mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-23 08:28:21 +01:00
oobabooga
GitHub
commit
7be09836fc
@ -15,7 +15,7 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
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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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* Precise chat templates for instruction-following models, including Llama-2-chat, Alpaca, Vicuna, Mistral, and many others.
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* Precise chat templates for instruction-following models, including Llama-2-chat, Alpaca, Vicuna, Mistral.
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* LoRA: train new LoRAs with your own data, load/unload LoRAs on the fly for generation.
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* Transformers library integration: load models in 4-bit or 8-bit precision through bitsandbytes, use llama.cpp with transformers samplers (`llamacpp_HF` loader), CPU inference in 32-bit precision using PyTorch.
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* OpenAI-compatible API server with Chat and Completions endpoints -- see the [examples](https://github.com/oobabooga/text-generation-webui/wiki/12-%E2%80%90-OpenAI-API#examples).
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@ -274,6 +274,7 @@ List of command-line flags
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|`--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. |
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|`--no_flash_attn` | Force flash-attention to not be used. |
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|`--cache_8bit` | Use 8-bit cache to save VRAM. |
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|`--num_experts_per_token NUM_EXPERTS_PER_TOKEN` | Number of experts to use for generation. Applies to MoE models like Mixtral. |
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#### AutoGPTQ
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@ -377,7 +378,7 @@ text-generation-webui
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└── llama-2-13b-chat.Q4_K_M.gguf
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```
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* Other models (like 16-bit transformers models and GPTQ models) are made of several files and must be placed in a subfolder. Example:
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* The remaining model types (like 16-bit transformers models and GPTQ models) are made of several files and must be placed in a subfolder. Example:
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```
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text-generation-webui
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@ -1,10 +1,18 @@
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.chat {
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background: var(--block-background-fill);
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padding: 24px 19px;
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padding-right: 19px !important;
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border: 1px solid var(--block-border-color);
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border-radius: 8px;
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}
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.message {
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display: grid;
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grid-template-columns: 60px 1fr;
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padding-bottom: 25px;
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font-size: 15px;
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font-family: 'Noto Sans', Helvetica, Arial, sans-serif;
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line-height: 22px;
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line-height: 24px;
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}
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.username {
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@ -13,11 +21,16 @@
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.message-body p, .message-body li {
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font-size: 15px !important;
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line-height: 22.5px !important;
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line-height: 24px !important;
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list-style-position: outside;
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}
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.message-body p, .chat .message-body ul, .chat .message-body ol {
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margin-bottom: 23.4375px !important;
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margin-bottom: 16px !important;
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}
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.chat .message-body ul, .chat .message-body ol {
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padding-inline-start: 2em;
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}
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.message-body p:last-child, .chat .message-body ul:last-child, .chat .message-body ol:last-child {
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@ -34,34 +47,34 @@
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.gradio-container .chat .assistant-message {
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padding: 20px;
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border-radius: 20px;
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background-color: #0000000f;
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margin-top: 9px !important;
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margin-bottom: 18px !important;
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background: var(--background-fill-secondary);
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margin-top: 12px !important;
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margin-bottom: 24px !important;
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margin-right: 16px;
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border-radius: 22px;
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border-bottom-left-radius: 0;
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border: 1px solid var(--border-color-primary);
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}
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.gradio-container .chat .user-message {
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padding: 20px;
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background-color: var(--color-accent-soft);
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border-radius: 20px;
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margin-bottom: 9px !important;
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margin-bottom: 12px !important;
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margin-left: 16px;
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border-radius: 22px;
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border-bottom-right-radius: 0;
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border: 1px solid var(--border-color-accent-subdued);
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}
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.gradio-container .chat .assistant-message:last-child, .gradio-container .chat .user-message:last-child {
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margin-bottom: 0 !important;
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}
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.dark .chat .assistant-message {
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background-color: #1f2937;
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}
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.dark .chat .user-message {
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background-color: transparent;
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}
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code {
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background-color: white !important;
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background-color: #f3f4f6 !important;
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}
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.dark code {
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background-color: #0e1321 !important;
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background-color: #1f2937 !important;
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}
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@ -332,7 +332,7 @@ div.svelte-362y77>*, div.svelte-362y77>.form>* {
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margin-left: auto;
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margin-right: auto;
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max-width: 880px;
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height: 100%;
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min-height: var(--chat-height);
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overflow-y: auto;
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padding-right: 15px;
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display: flex;
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@ -1,7 +0,0 @@
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# A probably incorrect grammar for Japanese
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root ::= jp-char+ ([ \t\n] jp-char+)*
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jp-char ::= hiragana | katakana | punctuation | cjk
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hiragana ::= [ぁ-ゟ]
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katakana ::= [ァ-ヿ]
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punctuation ::= [、-〾]
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cjk ::= [一-鿿]
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@ -1,25 +1,14 @@
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root ::= object
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object ::= "{" ws ( string ":" ws value ("," ws string ":" ws value)* )? "}"
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value ::= object | array | string | number | ("true" | "false" | "null") ws
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object ::=
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"{" ws (
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string ":" ws value
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("," ws string ":" ws value)*
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)? "}" ws
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array ::= "[" ws ( value ("," ws value)* )? "]" ws
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array ::=
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"[" ws (
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value
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("," ws value)*
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)? "]" ws
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string ::=
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"\"" (
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[^"\\] |
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"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
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)* "\"" ws
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string ::= "\"" ( [a-zA-Z0-9] )* "\"" ws
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number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
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# Optional space: by convention, applied in this grammar after literal chars when allowed
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ws ::= ([ \t\n] ws)?
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@ -1,34 +0,0 @@
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# This is the same as json.gbnf but we restrict whitespaces at the end of the root array
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# Useful for generating JSON arrays
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root ::= arr
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value ::= object | array | string | number | ("true" | "false" | "null") ws
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arr ::=
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"[\n" ws (
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value
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(",\n" ws value)*
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)? "]"
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object ::=
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"{" ws (
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string ":" ws value
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("," ws string ":" ws value)*
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)? "}" ws
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array ::=
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"[" ws (
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value
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("," ws value)*
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)? "]" ws
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string ::=
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"\"" (
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[^"\\] |
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"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
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)* "\"" ws
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number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
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# Optional space: by convention, applied in this grammar after literal chars when allowed
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ws ::= ([ \t\n] ws)?
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14
grammars/json_w_trailing_space.gbnf
Normal file
14
grammars/json_w_trailing_space.gbnf
Normal file
@ -0,0 +1,14 @@
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root ::= object
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object ::= "{" ws ( string ":" ws value ("," ws string ":" ws value)* )? "}" ws
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value ::= object | array | string | number | ("true" | "false" | "null") ws
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array ::= "[" ws ( value ("," ws value)* )? "]" ws
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string ::= "\"" ( [a-zA-Z0-9] )* "\"" ws
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number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
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ws ::= ([ \t\n] ws)?
