Debugging (#739)
* Update gemma2.py * Update llama.py * Update llama.py * Update gemma2.py * init * Update gemma2.py * Update gemma2.py * Update _utils.py * Update _utils.py * Update _utils.py * Update _utils.py * Update _utils.py * Update gemma2.py * Update gemma2.py * Update gemma2.py * All RoPE Scaling support * cleanup * Update llama.py * Update llama.py * Update _utils.py * Update _utils.py * exec * exec * Attention_Module * attention_module * imports * exec * Update llama.py * Update llama.py * boolean mask * revert masking * Update llama.py * Update save.py * Update llama.py * Update gemma2.py * Update gemma2.py * Update gemma2.py * Update utils.py * retry * Update gemma2.py * Update gemma2.py * Update gemma2.py * Update _utils.py * Update _utils.py * Update gemma2.py * Update chat_templates.py * Gemma 2 Ollama support * Update llama.py * Update llama.py * error handling * Update _utils.py * Update _utils.py * Stats for debugging * Update _utils.py * Update _utils.py * Debugging * Update tokenizer_utils.py * Update _utils.py
This commit is contained in:
Daniel Han
GitHub
parent
4be284bd79
commit
28e9de27d7
4 changed files with 76 additions and 61 deletions
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@ -39,10 +39,7 @@ if "CUDA_VISIBLE_DEVICES" in os.environ:
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first_id = devices.split(",")[0]
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warnings.warn(
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f"Unsloth: 'CUDA_VISIBLE_DEVICES' is currently {devices} \n"\
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"Unsloth currently does not work on multi GPU setups - sadly we are a 2 brother team so "\
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"enabling it will require much more work, so we have to prioritize. Please understand!"\
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"We do have a beta version, which you can contact us about!\n"\
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"Thank you for your understanding and we appreciate it immensely!\n\n"\
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"Unsloth currently does not support multi GPU setups - but we are working on it!\n"\
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"Multiple CUDA devices detected but we require a single device.\n"\
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f"We will override CUDA_VISIBLE_DEVICES to first device: {first_id}."
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)
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@ -21,6 +21,7 @@ __all__ = [
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"xformers_version",
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"__version__",
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"HAS_FLASH_ATTENTION",
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"PRE_CHECK",
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"platform_system",
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"patch_tokenizer",
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"get_statistics",
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@ -32,30 +33,27 @@ __all__ = [
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"unsloth_offloaded_gradient_checkpoint",
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"torch_compile_options",
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"patch_linear_scaling",
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"check_nvidia",
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"create_boolean_mask",
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]
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import torch
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from typing import Union, Optional, List, Any, Callable, Tuple
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import warnings
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from platform import system as platform_system
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platform_system = platform_system()
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import math
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import numpy as np
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import os
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import psutil
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import inspect
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import re
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import warnings, subprocess, re, inspect, psutil, os, math
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# =============================================
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# Disable some warnings which can get annoying
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warnings.filterwarnings(action = "ignore", category = UserWarning, module = "torch")
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warnings.filterwarnings(action = "ignore", category = UserWarning, module = "huggingface_hub")
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warnings.filterwarnings(action = "ignore", category = FutureWarning, module = "huggingface_hub")
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warnings.filterwarnings(action = "ignore", category = RuntimeWarning, module = "subprocess")
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warnings.filterwarnings(action = "ignore", category = UserWarning, module = "transformers")
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warnings.filterwarnings(action = "ignore", category = FutureWarning, module = "accelerate")
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warnings.filterwarnings(action = "ignore", category = FutureWarning, module = "huggingface_hub")
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warnings.filterwarnings(action = "ignore", category = RuntimeWarning, module = "multiprocessing")
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warnings.filterwarnings(action = "ignore", category = RuntimeWarning, module = "multiprocess")
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# Stop "Special tokens have been added in the vocabulary, ..."
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import logging
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@ -74,7 +72,10 @@ for model_name in model_architectures:
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config_filename = f"{model_name.title()}Config"
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exec(f"from {config_filepath} import {config_filename}", globals())
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config = inspect.getsource(eval(config_filename))
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try:
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config = inspect.getsource(eval(config_filename))
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except:
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continue
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if "rope_scaling" in config: continue
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config = re.sub(
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r"(\*\*kwargs)[\s]{0,}\,[\s]{0,}\)[\s]{0,}\:",
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@ -345,7 +346,6 @@ def get_statistics():
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# We simply download a README.md file from HF - all data is made public.
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# This is simply so we can check if some envs are broken or not.
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try:
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from huggingface_hub import hf_hub_download
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from huggingface_hub.utils import disable_progress_bars, enable_progress_bars, are_progress_bars_disabled
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import psutil
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n_cpus = psutil.cpu_count(logical = False)
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@ -367,7 +367,13 @@ def get_statistics():
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disable_progress_bars()
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disabled = True
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pass
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hf_hub_download(f"unslothai/statistics-{statistics}", "README.md", force_download = True)
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from transformers import AutoModelForCausalLM
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stats_model = AutoModelForCausalLM.from_pretrained(
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f"unslothai/statistics-{statistics}",
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force_download = True,
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)
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del stats_model
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if disabled:
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enable_progress_bars()
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pass
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@ -659,6 +665,19 @@ def patch_linear_scaling(
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pass
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def check_nvidia():
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# Unsloth doesn't work yet on AMD devices - we're working on it!
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try:
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output = subprocess.check_output("nvidia-smi --query-gpu=memory.used --format=csv", shell = True)
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except:
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raise RuntimeError("Unsloth: We do not support AMD / Intel machines yet - it is a work in progress!")
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output = re.findall(rb'([\d]{1,})[\s]{1,}M', output)
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output = np.array([int(x.decode('utf-8'))/1024 for x in output])
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return output
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pass
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PRE_CHECK = check_nvidia()
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def create_boolean_mask(n = 4096, sliding_window = 2048):
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# Creates a boolean mask for attention
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mask = torch.ones(n, n, dtype = torch.bool)
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@ -83,7 +83,8 @@ def _fast_prepare_inputs_for_generation(self, input_ids, **kwargs,):
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if "past_key_values" in kwargs:
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input_ids = input_ids[:,[-1]]
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kwargs["attention_mask"] = kwargs["attention_mask"][:,[-1]]
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kwargs["position_ids"] = kwargs["cache_position"]
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if "cache_position" in kwargs:
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kwargs["position_ids"] = kwargs["cache_position"]
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return { "input_ids" : input_ids, **kwargs, }
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pass
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@ -1128,7 +1129,7 @@ class FastLlamaModel:
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f' "-____-" Free Apache license: http://github.com/unslothai/unsloth'
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print(statistics)
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model_patcher.pre_patch()
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# get_statistics()
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get_statistics() # For debugging - we use a download counter to see if environments are not breaking
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if dtype is None:
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dtype = torch.float16 if not SUPPORTS_BFLOAT16 else torch.bfloat16
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@ -1180,6 +1181,8 @@ class FastLlamaModel:
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# Add to kwargs
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kwargs["rope_scaling"] = rope_scaling
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pass
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# We currently only support NVIDIA GPUs - AMD / Intel is a work in progress!
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pre_check = check_nvidia()
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bnb_config = None
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if load_in_4bit:
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@ -1206,6 +1209,8 @@ class FastLlamaModel:
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attn_implementation = "eager",
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**kwargs,
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)
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# We currently only support NVIDIA GPUs - AMD / Intel is a work in progress!
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post_check = check_nvidia()
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# Counteract saved tokenizers
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tokenizer_name = model_name if tokenizer_name is None else tokenizer_name
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@ -1235,14 +1240,12 @@ class FastLlamaModel:
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else:
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inner_training_loop = Trainer._original_training_loop
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except:
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raise RuntimeError(
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'Unsloth currently does not work on multi GPU setups - sadly we are a 2 brother team so '\
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'enabling it will require much more work, so we have to prioritize. Please understand!\n'\
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'We do have a separate beta version, which you can contact us about!\n'\
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'Thank you for your understanding and we appreciate it immensely!'
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)
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raise RuntimeError('Unsloth currently does not support multi GPU setups - but we are working on it!')
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pass
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if ((post_check - post_check) >= 1).sum() > 1:
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raise RuntimeError('Unsloth currently does not support multi GPU setups - but we are working on it!')
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import transformers.trainer
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items_in_trainer = dir(transformers.trainer)
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good_items = []
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@ -1266,16 +1269,15 @@ class FastLlamaModel:
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f"\\ / Total batch size = {total_train_batch_size:,} | Total steps = {max_steps:,}\\n"\\
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f' "-____-" Number of trainable parameters = {get_model_param_count(model, trainable_only=True):,}'
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logger.warning(debug_info)
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import subprocess, re, gc
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output = subprocess.check_output(
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'nvidia-smi --query-gpu=memory.used --format=csv', shell = True)
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output = re.findall(rb'([\\d]{1,})[\\s]{1,}M', output)
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output = sum(int(x.decode('utf-8'))/1024 > 4 for x in output)
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if output > 1: print(
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'********************\\nUnsloth currently does not work on multi GPU setups - sadly we are a 2 brother team so '\\
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'enabling it will require much more work, so we have to prioritize. Please understand!\\n'\\
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'********************\\nWe do have a separate beta version, which you can contact us about!\\n'\\
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'********************\\nThank you for your understanding and we appreciate it immensely!')
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import subprocess, re, gc, numpy as np
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try:
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a = subprocess.check_output('nvidia-smi --query-gpu=memory.used --format=csv', shell = True)
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except:
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raise RuntimeError('Unsloth: We do not support AMD / Intel machines yet - it is a work in progress!')
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a = re.findall(rb'([\\d]{1,})[\\s]{1,}M', a)
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a = np.array([int(x.decode('utf-8'))/1024 for x in a])
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if ((a - PRE_CHECK) >= 1).sum() > 1:
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raise RuntimeError('Unsloth currently does not support multi GPU setups - but we are working on it!')
