ttnn.empty_like
- ttnn.empty_like = Operation(python_fully_qualified_name='ttnn.empty_like', function=<ttnn._ttnn.operations.creation.empty_like_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function _golden_function>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)
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Creates a new tensor with the same shape as the given reference, but without initializing its values. The data type, layout, device, and memory configuration of the new tensor can be specified.
- Parameters:
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reference (ttnn.Tensor) – The reference tensor whose shape will be used for the output tensor.
- Keyword Arguments:
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dtype (ttnn.DataType, optional) – The desired data type of the output tensor. Defaults to ttnn.bfloat16.
layout (ttnn.Layout, optional) – The desired layout of the output tensor. Defaults to ttnn.ROW_MAJOR.
device (ttnn.Device | ttnn.MeshDevice, optional) – The device where the tensor will be allocated. Defaults to None.
memory_config (ttnn.MemoryConfig, optional) – The memory configuration for the operation. Defaults to ttnn.DRAM_MEMORY_CONFIG.
- Returns:
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ttnn.Tensor – The output uninitialized tensor with the same shape as the reference tensor.
Example
>>> reference = ttnn.from_torch(torch.randn(2, 3), dtype=ttnn.bfloat16) >>> tensor = ttnn.empty_like(reference, dtype=ttnn.float32) >>> print(tensor) ttnn.Tensor([[[[0.87, 0.45, 0.22], [0.60, 0.75, 0.25]]]], shape=Shape([2, 3]), dtype=DataType::BFLOAT16, layout=Layout::ROW_MAJOR)