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(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_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)