init
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transformers/tests/quantization/bnb/README.md
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transformers/tests/quantization/bnb/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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## Virtual 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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```bash
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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 attribute .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 prepending `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 versions with:
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```bash
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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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```bash
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ls -l $CONDA_PREFIX/lib/libcudart.so
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```
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or
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```bash
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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
transformers/tests/quantization/bnb/__init__.py
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0
transformers/tests/quantization/bnb/__init__.py
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868
transformers/tests/quantization/bnb/test_4bit.py
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868
transformers/tests/quantization/bnb/test_4bit.py
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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 importlib.metadata
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import tempfile
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import unittest
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import pytest
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from packaging import version
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from transformers import (
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AutoConfig,
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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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set_seed,
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)
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from transformers.models.opt.modeling_opt import OPTAttention
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from transformers.testing_utils import (
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apply_skip_if_not_implemented,
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backend_empty_cache,
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backend_torch_accelerator_module,
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is_bitsandbytes_available,
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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_if_bnb_not_multi_backend_enabled,
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require_torch_multi_accelerator,
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slow,
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torch_device,
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)
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def get_some_linear_layer(model):
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if model.config.model_type == "gpt2":
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return model.transformer.h[0].mlp.c_fc
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elif model.config.model_type == "opt":
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try:
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return model.decoder.layers[0].fc1
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except AttributeError:
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# for AutoModelforCausalLM
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return model.model.decoder.layers[0].fc1
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elif model.config.model_type == "llama":
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return model.model.layers[0].mlp.gate_proj
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else:
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return model.transformer.h[0].mlp.dense_4h_to_h
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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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if is_bitsandbytes_available():
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import bitsandbytes as bnb
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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_if_bnb_not_multi_backend_enabled
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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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EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University")
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EXPECTED_OUTPUTS.add("Hello my name is John and I am 25 years old.")
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EXPECTED_OUTPUTS.add("Hello my name is John and I am a student at the University of")
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# Expected values on Intel XPU and NV A100
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EXPECTED_OUTPUTS.add("Hello my name is Alina. I have been working as a professional")
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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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@apply_skip_if_not_implemented
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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(self.model_name, dtype=torch.float16, device_map="auto")
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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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backend_empty_cache(torch_device)
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def test_quantization_num_parameters(self):
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r"""
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Test if the number of returned parameters is correct
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See: https://github.com/huggingface/transformers/issues/25978
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"""
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num_params_4bit = self.model_4bit.num_parameters()
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num_params_fp16 = self.model_fp16.num_parameters()
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self.assertEqual(num_params_4bit, num_params_fp16)
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def test_quantization_config_json_serialization(self):
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r"""
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A simple test to check if the quantization config is correctly serialized and deserialized
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"""
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config = self.model_4bit.config
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self.assertTrue(hasattr(config, "quantization_config"))
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_ = config.to_dict()
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_ = config.to_diff_dict()
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_ = config.to_json_string()
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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, delta=1e-5)
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linear = get_some_linear_layer(self.model_4bit)
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self.assertTrue(linear.weight.__class__ == Params4bit)
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def test_original_dtype(self):
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r"""
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A simple test to check if the model successfully stores the original dtype
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"""
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self.assertTrue(hasattr(self.model_4bit.config, "_pre_quantization_dtype"))
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self.assertFalse(hasattr(self.model_fp16.config, "_pre_quantization_dtype"))
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self.assertTrue(self.model_4bit.config._pre_quantization_dtype == torch.float16)
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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_rwkv_4bit(self):
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r"""
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A simple test to check if 4-bit RWKV inference works as expected.
