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eleuther_eval.py
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eleuther_eval.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import importlib.metadata
import sys
import time
from typing import Dict, List, Tuple, Union
import PIL
import torch
from omegaconf import DictConfig
from torchtune import config, training, utils
from torchtune.data import (
format_content_with_images,
left_pad_sequence,
Message,
padded_collate_tiled_images_and_mask,
)
from torchtune.generation import generate, sample
from torchtune.modules import TransformerDecoder
from torchtune.modules.model_fusion import DeepFusionModel
from torchtune.modules.tokenizers import ModelTokenizer
from torchtune.modules.transforms import Transform
from torchtune.recipe_interfaces import EvalRecipeInterface
from torchtune.training import FullModelTorchTuneCheckpointer
try:
import lm_eval
except ImportError:
print(
"You must install the EleutherAI Eval Harness to run this recipe. "
"Please install with `pip install lm_eval>=0.4.2`"
)
sys.exit(1)
lm_eval_version = importlib.metadata.version("lm_eval")
if not lm_eval_version >= "0.4.2":
print(
"You must install the EleutherAI Eval Harness >= v0.4.2 to run this recipe. "
"Please install with `pip install lm_eval>=0.4.2`"
)
sys.exit(1)
from lm_eval.evaluator import evaluate, get_task_list
# User doesn't have to have nightlies installed, they just won't be able
# to use the multimodal model
try:
from lm_eval.models.hf_vlms import HFMultimodalLM
except ImportError as e:
# Create a dummy class to avoid having to import the HF models
# TODO (@joecummings): Remove this once v0.4.5 patch is released
class HFMultimodalLM:
def __init__(self, *args, **kwargs):
pass
from lm_eval.models.huggingface import HFLM
from lm_eval.tasks import get_task_dict, TaskManager
class _VLMEvalWrapper(HFMultimodalLM):
"""An EvalWrapper for EleutherAI's eval harness based on gpt-fast's
EvalWrapper: https://github.com/pytorch-labs/gpt-fast/blob/main/eval.py.
Note:
This is ONLY for vision-language models.
Args:
model (DeepFusionModel): The VLM to evaluate.
transform (Transform): The transform (tokenizer) to use for preprocessing.
device (torch.device): The device to use.
max_seq_length (int): The maximum sequence length.
batch_size (int): The batch size.
dtype (torch.dtype): dtype for the model caches during generation.
enable_kv_cache (bool): Whether to enable KV cache for generation.
image_tag (str): The string to use for the image token. Default is "<image>", which
is the default used by the MMMU dataset.
max_images_per_sample (int): The maximum number of images per sample. Defaults to
the max number of images in MMMU.
"""
def __init__(
self,
model: DeepFusionModel,
transform: Transform,
*,
device: torch.device,
max_seq_length: int = 4096,
batch_size: int = 8,
dtype: torch.dtype = torch.bfloat16,
enable_kv_cache: bool = True,
# TODO (@joecummings): Update these defaults once more multimodal
# tasks are added to the eval harness
image_tag: str = "<image>",
max_images_per_sample: int = 7,
):
self._model = model
self._transform = transform
self._device = device
self._max_seq_length = max_seq_length
self._batch_size = batch_size
self._dtype = dtype
# Defaulting KV cache to True for multimodal
self._enable_kv_cache = True
self._image_tag = image_tag
self._max_images_per_sample = max_images_per_sample
@property
def model(self):
# Not actually changing the dtype here, just adding it as a
# property on the model
self._model.dtype = self._dtype
return self._model
@property
def model_transform(self):
return self._transform
@property
def device(self):
return self._device
@property
def cache_hook(self):
# Dummy class to appease the Harness
class DummyCacheHook:
def __init__(self):
self.add_partial = lambda x, y, z: True
return DummyCacheHook()
@property
def rank(self):
# Hardcoded for now b/c we only support single GPU eval
return 0
@property
def world_size(self):
# Hardcoded for now b/c we only support single GPU eval
return 1
@property
def batch_size(self):
return self._batch_size
@property
def eos_token_id(self):
return self._transform.tokenizer.eos_id
@property
def eot_token_id(self):
return self._transform.tokenizer.eot_id
@property
def max_length(self):
return self._max_seq_length
@property
def truncation(self):
return True
def tok_encode(self, string, **kwargs) -> List[int]:
