From a06f512abf0bd018ad1e7cd0597ce9a6d28a6019 Mon Sep 17 00:00:00 2001 From: niuliling123 Date: Wed, 17 Aug 2022 14:51:51 +0800 Subject: [PATCH] update --- ppdet/data/reader.py | 4 +++ ppdet/engine/trainer.py | 62 ++++++++++++++++++++++++++++++++++++++--- 2 files changed, 62 insertions(+), 4 deletions(-) diff --git a/ppdet/data/reader.py b/ppdet/data/reader.py index f04fd6b33..a5489f40c 100644 --- a/ppdet/data/reader.py +++ b/ppdet/data/reader.py @@ -140,6 +140,7 @@ class BaseDataLoader(object): collate_batch=True, use_shared_memory=False, **kwargs): + print("[BaseDataLoader] batch_size={}, shuffle={}, use_shared_memory={}".format(batch_size, shuffle, use_shared_memory)) # sample transform self._sample_transforms = Compose( sample_transforms, num_classes=num_classes) @@ -187,6 +188,9 @@ class BaseDataLoader(object): "disable shared_memory in DataLoader") use_shared_memory = False + print("==========================================================================") + print("worker_num={}, use_shared_memory={}".format(worker_num, use_shared_memory)) + print("==========================================================================") self.dataloader = DataLoader( dataset=self.dataset, batch_sampler=self._batch_sampler, diff --git a/ppdet/engine/trainer.py b/ppdet/engine/trainer.py index c253b40aa..d96e501bc 100644 --- a/ppdet/engine/trainer.py +++ b/ppdet/engine/trainer.py @@ -34,7 +34,6 @@ import paddle.distributed as dist from paddle.distributed import fleet from paddle.static import InputSpec from ppdet.optimizer import ModelEMA - from ppdet.core.workspace import create from ppdet.utils.checkpoint import load_weight, load_pretrain_weight from ppdet.utils.visualizer import visualize_results, save_result @@ -58,6 +57,41 @@ __all__ = ['Trainer'] MOT_ARCH = ['DeepSORT', 'JDE', 'FairMOT', 'ByteTrack'] +GLOBAL_PROFILE_STATE = False +def add_nvtx_event(event_name, is_first=False, is_last=False): + global GLOBAL_PROFILE_STATE + if not GLOBAL_PROFILE_STATE: + return + + if not is_first: + paddle.fluid.core.nvprof_nvtx_pop() + if not is_last: + paddle.fluid.core.nvprof_nvtx_push(event_name) + +def switch_profile(start, end, step_idx, event_name=None): + global GLOBAL_PROFILE_STATE + if step_idx > start and step_idx < end: + GLOBAL_PROFILE_STATE = True + else: + GLOBAL_PROFILE_STATE = False + + #if step_idx == start: + # paddle.utils.profiler.start_profiler("All", "Default") + #elif step_idx == end: + # paddle.utils.profiler.stop_profiler("total", "tmp.profile") + + if event_name is None: + event_name = str(step_idx) + if step_idx == start: + paddle.fluid.core.nvprof_start() + paddle.fluid.core.nvprof_enable_record_event() + paddle.fluid.core.nvprof_nvtx_push(event_name) + elif step_idx == end: + paddle.fluid.core.nvprof_nvtx_pop() + paddle.fluid.core.nvprof_stop() + elif step_idx > start and step_idx < end: + paddle.fluid.core.nvprof_nvtx_pop() + paddle.fluid.core.nvprof_nvtx_push(event_name) class Trainer(object): def __init__(self, cfg, mode='train'): @@ -403,6 +437,7 @@ class Trainer(object): model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(model) # enabel auto mixed precision mode + print("use_amp={}, amp_level={}".format(self.use_amp, self.amp_level)) if self.use_amp: scaler = paddle.amp.GradScaler( enable=self.cfg.use_gpu or self.cfg.use_npu, @@ -436,10 +471,13 @@ class Trainer(object): profiler_options = self.cfg.get('profiler_options', None) self._compose_callback.on_train_begin(self.status) - + train_batch_size = self.cfg.TrainReader['batch_size'] use_fused_allreduce_gradients = self.cfg[ 'use_fused_allreduce_gradients'] if 'use_fused_allreduce_gradients' in self.cfg else False - + prof = paddle.profiler.Profiler(targets=[paddle.profiler.ProfilerTarget.CPU,paddle. paddle.profiler.ProfilerTarget.GPU], + scheduler=[60, 70], + timer_only=True) + prof.start() for epoch_id in range(self.start_epoch, self.cfg.epoch): self.status['mode'] = 'train' self.status['epoch_id'] = epoch_id @@ -451,6 +489,7 @@ class Trainer(object): self.status['data_time'].update(time.time() - iter_tic) self.status['step_id'] = step_id profiler.add_profiler_step(profiler_options) + #switch_profile(60, 70, step_id,"(iter is ={})".format(step_id)) self._compose_callback.on_step_begin(self.status) data['epoch_id'] = epoch_id @@ -479,12 +518,17 @@ class Trainer(object): custom_black_list=self.custom_black_list, level=self.amp_level): # model forward + add_nvtx_event("forward", is_first=True, is_last=False) outputs = model(data) + add_nvtx_event("loss", is_first=False, is_last=False) loss = outputs['loss'] # model backward + add_nvtx_event("scaleloss", is_first=False, is_last=False) scaled_loss = scaler.scale(loss) + add_nvtx_event("backward", is_first=False, is_last=False) scaled_loss.backward() # in dygraph mode, optimizer.minimize is equal to optimizer.step + add_nvtx_event("optimizer", is_first=False, is_last=False) scaler.minimize(self.optimizer, scaled_loss) else: if isinstance( @@ -500,22 +544,31 @@ class Trainer(object): list(model.parameters()), None) else: # model forward + add_nvtx_event("forward", is_first=True, is_last=False) outputs = model(data) + add_nvtx_event("loss", is_first=False, is_last=False) loss = outputs['loss'] # model backward + add_nvtx_event("backward", is_first=False, is_last=False) loss.backward() + add_nvtx_event("optimizer", is_first=False, is_last=False) self.optimizer.step() + add_nvtx_event("curr_lr", is_first=False, is_last=False) curr_lr = self.optimizer.get_lr() self.lr.step() if self.cfg.get('unstructured_prune'): self.pruner.step() + add_nvtx_event("clear_grad", is_first=False, is_last=False) self.optimizer.clear_grad() + add_nvtx_event("status", is_first=False, is_last=False) self.status['learning_rate'] = curr_lr if self._nranks < 2 or self._local_rank == 0: self.status['training_staus'].update(outputs) self.status['batch_time'].update(time.time() - iter_tic) + add_nvtx_event("other", is_first=False, is_last=True) + prof.step(num_samples=train_batch_size) self._compose_callback.on_step_end(self.status) if self.use_ema: self.ema.update() @@ -564,7 +617,8 @@ class Trainer(object): # reset original weight self.model.set_dict(weight) self.status.pop('weight') - + prof.stop() + prof.summary(op_detail=True) self._compose_callback.on_train_end(self.status) def _eval_with_loader(self, loader): -- GitLab