[Mlir-commits] [mlir] [Linalg] Add *Conv2D* matchers (PR #168362)
Abhishek Varma
llvmlistbot at llvm.org
Sat Nov 29 08:46:33 PST 2025
https://github.com/Abhishek-Varma updated https://github.com/llvm/llvm-project/pull/168362
>From 968cb69151838a6cd97ec82dcecc61a11526cf57 Mon Sep 17 00:00:00 2001
From: Abhishek Varma <abhvarma at amd.com>
Date: Fri, 28 Nov 2025 01:48:46 -0600
Subject: [PATCH] [Linalg] Add *Conv2D* matchers
-- This commit is the fourth in the series of adding matchers
for linalg.conv/pool. Refer: #163724
-- In this commit all variants of Conv2D convolution ops have been
added.
Signed-off-by: Abhishek Varma <abhvarma at amd.com>
---
.../Dialect/Linalg/Transforms/Specialize.cpp | 15 +
mlir/lib/Dialect/Linalg/Utils/Utils.cpp | 549 +++++++++++++++++-
.../convolution/roundtrip-convolution.mlir | 195 +++++++
3 files changed, 748 insertions(+), 11 deletions(-)
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp b/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
index c2485a08932dd..bbfbd2e9736a1 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
@@ -279,6 +279,17 @@ static FailureOr<LinalgOp> specializeLinalgConvolutions(RewriterBase &rewriter,
CONV_OP_SPECIALIZER(linalg::Conv1DNwcWcfOp);
CONV_OP_SPECIALIZER(linalg::Conv1DNcwFcwOp);
CONV_OP_SPECIALIZER(linalg::Conv2DOp);
+ CONV_OP_SPECIALIZER(linalg::Conv2DNhwcHwcfOp);
+ CONV_OP_SPECIALIZER(linalg::Conv2DNhwcHwcfQOp);
+ CONV_OP_SPECIALIZER(linalg::Conv2DNhwcFhwcOp);
+ CONV_OP_SPECIALIZER(linalg::Conv2DNhwcFhwcQOp);
+ CONV_OP_SPECIALIZER(linalg::Conv2DNchwFchwOp);
+ CONV_OP_SPECIALIZER(linalg::Conv2DNchwFchwQOp);
+ CONV_OP_SPECIALIZER(linalg::Conv2DNgchwFgchwOp);
+ CONV_OP_SPECIALIZER(linalg::Conv2DNgchwGfchwOp);
+ CONV_OP_SPECIALIZER(linalg::Conv2DNgchwGfchwQOp);
+ CONV_OP_SPECIALIZER(linalg::Conv2DNhwgcGfhwcOp);
+ CONV_OP_SPECIALIZER(linalg::Conv2DNhwgcGfhwcQOp);
CONV_OP_SPECIALIZER(linalg::Conv3DOp);
// -----------------------------
// Depthwise Convolution ops.
@@ -287,6 +298,10 @@ static FailureOr<LinalgOp> specializeLinalgConvolutions(RewriterBase &rewriter,
CONV_OP_SPECIALIZER(linalg::DepthwiseConv1DNwcWcOp);
CONV_OP_SPECIALIZER(linalg::DepthwiseConv1DNwcWcmOp);
CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNchwChwOp);
+ CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcOp);
+ CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcQOp);
+ CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcmOp);
+ CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcmQOp);
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 e85a2ab26bd32..a59b2663f2998 100644
--- a/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
+++ b/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
@@ -240,8 +240,8 @@ bool isReductionIterator(utils::IteratorType iteratorType) {
//===----------------------------------------------------------------------===//
/// Returns the BlockArgument that leads to `val`, if any. Traverses optional
-/// ext* ops.
-static BlockArgument getBlockArgumentWithOptionalExtOps(Value val) {
+/// ext*/sitofp ops.
+static BlockArgument getBlockArgumentWithOptionalCastOps(Value val) {
BlockArgument blockArg = dyn_cast<BlockArgument>(val);
if ((blockArg))
return blockArg;
@@ -249,18 +249,62 @@ static BlockArgument getBlockArgumentWithOptionalExtOps(Value val) {
Operation *defOp = val.getDefiningOp();
if (!dyn_cast_if_present<arith::ExtFOp>(defOp) &&
!dyn_cast_if_present<arith::ExtSIOp>(defOp) &&
- !dyn_cast_if_present<arith::ExtUIOp>(defOp)) {
+ !dyn_cast_if_present<arith::ExtUIOp>(defOp) &&
+ !dyn_cast_if_present<arith::SIToFPOp>(defOp)) {
return nullptr;
}
return dyn_cast<BlockArgument>(defOp->getOperand(0));
}
+/// Utility function to match the zero point offset body of convolution ops.
+/// It takes input the addition op and multiplication op expected in every
+/// convolution op and matches the following for both operands of multiplication
+/// op :-
+/// %a - %b
+/// where, %a and %b can have optional upcast operation.
