diff --git a/README.md b/README.md
index 1a71d2741d5b522728f5846a62a2a1659fdb216a..5c2134d05934e08d63a00bcbfc29c92cbea951a4 100644
--- a/README.md
+++ b/README.md
@@ -233,3 +233,6 @@ Instructions to run the image can be found in the [official documentation](https
- Use Mistral models on [Mistral AI official API](https://console.mistral.ai/) (La Plateforme)
- Use Mistral models via [cloud providers](https://docs.mistral.ai/deployment/cloud/overview/)
+## References
+
+[1]: [LoRA](https://arxiv.org/abs/2106.09685): Low-Rank Adaptation of Large Language Models, Hu et al. 2021
diff --git a/poetry.lock b/poetry.lock
index 93590547d2360667be406648c0834e1780d0f743..4ed0489e8c78d18c1ff415662e10ad159c31ecef 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,5 +1,35 @@
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
+[[package]]
+name = "attrs"
+version = "23.2.0"
+description = "Classes Without Boilerplate"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.extras]
+cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
+dev = ["attrs[tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+
[[package]]
name = "colorama"
version = "0.4.6"
@@ -52,15 +82,29 @@ docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1
testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
typing = ["typing-extensions (>=4.8)"]
+[[package]]
+name = "fire"
+version = "0.6.0"
+description = "A library for automatically generating command line interfaces."
+optional = false
+python-versions = "*"
+files = [
+ {file = "fire-0.6.0.tar.gz", hash = "sha256:54ec5b996ecdd3c0309c800324a0703d6da512241bc73b553db959d98de0aa66"},
+]
+
+[package.dependencies]
+six = "*"
+termcolor = "*"
+
[[package]]
name = "fsspec"
-version = "2024.3.1"
+version = "2024.5.0"
description = "File-system specification"
optional = false
python-versions = ">=3.8"
files = [
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[package.extras]
@@ -68,7 +112,7 @@ abfs = ["adlfs"]
adl = ["adlfs"]
arrow = ["pyarrow (>=1)"]
dask = ["dask", "distributed"]
-devel = ["pytest", "pytest-cov"]
+dev = ["pre-commit", "ruff"]
dropbox = ["dropbox", "dropboxdrivefs", "requests"]
full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
fuse = ["fusepy"]
@@ -85,6 +129,9 @@ s3 = ["s3fs"]
sftp = ["paramiko"]
smb = ["smbprotocol"]
ssh = ["paramiko"]
+test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
+test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
+test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
tqdm = ["tqdm"]
[[package]]
@@ -98,6 +145,20 @@ files = [
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]
+[[package]]
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+version = "2021.4.0"
+description = "Intel OpenMP* Runtime Library"
+optional = false
+python-versions = "*"
+files = [
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+]
+
[[package]]
name = "jinja2"
version = "3.1.4"
@@ -115,6 +176,41 @@ MarkupSafe = ">=2.0"
[package.extras]
i18n = ["Babel (>=2.7)"]
+[[package]]
+name = "jsonschema"
+version = "4.21.1"
+description = "An implementation of JSON Schema validation for Python"
+optional = false
+python-versions = ">=3.8"
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+referencing = ">=0.28.4"
+rpds-py = ">=0.7.1"
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+
+[[package]]
+name = "jsonschema-specifications"
+version = "2023.12.1"
+description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
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+
[[package]]
name = "markupsafe"
version = "2.1.5"
@@ -184,6 +280,41 @@ files = [
{file = "MarkupSafe-2.1.5.tar.gz", hash = "sha256:d283d37a890ba4c1ae73ffadf8046435c76e7bc2247bbb63c00bd1a709c6544b"},
]
+[[package]]
+name = "mistral-common"
