<table border="1" cellspacing="0" cellpadding="8">
<tr>
<th>Issue</th>
<td>
<a href=https://github.com/llvm/llvm-project/issues/121076>121076</a>
</td>
</tr>
<tr>
<th>Summary</th>
<td>
Error: convert-arith-to-emitc
</td>
</tr>
<tr>
<th>Labels</th>
<td>
new issue
</td>
</tr>
<tr>
<th>Assignees</th>
<td>
</td>
</tr>
<tr>
<th>Reporter</th>
<td>
pyl3000
</td>
</tr>
</table>
<pre>
When I drop the arith dialect to the emitc dialect using `mlir-opt --convert-arith-to-emitc`, I get the following error:
The input` 7-conv-emitc.mlir` as follow：
```
#map = affine_map<(d0, d1) -> (d0 + d1)>
module {
memref.global "private" @global_seed : memref<i64> = dense<0>
func.func @torch.aten.convolution$nobias(%arg0: tensor<1x24x16x128x128xf16>, %arg1: tensor<54x24x1x1x1xf16>) -> tensor<1x54x16x128x128xf16> {
%c24 = arith.constant 24 : index
%c128 = arith.constant 128 : index
%c16 = arith.constant 16 : index
%c54 = arith.constant 54 : index
%c1 = arith.constant 1 : index
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%0 = bufferization.to_memref %arg1 : memref<54x24x1x1x1xf16, strided<[?, ?, ?, ?, ?], offset: ?>>
%1 = bufferization.to_memref %arg0 : memref<1x24x16x128x128xf16, strided<[?, ?, ?, ?, ?], offset: ?>>
%alloc = memref.alloc() {alignment = 64 : i64} : memref<1x54x16x128x128xf32>
emitc.for %arg2 = %c0 to %c1 step %c1 {
emitc.for %arg3 = %c0 to %c54 step %c1 {
emitc.for %arg4 = %c0 to %c16 step %c1 {
emitc.for %arg5 = %c0 to %c128 step %c1 {
emitc.for %arg6 = %c0 to %c128 step %c1 {
memref.store %cst, %alloc[%arg2, %arg3, %arg4, %arg5, %arg6] : memref<1x54x16x128x128xf32>
}
}
}
}
}
emitc.for %arg2 = %c0 to %c1 step %c1 {
emitc.for %arg3 = %c0 to %c16 step %c1 {
emitc.for %arg4 = %c0 to %c128 step %c1 {
emitc.for %arg5 = %c0 to %c128 step %c1 {
emitc.for %arg6 = %c0 to %c54 step %c1 {
emitc.for %arg7 = %c0 to %c1 step %c1 {
emitc.for %arg8 = %c0 to %c1 step %c1 {
emitc.for %arg9 = %c0 to %c1 step %c1 {
emitc.for %arg10 = %c0 to %c24 step %c1 {
%3 = affine.apply #map(%arg3, %arg7)
%4 = affine.apply #map(%arg4, %arg8)
%5 = affine.apply #map(%arg5, %arg9)
%6 = memref.load %1[%arg2, %arg10, %3, %4, %5] : memref<1x24x16x128x128xf16, strided<[?, ?, ?, ?, ?], offset: ?>>
%7 = memref.load %0[%arg6, %arg10, %arg7, %arg8, %arg9] : memref<54x24x1x1x1xf16, strided<[?, ?, ?, ?, ?], offset: ?>>
%8 = memref.load %alloc[%arg2, %arg6, %arg3, %arg4, %arg5] : memref<1x54x16x128x128xf32>
%9 = arith.extf %6 : f16 to f32
%10 = arith.extf %7 : f16 to f32
%11 = arith.mulf %9, %10 : f32
%12 = arith.addf %8, %11 : f32
memref.store %12, %alloc[%arg2, %arg6, %arg3, %arg4, %arg5] : memref<1x54x16x128x128xf32>
}
}
}
}
}
}
}
}
}
%alloc_0 = memref.alloc() {alignment = 64 : i64} : memref<1x54x16x128x128xf16>
emitc.for %arg2 = %c0 to %c1 step %c1 {
emitc.for %arg3 = %c0 to %c54 step %c1 {
emitc.for %arg4 = %c0 to %c16 step %c1 {
emitc.for %arg5 = %c0 to %c128 step %c1 {
emitc.for %arg6 = %c0 to %c128 step %c1 {
