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Integrate LLVM at llvm/llvm-project@d1978fa4bf

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Updates LLVM usage to match
[d1978fa4bf0d](https://github.com/llvm/llvm-project/commit/d1978fa4bf0d)

PiperOrigin-RevId: 355848094
Change-Id: Ie734789487e9e5ec5cabd691949eb5155bd37aef
This commit is contained in:
Adrian Kuegel 2021-02-05 07:40:43 -08:00 committed by TensorFlower Gardener
parent e84e4b3c84
commit bf622b7a04
23 changed files with 40 additions and 42 deletions
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2
tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/legalize_gather_to_torch_index_select.cc
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@ -133,7 +133,7 @@ struct LegalizeGatherToTorchIndexSelectPass
void runOnFunction() override { void runOnFunction() override {
OwningRewritePatternList patterns; OwningRewritePatternList patterns;
PopulateGatherToTorchIndexSelectPatterns(&getContext(), &patterns); PopulateGatherToTorchIndexSelectPatterns(&getContext(), &patterns);
applyPatternsAndFoldGreedily(getFunction(), std::move(patterns)); (void)applyPatternsAndFoldGreedily(getFunction(), std::move(patterns));
} }
}; };
} // namespace } // namespace

2
tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/legalize_to_standard.cc
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@ -201,7 +201,7 @@ void PopulateMhloToStdPatterns(OwningRewritePatternList *patterns,
void LegalizeToStandardPass::runOnFunction() { void LegalizeToStandardPass::runOnFunction() {
OwningRewritePatternList patterns; OwningRewritePatternList patterns;
mlir::mhlo::PopulateMhloToStdPatterns(&patterns, &getContext()); mlir::mhlo::PopulateMhloToStdPatterns(&patterns, &getContext());
applyPatternsAndFoldGreedily(getFunction(), std::move(patterns)); (void)applyPatternsAndFoldGreedily(getFunction(), std::move(patterns));
} }
} // end namespace mhlo } // end namespace mhlo

2
tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/legalize_trigonometric_to_approximation.cc
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@ -259,7 +259,7 @@ struct LegalizeTrigonometricToApproximationPass
void runOnFunction() override { void runOnFunction() override {
OwningRewritePatternList patterns; OwningRewritePatternList patterns;
PopulateTrigonometricToApproximationPatterns(&getContext(), &patterns); PopulateTrigonometricToApproximationPatterns(&getContext(), &patterns);
applyPatternsAndFoldGreedily(getFunction(), std::move(patterns)); (void)applyPatternsAndFoldGreedily(getFunction(), std::move(patterns));
} }
}; };

4
tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/lhlo_fuse_linalg.cc
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@ -164,7 +164,7 @@ class LhloFuseLinalgPass
} }
}); });
auto patterns = linalg::getLinalgTilingCanonicalizationPatterns(ctx); auto patterns = linalg::getLinalgTilingCanonicalizationPatterns(ctx);
applyPatternsAndFoldGreedily(func, std::move(patterns)); (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
// Fuse producers of tiled linalg ops. // Fuse producers of tiled linalg ops.
llvm::SmallDenseSet<Operation*> erase_set; llvm::SmallDenseSet<Operation*> erase_set;
@ -185,7 +185,7 @@ class LhloFuseLinalgPass
} }
auto patterns = linalg::getLinalgTilingCanonicalizationPatterns(ctx); auto patterns = linalg::getLinalgTilingCanonicalizationPatterns(ctx);
applyPatternsAndFoldGreedily(func, std::move(patterns)); (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
} }
for (auto* e : erase_set) e->erase(); for (auto* e : erase_set) e->erase();
} }