# This is only used to get a number of tokens for use in sorting samples in dataset
# These values will not actually be used for eval
return self._transform.tokenizer.encode(string, add_bos=False, add_eos=False)
def tok_decode(self, tokens, skip_special_tokens=True) -> str:
if isinstance(tokens, int):
tokens = [tokens]
return self._transform.tokenizer.decode(
tokens, skip_special_tokens=skip_special_tokens
)
def tok_batch_multimodal_encode(
self,
all_texts: List[str],
all_images: List[List[PIL.Image.Image]],
left_truncate_len: int = None,
*args,
**kwargs,
):
# Eleuther already parses out the text and images, so we just need to get
# it into a Message format for our tokenizer
all_encoded_messages = []
for text, images in zip(all_texts, all_images):
# Ensure images are all RGB
proper_images = []
for image in images:
if image.mode != "RGB":
image = image.convert("RGB")
proper_images.append(image)
# Construct the messages
messages = []
content = format_content_with_images(
text, image_tag=self._image_tag, images=proper_images
)
messages.append(Message(role="user", content=content))
messages.append(Message(role="assistant", content=""))
# Transform the messages
tok_batch = self.model_transform({"messages": messages}, inference=True)
all_encoded_messages.append(tok_batch)
# Pad the encoded messages
tok_batch = padded_collate_tiled_images_and_mask(
all_encoded_messages,
pad_direction="left",
pad_max_images=self._max_images_per_sample,
)
utils.batch_to_device(tok_batch, self.device)
# Convert the batch to the format expected by the HF
tok_batch["input_ids"] = tok_batch.pop("tokens")
# the harness will use left_truncate_len to indicate that the current batch
# needs to be truncated to self.max_seq_len - self.max_gen_toks
if left_truncate_len is not None:
tok_batch["input_ids"] = tok_batch["input_ids"][:, -left_truncate_len:]
return tok_batch
@torch.inference_mode()
def _model_multimodal_generate(
self,
batch: Dict[str, torch.Tensor],
max_length: int,
stop: List[str],
**generation_kwargs,
):
# 1. Validate inputs
prompt = batch.pop("input_ids")
bsz, seq_len = prompt.shape
temperature = generation_kwargs.get("temperature", 0.0)
do_sample = generation_kwargs.get("do_sample", False)
if do_sample or temperature != 0.0:
raise RuntimeError(
"Any decoding strategy other than greedy is not supported."
)
if bsz > 1:
raise ValueError(
f"Got a batch size of '{bsz}'. Batch size > 1 is not yet supported for "
"multimodal generation."
)
# 2. Setup KV cache and masks for bsz 1
with self.device:
if self.model.caches_are_enabled():
self.model.reset_caches()
else:
self.model.setup_caches(
batch_size=1,
dtype=self._dtype,
encoder_max_seq_len=self.model_transform.image_seq_len
* self._max_images_per_sample,
decoder_max_seq_len=self.max_length,
)
causal_mask = torch.tril(
torch.ones(
size=(self.max_length, self.max_length),
dtype=torch.bool,
)
)
input_pos = torch.arange(self.max_length)
batch["input_pos"] = input_pos[None, :seq_len]
batch["mask"] = causal_mask[None, :seq_len]
# 3. Prefill step
generated_tokens = []
logits = self.model(prompt, **batch)[:, -1]
token = sample(logits, temperature=0.0, top_k=None)
generated_tokens.append(token.item())
cache_mask = batch["encoder_mask"][:, -1:]
# 4. Continue generating
for _ in range(max_length):
if token.item() in self.model_transform.stop_tokens:
break
logits = self.model(
token,
mask=causal_mask[None, seq_len, None, :],
encoder_input=None,
encoder_mask=cache_mask,
input_pos=input_pos[None, seq_len],
)[:, -1]
token = sample(logits, temperature=0.0, top_k=None)
generated_tokens.append(token.item())
seq_len += 1
# 5. Return generated tokens
return torch.tensor(generated_tokens, dtype=torch.int32).unsqueeze(0)
class _LLMEvalWrapper(HFLM):
"""An EvalWrapper for EleutherAI's eval harness based on gpt-fast's
EvalWrapper: https://github.com/pytorch-labs/gpt-fast/blob/main/eval.py.
Note:
This is for text-only decoder models.
Args:
model (TransformerDecoder): The model to evaluate.
tokenizer (ModelTokenizer): Tokenizer associated with the model being evaluated.
This should be the same tokenizer used when fine-tuning the model.
device (torch.device): The device to use.
max_seq_length (int): The maximum sequence length to use.
batch_size (int): The batch size per GPU to use.
dtype (torch.dtype): dtype for the model caches during generation.
enable_kv_cache (bool): Whether to enable KV cache for generation.
