Merge 5f95dccea8 into a5b57b08ce

* ggml: CUDA unary op EXP

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* ggml: rwkv_wkv op CUDA impl

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

---------

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

common/arg.cpp

@@ -391,7 +391,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         [](gpt_params & params) {
             params.verbose_prompt = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ));
     add_opt(llama_arg(
         {"--no-display-prompt"},
         format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
@@ -1098,11 +1098,12 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
         [](gpt_params & params, const std::string & value) {
             /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
             else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
-            else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
+            else if (value == "cls")  { params.pooling_type = LLAMA_POOLING_TYPE_CLS;  }
             else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
+            else if (value == "rank") { params.pooling_type = LLAMA_POOLING_TYPE_RANK; }
             else { throw std::invalid_argument("invalid value"); }
         }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
     add_opt(llama_arg(
         {"--attention"}, "{causal,non,causal}",
         "attention type for embeddings, use model default if unspecified",

convert_hf_to_gguf.py

@@ -291,8 +291,13 @@ class Model:
                     bid = int(part)
                     break
 
-            for new_name, data in ((n, d.squeeze().numpy()) for n, d in self.modify_tensors(data_torch, name, bid)):
-                data: np.ndarray  # type hint
+            for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
+                data = data_torch.squeeze().numpy()
+
+                # if data ends up empty, it means data_torch was a scalar tensor -> restore
+                if len(data.shape) == 0:
+                    data = data_torch.numpy()
+
                 n_dims = len(data.shape)
                 data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims)
 
@@ -2598,7 +2603,7 @@ class NomicBertModel(BertModel):
         self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
 
 
-@Model.register("XLMRobertaModel")
+@Model.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
 class XLMRobertaModel(BertModel):
     model_arch = gguf.MODEL_ARCH.BERT
 
@@ -2696,6 +2701,11 @@ class XLMRobertaModel(BertModel):
         self.gguf_writer.add_add_eos_token(True)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # if name starts with "roberta.", remove the prefix
+        # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main
+        if name.startswith("roberta."):
+            name = name[8:]
+
         # position embeddings start at pad_token_id + 1, so just chop down the weight tensor
         if name == "embeddings.position_embeddings.weight":
             if self._position_offset is not None:

examples/embedding/embedding.cpp

@@ -234,6 +234,10 @@ int main(int argc, char ** argv) {
                 }
                 LOG("\n");
             }
+        } else if (pooling_type == LLAMA_POOLING_TYPE_RANK) {
+            for (int j = 0; j < n_embd_count; j++) {
+                LOG("rank score %d: %8.3f\n", j, emb[j * n_embd]);
+            }
         } else {
             // print the first part of the embeddings or for a single prompt, the full embedding
             for (int j = 0; j < n_prompts; j++) {

examples/server/server.cpp

@@ -92,6 +92,7 @@ enum server_task_type {
 enum server_task_cmpl_type {
     SERVER_TASK_CMPL_TYPE_NORMAL,
     SERVER_TASK_CMPL_TYPE_EMBEDDING,
+    SERVER_TASK_CMPL_TYPE_RERANK,
     SERVER_TASK_CMPL_TYPE_INFILL,
 };
 
@@ -172,6 +173,7 @@ struct server_slot {
     std::vector<completion_token_output> generated_token_probs;
 
     server_task_cmpl_type cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL;
+
     bool has_next_token = true;
     bool truncated      = false;
     bool stopped_eos    = false;
@@ -954,8 +956,17 @@ struct server_context {
                 slot.prompt = *prompt;
             } else if (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_array()) {
                 slot.prompt = prompt->at(0);
+            } else if (prompt->is_array() && prompt->size() > 1) {
+                // array of strings
+                for (const auto & el : *prompt) {
+                    if (!el.is_string()) {
+                        send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST);
+                        return false;
+                    }
+                }
+                slot.prompt = *prompt;
             } else {
-                send_error(task, "\"prompt\" must be a string or an array of integers", ERROR_TYPE_INVALID_REQUEST);
+                send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST);
                 return false;
             }
         }
@@ -1380,6 +1391,7 @@ struct server_context {
 
                 res.data = json {
                     {"embedding", std::vector<float>(n_embd, 0.0f)},
+                    {"index",     slot.index},
                 };
 
                 continue;
@@ -1398,6 +1410,44 @@ struct server_context {
         queue_results.send(res);
     }
 
