scalar_arg: B
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
- name: conv_2d_input_nhwc_filter_ohwi_poly
- cpp_class_name: Conv2DInputNhwcFilterOhwiPolyOp
+ name: conv_2d_nchw
+ cpp_class_name: Conv2DNchwOp
doc: |-
- Performs a 2-D convolution.
+ Performs 2-D convolution.
Numeric casting is performed on the operands to the inner multiply, promoting
them to the same data type as the accumulator/output.
usage: InputOperand
type_var: T2
shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
- -> (s4, s5, s6, s3)>
+ -> (s4, s1, s5, s6)>
- !LinalgOperandDefConfig
name: O
usage: OutputOperand
type_var: U
shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
- -> (s0, s7, s8, s4)>
+ -> (s0, s4, s7, s8, s1)>
- !LinalgOperandDefConfig
name: strides
usage: IndexAttribute
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d0, d1 * s9 + d3 * s11, d2 * s10 + d4 * s12, d6)>
+ s9, s10, s11, s12] -> (d0, d4, d2 * s9 + d5 * s11, d3 * s10 + d6 * s12)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d5, d3, d4, d6)>
+ s9, s10, s11, s12] -> (d1, d4, d5, d6)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d0, d1, d2, d5)>
+ s9, s10, s11, s12] -> (d0, d1, d2, d3)>
iterator_types:
- parallel
- parallel
- parallel
+ - parallel
- reduction
- reduction
- - parallel
- reduction
assignments:
- !ScalarAssign
scalar_arg: K
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
- name: conv_2d_input_nhwc_filter_ohwi_poly_q
- cpp_class_name: Conv2DInputNhwcFilterOhwiPolyQOp
+ name: conv_2d_nhwc_hwcf
+ cpp_class_name: Conv2DNhwcHwcfOp
doc: |-
- Performs a 2-D quantized convolution.
+ Performs 2-D convolution.
Numeric casting is performed on the operands to the inner multiply, promoting
- them to the same data type as the accumulator/output. Includes zero point
- adjustment for quantization.
+ them to the same data type as the accumulator/output.
structured_op: !LinalgStructuredOpConfig
args:
- !LinalgOperandDefConfig
usage: InputOperand
type_var: T2
shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
- -> (s4, s5, s6, s3)>
- - !LinalgOperandDefConfig
- name: IZp
- usage: InputOperand
- type_var: I32
- - !LinalgOperandDefConfig
- name: KZp
- usage: InputOperand
- type_var: I32
+ -> (s4, s5, s3, s6)>
- !LinalgOperandDefConfig
name: O
usage: OutputOperand
type_var: U
shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
- -> (s0, s7, s8, s4)>
+ -> (s0, s7, s8, s6)>
- !LinalgOperandDefConfig
name: strides
usage: IndexAttribute
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d0, d1 * s9 + d3 * s11, d2 * s10 + d4 * s12, d6)>
- - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d5, d3, d4, d6)>
+ s9, s10, s11, s12] -> (d0, d1 * s9 + d4 * s11, d2 * s10 + d5 * s12, d6)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> ()>
- - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> ()>
+ s9, s10, s11, s12] -> (d4, d5, d6, d3)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d0, d1, d2, d5)>
+ s9, s10, s11, s12] -> (d0, d1, d2, d3)>
iterator_types:
- parallel
- parallel
- parallel
+ - parallel
- reduction
- reduction
- - parallel
- reduction
assignments:
- !ScalarAssign
fn_name: mul
operands:
- !ScalarExpression
- scalar_apply:
- fn_name: sub
+ symbolic_cast:
+ type_var: U
operands:
- !ScalarExpression
- symbolic_cast:
- type_var: U
- operands:
- - !ScalarExpression
- scalar_arg: I
- - !ScalarExpression
- symbolic_cast:
- type_var: U
- operands:
- - !ScalarExpression
- scalar_arg: IZp
+ scalar_arg: I
- !ScalarExpression
- scalar_apply:
- fn_name: sub
+ symbolic_cast:
+ type_var: U
operands:
- !ScalarExpression
- symbolic_cast:
- type_var: U
- operands:
- - !ScalarExpression
- scalar_arg: K
- - !ScalarExpression
- symbolic_cast:
- type_var: U
- operands:
- - !ScalarExpression
- scalar_arg: KZp
+ scalar_arg: K
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: depthwise_conv_2d_input_nhwc_filter_hwc_poly
scalar_arg: K
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
- name: depthwise_conv_2D_nchw
- cpp_class_name: DepthwiseConv2DNchwOp
+ name: conv_2d_nhwc_hwcf_q
+ cpp_class_name: Conv2DNhwcHwcfQOp
doc: |-
- Performs depth-wise 2-D convolution.
+ Performs 2-D convolution with zero point offsets.
Numeric casting is performed on the operands to the inner multiply, promoting
- them to the same data type as the accumulator/output.
