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Reorganize model loading UI completely (#2720)
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@ -8,6 +8,7 @@ extensions/multimodal/pipelines
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logs
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loras
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models
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presets
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repositories
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softprompts
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torch-dumps
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@ -211,6 +211,12 @@ Optionally, you can use the following command-line flags:
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| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
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| `--verbose` | Print the prompts to the terminal. |
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#### Model loader
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| Flag | Description |
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|--------------------------------------------|-------------|
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| `--loader LOADER` | Choose the model loader manually, otherwise, it will get autodetected. Valid options: autogptq, gptq-for-llama, transformers, llamacpp, rwkv, flexgen |
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#### Accelerate/transformers
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| Flag | Description |
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@ -265,7 +271,6 @@ Optionally, you can use the following command-line flags:
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| Flag | Description |
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|---------------------------|-------------|
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| `--gptq-for-llama` | Use GPTQ-for-LLaMa to load the GPTQ model instead of AutoGPTQ. |
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| `--wbits WBITS` | Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
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| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. |
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| `--groupsize GROUPSIZE` | Group size. |
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@ -280,7 +285,6 @@ Optionally, you can use the following command-line flags:
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| Flag | Description |
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|------------------|-------------|
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| `--flexgen` | Enable the use of FlexGen offloading. |
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| `--percent PERCENT [PERCENT ...]` | FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0). |
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| `--compress-weight` | FlexGen: Whether to compress weight (default: False).|
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| `--pin-weight [PIN_WEIGHT]` | FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%). |
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@ -21,13 +21,13 @@ The output will be saved to `models/opt-1.3b-np/`.
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The basic command is the following:
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```
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python server.py --model opt-1.3b --flexgen
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python server.py --model opt-1.3b --loader flexgen
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```
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For large models, the RAM usage may be too high and your computer may freeze. If that happens, you can try this:
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```
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python server.py --model opt-1.3b --flexgen --compress-weight
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python server.py --model opt-1.3b --loader flexgen --compress-weight
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```
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With this second command, I was able to run both OPT-6.7b and OPT-13B with **2GB VRAM**, and the speed was good in both cases.
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@ -35,7 +35,7 @@ With this second command, I was able to run both OPT-6.7b and OPT-13B with **2GB
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You can also manually set the offload strategy with
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```
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python server.py --model opt-1.3b --flexgen --percent 0 100 100 0 100 0
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python server.py --model opt-1.3b --loader flexgen --percent 0 100 100 0 100 0
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```
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where the six numbers after `--percent` are:
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@ -55,8 +55,8 @@ You should typically only change the first two numbers. If their sum is less tha
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In my experiments with OPT-30B using a RTX 3090 on Linux, I have obtained these results:
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* `--flexgen --compress-weight --percent 0 100 100 0 100 0`: 0.99 seconds per token.
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* `--flexgen --compress-weight --percent 100 0 100 0 100 0`: 0.765 seconds per token.
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* `--loader flexgen --compress-weight --percent 0 100 100 0 100 0`: 0.99 seconds per token.
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* `--loader flexgen --compress-weight --percent 100 0 100 0 100 0`: 0.765 seconds per token.
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## Limitations
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@ -7,10 +7,11 @@ from modules import shared
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from modules.chat import generate_chat_reply
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from modules.LoRA import add_lora_to_model
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from modules.models import load_model, unload_model
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from modules.models_settings import (get_model_settings_from_yamls,
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update_model_parameters)
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from modules.text_generation import (encode, generate_reply,
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stop_everything_event)
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from modules.utils import get_available_models
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from server import get_model_specific_settings, update_model_parameters
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def get_model_info():
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@ -22,6 +23,7 @@ def get_model_info():
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'shared.args': vars(shared.args),
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}
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class Handler(BaseHTTPRequestHandler):
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def do_GET(self):
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if self.path == '/api/v1/model':
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@ -126,7 +128,7 @@ class Handler(BaseHTTPRequestHandler):
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shared.model_name = model_name
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unload_model()
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model_settings = get_model_specific_settings(shared.model_name)
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model_settings = get_model_settings_from_yamls(shared.model_name)
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shared.settings.update(model_settings)
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update_model_parameters(model_settings, initial=True)
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@ -139,7 +141,7 @@ class Handler(BaseHTTPRequestHandler):
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add_lora_to_model(shared.args.lora) # list
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except Exception as e:
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response = json.dumps({'error': { 'message': repr(e) } })
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response = json.dumps({'error': {'message': repr(e)}})
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self.wfile.write(response.encode('utf-8'))
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raise e
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@ -77,8 +77,7 @@ def add_lora_to_model(lora_names):
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elif shared.args.load_in_8bit:
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params['device_map'] = {'': 0}
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shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"),adapter_name=lora_names[0], **params)
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shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), adapter_name=lora_names[0], **params)
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for lora in lora_names[1:]:
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shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
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@ -88,8 +88,8 @@ class RWKVModel:
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out, state = self.model.forward(tokens[:args.chunk_len], state)
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tokens = tokens[args.chunk_len:]
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if i == 0:
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begin_token= len(all_tokens)
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last_token_posi=begin_token
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begin_token = len(all_tokens)
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last_token_posi = begin_token
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# cache the model state after scanning the context
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# we don't cache the state after processing our own generated tokens because
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# the output string might be post-processed arbitrarily. Therefore, what's fed into the model
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@ -8,8 +8,9 @@ from tqdm import tqdm
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from modules import shared
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from modules.models import load_model, unload_model
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from modules.models_settings import (get_model_settings_from_yamls,
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update_model_parameters)
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from modules.text_generation import encode
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from server import get_model_specific_settings, update_model_parameters
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def load_past_evaluations():
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@ -66,7 +67,7 @@ def calculate_perplexity(models, input_dataset, stride, _max_length):
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if model != 'current model':
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try:
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yield cumulative_log + f"Loading {model}...\n\n"
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model_settings = get_model_specific_settings(model)
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model_settings = get_model_settings_from_yamls(model)
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shared.settings.update(model_settings) # hijacking the interface defaults
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update_model_parameters(model_settings) # hijacking the command-line arguments
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shared.model_name = model
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@ -1,6 +1,7 @@
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import os
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import subprocess
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def clone_or_pull_repository(github_url):
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repository_folder = "extensions"
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repo_name = github_url.split("/")[-1].split(".")[0]
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86
modules/loaders.py
Normal file
86
modules/loaders.py
Normal file
@ -0,0 +1,86 @@
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import functools
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import gradio as gr
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from modules import shared
