Merge pull request #25076 from fengyuentau:improve_attention

dnn: try improving performance of Attention layer #25076

Checklist:

- [x] Use `Mat` over `Mat::zeros` for temporary buffer in forward
- [x] Use layer internal buffer over temporary Mat buffer
- [x] Try a single fastGemmBatch on the Q/K/V calculation

Performance:

Performance test case is `Layer_Attention.VisionTransformer/0`, which has input of shape {1, 197, 768}, weight of shape {768, 2304} and bias {2304}.

Data is in millisecond.

| | macOS 14.2.1, Apple M1 | Ubuntu 22.04.2, Intel i7 12700K |
| - | - | - |
| Current | 10.96 | 1.58 |
| w/ Mat | 6.27 | 1.41 |
| w/ Internals | 5.87 | 1.38 |
| w/ fastGemmBatch | 6.12 | 2.14 |


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake

modules/dnn/src/layers/attention_layer.cpp

@@ -83,6 +83,19 @@ class AttentionLayerImpl CV_FINAL : public AttentionLayer {
         } else {
             CV_Error(Error::StsBadArg, format("DNN/Attention: invalid output dimension %zu, valid value is 2 or 3", output_ndims));
         }
+
+        const int batch_size_ = input_shape[0], seq_len_ = input_shape[1],
+                  hidden_size_ = weight_shape.back(),
+                  num_heads_ = static_cast<int>(num_heads),
+                  v_head_size_ = static_cast<int>((hidden_size_ - qkv_hidden_sizes[0] - qkv_hidden_sizes[1]) / num_heads);
+
+        MatShape gemm_buffer_shape{batch_size_, seq_len_, hidden_size_},
+                 attention_prob_shape{batch_size_ * num_heads_, seq_len_, seq_len_},
+                 output_buffer_shape{batch_size_ * num_heads_, seq_len_, v_head_size_};
+        internals.assign(1, gemm_buffer_shape);
+        internals.push_back(attention_prob_shape);
+        internals.push_back(output_buffer_shape);
+
         return false;
     }
 
@@ -112,9 +125,10 @@ class AttentionLayerImpl CV_FINAL : public AttentionLayer {
             return;
         }
 
-        std::vector<Mat> inputs, outputs;
+        std::vector<Mat> inputs, outputs, internals;
         inputs_arr.getMatVector(inputs);
         outputs_arr.getMatVector(outputs);
+        internals_arr.getMatVector(internals);
 
         // prepack weights
         if (!is_prepacked) {
@@ -131,7 +145,8 @@ class AttentionLayerImpl CV_FINAL : public AttentionLayer {
         float *packed_weights[3] = {packed_weight_q.data(), packed_weight_k.data(), packed_weight_v.data()};
         size_t packed_weights_size[3] = {packed_weight_q.size() / num_heads, packed_weight_k.size() / num_heads, packed_weight_v.size() / num_heads};
 
-        Mat gemm_buffer = Mat::zeros(1, int(batch_size * seq_len * hidden_size), CV_32F);
+        // Compute Q/K/V
+        auto &gemm_buffer = internals[0];
         auto *Q = gemm_buffer.ptr<float>();
         auto *K = Q + batch_size * seq_len * qkv_hidden_sizes[0];
         auto *V = K + batch_size * seq_len * qkv_hidden_sizes[1];
@@ -177,9 +192,8 @@ class AttentionLayerImpl CV_FINAL : public AttentionLayer {
             parallel_for_(Range(0, loops), fn, nstripes);
         }
 
-        // Compute softmax(scale * matmul(Q, K))
-        std::vector<int> attention_prob_shape{int(batch_size * num_heads), int(seq_len), int(seq_len)};
-        Mat attention_prob = Mat::zeros(attention_prob_shape.size(), attention_prob_shape.data(), CV_32F);
+        // Compute Softmax(scale * MatMul(Q, K))
+        auto &attention_prob = internals[1];
         {
             auto *output = attention_prob.ptr<float>();
 
@@ -202,12 +216,12 @@ class AttentionLayerImpl CV_FINAL : public AttentionLayer {
                 }
             }, loops * seq_len * qk_head_size * seq_len * (1 / 1024.0));
 
-            // Compute softmax
-            softmax(attention_prob, attention_prob, attention_prob_shape.size() - 1);
+            // Compute softmax on the last dimension
+            softmax(attention_prob, attention_prob, shape(attention_prob).size() - 1);
         }
 
-        // Compute np.matmul(attention_prob, V)
-        Mat output_buffer = Mat::zeros(1, int(batch_size * num_heads * seq_len * qkv_head_sizes[2]), CV_32F);
+        // Compute MatMul(attention_prob, V)
+        auto &output_buffer = internals[2];
         {
             auto *output = outputs[0].ptr<float>();
             auto *output_buff = output_buffer.ptr<float>();