ggml_vk_create_pipeline(device, device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_group_norm_f32, "group_norm_f32", group_norm_f32_len, group_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
- ggml_vk_create_pipeline(device, device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
+ ggml_vk_create_pipeline(device, device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_rms_norm_back_f32, "rms_norm_back_f32", rms_norm_back_f32_len, rms_norm_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_l2_norm_f32, "l2_norm_f32", l2_norm_f32_len, l2_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
case GGML_OP_REPEAT:
case GGML_OP_REPEAT_BACK:
case GGML_OP_ROPE:
+ case GGML_OP_RMS_NORM:
return true;
default:
return false;
switch (op) {
case GGML_OP_NORM:
- case GGML_OP_RMS_NORM:
case GGML_OP_RMS_NORM_BACK:
case GGML_OP_L2_NORM:
case GGML_OP_SOFT_MAX:
elements = { nr, 1, 1 };
}
} break;
+ case GGML_OP_RMS_NORM:
+ elements = { (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne03 };
+ break;
+
case GGML_OP_SUM:
// We use GGML_OP_SUM_ROWS with 1 row.
elements = { 1, 1, 1 };
static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
float * op_params = (float *)dst->op_params;
- ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }, dryrun);
+ const uint32_t src0_type_size = ggml_type_size(src0->type);
+ const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+ ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, {
+ (uint32_t)ggml_nelements(src0),
+ (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+ (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
+ 0,
+ op_params[0], 0.0f,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ }, dryrun);
}
static void ggml_vk_rms_norm_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
+ case GGML_OP_RMS_NORM:
return true;
case GGML_OP_NORM:
case GGML_OP_GROUP_NORM:
- case GGML_OP_RMS_NORM:
case GGML_OP_L2_NORM:
return ggml_is_contiguous(op->src[0]);
case GGML_OP_ADD:
#version 450
-#include "generic_head.comp"
+#include "generic_unary_head.comp"
#include "types.comp"
#extension GL_EXT_control_flow_attributes : enable
layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
shared FLOAT_TYPE sum[BLOCK_SIZE];
void main() {
- const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
- const uint tid = gl_LocalInvocationID.x;
+ const uint ncols = p.ne00;
+ const uint nrows = gl_NumWorkGroups.x;
+ const uint nchannels = gl_NumWorkGroups.y;
+
+ const uint row = gl_WorkGroupID.x;
+ const uint channel = gl_WorkGroupID.y;
+ const uint samp = gl_WorkGroupID.z;
+ const uint tid = gl_LocalInvocationID.x;
+
+ const uint stride_row = p.nb01;
+ const uint stride_channel = p.nb02;
+ const uint stride_sample = p.nb03;
+
+ uint32_t a_offset = samp*stride_sample + channel*stride_channel + row*stride_row + get_aoffset();
+ uint32_t d_offset = ((samp*nchannels + channel)*nrows + row)*ncols + get_doffset();
sum[tid] = FLOAT_TYPE(0.0f); // partial sum for thread in warp
- [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
- const FLOAT_TYPE xi = FLOAT_TYPE(data_a[row*p.KX + col]);
+ [[unroll]] for (uint col = tid; col < ncols; col += BLOCK_SIZE) {
+ const FLOAT_TYPE xi = FLOAT_TYPE(data_a[a_offset + col]);
sum[tid] += xi * xi;
}
barrier();
}
- const FLOAT_TYPE mean = sum[0] / FLOAT_TYPE(p.KX);
+ const FLOAT_TYPE mean = sum[0] / FLOAT_TYPE(ncols);
const FLOAT_TYPE scale = inversesqrt(mean + FLOAT_TYPE(p.param1));
- [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
- data_d[row*p.KX + col] = D_TYPE(scale * FLOAT_TYPE(data_a[row*p.KX + col]));
+ [[unroll]] for (uint col = tid; col < ncols; col += BLOCK_SIZE) {
+ data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]));
}
}