Add contributing guideline and mypy config (#122)

cacheflow/model_executor/layers/attention.py

@@ -61,7 +61,7 @@ class GPTCacheFlowAttention(nn.Module):
         key: torch.Tensor,                      # [num_prompt_tokens, num_heads, head_size]
         value: torch.Tensor,                    # [num_prompt_tokens, num_heads, head_size]
         attn_bias: xops.AttentionBias,
-    ) -> None:
+    ) -> torch.Tensor:
         # TODO(woosuk): The unsqueeze op may incur some CPU overhead. Optimize.
         out = xops.memory_efficient_attention_forward(
             query.unsqueeze(0),
@@ -197,7 +197,7 @@ class GPTNeoXCacheFlowAttention(GPTCacheFlowAttention):
 
     def forward(
         self,
-        positions: torch.LongTensor,            # [num_tokens]
+        positions: torch.Tensor,                # [num_tokens]
         query: torch.Tensor,                    # [num_tokens, num_heads * head_size]
         key: torch.Tensor,                      # [num_tokens, num_heads * head_size]
         value: torch.Tensor,                    # [num_tokens, num_heads * head_size]

cacheflow/model_executor/layers/sampler.py

@@ -347,7 +347,7 @@ def _sample_from_generation_tokens(
         # Greedy sampling.
         assert len(seq_ids) == 1
         next_token_id = torch.argmax(probs, dim=-1)
-        next_token_ids = [next_token_id.item()]
+        next_token_ids = [int(next_token_id.item())]
         parent_seq_ids = seq_ids
     else:
         # Random sampling.