mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-24 08:56:52 +01:00
Backend cleanup (#6025)
This commit is contained in:
oobabooga
GitHub
parent
6a1682aa95
commit
bd7cc4234d
27
README.md
27
README.md
@ -11,7 +11,7 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
|
||||
## Features
|
||||
|
||||
* 3 interface modes: default (two columns), notebook, and chat.
|
||||
* Multiple model backends: [Transformers](https://github.com/huggingface/transformers), [llama.cpp](https://github.com/ggerganov/llama.cpp) (through [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)), [ExLlamaV2](https://github.com/turboderp/exllamav2), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [QuIP#](https://github.com/Cornell-RelaxML/quip-sharp).
|
||||
* Multiple model backends: [Transformers](https://github.com/huggingface/transformers), [llama.cpp](https://github.com/ggerganov/llama.cpp) (through [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)), [ExLlamaV2](https://github.com/turboderp/exllamav2), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ).
|
||||
* Dropdown menu for quickly switching between different models.
|
||||
* Large number of extensions (built-in and user-contributed), including Coqui TTS for realistic voice outputs, Whisper STT for voice inputs, translation, [multimodal pipelines](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal), vector databases, Stable Diffusion integration, and a lot more. See [the wiki](https://github.com/oobabooga/text-generation-webui/wiki/07-%E2%80%90-Extensions) and [the extensions directory](https://github.com/oobabooga/text-generation-webui-extensions) for details.
|
||||
* [Chat with custom characters](https://github.com/oobabooga/text-generation-webui/wiki/03-%E2%80%90-Parameters-Tab#character).
|
||||
@ -208,12 +208,12 @@ usage: server.py [-h] [--multi-user] [--character CHARACTER] [--model MODEL] [--
|
||||
[--tensorcores] [--n_ctx N_CTX] [--threads THREADS] [--threads-batch THREADS_BATCH] [--no_mul_mat_q] [--n_batch N_BATCH] [--no-mmap] [--mlock] [--n-gpu-layers N_GPU_LAYERS]
|
||||
[--tensor_split TENSOR_SPLIT] [--numa] [--logits_all] [--no_offload_kqv] [--cache-capacity CACHE_CAPACITY] [--row_split] [--streaming-llm] [--attention-sink-size ATTENTION_SINK_SIZE]
|
||||
[--gpu-split GPU_SPLIT] [--autosplit] [--max_seq_len MAX_SEQ_LEN] [--cfg-cache] [--no_flash_attn] [--cache_8bit] [--cache_4bit] [--num_experts_per_token NUM_EXPERTS_PER_TOKEN]
|
||||
[--triton] [--no_inject_fused_attention] [--no_inject_fused_mlp] [--no_use_cuda_fp16] [--desc_act] [--disable_exllama] [--disable_exllamav2] [--wbits WBITS] [--model_type MODEL_TYPE]
|
||||
[--groupsize GROUPSIZE] [--pre_layer PRE_LAYER [PRE_LAYER ...]] [--checkpoint CHECKPOINT] [--monkey-patch] [--hqq-backend HQQ_BACKEND] [--deepspeed]
|
||||
[--nvme-offload-dir NVME_OFFLOAD_DIR] [--local_rank LOCAL_RANK] [--alpha_value ALPHA_VALUE] [--rope_freq_base ROPE_FREQ_BASE] [--compress_pos_emb COMPRESS_POS_EMB] [--listen]
|
||||
[--listen-port LISTEN_PORT] [--listen-host LISTEN_HOST] [--share] [--auto-launch] [--gradio-auth GRADIO_AUTH] [--gradio-auth-path GRADIO_AUTH_PATH] [--ssl-keyfile SSL_KEYFILE]
|
||||
[--ssl-certfile SSL_CERTFILE] [--api] [--public-api] [--public-api-id PUBLIC_API_ID] [--api-port API_PORT] [--api-key API_KEY] [--admin-key ADMIN_KEY] [--nowebui]
|
||||
[--multimodal-pipeline MULTIMODAL_PIPELINE]
|
||||
[--triton] [--no_inject_fused_mlp] [--no_use_cuda_fp16] [--desc_act] [--disable_exllama] [--disable_exllamav2] [--wbits WBITS] [--groupsize GROUPSIZE] [--no_inject_fused_attention]
|
||||
[--hqq-backend HQQ_BACKEND] [--deepspeed] [--nvme-offload-dir NVME_OFFLOAD_DIR] [--local_rank LOCAL_RANK] [--alpha_value ALPHA_VALUE] [--rope_freq_base ROPE_FREQ_BASE]
|
||||
[--compress_pos_emb COMPRESS_POS_EMB] [--listen] [--listen-port LISTEN_PORT] [--listen-host LISTEN_HOST] [--share] [--auto-launch] [--gradio-auth GRADIO_AUTH]
|
||||
[--gradio-auth-path GRADIO_AUTH_PATH] [--ssl-keyfile SSL_KEYFILE] [--ssl-certfile SSL_CERTFILE] [--api] [--public-api] [--public-api-id PUBLIC_API_ID] [--api-port API_PORT]
|
||||
[--api-key API_KEY] [--admin-key ADMIN_KEY] [--nowebui] [--multimodal-pipeline MULTIMODAL_PIPELINE] [--model_type MODEL_TYPE] [--pre_layer PRE_LAYER [PRE_LAYER ...]]
|
||||
[--checkpoint CHECKPOINT] [--monkey-patch]
|
||||
|
||||
Text generation web UI
|
||||
|
||||
@ -237,7 +237,7 @@ Basic settings:
|
||||
|
||||
Model loader:
|
||||
--loader LOADER Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, llamacpp_HF, ExLlamav2_HF, ExLlamav2,
|
||||
AutoGPTQ, AutoAWQ, GPTQ-for-LLaMa, QuIP#.
|
||||
AutoGPTQ, AutoAWQ.
|
||||
|
||||
Transformers/Accelerate:
|
||||
--cpu Use the CPU to generate text. Warning: Training on CPU is extremely slow.
|
||||
@ -293,21 +293,16 @@ ExLlamaV2:
|
||||
|
||||
AutoGPTQ:
|
||||
--triton Use triton.
|
||||
--no_inject_fused_attention Disable the use of fused attention, which will use less VRAM at the cost of slower inference.
