CUDA: use mma FA kernel for gqa > 4 on RTX 4000 (#15035 )

cuda: make im2col a little faster (#15025 )
kv-cache : skip alignment of n_stream in kv-cache log msg [no ci] (#15040 )
2026-02-05 13:53:23 +02:00 · 2025-08-02 16:37:08 +02:00 · 2025-08-02 17:15:36 +03:00 · 2025-08-02 17:14:57 +03:00 · 2025-08-02 17:14:21 +03:00 · 2025-08-02 17:13:05 +03:00
39 changed files with 1320 additions and 320 deletions
--- a/.github/workflows/pre-tokenizer-hashes.yml
+++ b/.github/workflows/pre-tokenizer-hashes.yml
@@ -0,0 +1,45 @@
+name: Check Pre-Tokenizer Hashes
+
+on:
+    push:
+        paths:
+            - 'convert_hf_to_gguf.py'
+            - 'convert_hf_to_gguf_update.py'
+    pull_request:
+        paths:
+            - 'convert_hf_to_gguf.py'
+            - 'convert_hf_to_gguf_update.py'
+
+jobs:
+    pre-tokenizer-hashes:
+        runs-on: ubuntu-latest
+
+        steps:
+        - name: Checkout repository
+          uses: actions/checkout@v4
+
+        - name: Set up Python
+          uses: actions/setup-python@v5
+          with:
+              python-version: '3.11'
+
+        - name: Install Python dependencies
+          run: |
+              python3 -m venv .venv
+              .venv/bin/pip install -r requirements/requirements-convert_hf_to_gguf_update.txt
+
+        - name: Update pre-tokenizer hashes
+          run: |
+              cp convert_hf_to_gguf.py /tmp
+              .venv/bin/python convert_hf_to_gguf_update.py --check-missing
+
+        - name: Check if committed pre-tokenizer hashes matches generated version
+          run: |
+              if ! diff -q convert_hf_to_gguf.py /tmp/convert_hf_to_gguf.py; then
+                  echo "Model pre-tokenizer hashes (in convert_hf_to_gguf.py) do not match generated hashes (from convert_hf_to_gguf_update.py)."
+                  echo "To fix: run ./convert_hf_to_gguf_update.py and commit the updated convert_hf_to_gguf.py along with your changes"
+                  echo "Differences found:"
+                  diff convert_hf_to_gguf.py /tmp/convert_hf_to_gguf.py || true
+                  exit 1
+              fi
+              echo "Model pre-tokenizer hashes are up to date."
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -1646,7 +1646,7 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
        "|<function name=\"([^\"]+)\">"  // match 5 (function name again)
    );

-    if (auto res = builder.try_find_regex(open_regex)) {
+    while (auto res = builder.try_find_regex(open_regex)) {
        const auto & block_start = res->groups[1];
        std::string block_end = block_start.empty() ? "" : "```";

@@ -1668,7 +1668,6 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
                    builder.consume_literal(block_end);
                    builder.consume_spaces();
                }
-                builder.add_content(builder.consume_rest());
            } else {
                throw common_chat_msg_partial_exception("failed to parse tool call");
            }
@@ -1693,11 +1692,10 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
                    builder.consume_spaces();
                }
            }
-            builder.add_content(builder.consume_rest());
        }
-    } else {
-        builder.add_content(builder.consume_rest());
    }
+
+    builder.add_content(builder.consume_rest());
 }

 static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct templates_params & inputs) {
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -684,6 +684,9 @@ class TextModel(ModelBase):
        if chkhsh == "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664":
            # ref: https://huggingface.co/tencent/Hunyuan-A13B-Instruct
            res = "hunyuan"
+        if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6":
+            # ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct
+            res = "hunyuan-dense"
        if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
            # ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
            res = "falcon-h1"
@@ -699,6 +702,9 @@ class TextModel(ModelBase):
        if chkhsh == "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890":
            # ref: https://huggingface.co/moonshotai/Kimi-K2-Base
            res = "kimi-k2"
+        if chkhsh == "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c":
+            # ref: https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
+            res = "qwen2"
        if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
            # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
            res = "llama-bpe"
@@ -7553,11 +7559,6 @@ class FalconH1Model(Mamba2Model):
 class HunYuanMoEModel(TextModel):
    model_arch = gguf.MODEL_ARCH.HUNYUAN_MOE

-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # For handling tied embeddings
-        self._tok_embd = None
-
    def set_vocab(self):
        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
@@ -7651,9 +7652,6 @@ class HunYuanMoEModel(TextModel):
    _experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name == "model.embed_tokens.weight":
-            self._tok_embd = data_torch.clone()
-
        if name == "lm_head.weight":
            if self.hparams.get("tie_word_embeddings", False):
                logger.info("Skipping tied output layer 'lm_head.weight'")
@@ -7698,6 +7696,98 @@ class HunYuanMoEModel(TextModel):
                raise ValueError(f"Unprocessed experts: {experts}")


+@ModelBase.register("HunYuanDenseV1ForCausalLM")
+class HunYuanModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE
+
+    def set_vocab(self):
+        if (self.dir_model / "tokenizer.json").is_file():
+            self._set_vocab_gpt2()
+        else:
+            from transformers import AutoTokenizer
+            tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+
+            # 1. Get the pre-tokenizer identifier hash
+            tokpre = self.get_vocab_base_pre(tokenizer)
+
+            # 2. Reverse-engineer the merges list from mergeable_ranks
+            merges = []
+            vocab = {}
+            mergeable_ranks = tokenizer.mergeable_ranks
+            for token, rank in mergeable_ranks.items():
+                vocab[QwenModel.token_bytes_to_string(token)] = rank
+                if len(token) == 1:
+                    continue
+                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
+                if len(merged) == 2:
+                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
+
+            # 3. Generate the tokens and toktypes lists
+            vocab_size = self.hparams["vocab_size"]
+            assert tokenizer.vocab_size == vocab_size
+            special_tokens = tokenizer.special_tokens
+            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
+            tokens: list[str] = []
+            toktypes: list[int] = []
+            for i in range(vocab_size):
+                if i not in reverse_vocab:
+                    tokens.append(f"[PAD{i}]")
+                    toktypes.append(gguf.TokenType.UNUSED)
+                else:
+                    token = reverse_vocab[i]
+                    tokens.append(token)
+                    if i in special_tokens.values():
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.NORMAL)
+
+            # 4. Write all vocab-related fields to the GGUF writer
+            self.gguf_writer.add_tokenizer_model("gpt2")
+            self.gguf_writer.add_tokenizer_pre(tokpre)
+            self.gguf_writer.add_token_list(tokens)
+            self.gguf_writer.add_token_types(toktypes)
+            self.gguf_writer.add_token_merges(merges)
+
+            # 5. Add special tokens and chat templates
+            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+            special_vocab.add_to_gguf(self.gguf_writer)
+            # FIX for BOS token: Overwrite incorrect id read from config.json
+            if self.hparams['hidden_size'] == 4096:
+                self.gguf_writer.add_bos_token_id(127958) # only for 7b dense, fix <|bos|> token
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        # Rope
+        rope_scaling = hparams.get("rope_scaling", {})
+        if rope_scaling.get("type") == "dynamic":
+            # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+            # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
+            alpha = rope_scaling.get("alpha", 50)
+            base = hparams.get("rope_theta", 10000.0)
+            dim = hparams["head_dim"]
+            scaled_base = base * (alpha ** (dim / (dim - 2)))
+            self.gguf_writer.add_rope_freq_base(scaled_base)
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+            self.gguf_writer.add_rope_scaling_factor(1)
+            # There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length
+            self.gguf_writer.add_context_length(256 * 1024) # 256k context length
+
+            # if any of our assumptions about the values are wrong, something has changed and this may need to be updated
+            assert base == 10000.0 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \
+                "HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually"
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "lm_head.weight":
+            if self.hparams.get("tie_word_embeddings", False):
+                logger.info("Skipping tied output layer 'lm_head.weight'")
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
@ModelBase.register("SmolLM3ForCausalLM")
 class SmolLM3Model(LlamaModel):
    model_arch = gguf.MODEL_ARCH.SMOLLM3
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -59,6 +59,10 @@ parser.add_argument(
    "--full", action="store_true",
    help="download full list of models - make sure you have access to all of them",
 )
+parser.add_argument(
+    "--check-missing", action="store_true",
+    help="only check for missing pre-tokenizer hashes",
+)
 parser.add_argument(
    "hf_token",
    help="optional HF token",
@@ -70,6 +74,10 @@ hf_token = args.hf_token if args.hf_token is not None else hf_token
 if hf_token is None:
    logger.warning("HF token not found. You can provide it as an argument or set it in ~/.cache/huggingface/token")

+if args.check_missing and args.full:
+    logger.warning("Downloading full list of models requested, ignoring --check-missing!")
+    args.check_missing = False
+
 # TODO: this string has to exercise as much pre-tokenizer functionality as possible
 #       will be updated with time - contributions welcome
 CHK_TXT = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
@@ -140,12 +148,14 @@ pre_computed_hashes = [
    {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"},
    {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
    {"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
+    {"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"},
    # falcon-h1 series uses 4 different tokenizers across model sizes (0.5b - 34b), hence we need to define 4 different hashes
    {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base", "chkhsh": "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6"},
    {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-1B-Base", "chkhsh": "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86"},
    {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-7B-Base", "chkhsh": "3eda48b4c4dc7de733d1a8b3e3b4a85243dbbf704da2ee9d42c6beced8897896"},
    {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-34B-Base", "chkhsh": "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b"},
    {"name": "kimi-k2",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base",   "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"},
+    {"name": "qwen2",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"},
 ]


@@ -220,12 +230,13 @@ if not args.full:
    all_models = models.copy()
    models = [model for model in all_models if model["name"] not in existing_models]

-logging.info(f"Downloading {len(models)} models...")
-for model in models:
-    try:
-        download_model(model)
-    except Exception as e:
-        logger.error(f"Failed to download model {model['name']}. Error: {e}")
+if not args.check_missing:
+    logging.info(f"Downloading {len(models)} models...")
+    for model in models:
+        try:
+            download_model(model)
+        except Exception as e:
+            logger.error(f"Failed to download model {model['name']}. Error: {e}")


 # generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function:
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -315,8 +315,9 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst

    const bool gqa_opt_applies = ((Q->ne[2] / K->ne[2]) % 2 == 0) && mask; // The mma-based kernels have GQA-specific optimizations
    const bool mma_needs_data_conversion = K->type != GGML_TYPE_F16 || V->type != GGML_TYPE_F16;
-    const bool mma_faster_for_bs1 = new_mma_available(cc) && gqa_opt_applies &&
-        (Q->ne[3] > 1 || cc < GGML_CUDA_CC_ADA_LOVELACE) && !mma_needs_data_conversion;
+    const bool mma_faster_for_rtx4000 = Q->ne[3] > 1 || (Q->ne[2] > 4*K->ne[2] && K->ne[1] >= 8192);
+    const bool mma_faster_for_bs1 = new_mma_available(cc) && gqa_opt_applies && !mma_needs_data_conversion &&
+        (cc < GGML_CUDA_CC_ADA_LOVELACE || mma_faster_for_rtx4000);
    const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % (2*warp_size) == 0;
    if (Q->ne[1] == 1 && can_use_vector_kernel && !mma_faster_for_bs1) {
        if (prec == GGML_PREC_DEFAULT) {
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -1852,6 +1852,9 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
    ggml_cuda_pool_alloc<cuda_t> src0_alloc(ctx.pool());
    ggml_cuda_pool_alloc<cuda_t> src1_alloc(ctx.pool());

+    bool is_src0_cont_2 = ggml_is_contiguous_2(src0);
+    bool is_src1_cont_2 = ggml_is_contiguous_2(src1);
+
    // Handle src0
    src0_ptr = (const cuda_t *) src0->data;

@@ -1870,6 +1873,8 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
        s11 = ne10;
        s12 = ne11*s11;
        s13 = ne12*s12;
+
+        is_src1_cont_2 = true;
    }

    // Setup destination buffer
@@ -1918,15 +1923,19 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
    const int64_t r2 = ne12/ne02;
    const int64_t r3 = ne13/ne03;

-    if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) {
+    if (r2 == 1 && r3 == 1 && is_src0_cont_2 && is_src1_cont_2) {
+        // with a [0, 2, 1, 3] perm. and ne02==1 the matrix strides need to be determined from dim 3:
+        const int64_t sma = ne02 == 1 ? nb03/nb00 : nb02/nb00;
+        const int64_t smb = ne12 == 1 ? s13       : s12;
+
        // there is no broadcast and src0, src1 are contiguous across dims 2, 3
        // use cublasGemmStridedBatchedEx
        CUBLAS_CHECK(
        cublasGemmStridedBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N,
                ne01, ne11, ne10,
-                alpha, src0_ptr, cu_data_type_a, nb01/nb00, nb02/nb00, // strideA
-                       src1_ptr, cu_data_type_b, s11,       s12,       // strideB
-                beta,     dst_t, cu_data_type,   ne0,       ne1*ne0,   // strideC
+                alpha, src0_ptr, cu_data_type_a, nb01/nb00, sma,     // strideA
+                       src1_ptr, cu_data_type_b, s11,       smb,     // strideB
+                beta,     dst_t, cu_data_type,   ne0,       ne1*ne0, // strideC
                ne12*ne13,
                cu_compute_type,
                CUBLAS_GEMM_DEFAULT_TENSOR_OP));
--- a/ggml/src/ggml-cuda/im2col.cu
+++ b/ggml/src/ggml-cuda/im2col.cu
@@ -1,65 +1,75 @@
 #include "im2col.cuh"

+#define MIN(a, b) (a) < (b) ? (a) : (b)
+
+#define MAX_GRIDDIM_Z 65535
+
 template <typename T>
 static  __global__ void im2col_kernel(
-        const float * x, T * dst, int64_t batch_offset,
-        int64_t offset_delta, int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW,
+        const float * x, T * dst,
+        int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH,
+        int64_t IC_IH_IW, int64_t IH_IW, int64_t N_OH, int64_t KH_KW, int64_t IC_KH_KW,
        int s0, int s1, int p0, int p1, int d0, int d1) {
    const int64_t i = threadIdx.x + blockIdx.x * blockDim.x;
-    if (i >= pelements) {
+    if (i >= IC_KH_KW) {
        return;
    }

-    const int64_t  ksize = OW * KH;
-    const int64_t  kx = i / ksize;
-    const int64_t  kd = kx * ksize;
-    const int64_t  ky = (i - kd) / OW;
-    const int64_t  ix = i % OW;
+    const int64_t iic = i / (KH_KW);
+    const int64_t rem = i - iic * KH_KW;
+    const int64_t ikh = rem / KW;
+    const int64_t ikw = rem - ikh * KW;

-    const int64_t  oh = blockIdx.y;
-    const int64_t  batch = blockIdx.z / IC;
-    const int64_t  ic = blockIdx.z % IC;
+    const int64_t  iow = blockIdx.y;
+    for (int64_t iz = blockIdx.z; iz < N_OH; iz+=MAX_GRIDDIM_Z) {
+        const int64_t  in = iz / OH;
+        const int64_t  ioh = iz - in * OH;

-    const int64_t iiw = ix * s0 + kx * d0 - p0;
-    const int64_t iih = oh * s1 + ky * d1 - p1;
+        const int64_t iiw = iow * s0 + ikw * d0 - p0;
+        const int64_t iih = ioh * s1 + ikh * d1 - p1;

-    const int64_t offset_dst =
-        ((batch * OH + oh) * OW + ix) * CHW +
-        (ic * (KW * KH) + ky * KW + kx);
+        const int64_t offset_dst =
+            ((in * OH + ioh) * OW + iow) * IC_KH_KW + iic * KH_KW + ikh * KW + ikw;

