refactor(tools): move interface.py and import it to web_demo (#195)

* move interface.py and import it to web_demo

* typo

tools/pal_inference.py

@@ -27,7 +27,7 @@ import tqdm
 from datasets import load_dataset
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-from internlm.utils.interface import GenerationConfig, generation_iterator
+from tools.transformers.interface import GenerationConfig, generate_interactive
 from internlm.utils.timeout import Timeout
 
 
@@ -115,7 +115,7 @@ class GenericRuntime:
 
 
 class PALInterface:
-    """PAL interface wrap fun:`generation_iterator` to extract and execute
+    """PAL interface wrap fun:`generate_interactive` to extract and execute
     generated code.
 
     Adapted from https://github.com/reasoning-machines/pal
@@ -150,7 +150,7 @@ class PALInterface:
     def generate(self, prompt):
         # The api will generate response word by word
         # we only need the last generation as the final results
-        for cur_gen in generation_iterator(
+        for cur_gen in generate_interactive(
             model=self.model,
             tokenizer=self.tokenizer,
             prompt=prompt,

tools/transformers/interface.py

@@ -4,6 +4,7 @@ from dataclasses import dataclass
 from typing import Callable, List, Optional
 
 import torch
+from torch import nn
 from transformers import AutoModel, AutoTokenizer
 from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
 from transformers.utils import logging
@@ -21,10 +22,10 @@ class GenerationConfig:
 
 
 @torch.inference_mode()
-def generation_iterator(
+def generate_interactive(
-    model: AutoModel,
+    model, 
-    tokenizer: AutoTokenizer,
+    tokenizer,
-    prompt: str,
+    prompt,
     generation_config: Optional[GenerationConfig] = None,
     logits_processor: Optional[LogitsProcessorList] = None,
     stopping_criteria: Optional[StoppingCriteriaList] = None,
@@ -37,12 +38,12 @@ def generation_iterator(
     for k, v in inputs.items():
         inputs[k] = v.cuda()
     input_ids = inputs["input_ids"]
-    input_ids_seq_length = input_ids.shape[-1]
+    batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
     if generation_config is None:
         generation_config = model.generation_config
     generation_config = copy.deepcopy(generation_config)
     model_kwargs = generation_config.update(**kwargs)
-    eos_token_id = generation_config.eos_token_id
+    bos_token_id, eos_token_id = 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:
@@ -58,24 +59,20 @@ def generation_iterator(
     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.warning(
+            logger.warn(
-                "Both `max_new_tokens` (={%s}) and `max_length`(="
+                f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
-                "{%s}) seem to have been set. `max_new_tokens` will take precedence. "
+                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)",
-                generation_config.max_new_tokens,
+                UserWarning,
-                generation_config.max_length,
             )
 
     if input_ids_seq_length >= generation_config.max_length:
         input_ids_string = "input_ids"
         logger.warning(
-            "Input length of {%s} is {%s}, but `max_length` is set to"
+            f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
-            " {%s}. This can lead to unexpected behavior. You should consider"
+            f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
-            " increasing `max_new_tokens`.",
+            " increasing `max_new_tokens`."
-            input_ids_string,
-            input_ids_seq_length,
-            generation_config.max_length,
         )
 
     # 2. Set generation parameters if not already defined
@@ -114,7 +111,7 @@ def generation_iterator(
         next_token_scores = logits_warper(input_ids, next_token_scores)
 
         # sample
-        probs = next_token_scores.softmax(dim=-1)
+        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:
@@ -122,7 +119,9 @@ def generation_iterator(
 
         # 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)
+        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()

web_demo.py

@@ -8,7 +8,6 @@ Please refer to these links below for more information:
 
 import streamlit as st
 import torch
-import torch.nn as nn
 from dataclasses import dataclass, asdict
 from typing import List, Optional, Callable, Optional
 import copy
@@ -16,140 +15,16 @@ import warnings
 import logging
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from transformers.utils import logging
-from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
+from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
+
+from tools.transformers.interface import generate_interactive, GenerationConfig
 
 logger = logging.get_logger(__name__)
 
 
-@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]
-    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 = 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(
-                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
 
-
-@dataclass
-class GenerationConfig:
-    max_length: Optional[int] = None
-    top_p: Optional[float] = None
-    temperature: Optional[float] = None
-    do_sample: Optional[bool] = True
-    repetition_penalty: Optional[float] = 1.0
-
-
 @st.cache_resource
 def load_model():
     model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16).cuda()