untitled32.py

import random
import gradio as gr
import openai
import os

openai.api_key = os.environ.get("open_ai_key")

prompt = ['''
You are a ''', 
'''
machine learning developer, trying to debug this code:

StackTrace: 

    Traceback (most recent call last):
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/process.py”, line 258, in _bootstrap
    self.run()
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/process.py”, line 93, in run
    self._target(*self._args, **self._kwargs)
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/utils/data/dataloader.py”, line 61, in _worker_loop
    data_queue.put((idx, samples))
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/queues.py”, line 341, in put
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/reduction.py”, line 51, in dumps
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/multiprocessing/reductions.py”, line 121, in reduce_storage
    RuntimeError: unable to open shared memory object </torch_54163_3383444026> in read-write mode at /opt/conda/conda-bld/pytorch_1525909934016/work/aten/src/TH/THAllocator.c:342

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last):
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/util.py”, line 262, in _run_finalizers
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/util.py”, line 186, in call
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/shutil.py”, line 476, in rmtree
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/shutil.py”, line 474, in rmtree
    OSError: [Errno 24] Too many open files: ‘/tmp/pymp-sgew4xdn’
    Process Process-1:
    Traceback (most recent call last):
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/process.py”, line 258, in _bootstrap
    self.run()
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/process.py”, line 93, in run
    self._target(*self._args, **self._kwargs)
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/utils/data/dataloader.py”, line 61, in _worker_loop
    data_queue.put((idx, samples))
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/queues.py”, line 341, in put
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/reduction.py”, line 51, in dumps
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/multiprocessing/reductions.py”, line 121, in reduce_storage
    RuntimeError: unable to open shared memory object </torch_54163_3383444026> in read-write mode at /opt/conda/conda-bld/pytorch_1525909934016/work/aten/src/TH/THAllocator.c:342
    Traceback (most recent call last):
    File “/home/nlpgpu3/LinoHong/FakeNewsByTitle/main.py”, line 25, in
    for mini_batch in trainloader :
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/utils/data/dataloader.py”, line 280, in next
    idx, batch = self._get_batch()
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/utils/data/dataloader.py”, line 259, in _get_batch
    return self.data_queue.get()
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/queues.py”, line 335, in get
    res = self._reader.recv_bytes()
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/connection.py”, line 216, in recv_bytes
    buf = self._recv_bytes(maxlength)
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/connection.py”, line 407, in _recv_bytes
    buf = self._recv(4)
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/multiprocessing/connection.py”, line 379, in _recv
    chunk = read(handle, remaining)
    File “/home/nlpgpu3/anaconda3/envs/linohong3/lib/python3.6/site-packages/torch/utils/data/dataloader.py”, line 178, in handler
    _error_if_any_worker_fails()
    RuntimeError: DataLoader worker (pid 54163) exited unexpectedly with exit code 1.

    Process finished with exit code 1

Question: Any idea how I can solve this problem?
Are follow up questions needed here: Yes
Follow up: Does your code run with less num_workers or num_workers=0? 
Intermediate Answer: It worked when I set num_workers equals to 0, but doesn’t work greater or equal to 1
Follow up: Could you try to increase the shared memory and try setting num_workers>0 again?
Intermediate Answer: It worked! Can you explain what happened here? 
So the final answer is: The error usually means that your system doesn’t provide enough shared memory for multiple workers (used via num_workers>0). Check the shared memory limitation of your system and try to increase it.


StackTrace: 

RuntimeError: size mismatch (got input: [100000], target: [1000]) 

Question: Any idea how I can solve this problem?
Are follow up questions needed here: No
So the final answer is: I don’t know which line of code creates the shape mismatch but would guess it’s raised in the loss calculation: loss = criterion(output.view(-1), batch['input_ids'].view(-1).to(device)). Print the shapes of both tensors and make sure they are expected in the used criterion. PS you can post code snippets by wrapping them into three backticks ```, which would make your code easier to read.

