ttnn.maximum
- ttnn.maximum = Operation(python_fully_qualified_name='ttnn.maximum', function=<ttnn._ttnn.operations.binary.maximum_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function _golden_function_maximum>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)
-
Computes maximum for
input_tensor_a
andinput_tensor_b
and returns the tensor with the same layout asinput_tensor_a
- Parameters:
-
input_tensor_a (ttnn.Tensor) – the input tensor.
input_tensor_b (ttnn.Tensor or Number) – the input tensor.
- Keyword Arguments:
-
memory_config (ttnn.MemoryConfig, optional) – memory configuration for the operation. Defaults to None.
dtype (ttnn.DataType, optional) – data type for the output tensor. Defaults to None.
output_tensor (ttnn.Tensor, optional) – preallocated output tensor. Defaults to None.
activations (List[str], optional) – list of activation functions to apply to the output tensor Defaults to None.
queue_id (int, optional) – command queue id. Defaults to 0.
- Returns:
-
ttnn.Tensor – the output tensor.
Note
Supported dtypes, layouts, and ranks:
Dtypes
Layouts
Ranks
BFLOAT16, FLOAT32
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) >>> output = ttnn.maximum(tensor1, tensor2/scalar)