def create_orttrainer_and_load_checkpoint(device, trainer_opts, checkpoint_dir, use_lamb=True):
"""Instantiate and load checkpoint into trainer
- Instantiates the ORTTrainer with given input trainer_opts configuration for a simple transformer model
- Loads the checkpoint from directory checkpoint_dir into the trainer
- Runs eval_step on the trainer so the trainer onnx graph is initialized
- Returns the trainer state_dict and the pytorch model
"""
seed = 1
torch.manual_seed(seed)
set_seed(seed)
# PyTorch transformer model setup
learning_rate = 0.1
optim_config = optim.LambConfig(lr=learning_rate) if use_lamb else optim.AdamConfig(lr=learning_rate)
model, model_desc, loss_fn, batcher_fn, train_data, _, _ = _load_pytorch_transformer_model(device)
trainer = orttrainer.ORTTrainer(model, model_desc, optim_config, loss_fn=loss_fn, options=orttrainer.ORTTrainerOptions(trainer_opts))
# load checkpoint into trainer
checkpoint_file_name = 'checkpoint*.ortcp'
checkpoint_files = glob.glob(os.path.join(checkpoint_dir, checkpoint_file_name))
trainer.load_checkpoint(*checkpoint_files)
# run an eval step to innitialize the graph
torch.manual_seed(seed)
set_seed(seed)
data, targets = batcher_fn(train_data, 0)
trainer.eval_step(data, targets)
return trainer.state_dict(), model
def testToyBERTModelMixedPrecisionLossScalerLegacyExperimental(
loss_scaler, legacy_loss_scaler):
# Common setup
total_steps = 128
device = "cuda"
seed = 1
# EXPERIMENTAL IMPLEMENTATION
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
model_desc = bert_model_description()
model = load_bert_onnx_model()
optim_config = optim.AdamConfig(lr=0.001)
opts = orttrainer.ORTTrainerOptions({
'debug': {
'deterministic_compute': True
},
'device': {
'id': device,
},
'mixed_precision': {
'enabled': True,
'loss_scaler': loss_scaler
}
})
trainer = orttrainer.ORTTrainer(model,
model_desc,
optim_config,
options=opts)
experimental_losses = []
for i in range(total_steps):
sample_input = generate_random_input_from_model_desc(model_desc, i)
experimental_losses.append(
trainer.train_step(*sample_input).cpu().item())
# LEGACY IMPLEMENTATION
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
device = torch.device(device)
model = load_bert_onnx_model()
legacy_model_desc, learning_rate_description, learning_rate = legacy_model_params(
optim_config.lr)
legacy_trainer = Legacy_ORTTrainer(model,
None,
legacy_model_desc,
"AdamOptimizer",
None,
learning_rate_description,
device,
_use_deterministic_compute=True,
use_mixed_precision=True,
loss_scaler=legacy_loss_scaler)
legacy_losses = []
for i in range(total_steps):
sample_input = generate_random_input_from_model_desc(model_desc, i)
leg_loss = legacy_trainer.train_step(*sample_input, learning_rate)
legacy_losses.append(leg_loss.cpu().item())
# Check results
_test_helpers.assert_model_outputs(experimental_losses, legacy_losses)
def create_orttrainer_and_save_checkpoint(device,
trainer_opts,
checkpoint_dir,
state_dict_key_name='state_dict',
use_lamb=True):
learning_rate = 0.1
seed = 1
torch.manual_seed(seed)
set_seed(seed)
optim_config = optim.LambConfig(
lr=learning_rate) if use_lamb else optim.AdamConfig(lr=learning_rate)
model, model_desc, loss_fn, batcher_fn, train_data, _, _ = _load_pytorch_transformer_model(
device)
trainer = orttrainer.ORTTrainer(
model,
model_desc,
optim_config,
loss_fn=loss_fn,
options=orttrainer.ORTTrainerOptions(trainer_opts))
if 'distributed' in trainer_opts:
train_data = next(
islice(
_chunkify(train_data,
trainer_opts['distributed']['world_size']),
trainer_opts['distributed']['world_rank'], None))
# run train steps
_train(trainer, train_data, batcher_fn)
# save current model parameters as a checkpoint
if checkpoint_dir:
_save(trainer, checkpoint_dir, state_dict_key_name)
def load_model_optim_state_and_eval(device,
trainer_opts,
use_lamb=True,
seed=1,
learning_rate=0.1):
torch.manual_seed(seed)
set_seed(seed)
optim_config = optim.LambConfig(
lr=learning_rate) if use_lamb else optim.AdamConfig(lr=learning_rate)
model, model_desc, loss_fn, batcher_fn, train_data, _, _ = _load_pytorch_transformer_model(
device)
trainer = orttrainer.ORTTrainer(
model,
model_desc,
optim_config,
loss_fn=loss_fn,
options=orttrainer.ORTTrainerOptions(trainer_opts))
# load dummy state
dummy_init_state = generate_dummy_optim_state(model, optim_config)
trainer.load_state_dict(dummy_init_state)
# run an eval step to innitialize the graph
data, targets = batcher_fn(train_data, 0)
trainer.eval_step(data, targets)
optimizer_state_dict = trainer.state_dict()
del optimizer_state_dict["model"]
return dummy_init_state, optimizer_state_dict
def testToyBERTModelGradientAccumulationLegacyExperimental(
gradient_accumulation_steps):
# Common setup
total_steps = 128
device = "cuda"
seed = 1
# EXPERIMENTAL IMPLEMENTATION
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
model_desc = bert_model_description()
model = load_bert_onnx_model()
optim_config = optim.AdamConfig()
opts = orttrainer.ORTTrainerOptions({
'debug': {
'deterministic_compute': True
},
'device': {
'id': device,
},
'batch': {
'gradient_accumulation_steps': gradient_accumulation_steps
},
})
trainer = orttrainer.ORTTrainer(model,
model_desc,
optim_config,
options=opts)
experimental_losses = []
for i in range(total_steps):
sample_input = generate_random_input_from_model_desc(model_desc, i)
loss = trainer.train_step(*sample_input)
experimental_losses.append(loss.cpu().item())
# LEGACY IMPLEMENTATION
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
device = torch.device(device)
model = load_bert_onnx_model()
legacy_model_desc, learning_rate_description, learning_rate = legacy_model_params(
optim_config.lr)
legacy_trainer = Legacy_ORTTrainer(
model,
None,
legacy_model_desc,
"AdamOptimizer",
None,
learning_rate_description,
device,
_use_deterministic_compute=True,
gradient_accumulation_steps=gradient_accumulation_steps)
legacy_losses = []
for i in range(total_steps):
sample_input = generate_random_input_from_model_desc(model_desc, i)
leg_loss = legacy_trainer.train_step(*sample_input, learning_rate)
legacy_losses.append(leg_loss.cpu().item())
# Check results
_test_helpers.assert_model_outputs(experimental_losses, legacy_losses)
def create_orttrainer_and_save_checkpoint_bart(
device,
trainer_opts,
checkpoint_dir,
state_dict_key_name="state_dict",
use_lamb=True,
seed=1,
learning_rate=0.1):
"""Instantiate trainer and save checkpoint for BART.
- Instantiates the ORTTrainer with given input trainer_opts configuration for a simple BART model
- Loads a dummy optimizer state into the trainer
- Runs eval_step on the trainer so the trainer onnx graph is initialized
- Returns the trainer state_dict, the expected state dict if present, and the onnx model
"""
torch.manual_seed(seed)
set_seed(seed)
ort_trainer_opts = orttrainer.ORTTrainerOptions(trainer_opts)
optim_config = optim.LambConfig(
lr=learning_rate) if use_lamb else optim.AdamConfig(lr=learning_rate)
model, model_desc = _load_bart_model()
trainer = orttrainer.ORTTrainer(model,
model_desc,
optim_config,
options=ort_trainer_opts)
# load dummy optimizer state as we are not going to run real training
dummy_init_state = generate_dummy_optim_state(model, optim_config)
init_state = copy.deepcopy(dummy_init_state)
trainer.load_state_dict(dummy_init_state)
# run an eval step to innitialize the graph
src_tokens, prev_output_tokens, target = generate_random_input_from_bart_model_desc(
model_desc, seed=seed)
trainer.eval_step(src_tokens, prev_output_tokens, target)
# save current model parameters as a checkpoint
if checkpoint_dir:
if _is_model_parallel_run(ort_trainer_opts):
_save(trainer,
checkpoint_dir,
state_dict_key_name,
world_rank=ort_trainer_opts.distributed.world_rank)
# save the initial complete model and optimizer states
if ort_trainer_opts.distributed.world_rank == 0:
init_state["model"] = {"full_precision": dict()}
for initializer in model.graph.initializer:
init_state["model"]["full_precision"][
initializer.name] = numpy_helper.to_array(initializer)
with open(
os.path.join(checkpoint_dir,
"expected_state_dict.pkl"), "wb") as f:
pickle.dump(init_state, f)
else:
_save(trainer, checkpoint_dir, state_dict_key_name)
def testToyBERTModelLegacyExperimentalCustomOptimParameters(params, legacy_optim_map):
# Common setup
total_steps = 128
device = "cuda"
seed = 1
# EXPERIMENTAL API
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
model_desc = bert_model_description()
model = load_bert_onnx_model()
optim_config = optim.AdamConfig(
params, alpha=0.9, beta=0.999, lambda_coef=0.01, epsilon=1e-6, do_bias_correction=False
)
opts = orttrainer.ORTTrainerOptions(
{
"debug": {"deterministic_compute": True},
"device": {
"id": device,
},
}
)
trainer = orttrainer.ORTTrainer(model, model_desc, optim_config, options=opts)
experimental_losses = []
for i in range(total_steps):
sample_input = generate_random_input_from_model_desc(model_desc, i)
experimental_losses.append(trainer.train_step(*sample_input).cpu().item())
# LEGACY IMPLEMENTATION
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
device = torch.device(device)
model = load_bert_onnx_model()
legacy_model_desc, learning_rate_description, learning_rate = legacy_model_params(trainer.optim_config.lr)
legacy_trainer = Legacy_ORTTrainer(
model,
None,
legacy_model_desc,
"AdamOptimizer",
legacy_optim_map,
learning_rate_description,
device,
_use_deterministic_compute=True,
)
legacy_losses = []
for i in range(total_steps):
sample_input = generate_random_input_from_model_desc(model_desc, i)
legacy_sample_input = [*sample_input, learning_rate]
legacy_losses.append(legacy_trainer.train_step(legacy_sample_input).cpu().item())
# Check results
_test_helpers.assert_model_outputs(experimental_losses, legacy_losses)
def prepare_model(args, device):
config = BertConfig.from_pretrained(args.bert_model, cache_dir=args.cache_dir)
# config.num_hidden_layers = 12
if args.force_num_hidden_layers:
logger.info("Modifying model config with num_hidden_layers to %d", args.force_num_hidden_layers)
config.num_hidden_layers = args.force_num_hidden_layers
model = BertForPreTraining(config)
if args.init_state_dict is not None:
model.load_state_dict(args.init_state_dict)
model_desc = bert_model_description(config)
lr_scheduler = LinearWarmupLRScheduler(total_steps=int(args.max_steps), warmup=args.warmup_proportion)
loss_scaler = amp.DynamicLossScaler() if args.fp16 else None
