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https://github.com/oobabooga/text-generation-webui.git
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Add q-cache 6 and 8 support for Exllamav2
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randoentity
parent
608545d282
commit
3d17c80954
@ -7,6 +7,8 @@ from exllamav2 import (
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ExLlamaV2Cache,
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ExLlamaV2Cache_8bit,
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ExLlamaV2Cache_Q4,
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ExLlamaV2Cache_Q6,
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ExLlamaV2Cache_Q8,
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ExLlamaV2Config,
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ExLlamaV2Tokenizer
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)
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@ -63,8 +65,12 @@ class Exllamav2Model:
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if shared.args.cache_8bit:
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cache = ExLlamaV2Cache_8bit(model, lazy=shared.args.autosplit)
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elif shared.args.cache_4bit:
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elif shared.args.cache_q4:
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cache = ExLlamaV2Cache_Q4(model, lazy=shared.args.autosplit)
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elif shared.args.cache_q6:
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cache = ExLlamaV2Cache_Q6(model, lazy=shared.args.autosplit)
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elif shared.args.cache_q8:
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cache = ExLlamaV2Cache_Q8(model, lazy=shared.args.autosplit)
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else:
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cache = ExLlamaV2Cache(model, lazy=shared.args.autosplit)
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@ -9,6 +9,8 @@ from exllamav2 import (
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ExLlamaV2Cache,
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ExLlamaV2Cache_8bit,
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ExLlamaV2Cache_Q4,
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ExLlamaV2Cache_Q6,
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ExLlamaV2Cache_Q8,
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ExLlamaV2Config
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)
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from torch.nn import CrossEntropyLoss
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@ -51,8 +53,12 @@ class Exllamav2HF(PreTrainedModel):
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if shared.args.cache_8bit:
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self.ex_cache = ExLlamaV2Cache_8bit(self.ex_model, lazy=shared.args.autosplit)
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elif shared.args.cache_4bit:
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elif shared.args.cache_q4:
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self.ex_cache = ExLlamaV2Cache_Q4(self.ex_model, lazy=shared.args.autosplit)
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elif shared.args.cache_q6:
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self.ex_cache = ExLlamaV2Cache_Q6(self.ex_model, lazy=shared.args.autosplit)
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elif shared.args.cache_q8:
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self.ex_cache = ExLlamaV2Cache_Q8(self.ex_model, lazy=shared.args.autosplit)
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else:
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self.ex_cache = ExLlamaV2Cache(self.ex_model, lazy=shared.args.autosplit)
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@ -63,8 +69,12 @@ class Exllamav2HF(PreTrainedModel):
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if shared.args.cfg_cache:
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if shared.args.cache_8bit:
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self.ex_cache_negative = ExLlamaV2Cache_8bit(self.ex_model)
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elif shared.args.cache_4bit:
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elif shared.args.cache_q4:
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self.ex_cache_negative = ExLlamaV2Cache_Q4(self.ex_model)
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elif shared.args.cache_q6:
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self.ex_cache_negative = ExLlamaV2Cache_Q6(self.ex_model)
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elif shared.args.cache_q8:
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self.ex_cache_negative = ExLlamaV2Cache_Q8(self.ex_model)
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else:
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self.ex_cache_negative = ExLlamaV2Cache(self.ex_model)
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@ -88,7 +88,9 @@ loaders_and_params = OrderedDict({
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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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'cache_q4',
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'cache_q6',
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'cache_q8',
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'autosplit',
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'alpha_value',
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'compress_pos_emb',
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@ -103,7 +105,9 @@ loaders_and_params = OrderedDict({
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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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'cache_q4',
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'cache_q6',
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'cache_q8',
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'autosplit',
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'alpha_value',
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'compress_pos_emb',
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@ -142,8 +142,11 @@ group.add_argument('--cfg-cache', action='store_true', help='ExLlamav2_HF: Creat
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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('--cache_4bit', action='store_true', help='Use 4-bit cache to save VRAM (llama.cpp).')
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group.add_argument('--cache_8bit', action='store_true', help='Use 8-bit (FP8) cache to save VRAM.')
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group.add_argument('--cache_q4', action='store_true', help='Use Q4 cache to save VRAM.')
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group.add_argument('--cache_q6', action='store_true', help='Use Q6 cache to save VRAM.')
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group.add_argument('--cache_q8', action='store_true', help='Use Q8 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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# AutoGPTQ
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@ -89,6 +89,9 @@ def list_model_elements():
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'num_experts_per_token',
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'cache_8bit',
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'cache_4bit',
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'cache_q4',
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'cache_q6',
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'cache_q8',
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'autosplit',
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'threads',
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'threads_batch',
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@ -118,8 +118,11 @@ def create_ui():
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shared.gradio['flash_attn'] = gr.Checkbox(label="flash_attn", value=shared.args.flash_attn, info='Use flash-attention.')
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shared.gradio['auto_devices'] = gr.Checkbox(label="auto-devices", value=shared.args.auto_devices)
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shared.gradio['tensorcores'] = gr.Checkbox(label="tensorcores", value=shared.args.tensorcores, info='NVIDIA only: use llama-cpp-python compiled with tensor cores support. This may increase performance on newer cards.')
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shared.gradio['cache_8bit'] = gr.Checkbox(label="cache_8bit", value=shared.args.cache_8bit, info='Use 8-bit cache to save VRAM.')
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shared.gradio['cache_4bit'] = gr.Checkbox(label="cache_4bit", value=shared.args.cache_4bit, info='Use Q4 cache to save VRAM.')
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shared.gradio['cache_4bit'] = gr.Checkbox(label="cache_4bit", value=shared.args.cache_8bit, info='Use 4-bit (FP4) cache to save VRAM.')
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shared.gradio['cache_8bit'] = gr.Checkbox(label="cache_8bit", value=shared.args.cache_8bit, info='Use 8-bit (FP8) cache to save VRAM.')
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shared.gradio['cache_q4'] = gr.Checkbox(label="cache_q4", value=shared.args.cache_q4, info='Use Q4 cache to save VRAM.')
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shared.gradio['cache_q6'] = gr.Checkbox(label="cache_q6", value=shared.args.cache_q6, info='Use Q6 cache to save VRAM.')
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shared.gradio['cache_q8'] = gr.Checkbox(label="cache_q8", value=shared.args.cache_q8, info='Use Q8 cache to save VRAM.')
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shared.gradio['streaming_llm'] = gr.Checkbox(label="streaming_llm", value=shared.args.streaming_llm, info='(experimental) Activate StreamingLLM to avoid re-evaluating the entire prompt when old messages are removed.')
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shared.gradio['attention_sink_size'] = gr.Number(label="attention_sink_size", value=shared.args.attention_sink_size, precision=0, info='StreamingLLM: number of sink tokens. Only used if the trimmed prompt doesn\'t share a prefix with the old prompt.')
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shared.gradio['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu, info='llama.cpp: Use llama-cpp-python compiled without GPU acceleration. Transformers: use PyTorch in CPU mode.')
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