[BUG CLIENT]: 'generator' object has no attribute 'iter_bytes'

[leejoeee]

Python -VV

AttributeError: 'generator' object has no attribute 'iter_bytes'

Pip Freeze

pip mistralai

Reproduction Steps

just run the code

Expected Behavior

expect run the code

Additional Context

import os
import torch
os.environ['OMP_NUM_THREADS'] = '4'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import openai
openai.api_key = os.environ['OPENAI_API_KEY']
openai.api_base = os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1")
from data.serialize import SerializerSettings
from models.utils import grid_iter
from models.promptcast import get_promptcast_predictions_data
from models.darts import get_arima_predictions_data
from models.llmtime import get_llmtime_predictions_data
from data.small_context import get_datasets
from models.validation_likelihood_tuning import get_autotuned_predictions_data

#os.environ['MISTRAL_KEY'] = 'CeDIlKzDq13swZtTAiCIMNdpK9GxmQwd'

def plot_preds(train, test, pred_dict, model_name, show_samples=False):
pred = pred_dict['median']
pred = pd.Series(pred, index=test.index)
plt.figure(figsize=(8, 6), dpi=100)
plt.plot(train)
plt.plot(test, label='Truth', color='black')
plt.plot(pred, label=model_name, color='purple')
# shade 90% confidence interval
samples = pred_dict['samples']
lower = np.quantile(samples, 0.05, axis=0)
upper = np.quantile(samples, 0.95, axis=0)
plt.fill_between(pred.index, lower, upper, alpha=0.3, color='purple')
if show_samples:
samples = pred_dict['samples']
# convert df to numpy array
samples = samples.values if isinstance(samples, pd.DataFrame) else samples
for i in range(min(10, samples.shape[0])):
plt.plot(pred.index, samples[i], color='purple', alpha=0.3, linewidth=1)
plt.legend(loc='upper left')
if 'NLL/D' in pred_dict:
nll = pred_dict['NLL/D']
if nll is not None:
plt.text(0.03, 0.85, f'NLL/D: {nll:.2f}', transform=plt.gca().transAxes, bbox=dict(facecolor='white', alpha=0.5))
plt.show()

print(torch.cuda.max_memory_allocated())
print()

gpt4_hypers = dict(
alpha=0.3,
basic=True,
temp=1.0,
top_p=0.8,
settings=SerializerSettings(base=10, prec=3, signed=True, time_sep=', ', bit_sep='', minus_sign='-')
)

mistral_api_hypers = dict(
alpha=0.3,
basic=True,
temp=1.0,
top_p=0.8,
settings=SerializerSettings(base=10, prec=3, signed=True, time_sep=', ', bit_sep='', minus_sign='-')
)

gpt3_hypers = dict(
temp=0.7,
alpha=0.95,
beta=0.3,
basic=False,
settings=SerializerSettings(base=10, prec=3, signed=True, half_bin_correction=True)
)

llma2_hypers = dict(
temp=0.7,
alpha=0.95,
beta=0.3,
basic=False,
settings=SerializerSettings(base=10, prec=3, signed=True, half_bin_correction=True)
)

promptcast_hypers = dict(
temp=0.7,
settings=SerializerSettings(base=10, prec=0, signed=True,
time_sep=', ',
bit_sep='',
plus_sign='',
minus_sign='-',
half_bin_correction=False,
decimal_point='')
)

arima_hypers = dict(p=[12,30], d=[1,2], q=[0])

model_hypers = {
'LLMTime GPT-3.5': {'model': 'gpt-3.5-turbo-instruct', **gpt3_hypers},
'LLMTime GPT-4': {'model': 'gpt-4', **gpt4_hypers},
'LLMTime GPT-3': {'model': 'text-davinci-003', **gpt3_hypers},
'PromptCast GPT-3': {'model': 'text-davinci-003', **promptcast_hypers},
'LLMA2': {'model': 'llama-7b', **llma2_hypers},
'mistral': {'model': 'mistral', **llma2_hypers},
'mistral-api-tiny': {'model': 'mistral-api-tiny', **mistral_api_hypers},
'mistral-api-small': {'model': 'mistral-api-tiny', **mistral_api_hypers},
'mistral-api-medium': {'model': 'mistral-api-tiny', **mistral_api_hypers},
'ARIMA': arima_hypers,

}

model_predict_fns = {
#'LLMA2': get_llmtime_predictions_data,
#'mistral': get_llmtime_predictions_data,
#'LLMTime GPT-4': get_llmtime_predictions_data,
'mistral-api-tiny': get_llmtime_predictions_data
}

model_names = list(model_predict_fns.keys())

datasets = get_datasets()
ds_name = 'AirPassengersDataset'

data = datasets[ds_name]
train, test = data # or change to your own data
out = {}

for model in model_names: # GPT-4 takes a about a minute to run
model_hypers[model].update({'dataset_name': ds_name}) # for promptcast
hypers = list(grid_iter(model_hypers[model]))
num_samples = 10
pred_dict = get_autotuned_predictions_data(train, test, hypers, num_samples, model_predict_fns[model], verbose=False, parallel=False)
out[model] = pred_dict
plot_preds(train, test, pred_dict, model, show_samples=True)

Suggested Solutions

Problem 'generator' object has no attribute 'iter_bytes' has not occurred before, is it because the version is updated? May I ask which version will not have this problem