colossalai.nn.layer.parallel_2d

colossalai.nn.layer.parallel_2d.split_tensor_2d(input_, dim=0)

Splits 2D tensor in specified dimension across cols :param input_: Input tensor :param dim: Specified dimension in which to split :type input_: torch.Tensor :type dim: int, optional :return output: Splitted tensor :rtype output: torch.Tensor

colossalai.nn.layer.parallel_2d.reduce_by_batch_2d(input_, reduce_mean=False)

All-reduce the input from the model parallel region.

Parameters

  • input (torch.tensor) – input maxtrix

  • reduce_mean (bool, optional) – If set to True, it will divide the output by column parallel size, default to False

class colossalai.nn.layer.parallel_2d.Linear2D(in_features, out_features, bias=True, dtype=None, skip_bias_add=False, weight_initializer=<function kaiming_uniform_.<locals>.initializer>, bias_initializer=<function xavier_uniform_.<locals>.initializer>)

Linear layer for 2D parallelism

Parameters

  • in_features (int) – size of each input sample

  • out_features (int) – size of each output sample

  • bias (bool, optional) – If set to False, the layer will not learn an additive bias, defaults to True

  • dtype (torch.dtype, optional) – The dtype of parameters, defaults to None

  • skip_bias_add (bool, optional) – If set to True, it will skip bias add for linear layer, which is preserved for kernel fusion, defaults to False

  • weight_initializer (Callable, optional) – The intializer of weight, defaults to kaiming uniform initializer

  • bias_initializer (Callable, optional) – The intializer of bias, defaults to xavier uniform initializer

class colossalai.nn.layer.parallel_2d.LayerNorm2D(normalized_shape, eps=1e-05, dtype=None)

Layer Normalization for 2D parallelism

Parameters

  • normalized_shape (int) – input shape from an expected input of size. \([* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]\) If a single integer is used, it is treated as a singleton list, and this module will normalize over the last dimension which is expected to be of that specific size.

  • eps (float, optional) – a value added to the denominator for numerical stability, defaults to 1e-05

  • dtype (torch.dtype, optional) – The dtype of parameters, defaults to None

class colossalai.nn.layer.parallel_2d.Classifier2D(in_features, num_classes, weight=None, bias=True, dtype=None, weight_initializer=<function kaiming_uniform_.<locals>.initializer>, bias_initializer=<function xavier_uniform_.<locals>.initializer>)

Classifier for 2D parallelism

Parameters

  • in_features (int) – size of each input sample

  • num_classes (int) – number of classes

  • weight (torch.nn.Parameter, optional) – weight of the classifier, defaults to True

  • bias (bool, optional) – If set to False, the layer will not learn an additive bias, defaults to True

  • dtype (torch.dtype, optional) – The dtype of parameters, defaults to None

  • weight_initializer (Callable, optional) – The intializer of weight, defaults to kaiming uniform initializer

  • bias_initializer (Callable, optional) – The intializer of bias, defaults to xavier uniform initializer

class colossalai.nn.layer.parallel_2d.PatchEmbedding2D(img_size, patch_size, in_chans, embed_size, flatten=True, dtype=None, weight_initializer=<function kaiming_uniform_.<locals>.initializer>, bias_initializer=<function xavier_uniform_.<locals>.initializer>, position_embed_initializer=<function zeros_.<locals>.initializer>)

2D Image to Patch Embedding

Parameters

  • img_size (int) – image size

  • patch_size (int) – patch size

  • in_chans (int) – number of channels of input image

  • embed_size (int) – size of embedding

  • dtype (torch.dtype, optional) – The dtype of parameters, defaults to None

  • flatten (bool, optional) – whether to flatten output tensor, defaults to True

  • weight_initializer (Callable, optional) – The intializer of weight, defaults to kaiming uniform initializer

  • bias_initializer (Callable, optional) – The intializer of bias, defaults to xavier uniform initializer

  • position_embed_initializer (Callable, optional) – The intializer of position embedding, defaults to zero

class colossalai.nn.layer.parallel_2d.Embedding2D(num_embeddings, embedding_dim, padding_idx=None, dtype=None, weight_initializer=<function normal_.<locals>.initializer>, *args, **kwargs)

Embedding for 2D parallelism

Parameters

  • num_embeddings (int) – number of embeddings

  • embedding_dim (int) – dimension of embedding

  • padding_idx (int, optional) – index of padding, defaults to None

  • dtype (torch.dtype, optional) – The dtype of parameters, defaults to None

  • weight_initializer (Callable, optional) – The intializer of weight, defaults to normal initializer

  • args – Args used in F.embedding

  • kwargs – Kwargs used in F.embedding

class colossalai.nn.layer.parallel_2d.VocabParallelEmbedding2D(num_embeddings, embedding_dim, padding_idx=None, dtype=None, weight_initializer=<function normal_.<locals>.initializer>, *args, **kwargs)

Embedding parallelized in the vocabulary dimension.

Parameters

  • num_embeddings (int) – number of embeddings

  • embedding_dim (int) – dimension of embedding

  • padding_idx (int, optional) – index of padding, defaults to None

  • dtype (torch.dtype, optional) – The dtype of parameters, defaults to None

  • weight_initializer (Callable, optional) – The intializer of weight, defaults to normal initializer

  • args – Args used in F.embedding

  • kwargs – Kwargs used in F.embedding

class colossalai.nn.layer.parallel_2d.VocabParallelClassifier2D(in_features, num_classes, weight=None, bias=True, dtype=None, weight_initializer=<function kaiming_uniform_.<locals>.initializer>, bias_initializer=<function xavier_uniform_.<locals>.initializer>)

Vocab parallel classifier layer for 2D parallelism

Parameters

  • in_features (int) – size of each input sample

  • num_classes (int) – number of classes

  • weight (torch.nn.Parameter, optional) – weight of the classifier, defaults to True

  • bias (bool, optional) – If set to False, the layer will not learn an additive bias, defaults to True

  • dtype (torch.dtype, optional) – The dtype of parameters, defaults to None

  • weight_initializer (Callable, optional) – The intializer of weight, defaults to kaiming uniform initializer

  • bias_initializer (Callable, optional) – The intializer of bias, defaults to xavier uniform initializer