Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	docs/multimodal/minicpmv4.0.md
#	examples/model-conversion/Makefile
#	examples/model-conversion/README.md
#	examples/model-conversion/logits.cpp
#	examples/model-conversion/scripts/causal/modelcard.template
#	examples/model-conversion/scripts/utils/hf-create-model.py
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	tests/test-backend-ops.cpp
#	tools/batched-bench/batched-bench.cpp

Author: LostRuins

convert_hf_to_gguf.py

@@ -3159,7 +3159,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         yield from super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("Ernie4_5_ForCausalLM")
+@ModelBase.register("Ernie4_5_ForCausalLM", "Ernie4_5ForCausalLM")
 class Ernie4_5Model(TextModel):
     model_arch = gguf.MODEL_ARCH.ERNIE4_5
 
@@ -6254,9 +6254,11 @@ def prepare_tensors(self):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
-@ModelBase.register("DeepseekV2ForCausalLM")
-@ModelBase.register("DeepseekV3ForCausalLM")
-@ModelBase.register("KimiVLForConditionalGeneration")
+@ModelBase.register(
+    "DeepseekV2ForCausalLM",
+    "DeepseekV3ForCausalLM",
+    "KimiVLForConditionalGeneration",
+)
 class DeepseekV2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK2
 
@@ -8507,6 +8509,43 @@ def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", "
             return "mm.2.weight"
         return super().map_tensor_name(name, try_suffixes)
 
+
+@ModelBase.register("KimiVLForConditionalGeneration")
+class KimiVLModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["image_size"] = 64 * 14 # for compatibility
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIVL)
+        self.gguf_writer.add_vision_use_gelu(True)
+        self.gguf_writer.add_vision_projector_scale_factor(2)
+        # eps is the same as pytorch's default value
+        assert self.hparams_vision is not None
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-5))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+        is_vision_tensor = "vision_tower" in name or "multi_modal_projector" in name
+
+        if is_vision_tensor:
+            if "pos_emb.weight" in name:
+                data_torch = data_torch.view(data_torch.shape[0] * data_torch.shape[1], data_torch.shape[2])
+            elif "wqkv" in name:
+                split_dim = 0 if "weight" in name else -1
+                wq, wk, wv = data_torch.chunk(3, dim=split_dim)
+                return [
+                    (self.map_tensor_name(name.replace("wqkv", "wq")), wq),
+                    (self.map_tensor_name(name.replace("wqkv", "wk")), wk),
+                    (self.map_tensor_name(name.replace("wqkv", "wv")), wv)
+                ]
+
+            return [(self.map_tensor_name(name), data_torch)]
+
+        return [] # skip other tensors
+
 ###### CONVERSION LOGIC ######
 
 

ggml/src/ggml-cuda/common.cuh

@@ -424,16 +424,28 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
 
 template<int width = WARP_SIZE>
 static __device__ __forceinline__ int warp_reduce_all(int x) {
-#ifdef GGML_USE_HIP
+    if (width == ggml_cuda_get_physical_warp_size()) {
+        return __all_sync(0xffffffff, x);
+    } else {
 #pragma unroll
-    for (int offset = width/2; offset > 0; offset >>= 1) {
-        x = x && __shfl_xor_sync(0xffffffff, x, offset, width);
+        for (int offset = width/2; offset > 0; offset >>= 1) {
+            x = __shfl_xor_sync(0xffffffff, x, offset, width) && x;
+        }
+        return x;
+    }
+}
+
+template<int width = WARP_SIZE>
+static __device__ __forceinline__ int warp_reduce_any(int x) {
+    if (width == ggml_cuda_get_physical_warp_size()) {
+        return __any_sync(0xffffffff, x);
+    } else {
+#pragma unroll
+        for (int offset = width/2; offset > 0; offset >>= 1) {
+            x = __shfl_xor_sync(0xffffffff, x, offset, width) || x;
+        }
+        return x;
     }
-    return x;
-#else
-    static_assert(width == WARP_SIZE, "width != WARP_SIZE not implemented");
-    return __all_sync(0xffffffff, x);
-#endif // GGML_USE_HIP
 }
 
 template<int width = WARP_SIZE>

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -207,6 +207,8 @@ static ggml_cuda_device_info ggml_cuda_init() {
 //#endif // GGML_CUDA_FORCE_CUBLAS
     GGML_LOG_INFO("---\nInitializing CUDA/HIP, please wait, the following step may take a few minutes (only for first launch)...\n---\n");
     GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
+
+    std::vector<std::pair<int, std::string>> turing_devices_without_mma;
     for (int id = 0; id < info.device_count; ++id) {
         int device_vmm = 0;
 
@@ -264,7 +266,25 @@ static ggml_cuda_device_info ggml_cuda_init() {
         info.devices[id].cc = 100*prop.major + 10*prop.minor;
         GGML_LOG_INFO("  Device %d: %s, compute capability %d.%d, VMM: %s\n",
                         id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
-#endif // defined(GGML_USE_HIP)
+        std::string device_name(prop.name);
+        if (device_name == "NVIDIA GeForce MX450") {
+            turing_devices_without_mma.push_back({ id, device_name });
+        } else if (device_name == "NVIDIA GeForce MX550") {
+            turing_devices_without_mma.push_back({ id, device_name });
+        } else if (device_name.substr(0, 21) == "NVIDIA GeForce GTX 16") {
+            turing_devices_without_mma.push_back({ id, device_name });
+        }
+#endif  // defined(GGML_USE_HIP)
+    }
+
+    if (ggml_cuda_highest_compiled_arch(GGML_CUDA_CC_TURING) >= GGML_CUDA_CC_TURING && !turing_devices_without_mma.empty()) {
+        GGML_LOG_INFO("The following devices will have suboptimal performance due to a lack of tensor cores:\n");
+        for (size_t device_pos = 0; device_pos < turing_devices_without_mma.size(); device_pos++) {
+            GGML_LOG_INFO(
+                "  Device %d: %s\n", turing_devices_without_mma[device_pos].first, turing_devices_without_mma[device_pos].second.c_str());
+        }
+        GGML_LOG_INFO(
+            "Consider compiling with CMAKE_CUDA_ARCHITECTURES=61-virtual;80-virtual and DGGML_CUDA_FORCE_MMQ to force the use of the Pascal code for Turing.\n");
     }
 
     for (int id = 0; id < info.device_count; ++id) {