[Mlir-commits] [mlir] [mlir][tensor] Restrict the verifier for tensor.pack/tensor.unpack (PR #113108)
Andrzej Warzyński
llvmlistbot at llvm.org
Sun Oct 20 21:17:02 PDT 2024
https://github.com/banach-space updated https://github.com/llvm/llvm-project/pull/113108
>From 4d74d843b7df93d9f41246cecdcd5971f8e58f7e Mon Sep 17 00:00:00 2001
From: Andrzej Warzynski <andrzej.warzynski at arm.com>
Date: Sun, 20 Oct 2024 19:43:46 -0700
Subject: [PATCH 1/3] Revert "[MLIR] Make `OneShotModuleBufferize` use
`OpInterface` (#110322)"
This reverts commit 2026501cf107fcb3cbd51026ba25fda3af823941.
---
.../IR/BufferizableOpInterface.h | 7 +-
.../FuncBufferizableOpInterfaceImpl.h | 12 +-
.../IR/BufferizableOpInterface.cpp | 5 +-
.../FuncBufferizableOpInterfaceImpl.cpp | 2 +-
.../Transforms/OneShotModuleBufferize.cpp | 112 ++++++-------
.../Transforms/transform-ops.mlir | 142 ++++++++---------
mlir/test/Dialect/LLVM/transform-e2e.mlir | 22 ++-
.../Linalg/matmul-shared-memory-padding.mlir | 52 +++---
.../Linalg/pad-to-specific-memory-space.mlir | 148 +++++++++---------
.../test/Dialect/Vector/transform-vector.mlir | 84 +++++-----
mlir/test/Examples/transform/ChH/full.mlir | 11 +-
11 files changed, 281 insertions(+), 316 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h b/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
index d19687ec9afee1..aceb9d059b95f3 100644
--- a/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
+++ b/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
@@ -11,7 +11,6 @@
#include "mlir/IR/Operation.h"
#include "mlir/IR/PatternMatch.h"
-#include "mlir/Interfaces/FunctionInterfaces.h"
#include "mlir/Support/LLVM.h"
#include "llvm/ADT/DenseMapInfoVariant.h"
#include "llvm/ADT/SetVector.h"
@@ -261,9 +260,9 @@ struct BufferizationOptions {
using AnalysisStateInitFn = std::function<void(AnalysisState &)>;
/// Tensor -> MemRef type converter.
/// Parameters: Value, memory space, func op, bufferization options
- using FunctionArgTypeConverterFn = std::function<BaseMemRefType(
- TensorType, Attribute memorySpace, FunctionOpInterface,
- const BufferizationOptions &)>;
+ using FunctionArgTypeConverterFn =
+ std::function<BaseMemRefType(TensorType, Attribute memorySpace,
+ func::FuncOp, const BufferizationOptions &)>;
/// Tensor -> MemRef type converter.
/// Parameters: Value, memory space, bufferization options
using UnknownTypeConverterFn = std::function<BaseMemRefType(
diff --git a/mlir/include/mlir/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.h b/mlir/include/mlir/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.h
index 8bed0dfc5814b7..0b91d3d675b7c9 100644
--- a/mlir/include/mlir/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.h
+++ b/mlir/include/mlir/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.h
@@ -50,24 +50,24 @@ struct FuncAnalysisState : public OneShotAnalysisState::Extension {
/// A mapping of ReturnOp OpOperand indices to equivalent FuncOp BBArg
/// indices.
- DenseMap<FunctionOpInterface, IndexMapping> equivalentFuncArgs;
+ DenseMap<FuncOp, IndexMapping> equivalentFuncArgs;
/// A mapping of FuncOp BBArg indices to aliasing ReturnOp OpOperand indices.
- DenseMap<FunctionOpInterface, IndexToIndexListMapping> aliasingReturnVals;
+ DenseMap<FuncOp, IndexToIndexListMapping> aliasingReturnVals;
/// A set of all read BlockArguments of FuncOps.
- DenseMap<FunctionOpInterface, BbArgIndexSet> readBbArgs;
+ DenseMap<FuncOp, BbArgIndexSet> readBbArgs;
/// A set of all written-to BlockArguments of FuncOps.
- DenseMap<FunctionOpInterface, BbArgIndexSet> writtenBbArgs;
+ DenseMap<FuncOp, BbArgIndexSet> writtenBbArgs;
/// Keep track of which FuncOps are fully analyzed or currently being
/// analyzed.
- DenseMap<FunctionOpInterface, FuncOpAnalysisState> analyzedFuncOps;
+ DenseMap<FuncOp, FuncOpAnalysisState> analyzedFuncOps;
/// This function is called right before analyzing the given FuncOp. It
/// initializes the data structures for the FuncOp in this state object.
- void startFunctionAnalysis(FunctionOpInterface funcOp);
+ void startFunctionAnalysis(FuncOp funcOp);
};
void registerBufferizableOpInterfaceExternalModels(DialectRegistry ®istry);
diff --git a/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp b/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp
index 92f757111cbaf7..85604eef2f2830 100644
--- a/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp
+++ b/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp
@@ -18,7 +18,6 @@
#include "mlir/IR/TypeUtilities.h"
#include "mlir/IR/Value.h"
#include "mlir/Interfaces/ControlFlowInterfaces.h"
-#include "mlir/Interfaces/FunctionInterfaces.h"
#include "llvm/ADT/ScopeExit.h"
#include "llvm/Support/Debug.h"
@@ -315,7 +314,7 @@ namespace {
/// Default function arg type converter: Use a fully dynamic layout map.
BaseMemRefType
defaultFunctionArgTypeConverter(TensorType type, Attribute memorySpace,
- FunctionOpInterface funcOp,
+ func::FuncOp funcOp,
const BufferizationOptions &options) {
return getMemRefTypeWithFullyDynamicLayout(type, memorySpace);
}
@@ -362,7 +361,7 @@ BufferizationOptions::dynCastBufferizableOp(Value value) const {
void BufferizationOptions::setFunctionBoundaryTypeConversion(
LayoutMapOption layoutMapOption) {
functionArgTypeConverterFn = [=](TensorType tensorType, Attribute memorySpace,
- FunctionOpInterface funcOp,
+ func::FuncOp funcOp,
const BufferizationOptions &options) {
if (layoutMapOption == LayoutMapOption::IdentityLayoutMap)
return bufferization::getMemRefTypeWithStaticIdentityLayout(tensorType,
diff --git a/mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp b/mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp
index 9749a71f3514bc..9fbe574ec392dc 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp
@@ -22,7 +22,7 @@ namespace mlir {
namespace bufferization {
namespace func_ext {
-void FuncAnalysisState::startFunctionAnalysis(FunctionOpInterface funcOp) {
+void FuncAnalysisState::startFunctionAnalysis(FuncOp funcOp) {
analyzedFuncOps[funcOp] = FuncOpAnalysisState::InProgress;
auto createdEquiv = equivalentFuncArgs.try_emplace(funcOp, IndexMapping());
auto createdAliasingResults =
diff --git a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
index a0e5c7fff7690f..0a4072605c265f 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp
@@ -75,7 +75,7 @@ using namespace mlir::bufferization;
using namespace mlir::bufferization::func_ext;
/// A mapping of FuncOps to their callers.
-using FuncCallerMap = DenseMap<FunctionOpInterface, DenseSet<Operation *>>;
+using FuncCallerMap = DenseMap<func::FuncOp, DenseSet<Operation *>>;
/// Get or create FuncAnalysisState.
static FuncAnalysisState &
@@ -88,11 +88,10 @@ getOrCreateFuncAnalysisState(OneShotAnalysisState &state) {
/// Return the unique ReturnOp that terminates `funcOp`.
/// Return nullptr if there is no such unique ReturnOp.
-static Operation *getAssumedUniqueReturnOp(FunctionOpInterface funcOp) {
- Operation *returnOp = nullptr;
- for (Block &b : funcOp.getFunctionBody()) {
- auto candidateOp = b.getTerminator();
- if (candidateOp && candidateOp->hasTrait<OpTrait::ReturnLike>()) {
+static func::ReturnOp getAssumedUniqueReturnOp(func::FuncOp funcOp) {
+ func::ReturnOp returnOp;
+ for (Block &b : funcOp.getBody()) {
+ if (auto candidateOp = dyn_cast<func::ReturnOp>(b.getTerminator())) {
if (returnOp)
return nullptr;
returnOp = candidateOp;
@@ -127,16 +126,16 @@ static void annotateEquivalentReturnBbArg(OpOperand &returnVal,
/// Store function BlockArguments that are equivalent to/aliasing a returned
/// value in FuncAnalysisState.
static LogicalResult
-aliasingFuncOpBBArgsAnalysis(FunctionOpInterface funcOp,
- OneShotAnalysisState &state,
+aliasingFuncOpBBArgsAnalysis(FuncOp funcOp, OneShotAnalysisState &state,
FuncAnalysisState &funcState) {
- if (funcOp.getFunctionBody().empty()) {
+ if (funcOp.getBody().empty()) {
// No function body available. Conservatively assume that every tensor
// return value may alias with any tensor bbArg.