"""
def __init__(
self,
model: TransformerDecoder,
tokenizer: ModelTokenizer,
*,
device: torch.device,
max_seq_length: int = 4096,
batch_size: int = 8,
dtype: torch.dtype = torch.float32,
enable_kv_cache: bool = True,
):
# TODO (@joecummings): Remove this init function so we don't load in extraneous stuff
super().__init__(pretrained="gpt2", device=str(device))
self._model = model
self._tokenizer = tokenizer
self._max_seq_length = max_seq_length
self._batch_size = batch_size
self._dtype = dtype
self._enable_kv_cache = enable_kv_cache
@property
def model(self):
return self._model
@property
def eot_token_id(self):
return self._tokenizer.eos_id
@property
def max_length(self):
return self._max_seq_length
@property
def max_gen_toks(self):
return 256
@property
def batch_size(self):
return self._batch_size
@property
def device(self):
return self._device
@property
def enable_kv_cache(self):
return self._enable_kv_cache
def tok_encode(self, text: str, **kwargs) -> List[int]:
# Note on add_bos flag: setting to False as this gives better results, for example
# +1% on truthfulqa_mc2 with a LoRA finetune. lit-gpt also sets this to False,
# see https://github.com/Lightning-AI/lit-gpt/blob/main/eval/lm_eval_harness.py#L66,
# though notably fast-gpt does the opposite
# https://github.com/pytorch-labs/gpt-fast/blob/main/eval.py#L123.
return self._tokenizer.encode(text=text, add_bos=False, add_eos=False)
def tok_batch_encode(
self, text: List[str], left_truncate_len: int = None, **kwargs
) -> Tuple[torch.Tensor, torch.Tensor]:
tokenized_text = [self.tok_encode(x) for x in text]
# pad left
x = left_pad_sequence(
[torch.tensor(x) for x in tokenized_text],
batch_first=True,
padding_value=self._tokenizer.pad_id,
)
# the harness will use left_truncate_len to indicate that the current batch
# needs to be truncated to self.max_seq_len - self.max_gen_toks
if left_truncate_len is not None:
x = x[:, -left_truncate_len:]
return x, torch.ones_like(x) # return 'mask' b/c it's expected by the harness
def tok_decode(self, tokens: Union[List[int], int], **kwargs) -> str:
if isinstance(tokens, int):
tokens = [tokens]
return self._tokenizer.decode(tokens)
def _model_call(self, inps: torch.Tensor, **kwargs) -> torch.Tensor:
return self._model(inps)
@torch.inference_mode()
def _model_generate(
self, context: torch.Tensor, **generation_kwargs
) -> torch.Tensor:
bsz, seq_len = context.shape
temperature = generation_kwargs.get("temperature", 0.0)
do_sample = generation_kwargs.get("do_sample", False)
if do_sample or temperature != 0.0:
raise RuntimeError(
"Any decoding strategy other than greedy is not supported."
)
# Setup KV caches OR reset them if they're already set up
if self.enable_kv_cache:
if self.model.caches_are_enabled():
self.model.reset_caches()
else:
with self.device:
self.model.setup_caches(
batch_size=self.batch_size,
dtype=self._dtype,
decoder_max_seq_len=self.max_length,
)
# if we've recieved fewer than self._batch_size samples in the current
# batch we need to pad the batch out. here we're padding the end of the
# current batch to the correct length. this is because when we use static
# KV-caches, the model will expect a fixed batch size for all samples.
maybe_padded_context = torch.nn.functional.pad(
context,
(0, 0, 0, self._batch_size - bsz),
value=self._tokenizer.eos_id, # pad with one of the tokenizer's stop tokens so generation can stop early
)
toks, _ = generate(
self.model,
maybe_padded_context,
max_generated_tokens=self.max_gen_toks,
temperature=temperature,
top_k=None,
stop_tokens=self._tokenizer.stop_tokens,
)
return toks[:bsz]
class EleutherEvalRecipe(EvalRecipeInterface):
"""
This recipe runs evaluation on a trained model using EleutherAI's eval harness.
This assumes the user has the EleutherAI eval harness installed. See
https://github.com/EleutherAI/lm-evaluation-harness for more details.
Features:
- Single GPU evaluation. Multi-GPU evaluation is currently not supported.
- Quantization (for text-only models) is supported.