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@ -1,4 +1,2 @@
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root ::= item+
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# Excludes various line break characters
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item ::= "- " [^\r\n\x0b\x0c\x85\u2028\u2029]+ "\n"
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root ::= "1. " paragraph "\n" ([0-9] [0-9]? ". " paragraph "\n")+
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paragraph ::= [a-zA-Z'.,; ]+
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7
grammars/simple_arithmetic.gbnf
Normal file
7
grammars/simple_arithmetic.gbnf
Normal file
@ -0,0 +1,7 @@
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root ::= (expr "=" ws term "\n")+
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expr ::= term ([-+*/] term)*
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term ::= num | "(" ws expr ")" ws
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num ::= [0-9]+ ws
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ws ::= [ \t\n]*
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# this is a comment
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64
js/main.js
64
js/main.js
@ -123,6 +123,8 @@ targetElement.addEventListener("scroll", function() {
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// Create a MutationObserver instance
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const observer = new MutationObserver(function(mutations) {
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mutations.forEach(function(mutation) {
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updateChatHeight();
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if(!isScrolled) {
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targetElement.scrollTop = targetElement.scrollHeight;
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}
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@ -153,56 +155,6 @@ const config = {
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// Start observing the target element
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observer.observe(targetElement, config);
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//------------------------------------------------
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// Notebook box scrolling
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//------------------------------------------------
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const notebookElement = document.querySelector("#textbox-notebook textarea");
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let notebookScrolled = false;
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notebookElement.addEventListener("scroll", function() {
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let diff = notebookElement.scrollHeight - notebookElement.clientHeight;
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if(Math.abs(notebookElement.scrollTop - diff) <= 10 || diff == 0) {
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notebookScrolled = false;
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} else {
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notebookScrolled = true;
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}
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});
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const notebookObserver = new MutationObserver(function(mutations) {
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mutations.forEach(function(mutation) {
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if(!notebookScrolled) {
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notebookElement.scrollTop = notebookElement.scrollHeight;
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}
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});
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});
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notebookObserver.observe(notebookElement.parentNode.parentNode.parentNode, config);
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//------------------------------------------------
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// Default box scrolling
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//------------------------------------------------
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const defaultElement = document.querySelector("#textbox-default textarea");
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let defaultScrolled = false;
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defaultElement.addEventListener("scroll", function() {
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let diff = defaultElement.scrollHeight - defaultElement.clientHeight;
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if(Math.abs(defaultElement.scrollTop - diff) <= 10 || diff == 0) {
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defaultScrolled = false;
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} else {
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defaultScrolled = true;
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}
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});
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const defaultObserver = new MutationObserver(function(mutations) {
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mutations.forEach(function(mutation) {
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if(!defaultScrolled) {
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defaultElement.scrollTop = defaultElement.scrollHeight;
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}
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});
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});
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defaultObserver.observe(defaultElement.parentNode.parentNode.parentNode, config);
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//------------------------------------------------
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// Add some scrollbars
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//------------------------------------------------
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@ -373,3 +325,15 @@ function toggleBigPicture() {
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}
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}
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//------------------------------------------------
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// Define the --chat-height global CSS variable to
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// the height of the chat parent
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//------------------------------------------------
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function updateChatHeight() {
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const chatContainer = document.getElementById('chat').parentNode.parentNode.parentNode;
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const newChatHeight = `${chatContainer.clientHeight}px`;
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document.documentElement.style.setProperty('--chat-height', newChatHeight);
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}
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window.addEventListener('resize', updateChatHeight);
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@ -210,10 +210,6 @@ def chatbot_wrapper(text, state, regenerate=False, _continue=False, loading_mess
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output = copy.deepcopy(history)
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output = apply_extensions('history', output)
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state = apply_extensions('state', state)
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if shared.model_name == 'None' or shared.model is None:
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logger.error("No model is loaded! Select one in the Model tab.")
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yield output
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return
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visible_text = None
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stopping_strings = get_stopping_strings(state)
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@ -252,6 +248,9 @@ def chatbot_wrapper(text, state, regenerate=False, _continue=False, loading_mess
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'internal': output['internal']
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}
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if shared.model_name == 'None' or shared.model is None:
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raise ValueError("No model is loaded! Select one in the Model tab.")
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# Generate the prompt
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kwargs = {
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'_continue': _continue,
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@ -48,6 +48,7 @@ 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.num_experts_per_token = int(shared.args.num_experts_per_token)
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model = ExLlamaV2(config)
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@ -165,5 +165,6 @@ 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.num_experts_per_token = int(shared.args.num_experts_per_token)
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return Exllamav2HF(config)
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@ -1,33 +0,0 @@
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from torch_grammar import GrammarSampler
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from transformers.generation.logits_process import LogitsProcessor
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from modules import shared
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sampler = None
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grammar = None
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grammar_string = ''
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class GrammarLogitsProcessor(LogitsProcessor):
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def __init__(self, string):
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global sampler, grammar, grammar_string
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if string != grammar_string:
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grammar_string = string
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if string.strip() != '':
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string = string.strip() + '\n'
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sampler = GrammarSampler(string, 'root', shared.tokenizer)
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else:
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sampler = None
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if sampler is not None:
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grammar = sampler.logits_processor()
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else:
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grammar = None
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def __call__(self, input_ids, scores):
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if grammar is not None:
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scores = grammar(input_ids, scores)
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return scores
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687
modules/grammar/grammar_utils.py
Normal file
687
modules/grammar/grammar_utils.py
Normal file
@ -0,0 +1,687 @@
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'''
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This file has been 100% copied from this PR to the Transformers library:
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https://github.com/huggingface/transformers/pull/27557
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Author: Saibo-creator
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Author GitHub: https://github.com/Saibo-creator
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|
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All credits go to the author.
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'''
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import logging
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import re
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import time
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from abc import ABC
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from functools import lru_cache
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from typing import Dict, List
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|
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import torch
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from modules import shared
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logger = logging.getLogger(__name__)
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||||
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########################
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# EBNF Grammar Parsing #
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########################
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END_OF_ALTERNATE_MARKER = 0
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END_OF_RULE_MARKER = 0
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TO_BE_FILLED_MARKER = 0
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REF_RULE_MARKER = 1
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LITERAL_MARKER = 2
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class ParseState:
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def __init__(self):
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self.symbol_ids = {}
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self.grammar_encoding = [] # old name: out_grammar
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|
||||
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||||
def get_symbol_id(state, src):
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if src not in state.symbol_ids:
|
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state.symbol_ids[src] = len(state.symbol_ids)
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return state.symbol_ids[src]
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|
||||
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def generate_symbol_id(state, base_name):
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next_id = len(state.symbol_ids)
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state.symbol_ids[base_name + "_" + str(next_id)] = next_id
|
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return next_id
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|
||||
|
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def is_word_char(c):
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return c.isalnum() or c == "-" or c == "_"
|
||||
|
||||
|
||||
def hex_to_int(c):
|
||||
if c.isdigit():
|
||||
return int(c)
|
||||
elif "a" <= c.lower() <= "f":
|
||||
return ord(c.lower()) - ord("a") + 10
|
||||
return -1
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||||
|
||||
|
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def remove_leading_white_space(src, newline_ok):
|
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"""
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||||
Skips over whitespace and comments in the input string.
|
||||
This function processes the input string, skipping over any spaces, tabs,
|
||||
and content following a '#' character, which denotes a comment. The parsing
|
||||
of a comment continues until the end of the line (denoted by newline characters
|
||||
'\r' or '\n'). If the 'newline_ok' parameter is set to False, the function
|
||||
will stop processing and return the remaining string upon encountering a
|
||||
newline character, otherwise it will skip over newline characters as well.
|
||||
Parameters:
|
||||
src (str): The input string to be processed.
