torch-mlir/lib/Dialect/TorchConversion/Transforms/VerifyLinalgOnTensorsBacken...

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Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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//===----------------------------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// Also available under a BSD-style license. See LICENSE.
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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//
//===----------------------------------------------------------------------===//
#include "PassDetail.h"
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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#include "mlir/IR/OpDefinition.h"
#include "mlir/Transforms/DialectConversion.h"
#include "torch-mlir/Dialect/TorchConversion/Transforms/Passes.h"
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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#include "mlir/IR/BuiltinOps.h"
using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::TorchConversion;
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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namespace {
class VerifyLinalgOnTensorsBackendContractPass
: public VerifyLinalgOnTensorsBackendContractBase<
VerifyLinalgOnTensorsBackendContractPass> {
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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void runOnOperation() override {
MLIRContext *context = &getContext();
auto module = getOperation();
TypeConverter converter;
converter.addConversion([](RankedTensorType type) -> Type {
if (BaseMemRefType::isValidElementType(type.getElementType()))
return type;
return nullptr;
});
TypeConverter scalarConverter;
for (TypeConverter *c : {&converter, &scalarConverter}) {
c->addConversion([](FloatType type) { return type; });
c->addConversion([](IntegerType type) { return type; });
c->addConversion([](IndexType type) { return type; });
}
auto opHasLegalTypes = [&](Operation *op) { return converter.isLegal(op); };
auto isLegalScalarOp = [&](Operation *op) {
// We recognize basic scalar ops by them having the trait "Elementwise",
// even though we don't expect them to operate on tensors.
return scalarConverter.isLegal(op) &&
op->hasTrait<OpTrait::Elementwise>();
};
ConversionTarget target(*context);
// Structural operations.
target.addDynamicallyLegalOp<ModuleOp, FuncOp, ReturnOp>(opHasLegalTypes);
// Basic scalar operations.
target.addDynamicallyLegalDialect<StandardOpsDialect>(isLegalScalarOp);
target.addDynamicallyLegalDialect<math::MathDialect>(isLegalScalarOp);
target.addDynamicallyLegalDialect<arith::ArithmeticDialect>(
isLegalScalarOp);
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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// Tensor operations should go through linalg and the tensor dialect.
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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target.addDynamicallyLegalDialect<linalg::LinalgDialect>(opHasLegalTypes);
target.addDynamicallyLegalDialect<tensor::TensorDialect>(opHasLegalTypes);
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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// AssertOp is used to terminate the program for error guards.
target.addLegalOp<AssertOp>();
// ConstantOp is used for tensors and for scalars.
target.addDynamicallyLegalOp<arith::ConstantOp>(opHasLegalTypes);
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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RewritePatternSet patterns(context);
if (failed(applyFullConversion(module, target, std::move(patterns)))) {
// We avoid `module.emitError()` so that mlir-print-op-on-diagnostics
// doesn't unnecessarily spew out the entire module.
emitError(module.getLoc())
<< "Module does not conform to the linalg-on-tensors backend contract. "
"See dialect conversion legality information above.";
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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return signalPassFailure();
}
}
};
} // namespace
std::unique_ptr<OperationPass<ModuleOp>>
mlir::torch::TorchConversion::createVerifyLinalgOnTensorsBackendContractPass() {
return std::make_unique<VerifyLinalgOnTensorsBackendContractPass>();
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
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}