|
||||
--no_inject_fused_mlp Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference.
|
||||
--no_use_cuda_fp16 This can make models faster on some systems.
|
||||
--desc_act For models that do not have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig.
|
||||
--disable_exllama Disable ExLlama kernel, which can improve inference speed on some systems.
|
||||
--disable_exllamav2 Disable ExLlamav2 kernel.
|
||||
|
||||
GPTQ-for-LLaMa:
|
||||
--wbits WBITS Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.
|
||||
--model_type MODEL_TYPE Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.
|
||||
--groupsize GROUPSIZE Group size.
|
||||
--pre_layer PRE_LAYER [PRE_LAYER ...] The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated
|
||||
by spaces, eg --pre_layer 30 60.
|
||||
--checkpoint CHECKPOINT The path to the quantized checkpoint file. If not specified, it will be automatically detected.
|
||||
--monkey-patch Apply the monkey patch for using LoRAs with quantized models.
|
||||
|
||||
AutoAWQ:
|
||||
--no_inject_fused_attention Disable the use of fused attention, which will use less VRAM at the cost of slower inference.
|
||||
|
||||
HQQ:
|
||||
--hqq-backend HQQ_BACKEND Backend for the HQQ loader. Valid options: PYTORCH, PYTORCH_COMPILE, ATEN.
|
||||
|
||||
@ -64,14 +64,6 @@ Loads: GPTQ models.
|
||||
* **no_use_cuda_fp16**: On some systems, the performance can be very bad with this unset. Can usually be ignored.
|
||||
* **desc_act**: For ancient models without proper metadata, sets the model "act-order" parameter manually. Can usually be ignored.
|
||||
|
||||
### GPTQ-for-LLaMa
|
||||
|
||||
Loads: GPTQ models.
|
||||
|
||||
Ancient loader, the first one to implement 4-bit quantization. It works on older GPUs for which ExLlamaV2 and AutoGPTQ do not work, and it doesn't work with "act-order", so you should use it with simple 4-bit-128g models.
|
||||
|
||||
* **pre_layer**: Used for CPU offloading. The higher the number, the more layers will be sent to the GPU. GPTQ-for-LLaMa CPU offloading was faster than the one implemented in AutoGPTQ the last time I checked.
|
||||
|
||||
### llama.cpp
|
||||
|
||||
Loads: GGUF models. Note: GGML models have been deprecated and do not work anymore.
|
||||
|
||||
@ -13,28 +13,6 @@ Source: https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/1126
|
||||
|
||||
This file will be automatically detected the next time you start the web UI.
|
||||
|
||||
## Using LoRAs with GPTQ-for-LLaMa
|
||||
|
||||
This requires using a monkey patch that is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit
|
||||
|
||||
To use it:
|
||||
|
||||
Install alpaca_lora_4bit using pip
|
||||
|
||||
```
|
||||
git clone https://github.com/johnsmith0031/alpaca_lora_4bit.git
|
||||
cd alpaca_lora_4bit
|
||||
git fetch origin winglian-setup_pip
|
||||
git checkout winglian-setup_pip
|
||||
pip install .
|
||||
```
|
||||
|
||||
Start the UI with the --monkey-patch flag:
|
||||
|
||||
```
|
||||
python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch
|
||||
```
|
||||
|
||||
## DeepSpeed
|
||||
|
||||
`DeepSpeed ZeRO-3` is an alternative offloading strategy for full-precision (16-bit) transformers models.
|
||||
|
||||
@ -2,15 +2,13 @@
|
||||
|
||||
| Loader | Loading 1 LoRA | Loading 2 or more LoRAs | Training LoRAs | Multimodal extension | Perplexity evaluation |
|
||||
|----------------|----------------|-------------------------|----------------|----------------------|-----------------------|
|
||||
| Transformers | ✅ | ✅\*\*\* | ✅\* | ✅ | ✅ |
|
||||
| Transformers | ✅ | ✅\*\* | ✅\* | ✅ | ✅ |
|
||||
| llama.cpp | ❌ | ❌ | ❌ | ❌ | use llamacpp_HF |
|
||||
| llamacpp_HF | ❌ | ❌ | ❌ | ❌ | ✅ |
|
||||
| ExLlamav2_HF | ✅ | ✅ | ❌ | ❌ | ✅ |
|
||||
| ExLlamav2 | ✅ | ✅ | ❌ | ❌ | use ExLlamav2_HF |
|
||||
| AutoGPTQ | ✅ | ❌ | ❌ | ✅ | ✅ |
|
||||
| AutoAWQ | ? | ❌ | ? | ? | ✅ |
|
||||
| GPTQ-for-LLaMa | ✅\*\* | ✅\*\*\* | ✅ | ✅ | ✅ |
|
||||
| QuIP# | ? | ? | ? | ? | ✅ |
|
||||
| HQQ | ? | ? | ? | ? | ✅ |
|
||||
|
||||
❌ = not implemented
|
||||
@ -19,6 +17,4 @@
|
||||
|
||||
\* Training LoRAs with GPTQ models also works with the Transformers loader. Make sure to check "auto-devices" and "disable_exllama" before loading the model.
|
||||
|
||||
\*\* Requires the monkey-patch. The instructions can be found [here](https://github.com/oobabooga/text-generation-webui/wiki/08-%E2%80%90-Additional-Tips#using-loras-with-gptq-for-llama).
|
||||
|
||||
\*\*\* Multi-LoRA in PEFT is tricky and the current implementation does not work reliably in all cases.
|
||||
\*\* Multi-LoRA in PEFT is tricky and the current implementation does not work reliably in all cases.
