[mlir][tosa-to-linalg] Add acc_type lowering Support #134267
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Add support for lowering of convolution operations where the `acc_type` attribute differs from the result type of the operation. The only case of this in for convolutions in the TOSA-v1.0 specification is an fp16 convolution which internally uses an fp32 accumulator; all other operations have accumulator types that match their output/result types. Add lit tests for the fp16 convolution with fp32 accumulator operators described above. Signed-off-by: Jack Frankland <[email protected]>
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@llvm/pr-subscribers-mlir @llvm/pr-subscribers-mlir-linalg Author: Jack Frankland (FranklandJack) ChangesAdd support for lowering of convolution operations where the Add lit tests for the fp16 convolution with fp32 accumulator operators described above. Full diff: https://github.com/llvm/llvm-project/pull/134267.diff 2 Files Affected:
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index fc1cad2423450..86f5e9baf4a94 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -119,10 +119,11 @@ static AffineMap getBroadcastingMap(PatternRewriter &rewriter, Value source,
}
// 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) {
+// If required, the element type is expanded using an arith.extsi or arith.extf
+// operation as appropriate.
+static mlir::Value linalgBroadcastAndMaybeExt(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.
@@ -135,11 +136,16 @@ static mlir::Value linalgBroadcastAndMaybeExtSI(PatternRewriter &rewriter,
.create<linalg::GenericOp>(
loc, resultTy, ValueRange({source}), result, indexingMaps,
getNParallelLoopsAttrs(resultTy.getRank()),
- [](OpBuilder &builder, Location loc, ValueRange args) {
+ [&resultTy](OpBuilder &builder, Location loc, ValueRange args) {
Value biasVal = args[0];
Type resType = args[1].getType();
if (resType != biasVal.getType()) {
- biasVal = builder.create<arith::ExtSIOp>(loc, resType, biasVal);
+ biasVal =
+ resultTy.getElementType().isFloat()
+ ? builder.create<arith::ExtFOp>(loc, resType, biasVal)
+ .getResult()
+ : builder.create<arith::ExtSIOp>(loc, resType, biasVal)
+ .getResult();
}
builder.create<linalg::YieldOp>(loc, biasVal);
})
@@ -253,12 +259,14 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
ShapedType resultTy = cast<ShapedType>(op->getResult(0).getType());
Type inputETy = inputTy.getElementType();
- Type resultETy = resultTy.getElementType();
DenseI64ArrayAttr padAttr = op.getPadAttr();
DenseI64ArrayAttr strideTosaAttr = op.getStrideAttr();
DenseI64ArrayAttr dilationTosaAttr = op.getDilationAttr();
+ Type accETy = op.getAccType();
+ Type accTy = RankedTensorType::get(resultTy.getShape(), accETy);
+
// Get and verify zero points.
FailureOr<int64_t> maybeIZp = op.getInputZeroPoint();
if (failed(maybeIZp))
@@ -385,10 +393,10 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
auto dilationAttr = rewriter.getI64TensorAttr(dilation);
Value biasEmptyTensor = rewriter.create<tensor::EmptyOp>(
- loc, resultTy.getShape(), resultETy, filteredDims);
+ loc, resultTy.getShape(), accETy, filteredDims);
Value broadcastBias =
- linalgBroadcastAndMaybeExtSI(rewriter, loc, bias, biasEmptyTensor);
+ linalgBroadcastAndMaybeExt(rewriter, loc, bias, biasEmptyTensor);
if (hasZp) {
auto iZp = rewriter.getI32IntegerAttr(inputZpVal);
@@ -410,10 +418,15 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
Value conv = rewriter
.create<LinalgConvOp>(
- loc, resultTy, ValueRange{input, weight},
+ loc, accTy, ValueRange{input, weight},
ValueRange{broadcastBias}, strideAttr, dilationAttr)
->getResult(0);
+ // We may need to truncate back to the result type if the accumulator was
+ // wider than the result.
