diff --git a/aiserver.py b/aiserver.py
index fefab9b8..6267aec2 100644
--- a/aiserver.py
+++ b/aiserver.py
@@ -90,7 +90,6 @@ mainmenu = [
["Adventure Models", "adventurelist", ""],
["Novel Models", "novellist", ""],
["NSFW Models", "nsfwlist", ""],
- ["Chatbot Models", "chatlist", ""],
["Untuned GPT-Neo/J", "gptneolist", ""],
["Untuned Fairseq Dense", "fsdlist", ""],
["Untuned OPT", "optlist", ""],
@@ -212,6 +211,7 @@ class vars:
temp = 0.5 # Default generator temperature
top_p = 0.9 # Default generator top_p
top_k = 0 # Default generator top_k
+ top_a = 0.0 # Default generator top-a
tfs = 1.0 # Default generator tfs (tail-free sampling)
typical = 1.0 # Default generator typical sampling threshold
numseqs = 1 # Number of sequences to ask the generator to create
@@ -577,6 +577,8 @@ def loadmodelsettings():
vars.tfs = js["tfs"]
if("typical" in js):
vars.typical = js["typical"]
+ if("top_a" in js):
+ vars.top_a = js["top_a"]
if("rep_pen" in js):
vars.rep_pen = js["rep_pen"]
if("rep_pen_slope" in js):
@@ -613,6 +615,7 @@ def savesettings():
js["top_k"] = vars.top_k
js["tfs"] = vars.tfs
js["typical"] = vars.typical
+ js["top_a"] = vars.top_a
js["rep_pen"] = vars.rep_pen
js["rep_pen_slope"] = vars.rep_pen_slope
js["rep_pen_range"] = vars.rep_pen_range
@@ -693,6 +696,8 @@ def processsettings(js):
vars.tfs = js["tfs"]
if("typical" in js):
vars.typical = js["typical"]
+ if("top_a" in js):
+ vars.top_a = js["top_a"]
if("rep_pen" in js):
vars.rep_pen = js["rep_pen"]
if("rep_pen_slope" in js):
@@ -1379,7 +1384,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go
# Patch transformers to use our custom logit warpers
from transformers import LogitsProcessorList, LogitsWarper, LogitsProcessor, TopKLogitsWarper, TopPLogitsWarper, TemperatureLogitsWarper, RepetitionPenaltyLogitsProcessor
- from warpers import AdvancedRepetitionPenaltyLogitsProcessor, TailFreeLogitsWarper, TypicalLogitsWarper
+ from warpers import AdvancedRepetitionPenaltyLogitsProcessor, TailFreeLogitsWarper, TypicalLogitsWarper, TopALogitsWarper
def dynamic_processor_wrap(cls, field_name, var_name, cond=None):
old_call = cls.__call__
@@ -1399,6 +1404,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go
cls.__call__ = new_call
dynamic_processor_wrap(AdvancedRepetitionPenaltyLogitsProcessor, ("penalty", "penalty_slope", "penalty_range"), ("rep_pen", "rep_pen_slope", "rep_pen_range"), cond=lambda x: x[0] != 1.0)
dynamic_processor_wrap(TopKLogitsWarper, "top_k", "top_k", cond=lambda x: x > 0)
+ dynamic_processor_wrap(TopALogitsWarper, "top_a", "top_a", cond=lambda x: x > 0.0)
dynamic_processor_wrap(TopPLogitsWarper, "top_p", "top_p", cond=lambda x: x < 1.0)
dynamic_processor_wrap(TailFreeLogitsWarper, "tfs", "tfs", cond=lambda x: x < 1.0)
dynamic_processor_wrap(TypicalLogitsWarper, "typical", "typical", cond=lambda x: x < 1.0)
@@ -1445,6 +1451,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go
def new_get_logits_warper(beams: int = 1,) -> LogitsProcessorList:
warper_list = LogitsProcessorList()
warper_list.append(TopKLogitsWarper(top_k=1, min_tokens_to_keep=1 + (beams > 1)))
+ warper_list.append(TopALogitsWarper(top_a=0.5, min_tokens_to_keep=1 + (beams > 1)))
warper_list.append(TopPLogitsWarper(top_p=0.5, min_tokens_to_keep=1 + (beams > 1)))
warper_list.append(TailFreeLogitsWarper(tfs=0.5, min_tokens_to_keep=1 + (beams > 1)))
warper_list.append(TypicalLogitsWarper(typical=0.5, min_tokens_to_keep=1 + (beams > 1)))
@@ -1814,6 +1821,7 @@ else:
"top_k": int(vars.top_k),
"tfs": float(vars.tfs),
"typical": float(vars.typical),
+ "top_a": float(vars.top_a),
"repetition_penalty": float(vars.rep_pen),
"rpslope": float(vars.rep_pen_slope),
"rprange": int(vars.rep_pen_range),
@@ -2176,6 +2184,7 @@ def lua_has_setting(setting):
"settopk",
"settfs",
"settypical",
+ "settopa",
"setreppen",
"setreppenslope",
"setreppenrange",
