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HIGGS Quantization Support (#34997)
* higgs init * working with crunches * per-model workspaces * style * style 2 * tests and style * higgs tests passing * protecting torch import * removed torch.Tensor type annotations * torch.nn.Module inheritance fix maybe * hide inputs inside quantizer calls * style structure something * Update src/transformers/quantizers/quantizer_higgs.py Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * reworked num_sms * Update src/transformers/integrations/higgs.py Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * revamped device checks * docstring upd * Update src/transformers/quantizers/quantizer_higgs.py Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com> * edited tests and device map assertions * minor edits * updated flute cuda version in docker * Added p=1 and 2,3bit HIGGS * flute version check update * incorporated `modules_to_not_convert` * less hardcoding * Fixed comment * Added docs * Fixed gemma support * example in docs * fixed torch_dtype for HIGGS * Update docs/source/en/quantization/higgs.md Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * Collection link * dequantize interface * newer flute version, torch.compile support * unittest message fix * docs update compile * isort * ValueError instead of assert --------- Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
This commit is contained in:
Andrei Panferov
GitHub
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16 changed files with 1249 additions and 0 deletions
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@ -69,6 +69,10 @@ RUN python3 -m pip install --no-cache-dir optimum-quanto
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# Add eetq for quantization testing
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RUN python3 -m pip install git+https://github.com/NetEase-FuXi/EETQ.git
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# Add flute-kernel and fast_hadamard_transform for quantization testing
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RUN python3 -m pip install --no-cache-dir flute-kernel==0.3.0 -i https://flute-ai.github.io/whl/cu118
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RUN python3 -m pip install --no-cache-dir fast_hadamard_transform==1.0.4.post1
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# When installing in editable mode, `transformers` is not recognized as a package.
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# this line must be added in order for python to be aware of transformers.
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RUN cd transformers && python3 setup.py develop
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@ -173,6 +173,8 @@
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title: Quanto
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- local: quantization/eetq
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title: EETQ
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- local: quantization/higgs
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title: HIGGS
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- local: quantization/hqq
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title: HQQ
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- local: quantization/fbgemm_fp8
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@ -57,6 +57,10 @@ Learn how to quantize models in the [Quantization](../quantization) guide.
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[[autodoc]] quantizers.base.HfQuantizer
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## HiggsConfig
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[[autodoc]] HiggsConfig
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## HqqConfig
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[[autodoc]] HqqConfig
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66
docs/source/en/quantization/higgs.md
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66
docs/source/en/quantization/higgs.md
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@ -0,0 +1,66 @@
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<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# HIGGS
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HIGGS is a 0-shot quantization algorithm that combines Hadamard preprocessing with MSE-Optimal quantization grids to achieve lower quantization error and SOTA performance. You can find more information in the paper [arxiv.org/abs/2411.17525](https://arxiv.org/abs/2411.17525).
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Runtime support for HIGGS is implemented through [FLUTE](https://arxiv.org/abs/2407.10960), and its [library](https://github.com/HanGuo97/flute).
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## Quantization Example
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, HiggsConfig
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model = AutoModelForCausalLM.from_pretrained(
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"google/gemma-2-9b-it",
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quantization_config=HiggsConfig(bits=4),
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
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tokenizer.decode(model.generate(
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**tokenizer("Hi,", return_tensors="pt").to(model.device),
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temperature=0.5,
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top_p=0.80,
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)[0])
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```
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## Pre-quantized models
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Some pre-quantized models can be found in the [official collection](https://huggingface.co/collections/ISTA-DASLab/higgs-675308e432fd56b7f6dab94e) on Hugging Face Hub.
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## Current Limitations
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**Architectures**
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Currently, FLUTE, and HIGGS by extension, **only support Llama 3 and 3.0 of 8B, 70B and 405B parameters, as well as Gemma-2 9B and 27B**. We're working on allowing to run more diverse models as well as allow arbitrary models by modifying the FLUTE compilation procedure.
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**torch.compile**
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HIGGS is fully compatible with `torch.compile`. Compiling `model.forward`, as described [here](../perf_torch_compile.md), here're the speedups it provides on RTX 4090 for `Llama-3.1-8B-Instruct` (forward passes/sec):
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| Batch Size | BF16 (With `torch.compile`) | HIGGS 4bit (No `torch.compile`) | HIGGS 4bit (With `torch.compile`) |
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|------------|-----------------------------|----------------------------------|-----------------------------------|
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| 1 | 59 | 41 | 124 |
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| 4 | 57 | 42 | 123 |
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| 16 | 56 | 41 | 120 |
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**Quantized training**
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Currently, HIGGS doesn't support quantized training (and backward passes in general). We're working on adding support for it.
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@ -54,6 +54,7 @@ Use the table below to help you decide which quantization method to use.
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| [EETQ](./eetq) | 🟢 | 🔴 | 🟢 | 🔴 | 🔴 | 🔴 | ? | 8 | 🟢 | 🟢 | 🟢 | https://github.com/NetEase-FuXi/EETQ |
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| GGUF / GGML (llama.cpp) | 🟢 | 🟢 | 🟢 | 🔴 | 🟢 | 🔴 | 🔴 | 1 - 8 | 🔴 | [See GGUF section](../gguf) | [See GGUF section](../gguf) | https://github.com/ggerganov/llama.cpp |
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| [GPTQ](./gptq) | 🔴 | 🔴 | 🟢 | 🟢 | 🔴 | 🔴 | 🔴 | 2 - 3 - 4 - 8 | 🟢 | 🟢 | 🟢 | https://github.com/AutoGPTQ/AutoGPTQ |
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| [HIGGS](./higgs) | 🟢 | 🔴 | 🟢 | 🔴 | 🔴 | 🔴 | 🟢 | 2 - 4 | 🔴 | 🟢 | 🟢 | https://github.com/HanGuo97/flute |
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| [HQQ](./hqq) | 🟢 | 🟢 | 🟢 | 🔴 | 🔴 | 🔴 | 🟢 | 1 - 8 | 🟢 | 🔴 | 🟢 | https://github.com/mobiusml/hqq/ |
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| [optimum-quanto](./quanto) | 🟢 | 🟢 | 🟢 | 🔴 | 🟢 | 🔴 | 🟢 | 2 / 4 / 8 | 🔴 | 🔴 | 🟢 | https://github.com/huggingface/optimum-quanto |
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| [FBGEMM_FP8](./fbgemm_fp8.md) | 🟢 | 🔴 | 🟢 | 🔴 | 🔴 | 🔴 | 🔴 | 8 | 🔴 | 🟢 | 🟢 | https://github.com/pytorch/FBGEMM |
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@ -998,6 +998,7 @@ _import_structure = {
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"EetqConfig",
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"FbgemmFp8Config",
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"GPTQConfig",
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"HiggsConfig",
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"HqqConfig",
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"QuantoConfig",
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"TorchAoConfig",
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@ -6023,6 +6024,7 @@ if TYPE_CHECKING:
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EetqConfig,
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FbgemmFp8Config,
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GPTQConfig,
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HiggsConfig,
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HqqConfig,
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QuantoConfig,
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TorchAoConfig,
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@ -63,6 +63,7 @@ _import_structure = {
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"load_dequant_gguf_tensor",
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"load_gguf",
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],
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"higgs": ["HiggsLinear", "dequantize_higgs", "quantize_with_higgs", "replace_with_higgs_linear"],
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"hqq": ["prepare_for_hqq_linear"],
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"integration_utils": [
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"INTEGRATION_TO_CALLBACK",
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@ -166,6 +167,7 @@ if TYPE_CHECKING:
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load_dequant_gguf_tensor,
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load_gguf,
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)
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from .higgs import HiggsLinear, dequantize_higgs, quantize_with_higgs, replace_with_higgs_linear
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from .hqq import prepare_for_hqq_linear
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from .integration_utils import (
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INTEGRATION_TO_CALLBACK,
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657
src/transformers/integrations/higgs.py
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657
src/transformers/integrations/higgs.py
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@ -0,0 +1,657 @@
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# Copyright 2024 The HuggingFace Team. All rights reserved.
