llvm/mlir/examples/Linalg/Linalg3/Execution.cpp
Alex Zinenko 4408228269 ExecutionEngine: drop PassManager integration
Originally, ExecutionEngine was created before MLIR had a proper pass
    management infrastructure or an LLVM IR dialect (using the LLVM target
    directly).  It has been running a bunch of lowering passes to convert the input
    IR from Standard+Affine dialects to LLVM IR and, later, to the LLVM IR dialect.
    This is no longer necessary and is even undesirable for compilation flows that
    perform their own conversion to the LLVM IR dialect.  Drop this integration and
    make ExecutionEngine accept only the LLVM IR dialect.  Users of the
    ExecutionEngine can call the relevant passes themselves.

--

PiperOrigin-RevId: 249004676
2019-05-20 13:48:45 -07:00

163 lines
5.2 KiB
C++

//===- Conversion.cpp - Linalg to LLVM execution driver -------------------===//
//
// Copyright 2019 The MLIR Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// =============================================================================
#include "TestHarness.h"
#include "linalg1/Common.h"
#include "linalg1/Dialect.h"
#include "linalg2/Intrinsics.h"
#include "linalg3/ConvertToLLVMDialect.h"
#include "linalg3/Ops.h"
#include "linalg3/Transforms.h"
#include "llvm/Support/TargetSelect.h"
#include "mlir/ExecutionEngine/ExecutionEngine.h"
// RUN: %p/execution | FileCheck %s
using namespace mlir;
using namespace mlir::edsc;
using namespace mlir::edsc::intrinsics;
using namespace linalg;
using namespace linalg::common;
using namespace linalg::intrinsics;
Function *makeFunctionWithAMatmulOp(Module &module, StringRef name) {
MLIRContext *context = module.getContext();
auto dynamic2DMemRefType = floatMemRefType<2>(context);
mlir::Function *f = linalg::common::makeFunction(
module, name,
{dynamic2DMemRefType, dynamic2DMemRefType, dynamic2DMemRefType}, {});
mlir::FuncBuilder builder(f);
ScopedContext scope(builder, f->getLoc());
// clang-format off
ValueHandle
M = dim(f->getArgument(0), 0),
N = dim(f->getArgument(2), 1),
K = dim(f->getArgument(0), 1),
rM = range(constant_index(0), M, constant_index(1)),
rN = range(constant_index(0), N, constant_index(1)),
rK = range(constant_index(0), K, constant_index(1)),
vA = view(f->getArgument(0), {rM, rK}),
vB = view(f->getArgument(1), {rK, rN}),
vC = view(f->getArgument(2), {rM, rN});
matmul(vA, vB, vC);
ret();
// clang-format on
return f;
}
// Representation of a Memref descriptor for a 2D dynamically-sized Memref in C.
// This is equivalent to the structure that the conversion produces.
struct MemRefDescriptor2D {
float *ptr;
int64_t sz1;
int64_t sz2;
};
// Alocate a 2D memref of the given size, store the sizes in the descriptor and
// initialize all values with 1.0f.
static MemRefDescriptor2D allocateInit2DMemref(int64_t sz1, int64_t sz2) {
MemRefDescriptor2D descriptor;
descriptor.ptr = static_cast<float *>(malloc(sizeof(float) * sz1 * sz2));
descriptor.sz1 = sz1;
descriptor.sz2 = sz2;
for (int64_t i = 0, e = sz1 * sz2; i < e; ++i)
descriptor.ptr[i] = 1.0f;
return descriptor;
}
// Print the contents of the memref given its descriptor.
static void print2DMemref(const MemRefDescriptor2D &descriptor) {
for (int64_t i = 0; i < descriptor.sz1; ++i) {
llvm::outs() << '[';
for (int64_t j = 0; j < descriptor.sz2; ++j) {
if (j != 0)
llvm::outs() << ", ";
llvm::outs() << descriptor.ptr[i * descriptor.sz2 + j];
}
llvm::outs() << "]\n";
}
}
// Free a 2D memref given its descriptor. Resets the pointer in the descriptor
// to nullptr.
static void free2DMemref(MemRefDescriptor2D &descriptor) {
free(descriptor.ptr);
descriptor.ptr = nullptr;
}
TEST_FUNC(execution) {
// Create an MLIR module, create a function "matmul_as_loops" containing a
// linalg.matmul operation and lower it all the way down to the LLVM IR
// dialect through partial conversions.
MLIRContext context;
Module module(&context);
mlir::Function *f = makeFunctionWithAMatmulOp(module, "matmul_as_loops");
lowerToLoops(f);
convertLinalg3ToLLVM(module);
// Create an MLIR execution engine. The execution engine eagerly JIT-compiles
// the module.
auto maybeEngine = mlir::ExecutionEngine::create(&module);
assert(maybeEngine && "failed to construct an execution engine");
auto &engine = maybeEngine.get();
// Prepare arguments for the function invocation: allocate input and output
// buffers.
auto A = allocateInit2DMemref(5, 3);
auto B = allocateInit2DMemref(3, 2);
auto C = allocateInit2DMemref(5, 2);
llvm::SmallVector<void *, 4> args;
args.push_back(&A);
args.push_back(&B);
args.push_back(&C);
// Invoke the JIT-compiled function with the arguments. Note that, for API
// uniformity reasons, it takes a list of type-erased pointers to arguments.
auto invocationResult =
engine->invoke("matmul_as_loops", MutableArrayRef<void *>(args));
assert(!invocationResult && "call failed");
// clang-format off
// CHECK: [3.000000e+00, 3.000000e+00]
// CHECK-NEXT: [3.000000e+00, 3.000000e+00]
// CHECK-NEXT: [3.000000e+00, 3.000000e+00]
// CHECK-NEXT: [3.000000e+00, 3.000000e+00]
// CHECK-NEXT: [3.000000e+00, 3.000000e+00]
// clang-format on
print2DMemref(C);
// Cleanup.
free2DMemref(A);
free2DMemref(B);
free2DMemref(C);
}
int main() {
mlir::registerDialect<linalg::LinalgDialect>();
// Initialize LLVM targets.
llvm::InitializeNativeTarget();
llvm::InitializeNativeTargetAsmPrinter();
RUN_TESTS();
return 0;
}