def transpose_checkpoint_embedding(checkpoint):
print(f"Loading checkpoint: {checkpoint}")
initializers = utils.load_initializers_from_onnx(checkpoint)
model = bert.Bert(config,
initializers=initializers,
pipeline=args.pipeline)
indices, positions, segments, masks, _ = bert.bert_add_inputs(args, model)
bert.bert_logits_graph(model, indices, positions, segments, masks,
args.pipeline)
proto = model.builder.getModelProto()
onnx_proto = onnx.load_from_string(proto)
output_path = os.path.join(pargs.output_dir, os.path.basename(checkpoint))
print(f"Saving to: {output_path}")
onnx.save(onnx_proto, output_path)
def training_run(bert_args, config, initializers, checkpoint_paths):
logger.info("Building Model")
model = Bert(config,
builder=popart.Builder(opsets={
"ai.onnx": 9,
"ai.onnx.ml": 1,
"ai.graphcore": 1
}),
initializers=initializers,
execution_mode=bert_args.execution_mode)
indices, positions, segments, masks, labels = bert_add_inputs(
bert_args, model)
logits = bert_logits_graph(model, indices, positions, segments, masks,
bert_args.execution_mode)
predictions, probs = bert_infer_graph(model, logits)
losses = bert_loss_graph(model, probs, labels)
outputs = bert_add_validation_outputs(model, predictions, losses)
embedding_dict, positional_dict = model.get_model_embeddings()
dataset = get_bert_dataset(model, bert_args,
[indices, positions, segments, masks, labels],
embedding_dict, positional_dict)
data_flow = popart.DataFlow(dataset.batches_per_step, outputs)
request_ipus, _ = calc_required_ipus(bert_args, model)
device = acquire_device(bert_args, request_ipus)
logger.info(f"Dataset length: {len(dataset)}")
writer = bert_writer(bert_args)
iteration = Iteration(
bert_args,
batches_per_step=dataset.batches_per_step,
steps_per_epoch=len(dataset),
writer=writer,
recording_steps=bert_args.aggregate_metrics_over_steps)
optimizer_factory = ScheduledOptimizerFactory(bert_args, iteration, "SGD",
model.tensors)
session, anchors = bert_training_session(model, bert_args, data_flow,
losses, device, optimizer_factory)
for path in checkpoint_paths:
ckpt_name = os.path.splitext(os.path.basename(path))[0]
session.resetHostWeights(os.path.abspath(path))
session.weightsFromHost()
logger.info(f"Fine-tuning started for checkpoint: {path}")
run_fine_tuning_store_ckpt(bert_args, model, ckpt_name, session,
dataset, predictions, losses, labels,
anchors)
device.detach()
def transpose_checkpoint_embedding(checkpoint):
print(f"Loading checkpoint: {checkpoint}")
initializers = utils.load_initializers_from_onnx(checkpoint)
model = bert.Bert(config,
builder=popart.Builder(opsets={
"ai.onnx": 9,
"ai.onnx.ml": 1,
"ai.graphcore": 1
}),
initializers=initializers,
execution_mode=args.execution_mode)
indices, positions, segments, masks, _ = bert.bert_add_inputs(args, model)
bert.bert_logits_graph(model, indices, positions, segments, masks)
proto = model.builder.getModelProto()
onnx_proto = onnx.load_from_string(proto)
output_path = os.path.join(pargs.output_dir, os.path.basename(checkpoint))
print(f"Saving to: {output_path}")
onnx.save(onnx_proto, output_path)
def pooled_validation_run(bert_args,
config,
initializers,
checkpoint_paths,
num_processes=1,
available_ipus=16):
logger.info("Building Model")
model = Bert(config,
builder=popart.Builder(
opsets={"ai.onnx": 9, "ai.onnx.ml": 1, "ai.graphcore": 1}),
initializers=initializers)
indices, positions, segments, masks, labels = bert_add_inputs(
bert_args, model)
logits = bert_logits_graph(model, indices, positions, segments, masks, bert_args.execution_mode)
inputs = [indices, positions, segments, *masks]
outputs = bert_add_logit_outputs(model, logits)
with tempfile.TemporaryDirectory() as temp_results_path:
# Inject the checkpoint-specific squad results directory into the dataset args otherwise
# they overwrite each other when multithreaded
bert_args.squad_results_dir = temp_results_path
dataset = get_bert_dataset(
model, bert_args, [indices, positions, segments, masks, labels])
logger.info(f"Dataset length: {len(dataset)}")
data_flow = popart.DataFlow(dataset.batches_per_step, outputs)
iteration = Iteration(
bert_args,
batches_per_step=dataset.batches_per_step,
steps_per_epoch=len(dataset),
writer=None,
recording_steps=bert_args.aggregate_metrics_over_steps)
request_ipus, _ = calc_required_ipus(bert_args, model)
if request_ipus * num_processes > available_ipus:
raise ValueError(
"Cannot run with requested number of processes - too many IPUs required")
device = acquire_device(bert_args, request_ipus)
session, anchors = bert_inference_session(
model, bert_args, data_flow, device)
model_results = recursive_defaultdict()
for path in checkpoint_paths:
session.resetHostWeights(str(path.absolute()))
session.weightsFromHost()
logger.info(f"Inference started for checkpoint: {path.absolute()}")
result = run_inference_extract_result(bert_args,
session,
dataset,
inputs,
logits,
anchors,
iteration)
result_into_recursive_path(model_results, path, bert_args.checkpoint_dir, result)
device.detach()
return model_results