+static bool bodyMatcherForZeroPointOffsets(Operation *addOp, Operation *mulOp,
+ Block *body) {
+ Operation *subOp1 = mulOp->getOperand(0).getDefiningOp();
+ if (!isa_and_present<arith::SubIOp, arith::SubFOp>(subOp1))
+ return false;
+ Operation *subOp2 = mulOp->getOperand(1).getDefiningOp();
+ if (!isa_and_present<arith::SubIOp, arith::SubFOp>(subOp2))
+ return false;
+ BlockArgument inputBlockArg =
+ getBlockArgumentWithOptionalCastOps(subOp1->getOperand(0));
+ BlockArgument inputScalarBlockArg =
+ getBlockArgumentWithOptionalCastOps(subOp1->getOperand(1));
+ BlockArgument filterBlockArg =
+ getBlockArgumentWithOptionalCastOps(subOp2->getOperand(0));
+ BlockArgument filterScalarBlockArg =
+ getBlockArgumentWithOptionalCastOps(subOp2->getOperand(1));
+ BlockArgument outBlockArg =
+ getBlockArgumentWithOptionalCastOps(addOp->getOperand(0));
+ if (!inputBlockArg || !inputScalarBlockArg || !filterBlockArg ||
+ !filterScalarBlockArg || !outBlockArg ||
+ inputBlockArg.getOwner() != body ||
+ inputScalarBlockArg.getOwner() != body ||
+ filterBlockArg.getOwner() != body ||
+ filterScalarBlockArg.getOwner() != body ||
+ outBlockArg.getOwner() != body || inputBlockArg.getArgNumber() != 0 ||
+ inputScalarBlockArg.getArgNumber() != 2 ||
+ filterBlockArg.getArgNumber() != 1 ||
+ filterScalarBlockArg.getArgNumber() != 3 ||
+ outBlockArg.getArgNumber() != 4)
+ return false;
+ return true;
+}
+
/// Utility to match block body for convolution ops.
/// The body is thus expected to yield :-
/// %out + (%lhs * %rhs)
/// where: %lhs, %rhs and %out are block arguments and
/// %lhs and %rhs can have optional upcast operation.
-static bool bodyMatcherForConvolutionOps(Value yieldVal, Block *body) {
+/// NOTE: In case of zero point offset convolution ops %lhs and %rhs would be :-
+/// %input - %input_scalar
+/// where, %input_scalar can have optional upcast operation.
+static bool bodyMatcherForConvolutionOps(Value yieldVal, Block *body,
+ bool zeroPointOffset = false) {
Operation *addOp = yieldVal.getDefiningOp();
if (!isa_and_present<arith::AddIOp, arith::AddFOp>(addOp))
return false;
@@ -269,12 +313,15 @@ static bool bodyMatcherForConvolutionOps(Value yieldVal, Block *body) {
if (!isa_and_present<arith::MulIOp, arith::MulFOp>(mulOp))
return false;
+ if (zeroPointOffset) {
+ return bodyMatcherForZeroPointOffsets(addOp, mulOp, body);
+ }
BlockArgument lhsBlockArg =
- getBlockArgumentWithOptionalExtOps(mulOp->getOperand(0));
+ getBlockArgumentWithOptionalCastOps(mulOp->getOperand(0));
BlockArgument rhsBlockArg =
- getBlockArgumentWithOptionalExtOps(mulOp->getOperand(1));
+ getBlockArgumentWithOptionalCastOps(mulOp->getOperand(1));
BlockArgument outBlockArg =
- getBlockArgumentWithOptionalExtOps(addOp->getOperand(0));
+ getBlockArgumentWithOptionalCastOps(addOp->getOperand(0));
if (!lhsBlockArg || !rhsBlockArg || !outBlockArg ||
lhsBlockArg.getOwner() != body || rhsBlockArg.getOwner() != body ||
outBlockArg.getOwner() != body || lhsBlockArg.getArgNumber() != 0 ||
@@ -291,9 +338,9 @@ static bool bodyMatcherForPoolOps(Value yieldVal, Block *body) {
return false;
BlockArgument lhsArg =
- getBlockArgumentWithOptionalExtOps(defOp->getOperand(0));
+ getBlockArgumentWithOptionalCastOps(defOp->getOperand(0));
BlockArgument rhsArg =
- getBlockArgumentWithOptionalExtOps(defOp->getOperand(1));
+ getBlockArgumentWithOptionalCastOps(defOp->getOperand(1));
if (!lhsArg || !rhsArg || lhsArg.getOwner() != body ||
rhsArg.getOwner() != body || lhsArg.getArgNumber() != 2 ||
rhsArg.getArgNumber() != 0)
@@ -488,14 +535,15 @@ class ConvMatcherBuilder {
}
/// Match body pattern. This should be called last.
