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vulkan: implement GGML_OP_ARGMAX

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This commit is contained in:
Rémy O 2025-02-08 11:58:36 +01:00
parent abf4c2ef74
commit deb15e3f53
3 changed files with 75 additions and 0 deletions
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23
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View file

@ -252,6 +252,7 @@ struct vk_device_struct {
vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
vk_pipeline pipeline_argsort_f32;
vk_pipeline pipeline_sum_rows_f32;
vk_pipeline pipeline_argmax_f32;
vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16;
vk_pipeline pipeline_timestep_embedding_f32;
vk_pipeline pipeline_pool2d_f32;
@ -2149,6 +2150,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
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);
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);
ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
ggml_vk_create_pipeline(device, device->pipeline_im2col_f32, "im2col_f32", im2col_f32_len, im2col_f32_data, "main", 2, sizeof(vk_op_im2col_push_constants), {512, 1, 1}, { device->subgroup_size }, 1, true);
@ -5282,6 +5285,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
return ctx->device->pipeline_sum_rows_f32;
}
return nullptr;
case GGML_OP_ARGMAX:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) {
return ctx->device->pipeline_argmax_f32;
}
return nullptr;
case GGML_OP_IM2COL:
if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_im2col_f32;
@ -5545,6 +5553,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
case GGML_OP_RMS_NORM:
case GGML_OP_SOFT_MAX:
case GGML_OP_SUM_ROWS:
case GGML_OP_ARGMAX:
{
const uint32_t nr = ggml_nrows(src0);
if (nr > 262144) {
@ -6149,6 +6158,10 @@ static void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context& subctx,
ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM_ROWS, { (uint32_t)src0->ne[0], 0, 0.0f, 0.0f }, dryrun);
}
static void ggml_vk_argmax(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGMAX, { (uint32_t)src0->ne[0], 0, 0.0f, 0.0f }, dryrun);
}
static void ggml_vk_im2col(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
const int32_t s0 = dst->op_params[0];
const int32_t s1 = dst->op_params[1];
@ -7040,6 +7053,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_ARGSORT:
case GGML_OP_SUM:
case GGML_OP_SUM_ROWS:
case GGML_OP_ARGMAX:
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
@ -7092,6 +7106,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_ARGSORT:
case GGML_OP_SUM:
case GGML_OP_SUM_ROWS:
case GGML_OP_ARGMAX:
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
@ -7219,6 +7234,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_SUM_ROWS:
ggml_vk_sum_rows(ctx, compute_ctx, src0, node, dryrun);
break;
case GGML_OP_ARGMAX:
ggml_vk_argmax(ctx, compute_ctx, src0, node, dryrun);
break;
case GGML_OP_IM2COL:
ggml_vk_im2col(ctx, compute_ctx, src0, src1, node, dryrun);
@ -7331,6 +7350,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *
case GGML_OP_ARGSORT:
case GGML_OP_SUM:
case GGML_OP_SUM_ROWS:
case GGML_OP_ARGMAX:
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
@ -8266,6 +8286,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
case GGML_OP_ARGSORT:
case GGML_OP_SUM:
case GGML_OP_SUM_ROWS:
case GGML_OP_ARGMAX:
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
@ -8840,6 +8861,8 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
tensor_clone = ggml_sum(ggml_ctx, src0_clone);
} else if (tensor->op == GGML_OP_SUM_ROWS) {
tensor_clone = ggml_sum_rows(ggml_ctx, src0_clone);
} else if (tensor->op == GGML_OP_ARGMAX) {
tensor_clone = ggml_argmax(ggml_ctx, src0_clone);
} else if (tensor->op == GGML_OP_IM2COL) {
const int32_t s0 = tensor->op_params[0];
const int32_t s1 = tensor->op_params[1];

51
ggml/src/ggml-vulkan/vulkan-shaders/argmax.comp Normal file
View file

@ -0,0 +1,51 @@
#version 450
#include "generic_head.comp"
#include "types.comp"
#extension GL_EXT_control_flow_attributes : enable
layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
layout (constant_id = 0) const uint BLOCK_SIZE = 32;
shared FLOAT_TYPE tmpmax[BLOCK_SIZE];
shared uint tmp[BLOCK_SIZE];
void main() {
const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
const uint col = gl_LocalInvocationID.x;
if (col >= p.KX) {
return;
}
A_TYPE amax = data_a[row*p.KX + col];
tmp[col] = col;
for (uint i = col + BLOCK_SIZE; i < p.KX; i += BLOCK_SIZE) {
A_TYPE val = data_a[row*p.KX + i];
if (val > amax) {
amax = val;
tmp[col] = i;
}
}
tmpmax[col] = amax;
barrier();
[[unroll]] for (int s = int(BLOCK_SIZE) / 2; s > 0; s >>= 1) {
if (col < s && col + s < p.KX) {
if (tmpmax[col] < tmpmax[col + s]) {
tmpmax[col] = tmpmax[col + s];
tmp[col] = tmp[col + s];
}
}
barrier();
}
if (col == 0) {
data_d[row] = D_TYPE(tmp[0]);
}
}

1
ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
View file

@ -484,6 +484,7 @@ void process_shaders() {
string_to_spv("argsort_f32", "argsort.comp", {{"A_TYPE", "float"}});
string_to_spv("argmax_f32", "argmax.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "int"}}));
string_to_spv("sum_rows_f32", "sum_rows.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("im2col_f32", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));