ttnn.batch_norm

ttnn.batch_norm = Operation(python_fully_qualified_name='ttnn.batch_norm', function=<ttnn._ttnn.operations.normalization.batch_norm_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)

Applies Spatial Batch Normalization over each channel on input_tensor. Inputs must be must be tilized and interleaved.

Parameters:

input_tensor (ttnn.Tensor) – the input tensor of shape [N, C, H, W].

Keyword Arguments:

  • eps (float, optional) – Epsilon value. Defaults to 1e-05.

  • momentum (float, optional) – Momentum value. Defaults to 0.1.

  • running_mean (ttnn.Tensor, optional) – the running_mean of shape [1, C, 1, 1], required in inference mode. When in training mode, this tensor is optional and the updated running mean value is stored in-place based on the inputs provided. Defaults to None.

  • running_var (ttnn.Tensor, optional) – the running_var of shape [1, C, 1, 1], required in inference mode. When in training mode, this tensor is optional and the updated running variance value is stored in-place based on the inputs provided. Defaults to None.

  • weight (ttnn.Tensor, optional) – the weight or gamma value of shape [1, C, 1, 1]. Defaults to None.

  • bias (ttnn.Tensor, optional) – the bias or beta value of shape [1, C, 1, 1]. Defaults to None.

  • training (bool, optional) – Selection between training mode and inference (evaluation) mode. Defaults to False (Inference mode).

  • output (ttnn.Tensor, optional) – Preallocated output tensor to store batch norm result of shape [N, C, H, W]. Defaults to None.

  • memory_config (ttnn.MemoryConfig, optional) – memory configuration for the operation. 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

4

Example

>>> input_tensor = ttnn.from_torch(torch.rand([2, 3, 4, 5], dtype=torch.bfloat16)), layout=ttnn.TILE_LAYOUT, device=device)
>>> running_mean = ttnn.from_torch(torch.rand([1, 3, 1, 1], dtype=torch.bfloat16)), layout=ttnn.TILE_LAYOUT, device=device)
>>> running_var = ttnn.from_torch(torch.rand([1, 3, 1, 1], dtype=torch.bfloat16)), layout=ttnn.TILE_LAYOUT, device=device)
>>> weight = ttnn.from_torch(torch.rand([1, 3, 1, 1], dtype=torch.bfloat16)), layout=ttnn.TILE_LAYOUT, device=device)
>>> bias = ttnn.from_torch(torch.rand([1, 3, 1, 1], dtype=torch.bfloat16)), layout=ttnn.TILE_LAYOUT, device=device)
>>> eps = 1e-05
>>> momentum = 0.1
>>> output = ttnn.batch_norm(input_tensor, running_mean = running_mean, running_var = running_var, weight = weight, bias = bias, eps = eps, momentum = momentum, training = True)