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Unverified Commit 4b2ca0db authored by Scott Cyphers's avatar Scott Cyphers Committed by GitHub
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Merge branch 'master' into ayzhuang/cf_gather

parents e9e53c5d c59ea84c
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Showing with 189 additions and 136 deletions
cmake/external_mlir.cmake
View file @ 4b2ca0db
......@@ -20,8 +20,8 @@ set(MLIR_LLVM_REPO_URL https://github.com/llvm/llvm-project.git)
set(MLIR_REPO_URL https://github.com/tensorflow/mlir.git)
# Change these commit IDs to move to latest stable versions
set(MLIR_LLVM_COMMIT_ID 0845ac7331e)
set(MLIR_COMMIT_ID 1f7893e0)
set(MLIR_LLVM_COMMIT_ID e0f1d9d8729)
set(MLIR_COMMIT_ID c61db4bb)
# MLIR environment variables. Some of them are used by LIT tool.
set(MLIR_PROJECT_ROOT ${CMAKE_CURRENT_BINARY_DIR}/mlir_project)
......
src/contrib/mlir/backend/pass/affine_lowerer.cpp
View file @ 4b2ca0db
......@@ -521,7 +521,7 @@ namespace
NGRAPH_CHECK(lhs->getType().isa<MemRefType>());
Type elemTy = lhs->getType().dyn_cast<MemRefType>().getElementType();
LoopNestBuilder(pivs, lbs, ubs, steps)([&] {
AffineLoopNestBuilder(pivs, lbs, ubs, steps)([&] {
ValueHandle val = iLHS(ivs);
ValueHandle zero = createZeroConstant(elemTy);
iRes(ivs) = intrinsics::select(val > zero, val, zero);
......@@ -591,12 +591,14 @@ namespace
{
IndexHandle n, k;
LoopBuilder(&n, nLb, nUb, nStep)(
[&] { LoopBuilder(&k, kLb, kUb, kStep)([&] { iRes(n, k) = zeroInit; }); });
LoopBuilder::makeAffine(&n, nLb, nUb, nStep)([&] {
LoopBuilder::makeAffine(&k, kLb, kUb, kStep)([&] { iRes(n, k) = zeroInit; });
});
}
LoopBuilder(&n, nLb, nUb, nStep)([&] {
LoopBuilder(&m, mLb, mUb, mStep)([&] {
LoopBuilder(&k, kLb, kUb, kStep)([&] { iRes(n, k) += iLhs(n, m) * iRhs(m, k); });
LoopBuilder::makeAffine(&n, nLb, nUb, nStep)([&] {
LoopBuilder::makeAffine(&m, mLb, mUb, mStep)([&] {
LoopBuilder::makeAffine(&k, kLb, kUb, kStep)(
[&] { iRes(n, k) += iLhs(n, m) * iRhs(m, k); });
});
});
......@@ -658,7 +660,7 @@ namespace
indexVarSteps.push_back(vOperand.step(i));
}
LoopNestBuilder(indexVarPtrs, indexVarLbs, indexVarUbs, indexVarSteps)([&] {
AffineLoopNestBuilder(indexVarPtrs, indexVarLbs, indexVarUbs, indexVarSteps)([&] {
IndexedValue ivRes(result);
IndexedValue ivOperand(operand);
......@@ -758,12 +760,12 @@ namespace
// params[P_0, P_1, .. P_(A-1), indices[I_0, .., I_(M-1)],
// P_(A+1), ... P_(N-1)];
LoopNestBuilder(indicesIVPtrs, indicesLbs, indicesUbs, indicesSteps)([&] {
AffineLoopNestBuilder(indicesIVPtrs, indicesLbs, indicesUbs, indicesSteps)([&] {
// Load axis value from indices array and cast it to Index Type
ValueHandle axisIdx = ValueHandle::create<IndexCastOp>(
(ValueHandle)iIndices(indicesIVs), rewriter.getIndexType());
LoopNestBuilder(paramsIVPtrs, paramsLbs, paramsUbs, paramsSteps)([&] {
AffineLoopNestBuilder(paramsIVPtrs, paramsLbs, paramsUbs, paramsSteps)([&] {
// construct indices for param
// [P_0, P_1, .. P_axis-1, Indices[I0, I1, .. I_k-1], P_axis+1, P_axis+2, .. P_n-1]
for (auto i = 0, j = 0; i < vParams.rank(); i++)
......@@ -965,8 +967,7 @@ namespace
NGRAPH_CHECK(affineExprs.size() == isEq.size() && isEq.size() == 2 * spatialRank,
