test / src /export_hf_checkpoint.py
iblfe's picture
Upload folder using huggingface_hub
b585c7f verified
import os
import json
import shutil
import subprocess
import torch
from accelerate import infer_auto_device_map, dispatch_model
from accelerate.utils import get_balanced_memory
from peft import PeftModel
from transformers import PreTrainedModel
def do_export():
BASE_MODEL = 'h2oai/h2ogpt-4096-llama2-13b-chat'
LORA_WEIGHTS = 'Llama-2-13b-chat-hf.h2oaiopenassistant_oasst1_h2ogpt_llama2_chat.1_epochs.b2aed9250804d815c258976c98ce968bacd88389.7'
OUTPUT_NAME = "h2ogpt-oasst1-4096-llama2-13b"
BASE_MODEL = 'meta-llama/Llama-2-7b-chat-hf'
LORA_WEIGHTS = 'Llama-2-7b-chat-hf.h2oaiopenassistant_oasst1_h2ogpt_llama2_chat.1_epochs.0c6b906f73b5639fd1d53c74fecbc9cf64f0f225.8'
OUTPUT_NAME = "h2ogpt-oasst1-4096-llama2-7b"
BASE_MODEL = 'meta-llama/Llama-2-70b-chat-hf'
LORA_WEIGHTS = 'Llama-2-70b-chat-hf.h2oaiopenassistant_oasst1_h2ogpt_llama2_chat.1_epochs.0c6b906f73b5639fd1d53c74fecbc9cf64f0f225.6'
OUTPUT_NAME = "h2ogpt-oasst1-4096-llama2-70b"
base_model = os.getenv('BASE_MODEL')
output = os.getenv('MODEL')
# for testing
if base_model and output:
BASE_MODEL = base_model
LORA_WEIGHTS = output + ".lora"
OUTPUT_NAME = output
llama_type = "llama" in BASE_MODEL
as_pytorch = False # False -> HF
from loaders import get_loaders
model_loader, tokenizer_loader, conditional_type = (
get_loaders(model_name=BASE_MODEL, reward_type=False, llama_type=llama_type))
tokenizer = tokenizer_loader.from_pretrained(
BASE_MODEL,
local_files_only=False,
resume_download=True,
)
tokenizer.save_pretrained(OUTPUT_NAME)
base_model = model_loader(
BASE_MODEL,
load_in_8bit=False,
trust_remote_code=True,
torch_dtype=torch.float16,
device_map={"": "cpu"},
)
print(base_model)
if llama_type:
layers = base_model.model.layers
first_weight = layers[0].self_attn.q_proj.weight
else:
if any([x in BASE_MODEL.lower() for x in ["pythia", "h2ogpt", "gpt-neox"]]):
layers = base_model.gpt_neox.base_model.layers
first_weight = layers[0].attention.query_key_value.weight
elif any([x in BASE_MODEL.lower() for x in ["falcon"]]):
first_weight = base_model.transformer.h._modules['0'].self_attention.query_key_value.weight
else:
layers = base_model.transformer.base_model.h
first_weight = layers[0].attn.q_proj.weight
first_weight_old = first_weight.clone()
lora_model = PeftModel.from_pretrained(
base_model,
LORA_WEIGHTS,
device_map={"": "cpu"},
torch_dtype=torch.float16,
)
assert torch.allclose(first_weight_old, first_weight)
# merge weights TODO: include all lora_target_modules, not just default ones
if llama_type:
merged_model = lora_model.merge_and_unload()
# for layer in lora_model.base_model.model.model.layers:
# layer.self_attn.q_proj.merge_weights = True
# layer.self_attn.k_proj.merge_weights = True
# layer.self_attn.v_proj.merge_weights = True
# layer.self_attn.o_proj.merge_weights = True
else:
if any([x in BASE_MODEL.lower() for x in ["pythia", "gpt-neox"]]):
for layer in lora_model.base_model.gpt_neox.base_model.layers:
layer.attention.query_key_value.merge_weights = True
merged_model = lora_model
else:
merged_model = lora_model.merge_and_unload()
# for layer in lora_model.base_model.transformer.base_model.h:
# layer.attn.q_proj.merge_weights = True
# layer.attn.v_proj.merge_weights = True
# max_memory = get_balanced_memory(merged_model)
# device_map = infer_auto_device_map(merged_model, max_memory=max_memory)
# merged_model = dispatch_model(
# merged_model,
# device_map=device_map,
# )
merged_model.eval()
print(merged_model)