options = orttrainer.ORTTrainerOptions({'batch': {
'gradient_accumulation_steps': args.gradient_accumulation_steps},
'device': {'id': str(device)},
'mixed_precision': {
'enabled': args.fp16,
'loss_scaler': loss_scaler},
'graph_transformer': {
'attn_dropout_recompute': args.attn_dropout_recompute,
'gelu_recompute': args.gelu_recompute,
'transformer_layer_recompute': args.transformer_layer_recompute,
},
'debug': {'deterministic_compute': True, },
'utils': {
'grad_norm_clip': True},
'distributed': {
'world_rank': max(0, args.local_rank),
'world_size': args.world_size,
'local_rank': max(0, args.local_rank),
'allreduce_post_accumulation': args.allreduce_post_accumulation,
'deepspeed_zero_optimization': {'stage': args.deepspeed_zero_stage},
'enable_adasum': False},
'lr_scheduler': lr_scheduler
})
param_optimizer = list(model.named_parameters())
no_decay_keys = ["bias", "gamma", "beta", "LayerNorm"]
params = [{
'params': [n for n, p in param_optimizer if any(no_decay_key in n for no_decay_key in no_decay_keys)],
"alpha": 0.9, "beta": 0.999, "lambda": 0.0, "epsilon": 1e-6}, {
'params': [n for n, p in param_optimizer if not any(no_decay_key in n for no_decay_key in no_decay_keys)],
"alpha": 0.9, "beta": 0.999, "lambda": 0.0, "epsilon": 1e-6}]
optim_config = optim.AdamConfig(params=params, lr=2e-5, do_bias_correction=True)
model = orttrainer.ORTTrainer(model, model_desc, optim_config, options=options)
return model
def testToyBERTModelLRScheduler(initial_lr, lr_scheduler, expected_learning_rates, expected_losses):
return # TODO: re-enable after nondeterminism on backend is fixed
# Common setup
device = "cuda"
total_steps = 10
seed = 1
warmup = 0.05
cycles = 0.5
power = 1.0
lr_end = 1e-7
rtol = 1e-3
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
# Setup LR Schedulers
if (
lr_scheduler == optim.lr_scheduler.ConstantWarmupLRScheduler
or lr_scheduler == optim.lr_scheduler.LinearWarmupLRScheduler
):
lr_scheduler = lr_scheduler(total_steps=total_steps, warmup=warmup)
elif lr_scheduler == optim.lr_scheduler.CosineWarmupLRScheduler:
lr_scheduler = lr_scheduler(total_steps=total_steps, warmup=warmup, cycles=cycles)
elif lr_scheduler == optim.lr_scheduler.PolyWarmupLRScheduler:
lr_scheduler = lr_scheduler(total_steps=total_steps, warmup=warmup, power=power, lr_end=lr_end)
else:
raise RuntimeError("Invalid lr_scheduler")
# Modeling
model_desc = bert_model_description()
model = load_bert_onnx_model()
optim_config = optim.AdamConfig(lr=initial_lr)
opts = orttrainer.ORTTrainerOptions(
{
"debug": {"deterministic_compute": True},
"device": {
"id": device,
},
"lr_scheduler": lr_scheduler,
}
)
trainer = orttrainer.ORTTrainer(model, model_desc, optim_config, options=opts)
# Train
losses = []
learning_rates = []
for i in range(total_steps):
sample_input = generate_random_input_from_model_desc(model_desc, i)
losses.append(trainer.train_step(*sample_input).cpu().item())
learning_rates.append(trainer.options.lr_scheduler.get_last_lr()[0])
# Check output
_test_helpers.assert_model_outputs(learning_rates, expected_learning_rates, rtol=rtol)
_test_helpers.assert_model_outputs(losses, expected_losses, rtol=rtol)
def create_initialized_orttrainer(device, trainer_opts, use_lamb=True):
seed = 1
torch.manual_seed(seed)
set_seed(seed)
learning_rate = 1e-10
optim_config = optim.LambConfig(lr=learning_rate) if use_lamb else optim.AdamConfig(lr=learning_rate)
model, model_desc, loss_fn, batcher_fn, train_data, _, _ = _load_pytorch_transformer_model(device)
trainer = orttrainer.ORTTrainer(model, model_desc, optim_config, loss_fn=loss_fn, options=orttrainer.ORTTrainerOptions(trainer_opts))
_train(trainer, train_data, batcher_fn)
return trainer
def verify_optimizer_state_match(device,
opts,
checkpoint_dir,
world_rank,
use_lamb=False):
expected_optim_state, trainer_state = load_model_optim_state_and_eval(
device, opts, use_lamb)
trainer_state = split_state_dict(trainer_state)
