Benchmarks (#4912)

* finish benchmark

* fix isort

* fix setup cfg

* retab

* fix time measuring of tf graph mode

* fix tf cuda

* clean code

* better error message

tests/test_benchmark_tf.py

@@ -0,0 +1,165 @@
+import os
+import tempfile
+import unittest
+from pathlib import Path
+
+from transformers import AutoConfig, is_tf_available
+
+from .utils import require_tf
+
+
+if is_tf_available():
+    import tensorflow as tf
+    from transformers import TensorflowBenchmark, TensorflowBenchmarkArguments
+
+
+@require_tf
+class TFBenchmarkTest(unittest.TestCase):
+    def check_results_dict_not_empty(self, results):
+        for model_result in results.values():
+            for batch_size, sequence_length in zip(model_result["bs"], model_result["ss"]):
+                result = model_result["result"][batch_size][sequence_length]
+                self.assertIsNotNone(result)
+
+    def test_inference_no_configs_eager(self):
+        MODEL_ID = "sshleifer/tiny-gpt2"
+        benchmark_args = TensorflowBenchmarkArguments(
+            models=[MODEL_ID],
+            training=False,
+            no_inference=False,
+            sequence_lengths=[8],
+            batch_sizes=[1],
+            eager_mode=True,
+            no_multi_process=True,
+        )
+        benchmark = TensorflowBenchmark(benchmark_args)
+        results = benchmark.run()
+        self.check_results_dict_not_empty(results.time_inference_result)
+        self.check_results_dict_not_empty(results.memory_inference_result)
+
+    def test_inference_no_configs_graph(self):
+        MODEL_ID = "sshleifer/tiny-gpt2"
+        benchmark_args = TensorflowBenchmarkArguments(
+            models=[MODEL_ID],
+            training=False,
+            no_inference=False,
+            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_inference_result)
+        self.check_results_dict_not_empty(results.memory_inference_result)
+
+    def test_inference_with_configs_eager(self):
+        MODEL_ID = "sshleifer/tiny-gpt2"
+        config = AutoConfig.from_pretrained(MODEL_ID)
+        benchmark_args = TensorflowBenchmarkArguments(
+            models=[MODEL_ID],
+            training=False,
+            no_inference=False,
+            sequence_lengths=[8],
+            batch_sizes=[1],
+            eager_mode=True,
+            no_multi_process=True,
+        )
+        benchmark = TensorflowBenchmark(benchmark_args, [config])
+        results = benchmark.run()
+        self.check_results_dict_not_empty(results.time_inference_result)
+        self.check_results_dict_not_empty(results.memory_inference_result)
+
+    def test_inference_with_configs_graph(self):
+        MODEL_ID = "sshleifer/tiny-gpt2"
+        config = AutoConfig.from_pretrained(MODEL_ID)
+        benchmark_args = TensorflowBenchmarkArguments(
+            models=[MODEL_ID],
+            training=False,
+            no_inference=False,
+            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_inference_result)
+        self.check_results_dict_not_empty(results.memory_inference_result)
+
+    def test_inference_encoder_decoder_with_configs(self):
+        MODEL_ID = "patrickvonplaten/t5-tiny-random"
+        config = AutoConfig.from_pretrained(MODEL_ID)
+        benchmark_args = TensorflowBenchmarkArguments(
+            models=[MODEL_ID],
+            training=False,
+            no_inference=False,
+            sequence_lengths=[8],
+            batch_sizes=[1],
+            no_multi_process=True,
+        )
+        benchmark = TensorflowBenchmark(benchmark_args, configs=[config])
+        results = benchmark.run()
+        self.check_results_dict_not_empty(results.time_inference_result)
+        self.check_results_dict_not_empty(results.memory_inference_result)
+
+    @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU")) == 0, "Cannot do xla on CPU.")
+    def test_inference_no_configs_xla(self):
+        MODEL_ID = "sshleifer/tiny-gpt2"
+        benchmark_args = TensorflowBenchmarkArguments(
+            models=[MODEL_ID],
+            training=False,
+            no_inference=False,
+            sequence_lengths=[8],
+            batch_sizes=[1],
+            use_xla=True,
+            no_multi_process=True,
+        )
+        benchmark = TensorflowBenchmark(benchmark_args)
+        results = benchmark.run()
+        self.check_results_dict_not_empty(results.time_inference_result)
+        self.check_results_dict_not_empty(results.memory_inference_result)
+
+    def test_save_csv_files(self):
+        MODEL_ID = "sshleifer/tiny-gpt2"
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            benchmark_args = TensorflowBenchmarkArguments(
+                models=[MODEL_ID],
+                no_inference=False,
+                save_to_csv=True,
+                sequence_lengths=[8],
+                batch_sizes=[1],
+                inference_time_csv_file=os.path.join(tmp_dir, "inf_time.csv"),
+                inference_memory_csv_file=os.path.join(tmp_dir, "inf_mem.csv"),
+                env_info_csv_file=os.path.join(tmp_dir, "env.csv"),
+                no_multi_process=True,
+            )
+            benchmark = TensorflowBenchmark(benchmark_args)
+            benchmark.run()
+            self.assertTrue(Path(os.path.join(tmp_dir, "inf_time.csv")).exists())
+            self.assertTrue(Path(os.path.join(tmp_dir, "inf_mem.csv")).exists())
+            self.assertTrue(Path(os.path.join(tmp_dir, "env.csv")).exists())
+
+    def test_trace_memory(self):
+        MODEL_ID = "sshleifer/tiny-gpt2"
+
+        def _check_summary_is_not_empty(summary):
+            self.assertTrue(hasattr(summary, "sequential"))
+            self.assertTrue(hasattr(summary, "cumulative"))
+            self.assertTrue(hasattr(summary, "current"))
+            self.assertTrue(hasattr(summary, "total"))
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            benchmark_args = TensorflowBenchmarkArguments(
+                models=[MODEL_ID],
+                no_inference=False,
+                sequence_lengths=[8],
+                batch_sizes=[1],
+                log_filename=os.path.join(tmp_dir, "log.txt"),
+                log_print=True,
+                trace_memory_line_by_line=True,
+                eager_mode=True,
+                no_multi_process=True,
+            )
+            benchmark = TensorflowBenchmark(benchmark_args)
+            result = benchmark.run()
+            _check_summary_is_not_empty(result.inference_summary)
+            self.assertTrue(Path(os.path.join(tmp_dir, "log.txt")).exists())