Add Flax example tests

.circleci/config.yml

@@ -613,6 +613,69 @@ jobs:
             - store_artifacts:
                   path: ~/transformers/reports
 
+    run_examples_flax:
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.7
+        environment:
+            OMP_NUM_THREADS: 1
+            TRANSFORMERS_IS_CI: yes
+        resource_class: xlarge
+        parallelism: 1
+        steps:
+            - checkout
+            - restore_cache:
+                keys:
+                    - v0.4-flax_examples-{{ checksum "setup.py" }}
+                    - v0.4-{{ checksum "setup.py" }}
+            - run: pip install --upgrade pip
+            - run: sudo pip install .[flax,testing,sentencepiece]
+            - run: pip install -r examples/flax/_tests_requirements.txt
+            - save_cache:
+                  key: v0.4-flax_examples-{{ checksum "setup.py" }}
+                  paths:
+                      - '~/.cache/pip'
+            - run: python utils/tests_fetcher.py --filters examples tests | tee test_preparation.txt
+            - store_artifacts:
+                  path: ~/transformers/test_preparation.txt
+            - run: |
+                  if [ -f test_list.txt ]; then
+                    python -m pytest -n 8 --dist=loadfile -s --make-reports=examples_flax ./examples/flax/ | tee tests_output.txt
+                  fi
+            - store_artifacts:
+                  path: ~/transformers/flax_examples_output.txt
+            - store_artifacts:
+                  path: ~/transformers/reports
+
+    run_examples_flax_all:
+        working_directory: ~/transformers
+        docker:
+            - image: circleci/python:3.7
+        environment:
+            OMP_NUM_THREADS: 1
+            TRANSFORMERS_IS_CI: yes
+        resource_class: xlarge
+        parallelism: 1
+        steps:
+            - checkout
+            - restore_cache:
+                keys:
+                    - v0.4-flax_examples-{{ checksum "setup.py" }}
+                    - v0.4-{{ checksum "setup.py" }}
+            - run: pip install --upgrade pip
+            - run: sudo pip install .[flax,testing,sentencepiece]
+            - run: pip install -r examples/flax/_tests_requirements.txt
+            - save_cache:
+                  key: v0.4-flax_examples-{{ checksum "setup.py" }}
+                  paths:
+                      - '~/.cache/pip'
+            - run: |
+                  TRANSFORMERS_IS_CI=1 python -m pytest -n 8 --dist=loadfile -s --make-reports=examples_flax ./examples/flax/ | tee examples_output.txt
+            - store_artifacts:
+                  path: ~/transformers/flax_examples_output.txt
+            - store_artifacts:
+                  path: ~/transformers/reports
+
     run_tests_hub:
         working_directory: ~/transformers
         docker:

examples/flax/_tests_requirements.txt

@@ -0,0 +1,7 @@
+datasets >= 1.1.3
+pytest
+conllu
+nltk
+rouge-score
+seqeval
+tensorboard

examples/flax/language-modeling/run_clm_flax.py

@@ -21,6 +21,7 @@
 """
 # You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
 
+import json
 import logging
 import math
 import os
@@ -672,6 +673,32 @@ def eval_step(params, batch):
                     if training_args.push_to_hub:
                         repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
 
+    # Eval after training
+    if training_args.do_eval:
+        eval_metrics = []
+        eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
+        eval_steps = len(eval_dataset) // eval_batch_size
+        for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
+            # Model forward
+            batch = shard(next(eval_loader))
+            metrics = p_eval_step(state.params, batch)
+            eval_metrics.append(metrics)
+
+        # normalize eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+        eval_metrics = jax.tree_map(lambda x: jnp.mean(x).item(), eval_metrics)
+
+        try:
+            eval_metrics["perplexity"] = math.exp(eval_metrics["loss"])
+        except OverflowError:
+            eval_metrics["perplexity"] = float("inf")
+
+        if jax.process_index() == 0:
+            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+            path = os.path.join(training_args.output_dir, "eval_results.json")
+            with open(path, "w") as f:
+                json.dump(eval_metrics, f, indent=4, sort_keys=True)
+
 
 if __name__ == "__main__":
     main()

examples/flax/language-modeling/run_mlm_flax.py

@@ -20,7 +20,9 @@
 Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
 https://huggingface.co/models?filter=masked-lm
 """
+import json
 import logging
+import math
 import os
 import sys
 import time
@@ -271,7 +273,7 @@ def write_eval_metric(summary_writer, eval_metrics, step):
         summary_writer.scalar(f"eval_{metric_name}", value, step)
 
 
-if __name__ == "__main__":
+def main():
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.
@@ -700,3 +702,41 @@ def eval_step(params, batch):
                     tokenizer.save_pretrained(training_args.output_dir)
                     if training_args.push_to_hub:
                         repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
+
+    # Eval after training
+    if training_args.do_eval:
+        num_eval_samples = len(tokenized_datasets["validation"])
+        eval_samples_idx = jnp.arange(num_eval_samples)
+        eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
+
+        eval_metrics = []
+        for _, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
+            samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
+            model_inputs = data_collator(samples, pad_to_multiple_of=16)
+
+            # Model forward
+            model_inputs = shard(model_inputs.data)
+            metrics = p_eval_step(state.params, model_inputs)
+            eval_metrics.append(metrics)
+
+        # normalize eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+        eval_metrics = jax.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics)
+        eval_normalizer = eval_metrics.pop("normalizer")
+        eval_metrics = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics)
+
+        try:
+            perplexity = math.exp(eval_metrics["loss"])
+        except OverflowError:
+            perplexity = float("inf")
+        eval_metrics["perplexity"] = perplexity
+
+        if jax.process_index() == 0:
+            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+            path = os.path.join(training_args.output_dir, "eval_results.json")
+            with open(path, "w") as f:
+                json.dump(eval_metrics, f, indent=4, sort_keys=True)
+
+
+if __name__ == "__main__":
+    main()

examples/flax/language-modeling/run_t5_mlm_flax.py

@@ -20,6 +20,7 @@
 https://huggingface.co/models?filter=t5
 """
 # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
+import json
 import logging
 import os
 import sys
@@ -401,7 +402,7 @@ def write_eval_metric(summary_writer, eval_metrics, step):
         summary_writer.scalar(f"eval_{metric_name}", value, step)
 
 
-if __name__ == "__main__":
+def main():
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.
@@ -522,9 +523,7 @@ def write_eval_metric(summary_writer, eval_metrics, step):
             model_args.config_name, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
         )
     elif model_args.model_name_or_path:
-        config = T5Config.from_pretrained(
-            model_args.model_name_or_path, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
-        )
+        config = T5Config.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
@@ -617,6 +616,7 @@ def group_texts(examples):
             model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
         )
     else:
+        config.vocab_size = len(tokenizer)
         model = FlaxT5ForConditionalGeneration(config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))
 
     # Data collator
@@ -808,3 +808,33 @@ def eval_step(params, batch):
                     tokenizer.save_pretrained(training_args.output_dir)
                     if training_args.push_to_hub:
                         repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
+
+    # Eval after training
+    if training_args.do_eval:
+        num_eval_samples = len(tokenized_datasets["validation"])
+        eval_samples_idx = jnp.arange(num_eval_samples)
+        eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
+
+        eval_metrics = []
+        for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
+            samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
+            model_inputs = data_collator(samples)
+
+            # Model forward
+            model_inputs = shard(model_inputs.data)
+            metrics = p_eval_step(state.params, model_inputs)
+            eval_metrics.append(metrics)
+
+        # get eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+        eval_metrics = jax.tree_map(lambda metric: jnp.mean(metric).item(), eval_metrics)
+
+        if jax.process_index() == 0:
+            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+            path = os.path.join(training_args.output_dir, "eval_results.json")
+            with open(path, "w") as f:
+                json.dump(eval_metrics, f, indent=4, sort_keys=True)
+
+
+if __name__ == "__main__":
+    main()

examples/flax/question-answering/run_qa.py

@@ -18,6 +18,7 @@
 """
 # You can also adapt this script on your own question answering task. Pointers for this are left as comments.
 
+import json
 import logging
 import os
 import random
@@ -911,6 +912,58 @@ def eval_step(state, batch):
         epochs.desc = f"Epoch ... {epoch + 1}/{num_epochs}"
     # endregion
 
+    # Eval after training
+    if training_args.do_eval:
+        eval_metrics = {}
+        all_start_logits = []
+        all_end_logits = []
+
+        eva_loader = eval_data_collator(eval_dataset, eval_batch_size)
+        for batch in tqdm(eva_loader, total=len(eval_dataset) // eval_batch_size, desc="Evaluating ...", position=2):
+            _ = batch.pop("example_id")
+            _ = batch.pop("offset_mapping")
+            predictions = p_eval_step(state, batch)
+            start_logits = np.array([pred for pred in chain(*predictions[0])])
+            end_logits = np.array([pred for pred in chain(*predictions[1])])
+            all_start_logits.append(start_logits)
+            all_end_logits.append(end_logits)
+
+        # evaluate also on leftover examples (not divisible by batch_size)
+        num_leftover_samples = len(eval_dataset) % eval_batch_size
+
+        # make sure leftover batch is evaluated on one device
+        if num_leftover_samples > 0 and jax.process_index() == 0:
+            # take leftover samples
+            batch = eval_dataset[-num_leftover_samples:]
+            batch = {k: np.array(v) for k, v in batch.items()}
+            _ = batch.pop("example_id")
+            _ = batch.pop("offset_mapping")
+
+            predictions = eval_step(unreplicate(state), batch)
+            start_logits = np.array([pred for pred in predictions[0]])
+            end_logits = np.array([pred for pred in predictions[1]])
+            all_start_logits.append(start_logits)
+            all_end_logits.append(end_logits)
+
+        max_len = max([x.shape[1] for x in all_start_logits])  # Get the max_length of the tensor
+
+        # concatenate the numpy array
+        start_logits_concat = create_and_fill_np_array(all_start_logits, eval_dataset, max_len)
+        end_logits_concat = create_and_fill_np_array(all_end_logits, eval_dataset, max_len)
+
+        # delete the list of numpy arrays
+        del all_start_logits
+        del all_end_logits
+        outputs_numpy = (start_logits_concat, end_logits_concat)
+        prediction = post_processing_function(eval_examples, eval_dataset, outputs_numpy)
+        eval_metrics = compute_metrics(prediction)
+
+        if jax.process_index() == 0:
+            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+            path = os.path.join(training_args.output_dir, "eval_results.json")
+            with open(path, "w") as f:
+                json.dump(eval_metrics, f, indent=4, sort_keys=True)
+
 
 if __name__ == "__main__":
     main()

examples/flax/summarization/run_summarization_flax.py

@@ -18,6 +18,7 @@
 """
 # You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
 
+import json
 import logging
 import os
 import sys
@@ -816,6 +817,13 @@ def generate_step(params, batch):
         desc = f"Predict Loss: {pred_metrics['loss']} | {rouge_desc})"
         logger.info(desc)
 
+        # save final metrics in json
+        if jax.process_index() == 0:
+            rouge_metrics = {f"test_{metric_name}": value for metric_name, value in rouge_metrics.items()}
+            path = os.path.join(training_args.output_dir, "test_results.json")
+            with open(path, "w") as f:
+                json.dump(rouge_metrics, f, indent=4, sort_keys=True)
+
 
 if __name__ == "__main__":
     main()