+    void send_rank(const server_slot & slot, const llama_batch & batch) {
+        server_task_result res;
+        res.id       = slot.id_task;
+        res.error    = false;
+        res.stop     = true;
+
+        for (int i = 0; i < batch.n_tokens; ++i) {
+            if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) {
+                continue;
+            }
+
+            const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
+            if (embd == NULL) {
+                embd = llama_get_embeddings_ith(ctx, i);
+            }
+
+            if (embd == NULL) {
+                SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]);
+
+                res.data = json {
+                    {"index", slot.index},
+                    {"rank",  -1e6},
+                };
+
+                continue;
+            }
+
+            res.data = json {
+                {"index", slot.index},
+                {"rank",  embd[0]},
+            };
+        }
+
+        SLT_DBG(slot, "sending rank, res = '%s'\n", res.data.dump().c_str());
+
+        queue_results.send(res);
+    }
+
     //
     // Functions to create new task(s) and receive result(s)
     //
@@ -1433,13 +1483,23 @@ struct server_context {
         // otherwise, it's a multiple-prompt task, we break it into smaller tasks
         else if (prompt.is_array()) {
             std::vector<json> prompts = prompt;
-            for (size_t i = 0; i < prompts.size(); i++) {
-                const auto & e = prompts[i];
-                if (e.is_string() || json_is_array_of_numbers(e)) {
-                    data["index"] = i;
-                    create_task(data, true, e);
-                } else {
-                    throw std::runtime_error(error_msg);
+            if (cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
+                for (size_t i = 1; i < prompts.size(); i++) {
+                    json qd;
+                    qd.push_back(prompts[0]);
+                    qd.push_back(prompts[i]);
+                    data["index"] = i - 1;
+                    create_task(data, true, qd);
+                }
+            } else {
+                for (size_t i = 0; i < prompts.size(); i++) {
+                    const auto & e = prompts[i];
+                    if (e.is_string() || json_is_array_of_numbers(e)) {
+                        data["index"] = i;
+                        create_task(data, true, e);
+                    } else {
+                        throw std::runtime_error(error_msg);
+                    }
                 }
             }
         }
@@ -1483,7 +1543,9 @@ struct server_context {
                 break;
             }
 
-            size_t idx = result.data["index"];
+            const size_t idx = result.data["index"];
+            GGML_ASSERT(idx < results.size() && "index out of range");
+
             results[idx] = result;
         }
         result_handler(results);
@@ -1934,6 +1996,29 @@ struct server_context {
                             }
 
                             prompt_tokens = embd_inp;
+                        } else if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
+                            // require slot.prompt to be array of 2 strings
+                            if (!slot.prompt.is_array() || slot.prompt.size() != 2) {
+                                SLT_ERR(slot, "%s", "invalid prompt for rerank task\n");
+                                slot.release();
+                                send_error(slot, "invalid prompt for rerank task", ERROR_TYPE_INVALID_REQUEST);
+                                continue;
+                            }
+
+                            // prompt: <s>query</s><s>doc</s>
+                            prompt_tokens.clear();
+                            prompt_tokens.push_back(llama_token_bos(model));
+                            {
+                                const auto part = tokenize(slot.prompt[0], false);
+                                prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
+                            }
+                            prompt_tokens.push_back(llama_token_eos(model));
+                            prompt_tokens.push_back(llama_token_bos(model));
+                            {
+                                const auto part = tokenize(slot.prompt[1], false);
+                                prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
+                            }
+                            prompt_tokens.push_back(llama_token_eos(model));
                         } else {
                             prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
                         }
@@ -1953,7 +2038,7 @@ struct server_context {
                             continue;
                         }
 
-                        if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING) {
+                        if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
                             // this prompt is too large to process - discard it
                             if (slot.n_prompt_tokens > n_ubatch) {
                                 slot.release();
@@ -2023,7 +2108,7 @@ struct server_context {
                         slot.n_prompt_tokens_processed = 0;
                     }
 