|
||||
|
||||
@ -44,7 +44,7 @@ def load_quantized(model_name):
|
||||
'model_basename': pt_path.stem,
|
||||
'device': "xpu:0" if is_xpu_available() else "cuda:0" if not shared.args.cpu else "cpu",
|
||||
'use_triton': shared.args.triton,
|
||||
'inject_fused_attention': not shared.args.no_inject_fused_attention,
|
||||
'inject_fused_attention': False,
|
||||
'inject_fused_mlp': not shared.args.no_inject_fused_mlp,
|
||||
'use_safetensors': use_safetensors,
|
||||
'trust_remote_code': shared.args.trust_remote_code,
|
||||
|
||||
@ -1,171 +0,0 @@
|
||||
import inspect
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
import accelerate
|
||||
import torch
|
||||
import transformers
|
||||
from accelerate.utils import is_xpu_available
|
||||
from gptq_for_llama import llama_inference_offload
|
||||
from gptq_for_llama.modelutils import find_layers
|
||||
from gptq_for_llama.quant import make_quant
|
||||
from transformers import AutoConfig, AutoModelForCausalLM
|
||||
|
||||
import modules.shared as shared
|
||||
from modules.logging_colors import logger
|
||||
|
||||
|
||||
# This function is a replacement for the load_quant function in the
|
||||
# GPTQ-for_LLaMa repository. It supports more models and branches.
|
||||
def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=None, kernel_switch_threshold=128, eval=True):
|
||||
exclude_layers = exclude_layers or ['lm_head']
|
||||
|
||||
def noop(*args, **kwargs):
|
||||
pass
|
||||
|
||||
config = AutoConfig.from_pretrained(model, trust_remote_code=shared.args.trust_remote_code)
|
||||
torch.nn.init.kaiming_uniform_ = noop
|
||||
torch.nn.init.uniform_ = noop
|
||||
torch.nn.init.normal_ = noop
|
||||
|
||||
torch.set_default_dtype(torch.half)
|
||||
transformers.modeling_utils._init_weights = False
|
||||
torch.set_default_dtype(torch.half)
|
||||
model = AutoModelForCausalLM.from_config(config, trust_remote_code=shared.args.trust_remote_code)
|
||||
torch.set_default_dtype(torch.float)
|
||||
if eval:
|
||||
model = model.eval()
|
||||
|
||||
layers = find_layers(model)
|
||||
for name in exclude_layers:
|
||||
if name in layers:
|
||||
del layers[name]
|
||||
|
||||
gptq_args = inspect.getfullargspec(make_quant).args
|
||||
|
||||
make_quant_kwargs = {
|
||||
'module': model,
|
||||
'names': layers,
|
||||
'bits': wbits,
|
||||
}
|
||||
if 'groupsize' in gptq_args:
|
||||
make_quant_kwargs['groupsize'] = groupsize
|
||||
if 'faster' in gptq_args:
|
||||
make_quant_kwargs['faster'] = faster_kernel
|
||||
if 'kernel_switch_threshold' in gptq_args:
|
||||
make_quant_kwargs['kernel_switch_threshold'] = kernel_switch_threshold
|
||||
|
||||
make_quant(**make_quant_kwargs)
|
||||
|
||||
del layers
|
||||
if checkpoint.endswith('.safetensors'):
|
||||
from safetensors.torch import load_file as safe_load
|
||||
model.load_state_dict(safe_load(checkpoint), strict=False)
|
||||
else:
|
||||
model.load_state_dict(torch.load(checkpoint, weights_only=True), strict=False)
|
||||
|
||||
model.seqlen = 2048
|
||||
return model
|
||||
|
||||
|
||||
# Used to locate the .pt/.safetensors quantized file
|
||||
def find_quantized_model_file(model_name):
|
||||
if shared.args.checkpoint:
|
||||
return Path(shared.args.checkpoint)
|
||||
|
||||
path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
|
||||
pt_path = None
|
||||
priority_name_list = [
|
||||
Path(f'{shared.args.model_dir}/{model_name}{hyphen}{shared.args.wbits}bit{group}{ext}')
|
||||
for group in ([f'-{shared.args.groupsize}g', ''] if shared.args.groupsize > 0 else [''])
|
||||
for ext in ['.safetensors', '.pt']
|
||||
for hyphen in ['-', f'/{model_name}-', '/']
|
||||
]
|
||||
|
||||
for path in priority_name_list:
|
||||
if path.exists():
|
||||
pt_path = path
|
||||
break
|
||||
|
||||
# If the model hasn't been found with a well-behaved name, pick the last .pt
|
||||
# or the last .safetensors found in its folder as a last resort
|
||||
if not pt_path:
|
||||
for ext in ['.pt', '.safetensors']:
|
||||
found = list(path_to_model.glob(f"*{ext}"))
|
||||
if len(found) > 0:
|
||||
if len(found) > 1:
|
||||
logger.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.')
|
||||
|
||||
pt_path = found[-1]
|
||||
break
|
||||
|
||||
return pt_path
|
||||
|
||||
|
||||
# The function that loads the model in modules/models.py
|
||||
def load_quantized(model_name):
|
||||
if shared.args.model_type is None:
|
||||
logger.error("The model could not be loaded because its type could not be inferred from its name.")
|
||||
logger.error("Please specify the type manually using the --model_type argument.")
|
||||
return None
|
||||
|
||||
# Select the appropriate load_quant function
|
||||
model_type = shared.args.model_type.lower()
|
||||
if shared.args.pre_layer and model_type == 'llama':
|
||||
load_quant = llama_inference_offload.load_quant
|
||||
elif model_type in ('llama', 'opt', 'gptj'):
|
||||
if shared.args.pre_layer:
|
||||
logger.warning("Ignoring --pre_layer because it only works for llama model type.")
|
||||
|
||||
load_quant = _load_quant
|
||||
else:
|
||||
logger.error("Unknown pre-quantized model type specified. Only 'llama', 'opt' and 'gptj' are supported")
|
||||
exit()
|
||||
|
||||
# Find the quantized model weights file (.pt/.safetensors)
|
||||
path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
|
||||
pt_path = find_quantized_model_file(model_name)
|
||||
if not pt_path:
|
||||
logger.error("Could not find the quantized model in .pt or .safetensors format. Exiting.")
