def testToyBERTModelLRScheduler(initial_lr, lr_scheduler, expected_learning_rates, expected_losses):
# Common setup
device = 'cuda'
total_steps = 10
seed = 1
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
# 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(total_steps=total_steps, warmup=0.5)
})
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=1e-6)
_test_helpers.assert_model_outputs(losses, expected_losses, rtol=1e-6)
def testToyBERTModelMixedPrecisionLossScaler(loss_scaler, expected_losses):
# Common setup
total_steps = 10
device = 'cuda'
seed = 1
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
# Modeling
model_desc = bert_model_description()
model = load_bert_onnx_model()
optim_config = optim.LambConfig()
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)
# Train
losses = []
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())
# Check output
_test_helpers.assert_model_outputs(losses, expected_losses, rtol=1e-4)
def testToyBERTDeterministicCheck(expected_losses):
# Common setup
train_steps = 10
device = 'cuda'
seed = 1
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
# Modeling
model_desc = bert_model_description()
model = load_bert_onnx_model()
params = optimizer_parameters(model)
optim_config = optim.LambConfig()
opts = orttrainer.ORTTrainerOptions({
'debug' : {
'deterministic_compute': True
},
'device': {
'id': device,
},
})
trainer = orttrainer.ORTTrainer(model, model_desc, optim_config, options=opts)
# Train
experimental_losses = []
for i in range(train_steps):
sample_input = generate_random_input_from_model_desc(model_desc, i)
experimental_losses.append(trainer.train_step(*sample_input).cpu().item())
# Check output
_test_helpers.assert_model_outputs(experimental_losses, expected_losses, rtol=1e-6)
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 testToyBERTModelGradientAccumulation(gradient_accumulation_steps, expected_losses):
# Common setup
total_steps = 10
device = "cuda"
seed = 1
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
# Modeling
model_desc = bert_model_description()
model = load_bert_onnx_model()
optim_config = optim.LambConfig()
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)
# Train
losses = []
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())
# Check output
_test_helpers.assert_model_outputs(losses, expected_losses, rtol=1e-6)
def testToyBERTModelGradientAccumulationLegacyExperimental(
gradient_accumulation_steps):
# Common setup
total_steps = 10
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.LambConfig()
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
device = torch.device(device)
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
legacy_model_desc, learning_rate_description, learning_rate = legacy_model_params(
optim_config.lr)
legacy_trainer = Legacy_ORTTrainer(
model,
None,
legacy_model_desc,
"LambOptimizer",
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,
rtol=1e-6)
def testToyBERTModelLRScheduler(initial_lr, lr_scheduler,
expected_learning_rates, expected_losses):
# Common setup
device = 'cuda'
total_steps = 10
seed = 1
warmup = 0.05
cycles = 0.5
power = 1.
lr_end = 1e-7
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=1e-6)
_test_helpers.assert_model_outputs(losses, expected_losses, rtol=1e-6)
def testToyBERTModelLegacyExperimentalBasicTraining(optimizer_config):
# Common setup
train_steps = 512
device = 'cuda'
seed = 1
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
# EXPERIMENTAL API
model_desc = bert_model_description()
model = load_bert_onnx_model()
opts = orttrainer.ORTTrainerOptions({
'debug' : {
'deterministic_compute': True
},
'device': {
'id': device,
},
})
optim_config = optimizer_config(lr=0.01)
trainer = orttrainer.ORTTrainer(model, model_desc, optim_config, options=opts)
experimental_losses = []
for i in range(train_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)
if optimizer_config == optim.AdamConfig:
legacy_optimizer = 'AdamOptimizer'
elif optimizer_config == optim.LambConfig:
legacy_optimizer = 'LambOptimizer'
elif optimizer_config == optim.SGDConfig:
legacy_optimizer = 'SGDOptimizer'
else:
raise RuntimeError("Invalid optimizer_config")
device = torch.device(device)
model = load_bert_onnx_model()
legacy_model_desc, learning_rate_description, learning_rate = legacy_model_params(lr=optim_config.lr)
legacy_trainer = Legacy_ORTTrainer(model, None, legacy_model_desc, legacy_optimizer,
None,
learning_rate_description,
device,
_use_deterministic_compute=True)
legacy_losses = []
for i in range(train_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, True)
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 testToyBERTModelLegacyExperimentalBasicTraining():
# Common setup
train_steps = 10
