mirror of https://github.com/llvm/torch-mlir
Revert "[LINALG] Decompose `aten.batch_norm` into `aten.native_batch_norm`"
This reverts commit 442ff4605c.
pull/599/head
Gaurav Shukla
parent
ba29d4f250
commit
056cd2078d
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@ -89,32 +89,6 @@ def BatchNorm3DModule_basic(module, tu: TestUtils):
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# ==============================================================================
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class BatchNormModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args([
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None,
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([-1, -1, -1, -1], torch.float32, True),
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([-1], torch.float32, True),
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([-1], torch.float32, True),
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([-1], torch.float32, True),
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([-1], torch.float32, True),
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])
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def forward(self, x, weight, bias, running_mean, running_var):
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return torch.ops.aten.batch_norm(
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x, weight, bias, running_mean, running_var, training=False,
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momentum=0.1, eps=0.00001, cudnn_enabled=False)
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@register_test_case(module_factory=lambda: BatchNormModule())
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def BatchNormModule_basic(module, tu: TestUtils):
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module.forward(
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tu.rand(2, 5, 3, 2), tu.rand(5), tu.rand(5), tu.rand(5), tu.rand(5))
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# ==============================================================================
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class NativeBatchNorm1DModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@ -193,7 +167,7 @@ def NativeBatchNorm3DModule_basic(module, tu: TestUtils):
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# ==============================================================================
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class NativeBatchNormWeightNoneModule(torch.nn.Module):
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class NativeBatchNormNoneWeightModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@ -207,39 +181,16 @@ class NativeBatchNormWeightNoneModule(torch.nn.Module):
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])
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def forward(self, x, bias, running_mean, running_var):
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return torch.ops.aten.native_batch_norm(
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x, weight=None, bias=bias, running_mean=running_mean,
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running_var=running_var, training=False, momentum=0.1, eps=0.00001)
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x, None, bias, running_mean, running_var, training=False,
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momentum=0.1, eps=0.00001)
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@register_test_case(module_factory=lambda: NativeBatchNormWeightNoneModule())
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def NativeBatchNormWeightNoneModule_basic(module, tu: TestUtils):
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@register_test_case(module_factory=lambda: NativeBatchNormNoneWeightModule())
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def NativeBatchNormNoneWeightModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(2, 5, 2, 2, 3), tu.rand(5), tu.rand(5), tu.rand(5))
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# ==============================================================================
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class NativeBatchNormWeightNoneBiasNoneModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args([
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None,
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([-1, -1], torch.float32, True),
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([-1], torch.float32, True),
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([-1], torch.float32, True),
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])
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def forward(self, x, running_mean, running_var):
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return torch.ops.aten.native_batch_norm(
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x, weight=None, bias=None, running_mean=running_mean,
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running_var=running_var, training=False, momentum=0.1, eps=0.00001)
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@register_test_case(module_factory=lambda: NativeBatchNormWeightNoneBiasNoneModule())
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def NativeBatchNormWeightNoneBiasNoneModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(2, 5), tu.rand(5), tu.rand(5))
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# ==============================================================================
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class NativeLayerNormModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@ -86,11 +86,15 @@ TOSA_PASS_SET = {
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"ElementwiseReciprocalModule_basic",
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"TypePromotionAlphaWiderModule_basic",
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"Conv2dWithPaddingDilationStrideStaticModule_basic",
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"BatchNorm1DModule_basic",
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"BatchNorm2DModule_basic",
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"BatchNorm3DModule_basic",
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"FlattenStaticModule_basic",
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"FlattenRank0Module_basic",
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"ElementwiseFlattenBroadcastModule_basic",
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"SquareModule_basic",
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"MaxPool2dStaticModule_basic",
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"ResNet18StaticModule_basic",
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"NativeLayerNormModule4D_basic",
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"LayerNormNormalizeOverAllDimsModule_basic",
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"PermuteModule_basic",
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@ -9,7 +9,6 @@
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#ifndef TORCHMLIR_DIALECT_TORCH_UTILS_H
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#define TORCHMLIR_DIALECT_TORCH_UTILS_H
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/IR/Value.h"
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#include "mlir/Support/LLVM.h"
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#include "torch-mlir/Dialect/Torch/Utils/TorchUpstream.h"
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@ -23,7 +22,6 @@ int64_t toPositiveDim(int64_t dim, int64_t inputRank);
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bool isValidDim(int64_t dim, int64_t inputRank);
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bool getListConstructElements(Value v, SmallVectorImpl<Value> &elems);
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torch_upstream::ScalarType getScalarTypeForType(Type type);
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LogicalResult checkNotNone(PatternRewriter &rewriter, Operation *op, Value v);
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} // namespace Torch
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} // namespace torch
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@ -70,6 +70,15 @@ static LogicalResult verifyLinalgCompatibleTypes(Operation *op,
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return success();
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}
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static LogicalResult checkNotNone(PatternRewriter &rewriter, Operation *op,
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Value v) {
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Type type = v.getType();
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if (type.isa<OptionalType>() || type.isa<Torch::NoneType>() ||
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type.isa<mlir::NoneType>())
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return rewriter.notifyMatchFailure(op, "unimplemented None type arg");
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return success();
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}
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// Generate IR: dim = dim >= 0 ? dim : dim + inputRank
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static Value toPositiveDimDynamic(OpBuilder &b, Location loc, Value dim,
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Value inputRank) {
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@ -599,6 +608,111 @@ static void createLinalgPayloadCalculationForGatherOps(
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b.create<linalg::YieldOp>(loc, extract);
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}
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namespace {
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class ConvertAtenBatchNormOp : public OpConversionPattern<AtenBatchNormOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(AtenBatchNormOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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MLIRContext *context = op->getContext();
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Location loc = op->getLoc();
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Value input = adaptor.input();
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Value weight = adaptor.weight();
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Value bias = adaptor.bias();
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Value runningMean = adaptor.running_mean();
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Value runningVar = adaptor.running_var();
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Value training = adaptor.training();
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Value eps = adaptor.eps();
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if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
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return failure();
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// TODO: Handle the None cases for the optional parameters:
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// weight, bias.
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if (failed(checkNotNone(rewriter, op, weight)) ||
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failed(checkNotNone(rewriter, op, bias)) ||
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failed(checkNotNone(rewriter, op, runningMean)) ||
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failed(checkNotNone(rewriter, op, runningVar)))
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return failure();
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auto inputType = input.getType().cast<RankedTensorType>();
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auto weightType = weight.getType().cast<RankedTensorType>();
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auto biasType = bias.getType().cast<RankedTensorType>();
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auto runningMeanType = runningMean.getType().cast<RankedTensorType>();
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auto runningVarType = runningVar.getType().cast<RankedTensorType>();
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auto inputRank = inputType.getRank();
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if (inputRank <= 2)
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return rewriter.notifyMatchFailure(
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op, "input should have rank larger than 2");
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if (weightType.getRank() != 1 || biasType.getRank() != 1 ||
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runningMeanType.getRank() != 1 || runningVarType.getRank() != 1) {
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return rewriter.notifyMatchFailure(
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op, "expect weight, bias, running_mean and running_var to be rank 1");
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}
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// TODO: Add support for training.
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auto constFalse = rewriter.create<arith::ConstantOp>(
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loc, IntegerAttr::get(IntegerType::get(context, 1), 0));
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auto trainingFalse = rewriter.create<arith::CmpIOp>(
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loc, arith::CmpIPredicate::eq, training, constFalse);
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rewriter.create<cf::AssertOp>(
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loc, trainingFalse,
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rewriter.getStringAttr("training is not supported for now"));
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// num_features – C from an expected input of size (N,C,D,H,W ...)