|
||||
exit()
|
||||
else:
|
||||
logger.info(f"Found the following quantized model: {pt_path}")
|
||||
|
||||
# qwopqwop200's offload
|
||||
if model_type == 'llama' and shared.args.pre_layer:
|
||||
if len(shared.args.pre_layer) == 1:
|
||||
pre_layer = shared.args.pre_layer[0]
|
||||
else:
|
||||
pre_layer = shared.args.pre_layer
|
||||
|
||||
model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, pre_layer)
|
||||
else:
|
||||
threshold = False if model_type == 'gptj' else 128
|
||||
model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, kernel_switch_threshold=threshold)
|
||||
|
||||
# accelerate offload (doesn't work properly)
|
||||
if shared.args.gpu_memory or torch.cuda.device_count() > 1 or (is_xpu_available() and torch.xpu.device_count() > 1):
|
||||
if shared.args.gpu_memory:
|
||||
memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory))
|
||||
max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
|
||||
max_memory = {}
|
||||
for i in range(len(memory_map)):
|
||||
max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i]
|
||||
|
||||
max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory
|
||||
else:
|
||||
max_memory = accelerate.utils.get_balanced_memory(model)
|
||||
|
||||
device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
|
||||
logger.info("Using the following device map for the quantized model:", device_map)
|
||||
# https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model
|
||||
model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True)
|
||||
|
||||
# No offload
|
||||
elif not shared.args.cpu:
|
||||
if is_xpu_available():
|
||||
model = model.to(torch.device("xpu:0"))
|
||||
else:
|
||||
model = model.to(torch.device('cuda:0'))
|
||||
|
||||
return model
|
||||
@ -105,7 +105,6 @@ loaders_and_params = OrderedDict({
|
||||
],
|
||||
'AutoGPTQ': [
|
||||
'triton',
|
||||
'no_inject_fused_attention',
|
||||
'no_inject_fused_mlp',
|
||||
'no_use_cuda_fp16',
|
||||
'wbits',
|
||||
@ -131,21 +130,6 @@ loaders_and_params = OrderedDict({
|
||||
'trust_remote_code',
|
||||
'no_use_fast',
|
||||
],
|
||||
'GPTQ-for-LLaMa': [
|
||||
'wbits',
|
||||
'groupsize',
|
||||
'model_type',
|
||||
'pre_layer',
|
||||
'trust_remote_code',
|
||||
'no_use_fast',
|
||||
'gptq_for_llama_info',
|
||||
],
|
||||
'QuIP#': [
|
||||
'trust_remote_code',
|
||||
'no_use_fast',
|
||||
'no_flash_attn',
|
||||
'quipsharp_info',
|
||||
],
|
||||
'HQQ': [
|
||||
'hqq_backend',
|
||||
'trust_remote_code',
|
||||
@ -205,9 +189,7 @@ def transformers_samplers():
|
||||
loaders_samplers = {
|
||||
'Transformers': transformers_samplers(),
|
||||
'AutoGPTQ': transformers_samplers(),
|
||||
'GPTQ-for-LLaMa': transformers_samplers(),
|
||||
'AutoAWQ': transformers_samplers(),
|
||||
'QuIP#': transformers_samplers(),
|
||||
'HQQ': transformers_samplers(),
|
||||
'ExLlamav2': {
|
||||
'temperature',
|
||||
@ -339,15 +321,6 @@ loaders_samplers = {
|
||||
},
|
||||
}
|
||||
|
||||
loaders_model_types = {
|
||||
'GPTQ-for-LLaMa': [
|
||||
"None",
|
||||
"llama",
|
||||
"opt",
|
||||
"gptj"
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
@functools.cache
|
||||
def list_all_samplers():
|
||||
@ -375,13 +348,6 @@ def blacklist_samplers(loader, dynamic_temperature):
|
||||
return output
|
||||
|
||||
|
||||
def get_model_types(loader):
|
||||
if loader in loaders_model_types:
|
||||
return loaders_model_types[loader]
|
||||
|
||||
return ["None"]
|
||||
|
||||
|
||||
def get_gpu_memory_keys():
|
||||
return [k for k in shared.gradio if k.startswith('gpu_memory')]
|
||||
|
||||
|
||||
@ -73,13 +73,11 @@ def load_model(model_name, loader=None):
|
||||
load_func_map = {
|
||||
'Transformers': huggingface_loader,
|
||||
'AutoGPTQ': AutoGPTQ_loader,
|
||||
'GPTQ-for-LLaMa': GPTQ_loader,
|
||||
'llama.cpp': llamacpp_loader,
|
||||
'llamacpp_HF': llamacpp_HF_loader,
|
||||
'ExLlamav2': ExLlamav2_loader,
|
||||
'ExLlamav2_HF': ExLlamav2_HF_loader,
|
||||
'AutoAWQ': AutoAWQ_loader,
|
||||
'QuIP#': QuipSharp_loader,
|
||||
'HQQ': HQQ_loader,
|
||||
}
|
||||
|
||||
@ -310,55 +308,6 @@ def AutoAWQ_loader(model_name):
|
||||
return model
|
||||
|
||||
|
||||
def QuipSharp_loader(model_name):
|
||||
try:
|
||||
with RelativeImport("repositories/quip-sharp"):
|
||||
from lib.utils.unsafe_import import model_from_hf_path
|
||||
except:
|
||||
logger.error(
|
||||
"\nQuIP# has not been found. It must be installed manually for now.\n"
|
||||
"For instructions on how to do that, please consult:\n"
|
||||
"https://github.com/oobabooga/text-generation-webui/pull/4803\n"
|
||||
)
|
||||
return None, None
|
||||
|
||||
# This fixes duplicate logging messages after the import above.
|
||||
handlers = logging.getLogger().handlers
|
||||
if len(handlers) > 1:
|
||||
logging.getLogger().removeHandler(handlers[1])
|
||||
|
||||
model_dir = Path(f'{shared.args.model_dir}/{model_name}')
|
||||
if not all((model_dir / file).exists() for file in ['tokenizer_config.json', 'special_tokens_map.json', 'tokenizer.model']):
|
||||
logger.error(f"Could not load the model because the tokenizer files could not be found in the model folder. Please download the following files from the original (unquantized) model into {model_dir}: special_tokens_map.json, tokenizer.json, tokenizer.model, tokenizer_config.json.")
|
||||
return None, None
|
||||
|
||||
model, model_str = model_from_hf_path(
|
||||
model_dir,
|
||||
use_cuda_graph=False,
|
||||
use_flash_attn=not shared.args.no_flash_attn
|
||||
)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
def GPTQ_loader(model_name):
|
||||
|
||||
# Monkey patch
|
||||
if shared.args.monkey_patch:
|
||||
logger.warning("Applying the monkey patch for using LoRAs with GPTQ models. It may cause undefined behavior outside its intended scope.")
