Benchmarks: Add Benchmark - Add GPT2 model benchmark. (#57)

* Benchmarks: Add Benchmark - Add GPT2 model benchmark.

examples/benchmarks/pytorch_gpt2_large.py

@@ -0,0 +1,41 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+"""Model benchmark example for gpt2-large (36-layer, 1280-hidden, 20-heads, 774M parameters).
+
+Commands to run:
+  python3 examples/benchmarks/pytorch_gpt2_large.py (Single GPU)
+  python3 -m torch.distributed.launch --use_env --nproc_per_node=8 examples/benchmarks/pytorch_gpt2_large.py \
+      --distributed (Distributed)
+"""
+
+import argparse
+
+from superbench.benchmarks import Platform, Framework, BenchmarkRegistry
+from superbench.common.utils import logger
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--distributed', action='store_true', default=False, help='Whether to enable distributed training.'
+    )
+    args = parser.parse_args()
+
+    # Specify the model name and benchmark parameters.
+    model_name = 'gpt2-large'
+    parameters = '--batch_size 1 --duration 120 --seq_len 128 --precision float32 --run_count 2'
+    if args.distributed:
+        parameters += ' --distributed_impl ddp --distributed_backend nccl'
+
+    # Create context for gpt2-large benchmark and run it for 120 * 2 seconds.
+    context = BenchmarkRegistry.create_benchmark_context(
+        model_name, platform=Platform.CUDA, parameters=parameters, framework=Framework.PYTORCH
+    )
+
+    benchmark = BenchmarkRegistry.launch_benchmark(context)
+    if benchmark:
+        logger.info(
+            'benchmark: {}, return code: {}, result: {}'.format(
+                benchmark.name, benchmark.return_code, benchmark.result
+            )
+        )

superbench/benchmarks/model_benchmarks/__init__.py

@@ -5,5 +5,6 @@
 
 from superbench.benchmarks.model_benchmarks.model_base import ModelBenchmark
 from superbench.benchmarks.model_benchmarks.pytorch_bert import PytorchBERT
+from superbench.benchmarks.model_benchmarks.pytorch_gpt2 import PytorchGPT2
 
-__all__ = ['ModelBenchmark', 'PytorchBERT']
+__all__ = ['ModelBenchmark', 'PytorchBERT', 'PytorchGPT2']