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"""
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model_id = "RWKV/rwkv-4-169m-pile"
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quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_use_double_quant=True)
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model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config)
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tok = AutoTokenizer.from_pretrained(model_id)
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text = "Hello my name is"
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input_ids = tok.encode(text, return_tensors="pt").to(torch_device)
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_ = model.generate(input_ids, max_new_tokens=30)
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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(
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input_ids=encoded_input["input_ids"].to(self.model_4bit.device), 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_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(model_4bit_from_config.device), 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_generate_quality_dequantize(self):
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r"""
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Test that loading the model and unquantize it produce correct results
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"""
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bnb_config = BitsAndBytesConfig(load_in_4bit=True)
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model_4bit = 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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model_4bit.dequantize()
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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output_sequences = model_4bit.generate(
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input_ids=encoded_input["input_ids"].to(model_4bit.device), 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_clear_quantization_trace(self):
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r"""
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Test that dequantizing the model won't leave any attribute relative to quantization in the model's configuration
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"""
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bnb_config = BitsAndBytesConfig(load_in_4bit=True)
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model_4bit = 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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model_4bit.dequantize()
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self.assertFalse(hasattr(model_4bit, "hf_quantizer"))
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self.assertFalse(hasattr(model_4bit.config, "quantization_config"))
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self.assertFalse(hasattr(model_4bit.config, "_pre_quantization_dtype"))
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self.assertFalse(hasattr(model_4bit, "quantization_method"))
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self.assertFalse(model_4bit.is_quantized)
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||||
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def test_to_device_dequantized(self):
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r"""
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Test that dequantizing the model won't prevent converting it to a different dtype
|
||||
"""
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||||
bnb_config = BitsAndBytesConfig(load_in_4bit=True)
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model_4bit = 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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model_4bit.dequantize()
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model_4bit.to(dtype=torch.float16)
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||||
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||||
def test_device_assignment(self):
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if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.43.2"):
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self.skipTest(reason="This test requires bitsandbytes >= 0.43.2")
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||||
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||||
mem_before = self.model_4bit.get_memory_footprint()
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||||
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# Move to CPU
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self.model_4bit.to("cpu")
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self.assertEqual(self.model_4bit.device.type, "cpu")
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||||
self.assertAlmostEqual(self.model_4bit.get_memory_footprint(), mem_before)
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||||
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||||
if torch_device in ["cuda", "xpu"]:
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||||
# Move back to CUDA device
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||||
self.model_4bit.to(torch_device)
|
||||
self.assertEqual(self.model_4bit.device.type, torch_device)
|
||||
self.assertAlmostEqual(self.model_4bit.get_memory_footprint(), mem_before)
|
||||
|
||||
def test_device_and_dtype_assignment(self):
|
||||
r"""
|
||||
Test whether attempting to change the device or cast the dtype of a model
|
||||
after converting it to 4-bit precision will raise an appropriate error.
|
||||
The test ensures that such operations are prohibited on 4-bit models
|
||||
to prevent invalid conversions.
|
||||
"""
|
||||
|
||||
# Moving with `to` or `cuda` is not supported with versions < 0.43.2.
|
||||
if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.43.2"):
|
||||
with self.assertRaises(ValueError):
|
||||
# Tries with `str`
|
||||
self.model_4bit.to("cpu")
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
# Tries with a `device`
|
||||
self.model_4bit.to(torch.device("cuda:0"))
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
# Tries with `cuda`
|
||||
self.model_4bit.cuda()
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
# Tries with a `dtype`
|
||||
self.model_4bit.to(torch.float16)
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
# Tries to cast the 4-bit model to float32 using `float()`
|
||||
self.model_4bit.float()
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
# Tries to cast the 4-bit model to float16 using `half()`
|
||||
self.model_4bit.half()
|
||||
|
||||
# Test if we did not break anything
|
||||
self.model_4bit.to(torch.device(torch_device))
|
||||
|
||||
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(self.model_fp16.device), max_new_tokens=10
|
||||
)
|
||||
|
||||
if torch_device in ["cuda", "xpu"]:
|
||||
# Check that this does not throw an error
|
||||
_ = self.model_fp16.to(torch_device)
|
||||
|
||||
# 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_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("google-t5/t5-small", load_in_4bit=True, device_map="auto")
|
||||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
|
||||
|
||||
def test_bnb_4bit_wrong_config(self):
|
||||
r"""
|
||||
Test whether creating a bnb config with unsupported values leads to errors.
