Fix wrong dtype in PagedAttentionWithALiBi bias (#996)

---------

Signed-off-by: Antoni Baum <[email protected]>

Author: Yard1

vllm/model_executor/layers/attention.py

@@ -73,7 +73,12 @@ def __init__(self,
             raise ValueError(f"head_size ({self.head_size}) is not supported. "
                              f"Supported head sizes: {_SUPPORTED_HEAD_SIZES}.")
 
-    def set_attn_bias(self, input_metadata: InputMetadata) -> None:
+    def set_attn_bias(
+        self,
+        input_metadata: InputMetadata,
+        dtype: torch.dtype,
+    ) -> None:
+        del dtype  # Unused.
         if input_metadata.attn_bias:
             # Already set by a previous layer.
             return
@@ -196,7 +201,7 @@ def forward(
         if num_prompt_tokens > 0:
             # Prompt run.
             assert input_metadata.num_generation_tokens == 0
-            self.set_attn_bias(input_metadata)
+            self.set_attn_bias(input_metadata, dtype=query.dtype)
             self.multi_query_kv_attention(
                 output[:num_prompt_tokens],
                 query[:num_prompt_tokens],
@@ -340,13 +345,14 @@ def __init__(self,
         slopes = torch.tensor(slopes, dtype=torch.float32)
         self.register_buffer("alibi_slopes", slopes, persistent=False)
 
-    def set_attn_bias(self, input_metadata: InputMetadata) -> None:
+    def set_attn_bias(self, input_metadata: InputMetadata,
+                      dtype: torch.dtype) -> None:
         if input_metadata.attn_bias:
             # Already set by a previous layer.
             return
         # Generates ALiBi mask for each prompt.
         for prompt_len in input_metadata.prompt_lens:
-            bias = torch.arange(prompt_len)
+            bias = torch.arange(prompt_len, dtype=dtype)
             # Note(zhuohan): HF uses
             #     `bias = bias[None, :].repeat(prompt_len, 1)`
             # here. We find that both biases give the same results, but
@@ -364,6 +370,7 @@ def set_attn_bias(self, input_metadata: InputMetadata) -> None:
                 prompt_len,
                 padded_len,
                 device=self.alibi_slopes.device,
+                dtype=dtype,
             )[:, :, :, :prompt_len].copy_(bias)
             bias.mul_(self.alibi_slopes[:, None, None])
             attn_bias = LowerTriangularMaskWithTensorBias(bias)