4-bit QLoRA via bitsandbytes (4-bit base model + LoRA) (#23479)
* Added lion and paged optimizers and made original tests pass. * Added tests for paged and lion optimizers. * Added and fixed optimizer tests. * Style and quality checks. * Initial draft. Some tests fail. * Fixed dtype bug. * Fixed bug caused by torch_dtype='auto'. * All test green for 8-bit and 4-bit layers. * Added fix for fp32 layer norms and bf16 compute in LLaMA. * Initial draft. Some tests fail. * Fixed dtype bug. * Fixed bug caused by torch_dtype='auto'. * All test green for 8-bit and 4-bit layers. * Added lion and paged optimizers and made original tests pass. * Added tests for paged and lion optimizers. * Added and fixed optimizer tests. * Style and quality checks. * Fixing issues for PR #23479. * Added fix for fp32 layer norms and bf16 compute in LLaMA. * Reverted variable name change. * Initial draft. Some tests fail. * Fixed dtype bug. * Fixed bug caused by torch_dtype='auto'. * All test green for 8-bit and 4-bit layers. * Added lion and paged optimizers and made original tests pass. * Added tests for paged and lion optimizers. * Added and fixed optimizer tests. * Style and quality checks. * Added missing tests. * Fixup changes. * Added fixup changes. * Missed some variables to rename. * revert trainer tests * revert test trainer * another revert * fix tests and safety checkers * protect import * simplify a bit * Update src/transformers/trainer.py * few fixes * add warning * replace with `load_in_kbit = load_in_4bit or load_in_8bit` * fix test * fix tests * this time fix tests * safety checker * add docs * revert torch_dtype * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * multiple fixes * update docs * version checks and multiple fixes * replace `is_loaded_in_kbit` * replace `load_in_kbit` * change methods names * better checks * oops * oops * address final comments --------- Co-authored-by: younesbelkada <younesbelkada@gmail.com> Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Tim Dettmers
120
tests/bitsandbytes/README.md
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120
tests/bitsandbytes/README.md
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# Testing mixed int8 quantization
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The following is the recipe on how to effectively debug `bitsandbytes` integration on Hugging Face `transformers`.
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## Library requirements
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+ `transformers>=4.22.0`
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+ `accelerate>=0.12.0`
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+ `bitsandbytes>=0.31.5`.
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## Hardware requirements
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The following instructions are tested with 2 NVIDIA-Tesla T4 GPUs. To run successfully `bitsandbytes` you would need a 8-bit core tensor supported GPU. Note that Turing, Ampere or newer architectures - e.g. T4, RTX20s RTX30s, A40-A100, A6000 should be supported.
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## Virutal envs
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```bash
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conda create --name int8-testing python==3.8
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pip install bitsandbytes>=0.31.5
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pip install accelerate>=0.12.0
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pip install transformers>=4.23.0
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```
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if `transformers>=4.23.0` is not released yet, then use:
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```
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pip install git+https://github.com/huggingface/transformers.git
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```
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## Troubleshooting
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A list of common errors:
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### Torch does not correctly do the operations on GPU
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First check that:
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```py
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import torch
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vec = torch.randn(1, 2, 3).to(0)
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```
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Works without any error. If not, install torch using `conda` like:
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```bash
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conda create --name int8-testing python==3.8
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conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
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pip install bitsandbytes>=0.31.5
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pip install accelerate>=0.12.0
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pip install transformers>=4.23.0
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```
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For the latest pytorch instructions please see [this](https://pytorch.org/get-started/locally/)
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and the snippet above should work.
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### ` bitsandbytes operations are not supported under CPU!`
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This happens when some Linear weights are set to the CPU when using `accelerate`. Please check carefully `model.hf_device_map` and make sure that there is no `Linear` module that is assigned to CPU. It is fine to have the last module (usually the Lm_head) set on CPU.
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### `To use the type as a Parameter, please correct the detach() semantics defined by __torch_dispatch__() implementation.`
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Use the latest version of `accelerate` with a command such as: `pip install -U accelerate` and the problem should be solved.
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### `Parameter has no attribue .CB`
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Same solution as above.
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### `RuntimeError: CUDA error: an illegal memory access was encountered ... consider passing CUDA_LAUNCH_BLOCKING=1`
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Run your script by pre-pending `CUDA_LAUNCH_BLOCKING=1` and you should observe an error as described in the next section.
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### `CUDA illegal memory error: an illegal memory access at line...`:
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Check the CUDA verisons with:
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```
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nvcc --version
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```
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and confirm it is the same version as the one detected by `bitsandbytes`. If not, run:
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```
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ls -l $CONDA_PREFIX/lib/libcudart.so
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```
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or
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```
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ls -l $LD_LIBRARY_PATH
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```
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Check if `libcudart.so` has a correct symlink that is set. Sometimes `nvcc` detects the correct CUDA version but `bitsandbytes` doesn't. You have to make sure that the symlink that is set for the file `libcudart.so` is redirected to the correct CUDA file.
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Here is an example of a badly configured CUDA installation:
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`nvcc --version` gives:
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which means that the detected CUDA version is 11.3 but `bitsandbytes` outputs:
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First check:
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```bash
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echo $LD_LIBRARY_PATH
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```
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If this contains multiple paths separated by `:`. Then you have to make sure that the correct CUDA version is set. By doing:
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```bash
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ls -l $path/libcudart.so
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```
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On each path (`$path`) separated by `:`.
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If not, simply run
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```bash
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ls -l $LD_LIBRARY_PATH/libcudart.so
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```
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and you can see
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If you see that the file is linked to the wrong CUDA version (here 10.2), find the correct location for `libcudart.so` (`find --name libcudart.so`) and replace the environment variable `LD_LIBRARY_PATH` with the one containing the correct `libcudart.so` file.