-    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
-        dst[offset_dst] = 0.0f;
-    } else {
-        const int64_t offset_src = ic * offset_delta + batch * batch_offset;
-        dst[offset_dst] = x[offset_src + iih * IW + iiw];
+        if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+            dst[offset_dst] = 0.0f;
+        } else {
+            const int64_t offset_src = iic * IC_IH_IW + in * IH_IW;
+            dst[offset_dst] = x[offset_src + iih * IW + iiw];
+        }
    }
 }

+// im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
 template <typename T>
 static void im2col_cuda(const float * x, T* dst,
    int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
-    int64_t batch, int64_t batch_offset, int64_t offset_delta,
+    int64_t N, int64_t IC_IH_IW, int64_t IH_IW,
    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
-    const int parallel_elements = OW * KW * KH;
-    const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
-    dim3 block_nums(num_blocks, OH, batch * IC);
-    im2col_kernel<<<block_nums, CUDA_IM2COL_BLOCK_SIZE, 0, stream>>>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1);
+    const int64_t IC_KH_KW = IC * KH * KW;
+    const int64_t num_blocks = (IC_KH_KW + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
+    const int64_t N_OH = N * OH;
+    const int64_t KH_KW = KW*KH;
+    dim3 block_nums(num_blocks, OW, MIN(N_OH, MAX_GRIDDIM_Z));
+    im2col_kernel<<<block_nums, MIN(IC_KH_KW, CUDA_IM2COL_BLOCK_SIZE) , 0, stream>>>(x, dst, IC, IW, IH, OH, OW, KW, KH,
+                                                                                     IC_IH_IW, IH_IW, N_OH, KH_KW, IC_KH_KW,
+                                                                                     s0, s1, p0, p1, d0, d1);
 }

 static void im2col_cuda_f16(const float * x, half * dst,
    int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
-    int64_t batch, int64_t batch_offset, int64_t offset_delta,
+    int64_t N, int64_t IC_IH_IW, int64_t IH_IW,
    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {

-    im2col_cuda<half>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0, p1, d0, d1, stream);
+    im2col_cuda<half>(x, dst, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
 }

 static void im2col_cuda_f32(const float * x, float * dst,
    int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
-    int64_t batch, int64_t batch_offset, int64_t offset_delta,
+    int64_t N, int64_t IC_IH_IW, int64_t IH_IW,
    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {

-    im2col_cuda<float>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0, p1, d0, d1, stream);
+    im2col_cuda<float>(x, dst, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
 }

 void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -91,13 +101,13 @@ void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    const int64_t OH = is_2D ? dst->ne[2] : 1;
    const int64_t OW =         dst->ne[1];

-    const size_t  delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
-    const int64_t batch        = src1->ne[is_2D ? 3 : 2];
-    const size_t  batch_offset = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32
+    const int64_t IC_IH_IW = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
+    const int64_t N        = src1->ne[is_2D ? 3 : 2];
+    const int64_t IH_IW    = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32

    if(dst->type == GGML_TYPE_F16) {
-        im2col_cuda_f16(src1_d, (half *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
+        im2col_cuda_f16(src1_d, (half *) dst_d, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
    } else {
-        im2col_cuda_f32(src1_d, (float *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
+        im2col_cuda_f32(src1_d, (float *) dst_d, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
    }
 }
--- a/ggml/src/ggml-cuda/mmq.cuh
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -251,25 +251,21 @@ static constexpr __device__ int mmq_get_granularity_device(const int /*mmq_x*/)
 #endif // AMD_MFMA_AVAILABLE

 #if defined(GGML_USE_HIP)
-static int mmq_get_nwarps_host(const int cc) {
-    return amd_mfma_available(cc) ? 8 : 4;
+static int mmq_get_nwarps_host(const int cc, const int warp_size) {
+    return amd_mfma_available(cc) ? 8 : 256/warp_size;
 }
 #else
-static int mmq_get_nwarps_host(const int /*cc*/) {
-    return 8;
+static int mmq_get_nwarps_host(const int /*cc*/, const int warp_size) {
+    return 256/warp_size;
 }
 #endif // (GGML_USE_HIP)

 static constexpr __device__ int mmq_get_nwarps_device() {
-#if defined(GGML_USE_HIP)
 #if defined(AMD_MFMA_AVAILABLE)
    return 8;
 #else
-    return 4;
+    return 256/ggml_cuda_get_physical_warp_size();
 #endif // AMD_MFMA_AVAILABLE
-#else
-    return 8;
-#endif // defined(GGML_USE_HIP)
 }

 // ------------------------------------------------------------
@@ -3472,7 +3468,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
    const int cc = ggml_cuda_info().devices[id].cc;
    const int nsm = ggml_cuda_info().devices[id].nsm;
    const int warp_size = ggml_cuda_info().devices[id].warp_size;
-    const int nwarps = mmq_get_nwarps_host(cc);
+    const int nwarps = mmq_get_nwarps_host(cc, warp_size);
    const int mmq_y = get_mmq_y_host(cc);

    const dim3 block_dims(warp_size, nwarps, 1);
@@ -3559,7 +3555,7 @@ void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cuda
    const int    cc     = ggml_cuda_info().devices[id].cc;
    const size_t smpbo  = ggml_cuda_info().devices[id].smpbo;
    const int warp_size = ggml_cuda_info().devices[id].warp_size;
-    const int nwarps    = mmq_get_nwarps_host(cc);
+    const int nwarps    = mmq_get_nwarps_host(cc, warp_size);

    const int mmq_x_max = get_mmq_x_max_host(cc);
    const int mmq_y = get_mmq_y_host(cc);
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -400,10 +400,10 @@ struct ggml_backend_opencl_context {
    cl_program program_mul_mm_f32_f32_l4_lm;
    cl_program program_mul_mm_f16_f32_l4_lm;

-    cl_kernel kernel_add, kernel_add_row;
-    cl_kernel kernel_mul, kernel_mul_row;
-    cl_kernel kernel_div, kernel_div_row;
-    cl_kernel kernel_sub, kernel_sub_row;
+    cl_kernel kernel_add, kernel_add_row, kernel_add_f16, kernel_add_row_f16;
+    cl_kernel kernel_mul, kernel_mul_row, kernel_mul_f16, kernel_mul_row_f16;
+    cl_kernel kernel_div, kernel_div_row, kernel_div_f16, kernel_div_row_f16;
+    cl_kernel kernel_sub, kernel_sub_row, kernel_sub_f16, kernel_sub_row_f16;
    cl_kernel kernel_scale;
    cl_kernel kernel_silu, kernel_silu_4;
    cl_kernel kernel_gelu, kernel_gelu_4;
@@ -674,8 +674,10 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
        backend_ctx->program_add =
            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);

-        CL_CHECK((backend_ctx->kernel_add     = clCreateKernel(backend_ctx->program_add, "kernel_add", &err), err));
-        CL_CHECK((backend_ctx->kernel_add_row = clCreateKernel(backend_ctx->program_add, "kernel_add_row", &err), err));
+        CL_CHECK((backend_ctx->kernel_add         = clCreateKernel(backend_ctx->program_add, "kernel_add", &err), err));
+        CL_CHECK((backend_ctx->kernel_add_row     = clCreateKernel(backend_ctx->program_add, "kernel_add_row", &err), err));
+        CL_CHECK((backend_ctx->kernel_add_f16     = clCreateKernel(backend_ctx->program_add, "kernel_add_f16", &err), err));
+        CL_CHECK((backend_ctx->kernel_add_row_f16 = clCreateKernel(backend_ctx->program_add, "kernel_add_row_f16", &err), err));
        GGML_LOG_CONT(".");
    }

@@ -1089,8 +1091,10 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
        backend_ctx->program_mul =
            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);

-        CL_CHECK((backend_ctx->kernel_mul     = clCreateKernel(backend_ctx->program_mul, "kernel_mul", &err), err));
-        CL_CHECK((backend_ctx->kernel_mul_row = clCreateKernel(backend_ctx->program_mul, "kernel_mul_row", &err), err));
+        CL_CHECK((backend_ctx->kernel_mul         = clCreateKernel(backend_ctx->program_mul, "kernel_mul", &err), err));
+        CL_CHECK((backend_ctx->kernel_mul_row     = clCreateKernel(backend_ctx->program_mul, "kernel_mul_row", &err), err));
+        CL_CHECK((backend_ctx->kernel_mul_f16     = clCreateKernel(backend_ctx->program_mul, "kernel_mul_f16", &err), err));
+        CL_CHECK((backend_ctx->kernel_mul_row_f16 = clCreateKernel(backend_ctx->program_mul, "kernel_mul_row_f16", &err), err));
        GGML_LOG_CONT(".");
    }

@@ -1288,11 +1292,16 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
 #else
        const std::string kernel_src = read_file("div.cl");
 #endif
+        std::string compile_opts = std::string("-cl-std=") + opencl_c_std +
+                               " -cl-mad-enable -cl-finite-math-only ";
+
        backend_ctx->program_div =
            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);

-        CL_CHECK((backend_ctx->kernel_div     = clCreateKernel(backend_ctx->program_div, "kernel_div", &err), err));
-        CL_CHECK((backend_ctx->kernel_div_row = clCreateKernel(backend_ctx->program_div, "kernel_div_row", &err), err));
+        CL_CHECK((backend_ctx->kernel_div         = clCreateKernel(backend_ctx->program_div, "kernel_div", &err), err));
+        CL_CHECK((backend_ctx->kernel_div_row     = clCreateKernel(backend_ctx->program_div, "kernel_div_row", &err), err));
+        CL_CHECK((backend_ctx->kernel_div_f16     = clCreateKernel(backend_ctx->program_div, "kernel_div_f16", &err), err));
+        CL_CHECK((backend_ctx->kernel_div_row_f16 = clCreateKernel(backend_ctx->program_div, "kernel_div_row_f16", &err), err));
        GGML_LOG_CONT(".");
    }

@@ -1308,8 +1317,10 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
        backend_ctx->program_sub =
            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);

-        CL_CHECK((backend_ctx->kernel_sub     = clCreateKernel(backend_ctx->program_sub, "kernel_sub", &err), err));
-        CL_CHECK((backend_ctx->kernel_sub_row = clCreateKernel(backend_ctx->program_sub, "kernel_sub_row", &err), err));
+        CL_CHECK((backend_ctx->kernel_sub         = clCreateKernel(backend_ctx->program_sub, "kernel_sub", &err), err));
+        CL_CHECK((backend_ctx->kernel_sub_row     = clCreateKernel(backend_ctx->program_sub, "kernel_sub_row", &err), err));
+        CL_CHECK((backend_ctx->kernel_sub_f16     = clCreateKernel(backend_ctx->program_sub, "kernel_sub_f16", &err), err));
+        CL_CHECK((backend_ctx->kernel_sub_row_f16 = clCreateKernel(backend_ctx->program_sub, "kernel_sub_row_f16", &err), err));
        GGML_LOG_CONT(".");
    }

@@ -2447,12 +2458,15 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                default:
                    return false;
            }
-        case GGML_OP_ADD:
        case GGML_OP_SCALE:
+            return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]);
+        case GGML_OP_ADD:
        case GGML_OP_MUL:
        case GGML_OP_DIV:
        case GGML_OP_SUB:
-            return op->src[0]->type == GGML_TYPE_F32;
+            return (op->src[0]->type == op->src[1]->type) &&
+                   (op->src[0]->type == op->type) &&
+                   (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16);
        case GGML_OP_UNARY:
            switch (ggml_get_unary_op(op)) {
                case GGML_UNARY_OP_GELU:
@@ -3680,35 +3694,39 @@ static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

-    const int  ne00 = src0 ? src0->ne[0] : 0;
-    const int  ne01 = src0 ? src0->ne[1] : 0;
-    const int  ne02 = src0 ? src0->ne[2] : 0;
-    const int  ne03 = src0 ? src0->ne[3] : 0;
+    GGML_ASSERT(src0->type == src1->type);
+    GGML_ASSERT(src0->type == dst->type);
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);

-    const cl_ulong nb00 = src0 ? src0->nb[0] : 0;
-    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
-    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
-    const cl_ulong nb03 = src0 ? src0->nb[3] : 0;
+    const int  ne00 = src0->ne[0];
+    const int  ne01 = src0->ne[1];
+    const int  ne02 = src0->ne[2];
+    const int  ne03 = src0->ne[3];

-    const int  ne10 = src1 ? src1->ne[0] : 0;
-    const int  ne11 = src1 ? src1->ne[1] : 0;
-    const int  ne12 = src1 ? src1->ne[2] : 0;
-    const int  ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);
+    const cl_ulong nb00 = src0->nb[0];
+    const cl_ulong nb01 = src0->nb[1];
+    const cl_ulong nb02 = src0->nb[2];
+    const cl_ulong nb03 = src0->nb[3];

-    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
-    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
-    const cl_ulong nb12 = src1 ? src1->nb[2] : 0;
-    const cl_ulong nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13);
+    const int  ne10 = src1->ne[0];
+    const int  ne11 = src1->ne[1];
+    const int  ne12 = src1->ne[2];
+    const int  ne13 = src1->ne[3]; UNUSED(ne13);

-    const int  ne0  = dst ? dst->ne[0] : 0;
-    const int  ne1  = dst ? dst->ne[1] : 0;
-    const int  ne2  = dst ? dst->ne[2] : 0;
-    const int  ne3  = dst ? dst->ne[3] : 0;
+    const cl_ulong nb10 = src1->nb[0];
+    const cl_ulong nb11 = src1->nb[1];
+    const cl_ulong nb12 = src1->nb[2];
+    const cl_ulong nb13 = src1->nb[3]; UNUSED(nb13);

-    const cl_ulong nb0  = dst ? dst->nb[0] : 0;
-    const cl_ulong nb1  = dst ? dst->nb[1] : 0;
-    const cl_ulong nb2  = dst ? dst->nb[2] : 0;
-    const cl_ulong nb3  = dst ? dst->nb[3] : 0;
+    const int  ne0  = dst->ne[0];
+    const int  ne1  = dst->ne[1];
+    const int  ne2  = dst->ne[2];
+    const int  ne3  = dst->ne[3];
+
+    const cl_ulong nb0  = dst->nb[0];
+    const cl_ulong nb1  = dst->nb[1];
+    const cl_ulong nb2  = dst->nb[2];
+    const cl_ulong nb3  = dst->nb[3];

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;

@@ -3731,7 +3749,12 @@ static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const

        bcast_row = true;
        int ne = ne00 / 4;
-        kernel = backend_ctx->kernel_add_row;
+
+        if (src0->type == GGML_TYPE_F32) {
+            kernel = backend_ctx->kernel_add_row;
+        } else {
+            kernel = backend_ctx->kernel_add_row_f16;
+        }

        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
@@ -3741,7 +3764,11 @@ static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const
        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
    } else {
-        kernel = backend_ctx->kernel_add;
+        if (src0->type == GGML_TYPE_F32) {
+            kernel = backend_ctx->kernel_add;
+        } else {
+            kernel = backend_ctx->kernel_add_f16;
+        }

        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
@@ -3803,35 +3830,39 @@ static void ggml_cl_mul(ggml_backend_t backend, const ggml_tensor * src0, const
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

-    const int ne00 = src0 ? src0->ne[0] : 0;
-    const int ne01 = src0 ? src0->ne[1] : 0;
-    const int ne02 = src0 ? src0->ne[2] : 0;
-    const int ne03 = src0 ? src0->ne[3] : 0;
+    GGML_ASSERT(src0->type == src1->type);
+    GGML_ASSERT(src0->type == dst->type);
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);

-    const cl_ulong nb00 = src0 ? src0->nb[0] : 0;
-    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
-    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
-    const cl_ulong nb03 = src0 ? src0->nb[3] : 0;
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+    const int ne03 = src0->ne[3];

-    const int ne10 = src1 ? src1->ne[0] : 0;
-    const int ne11 = src1 ? src1->ne[1] : 0;
-    const int ne12 = src1 ? src1->ne[2] : 0;
-    const int ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);
+    const cl_ulong nb00 = src0->nb[0];
+    const cl_ulong nb01 = src0->nb[1];
+    const cl_ulong nb02 = src0->nb[2];
+    const cl_ulong nb03 = src0->nb[3];

