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Inter common pad (#26) 

* common pad handling in shape inference for conv and maxpool

* common pads

Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
This commit is contained in:
Alexandre Eichenberger 2020-03-11 18:36:02 -04:00 committed by GitHub
parent 391f565a66
commit 811b63e031
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2 changed files with 246 additions and 282 deletions
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468
src/dialect/onnx/onnx_ops.cpp
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@ -765,11 +765,150 @@ void ONNXReduceSumOp::inferShapes() {
getResult().setType(getReductionOutputType(operandTy, axes(), keepdims())); getResult().setType(getReductionOutputType(operandTy, axes(), keepdims()));
} }
//===----------------------------------------------------------------------===//
// Conv
// Support function that computes default values for dilations, strides, and
// pads.
template <class T>
static void processConvTypeParams(T *op, Value inputOperand) {
auto builder = mlir::Builder(op->getContext());
// 1) Get shape of input.
auto inputShape = inputOperand.getType().cast<RankedTensorType>().getShape();
auto inputRank = inputShape.size();
// 2) Get kernel sizes from kernel_shape attribute.
auto kernelShape = op->kernel_shape();
auto kernelRank = ArrayAttrSize(kernelShape);
auto kernelOffset = inputRank - kernelRank;
// Dilatation.
auto dilationsOpt = op->dilations();
if (dilationsOpt.hasValue()) {
if (ArrayAttrSize(dilationsOpt) != kernelRank)
op->emitError("dialation rank is not the same as the spatial rank");
// Test values to be greater than 0.
for (int i = 0; i < kernelRank; ++i) {
if (ArrayAttrIntVal(dilationsOpt, i) < 1)
op->emitError("dialation value must be nonzero positive");
}
} else {
// Default dilatation is needed, all dimensions init with 1.
SmallVector<int64_t, 4> defaultVals(kernelRank, 1);
// Convert to ArrayRef, then build attribute, then store attribute.
ArrayRef<int64_t> defaultRefs(defaultVals);
op->dilationsAttr(builder.getI64ArrayAttr(defaultRefs));
}
// Strides.
auto stridesOpt = op->strides();
if (stridesOpt.hasValue()) {
if (ArrayAttrSize(stridesOpt) != kernelRank)
op->emitError("strides rank is not the same as the spatial rank");
// Check values to be greater than 0.
for (int i = 0; i < kernelRank; ++i) {
if (ArrayAttrIntVal(stridesOpt, i) < 1)
op->emitError("strides value must be nonzero positive");
}
} else {
// Default stride is needed, all dimensions init with 1.
SmallVector<int64_t, 4> defaultVals(kernelRank, 1);
// Convert to ArrayRef, then build attribute, then store attribute.
ArrayRef<int64_t> defaultRefs(defaultVals);
op->stridesAttr(builder.getI64ArrayAttr(defaultRefs));
}
// Now try to find padding, getting auto_pad attribute first.
auto autoPad = op->auto_pad();
// And then investigate the various different cases. Prefill pad values with
// zeros, the most common case.
SmallVector<int64_t, 4> actualPads(2 * kernelRank, 0);
bool updatedPad = false;
if (autoPad == "NOTSET") {
auto padsOpt = op->pads();
if (padsOpt.hasValue()) {
// Only option where pads are not updated. Pads consists of two entries
// for each spatial axis.
if (ArrayAttrSize(padsOpt) != 2 * kernelRank)
op->emitError("pads rank is not twice the spatial rank");
// Check values, pads cannot be negative.
for (int i = 0; i < 2 * kernelRank; ++i) {
if (ArrayAttrIntVal(padsOpt, i) < 0)
op->emitError("pads value must be nonnegative");
}
} else {
// We have notset with no pads, they are assumed to be all zero.
updatedPad = true;
}
} else if (autoPad == "SAME_UPPER" || autoPad == "SAME_LOWER") {
// Reload dialtion and strides as they may have gotten default values.