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0
tests/bitsandbytes/__init__.py
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0
tests/bitsandbytes/__init__.py
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460
tests/bitsandbytes/test_4bit.py
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460
tests/bitsandbytes/test_4bit.py
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# coding=utf-8
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# Copyright 2022 The HuggingFace Team Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a clone of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import tempfile
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import unittest
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from packaging import version
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from transformers import (
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AutoModel,
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AutoModelForCausalLM,
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AutoModelForSeq2SeqLM,
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AutoModelForSequenceClassification,
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AutoTokenizer,
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BitsAndBytesConfig,
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pipeline,
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)
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from transformers.testing_utils import (
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is_torch_available,
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require_accelerate,
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require_bitsandbytes,
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require_torch,
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require_torch_gpu,
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require_torch_multi_gpu,
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slow,
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)
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from transformers.utils.versions import importlib_metadata
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if is_torch_available():
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import torch
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import torch.nn as nn
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class LoRALayer(nn.Module):
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"""Wraps a linear layer with LoRA-like adapter - Used for testing purposes only"""
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def __init__(self, module: nn.Module, rank: int):
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super().__init__()
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self.module = module
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self.adapter = nn.Sequential(
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nn.Linear(module.in_features, rank, bias=False),
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nn.Linear(rank, module.out_features, bias=False),
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)
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small_std = (2.0 / (5 * min(module.in_features, module.out_features))) ** 0.5
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nn.init.normal_(self.adapter[0].weight, std=small_std)
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nn.init.zeros_(self.adapter[1].weight)
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self.adapter.to(module.weight.device)
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def forward(self, input, *args, **kwargs):
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return self.module(input, *args, **kwargs) + self.adapter(input)
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@require_bitsandbytes
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@require_accelerate
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@require_torch
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@require_torch_gpu
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@slow
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class Base4bitTest(unittest.TestCase):
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# We keep the constants inside the init function and model loading inside setUp function
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# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
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# Therefore here we use only bloom-1b3 to test our module
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model_name = "bigscience/bloom-1b7"
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# Constant values
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EXPECTED_RELATIVE_DIFFERENCE = (
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2.109659552692574 # This was obtained on a RTX Titan so the number might slightly change
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)
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input_text = "Hello my name is"
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EXPECTED_OUTPUTS = set()
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EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I")
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EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n")
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MAX_NEW_TOKENS = 10
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def setUp(self):
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# Models and tokenizer
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
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class Bnb4BitTest(Base4bitTest):
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def setUp(self):
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super().setUp()
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# Models and tokenizer
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self.model_fp16 = AutoModelForCausalLM.from_pretrained(
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self.model_name, torch_dtype=torch.float16, device_map="auto"
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)
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self.model_4bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_4bit=True, device_map="auto")
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def tearDown(self):
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r"""
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TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
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avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
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"""
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del self.model_fp16
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del self.model_4bit
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gc.collect()
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torch.cuda.empty_cache()
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def test_memory_footprint(self):
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r"""
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A simple test to check if the model conversion has been done correctly by checking on the
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memory footprint of the converted model and the class type of the linear layers of the converted models
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"""
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from bitsandbytes.nn import Params4bit
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mem_fp16 = self.model_fp16.get_memory_footprint()
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mem_4bit = self.model_4bit.get_memory_footprint()
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self.assertAlmostEqual(mem_fp16 / mem_4bit, self.EXPECTED_RELATIVE_DIFFERENCE)
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self.assertTrue(self.model_4bit.transformer.h[0].mlp.dense_4h_to_h.weight.__class__ == Params4bit)
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def test_linear_are_4bit(self):
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r"""
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A simple test to check if the model conversion has been done correctly by checking on the
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memory footprint of the converted model and the class type of the linear layers of the converted models
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"""
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from transformers import T5PreTrainedModel
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self.model_fp16.get_memory_footprint()
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self.model_4bit.get_memory_footprint()
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for name, module in self.model_4bit.named_modules():
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if isinstance(module, torch.nn.Linear):
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if name not in ["lm_head"] + T5PreTrainedModel._keep_in_fp32_modules:
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# 4-bit parameters are packed in uint8 variables
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self.assertTrue(module.weight.dtype == torch.uint8)
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def test_generate_quality(self):
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r"""
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Test the generation quality of the quantized model and see that we are matching the expected output.
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Given that we are operating on small numbers + the testing model is relatively small, we might not get
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the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
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"""
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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output_sequences = self.model_4bit.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
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self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
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def test_generate_quality_config(self):
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r"""
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Test that loading the model with the config is equivalent
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"""
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bnb_config = BitsAndBytesConfig()
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bnb_config.load_in_4bit = True
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model_4bit_from_config = AutoModelForCausalLM.from_pretrained(
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self.model_name, quantization_config=bnb_config, device_map="auto"
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)
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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output_sequences = model_4bit_from_config.generate(
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input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10
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)
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self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
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def test_raise_on_save_pretrained(self):
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r"""
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Test whether trying to save a model after converting it in 8-bit will throw a warning.
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"""
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with self.assertRaises(NotImplementedError), tempfile.TemporaryDirectory() as tmpdirname:
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self.model_4bit.save_pretrained(tmpdirname)
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def test_raise_if_config_and_load_in_4bit(self):
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r"""
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Test that loading the model with the config and `load_in_4bit` raises an error
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"""
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bnb_config = BitsAndBytesConfig()
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with self.assertRaises(ValueError):
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_ = AutoModelForCausalLM.from_pretrained(
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self.model_name,
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quantization_config=bnb_config,
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load_in_4bit=True,
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device_map="auto",
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bnb_4bit_quant_type="nf4",
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)
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def test_device_and_dtype_assignment(self):
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r"""
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Test whether trying to cast (or assigning a device to) a model after converting it in 8-bit will throw an error.
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Checks also if other models are casted correctly.
