Add seq2seq eval benchmark callback

README.md

@@ -782,7 +782,8 @@ save_total_limit: # Checkpoints saved at a time
 max_steps:
 
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
-eval_table_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
+eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
+eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", chrf]
 
 loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
 loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)

examples/llama-2/loftq.yml

@@ -60,7 +60,7 @@ s2_attention:
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_table_max_new_tokens: 128
+eval_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:

examples/llama-2/lora.yml

@@ -57,7 +57,7 @@ s2_attention:
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_table_max_new_tokens: 128
+eval_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:

examples/mamba/config.yml

@@ -49,7 +49,7 @@ flash_attention:
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_table_max_new_tokens: 128
+eval_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:

examples/mistral/Mistral-7b-example/config.yml

@@ -61,7 +61,7 @@ flash_attention: true
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_table_max_new_tokens: 128
+eval_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 #default deepspeed, can use more aggresive if needed like zero2, zero3

examples/mistral/config.yml

@@ -49,7 +49,7 @@ flash_attention: true
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_table_max_new_tokens: 128
+eval_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:

examples/mistral/mixtral.yml

@@ -81,7 +81,7 @@ loss_watchdog_patience: 3
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_table_max_new_tokens: 128
+eval_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed: deepspeed_configs/zero2.json

examples/mistral/qlora.yml

@@ -68,7 +68,7 @@ loss_watchdog_patience: 3
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_table_max_new_tokens: 128
+eval_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:

examples/qwen/lora.yml

@@ -58,7 +58,7 @@ flash_attention:
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_table_max_new_tokens: 128
+eval_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:

examples/qwen/qlora.yml

@@ -58,7 +58,7 @@ flash_attention:
 warmup_steps: 10
 evals_per_epoch: 4
 eval_table_size:
-eval_table_max_new_tokens: 128
+eval_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 deepspeed:

examples/yi-34B-chat/qlora.yml

@@ -29,7 +29,7 @@ num_epochs: 1
 val_set_size: 0.1
 evals_per_epoch: 5
 eval_table_size:
-eval_table_max_new_tokens: 128
+eval_max_new_tokens: 128
 eval_sample_packing: false
 eval_batch_size: 1
 

requirements.txt

@@ -23,7 +23,7 @@ numba
 numpy>=1.24.4
 mlflow
 # qlora things
-evaluate==0.4.0
+evaluate==0.4.1
 scipy
 scikit-learn==1.2.2
 pynvml

src/axolotl/core/trainer_builder.py

@@ -38,6 +38,7 @@
     SaveAxolotlConfigtoWandBCallback,
     SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
+    causal_lm_bench_eval_callback_factory,
     log_prediction_callback_factory,
 )
 from axolotl.utils.collators import (
@@ -143,6 +144,9 @@ class AxolotlTrainingArguments(TrainingArguments):
     do_bench_eval: Optional[bool] = field(
         default=False, metadata={"help": "Whether to run the Benchmark evaluation."}
     )
+    do_causal_lm_eval: Optional[bool] = field(
+        default=False, metadata={"help": "Whether to run the Causal LM evaluation."}
+    )
     max_bench_samples: Optional[int] = field(
         default=None,
         metadata={
@@ -643,6 +647,11 @@ def get_post_trainer_create_callbacks(self, trainer):
 
         if self.cfg.do_bench_eval:
             callbacks.append(bench_eval_callback_factory(trainer, self.tokenizer))
+        if self.cfg.do_causal_lm_eval:
+            CausalLMBenchEvalCallback = causal_lm_bench_eval_callback_factory(
+                trainer, self.tokenizer
+            )
+            callbacks.append(CausalLMBenchEvalCallback(self.cfg))
 
         if self.cfg.early_stopping_patience:
             early_stop_cb = EarlyStoppingCallback(
@@ -791,6 +800,8 @@ def build(self, total_num_steps):
             training_arguments_kwargs["do_bench_eval"] = self.cfg.do_bench_eval
             if self.cfg.bench_dataset:
                 training_arguments_kwargs["bench_dataset"] = self.cfg.bench_dataset
+        if self.cfg.do_causal_lm_eval:
+            training_arguments_kwargs["do_causal_lm_eval"] = self.cfg.do_causal_lm_eval
         if self.cfg.metric_for_best_model:
             training_arguments_kwargs[
                 "metric_for_best_model"