updatedPad = true;
dilationsOpt = op->dilations();
stridesOpt = op->strides();
for (int i = 0; i < kernelRank; ++i) {
auto inputSize = inputShape[kernelOffset + i];
auto kernelSize = ArrayAttrIntVal(kernelShape, i);
auto dilationVal = ArrayAttrIntVal(dilationsOpt, i);
auto strideVal = ArrayAttrIntVal(stridesOpt, i);
// Output size is input size divided by stride. When stride is 1, then
// input and output are the same size, which is the usual case. When
// stride is greater than 1, take the ceil to be sure to have each input
// value used, as padding will be used to fill the gaps.
int64_t outputSize = ceil((1.0 * inputSize) / (1.0 * strideVal));
// Forumla is from ONNX MaxPool, and can be explained as follows. Pads is
// the difference between the needed values for the computations, minus
// the input values. The needed values for the computation is the
// effective side of the kernel plus the number of times we jump to the
// next kernel. Number of time we jump is (outputSize - 1). That number is
// multiplied with the size of the jump, namely strideVal. Now for the
// effective kernel size. It is the kernelSize + the number of times we
// have dilation holes time the dialtion. The number of dialtion holes is
// (kernelSize -1). Thus the effective size is "kernelSize +
// (kernelSize-1)*dialation". This simplifies to "(kernelSize
// -1)*dialation + 1".
auto sumOfPad = (outputSize - 1) * strideVal +
((kernelSize - 1) * dilationVal + 1) - inputSize;
// Pad values are assumed equal on both size, at half the total value.
actualPads[i] = actualPads[kernelRank + i] = sumOfPad / 2;
// But if the total pad value is odd, we add 1 to begining or end
// depending on autoPad value.
if (sumOfPad % 2 != 0) {
if (autoPad == "SAME_UPPER") {
actualPads[kernelRank + i] += 1;
} else {
actualPads[i] += 1;
}
}
}
} else if (autoPad == "VALID") {
// No pad, default value was set to zero, we are all set.
updatedPad = true;
} else {
op->emitError("auto_pad of unknown / unsupported value");
}
// Set pads values in attributes, if it is needed.
if (updatedPad) {
ArrayRef<int64_t> defaultRefs(actualPads);
op->padsAttr(builder.getI64ArrayAttr(defaultRefs));
}
// In all cases now, the acutal pad values are found in the pads attribute.
op->auto_padAttr(builder.getStringAttr("NOTSET"));
}
// Conv // Conv
// For this operation, we define the attributes once in the original Conv // For this operation, we define the attributes once in the original Conv
// operation class. There is no need to redefine the attribute names for the // operation class. There is no need to redefine the attribute names for the
// other classes based on Conv. // other classes based on Conv.
// Conv attributes output:
// - auto_pad set to NOTSET;
// - dilations, strides: set to 1 if not defined by user;
// - kernelShape: inferred from weight matrix if not defined by user;
// - pads: set to proper value, 0 if not defined by user.
void ONNXConvNoBiasOp::inferShapes() { void ONNXConvNoBiasOp::inferShapes() {
// Generic shape for data input X and weight tensor W: // Generic shape for data input X and weight tensor W:
// X: (N x C x D1 x D2 ... x Dn) // X: (N x C x D1 x D2 ... x Dn)
@ -780,179 +919,86 @@ void ONNXConvNoBiasOp::inferShapes() {
!W().getType().isa<RankedTensorType>()) !W().getType().isa<RankedTensorType>())
return; return;
auto dataTy = X().getType().cast<RankedTensorType>(); auto xTy = X().getType().cast<RankedTensorType>();
auto xShape = xTy.getShape();
auto weightTy = W().getType().cast<RankedTensorType>(); auto weightTy = W().getType().cast<RankedTensorType>();
auto inDataShape = dataTy.getShape();
auto weightShape = weightTy.getShape(); auto weightShape = weightTy.getShape();
// Lowest supported convolution is a one dimensional convolution. // Lowest supported convolution is a one dimensional convolution.
if (inDataShape.size() < 3) if (xShape.size() < 3)
emitError("Data input shape must be at least (NxCxD1)"); emitError("Data input shape must be at least (NxCxD1)");
// Check that shape of weight and data have same length. // Check that shape of weight and data have same length.
if (inDataShape.size() != weightShape.size()) if (xShape.size() != weightShape.size())
emitError("Weight size not compatible with data size"); emitError("Weight size not compatible with data size");
// Required attribute auto_pad defaults to NOTSET.