2
tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/lhlo_legalize_to_affine.cc
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@ -158,7 +158,7 @@ struct LhloLegalizeToAffinePass
OwningRewritePatternList patterns; OwningRewritePatternList patterns;
auto func = getFunction(); auto func = getFunction();
populateLHLOToAffineConversionPattern(func.getContext(), &patterns); populateLHLOToAffineConversionPattern(func.getContext(), &patterns);
applyPatternsAndFoldGreedily(func, std::move(patterns)); (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
} }
}; };

2
tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/lower_complex.cc
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@ -70,7 +70,7 @@ void LowerComplexPass::runOnFunction() {
OwningRewritePatternList patterns; OwningRewritePatternList patterns;
mlir::mhlo::PopulateComplexLoweringPatterns(&getContext(), &patterns); mlir::mhlo::PopulateComplexLoweringPatterns(&getContext(), &patterns);
applyPatternsAndFoldGreedily(getFunction(), std::move(patterns)); (void)applyPatternsAndFoldGreedily(getFunction(), std::move(patterns));
} }
std::unique_ptr<FunctionPass> mlir::mhlo::createLowerComplexPass() { std::unique_ptr<FunctionPass> mlir::mhlo::createLowerComplexPass() {

2
tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/lower_general_dot.cc
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@ -182,7 +182,7 @@ struct LegalizeGeneralDotPass
void runOnFunction() override { void runOnFunction() override {
OwningRewritePatternList patterns; OwningRewritePatternList patterns;
mlir::mhlo::PopulateGeneralDotOpLoweringPatterns(&patterns, &getContext()); mlir::mhlo::PopulateGeneralDotOpLoweringPatterns(&patterns, &getContext());
applyPatternsAndFoldGreedily(getFunction(), std::move(patterns)); (void)applyPatternsAndFoldGreedily(getFunction(), std::move(patterns));
} }
}; };

2
tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/optimize_mhlo_pass.cc
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@ -42,7 +42,7 @@ void OptimizeMhloPass::runOnFunction() {
mlir::OwningRewritePatternList patterns; mlir::OwningRewritePatternList patterns;
mlir::mhlo::PopulateOptimizeMHLOPatterns(&getContext(), &patterns); mlir::mhlo::PopulateOptimizeMHLOPatterns(&getContext(), &patterns);
applyPatternsAndFoldGreedily(getFunction(), std::move(patterns)); (void)applyPatternsAndFoldGreedily(getFunction(), std::move(patterns));
} }
std::unique_ptr<mlir::FunctionPass> mlir::mhlo::createOptimizeMhloPass() { std::unique_ptr<mlir::FunctionPass> mlir::mhlo::createOptimizeMhloPass() {

2
tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/test_infer_shaped_type_pass.cc
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@ -87,7 +87,7 @@ struct TestInferShapedTypeMethodsPass
OwningRewritePatternList patterns; OwningRewritePatternList patterns;
patterns.insert<ReifyReturnTypeShapesPattern>(&getContext()); patterns.insert<ReifyReturnTypeShapesPattern>(&getContext());
patterns.insert<InferReturnTypeComponentsPattern>(&getContext()); patterns.insert<InferReturnTypeComponentsPattern>(&getContext());
applyPatternsAndFoldGreedily(getFunction(), std::move(patterns)); (void)applyPatternsAndFoldGreedily(getFunction(), std::move(patterns));
} }
}; };

2
tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/unfuse_batch_norm_pass.cc
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@ -32,7 +32,7 @@ struct TestUnfuseBatchNormPass
void runOnOperation() override { void runOnOperation() override {
OwningRewritePatternList patterns; OwningRewritePatternList patterns;
PopulateUnfuseBatchNormPatterns(&getContext(), &patterns); PopulateUnfuseBatchNormPatterns(&getContext(), &patterns);
applyPatternsAndFoldGreedily(getOperation(), std::move(patterns)); (void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns));
} }
}; };

