Allow changing sampler seed and sampler order from API

aiserver.py

@@ -377,6 +377,7 @@ class vars:
     comregex_ai = re.compile(r'(?:\n<\|(?:.|\n)*?\|>(?=\n|$))|(?:<\|(?:.|\n)*?\|>\n?)')  # Pattern for matching comments to remove them before sending them to the AI
     comregex_ui = re.compile(r'(&lt;\|(?:.|\n)*?\|&gt;)')  # Pattern for matching comments in the editor
     sampler_order = utils.default_sampler_order.copy()
+    rng_states  = {}  # Used by the POST /generate endpoint to store sampler RNG states
     chatmode    = False
     chatname    = "You"
     adventure   = False
@@ -630,7 +631,7 @@ tags = [
 api_version = None  # This gets set automatically so don't change this value
 
 api_v1 = KoboldAPISpec(
-    version="1.1.4",
+    version="1.2.0",
     prefixes=["/api/v1", "/api/latest"],
     tags=tags,
 )
@@ -7450,6 +7451,13 @@ def story_load_validator(name: str):
         raise ValidationError("Must be a valid story name.")
     return True
 
+def permutation_validator(lst: list):
+    if any(not isinstance(e, int) for e in lst):
+        return
+    if min(lst) != 0 or max(lst) != len(lst) - 1 or len(set(lst)) != len(lst):
+        raise ValidationError("Must be a permutation of the first N non-negative integers, where N is the length of this array")
+    return True
+
 class GenerationInputSchema(SamplerSettingsSchema):
     prompt: str = fields.String(required=True, metadata={"description": "This is the submission."})
     use_memory: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the memory from the KoboldAI GUI when generating text."})
@@ -7469,6 +7477,9 @@ class GenerationInputSchema(SamplerSettingsSchema):
     disable_input_formatting: bool = fields.Boolean(load_default=True, metadata={"description": "When enabled, all input formatting options default to `false` instead of the value in the KoboldAI GUI"})
     frmtadsnsp: Optional[bool] = fields.Boolean(metadata={"description": "Input formatting option. When enabled, adds a leading space to your input if there is no trailing whitespace at the end of the previous action.\n\nIf `disable_input_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."})
     quiet: Optional[bool] = fields.Boolean(metadata={"description": "When enabled, Generated output will not be displayed in the console."})
+    sampler_order: Optional[List[int]] = fields.List(fields.Integer(), validate=[validate.Length(min=6), permutation_validator], metadata={"description": "Sampler order to be used. If N is the length of this array, then N must be greater than or equal to 6 and the array must be a permutation of the first N non-negative integers."})
+    sampler_seed: Optional[int] = fields.Integer(metadata={"description": "RNG seed to use for sampling. If not specified, the global RNG will be used."})
+    sampler_full_determinism: Optional[bool] = fields.Boolean(metadata={"description": "If enabled, the generated text will always be the same as long as you use the same RNG seed, input and settings. If disabled, only the *sequence* of generated texts that you get when repeatedly generating text will be the same given the same RNG seed, input and settings."})
 
 class GenerationResultSchema(KoboldSchema):
     text: str = fields.String(required=True, metadata={"description": "Generated output as plain text."})
@@ -7559,6 +7570,29 @@ def _generate_text(body: GenerationInputSchema):
             "msg": "Server is busy; please try again later.",
             "type": "service_unavailable",
         }}), mimetype="application/json", status=503))
+    if vars.use_colab_tpu:
+        import tpu_mtj_backend
+    if hasattr(body, "sampler_seed"):
+        # If a seed was specified, we need to save the global RNG state so we
+        # can restore it later
+        old_seed = vars.seed
+        old_rng_state = tpu_mtj_backend.get_rng_state() if vars.use_colab_tpu else torch.get_rng_state()
+        vars.seed = body.sampler_seed
+        # We should try to use a previously saved RNG state with the same seed
+        if body.sampler_seed in vars.rng_states:
+            if vars.use_colab_tpu:
+                tpu_mtj_backend.set_rng_state(vars.rng_states[body.sampler_seed])
+            else:
+                torch.set_rng_state(vars.rng_states[body.sampler_seed])
+        else:
+            if vars.use_colab_tpu:
+                tpu_mtj_backend.set_rng_state(tpu_mtj_backend.new_rng_state(body.sampler_seed))
+            else:
+                torch.manual_seed(body.sampler_seed)
+        vars.rng_states[body.sampler_seed] = tpu_mtj_backend.get_rng_state() if vars.use_colab_tpu else torch.get_rng_state()
+    if hasattr(body, "sampler_order"):
+        if len(body.sampler_order) < 7:
+            body.sampler_order = [6] + body.sampler_order
     # This maps each property of the setting to use when sending the generate idempotently
     # To the object which typically contains it's value
     # This allows to set the property only for the API generation, and then revert the setting
@@ -7584,6 +7618,8 @@ def _generate_text(body: GenerationInputSchema):
         "max_context_length": ("vars", "max_length", None),
         "n": ("vars", "numseqs", None),
         "quiet": ("vars", "quiet", None),
+        "sampler_order": ("vars", "sampler_order", None),
+        "sampler_full_determinism": ("vars", "full_determinism", None),
     }
     saved_settings = {}
     set_aibusy(1)
@@ -7633,6 +7669,12 @@ def _generate_text(body: GenerationInputSchema):
         vars.output_streaming = output_streaming
         if vars.allowsp and getattr(body, "soft_prompt", None) is not None:
             spRequest(old_spfilename)
+        if hasattr(body, "sampler_seed"):
+            vars.seed = old_seed
+            if vars.use_colab_tpu:
+                tpu_mtj_backend.set_rng_state(old_rng_state)
+            else:
+                torch.set_rng_state(old_rng_state)
         set_aibusy(0)
     return output
 
@@ -10035,6 +10077,15 @@ class AddSentenceSpacingSettingsSchema(KoboldSchema):
         name = "add sentence spacing (input formatting)"
         example_yaml_value = "false"
 
+@config_endpoint_schema
+class SamplerOrderSettingSchema(KoboldSchema):
+    value = fields.List(fields.Integer(), validate=[validate.Length(min=6), permutation_validator], required=True)
+    class KoboldMeta:
+        route_name = "sampler_order"
+        obj = "vars"
+        var_name = "sampler_order"
+        name = "sampler order"
+        example_yaml_value = "[6, 0, 1, 2, 3, 4, 5]"
 
 
 for schema in config_endpoint_schemas:

tpu_mtj_backend.py

@@ -71,6 +71,15 @@ def set_rng_seed(seed: int):
 def randomize_rng_seed():
     return set_rng_seed(random.randrange(sys.maxsize))
 
+def get_rng_state():
+    return rng
+
+def set_rng_state(state):
+    global rng
+    rng = state
+
+def new_rng_state(seed: int):
+    return random.Random(seed)
 
 def warper_callback(logits) -> np.array:
     raise NotImplementedError("`tpu_mtj_backend.warper_callback()` needs to be defined")