From e98cc3cb16a20318ece08bd8ef275ff6eeb57705 Mon Sep 17 00:00:00 2001 From: Henk Date: Thu, 12 May 2022 23:55:21 +0200 Subject: [PATCH 01/63] OPT models --- aiserver.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/aiserver.py b/aiserver.py index b52b7eb5..6c9840fe 100644 --- a/aiserver.py +++ b/aiserver.py @@ -79,6 +79,7 @@ mainmenu = [ ["Chatbot Models", "chatlist", ""], ["Untuned GPT-Neo/J", "gptneolist", ""], ["Untuned Fairseq Dense", "fsdlist", ""], + ["Untuned OPT", "optlist", ""], ["Untuned XGLM", "xglmlist", ""], ["Untuned GPT2", "gpt2list", ""], ["Online Services", "apilist", ""], @@ -137,6 +138,17 @@ gpt2list = [ ["Return to Main Menu", "Return", ""], ] +optlist = [ + ["OPT 30B", "facebook/opt-30b", ""], + ["OPT 13B", "facebook/opt-13b", "32GB"], + ["OPT 6.7B", "facebook/opt-6.7b", "16GB"], + ["OPT 2.7B", "facebook/opt-2.7b", "8GB"], + ["OPT 1.3B", "facebook/opt-1.3b", "4GB"], + ["OPT 355M", "facebook/opt-350m", "2GB"], + ["OPT 125M", "facebook/opt-125m", "1GB"], + ["Return to Main Menu", "Return", ""], + ] + fsdlist = [ ["Fairseq Dense 13B", "KoboldAI/fairseq-dense-13B", "32GB"], ["Fairseq Dense 6.7B", "KoboldAI/fairseq-dense-6.7B", "16GB"], From f5e689a725eb3ad450e80aa4a05d9890b84b5630 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Thu, 12 May 2022 19:09:31 -0400 Subject: [PATCH 02/63] Upload maps/opt.json and update requirements --- maps/opt.json | 32 ++++++++++++++++++++++++++++++++ requirements.txt | 2 +- requirements_mtj.txt | 4 ++-- tpu_mtj_backend.py | 2 ++ 4 files changed, 37 insertions(+), 3 deletions(-) create mode 100644 maps/opt.json diff --git a/maps/opt.json b/maps/opt.json new file mode 100644 index 00000000..b9667ac9 --- /dev/null +++ b/maps/opt.json @@ -0,0 +1,32 @@ +{ + "mtj_compat": "opt", + "mtj_pe": "fixed", + "mtj_config_map": { + "do_layer_norm_before": ["do_layer_norm_before", true], + "d_model": "hidden_size", + "n_heads": "num_attention_heads", + "layers": "num_hidden_layers" + }, + "static_weights": { + "decoder.embed_tokens.weight": {"mtj": {"module": "embedding_shard/~/linear", "param": "w", "transforms": ["no_transpose", "vocab_pad"]}}, + "decoder.embed_positions.weight": {"mtj": {"module": "embedding_shard", "param": "pos_embs", "transforms": ["no_transpose", "remove_first_two_rows"]}} + }, + "layer_weights": { + "decoder.layers.{layer}.self_attn.q_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear", "param": "w"}}, + "decoder.layers.{layer}.self_attn.q_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear", "param": "b"}}, + "decoder.layers.{layer}.self_attn.v_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_1", "param": "w"}}, + "decoder.layers.{layer}.self_attn.v_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear_1", "param": "b"}}, + "decoder.layers.{layer}.self_attn.k_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_2", "param": "w"}}, + "decoder.layers.{layer}.self_attn.k_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear_2", "param": "b"}}, + "decoder.layers.{layer}.self_attn.out_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "w"}}, + "decoder.layers.{layer}.self_attn.out_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "b", "transforms": ["divide_by_shards"]}}, + "decoder.layers.{layer}.fc1.weight": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "w"}}, + "decoder.layers.{layer}.fc1.bias": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "b"}}, + "decoder.layers.{layer}.fc2.weight": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "w"}}, + "decoder.layers.{layer}.fc2.bias": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "b", "transforms": ["divide_by_shards"]}}, + "decoder.layers.{layer}.self_attn_layer_norm.weight": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm", "param": "scale"}}, + "decoder.layers.{layer}.self_attn_layer_norm.bias": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm", "param": "offset"}}, + "decoder.layers.{layer}.final_layer_norm.weight": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm_1", "param": "scale"}}, + "decoder.layers.{layer}.final_layer_norm.bias": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm_1", "param": "offset"}} + } +} diff --git a/requirements.txt b/requirements.txt index 897f9e8e..7b5b967c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -transformers>=4.17 +transformers>=4.19 Flask Flask-SocketIO requests diff --git a/requirements_mtj.txt b/requirements_mtj.txt index 416a06a4..e2a6c4e1 100644 --- a/requirements_mtj.txt +++ b/requirements_mtj.txt @@ -5,9 +5,9 @@ requests optax >= 0.0.5, <= 0.0.9 dm-haiku == 0.0.5 jax == 0.2.21 -transformers >= 4.17 +transformers >= 4.19 progressbar2 -git+https://github.com/VE-FORBRYDERNE/mesh-transformer-jax@ck +git+https://github.com/VE-FORBRYDERNE/mesh-transformer-jax@ck-staging flask Flask-SocketIO flask-cloudflared >= 0.0.5 diff --git a/tpu_mtj_backend.py b/tpu_mtj_backend.py index 1f67763f..75b4ee9c 100644 --- a/tpu_mtj_backend.py +++ b/tpu_mtj_backend.py @@ -1200,6 +1200,8 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo # MTJ requires certain mathematical operations to be performed # on tensors in order for them to be in the correct format + if "remove_first_two_rows" in transforms: + tensor = tensor[2:] if "divide_by_shards" in transforms: tensor /= params["cores_per_replica"] if "vocab_pad" in transforms: From 5c4a08797062d39e0df412928219f96b63115c88 Mon Sep 17 00:00:00 2001 From: Henk Date: Fri, 13 May 2022 01:47:59 +0200 Subject: [PATCH 03/63] Disable S mode for OPT --- aiserver.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aiserver.py b/aiserver.py index 6c9840fe..0dc19c5a 100644 --- a/aiserver.py +++ b/aiserver.py @@ -527,7 +527,7 @@ def loadmodelsettings(): js = json.load(open(vars.custmodpth.replace('/', '_') + "/config.json", "r")) except Exception as e: js = {} - if vars.model_type == "xglm" or vars.model_type == "opt" or js.get("compat", "j") == "fairseq_lm": + if vars.model_type == "xglm" or js.get("compat", "j") == "fairseq_lm": vars.newlinemode = "s" # Default to newline mode if using XGLM vars.modelconfig = js if("badwordsids" in js): From 4fa5f1cd6afb3486704870c2e56e84f5888e7f71 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Thu, 12 May 2022 22:21:15 -0400 Subject: [PATCH 04/63] Add TPU support for OPT-350M The 350M model seems to have a different structure than the other ones ??? --- aiserver.py | 8 ++++---- maps/opt.json | 5 ++++- tpu_mtj_backend.py | 2 +- 3 files changed, 9 insertions(+), 6 deletions(-) diff --git a/aiserver.py b/aiserver.py index 0dc19c5a..1f105701 100644 --- a/aiserver.py +++ b/aiserver.py @@ -772,7 +772,7 @@ def spRequest(filename): tensor = tensor.reshape( tpu_mtj_backend.params["cores_per_replica"], -1, - tpu_mtj_backend.params["d_model"], + tpu_mtj_backend.params.get("d_embed", tpu_mtj_backend.params["d_model"]), ) vars.sp = tpu_mtj_backend.shard_xmap(np.float32(tensor)) else: @@ -1574,14 +1574,14 @@ else: global np if 'np' not in globals(): import numpy as np - tensor = np.zeros((1, tpu_mtj_backend.params["d_model"]), dtype=np.float32) + tensor = np.zeros((1, tpu_mtj_backend.params.get("d_embed", tpu_mtj_backend.params["d_model"])), dtype=np.float32) rows = tensor.shape[0] padding_amount = tpu_mtj_backend.params["seq"] - (tpu_mtj_backend.params["seq"] % -tpu_mtj_backend.params["cores_per_replica"]) - rows tensor = np.pad(tensor, ((0, padding_amount), (0, 0))) tensor = tensor.reshape( tpu_mtj_backend.params["cores_per_replica"], -1, - tpu_mtj_backend.params["d_model"], + tpu_mtj_backend.params.get("d_embed", tpu_mtj_backend.params["d_model"]), ) vars.sp = tpu_mtj_backend.shard_xmap(tensor) soft_tokens = np.arange( @@ -1682,7 +1682,7 @@ else: loadmodelsettings() loadsettings() tpu_mtj_backend.load_model(vars.custmodpth, hf_checkpoint=vars.model not in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") and vars.use_colab_tpu, **vars.modelconfig) - vars.modeldim = int(tpu_mtj_backend.params["d_model"]) + vars.modeldim = int(tpu_mtj_backend.params.get("d_embed", tpu_mtj_backend.params["d_model"])) tokenizer = tpu_mtj_backend.tokenizer else: loadsettings() diff --git a/maps/opt.json b/maps/opt.json index b9667ac9..c99ae19f 100644 --- a/maps/opt.json +++ b/maps/opt.json @@ -3,13 +3,16 @@ "mtj_pe": "fixed", "mtj_config_map": { "do_layer_norm_before": ["do_layer_norm_before", true], + "d_embed": "word_embed_proj_dim", "d_model": "hidden_size", "n_heads": "num_attention_heads", "layers": "num_hidden_layers" }, "static_weights": { "decoder.embed_tokens.weight": {"mtj": {"module": "embedding_shard/~/linear", "param": "w", "transforms": ["no_transpose", "vocab_pad"]}}, - "decoder.embed_positions.weight": {"mtj": {"module": "embedding_shard", "param": "pos_embs", "transforms": ["no_transpose", "remove_first_two_rows"]}} + "decoder.project_in.weight": {"mtj": {"module": "embedding_shard", "param": "project_in"}}, + "decoder.embed_positions.weight": {"mtj": {"module": "embedding_shard", "param": "pos_embs", "transforms": ["no_transpose", "remove_first_two_rows"]}}, + "decoder.project_out.weight": {"mtj": {"module": "projection_shard", "param": "project_out"}} }, "layer_weights": { "decoder.layers.{layer}.self_attn.q_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear", "param": "w"}}, diff --git a/tpu_mtj_backend.py b/tpu_mtj_backend.py index 75b4ee9c..b956648b 100644 --- a/tpu_mtj_backend.py +++ b/tpu_mtj_backend.py @@ -1054,7 +1054,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo # by the number of TPU cores, and fall back to one core if an even # number of TPU cores is not possible. for c in (8, 6, 4, 2, 1): - if 0 == params["n_heads"] % c == params["d_model"] % c: + if 0 == params["n_heads"] % c == params.get("d_embed", params["d_model"]) % c: params["cores_per_replica"] = c break From b1d8797a54d4e22b6d43062930ed63765586bdc5 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Thu, 12 May 2022 23:51:40 -0400 Subject: [PATCH 05/63] Allow TPU Colab to load sharded HF models --- aiserver.py | 20 +++++++++++++++++++- requirements_mtj.txt | 2 +- tpu_mtj_backend.py | 12 ++++++++++-- utils.py | 14 +++++++++++++- 4 files changed, 43 insertions(+), 5 deletions(-) diff --git a/aiserver.py b/aiserver.py index 1f105701..6c9401e2 100644 --- a/aiserver.py +++ b/aiserver.py @@ -1127,13 +1127,21 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go from transformers import __version__ as transformers_version from transformers import PreTrainedModel + from transformers import modeling_utils old_from_pretrained = PreTrainedModel.from_pretrained.__func__ @classmethod def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): + utils.num_shards = None + utils.current_shard = 0 if not args.no_aria2: utils.aria2_hook(pretrained_model_name_or_path, **kwargs) return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) PreTrainedModel.from_pretrained = new_from_pretrained + old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files + def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): + utils.num_shards = utils.get_num_shards(index_filename) + return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs) + modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files # Lazy loader import torch_lazy_loader @@ -1170,7 +1178,9 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go last_storage_key = None f = None current_offset = 0 - for key in tqdm(sorted(device_map.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)), desc="Loading model tensors"): + if utils.num_shards is not None: + utils.current_shard += 1 + for key in tqdm(sorted(device_map.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)), desc="Loading model tensors" + (f" (shard {utils.current_shard}/{utils.num_shards})" if utils.num_shards is not None else "")): storage_key = model_dict[key].key if storage_key != last_storage_key or model_dict[key].seek_offset < current_offset: last_storage_key = storage_key @@ -1560,13 +1570,21 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache") else: from transformers import PreTrainedModel + from transformers import modeling_utils old_from_pretrained = PreTrainedModel.from_pretrained.__func__ @classmethod def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): + utils.num_shards = None + utils.current_shard = 0 if not args.no_aria2: utils.aria2_hook(pretrained_model_name_or_path, **kwargs) return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) PreTrainedModel.from_pretrained = new_from_pretrained + old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files + def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): + utils.num_shards = utils.get_num_shards(index_filename) + return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs) + modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files def tpumtjgetsofttokens(): soft_tokens = None diff --git a/requirements_mtj.txt b/requirements_mtj.txt index e2a6c4e1..0f723a49 100644 --- a/requirements_mtj.txt +++ b/requirements_mtj.txt @@ -7,7 +7,7 @@ dm-haiku == 0.0.5 jax == 0.2.21 transformers >= 4.19 progressbar2 -git+https://github.com/VE-FORBRYDERNE/mesh-transformer-jax@ck-staging +git+https://github.com/VE-FORBRYDERNE/mesh-transformer-jax@ck flask Flask-SocketIO flask-cloudflared >= 0.0.5 diff --git a/tpu_mtj_backend.py b/tpu_mtj_backend.py index b956648b..2fa149d7 100644 --- a/tpu_mtj_backend.py +++ b/tpu_mtj_backend.py @@ -27,6 +27,8 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ''' +import utils + import multiprocessing from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar import progressbar @@ -1163,8 +1165,11 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo last_storage_key = None f = None current_offset = 0 - print("\n\n\nThis model has ", f"{hk.data_structures.tree_size(network.state['params']):,d}".replace(",", " "), " parameters.\n") - for key in tqdm(sorted(model_dict.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)), desc="Loading model tensors"): + if utils.current_shard == 0: + print("\n\n\nThis model has ", f"{hk.data_structures.tree_size(network.state['params']):,d}".replace(",", " "), " parameters.\n") + if utils.num_shards is not None: + utils.current_shard += 1 + for key in tqdm(sorted(model_dict.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)), desc="Loading model tensors" + (f" (shard {utils.current_shard}/{utils.num_shards})" if utils.num_shards is not None else "")): # Some model weights are used by transformers but not by MTJ. # We have to materialize these weights anyways because @@ -1225,6 +1230,9 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo np.empty(params["cores_per_replica"]), ) + if utils.num_shards is not None and utils.current_shard < utils.num_shards: + return + # Check for tensors that MTJ needs that were not provided in the # HF model for mk, mv in network.state["params"].items(): diff --git a/utils.py b/utils.py index c6eb85ec..0fdfa125 100644 --- a/utils.py +++ b/utils.py @@ -6,8 +6,11 @@ import subprocess import tempfile import requests import os +from typing import Optional vars = None +num_shards: Optional[int] = None +current_shard = 0 #==================================================================# # Decorator to prevent a function's actions from being run until @@ -133,7 +136,7 @@ def decodenewlines(txt): return txt #==================================================================# -# Downloads sharded huggingface checkpoints using aria2c if possible +# Downloads huggingface checkpoints using aria2c if possible #==================================================================# def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_dir=None, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, mirror=None, **kwargs): import transformers @@ -225,3 +228,12 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d os.rename(os.path.join(_cache_dir, "kai-tempfile." + n), os.path.join(_cache_dir, n)) with open(os.path.join(_cache_dir, n + ".json"), "w") as f: json.dump({"url": u, "etag": t}, f) + +#==================================================================# +# Given the path to a pytorch_model.bin.index.json, returns how many +# shards there are in the model +#==================================================================# +def get_num_shards(filename): + with open(filename) as f: + map_data = json.load(f) + return len(set(map_data["weight_map"].values())) From defbb53b689fa448b4848eb257046934145c1d9b Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Fri, 13 May 2022 01:03:38 -0400 Subject: [PATCH 06/63] OPT breakmodel --- aiserver.py | 49 ++++++++++---- breakmodel.py | 182 ++++++++++++++++++++++++++++++++++++++++++++++++-- utils.py | 6 ++ 3 files changed, 220 insertions(+), 17 deletions(-) diff --git a/aiserver.py b/aiserver.py index 6c9401e2..f988e6ba 100644 --- a/aiserver.py +++ b/aiserver.py @@ -274,7 +274,7 @@ class vars: recentrngm = None # If a new random game was recently generated without Submitting after, this is the memory used (as a string), otherwise this is None useprompt = False # Whether to send the full prompt with every submit action breakmodel = False # For GPU users, whether to use both system RAM and VRAM to conserve VRAM while offering speedup compared to CPU-only - bmsupported = False # Whether the breakmodel option is supported (GPT-Neo/GPT-J/XGLM only, currently) + bmsupported = False # Whether the breakmodel option is supported (GPT-Neo/GPT-J/XGLM/OPT only, currently) nobreakmodel = False # Something specifically requested Breakmodel to be disabled (For example a models config) smandelete = False # Whether stories can be deleted from inside the browser smanrename = False # Whether stories can be renamed from inside the browser @@ -391,7 +391,7 @@ def device_list(n_layers, primary=None, selected=None): def device_config(config): global breakmodel, generator import breakmodel - n_layers = config.num_layers if hasattr(config, "num_layers") else config.n_layer + n_layers = utils.num_layers(config) if(args.breakmodel_gpulayers is not None): try: breakmodel.gpu_blocks = list(map(int, args.breakmodel_gpulayers.split(','))) @@ -464,7 +464,7 @@ def device_config(config): # If all layers are on the same device, use the old GPU generation mode while(len(breakmodel.gpu_blocks) and breakmodel.gpu_blocks[-1] == 0): breakmodel.gpu_blocks.pop() - if(len(breakmodel.gpu_blocks) and breakmodel.gpu_blocks[-1] in (-1, config.num_layers if hasattr(config, "num_layers") else config.n_layer)): + if(len(breakmodel.gpu_blocks) and breakmodel.gpu_blocks[-1] in (-1, utils.num_layers(config))): vars.breakmodel = False vars.usegpu = True vars.gpu_device = len(breakmodel.gpu_blocks)-1 @@ -496,22 +496,33 @@ def move_model_to_devices(model): model.lm_head.to(breakmodel.primary_device) if(hasattr(model.transformer, 'wpe')): model.transformer.wpe.to(breakmodel.primary_device) - else: + elif(not hasattr(model.model, "decoder")): model.model.embed_tokens.to(breakmodel.primary_device) model.model.layer_norm.to(breakmodel.primary_device) model.lm_head.to(breakmodel.primary_device) model.model.embed_positions.to(breakmodel.primary_device) + else: + model.model.decoder.embed_tokens.to(breakmodel.primary_device) + if(model.model.decoder.project_in is not None): + model.model.decoder.project_in.to(breakmodel.primary_device) + if(model.model.decoder.project_out is not None): + model.model.decoder.project_out.to(breakmodel.primary_device) + model.model.decoder.embed_positions.to(breakmodel.primary_device) gc.collect() GPTNeoModel.forward = breakmodel.new_forward_neo if("GPTJModel" in globals()): GPTJModel.forward = breakmodel.new_forward_neo # type: ignore if("XGLMModel" in globals()): XGLMModel.forward = breakmodel.new_forward_xglm # type: ignore + if("OPTDecoder" in globals()): + OPTDecoder.forward = breakmodel.new_forward_opt # type: ignore generator = model.generate if(hasattr(model, "transformer")): breakmodel.move_hidden_layers(model.transformer) - else: + elif(not hasattr(model.model, "decoder")): breakmodel.move_hidden_layers(model.model, model.model.layers) + else: + breakmodel.move_hidden_layers(model.model.decoder, model.model.decoder.layers) #==================================================================# # Allow the models to override some settings @@ -911,7 +922,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go loadsettings() print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="") vars.hascuda = torch.cuda.is_available() - vars.bmsupported = vars.model_type in ("gpt_neo", "gptj", "xglm") and not vars.nobreakmodel + vars.bmsupported = vars.model_type in ("gpt_neo", "gptj", "xglm", "opt") and not vars.nobreakmodel if(args.breakmodel is not None and args.breakmodel): print("WARNING: --breakmodel is no longer supported. Breakmodel mode is now automatically enabled when --breakmodel_gpulayers is used (see --help for details).", file=sys.stderr) if(args.breakmodel_layers is not None): @@ -1123,6 +1134,10 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go globals()[m] = getattr(__import__("transformers"), m) except: pass + try: + from transformers.models.opt.modeling_opt import OPTDecoder + except: + pass import transformers.generation_utils from transformers import __version__ as transformers_version @@ -1253,8 +1268,10 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go input_ids.clamp_(max=self.config.vocab_size-1) if(hasattr(self, "transformer")): inputs_embeds = self.transformer.wte(input_ids) - else: + elif(not hasattr(model.model, "decoder")): inputs_embeds = self.model.embed_tokens(input_ids) + else: + inputs_embeds = self.model.decoder.embed_tokens(input_ids) if(vars.sp is not None): vars.sp = vars.sp.to(inputs_embeds.dtype).to(inputs_embeds.device) inputs_embeds = torch.where( @@ -1262,14 +1279,14 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go vars.sp[shifted_input_ids.clamp(min=0)], inputs_embeds, ) - if(not hasattr(self, "transformer")): + if(hasattr(self.model, "embed_scale")): inputs_embeds *= self.model.embed_scale kwargs['inputs_embeds'] = inputs_embeds return old_forward(self, *args, **kwargs) cls.forward = new_causallm_forward for cls in (GPT2LMHeadModel, GPTNeoForCausalLM): patch_causallm(cls) - for c in ("GPTJForCausalLM", "XGLMForCausalLM"): + for c in ("GPTJForCausalLM", "XGLMForCausalLM", "OPTForCausalLM"): try: patch_causallm(getattr(__import__("transformers"), c)) except: @@ -1430,12 +1447,18 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go def get_hidden_size_from_model(model): try: - return int(model.transformer.hidden_size) + return int(model.model.decoder.project_in.in_features) except: try: - return int(model.transformer.embed_dim) + return int(model.model.decoder.embed_tokens.out_features) except: - return int(model.lm_head.in_features) + try: + return int(model.transformer.hidden_size) + except: + try: + return int(model.transformer.embed_dim) + except: + return int(model.lm_head.in_features) def maybe_low_cpu_mem_usage() -> Dict[str, Any]: if(packaging.version.parse(transformers_version) < packaging.version.parse("4.11.0")): @@ -1490,7 +1513,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go import shutil shutil.move(vars.model.replace('/', '_'), "models/{}".format(vars.model.replace('/', '_'))) print("\n", flush=True) - with maybe_use_float16(), torch_lazy_loader.use_lazy_torch_load(enable=vars.lazy_load, callback=get_lazy_load_callback(model_config.num_layers if hasattr(model_config, "num_layers") else model_config.n_layer) if vars.lazy_load else None, dematerialized_modules=True): + with maybe_use_float16(), torch_lazy_loader.use_lazy_torch_load(enable=vars.lazy_load, callback=get_lazy_load_callback(utils.num_layers(model_config)) if vars.lazy_load else None, dematerialized_modules=True): if(vars.lazy_load): # torch_lazy_loader.py and low_cpu_mem_usage can't be used at the same time lowmem = {} if(os.path.isdir(vars.custmodpth)): diff --git a/breakmodel.py b/breakmodel.py index 9818e6d9..262d39bd 100644 --- a/breakmodel.py +++ b/breakmodel.py @@ -633,11 +633,11 @@ def new_forward_xglm( layer_outputs = decoder_layer( hidden_states.to(device) if breakmodel and hidden_states is not None else hidden_states, attention_mask=attention_mask.to(device) if breakmodel and attention_mask is not None else attention_mask, - encoder_hidden_states=encoder_hidden_states.to(device) if encoder_hidden_states is not None else None, - encoder_attention_mask=encoder_attention_mask.to(device) if encoder_attention_mask is not None else None, - layer_head_mask=((head_mask[idx].to(device) if head_mask[idx] is not None else None) if head_mask is not None else None), + encoder_hidden_states=encoder_hidden_states.to(device) if breakmodel and encoder_hidden_states is not None else encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask.to(device) if breakmodel and encoder_attention_mask is not None else encoder_attention_mask, + layer_head_mask=((head_mask[idx].to(device) if breakmodel and head_mask[idx] is not None else head_mask[idx]) if head_mask is not None else None), cross_attn_layer_head_mask=( - (cross_attn_head_mask[idx].to(device) if cross_attn_head_mask[idx] is not None else None) if cross_attn_head_mask is not None else None + (cross_attn_head_mask[idx].to(device) if breakmodel and cross_attn_head_mask[idx] is not None else cross_attn_head_mask[idx]) if cross_attn_head_mask is not None else None ), past_key_value=tuple(v.to(device) for v in past_key_value if v is not None) if breakmodel and past_key_value is not None and i >= ram_blocks and len(past_key_value) and past_key_value[0].device.index != device else past_key_value, output_attentions=output_attentions, @@ -686,3 +686,177 @@ def new_forward_xglm( attentions=all_self_attns, cross_attentions=all_cross_attentions, ) + + +def new_forward_opt( + self, + input_ids=None, + attention_mask=None, + head_mask=None, + past_key_values=None, + inputs_embeds=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, +): + assert len(gpu_blocks) <= torch.cuda.device_count() + assert sum(gpu_blocks) <= len(self.layers) + ram_blocks = len(self.layers) - sum(gpu_blocks) + cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks)) + + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if inputs_embeds is None: + if breakmodel: + input_ids = input_ids.to(primary_device) + inputs_embeds = self.embed_tokens(input_ids) + + # embed positions + if breakmodel: + inputs_embeds = inputs_embeds.to(primary_device) + if attention_mask is None: + attention_mask = torch.ones(inputs_embeds.shape[:2], dtype=torch.bool, device=inputs_embeds.device) + + positions = self.embed_positions(attention_mask)[:, past_key_values_length:, :] + if breakmodel: + positions = positions.to(primary_device) + + attention_mask = self._prepare_decoder_attention_mask( + attention_mask, input_shape, inputs_embeds, past_key_values_length + ) + + if self.project_in is not None: + inputs_embeds = self.project_in(inputs_embeds) + + hidden_states = inputs_embeds + positions + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + if breakmodel and ram_blocks: + copystream = torch.cuda.Stream(device=primary_device, priority=-1) + + # check if head_mask has a correct number of layers specified if desired + for attn_mask, mask_name in zip([head_mask], ["head_mask"]): + if attn_mask is not None: + if attn_mask.size()[0] != (len(self.layers)): + raise ValueError( + f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" + f" {head_mask.size()[0]}." + ) + + for idx, decoder_layer in enumerate(self.layers): + i = idx + if breakmodel: + if i in range(ram_blocks): + index1 = (i+1)%ram_blocks + for param1,param2 in zip(self.layers[index1].parameters(),self.layers[(i-1)%ram_blocks].parameters()): + param1.data = param2.data + for param1,param2 in zip(self.layers[index1].parameters(),self.extrastorage[index1].parameters()): + with torch.cuda.stream(copystream): + torch.cuda.comm.broadcast(param2.data,out = [param1.data]) + + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + dropout_probability = random.uniform(0, 1) + if self.training and (dropout_probability < self.layerdrop): + continue + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + attention_mask, + head_mask[idx] if head_mask is not None else None, + None, + ) + else: + if breakmodel: + device = primary_device if i < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, i - ram_blocks) + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask.to(device) if breakmodel and attention_mask is not None else attention_mask, + layer_head_mask=((head_mask[idx].to(device) if breakmodel and head_mask[idx] is not None else head_mask[idx]) if head_mask is not None else None), + past_key_value=tuple(v.to(device) for v in past_key_value if v is not None) if breakmodel and past_key_value is not None and i >= ram_blocks and len(past_key_value) and past_key_value[0].device.index != device else past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if breakmodel: + if i in range(ram_blocks): + torch.cuda.synchronize() + torch.cuda.empty_cache() + + if breakmodel: + if ram_blocks: + del copystream + torch.cuda.empty_cache() + hidden_states = hidden_states.to(primary_device) + if self.project_out is not None: + hidden_states = self.project_out(hidden_states) + if breakmodel: + hidden_states = hidden_states.to(primary_device) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) diff --git a/utils.py b/utils.py index 0fdfa125..ea3cab94 100644 --- a/utils.py +++ b/utils.py @@ -135,6 +135,12 @@ def decodenewlines(txt): return txt.replace("", '\n') return txt +#==================================================================# +# Returns number of layers given an HF model config +#==================================================================# +def num_layers(config): + return config.num_layers if hasattr(config, "num_layers") else config.n_layer if hasattr(config, "n_layer") else config.num_hidden_layers + #==================================================================# # Downloads huggingface checkpoints using aria2c if possible #==================================================================# From 29bb3f569b550dbac4dfaa999c641115ecafec64 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Fri, 13 May 2022 01:37:17 -0400 Subject: [PATCH 07/63] Fix a bug in OPTForCausalLM where self.lm_head is the wrong size --- aiserver.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/aiserver.py b/aiserver.py index f988e6ba..102b24f5 100644 --- a/aiserver.py +++ b/aiserver.py @@ -1293,6 +1293,25 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go pass + # Fix a bug in OPTForCausalLM where self.lm_head is the wrong size + if(transformers_version == "4.19.0"): + try: + from transformers import OPTForCausalLM, OPTModel + except ImportError: + pass + else: + # This is the same as the original __init__ but with + # config.hidden_size + # replaced with + # config.word_embed_proj_dim + def new_init(self, config): + super(OPTForCausalLM, self).__init__(config) + self.model = OPTModel(config) + self.lm_head = torch.nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False) + self.post_init() + OPTForCausalLM.__init__ = new_init + + # 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 55079f672a0002513c0c61caf7d2fb5f35289f1d Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Fri, 13 May 2022 01:51:55 -0400 Subject: [PATCH 08/63] Fix typo in soft prompt patching code --- aiserver.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/aiserver.py b/aiserver.py index 102b24f5..150c8b68 100644 --- a/aiserver.py +++ b/aiserver.py @@ -1268,7 +1268,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go input_ids.clamp_(max=self.config.vocab_size-1) if(hasattr(self, "transformer")): inputs_embeds = self.transformer.wte(input_ids) - elif(not hasattr(model.model, "decoder")): + elif(not hasattr(self.model, "decoder")): inputs_embeds = self.model.embed_tokens(input_ids) else: inputs_embeds = self.model.decoder.embed_tokens(input_ids) @@ -1279,7 +1279,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go vars.sp[shifted_input_ids.clamp(min=0)], inputs_embeds, ) - if(hasattr(self.model, "embed_scale")): + if(hasattr(self, "model") and hasattr(self.model, "embed_scale")): inputs_embeds *= self.model.embed_scale kwargs['inputs_embeds'] = inputs_embeds return old_forward(self, *args, **kwargs) From 8376f12e2135bc8ae64c3bb395d39aac67114b81 Mon Sep 17 00:00:00 2001 From: Henk Date: Fri, 13 May 2022 10:44:12 +0200 Subject: [PATCH 09/63] Add NS mode OPT supports newlines, but it also needs some of the behavior we use in S mode. NS mode is a more limited version of S mode that still handles the token, but instead of replacing it with a new line we replace it empty and newlines are not converted. In future if your Fairseq style model has newline support use NS mode, while if it needs artifically inserted newlines use S mode. This also means that people finetuning fairseq models to include newlines might benefit from testing their models on ns mode. --- aiserver.py | 6 ++++-- utils.py | 2 ++ 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/aiserver.py b/aiserver.py index 0dc19c5a..ed6f4d9f 100644 --- a/aiserver.py +++ b/aiserver.py @@ -144,7 +144,7 @@ optlist = [ ["OPT 6.7B", "facebook/opt-6.7b", "16GB"], ["OPT 2.7B", "facebook/opt-2.7b", "8GB"], ["OPT 1.3B", "facebook/opt-1.3b", "4GB"], - ["OPT 355M", "facebook/opt-350m", "2GB"], + ["OPT 350M", "facebook/opt-350m", "2GB"], ["OPT 125M", "facebook/opt-125m", "1GB"], ["Return to Main Menu", "Return", ""], ] @@ -529,6 +529,8 @@ def loadmodelsettings(): js = {} if vars.model_type == "xglm" or js.get("compat", "j") == "fairseq_lm": vars.newlinemode = "s" # Default to newline mode if using XGLM + if vars.model_type == "opt": + vars.newlinemode = "ns" # Default to newline mode if using XGLM vars.modelconfig = js if("badwordsids" in js): vars.badwordsids = js["badwordsids"] @@ -1345,7 +1347,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go kwargs["logits_warper"] = new_get_logits_warper( beams=1, ) - if(vars.newlinemode == "s"): + if(vars.newlinemode == "s") or (vars.newlinemode == "ns"): kwargs["eos_token_id"] = -1 kwargs.setdefault("pad_token_id", 2) return new_sample.old_sample(self, *args, **kwargs) diff --git a/utils.py b/utils.py index c6eb85ec..52cc7c6f 100644 --- a/utils.py +++ b/utils.py @@ -130,6 +130,8 @@ def encodenewlines(txt): def decodenewlines(txt): if(vars.newlinemode == "s"): return txt.replace("", '\n') + if(vars.newlinemode == "ns"): + return txt.replace("", '') return txt #==================================================================# From d5fa782483aa2948c1b58a188e5226b32d804527 Mon Sep 17 00:00:00 2001 From: Henk Date: Fri, 13 May 2022 10:53:19 +0200 Subject: [PATCH 10/63] NS Mode (comment fix) --- aiserver.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aiserver.py b/aiserver.py index ed6f4d9f..3c96e80b 100644 --- a/aiserver.py +++ b/aiserver.py @@ -530,7 +530,7 @@ def loadmodelsettings(): if vars.model_type == "xglm" or js.get("compat", "j") == "fairseq_lm": vars.newlinemode = "s" # Default to newline mode if using XGLM if vars.model_type == "opt": - vars.newlinemode = "ns" # Default to newline mode if using XGLM + vars.newlinemode = "ns" # Handle but don't convert newlines if using Fairseq models that have newlines trained in them vars.modelconfig = js if("badwordsids" in js): vars.badwordsids = js["badwordsids"] From 1200173386a0f5fb9eb06d30f3b3a7ab5ab1538c Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Fri, 13 May 2022 10:45:28 -0400 Subject: [PATCH 11/63] Custom badwords for OPT Generated using: ``` import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("facebook/opt-350m", fast=False) badwordsids_opt = [[v] for k, v in tokenizer.vocab.items() if any(c in k for c in "<>[]")] ``` --- aiserver.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/aiserver.py b/aiserver.py index 150c8b68..bbe5ec9a 100644 --- a/aiserver.py +++ b/aiserver.py @@ -240,6 +240,7 @@ class vars: # badwords = [] # Array of str/chr values that should be removed from output badwordsids = [[13460], [6880], [50256], [42496], [4613], [17414], [22039], [16410], [27], [29], [38430], [37922], [15913], [24618], [28725], [58], [47175], [36937], [26700], [12878], [16471], [37981], [5218], [29795], [13412], [45160], [3693], [49778], [4211], [20598], [36475], [33409], [44167], [32406], [29847], [29342], [42669], [685], [25787], [7359], [3784], [5320], [33994], [33490], [34516], [43734], [17635], [24293], [9959], [23785], [21737], [28401], [18161], [26358], [32509], [1279], [38155], [18189], [26894], [6927], [14610], [23834], [11037], [14631], [26933], [46904], [22330], [25915], [47934], [38214], [1875], [14692], [41832], [13163], [25970], [29565], [44926], [19841], [37250], [49029], [9609], [44438], [16791], [17816], [30109], [41888], [47527], [42924], [23984], [49074], [33717], [31161], [49082], [30138], [31175], [12240], [14804], [7131], [26076], [33250], [3556], [38381], [36338], [32756], [46581], [17912], [49146]] # Tokenized array of badwords used to prevent AI artifacting badwordsids_neox = [[0], [1], [44162], [9502], [12520], [31841], [36320], [49824], [34417], [6038], [34494], [24815], [26635], [24345], [3455], [28905], [44270], [17278], [32666], [46880], [7086], [43189], [37322], [17778], [20879], [49821], [3138], [14490], [4681], [21391], [26786], [43134], [9336], [683], [48074], [41256], [19181], [29650], [28532], [36487], [45114], [46275], [16445], [15104], [11337], [1168], [5647], [29], [27482], [44965], [43782], [31011], [42944], [47389], [6334], [17548], [38329], [32044], [35487], [2239], [34761], [7444], [1084], [12399], [18990], [17636], [39083], [1184], [35830], [28365], [16731], [43467], [47744], [1138], [16079], [40116], [45564], [18297], [42368], [5456], [18022], [42696], [34476], [23505], [23741], [39334], [37944], [45382], [38709], [33440], [26077], [43600], [34418], [36033], [6660], [48167], [48471], [15775], [19884], [41533], [1008], [31053], [36692], [46576], [20095], [20629], [31759], [46410], [41000], [13488], [30952], [39258], [16160], [27655], [22367], [42767], [43736], [49694], [13811], [12004], [46768], [6257], [37471], [5264], [44153], [33805], [20977], [21083], [25416], [14277], [31096], [42041], [18331], [33376], [22372], [46294], [28379], [38475], [1656], [5204], [27075], [50001], [16616], [11396], [7748], [48744], [35402], [28120], [41512], [4207], [43144], [14767], [15640], [16595], [41305], [44479], [38958], [18474], [22734], [30522], [46267], [60], [13976], [31830], [48701], [39822], [9014], [21966], [31422], [28052], [34607], [2479], [3851], [32214], [44082], [45507], [3001], [34368], [34758], [13380], [38363], [4299], [46802], [30996], [12630], [49236], [7082], [8795], [5218], [44740], [9686], [9983], [45301], [27114], [40125], [1570], [26997], [544], [5290], [49193], [23781], [14193], [40000], [2947], [43781], [9102], [48064], [42274], [18772], [49384], [9884], [45635], [43521], [31258], [32056], [47686], [21760], [13143], [10148], [26119], [44308], [31379], [36399], [23983], [46694], [36134], [8562], [12977], [35117], [28591], [49021], [47093], [28653], [29013], [46468], [8605], [7254], [25896], [5032], [8168], [36893], [38270], [20499], [27501], [34419], [29547], [28571], [36586], [20871], [30537], [26842], [21375], [31148], [27618], [33094], [3291], [31789], [28391], [870], [9793], [41361], [47916], [27468], [43856], [8850], [35237], [15707], [47552], [2730], [41449], [45488], [3073], [49806], [21938], [24430], [22747], [20924], [46145], [20481], [20197], [8239], [28231], [17987], [42804], [47269], [29972], [49884], [21382], [46295], [36676], [34616], [3921], [26991], [27720], [46265], [654], [9855], [40354], [5291], [34904], [44342], [2470], [14598], [880], [19282], [2498], [24237], [21431], [16369], [8994], [44524], [45662], [13663], [37077], [1447], [37786], [30863], [42854], [1019], [20322], [4398], [12159], [44072], [48664], [31547], [18736], [9259], [31], [16354], [21810], [4357], [37982], [5064], [2033], [32871], [47446], [62], [22158], [37387], [8743], [47007], [17981], [11049], [4622], [37916], [36786], [35138], [29925], [14157], [18095], [27829], [1181], [22226], [5709], [4725], [30189], [37014], [1254], [11380], [42989], [696], [24576], [39487], [30119], [1092], [8088], [2194], [9899], [14412], [21828], [3725], [13544], [5180], [44679], [34398], [3891], [28739], [14219], [37594], [49550], [11326], [6904], [17266], [5749], [10174], [23405], [9955], [38271], [41018], [13011], [48392], [36784], [24254], [21687], [23734], [5413], [41447], [45472], [10122], [17555], [15830], [47384], [12084], [31350], [47940], [11661], [27988], [45443], [905], [49651], [16614], [34993], [6781], [30803], [35869], [8001], [41604], [28118], [46462], [46762], [16262], [17281], [5774], [10943], [5013], [18257], [6750], [4713], [3951], [11899], [38791], [16943], [37596], [9318], [18413], [40473], [13208], [16375]] + badwordsids_opt = [[44717], [46613], [48513], [49923], [50185], [48755], [8488], [43303], [49659], [48601], [49817], [45405], [48742], [49925], [47720], [11227], [48937], [48784], [50017], [42248], [49310], [48082], [49895], [50025], [49092], [49007], [8061], [44226], [0], [742], [28578], [15698], [49784], [46679], [39365], [49281], [49609], [48081], [48906], [46161], [48554], [49670], [48677], [49721], [49632], [48610], [48462], [47457], [10975], [46077], [28696], [48709], [43839], [49798], [49154], [48203], [49625], [48395], [50155], [47161], [49095], [48833], [49420], [49666], [48443], [22176], [49242], [48651], [49138], [49750], [40389], [48021], [21838], [49070], [45333], [40862], [1], [49915], [33525], [49858], [50254], [44403], [48992], [48872], [46117], [49853], [47567], [50206], [41552], [50068], [48999], [49703], [49940], [49329], [47620], [49868], [49962], [2], [44082], [50236], [31274], [50260], [47052], [42645], [49177], [17523], [48691], [49900], [49069], [49358], [48794], [47529], [46479], [48457], [646], [49910], [48077], [48935], [46386], [48902], [49151], [48759], [49803], [45587], [48392], [47789], [48654], [49836], [49230], [48188], [50264], [46844], [44690], [48505], [50161], [27779], [49995], [41833], [50154], [49097], [48520], [50018], [8174], [50084], [49366], [49526], [50193], [7479], [49982], [3]] deletewi = None # Temporary storage for UID to delete wirmvwhtsp = False # Whether to remove leading whitespace from WI entries widepth = 3 # How many historical actions to scan for WI hits @@ -917,6 +918,9 @@ if(vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMe print("WARNING: No model type detected, assuming Neo (If this is a GPT2 model use the other menu option or --model GPT2Custom)") vars.model_type = "gpt_neo" + if(vars.model_type == "opt"): + vars.badwordsids = vars.badwordsids_opt + if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): loadmodelsettings() loadsettings() @@ -1730,7 +1734,7 @@ else: if vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") and (not vars.custmodpth or not os.path.isdir(vars.custmodpth)): raise FileNotFoundError(f"The specified model path {repr(vars.custmodpth)} is not the path to a valid folder") import tpu_mtj_backend - if(vars.model == "TPUMeshTransformerGPTNeoX"): + if(vars.model == "TPUMeshTransformerGPTNeoX" or vars.model_type == "opt"): tpu_mtj_backend.pad_token_id = 1 tpu_mtj_backend.vars = vars tpu_mtj_backend.warper_callback = tpumtjgenerate_warper_callback From a051bf4397fb1e44d0ad9d5597442ea16b369d71 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Fri, 13 May 2022 10:45:57 -0400 Subject: [PATCH 12/63] OPT breakmodel bug fix --- breakmodel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/breakmodel.py b/breakmodel.py index 262d39bd..eb49e669 100644 --- a/breakmodel.py +++ b/breakmodel.py @@ -816,7 +816,7 @@ def new_forward_opt( if breakmodel: device = primary_device if i < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, i - ram_blocks) layer_outputs = decoder_layer( - hidden_states, + hidden_states.to(device) if breakmodel and hidden_states is not None else hidden_states, attention_mask=attention_mask.to(device) if breakmodel and attention_mask is not None else attention_mask, layer_head_mask=((head_mask[idx].to(device) if breakmodel and head_mask[idx] is not None else head_mask[idx]) if head_mask is not None else None), past_key_value=tuple(v.to(device) for v in past_key_value if v is not None) if breakmodel and past_key_value is not None and i >= ram_blocks and len(past_key_value) and past_key_value[0].device.index != device else past_key_value, From 91d367244651fc10b54fe71682a4d55a36668f62 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Fri, 13 May 2022 17:00:10 -0400 Subject: [PATCH 13/63] Proper progress bar for aria2 downloads --- aiserver.py | 6 ++++++ utils.py | 55 +++++++++++++++++++++++++++++++++++++++++++---------- 2 files changed, 51 insertions(+), 10 deletions(-) diff --git a/aiserver.py b/aiserver.py index a289409c..74adef38 100644 --- a/aiserver.py +++ b/aiserver.py @@ -16,6 +16,9 @@ os.environ['EVENTLET_THREADPOOL_SIZE'] = '1' os.environ['TOKENIZERS_PARALLELISM'] = 'false' from eventlet import tpool +import logging +logging.getLogger("urllib3").setLevel(logging.ERROR) + from os import path, getcwd import time import re @@ -808,6 +811,7 @@ parser.add_argument("--ngrok", action='store_true', help="Optimizes KoboldAI for parser.add_argument("--localtunnel", action='store_true', help="Optimizes KoboldAI for Remote Play using Localtunnel") parser.add_argument("--host", action='store_true', help="Optimizes KoboldAI for Remote Play without using a proxy service") parser.add_argument("--port", type=int, help="Specify the port on which the application will be joinable") +parser.add_argument("--aria2_port", type=int, help="Specify the port on which aria2's RPC interface will be open if aria2 is installed (defaults to 6799)") parser.add_argument("--model", help="Specify the Model Type to skip the Menu") parser.add_argument("--path", help="Specify the Path for local models (For model NeoCustom or GPT2Custom)") parser.add_argument("--revision", help="Specify the model revision for huggingface models (can be a git branch/tag name or a git commit hash)") @@ -867,6 +871,8 @@ if args.cpu: vars.smandelete = vars.host == args.override_delete vars.smanrename = vars.host == args.override_rename +vars.aria2_port = args.aria2_port or 6799 + # Select a model to run if args.model: print("Welcome to KoboldAI!\nYou have selected the following Model:", vars.model) diff --git a/utils.py b/utils.py index be064855..9565eaa4 100644 --- a/utils.py +++ b/utils.py @@ -5,6 +5,9 @@ import json import subprocess import tempfile import requests +import requests.adapters +import time +from tqdm.auto import tqdm import os from typing import Optional @@ -202,6 +205,7 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d if not urls: return etags = [h.get("X-Linked-Etag") or h.get("ETag") for u in urls for h in [requests.head(u, headers=headers, allow_redirects=False, proxies=proxies, timeout=10).headers]] + headers = [requests.head(u, headers=headers, allow_redirects=True, proxies=proxies, timeout=10).headers for u in urls] filenames = [transformers.file_utils.url_to_filename(u, t) for u, t in zip(urls, etags)] for n in filenames: path = os.path.join(_cache_dir, "kai-tempfile." + n + ".aria2") @@ -217,18 +221,49 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d path = os.path.join(_cache_dir, n) if os.path.exists(path): os.remove(path) + total_length = sum(int(h["Content-Length"]) for h in headers) + lengths = {} aria2_config = "\n".join(f"{u}\n out=kai-tempfile.{n}" for u, n in zip(urls, filenames)).encode() - with tempfile.NamedTemporaryFile("w+b", delete=False) as f: - f.write(aria2_config) - f.flush() - p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "--disable-ipv6", "--file-allocation=trunc", "--allow-overwrite", "--auto-file-renaming", "false", "-d", _cache_dir, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {token}'"] if use_auth_token else []), stdout=subprocess.PIPE, stderr=subprocess.STDOUT) - for line in p.stdout: - print(line.decode(), end="", flush=True) - path = f.name + s = requests.Session() + s.mount("http://", requests.adapters.HTTPAdapter(max_retries=requests.adapters.Retry(total=120, backoff_factor=1))) + bar = None + secret = os.urandom(17).hex() try: - os.remove(path) - except OSError: - pass + with tempfile.NamedTemporaryFile("w+b", delete=False) as f: + f.write(aria2_config) + f.flush() + p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "--enable-rpc=true", f"--rpc-secret={secret}", "--rpc-listen-port", str(vars.aria2_port), "--disable-ipv6", "--file-allocation=trunc", "--allow-overwrite", "--auto-file-renaming=false", "-d", _cache_dir, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {token}'"] if use_auth_token else []), stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + while p.poll() is None: + r = s.post(f"http://localhost:{vars.aria2_port}/jsonrpc", json={"jsonrpc": "2.0", "id": "kai", "method": "aria2.tellActive", "params": [f"token:{secret}"]}).json()["result"] + if not r: + s.close() + if bar is not None: + bar.n = bar.total + bar.close() + p.terminate() + break + if bar is None: + bar = tqdm(total=total_length, desc=f"[aria2] Downloading model", unit="B", unit_scale=True, unit_divisor=1000) + visited = set() + for x in r: + filename = x["files"][0]["path"] + lengths[filename] = (int(x["completedLength"]), int(x["totalLength"])) + visited.add(filename) + for k, v in lengths.items(): + if k not in visited: + lengths[k] = (v[1], v[1]) + bar.n = sum(v[0] for v in lengths.values()) + bar.update() + time.sleep(0.1) + path = f.name + except Exception as e: + p.terminate() + raise e + finally: + try: + os.remove(path) + except OSError: + pass code = p.wait() if code: raise OSError(f"aria2 exited with exit code {code}") From f9f1a5f3a90b27eefabf750c19afe934a0ae01a7 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Fri, 13 May 2022 17:37:45 -0400 Subject: [PATCH 14/63] Make sure tqdm progress bars display properly in Colab --- aiserver.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/aiserver.py b/aiserver.py index 74adef38..23c8f5e1 100644 --- a/aiserver.py +++ b/aiserver.py @@ -57,6 +57,16 @@ if lupa.LUA_VERSION[:2] != (5, 4): print(f"Please install lupa==1.10. You have lupa {lupa.__version__}.", file=sys.stderr) +# Make sure tqdm progress bars display properly in Colab +from tqdm.auto import tqdm +old_init = tqdm.__init__ +def new_init(self, *args, **kwargs): + old_init(self, *args, **kwargs) + if(self.ncols == 0 and kwargs.get("ncols") != 0): + self.ncols = 99 +tqdm.__init__ = new_init + + #==================================================================# # Variables & Storage #==================================================================# From 0c5ca5261e33461f132ae012a670aeb08510c548 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Fri, 13 May 2022 23:32:16 -0400 Subject: [PATCH 15/63] Loading a sharded model will now display only one progress bar --- aiserver.py | 29 ++++++++++++++++++++++++++++- tpu_mtj_backend.py | 24 +++++++++++++++++++++++- utils.py | 16 ++++++++++++++++ 3 files changed, 67 insertions(+), 2 deletions(-) diff --git a/aiserver.py b/aiserver.py index 23c8f5e1..f3e1c4ed 100644 --- a/aiserver.py +++ b/aiserver.py @@ -1170,6 +1170,10 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): utils.num_shards = None utils.current_shard = 0 + utils.from_pretrained_model_name = pretrained_model_name_or_path + utils.from_pretrained_index_filename = None + utils.from_pretrained_kwargs = kwargs + utils.bar = None if not args.no_aria2: utils.aria2_hook(pretrained_model_name_or_path, **kwargs) return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) @@ -1177,6 +1181,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): utils.num_shards = utils.get_num_shards(index_filename) + utils.from_pretrained_index_filename = index_filename return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs) modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files @@ -1196,6 +1201,10 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go ram_blocks = gpu_blocks = cumulative_gpu_blocks = None def lazy_load_callback(model_dict, f, **_): + if lazy_load_callback.nested: + return + lazy_load_callback.nested = True + device_map = {} for _key, spec in lazy_load_spec.get("layer_weights", {}).items(): @@ -1210,6 +1219,13 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go if isinstance(value, torch_lazy_loader.LazyTensor) and key not in device_map: device_map[key] = vars.gpu_device if vars.hascuda and vars.usegpu else "cpu" + if utils.num_shards is None or utils.current_shard == 0: + if utils.num_shards is not None: + num_tensors = len(utils.get_sharded_checkpoint_num_tensors(utils.from_pretrained_model_name, utils.from_pretrained_index_filename, **utils.from_pretrained_kwargs)) + else: + num_tensors = len(device_map) + utils.bar = tqdm(total=num_tensors, desc="Loading model tensors") + with zipfile.ZipFile(f, "r") as z: try: last_storage_key = None @@ -1217,7 +1233,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go current_offset = 0 if utils.num_shards is not None: utils.current_shard += 1 - for key in tqdm(sorted(device_map.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)), desc="Loading model tensors" + (f" (shard {utils.current_shard}/{utils.num_shards})" if utils.num_shards is not None else "")): + for key in sorted(device_map.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)): storage_key = model_dict[key].key if storage_key != last_storage_key or model_dict[key].seek_offset < current_offset: last_storage_key = storage_key @@ -1241,10 +1257,16 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go model_dict[key] = model_dict[key].to(device) #print("OK", flush=True) current_offset += nbytes + utils.bar.update(1) finally: + if utils.num_shards is None or utils.current_shard >= utils.num_shards: + utils.bar.close() + utils.bar = None + lazy_load_callback.nested = False if isinstance(f, zipfile.ZipExtFile): f.close() + lazy_load_callback.nested = False return lazy_load_callback lazy_load_config_path = os.path.join("maps", vars.model_type + ".json") @@ -1640,6 +1662,10 @@ else: def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): utils.num_shards = None utils.current_shard = 0 + utils.from_pretrained_model_name = pretrained_model_name_or_path + utils.from_pretrained_index_filename = None + utils.from_pretrained_kwargs = kwargs + utils.bar = None if not args.no_aria2: utils.aria2_hook(pretrained_model_name_or_path, **kwargs) return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) @@ -1647,6 +1673,7 @@ else: old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): utils.num_shards = utils.get_num_shards(index_filename) + utils.from_pretrained_index_filename = index_filename return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs) modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files diff --git a/tpu_mtj_backend.py b/tpu_mtj_backend.py index 2fa149d7..3d7bf735 100644 --- a/tpu_mtj_backend.py +++ b/tpu_mtj_backend.py @@ -1160,6 +1160,9 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo import functools def callback(model_dict, f, **_): + if callback.nested: + return + callback.nested = True with zipfile.ZipFile(f, "r") as z: try: last_storage_key = None @@ -1167,9 +1170,17 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo current_offset = 0 if utils.current_shard == 0: print("\n\n\nThis model has ", f"{hk.data_structures.tree_size(network.state['params']):,d}".replace(",", " "), " parameters.\n") + + if utils.num_shards is None or utils.current_shard == 0: + if utils.num_shards is not None: + num_tensors = len(utils.get_sharded_checkpoint_num_tensors(utils.from_pretrained_model_name, utils.from_pretrained_index_filename, **utils.from_pretrained_kwargs)) + else: + num_tensors = len(model_dict) + utils.bar = tqdm(total=num_tensors, desc="Loading model tensors") + if utils.num_shards is not None: utils.current_shard += 1 - for key in tqdm(sorted(model_dict.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)), desc="Loading model tensors" + (f" (shard {utils.current_shard}/{utils.num_shards})" if utils.num_shards is not None else "")): + for key in sorted(model_dict.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)): # Some model weights are used by transformers but not by MTJ. # We have to materialize these weights anyways because @@ -1178,6 +1189,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo # tensors, which don't take up any actual CPU or TPU memory. if key not in model_spec: model_dict[key] = torch.empty(model_dict[key].shape, dtype=model_dict[key].dtype, device="meta") + utils.bar.update(1) continue storage_key = model_dict[key].key @@ -1230,6 +1242,8 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo np.empty(params["cores_per_replica"]), ) + utils.bar.update(1) + if utils.num_shards is not None and utils.current_shard < utils.num_shards: return @@ -1250,9 +1264,17 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo error = f"{mk} {pk} could not be found in the model checkpoint" print("\n\nERROR: " + error, file=sys.stderr) raise RuntimeError(error) + except: + import traceback + traceback.print_exc() finally: + if utils.num_shards is None or utils.current_shard >= utils.num_shards: + utils.bar.close() + utils.bar = None + callback.nested = False if isinstance(f, zipfile.ZipExtFile): f.close() + callback.nested = False if os.path.isdir(vars.model.replace('/', '_')): import shutil diff --git a/utils.py b/utils.py index 9565eaa4..0e4299de 100644 --- a/utils.py +++ b/utils.py @@ -9,11 +9,16 @@ import requests.adapters import time from tqdm.auto import tqdm import os +import itertools from typing import Optional vars = None num_shards: Optional[int] = None current_shard = 0 +from_pretrained_model_name = "" +from_pretrained_index_filename: Optional[str] = None +from_pretrained_kwargs = {} +bar = None #==================================================================# # Decorator to prevent a function's actions from being run until @@ -280,3 +285,14 @@ def get_num_shards(filename): with open(filename) as f: map_data = json.load(f) return len(set(map_data["weight_map"].values())) + +#==================================================================# +# Given the name/path of a sharded model and the path to a +# pytorch_model.bin.index.json, returns a list of weight names in the +# sharded model. Requires lazy loader to be enabled to work properl +#==================================================================# +def get_sharded_checkpoint_num_tensors(pretrained_model_name_or_path, filename, cache_dir=None, force_download=False, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, mirror=None, **kwargs): + import transformers.modeling_utils + import torch + shard_paths, _ = transformers.modeling_utils.get_checkpoint_shard_files(pretrained_model_name_or_path, filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent, revision=revision, mirror=mirror) + return list(itertools.chain(*(torch.load(p, map_location="cpu").keys() for p in shard_paths))) From 1476e76cfcdc2244bd46efa8c34f43d63c88e6b6 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Sat, 14 May 2022 00:45:43 -0400 Subject: [PATCH 16/63] Copy fp16 model files instead of resaving them --- aiserver.py | 41 +++++++++++++++++++++++++++++++++++++++-- 1 file changed, 39 insertions(+), 2 deletions(-) diff --git a/aiserver.py b/aiserver.py index f3e1c4ed..e8845272 100644 --- a/aiserver.py +++ b/aiserver.py @@ -254,6 +254,7 @@ class vars: badwordsids = [[13460], [6880], [50256], [42496], [4613], [17414], [22039], [16410], [27], [29], [38430], [37922], [15913], [24618], [28725], [58], [47175], [36937], [26700], [12878], [16471], [37981], [5218], [29795], [13412], [45160], [3693], [49778], [4211], [20598], [36475], [33409], [44167], [32406], [29847], [29342], [42669], [685], [25787], [7359], [3784], [5320], [33994], [33490], [34516], [43734], [17635], [24293], [9959], [23785], [21737], [28401], [18161], [26358], [32509], [1279], [38155], [18189], [26894], [6927], [14610], [23834], [11037], [14631], [26933], [46904], [22330], [25915], [47934], [38214], [1875], [14692], [41832], [13163], [25970], [29565], [44926], [19841], [37250], [49029], [9609], [44438], [16791], [17816], [30109], [41888], [47527], [42924], [23984], [49074], [33717], [31161], [49082], [30138], [31175], [12240], [14804], [7131], [26076], [33250], [3556], [38381], [36338], [32756], [46581], [17912], [49146]] # Tokenized array of badwords used to prevent AI artifacting badwordsids_neox = [[0], [1], [44162], [9502], [12520], [31841], [36320], [49824], [34417], [6038], [34494], [24815], [26635], [24345], [3455], [28905], [44270], [17278], [32666], [46880], [7086], [43189], [37322], [17778], [20879], [49821], [3138], [14490], [4681], [21391], [26786], [43134], [9336], [683], [48074], [41256], [19181], [29650], [28532], [36487], [45114], [46275], [16445], [15104], [11337], [1168], [5647], [29], [27482], [44965], [43782], [31011], [42944], [47389], [6334], [17548], [38329], [32044], [35487], [2239], [34761], [7444], [1084], [12399], [18990], [17636], [39083], [1184], [35830], [28365], [16731], [43467], [47744], [1138], [16079], [40116], [45564], [18297], [42368], [5456], [18022], [42696], [34476], [23505], [23741], [39334], [37944], [45382], [38709], [33440], [26077], [43600], [34418], [36033], [6660], [48167], [48471], [15775], [19884], [41533], [1008], [31053], [36692], [46576], [20095], [20629], [31759], [46410], [41000], [13488], [30952], [39258], [16160], [27655], [22367], [42767], [43736], [49694], [13811], [12004], [46768], [6257], [37471], [5264], [44153], [33805], [20977], [21083], [25416], [14277], [31096], [42041], [18331], [33376], [22372], [46294], [28379], [38475], [1656], [5204], [27075], [50001], [16616], [11396], [7748], [48744], [35402], [28120], [41512], [4207], [43144], [14767], [15640], [16595], [41305], [44479], [38958], [18474], [22734], [30522], [46267], [60], [13976], [31830], [48701], [39822], [9014], [21966], [31422], [28052], [34607], [2479], [3851], [32214], [44082], [45507], [3001], [34368], [34758], [13380], [38363], [4299], [46802], [30996], [12630], [49236], [7082], [8795], [5218], [44740], [9686], [9983], [45301], [27114], [40125], [1570], [26997], [544], [5290], [49193], [23781], [14193], [40000], [2947], [43781], [9102], [48064], [42274], [18772], [49384], [9884], [45635], [43521], [31258], [32056], [47686], [21760], [13143], [10148], [26119], [44308], [31379], [36399], [23983], [46694], [36134], [8562], [12977], [35117], [28591], [49021], [47093], [28653], [29013], [46468], [8605], [7254], [25896], [5032], [8168], [36893], [38270], [20499], [27501], [34419], [29547], [28571], [36586], [20871], [30537], [26842], [21375], [31148], [27618], [33094], [3291], [31789], [28391], [870], [9793], [41361], [47916], [27468], [43856], [8850], [35237], [15707], [47552], [2730], [41449], [45488], [3073], [49806], [21938], [24430], [22747], [20924], [46145], [20481], [20197], [8239], [28231], [17987], [42804], [47269], [29972], [49884], [21382], [46295], [36676], [34616], [3921], [26991], [27720], [46265], [654], [9855], [40354], [5291], [34904], [44342], [2470], [14598], [880], [19282], [2498], [24237], [21431], [16369], [8994], [44524], [45662], [13663], [37077], [1447], [37786], [30863], [42854], [1019], [20322], [4398], [12159], [44072], [48664], [31547], [18736], [9259], [31], [16354], [21810], [4357], [37982], [5064], [2033], [32871], [47446], [62], [22158], [37387], [8743], [47007], [17981], [11049], [4622], [37916], [36786], [35138], [29925], [14157], [18095], [27829], [1181], [22226], [5709], [4725], [30189], [37014], [1254], [11380], [42989], [696], [24576], [39487], [30119], [1092], [8088], [2194], [9899], [14412], [21828], [3725], [13544], [5180], [44679], [34398], [3891], [28739], [14219], [37594], [49550], [11326], [6904], [17266], [5749], [10174], [23405], [9955], [38271], [41018], [13011], [48392], [36784], [24254], [21687], [23734], [5413], [41447], [45472], [10122], [17555], [15830], [47384], [12084], [31350], [47940], [11661], [27988], [45443], [905], [49651], [16614], [34993], [6781], [30803], [35869], [8001], [41604], [28118], [46462], [46762], [16262], [17281], [5774], [10943], [5013], [18257], [6750], [4713], [3951], [11899], [38791], [16943], [37596], [9318], [18413], [40473], [13208], [16375]] badwordsids_opt = [[44717], [46613], [48513], [49923], [50185], [48755], [8488], [43303], [49659], [48601], [49817], [45405], [48742], [49925], [47720], [11227], [48937], [48784], [50017], [42248], [49310], [48082], [49895], [50025], [49092], [49007], [8061], [44226], [0], [742], [28578], [15698], [49784], [46679], [39365], [49281], [49609], [48081], [48906], [46161], [48554], [49670], [48677], [49721], [49632], [48610], [48462], [47457], [10975], [46077], [28696], [48709], [43839], [49798], [49154], [48203], [49625], [48395], [50155], [47161], [49095], [48833], [49420], [49666], [48443], [22176], [49242], [48651], [49138], [49750], [40389], [48021], [21838], [49070], [45333], [40862], [1], [49915], [33525], [49858], [50254], [44403], [48992], [48872], [46117], [49853], [47567], [50206], [41552], [50068], [48999], [49703], [49940], [49329], [47620], [49868], [49962], [2], [44082], [50236], [31274], [50260], [47052], [42645], [49177], [17523], [48691], [49900], [49069], [49358], [48794], [47529], [46479], [48457], [646], [49910], [48077], [48935], [46386], [48902], [49151], [48759], [49803], [45587], [48392], [47789], [48654], [49836], [49230], [48188], [50264], [46844], [44690], [48505], [50161], [27779], [49995], [41833], [50154], [49097], [48520], [50018], [8174], [50084], [49366], [49526], [50193], [7479], [49982], [3]] + fp32_model = False # Whether or not the most recently loaded HF model was in fp32 format deletewi = None # Temporary storage for UID to delete wirmvwhtsp = False # Whether to remove leading whitespace from WI entries widepth = 3 # How many historical actions to scan for WI hits @@ -1168,6 +1169,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go old_from_pretrained = PreTrainedModel.from_pretrained.