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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 copy 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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"HIGGS through FLUTE (Flexible Lookup Table Engine for LUT-quantized LLMs) integration file"
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from math import sqrt
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from ..utils import (
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is_flute_available,
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is_hadamard_available,
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is_torch_available,
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)
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if is_torch_available():
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import torch
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from torch import nn
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if is_flute_available():
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import flute.utils
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if is_hadamard_available():
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from fast_hadamard_transform import hadamard_transform
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if is_flute_available():
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import flute.utils
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from flute.integrations.higgs import prepare_data_transposed
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def pad_to_block(tensor, dims, had_block_size, value=0):
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pad_dims = [0 for _ in range(2 * len(tensor.shape))]
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for dim in dims:
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size = tensor.shape[dim]
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next_multiple_of_1024 = ((size - 1) // had_block_size + 1) * had_block_size
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delta = next_multiple_of_1024 - size
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pad_dims[-2 * dim - 1] = delta
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return nn.functional.pad(tensor, pad_dims, "constant", value)
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def get_higgs_grid(p: int, n: int):
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if (p, n) == (2, 256):
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return torch.tensor(
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[
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[-2.501467704772949, 0.17954708635807037],
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[-0.6761789321899414, 1.2728623151779175],
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[-1.8025816679000854, 0.7613157629966736],
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[-0.538287878036499, -2.6028504371643066],
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[0.8415029644966125, -0.8600977659225464],
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[0.7023013234138489, 3.3138747215270996],
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[0.5699077844619751, 2.5782253742218018],
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[3.292393207550049, -0.6016128063201904],
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[0.5561617016792297, -1.7723814249038696],
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[-2.1012380123138428, 0.020958125591278076],
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[0.46085724234580994, 0.8428705334663391],
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[1.4548040628433228, -0.6156039237976074],
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[3.210029363632202, 0.3546904921531677],
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[0.8893890976905823, -0.5967988967895508],
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[0.8618854284286499, -3.2061192989349365],
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[1.1360996961593628, -0.23852407932281494],
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[1.6646337509155273, -0.9265465140342712],
|
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[1.4767773151397705, 1.2476022243499756],
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[-1.0511897802352905, 1.94503915309906],
|
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[-1.56318998336792, -0.3264186680316925],
|
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[-0.1829211413860321, 0.2922491431236267],
|
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[-0.8950616717338562, -1.3887052536010742],
|
||||
[-0.08206957578659058, -1.329533576965332],
|
||||
[-0.487422913312912, 1.4817842245101929],
|
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[-1.6769757270812988, -2.8269758224487305],
|
||||
[-1.5057679414749146, 1.8905963897705078],
|
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[1.8335362672805786, 1.0515104532241821],
|
||||
[0.3273945450782776, 1.0491033792495728],
|
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[-3.295924186706543, -0.7021600008010864],
|
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[-1.8428784608840942, -1.2315762042999268],
|
||||
[-0.8575026392936707, -1.7005949020385742],
|
||||
[-1.120667815208435, 0.6467998027801514],
|
||||
[-0.1588846743106842, -1.804071068763733],
|
||||
[-0.8539647459983826, 0.5645008683204651],
|
||||
[-1.4192019701004028, -0.6175029873847961],
|
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[1.0799058675765991, 1.7871345281600952],
|
||||
[1.171311855316162, 0.7511613965034485],
|
||||
[2.162078380584717, 0.8044339418411255],
|
||||
[1.3969420194625854, -1.243762493133545],
|
||||
[-0.23818807303905487, 0.053944624960422516],
|
||||
[2.304199457168579, -1.2667627334594727],
|
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[1.4225027561187744, 0.568610668182373],
|
||||
[0.376836895942688, -0.7134661674499512],
|
||||
[2.0404467582702637, 0.4087389409542084],
|
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[0.7639489769935608, -1.1367933750152588],
|
||||
[0.3622530400753021, -1.4827953577041626],
|
||||
[0.4100743532180786, 0.36108437180519104],
|
||||
[-1.5867475271224976, -1.618212342262268],
|
||||
[-2.2769672870635986, -1.2132309675216675],
|
||||
[0.9184022545814514, -0.34428009390830994],
|
||||
[-0.3902314603328705, 0.21785245835781097],
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||||
[3.120687484741211, 1.3077973127365112],
|
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[1.587440848350525, -1.6506884098052979],
|
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[-1.718808889389038, -0.038405973464250565],
|
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[-0.6888407468795776, -0.8402308821678162],
|
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[-0.7981445789337158, -1.1117373704910278],
|
||||
[-2.4124443531036377, 1.3419722318649292],
|
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[-0.6611530184745789, 0.9939885139465332],
|
||||
[-0.33103418350219727, -0.16702833771705627],
|
||||
[-2.4091389179229736, -2.326857566833496],
|
||||
[1.6610108613967896, -2.159703254699707],
|
||||
[0.014884627424180508, 0.3887578248977661],
