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Backend cleanup (#6025)
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README.md
27
README.md
@ -11,7 +11,7 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
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## Features
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* 3 interface modes: default (two columns), notebook, and chat.
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* 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).
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* 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).
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* Dropdown menu for quickly switching between different models.
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* 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.
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* [Chat with custom characters](https://github.com/oobabooga/text-generation-webui/wiki/03-%E2%80%90-Parameters-Tab#character).
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@ -208,12 +208,12 @@ usage: server.py [-h] [--multi-user] [--character CHARACTER] [--model MODEL] [--
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[--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]
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[--tensor_split TENSOR_SPLIT] [--numa] [--logits_all] [--no_offload_kqv] [--cache-capacity CACHE_CAPACITY] [--row_split] [--streaming-llm] [--attention-sink-size ATTENTION_SINK_SIZE]
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[--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]
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[--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]
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[--groupsize GROUPSIZE] [--pre_layer PRE_LAYER [PRE_LAYER ...]] [--checkpoint CHECKPOINT] [--monkey-patch] [--hqq-backend HQQ_BACKEND] [--deepspeed]
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[--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]
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[--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]
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[--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]
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[--multimodal-pipeline MULTIMODAL_PIPELINE]
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[--triton] [--no_inject_fused_mlp] [--no_use_cuda_fp16] [--desc_act] [--disable_exllama] [--disable_exllamav2] [--wbits WBITS] [--groupsize GROUPSIZE] [--no_inject_fused_attention]
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[--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]
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[--compress_pos_emb COMPRESS_POS_EMB] [--listen] [--listen-port LISTEN_PORT] [--listen-host LISTEN_HOST] [--share] [--auto-launch] [--gradio-auth GRADIO_AUTH]
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[--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]
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[--api-key API_KEY] [--admin-key ADMIN_KEY] [--nowebui] [--multimodal-pipeline MULTIMODAL_PIPELINE] [--model_type MODEL_TYPE] [--pre_layer PRE_LAYER [PRE_LAYER ...]]
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[--checkpoint CHECKPOINT] [--monkey-patch]
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Text generation web UI
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@ -237,7 +237,7 @@ Basic settings:
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Model loader:
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--loader LOADER Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, llamacpp_HF, ExLlamav2_HF, ExLlamav2,
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AutoGPTQ, AutoAWQ, GPTQ-for-LLaMa, QuIP#.
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AutoGPTQ, AutoAWQ.
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Transformers/Accelerate:
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--cpu Use the CPU to generate text. Warning: Training on CPU is extremely slow.
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@ -293,21 +293,16 @@ ExLlamaV2:
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AutoGPTQ:
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--triton Use triton.
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--no_inject_fused_attention Disable the use of fused attention, which will use less VRAM at the cost of slower inference.
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--no_inject_fused_mlp Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference.
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--no_use_cuda_fp16 This can make models faster on some systems.
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--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.
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--disable_exllama Disable ExLlama kernel, which can improve inference speed on some systems.
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--disable_exllamav2 Disable ExLlamav2 kernel.
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GPTQ-for-LLaMa:
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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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--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
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by spaces, eg --pre_layer 30 60.
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--checkpoint CHECKPOINT The path to the quantized checkpoint file. If not specified, it will be automatically detected.
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--monkey-patch Apply the monkey patch for using LoRAs with quantized models.
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AutoAWQ:
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--no_inject_fused_attention Disable the use of fused attention, which will use less VRAM at the cost of slower inference.
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HQQ:
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--hqq-backend HQQ_BACKEND Backend for the HQQ loader. Valid options: PYTORCH, PYTORCH_COMPILE, ATEN.
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@ -64,14 +64,6 @@ Loads: GPTQ models.
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* **no_use_cuda_fp16**: On some systems, the performance can be very bad with this unset. Can usually be ignored.
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* **desc_act**: For ancient models without proper metadata, sets the model "act-order" parameter manually. Can usually be ignored.
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### GPTQ-for-LLaMa
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Loads: GPTQ models.
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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.
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* **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.
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### llama.cpp
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Loads: GGUF models. Note: GGML models have been deprecated and do not work anymore.
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@ -13,28 +13,6 @@ Source: https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/1126
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This file will be automatically detected the next time you start the web UI.