__func__ @classmethod def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): + vars.fp32_model = False utils.num_shards = None utils.current_shard = 0 utils.from_pretrained_model_name = pretrained_model_name_or_path @@ -1224,6 +1226,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go num_tensors = len(utils.get_sharded_checkpoint_num_tensors(utils.from_pretrained_model_name, utils.from_pretrained_index_filename, **utils.from_pretrained_kwargs)) else: num_tensors = len(device_map) + print(flush=True) utils.bar = tqdm(total=num_tensors, desc="Loading model tensors") with zipfile.ZipFile(f, "r") as z: @@ -1250,6 +1253,8 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go nbytes = size if dtype is torch.bool else size * ((torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits >> 3) #print(f"Transferring <{key}> to {'(CPU)' if device == 'cpu' else '[device ' + str(device) + ']'} ... ", end="", flush=True) model_dict[key] = model_dict[key].materialize(f, map_location="cpu") + if model_dict[key].dtype is torch.float32: + vars.fp32_model = True if convert_to_float16 and vars.hascuda and (vars.breakmodel or vars.usegpu) and model_dict[key].dtype is torch.float32: model_dict[key] = model_dict[key].to(torch.float16) if not vars.usegpu and not vars.breakmodel and model_dict[key].dtype is torch.float16: @@ -1604,6 +1609,16 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go except Exception as e: model = GPTNeoForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache", **lowmem) else: + old_rebuild_tensor = torch._utils._rebuild_tensor + def new_rebuild_tensor(storage, storage_offset, shape, stride): + dtype = storage.storage_type.dtype + if(not isinstance(dtype, torch.dtype)): + dtype = storage.storage_type(0).dtype + if(dtype is torch.float32 and len(shape) >= 2): + vars.fp32_model = True + return old_rebuild_tensor(storage, storage_offset, shape, stride) + torch._utils._rebuild_tensor = new_rebuild_tensor + try: tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache") except Exception as e: @@ -1616,11 +1631,32 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go except Exception as e: model = GPTNeoForCausalLM.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache", **lowmem) + torch._utils._rebuild_tensor = old_rebuild_tensor + if not args.colab or args.savemodel: import shutil - model = model.half() - model.save_pretrained("models/{}".format(vars.model.replace('/', '_')), max_shard_size="500MiB") tokenizer.save_pretrained("models/{}".format(vars.model.replace('/', '_'))) + if(vars.fp32_model): # Use save_pretrained to convert fp32 models to fp16 + model = model.half() + model.save_pretrained("models/{}".format(vars.model.replace('/', '_')), max_shard_size="500MiB") + else: # For fp16 models, we can just copy the model files directly + import transformers.configuration_utils + import transformers.modeling_utils + import transformers.file_utils + # Save the config.json + shutil.move(transformers.file_utils.get_from_cache(transformers.file_utils.hf_bucket_url(vars.model, transformers.configuration_utils.CONFIG_NAME, revision=vars.revision), cache_dir="cache", local_files_only=True), os.path.join("models/{}".format(vars.model.replace('/', '_')), transformers.configuration_utils.CONFIG_NAME)) + if(utils.num_shards is None): + # Save the pytorch_model.bin of an unsharded model + shutil.move(transformers.file_utils.get_from_cache(transformers.file_utils.hf_bucket_url(vars.model, transformers.modeling_utils.WEIGHTS_NAME, revision=vars.revision), cache_dir="cache", local_files_only=True), os.path.join("models/{}".format(vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_NAME)) + else: + with open(utils.from_pretrained_index_filename) as f: + map_data = json.load(f) + filenames = set(map_data["weight_map"].values()) + # Save the pytorch_model.bin.index.json of a sharded model + shutil.move(utils.from_pretrained_index_filename, os.path.join("models/{}".format(vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_INDEX_NAME)) + # Then save the pytorch_model-#####-of-#####.bin files + for filename in filenames: + shutil.move(transformers.file_utils.get_from_cache(transformers.file_utils.hf_bucket_url(vars.model, filename, revision=vars.revision), cache_dir="cache", local_files_only=True), os.path.join("models/{}".format(vars.model.replace('/', '_')), filename)) shutil.rmtree("cache/") if(vars.hascuda): @@ -1660,6 +1696,7 @@ else: old_from_pretrained = PreTrainedModel.from_pretrained.__func__ @classmethod def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): + vars.fp32_model = False utils.num_shards = None utils.current_shard = 0 utils.from_pretrained_model_name = pretrained_model_name_or_path From 9eaa76c72b31ab356a22da863525cd8c9f8e6036 Mon Sep 17 00:00:00 2001 From: henk717 Date: Sat, 14 May 2022 07:55:47 +0200 Subject: [PATCH 17/63] Add OPT 13B to the models --- colab/TPU.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/colab/TPU.ipynb b/colab/TPU.ipynb index 0ff1fcc1..bb87a339 100644 --- a/colab/TPU.ipynb +++ b/colab/TPU.ipynb @@ -65,7 +65,7 @@ "#@markdown You can find a description of the models below along with instructions on how to start KoboldAI.\n", "\n", "#@title <-- Click this to start KoboldAI\n", - "Model = \"Janeway 13B\" #@param [\"Janeway 13B\", \"Shinen 13B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Shinen 6B\", \"Lit 6B\", \"Convo 6B\", \"C1 6B\", \"NeoX 20B\", \"KoboldAI/fairseq-dense-13B\", \"EleutherAI/gpt-j-6B\"] {allow-input: true}\n", + "Model = \"Janeway 13B\" #@param [\"Janeway 13B\", \"Shinen 13B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Shinen 6B\", \"Lit 6B\", \"Convo 6B\", \"C1 6B\", \"NeoX 20B\", \"facebook/opt-13b\", \"KoboldAI/fairseq-dense-13B\", \"EleutherAI/gpt-j-6B\"] {allow-input: true}\n", "Version = \"United\" #@param [\"Official\", \"United\"] {allow-input: true}\n", "Provider = \"Localtunnel\" #@param [\"Localtunnel\", \"Cloudflare\"]\n", "\n", From 6e82f205b4892ea60f0472646b86cea7a7599bf9 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Sat, 14 May 2022 11:44:28 -0400 Subject: [PATCH 18/63] Aria2 bug fix for Windows users --- utils.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/utils.py b/utils.py index 0e4299de..bc085412 100644 --- a/utils.py +++ b/utils.py @@ -232,6 +232,7 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d s = requests.Session() s.mount("http://", requests.adapters.HTTPAdapter(max_retries=requests.adapters.Retry(total=120, backoff_factor=1))) bar = None + done = False secret = os.urandom(17).hex() try: with tempfile.NamedTemporaryFile("w+b", delete=False) as f: @@ -246,6 +247,7 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d bar.n = bar.total bar.close() p.terminate() + done = True break if bar is None: bar = tqdm(total=total_length, desc=f"[aria2] Downloading model", unit="B", unit_scale=True, unit_divisor=1000) @@ -270,7 +272,7 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d except OSError: pass code = p.wait() - if code: + if not done and code: raise OSError(f"aria2 exited with exit code {code}") for u, t, n in zip(urls, etags, filenames): os.rename(os.path.join(_cache_dir, "kai-tempfile." + n), os.path.join(_cache_dir, n)) From d5ab3ef5b1de11a337e7fecfd3ca2fed2d7e11c0 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Sat, 14 May 2022 11:49:04 -0400 Subject: [PATCH 19/63] Fix `no attribute get_checkpoint_shard_files` --- aiserver.py | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/aiserver.py b/aiserver.py index e8845272..1f43c32c 100644 --- a/aiserver.py +++ b/aiserver.py @@ -1180,12 +1180,13 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go utils.aria2_hook(pretrained_model_name_or_path, **kwargs) return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) PreTrainedModel.from_pretrained = new_from_pretrained - old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files - def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): - utils.num_shards = utils.get_num_shards(index_filename) - utils.from_pretrained_index_filename = index_filename - return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs) - modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files + if(hasattr(modeling_utils, "get_checkpoint_shard_files")): + old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files + def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): + utils.num_shards = utils.get_num_shards(index_filename) + utils.from_pretrained_index_filename = index_filename + return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs) + modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files # Lazy loader import torch_lazy_loader @@ -1707,12 +1708,13 @@ else: utils.aria2_hook(pretrained_model_name_or_path, **kwargs) return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) PreTrainedModel.from_pretrained = new_from_pretrained - old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files - def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): - utils.num_shards = utils.get_num_shards(index_filename) - utils.from_pretrained_index_filename = index_filename - return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs) - modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files + if(hasattr(modeling_utils, "get_checkpoint_shard_files")): + old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files + def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): + utils.num_shards = utils.get_num_shards(index_filename) + utils.from_pretrained_index_filename = index_filename + return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs) + modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files def tpumtjgetsofttokens(): soft_tokens = None From d4e8f567897a99789039cff3b1773decedcf0570 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Sat, 14 May 2022 12:00:44 -0400 Subject: [PATCH 20/63] Remove debugging code from tpu_mtj_backend.py --- tpu_mtj_backend.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/tpu_mtj_backend.py b/tpu_mtj_backend.py index 3d7bf735..41746d37 100644 --- a/tpu_mtj_backend.py +++ b/tpu_mtj_backend.py @@ -1264,9 +1264,6 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo error = f"{mk} {pk} could not be found in the model checkpoint" print("\n\nERROR: " + error, file=sys.stderr) raise RuntimeError(error) - except: - import traceback - traceback.print_exc() finally: if utils.num_shards is None or utils.current_shard >= utils.num_shards: utils.bar.close() From f0df3de61017bed718f45702b30c0d6408fcd699 Mon Sep 17 00:00:00 2001 From: Julius ter Pelkwijk <1099127+mrseeker@users.noreply.github.com> Date: Mon, 16 May 2022 09:50:45 +0200 Subject: [PATCH 21/63] Adding Nerys model 2.7B --- aiserver.py | 1 + 1 file changed, 1 insertion(+) diff --git a/aiserver.py b/aiserver.py index 1f43c32c..79ca0937 100644 --- a/aiserver.py +++ b/aiserver.py @@ -114,6 +114,7 @@ novellist= [ ["Janeway Neo 6B", "KoboldAI/GPT-J-6B-Janeway", "16GB"], ["Janeway Neo 2.7B", "KoboldAI/GPT-Neo-2.7B-Janeway", "8GB"], ["Janeway FSD 2.7B", "KoboldAI/fairseq-dense-2.7B-Janeway", "8GB"], + ["Nerys FSD 2.7B", "KoboldAI/fairseq-dense-2.7B-Nerys", "8GB"], ["Horni-LN 2.7B", "KoboldAI/GPT-Neo-2.7B-Horni-LN", "8GB"], ["Picard 2.7B (Older Janeway)", "KoboldAI/GPT-Neo-2.7B-Picard", "8GB"], ["Return to Main Menu", "Return", ""], From 6ae7b48b69cc8b7d8704d4d223b2f46e7ce0c930 Mon Sep 17 00:00:00 2001 From: Julius ter Pelkwijk <1099127+mrseeker@users.noreply.github.com> Date: Wed, 18 May 2022 13:50:57 +0200 Subject: [PATCH 22/63] Adding Nerys model 13B --- aiserver.py | 1 + 1 file changed, 1 insertion(+) diff --git a/aiserver.py b/aiserver.py index 79ca0937..b9b9267b 100644 --- a/aiserver.py +++ b/aiserver.py @@ -110,6 +110,7 @@ adventurelist= [ novellist= [ ["Janeway FSD 13B", "KoboldAI/fairseq-dense-13B-Janeway", "32GB"], + ["Nerys FSD 13B", "KoboldAI/fairseq-dense-13B-Nerys", "32GB"], ["Janeway FSD 6.7B", "KoboldAI/fairseq-dense-6.7B-Janeway", "16GB"], ["Janeway Neo 6B", "KoboldAI/GPT-J-6B-Janeway", "16GB"], ["Janeway Neo 2.7B", "KoboldAI/GPT-Neo-2.7B-Janeway", "8GB"], From c692987e40cdc128c0138ab04f7f7b2ca6549409 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Fri, 20 May 2022 14:54:49 -0400 Subject: [PATCH 23/63] Fix an error that occurs when loading GPT-2 models I forgot that this new_rebuild_tensor function's first argument's type is different when loading GPT-2 models. --- aiserver.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/aiserver.py b/aiserver.py index b9b9267b..b1fce2cb 100644 --- a/aiserver.py +++ b/aiserver.py @@ -1613,10 +1613,13 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go model = GPTNeoForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache", **lowmem) else: old_rebuild_tensor = torch._utils._rebuild_tensor - def new_rebuild_tensor(storage, storage_offset, shape, stride): - dtype = storage.storage_type.dtype - if(not isinstance(dtype, torch.dtype)): - dtype = storage.storage_type(0).dtype + def new_rebuild_tensor(storage: Union[torch_lazy_loader.LazyTensor, torch.Storage], storage_offset, shape, stride): + if(not isinstance(storage, torch_lazy_loader.LazyTensor)): + dtype = storage.dtype + else: + dtype = storage.storage_type.dtype + if(not isinstance(dtype, torch.dtype)): + dtype = storage.storage_type(0).dtype if(dtype is torch.float32 and len(shape) >= 2): vars.fp32_model = True return old_rebuild_tensor(storage, storage_offset, shape, stride) From f47db6d1556cdc156038744f98c89eed4295a88c Mon Sep 17 00:00:00 2001 From: henk717 Date: Fri, 27 May 2022 00:12:35 +0200 Subject: [PATCH 24/63] Nerys 13B --- colab/TPU.ipynb | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/colab/TPU.ipynb b/colab/TPU.ipynb index bb87a339..bf9a430c 100644 --- a/colab/TPU.ipynb +++ b/colab/TPU.ipynb @@ -65,7 +65,7 @@ "#@markdown You can find a description of the models below along with instructions on how to start KoboldAI.\n", "\n", "#@title <-- Click this to start KoboldAI\n", - "Model = \"Janeway 13B\" #@param [\"Janeway 13B\", \"Shinen 13B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Shinen 6B\", \"Lit 6B\", \"Convo 6B\", \"C1 6B\", \"NeoX 20B\", \"facebook/opt-13b\", \"KoboldAI/fairseq-dense-13B\", \"EleutherAI/gpt-j-6B\"] {allow-input: true}\n", + "Model = \"Nerys 13B\" #@param [\"Nerys 13B\", \"Janeway 13B\", \"Shinen 13B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Shinen 6B\", \"Lit 6B\", \"Convo 6B\", \"C1 6B\", \"NeoX 20B\", \"facebook/opt-13b\", \"KoboldAI/fairseq-dense-13B\", \"EleutherAI/gpt-j-6B\"] {allow-input: true}\n", "Version = \"United\" #@param [\"Official\", \"United\"] {allow-input: true}\n", "Provider = \"Localtunnel\" #@param [\"Localtunnel\", \"Cloudflare\"]\n", "\n", @@ -84,6 +84,10 @@ " Model = \"KoboldAI/fairseq-dense-13B-Janeway\"\n", " path = \"\"\n", " download = \"\"\n", + "elif Model == \"Nerys 13B\":\n", + " Model = \"KoboldAI/fairseq-dense-13B-Nerys\"\n", + " path = \"\"\n", + " download = \"\"\n", "elif Model == \"Shinen 13B\":\n", " Model = \"KoboldAI/fairseq-dense-13B-Shinen\"\n", " path = \"\"\n", From b30370bf4b1959abb28b0ab79e84725b9c651e85 Mon Sep 17 00:00:00 2001 From: Henk Date: Fri, 27 May 2022 01:23:48 +0200 Subject: [PATCH 25/63] 2048 maxtoken default Almost everyone prefers 2048 max tokens because of the superior coherency. It should only be lower due to ram limits, but the menu already shows the optimal ram for 2048. Negatively effected users can turn it down themselves, for everyone else especially on rented machines or colab 2048 is a better default. --- aiserver.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aiserver.py b/aiserver.py index b1fce2cb..1fa91e19 100644 --- a/aiserver.py +++ b/aiserver.py @@ -199,7 +199,7 @@ class vars: model_type = "" # Model Type (Automatically taken from the model config) noai = False # Runs the script without starting up the transformers pipeline aibusy = False # Stops submissions while the AI is working - max_length = 1024 # Maximum number of tokens to submit per action + max_length = 2048 # Maximum number of tokens to submit per action ikmax = 3000 # Maximum number of characters to submit to InferKit genamt = 80 # Amount of text for each action to generate ikgen = 200 # Number of characters for InferKit to generate From 6e0510e1f51e4dc6fecdb836a7893532470afb69 Mon Sep 17 00:00:00 2001 From: henk717 Date: Fri, 27 May 2022 01:35:15 +0200 Subject: [PATCH 26/63] Replaced Jax models with HF models where possible --- colab/TPU.ipynb | 40 ++++++++++++---------------------------- 1 file changed, 12 insertions(+), 28 deletions(-) diff --git a/colab/TPU.ipynb b/colab/TPU.ipynb index bf9a430c..d0e4df20 100644 --- a/colab/TPU.ipynb +++ b/colab/TPU.ipynb @@ -101,41 +101,25 @@ " Drive = \"Unextracted (Less Space)\"\n", " ![[ -f /content/drive/MyDrive/KoboldAI/settings/gpt-neox-20b-jax.settings ]] || echo -e \"{\\n \\\"apikey\\\": \\\"\\\",\\n \\\"andepth\\\": 3,\\n \\\"temp\\\": 0.5,\\n \\\"top_p\\\": 0.9,\\n \\\"top_k\\\": 0,\\n \\\"tfs\\\": 1.0,\\n \\\"rep_pen\\\": 1.03,\\n \\\"genamt\\\": 80,\\n \\\"max_length\\\": 2048,\\n \\\"ikgen\\\": 200,\\n \\\"formatoptns\\\": {\\n \\\"frmttriminc\\\": true,\\n \\\"frmtrmblln\\\": false,\\n \\\"frmtrmspch\\\": false,\\n \\\"frmtadsnsp\\\": false\\n },\\n \\\"numseqs\\\": 1,\\n \\\"widepth\\\": 3,\\n \\\"useprompt\\\": true,\\n \\\"adventure\\\": false\\n}\" > /content/drive/MyDrive/KoboldAI/settings/gpt-neox-20b-jax.settings\n", "elif Model == \"Skein 6B\":\n", - " Model = \"TPUMeshTransformerGPTJ\"\n", - " path = \" -p gpt-j-6b-skein-jax\"\n", - " location = \"colab\"\n", - " download = \" -a https://storage.henk.tech/KoboldAI/skein-jax.txt\"\n", - " extract = \"\"\n", - " Drive = \"Unextracted (Less Space)\"\n", - " ![[ -f /content/drive/MyDrive/KoboldAI/settings/gpt-j-6b-skein-jax.settings ]] || echo -e \"{\\n \\\"apikey\\\": \\\"\\\",\\n \\\"andepth\\\": 3,\\n \\\"temp\\\": 0.5,\\n \\\"top_p\\\": 0.9,\\n \\\"top_k\\\": 0,\\n \\\"tfs\\\": 1.0,\\n \\\"rep_pen\\\": 1.1,\\n \\\"genamt\\\": 80,\\n \\\"max_length\\\": 2048,\\n \\\"ikgen\\\": 200,\\n \\\"formatoptns\\\": {\\n \\\"frmttriminc\\\": true,\\n \\\"frmtrmblln\\\": false,\\n \\\"frmtrmspch\\\": false,\\n \\\"frmtadsnsp\\\": false\\n },\\n \\\"numseqs\\\": 1,\\n \\\"widepth\\\": 3,\\n \\\"useprompt\\\": true,\\n \\\"adventure\\\": false\\n}\" > /content/drive/MyDrive/KoboldAI/settings/gpt-j-6b-skein-jax.settings\n", + " Model = \"KoboldAI/GPT-J-6B-Skein\"\n", + " path = \"\"\n", + " download = \"\"\n", "elif Model == \"Janeway 6B\":\n", - " Model = \"TPUMeshTransformerGPTJ\"\n", - " path = \" -p gpt-j-6b-janeway-jax\"\n", - " location = \"colab\"\n", - " download = \" -a https://storage.henk.tech/KoboldAI/janeway-jax.txt\"\n", - " extract = \"\"\n", - " Drive = \"Unextracted (Less Space)\"\n", - " ![[ -f /content/drive/MyDrive/KoboldAI/settings/gpt-j-6b-janeway-jax.settings ]] || echo -e \"{\\n \\\"apikey\\\": \\\"\\\",\\n \\\"andepth\\\": 3,\\n \\\"temp\\\": 0.5,\\n \\\"top_p\\\": 0.9,\\n \\\"top_k\\\": 0,\\n \\\"tfs\\\": 1.0,\\n \\\"rep_pen\\\": 1.1,\\n \\\"rep_pen_slope\\\": 0.7,\\n \\\"rep_pen_range\\\": 1024.0,\\n \\\"genamt\\\": 80,\\n \\\"max_length\\\": 2048,\\n \\\"ikgen\\\": 200,\\n \\\"formatoptns\\\": {\\n \\\"frmttriminc\\\": true,\\n \\\"frmtrmblln\\\": false,\\n \\\"frmtrmspch\\\": false,\\n \\\"frmtadsnsp\\\": false,\\n \\\"singleline\\\": false\\n },\\n \\\"numseqs\\\": 1,\\n \\\"widepth\\\": 3,\\n \\\"useprompt\\\": true,\\n \\\"adventure\\\": false,\\n \\\"chatmode\\\": false,\\n \\\"chatname\\\": \\\"You\\\",\\n \\\"dynamicscan\\\": false,\\n \\\"nopromptgen\\\": false,\\n \\\"rngpersist\\\": false,\\n \\\"nogenmod\\\": false,\\n \\\"autosave\\\": false,\\n \\\"welcome\\\": false,\\n \\\"newlinemode\\\": \\\"n\\\",\\n \\\"antemplate\\\": \\\"[Genre: <|>]\\\",\\n \\\"userscripts\\\": [],\\n \\\"corescript\\\": \\\"default.lua\\\",\\n \\\"softprompt\\\": \\\"\\\"\\n}\" > /content/drive/MyDrive/KoboldAI/settings/gpt-j-6b-janeway-jax.settings\n", + " Model = \"KoboldAI/GPT-J-6B-Janeway\"\n", + " path = \"\"\n", + " download = \"\"\n", "elif Model == \"Adventure 6B\":\n", - " Model = \"TPUMeshTransformerGPTJ\"\n", - " path = \" -p gpt-j-6b-adventure-jax\"\n", - " location = \"colab\"\n", - " download = \" -a https://api.wandb.ai/files/ve-forbryderne/adventure/carol-data/models/gpt-j-6b-adventure-jax/aria2.txt\"\n", - " extract = \"\"\n", - " Drive = \"Unextracted (Less Space)\"\n", - " ![[ -f /content/drive/MyDrive/KoboldAI/settings/gpt-j-6b-adventure-jax.settings ]] || echo -e \"{\\n \\\"apikey\\\": \\\"\\\",\\n \\\"andepth\\\": 3,\\n \\\"temp\\\": 0.5,\\n \\\"top_p\\\": 0.9,\\n \\\"top_k\\\": 0,\\n \\\"tfs\\\": 1.0,\\n \\\"rep_pen\\\": 1.1,\\n \\\"genamt\\\": 80,\\n \\\"max_length\\\": 2048,\\n \\\"ikgen\\\": 200,\\n \\\"formatoptns\\\": {\\n \\\"frmttriminc\\\": true,\\n \\\"frmtrmblln\\\": false,\\n \\\"frmtrmspch\\\": false,\\n \\\"frmtadsnsp\\\": false\\n },\\n \\\"numseqs\\\": 1,\\n \\\"widepth\\\": 3,\\n \\\"useprompt\\\": true,\\n \\\"adventure\\\": true\\n}\" > /content/drive/MyDrive/KoboldAI/settings/gpt-j-6b-adventure-jax.settings\n", + " Model = \"KoboldAI/GPT-J-6B-Adventure\"\n", + " path = \"\"\n", + " download = \"\"\n", "elif Model == \"Lit 6B\":\n", " Model = \"hakurei/lit-6B\"\n", " path = \"\"\n", " download = \"\"\n", "elif Model == \"Shinen 6B\":\n", - " Model = \"TPUMeshTransformerGPTJ\"\n", - " path = \" -p gpt-j-6b-shinen-jax\"\n", - " location = \"colab\"\n", - " download = \" -a https://storage.henk.tech/KoboldAI/shinen-jax.txt\"\n", - " extract = \"\"\n", - " Drive = \"Unextracted (Less Space)\"\n", - " ![[ -f /content/drive/MyDrive/KoboldAI/settings/gpt-j-6b-shinen-jax.settings ]] || echo -e \"{\\n \\\"apikey\\\": \\\"\\\",\\n \\\"andepth\\\": 3,\\n \\\"temp\\\": 0.5,\\n \\\"top_p\\\": 0.9,\\n \\\"top_k\\\": 0,\\n \\\"tfs\\\": 1.0,\\n \\\"rep_pen\\\": 1.1,\\n \\\"rep_pen_slope\\\": 0.7,\\n \\\"rep_pen_range\\\": 1024.0,\\n \\\"genamt\\\": 80,\\n \\\"max_length\\\": 2048,\\n \\\"ikgen\\\": 200,\\n \\\"formatoptns\\\": {\\n \\\"frmttriminc\\\": true,\\n \\\"frmtrmblln\\\": false,\\n \\\"frmtrmspch\\\": false,\\n \\\"frmtadsnsp\\\": false,\\n \\\"singleline\\\": false\\n },\\n \\\"numseqs\\\": 1,\\n \\\"widepth\\\": 3,\\n \\\"useprompt\\\": true,\\n \\\"adventure\\\": false,\\n \\\"chatmode\\\": false,\\n \\\"chatname\\\": \\\"You\\\",\\n \\\"dynamicscan\\\": false,\\n \\\"nopromptgen\\\": false,\\n \\\"rngpersist\\\": false,\\n \\\"nogenmod\\\": false,\\n \\\"autosave\\\": false,\\n \\\"welcome\\\": false,\\n \\\"newlinemode\\\": \\\"n\\\",\\n \\\"antemplate\\\": \\\"[Genre: <|>]\\\",\\n \\\"userscripts\\\": [],\\n \\\"corescript\\\": \\\"default.lua\\\",\\n \\\"softprompt\\\": \\\"\\\"\\n}\" > /content/drive/MyDrive/KoboldAI/settings/gpt-j-6b-shinen-jax.settings\n", + " Model = \"KoboldAI/GPT-J-6B-Shinen\"\n", + " path = \"\"\n", + " download = \"\"\n", "elif Model == \"Convo 6B\":\n", " Model = \"hitomi-team/convo-6B\"\n", " path = \"\"\n", From 4b65ce9c76aaabafb3f89095b7e2bba43d836217 Mon Sep 17 00:00:00 2001 From: Henk Date: Sat, 28 May 2022 19:39:05 +0200 Subject: [PATCH 27/63] 1.18 version bump --- aiserver.py | 8 +++++--- koboldai.ico | Bin 0 -> 143311 bytes templates/index.html | 4 ++-- 3 files changed, 7 insertions(+), 5 deletions(-) create mode 100644 koboldai.ico diff --git a/aiserver.py b/aiserver.py index 1fa91e19..0c1c5b95 100644 --- a/aiserver.py +++ b/aiserver.py @@ -1,7 +1,7 @@ #!/usr/bin/python3 #==================================================================# # KoboldAI -# Version: 1.17.0 +# Version: 1.18.0 # By: KoboldAIDev and the KoboldAI Community #==================================================================# @@ -100,8 +100,10 @@ mainmenu = [ ] adventurelist= [ + ["Nerys FSD 13B (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys", "32GB"], ["Skein 6B", "KoboldAI/GPT-J-6B-Skein", "16GB"], ["Adventure 6B", "KoboldAI/GPT-J-6B-Adventure", "16GB"], + ["Nerys FSD 2.7B (Hybrid)", "KoboldAI/fairseq-dense-2.7B-Nerys", "8GB"], ["Adventure 2.7B", "KoboldAI/GPT-Neo-2.7B-AID", "8GB"], ["Adventure 1.3B", "KoboldAI/GPT-Neo-1.3B-Adventure", "6GB"], ["Adventure 125M (Mia)", "Merry/AID-Neo-125M", "2GB"], @@ -109,13 +111,13 @@ adventurelist= [ ] novellist= [ + ["Nerys FSD 13B (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys", "32GB"], ["Janeway FSD 13B", "KoboldAI/fairseq-dense-13B-Janeway", "32GB"], - ["Nerys FSD 13B", "KoboldAI/fairseq-dense-13B-Nerys", "32GB"], ["Janeway FSD 6.7B", "KoboldAI/fairseq-dense-6.7B-Janeway", "16GB"], ["Janeway Neo 6B", "KoboldAI/GPT-J-6B-Janeway", "16GB"], ["Janeway Neo 2.7B", "KoboldAI/GPT-Neo-2.7B-Janeway", "8GB"], ["Janeway FSD 2.7B", "KoboldAI/fairseq-dense-2.7B-Janeway", "8GB"], - ["Nerys FSD 2.7B", "KoboldAI/fairseq-dense-2.7B-Nerys", "8GB"], + ["Nerys FSD 2.7B (Hybrid)", "KoboldAI/fairseq-dense-2.7B-Nerys", "8GB"], ["Horni-LN 2.7B", "KoboldAI/GPT-Neo-2.7B-Horni-LN", "8GB"], ["Picard 2.7B (Older Janeway)", "KoboldAI/GPT-Neo-2.7B-Picard", "8GB"], ["Return to Main Menu", "Return", ""], diff --git a/koboldai.ico b/koboldai.ico new file mode 100644 index 0000000000000000000000000000000000000000..876f49764c88f17e9dbdd8ba9d82039ebe116dd9 GIT binary patch literal 143311 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a/templates/index.html b/templates/index.html index 92b70753..11732133 100644 --- a/templates/index.html +++ b/templates/index.html @@ -9,7 +9,7 @@ - + @@ -17,7 +17,7 @@ - + From bab0cd6362816e9470925193a3e2d6f01eb0ed05 Mon Sep 17 00:00:00 2001 From: henk717 Date: Sat, 28 May 2022 19:45:32 +0200 Subject: [PATCH 28/63] Nerys Description --- colab/TPU.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/colab/TPU.ipynb b/colab/TPU.ipynb index d0e4df20..0bbbc15e 100644 --- a/colab/TPU.ipynb +++ b/colab/TPU.ipynb @@ -147,6 +147,7 @@ "\n", "| Model | Size | Style | Description |\n", "| --- | --- | --- | --- |\n", + "| Nerys by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", "| Janeway by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", "| Shinen by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n", "| Skein by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n", From 97401026dd546d8e2d2782ecb646b218792b61b9 Mon Sep 17 00:00:00 2001 From: Henk Date: Sat, 28 May 2022 19:46:05 +0200 Subject: [PATCH 29/63] Nerys Description in Readme --- readme.md | 44 ++++++++++++++++++++++---------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/readme.md b/readme.md index 2dd4c57c..f454eda0 100644 --- a/readme.md +++ b/readme.md @@ -50,9 +50,9 @@ Each edition features different models and requires different hardware to run, t ### [Click here for the TPU Edition Colab](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb) - | Model | Size | Style | Description | | --- | --- | --- | --- | +| Nerys by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. | | Janeway by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. | | Shinen by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. | | Skein by VE\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. | @@ -64,35 +64,35 @@ Each edition features different models and requires different hardware to run, t | Fairseq Dense | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. | | GPT-J-6B by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. | +## [GPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/GPU.ipynb) -# [GPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/GPU.ipynb) - -| Model | Size | Style | Description | -| ------------------------------------------------------------ | -------- | ---------- | ------------------------------------------------------------ | +| Model | Size | Style | Description | +| --- | --- | --- | --- | | [GPT-Neo-2.7B-Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B GPU | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. | -| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B GPU | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. | +| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B GPU | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. | | [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B GPU | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. | | [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B GPU | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. | -| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B GPU | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. | -| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B GPU | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. | -| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B GPU | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. | +| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B GPU | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. | +| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B GPU | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. | +| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B GPU | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. | -| Style | Description | -| --------- | ------------------------------------------------------------ | -| Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. | -| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. | +| Style | Description | +| --- | --- | +| Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. | +| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. | | Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel style model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. | -| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. | -| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. | +| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. | +| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. | ---- ## Tips to get the most out of Google Colab -- Google will occationally show a Captcha, typically after it has been open for 30 minutes but it can be more frequent if you often use Colab. Make sure to do these properly, or you risk getting your instance shut down and getting a lower priority towards the TPU's. -- KoboldAI uses Google Drive to store your files and settings, if you wish to upload a softprompt or userscript this can be done directly on the Google Drive website. You can also use this to download backups of your KoboldAI related files or upload models of your own. -- Don't want to save your stories on Google Drive for privacy reasons? Do not use KoboldAI's save function and instead click Download as .json, this will automatically download the story to your own computer without ever touching Google's harddrives. You can load this back trough the Load from file option. -- Google shut your instance down unexpectedly? You can still make use of the Download as .json button to recover your story as long as you did not close the KoboldAI window. You can then load this back up in your next session. -- Done with KoboldAI? Go to the Runtime menu, click on Manage Sessions and terminate your open sessions that you no longer need. This trick can help you maintain higher priority towards getting a TPU. -- Models stored on Google Drive typically load faster than models we need to download from the internet. + +* Google will occationally show a Captcha, typically after it has been open for 30 minutes but it can be more frequent if you often use Colab. Make sure to do these properly, or you risk getting your instance shut down and getting a lower priority towards the TPU's. +* KoboldAI uses Google Drive to store your files and settings, if you wish to upload a softprompt or userscript this can be done directly on the Google Drive website. You can also use this to download backups of your KoboldAI related files or upload models of your own. +* Don't want to save your stories on Google Drive for privacy reasons? Do not use KoboldAI's save function and instead click Download as .json, this will automatically download the story to your own computer without ever touching Google's harddrives. You can load this back trough the Load from file option. +* Google shut your instance down unexpectedly? You can still make use of the Download as .json button to recover your story as long as you did not close the KoboldAI window. You can then load this back up in your next session. +* Done with KoboldAI? Go to the Runtime menu, click on Manage Sessions and terminate your open sessions that you no longer need. This trick can help you maintain higher priority towards getting a TPU. +* Models stored on Google Drive typically load faster than models we need to download from the internet. + ### [Click here for the GPU Edition Colab](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/GPU.ipynb) | Model | Size | Type | Description | From 8ae0c8311b3b1fa6d7d41ae8f02da7b604772554 Mon Sep 17 00:00:00 2001 From: henk717 Date: Sat, 28 May 2022 19:52:06 +0200 Subject: [PATCH 30/63] GPU Colab improvements --- colab/GPU.ipynb | 38 ++++++++++++++++++++++++-------------- 1 file changed, 24 insertions(+), 14 deletions(-) diff --git a/colab/GPU.ipynb b/colab/GPU.ipynb index 47c31f27..349c5635 100644 --- a/colab/GPU.ipynb +++ b/colab/GPU.ipynb @@ -7,7 +7,7 @@ "private_outputs": true, "provenance": [], "collapsed_sections": [], - "authorship_tag": "ABX9TyOKIa/NDLlYI5j63GXPtkXv", + "authorship_tag": "ABX9TyMClgsuyqYCEvk3ugme8XOZ", "include_colab_link": true }, "kernelspec": { @@ -27,7 +27,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -68,14 +68,20 @@ "#@title <-- Click this to start KoboldAI\n", "#@markdown You can find a description of the models below along with instructions on how to start KoboldAI.\n", "\n", - "Model = \"KoboldAI/GPT-Neo-2.7B-Janeway\" #@param [\"KoboldAI/GPT-Neo-2.7B-Janeway\", \"KoboldAI/GPT-Neo-2.7B-AID\", \"KoboldAI/GPT-Neo-2.7B-Picard\", \"KoboldAI/GPT-Neo-2.7B-Horni-LN\", \"KoboldAI/GPT-Neo-2.7B-Horni\", \"KoboldAI/GPT-Neo-2.7B-Shinen\", \"EleutherAI/gpt-neo-2.7B\"] {allow-input: true}\n", - "Version = \"Official\" #@param [\"Official\", \"United\"] {allow-input: true}\n", + "Model = \"KoboldAI/fairseq-dense-2.7B-Nerys\" #@param [\"KoboldAI/fairseq-dense-2.7B-Nerys\", \"KoboldAI/GPT-Neo-2.7B-Janeway\", \"KoboldAI/GPT-Neo-2.7B-AID\", \"KoboldAI/GPT-Neo-2.7B-Picard\", \"KoboldAI/GPT-Neo-2.7B-Horni-LN\", \"KoboldAI/GPT-Neo-2.7B-Horni\", \"KoboldAI/GPT-Neo-2.7B-Shinen\", \"EleutherAI/gpt-neo-2.7B\"] {allow-input: true}\n", + "Version = \"United\" #@param [\"Official\", \"United\"] {allow-input: true}\n", + "Provider = \"Localtunnel\" #@param [\"Localtunnel\", \"Cloudflare\"]\n", "\n", "!nvidia-smi\n", "from google.colab import drive\n", "drive.mount('/content/drive/')\n", "\n", - "!wget https://henk.tech/ckds -O - | bash /dev/stdin -m $Model -g $Version" + "if Provider == \"Localtunnel\":\n", + " tunnel = \"--localtunnel yes\"\n", + "else:\n", + " tunnel = \"\"\n", + "\n", + "!wget https://henk.tech/ckds -O - | bash /dev/stdin -m $Model -g $Version $tunnel" ], "execution_count": null, "outputs": [] @@ -96,15 +102,19 @@ "\n", "# [TPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)\n", "\n", - "| Model | Size | Style | Drive Space | Description |\n", - "| ------------------------------ | ------ | --------- | ----------- | ------------------------------------------------------------ |\n", - "| Skein 6B by VE_FORBDRYDERNE | 6B TPU | Hybrid | 0 GB | Skein is our flagship 6B model, it is a hybrid between a Adventure model and a Novel model. Best used with either Adventure mode or the You Bias userscript enabled. Skein has been trained on high quality Novels along with CYOA adventure stories and is not as wackey as the Adventure model. It also has tagging support. |\n", - "| Janeway 6B by Mr Seeker | 6B TPU | Novel | 0 GB | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", - "| Adventure 6B by VE_FORBRYDERNE | 6B TPU | Adventure | 0 GB | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |\n", - "| Lit 6B by Haru | 6B TPU | NSFW | 8 GB / 12 GB | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |\n", - "| Shinen 6B by Mr Seeker | 6B TPU | NSFW | 0 GB | Shinen is an alternative to the Lit model designed to be more explicit. If Lit is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n", - "| Generic 6B by EleutherAI | 6B TPU | Generic | 10 GB / 12 GB | GPT-J-6B is what all other models are based on, if you need something that has no specific bias towards any particular subject this is the model for you. Best used when the other models are not suitable for what you wish to do. Such as homework assistance, blog writing, coding and more. It needs more hand holding than other models and is more prone to undesirable formatting changes. |\n", - "| C1 6B by Haru | 6B TPU | Chatbot | 8 GB / 12 GB | C1 has been trained on various internet chatrooms, it makes the basis for an interesting chatbot model and has been optimized to be used in the Chatmode. |\n", + "| Model | Size | Style | Description |\n", + "| --- | --- | --- | --- |\n", + "| Nerys by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", + "| Janeway by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", + "| Shinen by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n", + "| Skein by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n", + "| Adventure by VE\\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |\n", + "| Lit by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |\n", + "| Convo | 6B | Chatbot | Convo-6B is a GPT-J 6B model fine-tuned on a collection of high quality open source datasets which amount to 6 million messages. The primary goal of the model is to provide improved performance and generalization when generating multi-turn dialogue for characters that were not present from within the fine tuning data. The prompted performance has especially improved over the predecessor model [C1-6B](https://huggingface.co/hakurei/c1-6B). |\n", + "| C1 by Haru | 6B | Chatbot | C1 has been trained on various internet chatrooms, it makes the basis for an interesting chatbot model and has been optimized to be used in the Chatmode. |\n", + "| Neo(X) by EleutherAI | 20B | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. |\n", + "| Fairseq Dense | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. |\n", + "| GPT-J-6B by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |\n", "\n", "\n", "| Style | Description |\n", From 6ae12f29e4828ea327f699cea928a7c393caeedd Mon Sep 17 00:00:00 2001 From: Henk Date: Sun, 29 May 2022 00:25:53 +0200 Subject: [PATCH 31/63] (Un)Installer improvements Inno Setup has proven limited in its ability to uninstall because its hard to keep up to date which folders it should ask or keep and not everyone has used the Offline Installer yet wishes to use an uninstaller. This commit adds a new uninstall script which my future versions of the uninstaller will look for to delete the files. Eliminating the risk of the setup accidentally deleting your stories. If the proper Inno Setup uninstaller is detected the script will terminate itself and launch that first, to ensure the entire uninstallation is handled. If not, it will warn you before removing anything. It will also get rid of the virtual KoboldAI drives and we can update it trough normal git means. --- Uninstall.bat | 32 ++++++++++++++++++++++++++++++++ update-koboldai.bat | 2 +- 2 files changed, 33 insertions(+), 1 deletion(-) create mode 100644 Uninstall.bat diff --git a/Uninstall.bat b/Uninstall.bat new file mode 100644 index 00000000..422655de --- /dev/null +++ b/Uninstall.bat @@ -0,0 +1,32 @@ +@echo off +cd /D %~dp0 +TITLE KoboldAI Uninstall Helper +SET /P M=nul +for /d %%D in (*) do if not "%%~nxD"=="stories" if not "%%~nxD"=="userscripts" if not "%%~nxD"=="settings" if not "%%~nxD"=="softprompts" if not "%%~nxD"=="models" if not "%%~nxD"=="Uninstall" rmdir /S /Q %%~nxD +for %%i in (*) do if not "%%i"=="Uninstall.bat" del /q "%%i" +set /P DM=Would you like to delete the models folder? (Y/n) : +IF %DM%==Y rmdir models /s /q +IF %DM%==y rmdir models /s /q +set DM=N +set /P DM=Would you like to delete all other user folders? (y/N) : +IF %DM%==Y rmdir stories userscripts settings softprompts /s /q +IF %DM%==y rmdir stories userscripts settings softprompts /s /q +del Uninstall.bat \ No newline at end of file diff --git a/update-koboldai.bat b/update-koboldai.bat index 05ae280e..26e9ec17 100644 --- a/update-koboldai.bat +++ b/update-koboldai.bat @@ -50,4 +50,4 @@ git remote add origin %origin% git fetch --all git checkout %branch% -f git reset --hard origin/%branch% -cmd /k \ No newline at end of file +%windir%\system32\timeout -t 10 \ No newline at end of file From 