Adjust the operand order of tt.dot to linalg.matmul converter #191
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MercuryChen
changed the title
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thanks for your pr! could you help me understand why swapping the order is necessary? does having the rhs as dps init like we have currently introduce any incorrect codegen? |
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This is the FileCheck case of dot Arith to linalg code I don't know why this bug does not cause triton-shared cpu backend got mismatch error, but in our customed SPIRV backend, it did. |
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From: ***@***.***>
Date: Fri, Nov 22, 2024 09:52 AM
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Subject: Re: [microsoft/triton-shared] Adjust the operand order of tt.dot tolinalg.matmul converter (PR #191)
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@MercuryChen Would you be able to share the buggy IR after the |
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Thanks for your reply! #map = affine_map<(d0, d1) -> (d0, d1)>
module {
func.func @matmul_kernel(%arg0: memref<*xf32> {tt.divisibility = 16 : i32}, %arg1: memref<*xf32> {tt.divisibility = 16 : i32}, %arg2: memref<*xf32> {tt.divisibility = 16 : i32}, %arg3: i32 {tt.divisibility = 16 : i32}, %arg4: i32, %arg5: i32, %arg6: i32, %arg7: i32, %arg8: i32, %arg9: i32, %arg10: i32, %arg11: i32, %arg12: i32, %arg13: i32, %arg14: i32) {
%c8_i32 = arith.constant 8 : i32
%c32_i32 = arith.constant 32 : i32
%c64_i32 = arith.constant 64 : i32
%c16_i32 = arith.constant 16 : i32
%c0_i32 = arith.constant 0 : i32
%c1_i32 = arith.constant 1 : i32
%c31_i32 = arith.constant 31 : i32
%c63_i32 = arith.constant 63 : i32
%c15_i32 = arith.constant 15 : i32
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c16 = arith.constant 16 : index
%cst = arith.constant 0.000000e+00 : f32
%c32 = arith.constant 32 : index
%c64 = arith.constant 64 : index
%alloc = memref.alloc() {alignment = 64 : i64} : memref<32x64xf32>
linalg.fill ins(%cst : f32) outs(%alloc : memref<32x64xf32>)
%0 = arith.addi %arg3, %c31_i32 : i32
%1 = arith.divsi %0, %c32_i32 : i32
%2 = arith.addi %arg4, %c63_i32 : i32
%3 = arith.divsi %2, %c64_i32 : i32
%4 = arith.muli %3, %c8_i32 : i32
%5 = arith.divsi %arg12, %4 : i32
%6 = arith.muli %5, %c8_i32 : i32
%7 = arith.subi %1, %6 : i32
%8 = arith.minsi %7, %c8_i32 : i32
%9 = arith.remsi %arg12, %8 : i32
%10 = arith.addi %6, %9 : i32
%11 = arith.remsi %arg12, %4 : i32
%12 = arith.divsi %11, %8 : i32
%13 = arith.muli %10, %c32_i32 : i32
%14 = arith.index_cast %13 : i32 to index
%15 = arith.muli %12, %c64_i32 : i32
%16 = arith.index_cast %15 : i32 to index
%17 = arith.index_cast %arg3 : i32 to index
%18 = arith.index_cast %arg6 : i32 to index
%19 = arith.muli %14, %18 : index
%20 = arith.muli %17, %18 : index
%21 = arith.index_cast %arg7 : i32 to index
%22 = arith.index_cast %arg4 : i32 to index
%23 = arith.addi %arg5, %c15_i32 : i32
%24 = arith.divsi %23, %c16_i32 : i32
%25 = arith.muli %arg7, %c16_i32 : i32
%26 = arith.index_cast %25 : i32 to index
%alloc_0 = memref.alloc() {alignment = 64 : i64} : memref<32x64xf32>
memref.copy %alloc, %alloc_0 : memref<32x64xf32> to memref<32x64xf32>
%27:3 = scf.for %arg15 = %c0_i32 to %24 step %c1_i32 iter_args(%arg16 = %alloc_0, %arg17 = %19, %arg18 = %c0) -> (memref<32x64xf32>, index, index) : i32 {
%41 = arith.addi %arg18, %16 : index
%42 = arith.remsi %41, %22 : index
%43 = arith.subi %41, %42 : index
%44 = arith.addi %42, %c64 : index
%45 = arith.minsi %44, %22 : index
%46 = arith.subi %45, %42 : index
%reinterpret_cast_2 = memref.reinterpret_cast %arg1 to offset: [%41], sizes: [%c16, %46], strides: [%21, %c1] : memref<*xf32> to memref<16x?xf32, strided<[?, ?], offset: ?>>
%47 = arith.subi %c64, %46 : index
%reinterpret_cast_3 = memref.reinterpret_cast %arg1 to offset: [%43], sizes: [%c16, %47], strides: [%21, %c1] : memref<*xf32> to memref<16x?xf32, strided<[?, ?], offset: ?>>
%48 = arith.remsi %arg17, %18 : index
%49 = arith.addi %20, %48 : index
%50 = arith.subi %49, %arg17 : index
%51 = arith.divsi %50, %18 : index
