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add new gemma model (#11204)

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* update patches

* cherry pick metal mean kernel

* cherry pick cuda mean kernel

* gemma3n
This commit is contained in:
Michael Yang
2025-06-25 21:47:09 -07:00
committed by GitHub
parent ad118d8b13
commit 73b642e6f3
25 changed files with 6084 additions and 54 deletions
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2
llama/patches/0005-solar-pro.patch
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@@ -150,7 +150,7 @@ index 4cce5166..7f6617fa 100644
llama_model_loader::llama_model_loader(
const std::string & fname,
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 3a4e72a3..831b68c0 100644
index 3a4e72a3..db62973f 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1402,6 +1402,21 @@ void llama_model::load_hparams(llama_model_loader & ml) {

6
llama/patches/0008-ensure-KV-cache-is-fully-defragmented.patch
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@@ -22,10 +22,10 @@ multiple batches of processing until everything is complete.
4 files changed, 59 insertions(+), 79 deletions(-)
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index c22687e4..c5948e8f 100644
index dca22d8b..1f3a3956 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -950,9 +950,12 @@ int llama_context::decode(llama_batch & inp_batch) {
@@ -947,9 +947,12 @@ int llama_context::decode(llama_batch & inp_batch) {
// find KV slot
if (!kv_self->find_slot(ubatch)) {
@@ -41,7 +41,7 @@ index c22687e4..c5948e8f 100644
}
ggml_backend_sched_reset(sched.get());
@@ -1967,9 +1970,12 @@ void llama_context::opt_epoch_iter(
@@ -1965,9 +1968,12 @@ void llama_context::opt_epoch_iter(
// TODO: not sure if this is needed
if (!kv_self->find_slot(ubatch)) {

12
llama/patches/0015-add-argsort-and-cuda-copy-for-i32.patch
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@@ -10,10 +10,10 @@ Subject: [PATCH] add argsort and cuda copy for i32
3 files changed, 192 insertions(+), 2 deletions(-)
diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index becdae07..7a44b6cf 100644
index 955fec59..654e2f28 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -6890,6 +6890,45 @@ static void ggml_compute_forward_argsort_f32(
@@ -6822,6 +6822,45 @@ static void ggml_compute_forward_argsort_f32(
}
}
@@ -59,7 +59,7 @@ index becdae07..7a44b6cf 100644
void ggml_compute_forward_argsort(
const ggml_compute_params * params,
ggml_tensor * dst) {
@@ -6901,6 +6940,10 @@ void ggml_compute_forward_argsort(
@@ -6833,6 +6872,10 @@ void ggml_compute_forward_argsort(
{
ggml_compute_forward_argsort_f32(params, dst);
} break;
@@ -195,7 +195,7 @@ index 607ded85..53b02634 100644
+ }
}
diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu
index 2d46176e..47383486 100644
index d027271f..4abd01d7 100644
--- a/ggml/src/ggml-cuda/cpy.cu
+++ b/ggml/src/ggml-cuda/cpy.cu
@@ -38,6 +38,13 @@ static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) {
@@ -257,7 +257,7 @@ index 2d46176e..47383486 100644
static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
const float * xi = (const float *) cxi;
block_q8_0 * dsti = (block_q8_0 *) cdsti;
@@ -631,6 +676,8 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
@@ -633,6 +678,8 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
@@ -266,7 +266,7 @@ index 2d46176e..47383486 100644
} else {
GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__,
ggml_type_name(src0->type), ggml_type_name(src1->type));
@@ -686,6 +733,8 @@ void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
@@ -688,6 +735,8 @@ void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
return (void*) cpy_f32_f16<cpy_1_f16_f32>;

