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Phi-3 conversation format, example training script and perplexity metric (#1582)

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* phi-3 support and perplexity metric

* phi-3 chat template

* metrics updates

* chore: lint

* fix assertion on Tensor

* fix tests since tokenization happens in the metric

* fix perplexity value of shorter passage

---------

Co-authored-by: Wing Lian <wing.lian@gmail.com>
This commit is contained in:
Brian Fitzgerald
2024-06-04 15:11:56 -05:00
committed by GitHub
parent c996881ec2
commit cf64284a04
10 changed files with 243 additions and 26 deletions
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6
.gitignore vendored
View File

@@ -176,3 +176,9 @@ qlora-out/*
mlruns/*
/.quarto/
prepared-datasets/
submit.sh
*.out*
typings/
out/

64
examples/phi/phi3-ft.yml Normal file
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@@ -0,0 +1,64 @@
base_model: microsoft/Phi-3-mini-4k-instruct
trust_remote_code: true
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
chat_template: phi_3
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: garage-bAInd/Open-Platypus
type: alpaca:phi
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_torch
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 5.0e-6
train_on_inputs: false
group_by_length: false
bf16: auto
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: True
early_stopping_patience: 3
logging_steps: 1
flash_attention: true
eval_steps: 1000
save_steps: 5000
eval_table_size: 2
eval_batch_size: 2
eval_sample_packing: false
eval_max_new_tokens: 32
eval_causal_lm_metrics: ["perplexity"]
do_causal_lm_eval: true
warmup_ratio: 0.2
debug: true
weight_decay: 0.1
resize_token_embeddings_to_32x: true

15
src/axolotl/prompters.py
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@@ -20,6 +20,7 @@ class PromptStyle(Enum):
INSTRUCT = "instruct"
CHAT = "chat"
CHATML = "chatml"
PHI = "phi"
class Prompter:
@@ -38,9 +39,9 @@ class AlpacaPrompter(Prompter):
system_format: str = "{system}"
turn_format: str
turn_no_input_format: str
prompt_style: Optional[PromptStyle] = None
prompt_style: Optional[str] = None
def __init__(self, prompt_style=PromptStyle.INSTRUCT.value):
def __init__(self, prompt_style: Optional[str] = PromptStyle.INSTRUCT.value):
self.prompt_style = prompt_style if prompt_style else PromptStyle.INSTRUCT.value
self.match_prompt_style()
@@ -52,16 +53,20 @@ class AlpacaPrompter(Prompter):
"### Instruction:\n{instruction}\n\n### Response:\n"
)
self.system_format = "{system}\n\n"
if self.prompt_style == PromptStyle.CHAT.value:
elif self.prompt_style == PromptStyle.CHAT.value:
self.turn_format = "USER: {instruction}\n{input}\nASSISTANT:"
self.turn_no_input_format = "USER: {instruction}\nASSISTANT:"
self.system_format = "SYSTEM: {system}\n"
if self.prompt_style == PromptStyle.CHATML.value:
elif self.prompt_style == PromptStyle.CHATML.value:
self.turn_format = "<|im_start|>user\n{instruction}\n{input}<|im_end|>\n<|im_start|>assistant\n"
self.turn_no_input_format = (
"<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
)
self.system_format = "<|im_start|>system\n{system}<|im_end|>\n"
elif self.prompt_style == PromptStyle.PHI.value:
self.turn_format = "<|user|>\n{instruction}<|end|>{input}<|assistant|>"
self.turn_no_input_format = "<|user|>\n{instruction}<|end|><|assistant|>"
self.system_format = "<|system|>{system}\n"
def _build_result(self, instruction, input_text, output):
# returns the full prompt from instruction and optional input
@@ -381,12 +386,14 @@ class ShareGPTPrompterV2(ShareGPTPrompter):
conversation: Optional[Union[str, Conversation]] = None,
role_key_human: Optional[str] = None,
role_key_model: Optional[str] = None,
role_key_tool: Optional[str] = None,
roles: Optional[dict] = None,
):
super().__init__(
conversation=conversation,
role_key_human=role_key_human,
role_key_model=role_key_model,
role_key_tool=role_key_tool,
roles=roles,
)

