44 lines
2.6 KiB
Python
44 lines
2.6 KiB
Python
# code adapted from https://huggingface.co/kaiokendev/superhot-13b-8k-no-rlhf-test/blob/main/llama_rope_scaled_monkey_patch.py
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import torch
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import transformers
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import transformers.models.llama.modeling_llama
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from functools import partial
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class CondenseRotaryEmbedding(torch.nn.Module):
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def __init__(self, dim, ratio, max_position_embeddings=2048, base=10000, device=None):
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super().__init__()
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inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
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self.register_buffer("inv_freq", inv_freq)
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# Build here to make `torch.jit.trace` work.
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self.ratio = ratio
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max_position_embeddings *= ratio
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print(f"Condensing Positional embeddings from {max_position_embeddings} to {max_position_embeddings // ratio}")
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self.max_seq_len_cached = max_position_embeddings
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t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype) / ratio
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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dtype = torch.get_default_dtype()
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self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
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def forward(self, x, seq_len=None):
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# x: [bs, num_attention_heads, seq_len, head_size]
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# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
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if seq_len > self.max_seq_len_cached:
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self.max_seq_len_cached = seq_len
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t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) / self.ratio
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
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self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(x.dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(x.dtype), persistent=False)
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return (
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self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
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self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
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)
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def replace_llama_with_condense(ratio):
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transformers.models.llama.modeling_llama.LlamaRotaryEmbedding = partial(CondenseRotaryEmbedding, ratio=ratio)
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