69a28210e967ba951386d2ceeb271dddd3004326 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Sat, 28 May 2022 18:33:57 -0400 Subject: [PATCH 32/63] Don't cap setting values when manually entered by user --- static/application.js | 12 +++++++++--- templates/index.html | 2 +- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/static/application.js b/static/application.js index 63f804a3..c1e37b4e 100644 --- a/static/application.js +++ b/static/application.js @@ -175,18 +175,24 @@ function addSetting(ob) { // Add event function to input var send = function () { sliders_throttle(ob.id, function () { - socket.send({'cmd': $(refin).attr('id'), 'data': $(refin).val()}); + socket.send({'cmd': $(refin).attr('id'), 'data': $(reflb).val()}); }); - reflb.val($(refin).val()); } - refin.on("input", send); + refin.on("input", function (event) { + reflb.val(refin.val()); + send(); + }); reflb.on("change", function (event) { var value = (ob.unit === "float" ? parseFloat : parseInt)(event.target.value); if(Number.isNaN(value) || value > ob.max || value < ob.min) { event.target.value = refin.val(); return; } + if (ob.unit === "float") { + value = parseFloat(value.toFixed(3)); // Round to 3 decimal places to help avoid the number being too long to fit in the box + } refin.val(value); + reflb.val(value); send(); }); } else if(ob.uitype == "toggle"){ diff --git a/templates/index.html b/templates/index.html index 92b70753..16b47c6d 100644 --- a/templates/index.html +++ b/templates/index.html @@ -17,7 +17,7 @@ - + From 2798cf11da3749dd24853a30a835425899aef5d2 Mon Sep 17 00:00:00 2001 From: Henk Date: Sun, 29 May 2022 00:53:22 +0200 Subject: [PATCH 33/63] Hide drive error Fixes a small issue where the user gets an error if the drive is not mounted. --- Uninstall.bat | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Uninstall.bat b/Uninstall.bat index 422655de..ed143996 100644 --- a/Uninstall.bat +++ b/Uninstall.bat @@ -2,8 +2,8 @@ cd /D %~dp0 TITLE KoboldAI Uninstall Helper SET /P M=nul +IF %M%==1 subst /D K: >nul IF "%1" == "FORCE" GOTO UNINSTALL From b9c6c0b3bd1ed0bb1bb941d7a0cff7271abf6697 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Sat, 28 May 2022 19:14:26 -0400 Subject: [PATCH 34/63] Out-of-bounds setting values are now shown in red --- static/application.js | 40 +++++++++++++++++++++++++--------------- static/custom.css | 8 ++++++++ templates/index.html | 4 ++-- 3 files changed, 35 insertions(+), 17 deletions(-) diff --git a/static/application.js b/static/application.js index c1e37b4e..cfb25883 100644 --- a/static/application.js +++ b/static/application.js @@ -173,6 +173,14 @@ function addSetting(ob) { window["setting_"+ob.id] = refin; // Is this still needed? window["label_"+ob.id] = reflb; // Is this still needed? // Add event function to input + var updateLabelColor = function () { + var value = (ob.unit === "float" ? parseFloat : parseInt)(reflb.val()); + if(value > ob.max || value < ob.min) { + reflb.addClass("setting-value-warning"); + } else { + reflb.removeClass("setting-value-warning"); + } + } var send = function () { sliders_throttle(ob.id, function () { socket.send({'cmd': $(refin).attr('id'), 'data': $(reflb).val()}); @@ -180,11 +188,12 @@ function addSetting(ob) { } refin.on("input", function (event) { reflb.val(refin.val()); + updateLabelColor(); send(); - }); + }).on("change", updateLabelColor); reflb.on("change", function (event) { var value = (ob.unit === "float" ? parseFloat : parseInt)(event.target.value); - if(Number.isNaN(value) || value > ob.max || value < ob.min) { + if(Number.isNaN(value) || (ob.min >= 0 && value < 0)) { event.target.value = refin.val(); return; } @@ -193,6 +202,7 @@ function addSetting(ob) { } refin.val(value); reflb.val(value); + updateLabelColor(); send(); }); } else if(ob.uitype == "toggle"){ @@ -2065,48 +2075,48 @@ $(document).ready(function(){ newTextHighlight($("#n"+msg.data)) } else if(msg.cmd == "updatetemp") { // Send current temp value to input - $("#settemp").val(parseFloat(msg.data)); $("#settempcur").val(msg.data); + $("#settemp").val(parseFloat(msg.data)).trigger("change"); } else if(msg.cmd == "updatetopp") { // Send current top p value to input - $("#settopp").val(parseFloat(msg.data)); $("#settoppcur").val(msg.data); + $("#settopp").val(parseFloat(msg.data)).trigger("change"); } else if(msg.cmd == "updatetopk") { // Send current top k value to input - $("#settopk").val(parseFloat(msg.data)); $("#settopkcur").val(msg.data); + $("#settopk").val(parseFloat(msg.data)).trigger("change"); } else if(msg.cmd == "updatetfs") { // Send current tfs value to input - $("#settfs").val(parseFloat(msg.data)); $("#settfscur").val(msg.data); + $("#settfs").val(parseFloat(msg.data)).trigger("change"); } else if(msg.cmd == "updatetypical") { // Send current typical value to input - $("#settypical").val(parseFloat(msg.data)); $("#settypicalcur").val(msg.data); + $("#settypical").val(parseFloat(msg.data)).trigger("change"); } else if(msg.cmd == "updatereppen") { // Send current rep pen value to input - $("#setreppen").val(parseFloat(msg.data)); $("#setreppencur").val(msg.data); + $("#setreppen").val(parseFloat(msg.data)).trigger("change"); } else if(msg.cmd == "updatereppenslope") { // Send current rep pen value to input - $("#setreppenslope").val(parseFloat(msg.data)); $("#setreppenslopecur").val(msg.data); + $("#setreppenslope").val(parseFloat(msg.data)).trigger("change"); } else if(msg.cmd == "updatereppenrange") { // Send current rep pen value to input - $("#setreppenrange").val(parseFloat(msg.data)); $("#setreppenrangecur").val(msg.data); + $("#setreppenrange").val(parseFloat(msg.data)).trigger("change"); } else if(msg.cmd == "updateoutlen") { // Send current output amt value to input - $("#setoutput").val(parseInt(msg.data)); $("#setoutputcur").val(msg.data); + $("#setoutput").val(parseInt(msg.data)).trigger("change"); } else if(msg.cmd == "updatetknmax") { // Send current max tokens value to input - $("#settknmax").val(parseInt(msg.data)); $("#settknmaxcur").val(msg.data); + $("#settknmax").val(parseInt(msg.data)).trigger("change"); } else if(msg.cmd == "updateikgen") { // Send current max tokens value to input - $("#setikgen").val(parseInt(msg.data)); $("#setikgencur").val(msg.data); + $("#setikgen").val(parseInt(msg.data)).trigger("change"); } else if(msg.cmd == "setlabeltemp") { // Update setting label with value from server $("#settempcur").val(msg.data); @@ -2310,15 +2320,15 @@ $(document).ready(function(){ $("#setnumseqcur").html(msg.data); } else if(msg.cmd == "updatenumseq") { // Send current max tokens value to input - $("#setnumseq").val(parseInt(msg.data)); $("#setnumseqcur").html(msg.data); + $("#setnumseq").val(parseInt(msg.data)).trigger("change"); } else if(msg.cmd == "setlabelwidepth") { // Update setting label with value from server $("#setwidepthcur").html(msg.data); } else if(msg.cmd == "updatewidepth") { // Send current max tokens value to input - $("#setwidepth").val(parseInt(msg.data)); $("#setwidepthcur").html(msg.data); + $("#setwidepth").val(parseInt(msg.data)).trigger("change"); } else if(msg.cmd == "updateuseprompt") { // Update toggle state $("#setuseprompt").prop('checked', msg.data).change(); diff --git a/static/custom.css b/static/custom.css index 677fc028..d70fd34e 100644 --- a/static/custom.css +++ b/static/custom.css @@ -22,6 +22,14 @@ chunk.editing, chunk.editing * { font-style: normal !important; } +.setting-value-warning { + color: #ff7777; +} + +.setting-value-warning:focus { + color: #ffaaaa !important; +} + .settinglabel input { width: 5ch; background-color: inherit; diff --git a/templates/index.html b/templates/index.html index 16b47c6d..4d2d57b9 100644 --- a/templates/index.html +++ b/templates/index.html @@ -9,7 +9,7 @@ - + @@ -17,7 +17,7 @@ - + From 52b977a3d3c5a3048f5e744ef9cee04fbf57d65c Mon Sep 17 00:00:00 2001 From: Henk Date: Sun, 29 May 2022 02:20:06 +0200 Subject: [PATCH 35/63] Better Icon Thanks to Spock for all the effort in refining it --- koboldai.ico | Bin 143311 -> 153569 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/koboldai.ico b/koboldai.ico index 876f49764c88f17e9dbdd8ba9d82039ebe116dd9..4e39d0de95851532ebe8327af57628c8d69ea877 100644 GIT binary patch literal 153569 zcmXV11ymbdm>oR0I}~?{yF+nzY0={D?%LvR#l1j*0>vdz+-Y%#;u;{hF8}W4oSd1L z%z2jY-B0cf0Kfol`UsE5(plYG)bD0B!NR2 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69da5b7bc2bf72479f79c4f84d0d4e2419869ec2 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Sat, 28 May 2022 23:44:19 -0400 Subject: [PATCH 36/63] Update list of transformers versions that have broken OPT --- aiserver.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/aiserver.py b/aiserver.py index 0c1c5b95..2737cd69 100644 --- a/aiserver.py +++ b/aiserver.py @@ -26,6 +26,7 @@ import json import collections import zipfile import packaging +import packaging.version import contextlib import traceback import threading @@ -1348,7 +1349,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go # Fix a bug in OPTForCausalLM where self.lm_head is the wrong size - if(transformers_version == "4.19.0"): + if(packaging.version.parse("4.19.0.dev0") <= packaging.version.parse(transformers_version) <= packaging.version.parse("4.19.2")): try: from transformers import OPTForCausalLM, OPTModel except ImportError: From 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z*(b{QRUYSkaAe)45h&5!_|E}*u-%xrXz9b)j4nUdsaJ1T?Rxcg#;2tIG~?Cn58VSp z7TP ztB-m(=Z3OPs?|d|Pc{kjE%{$|UJP`_2i$EE(BLNCr#yPajc)T$%P;%9m8{wov|D?h zbK5#BjV&?{H-4E9f(kM%^{+neoo#;Vdn!)Nf9$$7T3BB8ME#rlN^q_#_nXOB9xrK2 zBh?%|&M$T8+T>@v%P=ZmwN|AaNh<>+v0sz~hPIaY>~IMk>??u2pa-F68wu|3E0LYT zBr?oL(z~Zh_)HJ#qThvgRrpq`U3q86nGtgO@L`#axmKqZu9krXI`@>n-bT*rTt9wCdJebAM02OrHlf!B)}|^Au%e@fs(W^Sm_WMa1rg9sX-DxEI_;m*)jg|-I9!>QLXAXyb^sMHjAz-=*f76Bg?1qIPa_O>C$!)H{V9BfaE<>u7 F{|AMNoE-oF literal 0 HcmV?d00001 From 1a1f2f64282e5781de474767a2ec2e714b0d1642 Mon Sep 17 00:00:00 2001 From: Henk Date: Tue, 31 May 2022 13:17:06 +0200 Subject: [PATCH 38/63] 30B ram requirements --- aiserver.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aiserver.py b/aiserver.py index 2737cd69..09e89a1a 100644 --- a/aiserver.py +++ b/aiserver.py @@ -157,7 +157,7 @@ gpt2list = [ ] optlist = [ - ["OPT 30B", "facebook/opt-30b", ""], + ["OPT 30B", "facebook/opt-30b", "64GB"], ["OPT 13B", "facebook/opt-13b", "32GB"], ["OPT 6.7B", "facebook/opt-6.7b", "16GB"], ["OPT 2.7B", "facebook/opt-2.7b", "8GB"], From 707316de31c0dd6f67f06dd32bf8e1f21a18dd6f Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Tue, 31 May 2022 12:20:16 -0400 Subject: [PATCH 39/63] Kaggle TPU support --- aiserver.py | 2 +- tpu_mtj_backend.py | 13 +++++++++---- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/aiserver.py b/aiserver.py index 09e89a1a..cead5e44 100644 --- a/aiserver.py +++ b/aiserver.py @@ -320,7 +320,7 @@ class vars: quiet = False # If set will suppress any story text from being printed to the console (will only be seen on the client web page) debug = False # If set to true, will send debug information to the client for display lazy_load = True # Whether or not to use torch_lazy_loader.py for transformers models in order to reduce CPU memory usage - use_colab_tpu = os.environ.get("COLAB_TPU_ADDR", "") != "" # Whether or not we're in a Colab TPU instance and are going to use the TPU rather than the CPU + use_colab_tpu = os.environ.get("COLAB_TPU_ADDR", "") != "" or os.environ.get("TPU_NAME", "") != "" # Whether or not we're in a Colab TPU instance or Kaggle TPU instance and are going to use the TPU rather than the CPU utils.vars = vars diff --git a/tpu_mtj_backend.py b/tpu_mtj_backend.py index 41746d37..fb2dc7ae 100644 --- a/tpu_mtj_backend.py +++ b/tpu_mtj_backend.py @@ -1094,13 +1094,18 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo print("Connecting to your Colab instance's TPU", flush=True) spinner = multiprocessing.Process(target=show_spinner, args=()) spinner.start() - colab_tpu_addr = os.environ['COLAB_TPU_ADDR'].split(':')[0] - url = f'http://{colab_tpu_addr}:8475/requestversion/{driver_version}' + if os.environ.get('COLAB_TPU_ADDR', '') != '': + tpu_address = os.environ['COLAB_TPU_ADDR'] # Colab + else: + tpu_address = os.environ['TPU_NAME'] # Kaggle + tpu_address = tpu_address.replace("grpc://", "") + tpu_address_without_port = tpu_address.split(':', 1)[0] + url = f'http://{tpu_address_without_port}:8475/requestversion/{driver_version}' + config.FLAGS.jax_xla_backend = "tpu_driver" + config.FLAGS.jax_backend_target = "grpc://" + tpu_address requests.post(url) spinner.terminate() print() - config.FLAGS.jax_xla_backend = "tpu_driver" - config.FLAGS.jax_backend_target = "grpc://" + os.environ['COLAB_TPU_ADDR'] cores_per_replica = params["cores_per_replica"] seq = params["seq"] From 714fc1729b99f17f041cb975b66d06b7f7d02fad Mon Sep 17 00:00:00 2001 From: Henk Date: Wed, 1 Jun 2022 10:20:24 +0200 Subject: [PATCH 40/63] Updated model list --- readme.md | 35 ++++++++++++++++++----------------- 1 file changed, 18 insertions(+), 17 deletions(-) diff --git a/readme.md b/readme.md index f454eda0..a136c856 100644 --- a/readme.md +++ b/readme.md @@ -52,29 +52,30 @@ Each edition features different models and requires different hardware to run, t | Model | Size | Style | Description | | --- | --- | --- | --- | -| Nerys by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. | -| Janeway by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. | -| Shinen by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. | -| Skein by VE\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. | -| Adventure by VE\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). | -| Lit by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. | -| Convo | 6B | Chatbot | Convo-6B is a GPT-J 6B model fine-tuned on a collection of high quality open source datasets which amount to 6 million messages. The primary goal of the model is to provide improved performance and generalization when generating multi-turn dialogue for characters that were not present from within the fine tuning data. The prompted performance has especially improved over the predecessor model [C1-6B](https://huggingface.co/hakurei/c1-6B). | -| C1 by Haru | 6B | Chatbot | C1 has been trained on various internet chatrooms, it makes the basis for an interesting chatbot model and has been optimized to be used in the Chatmode. | +| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. | +| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. | +| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. | +| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. | +| [Adventure](https://huggingface.co/KoboldAI/GPT-J-6B-Adventure) by VE\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). | +| [Lit](https://huggingface.co/hakurei/lit-6B) by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. | +| [Convo](https://huggingface.co/hitomi-team/convo-6B) by Hitomi Team | 6B | Chatbot | Convo-6B is a GPT-J 6B model fine-tuned on a collection of high quality open source datasets which amount to 6 million messages. The primary goal of the model is to provide improved performance and generalization when generating multi-turn dialogue for characters that were not present from within the fine tuning data. The prompted performance has especially improved over the predecessor model [C1-6B](https://huggingface.co/hakurei/c1-6B). | +| [C1](https://huggingface.co/hakurei/c1-6B) by Haru | 6B | Chatbot | C1 has been trained on various internet chatrooms, it makes the basis for an interesting chatbot model and has been optimized to be used in the Chatmode. | | Neo(X) by EleutherAI | 20B | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. | -| Fairseq Dense | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. | -| GPT-J-6B by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. | +| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-13B) | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. | +| [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B) by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. | ## [GPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/GPU.ipynb) | Model | Size | Style | Description | | --- | --- | --- | --- | -| [GPT-Neo-2.7B-Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B GPU | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. | -| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B GPU | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. | -| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B GPU | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. | -| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B GPU | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. | -| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B GPU | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. | -| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B GPU | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. | -| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B GPU | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. | +| [Fairseq-Dense-2.7B-Nerys](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. | +| [GPT-Neo-2.7B-Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. | +| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. | +| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. | +| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. | +| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. | +| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. | +| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. | | Style | Description | | --- | --- | From e5dcf91a086b356da800229edb9017af38864ebd Mon Sep 17 00:00:00 2001 From: Henk Date: Wed, 1 Jun 2022 10:34:16 +0200 Subject: [PATCH 41/63] Defaults Support This adds support for loading settings from the defaults folder, settings are loaded in the following order and overwritten if needed by the higher number. 1. The model config file. 2. The defaults folder. 3. The users defined settings file. With this support we can begin to ship better defaults for models we do not manage. Our community tuners have been most helpful at adding good defaults to their configuration files, but for other models such as the base models this gives us the flexibility to define better settings for each model without messing with a users desired settings if they already exist. --- .gitignore | 3 + aiserver.py | 160 ++++++++++++++++++++++++++++------------------------ 2 files changed, 88 insertions(+), 75 deletions(-) diff --git a/.gitignore b/.gitignore index 9b79234b..5b024bd8 100644 --- a/.gitignore +++ b/.gitignore @@ -31,3 +31,6 @@ Uninstall # Ignore compiled Python files. *.pyc + +# Don't ignore defaults +!defaults/* \ No newline at end of file diff --git a/aiserver.py b/aiserver.py index cead5e44..4e33332b 100644 --- a/aiserver.py +++ b/aiserver.py @@ -660,88 +660,98 @@ def settingschanged(): #==================================================================# # Read settings from client file JSON and send to vars #==================================================================# + def loadsettings(): + if(path.exists("defaults/" + getmodelname().replace('/', '_') + ".settings")): + # Read file contents into JSON object + file = open("defaults/" + getmodelname().replace('/', '_') + ".settings", "r") + js = json.load(file) + + processsettings(js) + file.close() if(path.exists("settings/" + getmodelname().replace('/', '_') + ".settings")): # Read file contents into JSON object file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "r") js = json.load(file) - # Copy file contents to vars - if("apikey" in js): - vars.apikey = js["apikey"] - if("andepth" in js): - vars.andepth = js["andepth"] - if("temp" in js): - vars.temp = js["temp"] - if("top_p" in js): - vars.top_p = js["top_p"] - if("top_k" in js): - vars.top_k = js["top_k"] - if("tfs" in js): - vars.tfs = js["tfs"] - if("typical" in js): - vars.typical = js["typical"] - if("rep_pen" in js): - vars.rep_pen = js["rep_pen"] - if("rep_pen_slope" in js): - vars.rep_pen_slope = js["rep_pen_slope"] - if("rep_pen_range" in js): - vars.rep_pen_range = js["rep_pen_range"] - if("genamt" in js): - vars.genamt = js["genamt"] - if("max_length" in js): - vars.max_length = js["max_length"] - if("ikgen" in js): - vars.ikgen = js["ikgen"] - if("formatoptns" in js): - vars.formatoptns = js["formatoptns"] - if("numseqs" in js): - vars.numseqs = js["numseqs"] - if("widepth" in js): - vars.widepth = js["widepth"] - if("useprompt" in js): - vars.useprompt = js["useprompt"] - if("adventure" in js): - vars.adventure = js["adventure"] - if("chatmode" in js): - vars.chatmode = js["chatmode"] - if("chatname" in js): - vars.chatname = js["chatname"] - if("dynamicscan" in js): - vars.dynamicscan = js["dynamicscan"] - if("nopromptgen" in js): - vars.nopromptgen = js["nopromptgen"] - if("rngpersist" in js): - vars.rngpersist = js["rngpersist"] - if("nogenmod" in js): - vars.nogenmod = js["nogenmod"] - if("autosave" in js): - vars.autosave = js["autosave"] - if("newlinemode" in js): - vars.newlinemode = js["newlinemode"] - if("welcome" in js): - vars.welcome = js["welcome"] - - if("antemplate" in js): - vars.setauthornotetemplate = js["antemplate"] - if(not vars.gamestarted): - vars.authornotetemplate = vars.setauthornotetemplate - - if("userscripts" in