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Remove ExLlamaV2 backend

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- archived upstream: 7dc12af3a8
- replaced by ExLlamaV3, which has much better quantization accuracy
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oobabooga 2026-03-05 13:57:21 -08:00
parent 134ac8fc29
commit 2f08dce7b0
19 changed files with 22 additions and 713 deletions
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20
modules/shared.py
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@ -67,12 +67,12 @@ group.add_argument('--image-quant', type=str, default=None,
# 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, ExLlamav3_HF, ExLlamav2_HF, ExLlamav2, TensorRT-LLM.')
group.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, ExLlamav3_HF, ExLlamav3, TensorRT-LLM.')
# Cache
group = parser.add_argument_group('Context and cache')
group.add_argument('--ctx-size', '--n_ctx', '--max_seq_len', type=int, default=8192, metavar='N', help='Context size in tokens. llama.cpp: 0 = auto if gpu-layers is also -1.')
group.add_argument('--cache-type', '--cache_type', type=str, default='fp16', metavar='N', help='KV cache type; valid options: llama.cpp - fp16, q8_0, q4_0; ExLlamaV2 - fp16, fp8, q8, q6, q4; ExLlamaV3 - fp16, q2 to q8 (can specify k_bits and v_bits separately, e.g. q4_q8).')
group.add_argument('--cache-type', '--cache_type', type=str, default='fp16', metavar='N', help='KV cache type; valid options: llama.cpp - fp16, q8_0, q4_0; ExLlamaV3 - fp16, q2 to q8 (can specify k_bits and v_bits separately, e.g. q4_q8).')
# Speculative decoding
group = parser.add_argument_group('Speculative decoding')
@ -127,18 +127,10 @@ group.add_argument('--quant_type', type=str, default='nf4', help='quant_type for
# ExLlamaV3
group = parser.add_argument_group('ExLlamaV3')
group.add_argument('--gpu-split', type=str, help='Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7.')
group.add_argument('--enable-tp', '--enable_tp', action='store_true', help='Enable Tensor Parallelism (TP) to split the model across GPUs.')
group.add_argument('--tp-backend', type=str, default='native', help='The backend for tensor parallelism. Valid options: native, nccl. Default: native.')
# ExLlamaV2
group = parser.add_argument_group('ExLlamaV2')
group.add_argument('--gpu-split', type=str, help='Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7.')
group.add_argument('--autosplit', action='store_true', help='Autosplit the model tensors across the available GPUs. This causes --gpu-split to be ignored.')
group.add_argument('--cfg-cache', action='store_true', help='ExLlamav2_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader.')
group.add_argument('--no_flash_attn', action='store_true', help='Force flash-attention to not be used.')
group.add_argument('--no_xformers', action='store_true', help='Force xformers to not be used.')
group.add_argument('--no_sdpa', action='store_true', help='Force Torch SDPA to not be used.')
group.add_argument('--num_experts_per_token', type=int, default=2, metavar='N', help='Number of experts to use for generation. Applies to MoE models like Mixtral.')
group.add_argument('--cfg-cache', action='store_true', help='Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader.')
# TensorRT-LLM
group = parser.add_argument_group('TensorRT-LLM')
@ -379,10 +371,6 @@ def fix_loader_name(name):
return 'llama.cpp'
elif name in ['transformers', 'huggingface', 'hf', 'hugging_face', 'hugging face']:
return 'Transformers'
elif name in ['exllamav2', 'exllama-v2', 'ex_llama-v2', 'exlamav2', 'exlama-v2', 'exllama2', 'exllama-2']:
return 'ExLlamav2'
elif name in ['exllamav2-hf', 'exllamav2_hf', 'exllama-v2-hf', 'exllama_v2_hf', 'exllama-v2_hf', 'exllama2-hf', 'exllama2_hf', 'exllama-2-hf', 'exllama_2_hf', 'exllama-2_hf']:
return 'ExLlamav2_HF'
elif name in ['exllamav3-hf', 'exllamav3_hf', 'exllama-v3-hf', 'exllama_v3_hf', 'exllama-v3_hf', 'exllama3-hf', 'exllama3_hf', 'exllama-3-hf', 'exllama_3_hf', 'exllama-3_hf']:
return 'ExLlamav3_HF'
elif name in ['exllamav3']: