sz0 = X_cat.shape[0]
sz1 = X_cat.shape[1]
if use_gpu:
lS_i = [X_cat[:, i].pin_memory() for i in range(sz1)]
lS_o = [
torch.tensor(range(sz0)).pin_memory() for _ in range(sz1)
]
return X_int.pin_memory(), lS_o, lS_i, T.pin_memory()
else:
lS_i = [X_cat[:, i] for i in range(sz1)]
lS_o = [torch.tensor(range(sz0)) for _ in range(sz1)]
return X_int, lS_o, lS_i, T
train_data = dp.CriteoDataset(args.data_set, args.max_ind_range,
args.data_sub_sample_rate,
args.data_randomize, "train",
args.raw_data_file,
args.processed_data_file,
args.memory_map)
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.mini_batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_wrapper,
pin_memory=False,
drop_last=False, # True
)
nbatches = args.num_batches if args.num_batches > 0 else len(
train_loader)
test_data = dp.CriteoDataset(args.data_set, args.max_ind_range,
def __init__(self,
data_path,
name,
pre_process,
use_cache,
count=None,
samples_to_aggregate_fix=None,
samples_to_aggregate_min=None,
samples_to_aggregate_max=None,
samples_to_aggregate_quantile_file=None,
samples_to_aggregate_trace_file=None,
test_num_workers=0,
max_ind_range=-1,
sub_sample_rate=0.0,
mlperf_bin_loader=False,
randomize="total",
memory_map=False):
super().__init__()
self.count = count
self.random_offsets = []
self.use_fixed_size = ((samples_to_aggregate_quantile_file is None) and
(samples_to_aggregate_min is None or samples_to_aggregate_max is None))
if self.use_fixed_size:
# fixed size queries
self.samples_to_aggregate = 1 if samples_to_aggregate_fix is None else samples_to_aggregate_fix
self.samples_to_aggregate_min = None
self.samples_to_aggregate_max = None
else:
# variable size queries
self.samples_to_aggregate = 1
self.samples_to_aggregate_min = samples_to_aggregate_min
self.samples_to_aggregate_max = samples_to_aggregate_max
self.samples_to_aggregate_quantile_file = samples_to_aggregate_quantile_file
if name == "kaggle":
raw_data_file = data_path + "/train.txt"
processed_data_file = data_path + "/kaggleAdDisplayChallenge_processed.npz"
elif name == "terabyte":
raw_data_file = data_path + "/day"
processed_data_file = data_path + "/terabyte_processed.npz"
else:
raise ValueError("only kaggle|terabyte dataset options are supported")
self.use_mlperf_bin_loader = mlperf_bin_loader and memory_map and name == "terabyte"
# debug prints
# print("dataset filenames", raw_data_file, processed_data_file)
self.test_data = dp.CriteoDataset(
dataset=name,
max_ind_range=max_ind_range,
sub_sample_rate=sub_sample_rate,
randomize=randomize,
split="test",
raw_path=raw_data_file,
pro_data=processed_data_file,
memory_map=memory_map
)
self.num_individual_samples = len(self.test_data)
if self.use_mlperf_bin_loader:
test_file = data_path + "/terabyte_processed_test.bin"
counts_file = raw_data_file + '_fea_count.npz'
data_loader_terabyte.numpy_to_binary(
input_files=[raw_data_file + '_23_reordered.npz'],
output_file_path=data_path + "/terabyte_processed_test.bin",
split="test")
self.test_data = data_loader_terabyte.CriteoBinDataset(
data_file=test_file,
counts_file=counts_file,
batch_size=self.samples_to_aggregate,
max_ind_range=max_ind_range
)
self.test_loader = torch.utils.data.DataLoader(
self.test_data,
batch_size=None,
batch_sampler=None,
shuffle=False,
num_workers=0,
collate_fn=None,
pin_memory=False,
drop_last=False,
)
else:
self.test_loader = torch.utils.data.DataLoader(
self.test_data,
batch_size=self.samples_to_aggregate,
shuffle=False,
num_workers=test_num_workers,
collate_fn=dp.collate_wrapper_criteo,
pin_memory=False,
drop_last=False,
)