%reinterpret_cast_4 = memref.reinterpret_cast %arg0 to offset: [%arg17], sizes: [%51, %c16], strides: [%18, %c1] : memref<*xf32> to memref<?x16xf32, strided<[?, ?], offset: ?>>
%52 = arith.subi %c32, %51 : index
%reinterpret_cast_5 = memref.reinterpret_cast %arg0 to offset: [%48], sizes: [%52, %c16], strides: [%18, %c1] : memref<*xf32> to memref<?x16xf32, strided<[?, ?], offset: ?>>
%53 = arith.muli %arg15, %c16_i32 : i32
%54 = arith.subi %arg5, %53 : i32
%55 = arith.index_cast %54 : i32 to index
%56 = arith.minsi %55, %c16 : index
%57 = arith.maxsi %56, %c0 : index
%alloc_6 = memref.alloc() : memref<32x16xf32>
%58 = arith.cmpi slt, %57, %c16 : index
scf.if %58 {
linalg.fill ins(%cst : f32) outs(%alloc_6 : memref<32x16xf32>)
}
%59 = arith.minsi %51, %c32 : index
%60 = arith.subi %c32, %59 : index
%subview_7 = memref.subview %reinterpret_cast_4[0, 0] [%59, %57] [1, 1] : memref<?x16xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_8 = memref.subview %reinterpret_cast_5[0, 0] [%60, %57] [1, 1] : memref<?x16xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_9 = memref.subview %alloc_6[0, 0] [%59, %57] [1, 1] : memref<32x16xf32> to memref<?x?xf32, strided<[16, 1]>>
%subview_10 = memref.subview %alloc_6[%59, 0] [%60, %57] [1, 1] : memref<32x16xf32> to memref<?x?xf32, strided<[16, 1], offset: ?>>
memref.copy %subview_7, %subview_9 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[16, 1]>>
memref.copy %subview_8, %subview_10 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[16, 1], offset: ?>>
%alloc_11 = memref.alloc() : memref<16x64xf32>
scf.if %58 {
linalg.fill ins(%cst : f32) outs(%alloc_11 : memref<16x64xf32>)
}
%61 = arith.minsi %46, %c64 : index
%62 = arith.subi %c64, %61 : index
%subview_12 = memref.subview %reinterpret_cast_2[0, 0] [%57, %61] [1, 1] : memref<16x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_13 = memref.subview %reinterpret_cast_3[0, 0] [%57, %62] [1, 1] : memref<16x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_14 = memref.subview %alloc_11[0, 0] [%57, %61] [1, 1] : memref<16x64xf32> to memref<?x?xf32, strided<[64, 1]>>
%subview_15 = memref.subview %alloc_11[0, %61] [%57, %62] [1, 1] : memref<16x64xf32> to memref<?x?xf32, strided<[64, 1], offset: ?>>
memref.copy %subview_12, %subview_14 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[64, 1]>>
memref.copy %subview_13, %subview_15 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[64, 1], offset: ?>>
%alloc_16 = memref.alloc() {alignment = 64 : i64} : memref<32x64xf32>
memref.copy %alloc, %alloc_16 : memref<32x64xf32> to memref<32x64xf32>
linalg.matmul ins(%alloc_6, %alloc_11 : memref<32x16xf32>, memref<16x64xf32>) outs(%alloc_16 : memref<32x64xf32>)
linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg16, %alloc_16 : memref<32x64xf32>, memref<32x64xf32>) outs(%arg16: memref<32x64xf32>) {
^bb0(%in: f32, %in_17: f32, %out: f32):
%65 = arith.addf %in, %in_17 : f32
linalg.yield %65 : f32
}
%63 = arith.addi %arg17, %c16 : index
%64 = arith.addi %arg18, %26 : index
scf.yield %alloc_16, %63, %64 : memref<32x64xf32>, index, index
}
%28 = arith.index_cast %arg8 : i32 to index
%29 = arith.muli %14, %28 : index
%30 = arith.addi %29, %16 : index
%reinterpret_cast = memref.reinterpret_cast %arg2 to offset: [%30], sizes: [32, 64], strides: [%28, 1] : memref<*xf32> to memref<32x64xf32, strided<[?, 1], offset: ?>>
%31 = arith.addi %14, %c32 : index
%32 = arith.minsi %31, %17 : index
%33 = arith.maxsi %32, %14 : index
%34 = arith.subi %33, %14 : index
%35 = arith.addi %16, %c64 : index
%36 = arith.minsi %35, %22 : index
%37 = arith.maxsi %36, %16 : index
%38 = arith.subi %37, %16 : index
%39 = arith.minsi %34, %c32 : index
%40 = arith.minsi %38, %c64 : index
%subview = memref.subview %27#0[0, 0] [%39, %40] [1, 1] : memref<32x64xf32> to memref<?x?xf32, strided<[64, 1]>>
%subview_1 = memref.subview %reinterpret_cast[0, 0] [%39, %40] [1, 1] : memref<32x64xf32, strided<[?, 1], offset: ?>> to memref<?x?xf32, strided<[?, 1], offset: ?>>
memref.copy %subview, %subview_1 : memref<?x?xf32, strided<[64, 1]>> to memref<?x?xf32, strided<[?, 1], offset: ?>>
return
}
}The IR before change: ...
// just replace the `out` from %alloc_16 to %arg16
linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%alloc_16, %arg16 : memref<32x64xf32>, memref<32x64xf32>) outs(%alloc_16: memref<32x64xf32>) {
^bb0(%in: f32, %in_17: f32, %out: f32):
%65 = arith.addf %in, %in_17 : f32
linalg.yield %65 : f32
}
...And the correct IR before bufferize: #map = affine_map<(d0, d1) -> (d0, d1)>
module {
func.func @matmul_kernel(%arg0: memref<*xf32> {tt.divisibility = 16 : i32}, %arg1: memref<*xf32> {tt.divisibility = 16 : i32}, %arg2: memref<*xf32> {tt.divisibility = 16 : i32}, %arg3: i32 {tt.divisibility = 16 : i32}, %arg4: i32, %arg5: i32, %arg6: i32, %arg7: i32, %arg8: i32, %arg9: i32, %arg10: i32, %arg11: i32, %arg12: i32, %arg13: i32, %arg14: i32) {
%c8_i32 = arith.constant 8 : i32
%c32_i32 = arith.constant 32 : i32
%c64_i32 = arith.constant 64 : i32
%c16_i32 = arith.constant 16 : i32
%c0_i32 = arith.constant 0 : i32
%c1_i32 = arith.constant 1 : i32
%c31_i32 = arith.constant 31 : i32
%c63_i32 = arith.constant 63 : i32
%c15_i32 = arith.constant 15 : i32
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c16 = arith.constant 16 : index
%cst = arith.constant 0.000000e+00 : f32
%c32 = arith.constant 32 : index
%c64 = arith.constant 64 : index
%0 = tensor.empty() : tensor<32x64xf32>
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<32x64xf32>) -> tensor<32x64xf32>
%2 = arith.addi %arg3, %c31_i32 : i32
%3 = arith.divsi %2, %c32_i32 : i32
%4 = arith.addi %arg4, %c63_i32 : i32
%5 = arith.divsi %4, %c64_i32 : i32
%6 = arith.muli %5, %c8_i32 : i32
%7 = arith.divsi %arg12, %6 : i32
%8 = arith.muli %7, %c8_i32 : i32
%9 = arith.subi %3, %8 : i32
%10 = arith.minsi %9, %c8_i32 : i32
%11 = arith.remsi %arg12, %10 : i32
%12 = arith.addi %8, %11 : i32
%13 = arith.remsi %arg12, %6 : i32
%14 = arith.divsi %13, %10 : i32
%15 = arith.muli %12, %c32_i32 : i32
%16 = arith.index_cast %15 : i32 to index
%17 = arith.muli %14, %c64_i32 : i32
%18 = arith.index_cast %17 : i32 to index
%19 = arith.index_cast %arg3 : i32 to index
%20 = arith.index_cast %arg6 : i32 to index
%21 = arith.muli %16, %20 : index
%22 = arith.muli %19, %20 : index
%23 = arith.index_cast %arg7 : i32 to index
%24 = arith.index_cast %arg4 : i32 to index
%25 = arith.addi %arg5, %c15_i32 : i32
%26 = arith.divsi %25, %c16_i32 : i32
%27 = arith.muli %arg7, %c16_i32 : i32
%28 = arith.index_cast %27 : i32 to index
%29:3 = scf.for %arg15 = %c0_i32 to %26 step %c1_i32 iter_args(%arg16 = %1, %arg17 = %21, %arg18 = %c0) -> (tensor<32x64xf32>, index, index) : i32 {
%43 = arith.addi %arg18, %18 : index
%44 = arith.remsi %43, %24 : index
%45 = arith.subi %43, %44 : index
%46 = arith.addi %44, %c64 : index
%47 = arith.minsi %46, %24 : index
%48 = arith.subi %47, %44 : index
%reinterpret_cast_0 = memref.reinterpret_cast %arg1 to offset: [%43], sizes: [%c16, %48], strides: [%23, %c1] : memref<*xf32> to memref<16x?xf32, strided<[?, ?], offset: ?>>
%49 = arith.subi %c64, %48 : index
%reinterpret_cast_1 = memref.reinterpret_cast %arg1 to offset: [%45], sizes: [%c16, %49], strides: [%23, %c1] : memref<*xf32> to memref<16x?xf32, strided<[?, ?], offset: ?>>
%50 = arith.remsi %arg17, %20 : index
%51 = arith.addi %22, %50 : index
%52 = arith.subi %51, %arg17 : index
%53 = arith.divsi %52, %20 : index
%reinterpret_cast_2 = memref.reinterpret_cast %arg0 to offset: [%arg17], sizes: [%53, %c16], strides: [%20, %c1] : memref<*xf32> to memref<?x16xf32, strided<[?, ?], offset: ?>>
%54 = arith.subi %c32, %53 : index
%reinterpret_cast_3 = memref.reinterpret_cast %arg0 to offset: [%50], sizes: [%54, %c16], strides: [%20, %c1] : memref<*xf32> to memref<?x16xf32, strided<[?, ?], offset: ?>>
%55 = arith.muli %arg15, %c16_i32 : i32
%56 = arith.subi %arg5, %55 : i32
%57 = arith.index_cast %56 : i32 to index
%58 = arith.minsi %57, %c16 : index
%59 = arith.maxsi %58, %c0 : index
%alloc = memref.alloc() : memref<32x16xf32>
%60 = arith.cmpi slt, %59, %c16 : index
scf.if %60 {
linalg.fill ins(%cst : f32) outs(%alloc : memref<32x16xf32>)
}
%61 = arith.minsi %53, %c32 : index
%62 = arith.subi %c32, %61 : index
%subview_4 = memref.subview %reinterpret_cast_2[0, 0] [%61, %59] [1, 1] : memref<?x16xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_5 = memref.subview %reinterpret_cast_3[0, 0] [%62, %59] [1, 1] : memref<?x16xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_6 = memref.subview %alloc[0, 0] [%61, %59] [1, 1] : memref<32x16xf32> to memref<?x?xf32, strided<[16, 1]>>
%subview_7 = memref.subview %alloc[%61, 0] [%62, %59] [1, 1] : memref<32x16xf32> to memref<?x?xf32, strided<[16, 1], offset: ?>>
memref.copy %subview_4, %subview_6 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[16, 1]>>
memref.copy %subview_5, %subview_7 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[16, 1], offset: ?>>
%63 = bufferization.to_tensor %alloc restrict writable : memref<32x16xf32>
%alloc_8 = memref.alloc() : memref<16x64xf32>
scf.if %60 {
linalg.fill ins(%cst : f32) outs(%alloc_8 : memref<16x64xf32>)
}
%64 = arith.minsi %48, %c64 : index
%65 = arith.subi %c64, %64 : index
%subview_9 = memref.subview %reinterpret_cast_0[0, 0] [%59, %64] [1, 1] : memref<16x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_10 = memref.subview %reinterpret_cast_1[0, 0] [%59, %65] [1, 1] : memref<16x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_11 = memref.subview %alloc_8[0, 0] [%59, %64] [1, 1] : memref<16x64xf32> to memref<?x?xf32, strided<[64, 1]>>
%subview_12 = memref.subview %alloc_8[0, %64] [%59, %65] [1, 1] : memref<16x64xf32> to memref<?x?xf32, strided<[64, 1], offset: ?>>
memref.copy %subview_9, %subview_11 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[64, 1]>>
memref.copy %subview_10, %subview_12 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[64, 1], offset: ?>>
%66 = bufferization.to_tensor %alloc_8 restrict writable : memref<16x64xf32>
%67 = linalg.matmul ins(%63, %66 : tensor<32x16xf32>, tensor<16x64xf32>) outs(%1 : tensor<32x64xf32>) -> tensor<32x64xf32>
%68 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg16, %67 : tensor<32x64xf32>, tensor<32x64xf32>) outs(%arg16 : tensor<32x64xf32>) {
^bb0(%in: f32, %in_13: f32, %out: f32):
%71 = arith.addf %in, %in_13 : f32
linalg.yield %71 : f32
} -> tensor<32x64xf32>
%69 = arith.addi %arg17, %c16 : index
%70 = arith.addi %arg18, %28 : index
scf.yield %68, %69, %70 : tensor<32x64xf32>, index, index
}
%30 = arith.index_cast %arg8 : i32 to index
%31 = arith.muli %16, %30 : index
%32 = arith.addi %31, %18 : index
%reinterpret_cast = memref.reinterpret_cast %arg2 to offset: [%32], sizes: [32, 64], strides: [%30, 1] : memref<*xf32> to memref<32x64xf32, strided<[?, 1], offset: ?>>
%33 = arith.addi %16, %c32 : index
%34 = arith.minsi %33, %19 : index
%35 = arith.maxsi %34, %16 : index
%36 = arith.subi %35, %16 : index
%37 = arith.addi %18, %c64 : index
%38 = arith.minsi %37, %24 : index
%39 = arith.maxsi %38, %18 : index
%40 = arith.subi %39, %18 : index
%41 = arith.minsi %36, %c32 : index
%42 = arith.minsi %40, %c64 : index
%extracted_slice = tensor.extract_slice %29#0[0, 0] [%41, %42] [1, 1] : tensor<32x64xf32> to tensor<?x?xf32>
%subview = memref.subview %reinterpret_cast[0, 0] [%41, %42] [1, 1] : memref<32x64xf32, strided<[?, 1], offset: ?>> to memref<?x?xf32, strided<[?, 1], offset: ?>>
bufferization.materialize_in_destination %extracted_slice in writable %subview : (tensor<?x?xf32>, memref<?x?xf32, strided<[?, 1], offset: ?>>) -> ()
return
}
}As the IR shows,
In the tensor semantic, it's SSA, so maybe the proper way is create an new empty tensor for the DPS init. |
|
Thanks for your quick response. Would you mind also sharing the IR before bufferization too? Sorry I should have asked in the previous reply. 😄 |
This seems like a good idea to try too. I would be curious to see what the bufferization result looks like if you manually create a linalg.generic {addf} with an empty tensor as its |
|
Sorry for my mistake accidentally close the PR. To use the new empty tensor as |
What if you simply use an empty tensor as out without explicitly inserting linalg.copy? What does the bufferization output look like? The reason why I'm asking this question is there seems to be a fundamental issue with how we use the out params which could produce incorrect codegen in various scenarios. While swapping the order of the two operands would fix this matmul bug, it would be great if we can figure out the correct bufferization behaviour. |
|
Yes, do not need insert #map = affine_map<(d0, d1) -> (d0, d1)>
module {
func.func @matmul_kernel(%arg0: memref<*xf32> {tt.divisibility = 16 : i32}, %arg1: memref<*xf32> {tt.divisibility = 16 : i32}, %arg2: memref<*xf32> {tt.divisibility = 16 : i32}, %arg3: i32 {tt.divisibility = 16 : i32}, %arg4: i32, %arg5: i32, %arg6: i32, %arg7: i32, %arg8: i32, %arg9: i32, %arg10: i32, %arg11: i32, %arg12: i32, %arg13: i32, %arg14: i32) {
%c8_i32 = arith.constant 8 : i32
%c32_i32 = arith.constant 32 : i32
%c64_i32 = arith.constant 64 : i32
%c16_i32 = arith.constant 16 : i32
%c0_i32 = arith.constant 0 : i32
%c1_i32 = arith.constant 1 : i32
%c31_i32 = arith.constant 31 : i32
%c63_i32 = arith.constant 63 : i32
%c15_i32 = arith.constant 15 : i32
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c16 = arith.constant 16 : index
%cst = arith.constant 0.000000e+00 : f32
%c32 = arith.constant 32 : index
%c64 = arith.constant 64 : index
%0 = tensor.empty() : tensor<32x64xf32>
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<32x64xf32>) -> tensor<32x64xf32>
%2 = arith.addi %arg3, %c31_i32 : i32
%3 = arith.divsi %2, %c32_i32 : i32
%4 = arith.addi %arg4, %c63_i32 : i32
%5 = arith.divsi %4, %c64_i32 : i32
%6 = arith.muli %5, %c8_i32 : i32
%7 = arith.divsi %arg12, %6 : i32
%8 = arith.muli %7, %c8_i32 : i32
%9 = arith.subi %3, %8 : i32
%10 = arith.minsi %9, %c8_i32 : i32
%11 = arith.remsi %arg12, %10 : i32
%12 = arith.addi %8, %11 : i32
%13 = arith.remsi %arg12, %6 : i32
%14 = arith.divsi %13, %10 : i32
%15 = arith.muli %12, %c32_i32 : i32
%16 = arith.index_cast %15 : i32 to index
%17 = arith.muli %14, %c64_i32 : i32
%18 = arith.index_cast %17 : i32 to index
%19 = arith.index_cast %arg3 : i32 to index
%20 = arith.index_cast %arg6 : i32 to index
%21 = arith.muli %16, %20 : index
%22 = arith.muli %19, %20 : index
%23 = arith.index_cast %arg7 : i32 to index
%24 = arith.index_cast %arg4 : i32 to index
%25 = arith.addi %arg5, %c15_i32 : i32
%26 = arith.divsi %25, %c16_i32 : i32
%27 = arith.muli %arg7, %c16_i32 : i32
%28 = arith.index_cast %27 : i32 to index
%29:3 = scf.for %arg15 = %c0_i32 to %26 step %c1_i32 iter_args(%arg16 = %1, %arg17 = %21, %arg18 = %c0) -> (tensor<32x64xf32>, index, index) : i32 {
%43 = arith.addi %arg18, %18 : index
%44 = arith.remsi %43, %24 : index
%45 = arith.subi %43, %44 : index
%46 = arith.addi %44, %c64 : index
%47 = arith.minsi %46, %24 : index
%48 = arith.subi %47, %44 : index
%reinterpret_cast_0 = memref.reinterpret_cast %arg1 to offset: [%43], sizes: [%c16, %48], strides: [%23, %c1] : memref<*xf32> to memref<16x?xf32, strided<[?, ?], offset: ?>>
%49 = arith.subi %c64, %48 : index
%reinterpret_cast_1 = memref.reinterpret_cast %arg1 to offset: [%45], sizes: [%c16, %49], strides: [%23, %c1] : memref<*xf32> to memref<16x?xf32, strided<[?, ?], offset: ?>>
%50 = arith.remsi %arg17, %20 : index
%51 = arith.addi %22, %50 : index
%52 = arith.subi %51, %arg17 : index
%53 = arith.divsi %52, %20 : index
%reinterpret_cast_2 = memref.reinterpret_cast %arg0 to offset: [%arg17], sizes: [%53, %c16], strides: [%20, %c1] : memref<*xf32> to memref<?x16xf32, strided<[?, ?], offset: ?>>
%54 = arith.subi %c32, %53 : index
%reinterpret_cast_3 = memref.reinterpret_cast %arg0 to offset: [%50], sizes: [%54, %c16], strides: [%20, %c1] : memref<*xf32> to memref<?x16xf32, strided<[?, ?], offset: ?>>
%55 = arith.muli %arg15, %c16_i32 : i32
%56 = arith.subi %arg5, %55 : i32
%57 = arith.index_cast %56 : i32 to index
%58 = arith.minsi %57, %c16 : index
%59 = arith.maxsi %58, %c0 : index
%alloc = memref.alloc() : memref<32x16xf32>
%60 = arith.cmpi slt, %59, %c16 : index
scf.if %60 {
linalg.fill ins(%cst : f32) outs(%alloc : memref<32x16xf32>)
}
%61 = arith.minsi %53, %c32 : index
%62 = arith.subi %c32, %61 : index
%subview_4 = memref.subview %reinterpret_cast_2[0, 0] [%61, %59] [1, 1] : memref<?x16xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_5 = memref.subview %reinterpret_cast_3[0, 0] [%62, %59] [1, 1] : memref<?x16xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_6 = memref.subview %alloc[0, 0] [%61, %59] [1, 1] : memref<32x16xf32> to memref<?x?xf32, strided<[16, 1]>>
%subview_7 = memref.subview %alloc[%61, 0] [%62, %59] [1, 1] : memref<32x16xf32> to memref<?x?xf32, strided<[16, 1], offset: ?>>
memref.copy %subview_4, %subview_6 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[16, 1]>>
memref.copy %subview_5, %subview_7 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[16, 1], offset: ?>>
%63 = bufferization.to_tensor %alloc restrict writable : memref<32x16xf32>
%alloc_8 = memref.alloc() : memref<16x64xf32>
scf.if %60 {
linalg.fill ins(%cst : f32) outs(%alloc_8 : memref<16x64xf32>)
}
%64 = arith.minsi %48, %c64 : index
%65 = arith.subi %c64, %64 : index
%subview_9 = memref.subview %reinterpret_cast_0[0, 0] [%59, %64] [1, 1] : memref<16x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_10 = memref.subview %reinterpret_cast_1[0, 0] [%59, %65] [1, 1] : memref<16x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_11 = memref.subview %alloc_8[0, 0] [%59, %64] [1, 1] : memref<16x64xf32> to memref<?x?xf32, strided<[64, 1]>>
%subview_12 = memref.subview %alloc_8[0, %64] [%59, %65] [1, 1] : memref<16x64xf32> to memref<?x?xf32, strided<[64, 1], offset: ?>>
memref.copy %subview_9, %subview_11 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[64, 1]>>
memref.copy %subview_10, %subview_12 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[64, 1], offset: ?>>
%66 = bufferization.to_tensor %alloc_8 restrict writable : memref<16x64xf32>
%67 = linalg.matmul ins(%63, %66 : tensor<32x16xf32>, tensor<16x64xf32>) outs(%1 : tensor<32x64xf32>) -> tensor<32x64xf32>
%manual_empty = tensor.empty() : tensor<32x64xf32>
%68 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg16, %67 : tensor<32x64xf32>, tensor<32x64xf32>) outs(%manual_empty : tensor<32x64xf32>) {
^bb0(%in: f32, %in_13: f32, %out: f32):
%71 = arith.addf %in, %in_13 : f32
linalg.yield %71 : f32
} -> tensor<32x64xf32>
%69 = arith.addi %arg17, %c16 : index
%70 = arith.addi %arg18, %28 : index
scf.yield %68, %69, %70 : tensor<32x64xf32>, index, index
}
%30 = arith.index_cast %arg8 : i32 to index
%31 = arith.muli %16, %30 : index
%32 = arith.addi %31, %18 : index
%reinterpret_cast = memref.reinterpret_cast %arg2 to offset: [%32], sizes: [32, 64], strides: [%30, 1] : memref<*xf32> to memref<32x64xf32, strided<[?, 1], offset: ?>>
%33 = arith.addi %16, %c32 : index
%34 = arith.minsi %33, %19 : index
%35 = arith.maxsi %34, %16 : index
%36 = arith.subi %35, %16 : index
%37 = arith.addi %18, %c64 : index
%38 = arith.minsi %37, %24 : index
%39 = arith.maxsi %38, %18 : index
%40 = arith.subi %39, %18 : index
%41 = arith.minsi %36, %c32 : index
%42 = arith.minsi %40, %c64 : index
%extracted_slice = tensor.extract_slice %29#0[0, 0] [%41, %42] [1, 1] : tensor<32x64xf32> to tensor<?x?xf32>
%subview = memref.subview %reinterpret_cast[0, 0] [%41, %42] [1, 1] : memref<32x64xf32, strided<[?, 1], offset: ?>> to memref<?x?xf32, strided<[?, 1], offset: ?>>
bufferization.materialize_in_destination %extracted_slice in writable %subview : (tensor<?x?xf32>, memref<?x?xf32, strided<[?, 1], offset: ?>>) -> ()
return
}
}
After bufferization: #map = affine_map<(d0, d1) -> (d0, d1)>
module {
func.func @matmul_kernel(%arg0: memref<*xf32> {tt.divisibility = 16 : i32}, %arg1: memref<*xf32> {tt.divisibility = 16 : i32}, %arg2: memref<*xf32> {tt.divisibility = 16 : i32}, %arg3: i32 {tt.divisibility = 16 : i32}, %arg4: i32, %arg5: i32, %arg6: i32, %arg7: i32, %arg8: i32, %arg9: i32, %arg10: i32, %arg11: i32, %arg12: i32, %arg13: i32, %arg14: i32) {
%c8_i32 = arith.constant 8 : i32
%c32_i32 = arith.constant 32 : i32
%c64_i32 = arith.constant 64 : i32
%c16_i32 = arith.constant 16 : i32
%c0_i32 = arith.constant 0 : i32
%c1_i32 = arith.constant 1 : i32
%c31_i32 = arith.constant 31 : i32
%c63_i32 = arith.constant 63 : i32
%c15_i32 = arith.constant 15 : i32
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c16 = arith.constant 16 : index
%cst = arith.constant 0.000000e+00 : f32
%c32 = arith.constant 32 : index
%c64 = arith.constant 64 : index
%alloc = memref.alloc() {alignment = 64 : i64} : memref<32x64xf32>
linalg.fill ins(%cst : f32) outs(%alloc : memref<32x64xf32>)
%0 = arith.addi %arg3, %c31_i32 : i32
%1 = arith.divsi %0, %c32_i32 : i32
%2 = arith.addi %arg4, %c63_i32 : i32
%3 = arith.divsi %2, %c64_i32 : i32
%4 = arith.muli %3, %c8_i32 : i32
%5 = arith.divsi %arg12, %4 : i32
%6 = arith.muli %5, %c8_i32 : i32
%7 = arith.subi %1, %6 : i32
%8 = arith.minsi %7, %c8_i32 : i32
%9 = arith.remsi %arg12, %8 : i32
%10 = arith.addi %6, %9 : i32
%11 = arith.remsi %arg12, %4 : i32
%12 = arith.divsi %11, %8 : i32
%13 = arith.muli %10, %c32_i32 : i32
%14 = arith.index_cast %13 : i32 to index
%15 = arith.muli %12, %c64_i32 : i32
%16 = arith.index_cast %15 : i32 to index
%17 = arith.index_cast %arg3 : i32 to index
%18 = arith.index_cast %arg6 : i32 to index
%19 = arith.muli %14, %18 : index
%20 = arith.muli %17, %18 : index
%21 = arith.index_cast %arg7 : i32 to index
%22 = arith.index_cast %arg4 : i32 to index
%23 = arith.addi %arg5, %c15_i32 : i32
%24 = arith.divsi %23, %c16_i32 : i32
%25 = arith.muli %arg7, %c16_i32 : i32
%26 = arith.index_cast %25 : i32 to index
%alloc_0 = memref.alloc() {alignment = 64 : i64} : memref<32x64xf32>
memref.copy %alloc, %alloc_0 : memref<32x64xf32> to memref<32x64xf32>
%27:3 = scf.for %arg15 = %c0_i32 to %24 step %c1_i32 iter_args(%arg16 = %alloc_0, %arg17 = %19, %arg18 = %c0) -> (memref<32x64xf32>, index, index) : i32 {
%41 = arith.addi %arg18, %16 : index
%42 = arith.remsi %41, %22 : index
%43 = arith.subi %41, %42 : index
%44 = arith.addi %42, %c64 : index
%45 = arith.minsi %44, %22 : index
%46 = arith.subi %45, %42 : index
%reinterpret_cast_2 = memref.reinterpret_cast %arg1 to offset: [%41], sizes: [%c16, %46], strides: [%21, %c1] : memref<*xf32> to memref<16x?xf32, strided<[?, ?], offset: ?>>
%47 = arith.subi %c64, %46 : index
%reinterpret_cast_3 = memref.reinterpret_cast %arg1 to offset: [%43], sizes: [%c16, %47], strides: [%21, %c1] : memref<*xf32> to memref<16x?xf32, strided<[?, ?], offset: ?>>
%48 = arith.remsi %arg17, %18 : index
%49 = arith.addi %20, %48 : index
%50 = arith.subi %49, %arg17 : index
%51 = arith.divsi %50, %18 : index
%reinterpret_cast_4 = memref.reinterpret_cast %arg0 to offset: [%arg17], sizes: [%51, %c16], strides: [%18, %c1] : memref<*xf32> to memref<?x16xf32, strided<[?, ?], offset: ?>>
%52 = arith.subi %c32, %51 : index
%reinterpret_cast_5 = memref.reinterpret_cast %arg0 to offset: [%48], sizes: [%52, %c16], strides: [%18, %c1] : memref<*xf32> to memref<?x16xf32, strided<[?, ?], offset: ?>>
%53 = arith.muli %arg15, %c16_i32 : i32
%54 = arith.subi %arg5, %53 : i32
%55 = arith.index_cast %54 : i32 to index
%56 = arith.minsi %55, %c16 : index
%57 = arith.maxsi %56, %c0 : index
%alloc_6 = memref.alloc() : memref<32x16xf32>
%58 = arith.cmpi slt, %57, %c16 : index
scf.if %58 {
linalg.fill ins(%cst : f32) outs(%alloc_6 : memref<32x16xf32>)
}
%59 = arith.minsi %51, %c32 : index
%60 = arith.subi %c32, %59 : index
%subview_7 = memref.subview %reinterpret_cast_4[0, 0] [%59, %57] [1, 1] : memref<?x16xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_8 = memref.subview %reinterpret_cast_5[0, 0] [%60, %57] [1, 1] : memref<?x16xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_9 = memref.subview %alloc_6[0, 0] [%59, %57] [1, 1] : memref<32x16xf32> to memref<?x?xf32, strided<[16, 1]>>
%subview_10 = memref.subview %alloc_6[%59, 0] [%60, %57] [1, 1] : memref<32x16xf32> to memref<?x?xf32, strided<[16, 1], offset: ?>>
memref.copy %subview_7, %subview_9 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[16, 1]>>
memref.copy %subview_8, %subview_10 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[16, 1], offset: ?>>
%alloc_11 = memref.alloc() : memref<16x64xf32>
scf.if %58 {
linalg.fill ins(%cst : f32) outs(%alloc_11 : memref<16x64xf32>)
}
%61 = arith.minsi %46, %c64 : index
%62 = arith.subi %c64, %61 : index
%subview_12 = memref.subview %reinterpret_cast_2[0, 0] [%57, %61] [1, 1] : memref<16x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_13 = memref.subview %reinterpret_cast_3[0, 0] [%57, %62] [1, 1] : memref<16x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
%subview_14 = memref.subview %alloc_11[0, 0] [%57, %61] [1, 1] : memref<16x64xf32> to memref<?x?xf32, strided<[64, 1]>>
%subview_15 = memref.subview %alloc_11[0, %61] [%57, %62] [1, 1] : memref<16x64xf32> to memref<?x?xf32, strided<[64, 1], offset: ?>>
memref.copy %subview_12, %subview_14 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[64, 1]>>
memref.copy %subview_13, %subview_15 : memref<?x?xf32, strided<[?, ?], offset: ?>> to memref<?x?xf32, strided<[64, 1], offset: ?>>
%alloc_16 = memref.alloc() {alignment = 64 : i64} : memref<32x64xf32>
memref.copy %alloc, %alloc_16 : memref<32x64xf32> to memref<32x64xf32>
linalg.matmul ins(%alloc_6, %alloc_11 : memref<32x16xf32>, memref<16x64xf32>) outs(%alloc_16 : memref<32x64xf32>)
%alloc_17 = memref.alloc() {alignment = 64 : i64} : memref<32x64xf32>
linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg16, %alloc_16 : memref<32x64xf32>, memref<32x64xf32>) outs(%alloc_17 : memref<32x64xf32>) {
^bb0(%in: f32, %in_18: f32, %out: f32):
%65 = arith.addf %in, %in_18 : f32
linalg.yield %65 : f32
}
%63 = arith.addi %arg17, %c16 : index
%64 = arith.addi %arg18, %26 : index
scf.yield %alloc_17, %63, %64 : memref<32x64xf32>, index, index
}
%28 = arith.index_cast %arg8 : i32 to index
%29 = arith.muli %14, %28 : index
%30 = arith.addi %29, %16 : index
%reinterpret_cast = memref.reinterpret_cast %arg2 to offset: [%30], sizes: [32, 64], strides: [%28, 1] : memref<*xf32> to memref<32x64xf32, strided<[?, 1], offset: ?>>
%31 = arith.addi %14, %c32 : index
%32 = arith.minsi %31, %17 : index
%33 = arith.maxsi %32, %14 : index
%34 = arith.subi %33, %14 : index
%35 = arith.addi %16, %c64 : index
%36 = arith.minsi %35, %22 : index
%37 = arith.maxsi %36, %16 : index
%38 = arith.subi %37, %16 : index
%39 = arith.minsi %34, %c32 : index
%40 = arith.minsi %38, %c64 : index
%subview = memref.subview %27#0[0, 0] [%39, %40] [1, 1] : memref<32x64xf32> to memref<?x?xf32, strided<[64, 1]>>
%subview_1 = memref.subview %reinterpret_cast[0, 0] [%39, %40] [1, 1] : memref<32x64xf32, strided<[?, 1], offset: ?>> to memref<?x?xf32, strided<[?, 1], offset: ?>>
memref.copy %subview, %subview_1 : memref<?x?xf32, strided<[64, 1]>> to memref<?x?xf32, strided<[?, 1], offset: ?>>
return
}
}The real problem appeared on the IR after Thanks for your time on this problem. We can keep this change on our branch. If you are willing to merge it, we will be very appreciate. |
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thanks for your explanation. i'm ok with merging your patch as a temporary fix, but would you mind adding a comment explaining the issue with your findings regarding using tensor.empty as the out param and linking to this issue here: #196. we will likely need to find the correct fix for all cases at some point. |
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@MercuryChen Sorry about the confusion, I meant updating your code to include a comment about swapping the order of the operands with a link to the issue above. But thank you for your Github comment anyway, it helps make the issue clearer. The test also now has a merge conflict, could you take a look in addition to adding the comment like I suggested above? Thanks! |
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the tt.dot with accumulator will lower to linalg.matmul and arith.add, and the arith.add will further lower to linalg.generic, generic will take the lhs of add as the DPS init, so the lhs should be the matmul accumulator. This is a temporary fix for issue microsoft#196.
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@nhat-nguyen Updated. Thanks! |
nhat-nguyen
approved these changes
Dec 30, 2024
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The tt.dot with accmulator will lower to linalg.matmul and arith.add, and the arith.add will further lower to linalg.generic, generic will take the lhs of add as the DPS init, so the lhs of add must be the accmulator.