mirror of https://github.com/llvm/torch-mlir
Adds misc fixes for some padding related issues (#3528)
This patch adds a few misc pad op related changes: 1. Addresses issue <https://github.com/llvm/torch-mlir/issues/3457> 2. Addresses issue <https://github.com/llvm/torch-mlir/issues/3442> 3. Fixes the padding order for asymmetrically padded onnx.Conv ops 4. Enables passing quantization through those onnx.Conv op pre-paddings 5. Modifies the torch-to-linalg lowering of AtenReplicationPad2d op to enable support for input rank != 4 Unfortunately, even with all of these changes, the e2e tests for the ReplicationPad2d still fail the onnx config, since the torch export procedure for rearranging the pad order is complicated enough that the padding ints end up not being able to fold back to constants.pull/3541/head
zjgarvey
GitHub
parent
b38585e077
commit
0fb8b017d8
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@ -1343,11 +1343,12 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
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SmallVector<Value> padsRearrange;
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SmallVector<Value> inputPaddingList;
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for (uint32_t i = 0; i < padding.size() / 2; i++) {
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padsRearrange.emplace_back(rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(padding[i])));
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padsRearrange.emplace_back(rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(
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padding[(padding.size() / 2) + i])));
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padding[padding.size() / 2 - i - 1])));
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padsRearrange.emplace_back(rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(),
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rewriter.getI64IntegerAttr(padding[padding.size() - i - 1])));
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inputPaddingList.emplace_back(
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rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(0)));
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@ -2233,41 +2233,84 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
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binder.op, "The axes parameter is not supported yet");
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}
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if (binder.tensorOperandAtIndex(data, 0) ||
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binder.tensorOperandAtIndex(pads, 1) ||
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binder.tensorResultType(resultType) ||
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binder.customOpNameStringAttr(mode, "mode", "constant"))
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return failure();
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bool cstMode = (mode == "constant");
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// get input rank
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auto dataOpTy = cast<Torch::ValueTensorType>(data.getType());
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TensorType dataTensor = dataOpTy.toBuiltinTensor();
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if (!dataTensor || !dataTensor.hasRank())
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return rewriter.notifyMatchFailure(
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binder.op, "pad length unknown and data operand unranked");
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int64_t dataRank = dataTensor.getRank();
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int64_t padsSize = 2 * dataRank;
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Location loc = binder.getLoc();
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// Get pads shape and rank. The pads tensor is expected to be 1-D
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// tensor.
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auto padsTensorType = cast<Torch::ValueTensorType>(pads.getType());
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if (!padsTensorType || !padsTensorType.hasSizes()) {
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return rewriter.notifyMatchFailure(binder.op,
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"Expect non empty pad tensor");
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}
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ArrayRef<int64_t> padsShape = padsTensorType.getSizes();
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int64_t padsRank = padsShape.size();
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if (padsRank != 1)
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return rewriter.notifyMatchFailure(binder.op,
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"expect 1-d pad tensor");
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// get pads (earlier versions use an attribute, newer versions use a
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// tensor input)
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SmallVector<Value> padsTensorValue;
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if (binder.tensorOperandAtIndex(pads, 1)) {
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SmallVector<int64_t> defaultPads(2 * dataRank, 0);
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SmallVector<int64_t> padInts;
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if (binder.s64IntegerArrayAttr(padInts, "pads", defaultPads))
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return rewriter.notifyMatchFailure(binder.op,
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"pads binder failure");
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// opset_version 1 uses the attribute name "paddings"
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if (padInts == defaultPads) {
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SmallVector<int64_t> paddingsInts;
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if (binder.s64IntegerArrayAttr(paddingsInts, "paddings",
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defaultPads))
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return rewriter.notifyMatchFailure(binder.op,
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"paddings binder failure");
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padInts = paddingsInts;
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}
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for (auto p : padInts)
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padsTensorValue.push_back(rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(p)));
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} else {
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// Get pads shape and rank. The pads tensor is expected to be 1-D
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// tensor.
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auto padsTensorType = cast<Torch::ValueTensorType>(pads.getType());
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if (!padsTensorType || !padsTensorType.hasSizes()) {
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return rewriter.notifyMatchFailure(binder.op,
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"Expect non empty pad tensor");
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}
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ArrayRef<int64_t> padsShape = padsTensorType.getSizes();
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int64_t padsRank = padsShape.size();
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if (padsRank != 1)
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return rewriter.notifyMatchFailure(binder.op,
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"expect 1-d pad tensor");
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if (padsShape[0] != Torch::kUnknownSize) {
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// As per onnx.Pad documentation, padSize = 2*num_data_axes
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// (if axes param not passed). Need to be updated when adding
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// support for `axes` param.
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padsSize = padsShape[0];
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}
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int64_t padsSize = padsShape[0];
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if (padsSize == Torch::kUnknownSize) {
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// As per onnx.Pad documentation, padSize = 2*num_data_axes
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// (if axes param not passed). Need to be updated when adding
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// support for `axes` param.
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auto dataOpTy = cast<Torch::ValueTensorType>(data.getType());
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TensorType dataTensor = dataOpTy.toBuiltinTensor();
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if (!dataTensor || !dataTensor.hasRank())
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return rewriter.notifyMatchFailure(
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binder.op, "pad length unknown and data operand unranked");
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int64_t dataRank = dataTensor.getRank();
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padsSize = 2 * dataRank;
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// Extract all the values of 1-D pad tensor and create a list of all
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// these values as torch.pad op expects pad list.
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Value constZero = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(0));
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SmallVector<int64_t> emptyShape;
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Type padsElemType = Torch::ValueTensorType::get(
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padsTensorType.getContext(), emptyShape,
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padsTensorType.getOptionalDtype());
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for (uint32_t i = 0; i < padsSize; ++i) {
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Value index = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(i));
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auto select = rewriter.create<Torch::AtenSelectIntOp>(
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loc, padsElemType, pads, constZero, index);
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Value selectInt = rewriter.create<Torch::AtenItemOp>(
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loc, rewriter.getType<Torch::IntType>(), select);
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padsTensorValue.push_back(selectInt);
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}
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}
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Value constantValue;
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if (binder.getNumOperands() >= 3) {
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if (binder.getNumOperands() >= 3 && cstMode) {
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if (!binder.tensorOperandAtIndex(constantValue, 2)) {
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auto constTy =
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dyn_cast<Torch::BaseTensorType>(constantValue.getType());
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@ -2283,38 +2326,28 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
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}
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}
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if (!constantValue) {
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if (!constantValue && cstMode) {
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auto dataTensorType = cast<Torch::ValueTensorType>(data.getType());
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if (isa<IntegerType>(dataTensorType.getDtype()))
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constantValue = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(0));
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if (isa<FloatType>(dataTensorType.getDtype()))
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// Earlier versions used a FLOAT attribute to store the constant
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// value. The following will pick up on any non-default value attr if
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// provided.
