Add mem_tracker in tools.
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Colin
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3233616aac
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45c2f532ff
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@ -1,2 +1,3 @@
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__pycache__
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.vscode
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.vscode
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*.txt
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@ -1 +1,2 @@
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from tools import show
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from tools import show
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from tools import mem_tracker
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import gc
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import datetime
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import inspect
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import torch
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import numpy as np
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import torch.nn as nn
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dtype_memory_size_dict = {
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torch.float64: 64 / 8,
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torch.double: 64 / 8,
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torch.float32: 32 / 8,
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torch.float: 32 / 8,
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torch.float16: 16 / 8,
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torch.half: 16 / 8,
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torch.int64: 64 / 8,
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torch.long: 64 / 8,
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torch.int32: 32 / 8,
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torch.int: 32 / 8,
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torch.int16: 16 / 8,
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torch.short: 16 / 6,
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torch.uint8: 8 / 8,
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torch.int8: 8 / 8,
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}
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# compatibility of torch1.0
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if getattr(torch, "bfloat16", None) is not None:
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dtype_memory_size_dict[torch.bfloat16] = 16 / 8
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if getattr(torch, "bool", None) is not None:
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dtype_memory_size_dict[torch.bool] = (
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8 / 8
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) # pytorch use 1 byte for a bool, see https://github.com/pytorch/pytorch/issues/41571
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def get_mem_space(x):
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try:
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ret = dtype_memory_size_dict[x]
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except KeyError:
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print(f"dtype {x} is not supported!")
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return ret
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class MemTracker(object):
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"""
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Class used to track pytorch memory usage
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Arguments:
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detail(bool, default True): whether the function shows the detail gpu memory usage
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path(str): where to save log file
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verbose(bool, default False): whether show the trivial exception
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device(int): GPU number, default is 0
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"""
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def __init__(self, detail=True, path="", verbose=False, device=0):
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self.print_detail = detail
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self.last_tensor_sizes = set()
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self.gpu_profile_fn = path + f"{datetime.datetime.now():%d-%b-%y-%H:%M:%S}-gpu_mem_track.txt"
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self.verbose = verbose
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self.begin = True
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self.device = device
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def get_tensors(self):
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for obj in gc.get_objects():
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try:
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if torch.is_tensor(obj) or (hasattr(obj, "data") and torch.is_tensor(obj.data)):
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tensor = obj
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else:
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continue
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if tensor.is_cuda:
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yield tensor
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except Exception as e:
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if self.verbose:
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print("A trivial exception occured: {}".format(e))
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def get_tensor_usage(self):
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sizes = [np.prod(np.array(tensor.size())) * get_mem_space(tensor.dtype) for tensor in self.get_tensors()]
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return np.sum(sizes) / 1024**2
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def get_allocate_usage(self):
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return torch.cuda.memory_allocated() / 1024**2
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def clear_cache(self):
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gc.collect()
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torch.cuda.empty_cache()
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def print_all_gpu_tensor(self, file=None):
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for x in self.get_tensors():
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print(x.size(), x.dtype, np.prod(np.array(x.size())) * get_mem_space(x.dtype) / 1024**2, file=file)
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def track(self):
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"""
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Track the GPU memory usage
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"""
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frameinfo = inspect.stack()[1]
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where_str = frameinfo.filename + " line " + str(frameinfo.lineno) + ": " + frameinfo.function
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with open(self.gpu_profile_fn, "a+") as f:
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if self.begin:
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f.write(
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f"GPU Memory Track | {datetime.datetime.now():%d-%b-%y-%H:%M:%S} |"
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f" Total Tensor Used Memory:{self.get_tensor_usage():<7.1f}Mb"
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f" Total Allocated Memory:{self.get_allocate_usage():<7.1f}Mb\n\n"
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)
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self.begin = False
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if self.print_detail is True:
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ts_list = [(tensor.size(), tensor.dtype) for tensor in self.get_tensors()]
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new_tensor_sizes = {
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(
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type(x),
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tuple(x.size()),
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ts_list.count((x.size(), x.dtype)),
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np.prod(np.array(x.size())) * get_mem_space(x.dtype) / 1024**2,
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x.dtype,
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)
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for x in self.get_tensors()
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}
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for t, s, n, m, data_type in new_tensor_sizes - self.last_tensor_sizes:
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f.write(
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f"+ | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} | {data_type}\n"
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)
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for t, s, n, m, data_type in self.last_tensor_sizes - new_tensor_sizes:
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f.write(
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f"- | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} | {data_type}\n"
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)
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self.last_tensor_sizes = new_tensor_sizes
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f.write(
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f"\nAt {where_str:<50}"
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f" Total Tensor Used Memory:{self.get_tensor_usage():<7.1f}Mb"
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f" Total Allocated Memory:{self.get_allocate_usage():<7.1f}Mb\n\n"
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)
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def ModelSize(model, input, type_size=4):
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para = sum([np.prod(list(p.size())) for p in model.parameters()])
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# print('Model {} : Number of params: {}'.format(model._get_name(), para))
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print("Model {} : params: {:4f}M".format(model._get_name(), para * type_size / 1000 / 1000))
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input_ = input.clone()
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input_.requires_grad_(requires_grad=False)
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mods = list(model.modules())
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out_sizes = []
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for i in range(1, len(mods)):
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m = mods[i]
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if isinstance(m, nn.ReLU):
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if m.inplace:
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continue
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out = m(input_)
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out_sizes.append(np.array(out.size()))
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input_ = out
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total_nums = 0
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for i in range(len(out_sizes)):
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s = out_sizes[i]
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nums = np.prod(np.array(s))
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total_nums += nums
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# print('Model {} : Number of intermedite variables without backward: {}'.format(model._get_name(), total_nums))
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# print('Model {} : Number of intermedite variables with backward: {}'.format(model._get_name(), total_nums*2))
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print(
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"Model {} : intermedite variables: {:3f} M (without backward)".format(
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model._get_name(), total_nums * type_size / 1000 / 1000
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)
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)
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print(
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"Model {} : intermedite variables: {:3f} M (with backward)".format(
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model._get_name(), total_nums * type_size * 2 / 1000 / 1000
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)
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)
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@ -1,5 +1,6 @@
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import show
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import torch
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import mem_tracker
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# radata = torch.randn(8192, 128)
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@ -10,11 +11,10 @@ radata = torch.randn(127)
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show.DumpTensorToImage(radata, "test.png")
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radata = torch.randn(3,127,127)
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radata = torch.randn(3, 127, 127)
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show.DumpTensorToImage(radata, "test.png")
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radata = torch.randn(127, 127)
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show.DumpTensorToLog(radata, "test.log")
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@ -22,3 +22,7 @@ show.DumpTensorToLog(radata, "test.log")
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radata = torch.randn(127, 127) - 0.5
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show.ProbGE0(radata)
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show.DumpProb()
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radata = torch.randn(127, 127).cuda()
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tracker = mem_tracker.MemTracker()
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tracker.track()
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