[Benchmark] Add benchmarks for TF Training (huggingface#5594)

* tf_train

* adapt timing for tpu

* fix timing

* fix timing

* fix timing

* fix timing

* update notebook

* add tests

notebooks/05-benchmark.ipynb

@@ -312,16 +312,16 @@
         ":-- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n",
         "**Speed - Inference** | ✔ | ✔ | ✔ | ✔ | ✔ | ✘ | ✔ |\n",
         "**Memory - Inference** | ✔ | ✔ | ✔ | ✔ | ✔ | ✘ | ✘ |\n",
-        "**Speed - Train** | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ |\n",
-        "**Memory - Train** | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ |\n",
+        "**Speed - Train** | ✔ | ✘ | ✔ | ✘ | ✘ | ✘ | ✔ |\n",
+        "**Memory - Train** | ✔ | ✘ | ✔ | ✘ | ✘ | ✘ | ✘ |\n",
         "\n",
         "*   *eager execution* means that the function is run in the eager execution environment of TensorFlow 2, see [here](https://www.tensorflow.org/guide/eager).\n",
         "\n",
         "*   *XLA* stands for TensorFlow's Accelerated Linear Algebra (XLA) compiler, see [here](https://www.tensorflow.org/xla)\n",
         "\n",
         "*   *FP16* stands for TensorFlow's mixed-precision package and is analogous to PyTorch's FP16 feature, see [here](https://www.tensorflow.org/guide/mixed_precision).\n",
         "\n",
-        "***Note***: In ~1,2 weeks it will also be possible to benchmark training in TensorFlow.\n",
+        "***Note***: Benchmark training in TensorFlow is not included in v3.0.2, but available in master.\n",
         "\n",
         "\n",
         "This notebook will show the user how to use `PyTorchBenchmark` and `TensorFlowBenchmark` for two different scenarios:\n",

src/transformers/benchmark/benchmark.py

@@ -157,7 +157,7 @@ def _prepare_train_func(self, model_name: str, batch_size: int, sequence_length:
         else:
             train_model = model
 
-        model.eval()
+        model.train()
         model.to(self.args.device)
 
         # encoder-decoder has vocab size saved differently
@@ -175,12 +175,12 @@ def _prepare_train_func(self, model_name: str, batch_size: int, sequence_length:
         def compute_loss_and_backprob_encoder():
             loss = train_model(input_ids, labels=input_ids)[0]
             loss.backward()
-            train_model.zero_grad()
+            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()
-            train_model.zero_grad()
+            return loss
 
         _train = (
             compute_loss_and_backprob_encoder_decoder

src/transformers/benchmark/benchmark_tf.py

@@ -24,7 +24,13 @@
 from functools import wraps
 from typing import Callable, Optional
 
-from transformers import TF_MODEL_MAPPING, PretrainedConfig, is_py3nvml_available, is_tf_available
+from transformers import (
+    TF_MODEL_MAPPING,
+    TF_MODEL_WITH_LM_HEAD_MAPPING,
+    PretrainedConfig,
+    is_py3nvml_available,
+    is_tf_available,
+)
 
 from .benchmark_utils import (
     Benchmark,
@@ -92,10 +98,11 @@ def _inference_speed(self, model_name: str, batch_size: int, sequence_length: in
         _inference = self._prepare_inference_func(model_name, batch_size, sequence_length)
         return self._measure_speed(_inference)
 
-    def _train_speed(self, model_name, batch_size, sequence_length):
-        raise NotImplementedError(
-            "Training is currently not really implemented." "Wait for TFTrainer to support CLM and MLM."
-        )
+    def _train_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
+        strategy = self.args.strategy
+        assert strategy is not None, "A device strategy has to be initialized before using TensorFlow."
+        _train = self._prepare_train_func(model_name, batch_size, sequence_length)
+        return self._measure_speed(_train)
 
     def _inference_memory(
         self, model_name: str, batch_size: int, sequence_length: int
@@ -108,10 +115,16 @@ def _inference_memory(
         _inference = self._prepare_inference_func(model_name, batch_size, sequence_length)
         return self._measure_memory(_inference)
 