superbench/benchmarks/model_benchmarks/pytorch_gpt2.py

@@ -0,0 +1,205 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+"""Module of the Pytorch GPT2 model."""
+
+import time
+
+import torch
+from transformers import GPT2Model, GPT2Config
+
+from superbench.common.utils import logger
+from superbench.benchmarks import BenchmarkRegistry, Precision
+from superbench.benchmarks.model_benchmarks.model_base import Optimizer
+from superbench.benchmarks.model_benchmarks.pytorch_base import PytorchBase
+from superbench.benchmarks.model_benchmarks.random_dataset import TorchRandomDataset
+
+
+class GPT2BenchmarkModel(torch.nn.Module):
+    """The GPT2 model for benchmarking."""
+    def __init__(self, config, num_class):
+        """Constructor.
+
+        Args:
+            config (GPT2Config): Configurations of GPT2 model.
+            num_class (int): The number of objects for classification.
+        """
+        super().__init__()
+        self._bert = GPT2Model(config)
+        self._linear = torch.nn.Linear(config.hidden_size, num_class)
+
+    def forward(self, input):
+        """Forward propagation function.
+
+        Args:
+            input (torch.LongTensor): Indices of input sequence tokens in the vocabulary,
+              shape (batch_size, sequence_length).
+
+        Return:
+            result (torch.FloatTensor): Last layer hidden-state of the first token of the sequence
+              (classification token) further processed by a Linear layer, shape (batch_size, hidden_size).
+        """
+        outputs = self._bert(input)
+        result = self._linear(outputs[0])
+        return result
+
+
+class PytorchGPT2(PytorchBase):
+    """The GPT2 benchmark class."""
+    def __init__(self, name, parameters=''):
+        """Constructor.
+
+        Args:
+            name (str): benchmark name.
+            parameters (str): benchmark parameters.
+        """
+        super().__init__(name, parameters)
+        self._config = None
+        self._supported_precision = [Precision.FLOAT32, Precision.FLOAT16]
+        self._optimizer_type = Optimizer.ADAMW
+        self._loss_fn = torch.nn.CrossEntropyLoss()
+
+    def add_parser_arguments(self):
+        """Add the GPT2-specified arguments.
+
+        GPT2 model reference: https://huggingface.co/transformers/model_doc/gpt2.html
+        """
+        super().add_parser_arguments()
+
+        self._parser.add_argument('--num_classes', type=int, default=100, required=False, help='Num of class.')
+        self._parser.add_argument('--hidden_size', type=int, default=1280, required=False, help='Hidden size.')
+        self._parser.add_argument(
+            '--num_hidden_layers', type=int, default=36, required=False, help='The number of hidden layers.'
+        )
+        self._parser.add_argument(
+            '--num_attention_heads', type=int, default=20, required=False, help='The number of attention heads.'
+        )
+        self._parser.add_argument('--seq_len', type=int, default=512, required=False, help='Sequence length.')
+
+    def _generate_dataset(self):
+        """Generate dataset for benchmarking according to shape info.
+
+        Return:
+            True if dataset is created successfully.
+        """
+        self._dataset = TorchRandomDataset(
+            [self._args.sample_count, self._args.seq_len], self._world_size, dtype=torch.long
+        )
+        if len(self._dataset) == 0:
+            logger.error('Generate random dataset failed - model: {}'.format(self._name))
+            return False
+
+        return True
+
+    def _create_model(self, precision):
+        """Construct the model for benchmarking.
+
+        Args:
+            precision (Precision): precision of model and input data, such as float32, float16.
+        """
+        self._config = GPT2Config(
+            n_embd=self._args.hidden_size, n_layer=self._args.num_hidden_layers, n_head=self._args.num_attention_heads
+        )
+
+        try:
+            self._model = GPT2BenchmarkModel(self._config, self._args.num_classes)
+            self._model = self._model.to(dtype=getattr(torch, precision.value))
+            if self._gpu_available:
+                self._model = self._model.cuda()
+        except BaseException as e:
+            logger.error(
+                'Create model with specified precision failed - model: {}, precision: {}, message: {}.'.format(
+                    self._name, precision, str(e)
+                )
+            )
+            return False
+
+        self._target = torch.LongTensor(self._args.batch_size).random_(self._args.num_classes)
+        if self._gpu_available:
+            self._target = self._target.cuda()
+
+        return True
+
+    def _train_step(self, precision):
+        """Define the training process.
+
+        Args:
+            precision (Precision): precision of model and input data, such as float32, float16.
+
+        Return:
+            The step-time list of every training step.
+        """
+        duration = []
+        curr_step = 0
+        while True:
+            for idx, sample in enumerate(self._dataloader):
+                start = time.time()
+                if self._gpu_available:
+                    sample = sample.cuda()
+                self._optimizer.zero_grad()
+                output = self._model(sample)
+                loss = self._loss_fn(output[range(self._args.batch_size), -1], self._target)
+                loss.backward()
+                self._optimizer.step()
+                end = time.time()
+                curr_step += 1
+                if curr_step > self._args.num_warmup:
+                    # Save the step time of every training/inference step, unit is millisecond.
+                    duration.append((end - start) * 1000)
+                if self._is_finished(curr_step, end):
+                    return duration
+
+    def _inference_step(self, precision):
+        """Define the inference process.
+
+        Args:
+            precision (Precision): precision of model and input data,
+              such as float32, float16.
+
+        Return:
+            The latency list of every inference operation.
+        """
+        duration = []
+        curr_step = 0
+        with torch.no_grad():
+            self._model.eval()
+            while True:
+                for idx, sample in enumerate(self._dataloader):
+                    start = time.time()
+                    if self._gpu_available:
+                        sample = sample.cuda()
+                    self._model(sample)
+                    if self._gpu_available:
+                        torch.cuda.synchronize()
+                    end = time.time()
+                    curr_step += 1
+                    if curr_step > self._args.num_warmup:
+                        # Save the step time of every training/inference step, unit is millisecond.
+                        duration.append((end - start) * 1000)
+                    if self._is_finished(curr_step, end):
+                        return duration
+
+
+# Register GPT2 benchmark with 117M parameters.
+# Reference: https://huggingface.co/transformers/pretrained_models.html
+BenchmarkRegistry.register_benchmark(
+    'pytorch-gpt2-small', PytorchGPT2, parameters='--hidden_size=768 --num_hidden_layers=12 --num_attention_heads=12'
+)
+
+# Register GPT2 benchmark with 345M parameters.
+# Reference: https://huggingface.co/transformers/pretrained_models.html
+BenchmarkRegistry.register_benchmark(
+    'pytorch-gpt2-medium', PytorchGPT2, parameters='--hidden_size=1024 --num_hidden_layers=24 --num_attention_heads=16'
+)
+
+# Register GPT2 benchmark with 774M parameters.
+# Reference: https://huggingface.co/transformers/pretrained_models.html
+BenchmarkRegistry.register_benchmark(
+    'pytorch-gpt2-large', PytorchGPT2, parameters='--hidden_size=1280 --num_hidden_layers=36 --num_attention_heads=20'
+)
+
+# Register GPT2 benchmark with 1558M parameters.
+# Reference: https://huggingface.co/transformers/pretrained_models.html
+BenchmarkRegistry.register_benchmark(
+    'pytorch-gpt2-xl', PytorchGPT2, parameters='--hidden_size=1600 --num_hidden_layers=48 --num_attention_heads=25'
+)