- bool matchBody() {
+ bool matchBody(bool zeroPointOffset = false) {
if (!matched)
return false;
Block *body = op.getBlock();
auto yieldOp = cast<linalg::YieldOp>(body->getTerminator());
switch (poolingType) {
case PoolingType::NONE:
- return bodyMatcherForConvolutionOps(yieldOp.getOperand(0), body);
+ return bodyMatcherForConvolutionOps(yieldOp.getOperand(0), body,
+ zeroPointOffset);
case PoolingType::MAX_SIGNED:
return bodyMatcherForMaxSignedPoolOps(yieldOp.getOperand(0), body);
case PoolingType::MAX_UNSIGNED:
@@ -620,6 +668,361 @@ bool isaConvolutionOpOfType<linalg::Conv2DOp>(LinalgOp op,
.matchBody();
}
+// #inputMap = affine_map<(N, H, W, F, h, w, c) -> (N, H + h, W + w, c)>
+// #filterMap = affine_map<(N, H, W, F, h, w, c) -> (h, w, c, F)>
+// #outputMap = affine_map<(N, H, W, F, h, w, c) -> (N, H, W, F)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv2DNhwcHwcfOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv2DNhwcHwcfOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr H = m.dim(1);
+ AffineExpr W = m.dim(2);
+ AffineExpr F = m.dim(3);
+ AffineExpr h = m.dim(4);
+ AffineExpr w = m.dim(5);
+ AffineExpr c = m.dim(6);
+
+ return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
+ .matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/1)
+ .expectMaps({/*inputMap=*/{N, m.strided(H, h, 0), m.strided(W, w, 1), c},
+ /*filterMap=*/{h, w, c, F},
+ /*outputMap=*/{N, H, W, F}})
+ .matchBody();
+}
+
+// #inputMap = affine_map<(N, H, W, F, h, w, c) -> (N, H + h, W + w, c)>
+// #filterMap = affine_map<(N, H, W, F, h, w, c) -> (h, w, c, F)>
+// #scalarMap = affine_map<(N, H, W, F, h, w, c) -> ()>
+// #outputMap = affine_map<(N, H, W, F, h, w, c) -> (N, H, W, F)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv2DNhwcHwcfQOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv2DNhwcHwcfQOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr H = m.dim(1);
+ AffineExpr W = m.dim(2);
+ AffineExpr F = m.dim(3);
+ AffineExpr h = m.dim(4);
+ AffineExpr w = m.dim(5);
+ AffineExpr c = m.dim(6);
+
+ return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
+ .matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/1)
+ .expectMaps({/*inputMap=*/{N, m.strided(H, h, 0), m.strided(W, w, 1), c},
+ /*filterMap=*/{h, w, c, F},
+ /*scalarMap=*/{},
+ /*scalarMap=*/{},
+ /*outputMap=*/{N, H, W, F}})
+ .matchBody(/*zeroPointOffset=*/true);
+}
+
+// #inputMap = affine_map<(N, H, W, F, h, w, c) -> (N, H + h, W + w, c)>
+// #filterMap = affine_map<(N, H, W, F, h, w, c) -> (F, h, w, c)>
+// #outputMap = affine_map<(N, H, W, F, h, w, c) -> (N, H, W, F)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv2DNhwcFhwcOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv2DNhwcFhwcOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr H = m.dim(1);
+ AffineExpr W = m.dim(2);
+ AffineExpr F = m.dim(3);
+ AffineExpr h = m.dim(4);
+ AffineExpr w = m.dim(5);
+ AffineExpr c = m.dim(6);
+
+ return m.matchStride(/*iDim=*/1, /*fDim=*/1, /*oDim=*/1, /*idx=*/0)
+ .matchStride(/*iDim=*/2, /*fDim=*/2, /*oDim=*/2, /*idx=*/1)
+ .expectMaps({/*inputMap=*/{N, m.strided(H, h, 0), m.strided(W, w, 1), c},
+ /*filterMap=*/{F, h, w, c},
+ /*outputMap=*/{N, H, W, F}})
+ .matchBody();
+}
+
+// #inputMap = affine_map<(N, H, W, F, h, w, c) -> (N, H + h, W + w, c)>
+// #filterMap = affine_map<(N, H, W, F, h, w, c) -> (F, h, w, c)>
+// #scalarMap = affine_map<(N, H, W, F, h, w, c) -> ()>
+// #outputMap = affine_map<(N, H, W, F, h, w, c) -> (N, H, W, F)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv2DNhwcFhwcQOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv2DNhwcFhwcQOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr H = m.dim(1);
+ AffineExpr W = m.dim(2);
+ AffineExpr F = m.dim(3);
+ AffineExpr h = m.dim(4);
+ AffineExpr w = m.dim(5);
+ AffineExpr c = m.dim(6);
+
+ return m.matchStride(/*iDim=*/1, /*fDim=*/1, /*oDim=*/1, /*idx=*/0)
