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#14448: Update examples of binary ops #14468

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Oct 30, 2024
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#14448: Update examples of binary ops #14468

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56 changes: 28 additions & 28 deletions ttnn/cpp/ttnn/operations/eltwise/binary/binary_pybind.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -44,8 +44,8 @@ void bind_primitive_binary_operation(py::module& module, const binary_operation_

Example:

>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device)
>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device)
>>> output = {1}(tensor1, tensor2)
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What was the issue ? Is it zero or the format how it passed?

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The format that we passed.
torch.tensor((1, 2), dtype=torch.bfloat16) creates a tensor of rank 1 with elements [1 2].
According to the documentation, most of the ops support ranks of 2, 3, 4. That's why we passed the input as
[1 2
3 4]

)doc",
operation.base_name(),
Expand Down Expand Up @@ -110,8 +110,8 @@ void bind_binary_operation(py::module& module, const binary_operation_t& operati

Example:

>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device=device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device=device)
>>> output = {1}(tensor1, tensor2)
)doc",
operation.base_name(),
Expand Down Expand Up @@ -195,8 +195,8 @@ void bind_binary_composite(py::module& module, const binary_operation_t& operati
{4}

Example:
>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device=device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device=device)
>>> output = {1}(tensor1, tensor2)

)doc",
Expand Down Expand Up @@ -246,8 +246,8 @@ void bind_binary_composite_with_alpha(py::module& module, const binary_operation

Example:

>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device=device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device=device)
>>> output = {1}(tensor1, tensor2, alpha)
)doc",
operation.base_name(),
Expand Down Expand Up @@ -286,9 +286,9 @@ void bind_binary_composite_with_rtol_atol(py::module& module, const binary_opera
Args:
input_tensor_a (ttnn.Tensor): the input tensor.
input_tensor_b (ttnn.Tensor): the input tensor.
rtol (float)
atol (float)
equal_nan (bool)
rtol (float): relative tolerance.
atol (float): absolute tolerance.
equal_nan (bool): if NaN values should be treated as equal during comparison.

Keyword args:
memory_config (ttnn.MemoryConfig, optional): Memory configuration for the operation. Defaults to `None`.
Expand All @@ -297,8 +297,8 @@ void bind_binary_composite_with_rtol_atol(py::module& module, const binary_opera
ttnn.Tensor: the output tensor.

Example:
>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device=device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device=device)
>>> output = {1}(tensor1, tensor2, rtol, atol, equal_nan)

)doc",
Expand Down Expand Up @@ -352,8 +352,8 @@ void bind_binary_composite_overload(py::module& module, const binary_operation_t

Example:

>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device)
>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device)
>>> output = {1}(tensor1, tensor2/scalar)
)doc",
operation.base_name(),
Expand Down Expand Up @@ -414,8 +414,8 @@ void bind_div(py::module& module, const binary_operation_t& operation, const std
ttnn.Tensor: the output tensor.

Example:
>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor((0, 1), dtype=torch.bfloat16)), device=device)
>>> tensor1 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device=device)
>>> tensor2 = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device=device)
>>> output = {1}(tensor1, tensor2/scalar)

)doc",
Expand Down Expand Up @@ -481,7 +481,7 @@ void bind_polyval(py::module& module, const binary_operation_t& operation, const

Args:
input_tensor (ttnn.Tensor): the input tensor.
Coeffs (Vector of floats).
Coeffs (Vector of floats): coefficients of the polynomial.

Keyword args:
memory_config (ttnn.MemoryConfig, optional): Memory configuration for the operation. Defaults to `None`.
Expand All @@ -493,7 +493,7 @@ void bind_polyval(py::module& module, const binary_operation_t& operation, const
{4}

Example:
>>> tensor = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
>>> tensor = ttnn.to_device(ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16)), device=device)
>>> coeffs = (1, 2, 3, 4)
>>> output = {1}(tensor, coeffs)

Expand Down Expand Up @@ -538,8 +538,8 @@ void bind_binary_overload_operation(py::module& module, const binary_operation_t
Returns:
ttnn.Tensor: the output tensor.

Example::
>>> tensor = ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16), device=device)
Example:
>>> tensor = ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16), device=device)
>>> output = {1}(tensor1, tensor2)
)doc",
operation.base_name(),
Expand Down Expand Up @@ -590,10 +590,10 @@ void bind_inplace_operation(py::module& module, const binary_operation_t& operat
input_tensor_b (ttnn.Tensor or Number): the input tensor.

Returns:
List of ttnn.Tensor: the output tensor.
ttnn.Tensor: the output tensor.

Example::
>>> tensor = ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16), device=device)
Example:
>>> tensor = ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16), device=device)
>>> output = {1}(tensor1, tensor2)
)doc",
operation.base_name(),
Expand Down Expand Up @@ -639,14 +639,14 @@ void bind_logical_inplace_operation(py::module& module, const binary_operation_t
input_tensor_b (ttnn.Tensor): the input tensor.

Returns:
List of ttnn.Tensor: the output tensor.
ttnn.Tensor: the output tensor.

Note:
{4}

Example:
>>> tensor1 = ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16), device=device)
>>> tensor2 = ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16), device=device)
>>> tensor1 = ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16), device=device)
>>> tensor2 = ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16), device=device)
>>> output = {1}(tensor1, tensor2)
)doc",
operation.base_name(),
Expand Down Expand Up @@ -681,7 +681,7 @@ void bind_binary_inplace_operation(py::module& module, const binary_operation_t&
Keyword args:
* :attr:`activations` (Optional[List[str]]): list of activation functions to apply to the output tensor
Example::
>>> tensor = ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16), device=device)
>>> tensor = ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]]), dtype=torch.bfloat16), device=device)
>>> output = {1}(tensor1, tensor2)
)doc",
operation.base_name(),
Expand Down
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