34
src/axolotl/utils/callbacks/__init__.py
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@@ -5,6 +5,7 @@ from __future__ import annotations
import logging
import math
import os
import traceback
from shutil import copyfile
from tempfile import NamedTemporaryFile
from typing import TYPE_CHECKING, Any, Dict, List
@@ -30,6 +31,7 @@ from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
from axolotl.utils import is_mlflow_available
from axolotl.utils.bench import log_gpu_memory_usage
from axolotl.utils.callbacks.perplexity import Perplexity
from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
from axolotl.utils.distributed import (
barrier,
@@ -374,10 +376,14 @@ def causal_lm_bench_eval_callback_factory(trainer: Trainer, tokenizer):
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}")
if metric == "perplexity":
max_seq_len = self.cfg.eval_max_new_tokens
metrics[metric] = Perplexity(trainer.model, tokenizer, max_seq_len)
else:
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(
@@ -421,13 +427,20 @@ def causal_lm_bench_eval_callback_factory(trainer: Trainer, tokenizer):
# safely compute a metric and return the score if the format is correct
metric_score = None
try:
metric_score = metric.compute(**kwargs)
# Only pass the kwargs that are in the metric's feature list
metric_kwargs = {
k: kwargs[k]
for k in metric._feature_names() # pylint: disable=protected-access
if k in kwargs
}
metric_score = metric.compute(**metric_kwargs)
return (
metric_score["score"]
if "score" in metric_score
else metric_score["mean_score"]
)
except Exception: # pylint: disable=broad-exception-caught
traceback.print_exc()
LOG.debug(
f"Failed to compute metric {metric.name} with kwargs {kwargs.keys()}"
)
@@ -443,11 +456,12 @@ def causal_lm_bench_eval_callback_factory(trainer: Trainer, tokenizer):
predictions=predictions,
sources=sources,
)
score = score or compute(
metric,
references=[[r] for r in references],
predictions=predictions,
)
if score is None:
score = compute(
metric,
references=[[r] for r in references],
predictions=predictions,
)
scores[metric_name] = score
return scores

76
src/axolotl/utils/callbacks/perplexity.py Normal file
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@@ -0,0 +1,76 @@
"""callback to calculate perplexity as an evaluation metric."""
from typing import Dict, List, Optional
import torch
from torch import Tensor
from tqdm import tqdm
from transformers.modeling_outputs import CausalLMOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.tokenization_utils import PreTrainedTokenizer
class Perplexity:
"""
Calculate perplexity as defined in https://huggingface.co/docs/transformers/en/perplexity.
This is a custom variant that doesn't re-tokenize the input or re-load the model.
"""
def __init__(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
max_seq_len: int,
stride: int = 512,
) -> None:
self.max_seq_len = max_seq_len
self.stride = stride
self.model = model
self.tokenizer = tokenizer
self.device = model.device
self.name = "perplexity"
def _feature_names(self) -> List[str]:
return ["references"]
def compute(
self,
references: Optional[List[str]] = None,
) -> Dict[str, float]:
"""
Compute perplexity in a fixed length sliding window across the sequence.
"""
assert references is not None, "Missing parameter: references"
references_tokenized = self.tokenizer(
references, return_tensors="pt", padding=True, truncation=True
)
input_ids: Tensor = references_tokenized["input_ids"] # type: ignore
input_ids = input_ids.to(self.device)
sequence_length = input_ids.size(1)
losses = []
prev_end_loc = 0
for begin_loc in tqdm(range(0, sequence_length, self.stride)):
end_loc = min(begin_loc + self.max_seq_len, sequence_length)
trg_len = end_loc - prev_end_loc
input_ids_slice = input_ids[:, begin_loc:end_loc]
labels_slice = input_ids_slice.clone()
labels_slice[:, :-trg_len] = -100
with torch.no_grad():
outputs: CausalLMOutput = self.model(
input_ids=input_ids_slice, labels=labels_slice
)
losses.append(outputs.loss)
prev_end_loc = end_loc
if end_loc == sequence_length:
break
perplexity = torch.exp(torch.stack(losses).mean()).item()
return {
"score": perplexity,
}

1
src/axolotl/utils/chat_templates.py
View File

@@ -25,6 +25,7 @@ def chat_templates(user_choice: str):
"gemma": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
"cohere": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}",
"llama3": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
"phi_3": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|system|>' + '\n' + message['content'] + '<|end|>' + '\n'}}{% elif (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif message['role'] == 'assistant' %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
}
if user_choice in templates:

6
src/axolotl/utils/config/__init__.py
View File

@@ -10,6 +10,7 @@ from transformers.utils import is_torch_bf16_gpu_available
from axolotl.utils.bench import log_gpu_memory_usage
from axolotl.utils.config.models.input.v0_4_1 import (
SUPPORTED_METRICS,
AxolotlConfigWCapabilities,
AxolotlInputConfig,
)
@@ -586,13 +587,12 @@ def legacy_validate_config(cfg):
)
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):
if set(cfg.eval_causal_lm_metrics) - SUPPORTED_METRICS:
raise ValueError(
f"eval_causal_lm_metrics must be one of {supported_metrics}"
f"eval_causal_lm_metrics must be one of {SUPPORTED_METRICS}"
)
# TODO

