ttnn.concat_bw

ttnn.concat_bw = FastOperation(python_fully_qualified_name='ttnn.concat_bw', function=<ttnn._ttnn.operations.binary_backward.concat_bw_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function <lambda>>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)

Performs backward operations for concat on input_tensor_a and input_tensor_b with given grad_tensor.

Args:

grad_tensor (ttnn.Tensor): the input gradient tensor. input_tensor_a (ttnn.Tensor): the input tensor. input_tensor_b (ttnn.Tensor): the input tensor. dim (int): Dimension to concatenate. Defaults to 0.

Keyword args:

are_required_outputs (List[bool], optional): List of required outputs. Defaults to [True, True]. memory_config (ttnn.MemoryConfig, optional): Memory configuration for the operation. Defaults to None. input_grad (ttnn.Tensor, optional): Preallocated output tensor for gradient of input_tensor_a. Defaults to None. other_grad (ttnn.Tensor, optional): Preallocated output tensor for gradient of input_tensor_b. Defaults to None. queue_id (int, optional): command queue id. Defaults to 0.

Returns:

List of ttnn.Tensor: the output tensor.

Note:

Supported dtypes, layouts, and ranks:

Dtypes	Layouts	Ranks
BFLOAT16	TILE	4

bfloat8_b/bfloat4_b is only supported on TILE_LAYOUT

Example:

>>> grad_tensor = ttnn.from_torch(torch.rand([14, 1, 30, 32], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> tensor1 = ttnn.from_torch(torch.rand([12, 1, 30, 32], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
>>> tensor2 = ttnn.from_torch(torch.rand([2, 1, 30, 32], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
>>> dim = 0
>>> output = ttnn.concat_bw(grad_tensor, tensor1, tensor2, dim)