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import operator_benchmark as op_bench
import torch
import numpy
from pt import configs
"""Embedding and EmbeddingBag Operator Benchmark"""
class EmbeddingBagBenchmark(op_bench.TorchBenchmarkBase):
def init(self, embeddingbags, dim, mode, input_size, offset, sparse, include_last_offset, device):
self.embedding = torch.nn.EmbeddingBag(
num_embeddings=embeddingbags,
embedding_dim=dim,
mode=mode,
include_last_offset=include_last_offset,
sparse=sparse).to(device=device)
numpy.random.seed((1 << 32) - 1)
offsets = torch.LongTensor([offset], device=device)
input = torch.tensor(numpy.random.randint(0, embeddingbags, input_size), device=device).long()
self.inputs = {
"input": input,
"offset": torch.cat((offsets, torch.tensor([input.size(0)], dtype=torch.long)), 0)
}
self.set_module_name('embeddingbag')
def forward(self, input, offset):
return self.embedding(input, offset)
op_bench.generate_pt_test(configs.embeddingbag_short_configs, EmbeddingBagBenchmark)
op_bench.generate_pt_gradient_test(configs.embeddingbag_short_configs, EmbeddingBagBenchmark)
class EmbeddingBenchmark(op_bench.TorchBenchmarkBase):
def init(self, num_embeddings, embedding_dim, input_size, device):
self.embedding = torch.nn.Embedding(
num_embeddings=num_embeddings,
embedding_dim=embedding_dim).to(device=device)
numpy.random.seed((1 << 32) - 1)
input = torch.tensor(numpy.random.randint(0, num_embeddings, input_size), device=device).long()
self.inputs = {"input": input}
self.set_module_name('embedding')
def forward(self, input):
return self.embedding(input)
op_bench.generate_pt_test(configs.embedding_short_configs, EmbeddingBenchmark)
op_bench.generate_pt_gradient_test(configs.embedding_short_configs, EmbeddingBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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