|
||||
"""
|
||||
with self.assertRaises(ValueError):
|
||||
_ = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_storage="add")
|
||||
|
||||
|
||||
@require_bitsandbytes
|
||||
@require_accelerate
|
||||
@require_torch
|
||||
@require_torch_gpu_if_bnb_not_multi_backend_enabled
|
||||
@slow
|
||||
@apply_skip_if_not_implemented
|
||||
class Bnb4BitT5Test(unittest.TestCase):
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.model_name = "google-t5/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()
|
||||
backend_empty_cache(torch_device)
|
||||
|
||||
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 `google-t5/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 `google-t5/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(model.device)
|
||||
_ = 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(model.device)
|
||||
_ = 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 `google-t5/t5-small` uses `T5DenseReluDense`. We need to test
|
||||
both cases.
|
||||
"""
|
||||
from transformers import T5ForConditionalGeneration
|
||||
|
||||
# test with `google-t5/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(model.device)
|
||||
_ = 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(model.device)
|
||||
_ = model.generate(**encoded_input)
|
||||
|
||||
|
||||
@apply_skip_if_not_implemented
|
||||
class Classes4BitModelTest(Base4bitTest):
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
# model_name
|
||||
self.model_name = "bigscience/bloom-560m"
|
||||
self.seq_to_seq_name = "google-t5/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()
|
||||
backend_empty_cache(torch_device)
|
||||
|
||||
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)
|
||||
|
||||
|
||||
@apply_skip_if_not_implemented
|
||||
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
|
||||
"""
|
||||
if hasattr(self, "pipe"):
|
||||
del self.pipe
|
||||
|
||||
gc.collect()
|
||||
backend_empty_cache(torch_device)
|
||||
|
||||
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 pipeline for inference speed benchmarking we want to make sure that this feature does not break anything
|
||||
on pipeline.
|
||||
"""
|
||||
# self._clear_cuda_cache()
|
||||
self.pipe = pipeline(
|
||||
"text-generation",
|
||||
model=self.model_name,
|
||||
model_kwargs={
|
||||
"device_map": "auto",
|
||||
"load_in_4bit": True,
|
||||
# float16 isn't supported on CPU, use bfloat16 instead
|
||||
"dtype": torch.bfloat16 if torch_device == "cpu" else torch.float16,
|
||||
},
|
||||
max_new_tokens=self.MAX_NEW_TOKENS,
|
||||
)
|
||||
|
||||
# Avoid sampling different outputs
|
||||
set_seed(42)
|
||||
# Real second forward pass
|
||||
pipeline_output = self.pipe(self.input_text)
|
||||
self.assertIn(pipeline_output[0]["generated_text"], self.EXPECTED_OUTPUTS)
|
||||
|
||||
|
||||
@require_torch_multi_accelerator
|
||||
@apply_skip_if_not_implemented
|
||||
class Bnb4bitTestMultiAccelerator(Base4bitTest):
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
|
||||
def test_multi_accelerator_loading(self):
|
||||
r"""
|
||||
This tests that the model has been loaded and can be used correctly on a multi-accelerator setup.