+ them to the same data type as the accumulator/output. This includes the zero
+ point offsets common to quantized operations.
structured_op: !LinalgStructuredOpConfig
args:
- !LinalgOperandDefConfig
type_var: T2
shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
-> (s4, s5, s3, s6)>
+ - !LinalgOperandDefConfig
+ name: IZp
+ usage: InputOperand
+ type_var: I32
+ - !LinalgOperandDefConfig
+ name: KZp
+ usage: InputOperand
+ type_var: I32
- !LinalgOperandDefConfig
name: O
usage: OutputOperand
type_var: U
shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
- -> (s0, s7, s8, s3, s6)>
+ -> (s0, s7, s8, s6)>
- !LinalgOperandDefConfig
name: strides
usage: IndexAttribute
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d0, d1 * s9 + d3 * s11, d2 * s10 + d4 * s12, d5)>
+ s9, s10, s11, s12] -> (d0, d1 * s9 + d4 * s11, d2 * s10 + d5 * s12, d6)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d3, d4, d5, d6)>
+ s9, s10, s11, s12] -> (d4, d5, d6, d3)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d0, d1, d2, d5, d6)>
+ s9, s10, s11, s12] -> ()>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+ s9, s10, s11, s12] -> ()>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+ s9, s10, s11, s12] -> (d0, d1, d2, d3)>
iterator_types:
- parallel
- parallel
- parallel
+ - parallel
+ - reduction
- reduction
- reduction
- - parallel
- - parallel
assignments:
- !ScalarAssign
arg: O
fn_name: mul
operands:
- !ScalarExpression
- symbolic_cast:
- type_var: U
+ scalar_apply:
+ fn_name: sub
operands:
- !ScalarExpression
- scalar_arg: I
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: I
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: IZp
- !ScalarExpression
- symbolic_cast:
- type_var: U
+ scalar_apply:
+ fn_name: sub
operands:
- !ScalarExpression
- scalar_arg: K
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: K
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: KZp
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
- name: depthwise_conv2D_nchw_q
- cpp_class_name: DepthwiseConv2DNchwQOp
+ name: depthwise_conv2D_nchw
+ cpp_class_name: DepthwiseConv2DNchwOp
doc: |-
Performs depth-wise 2-D convolution.
type_var: T2
shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
-> (s4, s5, s3, s6)>
- - !LinalgOperandDefConfig
- name: IZp
- usage: InputOperand
- type_var: I32
- - !LinalgOperandDefConfig
- name: KZp
- usage: InputOperand
- type_var: I32
- !LinalgOperandDefConfig
name: O
usage: OutputOperand
s9, s10, s11, s12] -> (d0, d1 * s9 + d3 * s11, d2 * s10 + d4 * s12, d5)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
s9, s10, s11, s12] -> (d3, d4, d5, d6)>
- - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> ()>
- - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> ()>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
s9, s10, s11, s12] -> (d0, d1, d2, d5, d6)>
iterator_types:
fn_name: mul
operands:
- !ScalarExpression
- scalar_apply:
- fn_name: sub
+ symbolic_cast:
+ type_var: U
operands:
- !ScalarExpression
- symbolic_cast:
- type_var: U
- operands:
- - !ScalarExpression
- scalar_arg: I
- - !ScalarExpression
- symbolic_cast:
- type_var: U
- operands:
- - !ScalarExpression
- scalar_arg: IZp
+ scalar_arg: I
- !ScalarExpression
- scalar_apply:
- fn_name: sub
+ symbolic_cast:
+ type_var: U
operands:
- !ScalarExpression
- symbolic_cast:
- type_var: U
- operands:
- - !ScalarExpression
- scalar_arg: K
- - !ScalarExpression
- symbolic_cast:
- type_var: U
- operands:
- - !ScalarExpression
- scalar_arg: KZp
+ scalar_arg: K
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
- name: conv_2d_nchw
- cpp_class_name: Conv2DNchwOp
+ name: depthwise_conv2D_nchw_q
+ cpp_class_name: DepthwiseConv2DNchwQOp
doc: |-
- Performs 2-D convolution.
+ Performs depth-wise 2-D convolution.
Numeric casting is performed on the operands to the inner multiply, promoting
them to the same data type as the accumulator/output.