-    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
-    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
-    const cl_ulong nb12 = src1 ? src1->nb[2] : 0;
-    const cl_ulong nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13);
+    const int ne10 = src1->ne[0];
+    const int ne11 = src1->ne[1];
+    const int ne12 = src1->ne[2];
+    const int ne13 = src1->ne[3]; UNUSED(ne13);

-    const int ne0  = dst ? dst->ne[0] : 0;
-    const int ne1  = dst ? dst->ne[1] : 0;
-    const int ne2  = dst ? dst->ne[2] : 0;
-    const int ne3  = dst ? dst->ne[3] : 0;
+    const cl_ulong nb10 = src1->nb[0];
+    const cl_ulong nb11 = src1->nb[1];
+    const cl_ulong nb12 = src1->nb[2];
+    const cl_ulong nb13 = src1->nb[3]; UNUSED(nb13);

-    const cl_ulong nb0  = dst ? dst->nb[0] : 0;
-    const cl_ulong nb1  = dst ? dst->nb[1] : 0;
-    const cl_ulong nb2  = dst ? dst->nb[2] : 0;
-    const cl_ulong nb3  = dst ? dst->nb[3] : 0;
+    const int ne0  = dst->ne[0];
+    const int ne1  = dst->ne[1];
+    const int ne2  = dst->ne[2];
+    const int ne3  = dst->ne[3];
+
+    const cl_ulong nb0  = dst->nb[0];
+    const cl_ulong nb1  = dst->nb[1];
+    const cl_ulong nb2  = dst->nb[2];
+    const cl_ulong nb3  = dst->nb[3];

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;

@@ -3854,7 +3885,12 @@ static void ggml_cl_mul(ggml_backend_t backend, const ggml_tensor * src0, const

        bcast_row = true;
        int ne = ne00 / 4;
-        kernel = backend_ctx->kernel_mul_row;
+
+        if (src0->type == GGML_TYPE_F32) {
+            kernel = backend_ctx->kernel_mul_row;
+        } else {
+            kernel = backend_ctx->kernel_mul_row_f16;
+        }

        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
@@ -3864,7 +3900,11 @@ static void ggml_cl_mul(ggml_backend_t backend, const ggml_tensor * src0, const
        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
    } else {
-        kernel = backend_ctx->kernel_mul;
+        if (src0->type == GGML_TYPE_F32) {
+            kernel = backend_ctx->kernel_mul;
+        } else {
+            kernel = backend_ctx->kernel_mul_f16;
+        }

        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
@@ -3926,6 +3966,10 @@ static void ggml_cl_div(ggml_backend_t backend, const ggml_tensor * src0, const
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

+    GGML_ASSERT(src0->type == src1->type);
+    GGML_ASSERT(src0->type == dst->type);
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+
    const int ne00 = src0->ne[0];
    const int ne01 = src0->ne[1];
    const int ne02 = src0->ne[2];
@@ -3974,7 +4018,12 @@ static void ggml_cl_div(ggml_backend_t backend, const ggml_tensor * src0, const

        bcast_row = true;
        int ne = ne00 / 4;
-        kernel = backend_ctx->kernel_div_row;
+
+        if (src0->type == GGML_TYPE_F32) {
+            kernel = backend_ctx->kernel_div_row;
+        } else {
+            kernel = backend_ctx->kernel_div_row_f16;
+        }

        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
@@ -3984,7 +4033,11 @@ static void ggml_cl_div(ggml_backend_t backend, const ggml_tensor * src0, const
        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
    } else {
-        kernel = backend_ctx->kernel_div;
+        if (src0->type == GGML_TYPE_F32) {
+            kernel = backend_ctx->kernel_div;
+        } else {
+            kernel = backend_ctx->kernel_div_f16;
+        }

        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
@@ -4034,6 +4087,10 @@ static void ggml_cl_sub(ggml_backend_t backend, const ggml_tensor * src0, const
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

+    GGML_ASSERT(src0->type == src1->type);
+    GGML_ASSERT(src0->type == dst->type);
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+
    const int ne00 = src0->ne[0];
    const int ne01 = src0->ne[1];
    const int ne02 = src0->ne[2];
@@ -4082,7 +4139,12 @@ static void ggml_cl_sub(ggml_backend_t backend, const ggml_tensor * src0, const

        bcast_row = true;
        int ne = ne00 / 4;
-        kernel = backend_ctx->kernel_sub_row;
+
+        if (src0->type == GGML_TYPE_F32) {
+            kernel = backend_ctx->kernel_sub_row;
+        } else {
+            kernel = backend_ctx->kernel_sub_row_f16;
+        }

        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
@@ -4092,7 +4154,11 @@ static void ggml_cl_sub(ggml_backend_t backend, const ggml_tensor * src0, const
        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
    } else {
-        kernel = backend_ctx->kernel_sub;
+        if (src0->type == GGML_TYPE_F32) {
+            kernel = backend_ctx->kernel_sub;
+        } else {
+            kernel = backend_ctx->kernel_sub_f16;
+        }

        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
--- a/ggml/src/ggml-opencl/kernels/add.cl
+++ b/ggml/src/ggml-opencl/kernels/add.cl
@@ -81,3 +81,76 @@ kernel void kernel_add_row(
    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
    dst[gid] = src0[gid] + src1[idx1];
 }
+
+kernel void kernel_add_f16(
+        global char * src0,
+        ulong  offset0,
+        global char * src1,
+        ulong  offset1,
+        global char * dst,
+        ulong  offsetd,
+        int   ne00,
+        int   ne01,
+        int   ne02,
+        int   ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int   ne10,
+        int   ne11,
+        int   ne12,
+        int   ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int   ne0,
+        int   ne1,
+        int   ne2,
+        int   ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst = dst + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int i13 = i03 % ne13;
+    int i12 = i02 % ne12;
+    int i11 = i01 % ne11;
+
+    global char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01;
+    global char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11;
+    global char * dst_ptr  = dst  + i03*nb3  + i02*nb2  + i01*nb1;
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        const int i10 = i0 % ne10;
+        *((global half *)(dst_ptr + i0*nb0)) = *((global half *)(src0_ptr + i0*nb00)) + *((global half *)(src1_ptr + i10*nb10));
+    }
+}
+
+kernel void kernel_add_row_f16(
+        global half4 * src0,
+        ulong  offset0,
+        global half4 * src1,
+        ulong  offset1,
+        global half4 * dst,
+        ulong  offsetd,
+        int ne
+) {
+    src0 = (global half4*)((global char*)src0 + offset0);
+    src1 = (global half4*)((global char*)src1 + offset1);
+    dst = (global half4*)((global char*)dst + offsetd);
+
+    // This performs better than using %.
+    uint gid = get_global_id(0);
+    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
+    dst[gid] = src0[gid] + src1[idx1];
+}
--- a/ggml/src/ggml-opencl/kernels/div.cl
+++ b/ggml/src/ggml-opencl/kernels/div.cl
@@ -70,3 +70,69 @@ kernel void kernel_div_row(
    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
    dst[gid] = src0[gid] / src1[idx1];
 }
+
+kernel void kernel_div_f16(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global char * dst,
+        ulong offsetd,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        int ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int i13 = i03 % ne13;
+    int i12 = i02 % ne12;
+    int i11 = i01 % ne11;
+
+    global char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01;
+    global char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11;
+    global char * dst_ptr  = dst  + i03*nb3  + i02*nb2  + i01*nb1;
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        const int i10 = i0 % ne10;
+        *((global half *)(dst_ptr + i0*nb0)) = *((global half *)(src0_ptr + i0*nb00)) / *((global half *)(src1_ptr + i10*nb10));
+    }
+}
+
+kernel void kernel_div_row_f16(
+        global half4 * src0,
+        ulong offset0,
+        global half4 * src1,
+        ulong offset1,
+        global half4 * dst,
+        ulong offsetd,
+        int ne
+) {
+    src0 = (global half4*)((global char*)src0 + offset0);
+    src1 = (global half4*)((global char*)src1 + offset1);
+    dst = (global half4*)((global char*)dst + offsetd);
+
+    // This performs better than using %.
+    uint gid = get_global_id(0);
+    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
+    dst[gid] = src0[gid] / src1[idx1];
+}
--- a/ggml/src/ggml-opencl/kernels/mul.cl
+++ b/ggml/src/ggml-opencl/kernels/mul.cl
@@ -77,3 +77,76 @@ kernel void kernel_mul_row(
    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
    dst[gid] = src0[gid] * src1[idx1];
 }
+
+kernel void kernel_mul_f16(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global char * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        int ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int i13 = i03 % ne13;
+    int i12 = i02 % ne12;
+    int i11 = i01 % ne11;
+
+    global char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01;
+    global char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11;
+    global char * dst_ptr  = dst  + i03*nb3  + i02*nb2  + i01*nb1;
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        const int i10 = i0 % ne10;
+        *((global half *)(dst_ptr + i0*nb0)) = *((global half *)(src0_ptr + i0*nb00)) * *((global half *)(src1_ptr + i10*nb10));
+    }
+}
+
+kernel void kernel_mul_row_f16(
+        global half4 * src0,
+        ulong offset0,
+        global half4 * src1,
+        ulong offset1,
+        global half4 * dst,
+        ulong offsetd,
+        int ne
+) {
+    src0 = (global half4*)((global char*)src0 + offset0);
+    src1 = (global half4*)((global char*)src1 + offset1);
+    dst = (global half4*)((global char*)dst + offsetd);
+
+    // This performs better than using %.
+    uint gid = get_global_id(0);
+    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
+    dst[gid] = src0[gid] * src1[idx1];
+}
--- a/ggml/src/ggml-opencl/kernels/sub.cl
+++ b/ggml/src/ggml-opencl/kernels/sub.cl
@@ -70,3 +70,69 @@ kernel void kernel_sub_row(
    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
    dst[gid] = src0[gid] - src1[idx1];
 }
+
+kernel void kernel_sub_f16(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global char * dst,
+        ulong offsetd,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        int ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int i13 = i03 % ne13;
+    int i12 = i02 % ne12;
+    int i11 = i01 % ne11;
+
+    global char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01;
+    global char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11;
+    global char * dst_ptr  = dst  + i03*nb3  + i02*nb2  + i01*nb1;
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        const int i10 = i0 % ne10;
+        *((global half *)(dst_ptr + i0*nb0)) = *((global half *)(src0_ptr + i0*nb00)) - *((global half *)(src1_ptr + i10*nb10));
+    }
+}
+
+kernel void kernel_sub_row_f16(
+        global half4 * src0,
+        ulong offset0,
+        global half4 * src1,
+        ulong offset1,
+        global half4 * dst,
+        ulong offsetd,
+        int ne
+) {
+    src0 = (global half4*)((global char*)src0 + offset0);
+    src1 = (global half4*)((global char*)src1 + offset1);
+    dst = (global half4*)((global char*)dst + offsetd);
+
+    // This performs better than using %.
+    uint gid = get_global_id(0);
+    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
+    dst[gid] = src0[gid] - src1[idx1];
+}
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -2688,6 +2688,9 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
    const size_t       type_size_src0 = ggml_type_size(src0->type);
    const size_t       type_size_src1 = ggml_type_size(src1->type);

+    bool is_src0_cont_2 = ggml_is_contiguous_2(src0);
+    bool is_src1_cont_2 = ggml_is_contiguous_2(src1);
+
    // SRC1 strides
    int64_t                          s11 = nb11 / type_size_src1;
    int64_t                          s12 = nb12 / type_size_src1;
@@ -2737,6 +2740,8 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
        s11      = ne10;
        s12      = ne11 * s11;
        s13      = ne12 * s12;
+
+        is_src1_cont_2 = true;
    }

    ggml_sycl_pool_alloc<sycl::half> dst_f16(ctx.pool());
@@ -2852,12 +2857,16 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
    else
 #endif
    {
-        if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) {
+        if (r2 == 1 && r3 == 1 && is_src0_cont_2 && is_src1_cont_2) {
+            // with a [0, 2, 1, 3] perm. and ne02==1 the matrix strides need to be determined from dim 3:
+            const int64_t sma = ne02 == 1 ? nb03/nb00 : nb02/nb00;
+            const int64_t smb = ne12 == 1 ? s13       : s12;
+
            // there is no broadcast and src0, src1 are contiguous across dims 2, 3
            SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(*queue, oneapi::math::transpose::trans,
                                                        oneapi::math::transpose::nontrans, ne01, ne11, ne10, alpha,
-                                                        src0_f16, dpct::library_data_t::real_half, nb01 / nb00, nb02 / nb00,
-                                                        src1_f16, dpct::library_data_t::real_half, s11, s12, beta, dst_ddf,
+                                                        src0_f16, dpct::library_data_t::real_half, nb01 / nb00, sma,
+                                                        src1_f16, dpct::library_data_t::real_half, s11, smb, beta, dst_ddf,
                                                        mkl_data_type, ne0, ne1 * ne0, ne12 * ne13, mkl_compute_type)));
        } else {
            const int ne23 = ne12 * ne13;
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -222,6 +222,7 @@ enum vk_device_architecture {
    AMD_RDNA2,
    AMD_RDNA3,
    INTEL_XE2,
+    NVIDIA_PRE_TURING,
 };

 // HSK x HSV
@@ -315,10 +316,33 @@ static vk_device_architecture get_device_architecture(const vk::PhysicalDevice&
            // https://www.intel.com/content/www/us/en/docs/oneapi/optimization-guide-gpu/2025-0/intel-xe-gpu-architecture.html
            return vk_device_architecture::INTEL_XE2;
        }
+    } else if (props.vendorID == VK_VENDOR_ID_NVIDIA) {
+        const std::vector<vk::ExtensionProperties> ext_props = device.enumerateDeviceExtensionProperties();
+
+        bool cooperative_matrix = false;
+
+        // Detect "pre-turing" based on lack of coopmat support.
+        for (const auto& properties : ext_props) {
+            if (strcmp("VK_KHR_cooperative_matrix", properties.extensionName) == 0) {
+                cooperative_matrix = true;
+                break;
+            }
+        }
+
+        if (!cooperative_matrix) {
+            return vk_device_architecture::NVIDIA_PRE_TURING;
+        }
    }
    return vk_device_architecture::OTHER;
 }

+enum vk_conv_shapes {
+    CONV_SHAPE_128x128,
+    CONV_SHAPE_64x32,
+    CONV_SHAPE_32x256,
+    CONV_SHAPE_COUNT,
+};
+
 struct vk_device_struct {
    std::recursive_mutex mutex;

@@ -483,8 +507,8 @@ struct vk_device_struct {
    vk_pipeline pipeline_rwkv_wkv6_f32;
    vk_pipeline pipeline_rwkv_wkv7_f32;
    vk_pipeline pipeline_opt_step_adamw_f32;
-    vk_pipeline pipeline_conv2d_f32;
-    vk_pipeline pipeline_conv2d_f16_f32;
+    vk_pipeline pipeline_conv2d_f32[CONV_SHAPE_COUNT];
+    vk_pipeline pipeline_conv2d_f16_f32[CONV_SHAPE_COUNT];
    vk_pipeline pipeline_conv2d_dw_whcn_f32;
    vk_pipeline pipeline_conv2d_dw_cwhn_f32;

@@ -908,8 +932,22 @@ struct vk_op_conv2d_push_constants {
    uint32_t nb1;
    uint32_t nb2;
    uint32_t nb3;
+
+    // init_fastdiv_values constants for dividing by KW, KW*KH, OW, OW*OH
+    uint32_t KWmp;   uint32_t KWL;
+    uint32_t KWKHmp; uint32_t KWKHL;
+    uint32_t OWmp;   uint32_t OWL;
+    uint32_t OWOHmp; uint32_t OWOHL;
 };