- for (const auto &inputIt : llvm::enumerate(funcOp.getArgumentTypes())) {
+ FunctionType type = funcOp.getFunctionType();
+ for (const auto &inputIt : llvm::enumerate(type.getInputs())) {
if (!isa<TensorType>(inputIt.value()))
continue;
- for (const auto &resultIt : llvm::enumerate(funcOp.getResultTypes())) {
+ for (const auto &resultIt : llvm::enumerate(type.getResults())) {
if (!isa<TensorType>(resultIt.value()))
continue;
int64_t returnIdx = resultIt.index();
@@ -148,7 +147,7 @@ aliasingFuncOpBBArgsAnalysis(FunctionOpInterface funcOp,
}
// Support only single return-terminated block in the function.
- Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
+ func::ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
assert(returnOp && "expected func with single return op");
for (OpOperand &returnVal : returnOp->getOpOperands())
@@ -169,8 +168,8 @@ aliasingFuncOpBBArgsAnalysis(FunctionOpInterface funcOp,
return success();
}
-static void annotateFuncArgAccess(FunctionOpInterface funcOp, int64_t idx,
- bool isRead, bool isWritten) {
+static void annotateFuncArgAccess(func::FuncOp funcOp, int64_t idx, bool isRead,
+ bool isWritten) {
OpBuilder b(funcOp.getContext());
Attribute accessType;
if (isRead && isWritten) {
@@ -190,12 +189,12 @@ static void annotateFuncArgAccess(FunctionOpInterface funcOp, int64_t idx,
/// function with unknown ops, we conservatively assume that such ops bufferize
/// to a read + write.
static LogicalResult
-funcOpBbArgReadWriteAnalysis(FunctionOpInterface funcOp,
- OneShotAnalysisState &state,
+funcOpBbArgReadWriteAnalysis(FuncOp funcOp, OneShotAnalysisState &state,
FuncAnalysisState &funcState) {
- for (int64_t idx = 0, e = funcOp.getNumArguments(); idx < e; ++idx) {
+ for (int64_t idx = 0, e = funcOp.getFunctionType().getNumInputs(); idx < e;
+ ++idx) {
// Skip non-tensor arguments.
- if (!isa<TensorType>(funcOp.getArgumentTypes()[idx]))
+ if (!isa<TensorType>(funcOp.getFunctionType().getInput(idx)))
continue;
bool isRead;
bool isWritten;
@@ -205,7 +204,7 @@ funcOpBbArgReadWriteAnalysis(FunctionOpInterface funcOp,
StringRef str = accessAttr.getValue();
isRead = str == "read" || str == "read-write";
isWritten = str == "write" || str == "read-write";
- } else if (funcOp.getFunctionBody().empty()) {
+ } else if (funcOp.getBody().empty()) {
// If the function has no body, conservatively assume that all args are
// read + written.
isRead = true;
@@ -231,19 +230,20 @@ funcOpBbArgReadWriteAnalysis(FunctionOpInterface funcOp,
/// Remove bufferization attributes on FuncOp arguments.
static void removeBufferizationAttributes(BlockArgument bbArg) {
- auto funcOp = cast<FunctionOpInterface>(bbArg.getOwner()->getParentOp());
+ auto funcOp = cast<func::FuncOp>(bbArg.getOwner()->getParentOp());
funcOp.removeArgAttr(bbArg.getArgNumber(),
BufferizationDialect::kBufferLayoutAttrName);
funcOp.removeArgAttr(bbArg.getArgNumber(),
BufferizationDialect::kWritableAttrName);
}
-static FunctionOpInterface getCalledFunction(CallOpInterface callOp) {
+/// Return the func::FuncOp called by `callOp`.
+static func::FuncOp getCalledFunction(func::CallOp callOp) {
SymbolRefAttr sym =
llvm::dyn_cast_if_present<SymbolRefAttr>(callOp.getCallableForCallee());
if (!sym)
return nullptr;
- return dyn_cast_or_null<FunctionOpInterface>(
+ return dyn_cast_or_null<func::FuncOp>(
SymbolTable::lookupNearestSymbolFrom(callOp, sym));
}
@@ -251,13 +251,12 @@ static FunctionOpInterface getCalledFunction(CallOpInterface callOp) {
/// Note: This only adds new equivalence info if the called function was already
/// analyzed.
// TODO: This does not handle cyclic function call graphs etc.
-static void equivalenceAnalysis(FunctionOpInterface funcOp,
+static void equivalenceAnalysis(func::FuncOp funcOp,
OneShotAnalysisState &state,
FuncAnalysisState &funcState) {
- funcOp->walk([&](CallOpInterface callOp) {
- FunctionOpInterface calledFunction = getCalledFunction(callOp);
- if (!calledFunction)
- return WalkResult::skip();
+ funcOp->walk([&](func::CallOp callOp) {
+ func::FuncOp calledFunction = getCalledFunction(callOp);
+ assert(calledFunction && "could not retrieved called func::FuncOp");
// No equivalence info available for the called function.
if (!funcState.equivalentFuncArgs.count(calledFunction))
@@ -268,7 +267,7 @@ static void equivalenceAnalysis(FunctionOpInterface funcOp,
int64_t bbargIdx = it.second;
if (!state.isInPlace(callOp->getOpOperand(bbargIdx)))
continue;
- Value returnVal = callOp->getResult(returnIdx);
+ Value returnVal = callOp.getResult(returnIdx);
Value argVal = callOp->getOperand(bbargIdx);
state.unionEquivalenceClasses(returnVal, argVal);
}
@@ -278,9 +277,11 @@ static void equivalenceAnalysis(FunctionOpInterface funcOp,
}
/// Return "true" if the given function signature has tensor semantics.
-static bool hasTensorSignature(FunctionOpInterface funcOp) {
- return llvm::any_of(funcOp.getArgumentTypes(), llvm::IsaPred<TensorType>) ||
- llvm::any_of(funcOp.getResultTypes(), llvm::IsaPred<TensorType>);
+static bool hasTensorSignature(func::FuncOp funcOp) {
+ return llvm::any_of(funcOp.getFunctionType().getInputs(),
+ llvm::IsaPred<TensorType>) ||
+ llvm::any_of(funcOp.getFunctionType().getResults(),
+ llvm::IsaPred<TensorType>);
}
/// Store all functions of the `moduleOp` in `orderedFuncOps`, sorted by
@@ -290,16 +291,16 @@ static bool hasTensorSignature(FunctionOpInterface funcOp) {
/// retrieve the called FuncOp from any func::CallOp.
static LogicalResult
getFuncOpsOrderedByCalls(ModuleOp moduleOp,
- SmallVectorImpl<FunctionOpInterface> &orderedFuncOps,
+ SmallVectorImpl<func::FuncOp> &orderedFuncOps,
FuncCallerMap &callerMap) {
// For each FuncOp, the set of functions called by it (i.e. the union of
// symbols of all nested func::CallOp).
- DenseMap<FunctionOpInterface, DenseSet<FunctionOpInterface>> calledBy;
+ DenseMap<func::FuncOp, DenseSet<func::FuncOp>> calledBy;
// For each FuncOp, the number of func::CallOp it contains.
- DenseMap<FunctionOpInterface, unsigned> numberCallOpsContainedInFuncOp;
- WalkResult res = moduleOp.walk([&](FunctionOpInterface funcOp) -> WalkResult {
- if (!funcOp.getFunctionBody().empty()) {
- Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
+ DenseMap<func::FuncOp, unsigned> numberCallOpsContainedInFuncOp;
+ WalkResult res = moduleOp.walk([&](func::FuncOp funcOp) -> WalkResult {
+ if (!funcOp.getBody().empty()) {
+ func::ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
if (!returnOp)
return funcOp->emitError()
<< "cannot bufferize a FuncOp with tensors and "
@@ -308,10 +309,9 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
// Collect function calls and populate the caller map.
numberCallOpsContainedInFuncOp[funcOp] = 0;
- return funcOp.walk([&](CallOpInterface callOp) -> WalkResult {
- FunctionOpInterface calledFunction = getCalledFunction(callOp);
- if (!calledFunction)
- return WalkResult::skip();
+ return funcOp.walk([&](func::CallOp callOp) -> WalkResult {
+ func::FuncOp calledFunction = getCalledFunction(callOp);
+ assert(calledFunction && "could not retrieved called func::FuncOp");
// If the called function does not have any tensors in its signature, then
// it is not necessary to bufferize the callee before the caller.
if (!hasTensorSignature(calledFunction))
@@ -349,11 +349,11 @@ getFuncOpsOrderedByCalls(ModuleOp moduleOp,
/// most generic layout map as function return types. After bufferizing the
/// entire function body, a more concise memref type can potentially be used for
/// the return type of the function.