+version = "1.1.0"
+description = ""
+optional = false
+python-versions = "<4.0.0,>=3.8.10"
+files = [
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+ {file = "mistral_common-1.1.0.tar.gz", hash = "sha256:fd919c19309deccf11476a38d975048380aa8950d06390084a6ebbf9bece2805"},
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+typing-extensions = ">=4.11.0,<5.0.0"
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+version = "2021.4.0"
+description = "Intel® oneAPI Math Kernel Library"
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+python-versions = "*"
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+[package.dependencies]
+intel-openmp = "==2021.*"
+tbb = "==2021.*"
+
[[package]]
name = "mpmath"
version = "1.3.0"
@@ -448,23 +579,24 @@ nvidia-nvjitlink-cu12 = "*"
[[package]]
name = "nvidia-nccl-cu12"
-version = "2.19.3"
+version = "2.20.5"
description = "NVIDIA Collective Communication Library (NCCL) Runtime"
optional = false
python-versions = ">=3"
files = [
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[[package]]
name = "nvidia-nvjitlink-cu12"
-version = "12.4.127"
+version = "12.5.40"
description = "Nvidia JIT LTO Library"
optional = false
python-versions = ">=3"
files = [
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@@ -506,24 +638,134 @@ testing = ["pytest", "pytest-benchmark"]
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+ {file = "torch-2.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:4fb27b35dbb32303c2927da86e27b54a92209ddfb7234afb1949ea2b3effffea"},
+ {file = "torch-2.3.0-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:760f8bedff506ce9e6e103498f9b1e9e15809e008368594c3a66bf74a8a51380"},
]
[package.dependencies]
filelock = "*"
fsspec = "*"
jinja2 = "*"
+mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""}
networkx = "*"
nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
@@ -666,10 +1241,10 @@ nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linu
nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu12 = {version = "2.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
sympy = "*"
-triton = {version = "2.2.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
+triton = {version = "2.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
typing-extensions = ">=4.8.0"
[package.extras]
@@ -678,17 +1253,17 @@ optree = ["optree (>=0.9.1)"]
[[package]]
name = "triton"
-version = "2.2.0"
+version = "2.3.0"
description = "A language and compiler for custom Deep Learning operations"
optional = false
python-versions = "*"
files = [
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+ {file = "triton-2.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ce4b8ff70c48e47274c66f269cce8861cf1dc347ceeb7a67414ca151b1822d8"},
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+ {file = "triton-2.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:218d742e67480d9581bafb73ed598416cc8a56f6316152e5562ee65e33de01c0"},
+ {file = "triton-2.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:381ec6b3dac06922d3e4099cfc943ef032893b25415de295e82b1a82b0359d2c"},
+ {file = "triton-2.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:038e06a09c06a164fef9c48de3af1e13a63dc1ba3c792871e61a8e79720ea440"},
+ {file = "triton-2.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d8f636e0341ac348899a47a057c3daea99ea7db31528a225a3ba4ded28ccc65"},
]
[package.dependencies]
@@ -712,38 +1287,38 @@ files = [
[[package]]
name = "typing-extensions"
-version = "4.11.0"
+version = "4.12.0"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.8"
files = [
- {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
- {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