+ if (resultTy != accTy)
+ conv = rewriter.create<tosa::CastOp>(loc, resultTy, conv);
+
rewriter.replaceOp(op, conv);
return success();
}
@@ -444,6 +457,8 @@ class DepthwiseConvConverter
auto strideTosaAttr = cast<DenseI64ArrayAttr>(op->getAttr("stride"));
auto dilationTosaAttr = cast<DenseI64ArrayAttr>(op->getAttr("dilation"));
+ Type accETy = op.getAccType();
+
if (!weightTy.hasStaticShape() || !biasTy.hasStaticShape())
return rewriter.notifyMatchFailure(
op, "tosa.depthwise_conv ops require static shapes");
@@ -516,11 +531,11 @@ class DepthwiseConvConverter
ShapedType linalgConvTy =
RankedTensorType::get({resultShape[0], resultShape[1], resultShape[2],
weightShape[2], weightShape[3]},
- resultETy);
+ accETy);
- auto resultZeroAttr = rewriter.getZeroAttr(resultETy);
+ auto resultZeroAttr = rewriter.getZeroAttr(accETy);
Value emptyTensor = rewriter.create<tensor::EmptyOp>(
- loc, linalgConvTy.getShape(), resultETy, filteredDims);
+ loc, linalgConvTy.getShape(), accETy, filteredDims);
Value zero = rewriter.create<arith::ConstantOp>(loc, resultZeroAttr);
Value zeroTensor = rewriter
.create<linalg::FillOp>(loc, ValueRange{zero},
@@ -543,6 +558,15 @@ class DepthwiseConvConverter
ValueRange{zeroTensor}, strideAttr, dilationAttr)
.getResult(0);
+ // We may need to truncate back to the result type if the accumulator was
+ // wider than the result.
+ if (accETy != resultETy)
+ conv = rewriter.create<tosa::CastOp>(
+ loc,
+ RankedTensorType::get(cast<ShapedType>(conv.getType()).getShape(),
+ resultETy),
+ conv);
+
SmallVector<ReassociationExprs, 4> reassociationMap;
createDepthwiseConvCollapseMap(resultRank, reassociationMap, rewriter);
Value convReshape = rewriter.create<tensor::CollapseShapeOp>(
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
index 19c12ba3edbd4..242772fe5cdcf 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -658,6 +658,20 @@ func.func @conv2d_quant(%arg0 : tensor<1x12x12x1xi8>, %arg1 : tensor<1024x3x3x1x
// -----
+// CHECK-LABEL: @conv2d_f16_f32_acc
+func.func @conv2d_f16_f32_acc(%input: tensor<1x49x42x27xf16>, %weights: tensor<28x3x3x27xf16>, %bias: tensor<28xf16>) -> () {
+ %input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ // CHECK: linalg.generic {{{.*}}} ins(%{{.*}} : tensor<28xf16>) outs(%{{.*}} : tensor<1x45x40x28xf32>)
+ // CHECK: arith.extf %{{.*}} : f16 to f32
+ // CHECK: %[[CONV:.*]] = linalg.conv_2d_nhwc_fhwc {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x49x42x27xf16>, tensor<28x3x3x27xf16>) outs(%{{.*}} : tensor<1x45x40x28xf32>) -> tensor<1x45x40x28xf32>
+ // CHECK: tosa.cast %[[CONV]] : (tensor<1x45x40x28xf32>) -> tensor<1x45x40x28xf16>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x49x42x27xf16>, tensor<28x3x3x27xf16>, tensor<28xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x45x40x28xf16>
+ return
+}
+
+// -----
+
// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
@@ -848,6 +862,18 @@ func.func @depthwise_int_conv_zero_zp(%arg0 : tensor<1x7x5x3xi8>, %arg1 : tensor
// -----
+// CHECK-LABEL: @depthwise_conv2d_f16_f32_acc
+func.func @depthwise_conv2d_f16_f32_acc(%arg0 : tensor<1x7x5x3xf16>, %arg1 : tensor<3x1x3x11xf16>, %arg2 : tensor<33xf16>) -> () {
+ %input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ // CHECK: %[[CONV:.*]] = linalg.depthwise_conv_2d_nhwc_hwcm {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x7x5x3xf16>, tensor<3x1x3x11xf16>) outs(%{{.*}} : tensor<1x5x5x3x11xf32>) -> tensor<1x5x5x3x11xf32>