+ .matchStride(/*iDim=*/2, /*fDim=*/2, /*oDim=*/2, /*idx=*/1)
+ .expectMaps({/*inputMap=*/{N, m.strided(H, h, 0), m.strided(W, w, 1), c},
+ /*filterMap=*/{F, h, w, c},
+ /*scalarMap=*/{},
+ /*scalarMap=*/{},
+ /*outputMap=*/{N, H, W, F}})
+ .matchBody(/*zeroPointOffset=*/true);
+}
+
+// #inputMap = affine_map<(N, F, H, W, c, h, w) -> (N, c, H + h, W + w)>
+// #filterMap = affine_map<(N, F, H, W, c, h, w) -> (F, c, h, w)>
+// #outputMap = affine_map<(N, F, H, W, c, h, w) -> (N, F, H, W)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv2DNchwFchwOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv2DNchwFchwOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr F = m.dim(1);
+ AffineExpr H = m.dim(2);
+ AffineExpr W = m.dim(3);
+ AffineExpr c = m.dim(4);
+ AffineExpr h = m.dim(5);
+ AffineExpr w = m.dim(6);
+
+ return m.matchStride(/*iDim=*/2, /*fDim=*/2, /*oDim=*/2, /*idx=*/0)
+ .matchStride(/*iDim=*/3, /*fDim=*/3, /*oDim=*/3, /*idx=*/1)
+ .expectMaps({/*inputMap=*/{N, c, m.strided(H, h, 0), m.strided(W, w, 1)},
+ /*filterMap=*/{F, c, h, w},
+ /*outputMap=*/{N, F, H, W}})
+ .matchBody();
+}
+
+// #inputMap = affine_map<(N, F, H, W, c, h, w) -> (N, c, H + h, W + w)>
+// #filterMap = affine_map<(N, F, H, W, c, h, w) -> (F, c, h, w)>
+// #scalarMap = affine_map<(N, F, H, W, c, h, w) -> ()>
+// #outputMap = affine_map<(N, F, H, W, c, h, w) -> (N, F, H, W)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv2DNchwFchwQOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv2DNchwFchwQOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr F = m.dim(1);
+ AffineExpr H = m.dim(2);
+ AffineExpr W = m.dim(3);
+ AffineExpr c = m.dim(4);
+ AffineExpr h = m.dim(5);
+ AffineExpr w = m.dim(6);
+
+ return m.matchStride(/*iDim=*/2, /*fDim=*/2, /*oDim=*/2, /*idx=*/0)
+ .matchStride(/*iDim=*/3, /*fDim=*/3, /*oDim=*/3, /*idx=*/1)
+ .expectMaps({/*inputMap=*/{N, c, m.strided(H, h, 0), m.strided(W, w, 1)},
+ /*filterMap=*/{F, c, h, w},
+ /*scalarMap=*/{},
+ /*scalarMap=*/{},
+ /*outputMap=*/{N, F, H, W}})
+ .matchBody(/*zeroPointOffset=*/true);
+}
+
+// #inputMap = affine_map<(N, G, F, H, W, c, h, w) -> (N, G, c, H + h, W + w)>
+// #filterMap = affine_map<(N, G, F, H, W, c, h, w) -> (F, G, c, h, w)>
+// #outputMap = affine_map<(N, G, F, H, W, c, h, w) -> (N, G, F, H, W)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv2DNgchwFgchwOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv2DNgchwFgchwOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr G = m.dim(1);
+ AffineExpr F = m.dim(2);
+ AffineExpr H = m.dim(3);
+ AffineExpr W = m.dim(4);
+ AffineExpr c = m.dim(5);
+ AffineExpr h = m.dim(6);
+ AffineExpr w = m.dim(7);
+
+ return m.matchStride(/*iDim=*/3, /*fDim=*/3, /*oDim=*/3, /*idx=*/0)
+ .matchStride(/*iDim=*/4, /*fDim=*/4, /*oDim=*/4, /*idx=*/1)
+ .expectMaps(
+ {/*inputMap=*/{N, G, c, m.strided(H, h, 0), m.strided(W, w, 1)},
+ /*filterMap=*/{F, G, c, h, w},
+ /*outputMap=*/{N, G, F, H, W}})
+ .matchBody();
+}
+
+// #inputMap = affine_map<(N, G, F, H, W, c, h, w) -> (N, G, c, H + h, W + w)>
+// #filterMap = affine_map<(N, G, F, H, W, c, h, w) -> (G, F, c, h, w)>
+// #outputMap = affine_map<(N, G, F, H, W, c, h, w) -> (N, G, F, H, W)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv2DNgchwGfchwOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv2DNgchwGfchwOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr G = m.dim(1);
+ AffineExpr F = m.dim(2);
+ AffineExpr H = m.dim(3);
+ AffineExpr W = m.dim(4);
+ AffineExpr c = m.dim(5);
+ AffineExpr h = m.dim(6);
+ AffineExpr w = m.dim(7);
+
+ return m.matchStride(/*iDim=*/3, /*fDim=*/3, /*oDim=*/3, /*idx=*/0)
+ .matchStride(/*iDim=*/4, /*fDim=*/4, /*oDim=*/4, /*idx=*/1)
+ .expectMaps(
+ {/*inputMap=*/{N, G, c, m.strided(H, h, 0), m.strided(W, w, 1)},
+ /*filterMap=*/{G, F, c, h, w},
+ /*outputMap=*/{N, G, F, H, W}})
+ .matchBody();
+}
+
