Merge remote-tracking branch 'origin/master' into matmul-shape
This commit is contained in:
Doru Bercea
commit
07d28769d3
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@ -104,7 +104,7 @@ def ONNXGemmNoBiasOp: ONNX_Op<"GemmNoBias",
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}
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def ONNXConvNoBiasOp:ONNX_Op<"ConvNoBias",
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[NoSideEffect]> {
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[NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
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let summary = "ONNX Conv operation with no Bias operand.";
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let description = [{
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"The convolution operator consumes an input tensor and a filter, and"
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@ -112,6 +112,8 @@ def ONNXConvNoBiasOp:ONNX_Op<"ConvNoBias",
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}];
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let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$W);
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let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
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let verifier = [{ return ::verify(*this); }];
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}
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def ONNXMaxPoolSingleOutOp: ONNX_Op<"MaxPoolSingleOut",
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@ -489,7 +489,7 @@ void ONNXReshapeOp::inferShapes() {
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void ONNXTransposeOp::inferShapes() {
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// Cannot infer shape if no shape exists.
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if (!getOperand().getType().isa<RankedTensorType>())
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emitError("Shape tensor not ranked.");
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return;
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// Naive transposition which handles the default case of
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// reversing the shape of the tensor (similar to numpy.transpose).
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@ -525,6 +525,181 @@ LogicalResult verify(ONNXTransposeOp op) {
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return success();
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}
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//===----------------------------------------------------------------------===//
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// Conv
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// For this operation, we define the attributes once in the original Conv
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// operation class. There is no need to redefine the attribute names for the
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// other classes based on Conv.
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void ONNXConvNoBiasOp::inferShapes() {
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// Generic shape for data input X and weight tensor W:
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// X: (N x C x D1 x D2 ... x Dn)
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// W: (M x C/group x k1 x k2 x ... x kn)
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// Cannot infer shape if no shape exists.
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if (!getOperand(0).getType().isa<RankedTensorType>() ||
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!getOperand(1).getType().isa<RankedTensorType>())
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return;
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auto dataTy = getOperand(0).getType().cast<RankedTensorType>();
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auto weightTy = getOperand(1).getType().cast<RankedTensorType>();
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auto dataShape = dataTy.getShape();
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auto weightShape = weightTy.getShape();
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// Check that shape of weight and data have same length.
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if (dataShape.size() != weightShape.size())
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emitError("Weight size not compatible with data size.");
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// Required attribute auto_pad defaults to NOTSET.
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auto autoPad = getAttrOfType<StringAttr>(
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ONNXConvOp::getAutoPadAttrName()).getValue();
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// Group is a required attribute and should have default value of 1.
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int64_t group = getAttrOfType<IntegerAttr>(
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ONNXConvOp::getGroupAttrName()).getInt();
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// Check that the X.shape[1] == (W.shape[1] * group) == C condition holds.
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if (dataShape[1] != (weightShape[1] * group))
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emitError("Channel dimension mismatch.");
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// Note: the value of the group attribut only impacts the way the
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// computation is carried out and not the actual output size.
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// First two output dimensions consist of the number of batches and the
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// number of kernels being applied.
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//
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SmallVector<int64_t, 2> dims;
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// Insert batch size.
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dims.emplace_back(dataShape[0]);
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// Insert number of filters being applied (number of output channels).
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dims.emplace_back(weightShape[0]);
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// Spatial dimensions of the output are computed using the formula:
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//
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// dim = (inputDim - kernelDim + startPadding + endPadding) / stride + 1
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//
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SmallVector<int64_t, 2> outSpatialDims;
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// Number of spatial dimensions.
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int32_t nDims = dataShape.size() - 2;
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// Initialize dimenions based on the input spatial dimensions.
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for (int i = 2; i < dataShape.size(); ++i)
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outSpatialDims.emplace_back(dataShape[i]);
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// Use kernel_shape attribute if present otherwise use size from weight
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// argument.
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SmallVector<int64_t, 2> kernelDims;
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if (auto kernelShape = getAttrOfType<ArrayAttr>(
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ONNXConvOp::getKernelShapeAttrName())) {
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if (kernelShape.getValue().size() != nDims)
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emitError("kernel_shape length incompatible with spatial dimensions.");
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for (int i = 0; i < nDims; ++i)
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kernelDims.emplace_back(
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(kernelShape.getValue()[i]).cast<IntegerAttr>().getInt());
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} else {
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for (int i = 0; i < nDims; ++i)
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kernelDims.emplace_back(weightShape[i + 2]);
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}
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// Check if dilations attribute is present.