|
||||
newline_ok (bool): A flag indicating whether encountering a newline character
|
||||
should stop the parsing (False) or if it should be skipped (True).
|
||||
Returns:
|
||||
str: The remaining portion of the input string after skipping whitespace and comments.
|
||||
"""
|
||||
pos = 0
|
||||
while pos < len(src) and (src[pos].isspace() or src[pos] == "#"):
|
||||
if src[pos] == "#":
|
||||
while pos < len(src) and src[pos] not in ("\r", "\n"):
|
||||
pos += 1
|
||||
else:
|
||||
if not newline_ok and src[pos] in ("\r", "\n"):
|
||||
break
|
||||
pos += 1
|
||||
return src[pos:]
|
||||
|
||||
|
||||
def parse_name(src):
|
||||
pos = 0
|
||||
while pos < len(src) and is_word_char(src[pos]):
|
||||
pos += 1
|
||||
if pos == 0:
|
||||
raise RuntimeError("expecting name at " + src)
|
||||
return src[:pos], src[pos:]
|
||||
|
||||
|
||||
def parse_char(src):
|
||||
"""
|
||||
parse the leading char from the input string
|
||||
:param src:
|
||||
:return: char, remaining_src
|
||||
"""
|
||||
|
||||
# if we have a backslash, it's maybe an escape
|
||||
if src[0] == "\\":
|
||||
esc = src[1]
|
||||
if esc == "x":
|
||||
first = hex_to_int(src[2])
|
||||
if first > -1:
|
||||
second = hex_to_int(src[3])
|
||||
if second > -1:
|
||||
return (first << 4) + second, src[4:]
|
||||
raise RuntimeError("expecting \\xNN at " + src)
|
||||
elif esc in ('"', "[", "]"):
|
||||
return esc, src[2:]
|
||||
elif esc == "r":
|
||||
return "\r", src[2:]
|
||||
elif esc == "n":
|
||||
return "\n", src[2:]
|
||||
elif esc == "t":
|
||||
return "\t", src[2:]
|
||||
raise RuntimeError("unknown escape at " + src)
|
||||
elif src:
|
||||
return src[0], src[1:]
|
||||
raise RuntimeError("unexpected end of input")
|
||||
|
||||
|
||||
def parse_sequence(state, src, rule_name, outbuf, is_nested):
|
||||
out_start_pos = len(outbuf)
|
||||
|
||||
# sequence size, will be replaced at end when known
|
||||
outbuf.append(TO_BE_FILLED_MARKER)
|
||||
|
||||
last_sym_start = len(outbuf)
|
||||
remaining_src = src
|
||||
while remaining_src:
|
||||
if remaining_src[0] == '"': # literal string
|
||||
remaining_src = remaining_src[1:]
|
||||
last_sym_start = len(outbuf)
|
||||
while remaining_src[0] != '"':
|
||||
char, remaining_src = parse_char(remaining_src)
|
||||
|
||||
# each char of a literal is encoded as a "range" of char - char
|
||||
outbuf.append(LITERAL_MARKER)
|
||||
outbuf.append(ord(char))
|
||||
outbuf.append(ord(char))
|
||||
remaining_src = remove_leading_white_space(remaining_src[1:], is_nested)
|
||||
elif remaining_src[0] == "[": # char range(s)
|
||||
remaining_src = remaining_src[1:]
|
||||
last_sym_start = len(outbuf)
|
||||
# num chars in range - replaced at end of loop
|
||||
outbuf.append(TO_BE_FILLED_MARKER)
|
||||
while remaining_src[0] != "]":
|
||||
char, remaining_src = parse_char(remaining_src)
|
||||
|
||||
outbuf.append(ord(char))
|
||||
if remaining_src[0] == "-" and remaining_src[1] != "]":
|
||||
endchar_pair, remaining_src = parse_char(remaining_src[1:])
|
||||
outbuf.append(ord(endchar_pair))
|
||||
else:
|
||||
# chars that aren't part of a c1-c2 range are just doubled (i.e., c-c)
|
||||
outbuf.append(ord(char))
|
||||
# replace num chars with actual
|
||||
outbuf[last_sym_start] = len(outbuf) - last_sym_start - 1
|
||||
remaining_src = remove_leading_white_space(remaining_src[1:], is_nested)
|
||||
elif is_word_char(remaining_src[0]): # rule reference
|
||||
name, remaining_src = parse_name(remaining_src)
|
||||
ref_rule_id = get_symbol_id(state, name)
|
||||
remaining_src = remove_leading_white_space(remaining_src, is_nested)
|
||||
last_sym_start = len(outbuf)
|
||||
outbuf.append(REF_RULE_MARKER)
|
||||
outbuf.append(ref_rule_id)
|
||||
elif remaining_src[0] == "(": # grouping
|
||||
# parse nested alternates into synthesized rule
|
||||
remaining_src = remove_leading_white_space(remaining_src[1:], True)
|
||||
sub_rule_id = generate_symbol_id(state, rule_name)
|
||||
remaining_src = parse_alternates(state, remaining_src, rule_name, sub_rule_id, True)
|
||||
last_sym_start = len(outbuf)
|
||||
# output reference to synthesized rule
|
||||
outbuf.append(REF_RULE_MARKER)
|
||||
outbuf.append(sub_rule_id)
|
||||
if remaining_src[0] != ")":
|
||||
raise RuntimeError("expecting ')' at " + remaining_src)
|
||||
remaining_src = remove_leading_white_space(remaining_src[1:], is_nested)
|
||||
elif remaining_src[0] in ("*", "+", "?"): # repetition operator
|
||||
if len(outbuf) - out_start_pos - 1 == 0:
|
||||
raise RuntimeError("expecting preceeding item to */+/? at " + remaining_src)
|
||||
out_grammar = state.grammar_encoding
|
||||
|
||||
# apply transformation to previous symbol (last_sym_start -
|
||||
# end) according to rewrite rules:
|
||||
# S* --> S' ::= S S' |
|
||||
# S+ --> S' ::= S S' | S
|
||||
# S? --> S' ::= S |
|
||||
sub_rule_id = generate_symbol_id(state, rule_name)
|
||||
out_grammar.append(sub_rule_id)
|
||||
sub_rule_start = len(out_grammar)
|
||||
# placeholder for size of 1st alternate
|
||||
out_grammar.append(TO_BE_FILLED_MARKER)
|
||||
# add preceding symbol to generated rule
|
||||
out_grammar.extend(outbuf[last_sym_start:])
|
||||
if remaining_src[0] in ("*", "+"):
|
||||
# cause generated rule to recurse
|
||||
out_grammar.append(REF_RULE_MARKER)
|
||||
out_grammar.append(sub_rule_id)
|
||||
# apply actual size
|
||||
out_grammar[sub_rule_start] = len(out_grammar) - sub_rule_start
|
||||
# mark end of 1st alternate
|
||||
out_grammar.append(END_OF_ALTERNATE_MARKER)
|
||||
sub_rule_start = len(out_grammar)
|
||||
# placeholder for size of 2nd alternate
|
||||
out_grammar.append(TO_BE_FILLED_MARKER)
|
||||
if remaining_src[0] == "+":
|
||||
# add preceding symbol as alternate only for '+'
|
||||
out_grammar.extend(outbuf[last_sym_start:])
|
||||