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loaders_and_params = {
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'AutoGPTQ': [
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'triton',
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'no_inject_fused_attention',
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'no_inject_fused_mlp',
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'wbits',
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'groupsize',
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'desc_act',
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'gpu_memory',
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'cpu_memory',
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'cpu',
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'disk',
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'auto_devices',
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'trust_remote_code',
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'autogptq_info',
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],
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'GPTQ-for-LLaMa': [
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'wbits',
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'groupsize',
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'model_type',
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'pre_layer',
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'gptq_for_llama_info',
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],
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'llama.cpp': [
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'n_ctx',
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'n_gpu_layers',
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'n_batch',
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'threads',
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'no_mmap',
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'mlock',
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'llama_cpp_seed',
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],
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'Transformers': [
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'cpu_memory',
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'gpu_memory',
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'trust_remote_code',
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'load_in_8bit',
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'bf16',
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'cpu',
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'disk',
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'auto_devices',
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'load_in_4bit',
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'use_double_quant',
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'quant_type',
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'compute_dtype',
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'trust_remote_code',
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],
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}
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def get_gpu_memory_keys():
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return [k for k in shared.gradio if k.startswith('gpu_memory')]
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@functools.cache
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def get_all_params():
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all_params = set()
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for k in loaders_and_params:
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for el in loaders_and_params[k]:
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all_params.add(el)
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if 'gpu_memory' in all_params:
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all_params.remove('gpu_memory')
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for k in get_gpu_memory_keys():
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all_params.add(k)
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return sorted(all_params)
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def make_loader_params_visible(loader):
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params = []
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all_params = get_all_params()
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if loader in loaders_and_params:
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params = loaders_and_params[loader]
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if 'gpu_memory' in params:
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params.remove('gpu_memory')
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params += get_gpu_memory_keys()
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return [gr.update(visible=True) if k in params else gr.update(visible=False) for k in all_params]
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@ -14,6 +14,7 @@ from transformers import (AutoConfig, AutoModel, AutoModelForCausalLM,
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import modules.shared as shared
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from modules import llama_attn_hijack, sampler_hijack
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from modules.logging_colors import logger
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from modules.models_settings import infer_loader
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transformers.logging.set_verbosity_error()
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@ -36,62 +37,31 @@ if shared.args.deepspeed:
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sampler_hijack.hijack_samplers()
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# Some models require special treatment in various parts of the code.
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# This function detects those models
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def find_model_type(model_name):
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path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
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if not path_to_model.exists():
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return 'None'
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model_name_lower = model_name.lower()
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if re.match('.*rwkv.*\.pth', model_name_lower):
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return 'rwkv'
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elif len(list(path_to_model.glob('*ggml*.bin'))) > 0:
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return 'llamacpp'
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elif re.match('.*ggml.*\.bin', model_name_lower):
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return 'llamacpp'
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elif 'chatglm' in model_name_lower:
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return 'chatglm'
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elif 'galactica' in model_name_lower:
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return 'galactica'
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elif 'llava' in model_name_lower:
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return 'llava'
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elif 'oasst' in model_name_lower:
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return 'oasst'
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elif any((k in model_name_lower for k in ['gpt4chan', 'gpt-4chan'])):
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return 'gpt4chan'
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else:
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config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code)
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# Not a "catch all", but fairly accurate
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if config.to_dict().get("is_encoder_decoder", False):
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return 'HF_seq2seq'
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else:
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return 'HF_generic'
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def load_model(model_name):
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def load_model(model_name, loader=None):
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logger.info(f"Loading {model_name}...")
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t0 = time.time()
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shared.model_type = find_model_type(model_name)
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if shared.model_type == 'None':
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shared.is_seq2seq = False
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load_func_map = {
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'Transformers': huggingface_loader,
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'AutoGPTQ': AutoGPTQ_loader,
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'GPTQ-for-LLaMa': GPTQ_loader,
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'llama.cpp': llamacpp_loader,
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'FlexGen': flexgen_loader,
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'RWKV': RWKV_loader
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}
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if loader is None:
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if shared.args.loader is not None:
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loader = shared.args.loader
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else:
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loader = infer_loader(model_name)
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if loader is None:
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logger.error('The path to the model does not exist. Exiting.')
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return None, None
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if shared.args.gptq_for_llama:
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load_func = GPTQ_loader
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elif Path(f'{shared.args.model_dir}/{model_name}/quantize_config.json').exists() or shared.args.wbits > 0:
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load_func = AutoGPTQ_loader
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elif shared.model_type == 'llamacpp':
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load_func = llamacpp_loader
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elif shared.model_type == 'rwkv':
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load_func = RWKV_loader
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elif shared.args.flexgen:
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load_func = flexgen_loader
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else:
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load_func = huggingface_loader
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output = load_func(model_name)
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shared.args.loader = loader
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output = load_func_map[loader](model_name)
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if type(output) is tuple:
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model, tokenizer = output
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else:
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@ -111,11 +81,11 @@ def load_model(model_name):
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def load_tokenizer(model_name, model):
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tokenizer = None
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if shared.model_type == 'gpt4chan' and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists():
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if any(s in model_name.lower() for s in ['gpt-4chan', 'gpt4chan']) and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists():
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tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/"))
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elif type(model) is transformers.LlamaForCausalLM or "LlamaGPTQForCausalLM" in str(type(model)):
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# Try to load an universal LLaMA tokenizer
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if shared.model_type not in ['llava', 'oasst']:
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if any(s in shared.model_name.lower() for s in ['llava', 'oasst']):
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for p in [Path(f"{shared.args.model_dir}/llama-tokenizer/"), Path(f"{shared.args.model_dir}/oobabooga_llama-tokenizer/")]:
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if p.exists():
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logger.info(f"Loading the universal LLaMA tokenizer from {p}...")
