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Update traits (NoSideEffect)

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This commit is contained in:
Yan Da
2022-04-27 19:41:07 +08:00
parent 8dfe78f6cf
commit edca91bf8f
6 changed files with 17 additions and 272 deletions
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4
include/triton/Dialect/Triton/IR/Dialect.h
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@@ -9,8 +9,8 @@
#include "mlir/Dialect/SCF/SCF.h"
#include "triton/Dialect/Triton/IR/Traits.h"
#include "triton/Dialect/Triton/Dialect.h.inc"
#include "triton/Dialect/Triton/OpsEnums.h.inc"
#include "triton/Dialect/Triton/IR/Dialect.h.inc"
#include "triton/Dialect/Triton/IR/OpsEnums.h.inc"
#define GET_OP_CLASSES
#include "triton/Dialect/Triton/IR/Ops.h.inc"

6
include/triton/Dialect/Triton/IR/TritonOps.td
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@@ -198,7 +198,7 @@ def TT_GEPOp : TT_Op<"getelementptr",
//
// Shape Manipulation Ops
//
def TT_ReshapeOp : TT_Op<"reshape", [SameOperandsAndResultElementType]> {
def TT_ReshapeOp : TT_Op<"reshape", [NoSideEffect, SameOperandsAndResultElementType]> {
let summary = "reshape";
let arguments = (ins TT_Tensor:$src);
@@ -208,7 +208,7 @@ def TT_ReshapeOp : TT_Op<"reshape", [SameOperandsAndResultElementType]> {
let assemblyFormat = "$src attr-dict `:` functional-type(operands, results)";
}
def TT_BroadcastOp : TT_Op<"broadcast", [SameOperandsAndResultElementType]> {
def TT_BroadcastOp : TT_Op<"broadcast", [NoSideEffect, SameOperandsAndResultElementType]> {
let summary = "broadcast";
let arguments = (ins TT_Type:$src);
@@ -220,7 +220,7 @@ def TT_BroadcastOp : TT_Op<"broadcast", [SameOperandsAndResultElementType]> {
let hasFolder = 1;
}
def TT_CatOp : TT_Op<"cat", [SameOperandsAndResultElementType]> {
def TT_CatOp : TT_Op<"cat", [NoSideEffect, SameOperandsAndResultElementType]> {
let summary = "concatenate 2 tensors";
let arguments = (ins TT_Tensor:$lhs, TT_Tensor:$rhs);

12
include/triton/transforms/Passes.h
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@@ -1,12 +0,0 @@
#ifndef TRITON_TRANSFORMS_PASSES_H_
#define TRITON_TRANSFORMS_PASSES_H_
#include "mlir/Pass/Pass.h"
std::unique_ptr<Pass> createCombineOpsPass();
// // Registration
// #define GEN_PASS_REGISTRATION
// #include
#endif // TRITON_TRANSFORMS_PASSES_H_

4
lib/Dialect/Triton/IR/Dialect.cpp
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@@ -8,7 +8,7 @@
#include "mlir/IR/DialectImplementation.h"
#include "triton/Dialect/Triton/Dialect.cpp.inc"
#include "triton/Dialect/Triton/IR/Dialect.cpp.inc"
using namespace mlir;
using namespace mlir::triton;
@@ -18,7 +18,7 @@ void TritonDialect::initialize() {
addOperations<
#define GET_OP_LIST
#include "triton/Dialect/Triton/Ops.cpp.inc"
#include "triton/Dialect/Triton/IR/Ops.cpp.inc"
>();
// We can also add interface here.

