Unverified Commit 49a9c9ba authored by Daniel Hiltgen's avatar Daniel Hiltgen Committed by GitHub
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GGML update to ec98e2002 (#13451)

* Revert "add support for NVIDIA Nemotron 3 Nano"

This reverts commit e7d2ae9d69421012e9a8765c06a3fdf0e45b12f3.

* GGML update to 380b4c984

Remove MaskBatchPadding as GGML_KQ_MASK_PAD is no longer present (no
padding required)

* update to c45f89d55

* ec98e2002

solar pro needed more adjusting - needs verification

* review comments
parent 1c094038
#version 450
#include "soft_max_large_common.glsl"
void main() {
const uint tid = gl_LocalInvocationID.x;
const uint rowx = gl_WorkGroupID.y;
const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters;
const uint32_t i03 = rowx / (p.ne01 * p.ne02);
const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01;
const uint32_t i01 = rowx % p.ne01;
uint rowy_start = 0;
if (p.KY > 0) {
rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13;
}
if (rowx >= p.nrows_x) {
return;
}
float slope = get_slope(rowx);
// Find max
FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02];
[[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
const uint col = col0 + tid;
FLOAT_TYPE a = FLOAT_TYPE(0);
if (col < p.KX) {
a = data_a[rowx * p.KX + col];
}
FLOAT_TYPE b = FLOAT_TYPE(0);
if (p.KY > 0 && col < p.KX) {
b = data_b[rowy_start + col];
}
FLOAT_TYPE v = a * p.scale + slope * b;
if (col < p.KX) {
max_val = max(max_val, v);
}
}
// reduce across the workgroup
vals[tid] = max_val;
barrier();
[[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) {
vals[tid] = max(vals[tid], vals[tid + s]);
}
barrier();
}
if (tid == 0) {
max_val = vals[0];
data_m[rowx * gl_NumWorkGroups.x + gl_WorkGroupID.x] = max_val;
}
}
#version 450
#include "soft_max_large_common.glsl"
void main() {
const uint tid = gl_LocalInvocationID.x;
const uint rowx = gl_WorkGroupID.y;
const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters;
const uint32_t i03 = rowx / (p.ne01 * p.ne02);
const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01;
const uint32_t i01 = rowx % p.ne01;
uint rowy_start = 0;
if (p.KY > 0) {
rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13;
}
if (rowx >= p.nrows_x) {
return;
}
float slope = get_slope(rowx);
// Find max
FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02];
[[unroll]] for (uint i = 0; i < gl_NumWorkGroups.x; i += BLOCK_SIZE) {
if (i + tid < gl_NumWorkGroups.x) {
max_val = max(max_val, data_m[rowx * gl_NumWorkGroups.x + i + tid]);
}
}
// reduce across the workgroup
vals[tid] = max_val;
barrier();
[[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) {
vals[tid] = max(max_val, vals[tid + s]);
}
barrier();
}
max_val = vals[0];
barrier();
FLOAT_TYPE sum = FLOAT_TYPE(0.0f);
// Compute sum{exp(x - max)}
[[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
const uint col = col0 + tid;
if (col >= p.KX) {
break;
}
// compute exp(a*scale+b*slope), add it to sum
const uint i = rowx * p.KX + col;
FLOAT_TYPE val;
val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy_start + col]) : FLOAT_TYPE(0.0f)) - max_val);
sum += val;
data_d[i] = D_TYPE(val);
}
// reduce across the workgroup
vals[tid] = sum;
barrier();
[[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) {
vals[tid] += vals[tid + s];
}
barrier();
}
if (tid == 0) {
sum = vals[0];
data_s[rowx * gl_NumWorkGroups.x + gl_WorkGroupID.x] = sum;
}
}
#version 450
#include "soft_max_large_common.glsl"
shared FLOAT_TYPE sumsh[BLOCK_SIZE];
void main() {
const uint tid = gl_LocalInvocationID.x;
const uint rowx = gl_WorkGroupID.y;
const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters;
const uint32_t i03 = rowx / (p.ne01 * p.ne02);
const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01;
const uint32_t i01 = rowx % p.ne01;
uint rowy_start = 0;
if (p.KY > 0) {
rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13;
}
if (rowx >= p.nrows_x) {
return;
}
FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02];
FLOAT_TYPE sum = FLOAT_TYPE(0.0f);
[[unroll]] for (uint i = 0; i < gl_NumWorkGroups.x; i += BLOCK_SIZE) {
if (i + tid < gl_NumWorkGroups.x) {
max_val = max(max_val, data_m[rowx * gl_NumWorkGroups.x + i + tid]);
sum += data_s[rowx * gl_NumWorkGroups.x + i + tid];
}
}
// reduce across the workgroup
vals[tid] = max_val;
sumsh[tid] = sum;
barrier();
[[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) {
vals[tid] = max(max_val, vals[tid + s]);
sumsh[tid] += sumsh[tid + s];
}
barrier();
}
max_val = vals[0];
sum = sumsh[0];
if (p.has_sinks != 0) {
sum += FLOAT_TYPE(exp(FLOAT_TYPE(data_c[i02]) - max_val));
}
FLOAT_TYPE rcpdivisor = 1.0/sum;
[[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
const uint col = col0 + tid;
if (col >= p.KX) {
continue;
}
data_d[rowx*p.KX + col] *= D_TYPE(rcpdivisor);
}
}
#extension GL_EXT_control_flow_attributes : enable
layout (push_constant) uniform parameter
{
uint KX;
uint KY;
uint ne00;
uint ne01;
uint ne02;
uint ne12;
uint ne13;
uint nb11;
uint nb12;
uint nb13;
float scale;
float max_bias;
float m0;
float m1;
uint n_head_log2;
uint nrows_x;
uint has_sinks;
} p;
#include "types.glsl"
layout(constant_id = 0) const uint BLOCK_SIZE = 128;
layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
layout(constant_id = 1) const uint num_iters = 4;
layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
