Request i1 type support in Interpreter

[qingyunqu]

Request description

It failed when I run some case like:

module {
  func.func @forward(%arg0: tensor<4xf32>) -> tensor<4xi1> {
    %0 = stablehlo.convert %arg0 : (tensor<4xf32>) -> tensor<4xi1>
    return %0 : tensor<4xi1>
  }
}

[sdasgup3]

Can you please elaborate on the error message?

Actually, the i1 type is supported in the interpreter. For example,

$ cat test.mlir
module {
  func.func @forward() -> tensor<4xi1> {
     %cst = stablehlo.constant dense<3.0> : tensor<4xf32>
    %0 = stablehlo.convert %cst : (tensor<4xf32>) -> tensor<4xi1>
    return %0 : tensor<4xi1>
  }
}
$ stablehlo-translate --interpret test.mlir
tensor<4xi1> {
  [true, true, true, true]
}

Note: I have modified the above program to specify some input to convert op.

If I run the above mentioned program as is we will get something like (from cs)

error: incorrect number of arguments specified, provided 0 inputs but function expected 1

which means that we have not provided constant inputs equal to the number of program arguments.
Please refer to

stablehlo/examples/c++/ExampleAdd.cpp

Line 91 in 726ac03

auto results = evalModule(*module, {inputValue1, inputValue2}, config);

and

stablehlo/stablehlo/integrations/python/tests/stablehlo.py

Line 283 in 726ac03

actual = np.array(stablehlo.eval_module(m, args)[0])

on how to use the interpreter programatcally using c+ and python APIs resp.

Please let me know if this helps.

[qingyunqu]

Can you please elaborate on the error message?

Actually, the i1 type is supported in the interpreter. For example,

$ cat test.mlir
module {
  func.func @forward() -> tensor<4xi1> {
     %cst = stablehlo.constant dense<3.0> : tensor<4xf32>
    %0 = stablehlo.convert %cst : (tensor<4xf32>) -> tensor<4xi1>
    return %0 : tensor<4xi1>
  }
}
$ stablehlo-translate --interpret test.mlir
tensor<4xi1> {
  [true, true, true, true]
}

Note: I have modified the above program to specify some input to convert op.

If I run the above mentioned program as is we will get something like (from cs)

error: incorrect number of arguments specified, provided 0 inputs but function expected 1

which means that we have not provided constant inputs equal to the number of program arguments. Please refer to

stablehlo/examples/c++/ExampleAdd.cpp

Line 91 in 726ac03

auto results = evalModule(*module, {inputValue1, inputValue2}, config);

and

stablehlo/stablehlo/integrations/python/tests/stablehlo.py

Line 283 in 726ac03

actual = np.array(stablehlo.eval_module(m, args)[0])

on how to use the interpreter programatcally using c+ and python APIs resp.
Please let me know if this helps.

Hi, I have got the failed example:

module attributes {torch.debug_module_name = "ElementwiseAtenLogicalAndOpPromoteBroadcastModule"} {
  func.func @forward(%arg0: tensor<?xf32>, %arg1: tensor<?x?xi64>) -> tensor<?x?xi1> {
    %c = stablehlo.constant dense<1> : tensor<1xi32>
    %0 = stablehlo.convert %arg0 : (tensor<?xf32>) -> tensor<?xi1>
    %1 = stablehlo.convert %arg1 : (tensor<?x?xi64>) -> tensor<?x?xi1>
    %2 = stablehlo.get_dimension_size %0, dim = 0 : (tensor<?xi1>) -> tensor<i32>
    %3 = stablehlo.reshape %2 : (tensor<i32>) -> tensor<1xi32>
    %4 = stablehlo.concatenate %3, dim = 0 : (tensor<1xi32>) -> tensor<1xi32>
    %5 = stablehlo.get_dimension_size %1, dim = 0 : (tensor<?x?xi1>) -> tensor<i32>
    %6 = stablehlo.reshape %5 : (tensor<i32>) -> tensor<1xi32>
    %7 = stablehlo.get_dimension_size %1, dim = 1 : (tensor<?x?xi1>) -> tensor<i32>
    %8 = stablehlo.reshape %7 : (tensor<i32>) -> tensor<1xi32>
    %9 = stablehlo.concatenate %6, %8, dim = 0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>
    %10 = stablehlo.concatenate %c, %4, dim = 0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>
    %11 = stablehlo.maximum %10, %9 : tensor<2xi32>
    %12 = stablehlo.dynamic_broadcast_in_dim %0, %11, dims = [1] : (tensor<?xi1>, tensor<2xi32>) -> tensor<?x?xi1>
    %13 = stablehlo.dynamic_broadcast_in_dim %1, %11, dims = [0, 1] : (tensor<?x?xi1>, tensor<2xi32>) -> tensor<?x?xi1>
    %14 = stablehlo.and %12, %13 : tensor<?x?xi1>
    return %14 : tensor<?x?xi1>
  }
}

The error message is LLVM ERROR: Element is not an integer.

[ghpvnist]

Hi, I'm able to interpret this fine on my machine (Note the 1.0 for floats. Maybe this is the issue?)

// RUN: stablehlo-translate %s --interpret --args="[dense<1.0> : tensor<4xf32>, dense<1> : tensor<4x4xi64>]"

module attributes {torch.debug_module_name = "ElementwiseAtenLogicalAndOpPromoteBroadcastModule"} {
  func.func @forward(%arg0: tensor<?xf32>, %arg1: tensor<?x?xi64>) -> tensor<?x?xi1> {
    %c = stablehlo.constant dense<1> : tensor<1xi32>
    %0 = stablehlo.convert %arg0 : (tensor<?xf32>) -> tensor<?xi1>
    %1 = stablehlo.convert %arg1 : (tensor<?x?xi64>) -> tensor<?x?xi1>
    %2 = stablehlo.get_dimension_size %0, dim = 0 : (tensor<?xi1>) -> tensor<i32>
    %3 = stablehlo.reshape %2 : (tensor<i32>) -> tensor<1xi32>
    %4 = stablehlo.concatenate %3, dim = 0 : (tensor<1xi32>) -> tensor<1xi32>
    %5 = stablehlo.get_dimension_size %1, dim = 0 : (tensor<?x?xi1>) -> tensor<i32>
    %6 = stablehlo.reshape %5 : (tensor<i32>) -> tensor<1xi32>
    %7 = stablehlo.get_dimension_size %1, dim = 1 : (tensor<?x?xi1>) -> tensor<i32>
    %8 = stablehlo.reshape %7 : (tensor<i32>) -> tensor<1xi32>
    %9 = stablehlo.concatenate %6, %8, dim = 0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>
    %10 = stablehlo.concatenate %c, %4, dim = 0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>
    %11 = stablehlo.maximum %10, %9 : tensor<2xi32>
    %12 = stablehlo.dynamic_broadcast_in_dim %0, %11, dims = [1] : (tensor<?xi1>, tensor<2xi32>) -> tensor<?x?xi1>
    %13 = stablehlo.dynamic_broadcast_in_dim %1, %11, dims = [0, 1] : (tensor<?x?xi1>, tensor<2xi32>) -> tensor<?x?xi1>
    %14 = stablehlo.and %12, %13 : tensor<?x?xi1>
    return %14 : tensor<?x?xi1>
  }
}