def testToyBertCheckpointLoadZero():
# Common setup
rtol = 1e-03
device = 'cuda'
seed = 1
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
optim_config = optim.LambConfig()
opts = orttrainer.ORTTrainerOptions({'debug' : {'deterministic_compute': True},
'device' : {'id' : device},
'distributed' : {'allreduce_post_accumulation' : True}})
# Create ORTTrainer and save initial state in a dict
model = load_bert_onnx_model()
model_desc = bert_model_description()
trainer = orttrainer.ORTTrainer(model, model_desc, optim_config, options=opts)
ckpt_dir = _test_helpers._get_name("ort_ckpt")
checkpoint.experimental_load_checkpoint(trainer, ckpt_dir, 'bert_toy_lamb')
# Expected values
expected_eval_loss = [10.997552871]
input_ids = torch.tensor([[26598],[21379],[19922],[ 5219],[ 5644],[20559],[23777],[25672],[22969],[16824],[16822],[635],[27399],[20647],[18519],[15546]], device=device)
segment_ids = torch.tensor([[0],[1],[0],[1],[0],[0],[1],[0],[0],[1],[1],[0],[0],[1],[1],[1]], device=device)
input_mask = torch.tensor([[0],[0],[0],[0],[1],[1],[1],[0],[1],[1],[0],[0],[0],[1],[0],[0]], device=device)
masked_lm_labels = torch.tensor([[25496],[16184],[11005],[16228],[14884],[21660],[ 8678],[23083],[ 4027],[ 8397],[11921],[ 1333],[26482],[ 1666],[17925],[27978]], device=device)
next_sentence_labels = torch.tensor([0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0], device=device)
# Actual values
actual_eval_loss = trainer.eval_step(input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels)
actual_eval_loss = actual_eval_loss.cpu().numpy().item(0)
# Check results
assert_allclose(expected_eval_loss, actual_eval_loss, rtol=rtol)
def testToyBertCheckpointBasic():
# Common setup
seed = 1
torch.manual_seed(seed)
onnxruntime.set_seed(seed)
optim_config = optim.LambConfig()
opts = orttrainer.ORTTrainerOptions(
{'debug': {
'deterministic_compute': True
}})
# Create ORTTrainer and save initial state in a dict
model = load_bert_onnx_model()
model_desc = bert_model_description()
trainer = orttrainer.ORTTrainer(model,
model_desc,
optim_config,
options=opts)
sd = checkpoint.experimental_state_dict(trainer)
## All initializers must be present in the state_dict
## when the specified model for ORTTRainer is an ONNX model
for param in trainer._onnx_model.graph.initializer:
assert param.name in sd
## Modify one of the state values and load into ORTTrainer
sd['bert.encoder.layer.0.attention.output.LayerNorm.weight'] += 10
checkpoint.experimental_load_state_dict(trainer, sd)
## Save a checkpoint
ckpt_dir = _test_helpers._get_name("ort_ckpt")
checkpoint.experimental_save_checkpoint(trainer, ckpt_dir,
'bert_toy_save_test')
del trainer
del model
# Create a new ORTTrainer and load the checkpoint from previous ORTTrainer
model2 = load_bert_onnx_model()
model_desc2 = bert_model_description()
trainer2 = orttrainer.ORTTrainer(model2,
model_desc2,
optim_config,
options=opts)
checkpoint.experimental_load_checkpoint(trainer2, ckpt_dir,
'bert_toy_save_test')
loaded_sd = checkpoint.experimental_state_dict(trainer2)
# Assert whether original state and the one loaded from checkpoint matches
for k, v in loaded_sd.items():
assert torch.all(torch.eq(v, sd[k]))