4
lib/Dialect/Triton/IR/Ops.cpp
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@@ -38,10 +38,10 @@ static Type getPointerTypeFromTensor(Type type) {
}
#define GET_OP_CLASSES
#include "triton/Dialect/Triton/Ops.cpp.inc"
#include "triton/Dialect/Triton/IR/Ops.cpp.inc"
// enum attribute definitions
#include "triton/Dialect/Triton/OpsEnums.cpp.inc"
#include "triton/Dialect/Triton/IR/OpsEnums.cpp.inc"
namespace mlir {
namespace triton {

259
rewrite-test/jit/matmul/matmul.mlir
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@@ -1,251 +1,8 @@
module {
func @matmul_kernel__Pfp16_Pfp16_Pfp16_i32_i32_i32_i32_i32_i32__7c1_9c1_11c1_12c64_13c64_14c32_15c8(%arg0: !tt.ptr<f16>, %arg1: !tt.ptr<f16>, %arg2: !tt.ptr<f16>, %arg3: i32, %arg4: i32, %arg5: i32, %arg6: i32, %arg7: i32, %arg8: i32) {
%0 = tt.get_program_id {axis = 0 : i32} : i32
%1 = call @"cdiv__i32__1cconstexpr[64]"(%arg3) : (i32) -> i32
%2 = call @"cdiv__i32__1cconstexpr[64]"(%arg4) : (i32) -> i32
%c8_i32 = arith.constant 8 : i32
%3 = arith.muli %2, %c8_i32 : i32
%4 = arith.divsi %0, %3 : i32
%c8_i32_0 = arith.constant 8 : i32
%5 = arith.muli %4, %c8_i32_0 : i32
%6 = arith.subi %1, %5 : i32
%7 = call @"minimum__i32__1cconstexpr[8]"(%6) : (i32) -> i32
%8 = arith.remsi %0, %7 : i32
%9 = arith.addi %5, %8 : i32
%10 = arith.remsi %0, %3 : i32
%11 = arith.divsi %10, %7 : i32
%c64_i32 = arith.constant 64 : i32
%12 = arith.muli %9, %c64_i32 : i32
%13 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
%14 = tt.broadcast %12 : (i32) -> tensor<64xi32>
%15 = arith.addi %14, %13 : tensor<64xi32>
%c64_i32_1 = arith.constant 64 : i32
%16 = arith.muli %11, %c64_i32_1 : i32
%17 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
%18 = tt.broadcast %16 : (i32) -> tensor<64xi32>
%19 = arith.addi %18, %17 : tensor<64xi32>
%20 = tt.make_range {end = 32 : i32, start = 0 : i32} : tensor<32xi32>
%21 = tt.reshape %15 : (tensor<64xi32>) -> tensor<64x1xi32>
%22 = tt.broadcast %arg6 : (i32) -> tensor<64x1xi32>
%23 = arith.muli %21, %22 : tensor<64x1xi32>
%24 = tt.reshape %20 : (tensor<32xi32>) -> tensor<1x32xi32>
%c1_i32 = arith.constant 1 : i32
%25 = tt.broadcast %c1_i32 : (i32) -> tensor<1x32xi32>
%26 = arith.muli %24, %25 : tensor<1x32xi32>
%27 = tt.broadcast %23 : (tensor<64x1xi32>) -> tensor<64x32xi32>
%28 = tt.broadcast %26 : (tensor<1x32xi32>) -> tensor<64x32xi32>
%29 = arith.addi %27, %28 : tensor<64x32xi32>
%30 = tt.broadcast %arg0 : (!tt.ptr<f16>) -> tensor<64x32x!tt.ptr<f16>>
%31 = tt.getelementptr %30, %29, : tensor<64x32x!tt.ptr<f16>>
%32 = tt.reshape %20 : (tensor<32xi32>) -> tensor<32x1xi32>
%33 = tt.broadcast %arg7 : (i32) -> tensor<32x1xi32>
%34 = arith.muli %32, %33 : tensor<32x1xi32>
%35 = tt.reshape %19 : (tensor<64xi32>) -> tensor<1x64xi32>
%c1_i32_2 = arith.constant 1 : i32
