[Fix/Inference] Fix format of input prompts and input model in inference engine (#5395)

* Fix bugs in inference_engine

* fix bugs in engine.py

* rm  CUDA_VISIBLE_DEVICES

* add request_ids in generate

* fix bug in engine.py

* add logger.debug for BatchBucket

colossalai/inference/batch_bucket.py

@@ -447,3 +447,6 @@ class BatchBucket:
     def fd_inter_tensor(self) -> None:
         assert self.fd_interm_tensor is not None, "fd_interm_tensor is not provided"
         return self.fd_interm_tensor
+
+    def __repr__(self) -> str:
+        return f"(sequences_dict={self._sequences_dict}, is_prompts={self.is_prompts})"

colossalai/inference/core/engine.py

@@ -57,6 +57,7 @@ class InferenceEngine:
         self.tokenizer.pad_token = self.tokenizer.eos_token
         self.generation_config = inference_config.to_generation_config(self.model_config)
         model = model.eval()
+        model = model.cuda()
         model.to(self.dtype)
 
         if model_policy is None:
@@ -133,12 +134,13 @@ class InferenceEngine:
         )
         shardformer = ShardFormer(shard_config=shardconfig)
         shard_model, _ = shardformer.optimize(model, model_policy)
-        return shard_model.cuda()
+        return shard_model
 
     def generate(
         self,
         prompts: List[str] = None,
         prompts_token_ids: Union[List[int], torch.Tensor, np.ndarray] = None,
+        request_ids: List[int] = None,
         return_token_ids: bool = False,
         generation_config: Optional[GenerationConfig] = None,
     ) -> List[str]:
@@ -148,6 +150,7 @@ class InferenceEngine:
         Args:
             prompts (Union[List[str], optional): Input prompts. Defaults to None.
             prompts_token_ids (List[List[int]], optional): token ids of input prompts. Defaults to None.
+            request_ids (List[int], optional): The request ID. Defaults to None.
             return_token_ids (bool): Whether to return output token ids. Defaults to False.
             generation_config (GenerationConfig, optional): Huggingface GenerationConfig used for inference. Defaults to None.
 
@@ -157,7 +160,7 @@ class InferenceEngine:
         with torch.inference_mode():
             self.generation_config = generation_config
             if prompts is not None or prompts_token_ids is not None:
-                self.add_request(prompts=prompts, prompts_token_ids=prompts_token_ids)
+                self.add_request(request_ids=request_ids, prompts=prompts, prompts_token_ids=prompts_token_ids)
 
             output_seqs_list = []
             total_tokens_list = []
@@ -204,7 +207,7 @@ class InferenceEngine:
 
     def add_request(
         self,
-        requests_id: List[int] = None,
+        request_ids: List[int] = None,
         prompts: List[str] = None,
         prompts_token_ids: Union[List[int], torch.Tensor, np.ndarray] = None,
     ) -> None:
@@ -212,7 +215,7 @@ class InferenceEngine:
         Add requests.
 
         Args:
-            requests_id (List[int], optional): The request ID. Defaults to None.
+            request_ids (List[int], optional): The request ID. Defaults to None.
             prompts (Union[List[str], optional): Input prompts. Defaults to None.
             prompts_token_ids (List[List[int]], optional): token ids of input prompts. Defaults to None.
         """
@@ -223,6 +226,9 @@ class InferenceEngine:
 
         block_size = self.inference_config.block_size
 
+        if prompts is not None and not isinstance(prompts, list):
+            prompts = [prompts]
+
         if prompts_token_ids is None:
             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=self.inference_config.pad_input)[
@@ -245,8 +251,14 @@ class InferenceEngine:
         prompts_num = len(prompts_token_ids)
 
         for i in range(prompts_num):
-            if requests_id:
+            if request_ids:
-                request_id = requests_id[i]
+                if not isinstance(request_ids, list):
+                    request_ids = [request_ids]
+                assert isinstance(
+                    request_ids[0], int
+                ), f"The request_id type must be int, but got {type(request_ids[0])}"
+                assert len(request_ids) == prompts_num
+                request_id = request_ids[i]
             else:
                 request_id = next(self.counter)
             if prompts == None:

colossalai/inference/struct.py

@@ -157,7 +157,7 @@ class Sequence:
             f"prompt={self.prompt}, "
             f"status={self.status.name}, "
             f"sample_params={self.sample_params}, "
-            f"input_len={self.input_len}),"
+            f"input_len={self.input_len},"
             f"output_len={self.output_len})"
         )
 

examples/inference/run_benchmark.sh

@@ -27,7 +27,7 @@ CUDA_VISIBLE_DEVICES_set_n_least_memory_usage 1
 for input_len in  128 512 1024; do
     for output_len in 128 256; do
         for bsz in 16 32 64; do
-            python3 ${PY_SCRIPT} -m llama2-7b --tp_size 1 --pp_size 1 -b ${bsz} -s ${input_len} --output_len ${output_len} --mode ${mode} --model_path "/home/caidi/llama_model/" | tee logs/${input_len}_${output_len}_${mode}_${GPU}_${bsz}.txt
+            python3 ${PY_SCRIPT} -m llama2-7b --tp_size 1 --pp_size 1 -b ${bsz} -s ${input_len} --output_len ${output_len} --mode ${mode} --test_random_weight | tee logs/${input_len}_${output_len}_${mode}_${GPU}_${bsz}.txt
         done
     done
 done

tests/test_infer/test_batch_bucket.py

@@ -5,8 +5,11 @@ from colossalai.inference.batch_bucket import BatchBucket
 from colossalai.inference.config import InferenceConfig
 from colossalai.inference.kv_cache import KVCacheManager
 from colossalai.inference.struct import Sequence
+from colossalai.logging import get_dist_logger
 from colossalai.testing import parameterize
 
+logger = get_dist_logger(__name__)
+
 
 @parameterize(
     "test_config",
@@ -83,6 +86,7 @@ def test_bucket(test_config):
         num_heads, cache_manager.get_head_size(), max_batch_size, max_length, block_size, kv_max_split_num=2
     )
     block_tables = bb.add_seqs([seq1, seq2])
+    logger.debug(f"bb information: {bb}")
     assert block_tables.shape == (2, cache_manager.max_blocks_per_sequence)
     assert torch.all(block_tables < 0), "Initialized block_tables should be negative values"