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Zero
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from collections import defaultdict
import os
from pathlib import Path
import time
import uuid
import warnings
from functools import wraps
from typing import Callable, Literal
import statistics
import gc
import numpy as np
from PIL import Image
import torch
from torchvision.utils import save_image
DEBUG = True
class ProfileReport:
def __init__(self):
self.clear()
def record(self,name,time):
if not self.active:
return
self.recorded_times[name].append(time)
def __getitem__(self, name):
return self.recorded_times[name]
def summary(self):
return {
name: statistics.mean(times)
for name, times in self.recorded_times.items()
}
def __str__(self):
return "ProfileReport: "+str(self.summary())
def clear(self):
self.recorded_times = defaultdict(list)
self.active = True
profile_report = ProfileReport()
class ProfileSession:
# def __init__(self):
# pass
def __enter__(self):
return self.start()
def __exit__(self, exc_type, exc_value, traceback):
return self.stop()
def start(self):
global profile_report
profile_report.clear()
return profile_report
def stop(self):
global profile_report
print(profile_report)
profile_report.clear()
def pause(self):
global profile_report
profile_report.active = False
def unpause(self):
global profile_report
profile_report.active = True
def ftimed(func=None):
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
if not DEBUG:
return func(*args, **kwargs)
else:
start_time = time.perf_counter()
result = func(*args, **kwargs)
end_time = time.perf_counter()
print(f"Time taken by {func.__qualname__}: {end_time - start_time} seconds")
profile_report.record(func.__qualname__, end_time - start_time)
return result
return wrapper
if func is None:
return decorator
else:
return decorator(func)
class ctimed:
"""
Context manager for timing lines of code. Use like:
```
with ctimed(name="Model Forward"):
y = model(x)
```
"""
def __init__(self, name=None):
self.name = name if name is not None else "unnamed"
self.start_time = None
def __enter__(self):
if DEBUG:
self.start_time = time.perf_counter()
return self
def __exit__(self, exc_type, exc_value, traceback):
if DEBUG:
end_time = time.perf_counter()
print(f"Time taken by {self.name}: {end_time - self.start_time} seconds")
profile_report.record(self.name, end_time - self.start_time)
def print_gpu_memory(clear_mem: Literal["pre", "post", None] = "pre"):
if not torch.cuda.is_available():
warnings.warn("Warning: CUDA device not available. Running on CPU.")
return
if clear_mem == "pre":
clear_cuda_memory()
allocated = torch.cuda.memory_allocated()
reserved = torch.cuda.memory_reserved()
total = torch.cuda.get_device_properties(0).total_memory
print(f"Memory allocated: {allocated / (1024**2):.2f} MB")
print(f"Memory reserved: {reserved / (1024**2):.2f} MB")
print(f"Total memory: {total / (1024**2):.2f} MB")
if clear_mem == "post":
clear_cuda_memory()
def clear_cuda_memory():
if not torch.cuda.is_available():
return
gc.collect()
torch.cuda.empty_cache()
torch.cuda.synchronize()
torch.cuda.reset_peak_memory_stats()
def cuda_empty_cache(func):
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
if torch.cuda.is_available():
torch.cuda.empty_cache()
return result
return wrapper
def print_first_param(module):
print(list(module.parameters())[0])
def first_param(module):
return list(module.parameters())[0]
def fdebug(func=None, *, exclude=None, print_input=True, print_output=True):
if exclude is None:
exclude = []
elif isinstance(exclude, str):
exclude = [exclude]
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
arg_names = func.__code__.co_varnames[:func.__code__.co_argcount]
arg_vals = args[:len(arg_names)]
arg_vals = [
(str(value)+str(value.shape) if isinstance(value, torch.Tensor) else value)
for value in arg_vals
]
args_pairs = ", ".join(f"{name}={value}" for name, value in zip(arg_names, arg_vals) if name not in exclude)
kwargs_pairs = ", ".join(f"{k}={v}" for k, v in kwargs.items() if k not in exclude)
all_args = ", ".join(filter(None, [args_pairs, kwargs_pairs]))
if print_input:
print(f"Calling {func.__name__}({all_args})")
result = func(*args, **kwargs)
if print_output:
print(f"{func.__name__} returned {str(result)+str(result.shape) if isinstance(result, torch.Tensor) else result}")
return result
return wrapper
if func is None:
return decorator
else:
return decorator(func)
class IncrementIndex:
def __init__(self, max:int=100):
self.retry_max = max
self.retries = 0
def __call__(self, index):
if self.retries > self.retry_max:
raise RuntimeError(f"Retried too many times, max:{self.retry_max}")
else:
self.retries += 1
index += 1
return index
_identity = lambda x: x
def fretry(func=None, *, exceptions=(Exception,), mod_args:tuple[Callable|None, ...]=tuple(), mod_kwargs:dict[str,Callable|None]=dict()):
def decorator(func):
@wraps(func)
def fretry_wrapper(*args, **kwargs):
try:
out = func(*args, **kwargs)
except exceptions as e:
new_args = []
for i, arg in enumerate(args):
if i < len(mod_args):
mod_func = mod_args[i] or _identity
new_args.append(mod_func(arg))
else:
new_args.append(arg)
new_kwargs = {}
for k, kwarg in kwargs.items():
if k in mod_kwargs:
mod_func = mod_kwargs[k] or _identity
new_kwargs[k] = mod_func(kwarg)
kwargs.update(new_kwargs)
import traceback
traceback.print_exc()
warnings.warn(
f"Function {func} failed due to {e} with inputs {args}, {kwargs}, "
f"retrying with modified inputs {new_args}, {new_kwargs}"
)
out = fretry_wrapper(*new_args, **new_kwargs)
return out
return fretry_wrapper
if func is None:
return decorator
else:
return decorator(func)
def texam(t: torch.Tensor, name=None):
if name is None:
name = ""
else:
name += " " # spacing
print(f"{name}Shape: {tuple(t.shape)}")
if t.dtype.is_floating_point or t.dtype.is_complex:
mean_val = t.mean().item()
else:
mean_val = "N/A"
print(f"{name}Min: {t.min().item()}, Max: {t.max().item()}, Mean: {mean_val}")
print(f"{name}Device: {t.device}, Dtype: {t.dtype}, Requires Grad: {t.requires_grad}")
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