b6fc46ada8
* add grpo scale_rewards config for trl#3135 * options to connect to vllm server directly w grpo trl#3094 * temperature support trl#3029 * sampling/generation kwargs for grpo trl#2989 * make vllm_enable_prefix_caching a config param trl#2900 * grpo multi-step optimizeations trl#2899 * remove overrides for grpo trainer * bump trl to 0.16.0 * add cli to start vllm-serve via trl * call the python module directly * update to use vllm with 2.6.0 too now and call trl vllm serve from module * vllm 0.8.1 * use python3 * use sys.executable * remove context and wait for start * fixes to make it actually work * fixes so the grpo tests pass with new vllm paradigm * explicit host/port and check in start vllm * make sure that vllm doesn't hang by setting quiet so outouts go to dev null * also bump bnb to latest release * add option for wait from cli and nccl debugging for ci * grpo + vllm test on separate devices for now * make sure grpo + vllm tests runs single worker since pynccl comms would conflict * fix cli * remove wait and add caching for argilla dataset * refactoring configs * chore: lint * add vllm config * fixup vllm grpo args * fix one more incorrect schema/config path * fix another vlllm reference and increase timeout * make the tests run a bit faster * change mbsz back so it is correct for grpo * another change mbsz back so it is correct for grpo * fixing cli args * nits * adding docs * docs * include tensor parallel size for vllm in pydantic schema * moving start_vllm, more docs * limit output len for grpo vllm * vllm enable_prefix_caching isn't a bool cli arg * fix env ordering in tests and also use pid check when looking for vllm --------- Co-authored-by: Salman Mohammadi <salman.mohammadi@outlook.com>
141 lines
4.3 KiB
Python
141 lines
4.3 KiB
Python
"""
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E2E tests for multigpu post-training use Ray Train
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"""
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import logging
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import os
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from pathlib import Path
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import pytest
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import yaml
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from accelerate.test_utils import execute_subprocess_async
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from axolotl.utils.dict import DictDefault
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from tests.e2e.utils import check_tensorboard, require_torch_lt_2_6_0
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LOG = logging.getLogger(__name__)
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os.environ["WANDB_DISABLED"] = "true"
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AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
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class TestMultiGPURay:
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"""
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Test cases for AnyScale Ray post training
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"""
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@require_torch_lt_2_6_0
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def test_lora_ddp(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"sequence_len": 2048,
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"adapter": "lora",
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.05,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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"datasets": [
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{
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"path": "tatsu-lab/alpaca",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 4,
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"gradient_accumulation_steps": 2,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_8bit",
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"lr_scheduler": "cosine",
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"flash_attention": True,
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"use_tensorboard": True,
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"use_ray": True,
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"ray_num_workers": 2,
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}
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)
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# write cfg to yaml file
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Path(temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
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fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
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execute_subprocess_async(
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[
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"axolotl",
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"train",
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str(Path(temp_dir) / "config.yaml"),
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"--use-ray",
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"--ray-num-workers",
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"2",
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]
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)
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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
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)
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@require_torch_lt_2_6_0
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@pytest.mark.parametrize(
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"gradient_accumulation_steps",
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[1, 2],
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)
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def test_ds_zero2_packed(self, temp_dir, gradient_accumulation_steps):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"sample_packing": True,
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"pad_to_sequence_len": True,
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"sequence_len": 2048,
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"val_set_size": 0.05,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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"datasets": [
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{
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"path": "tatsu-lab/alpaca",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": gradient_accumulation_steps,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch",
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"lr_scheduler": "cosine",
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"flash_attention": True,
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"deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero2.json"),
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"use_tensorboard": True,
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}
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)
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# write cfg to yaml file
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Path(temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
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fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
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execute_subprocess_async(
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[
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"axolotl",
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"train",
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str(Path(temp_dir) / "config.yaml"),
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"--use-ray",
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"--ray-num-workers",
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"2",
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]
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)
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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
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)
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