# round about way of checking optimizer states. Save state dicts into temporary folder, read them and aggregate them.
with open(
os.path.join(checkpoint_dir,
'distributed_state_' + str(world_rank) + '.pkl'),
"wb") as f:
pickle.dump(trainer_state, f)
dist.barrier()
if world_rank == 0:
num_states = len(glob.glob1(checkpoint_dir, "distributed_state*"))
optimizer_states = dict()
for rank in range(num_states):
rank_state_dict = None
with open(
os.path.join(checkpoint_dir,
'distributed_state_' + str(rank) + '.pkl'),
'rb') as f:
rank_state_dict = pickle.load(f)
# collect optimizer states for later comparison since they are sharded
aggregate_states(optimizer_states, rank_state_dict['optimizer'])
# compare optimizer states
optimizer_config = optim.LambConfig(
) if use_lamb else optim.AdamConfig()
actual_optim_state = get_optim_state_from_state_dict(
optimizer_states, optimizer_config)
assert actual_optim_state.keys() == expected_optim_state.keys()
for param_name, a_state in actual_optim_state.items():
for k, v in a_state.items():
assert_allclose(
v.reshape(expected_optim_state[param_name][k].shape),
expected_optim_state[param_name][k],
err_msg=
f"Optimizer state mismatch for param {param_name}, key {k}"
)
dist.barrier()
os.remove(
os.path.join(checkpoint_dir,
'distributed_state_' + str(world_rank) + '.pkl'))
def create_orttrainer_and_load_checkpoint_bart(device,
trainer_opts,
checkpoint_dir,
use_lamb=True,
seed=1,
learning_rate=0.1):
"""Instantiate and load checkpoint into trainer
- Instantiates the ORTTrainer with given input trainer_opts configuration for a simple BART model
- Loads the checkpoint from directory checkpoint_dir into the trainer
- Runs eval_step on the trainer so the trainer onnx graph is initialized
- Returns the trainer state_dict, the expected state dict if present, and the onnx model
"""
torch.manual_seed(seed)
set_seed(seed)
# model setup
optim_config = optim.LambConfig(
lr=learning_rate) if use_lamb else optim.AdamConfig(lr=learning_rate)
model, model_desc = _load_bart_model()
trainer = orttrainer.ORTTrainer(
model,
model_desc,
optim_config,
options=orttrainer.ORTTrainerOptions(trainer_opts))
# load checkpoint into trainer
checkpoint_file_name = "checkpoint*.ortcp"
checkpoint_files = glob.glob(
os.path.join(checkpoint_dir, checkpoint_file_name))
trainer.load_checkpoint(*checkpoint_files)
# run an eval step to innitialize the graph
src_tokens, prev_output_tokens, target = generate_random_input_from_bart_model_desc(
model_desc, seed=seed)
trainer.eval_step(src_tokens, prev_output_tokens, target)
expected_state_dict = None
fname = os.path.join(checkpoint_dir, "expected_state_dict.pkl")
if os.path.isfile(fname):
with open(fname, "rb") as f:
expected_state_dict = pickle.load(f)
return trainer.state_dict(), expected_state_dict, model
def create_orttrainer_and_save_checkpoint(device,
trainer_opts,
checkpoint_dir,
state_dict_key_name="state_dict",
use_lamb=True,
seed=1,
learning_rate=0.1):
torch.manual_seed(seed)
set_seed(seed)
ort_trainer_opts = orttrainer.ORTTrainerOptions(trainer_opts)
optim_config = optim.LambConfig(
lr=learning_rate) if use_lamb else optim.AdamConfig(lr=learning_rate)
model, model_desc, loss_fn, batcher_fn, train_data, _, _ = _load_pytorch_transformer_model(
device)
trainer = orttrainer.ORTTrainer(model,
model_desc,
optim_config,
loss_fn=loss_fn,
options=ort_trainer_opts)
if "distributed" in trainer_opts:
train_data = next(
islice(
_chunkify(train_data,
trainer_opts["distributed"]["world_size"]),
trainer_opts["distributed"]["world_rank"],
None,
))
# run train steps
_train(trainer, train_data, batcher_fn)
# save current model parameters as a checkpoint
if checkpoint_dir:
if _is_model_parallel_run(ort_trainer_opts):
_save(trainer,
checkpoint_dir,
state_dict_key_name,
world_rank=ort_trainer_opts.distributed.world_rank)
else:
_save(trainer, checkpoint_dir, state_dict_key_name)
def train(self, model_path: Optional[str] = None):
"""
Main training entry point.
Args:
model_path:
(Optional) Local path to model if model to train has been instantiated from a local path
If present, we will try reloading the optimizer/scheduler states from there.
"""
train_dataloader = self.get_train_dataloader()
if self.args.max_steps > 0:
t_total = self.args.max_steps
num_train_epochs = (self.args.max_steps //
(len(train_dataloader) //
self.args.gradient_accumulation_steps) + 1)
else:
t_total = int(
len(train_dataloader) //
self.args.gradient_accumulation_steps *
self.args.num_train_epochs)
num_train_epochs = self.args.num_train_epochs
config = self.model.config
model_desc = self.gpt2_model_description(
config.n_head, config.vocab_size, config.n_embd, config.n_layer,
config.n_ctx, self.args.per_gpu_train_batch_size)
from onnxruntime.capi._pybind_state import set_arena_extend_strategy, ArenaExtendStrategy
set_arena_extend_strategy(ArenaExtendStrategy.kSameAsRequested)
param_optimizer = list(self.model.named_parameters())
no_decay = ['bias', 'gamma', 'beta', 'LayerNorm']
optim_config = optim.AdamConfig(params=[{
'params':
[n for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'lambda_coef':
0.0
}],
lr=self.args.learning_rate,
alpha=0.9,
beta=0.999,
lambda_coef=self.args.weight_decay,
epsilon=self.args.adam_epsilon)
warmup = self.args.warmup_steps / t_total
lr_scheduler = optim.lr_scheduler.LinearWarmupLRScheduler(
total_steps=t_total, warmup=warmup)
loss_scaler = amp.DynamicLossScaler(
automatic_update=True,
loss_scale=float(1 << 20),
up_scale_window=2000,
min_loss_scale=1.0,
max_loss_scale=float(1 << 24)) if self.args.fp16 else None
opts = orttrainer.ORTTrainerOptions({
'device': {
'id': str(self.args.device)
},
'distributed': {
'world_rank': self.args.world_rank,
'world_size': self.args.world_size,
'local_rank': self.args.local_rank,
'allreduce_post_accumulation': True
},
'mixed_precision': {
'enabled': self.args.fp16,
'loss_scaler': loss_scaler
},
'batch': {
'gradient_accumulation_steps':
self.args.gradient_accumulation_steps
},
'lr_scheduler': lr_scheduler
})
self.ort_model = orttrainer.ORTTrainer(self.model,
model_desc,
optim_config,
None,
options=opts)
logger.info("****************************Model converted to ORT")
model = self.ort_model
if self.tb_writer is not None:
self.tb_writer.add_text("args", self.args.to_json_string())
