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73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
"""
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PyTorch BLOOM model that implements several memory-efficient modes.
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Based on https://github.com/huggingface/transformers/commit/ca2a55e9dfb245527b5e1c954fec6ffbb7aef07b
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See commit history for authorship.
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"""
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from hivemind import get_logger
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from torch import nn
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from transformers import BloomConfig
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logger = get_logger(__file__)
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class LMHead(nn.Module):
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"""
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The modified language modeling head which does not create extra tensor for the linear layer with weights tied to the input
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embeddings. Thus, it reduces initial memory consumption which might be crucial for large dictionaries.
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In addition, it provides an effcient way to deal with half-precision word embeddings on CPU.
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"""
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def __init__(self, config: BloomConfig, word_embeddings: nn.Embedding):
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super().__init__()
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self.word_embeddings = word_embeddings
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self.chunk_size = config.chunk_size_for_efficient_fp16_on_cpu
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@property
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def in_features(self) -> int:
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return self.word_embeddings.num_embeddings
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@property
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def out_features(self) -> int:
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return self.word_embeddings.embedding_dim
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@property
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def weight(self):
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return self.word_embeddings.weight
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@property
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def bias(self):
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return None
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def forward(self, hidden_states):
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word_embeddings = self.word_embeddings.weight
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# We use 'chunked_forward' only when embeddings are in half-precision on CPU.
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if word_embeddings.dtype in [torch.float16, torch.bfloat16] and word_embeddings.device.type == "cpu":
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lm_logits = self.chunked_forward(hidden_states)
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else:
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# Switch dtype in case word_embeddings are fp16/bf16
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hidden_states = hidden_states.to(word_embeddings.dtype)
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lm_logits = F.linear(hidden_states, word_embeddings)
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return lm_logits
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def chunked_forward(self, hidden_states):
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"""Splits word embeddings on chunks and iteratively casts them into fp32 to perform matmul more efficiently on CPU.
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chunk_size: provides trade-off between efficiency and extra memory consumption.
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"""
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assert self.chunk_size > 0, "Chunk size for chunked forward must be positive"
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word_embeddings = self.word_embeddings.weight
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num_embeddings = self.word_embeddings.num_embeddings
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hidden_states = hidden_states.float()
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output = torch.empty(*hidden_states.shape[:-1], num_embeddings)
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for i in range(0, num_embeddings, self.chunk_size):
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chunk = word_embeddings[i : i + self.chunk_size].float()
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output[..., i : i + self.chunk_size] = F.linear(hidden_states, chunk)
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return output
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