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@ -140,10 +110,14 @@ def load_tokenizer(model_name, model):
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def huggingface_loader(model_name):
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if shared.model_type == 'chatglm':
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path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
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if 'chatglm' in model_name.lower():
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LoaderClass = AutoModel
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elif shared.model_type == 'HF_seq2seq':
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else:
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config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code)
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if config.to_dict().get("is_encoder_decoder", False):
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LoaderClass = AutoModelForSeq2SeqLM
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shared.is_seq2seq = True
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else:
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LoaderClass = AutoModelForCausalLM
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134
modules/models_settings.py
Normal file
134
modules/models_settings.py
Normal file
@ -0,0 +1,134 @@
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import re
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from pathlib import Path
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import yaml
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from modules import shared, ui
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def get_model_settings_from_yamls(model):
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settings = shared.model_config
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model_settings = {}
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for pat in settings:
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if re.match(pat.lower(), model.lower()):
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for k in settings[pat]:
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model_settings[k] = settings[pat][k]
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return model_settings
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def infer_loader(model_name):
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path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
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model_settings = get_model_settings_from_yamls(model_name)
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if not path_to_model.exists():
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loader = None
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elif Path(f'{shared.args.model_dir}/{model_name}/quantize_config.json').exists() or ('wbits' in model_settings and type(model_settings['wbits']) is int and model_settings['wbits'] > 0):
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loader = 'AutoGPTQ'
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elif len(list(path_to_model.glob('*ggml*.bin'))) > 0:
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loader = 'llama.cpp'
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elif re.match('.*ggml.*\.bin', model_name.lower()):
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loader = 'llama.cpp'
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elif re.match('.*rwkv.*\.pth', model_name.lower()):
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loader = 'RWKV'
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elif shared.args.flexgen:
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loader = 'FlexGen'
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else:
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loader = 'Transformers'
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return loader
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# UI: update the command-line arguments based on the interface values
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def update_model_parameters(state, initial=False):
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elements = ui.list_model_elements() # the names of the parameters
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gpu_memories = []
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for i, element in enumerate(elements):
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if element not in state:
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continue
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value = state[element]
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if element.startswith('gpu_memory'):
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gpu_memories.append(value)
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continue
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if initial and vars(shared.args)[element] != vars(shared.args_defaults)[element]:
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continue
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||||
|
||||
# Setting null defaults
|
||||
if element in ['wbits', 'groupsize', 'model_type'] and value == 'None':
|
||||
value = vars(shared.args_defaults)[element]
|
||||
elif element in ['cpu_memory'] and value == 0:
|
||||
value = vars(shared.args_defaults)[element]
|
||||
|
||||
# Making some simple conversions
|
||||
if element in ['wbits', 'groupsize', 'pre_layer']:
|
||||
value = int(value)
|
||||
elif element == 'cpu_memory' and value is not None:
|
||||
value = f"{value}MiB"
|
||||
|
||||
if element in ['pre_layer']:
|
||||
value = [value] if value > 0 else None
|
||||
|
||||
setattr(shared.args, element, value)
|
||||
|
||||
found_positive = False
|
||||
for i in gpu_memories:
|
||||
if i > 0:
|
||||
found_positive = True
|
||||
break
|
||||
|
||||
if not (initial and vars(shared.args)['gpu_memory'] != vars(shared.args_defaults)['gpu_memory']):
|
||||
if found_positive:
|
||||
shared.args.gpu_memory = [f"{i}MiB" for i in gpu_memories]
|
||||
else:
|
||||
shared.args.gpu_memory = None
|
||||
|
||||
|
||||
# UI: update the state variable with the model settings
|
||||
def apply_model_settings_to_state(model, state):
|
||||
model_settings = get_model_settings_from_yamls(model)
|
||||
if 'loader' not in model_settings:
|
||||
loader = infer_loader(model)
|
||||
if 'wbits' in model_settings and type(model_settings['wbits']) is int and model_settings['wbits'] > 0:
|
||||
loader = 'AutoGPTQ'
|
||||
|
||||
# If the user is using an alternative GPTQ loader, let them keep using it
|
||||
if not (loader == 'AutoGPTQ' and state['loader'] in ['GPTQ-for-LLaMa', 'exllama']):
|
||||
state['loader'] = loader
|
||||
|
||||
for k in model_settings:
|
||||
if k in state:
|
||||
state[k] = model_settings[k]
|
||||
|
||||
return state
|
||||
|
||||
|
||||
# Save the settings for this model to models/config-user.yaml
|
||||
def save_model_settings(model, state):
|
||||
if model == 'None':
|
||||
yield ("Not saving the settings because no model is loaded.")
