File size: 6,802 Bytes
055eba4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | # Copyright 2024 Google LLC
# SPDX-License-Identifier: Apache-2.0
#
# 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
#
# https://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 collections import defaultdict
import torch
from gemma import config
from gemma import model as gemma_model
import numpy as np
import argparse
import os
# Requires torch 2.2 and gemma package from https://github.com/google/gemma_pytorch
def check_file_exists(value):
if not os.path.exists(str(value)):
raise argparse.ArgumentTypeError("The file %s does not appear to exist." % value)
return value
def check_model_types(value):
if str(value).lower() not in ["2b", "7b"]:
raise argparse.ArgumentTypeError("Model type value %s is not in [2b, 7b]." % value)
return value
parser = argparse.ArgumentParser()
parser.add_argument(
"--tokenizer",
dest="tokenizer",
default="models/tokenizer.spm",
help="Location of tokenizer file (.model or .spm)",
type=check_file_exists,
)
parser.add_argument(
"--weights",
dest="weights",
default="models/gemma-2b-it.ckpt",
help="Location of input checkpoint file (.ckpt)",
type=check_file_exists,
)
parser.add_argument(
"--output_file",
dest="output_file",
default="2bit-f32.sbs",
help="Location to write converted weights",
type=str,
)
parser.add_argument(
"--model_type",
dest="model_type",
default="2b",
help="Model size / type (2b, 7b)",
type=check_model_types,
)
args = parser.parse_args()
TRANSFORMATIONS = {
"2b":defaultdict(
lambda: lambda x: x,
{
"embedder.weight": lambda x: x,
"self_attn.qkv_proj.weight": lambda x: x.reshape((10, 256, 2048)),
"self_attn.o_proj.weight": lambda x: x.reshape((2048, 8, 256)).transpose([1,0,2]),
"mlp.gate_proj.weight": lambda x: x[np.newaxis, :, :],
"mlp.up_proj.weight": lambda x: x[np.newaxis, :, :],
"mlp.down_proj.weight": lambda x: x,
}
),
"7b":defaultdict(
lambda: lambda x: x,
{
"embedder.weight": lambda x: x,
"self_attn.qkv_proj.weight": lambda x: x.reshape((3, 16, 256, 3072)).transpose([1,0,2,3]),
"self_attn.o_proj.weight": lambda x: x.reshape((3072, 16, 256)).transpose([1,0,2]),
"mlp.gate_proj.weight": lambda x: x[np.newaxis, :, :],
"mlp.up_proj.weight": lambda x: x[np.newaxis, :, :],
"mlp.down_proj.weight": lambda x: x,
}
),
}
VALIDATIONS = {
"2b": {
"embedder.weight": lambda x: x.shape == (256000, 2048),
"model.norm.weight": lambda x: x.shape == (2048,),
"self_attn.qkv_proj.weight": lambda x: x.shape == (10, 256, 2048),
"self_attn.o_proj.weight": lambda x: x.shape == (8, 2048, 256),
"mlp.gate_proj.weight": lambda x: x.shape == (1, 16384, 2048),
"mlp.up_proj.weight": lambda x: x.shape == (1, 16384, 2048),
"mlp.down_proj.weight": lambda x: x.shape == (2048, 16384),
"input_layernorm.weight": lambda x: x.shape == (2048,),
"post_attention_layernorm.weight": lambda x: x.shape == (2048,),
},
"7b": {
"embedder.weight": lambda x: x.shape == (256000, 3072),
"model.norm.weight": lambda x: x.shape == (3072,),
"self_attn.qkv_proj.weight": lambda x: x.shape == (16, 3, 256, 3072),
"self_attn.o_proj.weight": lambda x: x.shape == (16, 3072, 256),
"mlp.gate_proj.weight": lambda x: x.shape == (1, 24576, 3072),
"mlp.up_proj.weight": lambda x: x.shape == (1, 24576, 3072),
"mlp.down_proj.weight": lambda x: x.shape == (3072, 24576),
"input_layernorm.weight": lambda x: x.shape == (3072,),
"post_attention_layernorm.weight": lambda x: x.shape == (3072,),
},
}
def param_names(num_hidden_layers: int):
"""Return parameter names in the order they are expected for deserialization."""
# note *weight_scaler params are ignored in the forward computation unless
# quantization is being used.
#
# since we are working with the full precision weights as input, don't
# include these in the parameters being iterated over.
# fmt: off
names = [
("embedder.weight", ) * 2, # embedder_input_embedding
("model.norm.weight", ) * 2 # final_norm_scale
]
layer_params = [
"self_attn.o_proj.weight", # attn_vec_einsum_w
"self_attn.qkv_proj.weight", # qkv_einsum_w
"mlp.gate_proj.weight", # gating_einsum_w
"mlp.up_proj.weight",
"mlp.down_proj.weight", # linear_w
"input_layernorm.weight", # pre_attention_norm_scale
"post_attention_layernorm.weight", # pre_ffw_norm_scale
]
# fmt: on
for layer in range(num_hidden_layers):
for layer_param in layer_params:
names = names + [(f"model.layers.{layer}.{layer_param}", layer_param)]
return names
def convert_weights():
model_type = args.model_type
output_file = args.output_file
model_config = config.get_model_config(model_type)
model_config.dtype = "float32"
model_config.tokenizer = args.tokenizer
device = torch.device("cpu")
torch.set_default_dtype(torch.float)
model = gemma_model.GemmaForCausalLM(model_config)
model.load_weights(args.weights)
model.to(device).eval()
model_dict = dict(model.named_parameters())
param_order = param_names(model_config.num_hidden_layers)
all_ok = True
print("Checking transformations ...")
for name, layer_name in param_order:
arr = model_dict[name].detach().numpy()
arr = TRANSFORMATIONS[model_type][layer_name](arr)
check = "OK" if VALIDATIONS[model_type][layer_name](arr) else "FAILED"
if check == "FAILED":
all_ok = False
print(f" {name : <60}{str(arr.shape) : <20}{check}")
if all_ok:
print("Writing parameters ...")
gate = None
with open(output_file, "wb") as bin_handle:
for name, layer_name in param_order:
arr = model_dict[name].detach().numpy()
arr = TRANSFORMATIONS[model_type][layer_name](arr)
check = "OK" if VALIDATIONS[model_type][layer_name](arr) else "FAILED"
print(f" {name : <60}{str(arr.shape) : <20}{check}")
arr.flatten().astype(np.float32).tofile(bin_handle)
if __name__ == "__main__":
convert_weights()
print("Done")
|