def run_embedding_layer(args):
set_library_seeds(args.seed)
config = bert_config_from_args(args)
initializers = bert_pretrained_initialisers(config, args)
logger.info("Building Model")
# Specifying ai.onnx opset9 for the slice syntax
# TODO: Change slice to opset10
model = Bert(config,
builder=popart.Builder(opsets={
"ai.onnx": 9,
"ai.onnx.ml": 1,
"ai.graphcore": 1
}),
initializers=initializers,
execution_mode=args.execution_mode)
# If config.host_embedding is enabled, indices and positions will have the matrices instead of the index vector.
indices, positions, segments, masks, labels = bert_add_inputs(args, model)
logits = tuple([model.embedding(indices, positions, segments)])
if args.inference:
outputs = bert_add_logit_outputs(model, logits)
writer = None
dataset = get_bert_dataset(
model, args, [indices, positions, segments, masks, labels])
data_flow = popart.DataFlow(dataset.batches_per_step, outputs)
iteration = Iteration(
args,
steps_per_epoch=len(dataset),
writer=writer,
recording_steps=args.aggregate_metrics_over_steps)
request_ipus = bert_required_ipus(args, model)
device = acquire_device(args, request_ipus)
session, anchors = bert_inference_session(model, args, data_flow,
device)
logger.info("Inference Started")
inputs = [indices, positions, segments, *masks]
"""bert_infer_loop(args, session,
dataset, inputs, logits, anchors,
iteration)"""
save_results = args.task == "SQUAD" and not (args.synthetic_data
or args.generated_data)
start_times = defaultdict(list)
end_times = defaultdict(list)
# Create the stepio once outside of the inference loop:
static_data = {}
if args.low_latency_inference and args.task == "SQUAD":
stepio = create_callback_stepio(static_data, anchors, start_times,
end_times,
dataset.batches_per_step)
else:
stepio = None
output = []
logger.info(dataset)
for data in dataset:
static_data.update({t: data[t] for t in inputs})
result = bert_process_infer_data(args, session, static_data,
anchors, logits, iteration,
start_times, end_times, stepio)
if save_results:
output.append(result)
break
device.detach()
return output
return None
def test_embedding_fwd(custom_ops):
# ------------------- PopART --------------------
config = BertConfig(task="SQUAD",
vocab_length=9728,
micro_batch_size=1,
hidden_size=768,
sequence_length=128,
activation_type='relu',
popart_dtype="FLOAT",
no_dropout=True,
inference=True)
popart_model = Bert(config)
sequence_info = popart.TensorInfo(
"UINT32", [config.micro_batch_size * config.sequence_length])
indices = popart_model.builder.addInputTensor(sequence_info)
positions = popart_model.builder.addInputTensor(sequence_info)
segments = popart_model.builder.addInputTensor(sequence_info)
data = {
indices:
np.random.randint(
0, config.vocab_length,
(config.micro_batch_size * config.sequence_length)).astype(
np.uint32),
positions:
np.random.randint(
0, config.max_positional_length,
(config.micro_batch_size * config.sequence_length)).astype(
np.uint32),
segments:
np.random.randint(
0, 2, (config.micro_batch_size * config.sequence_length)).astype(
np.uint32)
}
user_options = {"enableStochasticRounding": True}
with popart_model.builder.nameScope("Embedding"):
output = popart_model.embedding(indices, positions, segments)
proto = popart_model.builder.getModelProto()
outputs, post_proto = run_py(proto,
data,
output,
user_options=user_options)
# ----------------- PopART -> PyTorch ----------------
proto = onnx.load_model_from_string(proto)
inputs = [
data[t].reshape(config.micro_batch_size,
config.sequence_length).astype(np.int32)
for t in [indices, positions, segments]
]
# ------------------- PyTorch -------------------------
torch_model = BertEmbeddings(
TorchBertConfig(config.vocab_length,
config.hidden_size,
max_position_embeddings=config.max_positional_length,
layer_norm_eps=config.layer_norm_eps))
torch_model.eval()
copy_weights_to_torch(torch_model, proto, TORCH_TO_ONNX, {})
torch_outputs = run_fwd_model(inputs, torch_model)
check_tensors(torch_outputs, outputs, margin=5e-7)
def test_embedding_bwd(custom_ops):
# ------------------- PopART --------------------
config = BertConfig(task="SQUAD",
vocab_length=9728,
micro_batch_size=1,
hidden_size=768,
sequence_length=128,
activation_type='relu',
popart_dtype="FLOAT",
no_dropout=True,
update_embedding_dict=True)
popart_model = Bert(config)
# Prevent virtualGraph attributes being added to the ops
sequence_info = popart.TensorInfo(
"UINT32", [config.micro_batch_size * config.sequence_length])
indices = popart_model.builder.addInputTensor(sequence_info)
positions = popart_model.builder.addInputTensor(sequence_info)
segments = popart_model.builder.addInputTensor(sequence_info)
data = {
indices:
