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import numpy
from pt import configs
import operator_benchmark as op_bench
import torch
import torch.ao.nn.quantized as nnq
"""
Microbenchmarks for qEmbeddingBag operators.
"""
class QEmbeddingBagBenchmark(op_bench.TorchBenchmarkBase):
def init(
self,
embeddingbags,
dim,
mode,
input_size,
offset,
sparse,
include_last_offset,
device,
):
self.embedding = nnq.EmbeddingBag(
num_embeddings=embeddingbags,
embedding_dim=dim,
mode=mode,
include_last_offset=include_last_offset,
).to(device=device)
numpy.random.seed((1 << 32) - 1)
self.input = torch.tensor(
numpy.random.randint(0, embeddingbags, input_size), device=device
).long()
offset = torch.LongTensor([offset], device=device)
self.offset = torch.cat(
(offset, torch.tensor([self.input.size(0)], dtype=torch.long)), 0
)
self.inputs = {"input": self.input, "offset": self.offset}
self.set_module_name("qEmbeddingBag")
def forward(self, input, offset):
return self.embedding(input, offset)
op_bench.generate_pt_test(configs.embeddingbag_short_configs, QEmbeddingBagBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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