usage: InputOperand
type_var: T2
shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
- -> (s4, s1, s5, s6)>
+ -> (s4, s5, s3, s6)>
+ - !LinalgOperandDefConfig
+ name: IZp
+ usage: InputOperand
+ type_var: I32
+ - !LinalgOperandDefConfig
+ name: KZp
+ usage: InputOperand
+ type_var: I32
- !LinalgOperandDefConfig
name: O
usage: OutputOperand
type_var: U
shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12]
- -> (s0, s4, s7, s8, s1)>
+ -> (s0, s7, s8, s3, s6)>
- !LinalgOperandDefConfig
name: strides
usage: IndexAttribute
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d0, d4, d2 * s9 + d5 * s11, d3 * s10 + d6 * s12)>
+ s9, s10, s11, s12] -> (d0, d1 * s9 + d3 * s11, d2 * s10 + d4 * s12, d5)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d1, d4, d5, d6)>
+ s9, s10, s11, s12] -> (d3, d4, d5, d6)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
- s9, s10, s11, s12] -> (d0, d1, d2, d3)>
+ s9, s10, s11, s12] -> ()>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+ s9, s10, s11, s12] -> ()>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+ s9, s10, s11, s12] -> (d0, d1, d2, d5, d6)>
iterator_types:
- parallel
- parallel
- parallel
- - parallel
- - reduction
- reduction
- reduction
+ - parallel
+ - parallel
assignments:
- !ScalarAssign
arg: O
fn_name: mul
operands:
- !ScalarExpression
- symbolic_cast:
- type_var: U
+ scalar_apply:
+ fn_name: sub
operands:
- !ScalarExpression
- scalar_arg: I
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: I
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: IZp
- !ScalarExpression
- symbolic_cast:
- type_var: U
+ scalar_apply:
+ fn_name: sub
operands:
- !ScalarExpression
- scalar_arg: K
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: K
+ - !ScalarExpression
+ symbolic_cast:
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: KZp
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: pooling_nhwc_sum
operands:
- !ScalarExpression
scalar_arg: I
+
return success();
}
-static LogicalResult
-convolutionMatchAndRewriterHelper(Operation *op,
- ConversionPatternRewriter &rewriter) {
- Location loc = op->getLoc();
- Value input = op->getOperand(0);
- Value weight = op->getOperand(1);
- Value bias = op->getOperand(2);
+namespace {
- ShapedType inputTy = input.getType().cast<ShapedType>();
- ShapedType weightTy = weight.getType().cast<ShapedType>();
- ShapedType biasTy = bias.getType().cast<ShapedType>();
- ShapedType resultTy = op->getResult(0).getType().cast<ShapedType>();
+template <typename SrcOp>
+class PointwiseConverter : public OpRewritePattern<SrcOp> {
+public:
+ using OpRewritePattern<SrcOp>::OpRewritePattern;
- Type inputETy = inputTy.getElementType();
- Type resultETy = resultTy.getElementType();
-
- auto padAttr = op->getAttr("pad").cast<ArrayAttr>();
- auto strideTosaAttr = op->getAttr("stride").cast<ArrayAttr>();
- auto dilationTosaAttr = op->getAttr("dilation").cast<ArrayAttr>();
-
- bool isQuantized = op->hasAttr("quantization_info");
- IntegerAttr iZp;
- IntegerAttr kZp;
- if (isQuantized) {
- auto quantizationInfo =
- op->getAttr("quantization_info").cast<tosa::ConvOpQuantizationAttr>();
- iZp = rewriter.getI32IntegerAttr(
- quantizationInfo.input_zp().getValue().getSExtValue());
- kZp = rewriter.getI32IntegerAttr(
- quantizationInfo.weight_zp().getValue().getSExtValue());
+ LogicalResult matchAndRewrite(SrcOp op,
+ PatternRewriter &rewriter) const final {
+ return elementwiseMatchAndRewriteHelper(op, rewriter);
}
+};
- if (!inputTy.hasStaticShape() || !weightTy.hasStaticShape() ||
- !biasTy.hasStaticShape() || !resultTy.hasStaticShape())
- return rewriter.notifyMatchFailure(op,
- "tosa.conv ops require static shapes");
+class ConvConverter : public OpConversionPattern<tosa::Conv2DOp> {
+public:
+ using OpConversionPattern<tosa::Conv2DOp>::OpConversionPattern;
+ LogicalResult
+ matchAndRewrite(tosa::Conv2DOp op, ArrayRef<Value> args,
+ ConversionPatternRewriter &rewriter) const final {
+ Location loc = op->getLoc();
+ Value input = op->getOperand(0);
+ Value weight = op->getOperand(1);
+ Value bias = op->getOperand(2);
- auto weightShape = weightTy.getShape();
- auto resultShape = resultTy.getShape();
+ ShapedType inputTy = input.getType().cast<ShapedType>();
+ ShapedType weightTy = weight.getType().cast<ShapedType>();
+ ShapedType biasTy = bias.getType().cast<ShapedType>();
+ ShapedType resultTy = op->getResult(0).getType().cast<ShapedType>();
- // Apply padding as necessary.
- Attribute zeroAttr = rewriter.getZeroAttr(inputETy);
- llvm::SmallVector<int64_t> pad;
- pad.resize(2, 0);
- getValuesFromIntArrayAttribute(padAttr, pad);
- pad.resize(pad.size() + 2, 0);
+ Type inputETy = inputTy.getElementType();
+ Type resultETy = resultTy.getElementType();
- input = applyPad(loc, input, pad, zeroAttr, rewriter);
+ auto padAttr = op->getAttr("pad").cast<ArrayAttr>();
+ auto strideTosaAttr = op->getAttr("stride").cast<ArrayAttr>();
+ auto dilationTosaAttr = op->getAttr("dilation").cast<ArrayAttr>();
+ bool isQuantized = op->hasAttr("quantization_info");
- // Broadcast the initial value to the output tensor before convolving.
- SmallVector<AffineMap, 4> indexingMaps;
- indexingMaps.push_back(AffineMap::get(
- /*dimCount=*/resultTy.getRank(), /*symbolCount=*/0,
- {rewriter.getAffineDimExpr(3)}, rewriter.getContext()));
- indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultTy.getRank()));
+ if (!inputTy.hasStaticShape() || !weightTy.hasStaticShape() ||
+ !biasTy.hasStaticShape() || !resultTy.hasStaticShape())
+ return rewriter.notifyMatchFailure(op,
+ "tosa.conv ops require static shapes");
- Value initTensor = rewriter.create<linalg::InitTensorOp>(
- loc, resultTy.getShape(), resultTy.getElementType());
+ auto weightShape = weightTy.getShape();
- Value biasBroadcast =
- rewriter
- .create<linalg::GenericOp>(
- loc, resultTy, bias, initTensor, indexingMaps,
- getNParallelLoopsAttrs(resultTy.getRank()),
- [&](OpBuilder &nestedBuilder, Location nestedLoc,
- ValueRange args) {
- nestedBuilder.create<linalg::YieldOp>(nestedLoc, args[0]);
- })
- .getResult(0);
-
- // Extract the attributes for convolution.
- llvm::SmallVector<int64_t> stride, dilation;
- getValuesFromIntArrayAttribute(strideTosaAttr, stride);
- getValuesFromIntArrayAttribute(dilationTosaAttr, dilation);
-
- // Create the convolution op.
- auto strideAttr = DenseIntElementsAttr::get(
- RankedTensorType::get({2}, rewriter.getI64Type()), stride);
- auto dilationAttr = DenseIntElementsAttr::get(
- RankedTensorType::get({2}, rewriter.getI64Type()), dilation);
-
- if (isa<tosa::Conv2DOp>(op) && !isQuantized) {
- rewriter.replaceOpWithNewOp<linalg::Conv2DInputNhwcFilterOhwiPolyOp>(
+ // Apply padding as necessary.
+ Attribute zeroAttr = rewriter.getZeroAttr(inputETy);
+ llvm::SmallVector<int64_t> pad;
+ pad.resize(2, 0);
+ getValuesFromIntArrayAttribute(padAttr, pad);
+ pad.resize(pad.size() + 2, 0);
+ input = applyPad(loc, input, pad, zeroAttr, rewriter);
+
+ // Transpose the kernel to match dimension ordering of the linalg
+ // convolution operation.
+ // TODO(suderman): See if this can be efficiently folded - check whether
+ // the input is used anywhere else, if not fold the constant.
+ SmallVector<int64_t> weightPerm{1, 2, 3, 0};
+ SmallVector<int64_t> newWeightShape{weightShape[1], weightShape[2],
+ weightShape[3], weightShape[0]};
+ auto weightPermAttr = DenseIntElementsAttr::get(
+ RankedTensorType::get({4}, rewriter.getI64Type()), weightPerm);
+ Value weightPermValue = rewriter.create<ConstantOp>(loc, weightPermAttr);
+ Type newWeightTy =
+ RankedTensorType::get(newWeightShape, weightTy.getElementType());
+ weight = rewriter.create<tosa::TransposeOp>(loc, newWeightTy, weight,
+ weightPermValue);
+
+ // Broadcast the initial value to the output tensor before convolving.
+ SmallVector<AffineMap, 4> indexingMaps;
+ indexingMaps.push_back(AffineMap::get(
+ /*dimCount=*/resultTy.getRank(), /*symbolCount=*/0,
+ {rewriter.getAffineDimExpr(3)}, rewriter.getContext()));
+ indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultTy.getRank()));
+
+ Value initTensor = rewriter.create<linalg::InitTensorOp>(
+ loc, resultTy.getShape(), resultETy);
+
+ Value biasBroadcast =
+ rewriter
+ .create<linalg::GenericOp>(
+ loc, resultTy, bias, initTensor, indexingMaps,
+ getNParallelLoopsAttrs(resultTy.getRank()),
+ [&](OpBuilder &nestedBuilder, Location nestedLoc,
+ ValueRange args) {
+ nestedBuilder.create<linalg::YieldOp>(nestedLoc, args[0]);
+ })
+ .getResult(0);
+
+ // Extract the attributes for convolution.
+ llvm::SmallVector<int64_t> stride, dilation;
+ getValuesFromIntArrayAttribute(strideTosaAttr, stride);
+ getValuesFromIntArrayAttribute(dilationTosaAttr, dilation);
+
+ // Create the convolution op.