%36 = tt.broadcast %c1_i32_2 : (i32) -> tensor<1x64xi32>
%37 = arith.muli %35, %36 : tensor<1x64xi32>
%38 = tt.broadcast %34 : (tensor<32x1xi32>) -> tensor<32x64xi32>
%39 = tt.broadcast %37 : (tensor<1x64xi32>) -> tensor<32x64xi32>
%40 = arith.addi %38, %39 : tensor<32x64xi32>
%41 = tt.broadcast %arg1 : (!tt.ptr<f16>) -> tensor<32x64x!tt.ptr<f16>>
%42 = tt.getelementptr %41, %40, : tensor<32x64x!tt.ptr<f16>>
%cst = arith.constant 0.000000e+00 : f32
%43 = tt.broadcast %cst : (f32) -> tensor<64x64xf32>
%c0_i32 = arith.constant 0 : i32
%c32_i32 = arith.constant 32 : i32
%44 = arith.index_cast %c0_i32 : i32 to index
%45 = arith.index_cast %arg5 : i32 to index
%46 = arith.index_cast %c32_i32 : i32 to index
%47:3 = scf.for %arg9 = %44 to %45 step %46 iter_args(%arg10 = %43, %arg11 = %31, %arg12 = %42) -> (tensor<64x64xf32>, tensor<64x32x!tt.ptr<f16>>, tensor<32x64x!tt.ptr<f16>>) {
%cst_6 = arith.constant dense<true> : tensor<64x32xi1>
%cst_7 = arith.constant dense<0.000000e+00> : tensor<64x32xf16>
%77 = tt.load %arg11, %cst_6, %cst_7 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<64x32xf16>
%cst_8 = arith.constant dense<true> : tensor<32x64xi1>
%cst_9 = arith.constant dense<0.000000e+00> : tensor<32x64xf16>
%78 = tt.load %arg12, %cst_8, %cst_9 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<32x64xf16>
%cst_10 = arith.constant 0.000000e+00 : f32
%79 = tt.broadcast %cst_10 : (f32) -> tensor<64x64xf32>
%80 = tt.dot %77, %78, %79 {allowTF32 = true} : tensor<64x32xf16> * tensor<32x64xf16> -> tensor<64x64xf32>
%81 = arith.addf %arg10, %80 : tensor<64x64xf32>
%c32_i32_11 = arith.constant 32 : i32
%82 = tt.broadcast %c32_i32_11 : (i32) -> tensor<64x32xi32>
%83 = tt.getelementptr %arg11, %82, : tensor<64x32x!tt.ptr<f16>>
%c32_i32_12 = arith.constant 32 : i32
%84 = arith.muli %arg7, %c32_i32_12 : i32
%85 = tt.broadcast %84 : (i32) -> tensor<32x64xi32>
%86 = tt.getelementptr %arg12, %85, : tensor<32x64x!tt.ptr<f16>>
scf.yield %81, %83, %86 : tensor<64x64xf32>, tensor<64x32x!tt.ptr<f16>>, tensor<32x64x!tt.ptr<f16>>
}
%48 = arith.truncf %47#0 : tensor<64x64xf32> to tensor<64x64xf16>
%c64_i32_3 = arith.constant 64 : i32
%49 = arith.muli %9, %c64_i32_3 : i32
%50 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
%51 = tt.broadcast %49 : (i32) -> tensor<64xi32>
%52 = arith.addi %51, %50 : tensor<64xi32>
%c64_i32_4 = arith.constant 64 : i32
%53 = arith.muli %11, %c64_i32_4 : i32
%54 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
%55 = tt.broadcast %53 : (i32) -> tensor<64xi32>
%56 = arith.addi %55, %54 : tensor<64xi32>
%57 = tt.reshape %52 : (tensor<64xi32>) -> tensor<64x1xi32>
%58 = tt.broadcast %arg8 : (i32) -> tensor<64x1xi32>
%59 = arith.muli %58, %57 : tensor<64x1xi32>
%60 = tt.broadcast %arg2 : (!tt.ptr<f16>) -> tensor<64x1x!tt.ptr<f16>>