device = 'cuda'
seed = 1
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
# EXPERIMENTAL API
model_desc = bert_model_description()
model = load_bert_onnx_model()
params = optimizer_parameters(model)
optim_config = optim.LambConfig()
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(train_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)
legacy_model_desc, learning_rate_description, learning_rate = legacy_model_params(
lr=0.001)
legacy_trainer = Legacy_ORTTrainer(model, None, legacy_model_desc,
"LambOptimizer", None,
learning_rate_description, device)
legacy_losses = []
for i in range(train_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,
True,
rtol=1e-5)
def testORTTrainerLegacyAndExperimentalPrecisionLossScaler(seed, device):
# Common data
total_steps = 5
bptt=35
# Setup experimental API
torch.manual_seed(seed)
set_seed(seed)
loss_scaler = amp.DynamicLossScaler()
options = orttrainer.ORTTrainerOptions({'device' : {'id' : device},
'mixed_precision' : {
'enabled' : True,
'loss_scaler' : loss_scaler},
'debug' : {'deterministic_compute' : True,}})
model, model_desc, my_loss, batcher_fn, train_data, val_data, _ = _load_pytorch_transformer_model(device)
optim_config = optim.LambConfig(lr=0.001)
trainer = orttrainer.ORTTrainer(model, model_desc, optim_config, loss_fn=my_loss, options=options)
# Training loop
experimental_loss = []
experimental_preds_dtype = []
for i in range(total_steps):
data, targets = batcher_fn(train_data, i)
exp_loss, exp_preds = trainer.train_step(data, targets)
experimental_loss.append(exp_loss.cpu())
experimental_preds_dtype.append(exp_preds.dtype)
# Setup legacy API
torch.manual_seed(seed)
set_seed(seed)
model, (model_desc, lr_desc), _, _, _, _, _ = _load_pytorch_transformer_model(device, legacy_api=True)
loss_scaler = Legacy_LossScaler('ort_test_input_loss_scalar', True)
legacy_trainer = Legacy_ORTTrainer(model, my_loss, model_desc, "LambOptimizer",
None, lr_desc, device=device,
_use_deterministic_compute=True,
use_mixed_precision=True,
loss_scaler=loss_scaler)
# Training loop
legacy_loss = []
legacy_preds_dtype = []
for i in range(total_steps):
data, targets = batcher_fn(train_data, i)
leg_loss, leg_preds = legacy_trainer.train_step(data, targets, torch.tensor([optim_config.lr]))
legacy_loss.append(leg_loss.cpu())
legacy_preds_dtype.append(leg_preds.dtype)
# Compare legacy vs experimental APIs
assert experimental_preds_dtype == legacy_preds_dtype
_test_helpers.assert_legacy_onnx_weights(trainer, legacy_trainer, rtol=1e-4, atol=1e-2)
_test_helpers.assert_model_outputs(legacy_loss, experimental_loss, rtol=1e-4)
def testORTTrainerGradientAccumulation(seed, device, gradient_accumulation_steps, total_steps, expected_loss):
torch.manual_seed(seed)
set_seed(seed)
# Setup ORTTrainer
options = orttrainer.ORTTrainerOptions({'device' : {'id' : device},
'batch' : {'gradient_accumulation_steps' : gradient_accumulation_steps},
'debug' : {'deterministic_compute' : True}})
model, model_desc, my_loss, batcher_fn, train_data, val_data, _ = _load_pytorch_transformer_model(device)
optim_config = optim.LambConfig(lr=0.001)
trainer = orttrainer.ORTTrainer(model, model_desc, optim_config, loss_fn=my_loss, options=options)
# Training loop
actual_loss = []
for i in range(total_steps):
data, targets = batcher_fn(train_data, i)
loss, _ = trainer.train_step(data, targets)
actual_loss.append(loss.cpu())
# Compare legacy vs experimental APIs
_test_helpers.assert_model_outputs(expected_loss, actual_loss, rtol=1e-6)
def testORTTrainerMixedPrecisionLossScaler(seed, device, expected_loss, fetches):
total_steps = len(expected_loss)
torch.manual_seed(seed)
set_seed(seed)
bptt=35
# Setup ORTTrainer
loss_scaler = amp.DynamicLossScaler()
options = orttrainer.ORTTrainerOptions({'device' : {'id' : device},
'mixed_precision' : {
'enabled' : True,
'loss_scaler' : loss_scaler},
'debug' : {'deterministic_compute' : True}})
model, model_desc, my_loss, batcher_fn, train_data, val_data, _ = _load_pytorch_transformer_model(device)
optim_config = optim.LambConfig(lr=0.001)
trainer = orttrainer.ORTTrainer(model, model_desc, optim_config, loss_fn=my_loss, options=options)
# Training loop
actual_loss = []
for i in range(total_steps):
data, targets = batcher_fn(train_data, i)
if fetches:
trainer._train_step_info.fetches=['loss']
loss = trainer.train_step(data, targets)
else:
loss, _ = trainer.train_step(data, targets)
actual_loss.append(loss.cpu())
# Eval once just to test fetches in action
val_data, val_targets = batcher_fn(val_data, 0)
if fetches:
trainer._train_step_info.fetches=['loss']
loss = trainer.eval_step(val_data, val_targets)
trainer._train_step_info.fetches=[]
loss, preds = trainer.eval_step(val_data, val_targets)