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## Using LoRAs with GPTQ-for-LLaMa
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This requires using a monkey patch that is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit
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To use it:
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Install alpaca_lora_4bit using pip
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```
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git clone https://github.com/johnsmith0031/alpaca_lora_4bit.git
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cd alpaca_lora_4bit
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git fetch origin winglian-setup_pip
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git checkout winglian-setup_pip
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pip install .
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```
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Start the UI with the --monkey-patch flag:
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```
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python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch
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```
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## DeepSpeed
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`DeepSpeed ZeRO-3` is an alternative offloading strategy for full-precision (16-bit) transformers models.
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@ -2,15 +2,13 @@
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| Loader | Loading 1 LoRA | Loading 2 or more LoRAs | Training LoRAs | Multimodal extension | Perplexity evaluation |
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|----------------|----------------|-------------------------|----------------|----------------------|-----------------------|
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| Transformers | ✅ | ✅\*\*\* | ✅\* | ✅ | ✅ |
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| Transformers | ✅ | ✅\*\* | ✅\* | ✅ | ✅ |
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| llama.cpp | ❌ | ❌ | ❌ | ❌ | use llamacpp_HF |
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| llamacpp_HF | ❌ | ❌ | ❌ | ❌ | ✅ |
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| ExLlamav2_HF | ✅ | ✅ | ❌ | ❌ | ✅ |
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| ExLlamav2 | ✅ | ✅ | ❌ | ❌ | use ExLlamav2_HF |
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| AutoGPTQ | ✅ | ❌ | ❌ | ✅ | ✅ |
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| AutoAWQ | ? | ❌ | ? | ? | ✅ |
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| GPTQ-for-LLaMa | ✅\*\* | ✅\*\*\* | ✅ | ✅ | ✅ |
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| QuIP# | ? | ? | ? | ? | ✅ |
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| HQQ | ? | ? | ? | ? | ✅ |
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❌ = not implemented
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@ -19,6 +17,4 @@
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\* Training LoRAs with GPTQ models also works with the Transformers loader. Make sure to check "auto-devices" and "disable_exllama" before loading the model.
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\*\* 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).
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\*\*\* Multi-LoRA in PEFT is tricky and the current implementation does not work reliably in all cases.
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\*\* Multi-LoRA in PEFT is tricky and the current implementation does not work reliably in all cases.
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@ -44,7 +44,7 @@ def load_quantized(model_name):
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'model_basename': pt_path.stem,
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'device': "xpu:0" if is_xpu_available() else "cuda:0" if not shared.args.cpu else "cpu",
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'use_triton': shared.args.triton,
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'inject_fused_attention': not shared.args.no_inject_fused_attention,
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'inject_fused_attention': False,
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'inject_fused_mlp': not shared.args.no_inject_fused_mlp,
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'use_safetensors': use_safetensors,
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'trust_remote_code': shared.args.trust_remote_code,
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@ -1,171 +0,0 @@
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import inspect
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import re
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from pathlib import Path
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import accelerate
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import torch
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import transformers
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from accelerate.utils import is_xpu_available
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from gptq_for_llama import llama_inference_offload
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from gptq_for_llama.modelutils import find_layers
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from gptq_for_llama.quant import make_quant
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from transformers import AutoConfig, AutoModelForCausalLM
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import modules.shared as shared
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from modules.logging_colors import logger
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# This function is a replacement for the load_quant function in the
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# GPTQ-for_LLaMa repository. It supports more models and branches.
