Merge branch 'ggml-org:master' into n_past_max

Author: okuvshynov

.github/workflows/build.yml

@@ -1070,7 +1070,8 @@ jobs:
           write-host "Downloading AMD HIP SDK Installer"
           Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP SDK"
-          Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
+          $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru
+          $proc.WaitForExit(600000)
           write-host "Completed AMD HIP SDK installation"
 
       - name: Verify ROCm

.github/workflows/release.yml

@@ -557,7 +557,8 @@ jobs:
           write-host "Downloading AMD HIP SDK Installer"
           Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP SDK"
-          Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
+          $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru
+          $proc.WaitForExit(600000)
           write-host "Completed AMD HIP SDK installation"
 
       - name: Verify ROCm

convert_hf_to_gguf.py

@@ -1334,6 +1334,12 @@ def _find_param(self, obj: dict[str, Any], keys: Iterable[str], optional: bool =
             return None
         raise KeyError(f"could not find any of: {keys}")
 
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        del bid, name, n_dims  # unused
+        if ".patch_embd.weight" in new_name:
+            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
+        return False
+
 
 @ModelBase.register("GPTNeoXForCausalLM")
 class GPTNeoXModel(TextModel):
@@ -2305,10 +2311,9 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_vision_use_gelu(True)
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, new_name, n_dims  # unused
         if ".embeddings." in name:
             return gguf.GGMLQuantizationType.F32
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
@@ -3296,12 +3301,9 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_vision_attention_layernorm_eps(self.global_config.get("rms_norm_eps", 1e-6))
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, name, n_dims  # unused
-        if ".patch_embd." in new_name:
-            return gguf.GGMLQuantizationType.F16
         if ".position_embd." in new_name:
             return gguf.GGMLQuantizationType.F32
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
@@ -3374,10 +3376,9 @@ def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
         yield ("audio_tower.embed_positions.weight", pos_embd)
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, new_name, n_dims  # unused
         if ".conv" in name and ".weight" in name:
             return gguf.GGMLQuantizationType.F16
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         if name.startswith("thinker."):
@@ -3423,12 +3424,9 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, name, n_dims  # unused
-        if ".patch_embd." in new_name:
-            return gguf.GGMLQuantizationType.F16
         if ".position_embd." in new_name:
             return gguf.GGMLQuantizationType.F32
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def _mapping_interns1_name(self, name):
         names_map = {
@@ -5062,13 +5060,12 @@ def set_gguf_parameters(self):
             self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, new_name, n_dims  # unused
         # related to https://github.com/ggml-org/llama.cpp/issues/13025
         if "input_projection" in name:
             return gguf.GGMLQuantizationType.F16
         if ".embeddings." in name:
             return gguf.GGMLQuantizationType.F32
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
@@ -7727,10 +7724,9 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, new_name, n_dims  # unused
         if ".conv" in name and ".weight" in name:
             return gguf.GGMLQuantizationType.F16
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -345,6 +345,9 @@ enum vk_conv_shapes {
     CONV_SHAPE_COUNT,
 };
 
+static constexpr uint32_t num_argsort_pipelines = 11;
+static constexpr uint32_t max_argsort_cols = 1 << (num_argsort_pipelines-1);
+
 struct vk_device_struct {
     std::recursive_mutex mutex;
 
@@ -461,6 +464,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_upscale_nearest_f32, pipeline_upscale_bilinear_f32, pipeline_upscale_bilinear_ac_f32;
     vk_pipeline pipeline_scale_f32;
     vk_pipeline pipeline_sqr_f32;
+    vk_pipeline pipeline_sqrt_f32;
     vk_pipeline pipeline_sin_f32;
     vk_pipeline pipeline_cos_f32;
     vk_pipeline pipeline_clamp_f32;
@@ -505,7 +509,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
     vk_pipeline pipeline_rope_multi_f32, pipeline_rope_multi_f16;
     vk_pipeline pipeline_rope_vision_f32, pipeline_rope_vision_f16;
-    vk_pipeline pipeline_argsort_f32;
+    vk_pipeline pipeline_argsort_f32[num_argsort_pipelines];
     vk_pipeline pipeline_sum_rows_f32;
     vk_pipeline pipeline_argmax_f32;
     vk_pipeline pipeline_count_equal_i32;
@@ -870,7 +874,6 @@ struct vk_op_soft_max_push_constants {
 
 struct vk_op_argsort_push_constants {
     uint32_t ncols;
-    uint32_t ncols_pad;
     int32_t order;
 };
 
@@ -3029,6 +3032,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_sqrt_f32, "sqrt_f32", sqrt_f32_len, sqrt_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_sin_f32, "sin_f32", sin_f32_len, sin_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_cos_f32, "cos_f32", cos_f32_len, cos_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
@@ -3099,7 +3103,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_rope_vision_f16, "rope_vision_f16", rope_vision_f16_len, rope_vision_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
     }
 
-    ggml_vk_create_pipeline(device, device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1);
+    for (uint32_t i = 0; i < num_argsort_pipelines; ++i) {
+        ggml_vk_create_pipeline(device, device->pipeline_argsort_f32[i], "argsort_f32_"+std::to_string(i), argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1u<<i, 1, 1}, {1u<<i, i}, 1, true);
+    }
 
     ggml_vk_create_pipeline(device, device->pipeline_argmax_f32, "argmax_f32", argmax_f32_len, argmax_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
 
