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Extend context size without fine-tuning #705

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Jul 5, 2023
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Extend context size without fine-tuning #705

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8168a44
add Position Interpolation for inference scripts
airaria Jul 3, 2023
8556b8f
replace position interploation with NTK method
airaria Jul 5, 2023
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30 changes: 30 additions & 0 deletions scripts/inference/gradio_demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,36 @@
import argparse
import os

import transformers
old_init = transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__
def adaptive_ntk_init(self, dim, max_position_embeddings=2048, base=10000, device=None):
self.dim = dim
self.base = base
old_init(self, dim, max_position_embeddings, base, device)

def adaptive_ntk_forward(self, x, seq_len=None):
if seq_len > self.max_seq_len_cached:
t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
inv_freq = self.inv_freq
dim = self.dim
alpha = seq_len / 1024 - 1
base = self.base * alpha ** (dim / (dim-2))
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(x.device) / dim ))

freqs = torch.einsum("i,j->ij", t, inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
cos_cached = emb.cos()[None, None, :, :]
sin_cached = emb.sin()[None, None, :, :]
return (
cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype)
)
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype)
)
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.forward = adaptive_ntk_forward
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__ = adaptive_ntk_init

# Parse command-line arguments
parser = argparse.ArgumentParser()
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30 changes: 30 additions & 0 deletions scripts/inference/inference_hf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,36 @@
from transformers import LlamaForCausalLM, LlamaTokenizer
from peft import PeftModel

import transformers
old_init = transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__
def adaptive_ntk_init(self, dim, max_position_embeddings=2048, base=10000, device=None):
self.dim = dim
self.base = base
old_init(self, dim, max_position_embeddings, base, device)

def adaptive_ntk_forward(self, x, seq_len=None):
if seq_len > self.max_seq_len_cached:
t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
inv_freq = self.inv_freq
dim = self.dim
alpha = seq_len / 1024 - 1
base = self.base * alpha ** (dim / (dim-2))
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(x.device) / dim ))

freqs = torch.einsum("i,j->ij", t, inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
cos_cached = emb.cos()[None, None, :, :]
sin_cached = emb.sin()[None, None, :, :]
return (
cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype)
)
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype)
)
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.forward = adaptive_ntk_forward
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__ = adaptive_ntk_init

generation_config = dict(
temperature=0.2,
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31 changes: 31 additions & 0 deletions scripts/openai_server_demo/openai_api_server.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,37 @@
from transformers import LlamaForCausalLM, LlamaTokenizer, GenerationConfig
from peft import PeftModel

import transformers
old_init = transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__
def adaptive_ntk_init(self, dim, max_position_embeddings=2048, base=10000, device=None):
self.dim = dim
self.base = base
old_init(self, dim, max_position_embeddings, base, device)

def adaptive_ntk_forward(self, x, seq_len=None):
if seq_len > self.max_seq_len_cached:
t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
inv_freq = self.inv_freq
dim = self.dim
alpha = seq_len / 1024 - 1
base = self.base * alpha ** (dim / (dim-2))
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(x.device) / dim ))

freqs = torch.einsum("i,j->ij", t, inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
cos_cached = emb.cos()[None, None, :, :]
sin_cached = emb.sin()[None, None, :, :]
return (
cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype)
)
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype)
)
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.forward = adaptive_ntk_forward
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__ = adaptive_ntk_init

from openai_api_protocol import (
ChatCompletionRequest,
ChatCompletionResponse,
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