|
||||
Let's just try to load a model on 2 accelerators and see if it works. The model we test has ~2GB of total, 3GB should suffice
|
||||
"""
|
||||
device_map = {
|
||||
"transformer.word_embeddings": 0,
|
||||
"transformer.word_embeddings_layernorm": 0,
|
||||
"lm_head": 0,
|
||||
"transformer.h.0": 0,
|
||||
"transformer.h.1": 0,
|
||||
"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": 0,
|
||||
"transformer.h.10": 1,
|
||||
"transformer.h.11": 1,
|
||||
"transformer.h.12": 1,
|
||||
"transformer.h.13": 1,
|
||||
"transformer.h.14": 1,
|
||||
"transformer.h.15": 1,
|
||||
"transformer.h.16": 1,
|
||||
"transformer.h.17": 0,
|
||||
"transformer.h.18": 0,
|
||||
"transformer.h.19": 0,
|
||||
"transformer.h.20": 0,
|
||||
"transformer.h.21": 0,
|
||||
"transformer.h.22": 0,
|
||||
"transformer.h.23": 1,
|
||||
"transformer.ln_f": 0,
|
||||
}
|
||||
|
||||
model_parallel = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name, load_in_4bit=True, device_map=device_map
|
||||
)
|
||||
|
||||
# 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(torch_device), max_new_tokens=10
|
||||
)
|
||||
self.assertIn(self.tokenizer.decode(output_parallel[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
|
||||
|
||||
|
||||
@apply_skip_if_not_implemented
|
||||
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"):
|
||||
self.skipTest(reason="This test requires bitsandbytes >= 0.37.0")
|
||||
|
||||
# Step 1: freeze all parameters
|
||||
model = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_4bit=True)
|
||||
|
||||
if torch_device in ["cuda", "xpu"]:
|
||||
self.assertEqual(
|
||||
set(model.hf_device_map.values()), {backend_torch_accelerator_module(torch_device).current_device()}
|
||||
)
|
||||
else:
|
||||
self.assertTrue(all(param.device.type == "cpu" for param in model.parameters()))
|
||||
|
||||
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 isinstance(module, OPTAttention):
|
||||
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(torch_device)
|
||||
|
||||
# Step 4: Check if the gradient is not None
|
||||
with torch.autocast(torch_device):
|
||||
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)
|
||||
|
||||
|
||||
@apply_skip_if_not_implemented
|
||||
class Bnb4BitGPT2Test(Bnb4BitTest):
|
||||
model_name = "openai-community/gpt2-xl"
|
||||
EXPECTED_RELATIVE_DIFFERENCE = 3.3191854854152187
|
||||
|
||||
|
||||
@apply_skip_if_not_implemented
|
||||
class Bnb4BitLlamaTest(Bnb4BitTest):
|
||||
model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
|
||||
EXPECTED_RELATIVE_DIFFERENCE = 2.9461410686392764
|
||||
|
||||
|
||||
@require_bitsandbytes
|
||||
@require_accelerate
|
||||
@require_torch
|
||||
@require_torch_gpu_if_bnb_not_multi_backend_enabled
|
||||
@slow
|
||||
@apply_skip_if_not_implemented
|
||||
class BaseSerializationTest(unittest.TestCase):
|
||||
model_name = "facebook/opt-125m"
|
||||
input_text = "Mars colonists' favorite meals are"
|
||||
|
||||
def tearDown(self):
|
||||
gc.collect()
|
||||
backend_empty_cache(torch_device)
|
||||
|
||||
def test_serialization(self, quant_type="nf4", double_quant=True, safe_serialization=True):
|
||||
r"""
|
||||
Test whether it is possible to serialize a model in 4-bit. Uses most typical params as default.
|
||||
See ExtendedSerializationTest class for more params combinations.