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"""
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with self.assertRaises(ValueError):
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# Tries with `str`
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self.model_4bit.to("cpu")
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|
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with self.assertRaises(ValueError):
|
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# Tries with a `dtype``
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self.model_4bit.to(torch.float16)
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|
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with self.assertRaises(ValueError):
|
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# Tries with a `device`
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self.model_4bit.to(torch.device("cuda:0"))
|
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|
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with self.assertRaises(ValueError):
|
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# Tries with a `device`
|
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self.model_4bit.float()
|
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|
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with self.assertRaises(ValueError):
|
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# Tries with a `device`
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self.model_4bit.half()
|
||||
|
||||
# Test if we did not break anything
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
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self.model_fp16 = self.model_fp16.to(torch.float32)
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_ = self.model_fp16.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
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||||
# Check this does not throw an error
|
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_ = self.model_fp16.to("cpu")
|
||||
|
||||
# Check this does not throw an error
|
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_ = self.model_fp16.half()
|
||||
|
||||
# Check this does not throw an error
|
||||
_ = self.model_fp16.float()
|
||||
|
||||
def test_fp32_4bit_conversion(self):
|
||||
r"""
|
||||
Test whether it is possible to mix both `4bit` and `fp32` weights when using `keep_in_fp32_modules` correctly.
|
||||
"""
|
||||
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small", load_in_4bit=True, device_map="auto")
|
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self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
|
||||
|
||||
|
||||
@require_bitsandbytes
|
||||
@require_accelerate
|
||||
@require_torch
|
||||
@require_torch_gpu
|
||||
@slow
|
||||
class Bnb4BitT5Test(unittest.TestCase):
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
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cls.model_name = "t5-small"
|
||||
cls.dense_act_model_name = "google/flan-t5-small" # flan-t5 uses dense-act instead of dense-relu-dense
|
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cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
|
||||
cls.input_text = "Translate in German: Hello, my dog is cute"
|
||||
|
||||
def tearDown(self):
|
||||
r"""
|
||||
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
|
||||
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
|
||||
"""
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def test_inference_without_keep_in_fp32(self):
|
||||
r"""
|
||||
Test whether it is possible to mix both `4bit` and `fp32` weights when using `keep_in_fp32_modules` correctly.
|
||||
`flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
|
||||
both cases.
|
||||
"""
|
||||
from transformers import T5ForConditionalGeneration
|
||||
|
||||
modules = T5ForConditionalGeneration._keep_in_fp32_modules
|
||||
T5ForConditionalGeneration._keep_in_fp32_modules = None
|
||||
|
||||
# test with `t5-small`
|
||||
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_4bit=True, device_map="auto")
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
||||
_ = model.generate(**encoded_input)
|
||||
|
||||
# test with `flan-t5-small`
|
||||
model = T5ForConditionalGeneration.from_pretrained(
|
||||
self.dense_act_model_name, load_in_4bit=True, device_map="auto"
|
||||
)
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
||||
_ = model.generate(**encoded_input)
|
||||
T5ForConditionalGeneration._keep_in_fp32_modules = modules
|
||||
|
||||
def test_inference_with_keep_in_fp32(self):
|
||||
r"""
|
||||
Test whether it is possible to mix both `4bit` and `fp32` weights when using `keep_in_fp32_modules` correctly.
|
||||
`flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
|
||||
both cases.
|
||||
"""
|
||||
import bitsandbytes as bnb
|
||||
|
||||
from transformers import T5ForConditionalGeneration
|
||||
|
||||
# test with `t5-small`
|
||||
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_4bit=True, device_map="auto")
|
||||
|
||||
# there was a bug with decoders - this test checks that it is fixed
|
||||
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q, bnb.nn.Linear4bit))
|
||||
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
||||
_ = model.generate(**encoded_input)
|
||||
|
||||
# test with `flan-t5-small`
|
||||
model = T5ForConditionalGeneration.from_pretrained(
|
||||
self.dense_act_model_name, load_in_4bit=True, device_map="auto"
|
||||
)
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
||||
_ = model.generate(**encoded_input)
|
||||
|
||||
|
||||
class Classes4BitModelTest(Base4bitTest):
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
# model_name
|
||||
self.model_name = "bigscience/bloom-560m"
|
||||
self.seq_to_seq_name = "t5-small"
|
||||
|
||||
# Different types of model
|
||||
|
||||
self.base_model = AutoModel.from_pretrained(self.model_name, load_in_4bit=True, device_map="auto")
|
||||
# Sequence classification model
|
||||
self.sequence_model = AutoModelForSequenceClassification.from_pretrained(
|
||||
self.model_name, load_in_4bit=True, device_map="auto"
|
||||
)
|
||||
# CausalLM model
|
||||
self.model_4bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_4bit=True, device_map="auto")
|
||||
# Seq2seq model
|
||||
self.seq_to_seq_model = AutoModelForSeq2SeqLM.from_pretrained(
|
||||
self.seq_to_seq_name, load_in_4bit=True, device_map="auto"
|
||||
)
|
||||
|
||||
def tearDown(self):
|
||||
r"""
|
||||
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
|
||||
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
|
||||
"""
|
||||
del self.base_model
|
||||
del self.sequence_model
|
||||
del self.model_4bit
|
||||
del self.seq_to_seq_model
|
||||
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def test_correct_head_class(self):
|
||||
r"""
|
||||
A simple test to check if the last modules for some classes (AutoModelForCausalLM or SequenceClassification)
|
||||
are kept in their native class.
|
||||
"""
|
||||
from bitsandbytes.nn import Params4bit
|
||||
|
||||
self.assertTrue(self.base_model.h[-1].mlp.dense_4h_to_h.weight.__class__ == Params4bit)
|
||||
|
||||
# Other heads should be nn.Parameter
|
||||
self.assertTrue(self.model_4bit.lm_head.weight.__class__ == torch.nn.Parameter)
|
||||
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
|
||||
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)
|
||||
|
||||
|
||||
class Pipeline4BitTest(Base4bitTest):
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
|
||||
def tearDown(self):
|
||||
r"""
|
||||
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
|
||||
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
|
||||
"""
|
||||
del self.pipe
|
||||
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def test_pipeline(self):
|
||||
r"""
|
||||
The aim of this test is to verify that the mixed 4bit is compatible with `pipeline` from transformers. Since
|
||||
we used pipline for inference speed benchmarking we want to make sure that this feature does not break anything
|
||||
on pipline.