-                    if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING) {
+                    if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
                         // cannot fit the prompt in the current batch - will try next iter
                         if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
                             continue;
@@ -2031,7 +2116,10 @@ struct server_context {
                     }
 
                     // check that we are in the right batch_type, if not defer the slot
-                    bool slot_type = slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING ? 1 : 0;
+                    const bool slot_type =
+                        slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING ||
+                        slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK     ? 1 : 0;
+
                     if (batch_type == -1) {
                         batch_type = slot_type;
                     } else if (batch_type != slot_type) {
@@ -2204,6 +2292,13 @@ struct server_context {
                         continue; // continue loop of slots
                     }
 
+                    if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
+                        send_rank(slot, batch_view);
+                        slot.release();
+                        slot.i_batch = -1;
+                        continue; // continue loop of slots
+                    }
+
                     // prompt evaluated for next-token prediction
                     slot.state = SLOT_STATE_GENERATING;
                 } else if (slot.state != SLOT_STATE_GENERATING) {
@@ -2994,6 +3089,82 @@ int main(int argc, char ** argv) {
         res_ok(res, root);
     };
 
+    const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
+        const json body = json::parse(req.body);
+
+        // TODO: implement
+        //int top_n = 1;
+        //if (body.count("top_n") != 1) {
+        //    top_n = body.at("top_n");
+        //} else {
+        //    res_error(res, format_error_response("\"top_n\" must be provided", ERROR_TYPE_INVALID_REQUEST));
+        //    return;
+        //}
+
+        json query;
+        if (body.count("query") == 1) {
+            query = body.at("query");
+            if (!query.is_string()) {
+                res_error(res, format_error_response("\"query\" must be a string", ERROR_TYPE_INVALID_REQUEST));
+                return;
+            }
+        } else {
+            exit(0);
+            res_error(res, format_error_response("\"query\" must be provided", ERROR_TYPE_INVALID_REQUEST));
+            return;
+        }
+
+        json documents;
+        if (body.count("documents") != 0) {
+            documents = body.at("documents");
+            if (!documents.is_array() || documents.size() == 0) {
+                res_error(res, format_error_response("\"documents\" must be a non-empty string array", ERROR_TYPE_INVALID_REQUEST));
+                return;
+            }
+        } else {
+            res_error(res, format_error_response("\"documents\" must be provided", ERROR_TYPE_INVALID_REQUEST));
+            return;
+        }
+
+        // construct prompt object: array of ["query", "doc0", "doc1", ...]
+        json prompt;
+        prompt.push_back(query);
+        for (const auto & doc : documents) {
+            prompt.push_back(doc);
+        }
+
+        LOG_DBG("rerank prompt: %s\n", prompt.dump().c_str());
+
+        // create and queue the task
+        json responses = json::array();
+        bool error = false;
+        {
+            std::vector<server_task> tasks = ctx_server.create_tasks_cmpl({{"prompt", prompt}}, SERVER_TASK_CMPL_TYPE_RERANK);
+            ctx_server.queue_results.add_waiting_tasks(tasks);
+            ctx_server.queue_tasks.post(tasks);
+
+            // get the result
+            std::unordered_set<int> task_ids = server_task::get_list_id(tasks);
+
+            ctx_server.receive_cmpl_results(task_ids, [&](std::vector<server_task_result> & results) {
+                for (const auto & res : results) {
+                    responses.push_back(res.data);
+                }
+            }, [&](const json & error_data) {
+                res_error(res, error_data);
+                error = true;
+            });
+        }
+
+        if (error) {
+            return;
+        }
+
+        // write JSON response
+        json root = format_response_rerank(body, responses);
+        res_ok(res, root);
+    };
+
     const auto handle_lora_adapters_list = [&](const httplib::Request &, httplib::Response & res) {
         json result = json::array();
         for (size_t i = 0; i < ctx_server.loras.size(); ++i) {
@@ -3090,6 +3261,7 @@ int main(int argc, char ** argv) {
     svr->Post("/embedding",           handle_embeddings); // legacy
     svr->Post("/embeddings",          handle_embeddings);
     svr->Post("/v1/embeddings",       handle_embeddings);
+    svr->Post("/v1/rerank",           handle_rerank);
     svr->Post("/tokenize",            handle_tokenize);
     svr->Post("/detokenize",          handle_detokenize);
     // LoRA adapters hotswap