-static void foldMemRefCasts(FunctionOpInterface funcOp) {
- if (funcOp.getFunctionBody().empty())
+static void foldMemRefCasts(func::FuncOp funcOp) {
+ if (funcOp.getBody().empty())
return;
- Operation *returnOp = getAssumedUniqueReturnOp(funcOp);
+ func::ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
SmallVector<Type> resultTypes;
for (OpOperand &operand : returnOp->getOpOperands()) {
@@ -365,8 +365,8 @@ static void foldMemRefCasts(FunctionOpInterface funcOp) {
}
}
- auto newFuncType = FunctionType::get(funcOp.getContext(),
- funcOp.getArgumentTypes(), resultTypes);
+ auto newFuncType = FunctionType::get(
+ funcOp.getContext(), funcOp.getFunctionType().getInputs(), resultTypes);
funcOp.setType(newFuncType);
}
@@ -379,7 +379,7 @@ mlir::bufferization::analyzeModuleOp(ModuleOp moduleOp,
FuncAnalysisState &funcState = getOrCreateFuncAnalysisState(state);
// A list of functions in the order in which they are analyzed + bufferized.
- SmallVector<FunctionOpInterface> orderedFuncOps;
+ SmallVector<func::FuncOp> orderedFuncOps;
// A mapping of FuncOps to their callers.
FuncCallerMap callerMap;
@@ -388,7 +388,7 @@ mlir::bufferization::analyzeModuleOp(ModuleOp moduleOp,
return failure();
// Analyze ops.
- for (FunctionOpInterface funcOp : orderedFuncOps) {
+ for (func::FuncOp funcOp : orderedFuncOps) {
if (!state.getOptions().isOpAllowed(funcOp))
continue;
@@ -416,7 +416,7 @@ mlir::bufferization::analyzeModuleOp(ModuleOp moduleOp,
void mlir::bufferization::removeBufferizationAttributesInModule(
ModuleOp moduleOp) {
- moduleOp.walk([&](FunctionOpInterface op) {
+ moduleOp.walk([&](func::FuncOp op) {
for (BlockArgument bbArg : op.getArguments())
removeBufferizationAttributes(bbArg);
});
@@ -430,7 +430,7 @@ LogicalResult mlir::bufferization::bufferizeModuleOp(
IRRewriter rewriter(moduleOp.getContext());
// A list of functions in the order in which they are analyzed + bufferized.
- SmallVector<FunctionOpInterface> orderedFuncOps;
+ SmallVector<func::FuncOp> orderedFuncOps;
// A mapping of FuncOps to their callers.
FuncCallerMap callerMap;
@@ -439,11 +439,11 @@ LogicalResult mlir::bufferization::bufferizeModuleOp(
return failure();
// Bufferize functions.
- for (FunctionOpInterface funcOp : orderedFuncOps) {
+ for (func::FuncOp funcOp : orderedFuncOps) {
// Note: It would be good to apply cleanups here but we cannot as aliasInfo
// would be invalidated.
- if (llvm::is_contained(options.noAnalysisFuncFilter, funcOp.getName())) {
+ if (llvm::is_contained(options.noAnalysisFuncFilter, funcOp.getSymName())) {
// This function was not analyzed and RaW conflicts were not resolved.
// Buffer copies must be inserted before every write.
OneShotBufferizationOptions updatedOptions = options;
@@ -463,7 +463,7 @@ LogicalResult mlir::bufferization::bufferizeModuleOp(
// Bufferize all other ops.
for (Operation &op : llvm::make_early_inc_range(moduleOp.getOps())) {
// Functions were already bufferized.
- if (isa<FunctionOpInterface>(&op))
+ if (isa<func::FuncOp>(&op))
continue;
if (failed(bufferizeOp(&op, options, statistics)))
return failure();
@@ -490,12 +490,12 @@ LogicalResult mlir::bufferization::runOneShotModuleBufferize(
// FuncOps whose names are specified in options.noAnalysisFuncFilter will
// not be analyzed. Ops in these FuncOps will not be analyzed as well.
OpFilter::Entry::FilterFn analysisFilterFn = [=](Operation *op) {
- auto func = dyn_cast<FunctionOpInterface>(op);
+ auto func = dyn_cast<func::FuncOp>(op);
if (!func)
- func = op->getParentOfType<FunctionOpInterface>();
+ func = op->getParentOfType<func::FuncOp>();
if (func)
return llvm::is_contained(options.noAnalysisFuncFilter,
- func.getName());
+ func.getSymName());
return false;
};
OneShotBufferizationOptions updatedOptions(options);
diff --git a/mlir/test/Dialect/Bufferization/Transforms/transform-ops.mlir b/mlir/test/Dialect/Bufferization/Transforms/transform-ops.mlir
index 588aa8a85a84e6..3c50a9e72d9d9b 100644
--- a/mlir/test/Dialect/Bufferization/Transforms/transform-ops.mlir
+++ b/mlir/test/Dialect/Bufferization/Transforms/transform-ops.mlir
@@ -1,4 +1,4 @@
-// RUN: mlir-opt --transform-interpreter="debug-payload-root-tag=payload" %s -split-input-file -verify-diagnostics | FileCheck %s
+// RUN: mlir-opt --transform-interpreter %s -split-input-file -verify-diagnostics | FileCheck %s
// Test One-Shot Bufferize.
@@ -12,21 +12,19 @@ module attributes {transform.with_named_sequence} {
// CHECK-LABEL: func @test_function(
// CHECK-SAME: %[[A:.*]]: tensor<?xf32>
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @test_function(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
- %c0 = arith.constant 0 : index
+func.func @test_function(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
+ %c0 = arith.constant 0 : index
- // CHECK: %[[A_memref:.*]] = bufferization.to_memref %[[A]]
- // CHECK: %[[dim:.*]] = memref.dim %[[A_memref]]
- // CHECK: %[[alloc:.*]] = memref.alloc(%[[dim]])
- // CHECK: memref.copy %[[A_memref]], %[[alloc]]
- // CHECK: vector.transfer_write %{{.*}}, %[[alloc]]
- // CHECK: %[[res_tensor:.*]] = bufferization.to_tensor %[[alloc]]
- %0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
+ // CHECK: %[[A_memref:.*]] = bufferization.to_memref %[[A]]
+ // CHECK: %[[dim:.*]] = memref.dim %[[A_memref]]
+ // CHECK: %[[alloc:.*]] = memref.alloc(%[[dim]])
+ // CHECK: memref.copy %[[A_memref]], %[[alloc]]
+ // CHECK: vector.transfer_write %{{.*}}, %[[alloc]]
+ // CHECK: %[[res_tensor:.*]] = bufferization.to_tensor %[[alloc]]
+ %0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
- // CHECK: return %[[res_tensor]]
- return %0 : tensor<?xf32>
- }
+ // CHECK: return %[[res_tensor]]
+ return %0 : tensor<?xf32>
}
// -----
@@ -44,21 +42,19 @@ module attributes {transform.with_named_sequence} {
// CHECK-LABEL: func @test_function(
// CHECK-SAME: %[[A:.*]]: tensor<?xf32>
// CHECK-NOT: memref.copy
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @test_function(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
- %c0 = arith.constant 0 : index
+func.func @test_function(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
+ %c0 = arith.constant 0 : index
- // CHECK: %[[A_memref:.*]] = bufferization.to_memref %[[A]]
- // CHECK: %[[dim:.*]] = memref.dim %[[A_memref]]
- // CHECK: %[[alloc:.*]] = memref.alloc(%[[dim]])
- // CHECK: linalg.copy ins(%[[A_memref]] : memref<{{.*}}>) outs(%[[alloc]]
- // CHECK: vector.transfer_write %{{.*}}, %[[alloc]]
- // CHECK: %[[res_tensor:.*]] = bufferization.to_tensor %[[alloc]]
- %0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
+ // CHECK: %[[A_memref:.*]] = bufferization.to_memref %[[A]]
+ // CHECK: %[[dim:.*]] = memref.dim %[[A_memref]]
+ // CHECK: %[[alloc:.*]] = memref.alloc(%[[dim]])
+ // CHECK: linalg.copy ins(%[[A_memref]] : memref<{{.*}}>) outs(%[[alloc]]
+ // CHECK: vector.transfer_write %{{.*}}, %[[alloc]]
+ // CHECK: %[[res_tensor:.*]] = bufferization.to_tensor %[[alloc]]
+ %0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
- // CHECK: return %[[res_tensor]]
- return %0 : tensor<?xf32>
- }
+ // CHECK: return %[[res_tensor]]
