[mlir] Add Scalar Broadcast TOSA Depthwise Conv #110806
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Support broadcasting of depthwise conv2d bias in tosa->linalg named lowering in the case that bias is a rank-1 tensor with exactly 1 element. In this case TOSA specifies the value should first be broadcast across the bias dimension and then across the result tensor. Add `lit` tests for depthwise conv2d with scalar bias and for conv3d which was already supported but missing coverage. Signed-off-by: Jack Frankland <jack.frankland@arm.com>
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@llvm/pr-subscribers-mlir-linalg @llvm/pr-subscribers-mlir Author: Jack Frankland (FranklandJack) ChangesSupport broadcasting of depthwise conv2d bias in tosa->linalg named lowering in the case that bias is a rank-1 tensor with exactly 1 element. In this case TOSA specifies the value should first be broadcast across the bias dimension and then across the result tensor. Add Full diff: https://github.com/llvm/llvm-project/pull/110806.diff 2 Files Affected:
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index fe53b499674324..d537aef5791031 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -88,15 +88,14 @@ linalgIntBroadcastExtSIAdd(PatternRewriter &rewriter, Location loc, Value bias,
.getResult(0);
}
-// Broadcast the source value to all the outer dimensions of the result value.
-// If required, the element type is expanded using an arith.extsi operation.
-static mlir::Value linalgBroadcastAndMaybeExtSI(PatternRewriter &rewriter,
- Location loc, Value source,
- Value result) {
+// Construct the affine map that a linalg generic would use to broadcast the
+// source tensor into the shape of the result tensor.
+static AffineMap getBroadcastingMap(PatternRewriter &rewriter, Value source,
+ Value result) {
ShapedType resultTy = cast<ShapedType>(result.getType());
ShapedType sourceTy = cast<ShapedType>(source.getType());
- int64_t resultRank = resultTy.getRank();
- int64_t sourceRank = sourceTy.getRank();
+ const int64_t resultRank = resultTy.getRank();
+ const int64_t sourceRank = sourceTy.getRank();
// The source tensor is broadcast to all the outer dimensions of the
// result tensor.
@@ -115,14 +114,21 @@ static mlir::Value linalgBroadcastAndMaybeExtSI(PatternRewriter &rewriter,
}
}
- // Creating maps for the input and output of the broacast-like generic op.
- SmallVector<AffineMap, 2> indexingMaps = {
- // Broadcast the last dimension of the bias to all output dimensions.
- AffineMap::get(/*dimCount=*/resultRank,
- /*symbolCount=*/0, sourceDims, rewriter.getContext()),
+ return AffineMap::get(/*dimCount=*/resultRank,
+ /*symbolCount=*/0, sourceDims, rewriter.getContext());
+}
- // Output indexing map.
- rewriter.getMultiDimIdentityMap(resultRank)};
+// Broadcast the source value to all the outer dimensions of the result value.
+// If required, the element type is expanded using an arith.extsi operation.
+static mlir::Value linalgBroadcastAndMaybeExtSI(PatternRewriter &rewriter,
+ Location loc, Value source,
+ Value result) {
+ ShapedType resultTy = cast<ShapedType>(result.getType());
+ const int64_t resultRank = resultTy.getRank();
+ // Creating maps for the input and output of the broacast-like generic op.
+ SmallVector<AffineMap, 2> indexingMaps;
+ indexingMaps.push_back(getBroadcastingMap(rewriter, source, result));
+ indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
// Build the broadcast-like operation as a linalg.generic.
return rewriter
@@ -488,14 +494,6 @@ class DepthwiseConvConverter
weightShape[2], weightShape[3]},
resultETy);
- // Broadcast the initial value to the output tensor before convolving.
- SmallVector<AffineMap, 4> indexingMaps;
- indexingMaps.push_back(AffineMap::get(
- /*dimCount=*/resultRank, /*symbolCount=*/0,
- {rewriter.getAffineDimExpr(3)}, rewriter.getContext()));
- indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
- indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
-
auto resultZeroAttr = rewriter.getZeroAttr(resultETy);
Value emptyTensor = rewriter.create<tensor::EmptyOp>(
loc, linalgConvTy.getShape(), resultETy, filteredDims);
@@ -507,6 +505,13 @@ class DepthwiseConvConverter
Value biasEmptyTensor = rewriter.create<tensor::EmptyOp>(
loc, resultTy.getShape(), resultETy, filteredDims);
+
+ // Broadcast the initial value to the output tensor before convolving.
+ SmallVector<AffineMap, 4> indexingMaps;
+ indexingMaps.push_back(getBroadcastingMap(rewriter, bias, biasEmptyTensor));
+ indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
+ indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
+
if (!isQuantized) {
Value conv = rewriter
.create<linalg::DepthwiseConv2DNhwcHwcmOp>(
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
index 0d55d1899c713e..bfdc72ee07e97f 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -702,6 +702,22 @@ func.func @depthwise_conv(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tensor<3x1x3x11xf
// -----
+// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (0)>
+// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+
+// CHECK-LABEL: @depthwise_conv_scalar_bias
+func.func @depthwise_conv_scalar_bias(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tensor<3x1x3x11xf32>, %arg2 : tensor<1xf32>) -> () {
+ // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, %{{.*}} : tensor<1xf32>, tensor<1x5x5x33xf32>) outs(%{{.*}} : tensor<1x5x5x33xf32>) {
+ // CHECK: ^bb0(%[[ARG3:[0-9a-zA-Z_]+]]: f32, %[[ARG4:[0-9a-zA-Z_]+]]: f32, %{{.*}}: f32):
+ // CHECK: [[ADD:%.+]] = arith.addf %[[ARG3]], %[[ARG4]] : f32
+ // CHECK: linalg.yield [[ADD]] : f32
+ // CHECK: } -> tensor<1x5x5x33xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 { pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
+ return
+}
+
+// -----
+
// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
@@ -840,6 +856,20 @@ func.func @conv3d_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<28x3x4
// -----
+// CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (0)>
+// CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
+
+// CHECK-LABEL: @conv3d_scalar_bias_f32
+func.func @conv3d_scalar_bias_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<28x3x4x5x27xf32>, %bias: tensor<1xf32>) -> () {
+ // CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x28xf32>
+ // CHECK: %[[BROADCAST:.+]] = linalg.generic
+ // CHECK-SAME: {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
+ %0 = tosa.conv3d %input, %weights, %bias {pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf32>, tensor<28x3x4x5x27xf32>, tensor<1xf32>) -> tensor<1x47x45x43x28xf32>
+ return
+}
+
+// -----
+
// CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d4)>
// CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
|
|
@llvm/pr-subscribers-mlir-tosa Author: Jack Frankland (FranklandJack) ChangesSupport broadcasting of depthwise conv2d bias in tosa->linalg named lowering in the case that bias is a rank-1 tensor with exactly 1 element. In this case TOSA specifies the value should first be broadcast across the bias dimension and then across the result tensor. Add Full diff: https://github.com/llvm/llvm-project/pull/110806.diff 2 Files Affected:
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index fe53b499674324..d537aef5791031 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -88,15 +88,14 @@ linalgIntBroadcastExtSIAdd(PatternRewriter &rewriter, Location loc, Value bias,
.getResult(0);
}
-// Broadcast the source value to all the outer dimensions of the result value.
-// If required, the element type is expanded using an arith.extsi operation.
-static mlir::Value linalgBroadcastAndMaybeExtSI(PatternRewriter &rewriter,
- Location loc, Value source,
- Value result) {
+// Construct the affine map that a linalg generic would use to broadcast the
+// source tensor into the shape of the result tensor.
+static AffineMap getBroadcastingMap(PatternRewriter &rewriter, Value source,
+ Value result) {
ShapedType resultTy = cast<ShapedType>(result.getType());
ShapedType sourceTy = cast<ShapedType>(source.getType());
- int64_t resultRank = resultTy.getRank();
- int64_t sourceRank = sourceTy.getRank();
+ const int64_t resultRank = resultTy.getRank();
+ const int64_t sourceRank = sourceTy.getRank();
// The source tensor is broadcast to all the outer dimensions of the
// result tensor.
@@ -115,14 +114,21 @@ static mlir::Value linalgBroadcastAndMaybeExtSI(PatternRewriter &rewriter,
}
}
- // Creating maps for the input and output of the broacast-like generic op.
- SmallVector<AffineMap, 2> indexingMaps = {
- // Broadcast the last dimension of the bias to all output dimensions.
- AffineMap::get(/*dimCount=*/resultRank,
- /*symbolCount=*/0, sourceDims, rewriter.getContext()),
+ return AffineMap::get(/*dimCount=*/resultRank,
+ /*symbolCount=*/0, sourceDims, rewriter.getContext());
+}
- // Output indexing map.
- rewriter.getMultiDimIdentityMap(resultRank)};
+// Broadcast the source value to all the outer dimensions of the result value.
+// If required, the element type is expanded using an arith.extsi operation.