+template <> void init_pushconst_fastdiv(vk_op_conv2d_push_constants &p) {
+    // Compute magic values to divide by KW, KW*KH, OW, OW*OH
+    init_fastdiv_values(p.KW,       p.KWmp,    p.KWL);
+    init_fastdiv_values(p.KW*p.KH,  p.KWKHmp,  p.KWKHL);
+    init_fastdiv_values(p.OW,       p.OWmp,    p.OWL);
+    init_fastdiv_values(p.OW*p.OH,  p.OWOHmp,  p.OWOHL);
+}
+
 struct vk_op_conv2d_dw_push_constants {
    uint32_t ne;
    uint32_t batches;
@@ -2068,12 +2106,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
        s_mmq_wg_denoms = { 32,  64,  1 };

        // spec constants and tile sizes for quant matmul (Qi_K)
-        l_warptile_mmq_k = { 256, 64, 128, 64,  1 };
-        m_warptile_mmq_k = { 256, 32,  64, 64,  0 };
-        s_warptile_mmq_k = { 256, 32,  32, 128, 0 };
-        l_mmq_wg_denoms_k = { 64, 128, 1 };
-        m_mmq_wg_denoms_k = { 32,  64, 1 };
-        s_mmq_wg_denoms_k = { 32,  32, 1 };
+        l_warptile_mmq_k = { 256, 128, 256, 64, 1 };
+        m_warptile_mmq_k = { 256, 128, 128, 64, 1 };
+        s_warptile_mmq_k = { 256, 32,  64, 128, 0 };
+        l_mmq_wg_denoms_k = { 128, 256, 1 };
+        m_mmq_wg_denoms_k = { 128, 128, 1 };
+        s_mmq_wg_denoms_k = { 32,  64,  1 };

        // spec constants and tile sizes for quant matmul_id
        l_warptile_mmqid = { 256, 128, 128, 16, 0 };
@@ -2847,7 +2885,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
            ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_len,              mul_mat_vec_p021_f16_f32_data,              "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true);
        }
    }
-    ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 9 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 12 * sizeof(uint32_t), {1, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_group_norm_f32, "group_norm_f32", group_norm_f32_len, group_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
@@ -3048,48 +3086,89 @@ static void ggml_vk_load_shaders(vk_device& device) {
    ggml_vk_create_pipeline(device, device->pipeline_opt_step_adamw_f32, "opt_step_adamw_f32", opt_step_adamw_f32_len, opt_step_adamw_f32_data, "main", 5, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);

    // conv2d
-    uint32_t conv2d_WG_SIZE  = 256;
-    uint32_t conv2d_BS_K     = 128;
-    uint32_t conv2d_BS_CRS   = 16;
-    uint32_t use_collectives = 0;  // Enables subgroup ops for preventing the re-calculation of indices.
-    if (device->subgroup_shuffle &&
-        device->vendor_id != VK_VENDOR_ID_INTEL) {  // Do not enable collectives on Intel, see PR 14316
-        use_collectives = 1;
-        conv2d_BS_CRS   = std::min(
-            device->subgroup_size,
-            conv2d_BS_CRS);  // CRS block size should be capped at sugroup size for correctness when shuffle is used.
-    }
-    uint32_t conv2d_BS_NPQ = 128;
-    uint32_t conv2d_TS_K   = 8;
-    uint32_t conv2d_shmem_req =
-        (conv2d_BS_K * (conv2d_BS_CRS + 1) + conv2d_BS_CRS * (conv2d_BS_NPQ + 1)) * sizeof(float);
-    if (device->properties.limits.maxComputeSharedMemorySize < conv2d_shmem_req) {
-        conv2d_BS_CRS = 8;
-        if (use_collectives) {
-            conv2d_BS_CRS = std::min(device->subgroup_size, conv2d_BS_CRS);
-        }
-    }
+    for (uint32_t s = 0; s < CONV_SHAPE_COUNT; ++s) {
+        uint32_t conv2d_WG_SIZE  = 256;
+        uint32_t conv2d_BS_K     = 128;
+        uint32_t conv2d_BS_CRS   = 16;
+        uint32_t use_collectives = 0;  // Enables subgroup ops for preventing the re-calculation of indices.
+        uint32_t conv2d_BS_NPQ = 128;
+        uint32_t conv2d_TS_K   = 8;
+        uint32_t conv2d_SHMEM_PAD = 4;
+        bool conv2d_UNROLL = true;

-    if (use_collectives) {
-        ggml_vk_create_pipeline(
-            device, device->pipeline_conv2d_f32, "conv2d_f32", conv2d_f32_len, conv2d_f32_data, "main", 3,
-            sizeof(vk_op_conv2d_push_constants), { conv2d_BS_K, conv2d_BS_NPQ, 1 },
-            { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives }, 1, true, true);
-        ggml_vk_create_pipeline(
-            device, device->pipeline_conv2d_f16_f32, "conv2d_f16_f32", conv2d_f16_f32_len, conv2d_f16_f32_data, "main", 3,
-            sizeof(vk_op_conv2d_push_constants), { conv2d_BS_K, conv2d_BS_NPQ, 1 },
-            { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives }, 1, true, true);
-    } else {
-        ggml_vk_create_pipeline(
-            device, device->pipeline_conv2d_f32, "conv2d_f32", conv2d_f32_len, conv2d_f32_data, "main", 3,
-            sizeof(vk_op_conv2d_push_constants), { conv2d_BS_K, conv2d_BS_NPQ, 1 },
-            { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives }, 1, true,
-            false);
-        ggml_vk_create_pipeline(
-            device, device->pipeline_conv2d_f16_f32, "conv2d_f16_f32", conv2d_f16_f32_len, conv2d_f16_f32_data, "main", 3,
-            sizeof(vk_op_conv2d_push_constants), { conv2d_BS_K, conv2d_BS_NPQ, 1 },
-            { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives }, 1, true,
-            false);
+        if (device->vendor_id == VK_VENDOR_ID_INTEL) {
+            conv2d_SHMEM_PAD = 0;
+            conv2d_UNROLL = false;
+        } else if (device->vendor_id == VK_VENDOR_ID_AMD) {
+            conv2d_SHMEM_PAD = device->architecture == vk_device_architecture::AMD_GCN ? 1 : 4;
+        }
+
+        switch (s) {
+        default:
+        case CONV_SHAPE_128x128:
+            conv2d_BS_K = 128;
+            conv2d_BS_NPQ = 128;
+            conv2d_BS_CRS = 16;
+            if (device->vendor_id == VK_VENDOR_ID_AMD && device->architecture != vk_device_architecture::AMD_GCN) {
+                conv2d_UNROLL = false;
+            }
+            break;
+        case CONV_SHAPE_64x32:
+            conv2d_BS_K = 64;
+            conv2d_BS_NPQ = 32;
+            conv2d_BS_CRS = 32;
+            conv2d_TS_K   = 4;
+            break;
+        case CONV_SHAPE_32x256:
+            conv2d_BS_K = 32;
+            conv2d_BS_NPQ = 256;
+            conv2d_BS_CRS = 16;
+            break;
+        }
+
+        // Use collectives on pre-Turing NVIDIA GPUs and GCN AMD cards, which had slower integer math.
+        bool allow_collectives_nv = device->vendor_id != VK_VENDOR_ID_NVIDIA ||
+                                    device->architecture == vk_device_architecture::NVIDIA_PRE_TURING;
+        bool allow_collectives_amd = device->vendor_id != VK_VENDOR_ID_AMD ||
+                                     device->architecture == vk_device_architecture::AMD_GCN;
+
+        if (device->subgroup_shuffle &&
+            device->vendor_id != VK_VENDOR_ID_INTEL &&   // Do not enable collectives on Intel, see PR 14316.
+            allow_collectives_nv &&
+            allow_collectives_amd) {
+            use_collectives = 1;
+            conv2d_BS_CRS   = std::min(
+                device->subgroup_size,
+                conv2d_BS_CRS);  // CRS block size should be capped at subgroup size for correctness when shuffle is used.
+        }
+
+        uint32_t conv2d_shmem_req =
+            (conv2d_BS_K * (conv2d_BS_CRS + conv2d_SHMEM_PAD) + conv2d_BS_CRS * (conv2d_BS_NPQ + conv2d_SHMEM_PAD)) * sizeof(float);
+        if (device->properties.limits.maxComputeSharedMemorySize < conv2d_shmem_req) {
+            conv2d_BS_CRS = 8;
+            if (use_collectives) {
+                conv2d_BS_CRS = std::min(device->subgroup_size, conv2d_BS_CRS);
+            }
+        }
+
+        std::array<uint32_t, 3> wg_denoms = { conv2d_BS_K, conv2d_BS_NPQ, 1 };
+        std::vector<uint32_t> spec_constants = { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives, conv2d_SHMEM_PAD };
+
+        if (conv2d_UNROLL) {
+            ggml_vk_create_pipeline(
+                device, device->pipeline_conv2d_f32[s], "conv2d_f32", conv2d_f32_unroll_len, conv2d_f32_unroll_data, "main", 3,
+                sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
+            ggml_vk_create_pipeline(
+                device, device->pipeline_conv2d_f16_f32[s], "conv2d_f16_f32", conv2d_f16_f32_unroll_len, conv2d_f16_f32_unroll_data, "main", 3,
+                sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
+        } else {
+            ggml_vk_create_pipeline(
+                device, device->pipeline_conv2d_f32[s], "conv2d_f32", conv2d_f32_len, conv2d_f32_data, "main", 3,
+                sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
+            ggml_vk_create_pipeline(
+                device, device->pipeline_conv2d_f16_f32[s], "conv2d_f16_f32", conv2d_f16_f32_len, conv2d_f16_f32_data, "main", 3,
+                sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
+        }
    }

    ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f32, "conv2d_dw_whcn_f32", conv2d_dw_whcn_f32_len, conv2d_dw_whcn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
@@ -4943,26 +5022,37 @@ static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, siz
    ggml_vk_queue_command_pools_cleanup(dst->device);
 }

-static uint32_t ggml_vk_guess_split_k(ggml_backend_vk_context * ctx, int m, int n, int k, const vk_pipeline& pipeline) {
+static uint32_t ggml_vk_guess_split_k(ggml_backend_vk_context * ctx, uint32_t m, uint32_t n, uint32_t k, const vk_pipeline& pipeline) {
    VK_LOG_DEBUG("ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")");

    uint32_t split_k = 1;
-    if (ctx->device->shader_core_count != 0 && m >= (int)pipeline->wg_denoms[0] && n >= (int)pipeline->wg_denoms[1]) {
+    if (ctx->device->shader_core_count != 0 && m >= pipeline->wg_denoms[0] && n >= pipeline->wg_denoms[1]) {
        // If k is 'large' and the SMs will fill less than halfway, use split_k.
        uint32_t m_tiles = CEIL_DIV(m, pipeline->wg_denoms[0]);
        uint32_t n_tiles = CEIL_DIV(n, pipeline->wg_denoms[1]);
-        if (k >= 2048 && m_tiles * n_tiles < ctx->device->shader_core_count / 2) {
-            split_k = ctx->device->shader_core_count / (m_tiles * n_tiles);
-            // Clamp to 2 or 4
-            split_k = std::min(split_k, 4u);
-            if (split_k == 3) {
-                split_k = 2;
+
+        if (k >= 2048) {
+            if (m_tiles * n_tiles <= ctx->device->shader_core_count / 2) {
+                split_k = ctx->device->shader_core_count / (m_tiles * n_tiles);
+            } else if (m_tiles * n_tiles <= ctx->device->shader_core_count * 2 / 3) {
+                split_k = 3;
            }
-            if (ctx->device->coopmat2) {
-                // coopmat2 shader expects splits to be aligned to 256
-                while (split_k > 1 && ((k / split_k) % 256) != 0) {
-                    split_k /= 2;
+            // Cap the split at 8x. Unless k is huge this is a lot of overhead.
+            split_k = std::min(split_k, 8u);
+
+            // ggml_vk_matmul will align the splits to be a multiple of 256.
+            // If this rounded up size would cause the last split to be empty,
+            // then reduce the split count.
+            while (true) {
+                if (split_k == 1) {
+                    break;
                }
+                uint32_t k_split = CEIL_DIV(k, split_k);
+                k_split = ROUNDUP_POW2(k_split, 256);
+                if (k_split * (split_k - 1) < k) {
+                    break;
+                }
+                split_k--;
            }
        }
    }
@@ -4974,9 +5064,22 @@ static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx,
    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")");

    if (ctx->device->coopmat2) {
+        const uint32_t shader_core_count = ctx->device->shader_core_count;
+        const uint32_t tiles_l = CEIL_DIV(m, mmp->a_l->wg_denoms[0]) * CEIL_DIV(n, mmp->a_l->wg_denoms[1]);
+        const uint32_t tiles_m = CEIL_DIV(m, mmp->a_m->wg_denoms[0]) * CEIL_DIV(n, mmp->a_m->wg_denoms[1]);
+
        // Use large shader when the N dimension is greater than the medium shader's tile size
        uint32_t crossover_large = mmp->m->wg_denoms[1];
-        if ((ctx->device->mul_mat_l[src0_type] && (n > crossover_large)) || (!ctx->device->mul_mat_m[src0_type] && !ctx->device->mul_mat_s[src0_type])) {
+
+        // Prefer large over medium if either:
+        // - medium or large tiles would overfill the GPU
+        // - large tiles with a split_k==3 fits in the GPU and medium tiles with split_k==2 does not
+        //   (medium with split_k==2 is probably better if it fits - more workgroups running and less split_k overhead)
+        bool prefer_large = tiles_m > shader_core_count || tiles_l > shader_core_count ||
+                            // split_k==3 with large tiles likely better than medium tiles with no split_k.
+                            (tiles_l <= shader_core_count / 3 && tiles_m > shader_core_count / 2);
+
+        if ((ctx->device->mul_mat_l[src0_type] && (n > crossover_large && prefer_large)) || (!ctx->device->mul_mat_m[src0_type] && !ctx->device->mul_mat_s[src0_type])) {
            return aligned ? mmp->a_l : mmp->l;
        }
        // Use medium shader when the N dimension is greater than the small shader's tile size
@@ -5020,7 +5123,11 @@ static void ggml_vk_matmul(

    GGML_ASSERT(batch_stride_d == m * n);

-    const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, padded_n };
+    // Round the split size up to a multiple of 256 (k-quant alignment)
+    uint32_t k_split = CEIL_DIV(k, split_k);
+    k_split = ROUNDUP_POW2(k_split, 256);
+
+    const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, k_split, ne02, ne12, broadcast2, broadcast3, padded_n };
    // Make sure enough workgroups get assigned for split k to work
    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch });
    ggml_vk_sync_buffers(subctx);
@@ -5742,7 +5849,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
    const uint64_t ne00 = src0->ne[0];
    const uint64_t ne01 = src0->ne[1];
    const uint64_t ne02 = src0->ne[2];
-    // const uint64_t ne03 = src0->ne[3];
+    const uint64_t ne03 = src0->ne[3];

    const uint64_t nb01 = src0->nb[1];
    const uint64_t nb02 = src0->nb[2];
@@ -5754,7 +5861,12 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
    const uint64_t ne12 = src1->ne[2];
    // const uint64_t ne13 = src1->ne[3];

+    const uint32_t nb03 = (uint32_t)(src0->nb[3] / sizeof(ggml_fp16_t));
+    const uint32_t nb13 = (uint32_t)(src1->nb[3] / sizeof(float));
+    const uint32_t nb23 = (uint32_t)(dst->nb[3] / sizeof(float));
+
    GGML_ASSERT(ne11 == 1);
+    GGML_ASSERT(src0->ne[3] == src1->ne[3]); // checked in supports_op

    ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
    ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context;
@@ -5770,7 +5882,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
        src1_uma = d_Qy != nullptr;
    }

