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c0ed38e644
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129754 Approved by: https://github.com/ezyang
96 lines
2.7 KiB
Python
96 lines
2.7 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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import torch.ao.nn.qat as nnqat
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from torch.ao.quantization import default_embedding_qat_qconfig
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"""
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Microbenchmarks for QAT Embedding + EmbeddingBag operators.
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"""
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class QATEmbeddingBagBenchmark(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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qconfig = default_embedding_qat_qconfig
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self.embedding = nnqat.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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device=device,
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qconfig=qconfig,
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)
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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("qatEmbeddingBag")
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def forward(self, input, offset):
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return self.embedding(input, offset)
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# Currently, EmbeddingBag QAT does not support sparse embeddings.
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embeddingbag_short_dense_configs = [
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config
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for config in configs.embeddingbag_short_configs
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if {"sparse": True} not in config
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]
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op_bench.generate_pt_test(embeddingbag_short_dense_configs, QATEmbeddingBagBenchmark)
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op_bench.generate_pt_gradient_test(
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embeddingbag_short_dense_configs, QATEmbeddingBagBenchmark
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)
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class QATEmbeddingBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, num_embeddings, embedding_dim, input_size, device):
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qconfig = default_embedding_qat_qconfig
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self.embedding = nnqat.Embedding(
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num_embeddings=num_embeddings,
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embedding_dim=embedding_dim,
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qconfig=qconfig,
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device=device,
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)
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self.embedding.qconfig = default_embedding_qat_qconfig
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numpy.random.seed((1 << 32) - 1)
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self.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": self.input}
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self.set_module_name("qatEmbedding")
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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, QATEmbeddingBenchmark)
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op_bench.generate_pt_gradient_test(
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configs.embedding_short_configs, QATEmbeddingBenchmark
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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