# Train!
if self.is_world_master():
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataloader.dataset))
logger.info(" Num Epochs = %d", num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
self.args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
self.args.train_batch_size *
self.args.gradient_accumulation_steps *
(self.args.world_size if self.args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d",
self.args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
epochs_trained = 0
steps_trained_in_current_epoch = 0
# Check if continuing training from a checkpoint
if model_path is not None:
# set global_step to global_step of last saved checkpoint from model path
try:
global_step = int(model_path.split("-")[-1].split("/")[0])
epochs_trained = global_step // (
len(train_dataloader) //
self.args.gradient_accumulation_steps)
steps_trained_in_current_epoch = global_step % (
len(train_dataloader) //
self.args.gradient_accumulation_steps)
logger.info(
" Continuing training from checkpoint, will skip to saved global_step"
)
logger.info(" Continuing training from epoch %d",
epochs_trained)
logger.info(" Continuing training from global step %d",
global_step)
logger.info(
" Will skip the first %d steps in the first epoch",
steps_trained_in_current_epoch)
except ValueError:
global_step = 0
logger.info(" Starting fine-tuning.")
tr_loss = 0.0
logging_loss = 0.0
global_batch_train_start = time.time()
train_iterator = trange(
epochs_trained,
int(num_train_epochs),
desc="Epoch",
disable=self.args.local_rank not in [-1, 0],
)
for epoch in train_iterator:
epoch_iterator = tqdm(train_dataloader,
desc="Iteration",
disable=self.args.local_rank not in [-1, 0])
for step, inputs in enumerate(epoch_iterator):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
if len(inputs['input_ids']
) < self.args.per_gpu_train_batch_size:
# skip incomplete batch
logger.info('Skipping incomplete batch...')
continue
tr_loss += self._training_step(model, inputs)
if (step + 1) % self.args.gradient_accumulation_steps == 0 or (
# last step in epoch but step is always smaller than gradient_accumulation_steps
len(epoch_iterator) <=
self.args.gradient_accumulation_steps and
(step + 1) == len(epoch_iterator)):
global_step += 1
global_batch_train_duration = time.time(
) - global_batch_train_start
global_batch_train_start = time.time()
if self.args.local_rank in [-1, 0]:
if (self.args.logging_steps > 0
and global_step % self.args.logging_steps
== 0) or (global_step == 1
and self.args.logging_first_step):
logs = {}
loss_avg = (tr_loss - logging_loss) / (
self.args.logging_steps *
self.args.gradient_accumulation_steps)
logs["learning_rate"] = lr_scheduler.get_last_lr(
)[0]
logs["loss"] = loss_avg.item()
logs["global_step"] = global_step
logs[
"global_step_time"] = global_batch_train_duration
logging_loss = tr_loss.clone()
if self.tb_writer:
for k, v in logs.items():
self.tb_writer.add_scalar(
k, v, global_step)
run.log(k, v)
epoch_iterator.write(
json.dumps({
**logs,
**{
"step": global_step
}
}))
if self.args.save_steps > 0 and global_step % self.args.save_steps == 0:
# In all cases (even distributed/parallel), self.model is always a reference
# to the model we want to save.
if hasattr(model, "module"):
assert model.module is self.ort_model
else:
assert model is self.ort_model
# Save model checkpoint
output_dir = os.path.join(
self.args.output_dir,
f"{PREFIX_CHECKPOINT_DIR}-{global_step}")
self.save_model(output_dir)
self._rotate_checkpoints()
if self.args.max_steps > 0 and global_step > self.args.max_steps:
epoch_iterator.close()
break
if self.args.max_steps > 0 and global_step > self.args.max_steps:
train_iterator.close()
break
if self.tb_writer:
self.tb_writer.close()
self.update_torch_model()
logger.info(
"\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n"
)
return TrainOutput(global_step, tr_loss / global_step)
def testToyBERTModelLegacyExperimentalLRScheduler(initial_lr, lr_scheduler,
legacy_lr_scheduler):
############################################################################
# These tests require hard-coded values for 'total_steps' and 'initial_lr' #
############################################################################
# Common setup
total_steps = 128
device = 'cuda'
seed = 1
warmup = 0.05
cycles = 0.5
power = 1.