-    const uint64_t d_ne = ne01 * ne11 * ne12;
+    const uint64_t d_ne = ne01 * ne11 * ne12 * ne03;

    const uint32_t row_stride_x = nb01 / sizeof(ggml_fp16_t);
    const uint32_t channel_stride_x = nb02 / sizeof(ggml_fp16_t);
@@ -5805,10 +5917,10 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
    const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;

    // compute
-    const std::array<uint32_t, 9> pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, channel_stride_y, (uint32_t)(ne12 / ne02), (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
+    const std::array<uint32_t, 12> pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, channel_stride_y, (uint32_t)(ne12 / ne02), (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)), nb03, nb13, nb23 };
    ggml_vk_sync_buffers(subctx);
    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32,
-        { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
+        { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, pc, { (uint32_t)ne03, (uint32_t)ne01, (uint32_t)ne12 });
 }

 static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
@@ -6641,6 +6753,34 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
    }
 }

+static std::array<uint32_t, 3> ggml_vk_get_conv_elements(const ggml_tensor *dst) {
+    const ggml_tensor *src0 = dst->src[0];
+    const ggml_tensor *src1 = dst->src[1];
+
+    // src0 - kernel:   [KW, KH, Cin, Cout]
+    // src1 - input:    [W, H, Cin, N]
+    // dst - result:    [OW, OH, Cout, N]
+
+    // Copied from ggml.c: int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d)
+    auto calc_conv_output_size = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t {
+        return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
+    };
+    // parallelize in {OW/BS_K, OH/BS_NPQ, 1}
+    int64_t W    = src1->ne[0];
+    int64_t H    = src1->ne[1];
+    int64_t KW   = src0->ne[0];
+    int64_t KH   = src0->ne[1];
+    int64_t Cout = src0->ne[3];
+    int64_t N    = src1->ne[3];
+    int64_t OH   = calc_conv_output_size(H, KH, dst->op_params[1], dst->op_params[3], dst->op_params[5]);
+    int64_t OW   = calc_conv_output_size(W, KW, dst->op_params[0], dst->op_params[2], dst->op_params[4]);
+    int64_t NPQ  = N * OW * OH;
+
+    // Tile output matrix to (K/NB_K, NPQ/NB_NPQ, 1) workgroups
+    std::array<uint32_t, 3> elements = { static_cast<uint32_t>(Cout), static_cast<uint32_t>(NPQ), 1 };
+    return elements;
+}
+
 static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) {
    switch (op) {
    case GGML_OP_GET_ROWS:
@@ -6970,10 +7110,30 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
    case GGML_OP_CONV_2D:
        if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 &&
            ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
+            auto elements = ggml_vk_get_conv_elements(dst);
+            vk_conv_shapes shape;
+
+            uint32_t tiles[CONV_SHAPE_COUNT];
+            for (uint32_t i = 0; i < CONV_SHAPE_COUNT; ++i) {
+                tiles[i] = CEIL_DIV(elements[0], ctx->device->pipeline_conv2d_f32[i]->wg_denoms[0]) * CEIL_DIV(elements[1], ctx->device->pipeline_conv2d_f32[i]->wg_denoms[1]);
+            }
+
+            // We can't query number of shader cores on Intel, use 32 as a placeholder
+            // so small convolutions will still choose a smaller tile.
+            const uint32_t shader_core_count = ctx->device->shader_core_count > 0 ? ctx->device->shader_core_count : 32;
+
+            if (elements[0] > 64 && tiles[CONV_SHAPE_128x128] >= shader_core_count * 2) {
+                shape = CONV_SHAPE_128x128;
+            } else if (elements[0] <= 32 && tiles[CONV_SHAPE_32x256] >= shader_core_count * 2) {
+                shape = CONV_SHAPE_32x256;
+            } else {
+                shape = CONV_SHAPE_64x32;
+            }
+
            if (src0->type == GGML_TYPE_F32) {
-                return ctx->device->pipeline_conv2d_f32;
+                return ctx->device->pipeline_conv2d_f32[shape];
            } else if (src0->type == GGML_TYPE_F16) {
-                return ctx->device->pipeline_conv2d_f16_f32;
+                return ctx->device->pipeline_conv2d_f16_f32[shape];
            }
        }
        return nullptr;
@@ -7301,29 +7461,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
        } break;
    case GGML_OP_CONV_2D:
        {
-            // src0 - kernel:   [KW, KH, Cin, Cout]
-            // src1 - input:    [W, H, Cin, N]
-            // dst - result:    [OW, OH, Cout, N]
-
-            // Copied from ggml.c: int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d)
-            auto calc_conv_output_size = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t {
-                return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
-            };
-            // parallelize in {OW/BS_K, OH/BS_NPQ, 1}
-            int64_t W    = src1->ne[0];
-            int64_t H    = src1->ne[1];
-            int64_t KW   = src0->ne[0];
-            int64_t KH   = src0->ne[1];
-            int64_t Cout = src0->ne[3];
-            int64_t N    = src1->ne[3];
-            int64_t OH   = calc_conv_output_size(H, KH, dst->op_params[1], dst->op_params[3], dst->op_params[5]);
-            int64_t OW   = calc_conv_output_size(W, KW, dst->op_params[0], dst->op_params[2], dst->op_params[4]);
-            int64_t NPQ  = N * OW * OH;
-
-            // Tile output matrix to (K/NB_K, NPQ/NB_NPQ, 1) workgroups
-            elements = { static_cast<uint32_t>(Cout), static_cast<uint32_t>(NPQ), 1 };
-        }
-        break;
+            elements = ggml_vk_get_conv_elements(dst);
+        } break;
    case GGML_OP_ADD:
    case GGML_OP_SUB:
    case GGML_OP_DIV:
--- a/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp
@@ -1,14 +1,13 @@
 #version 450

+#extension GL_EXT_control_flow_attributes : enable
+
 #ifdef USE_COLLECTIVES
 #    extension GL_KHR_shader_subgroup_shuffle : enable
 #endif

 #include "types.comp"

-// Make spec constant
-#define SHMEM_PAD 0
-
 // shape notation: [dim(N), ..., dim(0)] -- stride(dim(j)) >= stride(dim(i)) if i > j
 layout(binding = 0) readonly buffer A {
    A_TYPE knl_data[];
@@ -56,6 +55,12 @@ layout(push_constant) uniform parameter {
    uint32_t nb1;
    uint32_t nb2;
    uint32_t nb3;
+
+    // fastdiv helper values
+    uint32_t KWmp;   uint32_t KWL;
+    uint32_t KWKHmp; uint32_t KWKHL;
+    uint32_t OWmp;   uint32_t OWL;
+    uint32_t OWOHmp; uint32_t OWOHL;
 }

 p;
@@ -68,6 +73,7 @@ layout(constant_id = 3) const uint BS_NPQ          = 128;
 // Thread-tile sizes
 layout(constant_id = 4) const uint TS_K            = 8;
 layout(constant_id = 5) const uint use_collectives = 1;
+layout(constant_id = 6) const uint SHMEM_PAD       = 4;

 uint32_t       tid     = gl_LocalInvocationID.x;
 const uint32_t WG_SIZE = gl_WorkGroupSize.x;
@@ -131,6 +137,14 @@ uint32_t       Br    = tid / BS_NPQ;
 uint32_t       Bc    = tid % BS_NPQ;
 const uint32_t BrpWg = WG_SIZE / BS_NPQ;

+// see init_fastdiv_values in ggml-vulkan.cpp
+uint fastdiv(uint n, uint mp, uint L) {
+    uint msbs, lsbs;
+    // msbs = mulhi(n, mp)
+    umulExtended(n, mp, msbs, lsbs);
+    return (msbs + n) >> L;
+}
+
 void main() {
    for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
        for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
@@ -151,9 +165,9 @@ void main() {
        uint32_t cached_KW_idx;
        if (use_collectives == 1) {
            cached_CRS_idx                = B_idx_CRS * BS_CRS + gl_SubgroupInvocationID;
-            cached_Cin_idx                = cached_CRS_idx / (p.KW * p.KH);
+            cached_Cin_idx                = fastdiv(cached_CRS_idx, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
            uint32_t cached_CRS_remainder = (cached_CRS_idx - cached_Cin_idx * p.KW * p.KH);
-            cached_KH_idx                 = cached_CRS_remainder / p.KW;
+            cached_KH_idx                 = fastdiv(cached_CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
            cached_KW_idx                 = cached_CRS_remainder - cached_KH_idx * p.KW;

            CRS_idx_a = subgroupShuffle(cached_CRS_idx, Ac);
@@ -162,16 +176,16 @@ void main() {
            KW_idx_a  = subgroupShuffle(cached_KW_idx, Ac);
        } else {
            CRS_idx_a              = B_idx_CRS * BS_CRS + Ac;  // Global CRS_idx_a (column index of A)
-            Cin_idx_a              = CRS_idx_a / (p.KW * p.KH);
+            Cin_idx_a              = fastdiv(CRS_idx_a, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
            uint32_t CRS_remainder = CRS_idx_a - Cin_idx_a * p.KW * p.KH;
-            KH_idx_a               = CRS_remainder / p.KW;
+            KH_idx_a               = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
            KW_idx_a               = CRS_remainder - KH_idx_a * p.KW;
        }
 #else
        CRS_idx_a     = B_idx_CRS * BS_CRS + Ac;  // Global CRS_idx_a (column index of A)
-        Cin_idx_a     = CRS_idx_a / (p.KW * p.KH);
+        Cin_idx_a     = fastdiv(CRS_idx_a, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH); / (p.KW * p.KH);
        CRS_remainder = CRS_idx_a - Cin_idx_a * p.KW * p.KH;
-        KH_idx_a      = CRS_remainder / p.KW;
+        KH_idx_a      = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
        KW_idx_a      = CRS_remainder - KH_idx_a * p.KW;
 #endif

@@ -188,13 +202,13 @@ void main() {
            Ash[B_ly * Ash_stride + B_lx] = val;
        }
        /* Load input to B_block: (BS_CRS x BS_NPQ) */
-        for (uint32_t r_offset = 0; r_offset < BS_CRS; r_offset += BrpWg) {
+        UNROLL for (uint32_t r_offset = 0; r_offset < BS_CRS; r_offset += BrpWg) {
            uint32_t B_ly          = r_offset + Br;             /* Row index of B block */
            uint32_t B_lx          = Bc;
            uint32_t NPQ_idx       = B_idx_NPQ * BS_NPQ + B_lx; /* Global NPQ index (column index of B) */
-            uint32_t N_idx         = NPQ_idx / (p.OH * p.OW);
+            uint32_t N_idx         = fastdiv(NPQ_idx, p.OWOHmp, p.OWOHL); // divide by p.OH * p.OW;
            uint32_t NPQ_remainder = NPQ_idx - N_idx * p.OH * p.OW;
-            uint32_t OH_idx        = NPQ_remainder / p.OW;
+            uint32_t OH_idx        = fastdiv(NPQ_remainder, p.OWmp, p.OWL); // divide by p.OW;
            uint32_t OW_idx        = NPQ_remainder - OH_idx * p.OW;

            uint32_t CRS_idx_b;
@@ -209,16 +223,16 @@ void main() {
                KW_idx_b  = subgroupShuffle(cached_KW_idx, r_offset + Br);
            } else {
                CRS_idx_b              = B_idx_CRS * BS_CRS + B_ly; /* Global CRS index (row index of B) */
-                Cin_idx_b              = CRS_idx_b / (p.KW * p.KH);
+                Cin_idx_b              = fastdiv(CRS_idx_b, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
                uint32_t CRS_remainder = CRS_idx_b - Cin_idx_b * p.KW * p.KH;
-                KH_idx_b               = CRS_remainder / p.KW;
+                KH_idx_b               = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
                KW_idx_b               = CRS_remainder - KH_idx_b * p.KW;
            }
 #else
            CRS_idx_b              = B_idx_CRS * BS_CRS + B_ly; /* Global CRS index (row index of B) */
-            Cin_idx_b              = CRS_idx_b / (p.KW * p.KH);
+            Cin_idx_b              = fastdiv(CRS_idx_b, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
            uint32_t CRS_remainder = CRS_idx_b - Cin_idx_b * p.KW * p.KH;
-            KH_idx_b               = CRS_remainder / p.KW;
+            KH_idx_b               = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
            KW_idx_b               = CRS_remainder - KH_idx_b * p.KW;
 #endif

@@ -233,32 +247,36 @@ void main() {
            Bsh[B_ly * Bsh_stride + B_lx] = val;
        }
        barrier();
-        for (uint32_t CRS_lidx = 0; CRS_lidx < BS_CRS; CRS_lidx++) {
-            for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
-                regA[T_ly] = Ash[(T_y * TS_K + T_ly) * Ash_stride + CRS_lidx];
-            }
-            for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
-                regB[T_lx] = Bsh[CRS_lidx * Bsh_stride + T_x * TS_NPQ + T_lx];
-            }
-            for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
+        if (T_y * TS_K < K) {
+            UNROLL for (uint32_t CRS_lidx = 0; CRS_lidx < BS_CRS; CRS_lidx++) {
+                for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
+                    regA[T_ly] = Ash[(T_y * TS_K + T_ly) * Ash_stride + CRS_lidx];
+                }
                for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
-                    regC[T_ly][T_lx] = fma(regA[T_ly], regB[T_lx], regC[T_ly][T_lx]);
+                    regB[T_lx] = Bsh[CRS_lidx * Bsh_stride + T_x * TS_NPQ + T_lx];
+                }
+                for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
+                    for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
+                        regC[T_ly][T_lx] = fma(regA[T_ly], regB[T_lx], regC[T_ly][T_lx]);
+                    }
                }
            }
        }
        barrier();
    }
    /* Save C* */
-    for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
-        for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
-            uint32_t K_idx   = B_idx_K * BS_K + T_y * TS_K + T_ly;
-            uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + T_x * TS_NPQ + T_lx;
-            uint32_t N_idx   = NPQ_idx / (p.OH * p.OW);
-            uint32_t OH_idx  = (NPQ_idx - N_idx * p.OH * p.OW) / p.OW;
-            uint32_t OW_idx  = NPQ_idx - N_idx * p.OH * p.OW - OH_idx * p.OW;
-            uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + K_idx * p.nb2 + N_idx * p.nb3;
-            if (K_idx < K && NPQ_idx < NPQ) {
-                dst_data[dst_idx] = regC[T_ly][T_lx];
+    if (T_y * TS_K < K) {
+        for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
+            for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
+                uint32_t K_idx   = B_idx_K * BS_K + T_y * TS_K + T_ly;
+                uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + T_x * TS_NPQ + T_lx;
+                uint32_t N_idx   = fastdiv(NPQ_idx, p.OWOHmp, p.OWOHL); // divide by p.OH * p.OW;
+                uint32_t OH_idx  = fastdiv(NPQ_idx - N_idx * p.OH * p.OW, p.OWmp, p.OWL); // divide by p.OW;
+                uint32_t OW_idx  = NPQ_idx - N_idx * p.OH * p.OW - OH_idx * p.OW;
+                uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + K_idx * p.nb2 + N_idx * p.nb3;
+                if (K_idx < K && NPQ_idx < NPQ) {
+                    dst_data[dst_idx] = regC[T_ly][T_lx];
+                }
            }
        }
    }
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp
@@ -26,6 +26,9 @@ layout (push_constant) uniform parameter
    uint ne12;
    uint b_offset;
    uint d_offset;
+    uint nb03;
+    uint nb13;
+    uint nb23;
 } p;

 shared FLOAT_TYPE tmp[BLOCK_SIZE];
@@ -34,6 +37,7 @@ void main() {
    const uint tid       = gl_LocalInvocationID.x;
    const uint row_x     = gl_GlobalInvocationID.y;
    const uint channel   = gl_GlobalInvocationID.z;
+    const uint i3        = gl_WorkGroupID.x;
    const uint channel_x = channel / p.channel_x_divisor;
    const uint channel_y = channel % p.ne12;

@@ -41,7 +45,7 @@ void main() {
    const uint nrows_dst = p.nrows_x;
    const uint row_dst   = row_x;

