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Reland: [mlir][Affine][Vector] Add initial support for 'iter_args' to Affine vectorizer. 

This patch adds support for vectorizing loops with 'iter_args' when those loops
are not a vector dimension. This allows vectorizing outer loops with an inner
'iter_args' loop (e.g., reductions). Vectorizing scenarios where 'iter_args'
loops are vector dimensions would require more work (e.g., analysis,
generating horizontal reduction, etc.) not included in this patch.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D97892
This commit is contained in:
Diego Caballero 2021-03-10 20:39:39 +02:00
parent b552adf8b3
commit 0fd0fb5329
2 changed files with 113 additions and 26 deletions
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67
mlir/lib/Dialect/Affine/Transforms/SuperVectorize.cpp
View File

@ -254,10 +254,11 @@ using namespace vector;
/// * Uniform operands (only operands defined outside of the loop nest,
/// for now) are broadcasted to a vector.
/// TODO: Support more uniform cases.
/// * Affine for operations with 'iter_args' are vectorized by
/// vectorizing their 'iter_args' operands and results.
/// TODO: Support more complex loops with divergent lbs and/or ubs.
/// * The remaining operations in the loop nest are vectorized by
/// widening their scalar types to vector types.
/// * TODO: Add vectorization support for loops with 'iter_args' and
/// more complex loops with divergent lbs and/or ubs.
/// b. if everything under the root AffineForOp in the current pattern
/// is vectorized properly, we commit that loop to the IR and remove the
/// scalar loop. Otherwise, we discard the vectorized loop and keep the
@ -620,6 +621,14 @@ struct VectorizationState {
/// * 'replacement': %0 = vector.broadcast %1 : f32 to vector<128xf32>
void registerValueVectorReplacement(Value replaced, Operation *replacement);
/// Registers the vector replacement of a block argument (e.g., iter_args).
///
/// Example:
/// * 'replaced': 'iter_arg' block argument.
/// * 'replacement': vectorized 'iter_arg' block argument.
void registerBlockArgVectorReplacement(BlockArgument replaced,
BlockArgument replacement);
/// Registers the scalar replacement of a scalar value. 'replacement' must be
/// scalar. Both values must be block arguments. Operation results should be
/// replaced using the 'registerOp*' utilitites.
@ -685,15 +694,15 @@ void VectorizationState::registerOpVectorReplacement(Operation *replaced,
LLVM_DEBUG(dbgs() << "into\n");
LLVM_DEBUG(dbgs() << *replacement << "\n");
assert(replaced->getNumResults() <= 1 && "Unsupported multi-result op");
assert(replaced->getNumResults() == replacement->getNumResults() &&
"Unexpected replaced and replacement results");
assert(opVectorReplacement.count(replaced) == 0 && "already registered");
opVectorReplacement[replaced] = replacement;
if (replaced->getNumResults() > 0)
registerValueVectorReplacementImpl(replaced->getResult(0),
replacement->getResult(0));
for (auto resultTuple :
llvm::zip(replaced->getResults(), replacement->getResults()))
registerValueVectorReplacementImpl(std::get<0>(resultTuple),
std::get<1>(resultTuple));
}
/// Registers the vector replacement of a scalar value. The replacement
@ -716,6 +725,16 @@ void VectorizationState::registerValueVectorReplacement(
registerValueVectorReplacementImpl(replaced, replacement->getResult(0));
}
/// Registers the vector replacement of a block argument (e.g., iter_args).
///
/// Example:
/// * 'replaced': 'iter_arg' block argument.
/// * 'replacement': vectorized 'iter_arg' block argument.
void VectorizationState::registerBlockArgVectorReplacement(
BlockArgument replaced, BlockArgument replacement) {
registerValueVectorReplacementImpl(replaced, replacement);
}
void VectorizationState::registerValueVectorReplacementImpl(Value replaced,
Value replacement) {
assert(!valueVectorReplacement.contains(replaced) &&
@ -1013,16 +1032,20 @@ static Operation *vectorizeAffineStore(AffineStoreOp storeOp,
// vectorized at this point.
static Operation *vectorizeAffineForOp(AffineForOp forOp,
VectorizationState &state) {
// 'iter_args' not supported yet.
if (forOp.getNumIterOperands() > 0)
const VectorizationStrategy &strategy = *state.strategy;
auto loopToVecDimIt = strategy.loopToVectorDim.find(forOp);
bool isLoopVecDim = loopToVecDimIt != strategy.loopToVectorDim.end();
// We only support 'iter_args' when the loop is not one of the vector
// dimensions.
