Feat: Added Gradio support (#812)

* Added gradio support

* queuing and title

* pre-commit run

README.md

@@ -97,6 +97,10 @@ accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml
 # inference
 accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
     --lora_model_dir="./lora-out"
+
+# gradio
+accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
+    --lora_model_dir="./lora-out" --gradio
 ```
 
 ## Installation
@@ -919,6 +923,10 @@ Pass the appropriate flag to the train command:
   cat /tmp/prompt.txt | python -m axolotl.cli.inference examples/your_config.yml \
     --base_model="./completed-model" --prompter=None --load_in_8bit=True
   ```
+-- With gradio hosting
+  ```bash
+  python -m axolotl.cli.inference examples/your_config.yml --gradio
+  ```
 
 Please use `--sample_packing False` if you have it on and receive the error similar to below:
 

requirements.txt

@@ -31,3 +31,4 @@ scikit-learn==1.2.2
 pynvml
 art
 fschat==0.2.29
+gradio

src/axolotl/cli/__init__.py

@@ -6,8 +6,10 @@ import os
 import random
 import sys
 from pathlib import Path
+from threading import Thread
 from typing import Any, Dict, List, Optional, Union
 
+import gradio as gr
 import torch
 import yaml
 
@@ -16,7 +18,7 @@ from accelerate.commands.config import config_args
 from art import text2art
 from huggingface_hub import HfApi
 from huggingface_hub.utils import LocalTokenNotFoundError
-from transformers import GenerationConfig, TextStreamer
+from transformers import GenerationConfig, TextIteratorStreamer, TextStreamer
 
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.logging_config import configure_logging
@@ -153,6 +155,91 @@ def do_inference(
         print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
 
 
+def do_inference_gradio(
+    *,
+    cfg: DictDefault,
+    cli_args: TrainerCliArgs,
+):
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
+    prompter = cli_args.prompter
+    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+
+    for token, symbol in default_tokens.items():
+        # If the token isn't already specified in the config, add it
+        if not (cfg.special_tokens and token in cfg.special_tokens):
+            tokenizer.add_special_tokens({token: symbol})
+
+    prompter_module = None
+    if prompter:
+        prompter_module = getattr(
+            importlib.import_module("axolotl.prompters"), prompter
+        )
+
+    if cfg.landmark_attention:
+        from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id
+
+        set_model_mem_id(model, tokenizer)
+        model.set_mem_cache_args(
+            max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
+        )
+
+    model = model.to(cfg.device)
+
+    def generate(instruction):
+        if not instruction:
+            return
+        if prompter_module:
+            # pylint: disable=stop-iteration-return
+            prompt: str = next(
+                prompter_module().build_prompt(instruction=instruction.strip("\n"))
+            )
+        else:
+            prompt = instruction.strip()
+        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+
+        model.eval()
+        with torch.no_grad():
+            generation_config = GenerationConfig(
+                repetition_penalty=1.1,
+                max_new_tokens=1024,
+                temperature=0.9,
+                top_p=0.95,
+                top_k=40,
+                bos_token_id=tokenizer.bos_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                pad_token_id=tokenizer.pad_token_id,
+                do_sample=True,
+                use_cache=True,
+                return_dict_in_generate=True,
+                output_attentions=False,
+                output_hidden_states=False,
+                output_scores=False,
+            )
+            streamer = TextIteratorStreamer(tokenizer)
+            generation_kwargs = {
+                "inputs": batch["input_ids"].to(cfg.device),
+                "generation_config": generation_config,
+                "streamer": streamer,
+            }
+
+            thread = Thread(target=model.generate, kwargs=generation_kwargs)
+            thread.start()
+
+            all_text = ""
+
+            for new_text in streamer:
+                all_text += new_text
+                yield all_text
+
+    demo = gr.Interface(
+        fn=generate,
+        inputs="textbox",
+        outputs="text",
+        title=cfg.get("gradio_title", "Axolotl Gradio Interface"),
+    )
+    demo.queue().launch(show_api=False, share=True)
+
+
 def choose_config(path: Path):
     yaml_files = list(path.glob("*.yml"))
 

src/axolotl/cli/inference.py

@@ -6,11 +6,16 @@ from pathlib import Path
 import fire
 import transformers
 
-from axolotl.cli import do_inference, load_cfg, print_axolotl_text_art
+from axolotl.cli import (
+    do_inference,
+    do_inference_gradio,
+    load_cfg,
+    print_axolotl_text_art,
+)
 from axolotl.common.cli import TrainerCliArgs
 
 
-def do_cli(config: Path = Path("examples/"), **kwargs):
+def do_cli(config: Path = Path("examples/"), gradio=False, **kwargs):
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
     parsed_cfg = load_cfg(config, **kwargs)
@@ -21,7 +26,10 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
     )
     parsed_cli_args.inference = True
 
-    do_inference(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    if gradio:
+        do_inference_gradio(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    else:
+        do_inference(cfg=parsed_cfg, cli_args=parsed_cli_args)
 
 
 if __name__ == "__main__":