"Invalid number of expressions in the IntegerSet");
nonPaddedRange =
rewriter.getIntegerSet(spatialRank, 2 * spatialRank, affineExprs, isEq);
nonPaddedRange = IntegerSet::get(spatialRank, 2 * spatialRank, affineExprs, isEq);
}
// Initialize output to zero
......@@ -975,9 +976,9 @@ namespace
auto resSpatialIndices = makeIndexHandles(spatialRank);
auto resSpatialIndicesPtrs = makeIndexHandlePointers(resSpatialIndices);
LoopBuilder(&n, batchLb, batchUb, 1)([&] {
LoopBuilder(&k, numFiltersLb, numFiltersUb, 1)([&] {
LoopNestBuilder(
LoopBuilder::makeAffine(&n, batchLb, batchUb, 1)([&] {
LoopBuilder::makeAffine(&k, numFiltersLb, numFiltersUb, 1)([&] {
AffineLoopNestBuilder(
resSpatialIndicesPtrs, resSpatialLbs, resSpatialUbs, resSteps)([&] {
SmallVector<IndexHandle, 4> resIndices;
// Result indices
......@@ -994,13 +995,13 @@ namespace
IndexHandle n, k, c;
// Convolution loop
LoopBuilder(&n, batchLb, batchUb, 1)([&] {
LoopBuilder::makeAffine(&n, batchLb, batchUb, 1)([&] {
// Number of filters loop
LoopBuilder(&k, numFiltersLb, numFiltersUb, 1)([&] {
LoopBuilder::makeAffine(&k, numFiltersLb, numFiltersUb, 1)([&] {
// Channels loop
LoopBuilder(&c, numChannelsLb, numChannelsUb, 1)([&] {
LoopBuilder::makeAffine(&c, numChannelsLb, numChannelsUb, 1)([&] {
// Results loop
LoopNestBuilder(
AffineLoopNestBuilder(
resSpatialIndicesPtrs, resSpatialLbs, resSpatialUbs, resSteps)([&] {
// Compute image start indices
SmallVector<IndexHandle, 4> imgStartIndices;
......@@ -1017,10 +1018,10 @@ namespace
resIndices.insert(
resIndices.end(), resSpatialIndices.begin(), resSpatialIndices.end());
// Filters spatial loop
LoopNestBuilder(filtersSpatialIndicesPtrs,
filtersSpatialLbs,
filtersSpatialUbs,
filtersSteps)([&] {
AffineLoopNestBuilder(filtersSpatialIndicesPtrs,
filtersSpatialLbs,
filtersSpatialUbs,
filtersSteps)([&] {
SmallVector<IndexHandle, 4> imgIndices, filtersIndices;
// Image indices
// Here we compute the virtual start index into the padded image.
......@@ -1131,7 +1132,7 @@ namespace
NGRAPH_CHECK(lhs->getType().isa<MemRefType>());
Type elemTy = lhs->getType().cast<MemRefType>().getElementType();
LoopNestBuilder(pivs, lbs, ubs, steps)([&] {
AffineLoopNestBuilder(pivs, lbs, ubs, steps)([&] {
ValueHandle val = iLHS(ivs);
if (isa<NGNegOp>(op))
{
......@@ -1173,7 +1174,7 @@ namespace
auto pivs = makeIndexHandlePointers(ivs);
// Steps
auto steps = vLHS.getSteps();
LoopNestBuilder(pivs, lbs, ubs, steps)(
AffineLoopNestBuilder(pivs, lbs, ubs, steps)(
// single stmt body
[&] {
if (isa<NGAddOp>(op))
......@@ -1266,7 +1267,7 @@ namespace
auto pivs = makeIndexHandlePointers(ivs);
auto steps = vRes.getSteps();
auto initVal = vArg.lb(axis);
LoopNestBuilder(pivs, resLbs, resUbs, steps)(
AffineLoopNestBuilder(pivs, resLbs, resUbs, steps)(
[&] { iRes(ivs) = ValueHandle::create<IndexCastOp>(initVal, resTy); });
}
......@@ -1282,7 +1283,7 @@ namespace
"Expected integer result type in index reduction");
// iterate over all argument dimensions
LoopNestBuilder(pAllIVs, argLbs, argUbs, steps)([&] {
AffineLoopNestBuilder(pAllIVs, argLbs, argUbs, steps)([&] {
// build a list of non-reduction IVs
for (auto i = 0; i < vArg.rank(); i++)
{
......
