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