+ auto strideAttr = DenseIntElementsAttr::get(
+ RankedTensorType::get({2}, rewriter.getI64Type()), stride);
+ auto dilationAttr = DenseIntElementsAttr::get(
+ RankedTensorType::get({2}, rewriter.getI64Type()), dilation);
+
+ Value conv;
+ if (isQuantized) {
+ auto quantizationInfo =
+ op->getAttr("quantization_info").cast<tosa::ConvOpQuantizationAttr>();
+ auto iZp = rewriter.getI32IntegerAttr(
+ quantizationInfo.input_zp().getValue().getSExtValue());
+ auto kZp = rewriter.getI32IntegerAttr(
+ quantizationInfo.weight_zp().getValue().getSExtValue());
+
+ auto iZpVal = rewriter.create<ConstantOp>(loc, iZp);
+ auto kZpVal = rewriter.create<ConstantOp>(loc, kZp);
+ rewriter.replaceOpWithNewOp<linalg::Conv2DNhwcHwcfQOp>(
+ op, resultTy, ValueRange{input, weight, iZpVal, kZpVal},
+ ValueRange{biasBroadcast}, strideAttr, dilationAttr);
+ return success();
+ }
+
+ rewriter.replaceOpWithNewOp<linalg::Conv2DNhwcHwcfOp>(
op, resultTy, ValueRange{input, weight}, ValueRange{biasBroadcast},
strideAttr, dilationAttr);
return success();
}
+};
- if (isa<tosa::Conv2DOp>(op) && isQuantized) {
- auto iZpVal = rewriter.create<ConstantOp>(loc, iZp);
- auto kZpVal = rewriter.create<ConstantOp>(loc, kZp);
- rewriter.replaceOpWithNewOp<linalg::Conv2DInputNhwcFilterOhwiPolyQOp>(
- op, resultTy, ValueRange{input, weight, iZpVal, kZpVal},
- ValueRange{biasBroadcast}, strideAttr, dilationAttr);
- return success();
- }
+class DepthwiseConvConverter
+ : public OpConversionPattern<tosa::DepthwiseConv2DOp> {
+public:
+ using OpConversionPattern<tosa::DepthwiseConv2DOp>::OpConversionPattern;
+ LogicalResult
+ matchAndRewrite(tosa::DepthwiseConv2DOp op, ArrayRef<Value> args,
+ ConversionPatternRewriter &rewriter) const final {
+ Location loc = op->getLoc();
+ Value input = op->getOperand(0);
+ Value weight = op->getOperand(1);
+ Value bias = op->getOperand(2);
+
+ ShapedType inputTy = input.getType().cast<ShapedType>();
+ ShapedType weightTy = weight.getType().cast<ShapedType>();
+ ShapedType biasTy = bias.getType().cast<ShapedType>();
+ ShapedType resultTy = op->getResult(0).getType().cast<ShapedType>();
- if (isa<tosa::DepthwiseConv2DOp>(op)) {
+ Type inputETy = inputTy.getElementType();
+ Type resultETy = resultTy.getElementType();
+
+ auto padAttr = op->getAttr("pad").cast<ArrayAttr>();
+ auto strideTosaAttr = op->getAttr("stride").cast<ArrayAttr>();
+ auto dilationTosaAttr = op->getAttr("dilation").cast<ArrayAttr>();
+
+ bool isQuantized = op->hasAttr("quantization_info");
+ IntegerAttr iZp;
+ IntegerAttr kZp;
+ if (isQuantized) {
+ auto quantizationInfo =
+ op->getAttr("quantization_info").cast<tosa::ConvOpQuantizationAttr>();
+ iZp = rewriter.getI32IntegerAttr(
+ quantizationInfo.input_zp().getValue().getSExtValue());
+ kZp = rewriter.getI32IntegerAttr(
+ quantizationInfo.weight_zp().getValue().getSExtValue());
+ }
+
+ if (!inputTy.hasStaticShape() || !weightTy.hasStaticShape() ||
+ !biasTy.hasStaticShape() || !resultTy.hasStaticShape())
+ return rewriter.notifyMatchFailure(op,
+ "tosa.conv ops require static shapes");
+
+ auto weightShape = weightTy.getShape();
+ auto resultShape = resultTy.getShape();
+
+ // Apply padding as necessary.
+ Attribute zeroAttr = rewriter.getZeroAttr(inputETy);
+ llvm::SmallVector<int64_t> pad;
+ pad.resize(2, 0);
+ getValuesFromIntArrayAttribute(padAttr, pad);
+ pad.resize(pad.size() + 2, 0);
+
+ input = applyPad(loc, input, pad, zeroAttr, rewriter);
+
+ // Broadcast the initial value to the output tensor before convolving.
+ SmallVector<AffineMap, 4> indexingMaps;
+ indexingMaps.push_back(AffineMap::get(
+ /*dimCount=*/resultTy.getRank(), /*symbolCount=*/0,
+ {rewriter.getAffineDimExpr(3)}, rewriter.getContext()));
+ indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultTy.getRank()));
+
+ Value initTensor =
+ rewriter.create<linalg::InitTensorOp>(loc, resultShape, resultETy);
+
+ Value biasBroadcast =
+ rewriter
+ .create<linalg::GenericOp>(
+ loc, resultTy, bias, initTensor, indexingMaps,
+ getNParallelLoopsAttrs(resultTy.getRank()),
+ [&](OpBuilder &nestedBuilder, Location nestedLoc,
+ ValueRange args) {
+ nestedBuilder.create<linalg::YieldOp>(nestedLoc, args[0]);
+ })
+ .getResult(0);
+
+ // Extract the attributes for convolution.
+ llvm::SmallVector<int64_t> stride, dilation;
+ getValuesFromIntArrayAttribute(strideTosaAttr, stride);
+ getValuesFromIntArrayAttribute(dilationTosaAttr, dilation);
+
+ // Create the convolution op.
+ auto strideAttr = DenseIntElementsAttr::get(
+ RankedTensorType::get({2}, rewriter.getI64Type()), stride);
+ auto dilationAttr = DenseIntElementsAttr::get(
+ RankedTensorType::get({2}, rewriter.getI64Type()), dilation);
ShapedType linalgConvTy =
RankedTensorType::get({resultShape[0], resultShape[1], resultShape[2],
weightShape[2], weightShape[3]},
rewriter.replaceOp(op, reshape);
return success();
}
-
- return failure();
-}
-
-namespace {
-
-template <typename SrcOp>
-class PointwiseConverter : public OpRewritePattern<SrcOp> {
-public:
- using OpRewritePattern<SrcOp>::OpRewritePattern;
-
- LogicalResult matchAndRewrite(SrcOp op,
- PatternRewriter &rewriter) const final {
- return elementwiseMatchAndRewriteHelper(op, rewriter);
- }
-};
-
-template <typename T>
-class ConvConverter : public OpConversionPattern<T> {
-public:
- using OpConversionPattern<T>::OpConversionPattern;
- LogicalResult
- matchAndRewrite(T op, ArrayRef<Value> args,
- ConversionPatternRewriter &rewriter) const final {
- return convolutionMatchAndRewriterHelper(op, rewriter);
- }
};
class TransposeConvConverter
ReduceConverter<tosa::ReduceProdOp>,
ArgMaxConverter,
ConcatConverter,
- ConvConverter<tosa::Conv2DOp>,
- ConvConverter<tosa::DepthwiseConv2DOp>,
+ ConvConverter,
+ DepthwiseConvConverter,
TransposeConvConverter,
GatherConverter,
PadConverter,
implements(ContractionOpInterface)
C[None] += cast(U, A[D.m]) * cast(U, B[D.m])
-
@linalg_structured_op
-def conv_2d_input_nhwc_filter_ohwi_poly(
- I=TensorDef(T1, S.N, S.IH, S.IW, S.IC),
- K=TensorDef(T2, S.OC, S.KH, S.KW, S.IC),
- O=TensorDef(U, S.N, S.OH, S.OW, S.OC, output=True),
+def conv_2d_nchw(
+ I=TensorDef(T1, S.N, S.C, S.IH, S.IW),
+ K=TensorDef(T2, S.F, S.C, S.KH, S.KW),
+ O=TensorDef(U, S.N, S.F, S.OH, S.OW, S.C, output=True),
strides=AttributeDef(S.SH, S.SW),
dilations=AttributeDef(S.DH, S.DW)):
- """Performs a 2-D convolution.
+ """Performs 2-D convolution.
Numeric casting is performed on the operands to the inner multiply, promoting
them to the same data type as the accumulator/output.
"""
- domain(D.n, D.oh, D.ow, D.kh, D.kw, D.oc, D.ic)
- O[D.n, D.oh, D.ow, D.oc] += cast(
- U, I[D.n,
- D.oh * S.SH + D.kh * S.DH,
- D.ow * S.SW + D.kw * S.DW,
- D.ic]) * cast(U, K[D.oc, D.kh, D.kw, D.ic])
+ domain(D.n, D.f, D.oh, D.ow, D.c, D.kh, D.kw)
+ O[D.n, D.f, D.oh, D.ow] += cast(
+ U, I[D.n, D.c, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW,
+ ]) * cast(U, K[D.f, D.c, D.kh, D.kw])
@linalg_structured_op
-def conv_2d_input_nhwc_filter_ohwi_poly_q(
- I=TensorDef(T1, S.N, S.IH, S.IW, S.IC),
- K=TensorDef(T2, S.OC, S.KH, S.KW, S.IC),
- IZp=ScalarDef(I32),
- KZp=ScalarDef(I32),
- O=TensorDef(U, S.N, S.OH, S.OW, S.OC, output=True),
+def conv_2d_nhwc_hwcf(
+ I=TensorDef(T1, S.N, S.IH, S.IW, S.C),
+ K=TensorDef(T2, S.KH, S.KW, S.C, S.F),
+ O=TensorDef(U, S.N, S.OH, S.OW, S.F, output=True),
strides=AttributeDef(S.SH, S.SW),
dilations=AttributeDef(S.DH, S.DW)):
- """Performs a 2-D quantized convolution.
+ """Performs 2-D convolution.
Numeric casting is performed on the operands to the inner multiply, promoting
- them to the same data type as the accumulator/output. Includes zero point
- adjustment for quantization.
+ them to the same data type as the accumulator/output.
"""
- domain(D.n, D.oh, D.ow, D.kh, D.kw, D.oc, D.ic)
- O[D.n, D.oh, D.ow, D.oc] += ((cast(
- U, I[D.n,
- D.oh * S.SH + D.kh * S.DH,
- D.ow * S.SW + D.kw * S.DW,
- D.ic]) - cast(U, IZp)) *
- (cast(U, K[D.oc, D.kh, D.kw, D.ic]) - cast(U, KZp)))
-
+ domain(D.n, D.oh, D.ow, D.f, D.kh, D.kw, D.c)
+ O[D.n, D.oh, D.ow, D.f] += cast(
+ U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, D.c
+ ]) * cast(U, K[D.kh, D.kw, D.c, D.f])
@linalg_structured_op
def depthwise_conv_2d_input_nhwc_filter_hwc_poly(
D.c]) * cast(U, K[D.kh, D.kw, D.c])
@linalg_structured_op
-def conv_2d_nchw(
- I=TensorDef(T1, S.N, S.C, S.IH, S.IW),
- K=TensorDef(T2, S.F, S.C, S.KH, S.KW),
- O=TensorDef(U, S.N, S.F, S.OH, S.OW, S.C, output=True),
+def conv_2d_nhwc_hwcf_q(
+ I=TensorDef(T1, S.N, S.IH, S.IW, S.C),
+ K=TensorDef(T2, S.KH, S.KW, S.C, S.F),
+ IZp=ScalarDef(I32),
+ KZp=ScalarDef(I32),
+ O=TensorDef(U, S.N, S.OH, S.OW, S.F, output=True),
strides=AttributeDef(S.SH, S.SW),
dilations=AttributeDef(S.DH, S.DW)):
- """Performs 2-D convolution.
+ """Performs 2-D convolution with zero point offsets.
Numeric casting is performed on the operands to the inner multiply, promoting
- them to the same data type as the accumulator/output.
+ them to the same data type as the accumulator/output. This includes the zero
+ point offsets common to quantized operations.
"""
- domain(D.n, D.f, D.oh, D.ow, D.c, D.kh, D.kw)
- O[D.n, D.f, D.oh, D.ow] += cast(
- U, I[D.n, D.c, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW,
- ]) * cast(U, K[D.f, D.c, D.kh, D.kw])
+ domain(D.n, D.oh, D.ow, D.f, D.kh, D.kw, D.c)
+ O[D.n, D.oh, D.ow, D.f] += (cast(
+ U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, D.c
+ ]) - cast(U, IZp)) * (cast(U, K[D.kh, D.kw, D.c, D.f]) - cast(U, KZp))
-
-def depthwise_conv2D_nchw( #TODO: Fix name
+@linalg_structured_op
+def depthwise_conv2D_nchw(
I=TensorDef(T1, S.N, S.IH, S.IW, S.IC),
K=TensorDef(T2, S.KH, S.KW, S.IC, S.CM),
O=TensorDef(U, S.N, S.OH, S.OW, S.IC, S.CM, output=True),
U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW,
D.ic]) * cast(U, K[D.kh, D.kw, D.ic, D.cm])
-
-def depthwise_conv2D_nchw_q( #TODO: Fix name
+@linalg_structured_op
+def depthwise_conv2D_nchw_q(
I=TensorDef(T1, S.N, S.IH, S.IW, S.IC),
K=TensorDef(T2, S.KH, S.KW, S.IC, S.CM),
IZp=ScalarDef(I32),
// -----
-// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
-// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+// CHECK: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3) -> (d3, d0, d1, d2)>
+// CHECK: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+// CHECK: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3) -> (d3)>
-// CHECK-LABEL: @conv2d_f32
+// CHECK-LABEL @conv2d_f32
func @conv2d_f32(%input: tensor<1x49x42x27xf32>, %weights: tensor<28x3x3x27xf32>, %bias: tensor<28xf32>) -> () {
- // CHECK: %[[INIT:.+]] = linalg.init_tensor [1, 45, 40, 28]
- // CHECK: %[[BROADCAST:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<28xf32>) outs(%[[INIT]] : tensor<1x45x40x28xf32>)
- // CHECK: linalg.conv_2d_input_nhwc_filter_ohwi_poly {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>) outs(%[[BROADCAST]] : tensor<1x45x40x28xf32>)
+ // CHECK: %[[W_IN:.+]] = linalg.init_tensor [3, 3, 27, 28]
+ // CHECK: %[[W:.+]] = linalg.generic {indexing_maps = [#[[MAP0]], #[[MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg1 : tensor<28x3x3x27xf32>) outs(%[[W_IN]] : tensor<3x3x27x28xf32>)
+ // CHECK: linalg.yield %arg3 : f32
+ // CHECK: %[[B_IN:.+]] = linalg.init_tensor [1, 45, 40, 28]
+ // CHECK: %[[B:.+]] = linalg.generic {indexing_maps = [#[[MAP2]], #[[MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<28xf32>) outs(%[[B_IN]] : tensor<1x45x40x28xf32>)
+ // CHECK: linalg.yield %arg3 : f32
+ // CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %1 : tensor<1x49x42x27xf32>, tensor<3x3x27x28xf32>) outs(%[[B]] : tensor<1x45x40x28xf32>)
%0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [2, 1]} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>) -> (tensor<1x45x40x28xf32>)
return
}
// CHECK-LABEL: @conv2d_padded_f32
func @conv2d_padded_f32(%input: tensor<1x47x40x28xf32>, %weights: tensor<28x3x3x28xf32>, %bias: tensor<28xf32>) -> () {
- // CHECK: linalg.pad_tensor %arg0
- // CHECK: linalg.conv_2d_input_nhwc_filter_ohwi_poly
+ // CHECK: linalg.pad_tensor %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0]
+ // CHECK: linalg.conv_2d_nhwc_hwcf
%0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [1, 1, 1, 1], stride = [1, 1], dilation = [2, 1]} : (tensor<1x47x40x28xf32>, tensor<28x3x3x28xf32>, tensor<28xf32>) -> (tensor<1x45x40x28xf32>)
return
}
// -----
-// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
-// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
-
// CHECK-LABEL: @conv2d_quant
func @conv2d_quant(%arg0 : tensor<1x12x12x1xi8>, %arg1 : tensor<1024x3x3x1xi8>, %arg2 : tensor<1024xi32>) -> () {
- // CHECK: %[[INIT:.+]] = linalg.init_tensor [1, 10, 10, 1024]
- // CHECK: %[[BROADCAST:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<1024xi32>) outs(%[[INIT]] : tensor<1x10x10x1024xi32>)