src/axolotl/utils/callbacks.py

@@ -361,6 +361,187 @@ def on_evaluate(
     return BenchEvalCallback
 
 
+def causal_lm_bench_eval_callback_factory(trainer: Trainer, tokenizer):
+    class CausalLMBenchEvalCallback(TrainerCallback):
+        """Callback to log prediction values during each evaluation"""
+
+        def __init__(self, cfg):
+            self.cfg = cfg
+            self.logged = False
+            self.metrics = self.__maybe_load_metrics()
+
+        def __maybe_load_metrics(self):
+            metrics = {}
+            for metric in self.cfg.eval_causal_lm_metrics:
+                try:
+                    metrics[metric] = evaluate.load(metric)
+                except Exception as exc:  # pylint: disable=broad-exception-caught
+                    LOG.warning(f"{metric}: {exc.args}")
+            return metrics
+
+        def on_evaluate(
+            self,
+            args: AxolotlTrainingArguments,  # pylint: disable=unused-argument
+            state: TrainerState,
+            control: TrainerControl,
+            train_dataloader,  # pylint: disable=unused-argument
+            eval_dataloader,
+            **kwargs,  # pylint: disable=unused-argument
+        ):
+            trainer.model.eval()
+            device = torch.device(self.cfg.device)
+
+            # pylint: disable=duplicate-code
+            generation_config = GenerationConfig(
+                max_new_tokens=self.cfg.eval_max_new_tokens,
+                bos_token_id=tokenizer.bos_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                pad_token_id=tokenizer.pad_token_id,
+                do_sample=False,
+                use_cache=True,
+                return_dict_in_generate=True,
+                output_attentions=False,
+                output_hidden_states=False,
+                output_scores=False,
+            )
+
+            def find_ranges(lst):
+                ranges = []
+                start = 0
+                for i in range(1, len(lst)):
+                    if lst[i] == 0:
+                        ranges.append((start, i - 1))
+                        start = i
+                end = len(lst) - 1
+                ranges.append((start, end))
+                return ranges
+
+            def compute(metric: evaluate.Metric, **kwargs):
+                # safely compute a metric and return the score if the format is correct
+                metric_score = None
+                try:
+                    metric_score = metric.compute(**kwargs)
+                    return (
+                        metric_score["score"]
+                        if "score" in metric_score
+                        else metric_score["mean_score"]
+                    )
+                except Exception:  # pylint: disable=broad-exception-caught
+                    LOG.debug(
+                        f"Failed to compute metric {metric.name} with kwargs {kwargs.keys()}"
+                    )
+                return metric_score
+
+            def evaluate_preds(sources, predictions, references):
+                scores = {}
+
+                for metric_name, metric in self.metrics.items():
+                    score = compute(
+                        metric,
+                        references=references,
+                        predictions=predictions,
+                        sources=sources,
+                    )
+                    score = score or compute(
+                        metric,
+                        references=[[r] for r in references],
+                        predictions=predictions,
+                    )
+                    scores[metric_name] = score
+                return scores
+
+            def predict_with_generate():
+                eval_src, eval_pred, eval_ref = [], [], []
+
+                for batch in tqdm(eval_dataloader):
+                    batch_labels = batch["labels"].to(device)
+                    batch_input_ids = batch["input_ids"].to(device)
+
+                    if "position_ids" in batch:
+                        batch_pos_ids = batch["position_ids"].tolist()
+                    else:
+                        batch_pos_ids = [None] * len(batch["input_ids"])
+
+                    prompt_token_ids_list = []
+                    completion_token_ids_list = []
+
+                    for input_ids_all, labels_all, pos_ids in zip(
+                        batch_input_ids,
+                        batch_labels,
+                        batch_pos_ids,
+                    ):
+                        if pos_ids is None:
+                            pos_ranges = [(0, len(input_ids_all) - 1)]
+                        else:
+                            pos_ranges = find_ranges(pos_ids)
+
+                        for pos_range in pos_ranges:
+                            start, end = pos_range
+                            if start == end:
+                                continue
+
+                            input_ids = input_ids_all[start : end + 1]
+                            labels = labels_all[start : end + 1]
+
+                            tokens_without_loss = labels == IGNORE_INDEX
+                            tokens_with_loss = labels != IGNORE_INDEX
+                            tokens_exclude_padding = input_ids != tokenizer.pad_token_id
+                            prompt_token_includes = (
+                                tokens_without_loss & tokens_exclude_padding
+                            )
+
+                            prompt_token_ids = input_ids[prompt_token_includes]
+                            prompt_token_ids_list.append(prompt_token_ids)
+
+                            completion_token_ids = input_ids[tokens_with_loss]
+                            completion_token_ids_list.append(completion_token_ids)
+
+                    prompt_texts = tokenizer.batch_decode(
+                        prompt_token_ids_list, skip_special_tokens=True
+                    )
+                    completion_texts = tokenizer.batch_decode(
+                        completion_token_ids_list, skip_special_tokens=True
+                    )
+
+                    with torch.no_grad():
+                        prompt_encoding = tokenizer(
+                            prompt_texts, padding=True, return_tensors="pt"
+                        ).to(self.cfg.device)
+                        predictions = trainer.model.generate(
+                            **prompt_encoding, generation_config=generation_config
+                        )
+
+                    prediction_all_tokens = predictions["sequences"].cpu().tolist()
+                    prediction_without_prompt_tokens_list = []
+                    for prompt_token_ids, prediction_tokens in zip(
+                        prompt_token_ids_list, prediction_all_tokens
+                    ):
+                        prediction_without_prompt_tokens = prediction_tokens[
+                            len(prompt_token_ids) :
+                        ]
+                        prediction_without_prompt_tokens_list.append(
+                            prediction_without_prompt_tokens
+                        )
+
+                    predicted_texts = tokenizer.batch_decode(
+                        prediction_without_prompt_tokens_list, skip_special_tokens=True
+                    )
+
+                    eval_src.extend(prompt_texts)
+                    eval_pred.extend(predicted_texts)
+                    eval_ref.extend(completion_texts)
+
+                return eval_src, eval_pred, eval_ref
+
+            if is_main_process():
+                eval_preds = predict_with_generate()
+                trainer.log(evaluate_preds(*eval_preds))
+
+            return control
+
+    return CausalLMBenchEvalCallback
+
+
 def log_prediction_callback_factory(trainer: Trainer, tokenizer):
     class LogPredictionCallback(TrainerCallback):
         """Callback to log prediction values during each evaluation"""
@@ -388,7 +569,7 @@ def on_evaluate(
 