+ return %0 : tensor<?xf32>
}
// -----
@@ -76,15 +72,13 @@ module attributes {transform.with_named_sequence} {
// CHECK-LABEL: func @test_function_analysis(
// CHECK-SAME: %[[A:.*]]: tensor<?xf32>
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @test_function_analysis(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
- %c0 = arith.constant 0 : index
- // CHECK: vector.transfer_write
- // CHECK-SAME: {__inplace_operands_attr__ = ["none", "false", "none"]}
- // CHECK-SAME: tensor<?xf32>
- %0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
- return %0 : tensor<?xf32>
- }
+func.func @test_function_analysis(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
+ %c0 = arith.constant 0 : index
+ // CHECK: vector.transfer_write
+ // CHECK-SAME: {__inplace_operands_attr__ = ["none", "false", "none"]}
+ // CHECK-SAME: tensor<?xf32>
+ %0 = vector.transfer_write %v, %A[%c0] : vector<4xf32>, tensor<?xf32>
+ return %0 : tensor<?xf32>
}
// -----
@@ -101,12 +95,10 @@ module attributes {transform.with_named_sequence} {
}
}
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @test_unknown_op_failure() -> (tensor<?xf32>) {
- // expected-error @+1 {{op was not bufferized}}
- %0 = "test.dummy_op"() : () -> (tensor<?xf32>)
- return %0 : tensor<?xf32>
- }
+func.func @test_unknown_op_failure() -> (tensor<?xf32>) {
+ // expected-error @+1 {{op was not bufferized}}
+ %0 = "test.dummy_op"() : () -> (tensor<?xf32>)
+ return %0 : tensor<?xf32>
}
// -----
@@ -119,7 +111,7 @@ module attributes {transform.with_named_sequence} {
}
}
-module @payload attributes { transform.target_tag = "payload" } {
+module {
// CHECK-LABEL: func @test_function(
// CHECK-SAME: %[[A:.*]]: tensor<?xf32>
func.func @test_function(%A : tensor<?xf32>, %v : vector<4xf32>) -> (tensor<?xf32>) {
@@ -154,13 +146,11 @@ module attributes {transform.with_named_sequence} {
// CHECK-SAME: %[[A:.*]]: memref<12x9xf32>,
// CHECK-SAME: %[[B:.*]]: memref<9x6xf32>,
// CHECK-SAME: %[[C:.*]]: memref<12x6xf32>) -> memref<12x6xf32> {
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @matmul(%A: tensor<12x9xf32>, %B: tensor<9x6xf32>, %C: tensor<12x6xf32>) -> tensor<12x6xf32> {
- // CHECK: linalg.matmul ins(%[[A]], %[[B]] : memref<12x9xf32>, memref<9x6xf32>) outs(%[[C]] : memref<12x6xf32>)
- %D = linalg.matmul ins(%A, %B: tensor<12x9xf32>, tensor<9x6xf32>) outs(%C: tensor<12x6xf32>) -> tensor<12x6xf32>
- // CHECK: return %[[C]] : memref<12x6xf32>
- return %D : tensor<12x6xf32>
- }
+func.func @matmul(%A: tensor<12x9xf32>, %B: tensor<9x6xf32>, %C: tensor<12x6xf32>) -> tensor<12x6xf32> {
+ // CHECK: linalg.matmul ins(%[[A]], %[[B]] : memref<12x9xf32>, memref<9x6xf32>) outs(%[[C]] : memref<12x6xf32>)
+ %D = linalg.matmul ins(%A, %B: tensor<12x9xf32>, tensor<9x6xf32>) outs(%C: tensor<12x6xf32>) -> tensor<12x6xf32>
+ // CHECK: return %[[C]] : memref<12x6xf32>
+ return %D : tensor<12x6xf32>
}
// -----
@@ -175,12 +165,10 @@ module attributes {transform.with_named_sequence} {
}
// Expect `bufferization.empty_tensor_to_alloc_tensor` to replace the tensor.empty.
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @empty_to_tensor_alloc() -> tensor<2x2xf32> {
- // CHECK: bufferization.alloc_tensor
- %0 = tensor.empty() : tensor<2x2xf32>
- return %0 : tensor<2x2xf32>
- }
+func.func @empty_to_tensor_alloc() -> tensor<2x2xf32> {
+ // CHECK: bufferization.alloc_tensor
+ %0 = tensor.empty() : tensor<2x2xf32>
+ return %0 : tensor<2x2xf32>
}
// -----
@@ -197,15 +185,13 @@ module attributes {transform.with_named_sequence} {
// CHECK: tensor.extract_slice
// CHECK: linalg.fill
// CHECK: tensor.insert_slice
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @empty_tensor_elimination(
- %t: tensor<10xf32>, %f: f32) -> tensor<10xf32> {
- %0 = tensor.empty() : tensor<5xf32>
- %1 = linalg.fill ins(%f : f32) outs(%0 : tensor<5xf32>) -> tensor<5xf32>
- %2 = tensor.insert_slice %1 into %t [1][5][1]
- : tensor<5xf32> into tensor<10xf32>
- return %2 : tensor<10xf32>
- }
+func.func @empty_tensor_elimination(
+ %t: tensor<10xf32>, %f: f32) -> tensor<10xf32> {
+ %0 = tensor.empty() : tensor<5xf32>
+ %1 = linalg.fill ins(%f : f32) outs(%0 : tensor<5xf32>) -> tensor<5xf32>
+ %2 = tensor.insert_slice %1 into %t [1][5][1]
+ : tensor<5xf32> into tensor<10xf32>
+ return %2 : tensor<10xf32>
}
// -----
@@ -222,14 +208,12 @@ module attributes {transform.with_named_sequence} {
// CHECK: memref.alloca
// CHECK: scf.for
// CHECK: memref.store
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @buffer_loop_hoisting(%lb: index, %ub: index, %step: index, %f: f32, %pos: index) {
- scf.for %iv = %lb to %ub step %step {
- %0 = memref.alloca() : memref<5xf32>
- memref.store %f, %0[%pos] : memref<5xf32>
- }
- return
+func.func @buffer_loop_hoisting(%lb: index, %ub: index, %step: index, %f: f32, %pos: index) {
+ scf.for %iv = %lb to %ub step %step {
+ %0 = memref.alloca() : memref<5xf32>
+ memref.store %f, %0[%pos] : memref<5xf32>
}
+ return
}
// -----
@@ -247,12 +231,10 @@ module attributes {transform.with_named_sequence} {
// Expect `bufferization.bufferize_to_allocation` to create an alloc.
// CHECK-LABEL: func.func @empty_to_tensor_alloc()
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @empty_to_tensor_alloc() -> tensor<2x2xf32> {
- // CHECK-NEXT: %[[alloca:.*]] = memref.alloca() : memref<2x2xf32>
- // CHECK-NEXT: %[[tensor:.*]] = bufferization.to_tensor %[[alloca]] restrict writable : memref<2x2xf32>
- // CHECK-NEXT: return %[[tensor]] : tensor<2x2xf32>
- %0 = bufferization.alloc_tensor() : tensor<2x2xf32>
- return %0 : tensor<2x2xf32>
- }
+func.func @empty_to_tensor_alloc() -> tensor<2x2xf32> {
+ // CHECK-NEXT: %[[alloca:.*]] = memref.alloca() : memref<2x2xf32>
+ // CHECK-NEXT: %[[tensor:.*]] = bufferization.to_tensor %[[alloca]] restrict writable : memref<2x2xf32>
+ // CHECK-NEXT: return %[[tensor]] : tensor<2x2xf32>
+ %0 = bufferization.alloc_tensor() : tensor<2x2xf32>
+ return %0 : tensor<2x2xf32>
}
diff --git a/mlir/test/Dialect/LLVM/transform-e2e.mlir b/mlir/test/Dialect/LLVM/transform-e2e.mlir
index 3e637a3ec49a42..c00b47fb936e97 100644
--- a/mlir/test/Dialect/LLVM/transform-e2e.mlir
+++ b/mlir/test/Dialect/LLVM/transform-e2e.mlir
@@ -1,17 +1,15 @@
-// RUN: mlir-opt %s --transform-interpreter="debug-payload-root-tag=payload" -test-transform-dialect-erase-schedule --test-lower-to-llvm --split-input-file | FileCheck %s
+// RUN: mlir-opt %s --transform-interpreter -test-transform-dialect-erase-schedule --test-lower-to-llvm --split-input-file | FileCheck %s
// CHECK-LABEL: llvm.func @matmul_tensors
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @matmul_tensors(
- %arg0: tensor<2x4xf32>, %arg1: tensor<4x6xf32>, %arg2: tensor<2x6xf32>)
- -> tensor<2x6xf32> {
- // CHECK-NOT: linalg
- // CHECK: llvm.intr.fmuladd{{.*}}
- %0 = linalg.matmul ins(%arg0, %arg1: tensor<2x4xf32>, tensor<4x6xf32>)
- outs(%arg2: tensor<2x6xf32>)
- -> tensor<2x6xf32>
- return %0 : tensor<2x6xf32>
- }
+func.func @matmul_tensors(
+ %arg0: tensor<2x4xf32>, %arg1: tensor<4x6xf32>, %arg2: tensor<2x6xf32>)
+ -> tensor<2x6xf32> {
+// CHECK-NOT: linalg
+// CHECK: llvm.intr.fmuladd{{.*}}
