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renamed vars to koboldai_vars

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This commit is contained in:
ebolam
2022-08-17 09:26:45 -04:00
parent 85337ccf11
commit 21c08353b9
4 changed files with 1867 additions and 1867 deletions
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64
tpu_mtj_backend.py
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@@ -563,7 +563,7 @@ class PenalizingCausalTransformer(CausalTransformer):
compiling_callback()
numseqs = numseqs_aux.shape[0]
# These are the tokens that we don't want the AI to ever write
badwords = jnp.array(vars.badwordsids).squeeze()
badwords = jnp.array(koboldai_vars.badwordsids).squeeze()
@hk.transform
def generate_sample(context, ctx_length):
# Give the initial context to the transformer
@@ -1041,8 +1041,8 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
elif "eos_token_id" in kwargs:
pad_token_id = kwargs["eos_token_id"]
if not hasattr(vars, "sampler_order") or not vars.sampler_order:
vars.sampler_order = utils.default_sampler_order.copy()
if not hasattr(koboldai_vars, "sampler_order") or not koboldai_vars.sampler_order:
koboldai_vars.sampler_order = utils.default_sampler_order.copy()
default_params = {
"compat": "j",
@@ -1061,7 +1061,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
}
params = kwargs
if vars.model == "TPUMeshTransformerGPTNeoX":
if koboldai_vars.model == "TPUMeshTransformerGPTNeoX":
default_params = {
"compat": "neox",
"layers": 44,
@@ -1080,9 +1080,9 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
# Try to convert HF config.json to MTJ config
if hf_checkpoint:
spec_path = os.path.join("maps", vars.model_type + ".json")
spec_path = os.path.join("maps", koboldai_vars.model_type + ".json")
if not os.path.isfile(spec_path):
raise NotImplementedError(f"Unsupported model type {repr(vars.model_type)}")
raise NotImplementedError(f"Unsupported model type {repr(koboldai_vars.model_type)}")
with open(spec_path) as f:
lazy_load_spec = json.load(f)
@@ -1133,7 +1133,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
params[param] = default_params[param]
# Load tokenizer
if vars.model == "TPUMeshTransformerGPTNeoX":
if koboldai_vars.model == "TPUMeshTransformerGPTNeoX":
tokenizer = Tokenizer.from_file(os.path.join(path, "20B_tokenizer.json"))
def new_encode(old_encode):
def encode(s, *args, **kwargs):
@@ -1181,19 +1181,19 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
global badwords
# These are the tokens that we don't want the AI to ever write
badwords = jnp.array(vars.badwordsids).squeeze()
badwords = jnp.array(koboldai_vars.badwordsids).squeeze()
if not path.endswith("/"):
path += "/"
network = PenalizingCausalTransformer(params, dematerialized=True)
if not hf_checkpoint and vars.model != "TPUMeshTransformerGPTNeoX":
if not hf_checkpoint and koboldai_vars.model != "TPUMeshTransformerGPTNeoX":
network.state = read_ckpt_lowmem(network.state, path, devices.shape[1])
#network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
return
if vars.model == "TPUMeshTransformerGPTNeoX":
if koboldai_vars.model == "TPUMeshTransformerGPTNeoX":
print("\n\n\nThis model has ", f"{hk.data_structures.tree_size(network.state['params']):,d}".replace(",", " "), " parameters.\n")
read_neox_checkpoint(network.state, path, params)
return
@@ -1339,58 +1339,58 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
f.close()
callback.nested = False
if os.path.isdir(vars.model.replace('/', '_')):
if os.path.isdir(koboldai_vars.model.replace('/', '_')):
import shutil
shutil.move(vars.model.replace('/', '_'), "models/{}".format(vars.model.replace('/', '_')))
shutil.move(koboldai_vars.model.replace('/', '_'), "models/{}".format(koboldai_vars.model.replace('/', '_')))
print("\n", flush=True)
with torch_lazy_loader.use_lazy_torch_load(callback=callback, dematerialized_modules=True):
if(os.path.isdir(vars.custmodpth)):
if(os.path.isdir(koboldai_vars.custmodpth)):
try:
tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache", use_fast=False)
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
tokenizer = GPT2TokenizerFast.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
model = AutoModelForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))):
model = GPTNeoForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
elif(os.path.isdir("models/{}".format(koboldai_vars.model.replace('/', '_')))):
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache", use_fast=False)
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
model = AutoModelForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
model = GPTNeoForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
model = GPTNeoForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
else:
try:
tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache", use_fast=False)
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
tokenizer = GPT2TokenizerFast.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
model = AutoModelForCausalLM.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
model = GPTNeoForCausalLM.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
model = GPTNeoForCausalLM.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
#network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))