src/contrib/mlir/core/compiler.cpp
View file @ 4b2ca0db
......@@ -64,7 +64,6 @@
#include <mlir/Conversion/StandardToLLVM/ConvertStandardToLLVMPass.h>
#include <mlir/Dialect/LLVMIR/LLVMDialect.h>
#include <mlir/ExecutionEngine/ExecutionEngine.h>
#include <mlir/ExecutionEngine/MemRefUtils.h>
#include <mlir/ExecutionEngine/OptUtils.h>
#include <mlir/Pass/PassManager.h>
#include <mlir/Target/LLVMIR.h>
......
src/contrib/mlir/core/compiler.hpp
View file @ 4b2ca0db
......@@ -24,7 +24,6 @@
#include "ngraph/descriptor/tensor.hpp"
#include "ngraph/node.hpp"
#include <mlir/ExecutionEngine/MemRefUtils.h>
#include <mlir/IR/Builders.h>
#include <mlir/IR/Module.h>
#include <mlir/IR/Types.h>
......
src/contrib/mlir/core/ngraph_dialect/dialect.cpp
View file @ 4b2ca0db
......@@ -18,12 +18,12 @@
// not expose public API to the rest of nGraph codebase and heavily depends on MLIR API.
#include "dialect.hpp"
#include <mlir/IR/DialectImplementation.h>
#include <mlir/Parser.h>
#include "ngraph/check.hpp"
#include "ops.hpp"
#include "type.hpp"
#include <mlir/Parser.h>
using namespace mlir;
NGraphOpsDialect::NGraphOpsDialect(mlir::MLIRContext* ctx)
......@@ -39,63 +39,64 @@ NGraphOpsDialect::NGraphOpsDialect(mlir::MLIRContext* ctx)
>();
}
mlir::Type NGraphOpsDialect::parseType(llvm::StringRef tyData, mlir::Location loc) const
mlir::Type NGraphOpsDialect::parseType(mlir::DialectAsmParser& parser) const
{
StringRef origTypeStr = tyData;
MLIRContext* context = getContext();
// Process nGraph tensor type.
if (tyData.consume_front("tensor"))
// failure is true
if (!parser.parseOptionalKeyword("tensor"))
{
if (!tyData.consume_front("<") || !tyData.consume_back(">"))
llvm::SMLoc typeLoc = parser.getCurrentLocation();
if (parser.parseLess())
{
return (emitError(loc, "expected '<' and '>' enclosing the tensor shape: " + tyData),
Type());
parser.emitError(typeLoc, "expected '<' and '>' enclosing the tensor shape");
return Type();
}
// Get x-separated sub-strings.
SmallVector<StringRef, 8> subStrings;
tyData.split(subStrings, "x");
// Parse shape dimensions.
SmallVector<int64_t, 4> shape;
for (unsigned i = 0, end = subStrings.size() - 1; i < end; ++i)
{
StringRef dimStr = subStrings[i];
int64_t dim = -1;
// NOTE: `consumeInteger` returns false if an integer was parsed successfully.
if (dimStr.consumeInteger(/*Radix=*/10, dim) || !dimStr.empty())
{
return (
emitError(loc, "expected a list of '[0-9]+x' dimension specifiers: " + tyData),
Type());
}
shape.push_back(dim);
}
parser.parseDimensionList(shape);
// Parse the current element type.
Type eltType;
// Parse nGraph element type.
auto elem_ty = mlir::parseType(subStrings.back(), context);
if (!elem_ty)
parser.parseType(eltType);
if (!eltType)
{
return (emitError(loc, "Unexpected element type in tensor type: " + tyData), Type());
typeLoc = parser.getCurrentLocation();
parser.emitError(typeLoc, "Invalid tensor element type");
}
return NGTensorType::get(context, elem_ty, shape);
parser.parseGreater();
return NGTensorType::get(context, eltType, shape);
}
else
{
// parse nGraph scalar type
return parseEltType(parser);
}
}
mlir::Type NGraphOpsDialect::parseEltType(mlir::DialectAsmParser& parser) const
{
// Process nGraph integer element types.
