mirror of https://github.com/llvm/torch-mlir
[MHLO] Add convolution op pattern (#1152)
Co-authored-by: Bairen Yi <yibairen.byron@bytedance.com> Co-authored-by: Jiawei Wu <xremold@gmail.com> Co-authored-by: Tianyou Guo <tianyou.gty@alibaba-inc.com> Co-authored-by: Xu Yan <yancey.yx@alibaba-inc.com> Co-authored-by: Ziheng Jiang <ziheng.jiang@bytedance.com>pull/1156/head snapshot-20220804.554
武家伟
GitHub
parent
08fc2d89bb
commit
c94431f71c
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@ -379,6 +379,171 @@ public:
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} // namespace
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// AtenConvolutionOp
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namespace {
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class ConvertAtenConvlutionOp : public OpConversionPattern<AtenConvolutionOp> {
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public:
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using OpConversionPattern<AtenConvolutionOp>::OpConversionPattern;
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using OpAdaptor = typename AtenConvolutionOp::Adaptor;
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LogicalResult
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matchAndRewrite(AtenConvolutionOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Value input = adaptor.input();
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Value weight = adaptor.weight();
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// The input shape is [N, C, H, W]
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auto inputTy = input.getType().template cast<RankedTensorType>();
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// The weight shape is [OC, (IC // groups), KH, KW]
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// If tranposed is set to true, the weight shape changes to [IC, (OC //
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// groups), KH, KW]
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auto weightTy = weight.getType().template cast<RankedTensorType>();
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auto outTy = getTypeConverter()
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->convertType(op.getType())
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.template cast<RankedTensorType>();
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if (!inputTy || !weightTy || !outTy) {
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return op.emitError("input, weight and output must be ranked tensors");
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}
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if (inputTy.getRank() < 3)
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return op.emitError("only input with at least 3 dims valid");
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SmallVector<int64_t> stride;
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if (!matchPattern(op.stride(), m_TorchConstantIntList(stride))) {
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return rewriter.notifyMatchFailure(op,
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"non-const stride list unsupported");
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}
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SmallVector<int64_t> padding;
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if (!matchPattern(op.padding(), m_TorchConstantIntList(padding))) {
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return rewriter.notifyMatchFailure(op,
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"non-const padding list unsupported");
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}
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SmallVector<int64_t> dilation;
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if (!matchPattern(op.dilation(), m_TorchConstantIntList(dilation))) {
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return rewriter.notifyMatchFailure(op,
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"non-const dilation list unsupported");
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}
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SmallVector<int64_t> outputPadding;
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if (!matchPattern(op.output_padding(),
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m_TorchConstantIntList(outputPadding))) {
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return rewriter.notifyMatchFailure(
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op, "non-const output_padding list unsupported");
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}
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// Just ignore the outputPadding attribute
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for (int64_t item : outputPadding) {
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if (item != 0)
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return rewriter.notifyMatchFailure(
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op, "only zero output_padding list supported");
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}
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int64_t groups;
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if (!matchPattern(op.groups(), m_TorchConstantInt(&groups))) {
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return rewriter.notifyMatchFailure(op, "non-int groups unsupported");
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}
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bool transposed;
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if (!matchPattern(op.transposed(), m_TorchConstantBool(&transposed))) {
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return rewriter.notifyMatchFailure(op, "non-bool transposed unsupported");
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}
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if (transposed) {
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return rewriter.notifyMatchFailure(
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op, "only param tranposed of value 'false' supported!");
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}
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assert(padding.size() == dilation.size() &&
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padding.size() == stride.size() &&
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padding.size() == static_cast<size_t>(inputTy.getRank()) - 2);
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int64_t nSpatialDims = padding.size();
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// Get mhlo::ConvolutionOp attributes
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DenseIntElementsAttr mhloWindowStride = DenseIntElementsAttr::get(
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RankedTensorType::get({static_cast<long int>(stride.size())},
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rewriter.getI64Type()),
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stride);
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std::vector<int64_t> mhloPaddingVec;
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for (size_t i = 0; i < padding.size(); i++) {
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mhloPaddingVec.emplace_back(padding[i]);
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mhloPaddingVec.emplace_back(padding[i]);
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}
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DenseIntElementsAttr mhloPadding = DenseIntElementsAttr::get(
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RankedTensorType::get(
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{static_cast<long int>(padding.size()), static_cast<long int>(2)},
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rewriter.getI64Type()),
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mhloPaddingVec);
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DenseIntElementsAttr mhloRhsDilation = DenseIntElementsAttr::get(
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RankedTensorType::get({static_cast<long int>(dilation.size())},
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rewriter.getI64Type()),
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dilation);
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SmallVector<int64_t> spatialDimensions;
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for (int64_t i = 2; i < inputTy.getRank(); i++) {
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spatialDimensions.emplace_back(i);
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}
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mhlo::ConvDimensionNumbersAttr dimensionNumbers =
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mhlo::ConvDimensionNumbersAttr::get(
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/*context=*/rewriter.getContext(), /*inputBatchDimension=*/0,
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/*inputFeatureDimension=*/1,
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/*inputSpatialDimensions=*/spatialDimensions,
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/*kernelInputFeatureDimension=*/1,
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/*kernelOutputFeatureDimension=*/0,
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/*kernelSpatialDimensions=*/spatialDimensions,
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/*outputBatchDimension=*/0, /*outputFeatureDimension=*/1,
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/*outputSpatialDimensions=*/spatialDimensions);
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IntegerAttr featureGroupCount =
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IntegerAttr::get(rewriter.getI64Type(), groups);
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IntegerAttr batchGroupCount = IntegerAttr::get(rewriter.getI64Type(), 1);
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// mhlo::ConvolutionOp's optional attributes, leave them as default
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DenseIntElementsAttr mhloLhsDilation;
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DenseElementsAttr windowReversal;
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ArrayAttr precisionConfig;
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auto mhloConvOp = rewriter.create<mhlo::ConvolutionOp>(
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op->getLoc(), outTy, input, weight, mhloWindowStride, mhloPadding,
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mhloLhsDilation, mhloRhsDilation, windowReversal, dimensionNumbers,
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featureGroupCount, batchGroupCount, precisionConfig);
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auto bias = adaptor.bias();
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// No bias provided
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if (failed(checkNotNone(rewriter, op, op.bias()))) {
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rewriter.replaceOp(op, mhloConvOp.getResult());
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return success();
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}
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// Handle bias
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if (!bias.getType().cast<RankedTensorType>()) {
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return op.emitError("bias provided but not a ranked tensor");
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}
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auto biasTy = bias.getType().template cast<RankedTensorType>();
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if (!biasTy.getElementType().isIntOrFloat()) {
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return op.emitError("only floating-point or integer datatype "
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"legalization for bias supported");
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}
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assert(biasTy.getRank() <= 1);
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// Reshape and promote bias
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auto inputUnsqzDims =
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llvm::to_vector<4>(llvm::seq<int64_t>(-nSpatialDims, 0));
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bias = *mhlo::unsqueezeTensor(rewriter, op, bias, inputUnsqzDims);
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bias = mhlo::promoteType(rewriter, bias, outTy);
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DenseIntElementsAttr bcastDimensions;
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rewriter.replaceOpWithNewOp<chlo::BroadcastAddOp>(
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op, outTy, mhloConvOp.getResult(), bias, bcastDimensions);
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return success();
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}
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};
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} // namespace
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void mlir::torch::torch_to_mhlo::populateLinearOpPatternsAndLegality(
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TypeConverter &typeConverter, RewritePatternSet &patterns,
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ConversionTarget &target) {
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@ -402,4 +567,10 @@ void mlir::torch::torch_to_mhlo::populateLinearOpPatternsAndLegality(
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patterns.add<ConvertAtenLinearOp<AtenOp>>(typeConverter, context);
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INSERT_LINEAR_ATENOP_PATTERN(AtenLinearOp);
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#undef INSERT_LINEAR_ATEMOP_PATTERN
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#define INSERT_CONVOLUTION_ATENOP_PATTERN(AtenOp) \
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target.addIllegalOp<AtenOp>(); \
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patterns.add<ConvertAtenConvlutionOp>(typeConverter, context);
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INSERT_CONVOLUTION_ATENOP_PATTERN(AtenConvolutionOp);
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#undef INSERT_CONVOLUTION_ATENOP_PATTERN
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}
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@ -266,3 +266,82 @@ func.func @torch.aten.mm$proj(%arg0: !torch.vtensor<[?,256],f32>) -> !torch.vten
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return %1 : !torch.vtensor<[?,256],f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.convolution(
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// CHECK-SAME: %[[ARG_0:.*]]: !torch.vtensor<[?,?,?,?],f32>,
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// CHECK-SAME: %[[ARG_1:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
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// CHECK: %[[T_0:.*]] = torch_c.to_builtin_tensor %[[ARG_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
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// CHECK: %[[T_1:.*]] = torch_c.to_builtin_tensor %[[ARG_1]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
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// CHECK: %[[T_2:.*]] = torch.constant.none
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// CHECK: %[[T_4:.*]] = torch.constant.int 2
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// CHECK: %[[T_5:.*]] = torch.constant.int 1
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// CHECK: %[[T_6:.*]] = torch.constant.int 4
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// CHECK: %[[T_7:.*]] = torch.constant.int 3
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// CHECK: %[[T_8:.*]] = torch_c.to_i64 %[[T_7]]
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// CHECK: %[[T_9:.*]] = torch.prim.ListConstruct %[[T_4]], %[[T_5]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[T_10:.*]] = torch.prim.ListConstruct %[[T_6]], %[[T_4]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[T_11:.*]] = torch.prim.ListConstruct %[[T_7]], %[[T_5]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[T_12:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
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// CHECK: %[[T_13:.*]] = torch.constant.bool false
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// CHECK: %[[T_14:.*]] = mhlo.convolution(%[[T_0]], %[[T_1]])
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// CHECK-SAME{LITERAL}: dim_numbers = [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1], window = {stride = [2, 1], pad = [[4, 4], [2, 2]], rhs_dilate = [3, 1]} {batch_group_count = 1 : i64, feature_group_count = 3 : i64} : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
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// CHECK: %[[T_15:.*]] = torch_c.from_builtin_tensor %[[T_14]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
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// CHECK: return %[[T_15]] : !torch.vtensor<[?,?,?,?],f32>
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func.func @torch.aten.convolution(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
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%none = torch.constant.none
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%int2 = torch.constant.int 2
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%int1 = torch.constant.int 1
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%int4 = torch.constant.int 4
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%int3 = torch.constant.int 3
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%1 = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
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%2 = torch.prim.ListConstruct %int4, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
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%3 = torch.prim.ListConstruct %int3, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
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%4 = torch.prim.ListConstruct : () -> !torch.list<int>
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%false = torch.constant.bool false
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%5 = torch.aten.convolution %arg0, %arg1, %none, %1, %2, %3, %false, %4, %int3 : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[?,?,?,?],f32>
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return %5 : !torch.vtensor<[?,?,?,?],f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.convolution$bias(
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// CHECK-SAME: %[[ARG_0:.*]]: !torch.vtensor<[?,?,?,?],f32>, %[[ARG_1:.*]]: !torch.vtensor<[?,?,?,?],f32>,
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// CHECK-SAME: %[[ARG_2:.*]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
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// CHECK: %[[T_0:.*]] = torch_c.to_builtin_tensor %[[ARG_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
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// CHECK: %[[T_1:.*]] = torch_c.to_builtin_tensor %[[ARG_1]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
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// CHECK: %[[T_2:.*]] = torch_c.to_builtin_tensor %[[ARG_2]] : !torch.vtensor<[?],f32> -> tensor<?xf32>
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// CHECK: %int2 = torch.constant.int 2
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// CHECK: %int1 = torch.constant.int 1
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// CHECK: %int4 = torch.constant.int 4
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// CHECK: %int3 = torch.constant.int 3
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// CHECK: %[[T_3:.*]] = torch_c.to_i64 %int3
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// CHECK: %[[T_4:.*]] = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[T_5:.*]] = torch.prim.ListConstruct %int4, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[T_6:.*]] = torch.prim.ListConstruct %int3, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[T_7:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
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// CHECK: %false = torch.constant.bool false
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// CHECK: %[[T_8:.*]] = mhlo.convolution(%[[T_0]], %[[T_1]])
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// CHECK{LITERAL}: dim_numbers = [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1], window = {stride = [2, 1], pad = [[4, 4], [2, 2]], rhs_dilate = [3, 1]} {batch_group_count = 1 : i64, feature_group_count = 3 : i64} : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
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// CHECK: %[[IDX_0:.*]] = arith.constant 0 : index
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// CHECK: %[[T_9:.*]] = tensor.dim %[[T_2]], %[[IDX_0]] : tensor<?xf32>
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// CHECK: %[[T_10:.*]] = arith.index_cast %[[T_9]] : index to i64
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// CHECK: %[[VAL_0:.*]] = arith.constant 1 : i64
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// CHECK: %[[T_11:.*]] = tensor.from_elements %[[T_10]], %[[VAL_0]], %[[VAL_0]] : tensor<3xi64>
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// CHECK: %[[T_12:.*]] = "mhlo.dynamic_reshape"(%[[T_2]], %[[T_11]]) : (tensor<?xf32>, tensor<3xi64>) -> tensor<?x1x1xf32>
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// CHECK: %[[T_13:.*]] = chlo.broadcast_add %[[T_8]], %[[T_12]] : (tensor<?x?x?x?xf32>, tensor<?x1x1xf32>) -> tensor<?x?x?x?xf32>
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// CHECK: %[[T_14:.*]] = torch_c.from_builtin_tensor %[[T_13]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
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// CHECK: return %[[T_14]] : !torch.vtensor<[?,?,?,?],f32>
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func.func @torch.aten.convolution$bias(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,?,?],f32>, %arg2: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
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%int2 = torch.constant.int 2
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%int1 = torch.constant.int 1
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%int4 = torch.constant.int 4
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%int3 = torch.constant.int 3
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%1 = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
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%2 = torch.prim.ListConstruct %int4, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
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%3 = torch.prim.ListConstruct %int3, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
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%4 = torch.prim.ListConstruct : () -> !torch.list<int>
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%false = torch.constant.bool false
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%5 = torch.aten.convolution %arg0, %arg1, %arg2, %1, %2, %3, %false, %4, %int3 : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[?,?,?,?],f32>
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return %5 : !torch.vtensor<[?,?,?,?],f32>
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}
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