support fa3 rope3d (#3622)

custom_ops/gpu_ops/append_attn/gqa_rope_write_cache.cu

@@ -37,7 +37,8 @@ __global__ void GQAVariableLengthRotarySplitKernel(
     const int q_num_head,
     const int kv_num_head,
     const int seq_len,
-    const int last_dim) {
+    const int last_dim,
+    const bool rope_3d) {
   using LoadT = AlignedVector<T, VecSize>;
   constexpr int HalfVecSize = VecSize / 2;
   using LoadEmbT = AlignedVector<float, HalfVecSize>;
@@ -62,6 +63,7 @@ __global__ void GQAVariableLengthRotarySplitKernel(
     const int kv_write_idx = cu_seqlens_k[ori_bi] + ori_seq_id;
 
     const int64_t emb_idx = ori_seq_id * half_lastdim + h_bias / 2;
+    int64_t new_emb_idx = rope_3d ? emb_idx + ori_bi * last_dim * seq_len : emb_idx;
     const int64_t base_idx =
         token_idx * (q_num_head + 2 * kv_num_head) * last_dim + hi * last_dim +
         h_bias;
@@ -80,8 +82,8 @@ __global__ void GQAVariableLengthRotarySplitKernel(
     Load<T, VecSize>(&qkv[base_idx], &src_vec);
     // do rope
     if (hi < q_num_head + kv_num_head) {
-      Load<float, HalfVecSize>(&cos_emb[emb_idx], &cos_emb_vec);
+      Load<float, HalfVecSize>(&cos_emb[new_emb_idx], &cos_emb_vec);
-      Load<float, HalfVecSize>(&sin_emb[emb_idx], &sin_emb_vec);
+      Load<float, HalfVecSize>(&sin_emb[new_emb_idx], &sin_emb_vec);
 #pragma unroll
       for (int i = 0; i < HalfVecSize; i++) {
         const float input_left = static_cast<float>(src_vec[2 * i]);
@@ -118,6 +120,7 @@ void gqa_rotary_qk_split_variable(
     const int seq_len,
     const int input_output_len,
     const int dim_head,
+    const bool rope_3d,
     const cudaStream_t &stream) {
   int64_t elem_nums = token_num * (num_heads + 2 * kv_num_heads) * dim_head;
   constexpr int PackSize = 16 / sizeof(T);
@@ -146,7 +149,8 @@ void gqa_rotary_qk_split_variable(
             num_heads,
             kv_num_heads,
             seq_len,
-            dim_head);
+            dim_head,
+            rope_3d);
 }
 
 template <typename T,
@@ -890,7 +894,8 @@ std::vector<paddle::Tensor> GQARopeWriteCacheKernel(
     const paddle::optional<paddle::Tensor>& kv_signal_data,
     const int kv_token_num,
     const int max_seq_len,
-    const std::string& cache_quant_type) {
+    const std::string& cache_quant_type,
+    const bool rope_3d) {
   typedef PDTraits<paddle::DataType::BFLOAT16> traits_;
   typedef typename traits_::DataType DataType_;
   typedef typename traits_::data_t data_t;
@@ -953,8 +958,9 @@ std::vector<paddle::Tensor> GQARopeWriteCacheKernel(
         num_heads,
         kv_num_heads,
         max_seq_len,
-        rotary_embs.dims()[2],
+        rope_3d ? rotary_embs.dims()[3] : rotary_embs.dims()[2],
         head_dim,
+        rope_3d,
         stream);
 
   if (token_num < kv_token_num) {

custom_ops/gpu_ops/cpp_extensions.cc

@@ -111,7 +111,8 @@ std::vector<paddle::Tensor> GQARopeWriteCacheKernel(
     const paddle::optional<paddle::Tensor> &cache_v_zp,
     const paddle::optional<paddle::Tensor> &kv_signal_data,
     const int kv_token_num, const int max_seq_len,
-    const std::string &cache_quant_type);
+    const std::string &cache_quant_type,
+    const bool rope_3d);
 
 std::vector<paddle::Tensor>
 PreCacheLenConcat(const paddle::Tensor &seq_lens_decoder,

fastdeploy/model_executor/layers/attention/flash_attn_backend.py

@@ -311,6 +311,7 @@ class FlashAttentionBackend(AttentionBackend):
                 metadata.kv_token_num_cpu[0].item(),
                 self.max_seq_len,
                 getattr(layer, "cache_quant_type_str", "none"),
+                self.rope_3d,
             )
 
             res_encoder = self.flash_attn_func(

fastdeploy/model_executor/layers/attention/ops/gqa_rope_write_cache.py

@@ -49,6 +49,7 @@ def gqa_rope_write_cache(
     kv_token_num: int = 1,
     max_seq_len: int = 0,
     cache_quant_type: str = "none",
+    rope_3d: bool = False,
 ):
     if current_platform.is_cuda():
         from fastdeploy.model_executor.ops.gpu import gqa_rope_write_cache
@@ -81,6 +82,7 @@ def gqa_rope_write_cache(
             kv_token_num,
             max_seq_len,
             cache_quant_type,
+            rope_3d,
         )
         return q, k, v, qkv_
     else: