[Inference]Adapt repetition_penalty and no_repeat_ngram_size (#5708)

* Adapt repetition_penalty and no_repeat_ngram_size

* fix no_repeat_ngram_size_logit_process

* remove batch_updated

* fix annotation

* modified codes based on the review feedback.

* rm get_batch_token_ids

colossalai/inference/batch_bucket.py

@@ -102,6 +102,13 @@ class BatchBucket:
     def num_tokens_to_verify(self) -> int:
         return self._num_tokens_to_verify
 
+    @property
+    def batch_token_ids(self) -> List[List[int]]:
+        out = []
+        for seq in self.seqs_li:
+            out.append(seq.input_token_id + seq.output_token_id)
+        return out
+
     def set_use_spec_dec(self, num_tokens_to_verify: int = 5) -> None:
         """Set batch bucket to use speculatvie decoding.
         This will notify the adjust the lengths of inputs during modeling,
@@ -328,6 +335,7 @@ class BatchBucket:
             seqs.append(seq)
         if not self.is_compact:
             self._make_compact()
+
         return seqs, block_tables
 
     def pop_finished(
@@ -432,6 +440,7 @@ class BatchBucket:
             block_tables = torch.stack(block_tables_li)
             self.add_seqs(seqs, alloc_block_tables=block_tables)
             unmerged_ids = other.seqs_ids
+
         return unmerged_ids
 
     ########## The following methods are expected to be used in modeling ###########

colossalai/inference/config.py

@@ -99,7 +99,9 @@ class InferenceConfig:
         early_stopping (Optional[bool]): Whether to stop the generation when all beam hypotheses have finished or not, defaults to False.
         top_k (Optional[int]): The number of highest probability vocabulary tokens to keep for top-k-filtering, defaults to None.
         top_p (Optional[float]): The cumulative probability threshold for retaining tokens with a total probability above it, defaults to None.
-        min_p (Optional[float]): The minimum probability to keep for top-p filtering, defaults to None.
+        temperature (Optional[float]): Randomness used to control randomization, defaults to 1.0.
+        repetition_penalty (Optional[float]): The parameter that influences the model's treatment of new tokens in relation to their appearance in the prompt and the generated text. Values greater than 1 incentivize the model to introduce new tokens, whereas values less than 1 incentivize token repetition., defaults to 1.0.
+        no_repeat_ngram_size (Optional[int]): If no_repeat_ngram_size > 0, the consecutive tokens of ngram size can only appear once in inference sentences.
         n_spec_tokens (int): The maximum number of speculating tokens, defaults to None.
         glimpse_large_kv (bool): Whether to use large KV in drafter model, defaults to False.
         block_size (int): The number of blocks in a logical block, defaults to 16.
@@ -136,7 +138,9 @@ class InferenceConfig:
     early_stopping: Optional[bool] = False
     top_k: Optional[int] = None
     top_p: Optional[float] = None
-    min_p: Optional[float] = None
+    temperature: Optional[float] = 1.0
+    no_repeat_ngram_size: Optional[int] = 0
+    repetition_penalty: Optional[float] = 1.0
 
     # speculative decoding configs
     max_n_spec_tokens: int = 5
@@ -213,7 +217,7 @@ class InferenceConfig:
             "do_sample": self.do_sample,
             "num_beams": self.beam_width,
         }
-        for type in ["top_k", "top_p", "min_p"]:
+        for type in ["repetition_penalty", "no_repeat_ngram_size", "temperature", "top_k", "top_p"]:
             if hasattr(self, type):
                 meta_config[type] = getattr(self, type)
         for type in ["pad_token_id", "bos_token_id", "eos_token_id"]:

colossalai/inference/core/engine.py

@@ -424,7 +424,7 @@ class InferenceEngine:
 
         # 2. Prefill main model (Verifier) - fill past kv cache for main model
         logits = model_executable(input_token_ids, output_tensor, input_meta_data, self.k_cache, self.v_cache)
-        next_tokens = self.request_handler.search_tokens(self.generation_config, logits)
+        next_tokens = self.request_handler.search_tokens(self.generation_config, logits, batch)
         # append new inputs to the batch, temporarily
         batch.append_batch_tokens(next_tokens)
         self.request_handler.allocate_batch_spec_dec(batch, 1)
@@ -472,7 +472,7 @@ class InferenceEngine:
             input_token_ids, output_tensor, input_meta_data = self.prepare_input(batch)
             logits = model_executable(input_token_ids, output_tensor, input_meta_data, self.k_cache, self.v_cache)
 
-            next_tokens = self.request_handler.search_tokens(self.generation_config, logits)
+            next_tokens = self.request_handler.search_tokens(self.generation_config, logits, batch)
 
             # 5. Compare and process the results
             diff_indexes = torch.nonzero(~(next_tokens[:-1] == next_token_ids_spec))
@@ -738,7 +738,7 @@ class InferenceEngine:
         logits = model_executable(input_token_ids, output_tensor, input_meta_data, self.k_cache, self.v_cache)
         if self.inference_config.pad_input:
             logits = logits[:, -1, :]
-        next_tokens = self.request_handler.search_tokens(self.generation_config, logits)
+        next_tokens = self.request_handler.search_tokens(self.generation_config, logits, batch)
         self.request_handler.append_next_tokens(next_tokens)
         finished_sequences = self.request_handler.update()
 

colossalai/inference/core/request_handler.py

@@ -11,12 +11,9 @@ from colossalai.inference.kv_cache import KVCacheManager
 from colossalai.inference.logit_processors import logit_processor
 from colossalai.inference.sampler import *
 from colossalai.inference.struct import RequestStatus, Sequence
-from colossalai.logging import get_dist_logger
 