-    const uint idst = channel*nrows_dst + row_dst;
+    const uint idst = i3*p.nb23 + channel*nrows_dst + row_dst;

    FLOAT_TYPE temp = 0.0f;

@@ -58,8 +62,8 @@ void main() {

                const uint row_y = col_x;

-                const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
-                const uint iy = channel_y*p.channel_stride_y + row_y;
+                const uint ix = i3*p.nb03 + channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
+                const uint iy = i3*p.nb13 + channel_y*p.channel_stride_y + row_y;

                const vec4 av4 = vec4(data_a_v4[ix / 4]);
                const vec4 bv4 = vec4(data_b_v4[iy / 4]);
@@ -74,8 +78,8 @@ void main() {

            const uint row_y = col_x;

-            const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
-            const uint iy = channel_y*p.channel_stride_y + row_y;
+            const uint ix = i3*p.nb03 + channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
+            const uint iy = i3*p.nb13 + channel_y*p.channel_stride_y + row_y;

            const vec4 av4 = vec4(data_a_v4[ix / 4]);
            const vec4 bv4 = vec4(data_b_v4[iy / 4]);
@@ -91,8 +95,8 @@ void main() {

            const uint row_y = col_x;

-            const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
-            const uint iy = channel_y*p.channel_stride_y + row_y;
+            const uint ix = i3*p.nb03 + channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
+            const uint iy = i3*p.nb13 + channel_y*p.channel_stride_y + row_y;

            const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]);

--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -655,8 +655,11 @@ void process_shaders() {

    string_to_spv("opt_step_adamw_f32", "opt_step_adamw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));

-    string_to_spv("conv2d_f32", "conv2d_mm.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}});
-    string_to_spv("conv2d_f16_f32", "conv2d_mm.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}});
+    string_to_spv("conv2d_f32_unroll", "conv2d_mm.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", "[[unroll]]"}});
+    string_to_spv("conv2d_f16_f32_unroll", "conv2d_mm.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", "[[unroll]]"}});
+
+    string_to_spv("conv2d_f32", "conv2d_mm.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", ""}});
+    string_to_spv("conv2d_f16_f32", "conv2d_mm.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", ""}});

    string_to_spv("conv2d_dw_whcn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"WHCN", "1"}}));
    string_to_spv("conv2d_dw_cwhn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"CWHN", "1"}}));
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -376,6 +376,7 @@ class MODEL_ARCH(IntEnum):
    ERNIE4_5         = auto()
    ERNIE4_5_MOE     = auto()
    HUNYUAN_MOE      = auto()
+    HUNYUAN_DENSE    = auto()
    SMOLLM3          = auto()
    LFM2             = auto()
    DREAM            = auto()
@@ -697,6 +698,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
    MODEL_ARCH.ERNIE4_5_MOE:     "ernie4_5-moe",
    MODEL_ARCH.FALCON_H1:        "falcon-h1",
    MODEL_ARCH.HUNYUAN_MOE:      "hunyuan-moe",
+    MODEL_ARCH.HUNYUAN_DENSE:    "hunyuan-dense",
    MODEL_ARCH.SMOLLM3:          "smollm3",
    MODEL_ARCH.LFM2:             "lfm2",
    MODEL_ARCH.DREAM:            "dream",
@@ -2471,6 +2473,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.FFN_DOWN_SHEXP,
        MODEL_TENSOR.FFN_UP_SHEXP,
    ],
+    MODEL_ARCH.HUNYUAN_DENSE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
    MODEL_ARCH.SMOLLM3: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -33,6 +33,7 @@ class TensorNameMap:
            "language_model.model.embed_tokens",         # llama4
            "encoder",                                   # neobert
            "model.transformer.wte",                     # llada
+            "embed_tokens",                              # qwen3-embedding
        ),

        # Token type embeddings
@@ -143,6 +144,7 @@ class TensorNameMap:
            "transformer_encoder.{bid}.attention_norm",             # neobert
            "model.layers.{bid}.operator_norm",                     # lfm2
            "model.transformer.blocks.{bid}.attn_norm",             # llada
+            "layers.{bid}.input_layernorm",                         # qwen3-embedding
        ),

        # Attention norm 2
@@ -188,6 +190,7 @@ class TensorNameMap:
            "transformer.h.{bid}.attn.attention.q_proj",                 # exaone
            "model.layers.{bid}.self_attn.q_proj",                       # llama4
            "model.transformer.blocks.{bid}.q_proj",                     # llada
+            "layers.{bid}.self_attn.q_proj",                             # qwen3-embedding
        ),

        # Attention key
@@ -205,6 +208,7 @@ class TensorNameMap:
            "transformer.h.{bid}.attn.attention.k_proj",               # exaone
            "model.layers.{bid}.self_attn.k_proj",                     # llama4
            "model.transformer.blocks.{bid}.k_proj",                   # llada
+            "layers.{bid}.self_attn.k_proj",                           # qwen3-embedding
        ),

        # Attention value
@@ -221,6 +225,7 @@ class TensorNameMap:
            "transformer.h.{bid}.attn.attention.v_proj",                 # exaone
            "model.layers.{bid}.self_attn.v_proj",                       # llama4
            "model.transformer.blocks.{bid}.v_proj",                     # llada
+            "layers.{bid}.self_attn.v_proj",                             # qwen3-embedding
        ),

        # Attention output
@@ -254,6 +259,7 @@ class TensorNameMap:
            "model.layers.{bid}.self_attn.o_proj",                          # llama4
            "transformer_encoder.{bid}.wo",                                 # neobert
            "model.transformer.blocks.{bid}.attn_out",                      # llada
+            "layers.{bid}.self_attn.o_proj",                                # qwen3-embedding
        ),

        # Attention output norm
@@ -300,6 +306,7 @@ class TensorNameMap:
            "transformer_encoder.{bid}.ffn_norm",                            # neobert
            "model.layers.layers.{bid}.pre_mlp_norm",                        # plamo2
            "model.transformer.blocks.{bid}.ff_norm",                        # llada
+            "layers.{bid}.post_attention_layernorm",                         # qwen3-embedding
        ),

        # Post feed-forward norm
@@ -373,7 +380,8 @@ class TensorNameMap:
            "model.layers.{bid}.feed_forward.up_proj",                # llama4 jamba granite-hybrid
            "transformer_encoder.{bid}.ffn.w12",                      # neobert
            "model.layers.{bid}.block_sparse_moe.up",                 # smallthinker
-            "model.transformer.blocks.{bid}.up_proj",                  # llada
+            "model.transformer.blocks.{bid}.up_proj",                 # llada
+            "layers.{bid}.mlp.up_proj",                               # qwen3-embedding
        ),

        MODEL_TENSOR.FFN_UP_EXP: (
@@ -416,6 +424,7 @@ class TensorNameMap:
            "model.layers.{bid}.feed_forward.gate_proj",  # llama4 jamba granite-hybrid
            "model.layers.{bid}.block_sparse_moe.gate",   # smallthinker
            "model.transformer.blocks.{bid}.ff_proj",     # llada
+            "layers.{bid}.mlp.gate_proj",                 # qwen3-embedding
        ),

        MODEL_TENSOR.FFN_GATE_EXP: (
@@ -465,7 +474,8 @@ class TensorNameMap:
            "model.layers.{bid}.feed_forward.down_proj",              # llama4 jamba granite-hybrid
            "transformer_encoder.{bid}.ffn.w3",                       # neobert
            "model.layers.{bid}.block_sparse_moe.down",               # smallthinker
-            "model.transformer.blocks.{bid}.ff_out",                   # llada
+            "model.transformer.blocks.{bid}.ff_out",                  # llada
+            "layers.{bid}.mlp.down_proj",                             # qwen3-embedding
        ),

        MODEL_TENSOR.FFN_DOWN_EXP: (
@@ -497,6 +507,7 @@ class TensorNameMap:
            "encoder.layer.{bid}.attention.self.layer_norm_q",                # jina-bert-v2
            "transformer.layers.{bid}.attn.q_norm",                           # openelm
            "model.layers.layers.{bid}.mixer.q",                              # plamo2
+            "layers.{bid}.self_attn.q_norm",                                  # qwen3-embedding
        ),

        MODEL_TENSOR.ATTN_K_NORM: (
@@ -508,6 +519,7 @@ class TensorNameMap:
            "encoder.layer.{bid}.attention.self.layer_norm_k",                # jina-bert-v2
            "transformer.layers.{bid}.attn.k_norm",                           # openelm
            "model.layers.layers.{bid}.mixer.k",                              # plamo2
+            "layers.{bid}.self_attn.k_norm",                                  # qwen3-embedding
        ),

        MODEL_TENSOR.ROPE_FREQS: (
--- a/requirements/requirements-convert_hf_to_gguf_update.txt
+++ b/requirements/requirements-convert_hf_to_gguf_update.txt
@@ -1,7 +1 @@
 -r ./requirements-convert_legacy_llama.txt
--extra-index-url https://download.pytorch.org/whl/cpu
-torch~=2.2.1; platform_machine != "s390x"
-
-# torch s390x packages can only be found from nightly builds
--extra-index-url https://download.pytorch.org/whl/nightly
-torch>=0.0.0.dev0; platform_machine == "s390x"
--- a/scripts/compare-llama-bench.py
+++ b/scripts/compare-llama-bench.py
@@ -326,7 +326,7 @@ class LlamaBenchDataSQLite3(LlamaBenchData):

        # Set table name and schema based on tool
        if self.tool == "llama-bench":
-            self.table_name = "test"
+            self.table_name = "llama_bench"
            db_fields = LLAMA_BENCH_DB_FIELDS
            db_types = LLAMA_BENCH_DB_TYPES
        elif self.tool == "test-backend-ops":
@@ -409,8 +409,8 @@ class LlamaBenchDataSQLite3File(LlamaBenchDataSQLite3):

        # Tool selection logic
        if tool is None:
-            if "test" in table_names:
-                self.table_name = "test"
+            if "llama_bench" in table_names:
+                self.table_name = "llama_bench"
                self.tool = "llama-bench"
            elif "test_backend_ops" in table_names:
                self.table_name = "test_backend_ops"
@@ -418,8 +418,8 @@ class LlamaBenchDataSQLite3File(LlamaBenchDataSQLite3):
            else:
                raise RuntimeError(f"No suitable table found in database. Available tables: {table_names}")
        elif tool == "llama-bench":
-            if "test" in table_names:
-                self.table_name = "test"
+            if "llama_bench" in table_names:
+                self.table_name = "llama_bench"
                self.tool = "llama-bench"
            else:
                raise RuntimeError(f"Table 'test' not found for tool 'llama-bench'. Available tables: {table_names}")
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -85,6 +85,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
    { LLM_ARCH_ERNIE4_5,         "ernie4_5"         },
    { LLM_ARCH_ERNIE4_5_MOE,     "ernie4_5-moe"     },
    { LLM_ARCH_HUNYUAN_MOE,      "hunyuan-moe"      },
+    { LLM_ARCH_HUNYUAN_DENSE,    "hunyuan-dense"    },
    { LLM_ARCH_SMOLLM3,          "smollm3"          },
    { LLM_ARCH_LFM2,             "lfm2"             },
    { LLM_ARCH_DREAM,            "dream"            },
@@ -1897,6 +1898,26 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
        },
    },
+    {
+        LLM_ARCH_HUNYUAN_DENSE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+
+        },
+    },
    {
        LLM_ARCH_SMOLLM3,
        {
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -89,6 +89,7 @@ enum llm_arch {
    LLM_ARCH_ERNIE4_5,
    LLM_ARCH_ERNIE4_5_MOE,
    LLM_ARCH_HUNYUAN_MOE,
+    LLM_ARCH_HUNYUAN_DENSE,
    LLM_ARCH_SMOLLM3,
    LLM_ARCH_LFM2,
    LLM_ARCH_DREAM,
--- a/src/llama-chat.cpp
+++ b/src/llama-chat.cpp
@@ -66,6 +66,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
    { "llama4",            LLM_CHAT_TEMPLATE_LLAMA4            },
    { "smolvlm",           LLM_CHAT_TEMPLATE_SMOLVLM           },
    { "hunyuan-moe",       LLM_CHAT_TEMPLATE_HUNYUAN_MOE       },
+    { "hunyuan-dense",     LLM_CHAT_TEMPLATE_HUNYUAN_DENSE     },
    { "kimi-k2",           LLM_CHAT_TEMPLATE_KIMI_K2           },
 };

@@ -193,6 +194,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
        return LLM_CHAT_TEMPLATE_DOTS1;
    } else if (tmpl_contains("<|startoftext|>") && tmpl_contains("<|extra_4|>")) {
        return LLM_CHAT_TEMPLATE_HUNYUAN_MOE;
+    } else if (tmpl_contains("<｜hy_place▁holder▁no▁2｜>") && tmpl_contains("<｜hy_place▁holder▁no▁3｜>")) {
+        return LLM_CHAT_TEMPLATE_HUNYUAN_DENSE;
    } else if (tmpl_contains("<|im_assistant|>assistant<|im_middle|>")) {
        return LLM_CHAT_TEMPLATE_KIMI_K2;
    }
@@ -698,11 +701,27 @@ int32_t llm_chat_apply_template(
            if (role == "system") {
                ss << "<|startoftext|>" << message->content << "<|extra_4|>";
            } else if (role == "assistant") {
-                ss << "<|startoftext|>" << message->content << "<|eos|>";
+                ss << message->content << "<|eos|>";
            } else {
                ss << "<|startoftext|>" << message->content << "<|extra_0|>";
            }
        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_HUNYUAN_DENSE) {
+        // tencent/Hunyuan-4B-Instruct
+        for (size_t i = 0; i < chat.size(); i++) {
+            std::string role(chat[i]->role);
+            if (i == 0) {
+                if (role == "system") {
+                    ss << chat[i]->content << "<｜hy_place▁holder▁no▁3｜>";
+                }
+            }
+
+            if (role == "assistant") {
+                ss << "<｜hy_Assistant｜>" << chat[i]->content << "<｜hy_place▁holder▁no▁2｜>";
+            } else if (role == "user") {
+                ss << "<｜hy_User｜>" << chat[i]->content << "<｜hy_Assistant｜>";
+            }
+        }
    } else if (tmpl == LLM_CHAT_TEMPLATE_KIMI_K2) {
        // moonshotai/Kimi-K2-Instruct
        for (auto message : chat) {
--- a/src/llama-chat.h
+++ b/src/llama-chat.h
@@ -46,6 +46,7 @@ enum llm_chat_template {
    LLM_CHAT_TEMPLATE_SMOLVLM,
    LLM_CHAT_TEMPLATE_DOTS1,
    LLM_CHAT_TEMPLATE_HUNYUAN_MOE,
+    LLM_CHAT_TEMPLATE_HUNYUAN_DENSE,
    LLM_CHAT_TEMPLATE_KIMI_K2,
    LLM_CHAT_TEMPLATE_UNKNOWN,
 };
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -105,7 +105,7 @@ llama_context::llama_context(

    {
        const char * LLAMA_SET_ROWS = getenv("LLAMA_SET_ROWS");
-        supports_set_rows = LLAMA_SET_ROWS ? (atoi(LLAMA_SET_ROWS) != 0) : false;
+        supports_set_rows = LLAMA_SET_ROWS ? (atoi(LLAMA_SET_ROWS) != 0) : supports_set_rows;

        if (!supports_set_rows && !cparams.kv_unified) {
            LLAMA_LOG_WARN("%s: non-unified KV cache requires ggml_set_rows() - forcing unified KV cache\n", __func__);
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -289,7 +289,7 @@ private:

    // env: LLAMA_SET_ROWS (temporary)
    // ref: https://github.com/ggml-org/llama.cpp/pull/14285
-    bool supports_set_rows = false;
+    bool supports_set_rows = true;