|
||||
[0.029668325558304787, 1.8786455392837524],
|
||||
[1.180362582206726, 2.699317216873169],
|
||||
[1.821286678314209, -0.5960053205490112],
|
||||
[-0.44835323095321655, 3.327436685562134],
|
||||
[-0.3714401423931122, -2.1466753482818604],
|
||||
[-1.1103475093841553, -2.4536871910095215],
|
||||
[-0.39110705256462097, 0.6670510172843933],
|
||||
[0.474752813577652, -1.1959707736968994],
|
||||
[-0.013110585510730743, -2.52519154548645],
|
||||
[-2.0836575031280518, -1.703289270401001],
|
||||
[-1.1077687740325928, -0.1252644956111908],
|
||||
[-0.4138077199459076, 1.1837692260742188],
|
||||
[-1.977599024772644, 1.688241720199585],
|
||||
[-1.659559965133667, -2.1387736797332764],
|
||||
[0.03242531046271324, 0.6526556015014648],
|
||||
[0.9127950072288513, 0.6099498867988586],
|
||||
[-0.38478314876556396, 0.433487206697464],
|
||||
[0.27454206347465515, -0.27719801664352417],
|
||||
[0.10388526320457458, 2.2812814712524414],
|
||||
[-0.014394169673323631, -3.177137613296509],
|
||||
[-1.2871228456497192, -0.8961855173110962],
|
||||
[0.5720916986465454, -0.921597957611084],
|
||||
[1.1159656047821045, -0.7609877586364746],
|
||||
[2.4383342266082764, -2.2983546257019043],
|
||||
[-0.294057160615921, -0.9770799875259399],
|
||||
[-0.9342701435089111, 1.107579231262207],
|
||||
[-1.549338698387146, 3.090520143508911],
|
||||
[2.6076579093933105, 2.051239013671875],
|
||||
[-0.9259037375450134, 1.407211184501648],
|
||||
[-0.1747353971004486, 0.540488600730896],
|
||||
[-0.8963701725006104, 0.8271111249923706],
|
||||
[0.6480194926261902, 1.0128909349441528],
|
||||
[0.980783998966217, -0.06156221032142639],
|
||||
[-0.16883476078510284, 1.0601658821105957],
|
||||
[0.5839992761611938, 0.004697148688137531],
|
||||
[-0.34228450059890747, -1.2423977851867676],
|
||||
[2.500824451446533, 0.3665279746055603],
|
||||
[-0.17641609907150269, 1.3529551029205322],
|
||||
[0.05378641560673714, 2.817232847213745],
|
||||
[-1.2391047477722168, 2.354328155517578],
|
||||
[0.630434513092041, -0.668536365032196],
|
||||
[1.7576488256454468, 0.6738647818565369],
|
||||
[0.4435231387615204, 0.6000469326972961],
|
||||
[-0.08794835954904556, -0.11511358618736267],
|
||||
[1.6540337800979614, 0.33995017409324646],
|
||||
[-0.04202975332736969, -0.5375117063522339],
|
||||
[-0.4247745871543884, -0.7897617220878601],
|
||||
[0.06695003807544708, 1.2000739574432373],
|
||||
[-3.2508881092071533, 0.28734830021858215],
|
||||
[-1.613816261291504, 0.4944162368774414],
|
||||
[1.3598989248275757, 0.26117825508117676],
|
||||
[2.308382511138916, 1.3462618589401245],
|
||||
[-1.2137469053268433, -1.9254342317581177],
|
||||
[-0.4889402985572815, 1.8136259317398071],
|
||||
[-0.1870335340499878, -0.3480615019798279],
|
||||
[1.0766386985778809, -1.0627082586288452],
|
||||
[0.4651014506816864, 2.131748914718628],
|
||||
[-0.1306295394897461, -0.7811847925186157],
|
||||
[0.06433182954788208, -1.5397958755493164],
|
||||
[-0.2894323468208313, -0.5789554715156555],
|
||||
[-0.6081662178039551, 0.4845278263092041],
|
||||
[2.697964668273926, -0.18515698611736298],
|
||||
[0.1277363896369934, -0.7221432328224182],
|
||||
[0.8700758218765259, 0.35042452812194824],
|
||||
[0.22088994085788727, 0.495242178440094],
|
||||
[-2.5843818187713623, -0.8000828623771667],
|
||||
[0.6732649803161621, -1.4362232685089111],
|
||||
[-1.5286413431167603, 1.0417330265045166],
|
||||
[-1.1222513914108276, -0.6269875764846802],
|
||||
[-0.9752035140991211, -0.8750635385513306],
|
||||
[-2.6369473934173584, 0.6918523907661438],
|
||||
[0.14478731155395508, -0.041986867785453796],
|
||||
[-1.5629483461380005, 1.4369450807571411],
|
||||
[0.38952457904815674, -2.16428804397583],
|
||||
[-0.16885095834732056, 0.7976621985435486],
|
||||
[-3.12416934967041, 1.256506085395813],
|
||||
[0.6843105554580688, -0.4203019142150879],
|
||||
[1.9345275163650513, 1.934950351715088],
|
||||
[0.012184220366179943, -2.1080918312072754],
|
||||
[-0.6350273489952087, 0.7358828186988831],
|
||||
[-0.837304949760437, -0.6214472651481628],
|
||||
[0.08211923390626907, -0.9472538232803345],
|
||||
[2.9332995414733887, -1.4956780672073364],
|
||||
[1.3806978464126587, -0.2916182279586792],
|
||||
[0.06773144006729126, 0.9285762310028076],
|
||||
[-1.1943119764328003, 1.5963770151138306],
|
||||
[1.6395620107650757, -0.32285431027412415],
|
||||
[-1.390851378440857, -0.08273141086101532],
|
||||
[1.816330909729004, -1.2812227010726929],
|
||||
[0.7921574711799622, -2.1135804653167725],
|
||||
[0.5817914605140686, 1.2644577026367188],
|
||||
[1.929347038269043, -0.2386285960674286],
|
||||
[0.8877345323562622, 1.190008521080017],
|
||||
[1.4732073545455933, 0.8935023546218872],
|
||||
[-2.8518524169921875, -1.5478795766830444],
|
||||
[0.2439267635345459, 0.7576767802238464],
|
||||
[0.5246709585189819, -2.606659412384033],
|
||||
[1.150876760482788, 1.4073830842971802],
|
||||
[-0.2643202245235443, 2.0634236335754395],
|
||||
[1.555483341217041, -0.0023102816194295883],
|
||||
[2.0830578804016113, -1.7225427627563477],
|
||||
[-0.5424830317497253, -1.070199728012085],
|
||||
[0.9168899655342102, 0.8955540060997009],
|
||||
[-0.8120972514152527, 2.696739912033081],
|
||||
[-0.29908373951911926, -1.5310651063919067],
|
||||
[1.2320337295532227, -1.556247353553772],
|
||||
[1.8612544536590576, 0.08704725652933121],
|
||||
[0.22133447229862213, -1.8091708421707153],
|
||||
[-0.4403655230998993, -0.38571012020111084],
|
||||
[-1.88539457321167, 1.192205786705017],
|
||||
[2.239687919616699, 0.004709010478109121],
|
||||
[1.139495611190796, 0.45733731985092163],
|
||||
[-1.507995367050171, 0.19716016948223114],
|
||||
[0.46986445784568787, 1.5422041416168213],
|
||||
[-1.2573751211166382, -0.35984551906585693],
|
||||
[-1.7415345907211304, -0.6020717024803162],
|
||||
[1.0751984119415283, 0.19006384909152985],
|
||||
[2.24186635017395, -0.46343153715133667],
|
||||
[0.3610347509384155, -0.07658443599939346],
|
||||
[-1.3111497163772583, 0.432013601064682],
|
||||
[0.6164408326148987, 0.24538464844226837],
|
||||
[-1.9266542196273804, -0.3256155550479889],
|
||||
[-0.5870336890220642, -0.1879584938287735],
|
||||
[-1.0476511716842651, 0.3677721917629242],
|
||||
[-1.229940414428711, 1.2433830499649048],
|
||||
[0.18550436198711395, 0.22753673791885376],
|
||||
[-0.017921989783644676, 0.12625974416732788],
|
||||
[1.1659504175186157, -0.5020995736122131],
|
||||
[-0.5983408093452454, -1.40438973903656],
|
||||
[0.7519024014472961, -0.16282692551612854],
|
||||
[0.9920787811279297, -1.344896912574768],
|
||||
[-0.8103678226470947, 0.3064485788345337],
|
||||
[0.6956969499588013, 1.8208192586898804],
|
||||
[-2.7830491065979004, -0.2299390584230423],
|
||||
[-0.34681546688079834, 2.4890666007995605],
|
||||
[-1.4452646970748901, -1.2216600179672241],
|
||||
[-2.1872897148132324, 0.8926076292991638],
|
||||
[1.706072211265564, -2.8440372943878174],
|
||||
[1.1119003295898438, -2.4923460483551025],
|
||||
[-2.582794666290283, 2.0973289012908936],
|
||||
[0.04987720400094986, -0.2964983284473419],
|
||||
[-2.063807487487793, -0.7847916483879089],
|
||||
[-0.4068813621997833, 0.9135897755622864],
|
||||
[-0.9814359545707703, -0.3874954879283905],
|
||||
[-1.4227229356765747, 0.7337291240692139],
|
||||
[0.3065044581890106, 1.3125417232513428],
|
||||
[1.2160996198654175, -1.9643305540084839],