%61 = tt.getelementptr %60, %59, : tensor<64x1x!tt.ptr<f16>>
%62 = tt.reshape %56 : (tensor<64xi32>) -> tensor<1x64xi32>
%c1_i32_5 = arith.constant 1 : i32
%63 = tt.broadcast %c1_i32_5 : (i32) -> tensor<1x64xi32>
%64 = arith.muli %62, %63 : tensor<1x64xi32>
%65 = tt.broadcast %61 : (tensor<64x1x!tt.ptr<f16>>) -> tensor<64x64x!tt.ptr<f16>>
%66 = tt.broadcast %64 : (tensor<1x64xi32>) -> tensor<64x64xi32>
%67 = tt.getelementptr %65, %66, : tensor<64x64x!tt.ptr<f16>>
%68 = tt.reshape %52 : (tensor<64xi32>) -> tensor<64x1xi32>
%69 = tt.broadcast %arg3 : (i32) -> tensor<64x1xi32>
%70 = arith.cmpi slt, %68, %69 : tensor<64x1xi32>
%71 = tt.reshape %56 : (tensor<64xi32>) -> tensor<1x64xi32>
%72 = tt.broadcast %arg4 : (i32) -> tensor<1x64xi32>
%73 = arith.cmpi slt, %71, %72 : tensor<1x64xi32>
%74 = tt.broadcast %70 : (tensor<64x1xi1>) -> tensor<64x64xi1>
%75 = tt.broadcast %73 : (tensor<1x64xi1>) -> tensor<64x64xi1>
%76 = arith.andi %74, %75 : tensor<64x64xi1>
tt.store %67, %48, %76, : tensor<64x64xf16>
return
}
func @"cdiv__i32__1cconstexpr[64]"(%arg0: i32) -> i32 {
%c64_i32 = arith.constant 64 : i32
%0 = arith.addi %arg0, %c64_i32 : i32
%c1_i32 = arith.constant 1 : i32
%1 = arith.subi %0, %c1_i32 : i32
%c64_i32_0 = arith.constant 64 : i32
%2 = arith.divsi %1, %c64_i32_0 : i32
return %2 : i32
}
func @"minimum__i32__1cconstexpr[8]"(%arg0: i32) -> i32 {
%c8_i32 = arith.constant 8 : i32
%0 = arith.cmpi slt, %arg0, %c8_i32 : i32
%c8_i32_0 = arith.constant 8 : i32
%1 = select %0, %arg0, %c8_i32_0 : i32
return %1 : i32
}
}
module {
func @matmul_kernel__Pfp16_Pfp16_Pfp16_i32_i32_i32_i32_i32_i32__7c1_9c1_11c1_12c64_13c64_14c32_15c8(%arg0: !tt.ptr<f16>, %arg1: !tt.ptr<f16>, %arg2: !tt.ptr<f16>, %arg3: i32, %arg4: i32, %arg5: i32, %arg6: i32, %arg7: i32, %arg8: i32) {
%c1_i32 = arith.constant 1 : i32
%c32_i32 = arith.constant 32 : i32
%cst = arith.constant dense<0.000000e+00> : tensor<32x64xf16>
%cst_0 = arith.constant dense<true> : tensor<32x64xi1>
%cst_1 = arith.constant dense<0.000000e+00> : tensor<64x32xf16>
%cst_2 = arith.constant dense<true> : tensor<64x32xi1>
%c32 = arith.constant 32 : index
%c0 = arith.constant 0 : index
%cst_3 = arith.constant 0.000000e+00 : f32
%c64_i32 = arith.constant 64 : i32
%c63_i32 = arith.constant 63 : i32
%c8_i32 = arith.constant 8 : i32
%0 = tt.get_program_id {axis = 0 : i32} : i32
%1 = arith.addi %arg3, %c63_i32 : i32
%2 = arith.divsi %1, %c64_i32 : i32
%3 = arith.addi %arg4, %c63_i32 : i32
%4 = arith.divsi %3, %c64_i32 : i32
%5 = arith.muli %4, %c8_i32 : i32
%6 = arith.divsi %0, %5 : i32
%7 = arith.muli %6, %c8_i32 : i32
%8 = arith.subi %2, %7 : i32
%9 = arith.cmpi slt, %8, %c8_i32 : i32
%10 = select %9, %8, %c8_i32 : i32
%11 = arith.remsi %0, %10 : i32
%12 = arith.addi %7, %11 : i32
%13 = arith.remsi %0, %5 : i32
%14 = arith.divsi %13, %10 : i32