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def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=None, kernel_switch_threshold=128, eval=True):
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exclude_layers = exclude_layers or ['lm_head']
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def noop(*args, **kwargs):
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pass
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config = AutoConfig.from_pretrained(model, trust_remote_code=shared.args.trust_remote_code)
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torch.nn.init.kaiming_uniform_ = noop
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torch.nn.init.uniform_ = noop
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torch.nn.init.normal_ = noop
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torch.set_default_dtype(torch.half)
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transformers.modeling_utils._init_weights = False
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torch.set_default_dtype(torch.half)
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model = AutoModelForCausalLM.from_config(config, trust_remote_code=shared.args.trust_remote_code)
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torch.set_default_dtype(torch.float)
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if eval:
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model = model.eval()
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layers = find_layers(model)
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for name in exclude_layers:
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if name in layers:
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del layers[name]
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gptq_args = inspect.getfullargspec(make_quant).args
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make_quant_kwargs = {
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'module': model,
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'names': layers,
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'bits': wbits,
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}
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if 'groupsize' in gptq_args:
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make_quant_kwargs['groupsize'] = groupsize
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if 'faster' in gptq_args:
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make_quant_kwargs['faster'] = faster_kernel
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if 'kernel_switch_threshold' in gptq_args:
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make_quant_kwargs['kernel_switch_threshold'] = kernel_switch_threshold
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make_quant(**make_quant_kwargs)
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del layers
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if checkpoint.endswith('.safetensors'):
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from safetensors.torch import load_file as safe_load
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model.load_state_dict(safe_load(checkpoint), strict=False)
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else:
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model.load_state_dict(torch.load(checkpoint, weights_only=True), strict=False)
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model.seqlen = 2048
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return model
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# Used to locate the .pt/.safetensors quantized file
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def find_quantized_model_file(model_name):
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if shared.args.checkpoint:
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return Path(shared.args.checkpoint)
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path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
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pt_path = None
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priority_name_list = [
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Path(f'{shared.args.model_dir}/{model_name}{hyphen}{shared.args.wbits}bit{group}{ext}')
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for group in ([f'-{shared.args.groupsize}g', ''] if shared.args.groupsize > 0 else [''])
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for ext in ['.safetensors', '.pt']
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for hyphen in ['-', f'/{model_name}-', '/']
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]
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for path in priority_name_list:
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if path.exists():
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pt_path = path
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break
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# If the model hasn't been found with a well-behaved name, pick the last .pt
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# or the last .safetensors found in its folder as a last resort
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if not pt_path:
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for ext in ['.pt', '.safetensors']:
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found = list(path_to_model.glob(f"*{ext}"))
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if len(found) > 0:
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if len(found) > 1:
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logger.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.')
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pt_path = found[-1]
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break
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return pt_path
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# The function that loads the model in modules/models.py
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def load_quantized(model_name):
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if shared.args.model_type is None:
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logger.error("The model could not be loaded because its type could not be inferred from its name.")
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logger.error("Please specify the type manually using the --model_type argument.")
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return None
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# Select the appropriate load_quant function
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model_type = shared.args.model_type.lower()
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if shared.args.pre_layer and model_type == 'llama':
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load_quant = llama_inference_offload.load_quant
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elif model_type in ('llama', 'opt', 'gptj'):
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if shared.args.pre_layer:
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logger.warning("Ignoring --pre_layer because it only works for llama model type.")
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load_quant = _load_quant
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else:
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logger.error("Unknown pre-quantized model type specified. Only 'llama', 'opt' and 'gptj' are supported")
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exit()
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# Find the quantized model weights file (.pt/.safetensors)
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path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
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pt_path = find_quantized_model_file(model_name)
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if not pt_path:
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logger.error("Could not find the quantized model in .pt or .safetensors format. Exiting.")
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exit()
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else:
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logger.info(f"Found the following quantized model: {pt_path}")
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# qwopqwop200's offload
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if model_type == 'llama' and shared.args.pre_layer:
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if len(shared.args.pre_layer) == 1:
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pre_layer = shared.args.pre_layer[0]
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else:
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pre_layer = shared.args.pre_layer
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model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, pre_layer)
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else:
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threshold = False if model_type == 'gptj' else 128
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model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, kernel_switch_threshold=threshold)
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# accelerate offload (doesn't work properly)
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if shared.args.gpu_memory or torch.cuda.device_count() > 1 or (is_xpu_available() and torch.xpu.device_count() > 1):
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if shared.args.gpu_memory:
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memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory))
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max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
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max_memory = {}
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for i in range(len(memory_map)):
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max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i]
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max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory
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else:
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max_memory = accelerate.utils.get_balanced_memory(model)
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device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
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logger.info("Using the following device map for the quantized model:", device_map)
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# https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model
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model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True)
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# No offload
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elif not shared.args.cpu:
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if is_xpu_available():
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model = model.to(torch.device("xpu:0"))
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else:
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model = model.to(torch.device('cuda:0'))
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return model
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@ -105,7 +105,6 @@ loaders_and_params = OrderedDict({
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],
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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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'no_use_cuda_fp16',
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'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