MLIRContext* context = getContext();
int width = 0;
bool isSigned = false;
llvm::SMLoc loc = parser.getCurrentLocation();
StringRef tyData = parser.getFullSymbolSpec();
StringRef origTypeStr = tyData;
if (tyData.startswith("i") || tyData.startswith("u"))
{
bool isSigned = tyData.consume_front("i");
bool isUnsigned = tyData.consume_front("u");
NGRAPH_CHECK(isSigned != isUnsigned, "nGraph integer cannot be signed and unsigned");
isSigned = tyData.consume_front("i");
tyData.consume_front("u");
unsigned width = 0;
// NOTE: `consumeInteger` returns false if an integer was parsed successfully.
if (tyData.consumeInteger(/*Radix=*/10, width) || width == 0 || !tyData.empty())
{
return (emitError(loc, "Unexpected nGraph integer type: " + origTypeStr), Type());
parser.emitError(loc, "Unexpected nGraph integer type: " + origTypeStr);
}
switch (width)
......@@ -108,9 +109,7 @@ mlir::Type NGraphOpsDialect::parseType(llvm::StringRef tyData, mlir::Location lo
return isSigned ? NGIntegerType::getInt32(context) : NGIntegerType::getUInt32(context);
case 64:
return isSigned ? NGIntegerType::getInt64(context) : NGIntegerType::getUInt64(context);
default:
return (emitError(loc, "Unexpected width for nGraph integer type: " + origTypeStr),
Type());
default: parser.emitError(loc, "Unexpected width for nGraph integer type: " + origTypeStr);
}
}
......@@ -119,43 +118,49 @@ mlir::Type NGraphOpsDialect::parseType(llvm::StringRef tyData, mlir::Location lo
"Floating point types should be processed by standard parser");
// NOTE: We may hit this error if the nGraph type is not yet supported in parser.
return (emitError(loc, "Unknown nGraph type: " + origTypeStr), Type());
parser.emitError(loc, "Unknown nGraph type: " + origTypeStr);
return Type();
}
void NGraphOpsDialect::printType(mlir::Type type, raw_ostream& os) const
void NGraphOpsDialect::printType(mlir::Type type, mlir::DialectAsmPrinter& printer) const
{
switch (type.getKind())
{
case NG_TENSOR_TYPE_ID:
{
os << "tensor<";
printer << "tensor<";
auto tensorTy = type.cast<NGTensorType>();
for (auto dim : tensorTy.getShape())
{
os << dim << 'x';
printer << dim << 'x';
}
os << tensorTy.getElementType() << '>';
printer << tensorTy.getElementType() << '>';
return;
}
case NG_I8_TYPE_ID:
case NG_I16_TYPE_ID:
case NG_I32_TYPE_ID:
case NG_I64_TYPE_ID:
{
auto intTy = type.cast<NGIntegerType>();
printer << "i" << intTy.getWidth();
return;
}
case NG_U8_TYPE_ID:
case NG_U16_TYPE_ID:
case NG_U32_TYPE_ID:
case NG_U64_TYPE_ID:
{
auto intTy = type.cast<NGIntegerType>();
os << "i" << intTy.getWidth();
printer << "u" << intTy.getWidth();
return;
}
case NG_BOOL_TYPE_ID:
{
os << "bool";
printer << "bool";
return;
}
default: { NGRAPH_CHECK(false, "Incorrect type to print?");
}
default: NGRAPH_UNREACHABLE("Incorrect type to print?");
}
}
src/contrib/mlir/core/ngraph_dialect/dialect.hpp
View file @ 4b2ca0db
......@@ -34,9 +34,12 @@ namespace mlir
{
public:
explicit NGraphOpsDialect(mlir::MLIRContext* ctx);
mlir::Type parseType(llvm::StringRef tyData, mlir::Location loc) const override;
void printType(mlir::Type type, llvm::raw_ostream& os) const override;
mlir::Type parseType(mlir::DialectAsmParser& parser) const override;