+static mlir::Value linalgBroadcastAndMaybeExtSI(PatternRewriter &rewriter,
+ Location loc, Value source,
+ Value result) {
+ ShapedType resultTy = cast<ShapedType>(result.getType());
+ const int64_t resultRank = resultTy.getRank();
+ // Creating maps for the input and output of the broacast-like generic op.
+ SmallVector<AffineMap, 2> indexingMaps;
+ indexingMaps.push_back(getBroadcastingMap(rewriter, source, result));
+ indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
// Build the broadcast-like operation as a linalg.generic.
return rewriter
@@ -488,14 +494,6 @@ class DepthwiseConvConverter
weightShape[2], weightShape[3]},
resultETy);
- // Broadcast the initial value to the output tensor before convolving.
- SmallVector<AffineMap, 4> indexingMaps;
- indexingMaps.push_back(AffineMap::get(
- /*dimCount=*/resultRank, /*symbolCount=*/0,
- {rewriter.getAffineDimExpr(3)}, rewriter.getContext()));
- indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
- indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
-
auto resultZeroAttr = rewriter.getZeroAttr(resultETy);
Value emptyTensor = rewriter.create<tensor::EmptyOp>(
loc, linalgConvTy.getShape(), resultETy, filteredDims);
@@ -507,6 +505,13 @@ class DepthwiseConvConverter
Value biasEmptyTensor = rewriter.create<tensor::EmptyOp>(
loc, resultTy.getShape(), resultETy, filteredDims);
+
+ // Broadcast the initial value to the output tensor before convolving.
+ SmallVector<AffineMap, 4> indexingMaps;
+ indexingMaps.push_back(getBroadcastingMap(rewriter, bias, biasEmptyTensor));
+ indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
+ indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
+
if (!isQuantized) {
Value conv = rewriter
.create<linalg::DepthwiseConv2DNhwcHwcmOp>(
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
index 0d55d1899c713e..bfdc72ee07e97f 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -702,6 +702,22 @@ func.func @depthwise_conv(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tensor<3x1x3x11xf
// -----
+// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (0)>
+// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+
+// CHECK-LABEL: @depthwise_conv_scalar_bias
+func.func @depthwise_conv_scalar_bias(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tensor<3x1x3x11xf32>, %arg2 : tensor<1xf32>) -> () {
+ // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, %{{.*}} : tensor<1xf32>, tensor<1x5x5x33xf32>) outs(%{{.*}} : tensor<1x5x5x33xf32>) {
+ // CHECK: ^bb0(%[[ARG3:[0-9a-zA-Z_]+]]: f32, %[[ARG4:[0-9a-zA-Z_]+]]: f32, %{{.*}}: f32):
+ // CHECK: [[ADD:%.+]] = arith.addf %[[ARG3]], %[[ARG4]] : f32
+ // CHECK: linalg.yield [[ADD]] : f32
+ // CHECK: } -> tensor<1x5x5x33xf32>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2 { pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<1xf32>) -> tensor<1x5x5x33xf32>
+ return
+}
+
+// -----
+
// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
@@ -840,6 +856,20 @@ func.func @conv3d_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<28x3x4
// -----
+// CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (0)>
+// CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
+
+// CHECK-LABEL: @conv3d_scalar_bias_f32
+func.func @conv3d_scalar_bias_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<28x3x4x5x27xf32>, %bias: tensor<1xf32>) -> () {
+ // CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x28xf32>
+ // CHECK: %[[BROADCAST:.+]] = linalg.generic
+ // CHECK-SAME: {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
+ %0 = tosa.conv3d %input, %weights, %bias {pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf32>, tensor<28x3x4x5x27xf32>, tensor<1xf32>) -> tensor<1x47x45x43x28xf32>
+ return
+}
+
+// -----
+
// CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d4)>
// CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
|
GeorgeARM
approved these changes
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requested review from
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sjarus,
eric-k256 and
kuhar
and removed request for
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approved these changes
Oct 2, 2024
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Oct 4, 2024
Support broadcasting of depthwise conv2d bias in tosa->linalg named lowering in the case that bias is a rank-1 tensor with exactly 1 element. In this case TOSA specifies the value should first be broadcast across the bias dimension and then across the result tensor. Add `lit` tests for depthwise conv2d with scalar bias and for conv3d which was already supported but missing coverage. Signed-off-by: Jack Frankland <jack.frankland@arm.com>
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Support broadcasting of depthwise conv2d bias in tosa->linalg named lowering in the case that bias is a rank-1 tensor with exactly 1 element. In this case TOSA specifies the value should first be broadcast across the bias dimension and then across the result tensor.
Add
littests for depthwise conv2d with scalar bias and for conv3d which was already supported but missing coverage.