auto autoPad = auto_pad();
// Group is a required attribute and should have default value of 1. // Group is a required attribute and should have default value of 1.
int64_t group = int64_t group = ONNXConvNoBiasOp::group().getSExtValue();
ONNXConvNoBiasOp::group().getSExtValue(); //.getLimitedValue();
// Check that the X.shape[1] == (W.shape[1] * group) == C condition holds. // Check that the X.shape[1] == (W.shape[1] * group) == C condition holds.
if (inDataShape[1] != -1 && weightShape[1] != -1 && if (xShape[1] != -1 && weightShape[1] != -1 &&
inDataShape[1] != (weightShape[1] * group)) xShape[1] != (weightShape[1] * group))
emitError("Channel dimension mismatch"); emitError("Channel dimension mismatch");
// Note: the value of the group attribut only impacts the way the // Note: the value of the group attribut only impacts the way the
// computation is carried out and not the actual output size. // computation is carried out and not the actual output size.
// First two output dimensions consist of the number of batches and the
// number of kernels being applied.
//
SmallVector<int64_t, 2> dims;
// Insert batch size.
dims.emplace_back(inDataShape[0]);
// Insert number of filters being applied (number of output channels).
dims.emplace_back(weightShape[0]);
// Spatial dimensions of the output are computed using the formula:
//
// dim = (inputDim - kernelDim + startPadding + endPadding) / stride + 1
//
SmallVector<int64_t, 2> outSpatialDims;
// Number of spatial dimensions. // Number of spatial dimensions.
int32_t nSpatialDims = inDataShape.size() - 2; auto spatialOffset = 2;
int32_t spatialRank = xShape.size() - spatialOffset;
// Initialize dimenions based on the input spatial dimensions.
for (int i = 2; i < inDataShape.size(); ++i)
outSpatialDims.emplace_back(inDataShape[i]);
// Use kernel_shape attribute if present otherwise use size from weight // Use kernel_shape attribute if present otherwise use size from weight
// argument. // argument.
SmallVector<int64_t, 2> kernelDims; auto kernelShape = kernel_shape();
if (auto kernelShape = kernel_shapeAttr()) { if (kernelShape.hasValue()) {
if (ArrayAttrSize(kernelShape) != nSpatialDims) if (ArrayAttrSize(kernelShape) != spatialRank)
emitError("kernel_shape length incompatible with spatial dimensions"); emitError("kernel_shape length incompatible with spatial dimensions");
for (int i = 0; i < nSpatialDims; ++i) // Have the right number of values, check them.
kernelDims.emplace_back(ArrayAttrIntVal(kernelShape, i)); for (int i = 0; i < spatialRank; ++i)
if (ArrayAttrIntVal(kernelShape, i) < 1)
emitError("bad kernel_shape value");
} else { } else {
for (int i = 0; i < nSpatialDims; ++i) // Deduce shape from weight input.
kernelDims.emplace_back(weightShape[i + 2]); SmallVector<int64_t, 2> defaultVals;
for (int i = 0; i < spatialRank; ++i)
defaultVals.emplace_back(weightShape[spatialOffset + i]);
// Convert to ArrayRef, then build attribute, then store attribute.
ArrayRef<int64_t> defaultRefs(defaultVals);
auto builder = mlir::Builder(getContext());
kernel_shapeAttr(builder.getI64ArrayAttr(defaultRefs));
kernelShape = kernel_shape();
} }
// Check if dilations attribute is present. // Process strides, dilations, and pads.
// If it is then compute new kernel size that includes the receptive field. processConvTypeParams<>(this, X());
// In this calculation we assume that the receptive field pixels must all be auto dilationsOpt = dilations();
// within the bounds of the image. In this case the new kernel size is given auto stridesOpt = strides();
// by: auto padsOpt = pads();
//
// ( K + 1 ) * d - 1 // First two output dimensions consist of the number of batches and the
// where K is a kernel dimension and d is the dilation along that axis. // number of kernels being applied.
// SmallVector<int64_t, 4> outputDims;
// From a dimensionality perspective the kernel size becomes the dilated // Insert batch size.