np.random.randint(
0, config.vocab_length,
(config.micro_batch_size * config.sequence_length)).astype(
np.uint32),
positions:
np.random.randint(
0, config.max_positional_length,
(config.micro_batch_size * config.sequence_length)).astype(
np.uint32),
segments:
np.random.randint(
0, 2, (config.micro_batch_size * config.sequence_length)).astype(
np.uint32)
}
optimizer = popart.ConstSGD(0.01)
l1_lambda = 0.1
with popart_model.builder.nameScope("Embedding"):
output = popart_model.embedding(indices, positions, segments)
l1 = popart_model.builder.aiGraphcore.l1loss(
[output],
l1_lambda,
debugContext="l1LossVal",
reduction=popart.ReductionType.Sum)
num_reps = 5
proto = popart_model.builder.getModelProto()
outputs, post_proto = run_py(proto,
data,
output,
ipus=1,
loss=l1,
num_reps=num_reps,
optimizer=optimizer)
# ----------------- PopART -> PyTorch ----------------
proto = onnx.load_model_from_string(proto)
inputs = [
data[t].reshape(config.micro_batch_size,
config.sequence_length).astype(np.int32)
for t in [indices, positions, segments]
]
# ------------------- PyTorch -------------------------
torch_model = BertEmbeddings(
TorchBertConfig(config.vocab_length,
config.hidden_size,
max_position_embeddings=config.max_positional_length,
layer_norm_eps=config.layer_norm_eps,
update_embedding_dict=config.update_embedding_dict))
# Turn off dropout
torch_model.eval()
copy_weights_to_torch(torch_model, proto, TORCH_TO_ONNX, {})
optim = torch.optim.SGD(torch_model.parameters(), 0.01)
for _ in range(num_reps):
torch_output = torch_model(
*[torch.from_numpy(t).long() for t in inputs])
torch_loss = l1_lambda * torch.norm(torch_output, 1)
torch_loss.backward()
optim.step()
optim.zero_grad()
torch_outputs = [torch_output.detach().numpy()]
check_tensors(torch_outputs, outputs, margin=7e-6)
check_model(torch_model, post_proto, TORCH_TO_ONNX, {}, margin=7e-06)
def test_embedding_fwd(custom_ops):
# ------------------- PopART --------------------
builder = popart.Builder(opsets={
"ai.onnx": 9,
"ai.onnx.ml": 1,
"ai.graphcore": 1
})
config = BertConfig(vocab_length=9728,
batch_size=1,
hidden_size=768,
sequence_length=128,
activation_type='relu',
popart_dtype="FLOAT",
no_dropout=True,
custom_ops=['gather'],
inference=True)
popart_model = Bert(config, builder=builder)
# Prevent virtualGraph attributes being added to the ops.
popart_model.embedding_scope = popart_model.device_scope(None, None)
popart_model.embedding_split_scope = popart_model.embedding_scope
sequence_info = popart.TensorInfo(
"UINT32", [config.batch_size * config.sequence_length])
indices = builder.addInputTensor(sequence_info)
positions = builder.addInputTensor(sequence_info)
segments = builder.addInputTensor(sequence_info)
data = {
indices:
np.random.randint(0, config.vocab_length,
(config.batch_size * config.sequence_length)).astype(
np.uint32),
positions:
np.random.randint(0, config.max_positional_length,
(config.batch_size * config.sequence_length)).astype(
np.uint32),
segments:
np.random.randint(0, 2,
(config.batch_size * config.sequence_length)).astype(
np.uint32)
}
# Use the custom embedding for layout
output = popart_model.embedding(indices, positions, segments)
proto = builder.getModelProto()
outputs, post_proto = run_py(
proto, data, output, user_options={"enableStochasticRounding": True})
# ----------------- PopART -> PyTorch ----------------
proto = onnx.load_model_from_string(proto)
inputs = [
data[t].reshape(config.batch_size,
config.sequence_length).astype(np.int32)
for t in [indices, positions, segments]
]
torch_to_onnx = {
"word_embeddings.weight": "Embedding_Dict",
"position_embeddings.weight": "Positional_Dict",
"token_type_embeddings.weight": "Segment_Dict",
"LayerNorm.weight": "Gamma",
"LayerNorm.bias": "Beta"
}
transposed_weights = {
"word_embeddings.weight": np.transpose,
"position_embeddings.weight": np.transpose,
}
# ------------------- PyTorch -------------------------
torch_model = BertEmbeddings(
TorchBertConfig(config.vocab_length,
config.hidden_size,
max_position_embeddings=config.max_positional_length,
layer_norm_eps=config.layer_norm_eps))
torch_model.eval()
copy_weights_to_torch(torch_model, proto, torch_to_onnx,
transposed_weights)
torch_outputs = run_fwd_model(inputs, torch_model)
check_tensors(torch_outputs, outputs)
def test_embedding_bwd(custom_ops):
l1_lambda = 0.1
# ------------------- PopART --------------------
builder = popart.Builder(opsets={
"ai.onnx": 9,
"ai.onnx.ml": 1,
"ai.graphcore": 1
})
config = BertConfig(vocab_length=9728,
batch_size=1,
hidden_size=768,
sequence_length=128,
activation_type='relu',
popart_dtype="FLOAT",
no_dropout=True,
custom_ops=['gather'])
popart_model = Bert(config, builder=builder)
