ttnn.prod_bw

ttnn.prod_bw = Operation(python_fully_qualified_name='ttnn.prod_bw', function=<ttnn._ttnn.operations.unary_backward.prod_bw_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=None, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)

Performs backward operations for prod on input_tensor with given grad_tensor along a particular dim. If no dim is provided, the prod is taken over all dimensions.

Parameters:

  • grad_tensor (ttnn.Tensor) – the input gradient tensor.

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

Keyword Arguments:

  • dim (int, optional) – dimension to perform prod backward. Defaults to None.

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

Returns:

List of ttnn.Tensor – the output tensor.

Note

Supported dtypes, layouts, and ranks:

Dtypes

Layouts

Ranks

BFLOAT16

TILE

4

For more details about BFLOAT8_B, refer to the BFLOAT8_B limitations.

Example

>>> grad_tensor = ttnn.from_torch(torch.rand([1, 1, 32, 32], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> input = ttnn.from_torch(torch.rand([1, 1, 32, 32], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
>>> dim =0
>>> output = ttnn.prod_bw(grad_tensor, input, dim)
>>> all_dims_output = ttnn.prod_bw(grad_tensor, input)