|
||||
return
|
||||
|
||||
with Path(f'{shared.args.model_dir}/config-user.yaml') as p:
|
||||
if p.exists():
|
||||
user_config = yaml.safe_load(open(p, 'r').read())
|
||||
else:
|
||||
user_config = {}
|
||||
|
||||
model_regex = model + '$' # For exact matches
|
||||
for _dict in [user_config, shared.model_config]:
|
||||
if model_regex not in _dict:
|
||||
_dict[model_regex] = {}
|
||||
|
||||
if model_regex not in user_config:
|
||||
user_config[model_regex] = {}
|
||||
|
||||
for k in ui.list_model_elements():
|
||||
user_config[model_regex][k] = state[k]
|
||||
shared.model_config[model_regex][k] = state[k]
|
||||
|
||||
with open(p, 'w') as f:
|
||||
f.write(yaml.dump(user_config, sort_keys=False))
|
||||
|
||||
yield (f"Settings for {model} saved to {p}")
|
||||
@ -52,4 +52,3 @@ def load_preset_for_ui(name, state):
|
||||
def generate_preset_yaml(state):
|
||||
data = {k: state[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']}
|
||||
return yaml.dump(data, sort_keys=False)
|
||||
|
||||
|
||||
@ -10,7 +10,6 @@ generation_lock = None
|
||||
model = None
|
||||
tokenizer = None
|
||||
model_name = "None"
|
||||
model_type = None
|
||||
lora_names = []
|
||||
|
||||
# Chat variables
|
||||
@ -97,6 +96,9 @@ parser.add_argument('--settings', type=str, help='Load the default interface set
|
||||
parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.')
|
||||
parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
|
||||
|
||||
# Model loader
|
||||
parser.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: autogptq, gptq-for-llama, transformers, llamacpp, rwkv, flexgen')
|
||||
|
||||
# Accelerate/transformers
|
||||
parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.')
|
||||
parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
|
||||
@ -139,7 +141,7 @@ parser.add_argument('--warmup_autotune', action='store_true', help='(triton) Ena
|
||||
parser.add_argument('--fused_mlp', action='store_true', help='(triton) Enable fused mlp.')
|
||||
|
||||
# AutoGPTQ
|
||||
parser.add_argument('--gptq-for-llama', action='store_true', help='Use GPTQ-for-LLaMa to load the GPTQ model instead of AutoGPTQ.')
|
||||
parser.add_argument('--gptq-for-llama', action='store_true', help='DEPRECATED')
|
||||
parser.add_argument('--autogptq', action='store_true', help='DEPRECATED')
|
||||
parser.add_argument('--triton', action='store_true', help='Use triton.')
|
||||
parser.add_argument('--no_inject_fused_attention', action='store_true', help='Do not use fused attention (lowers VRAM requirements).')
|
||||
@ -147,7 +149,7 @@ parser.add_argument('--no_inject_fused_mlp', action='store_true', help='Triton m
|
||||
parser.add_argument('--desc_act', action='store_true', help='For models that don\'t have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig.')
|
||||
|
||||
# FlexGen
|
||||
parser.add_argument('--flexgen', action='store_true', help='Enable the use of FlexGen offloading.')
|
||||
parser.add_argument('--flexgen', action='store_true', help='DEPRECATED')
|
||||
parser.add_argument('--percent', type=int, nargs="+", default=[0, 100, 100, 0, 100, 0], help='FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0).')
|
||||
parser.add_argument("--compress-weight", action="store_true", help="FlexGen: activate weight compression.")
|
||||
parser.add_argument("--pin-weight", type=str2bool, nargs="?", const=True, default=True, help="FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%%).")
|
||||
@ -184,7 +186,14 @@ args_defaults = parser.parse_args([])
|
||||
|
||||
# Deprecation warnings
|
||||
if args.autogptq:
|
||||
logger.warning('--autogptq has been deprecated and will be removed soon. AutoGPTQ is now used by default for GPTQ models.')
|
||||
logger.warning('--autogptq has been deprecated and will be removed soon. Use --loader autogptq instead.')
|
||||
args.loader = 'autogptq'
|
||||
if args.gptq_for_llama:
|
||||
logger.warning('--gptq-for-llama has been deprecated and will be removed soon. Use --loader gptq-for-llama instead.')
|
||||
args.loader = 'gptq-for-llama'
|
||||
if args.flexgen:
|
||||
logger.warning('--flexgen has been deprecated and will be removed soon. Use --loader flexgen instead.')
|
||||
args.loader = 'FlexGen'
|
||||
|
||||
# Security warnings
|
||||
if args.trust_remote_code:
|
||||
@ -193,6 +202,22 @@ if args.share:
|
||||
logger.warning("The gradio \"share link\" feature uses a proprietary executable to create a reverse tunnel. Use it with care.")
|
||||
|
||||
|
||||
def fix_loader_name(name):
|
||||
name = name.lower()
|
||||
if name in ['llamacpp', 'llama.cpp', 'llama-cpp', 'llama cpp']:
|
||||
return 'llama.cpp'
|
||||
elif name in ['transformers', 'huggingface', 'hf', 'hugging_face', 'hugging face']:
|
||||
return 'Transformers'
|
||||
elif name in ['autogptq', 'auto-gptq', 'auto_gptq', 'auto gptq']:
|
||||
return 'AutoGPTQ'
|
||||
elif name in ['gptq-for-llama', 'gptqforllama', 'gptqllama', 'gptq for llama', 'gptq_for_llama']:
|
||||
return 'GPTQ-for-LLaMa'
|
||||
|
||||
|
||||
if args.loader is not None:
|
||||
args.loader = fix_loader_name(args.loader)
|
||||
|
||||
|
||||
def add_extension(name):
|
||||
if args.extensions is None:
|
||||
args.extensions = [name]
|
||||
|
||||
@ -31,7 +31,7 @@ def get_max_prompt_length(state):
|
||||
|
||||
|
||||
def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None):
|
||||
if shared.model_type in ['rwkv', 'llamacpp']:
|
||||
if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel']:
|
||||
input_ids = shared.tokenizer.encode(str(prompt))
|
||||
input_ids = np.array(input_ids).reshape(1, len(input_ids))
|
||||
return input_ids
|
||||
@ -51,7 +51,7 @@ def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_lengt
|
||||
