rixty
/
KoboldAI-Client
mirror of https://github.com/KoboldAI/KoboldAI-Client.git
1
1
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #145 from VE-FORBRYDERNE/top-a 

Top-a sampling
This commit is contained in:
henk717 2022-06-11 09:33:51 +02:00 committed by GitHub
parent 98c2aad072 42b7a327b2
commit 8a38b258f4
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 108 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

23
aiserver.py
View File

@ -212,6 +212,7 @@ 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 +578,8 @@ 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 +616,7 @@ 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 +697,8 @@ 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 +1385,7 @@ 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 +1405,7 @@ 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 +1452,7 @@ 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 +1822,7 @@ 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 +2185,7 @@ def lua_has_setting(setting):
"settopk",
"settfs",
"settypical",
"settopa",
"setreppen",
"setreppenslope",
"setreppenrange",
@ -2195,6 +2205,7 @@ def lua_has_setting(setting):
"top_k",
"tfs",
"typical",
"topa",
"reppen",
"reppenslope",
"reppenrange",
@ -2229,6 +2240,7 @@ 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 +2276,7 @@ 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 +2701,11 @@ 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 +3766,7 @@ 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 +3904,7 @@ 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 +4091,7 @@ 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)

2
bridge.lua
View File

@ -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

11
gensettings.py
View File

@ -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",

7
static/application.js
View File

@ -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);

2
templates/index.html
View File

@ -17,7 +17,7 @@
<script src="static/bootstrap.min.js"></script>
<script src="static/bootstrap-toggle.min.js"></script>
<script src="static/rangy-core.min.js"></script>
<script src="static/application.js?ver=1.18c"></script>
<script src="static/application.js?ver=1.18d"></script>
</head>
<body>
<input type="file" id="remote-save-select" accept="application/json" style="display:none">

42
tpu_mtj_backend.py
View File

@ -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),

29
warpers.py
View File

@ -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