void printType(mlir::Type type, mlir::DialectAsmPrinter& printer) const override;
static StringRef getDialectNamespace() { return "ng"; }
private:
mlir::Type parseEltType(mlir::DialectAsmParser& parser) const;
};
}
src/contrib/mlir/core/pass/ng_dialect_builder.cpp
View file @ 4b2ca0db
......@@ -46,32 +46,6 @@
#include "ngraph/op/util/index_reduction.hpp"
#include "ngraph/type/element_type.hpp"
#include "contrib/mlir/utils.hpp"
#include <llvm/ADT/STLExtras.h>
#include <llvm/Analysis/TargetTransformInfo.h>
#include <llvm/ExecutionEngine/Orc/JITTargetMachineBuilder.h>
#include <llvm/IR/Module.h>
#include <llvm/Support/ErrorOr.h>
#include <llvm/Support/MemoryBuffer.h>
#include <llvm/Support/SourceMgr.h>
#include <llvm/Support/TargetSelect.h>
#include <llvm/Target/TargetMachine.h>
#include <mlir/Conversion/LoopToStandard/ConvertLoopToStandard.h>
#include <mlir/Conversion/StandardToLLVM/ConvertStandardToLLVM.h>
#include <mlir/Conversion/StandardToLLVM/ConvertStandardToLLVMPass.h>
#include <mlir/Dialect/LLVMIR/LLVMDialect.h>
#include <mlir/ExecutionEngine/ExecutionEngine.h>
#include <mlir/ExecutionEngine/MemRefUtils.h>
#include <mlir/ExecutionEngine/OptUtils.h>
#include <mlir/Pass/PassManager.h>
#include <mlir/Target/LLVMIR.h>
#include <mlir/Transforms/DialectConversion.h>
#include <mlir/Transforms/Passes.h>
#include <memory>
#include <mutex>
// Defines a new LLVM debug type for this file to be used by LLVM_DEBUG macro.
#define DEBUG_TYPE "mlir-compiler"
......
src/contrib/mlir/core/pass/ng_dialect_builder.hpp
View file @ 4b2ca0db
......@@ -20,21 +20,13 @@
#pragma once
#include "contrib/mlir/core/compiler.hpp"
#include "contrib/mlir/runtime/cpu/memory_manager.hpp"
#include "ngraph/check.hpp"
#include "ngraph/descriptor/tensor.hpp"
#include "ngraph/node.hpp"
#include <mlir/ExecutionEngine/MemRefUtils.h>
#include <mlir/IR/Builders.h>
#include <mlir/IR/Module.h>
#include <mlir/IR/Types.h>
#include <mlir/Pass/Pass.h>
#include <typeindex>
#include <unordered_map>
#include <vector>
using namespace ngraph::runtime::ngmlir;
namespace ngraph
......
src/contrib/mlir/runtime/cpu/cpu_runtime.cpp
View file @ 4b2ca0db
......@@ -30,8 +30,8 @@
#include <llvm/Support/SourceMgr.h>
#include <llvm/Support/TargetSelect.h>
#include <llvm/Target/TargetMachine.h>
#include <mlir/Dialect/LLVMIR/LLVMDialect.h>
#include <mlir/ExecutionEngine/ExecutionEngine.h>
#include <mlir/ExecutionEngine/MemRefUtils.h>
#include <mlir/ExecutionEngine/OptUtils.h>
#include <mlir/IR/Function.h>
......@@ -81,7 +81,7 @@ void MLIRCPURuntime::bindArguments(std::vector<void*>& externalTensors)
{
NGRAPH_CHECK(m_module, "MLIR module is not ready.");
mlir::FuncOp func = m_module->lookupSymbol<mlir::FuncOp>("main");
auto func = m_module->lookupSymbol<mlir::LLVM::LLVMFuncOp>("main");
NGRAPH_CHECK(func && !func.getBlocks().empty(), "Function not found");
// Set external arguments
......@@ -90,7 +90,7 @@ void MLIRCPURuntime::bindArguments(std::vector<void*>& externalTensors)
// Create list with a type-erased double pointer for each invocation arguments.
// We currently use 'allocateMemrefArgs', which creates the arguments list per call ABI (see
// comment below).
// StaticFloatMemref is just a struct with the actual pointer to the data.
// StaticMemRef is just a struct with the actual pointer to the data.
auto expectedArguments = allocateMemrefArgs();
NGRAPH_CHECK(expectedArguments.size(), "Arguments can't be created");
......@@ -102,7 +102,7 @@ void MLIRCPURuntime::bindArguments(std::vector<void*>& externalTensors)
// Assign external tensor pointers to invocation arguments.
for (size_t i = 0, numArgs = m_invokeArgs.size(); i < numArgs; ++i)
{
auto* memRefArg = *(reinterpret_cast<mlir::StaticFloatMemRef**>(m_invokeArgs[i]));
auto* memRefArg = *(reinterpret_cast<StaticMemRef**>(m_invokeArgs[i]));
memRefArg->data = reinterpret_cast<float*>((*m_externalTensors)[i]);
}
}
......@@ -129,18 +129,18 @@ void MLIRCPURuntime::cleanup()
// Free void double pointer arguments without freeing external tensor data.
for (auto* arg : m_invokeArgs)
{
auto* memRefArg = *(reinterpret_cast<mlir::StaticFloatMemRef**>(arg));
auto* memRefArg = *(reinterpret_cast<StaticMemRef**>(arg));
free(memRefArg);
free(arg);
}
}
// The current call ABI takes a single arg pointer (argPtr) pointing to a list of args.
// Each arg is a pointer to a StaticFloatMemRef which contains a data pointer
// Each arg is a pointer to a StaticMemRef which contains a data pointer
//
// The args are laid out as follows
// argPtr-> arg[0]-> StaticFloatMemRef -> <data>
// arg[1]-> StaticFloatMemRef -> <data>
// argPtr-> arg[0]-> StaticMemRef -> <data>
// arg[1]-> StaticMemRef -> <data>
// ...
SmallVector<void*, 8> MLIRCPURuntime::allocateMemrefArgs()
{
......@@ -148,20 +148,18 @@ SmallVector<void*, 8> MLIRCPURuntime::allocateMemrefArgs()
for (auto i = 0; i < m_externalTensors->size(); i++)
{
auto descriptor = allocateMemrefDescriptor();
mlir::StaticFloatMemRef** arg =
reinterpret_cast<mlir::StaticFloatMemRef**>(malloc(sizeof(mlir::StaticFloatMemRef*)));
StaticMemRef** arg = reinterpret_cast<StaticMemRef**>(malloc(sizeof(StaticMemRef*)));
*arg = descriptor;
args.push_back(arg);
}
return args;
}
mlir::StaticFloatMemRef* MLIRCPURuntime::allocateMemrefDescriptor()
StaticMemRef* MLIRCPURuntime::allocateMemrefDescriptor()
{
// We only use StaticFloatMemRef because that's what MLIR currently offers.
// We only use StaticMemRef because that's what MLIR currently offers.
// We should expand this with different types and dynamic MemRefs
auto* descriptor =
reinterpret_cast<mlir::StaticFloatMemRef*>(malloc(sizeof(mlir::StaticFloatMemRef)));
auto* descriptor = reinterpret_cast<StaticMemRef*>(malloc(sizeof(StaticMemRef)));
NGRAPH_CHECK(descriptor != nullptr, "NULL MemRef descriptor");
descriptor->data = nullptr;
return descriptor;
......
src/contrib/mlir/runtime/cpu/cpu_runtime.hpp
View file @ 4b2ca0db
......@@ -21,7 +21,6 @@
#include <memory>
#include <mlir/ExecutionEngine/ExecutionEngine.h>
#include <mlir/ExecutionEngine/MemRefUtils.h>
#include <mlir/IR/Builders.h>
#include <mlir/IR/Module.h>
#include <mlir/IR/Types.h>
......@@ -34,6 +33,10 @@ namespace ngraph
{
namespace ngmlir
{
struct StaticMemRef
{
void* data;
};
/// A CPU Runtime is an MLIR runtime that owns an MLIR context and a module
/// The module should be in LLVM dialect and ready to be lowered via an MLIR
/// ExecutionEngine. The runtime owns the context and must out-live any MLIR
......@@ -57,7 +60,7 @@ namespace ngraph
llvm::SmallVector<void*, 8> allocateMemrefArgs();
/// Helper to allocate a mem ref object. Handles static shapes only for now.
mlir::StaticFloatMemRef* allocateMemrefDescriptor();
StaticMemRef* allocateMemrefDescriptor();
private:
// Pointers to externally allocated memory for sub-graph's input and output tensors.