examples/server/utils.hpp

@@ -537,7 +537,7 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
     json res = json {
         {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
         {"object", "list"},
-        {"usage", json {
+        {"usage", json { // TODO: fill
             {"prompt_tokens", 0},
             {"total_tokens", 0}
         }},
@@ -547,6 +547,29 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
     return res;
 }
 
+static json format_response_rerank(const json & request, const json & ranks) {
+    json data = json::array();
+    int i = 0;
+    for (const auto & rank : ranks) {
+        data.push_back(json{
+            {"index",    i++},
+            {"relevance_score", json_value(rank, "rank", 0.0)},
+        });
+    }
+
+    json res = json {
+        {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
+        {"object", "list"},
+        {"usage", json { // TODO: fill
+            {"prompt_tokens", 0},
+            {"total_tokens", 0}
+        }},
+        {"results", data}
+    };
+
+    return res;
+}
+
 static bool is_valid_utf8(const std::string & str) {
     const unsigned char* bytes = reinterpret_cast<const unsigned char*>(str.data());
     const unsigned char* end = bytes + str.length();

ggml/src/ggml-cuda.cu

@@ -34,6 +34,7 @@
 #include "ggml-cuda/tsembd.cuh"
 #include "ggml-cuda/unary.cuh"
 #include "ggml-cuda/upscale.cuh"
+#include "ggml-cuda/rwkv-wkv.cuh"
 
 #include <algorithm>
 #include <array>
@@ -2243,6 +2244,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
                 case GGML_UNARY_OP_HARDSWISH:
                     ggml_cuda_op_hardswish(ctx, dst);
                     break;
+                case GGML_UNARY_OP_EXP:
+                    ggml_cuda_op_exp(ctx, dst);
+                    break;
                 default:
                     return false;
             }
@@ -2345,6 +2349,8 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_CROSS_ENTROPY_LOSS:
             ggml_cuda_cross_entropy_loss(ctx, dst);
             break;
+        case GGML_OP_RWKV_WKV:
+            ggml_cuda_op_rwkv_wkv(ctx, dst);
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
             ggml_cuda_cross_entropy_loss_back(ctx, dst);
             break;
@@ -2806,6 +2812,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
                 case GGML_UNARY_OP_HARDSWISH:
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_TANH:
+                case GGML_UNARY_OP_EXP:
                     return ggml_is_contiguous(op->src[0]);
                 default:
                     return false;
@@ -2967,20 +2974,21 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
         case GGML_OP_ARANGE:
         case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_LEAKY_RELU:
+        case GGML_OP_RWKV_WKV:
             return true;
-        case GGML_OP_FLASH_ATTN_EXT:
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-            return (op->src[0]->ne[0] == 64 && op->src[1]->type == GGML_TYPE_F16) || op->src[0]->ne[0] == 128;
-#else
-            if (op->src[0]->ne[0] == 128) {
-                return true;
-            }
+        case GGML_OP_FLASH_ATTN_EXT: {
             if (op->src[0]->ne[0] ==  64 && op->src[1]->type == GGML_TYPE_F16) {
                 return true;
             }
-            return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA &&
-                op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16;
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+            if (op->src[0]->ne[0] == 128) {
+                return true;
+            }
+            if (op->src[0]->ne[0] == 256 && op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16) {
+                return true;
+            }
+            const int cc = ggml_cuda_info().devices[cuda_ctx->device].cc;
+            return cc >= CC_VOLTA && cc < CC_OFFSET_AMD && op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16;
+        }
         case GGML_OP_CROSS_ENTROPY_LOSS:
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
         case GGML_OP_OPT_STEP_ADAMW:

ggml/src/ggml-cuda/fattn.cu

@@ -314,7 +314,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     }
 
     if (!fast_fp16_available(cc)) {
-        if (Q->ne[1] <= 8) {
+        if (Q->ne[1] <= 8 || Q->ne[0] == 256) {
             ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
         } else {
             ggml_cuda_flash_attn_ext_tile_f32(ctx, dst);

