Files
clang-p2996/mlir/lib/Dialect/GPU/Transforms/MemoryPromotion.cpp
Christopher Bate 6ca1a09f03 [mlir][gpu] Migrate hard-coded address space integers to an enum attribute (gpu::AddressSpaceAttr)
This is a purely mechanical change that introduces an enum attribute in the GPU
dialect to represent the various memref memory spaces as opposed to the
hard-coded integer attributes that are currently used.

The following steps were taken to make the transition across the codebase:

1. Introduce a pass "gpu-lower-memory-space-attributes":

The pass updates all memref types that have a memory space attribute that is a
`gpu::AddressSpaceAttr`. These attributes are changed to `IntegerAttr`'s using a
mapping that is given by the caller. This pass is based on the
"map-memref-spirv-storage-class" pass and the common functions can probably
be refactored into a set of utilities under the MemRef dialect.

2. Update the verifiers of GPU/NVGPU dialect operations.

If a verifier currently checks the address space of an operand using
e.g.`getWorkspaceAddressSpace`, then it can continue to do so. However, the
checks are changed to only fail if the memory space is either missing or a wrong
value of type `gpu::AddressSpaceAttr`. Otherwise, it just assumes the address
space is correct because it was specifically lowered to something other than a
`gpu::AddressSpaceAttr`.

3. Update existing gpu-to-llvm conversion infrastructure.

In the existing gpu-to-X passes, we add a full conversion equivalent to
`gpu-lower-memory-space-attributes` just before doing the conversion to the
LLVMDialect. This is done because currently both the gpu-to-llvm passes
(rocdl,nvvm) run gpu-to-gpu rewrites within the pass, which introduce
`AddressSpaceAttr` memory space annotations. Therefore, I inserted the
memory space conversion between the gpu-to-gpu rewrites and the LLVM
conversion.

For more context see the below discourse discussion:
https://discourse.llvm.org/t/gpu-workgroup-shared-memory-address-space-is-hard-coded/

Reviewed By: ftynse

Differential Revision: https://reviews.llvm.org/D140644
2023-01-13 11:00:10 -07:00

