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Initial GPTQ model commit

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  1. llama_rope_scaled_monkey_patch.py +65 -0
llama_rope_scaled_monkey_patch.py ADDED
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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 einops import rearrange
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+ import random
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+
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+ # This monkey patch file is not needed if using ExLlama, or if using `trust_remote_code=True``
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+
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+ class ScaledRotaryEmbedding(torch.nn.Module):
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+ def __init__(self, dim, 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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+
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+ max_position_embeddings = 8192
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+
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+ # Build here to make `torch.jit.trace` work.
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+ self.max_seq_len_cached = max_position_embeddings
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+ t = torch.arange(
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+ self.max_seq_len_cached,
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+ device=self.inv_freq.device,
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+ dtype=self.inv_freq.dtype,
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+ )
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+
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+ self.scale = 1 / 4
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+ t *= self.scale
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+
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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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+ self.register_buffer(
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+ "cos_cached", emb.cos()[None, None, :, :], persistent=False
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+ )
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+ self.register_buffer(
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+ "sin_cached", emb.sin()[None, None, :, :], persistent=False
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+ )
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+
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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(
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+ self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype
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+ )
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+ t *= self.scale
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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(
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+ "cos_cached", emb.cos()[None, None, :, :], persistent=False
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+ )
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+ self.register_buffer(
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+ "sin_cached", emb.sin()[None, None, :, :], persistent=False
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+ )
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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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+
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+
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+ def replace_llama_rope_with_scaled_rope():
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+ transformers.models.llama.modeling_llama.LlamaRotaryEmbedding = (
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+ ScaledRotaryEmbedding
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+ )