+ %0 = linalg.matmul ins(%arg0, %arg1: tensor<2x4xf32>, tensor<4x6xf32>)
+ outs(%arg2: tensor<2x6xf32>)
+ -> tensor<2x6xf32>
+ return %0 : tensor<2x6xf32>
}
module attributes {transform.with_named_sequence} {
diff --git a/mlir/test/Dialect/Linalg/matmul-shared-memory-padding.mlir b/mlir/test/Dialect/Linalg/matmul-shared-memory-padding.mlir
index 8a3bb1bc52dc5d..d6c400dcbf2b93 100644
--- a/mlir/test/Dialect/Linalg/matmul-shared-memory-padding.mlir
+++ b/mlir/test/Dialect/Linalg/matmul-shared-memory-padding.mlir
@@ -1,4 +1,4 @@
-// RUN: mlir-opt --split-input-file --transform-interpreter="debug-payload-root-tag=payload" %s | FileCheck %s
+// RUN: mlir-opt --split-input-file --transform-interpreter %s | FileCheck %s
// CHECK-LABEL: func @matmul_divisible
// CHECK: scf.forall
@@ -24,21 +24,19 @@
// CHECK: scf.forall
// CHECK: vector.transfer_read
// CHECK: vector.transfer_write
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @matmul_divisible(%A: tensor<1024x1024xf32>,
- %B: tensor<1024x1024xf32>,
- %C: tensor<1024x1024xf32>)
+func.func @matmul_divisible(%A: tensor<1024x1024xf32>,
+ %B: tensor<1024x1024xf32>,
+ %C: tensor<1024x1024xf32>)
+ -> tensor<1024x1024xf32>
+{
+ %cst = arith.constant 0.000000e+00 : f32
+ %0 = linalg.fill ins(%cst : f32)
+ outs(%C : tensor<1024x1024xf32>)
-> tensor<1024x1024xf32>
- {
- %cst = arith.constant 0.000000e+00 : f32
- %0 = linalg.fill ins(%cst : f32)
- outs(%C : tensor<1024x1024xf32>)
- -> tensor<1024x1024xf32>
- %1 = linalg.matmul ins(%A, %B : tensor<1024x1024xf32>, tensor<1024x1024xf32>)
- outs(%0 : tensor<1024x1024xf32>)
- -> tensor<1024x1024xf32>
- return %1 : tensor<1024x1024xf32>
- }
+ %1 = linalg.matmul ins(%A, %B : tensor<1024x1024xf32>, tensor<1024x1024xf32>)
+ outs(%0 : tensor<1024x1024xf32>)
+ -> tensor<1024x1024xf32>
+ return %1 : tensor<1024x1024xf32>
}
module attributes {transform.with_named_sequence} {
@@ -145,21 +143,19 @@ module attributes {transform.with_named_sequence} {
// CHECK: linalg.matmul
// CHECK: vector.transfer_read
// CHECK: vector.transfer_write
-module @payload attributes { transform.target_tag = "payload" } {
func.func @matmul_not_divisible(%A: tensor<1023x1023xf32>,
- %B: tensor<1023x1023xf32>,
- %C: tensor<1023x1023xf32>)
+ %B: tensor<1023x1023xf32>,
+ %C: tensor<1023x1023xf32>)
+ -> tensor<1023x1023xf32>
+{
+ %cst = arith.constant 0.000000e+00 : f32
+ %0 = linalg.fill ins(%cst : f32)
+ outs(%C : tensor<1023x1023xf32>)
-> tensor<1023x1023xf32>
- {
- %cst = arith.constant 0.000000e+00 : f32
- %0 = linalg.fill ins(%cst : f32)
- outs(%C : tensor<1023x1023xf32>)
- -> tensor<1023x1023xf32>
- %1 = linalg.matmul ins(%A, %B : tensor<1023x1023xf32>, tensor<1023x1023xf32>)
- outs(%0 : tensor<1023x1023xf32>)
- -> tensor<1023x1023xf32>
- return %1 : tensor<1023x1023xf32>
- }
+ %1 = linalg.matmul ins(%A, %B : tensor<1023x1023xf32>, tensor<1023x1023xf32>)
+ outs(%0 : tensor<1023x1023xf32>)
+ -> tensor<1023x1023xf32>
+ return %1 : tensor<1023x1023xf32>
}
module attributes {transform.with_named_sequence} {
diff --git a/mlir/test/Dialect/Linalg/pad-to-specific-memory-space.mlir b/mlir/test/Dialect/Linalg/pad-to-specific-memory-space.mlir
index 373ed5f0d7908a..9f52cf8aa862ab 100644
--- a/mlir/test/Dialect/Linalg/pad-to-specific-memory-space.mlir
+++ b/mlir/test/Dialect/Linalg/pad-to-specific-memory-space.mlir
@@ -1,5 +1,5 @@
-// RUN: mlir-opt --transform-interpreter="debug-payload-root-tag=payload" -cse -canonicalize -split-input-file -verify-diagnostics %s | FileCheck %s
+// RUN: mlir-opt --transform-interpreter -cse -canonicalize -split-input-file -verify-diagnostics %s | FileCheck %s
#map = affine_map<()[s0] -> (-s0 + 12, 7)>
@@ -7,45 +7,43 @@
// CHECK-SAME: %[[arg0:.*]]: memref<24x12xf32, strided<[?, ?], offset: ?>>,
// CHECK-SAME: %[[arg1:.*]]: memref<12x25xf32, strided<[?, ?], offset: ?>>,
// CHECK-SAME: %[[arg2:.*]]: memref<24x25xf32, strided<[?, ?], offset: ?>>,
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @pad_to_memory_space(%arg0: tensor<24x12xf32>,
- %arg1: tensor<12x25xf32>,
- %arg2: tensor<24x25xf32>,
- %iv0 : index, %iv1 : index,
- %iv2 : index) -> tensor<24x25xf32> {
- %0 = affine.min #map()[%iv2]
-
- // CHECK: %[[s0:.*]] = memref.subview %[[arg0]]
- %1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>
- // CHECK: %[[s1:.*]] = memref.subview %[[arg1]]
- %2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>
- // CHECK: %[[s2:.*]] = memref.subview %[[arg2]]
- %3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
-
- // CHECK: %[[alloc0:.*]] = memref.alloc() : memref<4x7xf32, 3>
- // CHECK: linalg.fill {{.*}} outs(%[[alloc0]]
- // CHECK: %[[alloc0_view:.*]] = memref.subview %[[alloc0]][0, 0] [4, %{{.*}}] [1, 1]
- // CHECK: memref.copy %[[s0]], %[[alloc0_view]]
-
- // CHECK: %[[alloc1:.*]] = memref.alloc() : memref<7x5xf32, 3>
- // CHECK: linalg.fill {{.*}} outs(%[[alloc1]]
- // CHECK: %[[alloc1_view:.*]] = memref.subview %[[alloc1]][0, 0] [%{{.*}}, 5] [1, 1]
- // CHECK: memref.copy %[[s1]], %[[alloc1_view]]
-
- // CHECK: %[[alloc2:.*]] = memref.alloc() : memref<4x5xf32, 3>
- // CHECK-NOT: linalg.fill {{.*}} outs(%[[alloc2]]
- // No subview because there is 0 padding
- // CHECK: memref.copy %[[s2]], %[[alloc2]]
-
- // CHECK: linalg.matmul ins(%[[alloc0]], %[[alloc1]] : {{.*}}) outs(%[[alloc2]] : {{.*}})
- // Copy back result.
- // CHECK: memref.copy %[[alloc2]], %[[s2]]
- %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
-
- // insert_slice bufferizes to a no-op.
- %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
- func.return %5 : tensor<24x25xf32>
- }
+func.func @pad_to_memory_space(%arg0: tensor<24x12xf32>,
+ %arg1: tensor<12x25xf32>,
+ %arg2: tensor<24x25xf32>,
+ %iv0 : index, %iv1 : index,
+ %iv2 : index) -> tensor<24x25xf32> {
+ %0 = affine.min #map()[%iv2]
+
+ // CHECK: %[[s0:.*]] = memref.subview %[[arg0]]
+ %1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>
+ // CHECK: %[[s1:.*]] = memref.subview %[[arg1]]
+ %2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>
+ // CHECK: %[[s2:.*]] = memref.subview %[[arg2]]
+ %3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
+
+ // CHECK: %[[alloc0:.*]] = memref.alloc() : memref<4x7xf32, 3>
+ // CHECK: linalg.fill {{.*}} outs(%[[alloc0]]
+ // CHECK: %[[alloc0_view:.*]] = memref.subview %[[alloc0]][0, 0] [4, %{{.*}}] [1, 1]
+ // CHECK: memref.copy %[[s0]], %[[alloc0_view]]
+
+ // CHECK: %[[alloc1:.*]] = memref.alloc() : memref<7x5xf32, 3>
+ // CHECK: linalg.fill {{.*}} outs(%[[alloc1]]
+ // CHECK: %[[alloc1_view:.*]] = memref.subview %[[alloc1]][0, 0] [%{{.*}}, 5] [1, 1]
+ // CHECK: memref.copy %[[s1]], %[[alloc1_view]]
+
+ // CHECK: %[[alloc2:.*]] = memref.alloc() : memref<4x5xf32, 3>
+ // CHECK-NOT: linalg.fill {{.*}} outs(%[[alloc2]]
+ // No subview because there is 0 padding
+ // CHECK: memref.copy %[[s2]], %[[alloc2]]
+
+ // CHECK: linalg.matmul ins(%[[alloc0]], %[[alloc1]] : {{.*}}) outs(%[[alloc2]] : {{.*}})
+ // Copy back result.
+ // CHECK: memref.copy %[[alloc2]], %[[s2]]
+ %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
+
+ // insert_slice bufferizes to a no-op.