// kernel size. outputDims.emplace_back(xShape[0]);
if (auto dilations = dilationsAttr()) { // Insert number of filters being applied (number of output channels).
if (ArrayAttrSize(dilations) != nSpatialDims) outputDims.emplace_back(weightShape[0]);
emitError("dilations length incompatible with spatial dimensions");
for (int i = 0; i < nSpatialDims; ++i) // Then the spatial dimensions of the output are computed.
kernelDims[i] = for (int i = 0; i < spatialRank; ++i) {
(kernelDims[i] + 1) * ArrayAttrIntVal(dilations, i) - 1; auto inputSize = xShape[spatialOffset + i];
auto sumOfPads =
ArrayAttrIntVal(padsOpt, i) + ArrayAttrIntVal(padsOpt, spatialRank + i);
auto kernelSize = ArrayAttrIntVal(kernelShape, i);
auto dilationVal = ArrayAttrIntVal(dilationsOpt, i);
auto strideVal = ArrayAttrIntVal(stridesOpt, i);
// Number of useful values: input plus pad - effective size of kernel (see
// processConvTypeParams comments to see how this value is derived).
double numerator =
inputSize + sumOfPads - ((kernelSize - 1) * dilationVal + 1);
// Useful number is divided by the strides.
double denominator = strideVal;
outputDims.emplace_back(floor(numerator / denominator) + 1);
} }
getResult().setType(RankedTensorType::get(outputDims, xTy.getElementType()));
// Subtract kernel dimensions from input data dimensions.
for (int i = 0; i < nSpatialDims; ++i)
outSpatialDims[i] -= kernelDims[i];
// Array which holds the padding information.
SmallVector<int64_t, 2> actualPads(2 * nSpatialDims, 0);
auto stridesAttr = ONNXConvNoBiasOp::stridesAttr();
// Add padding information.
if (autoPad == "NOTSET") {
// Use pads to to determine the padding. If attribute is not
// present then pads is considered to be all zeros (no padding).
if (auto pads = padsAttr()) {
// pads consists of two entries for each spatial axis.
if (ArrayAttrSize(pads) != 2 * nSpatialDims)
emitError("pads size is not twice the spatial size");
for (int i = 0; i < nSpatialDims; ++i) {
// Padding for beginning of axis.
outSpatialDims[i] += ArrayAttrIntVal(pads, i);
// Padding for end of axis.
outSpatialDims[i] += ArrayAttrIntVal(pads, i + nSpatialDims);
}
}
} else if (autoPad == "SAME_UPPER" || autoPad == "SAME_LOWER") {
// Pad input so that output size matches input size.
// Each spatial dimension needs to be padded by a total of:
//
// stride * (InDim - 1) + KerDim - InDim
//
// where K is a kernel spatial dimension.
for (int i = 0; i < nSpatialDims; ++i) {
// If strides are given use them otherwise stride is 1.
int64_t stride = 1;
if (stridesAttr)
stride = ArrayAttrIntVal(stridesAttr, i);
// Compute necessary padding. The input dimensions are stored in
// inDataShape.
int64_t totalPadding = stride * (inDataShape[i + 2] - 1) +
kernelDims[i] - inDataShape[i + 2];
// Adjust current output value with the value of the padding.
// When dividing by stride later on, the output dimension should
// be equal to the input dimension.
outSpatialDims[i] += totalPadding;
// Record the upper and lower axis padding.
actualPads[i] = actualPads[i + nSpatialDims] = totalPadding / 2;
if (totalPadding % 2 != 0) {
if (autoPad == "SAME_LOWER") {
actualPads[i]++;
} else {
actualPads[i + nSpatialDims]++;
}
}
}
} else if (autoPad == "VALID") {
// No padding
} else {
emitError("Unexpected attribute value for auto_pad");
}
// Strides
if (stridesAttr) {
if (ArrayAttrSize(stridesAttr) != nSpatialDims)
emitError("strides length incompatible with spatial dimensions");
for (int i = 0; i < nSpatialDims; ++i) {
int64_t stride = ArrayAttrIntVal(stridesAttr, i);
outSpatialDims[i] = floor(outSpatialDims[i] / stride);
}
}
for (int i = 0; i < nSpatialDims; ++i)
outSpatialDims[i] += 1;
// Check input and output sizes match.
if (autoPad == "SAME_UPPER" || autoPad == "SAME_LOWER") {
for (int i = 0; i < nSpatialDims; ++i)
if (outSpatialDims[i] != inDataShape[i + 2])
emitError("input and output spatial dimension mismatch");
// Set pads values in attributes.
auto builder = mlir::Builder(this->getContext());
ArrayRef<int64_t> defaultRefs(actualPads);
padsAttr(builder.getI64ArrayAttr(defaultRefs));
// Change auto padding attribute to NOTSET since padding values
// are now explicitly included in the operation.
auto_padAttr(builder.getStringAttr("NOTSET"));
}
dims.append(outSpatialDims.begin(), outSpatialDims.end());
getResult().setType(RankedTensorType::get(dims, dataTy.getElementType()));
} }
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//
@ -987,112 +1033,29 @@ void ONNXMaxPoolSingleOutOp::inferShapes() {
// Ceil mode. // Ceil mode.
auto ceilMode = ceil_mode().getSExtValue(); auto ceilMode = ceil_mode().getSExtValue();
// Dilatation.
auto dilationsOpt = dilations();
if (dilationsOpt.hasValue()) {
if (ArrayAttrSize(dilationsOpt) != kernelRank)
emitError("dialation rank is not the same as the spatial rank");
// Test values.
for (int i = 0; i < kernelRank; ++i) {
if (ArrayAttrIntVal(dilationsOpt, i) < 1)
emitError("dialation value must be nonzero positive");
}
} else {
// Default dilatation is needed.
SmallVector<int64_t, 4> defaultVals(kernelRank, 1);
// Convert to ArrayRef, then build attribute, then store attribute.
ArrayRef<int64_t> defaultRefs(defaultVals);
auto defaultAttr = builder.getI64ArrayAttr(defaultRefs);
dilationsAttr(defaultAttr);
dilationsOpt = dilations();
}
// Storage order. // Storage order.
auto storageOrder = storage_order().getSExtValue(); auto storageOrder = storage_order().getSExtValue();
if (storageOrder != 0) if (storageOrder != 0)
emitError("column major storage order not supported at this time"); emitError("column major storage order not supported at this time");
// Strides. processConvTypeParams<ONNXMaxPoolSingleOutOp>(this, X());
auto stridesOpt = strides();
if (stridesOpt.hasValue()) {
if (ArrayAttrSize(stridesOpt) != kernelRank)
emitError("strides rank is not the same as the spatial rank");
// Check values.
for (int i = 0; i < kernelRank; ++i) {
if (ArrayAttrIntVal(stridesOpt, i) < 1)
emitError("strides value must be nonzero positive");
}
} else {
SmallVector<int64_t, 4> defaultVals(kernelRank, 1);
// Convert to ArrayRef, then build attribute, then store attribute.
ArrayRef<int64_t> defaultRefs(defaultVals);
auto defaultAttr = builder.getI64ArrayAttr(defaultRefs);
stridesAttr(defaultAttr);
stridesOpt = strides();
}
// Now try to find padding, getting auto_pad attribute first.
auto autoPad = auto_pad();
// And then investigate the various different cases.
SmallVector<int64_t, 4> actualPads(2 * kernelRank, 0);
if (autoPad == "NOTSET") {
auto padsOpt = pads();
if (padsOpt.hasValue()) {
// Pads consists of two entries for each spatial axis.
if (ArrayAttrSize(padsOpt) != 2 * kernelRank)
emitError("pads rank is not twice the spatial rank");
// Check values
for (int i = 0; i < 2 * kernelRank; ++i) {
int64_t p = ArrayAttrIntVal(padsOpt, i);
if (p < 0)
emitError("pads value must be nonnegative");
actualPads[i] = p;
}
}
} else if (autoPad == "SAME_UPPER" || autoPad == "SAME_LOWER") {
for (int i = 0; i < kernelRank; ++i) {
auto inputSpatialShape = xShape[kernelOffset + i];
auto kernelSpatialShape = ArrayAttrIntVal(kernelShape, i);
auto dilations = ArrayAttrIntVal(dilationsOpt, i);
auto strideSpatialShape = ArrayAttrIntVal(stridesOpt, i);
int64_t outputSpatialShape =
ceil((1.0 * inputSpatialShape) / (1.0 * strideSpatialShape));
auto sumOfPad = (outputSpatialShape - 1) * strideSpatialShape +
((kernelSpatialShape - 1) * dilations + 1) -
inputSpatialShape;
actualPads[i] = actualPads[kernelRank + i] = sumOfPad / 2;
if (sumOfPad % 2 != 0) {
if (autoPad == "SAME_UPPER") {
actualPads[kernelRank + i] += 1;
} else {
actualPads[i] += 1;
}
}
}
} else if (autoPad != "VALID") {
emitError("auto_pad of unknown / unsupported value");
}
// Set pads values in attributes.
{
ArrayRef<int64_t> defaultRefs(actualPads);
auto defaultAttr = builder.getI64ArrayAttr(defaultRefs);
padsAttr(defaultAttr);
auto defaultAutoPadAttr = builder.getStringAttr("NOTSET");
auto_padAttr(defaultAutoPadAttr);
}
// Initialize output shape. // Initialize output shape.
SmallVector<int64_t, 4> yShape(xShape.begin(), xShape.end()); SmallVector<int64_t, 4> yShape(xShape.begin(), xShape.end());
auto dilationsOpt = dilations();
auto stridesOpt = strides();
auto padsOpt = pads();
// Process for all kernel dimensions. // Process for all kernel dimensions.
for (int i = 0; i < kernelRank; ++i) { for (int i = 0; i < kernelRank; ++i) {
auto inputSpatialShape = xShape[kernelOffset + i]; auto inputSize = xShape[kernelOffset + i];
auto padShape = actualPads[i] + actualPads[kernelRank + i]; auto sumOfPads =
auto kernelSpatialShape = ArrayAttrIntVal(kernelShape, i); ArrayAttrIntVal(padsOpt, i) + ArrayAttrIntVal(padsOpt, kernelRank + i);
auto dilations = ArrayAttrIntVal(dilationsOpt, i); auto kernelSize = ArrayAttrIntVal(kernelShape, i);
auto strideSpatialShape = ArrayAttrIntVal(stridesOpt, i); auto dilationVal = ArrayAttrIntVal(dilationsOpt, i);
double numerator = inputSpatialShape + padShape - auto strideVal = ArrayAttrIntVal(stridesOpt, i);
((kernelSpatialShape - 1) * dilations + 1); double numerator =
double denominator = strideSpatialShape; inputSize + sumOfPads - ((kernelSize - 1) * dilationVal + 1);
double denominator = strideVal;
int64_t res; int64_t res;
if (ceilMode) { if (ceilMode) {
res = ceil(numerator / denominator) + 1; res = ceil(numerator / denominator) + 1;
@ -1118,14 +1081,15 @@ static Type padShapeInferenceHelper(Value data, ArrayAttr padsOpt) {
if (padsOpt) { if (padsOpt) {
auto padsArray = padsOpt.getValue(); auto padsArray = padsOpt.getValue();
// Pads consists of two values for each axis of data. // Pads consists of two values for each axis of data.
// The two values specify the number of elements padded before and after respectively. // The two values specify the number of elements padded before and after
// respectively.
