forked from mrq/DL-Art-School
120 lines
5.0 KiB
Python
120 lines
5.0 KiB
Python
"""
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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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max_mel_seq_len: Maximum mel sequence length to attend to.
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max_text_seq_len: Maximum text sequence length to attend to.
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checkpointing: Whether or not the underlying implementation should support gradient checkpointing.
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Returns:
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(model, global_mel_pos_embedding, global_text_pos_embedding, local_mel_pos_embedding, local_text_pos_embedding)
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model: The transformer model
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global_mel_pos_embedding: A global embedding function (that takes the MEL sequence as input) which should be added on to the MEL embeddings.
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global_text_pos_embedding: The global embedding function for text tokens.
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local_mel_pos_embedding: A local embedding function which, if not None, should be concatenated with the local text position embeddings and fed to the transformer.
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local_text_pos_embedding: The local embedding function for text positions which will be None if local_mel_pos_embedding=None.
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"""
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import functools
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import random
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from time import time
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import torch
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import torch.nn as nn
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from tqdm import tqdm
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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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class LearnedPositionEmbeddings(nn.Module):
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def __init__(self, seq_len, model_dim, init=.02, relative=True):
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super().__init__()
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self.emb = nn.Embedding(seq_len, model_dim)
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# Initializing this way is standard for GPT-2
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self.emb.weight.data.normal_(mean=0.0, std=init)
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self.relative = relative
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self.seq_len = seq_len
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def forward(self, x):
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sl = x.shape[1]
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if self.relative:
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start = random.randint(sl, self.seq_len) - sl
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return self.emb(torch.arange(start, start+sl, device=x.device))
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else:
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return self.emb(torch.arange(0, sl, device=x.device))
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def get_fixed_embedding(self, ind, dev):
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return self.emb(torch.tensor([ind], device=dev)).unsqueeze(0)
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def build_hf_gpt_transformer(layers, model_dim, heads, max_mel_seq_len, max_text_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=256, # Unused.
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n_positions=max_mel_seq_len+max_text_seq_len,
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n_ctx=max_mel_seq_len+max_text_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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# Built-in token embeddings are unused.
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del gpt.wte
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mel_pos_emb = LearnedPositionEmbeddings(max_mel_seq_len, model_dim) if max_mel_seq_len != -1 else functools.partial(null_position_embeddings, dim=model_dim)
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text_pos_emb = LearnedPositionEmbeddings(max_text_seq_len, model_dim) if max_mel_seq_len != -1 else functools.partial(null_position_embeddings, dim=model_dim)
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return gpt, mel_pos_emb, text_pos_emb, None, None
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def build_lr_performer(layers, model_dim, heads, max_mel_seq_len, max_text_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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from models.lucidrains.performer.performer_pytorch import Performer
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model = Performer(dim=model_dim, depth=layers, heads=heads, dim_head=model_dim, causal=True)
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return model
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def build_lr_reformer(layers, model_dim, heads, max_mel_seq_len, max_text_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, max_mel_seq_len, max_text_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,
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build_lr_performer,]
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# build_lr_reformer,
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# build_lr_xformer]
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for builder in transformer_builders:
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model = builder(**kwargs)
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start = time()
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args = torch.randint(0, 8192, (16,450))
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for k in tqdm(range(10)):
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model(args)
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stop = time()
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print(f"Model: {str(builder)}; Elapsed: {stop-start}")
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if __name__ == '__main__':
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test_all_performance(layers=12, model_dim=512, heads=8, num_tokens=8192, max_seq_len=1000, checkpointing=False) |