mirror of https://github.com/hpcaitech/ColossalAI
polished output format for communication profiler and pcie profiler (#404)
fixed typing errorpull/411/head
parent
aaead33cfe
commit
dfd0363f68
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@ -6,20 +6,25 @@ from torch.autograd.profiler import profile
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import torch.distributed as dist
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from torch.distributed import ReduceOp
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from colossalai.utils import get_current_device
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from .prof_utils import BaseProfiler, _format_time, _format_memory, _format_bandwith
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from .prof_utils import BaseProfiler, _format_time, _format_memory, _format_bandwidth
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from typing import List, Optional
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def _get_code_location(depth: int):
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ret = ""
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length = len(inspect.stack())
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for i in range(3, min(length, depth + 1)):
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ret = []
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length = min(len(inspect.stack()), depth + 1)
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for i in range(3, length):
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upper_frame = inspect.stack()[i]
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function_name = inspect.stack()[i - 1].function
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info = upper_frame.filename + "(" + str(upper_frame.lineno) + "): " + function_name + "\n"
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ret += info
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ret.append(upper_frame.filename)
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ret.append('(')
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ret.append(str(upper_frame.lineno))
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ret.append('): ')
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ret.append(function_name)
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if i != length - 1:
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ret.append('\n')
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return ret
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return ''.join(ret)
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torch_all_reduce = dist.all_reduce
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@ -100,8 +105,9 @@ class CommProfiler(BaseProfiler):
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def result_list(self, sep: str = "\n"):
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res = []
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def append(s: str):
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res.append(s)
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def append(s: str = None):
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if s is not None:
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res.append(s)
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res.append(sep)
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if self.warn_flag:
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@ -110,19 +116,26 @@ class CommProfiler(BaseProfiler):
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append("Collective communication profiling result:")
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append("total cuda time: {}".format(_format_time(self.total_cuda_time)))
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append("average bandwith: {}".format(_format_bandwith(self.total_comm_vol, self.total_cuda_time)))
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append("average bandwidth: {}".format(_format_bandwidth(self.total_comm_vol, self.total_cuda_time)))
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append("total number of calls: {}".format(self.total_count))
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append("All events:\n----------------------------------------")
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append("All events:")
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seperation = '-' * 74
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row_format = '{:^10}' + '{:^12}' * 2 + '{:^16}' + '{:^12}' * 2
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append(seperation)
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append(row_format.format('Location', 'GPU time', 'Percentage', 'Comm volume', 'Bandwidth', 'Num of calls'))
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append(seperation)
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show_list = sorted(self.ops_record.items(), key=lambda kv: -kv[1].self_cuda_time)
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for location, event in show_list:
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append(location)
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append("self cuda time: {}".format(_format_time(event.self_cuda_time)))
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append("{:.1f}% of total communication time".format(event.self_cuda_time / self.total_cuda_time * 100.0))
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append("self communication volme: {}".format(_format_memory(event.self_comm_vol)))
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append("average bandwith: {}".format(_format_bandwith(event.self_comm_vol, event.self_cuda_time)))
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append("number of calls: {}".format(event.self_count))
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append("----------------------------------------")
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append(
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row_format.format('', _format_time(event.self_cuda_time),
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'{:.1f}%'.format(event.self_cuda_time / self.total_cuda_time * 100.0),
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_format_memory(event.self_comm_vol),
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_format_bandwidth(event.self_comm_vol, event.self_cuda_time), event.self_count))
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append()
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return ''.join(res)
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@ -1,6 +1,6 @@
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from pathlib import Path
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from torch.autograd.profiler import profile
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from .prof_utils import BaseProfiler, _format_time, _format_memory, _format_bandwith
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from .prof_utils import BaseProfiler, _format_time, _format_memory, _format_bandwidth
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from typing import List
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@ -24,6 +24,7 @@ def _reduce_location(locations: List[str]) -> str:
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for lo in locations:
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ret.append(lo)
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ret.append("\n")
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ret = ret[:-1]
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return ''.join(ret)
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@ -48,18 +49,23 @@ class PcieProfiler(BaseProfiler):
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TODO: Merge pcie profiler into communication profiler
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"""
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def __init__(self,
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dtype: str = "fp32",
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depth: int = 1,
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total_count: int = 0,
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total_pcie_vol: int = 0,
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total_cuda_time: int = 0):
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def __init__(self, dtype: str = "fp32", depth: int = 1):
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super().__init__(profiler_name="Pcie", priority=10)
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self.depth = depth
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self.data_size = _get_size(dtype)
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self.total_count = total_count
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self.total_pcie_vol = total_pcie_vol
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self.total_cuda_time = total_cuda_time
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self.h2d_count = 0
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self.h2d_time = 0
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self.d2h_count = 0
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self.d2h_time = 0
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self.ops_record = dict()
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self.profiler = None
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def reset(self):
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self.h2d_count = 0
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self.h2d_time = 0
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self.d2h_count = 0
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self.d2h_time = 0
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self.ops_record = dict()
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self.profiler = None
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@ -81,17 +87,20 @@ class PcieProfiler(BaseProfiler):
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for event in events:
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if event.name == "aten::copy_":
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t_shape = event.input_shapes[0]
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if len(t_shape) == 0 or event.cuda_time_total == 0:
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if len(t_shape) == 0 or event.cuda_time_total == 0 or len(event.stack) == 0:
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continue
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current_comm_event = PcieEvent(1, self.data_size * _get_numel(t_shape), event.cuda_time_total)
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self.total_count += current_comm_event.count
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self.total_pcie_vol += current_comm_event.pcie_vol
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self.total_cuda_time += current_comm_event.cuda_time
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code_location = _reduce_location(event.stack[:self.depth])
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if code_location in self.ops_record:
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self.ops_record[code_location].add(current_comm_event)
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else:
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self.ops_record[code_location] = current_comm_event
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elif 'Memcpy HtoD' in event.name:
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self.h2d_count += 1
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self.h2d_time += event.cuda_time_total
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elif 'Memcpy DtoH' in event.name:
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self.d2h_count += 1
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self.d2h_time += event.cuda_time_total
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self.profiler = None
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@ -108,24 +117,32 @@ class PcieProfiler(BaseProfiler):
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def result_list(self, sep: str = "\n"):
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res = []
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def append(s: str):
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res.append(s)
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def append(s: str = None):
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if s is not None:
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res.append(s)
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res.append(sep)
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append("Pcie profiling result:")
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append("total cuda time: {}".format(_format_time(self.total_cuda_time)))
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append("average bandwith: {}".format(_format_bandwith(self.total_pcie_vol, self.total_cuda_time)))
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append("total number of calls: {}".format(self.total_count))
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append("All events:\n----------------------------------------")
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append("time of data transmission (CPU -> GPU): {}".format(_format_time(self.h2d_time)))
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append("number of transmission (CPU -> GPU): {}".format(self.h2d_count))
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append("time of data transmission (GPU -> CPU): {}".format(_format_time(self.d2h_time)))
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append("number of transmission (GPU -> CPU): {}".format(self.d2h_count))
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append("Possible data transmission events in PCIE:")
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seperation = '-' * 62
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row_format = '{:^10}' + '{:^12}' + '{:^16}' + '{:^12}' * 2
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append(seperation)
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append(row_format.format('Location', 'GPU time', 'Trans volume', 'Bandwidth', 'Num of calls'))
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append(seperation)
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show_list = sorted(self.ops_record.items(), key=lambda kv: -kv[1].cuda_time)
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for location, event in show_list:
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append(location)
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append("cuda time: {}".format(_format_time(event.cuda_time)))
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append("{:.1f}% of total pcie time".format(event.cuda_time / self.total_cuda_time * 100.0))
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append("pcie volme: {}".format(_format_memory(event.pcie_vol)))
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append("average bandwith: {}".format(_format_bandwith(event.pcie_vol, event.cuda_time)))
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append("number of calls: {}".format(event.count))
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append("----------------------------------------")
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append(
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row_format.format('', _format_time(event.cuda_time), _format_memory(event.pcie_vol),
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_format_bandwidth(event.pcie_vol, event.cuda_time), event.count))
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append()
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return ''.join(res)
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@ -32,7 +32,7 @@ def _format_memory(nbytes):
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return str(nbytes) + ' B'
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def _format_bandwith(volme: float or int, time_us: int):
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def _format_bandwidth(volme: float or int, time_us: int):
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sec_div_mb = (1000.0 / 1024.0)**2
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mb_per_sec = volme / time_us * sec_div_mb
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