layout (binding = 2) readonly buffer Z {float data_c[];};
layout (binding = 3) buffer D {D_TYPE data_d[];};
layout (binding = 4) buffer M {float data_m[];};
layout (binding = 5) buffer S {float data_s[];};
shared FLOAT_TYPE vals[BLOCK_SIZE];
float get_slope(uint rowx) {
float slope = 1.0f;
// ALiBi
if (p.max_bias > 0.0f) {
const uint h = (rowx / p.ne01) % p.ne02; // head index
const float base = h < p.n_head_log2 ? p.m0 : p.m1;
const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;
slope = pow(base, exp);
}
return slope;
}
......@@ -10,6 +10,7 @@
layout (push_constant) uniform parameter
{
uint n_rows;
uint n_experts_push;
uint n_expert_used;
float clamp_min;
float clamp_max;
......@@ -18,11 +19,16 @@ layout (push_constant) uniform parameter
layout(local_size_x_id = 0, local_size_y = 4, local_size_z = 1) in;
layout(constant_id = 0) const uint WARP_SIZE = 32;
layout(constant_id = 1) const uint n_experts = 512;
layout(constant_id = 1) const uint n_experts_spec = 512;
layout(constant_id = 2) const bool with_norm = true;
layout(constant_id = 3) const bool late_softmax = false;
layout(constant_id = 4) const bool nexperts_use_push = false;
const uint experts_per_thread = (n_experts > WARP_SIZE) ? n_experts / WARP_SIZE : 1;
uint n_experts = nexperts_use_push ? n_experts_push : n_experts_spec;
#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
const uint experts_per_thread = CEIL_DIV(n_experts_spec, WARP_SIZE);
layout (binding = 0, std430) readonly buffer Logits {float logits[];};
layout (binding = 1, std430) writeonly buffer Weights {float weights[];};
......@@ -94,7 +100,7 @@ void main() {
}
if (!late_softmax) {
softmax_warp_inplace(wt, n_experts, lane, false);
softmax_warp_inplace(wt, n_experts, lane, nexperts_use_push);
}
// at this point, each thread holds a portion of softmax,
......
......@@ -704,13 +704,15 @@ void process_shaders() {
shader = (tname == "f32" || tname == "f16" || tname == "bf16") ? "get_rows.comp" : "get_rows_quant.comp";
if (tname == "f16") {
string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}));
string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}));
} else {
string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}}));
string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}}));
}
string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}}));
string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}}));
}
string_to_spv("get_rows_i32", "get_rows.comp", {{"TEMP_TYPE", "uint"}, {"A_TYPE", "uint"}, {"B_TYPE", "int"}, {"D_TYPE", "uint"}});
string_to_spv("mul_mat_vec_p021_f16_f32_subgroup_add", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}});
string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}});
string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}});
......@@ -854,6 +856,8 @@ void process_shaders() {
string_to_spv("tri_f16", "tri.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
string_to_spv("tri_f32", "tri.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
string_to_spv("diag_f16", "diag.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
string_to_spv("diag_f32", "diag.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
string_to_spv("softplus_f16", "softplus.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
string_to_spv("softplus_f32", "softplus.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
......@@ -899,6 +903,13 @@ void process_shaders() {
string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
string_to_spv("soft_max_back_f32", "soft_max_back.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("soft_max_large1_f32", "soft_max_large1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("soft_max_large2_f32", "soft_max_large2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("soft_max_large3_f32", "soft_max_large3.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("soft_max_large1_f32_f16", "soft_max_large1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
string_to_spv("soft_max_large2_f32_f16", "soft_max_large2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
string_to_spv("soft_max_large3_f32_f16", "soft_max_large3.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}});
string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}});
string_to_spv("rope_norm_f16_rte", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
......
......@@ -5265,8 +5265,6 @@ struct ggml_tensor * ggml_flash_attn_ext(
if (mask) {
GGML_ASSERT(ggml_is_contiguous(mask));
GGML_ASSERT(mask->ne[1] >= GGML_PAD(q->ne[1], GGML_KQ_MASK_PAD) &&
"the Flash-Attention kernel requires the mask to be padded to GGML_KQ_MASK_PAD and at least n_queries big");
//GGML_ASSERT(ggml_can_repeat_rows(mask, qk));
GGML_ASSERT(q->ne[2] % mask->ne[2] == 0);
......@@ -7573,6 +7571,11 @@ size_t ggml_quantize_chunk(
////////////////////////////////////////////////////////////////////////////////
void ggml_log_get(ggml_log_callback * log_callback, void ** user_data) {
*log_callback = g_logger_state.log_callback;
*user_data = g_logger_state.log_callback_user_data;
}
void ggml_log_set(ggml_log_callback log_callback, void * user_data) {
g_logger_state.log_callback = log_callback ? log_callback : ggml_log_callback_default;
g_logger_state.log_callback_user_data = user_data;
......
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