-    def _train_memory(self, model_name, batch_size, sequence_length):
-        raise NotImplementedError(
-            "Training is currently not really implemented. Wait for TFTrainer to support CLM and MLM."
-        )
+    def _train_memory(
+        self, model_name: str, batch_size: int, sequence_length: int
+    ) -> [Memory, Optional[MemorySummary]]:
+        if self.args.is_gpu:
+            tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx], True)
+        strategy = self.args.strategy
+        assert strategy is not None, "A device strategy has to be initialized before using TensorFlow."
+
+        _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]
@@ -149,6 +162,50 @@ def encoder_forward():
 
         return _inference
 
+    def _prepare_train_func(self, model_name: str, batch_size: int, sequence_length: int) -> Callable[[], None]:
+        config = self.config_dict[model_name]
+
+        assert (
+            self.args.eager_mode is False
+        ), "Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."
+
+        if self.args.fp16:
+            raise NotImplementedError("Mixed precision is currently not supported.")
+
+        has_model_class_in_config = hasattr(config, "architecture") and len(config.architectures) > 1
+        if not self.args.only_pretrain_model and has_model_class_in_config:
+            try:
+                model_class = "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
+                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 = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](config)
+
+        # encoder-decoder has vocab size saved differently
+        vocab_size = config.vocab_size if hasattr(config, "vocab_size") else config.encoder.vocab_size
+        input_ids = random_input_ids(batch_size, sequence_length, vocab_size)
+
+        @run_with_tf_optimizations(self.args.eager_mode, self.args.use_xla)
+        def encoder_decoder_train():
+            loss = model(input_ids, decoder_input_ids=input_ids, labels=input_ids, training=True)[0]
+            gradients = tf.gradients(loss, model.trainable_variables)
+            return gradients
+
+        @run_with_tf_optimizations(self.args.eager_mode, self.args.use_xla)
+        def encoder_train():
+            loss = model(input_ids, labels=input_ids, training=True)[0]
+            gradients = tf.gradients(loss, model.trainable_variables)
+            return gradients
+
+        _train = encoder_decoder_train if config.is_encoder_decoder else encoder_train
+
+        return _train
+
     def _measure_speed(self, func) -> float:
         with self.args.strategy.scope():
             try:

tests/test_benchmark_tf.py

@@ -100,6 +100,37 @@ def test_inference_with_configs_graph(self):
         self.check_results_dict_not_empty(results.time_inference_result)
         self.check_results_dict_not_empty(results.memory_inference_result)
 
+    def test_train_no_configs(self):
+        MODEL_ID = "sshleifer/tiny-gpt2"
+        benchmark_args = TensorFlowBenchmarkArguments(
+            models=[MODEL_ID],
+            training=True,
+            no_inference=True,
+            sequence_lengths=[8],
+            batch_sizes=[1],
+            no_multi_process=True,
+        )
+        benchmark = TensorFlowBenchmark(benchmark_args)
+        results = benchmark.run()
+        self.check_results_dict_not_empty(results.time_train_result)
+        self.check_results_dict_not_empty(results.memory_train_result)
+
+    def test_train_with_configs(self):
+        MODEL_ID = "sshleifer/tiny-gpt2"
+        config = AutoConfig.from_pretrained(MODEL_ID)
+        benchmark_args = TensorFlowBenchmarkArguments(
+            models=[MODEL_ID],
+            training=True,
+            no_inference=True,
+            sequence_lengths=[8],
+            batch_sizes=[1],
+            no_multi_process=True,
+        )
+        benchmark = TensorFlowBenchmark(benchmark_args, [config])
+        results = benchmark.run()
+        self.check_results_dict_not_empty(results.time_train_result)
+        self.check_results_dict_not_empty(results.memory_train_result)
+
     def test_inference_encoder_decoder_with_configs(self):
         MODEL_ID = "patrickvonplaten/t5-tiny-random"
         config = AutoConfig.from_pretrained(MODEL_ID)