|
||||
"""
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(self.model_name)
|
||||
|
||||
self.quantization_config = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
bnb_4bit_quant_type=quant_type,
|
||||
bnb_4bit_use_double_quant=double_quant,
|
||||
bnb_4bit_compute_dtype=torch.bfloat16,
|
||||
)
|
||||
model_0 = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name,
|
||||
quantization_config=self.quantization_config,
|
||||
device_map=torch_device,
|
||||
)
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
model_0.save_pretrained(tmpdirname, safe_serialization=safe_serialization)
|
||||
|
||||
config = AutoConfig.from_pretrained(tmpdirname)
|
||||
self.assertTrue(hasattr(config, "quantization_config"))
|
||||
|
||||
model_1 = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map=torch_device)
|
||||
|
||||
# checking quantized linear module weight
|
||||
linear = get_some_linear_layer(model_1)
|
||||
self.assertTrue(linear.weight.__class__ == bnb.nn.Params4bit)
|
||||
self.assertTrue(hasattr(linear.weight, "quant_state"))
|
||||
self.assertTrue(linear.weight.quant_state.__class__ == bnb.functional.QuantState)
|
||||
|
||||
# checking memory footpring
|
||||
self.assertAlmostEqual(model_0.get_memory_footprint() / model_1.get_memory_footprint(), 1, places=2)
|
||||
|
||||
# Matching all parameters and their quant_state items:
|
||||
d0 = dict(model_0.named_parameters())
|
||||
d1 = dict(model_1.named_parameters())
|
||||
self.assertTrue(d0.keys() == d1.keys())
|
||||
|
||||
for k in d0:
|
||||
self.assertTrue(d0[k].shape == d1[k].shape)
|
||||
self.assertTrue(d0[k].device.type == d1[k].device.type)
|
||||
self.assertTrue(d0[k].device == d1[k].device)
|
||||
self.assertTrue(d0[k].dtype == d1[k].dtype)
|
||||
self.assertTrue(torch.equal(d0[k], d1[k].to(d0[k].device)))
|
||||
|
||||
if isinstance(d0[k], bnb.nn.modules.Params4bit):
|
||||
for v0, v1 in zip(
|
||||
d0[k].quant_state.as_dict().values(),
|
||||
d1[k].quant_state.as_dict().values(),
|
||||
):
|
||||
if isinstance(v0, torch.Tensor):
|
||||
# The absmax will not be saved in the quant_state when using NF4 in CPU
|
||||
if v0.numel() != 0:
|
||||
self.assertTrue(torch.equal(v0, v1.to(v0.device)))
|
||||
else:
|
||||
self.assertTrue(v0 == v1)
|
||||
|
||||
# comparing forward() outputs
|
||||
encoded_input = tokenizer(self.input_text, return_tensors="pt").to(torch_device)
|
||||
out_0 = model_0(**encoded_input)
|
||||
out_1 = model_1(**encoded_input)
|
||||
torch.testing.assert_close(out_0["logits"], out_1["logits"], rtol=0.05, atol=0.05)
|
||||
|
||||
# comparing generate() outputs
|
||||
encoded_input = tokenizer(self.input_text, return_tensors="pt").to(torch_device)
|
||||
output_sequences_0 = model_0.generate(**encoded_input, max_new_tokens=10)
|
||||
output_sequences_1 = model_1.generate(**encoded_input, max_new_tokens=10)
|
||||
|
||||
def _decode(token):
|
||||
return tokenizer.decode(token, skip_special_tokens=True)
|
||||
|
||||
self.assertEqual(
|
||||
[_decode(x) for x in output_sequences_0],
|
||||
[_decode(x) for x in output_sequences_1],
|
||||
)
|
||||
|
||||
|
||||
@apply_skip_if_not_implemented
|
||||
class ExtendedSerializationTest(BaseSerializationTest):
|
||||
"""
|
||||
tests more combinations of parameters
|
||||
"""
|
||||
|
||||
def test_nf4_single_unsafe(self):
|
||||
self.test_serialization(quant_type="nf4", double_quant=False, safe_serialization=False)
|
||||
|
||||
def test_nf4_single_safe(self):
|
||||
self.test_serialization(quant_type="nf4", double_quant=False, safe_serialization=True)
|
||||
|
||||
def test_nf4_double_unsafe(self):
|
||||
self.test_serialization(quant_type="nf4", double_quant=True, safe_serialization=False)
|
||||
|
||||
# nf4 double safetensors quantization is tested in test_serialization() method from the parent class
|
||||
|
||||
def test_fp4_single_unsafe(self):
|
||||
self.test_serialization(quant_type="fp4", double_quant=False, safe_serialization=False)
|
||||
|
||||
def test_fp4_single_safe(self):
|
||||
self.test_serialization(quant_type="fp4", double_quant=False, safe_serialization=True)
|
||||
|
||||
def test_fp4_double_unsafe(self):
|
||||
self.test_serialization(quant_type="fp4", double_quant=True, safe_serialization=False)
|
||||
|
||||
def test_fp4_double_safe(self):
|
||||
self.test_serialization(quant_type="fp4", double_quant=True, safe_serialization=True)
|
||||
|
||||
|
||||
class BloomSerializationTest(BaseSerializationTest):
|
||||
"""
|
||||
default BaseSerializationTest config tested with Bloom family model
|
||||
"""
|
||||
|
||||
model_name = "bigscience/bloom-560m"
|
||||
|
||||
|
||||
class GPTSerializationTest(BaseSerializationTest):
|
||||
"""
|
||||
default BaseSerializationTest config tested with GPT family model
|
||||
"""
|
||||
|
||||
model_name = "openai-community/gpt2-xl"
|
||||
|
||||
|
||||
class LlamaSerializationTest(BaseSerializationTest):
|
||||
"""
|
||||
default BaseSerializationTest config tested with Llama family model
|
||||
"""
|
||||
|
||||
model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
|
||||
|
||||
|
||||
@require_bitsandbytes
|
||||
@require_accelerate
|
||||
@require_torch_gpu_if_bnb_not_multi_backend_enabled
|
||||
@slow
|
||||
@apply_skip_if_not_implemented
|
||||
class Bnb4BitTestBasicConfigTest(unittest.TestCase):
|
||||
def test_load_in_4_and_8_bit_fails(self):
|
||||
with self.assertRaisesRegex(ValueError, "load_in_4bit and load_in_8bit are both True"):
|
||||
AutoModelForCausalLM.from_pretrained("facebook/opt-125m", load_in_4bit=True, load_in_8bit=True)
|
||||
|
||||
def test_set_load_in_8_bit(self):
|
||||
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
|
||||
with self.assertRaisesRegex(ValueError, "load_in_4bit and load_in_8bit are both True"):
|
||||
quantization_config.load_in_8bit = True
|
||||
|
||||
|
||||
@require_bitsandbytes
|
||||
@require_accelerate
|
||||
@require_torch_gpu_if_bnb_not_multi_backend_enabled
|
||||
@slow
|
||||
@apply_skip_if_not_implemented
|
||||
class Bnb4bitCompile(unittest.TestCase):
|
||||
model_name = "hf-internal-testing/tiny-random-LlamaForCausalLM"
|
||||
input_text = "Hello my name is"
|
||||
|
||||
def setUp(self):
|
||||
# Models and tokenizer
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
|
||||
self.model_4bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_4bit=True)
|
||||
|
||||
@pytest.mark.torch_compile_test
|
||||
def test_generate_compile(self):
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
||||
|
||||
# if nothing is set, compile will be disabled for bnb
|
||||
self.model_4bit.generate(
|
||||
input_ids=encoded_input["input_ids"].to(self.model_4bit.device),
|
||||
max_new_tokens=10,
|
||||
cache_implementation="static",
|
||||
)
|
||||
with self.assertRaises(Exception):
|
||||
# overwrite property
|
||||
object.__setattr__(self.model_4bit.hf_quantizer, "is_compileable", True)
|
||||
self.model_4bit.generate(
|
||||
input_ids=encoded_input["input_ids"].to(self.model_4bit.device),
|
||||
max_new_tokens=10,
|
||||
cache_implementation="static",
|
||||
)
|
||||
1015
transformers/tests/quantization/bnb/test_mixed_int8.py
Normal file
1015
transformers/tests/quantization/bnb/test_mixed_int8.py
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user