tests/benchmarks/model_benchmarks/test_pytorch_gpt2.py

@@ -0,0 +1,58 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Tests for GPT2 model benchmarks."""
+
+from tests.helper import decorator
+from superbench.benchmarks import BenchmarkRegistry, Platform, Framework, BenchmarkType, ReturnCode
+from superbench.benchmarks.model_benchmarks.pytorch_gpt2 import PytorchGPT2
+
+
+@decorator.cuda_test
+@decorator.pytorch_test
+def test_pytorch_gpt2_small():
+    """Test pytorch-gpt2-small benchmark."""
+    context = BenchmarkRegistry.create_benchmark_context(
+        'gpt2-small',
+        platform=Platform.CUDA,
+        parameters='--batch_size 1 --num_classes 5 --seq_len 8 --num_warmup 2 --num_steps 4 \
+            --model_action train inference',
+        framework=Framework.PYTORCH
+    )
+
+    assert (BenchmarkRegistry.is_benchmark_context_valid(context))
+
+    benchmark = BenchmarkRegistry.launch_benchmark(context)
+
+    # Check basic information.
+    assert (benchmark)
+    assert (isinstance(benchmark, PytorchGPT2))
+    assert (benchmark.name == 'pytorch-gpt2-small')
+    assert (benchmark.type == BenchmarkType.MODEL)
+
+    # Check predefined parameters of gpt2-large model.
+    assert (benchmark._args.hidden_size == 768)
+    assert (benchmark._args.num_hidden_layers == 12)
+    assert (benchmark._args.num_attention_heads == 12)
+
+    # Check parameters specified in BenchmarkContext.
+    assert (benchmark._args.batch_size == 1)
+    assert (benchmark._args.num_classes == 5)
+    assert (benchmark._args.seq_len == 8)
+    assert (benchmark._args.num_warmup == 2)
+    assert (benchmark._args.num_steps == 4)
+
+    # Test Dataset.
+    assert (len(benchmark._dataset) == benchmark._args.sample_count * benchmark._world_size)
+
+    # Check results and metrics.
+    assert (benchmark.run_count == 1)
+    assert (benchmark.return_code == ReturnCode.SUCCESS)
+    for metric in [
+        'steptime_train_float32', 'throughput_train_float32', 'steptime_train_float16', 'throughput_train_float16',
+        'steptime_inference_float32', 'throughput_inference_float32', 'steptime_inference_float16',
+        'throughput_inference_float16'
+    ]:
+        assert (len(benchmark.raw_data[metric]) == benchmark.run_count)
+        assert (len(benchmark.raw_data[metric][0]) == benchmark._args.num_steps)
+        assert (len(benchmark.result[metric]) == benchmark.run_count)