8
src/axolotl/utils/config/models/input/v0_4_1/__init__.py
View File

@@ -17,6 +17,8 @@ from axolotl.utils.config.models.internals import GPUCapabilities
LOG = logging.getLogger("axolotl.utils.config.models.input")
SUPPORTED_METRICS = {"sacrebleu", "comet", "ter", "chrf", "perplexity"}
class DeprecatedParameters(BaseModel):
"""configurations that are deprecated"""
@@ -176,6 +178,7 @@ class ChatTemplate(str, Enum):
gemma = "gemma" # pylint: disable=invalid-name
cohere = "cohere" # pylint: disable=invalid-name
llama3 = "llama3" # pylint: disable=invalid-name
phi_3 = "phi_3" # pylint: disable=invalid-name
class LoftQConfig(BaseModel):
@@ -1073,13 +1076,12 @@ class AxolotlInputConfig(
)
if data.get("eval_causal_lm_metrics"):
supported_metrics = ["sacrebleu", "comet", "ter", "chrf"]
if not isinstance(data.get("eval_causal_lm_metrics"), list):
raise ValueError("eval_causal_lm_metrics must be a list")
# only ["sacrebleu", "comet", "ter", "chrf"] supported
if set(data.get("eval_causal_lm_metrics")) - set(supported_metrics):
if set(data.get("eval_causal_lm_metrics")) - SUPPORTED_METRICS:
raise ValueError(
f"eval_causal_lm_metrics must be one of {supported_metrics}"
f"eval_causal_lm_metrics must be one of {SUPPORTED_METRICS}"
)
return data

18
src/axolotl/utils/data/sft.py
View File

@@ -474,12 +474,16 @@ def load_prepare_datasets(
index=cfg.dataset_shard_idx,
)
if split == "train" and cfg.val_set_size:
val_set_size = (
int(cfg.val_set_size) if cfg.val_set_size > 1 else float(cfg.val_set_size)
)
if split == "train" and val_set_size:
# ensure we end up with the same fingerprint by doing rank0 first and being able to cache
to_hash_train = (
dataset._fingerprint # pylint: disable=protected-access
+ "|"
+ str(cfg.val_set_size)
+ str(val_set_size)
+ "|"
+ "train"
+ "|"
@@ -488,7 +492,7 @@ def load_prepare_datasets(
to_hash_test = (
dataset._fingerprint # pylint: disable=protected-access
+ "|"
+ str(cfg.val_set_size)
+ str(val_set_size)
+ "|"
+ "test"
+ "|"
@@ -498,9 +502,7 @@ def load_prepare_datasets(
test_fingerprint = md5(to_hash_test)
dataset = dataset.train_test_split(
test_size=int(cfg.val_set_size)
if cfg.val_set_size == int(cfg.val_set_size)
else cfg.val_set_size,
test_size=val_set_size,
shuffle=False,
seed=cfg.seed or 42,
train_new_fingerprint=train_fingerprint,
@@ -535,6 +537,10 @@ def get_dataset_wrapper(
"keep_in_memory": cfg.dataset_keep_in_memory is True,
}
LOG.info(
f"Loading dataset with base_type: {d_base_type} and prompt_style: {d_prompt_style}"
)
if (
isinstance(dataset, Dataset)
and "input_ids" in dataset.features

41
tests/test_perplexity.py Normal file
View File

@@ -0,0 +1,41 @@
"""unit tests for perplexity eval callback"""
# pylint: disable=redefined-outer-name
from pytest import fixture
from transformers.models.auto.modeling_auto import AutoModelForCausalLM
from transformers.models.auto.tokenization_auto import AutoTokenizer
from axolotl.utils.callbacks.perplexity import Perplexity
MODEL_NAME = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
@fixture()
def metric(tokenizer):
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True)
return Perplexity(model, tokenizer, 512)
@fixture()
def tokenizer():
return AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
def test_perplexity_longer_than_stride(metric):
# taken from https://huggingface.co/datasets/roneneldan/TinyStories
sample_text = """
Once upon a time, there was a little car named Beep. Beep loved to go fast and play in the sun. Beep was a healthy car because he always had good fuel. Good fuel made Beep happy and strong. One day, Beep was driving in the park when he saw a big tree. The tree had many leaves that were falling. Beep liked how the leaves fall and wanted to play with them. Beep drove under the tree and watched the leaves fall on him. He laughed and beeped his horn. Beep played with the falling leaves all day. When it was time to go home, Beep knew he needed more fuel. He went to the fuel place and got more healthy fuel. Now, Beep was ready to go fast and play again the next day. And Beep lived happily ever after.
One day, a little fish named Fin was swimming near the shore. He saw a big crab and wanted to be friends. "Hi, I am Fin. Do you want to play?" asked the little fish. The crab looked at Fin and said, "No, I don't want to play. I am cold and I don't feel fine." Fin felt sad but wanted to help the crab feel better. He swam away and thought of a plan. He remembered that the sun could make things warm. So, Fin swam to the top of the water and called to the sun, "Please, sun, help my new friend feel fine and not freeze!" The sun heard Fin's call and shone its warm light on the shore. The crab started to feel better and not so cold. He saw Fin and said, "Thank you, little fish, for making me feel fine. I don't feel like I will freeze now. Let's play together!" And so, Fin and the crab played and became good friends.
"""
result = metric.compute([sample_text])
ppl = result["score"]
assert round(ppl, 2) == 5.37
def test_perplexity_short(metric):
# taken from https://huggingface.co/datasets/roneneldan/TinyStories
sample_text = "Once upon a time, there was a little car named Beep. Beep loved to go fast and play in the sun."
result = metric.compute([sample_text])
ppl = result["score"]
assert round(ppl, 2) == 10.02