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Merge pull request #617 from matthoffner/master
Add GGML model
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import requests | ||
import logging | ||
import time | ||
from tqdm import tqdm | ||
from requests.exceptions import RequestException | ||
import transformers | ||
from lm_eval.utils import Reorderer | ||
from lm_eval.base import BaseLM | ||
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logger = logging.getLogger(__name__) | ||
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def get_result(logprobs, context_length): | ||
is_greedy = True | ||
offsets = logprobs['text_offset'] | ||
tokens = logprobs['tokens'] | ||
tokens_logprobs = logprobs['token_logprobs'] | ||
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idx = 0 | ||
while offsets[idx] < context_length: | ||
idx += 1 | ||
continuation_logprobs = sum(tokens_logprobs[idx:-1]) | ||
for i in range(idx, len(tokens)): | ||
token = tokens[i] | ||
top_tokens = logprobs["top_logprobs"][i] | ||
top_token = max(top_tokens.keys(), key=lambda x: top_tokens[x]) | ||
if top_token != token: | ||
is_greedy = False | ||
break | ||
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return continuation_logprobs, is_greedy | ||
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class GGUFLM(BaseLM): | ||
def __init__(self, base_url, max_length=2048): | ||
super().__init__() | ||
self.base_url = base_url | ||
self.logprobs = 10 | ||
self.temperature = 0.0 | ||
self.max_length = max_length | ||
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def gguf_completion(self, context, continuation=None, stop=None, retries=3, delay=5, **kwargs): | ||
for _ in range(retries): | ||
try: | ||
prompt = context | ||
request = {'prompt': prompt, 'logprobs': self.logprobs, | ||
'temperature': self.temperature} | ||
if continuation: | ||
prompt += continuation | ||
request.update({'prompt': prompt, 'max_tokens': 1, 'echo': True}) | ||
if stop is not None: | ||
request['stop'] = stop | ||
response = requests.post(f"{self.base_url}/v1/completions", json=request) | ||
response.raise_for_status() | ||
return response.json() | ||
except RequestException as e: | ||
logger.error(f"RequestException: {e}") | ||
time.sleep(delay) # wait before retrying | ||
else: | ||
raise Exception(f"Failed to get a valid response after {retries} retries.") | ||
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def loglikelihood(self, requests): | ||
if not requests: | ||
return [] | ||
res = [] | ||
for context, continuation in tqdm(requests): | ||
response = self.gguf_completion(context=context, continuation=continuation) | ||
if response and "choices" in response and response["choices"]: | ||
choice = response["choices"][0] | ||
logprobs = choice.get("logprobs") | ||
if logprobs and "token_logprobs" in logprobs and logprobs["token_logprobs"]: | ||
logprob, is_greedy = get_result(logprobs, len(context)) | ||
res.append((logprob, is_greedy)) | ||
else: | ||
logger.warning("Invalid logprobs data. Expected 'logprobs' to contain 'token_logprobs' list.") | ||
else: | ||
logger.error(f"Invalid response for loglikelihood. Response: {response}") | ||
assert False | ||
return res | ||
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def greedy_until(self, requests): | ||
if not requests: | ||
return [] | ||
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res = [] | ||
for request in tqdm(requests): | ||
inp = request[0] | ||
request_args = request[1] | ||
until = request_args["until"] | ||
response = self.gguf_completion(context=inp, stop=until) | ||
if response and "choices" in response and response["choices"]: | ||
choice = response["choices"][0] | ||
if "text" in choice: | ||
generated_text = choice["text"].strip() | ||
res.append(generated_text) | ||
else: | ||
logger.error(f"Invalid response for greedy_until. Response: {response}") | ||
res.append(None) # Add default value in case of error | ||
else: | ||
logger.error(f"Invalid response for greedy_until. Response: {response}") | ||
res.append(None) # Add default value in case of error | ||
return res | ||
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def loglikelihood_rolling(self, requests): | ||
raise NotImplementedError("loglikelihood_rolling not yet supported for GGUF models") | ||
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def _model_call(self, inps): | ||
# Placeholder implementation | ||
raise NotImplementedError() | ||
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def _model_generate(self, context, max_length, eos_token_id): | ||
# Placeholder implementation | ||
raise NotImplementedError() | ||
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def tok_encode(self, string: str): | ||
raise NotImplementedError() | ||
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def tok_decode(self, tokens): | ||
raise NotImplementedError() | ||
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@property | ||
def batch_size(self): | ||
# Placeholder implementation | ||
raise NotImplementedError() | ||
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@property | ||
def device(self): | ||
# Placeholder implementation | ||
raise NotImplementedError() | ||
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@property | ||
def eot_token_id(self): | ||
# Placeholder implementation | ||
raise NotImplementedError() | ||
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def max_length(self): | ||
return self.max_length | ||
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@property | ||
def max_gen_toks(self): | ||
# Placeholder implementation | ||
raise NotImplementedError() |
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import unittest | ||
from unittest.mock import patch | ||
import hashlib | ||
import json | ||
import os | ||
import pickle | ||
from lm_eval.models.gguf import GGUFLM | ||
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base_url = "https://matthoffner-ggml-llm-api.hf.space" | ||
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def gguf_completion_mock(base_url, **kwargs): | ||
# Generate a hash from the parameters | ||
hash_kwargs = {'base_url': base_url, **kwargs} | ||
hash = hashlib.sha256(json.dumps(hash_kwargs, sort_keys=True).encode('utf-8')).hexdigest() | ||
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fname = f"./tests/testdata/ggml_test_{hash}.pkl" | ||
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if os.path.exists(fname): | ||
with open(fname, "rb") as fh: | ||
return pickle.load(fh) | ||
else: | ||
print("The file does not exist, attempting to write...") | ||
if 'stop' in kwargs: | ||
result = {"choices": [{"text": f"generated text until {kwargs['stop']}", "logprobs": {"token_logprobs": [-1.2345]}, "finish_reason": "length"}]} | ||
else: | ||
result = {"choices": [{"logprobs": {"token_logprobs": [-1.2345]}, "finish_reason": "length"}]} | ||
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try: | ||
os.makedirs(os.path.dirname(fname), exist_ok=True) | ||
print('Writing file at', fname) | ||
with open(fname, "wb") as fh: | ||
pickle.dump(result, fh) | ||
print('File written successfully') | ||
except Exception as e: | ||
print('File writing failed:', e) | ||
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return result | ||
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class GGUFLMTest(unittest.TestCase): | ||
@patch('lm_eval.models.gguf.GGUFLM.gguf_completion', side_effect=gguf_completion_mock) | ||
def test_loglikelihood(self, gguf_completion_mock): | ||
lm = GGUFLM(base_url) | ||
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# Test loglikelihood | ||
requests = [("context1", "continuation1"), ("context2", "continuation2")] | ||
res = lm.loglikelihood(requests) | ||
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# Assert the loglikelihood response is correct | ||
expected_res = [(logprob, True) for logprob in [-1.2345, -1.2345]] | ||
self.assertEqual(res, expected_res) | ||
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@patch('lm_eval.models.gguf.GGUFLM.gguf_completion', side_effect=gguf_completion_mock) | ||
def test_greedy_until(self, gguf_completion_mock): | ||
lm = GGUFLM(base_url) | ||
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# Test greedy_until | ||
requests = [("input1", {"until": "stop1"}), ("input2", {"until": "stop2"})] | ||
res = lm.greedy_until(requests) | ||
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# Assert the greedy_until response is correct | ||
expected_res = ["generated text until stop1", "generated text until stop2"] | ||
self.assertEqual(res, expected_res) | ||
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@patch('lm_eval.models.gguf.GGUFLM.gguf_completion', side_effect=gguf_completion_mock) | ||
def test_loglikelihood_rolling(self, gguf_completion_mock): | ||
lm = GGUFLM(base_url) | ||
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# Test loglikelihood_rolling | ||
requests = ["input1", "input2"] | ||
res = lm.loglikelihood_rolling(requests) | ||
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# Assert the loglikelihood_rolling response is correct | ||
expected_res = [(-1.2345, True), (-1.2345, True)] | ||
self.assertEqual(res, expected_res) | ||
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if __name__ == "__main__": | ||
unittest.main() |
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