......
src/contrib/mlir/runtime/runtime.hpp
View file @ 4b2ca0db
......@@ -23,7 +23,6 @@
#include <memory>
#include <mlir/ExecutionEngine/ExecutionEngine.h>
#include <mlir/ExecutionEngine/MemRefUtils.h>
#include <mlir/IR/Builders.h>
#include <mlir/IR/Module.h>
#include <mlir/IR/Types.h>
......
test/mlir/ngraph_dialect/types.mlir
View file @ 4b2ca0db
......@@ -55,7 +55,7 @@ func @i64(%arg0: !ng.i64) {
// -----
// CHECK-LABEL: func @u8
// CHECK-SAME: (%{{.*}}: !ng.i8)
// CHECK-SAME: (%{{.*}}: !ng.u8)
func @u8(%arg0: !ng.u8) {
"ng.return"() : () -> ()
}
......@@ -63,7 +63,7 @@ func @u8(%arg0: !ng.u8) {
// -----
// CHECK-LABEL: func @u16
// CHECK-SAME: (%{{.*}}: !ng.i16)
// CHECK-SAME: (%{{.*}}: !ng.u16)
func @u16(%arg0: !ng.u16) {
"ng.return"() : () -> ()
}
......@@ -71,7 +71,7 @@ func @u16(%arg0: !ng.u16) {
// -----
// CHECK-LABEL: func @u32
// CHECK-SAME: (%{{.*}}: !ng.i32)
// CHECK-SAME: (%{{.*}}: !ng.u32)
func @u32(%arg0: !ng.u32) {
"ng.return"() : () -> ()
}
......@@ -83,3 +83,83 @@ func @u32(%arg0: !ng.u32) {
func @u64(%arg0: !ng.u64) {
"ng.return"() : () -> ()
}
// -----
// CHECK: func @tensor_i8
// CHECK-SAME: (%{{.*}}: !ng.tensor<2x2x!ng.i8>)
func @tensor_i8(%arg0: !ng.tensor<2x2x!ng.i8>) {
"ng.return"() : () -> ()
}
// -----
// CHECK: func @tensor_i16
// CHECK-SAME: (%{{.*}}: !ng.tensor<2x2x!ng.i16>)
func @tensor_i16(%arg0: !ng.tensor<2x2x!ng.i16>) {
"ng.return"() : () -> ()
}
// -----
// CHECK: func @tensor_i32
// CHECK-SAME: (%{{.*}}: !ng.tensor<2x2x!ng.i32>)
func @tensor_i32(%arg0: !ng.tensor<2x2x!ng.i32>) {
"ng.return"() : () -> ()
}
// -----
// CHECK: func @tensor_i64
// CHECK-SAME: (%{{.*}}: !ng.tensor<2x2x!ng.i64>)
func @tensor_i64(%arg0: !ng.tensor<2x2x!ng.i64>) {
"ng.return"() : () -> ()
}
// -----
// CHECK: func @tensor_u8
// CHECK-SAME: (%{{.*}}: !ng.tensor<2x2x!ng.u8>)
func @tensor_u8(%arg0: !ng.tensor<2x2x!ng.u8>) {
"ng.return"() : () -> ()
}
// -----
// CHECK: func @tensor_u16
// CHECK-SAME: (%{{.*}}: !ng.tensor<2x2x!ng.u16>)
func @tensor_u16(%arg0: !ng.tensor<2x2x!ng.u16>) {
"ng.return"() : () -> ()
}
// -----
// CHECK: func @tensor_u32
// CHECK-SAME: (%{{.*}}: !ng.tensor<2x2x!ng.u32>)
func @tensor_u32(%arg0: !ng.tensor<2x2x!ng.u32>) {
"ng.return"() : () -> ()
}
// -----
// CHECK: func @tensor_u64
// CHECK-SAME: (%{{.*}}: !ng.tensor<2x2x!ng.u64>)
func @tensor_u64(%arg0: !ng.tensor<2x2x!ng.u64>) {
"ng.return"() : () -> ()
}
// -----
// CHECK: func @tensor_f32
// CHECK-SAME: (%{{.*}}: !ng.tensor<2x2xf32>)
func @tensor_f32(%arg0: !ng.tensor<2x2xf32>) {
"ng.return"() : () -> ()
}
// -----
// CHECK: func @tensor_f64
// CHECK-SAME: (%{{.*}}: !ng.tensor<2x2xf64>)
func @tensor_f64(%arg0: !ng.tensor<2x2xf64>) {
"ng.return"() : () -> ()
}
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