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import argparse
import datetime
import numpy as np
from caffe2.python import core, workspace
DTYPES = {
"uint8": np.uint8,
"uint8_fused": np.uint8,
"float": np.float32,
"float16": np.float16,
}
def benchmark_sparse_lengths_sum(
dtype_str,
categorical_limit,
embedding_size,
average_len,
batch_size,
iterations,
flush_cache,
):
print("Preparing lookup table. " + str(datetime.datetime.now()))
# We will use a constant, but non-trivial value so we save initialization
# time.
data = np.ones([categorical_limit, embedding_size], dtype=np.float32)
data *= 17.01
if dtype_str == "uint8":
scale_bias = np.random.rand(categorical_limit, 2).astype(np.float32)
workspace.FeedBlob("scale_bias", scale_bias.astype(np.float32))
elif dtype_str == "uint8_fused":
scale_bias = np.random.randint(255, size=(categorical_limit, 8))
data = np.concatenate([data, scale_bias], axis=1)
print("Data has shape {} {}".format(data.shape, datetime.datetime.now()))
workspace.FeedBlob("X", data.astype(DTYPES[dtype_str]))
# In order to produce truly random lengths and indices, we will embed a
# Python operator in the net to generate them.
def f(_, outputs):
lengths = np.random.randint(
int(np.round(average_len * 0.75)),
int(np.round(average_len * 1.25)) + 1,
batch_size,
).astype(np.int32)
indices = np.random.randint(0, categorical_limit, np.sum(lengths)).astype(
np.int64
)
outputs[0].feed(indices)
outputs[1].feed(lengths)
init_net = core.Net("init_net")
init_net.Python(f)([], ["indices", "lengths"])
workspace.RunNetOnce(init_net)
net = core.Net("mynet")
if flush_cache:
l3_cache_size = 30 * 2 ** 20 // 4
workspace.FeedBlob(
"huge_blob", np.random.randn(l3_cache_size).astype(np.float32)
)
net.Scale("huge_blob", "huge_blob_2x", value=2.0)
if dtype_str == "uint8":
net.SparseLengthsSum8BitsRowwise(["X", "indices", "lengths", "scale_bias"], "Y")
elif dtype_str == "uint8_fused":
net.SparseLengthsSumFused8BitRowwise(["X", "indices", "lengths"], "Y")
else:
net.SparseLengthsSum(["X", "indices", "lengths"], "Y")
workspace.CreateNet(net)
# Set random seed, so that repeated runs will keep the same sequence of
# random indices.
np.random.seed(1701)
print("Preparation finished. " + str(datetime.datetime.now()))
runtimes = workspace.BenchmarkNet(net.Name(), 1, iterations, True)
print(
"{} billion sums per cycle".format(
embedding_size
* workspace.FetchBlob("indices").size
/ runtimes[2 if flush_cache else 1]
/ 1e6
)
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="minimal benchmark for sparse lengths sum."
)
parser.add_argument(
"-d",
"--dtype",
choices=list(DTYPES.keys()),
default="float",
help="The data type for the input lookup table.",
)
parser.add_argument(
"-e", "--embedding-size", type=int, default=6000000, help="Lookup table size."
)
parser.add_argument(
"--embedding-dim", type=int, default=128, help="Embedding dimension."
)
parser.add_argument(
"--average-len",
type=int,
default=27,
help="Sparse feature average lengths, default is 27",
)
parser.add_argument("--batch-size", type=int, default=100, help="The batch size.")
parser.add_argument(
"-i", "--iteration", type=int, default=100000, help="The number of iterations."
)
parser.add_argument(
"--flush-cache", action="store_true", help="If true, flush cache"
)
args, extra_args = parser.parse_known_args()
core.GlobalInit(["python"] + extra_args)
benchmark_sparse_lengths_sum(
args.dtype,
args.embedding_size,
args.embedding_dim,
args.average_len,
args.batch_size,
args.iteration,
args.flush_cache,
)
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