[chore] Remove obsolete comments

vllm_kunlun/compilation/wrapper.py

@@ -126,12 +126,7 @@ class TorchCompileWrapperWithCustomDispatcher:
                                  decompiled_file)
                 except Exception:
                     pass
-        # if self.vllm_config.compilation_config.use_cudagraph and \
+
-        #     "update" in new_code.co_names:
-        #     import depyf
-        #     src = depyf.decompile(new_code)
-        #     msg = "Assigning / modifying buffers of nn.Module during forward pass is not allowed when using cudagraph inside the compiler because it will cause silent errors. Please use eager mode or fix the code. The following code contains clues about which buffer is being modified (please search for the usage of the function `update`):\n" + src  # noqa
-        #     raise RuntimeError(msg)
 
     @contextmanager
     def dispatch_to_code(self, index: int):

vllm_kunlun/models/interns1_vit.py

@@ -253,7 +253,6 @@ class InternS1VisionMLP(nn.Module):
 
         self.config = config
         self.activation_fn = get_act_fn(config.hidden_act)
-        # self.activation_fn = GeluAndMul()
         self.fc1 = ColumnParallelLinear(config.hidden_size,
                                         config.intermediate_size,
                                         bias=True,

vllm_kunlun/ops/_kunlun_ops.py

@@ -73,7 +73,6 @@ class KunlunOps:
         alibi_sqrt=False,
     ):
         """PagedAttentionV1"""
-        # block_size = value_cache.shape[2]
         xtorch_ops.paged_attention(
             x=query,
             k_cache=key_cache,
@@ -116,7 +115,6 @@ class KunlunOps:
         alibi_sqrt=False,
     ):
         """PagedAttentionV2"""
-        # block_size = value_cache.shape[2]
         xtorch_ops.paged_attention(
             x=query,
             k_cache=key_cache,
@@ -221,17 +219,6 @@ class KunlunOps:
         num_heads = query_x.shape[1] // head_size
         num_kv_heads = key_x.shape[1] // head_size
 
-        # # [num_tokens, num_heads * head_size] -> [num_tokens, num_heads, head_size]
-        # query_x = query_x.view(num_tokens, num_heads, head_size)
-        # # [num_tokens, num_kv_heads * head_size] -> [num_tokens, num_kv_heads, head_size]
-        # key_x = key_x.view(num_tokens, num_kv_heads, head_size)
-
-        # # Ensure shapes are correct
-        # assert query_x.shape == (num_tokens, num_heads, head_size), \
-        #     f"Expected query shape [{num_tokens}, {num_heads}, {head_size}], got {query_x.shape}"
-        # assert key_x.shape == (num_tokens, num_kv_heads, head_size), \
-        #     f"Expected key shape [{num_tokens}, {num_kv_heads}, {head_size}], got {key_x.shape}"
-
         torch.ops._C.rotary_embedding(
             positions, query_x, key_x, head_size, cos_sin_cache, is_neox_style
         )
@@ -239,8 +226,6 @@ class KunlunOps:
         query_x = query_x.view(num_tokens, num_heads * head_size)
         key_x = key_x.view(num_tokens, num_kv_heads * head_size)
 
-        # query.data = query_x
-        # key.data  = key_x
         return query_x, key_x
 
     # Rotary embedding
@@ -290,7 +275,6 @@ class KunlunOps:
         kv_cache_dtype,
     ):
         """reshape_and_cache"""
-        # slot_mapping_cast = slot_mapping.to(torch.int32)
         xtorch_ops.reshape_and_cache(key, value, key_cache, value_cache, slot_mapping)
 
     @staticmethod

vllm_kunlun/ops/attention/backends/kunlun_attn.py

@@ -271,13 +271,6 @@ class KunlunMetadata(AttentionMetadata, PagedAttentionMetadata):
             if self.context_lens_tensor is None
             else self.context_lens_tensor[: self.num_prefills]
         )
-        # for prefix cache, block table only contains blocks that hit
-        # if self.block_tables is None:
-        #     block_tables = None
-        # elif self.block_tables.shape[1] == 0:
-        #     block_tables = self.block_tables[:self.num_prefills]
-        # else:
-        #     block_tables = self.block_tables[:self.num_prefills][:, -1].clone()
 
         block_tables = (
             None
@@ -442,7 +435,6 @@ class KunlunMetadataBuilder(CommonMetadataBuilder[KunlunMetadata]):
             if inter_data.prefix_cache_hit:
                 assert context_len != 0
                 assert context_len % self.block_size == 0
-                # block_table = block_tables[seq_id]
                 block_table = block_tables[seq_id][: context_len // self.block_size]
             elif (not is_prompt) and block_tables is not None:
                 if curr_sliding_window_block == 0:
@@ -483,7 +475,6 @@ class KunlunMetadataBuilder(CommonMetadataBuilder[KunlunMetadata]):
             query_start_loc, dtype=torch.int32, device="cpu"
         )
         attn_meta.query_start_loc_host = query_start_loc_host
-        # max_kv_len = max(query_lens + prefix_cache_kv_lens)
         attn_meta.max_kv_len = max(self.prefix_cache_kv_lens + attn_meta.seq_lens)
 
         # If kv cache is included and there is a hit

vllm_kunlun/ops/sample/sampler.py

@@ -516,10 +516,6 @@ def _apply_top_k_top_p(
     top_p_mask[:, -1] = False
     logits_sort.masked_fill_(top_p_mask, -float("inf"))
 
-    # Re-sort the probabilities.
-    # logits = torch.empty_like(logits_sort).scatter_(dim=-1,
-    #                                                 index=logits_idx,
-    #                                                 src=logits_sort)
     return logits_sort, logits_idx
 
 
@@ -883,7 +879,6 @@ def _sample_with_torch(
                     seq_groups=seq_groups_arg,
                 )
                 if logits_idx is not None:
-                    # multinomial_samples[sampling_type] = logits_idx[:, result_idx[:][0]]
                     token_ids = logits_idx[long_sample_indices].gather(
                         dim=1, index=result_idx.to(logits_idx.device)
                     )

vllm_kunlun/utils.py

@@ -200,7 +200,6 @@ class ModuleLoggingHookPost(object):
         self.name_list.pop()
 
 
-# if os.environ.get("ENABLE_VLLM_MODULE_HOOK", "0") == "1":
 if xenvs.ENABLE_VLLM_MODULE_HOOK:
     from torch.nn.modules.module import (
         register_module_forward_pre_hook,