2
tensorflow/compiler/mlir/lite/quantization/device_target.h
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@ -92,7 +92,7 @@ class KernelSpecs {
KernelSpecs& WithSignature(const KernelSpecs::Signature& signature, KernelSpecs& WithSignature(const KernelSpecs::Signature& signature,
const ScaleFn& fn) { const ScaleFn& fn) {
Add(signature, {ScaleConstraintType::CustomScale, fn}); (void)Add(signature, {ScaleConstraintType::CustomScale, fn});
return *this; return *this;
} }

4
tensorflow/compiler/mlir/lite/quantization/tensorflow/tf_to_quant.cc
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@ -113,7 +113,7 @@ struct InsertQuantOpsAfterTFFakeQuantOp
TypeAttr qtype = quant::GetQuantizedTypeAttr( TypeAttr qtype = quant::GetQuantizedTypeAttr(
rewriter, res_type, min_value, max_value, quant_dim, num_bits, rewriter, res_type, min_value, max_value, quant_dim, num_bits,
narrow_range, /*is_signed=*/true); narrow_range, /*is_signed=*/true);
if (!qtype) failure(); if (!qtype) return failure();
// Finally, use the quantization parameter to create the quantize and // Finally, use the quantization parameter to create the quantize and
// dequantize ops, and insert them between the tf.FakeQuantWithMinMaxVarsOp // dequantize ops, and insert them between the tf.FakeQuantWithMinMaxVarsOp
@ -145,7 +145,7 @@ void LegalizeTFToQuant::runOnFunction() {
auto func = getFunction(); auto func = getFunction();
auto *ctx = func.getContext(); auto *ctx = func.getContext();
patterns.insert<PreparePerTensorFakeQuant, PreparePerChannelFakeQuant>(ctx); patterns.insert<PreparePerTensorFakeQuant, PreparePerChannelFakeQuant>(ctx);
applyPatternsAndFoldGreedily(func, std::move(patterns)); (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
} }
} // namespace } // namespace

6
tensorflow/compiler/mlir/lite/transforms/lower_static_tensor_list.cc
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@ -331,12 +331,11 @@ struct ConvertTensorListInitOp : public OpConversionPattern<OpT> {
if (!(dtype.isF16() || dtype.isF32() || dtype.isF64() || if (!(dtype.isF16() || dtype.isF32() || dtype.isF64() ||
dtype.isInteger(1) || dtype.isInteger(8) || dtype.isInteger(16) || dtype.isInteger(1) || dtype.isInteger(8) || dtype.isInteger(16) ||
dtype.isInteger(32) || dtype.isInteger(64))) { dtype.isInteger(32) || dtype.isInteger(64))) {
(void)rewriter.notifyMatchFailure( return rewriter.notifyMatchFailure(
op, op,
"requires element_dtype to be 1-bit/8-bit/16-bit/32-bit/64-bit " "requires element_dtype to be 1-bit/8-bit/16-bit/32-bit/64-bit "
"integer or 16-bit/32-bit/64-bit float type during TF Lite " "integer or 16-bit/32-bit/64-bit float type during TF Lite "
"transformation pass"); "transformation pass");
return failure();
} }
Value element_shape = operands[0]; Value element_shape = operands[0];
@ -390,11 +389,10 @@ struct ConvertTensorListInitOp : public OpConversionPattern<OpT> {
if (element_shape_acquired) break; if (element_shape_acquired) break;
} }
if (!element_shape_acquired) { if (!element_shape_acquired) {
(void)rewriter.notifyMatchFailure( return rewriter.notifyMatchFailure(
op, op,
"requires element_shape to be 1D tensor during TF Lite " "requires element_shape to be 1D tensor during TF Lite "
"transformation pass"); "transformation pass");
return failure();
} }
} }
} }

