[Fix]: Update web demo to be self-contained (#624)

chat/web_demo.py

@@ -7,17 +7,148 @@ Please refer to these links below for more information:
     3. transformers: https://github.com/huggingface/transformers
 """
 
-from dataclasses import asdict
+import copy
+import warnings
+from dataclasses import asdict, dataclass
+from typing import Callable, List, Optional
 
 import streamlit as st
 import torch
-from tools.transformers.interface import GenerationConfig, generate_interactive
+from torch import nn
 from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
 from transformers.utils import logging
 
 logger = logging.get_logger(__name__)
 
 
+@dataclass
+class GenerationConfig:
+    # this config is used for chat to provide more diversity
+    max_length: int = 32768
+    top_p: float = 0.8
+    temperature: float = 0.8
+    do_sample: bool = True
+    repetition_penalty: float = 1.005
+
+
+@torch.inference_mode()
+def generate_interactive(
+    model,
+    tokenizer,
+    prompt,
+    generation_config: Optional[GenerationConfig] = None,
+    logits_processor: Optional[LogitsProcessorList] = None,
+    stopping_criteria: Optional[StoppingCriteriaList] = None,
+    prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
+    additional_eos_token_id: Optional[int] = None,
+    **kwargs,
+):
+    inputs = tokenizer([prompt], padding=True, return_tensors="pt")
+    input_length = len(inputs["input_ids"][0])
+    for k, v in inputs.items():
+        inputs[k] = v.cuda()
+    input_ids = inputs["input_ids"]
+    batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]  # noqa: F841  # pylint: disable=W0612
+    if generation_config is None:
+        generation_config = model.generation_config
+    generation_config = copy.deepcopy(generation_config)
+    model_kwargs = generation_config.update(**kwargs)
+    bos_token_id, eos_token_id = (  # noqa: F841  # pylint: disable=W0612
+        generation_config.bos_token_id,
+        generation_config.eos_token_id,
+    )
+    if isinstance(eos_token_id, int):
+        eos_token_id = [eos_token_id]
+    if additional_eos_token_id is not None:
+        eos_token_id.append(additional_eos_token_id)
+    has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
+    if has_default_max_length and generation_config.max_new_tokens is None:
+        warnings.warn(
+            f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
+            "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
+            " recommend using `max_new_tokens` to control the maximum length of the generation.",
+            UserWarning,
+        )
+    elif generation_config.max_new_tokens is not None:
+        generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
+        if not has_default_max_length:
+            logger.warn(  # pylint: disable=W4902
+                f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
+                f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
+                "Please refer to the documentation for more information. "
+                "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
+                UserWarning,
+            )
+
+    if input_ids_seq_length >= generation_config.max_length:
+        input_ids_string = "input_ids"
+        logger.warning(
+            f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
+            f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
+            " increasing `max_new_tokens`."
+        )
+
+    # 2. Set generation parameters if not already defined
+    logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+    stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+
+    logits_processor = model._get_logits_processor(
+        generation_config=generation_config,
+        input_ids_seq_length=input_ids_seq_length,
+        encoder_input_ids=input_ids,
+        prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+        logits_processor=logits_processor,
+    )
+
+    stopping_criteria = model._get_stopping_criteria(
+        generation_config=generation_config, stopping_criteria=stopping_criteria
+    )
+    logits_warper = model._get_logits_warper(generation_config)
+
+    unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
+    scores = None
+    while True:
+        model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)
+        # forward pass to get next token
+        outputs = model(
+            **model_inputs,
+            return_dict=True,
+            output_attentions=False,
+            output_hidden_states=False,
+        )
+
+        next_token_logits = outputs.logits[:, -1, :]
+
+        # pre-process distribution
+        next_token_scores = logits_processor(input_ids, next_token_logits)
+        next_token_scores = logits_warper(input_ids, next_token_scores)
+
+        # sample
+        probs = nn.functional.softmax(next_token_scores, dim=-1)
+        if generation_config.do_sample:
+            next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+        else:
+            next_tokens = torch.argmax(probs, dim=-1)
+
+        # update generated ids, model inputs, and length for next step
+        input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+        model_kwargs = model._update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False)
+        unfinished_sequences = unfinished_sequences.mul((min(next_tokens != i for i in eos_token_id)).long())
+
+        output_token_ids = input_ids[0].cpu().tolist()
+        output_token_ids = output_token_ids[input_length:]
+        for each_eos_token_id in eos_token_id:
+            if output_token_ids[-1] == each_eos_token_id:
+                output_token_ids = output_token_ids[:-1]
+        response = tokenizer.decode(output_token_ids)
+
+        yield response
+        # stop when each sentence is finished, or if we exceed the maximum length
+        if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
+            break
+
+
 def on_btn_click():
     del st.session_state.messages
 
@@ -35,7 +166,7 @@ def load_model():
 
 def prepare_generation_config():
     with st.sidebar:
-        max_length = st.slider("Max Length", min_value=32, max_value=2048, value=2048)
+        max_length = st.slider("Max Length", min_value=8, max_value=32768, value=32768)
         top_p = st.slider("Top P", 0.0, 1.0, 0.8, step=0.01)
         temperature = st.slider("Temperature", 0.0, 1.0, 0.7, step=0.01)
         st.button("Clear Chat History", on_click=on_btn_click)
@@ -52,17 +183,21 @@ cur_query_prompt = "[UNUSED_TOKEN_146]user\n{user}[UNUSED_TOKEN_145]\n[UNUSED_TO
 
 def combine_history(prompt):
     messages = st.session_state.messages
-    total_prompt = ""
+    meta_instruction = (
+        "You are InternLM (书生·浦语), a helpful, honest, and harmless AI assistant developed by Shanghai "
+        "AI Laboratory (上海人工智能实验室)."
+    )
+    total_prompt = f"<s>[UNUSED_TOKEN_146]system\n{meta_instruction}[UNUSED_TOKEN_145]\n"
     for message in messages:
         cur_content = message["content"]
         if message["role"] == "user":
-            cur_prompt = user_prompt.replace("{user}", cur_content)
+            cur_prompt = user_prompt.format(user=cur_content)
         elif message["role"] == "robot":
-            cur_prompt = robot_prompt.replace("{robot}", cur_content)
+            cur_prompt = robot_prompt.format(robot=cur_content)
         else:
             raise RuntimeError
         total_prompt += cur_prompt
-    total_prompt = total_prompt + cur_query_prompt.replace("{user}", prompt)
+    total_prompt = total_prompt + cur_query_prompt.format(user=prompt)
     return total_prompt