mirror of https://github.com/llvm/torch-mlir
debugging and hacks
dan
parent
f70978c034
commit
1e3df9c943
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@ -366,11 +366,7 @@ public:
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auto [inputShape, outputShape] =
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getInputAndOutputShape(op.getSelf(), outputSizeTorchInt, resultType.getShape());
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llvm::errs() << "inputShape: ";
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llvm::interleaveComma(inputShape, llvm::errs());
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llvm::errs() << " \noutput shape: ";
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llvm::interleaveComma(outputShape, llvm::errs());
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// Currently, we only handle the cases where each dimension is either
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// being expanded or collapsed. We do not handle cases where it's neither
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@ -395,10 +391,6 @@ public:
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m_TorchTensorSizeInt(op.getSelf(), &inputDim))) {
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unchangedDims.push_back(std::make_pair(inputDim, outputDim));
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llvm::errs() << "inputDim: ";
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llvm::errs() << inputDim;
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llvm::errs() << " \noutput Dim: ";
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llvm::errs() << outputDim;
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}
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}
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// Mark the end of the input/output shapes
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@ -440,10 +432,10 @@ public:
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// Used for ensuring that we don't have an ambiguous expansion
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bool assumedDynamicDimNotSplit = false;
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while (inputDim < nextUnchangedInput && outputDim < nextUnchangedOutput) {
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auto inputShapeSlice = // 1, ?, 4096
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auto inputShapeSlice = // ?, 4096
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MutableArrayRef<int64_t>(inputShape)
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.slice(inputDim, nextUnchangedInput - inputDim);
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auto outputShapeSlice = // ?, 4096
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auto outputShapeSlice = //1, ?, 4096
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MutableArrayRef<int64_t>(outputShape)
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.slice(outputDim, nextUnchangedOutput - outputDim);
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SmallVector<int64_t> inputSliceIndices;
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@ -483,13 +475,21 @@ public:
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/// known to have the same number of elements.
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} else if (inputShapeSlice[0] == kUnknownSize) {
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// If the input is dynamic, assume it is not split
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checkDimEqualHelper(rewriter, loc, inputSize[inputDim],
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outputSizeInt[outputDim]);
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// Elide any unit dims in the output and checkDimEqual on dynamic dims
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int64_t idx = 0;
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while (idx < outputShapeSlice.size() && outputShapeSlice[idx] == 1) {
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outputSliceIndices.push_back(idx++);
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}
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//checkDimEqualHelper(rewriter, loc, inputSize[inputDim],
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// outputSizeInt[idx]);
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// If output dimension is not dynamic, improve static information of
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// input
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inputShape[inputDim] = outputShape[outputDim];
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inputShape[inputDim] = outputShape[idx];
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inputSliceIndices.push_back(0);
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outputSliceIndices.push_back(0);
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outputSliceIndices.push_back(idx);
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assumedDynamicDimNotSplit = true;
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} else if (inputShapeSlice[0] == 1 && outputShapeSlice[0] == kUnknownSize) {
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int64_t idx = 0;
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@ -1543,6 +1543,8 @@ public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(AtenMaskedFillScalarOp op,
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PatternRewriter &rewriter) const override {
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llvm::errs() << "dbg decompose fill_scalar\n";
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Location loc = op.getLoc();
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auto resType = op.getType().cast<BaseTensorType>();
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if (!resType.hasDtype()) {
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@ -4429,13 +4431,18 @@ public:
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LogicalResult matchAndRewrite(AtenEmptyStridedOp op,
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PatternRewriter &rewriter) const override {
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SmallVector<int64_t> sizeListInts, strideListInts;
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if (!matchPattern(op.getSize(), m_TorchListOfConstantInts(sizeListInts)))
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return rewriter.notifyMatchFailure(
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op, "all size list elements must be constant ints");
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if (!matchPattern(op.getStride(),
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m_TorchListOfConstantInts(strideListInts)))
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return rewriter.notifyMatchFailure(
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op, "all stride list elements must be constant ints");
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llvm::errs() << "dbg Try decompose empty strided\n";
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//if (!matchPattern(op.getSize(), m_TorchListOfConstantInts(sizeListInts))){
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// llvm::errs() << "dbg all size list ele const ints\n";
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// return rewriter.notifyMatchFailure(
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// op, "all size list elements must be constant ints");
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//}
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//if (!matchPattern(op.getStride(),
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// m_TorchListOfConstantInts(strideListInts))){
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// llvm::errs() << "dbg all stride list ele const ints\n";
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// return rewriter.notifyMatchFailure(
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// op, "all stride list elements must be constant ints");}
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// We only support the cases with default stride values.
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// For ex: aten.new_empty_strided(self, size=[2, 3, 4], stride=[12, 4, 1])
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@ -4451,16 +4458,18 @@ public:
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break;
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}
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}
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if (!isDefaultStride)
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if (!isDefaultStride){
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llvm::errs() << "dbg non default strides\n";
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return rewriter.notifyMatchFailure(
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op, "only default strides supported for new_empty_strided op");
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}
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Value noneVal = rewriter.create<ConstantNoneOp>(op.getLoc());
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rewriter.replaceOpWithNewOp<AtenEmptyMemoryFormatOp>(
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op, op.getType(), op.getSize(), op.getDtype(), op.getLayout(), op.getDevice(),
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op.getPinMemory(), /*memoryFormat=*/noneVal);
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llvm::errs() << "dbg success\n";
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return success();
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@ -227,12 +227,14 @@ public:
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if (!op->hasTrait<Torch::OpTrait::IsTrailingUnderscoreInplaceVariant>())
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return rewriter.notifyMatchFailure(op, "is not trailing_ variant");
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llvm::errs() << "dbg op name: " << op->getName().getStringRef() << "\n";
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SmallVector<StringRef> fragments;
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llvm::SplitString(op->getName().getStringRef(), fragments, ".");
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assert(fragments.size() >= 3 && fragments[2].endswith("_") &&
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"IsTrailingUnderscoreInplaceVariant incorrectly applied");
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fragments[2] = fragments[2].drop_back();
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std::string noUnderscoreName = llvm::join(fragments, ".");
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llvm::errs() << "dbg fragments good\n";
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OperationState state(op->getLoc(), noUnderscoreName);
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state.addTypes(op->getResultTypes());
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@ -241,14 +243,19 @@ public:
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// Note: No successors or regions. Torch JIT operators don't have any.
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assert(op->getNumRegions() == 0 && op->getNumSuccessors() == 0 &&
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"Torch JIT operators shouldn't have regions or successors");
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llvm::errs() << "dbg good regions, good successors\n";
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Operation *newOp = rewriter.create(state);
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auto tensor =
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rewriter.create<CopyToValueTensorOp>(op->getLoc(), newOp->getResult(0));
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llvm::errs() << "dbg create copy_to_value_tensor\n";
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createOverwriteTensorContents(rewriter, op->getLoc(), tensor,
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op->getOperand(0));
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rewriter.replaceOp(op, op->getOperand(0));
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llvm::errs() << "dbg success\n";
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return success();
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}
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};
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