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90 lines
3.3 KiB
Python
90 lines
3.3 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 platform
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import psutil
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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(__name__)
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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.use_chunked_forward = config.use_chunked_forward
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self.chunked_forward_step = config.chunked_forward_step
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self._bf16_warning_shown = False
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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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if (
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word_embeddings.dtype in [torch.float16, torch.bfloat16]
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and word_embeddings.device.type == "cpu"
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and self.use_chunked_forward
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):
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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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chunked_forward_step: provides trade-off between efficiency and extra memory consumption.
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"""
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assert self.chunked_forward_step > 0, "Chunk size for chunked forward must be positive"
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if not self._bf16_warning_shown:
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if self.word_embeddings.weight.numel() * 4 < 0.9 * psutil.virtual_memory().total:
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logger.warning(
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"Running the client with dtype bfloat16 on CPU may be slow, since your CPU doesn't support AVX512. "
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"Consider loading the model with torch_dtype='float32'"
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)
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self._bf16_warning_shown = True
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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.chunked_forward_step):
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chunk = word_embeddings[i : i + self.chunked_forward_step].float()
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output[..., i : i + self.chunked_forward_step] = F.linear(hidden_states, chunk)
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return output
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