forked from mrq/DL-Art-School
70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
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"""
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A list of functions that map a unified set of arguments to a fully built transformer. Also includes some testing
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utilities for measuring parameter count, FLOPS, and general performance of each type.
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Every function contains the following arguments:
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layers: Net number of layers in the transformer.
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model_dim: Hidden dimensionality of the model.
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heads: Number of attention heads.
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num_tokens: Number of possible tokens in the transformer's dictionary. Do not use this in future releases.
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max_seq_len: Maximum sequence length to attend to.
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checkpointing: Whether or not the underlying implementation should support gradient checkpointing.
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"""
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import functools
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import torch
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def null_position_embeddings(range, dim):
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return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
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def build_hf_gpt_transformer(layers, model_dim, heads, num_tokens, max_seq_len, checkpointing):
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"""
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GPT-2 implemented by the HuggingFace library.
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"""
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from transformers import GPT2Config, GPT2Model
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gpt_config = GPT2Config(vocab_size=num_tokens,
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n_positions=max_seq_len,
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n_ctx=max_seq_len,
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n_embd=model_dim,
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n_layer=layers,
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n_head=heads,
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gradient_checkpointing=checkpointing,
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use_cache=not checkpointing)
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gpt = GPT2Model(gpt_config)
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# Override the built in positional embeddings
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del gpt.wpe
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gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim)
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return gpt
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def build_lr_performer(layers, model_dim, heads, num_tokens, max_seq_len, checkpointing):
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"""
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lucidrains Performer implementation, https://github.com/lucidrains/performer-pytorch
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"""
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pass
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def build_lr_reformer(layers, model_dim, heads, num_tokens, max_seq_len, checkpointing):
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"""
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lucidrains Reformer implementation, https://github.com/lucidrains/reformer-pytorch
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"""
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pass
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def build_lr_xformer(layers, model_dim, heads, num_tokens, max_seq_len, checkpointing):
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"""
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lucidrains x-transformer implementation, https://github.com/lucidrains/x-transformers
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"""
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pass
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def test_all_performance(**kwargs):
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transformer_builders = [build_hf_gpt_transformer, build_lr_performer, build_lr_reformer, build_lr_xformer]
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for builder in transformer_builders:
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model = builder(**kwargs)
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if __name__ == '__main__':
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test_all_performance(12, 512, 8, 8192, 1000, False)
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