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Revert "Basic Contrastive Search support"

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This reverts commit 0f52281af5.
This commit is contained in:
somebody
2022-11-14 18:01:16 -06:00
parent a523aa93e2
commit 36cee7768c
6 changed files with 4 additions and 48 deletions
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22
aiserver.py
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@@ -90,10 +90,6 @@ global tpu_mtj_backend
if lupa.LUA_VERSION[:2] != (5, 4):
logger.error(f"Please install lupa==1.10. You have lupa {lupa.__version__}.")
if packaging.version.parse(transformers_version) < packaging.version.parse("4.24.0"):
logger.warning(f"Please upgrade to transformers 4.24.0 or later for Contrastive Search. You have transformers {transformers_version}.")
patch_causallm_patched = False
# Make sure tqdm progress bars display properly in Colab
@@ -1186,9 +1182,6 @@ def loadmodelsettings():
if("top_a" in js):
koboldai_vars.top_a = js["top_a"]
koboldai_vars.default_preset['top_a'] = js["top_a"]
if("penalty_alpha" in js):
koboldai_vars.penalty_alpha = js["penalty_alpha"]
koboldai_vars.default_preset['penalty_alpha'] = js["penalty_alpha"]
if("rep_pen" in js):
koboldai_vars.rep_pen = js["rep_pen"]
koboldai_vars.default_preset['rep_pen'] = js["rep_pen"]
@@ -2440,7 +2433,6 @@ def reset_model_settings():
koboldai_vars.top_a = 0.0 # Default generator top-a
koboldai_vars.tfs = 1.0 # Default generator tfs (tail-free sampling)
koboldai_vars.typical = 1.0 # Default generator typical sampling threshold
koboldai_vars.penalty_alpha = 0.0 # Default generator penalty_alpha (contrastive search)
koboldai_vars.numseqs = 1 # Number of sequences to ask the generator to create
koboldai_vars.generated_tkns = 0 # If using a backend that supports Lua generation modifiers, how many tokens have already been generated, otherwise 0
koboldai_vars.badwordsids = []
@@ -3622,7 +3614,6 @@ def lua_has_setting(setting):
"tfs",
"typical",
"topa",
"penalty_alpha",
"reppen",
"reppenslope",
"reppenrange",
@@ -3660,7 +3651,6 @@ def lua_get_setting(setting):
if(setting in ("settfs", "tfs")): return koboldai_vars.tfs
if(setting in ("settypical", "typical")): return koboldai_vars.typical
if(setting in ("settopa", "topa")): return koboldai_vars.top_a
if(setting in ("setpenaltyalpha", "penalty_alpha")): return koboldai_vars.penalty_alpha
if(setting in ("setreppen", "reppen")): return koboldai_vars.rep_pen
if(setting in ("setreppenslope", "reppenslope")): return koboldai_vars.rep_pen_slope
if(setting in ("setreppenrange", "reppenrange")): return koboldai_vars.rep_pen_range
@@ -3699,7 +3689,6 @@ def lua_set_setting(setting, v):
if(setting in ("settfs", "tfs")): koboldai_vars.tfs = v
if(setting in ("settypical", "typical")): koboldai_vars.typical = v
if(setting in ("settopa", "topa")): koboldai_vars.top_a = v
if(setting in ("setpenaltyalpha", "penalty_alpha")): koboldai_vars.penalty_alpha = v
if(setting in ("setreppen", "reppen")): koboldai_vars.rep_pen = v
if(setting in ("setreppenslope", "reppenslope")): koboldai_vars.rep_pen_slope = v
if(setting in ("setreppenrange", "reppenrange")): koboldai_vars.rep_pen_range = v
@@ -4102,11 +4091,6 @@ def get_message(msg):
emit('from_server', {'cmd': 'setlabeltopa', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setpenaltyalpha'):
koboldai_vars.penalty_alpha = float(msg['data'])
emit('from_server', {'cmd': 'setlabelpenaltyalpha', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setreppen'):
koboldai_vars.rep_pen = float(msg['data'])
emit('from_server', {'cmd': 'setlabelreppen', 'data': msg['data']}, broadcast=True, room="UI_1")
@@ -5373,7 +5357,6 @@ class GenerationSettings:
"tfs",
"typical",
"top_a",
"penalty_alpha",
"rep_pen",
"rep_pen_slope",
"rep_pen_range",
@@ -5568,7 +5551,6 @@ def torch_raw_generate(
bad_words_ids=koboldai_vars.badwordsids,
use_cache=True,
num_return_sequences=batch_count,
penalty_alpha=koboldai_vars.penalty_alpha,
)
logger.debug("torch_raw_generate: run generator {}s".format(time.time()-start_time))
@@ -6469,7 +6451,6 @@ def refresh_settings():
emit('from_server', {'cmd': 'updatetfs', 'data': koboldai_vars.tfs}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatetypical', 'data': koboldai_vars.typical}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatetopa', 'data': koboldai_vars.top_a}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatepenaltyalpha', 'data': koboldai_vars.penalty_alpha}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatereppen', 'data': koboldai_vars.rep_pen}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatereppenslope', 'data': koboldai_vars.rep_pen_slope}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatereppenrange', 'data': koboldai_vars.rep_pen_range}, broadcast=True, room="UI_1")
@@ -9084,7 +9065,7 @@ def UI_2_load_cookies():
def UI_2_save_new_preset(data):
preset = {}
#Data to get from current settings
for item in ["genamt", "rep_pen", "rep_pen_range", "rep_pen_slope", "sampler_order", "temp", "tfs", "top_a", "top_k", "top_p", "typical", "penalty_alpha"]:
for item in ["genamt", "rep_pen", "rep_pen_range", "rep_pen_slope", "sampler_order", "temp", "tfs", "top_a", "top_k", "top_p", "typical"]:
preset[item] = getattr(koboldai_vars, item)
#Data to get from UI
for item in ['preset', 'description']:
@@ -9895,7 +9876,6 @@ def _generate_text(body: GenerationInputSchema):
"top_k": ("koboldai_vars", "top_k", None),
"top_a": ("koboldai_vars", "top_a", None),
"top_p": ("koboldai_vars", "top_p", None),
"penalty_alpha": ("koboldai_vars", "penalty_alpha", None),
"tfs": ("koboldai_vars", "tfs", None),
"typical": ("koboldai_vars", "typical", None),
"temperature": ("koboldai_vars", "temp", None),