 __all__ = ["RunningList", "RequestHandler"]
 
-logger = get_dist_logger(__name__)
-
 
 class RunningList:
     """
@@ -331,15 +328,21 @@ class RequestHandler:
     def total_requests_in_batch_bucket(self) -> int:
         return self.prefill_bb.current_batch_size + self.running_bb.current_batch_size
 
-    def search_tokens(self, generation_config: GenerationConfig, logits):
+    def search_tokens(self, generation_config: GenerationConfig, logits, cur_batch: BatchBucket):
         """
         Sample tokens for finished requests.
         """
 
-        # do logit processor
-        if generation_config.do_sample:
         # NOTE: need to decide the granularity to process logits (sequence or batch)
         config_dict = generation_config.to_dict()
+        # process repetition_penalty, no_repeat_ngram_size
+        for type in ["repetition_penalty", "no_repeat_ngram_size"]:
+            if type in config_dict and config_dict[type] is not None:
+                logits = logit_processor(type, logits, config_dict[type], cur_batch)
+
+        # do logit processor
+        if generation_config.do_sample:
+            # process temperature, top_k, top_p
             for type in ["temperature", "top_k", "top_p"]:
                 if type in config_dict and config_dict[type] is not None:
                     logits = logit_processor(type, logits, config_dict[type])

colossalai/inference/logit_processors.py

@@ -1,6 +1,10 @@
+# This code is adapted from huggingface transformers: https://github.com/huggingface/transformers/blob/v4.36.2/src/transformers/generation/logits_process.py
+
 import torch
 import torch.nn.functional as F
 
+from colossalai.inference.batch_bucket import BatchBucket
+
 _LOGIT_PROCESSOR_MAP = {}
 
 
@@ -17,6 +21,66 @@ def register_logit_processor(process_type):
     return register
 
 
+@register_logit_processor("no_repeat_ngram_size")
+def no_repeat_ngram_size_logit_process(logits, ngram_size: int, batch: BatchBucket):
+    """
+    enforces no repetition of n-grams to avoid repetitions of word sequences.
+    """
+
+    if not isinstance(ngram_size, int) or ngram_size < 0:
+        raise ValueError(f"'temperature={ngram_size}' should be a strictly positive integer.")
+
+    if ngram_size != 0:
+        batch_token_ids = batch.batch_token_ids
+        batch_size = len(batch_token_ids)
+
+        for batch_id in range(batch_size):
+            current_token_ids = batch_token_ids[batch_id]
+            current_len = len(current_token_ids)
+            if current_len + 1 < ngram_size:
+                continue
+
+            ngrams_dict = {}
+
+            for ngram in zip(*[current_token_ids[i:] for i in range(ngram_size)]):
+                prev_ngram_tuple = tuple(ngram[:-1])
+                ngrams_dict[prev_ngram_tuple] = ngrams_dict.get(prev_ngram_tuple, []) + [ngram[-1]]
+
+            prev_ngrams = tuple(current_token_ids[current_len + 1 - ngram_size : current_len])
+            banned_token = ngrams_dict.get(prev_ngrams, [])
+
+            logits[batch_id, banned_token] = -float("inf")
+
+    return logits
+
+
+@register_logit_processor("repetition_penalty")
+def repetition_penalty_logit_process(logits, penalty: float, batch: BatchBucket):
+    """
+    apply the penalty to the tokens present in the prompt.
+    """
+
+    if not isinstance(penalty, float) or not (penalty > 0):
+        raise ValueError(f"'penalty={penalty}' has to be a strictly positive float and greater than 0.")
+
+    logit_list = []
+
+    # TODO(yuehuayingxueluo) This is only a temporary implementation. Later, we will implement presence_penalties, frequency_penalties, and repetition_penalties using CUDA kernels.
+    if penalty != 1.0:
+        batch_token_ids = batch.batch_token_ids
+        for batch_id in range(len(batch_token_ids)):
+            current_logit = logits[batch_id]
+            current_token = torch.tensor(batch_token_ids[batch_id], dtype=torch.long, device=logits.device)
+
+            curretn_socre = torch.gather(current_logit, 0, current_token)
+            curretn_socre = torch.where(curretn_socre < 0, curretn_socre * penalty, curretn_socre / penalty)
+            logit_list.append(current_logit.scatter(0, current_token, curretn_socre))
+
+        logits = torch.stack(logit_list)
+
+    return logits
+
+
 @register_logit_processor("temperature")
 def temperature_logit_process(logits, temperature: float):
     """
@@ -68,14 +132,13 @@ def top_p_logit_processor(logits, top_p: float):
     return logits
 
 
-def logit_processor(processor: str, logits, attrs):
+def logit_processor(processor: str, logits, *args, **kwargs):
     """
     do logit process for given logits.
 
     Args:
         processor(str): the type of logit processor
         logits(torch.Tensor): input logits
-        attrs(dict): attrs of the logit processor
 
     Returns:
         logits after process
@@ -84,8 +147,5 @@ def logit_processor(processor: str, logits, attrs):
         return logits
     else:
         func = _LOGIT_PROCESSOR_MAP[processor]
-        try:
+        logits = func(logits, *args, **kwargs)
-            logits = func(logits, attrs)
-        except Exception:
-            return logits
         return logits