ggml-cpu: add RVV vec dot kernels for quantization types (#18859)

* ggml-cpu: add rvv quantize_row_q8_K kernel

Co-authored-by: Rehan Qasim <rehan.qasim@10xengineers.ai>

* ggml-cpu: add rvv vec_dot for iq4_nl, mxfp4, iq2_xxs

Co-authored-by: Rehan Qasim <rehan.qasim@10xengineers.ai>

* ggml-cpu: add rvv vec_dot for iq4_xs, refactor

* ggml-cpu: remove ifunc for rvv vec dot

* ggml-cpu: add vec_dot for iq2_xs, iq3_xxs

Co-authored-by: Rehan Qasim <rehan.qasim@10xengineers.ai>

* ggml-cpu: refactor quants.c

---------

Co-authored-by: taimur-10x <taimur.ahmad@10xengineers.ai>
Co-authored-by: Rehan Qasim <rehan.qasim@10xengineers.ai>
Co-authored-by: Rehan Qasim <rehanbhatti0317@gmail.com>

CONTRIBUTING.md

@@ -30,14 +30,19 @@ Before submitting your PR:
 - Search for existing PRs to prevent duplicating efforts
 - llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier
 - Test your changes:
-    - Execute [the full CI locally on your machine](ci/README.md) before publishing
-    - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
-    - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
-    - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
+  - Execute [the full CI locally on your machine](ci/README.md) before publishing
+  - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
+  - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
+  - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
 - Create separate PRs for each feature or fix:
-    - Avoid combining unrelated changes in a single PR
-    - For intricate features, consider opening a feature request first to discuss and align expectations
-    - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs
+  - Avoid combining unrelated changes in a single PR
+  - For intricate features, consider opening a feature request first to discuss and align expectations
+  - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs
+  - In particular, adding new data types (extension of the `ggml_type` enum) carries with it a disproportionate maintenance burden. As such, to add a new quantization type you will need to meet the following *additional* criteria *at minimum*:
+    - convert a small model to GGUF using the new type and upload it to HuggingFace
+    - provide [perplexity](https://github.com/ggml-org/llama.cpp/tree/master/tools/perplexity) comparisons to FP16/BF16 (whichever is the native precision) as well as to types of similar size
+    - provide KL divergence data calculated vs. the FP16/BF16 (whichever is the native precision) version for both the new type as well as types of similar size
+    - provide [performance data](https://github.com/ggml-org/llama.cpp/tree/master/tools/llama-bench) for the new type in comparison to types of similar size on pure CPU
 - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
 - If you are a new contributor, limit your open PRs to 1.
 

convert_hf_to_gguf.py

@@ -2194,6 +2194,8 @@ class GPTNeoXModel(TextModel):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
+        assert n_head is not None
+        assert n_embed is not None
 
         if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name):
             # Map bloom-style qkv_linear to gpt-style qkv_linear
@@ -2231,6 +2233,8 @@ class BloomModel(TextModel):
     def set_gguf_parameters(self):
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
+        assert n_head is not None
+        assert n_embed is not None
         self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
         self.gguf_writer.add_embedding_length(n_embed)
         self.gguf_writer.add_feed_forward_length(4 * n_embed)
@@ -2243,6 +2247,8 @@ class BloomModel(TextModel):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
+        assert n_head is not None
+        assert n_embed is not None
 
         name = re.sub(r'transformer\.', '', name)
 
@@ -3853,6 +3859,7 @@ class LLaDAModel(TextModel):
 
         if (rope_dim := hparams.get("head_dim")) is None:
             n_heads = hparams.get("num_attention_heads", hparams.get("n_heads"))
+            assert n_heads is not None
             rope_dim = hparams.get("hidden_size", hparams.get("d_model")) // n_heads
         self.gguf_writer.add_rope_dimension_count(rope_dim)
 
