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metal : optimize pad + cpy (#23354)

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* metal : optimize pad

* metal : optinmize cpy

* cont : better row packing in threadgroup
This commit is contained in:
Georgi Gerganov 2026-05-20 09:42:00 +03:00 • committed by GitHub
parent 871b0b70f8
commit 57ebaf4edd
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GPG key ID: B5690EEEBB952194
4 changed files with 94 additions and 67 deletions
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8
ggml/src/ggml-metal/ggml-metal-device.cpp
View file

@ -1897,7 +1897,11 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_pad(ggml_metal_l
char base[256];
char name[256];
snprintf(base, 256, "kernel_pad_%s", ggml_type_name(op->src[0]->type));
// note: this is slower
//const bool is_c4 = op->src[0]->ne[0] % 4 == 0 && op->ne[0] % 4 == 0;
const bool is_c4 = false;
snprintf(base, 256, "kernel_pad_%s%s", ggml_type_name(op->src[0]->type), is_c4 ? "_4" : "");
snprintf(name, 256, "%s", base);
ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
@ -1907,6 +1911,8 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_pad(ggml_metal_l
res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
res.c4 = is_c4;
return res;
}

17
ggml/src/ggml-metal/ggml-metal-ops.cpp
View file

@ -816,9 +816,7 @@ int ggml_metal_op_unary(ggml_metal_op_t ctx, int idx) {
ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
} else {
const int nth_max = MIN(256, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
const int nth = MIN(args.ne00, nth_max);
const int nk0 = (args.ne00 + nth - 1)/nth;
ggml_metal_encoder_dispatch_threadgroups(enc, nk0*ne01, ne02, ne03, nth, 1, 1);
@ -1863,7 +1861,7 @@ int ggml_metal_op_cpy(ggml_metal_op_t ctx, int idx) {
nk0 = ne00/ggml_blck_size(op->type);
}
int nth = std::min<int>(nk0, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
int nth = std::min<int>(nk0*ne01, 256);
// when rows are small, we can batch them together in a single threadgroup
int nrptg = 1;
@ -1874,7 +1872,7 @@ int ggml_metal_op_cpy(ggml_metal_op_t ctx, int idx) {
nrptg = (nth + nk0 - 1)/nk0;
nth = nk0;
if (nrptg*nth > ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
if (nrptg*nth > 256) {
nrptg--;
}
}
@ -4039,14 +4037,21 @@ int ggml_metal_op_pad(ggml_metal_op_t ctx, int idx) {
auto pipeline = ggml_metal_library_get_pipeline_pad(lib, op);
const int nth = std::min(1024, ne0);
if (pipeline.c4) {
args.ne00 = ne00/4;
args.ne0 = ne0/4;
}
const int nth_max = MIN(64, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
const int nth = MIN(args.ne0, nth_max);
const int nk0 = (args.ne0 + 1024 - 1)/1024; // note: 1024 is hardcoded in the kernel!
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_dispatch_threadgroups(enc, ne1, ne2, ne3, nth, 1, 1);
ggml_metal_encoder_dispatch_threadgroups(enc, nk0*ne1, ne2, ne3, nth, 1, 1);
return 1;
}