+// #inputMap = affine_map<(N, G, F, H, W, c, h, w) -> (N, G, c, H + h, W + w)>
+// #filterMap = affine_map<(N, G, F, H, W, c, h, w) -> (G, F, c, h, w)>
+// #scalarMap = affine_map<(N, G, F, H, W, c, h, w) -> ()>
+// #outputMap = affine_map<(N, G, F, H, W, c, h, w) -> (N, G, F, H, W)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv2DNgchwGfchwQOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv2DNgchwGfchwQOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr G = m.dim(1);
+ AffineExpr F = m.dim(2);
+ AffineExpr H = m.dim(3);
+ AffineExpr W = m.dim(4);
+ AffineExpr c = m.dim(5);
+ AffineExpr h = m.dim(6);
+ AffineExpr w = m.dim(7);
+
+ return m.matchStride(/*iDim=*/3, /*fDim=*/3, /*oDim=*/3, /*idx=*/0)
+ .matchStride(/*iDim=*/4, /*fDim=*/4, /*oDim=*/4, /*idx=*/1)
+ .expectMaps(
+ {/*inputMap=*/{N, G, c, m.strided(H, h, 0), m.strided(W, w, 1)},
+ /*filterMap=*/{G, F, c, h, w},
+ /*scalarMap=*/{},
+ /*scalarMap=*/{},
+ /*outputMap=*/{N, G, F, H, W}})
+ .matchBody(/*zeroPointOffset=*/true);
+}
+
+// #inputMap = affine_map<(N, H, W, G, F, h, w, c) -> (N, H + h, W + w, G, c)>
+// #filterMap = affine_map<(N, H, W, G, F, h, w, c) -> (G, F, h, w, c)>
+// #outputMap = affine_map<(N, H, W, G, F, h, w, c) -> (N, H, W, G, F)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv2DNhwgcGfhwcOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv2DNhwgcGfhwcOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr H = m.dim(1);
+ AffineExpr W = m.dim(2);
+ AffineExpr G = m.dim(3);
+ AffineExpr F = m.dim(4);
+ AffineExpr h = m.dim(5);
+ AffineExpr w = m.dim(6);
+ AffineExpr c = m.dim(7);
+
+ return m.matchStride(/*iDim=*/1, /*fDim=*/2, /*oDim=*/1, /*idx=*/0)
+ .matchStride(/*iDim=*/2, /*fDim=*/3, /*oDim=*/2, /*idx=*/1)
+ .expectMaps(
+ {/*inputMap=*/{N, m.strided(H, h, 0), m.strided(W, w, 1), G, c},
+ /*filterMap=*/{G, F, h, w, c},
+ /*outputMap=*/{N, H, W, G, F}})
+ .matchBody();
+}
+
+// #inputMap = affine_map<(N, H, W, G, F, h, w, c) -> (N, H + h, W + w, G, c)>
+// #filterMap = affine_map<(N, H, W, G, F, h, w, c) -> (G, F, h, w, c)>
+// #scalarMap = affine_map<(N, H, W, G, F, h, w, c) -> ()>
+// #outputMap = affine_map<(N, H, W, G, F, h, w, c) -> (N, H, W, G, F)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv2DNhwgcGfhwcQOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::Conv2DNhwgcGfhwcQOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr H = m.dim(1);
+ AffineExpr W = m.dim(2);
+ AffineExpr G = m.dim(3);
+ AffineExpr F = m.dim(4);
+ AffineExpr h = m.dim(5);
+ AffineExpr w = m.dim(6);
+ AffineExpr c = m.dim(7);
+
+ return m.matchStride(/*iDim=*/1, /*fDim=*/2, /*oDim=*/1, /*idx=*/0)
+ .matchStride(/*iDim=*/2, /*fDim=*/3, /*oDim=*/2, /*idx=*/1)
+ .expectMaps(
+ {/*inputMap=*/{N, m.strided(H, h, 0), m.strided(W, w, 1), G, c},
+ /*filterMap=*/{G, F, h, w, c},
+ /*scalarMap=*/{},
+ /*scalarMap=*/{},
+ /*outputMap=*/{N, H, W, G, F}})
+ .matchBody(/*zeroPointOffset=*/true);
+}
+
// #inputMap = affine_map<(D, H, W, d, h, w) -> (D + d, H + h, W + w)>
// #filterMap = affine_map<(D, H, W, d, h, w) -> (d, h, w)>
// #outputMap = affine_map<(D, H, W, d, h, w) -> (D, H, W)>
@@ -759,6 +1162,130 @@ bool isaConvolutionOpOfType<linalg::DepthwiseConv2DNchwChwOp>(
.matchBody();
}
+// #inputMap = affine_map<(N, H, W, C, h, w) -> (N, H + h, W + w, C)>
+// #filterMap = affine_map<(N, H, W, C, h, w) -> (h, w, C)>
+// #outputMap = affine_map<(N, H, W, C, h, w) -> (N, H, W, C)>
+template <>
+bool isaConvolutionOpOfType<linalg::DepthwiseConv2DNhwcHwcOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::DepthwiseConv2DNhwcHwcOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr H = m.dim(1);
+ AffineExpr W = m.dim(2);
+ AffineExpr C = m.dim(3);
+ AffineExpr h = m.dim(4);
+ AffineExpr w = m.dim(5);
+
+ return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
+ .matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/1)