lr_end = 1e-7
# Setup both Experimental and Legacy LR Schedulers before the experimental loop
if legacy_lr_scheduler == _test_commons.legacy_constant_lr_scheduler or legacy_lr_scheduler == _test_commons.legacy_linear_lr_scheduler:
legacy_lr_scheduler = partial(legacy_lr_scheduler,
initial_lr=initial_lr,
total_steps=total_steps,
warmup=warmup)
elif legacy_lr_scheduler == _test_commons.legacy_cosine_lr_scheduler:
legacy_lr_scheduler = partial(legacy_lr_scheduler,
initial_lr=initial_lr,
total_steps=total_steps,
warmup=warmup,
cycles=cycles)
elif legacy_lr_scheduler == _test_commons.legacy_poly_lr_scheduler:
legacy_lr_scheduler = partial(legacy_lr_scheduler,
initial_lr=initial_lr,
total_steps=total_steps,
warmup=warmup,
power=power,
lr_end=lr_end)
else:
raise RuntimeError("Invalid legacy_lr_scheduler")
if lr_scheduler == optim.lr_scheduler.ConstantWarmupLRScheduler or lr_scheduler == optim.lr_scheduler.LinearWarmupLRScheduler:
lr_scheduler = lr_scheduler(total_steps=total_steps, warmup=warmup)
elif lr_scheduler == optim.lr_scheduler.CosineWarmupLRScheduler:
lr_scheduler = lr_scheduler(total_steps=total_steps,
warmup=warmup,
cycles=cycles)
elif lr_scheduler == optim.lr_scheduler.PolyWarmupLRScheduler:
lr_scheduler = lr_scheduler(total_steps=total_steps,
warmup=warmup,
power=power,
lr_end=lr_end)
else:
raise RuntimeError("Invalid lr_scheduler")
# EXPERIMENTAL API
model_desc = bert_model_description()
model = load_bert_onnx_model()
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
optim_config = optim.AdamConfig(lr=initial_lr)
opts = orttrainer.ORTTrainerOptions({
'debug': {
'deterministic_compute': True
},
'device': {
'id': device,
},
'lr_scheduler': lr_scheduler
})
trainer = orttrainer.ORTTrainer(model,
model_desc,
optim_config,
options=opts)
experimental_losses = []
for i in range(total_steps):
sample_input = generate_random_input_from_model_desc(model_desc, i)
experimental_losses.append(
trainer.train_step(*sample_input).cpu().item())
assert_allclose(trainer.options.lr_scheduler.get_last_lr()[0],
legacy_lr_scheduler(i))
# LEGACY IMPLEMENTATION
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
device = torch.device(device)
model = load_bert_onnx_model()
legacy_model_desc, learning_rate_description, learning_rate = legacy_model_params(
initial_lr)
legacy_trainer = Legacy_ORTTrainer(model,
None,
legacy_model_desc,
"AdamOptimizer",
None,
learning_rate_description,
device,
_use_deterministic_compute=True,
get_lr_this_step=legacy_lr_scheduler)
legacy_losses = []
for i in range(total_steps):
sample_input = generate_random_input_from_model_desc(model_desc, i)
leg_loss = legacy_trainer.train_step(*sample_input)
legacy_losses.append(leg_loss.cpu().item())
# Check results
_test_helpers.assert_model_outputs(experimental_losses, legacy_losses)
total_loss += len(data) * loss.item()
return total_loss / (len(data_source) - 1)
best_val_loss = float("inf")
epochs = 3 # The number of epochs
best_model = None
model_description = {
'inputs': [('src', ['bptt', 'batch_size']),
('label', ['bptt_x_batch_size'])],
'outputs': [('loss', [], True), ('output', ['bptt', 'batch_size',
ntokens])]
}
optimizer_config = optim.AdamConfig(lr=learning_rate)
trainer = ORTTrainer(
model, # model
model_description, # model description
optimizer_config, # optimizer configuration
loss_with_flat_output) # loss function
for epoch in range(1, epochs + 1):
epoch_start_time = time.time()
train()
val_loss = evaluate(model, val_data)
print('-' * 89)
print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
val_loss, math.exp(val_loss)))
model_desc2,
optim_config2,
options=opts)
trainer2.load_state_dict(state_dict)
# Evaluate once to get a base loss
ckpt_loss = trainer2.eval_step(*sample_input)
# Must match as both trainers have the same dict state
assert_allclose(loss.cpu(), ckpt_loss.cpu())
loaded_state_dict = trainer2.state_dict()
_test_commons.assert_all_states_close_ort(state_dict, loaded_state_dict)
@pytest.mark.parametrize("optimizer, mixedprecision_enabled", [
(optim.LambConfig(), False),
(optim.AdamConfig(), False),
(optim.LambConfig(), True),
(optim.AdamConfig(), True),
])
def testToyBertLoadOptimState(optimizer, mixedprecision_enabled):
# Common setup
rtol = 1e-03
device = 'cuda'
seed = 1
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
optim_config = optimizer
opts = orttrainer.ORTTrainerOptions({
'debug': {
'deterministic_compute': True
},
def prepare_model(args, device):
config = BertConfig.from_pretrained('bert-base-uncased',
cache_dir=args.cache_dir)
if args.force_num_hidden_layers:
logger.info("Modifying model config with num_hidden_layers to %d",
args.force_num_hidden_layers)
config.num_hidden_layers = args.force_num_hidden_layers
model = BertForPreTraining(config)
model_desc = bert_model_description(config)
lr_scheduler = PolyWarmupLRScheduler(total_steps=int(args.max_steps))
loss_scaler = amp.DynamicLossScaler() if args.fp16 else None
options = orttrainer.ORTTrainerOptions({
'batch': {
'gradient_accumulation_steps': args.gradient_accumulation_steps
},
'device': {
'id': str(device)
},
'mixed_precision': {
'enabled': args.fp16,
'loss_scaler': loss_scaler
},
'debug': {
'deterministic_compute': True,
},
'utils': {
'grad_norm_clip': True
},
'distributed': {
'allreduce_post_accumulation': True
},
'lr_scheduler': lr_scheduler
})
param_optimizer = list(model.named_parameters())
no_decay_keys = ["bias", "gamma", "beta", "LayerNorm"]
params = [{
'params': [
n for n, p in param_optimizer
if any(no_decay_key in n for no_decay_key in no_decay_keys)
],
"alpha":
0.9,
"beta":
0.999,
"lambda":
0.0,
"epsilon":
1e-6
}, {
'params': [
n for n, p in param_optimizer
if not any(no_decay_key in n for no_decay_key in no_decay_keys)
],
"alpha":
0.9,
"beta":
0.999,
"lambda":
0.0,
"epsilon":
1e-6
}]
optim_config = optim.AdamConfig(params=params,
lr=2e-5,
do_bias_correction=True)
model = orttrainer.ORTTrainer(model,
model_desc,
optim_config,
options=options)