@@ -2195,6 +2204,7 @@ def lua_has_setting(setting):
"top_k",
"tfs",
"typical",
+ "topa",
"reppen",
"reppenslope",
"reppenrange",
@@ -2229,6 +2239,7 @@ def lua_get_setting(setting):
if(setting in ("settopk", "topk", "top_k")): return vars.top_k
if(setting in ("settfs", "tfs")): return vars.tfs
if(setting in ("settypical", "typical")): return vars.typical
+ if(setting in ("settopa", "topa")): return vars.top_a
if(setting in ("setreppen", "reppen")): return vars.rep_pen
if(setting in ("setreppenslope", "reppenslope")): return vars.rep_pen_slope
if(setting in ("setreppenrange", "reppenrange")): return vars.rep_pen_range
@@ -2264,6 +2275,7 @@ def lua_set_setting(setting, v):
if(setting in ("settopk", "topk")): vars.top_k = v
if(setting in ("settfs", "tfs")): vars.tfs = v
if(setting in ("settypical", "typical")): vars.typical = v
+ if(setting in ("settopa", "topa")): vars.top_a = v
if(setting in ("setreppen", "reppen")): vars.rep_pen = v
if(setting in ("setreppenslope", "reppenslope")): vars.rep_pen_slope = v
if(setting in ("setreppenrange", "reppenrange")): vars.rep_pen_range = v
@@ -2688,6 +2700,11 @@ def get_message(msg):
emit('from_server', {'cmd': 'setlabeltypical', 'data': msg['data']}, broadcast=True)
settingschanged()
refresh_settings()
+ elif(msg['cmd'] == 'settopa'):
+ vars.top_a = float(msg['data'])
+ emit('from_server', {'cmd': 'setlabeltopa', 'data': msg['data']}, broadcast=True)
+ settingschanged()
+ refresh_settings()
elif(msg['cmd'] == 'setreppen'):
vars.rep_pen = float(msg['data'])
emit('from_server', {'cmd': 'setlabelreppen', 'data': msg['data']}, broadcast=True)
@@ -3748,6 +3765,7 @@ def sendtocolab(txt, min, max):
'top_k': vars.top_k,
'tfs': vars.tfs,
'typical': vars.typical,
+ 'topa': vars.top_a,
'numseqs': vars.numseqs,
'retfultxt': False
}
@@ -3885,6 +3903,7 @@ def tpumtjgenerate(txt, minimum, maximum, found_entries=None):
top_k=vars.top_k,
tfs=vars.tfs,
typical=vars.typical,
+ top_a=vars.top_a,
numseqs=vars.numseqs,
repetition_penalty=vars.rep_pen,
rpslope=vars.rep_pen_slope,
@@ -4071,6 +4090,7 @@ def refresh_settings():
emit('from_server', {'cmd': 'updatetopk', 'data': vars.top_k}, broadcast=True)
emit('from_server', {'cmd': 'updatetfs', 'data': vars.tfs}, broadcast=True)
emit('from_server', {'cmd': 'updatetypical', 'data': vars.typical}, broadcast=True)
+ emit('from_server', {'cmd': 'updatetopa', 'data': vars.top_a}, broadcast=True)
emit('from_server', {'cmd': 'updatereppen', 'data': vars.rep_pen}, broadcast=True)
emit('from_server', {'cmd': 'updatereppenslope', 'data': vars.rep_pen_slope}, broadcast=True)
emit('from_server', {'cmd': 'updatereppenrange', 'data': vars.rep_pen_range}, broadcast=True)
@@ -4647,6 +4667,7 @@ def oairequest(txt, min, max):
'prompt': txt,
'max_tokens': vars.genamt,
'temperature': vars.temp,
+ 'top_a': vars.top_a,
'top_p': vars.top_p,
'top_k': vars.top_k,
'tfs': vars.tfs,
diff --git a/bridge.lua b/bridge.lua
index ed0941c6..fc6c8823 100644
--- a/bridge.lua
+++ b/bridge.lua
@@ -867,6 +867,7 @@ return function(_python, _bridged)
---@field settopk integer
---@field settfs number
---@field settypical number
+ ---@field settopa number
---@field setreppen number
---@field setreppenslope number
---@field setreppenrange number
@@ -884,6 +885,7 @@ return function(_python, _bridged)
---@field top_k integer
---@field tfs number
---@field typical number
+ ---@field topa number
---@field reppen number
---@field reppenslope number
---@field reppenrange number
diff --git a/gensettings.py b/gensettings.py
index e8d4e566..b3007c91 100644
--- a/gensettings.py
+++ b/gensettings.py
@@ -64,6 +64,17 @@ gensettingstf = [
"step": 0.05,
"default": 1.0,
"tooltip": "Alternative sampling method described in the paper \"Typical Decoding for Natural Language Generation\" (10.48550/ARXIV.2202.00666). The paper suggests 0.2 as a good value for this setting. Set this setting to 1 to disable its effect."
