CS348Project/llama.cpp
5
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2025-10-29 08:41:22 +00:00
Code Issues Packages Projects Releases Wiki Activity

vulkan: Optimize argsort (#15354)

Browse Source 
- Launch an appropriate number of invocations (next larger power of two).
32 invocations is common and the barrier is much cheaper there.
- Specialize for "needs bounds checking" vs not.
- Make the code less branchy and [[unroll]] the loops. In the final code,
I see no branches inside the main loop (only predicated stores) when
needs_bounds_check is false.
- Always sort ascending, then apply the ascending vs descending option when
doing the final stores to memory.
- Copy the values into shared memory, makes them slightly cheaper to access.
This commit is contained in:
Jeff Bolz
2025-08-17 03:41:45 -05:00
committed by GitHub
parent 65349f26f2
commit de5627910d
3 changed files with 51 additions and 42 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View File

@@ -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;
@@ -505,7 +508,7 @@ 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 +873,6 @@ struct vk_op_soft_max_push_constants {
struct vk_op_argsort_push_constants {
uint32_t ncols;
uint32_t ncols_pad;
int32_t order;
};
@@ -3099,7 +3101,9 @@ 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);
@@ -7160,7 +7164,8 @@ 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:
@@ -8485,16 +8490,8 @@ 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);
}
@@ -11367,6 +11364,8 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
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 +11375,6 @@ 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: