Ensure classmethods use cls instead of the class directly

server/text_generation_server/models/bloom.py

@@ -30,7 +30,7 @@ class BloomCausalLMBatch(CausalLMBatch):
         tokenizer: PreTrainedTokenizerBase,
         dtype: torch.dtype,
         device: torch.device,
-    ) -> "CausalLMBatch":
+    ) -> "BloomCausalLMBatch":
         batch = super().from_pb(pb=pb, tokenizer=tokenizer, dtype=dtype, device=device)
         batch.keys_head_dim_last = False
         return batch

server/text_generation_server/models/flash_causal_lm.py

@@ -646,7 +646,7 @@ class FlashCausalLMBatch(Batch):
         for b in batches:
             b.block_tables = None
 
-        return FlashCausalLMBatch(
+        return cls(
             batch_id=batches[0].batch_id,
             requests=requests,
             requests_idx_mapping=requests_idx_mapping,

server/text_generation_server/utils/tokens.py

@@ -80,7 +80,7 @@ class NextTokenChooser:
         pb: generate_pb2.NextTokenChooserParameters,
         device: torch.device,
     ) -> "NextTokenChooser":
-        return NextTokenChooser(
+        return cls(
             watermark=pb.watermark,
             temperature=pb.temperature,
             repetition_penalty=pb.repetition_penalty,
@@ -143,7 +143,7 @@ class StoppingCriteria:
         stop_sequence_criterias = [
             StopSequenceCriteria(sequence) for sequence in pb.stop_sequences
         ]
-        return StoppingCriteria(
+        return cls(
             tokenizer.eos_token_id,
             stop_sequence_criterias,
             pb.max_new_tokens,
@@ -266,7 +266,7 @@ class HeterogeneousNextTokenChooser:
         dtype: torch.dtype,
         device: torch.device,
     ) -> "HeterogeneousNextTokenChooser":
-        return HeterogeneousNextTokenChooser(
+        return cls(
             watermark=[pb_.watermark for pb_ in pb],
             temperature=[pb_.temperature for pb_ in pb],
             repetition_penalty=[pb_.repetition_penalty for pb_ in pb],