num_workers=0)
EMBEDDING_TABLE_SHAPES_TUPLE = (
{
CATEGORICAL_COLUMNS[0]: EMBEDDING_TABLE_SHAPES[CATEGORICAL_COLUMNS[0]],
CATEGORICAL_COLUMNS[1]: EMBEDDING_TABLE_SHAPES[CATEGORICAL_COLUMNS[1]],
},
{
CATEGORICAL_MH_COLUMNS[0]:
EMBEDDING_TABLE_SHAPES[CATEGORICAL_MH_COLUMNS[0]]
},
)
model = Model(
embedding_table_shapes=EMBEDDING_TABLE_SHAPES_TUPLE,
num_continuous=0,
emb_dropout=0.0,
layer_hidden_dims=[128, 128, 128],
layer_dropout_rates=[0.0, 0.0, 0.0],
).cuda()
lr_scaler = hvd.size()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01 * lr_scaler)
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
optimizer = hvd.DistributedOptimizer(optimizer,
named_parameters=model.named_parameters())
for epoch in range(args.epochs):
def test_empty_cols(tmpdir, engine, cat_names, mh_names, cont_names,
label_name, num_rows):
json_sample["num_rows"] = num_rows
cols = datagen._get_cols_from_schema(json_sample)
df_gen = datagen.DatasetGen(datagen.PowerLawDistro(0.1))
dataset = df_gen.create_df(num_rows, cols)
dataset = nvt.Dataset(dataset)
features = []
if cont_names:
features.append(cont_names >> ops.FillMedian() >> ops.Normalize())
if cat_names or mh_names:
features.append(cat_names + mh_names >> ops.Categorify())
# test out https://github.com/NVIDIA/NVTabular/issues/149 making sure we can iterate over
# empty cats/conts
graph = sum(features, nvt.WorkflowNode(label_name))
processor = nvt.Workflow(graph)
output_train = os.path.join(tmpdir, "train/")
os.mkdir(output_train)
df_out = processor.fit_transform(dataset).to_ddf().compute(
scheduler="synchronous")
if processor.output_node.output_schema.apply_inverse(
ColumnSelector("lab_1")):
# if we don't have conts/cats/labels we're done
return
data_itr = None
with pytest.raises(ValueError) as exc_info:
data_itr = torch_dataloader.TorchAsyncItr(
nvt.Dataset(df_out),
cats=cat_names + mh_names,
conts=cont_names,
labels=label_name,
batch_size=2,
)
assert "Neither Categorical or Continuous columns were found by the dataloader. " in str(
exc_info.value)
if data_itr:
for nvt_batch in data_itr:
cats_conts, labels = nvt_batch
if cat_names:
assert set(cat_names).issubset(set(list(cats_conts.keys())))
if cont_names:
assert set(cont_names).issubset(set(list(cats_conts.keys())))
if cat_names or cont_names or mh_names:
emb_sizes = nvt.ops.get_embedding_sizes(processor)
EMBEDDING_DROPOUT_RATE = 0.04
DROPOUT_RATES = [0.001, 0.01]
HIDDEN_DIMS = [1000, 500]
LEARNING_RATE = 0.001
model = Model(
embedding_table_shapes=emb_sizes,
num_continuous=len(cont_names),
emb_dropout=EMBEDDING_DROPOUT_RATE,
layer_hidden_dims=HIDDEN_DIMS,
layer_dropout_rates=DROPOUT_RATES,
).cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
def rmspe_func(y_pred, y):
"Return y_pred and y to non-log space and compute RMSPE"
y_pred, y = torch.exp(y_pred) - 1, torch.exp(y) - 1
pct_var = (y_pred - y) / y
return (pct_var**2).mean().pow(0.5)
train_loss, y_pred, y = process_epoch(
data_itr,
model,
train=True,
optimizer=optimizer,
amp=False,
)
train_rmspe = None
train_rmspe = rmspe_func(y_pred, y)
assert train_rmspe is not None
assert len(y_pred) > 0
assert len(y) > 0
def test_mh_model_support(tmpdir):
df = cudf.DataFrame({
"Authors": [["User_A"], ["User_A", "User_E"], ["User_B", "User_C"],
["User_C"]],
"Reviewers": [["User_A"], ["User_A", "User_E"], ["User_B", "User_C"],
["User_C"]],
"Engaging User": ["User_B", "User_B", "User_A", "User_D"],
"Null User": ["User_B", "User_B", "User_A", "User_D"],
"Post": [1, 2, 3, 4],
"Cont1": [0.3, 0.4, 0.5, 0.6],
"Cont2": [0.3, 0.4, 0.5, 0.6],
"Cat1": ["A", "B", "A", "C"],
})
cat_names = ["Cat1", "Null User", "Authors",
"Reviewers"] # , "Engaging User"]
cont_names = ["Cont1", "Cont2"]
label_name = ["Post"]
out_path = os.path.join(tmpdir, "train/")
os.mkdir(out_path)
cats = cat_names >> ops.Categorify()
conts = cont_names >> ops.Normalize()
processor = nvt.Workflow(cats + conts + label_name)
df_out = processor.fit_transform(nvt.Dataset(df)).to_ddf().compute()
data_itr = torch_dataloader.TorchAsyncItr(
nvt.Dataset(df_out),
cats=cat_names,
conts=cont_names,
labels=label_name,
batch_size=2,
)
emb_sizes = nvt.ops.get_embedding_sizes(processor)
EMBEDDING_DROPOUT_RATE = 0.04
DROPOUT_RATES = [0.001, 0.01]
HIDDEN_DIMS = [1000, 500]
LEARNING_RATE = 0.001
model = Model(
embedding_table_shapes=emb_sizes,
num_continuous=len(cont_names),
emb_dropout=EMBEDDING_DROPOUT_RATE,
layer_hidden_dims=HIDDEN_DIMS,
layer_dropout_rates=DROPOUT_RATES,
).cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
def rmspe_func(y_pred, y):
"Return y_pred and y to non-log space and compute RMSPE"
y_pred, y = torch.exp(y_pred) - 1, torch.exp(y) - 1
pct_var = (y_pred - y) / y
return (pct_var**2).mean().pow(0.5)
train_loss, y_pred, y = process_epoch(
data_itr,
model,
train=True,
optimizer=optimizer,
# transform=batch_transform,
amp=False,
)
train_rmspe = None
train_rmspe = rmspe_func(y_pred, y)
assert train_rmspe is not None
assert len(y_pred) > 0
assert len(y) > 0
def runner(rank, world_size):
setup(rank, world_size)
train_dataset = TorchAsyncItr(
nvt.Dataset(TRAIN_PATHS),
batch_size=BATCH_SIZE,
cats=CATEGORICAL_COLUMNS + CATEGORICAL_MH_COLUMNS,
conts=NUMERIC_COLUMNS,
labels=["rating"],
device=rank,
global_size=world_size,
global_rank=rank,
shuffle=True,
seed_fn=seed_fn,
)
train_loader = DLDataLoader(
train_dataset, batch_size=None, collate_fn=collate_fn, pin_memory=False, num_workers=0
)
EMBEDDING_TABLE_SHAPES_TUPLE = (
{
CATEGORICAL_COLUMNS[0]: EMBEDDING_TABLE_SHAPES[CATEGORICAL_COLUMNS[0]],
CATEGORICAL_COLUMNS[1]: EMBEDDING_TABLE_SHAPES[CATEGORICAL_COLUMNS[1]],
},
{CATEGORICAL_MH_COLUMNS[0]: EMBEDDING_TABLE_SHAPES[CATEGORICAL_MH_COLUMNS[0]]},
)
model = Model(
embedding_table_shapes=EMBEDDING_TABLE_SHAPES_TUPLE,
num_continuous=0,
emb_dropout=0.0,
layer_hidden_dims=[128, 128, 128],
layer_dropout_rates=[0.0, 0.0, 0.0],
).cuda()
model = nn.parallel.DistributedDataParallel(
model, device_ids=[rank], find_unused_parameters=True
)
lr_scaler = world_size
# optimizer = DistributedOptimizer(torch.optim.Adam, model.parameters(), lr=0.01 * lr_scaler)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01 * lr_scaler)
total_rows = 0
t_final = 0
for epoch in range(args.epochs):
start = time()
with model.join():
train_loss, y_pred, y = process_epoch(
train_loader,
model,
train=True,
optimizer=optimizer,
)
# hvd.join(gpu_to_use)
# hvd.broadcast_parameters(model.state_dict(), root_rank=0)
print(f"Epoch {epoch:02d}. Train loss: {train_loss:.4f}.")
t_final += time() - start
total_rows += train_dataset.num_rows_processed
print(
f"run_time: {t_final} - rows: {total_rows} - "
f"epochs: {epoch} - dl_thru: {total_rows / t_final}"
)