|
||||
from modules.monkey_patch_gptq_lora import load_model_llama
|
||||
|
||||
model, _ = load_model_llama(model_name)
|
||||
|
||||
# No monkey patch
|
||||
else:
|
||||
import modules.GPTQ_loader
|
||||
|
||||
model = modules.GPTQ_loader.load_quantized(model_name)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
def AutoGPTQ_loader(model_name):
|
||||
import modules.AutoGPTQ_loader
|
||||
|
||||
@ -380,12 +329,12 @@ def ExLlamav2_HF_loader(model_name):
|
||||
|
||||
def HQQ_loader(model_name):
|
||||
from hqq.core.quantize import HQQBackend, HQQLinear
|
||||
from hqq.engine.hf import HQQModelForCausalLM
|
||||
from hqq.models.hf.base import AutoHQQHFModel
|
||||
|
||||
logger.info(f"Loading HQQ model with backend: \"{shared.args.hqq_backend}\"")
|
||||
|
||||
model_dir = Path(f'{shared.args.model_dir}/{model_name}')
|
||||
model = HQQModelForCausalLM.from_quantized(str(model_dir))
|
||||
model = AutoHQQHFModel.from_quantized(str(model_dir))
|
||||
HQQLinear.set_backend(getattr(HQQBackend, shared.args.hqq_backend))
|
||||
return model
|
||||
|
||||
|
||||
@ -40,12 +40,7 @@ def get_model_metadata(model):
|
||||
hf_metadata = None
|
||||
|
||||
if 'loader' not in model_settings:
|
||||
if hf_metadata is not None and 'quip_params' in hf_metadata:
|
||||
loader = 'QuIP#'
|
||||
else:
|
||||
loader = infer_loader(model, model_settings)
|
||||
|
||||
model_settings['loader'] = loader
|
||||
model_settings['loader'] = infer_loader(model, model_settings)
|
||||
|
||||
# GGUF metadata
|
||||
if model_settings['loader'] in ['llama.cpp', 'llamacpp_HF']:
|
||||
@ -242,7 +237,7 @@ def apply_model_settings_to_state(model, state):
|
||||
loader = model_settings.pop('loader')
|
||||
|
||||
# If the user is using an alternative loader for the same model type, let them keep using it
|
||||
if not (loader == 'ExLlamav2_HF' and state['loader'] in ['GPTQ-for-LLaMa', 'ExLlamav2', 'AutoGPTQ']):
|
||||
if not (loader == 'ExLlamav2_HF' and state['loader'] in ['ExLlamav2', 'AutoGPTQ']):
|
||||
state['loader'] = loader
|
||||
|
||||
for k in model_settings:
|
||||
|
||||
@ -1,39 +0,0 @@
|
||||
# Copied from https://github.com/johnsmith0031/alpaca_lora_4bit
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import alpaca_lora_4bit.autograd_4bit as autograd_4bit
|
||||
from alpaca_lora_4bit.amp_wrapper import AMPWrapper
|
||||
from alpaca_lora_4bit.autograd_4bit import (
|
||||
Autograd4bitQuantLinear,
|
||||
load_llama_model_4bit_low_ram
|
||||
)
|
||||
from alpaca_lora_4bit.models import Linear4bitLt
|
||||
from alpaca_lora_4bit.monkeypatch.peft_tuners_lora_monkey_patch import (
|
||||
replace_peft_model_with_int4_lora_model
|
||||
)
|
||||
|
||||
from modules import shared
|
||||
from modules.GPTQ_loader import find_quantized_model_file
|
||||
|
||||
replace_peft_model_with_int4_lora_model()
|
||||
|
||||
|
||||
def load_model_llama(model_name):
|
||||
config_path = str(Path(f'{shared.args.model_dir}/{model_name}'))
|
||||
model_path = str(find_quantized_model_file(model_name))
|
||||
model, tokenizer = load_llama_model_4bit_low_ram(config_path, model_path, groupsize=shared.args.groupsize, is_v1_model=False)
|
||||
for _, m in model.named_modules():
|
||||
if isinstance(m, Autograd4bitQuantLinear) or isinstance(m, Linear4bitLt):
|
||||
if m.is_v1_model:
|
||||
m.zeros = m.zeros.half()
|
||||
m.scales = m.scales.half()
|
||||
m.bias = m.bias.half()
|
||||
|
||||
autograd_4bit.auto_switch = True
|
||||
|
||||
model.half()
|
||||
wrapper = AMPWrapper(model)
|
||||
wrapper.apply_generate()
|
||||
|
||||
return model, tokenizer
|
||||
@ -89,7 +89,7 @@ group.add_argument('--idle-timeout', type=int, default=0, help='Unload model aft
|
||||
|
||||
# Model loader
|
||||
group = parser.add_argument_group('Model loader')
|
||||
group.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, llamacpp_HF, ExLlamav2_HF, ExLlamav2, AutoGPTQ, AutoAWQ, GPTQ-for-LLaMa, QuIP#.')
|
||||
group.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, llamacpp_HF, ExLlamav2_HF, ExLlamav2, AutoGPTQ, AutoAWQ.')
|
||||
|
||||
# Transformers/Accelerate
|
||||
group = parser.add_argument_group('Transformers/Accelerate')
|
||||
@ -149,21 +149,17 @@ group.add_argument('--num_experts_per_token', type=int, default=2, help='Number
|
||||
# AutoGPTQ
|
||||
group = parser.add_argument_group('AutoGPTQ')
|
||||
group.add_argument('--triton', action='store_true', help='Use triton.')
|
||||
group.add_argument('--no_inject_fused_attention', action='store_true', help='Disable the use of fused attention, which will use less VRAM at the cost of slower inference.')
|
||||
group.add_argument('--no_inject_fused_mlp', action='store_true', help='Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference.')
|
||||
group.add_argument('--no_use_cuda_fp16', action='store_true', help='This can make models faster on some systems.')
|
||||
group.add_argument('--desc_act', action='store_true', help='For models that do not have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig.')
|
||||
group.add_argument('--disable_exllama', action='store_true', help='Disable ExLlama kernel, which can improve inference speed on some systems.')
|
||||
group.add_argument('--disable_exllamav2', action='store_true', help='Disable ExLlamav2 kernel.')
|
||||
|
||||
# GPTQ-for-LLaMa
|
||||
group = parser.add_argument_group('GPTQ-for-LLaMa')
|
||||
group.add_argument('--wbits', type=int, default=0, help='Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.')