+ .expectMaps({/*inputMap=*/{N, m.strided(H, h, 0), m.strided(W, w, 1), C},
+ /*filterMap=*/{h, w, C},
+ /*outputMap=*/{N, H, W, C}})
+ .matchBody();
+}
+
+// #inputMap = affine_map<(N, H, W, C, h, w) -> (N, H + h, W + w, C)>
+// #filterMap = affine_map<(N, H, W, C, h, w) -> (h, w, C)>
+// #scalarMap = affine_map<(N, H, W, C, h, w) -> ()>
+// #outputMap = affine_map<(N, H, W, C, h, w) -> (N, H, W, C)>
+template <>
+bool isaConvolutionOpOfType<linalg::DepthwiseConv2DNhwcHwcQOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::DepthwiseConv2DNhwcHwcQOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr H = m.dim(1);
+ AffineExpr W = m.dim(2);
+ AffineExpr C = m.dim(3);
+ AffineExpr h = m.dim(4);
+ AffineExpr w = m.dim(5);
+
+ return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
+ .matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/1)
+ .expectMaps({/*inputMap=*/{N, m.strided(H, h, 0), m.strided(W, w, 1), C},
+ /*filterMap=*/{h, w, C},
+ /*scalarMap=*/{},
+ /*scalarMap=*/{},
+ /*outputMap=*/{N, H, W, C}})
+ .matchBody(/*zeroPointOffset=*/true);
+}
+
+// #inputMap = affine_map<(N, H, W, C, CM, h, w) -> (N, H + h, W + w, C)>
+// #filterMap = affine_map<(N, H, W, C, CM, h, w) -> (h, w, C, CM)>
+// #outputMap = affine_map<(N, H, W, C, CM, h, w) -> (N, H, W, C, CM)>
+template <>
+bool isaConvolutionOpOfType<linalg::DepthwiseConv2DNhwcHwcmOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::DepthwiseConv2DNhwcHwcmOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr H = m.dim(1);
+ AffineExpr W = m.dim(2);
+ AffineExpr C = m.dim(3);
+ AffineExpr CM = m.dim(4);
+ AffineExpr h = m.dim(5);
+ AffineExpr w = m.dim(6);
+
+ return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
+ .matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/1)
+ .expectMaps({/*inputMap=*/{N, m.strided(H, h, 0), m.strided(W, w, 1), C},
+ /*filterMap=*/{h, w, C, CM},
+ /*outputMap=*/{N, H, W, C, CM}})
+ .matchBody();
+}
+
+// #inputMap = affine_map<(N, H, W, C, CM, h, w) -> (N, H + h, W + w, C)>
+// #filterMap = affine_map<(N, H, W, C, CM, h, w) -> (h, w, C, CM)>
+// #scalarMap = affine_map<(N, H, W, C, CM, h, w) -> ()>
+// #outputMap = affine_map<(N, H, W, C, CM, h, w) -> (N, H, W, C, CM)>
+template <>
+bool isaConvolutionOpOfType<linalg::DepthwiseConv2DNhwcHwcmQOp>(
+ LinalgOp op, SmallVector<int64_t> *dilations,
+ SmallVector<int64_t> *strides) {
+ if (isa<linalg::DepthwiseConv2DNhwcHwcmQOp>(op))
+ return true;
+
+ assert(isaConvolutionOpInterface(op) &&
+ "expected op to implement ConvolutionOpInterface");
+
+ ConvMatcherBuilder m(op, /*spatialRank=*/2, dilations, strides);
+ AffineExpr N = m.dim(0);
+ AffineExpr H = m.dim(1);
+ AffineExpr W = m.dim(2);
+ AffineExpr C = m.dim(3);
+ AffineExpr CM = m.dim(4);
+ AffineExpr h = m.dim(5);
+ AffineExpr w = m.dim(6);
+
+ return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
+ .matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/1)
+ .expectMaps({/*inputMap=*/{N, m.strided(H, h, 0), m.strided(W, w, 1), C},
+ /*filterMap=*/{h, w, C, CM},
+ /*scalarMap=*/{},
+ /*scalarMap=*/{},
+ /*outputMap=*/{N, H, W, C, CM}})
+ .matchBody(/*zeroPointOffset=*/true);
+}
+
// #inputMap = affine_map<(N, D, H, W, CM, d, h, w, C)
// -> (N, D + d, H + h, W + w, C)>
// #filterMap = 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 4b2d42a3ae4e0..289d55ce9911a 100644
--- a/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir
+++ b/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir
@@ -55,6 +55,149 @@ func.func @conv_2d(%in : tensor<?x?xf32>, %filter : tensor<?x?xf32>, %out : tens
// -----
+func.func @conv_2d_nhwc_hwcf(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %output: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
+ %0 = linalg.conv_2d_nhwc_hwcf
+ {dilations = dense<2> : tensor<2xi64>, strides = dense<3> : tensor<2xi64>}
