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A simple Pytorch memory usages profiler
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import gc | |
import torch | |
## MEM utils ## | |
def mem_report(): | |
'''Report the memory usage of the tensor.storage in pytorch | |
Both on CPUs and GPUs are reported''' | |
def _mem_report(tensors, mem_type): | |
'''Print the selected tensors of type | |
There are two major storage types in our major concern: | |
- GPU: tensors transferred to CUDA devices | |
- CPU: tensors remaining on the system memory (usually unimportant) | |
Args: | |
- tensors: the tensors of specified type | |
- mem_type: 'CPU' or 'GPU' in current implementation ''' | |
print('Storage on %s' %(mem_type)) | |
print('-'*LEN) | |
total_numel = 0 | |
total_mem = 0 | |
visited_data = [] | |
for tensor in tensors: | |
if tensor.is_sparse: | |
continue | |
# a data_ptr indicates a memory block allocated | |
data_ptr = tensor.storage().data_ptr() | |
if data_ptr in visited_data: | |
continue | |
visited_data.append(data_ptr) | |
numel = tensor.storage().size() | |
total_numel += numel | |
element_size = tensor.storage().element_size() | |
mem = numel*element_size /1024/1024 # 32bit=4Byte, MByte | |
total_mem += mem | |
element_type = type(tensor).__name__ | |
size = tuple(tensor.size()) | |
print('%s\t\t%s\t\t%.2f' % ( | |
element_type, | |
size, | |
mem) ) | |
print('-'*LEN) | |
print('Total Tensors: %d \tUsed Memory Space: %.2f MBytes' % (total_numel, total_mem) ) | |
print('-'*LEN) | |
LEN = 65 | |
print('='*LEN) | |
objects = gc.get_objects() | |
print('%s\t%s\t\t\t%s' %('Element type', 'Size', 'Used MEM(MBytes)') ) | |
tensors = [obj for obj in objects if torch.is_tensor(obj)] | |
cuda_tensors = [t for t in tensors if t.is_cuda] | |
host_tensors = [t for t in tensors if not t.is_cuda] | |
_mem_report(cuda_tensors, 'GPU') | |
_mem_report(host_tensors, 'CPU') | |
print('='*LEN) |
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I wrote a more powerful and pip installable tool recently, you can check this out:
https://github.com/stonesjtu/pytorch_memlab