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llama.cpp
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Merge remote-tracking branch 'upstream/master' into fix-x86-os-support

This commit is contained in:
giorgiopapini 2026-02-11 15:54:04 +01:00
parent 61b0baf45a 53de59f67d
commit 65a65196fa
7 changed files with 77 additions and 32 deletions
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2
CONTRIBUTING.md
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@ -20,7 +20,7 @@ If AI is used to generate any portion of the code, contributors must adhere to t
1. Explicitly disclose the manner in which AI was employed.
2. Perform a comprehensive manual review prior to submitting the pull request.
3. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
4. Using AI to write pull request descriptions or to respond to human reviewers is strictly prohibited.
4. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...).
For more info, please refer to the [AGENTS.md](AGENTS.md) file.

2
build-xcframework.sh
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@ -534,7 +534,7 @@ xcodebuild -create-xcframework \
-framework $(pwd)/build-ios-device/framework/llama.framework \
-debug-symbols $(pwd)/build-ios-device/dSYMs/llama.dSYM \
-framework $(pwd)/build-macos/framework/llama.framework \
-debug-symbols $(pwd)/build-macos/dSYMS/llama.dSYM \
-debug-symbols $(pwd)/build-macos/dSYMs/llama.dSYM \
-framework $(pwd)/build-visionos/framework/llama.framework \
-debug-symbols $(pwd)/build-visionos/dSYMs/llama.dSYM \
-framework $(pwd)/build-visionos-sim/framework/llama.framework \

11
ggml/src/ggml-metal/ggml-metal-device.cpp
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@ -1480,13 +1480,15 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin_one(ggml_met
ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm(ggml_metal_library_t lib, const ggml_tensor * op) {
assert(op->op == GGML_OP_L2_NORM);
GGML_ASSERT(op->src[0]->ne[0] % 4 == 0);
GGML_ASSERT(ggml_is_contiguous_1(op->src[0]));
char base[256];
char name[256];
snprintf(base, 256, "kernel_l2_norm_f32");
const bool is_c4 = op->src[0]->ne[0] % 4 == 0;
const char * t0_str = ggml_type_name(op->src[0]->type);
const char * t_str = ggml_type_name(op->type);
snprintf(base, 256, "kernel_l2_norm_%s_%s%s", t0_str, t_str, is_c4 ? "_4" : "");
snprintf(name, 256, "%s", base);
ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
@ -1494,6 +1496,7 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm(ggml_met
res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
}
res.c4 = is_c4;
res.smem = 32*sizeof(float);
return res;

3
ggml/src/ggml-metal/ggml-metal-device.m
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@ -1086,9 +1086,8 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
case GGML_OP_MEAN:
case GGML_OP_SOFT_MAX:
case GGML_OP_GROUP_NORM:
return has_simdgroup_reduction && ggml_is_contiguous_rows(op->src[0]);
case GGML_OP_L2_NORM:
return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
return has_simdgroup_reduction && ggml_is_contiguous_rows(op->src[0]);
case GGML_OP_COUNT_EQUAL:
return has_simdgroup_reduction &&
op->src[0]->type == GGML_TYPE_I32 &&

15
ggml/src/ggml-metal/ggml-metal-impl.h
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@ -539,8 +539,21 @@ typedef struct {
typedef struct {
int32_t ne00;
int32_t ne00_4;
int32_t ne01;
int32_t ne02;
int32_t ne03;
uint64_t nb00;
uint64_t nb01;
uint64_t nb02;
uint64_t nb03;
int32_t ne0;
int32_t ne1;
int32_t ne2;
int32_t ne3;
uint64_t nb0;
uint64_t nb1;
uint64_t nb2;
uint64_t nb3;
float eps;
} ggml_metal_kargs_l2_norm;

46
ggml/src/ggml-metal/ggml-metal-ops.cpp
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@ -2979,39 +2979,59 @@ int ggml_metal_op_l2_norm(ggml_metal_op_t ctx, int idx) {
GGML_TENSOR_LOCALS( int32_t, ne, op, ne);
GGML_TENSOR_LOCALS(uint64_t, nb, op, nb);
GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
ggml_metal_buffer_id bid_dst = ggml_metal_get_buffer_id(op);
float eps;
memcpy(&eps, op->op_params, sizeof(float));
int nth = 32; // SIMD width
ggml_metal_kargs_l2_norm args = {
/*.ne00 =*/ ne00,
/*.ne00_4 =*/ ne00/4,
/*.nb01 =*/ nb01,
/*.eps =*/ eps,
/*.ne00 =*/ ne00,
/*.ne01 =*/ ne01,
/*.ne02 =*/ ne02,
/*.ne03 =*/ ne03,
/*.nb00 =*/ nb00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.nb03 =*/ nb03,
/*.ne0 =*/ ne0,
/*.ne1 =*/ ne1,
/*.ne2 =*/ ne2,
/*.ne3 =*/ ne3,
/*.nb0 =*/ nb0,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
/*.nb3 =*/ nb3,
/*.eps =*/ eps,
};
auto pipeline = ggml_metal_library_get_pipeline_l2_norm(lib, op);
while (nth < ne00/4 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
if (pipeline.c4) {
args.ne00 = ne00/4;
args.ne0 = ne0/4;
}
int nth = 32; // SIMD width
while (nth < ne00 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
nth *= 2;
}
nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
nth = std::min(nth, ne00/4);
const size_t smem = pipeline.smem;
const int64_t nrows = ggml_nrows(op->src[0]);
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 2);
ggml_metal_encoder_set_buffer (enc, bid_src0, 1);
ggml_metal_encoder_set_buffer (enc, bid_dst, 2);
ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
ggml_metal_encoder_dispatch_threadgroups(enc, nrows, 1, 1, nth, 1, 1);
ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne02, ne03, nth, 1, 1);
return 1;
}

30
ggml/src/ggml-metal/ggml-metal.metal
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@ -2706,26 +2706,32 @@ template [[host_name("kernel_rms_norm_f32_4")]] kernel kernel_rms_norm_f
template [[host_name("kernel_rms_norm_mul_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 2>;
template [[host_name("kernel_rms_norm_mul_add_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 3>;
kernel void kernel_l2_norm_f32(
template <typename T0, typename T>
kernel void kernel_l2_norm_impl(
constant ggml_metal_kargs_l2_norm & args,
device const char * src0,
device char * dst,
threadgroup float * shmem_f32 [[threadgroup(0)]],
uint tgpig[[threadgroup_position_in_grid]],
ushort tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort ntg[[threads_per_threadgroup]]) {
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int i03 = tgpig.z;
const int i02 = tgpig.y;
const int i01 = tgpig.x;
if (sgitg == 0) {
shmem_f32[tiisg] = 0.0f;
}
device const float4 * x = (device const float4 *) (src0 + tgpig*args.nb01);
device const T0 * x = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
device T * y = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
float sumf = 0.0f;
// parallel sum
for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) {
for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
sumf += dot(x[i00], x[i00]);
}
sumf = simd_sum(sumf);
@ -2743,12 +2749,16 @@ kernel void kernel_l2_norm_f32(
const float scale = 1.0f/sqrt(max(sumf, args.eps));
device float4 * y = (device float4 *) dst + tgpig*args.ne00_4;
for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) {
for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
y[i00] = x[i00] * scale;
}
}
typedef decltype(kernel_l2_norm_impl<float, float>) kernel_l2_norm_t;
template [[host_name("kernel_l2_norm_f32_f32")]] kernel kernel_l2_norm_t kernel_l2_norm_impl<float, float>;
template [[host_name("kernel_l2_norm_f32_f32_4")]] kernel kernel_l2_norm_t kernel_l2_norm_impl<float4, float4>;
kernel void kernel_group_norm_f32(
constant ggml_metal_kargs_group_norm & args,
device const float * src0,