24
tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc
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@ -1056,8 +1056,8 @@ struct FusedBatchNormV3Pat : public ::mlir::RewritePattern {
/*x=*/tblgen_value_0, /*x=*/tblgen_value_0,
/*y=*/tblgen_value_1); /*y=*/tblgen_value_1);
// We need to make sure the Add operands are broadcastable. // We need to make sure the Add operands are broadcastable.
if (mlir::OpTrait::impl::verifyCompatibleOperandBroadcast(add_op_1) if (mlir::failed(mlir::OpTrait::impl::verifyCompatibleOperandBroadcast(
.value == LogicalResult::Failure) { add_op_1))) {
return failure(); return failure();
} }
} }
@ -1077,8 +1077,8 @@ struct FusedBatchNormV3Pat : public ::mlir::RewritePattern {
multiplier = rewriter.create<::mlir::TF::MulOp>(odsLoc, multiplier = rewriter.create<::mlir::TF::MulOp>(odsLoc,
/*x=*/tblgen_value_0, /*x=*/tblgen_value_0,
/*y=*/tblgen_value_1); /*y=*/tblgen_value_1);
if (mlir::OpTrait::impl::verifyCompatibleOperandBroadcast(multiplier) if (mlir::failed(mlir::OpTrait::impl::verifyCompatibleOperandBroadcast(
.value == LogicalResult::Failure) { multiplier))) {
return failure(); return failure();
} }
} }
@ -1090,8 +1090,8 @@ struct FusedBatchNormV3Pat : public ::mlir::RewritePattern {
/*x=*/tblgen_value_0, /*x=*/tblgen_value_0,
/*y=*/tblgen_value_1); /*y=*/tblgen_value_1);
// We need to make sure the Mul operands are broadcastable. // We need to make sure the Mul operands are broadcastable.
if (mlir::OpTrait::impl::verifyCompatibleOperandBroadcast(mul_op_1) if (mlir::failed(mlir::OpTrait::impl::verifyCompatibleOperandBroadcast(
.value == LogicalResult::Failure) { mul_op_1))) {
return failure(); return failure();
} }
} }
@ -1102,8 +1102,8 @@ struct FusedBatchNormV3Pat : public ::mlir::RewritePattern {
mul_op_2 = rewriter.create<::mlir::TF::MulOp>(odsLoc, mul_op_2 = rewriter.create<::mlir::TF::MulOp>(odsLoc,
/*x=*/tblgen_value_0, /*x=*/tblgen_value_0,
/*y=*/tblgen_value_1); /*y=*/tblgen_value_1);
if (mlir::OpTrait::impl::verifyCompatibleOperandBroadcast(mul_op_2) if (mlir::failed(mlir::OpTrait::impl::verifyCompatibleOperandBroadcast(
.value == LogicalResult::Failure) { mul_op_2))) {
return failure(); return failure();
} }
} }
@ -1114,8 +1114,8 @@ struct FusedBatchNormV3Pat : public ::mlir::RewritePattern {
sub_op = rewriter.create<::mlir::TF::SubOp>(odsLoc, sub_op = rewriter.create<::mlir::TF::SubOp>(odsLoc,
/*x=*/tblgen_value_0, /*x=*/tblgen_value_0,
/*y=*/tblgen_value_1); /*y=*/tblgen_value_1);
if (mlir::OpTrait::impl::verifyCompatibleOperandBroadcast(sub_op).value == if (failed(
LogicalResult::Failure) { mlir::OpTrait::impl::verifyCompatibleOperandBroadcast(sub_op))) {
return failure(); return failure();
} }
} }
@ -1132,8 +1132,8 @@ struct FusedBatchNormV3Pat : public ::mlir::RewritePattern {
add_op_2 = rewriter.create<::mlir::TF::AddOp>( add_op_2 = rewriter.create<::mlir::TF::AddOp>(
odsLoc, tblgen_types, tblgen_values, tblgen_attrs); odsLoc, tblgen_types, tblgen_values, tblgen_attrs);
// We need to make sure the Add operands are broadcastable. // We need to make sure the Add operands are broadcastable.
if (mlir::OpTrait::impl::verifyCompatibleOperandBroadcast(add_op_2) if (mlir::failed(mlir::OpTrait::impl::verifyCompatibleOperandBroadcast(
.value == LogicalResult::Failure) { add_op_2))) {
return failure(); return failure();
} }
} }