%15 = arith.muli %12, %c64_i32 : i32
%16 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
%17 = tt.broadcast %15 : (i32) -> tensor<64xi32>
%18 = arith.addi %17, %16 : tensor<64xi32>
%19 = arith.muli %14, %c64_i32 : i32
%20 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
%21 = tt.broadcast %19 : (i32) -> tensor<64xi32>
%22 = arith.addi %21, %20 : tensor<64xi32>
%23 = tt.make_range {end = 32 : i32, start = 0 : i32} : tensor<32xi32>
%24 = tt.reshape %18 : (tensor<64xi32>) -> tensor<64x1xi32>
%25 = tt.broadcast %arg6 : (i32) -> tensor<64x1xi32>
%26 = arith.muli %24, %25 : tensor<64x1xi32>
%27 = tt.reshape %23 : (tensor<32xi32>) -> tensor<1x32xi32>
%28 = tt.broadcast %c1_i32 : (i32) -> tensor<1x32xi32>
%29 = arith.muli %27, %28 : tensor<1x32xi32>
%30 = tt.broadcast %26 : (tensor<64x1xi32>) -> tensor<64x32xi32>
%31 = tt.broadcast %29 : (tensor<1x32xi32>) -> tensor<64x32xi32>
%32 = arith.addi %30, %31 : tensor<64x32xi32>
%33 = tt.broadcast %arg0 : (!tt.ptr<f16>) -> tensor<64x32x!tt.ptr<f16>>
%34 = tt.getelementptr %33, %32, : tensor<64x32x!tt.ptr<f16>>
%35 = tt.reshape %23 : (tensor<32xi32>) -> tensor<32x1xi32>
%36 = tt.broadcast %arg7 : (i32) -> tensor<32x1xi32>
%37 = arith.muli %35, %36 : tensor<32x1xi32>
%38 = tt.reshape %22 : (tensor<64xi32>) -> tensor<1x64xi32>
%39 = tt.broadcast %c1_i32 : (i32) -> tensor<1x64xi32>
%40 = arith.muli %38, %39 : tensor<1x64xi32>
%41 = tt.broadcast %37 : (tensor<32x1xi32>) -> tensor<32x64xi32>
%42 = tt.broadcast %40 : (tensor<1x64xi32>) -> tensor<32x64xi32>
%43 = arith.addi %41, %42 : tensor<32x64xi32>
%44 = tt.broadcast %arg1 : (!tt.ptr<f16>) -> tensor<32x64x!tt.ptr<f16>>
%45 = tt.getelementptr %44, %43, : tensor<32x64x!tt.ptr<f16>>
%46 = tt.broadcast %cst_3 : (f32) -> tensor<64x64xf32>
%47 = arith.index_cast %arg5 : i32 to index
%48:3 = scf.for %arg9 = %c0 to %47 step %c32 iter_args(%arg10 = %46, %arg11 = %34, %arg12 = %45) -> (tensor<64x64xf32>, tensor<64x32x!tt.ptr<f16>>, tensor<32x64x!tt.ptr<f16>>) {
%78 = tt.load %arg11, %cst_2, %cst_1 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<64x32xf16>
%79 = tt.load %arg12, %cst_0, %cst {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<32x64xf16>
%80 = tt.broadcast %cst_3 : (f32) -> tensor<64x64xf32>
%81 = tt.dot %78, %79, %80 {allowTF32 = true} : tensor<64x32xf16> * tensor<32x64xf16> -> tensor<64x64xf32>
%82 = arith.addf %arg10, %81 : tensor<64x64xf32>
%83 = tt.broadcast %c32_i32 : (i32) -> tensor<64x32xi32>
%84 = tt.getelementptr %arg11, %83, : tensor<64x32x!tt.ptr<f16>>
%85 = arith.muli %arg7, %c32_i32 : i32
%86 = tt.broadcast %85 : (i32) -> tensor<32x64xi32>
%87 = tt.getelementptr %arg12, %86, : tensor<32x64x!tt.ptr<f16>>
scf.yield %82, %84, %87 : tensor<64x64xf32>, tensor<64x32x!tt.ptr<f16>>, tensor<32x64x!tt.ptr<f16>>
}
%49 = arith.truncf %48#0 : tensor<64x64xf32> to tensor<64x64xf16>
