python : add check-requirements.sh and GitHub workflow (#4585)
* python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
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crasm
GitHub
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16 changed files with 360 additions and 130 deletions
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@ -47,95 +47,96 @@ def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_ty
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fout.seek((fout.tell() + 31) & -32)
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if len(sys.argv) < 2:
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print(f"Usage: python {sys.argv[0]} <path> [arch]")
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print(
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"Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
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)
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print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
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sys.exit(1)
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if __name__ == '__main__':
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if len(sys.argv) < 2:
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print(f"Usage: python {sys.argv[0]} <path> [arch]")
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print(
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"Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
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)
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print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
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sys.exit(1)
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input_json = os.path.join(sys.argv[1], "adapter_config.json")
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input_model = os.path.join(sys.argv[1], "adapter_model.bin")
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output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
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input_json = os.path.join(sys.argv[1], "adapter_config.json")
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input_model = os.path.join(sys.argv[1], "adapter_model.bin")
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output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
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model = torch.load(input_model, map_location="cpu")
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arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
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model = torch.load(input_model, map_location="cpu")
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arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
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if arch_name not in gguf.MODEL_ARCH_NAMES.values():
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print(f"Error: unsupported architecture {arch_name}")
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sys.exit(1)
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if arch_name not in gguf.MODEL_ARCH_NAMES.values():
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print(f"Error: unsupported architecture {arch_name}")
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sys.exit(1)
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arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
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name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
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arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
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name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
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with open(input_json, "r") as f:
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params = json.load(f)
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with open(input_json, "r") as f:
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params = json.load(f)
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if params["peft_type"] != "LORA":
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print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA")
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sys.exit(1)
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if params["peft_type"] != "LORA":
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print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA")
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sys.exit(1)
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if params["fan_in_fan_out"] is True:
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print("Error: param fan_in_fan_out is not supported")
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sys.exit(1)
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if params["fan_in_fan_out"] is True:
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print("Error: param fan_in_fan_out is not supported")
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sys.exit(1)
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if params["bias"] is not None and params["bias"] != "none":
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print("Error: param bias is not supported")
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sys.exit(1)
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if params["bias"] is not None and params["bias"] != "none":
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print("Error: param bias is not supported")
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sys.exit(1)
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# TODO: these seem to be layers that have been trained but without lora.
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# doesn't seem widely used but eventually should be supported
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if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0:
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print("Error: param modules_to_save is not supported")
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sys.exit(1)
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# TODO: these seem to be layers that have been trained but without lora.
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# doesn't seem widely used but eventually should be supported
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if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0:
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print("Error: param modules_to_save is not supported")
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sys.exit(1)
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with open(output_path, "wb") as fout:
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fout.truncate()
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with open(output_path, "wb") as fout:
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fout.truncate()
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write_file_header(fout, params)
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for k, v in model.items():
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orig_k = k
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if k.endswith(".default.weight"):
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k = k.replace(".default.weight", ".weight")
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if k in ["llama_proj.weight", "llama_proj.bias"]:
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continue
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if k.endswith("lora_A.weight"):
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if v.dtype != torch.float16 and v.dtype != torch.float32:
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write_file_header(fout, params)
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for k, v in model.items():
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orig_k = k
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if k.endswith(".default.weight"):
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k = k.replace(".default.weight", ".weight")
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if k in ["llama_proj.weight", "llama_proj.bias"]:
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continue
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if k.endswith("lora_A.weight"):
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if v.dtype != torch.float16 and v.dtype != torch.float32:
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v = v.float()
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v = v.T
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else:
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v = v.float()
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v = v.T
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else:
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v = v.float()
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t = v.detach().numpy()
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t = v.detach().numpy()
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prefix = "base_model.model."
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if k.startswith(prefix):
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k = k[len(prefix) :]
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prefix = "base_model.model."
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if k.startswith(prefix):
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k = k[len(prefix) :]
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lora_suffixes = (".lora_A.weight", ".lora_B.weight")
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if k.endswith(lora_suffixes):
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suffix = k[-len(lora_suffixes[0]):]
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k = k[: -len(lora_suffixes[0])]
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else:
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print(f"Error: unrecognized tensor name {orig_k}")
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sys.exit(1)
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lora_suffixes = (".lora_A.weight", ".lora_B.weight")
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if k.endswith(lora_suffixes):
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suffix = k[-len(lora_suffixes[0]):]
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k = k[: -len(lora_suffixes[0])]
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else:
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print(f"Error: unrecognized tensor name {orig_k}")
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sys.exit(1)
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tname = name_map.get_name(k)
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if tname is None:
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print(f"Error: could not map tensor name {orig_k}")
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print(" Note: the arch parameter must be specified if the model is not llama")
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sys.exit(1)
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tname = name_map.get_name(k)
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if tname is None:
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print(f"Error: could not map tensor name {orig_k}")
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print(" Note: the arch parameter must be specified if the model is not llama")
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sys.exit(1)
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if suffix == ".lora_A.weight":
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tname += ".weight.loraA"
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elif suffix == ".lora_B.weight":
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tname += ".weight.loraB"
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else:
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assert False
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if suffix == ".lora_A.weight":
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tname += ".weight.loraA"
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elif suffix == ".lora_B.weight":
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tname += ".weight.loraB"
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else:
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assert False
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print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
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write_tensor_header(fout, tname, t.shape, t.dtype)
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t.tofile(fout)
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print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
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write_tensor_header(fout, tname, t.shape, t.dtype)
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t.tofile(fout)
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print(f"Converted {input_json} and {input_model} to {output_path}")
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print(f"Converted {input_json} and {input_model} to {output_path}")
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