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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,119 +1,30 @@ | ||
| # Copyright 2022 MosaicML LLM Foundry authors | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
|
||
| """Periodically log generations to wandb from a set of prompts.""" | ||
| from typing import Any, List, Union, cast | ||
| """Deprecated Generate callback. | ||
| import torch | ||
| import wandb | ||
| from composer.core import Callback, State, get_precision_context | ||
| from composer.loggers import Logger, WandBLogger | ||
| from composer.utils import dist, ensure_tuple | ||
| Please use composer.callbacks.Generate instead. | ||
| """ | ||
| import warnings | ||
| from typing import Any, List, Union | ||
|
|
||
| from composer.callbacks import Generate as ComposerGenerate | ||
| from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast | ||
|
|
||
| Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast] | ||
|
|
||
|
|
||
| class Generate(Callback): | ||
| class Generate(ComposerGenerate): | ||
|
|
||
| def __init__(self, prompts: List[str], batch_log_interval: int, | ||
| **kwargs: Any): | ||
| """Periodically log generations to wandb from a set of prompts. | ||
| In the main view for a run, there will be a table that will show the _last_ logged generations. | ||
| To compare previous iterations of the generations, you need to | ||
| 1. Click on the run | ||
| 2. Click on "artifacts" in the menu on the left side of the screen | ||
| 3. Click on one of the artifacts called "predictions" | ||
| 4. Click on the "files" tab | ||
| 5. Click on "predictions.table.json" | ||
| 6. On the left hand side, there are different versions of the table produced throughout training. Select one of these. | ||
| 7. Now, when you hover over other versions, there will be a "compare" button, which will allow you to compare the currently | ||
| selected version to the version you add via compare. | ||
| Args: | ||
| prompts (List[str]): The list of prompts you would like to produce generations for | ||
| batch_log_interval (int): The interval (in batches) at which this callback runs | ||
| kwargs: All kwargs well be passed along to the call to generate. This is for things like `do_sample`, `top_p`, etc | ||
| """ | ||
| self.prompts = prompts | ||
| self.batch_log_interval = batch_log_interval | ||
| self.generate_kwargs = kwargs | ||
| self.wandb_logger = None | ||
|
|
||
| def init(self, state: State, logger: Logger): | ||
| if dist.get_global_rank() == 0: | ||
| for destination in ensure_tuple(logger.destinations): | ||
| if isinstance(destination, WandBLogger): | ||
| self.wandb_logger = destination | ||
|
|
||
| def batch_checkpoint(self, state: State, logger: Logger) -> None: | ||
| if (state.timestamp.batch.value % self.batch_log_interval) == 0: | ||
| self.generate(state, logger) | ||
|
|
||
| def generate(self, state: State, logger: Logger) -> None: | ||
| model = state.model | ||
| original_mode = model.training | ||
| model.eval() | ||
| tokenizer = cast(Tokenizer, state.model.tokenizer) | ||
| device = state.device | ||
|
|
||
| if not hasattr(model.model, 'generate'): | ||
| raise ValueError( | ||
| f'Cannot generate from model {model.model.__class__.__name__} because it does not have a `generate` method' | ||
| ) | ||
|
|
||
| # stash the original original value of padding_side because generation requires left padding | ||
| original_padding_side = tokenizer.padding_side | ||
| tokenizer.padding_side = 'left' | ||
| if tokenizer.pad_token_id is None: | ||
| tokenizer.pad_token_id = tokenizer.eos_token_id | ||
| tokenized_input = tokenizer(self.prompts, | ||
| return_tensors='pt', | ||
| padding=True) | ||
|
|
||
| for k, v in tokenized_input.items(): | ||
| tokenized_input[k] = device.tensor_to_device(v) | ||
|
|
||
| # dummy forward call needed for FSDP to work consistently | ||
| dummy_input = torch.tensor([[0]], dtype=torch.long) | ||
| dummy_input = device.tensor_to_device(dummy_input) | ||
| with get_precision_context(state.precision): | ||
| with torch.no_grad(): | ||
| assert isinstance(model.model, torch.nn.Module) | ||
| _ = model.model(input_ids=dummy_input) | ||
|
|
||
| output_token_ids = model.model.generate( # type: ignore | ||
| input_ids=tokenized_input['input_ids'], | ||
| attention_mask=tokenized_input['attention_mask'], | ||
| synced_gpus=True, | ||
| **self.generate_kwargs, | ||
| ) | ||
|
|
||
| if dist.get_global_rank() == 0: | ||
| if self.wandb_logger is not None: | ||
| assert wandb.run is not None, 'wandb should have started run' | ||
|
|
||
| artifact = wandb.Artifact('generate_samples_' + | ||
| str(wandb.run.id), | ||
| type='predictions') | ||
|
|
||
| rows = [] | ||
| for i in range(len(self.prompts)): | ||
| prompt = self.prompts[i] | ||
| output_tokens = output_token_ids[i][ | ||
| tokenized_input['input_ids'].shape[1]:] | ||
| output_text = tokenizer.decode(output_tokens, | ||
| skip_special_tokens=True) | ||
|
|
||
| rows.append([prompt, output_text]) | ||
|
|
||
| text_table = wandb.Table(data=rows, | ||
| columns=['prompt', 'generation']) | ||
| artifact.add(text_table, 'predictions') | ||
| wandb.log_artifact(artifact) | ||
| wandb.log({'generations': text_table}, | ||
| step=state.timestamp.batch.value) | ||
| warnings.warn( | ||
| ('Accessing llmfoundry.callbacks.generate_callback.Generate ' | ||
| 'is deprecated and will be removed in a future release. ' | ||
| 'Please use composer.callbacks.Generate instead.'), | ||
| DeprecationWarning, | ||
| ) | ||
|
|
||
| tokenizer.padding_side = original_padding_side | ||
| model.train(mode=original_mode) | ||
| interval = f'{batch_log_interval}ba' | ||
| super().__init__(prompts=prompts, interval=interval, **kwargs) |
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