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use axolotl contribs for fix_untrained_tokens (#2194) [skip ci]

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* use axolotl contribs for fix_untrained_tokens

* remove the module we're replacing

* Add check for using fix_untrained_tokens
This commit is contained in:
Wing Lian
2024-12-17 13:57:16 -05:00
committed by GitHub
parent 8ddc18ec8d
commit e246ceffa4
5 changed files with 55 additions and 279 deletions
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2
requirements.txt
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@@ -60,3 +60,5 @@ antlr4-python3-runtime==4.13.2
torchao==0.7.0 torchao==0.7.0
schedulefree==1.3.0 schedulefree==1.3.0
axolotl-contribs-lgpl==0.0.1b2

4
src/axolotl/__init__.py
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@@ -1,3 +1,7 @@
"""Axolotl - Train and fine-tune large language models""" """Axolotl - Train and fine-tune large language models"""
import pkgutil
__path__ = pkgutil.extend_path(__path__, __name__) # Make this a namespace package
__version__ = "0.6.0" __version__ = "0.6.0"

272
src/axolotl/core/tokenizer_utils.py
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@@ -1,272 +0,0 @@
"""
helper functions for fixing the embeddings/tokenizer
"""
# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
# GNU LESSER GENERAL PUBLIC LICENSE
# Version 3, 29 June 2007
#
# Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
# Everyone is permitted to copy and distribute verbatim copies
# of this license document, but changing it is not allowed.
import gc
import itertools
import logging
from collections import Counter
import datasets
import numpy as np
import torch
LOG = logging.getLogger("axolotl.core.tokenizer_utils")
@torch.inference_mode()
def fix_untrained_tokens( # pylint: disable=too-many-return-statements
model, tokenizer, train_dataset, ignored_tokenizer_names=None, eps=1e-16
):
"""
Llama-3 for eg has untrained vectors in the base model.
These include <|eot_id|>, <|start_header_id|>, <|end_header_id|>
We reset them to the mean of the rest of the tokens
"""
# Code licensed under LGPL
embedding_matrix = model.get_input_embeddings().weight
lm_head_matrix = model.get_output_embeddings().weight
chat_template = getattr(tokenizer, "chat_template", None)
tokenizer = tokenizer.tokenizer if hasattr(tokenizer, "tokenizer") else tokenizer
# Ignore some model checks for now
if not ignored_tokenizer_names:
ignored_tokenizer_names = []
if (
model.config._name_or_path # pylint: disable=protected-access
in ignored_tokenizer_names
):
return
# Sometimes the sizes can be different like in vision models
# Ie <image> is in input, but not in output
min_size = min(embedding_matrix.shape[1], lm_head_matrix.shape[1])
embedding_matrix = embedding_matrix[:, :min_size]
lm_head_matrix = lm_head_matrix[:, :min_size]
# Get untrained tokens
indicator_untrained1 = torch.amax(embedding_matrix, axis=1) <= eps
# Check lm_head as well
# Does NOT work for Llama 3.1!!
indicator_untrained2 = torch.amax(lm_head_matrix, axis=1) <= eps
# We instead check for repeated vectors
lm_head_where = torch.where(indicator_untrained1)[0]
lm_head_bad = lm_head_matrix[lm_head_where]
lm_head_bad = lm_head_bad.cpu().float().numpy().round(3)
counter = Counter()
for row in lm_head_bad:
counter[hash(row.data.tobytes())] += 1
counter = Counter({k: c for k, c in counter.items() if c >= 2})
lm_head_where = lm_head_where.cpu().numpy()
final_bad_lm_head = []
for j, row in enumerate(lm_head_bad):
if hash(row.data.tobytes()) in counter:
final_bad_lm_head.append(lm_head_where[j])
indicator_untrained2 = indicator_untrained2 | torch.zeros_like(indicator_untrained2)
indicator_untrained2[final_bad_lm_head] = True
# Combine both checks
indicator_untrained = indicator_untrained1 & indicator_untrained2
# Remove pad token possibility
if hasattr(tokenizer, "pad_token_id"):
pad_token_id = tokenizer.pad_token_id
if pad_token_id is not None and pad_token_id < indicator_untrained.shape[0]:
indicator_untrained[pad_token_id] = False
where_untrained = torch.where(indicator_untrained)[0]
n_untrained = where_untrained.shape[0]
n_trained = embedding_matrix.shape[0] - n_untrained
# Get set and actual tokens
where_untrained = where_untrained.tolist()
if len(where_untrained) == 0:
return
# Remove untrained indices where it's longer
where_untrained_set = frozenset(where_untrained)
actual_bad_tokens = tokenizer.convert_ids_to_tokens(where_untrained)
# Remove None items in actual_bad_tokens
actual_bad_tokens = [x for x in actual_bad_tokens if x is not None]
# Check if tokenizer and training datasets have bad tokens
if_bad_first = False
if_bad_second = False
# Check tokenizer's chat template for any untrained tokens
if chat_template is not None:
if_bad_first = any(x in chat_template for x in actual_bad_tokens)
if isinstance(train_dataset, datasets.IterableDataset):
# Skip the check, since the code below assumes
# an indexable dataset
return
# Check the first 250, last 250 input_ids
size_dataset = len(train_dataset)
size = min(size_dataset, 250)
for j in range(size):
input_ids = train_dataset[j]
if "input_ids" in input_ids:
input_ids = input_ids["input_ids"]
if_bad = any(item in where_untrained_set for item in input_ids)
if if_bad:
if_bad_second = True
break
# Check last 250
if not if_bad_second:
left = max(size_dataset - 250, 0)
for j in range(left, size_dataset):
input_ids = train_dataset[j]
if "input_ids" in input_ids:
input_ids = input_ids["input_ids"]
if_bad = any(item in where_untrained_set for item in input_ids)
if if_bad:
if_bad_second = True
break
# Check if bad tokens exists!
if not if_bad_first and not if_bad_second:
return
# Check if lm_head / embed_token are trainable!
bad_not_trainable = False
if not embedding_matrix.requires_grad:
bad_not_trainable = True
if not lm_head_matrix.requires_grad:
bad_not_trainable = True
if bad_not_trainable: # pylint: disable=too-many-nested-blocks
final_bad_items = []
# Re-check the first 250, last 250 input_ids
size_dataset = len(train_dataset)
size = min(size_dataset, 250)
for j in range(size):
input_ids = train_dataset[j]
if "input_ids" in input_ids:
input_ids = input_ids["input_ids"]
for item in input_ids:
if item in where_untrained_set:
final_bad_items.append(item)
# Re-check last 250
left = max(size_dataset - 250, 0)
for j in range(left, size_dataset):
input_ids = train_dataset[j]
if "input_ids" in input_ids:
input_ids = input_ids["input_ids"]
for item in input_ids:
if item in where_untrained_set:
final_bad_items.append(item)
# If no bad tokens, possibly chat template itself has issues?
if len(final_bad_items) == 0:
# Recheck 2000 and last 2000 items
size_dataset = len(train_dataset)
size = min(size_dataset, 2000)
for j in range(size):
input_ids = train_dataset[j]
if "input_ids" in input_ids:
input_ids = input_ids["input_ids"]
for item in input_ids:
if item in where_untrained_set:
final_bad_items.append(item)
# Re-check last 2000
left = max(size_dataset - 2000, 0)
for j in range(left, size_dataset):
input_ids = train_dataset[j]
if "input_ids" in input_ids:
input_ids = input_ids["input_ids"]
for item in input_ids:
if item in where_untrained_set:
final_bad_items.append(item)
# Most likely false signal!
if len(final_bad_items) == 0:
return
raise ValueError(
f"Untrained tokens of [{list(set(final_bad_items))}] found, but embed_tokens & lm_head not trainable, causing NaNs. "
)
# Count all the possible bad tokens
final_counts = np.zeros(
max(len(tokenizer), embedding_matrix.shape[0]), dtype=np.int64
)
def mapping(examples):
input_ids = examples["input_ids"]
counter = np.fromiter(itertools.chain.from_iterable(input_ids), dtype=np.int32)
np.add.at(final_counts, counter, 1)
train_dataset.map(mapping, batched=True, desc="Counting untrained tokens")
# Get counts for untrained tokens
counts_untrained = final_counts[where_untrained]
# Identify untrained tokens seen in train_dataset
indices_seen_in_train = np.where(counts_untrained > 0)[0]
tokens_to_update = [where_untrained[i] for i in indices_seen_in_train]
if len(tokens_to_update) == 0:
LOG.info(
"No untrained tokens found in train_dataset. No embeddings were modified."
)
return
# Log the token IDs that are being rescaled
LOG.info(
f"Rescaling embeddings for tokens seen in train_dataset: {tokens_to_update}"
)
# Get sum of all items
sum_embedding = torch.sum(embedding_matrix, dtype=torch.float32, axis=0)
sum_lm_head = torch.sum(lm_head_matrix, dtype=torch.float32, axis=0)
# Remove bad tokens
sum_embedding -= torch.sum(
embedding_matrix[where_untrained], dtype=torch.float32, axis=0
)
sum_lm_head -= torch.sum(
lm_head_matrix[where_untrained], dtype=torch.float32, axis=0
)
# Find correct average by dividing by sum of trained tokens
mean_embedding = sum_embedding / n_trained
mean_lm_head = sum_lm_head / n_trained
# Compute scaling for tokens to update
scaling = counts_untrained[indices_seen_in_train] / max(final_counts.max(), 1)
scaling = torch.tensor(scaling, device=mean_embedding.device).unsqueeze(1)
# Prepare mean embeddings for tokens to update
mean_embedding_repeated = (
mean_embedding.unsqueeze(0).repeat(len(tokens_to_update), 1) * scaling
)
mean_lm_head_repeated = (
mean_lm_head.unsqueeze(0).repeat(len(tokens_to_update), 1) * scaling
)
# Update embeddings only for tokens seen in train_dataset
embedding_matrix[tokens_to_update] = mean_embedding_repeated.to(
embedding_matrix.dtype
)
lm_head_matrix[tokens_to_update] = mean_lm_head_repeated.to(lm_head_matrix.dtype)
# Clean up
for _ in range(3):
gc.collect()
torch.cuda.empty_cache()
return