%50 = arith.muli %12, %c64_i32 : i32
%51 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
%52 = tt.broadcast %50 : (i32) -> tensor<64xi32>
%53 = arith.addi %52, %51 : tensor<64xi32>
%54 = arith.muli %14, %c64_i32 : i32
%55 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32>
%56 = tt.broadcast %54 : (i32) -> tensor<64xi32>
%57 = arith.addi %56, %55 : tensor<64xi32>
%58 = tt.reshape %53 : (tensor<64xi32>) -> tensor<64x1xi32>
%59 = tt.broadcast %arg8 : (i32) -> tensor<64x1xi32>
%60 = arith.muli %59, %58 : tensor<64x1xi32>
%61 = tt.broadcast %arg2 : (!tt.ptr<f16>) -> tensor<64x1x!tt.ptr<f16>>
%62 = tt.getelementptr %61, %60, : tensor<64x1x!tt.ptr<f16>>
%63 = tt.reshape %57 : (tensor<64xi32>) -> tensor<1x64xi32>
%64 = tt.broadcast %c1_i32 : (i32) -> tensor<1x64xi32>
%65 = arith.muli %63, %64 : tensor<1x64xi32>
%66 = tt.broadcast %62 : (tensor<64x1x!tt.ptr<f16>>) -> tensor<64x64x!tt.ptr<f16>>
%67 = tt.broadcast %65 : (tensor<1x64xi32>) -> tensor<64x64xi32>
%68 = tt.getelementptr %66, %67, : tensor<64x64x!tt.ptr<f16>>
%69 = tt.reshape %53 : (tensor<64xi32>) -> tensor<64x1xi32>
%70 = tt.broadcast %arg3 : (i32) -> tensor<64x1xi32>
%71 = arith.cmpi slt, %69, %70 : tensor<64x1xi32>
%72 = tt.reshape %57 : (tensor<64xi32>) -> tensor<1x64xi32>
%73 = tt.broadcast %arg4 : (i32) -> tensor<1x64xi32>
%74 = arith.cmpi slt, %72, %73 : tensor<1x64xi32>
%75 = tt.broadcast %71 : (tensor<64x1xi1>) -> tensor<64x64xi1>
%76 = tt.broadcast %74 : (tensor<1x64xi1>) -> tensor<64x64xi1>
%77 = arith.andi %75, %76 : tensor<64x64xi1>
tt.store %68, %49, %77, : tensor<64x64xf16>
return
}
func @"cdiv__i32__1cconstexpr[64]"(%arg0: i32) -> i32 {
%c63_i32 = arith.constant 63 : i32
%c64_i32 = arith.constant 64 : i32
%0 = arith.addi %arg0, %c63_i32 : i32
%1 = arith.divsi %0, %c64_i32 : i32
return %1 : i32
}
func @"minimum__i32__1cconstexpr[8]"(%arg0: i32) -> i32 {
%c8_i32 = arith.constant 8 : i32
%0 = arith.cmpi slt, %arg0, %c8_i32 : i32
%1 = select %0, %arg0, %c8_i32 : i32
return %1 : i32
}
}
Traceback (most recent call last):
File "matmul.py", line 1, in <module>
import triton
File "/home/da/miniconda3/envs/torch-src/lib/python3.7/site-packages/triton-2.0.0-py3.7-linux-x86_64.egg/triton/__init__.py", line 9, in <module>
from .code_gen import cdiv, next_power_of_2, jit, autotune, heuristics, \
File "/home/da/miniconda3/envs/torch-src/lib/python3.7/site-packages/triton-2.0.0-py3.7-linux-x86_64.egg/triton/code_gen.py", line 23, in <module>
import triton._C.libtriton.triton as _triton
ImportError: /home/da/miniconda3/envs/torch-src/lib/python3.7/site-packages/triton-2.0.0-py3.7-linux-x86_64.egg/triton/_C/libtriton.so: undefined symbol: _ZN4mlir6triton5CatOp10getEffectsERN4llvm15SmallVectorImplINS_11SideEffects14EffectInstanceINS_13MemoryEffects6EffectEEEEE