128
ggml/src/ggml-metal/ggml-metal.metal
View file

@ -2643,7 +2643,7 @@ kernel void kernel_gated_delta_net_impl(
b_ptr += args.ne21;
g_ptr += args.ne21*G;
if (K > 1u) {
if (K > 1) {
const int target_slot = (int)t - shift;
if (target_slot >= 0 && target_slot < (int)K) {
device float * dst_state = (device float *) (dst) + attn_size + (uint)target_slot * state_size_per_snap + state_out_base;
@ -2655,7 +2655,7 @@ kernel void kernel_gated_delta_net_impl(
}
}
if (K == 1u) {
if (K == 1) {
device float * dst_state = (device float *) (dst) + attn_size + state_out_base;
FOR_UNROLL (short j = 0; j < NSG; j++) {
const short is = tx*NSG + j;
@ -5104,7 +5104,7 @@ kernel void kernel_upscale_bilinear_f32(
for (int64_t sx = x_min; sx < x_max; ++sx) {
const float wx = MAX(0.0f, 1.0f - fabs((float)sx - f00) * invscale0);
const float w = wx * wy;
const device const float * src_ptr = (device const float *)(src0 + sy*args.nb01 + sx*args.nb00);
device const float * src_ptr = (device const float *)(src0 + sy*args.nb01 + sx*args.nb00);
sum += (*src_ptr) * w;
wsum += w;
}
@ -5286,7 +5286,7 @@ kernel void kernel_upscale_bicubic_f32(
const int64_t ix = MAX(0, MIN(args.ne00 - 1, i00 + dx));
const float wx = (dx == -1) ? w_x0 : (dx == 0) ? w_x1 : (dx == 1) ? w_x2 : w_x3;
const device const float * src_ptr = (device const float *)(src_slice + iy * args.nb01 + ix * args.nb00);
device const float * src_ptr = (device const float *)(src_slice + iy * args.nb01 + ix * args.nb00);
sum += (*src_ptr) * wx * wy;
}
}
@ -5329,42 +5329,46 @@ kernel void kernel_roll_f32(
}
}
kernel void kernel_pad_f32(
template <typename T>
kernel void kernel_pad_impl(
constant ggml_metal_kargs_pad & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int32_t i3 = tgpig.z;
const int32_t i2 = tgpig.y;
const int32_t k0 = tgpig.x/args.ne1;
const int32_t i1 = tgpig.x - k0*args.ne1;
const int64_t i3 = tgpig.z;
const int64_t i2 = tgpig.y;
const int64_t i1 = tgpig.x;
const int32_t i03 = i3;
const int32_t i02 = i2;
const int32_t i01 = i1;
const int64_t i03 = i3;
const int64_t i02 = i2;
const int64_t i01 = i1;
device const T * src0_ptr = (device const T *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
device T * dst_ptr = (device T *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1);
device const float * src0_ptr = (device const float *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
device float * dst_ptr = (device float *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1);
if (i1 < args.ne01 && i2 < args.ne02 && i3 < args.ne03) {
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
if (i0 < args.ne00) {
dst_ptr[i0] = src0_ptr[i0];
} else {
dst_ptr[i0] = 0.0f;
}
for (int32_t l0 = 0; l0 < 1024; l0 += ntg.x) {
const int32_t i0 = k0*1024 + tpitg.x + l0;
if (i0 >= args.ne0) {
break;
}
return;
}
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
dst_ptr[i0] = 0.0f;
if (i0 < args.ne00 && i1 < args.ne01 && i2 < args.ne02 && i3 < args.ne03) {
dst_ptr[i0] = src0_ptr[i0];
} else {
dst_ptr[i0] = 0.0f;
}
}
}
typedef decltype(kernel_pad_impl<float>) kernel_pad_t;
template [[host_name("kernel_pad_f32")]] kernel kernel_pad_t kernel_pad_impl<float>;
template [[host_name("kernel_pad_f32_4")]] kernel kernel_pad_t kernel_pad_impl<float4>;
// TODO: this is slow - optimize
kernel void kernel_pad_reflect_1d_f32(
constant ggml_metal_kargs_pad_reflect_1d & args,
device const char * src0,
@ -7328,23 +7332,27 @@ kernel void kernel_cpy_t_t(
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort tiitg[[thread_index_in_threadgroup]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int i03 = tgpig[2];
const int i02 = tgpig[1];
const int i01 = ntg[1] == 1 ? tgpig[0]%args.ne01 : tgpig[0]*ntg[1] + tiitg/ntg[0];
const int iw0 = ntg[1] == 1 ? tgpig[0]/args.ne01 : 0;
const int32_t i03 = tgpig[2];
const int32_t i02 = tgpig[1];
const int32_t i01 = ntg[1] == 1 ? tgpig[0]%args.ne01 : tgpig[0]*ntg[1] + tpitg.y;
const int32_t iw0 = ntg[1] == 1 ? tgpig[0]/args.ne01 : 0;
if (i01 >= args.ne01) {
return;
}
const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00;
const int64_t i3 = n/(args.ne2*args.ne1*args.ne0);
const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0)/(args.ne1*args.ne0);
const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0)/args.ne0;
const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0);
const int32_t i3 = n/(args.ne2*args.ne1*args.ne0);
const int32_t i2 = (n - i3*args.ne2*args.ne1*args.ne0)/(args.ne1*args.ne0);