StackTrace: ''',
'''
Question: ''', 
'''
Are follow up questions needed here:''',]



def extract_answer(generated):
    if '\n' not in generated:
        last_line =  generated
    else: 
        last_line = generated.split('\n')[-1]

    if ':' not in last_line:
        after_colon = last_line
    else:
        after_colon = generated.split(':')[-1]
    
    if ' ' == after_colon[0]:
        after_colon = after_colon[1:]
    if '.' == after_colon[-1]:
        after_colon = after_colon[:-1]

    return after_colon

def extract_question(generated):
    if '\n' not in generated:
        last_line =  generated
    else: 
        last_line = generated.split('\n')[-1]

    if 'Follow up:' not in last_line:
      print('we probably should never get here...' + generated)

    if ':' not in last_line:
        after_colon = last_line
    else:
        after_colon = generated.split(':')[-1]
    
    if ' ' == after_colon[0]:
        after_colon = after_colon[1:]
    if '?' != after_colon[-1]:
        print('we probably should never get here...' + generated)

    return after_colon

def get_last_line(generated):
    if '\n' not in generated:
        last_line =  generated
    else: 
        last_line = generated.split('\n')[-1]


    return last_line
  
def greenify(input):
  return "\x1b[102m" + input + "\x1b[0m"

def yellowfy(input):
  return "\x1b[106m" + input + "\x1b[0m"

def call_gpt(cur_prompt, stop):
    ans = openai.Completion.create(
                model="text-davinci-002",
                max_tokens=256,
                stop=stop,
                prompt=cur_prompt,
                temperature=0.7,
                top_p=1,
                frequency_penalty=0,
                presence_penalty=0
              )  
    returned = ans['choices'][0]['text']
    print( greenify(returned), end='')
    return returned

def initial_query_builder(language, code, question, intermediate = "\nIntermediate Answer:", followup = "\nFollow up:", finalans= '\nSo the final answer is:'):
  cur_prompt = prompt[0] +  language + prompt[1] + code + prompt[2] + question + prompt[3]

  # print("prompt: ", cur_prompt, end ='')

  ret_text = call_gpt(cur_prompt, intermediate)
  print("ret_text: ", ret_text)
  print("get_last_line(ret_text): ", get_last_line(ret_text))
  return ret_text

def subsequent_query_builder(curr_prompt, external_answer, intermediate = "\nIntermediate Answer:", followup = "\nFollow up:", finalans= '\nSo the final answer is:'):
  curr_prompt += intermediate + ' ' + external_answer + '.'
  print(intermediate + ' ' + yellowfy(external_answer) + '.', end='' )
  ret_text = call_gpt(curr_prompt, intermediate)
  return ret_text

"""subsequent query builder: 

the way to rebuild the prompt for each subsequent call: 

1. every user response is 'intermediate answer' 
2. until you hit 'so the final answer is: ' you're good
3. 
"""

def prompt_builder(history, intermediate = "\nIntermediate Answer:", followup = "\nFollow up:", finalans= '\nSo the final answer is:'): 
  #set language 
  language = history[1][0]
  #set stack trace 
  stacktrace = history[0][0]
  #set question (hardcoded)
  question = "Any idea how I can solve this problem?"

  # initial prompt
  curr_prompt = prompt[0] +  language + prompt[1] + stacktrace + prompt[2] + question + prompt[3]

  #set subsequent conversation thread
  if len(history) > 2: #subsequent conversations have occurred 
    curr_prompt += history[1][1] ## get the first response to the stacktrace prompt
    for conversation in history[2:]:
      #grab intermediate answer
      curr_prompt += intermediate + ' ' + conversation[0] + '.'
      #grab the follow up
      curr_prompt += conversation[1]
  return curr_prompt

def chat(message, history):
    history = history or []
    print(len(history))
    if len(history) == 0: ## just the stacktrace
      response = "which language is this in? (python, java, c++, kotlin, etc.)"
    elif len(history) == 1: ## stacktrace + just entered the language 
      # get stacktrace
      stacktrace = history[0][0]
      # get language
      language = message
      # set question (hardcoded for v1)
      question = "Any idea how I can solve this problem?"
      response = initial_query_builder(language, stacktrace, question)
    else: # subsequent prompts
      # get stacktrace
      stacktrace = history[0][0]
      # get language
      language = history[1][0]
      # set question (hardcoded for v1)
      question = "Any idea how I can solve this problem?"
      curr_prompt = prompt_builder(history)
      response = subsequent_query_builder(curr_prompt, message)
      # response = query_builder(language, stacktrace, question)
      print("response: ", response)
    history.append((message, response))
    return history, history

chatbot = gr.Chatbot().style(color_map=("green", "pink"))
demo = gr.Interface(
    chat,
    [gr.Textbox(placeholder="enter your stacktrace here"), "state"],
    [chatbot, "state"],
    allow_flagging="never",
)
if __name__ == "__main__":
    demo.launch(debug=True)