2
tensorflow/compiler/mlir/tensorflow/transforms/lower_tf_pass.cc
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@ -30,7 +30,7 @@ struct LowerTF : public PassWrapper<LowerTF, FunctionPass> {
OwningRewritePatternList patterns; OwningRewritePatternList patterns;
mlir::TF::PopulateLoweringTFPatterns(&getContext(), &patterns); mlir::TF::PopulateLoweringTFPatterns(&getContext(), &patterns);
applyPatternsAndFoldGreedily(getFunction(), std::move(patterns)); (void)applyPatternsAndFoldGreedily(getFunction(), std::move(patterns));
} }
}; };

4
tensorflow/compiler/mlir/tensorflow/utils/tpu_rewrite_device_util_test.cc
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@ -744,7 +744,7 @@ TEST(TPURewriteDeviceUtilTest, TestGetHostDeviceFailBadDeviceName) {
cluster->setAttr(kDeviceAssignmentAttr, builder.getArrayAttr({})); cluster->setAttr(kDeviceAssignmentAttr, builder.getArrayAttr({}));
mlir::TF::RuntimeDevices runtime_devices; mlir::TF::RuntimeDevices runtime_devices;
GetDevicesFromOp(*module_ref, &runtime_devices); (void)GetDevicesFromOp(*module_ref, &runtime_devices);
std::string host_device; std::string host_device;
EXPECT_TRUE(mlir::failed( EXPECT_TRUE(mlir::failed(
GetHostDeviceOutsideComputation(runtime_devices, cluster, &host_device))); GetHostDeviceOutsideComputation(runtime_devices, cluster, &host_device)));
@ -799,7 +799,7 @@ TEST(TPURewriteDeviceUtilTest, TestGetHostDeviceNotReplicated) {
cluster->setAttr(kDeviceAssignmentAttr, builder.getArrayAttr({})); cluster->setAttr(kDeviceAssignmentAttr, builder.getArrayAttr({}));
mlir::TF::RuntimeDevices runtime_devices; mlir::TF::RuntimeDevices runtime_devices;
GetDevicesFromOp(*module_ref, &runtime_devices); (void)GetDevicesFromOp(*module_ref, &runtime_devices);
std::string host_device; std::string host_device;
EXPECT_TRUE(mlir::succeeded( EXPECT_TRUE(mlir::succeeded(
GetHostDeviceOutsideComputation(runtime_devices, cluster, &host_device))); GetHostDeviceOutsideComputation(runtime_devices, cluster, &host_device)));

2
tensorflow/compiler/mlir/tfjs/transforms/optimize.cc
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@ -52,7 +52,7 @@ void Optimize::runOnFunction() {
auto func = getFunction(); auto func = getFunction();
populateWithGenerated(ctx, patterns); populateWithGenerated(ctx, patterns);
applyPatternsAndFoldGreedily(func, std::move(patterns)); (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
} }
} // namespace } // namespace

4
tensorflow/compiler/mlir/tosa/transforms/convert_tfl_uint8.cc
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@ -338,10 +338,10 @@ void ConvertUint8ToInt8::runOnFunction() {
// Convert uint8 const tensor. const needs to be handled specifically. // Convert uint8 const tensor. const needs to be handled specifically.
patterns.insert<ConvertUint8QConstOp>(&ctx); patterns.insert<ConvertUint8QConstOp>(&ctx);
applyPatternsAndFoldGreedily(func, std::move(patterns)); (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
// Replace uint8 tensor in the graph and insert rescale as needed. // Replace uint8 tensor in the graph and insert rescale as needed.
convert_graph_uint8_tensor(ctx, func); (void)convert_graph_uint8_tensor(ctx, func);
} }
} // anonymous namespace } // anonymous namespace