|
||||
"""
|
||||
# self._clear_cuda_cache()
|
||||
self.pipe = pipeline(
|
||||
"text-generation",
|
||||
model=self.model_name,
|
||||
model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.float16},
|
||||
max_new_tokens=self.MAX_NEW_TOKENS,
|
||||
)
|
||||
|
||||
# Real second forward pass
|
||||
pipeline_output = self.pipe(self.input_text)
|
||||
self.assertIn(pipeline_output[0]["generated_text"], self.EXPECTED_OUTPUTS)
|
||||
|
||||
|
||||
@require_torch_multi_gpu
|
||||
class Bnb4bitTestMultiGpu(Base4bitTest):
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
|
||||
def test_multi_gpu_loading(self):
|
||||
r"""
|
||||
This tests that the model has been loaded and can be used correctly on a multi-GPU setup.
|
||||
Let's just try to load a model on 2 GPUs and see if it works. The model we test has ~2GB of total, 3GB should suffice
|
||||
"""
|
||||
|
||||
model_parallel = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name, load_in_4bit=True, device_map="balanced"
|
||||
)
|
||||
|
||||
# Check correct device map
|
||||
self.assertEqual(set(model_parallel.hf_device_map.values()), {0, 1})
|
||||
|
||||
# Check that inference pass works on the model
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
||||
|
||||
# Second real batch
|
||||
output_parallel = model_parallel.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
|
||||
self.assertIn(self.tokenizer.decode(output_parallel[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
|
||||
|
||||
|
||||
class Bnb4BitTestTraining(Base4bitTest):
|
||||
def setUp(self):
|
||||
self.model_name = "facebook/opt-350m"
|
||||
super().setUp()
|
||||
|
||||
def test_training(self):
|
||||
if version.parse(importlib_metadata.version("bitsandbytes")) < version.parse("0.37.0"):
|
||||
return
|
||||
|
||||
# Step 1: freeze all parameters
|
||||
model = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_4bit=True, device_map="auto")
|
||||
|
||||
for param in model.parameters():
|
||||
param.requires_grad = False # freeze the model - train adapters later
|
||||
if param.ndim == 1:
|
||||
# cast the small parameters (e.g. layernorm) to fp32 for stability
|
||||
param.data = param.data.to(torch.float32)
|
||||
|
||||
# Step 2: add adapters
|
||||
for _, module in model.named_modules():
|
||||
if "OPTAttention" in repr(type(module)):
|
||||
module.q_proj = LoRALayer(module.q_proj, rank=16)
|
||||
module.k_proj = LoRALayer(module.k_proj, rank=16)
|
||||
module.v_proj = LoRALayer(module.v_proj, rank=16)
|
||||
|
||||
# Step 3: dummy batch
|
||||
batch = self.tokenizer("Test batch ", return_tensors="pt").to(0)
|
||||
|
||||
# Step 4: Check if the gradient is not None
|
||||
with torch.cuda.amp.autocast():
|
||||
out = model.forward(**batch)
|
||||
out.logits.norm().backward()
|
||||
|
||||
for module in model.modules():
|
||||
if isinstance(module, LoRALayer):
|
||||
self.assertTrue(module.adapter[1].weight.grad is not None)
|
||||
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
|
||||
elif isinstance(module, nn.Embedding):
|
||||
self.assertTrue(module.weight.grad is None)
|
||||
715
tests/bitsandbytes/test_mixed_int8.py
Normal file
715
tests/bitsandbytes/test_mixed_int8.py
Normal file
@@ -0,0 +1,715 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2022 The HuggingFace Team Inc.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a clone of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import gc
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
from packaging import version
|
||||
|
||||
from transformers import (
|
||||
AutoConfig,
|
||||
AutoModel,
|
||||
AutoModelForCausalLM,
|
||||
AutoModelForSeq2SeqLM,
|
||||
AutoModelForSequenceClassification,
|
||||
AutoTokenizer,
|
||||
BitsAndBytesConfig,
|
||||
pipeline,
|
||||
)
|
||||
from transformers.testing_utils import (
|
||||
is_accelerate_available,
|
||||
is_torch_available,
|
||||
require_accelerate,
|
||||
require_bitsandbytes,
|
||||
require_torch,
|
||||
require_torch_gpu,
|
||||
require_torch_multi_gpu,
|
||||
slow,
|
||||
)
|
||||
from transformers.utils.versions import importlib_metadata
|
||||
|
||||
|
||||
if is_accelerate_available():
|
||||
from accelerate import PartialState
|
||||
from accelerate.logging import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
_ = PartialState()
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
class LoRALayer(nn.Module):
|
||||
"""Wraps a linear layer with LoRA-like adapter - Used for testing purposes only"""
|
||||
|
||||
def __init__(self, module: nn.Module, rank: int):
|
||||
super().__init__()
|
||||
self.module = module
|
||||
self.adapter = nn.Sequential(
|
||||
nn.Linear(module.in_features, rank, bias=False),
|
||||
nn.Linear(rank, module.out_features, bias=False),
|
||||
)
|
||||
small_std = (2.0 / (5 * min(module.in_features, module.out_features))) ** 0.5
|
||||
nn.init.normal_(self.adapter[0].weight, std=small_std)
|
||||
nn.init.zeros_(self.adapter[1].weight)
|
||||
self.adapter.to(module.weight.device)
|
||||
|
||||
def forward(self, input, *args, **kwargs):
|
||||
return self.module(input, *args, **kwargs) + self.adapter(input)
|
||||
|
||||
|
||||
@require_bitsandbytes
|
||||
@require_accelerate
|
||||
@require_torch
|
||||
@require_torch_gpu
|
||||
@slow
|
||||
class BaseMixedInt8Test(unittest.TestCase):
|
||||
# We keep the constants inside the init function and model loading inside setUp function
|
||||
|
||||
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
|
||||
# Therefore here we use only bloom-1b3 to test our module
|
||||
model_name = "bigscience/bloom-1b7"
|
||||
|
||||
# Constant values
|
||||
EXPECTED_RELATIVE_DIFFERENCE = (
|
||||
1.540025 # This was obtained on a Quadro RTX 8000 so the number might slightly change
|
||||
)
|
||||
|
||||
input_text = "Hello my name is"
|
||||
EXPECTED_OUTPUT = "Hello my name is John.\nI am a friend of the family.\n"
|
||||
MAX_NEW_TOKENS = 10
|
||||
|
||||
def setUp(self):
|
||||
# Models and tokenizer
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
|
||||
|
||||
|
||||
class MixedInt8Test(BaseMixedInt8Test):
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
|
||||
# Models and tokenizer
|
||||
self.model_fp16 = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name, torch_dtype=torch.float16, device_map="auto"
|
||||
)
|
||||
self.model_8bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
|
||||
|
||||
def tearDown(self):
|
||||
r"""
|
||||
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
|
||||
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
|
||||
"""
|
||||
del self.model_fp16
|
||||
del self.model_8bit
|
||||
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def test_memory_footprint(self):
|
||||
r"""
|
||||
A simple test to check if the model conversion has been done correctly by checking on the
|
||||
memory footprint of the converted model and the class type of the linear layers of the converted models
|
||||
"""
|
||||
from bitsandbytes.nn import Int8Params
|
||||
|
||||
mem_fp16 = self.model_fp16.get_memory_footprint()
|
||||
mem_8bit = self.model_8bit.get_memory_footprint()
|
||||
|
||||
self.assertAlmostEqual(mem_fp16 / mem_8bit, self.EXPECTED_RELATIVE_DIFFERENCE)
|
||||
self.assertTrue(self.model_8bit.transformer.h[0].mlp.dense_4h_to_h.weight.__class__ == Int8Params)
|
||||
|
||||
def test_linear_are_8bit(self):
|
||||
r"""
|
||||
A simple test to check if the model conversion has been done correctly by checking on the
|
||||
memory footprint of the converted model and the class type of the linear layers of the converted models
|
||||
"""
|
||||
from transformers import T5PreTrainedModel
|
||||
|
||||
self.model_fp16.get_memory_footprint()
|
||||
self.model_8bit.get_memory_footprint()
|
||||
|
||||
for name, module in self.model_8bit.named_modules():
|
||||
if isinstance(module, torch.nn.Linear):
|
||||
if name not in ["lm_head"] + T5PreTrainedModel._keep_in_fp32_modules:
|
||||
self.assertTrue(module.weight.dtype == torch.int8)
|
||||
|
||||
def test_generate_quality(self):
|
||||
r"""
|
||||
Test the generation quality of the quantized model and see that we are matching the expected output.
|
||||
Given that we are operating on small numbers + the testing model is relatively small, we might not get
|
||||
the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
|
||||
"""
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
||||
output_sequences = self.model_8bit.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
|
||||
|
||||
self.assertEqual(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
|
||||
|
||||
def test_generate_quality_config(self):
|
||||
r"""
|
||||
Test that loading the model with the config is equivalent
|
||||
"""
|
||||
bnb_config = BitsAndBytesConfig()
|
||||
bnb_config.load_in_8bit = True
|
||||
|
||||
model_8bit_from_config = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name, quantization_config=bnb_config, device_map="auto"
|
||||
)
|
||||
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
||||
output_sequences = model_8bit_from_config.generate(
|
||||
input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10
|
||||
)
|
||||
|
||||
self.assertEqual(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
|
||||
|
||||
def test_warns_save_pretrained(self):
|
||||
r"""
|
||||
Test whether trying to save a model after converting it in 8-bit will throw a warning.
|
||||
"""
|
||||
with self.assertWarns(UserWarning), tempfile.TemporaryDirectory() as tmpdirname:
|
||||
self.model_8bit.save_pretrained(tmpdirname)
|
||||
|
||||
def test_raise_if_config_and_load_in_8bit(self):
|
||||
r"""
|
||||
Test that loading the model with the config and `load_in_8bit` raises an error
|
||||
"""
|
||||
bnb_config = BitsAndBytesConfig()
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
_ = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name,
|
||||
quantization_config=bnb_config,
|
||||
load_in_8bit=True,
|
||||
device_map="auto",
|
||||
llm_int8_enable_fp32_cpu_offload=True,
|
||||
)
|
||||
|
||||
def test_device_and_dtype_assignment(self):
|
||||
r"""
|
||||
Test whether trying to cast (or assigning a device to) a model after converting it in 8-bit will throw an error.
|
||||
Checks also if other models are casted correctly.
|
||||
"""
|
||||
with self.assertRaises(ValueError):
|
||||
# Tries with `str`
|
||||
self.model_8bit.to("cpu")
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
# Tries with a `dtype``
|
||||
self.model_8bit.to(torch.float16)
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
# Tries with a `device`
|
||||
self.model_8bit.to(torch.device("cuda:0"))
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
# Tries with a `device`
|
||||
self.model_8bit.float()
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
# Tries with a `device`
|
||||
self.model_8bit.half()
|
||||
|
||||
# Test if we did not break anything
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
||||
|
||||
self.model_fp16 = self.model_fp16.to(torch.float32)
|
||||
_ = self.model_fp16.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
|
||||
|
||||
# Check this does not throw an error
|
||||
_ = self.model_fp16.to("cpu")
|
||||
|
||||
# Check this does not throw an error
|
||||
_ = self.model_fp16.half()
|
||||
|
||||
# Check this does not throw an error
|
||||
_ = self.model_fp16.float()
|
||||
|
||||
def test_fp32_int8_conversion(self):
|
||||
r"""
|
||||
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
|
||||
"""
|
||||
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small", load_in_8bit=True, device_map="auto")
|
||||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
|
||||
|
||||
def test_int8_serialization(self):
|
||||
r"""
|
||||
Test whether it is possible to serialize a model in 8-bit.