# apply actual size of 2nd alternate
|
||||
out_grammar[sub_rule_start] = len(out_grammar) - sub_rule_start
|
||||
# mark end of 2nd alternate, then end of rule
|
||||
out_grammar.append(END_OF_ALTERNATE_MARKER)
|
||||
out_grammar.append(END_OF_RULE_MARKER)
|
||||
|
||||
# in original rule, replace previous symbol with reference to generated rule
|
||||
outbuf[last_sym_start:] = [1, sub_rule_id]
|
||||
|
||||
remaining_src = remove_leading_white_space(remaining_src[1:], is_nested)
|
||||
else:
|
||||
break
|
||||
# apply actual size of this alternate sequence
|
||||
outbuf[out_start_pos] = len(outbuf) - out_start_pos
|
||||
# mark end of alternate
|
||||
outbuf.append(END_OF_ALTERNATE_MARKER)
|
||||
return remaining_src
|
||||
|
||||
|
||||
def parse_alternates(state, src, rule_name, rule_id, is_nested):
|
||||
outbuf = []
|
||||
remaining_src = parse_sequence(state, src, rule_name, outbuf, is_nested)
|
||||
while remaining_src and remaining_src[0] == "|":
|
||||
remaining_src = remove_leading_white_space(remaining_src[1:], True)
|
||||
remaining_src = parse_sequence(state, remaining_src, rule_name, outbuf, is_nested)
|
||||
|
||||
state.grammar_encoding.append(rule_id)
|
||||
state.grammar_encoding.extend(outbuf)
|
||||
state.grammar_encoding.append(0)
|
||||
return remaining_src
|
||||
|
||||
|
||||
def parse_rule(state, src):
|
||||
name, remaining_src = parse_name(src)
|
||||
remaining_src = remove_leading_white_space(remaining_src, False)
|
||||
rule_id = get_symbol_id(state, name)
|
||||
|
||||
if remaining_src[:3] != "::=":
|
||||
raise RuntimeError("expecting ::= at " + remaining_src)
|
||||
remaining_src = remove_leading_white_space(remaining_src[3:], True)
|
||||
|
||||
remaining_src = parse_alternates(state, remaining_src, name, rule_id, False)
|
||||
|
||||
if remaining_src and remaining_src[0] == "\r":
|
||||
remaining_src = remaining_src[2:] if remaining_src[1] == "\n" else remaining_src[1:]
|
||||
elif remaining_src and remaining_src[0] == "\n":
|
||||
remaining_src = remaining_src[1:]
|
||||
elif remaining_src:
|
||||
raise RuntimeError("expecting newline or end at " + remaining_src)
|
||||
return remove_leading_white_space(remaining_src, True)
|
||||
|
||||
|
||||
def parse_ebnf(src):
|
||||
try:
|
||||
state = ParseState()
|
||||
grammar_repr = remove_leading_white_space(src, True)
|
||||
last_grammar_repr = ""
|
||||
while grammar_repr:
|
||||
if last_grammar_repr:
|
||||
last_parsed_rule_len = len(last_grammar_repr) - len(grammar_repr)
|
||||
logger.debug(f"last_parsed_rule: {last_grammar_repr[:last_parsed_rule_len]}")
|
||||
last_grammar_repr = grammar_repr
|
||||
grammar_repr = parse_rule(state, grammar_repr)
|
||||
state.grammar_encoding.append(0xFFFF)
|
||||
return state
|
||||
except RuntimeError as err:
|
||||
logger.warning("error parsing grammar:", err)
|
||||
return ParseState()
|
||||
|
||||
|
||||
def print_rule(file, grammar_encoding, index, symbol_id_names):
|
||||
rule_id = grammar_encoding[index]
|
||||
print(f"<{index}>{symbol_id_names[rule_id]} ::=", end=" ", file=file)
|
||||
pos = index + 1
|
||||
while grammar_encoding[pos]:
|
||||
if pos - 1 > index:
|
||||
print("|", end=" ", file=file)
|
||||
pos += 1 # sequence size, not needed here
|
||||
while grammar_encoding[pos]:
|
||||
if grammar_encoding[pos] == REF_RULE_MARKER:
|
||||
ref_rule_id = grammar_encoding[pos + 1]
|
||||
print(
|
||||
f"<{pos}>{symbol_id_names[ref_rule_id]}",
|
||||
end=" ",
|
||||
file=file,
|
||||
)
|
||||
pos += 2
|
||||
else:
|
||||
print("<{}>[".format(pos), end="", file=file)
|
||||
num_chars = grammar_encoding[pos]
|
||||
pos += 1
|
||||
|
||||
for i in range(0, num_chars, 2):
|
||||
print("{}-".format(chr(grammar_encoding[pos + i])), end="", file=file)
|
||||
if i + 1 < num_chars:
|
||||
print("{}".format(chr(grammar_encoding[pos + i + 1])), end="", file=file)
|
||||
print("]", end=" ", file=file)
|
||||
pos += num_chars
|
||||
pos += 1
|
||||
print(file=file)
|
||||
return pos + 1
|
||||
|
||||
|
||||
def print_grammar(file, state):
|
||||
pos = 0
|
||||
symbol_id_names = {v: k for k, v in state.symbol_ids.items()}
|
||||
print("Grammar Rules:", file=file)
|
||||
|
||||
while state.grammar_encoding[pos] != 0xFFFF:
|
||||
pos = print_rule(file, state.grammar_encoding, pos, symbol_id_names)
|
||||
pos = 0
|
||||
print("\nBinary representation:", file=file)
|
||||
while state.grammar_encoding[pos] != 0xFFFF:
|
||||
print(f"{state.grammar_encoding[pos]:04x}", end=" ", file=file)
|
||||
pos += 1
|
||||
print("ffff\n")
|
||||
|
||||
|
||||
###################################
|
||||
# EBNF Grammar Parsing ends here #
|
||||
###################################
|
||||
|
||||
|
||||
class GrammarConstraint(ABC):
|
||||
def __init__(self, grammar_str, start_rule_name, tokenizer):
|
||||
self.tt = 0
|
||||
self.nt = 0
|
||||
state = parse_ebnf(grammar_str)
|
||||
grammar_encoding = state.grammar_encoding
|
||||
self.start_rule_id = state.symbol_ids.get(start_rule_name)
|
||||
|
||||
self.eos_token_id = tokenizer.eos_token_id
|
||||
self.token_trie = TokenTrie(tokenizer)
|
||||
self.tokenizer = tokenizer
|
||||
self.grammar_encoding = grammar_encoding
|
||||
|
||||
pos = 0
|
||||
rules: Dict[int, int] = {}
|
||||
|
||||
while grammar_encoding[pos] != 0xFFFF:
|
||||
rule_id = grammar_encoding[pos]
|
||||
|
||||
# Store the current position in the 'rules' list at the index corresponding to rule_id.
|
||||
# This effectively maps each rule_id to its position in the grammar encoding.
|
||||
rules[rule_id] = pos
|
||||
pos += 1
|
||||
|
||||
# Continue to the next rule in the encoding.
|
||||
# The loop advances by the size indicated at the current position (grammar_encoding[pos])
|
||||
# plus one for the size field itself.
|
||||
while grammar_encoding[pos]:
|
||||
pos += 1 + grammar_encoding[pos]
