fix bugs in request_handler.py and engine.py

colossalai/inference/config.py

@@ -28,7 +28,6 @@ class InferenceConfig:
         dtype (Union[str, torch.dtype]): The data type for weights and activations.
         tp_size (int): Tensor parallel size.
         pp_size (int): Pipeline parallel size.
-        max_seq_len (int): Maximum length of input sentence.
         beam_width (int): The maximum beam width used to initialize KV Cache.
             During generation, the beam width provided as sampling parameter should be less than or equivalent to this value.
         prefill_ratio (Optional[float]): A controling ratio for prefill and decoding in running list, we will do a step of prefill
@@ -46,7 +45,6 @@ class InferenceConfig:
     dtype: Union[str, torch.dtype] = torch.float32
     tp_size: int = 1
     pp_size: int = 1
-    max_seq_len: int = 512
     # TODO: beam search is not support for now
     beam_width: int = 1
     # the ratio of prefill sequences to decoding sequences, we do prefill step once the actual value exceeds ratio
@@ -99,6 +97,3 @@ class InferenceConfig:
             "gptq",
             None,
         ], f"quant should be one of 'smoothquant', 'gptq', but got {self.quant_mode}."
-        assert (
-            self.max_input_len + self.max_output_len <= self.max_seq_len
-        ), f"The sum of max_input_len {self.max_input_len} and max_output_len {self.max_output_len} must be smaller than max_seq_len {self.max_seq_len}."

colossalai/inference/core/engine.py

@@ -1,6 +1,7 @@
 from itertools import count
 from typing import List, Optional, Union
 
+import numpy as np
 import torch
 import torch.nn as nn
 from transformers import GenerationConfig, PreTrainedTokenizer, PreTrainedTokenizerFast
@@ -159,7 +160,7 @@ class InferenceEngine:
         self,
         requests_id: List[int] = None,
         prompts: List[str] = None,
-        prompts_token_ids: List[int] = None,
+        prompts_token_ids: Union[List[int], torch.Tensor, np.ndarray] = None,
     ) -> None:
         """
         Add requests.
@@ -176,9 +177,18 @@ class InferenceEngine:
             assert prompts, "When the prompts_token_ids is none, the input prompt list must be provided."
             prompts_token_ids = self.tokenizer.batch_encode_plus(prompts, padding=True)["input_ids"]
 
+        if isinstance(prompts_token_ids, list):
+            pass
+        elif isinstance(prompts_token_ids, torch.Tensor) or isinstance(prompts_token_ids, np.ndarray):
+            prompts_token_ids = prompts_token_ids.tolist()
+        else:
+            raise TypeError(
+                f"The dtype of prompts_token_ids must be one of list, torch.Tensor, np.ndarray, but got {type(prompts_token_ids)}."
+            )
+
         assert (
-            len(prompts_token_ids[0]) < self.inference_config.max_input_len
+            len(prompts_token_ids[0]) <= self.inference_config.max_input_len
-        ), "The length of input prompts must be less than max_input_len."
+        ), f"The length of input prompts {len(prompts_token_ids[0])} must be less than max_input_len {self.inference_config.max_input_len}."
 
         prompts_num = len(prompts_token_ids)
 

colossalai/inference/core/request_handler.py

@@ -131,9 +131,9 @@ class RequestHandler:
         """
         assert not self._find_sequence(req.request_id), f"Sequence {req.request_id} already exists."
         assert (
-            req.input_len < self.inference_config.max_input_len
+            req.input_len <= self.inference_config.max_input_len
         ), f"Sequence {req.request_id} exceeds input length limit"
-        self.waiting_list[req.input_len * 3 // self.inference_config.max_input_len].append(req)
+        self.waiting_list[req.input_len * 3 // (self.inference_config.max_input_len + 1)].append(req)
 
     def abort_sequence(self, request_id: str):
         """

colossalai/inference/kv_cache/kvcache_manager.py

@@ -58,7 +58,12 @@ class KVCacheManager:
         # Parallel settings
         self.tp_size = config.tp_size
         # Model settings
-        self.dtype = config.dtype
+        if config.dtype == "fp32" or config.dtype == torch.float32:
+            self.dtype = torch.float32
+        elif config.dtype == "fp16" or config.dtype == torch.float16:
+            self.dtype = torch.float16
+        else:
+            self.dtype = torch.bfloat16
         self.elem_size_in_bytes = torch.tensor([], dtype=self.dtype).element_size()
         self.num_layers = get_model_config_attr(model_config, "num_hidden_layers")
         # For now we focus on MHA only, TODO add handling for MQA and GQA