ggml/src/ggml-cuda/rwkv-wkv.cu

@@ -0,0 +1,89 @@
+#include "common.cuh"
+#include "rwkv-wkv.cuh"
+
+static __global__ void rwkv_wkv_f32(const int B, const int T, const int C, const int H, const float * k, const float * v, const float * r, const float * tf, const float * td, const float * s, float * dst) {
+    const int tid = threadIdx.x;
+    const int bid = blockIdx.x;
+
+    const int head_size = CUDA_WKV_BLOCK_SIZE;
+    const int batch_i = bid / H;
+    const int head_i = bid % H;
+    const int state_size = C * head_size;
+    const int n_seq_tokens = T / B;
+
+    float state[head_size];
+    __shared__ float _k[head_size], _r[head_size], _tf[head_size], _td[head_size];
+
+    #pragma unroll
+    for (int i = 0; i < head_size; i++) {
+        state[i] = s[batch_i * state_size + head_i * head_size * head_size + i * head_size + tid];
+    }
+
+    __syncthreads();
+    _tf[tid] = tf[head_i * head_size + tid];
+    __syncthreads();
+
+    for (int t = batch_i * n_seq_tokens * C + head_i * head_size + tid; t < (batch_i + 1) * n_seq_tokens * C + head_i * head_size + tid; t += C) {
+        __syncthreads();
+        _k[tid] = k[t];
+        _r[tid] = r[t];
+        _td[tid] = td[t];
+        __syncthreads();
+
+        const float _v = v[t];
+        float y = 0;
+        for (int j = 0; j < head_size; j += 4) {
+            const float4& k = (float4&)(_k[j]);
+            const float4& r = (float4&)(_r[j]);
+            const float4& tf = (float4&)(_tf[j]);
+            const float4& td = (float4&)(_td[j]);
+            float4& s = (float4&)(state[j]);
+            float4 kv;
+
+            kv.x = k.x * _v;
+            kv.y = k.y * _v;
+            kv.z = k.z * _v;
+            kv.w = k.w * _v;
+
+            y += r.x * (tf.x * kv.x + s.x);
+            y += r.y * (tf.y * kv.y + s.y);
+            y += r.z * (tf.z * kv.z + s.z);
+            y += r.w * (tf.w * kv.w + s.w);
+
+            s.x = s.x * td.x + kv.x;
+            s.y = s.y * td.y + kv.y;
+            s.z = s.z * td.z + kv.z;
+            s.w = s.w * td.w + kv.w;
+        }
+        dst[t] = y;
+    }
+
+    #pragma unroll
+    for (int i = 0; i < head_size; i++) {
+        dst[T * C + batch_i * state_size + head_i * head_size * head_size + i * head_size + tid] = state[i];
+    }
+}
+
+void ggml_cuda_op_rwkv_wkv(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const float * k_d  = (const float *)dst->src[0]->data;
+    const float * v_d  = (const float *)dst->src[1]->data;
+    const float * r_d  = (const float *)dst->src[2]->data;
+    const float * tf_d = (const float *)dst->src[3]->data;
+    const float * td_d = (const float *)dst->src[4]->data;
+    const float * s_d  = (const float *)dst->src[5]->data;
+
+    const int64_t B = dst->src[5]->ne[1];
+    const int64_t T = dst->src[0]->ne[3];
+    const int64_t C = dst->ne[0];
+    const int64_t H = dst->src[0]->ne[2];
+
+    float * dst_d = (float *)dst->data;
+
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(dst->src[5]->type == GGML_TYPE_F32);
+    GGML_ASSERT(C % H == 0);
+    GGML_ASSERT(C / H == CUDA_WKV_BLOCK_SIZE);
+
+    rwkv_wkv_f32<<<B * H, C / H, 0, stream>>>(B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d);
+}

ggml/src/ggml-cuda/rwkv-wkv.cuh

@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_WKV_BLOCK_SIZE 64
+
+void ggml_cuda_op_rwkv_wkv(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ggml/src/ggml-cuda/unary.cu