|
||||
"""
|
||||
from bitsandbytes.nn import Int8Params
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
self.model_8bit.save_pretrained(tmpdirname)
|
||||
|
||||
# check that the file `quantization_config` is present
|
||||
config = AutoConfig.from_pretrained(tmpdirname)
|
||||
self.assertTrue(hasattr(config, "quantization_config"))
|
||||
|
||||
model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, load_in_8bit=True, device_map="auto")
|
||||
|
||||
self.assertTrue(model_from_saved.transformer.h[0].mlp.dense_4h_to_h.weight.__class__ == Int8Params)
|
||||
self.assertTrue(hasattr(model_from_saved.transformer.h[0].mlp.dense_4h_to_h.weight, "SCB"))
|
||||
|
||||
# generate
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
||||
output_sequences = model_from_saved.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
|
||||
|
||||
self.assertEqual(
|
||||
self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUT
|
||||
)
|
||||
|
||||
def test_int8_serialization_sharded(self):
|
||||
r"""
|
||||
Test whether it is possible to serialize a model in 8-bit - sharded version.
|
||||
"""
|
||||
from bitsandbytes.nn import Int8Params
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
self.model_8bit.save_pretrained(tmpdirname, max_shard_size="200MB")
|
||||
|
||||
# check that the file `quantization_config` is present
|
||||
config = AutoConfig.from_pretrained(tmpdirname)
|
||||
self.assertTrue(hasattr(config, "quantization_config"))
|
||||
|
||||
model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname)
|
||||
|
||||
self.assertTrue(model_from_saved.transformer.h[0].mlp.dense_4h_to_h.weight.__class__ == Int8Params)
|
||||
self.assertTrue(hasattr(model_from_saved.transformer.h[0].mlp.dense_4h_to_h.weight, "SCB"))
|
||||
|
||||
# generate
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
||||
output_sequences = model_from_saved.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
|
||||
|
||||
self.assertEqual(
|
||||
self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUT
|
||||
)
|
||||
|
||||
def test_int8_from_pretrained(self):
|
||||
r"""
|
||||
Test whether loading a 8bit model from the Hub works as expected
|
||||
"""
|
||||
from bitsandbytes.nn import Int8Params
|
||||
|
||||
model_id = "ybelkada/bloom-1b7-8bit"
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(model_id)
|
||||
|
||||
self.assertTrue(model.transformer.h[0].mlp.dense_4h_to_h.weight.__class__ == Int8Params)
|
||||
self.assertTrue(hasattr(model.transformer.h[0].mlp.dense_4h_to_h.weight, "SCB"))
|
||||
|
||||
# generate
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
||||
output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
|
||||
|
||||
self.assertEqual(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
|
||||
|
||||
|
||||
@require_bitsandbytes
|
||||
@require_accelerate
|
||||
@require_torch
|
||||
@require_torch_gpu
|
||||
@slow
|
||||
class MixedInt8T5Test(unittest.TestCase):
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.model_name = "t5-small"
|
||||
cls.dense_act_model_name = "google/flan-t5-small" # flan-t5 uses dense-act instead of dense-relu-dense
|
||||
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
|
||||
cls.input_text = "Translate in German: Hello, my dog is cute"
|
||||
|
||||
def tearDown(self):
|
||||
r"""
|
||||
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
|
||||
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
|
||||
"""
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def test_inference_without_keep_in_fp32(self):
|
||||
r"""
|
||||
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
|
||||
`flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
|
||||
both cases.
|
||||
"""
|
||||
from transformers import T5ForConditionalGeneration
|
||||
|
||||
modules = T5ForConditionalGeneration._keep_in_fp32_modules
|
||||
T5ForConditionalGeneration._keep_in_fp32_modules = None
|
||||
|
||||
# test with `t5-small`
|
||||
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
||||
_ = model.generate(**encoded_input)
|
||||
|
||||
# test with `flan-t5-small`
|
||||
model = T5ForConditionalGeneration.from_pretrained(
|
||||
self.dense_act_model_name, load_in_8bit=True, device_map="auto"
|
||||
)
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
||||
_ = model.generate(**encoded_input)
|
||||
T5ForConditionalGeneration._keep_in_fp32_modules = modules
|
||||
|
||||
def test_inference_with_keep_in_fp32(self):
|
||||
r"""
|
||||
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
|
||||
`flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
|
||||
both cases.
|
||||
"""
|
||||
import bitsandbytes as bnb
|
||||
|
||||
from transformers import T5ForConditionalGeneration
|
||||
|
||||
# test with `t5-small`
|
||||
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
|
||||
|
||||
# there was a bug with decoders - this test checks that it is fixed
|
||||
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q, bnb.nn.Linear8bitLt))
|
||||
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
||||
_ = model.generate(**encoded_input)
|
||||
|
||||
# test with `flan-t5-small`
|
||||
model = T5ForConditionalGeneration.from_pretrained(
|
||||
self.dense_act_model_name, load_in_8bit=True, device_map="auto"
|
||||
)
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
||||
_ = model.generate(**encoded_input)
|
||||
|
||||
def test_inference_with_keep_in_fp32_serialized(self):
|
||||
r"""
|
||||
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly on
|
||||
a serialized model.
|
||||
`flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
|
||||
both cases.
|
||||
"""
|
||||
import bitsandbytes as bnb
|
||||
|
||||
from transformers import T5ForConditionalGeneration
|
||||
|
||||
# test with `t5-small`
|
||||
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
model.save_pretrained(tmp_dir)
|
||||
|
||||
model = T5ForConditionalGeneration.from_pretrained(tmp_dir)
|
||||
|
||||
# there was a bug with decoders - this test checks that it is fixed
|
||||
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q, bnb.nn.Linear8bitLt))
|
||||
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
||||
_ = model.generate(**encoded_input)
|
||||
|
||||
# test with `flan-t5-small`
|
||||
model = T5ForConditionalGeneration.from_pretrained(
|
||||
self.dense_act_model_name, load_in_8bit=True, device_map="auto"
|
||||
)
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
||||
_ = model.generate(**encoded_input)
|
||||
|
||||
|
||||
class MixedInt8ModelClassesTest(BaseMixedInt8Test):
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
# model_name
|
||||
self.model_name = "bigscience/bloom-560m"
|
||||
self.seq_to_seq_name = "t5-small"
|
||||
|
||||
# Different types of model
|
||||
|
||||
self.base_model = AutoModel.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
|
||||
# Sequence classification model
|
||||
self.sequence_model = AutoModelForSequenceClassification.from_pretrained(
|
||||
self.model_name, load_in_8bit=True, device_map="auto"
|
||||
)
|
||||
# CausalLM model
|
||||
self.model_8bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
|
||||
# Seq2seq model
|
||||
self.seq_to_seq_model = AutoModelForSeq2SeqLM.from_pretrained(
|
||||
self.seq_to_seq_name, load_in_8bit=True, device_map="auto"
|
||||
)
|
||||
|
||||
def tearDown(self):
|
||||
r"""
|
||||
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
|
||||
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
|
||||
"""
|
||||
del self.base_model
|
||||
del self.sequence_model
|
||||
del self.model_8bit
|
||||
del self.seq_to_seq_model
|
||||
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def test_correct_head_class(self):
|
||||
r"""
|
||||
A simple test to check if the last modules for some classes (AutoModelForCausalLM or SequenceClassification)
|
||||
are kept in their native class.
|
||||
"""
|
||||
from bitsandbytes.nn import Int8Params
|
||||
|
||||
# last param of a base model should be a linear8bit module
|
||||
self.assertTrue(self.base_model.h[-1].mlp.dense_4h_to_h.weight.__class__ == Int8Params)
|
||||
|
||||
# Other heads should be nn.Parameter
|
||||
self.assertTrue(self.model_8bit.lm_head.weight.__class__ == torch.nn.Parameter)
|
||||
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
|
||||
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)
|
||||
|
||||
|
||||
class MixedInt8TestPipeline(BaseMixedInt8Test):
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
|
||||
def tearDown(self):
|
||||
r"""
|
||||
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
|
||||
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
|
||||
"""
|
||||
del self.pipe
|
||||
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def test_pipeline(self):
|
||||
r"""
|
||||
The aim of this test is to verify that the mixed int8 is compatible with `pipeline` from transformers. Since
|
||||
we used pipline for inference speed benchmarking we want to make sure that this feature does not break anything
|
||||
on pipline.
|
||||
"""
|
||||
# self._clear_cuda_cache()
|
||||
self.pipe = pipeline(
|
||||
"text-generation",
|
||||
model=self.model_name,
|
||||
model_kwargs={"device_map": "auto", "load_in_8bit": True},
|
||||
max_new_tokens=self.MAX_NEW_TOKENS,
|
||||
)
|
||||
|
||||
# Real second forward pass
|
||||
pipeline_output = self.pipe(self.input_text)
|
||||
self.assertEqual(pipeline_output[0]["generated_text"], self.EXPECTED_OUTPUT)
|
||||
|
||||
|
||||
@require_torch_multi_gpu
|
||||
class MixedInt8TestMultiGpu(BaseMixedInt8Test):
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
|
||||
def test_multi_gpu_loading(self):
|
||||
r"""
|
||||
This tests that the model has been loaded and can be used correctly on a multi-GPU setup.
|
||||
Let's just try to load a model on 2 GPUs and see if it works. The model we test has ~2GB of total, 3GB should suffice
|
||||
"""
|
||||
|
||||
model_parallel = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name, load_in_8bit=True, device_map="balanced"
|
||||
)
|
||||
|
||||
# Check correct device map
|
||||
self.assertEqual(set(model_parallel.hf_device_map.values()), {0, 1})
|
||||
|
||||
# Check that inference pass works on the model
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
||||
|
||||
# Second real batch
|
||||
output_parallel = model_parallel.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
|
||||
self.assertEqual(self.tokenizer.decode(output_parallel[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
|
||||
|
||||
|
||||
@require_torch_multi_gpu
|
||||
class MixedInt8TestCpuGpu(BaseMixedInt8Test):
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
|
||||
def check_inference_correctness(self, model):
|
||||
# Check that inference pass works on the model
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
||||
|
||||
# Check the exactness of the results
|
||||
output_parallel = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
|
||||
|
||||
# Get the generation
|
||||
output_text = self.tokenizer.decode(output_parallel[0], skip_special_tokens=True)
|
||||
self.assertEqual(output_text, self.EXPECTED_OUTPUT)
|
||||
|
||||
def test_cpu_gpu_loading_random_device_map(self):
|
||||
r"""
|
||||
A test to check is dispatching a model on cpu & gpu works correctly using a random `device_map`.
