feat: support huggingface transformer style rope interface (#568)

Previously our rope apis assume the position indices of each request is
contiguous, which is not appropriate for applications such as
speculative decoding, this PR fixes the issue by supporting the
huggingface transformer-style API which use `pos_ids` argument to
specify positions.

This PR implements parts of the feature of #530 , other requests are
coming in later PRs.

cc @dreaming-panda @abcdabcd987 @ByronHsu

flashinfer-aot/csrc_aot/flashinfer_ops.cu

@@ -61,10 +61,11 @@ torch::Tensor top_k_renorm_probs(torch::Tensor probs, std::optional<torch::Tenso
 torch::Tensor top_k_mask_logits(torch::Tensor logits, std::optional<torch::Tensor> maybe_top_k_arr,
                                 unsigned int top_k_val);
 
-torch::Tensor chain_speculative_sampling(
-    torch::Tensor draft_probs, torch::Tensor draft_token_ids, torch::Tensor uniform_samples,
-    torch::Tensor target_probs, torch::Tensor output_accepted_token_num,
-    torch::Tensor output_emitted_token_num, bool deterministic);
+torch::Tensor chain_speculative_sampling(torch::Tensor draft_probs, torch::Tensor draft_token_ids,
+                                         torch::Tensor uniform_samples, torch::Tensor target_probs,
+                                         torch::Tensor output_accepted_token_num,
+                                         torch::Tensor output_emitted_token_num,
+                                         bool deterministic);
 
 void rmsnorm(torch::Tensor& out, torch::Tensor& input, torch::Tensor& weight, double eps);
 
@@ -82,24 +83,30 @@ void gelu_tanh_and_mul(torch::Tensor& out, torch::Tensor& input);
 
 void gelu_and_mul(torch::Tensor& out, torch::Tensor& input);
 
-void apply_rope_inplace(torch::Tensor q, torch::Tensor k, torch::Tensor indptr,
-                        torch::Tensor offsets, bool interleave, float rope_scale, float rope_theta);
-
-void apply_llama31_rope_inplace(torch::Tensor q, torch::Tensor k, torch::Tensor indptr,
-                                torch::Tensor offsets, bool interleave, float rope_scale,
-                                float rope_theta, float low_freq_factor, float high_freq_factor,
-                                float old_context_length);
-
-std::vector<torch::Tensor> apply_rope(torch::Tensor q, torch::Tensor k, torch::Tensor indptr,
+std::vector<torch::Tensor> apply_rope(torch::Tensor q, torch::Tensor k, torch::Tensor q_rope,
+                                      torch::Tensor k_rope, torch::Tensor indptr,
                                       torch::Tensor offsets, bool interleave, float rope_scale,
                                       float rope_theta);
 
 std::vector<torch::Tensor> apply_llama31_rope(torch::Tensor q, torch::Tensor k,
+                                              torch::Tensor q_rope, torch::Tensor k_rope,
                                               torch::Tensor indptr, torch::Tensor offsets,
                                               bool interleave, float rope_scale, float rope_theta,
                                               float low_freq_factor, float high_freq_factor,
                                               float old_context_length);
 
+std::vector<torch::Tensor> apply_rope_pos_ids(torch::Tensor q, torch::Tensor k,
+                                              torch::Tensor q_rope, torch::Tensor k_rope,
+                                              torch::Tensor pos_ids, bool interleave,
+                                              float rope_scale, float rope_theta);
+
+std::vector<torch::Tensor> apply_llama31_rope_pos_ids(torch::Tensor q, torch::Tensor k,
+                                                      torch::Tensor q_rope, torch::Tensor k_rope,
+                                                      torch::Tensor pos_ids, bool interleave,
+                                                      float rope_scale, float rope_theta,
+                                                      float low_freq_factor, float high_freq_factor,
+                                                      float old_context_length);
+
 torch::Tensor packbits(torch::Tensor x, const std::string& bitorder);
 
 torch::Tensor segment_packbits(torch::Tensor x, torch::Tensor input_indptr,
@@ -141,11 +148,11 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("silu_and_mul", &silu_and_mul, "Fused SiLU and Mul");
   m.def("gelu_tanh_and_mul", &gelu_tanh_and_mul, "Fused GeLU Tanh and Mul");
   m.def("gelu_and_mul", &gelu_and_mul, "Fused GeLU and Mul");
-  m.def("apply_rope_inplace", &apply_rope_inplace, "Apply RoPE in-place");
-  m.def("apply_llama31_rope_inplace", &apply_llama31_rope_inplace,
-        "Apply Llama 3.1 style RoPE in-place");
   m.def("apply_rope", &apply_rope, "Apply RoPE");
   m.def("apply_llama31_rope", &apply_llama31_rope, "Apply Llama 3.1 style RoPE");
+  m.def("apply_rope_pos_ids", &apply_rope_pos_ids, "Apply RoPE with positional ids");
+  m.def("apply_llama31_rope_pos_ids", &apply_llama31_rope_pos_ids,
+        "Apply Llama 3.1 style RoPE with positional ids");
   m.def("packbits", &packbits, "GPU packbits operator");
   m.def("segment_packbits", &segment_packbits, "GPU segment packbits operator");
   m.def("cutlass_segment_gemm", &CutlassSegmentGEMM, "Cutlass Segment GEMM operator");