             # pylint: disable=duplicate-code
             generation_config = GenerationConfig(
-                max_new_tokens=self.cfg.eval_table_max_new_tokens,
+                max_new_tokens=self.cfg.eval_max_new_tokens,
                 bos_token_id=tokenizer.bos_token_id,
                 eos_token_id=tokenizer.eos_token_id,
                 pad_token_id=tokenizer.pad_token_id,

src/axolotl/utils/config.py

@@ -56,7 +56,13 @@ def normalize_config(cfg):
     cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
     cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
     cfg.eval_table_size = cfg.eval_table_size or 0
-    cfg.eval_table_max_new_tokens = cfg.eval_table_max_new_tokens or 128
+    cfg.eval_max_new_tokens = cfg.eval_max_new_tokens or 128
+    cfg.eval_causal_lm_metrics = cfg.eval_causal_lm_metrics or [
+        "sacrebleu",
+        "comet",
+        "ter",
+        "chrf",
+    ]
     choose_device(cfg)
     cfg.ddp = cfg.ddp if cfg.ddp is not None else cfg.world_size != 1
     if cfg.ddp:
@@ -550,6 +556,21 @@ def validate_config(cfg):
     if cfg.fsdp and "bnb" in cfg.optimizer:
         raise ValueError(f"FSDP not compatible with {cfg.optimizer}")
 
+    if cfg.do_causal_lm_eval and cfg.eval_sample_packing:
+        raise ValueError(
+            "do_causal_lm_eval is enabled, eval_sample_packing must be set to False"
+        )
+
+    if cfg.eval_causal_lm_metrics:
+        supported_metrics = ["sacrebleu", "comet", "ter", "chrf"]
+        if not isinstance(cfg.eval_causal_lm_metrics, list):
+            raise ValueError("eval_causal_lm_metrics must be a list")
+        # only ["sacrebleu", "comet", "ter", "chrf"] supported
+        if set(cfg.eval_causal_lm_metrics) - set(supported_metrics):
+            raise ValueError(
+                f"eval_causal_lm_metrics must be one of {supported_metrics}"
+            )
+
     # TODO
     # MPT 7b
     # https://github.com/facebookresearch/bitsandbytes/issues/25