+ // CHECK: tosa.cast %[[CONV]] : (tensor<1x5x5x3x11xf32>) -> tensor<1x5x5x3x11xf16>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf16>, tensor<3x1x3x11xf16>, tensor<33xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x5x5x33xf16>
+ 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)>
@@ -918,6 +944,20 @@ func.func @conv3d_i8(%input: tensor<1x49x48x47x27xi8>, %weights: tensor<28x3x4x5
// -----
+// CHECK-LABEL: @conv3d_f16_f32_acc
+func.func @conv3d_f16_f32_acc(%input: tensor<1x49x48x47x27xf16>, %weights: tensor<28x3x4x5x27xf16>, %bias: tensor<28xf16>) -> () {
+ %input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ // CHECK: linalg.generic {{{.*}}} ins(%{{.*}} : tensor<28xf16>) outs(%{{.*}} : tensor<1x47x45x43x28xf32>)
+ // CHECK: arith.extf %{{.*}} : f16 to f32
+ // CHECK: %[[CONV:.*]] = linalg.conv_3d_ndhwc_dhwcf {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x49x48x47x27xf16>, tensor<3x4x5x27x28xf16>) outs(%{{.*}} : tensor<1x47x45x43x28xf32>) -> tensor<1x47x45x43x28xf32>
+ // CHECK: tosa.cast %[[CONV]] : (tensor<1x47x45x43x28xf32>) -> tensor<1x47x45x43x28xf16>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf16>, tensor<28x3x4x5x27xf16>, tensor<28xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x47x45x43x28xf16>
+ return
+}
+
+// -----
+
// CHECK-LABEL: @test_transpose
// CHECK-SAME: (%[[ARG0:.+]]: tensor<1x2x3xi32>)
func.func @test_transpose(%arg0: tensor<1x2x3xi32>) -> () {
|
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@llvm/pr-subscribers-mlir-tosa Author: Jack Frankland (FranklandJack) ChangesAdd support for lowering of convolution operations where the Add lit tests for the fp16 convolution with fp32 accumulator operators described above. Full diff: https://github.com/llvm/llvm-project/pull/134267.diff 2 Files Affected:
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index fc1cad2423450..86f5e9baf4a94 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -119,10 +119,11 @@ static AffineMap getBroadcastingMap(PatternRewriter &rewriter, Value source,
}
// 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) {
+// If required, the element type is expanded using an arith.extsi or arith.extf
+// operation as appropriate.
+static mlir::Value linalgBroadcastAndMaybeExt(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.
@@ -135,11 +136,16 @@ static mlir::Value linalgBroadcastAndMaybeExtSI(PatternRewriter &rewriter,
.create<linalg::GenericOp>(
loc, resultTy, ValueRange({source}), result, indexingMaps,
getNParallelLoopsAttrs(resultTy.getRank()),
- [](OpBuilder &builder, Location loc, ValueRange args) {
+ [&resultTy](OpBuilder &builder, Location loc, ValueRange args) {
Value biasVal = args[0];
Type resType = args[1].getType();
if (resType != biasVal.getType()) {
- biasVal = builder.create<arith::ExtSIOp>(loc, resType, biasVal);
+ biasVal =
+ resultTy.getElementType().isFloat()
+ ? builder.create<arith::ExtFOp>(loc, resType, biasVal)
+ .getResult()
+ : builder.create<arith::ExtSIOp>(loc, resType, biasVal)
+ .getResult();
}
builder.create<linalg::YieldOp>(loc, biasVal);
})
@@ -253,12 +259,14 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
ShapedType resultTy = cast<ShapedType>(op->getResult(0).getType());
Type inputETy = inputTy.getElementType();
- Type resultETy = resultTy.getElementType();
DenseI64ArrayAttr padAttr = op.getPadAttr();
DenseI64ArrayAttr strideTosaAttr = op.getStrideAttr();
DenseI64ArrayAttr dilationTosaAttr = op.getDilationAttr();
+ Type accETy = op.getAccType();
+ Type accTy = RankedTensorType::get(resultTy.getShape(), accETy);
+
// Get and verify zero points.