    // env: LLAMA_GRAPH_REUSE_DISABLE
    bool graph_reuse_disable = false;
--- a/src/llama-kv-cache-unified.cpp
+++ b/src/llama-kv-cache-unified.cpp
@@ -183,7 +183,7 @@ llama_kv_cache_unified::llama_kv_cache_unified(
        const size_t memory_size_k = size_k_bytes();
        const size_t memory_size_v = size_v_bytes();

-        LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6u cells, %3d layers, %2u/%2u seqs), K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
+        LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6u cells, %3d layers, %2u/%u seqs), K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
                (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), kv_size, (int) layers.size(), n_seq_max, n_stream,
                ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
                ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
@@ -193,7 +193,7 @@ llama_kv_cache_unified::llama_kv_cache_unified(
    debug = LLAMA_KV_CACHE_DEBUG ? atoi(LLAMA_KV_CACHE_DEBUG) : 0;

    const char * LLAMA_SET_ROWS = getenv("LLAMA_SET_ROWS");
-    supports_set_rows = LLAMA_SET_ROWS ? atoi(LLAMA_SET_ROWS) != 0 : 0;
+    supports_set_rows = LLAMA_SET_ROWS ? atoi(LLAMA_SET_ROWS) != 0 : supports_set_rows;

    if (!supports_set_rows) {
        // ref: https://github.com/ggml-org/llama.cpp/pull/14363
--- a/src/llama-kv-cache-unified.h
+++ b/src/llama-kv-cache-unified.h
@@ -230,7 +230,7 @@ private:

    // env: LLAMA_SET_ROWS (temporary)
    // ref: https://github.com/ggml-org/llama.cpp/pull/14285
-    bool supports_set_rows = false;
+    bool supports_set_rows = true;

    const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;

--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -899,6 +899,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
            } break;
        case LLM_ARCH_QWEN3:
            {
+                ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                switch (hparams.n_layer) {
                    case 28: type = hparams.n_embd == 1024 ? LLM_TYPE_0_6B : LLM_TYPE_1_7B; break;
@@ -1760,6 +1761,18 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                    default: type = LLM_TYPE_UNKNOWN;
                }
            } break;
+        case LLM_ARCH_HUNYUAN_DENSE:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_embd) {
+                    case 1024: type = LLM_TYPE_0_5B; break;
+                    case 2048: type = LLM_TYPE_1_8B; break;
+                    case 3072: type = LLM_TYPE_4B; break;
+                    case 4096: type = LLM_TYPE_7B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
        case LLM_ARCH_SMOLLM3:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -5195,6 +5208,39 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
                    }
                } break;
+            case LLM_ARCH_HUNYUAN_DENSE:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+                    }
+                } break;
            case LLM_ARCH_SMOLLM3:
                {
                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -16930,6 +16976,144 @@ struct llm_build_hunyuan_moe : public llm_graph_context {
    }
 };

+struct llm_build_hunyuan_dense : public llm_graph_context {
+    llm_build_hunyuan_dense(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv_unified();
+
+        const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+            // self-attention
+            {
+                // rope freq factors for llama3; may return nullptr for llama2 and other models
+                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = ggml_rope_ext(
+                         ctx0, Qcur, inp_pos, rope_factors,
+                         n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                         ext_factor, attn_factor, beta_fast, beta_slow
+                         );
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                Kcur = ggml_rope_ext(
+                         ctx0, Kcur, inp_pos, rope_factors,
+                         n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                         ext_factor, attn_factor, beta_fast, beta_slow
+                         );
+
+                Kcur = build_norm(Kcur,
+                         model.layers[il].attn_k_norm, nullptr,
+                         LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_norm", il);
+
+                Qcur = build_norm(Qcur,
+                         model.layers[il].attn_q_norm, nullptr,
+                         LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_norm", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+            }
+
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+            // feed-forward network (non-MoE)
+            ggml_tensor * cur_mlp = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur_mlp, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur_mlp, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
 struct llm_build_smollm3 : public llm_graph_context {
    llm_build_smollm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
        const int64_t n_embd_head = hparams.n_embd_head_v;
@@ -17797,6 +17981,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
            {
                llm = std::make_unique<llm_build_hunyuan_moe>(*this, params);
            } break;
+        case LLM_ARCH_HUNYUAN_DENSE:
+            {
+                llm = std::make_unique<llm_build_hunyuan_dense>(*this, params);
+            } break;
        case LLM_ARCH_SMOLLM3:
            {
                llm = std::make_unique<llm_build_smollm3>(*this, params);
@@ -18016,6 +18204,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
        case LLM_ARCH_MINICPM3:
        case LLM_ARCH_DOTS1:
        case LLM_ARCH_HUNYUAN_MOE:
+        case LLM_ARCH_HUNYUAN_DENSE:
        case LLM_ARCH_LFM2:
        case LLM_ARCH_SMALLTHINKER:
            return LLAMA_ROPE_TYPE_NEOX;
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -307,6 +307,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                };
                break;
            case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM:
+            case LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE:
                regex_exprs = {
                    "\\p{N}{1,3}",
                    "[一-龥぀-ゟ゠-ヿ]+",
@@ -1964,6 +1965,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                tokenizer_pre == "hunyuan") {
                pre_type = LLAMA_VOCAB_PRE_TYPE_HUNYUAN;
                clean_spaces = false;
+            } else if (
+                tokenizer_pre == "hunyuan-dense") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE;
+                clean_spaces = false;
            } else if (
                tokenizer_pre == "kimi-k2") {
                pre_type = LLAMA_VOCAB_PRE_TYPE_KIMI_K2;
--- a/src/llama-vocab.h
+++ b/src/llama-vocab.h
@@ -46,6 +46,7 @@ enum llama_vocab_pre_type {
    LLAMA_VOCAB_PRE_TYPE_SEED_CODER     = 35,
    LLAMA_VOCAB_PRE_TYPE_HUNYUAN        = 36,
    LLAMA_VOCAB_PRE_TYPE_KIMI_K2        = 37,
+    LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE  = 38,
 };

 struct LLM_KV;
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -5592,13 +5592,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 1056, 1, 193, {1,  1}, {4, 1}, {0, 2, 1, 3}));
    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 1056, 1, 67,  {1,  1}, {4, 1}, {0, 2, 1, 3}));

-    for (auto bs : {1,2,4,8}) {
-        for (auto nr : {1,4}) {
-            for (uint32_t m = 0; m < 2; ++m) {
-                for (uint32_t k = 0; k < 2; ++k) {
-                    for (ggml_type type: {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_F32}) {
-                        test_cases.emplace_back(new test_mul_mat(type, GGML_TYPE_F32, 1056 + m, 1, 128 + k,  {bs,  1}, {nr, 1}, {0, 2, 1, 3}));
-                        test_cases.emplace_back(new test_mul_mat(type, GGML_TYPE_F32, 128 + m,  1, 1056 + k, {bs,  1}, {nr, 1}, {0, 1, 2, 3}, true));
+    for (auto bs2 : {1,3}) {
+        for (auto bs : {1,2,4,8}) {
+            for (auto nr : {1,4}) {
+                for (uint32_t m = 0; m < 2; ++m) {
+                    for (uint32_t k = 0; k < 2; ++k) {
+                        for (ggml_type type: {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_F32}) {
+                            test_cases.emplace_back(new test_mul_mat(type, GGML_TYPE_F32, 1056 + m, 1, 128 + k,  {bs,  bs2}, {nr, 1}, {0, 2, 1, 3}));
+                            test_cases.emplace_back(new test_mul_mat(type, GGML_TYPE_F32, 128 + m,  1, 1056 + k, {bs,  bs2}, {nr, 1}, {0, 1, 2, 3}, true));
+                        }
                    }
                }
            }
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -953,6 +953,33 @@ static void test_template_output_parsers() {
                /* is_partial= */ false,
                {COMMON_CHAT_FORMAT_HERMES_2_PRO}));

+        // Test multiple tool calls
+        common_chat_msg message_assist_multiple_calls;
+        message_assist_multiple_calls.role = "assistant";
+        message_assist_multiple_calls.content = "";
+        message_assist_multiple_calls.tool_calls.push_back({"special_function", "{\"arg1\": 1}", ""});
+        message_assist_multiple_calls.tool_calls.push_back({"python", "{\"code\":\"print('hello')\"}", ""});
+
+        assert_msg_equals(
+            message_assist_multiple_calls,
+            common_chat_parse(
+                "<tool_call>\n"
+                "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n"
+                "</tool_call>\n"
+                "<tool_call>\n"
+                "{\"name\": \"python\", \"arguments\": {\"code\":\"print('hello')\"}}\n"
+                "</tool_call>",
+                /* is_partial= */ false,
+                {COMMON_CHAT_FORMAT_HERMES_2_PRO}));
+
+        assert_msg_equals(
+            message_assist_multiple_calls,
+            common_chat_parse(
+                "<function=special_function>{\"arg1\": 1}</function>\n"
+                "<function=python>{\"code\":\"print('hello')\"}</function>",
+                /* is_partial= */ false,
+                {COMMON_CHAT_FORMAT_HERMES_2_PRO}));
+
        assert_msg_equals(
            simple_assist_msg(
                "This is not a tool call:",
@@ -1039,6 +1066,22 @@ static void test_template_output_parsers() {
                      "<tool_call>\n"
                      "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n"
                      "</tool_call>");
+
+        // Test multiple tool calls with template
+        common_chat_msg message_assist_multiple_calls_template;
+        message_assist_multiple_calls_template.role = "assistant";
+        message_assist_multiple_calls_template.content = "";
+        message_assist_multiple_calls_template.tool_calls.push_back({"special_function", "{\"arg1\": 1}", ""});
+        message_assist_multiple_calls_template.tool_calls.push_back({"python", "{\"code\":\"print('test')\"}", ""});
+
+        test_templates(tmpls.get(), end_tokens, message_assist_multiple_calls_template, tools,
+                      "<tool_call>\n"
+                      "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n"
+                      "</tool_call>\n"
+                      "<tool_call>\n"
+                      "{\"name\": \"python\", \"arguments\": {\"code\":\"print('test')\"}}\n"
+                      "</tool_call>");
+
        test_templates(tmpls.get(), end_tokens, message_assist_call_python_lines, tools,
                      "<tool_call>\n"
                      "{\"name\": \"python\", \"arguments\": {\"code\":\"# This is a program:\\nprint('hey')\"}}\n"
--- a/tools/llama-bench/llama-bench.cpp
+++ b/tools/llama-bench/llama-bench.cpp
@@ -1738,7 +1738,7 @@ struct sql_printer : public printer {

    void print_header(const cmd_params & params) override {
        std::vector<std::string> fields = test::get_fields();
-        fprintf(fout, "CREATE TABLE IF NOT EXISTS test (\n");
+        fprintf(fout, "CREATE TABLE IF NOT EXISTS llama_bench (\n");
        for (size_t i = 0; i < fields.size(); i++) {
            fprintf(fout, "  %s %s%s\n", fields.at(i).c_str(), get_sql_field_type(fields.at(i)).c_str(),
                    i < fields.size() - 1 ? "," : "");
@@ -1749,7 +1749,7 @@ struct sql_printer : public printer {
    }

    void print_test(const test & t) override {
-        fprintf(fout, "INSERT INTO test (%s) ", join(test::get_fields(), ", ").c_str());
+        fprintf(fout, "INSERT INTO llama_bench (%s) ", join(test::get_fields(), ", ").c_str());
        fprintf(fout, "VALUES (");
        std::vector<std::string> values = t.get_values();
        for (size_t i = 0; i < values.size(); i++) {
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -4249,9 +4249,6 @@ int main(int argc, char ** argv) {

            // process prompt
            std::vector<server_tokens> inputs;
-            if (oaicompat && !prompt.is_string()) {
-                throw std::runtime_error("prompt must be a string");
-            }

            if (oaicompat && has_mtmd) {
                // multimodal
--- a/vendor/minja/chat-template.hpp
+++ b/vendor/minja/chat-template.hpp
@@ -162,10 +162,15 @@ class chat_template {
        }), false);
        caps_.supports_tools = contains(out, "some_tool");

+        auto out_empty = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", ""}}}), {}, false);
+        auto out_null = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", nullptr}}}), {}, false);
+        caps_.requires_non_null_content = contains(out_empty, user_needle) && !contains(out_null, user_needle);
+
+        json j_null;
        auto make_tool_calls_msg = [&](const json & tool_calls) {
            return json {
                {"role", "assistant"},
-                {"content", nullptr},
+                {"content", caps_.requires_non_null_content? "" : j_null},
                {"tool_calls", tool_calls},
            };
        };
@@ -195,9 +200,6 @@ class chat_template {

        caps_.supports_tool_calls = tool_call_renders_str_arguments || tool_call_renders_obj_arguments;
        caps_.requires_object_arguments = !tool_call_renders_str_arguments && tool_call_renders_obj_arguments;
-        auto out_empty = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", ""}}}), {}, false);
-        auto out_null = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", nullptr}}}), {}, false);
-        caps_.requires_non_null_content = contains(out_empty, user_needle) && !contains(out_null, user_needle);

        if (caps_.supports_tool_calls) {
            auto dummy_args = caps_.requires_object_arguments ? dummy_args_obj : json(dummy_args_obj.dump());
@@ -234,7 +236,7 @@ class chat_template {
                };
                const json tool_call_msg {
                    {"role", "assistant"},
-                    {"content", nullptr},
+                    {"content", caps_.requires_non_null_content ? "" : j_null},
                    {"tool_calls", json::array({
                        {
                            // TODO: detect if requires numerical id or fixed length == 6 like Nemo
--- a/vendor/minja/minja.hpp
+++ b/vendor/minja/minja.hpp
@@ -1355,8 +1355,13 @@ public:
              case Op::Gt:        return l > r;
              case Op::Le:        return l <= r;
              case Op::Ge:        return l >= r;
-              case Op::In:        return (r.is_array() || r.is_object()) && r.contains(l);
-              case Op::NotIn:     return !(r.is_array() && r.contains(l));
+              case Op::In:        return (((r.is_array() || r.is_object()) && r.contains(l)) ||
+                                          (l.is_string() && r.is_string() &&
+                                            r.to_str().find(l.to_str()) != std::string::npos));
+              case Op::NotIn:
+                                  return !(((r.is_array() || r.is_object()) && r.contains(l)) ||
+                                            (l.is_string() && r.is_string() &&
+                                              r.to_str().find(l.to_str()) != std::string::npos));
              default:            break;
          }
          throw std::runtime_error("Unknown binary operator");
@@ -1552,6 +1557,19 @@ public:
              else res[i] = std::tolower(res[i]);
            }
            return res;
+          } else if (method->get_name() == "replace") {
+            vargs.expectArgs("replace method", {2, 3}, {0, 0});
+            auto before = vargs.args[0].get<std::string>();
+            auto after = vargs.args[1].get<std::string>();
+            auto count = vargs.args.size() == 3 ? vargs.args[2].get<int64_t>()
+                                                : str.length();
+            size_t start_pos = 0;
+            while ((start_pos = str.find(before, start_pos)) != std::string::npos &&
+                  count-- > 0) {
+              str.replace(start_pos, before.length(), after);
+              start_pos += after.length();
+            }
+            return str;
          }
        }
        throw std::runtime_error("Unknown method: " + method->get_name());
@@ -2128,7 +2146,7 @@ private:
            }
          }

