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gemma.cpp
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Added 7B support and args parsing. Still todo: more testing of 7B conversion.

This commit is contained in:
Phil Culliton 2024-03-07 22:34:14 +00:00
parent c93e1a1e4d
commit 2161908f50
1 changed files with 110 additions and 58 deletions
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164
util/convert_weights.py
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@ -13,23 +13,74 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# WIP - DO NOT MERGE
# Requires torch 2.2 and gemma package from https://github.com/google/gemma_pytorch
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
# WIP - DO NOT MERGE
# Requires torch 2.2 and gemma package from https://github.com/google/gemma_pytorch
## parameters
## model, tokenizer, model type,
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()
def expand_qkv(qkv_proj: np.array) -> np.array:
"""This won't be needed anymore when MQA is implemented"""
## this will only be true for 2b
assert qkv_proj.shape == (2560, 2048)
qkv = qkv_proj.reshape((10, 256, 2048))
## based on line 230 of
## https://github.com/google/gemma_pytorch/blob/main/gemma/model.py
q_proj = qkv[:8].reshape((1,8,256,2048))
kv_proj = qkv[8:]
kv_proj = kv_proj[:, np.newaxis, :, :]
@ -39,23 +90,33 @@ def expand_qkv(qkv_proj: np.array) -> np.array:
qkv = np.transpose(qkv, axes=[1,0,2,3])
return qkv
TRANSFORMATIONS = defaultdict(
TRANSFORMATIONS = {
"2b":defaultdict(
lambda: lambda x: x,
{
## padding goes at end per discussion
"embedder.weight": lambda x: np.concatenate([x, np.zeros([128, 2048])], 0),
"self_attn.qkv_proj.weight": expand_qkv,
## based on line 234 of
## https://github.com/google/gemma_pytorch/blob/main/gemma/model.py
"self_attn.o_proj.weight": lambda x: x.reshape(2048, 8, 256).transpose([1,0,2]), # TODO: which of the 2048 is unpacked to 8 x 256, and which is model_dim?
"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: np.concatenate([x, np.zeros([128, 3072])], 0),
"self_attn.qkv_proj.weight": lambda x: x.reshape((16, 3, 256, 3072)),
"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 == (256128, 2048),
"model.norm.weight": lambda x: x.shape == (2048,),
"self_attn.qkv_proj.weight": lambda x: x.shape == (8, 3, 256, 2048),
@ -65,10 +126,22 @@ VALIDATIONS = {
"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 == (256128, 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():
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
@ -79,61 +152,49 @@ def param_names():
# fmt: off
names = [
("embedder.weight", ) * 2, # embedder_input_embedding (vocab=256000, model_dim=2048) -> (vocab=256128, model_dim=2048)
("model.norm.weight", ) * 2 # final_norm_scale (model_dim=2048)
("embedder.weight", ) * 2, # embedder_input_embedding
("model.norm.weight", ) * 2 # final_norm_scale
]
layer_params = [
# TODO(austinvhuang): transpositions here ...
"self_attn.o_proj.weight", # attn_vec_einsum_w (2048, 2048) -> (heads=8, model_dim=2048, qkv_dim=256)
# # ( q_heads = 8 + kv = 2 ) x qkv_dim = 2560
"self_attn.qkv_proj.weight", # qkv_einsum_w (2560, 2048) -> (heads=8, qkv=3, qkv_dim=256, model_dim=2048)
# these are the same without any change
"mlp.gate_proj.weight", # gating_einsum_w (16384, 2048) => (gate/up=2, hidden=16384, model_dim=2048)
"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 (2048, 16384) => (model_dim=2048, hidden=16384)
"input_layernorm.weight", # pre_attention_norm_scale (model_dim=2048)
"post_attention_layernorm.weight", # pre_ffw_norm_scale (model_dim=2048)
"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(18):
for layer in range(num_hidden_layers):
for layer_param in layer_params:
names = names + [(f"model.layers.{layer}.{layer_param}", layer_param)]
print("names:", names)
return names
def convert_weights():
# TODO: parameterize paths as CLI args instead of hard coding them
output_file = "2bit-f32.sbs"
model_config = config.get_model_config("2b")
model_config.dtype = "float32"
model_type = args.model_type
output_file = args.output_file
## this turns on int8 quantization
# model_config.quant = "store_true"
model_config.tokenizer = "models/tokenizer.spm"
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("models/gemma-2b-it.ckpt")
model.load_weights(args.weights)
model_dict = dict(model.named_parameters())
for layer_name in model_dict:
## Make sure we're not silently having int8 quantization turned on.
print(layer_name, model_dict[layer_name].max())
assert(model_dict[layer_name].max() > 0.0)
param_order = param_names()
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[layer_name](arr)
check = "OK" if VALIDATIONS[layer_name](arr) else "FAILED"
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:
@ -142,18 +203,9 @@ def convert_weights():
with open(output_file, "wb") as bin_handle:
for name, layer_name in param_order:
arr = model_dict[name].detach().numpy()
arr = TRANSFORMATIONS[layer_name](arr)
check = "OK" if VALIDATIONS[layer_name](arr) else "FAILED"
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}")
if "gate_proj" in name:
gate = arr
elif "up_proj" in name:
up = arr
f = np.concatenate([gate, up])
print (f.shape)
f.flatten().astype(np.float32).tofile(bin_handle)
else:
arr.flatten().astype(np.float32).tofile(bin_handle)