codingutf8 2018 the huggingface inc team c 2018 nvidia corporation 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 orglicenseslicense2 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 encoderdecoder has vocab size saved differently amp seems to have memory leaks so that memory usage is measured using half for now https github comnvidiaapexissues439 encoderdecoder has vocab size saved differently amp seems to have memory leaks so that memory usage is measured using half for now https github comnvidiaapexissues439 run additional 10 times to stabilize compilation for tpu and torchscript as written in https docs python org2librarytimeit htmltimeit timer repeat min should be taken rather than the average tpu init nvml shutdown nvml cpu coding utf 8 2018 the huggingface inc team c 2018 nvidia corporation 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 encoder decoder has vocab size saved differently amp seems to have memory leaks so that memory usage is measured using half for now https github com nvidia apex issues 439 encoder decoder has vocab size saved differently amp seems to have memory leaks so that memory usage is measured using half for now https github com nvidia apex issues 439 run additional 10 times to stabilize compilation for tpu and torchscript as written in https docs python org 2 library timeit html timeit timer repeat min should be taken rather than the average tpu init nvml shutdown nvml cpu
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"

    @property
    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)

        
        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.")
            
            
            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)

        
        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.")

            
            
            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:
                
                logger.info("Do inference on TPU or torchscript. Running model 5 times to stabilize compilation")
                timeit.repeat(
                    func,
                    repeat=1,
                    number=5,
                )

            
            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:
                
                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."
                    )
                    
                    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)
                    
                    nvml.nvmlShutdown()
            else:
                
                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