ttnn.isclose

ttnn.isclose = Operation(python_fully_qualified_name='ttnn.isclose', function=<ttnn._ttnn.operations.binary.isclose_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function _golden_function_isclose>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)

Computes isclose for input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a

\[\begin{split}\mathrm{output\_tensor} = \begin{cases} 1, & \text{if } |\mathrm{input\_tensor\_a} - \mathrm{input\_tensor\_b}| \leq (\mathrm{atol} + \mathrm{rtol} \times |\mathrm{input\_tensor\_b}|) \\ 0, & \text{otherwise} \end{cases}\end{split}\]
Parameters:

  • input_tensor_a (ttnn.Tensor) – the input tensor.

  • input_tensor_b (ttnn.Tensor) – the input tensor.

Keyword Arguments:

  • rtol (float) – relative tolerance. Defaults to 1e-05f.

  • atol (float) – absolute tolerance. Defaults to 1e-08f.

  • equal_nan (bool) – if NaN values should be treated as equal during comparison. Defaults to False.

  • memory_config (ttnn.MemoryConfig, optional) – memory configuration for the operation. Defaults to None.

Returns:

ttnn.Tensor – the output tensor.

Note

Supported dtypes, layouts, and ranks:

Dtypes

Layouts

Ranks

BFLOAT16

TILE

2, 3, 4

Example

>>> tensor1 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> tensor2 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> rtol = 1e-4
>>> atol = 1e-5
>>> equal_nan = False
>>> output = ttnn.isclose(tensor1, tensor2, rtol=rtol, atol=atol, equal_nan=equal_nan)