+ {file = "typing_extensions-4.12.0-py3-none-any.whl", hash = "sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594"},
+ {file = "typing_extensions-4.12.0.tar.gz", hash = "sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8"},
]
[[package]]
name = "xformers"
-version = "0.0.25.post1"
+version = "0.0.26.post1"
description = "XFormers: A collection of composable Transformer building blocks."
optional = false
python-versions = ">=3.7"
files = [
- {file = "xformers-0.0.25.post1-cp310-cp310-manylinux2014_x86_64.whl", hash = "sha256:cdfe9560848fa5ba75fc04d3da8803658e35997adc6075ee6bbf6d67c1f0fa5e"},
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+ {file = "xformers-0.0.26.post1-cp311-cp311-manylinux2014_x86_64.whl", hash = "sha256:0d35615870b9237077aec51802a5a821f9e9d2708730c8914d5cff301fb29558"},
+ {file = "xformers-0.0.26.post1-cp311-cp311-win_amd64.whl", hash = "sha256:d05c547b4ba603fc8e21fad03a342138eaaece35fe0a1692e6ee0d061ddd21ac"},
+ {file = "xformers-0.0.26.post1-cp38-cp38-manylinux2014_x86_64.whl", hash = "sha256:f3afa385266264d5f713e75ad1b31de99add9a719f2f461f98e032c259170cc9"},
+ {file = "xformers-0.0.26.post1-cp38-cp38-win_amd64.whl", hash = "sha256:e96e2c1a11e84a6aac8386a304e3e338057c95029c82b221bf6aa1658aeacdf0"},
+ {file = "xformers-0.0.26.post1-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:16dcfa801450a03f148d6d5f7ba2b5540a27c52517946570fe0e1f75238d73a1"},
+ {file = "xformers-0.0.26.post1-cp39-cp39-win_amd64.whl", hash = "sha256:cf267bac8f4454db1056e4e6ff9e4df1e02b2b31d8116d50913af15fa6ae7132"},
+ {file = "xformers-0.0.26.post1.tar.gz", hash = "sha256:1d14b5f999ede649198379b0470ebdd25007ba224ae336ef958124158a6de8b1"},
]
[package.dependencies]
numpy = "*"
-torch = "2.2.2"
+torch = "2.3.0"
[metadata]
lock-version = "2.0"
python-versions = "^3.9.10"
-content-hash = "136b7145f33a2c07f0ba120d580354e28c2c6b7be7a386832b61c6dfd417aa9a"
+content-hash = "8f0293f4b7c8bb7839f9285e1b4a0cd1f2c22c596c0312d5726ada09f3d10802"
diff --git a/pyproject.toml b/pyproject.toml
index 245fecf065e32360d280e3c65ef8b542a61df352..7bebab4a083c167919f8bc9b3fc99f064163fca6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "mistral_inference"
-version = "v1.0.4"
+version = "v1.1.0"
description = ""
authors = ["bam4d <bam4d@mistral.ai>"]
readme = "README.md"
@@ -24,7 +24,7 @@ exclude = ["docs", "tools", "build"]
[tool.poetry.dependencies]
python = "^3.9.10"
-xformers = ">=0.0.25"
+xformers = ">=0.0.24"
simple-parsing = ">=0.1.5"
fire = ">=0.6.0"
mistral_common = "^1.0.0"
diff --git a/src/mistral_inference/__init__.py b/src/mistral_inference/__init__.py
index 92192eed4fc88eaee9fdbafbc8e2139c43ba00e2..6849410aae0a8010e76d5f0a44ced13d750b0989 100644
--- a/src/mistral_inference/__init__.py
+++ b/src/mistral_inference/__init__.py
@@ -1 +1 @@
-__version__ = "1.0.4"
+__version__ = "1.1.0"
diff --git a/src/mistral_inference/lora.py b/src/mistral_inference/lora.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3846e2a59a4d2481c0e834282977d8e98627525
--- /dev/null
+++ b/src/mistral_inference/lora.py
@@ -0,0 +1,166 @@
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, NamedTuple, Union
+
+import safetensors.torch
+import torch
+import torch.nn as nn
+from simple_parsing.helpers import Serializable
+
+
+@dataclass
+class LoraArgs(Serializable):
+ rank: int
+ scaling: float
+
+ def __post_init__(self):
+ assert self.rank > 0
+ assert self.scaling > 0.0
+
+
+class LoRALinear(nn.Module):
+ """
+ Implementation of:
+ - LoRA: https://arxiv.org/abs/2106.09685
+
+ Notes:
+ - Freezing is handled at network level, not layer level.
+ - Scaling factor controls relative importance of LoRA skip
+ connection versus original frozen weight. General guidance is
+ to keep it to 2.0 and sweep over learning rate when changing
+ the rank.
+ """
+
+ def __init__(
+ self,
+ in_features: int,
+ out_features: int,
+ rank: int,
+ scaling: float,
+ bias: bool = False,
+ ):
+ super().__init__()
+
+ self.in_features = in_features
+ self.out_features = out_features
+ assert not bias
+ self.bias = bias
+ self.rank = rank
+ self.scaling = scaling
+
+ self.lora_A = nn.Linear(
+ self.in_features,
+ self.rank,
+ bias=self.bias,
+ )
+ self.lora_B = nn.Linear(
+ self.rank,
+ self.out_features,
+ bias=self.bias,
+ )
+
+ self.linear = nn.Linear(self.in_features, self.out_features, bias=self.bias)
+
+ # make sure no LoRA weights are marked as "missing" in load_state_dict
+ def ignore_missing_keys(m: nn.Module, incompatible_keys: NamedTuple):
+ incompatible_keys.missing_keys[:] = [] # type: ignore
+
+ self.register_load_state_dict_post_hook(ignore_missing_keys)
+
+ def forward(self, x: torch.Tensor):
+ lora = self.lora_B(self.lora_A(x))
+ return self.linear(x) + lora * self.scaling
+
+ def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
+ key_name = prefix + "weight"
+
+ # full checkpoint
+ if key_name in state_dict:
+ w_ref = state_dict[key_name]
+
+ # load frozen weights
+ state_dict = {
+ "linear.weight": w_ref,
+ "lora_A.weight": torch.zeros_like(
+ self.lora_A.weight, device=w_ref.device, dtype=w_ref.dtype
+ ),
+ "lora_B.weight": torch.zeros_like(
+ self.lora_B.weight, device=w_ref.device, dtype=w_ref.dtype
+ ),
+ }
+ self.load_state_dict(state_dict, assign=True, strict=True)
+
+
+class LoRALoaderMixin:
+ def load_lora(self, lora_path: Union[Path, str], scaling: float = 2.0):
+ """Loads LoRA checkpoint"""
+
+ lora_path = Path(lora_path)
+ assert lora_path.is_file(), f"{lora_path} does not exist or is not a file"
+
+ state_dict = safetensors.torch.load_file(lora_path)
+
+ self._load_lora_state_dict(state_dict, scaling=scaling)
+
+ def _load_lora_state_dict(
+ self, lora_state_dict: Dict[str, torch.Tensor], scaling: float = 2.0
+ ):
+ """Loads LoRA state_dict"""
+
+ lora_dtypes = set([p.dtype for p in lora_state_dict.values()])
+ assert (
+ len(lora_dtypes) == 1
+ ), f"LoRA weights have multipe different dtypes {lora_dtypes}. All weights need to have the same dtype"
+ lora_dtype = lora_dtypes.pop()
+ assert (
+ lora_dtype == self.dtype
+ ), f"LoRA weights dtype differs from model's dtype {lora_dtype} != {self.dtype}"
+ assert all("lora" in key for key in lora_state_dict.keys())
+
+ # move tensors to device
+ lora_state_dict = {k: v.to(self.device) for k, v in lora_state_dict.items()}
+
+ state_dict = self.state_dict()
+
+ if self.args.lora is None:
+ logging.info("Loading and merging LoRA weights...")
+
+ # replace every nn.Linear with a LoRALinear with 'meta' device except the output layer
+ named_modules = dict(self.named_modules())
+ for name, module in named_modules.items():
+ if isinstance(module, nn.Linear) and name != "output":
+ layer_id = name.split(".")[1]
+ if layer_id not in self.layers:
+ logging.debug(
+ "Skipping parameter %s at pipeline rank %d",
+ name,
+ self.pipeline_rank,
+ )
+ else:
+ weight = (
+ module.weight
+ + (
+ lora_state_dict[name + ".lora_B.weight"]
+ @ lora_state_dict[name + ".lora_A.weight"]
+ )
+ * scaling
+ )
+
+ state_dict[name + ".weight"] = weight
+ else:
+ logging.info("Loading LoRA weights...")
+ for k, v in lora_state_dict.items():
+ state_dict.update(lora_state_dict)
+
+ layer_id = k.split(".")[1]
+ if layer_id in self.layers:
+ state_dict[k] = v
+ else:
+ logging.debug(
+ "Skipping parameter %s at pipeline rank %d",
+ k,
+ self.pipeline_rank,
+ )
+
+ self.load_state_dict(state_dict, strict=True)
diff --git a/src/mistral_inference/main.py b/src/mistral_inference/main.py
index 5ee3c8881b53496dd5b08d386ff86bc7f55be6c6..a5ef3a0ddfd5eb126f885316a1e94969b809e0c8 100644
--- a/src/mistral_inference/main.py
+++ b/src/mistral_inference/main.py
@@ -1,7 +1,7 @@
import logging
import os
from pathlib import Path
-from typing import List
+from typing import List, Optional
import fire # type: ignore
import torch
@@ -46,6 +46,7 @@ def interactive(
temperature: float = 0.7,
num_pipeline_ranks: int = 1,
instruct: bool = False,
+ lora_path: Optional[str] = None,
) -> None:
if is_torchrun():
torch.distributed.init_process_group()
@@ -64,6 +65,10 @@ def interactive(
Path(model_path), max_batch_size=3, num_pipeline_ranks=num_pipeline_ranks
)
+ # load LoRA
+ if lora_path is not None:
+ transformer.load_lora(Path(lora_path))
+
prompt: str = ""
messages: List[UserMessage | AssistantMessage] = []
@@ -117,6 +122,7 @@ def demo(
model_path: str,
max_tokens: int = 35,
temperature: float = 0,
+ lora_path: Optional[str] = None,
) -> None:
if is_torchrun():
torch.distributed.init_process_group()
@@ -131,6 +137,10 @@ def demo(
transformer = Transformer.from_folder(
Path(model_path), max_batch_size=3, num_pipeline_ranks=num_pipeline_ranks
)
+ # load LoRA
+ if lora_path is not None:
+ transformer.load_lora(Path(lora_path))
+
mistral_tokenizer: MistralTokenizer = load_tokenizer(Path(model_path))
tokenizer: Tokenizer = mistral_tokenizer.instruct_tokenizer.tokenizer
diff --git a/src/mistral_inference/model.py b/src/mistral_inference/model.py
index 43d2e95f8dfd35d3230173a62d7434fd21f77693..829579653b7e712dd6f0e5974cd3dfc4bb7cd134 100644
--- a/src/mistral_inference/model.py
+++ b/src/mistral_inference/model.py
@@ -2,6 +2,7 @@ import json
import logging
import math
from dataclasses import dataclass
+from functools import partial
from pathlib import Path
from typing import Any, List, Mapping, Optional, Tuple, Union
@@ -16,6 +17,7 @@ from mistral_inference.cache import (
CacheInputMetadata,
CacheView,
)
+from mistral_inference.lora import LoraArgs, LoRALinear, LoRALoaderMixin
from mistral_inference.moe import MoeArgs, MoeLayer
from mistral_inference.rope import apply_rotary_emb, precompute_freqs_cis
@@ -37,6 +39,8 @@ class ModelArgs(Serializable):
rope_theta: Optional[float] = None
# If this is set, we will use MoE layers instead of dense layers.
moe: Optional[MoeArgs] = None
+ # If this is set, we will load LoRA linear layers instead of linear layers.
+ lora: Optional[LoraArgs] = None
@dataclass
@@ -61,6 +65,13 @@ def repeat_kv(
return keys, values
+def maybe_lora(args: ModelArgs) -> Union[nn.Linear, LoRALinear]:
+ if args.lora is None:
+ return nn.Linear
+ else:
+ return partial(LoRALinear, rank=args.lora.rank, scaling=args.lora.scaling)
+
+
class Attention(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
@@ -74,10 +85,11 @@ class Attention(nn.Module):
self.scale = self.args.head_dim**-0.5
- self.wq = nn.Linear(args.dim, args.n_heads * args.head_dim, bias=False)
- self.wk = nn.Linear(args.dim, args.n_kv_heads * args.head_dim, bias=False)
- self.wv = nn.Linear(args.dim, args.n_kv_heads * args.head_dim, bias=False)
- self.wo = nn.Linear(args.n_heads * args.head_dim, args.dim, bias=False)
+ MaybeLora = maybe_lora(args)
+ self.wq = MaybeLora(args.dim, args.n_heads * args.head_dim, bias=False)
+ self.wk = MaybeLora(args.dim, args.n_kv_heads * args.head_dim, bias=False)
+ self.wv = MaybeLora(args.dim, args.n_kv_heads * args.head_dim, bias=False)
+ self.wo = MaybeLora(args.n_heads * args.head_dim, args.dim, bias=False)
def forward(
self,
@@ -127,9 +139,10 @@ class FeedForward(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
- self.w1 = nn.Linear(args.dim, args.hidden_dim, bias=False)
- self.w2 = nn.Linear(args.hidden_dim, args.dim, bias=False)
- self.w3 = nn.Linear(args.dim, args.hidden_dim, bias=False)
+ MaybeLora = maybe_lora(args)
+ self.w1 = MaybeLora(args.dim, args.hidden_dim, bias=False)
+ self.w2 = MaybeLora(args.hidden_dim, args.dim, bias=False)
+ self.w3 = MaybeLora(args.dim, args.hidden_dim, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x)) # type: ignore
@@ -179,7 +192,7 @@ class TransformerBlock(nn.Module):
return out
-class Transformer(nn.Module):
+class Transformer(nn.Module, LoRALoaderMixin):
def __init__(
self,
args: ModelArgs,