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// If it is then compute new kernel size that includes the receptive field.
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// In this calculation we assume that the receptive field pixels must all be
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// within the bounds of the image. In this case the new kernel size is given
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// by:
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//
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// ( K + 1 ) * d - 1
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// where K is a kernel dimension and d is the dilation along that axis.
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//
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// From a dimensionality perspective the kernel size becomes the dilated
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// kernel size.
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if (auto dilations = getAttrOfType<ArrayAttr>(
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ONNXConvOp::getDilationsAttrName())) {
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if (dilations.getValue().size() != nDims)
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emitError("dilations length incompatible with spatial dimensions.");
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for (int i = 0; i < nDims; ++i)
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kernelDims[i] = (kernelDims[i] + 1) *
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(dilations.getValue()[i]).cast<IntegerAttr>().getInt() - 1;
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}
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// Subtract kernel dimensions from input data dimensions.
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for (int i = 0; i < nDims; ++i)
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outSpatialDims[i] -= kernelDims[i];
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// Add padding information.
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if (autoPad == "NOTSET") {
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// Use pads to to determine the padding. If attribute is not
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// present then pads is considered to be all zeros (no padding).
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if (auto pads = getAttrOfType<ArrayAttr>(
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ONNXConvOp::getPadsAttrName())) {
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// pads consists of two entries for each spatial axis.
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if (pads.getValue().size() != 2 * nDims)
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emitError("pads size is not twice the spatial size.");
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for (int i = 0; i < nDims; ++i) {
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// Padding for beginning of axis.
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int32_t p = (pads.getValue()[i]).cast<IntegerAttr>().getInt();
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outSpatialDims[i] += p;
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// Padding for end of axis.
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p = (pads.getValue()[i + nDims]).cast<IntegerAttr>().getInt();
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outSpatialDims[i] += p;
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}
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}
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} else if (autoPad == "SAME_UPPER" || autoPad == "SAME_LOWER") {
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// Pad input so that output size matches input size.
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// Each spatial dimension needs to be padded by a total of:
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//
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// K - 1
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//
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// where K is a kernel spatial dimension.
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// Pad as if stride is 1.
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for (int i = 0; i < nDims; ++i)
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outSpatialDims[i] += kernelDims[i] - 1;
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} else if (autoPad == "VALID") {
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// No padding
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} else {
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emitError("Unexpected attribute value for auto_pad.");
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}
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// Strides
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if (auto strides = getAttrOfType<ArrayAttr>(
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ONNXConvOp::getStridesAttrName())) {
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if (strides.getValue().size() != nDims)
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emitError("strides length incompatible with spatial dimensions.");
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for (int i = 0; i < nDims; ++i) {
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int64_t stride =
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(strides.getValue()[i]).cast<IntegerAttr>().getInt();
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outSpatialDims[i] = floor(outSpatialDims[i] / stride);
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}
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}
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for (int i = 0; i < nDims; ++i)
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outSpatialDims[i] += 1;
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dims.append(outSpatialDims.begin(), outSpatialDims.end());
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getResult().setType(RankedTensorType::get(dims, dataTy.getElementType()));
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}
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LogicalResult verify(ONNXConvNoBiasOp op) {
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auto module = op.getParentOfType<ModuleOp>();
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if (!module)
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op.emitError("expected to belong to a module");
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auto autoPadAttr = op.getAttrOfType<StringAttr>(
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ONNXConvOp::getAutoPadAttrName());
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if (!autoPadAttr)
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op.emitError("auto_pad attribute not specified.");
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if (autoPadAttr.getValue() != "NOTSET")
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if (auto pads = op.getAttrOfType<ArrayAttr>(
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ONNXConvOp::getPadsAttrName()))
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op.emitError("auto_pad and pads are both set.");
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auto groupAttr =
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op.getAttrOfType<IntegerAttr>(ONNXConvOp::getGroupAttrName());
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if (!groupAttr)
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op.emitError("group attribute not specified.");
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return success();
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}
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//===----------------------------------------------------------------------===//
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// TableGen'd op method definitions
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//===----------------------------------------------------------------------===//
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@ -324,6 +324,15 @@ def ONNXConvOp:ONNX_Op<"Conv",
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}];
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let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$W, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
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let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
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let extraClassDeclaration = [{
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static StringRef getAutoPadAttrName() { return "auto_pad"; }
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static StringRef getDilationsAttrName() { return "dilations"; }
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static StringRef getGroupAttrName() { return "group"; }
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static StringRef getKernelShapeAttrName() { return "kernel_shape"; }
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static StringRef getPadsAttrName() { return "pads"; }
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static StringRef getStridesAttrName() { return "strides"; }
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}];
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}
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def ONNXConvIntegerOp:ONNX_Op<"ConvInteger",
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@ -117,7 +117,8 @@ public:
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op->getName().getStringRef() != "onnx.GemmNoBias" &&
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op->getName().getStringRef() != "onnx.Reshape" &&
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op->getName().getStringRef() != "onnx.Transpose" &&
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op->getName().getStringRef() != "onnx.Softmax")
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op->getName().getStringRef() != "onnx.Softmax" &&
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op->getName().getStringRef() != "onnx.ConvNoBias")
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return false;
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return llvm::any_of(op->getResultTypes(), [](Type result_type) {
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return !result_type.isa<RankedTensorType>();
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@ -144,8 +144,8 @@ test_to_enable = [
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#"test_sum_two_inputs_cpu", <- error
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# Reciprocal Op:
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#"test_reciprocal_cpu", <- error on shape inference.
