<table border="1" cellspacing="0" cellpadding="8">
    <tr>
        <th>Issue</th>
        <td>
            <a href=https://github.com/llvm/llvm-project/issues/118939>118939</a>
        </td>
    </tr>

    <tr>
        <th>Summary</th>
        <td>
            [MLIR] Inconsistent output when executing MLIR program with and without `-linalg-inline-scalar-operands`
        </td>
    </tr>

    <tr>
      <th>Labels</th>
      <td>
            mlir
      </td>
    </tr>

    <tr>
      <th>Assignees</th>
      <td>
      </td>
    </tr>

    <tr>
      <th>Reporter</th>
      <td>
          jzc-1122
      </td>
    </tr>
</table>

<pre>
    My git version is [35619c7](https://github.com/llvm/llvm-project/commit/35619c791d1f5128d16c7a8e099e8856e12ab39c).

## Description:
 I am experiencing an inconsistent result when executing the same MLIR program with and without the `-linalg-inline-scalar-operands`.

## Steps to Reproduce:

### 1. **MLIR Program (test.mlir)**:

test.mlir:

```
module {
  func.func private @printMemrefI32(tensor<*xi32>)
  func.func @main() -> () {
    %arg0 = "tosa.const"() {value = dense<83> : tensor<i32>} : () -> tensor<i32>
 %arg1 = "tosa.const"() {value = dense<36> : tensor<i32>} : () -> tensor<i32>
    %0 = tosa.arithmetic_right_shift %arg0, %arg1 {round = true} : (tensor<i32>, tensor<i32>) -> tensor<i32>
    %rtn1 = tensor.cast %0 : tensor<i32> to tensor<*xi32>
    call @printMemrefI32(%rtn1) : (tensor<*xi32>) -> ()
    return
  }
}
```

### 2. **Command to Run Without `-linalg-inline-scalar-operands`:**

```
/path/llvm-project/build/bin/mlir-opt test.mlir -pass-pipeline='builtin.module(func.func(tosa-to-linalg))' | \
/path/llvm-project/build/bin/mlir-opt -tosa-to-arith -one-shot-bufferize='bufferize-function-boundaries' -convert-linalg-to-loops -finalize-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
/path/llvm-project/build/bin/mlir-cpu-runner -e main -entry-point-result=void \
-shared-libs=/path/llvm-project/build/lib/libmlir_runner_utils.so \
-shared-libs=/path/llvm-project/build/lib/libmlir_c_runner_utils.so \
-shared-libs=/path/llvm-project/build/lib/libmlir_async_runtime.so
```

### 3. **Output Without `-linalg-inline-scalar-operands`:**
```
[5]
```

### 4. **Command to Run With `-linalg-inline-scalar-operands`:**
```
/path/llvm-project/build/bin/mlir-opt test.mlir -pass-pipeline='builtin.module(func.func(tosa-to-linalg))' | \
/path/llvm-project/build/bin/mlir-opt -linalg-inline-scalar-operands -tosa-to-arith -one-shot-bufferize='bufferize-function-boundaries' -convert-linalg-to-loops -finalize-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
/path/llvm-project/build/bin/mlir-cpu-runner -e main -entry-point-result=void \
-shared-libs=/path/llvm-project/build/lib/libmlir_runner_utils.so \
-shared-libs=/path/llvm-project/build/lib/libmlir_c_runner_utils.so \
-shared-libs=/path/llvm-project/build/lib/libmlir_async_runtime.so
```

### 5. **Output With `-linalg-inline-scalar-operands`:**
```
[0]
```

I'm not sure if there is any bug in my program or if the wrong usage of the above passes caused this result.
</pre>
<img width="1px" height="1px" alt="" src="http://email.email.llvm.org/o/eJzsV02PozgT_jXOpWRkbAjhkEM6mUgtvaN3NXvYY8tABTwCG_kjMz2_fmUg6U_1fPTuYaWREHFMuZ6nyvVYLumcajXiluQ3JD-sZPCdsdvP32qappyvKtPcbz_eQ6s8nNE6ZTQoByS_Efk6LeuC5AfCN533oyNiR_iR8GOrfBeqpDYD4ce-P19-6GjNZ6w94cfaDIOKg8VNmTbpKU_5pknXdSE3yMoSN5t8jSmXlShrwsuEsF18uCBcwAFdbdXoldERmO3gFuQA-HVEq1DXSrcgNShdG-2U86g9WHSh9_ClQw34Fevgo5XvEJwcED7-7_YTjNa0Vg7wRfkOpG6mgQl-MiNrRnulZd9SpXulkbpa9tJSM6KVunFkzZ7S_NPj6MAb-ISjNU2ocWZ7tYhGaQKE7wjfTQz-WBgQvvHofDL0yhJezhbX1Q-frv7WbHnYbjBN6BFIcRMzA6eg6yS-YLTqLD0CydholfYfcbB4uhV8QtPOWCL2hO--KsGJ-BBxnzogGRuk0oRvCC-BEvEBlvEFDIDwXNqWAREHIJx742QSt8ETzh-Mz7IPONk0qB0Ssd-IyZ3YwZXKQqM4TNOPUZ-bsN2Cm_40rli_D3cOeY53ApVW-W5Ar-o7q9rO37lOnfwlL4TvH6gWN9YE3cxrbcBHkM-R-P4F-HdJWa_nfMwWSS2dv5B9EW6s09eKYPZWy75_tW4WmCm9z5g_qaTH1bL4tOiD1dMfUhxiEc_vR6X8RCn8opS9GYaozqisoOGvRaQ_ItDpnJqeF7Ih_DhK3708r6qg-ib-xso_RtlRM3q4ihDoKJ2joxoxohJxILyIi7zSySxGwjdXFcUUGSepNwvbSd8l4QWQYg8k3_8CGXpxOVUfUBOj74ynVTid0KpvV1rLXxqpxAOUVrEEpVXoIgVaG31G6y-ZjCyNGR3QU5yIK4dp-6cv_Xl4WBE9PsxarI2uVY80aIvTyobGCnS_GGY9BmqD1miBIsRzCChqb-_paJT2dD7giTicjWoW_9R10mJDe1W5KQHfgetVNb8j4t2Mdhe86l3izD_is_5XvEp3ryfPXg2YOPOWjMRFRv8Pfgz-1-XzGCC_yeNt4A3Y7A31vg_7P63bN6P-Levfsv5RWeevyPq9mmavavqW8GIAbTy4YBHUKV6P48CB1PdQhRaUhuH-ep02djGCL9boFoKTLYKZp2RlzghRieiglsFhA75TbrmwJ6tmK5pSlHKF27QQIit4xrJVt82buqqZEFlWVWspMC0FFmlVMSwY32zkSm0541nK2Zpt2DrLEtasWcZPTJxkzZDlJGM4SNUncTMSY9uVci7gNk03pShXvaywd1N3xPl8EeexT7LbafOq0DqSsV457x48eOX7qaOK13mSH-D2cRNi5r151oS83Xv8yB6ugu23P92ITdE6wo9LwOct_zsAAP__oZZolg">