169
llama/patches/0019-metal-add-mean-kernel-14267.patch Normal file
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@@ -0,0 +1,169 @@
From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 19 Jun 2025 08:05:21 +0300
Subject: [PATCH] metal : add mean kernel (#14267)
* metal : add mean kernel
ggml-ci
* cont : dedup implementation
ggml-ci
---
ggml/src/ggml-metal/ggml-metal.m | 33 ++++++++++++++++---
ggml/src/ggml-metal/ggml-metal.metal | 48 ++++++++++++++++++++++------
2 files changed, 67 insertions(+), 14 deletions(-)
diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index ee4f2dcb..f20f5615 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -489,6 +489,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_COS,
GGML_METAL_KERNEL_TYPE_NEG,
GGML_METAL_KERNEL_TYPE_SUM_ROWS,
+ GGML_METAL_KERNEL_TYPE_MEAN,
GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32,
GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32,
GGML_METAL_KERNEL_TYPE_ARGMAX,
@@ -1436,6 +1437,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS, cos, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NEG, neg, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true);
+ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MEAN, mean, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGMAX, argmax, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32, pool_2d_avg_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32, pool_2d_max_f32, true);
@@ -1634,6 +1636,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
case GGML_OP_LOG:
return false; // TODO: implement
case GGML_OP_SUM_ROWS:
+ case GGML_OP_MEAN:
case GGML_OP_SOFT_MAX:
case GGML_OP_GROUP_NORM:
return has_simdgroup_reduction && ggml_is_contiguous(op->src[0]);
@@ -2362,11 +2365,30 @@ static bool ggml_metal_encode_node(
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SUM_ROWS:
+ case GGML_OP_MEAN:
{
GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
- id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline;
+ id<MTLComputePipelineState> pipeline = nil;
+
+ switch (dst->op) {
+ case GGML_OP_SUM_ROWS:
+ pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline;
+ break;
+ case GGML_OP_MEAN:
+ pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MEAN].pipeline;
+ break;
+ default:
+ GGML_ABORT("fatal error");
+ }
+
+ int nth = 32; // SIMD width
+
+ while (nth < ne00 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) {
+ nth *= 2;
+ }
+ nth = MIN(nth, ne00);
ggml_metal_kargs_sum_rows args = {
/*.ne00 =*/ ne00,
@@ -2396,11 +2418,12 @@ static bool ggml_metal_encode_node(
};
[encoder setComputePipelineState:pipeline];
- [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
- [encoder setBuffer:id_dst offset:offs_dst atIndex:1];
- [encoder setBytes:&args length:sizeof(args) atIndex:2];
+ [encoder setBytes:&args length:sizeof(args) atIndex:0];
+ [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
+ [encoder setBuffer:id_dst offset:offs_dst atIndex:2];
+ [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
- [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+ [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_SOFT_MAX:
{
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 9cfddf45..08e8d807 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -956,31 +956,61 @@ kernel void kernel_neg(
dst[tpig] = -src0[tpig];
}
+template <bool norm>
kernel void kernel_sum_rows(
+ constant ggml_metal_kargs_sum_rows & args,
device const float * src0,
device float * dst,
- constant ggml_metal_kargs_sum_rows & args,
- uint3 tpig[[thread_position_in_grid]]) {
- int64_t i3 = tpig.z;
- int64_t i2 = tpig.y;
- int64_t i1 = tpig.x;
+ threadgroup float * shmem_f32 [[threadgroup(0)]],
+ 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]]) {
+ int64_t i3 = tgpig.z;
+ int64_t i2 = tgpig.y;
+ int64_t i1 = tgpig.x;
if (i3 >= args.ne03 || i2 >= args.ne02 || i1 >= args.ne01) {
return;
}
+ if (sgitg == 0) {
+ shmem_f32[tiisg] = 0.0f;
+ }
+
device const float * src_row = (device const float *) ((device const char *) src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
device float * dst_row = (device float *) ((device char *) dst + i1*args.nb1 + i2*args.nb2 + i3*args.nb3);
- float row_sum = 0;
+ float sumf = 0;
- for (int64_t i0 = 0; i0 < args.ne00; i0++) {
- row_sum += src_row[i0];
+ for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
+ sumf += src_row[i0];
}
- dst_row[0] = row_sum;
+ sumf = simd_sum(sumf);
+
+ threadgroup_barrier(mem_flags::mem_threadgroup);
+
+ if (tiisg == 0) {
+ shmem_f32[sgitg] = sumf;
+ }
+
+ threadgroup_barrier(mem_flags::mem_threadgroup);
+
+ sumf = shmem_f32[tiisg];
+ sumf = simd_sum(sumf);
+
+ if (tpitg.x == 0) {
+ dst_row[0] = norm ? sumf / args.ne00 : sumf;
+ }
}
+typedef decltype(kernel_sum_rows<false>) kernel_sum_rows_t;
+
+template [[host_name("kernel_sum_rows")]] kernel kernel_sum_rows_t kernel_sum_rows<false>;
+template [[host_name("kernel_mean")]] kernel kernel_sum_rows_t kernel_sum_rows<true>;
+
template<typename T>
kernel void kernel_soft_max(
device const char * src0,

5089
llama/patches/0020-CUDA-add-mean-operation-14313.patch Normal file
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