ttnn.conv1d

ttnn.conv1d = Operation(python_fully_qualified_name='ttnn.conv1d', function=<ttnn._ttnn.operations.conv.conv1d_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 a 1D convolution over an input signal composed of several input planes. Implemented as a 2D Convolution of input height 1 and input width as input_length.

Parameters:

  • input_tensor (ttnn.Tensor) – The input tensor. This must be in the format [N, H, W, C]. It can be on host or device.

  • weight_tensor (ttnn.Tensor) – The weight tensor. The weights can be passed in the same format as PyTorch, [out_channels, in_channels, kernel_height, kernel_width]. The op w

  • bias_tensor (ttnn.Tensor, None) – Optional bias tensor. Default: None

  • device (ttnn.MeshDevice) – The device to use.

  • in_channels (int) – Number of input channels.

  • out_channels (int) – Number of output channels.

  • batch_size (int) – Batch size.

  • input_length (int) – Length of the input signal.

  • kernel_size (int) – Size of the convolving kernel.

  • stride (int) – Stride of the cross-correlation.

  • padding (int or tuple[int, int])) – Zero-padding added to both sides of the input. pad_length or [pad_left, pad_right].

  • dilation (int) – Spacing between kernel elements.

  • groups (int) – Number of blocked connections from input channels to output channels.

  • dtype (ttnn.DataType, None) – The data type of the input tensor. Default: None (will use the same dtype as input_tensor).

  • conv_config (ttnn.Conv2dConfig, None) – Configuration for convolution. Default: None

  • compute_config (ttnn.DeviceComputeKernelConfig, None) – Configuration for compute kernel. Default: None

  • memory_config (ttnn.MemoryConfig, None) – Output Tensor’s Memory Configuration. Default: None

  • return_output_dim (bool) – If true, the op also returns the height and width of the output tensor in [N, H, W, C] format,

  • return_weights_and_bias (bool) – If true, the op also returns the preprocessed weight and bias on device .

Returns:

The output tensor, output height and width, and the preprocessed weights and bias.

Return type:

[ttnn.Tensor]: The output tensor, when return_output_dim = False and return_weights_and_bias = False

Return type:

[ttnn.Tensor, int]: The output tensor, and it’s length, if return_output_dim = True

Return type:

[ttnn.Tensor, Tuple[ttnn.Tensor, ttnn.Tensor]]: The output tensor, it’s weights and biases, if return_weights_and_bias = True

Return type:

[ttnn.Tensor, int, Tuple[ttnn.Tensor, ttnn.Tensor]]: The output tensor,it’s length, it’s weights and biases, if return_output_dim = True and return_weights_and_bias = True