@@ -95,6 +95,15 @@ static __global__ void hardswish_f32(const float * x, float * dst, const int k)
     dst[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f));
 }
 
+static __global__ void exp_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = expf(x[i]);
+}
+
 static __global__ void leaky_relu_f32(const float * x, float * dst, const int k, const float negative_slope) {
     const int i  = blockDim.x*blockIdx.x + threadIdx.x;
     if (i >= k) {
@@ -189,6 +198,11 @@ static void hardswish_f32_cuda(const float * x, float * dst, const int k, cudaSt
     hardswish_f32<<<num_blocks, CUDA_HARDSWISH_BLOCK_SIZE, 0, stream>>>(x, dst, k);
 }
 
+static void exp_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_EXP_BLOCK_SIZE - 1) / CUDA_EXP_BLOCK_SIZE;
+    exp_f32<<<num_blocks, CUDA_EXP_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
 static void leaky_relu_f32_cuda(const float * x, float * dst, const int k, const float negative_slope, cudaStream_t stream) {
     const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE;
     leaky_relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k, negative_slope);
@@ -354,6 +368,20 @@ void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     hardswish_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
 }
 
+void ggml_cuda_op_exp(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    exp_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
 void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const float * src0_d = (const float *)src0->data;

ggml/src/ggml-cuda/unary.cuh

@@ -8,6 +8,7 @@
 #define CUDA_RELU_BLOCK_SIZE 256
 #define CUDA_SIGMOID_BLOCK_SIZE 256
 #define CUDA_HARDSIGMOID_BLOCK_SIZE 256
+#define CUDA_EXP_BLOCK_SIZE 256
 #define CUDA_HARDSWISH_BLOCK_SIZE 256
 #define CUDA_SQR_BLOCK_SIZE 256
 #define CUDA_SQRT_BLOCK_SIZE 256
@@ -32,6 +33,8 @@ void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
+void ggml_cuda_op_exp(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
 void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

gguf-py/gguf/constants.py

@@ -342,6 +342,8 @@ class MODEL_TENSOR(IntEnum):
     ENC_FFN_DOWN         = auto()
     ENC_FFN_UP           = auto()
     ENC_OUTPUT_NORM      = auto()
+    CLS                  = auto() # classifier
+    CLS_OUT              = auto() # classifier output projection
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -499,6 +501,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ENC_FFN_DOWN:              "enc.blk.{bid}.ffn_down",
     MODEL_TENSOR.ENC_FFN_UP:                "enc.blk.{bid}.ffn_up",
     MODEL_TENSOR.ENC_OUTPUT_NORM:           "enc.output_norm",
+    MODEL_TENSOR.CLS:                       "cls",
+    MODEL_TENSOR.CLS_OUT:                   "cls.output",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -608,6 +612,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
         MODEL_TENSOR.LAYER_OUT_NORM,
+        MODEL_TENSOR.CLS,
+        MODEL_TENSOR.CLS_OUT,
     ],
     MODEL_ARCH.NOMIC_BERT: [
         MODEL_TENSOR.TOKEN_EMBD,

gguf-py/gguf/tensor_mapping.py

@@ -677,6 +677,14 @@ class TensorNameMap:
         MODEL_TENSOR.ENC_OUTPUT_NORM: (
             "encoder.final_layer_norm", # t5
         ),
+
+        MODEL_TENSOR.CLS: (
+            "classifier.dense", # roberta
+        ),
+
+        MODEL_TENSOR.CLS_OUT: (
+            "classifier.out_proj", # roberta
+        ),
     }
 
     # architecture-specific block mappings

include/llama.h

@@ -192,6 +192,7 @@ extern "C" {
         LLAMA_POOLING_TYPE_MEAN = 1,
         LLAMA_POOLING_TYPE_CLS  = 2,
         LLAMA_POOLING_TYPE_LAST = 3,
+        LLAMA_POOLING_TYPE_RANK = 4,
     };
 
     enum llama_attention_type {

src/llama.cpp

@@ -600,6 +600,8 @@ enum llm_tensor {
     LLM_TENSOR_ENC_FFN_DOWN,
     LLM_TENSOR_ENC_FFN_UP,
     LLM_TENSOR_ENC_OUTPUT_NORM,
+    LLM_TENSOR_CLS,
+    LLM_TENSOR_CLS_OUT,
 };
 
 static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@@ -787,6 +789,8 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
             { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_CLS,             "cls" },
+            { LLM_TENSOR_CLS_OUT,         "cls.output" },
         },
     },
     {
@@ -2861,6 +2865,12 @@ struct llama_model {
     struct ggml_tensor * output_b;
     struct ggml_tensor * output_norm_enc;
 
+    // classifier
+    struct ggml_tensor * cls;
+    struct ggml_tensor * cls_b;
+    struct ggml_tensor * cls_out;
+    struct ggml_tensor * cls_out_b;
+
     std::vector<llama_layer> layers;
 
     llama_split_mode split_mode;
@@ -3056,18 +3066,14 @@ struct llama_sbatch {
         } else {
             // simple split
             if (batch->n_seq_id) {
-                for (size_t i = 0; i < length; ++i) {
-                    ubatch.n_seq_id = batch->n_seq_id + seq.offset;
-                }
+                ubatch.n_seq_id = batch->n_seq_id + seq.offset;
             } else {
                 for (size_t i = 0; i < length; ++i) {
                     ubatch.n_seq_id[ubatch.n_seqs + i] = 1;
                 }
             }
             if (batch->seq_id) {
-                for (size_t i = 0; i < length; ++i) {
-                    ubatch.seq_id = batch->seq_id + seq.offset;
-                }
+                ubatch.seq_id = batch->seq_id + seq.offset;
             } else {
                 for (size_t i = 0; i < length; ++i) {
                     ubatch.seq_id[ubatch.n_seqs + i] = &seq.all_seq_id;
@@ -7288,6 +7294,12 @@ static bool llm_load_tensors(
 
                     if (model.arch == LLM_ARCH_BERT) {
                         model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, n_ctx_train});
+
+                        model.cls   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        model.cls_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         llama_model_loader::TENSOR_NOT_REQUIRED);
+
+                        model.cls_out   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        model.cls_out_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "bias"),   {1},         llama_model_loader::TENSOR_NOT_REQUIRED);
                     }
 
                     model.tok_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
@@ -10115,6 +10127,10 @@ struct llm_build_context {
         struct ggml_tensor * cur;
 
         switch (pooling_type) {
+            case LLAMA_POOLING_TYPE_NONE:
+                {
+                    cur = inp;
+                } break;
             case LLAMA_POOLING_TYPE_MEAN:
                 {
                     struct ggml_tensor * inp_mean = build_inp_mean();
@@ -10126,9 +10142,24 @@ struct llm_build_context {
                     struct ggml_tensor * inp_cls = build_inp_cls();
                     cur = ggml_get_rows(ctx0, inp, inp_cls);
                 } break;
-            case LLAMA_POOLING_TYPE_NONE:
+            case LLAMA_POOLING_TYPE_RANK:
                 {
-                    cur = inp;
+                    struct ggml_tensor * inp_cls = build_inp_cls();
+                    inp = ggml_get_rows(ctx0, inp, inp_cls);
+
+                    // classification head
+                    // https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
+                    GGML_ASSERT(model.cls       != nullptr);
+                    GGML_ASSERT(model.cls_b     != nullptr);
+                    GGML_ASSERT(model.cls_out   != nullptr);
+                    GGML_ASSERT(model.cls_out_b != nullptr);
+
+                    cur = ggml_add (ctx0, ggml_mul_mat(ctx0, model.cls, inp), model.cls_b);
+                    cur = ggml_tanh(ctx0, cur);
+                    cur = ggml_add (ctx0, ggml_mul_mat(ctx0, model.cls_out, cur), model.cls_out_b);
+
+                    // broadcast across the embedding size to make it compatible with the llama_get_embeddings API
+                    cur = ggml_repeat(ctx0, cur, inp);
                 } break;
             default:
                 {
@@ -11357,8 +11388,8 @@ struct llm_build_context {
             inpL = cur;
         }
 