# Prevent virtualGraph attributes being added to the ops.
popart_model.embedding_scope = popart_model.device_scope(None, None)
popart_model.embedding_split_scope = popart_model.embedding_scope
sequence_info = popart.TensorInfo(
"UINT32", [config.batch_size * config.sequence_length])
indices = builder.addInputTensor(sequence_info)
positions = builder.addInputTensor(sequence_info)
segments = builder.addInputTensor(sequence_info)
data = {
indices:
np.random.randint(0, config.vocab_length,
(config.batch_size * config.sequence_length)).astype(
np.uint32),
positions:
np.random.randint(0, config.max_positional_length,
(config.batch_size * config.sequence_length)).astype(
np.uint32),
segments:
np.random.randint(0, 2,
(config.batch_size * config.sequence_length)).astype(
np.uint32)
}
output = popart_model.embedding(indices, positions, segments)
proto = builder.getModelProto()
l1 = popart.L1Loss(output, "l1LossVal", l1_lambda)
optimizer = popart.ConstSGD(0.01)
outputs, post_proto = run_py(
proto,
data,
output,
loss=l1,
optimizer=optimizer,
user_options={"enableStochasticRounding": True})
# ----------------- PopART -> PyTorch ----------------
proto = onnx.load_model_from_string(proto)
inputs = [
data[t].reshape(config.batch_size,
config.sequence_length).astype(np.int32)
for t in [indices, positions, segments]
]
torch_to_onnx = {
"word_embeddings.weight": "Embedding_Dict",
"position_embeddings.weight": "Positional_Dict",
"token_type_embeddings.weight": "Segment_Dict",
"LayerNorm.weight": "Gamma",
"LayerNorm.bias": "Beta"
}
transposed_weights = {
"word_embeddings.weight": np.transpose,
"position_embeddings.weight": np.transpose,
}
# ------------------- PyTorch -------------------------
torch_model = BertEmbeddings(
TorchBertConfig(config.vocab_length,
config.hidden_size,
max_position_embeddings=config.max_positional_length,
layer_norm_eps=config.layer_norm_eps))
# Turn off dropout
torch_model.eval()
copy_weights_to_torch(torch_model,
proto,
torch_to_onnx,
transform=transposed_weights)
optim = torch.optim.SGD(torch_model.parameters(),
0.01,
weight_decay=0.0,
momentum=0.0)
torch_output = torch_model(*[torch.from_numpy(t).long() for t in inputs])
torch_loss = l1_lambda * torch.norm(torch_output, 1)
torch_loss.backward()
optim.step()
torch_outputs = [torch_output.detach().numpy()]
check_tensors(torch_outputs, outputs)
check_model(torch_model,
post_proto,
torch_to_onnx,
transform=transposed_weights)
def test_embedding_fwd(custom_ops):
# ------------------- PopART --------------------
builder = popart.Builder(opsets={
"ai.onnx": 9,
"ai.onnx.ml": 1,
"ai.graphcore": 1
})
config = BertConfig(task="SQUAD",
vocab_length=9728,
batch_size=1,
hidden_size=768,
sequence_length=128,
activation_type='relu',
popart_dtype="FLOAT",
no_dropout=True,
inference=True)
popart_model = Bert(config, builder=builder)
# Prevent virtualGraph attributes being added to the ops.
popart_model.embedding_scope = popart_model.device_scope(None, None)
popart_model.embedding_split_scope = popart_model.embedding_scope
sequence_info = popart.TensorInfo(
"UINT32", [config.batch_size * config.sequence_length])
indices = builder.addInputTensor(sequence_info)
positions = builder.addInputTensor(sequence_info)
segments = builder.addInputTensor(sequence_info)
data = {
indices:
np.random.randint(0, config.vocab_length,
(config.batch_size * config.sequence_length)).astype(
np.uint32),
positions:
np.random.randint(0, config.max_positional_length,
(config.batch_size * config.sequence_length)).astype(
np.uint32),
segments:
np.random.randint(0, 2,
(config.batch_size * config.sequence_length)).astype(
np.uint32)
}
output = popart_model.embedding(indices, positions, segments)
proto = builder.getModelProto()
outputs, post_proto = run_py(proto, data, output)
# ----------------- PopART -> PyTorch ----------------
proto = onnx.load_model_from_string(proto)
inputs = [
data[t].reshape(config.batch_size,
config.sequence_length).astype(np.int32)
for t in [indices, positions, segments]
]
# ------------------- PyTorch -------------------------
torch_model = BertEmbeddings(
TorchBertConfig(config.vocab_length,
config.hidden_size,
max_position_embeddings=config.max_positional_length,
layer_norm_eps=config.layer_norm_eps))
torch_model.eval()
copy_weights_to_torch(torch_model, proto, TORCH_TO_ONNX, {})
torch_outputs = run_fwd_model(inputs, torch_model)
check_tensors(torch_outputs, outputs)