- // CHECK: ^bb0(%arg3: i32, %arg4: i32):
- // CHECK: linalg.yield %arg3 : i32
- // CHECK: %[[C128:.+]] = constant -128
- // CHECK: %[[C42:.+]] = constant 42
- // CHECK: linalg.conv_2d_input_nhwc_filter_ohwi_poly_q {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, %[[C128]], %[[C42]] : tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, i32, i32) outs(%1 : tensor<1x10x10x1024xi32>)
+ // CHECK: linalg.conv_2d_nhwc_hwcf_q
%0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], quantization_info = {input_zp = -128 : i32, weight_zp = 42 : i32}, stride = [1, 1]} : (tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, tensor<1024xi32>) -> tensor<1x10x10x1024xi32>
return
}
// CHECK: linalg.yield %arg3 : f32
// CHECK: } -> tensor<1x5x5x33xf32>
// CHECK: [[DBIAS:%.+]] = linalg.tensor_expand_shape [[BIAS]] {{\[}}[0], [1], [2], [3, 4]]
- // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv_2D_nchw {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>) outs([[DBIAS]] : tensor<1x5x5x3x11xf32>)
+ // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nchw {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>) outs([[DBIAS]] : tensor<1x5x5x3x11xf32>)
// CHECK: linalg.tensor_collapse_shape %3 {{\[}}[0], [1], [2], [3, 4]]
%2 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) { pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1] } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>) -> (tensor<1x5x5x33xf32>)
return
// CHECK-LABEL: @transpose_conv
func @transpose_conv(%arg0 : tensor<1x12x12x2xf32>, %arg1 : tensor<4x3x3x2xf32>, %arg2 : tensor<4xf32>) -> () {
- // CHECK: [[PAD:%.+]] = linalg.pad_tensor %arg0 low[0, 2, 2, 0] high[0, 2, 2, 0]
- // CHECK: linalg.conv_2d_input_nhwc_filter_ohwi_poly {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins([[PAD]], {{%.+}} : tensor<1x16x16x2xf32>, tensor<4x3x3x2xf32>)
+ // CHECK: linalg.pad_tensor %arg0 low[0, 2, 2, 0] high[0, 2, 2, 0]
+ // CHECK: linalg.conv_2d_nhwc_hwcf
%0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], out_shape = [1, 14, 14, 4], stride = [1, 1]} : (tensor<1x12x12x2xf32>, tensor<4x3x3x2xf32>, tensor<4xf32>) -> tensor<1x14x14x4xf32>
return
}
// CHECK-LABEL: @transpose_conv_dilated
func @transpose_conv_dilated(%arg0 : tensor<1x12x12x2xf32>, %arg1 : tensor<4x3x3x2xf32>, %arg2 : tensor<4xf32>) -> () {
// CHECK: [[PAD:%.+]] = linalg.pad_tensor %arg0 low[0, 4, 4, 0] high[0, 4, 4, 0]
- // CHECK: linalg.conv_2d_input_nhwc_filter_ohwi_poly {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins([[PAD]], {{%.+}} : tensor<1x20x20x2xf32>, tensor<4x3x3x2xf32>)
+ // CHECK: linalg.conv_2d_nhwc_hwcf {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins([[PAD]], {{%.+}} : tensor<1x20x20x2xf32>, tensor<3x3x2x4xf32>)
%0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [2, 2], out_pad = [0, 0], out_shape = [1, 16, 16, 4], stride = [1, 1]} : (tensor<1x12x12x2xf32>, tensor<4x3x3x2xf32>, tensor<4xf32>) -> tensor<1x16x16x4xf32>
return
}
// RUN: mlir-opt -split-input-file -verify-diagnostics %s | FileCheck %s
-// CHECK-LABEL: func @conv_2d_input_nhwc_filter_ohwi_poly_q_tensor
-func @conv_2d_input_nhwc_filter_ohwi_poly_q_tensor(%input: tensor<2x4x5x3xi8>, %filter: tensor<2x2x2x3xi8>) -> tensor<2x3x4x2xi32> {
- %zero = constant 0 : i32
- %init = linalg.init_tensor [2, 3, 4, 2] : tensor<2x3x4x2xi32>
- %fill = linalg.fill(%zero, %init) : i32, tensor<2x3x4x2xi32> -> tensor<2x3x4x2xi32>
- %c128 = constant -128 : i32
- %c42 = constant 42 : i32
- %0 = linalg.conv_2d_input_nhwc_filter_ohwi_poly_q
- { dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
- ins(%input, %filter, %c128, %c42 : tensor<2x4x5x3xi8>, tensor<2x2x2x3xi8>, i32, i32)
- outs(%fill : tensor<2x3x4x2xi32>) -> tensor<2x3x4x2xi32>
- return %0 : tensor<2x3x4x2xi32>
-}
-
// CHECK-LABEL: func @depthwise_conv_2d_input_nhwc_filter_hwcf_tensor
func @depthwise_conv_2d_input_nhwc_filter_hwcf_tensor(%input: tensor<2x4x5x2xf32>, %filter: tensor<2x2x2x3xf32>) -> tensor<2x3x4x2x3xf32> {
%zero = constant 0.000000e+00 : f32
projects / platform / upstream / llvm.git / commitdiff