mirror of https://github.com/llvm/torch-mlir
MLIR][TORCH] Fix GroupNorm decomposition by adding shape info (#3658)
This commit adds the shape info for the tensors created during the decomposition of GroupNorm op. Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>pull/3636/merge
Vivek Khandelwal
GitHub
parent
a980130676
commit
fcc5f444cd
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@ -6233,7 +6233,6 @@ class DecomposeAtenGroupNormOp : public OpRewritePattern<AtenGroupNormOp> {
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LogicalResult matchAndRewrite(AtenGroupNormOp op,
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PatternRewriter &rewriter) const override {
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Location loc = op.getLoc();
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MLIRContext *context = op.getContext();
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Value input = op.getInput();
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Value weight = op.getWeight();
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@ -6241,11 +6240,23 @@ class DecomposeAtenGroupNormOp : public OpRewritePattern<AtenGroupNormOp> {
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Value numGroups = op.getNumGroups();
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Value eps = op.getEps();
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int64_t numGroupsInt;
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if (!matchPattern(numGroups, m_TorchConstantInt(&numGroupsInt)))
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return rewriter.notifyMatchFailure(
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op, "unimplemented: num_groups must be a constant int");
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Value cstZero =
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rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(0));
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Value cstOne =
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rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
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auto baseType = ValueTensorType::getWithLeastStaticInformation(context);
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auto inputType = cast<BaseTensorType>(input.getType());
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if (!inputType.hasSizes())
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return rewriter.notifyMatchFailure(op, "input should have sizes.");
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SmallVector<int64_t> baseTypeSizes{inputType.getSizes()[0], numGroupsInt};
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auto baseType = inputType.getWithSizesAndDtype(
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baseTypeSizes, inputType.getOptionalDtype());
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Value N = rewriter.create<AtenSizeIntOp>(loc, input, cstZero);
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Value C = rewriter.create<AtenSizeIntOp>(loc, input, cstOne);
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@ -6299,7 +6310,6 @@ class DecomposeAtenNativeGroupNormOp
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rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(-1));
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Value cstTrue = rewriter.create<Torch::ConstantBoolOp>(loc, true);
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Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(loc, false);
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auto baseType = ValueTensorType::getWithLeastStaticInformation(context);
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// GroupNorm requires the channel dimension (C) to be exactly divisible by
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// the number of groups.
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@ -6313,12 +6323,34 @@ class DecomposeAtenNativeGroupNormOp
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"the number of groups"));
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// Reshape the input tensor to (N, numGroups, -1) to apply normalization.
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int64_t numGroupsInt;
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if (!matchPattern(numGroups, m_TorchConstantInt(&numGroupsInt)))
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return rewriter.notifyMatchFailure(
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op, "unimplemented: num_groups must be a constant int");
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SmallVector<Value> newShape;
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SmallVector<int64_t> inputShapeInt{inputType.getSizes()};
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SmallVector<int64_t> reshapeInputShape{inputShapeInt[0], numGroupsInt};
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int64_t reshapeInputLastDim = 1;
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for (size_t i = 1; i < inputShapeInt.size(); i++) {
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if (inputShapeInt[i] == Torch::kUnknownSize) {
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reshapeInputLastDim = Torch::kUnknownSize;
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break;
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}
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reshapeInputLastDim *= inputShapeInt[i];
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}
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reshapeInputLastDim = reshapeInputLastDim == Torch::kUnknownSize
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? reshapeInputLastDim
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: reshapeInputLastDim / numGroupsInt;
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reshapeInputShape.push_back(reshapeInputLastDim);
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newShape.push_back(rewriter.create<AtenSizeIntOp>(loc, input, cstZero));
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newShape.push_back(numGroups);
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newShape.push_back(cstNegtiveOne);
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Type reshapeInputType = inputType.getWithSizesAndDtype(
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reshapeInputShape, inputType.getOptionalDtype());
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Value reshapedInput = rewriter.create<AtenViewOp>(
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loc, baseType, input,
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loc, reshapeInputType, input,
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rewriter.create<PrimListConstructOp>(
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loc, Torch::ListType::get(IntType::get(context)), newShape));
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@ -6327,21 +6359,28 @@ class DecomposeAtenNativeGroupNormOp
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Value dimList = rewriter.create<PrimListConstructOp>(
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loc, Torch::ListType::get(Torch::IntType::get(op.getContext())),
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ArrayRef<Value>{cstNegtiveOne});
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auto mean = rewriter.create<AtenMeanDimOp>(
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loc, baseType, reshapedInput, /*dims=*/dimList, /*keepdim=*/cstTrue,
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/*dtype=*/none);
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auto var = rewriter.create<AtenVarDimOp>(
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loc, baseType, reshapedInput, /*dims=*/dimList, /*unbiased=*/cstFalse,
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/*keepdim=*/cstTrue);
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reshapeInputShape[2] = 1;
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Type reductionType = inputType.getWithSizesAndDtype(
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reshapeInputShape, inputType.getOptionalDtype());
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auto mean =
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rewriter.create<AtenMeanDimOp>(loc, reductionType, reshapedInput,
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/*dims=*/dimList, /*keepdim=*/cstTrue,
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/*dtype=*/none);
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auto var =
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rewriter.create<AtenVarDimOp>(loc, reductionType, reshapedInput,
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/*dims=*/dimList, /*unbiased=*/cstFalse,
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/*keepdim=*/cstTrue);
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// Compute the normalized output: (input - mean) * rsqrt(var + eps)
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auto varPlusEps = rewriter.create<AtenAddScalarOp>(loc, baseType, var, eps,
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/*alpha=*/cstOne);
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auto invStd = rewriter.create<AtenRsqrtOp>(loc, baseType, varPlusEps);
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auto varPlusEps =
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rewriter.create<AtenAddScalarOp>(loc, reductionType, var, eps,
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/*alpha=*/cstOne);
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auto invStd = rewriter.create<AtenRsqrtOp>(loc, reductionType, varPlusEps);
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auto inputSubMean = rewriter.create<AtenSubTensorOp>(
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loc, baseType, reshapedInput, mean, /*alpha=*/cstOne);
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auto normalizedOutput =
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rewriter.create<AtenMulTensorOp>(loc, baseType, inputSubMean, invStd);
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loc, reshapeInputType, reshapedInput, mean, /*alpha=*/cstOne);
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auto normalizedOutput = rewriter.create<AtenMulTensorOp>(
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loc, reshapeInputType, inputSubMean, invStd);
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// Reshape normalized output back to the original input shape
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auto inputShape = rewriter.create<AtenSizeOp>(
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@ -6352,22 +6391,26 @@ class DecomposeAtenNativeGroupNormOp
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// Apply weight and bias if they are not None
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// Reshape weight and bias to C,1,1,...
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SmallVector<Value> viewShape = {channel};
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SmallVector<int64_t> viewShapeInt{inputShapeInt[1]};
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for (unsigned i = 2; i < inputType.getSizes().size(); i++) {
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viewShape.push_back(cstOne);
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viewShapeInt.push_back(1);
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}
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Value viewShapeSizeList = rewriter.create<PrimListConstructOp>(
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loc, ListType::get(IntType::get(context)), viewShape);
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Type viewType = inputType.getWithSizesAndDtype(
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viewShapeInt, inputType.getOptionalDtype());
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Value groupNormOutput = reshapedOutput;
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if (!isa<Torch::NoneType>(weight.getType())) {
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auto weightReshaped = rewriter.create<AtenViewOp>(
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loc, baseType, weight, /*shape=*/viewShapeSizeList);
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loc, viewType, weight, /*shape=*/viewShapeSizeList);
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groupNormOutput = rewriter.create<AtenMulTensorOp>(
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loc, inputType, groupNormOutput, weightReshaped);
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}
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if (!isa<Torch::NoneType>(bias.getType())) {
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auto biasReshaped = rewriter.create<AtenViewOp>(
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loc, baseType, bias, /*shape=*/viewShapeSizeList);
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loc, viewType, bias, /*shape=*/viewShapeSizeList);
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groupNormOutput = rewriter.create<AtenAddTensorOp>(
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loc, inputType, groupNormOutput, biasReshaped,
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/*alpha=*/cstOne);
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@ -1626,25 +1626,25 @@ func.func @test_maxunpool3d_export_without_output_shape(%arg0: !torch.vtensor<[1
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// -----
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// CHECK-LABEL: func.func @test_group_normalization
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func.func @test_group_normalization(%arg0: !torch.vtensor<[3,4,2,2],f32>, %arg1: !torch.vtensor<[2],f32>, %arg2: !torch.vtensor<[2],f32>) -> !torch.vtensor<[3,4,2,2],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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func.func @test_group_normalization(%arg0: !torch.vtensor<[3,4,2,2],f32>, %arg1: !torch.vtensor<[4],f32>, %arg2: !torch.vtensor<[4],f32>) -> !torch.vtensor<[3,4,2,2],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[EPSILON:.*]] = torch.constant.float 9.9999997473787516E-6
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// CHECK: %[[INT2:.*]] = torch.constant.int 2
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// CHECK: %[[FALSE:.*]] = torch.constant.bool false
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// CHECK: %[[RESULT:.*]] = torch.aten.group_norm %arg0, %int2, %arg1, %arg2, %[[EPSILON]], %[[FALSE:.*]] : !torch.vtensor<[3,4,2,2],f32>, !torch.int, !torch.vtensor<[2],f32>, !torch.vtensor<[2],f32>, !torch.float, !torch.bool -> !torch.vtensor<[3,4,2,2],f32>
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// CHECK: %[[RESULT:.*]] = torch.aten.group_norm %arg0, %int2, %arg1, %arg2, %[[EPSILON]], %[[FALSE:.*]] : !torch.vtensor<[3,4,2,2],f32>, !torch.int, !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32>, !torch.float, !torch.bool -> !torch.vtensor<[3,4,2,2],f32>
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// CHECK: return %[[RESULT]] : !torch.vtensor<[3,4,2,2],f32>
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%0 = torch.operator "onnx.GroupNormalization"(%arg0, %arg1, %arg2) {torch.onnx.num_groups = 2 : si64} : (!torch.vtensor<[3,4,2,2],f32>, !torch.vtensor<[2],f32>, !torch.vtensor<[2],f32>) -> !torch.vtensor<[3,4,2,2],f32>
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%0 = torch.operator "onnx.GroupNormalization"(%arg0, %arg1, %arg2) {torch.onnx.num_groups = 2 : si64} : (!torch.vtensor<[3,4,2,2],f32>, !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32>) -> !torch.vtensor<[3,4,2,2],f32>
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return %0 : !torch.vtensor<[3,4,2,2],f32>
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}
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// -----
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func.func @test_group_normalization_epsilon(%arg0: !torch.vtensor<[3,4,2,2],f32>, %arg1: !torch.vtensor<[2],f32>, %arg2: !torch.vtensor<[2],f32>) -> !torch.vtensor<[3,4,2,2],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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func.func @test_group_normalization_epsilon(%arg0: !torch.vtensor<[3,4,2,2],f32>, %arg1: !torch.vtensor<[4],f32>, %arg2: !torch.vtensor<[4],f32>) -> !torch.vtensor<[3,4,2,2],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[EPSILON:.*]] = torch.constant.float 0.0099999997764825821
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// CHECK: %[[INT2:.*]] = torch.constant.int 2
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// CHECK: %[[FALSE:.*]] = torch.constant.bool false
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// CHECK: %[[RESULT:.*]] = torch.aten.group_norm %arg0, %int2, %arg1, %arg2, %[[EPSILON]], %[[FALSE:.*]] : !torch.vtensor<[3,4,2,2],f32>, !torch.int, !torch.vtensor<[2],f32>, !torch.vtensor<[2],f32>, !torch.float, !torch.bool -> !torch.vtensor<[3,4,2,2],f32>
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// CHECK: %[[RESULT:.*]] = torch.aten.group_norm %arg0, %int2, %arg1, %arg2, %[[EPSILON]], %[[FALSE:.*]] : !torch.vtensor<[3,4,2,2],f32>, !torch.int, !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32>, !torch.float, !torch.bool -> !torch.vtensor<[3,4,2,2],f32>
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// CHECK: return %[[RESULT]] : !torch.vtensor<[3,4,2,2],f32>
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%0 = torch.operator "onnx.GroupNormalization"(%arg0, %arg1, %arg2) {torch.onnx.epsilon = 0.00999999977 : f32, torch.onnx.num_groups = 2 : si64} : (!torch.vtensor<[3,4,2,2],f32>, !torch.vtensor<[2],f32>, !torch.vtensor<[2],f32>) -> !torch.vtensor<[3,4,2,2],f32>
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%0 = torch.operator "onnx.GroupNormalization"(%arg0, %arg1, %arg2) {torch.onnx.epsilon = 0.00999999977 : f32, torch.onnx.num_groups = 2 : si64} : (!torch.vtensor<[3,4,2,2],f32>, !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32>) -> !torch.vtensor<[3,4,2,2],f32>
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return %0 : !torch.vtensor<[3,4,2,2],f32>
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}
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