|
||||
# Now we're at the end of the rule,
|
||||
# so advance to the next rule by skipping the 0, which means 'end of rule'.
|
||||
pos += 1
|
||||
|
||||
self.start_rule_pos = rules[self.start_rule_id]
|
||||
self.rules_pos_dict: Dict[int, int] = rules
|
||||
|
||||
def init_stacks(self):
|
||||
# suppose the start rule position is 0, then grammar_encoding[0] = rule_id
|
||||
# grammar_encoding[1] = rule_size
|
||||
# grammar_encoding[2] = rule_type
|
||||
# this is why we need to add 2 to the start rule position
|
||||
stack = [self.start_rule_pos + 2]
|
||||
# convert to tuple for caching(immutable)
|
||||
return self.advance_stack(tuple(stack))
|
||||
|
||||
# For each stack, resolve rules to find the actual characters that are
|
||||
# accepted by this stack (not the set of sub-rules).
|
||||
# This is where the parsing happens.
|
||||
# The parsing is a top-down, left-to-right, depth-first traversal of the
|
||||
# grammar.
|
||||
@lru_cache(maxsize=32768)
|
||||
def advance_stack(self, stack):
|
||||
stack = list(stack)
|
||||
# If the stack is empty, we're done. Because no more tokens should be accepted.
|
||||
if len(stack) == 0:
|
||||
return [stack]
|
||||
|
||||
# Get the top of the stack.
|
||||
pos = stack[-1]
|
||||
|
||||
# If the stack head is a terminal(literal), we can resolve it immediately.
|
||||
# literal is marked with 2 in the grammar encoding.
|
||||
if self.grammar_encoding[pos] > 1:
|
||||
return [stack]
|
||||
|
||||
# The stack head is a nonterminal (a rule reference, 1 in the grammar encoding).
|
||||
# Resolving this rule gives a set of one or more possible positions
|
||||
# (e.g. two in `a ::= b | c`)
|
||||
# We pop the current rule off the stack and, for each option, push:
|
||||
# - the symbol following this symbol in the current rule; then
|
||||
# - the first symbol of the resolved rule.
|
||||
referenced_rule_id = self.grammar_encoding[pos + 1]
|
||||
|
||||
# subpos should points to the size of the subrule
|
||||
subpos = self.rules_pos_dict[referenced_rule_id] + 1
|
||||
stacks: List[List[int]] = []
|
||||
|
||||
# do depth-first search to find all possible rules and check the next terminal
|
||||
# When this value is non-zero, it indicates that subpos is not yet at the end of the rule, so we can continue.
|
||||
# here subpos is a pointer, and the value in the rule encoding can never be 0 except for the end of the rule.
|
||||
while self.grammar_encoding[subpos]:
|
||||
new_stack = stack[:-1]
|
||||
if self.grammar_encoding[pos + 2]:
|
||||
# check if there is a next symbol in the current rule, e.g. `a ::= b c | d`
|
||||
# if yes, push the pos to rule_size to the stack
|
||||
new_stack.append(pos + 2)
|
||||
|
||||
# if the type of the next symbol is not "empty", push the first symbol of the resolved rule to the stack
|
||||
if self.grammar_encoding[subpos + 1]:
|
||||
new_stack.append(subpos + 1)
|
||||
stacks.extend(self.advance_stack(tuple(new_stack)))
|
||||
# The increment subpos += self.grammar_encoding[subpos] + 1
|
||||
# moves subpos forward in the grammar encoding array to the next alternative in the current rule.
|
||||
subpos += self.grammar_encoding[subpos] + 1
|
||||
return stacks
|
||||
|
||||
def accept_char(self, *args, **kwargs):
|
||||
"""Process a byte according to the grammar rules."""
|
||||
raise NotImplementedError
|
||||
|
||||
def accept_token_id(self, *args, **kwargs):
|
||||
"""Process a token according to the grammar rules."""
|
||||
raise NotImplementedError
|
||||
|
||||
def filter_vocab(self, *args, **kwargs):
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class IncrementalGrammarConstraint(GrammarConstraint):
|
||||
def __init__(self, grammar_str, start_rule_name, tokenizer):
|
||||
super().__init__(grammar_str, start_rule_name, tokenizer)
|
||||
|
||||
def accept_char(self, byte, stacks):
|
||||
new_stacks = []
|
||||
for stack in stacks:
|
||||
# stack is empty
|
||||
if not stack:
|
||||
continue
|
||||
|
||||
pos = stack[-1]
|
||||
num_chars = self.grammar_encoding[pos]
|
||||
|
||||
# to make pos point to the size of the char range rule
|
||||
pos += 1
|
||||
found = False
|
||||
for i in range(0, num_chars, 2):
|
||||
if self.grammar_encoding[pos + i] <= byte and byte <= self.grammar_encoding[pos + i + 1]:
|
||||
found = True
|
||||
break
|
||||
if not found:
|
||||
continue
|
||||
|
||||
pos += num_chars
|
||||
new_stack = stack[:-1]
|
||||
if self.grammar_encoding[pos]:
|
||||
new_stack.append(pos)
|
||||
new_stacks.extend(self.advance_stack(tuple(new_stack)))
|
||||
|
||||
return new_stacks
|
||||
|
||||
def accept_string(self, string: str, stacks: List[List[int]]):
|
||||
_bytes = bytes(string, "utf-8")
|
||||
for byte in _bytes:
|
||||
stacks = self.accept_char(byte, stacks)
|
||||
return stacks
|
||||
|
||||
def accept_token_id(self, token_id: int, stacks: List[List[int]]):
|
||||
if token_id == self.eos_token_id:
|
||||
if stacks and all(len(stack) != 0 for stack in stacks):
|
||||
raise Exception(
|
||||
f"At least one of the stack should be empty when EOS is reached. However, "
|
||||
f"the stacks are {stacks}"
|
||||
)
|
||||
return []
|
||||
|
||||
for byte in self.token_trie.id2str(token_id):
|
||||
stacks = self.accept_char(byte, stacks)
|
||||
# check updated stacks
|
||||
# TODO, I commented this out because it will fail when the stack is empty
|
||||
# empty stack means the end of the grammar
|
||||
# assert stacks != []
|
||||
|
||||
return stacks
|
||||
|
||||
def accept_token_ids(self, token_ids: List[int], stacks: List[List[int]], as_string=True):
|
||||
if as_string:
|
||||
string = self.tokenizer.decode(token_ids)
|
||||
stacks = self.accept_string(string, stacks)
|
||||
else:
|
||||
for token_id in token_ids:
|
||||
stacks = self.accept_token_id(token_id, stacks)
|
||||
return stacks
|
||||
|
||||
def batch_filter_vocab(self, batch_stacks, device):
|
||||
batch_acceptance = []
|
||||
for stacks in batch_stacks:
|
||||
batch_acceptance.append(self.filter_vocab(stacks, device))
|
||||
return torch.stack(batch_acceptance)
|
||||
|
||||
def filter_vocab(self, stacks, device):
|
||||
if not stacks: # Check if stacks is empty
|
||||
# Handle the empty case: for example, return a tensor of False
|
||||
# The size of the tensor should match the size of your vocabulary
|
||||
vocab_size = len(self.token_trie)
|
||||
logger.debug(f"sum of acceptance: {0}")
|
||||
return torch.zeros(vocab_size, dtype=torch.bool, device=device)
|
||||
|
||||
acceptance_matrix = torch.cat([self.token_acceptance_for_stack(tuple(stack), device) for stack in stacks])
|
||||
# Merge stacks: any True => True
|
||||
acceptance = acceptance_matrix.reshape(len(stacks), -1).any(dim=0)
|
||||
logger.debug(f"sum of acceptance: {acceptance.sum()}")