+ %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
+ func.return %5 : tensor<24x25xf32>
}
module attributes {transform.with_named_sequence} {
@@ -71,42 +69,40 @@ module attributes {transform.with_named_sequence} {
// CHECK-SAME: %[[arg0:.*]]: memref<24x12xf32, strided<[?, ?], offset: ?>>,
// CHECK-SAME: %[[arg1:.*]]: memref<12x25xf32, strided<[?, ?], offset: ?>>,
// CHECK-SAME: %[[arg2:.*]]: memref<24x25xf32, strided<[?, ?], offset: ?>>,
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @vectorize_and_bufferize_pad(%arg0: tensor<24x12xf32>,
- %arg1: tensor<12x25xf32>,
- %arg2: tensor<24x25xf32>,
- %iv0 : index, %iv1 : index,
- %iv2 : index) -> tensor<24x25xf32> {
- %0 = affine.min #map()[%iv2]
-
- // CHECK: %[[s0:.*]] = memref.subview %[[arg0]]
- %1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>
- // CHECK: %[[s1:.*]] = memref.subview %[[arg1]]
- %2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>
- // CHECK: %[[s2:.*]] = memref.subview %[[arg2]]
- %3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
-
- // CHECK: %[[v0:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s0]]
- // CHECK: %[[alloc0:.*]] = memref.alloc() : memref<4x7xf32, 3>
- // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v0]], %[[alloc0]]
-
- // CHECK: %[[v1:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s1]]
- // CHECK: %[[alloc1:.*]] = memref.alloc() : memref<7x5xf32, 3>
- // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v1]], %[[alloc1]]
-
- // CHECK: %[[v2:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s2]]
- // CHECK: %[[alloc2:.*]] = memref.alloc() : memref<4x5xf32, 3>
- // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v2]], %[[alloc0]]
-
- // CHECK: linalg.matmul ins(%[[alloc0]], %[[alloc1]] : {{.*}}) outs(%[[alloc2]] : {{.*}})
- // Copy back result.
- // CHECK: memref.copy %[[alloc2]], %[[s2]]
- %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
-
- // insert_slice bufferizes to a no-op.
- %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
- func.return %5 : tensor<24x25xf32>
- }
+func.func @vectorize_and_bufferize_pad(%arg0: tensor<24x12xf32>,
+ %arg1: tensor<12x25xf32>,
+ %arg2: tensor<24x25xf32>,
+ %iv0 : index, %iv1 : index,
+ %iv2 : index) -> tensor<24x25xf32> {
+ %0 = affine.min #map()[%iv2]
+
+ // CHECK: %[[s0:.*]] = memref.subview %[[arg0]]
+ %1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>
+ // CHECK: %[[s1:.*]] = memref.subview %[[arg1]]
+ %2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>
+ // CHECK: %[[s2:.*]] = memref.subview %[[arg2]]
+ %3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
+
+ // CHECK: %[[v0:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s0]]
+ // CHECK: %[[alloc0:.*]] = memref.alloc() : memref<4x7xf32, 3>
+ // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v0]], %[[alloc0]]
+
+ // CHECK: %[[v1:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s1]]
+ // CHECK: %[[alloc1:.*]] = memref.alloc() : memref<7x5xf32, 3>
+ // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v1]], %[[alloc1]]
+
+ // CHECK: %[[v2:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s2]]
+ // CHECK: %[[alloc2:.*]] = memref.alloc() : memref<4x5xf32, 3>
+ // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v2]], %[[alloc0]]
+
+ // CHECK: linalg.matmul ins(%[[alloc0]], %[[alloc1]] : {{.*}}) outs(%[[alloc2]] : {{.*}})
+ // Copy back result.
+ // CHECK: memref.copy %[[alloc2]], %[[s2]]
+ %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
+
+ // insert_slice bufferizes to a no-op.
+ %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
+ func.return %5 : tensor<24x25xf32>
}
module attributes {transform.with_named_sequence} {
diff --git a/mlir/test/Dialect/Vector/transform-vector.mlir b/mlir/test/Dialect/Vector/transform-vector.mlir
index 0439844dc66cad..4b38db79bff3e1 100644
--- a/mlir/test/Dialect/Vector/transform-vector.mlir
+++ b/mlir/test/Dialect/Vector/transform-vector.mlir
@@ -1,18 +1,16 @@
-// RUN: mlir-opt --transform-interpreter="debug-payload-root-tag=payload" %s --split-input-file | FileCheck %s
+// RUN: mlir-opt %s --transform-interpreter --split-input-file | FileCheck %s
// CHECK-LABEL: func @matmul_tensors
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @matmul_tensors(
- %arg0: tensor<8x16xf32>, %arg1: tensor<16x32xf32>, %arg2: tensor<8x32xf32>)
- -> tensor<8x32xf32> {
- // CHECK-NOT: linalg
- // CHECK: vector.extract {{.*}} : vector<4xf32> from vector<8x4xf32>
- // CHECK: vector.store {{.*}} : memref<8x32xf32>, vector<4xf32>
- %0 = linalg.matmul ins(%arg0, %arg1: tensor<8x16xf32>, tensor<16x32xf32>)
- outs(%arg2: tensor<8x32xf32>)
- -> tensor<8x32xf32>
- return %0 : tensor<8x32xf32>
- }
+func.func @matmul_tensors(
+ %arg0: tensor<8x16xf32>, %arg1: tensor<16x32xf32>, %arg2: tensor<8x32xf32>)
+ -> tensor<8x32xf32> {
+// CHECK-NOT: linalg
+// CHECK: vector.extract {{.*}} : vector<4xf32> from vector<8x4xf32>
+// CHECK: vector.store {{.*}} : memref<8x32xf32>, vector<4xf32>
+ %0 = linalg.matmul ins(%arg0, %arg1: tensor<8x16xf32>, tensor<16x32xf32>)
+ outs(%arg2: tensor<8x32xf32>)
+ -> tensor<8x32xf32>
+ return %0 : tensor<8x32xf32>
}
module attributes {transform.with_named_sequence} {
@@ -78,13 +76,11 @@ module attributes {transform.with_named_sequence} {
// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>}
// CHECK-SAME: %[[ARG0]], %[[ARG1]], %[[ARG2]] : vector<64x64xf16>, vector<64x64xf16> into vector<64x64xf32>
// CHECK-NEXT: return %[[R]] : vector<64x64xf32>
-module @payload attributes { transform.target_tag = "payload" } {
- func.func @fold_arith_extf_into_contract(%arg0: vector<64x64xf16>, %arg1: vector<64x64xf16>, %arg2: vector<64x64xf32>) -> vector<64x64xf32> {
- %lhs_f32 = arith.extf %arg0 : vector<64x64xf16> to vector<64x64xf32>
- %rhs_f32 = arith.extf %arg1 : vector<64x64xf16> to vector<64x64xf32>
- %result = vector.contract {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>} %lhs_f32, %rhs_f32, %arg2 : vector<64x64xf32>, vector<64x64xf32> into vector<64x64xf32>
- return %result : vector<64x64xf32>
- }
+func.func @fold_arith_extf_into_contract(%arg0: vector<64x64xf16>, %arg1: vector<64x64xf16>, %arg2: vector<64x64xf32>) -> vector<64x64xf32> {
+ %lhs_f32 = arith.extf %arg0 : vector<64x64xf16> to vector<64x64xf32>
+ %rhs_f32 = arith.extf %arg1 : vector<64x64xf16> to vector<64x64xf32>
+ %result = vector.contract {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>} %lhs_f32, %rhs_f32, %arg2 : vector<64x64xf32>, vector<64x64xf32> into vector<64x64xf32>
+ return %result : vector<64x64xf32>
}
module attributes {transform.with_named_sequence} {
@@ -99,32 +95,30 @@ module attributes {transform.with_named_sequence} {
// -----
-module @payload attributes { transform.target_tag = "payload" } {
- // CHECK-LABEL: func.func @arith_to_outerproduct_scalable_i32
- // CHECK-SAME: %[[LHS:.*]]: vector<[4]xi32>,
