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Add --no_xformers and --no_sdpa flags for ExllamaV2
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@ -48,6 +48,8 @@ class Exllamav2Model:
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config.scale_pos_emb = shared.args.compress_pos_emb
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config.scale_alpha_value = shared.args.alpha_value
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config.no_flash_attn = shared.args.no_flash_attn
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config.no_xformers = shared.args.no_xformers
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config.no_sdpa = shared.args.no_sdpa
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config.num_experts_per_token = int(shared.args.num_experts_per_token)
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model = ExLlamaV2(config)
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@ -176,6 +176,8 @@ class Exllamav2HF(PreTrainedModel):
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config.scale_pos_emb = shared.args.compress_pos_emb
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config.scale_alpha_value = shared.args.alpha_value
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config.no_flash_attn = shared.args.no_flash_attn
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config.no_xformers = shared.args.no_xformers
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config.no_sdpa = shared.args.no_sdpa
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config.num_experts_per_token = int(shared.args.num_experts_per_token)
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return Exllamav2HF(config)
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@ -84,6 +84,8 @@ loaders_and_params = OrderedDict({
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'max_seq_len',
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'cfg_cache',
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'no_flash_attn',
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'no_xformers',
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'no_sdpa',
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'num_experts_per_token',
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'cache_8bit',
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'cache_4bit',
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@ -97,6 +99,8 @@ loaders_and_params = OrderedDict({
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'gpu_split',
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'max_seq_len',
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'no_flash_attn',
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'no_xformers',
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'no_sdpa',
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'num_experts_per_token',
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'cache_8bit',
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'cache_4bit',
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@ -143,6 +143,8 @@ group.add_argument('--autosplit', action='store_true', help='Autosplit the model
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group.add_argument('--max_seq_len', type=int, default=2048, help='Maximum sequence length.')
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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.')
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group.add_argument('--no_flash_attn', action='store_true', help='Force flash-attention to not be used.')
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group.add_argument('--no_xformers', action='store_true', help='Force xformers to not be used.')
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group.add_argument('--no_sdpa', action='store_true', help='Force Torch SDPA to not be used.')
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group.add_argument('--cache_8bit', action='store_true', help='Use 8-bit cache to save VRAM.')
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group.add_argument('--cache_4bit', action='store_true', help='Use Q4 cache to save VRAM.')
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group.add_argument('--num_experts_per_token', type=int, default=2, help='Number of experts to use for generation. Applies to MoE models like Mixtral.')
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@ -85,6 +85,8 @@ def list_model_elements():
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'disable_exllamav2',
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'cfg_cache',
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'no_flash_attn',
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'no_xformers',
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'no_sdpa',
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'num_experts_per_token',
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'cache_8bit',
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'cache_4bit',
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@ -138,7 +138,9 @@ def create_ui():
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shared.gradio['disk'] = gr.Checkbox(label="disk", value=shared.args.disk)
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shared.gradio['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16)
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shared.gradio['autosplit'] = gr.Checkbox(label="autosplit", value=shared.args.autosplit, info='Automatically split the model tensors across the available GPUs.')
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shared.gradio['no_flash_attn'] = gr.Checkbox(label="no_flash_attn", value=shared.args.no_flash_attn, info='Force flash-attention to not be used.')
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shared.gradio['no_flash_attn'] = gr.Checkbox(label="no_flash_attn", value=shared.args.no_flash_attn)
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shared.gradio['no_xformers'] = gr.Checkbox(label="no_xformers", value=shared.args.no_xformers)
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shared.gradio['no_sdpa'] = gr.Checkbox(label="no_sdpa", value=shared.args.no_sdpa)
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shared.gradio['cfg_cache'] = gr.Checkbox(label="cfg-cache", value=shared.args.cfg_cache, info='Necessary to use CFG with this loader.')
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shared.gradio['cpp_runner'] = gr.Checkbox(label="cpp-runner", value=shared.args.cpp_runner, info='Enable inference with ModelRunnerCpp, which is faster than the default ModelRunner.')
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shared.gradio['num_experts_per_token'] = gr.Number(label="Number of experts per token", value=shared.args.num_experts_per_token, info='Only applies to MoE models like Mixtral.')
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