-          if ((has_first_colon || has_second_colon) && (start || end || step)) {
+          if ((has_first_colon || has_second_colon)) {
            index = std::make_shared<SliceExpr>(slice_loc, std::move(start), std::move(end), std::move(step));
          } else {
            index = std::move(start);
Author	SHA1	Message	Date
Johannes Gäßler	03d4698218	CUDA: use mma FA kernel for gqa > 4 on RTX 4000 (#15035 )	2025-08-02 16:37:08 +02:00
leejet	3303c19b16	cuda: make im2col a little faster (#15025 )	2025-08-02 17:15:36 +03:00
Daniel Bevenius	4fdea540bd	kv-cache : skip alignment of n_stream in kv-cache log msg [no ci] (#15040 ) This commit removes the right alignment the `n_stream` value in the log message in the `llama_kv_cache_unified` constructor. The motivation for this change is to enhance the readability of log message. Currently the output looks like this: ```console llama_kv_cache_unified: size = 2048.00 MiB ( 4096 cells, 32 layers, 1/ 1 seqs), K (f16): 1024.00 MiB, V (f16): 1024.00 MiB ``` Notice that the `n_stream` value is right aligned, which makes it a little harder to read. With the change in this commit the output will look like ```console llama_kv_cache_unified: size = 2048.00 MiB ( 4096 cells, 32 layers, 1/1 seqs), K (f16): 1024.00 MiB, V (f16): 1024.00 MiB ```	2025-08-02 17:14:57 +03:00
Georgi Gerganov	a4569c41fd	llama : enable LLAMA_SET_ROWS=1 by default (#14959 ) ggml-ci	2025-08-02 17:14:21 +03:00
Georgi Gerganov	15e92fd337	cuda, sycl : fix batched gemm when ne02 == 1 && ne03 > 1 (#15038 ) * cuda, sycl : fix batched gemm when ne02 == 1 && ne03 > 1 ggml-ci * cont : fix cont types ggml-ci * cont : adopt variable names and comment from the other branch	2025-08-02 17:13:05 +03:00
Sigbjørn Skjæret	2bf3fbf0b5	ci : check that pre-tokenizer hashes are up-to-date (#15032 ) * torch is not required for convert_hf_to_gguf_update * add --check-missing parameter * check that pre-tokenizer hashes are up-to-date	2025-08-02 14:39:01 +02:00
Douglas Hanley	711d5e6fe6	convert : fix Qwen3-Embedding pre-tokenizer hash (#15030 ) Some checks are pending CI / macOS-latest-cmake-arm64 (push) Waiting to run CI / macOS-latest-cmake-x64 (push) Waiting to run CI / macOS-latest-cmake-arm64-webgpu (push) Waiting to run CI / ubuntu-cpu-cmake (arm64, ubuntu-22.04-arm) (push) Waiting to run CI / ubuntu-cpu-cmake (x64, ubuntu-22.04) (push) Waiting to run CI / ubuntu-latest-cmake-sanitizer (Debug, ADDRESS) (push) Waiting to run CI / ubuntu-latest-cmake-sanitizer (Debug, THREAD) (push) Waiting to run CI / ubuntu-latest-cmake-sanitizer (Debug, UNDEFINED) (push) Waiting to run CI / ubuntu-latest-llguidance (push) Waiting to run CI / ubuntu-latest-cmake-rpc (push) Waiting to run CI / ubuntu-22-cmake-vulkan (push) Waiting to run CI / ubuntu-22-cmake-webgpu (push) Waiting to run CI / ubuntu-22-cmake-hip (push) Waiting to run CI / ubuntu-22-cmake-musa (push) Waiting to run CI / ubuntu-22-cmake-sycl (push) Waiting to run CI / ubuntu-22-cmake-sycl-fp16 (push) Waiting to run CI / build-linux-cross (push) Waiting to run CI / build-cmake-pkg (push) Waiting to run CI / macOS-latest-cmake-ios (push) Waiting to run CI / macOS-latest-cmake-tvos (push) Waiting to run CI / macOS-latest-cmake-visionos (push) Waiting to run CI / macOS-latest-swift (generic/platform=iOS) (push) Waiting to run CI / macOS-latest-swift (generic/platform=macOS) (push) Waiting to run CI / macOS-latest-swift (generic/platform=tvOS) (push) Waiting to run CI / windows-msys2 (Release, clang-x86_64, CLANG64) (push) Waiting to run CI / windows-msys2 (Release, ucrt-x86_64, UCRT64) (push) Waiting to run CI / windows-latest-cmake (arm64, llvm-arm64, -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON) (push) Waiting to run CI / windows-latest-cmake (arm64, llvm-arm64-opencl-adreno, -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON) (push) Waiting to run CI / windows-latest-cmake (x64, cpu-x64 (static), -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF) (push) Waiting to run CI / windows-latest-cmake (x64, openblas-x64, -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF -DGGML_BLAS=… (push) Waiting to run CI / windows-latest-cmake (x64, vulkan-x64, -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_VULKAN=ON) (push) Waiting to run CI / ubuntu-latest-cmake-cuda (push) Waiting to run CI / windows-2022-cmake-cuda (12.4) (push) Waiting to run CI / windows-latest-cmake-sycl (push) Waiting to run CI / windows-latest-cmake-hip (push) Waiting to run CI / ios-xcode-build (push) Waiting to run CI / android-build (push) Waiting to run CI / openEuler-latest-cmake-cann (aarch64, Release, 8.1.RC1.alpha001-910b-openeuler22.03-py3.10, ascend910b3) (push) Waiting to run CI / openEuler-latest-cmake-cann (x86, Release, 8.1.RC1.alpha001-910b-openeuler22.03-py3.10, ascend910b3) (push) Waiting to run Python check requirements.txt / check-requirements (push) Waiting to run flake8 Lint / Lint (push) Waiting to run Python Type-Check / pyright type-check (push) Waiting to run	2025-08-02 12:51:02 +02:00
Jhen-Jie Hong	f738989dcb	chat : fix multiple tool_calls on hermes-2-pro (#14962 )	2025-08-02 18:04:48 +08:00
Jeff Bolz	4cb208c93c	vulkan: coopmat2 mul_mat optimizations (#14934 ) - Increase tile size for k-quants, to match non-k-quants - Choose more carefully between large and medium tiles, considering how it interacts with split_k - Allow larger/non-power of two split_k, and make the splits a multiple of 256 - Use split_k==3 to when >1/2 and <=2/3 of the SMs would hae been used	2025-08-02 11:21:37 +02:00
R0CKSTAR	3025b621d1	llama-bench: rename DB table name from test to llama_bench (#15003 ) Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>	2025-08-02 17:20:40 +08:00
Jeff Bolz	ec0b18802c	vulkan: Support ne[3]>1 in noncontig matrix-vector multiply (#15015 )	2025-08-02 10:48:30 +02:00
Douglas Hanley	339bd0268c	model : support Qwen3-Embedding (#15023 )	2025-08-02 10:44:50 +02:00
Johannes Gäßler	f906275537	server: enable token array inputs for OAI API (#15001 )	2025-08-02 10:12:41 +02:00
Jeff Bolz	a9f7541ec2	vulkan: optimizations for direct convolution (#14933 ) * vulkan: optimizations for direct convolution - Empirically choose a better tile size. Reducing BS_K/BS_NPQ helps fill the GPU. The new size should be amenable to using coopmat, too. - Fix shmem bank conflicts. 16B padding should work with coopmat. - Some explicit loop unrolling. - Skip math/stores work for parts of the tile that are OOB. - Apply fastdiv opt. - Disable shuffles for NV. * Three tiles sizes for CONV_2D, and a heuristic to choose * reallow collectives for pre-Turing * make SHMEM_PAD a spec constant * fixes for intel perf - no shmem padding, placeholder shader core count * shader variants with/without unrolling * 0cc4m's fixes for AMD perf Co-authored-by: 0cc4m <picard12@live.de> --------- Co-authored-by: 0cc4m <picard12@live.de>	2025-08-02 09:57:04 +02:00
Johannes Gäßler	9c35706b98	CUDA: fix MMQ nwarps for AMD with warp_size==32 (#15014 ) Some checks are pending CI / macOS-latest-cmake-arm64 (push) Waiting to run CI / macOS-latest-cmake-x64 (push) Waiting to run CI / macOS-latest-cmake-arm64-webgpu (push) Waiting to run CI / ubuntu-cpu-cmake (arm64, ubuntu-22.04-arm) (push) Waiting to run CI / ubuntu-cpu-cmake (x64, ubuntu-22.04) (push) Waiting to run CI / ubuntu-latest-cmake-sanitizer (Debug, ADDRESS) (push) Waiting to run CI / ubuntu-latest-cmake-sanitizer (Debug, THREAD) (push) Waiting to run CI / ubuntu-latest-cmake-sanitizer (Debug, UNDEFINED) (push) Waiting to run CI / ubuntu-latest-llguidance (push) Waiting to run CI / ubuntu-latest-cmake-rpc (push) Waiting to run CI / ubuntu-22-cmake-vulkan (push) Waiting to run CI / ubuntu-22-cmake-webgpu (push) Waiting to run CI / ubuntu-22-cmake-hip (push) Waiting to run CI / ubuntu-22-cmake-musa (push) Waiting to run CI / ubuntu-22-cmake-sycl (push) Waiting to run CI / ubuntu-22-cmake-sycl-fp16 (push) Waiting to run CI / build-linux-cross (push) Waiting to run CI / build-cmake-pkg (push) Waiting to run CI / macOS-latest-cmake-ios (push) Waiting to run CI / macOS-latest-cmake-tvos (push) Waiting to run CI / macOS-latest-cmake-visionos (push) Waiting to run CI / macOS-latest-swift (generic/platform=iOS) (push) Waiting to run CI / macOS-latest-swift (generic/platform=macOS) (push) Waiting to run CI / macOS-latest-swift (generic/platform=tvOS) (push) Waiting to run CI / windows-msys2 (Release, clang-x86_64, CLANG64) (push) Waiting to run CI / windows-msys2 (Release, ucrt-x86_64, UCRT64) (push) Waiting to run CI / windows-latest-cmake (arm64, llvm-arm64, -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON) (push) Waiting to run CI / windows-latest-cmake (arm64, llvm-arm64-opencl-adreno, -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON) (push) Waiting to run CI / windows-latest-cmake (x64, cpu-x64 (static), -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF) (push) Waiting to run CI / windows-latest-cmake (x64, openblas-x64, -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF -DGGML_BLAS=… (push) Waiting to run CI / windows-latest-cmake (x64, vulkan-x64, -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_VULKAN=ON) (push) Waiting to run CI / ubuntu-latest-cmake-cuda (push) Waiting to run CI / windows-2022-cmake-cuda (12.4) (push) Waiting to run CI / windows-latest-cmake-sycl (push) Waiting to run CI / windows-latest-cmake-hip (push) Waiting to run CI / ios-xcode-build (push) Waiting to run CI / android-build (push) Waiting to run CI / openEuler-latest-cmake-cann (aarch64, Release, 8.1.RC1.alpha001-910b-openeuler22.03-py3.10, ascend910b3) (push) Waiting to run CI / openEuler-latest-cmake-cann (x86, Release, 8.1.RC1.alpha001-910b-openeuler22.03-py3.10, ascend910b3) (push) Waiting to run Python check requirements.txt / check-requirements (push) Waiting to run flake8 Lint / Lint (push) Waiting to run Python Type-Check / pyright type-check (push) Waiting to run	2025-08-01 20:47:32 +02:00
l-austenfeld	c76b420e4c	vendor : update vendored copy of google/minja (#15011 ) * vendor : update vendored copy of google/minja Signed-off-by: Lennart Austenfeld <l.austenfeld@googlemail.com> * Re-remove trailing whitespace Signed-off-by: Lennart Austenfeld <l.austenfeld@googlemail.com> * Remove another trailing whitespace Signed-off-by: Lennart Austenfeld <l.austenfeld@googlemail.com> --------- Signed-off-by: Lennart Austenfeld <l.austenfeld@googlemail.com>	2025-08-01 16:59:06 +02:00
stevenkuang	0f5ccd6fd1	model : add hunyuan dense (#14878 ) * support hunyuan_v1_dense Signed-off-by: stevenkuang <stevenkuang@tencent.com> * update hunyuan_moe to hunyuan_v1_moe Signed-off-by: stevenkuang <stevenkuang@tencent.com> * fix rope alpha assert and bos token Signed-off-by: stevenkuang <stevenkuang@tencent.com> * add blank line Signed-off-by: stevenkuang <stevenkuang@tencent.com> * Revert "update hunyuan_moe to hunyuan_v1_moe" This reverts commit `aa973ca219`. * use hunyuan_dense instead of hunyuan_v1_dense Signed-off-by: stevenkuang <stevenkuang@tencent.com> * fix hunyuan_moe chat template Signed-off-by: stevenkuang <stevenkuang@tencent.com> * remove leftover code Signed-off-by: stevenkuang <stevenkuang@tencent.com> * update hunyuan dense chat template Signed-off-by: stevenkuang <stevenkuang@tencent.com> * fix hunyuan dense vocab and chat template Signed-off-by: stevenkuang <stevenkuang@tencent.com> --------- Signed-off-by: stevenkuang <stevenkuang@tencent.com>	2025-08-01 15:31:12 +02:00
lhez	1c872f71fb	opencl: add f16 for `add`, `sub`, `mul`, `div` (#14984 ) Some checks are pending CI / macOS-latest-cmake-arm64 (push) Waiting to run CI / macOS-latest-cmake-x64 (push) Waiting to run CI / macOS-latest-cmake-arm64-webgpu (push) Waiting to run CI / ubuntu-cpu-cmake (arm64, ubuntu-22.04-arm) (push) Waiting to run CI / ubuntu-cpu-cmake (x64, ubuntu-22.04) (push) Waiting to run CI / ubuntu-latest-cmake-sanitizer (Debug, ADDRESS) (push) Waiting to run CI / ubuntu-latest-cmake-sanitizer (Debug, THREAD) (push) Waiting to run CI / ubuntu-latest-cmake-sanitizer (Debug, UNDEFINED) (push) Waiting to run CI / ubuntu-latest-llguidance (push) Waiting to run CI / ubuntu-latest-cmake-rpc (push) Waiting to run CI / ubuntu-22-cmake-vulkan (push) Waiting to run CI / ubuntu-22-cmake-webgpu (push) Waiting to run CI / ubuntu-22-cmake-hip (push) Waiting to run CI / ubuntu-22-cmake-musa (push) Waiting to run CI / ubuntu-22-cmake-sycl (push) Waiting to run CI / ubuntu-22-cmake-sycl-fp16 (push) Waiting to run CI / build-linux-cross (push) Waiting to run CI / build-cmake-pkg (push) Waiting to run CI / macOS-latest-cmake-ios (push) Waiting to run CI / macOS-latest-cmake-tvos (push) Waiting to run CI / macOS-latest-cmake-visionos (push) Waiting to run CI / macOS-latest-swift (generic/platform=iOS) (push) Waiting to run CI / macOS-latest-swift (generic/platform=macOS) (push) Waiting to run CI / macOS-latest-swift (generic/platform=tvOS) (push) Waiting to run CI / windows-msys2 (Release, clang-x86_64, CLANG64) (push) Waiting to run CI / windows-msys2 (Release, ucrt-x86_64, UCRT64) (push) Waiting to run CI / windows-latest-cmake (arm64, llvm-arm64, -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON) (push) Waiting to run CI / windows-latest-cmake (arm64, llvm-arm64-opencl-adreno, -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON) (push) Waiting to run CI / windows-latest-cmake (x64, cpu-x64 (static), -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF) (push) Waiting to run CI / windows-latest-cmake (x64, openblas-x64, -G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF -DGGML_BLAS=… (push) Waiting to run CI / windows-latest-cmake (x64, vulkan-x64, -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_VULKAN=ON) (push) Waiting to run CI / ubuntu-latest-cmake-cuda (push) Waiting to run CI / windows-2022-cmake-cuda (12.4) (push) Waiting to run CI / windows-latest-cmake-sycl (push) Waiting to run CI / windows-latest-cmake-hip (push) Waiting to run CI / ios-xcode-build (push) Waiting to run CI / android-build (push) Waiting to run CI / openEuler-latest-cmake-cann (aarch64, Release, 8.1.RC1.alpha001-910b-openeuler22.03-py3.10, ascend910b3) (push) Waiting to run CI / openEuler-latest-cmake-cann (x86, Release, 8.1.RC1.alpha001-910b-openeuler22.03-py3.10, ascend910b3) (push) Waiting to run	2025-08-01 13:15:44 +02:00