2bb0b78975
* fix attetion mask with packing * set position ids and use block diagonal attn mask * fix expand mask for multiple batch items, make sure we pad position_ids * don't move masks to cpu * use multi pack dataloader w random sampler * add position_ids back * more fixes for dataloader integration * est total tokens, fix field loop * more fixes, position_ids seems broken * more fixes for sample packing * use distributed sampler, avoid accelerate prepare * use accelerator prepare for dataloader * fix for position_ids w packing * Update src/axolotl/utils/dataloader.py * validation for sample packing and doc * more fixes for 4k and optimizations * optimized expand mask fn * better handling of variance in multipack dataloader length and trainer hanging when it runs out of data * fix rounding of len of batches to int * better handling so that all devices have the same dataloader len * fix step calc for packing * pass sample packing efficiency to training args * add a test for the mask expansion for sequence packing * only process eval dataset for packing if not None * don't split batches when packing * weighted CE losses * weighted CEL fixes * limit packing to sequences of max seq len * seq_len_multiple for packing * make sure the chunk size is an int * sample_packing_seq_len_multiplier config * use cumulative seq len with var len flash attn v2 w packing * properly calculate max len * fix flash-attn, xformers, packing, support chatml * fix chatml system prompt for openorca, legacy tokenizer opts * add chatml * add unit tests for cum seq lens, add ability to build cu_seq_lens from positional ids, fix prompt test * fix test and pylint checks * more packing and dataset optimizations and fixes * filter w multiple cpus * more fixes and optimizations * fixes and go back to distributed sampler since batch sampler won't work * fix counts by accounting for num devices * fix steps calculation * previous accelerate is still most performant * add numba to requirements. * use custom distributed checks * fix sampler to prevent overfit w new epochs * let's not cleanup the cached datasets * calculate cum seq lens with pos_ids instead of mask, simplify packing params, fix distributed barrier * speed optimizations and set accelerate fsdp env vars * optimize dataset concatenation? * more optimizations for dataset handling * fix import for annotation * manual pre-commit fixes * another sum optimization and bug fix for calc steps * fix packing estimations * fix formatting * pylint problems * add back flash attention branch for handling unpacked sequences seperately * Address PR feedback * add optional sample packing config params to readme
230 lines
8.0 KiB
Python
230 lines
8.0 KiB
Python
"""Module for testing prompt tokenizers."""
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import json
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import logging
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import unittest
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from pathlib import Path
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from transformers import AutoTokenizer, LlamaTokenizer
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from axolotl.prompt_strategies.alpaca_chat import NoSystemPrompter
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from axolotl.prompt_strategies.alpaca_w_system import (
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InstructionWSystemPromptTokenizingStrategy,
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SystemDataPrompter,
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)
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from axolotl.prompt_strategies.llama2_chat import (
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Llama2ChatPrompter,
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LLama2ChatTokenizingStrategy,
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)
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from axolotl.prompt_tokenizers import (
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AlpacaPromptTokenizingStrategy,
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ShareGPTPromptTokenizingStrategy,
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)
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from axolotl.prompters import AlpacaPrompter, PromptStyle, ShareGPTPrompter
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LOG = logging.getLogger("axolotl")
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class TestPromptTokenizationStrategies(unittest.TestCase):
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"""
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Test class for prompt tokenization strategies.
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"""
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def setUp(self) -> None:
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# pylint: disable=duplicate-code
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self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
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self.tokenizer.add_special_tokens(
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{
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"bos_token": "<s>",
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"eos_token": "</s>",
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"unk_token": "<unk>",
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}
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)
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def test_sharegpt_integration(self):
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with open(
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Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
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) as fin:
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data = fin.read()
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conversation = json.loads(data)
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with open(
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Path(__file__).parent / "fixtures/conversation.tokenized.json",
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encoding="utf-8",
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) as fin:
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data = fin.read()
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tokenized_conversation = json.loads(data)
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prompter = ShareGPTPrompter("chat")
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strat = ShareGPTPromptTokenizingStrategy(
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prompter,
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self.tokenizer,
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False,
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2048,
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)
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example = strat.tokenize_prompt(conversation)
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for fields in ["input_ids", "attention_mask", "labels"]:
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self.assertEqual(len(example[fields]), len(tokenized_conversation[fields]))
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self.assertEqual(example[fields], tokenized_conversation[fields])
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def test_no_sys_prompt(self):
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"""
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tests the interface between the user and assistant parts
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"""
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prompter = NoSystemPrompter()
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# pylint: disable=duplicate-code
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strat = AlpacaPromptTokenizingStrategy(
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prompter,
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self.tokenizer,
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False,
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2048,
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)
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sample = {
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"instruction": "hello cruel. lorem ipsum dolor sit amet.",
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"output": "world!",
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}
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example = strat.tokenize_prompt(sample)
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world_idx = example["input_ids"].index(3186)
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assert example["labels"][world_idx] == 3186
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assert example["labels"][world_idx - 1] == -100
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def test_alpaca(self):
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"""
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tests the interface between the user and assistant parts
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"""
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# pylint: disable=duplicate-code
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prompter = AlpacaPrompter()
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strat = AlpacaPromptTokenizingStrategy(
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prompter,
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self.tokenizer,
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False,
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2048,
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)
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sample = {"instruction": "hello!", "output": "Hi! How can I help?"}
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example = strat.tokenize_prompt(sample)
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world_idx = example["input_ids"].index(6324)
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assert example["labels"][world_idx] == 6324
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assert example["labels"][world_idx - 1] == -100
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class InstructionWSystemPromptTokenizingStrategyTest(unittest.TestCase):
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"""
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Test class for prompt tokenization strategies with sys prompt from the dataset
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"""
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def setUp(self) -> None:
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# pylint: disable=duplicate-code
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self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
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self.tokenizer.add_special_tokens(
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{
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"bos_token": "<s>",
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"eos_token": "</s>",
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"unk_token": "<unk>",
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}
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)
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def test_system_alpaca(self):
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prompter = SystemDataPrompter(PromptStyle.CHAT.value)
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strat = InstructionWSystemPromptTokenizingStrategy(
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prompter,
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self.tokenizer,
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False,
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2048,
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)
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sample = {
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"system": "use cot",
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"instruction": "hello!",
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"output": "Hi! How can I help?",
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}
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example = strat.tokenize_prompt(sample)
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assert example["input_ids"][0:5] == [
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1,
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28962,
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1254,
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12665,
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29901,
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] # "<s>SYSTEM:"
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assert example["input_ids"][5:7] == [671, 20118] # " use cot"
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assert example["input_ids"][8] == 11889 # USER
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class Llama2ChatTokenizationTest(unittest.TestCase):
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"""
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Test class for prompt tokenization strategies with sys prompt from the dataset
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"""
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def setUp(self) -> None:
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# pylint: disable=duplicate-code
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self.tokenizer = LlamaTokenizer.from_pretrained("NousResearch/Llama-2-7b-hf")
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# woraround because official Meta repos are not open
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def test_llama2_chat_integration(self):
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with open(
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Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
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) as fin:
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data = fin.read()
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conversation = json.loads(data)
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with open(
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Path(__file__).parent / "fixtures/conversation.tokenized_llama2chat.json",
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encoding="utf-8",
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) as fin:
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data = fin.read()
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tokenized_conversation = json.loads(data)
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prompter = Llama2ChatPrompter()
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strat = LLama2ChatTokenizingStrategy(
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prompter,
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self.tokenizer,
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False,
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4096,
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)
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example = strat.tokenize_prompt(conversation)
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for fields in ["input_ids", "attention_mask", "labels"]:
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self.assertEqual(len(example[fields]), len(tokenized_conversation[fields]))
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self.assertEqual(example[fields], tokenized_conversation[fields])
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def compare_with_transformers_integration(self):
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# this needs transformers >= v4.31.0
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from transformers.models.llama.tokenization_llama import B_SYS, E_SYS
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from transformers.pipelines.conversational import Conversation
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# from transformers.models.llama.tokenization_llama import DEFAULT_SYSTEM_PROMPT
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# broken as of 23/7/20
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# see https://github.com/huggingface/transformers/pull/24935
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# pylint: disable=C0103
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DEFAULT_SYSTEM_PROMPT = """\
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You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
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If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
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with open(
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Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
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) as fin:
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data = fin.read()
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conversation = json.loads(data)
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with open(
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Path(__file__).parent / "fixtures/conversation.tokenized_llama2chat.json",
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encoding="utf-8",
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) as fin:
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data = fin.read()
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tokenized_conversation = json.loads(data)
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user_input = []
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answers = []
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for msg in conversation["conversations"]:
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if msg["from"] == "human":
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user_input.append(msg["value"])
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else:
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answers.append(msg["value"])
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hf_conf = Conversation(
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text=user_input[-1],
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past_user_inputs=[B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + user_input[0]]
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+ user_input[1:-1],
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generated_responses=answers,
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)
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# pylint: disable=W0212
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hf_tokens = self.tokenizer._build_conversation_input_ids(hf_conf)
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self.assertEqual(
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hf_tokens, tokenized_conversation["input_ids"][: len(hf_tokens)]
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)
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if __name__ == "__main__":
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unittest.main()
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