# WARNING: Note that the orignal dataset returns number of samples, while the
# binary dataset returns the number of batches. Therefore, when using a mini-batch
# of size samples_to_aggregate as an item we need to adjust the original dataset item_count.
# On the other hand, data loader always returns number of batches.
if self.use_fixed_size:
# the offsets for fixed query size will be generated on-the-fly later on
print("Using fixed query size: " + str(self.samples_to_aggregate))
if self.use_mlperf_bin_loader:
self.num_aggregated_samples = len(self.test_data)
# self.num_aggregated_samples2 = len(self.test_loader)
else:
self.num_aggregated_samples = (self.num_individual_samples + self.samples_to_aggregate - 1) // self.samples_to_aggregate
# self.num_aggregated_samples2 = len(self.test_loader)
else:
# the offsets for variable query sizes will be pre-generated here
if self.samples_to_aggregate_quantile_file is None:
# generate number of samples in a query from a uniform(min,max) distribution
print("Using variable query size: uniform distribution (" + str(self.samples_to_aggregate_min) + "," + str(self.samples_to_aggregate_max) + ")")
done = False
qo = 0
while done == False:
self.random_offsets.append(int(qo))
qs = random.randint(self.samples_to_aggregate_min, self.samples_to_aggregate_max)
qo = min(qo + qs, self.num_individual_samples)
if qo >= self.num_individual_samples:
done = True
self.random_offsets.append(int(qo))
# compute min and max number of samples
nas_max = (self.num_individual_samples + self.samples_to_aggregate_min - 1) // self.samples_to_aggregate_min
nas_min = (self.num_individual_samples + self.samples_to_aggregate_max - 1) // self.samples_to_aggregate_max
else:
# generate number of samples in a query from a custom distribution,
# with quantile (inverse of its cdf) given in the file. Note that
# quantile is related to the concept of percentile in statistics.
#
# For instance, assume that we have the following distribution for query length
# length = [100, 200, 300, 400, 500, 600, 700] # x
# pdf = [0.1, 0.6, 0.1, 0.05, 0.05, 0.05, 0.05] # p(x)
# cdf = [0.1, 0.7, 0.8, 0.85, 0.9, 0.95, 1.0] # f(x) = prefix-sum of p(x)
# The inverse of its cdf with granularity of 0.05 can be written as
# quantile_p = [.05, .10, .15, .20, .25, .30, .35, .40, .45, .50, .55, .60, .65, .70, .75, .80, .85, .90, .95, 1.0] # p
# quantile_x = [100, 100, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 300, 300, 400, 500, 600, 700] # q(p) = x, such that f(x) >= p
# Notice that once we have quantile, we can apply inverse transform sampling method.
print("Using variable query size: custom distribution (file " + str(samples_to_aggregate_quantile_file) + ")")
with open(self.samples_to_aggregate_quantile_file, 'r') as f:
line = f.readline()
quantile = np.fromstring(line, dtype=int, sep=", ")
# debug prints
# print(quantile)
# print(len(quantile))
l = len(quantile)
done = False
qo = 0
while done == False:
self.random_offsets.append(int(qo))
pr = np.random.randint(low=0, high=l)
qs = quantile[pr]
qo = min(qo + qs, self.num_individual_samples)
if qo >= self.num_individual_samples:
done = True
self.random_offsets.append(int(qo))
# compute min and max number of samples
nas_max = (self.num_individual_samples + quantile[0] - 1) // quantile[0]
nas_min = (self.num_individual_samples + quantile[-1]- 1) // quantile[-1]
# reset num_aggregated_samples
self.num_aggregated_samples = len(self.random_offsets) - 1
# check num_aggregated_samples
if self.num_aggregated_samples < nas_min or nas_max < self.num_aggregated_samples:
raise ValueError("Sannity check failed")
# limit number of items to count if needed
if self.count is not None:
self.num_aggregated_samples = min(self.count, self.num_aggregated_samples)
# dump the trace of aggregated samples
if samples_to_aggregate_trace_file is not None:
with open(samples_to_aggregate_trace_file, 'w') as f:
for l in range(self.num_aggregated_samples):
if self.use_fixed_size:
s = l * self.samples_to_aggregate
e = min((l + 1) * self.samples_to_aggregate, self.num_individual_samples)
else:
s = self.random_offsets[l]
e = self.random_offsets[l+1]
f.write(str(s) + ", " + str(e) + ", " + str(e-s) + "\n")
def __init__(self, data_path, name, pre_process, use_cache, count=None, test_num_workers=0, max_ind_range=-1, sub_sample_rate=0.0, mlperf_bin_loader=False, randomize="total", memory_map=False):
super().__init__()
# debug prints
# print('__init__', data_path, name, pre_process, use_cache, count, test_num_workers, max_ind_range, sub_sample_rate, randomize, memory_map)
self.count = count
if name == "kaggle":
raw_data_file = data_path + "/train.txt"
processed_data_file = data_path + "/kaggleAdDisplayChallenge_processed.npz"
elif name == "terabyte":
raw_data_file = data_path + "/day"
processed_data_file = data_path + "/terabyte_processed.npz"
else:
raise ValueError("only kaggle|terabyte dataset options are supported")
self.use_mlperf_bin_loader = mlperf_bin_loader and memory_map and name == "terabyte"
# debug prints
# print("dataset filenames", raw_data_file, processed_data_file)
self.test_data = dp.CriteoDataset(
dataset=name,
max_ind_range=max_ind_range,
sub_sample_rate=sub_sample_rate,
randomize=randomize,
split="test",
raw_path=raw_data_file,
pro_data=processed_data_file,
memory_map=memory_map
)
if self.use_mlperf_bin_loader:
test_file = data_path + "/terabyte_processed_test.bin"
counts_file = raw_data_file + '_fea_count.npz'
data_loader_terabyte.numpy_to_binary(
input_files=[raw_data_file + '_23_reordered.npz'],
output_file_path=data_path + "/terabyte_processed_test.bin",
split="test")
self.test_data = data_loader_terabyte.CriteoBinDataset(
data_file=test_file,
counts_file=counts_file,
batch_size=1, # FIGURE this out
max_ind_range=max_ind_range
)
self.test_loader = torch.utils.data.DataLoader(
self.test_data,
batch_size=None,
batch_sampler=None,
shuffle=False,
num_workers=0,
collate_fn=None,
pin_memory=False,
drop_last=False,
)
else:
self.test_loader = torch.utils.data.DataLoader(
self.test_data,
batch_size=1, # FIGURE this out
shuffle=False,
num_workers=test_num_workers,
collate_fn=dp.collate_wrapper_criteo,
pin_memory=False,
drop_last=False,
)
sz1 = X_cat.shape[1]
if use_gpu:
lS_i = [X_cat[:, i].pin_memory() for i in range(sz1)]
lS_o = [
torch.tensor(range(sz0)).pin_memory() for _ in range(sz1)
]
return X_int.pin_memory(), lS_o, lS_i, T.pin_memory()
else:
lS_i = [X_cat[:, i] for i in range(sz1)]
lS_o = [torch.tensor(range(sz0)) for _ in range(sz1)]
return X_int, lS_o, lS_i, T
train_data = dp.CriteoDataset(
args.data_set,
args.data_randomize,
"train",
args.raw_data_file,
args.processed_data_file,
)
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.mini_batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_wrapper,
pin_memory=False,
drop_last=False,
)
nbatches = args.num_batches if args.num_batches > 0 else len(
train_loader)
def __init__(self,
data_path,
name,
pre_process,
use_cache,
count=None,
samples_to_aggregate=None,
min_samples_to_aggregate=None,
max_samples_to_aggregate=None,
test_num_workers=0,
max_ind_range=-1,
sub_sample_rate=0.0,
mlperf_bin_loader=False,
randomize="total",
memory_map=False):
super().__init__()
self.count = count
self.random_offsets = []
self.use_fixed_size = min_samples_to_aggregate is None or max_samples_to_aggregate is None
if self.use_fixed_size:
# fixed size queries
self.samples_to_aggregate = 1 if samples_to_aggregate is None else samples_to_aggregate
self.min_samples_to_aggregate = None
self.max_samples_to_aggregate = None
else:
# variable size queries
self.samples_to_aggregate = 1
self.min_samples_to_aggregate = min_samples_to_aggregate
self.max_samples_to_aggregate = max_samples_to_aggregate
if name == "kaggle":
raw_data_file = data_path + "/train.txt"
processed_data_file = data_path + "/kaggleAdDisplayChallenge_processed.npz"
elif name == "terabyte":
raw_data_file = data_path + "/day"
processed_data_file = data_path + "/terabyte_processed.npz"
else:
raise ValueError(
"only kaggle|terabyte dataset options are supported")
self.use_mlperf_bin_loader = mlperf_bin_loader and memory_map and name == "terabyte"
# debug prints
# print("dataset filenames", raw_data_file, processed_data_file)
self.test_data = dp.CriteoDataset(dataset=name,
max_ind_range=max_ind_range,
sub_sample_rate=sub_sample_rate,
randomize=randomize,
split="test",
raw_path=raw_data_file,
pro_data=processed_data_file,
memory_map=memory_map)
self.num_individual_samples = len(self.test_data)
if self.use_mlperf_bin_loader:
test_file = data_path + "/terabyte_processed_test.bin"
counts_file = raw_data_file + '_fea_count.npz'
data_loader_terabyte.numpy_to_binary(
input_files=[raw_data_file + '_23_reordered.npz'],
output_file_path=data_path + "/terabyte_processed_test.bin",
split="test")
self.test_data = data_loader_terabyte.CriteoBinDataset(
data_file=test_file,
counts_file=counts_file,
batch_size=self.samples_to_aggregate,
max_ind_range=max_ind_range)
self.test_loader = torch.utils.data.DataLoader(
self.test_data,
batch_size=None,
batch_sampler=None,
shuffle=False,
num_workers=0,
collate_fn=None,
pin_memory=False,
drop_last=False,
)
else:
self.test_loader = torch.utils.data.DataLoader(
self.test_data,
batch_size=self.samples_to_aggregate,
shuffle=False,
num_workers=test_num_workers,
collate_fn=dp.collate_wrapper_criteo,
pin_memory=False,
drop_last=False,
)
# WARNING: Note that the orignal dataset returns number of samples, while the
# binary dataset returns the number of batches. Therefore, when using a mini-batch
# of size samples_to_aggregate as an item we need to adjust the original dataset item_count.
# On the other hand, data loader always returns number of batches.
if self.use_fixed_size:
# the offsets for fixed query size will be generated on-the-fly later on
if self.use_mlperf_bin_loader:
self.num_aggregated_samples = len(self.test_data)
# self.num_aggregated_samples2 = len(self.test_loader)
else:
self.num_aggregated_samples = (self.num_individual_samples +
self.samples_to_aggregate -
1) // self.samples_to_aggregate
# self.num_aggregated_samples2 = len(self.test_loader)
else:
# the offsets for variable query sizes will be pre-generated here
done = False
qo = 0
while done == False:
self.random_offsets.append(int(qo))
qs = random.randint(self.min_samples_to_aggregate,
self.max_samples_to_aggregate)
qo = min(qo + qs, self.num_individual_samples)
if qo >= self.num_individual_samples:
done = True
self.random_offsets.append(int(qo))
# reset num_aggregated_samples
self.num_aggregated_samples = len(self.random_offsets) - 1
# check num_aggregated_samples
nas_max = (self.num_individual_samples +
self.min_samples_to_aggregate -
1) // self.min_samples_to_aggregate
nas_min = (self.num_individual_samples +
self.max_samples_to_aggregate -
1) // self.max_samples_to_aggregate
if self.num_aggregated_samples < nas_min or nas_max < self.num_aggregated_samples:
raise ValueError("Sannity check failed")
# limit number of items to count if needed
if self.count is not None:
self.num_aggregated_samples = min(self.count,
self.num_aggregated_samples)
# dump the trace of aggregated samples
with open('dlrm_trace_of_aggregated_samples.txt', 'w') as f:
for l in range(self.num_aggregated_samples):
if self.use_fixed_size:
s = l * self.samples_to_aggregate
e = min((l + 1) * self.samples_to_aggregate,
self.num_individual_samples)
else:
s = self.random_offsets[l]
e = self.random_offsets[l + 1]
f.write(str(s) + ", " + str(e) + "\n")