diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile index 360602d65..77a9ddc14 100644 --- a/.devops/full-cuda.Dockerfile +++ b/.devops/full-cuda.Dockerfile @@ -14,7 +14,8 @@ ARG CUDA_DOCKER_ARCH=all RUN apt-get update && \ apt-get install -y build-essential python3 python3-pip git -COPY requirements.txt requirements.txt +COPY requirements.txt requirements.txt +COPY requirements requirements RUN pip install --upgrade pip setuptools wheel \ && pip install -r requirements.txt diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile index 6c521e9b4..8b9633dc4 100644 --- a/.devops/full-rocm.Dockerfile +++ b/.devops/full-rocm.Dockerfile @@ -23,7 +23,8 @@ ARG ROCM_DOCKER_ARCH=\ gfx1101 \ gfx1102 -COPY requirements.txt requirements.txt +COPY requirements.txt requirements.txt +COPY requirements requirements RUN pip install --upgrade pip setuptools wheel \ && pip install -r requirements.txt diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile index 687628b35..cef1297d3 100644 --- a/.devops/full.Dockerfile +++ b/.devops/full.Dockerfile @@ -5,7 +5,8 @@ FROM ubuntu:$UBUNTU_VERSION as build RUN apt-get update && \ apt-get install -y build-essential python3 python3-pip git -COPY requirements.txt requirements.txt +COPY requirements.txt requirements.txt +COPY requirements requirements RUN pip install --upgrade pip setuptools wheel \ && pip install -r requirements.txt diff --git a/.devops/main-rocm.Dockerfile b/.devops/main-rocm.Dockerfile index 789deff6d..0a706dc73 100644 --- a/.devops/main-rocm.Dockerfile +++ b/.devops/main-rocm.Dockerfile @@ -23,7 +23,8 @@ ARG ROCM_DOCKER_ARCH=\ gfx1101 \ gfx1102 -COPY requirements.txt requirements.txt +COPY requirements.txt requirements.txt +COPY requirements requirements RUN pip install --upgrade pip setuptools wheel \ && pip install -r requirements.txt diff --git a/.github/workflows/python-check-requirements.yml b/.github/workflows/python-check-requirements.yml new file mode 100644 index 000000000..92e1108b3 --- /dev/null +++ b/.github/workflows/python-check-requirements.yml @@ -0,0 +1,29 @@ +name: Python check requirements.txt + +on: + push: + paths: + - 'scripts/check-requirements.sh' + - 'convert*.py' + - 'requirements.txt' + - 'requirements/*.txt' + pull_request: + paths: + - 'scripts/check-requirements.sh' + - 'convert*.py' + - 'requirements.txt' + - 'requirements/*.txt' + +jobs: + python-check-requirements: + runs-on: ubuntu-latest + name: check-requirements + steps: + - name: Check out source repository + uses: actions/checkout@v3 + - name: Set up Python environment + uses: actions/setup-python@v4 + with: + python-version: "3.11" + - name: Run check-requirements.sh script + run: bash scripts/check-requirements.sh nocleanup diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 3557a825e..51724c0df 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -242,7 +242,7 @@ class Model: tokens: list[bytearray] = [] toktypes: list[int] = [] - from transformers import AutoTokenizer # type: ignore[attr-defined] + from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(dir_model) vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) assert max(tokenizer.vocab.values()) < vocab_size @@ -856,7 +856,7 @@ class StableLMModel(Model): hparams = self.hparams block_count = hparams["num_hidden_layers"] - self.gguf_writer.add_name(dir_model.name) + self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) @@ -902,7 +902,7 @@ class QwenModel(Model): tokens: list[bytearray] = [] toktypes: list[int] = [] - from transformers import AutoTokenizer # type: ignore[attr-defined] + from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) vocab_size = hparams["vocab_size"] assert max(tokenizer.get_vocab().values()) < vocab_size @@ -1185,57 +1185,62 @@ def parse_args() -> argparse.Namespace: return parser.parse_args() -args = parse_args() +def main() -> None: + args = parse_args() -dir_model = args.model + dir_model = args.model -if args.awq_path: - sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) - from awq.apply_awq import add_scale_weights - tmp_model_path = args.model / "weighted_model" - dir_model = tmp_model_path - if tmp_model_path.is_dir(): - print(f"{tmp_model_path} exists as a weighted model.") + if args.awq_path: + sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) + from awq.apply_awq import add_scale_weights + tmp_model_path = args.model / "weighted_model" + dir_model = tmp_model_path + if tmp_model_path.is_dir(): + print(f"{tmp_model_path} exists as a weighted model.") + else: + tmp_model_path.mkdir(parents=True, exist_ok=True) + print("Saving new weighted model ...") + add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) + print(f"Saved weighted model at {tmp_model_path}.") + + if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file=sys.stderr) + sys.exit(1) + + ftype_map = { + "f32": gguf.GGMLQuantizationType.F32, + "f16": gguf.GGMLQuantizationType.F16, + } + + if args.outfile is not None: + fname_out = args.outfile else: - tmp_model_path.mkdir(parents=True, exist_ok=True) - print("Saving new weighted model ...") - add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) - print(f"Saved weighted model at {tmp_model_path}.") + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{args.outtype}.gguf' -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file=sys.stderr) - sys.exit(1) + print(f"Loading model: {dir_model.name}") -ftype_map = { - "f32": gguf.GGMLQuantizationType.F32, - "f16": gguf.GGMLQuantizationType.F16, -} + hparams = Model.load_hparams(dir_model) -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{args.outtype}.gguf' + with torch.inference_mode(): + model_class = Model.from_model_architecture(hparams["architectures"][0]) + model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) -print(f"Loading model: {dir_model.name}") + print("Set model parameters") + model_instance.set_gguf_parameters() -hparams = Model.load_hparams(dir_model) + print("Set model tokenizer") + model_instance.set_vocab() -with torch.inference_mode(): - model_class = Model.from_model_architecture(hparams["architectures"][0]) - model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) + if args.vocab_only: + print(f"Exporting model vocab to '{fname_out}'") + model_instance.write_vocab() + else: + print(f"Exporting model to '{fname_out}'") + model_instance.write() - print("Set model parameters") - model_instance.set_gguf_parameters() + print(f"Model successfully exported to '{fname_out}'") - print("Set model tokenizer") - model_instance.set_vocab() - if args.vocab_only: - print(f"Exporting model vocab to '{fname_out}'") - model_instance.write_vocab() - else: - print(f"Exporting model to '{fname_out}'") - model_instance.write() - - print(f"Model successfully exported to '{fname_out}'") +if __name__ == '__main__': + main() diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py index 53bb8a3d9..35ce152f4 100755 --- a/convert-lora-to-ggml.py +++ b/convert-lora-to-ggml.py @@ -47,95 +47,96 @@ def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_ty fout.seek((fout.tell() + 31) & -32) -if len(sys.argv) < 2: - print(f"Usage: python {sys.argv[0]} [arch]") - print( - "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" - ) - print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") - sys.exit(1) +if __name__ == '__main__': + if len(sys.argv) < 2: + print(f"Usage: python {sys.argv[0]} [arch]") + print( + "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" + ) + print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") + sys.exit(1) -input_json = os.path.join(sys.argv[1], "adapter_config.json") -input_model = os.path.join(sys.argv[1], "adapter_model.bin") -output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") + input_json = os.path.join(sys.argv[1], "adapter_config.json") + input_model = os.path.join(sys.argv[1], "adapter_model.bin") + output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") -model = torch.load(input_model, map_location="cpu") -arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" + model = torch.load(input_model, map_location="cpu") + arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" -if arch_name not in gguf.MODEL_ARCH_NAMES.values(): - print(f"Error: unsupported architecture {arch_name}") - sys.exit(1) + if arch_name not in gguf.MODEL_ARCH_NAMES.values(): + print(f"Error: unsupported architecture {arch_name}") + sys.exit(1) -arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] -name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone + arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] + name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone -with open(input_json, "r") as f: - params = json.load(f) + with open(input_json, "r") as f: + params = json.load(f) -if params["peft_type"] != "LORA": - print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") - sys.exit(1) + if params["peft_type"] != "LORA": + print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") + sys.exit(1) -if params["fan_in_fan_out"] is True: - print("Error: param fan_in_fan_out is not supported") - sys.exit(1) + if params["fan_in_fan_out"] is True: + print("Error: param fan_in_fan_out is not supported") + sys.exit(1) -if params["bias"] is not None and params["bias"] != "none": - print("Error: param bias is not supported") - sys.exit(1) + if params["bias"] is not None and params["bias"] != "none": + print("Error: param bias is not supported") + sys.exit(1) -# TODO: these seem to be layers that have been trained but without lora. -# doesn't seem widely used but eventually should be supported -if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: - print("Error: param modules_to_save is not supported") - sys.exit(1) + # TODO: these seem to be layers that have been trained but without lora. + # doesn't seem widely used but eventually should be supported + if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: + print("Error: param modules_to_save is not supported") + sys.exit(1) -with open(output_path, "wb") as fout: - fout.truncate() + with open(output_path, "wb") as fout: + fout.truncate() - write_file_header(fout, params) - for k, v in model.items(): - orig_k = k - if k.endswith(".default.weight"): - k = k.replace(".default.weight", ".weight") - if k in ["llama_proj.weight", "llama_proj.bias"]: - continue - if k.endswith("lora_A.weight"): - if v.dtype != torch.float16 and v.dtype != torch.float32: + write_file_header(fout, params) + for k, v in model.items(): + orig_k = k + if k.endswith(".default.weight"): + k = k.replace(".default.weight", ".weight") + if k in ["llama_proj.weight", "llama_proj.bias"]: + continue + if k.endswith("lora_A.weight"): + if v.dtype != torch.float16 and v.dtype != torch.float32: + v = v.float() + v = v.T + else: v = v.float() - v = v.T - else: - v = v.float() - t = v.detach().numpy() + t = v.detach().numpy() - prefix = "base_model.model." - if k.startswith(prefix): - k = k[len(prefix) :] + prefix = "base_model.model." + if k.startswith(prefix): + k = k[len(prefix) :] - lora_suffixes = (".lora_A.weight", ".lora_B.weight") - if k.endswith(lora_suffixes): - suffix = k[-len(lora_suffixes[0]):] - k = k[: -len(lora_suffixes[0])] - else: - print(f"Error: unrecognized tensor name {orig_k}") - sys.exit(1) + lora_suffixes = (".lora_A.weight", ".lora_B.weight") + if k.endswith(lora_suffixes): + suffix = k[-len(lora_suffixes[0]):] + k = k[: -len(lora_suffixes[0])] + else: + print(f"Error: unrecognized tensor name {orig_k}") + sys.exit(1) - tname = name_map.get_name(k) - if tname is None: - print(f"Error: could not map tensor name {orig_k}") - print(" Note: the arch parameter must be specified if the model is not llama") - sys.exit(1) + tname = name_map.get_name(k) + if tname is None: + print(f"Error: could not map tensor name {orig_k}") + print(" Note: the arch parameter must be specified if the model is not llama") + sys.exit(1) - if suffix == ".lora_A.weight": - tname += ".weight.loraA" - elif suffix == ".lora_B.weight": - tname += ".weight.loraB" - else: - assert False + if suffix == ".lora_A.weight": + tname += ".weight.loraA" + elif suffix == ".lora_B.weight": + tname += ".weight.loraB" + else: + assert False - print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") - write_tensor_header(fout, tname, t.shape, t.dtype) - t.tofile(fout) + print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") + write_tensor_header(fout, tname, t.shape, t.dtype) + t.tofile(fout) -print(f"Converted {input_json} and {input_model} to {output_path}") + print(f"Converted {input_json} and {input_model} to {output_path}") diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py old mode 100644 new mode 100755 index 206b7d5ff..1ba5864dc --- a/convert-persimmon-to-gguf.py +++ b/convert-persimmon-to-gguf.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 import torch import os from pprint import pprint diff --git a/requirements-hf-to-gguf.txt b/requirements-hf-to-gguf.txt deleted file mode 100644 index f4600539e..000000000 --- a/requirements-hf-to-gguf.txt +++ /dev/null @@ -1,3 +0,0 @@ --r requirements.txt -torch==2.1.1 -transformers==4.35.2 diff --git a/requirements.txt b/requirements.txt index 1a1162566..d36f74520 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,12 @@ -numpy==1.24.4 -sentencepiece==0.1.98 -transformers>=4.34.0 -gguf>=0.1.0 -protobuf>=4.21.0 +# These requirements include all dependencies for all top-level python scripts +# for llama.cpp. Avoid adding packages here directly. +# +# Package versions must stay compatible across all top-level python scripts. +# + +-r ./requirements/requirements-convert.txt + +-r ./requirements/requirements-convert-hf-to-gguf.txt +-r ./requirements/requirements-convert-llama-ggml-to-gguf.txt +-r ./requirements/requirements-convert-lora-to-ggml.txt +-r ./requirements/requirements-convert-persimmon-to-gguf.txt diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert-hf-to-gguf.txt new file mode 100644 index 000000000..6ac402610 --- /dev/null +++ b/requirements/requirements-convert-hf-to-gguf.txt @@ -0,0 +1,2 @@ +-r ./requirements-convert.txt +torch~=2.1.1 diff --git a/requirements/requirements-convert-llama-ggml-to-gguf.txt b/requirements/requirements-convert-llama-ggml-to-gguf.txt new file mode 100644 index 000000000..a0f37cd1c --- /dev/null +++ b/requirements/requirements-convert-llama-ggml-to-gguf.txt @@ -0,0 +1 @@ +-r ./requirements-convert.txt diff --git a/requirements/requirements-convert-lora-to-ggml.txt b/requirements/requirements-convert-lora-to-ggml.txt new file mode 100644 index 000000000..6ac402610 --- /dev/null +++ b/requirements/requirements-convert-lora-to-ggml.txt @@ -0,0 +1,2 @@ +-r ./requirements-convert.txt +torch~=2.1.1 diff --git a/requirements/requirements-convert-persimmon-to-gguf.txt b/requirements/requirements-convert-persimmon-to-gguf.txt new file mode 100644 index 000000000..6ac402610 --- /dev/null +++ b/requirements/requirements-convert-persimmon-to-gguf.txt @@ -0,0 +1,2 @@ +-r ./requirements-convert.txt +torch~=2.1.1 diff --git a/requirements/requirements-convert.txt b/requirements/requirements-convert.txt new file mode 100644 index 000000000..a3d6ecec0 --- /dev/null +++ b/requirements/requirements-convert.txt @@ -0,0 +1,5 @@ +numpy~=1.24.4 +sentencepiece~=0.1.98 +transformers>=4.35.2,<5.0.0 +gguf>=0.1.0 +protobuf>=4.21.0,<5.0.0 diff --git a/scripts/check-requirements.sh b/scripts/check-requirements.sh new file mode 100755 index 000000000..af7bab753 --- /dev/null +++ b/scripts/check-requirements.sh @@ -0,0 +1,174 @@ +#!/bin/bash +set -euo pipefail + +# +# check-requirements.sh checks all requirements files for each top-level +# convert*.py script. +# +# WARNING: This is quite IO intensive, because a fresh venv is set up for every +# python script. As of 2023-12-22, this writes ~2.7GB of data. An adequately +# sized tmpfs /tmp or ramdisk is recommended if running this frequently. +# +# usage: check-requirements.sh [] +# check-requirements.sh nocleanup [] +# +# where: +# - is a directory that can be used as the base for +# setting up the venvs. Defaults to `/tmp`. +# - 'nocleanup' as the first argument will disable automatic cleanup +# of the files created by this script. +# +# requires: +# - bash >= 3.2.57 +# - shellcheck +# +# For each script, it creates a fresh venv, `pip install`s the requirements, and +# finally imports the python script to check for `ImportError`. +# + +log() { + local level=$1 msg=$2 + printf >&2 '%s: %s\n' "$level" "$msg" +} + +debug() { + log DEBUG "$@" +} + +info() { + log INFO "$@" +} + +fatal() { + log FATAL "$@" + exit 1 +} + +cleanup() { + if [[ -n ${workdir+x} && -d $workdir && -w $workdir ]]; then + info "Removing $workdir" + local count=0 + rm -rfv -- "$workdir" | while read -r; do + if (( count++ > 750 )); then + printf . + count=0 + fi + done + printf '\n' + info "Removed $workdir" + fi +} + +do_cleanup=1 +if [[ ${1-} == nocleanup ]]; then + do_cleanup=0; shift +fi + +if (( do_cleanup )); then + trap exit INT TERM + trap cleanup EXIT +fi + +this=$(realpath -- "$0"); readonly this +cd "$(dirname "$this")/.." # PWD should stay in llama.cpp project directory + +shellcheck "$this" + +readonly reqs_dir=requirements + +if [[ ${1+x} ]]; then + tmp_dir=$(realpath -- "$1") + if [[ ! ( -d $tmp_dir && -w $tmp_dir ) ]]; then + fatal "$tmp_dir is not a writable directory" + fi +else + tmp_dir=/tmp +fi + +workdir=$(mktemp -d "$tmp_dir/check-requirements.XXXX"); readonly workdir +info "Working directory: $workdir" + +check_requirements() { + local reqs=$1 + + info "$reqs: beginning check" + pip --disable-pip-version-check install -qr "$reqs" + info "$reqs: OK" +} + +check_convert_script() { + local py=$1 # e.g. ./convert-hf-to-gguf.py + local pyname=${py##*/} # e.g. convert-hf-to-gguf.py + pyname=${pyname%.py} # e.g. convert-hf-to-gguf + + info "$py: beginning check" + + local reqs="$reqs_dir/requirements-$pyname.txt" + if [[ ! -r $reqs ]]; then + fatal "$py missing requirements. Expected: $reqs" + fi + + local venv="$workdir/$pyname-venv" + python3 -m venv "$venv" + + ( + # shellcheck source=/dev/null + source "$venv/bin/activate" + + check_requirements "$reqs" + + python - "$py" "$pyname" <<'EOF' +import sys +from importlib.machinery import SourceFileLoader +py, pyname = sys.argv[1:] +SourceFileLoader(pyname, py).load_module() +EOF + ) + + if (( do_cleanup )); then + rm -rf -- "$venv" + fi + + info "$py: imports OK" +} + +readonly ignore_eq_eq='check_requirements: ignore "=="' + +for req in "$reqs_dir"/*; do + # Check that all sub-requirements are added to top-level requirements.txt + if ! grep -qF "$req" requirements.txt; then + fatal "$req needs to be added to requirements.txt" + fi + + # Make sure exact release versions aren't being pinned in the requirements + # Filters out the ignore string + if grep -vF "$ignore_eq_eq" "$req" | grep -q '=='; then + tab=$'\t' + cat >&2 <