optimize: vectorize transpose_padding (#48116)

paddle/fluid/inference/tensorrt/plugin/qkv_to_context_plugin.cu

@@ -78,8 +78,6 @@ __global__ void transpose_qkv_padding(
       qkv_id * head_num * size_per_head + head_id * size_per_head;
   if (seq_id < real_seq_len) {
     dst[threadIdx.x + dst_offset] = src[threadIdx.x + src_offset];
-  } else if (seq_id < seq_len) {
-    dst[threadIdx.x + dst_offset] = 0;
   }
 }
 
@@ -91,14 +89,69 @@ __global__ void transpose_qkv_unpadding(const T *src,
                                         const int head_num,
                                         const int size_per_head,
                                         const int real_seq_len) {
-  int batch_id = blockIdx.x / (head_num * real_seq_len);
-  int seq_id = blockIdx.x % real_seq_len;
-  int head_id = blockIdx.x % (head_num * real_seq_len) / real_seq_len;
-  dst[batch_id * head_num * real_seq_len * size_per_head +
-      seq_id * head_num * size_per_head + head_id * size_per_head +
-      threadIdx.x] = src[batch_id * head_num * seq_len * size_per_head +
+  int batch_id = blockIdx.y;
+  int seq_id = blockIdx.x;
+  int head_id = threadIdx.y;
+  const int src_offset = batch_id * head_num * seq_len * size_per_head +
                          head_id * seq_len * size_per_head +
-                         seq_id * size_per_head + threadIdx.x];
+                         seq_id * size_per_head;
+  const int dst_offset = batch_id * real_seq_len * head_num * size_per_head +
+                         seq_id * head_num * size_per_head +
+                         head_id * size_per_head;
+
+  dst[threadIdx.x + dst_offset] = src[threadIdx.x + src_offset];
+}
+
+#define LAUNCH_TRANSPOSE_KERNEL(TYPE, VECTOR_SIZE, PAD_TYPE)                \
+  do {                                                                      \
+    int h = head_size / VECTOR_SIZE;                                        \
+    const TYPE *input##VECTOR_SIZE = reinterpret_cast<const TYPE *>(input); \
+    TYPE *output##VECTOR_SIZE = reinterpret_cast<TYPE *>(output);           \
+    dim3 block(h, head_num, 1);                                             \
+    transpose_qkv_##PAD_TYPE<TYPE>                                          \
+        <<<grid, block, 0, stream>>>(input##VECTOR_SIZE,                    \
+                                     output##VECTOR_SIZE,                   \
+                                     batch,                                 \
+                                     seq_len,                               \
+                                     head_num,                              \
+                                     h,                                     \
+                                     real_seq_len);                         \
+  } while (0)
+
+inline void TransposePadding(const half *input,
+                             half *output,
+                             const int batch,
+                             const int seq_len,
+                             const int head_num,
+                             const int head_size,
+                             const int real_seq_len,
+                             cudaStream_t stream) {
+  const dim3 grid(seq_len, batch, 3);
+  if (head_size % 8 == 0) {
+    LAUNCH_TRANSPOSE_KERNEL(int4, 8, padding);
+  } else if (head_size % 2 == 0) {
+    LAUNCH_TRANSPOSE_KERNEL(half2, 2, padding);
+  } else {
+    LAUNCH_TRANSPOSE_KERNEL(half, 1, padding);
+  }
+}
+
+inline void TransposeUnPadding(const half *input,
+                               half *output,
+                               const int batch,
+                               const int seq_len,
+                               const int head_num,
+                               const int head_size,
+                               const int real_seq_len,
+                               cudaStream_t stream) {
+  const dim3 grid(real_seq_len, batch);
+  if (head_size % 8 == 0) {
+    LAUNCH_TRANSPOSE_KERNEL(int4, 8, unpadding);
+  } else if (head_size % 2 == 0) {
+    LAUNCH_TRANSPOSE_KERNEL(half2, 2, unpadding);
+  } else {
+    LAUNCH_TRANSPOSE_KERNEL(half, 1, unpadding);
+  }
 }
 
 int QkvToContextPluginDynamic::initialize() TRT_NOEXCEPT { return 0; }
@@ -381,15 +434,14 @@ int QkvToContextPluginDynamic::enqueue(
     const half *input1_data = static_cast<const half *>(qk_bias);
     // BxSx3xNxH => tptr: 3xBxNxSxH.
     if (need_padding) {
-      dim3 grid_p(seq_len, batch, 3);
-      dim3 block_p(head_size_, head_number_, 1);
-      transpose_qkv_padding<<<grid_p, block_p, 0, stream>>>(input0_data,
-                                                            tptr,
-                                                            batch,
-                                                            seq_len,
-                                                            head_number_,
-                                                            head_size_,
-                                                            real_seq_len);
+      TransposePadding(input0_data,
+                       tptr,
+                       batch,
+                       seq_len,
+                       head_number_,
+                       head_size_,
+                       real_seq_len,
+                       stream);
     } else {
       TransposeQKV(
           batch, seq_len, head_size_, head_number_, input0_data, tptr, stream);
@@ -424,10 +476,14 @@ int QkvToContextPluginDynamic::enqueue(
     int block = head_size_;
     half *output = static_cast<half *>(outputs[0]);
     if (need_padding) {
-      int grid_u = batch * head_number_ * real_seq_len;
-      int block_u = head_size_;
-      transpose_qkv_unpadding<half><<<grid_u, block_u, 0, stream>>>(
-          tptr, output, batch, seq_len, head_number_, head_size_, real_seq_len);
+      TransposeUnPadding(tptr,
+                         output,
+                         batch,
+                         seq_len,
+                         head_number_,
+                         head_size_,
+                         real_seq_len,
+                         stream);
     } else {
       transpose<half><<<grid, block, 0, stream>>>(
           tptr, output, batch, seq_len, head_number_, head_size_);