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| # coding=utf-8 | |
| # Copyright 2018 The HuggingFace Inc. team. | |
| # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ | |
| Benchmarking the library on inference and training in PyTorch. | |
| """ | |
| import timeit | |
| from typing import Callable, Optional | |
| from ..configuration_utils import PretrainedConfig | |
| from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING | |
| from ..utils import is_py3nvml_available, is_torch_available, logging | |
| from .benchmark_utils import ( | |
| Benchmark, | |
| Memory, | |
| MemorySummary, | |
| measure_peak_memory_cpu, | |
| start_memory_tracing, | |
| stop_memory_tracing, | |
| ) | |
| if is_torch_available(): | |
| import torch | |
| from .benchmark_args import PyTorchBenchmarkArguments | |
| if is_py3nvml_available(): | |
| import py3nvml.py3nvml as nvml | |
| logger = logging.get_logger(__name__) | |
| class PyTorchBenchmark(Benchmark): | |
| args: PyTorchBenchmarkArguments | |
| configs: PretrainedConfig | |
| framework: str = "PyTorch" | |
| def framework_version(self): | |
| return torch.__version__ | |
| def _inference_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float: | |
| _inference = self._prepare_inference_func(model_name, batch_size, sequence_length) | |
| return self._measure_speed(_inference) | |
| def _inference_memory( | |
| self, model_name: str, batch_size: int, sequence_length: int | |
| ) -> [Memory, Optional[MemorySummary]]: | |
| _inference = self._prepare_inference_func(model_name, batch_size, sequence_length) | |
| return self._measure_memory(_inference) | |
| def _train_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float: | |
| _train = self._prepare_train_func(model_name, batch_size, sequence_length) | |
| return self._measure_speed(_train) | |
| def _train_memory( | |
| self, model_name: str, batch_size: int, sequence_length: int | |
| ) -> [Memory, Optional[MemorySummary]]: | |
| _train = self._prepare_train_func(model_name, batch_size, sequence_length) | |
| return self._measure_memory(_train) | |
| def _prepare_inference_func(self, model_name: str, batch_size: int, sequence_length: int) -> Callable[[], None]: | |
| config = self.config_dict[model_name] | |
| if self.args.torchscript: | |
| config.torchscript = True | |
| has_model_class_in_config = ( | |
| hasattr(config, "architectures") | |
| and isinstance(config.architectures, list) | |
| and len(config.architectures) > 0 | |
| ) | |
| if not self.args.only_pretrain_model and has_model_class_in_config: | |
| try: | |
| model_class = config.architectures[0] | |
| transformers_module = __import__("transformers", fromlist=[model_class]) | |
| model_cls = getattr(transformers_module, model_class) | |
| model = model_cls(config) | |
| except ImportError: | |
| raise ImportError( | |
| f"{model_class} does not exist. If you just want to test the pretrained model, you might want to" | |
| " set `--only_pretrain_model` or `args.only_pretrain_model=True`." | |
| ) | |
| else: | |
| model = MODEL_MAPPING[config.__class__](config) | |
| model.eval() | |
| model.to(self.args.device) | |
| # encoder-decoder has vocab size saved differently | |
| vocab_size = config.vocab_size if hasattr(config, "vocab_size") else config.encoder.vocab_size | |
| input_ids = torch.randint(vocab_size, (batch_size, sequence_length), dtype=torch.long, device=self.args.device) | |
| if self.args.fp16: | |
| logger.info("Running training in Mixed Precision...") | |
| if not self.args.is_gpu: | |
| raise ValueError("Mixed precision is possible only for GPU.") | |
| # amp seems to have memory leaks so that memory usage | |
| # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439 | |
| model.half() | |
| if self.args.torchscript: | |
| with torch.no_grad(): | |
| inference_model = torch.jit.trace(model, input_ids) | |
| else: | |
| inference_model = model | |
| def encoder_decoder_forward(): | |
| with torch.no_grad(): | |
| outputs = inference_model(input_ids, decoder_input_ids=input_ids) | |
| return outputs | |
| def encoder_forward(): | |
| with torch.no_grad(): | |
| outputs = inference_model(input_ids) | |
| return outputs | |
| _forward = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward | |
| return _forward | |
| def _prepare_train_func(self, model_name: str, batch_size: int, sequence_length: int) -> Callable[[], None]: | |
| config = self.config_dict[model_name] | |