+ },
+ {
+ "uitype": "slider",
+ "unit": "float",
+ "label": "Top a Sampling",
+ "id": "settopa",
+ "min": 0.0,
+ "max": 1.0,
+ "step": 0.01,
+ "default": 0.0,
+ "tooltip": "Alternative sampling method that reduces the randomness of the AI whenever the probability of one token is much higher than all the others. Higher values have a stronger effect. Set this setting to 0 to disable its effect."
},
{
"uitype": "slider",
diff --git a/static/application.js b/static/application.js
index 3b50281c..55487f76 100644
--- a/static/application.js
+++ b/static/application.js
@@ -2096,6 +2096,10 @@ $(document).ready(function(){
// Send current typical value to input
$("#settypicalcur").val(msg.data);
$("#settypical").val(parseFloat(msg.data)).trigger("change");
+ } else if(msg.cmd == "updatetopa") {
+ // Send current top a value to input
+ $("#settopacur").val(msg.data);
+ $("#settopa").val(parseFloat(msg.data)).trigger("change");
} else if(msg.cmd == "updatereppen") {
// Send current rep pen value to input
$("#setreppencur").val(msg.data);
@@ -2135,6 +2139,9 @@ $(document).ready(function(){
} else if(msg.cmd == "setlabeltypical") {
// Update setting label with value from server
$("#settypicalcur").val(msg.data);
+ } else if(msg.cmd == "setlabeltypical") {
+ // Update setting label with value from server
+ $("#settopa").val(msg.data);
} else if(msg.cmd == "setlabelreppen") {
// Update setting label with value from server
$("#setreppencur").val(msg.data);
diff --git a/templates/index.html b/templates/index.html
index a3214ffa..690535f7 100644
--- a/templates/index.html
+++ b/templates/index.html
@@ -17,7 +17,7 @@
-
+
diff --git a/tpu_mtj_backend.py b/tpu_mtj_backend.py
index fb2dc7ae..f66ad53c 100644
--- a/tpu_mtj_backend.py
+++ b/tpu_mtj_backend.py
@@ -70,6 +70,7 @@ def settings_callback() -> dict:
"top_k": 0,
"tfs": 1.0,
"typical": 1.0,
+ "top_a": 0.0,
"repetition_penalty": 1.0,
"rpslope": 0.0,
"rprange": 0,
@@ -158,10 +159,10 @@ def apply_repetition_penalty_dynamic(logits, tokens, repetition_penalty, generat
logits[tokens] = penalty_logits
return logits
-def kobold_sample_dynamic(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typical=1.0):
+def kobold_sample_dynamic(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typical=1.0, top_a=0.0):
'''
- This gets called by generate_loop_fn to apply a series of 5 filters
- to the logits (top-k, then top-p, then TFS, then typical, then temperature)
+ This gets called by generate_loop_fn to apply a series of 6 filters
+ to the logits (top-k, then top-a, then top-p, then TFS, then typical, then temperature)
before picking one token using the modified logits
'''
# Top-k (keep only the k tokens with the highest logits and remove
@@ -182,6 +183,20 @@ def kobold_sample_dynamic(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, ty
return np.where(indices_to_remove, -np.inf, logits)
if top_k > 0:
logits = top_k_filter(logits)
+ # Top-a (remove all tokens that have softmax probability less than
+ # a*m^2 where m is the maximum softmax probability)
+ def top_a_filter(logits):
+ # Replace every element in the logits array
+ # with e (Euler's number) to the power of that element, and divide
+ # each element of the new array by the sum of the elements in the
+ # new array
+ probabilities = np.array(jax.nn.softmax(logits), copy=True)
+ # Find the largest probability
+ probs_max = probabilities.max()
+ # Remove tokens
+ return np.where(probabilities < probs_max * probs_max * top_a, -np.inf, logits)
+ if top_a > 0.0:
+ logits = top_a_filter(logits)
# Top-p (after sorting the remaining tokens again in descending order of
# logit, remove the ones that have cumulative softmax probability
# greater than p)
@@ -332,10 +347,10 @@ def apply_repetition_penalty_static(logits, tokens, repetition_penalty, generate
