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import operator_benchmark as op_bench
import torch
from torch import nn
"""
Microbenchmarks for RNNs.
"""
qrnn_configs = op_bench.config_list(
attrs=[
[1, 3, 1],
[5, 7, 4],
],
# names: input_size, hidden_size, num_layers
attr_names=["I", "H", "NL"],
cross_product_configs={
"B": (True,), # Bias always True for quantized
"D": (False, True), # Bidirectional
"dtype": (torch.qint8,), # Only qint8 dtype works for now
},
tags=["short"],
)
class LSTMBenchmark(op_bench.TorchBenchmarkBase):
def init(self, I, H, NL, B, D, dtype):
sequence_len = 128
batch_size = 16
# The quantized.dynamic.LSTM has a bug. That's why we create a regular
# LSTM, and quantize it later. See issue #31192.
cell_nn = nn.LSTM(
input_size=I,
hidden_size=H,
num_layers=NL,
bias=B,
batch_first=False,
dropout=0.0,
bidirectional=D,
)
cell_temp = nn.Sequential(cell_nn)
self.cell = torch.ao.quantization.quantize_dynamic(
cell_temp, {nn.LSTM, nn.Linear}, dtype=dtype
)[0]
x = torch.randn(
sequence_len, # sequence length
batch_size, # batch size
I, # Number of features in X
)
h = torch.randn(
NL * (D + 1), # layer_num * dir_num
batch_size, # batch size
H, # hidden size
)
c = torch.randn(
NL * (D + 1), # layer_num * dir_num
batch_size, # batch size
H, # hidden size
)
self.inputs = {"x": x, "h": h, "c": c}
self.set_module_name("QLSTM")
def forward(self, x, h, c):
return self.cell(x, (h, c))[0]
op_bench.generate_pt_test(qrnn_configs, LSTMBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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