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float constantFloat;
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if (isa<FloatType>(dataTensorType.getDtype()) &&
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!binder.f32FloatAttr(constantFloat, "value", 0.0f))
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constantValue = rewriter.create<Torch::ConstantFloatOp>(
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loc, rewriter.getF64FloatAttr(0.0f));
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loc, rewriter.getF64FloatAttr(constantFloat));
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if (!constantValue)
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return rewriter.notifyMatchFailure(
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binder.op, "expected integer or float data tensor");
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}
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// Extract all the values of 1-D pad tensor and create a list of all
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// these values as torch.pad op expects pad list.
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Value constZero = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(0));
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SmallVector<Value> padsTensorValue;
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SmallVector<int64_t> emptyShape;
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Type padsElemType =
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Torch::ValueTensorType::get(padsTensorType.getContext(), emptyShape,
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padsTensorType.getOptionalDtype());
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for (uint32_t i = 0; i < padsSize; ++i) {
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Value index = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(i));
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auto select = rewriter.create<Torch::AtenSelectIntOp>(
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loc, padsElemType, pads, constZero, index);
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Value selectInt = rewriter.create<Torch::AtenItemOp>(
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loc, rewriter.getType<Torch::IntType>(), select);
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padsTensorValue.push_back(selectInt);
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}
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// for modes other than "constant" a value is not required
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if (!cstMode)
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constantValue = rewriter.create<Torch::ConstantNoneOp>(loc);
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// The torch.pad op expects a different arrangement of padding pairs for
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// each dimension as compared to the onnx.pad op. Rearrange the pad
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@ -2335,6 +2368,20 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
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Torch::ListType::get(rewriter.getType<Torch::IntType>()),
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padsRearrange)
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.getResult();
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// lowering to AtenConstantPadNdOp directly allows passing any torch
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// scalar type for the value, whereas AtenPadOp takes an optional float
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// type.
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if (cstMode && !isa<Torch::NoneType>(constantValue.getType())) {
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rewriter.replaceOpWithNewOp<Torch::AtenConstantPadNdOp>(
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binder.op, resultType, data, padsSizeList, constantValue);
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return success();
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}
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// translate a few mismatching mode names ONNX -> Torch
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mode = (mode == "edge") ? "replicate" : mode;
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mode = (mode == "wrap") ? "circular" : mode;
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Value modeVal = rewriter.create<Torch::ConstantStrOp>(
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loc, rewriter.getStringAttr(mode));
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@ -97,8 +97,12 @@ public:
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Type newResultType = getTypeConverter()->convertType(op.getType());
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Type elementType = cast<RankedTensorType>(newResultType).getElementType();
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auto dstOriginalDtype =
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cast<Torch::ValueTensorType>(op.getType()).getDtype();
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Value castedValue =
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convertScalarToDtype(rewriter, loc, adaptor.getValue(), elementType);
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convertScalarToDtype(rewriter, loc, adaptor.getValue(), elementType,
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std::nullopt, dstOriginalDtype);
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Type padType = tensor::PadOp::inferResultType(
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cast<RankedTensorType>(self.getType()), staticLow, staticHigh);
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@ -209,26 +213,38 @@ public:
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Value one = getConstant(rewriter, loc, 1, indexType);
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Value hDimSizeMinusOne = createSub(hDimSize, one);
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Value vDimSizeMinusOne = createSub(vDimSize, one);
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SmallVector<Value> allOneStrides(numDims, one);
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SmallVector<Value> allOneStridesVal(numDims, one);
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SmallVector<OpFoldResult> allOneStrides =
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getAsOpFoldResult(allOneStridesVal);
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SmallVector<Value> extractOffsetsLT(numDims, zero);
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extractOffsetsLT[hDim] = zero;
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extractOffsetsLT[vDim] = zero;
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SmallVector<Value> extractShapeLR(numDims, one);
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extractShapeLR[hDim] = one;
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extractShapeLR[vDim] = vDimSize;
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SmallVector<Value> extractOffsetsLTVal(numDims, zero);
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extractOffsetsLTVal[hDim] = zero;
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extractOffsetsLTVal[vDim] = zero;
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SmallVector<OpFoldResult> extractOffsetsLT =
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getAsOpFoldResult(extractOffsetsLTVal);
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SmallVector<Value> extractShapeLRVal(numDims, one);
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extractShapeLRVal[hDim] = one;
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extractShapeLRVal[vDim] = vDimSize;
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SmallVector<OpFoldResult> extractShapeLR =
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getAsOpFoldResult(extractShapeLRVal);
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SmallVector<Value> extractOffsetsRight(numDims, zero);
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extractOffsetsRight[hDim] = hDimSizeMinusOne;
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extractOffsetsRight[vDim] = zero;
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SmallVector<Value> extractOffsetsRightVal(numDims, zero);
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extractOffsetsRightVal[hDim] = hDimSizeMinusOne;
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extractOffsetsRightVal[vDim] = zero;
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SmallVector<OpFoldResult> extractOffsetsRight =
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getAsOpFoldResult(extractOffsetsRightVal);
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SmallVector<Value> extractOffsetsBottom(numDims, zero);
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extractOffsetsBottom[hDim] = zero;
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extractOffsetsBottom[vDim] = vDimSizeMinusOne;
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SmallVector<Value> extractOffsetsBottomVal(numDims, zero);
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extractOffsetsBottomVal[hDim] = zero;
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extractOffsetsBottomVal[vDim] = vDimSizeMinusOne;
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SmallVector<OpFoldResult> extractOffsetsBottom =
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getAsOpFoldResult(extractOffsetsBottomVal);
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SmallVector<Value> extractShapeTB(numDims, one);
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extractShapeTB[hDim] = hDimSize;
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extractShapeTB[vDim] = one;
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SmallVector<Value> extractShapeTBVal(numDims, one);
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extractShapeTBVal[hDim] = hDimSize;
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extractShapeTBVal[vDim] = one;
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SmallVector<OpFoldResult> extractShapeTB =
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getAsOpFoldResult(extractShapeTBVal);
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SmallVector<Value> tensorsLeft;
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SmallVector<Value> tensorsRight;
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@ -240,24 +256,26 @@ public:
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Value vCenterLeftSlice = rewriter.create<tensor::ExtractSliceOp>(
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loc, input, extractOffsetsLT, extractShapeLR, allOneStrides);
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Value vLeftSlice = vCenterLeftSlice;
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SmallVector<Value> extractIndices(numDims, zero);
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if (hasTopPadding) {
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Value topLeftValue = rewriter.create<tensor::ExtractOp>(
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loc, input, ValueRange{zero, zero, zero, zero});
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Value topLeftValue =
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rewriter.create<tensor::ExtractOp>(loc, input, extractIndices);
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// pad vCenterLeftSlice on the top
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SmallVector<int64_t> lowPadding(4, 0);
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SmallVector<int64_t> highPadding(4, 0);
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lowPadding[2] = padInts[2];
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SmallVector<int64_t> lowPadding(numDims, 0);
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SmallVector<int64_t> highPadding(numDims, 0);
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lowPadding[vDim] = padInts[2];
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vLeftSlice = torch_to_linalg::getPaddedTensor(
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op, rewriter, vLeftSlice, lowPadding, highPadding, topLeftValue);
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}
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if (hasBottomPadding) {
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Value bottomLeftValue = rewriter.create<tensor::ExtractOp>(
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loc, input, ValueRange{zero, zero, vDimSizeMinusOne, zero});
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extractIndices[vDim] = vDimSizeMinusOne;
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Value bottomLeftValue =
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rewriter.create<tensor::ExtractOp>(loc, input, extractIndices);
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// pad vLeftSlice at the bottom
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SmallVector<int64_t> lowPadding(4, 0);
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SmallVector<int64_t> highPadding(4, 0);
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highPadding[2] = padInts[3];
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SmallVector<int64_t> lowPadding(numDims, 0);
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SmallVector<int64_t> highPadding(numDims, 0);
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highPadding[vDim] = padInts[3];
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vLeftSlice = torch_to_linalg::getPaddedTensor(
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op, rewriter, vLeftSlice, lowPadding, highPadding, bottomLeftValue);
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}
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@ -265,7 +283,7 @@ public:
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tensorsLeft.push_back(vLeftSlice);
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}
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Value leftPadTile =
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rewriter.create<tensor::ConcatOp>(loc, 3, tensorsLeft);
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rewriter.create<tensor::ConcatOp>(loc, hDim, tensorsLeft);
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tensorsRes.push_back(leftPadTile);
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}
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if (hasTopPadding) {
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@ -283,33 +301,35 @@ public:
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tensorsCenter.push_back(bottomHcenterSlice);
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}
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}
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centerTile = rewriter.create<tensor::ConcatOp>(loc, 2, tensorsCenter);
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centerTile = rewriter.create<tensor::ConcatOp>(loc, vDim, tensorsCenter);
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tensorsRes.push_back(centerTile);
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if (hasRightPadding) {
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Value vCenterRightSlice = rewriter.create<tensor::ExtractSliceOp>(
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loc, input, extractOffsetsRight, extractShapeLR, allOneStrides);
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Value vRightSlice = vCenterRightSlice;
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SmallVector<Value> extractIndices(numDims, zero);
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extractIndices[hDim] = hDimSizeMinusOne;
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if (hasTopPadding) {
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Value topRightValue = rewriter.create<tensor::ExtractOp>(
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loc, input, ValueRange{zero, zero, zero, hDimSizeMinusOne});
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// pad vCenterRightSlice on the top
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SmallVector<int64_t> lowPadding(4, 0);
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SmallVector<int64_t> highPadding(4, 0);
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lowPadding[2] = padInts[2];
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SmallVector<int64_t> lowPadding(numDims, 0);
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SmallVector<int64_t> highPadding(numDims, 0);
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lowPadding[vDim] = padInts[2];
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vRightSlice = torch_to_linalg::getPaddedTensor(
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op, rewriter, vRightSlice, lowPadding, highPadding, topRightValue);
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}
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if (hasBottomPadding) {
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Value bottomRightValue = rewriter.create<tensor::ExtractOp>(
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loc, input,
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ValueRange{zero, zero, vDimSizeMinusOne, hDimSizeMinusOne});
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extractIndices[vDim] = vDimSizeMinusOne;
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Value bottomRightValue =
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rewriter.create<tensor::ExtractOp>(loc, input, extractIndices);
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// Pad vCenterRightSlice or vRightTopPaddedSlice at the bottom.
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SmallVector<int64_t> lowPadding(4, 0);
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SmallVector<int64_t> highPadding(4, 0);
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highPadding[2] = padInts[3];
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SmallVector<int64_t> lowPadding(numDims, 0);
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SmallVector<int64_t> highPadding(numDims, 0);
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highPadding[vDim] = padInts[3];
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vRightSlice = torch_to_linalg::getPaddedTensor(
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op, rewriter, vRightSlice, lowPadding, highPadding,
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bottomRightValue);
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@ -318,10 +338,10 @@ public:
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tensorsRight.push_back(vRightSlice);
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}
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Value rightPadTile =
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rewriter.create<tensor::ConcatOp>(loc, 3, tensorsRight);
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rewriter.create<tensor::ConcatOp>(loc, hDim, tensorsRight);
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tensorsRes.push_back(rightPadTile);
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}
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Value resTensor = rewriter.create<tensor::ConcatOp>(loc, 3, tensorsRes);
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Value resTensor = rewriter.create<tensor::ConcatOp>(loc, hDim, tensorsRes);
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Type newResultType = getTypeConverter()->convertType(op.getType());
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rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, resTensor);
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return success();
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@ -6379,17 +6379,91 @@ class DecomposeAtenPadOp : public OpRewritePattern<AtenPadOp> {
|
|||
using OpRewritePattern::OpRewritePattern;
|
||||
LogicalResult matchAndRewrite(AtenPadOp op,
|
||||
PatternRewriter &rewriter) const override {
|
||||
std::string mode;
|
||||
if (!matchPattern(op.getMode(), m_TorchConstantStr(mode)))
|
||||
return rewriter.notifyMatchFailure(op, "mode must be a constant string");
|
||||
|
||||
Value value = op.getValue();
|
||||
if (isa<Torch::OptionalType>(value.getType()))
|
||||
return rewriter.notifyMatchFailure(op, "optional type not supported");
|
||||
if (isa<Torch::NoneType>(value.getType()))
|
||||
value = rewriter.create<Torch::ConstantFloatOp>(
|
||||
op.getLoc(), rewriter.getF64FloatAttr(0));
|
||||
if (mode == "constant") {
|
||||
Value value = op.getValue();
|
||||
if (isa<Torch::OptionalType>(value.getType()))
|
||||
return rewriter.notifyMatchFailure(op, "optional type not supported");
|
||||
if (isa<Torch::NoneType>(value.getType()))
|
||||
value = rewriter.create<Torch::ConstantFloatOp>(
|
||||
op.getLoc(), rewriter.getF64FloatAttr(0));
|
||||
|
||||
rewriter.replaceOpWithNewOp<AtenConstantPadNdOp>(
|
||||
op, op.getType(), op.getSelf(), op.getPad(), value);
|
||||
return success();
|
||||
rewriter.replaceOpWithNewOp<AtenConstantPadNdOp>(
|
||||
op, op.getType(), op.getSelf(), op.getPad(), value);
|
||||
return success();
|
||||
}
|
||||
|
||||
SmallVector<Value> padValues;
|
||||
if (!getListConstructElements(op.getPad(), padValues))
|
||||
return failure();
|
||||
SmallVector<int64_t> padInts;
|
||||
Value usefulPads = op.getPad();
|
||||
uint64_t usefulPadIndexEnd = padValues.size();
|
||||
|
||||
// try to reduce the number of padding dims if possible
|
||||
if (matchPattern(op.getPad(), m_TorchListOfConstantInts(padInts))) {
|
||||
if ((padInts.size() % 2) == 1)
|
||||
return rewriter.notifyMatchFailure(op,
|
||||
"expected an even number of pads");
|
||||
|
||||
for (uint64_t i = padInts.size() - 1; i > 0; i -= 2) {
|
||||
if (padInts[i] != 0 || padInts[i - 1] != 0)
|
||||
break;
|
||||
usefulPadIndexEnd = i - 1;
|
||||
}
|
||||
if (usefulPadIndexEnd == 0) {
|
||||
rewriter.replaceOp(op, op.getSelf());
|
||||
return success();
|
||||
}
|
||||
}
|
||||
|
||||
// we don't have support for 1-D replicate pad, so pass it as 2d if
|
||||
// possible.
|
||||
// TODO: add support for AtenReplicatePad1dOp and remove this.
|
||||
if (mode == "replicate" && usefulPadIndexEnd == 2 && padValues.size() >= 4)
|
||||
usefulPadIndexEnd = 4;
|
||||
|
||||
// make a new list of padding ints if dimensionality reduction can be
|
||||
// performed
|
||||
if (usefulPadIndexEnd < padValues.size()) {
|
||||
ArrayRef<Value> usefulPadValues(padValues.begin(),
|
||||
padValues.begin() + usefulPadIndexEnd);
|
||||
usefulPads = rewriter.create<PrimListConstructOp>(
|
||||
op.getLoc(),
|
||||
rewriter.getType<Torch::ListType>(rewriter.getType<Torch::IntType>()),
|
||||
usefulPadValues);
|
||||
}
|
||||
|
||||
uint64_t numPadDims = usefulPadIndexEnd / 2;
|
||||
|
||||
if (mode == "reflect") {
|
||||
// only support for relectionpad 1d and 2d
|
||||
if (numPadDims == 2) {
|
||||
rewriter.replaceOpWithNewOp<AtenReflectionPad2dOp>(
|
||||
op, op.getType(), op.getSelf(), usefulPads);
|
||||
return success();
|
||||
}
|
||||
if (numPadDims == 1) {
|
||||
rewriter.replaceOpWithNewOp<AtenReflectionPad1dOp>(
|
||||
op, op.getType(), op.getSelf(), usefulPads);
|
||||
return success();
|
||||
}
|
||||
return failure();
|
||||
}
|
||||
|
||||
if (mode == "replicate") {
|
||||
// only support for replication pad 2d
|
||||
if (numPadDims != 2)
|
||||
return failure();
|
||||
rewriter.replaceOpWithNewOp<AtenReplicationPad2dOp>(
|
||||
op, op.getType(), op.getSelf(), usefulPads);
|
||||
return success();
|
||||
}
|
||||
|
||||
return rewriter.notifyMatchFailure(op, "unsupported mode: " + mode);
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
|
|
|||
|
|
@ -40,7 +40,8 @@ bool isQCommutingOp(mlir::Operation *op) {
|
|||
// if adding a new commuting op here, be sure to add a
|
||||
// RemoveUnused pattern for that op to clean up afterwards
|
||||
return llvm::isa<AtenTransposeIntOp, AtenReshapeOp, AtenSliceTensorOp,
|
||||
PrimsCollapseOp, AtenViewOp>(op);
|
||||
PrimsCollapseOp, AtenViewOp, AtenPadOp, AtenConstantPadNdOp>(
|
||||
op);
|
||||
}
|
||||
|
||||
// The following conversion takes patterns of the form [op0 -> MPTQT -> dequant
|
||||
|
|
@ -65,7 +66,7 @@ public:
|
|||
for (unsigned i : QuantInfo<SrcOp>::operandsToQuantize) {
|
||||
Value operand = operands[i];
|
||||
std::stack<mlir::Operation *> commutingOpStack;
|
||||
Value dequantOpd, MPTQTOpd;
|
||||
Value dequantOpd, MPTQTOpd, scale, zeroPoint;
|
||||
for (unsigned k = 0; k < depth + 1; k++) {
|
||||
auto currOp = operand.getDefiningOp();
|
||||
// Case 0 : currOp is a nullptr (e.g., operand is a block argument)
|
||||
|
|
@ -84,6 +85,8 @@ public:
|
|||
auto MPTQTOp =
|
||||
dequantOpd.getDefiningOp<Aten_MakePerTensorQuantizedTensorOp>();
|
||||
MPTQTOpd = MPTQTOp.getOperand(0);
|
||||
scale = MPTQTOp.getOperand(1);
|
||||
zeroPoint = MPTQTOp.getOperand(2);
|
||||
}
|
||||
// either a dequant was found or chain broken, so break loop
|
||||
break;
|
||||
|
|
@ -107,6 +110,47 @@ public:
|
|||
commutingOpStack.pop();
|
||||
llvm::SmallVector<Value> currOperands(currOp->getOperands());
|
||||
currOperands[0] = oldOpd;
|
||||
// pad ops aren't quite commuting, so we include some extra logic to
|
||||
// quantize the padding value
|
||||
if (isa<Torch::AtenPadOp, Torch::AtenConstantPadNdOp>(currOp)) {
|
||||
Value floatPadValue = currOperands.back();
|
||||
Value quantPadValue;
|
||||
if (isa<Torch::NoneType>(floatPadValue.getType()))
|
||||
quantPadValue = rewriter.create<AtenFloatScalarOp>(loc, zeroPoint);
|
||||
else {
|
||||
floatPadValue =
|
||||
rewriter.create<AtenFloatScalarOp>(loc, floatPadValue);
|
||||
quantPadValue = rewriter.create<Torch::AtenDivFloatOp>(
|
||||
loc, floatPadValue, scale);
|
||||
quantPadValue = rewriter.create<Torch::AtenAddFloatIntOp>(
|
||||
loc, quantPadValue, zeroPoint);
|
||||
}
|
||||
// clamp pad value to qint range
|
||||
if (auto intType = dyn_cast<mlir::IntegerType>(intDType)) {
|
||||
bool isSigned = intType.isSignedInteger();
|
||||
int64_t width = intType.getWidth();
|
||||
assert(width < 64 &&
|
||||
"quantized int bitwidth should be less than 64");
|
||||
int64_t minInt = isSigned ? -(1 << (width - 1)) : 0;
|
||||
int64_t maxInt = isSigned ? -minInt - 1 : ((1 << width) - 1);
|
||||
Value minQValueFloat = rewriter.create<ConstantFloatOp>(
|
||||
loc, rewriter.getF64FloatAttr(minInt));
|
||||
Value maxQValueFloat = rewriter.create<ConstantFloatOp>(
|
||||
loc, rewriter.getF64FloatAttr(maxInt));
|
||||
SmallVector<int64_t> emptyShape;
|
||||
auto floatTensorType = rewriter.getType<Torch::ValueTensorType>(
|
||||
emptyShape, rewriter.getF64Type());
|
||||
Value quantPadValueTensor = createRank0Tensor(
|
||||
rewriter, loc, floatTensorType, quantPadValue);
|
||||
Value clampedTensor = rewriter.create<Torch::AtenClampOp>(
|
||||
loc, floatTensorType, quantPadValueTensor, minQValueFloat,
|
||||
maxQValueFloat);
|
||||
quantPadValue = rewriter.create<Torch::AtenItemOp>(
|
||||
loc, rewriter.getType<Torch::FloatType>(), clampedTensor);
|
||||
}
|
||||
// quantPadValue is a float, but will get converted/truncated
|
||||
currOperands.back() = quantPadValue;
|
||||
}
|
||||
// get new result type
|
||||
auto oldType = cast<ValueTensorType>(currOp->getResultTypes()[0]);
|
||||
auto intType =
|
||||
|
|
@ -374,7 +418,8 @@ public:
|
|||
RemoveUnused<Aten_MakePerTensorQuantizedTensorOp>,
|
||||
RemoveUnused<AtenTransposeIntOp>, RemoveUnused<AtenSliceTensorOp>,
|
||||
RemoveUnused<AtenReshapeOp>, RemoveUnused<PrimsCollapseOp>,
|
||||
RemoveUnused<AtenViewOp>,
|
||||
RemoveUnused<AtenViewOp>, RemoveUnused<AtenPadOp>,
|
||||
RemoveUnused<AtenConstantPadNdOp>,
|
||||
QuantizeOperandsPastCommutingOps<AtenConvolutionOp, 5>,
|
||||
QuantizeOperandsPastCommutingOps<AtenReluOp, 0>,
|
||||
QuantizeOperandsPastCommutingOps<AtenMatmulOp, 2>,
|
||||
|
|
|
|||
|
|
@ -1014,6 +1014,38 @@ func.func @test_conv_with_strides_padding(%arg0: !torch.vtensor<[1,1,7,5],f32>,
|
|||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: @test_conv_with_asymmetric_padding
|
||||
func.func @test_conv_with_asymmetric_padding(%arg0: !torch.vtensor<[1,1,7,5],f32>, %arg1: !torch.vtensor<[1,1,3,3],f32>) -> !torch.vtensor<[1,1,4,3],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK: %[[int0:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[int2:.*]] = torch.constant.int 2
|
||||
// CHECK: %[[int0_0:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[int2_1:.*]] = torch.constant.int 2
|
||||
// CHECK: %[[int0_2:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[int0_3:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[FakePADS:.*]] = torch.prim.ListConstruct %[[int0_0]], %[[int0_3]] : (!torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[OGPADS:.*]] = torch.prim.ListConstruct %[[int0]], %[[int2]], %[[int2_1]], %[[int0_2]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[str:.*]] = torch.constant.str "constant"
|
||||
// CHECK: %[[float0:.*]] = torch.constant.float 0.000
|
||||
// CHECK: %[[PrePad:.*]] = torch.aten.pad %arg0, %[[OGPADS]], %[[str]], %[[float0]] : !torch.vtensor<[1,1,7,5],f32>, !torch.list<int>, !torch.str, !torch.float -> !torch.vtensor<[1,1,9,7],f32>
|
||||
// CHECK: %[[C1_1:.*]] = torch.constant.int 1
|
||||
// CHECK: %[[C1_2:.*]] = torch.constant.int 1
|
||||
// CHECK: %[[C2:.*]] = torch.constant.int 2
|
||||
// CHECK: %[[C2_0:.*]] = torch.constant.int 2
|
||||
// CHECK: %[[C0:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C1_1]], %[[C1_2]] : (!torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C2]], %[[C2_0]] : (!torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0]] : (!torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool false
|
||||
// CHECK: %[[BIAS:.*]] = torch.constant.none
|
||||
// CHECK: %[[GROUPS:.*]] = torch.constant.int 1
|
||||
// CHECK: %[[Conv:.*]] = torch.aten.convolution %[[PrePad]], %arg1, %[[BIAS]], %[[STRIDE]], %[[FakePADS]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,9,7],f32>, !torch.vtensor<[1,1,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,4,3],f32>
|
||||
// CHECK: return %[[Conv]]
|
||||
%0 = torch.operator "onnx.Conv"(%arg0, %arg1) {torch.onnx.kernel_shape = [3 : si64, 3 : si64], torch.onnx.pads = [2 : si64, 0 : si64, 0 : si64, 2 : si64], torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[1,1,7,5],f32>, !torch.vtensor<[1,1,3,3],f32>) -> !torch.vtensor<[1,1,4,3],f32>
|
||||
return %0 : !torch.vtensor<[1,1,4,3],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: @test_conv_with_bias_strides_padding
|
||||
func.func @test_conv_with_bias_strides_padding(%arg0: !torch.vtensor<[?,?,224,224],f32>, %arg1: !torch.vtensor<[64,3,7,7],f32>, %arg2: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,64,112,112],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK: %[[C3:.*]] = torch.constant.int 3
|
||||
|
|
|
|||
|
|
@ -815,32 +815,40 @@ func.func @test_grid_sampler02(%arg0: !torch.vtensor<[5,10,10,4],f32>, %arg1: !t
|
|||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: @test_grid_sampler03
|
||||
// CHECK: %[[INT1:.*]] = torch.constant.int 1
|
||||
// CHECK: %[[INT0:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[B0:.*]] = torch.constant.bool true
|
||||
// CHECK: %[[A0:.*]] = torch.aten.grid_sampler %arg0, %arg1, %[[INT1]], %[[INT0]], %[[B0]] : !torch.vtensor<[5,10,10,4],f32>
|
||||
func.func @test_grid_sampler03(%arg0: !torch.vtensor<[5,10,10,4],f32>, %arg1: !torch.vtensor<[5,7,8,2],f32>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
%0 = torch.operator "onnx.GridSample" (%arg0, %arg1) {torch.onnx.align_corners = 1 : si64, torch.onnx.mode = "nearest", torch.onnx.padding_mode = "zeros"} : (!torch.vtensor<[5,10,10,4],f32>, !torch.vtensor<[5,7,8,2],f32>) -> !torch.vtensor<[?,?,?,?],f32>
|
||||
return %0 : !torch.vtensor<[?,?,?,?],f32>
|
||||
// CHECK-LABEL: func.func @test_oldest_pad
|
||||
func.func @test_oldest_pad(%arg0: !torch.vtensor<[3,4],f32>) -> !torch.vtensor<[5,4],f32> attributes {torch.onnx_meta.opset_version = 1 : si64} {
|
||||
// CHECK: %[[int0:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[int0_0:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[int2:.*]] = torch.constant.int 2
|
||||
// CHECK: %[[int0_1:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[float0:.*]] = torch.constant.float 0.000000e+00
|
||||
// CHECK: %[[list:.*]] = torch.prim.ListConstruct %[[int0_0]], %[[int0_1]], %[[int0]], %[[int2]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[pad:.*]] = torch.aten.constant_pad_nd %arg0, %[[list]], %[[float0]] : !torch.vtensor<[3,4],f32>, !torch.list<int>, !torch.float -> !torch.vtensor<[5,4],f32>
|
||||
// CHECK: return %[[pad]] : !torch.vtensor<[5,4],f32>
|
||||
%0 = torch.operator "onnx.Pad"(%arg0) {torch.onnx.mode = "constant", torch.onnx.paddings = [0 : si64, 0 : si64, 2 : si64, 0 : si64], torch.onnx.value = 0.000000e+00 : f32} : (!torch.vtensor<[3,4],f32>) -> !torch.vtensor<[5,4],f32>
|
||||
return %0 : !torch.vtensor<[5,4],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @test_less_or_equal
|
||||
func.func @test_less_or_equal(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],i1> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 16 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[3,4,5],f32>
|
||||
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: !torch.vtensor<[3,4,5],f32>
|
||||
// CHECK: torch.aten.le.Tensor %[[ARG0]], %[[ARG1]] : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],i1>
|
||||
%0 = torch.operator "onnx.LessOrEqual"(%arg0, %arg1) : (!torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],i1>
|
||||
return %0 : !torch.vtensor<[3,4,5],i1>
|
||||
// CHECK-LABEL: func.func @test_old_pad
|
||||
func.func @test_old_pad(%arg0: !torch.vtensor<[3,4],f32>) -> !torch.vtensor<[5,4],f32> attributes {torch.onnx_meta.opset_version = 11 : si64} {
|
||||
// CHECK: %[[int0:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[int0_0:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[int2:.*]] = torch.constant.int 2
|
||||
// CHECK: %[[int0_1:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[float0:.*]] = torch.constant.float 0.000000e+00
|
||||
// CHECK: %[[list:.*]] = torch.prim.ListConstruct %[[int0_0]], %[[int0_1]], %[[int0]], %[[int2]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[pad:.*]] = torch.aten.constant_pad_nd %arg0, %[[list]], %[[float0]] : !torch.vtensor<[3,4],f32>, !torch.list<int>, !torch.float -> !torch.vtensor<[5,4],f32>
|
||||
// CHECK: return %[[pad]] : !torch.vtensor<[5,4],f32>
|
||||
%0 = torch.operator "onnx.Pad"(%arg0) {torch.onnx.mode = "constant", torch.onnx.pads = [0 : si64, 0 : si64, 2 : si64, 0 : si64], torch.onnx.value = 0.000000e+00 : f32} : (!torch.vtensor<[3,4],f32>) -> !torch.vtensor<[5,4],f32>
|
||||
return %0 : !torch.vtensor<[5,4],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @test_pad
|
||||
func.func @test_pad(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[4], si64>, %arg2: !torch.vtensor<[], f32>) -> !torch.vtensor<[5,4],f32> attributes {torch.onnx_meta.opset_version = 19 : si64} {
|
||||
// CHECK: %[[VAL:.+]] = torch.aten.item %arg2 : !torch.vtensor<[],f32> -> !torch.float
|
||||
// CHECK: %[[INT0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[SELECT_0:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT0_0]] : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
|
||||
|
|
@ -854,9 +862,9 @@ func.func @test_pad(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[4],
|
|||
// CHECK: %[[INT3:.+]] = torch.constant.int 3
|
||||
// CHECK: %[[SELECT_3:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT3]] : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
|
||||
// CHECK: %[[ITEM_3:.+]] = torch.aten.item %[[SELECT_3]] : !torch.vtensor<[],si64> -> !torch.int
|
||||
// CHECK: %[[VAL:.+]] = torch.aten.item %arg2 : !torch.vtensor<[],f32> -> !torch.float
|
||||
// CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[ITEM_1]], %[[ITEM_3]], %[[ITEM_0]], %[[ITEM_2]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[STR:.+]] = torch.constant.str "constant"
|
||||
// CHECK: %[[PAD:.+]] = torch.aten.pad %arg0, %[[LIST]], %[[STR]], %[[VAL]] : !torch.vtensor<[3,4],f32>, !torch.list<int>, !torch.str, !torch.float -> !torch.vtensor<[5,4],f32>
|
||||