|
||||
return acceptance
|
||||
|
||||
# For each sub-rule in the grammar, cache whether each byte is accepted.
|
||||
@lru_cache(maxsize=None)
|
||||
def pos_char_acceptance(self, pos):
|
||||
acceptance = [False] * 256
|
||||
num_chars = self.grammar_encoding[pos]
|
||||
pos += 1
|
||||
for i in range(0, num_chars, 2):
|
||||
start = self.grammar_encoding[pos + i]
|
||||
end = self.grammar_encoding[pos + i + 1]
|
||||
for j in range(start, end + 1):
|
||||
acceptance[j] = True
|
||||
return acceptance
|
||||
|
||||
# Probably this should be configurable. If the grammar has an exceedingly
|
||||
# large number of states, the correct setting is a tradeoff between GPU
|
||||
# RAM usage and recomputation time.
|
||||
#
|
||||
# The main variable that pushes usage up here is number of states in the
|
||||
# grammar.
|
||||
@lru_cache(maxsize=32768)
|
||||
def token_acceptance_for_stack(self, stack, device):
|
||||
st = time.time()
|
||||
stack = list(stack) # needs to come in as a tuple for lru_cache
|
||||
|
||||
accepts = [False] * len(self.token_trie)
|
||||
accepts[self.eos_token_id] = len(stack) == 0
|
||||
if len(stack) == 0:
|
||||
logger.debug("empty stack")
|
||||
|
||||
def traverse_trie(trie, stacks):
|
||||
for byte, next_trie in trie.items():
|
||||
if byte == LEAF:
|
||||
token_id = next_trie
|
||||
if token_id != self.eos_token_id:
|
||||
accepts[token_id] = bool(stacks)
|
||||
continue
|
||||
|
||||
new_stacks = []
|
||||
for stk in stacks:
|
||||
if not stk:
|
||||
continue
|
||||
|
||||
pos = stk[-1]
|
||||
num_chars = self.grammar_encoding[pos]
|
||||
|
||||
if not self.pos_char_acceptance(pos)[byte]:
|
||||
continue
|
||||
|
||||
pos += num_chars + 1
|
||||
new_stack = stk[:-1]
|
||||
if self.grammar_encoding[pos]:
|
||||
new_stack.append(pos)
|
||||
new_stacks.extend(self.advance_stack(tuple(new_stack)))
|
||||
|
||||
if new_stacks:
|
||||
traverse_trie(next_trie, new_stacks)
|
||||
|
||||
traverse_trie(self.token_trie.trie, [stack])
|
||||
|
||||
et = time.time() - st
|
||||
x = torch.tensor(accepts, dtype=torch.bool, device=device)
|
||||
self.tt += et
|
||||
self.nt += 1
|
||||
return x
|
||||
|
||||
|
||||
class StaticGrammarConstraint(GrammarConstraint):
|
||||
def __init__(self, grammar_str, start_rule_name, tokenizer):
|
||||
super().__init__(grammar_str, start_rule_name, tokenizer)
|
||||
|
||||
def accept_char(self):
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
#################
|
||||
# DATA STRUCTURES
|
||||
#################
|
||||
|
||||
|
||||
LEAF = -1
|
||||
|
||||
|
||||
class TokenTrie:
|
||||
def __init__(self, tokenizer):
|
||||
self.eos_token_id = tokenizer.eos_token_id
|
||||
self.tokens = []
|
||||
self.trie = {}
|
||||
self.load_tokens(tokenizer)
|
||||
|
||||
def id2str(self, token_id):
|
||||
return self.tokens[token_id]
|
||||
|
||||
def __len__(self):
|
||||
return len(self.tokens)
|
||||
|
||||
def load_tokens(self, tokenizer):
|
||||
def replace_hex(match):
|
||||
hex_value = match.group(1)
|
||||
return chr(int(hex_value, 16))
|
||||
|
||||
if "gpt2" in tokenizer.__class__.__name__.lower():
|
||||
special = tokenizer.additional_special_tokens_ids
|
||||
|
||||
# Here, the decoder does a string replace on a bunch of sequences
|
||||
# like ' .' for '.'. This interferes with our assumptions, where a
|
||||
# token should always have exactly one representation.
|
||||
# Fortunately(?) text-generation-inference doesn't seem to run this
|
||||
# cleanup, so we get extraneous spaces. So, in order to generate
|
||||
# the right token set for TGI, we have to skip the space trimming.
|
||||
# See:
|
||||
# https://github.com/huggingface/transformers/blob/main/src/transformers/tokenization_utils_base.py#L3588-L3600
|
||||
def fmt_token(id):
|
||||
if id in special:
|
||||
return None
|
||||
return bytes(tokenizer.decode([id], clean_up_tokenization_spaces=False), "utf-8")
|
||||
|
||||
elif "llama" in tokenizer.__class__.__name__.lower():
|
||||
|
||||
def fmt_token(id):
|
||||
token = tokenizer.convert_ids_to_tokens(id)
|
||||
token = re.sub(r"<0x([0-9a-fA-F]{2})>", replace_hex, token)
|
||||
token = token.replace("▁", " ")
|
||||
return bytes(token, "utf-8")
|
||||
|
||||
else:
|
||||
print("Warning: unrecognized tokenizer: using default token formatting")
|
||||
|
||||
def fmt_token(id):
|
||||
token = tokenizer.convert_ids_to_tokens(id)
|
||||
return bytes(token, "utf-8")
|
||||
|
||||
# note: vocab_size doesn't work here because there are also
|
||||
# get_added_vocab() tokens
|
||||
self.tokens = [fmt_token(i) for i in range(len(tokenizer.get_vocab()))]
|
||||
for token_id, token_bytes in enumerate(self.tokens):
|
||||
if token_bytes is not None:
|
||||
self.insert_into_trie(self.trie, token_bytes, token_id)
|
||||
|
||||
def insert_into_trie(self, trie, token_bytes, token_id):
|
||||
current = trie
|
||||
for byte in token_bytes:
|
||||
if byte not in current:
|
||||
current[byte] = {}
|
||||
current = current[byte]
|
||||
current[LEAF] = token_id
|
||||
|
||||
|
||||
@lru_cache(maxsize=5)
|
||||
def initialize_grammar(grammar_string):
|
||||
return IncrementalGrammarConstraint(grammar_string.strip(), start_rule_name="root", tokenizer=shared.tokenizer)
|
||||
104
modules/grammar/logits_process.py
Normal file
104
modules/grammar/logits_process.py
Normal file
@ -0,0 +1,104 @@
|
||||
'''
|
||||
This file has been 100% copied from this PR to the Transformers library:
|
||||
https://github.com/huggingface/transformers/pull/27557
|
||||
|
||||
Author: Saibo-creator
|
||||
Author GitHub: https://github.com/Saibo-creator
|
||||
|
||||
All credits go to the author.