FailureOr<int64_t> maybeIZp = op.getInputZeroPoint();
if (failed(maybeIZp))
@@ -385,10 +393,10 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
auto dilationAttr = rewriter.getI64TensorAttr(dilation);
Value biasEmptyTensor = rewriter.create<tensor::EmptyOp>(
- loc, resultTy.getShape(), resultETy, filteredDims);
+ loc, resultTy.getShape(), accETy, filteredDims);
Value broadcastBias =
- linalgBroadcastAndMaybeExtSI(rewriter, loc, bias, biasEmptyTensor);
+ linalgBroadcastAndMaybeExt(rewriter, loc, bias, biasEmptyTensor);
if (hasZp) {
auto iZp = rewriter.getI32IntegerAttr(inputZpVal);
@@ -410,10 +418,15 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
Value conv = rewriter
.create<LinalgConvOp>(
- loc, resultTy, ValueRange{input, weight},
+ loc, accTy, ValueRange{input, weight},
ValueRange{broadcastBias}, strideAttr, dilationAttr)
->getResult(0);
+ // We may need to truncate back to the result type if the accumulator was
+ // wider than the result.
+ if (resultTy != accTy)
+ conv = rewriter.create<tosa::CastOp>(loc, resultTy, conv);
+
rewriter.replaceOp(op, conv);
return success();
}
@@ -444,6 +457,8 @@ class DepthwiseConvConverter
auto strideTosaAttr = cast<DenseI64ArrayAttr>(op->getAttr("stride"));
auto dilationTosaAttr = cast<DenseI64ArrayAttr>(op->getAttr("dilation"));
+ Type accETy = op.getAccType();
+
if (!weightTy.hasStaticShape() || !biasTy.hasStaticShape())
return rewriter.notifyMatchFailure(
op, "tosa.depthwise_conv ops require static shapes");
@@ -516,11 +531,11 @@ class DepthwiseConvConverter
ShapedType linalgConvTy =
RankedTensorType::get({resultShape[0], resultShape[1], resultShape[2],
weightShape[2], weightShape[3]},
- resultETy);
+ accETy);
- auto resultZeroAttr = rewriter.getZeroAttr(resultETy);
+ auto resultZeroAttr = rewriter.getZeroAttr(accETy);
Value emptyTensor = rewriter.create<tensor::EmptyOp>(
- loc, linalgConvTy.getShape(), resultETy, filteredDims);
+ loc, linalgConvTy.getShape(), accETy, filteredDims);
Value zero = rewriter.create<arith::ConstantOp>(loc, resultZeroAttr);
Value zeroTensor = rewriter
.create<linalg::FillOp>(loc, ValueRange{zero},
@@ -543,6 +558,15 @@ class DepthwiseConvConverter
ValueRange{zeroTensor}, strideAttr, dilationAttr)
.getResult(0);
+ // We may need to truncate back to the result type if the accumulator was
+ // wider than the result.