- Any task from the EleutherAI eval harness
We recommend launching evaluation using the tune CLI::
tune run eleuther_eval --config eleuther_evaluation \
tasks=["truthfulqa_mc2","hellaswag"] \
limit=50 \
"""
def __init__(self, cfg: DictConfig) -> None:
self.device = utils.get_device(device=cfg.device)
self.dtype = training.get_dtype(dtype=cfg.dtype, device=self.device)
self.logger = utils.get_logger(cfg.get("log_level", "info"))
training.set_seed(seed=cfg.seed)
# Eval specific variables
self.limit = cfg.limit
self.tasks = list(cfg.tasks)
self.batch_size = cfg.batch_size
self.enable_kv_cache = cfg.get("enable_kv_cache", True)
self.include_path = cfg.get("include_path", None)
def setup(self, cfg: DictConfig) -> None:
# Initialize quantizer and quantization mode
quantizer = config.instantiate(cfg.quantizer)
quantization_mode = training.get_quantizer_mode(quantizer)
# Load checkpoint
checkpointer = config.instantiate(cfg.checkpointer)
# Initialize model
with training.set_default_dtype(self.dtype), self.device:
model = config.instantiate(cfg.model)
# Quantize model if requested
if quantization_mode is not None:
if not isinstance(checkpointer, FullModelTorchTuneCheckpointer):
raise ValueError(
"Quantization is only supported for models quantized and saved with the "
"FullModelTorchTuneCheckpointer - please ensure you have quantized your "
"model and are using the quantized weights!"
)
if "qat" in quantization_mode:
raise ValueError(
"You have specified a quantizer with 'QAT' - "
"QAT quantizers should only be used during quantization aware training "
"and when quantizing models. Please use the corresponding post-training "
"quantizer e.g. Int8DynActInt4WeightQuantizer for Int8DynActInt4WeightQATQuantizer."
)
model = quantizer.quantize(model)
model = model.to(device=self.device, dtype=self.dtype)
ckpt_dict = checkpointer.load_checkpoint(weights_only=False)[
training.MODEL_KEY
]
for k, v in ckpt_dict.items():
ckpt_dict[k] = v.to(self.device)
model.load_state_dict(ckpt_dict, assign=True)
else:
ckpt_dict = checkpointer.load_checkpoint()[training.MODEL_KEY]
model.load_state_dict(ckpt_dict)
# Load model weights into initialized model
self.logger.info(f"Model is initialized with precision {self.dtype}.")
# Put model in eval mode.
# Note: This will not disable the dropout applied in SDPA,
# see https://github.com/pytorch/pytorch/issues/124464
model.eval()
# Initialize tokenizer/transform
model_transform = config.instantiate(cfg.tokenizer)
# Finally, we setup the actual EvalWrapper class
if isinstance(model, DeepFusionModel):
eleuther_model_wrapper = _VLMEvalWrapper
if not self.enable_kv_cache:
self.logger.debug(
"Received enable_kv_cache=False, but KV cache is required for running "
"multimodal generation in a timely manner. Setting enable_kv_cache=True."
)
elif isinstance(model, TransformerDecoder):
eleuther_model_wrapper = _LLMEvalWrapper
self.eleuther_model_wrapper = eleuther_model_wrapper(
model,
model_transform,
device=self.device,
max_seq_length=cfg.max_seq_length,
batch_size=self.batch_size,
dtype=self.dtype,
enable_kv_cache=self.enable_kv_cache,
)
def evaluate(self) -> None:
# Initialize tasks for the harness
task_manager = TaskManager(include_path=self.include_path)
task_dict = get_task_dict(self.tasks, task_manager)
task_types = set([t.task.OUTPUT_TYPE for t in get_task_list(task_dict)])
if len(task_types) > 1 and "generate_until" in task_types:
raise RuntimeError(
"Evaluating on multiple task types where any one task involves "
"generation is currently not supported. See the issue below for more info: "
"https://github.com/pytorch/torchtune/issues/1621"
)
# Run evaluation
t0 = time.time()
self.logger.info(f"Running evaluation on the following tasks: {self.tasks}")
output = evaluate(
self.eleuther_model_wrapper,
task_dict,
limit=self.limit,
)
t1 = time.time() - t0
# Log metrics
self.logger.info(f"Eval completed in {t1:.02f} seconds.")
self.logger.info(
f"Max memory allocated: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB"
)
formatted_output = lm_eval.utils.make_table(output)
self.logger.info(f"\n\n{formatted_output}\n")
@config.parse
def recipe_main(cfg: DictConfig) -> None:
"""Entry point for the recipe."""
config.log_config(recipe_name="EleutherEvalRecipe", cfg=cfg)
recipe = EleutherEvalRecipe(cfg=cfg)
recipe.setup(cfg=cfg)
recipe.evaluate()
if __name__ == "__main__":
sys.exit(recipe_main())