# Compare loss to ground truth computed from current ORTTrainer API
_test_helpers.assert_model_outputs(expected_loss, actual_loss, True, rtol=1e-4)
assert trainer._onnx_model is not None
def testORTTrainerLegacyAndExperimentalGradientAccumulation(seed, device, gradient_accumulation_steps, total_steps):
# Common data
torch.set_printoptions(precision=10)
# Setup experimental API
torch.manual_seed(seed)
set_seed(seed)
options = orttrainer.ORTTrainerOptions({'device' : {'id' : device},
'batch' : {'gradient_accumulation_steps' : gradient_accumulation_steps},
'debug' : {'deterministic_compute' : True}})
model, model_desc, my_loss, batcher_fn, train_data, val_data, _ = _load_pytorch_transformer_model(device)
optim_config = optim.LambConfig(lr=0.001)
trainer = orttrainer.ORTTrainer(model, model_desc, optim_config, loss_fn=my_loss, options=options)
# Training loop
experimental_loss = []
for i in range(total_steps):
data, targets = batcher_fn(train_data, i)
exp_loss, exp_preds = trainer.train_step(data, targets)
experimental_loss.append(exp_loss.cpu())
# Setup legacy API
torch.manual_seed(seed)
set_seed(seed)
model, (model_desc, lr_desc), _, _, _, _, _ = _load_pytorch_transformer_model(device, legacy_api=True)
legacy_trainer = Legacy_ORTTrainer(model, my_loss, model_desc, "LambOptimizer",
None, lr_desc, device=device,
_use_deterministic_compute=True,
gradient_accumulation_steps=gradient_accumulation_steps)
# Training loop
legacy_loss = []
for i in range(total_steps):
data, targets = batcher_fn(train_data, i)
leg_loss, leg_preds = legacy_trainer.train_step(data, targets, torch.tensor([optim_config.lr]))
legacy_loss.append(leg_loss.cpu())
# Compare legacy vs experimental APIs
_test_helpers.assert_model_outputs(legacy_loss, experimental_loss, rtol=1e-6)
def testORTTrainerLegacyAndExperimentalLRScheduler(seed, device, optimizer_config, lr_scheduler, get_lr_this_step):
# Common data
total_steps = 10
lr = 0.001
warmup = 0.5
cycles = 0.5
power = 1.
lr_end = 1e-7
torch.set_printoptions(precision=10)
# Setup experimental API
torch.manual_seed(seed)
set_seed(seed)
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")
options = orttrainer.ORTTrainerOptions({'device' : {'id' : device},
'debug' : {'deterministic_compute' : True},
'lr_scheduler' : lr_scheduler})
model, model_desc, my_loss, batcher_fn, train_data, val_data, _ = _load_pytorch_transformer_model(device)
optim_config = optimizer_config(lr=lr)
trainer = orttrainer.ORTTrainer(model, model_desc, optim_config, loss_fn=my_loss, options=options)
# Training loop
experimental_loss = []
for i in range(total_steps):
data, targets = batcher_fn(train_data, i)
exp_loss, exp_preds = trainer.train_step(data, targets)
experimental_loss.append(exp_loss.cpu())
# Setup legacy API
torch.manual_seed(seed)
set_seed(seed)
if optimizer_config == optim.AdamConfig:
legacy_optimizer_config = 'AdamOptimizer'
elif optimizer_config == optim.LambConfig:
legacy_optimizer_config = 'LambOptimizer'
elif optimizer_config == optim.SGDConfig:
legacy_optimizer_config = 'SGDOptimizer'
else:
raise RuntimeError("Invalid optimizer_config")
if get_lr_this_step == _test_commons.legacy_constant_lr_scheduler or get_lr_this_step == _test_commons.legacy_linear_lr_scheduler:
get_lr_this_step = partial(get_lr_this_step, initial_lr=lr, total_steps=total_steps, warmup=warmup)
elif get_lr_this_step == _test_commons.legacy_cosine_lr_scheduler:
get_lr_this_step = partial(get_lr_this_step, initial_lr=lr, total_steps=total_steps, warmup=warmup, cycles=cycles)
elif get_lr_this_step == _test_commons.legacy_poly_lr_scheduler:
get_lr_this_step = partial(get_lr_this_step, initial_lr=lr, total_steps=total_steps, warmup=warmup, power=power, lr_end=lr_end)
else:
raise RuntimeError("Invalid get_lr_this_step")
model, (model_desc, lr_desc), _, _, _, _, _ = _load_pytorch_transformer_model(device, legacy_api=True)
legacy_trainer = Legacy_ORTTrainer(model, my_loss, model_desc, legacy_optimizer_config,
None, lr_desc, device=device,
_use_deterministic_compute=True,
get_lr_this_step=get_lr_this_step)
# Training loop
legacy_loss = []
for i in range(total_steps):
data, targets = batcher_fn(train_data, i)
leg_loss, leg_preds = legacy_trainer.train_step(data, targets)
legacy_loss.append(leg_loss.cpu())
# Compare legacy vs experimental APIs
_test_helpers.assert_model_outputs(legacy_loss, experimental_loss)
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)
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 = 10
device = 'cuda'
seed = 1
# 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(total_steps=total_steps, warmup=0.5)
})
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)
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)
