fix CI bugs

colossalai/inference/core/engine.py

@@ -191,7 +191,14 @@ class InferenceEngine:
                 prompt = None
             else:
                 prompt = prompts[i]
-            block_table = torch.full([self.inference_config.max_seq_len], -1, device=self.device)
+
+            max_blocks_per_sequence = (
+                self.inference_config.max_input_len
+                + self.inference_config.max_output_len
+                + self.inference_config.block_size
+                - 1
+            ) // self.inference_config.block_size
+            block_table = torch.full([max_blocks_per_sequence], -1, device=self.device)
             sequence = Sequence(
                 request_id,
                 prompt,

colossalai/inference/core/request_handler.py

@@ -7,7 +7,7 @@ from colossalai.inference.config import InferenceConfig
 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 BatchInfo, Sequence
+from colossalai.inference.struct import BatchInfo, RequestStatus, Sequence
 from colossalai.logging import get_dist_logger
 
 logger = get_dist_logger(__name__)
@@ -104,7 +104,7 @@ class RequestHandler:
                                 f"the prompt(Request id = {seq.request_id}) length is longer than max_input_len, abort this sequence."
                             )
                             self.abort_sequence(seq.request_id)
-                            remove_list.append(seq)
+                            break
                         # Try to allocate cache blocks for the sequence.
                         if self.cache_manager.check_allocation(seq):
                             # If succeed, add the sequence to running list.
@@ -139,9 +139,10 @@ class RequestHandler:
         """
         Abort the request.
         """
-        seq, _ = self._find_sequence(request_id)
+        seq, priority = self._find_sequence(request_id)
-        if seq.status.is_waiting:
+        if seq.status == RequestStatus.WAITING:
             seq.mark_aborted()
+            self.waiting_list[priority].remove(seq)
         elif seq.status.is_running():
             self.cache_manager.free_block_table(seq.block_table)
             self.running_list.remove(seq)

colossalai/inference/modeling/layers/attention.py

@@ -217,6 +217,8 @@ class PagedAttention:
 
         attn_weights = torch.matmul(q, k.transpose(2, 3)) / math.sqrt(head_size)
 
+        padding_mask = None
+
         if attn_mask is not None:
             padding_mask = AttentionMaskConverter._expand_mask(attn_mask, q.dtype, seq_len)
 
@@ -279,11 +281,12 @@ class PagedAttention:
         if attn_weights.size() != (bsz, num_heads, 1, seq_len):
             raise ValueError(f"Got wrong attn_weights, should be in shape {(bsz,num_heads,1,seq_len)}.")
 
+        padding_mask = None
         if attn_mask is not None:
-            padding_mask = AttentionMaskConverter._expand_mask(attn_mask, q.dtype, query_length)
+            padding_mask = AttentionMaskConverter._expand_mask(attn_mask, q.dtype, q_length)
 
         attn_mask = AttentionMaskConverter._make_causal_mask(
-            (bsz, q_length), q.dtype, q.device, past_key_values_length=seq_len - query_length
+            (bsz, q_length), q.dtype, q.device, past_key_values_length=seq_len - q_length
         )
 
         if padding_mask is not None:

tests/test_infer/test_inference_engine.py

@@ -11,6 +11,7 @@ from colossalai.inference.config import InferenceConfig
 from colossalai.inference.core.engine import InferenceEngine
 from colossalai.testing import rerun_if_address_is_in_use, spawn
 
+
 def setup_seed(seed):
     torch.manual_seed(seed)
     torch.cuda.manual_seed_all(seed)
@@ -34,7 +35,7 @@ def check_inference_engine(test_cai=False):
         "介绍一下武汉,",
     ]
 
-    output_len = 128
+    output_len = 38
     do_sample = True
     top_p = 0.5
     top_k = 50

tests/test_infer/test_request_handler.py

@@ -57,7 +57,7 @@ def check_request_handler():
         block_size=16,
         eos_token_id=0,
         sample_params=None,
-        block_table=torch.tensor([0, 0]),
+        block_table=torch.tensor([-1, -1]),
     )
     request_handler.add_sequence(seq1)
     # the priority should be 1