const int32_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0)/args.ne0;
const int32_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0);
device T1 * dst_data = (device T1 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
for (int64_t i00 = iw0*ntg[0] + tiitg%ntg[0]; i00 < args.ne00; ) {
for (int32_t i00 = iw0*ntg[0] + tpitg.x; i00 < args.ne00;) {
device const T0 * src = (device T0 *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00);
dst_data[i00] = (T1) src[0];
break;
@ -7376,23 +7384,27 @@ kernel void kernel_cpy_f32_q(
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort tiitg[[thread_index_in_threadgroup]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int i03 = tgpig[2];
const int i02 = tgpig[1];
const int i01 = ntg[1] == 1 ? tgpig[0]%args.ne01 : tgpig[0]*ntg[1] + tiitg/ntg[0];
const int iw0 = ntg[1] == 1 ? tgpig[0]/args.ne01 : 0;
const int32_t i03 = tgpig[2];
const int32_t i02 = tgpig[1];
const int32_t i01 = ntg[1] == 1 ? tgpig[0]%args.ne01 : tgpig[0]*ntg[1] + tpitg.y;
const int32_t iw0 = ntg[1] == 1 ? tgpig[0]/args.ne01 : 0;
if (i01 >= args.ne01) {
return;
}
const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00;
const int64_t i3 = n / (args.ne2*args.ne1*args.ne0);
const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0);
const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0;
const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK;
const int32_t i3 = n / (args.ne2*args.ne1*args.ne0);
const int32_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0);
const int32_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0;
const int32_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK;
device block_q * dst_data = (device block_q *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
for (int64_t i00 = iw0*ntg[0] + tiitg%ntg[0]; i00 < args.nk0; ) {
for (int32_t i00 = iw0*ntg[0] + tpitg.x; i00 < args.nk0;) {
device const float * src = (device const float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + (i00*QK)*args.nb00);
quantize_func(src, dst_data[i00]);
@ -7417,24 +7429,28 @@ kernel void kernel_cpy_q_f32(
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort tiitg[[thread_index_in_threadgroup]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int i03 = tgpig[2];
const int i02 = tgpig[1];
const int i01 = ntg[1] == 1 ? tgpig[0]%args.ne01 : tgpig[0]*ntg[1] + tiitg/ntg[0];
const int iw0 = ntg[1] == 1 ? tgpig[0]/args.ne01 : 0;
const int32_t i03 = tgpig[2];
const int32_t i02 = tgpig[1];
const int32_t i01 = ntg[1] == 1 ? tgpig[0]%args.ne01 : tgpig[0]*ntg[1] + tpitg.y;
const int32_t iw0 = ntg[1] == 1 ? tgpig[0]/args.ne01 : 0;
if (i01 >= args.ne01) {
return;
}
const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00;
const int64_t i3 = n/(args.ne2*args.ne1*args.ne0);
const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0)/(args.ne1*args.ne0);
const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0)/args.ne0;
const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0);
const int32_t i3 = n/(args.ne2*args.ne1*args.ne0);
const int32_t i2 = (n - i3*args.ne2*args.ne1*args.ne0)/(args.ne1*args.ne0);
const int32_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0)/args.ne0;
const int32_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0);
device const block_q * src_data = (device const block_q *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
device T4x4 * dst_data = (device T4x4 *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
for (int64_t i00 = iw0*ntg[0] + tiitg%ntg[0]; i00 < args.nk0; ) {
for (int32_t i00 = iw0*ntg[0] + tpitg.x; i00 < args.nk0;) {
T4x4 temp;
dequantize_func(src_data + i00/nl, i00%nl, temp);
dst_data[i00] = temp;

8
src/models/delta-net-base.cpp
View file

@ -562,13 +562,13 @@ ggml_tensor * llm_build_delta_net_base::build_recurrent_attn(
}
const int64_t D = S_v * S_v * H_v;
const int64_t K = (int64_t) cparams.n_rs_seq + 1;
const int64_t K = cparams.n_rs_seq + 1;
// TODO: remove pad + simplify
ggml_tensor * state_in_3d = ggml_reshape_3d(ctx0, s, D, 1, n_seqs);
ggml_tensor * state_3d = ggml_pad(ctx0, state_in_3d, 0, K - 1, 0, 0);
ggml_tensor * s_3d = ggml_reshape_3d(ctx0, s, D, 1, n_seqs);
ggml_tensor * s_3d_pad = ggml_pad (ctx0, s_3d, 0, K - 1, 0, 0);
ggml_tensor * gdn_out = ggml_gated_delta_net(ctx0, q, k, v, g, b, state_3d);
ggml_tensor * gdn_out = ggml_gated_delta_net(ctx0, q, k, v, g, b, s_3d_pad);
if (n_seq_tokens > 1) {
cb(gdn_out, LLAMA_TENSOR_NAME_FGDN_CH, il);
} else {