// CHECK: %[[PAD:.+]] = torch.aten.constant_pad_nd %arg0, %[[LIST]], %[[VAL]] : !torch.vtensor<[3,4],f32>, !torch.list<int>, !torch.float -> !torch.vtensor<[5,4],f32>
|
||||
// CHECK: return %[[PAD]] : !torch.vtensor<[5,4],f32>
|
||||
%0 = torch.operator "onnx.Pad"(%arg0, %arg1, %arg2) {torch.onnx.mode = "constant"} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[4], si64>, !torch.vtensor<[], f32>) -> !torch.vtensor<[5,4],f32>
|
||||
return %0 : !torch.vtensor<[5,4],f32>
|
||||
|
|
@ -864,12 +872,36 @@ func.func @test_pad(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[4],
|
|||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @test_i32pad
|
||||
func.func @test_i32pad(%arg0: !torch.vtensor<[3,4],si32>, %arg1: !torch.vtensor<[4], si64>, %arg2: !torch.vtensor<[], si32>) -> !torch.vtensor<[5,4],si32> attributes {torch.onnx_meta.opset_version = 19 : si64} {
|
||||
// CHECK: %[[INT0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[SELECT_0:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT0_0]] : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
|
||||
// CHECK: %[[ITEM_0:.+]] = torch.aten.item %[[SELECT_0]] : !torch.vtensor<[],si64> -> !torch.int
|
||||
// CHECK: %[[INT1:.+]] = torch.constant.int 1
|
||||
// CHECK: %[[SELECT_1:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT1]] : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
|
||||
// CHECK: %[[ITEM_1:.+]] = torch.aten.item %[[SELECT_1]] : !torch.vtensor<[],si64> -> !torch.int
|
||||
// CHECK: %[[INT2:.+]] = torch.constant.int 2
|
||||
// CHECK: %[[SELECT_2:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT2]] : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
|
||||
// CHECK: %[[ITEM_2:.+]] = torch.aten.item %[[SELECT_2]] : !torch.vtensor<[],si64> -> !torch.int
|
||||
// CHECK: %[[INT3:.+]] = torch.constant.int 3
|
||||
// CHECK: %[[SELECT_3:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT3]] : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
|
||||
// CHECK: %[[ITEM_3:.+]] = torch.aten.item %[[SELECT_3]] : !torch.vtensor<[],si64> -> !torch.int
|
||||
// CHECK: %[[VAL:.+]] = torch.aten.item %arg2 : !torch.vtensor<[],si32> -> !torch.int
|
||||
// CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[ITEM_1]], %[[ITEM_3]], %[[ITEM_0]], %[[ITEM_2]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[PAD:.+]] = torch.aten.constant_pad_nd %arg0, %[[LIST]], %[[VAL]] : !torch.vtensor<[3,4],si32>, !torch.list<int>, !torch.int -> !torch.vtensor<[5,4],si32>
|
||||
// CHECK: return %[[PAD]] : !torch.vtensor<[5,4],si32>
|
||||
%0 = torch.operator "onnx.Pad"(%arg0, %arg1, %arg2) {torch.onnx.mode = "constant"} : (!torch.vtensor<[3,4],si32>, !torch.vtensor<[4], si64>, !torch.vtensor<[], si32>) -> !torch.vtensor<[5,4],si32>
|
||||
return %0 : !torch.vtensor<[5,4],si32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: @test_pad_optional_constant
|
||||
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[3,4],f32>
|
||||
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[4],si64>
|
||||
// CHECK: %[[VAL:.+]] = torch.constant.float 0
|
||||
// CHECK: %[[CONST_STR:.*]] = torch.constant.str "constant"
|
||||
// CHECK: torch.aten.pad %[[ARG0]], %{{.*}}, %[[CONST_STR]], %[[VAL]] : !torch.vtensor<[3,4],f32>, !torch.list<int>, !torch.str, !torch.float -> !torch.vtensor<[5,4],f32>
|
||||
// CHECK: torch.aten.constant_pad_nd %[[ARG0]], %{{.*}}, %[[VAL]] : !torch.vtensor<[3,4],f32>, !torch.list<int>, !torch.float -> !torch.vtensor<[5,4],f32>
|
||||
|
||||
func.func @test_pad_optional_constant(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[4], si64>) -> !torch.vtensor<[5,4],f32> attributes {torch.onnx_meta.opset_version = 19 : si64} {
|
||||
%0 = torch.operator "onnx.Pad"(%arg0, %arg1) {torch.onnx.mode = "constant"} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[4], si64>) -> !torch.vtensor<[5,4],f32>
|
||||
|
|
@ -878,6 +910,34 @@ func.func @test_pad_optional_constant(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !
|
|||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: @test_pad_wrap
|
||||
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[3,4],f32>
|
||||
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[4],si64>
|
||||
// CHECK: %[[VAL:.+]] = torch.constant.none
|
||||
// CHECK: %[[STR:.+]] = torch.constant.str "circular"
|
||||
// CHECK: torch.aten.pad %[[ARG0]], %{{.*}}, %[[STR]], %[[VAL]] : !torch.vtensor<[3,4],f32>, !torch.list<int>, !torch.str, !torch.none -> !torch.vtensor<[5,4],f32>
|
||||
|
||||
func.func @test_pad_wrap(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[4], si64>) -> !torch.vtensor<[5,4],f32> attributes {torch.onnx_meta.opset_version = 19 : si64} {
|
||||
%0 = torch.operator "onnx.Pad"(%arg0, %arg1) {torch.onnx.mode = "wrap"} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[4], si64>) -> !torch.vtensor<[5,4],f32>
|
||||
return %0 : !torch.vtensor<[5,4],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: @test_pad_edge
|
||||
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[3,4],f32>
|
||||
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[4],si64>
|
||||
// CHECK: %[[VAL:.+]] = torch.constant.none
|
||||
// CHECK: %[[STR:.+]] = torch.constant.str "replicate"
|
||||
// CHECK: torch.aten.pad %[[ARG0]], %{{.*}}, %[[STR]], %[[VAL]] : !torch.vtensor<[3,4],f32>, !torch.list<int>, !torch.str, !torch.none -> !torch.vtensor<[5,4],f32>
|
||||
|
||||
func.func @test_pad_edge(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[4], si64>) -> !torch.vtensor<[5,4],f32> attributes {torch.onnx_meta.opset_version = 19 : si64} {
|
||||
%0 = torch.operator "onnx.Pad"(%arg0, %arg1) {torch.onnx.mode = "edge"} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[4], si64>) -> !torch.vtensor<[5,4],f32>
|
||||
return %0 : !torch.vtensor<[5,4],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @test_pow
|
||||
func.func @test_pow(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 15 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK: torch.aten.pow.Tensor_Tensor %arg0, %arg1 : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
|
||||
|
|
|
|||
|
|
@ -82,6 +82,48 @@ func.func @matmul_commuting(%arg0: !torch.vtensor<[2,128,32,32],si8>) -> !torch.