- // CHECK-SAME: %[[RHS:.*]]: vector<[4]xi32>) -> vector<[4]x[4]xi32> {
- // CHECK: %[[RES:.*]] = vector.outerproduct %[[LHS]], %[[RHS]] : vector<[4]xi32>, vector<[4]xi32>
- // CHECK: return %[[RES]] : vector<[4]x[4]xi32>
- func.func @arith_to_outerproduct_scalable_i32(%lhs: vector<[4]xi32>, %rhs: vector<[4]xi32>) -> vector<[4]x[4]xi32> {
- %lhsBcast = vector.broadcast %lhs : vector<[4]xi32> to vector<[4]x[4]xi32>
- %lhsT = vector.transpose %lhsBcast, [1, 0] : vector<[4]x[4]xi32> to vector<[4]x[4]xi32>
- %rhsBcast = vector.broadcast %rhs : vector<[4]xi32> to vector<[4]x[4]xi32>
- %mul = arith.muli %lhsT, %rhsBcast : vector<[4]x[4]xi32>
- return %mul: vector<[4]x[4]xi32>
- }
+// CHECK-LABEL: func.func @arith_to_outerproduct_scalable_i32
+// CHECK-SAME: %[[LHS:.*]]: vector<[4]xi32>,
+// CHECK-SAME: %[[RHS:.*]]: vector<[4]xi32>) -> vector<[4]x[4]xi32> {
+// CHECK: %[[RES:.*]] = vector.outerproduct %[[LHS]], %[[RHS]] : vector<[4]xi32>, vector<[4]xi32>
+// CHECK: return %[[RES]] : vector<[4]x[4]xi32>
+func.func @arith_to_outerproduct_scalable_i32(%lhs: vector<[4]xi32>, %rhs: vector<[4]xi32>) -> vector<[4]x[4]xi32> {
+ %lhsBcast = vector.broadcast %lhs : vector<[4]xi32> to vector<[4]x[4]xi32>
+ %lhsT = vector.transpose %lhsBcast, [1, 0] : vector<[4]x[4]xi32> to vector<[4]x[4]xi32>
+ %rhsBcast = vector.broadcast %rhs : vector<[4]xi32> to vector<[4]x[4]xi32>
+ %mul = arith.muli %lhsT, %rhsBcast : vector<[4]x[4]xi32>
+ return %mul: vector<[4]x[4]xi32>
+}
- // CHECK-LABEL: func.func @arith_to_outerproduct_trans_rhs_f32
- // CHECK-SAME: %[[LHS:.*]]: vector<16xf32>,
- // CHECK-SAME: %[[RHS:.*]]: vector<8xf32>) -> vector<8x16xf32> {
- // CHECK: %[[RES:.*]] = vector.outerproduct %[[RHS]], %[[LHS]] : vector<8xf32>, vector<16xf32>
- // CHECK: return %[[RES]] : vector<8x16xf32>
- func.func @arith_to_outerproduct_trans_rhs_f32(%lhs: vector<16xf32>, %rhs: vector<8xf32>) -> vector<8x16xf32> {
- %rhsBcast = vector.broadcast %rhs : vector<8xf32> to vector<16x8xf32>
- %rhsT = vector.transpose %rhsBcast, [1, 0] : vector<16x8xf32> to vector<8x16xf32>
- %lhsBcast = vector.broadcast %lhs : vector<16xf32> to vector<8x16xf32>
- %mul = arith.mulf %lhsBcast, %rhsT : vector<8x16xf32>
- return %mul: vector<8x16xf32>
- }
+// CHECK-LABEL: func.func @arith_to_outerproduct_trans_rhs_f32
+// CHECK-SAME: %[[LHS:.*]]: vector<16xf32>,
+// CHECK-SAME: %[[RHS:.*]]: vector<8xf32>) -> vector<8x16xf32> {
+// CHECK: %[[RES:.*]] = vector.outerproduct %[[RHS]], %[[LHS]] : vector<8xf32>, vector<16xf32>
+// CHECK: return %[[RES]] : vector<8x16xf32>
+func.func @arith_to_outerproduct_trans_rhs_f32(%lhs: vector<16xf32>, %rhs: vector<8xf32>) -> vector<8x16xf32> {
+ %rhsBcast = vector.broadcast %rhs : vector<8xf32> to vector<16x8xf32>
+ %rhsT = vector.transpose %rhsBcast, [1, 0] : vector<16x8xf32> to vector<8x16xf32>
+ %lhsBcast = vector.broadcast %lhs : vector<16xf32> to vector<8x16xf32>
+ %mul = arith.mulf %lhsBcast, %rhsT : vector<8x16xf32>
+ return %mul: vector<8x16xf32>
}
module attributes {transform.with_named_sequence} {
diff --git a/mlir/test/Examples/transform/ChH/full.mlir b/mlir/test/Examples/transform/ChH/full.mlir
index 85dbf670233232..259475ebdbf49e 100644
--- a/mlir/test/Examples/transform/ChH/full.mlir
+++ b/mlir/test/Examples/transform/ChH/full.mlir
@@ -1,6 +1,8 @@
-// RUN: mlir-opt %s --transform-interpreter="debug-payload-root-tag=payload" \
-// RUN: --test-transform-dialect-erase-schedule |\
-// RUN: mlir-opt -pass-pipeline='builtin.module(builtin.module(math-uplift-to-fma,convert-bufferization-to-memref,test-lower-to-llvm))' - |\
+// RUN: mlir-opt %s --transform-interpreter \
+// RUN: --test-transform-dialect-erase-schedule \
+// RUN: --math-uplift-to-fma \
+// RUN: --convert-bufferization-to-memref \
+// RUN: --test-lower-to-llvm |\
// RUN: FileCheck %s
// Fixed-size tensor types to be used in convolution.
@@ -17,7 +19,6 @@
// tensors annotated with attributes from the `bufferization` dialect. These
// attributes hint the bufferization pass to assume buffers can be directly
// used for these tensors without reshaping.
-module @payload attributes { transform.target_tag = "payload" } {
func.func @conv(
%input: !tinput {bufferization.writable = false,
bufferization.access = "read",
@@ -83,7 +84,7 @@ func.func @conv(
return %relued : !toutput
}
-}
+
// Module containing the transformation script to be applied. The attribute
// is required to correctly verify the use of named (macro-like) sequences.
module attributes { transform.with_named_sequence } {
>From 2994363fa4298c975c2fa545bfccae599e1a98fb Mon Sep 17 00:00:00 2001
From: Andrzej Warzynski <andrzej.warzynski at arm.com>
Date: Sun, 20 Oct 2024 16:51:34 -0700
Subject: [PATCH 2/3] [mlir][tensor] Restrict the verifier for
tensor.pack/tensor.unpack
Restricts the verifier for tensor.pack and tensor.unpack Ops so that the
following is no longer allowed:
```mlir
%c8 = arith.constant 8 : index
%0 = tensor.pack %input inner_dims_pos = [0, 1] inner_tiles = [8, %c8] into %output : tensor<?x?xf32> -> tensor<?x?x8x8xf32>
```
Specifically, in line with other Tensor Ops, require:
* a dynamic dimensions for each (dynamic) SSA value,
* a static dimension for each static size (attribute).
In the example above, a static dimension (8) is mixed with a dynamic
size (%c8).
Note that this is mostly deleting existing code - that's because this
change simplifies the logic in verifier.
For more context:
* https://discourse.llvm.org/t/tensor-ops-with-dynamic-sizes-which-behaviour-is-more-correct
---
mlir/lib/Dialect/Tensor/IR/TensorOps.cpp | 21 +++++--------
mlir/test/Dialect/Tensor/invalid.mlir | 38 ++++++++++++++++++++++++
2 files changed, 45 insertions(+), 14 deletions(-)
diff --git a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
index 4d6c5965c4fcc3..60a04152848d88 100644
--- a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
+++ b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
@@ -3865,22 +3865,15 @@ static LogicalResult commonVerifierPackAndUnPackOp(OpTy packOrUnPack) {
llvm::zip(packedType.getShape().take_back(mixedTiles.size()),
mixedTiles),
[](std::tuple<int64_t, OpFoldResult> it) {
- std::optional<int64_t> constTileSize =
- getConstantIntValue(std::get<1>(it));
int64_t shape = std::get<0>(it);
- if (!constTileSize) {
- // If specified tile size is dynamic, output shape should
- // be dynamic too.
- return ShapedType::isDynamic(shape);
+ if (Attribute attr =
+ llvm::dyn_cast_if_present<Attribute>(std::get<1>(it))) {
+ if (IntegerAttr intAttr = dyn_cast_or_null<IntegerAttr>(attr)) {
+ int64_t staticTileSize = intAttr.getValue().getSExtValue();
+ return shape == staticTileSize;
+ }
}
- if (ShapedType::isDynamic(shape)) {
- // For the shape being dynamic when tile size is
- // specified, return true. In canonical form a constant
- // tile size should lead to constant shape of the tiled
- // dimension, but not needed for verification.