if truncation_length is not None:
|
||||
input_ids = input_ids[:, -truncation_length:]
|
||||
|
||||
if shared.model_type in ['rwkv', 'llamacpp'] or shared.args.cpu:
|
||||
if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel'] or shared.args.cpu:
|
||||
return input_ids
|
||||
elif shared.args.flexgen:
|
||||
return input_ids.numpy()
|
||||
@ -99,7 +99,7 @@ def fix_galactica(s):
|
||||
|
||||
|
||||
def get_reply_from_output_ids(output_ids, input_ids, original_question, state, is_chat=False):
|
||||
if shared.model_type == 'HF_seq2seq':
|
||||
if shared.is_seq2seq:
|
||||
reply = decode(output_ids, state['skip_special_tokens'])
|
||||
else:
|
||||
new_tokens = len(output_ids) - len(input_ids[0])
|
||||
@ -117,7 +117,7 @@ def get_reply_from_output_ids(output_ids, input_ids, original_question, state, i
|
||||
|
||||
|
||||
def formatted_outputs(reply, model_name):
|
||||
if shared.model_type == 'gpt4chan':
|
||||
if any(s in model_name for s in ['gpt-4chan', 'gpt4chan']):
|
||||
reply = fix_gpt4chan(reply)
|
||||
return reply, generate_4chan_html(reply)
|
||||
else:
|
||||
@ -142,7 +142,7 @@ def stop_everything_event():
|
||||
|
||||
def generate_reply_wrapper(question, state, eos_token=None, stopping_strings=None):
|
||||
for reply in generate_reply(question, state, eos_token, stopping_strings, is_chat=False):
|
||||
if shared.model_type not in ['HF_seq2seq']:
|
||||
if not shared.is_seq2seq:
|
||||
reply = question + reply
|
||||
|
||||
yield formatted_outputs(reply, shared.model_name)
|
||||
@ -157,7 +157,7 @@ def _generate_reply(question, state, eos_token=None, stopping_strings=None, is_c
|
||||
yield ''
|
||||
return
|
||||
|
||||
if shared.model_type in ['rwkv', 'llamacpp']:
|
||||
if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel']:
|
||||
generate_func = generate_reply_custom
|
||||
elif shared.args.flexgen:
|
||||
generate_func = generate_reply_flexgen
|
||||
@ -240,7 +240,7 @@ def generate_reply_HF(question, original_question, seed, state, eos_token=None,
|
||||
|
||||
t0 = time.time()
|
||||
try:
|
||||
if not is_chat and shared.model_type != 'HF_seq2seq':
|
||||
if not is_chat and not shared.is_seq2seq:
|
||||
yield ''
|
||||
|
||||
# Generate the entire reply at once.
|
||||
@ -276,7 +276,7 @@ def generate_reply_HF(question, original_question, seed, state, eos_token=None,
|
||||
finally:
|
||||
t1 = time.time()
|
||||
original_tokens = len(original_input_ids[0])
|
||||
new_tokens = len(output) - (original_tokens if shared.model_type != 'HF_seq2seq' else 0)
|
||||
new_tokens = len(output) - (original_tokens if not shared.is_seq2seq else 0)
|
||||
print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
|
||||
return
|
||||
|
||||
@ -287,7 +287,7 @@ def generate_reply_custom(question, original_question, seed, state, eos_token=No
|
||||
for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
|
||||
generate_params[k] = state[k]
|
||||
|
||||
if shared.model_type == 'llamacpp':
|
||||
if shared.model.__class__.__name__ in ['LlamaCppModel']:
|
||||
for k in ['mirostat_mode', 'mirostat_tau', 'mirostat_eta']:
|
||||
generate_params[k] = state[k]
|
||||
|
||||
@ -381,6 +381,6 @@ def generate_reply_flexgen(question, original_question, seed, state, eos_token=N
|
||||
finally:
|
||||
t1 = time.time()
|
||||
original_tokens = len(original_input_ids[0])
|
||||
new_tokens = len(output) - (original_tokens if shared.model_type != 'HF_seq2seq' else 0)
|
||||
new_tokens = len(output) - (original_tokens if not shared.is_seq2seq else 0)
|
||||
print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
|
||||
return
|
||||
|
||||
@ -30,7 +30,7 @@ theme = gr.themes.Default(
|
||||
|
||||
|
||||
def list_model_elements():
|
||||
elements = ['cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'trust_remote_code', 'load_in_4bit', 'compute_dtype', 'quant_type', 'use_double_quant', 'gptq_for_llama', 'wbits', 'groupsize', 'model_type', 'pre_layer', 'triton', 'desc_act', 'no_inject_fused_attention', 'no_inject_fused_mlp', 'threads', 'n_batch', 'no_mmap', 'mlock', 'n_gpu_layers', 'n_ctx', 'llama_cpp_seed']
|
||||
elements = ['loader', 'cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'trust_remote_code', 'load_in_4bit', 'compute_dtype', 'quant_type', 'use_double_quant', 'wbits', 'groupsize', 'model_type', 'pre_layer', 'triton', 'desc_act', 'no_inject_fused_attention', 'no_inject_fused_mlp', 'threads', 'n_batch', 'no_mmap', 'mlock', 'n_gpu_layers', 'n_ctx', 'llama_cpp_seed']
|
||||
for i in range(torch.cuda.device_count()):
|
||||
elements.append(f'gpu_memory_{i}')
|
||||
|
||||
|
||||
198
server.py
198
server.py
@ -43,17 +43,21 @@ import yaml
|
||||
from PIL import Image
|
||||
|
||||
import modules.extensions as extensions_module
|
||||
from modules import chat, presets, shared, training, ui, utils
|
||||
from modules import chat, loaders, presets, shared, training, ui, utils
|
||||
from modules.extensions import apply_extensions
|
||||
from modules.github import clone_or_pull_repository
|
||||
from modules.html_generator import chat_html_wrapper
|
||||
from modules.LoRA import add_lora_to_model
|
||||
from modules.models import load_model, unload_model
|
||||
from modules.models_settings import (apply_model_settings_to_state,
|
||||
get_model_settings_from_yamls,
|
||||
save_model_settings,
|
||||
update_model_parameters)
|
||||
from modules.text_generation import (generate_reply_wrapper,
|
||||
get_encoded_length, stop_everything_event)
|
||||
|
||||
|
||||
def load_model_wrapper(selected_model, autoload=False):
|
||||
def load_model_wrapper(selected_model, loader, autoload=False):
|
||||
if not autoload:
|
||||
yield f"The settings for {selected_model} have been updated.\nClick on \"Load the model\" to load it."
|
||||
return
|
||||
@ -66,9 +70,12 @@ def load_model_wrapper(selected_model, autoload=False):
|
||||
shared.model_name = selected_model
|
||||
unload_model()
|
||||
if selected_model != '':
|
||||
shared.model, shared.tokenizer = load_model(shared.model_name)
|
||||
shared.model, shared.tokenizer = load_model(shared.model_name, loader)
|
||||
|
||||
if shared.model is not None:
|
||||
yield f"Successfully loaded {selected_model}"
|
||||
else:
|
||||
yield f"Failed to load {selected_model}."
|
||||
except:
|
||||
yield traceback.format_exc()
|
||||
|
||||
@ -144,103 +151,6 @@ def download_model_wrapper(repo_id):
|
||||
yield traceback.format_exc()
|
||||
|
||||
|
||||
# Update the command-line arguments based on the interface values
|
||||
def update_model_parameters(state, initial=False):
|
||||
elements = ui.list_model_elements() # the names of the parameters
|
||||
gpu_memories = []
|
||||
|
||||
for i, element in enumerate(elements):
|
||||
if element not in state:
|
||||
continue
|
||||
|
||||
value = state[element]
|
||||
if element.startswith('gpu_memory'):
|
||||
gpu_memories.append(value)
|
||||
continue
|
||||
|
||||
if initial and vars(shared.args)[element] != vars(shared.args_defaults)[element]:
|
||||
continue
|
||||
|
||||
# Setting null defaults
|
||||
if element in ['wbits', 'groupsize', 'model_type'] and value == 'None':
|
||||
value = vars(shared.args_defaults)[element]
|
||||
elif element in ['cpu_memory'] and value == 0:
|
||||
value = vars(shared.args_defaults)[element]
|
||||
|
||||
# Making some simple conversions
|
||||
if element in ['wbits', 'groupsize', 'pre_layer']:
|
||||
value = int(value)
|
||||
elif element == 'cpu_memory' and value is not None:
|
||||
value = f"{value}MiB"
|
||||
|
||||
if element in ['pre_layer']:
|
||||
value = [value] if value > 0 else None
|
||||
|
||||
setattr(shared.args, element, value)
|
||||
|
||||
found_positive = False
|
||||
for i in gpu_memories:
|
||||
if i > 0:
|
||||
found_positive = True
|
||||
break
|
||||
|
||||
if not (initial and vars(shared.args)['gpu_memory'] != vars(shared.args_defaults)['gpu_memory']):
|
||||
if found_positive:
|
||||
shared.args.gpu_memory = [f"{i}MiB" for i in gpu_memories]
|
||||
else:
|
||||
shared.args.gpu_memory = None
|
||||
|
||||
|
||||
def get_model_specific_settings(model):
|
||||
settings = shared.model_config
|
||||
model_settings = {}
|
||||
|
||||
for pat in settings:
|
||||
if re.match(pat.lower(), model.lower()):
|
||||
for k in settings[pat]:
|
||||
model_settings[k] = settings[pat][k]
|
||||
|
||||
return model_settings
|
||||
|
||||
|
||||
def load_model_specific_settings(model, state):
|
||||
model_settings = get_model_specific_settings(model)
|
||||
for k in model_settings:
|
||||
if k in state:
|
||||
state[k] = model_settings[k]
|
||||
|
||||
return state
|
||||
|
||||
|
||||
def save_model_settings(model, state):
|
||||
if model == 'None':
|
||||
yield ("Not saving the settings because no model is loaded.")
|
||||
return
|
||||
|
||||
with Path(f'{shared.args.model_dir}/config-user.yaml') as p:
|
||||
if p.exists():
|
||||
user_config = yaml.safe_load(open(p, 'r').read())
|
||||
else:
|
||||
user_config = {}
|
||||
|
||||
model_regex = model + '$' # For exact matches
|
||||
for _dict in [user_config, shared.model_config]:
|
||||
if model_regex not in _dict:
|
||||
_dict[model_regex] = {}
|
||||
|
||||
if model_regex not in user_config:
|
||||
user_config[model_regex] = {}
|
||||
|
||||
for k in ui.list_model_elements():
|
||||
user_config[model_regex][k] = state[k]
|
||||
shared.model_config[model_regex][k] = state[k]
|
||||
|
||||
with open(p, 'w') as f:
|
||||
f.write(yaml.dump(user_config, sort_keys=False))
|
||||
|
||||
yield (f"Settings for {model} saved to {p}")
|
||||
|
||||
|
||||
def create_model_menus():
|
||||
# Finding the default values for the GPU and CPU memories
|
||||
total_mem = []
|
||||
@ -283,88 +193,70 @@ def create_model_menus():
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
shared.gradio['loader'] = gr.Dropdown(label="Model loader", choices=["Transformers", "AutoGPTQ", "GPTQ-for-LLaMa", "llama.cpp"], value=None)
|
||||
with gr.Box():
|
||||
gr.Markdown('Transformers')
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
for i in range(len(total_mem)):
|
||||
shared.gradio[f'gpu_memory_{i}'] = gr.Slider(label=f"gpu-memory in MiB for device :{i}", maximum=total_mem[i], value=default_gpu_mem[i])
|
||||
|
||||
shared.gradio['cpu_memory'] = gr.Slider(label="cpu-memory in MiB", maximum=total_cpu_mem, value=default_cpu_mem)
|
||||
|
||||
with gr.Column():
|
||||
shared.gradio['auto_devices'] = gr.Checkbox(label="auto-devices", value=shared.args.auto_devices)
|
||||
shared.gradio['disk'] = gr.Checkbox(label="disk", value=shared.args.disk)
|
||||
shared.gradio['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu)
|
||||
shared.gradio['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16)
|
||||
shared.gradio['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit)
|
||||
shared.gradio['trust_remote_code'] = gr.Checkbox(label="trust-remote-code", value=shared.args.trust_remote_code, info='Make sure to inspect the .py files inside the model folder before loading it with this option enabled.')
|
||||
|
||||
with gr.Box():
|
||||
gr.Markdown('Transformers 4-bit')
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
shared.gradio['load_in_4bit'] = gr.Checkbox(label="load-in-4bit", value=shared.args.load_in_4bit)
|
||||
shared.gradio['use_double_quant'] = gr.Checkbox(label="use_double_quant", value=shared.args.use_double_quant)
|
||||
|
||||
with gr.Column():
|
||||
shared.gradio['compute_dtype'] = gr.Dropdown(label="compute_dtype", choices=["bfloat16", "float16", "float32"], value=shared.args.compute_dtype)
|
||||
shared.gradio['quant_type'] = gr.Dropdown(label="quant_type", choices=["nf4", "fp4"], value=shared.args.quant_type)
|
||||
|
||||
shared.gradio['autoload_model'] = gr.Checkbox(value=shared.settings['autoload_model'], label='Autoload the model', info='Whether to load the model as soon as it is selected in the Model dropdown.')
|
||||
shared.gradio['custom_model_menu'] = gr.Textbox(label="Download custom model or LoRA", info="Enter the Hugging Face username/model path, for instance: facebook/galactica-125m. To specify a branch, add it at the end after a \":\" character like this: facebook/galactica-125m:main")
|
||||
shared.gradio['download_model_button'] = gr.Button("Download")
|
||||
|
||||
with gr.Column():
|
||||
with gr.Box():
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
gr.Markdown('GPTQ')
|
||||
shared.gradio['triton'] = gr.Checkbox(label="triton", value=shared.args.triton)
|
||||
shared.gradio['no_inject_fused_attention'] = gr.Checkbox(label="no_inject_fused_attention", value=shared.args.no_inject_fused_attention, info='Disable fused attention. Fused attention improves inference performance but uses more VRAM. Disable if running low on VRAM.')
|
||||
shared.gradio['no_inject_fused_mlp'] = gr.Checkbox(label="no_inject_fused_mlp", value=shared.args.no_inject_fused_mlp, info='Affects Triton only. Disable fused MLP. Fused MLP improves performance but uses more VRAM. Disable if running low on VRAM.')
|
||||
shared.gradio['desc_act'] = gr.Checkbox(label="desc_act", value=shared.args.desc_act, info='\'desc_act\', \'wbits\', and \'groupsize\' are used for old models without a quantize_config.json.')
|
||||
shared.gradio['gptq_for_llama'] = gr.Checkbox(label="gptq-for-llama", value=shared.args.gptq_for_llama, info='Use GPTQ-for-LLaMa loader instead of AutoGPTQ. pre_layer should be used for CPU offloading instead of gpu-memory.')
|
||||
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
shared.gradio['wbits'] = gr.Dropdown(label="wbits", choices=["None", 1, 2, 3, 4, 8], value=shared.args.wbits if shared.args.wbits > 0 else "None")
|
||||
shared.gradio['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128, 1024], value=shared.args.groupsize if shared.args.groupsize > 0 else "None")
|
||||
|
||||
shared.gradio['model_type'] = gr.Dropdown(label="model_type", choices=["None", "llama", "opt", "gptj"], value=shared.args.model_type or "None")
|
||||
shared.gradio['pre_layer'] = gr.Slider(label="pre_layer", minimum=0, maximum=100, value=shared.args.pre_layer[0] if shared.args.pre_layer is not None else 0)
|
||||
|
||||
with gr.Box():
|
||||
gr.Markdown('llama.cpp')
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
shared.gradio['threads'] = gr.Slider(label="threads", minimum=0, step=1, maximum=32, value=shared.args.threads)
|
||||
shared.gradio['n_batch'] = gr.Slider(label="n_batch", minimum=1, maximum=2048, value=shared.args.n_batch)
|
||||
shared.gradio['n_gpu_layers'] = gr.Slider(label="n-gpu-layers", minimum=0, maximum=128, value=shared.args.n_gpu_layers)
|
||||
shared.gradio['n_ctx'] = gr.Slider(minimum=0, maximum=8192, step=1, label="n_ctx", value=shared.args.n_ctx)
|
||||
shared.gradio['wbits'] = gr.Dropdown(label="wbits", choices=["None", 1, 2, 3, 4, 8], value=shared.args.wbits if shared.args.wbits > 0 else "None")
|
||||
shared.gradio['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128, 1024], value=shared.args.groupsize if shared.args.groupsize > 0 else "None")
|
||||
shared.gradio['model_type'] = gr.Dropdown(label="model_type", choices=["None", "llama", "opt", "gptj"], value=shared.args.model_type or "None")
|
||||
shared.gradio['pre_layer'] = gr.Slider(label="pre_layer", minimum=0, maximum=100, value=shared.args.pre_layer[0] if shared.args.pre_layer is not None else 0)
|
||||
shared.gradio['autogptq_info'] = gr.Markdown('On some systems, AutoGPTQ can be 2x slower than GPTQ-for-LLaMa. You can manually select the GPTQ-for-LLaMa loader above.')
|
||||
|
||||
with gr.Column():
|
||||
shared.gradio['triton'] = gr.Checkbox(label="triton", value=shared.args.triton)
|
||||
shared.gradio['no_inject_fused_attention'] = gr.Checkbox(label="no_inject_fused_attention", value=shared.args.no_inject_fused_attention, info='Disable fused attention. Fused attention improves inference performance but uses more VRAM. Disable if running low on VRAM.')
|
||||
shared.gradio['no_inject_fused_mlp'] = gr.Checkbox(label="no_inject_fused_mlp", value=shared.args.no_inject_fused_mlp, info='Affects Triton only. Disable fused MLP. Fused MLP improves performance but uses more VRAM. Disable if running low on VRAM.')
|
||||
shared.gradio['desc_act'] = gr.Checkbox(label="desc_act", value=shared.args.desc_act, info='\'desc_act\', \'wbits\', and \'groupsize\' are used for old models without a quantize_config.json.')
|
||||
shared.gradio['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit)
|
||||
shared.gradio['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16)
|
||||
shared.gradio['auto_devices'] = gr.Checkbox(label="auto-devices", value=shared.args.auto_devices)
|
||||
shared.gradio['disk'] = gr.Checkbox(label="disk", value=shared.args.disk)
|
||||
shared.gradio['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu)
|
||||
shared.gradio['load_in_4bit'] = gr.Checkbox(label="load-in-4bit")
|
||||
shared.gradio['use_double_quant'] = gr.Checkbox(label="use_double_quant", value=shared.args.use_double_quant)
|
||||
shared.gradio['no_mmap'] = gr.Checkbox(label="no-mmap", value=shared.args.no_mmap)
|
||||
shared.gradio['mlock'] = gr.Checkbox(label="mlock", value=shared.args.mlock)
|
||||
shared.gradio['llama_cpp_seed'] = gr.Number(label='Seed (0 for random)', value=shared.args.llama_cpp_seed)
|
||||
shared.gradio['trust_remote_code'] = gr.Checkbox(label="trust-remote-code", value=shared.args.trust_remote_code, info='Make sure to inspect the .py files inside the model folder before loading it with this option enabled.')
|
||||
shared.gradio['gptq_for_llama_info'] = gr.Markdown('GPTQ-for-LLaMa is currently 2x faster than AutoGPTQ on some systems. It is installed by default with the one-click installers. Otherwise, it has to be installed manually following the instructions here: [instructions](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#installation-1).')
|
||||
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
shared.gradio['autoload_model'] = gr.Checkbox(value=shared.settings['autoload_model'], label='Autoload the model', info='Whether to load the model as soon as it is selected in the Model dropdown.')
|
||||
|
||||
shared.gradio['custom_model_menu'] = gr.Textbox(label="Download custom model or LoRA", info="Enter the Hugging Face username/model path, for instance: facebook/galactica-125m. To specify a branch, add it at the end after a \":\" character like this: facebook/galactica-125m:main")
|
||||
shared.gradio['download_model_button'] = gr.Button("Download")
|
||||
|
||||
with gr.Row():
|
||||
shared.gradio['model_status'] = gr.Markdown('No model is loaded' if shared.model_name == 'None' else 'Ready')
|
||||
|
||||
shared.gradio['loader'].change(loaders.make_loader_params_visible, shared.gradio['loader'], [shared.gradio[k] for k in loaders.get_all_params()])
|
||||
|
||||
# In this event handler, the interface state is read and updated
|
||||
# with the model defaults (if any), and then the model is loaded
|
||||
# unless "autoload_model" is unchecked
|
||||
shared.gradio['model_menu'].change(
|
||||
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
|
||||
load_model_specific_settings, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['interface_state']).then(
|
||||
apply_model_settings_to_state, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['interface_state']).then(
|
||||
ui.apply_interface_values, shared.gradio['interface_state'], [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False).then(
|
||||
update_model_parameters, shared.gradio['interface_state'], None).then(
|
||||
load_model_wrapper, [shared.gradio[k] for k in ['model_menu', 'autoload_model']], shared.gradio['model_status'], show_progress=False)
|
||||
load_model_wrapper, [shared.gradio[k] for k in ['model_menu', 'loader', 'autoload_model']], shared.gradio['model_status'], show_progress=False)
|
||||
|
||||
load.click(
|
||||
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
|
||||
update_model_parameters, shared.gradio['interface_state'], None).then(
|
||||
partial(load_model_wrapper, autoload=True), shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=False)
|
||||
partial(load_model_wrapper, autoload=True), [shared.gradio[k] for k in ['model_menu', 'loader']], shared.gradio['model_status'], show_progress=False)
|
||||
|
||||
unload.click(
|
||||
unload_model, None, None).then(
|
||||
@ -374,7 +266,7 @@ def create_model_menus():
|
||||
unload_model, None, None).then(
|
||||
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
|
||||
update_model_parameters, shared.gradio['interface_state'], None).then(
|
||||
partial(load_model_wrapper, autoload=True), shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=False)
|
||||
partial(load_model_wrapper, autoload=True), [shared.gradio[k] for k in ['model_menu', 'loader']], shared.gradio['model_status'], show_progress=False)
|
||||
|
||||
save_settings.click(
|
||||
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
|
||||
@ -1100,7 +992,7 @@ if __name__ == "__main__":
|
||||
|
||||
# If any model has been selected, load it
|
||||
if shared.model_name != 'None':
|
||||
model_settings = get_model_specific_settings(shared.model_name)
|
||||
model_settings = get_model_settings_from_yamls(shared.model_name)
|
||||
shared.settings.update(model_settings) # hijacking the interface defaults
|
||||
update_model_parameters(model_settings, initial=True) # hijacking the command-line arguments
|
||||
|
||||
@ -1117,6 +1009,10 @@ if __name__ == "__main__":
|
||||
'instruction_template': shared.settings['instruction_template']
|
||||
})
|
||||
|
||||
shared.persistent_interface_state.update({
|
||||
'loader': shared.args.loader or 'Transformers',
|
||||
})
|
||||
|
||||
shared.generation_lock = Lock()
|
||||
# Launch the web UI
|
||||
create_interface()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user