+ ins (%input, %filter: tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
+ outs (%output: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?xf32>
+}
+// CHECK: @conv_2d_nhwc_hwcf
+// CHECK: linalg.conv_2d_nhwc_hwcf
+// CHECK-SAME: dilations = dense<2> : tensor<2xi64>, strides = dense<3> : tensor<2xi64>
+
+// -----
+
+func.func @conv_2d_nhwc_hwcf_q(%input: tensor<?x?x?x?xi8>, %filter: tensor<?x?x?x?xi8>, %output: tensor<?x?x?x?xi32>, %zp_input: i32, %zp_filter: i32) -> tensor<?x?x?x?xi32> {
+ %0 = linalg.conv_2d_nhwc_hwcf_q
+ {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+ ins (%input, %filter, %zp_input, %zp_filter : tensor<?x?x?x?xi8>, tensor<?x?x?x?xi8>, i32, i32)
+ outs (%output: tensor<?x?x?x?xi32>) -> tensor<?x?x?x?xi32>
+ return %0 : tensor<?x?x?x?xi32>
+}
+// CHECK: @conv_2d_nhwc_hwcf_q
+// CHECK: linalg.conv_2d_nhwc_hwcf_q
+// CHECK-SAME: dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>
+
+// -----
+
+func.func @conv_2d_nhwc_fhwc(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %output: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
+ %0 = linalg.conv_2d_nhwc_fhwc
+ {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>}
+ ins (%input, %filter: tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
+ outs (%output: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?xf32>
+}
+// CHECK: @conv_2d_nhwc_fhwc
+// CHECK: linalg.conv_2d_nhwc_fhwc
+// CHECK-SAME: dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>
+
+// -----
+
+func.func @conv_2d_nhwc_fhwc_q(%input: tensor<?x?x?x?xi8>, %filter: tensor<?x?x?x?xi8>, %output: tensor<?x?x?x?xi32>, %zp_input: i32, %zp_filter: i32) -> tensor<?x?x?x?xi32> {
+ %0 = linalg.conv_2d_nhwc_fhwc_q
+ {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+ ins (%input, %filter, %zp_input, %zp_filter : tensor<?x?x?x?xi8>, tensor<?x?x?x?xi8>, i32, i32)
+ outs (%output: tensor<?x?x?x?xi32>) -> tensor<?x?x?x?xi32>
+ return %0 : tensor<?x?x?x?xi32>
+}
+// CHECK: @conv_2d_nhwc_fhwc_q
+// CHECK: linalg.conv_2d_nhwc_fhwc_q
+// CHECK-SAME: dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>
+
+// -----
+
+func.func @conv_2d_nchw_fchw(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %output: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
+ %0 = linalg.conv_2d_nchw_fchw
+ {dilations = dense<[1, 2]> : tensor<2xi64>, strides = dense<[3, 4]> : tensor<2xi64>}
+ ins (%input, %filter: tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
+ outs (%output: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?xf32>
+}
+// CHECK: @conv_2d_nchw_fchw
+// CHECK: linalg.conv_2d_nchw_fchw
+// CHECK-SAME: dilations = dense<[1, 2]> : tensor<2xi64>, strides = dense<[3, 4]> : tensor<2xi64>
+
+// -----
+
+func.func @conv_2d_nchw_fchw_q(%input: tensor<?x?x?x?xi8>, %filter: tensor<?x?x?x?xi8>, %output: tensor<?x?x?x?xi32>, %zp_input: i32, %zp_filter: i32) -> tensor<?x?x?x?xi32> {
+ %0 = linalg.conv_2d_nchw_fchw_q
+ {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+ ins (%input, %filter, %zp_input, %zp_filter : tensor<?x?x?x?xi8>, tensor<?x?x?x?xi8>, i32, i32)
+ outs (%output: tensor<?x?x?x?xi32>) -> tensor<?x?x?x?xi32>
+ return %0 : tensor<?x?x?x?xi32>
+}
+// CHECK: @conv_2d_nchw_fchw_q
+// CHECK: linalg.conv_2d_nchw_fchw_q
+// CHECK-SAME: dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>
+
+// -----
+
+func.func @conv_2d_ngchw_fgchw(%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_2d_ngchw_fgchw
+ {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+ 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_2d_ngchw_fgchw
+// CHECK: linalg.conv_2d_ngchw_fgchw
+// CHECK-SAME: dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>
+
+// -----
+
+func.func @conv_2d_ngchw_gfchw(%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_2d_ngchw_gfchw
+ {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+ 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_2d_ngchw_gfchw
