
Signed-off-by: Abhishek Varma [email protected]