js): - vars.userscripts = [] - for userscript in js["userscripts"]: - if type(userscript) is not str: - continue - userscript = userscript.strip() - if len(userscript) != 0 and all(q not in userscript for q in ("..", ":")) and all(userscript[0] not in q for q in ("/", "\\")) and os.path.exists(fileops.uspath(userscript)): - vars.userscripts.append(userscript) - - if("corescript" in js and type(js["corescript"]) is str and all(q not in js["corescript"] for q in ("..", ":")) and all(js["corescript"][0] not in q for q in ("/", "\\"))): - vars.corescript = js["corescript"] - else: - vars.corescript = "default.lua" - + processsettings(js) file.close() + +def processsettings(js): +# Copy file contents to vars + if("apikey" in js): + vars.apikey = js["apikey"] + if("andepth" in js): + vars.andepth = js["andepth"] + if("temp" in js): + vars.temp = js["temp"] + if("top_p" in js): + vars.top_p = js["top_p"] + if("top_k" in js): + vars.top_k = js["top_k"] + if("tfs" in js): + vars.tfs = js["tfs"] + if("typical" in js): + vars.typical = js["typical"] + if("rep_pen" in js): + vars.rep_pen = js["rep_pen"] + if("rep_pen_slope" in js): + vars.rep_pen_slope = js["rep_pen_slope"] + if("rep_pen_range" in js): + vars.rep_pen_range = js["rep_pen_range"] + if("genamt" in js): + vars.genamt = js["genamt"] + if("max_length" in js): + vars.max_length = js["max_length"] + if("ikgen" in js): + vars.ikgen = js["ikgen"] + if("formatoptns" in js): + vars.formatoptns = js["formatoptns"] + if("numseqs" in js): + vars.numseqs = js["numseqs"] + if("widepth" in js): + vars.widepth = js["widepth"] + if("useprompt" in js): + vars.useprompt = js["useprompt"] + if("adventure" in js): + vars.adventure = js["adventure"] + if("chatmode" in js): + vars.chatmode = js["chatmode"] + if("chatname" in js): + vars.chatname = js["chatname"] + if("dynamicscan" in js): + vars.dynamicscan = js["dynamicscan"] + if("nopromptgen" in js): + vars.nopromptgen = js["nopromptgen"] + if("rngpersist" in js): + vars.rngpersist = js["rngpersist"] + if("nogenmod" in js): + vars.nogenmod = js["nogenmod"] + if("autosave" in js): + vars.autosave = js["autosave"] + if("newlinemode" in js): + vars.newlinemode = js["newlinemode"] + if("welcome" in js): + vars.welcome = js["welcome"] + + if("antemplate" in js): + vars.setauthornotetemplate = js["antemplate"] + if(not vars.gamestarted): + vars.authornotetemplate = vars.setauthornotetemplate + + if("userscripts" in js): + vars.userscripts = [] + for userscript in js["userscripts"]: + if type(userscript) is not str: + continue + userscript = userscript.strip() + if len(userscript) != 0 and all(q not in userscript for q in ("..", ":")) and all(userscript[0] not in q for q in ("/", "\\")) and os.path.exists(fileops.uspath(userscript)): + vars.userscripts.append(userscript) + + if("corescript" in js and type(js["corescript"]) is str and all(q not in js["corescript"] for q in ("..", ":")) and all(js["corescript"][0] not in q for q in ("/", "\\"))): + vars.corescript = js["corescript"] + else: + vars.corescript = "default.lua" #==================================================================# # Load a soft prompt from a file From 18cf8e620e84ebe2460c19838c37d09a29144f7b Mon Sep 17 00:00:00 2001 From: henk717 Date: Wed, 1 Jun 2022 19:51:59 +0200 Subject: [PATCH 42/63] Model Descriptions (TPU) --- colab/TPU.ipynb | 51 +++++++++++++++++++++++++------------------------ 1 file changed, 26 insertions(+), 25 deletions(-) diff --git a/colab/TPU.ipynb b/colab/TPU.ipynb index 0bbbc15e..ee7f9fd3 100644 --- a/colab/TPU.ipynb +++ b/colab/TPU.ipynb @@ -147,38 +147,39 @@ "\n", "| Model | Size | Style | Description |\n", "| --- | --- | --- | --- |\n", - "| Nerys by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", - "| Janeway by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", - "| Shinen by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n", - "| Skein by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n", - "| Adventure by VE\\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |\n", - "| Lit by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |\n", - "| Convo | 6B | Chatbot | Convo-6B is a GPT-J 6B model fine-tuned on a collection of high quality open source datasets which amount to 6 million messages. The primary goal of the model is to provide improved performance and generalization when generating multi-turn dialogue for characters that were not present from within the fine tuning data. The prompted performance has especially improved over the predecessor model [C1-6B](https://huggingface.co/hakurei/c1-6B). |\n", - "| C1 by Haru | 6B | Chatbot | C1 has been trained on various internet chatrooms, it makes the basis for an interesting chatbot model and has been optimized to be used in the Chatmode. |\n", + "| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", + "| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", + "| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n", + "| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n", + "| [Adventure](https://huggingface.co/KoboldAI/GPT-J-6B-Adventure) by VE\\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |\n", + "| [Lit](https://huggingface.co/hakurei/lit-6B) by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |\n", + "| [Convo](https://huggingface.co/hitomi-team/convo-6B) by Hitomi Team | 6B | Chatbot | Convo-6B is a GPT-J 6B model fine-tuned on a collection of high quality open source datasets which amount to 6 million messages. The primary goal of the model is to provide improved performance and generalization when generating multi-turn dialogue for characters that were not present from within the fine tuning data. The prompted performance has especially improved over the predecessor model [C1-6B](https://huggingface.co/hakurei/c1-6B). |\n", + "| [C1](https://huggingface.co/hakurei/c1-6B) by Haru | 6B | Chatbot | C1 has been trained on various internet chatrooms, it makes the basis for an interesting chatbot model and has been optimized to be used in the Chatmode. |\n", "| Neo(X) by EleutherAI | 20B | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. |\n", - "| Fairseq Dense | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. |\n", - "| GPT-J-6B by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |\n", + "| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-13B) | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. |\n", + "| [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B) by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |\n", "\n", "\n", "# [GPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/GPU.ipynb)\n", "\n", - "| Model | Size | Style | Description |\n", - "| ------------------------------------------------------------ | -------- | ---------- | ------------------------------------------------------------ |\n", - "| [GPT-Neo-2.7B-Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B GPU | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", - "| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B GPU | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n", - "| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B GPU | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n", - "| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B GPU | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n", - "| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B GPU | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n", - "| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B GPU | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n", - "| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B GPU | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n", + "| Model | Size | Style | Description |\n", + "| --- | --- | --- | --- |\n", + "| [Fairseq-Dense-2.7B-Nerys](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", + "| [GPT-Neo-2.7B-Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", + "| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n", + "| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n", + "| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n", + "| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n", + "| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n", + "| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n", "\n", - "| Style | Description |\n", - "| --------- | ------------------------------------------------------------ |\n", - "| Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. |\n", - "| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. |\n", + "| Style | Description |\n", + "| --- | --- |\n", + "| Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. |\n", + "| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. |\n", "| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel style model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |\n", - "| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. |\n", - "| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |\n", + "| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. |\n", + "| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |\n", "\n", "---\n", "## Tips to get the most out of Google Colab\n", From dd84a22fa9a25e4b12f185380d401aa968a0d692 Mon Sep 17 00:00:00 2001 From: henk717 Date: Wed, 1 Jun 2022 19:53:31 +0200 Subject: [PATCH 43/63] Model Descriptions (GPU) --- colab/GPU.ipynb | 42 +++++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/colab/GPU.ipynb b/colab/GPU.ipynb index 349c5635..d70e1fea 100644 --- a/colab/GPU.ipynb +++ b/colab/GPU.ipynb @@ -7,7 +7,7 @@ "private_outputs": true, "provenance": [], "collapsed_sections": [], - "authorship_tag": "ABX9TyMClgsuyqYCEvk3ugme8XOZ", + "authorship_tag": "ABX9TyN3BT4aQmdwrT2ibYyYjjMw", "include_colab_link": true }, "kernelspec": { @@ -90,31 +90,32 @@ "cell_type": "markdown", "source": [ "# GPU Edition Model Descriptions\n", - "| Model | Size | Style | Description |\n", - "| ------------------------------------------------------------ | -------- | ---------- | ------------------------------------------------------------ |\n", - "| [GPT-Neo-2.7B-Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B GPU | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", - "| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B GPU | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n", - "| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B GPU | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n", - "| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B GPU | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n", - "| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B GPU | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n", - "| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B GPU | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n", - "| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B GPU | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n", + "| Model | Size | Style | Description |\n", + "| --- | --- | --- | --- |\n", + "| [Fairseq-Dense-2.7B-Nerys](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", + "| [GPT-Neo-2.7B-Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", + "| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n", + "| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n", + "| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n", + "| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n", + "| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n", + "| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n", "\n", "# [TPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)\n", "\n", "| Model | Size | Style | Description |\n", "| --- | --- | --- | --- |\n", - "| Nerys by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", - "| Janeway by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", - "| Shinen by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n", - "| Skein by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n", - "| Adventure by VE\\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |\n", - "| Lit by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |\n", - "| Convo | 6B | Chatbot | Convo-6B is a GPT-J 6B model fine-tuned on a collection of high quality open source datasets which amount to 6 million messages. The primary goal of the model is to provide improved performance and generalization when generating multi-turn dialogue for characters that were not present from within the fine tuning data. The prompted performance has especially improved over the predecessor model [C1-6B](https://huggingface.co/hakurei/c1-6B). |\n", - "| C1 by Haru | 6B | Chatbot | C1 has been trained on various internet chatrooms, it makes the basis for an interesting chatbot model and has been optimized to be used in the Chatmode. |\n", + "| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", + "| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", + "| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n", + "| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n", + "| [Adventure](https://huggingface.co/KoboldAI/GPT-J-6B-Adventure) by VE\\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |\n", + "| [Lit](https://huggingface.co/hakurei/lit-6B) by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |\n", + "| [Convo](https://huggingface.co/hitomi-team/convo-6B) by Hitomi Team | 6B | Chatbot | Convo-6B is a GPT-J 6B model fine-tuned on a collection of high quality open source datasets which amount to 6 million messages. The primary goal of the model is to provide improved performance and generalization when generating multi-turn dialogue for characters that were not present from within the fine tuning data. The prompted performance has especially improved over the predecessor model [C1-6B](https://huggingface.co/hakurei/c1-6B). |\n", + "| [C1](https://huggingface.co/hakurei/c1-6B) by Haru | 6B | Chatbot | C1 has been trained on various internet chatrooms, it makes the basis for an interesting chatbot model and has been optimized to be used in the Chatmode. |\n", "| Neo(X) by EleutherAI | 20B | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. |\n", - "| Fairseq Dense | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. |\n", - "| GPT-J-6B by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |\n", + "| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-13B) | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. |\n", + "| [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B) by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |\n", "\n", "\n", "| Style | Description |\n", @@ -123,7 +124,6 @@ "| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. |\n", "| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel style model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |\n", "| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. |\n", - "| Hybrid | Hybrid models are a blend between different styles, for example they are trained on both Novel stories and Adventure stories. These models are great variety models that you can use for multiple different playstyles and modes, but depending on your usage you may need to enable Adventure Mode or the You bias (in userscripts). |\n", "| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |\n", "\n", "# How to start KoboldAI in 7 simple steps\n", From 1c12d8455400a3a37d34632878ab1ff8107e86d4 Mon Sep 17 00:00:00 2001 From: henk717 Date: Sat, 4 Jun 2022 13:11:54 +0200 Subject: [PATCH 44/63] Official as Default (GPU) --- colab/GPU.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/colab/GPU.ipynb b/colab/GPU.ipynb index d70e1fea..0e86c4a5 100644 --- a/colab/GPU.ipynb +++ b/colab/GPU.ipynb @@ -7,7 +7,7 @@ "private_outputs": true, "provenance": [], "collapsed_sections": [], - "authorship_tag": "ABX9TyN3BT4aQmdwrT2ibYyYjjMw", + "authorship_tag": "ABX9TyPbwW79K9/RkYH9i9rkYFyj", "include_colab_link": true }, "kernelspec": { @@ -27,7 +27,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -69,7 +69,7 @@ "#@markdown You can find a description of the models below along with instructions on how to start KoboldAI.\n", "\n", "Model = \"KoboldAI/fairseq-dense-2.7B-Nerys\" #@param [\"KoboldAI/fairseq-dense-2.7B-Nerys\", \"KoboldAI/GPT-Neo-2.7B-Janeway\", \"KoboldAI/GPT-Neo-2.7B-AID\", \"KoboldAI/GPT-Neo-2.7B-Picard\", \"KoboldAI/GPT-Neo-2.7B-Horni-LN\", \"KoboldAI/GPT-Neo-2.7B-Horni\", \"KoboldAI/GPT-Neo-2.7B-Shinen\", \"EleutherAI/gpt-neo-2.7B\"] {allow-input: true}\n", - "Version = \"United\" #@param [\"Official\", \"United\"] {allow-input: true}\n", + "Version = \"Official\" #@param [\"Official\", \"United\"] {allow-input: true}\n", "Provider = \"Localtunnel\" #@param [\"Localtunnel\", \"Cloudflare\"]\n", "\n", "!nvidia-smi\n", From 10d9ae88f34ef3c437fe09de4bb88417dbac3ff3 Mon Sep 17 00:00:00 2001 From: henk717 Date: Sat, 4 Jun 2022 13:41:09 +0200 Subject: [PATCH 45/63] Official as Default (TPU) --- colab/TPU.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/colab/TPU.ipynb b/colab/TPU.ipynb index ee7f9fd3..a7ba10e8 100644 --- a/colab/TPU.ipynb +++ b/colab/TPU.ipynb @@ -7,7 +7,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -66,7 +66,7 @@ "\n", "#@title <-- Click this to start KoboldAI\n", "Model = \"Nerys 13B\" #@param [\"Nerys 13B\", \"Janeway 13B\", \"Shinen 13B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Shinen 6B\", \"Lit 6B\", \"Convo 6B\", \"C1 6B\", \"NeoX 20B\", \"facebook/opt-13b\", \"KoboldAI/fairseq-dense-13B\", \"EleutherAI/gpt-j-6B\"] {allow-input: true}\n", - "Version = \"United\" #@param [\"Official\", \"United\"] {allow-input: true}\n", + "Version = \"Official\" #@param [\"Official\", \"United\"] {allow-input: true}\n", "Provider = \"Localtunnel\" #@param [\"Localtunnel\", \"Cloudflare\"]\n", "\n", "import os\n", From ce4a78544941eb99a12e95b9b4e535cb216f28b4 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Sun, 5 Jun 2022 21:06:09 -0400 Subject: [PATCH 46/63] Allow clicking on userscripts to enable/disable them (mobile friendly) --- static/application.js | 27 +++++++++++++++++++++++---- templates/index.html | 2 +- 2 files changed, 24 insertions(+), 5 deletions(-) diff --git a/static/application.js b/static/application.js index cfb25883..3b50281c 100644 --- a/static/application.js +++ b/static/application.js @@ -106,6 +106,9 @@ var using_webkit_patch = true; var shift_down = false; var do_clear_ent = false; +// Whether or not an entry in the Userscripts menu is being dragged +var us_dragging = false; + // Display vars var allowtoggle = false; var formatcount = 0; @@ -2412,9 +2415,25 @@ $(document).ready(function(){ }, 2); }); + var us_click_handler = function(ev) { + setTimeout(function() { + if (us_dragging) { + return; + } + var target = $(ev.target).closest(".uslistitem")[0]; + if ($.contains(document.getElementById("uslistunloaded"), target)) { + document.getElementById("uslistloaded").appendChild(target); + } else { + document.getElementById("uslistunloaded").appendChild(target); + } + }, 10); + } + // Make the userscripts menu sortable var us_sortable_settings = { placeholder: "ussortable-placeholder", + start: function() { us_dragging = true; }, + stop: function() { us_dragging = false; }, delay: 2, cursor: "move", tolerance: "pointer", @@ -2423,12 +2442,12 @@ $(document).ready(function(){ scrollSensitivity: 64, scrollSpeed: 10, } - $(usunloaded).sortable($.extend({ + usunloaded.sortable($.extend({ connectWith: "#uslistloaded", - }, us_sortable_settings)); - $(usloaded).sortable($.extend({ + }, us_sortable_settings)).on("click", ".uslistitem", us_click_handler); + usloaded.sortable($.extend({ connectWith: "#uslistunloaded", - }, us_sortable_settings)); + }, us_sortable_settings)).on("click", ".uslistitem", us_click_handler); // Bind actions to UI buttons button_send.on("click", function(ev) { diff --git a/templates/index.html b/templates/index.html index 07bcdcc5..a3214ffa 100644 --- a/templates/index.html +++ b/templates/index.html @@ -17,7 +17,7 @@ - + From fe619d4677b67eecb1a706c12c242f10074434b4 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Thu, 9 Jun 2022 17:42:46 -0400 Subject: [PATCH 47/63] Update list of versions with broken OPT again They released another version of transformers that still doesn't have the OPT patch so I decided it would be safer to just mark all 4.19 transformers versions as needing the OPT patch. --- aiserver.