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Add --num_experts_per_token parameter (ExLlamav2) (#4955)

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oobabooga 2023-12-17 12:08:33 -03:00 committed by GitHub
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7 changed files with 28 additions and 20 deletions
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1
README.md
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@ -274,6 +274,7 @@ List of command-line flags
|`--cfg-cache` | ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama. |
|`--no_flash_attn` | Force flash-attention to not be used. |
|`--cache_8bit` | Use 8-bit cache to save VRAM. |
|`--num_experts_per_token NUM_EXPERTS_PER_TOKEN` | Number of experts to use for generation. Applies to MoE models like Mixtral. |
#### AutoGPTQ

1
modules/exllamav2.py
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@ -48,6 +48,7 @@ class Exllamav2Model:
config.scale_pos_emb = shared.args.compress_pos_emb
config.scale_alpha_value = shared.args.alpha_value
config.no_flash_attn = shared.args.no_flash_attn
config.num_experts_per_token = int(shared.args.num_experts_per_token)
model = ExLlamaV2(config)

1
modules/exllamav2_hf.py
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@ -165,5 +165,6 @@ class Exllamav2HF(PreTrainedModel):
config.scale_pos_emb = shared.args.compress_pos_emb
config.scale_alpha_value = shared.args.alpha_value
config.no_flash_attn = shared.args.no_flash_attn
config.num_experts_per_token = int(shared.args.num_experts_per_token)
return Exllamav2HF(config)

42
modules/loaders.py
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@ -65,6 +65,18 @@ loaders_and_params = OrderedDict({
'logits_all',
'llamacpp_HF_info',
],
'ExLlamav2_HF': [
'gpu_split',
'max_seq_len',
'cfg_cache',
'no_flash_attn',
'num_experts_per_token',
'cache_8bit',
'alpha_value',
'compress_pos_emb',
'trust_remote_code',
'no_use_fast',
],
'ExLlama_HF': [
'gpu_split',
'max_seq_len',
@ -75,17 +87,6 @@ loaders_and_params = OrderedDict({
'trust_remote_code',
'no_use_fast',
],
'ExLlamav2_HF': [
'gpu_split',
'max_seq_len',
'cfg_cache',
'no_flash_attn',
'cache_8bit',
'alpha_value',
'compress_pos_emb',
'trust_remote_code',
'no_use_fast',
],
'AutoGPTQ': [
'triton',
'no_inject_fused_attention',
@ -123,6 +124,16 @@ loaders_and_params = OrderedDict({
'no_use_fast',
'gptq_for_llama_info',
],
'ExLlamav2': [
'gpu_split',
'max_seq_len',
'no_flash_attn',
'num_experts_per_token',
'cache_8bit',
'alpha_value',
'compress_pos_emb',
'exllamav2_info',
],
'ExLlama': [
'gpu_split',
'max_seq_len',
@ -131,15 +142,6 @@ loaders_and_params = OrderedDict({
'compress_pos_emb',
'exllama_info',
],
'ExLlamav2': [
'gpu_split',
'max_seq_len',
'no_flash_attn',
'cache_8bit',
'alpha_value',
'compress_pos_emb',
'exllamav2_info',
],
'ctransformers': [
'n_ctx',
'n_gpu_layers',

1
modules/shared.py
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@ -125,6 +125,7 @@ parser.add_argument('--max_seq_len', type=int, default=2048, help='Maximum seque
parser.add_argument('--cfg-cache', action='store_true', help='ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama.')
parser.add_argument('--no_flash_attn', action='store_true', help='Force flash-attention to not be used.')
parser.add_argument('--cache_8bit', action='store_true', help='Use 8-bit cache to save VRAM.')
parser.add_argument('--num_experts_per_token', type=int, default=2, help='Number of experts to use for generation. Applies to MoE models like Mixtral.')
# AutoGPTQ
parser.add_argument('--triton', action='store_true', help='Use triton.')

1
modules/ui.py
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@ -73,6 +73,7 @@ def list_model_elements():
'disable_exllamav2',
'cfg_cache',
'no_flash_attn',
'num_experts_per_token',
'cache_8bit',
'threads',
'threads_batch',

1
modules/ui_model_menu.py
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@ -129,6 +129,7 @@ def create_ui():
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.')
shared.gradio['cache_8bit'] = gr.Checkbox(label="cache_8bit", value=shared.args.cache_8bit, info='Use 8-bit cache to save VRAM.')
shared.gradio['no_use_fast'] = gr.Checkbox(label="no_use_fast", value=shared.args.no_use_fast, info='Set use_fast=False while loading the tokenizer.')
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.')
shared.gradio['gptq_for_llama_info'] = gr.Markdown('Legacy loader for compatibility with older GPUs. ExLlama_HF or AutoGPTQ are preferred for GPTQ models when supported.')
shared.gradio['exllama_info'] = gr.Markdown("ExLlama_HF is recommended over ExLlama for better integration with extensions and more consistent sampling behavior across loaders.")
shared.gradio['exllamav2_info'] = gr.Markdown("ExLlamav2_HF is recommended over ExLlamav2 for better integration with extensions and more consistent sampling behavior across loaders.")