@@ -6977,6 +6983,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             return ctx->device->pipeline_sqr_f32;
         }
         return nullptr;
+    case GGML_OP_SQRT:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_sqrt_f32;
+        }
+        return nullptr;
     case GGML_OP_SIN:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
             return ctx->device->pipeline_sin_f32;
@@ -7160,7 +7171,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         }
     case GGML_OP_ARGSORT:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) {
-            return ctx->device->pipeline_argsort_f32;
+            uint32_t idx = (uint32_t)ceilf(log2f(float(dst->ne[0])));
+            return ctx->device->pipeline_argsort_f32[idx];
         }
         return nullptr;
     case GGML_OP_SUM:
@@ -7285,6 +7297,7 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) {
     case GGML_OP_CONCAT:
     case GGML_OP_UPSCALE:
     case GGML_OP_SQR:
+    case GGML_OP_SQRT:
     case GGML_OP_SIN:
     case GGML_OP_COS:
     case GGML_OP_CLAMP:
@@ -7590,6 +7603,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
     case GGML_OP_MUL:
     case GGML_OP_SCALE:
     case GGML_OP_SQR:
+    case GGML_OP_SQRT:
     case GGML_OP_SIN:
     case GGML_OP_COS:
     case GGML_OP_CLAMP:
@@ -8237,6 +8251,10 @@ static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context& subctx, const
     ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, vk_op_unary_push_constants_init(src0, dst), dryrun);
 }
 
+static void ggml_vk_sqrt(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQRT, vk_op_unary_push_constants_init(src0, dst), dryrun);
+}
+
 static void ggml_vk_sin(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
     ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SIN, vk_op_unary_push_constants_init(src0, dst), dryrun);
 }
@@ -8485,16 +8503,8 @@ static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context& subctx, c
 
     uint32_t ncols = src0->ne[0];
 
-    uint32_t ncols_pad = 1;
-    while (ncols_pad < ncols) {
-        ncols_pad *= 2;
-    }
-
-    GGML_ASSERT(ncols_pad <= 1024);
-
     ggml_vk_op_f32<vk_op_argsort_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, {
         ncols,
-        ncols_pad,
         op_params[0],
     }, dryrun);
 }
@@ -9700,6 +9710,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_UPSCALE:
     case GGML_OP_SCALE:
     case GGML_OP_SQR:
+    case GGML_OP_SQRT:
     case GGML_OP_SIN:
     case GGML_OP_COS:
     case GGML_OP_CLAMP:
@@ -9769,6 +9780,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         case GGML_OP_UPSCALE:
         case GGML_OP_SCALE:
         case GGML_OP_SQR:
+        case GGML_OP_SQRT:
         case GGML_OP_SIN:
         case GGML_OP_COS:
         case GGML_OP_CLAMP:
@@ -9870,6 +9882,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_SQR:
         ggml_vk_sqr(ctx, compute_ctx, src0, node, dryrun);
 
+        break;
+    case GGML_OP_SQRT:
+        ggml_vk_sqrt(ctx, compute_ctx, src0, node, dryrun);
+
         break;
     case GGML_OP_SIN:
         ggml_vk_sin(ctx, compute_ctx, src0, node, dryrun);
@@ -10121,6 +10137,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
     case GGML_OP_UPSCALE:
     case GGML_OP_SCALE:
     case GGML_OP_SQR:
+    case GGML_OP_SQRT:
     case GGML_OP_SIN:
     case GGML_OP_COS:
     case GGML_OP_CLAMP:
@@ -11360,13 +11377,16 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_SILU_BACK:
         case GGML_OP_RMS_NORM_BACK:
         case GGML_OP_SQR:
+        case GGML_OP_SQRT:
         case GGML_OP_SIN:
         case GGML_OP_COS:
         case GGML_OP_CLAMP:
         case GGML_OP_LEAKY_RELU:
         case GGML_OP_OPT_STEP_ADAMW:
         case GGML_OP_OPT_STEP_SGD:
             return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_ARGSORT:
+            return op->ne[0] <= max_argsort_cols;
         case GGML_OP_UPSCALE:
         case GGML_OP_ACC:
         case GGML_OP_CONCAT:
@@ -11376,7 +11396,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
         case GGML_OP_SOFT_MAX_BACK:
-        case GGML_OP_ARGSORT:
         case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS:
         case GGML_OP_ARGMAX:
@@ -11803,6 +11822,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
         tensor_clone = ggml_scale_bias(ggml_ctx, src_clone[0], params[0], params[1]);
     } else if (tensor->op == GGML_OP_SQR) {
         tensor_clone = ggml_sqr(ggml_ctx, src_clone[0]);
+    } else if (tensor->op == GGML_OP_SQRT) {
+        tensor_clone = ggml_sqrt(ggml_ctx, src_clone[0]);
     } else if (tensor->op == GGML_OP_SIN) {
         tensor_clone = ggml_sin(ggml_ctx, src_clone[0]);
     } else if (tensor->op == GGML_OP_COS) {