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for _ in range(3):
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gc.collect()
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torch.cuda.empty_cache()"""
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@ -1287,12 +1289,7 @@ class FastLlamaModel:
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debug_info = """n_total_devices = total_train_batch_size // \\
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args.gradient_accumulation_steps // self._train_batch_size
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if n_total_devices > 1:
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logger.warning_once(
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'* Unsloth currently does not work on multi GPU setups - sadly we are a 2 brother team so ' \\
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'* enabling it will require much more work, so we have to prioritize. Please understand!\\n' \\
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'* We do have a separate beta version, which you can contact us about!\\n'\\
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'* Thank you for your understanding and we appreciate it immensely!'
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)
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logger.warning_once('Unsloth currently does not support multi GPU setups - but we are working on it!')
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debug_info ="""
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debug_info = debug_info.split('\n')
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debug_info = "\n".join([debug_info[0]] + [spaces + x[8:] for x in debug_info[1:]])
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@ -1317,12 +1314,7 @@ class FastLlamaModel:
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total_batches = bsz * ga * args.world_size
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n_total_devices = total_batches // ga // bsz
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if n_total_devices > 1:
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logger.warning_once(
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'* Unsloth currently does not work on multi GPU setups - sadly we are a 2 brother team so ' \\
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'* enabling it will require much more work, so we have to prioritize. Please understand!\\n' \\
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'* We do have a separate beta version, which you can contact us about!\\n'\\
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'* Thank you for your understanding and we appreciate it immensely!'
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)
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logger.warning_once('Unsloth currently does not support multi GPU setups - but we are working on it!')
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divisor = n_total_devices / 1
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bsz = self._train_batch_size = max(int(bsz / divisor), 1)
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if total_batches // ga // bsz > 1:
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@ -1346,12 +1338,7 @@ class FastLlamaModel:
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"False",
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)
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if "n_total_devices >" not in inner_training_loop:
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raise RuntimeError(
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'Unsloth currently does not work on multi GPU setups - sadly we are a 2 brother team so '\
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'enabling it will require much more work, so we have to prioritize. Please understand!\n'\
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'We do have a separate beta version, which you can contact us about!\n'\
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'Thank you for your understanding and we appreciate it immensely!'
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)
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raise RuntimeError('Unsloth currently does not support multi GPU setups - but we are working on it!')
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pass
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inner_training_loop = inner_training_loop.replace(
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"is_sagemaker_mp_enabled()",
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@ -24,6 +24,7 @@ import itertools
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import collections
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import numpy as np
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import gc
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import subprocess
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__all__ = [
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"load_correct_tokenizer",
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@ -907,6 +908,19 @@ def add_new_tokens(
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pass
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def check_nvidia():
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# Unsloth doesn't work yet on AMD devices - we're working on it!
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try:
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output = subprocess.check_output("nvidia-smi --query-gpu=memory.used --format=csv", shell = True)
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except:
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raise RuntimeError("Unsloth: We do not support AMD / Intel machines yet - it is a work in progress!")
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output = re.findall(rb'([\d]{1,})[\s]{1,}M', output)
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output = np.array([int(x.decode('utf-8'))/1024 for x in output])
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return output
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pass
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PRE_CHECK = check_nvidia()
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from inspect import getsource
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import trl.trainer.sft_trainer
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from trl.trainer.sft_trainer import *
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@ -957,17 +971,15 @@ def patch_sft_trainer_tokenizer():
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" 'Please do not edit specific areas of the Unsloth codebase or you will get CUDA segfaults.'\n"\
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" )\n"\
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"pass\n"\
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"n_devices = torch.cuda.device_count()\n"\
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"import subprocess, re\n"\
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"output = subprocess.check_output(\n"\
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" 'nvidia-smi --query-gpu=memory.used --format=csv', shell = True)\n"\
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"output = re.findall(rb'([\\d]{1,})[\\s]{1,}M', output)\n"\
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"output = sum(int(x.decode('utf-8'))/1024 > 4 for x in output)\n"\
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"if output > 1: print(\n"\
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" '********************\\nUnsloth currently does not work on multi GPU setups - sadly we are a 2 brother team so '\\\n"\
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" 'enabling it will require much more work, so we have to prioritize. Please understand!\\n'\\\n"\
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" '********************\\nWe do have a separate beta version, which you can contact us about!\\n'\\\n"\
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" '********************\\nThank you for your understanding and we appreciate it immensely!')\n"\
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"import subprocess, re, gc, numpy as np\n"\
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"try:\n"\
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" a = subprocess.check_output('nvidia-smi --query-gpu=memory.used --format=csv', shell = True)\n"\
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"except:\n"\
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" raise RuntimeError('Unsloth: We do not support AMD / Intel machines yet - it is a work in progress!')\n"\
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"a = re.findall(rb'([\\d]{1,})[\\s]{1,}M', a)\n"\
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"a = np.array([int(x.decode('utf-8'))/1024 for x in a])\n"\
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"if ((a - PRE_CHECK) >= 1).sum() > 1:\n"\
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" raise RuntimeError('Unsloth currently does not support multi GPU setups - but we are working on it!')\n"\
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"for _ in range(3):\n"\
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" gc.collect()\n"\
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" torch.cuda.empty_cache()\n"\
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