+ if (accETy != resultETy)
+ conv = rewriter.create<tosa::CastOp>(
+ loc,
+ RankedTensorType::get(cast<ShapedType>(conv.getType()).getShape(),
+ resultETy),
+ conv);
+
SmallVector<ReassociationExprs, 4> reassociationMap;
createDepthwiseConvCollapseMap(resultRank, reassociationMap, rewriter);
Value convReshape = rewriter.create<tensor::CollapseShapeOp>(
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
index 19c12ba3edbd4..242772fe5cdcf 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -658,6 +658,20 @@ func.func @conv2d_quant(%arg0 : tensor<1x12x12x1xi8>, %arg1 : tensor<1024x3x3x1x
// -----
+// CHECK-LABEL: @conv2d_f16_f32_acc
+func.func @conv2d_f16_f32_acc(%input: tensor<1x49x42x27xf16>, %weights: tensor<28x3x3x27xf16>, %bias: tensor<28xf16>) -> () {
+ %input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ // CHECK: linalg.generic {{{.*}}} ins(%{{.*}} : tensor<28xf16>) outs(%{{.*}} : tensor<1x45x40x28xf32>)
+ // CHECK: arith.extf %{{.*}} : f16 to f32
+ // CHECK: %[[CONV:.*]] = linalg.conv_2d_nhwc_fhwc {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x49x42x27xf16>, tensor<28x3x3x27xf16>) outs(%{{.*}} : tensor<1x45x40x28xf32>) -> tensor<1x45x40x28xf32>
+ // CHECK: tosa.cast %[[CONV]] : (tensor<1x45x40x28xf32>) -> tensor<1x45x40x28xf16>
+ %0 = tosa.conv2d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x49x42x27xf16>, tensor<28x3x3x27xf16>, tensor<28xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x45x40x28xf16>
+ return
+}
+
+// -----
+
// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
@@ -848,6 +862,18 @@ func.func @depthwise_int_conv_zero_zp(%arg0 : tensor<1x7x5x3xi8>, %arg1 : tensor
// -----
+// CHECK-LABEL: @depthwise_conv2d_f16_f32_acc
+func.func @depthwise_conv2d_f16_f32_acc(%arg0 : tensor<1x7x5x3xf16>, %arg1 : tensor<3x1x3x11xf16>, %arg2 : tensor<33xf16>) -> () {
+ %input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ // CHECK: %[[CONV:.*]] = linalg.depthwise_conv_2d_nhwc_hwcm {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x7x5x3xf16>, tensor<3x1x3x11xf16>) outs(%{{.*}} : tensor<1x5x5x3x11xf32>) -> tensor<1x5x5x3x11xf32>
+ // CHECK: tosa.cast %[[CONV]] : (tensor<1x5x5x3x11xf32>) -> tensor<1x5x5x3x11xf16>
+ %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xf16>, tensor<3x1x3x11xf16>, tensor<33xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x5x5x33xf16>
+ 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)>
@@ -918,6 +944,20 @@ func.func @conv3d_i8(%input: tensor<1x49x48x47x27xi8>, %weights: tensor<28x3x4x5
// -----
+// CHECK-LABEL: @conv3d_f16_f32_acc
+func.func @conv3d_f16_f32_acc(%input: tensor<1x49x48x47x27xf16>, %weights: tensor<28x3x4x5x27xf16>, %bias: tensor<28xf16>) -> () {
+ %input_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ %weight_zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf16>}> : () -> tensor<1xf16>
+ // CHECK: linalg.generic {{{.*}}} ins(%{{.*}} : tensor<28xf16>) outs(%{{.*}} : tensor<1x47x45x43x28xf32>)
+ // CHECK: arith.extf %{{.*}} : f16 to f32
+ // CHECK: %[[CONV:.*]] = linalg.conv_3d_ndhwc_dhwcf {{{.*}}} ins(%{{.*}}, %{{.*}} : tensor<1x49x48x47x27xf16>, tensor<3x4x5x27x28xf16>) outs(%{{.*}} : tensor<1x47x45x43x28xf32>) -> tensor<1x47x45x43x28xf32>
+ // CHECK: tosa.cast %[[CONV]] : (tensor<1x47x45x43x28xf32>) -> tensor<1x47x45x43x28xf16>
+ %0 = tosa.conv3d %input, %weights, %bias, %input_zp, %weight_zp {acc_type = f32, pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf16>, tensor<28x3x4x5x27xf16>, tensor<28xf16>, tensor<1xf16>, tensor<1xf16>) -> tensor<1x47x45x43x28xf16>
+ return
+}
+
+// -----
+
// CHECK-LABEL: @test_transpose
// CHECK-SAME: (%[[ARG0:.+]]: tensor<1x2x3xi32>)
func.func @test_transpose(%arg0: tensor<1x2x3xi32>) -> () {
|
lhutton1
reviewed
Apr 4, 2025
GeorgeARM
approved these changes
Apr 7, 2025
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Add support for lowering of convolution operations where the
acc_typeattribute differs from the result type of the operation. The only case of this in for convolutions in the TOSA-v1.0 specification is an fp16 convolution which internally uses an fp32 accumulator; all other operations have accumulator types that match their output/result types.Add lit tests for the fp16 convolution with fp32 accumulator operators described above.