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#"test_reciprocal_example_cpu", <- error on shape inference.
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"test_reciprocal_cpu",
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"test_reciprocal_example_cpu",
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]
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# Extract name of all test cases.
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@ -1,7 +1,10 @@
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// RUN: onnf-opt --shape-inference %s -split-input-file | FileCheck %s
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//===----------------------------------------------------------------------===//
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/// Test the default behavior of transpose when no information for the
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/// permutation of the axes is provided.
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/// permutation of the axes is provided and when a permutation is provided.
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//===----------------------------------------------------------------------===//
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func @test_default_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
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%0 = "onnx.Transpose"(%arg0) : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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@ -12,6 +15,7 @@ func @test_default_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
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}
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/// Test shape inference for transposition when perm attribute is specified.
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func @test_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
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%0 = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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@ -112,3 +116,128 @@ func @test_matmul_8(%arg0 : tensor<32x64xf32>, %arg1 : tensor<64x128xf32>) -> te
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// CHECK: [[RES8:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<32x64xf32>, tensor<64x128xf32>) -> tensor<32x128xf32>
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// CHECK: return [[RES8]] : tensor<32x128xf32>
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}
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//===----------------------------------------------------------------------===//
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/// Test shape inference for ConvNoBias operation and all its attributes.
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//===----------------------------------------------------------------------===//
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/// Default and required attributes.
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func @test_conv_no_bias_1(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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}
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// CHECK-LABEL: test_conv_no_bias_1
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x27x58xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xf32>
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/// kernel_shape attribute.
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func @test_conv_no_bias_2(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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}
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// CHECK-LABEL: test_conv_no_bias_2
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x25x56xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xf32>
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/// pads attribute.
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/// Use pads to make output size equal to input size by adding K - 1 to the result.
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func @test_conv_no_bias_3(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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}
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// CHECK-LABEL: test_conv_no_bias_3
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
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/// auto_pad set to SAME_UPPER and SAME_LOWER.
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func @test_conv_no_bias_4(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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}
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// CHECK-LABEL: test_conv_no_bias_4
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
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func @test_conv_no_bias_5(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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}
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// CHECK-LABEL: test_conv_no_bias_5
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
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/// auto_pad set to VALID.
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func @test_conv_no_bias_6(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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}
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// CHECK-LABEL: test_conv_no_bias_6
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x27x55xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xf32>
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/// With strides attribute.
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func @test_conv_no_bias_7(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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}
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// CHECK-LABEL: test_conv_no_bias_7
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x14x20xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xf32>
|
||||
|
||||
/// auto_pad set to SAME_UPPER with strides attribute.
|
||||
/// The auto_pad will pas as if stride is equal to 1.
|
||||
|
||||
func @test_conv_no_bias_8(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_8
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x16x22xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x16x22xf32>
|
||||
|
||||
/// dilations attribute.
|
||||
|
||||
func @test_conv_no_bias_9(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_9
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x20x42xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x20x42xf32>
|
||||
|
||||
/// dilations attribute with stride.
|
||||
|
||||
func @test_conv_no_bias_10(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, dilations = [2, 3], strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_10
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i32, strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x10x21xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x10x21xf32>
|
||||
|
||||
/// dilations attribute with auto_pad set to SAME_UPPER.
|
||||
|
||||
func @test_conv_no_bias_11(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_11
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", dilations = [2, 3], group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x32x64xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
|
||||
|
|
|
|||
Loading…
Reference in New Issue