return model
num_pipeline_stages = 2
# Compute batch size for micro-batches.
n_slice = int(n / num_pipeline_steps)
cuda_device = 'cuda:' + str(rank)
# Schema used when running the original batch.
schema = {'inputs': [('x', ['n', 'd_in']), ('target', ['n'])], 'outputs': [
('loss', [], True), ('output', ['n', d_out])]}
# Actual schema used when running micro-batches.
pipeline_schema = {'x': [n_slice, d_in], 'target': [
n_slice], 'output': [n_slice, d_out], 'loss': []}
# Describe which axis to slice along for each sliced tensor.
sliced_axes = {'x': 0, 'target': 0, 'output': 0}
adam_config = optim.AdamConfig(lr=0.1)
# # Specify configuration for pipeline parallel training.
trainer_config = ORTTrainerOptions({
'batch': {
'gradient_accumulation_steps': num_pipeline_steps
},
'device': {
'id': cuda_device
},
'distributed': {
'world_size': total_ranks,
'world_rank': rank,
'data_parallel_size': int(total_ranks / num_pipeline_stages),
'horizontal_parallel_size': 1,
'pipeline_parallel': {
def train(self):
"""
Main training entry point.
"""
train_dataloader = self.get_train_dataloader()
if self.args.max_steps > 0:
t_total = self.args.max_steps
num_train_epochs = (self.args.max_steps //
(len(train_dataloader) //
self.args.gradient_accumulation_steps) + 1)
else:
t_total = int(
len(train_dataloader) //
self.args.gradient_accumulation_steps *
self.args.num_train_epochs)
num_train_epochs = self.args.num_train_epochs
lr_scheduler = orttrainer.optim.LinearWarmupLRScheduler(
t_total, self.args.warmup_steps / float(t_total))
loss_scaler = amp.DynamicLossScaler() if self.args.fp16 else None
device = self.args.device.type
device = f"{device}:{self.args.device.index}" if self.args.device.index else f"{device}:0"
options = orttrainer.ORTTrainerOptions({
"batch": {
"gradient_accumulation_steps":
self.args.gradient_accumulation_steps
},
"device": {
"id": device
},
"mixed_precision": {
"enabled": self.args.fp16,
"loss_scaler": loss_scaler
},
"debug": {
"deterministic_compute": True,
},
"utils": {
"grad_norm_clip": False
},
"distributed": {
# we are running single node multi gpu test. thus world_rank = local_rank
# and world_size = self.args.n_gpu
"world_rank": max(0, self.args.local_rank),
"world_size": int(self.world_size),
"local_rank": max(0, self.args.local_rank),
"allreduce_post_accumulation": True,
},
"lr_scheduler": lr_scheduler,
})
param_optimizer = list(self.model.named_parameters())
params = [
{
"params": [
n for n, p in param_optimizer
if "bias" in n or "LayerNorm.weight" in n
],
"weight_decay_mode":
1,
},
{
"params": [
n for n, p in param_optimizer
if not ("bias" in n or "LayerNorm.weight" in n)
],
"weight_decay_mode":
1,
},
]
optim_config = optim.AdamConfig(params=params,
lr=2e-5,
do_bias_correction=True)
self.model = orttrainer.ORTTrainer(self.model,
self.model_desc,
optim_config,
options=options)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataloader.dataset))
logger.info(" Num Epochs = %d", num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
self.args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
self.args.train_batch_size *
self.args.gradient_accumulation_steps *
(torch.distributed.get_world_size()
if self.args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d",
self.args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
epochs_trained = 0
steps_trained_in_current_epoch = 0
tr_loss = 0.0
logging_loss = 0.0
train_iterator = trange(
epochs_trained,
int(num_train_epochs),
desc="Epoch",
disable=self.args.local_rank not in [-1, 0],
)
for epoch in train_iterator:
epoch_iterator = tqdm(train_dataloader,
desc="Iteration",
disable=self.args.local_rank not in [-1, 0])
for step, inputs in enumerate(epoch_iterator):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
tr_loss += self._training_step(self.model, inputs)
if (step + 1) % self.args.gradient_accumulation_steps == 0 or (
len(epoch_iterator) <=
self.args.gradient_accumulation_steps and
(step + 1) == len(epoch_iterator)):
global_step += 1
if self.args.local_rank in [-1, 0]:
if (self.args.logging_steps > 0
and global_step % self.args.logging_steps
== 0) or (global_step == 1
and self.args.logging_first_step):
logs = {}
if self.args.evaluate_during_training:
results = self.evaluate()
for key, value in results.items():
eval_key = "eval_{}".format(key)
logs[eval_key] = value
loss_scalar = (tr_loss - logging_loss
) / self.args.logging_steps
logs["loss"] = loss_scalar
logging_loss = tr_loss
epoch_iterator.write(
json.dumps({
**logs,
**{
"step": global_step
}
}))
if self.args.max_steps > 0 and global_step > self.args.max_steps:
epoch_iterator.close()
break
if self.args.max_steps > 0 and global_step > self.args.max_steps:
train_iterator.close()
break
logger.info("\n\nTraining completed. \n\n")
return TrainOutput(global_step, tr_loss / global_step)
def train(self):
"""
Main training entry point.