16
gensettings.py
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@@ -107,22 +107,6 @@ gensettingstf = [
},
{
"UI_V2_Only": True,
"uitype": "slider",
"unit": "float",
"label": "Penalty Alpha",
"id": "setpenaltyalpha",
"min": 0.0,
"max": 1.0,
"step": 0.01,
"default": 0.0,
"tooltip": "Alternative search method. Encourages diversity of output embeddings. To be used with Top-K. Does not work on TPU! (Put this value on 0 to disable its effect)",
"menu_path": "Settings",
"sub_path": "Sampling",
"classname": "model",
"name": "penalty_alpha",
"extra_classes": "var_sync_alt_system_use_colab_tpu"
},
{
"uitype": "slider",
"unit": "float",
"label": "Repetition Penalty",

1
koboldai_settings.py
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@@ -622,7 +622,6 @@ class model_settings(settings):
self.top_a = 0.0 # Default generator top-a
self.tfs = 1.0 # Default generator tfs (tail-free sampling)
self.typical = 1.0 # Default generator typical sampling threshold
self.penalty_alpha = 0.0 # Default generator penalty_alpha (contrastive search)
self.numseqs = 1 # Number of sequences to ask the generator to create
self.badwordsids = []
self.fp32_model = False # Whether or not the most recently loaded HF model was in fp32 format

2
requirements.txt
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@@ -1,4 +1,4 @@
transformers>=4.24.0
transformers>=4.20.1
Flask
Flask-SocketIO
requests

2
requirements_mtj.txt
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@@ -5,7 +5,7 @@ requests
dm-haiku == 0.0.5
jax == 0.2.21
jaxlib >= 0.1.69, <= 0.3.7
transformers >=4.24.0
transformers >=4.20.1
progressbar2
git+https://github.com/VE-FORBRYDERNE/mesh-transformer-jax@ck
flask

7
static/application.js
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@@ -2619,10 +2619,6 @@ $(document).ready(function(){
// Send current top a value to input
$("#settopacur").val(msg.data);
$("#settopa").val(parseFloat(msg.data)).trigger("change");
} else if(msg.cmd == "updatepenaltyalpha") {
// Send current top p value to input
$("#setpenaltyalphacur").val(msg.data);
$("#setpenaltyalpha").val(parseFloat(msg.data)).trigger("change");
} else if(msg.cmd == "updatereppen") {
// Send current rep pen value to input
$("#setreppencur").val(msg.data);
@@ -2653,9 +2649,6 @@ $(document).ready(function(){
} else if(msg.cmd == "setlabeltopp") {
// Update setting label with value from server
$("#settoppcur").val(msg.data);
} else if(msg.cmd == "setlabelpenaltyalpha") {
// Update setting label with value from server
$("#setpenaltyalphacur").val(msg.data);
} else if(msg.cmd == "setlabeltopk") {
// Update setting label with value from server
$("#settopkcur").val(msg.data);