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Value numFeatures = rewriter.create<tensor::DimOp>(loc, input, 1);
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auto contractingDim0EqualsNumFeatures = [&](Value v) {
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auto dim0 = rewriter.create<tensor::DimOp>(loc, v, 0);
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auto dim0Equal = rewriter.create<arith::CmpIOp>(
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loc, arith::CmpIPredicate::eq, numFeatures, dim0);
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rewriter.create<cf::AssertOp>(
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loc, dim0Equal,
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rewriter.getStringAttr(
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"expect the size of dim 0 equal to the number of features"));
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};
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contractingDim0EqualsNumFeatures(weight);
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contractingDim0EqualsNumFeatures(bias);
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contractingDim0EqualsNumFeatures(runningMean);
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contractingDim0EqualsNumFeatures(runningVar);
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auto indexingMap = AffineMap::get(
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/*dimCount=*/inputRank,
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/*symbolCount=*/0, rewriter.getAffineDimExpr(1), context);
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SmallVector<AffineMap> indexingMaps = {
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rewriter.getMultiDimIdentityMap(inputRank), // input
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indexingMap, // weight
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indexingMap, // bias
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indexingMap, // runningMean
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indexingMap, // runningVar
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rewriter.getMultiDimIdentityMap(inputRank), // output
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};
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SmallVector<StringRef> iteratorTypes(inputRank, "parallel");
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Value batchNorm =
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rewriter
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.create<linalg::GenericOp>(
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loc, input.getType(),
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ValueRange{input, weight, bias, runningMean, runningVar}, input,
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/*indexingMaps=*/indexingMaps,
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/*iteratorTypes=*/iteratorTypes,
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[&](OpBuilder &b, Location loc, ValueRange args) {
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Value input = args[0], weight = args[1], bias = args[2],
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mean = args[3], var = args[4];
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Value result = createLinalgPayloadCalculationForNormOps(
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b, loc, var.getType(), input, mean, var, eps, weight,
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bias);
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b.create<linalg::YieldOp>(loc, result);
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})
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.getResult(0);
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Type newResultType = getTypeConverter()->convertType(op.getType());
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rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, batchNorm);
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return success();
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}
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};
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} // namespace
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// For layernorm, the mean and standard-deviation are calculated separately over
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// the last certain number dimensions which have to be of the shape specified by
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// normalized_shape.
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@ -4532,6 +4646,8 @@ public:
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patterns.add<ConvertAtenBmmOp>(typeConverter, context);
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target.addIllegalOp<AtenLinearOp>();
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patterns.add<ConvertAtenLinearOp>(typeConverter, context);
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target.addIllegalOp<AtenBatchNormOp>();
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patterns.add<ConvertAtenBatchNormOp>(typeConverter, context);
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target.addIllegalOp<
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AtenTanhOp, AtenReluOp, AtenLeakyReluOp, AtenGeluOp, AtenGeluBackwardOp,
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AtenAddTensorOp, AtenMulTensorOp, AtenDivTensorOp, AtenSubTensorOp,
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@ -900,9 +900,7 @@ class DecomposeAtenAddCLikeOp : public OpRewritePattern<OpTy> {
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return success();
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}
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};
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} // namespace
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namespace {
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class DecomposeAtenLayerNormOp : public OpRewritePattern<AtenLayerNormOp> {
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using OpRewritePattern<AtenLayerNormOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(AtenLayerNormOp op,
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@ -931,40 +929,6 @@ class DecomposeAtenLayerNormOp : public OpRewritePattern<AtenLayerNormOp> {
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};
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} // namespace
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namespace {
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class DecomposeAtenBatchNormOp : public OpRewritePattern<AtenBatchNormOp> {
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using OpRewritePattern<AtenBatchNormOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(AtenBatchNormOp op,
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PatternRewriter &rewriter) const override {
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// TODO: Add support for `training` mode.
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bool training = false;
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if (!matchPattern(op.training(), m_TorchConstantBool(&training)) ||
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training)
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return rewriter.notifyMatchFailure(
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op, "unimplemented: training mode is not supported");
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// The `mean` and `invstd` outputs shape should be {0} in the inference
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// mode.
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BaseTensorType tensorType = op.getType().cast<BaseTensorType>();
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if (!tensorType.hasDtype() || !tensorType.getDtype().isa<mlir::FloatType>())
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return rewriter.notifyMatchFailure(
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op, "unimplemented: non-floating point type input");
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Type emptyType =
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tensorType.getWithSizesAndDtype({0}, tensorType.getDtype());
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// The first output tensor of the `AtenNativeBatchNormOp` is essentially
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// `AtenBatchNormOp` result.
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auto nativeBatchNorm = rewriter.create<AtenNativeBatchNormOp>(
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op.getLoc(), op.getType(), /*meanType=*/emptyType,
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/*invStdType=*/emptyType, op.input(), op.weight(), op.bias(),
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op.running_mean(), op.running_var(), op.training(), op.momentum(),
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op.eps());
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rewriter.replaceOp(op, nativeBatchNorm.getResult(0));
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return success();
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}
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};
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} // namespace
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namespace {
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// Decompose `aten.empty_like` op into `aten.size` and `aten.empty` ops.
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class DecomposeAtenEmptyLikeOp : public OpRewritePattern<AtenEmptyLikeOp> {
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@ -1063,14 +1027,6 @@ class DecomposeAtenNativeBatchNormOp
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Value runningVar = op.running_var();
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Value eps = op.eps();
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// TODO: Add support for optional type parameters.
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if (weight.getType().isa<OptionalType>() ||
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bias.getType().isa<OptionalType>() ||
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runningMean.getType().isa<OptionalType>() ||
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runningVar.getType().isa<OptionalType>())
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return rewriter.notifyMatchFailure(
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op, "unimplemented: optional type arg is not supported");
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// TODO: Add support for `training` mode.
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bool training = false;
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if (!matchPattern(op.training(), m_TorchConstantBool(&training)) ||
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@ -1097,24 +1053,13 @@ class DecomposeAtenNativeBatchNormOp
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return rewriter.notifyMatchFailure(
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op, "expected running_mean and running_var to be rank 1");
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// The shape of `runningMean` and `runningVar` must be (numFeatures). Here,
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// 'numFeatures' is C from an expected 'input' of size (N,C,D?,H?,W?).
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Value zero =
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rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(0));
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Value one =
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rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1));
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Value numFeatures = rewriter.create<AtenSizeIntOp>(loc, input, /*dim=*/one);
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auto dim0EqualsNumFeatures = [&](Value v) {
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Value dim0 = rewriter.create<AtenSizeIntOp>(loc, v, /*dim=*/zero);
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Value eqCmp = rewriter.create<AtenEqIntOp>(loc, BoolType::get(context),
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dim0, numFeatures);
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rewriter.create<RuntimeAssertOp>(
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loc, eqCmp,
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rewriter.getStringAttr("size of the 0th dimension must be equal to "
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"the number of features"));
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};
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dim0EqualsNumFeatures(runningMean);
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dim0EqualsNumFeatures(runningVar);
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// TODO: Add Runtime Asserts to check the shape of weight, bias,
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// running_mean and running_var to be (numFeatures).
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// The `runningMean` and `runningVar` must be reshaped to (1, C, 1?, 1?, 1?)
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// to make it broadcast-compatible with (N, C, D?, H?, W?).
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@ -1152,22 +1097,18 @@ class DecomposeAtenNativeBatchNormOp
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// 3. output = normalizedInput * weight + bias
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Value batchNormOutput = normalizedInput;
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if (!weight.getType().isa<Torch::NoneType>()) {
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// The shape of the `weight` tensor must be (numFeatures).
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// Rank of `weight` must be exactly 1.
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if (getTensorRank(weight) != 1)
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return rewriter.notifyMatchFailure(op, "expected weight to be rank 1");
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dim0EqualsNumFeatures(weight);
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weight = rewriter.create<AtenViewOp>(loc, reshapeType, weight,
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runningStatsSizeList);
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batchNormOutput = rewriter.create<AtenMulTensorOp>(
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loc, batchNormOutput.getType(), batchNormOutput, weight);
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}
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if (!bias.getType().isa<Torch::NoneType>()) {
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// The shape of the `bias` tensor must be (numFeatures).
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// Rank of `bias` must be exactly 1.
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if (getTensorRank(bias) != 1)
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return rewriter.notifyMatchFailure(op, "expected bias to be rank 1");
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dim0EqualsNumFeatures(bias);
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bias = rewriter.create<AtenViewOp>(loc, reshapeType, bias,
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runningStatsSizeList);
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batchNormOutput = rewriter.create<AtenAddTensorOp>(
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@ -1278,8 +1219,6 @@ class DecomposeComplexOpsPass
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patterns.add<DecomposeAtenLayerNormOp>(context);
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target.addIllegalOp<AtenNativeBatchNormOp>();
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patterns.add<DecomposeAtenNativeBatchNormOp>(context);
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target.addIllegalOp<AtenBatchNormOp>();
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patterns.add<DecomposeAtenBatchNormOp>(context);
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patterns.add<DecomposeAtenArangeOp>(context);
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target.addIllegalOp<AtenArangeOp>();
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patterns.add<DecomposeAtenArangeStartOp>(context);
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@ -1867,10 +1867,9 @@ ChangeResult TypeAnalyzer::visitAtenNativeBatchNormOp(
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meanKnowledge.dtype = input.dtype;
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invStdKnowledge.dtype = input.dtype;
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// Rank of the input tensor must be greater than or equal to 2. The shape
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// of the input tensor as well as the batch norm output tensor should be
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// (N, C, D?, H?, W?). In inference mode, the mean and inv-std outputs should
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// be empty tensors, whereas they should be of shape (C) in the training mode.
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// Rank of the input tensor must be greater than or equal to 2. The size of
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// the input tensor as well as the output tensor should be (N, C, D?, H?, W?).
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// The running_mean, running_var, weight, and bias should be of size (C).
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bool training = false;
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if (matchPattern(op.training(), m_TorchConstantBool(&training)) &&
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input.hasSizes && input.sizes.size() >= 2) {
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|
|
|||
|
|
@ -46,14 +46,6 @@ ScalarType getScalarTypeForType(Type type) {
|
|||
llvm::report_fatal_error("unhandled type for getScalarTypeForType");
|
||||
}
|
||||
|
||||
LogicalResult checkNotNone(PatternRewriter &rewriter, Operation *op, Value v) {
|
||||
Type type = v.getType();
|
||||
if (type.isa<OptionalType>() || type.isa<Torch::NoneType>() ||
|
||||
type.isa<mlir::NoneType>())
|
||||
return rewriter.notifyMatchFailure(op, "unimplemented None type arg");
|
||||
return success();
|
||||
}
|
||||
|
||||
} // namespace Torch
|
||||
} // namespace torch
|
||||
} // namespace mlir
|
||||
|
|
|
|||
|
|
@ -448,49 +448,3 @@ func @torch.aten.hardsigmoid(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor
|
|||
%0 = torch.aten.hardsigmoid %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
|
||||
return %0 : !torch.vtensor<[?,?],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
// CHECK-LABEL: func @torch.aten.batch_norm(
|
||||
// CHECK-SAME: %[[INPUT:.*]]: !torch.vtensor<[?,?,?,?],f32>,
|
||||
// CHECK-SAME: %[[WEIGHT:.*]]: !torch.vtensor<[?],f32>, %[[BIAS:.*]]: !torch.vtensor<[?],f32>, %[[RMEAN:.*]]: !torch.vtensor<[?],f32>, %[[RVAR:.*]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
|
||||
// CHECK: %[[EPS:.*]] = torch.constant.float 1.000000e-05
|
||||
// CHECK: %[[MOM:.*]] = torch.constant.float 1.000000e-01
|
||||
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
|
||||
// CHECK: %[[INT0:.*]] = torch.constant.int 0
|
||||
// CHECK: %[[INT1:.*]] = torch.constant.int 1
|
||||
// CHECK: %[[INPUT_DIM1:.*]] = torch.aten.size.int %[[INPUT]], %[[INT1]] : !torch.vtensor<[?,?,?,?],f32>, !torch.int -> !torch.int
|
||||
// CHECK: %[[RMEAN_DIM0:.*]] = torch.aten.size.int %[[RMEAN]], %[[INT0]] : !torch.vtensor<[?],f32>, !torch.int -> !torch.int
|
||||
// CHECK: %[[PRED_MEAN:.*]] = torch.aten.eq.int %[[RMEAN_DIM0]], %[[INPUT_DIM1]] : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: torch.runtime.assert %[[PRED_MEAN]], "size of the 0th dimension must be equal to the number of features"
|
||||
// CHECK: %[[RVAR_DIM0:.*]] = torch.aten.size.int %[[RVAR]], %[[INT0]] : !torch.vtensor<[?],f32>, !torch.int -> !torch.int
|
||||
// CHECK: %[[PRED_VAR:.*]] = torch.aten.eq.int %[[RVAR_DIM0]], %[[INPUT_DIM1]] : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: torch.runtime.assert %[[PRED_VAR]], "size of the 0th dimension must be equal to the number of features"
|
||||
// CHECK: %[[SIZE_LIST:.*]] = torch.prim.ListConstruct %[[INT1]], %[[INPUT_DIM1]], %[[INT1]], %[[INT1]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<!torch.int>
|
||||
// CHECK: %[[RMEAN_VIEW:.*]] = torch.aten.view %[[RMEAN]], %[[SIZE_LIST]] : !torch.vtensor<[?],f32>, !torch.list<!torch.int> -> !torch.vtensor<[1,?,1,1],f32>
|
||||
// CHECK: %[[RVAR_VIEW:.*]] = torch.aten.view %[[RVAR]], %[[SIZE_LIST]] : !torch.vtensor<[?],f32>, !torch.list<!torch.int> -> !torch.vtensor<[1,?,1,1],f32>
|
||||
// CHECK: %[[X_SUB_MEAN:.*]] = torch.aten.sub.Tensor %[[INPUT]], %[[RMEAN_VIEW]], %[[INT1]] : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[1,?,1,1],f32>, !torch.int -> !torch.vtensor<[?,?,?,?],f32>
|
||||
