def benchmark_map_filter():
times = {"num examples": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
features = nlp.Features({
"text": nlp.Value("string"),
"numbers": nlp.Value("float32")
})
dataset = generate_example_dataset(os.path.join(
tmp_dir, "dataset.arrow"),
features,
num_examples=SPEED_TEST_N_EXAMPLES)
tokenizer = transformers.AutoTokenizer.from_pretrained(
"bert-base-cased", use_fast=True)
def tokenize(examples):
return tokenizer(examples["text"])
times["map identity"] = map(dataset)
times["map identity batched"] = map(dataset, batched=True)
times["map no-op batched"] = map(dataset,
function=lambda x: None,
batched=True)
with dataset.formatted_as(type="numpy"):
times["map no-op batched numpy"] = map(dataset,
function=lambda x: None,
batched=True)
with dataset.formatted_as(type="pandas"):
times["map no-op batched pandas"] = map(dataset,
function=lambda x: None,
batched=True)
with dataset.formatted_as(type="torch", columns="numbers"):
times["map no-op batched pytorch"] = map(dataset,
function=lambda x: None,
batched=True)
with dataset.formatted_as(type="tensorflow", columns="numbers"):
times["map no-op batched tensorflow"] = map(
dataset, function=lambda x: None, batched=True)
times["map fast-tokenizer batched"] = map(dataset,
function=tokenize,
batched=True)
times["filter"] = filter(dataset)
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(RESULTS_FILE_PATH, "wb") as f:
f.write(json.dumps(times).encode("utf-8"))
def benchmark_indices_mapping():
times = {"num examples": SPEED_TEST_N_EXAMPLES}
functions = (select, sort, shuffle, train_test_split, shard)
with tempfile.TemporaryDirectory() as tmp_dir:
print("generating dataset")
features = nlp.Features({"text": nlp.Value("string"), "numbers": nlp.Value("float32")})
dataset = generate_example_dataset(
os.path.join(tmp_dir, "dataset.arrow"), features, num_examples=SPEED_TEST_N_EXAMPLES
)
print("Functions")
for func in functions:
print(func.__name__)
times[func.__name__] = func(dataset)
with open(RESULTS_FILE_PATH, "wb") as f:
f.write(json.dumps(times).encode("utf-8"))
def benchmark_iterating():
times = {"num examples": SPEED_TEST_N_EXAMPLES}
functions = [
(read, {
"length": SMALL_TEST
}),
(read, {
"length": SPEED_TEST_N_EXAMPLES
}),
(read_batch, {
"length": SPEED_TEST_N_EXAMPLES,
"batch_size": 10
}),
(read_batch, {
"length": SPEED_TEST_N_EXAMPLES,
"batch_size": 100
}),
(read_batch, {
"length": SPEED_TEST_N_EXAMPLES,
"batch_size": 1_000
}),
(read_formatted, {
"type": "numpy",
"length": SMALL_TEST
}),
(read_formatted, {
"type": "pandas",
"length": SMALL_TEST
}),
(read_formatted, {
"type": "torch",
"length": SMALL_TEST
}),
(read_formatted, {
"type": "tensorflow",
"length": SMALL_TEST
}),
(read_formatted_batch, {
"type": "numpy",
"length": SMALL_TEST,
"batch_size": 10
}),
(read_formatted_batch, {
"type": "numpy",
"length": SMALL_TEST,
"batch_size": 1_000
}),
]
functions_shuffled = [
(read, {
"length": SMALL_TEST
}),
(read, {
"length": SPEED_TEST_N_EXAMPLES
}),
(read_batch, {
"length": SPEED_TEST_N_EXAMPLES,
"batch_size": 10
}),
(read_batch, {
"length": SPEED_TEST_N_EXAMPLES,
"batch_size": 100
}),
(read_batch, {
"length": SPEED_TEST_N_EXAMPLES,
"batch_size": 1_000
}),
(read_formatted, {
"type": "numpy",
"length": SMALL_TEST
}),
(read_formatted_batch, {
"type": "numpy",
"length": SMALL_TEST,
"batch_size": 10
}),
(read_formatted_batch, {
"type": "numpy",
"length": SMALL_TEST,
"batch_size": 1_000
}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print("generating dataset")
features = datasets.Features({
"list":
datasets.Sequence(datasets.Value("float32")),
"numbers":
datasets.Value("float32")
})
dataset = generate_example_dataset(
os.path.join(tmp_dir, "dataset.arrow"),
features,
num_examples=SPEED_TEST_N_EXAMPLES,
seq_shapes={"list": (100, )},
)
print("first set of iterations")
for func, kwargs in functions:
print(func.__name__, str(kwargs))
times[func.__name__ + " " +
" ".join(str(v) for v in kwargs.values())] = func(
dataset, **kwargs)
print("shuffling dataset")
dataset = dataset.shuffle()
print("Second set of iterations (after shuffling")
for func, kwargs in functions_shuffled:
print("shuffled ", func.__name__, str(kwargs))
times["shuffled " + func.__name__ + " " +
" ".join(str(v) for v in kwargs.values())] = func(
dataset, **kwargs)
with open(RESULTS_FILE_PATH, "wb") as f:
f.write(json.dumps(times).encode("utf-8"))
