[Bug Report] invalid signbit result

[hschoi4448]

Describe the bug
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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,
    tt_lib_ops
)
from tests.ttnn.python_api_testing.sweep_tests import ttnn_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 = float('0')
    in_data, input_tensor = data_gen_pt_tt(input_shapes, device, True, val=val)
    
    print("input_tensor", input_tensor)
    
    golden_tensor = pytorch_ops.signbit(in_data)
    tt_output_tensor_on_device = ttnn.signbit(input_tensor)
    
    print("tt_output_tensor_on_device", tt_output_tensor_on_device)
    print("golden_tensor", golden_tensor)
    

2. Run with pytest

input_tensor ttnn.Tensor([[[[ 0.00000,  0.00000,  ...,  0.00000,  0.00000],
               [ 0.00000,  0.00000,  ...,  0.00000,  0.00000],
               ...,
               [ 0.00000,  0.00000,  ...,  0.00000,  0.00000],
               [ 0.00000,  0.00000,  ...,  0.00000,  0.00000]]]], shape=Shape([1, 1, 32, 32]), dtype=DataType::BFLOAT16, layout=Layout::TILE)
tt_output_tensor_on_device ttnn.Tensor([[[[ 1.00000,  1.00000,  ...,  1.00000,  1.00000],
               [ 1.00000,  1.00000,  ...,  1.00000,  1.00000],
               ...,
               [ 1.00000,  1.00000,  ...,  1.00000,  1.00000],
               [ 1.00000,  1.00000,  ...,  1.00000,  1.00000]]]], shape=Shape([1, 1, 32, 32]), dtype=DataType::BFLOAT16, layout=Layout::TILE)
golden_tensor tensor([[[[False, False, False,  ..., False, False, False],
          [False, False, False,  ..., False, False, False],
          [False, False, False,  ..., False, False, False],
          ...,
          [False, False, False,  ..., False, False, False],
          [False, False, False,  ..., False, False, False],
          [False, False, False,  ..., False, False, False]]]])

Expected behavior
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Screenshots
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Please complete the following environment information:

• OS: [e.g. Ubuntu 20.04]
• Version of software (eg. commit db6d456)
• Device: wormhole_b0

Additional context
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[umadevimcw]

@tt-aho @eyonland and @jliangTT Not able to check -0.0 in both GS and WHB0. How do we proceed with this? Suggestions will be helpful for us to proceed further.

[hschoi4448]

Is this issue still ongoing? Or is it an issue that cannot be addressed by TT? @tt-aho @eyonland @jliangTT @umadevimcw @razorback3

[eyonland]

There are hardware limitations that prevent -0.0 being handled. If there are use cases that cannot be handled with the current op, we need to identify them and create new ops to handle those special cases. See comments on #8944

[VirdhatchaniKN]

Hi @hschoi4448 , We ran the test as the fix has been merged. Now the test seems to pass.

Can you confirm ?

[VirdhatchaniKN]

Closing Issue as test file passes as fix is merged to main