maxjhandsome
/
llvm-project
forked from OSchip/llvm-project
1
0
Fork 0
Code Issues Pull Requests Packages Releases Wiki Activity

Revert "[mlir] add an option to print op stats in JSON" 

There is a failure from the python pass manager.

This reverts commit 1a19abf38c.
This commit is contained in:
Okwan Kwon 2022-06-14 14:06:54 -07:00
parent c67c4133ac
commit 28331c6097
4 changed files with 10 additions and 77 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
mlir/include/mlir/Transforms/Passes.td
View File

@ -145,10 +145,6 @@ def LoopInvariantCodeMotion : Pass<"loop-invariant-code-motion"> {
def PrintOpStats : Pass<"print-op-stats"> {
let summary = "Print statistics of operations";
let constructor = "mlir::createPrintOpStatsPass()";
let options = [
Option<"printAsJSON", "json", "bool", /*default=*/"false",
"print the stats as JSON">
];
}
def SCCP : Pass<"sccp"> {

28
mlir/lib/Transforms/OpStats.cpp
View File

@ -27,9 +27,6 @@ struct PrintOpStatsPass : public PrintOpStatsBase<PrintOpStatsPass> {
// Print summary of op stats.
void printSummary();
// Print symmary of op stats in JSON.
void printSummaryInJSON();
private:
llvm::StringMap<int64_t> opCount;
raw_ostream &os;
@ -40,12 +37,8 @@ void PrintOpStatsPass::runOnOperation() {
opCount.clear();
// Compute the operation statistics for the currently visited operation.
getOperation()->walk(
[&](Operation *op) { ++opCount[op->getName().getStringRef()]; });
if (printAsJSON) {
printSummaryInJSON();
} else
printSummary();
getOperation()->walk([&](Operation *op) { ++opCount[op->getName().getStringRef()]; });
printSummary();
}
void PrintOpStatsPass::printSummary() {
@ -87,23 +80,6 @@ void PrintOpStatsPass::printSummary() {
}
}
void PrintOpStatsPass::printSummaryInJSON() {
SmallVector<StringRef, 64> sorted(opCount.keys());
llvm::sort(sorted);
os << "{\n";
for (unsigned i = 0, e = sorted.size(); i != e; ++i) {
const auto &key = sorted[i];
os << " \"" << key << "\" : " << opCount[key];
if (i != e - 1)
os << ",\n";
else
os << "\n";
}
os << "}\n";
}
std::unique_ptr<Pass> mlir::createPrintOpStatsPass(raw_ostream &os) {
return std::make_unique<PrintOpStatsPass>(os);
}

18
mlir/test/CAPI/pass.c
View File

@ -138,14 +138,14 @@ void testPrintPassPipeline() {
mlirOpPassManagerAddOwnedPass(nestedFuncPm, printOpStatPass);
// Print the top level pass manager
// CHECK: Top-level: builtin.module(func.func(print-op-stats{json=false}))
// CHECK: Top-level: builtin.module(func.func(print-op-stats))
fprintf(stderr, "Top-level: ");
mlirPrintPassPipeline(mlirPassManagerGetAsOpPassManager(pm), printToStderr,
NULL);
fprintf(stderr, "\n");
// Print the pipeline nested one level down
// CHECK: Nested Module: func.func(print-op-stats{json=false})
// CHECK: Nested Module: func.func(print-op-stats)
fprintf(stderr, "Nested Module: ");
mlirPrintPassPipeline(nestedModulePm, printToStderr, NULL);
fprintf(stderr, "\n");
@ -166,9 +166,8 @@ void testParsePassPipeline() {
// Try parse a pipeline.
MlirLogicalResult status = mlirParsePassPipeline(
mlirPassManagerGetAsOpPassManager(pm),
mlirStringRefCreateFromCString(
"builtin.module(func.func(print-op-stats{json=false}),"
" func.func(print-op-stats{json=false}))"));
mlirStringRefCreateFromCString("builtin.module(func.func(print-op-stats),"
" func.func(print-op-stats))"));
// Expect a failure, we haven't registered the print-op-stats pass yet.
if (mlirLogicalResultIsSuccess(status)) {
fprintf(
@ -180,9 +179,8 @@ void testParsePassPipeline() {
mlirRegisterTransformsPrintOpStats();
status = mlirParsePassPipeline(
mlirPassManagerGetAsOpPassManager(pm),
mlirStringRefCreateFromCString(
"builtin.module(func.func(print-op-stats{json=false}),"
" func.func(print-op-stats{json=false}))"));
mlirStringRefCreateFromCString("builtin.module(func.func(print-op-stats),"
" func.func(print-op-stats))"));
// Expect a failure, we haven't registered the print-op-stats pass yet.
if (mlirLogicalResultIsFailure(status)) {
fprintf(stderr,
@ -190,8 +188,8 @@ void testParsePassPipeline() {
exit(EXIT_FAILURE);
}
// CHECK: Round-trip: builtin.module(func.func(print-op-stats{json=false}),
// func.func(print-op-stats{json=false}))
// CHECK: Round-trip: builtin.module(func.func(print-op-stats),
// func.func(print-op-stats))
fprintf(stderr, "Round-trip: ");
mlirPrintPassPipeline(mlirPassManagerGetAsOpPassManager(pm), printToStderr,
NULL);

37
mlir/test/IR/op-stats-json.mlir
View File

@ -1,37 +0,0 @@
// RUN: mlir-opt -allow-unregistered-dialect -print-op-stats=json %s -o=/dev/null 2>&1 | FileCheck %s
func.func @main(tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> {
^bb0(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>):
%0 = arith.addf %arg0, %arg1 : tensor<4xf32>
%1 = arith.addf %arg0, %arg1 : tensor<4xf32>
%2 = arith.addf %arg0, %arg1 : tensor<4xf32>
%3 = arith.addf %arg0, %arg1 : tensor<4xf32>
%4 = arith.addf %arg0, %arg1 : tensor<4xf32>
%5 = arith.addf %arg0, %arg1 : tensor<4xf32>
%10 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%11 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%12 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%13 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%14 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%15 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%16 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%17 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%18 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%19 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%20 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%21 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%22 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%23 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%24 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%25 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%26 = "xla.add"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
%30 = "long_op_name"(%0, %arg1) : (tensor<4xf32>,tensor<4xf32>)-> tensor<4xf32>
return %1 : tensor<4xf32>
}
// CHECK: {
// CHECK: "arith.addf" : 6,
// CHECK: "func.return" : 1,
// CHECK: "long_op_name" : 1,
// CHECK: "xla.add" : 17
// CHECK: }