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# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import sys
import os
on_step_begin = []
on_step_end = []
def trigger(phase):
[f() for f in phase]
def setup_profiler(args, device):
if args.profile is None:
return
start_step, end_step = map(int, args.profile_steps.split(','))
active_steps = end_step - start_step + 1
cur_step = 0
def on_step_begin_fn():
nonlocal cur_step
cur_step = cur_step + 1
on_step_begin.append(on_step_begin_fn)
def when(cond, clbk):
def fn():
if cond():
clbk()
return fn
def is_start_step():
return cur_step == start_step
def is_end_step():
return cur_step == end_step
def is_capture_step():
return cur_step >= start_step and cur_step <= end_step
if args.profile.startswith('pt'):
schedule = torch.profiler.schedule(wait=0, warmup=0, active=active_steps, repeat=1)
activities = [torch.profiler.ProfilerActivity.CPU]
activities.extend([torch.profiler.ProfilerActivity.HPU] if device.startswith("hpu") else [])
activities.extend([torch.profiler.ProfilerActivity.CUDA] if device.startswith("cuda") else [])
full = args.profile == 'pt-full'
profiler = torch.profiler.profile(
schedule=schedule,
activities=activities,
on_trace_ready=torch.profiler.tensorboard_trace_handler(args.tensorboard_dir, use_gzip=True),
with_stack=full)
on_step_begin.append(when(is_start_step, profiler.start))
on_step_end.append(when(is_capture_step, profiler.step))
on_step_end.append(when(is_end_step, profiler.stop))
elif args.profile == 'hltv':
sys.path.append(os.environ['PYTORCH_MODULES_ROOT_PATH'])
from topologies.tools import SynapseProfilerApi, TraceType
api = SynapseProfilerApi()
def on_start_step():
nonlocal api
api.profiler_start(TraceType.TraceAll, 0)
def on_end_step():
nonlocal api
import habana_frameworks.torch.hpu as hpu
hpu.synchronize()
api.profiler_stop(TraceType.TraceAll, 0)
api.profiler_get_trace_json(TraceType.TraceAll, 0)
on_step_begin.append(when(is_start_step, on_start_step))
on_step_end.append(when(is_end_step, on_end_step))
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