// CHECK: %[[VAR_EPS:.*]] = torch.aten.add.Scalar %[[RVAR_VIEW]], %[[EPS]], %[[INT1]] : !torch.vtensor<[1,?,1,1],f32>, !torch.float, !torch.int -> !torch.vtensor<[1,?,1,1],f32>
|
||||
// CHECK: %[[SQRT_VAR_EPS:.*]] = torch.aten.rsqrt %[[VAR_EPS]] : !torch.vtensor<[1,?,1,1],f32> -> !torch.vtensor<[1,?,1,1],f32>
|
||||
// CHECK: %[[NORM_INPUT:.*]] = torch.aten.mul.Tensor %[[X_SUB_MEAN]], %[[SQRT_VAR_EPS]] : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[1,?,1,1],f32> -> !torch.vtensor<[?,?,?,?],f32>
|
||||
// CHECK: %[[WEIGHT_DIM0:.*]] = torch.aten.size.int %[[WEIGHT]], %[[INT0]] : !torch.vtensor<[?],f32>, !torch.int -> !torch.int
|
||||
// CHECK: %[[PRED_WEIGHT:.*]] = torch.aten.eq.int %[[WEIGHT_DIM0]], %[[INPUT_DIM1]] : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: torch.runtime.assert %[[PRED_WEIGHT]], "size of the 0th dimension must be equal to the number of features"
|
||||
// CHECK: %[[WEIGHT_VIEW:.*]] = torch.aten.view %[[WEIGHT]], %[[SIZE_LIST]] : !torch.vtensor<[?],f32>, !torch.list<!torch.int> -> !torch.vtensor<[1,?,1,1],f32>
|
||||
// CHECK: %[[SCALED_INPUT:.*]] = torch.aten.mul.Tensor %[[NORM_INPUT]], %[[WEIGHT_VIEW]] : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[1,?,1,1],f32> -> !torch.vtensor<[?,?,?,?],f32>
|
||||
// CHECK: %[[BIAS_DIM0:.*]] = torch.aten.size.int %[[BIAS]], %[[INT0]] : !torch.vtensor<[?],f32>, !torch.int -> !torch.int
|
||||
// CHECK: %[[PRED_BIAS:.*]] = torch.aten.eq.int %[[BIAS_DIM0]], %[[INPUT_DIM1]] : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: torch.runtime.assert %[[PRED_BIAS]], "size of the 0th dimension must be equal to the number of features"
|
||||
// CHECK: %[[BIAS_VIEW:.*]] = torch.aten.view %[[BIAS]], %[[SIZE_LIST]] : !torch.vtensor<[?],f32>, !torch.list<!torch.int> -> !torch.vtensor<[1,?,1,1],f32>
|
||||
// CHECK: %[[OUTPUT:.*]] = torch.aten.add.Tensor %[[SCALED_INPUT]], %[[BIAS_VIEW]], %[[INT1]] : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[1,?,1,1],f32>, !torch.int -> !torch.vtensor<[?,?,?,?],f32>
|
||||
// CHECK: %[[ZERO_LIST:.*]] = torch.prim.ListConstruct %[[INT0]] : (!torch.int) -> !torch.list<!torch.int>
|
||||
// CHECK: %[[NONE:.*]] = torch.constant.none
|
||||
// CHECK: %[[MEAN_OUT:.*]] = torch.aten.empty.memory_format %[[ZERO_LIST]], %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]] : !torch.list<!torch.int>, !torch.none, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[0],f32>
|
||||
// CHECK: %[[INV_STD_OUT:.*]] = torch.aten.empty.memory_format %[[ZERO_LIST]], %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]] : !torch.list<!torch.int>, !torch.none, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[0],f32>
|
||||
// CHECK: return %[[OUTPUT]] : !torch.vtensor<[?,?,?,?],f32>
|
||||
func @torch.aten.batch_norm(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[?],f32>, %arg2: !torch.vtensor<[?],f32>, %arg3: !torch.vtensor<[?],f32>, %arg4: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
|
||||
%float1.000000e-05 = torch.constant.float 1.000000e-05
|
||||
%float1.000000e-01 = torch.constant.float 1.000000e-01
|
||||
%false = torch.constant.bool false
|
||||
%0 = torch.aten.batch_norm %arg0, %arg1, %arg2, %arg3, %arg4, %false, %float1.000000e-01, %float1.000000e-05, %false : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>, !torch.bool, !torch.float, !torch.float, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
|
||||
return %0 : !torch.vtensor<[?,?,?,?],f32>
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue