Create preprocess CLI (axolotl-ai-cloud#785)

* Create preprocess CLI

* Print prompt template if debugging

* Add print for unsupported prompters

* Formatting

* Formatting

* Refactor variables

* Formatting

* Formatting

* Formatting

* Formatting

README.md

@@ -32,7 +32,6 @@ Features:
   - [How to Use Custom Pretokenized Dataset](#how-to-use-your-custom-pretokenized-dataset)
 - [Config](#config)
   - [Train](#train)
-  - [Training w/ Deepspeed](#training-with-deepspeed)
   - [Inference](#inference)
   - [Merge LORA to Base](#merge-lora-to-base)
 - [Common Errors](#common-errors-)
@@ -824,14 +823,41 @@ Run
 accelerate launch -m axolotl.cli.train your_config.yml
 ```
 
-#### Multi-GPU
+#### Preprocess dataset
+
+You can optionally pre-tokenize dataset with the following before finetuning.
+This is recommended for large datasets.
+
+- Set `push_dataset_to_hub: hf_user/repo` to push it to Huggingface.
+- Use `--debug` to see preprocessed examples.
 
-You can optionally pre-tokenize dataset with the following before finetuning:
 ```bash
-CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train your_config.yml --prepare_ds_only
+python -m axolotl.cli.preprocess your_config.yml
 ```
 
-##### Config
+#### Multi-GPU
+
+Below are the options available in axolotl for training with multiple GPUs. Note that DeepSpeed
+is the recommended multi-GPU option currently because FSDP may experience
+[loss instability](https://github.com/huggingface/transformers/issues/26498).
+
+##### DeepSpeed
+
+Deepspeed is an optimization suite for multi-gpu systems allowing you to train much larger models than you
+might typically be able to fit into your GPU's VRAM. More information about the various optimization types
+for deepspeed is available at https://huggingface.co/docs/accelerate/main/en/usage_guides/deepspeed#what-is-integrated
+
+We provide several default deepspeed JSON configurations for ZeRO stage 1, 2, and 3.
+
+```yaml
+deepspeed: deepspeed/zero1.json
+```
+
+```shell
+accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed/zero1.json
+```
+
+##### FSDP
 
 - llama FSDP
 ```yaml
@@ -856,24 +882,6 @@ wandb_run_id:
 wandb_log_model:
 ```
 
-### Training with Deepspeed
-
-Deepspeed is an optimization suite for multi-gpu systems allowing you to train much larger models than you
-might typically be able to fit into your GPU's VRAM. More information about the various optimization types
-for deepspeed is available at https://huggingface.co/docs/accelerate/main/en/usage_guides/deepspeed#what-is-integrated
-
-We provide several default deepspeed JSON configurations for ZeRO stage 1, 2, and 3.
-
-```shell
-accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed/zero1.json
-```
-
-or
-
-```yaml
-deepspeed: deepspeed/zero1.json
-```
-
 ### Inference
 
 Pass the appropriate flag to the train command:

scripts/finetune.py

@@ -45,8 +45,6 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
         shard(cfg=parsed_cfg, cli_args=parsed_cli_args)
     else:
         dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
-        if parsed_cli_args.prepare_ds_only:
-            return
         train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
 
 

src/axolotl/cli/__init__.py

@@ -222,7 +222,9 @@ def load_datasets(
 ) -> TrainDatasetMeta:
     tokenizer = load_tokenizer(cfg)
 
-    train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)
+    train_dataset, eval_dataset, total_num_steps, prompters = prepare_dataset(
+        cfg, tokenizer
+    )
 
     if cli_args.debug or cfg.debug:
         LOG.info("check_dataset_labels...")
@@ -238,6 +240,10 @@ def load_datasets(
             text_only=cli_args.debug_text_only,
         )
 
+        LOG.info("printing prompters...")
+        for prompter in prompters:
+            LOG.info(prompter)
+
     return TrainDatasetMeta(
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,

src/axolotl/cli/preprocess.py

@@ -0,0 +1,53 @@
+"""
+CLI to run training on a model
+"""
+import logging
+from pathlib import Path
+
+import fire
+import transformers
+from colorama import Fore
+
+from axolotl.cli import (
+    check_accelerate_default_config,
+    check_user_token,
+    load_cfg,
+    load_datasets,
+    print_axolotl_text_art,
+)
+from axolotl.common.cli import PreprocessCliArgs
+from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
+
+LOG = logging.getLogger("axolotl.cli.preprocess")
+
+
+def do_cli(config: Path = Path("examples/"), **kwargs):
+    # pylint: disable=duplicate-code
+    print_axolotl_text_art()
+    parsed_cfg = load_cfg(config, **kwargs)
+    check_accelerate_default_config()
+    check_user_token()
+    parser = transformers.HfArgumentParser((PreprocessCliArgs))
+    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
+        return_remaining_strings=True
+    )
+    if not parsed_cfg.dataset_prepared_path:
+        msg = (
+            Fore.RED
+            + "preprocess CLI called without dataset_prepared_path set, "
+            + f"using default path: {DEFAULT_DATASET_PREPARED_PATH}"
+            + Fore.RESET
+        )
+        LOG.warning(msg)
+        parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
+
+    _ = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    LOG.info(
+        Fore.GREEN
+        + f"Success! Preprocessed data path: `dataset_prepared_path: {parsed_cfg.dataset_prepared_path}`"
+        + Fore.RESET
+    )
+
+
+if __name__ == "__main__":
+    fire.Fire(do_cli)

src/axolotl/cli/train.py

@@ -6,7 +6,6 @@
 
 import fire
 import transformers
-from colorama import Fore
 
 from axolotl.cli import (
     check_accelerate_default_config,
@@ -16,7 +15,6 @@
     print_axolotl_text_art,
 )
 from axolotl.common.cli import TrainerCliArgs
-from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.train import train
 
 LOG = logging.getLogger("axolotl.cli.train")
@@ -32,18 +30,7 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(
         return_remaining_strings=True
     )
-    if parsed_cli_args.prepare_ds_only and not parsed_cfg.dataset_prepared_path:
-        msg = (
-            Fore.RED
-            + "--prepare_ds_only called without dataset_prepared_path set."
-            + Fore.RESET
-        )
-        LOG.warning(msg)
-        parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
-
     dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
-    if parsed_cli_args.prepare_ds_only:
-        return
     train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
 
 

src/axolotl/common/cli.py

@@ -25,11 +25,22 @@ class TrainerCliArgs:
     debug_num_examples: int = field(default=5)
     inference: bool = field(default=False)
     merge_lora: bool = field(default=False)
-    prepare_ds_only: bool = field(default=False)
     prompter: Optional[str] = field(default=None)
     shard: bool = field(default=False)
 
 
+@dataclass
+class PreprocessCliArgs:
+    """
+    dataclass representing arguments for preprocessing only
+    """
+
+    debug: bool = field(default=False)
+    debug_text_only: bool = field(default=False)
+    debug_num_examples: int = field(default=1)
+    prompter: Optional[str] = field(default=None)
+
+
 def load_model_and_tokenizer(
     *,
     cfg: DictDefault,

src/axolotl/prompt_tokenizers.py

@@ -245,6 +245,7 @@ def parse_instruction_fields(self, prompt) -> Tuple[str, str, str, str, str]:
         raise NotImplementedError
 
     def tokenize_prompt(self, prompt):
+        # pylint: disable=duplicate-code
         (
             instruction,
             input,  # pylint: disable=redefined-builtin

src/axolotl/prompters.py

@@ -4,10 +4,12 @@
 from enum import Enum
 from typing import Generator, Optional, Union
 
+from colorama import Fore
 from fastchat.conversation import Conversation, get_conv_template
 
 LOG = logging.getLogger("axolotl")
 IGNORE_TOKEN_ID = -100
+REPR_TEMPLATE = "\n<start>\n" + Fore.CYAN + "{full_prompt}" + Fore.RESET + "\n<end>\n"
 
 
 class PromptStyle(Enum):
@@ -55,20 +57,15 @@ def match_prompt_style(self):
             )
             self.system_format = "<|im_start|>system\n{system}<|im_end|>\n"
 
-    def build_prompt(
-        self,
-        instruction: str,
-        input: Union[None, str] = None,  # pylint: disable=redefined-builtin
-        output: Union[None, str] = None,
-    ) -> Generator[str, None, None]:
+    def _build_result(self, instruction, input_text, output):
         # returns the full prompt from instruction and optional input
         # if a label (=response, =output) is provided, it's also appended.
-        if input:
+        if input_text:
             res = (
                 self.system_format.format(system=self.system_prompt)
                 if self.system_prompt
                 else ""
-            ) + self.turn_format.format(instruction=instruction, input=input)
+            ) + self.turn_format.format(instruction=instruction, input=input_text)
         else:
             res = (
                 self.system_format.format(system=self.system_no_input_prompt)
@@ -77,7 +74,21 @@ def build_prompt(
             ) + self.turn_no_input_format.format(instruction=instruction)
         if output:
             res = f"{res}{output}"
-        yield res
+
+        return res
+
+    def build_prompt(
+        self,
+        instruction: str,
+        input: Union[None, str] = None,  # pylint: disable=redefined-builtin
+        output: Union[None, str] = None,
+    ) -> Generator[str, None, None]:
+        yield self._build_result(instruction, input, output)
+
+    def __repr__(self) -> str:
+        return REPR_TEMPLATE.format(
+            full_prompt=self._build_result("{instruction}", "{input}", "{output}")
+        )
 
 
 class UnpromptedPrompter(AlpacaPrompter):
@@ -191,14 +202,14 @@ def match_prompt_style(self):
             )
             self.response_split = "ASSISTANT:"
 
-    def build_prompt(
+    def _build_result(
         self,
         instruction: str,
         input: Union[None, str] = None,  # pylint: disable=redefined-builtin
         output: Union[None, str] = None,
         reflection: Union[None, str] = None,
         corrected: Union[None, str] = None,
-    ) -> Generator[str, None, None]:
+    ):
         # returns the full prompt from instruction and optional input
         # if a label (=response, =output) is provided, it's also appended.
         if input:
@@ -212,7 +223,30 @@ def build_prompt(
                 corrected=corrected,
             )
             res = f"{res}{label}"
-        yield res
+
+        return res
+
+    def build_prompt(
+        self,
+        instruction: str,
+        input: Union[None, str] = None,  # pylint: disable=redefined-builtin
+        output: Union[None, str] = None,
+        reflection: Union[None, str] = None,
+        corrected: Union[None, str] = None,
+    ) -> Generator[str, None, None]:
+        # pylint: disable=duplicate-code
+        yield self._build_result(
+            instruction,
+            input,
+            output,
+            reflection,
+            corrected,
+        )
+
+    def __repr__(self) -> str:
+        return REPR_TEMPLATE.format(
+            full_prompt=self._build_result("{instruction}", "{input}", "{output}")
+        )
 
 
 SHAREGPT_ASSERTION_FAILED_ROLE = (
@@ -247,7 +281,7 @@ def __init__(
         if role_key_model:
             self.role_key_model = role_key_model
 
-    def build_prompt(self, source) -> Generator[str, None, None]:
+    def _build_result(self, source):
         if len(source) < 2:
             # If there isn't a back and forth conversation, ignore it
             # also happens on the data splitting leaving empty conversations
@@ -282,11 +316,20 @@ def build_prompt(self, source) -> Generator[str, None, None]:
                 LOG.warning(f"{SHAREGPT_ASSERTION_FAILED_ROLE}: {sentence}")
             conv.append_message(role, sentence["value"])
 
-        for part in conv.get_turns():
+        return conv.get_turns()
+
+    def build_prompt(self, source) -> Generator[str, None, None]:
+        turns = self._build_result(source)
+
+        for part in turns:
             if part[0] and not part[1]:
                 LOG.warning(f"role with empty message: {part[0]}")
             yield part
 
+    def __repr__(self) -> str:
+        turns = self._build_result([{"from": "{from}", "value": "{value}"}])
+        return "\n".join([REPR_TEMPLATE.format(full_prompt=part) for part in turns])
+
 
 class ShareGPTPrompterV2(ShareGPTPrompter):
     """
@@ -304,3 +347,15 @@ def __init__(
             role_key_human=role_key_human,
             role_key_model=role_key_model,
         )
+
+
+class UnsupportedPrompter:
+    """
+    A dummy class for custom prompters
+    """
+
+    def __init__(self) -> None:
+        pass
+
+    def __repr__(self):
+        return "Pre-tokenized or custom dataset types are unsupported for logging"