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[NFC] polish colossalai/utils/tensor_detector/tensor_detector.py code style (#1566)

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pull/1550/head
LuGY 2 years ago committed by Frank Lee
parent 0c4c9aa6e0
commit c7d4932956
1 changed files with 32 additions and 36 deletions
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48
colossalai/utils/tensor_detector/tensor_detector.py
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@ -5,18 +5,17 @@ import torch.nn as nn
from typing import Optional from typing import Optional
from collections import defaultdict from collections import defaultdict
LINE_WIDTH = 108 LINE_WIDTH = 108
LINE = '-' * LINE_WIDTH + '\n' LINE = '-' * LINE_WIDTH + '\n'
class TensorDetector(): class TensorDetector():
def __init__(self, def __init__(self,
show_info: bool = True, show_info: bool = True,
log: str = None, log: str = None,
include_cpu: bool = False, include_cpu: bool = False,
module: Optional[nn.Module] = None module: Optional[nn.Module] = None):
):
"""This class is a detector to detect tensor on different devices. """This class is a detector to detect tensor on different devices.
Args: Args:
@ -57,12 +56,12 @@ class TensorDetector():
def mem_format(self, real_memory_size): def mem_format(self, real_memory_size):
# format the tensor memory into a reasonal magnitude # format the tensor memory into a reasonal magnitude
if real_memory_size >= 2 ** 30: if real_memory_size >= 2**30:
return str(real_memory_size / (2 ** 30)) + ' GB' return str(real_memory_size / (2**30)) + ' GB'
if real_memory_size >= 2 ** 20: if real_memory_size >= 2**20:
return str(real_memory_size / (2 ** 20)) + ' MB' return str(real_memory_size / (2**20)) + ' MB'
if real_memory_size >= 2 ** 10: if real_memory_size >= 2**10:
return str(real_memory_size / (2 ** 10)) + ' KB' return str(real_memory_size / (2**10)) + ' KB'
return str(real_memory_size) + ' B' return str(real_memory_size) + ' B'
def collect_tensors_state(self): def collect_tensors_state(self):
@ -125,33 +124,30 @@ class TensorDetector():
minus = outdated + minus minus = outdated + minus
if len(self.order) > 0: if len(self.order) > 0:
for tensor_id in self.order: for tensor_id in self.order:
self.info += template_format.format('+', self.info += template_format.format('+', str(self.tensor_info[tensor_id][0]),
str(self.tensor_info[tensor_id][0]), str(self.tensor_info[tensor_id][1]),
str(self.tensor_info[tensor_id][1]), str(tuple(self.tensor_info[tensor_id][2])),
str(tuple(self.tensor_info[tensor_id][2])), str(self.tensor_info[tensor_id][3]),
str(self.tensor_info[tensor_id][3]), str(self.tensor_info[tensor_id][4]),
str(self.tensor_info[tensor_id][4]), str(self.tensor_info[tensor_id][5]))
str(self.tensor_info[tensor_id][5]))
self.info += '\n' self.info += '\n'
if len(self.order) > 0 and len(minus) > 0: if len(self.order) > 0 and len(minus) > 0:
self.info += '\n' self.info += '\n'
if len(minus) > 0: if len(minus) > 0:
for tensor_id in minus: for tensor_id in minus:
self.info += template_format.format('-', self.info += template_format.format('-', str(self.saved_tensor_info[tensor_id][0]),
str(self.saved_tensor_info[tensor_id][0]), str(self.saved_tensor_info[tensor_id][1]),
str(self.saved_tensor_info[tensor_id][1]), str(tuple(self.saved_tensor_info[tensor_id][2])),
str(tuple(self.saved_tensor_info[tensor_id][2])), str(self.saved_tensor_info[tensor_id][3]),
str(self.saved_tensor_info[tensor_id][3]), str(self.saved_tensor_info[tensor_id][4]),
str(self.saved_tensor_info[tensor_id][4]), str(self.saved_tensor_info[tensor_id][5]))
str(self.saved_tensor_info[tensor_id][5]))
self.info += '\n' self.info += '\n'
# deleted the updated tensor # deleted the updated tensor
self.saved_tensor_info.pop(tensor_id) self.saved_tensor_info.pop(tensor_id)
# trace where is the detect() # trace where is the detect()
locate_info = inspect.stack()[2] locate_info = inspect.stack()[2]
locate_msg = '"' + locate_info.filename + '" line ' + str(locate_info.lineno) locate_msg = '"' + locate_info.filename + '" line ' + str(locate_info.lineno)
self.info += LINE self.info += LINE
self.info += f"Detect Location: {locate_msg}\n" self.info += f"Detect Location: {locate_msg}\n"
@ -168,7 +164,7 @@ class TensorDetector():
with open(self.log + '.log', 'a') as f: with open(self.log + '.log', 'a') as f:
f.write(self.info) f.write(self.info)
def detect(self, include_cpu = False): def detect(self, include_cpu=False):
self.include_cpu = include_cpu self.include_cpu = include_cpu
self.collect_tensors_state() self.collect_tensors_state()
self.print_tensors_state() self.print_tensors_state()

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