Clean

Author: jackzhxng

extension/llm/modules/attention.py

@@ -364,11 +364,6 @@ def forward(
             k = k.unsqueeze(2).expand(expand_shape).flatten(1, 2)
             v = v.unsqueeze(2).expand(expand_shape).flatten(1, 2)
 
-        # print(f"q shape: {q.shape}")
-        # print(f"k shape: {k.shape}")
-        # print(f"v shape: {v.shape}")
-        # print(f"mask shape: {mask.shape}")
-
         output = self._attention_fn(
             q,
             k,

extension/llm/modules/kv_cache.py

@@ -105,7 +105,6 @@ def update(
                 f", but found new key tensors with batch size {k_val.shape[0]}!"
             )
 
-        # print(self.cache_pos[0], seq_len, self.max_seq_len)
         assert (self.cache_pos[0] + seq_len) <= self.max_seq_len
 
         k_out = self.k_cache

kernels/portable/cpu/util/broadcast_util.cpp

@@ -6,8 +6,6 @@
  * LICENSE file in the root directory of this source tree.
  */
 
-#include <iostream>
-
 #include <executorch/kernels/portable/cpu/util/repeat_util.h>
 #include <executorch/runtime/core/exec_aten/exec_aten.h>
 #include <executorch/runtime/core/exec_aten/util/scalar_type_util.h>
@@ -215,18 +213,6 @@ ET_NODISCARD Error get_broadcast_target_size(
     Tensor::SizesType* out_sizes,
     const size_t out_sizes_len,
     size_t* out_dim) {
-  // std::cout << "a_size: ";
-  // for (const auto& s : a_size) {
-  //   std::cout << s << " ";
-  // }
-  // std::cout << std::endl;
-
-  // std::cout << "b_size: ";
-  // for (const auto& s : b_size) {
-  //   std::cout << s << " ";
-  // }
-  // std::cout << std::endl;
-
   ET_CHECK_OR_RETURN_ERROR(
       tensors_are_broadcastable_between(a_size, b_size),
       InvalidArgument,