"""
train_dataloader = self.get_train_dataloader()
if self.args.max_steps > 0:
t_total = self.args.max_steps
num_train_epochs = (self.args.max_steps //
(len(train_dataloader) //
self.args.gradient_accumulation_steps) + 1)
else:
t_total = int(
len(train_dataloader) //
self.args.gradient_accumulation_steps *
self.args.num_train_epochs)
num_train_epochs = self.args.num_train_epochs
if self.use_new_api:
lr_scheduler = orttrainer.optim.LinearWarmupLRScheduler(
t_total, self.args.warmup_steps / float(t_total))
loss_scaler = amp.DynamicLossScaler() if self.args.fp16 else None
device = self.args.device.type
device = f'{device}:{self.args.device.index}' if self.args.device.index else f'{device}:0'
options = orttrainer.ORTTrainerOptions({
'batch': {
'gradient_accumulation_steps':
self.args.gradient_accumulation_steps
},
'device': {
'id': device
},
'mixed_precision': {
'enabled': self.args.fp16,
'loss_scaler': loss_scaler
},
'debug': {
'deterministic_compute': True,
},
'utils': {
'grad_norm_clip': False
},
'distributed': {
# we are running single node multi gpu test. thus world_rank = local_rank
# and world_size = self.args.n_gpu
'world_rank': max(0, self.args.local_rank),
'world_size': int(self.world_size),
'local_rank': max(0, self.args.local_rank),
'allreduce_post_accumulation': True
},
'lr_scheduler': lr_scheduler
})
param_optimizer = list(self.model.named_parameters())
params = [{
'params': [
n for n, p in param_optimizer
if "bias" in n or "LayerNorm.weight" in n
],
"weight_decay_mode":
1,
}, {
'params': [
n for n, p in param_optimizer
if not ("bias" in n or "LayerNorm.weight" in n)
],
"weight_decay_mode":
1,
}]
optim_config = optim.AdamConfig(params=params,
lr=2e-5,
do_bias_correction=True)
self.model = orttrainer.ORTTrainer(self.model,
self.new_model_desc,
optim_config,
options=options)
else:
def map_optimizer_attributes(name):
no_decay = "bias" in name or "LayerNorm.weight" in name
if no_decay:
return {"weight_decay_mode": 1}
else:
return {"weight_decay_mode": 1}
get_lr_this_step = get_linear_schedule_with_warmup(
self.args.warmup_steps, t_total, self.args.learning_rate)
loss_scaler = LossScaler(
'loss_scale_input_name', True,
up_scale_window=2000) if self.args.fp16 else None
self.model = ORTTrainer(
self.model,
None,
self.model_desc,
"AdamOptimizer",
map_optimizer_attributes=map_optimizer_attributes,
learning_rate_description=IODescription(
'Learning_Rate', [
1,
], torch.float32),
device=self.args.device,
gradient_accumulation_steps=self.args.
gradient_accumulation_steps,
world_rank=max(0, self.args.local_rank),
world_size=int(self.world_size),
use_mixed_precision=self.args.fp16,
allreduce_post_accumulation=True,
get_lr_this_step=get_lr_this_step,
loss_scaler=loss_scaler,
enable_grad_norm_clip=False,
_opset_version=12,
_use_deterministic_compute=True)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataloader.dataset))
logger.info(" Num Epochs = %d", num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
self.args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
self.args.train_batch_size *
self.args.gradient_accumulation_steps *
(torch.distributed.get_world_size()
if self.args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d",
self.args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
epochs_trained = 0
steps_trained_in_current_epoch = 0
tr_loss = 0.0
logging_loss = 0.0
train_iterator = trange(
epochs_trained,
int(num_train_epochs),
desc="Epoch",
disable=self.args.local_rank not in [-1, 0],
)
for epoch in train_iterator:
epoch_iterator = tqdm(train_dataloader,
desc="Iteration",
disable=self.args.local_rank not in [-1, 0])
for step, inputs in enumerate(epoch_iterator):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
tr_loss += self._training_step(self.model, inputs)
if (step + 1) % self.args.gradient_accumulation_steps == 0 or (
len(epoch_iterator) <=
self.args.gradient_accumulation_steps and
(step + 1) == len(epoch_iterator)):
global_step += 1
if self.args.local_rank in [-1, 0]:
if (self.args.logging_steps > 0
and global_step % self.args.logging_steps
== 0) or (global_step == 1
and self.args.logging_first_step):
logs = {}
if self.args.evaluate_during_training:
results = self.evaluate()
for key, value in results.items():
eval_key = "eval_{}".format(key)
logs[eval_key] = value
loss_scalar = (tr_loss - logging_loss
) / self.args.logging_steps
if not self.use_new_api:
learning_rate_scalar = get_lr_this_step(
global_step)
logs["learning_rate"] = learning_rate_scalar
logs["loss"] = loss_scalar
logging_loss = tr_loss
epoch_iterator.write(
json.dumps({
**logs,
**{
"step": global_step
}
}))
if self.args.max_steps > 0 and global_step > self.args.max_steps:
epoch_iterator.close()
break
if self.args.max_steps > 0 and global_step > self.args.max_steps:
train_iterator.close()
break
logger.info("\n\nTraining completed. \n\n")
return TrainOutput(global_step, tr_loss / global_step)
