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 givengrad_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)