+// CHECK: linalg.conv_2d_ngchw_gfchw
+// CHECK-SAME: dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>
+
+// -----
+
+func.func @conv_2d_ngchw_gfchw_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_2d_ngchw_gfchw_q
+ {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+ 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_2d_ngchw_gfchw_q
+// CHECK: linalg.conv_2d_ngchw_gfchw_q
+// CHECK-SAME: dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>
+
+// -----
+
+func.func @conv_2d_nhwgc_gfhwc(%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_2d_nhwgc_gfhwc
+ {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>}
+ 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_2d_nhwgc_gfhwc
+// CHECK: linalg.conv_2d_nhwgc_gfhwc
+// CHECK-SAME: dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>
+
+// -----
+
+func.func @conv_2d_nhwgc_gfhwc_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_2d_nhwgc_gfhwc_q
+ {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+ 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_2d_nhwgc_gfhwc_q
+// CHECK: linalg.conv_2d_nhwgc_gfhwc_q
+// CHECK-SAME: dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>
+
+// -----
+
func.func @conv_3d(%in : tensor<?x?x?xf32>, %filter : tensor<?x?x?xf32>, %out : tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
%0 = linalg.conv_3d
ins(%in, %filter : tensor<?x?x?xf32>, tensor<?x?x?xf32>)
@@ -121,6 +264,58 @@ func.func @depthwise_conv_2d_nchw_chw(%input: tensor<?x?x?x?xf16>, %filter: tens
// -----
+func.func @depthwise_conv_2d_nhwc_hwc(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?xf32>, %output: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
+ %0 = linalg.depthwise_conv_2d_nhwc_hwc
+ {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>}
+ ins (%input, %filter: tensor<?x?x?x?xf32>, tensor<?x?x?xf32>)
+ outs (%output: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+ return %0 : tensor<?x?x?x?xf32>
+}
+// CHECK: @depthwise_conv_2d_nhwc_hwc
+// CHECK: linalg.depthwise_conv_2d_nhwc_hwc
+// CHECK-SAME: dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>
+
+// -----
+
+func.func @depthwise_conv_2d_nhwc_hwc_q(%input: tensor<?x?x?x?xi8>, %filter: tensor<?x?x?xi8>, %output: tensor<?x?x?x?xi32>, %zp_input: i32, %zp_filter: i32) -> tensor<?x?x?x?xi32> {
+ %0 = linalg.depthwise_conv_2d_nhwc_hwc_q
+ {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+ ins (%input, %filter, %zp_input, %zp_filter : tensor<?x?x?x?xi8>, tensor<?x?x?xi8>, i32, i32)
+ outs (%output: tensor<?x?x?x?xi32>) -> tensor<?x?x?x?xi32>
+ return %0 : tensor<?x?x?x?xi32>
+}
+// CHECK: @depthwise_conv_2d_nhwc_hwc_q
+// CHECK: linalg.depthwise_conv_2d_nhwc_hwc_q
+// CHECK-SAME: dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>
+
+// -----
+
+func.func @depthwise_conv_2d_nhwc_hwcm(%input: tensor<?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_2d_nhwc_hwcm
+ {dilations = dense<[1, 2]> : tensor<2xi64>, strides = dense<[3, 1]> : tensor<2xi64>}
+ ins (%input, %filter: tensor<?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_2d_nhwc_hwcm
+// CHECK: linalg.depthwise_conv_2d_nhwc_hwcm
+// CHECK-SAME: dilations = dense<[1, 2]> : tensor<2xi64>, strides = dense<[3, 1]> : tensor<2xi64>
+
+// -----
+
+func.func @depthwise_conv_2d_nhwc_hwcm_q(%input: tensor<?x?x?x?xi8>, %filter: tensor<?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.depthwise_conv_2d_nhwc_hwcm_q
+ {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+ ins (%input, %filter, %zp_input, %zp_filter : tensor<?x?x?x?xi8>, tensor<?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: @depthwise_conv_2d_nhwc_hwcm_q
+// CHECK: linalg.depthwise_conv_2d_nhwc_hwcm_q
+// CHECK-SAME: dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>
+
+// -----
+
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>}
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