|
||||
group.add_argument('--model_type', type=str, help='Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.')
|
||||
group.add_argument('--groupsize', type=int, default=-1, help='Group size.')
|
||||
group.add_argument('--pre_layer', type=int, nargs='+', help='The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60.')
|
||||
group.add_argument('--checkpoint', type=str, help='The path to the quantized checkpoint file. If not specified, it will be automatically detected.')
|
||||
group.add_argument('--monkey-patch', action='store_true', help='Apply the monkey patch for using LoRAs with quantized models.')
|
||||
|
||||
# AutoAWQ
|
||||
group = parser.add_argument_group('AutoAWQ')
|
||||
group.add_argument('--no_inject_fused_attention', action='store_true', help='Disable the use of fused attention, which will use less VRAM at the cost of slower inference.')
|
||||
|
||||
# HQQ
|
||||
group = parser.add_argument_group('HQQ')
|
||||
@ -208,7 +204,11 @@ group = parser.add_argument_group('Multimodal')
|
||||
group.add_argument('--multimodal-pipeline', type=str, default=None, help='The multimodal pipeline to use. Examples: llava-7b, llava-13b.')
|
||||
|
||||
# Deprecated parameters
|
||||
# group = parser.add_argument_group('Deprecated')
|
||||
group = parser.add_argument_group('Deprecated')
|
||||
group.add_argument('--model_type', type=str, help='DEPRECATED')
|
||||
group.add_argument('--pre_layer', type=int, nargs='+', help='DEPRECATED')
|
||||
group.add_argument('--checkpoint', type=str, help='DEPRECATED')
|
||||
group.add_argument('--monkey-patch', action='store_true', help='DEPRECATED')
|
||||
|
||||
args = parser.parse_args()
|
||||
args_defaults = parser.parse_args([])
|
||||
@ -253,8 +253,6 @@ def fix_loader_name(name):
|
||||
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'
|
||||
elif name in ['exllama', 'ex-llama', 'ex_llama', 'exlama']:
|
||||
return 'ExLlama'
|
||||
elif name in ['exllamav2', 'exllama-v2', 'ex_llama-v2', 'exlamav2', 'exlama-v2', 'exllama2', 'exllama-2']:
|
||||
@ -263,8 +261,6 @@ def fix_loader_name(name):
|
||||
return 'ExLlamav2_HF'
|
||||
elif name in ['autoawq', 'awq', 'auto-awq']:
|
||||
return 'AutoAWQ'
|
||||
elif name in ['quip#', 'quip-sharp', 'quipsharp', 'quip_sharp']:
|
||||
return 'QuIP#'
|
||||
elif name in ['hqq']:
|
||||
return 'HQQ'
|
||||
|
||||
|
||||
@ -292,12 +292,6 @@ def calc_trainable_parameters(model):
|
||||
|
||||
def do_train(lora_name: str, always_override: bool, q_proj_en: bool, v_proj_en: bool, k_proj_en: bool, o_proj_en: bool, gate_proj_en: bool, down_proj_en: bool, up_proj_en: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, overlap_len: int, newline_favor_len: int, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str, stop_at_loss: float, add_eos_token: bool, min_chars: int, report_to: str):
|
||||
|
||||
if shared.args.monkey_patch:
|
||||
from alpaca_lora_4bit.monkeypatch.peft_tuners_lora_monkey_patch import (
|
||||
replace_peft_model_with_int4_lora_model
|
||||
)
|
||||
replace_peft_model_with_int4_lora_model()
|
||||
|
||||
global WANT_INTERRUPT
|
||||
WANT_INTERRUPT = False
|
||||
|
||||
@ -329,10 +323,6 @@ def do_train(lora_name: str, always_override: bool, q_proj_en: bool, v_proj_en:
|
||||
|
||||
time.sleep(5)
|
||||
|
||||
if shared.args.loader == 'GPTQ-for-LLaMa' and not shared.args.monkey_patch:
|
||||
yield "LoRA training with GPTQ-for-LLaMa requires loading with `--monkey-patch`"
|
||||
return
|
||||
|
||||
if cutoff_len <= 0 or micro_batch_size <= 0 or batch_size <= 0 or actual_lr <= 0 or lora_rank <= 0 or lora_alpha <= 0:
|
||||
yield "Cannot input zeroes."
|
||||
return
|
||||
@ -553,15 +543,6 @@ def do_train(lora_name: str, always_override: bool, q_proj_en: bool, v_proj_en:
|
||||
yield traceback.format_exc().replace('\n', '\n\n')
|
||||
return
|
||||
|
||||
if shared.args.monkey_patch:
|
||||
from alpaca_lora_4bit.autograd_4bit import Autograd4bitQuantLinear
|
||||
from alpaca_lora_4bit.models import Linear4bitLt
|
||||
for _, m in lora_model.named_modules():
|
||||
if isinstance(m, Autograd4bitQuantLinear) or isinstance(m, Linear4bitLt):
|
||||
if m.is_v1_model:
|
||||
m.zeros = m.zeros.half()
|
||||
m.scales = m.scales.half()
|
||||
|
||||
class Tracked():
|
||||
def __init__(self):
|
||||
self.current_steps = 0
|
||||
|
||||
@ -111,7 +111,6 @@ def create_ui():
|
||||
shared.gradio['compress_pos_emb'] = gr.Slider(label='compress_pos_emb', minimum=1, maximum=8, step=1, info='Positional embeddings compression factor. Should be set to (context length) / (model\'s original context length). Equal to 1/rope_freq_scale.', value=shared.args.compress_pos_emb)
|
||||
|
||||
shared.gradio['autogptq_info'] = gr.Markdown('ExLlamav2_HF is recommended over AutoGPTQ for models derived from Llama.')
|
||||
shared.gradio['quipsharp_info'] = gr.Markdown('QuIP# has to be installed manually at the moment.')
|
||||
|
||||
with gr.Column():
|
||||
shared.gradio['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit)
|
||||
|
||||
@ -388,7 +388,7 @@ def update_requirements(initial_installation=False, pull=True):
|
||||
# Prepare the requirements file
|
||||
textgen_requirements = open(requirements_file).read().splitlines()
|
||||
if is_cuda118:
|
||||
textgen_requirements = [req.replace('+cu121', '+cu118').replace('+cu122', '+cu118') for req in textgen_requirements]
|
||||
textgen_requirements = [req.replace('+cu121', '+cu118').replace('+cu122', '+cu118') for req in textgen_requirements if "auto-gptq" not in req]
|
||||
if is_windows() and is_cuda118: # No flash-attention on Windows for CUDA 11
|
||||
textgen_requirements = [req for req in textgen_requirements if 'oobabooga/flash-attention' not in req]
|
||||
|
||||
|
||||
@ -1,11 +1,12 @@
|
||||
accelerate==0.30.*
|
||||
aqlm[gpu,cpu]==1.1.3; platform_system == "Linux"
|
||||
aqlm[gpu,cpu]==1.1.5; platform_system == "Linux"
|
||||
auto-gptq==0.7.1
|
||||
bitsandbytes==0.43.*
|
||||
colorama
|
||||
datasets
|
||||
einops
|
||||
gradio==4.26.*
|
||||
hqq==0.1.5
|
||||
hqq==0.1.7.post2
|
||||
jinja2==3.1.2
|
||||
lm_eval==0.3.0
|
||||
markdown
|
||||
@ -23,7 +24,7 @@ safetensors==0.4.*
|
||||
scipy
|
||||
sentencepiece
|
||||
tensorboard
|
||||
transformers==4.40.*
|
||||
transformers==4.41.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
@ -52,10 +53,6 @@ https://github.com/oobabooga/llama-cpp-python-cuBLAS-wheels/releases/download/te
|
||||
https://github.com/oobabooga/llama-cpp-python-cuBLAS-wheels/releases/download/textgen-webui/llama_cpp_python_cuda_tensorcores-0.2.75+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
|
||||
# CUDA wheels
|
||||
https://github.com/jllllll/AutoGPTQ/releases/download/v0.6.0/auto_gptq-0.6.0+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
|
||||
https://github.com/jllllll/AutoGPTQ/releases/download/v0.6.0/auto_gptq-0.6.0+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/jllllll/AutoGPTQ/releases/download/v0.6.0/auto_gptq-0.6.0+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/jllllll/AutoGPTQ/releases/download/v0.6.0/auto_gptq-0.6.0+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/oobabooga/exllamav2/releases/download/v0.0.20/exllamav2-0.0.20+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
|
||||
https://github.com/oobabooga/exllamav2/releases/download/v0.0.20/exllamav2-0.0.20+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/oobabooga/exllamav2/releases/download/v0.0.20/exllamav2-0.0.20+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
@ -65,8 +62,4 @@ https://github.com/oobabooga/flash-attention/releases/download/v2.5.6/flash_attn
|
||||
https://github.com/oobabooga/flash-attention/releases/download/v2.5.6/flash_attn-2.5.6+cu122torch2.2.0cxx11abiFALSE-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/Dao-AILab/flash-attention/releases/download/v2.5.6/flash_attn-2.5.6+cu122torch2.2cxx11abiFALSE-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/Dao-AILab/flash-attention/releases/download/v2.5.6/flash_attn-2.5.6+cu122torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
|
||||
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
autoawq==0.2.3; platform_system == "Linux" or platform_system == "Windows"
|
||||
autoawq==0.2.5; platform_system == "Linux" or platform_system == "Windows"
|
||||
|
||||
@ -3,7 +3,7 @@ colorama
|
||||
datasets
|
||||
einops
|
||||
gradio==4.26.*
|
||||
hqq==0.1.5
|
||||
hqq==0.1.7.post2
|
||||
jinja2==3.1.2
|
||||
lm_eval==0.3.0
|
||||
markdown
|
||||
@ -21,7 +21,7 @@ safetensors==0.4.*
|
||||
scipy
|
||||
sentencepiece
|
||||
tensorboard
|
||||
transformers==4.40.*
|
||||
transformers==4.41.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
@ -40,12 +40,8 @@ https://github.com/oobabooga/llama-cpp-python-cuBLAS-wheels/releases/download/cp
|
||||
# AMD wheels
|
||||
https://github.com/oobabooga/llama-cpp-python-cuBLAS-wheels/releases/download/rocm/llama_cpp_python_cuda-0.2.75+rocm5.6.1-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/oobabooga/llama-cpp-python-cuBLAS-wheels/releases/download/rocm/llama_cpp_python_cuda-0.2.75+rocm5.6.1-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/jllllll/AutoGPTQ/releases/download/v0.6.0/auto_gptq-0.6.0+rocm5.6-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/jllllll/AutoGPTQ/releases/download/v0.6.0/auto_gptq-0.6.0+rocm5.6-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/oobabooga/exllamav2/releases/download/v0.0.20/exllamav2-0.0.20+rocm5.6-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/oobabooga/exllamav2/releases/download/v0.0.20/exllamav2-0.0.20+rocm5.6-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/oobabooga/exllamav2/releases/download/v0.0.20/exllamav2-0.0.20-py3-none-any.whl; platform_system != "Darwin" and platform_machine != "x86_64"
|
||||
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+rocm5.6-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+rocm5.6-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.3/autoawq-0.2.3+rocm561-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.3/autoawq-0.2.3+rocm561-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.5/autoawq-0.2.5+rocm561-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.5/autoawq-0.2.5+rocm561-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
|
||||
@ -3,7 +3,7 @@ colorama
|
||||
datasets
|
||||
einops
|
||||
gradio==4.26.*
|
||||
hqq==0.1.5
|
||||
hqq==0.1.7.post2
|
||||
jinja2==3.1.2
|
||||
lm_eval==0.3.0
|
||||
markdown
|
||||
@ -21,7 +21,7 @@ safetensors==0.4.*
|
||||
scipy
|
||||
sentencepiece
|
||||
tensorboard
|
||||
transformers==4.40.*
|
||||
transformers==4.41.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
@ -38,12 +38,8 @@ https://github.com/oobabooga/llama-cpp-python-cuBLAS-wheels/releases/download/cp
|
||||
https://github.com/oobabooga/llama-cpp-python-cuBLAS-wheels/releases/download/cpu/llama_cpp_python-0.2.75+cpuavx-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
|
||||
# AMD wheels
|
||||
https://github.com/jllllll/AutoGPTQ/releases/download/v0.6.0/auto_gptq-0.6.0+rocm5.6-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/jllllll/AutoGPTQ/releases/download/v0.6.0/auto_gptq-0.6.0+rocm5.6-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/oobabooga/exllamav2/releases/download/v0.0.20/exllamav2-0.0.20+rocm5.6-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/oobabooga/exllamav2/releases/download/v0.0.20/exllamav2-0.0.20+rocm5.6-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/oobabooga/exllamav2/releases/download/v0.0.20/exllamav2-0.0.20-py3-none-any.whl; platform_system != "Darwin" and platform_machine != "x86_64"
|
||||
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+rocm5.6-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+rocm5.6-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.3/autoawq-0.2.3+rocm561-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.3/autoawq-0.2.3+rocm561-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.5/autoawq-0.2.5+rocm561-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.5/autoawq-0.2.5+rocm561-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
|
||||
@ -3,7 +3,7 @@ colorama
|
||||
datasets
|
||||
einops
|
||||
gradio==4.26.*
|
||||
hqq==0.1.5
|
||||
hqq==0.1.7.post2
|
||||
jinja2==3.1.2
|
||||
lm_eval==0.3.0
|
||||
markdown
|
||||
@ -21,7 +21,7 @@ safetensors==0.4.*
|
||||
scipy
|
||||
sentencepiece
|
||||
tensorboard
|
||||
transformers==4.40.*
|
||||
transformers==4.41.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
|
||||
@ -3,7 +3,7 @@ colorama
|
||||
datasets
|
||||
einops
|
||||
gradio==4.26.*
|
||||
hqq==0.1.5
|
||||
hqq==0.1.7.post2
|
||||
jinja2==3.1.2
|
||||
lm_eval==0.3.0
|
||||
markdown
|
||||
@ -21,7 +21,7 @@ safetensors==0.4.*
|
||||
scipy
|
||||
sentencepiece
|
||||
tensorboard
|
||||
transformers==4.40.*
|
||||
transformers==4.41.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
|
||||
@ -3,7 +3,7 @@ colorama
|
||||
datasets
|
||||
einops
|
||||
gradio==4.26.*
|
||||
hqq==0.1.5
|
||||
hqq==0.1.7.post2
|
||||
jinja2==3.1.2
|
||||
lm_eval==0.3.0
|
||||
markdown
|
||||
@ -21,7 +21,7 @@ safetensors==0.4.*
|
||||
scipy
|
||||
sentencepiece
|
||||
tensorboard
|
||||
transformers==4.40.*
|
||||
transformers==4.41.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
|
||||
@ -3,7 +3,7 @@ colorama
|
||||
datasets
|
||||
einops
|
||||
gradio==4.26.*
|
||||
hqq==0.1.5
|
||||
hqq==0.1.7.post2
|
||||
jinja2==3.1.2
|
||||
lm_eval==0.3.0
|
||||
markdown
|
||||
@ -21,7 +21,7 @@ safetensors==0.4.*
|
||||
scipy
|
||||
sentencepiece
|
||||
tensorboard
|
||||
transformers==4.40.*
|
||||
transformers==4.41.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
|
||||
@ -1,11 +1,12 @@
|
||||
accelerate==0.30.*
|
||||
aqlm[gpu,cpu]==1.1.3; platform_system == "Linux"
|
||||
aqlm[gpu,cpu]==1.1.5; platform_system == "Linux"
|
||||
auto-gptq==0.7.1
|
||||
bitsandbytes==0.43.*
|
||||
colorama
|
||||
datasets
|
||||
einops
|
||||
gradio==4.26.*
|
||||
hqq==0.1.5
|
||||
hqq==0.1.7.post2
|
||||
jinja2==3.1.2
|
||||
lm_eval==0.3.0
|
||||
markdown
|
||||
@ -23,7 +24,7 @@ safetensors==0.4.*
|
||||
scipy
|
||||
sentencepiece
|
||||
tensorboard
|
||||
transformers==4.40.*
|
||||
transformers==4.41.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
@ -52,10 +53,6 @@ https://github.com/oobabooga/llama-cpp-python-cuBLAS-wheels/releases/download/te
|
||||
https://github.com/oobabooga/llama-cpp-python-cuBLAS-wheels/releases/download/textgen-webui/llama_cpp_python_cuda_tensorcores-0.2.75+cu121avx-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
|
||||
# CUDA wheels
|
||||
https://github.com/jllllll/AutoGPTQ/releases/download/v0.6.0/auto_gptq-0.6.0+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
|
||||
https://github.com/jllllll/AutoGPTQ/releases/download/v0.6.0/auto_gptq-0.6.0+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/jllllll/AutoGPTQ/releases/download/v0.6.0/auto_gptq-0.6.0+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/jllllll/AutoGPTQ/releases/download/v0.6.0/auto_gptq-0.6.0+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/oobabooga/exllamav2/releases/download/v0.0.20/exllamav2-0.0.20+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
|
||||
https://github.com/oobabooga/exllamav2/releases/download/v0.0.20/exllamav2-0.0.20+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/oobabooga/exllamav2/releases/download/v0.0.20/exllamav2-0.0.20+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
@ -65,8 +62,4 @@ https://github.com/oobabooga/flash-attention/releases/download/v2.5.6/flash_attn
|
||||
https://github.com/oobabooga/flash-attention/releases/download/v2.5.6/flash_attn-2.5.6+cu122torch2.2.0cxx11abiFALSE-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/Dao-AILab/flash-attention/releases/download/v2.5.6/flash_attn-2.5.6+cu122torch2.2cxx11abiFALSE-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/Dao-AILab/flash-attention/releases/download/v2.5.6/flash_attn-2.5.6+cu122torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
|
||||
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
autoawq==0.2.3; platform_system == "Linux" or platform_system == "Windows"
|
||||
autoawq==0.2.5; platform_system == "Linux" or platform_system == "Windows"
|
||||
|
||||
@ -3,7 +3,7 @@ colorama
|
||||
datasets
|
||||
einops
|
||||
gradio==4.26.*
|
||||
hqq==0.1.5
|
||||
hqq==0.1.7.post2
|
||||
jinja2==3.1.2
|
||||
lm_eval==0.3.0
|
||||
markdown
|
||||
@ -21,7 +21,7 @@ safetensors==0.4.*
|
||||
scipy
|
||||
sentencepiece
|
||||
tensorboard
|
||||
transformers==4.40.*
|
||||
transformers==4.41.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user