2
tensorflow/compiler/mlir/tosa/transforms/fuse_bias_tf.cc
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@ -113,7 +113,7 @@ void FuseBiasTF::runOnFunction() {
// Add the generated patterns to the list. // Add the generated patterns to the list.
patterns.insert<ConvertTFBiasAddOp>(ctx); patterns.insert<ConvertTFBiasAddOp>(ctx);
applyPatternsAndFoldGreedily(func, std::move(patterns)); (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
} }
} // anonymous namespace } // anonymous namespace

2
tensorflow/compiler/mlir/tosa/transforms/legalize_tf.cc
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@ -2112,7 +2112,7 @@ void LegalizeTF::runOnFunction() {
patterns.insert<ConvertTFReverseV2Op>(ctx); patterns.insert<ConvertTFReverseV2Op>(ctx);
patterns.insert<ConvertTFFakeQuantWithMinMaxArgsOp>(ctx); patterns.insert<ConvertTFFakeQuantWithMinMaxArgsOp>(ctx);
patterns.insert<ConvertTFFakeQuantWithMinMaxVarsOp>(ctx); patterns.insert<ConvertTFFakeQuantWithMinMaxVarsOp>(ctx);
applyPatternsAndFoldGreedily(func, std::move(patterns)); (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
} }
} // anonymous namespace } // anonymous namespace

2
tensorflow/compiler/mlir/tosa/transforms/legalize_tfl.cc
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@ -2962,7 +2962,7 @@ void LegalizeTFL::runOnFunction() {
DEF_PATTERN_INSERT(TFLQuantize); DEF_PATTERN_INSERT(TFLQuantize);
DEF_PATTERN_INSERT(TFLDequantize); DEF_PATTERN_INSERT(TFLDequantize);
DEF_PATTERN_INSERT(TFLQConst); DEF_PATTERN_INSERT(TFLQConst);
applyPatternsAndFoldGreedily(func, std::move(patterns)); (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
} }
} // namespace } // namespace

2
tensorflow/compiler/mlir/xla/experimental/conv_emitter/conv_emitter_test.cc
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@ -56,7 +56,7 @@ std::string CompileHloConvAndGetMlir(absl::string_view hlo_text) {
EmitConvolutionForwardAsMlir(conv, "Conv", &context).ValueOrDie(); EmitConvolutionForwardAsMlir(conv, "Conv", &context).ValueOrDie();
mlir_module->push_back(function); mlir_module->push_back(function);
mlir_module->verify(); (void)mlir_module->verify();
std::string mlir_text; std::string mlir_text;
{ {

4
tensorflow/workspace.bzl
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@ -685,8 +685,8 @@ def tf_repositories(path_prefix = "", tf_repo_name = ""):
) )
# Check out LLVM and MLIR from llvm-project. # Check out LLVM and MLIR from llvm-project.
LLVM_COMMIT = "91e7a17133324ac4beaf6ed45c170436c2d91c98" LLVM_COMMIT = "d1978fa4bf0d7da0b3cd88879e9411467edcb01f"
LLVM_SHA256 = "07ef834c47337dc7b38c765693a3b5a1835aac2f716fd9a06c374a71722c20de" LLVM_SHA256 = "7cf83492b954d713f727bbafb00aa93e412281cc6e0cab90a38a3c00ca63cc4a"
LLVM_URLS = [ LLVM_URLS = [
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/{commit}.tar.gz".format(commit = LLVM_COMMIT), "https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/{commit}.tar.gz".format(commit = LLVM_COMMIT),
"https://github.com/llvm/llvm-project/archive/{commit}.tar.gz".format(commit = LLVM_COMMIT), "https://github.com/llvm/llvm-project/archive/{commit}.tar.gz".format(commit = LLVM_COMMIT),