[Bug Report] invalid asin result

[hschoi4448]

Describe the bug A clear and concise description of what the bug is.

The asin function returns an invalid value.

To Reproduce Steps to reproduce the behavior:

1. Copy and past below code

   
   # SPDX-FileCopyrightText: © 2023 Tenstorrent Inc.

SPDX-License-Identifier: Apache-2.0

import torch import pytest import tt_lib from tests.tt_eager.python_api_testing.unit_testing.backward_ops.utility_funcs import data_gen_pt_tt, compare_results

import ttnn from tests.tt_eager.python_api_testing.sweep_tests import pytorch_ops

def data_gen_pt_tt(input_shapes, device, required_grad=False, val=1): pt_tensor = (torch.ones(input_shapes, requires_grad=required_grad) * val).bfloat16() tt_tensor = ( tt_lib.tensor.Tensor(pt_tensor, tt_lib.tensor.DataType.BFLOAT16).to(tt_lib.tensor.Layout.TILE).to(device) ) return pt_tensor, tt_tensor

@pytest.mark.parametrize( "input_shapes", ( (torch.Size([1, 1, 32, 32])), ), ) def test1(input_shapes, device): val = 90 in_data, input_tensor = data_gen_pt_tt(input_shapes, device, True, val=val)

print("input_tensor", input_tensor)

golden_tensor = pytorch_ops.asin(in_data)
tt_output_tensor_on_device = tt_lib.tensor.asin(input_tensor)

print("tt_output_tensor_on_device", tt_output_tensor_on_device)
print("golden_tensor", golden_tensor)

2. Run with pytest
```Python
input_tensor ttnn.Tensor([[[[90.00000, 90.00000,  ..., 90.00000, 90.00000],
               [90.00000, 90.00000,  ..., 90.00000, 90.00000],
               ...,
               [90.00000, 90.00000,  ..., 90.00000, 90.00000],
               [90.00000, 90.00000,  ..., 90.00000, 90.00000]]]], shape=Shape([1, 1, 32, 32]), dtype=DataType::BFLOAT16, layout=Layout::TILE)
tt_output_tensor_on_device ttnn.Tensor([[[[70039981404865953792.00000, 70039981404865953792.00000,  ..., 70039981404865953792.00000, 70039981404865953792.00000],
               [70039981404865953792.00000, 70039981404865953792.00000,  ..., 70039981404865953792.00000, 70039981404865953792.00000],
               ...,
               [70039981404865953792.00000, 70039981404865953792.00000,  ..., 70039981404865953792.00000, 70039981404865953792.00000],
               [70039981404865953792.00000, 70039981404865953792.00000,  ..., 70039981404865953792.00000, 70039981404865953792.00000]]]], shape=Shape([1, 1, 32, 32]), dtype=DataType::BFLOAT16, layout=Layout::TILE)
golden_tensor tensor([[[[nan, nan, nan,  ..., nan, nan, nan],
          [nan, nan, nan,  ..., nan, nan, nan],
          [nan, nan, nan,  ..., nan, nan, nan],
          ...,
          [nan, nan, nan,  ..., nan, nan, nan],
          [nan, nan, nan,  ..., nan, nan, nan],
          [nan, nan, nan,  ..., nan, nan, nan]]]], dtype=torch.bfloat16,
       grad_fn=<AsinBackward0>)

Expected behavior A clear and concise description of what you expected to happen.

Screenshots If applicable, add screenshots to help explain your problem.

Please complete the following environment information:

• OS: [e.g. Ubuntu 20.04]
• Version of software (eg. commit 2e5b96fae39079e7d120accec7b5f81c34f3924f)
• Device: wormhole_b0

Additional context Add any other context about the problem here.

[umadevimcw]

@hschoi4448 Can you label whether the bugs belong forward or backward? It will help us to categorise it.

[hschoi4448]

@hschoi4448 Can you label whether the bugs belong forward or backward? It will help us to categorise it.

Got it. However, I can't find the 'forward' label.

[umadevimcw]

@jliangTT Can we create a label name "forward" and use it? Also, It would be helpful for us whether the issue is P0/P1/P2 is added

[jliangTT]

let's make bug report from @hschoi4448 by default p1. And we can spend tuesday morning to look at the overall work priority and load-balance.

[umadevimcw]

@hschoi4448 Can you add the labels for other issues created by you? (use the same labels added in this issue and assign it to @umadevimcw ). Thanks in advance!

[umadevimcw]

@hschoi4448 @razorback3 https://github.com/tenstorrent/tt-metal/issues/8944, https://github.com/tenstorrent/tt-metal/issues/8945#issuecomment-2146247945, Please look at this comments for this issue

[umadevimcw]

Need to update the test files with the supported range and test it after migration

[umadevimcw]

@hschoi4448 Fix for this issue available in this PR (due to hardware limitations nan/inf are replaced with the numbers) https://github.com/tenstorrent/tt-metal/pull/11243 Kindly review it.