4
src/axolotl/train.py
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@@ -19,7 +19,9 @@ from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
from axolotl.common.cli import TrainerCliArgs from axolotl.common.cli import TrainerCliArgs
from axolotl.core.tokenizer_utils import fix_untrained_tokens from axolotl.contribs.lgpl.unsloth import ( # pylint: disable = no-name-in-module
fix_untrained_tokens,
)
from axolotl.logging_config import configure_logging from axolotl.logging_config import configure_logging
from axolotl.utils.dict import DictDefault from axolotl.utils.dict import DictDefault
from axolotl.utils.freeze import freeze_layers_except from axolotl.utils.freeze import freeze_layers_except

52
tests/e2e/test_llama.py
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@@ -4,7 +4,6 @@ E2E tests for llama
import logging import logging
import os import os
import unittest
from pathlib import Path from pathlib import Path
from axolotl.cli import load_datasets from axolotl.cli import load_datasets
@@ -13,18 +12,15 @@ from axolotl.train import train
from axolotl.utils.config import normalize_config from axolotl.utils.config import normalize_config
from axolotl.utils.dict import DictDefault from axolotl.utils.dict import DictDefault
from .utils import with_temp_dir
LOG = logging.getLogger("axolotl.tests.e2e") LOG = logging.getLogger("axolotl.tests.e2e")
os.environ["WANDB_DISABLED"] = "true" os.environ["WANDB_DISABLED"] = "true"
class TestLlama(unittest.TestCase): class TestLlama:
""" """
Test case for Llama models Test case for Llama models
""" """
@with_temp_dir
def test_fft_trust_remote_code(self, temp_dir): def test_fft_trust_remote_code(self, temp_dir):
# pylint: disable=duplicate-code # pylint: disable=duplicate-code
cfg = DictDefault( cfg = DictDefault(
@@ -46,7 +42,8 @@ class TestLlama(unittest.TestCase):
}, },
], ],
"num_epochs": 1, "num_epochs": 1,
"micro_batch_size": 8, "max_steps": 5,
"micro_batch_size": 2,
"gradient_accumulation_steps": 1, "gradient_accumulation_steps": 1,
"output_dir": temp_dir, "output_dir": temp_dir,
"learning_rate": 0.00001, "learning_rate": 0.00001,
@@ -64,3 +61,46 @@ class TestLlama(unittest.TestCase):
train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "model.safetensors").exists() assert (Path(temp_dir) / "model.safetensors").exists()
def test_fix_untrained_tokens(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "HuggingFaceTB/SmolLM2-135M",
"fix_untrained_tokens": True,
"sequence_len": 512,
"val_set_size": 0.0,
"special_tokens": {
"pad_token": "<|endoftext|>",
},
"chat_template": "chatml",
"datasets": [
{
"path": "mlabonne/FineTome-100k",
"type": "chat_template",
"split": "train[:10%]",
"field_messages": "conversations",
"message_field_role": "from",
"message_field_content": "value",
},
],
"num_epochs": 1,
"max_steps": 5,
"micro_batch_size": 1,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_8bit",
"lr_scheduler": "cosine",
"flash_attention": True,
"sample_packing": True,
"bf16": True,
"save_safetensors": True,
}
)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "model.safetensors").exists()