-        // final output
         cur = inpL;
+
         cb(cur, "result_embd", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -16335,7 +16366,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
         }
     }
 
-    if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
+    if (cparams.embeddings && (
+                cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
+                cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) {
         const int64_t n_tokens     = batch.n_tokens;
         const int64_t n_seq_tokens = batch.n_seq_tokens;
         const int64_t n_seqs       = batch.n_seqs;
@@ -16350,7 +16383,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
             const llama_seq_id seq_id = batch.seq_id[s][0];
 
             // TODO: adapt limits to n_seqs when batch.equal_seqs is true
-            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK");
 
             for (int i = 0; i < n_seq_tokens; ++i) {
                 const llama_pos pos = batch.pos[s*n_seq_tokens + i];
@@ -16877,6 +16910,7 @@ static int llama_decode_internal(
                 case LLAMA_POOLING_TYPE_MEAN:
                 case LLAMA_POOLING_TYPE_CLS:
                 case LLAMA_POOLING_TYPE_LAST:
+                case LLAMA_POOLING_TYPE_RANK:
                     {
                         // extract sequence embeddings (cleared before processing each batch)
                         auto & embd_seq_out = lctx.embd_seq;
@@ -17080,6 +17114,7 @@ static int llama_encode_internal(
                 case LLAMA_POOLING_TYPE_MEAN:
                 case LLAMA_POOLING_TYPE_CLS:
                 case LLAMA_POOLING_TYPE_LAST:
+                case LLAMA_POOLING_TYPE_RANK:
                     {
                         // extract sequence embeddings
                         auto & embd_seq_out = lctx.embd_seq;

tests/test-backend-ops.cpp

@@ -1543,6 +1543,36 @@ struct test_ssm_scan : public test_case {
     }
 };
 
+// GGML_OP_RWKV_WKV
+struct test_rwkv_wkv : public test_case {
+    const ggml_type type;
+
+    const int64_t head_count;
+    const int64_t head_size;
+    const int64_t n_seq_tokens;
+    const int64_t n_seqs;
+
+    std::string vars() override {
+        return VARS_TO_STR5(type, head_count, head_size, n_seq_tokens, n_seqs);
+    }
+
+    test_rwkv_wkv(ggml_type type = GGML_TYPE_F32,
+            int64_t head_count = 32, int64_t head_size = 64, int64_t n_seq_tokens = 32, int64_t n_seqs = 32)
+        : type(type), head_count(head_count), head_size(head_size), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        const int64_t n_tokens = n_seq_tokens * n_seqs;
+        ggml_tensor * r   = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ 1, head_size, head_count, n_tokens }.data());
+        ggml_tensor * k   = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ head_size, 1, head_count, n_tokens }.data());
+        ggml_tensor * v   = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ 1, head_size, head_count, n_tokens }.data());
+        ggml_tensor * tf  = ggml_new_tensor(ctx, type, 2, std::vector<int64_t>{ head_size, head_count }.data());
+        ggml_tensor * td  = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ 1, head_size, head_count, n_tokens }.data());
+        ggml_tensor * s   = ggml_new_tensor(ctx, type, 2, std::vector<int64_t>{ head_size * head_size * head_count, n_seqs }.data());
+        ggml_tensor * out = ggml_rwkv_wkv(ctx, k, v, r, tf, td, s);
+        return out;
+    }
+};
+
 // GGML_OP_MUL_MAT
 struct test_mul_mat : public test_case {
     const ggml_type type_a;
@@ -3337,6 +3367,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
 
     test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 16, 1024, 32, 4));
 
+    test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 1, 1));
+    test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 32, 1));
+    test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 32, 4));
+    test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 128, 4));
+
 #if 1
     for (ggml_type type_a : base_types) {
         for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
@@ -3564,7 +3599,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
                     if (hs != 128 && logit_softcap != 0.0f) continue;
                     for (int nh : { 32, }) {
                         for (int kv : { 512, 1024, }) {
-                            for (int nb : { 1, 2, 4, 8, }) {
+                            for (int nb : { 1, 3, 32, 35, }) {
                                 for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
                                     test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, logit_softcap, type_KV));
                                 }