# did we do anything?
assert not torch.allclose(first_weight_old, first_weight)
merged_model_sd = merged_model.state_dict()
if as_pytorch:
# FIXME - might not be generic enough still
params = {
"dim": base_model.config.hidden_size,
"n_heads": base_model.config.num_attention_heads,
"n_layers": base_model.config.num_hidden_layers,
"norm_eps": base_model.config.layer_norm_eps,
"vocab_size": base_model.config.vocab_size,
}
n_layers = params["n_layers"]
n_heads = params["n_heads"]
dim = params["dim"]
dims_per_head = dim // n_heads
base = 10000.0
inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
def permute(w):
return (
w.view(n_heads, dim // n_heads // 2, 2, dim).transpose(1, 2).reshape(dim, dim)
)
def unpermute(w):
return (
w.view(n_heads, 2, dim // n_heads // 2, dim).transpose(1, 2).reshape(dim, dim)
)
def translate_state_dict_key(k):
if "gpt-neoxt" in BASE_MODEL.lower():
k = k.replace("gpt_neox.model.", "")
else:
k = k.replace("base_model.model.", "")
if k == "model.embed_tokens.weight":
return "tok_embeddings.weight"
elif k == "model.norm.weight":
return "norm.weight"
elif k == "lm_head.weight":
return "output.weight"
elif k.startswith("model.layers."):
layer = k.split(".")[2]
if k.endswith(".self_attn.q_proj.weight"):
return f"layers.{layer}.attention.wq.weight"
elif k.endswith(".self_attn.k_proj.weight"):
return f"layers.{layer}.attention.wk.weight"
elif k.endswith(".self_attn.v_proj.weight"):
return f"layers.{layer}.attention.wv.weight"
elif k.endswith(".self_attn.o_proj.weight"):
return f"layers.{layer}.attention.wo.weight"
elif k.endswith(".mlp.gate_proj.weight"):
return f"layers.{layer}.feed_forward.w1.weight"
elif k.endswith(".mlp.down_proj.weight"):
return f"layers.{layer}.feed_forward.w2.weight"
elif k.endswith(".mlp.up_proj.weight"):
return f"layers.{layer}.feed_forward.w3.weight"
elif k.endswith(".input_layernorm.weight"):
return f"layers.{layer}.attention_norm.weight"
elif k.endswith(".post_attention_layernorm.weight"):
return f"layers.{layer}.ffn_norm.weight"
elif k.endswith("rotary_emb.inv_freq") or "lora" in k:
return None
else:
print(layer, k)
raise NotImplementedError
else:
print(k)
raise NotImplementedError
new_state_dict = {}
for k, v in merged_model_sd.items():
new_k = translate_state_dict_key(k)
if new_k is not None:
if "wq" in new_k or "wk" in new_k:
new_state_dict[new_k] = unpermute(v)
else:
new_state_dict[new_k] = v
os.makedirs("./ckpt", exist_ok=True)
torch.save(new_state_dict, "./ckpt/consolidated.00.pth")
with open("./ckpt/params.json", "w") as f:
json.dump(params, f)
else:
# deloreanized_sd = {
# k.replace("base_model.model.", ""): v
# for k, v in merged_model_sd.items()
# if "lora" not in k
# }
merged_model.config.custom_pipelines = {
"text-generation": {
"impl": "h2oai_pipeline.H2OTextGenerationPipeline",
"pt": "AutoModelForCausalLM"
}
}
PreTrainedModel.save_pretrained(
merged_model,
OUTPUT_NAME,
# state_dict=deloreanized_sd,
# max_shard_size="5GB",
)
do_copy(OUTPUT_NAME)
test_copy()
def do_copy(OUTPUT_NAME):
dest_file = os.path.join(OUTPUT_NAME, "h2oai_pipeline.py")
shutil.copyfile("src/h2oai_pipeline.py", dest_file)
os.system("""sed -i 's/from enums.*//g' %s""" % dest_file)
os.system("""sed -i 's/from stopping.*//g' %s""" % dest_file)
os.system("""sed -i 's/from prompter.*//g' %s""" % dest_file)
os.system("""cat %s|grep -v "from enums import PromptType" >> %s""" % ('src/enums.py', dest_file))
os.system("""cat %s|grep -v "from enums import PromptType" >> %s""" % ('src/prompter.py', dest_file))
os.system("""cat %s|grep -v "from enums import PromptType" >> %s""" % ('src/stopping.py', dest_file))
TEST_OUTPUT_NAME = "test_output"
def test_copy():
if os.path.isdir(TEST_OUTPUT_NAME):
shutil.rmtree(TEST_OUTPUT_NAME)
os.makedirs(TEST_OUTPUT_NAME, exist_ok=False)
do_copy(TEST_OUTPUT_NAME)
shutil.copy('src/export_hf_checkpoint.py', TEST_OUTPUT_NAME)
os.environ['DO_COPY_TEST'] = '1'
os.chdir(TEST_OUTPUT_NAME)
output = subprocess.check_output(['python', 'export_hf_checkpoint.py'])
print(output)
def inner_test_copy():
"""
pytest -s -v export_hf_checkpoint.py::test_copy
:return:
"""
# test imports
# below supposed to look bad in pycharm, don't fix!
from h2oai_pipeline import get_stopping, get_prompt, H2OTextGenerationPipeline
assert get_stopping
assert get_prompt
assert H2OTextGenerationPipeline
if __name__ == '__main__':
if os.getenv('DO_COPY_TEST'):
inner_test_copy()
else:
do_export()
# uncomment for raw isolated test, but test is done every time for each export now
# test_copy()