@@ -3884,6 +3891,7 @@ class LLaDAModel(TextModel):
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         n_head = self.hparams.get("num_attention_heads", self.hparams.get("n_heads"))
+        assert n_head is not None
         n_kv_head = self.hparams.get("num_key_value_heads", self.hparams.get("n_kv_heads"))
 
         if self.undo_permute:
@@ -9485,7 +9493,9 @@ class ChatGLMModel(TextModel):
 
     def set_gguf_parameters(self):
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
+        assert n_embed is not None
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
+        assert n_head is not None
         n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head))
         self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
         self.gguf_writer.add_embedding_length(n_embed)

ggml/CMakeLists.txt

@@ -253,7 +253,7 @@ option(GGML_OPENCL_PROFILING                "ggml: use OpenCL profiling (increas
 option(GGML_OPENCL_EMBED_KERNELS            "ggml: embed kernels"                             ON)
 option(GGML_OPENCL_USE_ADRENO_KERNELS       "ggml: use optimized kernels for Adreno"          ON)
 set   (GGML_OPENCL_TARGET_VERSION "300" CACHE STRING
-                                            "gmml: OpenCL API version to target")
+                                            "ggml: OpenCL API version to target")
 
 option(GGML_HEXAGON                         "ggml: enable Hexagon backend"                    OFF)
 set(GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE 128 CACHE STRING "ggml: quantize group size (32, 64, or 128)")

ggml/src/ggml-cpu/arch-fallback.h

@@ -199,13 +199,6 @@
 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
 #elif defined(__riscv)
 // quants.c
-#define quantize_row_q8_K_generic quantize_row_q8_K
-#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
-#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
-#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
-#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
-#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 #define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x1_generic ggml_quantize_mat_q8_0_4x1

ggml/src/ggml-cpu/ops.cpp

@@ -9624,7 +9624,7 @@ void ggml_compute_forward_win_unpart(
     }
 }
 
-//gmml_compute_forward_unary
+//ggml_compute_forward_unary
 
 void ggml_compute_forward_unary(
         const ggml_compute_params * params,

ggml/src/ggml-metal/ggml-metal-device.m

@@ -1156,7 +1156,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_RWKV_WKV7:
             return true;
         case GGML_OP_GATED_DELTA_NET:
-            return op->src[2]->ne[0] % 32 == 0;
+            return has_simdgroup_reduction && op->src[2]->ne[0] % 32 == 0;
         case GGML_OP_SOLVE_TRI:
         case GGML_OP_MUL_MAT:
         case GGML_OP_MUL_MAT_ID:

ggml/src/ggml-metal/ggml-metal.metal

@@ -3006,7 +3006,7 @@ kernel void kernel_l2_norm_impl(
     sumf = shmem_f32[tiisg];
     sumf = simd_sum(sumf);
 
-    const float scale = 1.0f/sqrt(max(sumf, args.eps));
+    const float scale = 1.0f/max(sqrt(sumf), args.eps);
 
     for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
         y[i00] = x[i00] * scale;

tools/mtmd/mtmd-helper.cpp

@@ -470,12 +470,12 @@ static bool decode_audio_from_buf(const unsigned char * buf_in, size_t len, int
 mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len) {
     if (audio_helpers::is_audio_file((const char *)buf, len)) {
         std::vector<float> pcmf32;
-        int bitrate = mtmd_get_audio_bitrate(ctx);
-        if (bitrate < 0) {
+        const int sample_rate = mtmd_get_audio_sample_rate(ctx);
+        if (sample_rate < 0) {
             LOG_ERR("This model does not support audio input\n");
             return nullptr;
         }
-        if (!audio_helpers::decode_audio_from_buf(buf, len, bitrate, pcmf32)) {
+        if (!audio_helpers::decode_audio_from_buf(buf, len, sample_rate, pcmf32)) {
             LOG_ERR("Unable to read WAV audio file from buffer\n");
             return nullptr;
         }

tools/mtmd/mtmd.cpp

@@ -912,7 +912,7 @@ bool mtmd_support_audio(mtmd_context * ctx) {
     return ctx->ctx_a != nullptr;
 }
 
-int mtmd_get_audio_bitrate(mtmd_context * ctx) {
+int mtmd_get_audio_sample_rate(mtmd_context * ctx) {
     if (!ctx->ctx_a) {
         return -1;
     }

tools/mtmd/mtmd.h

@@ -125,9 +125,9 @@ MTMD_API bool mtmd_support_vision(mtmd_context * ctx);
 // whether the current model supports audio input
 MTMD_API bool mtmd_support_audio(mtmd_context * ctx);
 
-// get audio bitrate in Hz, for example 16000 for Whisper
+// get audio sample rate in Hz, for example 16000 for Whisper
 // return -1 if audio is not supported
-MTMD_API int mtmd_get_audio_bitrate(mtmd_context * ctx);
+MTMD_API int mtmd_get_audio_sample_rate(mtmd_context * ctx);
 
 // mtmd_bitmap
 //