for (int i = 0; i < dataRank; ++i) { for (int i = 0; i < dataRank; ++i) {
int64_t p1 = (padsArray[2*i]).cast<IntegerAttr>().getInt(); int64_t p1 = (padsArray[2 * i]).cast<IntegerAttr>().getInt();
int64_t p2 = (padsArray[2*i+1]).cast<IntegerAttr>().getInt(); int64_t p2 = (padsArray[2 * i + 1]).cast<IntegerAttr>().getInt();
//Have to non-negative constant // Have to non-negative constant
if (p1 < 0 || p2 <0) if (p1 < 0 || p2 < 0)
return (Type)NULL; return (Type)NULL;
outputShape[i] += p1+p2; outputShape[i] += p1 + p2;
} }
return (RankedTensorType::get(outputShape, dataTy.getElementType())); return (RankedTensorType::get(outputShape, dataTy.getElementType()));
@ -1136,7 +1100,7 @@ static Type padShapeInferenceHelper(Value data, ArrayAttr padsOpt) {
// PadConstantPad // PadConstantPad
void ONNXPadConstantPadOp::inferShapes(){ void ONNXPadConstantPadOp::inferShapes() {
auto outputType = padShapeInferenceHelper(data(), pads()); auto outputType = padShapeInferenceHelper(data(), pads());
if (outputType) { if (outputType) {
getResult().setType(outputType); getResult().setType(outputType);
@ -1148,7 +1112,7 @@ void ONNXPadConstantPadOp::inferShapes(){
// PadConstantValuePad // PadConstantValuePad
void ONNXPadConstantValuePadOp::inferShapes(){ void ONNXPadConstantValuePadOp::inferShapes() {
auto outputType = padShapeInferenceHelper(data(), pads()); auto outputType = padShapeInferenceHelper(data(), pads());
if (outputType) { if (outputType) {
getResult().setType(outputType); getResult().setType(outputType);

58
test/mlir/onnx/onnx_shape_inference.mlir
View File

@ -150,7 +150,7 @@ func @test_conv_no_bias_0(%arg0 : tensor<1x2x32xf32>, %arg1 : tensor<5x2x6xf32>)
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_0 // CHECK-LABEL: test_conv_no_bias_0
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32xf32>, tensor<5x2x6xf32>) -> tensor<1x5x27xf32> // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [1], group = 1 : i64, kernel_shape = [6], pads = [0, 0], strides = [1]} : (tensor<1x2x32xf32>, tensor<5x2x6xf32>) -> tensor<1x5x27xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x27xf32> // CHECK: return [[RES_ATTR]] : tensor<1x5x27xf32>
} }
@ -161,7 +161,7 @@ func @test_conv_no_bias_1(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7x
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_1 // CHECK-LABEL: test_conv_no_bias_1
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x27x58xf32> // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 7], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x27x58xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xf32> // CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xf32>
} }
@ -172,7 +172,7 @@ func @test_conv_no_bias_2(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7x
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_2 // CHECK-LABEL: test_conv_no_bias_2
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x25x56xf32> // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [8, 9], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x25x56xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xf32> // CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xf32>
} }
@ -184,7 +184,7 @@ func @test_conv_no_bias_3(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_3 // CHECK-LABEL: test_conv_no_bias_3
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32> // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 10], pads = [2, 4, 3, 5], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32> // CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
} }
@ -195,7 +195,7 @@ func @test_conv_no_bias_4(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_4 // CHECK-LABEL: test_conv_no_bias_4
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32> // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 10], pads = [2, 4, 3, 5], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32> // CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
} }
@ -204,7 +204,7 @@ func @test_conv_no_bias_5(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_5 // CHECK-LABEL: test_conv_no_bias_5
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [3, 5, 2, 4]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32> // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 10], pads = [3, 5, 2, 4], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32> // CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
} }
@ -215,7 +215,7 @@ func @test_conv_no_bias_6(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_6 // CHECK-LABEL: test_conv_no_bias_6
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x27x55xf32> // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 10], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x27x55xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xf32> // CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xf32>
} }
@ -226,7 +226,7 @@ func @test_conv_no_bias_7(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7x
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_7 // CHECK-LABEL: test_conv_no_bias_7
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x14x20xf32> // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 7], pads = [0, 0, 0, 0], strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x14x20xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xf32> // CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xf32>
} }
@ -238,8 +238,8 @@ func @test_conv_no_bias_8(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7x
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_8 // CHECK-LABEL: test_conv_no_bias_8
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [18, 66, 18, 66], strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x32x64xf32> // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 7], pads = [2, 3, 2, 3], strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x16x22xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32> // CHECK: return [[RES_ATTR]] : tensor<1x5x16x22xf32>
} }
/// dilations attribute. /// dilations attribute.
@ -249,8 +249,8 @@ func @test_conv_no_bias_9(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7x
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_9 // CHECK-LABEL: test_conv_no_bias_9
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x20x42xf32> // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, kernel_shape = [6, 7], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x22x46xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x20x42xf32> // CHECK: return [[RES_ATTR]] : tensor<1x5x22x46xf32>
} }
/// dilations attribute with stride. /// dilations attribute with stride.
