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# Copyright 2023 Amirkeivan Mohtashami, Martin Jaggi
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import os
import random
import re
import requests
llama_weights_7b_base = "/llama_weights/7B_hf/"
llama_weights_7b_tuned = "/llama-redpajama-mem-15000-with-mem/"
cache_path = "/hf-cache/"
use_flash = False # using flash for inference is only implemented for when offloading kv to cpu
top_k = 5
dtype = torch.bfloat16
def make_llama_base_pipe():
from transformers import pipeline
from transformers.models.llama import LlamaForCausalLM
llama_base = LlamaForCausalLM.from_pretrained(
llama_weights_7b_base,
cache_dir=cache_path,
)
llama_base = llama_base.to('cuda:0')
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained(
llama_weights_7b_base,
cache_dir=cache_path,
model_max_length=2048,
padding_side="right",
use_fast=False,
)
llama_base_pipe = pipeline("text-generation", model=llama_base, tokenizer=tokenizer, device=llama_base.device)
return llama_base_pipe
llama_base_pipe = make_llama_base_pipe()
def make_llama_mem_pipe():
from llama_mem import LlamaForCausalLM
model = LlamaForCausalLM.from_pretrained(
llama_weights_7b_tuned,
cache_dir=cache_path,
torch_dtype=dtype
)
model.to('cuda:1')
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained(
llama_weights_7b_tuned,
cache_dir=cache_path,
model_max_length=model.config.train_context_length,
padding_side="right",
use_fast=False,
)
mem_id = tokenizer.convert_tokens_to_ids("<landmark>")
model.set_mem_id(mem_id)
from transformers import pipeline
llama_mem_pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=model.device,
offload_cache_to_cpu=use_flash, use_flash=use_flash,
cache_top_k=top_k)
return llama_mem_pipe
llama_mem_pipe = make_llama_mem_pipe()
pipes = {"base": llama_base_pipe, "mem": llama_mem_pipe}
def generate_prompt(n_garbage):
"""Generates a text file and inserts an execute line at a random position."""
n_garbage_prefix = random.randint(0, n_garbage)
n_garbage_suffix = n_garbage - n_garbage_prefix
task_description = "There is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there."
garbage = "The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again."
garbage_inf = " ".join([garbage] * 2000)
assert len(garbage_inf) >= n_garbage
garbage_prefix = garbage_inf[:n_garbage_prefix]
garbage_suffix = garbage_inf[:n_garbage_suffix]
pass_key = random.randint(1, 50000)
information_line = f"The pass key is {pass_key}. Remember it. {pass_key} is the pass key."
final_question = "What is the pass key? The pass key is"
lines = [
task_description,
garbage_prefix,
information_line,
garbage_suffix,
final_question
]
return "\n".join(lines), pass_key
def test_model(prompt_text, pass_key, model_name):
response = pipes[model_name](prompt_text,num_return_sequences=1, max_new_tokens=10)[0]["generated_text"][len(prompt_text):]
assert f"The pass key is {pass_key}" in prompt_text
try:
pass_key = int(re.search(r'\d+', response).group())
except:
pass_key = response[:20]
return pass_key
n_values = [0, 100, 500, 1000, 5000, 8000, 10000, 12000, 14000, 18000, 20000, 25000, 38000]
num_tests = 50
models = ["base", "mem"]
accuracies = {x: [] for x in models}
individual_results = {x: [] for x in models}
for n in n_values:
correct_count = {x: 0 for x in models}
n_results = {x: [] for x in models}
for i in range(num_tests):
print(f"\nRunning test {i + 1}/{num_tests} for n = {n}...")
prompt_text, pass_key = generate_prompt(n)
for model_name in models:
if pipes[model_name] is None:
continue
num_tokens = len(pipes[model_name].tokenizer.encode(prompt_text))
print("Number of tokens in this prompt: ", num_tokens)
model_output = test_model(prompt_text, pass_key, model_name)
print(f"Expected number in the prompt: {pass_key}, {model_name} output: {model_output}")
if pass_key == model_output:
correct_count[model_name] += 1
n_results[model_name].append(1)
print("Success!")
else:
n_results[model_name].append(0)
print("Fail.")
for model in models:
accuracy = (correct_count[model] / num_tests) * 100
print(f"Accuracy {model} for n = {n}: {accuracy}%")
accuracies[model].append(accuracy)
individual_results[model].append(n_results)
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