# positions in the logits array
return logits.at[tokens].set(penalty_logits)
-def kobold_sample_static(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typical=1.0):
+def kobold_sample_static(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typical=1.0, top_a=0.0):
'''
- This gets called by generate_loop_fn to apply a series of 5 filters
- to the logits (top-k, then top-p, then TFS, then typical, then temperature)
+ This gets called by generate_loop_fn to apply a series of 6 filters
+ to the logits (top-k, then top-a, then top-p, then TFS, then typical, then temperature)
before picking one token using the modified logits
'''
# Top-k (keep only the k tokens with the highest logits and remove
@@ -355,6 +370,19 @@ def kobold_sample_static(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typ
)
return jnp.where(indices_to_remove, -jnp.inf, logits)
logits = jax.lax.cond(top_k > 0, top_k_filter, lambda x: x, logits)
+ # Top-a (remove all tokens that have softmax probability less than
+ # a*m^2 where m is the maximum softmax probability)
+ def top_a_filter(logits):
+ # Replace every element in the logits array
+ # with e (Euler's number) to the power of that element, and divide
+ # each element of the new array by the sum of the elements in the
+ # new array
+ probabilities = jax.nn.softmax(logits)
+ # Find the largest probability
+ probs_max = probabilities.max()
+ # Remove tokens
+ return jnp.where(probabilities < probs_max * probs_max * top_a, -jnp.inf, logits)
+ logits = jax.lax.cond(top_a > 0.0, top_a_filter, lambda x: x, logits)
# Top-p (after sorting the remaining tokens again in descending order of
# logit, remove the ones that have cumulative softmax probability
# greater than p)
@@ -806,6 +834,7 @@ def infer_static(
top_k=0,
tfs=1.0,
typical=1.0,
+ top_a=0.0,
repetition_penalty=1.0,
rpslope=0.0,
rprange=0,
@@ -829,6 +858,7 @@ def infer_static(
"top_p": top_p * np.ones(total_batch),
"tfs": tfs * np.ones(total_batch),
"typical": typical * np.ones(total_batch),
+ "top_a": top_a * np.ones(total_batch),
"repetition_penalty": repetition_penalty * np.ones(total_batch),
"rpslope": rpslope * np.ones(total_batch),
"rprange": np.full(total_batch, rprange, dtype=np.uint32),
diff --git a/warpers.py b/warpers.py
index 7c4f854b..fb683f50 100644
--- a/warpers.py
+++ b/warpers.py
@@ -148,3 +148,32 @@ class TypicalLogitsWarper(LogitsWarper):
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
scores = scores.masked_fill(indices_to_remove, self.filter_value)
return scores
+
+
+class TopALogitsWarper(LogitsWarper):
+ def __init__(self, top_a: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
+ top_a = float(top_a)
+ if top_a < 0 or top_a > 1.0:
+ raise ValueError(f"`top_a` has to be a float >= 0 and <= 1, but is {top_a}")
+ self.top_a = top_a
+ self.filter_value = filter_value
+ self.min_tokens_to_keep = min_tokens_to_keep
+
+ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+ if self.filter_value >= 1.0:
+ return scores
+
+ sorted_logits, sorted_indices = torch.sort(scores, descending=True)
+ probs = sorted_logits.softmax(dim=-1)
+
+ # Remove tokens with probability less than top_a*(max(probs))^2 (token with 0 are kept)
+ probs_max = probs[..., 0, None]
+ sorted_indices_to_remove = probs < probs_max * probs_max * self.top_a
+
+ if self.min_tokens_to_keep > 1:
+ # Keep at least min_tokens_to_keep
+ sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
+
+ indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+ scores = scores.masked_fill(indices_to_remove, self.filter_value)
+ return scores