|
||||
[-1.2163853645324707, 0.14608727395534515],
|
||||
[-2.3030710220336914, -0.37558120489120483],
|
||||
[0.9232977628707886, 2.1843791007995605],
|
||||
[-0.1989777386188507, 1.651851773262024],
|
||||
[-0.714374840259552, -0.39365994930267334],
|
||||
[-0.7805715799331665, -2.099881887435913],
|
||||
[0.9015759229660034, -1.7053706645965576],
|
||||
[0.1033422127366066, 1.5256654024124146],
|
||||
[-1.8773194551467896, 2.324174165725708],
|
||||
[1.9227174520492554, 2.7441604137420654],
|
||||
[-0.5994020104408264, 0.23984014987945557],
|
||||
[1.3496100902557373, -0.9126054644584656],
|
||||
[-0.8765304088592529, -3.1877026557922363],
|
||||
[-1.2040035724639893, -1.5169521570205688],
|
||||
[1.4261796474456787, 2.150200128555298],
|
||||
[1.463774561882019, 1.6656692028045654],
|
||||
[0.20364105701446533, -0.4988172650337219],
|
||||
[0.5195154547691345, -0.24067887663841248],
|
||||
[-1.1116786003112793, -1.1599653959274292],
|
||||
[-0.8490808606147766, -0.1681060940027237],
|
||||
[0.3189965784549713, -0.9641751646995544],
|
||||
[-0.5664751529693604, -0.5951744318008423],
|
||||
[-1.6347930431365967, -0.9137664437294006],
|
||||
[0.44048091769218445, -0.47259435057640076],
|
||||
[-2.147747039794922, 0.47442489862442017],
|
||||
[1.834734320640564, 1.4462147951126099],
|
||||
[1.1777573823928833, 1.0659226179122925],
|
||||
[-0.9568989872932434, 0.09495053440332413],
|
||||
[-1.838529348373413, 0.2950586676597595],
|
||||
[-0.4800611734390259, 0.014894310384988785],
|
||||
[-0.5235516428947449, -1.7687653303146362],
|
||||
[2.0735011100769043, -0.8825281262397766],
|
||||
[2.637502431869507, 0.8455678224563599],
|
||||
[2.606602907180786, -0.7848446369171143],
|
||||
[-1.1886937618255615, 0.9330510497093201],
|
||||
[0.38082656264305115, 0.13328030705451965],
|
||||
[0.6847941875457764, 0.7384101152420044],
|
||||
[1.2638574838638306, -0.007309418171644211],
|
||||
[0.18292222917079926, -1.22371244430542],
|
||||
[0.8143821954727173, 1.4976691007614136],
|
||||
[0.6571850776672363, 0.48368802666664124],
|
||||
[-0.6991601586341858, 2.150190830230713],
|
||||
[0.8101756572723389, 0.10206498205661774],
|
||||
[-0.08768226951360703, -1.084917664527893],
|
||||
[-0.7208092212677002, 0.03657956421375275],
|
||||
[0.3211449086666107, 1.803687334060669],
|
||||
[-0.7835946083068848, 1.6869111061096191],
|
||||
]
|
||||
)
|
||||
if (p, n) == (2, 64):
|
||||
return torch.tensor(
|
||||
[
|
||||
[-2.7216711044311523, 0.14431366324424744],
|
||||
[-0.766914427280426, 1.7193410396575928],
|
||||
[-2.2575762271881104, 1.2476624250411987],
|
||||
[1.233758807182312, -2.3560616970062256],
|
||||
[0.8701965808868408, -0.2649352252483368],
|
||||
[1.4506438970565796, 2.1776366233825684],
|
||||
[-0.06305818259716034, 1.9049758911132812],
|
||||
[2.536226511001587, 0.563927412033081],
|
||||
[0.4599496126174927, -1.8745561838150024],
|
||||
[-1.900517225265503, -0.30703988671302795],
|
||||
[0.09386251866817474, 0.8755807280540466],
|
||||
[1.946500539779663, -0.6743080615997314],
|
||||
[2.1338934898376465, 1.4581491947174072],
|
||||
[0.9429940581321716, -0.8038390278816223],
|
||||
[2.0697755813598633, -1.614896535873413],
|
||||
[0.772676408290863, 0.22017823159694672],
|
||||
[1.0689979791641235, -1.525044322013855],
|
||||
[0.6813604831695557, 1.1345642805099487],
|
||||
[0.4706456661224365, 2.606626272201538],
|
||||
[-1.294018030166626, -0.4372096061706543],
|
||||
[-0.09134224057197571, 0.4610418677330017],
|
||||
[-0.7907772064208984, -0.48412787914276123],
|
||||
[0.060459110885858536, -0.9172890186309814],
|
||||
[-0.5855047702789307, 2.56172513961792],
|
||||
[0.11484206467866898, -2.659848213195801],
|
||||
[-1.5893300771713257, 2.188580274581909],
|
||||
[1.6750942468643188, 0.7089915871620178],
|
||||
[-0.445697546005249, 0.7452405095100403],
|
||||
[-1.8539940118789673, -1.8377939462661743],
|
||||
[-1.5791912078857422, -1.017285943031311],
|
||||
[-1.030419945716858, -1.5746369361877441],
|
||||
[-1.9511750936508179, 0.43696075677871704],
|
||||
[-0.3446580767631531, -1.8953213691711426],
|
||||
[-1.4219647645950317, 0.7676230669021606],
|
||||
[-0.9191089272499084, 0.5021472573280334],
|
||||
[0.20464491844177246, 1.3684605360031128],
|
||||
[0.5402919054031372, 0.6699410676956177],
|
||||
[1.8903915882110596, 0.03638288006186485],
|
||||
[0.4723062515258789, -0.6216739416122437],
|
||||
[-0.41345009207725525, -0.22752176225185394],
|
||||
[2.7119064331054688, -0.5111885070800781],
|
||||
[1.065286636352539, 0.6950305700302124],
|
||||
[0.40629103779792786, -0.14339995384216309],
|
||||
[1.2815024852752686, 0.17108257114887238],
|
||||
[0.01785222627222538, -0.43778058886528015],
|
||||
[0.054590027779340744, -1.4225547313690186],
|
||||
[0.3076786696910858, 0.30697619915008545],
|
||||
[-0.9498570561408997, -0.9576997756958008],
|
||||
[-2.4640724658966064, -0.9660449028015137],
|
||||
[1.3714425563812256, -0.39760473370552063],
|
||||
[-0.4857747256755829, 0.2386789172887802],
|
||||
[1.2797833681106567, 1.3097363710403442],
|
||||
[0.5508887767791748, -1.1777795553207397],
|
||||
[-1.384316325187683, 0.1465839296579361],
|
||||
[-0.46556955575942993, -1.2442727088928223],
|
||||
[-0.3915477693080902, -0.7319604158401489],
|
||||
[-1.4005504846572876, 1.3890998363494873],
|
||||
[-0.8647305965423584, 1.0617644786834717],
|
||||
[-0.8901953101158142, -0.01650036871433258],
|
||||
[-0.9893633723258972, -2.4662880897521973],
|
||||
[1.445534110069275, -1.049334168434143],
|
||||
[-0.041650623083114624, 0.012734669260680676],
|
||||
[-0.3302375078201294, 1.26217782497406],
|
||||
[0.6934980154037476, 1.7714335918426514],
|
||||
]
|
||||
)
|
||||
elif (p, n) == (2, 16):
|
||||
return torch.tensor(
|
||||
[
|
||||
[-0.8996632695198059, -1.6360418796539307],
|
||||
[-0.961183488368988, 1.5999565124511719],
|
||||
[-1.882026195526123, 0.678778350353241],
|
||||
[0.36300793290138245, -1.9667866230010986],
|
||||
[-0.6814072728157043, -0.576818585395813],
|
||||
[0.7270012497901917, 0.6186859607696533],
|
||||
[0.3359416127204895, 1.8371193408966064],
|
||||
[1.859930396080017, 0.036668598651885986],
|
||||
[0.17208248376846313, -0.9401724338531494],
|
||||
[-1.7599700689315796, -0.6244229674339294],
|
||||
[-0.8993809223175049, 0.32267823815345764],
|
||||
[0.839488685131073, -0.3017036020755768],
|
||||
[1.5314953327178955, 1.2942044734954834],
|
||||
[-0.0011779458727687597, 0.00022069070837460458],
|
||||
[1.4274526834487915, -1.207889199256897],
|
||||
[-0.16123905777931213, 0.8787511587142944],
|
||||
]
|
||||
)
|
||||
elif (p, n) == (1, 16):
|
||||
return torch.tensor(
|
||||
[
|
||||
[-2.7325894832611084],
|
||||
[-2.069017171859741],
|
||||
[-1.6180464029312134],
|
||||
[-1.2562311887741089],
|
||||
[-0.9423404335975647],
|
||||
[-0.6567591428756714],
|
||||
[-0.38804829120635986],
|
||||
[-0.12839503586292267],
|
||||
[0.12839503586292267],
|
||||
[0.38804829120635986],
|
||||
[0.6567591428756714],
|
||||
[0.9423404335975647],
|
||||
[1.2562311887741089],
|
||||
[1.6180464029312134],
|
||||
[2.069017171859741],
|
||||
[2.7325894832611084],
|
||||
]
|
||||
)
|
||||
elif (p, n) == (1, 8):
|
||||
return torch.tensor(
|
||||
[
|
||||
[-2.1519455909729004],
|
||||
[-1.3439092636108398],
|
||||
[-0.7560052871704102],
|
||||
[-0.2450941801071167],
|
||||
[0.2450941801071167],
|
||||
[0.7560052871704102],
|
||||
[1.3439092636108398],
|
||||
[2.1519455909729004],
|
||||
]
|
||||
)
|
||||
elif (p, n) == (1, 4):
|
||||
return torch.tensor([[-1.5104175806045532], [-0.4527800381183624], [0.4527800381183624], [1.5104175806045532]])
|
||||
else:
|
||||
raise NotImplementedError(f"Unsupported p={p}, n={n}")
|
||||
|
||||
|
||||
def quantize_with_higgs(weight, bits: int = 4, p: int = 2, group_size: int = 256, hadamard_size: int = 1024):
|
||||
assert len(weight.shape) == 2, "Only 2D weights are supported for now"
|
||||
|
||||
grid = get_higgs_grid(p, 2 ** (p * bits)).to(weight.device)
|
||||
grid_norm_2 = torch.linalg.norm(grid, axis=-1) ** 2
|
||||
|
||||
device = weight.device
|
||||
dtype = weight.dtype
|
||||
weight = weight.clone().float()
|
||||
# Pad to Hadamard transform size
|
||||
weight = pad_to_block(weight, [1], hadamard_size)
|
||||
|
||||
# Scale and Hadamard transform
|
||||
mult = weight.shape[1] // hadamard_size
|
||||
weight = weight.reshape(-1, mult, hadamard_size)
|
||||
scales = torch.linalg.norm(weight, axis=-1)
|
||||
weight = hadamard_transform(weight, 1) / scales[:, :, None]
|
||||
|
||||
# Pad to edenn_d and project
|
||||
weight = pad_to_block(weight, [2], p).reshape(weight.shape[0], mult, -1, p)
|
||||
|
||||
# Quantize
|
||||
codes = torch.empty(weight.shape[:-1], device=device, dtype=torch.uint8)
|
||||
for i in range(0, weight.shape[0], 64):
|
||||
codes[i : i + 64] = torch.argmax(2 * weight[i : i + 64] @ grid.T - grid_norm_2, dim=-1).to(torch.uint8)
|
||||
del weight
|
||||
|
||||
codes = codes.reshape(codes.shape[0], -1)
|
||||
scales = scales / sqrt(hadamard_size)
|
||||
|
||||
weight, scales, tables, tables2 = prepare_data_transposed(
|
||||
codes,
|
||||
torch.repeat_interleave(scales.to(dtype), hadamard_size // group_size, dim=1),
|
||||
grid.to(dtype),
|
||||
num_bits=bits,
|
||||
group_size=group_size,
|
||||
vector_size=p,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
)
|
||||
|
||||
return {
|
||||
"weight": weight,
|
||||
"scales": scales,
|
||||
"tables": tables,
|
||||
"tables2": tables2.view(dtype=torch.float16),
|
||||
}
|
||||
|
||||
|
||||
class HiggsLinear(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_features: int,
|
||||
out_features: int,
|
||||
num_bits: int,
|
||||
bias=True,
|
||||
dtype: torch.dtype = None,
|
||||
device: torch.device = None,
|
||||
group_size: int = 256,
|
||||
hadamard_size: int = 1024,
|
||||
):
|
||||
super().__init__()
|
||||
self.in_features = in_features
|
||||
self.out_features = out_features
|
||||
self.num_bits = num_bits
|
||||
self.group_size = group_size
|
||||
self.hadamard_size = hadamard_size
|
||||
self.num_sms_packed = nn.Parameter(torch.tensor(-1, dtype=torch.int32, device=device), requires_grad=False)
|
||||
|
||||
assert in_features % group_size == 0
|
||||
assert num_bits in [2, 3, 4]
|
||||
|
||||
self.weight = nn.Parameter(
|
||||
torch.empty((out_features * num_bits // 16, in_features), dtype=torch.int16, device=device),
|
||||
requires_grad=False,
|
||||
)
|
||||
self.scales = nn.Parameter(
|
||||
torch.empty((out_features, in_features // group_size), dtype=dtype, device=device), requires_grad=False
|
||||
)
|
||||
self.tables = nn.Parameter(torch.empty((2**num_bits,), dtype=dtype, device=device), requires_grad=False)
|
||||
self.tables2 = nn.Parameter(
|
||||
torch.empty((2**num_bits, 2**num_bits, 2), dtype=dtype, device=device), requires_grad=False
|
||||
)
|
||||
|
||||
if bias:
|
||||
self.bias = nn.Parameter(torch.empty(out_features, device=device, dtype=dtype), requires_grad=False)
|
||||
else:
|
||||
self.register_parameter("bias", None)
|
||||
|
||||
self.workspace = None # must be set externally to be reused among layers
|
||||
|
||||
def forward(self, x):
|
||||
x = pad_to_block(x, [-1], self.hadamard_size)
|
||||
|
||||
if self.workspace is None:
|
||||
raise Exception("Workspace must be set before calling forward")
|
||||
|
||||
return flute.qgemm_hadamard(
|
||||
x,
|
||||
self.weight,
|
||||
self.scales,
|
||||
self.tables,
|
||||
self.tables2.view(dtype=torch.float32),
|
||||
self.workspace,
|
||||
self.num_bits,
|
||||
self.group_size,
|
||||
self.hadamard_size,
|
||||
)
|
||||
|
||||
|
||||
def replace_with_higgs_linear(
|
||||
model,
|
||||
quantization_config=None,
|
||||
current_key_name=None,
|
||||
has_been_replaced=False,
|
||||
):
|
||||
"""
|
||||
Public method that recursively replaces the Linear layers of the given model with HIGGS quantized layers.
|
||||
`accelerate` is needed to use this method. Returns the converted model and a boolean that indicates if the
|
||||
conversion has been successfull or not.
|
||||
|
||||
Args:
|
||||
model (`torch.nn.Module`):
|
||||
The model to convert, can be any `torch.nn.Module` instance.
|
||||
quantization_config (`HiggsConfig`):
|
||||
The quantization config object that contains the quantization parameters.
|
||||
current_key_name (`list`, *optional*):
|
||||
A list that contains the current key name. This is used for recursion and should not be passed by the user.
|
||||
has_been_replaced (`bool`, *optional*):
|
||||
A boolean that indicates if the conversion has been successful or not. This is used for recursion and
|
||||
should not be passed by the user.
|
||||
"""
|
||||
|
||||
from accelerate import init_empty_weights
|
||||
|
||||
for name, module in model.named_children():
|
||||
if current_key_name is None:
|
||||
current_key_name = []
|
||||
current_key_name.append(name)
|
||||
|
||||
if isinstance(module, nn.Linear):
|
||||
# Check if the current key is not in the `quantization_config.modules_to_not_convert`
|
||||
current_key_name_str = ".".join(current_key_name)
|
||||
if not any(current_key_name_str.endswith(key) for key in quantization_config.modules_to_not_convert):
|
||||
with init_empty_weights():
|
||||
in_features = module.in_features
|
||||
out_features = module.out_features
|
||||
|
||||
model._modules[name] = HiggsLinear(
|
||||
in_features,
|
||||
out_features,
|
||||
bias=module.bias is not None,
|
||||
num_bits=quantization_config.bits,
|
||||
hadamard_size=quantization_config.hadamard_size,
|
||||
group_size=quantization_config.group_size,
|
||||
)
|
||||
has_been_replaced = True
|
||||
|
||||
# Store the module class in case we need to transpose the weight later
|
||||
model._modules[name].source_cls = type(module)
|
||||
# Force requires grad to False to avoid unexpected errors
|
||||
model._modules[name].requires_grad_(False)
|
||||
if len(list(module.children())) > 0:
|
||||
_, has_been_replaced = replace_with_higgs_linear(
|
||||
module,
|
||||
quantization_config=quantization_config,
|
||||
current_key_name=current_key_name,
|
||||
has_been_replaced=has_been_replaced,
|
||||
)
|
||||
# Remove the last key for recursion
|
||||
current_key_name.pop(-1)
|
||||
return model, has_been_replaced
|
||||
|
||||
|
||||
def dequantize_higgs(model, current_key_name=None):
|
||||
"""
|
||||
Dequantizes the HiggsLinear layers in the given model by replacing them with standard torch.nn.Linear layers.
|
||||
Args:
|
||||
model (torch.nn.Module): The model containing HiggsLinear layers to be dequantized.
|
||||
current_key_name (list, optional): A list to keep track of the current module names during recursion. Defaults to None.
|
||||
Returns:
|
||||
torch.nn.Module: The model with HiggsLinear layers replaced by torch.nn.Linear layers.
|
||||
"""
|
||||
|
||||
with torch.no_grad():
|
||||
for name, module in model.named_children():
|
||||
if current_key_name is None:
|
||||
current_key_name = []
|
||||
current_key_name.append(name)
|
||||
|
||||
if isinstance(module, HiggsLinear):
|
||||
in_features = module.in_features
|
||||
out_features = module.out_features
|
||||
|
||||
model._modules[name] = torch.nn.Linear(
|
||||
in_features,
|
||||
out_features,
|
||||
bias=module.bias is not None,
|
||||
device=module.scales.device,
|
||||
dtype=module.scales.dtype,
|
||||
)
|
||||
|
||||
model._modules[name].weight.data = module(
|
||||
torch.eye(in_features, device=module.scales.device, dtype=module.scales.dtype)
|
||||
).T.contiguous()
|
||||
|
||||
if len(list(module.children())) > 0:
|
||||
_ = dequantize_higgs(
|
||||
module,
|
||||
current_key_name=current_key_name,
|
||||
)
|
||||
# Remove the last key for recursion
|
||||
current_key_name.pop(-1)
|
||||
return model
|
||||
|
|
@ -24,6 +24,7 @@ from ..utils.quantization_config import (
|
|||
EetqConfig,
|
||||
FbgemmFp8Config,
|
||||
GPTQConfig,
|
||||
HiggsConfig,
|
||||
HqqConfig,
|
||||
QuantizationConfigMixin,
|
||||
QuantizationMethod,
|
||||
|
|
@ -40,6 +41,7 @@ from .quantizer_compressed_tensors import CompressedTensorsHfQuantizer
|
|||
from .quantizer_eetq import EetqHfQuantizer
|
||||
from .quantizer_fbgemm_fp8 import FbgemmFp8HfQuantizer
|
||||
from .quantizer_gptq import GptqHfQuantizer
|
||||
from .quantizer_higgs import HiggsHfQuantizer
|
||||
from .quantizer_hqq import HqqHfQuantizer
|
||||
from .quantizer_quanto import QuantoHfQuantizer
|
||||
from .quantizer_torchao import TorchAoHfQuantizer
|
||||
|
|
@ -54,6 +56,7 @@ AUTO_QUANTIZER_MAPPING = {
|
|||
"aqlm": AqlmHfQuantizer,
|
||||
"quanto": QuantoHfQuantizer,
|
||||
"eetq": EetqHfQuantizer,
|
||||
"higgs": HiggsHfQuantizer,
|
||||
"hqq": HqqHfQuantizer,
|
||||
"compressed-tensors": CompressedTensorsHfQuantizer,
|
||||
"fbgemm_fp8": FbgemmFp8HfQuantizer,
|
||||
|
|
@ -73,6 +76,7 @@ AUTO_QUANTIZATION_CONFIG_MAPPING = {
|
|||
"hqq": HqqConfig,
|
||||
"compressed-tensors": CompressedTensorsConfig,
|
||||
"fbgemm_fp8": FbgemmFp8Config,
|
||||
"higgs": HiggsConfig,
|
||||
"torchao": TorchAoConfig,
|
||||
"bitnet": BitNetConfig,
|
||||
"vptq": VptqConfig,
|
||||
|
|
|
|||
232
src/transformers/quantizers/quantizer_higgs.py
Normal file
232
src/transformers/quantizers/quantizer_higgs.py
Normal file
|
|
@ -0,0 +1,232 @@
|
|||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# 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 copy 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.
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional
|
||||
|
||||
from .base import HfQuantizer
|
||||
from .quantizers_utils import get_module_from_name
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ..modeling_utils import PreTrainedModel
|
||||
|
||||
from ..utils import is_accelerate_available, is_flute_available, is_hadamard_available, is_torch_available, logging
|
||||
from ..utils.quantization_config import QuantizationConfigMixin
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
def get_num_sms_from_device(device):
|
||||
target_device_cc = torch.cuda.get_device_capability(device=device)
|
||||
if target_device_cc == (8, 6):
|
||||
return 84
|
||||
elif target_device_cc == (8, 0):
|
||||
return 108
|
||||
elif target_device_cc == (8, 9):
|
||||
return 128
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
f"Device capability {target_device_cc} not supported for FLUTE (yet?) to verify your device capability check out https://developer.nvidia.com/cuda-gpus"
|
||||
)
|
||||
|
||||
|
||||
class HiggsHfQuantizer(HfQuantizer):
|
||||
"""
|
||||
Quantizer of the HIGGS method. Enables the loading of prequantized models and in-flight quantization of full-precision models.
|
||||
"""
|
||||
|
||||
requires_calibration = False
|
||||
requires_parameters_quantization = True
|
||||
required_packages = ["flute-kernel", "fast_hadamard_transform"]
|
||||
|
||||
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
|
||||
super().__init__(quantization_config, **kwargs)
|
||||
self.quantization_config = quantization_config
|
||||
|
||||
def validate_environment(self, device_map, **kwargs):
|
||||
if not torch.cuda.is_available():
|
||||
raise NotImplementedError("HIGGS quantization is only supported on GPU. Please use a different quantizer.")
|
||||
|
||||
if not is_accelerate_available():
|
||||
raise ImportError("Using `higgs` quantization requires Accelerate: `pip install accelerate`")
|
||||
|
||||
if not is_flute_available():
|
||||
raise ImportError("Using `higgs` quantization requires FLUTE: `pip install flute-kernel>=0.3.0`")
|
||||
|
||||
if not is_hadamard_available():
|
||||
raise ImportError(
|
||||
"Using `higgs` quantization requires fast_hadamard_transform: `pip install fast_hadamard_transform`"
|
||||
)
|
||||
|
||||
if device_map is None:
|
||||
raise ValueError(
|
||||
"You are attempting to load a HIGGS model without setting device_map."
|
||||
" Please set device_map comprised of 'cuda' devices."
|
||||
)
|
||||
elif isinstance(device_map, dict) and ("cpu" in device_map.values() or "disk" in device_map.values()):
|
||||
raise ValueError(
|
||||
"You are attempting to load a HIGGS model with a device_map that contains a CPU or disk device."
|
||||
" This is not supported. Please remove the CPU or disk device from the device_map."
|
||||
)
|
||||
|
||||
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
|
||||
if torch_dtype is None:
|
||||
logger.info("`torch_dtype` is None. Setting `torch_dtype=torch.float16` for FLUTE compatibility.")
|
||||
torch_dtype = torch.float16
|
||||
elif torch_dtype != torch.float16 and torch_dtype != torch.bfloat16:
|
||||
raise ValueError(
|
||||
f"Invalid `torch_dtype` {torch_dtype}. HIGGS quantization only supports `torch_dtype=torch.float16` or `torch_dtype=torch.bfloat16`."
|
||||
)
|
||||
|
||||
return torch_dtype
|
||||
|
||||
def create_quantized_param(
|
||||
self,
|
||||
model: "PreTrainedModel",
|
||||
param_value: "torch.Tensor",
|
||||
param_name: str,
|
||||
target_device: "torch.device",
|
||||
state_dict: Dict[str, Any],
|
||||
unexpected_keys: Optional[List[str]] = None,
|
||||
):
|
||||
from ..integrations import quantize_with_higgs
|
||||
|
||||
"""
|
||||
Quantizes weights into weight and weight_scale
|
||||
"""
|
||||
flute_dict = quantize_with_higgs(
|
||||
param_value.to(target_device),
|
||||
self.quantization_config.bits,
|
||||
self.quantization_config.p,
|
||||
self.quantization_config.group_size,
|
||||
self.quantization_config.hadamard_size,
|
||||
)
|
||||
|
||||
del param_value
|
||||
|
||||
module, tensor_name = get_module_from_name(model, param_name)
|
||||
for key, value in flute_dict.items():
|
||||
if key in module._parameters:
|
||||
module._parameters[key] = torch.nn.Parameter(value, requires_grad=False)
|
||||
elif key in module._buffers:
|
||||
module._buffers[key] = torch.nn.Buffer(value)
|
||||
else:
|
||||
raise ValueError(f"Unexpected key {key} in module {module}")
|
||||
|
||||
if unexpected_keys is not None and param_name in unexpected_keys:
|
||||
unexpected_keys.remove(param_name)
|
||||
|
||||
module.num_sms_packed = torch.nn.Parameter(
|
||||
torch.tensor(get_num_sms_from_device(target_device), device=target_device, dtype=torch.int32),
|
||||
requires_grad=False,
|
||||
)
|
||||
|
||||
def _process_model_before_weight_loading(
|
||||
self,
|
||||
model: "PreTrainedModel",
|
||||
**kwargs,
|
||||
):
|
||||
from ..integrations import replace_with_higgs_linear
|
||||
|
||||
replace_with_higgs_linear(
|
||||
model,
|
||||
quantization_config=self.quantization_config,
|
||||
)
|
||||
model.config.quantization_config = self.quantization_config
|
||||
|
||||
def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
|
||||
import flute.utils
|
||||
|
||||
from ..integrations import HiggsLinear
|
||||
|
||||
flute_workspaces = {}
|
||||
for name, module in model.named_modules():
|
||||
if isinstance(module, HiggsLinear):
|
||||
# Every HiggsLinear needs a "workspace": a buffer for the unpacking operation.
|
||||
# This buffer needs to be on the same device as the weights, but can be reused across modules otherwise.
|
||||
if module.weight.device not in flute_workspaces:
|
||||
flute_workspaces[module.weight.device] = flute.utils.make_workspace_streamk(
|
||||
device=module.weight.device
|
||||
)
|
||||
module.workspace = flute_workspaces[module.weight.device]
|
||||
|
||||
# FLUTE weights are packed in a way that is optimized for a specific number of SMs (GPU streaming multiprocessors).
|
||||
# If the model is loaded on a different device than the one it was saved on, we need to repack the weights.
|
||||
if module.num_sms_packed.item() != get_num_sms_from_device(module.weight.device):
|
||||
new_device = module.weight.device
|
||||
new_num_sms = get_num_sms_from_device(new_device)
|
||||
module.weight.data = flute.utils.pack(
|
||||
flute.utils.unpack(
|
||||
weight=module.weight.data,
|
||||
scales=module.scales.data,
|
||||
workspace=module.workspace,
|
||||
num_bits=module.num_bits,
|
||||
group_size=module.group_size,
|
||||
num_sms_packed=module.num_sms_packed.item(),
|
||||
).T.contiguous(),
|
||||
module.num_bits,
|
||||
module.group_size,
|
||||
)
|
||||
module.num_sms_packed = torch.nn.Parameter(
|
||||
torch.tensor(new_num_sms, device=new_device, dtype=torch.int32),
|
||||
requires_grad=False,
|
||||
)
|
||||
|
||||
def update_missing_keys(self, model, missing_keys: List[str], prefix: str) -> List[str]:
|
||||
from ..integrations import HiggsLinear
|
||||
|
||||
not_missing_keys = []
|
||||
for name, module in model.named_modules():
|
||||
if isinstance(module, HiggsLinear):
|
||||
for missing in missing_keys:
|
||||
if (
|
||||
(name in missing or name in f"{prefix}.{missing}")
|
||||
and not missing.endswith(".weight")
|
||||
and not missing.endswith(".bias")
|
||||
):
|
||||
not_missing_keys.append(missing)
|
||||
return [k for k in missing_keys if k not in not_missing_keys]
|
||||
|
||||
@property
|
||||
def is_trainable(self, model: Optional["PreTrainedModel"] = None):
|
||||
return False
|
||||
|
||||
def is_serializable(self, safe_serialization=None):
|
||||
return True
|
||||
|
||||
def check_quantized_param(
|
||||
self,
|
||||
model: "PreTrainedModel",
|
||||
param_value: "torch.Tensor",
|
||||
param_name: str,
|
||||
state_dict: Dict[str, Any],
|
||||
**kwargs,
|
||||
) -> bool:
|
||||
from ..integrations import HiggsLinear
|
||||
|
||||
module, tensor_name = get_module_from_name(model, param_name)
|
||||
if isinstance(module, HiggsLinear) and tensor_name == "weight" and param_value.dtype != torch.int16:
|
||||
# Only quantize weights of HiggsLinear modules that are not already quantized
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
def _dequantize(self, model):
|
||||
from ..integrations import dequantize_higgs
|
||||
|
||||
model = dequantize_higgs(model)
|
||||
return model
|
||||
|
|
@ -79,12 +79,14 @@ from .utils import (
|
|||
is_fbgemm_gpu_available,
|
||||
is_flash_attn_2_available,
|
||||
is_flax_available,
|
||||
is_flute_available,
|
||||
is_fsdp_available,
|
||||
is_ftfy_available,
|
||||
is_g2p_en_available,
|
||||
is_galore_torch_available,
|
||||
is_gguf_available,
|
||||
is_grokadamw_available,
|
||||
is_hadamard_available,
|
||||
is_ipex_available,
|
||||
is_jieba_available,
|
||||
is_jinja_available,
|
||||
|
|
@ -1239,6 +1241,15 @@ def require_fbgemm_gpu(test_case):
|
|||
return unittest.skipUnless(is_fbgemm_gpu_available(), "test requires fbgemm-gpu")(test_case)
|
||||
|
||||
|
||||
def require_flute_hadamard(test_case):
|
||||
"""
|
||||
Decorator marking a test that requires higgs and hadamard
|
||||
"""
|
||||
return unittest.skipUnless(
|
||||
is_flute_available() and is_hadamard_available(), "test requires flute and fast_hadamard_transform"
|
||||
)(test_case)
|
||||
|
||||
|
||||
def require_phonemizer(test_case):
|
||||
"""
|
||||
Decorator marking a test that requires phonemizer
|
||||
|
|
|
|||
|
|
@ -140,12 +140,14 @@ from .import_utils import (
|
|||
is_flash_attn_greater_or_equal,
|
||||
is_flash_attn_greater_or_equal_2_10,
|
||||
is_flax_available,
|
||||
is_flute_available,
|
||||
is_fsdp_available,
|
||||
is_ftfy_available,
|
||||
is_g2p_en_available,
|
||||
is_galore_torch_available,
|
||||
is_gguf_available,
|
||||
is_grokadamw_available,
|
||||
is_hadamard_available,
|
||||
is_hqq_available,
|
||||
is_in_notebook,
|
||||
is_ipex_available,
|
||||
|
|
|
|||
|
|
@ -128,6 +128,7 @@ except importlib.metadata.PackageNotFoundError:
|
|||
_faiss_available = False
|
||||
_ftfy_available = _is_package_available("ftfy")
|
||||
_g2p_en_available = _is_package_available("g2p_en")
|
||||
_hadamard_available = _is_package_available("fast_hadamard_transform")
|
||||
_ipex_available, _ipex_version = _is_package_available("intel_extension_for_pytorch", return_version=True)
|
||||
_jieba_available = _is_package_available("jieba")
|
||||
_jinja_available = _is_package_available("jinja2")
|
||||
|
|
@ -332,6 +333,10 @@ def is_torch_deterministic():
|
|||
return True
|
||||
|
||||
|
||||
def is_hadamard_available():
|
||||
return _hadamard_available
|
||||
|
||||
|
||||
def is_hqq_available(min_version: str = HQQ_MIN_VERSION):
|
||||
return _hqq_available and version.parse(_hqq_version) >= version.parse(min_version)
|
||||
|
||||
|
|
@ -615,6 +620,13 @@ def is_flax_available():
|
|||
return _flax_available
|
||||
|
||||
|
||||
def is_flute_available():
|
||||
try:
|
||||
return importlib.util.find_spec("flute") is not None and importlib.metadata.version("flute-kernel") >= "0.3.0"
|
||||
except importlib.metadata.PackageNotFoundError:
|
||||
return False
|
||||
|
||||
|
||||
def is_ftfy_available():
|
||||
return _ftfy_available
|
||||
|
||||
|
|
|
|||
|
|
@ -42,6 +42,7 @@ class QuantizationMethod(str, Enum):
|
|||
VPTQ = "vptq"
|
||||
QUANTO = "quanto"
|
||||
EETQ = "eetq"
|
||||
HIGGS = "higgs"
|
||||
HQQ = "hqq"
|
||||
COMPRESSED_TENSORS = "compressed-tensors"
|
||||
FBGEMM_FP8 = "fbgemm_fp8"
|
||||
|
|
@ -1340,6 +1341,58 @@ class FbgemmFp8Config(QuantizationConfigMixin):
|
|||
return loading_attibutes_dict
|
||||
|
||||
|
||||
@dataclass
|
||||
class HiggsConfig(QuantizationConfigMixin):
|
||||
"""
|
||||
HiggsConfig is a configuration class for quantization using the HIGGS method.
|
||||
|
||||
Args:
|
||||
bits (int, *optional*, defaults to 4):
|
||||
Number of bits to use for quantization. Can be 2, 3 or 4. Default is 4.
|
||||
p (int, *optional*, defaults to 2):
|
||||
Quantization grid dimension. 1 and 2 are supported. 2 is always better in practice. Default is 2.
|
||||
modules_to_not_convert (`list`, *optional*, default to ["lm_head"]):
|
||||
List of linear layers that should not be quantized.
|
||||
hadamard_size (int, *optional*, defaults to 512):
|
||||
Hadamard size for the HIGGS method. Default is 512. Input dimension of matrices is padded to this value. Decreasing this below 512 will reduce the quality of the quantization.
