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convert-gptneox-h5-to-gguf.py : load model in parts to save memory
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klosax
GitHub
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@ -1,4 +1,4 @@
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# Quick and dirty HF gptneox--> gguf conversion
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# HF gptneox--> gguf conversion
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import gguf
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import gguf_tensor_map as tmap
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@ -9,7 +9,8 @@ import json
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import numpy as np
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from typing import Any, List
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from pathlib import Path
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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from transformers import AutoTokenizer
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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def bytes_to_unicode():
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@ -33,6 +34,15 @@ def bytes_to_unicode():
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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def count_model_parts(dir_model: str) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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if filename.startswith("pytorch_model-"):
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num_parts += 1
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if num_parts > 0:
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print("gguf: found " + str(num_parts) + " model parts")
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return num_parts
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if len(sys.argv) < 3:
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print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
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@ -70,9 +80,8 @@ if hparams["architectures"][0] != "GPTNeoXForCausalLM":
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print("Model architecture not supported: " + hparams["architectures"][0] )
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sys.exit()
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model = AutoModelForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True, trust_remote_code=True)
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list_vars = model.state_dict()
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# get number of model parts
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num_parts = count_model_parts(dir_model)
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gguf_writer = gguf.GGUFWriter.open(fname_out)
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@ -183,37 +192,58 @@ tensor_map = tmap.get_tensor_map(block_count)
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# tensor info
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print("gguf: get tensor metadata")
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for name in list_vars.keys():
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data = list_vars[name].squeeze().numpy()
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if num_parts == 0:
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part_names = ("pytorch_model.bin",)
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else:
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part_names = (
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f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
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)
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# we don't need these
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if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
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continue
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for part_name in part_names:
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print("gguf: loading model part '"+ part_name + "'")
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model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
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# map tensor names
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if name.endswith(".weight") and name[:-7] in tensor_map:
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name = tensor_map[name[:-7]] + ".weight"
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elif name.endswith(".bias") and name[:-5] in tensor_map:
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name = tensor_map[name[:-5]] + ".bias"
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else:
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print( "Can not map tensor '" + name + "'" )
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sys.exit()
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for name in model_part.keys():
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data = model_part[name]
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# we don't need these
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if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
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continue
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# print( name + " dims " + str(n_dims) + " dtype " + str(data.dtype) )
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if data.dtype != np.float16 and data.dtype != np.float32:
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# convert any unsupported data types to float32
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data_dtype = np.float32
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elif ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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if data.dtype != torch.float16 and data.dtype != torch.float32:
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data = data.to(torch.float32)
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data = data.squeeze().numpy()
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# map tensor names
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if name.endswith(".weight") and name[:-7] in tensor_map:
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name = tensor_map[name[:-7]] + ".weight"
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elif name.endswith(".bias") and name[:-5] in tensor_map:
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name = tensor_map[name[:-5]] + ".bias"
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else:
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print( "Can not map tensor '" + name + "'" )
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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if ftype == 0 and data.dtype == np.float16:
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data_dtype = np.float32
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if ftype == 1 and data.dtype == np.float16 and n_dims == 1:
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data_dtype = np.float32
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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data_dtype = np.float16
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if ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data_dtype = np.float16
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data_nbytes = data.size * 2 if data_dtype == np.float16 else data.size * 4
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data_nbytes = data.size * 2 if data_dtype == np.float16 else data.size * 4
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gguf_writer.add_tensor_info(name, data.shape, data_dtype, data_nbytes)
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gguf_writer.add_tensor_info(name, data.shape, data_dtype, data_nbytes)
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print("gguf: write header")
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gguf_writer.write_header_to_file()
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@ -225,24 +255,59 @@ gguf_writer.write_ti_data_to_file()
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# tensor data
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print("gguf: convert and write tensor data")
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for name in list_vars.keys():
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data = list_vars[name].squeeze().numpy()
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if num_parts == 0:
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part_names = ("pytorch_model.bin",)
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else:
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part_names = (
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f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
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)
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# we don't need these
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if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
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continue
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for part_name in part_names:
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print("gguf: loading model part '"+ part_name + "'")
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model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
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n_dims = len(data.shape)
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data_dtype = data.dtype
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for name in model_part.keys():
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data = model_part[name]
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old_dtype = data.dtype
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# we don't need these
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if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
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continue
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if data_dtype != np.float16 and data_dtype != np.float32:
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# convert any unsupported data types to float32
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data = data.astype(np.float32)
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elif ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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data = data.astype(np.float16)
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if data.dtype != torch.float16 and data.dtype != torch.float32:
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data = data.to(torch.float32)
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gguf_writer.write_tensor_to_file(data)
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data = data.squeeze().numpy()
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# map tensor names
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if name.endswith(".weight") and name[:-7] in tensor_map:
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name = tensor_map[name[:-7]] + ".weight"
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elif name.endswith(".bias") and name[:-5] in tensor_map:
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name = tensor_map[name[:-5]] + ".bias"
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else:
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print( "Can not map tensor '" + name + "'" )
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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if ftype == 0 and data.dtype == np.float16:
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data = data.astype(np.float32)
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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print( name + ", shape " + str(len(data.shape)) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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gguf_writer.write_tensor_to_file(data)
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gguf_writer.close()
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