transfer

gpu_mem_track.py

@@ -0,0 +1,113 @@
+import gc
+import datetime
+import inspect
+
+import torch
+import numpy as np
+
+dtype_memory_size_dict = {
+    torch.float64: 64/8,
+    torch.double: 64/8,
+    torch.float32: 32/8,
+    torch.float: 32/8,
+    torch.float16: 16/8,
+    torch.half: 16/8,
+    torch.int64: 64/8,
+    torch.long: 64/8,
+    torch.int32: 32/8,
+    torch.int: 32/8,
+    torch.int16: 16/8,
+    torch.short: 16/6,
+    torch.uint8: 8/8,
+    torch.int8: 8/8,
+}
+# compatibility of torch1.0
+if getattr(torch, "bfloat16", None) is not None:
+    dtype_memory_size_dict[torch.bfloat16] = 16/8
+if getattr(torch, "bool", None) is not None:
+    dtype_memory_size_dict[torch.bool] = 8/8 # pytorch use 1 byte for a bool, see https://github.com/pytorch/pytorch/issues/41571
+
+def get_mem_space(x):
+    try:
+        ret = dtype_memory_size_dict[x]
+    except KeyError:
+        print(f"dtype {x} is not supported!")
+    return ret
+
+class MemTracker(object):
+    """
+    Class used to track pytorch memory usage
+    Arguments:
+        detail(bool, default True): whether the function shows the detail gpu memory usage
+        path(str): where to save log file
+        verbose(bool, default False): whether show the trivial exception
+        device(int): GPU number, default is 0
+    """
+    def __init__(self, detail=True, path='', verbose=False, device=0):
+        self.print_detail = detail
+        self.last_tensor_sizes = set()
+        self.gpu_profile_fn = path + f'{datetime.datetime.now():%d-%b-%y-%H:%M:%S}-gpu_mem_track.txt'
+        self.verbose = verbose
+        self.begin = True
+        self.device = device
+
+    def get_tensors(self):
+        for obj in gc.get_objects():
+            try:
+                if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
+                    tensor = obj
+                else:
+                    continue
+                if tensor.is_cuda:
+                    yield tensor
+            except Exception as e:
+                if self.verbose:
+                    print('A trivial exception occured: {}'.format(e))
+
+    def get_tensor_usage(self):
+        sizes = [np.prod(np.array(tensor.size())) * get_mem_space(tensor.dtype) for tensor in self.get_tensors()]
+        return np.sum(sizes) / 1024**2
+
+    def get_allocate_usage(self):
+        return torch.cuda.memory_allocated() / 1024**2
+
+    def clear_cache(self):
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def print_all_gpu_tensor(self, file=None):
+        for x in self.get_tensors():
+            print(x.size(), x.dtype, np.prod(np.array(x.size()))*get_mem_space(x.dtype)/1024**2, file=file)
+
+    def track(self):
+        """
+        Track the GPU memory usage
+        """
+        frameinfo = inspect.stack()[1]
+        where_str = frameinfo.filename + ' line ' + str(frameinfo.lineno) + ': ' + frameinfo.function
+
+        with open(self.gpu_profile_fn, 'a+') as f:
+
+            if self.begin:
+                f.write(f"GPU Memory Track | {datetime.datetime.now():%d-%b-%y-%H:%M:%S} |"
+                        f" Total Tensor Used Memory:{self.get_tensor_usage():<7.1f}Mb"
+                        f" Total Allocated Memory:{self.get_allocate_usage():<7.1f}Mb\n\n")
+                self.begin = False
+
+            if self.print_detail is True:
+                ts_list = [(tensor.size(), tensor.dtype) for tensor in self.get_tensors()]
+                new_tensor_sizes = {(type(x),
+                                    tuple(x.size()),
+                                    ts_list.count((x.size(), x.dtype)),
+                                    np.prod(np.array(x.size()))*get_mem_space(x.dtype)/1024**2,
+                                    x.dtype) for x in self.get_tensors()}
+                for t, s, n, m, data_type in new_tensor_sizes - self.last_tensor_sizes:
+                    f.write(f'+ | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} | {data_type}\n')
+                for t, s, n, m, data_type in self.last_tensor_sizes - new_tensor_sizes:
+                    f.write(f'- | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} | {data_type}\n')
+
+                self.last_tensor_sizes = new_tensor_sizes
+
+            f.write(f"\nAt {where_str:<50}"
+                    f" Total Tensor Used Memory:{self.get_tensor_usage():<7.1f}Mb"
+                    f" Total Allocated Memory:{self.get_allocate_usage():<7.1f}Mb\n\n")