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aiserver.py b/aiserver.py index 4e33332b..fefab9b8 100644 --- a/aiserver.py +++ b/aiserver.py @@ -1359,7 +1359,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go # Fix a bug in OPTForCausalLM where self.lm_head is the wrong size - if(packaging.version.parse("4.19.0.dev0") <= packaging.version.parse(transformers_version) <= packaging.version.parse("4.19.2")): + if(packaging.version.parse("4.19.0.dev0") <= packaging.version.parse(transformers_version) < packaging.version.parse("4.20.0")): try: from transformers import OPTForCausalLM, OPTModel except ImportError: From fdb2a7fa4ce87a586aae1590ded4630a1a03e48d Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Fri, 10 Jun 2022 22:28:20 -0400 Subject: [PATCH 48/63] Top-A sampling --- aiserver.py | 23 ++++++++++++++++++++++- bridge.lua | 2 ++ gensettings.py | 11 +++++++++++ static/application.js | 7 +++++++ templates/index.html | 2 +- tpu_mtj_backend.py | 42 ++++++++++++++++++++++++++++++++++++------ warpers.py | 29 +++++++++++++++++++++++++++++ 7 files changed, 108 insertions(+), 8 deletions(-) diff --git a/aiserver.py b/aiserver.py index fefab9b8..f14bbd77 100644 --- a/aiserver.py +++ b/aiserver.py @@ -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) 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..9c1f88eb 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 From 42b7a327b2440a3a48a969059beb06293a26e239 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Fri, 10 Jun 2022 22:34:14 -0400 Subject: [PATCH 49/63] Fix an unfortunate typo in top-a warper --- warpers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/warpers.py b/warpers.py index 9c1f88eb..fb683f50 100644 --- a/warpers.py +++ b/warpers.py @@ -168,7 +168,7 @@ class TopALogitsWarper(LogitsWarper): # 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 + 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 From 5c81374a48822cdaeb742d9aa007a152db30260d Mon Sep 17 00:00:00 2001 From: Henk Date: Sat, 11 Jun 2022 22:04:37 +0200 Subject: [PATCH 50/63] Top A for GooseAi --- aiserver.py | 1 + 1 file changed, 1 insertion(+) diff --git a/aiserver.py b/aiserver.py index f14bbd77..b51fe538 100644 --- a/aiserver.py +++ b/aiserver.py @@ -4668,6 +4668,7 @@ 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, From 66c0dda485d9be7df300e7e791d5b625f0c825e3 Mon Sep 17 00:00:00 2001 From: Henk Date: Sat, 11 Jun 2022 22:54:51 +0200 Subject: [PATCH 51/63] Hide (Broken) Chatbot Models Removing this option because they are currently unavailable. People who still have them can load them trough the load from file option. Once they have been retrained and reuploaded I will add the menu back. --- aiserver.py | 1 - 1 file changed, 1 deletion(-) diff --git a/aiserver.py b/aiserver.py index b51fe538..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", ""], From 2d3db7b4ba388f566aaec88a0e76678fe4fade8d Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Mon, 13 Jun 2022 19:12:23 -0400 Subject: [PATCH 52/63] Implement support for sampler order in the backend code --- aiserver.py | 27 +++++++++++++++++++-------- tpu_mtj_backend.py | 46 +++++++++++++++++++++++++++------------------- utils.py | 2 ++ 3 files changed, 48 insertions(+), 27 deletions(-) diff --git a/aiserver.py b/aiserver.py index 6267aec2..0bed5ad8 100644 --- a/aiserver.py +++ b/aiserver.py @@ -306,6 +306,7 @@ class vars: acregex_ui = re.compile(r'^ *(>.*)$', re.MULTILINE) # Pattern for matching actions in the HTML-escaped story so we can apply colouring, etc (make sure to encase part to format in parentheses) 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'(<\|(?:.|\n)*?\|>)') # Pattern for matching comments in the editor + sampler_order = utils.default_sampler_order.copy() chatmode = False chatname = "You" adventure = False @@ -1448,15 +1449,23 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go new_get_logits_processor.old_get_logits_processor = transformers.generation_utils.GenerationMixin._get_logits_processor transformers.generation_utils.GenerationMixin._get_logits_processor = new_get_logits_processor + class KoboldLogitsWarperList(LogitsProcessorList): + def __init__(self, beams: int = 1, **kwargs): + self.__warper_list: List[LogitsWarper] = [] + self.__warper_list.append(TopKLogitsWarper(top_k=1, min_tokens_to_keep=1 + (beams > 1))) + self.__warper_list.append(TopALogitsWarper(top_a=0.5, min_tokens_to_keep=1 + (beams > 1))) + self.__warper_list.append(TopPLogitsWarper(top_p=0.5, min_tokens_to_keep=1 + (beams > 1))) + self.__warper_list.append(TailFreeLogitsWarper(tfs=0.5, min_tokens_to_keep=1 + (beams > 1))) + self.__warper_list.append(TypicalLogitsWarper(typical=0.5, min_tokens_to_keep=1 + (beams > 1))) + self.__warper_list.append(TemperatureLogitsWarper(temperature=0.5)) + + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, *args, **kwargs): + for k in vars.sampler_order: + scores = self.__warper_list[k](input_ids, scores, *args, **kwargs) + return scores + 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))) - warper_list.append(TemperatureLogitsWarper(temperature=0.5)) - return warper_list + return KoboldLogitsWarperList(beams=beams) def new_sample(self, *args, **kwargs): assert kwargs.pop("logits_warper", None) is not None @@ -1816,6 +1825,7 @@ else: def tpumtjgenerate_settings_callback() -> dict: return { + "sampler_order": vars.sampler_order, "top_p": float(vars.top_p), "temp": float(vars.temp), "top_k": int(vars.top_k), @@ -3910,6 +3920,7 @@ def tpumtjgenerate(txt, minimum, maximum, found_entries=None): rprange=vars.rep_pen_range, soft_embeddings=vars.sp, soft_tokens=soft_tokens, + sampler_order=vars.sampler_order, ) past = genout for i in range(vars.numseqs): diff --git a/tpu_mtj_backend.py b/tpu_mtj_backend.py index f66ad53c..67e006d6 100644 --- a/tpu_mtj_backend.py +++ b/tpu_mtj_backend.py @@ -65,6 +65,7 @@ def stopping_callback(generated, n_generated, excluded_world_info) -> Tuple[List def settings_callback() -> dict: return { + "sampler_order": utils.default_sampler_order.copy(), "top_p": 0.9, "temp": 0.5, "top_k": 0, @@ -159,7 +160,7 @@ 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, top_a=0.0): +def kobold_sample_dynamic(key, logits, sampler_order: Optional[np.ndarray] = None, 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 6 filters to the logits (top-k, then top-a, then top-p, then TFS, then typical, then temperature) @@ -181,8 +182,6 @@ def kobold_sample_dynamic(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, ty sorted_indices_to_remove, ) 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): @@ -195,8 +194,6 @@ def kobold_sample_dynamic(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, ty 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) @@ -222,8 +219,6 @@ def kobold_sample_dynamic(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, ty sorted_indices_to_remove, ) return np.where(indices_to_remove, -np.inf, logits) - if top_p < 1.0: - logits = top_p_filter(logits) # Tail free sampling (basically top-p a second time on remaining tokens # except it's the "cumulative normalized absolute second finite # differences of the softmax probabilities" instead of just the @@ -262,8 +257,6 @@ def kobold_sample_dynamic(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, ty sorted_indices_to_remove, ) return np.where(indices_to_remove, -np.inf, logits) - if tfs < 1.0: - logits = tail_free_filter(logits) # Typical sampling (https://arxiv.org/pdf/2202.00666.pdf) def typical_filter(logits): # Compute softmax probabilities and the natural logarithms of them @@ -293,10 +286,16 @@ def kobold_sample_dynamic(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, ty sorted_indices_to_remove, ) return np.where(indices_to_remove, -jnp.inf, logits) - if typical < 1.0: - logits = typical_filter(logits) # Temperature (just divide the logits by the temperature) - logits /= temp + def temp_filter(logits): + return logits / temp + for k in sampler_order: + if k == 0 and top_k > 0: logits = top_k_filter(logits) + if k == 1 and top_a > 0.0: logits = top_a_filter(logits) + if k == 2 and top_p < 1.0: logits = top_p_filter(logits) + if k == 3 and tfs < 1.0: logits = tail_free_filter(logits) + if k == 4 and typical < 1.0: logits = typical_filter(logits) + if k == 5 and temp != 1.0: logits = temp_filter(logits) # Finally, pick one token using the softmax thingy again (it gives # an array whose elements sum to 1 so it can be used nicely as a # probability distribution) @@ -347,7 +346,7 @@ 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, top_a=0.0): +def kobold_sample_static(key, logits, sampler_order: Optional[np.ndarray] = None, 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 6 filters to the logits (top-k, then top-a, then top-p, then TFS, then typical, then temperature) @@ -369,7 +368,6 @@ def kobold_sample_static(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typ sorted_indices_to_remove, ) 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): @@ -382,7 +380,6 @@ def kobold_sample_static(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typ 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) @@ -408,7 +405,6 @@ def kobold_sample_static(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typ sorted_indices_to_remove, ) return jnp.where(indices_to_remove, -jnp.inf, logits) - logits = jax.lax.cond(top_p < 1.0, top_p_filter, lambda x: x, logits) # Tail free sampling (basically top-p a second time on remaining tokens # except it's the "cumulative normalized absolute second finite # differences of the softmax probabilities" instead of just the @@ -447,7 +443,6 @@ def kobold_sample_static(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typ sorted_indices_to_remove, ) return jnp.where(indices_to_remove, -jnp.inf, logits) - logits = jax.lax.cond(tfs < 1.0, tail_free_filter, lambda x: x, logits) # Typical sampling (https://arxiv.org/pdf/2202.00666.pdf) def typical_filter(logits): # Compute softmax probabilities and the natural logarithms of them @@ -476,11 +471,16 @@ def kobold_sample_static(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typ sorted_indices_to_remove, ) return jnp.where(indices_to_remove, -jnp.inf, logits) - logits = jax.lax.cond(typical < 1.0, typical_filter, lambda x: x, logits) # Temperature (just divide the logits by the temperature) def temp_filter(logits): return logits / temp - logits = jax.lax.cond(True, temp_filter, lambda x: x, logits) + for k in sampler_order: + logits = jax.lax.cond(jnp.logical_and(k == 0, top_k > 0), top_k_filter, lambda x: x, logits) + logits = jax.lax.cond(jnp.logical_and(k == 1, top_a > 0.0), top_a_filter, lambda x: x, logits) + logits = jax.lax.cond(jnp.logical_and(k == 2, top_p < 1.0), top_p_filter, lambda x: x, logits) + logits = jax.lax.cond(jnp.logical_and(k == 3, tfs < 1.0), tail_free_filter, lambda x: x, logits) + logits = jax.lax.cond(jnp.logical_and(k == 4, typical < 1.0), typical_filter, lambda x: x, logits) + logits = jax.lax.cond(jnp.logical_and(k == 5, temp != 1.0), temp_filter, lambda x: x, logits) # Finally, pick one token using the softmax thingy again (it gives # an array whose elements sum to 1 so it can be used nicely as a # probability distribution) @@ -842,8 +842,12 @@ def infer_static( gen_len=80, soft_embeddings: Optional[np.array] = None, soft_tokens: Optional[np.array] = None, + sampler_order: Optional[List[int]] = None, ) -> List[np.array]: maps.thread_resources.env = thread_resources_env + if sampler_order is None: + sampler_order = utils.default_sampler_order.copy() + sampler_order = np.uint32(sampler_order) total_batch = 1 tokens = context if(soft_tokens is not None): @@ -854,6 +858,7 @@ def infer_static( batched_tokens = np.array([padded_tokens] * total_batch) samples = [] batched_generator_params = { + "sampler_order": np.repeat(sampler_order[np.newaxis], total_batch, axis=0), "temp": temp * np.ones(total_batch), "top_p": top_p * np.ones(total_batch), "tfs": tfs * np.ones(total_batch), @@ -1015,6 +1020,9 @@ def read_neox_checkpoint(state, path, config, checkpoint_shards=2): def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpoint=False, **kwargs) -> None: global thread_resources_env, seq, tokenizer, network, params + if not hasattr(vars, "sampler_order") or not vars.sampler_order: + vars.sampler_order = utils.default_sampler_order.copy() + default_params = { "compat": "j", "layers": 28, diff --git a/utils.py b/utils.py index bc085412..96606269 100644 --- a/utils.py +++ b/utils.py @@ -20,6 +20,8 @@ from_pretrained_index_filename: Optional[str] = None from_pretrained_kwargs = {} bar = None +default_sampler_order = [0, 1, 2, 3, 4, 5] + #==================================================================# # Decorator to prevent a function's actions from being run until # at least x seconds have passed without the function being called From 4c7d6f42d99d557130511f5d185249b34f9db5a1 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Mon, 13 Jun 2022 19:14:38 -0400 Subject: [PATCH 53/63] Add `sampler_order` to settings file --- aiserver.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/aiserver.py b/aiserver.py index 0bed5ad8..abaffa77 100644 --- a/aiserver.py +++ b/aiserver.py @@ -568,6 +568,8 @@ def loadmodelsettings(): vars.badwordsids = js["badwordsids"] if("nobreakmodel" in js): vars.nobreakmodel = js["nobreakmodel"] + if("sampler_order" in js): + vars.sampler_order = js["sampler_order"] if("temp" in js): vars.temp = js["temp"] if("top_p" in js): @@ -611,6 +613,7 @@ def savesettings(): js = {} js["apikey"] = vars.apikey js["andepth"] = vars.andepth + js["sampler_order"] = vars.sampler_order js["temp"] = vars.temp js["top_p"] = vars.top_p js["top_k"] = vars.top_k @@ -687,6 +690,8 @@ def processsettings(js): vars.apikey = js["apikey"] if("andepth" in js): vars.andepth = js["andepth"] + if("sampler_order" in js): + vars.sampler_order = js["sampler_order"] if("temp" in js): vars.temp = js["temp"] if("top_p" in js): From 6231106f95221bdfa3ed452fdca0bb14b22aa453 Mon Sep 17 00:00:00 2001 From: Gnome Ann <> Date: Mon, 13 Jun 2022 20:18:09 -0400 Subject: [PATCH 54/63] Add Samplers menu --- aiserver.py | 12 ++++++ static/application.js | 90 ++++++++++++++++++++++++++++++++++++++++++- static/custom.css | 28 ++++++++++++-- templates/index.html | 20 +++++++++- 4 files changed, 143 insertions(+), 7 deletions(-) diff --git a/aiserver.py b/aiserver.py index abaffa77..06c65fc0 100644 --- a/aiserver.py +++ b/aiserver.py @@ -2873,6 +2873,8 @@ def get_message(msg): elif(msg['cmd'] == 'uslistrequest'): unloaded, loaded = getuslist() emit('from_server', {'cmd': 'buildus', 'data': {"unloaded": unloaded, "loaded": loaded}}) + elif(msg['cmd'] == 'samplerlistrequest'): + emit('from_server', {'cmd': 'buildsamplers', 'data': vars.sampler_order}) elif(msg['cmd'] == 'usloaded'): vars.userscripts = [] for userscript in msg['data']: @@ -2886,6 +2888,16 @@ def get_message(msg): load_lua_scripts() unloaded, loaded = getuslist() sendUSStatItems() + elif(msg['cmd'] == 'samplers'): + sampler_order = msg["data"] + if(not isinstance(sampler_order, list)): + raise ValueError(f"Sampler order must be a list, but got a {type(sampler_order)}") + if(len(sampler_order) != len(vars.sampler_order)): + raise ValueError(f"Sampler order must be a list of length {len(vars.sampler_order)}, but got a list of length {len(sampler_order)}") + if(not all(isinstance(e, int) for e in sampler_order)): + raise ValueError(f"Sampler order must be a list of ints, but got a list with at least one non-int element") + vars.sampler_order = sampler_order + settingschanged() elif(msg['cmd'] == 'loadselect'): vars.loadselect = msg["data"] elif(msg['cmd'] == 'spselect'): diff --git a/static/application.js b/static/application.js index 55487f76..3cddea87 100644 --- a/static/application.js +++ b/static/application.js @@ -20,6 +20,7 @@ var button_settings; var button_format; var button_softprompt; var button_userscripts; +var button_samplers; var button_mode; var button_mode_label; var button_send; @@ -109,6 +110,9 @@ var do_clear_ent = false; // Whether or not an entry in the Userscripts menu is being dragged var us_dragging = false; +// Whether or not an entry in the Samplers menu is being dragged +var samplers_dragging = false; + // Display vars var allowtoggle = false; var formatcount = 0; @@ -976,6 +980,16 @@ function hideUSPopup() { spcontent.html(""); } +function showSamplersPopup() { + samplerspopup.removeClass("hidden"); + samplerspopup.addClass("flex"); +} + +function hideSamplersPopup() { + samplerspopup.removeClass("flex"); + samplerspopup.addClass("hidden"); +} + function buildLoadList(ar) { disableButtons([load_accept]); loadcontent.html(""); @@ -1109,6 +1123,29 @@ function buildUSList(unloaded, loaded) { } } +function buildSamplerList(samplers) { + samplerslist.html(""); + showSamplersPopup(); + var i; + var samplers_lookup_table = [ + "Top-k Sampling", + "Top-a Sampling", + "Top-p Sampling", + "Tail-free Sampling", + "Typical Sampling", + "Temperature", + ] + for(i=0; i\ +

\ +
\ +
"+samplers_lookup_table[samplers[i]]+"
\ +
\ +
\ + "); + } +} + function highlightLoadLine(ref) { $("#loadlistcontent > div > div.popuplistselected").removeClass("popuplistselected"); ref.addClass("popuplistselected"); @@ -1838,6 +1875,7 @@ $(document).ready(function(){ button_format = $('#btn_format'); button_softprompt = $("#btn_softprompt"); button_userscripts= $("#btn_userscripts"); + button_samplers = $("#btn_samplers"); button_mode = $('#btnmode') button_mode_label = $('#btnmode_label') button_send = $('#btnsend'); @@ -1886,6 +1924,10 @@ $(document).ready(function(){ usloaded = $("#uslistloaded"); us_accept = $("#btn_usaccept"); us_close = $("#btn_usclose"); + samplerspopup = $("#samplerscontainer"); + samplerslist = $("#samplerslist"); + samplers_accept = $("#btn_samplersaccept"); + samplers_close = $("#btn_samplersclose"); nspopup = $("#newgamecontainer"); ns_accept = $("#btn_nsaccept"); ns_close = $("#btn_nsclose"); @@ -1908,7 +1950,7 @@ $(document).ready(function(){ modelname = msg.modelname; } refreshTitle(); - connect_status.html("Connected to KoboldAI Process!"); + connect_status.html("Connected to KoboldAI!"); connect_status.removeClass("color_orange"); connect_status.addClass("color_green"); // Reset Menus @@ -2310,6 +2352,8 @@ $(document).ready(function(){ buildSPList(msg.data); } else if(msg.cmd == "buildus") { buildUSList(msg.data.unloaded, msg.data.loaded); + } else if(msg.cmd == "buildsamplers") { + buildSamplerList(msg.data); } else if(msg.cmd == "askforoverwrite") { // Show overwrite warning show([$(".saveasoverwrite")]); @@ -2436,6 +2480,20 @@ $(document).ready(function(){ }, 10); } + var samplers_click_handler = function(ev) { + setTimeout(function() { + if (samplers_dragging) { + return; + } + var target = $(ev.target).closest(".samplerslistitem"); + var next = target.parent().next().find(".samplerslistitem"); + if (!next.length) { + return; + } + next.parent().after(target.parent()); + }, 10); + } + // Make the userscripts menu sortable var us_sortable_settings = { placeholder: "ussortable-placeholder", @@ -2456,6 +2514,22 @@ $(document).ready(function(){ connectWith: "#uslistunloaded", }, us_sortable_settings)).on("click", ".uslistitem", us_click_handler); + // Make the samplers menu sortable + var samplers_sortable_settings = { + placeholder: "samplerssortable-placeholder", + start: function() { samplers_dragging = true; }, + stop: function() { samplers_dragging = false; }, + delay: 2, + cursor: "move", + tolerance: "pointer", + opacity: 0.21, + revert: 173, + scrollSensitivity: 64, + scrollSpeed: 10, + } + samplerslist.sortable($.extend({ + }, samplers_sortable_settings)).on("click", ".samplerslistitem", samplers_click_handler); + // Bind actions to UI buttons button_send.on("click", function(ev) { dosubmit(); @@ -2590,6 +2664,10 @@ $(document).ready(function(){ button_userscripts.on("click", function(ev) { socket.send({'cmd': 'uslistrequest', 'data': ''}); }); + + button_samplers.on("click", function(ev) { + socket.send({'cmd': 'samplerlistrequest', 'data': ''}); + }); load_close.on("click", function(ev) { hideLoadPopup(); @@ -2623,6 +2701,16 @@ $(document).ready(function(){ socket.send({'cmd': 'usload', 'data': ''}); hideUSPopup(); }); + + samplers_close.on("click", function(ev) { + hideSamplersPopup(); + }); + + samplers_accept.on("click", function(ev) { + hideMessage(); + socket.send({'cmd': 'samplers', 'data': samplerslist.find(".samplerslistitem").map(function() { return parseInt($(this).attr("sid")); }).toArray()}); + hideSamplersPopup(); + }); button_newgame.on("click", function(ev) { if(connected) { diff --git a/static/custom.css b/static/custom.css index d70fd34e..640cb8db 100644 --- a/static/custom.css +++ b/static/custom.css @@ -457,6 +457,26 @@ body.connected #popupfooter, #popupfooter.always-available { overflow-wrap: anywhere; } +#samplerspopup { + width: 300px; + background-color: #262626; + margin-top: 100px; +} + +@media (max-width: 768px) { + #samplerspopup { + width: 100%; + background-color: #262626; + margin-top: 100px; + } +} + +#samplerslist { + height: 300px; + overflow-y: scroll; + overflow-wrap: anywhere; +} + #nspopup { width: 350px; background-color: #262626; @@ -750,7 +770,7 @@ body.connected .dropdown-item:hover, .dropdown-item.always-available:hover { background-color: #3bf723; } -.ussortable-placeholder { +.ussortable-placeholder, .samplerssortable-placeholder { height: 4px; background-color: #3bf723; } @@ -1340,7 +1360,7 @@ body.connected .popupfooter, .popupfooter.always-available { background-color: #688f1f; } -.uslistitem { +.uslistitem, .samplerslistitem { padding: 12px 10px 12px 10px; display: flex; flex-grow: 1; @@ -1352,11 +1372,11 @@ body.connected .popupfooter, .popupfooter.always-available { transition: background-color 0.25s ease-in; } -.uslistitemsub { +.uslistitemsub, .samplerslistitemsub { color: #ba9; } -.uslistitem:hover { +.uslistitem:hover, .samplerslistitem:hover { cursor: move; background-color: #688f1f; } diff --git a/templates/index.html b/templates/index.html index 690535f7..7ec9f66c 100644 --- a/templates/index.html +++ b/templates/index.html @@ -9,7 +9,7 @@ - + @@ -17,7 +17,7 @@ - + @@ -71,6 +71,9 @@
  • Formatting
    • +
  • + Samplers +
  • Userscripts
    • @@ -299,6 +302,19 @@ +