@ -260,8 +260,8 @@ func @test_conv_no_bias_10(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_10 // CHECK-LABEL: test_conv_no_bias_10
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x10x21xf32> // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, kernel_shape = [6, 7], pads = [0, 0, 0, 0], strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x11x23xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x10x21xf32> // CHECK: return [[RES_ATTR]] : tensor<1x5x11x23xf32>
} }
/// dilations attribute with auto_pad set to SAME_UPPER. /// dilations attribute with auto_pad set to SAME_UPPER.
@ -269,30 +269,30 @@ func @test_conv_no_bias_10(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7
func @test_conv_no_bias_11(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> { 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 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32> %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_11
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, pads = [6, 11, 6, 11]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
} }
// CHECK-LABEL: test_conv_no_bias_11
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, kernel_shape = [6, 7], pads = [5, 9, 5, 9], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
/// Test PadConstantValuePad //===----------------------------------------------------------------------===//
/// Test shape inference for PadConstantValuePad.
//===----------------------------------------------------------------------===//
/// Test PadConstantValuePad_1
func @test_PadConstantValuePad_1(%arg0 : tensor<16x13xf32>) -> tensor<*xf32> { func @test_PadConstantValuePad_1(%arg0 : tensor<16x13xf32>) -> tensor<*xf32> {
%0 = "onnx.PadConstantValuePad"(%arg0) {constant_value = 0.000000e+00 : f32, mode = "constant", pads = [0, 2, 0, 0]} : (tensor<16x13xf32>) -> tensor<*xf32> %0 = "onnx.PadConstantValuePad"(%arg0) {constant_value = 0.000000e+00 : f32, mode = "constant", pads = [0, 2, 0, 0]} : (tensor<16x13xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_PadConstantValuePad_1
// CHECK: [[RES:%.+]] = "onnx.PadConstantValuePad"(%arg0) {constant_value = 0.000000e+00 : f32, mode = "constant", pads = [0, 2, 0, 0]} : (tensor<16x13xf32>) -> tensor<18x13xf32>
// CHECK: return [[RES]] : tensor<18x13xf32>
} }
// CHECK-LABEL: test_PadConstantValuePad_1
// CHECK: [[RES:%.+]] = "onnx.PadConstantValuePad"(%arg0) {constant_value = 0.000000e+00 : f32, mode = "constant", pads = [0, 2, 0, 0]} : (tensor<16x13xf32>) -> tensor<18x13xf32>
// CHECK: return [[RES]] : tensor<18x13xf32>
/// Test PadConstantPad /// Test PadConstantPad_1
func @test_PadConstantPad_1(%arg0 : tensor<16x13xf32>, %arg1 : tensor<*xf32>) -> tensor<*xf32> { func @test_PadConstantPad_1(%arg0 : tensor<16x13xf32>, %arg1 : tensor<*xf32>) -> tensor<*xf32> {
%0 = "onnx.PadConstantPad"(%arg0, %arg1) {mode = "constant", pads = [0, 2, 3, 1]} : (tensor<16x13xf32>, tensor<*xf32>) -> tensor<*xf32> %0 = "onnx.PadConstantPad"(%arg0, %arg1) {mode = "constant", pads = [0, 2, 3, 1]} : (tensor<16x13xf32>, tensor<*xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> () "std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_PadConstantPad_1
// CHECK: [[RES:%.+]] = "onnx.PadConstantPad"(%arg0, %arg1) {mode = "constant", pads = [0, 2, 3, 1]} : (tensor<16x13xf32>, tensor<*xf32>) -> tensor<18x17xf32>
// CHECK: return [[RES]] : tensor<18x17xf32>
} }
// CHECK-LABEL: test_PadConstantPad_1
// CHECK: [[RES:%.+]] = "onnx.PadConstantPad"(%arg0, %arg1) {mode = "constant", pads = [0, 2, 3, 1]} : (tensor<16x13xf32>, tensor<*xf32>) -> tensor<18x17xf32>
// CHECK: return [[RES]] : tensor<18x17xf32>