- return true;
- }
- return shape == constTileSize.value();
+ return ShapedType::isDynamic(shape);
})) {
return op->emitError("mismatch in inner tile sizes specified and shaped of "
"tiled dimension in the packed type");
diff --git a/mlir/test/Dialect/Tensor/invalid.mlir b/mlir/test/Dialect/Tensor/invalid.mlir
index 921d7f9f1fefc3..be470ce2af9b31 100644
--- a/mlir/test/Dialect/Tensor/invalid.mlir
+++ b/mlir/test/Dialect/Tensor/invalid.mlir
@@ -755,9 +755,47 @@ func.func @pack_mismatch_inner_tile_size_and_output_shape(
// -----
+func.func @pack_dynamic_inner_tile_size_and_static_output_shape(
+ %input : tensor<?x?xf32>, %output : tensor<?x?x8x8xf32>) -> tensor<?x?x8x8xf32> {
+ %c8 = arith.constant 8 : index
+ // expected-error at +1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}}
+ %0 = tensor.pack %input inner_dims_pos = [0, 1] inner_tiles = [8, %c8] into %output : tensor<?x?xf32> -> tensor<?x?x8x8xf32>
+ return %0 : tensor<?x?x8x8xf32>
+}
+
+// -----
+
+func.func @pack_static_inner_tile_size_and_dynamic_output_shape(
+ %input : tensor<?x?xf32>, %output : tensor<?x?x8x?xf32>) -> tensor<?x?x8x?xf32> {
+ // expected-error at +1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}}
+ %0 = tensor.pack %input inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %output : tensor<?x?xf32> -> tensor<?x?x8x?xf32>
+ return %0 : tensor<?x?x8x?xf32>
+}
+
+// -----
+
func.func @unpack_mismatch_inner_tile_size_and_output_shape(
%input : tensor<?x?x8x8xf32>, %output : tensor<?x?xf32>) -> tensor<?x?xf32> {
// expected-error at +1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}}
%0 = tensor.unpack %input inner_dims_pos = [0, 1] inner_tiles = [8, 4] into %output : tensor<?x?x8x8xf32> -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
+
+// -----
+
+func.func @unpack_dynamic_inner_tile_size_and_static_output_shape(
+ %input : tensor<?x?x8x4xf32>, %output : tensor<?x?xf32>) -> tensor<?x?xf32> {
+ %c8 = arith.constant 8 : index
+ // expected-error at +1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}}
+ %0 = tensor.unpack %input inner_dims_pos = [0, 1] inner_tiles = [%c8, 4] into %output : tensor<?x?x8x4xf32> -> tensor<?x?xf32>
+ return %0 : tensor<?x?xf32>
+}
+
+// -----
+
+func.func @unpack_static_inner_tile_size_and_dynamic_output_shape(
+ %input : tensor<?x?x?x4xf32>, %output : tensor<?x?xf32>) -> tensor<?x?xf32> {
+ // expected-error at +1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}}
+ %0 = tensor.unpack %input inner_dims_pos = [0, 1] inner_tiles = [8, 4] into %output : tensor<?x?x?x4xf32> -> tensor<?x?xf32>
+ return %0 : tensor<?x?xf32>
+}
>From 2617fda75ca406ba6faa6a4eabee8899d3a258b1 Mon Sep 17 00:00:00 2001
From: Andrzej Warzynski <andrzej.warzynski at arm.com>
Date: Sun, 20 Oct 2024 21:16:07 -0700
Subject: [PATCH 3/3] fixup! [mlir][tensor] Restrict the verifier for
tensor.pack/tensor.unpack
Update tests
---
.../Dialect/Linalg/transform-lower-pack.mlir | 8 ++++----
mlir/test/Dialect/Tensor/fold-empty-op.mlir | 20 +++++++++----------
2 files changed, 13 insertions(+), 15 deletions(-)
diff --git a/mlir/test/Dialect/Linalg/transform-lower-pack.mlir b/mlir/test/Dialect/Linalg/transform-lower-pack.mlir
index 48bf1c151de8f5..7aadf190695630 100644
--- a/mlir/test/Dialect/Linalg/transform-lower-pack.mlir
+++ b/mlir/test/Dialect/Linalg/transform-lower-pack.mlir
@@ -586,7 +586,7 @@ module attributes {transform.with_named_sequence} {
// Check that we can lower unpack "as unpad" with dynamic dims.
// CHECK-LABEL: func.func @unpack_as_pad_dynamic(
-// CHECK-SAME: %[[ARG0:.*]]: tensor<1x1x1x1x?x?x?x?xf32>, %[[ARG1:.*]]: tensor<?x?x?x?xf32>
+// CHECK-SAME: %[[ARG0:.*]]: tensor<1x1x1x1x136x64x16x16xf32>, %[[ARG1:.*]]: tensor<?x?x?x?xf32>
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
@@ -602,10 +602,10 @@ module attributes {transform.with_named_sequence} {
// CHECK-SAME: [1, 1, 1, 1, %[[DIM0]], %[[DIM1]], %[[DIM2]], %[[DIM3]]]
// strides multiplers.
// CHECK-SAME: [1, 1, 1, 1, 1, 1, 1, 1]
-// CHECK-SAME: : tensor<1x1x1x1x?x?x?x?xf32> to tensor<?x?x?x?xf32>
-func.func @unpack_as_pad_dynamic(%arg0: tensor<1x1x1x1x?x?x?x?xf32>, %arg1: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
+// CHECK-SAME: : tensor<1x1x1x1x136x64x16x16xf32> to tensor<?x?x?x?xf32>
+func.func @unpack_as_pad_dynamic(%arg0: tensor<1x1x1x1x136x64x16x16xf32>, %arg1: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
%pack = tensor.unpack %arg0 inner_dims_pos = [0, 1, 2, 3] inner_tiles = [136, 64, 16, 16] into %arg1
- : tensor<1x1x1x1x?x?x?x?xf32> -> tensor<?x?x?x?xf32>
+ : tensor<1x1x1x1x136x64x16x16xf32> -> tensor<?x?x?x?xf32>
return %pack : tensor<?x?x?x?xf32>
}
diff --git a/mlir/test/Dialect/Tensor/fold-empty-op.mlir b/mlir/test/Dialect/Tensor/fold-empty-op.mlir
index 5beb8c250aa105..65ceb4ff3e3df4 100644
--- a/mlir/test/Dialect/Tensor/fold-empty-op.mlir
+++ b/mlir/test/Dialect/Tensor/fold-empty-op.mlir
@@ -77,20 +77,20 @@ func.func @pack_empty(%arg0: tensor<8x8x32x32xf32>) -> tensor<8x8x32x32xf32> {
// CHECK-NOT: tensor.pack
// CHECK: return %[[T]] : tensor<8x8x32x32xf32>
-func.func @pack_empty_dynamic(%arg0: tensor<?x?x?x?xf32>, %dim0: index, %dim1: index) -> tensor<?x?x?x?xf32> {
+func.func @pack_empty_dynamic(%arg0: tensor<?x?x32x32xf32>, %dim0: index, %dim1: index) -> tensor<?x?x32x32xf32> {
%empty_unpacked = tensor.empty(%dim0, %dim1) : tensor<?x?xf32>
%packed = tensor.pack %empty_unpacked
inner_dims_pos = [0, 1] inner_tiles = [32, 32]
- into %arg0 : tensor<?x?xf32> -> tensor<?x?x?x?xf32>
- return %packed : tensor<?x?x?x?xf32>
+ into %arg0 : tensor<?x?xf32> -> tensor<?x?x32x32xf32>
+ return %packed : tensor<?x?x32x32xf32>
}
// CHECK-LABEL: func.func @pack_empty_dynamic(
-// CHECK-SAME: %[[T:.+]]: tensor<?x?x?x?xf32>,
+// CHECK-SAME: %[[T:.+]]: tensor<?x?x32x32xf32>,
// CHECK-SAME: %[[DIM0:[a-zA-Z0-9_]+]]: index,
// CHECK-SAME: %[[DIM1:[a-zA-Z0-9_]+]]: index
// CHECK-NOT: tensor.pack
-// CHECK: return %[[T]] : tensor<?x?x?x?xf32>
+// CHECK: return %[[T]] : tensor<?x?x32x32xf32>
func.func @unpack_empty(%arg0: tensor<256x256xf32>) -> tensor<256x256xf32> {
%empty_packed = tensor.empty() : tensor<8x8x32x32xf32>
@@ -105,20 +105,18 @@ func.func @unpack_empty(%arg0: tensor<256x256xf32>) -> tensor<256x256xf32> {
// CHECK-NOT: tensor.unpack
// CHECK: return %[[T]] : tensor<256x256xf32>
-func.func @unpack_empty_dynamic(%arg0: tensor<?x?xf32>, %dim0: index, %dim1: index, %dim2: index, %dim3: index) -> tensor<?x?xf32> {
- %empty_packed = tensor.empty(%dim0, %dim1, %dim2, %dim3) : tensor<?x?x?x?xf32>
+func.func @unpack_empty_dynamic(%arg0: tensor<?x?xf32>, %dim0: index, %dim1: index) -> tensor<?x?xf32> {
+ %empty_packed = tensor.empty(%dim0, %dim1) : tensor<?x?x32x32xf32>
%unpacked = tensor.unpack %empty_packed
inner_dims_pos = [0, 1] inner_tiles = [32, 32]
- into %arg0 : tensor<?x?x?x?xf32> -> tensor<?x?xf32>
+ into %arg0 : tensor<?x?x32x32xf32> -> tensor<?x?xf32>
return %unpacked : tensor<?x?xf32>
}
// CHECK-LABEL: func.func @unpack_empty_dynamic(
// CHECK-SAME: %[[T:.+]]: tensor<?x?xf32>,
// CHECK-SAME: %[[DIM0:[a-zA-Z0-9_]+]]: index,
-// CHECK-SAME: %[[DIM1:[a-zA-Z0-9_]+]]: index,
-// CHECK-SAME: %[[DIM2:[a-zA-Z0-9_]+]]: index,
-// CHECK-SAME: %[[DIM3:[a-zA-Z0-9_]+]]: index
+// CHECK-SAME: %[[DIM1:[a-zA-Z0-9_]+]]: index
// CHECK-NOT: tensor.unpack
// CHECK: return %[[T]] : tensor<?x?xf32>
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