|
|||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @mm_pad_commute
|
||||
func.func @mm_pad_commute(%arg0: !torch.vtensor<[8,8],si8>, %arg1: !torch.vtensor<[11,4],si8>) -> !torch.vtensor<[9,4],f32> {
|
||||
// CHECK-DAG: %[[cstQuart:.*]] = torch.constant.float 2.500000e-01
|
||||
// CHECK-DAG: %[[int7:.*]] = torch.constant.int 7
|
||||
// CHECK-DAG: %[[none:.*]] = torch.constant.none
|
||||
// CHECK-DAG: %[[qMax:.*]] = torch.constant.float 1.270000e+02
|
||||
// CHECK-DAG: %[[qMin:.*]] = torch.constant.float -1.280000e+02
|
||||
// CHECK-DAG: %[[padVal:.*]] = torch.constant.float 8.000000e+00
|
||||
// CHECK-DAG: %[[str:.*]] = torch.constant.str "constant"
|
||||
// CHECK-DAG: %[[cstHalf:.*]] = torch.constant.float 5.000000e-01
|
||||
// CHECK-DAG: %[[int0:.*]] = torch.constant.int 0
|
||||
// CHECK-DAG: %[[int1:.*]] = torch.constant.int 1
|
||||
// CHECK-DAG: %[[int2:.*]] = torch.constant.int 2
|
||||
// CHECK: %[[PadList:.*]] = torch.prim.ListConstruct %[[int1]], %[[int2]], %[[int0]], %[[int1]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[EmptyList:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
|
||||
// CHECK: %[[Rank0:.*]] = torch.aten.full %[[EmptyList]], %[[padVal]], %[[int7]], %[[none]], %[[none]], %[[none]] : !torch.list<int>, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f64>
|
||||
// CHECK: %[[Clamp:.*]] = torch.aten.clamp %[[Rank0]], %[[qMin]], %[[qMax]] : !torch.vtensor<[],f64>, !torch.float, !torch.float -> !torch.vtensor<[],f64>
|
||||
// CHECK: %[[Item:.*]] = torch.aten.item %[[Clamp]] : !torch.vtensor<[],f64> -> !torch.float
|
||||
// CHECK: %[[NewPad:.*]] = torch.aten.pad %arg0, %[[PadList]], %[[str]], %[[Item]] : !torch.vtensor<[8,8],si8>, !torch.list<int>, !torch.str, !torch.float -> !torch.vtensor<[9,11],si8>
|
||||
// CHECK: %[[NewMPTQT:.*]] = torch.aten._make_per_tensor_quantized_tensor %[[NewPad]], %[[cstHalf]], %[[int1]] : !torch.vtensor<[9,11],si8>, !torch.float, !torch.int -> !torch.vtensor<[9,11],!torch.qint8>
|
||||
// CHECK: %[[OtherMPTQT:.*]] = torch.aten._make_per_tensor_quantized_tensor %arg1, %[[cstHalf]], %[[int0]] : !torch.vtensor<[11,4],si8>, !torch.float, !torch.int -> !torch.vtensor<[11,4],!torch.qint8>
|
||||
// CHECK: %[[MM:.*]] = torch.aten.mm %[[NewMPTQT]], %[[OtherMPTQT]] : !torch.vtensor<[9,11],!torch.qint8>, !torch.vtensor<[11,4],!torch.qint8> -> !torch.vtensor<[9,4],!torch.qint32>
|
||||
%scale = torch.constant.float 0.5
|
||||
%false = torch.constant.bool false
|
||||
%zero = torch.constant.int 0
|
||||
%one = torch.constant.int 1
|
||||
%two = torch.constant.int 2
|
||||
%floatpad = torch.constant.float 3.5
|
||||
%zp = torch.constant.int -128
|
||||
%6 = torch.aten._make_per_tensor_quantized_tensor %arg0, %scale, %one : !torch.vtensor<[8,8],si8>, !torch.float, !torch.int -> !torch.vtensor<[8,8],!torch.qint8>
|
||||
%7 = torch.aten.dequantize.tensor %6 : !torch.vtensor<[8,8],!torch.qint8> -> !torch.vtensor<[8,8],f32>
|
||||
%list = torch.prim.ListConstruct %one, %two, %zero, %one : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
|
||||
%str = torch.constant.str "constant"
|
||||
%pad = torch.aten.pad %7, %list, %str, %floatpad : !torch.vtensor<[8,8],f32>, !torch.list<int>, !torch.str, !torch.float -> !torch.vtensor<[9,11],f32>
|
||||
%12 = torch.aten._make_per_tensor_quantized_tensor %arg1, %scale, %zero : !torch.vtensor<[11,4],si8>, !torch.float, !torch.int -> !torch.vtensor<[11,4],!torch.qint8>
|
||||
%13 = torch.aten.dequantize.tensor %12 : !torch.vtensor<[11,4],!torch.qint8> -> !torch.vtensor<[11,4],f32>
|
||||
%16 = torch.aten.mm %pad, %13 : !torch.vtensor<[9,11],f32>, !torch.vtensor<[11,4],f32> -> !torch.vtensor<[9,4],f32>
|
||||
return %16 : !torch.vtensor<[9,4],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: @convolution_bias
|
||||
func.func @convolution_bias(%arg0: !torch.vtensor<[1,3,8,8],si8>, %arg1: !torch.vtensor<[3,3,2,2],si8>, %arg2 : !torch.vtensor<[3], f32>) -> !torch.vtensor<[1,3,7,7],f32> {
|
||||
%scale = torch.constant.float 0.5
|
||||
|
|
|
|||
Loading…
Reference in New Issue