| has_model_class_in_config = ( | |
| hasattr(config, "architectures") | |
| and isinstance(config.architectures, list) | |
| and len(config.architectures) > 0 | |
| ) | |
| if not self.args.only_pretrain_model and has_model_class_in_config: | |
| try: | |
| model_class = config.architectures[0] | |
| transformers_module = __import__("transformers", fromlist=[model_class]) | |
| model_cls = getattr(transformers_module, model_class) | |
| model = model_cls(config) | |
| except ImportError: | |
| raise ImportError( | |
| f"{model_class} does not exist. If you just want to test the pretrained model, you might want to" | |
| " set `--only_pretrain_model` or `args.only_pretrain_model=True`." | |
| ) | |
| else: | |
| model = MODEL_WITH_LM_HEAD_MAPPING[config.__class__](config) | |
| if self.args.torchscript: | |
| raise NotImplementedError("Training for torchscript is currently not implemented") | |
| else: | |
| train_model = model | |
| model.train() | |
| model.to(self.args.device) | |
| # encoder-decoder has vocab size saved differently | |
| vocab_size = config.vocab_size if hasattr(config, "vocab_size") else config.encoder.vocab_size | |
| input_ids = torch.randint(vocab_size, (batch_size, sequence_length), dtype=torch.long, device=self.args.device) | |
| if self.args.fp16: | |
| logger.info("Running training in Mixed Precision...") | |
| if not self.args.is_gpu: | |
| raise ValueError("Mixed precision is possible only for GPU.") | |
| # amp seems to have memory leaks so that memory usage | |
| # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439 | |
| model.half() | |
| def compute_loss_and_backprob_encoder(): | |
| loss = train_model(input_ids, labels=input_ids)[0] | |
| loss.backward() | |
| return loss | |
| def compute_loss_and_backprob_encoder_decoder(): | |
| loss = train_model(input_ids, decoder_input_ids=input_ids, labels=input_ids)[0] | |
| loss.backward() | |
| return loss | |
| _train = ( | |
| compute_loss_and_backprob_encoder_decoder | |
| if config.is_encoder_decoder | |
| else compute_loss_and_backprob_encoder | |
| ) | |
| return _train | |
| def _measure_speed(self, func) -> float: | |
| try: | |
| if self.args.is_tpu or self.args.torchscript: | |
| # run additional 10 times to stabilize compilation for tpu and torchscript | |
| logger.info("Do inference on TPU or torchscript. Running model 5 times to stabilize compilation") | |
| timeit.repeat( | |
| func, | |
| repeat=1, | |
| number=5, | |
| ) | |
| # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average | |
| runtimes = timeit.repeat( | |
| func, | |
| repeat=self.args.repeat, | |
| number=10, | |
| ) | |
| if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics: | |
| import torch_xla.debug.metrics as met | |
| self.print_fn(met.metrics_report()) | |
| return min(runtimes) / 10.0 | |
| except RuntimeError as e: | |
| self.print_fn(f"Doesn't fit on GPU. {e}") | |
| return "N/A" | |
| def _measure_memory(self, func: Callable[[], None]) -> [Memory, MemorySummary]: | |
| try: | |
| if self.args.trace_memory_line_by_line: | |
| trace = start_memory_tracing("transformers") | |
| if self.args.is_tpu: | |
| # tpu | |
| raise NotImplementedError( | |
| "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with" | |
| " `--no-memory` or `args.memory=False`" | |
| ) | |
| elif self.args.is_gpu: | |
| if not is_py3nvml_available(): | |
| logger.warning( | |
| "py3nvml not installed, we won't log GPU memory usage. " | |
| "Install py3nvml (pip install py3nvml) to log information about GPU." | |
| ) | |
| memory = "N/A" | |
| else: | |
| logger.info( | |
| "Measuring total GPU usage on GPU device. Make sure to not have additional processes running" | |
| " on the same GPU." | |
| ) | |
| # init nvml | |
| nvml.nvmlInit() | |
| func() | |
| handle = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx) | |
| meminfo = nvml.nvmlDeviceGetMemoryInfo(handle) | |
| max_bytes_in_use = meminfo.used | |
| memory = Memory(max_bytes_in_use) | |
| # shutdown nvml | |
| nvml.nvmlShutdown() | |
| else: | |
| # cpu | |
| memory_bytes = measure_peak_memory_cpu(func) | |
| memory = Memory(memory_bytes) if isinstance(memory_bytes, int) else memory_bytes | |
| if self.args.trace_memory_line_by_line: | |
| summary = stop_memory_tracing(trace) | |
| else: | |
| summary = None | |
| return memory, summary | |
| except RuntimeError as e: | |
| self.print_fn(f"Doesn't fit on GPU. {e}") | |
| return "N/A", None | |