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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 ghstack dependencies: #127122, #127123, #127124, #127125
73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
import numpy
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from pt import configs
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import operator_benchmark as op_bench
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import torch
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"""Embedding and EmbeddingBag Operator Benchmark"""
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class EmbeddingBagBenchmark(op_bench.TorchBenchmarkBase):
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def init(
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self,
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embeddingbags,
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dim,
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mode,
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input_size,
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offset,
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sparse,
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include_last_offset,
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device,
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):
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self.embedding = torch.nn.EmbeddingBag(
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num_embeddings=embeddingbags,
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embedding_dim=dim,
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mode=mode,
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include_last_offset=include_last_offset,
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sparse=sparse,
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).to(device=device)
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numpy.random.seed((1 << 32) - 1)
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offsets = torch.LongTensor([offset], device=device)
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input = torch.tensor(
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numpy.random.randint(0, embeddingbags, input_size), device=device
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).long()
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self.inputs = {
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"input": input,
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"offset": torch.cat(
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(offsets, torch.tensor([input.size(0)], dtype=torch.long)), 0
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),
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}
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self.set_module_name("embeddingbag")
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def forward(self, input, offset):
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return self.embedding(input, offset)
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op_bench.generate_pt_test(configs.embeddingbag_short_configs, EmbeddingBagBenchmark)
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op_bench.generate_pt_gradient_test(
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configs.embeddingbag_short_configs, EmbeddingBagBenchmark
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)
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class EmbeddingBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, num_embeddings, embedding_dim, input_size, device):
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self.embedding = torch.nn.Embedding(
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num_embeddings=num_embeddings, embedding_dim=embedding_dim
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).to(device=device)
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numpy.random.seed((1 << 32) - 1)
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input = torch.tensor(
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numpy.random.randint(0, num_embeddings, input_size), device=device
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).long()
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self.inputs = {"input": input}
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self.set_module_name("embedding")
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def forward(self, input):
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return self.embedding(input)
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op_bench.generate_pt_test(configs.embedding_short_configs, EmbeddingBenchmark)
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op_bench.generate_pt_gradient_test(configs.embedding_short_configs, EmbeddingBenchmark)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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