|
||||
'''
|
||||
|
||||
import math
|
||||
|
||||
import torch
|
||||
from transformers.generation.logits_process import LogitsProcessor
|
||||
from transformers.utils import add_start_docstrings
|
||||
|
||||
LOGITS_PROCESSOR_INPUTS_DOCSTRING = r"""
|
||||
Args:
|
||||
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
||||
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
|
||||
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
|
||||
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
|
||||
search or log softmax for each vocabulary token when using beam search
|
||||
|
||||
Return:
|
||||
`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
|
||||
|
||||
"""
|
||||
|
||||
|
||||
class GrammarConstrainedLogitsProcessor(LogitsProcessor):
|
||||
def __init__(self, grammar_constraint):
|
||||
self.last_size = None
|
||||
self.grammar_constraint = grammar_constraint
|
||||
self.batch_stacks = None
|
||||
|
||||
def filter_logits(self, logits, device):
|
||||
# resolve each stack to a tensor of True/False for each token
|
||||
# indicating acceptance
|
||||
# acceptance = self.grammar_acceptor.filter_vocab(self.stacks, device)
|
||||
acceptance = self.grammar_constraint.batch_filter_vocab(self.batch_stacks, device)
|
||||
# logger.debug(acceptance)
|
||||
# Logits to -inf where False
|
||||
logits[~acceptance] = -math.inf
|
||||
|
||||
# TODO: batching
|
||||
def process_logits(self, input_ids, scores, parse_start_index=None):
|
||||
"""
|
||||
:param input_ids:
|
||||
:param scores:
|
||||
:param parse_start_index: default None, which means generate from scratch. Set to 0 to parse all input_ids
|
||||
:return:
|
||||
"""
|
||||
# we dynamically create stacks at the first call, so that we know the batch size and beam size
|
||||
if self.batch_stacks is None:
|
||||
self.batch_stacks = [self.grammar_constraint.init_stacks() for _ in range(len(input_ids))]
|
||||
|
||||
# if self.last_size is not set (which would be the case when processing the first token).
|
||||
# In this case, do nothing.
|
||||
if self.last_size is None:
|
||||
prefix_to_parse = [
|
||||
single_input_ids[parse_start_index:] if parse_start_index is not None else []
|
||||
for single_input_ids in input_ids
|
||||
]
|
||||
# self.grammar_acceptor.accept_token_ids(prefix_to_parse, self.stacks)
|
||||
self.batch_stacks = [
|
||||
self.grammar_constraint.accept_token_ids(prefix, stack)
|
||||
for prefix, stack in zip(prefix_to_parse, self.batch_stacks)
|
||||
]
|
||||
# if the length of the current input IDs (input_ids[0]) is exactly one more than self.last_size.
|
||||
# This is expected in a scenario where inputs are processed incrementally, one token at a time.
|
||||
elif len(input_ids[0]) == self.last_size + 1:
|
||||
# self.stacks = self.grammar_acceptor.accept_token_id(input_ids[0][-1], self.stacks)
|
||||
self.batch_stacks = [
|
||||
self.grammar_constraint.accept_token_id(single_input_ids[-1], stack)
|
||||
for single_input_ids, stack in zip(input_ids, self.batch_stacks)
|
||||
]
|
||||
# ensure that the input size is consistent with the expected incremental processing
|
||||
# (i.e., one token at a time).
|
||||
else:
|
||||
# here we check if the input_ids are one token longer than the last time we processed
|
||||
# but we don't check if input_ids are actually valid.
|
||||
# Imagine a scenario where we generate 10 tokens, then we replace the 10 generated tokens with 10 new tokens.
|
||||
# In this case, the input_ids will be consistent with the last_size, but the input_ids are not valid.
|
||||
# However, should we really check if the input_ids are valid here?
|
||||
# If we do, then we need to reparse the whole input_ids at each call, which is not efficient.
|
||||
# Maybe we should just trust the user to provide valid input_ids?
|
||||
# The conclusion is that, we assume the input_ids are valid, and our generation will be correct.
|
||||
# If the input_ids are not valid, then the generation result will be wrong and we don't take responsibility for that.
|
||||
raise RuntimeError(
|
||||
"Input ID's length is inconsistent with the current state of "
|
||||
"the GrammarConstrainedLogitsProcessor. If you want to process "
|
||||
"another input sequence, please instantiate a new "
|
||||
"GrammarConstrainedLogitsProcessor."
|
||||
)
|
||||
|
||||
self.filter_logits(scores, scores.device)
|
||||
|
||||
self.last_size = len(input_ids[0])
|
||||
return scores
|
||||
|
||||
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
|
||||
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
|
||||
return self.process_logits(input_ids, scores)
|
||||
@ -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',
|
||||
|
||||
@ -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.')
|
||||
|
||||
@ -18,7 +18,8 @@ from modules.callbacks import (
|
||||
_StopEverythingStoppingCriteria
|
||||
)
|
||||
from modules.extensions import apply_extensions
|
||||
from modules.grammar import GrammarLogitsProcessor
|
||||
from modules.grammar.grammar_utils import initialize_grammar
|
||||
from modules.grammar.logits_process import GrammarConstrainedLogitsProcessor
|
||||
from modules.html_generator import generate_4chan_html, generate_basic_html
|
||||
from modules.logging_colors import logger
|
||||
from modules.models import clear_torch_cache, local_rank
|
||||
@ -317,11 +318,17 @@ def generate_reply_HF(question, original_question, seed, state, stopping_strings
|
||||
generate_params['stopping_criteria'] = transformers.StoppingCriteriaList()
|
||||
generate_params['stopping_criteria'].append(_StopEverythingStoppingCriteria())
|
||||
|
||||
# Logits processor
|
||||
processor = state.get('logits_processor', LogitsProcessorList([]))
|
||||
# In case a processor is passed by itself.
|
||||
if not isinstance(processor, LogitsProcessorList):
|
||||
processor = LogitsProcessorList([processor])
|
||||
processor.append(GrammarLogitsProcessor(state['grammar_string']))
|
||||
|
||||
# Grammar
|
||||
if state['grammar_string'].strip() != '':
|
||||
grammar = initialize_grammar(state['grammar_string'])
|
||||
grammar_processor = GrammarConstrainedLogitsProcessor(grammar)
|
||||
processor.append(grammar_processor)
|
||||
|
||||
apply_extensions('logits_processor', processor, input_ids)
|
||||
generate_params['logits_processor'] = processor
|
||||
|
||||
|
||||
@ -73,6 +73,7 @@ def list_model_elements():
|
||||
'disable_exllamav2',
|
||||
'cfg_cache',
|
||||
'no_flash_attn',
|
||||
'num_experts_per_token',
|
||||
'cache_8bit',
|
||||
'threads',
|
||||
'threads_batch',
|
||||
|
||||
@ -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.")