|
||||
"""
|
||||
device_map = {
|
||||
"transformer.word_embeddings": 0,
|
||||
"transformer.word_embeddings_layernorm": 0,
|
||||
"lm_head": 0,
|
||||
"transformer.h.0": "cpu",
|
||||
"transformer.h.1": "cpu",
|
||||
"transformer.h.2": 0,
|
||||
"transformer.h.3": 0,
|
||||
"transformer.h.4": 0,
|
||||
"transformer.h.5": 0,
|
||||
"transformer.h.6": 0,
|
||||
"transformer.h.7": 0,
|
||||
"transformer.h.8": 0,
|
||||
"transformer.h.9": 1,
|
||||
"transformer.h.10": 0,
|
||||
"transformer.h.11": 1,
|
||||
"transformer.h.12": 0,
|
||||
"transformer.h.13": 0,
|
||||
"transformer.h.14": 1,
|
||||
"transformer.h.15": 0,
|
||||
"transformer.h.16": 0,
|
||||
"transformer.h.17": 1,
|
||||
"transformer.h.18": 1,
|
||||
"transformer.h.19": 0,
|
||||
"transformer.h.20": 1,
|
||||
"transformer.h.21": 1,
|
||||
"transformer.h.22": 0,
|
||||
"transformer.h.23": 0,
|
||||
"transformer.ln_f": 1,
|
||||
}
|
||||
|
||||
bnb_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)
|
||||
|
||||
model_8bit = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name,
|
||||
device_map=device_map,
|
||||
quantization_config=bnb_config,
|
||||
)
|
||||
|
||||
# Check that the model has been correctly set on device 0, 1, and `cpu`.
|
||||
self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu"})
|
||||
|
||||
self.check_inference_correctness(model_8bit)
|
||||
|
||||
def test_cpu_gpu_loading_custom_device_map(self):
|
||||
r"""
|
||||
A test to check is dispatching a model on cpu & gpu works correctly using a custom `device_map`.
|
||||
This time the device map is more organized than the test above and uses the abstraction
|
||||
`transformer.h` to encapsulate all the decoder layers.
|
||||
"""
|
||||
device_map = {
|
||||
"transformer.word_embeddings": "cpu",
|
||||
"transformer.word_embeddings_layernorm": "cpu",
|
||||
"lm_head": "cpu",
|
||||
"transformer.h": 0,
|
||||
"transformer.ln_f": 1,
|
||||
}
|
||||
bnb_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)
|
||||
|
||||
# Load model
|
||||
model_8bit = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name,
|
||||
device_map=device_map,
|
||||
quantization_config=bnb_config,
|
||||
)
|
||||
|
||||
# Check that the model has been correctly set on device 0, 1, and `cpu`.
|
||||
self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu"})
|
||||
|
||||
self.check_inference_correctness(model_8bit)
|
||||
|
||||
def test_cpu_gpu_disk_loading_custom_device_map(self):
|
||||
r"""
|
||||
A test to check is dispatching a model on cpu & gpu works correctly using a custom `device_map`.
|
||||
This time we also add `disk` on the device_map.
|
||||
"""
|
||||
device_map = {
|
||||
"transformer.word_embeddings": 0,
|
||||
"transformer.word_embeddings_layernorm": "cpu",
|
||||
"lm_head": 0,
|
||||
"transformer.h": 1,
|
||||
"transformer.ln_f": "disk",
|
||||
}
|
||||
bnb_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
# Load model
|
||||
model_8bit = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name,
|
||||
device_map=device_map,
|
||||
quantization_config=bnb_config,
|
||||
offload_folder=tmpdirname,
|
||||
)
|
||||
|
||||
# Check that the model has been correctly set on device 0, 1, and `cpu`.
|
||||
self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu", "disk"})
|
||||
|
||||
self.check_inference_correctness(model_8bit)
|
||||
|
||||
def test_cpu_gpu_disk_loading_custom_device_map_kwargs(self):
|
||||
r"""
|
||||
A test to check is dispatching a model on cpu & gpu works correctly using a custom `device_map`.
|
||||
This time we also add `disk` on the device_map - using the kwargs directly instead of the quantization config
|
||||
"""
|
||||
device_map = {
|
||||
"transformer.word_embeddings": 0,
|
||||
"transformer.word_embeddings_layernorm": "cpu",
|
||||
"lm_head": 0,
|
||||
"transformer.h": 1,
|
||||
"transformer.ln_f": "disk",
|
||||
}
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
# Load model
|
||||
model_8bit = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name,
|
||||
device_map=device_map,
|
||||
llm_int8_enable_fp32_cpu_offload=True,
|
||||
offload_folder=tmpdirname,
|
||||
)
|
||||
|
||||
# Check that the model has been correctly set on device 0, 1, and `cpu`.
|
||||
self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu", "disk"})
|
||||
|
||||
self.check_inference_correctness(model_8bit)
|
||||
|
||||
|
||||
class MixedInt8TestTraining(BaseMixedInt8Test):
|
||||
def setUp(self):
|
||||
self.model_name = "facebook/opt-350m"
|
||||
super().setUp()
|
||||
|
||||
def test_training(self):
|
||||
if version.parse(importlib_metadata.version("bitsandbytes")) < version.parse("0.37.0"):
|
||||
return
|
||||
|
||||
# Step 1: freeze all parameters
|
||||
model = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
|
||||
|
||||
for param in model.parameters():
|
||||
param.requires_grad = False # freeze the model - train adapters later
|
||||
if param.ndim == 1:
|
||||
# cast the small parameters (e.g. layernorm) to fp32 for stability
|
||||
param.data = param.data.to(torch.float32)
|
||||
|
||||
# Step 2: add adapters
|
||||
for _, module in model.named_modules():
|
||||
if "OPTAttention" in repr(type(module)):
|
||||
module.q_proj = LoRALayer(module.q_proj, rank=16)
|
||||
module.k_proj = LoRALayer(module.k_proj, rank=16)
|
||||
module.v_proj = LoRALayer(module.v_proj, rank=16)
|
||||
|
||||
# Step 3: dummy batch
|
||||
batch = self.tokenizer("Test batch ", return_tensors="pt").to(0)
|
||||
|
||||
# Step 4: Check if the gradient is not None
|
||||
with torch.cuda.amp.autocast():
|
||||
out = model.forward(**batch)
|
||||
out.logits.norm().backward()
|
||||
|
||||
for module in model.modules():
|
||||
if isinstance(module, LoRALayer):
|
||||
self.assertTrue(module.adapter[1].weight.grad is not None)
|
||||
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
|
||||
elif isinstance(module, nn.Embedding):
|
||||
self.assertTrue(module.weight.grad is None)
|
||||
Reference in New Issue
Block a user