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6.6 KiB
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//===- MemoryPromotion.cpp - Utilities for moving data across GPU memories ===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// This file implements utilities that allow one to create IR moving the data
// across different levels of the GPU memory hierarchy.
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/GPU/Transforms/MemoryPromotion.h"
#include "mlir/Dialect/Affine/LoopUtils.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/GPU/IR/GPUDialect.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/IR/ImplicitLocOpBuilder.h"
#include "mlir/Pass/Pass.h"
using namespace mlir;
using namespace mlir::gpu;
/// Emits the (imperfect) loop nest performing the copy between "from" and "to"
/// values using the bounds derived from the "from" value. Emits at least
/// GPUDialect::getNumWorkgroupDimensions() loops, completing the nest with
/// single-iteration loops. Maps the innermost loops to thread dimensions, in
/// reverse order to enable access coalescing in the innermost loop.
static void insertCopyLoops(ImplicitLocOpBuilder &b, Value from, Value to) {
auto memRefType = from.getType().cast<MemRefType>();
auto rank = memRefType.getRank();
SmallVector<Value, 4> lbs, ubs, steps;
Value zero = b.create<arith::ConstantIndexOp>(0);
Value one = b.create<arith::ConstantIndexOp>(1);
// Make sure we have enough loops to use all thread dimensions, these trivial
// loops should be outermost and therefore inserted first.
if (rank < GPUDialect::getNumWorkgroupDimensions()) {
unsigned extraLoops = GPUDialect::getNumWorkgroupDimensions() - rank;
lbs.resize(extraLoops, zero);
ubs.resize(extraLoops, one);
steps.resize(extraLoops, one);
}
// Add existing bounds.
lbs.append(rank, zero);
ubs.reserve(lbs.size());
steps.reserve(lbs.size());
for (auto idx = 0; idx < rank; ++idx) {
ubs.push_back(b.createOrFold<memref::DimOp>(
from, b.create<arith::ConstantIndexOp>(idx)));
steps.push_back(one);
}
// Obtain thread identifiers and block sizes, necessary to map to them.
auto indexType = b.getIndexType();
SmallVector<Value, 3> threadIds, blockDims;
for (auto dim : {gpu::Dimension::x, gpu::Dimension::y, gpu::Dimension::z}) {
threadIds.push_back(b.create<gpu::ThreadIdOp>(indexType, dim));
blockDims.push_back(b.create<gpu::BlockDimOp>(indexType, dim));
}
// Produce the loop nest with copies.
SmallVector<Value, 8> ivs(lbs.size());
mlir::scf::buildLoopNest(
b, b.getLoc(), lbs, ubs, steps,
[&](OpBuilder &b, Location loc, ValueRange loopIvs) {
ivs.assign(loopIvs.begin(), loopIvs.end());
auto activeIvs = llvm::ArrayRef(ivs).take_back(rank);
Value loaded = b.create<memref::LoadOp>(loc, from, activeIvs);
b.create<memref::StoreOp>(loc, loaded, to, activeIvs);
});
// Map the innermost loops to threads in reverse order.
for (const auto &en :
llvm::enumerate(llvm::reverse(llvm::ArrayRef(ivs).take_back(
GPUDialect::getNumWorkgroupDimensions())))) {
Value v = en.value();
auto loop = cast<scf::ForOp>(v.getParentRegion()->getParentOp());
mapLoopToProcessorIds(loop, {threadIds[en.index()]},
{blockDims[en.index()]});
}
}
/// Emits the loop nests performing the copy to the designated location in the
/// beginning of the region, and from the designated location immediately before
/// the terminator of the first block of the region. The region is expected to
/// have one block. This boils down to the following structure
///
/// ^bb(...):
/// <loop-bound-computation>
/// for %arg0 = ... to ... step ... {
/// ...
/// for %argN = <thread-id-x> to ... step <block-dim-x> {
/// %0 = load %from[%arg0, ..., %argN]
/// store %0, %to[%arg0, ..., %argN]
/// }
/// ...
/// }
/// gpu.barrier
/// <... original body ...>
/// gpu.barrier
/// for %arg0 = ... to ... step ... {
/// ...
/// for %argN = <thread-id-x> to ... step <block-dim-x> {
/// %1 = load %to[%arg0, ..., %argN]
/// store %1, %from[%arg0, ..., %argN]
/// }
/// ...
/// }
///
/// Inserts the barriers unconditionally since different threads may be copying
/// values and reading them. An analysis would be required to eliminate barriers
/// in case where value is only used by the thread that copies it. Both copies
/// are inserted unconditionally, an analysis would be required to only copy
/// live-in and live-out values when necessary. This copies the entire memref
/// pointed to by "from". In case a smaller block would be sufficient, the
/// caller can create a subview of the memref and promote it instead.
static void insertCopies(Region &region, Location loc, Value from, Value to) {
auto fromType = from.getType().cast<MemRefType>();
auto toType = to.getType().cast<MemRefType>();
(void)fromType;
(void)toType;
assert(fromType.getShape() == toType.getShape());
assert(fromType.getRank() != 0);
assert(llvm::hasSingleElement(region) &&
"unstructured control flow not supported");
auto b = ImplicitLocOpBuilder::atBlockBegin(loc, &region.front());
insertCopyLoops(b, from, to);
b.create<gpu::BarrierOp>();
b.setInsertionPoint(&region.front().back());
b.create<gpu::BarrierOp>();
insertCopyLoops(b, to, from);
}
/// Promotes a function argument to workgroup memory in the given function. The
/// copies will be inserted in the beginning and in the end of the function.
void mlir::promoteToWorkgroupMemory(GPUFuncOp op, unsigned arg) {
Value value = op.getArgument(arg);
auto type = value.getType().dyn_cast<MemRefType>();
assert(type && type.hasStaticShape() && "can only promote memrefs");
// Get the type of the buffer in the workgroup memory.
auto workgroupMemoryAddressSpace = gpu::AddressSpaceAttr::get(
op->getContext(), gpu::AddressSpace::Workgroup);
auto bufferType = MemRefType::get(type.getShape(), type.getElementType(),
MemRefLayoutAttrInterface{},
Attribute(workgroupMemoryAddressSpace));
Value attribution = op.addWorkgroupAttribution(bufferType, value.getLoc());
// Replace the uses first since only the original uses are currently present.
// Then insert the copies.
value.replaceAllUsesWith(attribution);
insertCopies(op.getBody(), op.getLoc(), value, attribution);
}