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26f4f10ac8
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126 Approved by: https://github.com/kit1980
82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
from pt import configs
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import operator_benchmark as op_bench
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import torch
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import torch.ao.nn.quantized as nnq
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"""
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Microbenchmarks for qConv operators.
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"""
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class QConv1dBenchmark(op_bench.TorchBenchmarkBase):
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# def init(self, N, IC, OC, L, G, kernel, stride, pad):
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def init(self, IC, OC, kernel, stride, N, L, device):
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G = 1
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pad = 0
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self.scale = 1.0 / 255
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self.zero_point = 0
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X = torch.randn(N, IC, L, dtype=torch.float32)
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qX = torch.quantize_per_tensor(
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X, scale=self.scale, zero_point=self.zero_point, dtype=torch.quint8
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)
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# Convert the tensor to NHWC format
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W = torch.randn(OC, IC // G, kernel, dtype=torch.float32)
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self.qW = torch.quantize_per_tensor(
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W, scale=self.scale, zero_point=0, dtype=torch.qint8
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)
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self.inputs = {"input": qX}
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self.qconv1d = nnq.Conv1d(IC, OC, kernel, stride=stride, padding=pad, groups=G)
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self.qconv1d.set_weight_bias(self.qW, None)
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self.qconv1d.scale = torch.tensor(self.scale, dtype=torch.double)
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self.qconv1d.zero_point = torch.tensor(self.zero_point, dtype=torch.int)
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self.set_module_name("QConv1d")
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def forward(self, input):
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return self.qconv1d(input)
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class QConv2dBenchmark(op_bench.TorchBenchmarkBase):
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# def init(self, N, IC, OC, H, W, G, kernel, stride, pad):
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def init(self, IC, OC, kernel, stride, N, H, W, G, pad, device):
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# super().init(N, IC, OC, (H, W), G, (kernel, kernel), stride, pad)
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self.scale = 1.0 / 255
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self.zero_point = 0
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X = torch.randn(N, IC, H, W, dtype=torch.float32)
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qX = torch.quantize_per_tensor(
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X, scale=self.scale, zero_point=self.zero_point, dtype=torch.quint8
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)
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# Convert the tensor to NHWC format
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W = torch.randn(OC, IC // G, kernel, kernel, dtype=torch.float32)
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self.qW = torch.quantize_per_tensor(
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W, scale=self.scale, zero_point=0, dtype=torch.qint8
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)
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self.inputs = {"input": qX}
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self.qconv2d = nnq.Conv2d(IC, OC, kernel, stride=stride, padding=pad, groups=G)
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self.qconv2d.set_weight_bias(self.qW, None)
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self.qconv2d.scale = torch.tensor(self.scale, dtype=torch.double)
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self.qconv2d.zero_point = torch.tensor(self.zero_point, dtype=torch.int)
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self.set_module_name("QConv2d")
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def forward(self, input):
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return self.qconv2d(input)
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op_bench.generate_pt_test(
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configs.remove_cuda(configs.conv_1d_configs_short + configs.conv_1d_configs_long),
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QConv1dBenchmark,
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)
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op_bench.generate_pt_test(
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configs.remove_cuda(configs.conv_2d_configs_short + configs.conv_2d_configs_long),
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QConv2dBenchmark,
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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