// TODO: Support vector dimension loops. They require special handling:
// generate horizontal reduction, last-value extraction, etc.
if (forOp.getNumIterOperands() > 0 && isLoopVecDim)
return nullptr;
// If we are vectorizing a vector dimension, compute a new step for the new
// vectorized loop using the vectorization factor for the vector dimension.
// Otherwise, propagate the step of the scalar loop.
const VectorizationStrategy &strategy = *state.strategy;
auto loopToVecDimIt = strategy.loopToVectorDim.find(forOp);
bool isLoopVecDim = loopToVecDimIt != strategy.loopToVectorDim.end();
unsigned newStep;
if (isLoopVecDim) {
unsigned vectorDim = loopToVecDimIt->second;
@ -1033,10 +1056,15 @@ static Operation *vectorizeAffineForOp(AffineForOp forOp,
newStep = forOp.getStep();
}
// Vectorize 'iter_args'.
SmallVector<Value, 8> vecIterOperands;
for (auto operand : forOp.getIterOperands())
vecIterOperands.push_back(vectorizeOperand(operand, state));
auto vecForOp = state.builder.create<AffineForOp>(
forOp.getLoc(), forOp.getLowerBoundOperands(), forOp.getLowerBoundMap(),
forOp.getUpperBoundOperands(), forOp.getUpperBoundMap(), newStep,
forOp.getIterOperands(),
vecIterOperands,
/*bodyBuilder=*/[](OpBuilder &, Location, Value, ValueRange) {
// Make sure we don't create a default terminator in the loop body as
// the proper terminator will be added during vectorization.
@ -1051,11 +1079,16 @@ static Operation *vectorizeAffineForOp(AffineForOp forOp,
// since a scalar copy of the iv will prevail in the vectorized loop.
// TODO: A vector replacement will also be added in the future when
// vectorization of linear ops is supported.
// 3) TODO: Support 'iter_args' along non-vector dimensions.
// 3) The new 'iter_args' region arguments are registered as vector
// replacements since they have been vectorized.
state.registerOpVectorReplacement(forOp, vecForOp);
state.registerValueScalarReplacement(forOp.getInductionVar(),
vecForOp.getInductionVar());
// Map the new vectorized loop to its vector dimension.
for (auto iterTuple :
llvm ::zip(forOp.getRegionIterArgs(), vecForOp.getRegionIterArgs()))
state.registerBlockArgVectorReplacement(std::get<0>(iterTuple),
std::get<1>(iterTuple));
if (isLoopVecDim)
state.vecLoopToVecDim[vecForOp] = loopToVecDimIt->second;
@ -1102,12 +1135,6 @@ static Operation *widenOp(Operation *op, VectorizationState &state) {
/// operations after the parent op.
static Operation *vectorizeAffineYieldOp(AffineYieldOp yieldOp,
VectorizationState &state) {
// 'iter_args' not supported yet.
if (yieldOp.getNumOperands() > 0)
return nullptr;
// Vectorize the yield op and change the insertion point right after the new
// parent op.
Operation *newYieldOp = widenOp(yieldOp, state);
Operation *newParentOp = state.builder.getInsertionBlock()->getParentOp();
state.builder.setInsertionPointAfter(newParentOp);

72
mlir/test/Dialect/Affine/SuperVectorize/vectorize_1d.mlir
View File

@ -500,11 +500,11 @@ func @vec_rejected_unsupported_block_arg(%A : memref<512xi32>) {
// -----
// CHECK-LABEL: @vec_rejected_unsupported_reduction
func @vec_rejected_unsupported_reduction(%in: memref<128x256xf32>, %out: memref<256xf32>) {
// '%i' loop is vectorized, including the inner reduction over '%j'.
func @vec_non_vecdim_reduction(%in: memref<128x256xf32>, %out: memref<256xf32>) {
%cst = constant 0.000000e+00 : f32
affine.for %i = 0 to 256 {
// CHECK-NOT: vector
%final_red = affine.for %j = 0 to 128 iter_args(%red_iter = %cst) -> (f32) {
%ld = affine.load %in[%j, %i] : memref<128x256xf32>
%add = addf %red_iter, %ld : f32
@ -515,13 +515,63 @@ func @vec_rejected_unsupported_reduction(%in: memref<128x256xf32>, %out: memref<
return
}
// CHECK-LABEL: @vec_non_vecdim_reduction
// CHECK: affine.for %{{.*}} = 0 to 256 step 128 {
// CHECK: %[[vzero:.*]] = constant dense<0.000000e+00> : vector<128xf32>
// CHECK: %[[final_red:.*]] = affine.for %{{.*}} = 0 to 128 iter_args(%[[red_iter:.*]] = %[[vzero]]) -> (vector<128xf32>) {
// CHECK: %[[ld:.*]] = vector.transfer_read %{{.*}} : memref<128x256xf32>, vector<128xf32>
// CHECK: %[[add:.*]] = addf %[[red_iter]], %[[ld]] : vector<128xf32>
// CHECK: affine.yield %[[add]] : vector<128xf32>
// CHECK: }
// CHECK: vector.transfer_write %[[final_red]], %{{.*}} : vector<128xf32>, memref<256xf32>
// CHECK: }
// -----
// CHECK-LABEL: @vec_rejected_unsupported_last_value
func @vec_rejected_unsupported_last_value(%in: memref<128x256xf32>, %out: memref<256xf32>) {
// '%i' loop is vectorized, including the inner reductions over '%j'.