|
||||
|
||||
@ -2,7 +2,7 @@ accelerate==0.25.*
|
||||
colorama
|
||||
datasets
|
||||
einops
|
||||
exllamav2==0.0.10; platform_system != "Darwin" and platform_machine != "x86_64"
|
||||
exllamav2==0.0.11; platform_system != "Darwin" and platform_machine != "x86_64"
|
||||
gradio==3.50.*
|
||||
markdown
|
||||
numpy==1.24.*
|
||||
@ -20,8 +20,6 @@ transformers==4.36.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
git+https://github.com/oobabooga/torch-grammar.git
|
||||
|
||||
# bitsandbytes
|
||||
bitsandbytes==0.41.1; platform_system != "Windows"
|
||||
https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"
|
||||
@ -53,14 +51,14 @@ https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121
|
||||
https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
|
||||
https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp39-cp39-win_amd64.whl; platform_system == "Windows" and python_version == "3.9"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp38-cp38-win_amd64.whl; platform_system == "Windows" and python_version == "3.8"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp39-cp39-win_amd64.whl; platform_system == "Windows" and python_version == "3.9"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp38-cp38-win_amd64.whl; platform_system == "Windows" and python_version == "3.8"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
|
||||
https://github.com/jllllll/flash-attention/releases/download/v2.3.4/flash_attn-2.3.4+cu121torch2.1cxx11abiFALSE-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
|
||||
https://github.com/jllllll/flash-attention/releases/download/v2.3.4/flash_attn-2.3.4+cu121torch2.1cxx11abiFALSE-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/jllllll/flash-attention/releases/download/v2.3.4/flash_attn-2.3.4+cu121torch2.1cxx11abiFALSE-cp39-cp39-win_amd64.whl; platform_system == "Windows" and python_version == "3.9"
|
||||
|
||||
@ -2,7 +2,7 @@ accelerate==0.25.*
|
||||
colorama
|
||||
datasets
|
||||
einops
|
||||
exllamav2==0.0.10
|
||||
exllamav2==0.0.11
|
||||
gradio==3.50.*
|
||||
markdown
|
||||
numpy==1.24.*
|
||||
@ -20,8 +20,6 @@ transformers==4.36.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
git+https://github.com/oobabooga/torch-grammar.git
|
||||
|
||||
# bitsandbytes
|
||||
bitsandbytes==0.38.1; platform_system != "Windows"
|
||||
https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.38.1-py3-none-win_amd64.whl; platform_system == "Windows"
|
||||
|
||||
@ -2,7 +2,7 @@ accelerate==0.25.*
|
||||
colorama
|
||||
datasets
|
||||
einops
|
||||
exllamav2==0.0.10
|
||||
exllamav2==0.0.11
|
||||
gradio==3.50.*
|
||||
markdown
|
||||
numpy==1.24.*
|
||||
@ -20,8 +20,6 @@ transformers==4.36.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
git+https://github.com/oobabooga/torch-grammar.git
|
||||
|
||||
# bitsandbytes
|
||||
bitsandbytes==0.38.1; platform_system != "Windows"
|
||||
https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.38.1-py3-none-win_amd64.whl; platform_system == "Windows"
|
||||
|
||||
@ -2,7 +2,7 @@ accelerate==0.25.*
|
||||
colorama
|
||||
datasets
|
||||
einops
|
||||
exllamav2==0.0.10
|
||||
exllamav2==0.0.11
|
||||
gradio==3.50.*
|
||||
markdown
|
||||
numpy==1.24.*
|
||||
@ -20,8 +20,6 @@ transformers==4.36.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
git+https://github.com/oobabooga/torch-grammar.git
|
||||
|
||||
# bitsandbytes
|
||||
bitsandbytes==0.41.1; platform_system != "Windows"
|
||||
https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"
|
||||
|
||||
@ -2,7 +2,7 @@ accelerate==0.25.*
|
||||
colorama
|
||||
datasets
|
||||
einops
|
||||
exllamav2==0.0.10
|
||||
exllamav2==0.0.11
|
||||
gradio==3.50.*
|
||||
markdown
|
||||
numpy==1.24.*
|
||||
@ -20,8 +20,6 @@ transformers==4.36.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
git+https://github.com/oobabooga/torch-grammar.git
|
||||
|
||||
# bitsandbytes
|
||||
bitsandbytes==0.41.1; platform_system != "Windows"
|
||||
https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"
|
||||
|
||||
@ -2,7 +2,7 @@ accelerate==0.25.*
|
||||
colorama
|
||||
datasets
|
||||
einops
|
||||
exllamav2==0.0.10
|
||||
exllamav2==0.0.11
|
||||
gradio==3.50.*
|
||||
markdown
|
||||
numpy==1.24.*
|
||||
@ -20,8 +20,6 @@ transformers==4.36.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
git+https://github.com/oobabooga/torch-grammar.git
|
||||
|
||||
# bitsandbytes
|
||||
bitsandbytes==0.41.1; platform_system != "Windows"
|
||||
https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"
|
||||
|
||||
@ -2,7 +2,7 @@ accelerate==0.25.*
|
||||
colorama
|
||||
datasets
|
||||
einops
|
||||
exllamav2==0.0.10
|
||||
exllamav2==0.0.11
|
||||
gradio==3.50.*
|
||||
markdown
|
||||
numpy==1.24.*
|
||||
@ -20,8 +20,6 @@ transformers==4.36.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
git+https://github.com/oobabooga/torch-grammar.git
|
||||
|
||||
# bitsandbytes
|
||||
bitsandbytes==0.41.1; platform_system != "Windows"
|
||||
https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"
|
||||
|
||||
@ -2,7 +2,7 @@ accelerate==0.25.*
|
||||
colorama
|
||||
datasets
|
||||
einops
|
||||
exllamav2==0.0.10; platform_system != "Darwin" and platform_machine != "x86_64"
|
||||
exllamav2==0.0.11; platform_system != "Darwin" and platform_machine != "x86_64"
|
||||
gradio==3.50.*
|
||||
markdown
|
||||
numpy==1.24.*
|
||||
@ -20,8 +20,6 @@ transformers==4.36.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
git+https://github.com/oobabooga/torch-grammar.git
|
||||
|
||||
# bitsandbytes
|
||||
bitsandbytes==0.41.1; platform_system != "Windows"
|
||||
https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"
|
||||
@ -53,14 +51,14 @@ https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121
|
||||
https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
|
||||
https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp39-cp39-win_amd64.whl; platform_system == "Windows" and python_version == "3.9"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp38-cp38-win_amd64.whl; platform_system == "Windows" and python_version == "3.8"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.10/exllamav2-0.0.10+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp39-cp39-win_amd64.whl; platform_system == "Windows" and python_version == "3.9"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp38-cp38-win_amd64.whl; platform_system == "Windows" and python_version == "3.8"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
|
||||
https://github.com/turboderp/exllamav2/releases/download/v0.0.11/exllamav2-0.0.11+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
|
||||
https://github.com/jllllll/flash-attention/releases/download/v2.3.4/flash_attn-2.3.4+cu121torch2.1cxx11abiFALSE-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
|
||||
https://github.com/jllllll/flash-attention/releases/download/v2.3.4/flash_attn-2.3.4+cu121torch2.1cxx11abiFALSE-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
|
||||
https://github.com/jllllll/flash-attention/releases/download/v2.3.4/flash_attn-2.3.4+cu121torch2.1cxx11abiFALSE-cp39-cp39-win_amd64.whl; platform_system == "Windows" and python_version == "3.9"
|
||||
|
||||
@ -2,7 +2,7 @@ accelerate==0.25.*
|
||||
colorama
|
||||
datasets
|
||||
einops
|
||||
exllamav2==0.0.10
|
||||
exllamav2==0.0.11
|
||||
gradio==3.50.*
|
||||
markdown
|
||||
numpy==1.24.*
|
||||
@ -20,8 +20,6 @@ transformers==4.36.*
|
||||
tqdm
|
||||
wandb
|
||||
|
||||
git+https://github.com/oobabooga/torch-grammar.git
|
||||
|
||||
# bitsandbytes
|
||||
bitsandbytes==0.41.1; platform_system != "Windows"
|
||||
https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user