|
||||
group_size (int, *optional*, defaults to 256):
|
||||
Group size for the HIGGS method. Can be 64, 128 or 256. Decreasing it barely affects the performance. Default is 256. Must be a divisor of hadamard_size.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
bits: int = 4,
|
||||
p: int = 2,
|
||||
modules_to_not_convert: Optional[List[str]] = None,
|
||||
hadamard_size: int = 512,
|
||||
group_size: int = 256,
|
||||
**kwargs,
|
||||
):
|
||||
if modules_to_not_convert is None:
|
||||
modules_to_not_convert = ["lm_head"]
|
||||
self.quant_method = QuantizationMethod.HIGGS
|
||||
self.bits = bits
|
||||
self.p = p
|
||||
self.modules_to_not_convert = modules_to_not_convert
|
||||
self.hadamard_size = hadamard_size
|
||||
self.group_size = group_size
|
||||
|
||||
self.post_init()
|
||||
|
||||
def post_init(self):
|
||||
r"""
|
||||
Safety checker that arguments are correct - also replaces some NoneType arguments with their default values.
|
||||
"""
|
||||
if self.bits not in [2, 3, 4]:
|
||||
raise ValueError("bits must be 2, 3, or 4")
|
||||
if self.p not in [1, 2]:
|
||||
raise ValueError("p must be 1 or 2. 2 is always better in practice")
|
||||
if self.group_size not in [64, 128, 256]:
|
||||
raise ValueError("group_size must be 64, 128, or 256")
|
||||
if self.hadamard_size % self.group_size != 0:
|
||||
raise ValueError("hadamard_size must be divisible by group_size")
|
||||
|
||||
|
||||
@dataclass
|
||||
class TorchAoConfig(QuantizationConfigMixin):
|
||||
"""This is a config class for torchao quantization/sparsity techniques.
|
||||
|
|
|
|||
0
tests/quantization/higgs/__init__.py
Normal file
0
tests/quantization/higgs/__init__.py
Normal file
197
tests/quantization/higgs/test_higgs.py
Normal file
197
tests/quantization/higgs/test_higgs.py
Normal file
|
|
@ -0,0 +1,197 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# 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 copy 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 transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HiggsConfig, OPTForCausalLM
|
||||
from transformers.testing_utils import (
|
||||
require_accelerate,
|
||||
require_flute_hadamard,
|
||||
require_torch_gpu,
|
||||
require_torch_multi_gpu,
|
||||
slow,
|
||||
torch_device,
|
||||
)
|
||||
from transformers.utils import is_accelerate_available, is_torch_available
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
if is_accelerate_available():
|
||||
from accelerate import init_empty_weights
|
||||
|
||||
|
||||
@require_torch_gpu
|
||||
class HiggsConfigTest(unittest.TestCase):
|
||||
def test_to_dict(self):
|
||||
"""
|
||||
Simple test that checks if one uses a config and converts it to a dict, the dict is the same as the config object
|
||||
"""
|
||||
quantization_config = HiggsConfig()
|
||||
config_to_dict = quantization_config.to_dict()
|
||||
|
||||
for key in config_to_dict:
|
||||
self.assertEqual(getattr(quantization_config, key), config_to_dict[key])
|
||||
|
||||
def test_from_dict(self):
|
||||
"""
|
||||
Simple test that checks if one uses a dict and converts it to a config object, the config object is the same as the dict
|
||||
"""
|
||||
dict = {"modules_to_not_convert": ["embed_tokens", "lm_head"], "quant_method": "higgs"}
|
||||
quantization_config = HiggsConfig.from_dict(dict)
|
||||
|
||||
self.assertEqual(dict["modules_to_not_convert"], quantization_config.modules_to_not_convert)
|
||||
self.assertEqual(dict["quant_method"], quantization_config.quant_method)
|
||||
|
||||
|
||||
@slow
|
||||
@require_torch_gpu
|
||||
@require_flute_hadamard
|
||||
@require_accelerate
|
||||
# @require_read_token
|
||||
class HiggsTest(unittest.TestCase):
|
||||
model_name = "meta-llama/Meta-Llama-3.1-8B"
|
||||
|
||||
input_text = "A quick brown fox jumps over the"
|
||||
max_new_tokens = 2
|
||||
|
||||
EXPECTED_OUTPUT = "A quick brown fox jumps over the lazy dog"
|
||||
|
||||
device_map = "cuda"
|
||||
|
||||
# called only once for all test in this class
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
"""
|
||||
Setup quantized model
|
||||
"""
|
||||
quantization_config = HiggsConfig()
|
||||
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
|
||||
cls.quantized_model = AutoModelForCausalLM.from_pretrained(
|
||||
cls.model_name, device_map=cls.device_map, quantization_config=quantization_config
|
||||
)
|
||||
|
||||
def tearDown(self):
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
gc.collect()
|
||||
|
||||
def test_quantized_model_conversion(self):
|
||||
"""
|
||||
Simple test that checks if the quantized model has been converted properly
|
||||
"""
|
||||
|
||||
from transformers.integrations import HiggsLinear, replace_with_higgs_linear
|
||||
|
||||
model_id = "facebook/opt-350m"
|
||||
config = AutoConfig.from_pretrained(model_id, revision="cb32f77e905cccbca1d970436fb0f5e6b58ee3c5")
|
||||
quantization_config = HiggsConfig()
|
||||
|
||||
with init_empty_weights():
|
||||
model = OPTForCausalLM(config)
|
||||
|
||||
nb_linears = 0
|
||||
for module in model.modules():
|
||||
if isinstance(module, torch.nn.Linear):
|
||||
nb_linears += 1
|
||||
|
||||
model, _ = replace_with_higgs_linear(model, quantization_config=quantization_config)
|
||||
nb_higgs_linear = 0
|
||||
for module in model.modules():
|
||||
if isinstance(module, HiggsLinear):
|
||||
nb_higgs_linear += 1
|
||||
|
||||
self.assertEqual(nb_linears - 1, nb_higgs_linear)
|
||||
|
||||
with init_empty_weights():
|
||||
model = OPTForCausalLM(config)
|
||||
quantization_config = HiggsConfig(modules_to_not_convert=["fc1"])
|
||||
model, _ = replace_with_higgs_linear(model, quantization_config=quantization_config)
|
||||
nb_higgs_linear = 0
|
||||
for module in model.modules():
|
||||
if isinstance(module, HiggsLinear):
|
||||
nb_higgs_linear += 1
|
||||
|
||||
self.assertEqual(nb_linears - 24, nb_higgs_linear)
|
||||
|
||||
def test_quantized_model(self):
|
||||
"""
|
||||
Simple test that checks if the quantized model is working properly
|
||||
"""
|
||||
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
|
||||
|
||||
output = self.quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
|
||||
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
|
||||
|
||||
def test_save_pretrained(self):
|
||||
"""
|
||||
Simple test that checks if the quantized model is working properly after being saved and loaded
|
||||
"""
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
self.quantized_model.save_pretrained(tmpdirname)
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map=self.device_map)
|
||||
|
||||
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
|
||||
|
||||
output = model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
|
||||
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
|
||||
|
||||
@require_torch_multi_gpu
|
||||
def test_quantized_model_multi_gpu(self):
|
||||
"""
|
||||
Simple test that checks if the quantized model is working properly with multiple GPUs
|
||||
set CUDA_VISIBLE_DEVICES=0,1 if you have more than 2 GPUS
|
||||
"""
|
||||
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
|
||||
quantization_config = HiggsConfig()
|
||||
quantized_model = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name, device_map="auto", quantization_config=quantization_config
|
||||
)
|
||||
self.assertTrue(set(quantized_model.hf_device_map.values()) == {0, 1})
|
||||
|
||||
output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
|
||||
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
|
||||
|
||||
@require_torch_multi_gpu
|
||||
def test_save_pretrained_multi_gpu(self):
|
||||
"""
|
||||
Simple test that checks if the quantized model is working properly after being saved and loaded
|
||||
"""
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
self.quantized_model.save_pretrained(tmpdirname)
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map="auto")
|
||||
self.assertTrue(set(model.hf_device_map.values()) == {0, 1})
|
||||
|
||||
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
|
||||
|
||||
output = model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
|
||||
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
|
||||
|
||||
@unittest.skip("This will almost surely OOM. Enable when swithed to a smaller model")
|
||||
def test_dequantize(self):
|
||||
"""
|
||||
Test the ability to dequantize a model
|
||||
"""
|
||||
self.quantized_model.dequantize()
|
||||
|
||||
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
|
||||
|
||||
output = self.quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
|
||||
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
|
||||
Loading…
Reference in a new issue