func @vec_non_vecdim_reductions(%in0: memref<128x256xf32>, %in1: memref<128x256xi32>,
%out0: memref<256xf32>, %out1: memref<256xi32>) {
%zero = constant 0.000000e+00 : f32
%one = constant 1 : i32
affine.for %i = 0 to 256 {
%red0, %red1 = affine.for %j = 0 to 128
iter_args(%red_iter0 = %zero, %red_iter1 = %one) -> (f32, i32) {
%ld0 = affine.load %in0[%j, %i] : memref<128x256xf32>
%add = addf %red_iter0, %ld0 : f32
%ld1 = affine.load %in1[%j, %i] : memref<128x256xi32>
%mul = muli %red_iter1, %ld1 : i32
affine.yield %add, %mul : f32, i32
}
affine.store %red0, %out0[%i] : memref<256xf32>
affine.store %red1, %out1[%i] : memref<256xi32>
}
return
}
// CHECK-LABEL: @vec_non_vecdim_reductions
// CHECK: affine.for %{{.*}} = 0 to 256 step 128 {
// CHECK: %[[vzero:.*]] = constant dense<0.000000e+00> : vector<128xf32>
// CHECK: %[[vone:.*]] = constant dense<1> : vector<128xi32>
// CHECK: %[[reds:.*]]:2 = affine.for %{{.*}} = 0 to 128
// CHECK-SAME: iter_args(%[[red_iter0:.*]] = %[[vzero]], %[[red_iter1:.*]] = %[[vone]]) -> (vector<128xf32>, vector<128xi32>) {
// CHECK: %[[ld0:.*]] = vector.transfer_read %{{.*}} : memref<128x256xf32>, vector<128xf32>
// CHECK: %[[add:.*]] = addf %[[red_iter0]], %[[ld0]] : vector<128xf32>
// CHECK: %[[ld1:.*]] = vector.transfer_read %{{.*}} : memref<128x256xi32>, vector<128xi32>
// CHECK: %[[mul:.*]] = muli %[[red_iter1]], %[[ld1]] : vector<128xi32>
// CHECK: affine.yield %[[add]], %[[mul]] : vector<128xf32>, vector<128xi32>
// CHECK: }
// CHECK: vector.transfer_write %[[reds]]#0, %{{.*}} : vector<128xf32>, memref<256xf32>
// CHECK: vector.transfer_write %[[reds]]#1, %{{.*}} : vector<128xi32>, memref<256xi32>
// CHECK: }
// -----
// '%i' loop is vectorized, including the inner last value computation over '%j'.
func @vec_no_vecdim_last_value(%in: memref<128x256xf32>, %out: memref<256xf32>) {
%cst = constant 0.000000e+00 : f32
affine.for %i = 0 to 256 {
// CHECK-NOT: vector
%last_val = affine.for %j = 0 to 128 iter_args(%last_iter = %cst) -> (f32) {
%ld = affine.load %in[%j, %i] : memref<128x256xf32>
affine.yield %ld : f32
@ -530,3 +580,13 @@ func @vec_rejected_unsupported_last_value(%in: memref<128x256xf32>, %out: memref
}
return
}
// CHECK-LABEL: @vec_no_vecdim_last_value
// CHECK: affine.for %{{.*}} = 0 to 256 step 128 {
// CHECK: %[[vzero:.*]] = constant dense<0.000000e+00> : vector<128xf32>
// CHECK: %[[last_val:.*]] = affine.for %{{.*}} = 0 to 128 iter_args(%[[last_iter:.*]] = %[[vzero]]) -> (vector<128xf32>) {
// CHECK: %[[ld:.*]] = vector.transfer_read %{{.*}} : memref<128x256xf32>, vector<128xf32>
// CHECK: affine.yield %[[ld]] : vector<128xf32>
// CHECK: }
// CHECK: vector.transfer_write %[[last_val]], %{{.*}} : vector<128xf32>, memref<256xf32>
// CHECK: }