%3 = memref.load %alloc[%c0, %arg3, %arg4, %arg5, %arg6] : memref<1x54x16x128x128xf32>
%4 = arith.truncf %3 : f32 to f16
memref.store %4, %alloc_0[%arg2, %arg3, %arg4, %arg5, %arg6] : memref<1x54x16x128x128xf16>
}
}
}
}
}
%2 = bufferization.to_tensor %alloc_0 : memref<1x54x16x128x128xf16>
return %2 : tensor<1x54x16x128x128xf16>
}
func.func @q_conv_test(%arg0: tensor<?x?x?x?xi8>, %arg1: tensor<?x?x?x?xi8>, %arg2: tensor<?xf32>) -> tensor<?x?x?x?xf32> {
%c1 = arith.constant 1 : index
%c3_i32 = arith.constant 3 : i32
%c7_i32 = arith.constant 7 : i32
%cst = arith.constant 1.000000e-04 : f64
%cst_0 = arith.constant 0.000000e+00 : f32
%c3 = arith.constant 3 : index
%c2 = arith.constant 2 : index
%cst_1 = arith.constant 2.14748365E+9 : f32
%cst_2 = arith.constant -2.14748365E+9 : f32
%c0 = arith.constant 0 : index
%c1_i64 = arith.constant 1 : i64
%0 = bufferization.to_memref %arg1 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%1 = bufferization.to_memref %arg0 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%2 = bufferization.to_memref %arg1 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%3 = bufferization.to_memref %arg1 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%4 = bufferization.to_memref %arg1 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%5 = bufferization.to_memref %arg0 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%6 = bufferization.to_memref %arg0 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%7 = bufferization.to_memref %arg0 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%8 = bufferization.to_memref %arg2 : memref<?xf32, strided<[?], offset: ?>>
%9 = bufferization.to_memref %arg2 : memref<?xf32, strided<[?], offset: ?>>
%dim = memref.dim %9, %c0 : memref<?xf32, strided<[?], offset: ?>>
%alloc = memref.alloc(%dim) {alignment = 64 : i64} : memref<?xi32>
%dim_3 = memref.dim %8, %c0 : memref<?xf32, strided<[?], offset: ?>>
emitc.for %arg3 = %c0 to %dim_3 step %c1 {
%21 = memref.load %8[%arg3] : memref<?xf32, strided<[?], offset: ?>>
%22 = arith.truncf %cst : f64 to f32
%23 = arith.divf %21, %22 : f32
%24 = math.roundeven %23 : f32
%25 = arith.addf %24, %cst_0 : f32
%26 = arith.maximumf %25, %cst_2 : f32
%27 = arith.minimumf %26, %cst_1 : f32
%28 = arith.fptosi %27 : f32 to i32
memref.store %28, %alloc[%arg3] : memref<?xi32>
}
%alloc_4 = memref.alloc(%dim) {alignment = 64 : i64} : memref<?xf32>
emitc.for %arg3 = %c0 to %dim step %c1 {
%21 = memref.load %alloc[%arg3] : memref<?xi32>
%22 = arith.sitofp %21 : i32 to f32
%23 = arith.truncf %cst : f64 to f32
%24 = arith.mulf %22, %23 : f32
memref.store %24, %alloc_4[%arg3] : memref<?xf32>
}
emitc.for %arg3 = %c0 to %dim step %c1 {
%21 = memref.load %alloc_4[%arg3] : memref<?xf32>
%22 = arith.truncf %cst : f64 to f32
%23 = arith.divf %21, %22 : f32
%24 = math.roundeven %23 : f32
%25 = arith.addf %24, %cst_0 : f32
%26 = arith.maximumf %25, %cst_2 : f32
%27 = arith.minimumf %26, %cst_1 : f32
%28 = arith.fptosi %27 : f32 to i32
memref.store %28, %alloc[%arg3] : memref<?xi32>
}
%dim_5 = memref.dim %7, %c0 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%dim_6 = memref.dim %6, %c2 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%dim_7 = memref.dim %5, %c3 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%dim_8 = memref.dim %4, %c0 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%dim_9 = memref.dim %3, %c2 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%dim_10 = memref.dim %2, %c3 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%10 = arith.index_cast %dim_9 : index to i64
%11 = arith.index_cast %dim_6 : index to i64
%12 = arith.subi %10, %c1_i64 : i64
%13 = arith.subi %11, %12 : i64
%14 = arith.index_cast %13 : i64 to index
%15 = arith.index_cast %dim_10 : index to i64
%16 = arith.index_cast %dim_7 : index to i64
%17 = arith.subi %15, %c1_i64 : i64
%18 = arith.subi %16, %17 : i64
%19 = arith.index_cast %18 : i64 to index
%alloc_11 = memref.alloc(%dim_5, %dim_8, %14, %19) {alignment = 64 : i64} : memref<?x?x?x?xi32>
emitc.for %arg3 = %c0 to %dim_5 step %c1 {
emitc.for %arg4 = %c0 to %dim step %c1 {
emitc.for %arg5 = %c0 to %14 step %c1 {
emitc.for %arg6 = %c0 to %19 step %c1 {
%21 = memref.load %alloc[%arg4] : memref<?xi32>
memref.store %21, %alloc_11[%arg3, %arg4, %arg5, %arg6] : memref<?x?x?x?xi32>
}
}
}
}
%dim_12 = memref.dim %1, %c0 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%dim_13 = memref.dim %1, %c1 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%dim_14 = memref.dim %0, %c0 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%dim_15 = memref.dim %0, %c2 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%dim_16 = memref.dim %0, %c3 : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
emitc.for %arg3 = %c0 to %dim_12 step %c1 {
emitc.for %arg4 = %c0 to %dim_14 step %c1 {
emitc.for %arg5 = %c0 to %14 step %c1 {
emitc.for %arg6 = %c0 to %19 step %c1 {
emitc.for %arg7 = %c0 to %dim_13 step %c1 {
emitc.for %arg8 = %c0 to %dim_15 step %c1 {
emitc.for %arg9 = %c0 to %dim_16 step %c1 {
%21 = affine.apply #map(%arg5, %arg8)
%22 = affine.apply #map(%arg6, %arg9)
%23 = memref.load %1[%arg3, %arg7, %21, %22] : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%24 = memref.load %0[%arg4, %arg7, %arg8, %arg9] : memref<?x?x?x?xi8, strided<[?, ?, ?, ?], offset: ?>>
%25 = memref.load %alloc_11[%arg3, %arg4, %arg5, %arg6] : memref<?x?x?x?xi32>
%26 = arith.extsi %23 : i8 to i32
%27 = arith.subi %26, %c7_i32 : i32
%28 = arith.extsi %24 : i8 to i32
%29 = arith.subi %28, %c3_i32 : i32
%30 = arith.muli %27, %29 : i32
%31 = arith.addi %25, %30 : i32
memref.store %31, %alloc_11[%arg3, %arg4, %arg5, %arg6] : memref<?x?x?x?xi32>
}
}
}
}
}
}
}
%alloc_17 = memref.alloc(%dim_5, %dim_8, %14, %19) {alignment = 64 : i64} : memref<?x?x?x?xf32>
emitc.for %arg3 = %c0 to %dim_5 step %c1 {
emitc.for %arg4 = %c0 to %dim_8 step %c1 {
emitc.for %arg5 = %c0 to %14 step %c1 {
emitc.for %arg6 = %c0 to %19 step %c1 {
%21 = memref.load %alloc_11[%arg3, %arg4, %arg5, %arg6] : memref<?x?x?x?xi32>
%22 = arith.sitofp %21 : i32 to f32
%23 = arith.truncf %cst : f64 to f32
%24 = arith.mulf %22, %23 : f32
memref.store %24, %alloc_17[%arg3, %arg4, %arg5, %arg6] : memref<?x?x?x?xf32>
}
}
}
}
%20 = bufferization.to_tensor %alloc_17 : memref<?x?x?x?xf32>
return %20 : tensor<?x?x?x?xf32>
}
func.func @conv_broadcast(%arg0: tensor<1x80x3000xf32>, %arg1: tensor<1024x80x3xf32>, %arg2: tensor<1024xf32>) -> tensor<1x1024x3000xf32> {
%c3 = arith.constant 3 : index
%c3000 = arith.constant 3000 : index
%c1024 = arith.constant 1024 : index
%c3002 = arith.constant 3002 : index
%c80 = arith.constant 80 : index
%c1 = arith.constant 1 : index
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%0 = bufferization.to_memref %arg0 : memref<1x80x3000xf32, strided<[?, ?, ?], offset: ?>>
%1 = bufferization.to_memref %arg1 : memref<1024x80x3xf32, strided<[?, ?, ?], offset: ?>>
%2 = bufferization.to_memref %arg2 : memref<1024xf32, strided<[?], offset: ?>>
%alloc = memref.alloc() {alignment = 64 : i64} : memref<1x80x3002xf32>
emitc.for %arg3 = %c0 to %c1 step %c1 {
emitc.for %arg4 = %c0 to %c80 step %c1 {
emitc.for %arg5 = %c0 to %c3002 step %c1 {
memref.store %cst, %alloc[%arg3, %arg4, %arg5] : memref<1x80x3002xf32>
}
}
}
%reinterpret_cast = memref.reinterpret_cast %alloc to offset: [1], sizes: [1, 80, 3000], strides: [240160, 3002, 1] : memref<1x80x3002xf32> to memref<1x80x3000xf32, strided<[240160, 3002, 1], offset: 1>>
memref.copy %0, %reinterpret_cast : memref<1x80x3000xf32, strided<[?, ?, ?], offset: ?>> to memref<1x80x3000xf32, strided<[240160, 3002, 1], offset: 1>>
%alloc_0 = memref.alloc() {alignment = 64 : i64} : memref<1x1024x3000xf32>
emitc.for %arg3 = %c0 to %c1 step %c1 {
emitc.for %arg4 = %c0 to %c1024 step %c1 {
emitc.for %arg5 = %c0 to %c3000 step %c1 {
%4 = memref.load %2[%arg4] : memref<1024xf32, strided<[?], offset: ?>>
memref.store %4, %alloc_0[%arg3, %arg4, %arg5] : memref<1x1024x3000xf32>
}
}
}
emitc.for %arg3 = %c0 to %c1 step %c1 {
emitc.for %arg4 = %c0 to %c1024 step %c1 {
emitc.for %arg5 = %c0 to %c3000 step %c1 {
emitc.for %arg6 = %c0 to %c80 step %c1 {
emitc.for %arg7 = %c0 to %c3 step %c1 {
%4 = affine.apply #map(%arg5, %arg7)
%5 = memref.load %alloc[%arg3, %arg6, %4] : memref<1x80x3002xf32>
%6 = memref.load %1[%arg4, %arg6, %arg7] : memref<1024x80x3xf32, strided<[?, ?, ?], offset: ?>>
%7 = memref.load %alloc_0[%arg3, %arg4, %arg5] : memref<1x1024x3000xf32>
%8 = arith.mulf %5, %6 : f32
%9 = arith.addf %7, %8 : f32
memref.store %9, %alloc_0[%arg3, %arg4, %arg5] : memref<1x1024x3000xf32>
}
}
}
}
}
%3 = bufferization.to_tensor %alloc_0 : memref<1x1024x3000xf32>
return %3 : tensor<1x1024x3000xf32>
}
}
```
How to fix it？
</pre>
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