Forward step.

embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)

embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)

grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/

Parse all the args.

Return my rank for the data parallel group.

Return my rank for the node group.

Calculate the global rank corresponding to a local rank zero in the node group.

Return world size for the node group.

Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings).

This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches

r""" Samples from the Gumbel-Softmax distribution (`Link 1`_ `Link 2`_) and optionally discretizes. Args: logits: `[..., num_features]

Equivalent to nn.functional.interpolate, but with support for empty batch sizes. This will eventually be supported natively by PyTorch, and t

Check if all non-python dependencies are satisfied to build this op

This function loads partitions from rank 0. It takes less memory when world size is large.

Save a model checkpoint.

This function disables printing when not in master process

seq: [2, 3, 5, ..., -1(to be generated), -1, ...] mems: [num_layers, batch_size, len_mems(index), mem_hidden_size] cache,