vk_pipeline pipeline_tanh_f32;
vk_pipeline pipeline_diag_mask_inf_f32;
vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16;
+ vk_pipeline pipeline_soft_max_f32_wg512, pipeline_soft_max_f32_f16_wg512;
vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16;
vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
vk_pipeline pipeline_argsort_f32;
float m0;
float m1;
uint32_t n_head_log2;
+ uint32_t nrows_x;
};
struct vk_op_argsort_push_constants {
ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1);
- ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
- ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
+ ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+ ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_wg512, "soft_max_f32_wg512", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
+ ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+ ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f16, "rope_norm_f16", rope_norm_f16_len, rope_norm_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
- return ctx->device->pipeline_soft_max_f32;
+ return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_wg512 : ctx->device->pipeline_soft_max_f32;
}
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
- return ctx->device->pipeline_soft_max_f32_f16;
+ return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_f16_wg512 : ctx->device->pipeline_soft_max_f32_f16;
}
return nullptr;
case GGML_OP_ROPE:
scale, max_bias,
m0, m1,
n_head_log2,
+ nrows_x,
}, dryrun);
}
#version 450
-#extension GL_EXT_shader_16bit_storage : require
+#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
+#extension GL_EXT_control_flow_attributes : enable
layout (push_constant) uniform parameter
{
float m0;
float m1;
uint n_head_log2;
+ uint nrows_x;
} p;
#include "types.comp"
-#extension GL_EXT_control_flow_attributes : enable
-#define BLOCK_SIZE 512
-
-layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+layout(constant_id = 0) const uint BLOCK_SIZE = 32;
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
shared FLOAT_TYPE vals[BLOCK_SIZE];
-void main() {
+// num_iters is the number of BLOCK_SIZE loop iterations we need to iterate
+// over all the columns. The main function tries to pass a constant here,
+// as if it were a template function, to allow unrolling.
+void soft_max(uint num_iters) {
const uint tid = gl_LocalInvocationID.x;
const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
const uint rowy = rowx % p.KY;
+ if (rowx >= p.nrows_x) {
+ return;
+ }
+
float slope = 1.0f;
// ALiBi
// Find max
FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000);
- [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
+ // Cache values while we compute the max, so we don't need to read them
+ // again when we're ready to compute exp(x-max).
+ const uint DATA_CACHE_SIZE = 16;
+ FLOAT_TYPE data_cache[DATA_CACHE_SIZE];
+
+ [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
const uint col = col0 + tid;
- if (col >= p.KX) {
- break;
+ FLOAT_TYPE a = FLOAT_TYPE(0);
+ if (col < p.KX) {
+ a = data_a[rowx * p.KX + col];
}
- max_val = max(max_val, FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)));
+ FLOAT_TYPE b = FLOAT_TYPE(0);
+ if (p.KY > 0 && col < p.KX) {
+ b = data_b[rowy * p.KX + col];
+ }
+
+ FLOAT_TYPE v = a * p.scale + slope * b;
+
+ max_val = max(max_val, v);
+
+ if (idx < DATA_CACHE_SIZE) {
+ data_cache[idx] = v;
+ }
}
- vals[tid] = max_val;
+ // reduce across the workgroup
+ vals[tid] = max_val;
barrier();
- [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+ [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) {
vals[tid] = max(vals[tid], vals[tid + s]);
}
max_val = vals[0];
barrier();
- // Sum up values
- vals[tid] = FLOAT_TYPE(0.0f);
+ FLOAT_TYPE sum = FLOAT_TYPE(0.0f);
- [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
+ // Compute sum{exp(x - max)}
+ [[unroll]] for (uint col0 = 0, 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, and cache the new value
+ // in data_cache if possible.
const uint i = rowx * p.KX + col;
- const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val);
- vals[tid] += val;
- data_d[i] = D_TYPE(val);
+ FLOAT_TYPE val;
+ if (idx < DATA_CACHE_SIZE) {
+ val = exp(data_cache[idx] - max_val);
+ } else {
+ val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val);
+ }
+ sum += val;
+ if (idx < DATA_CACHE_SIZE) {
+ data_cache[idx] = val;
+ } else {
+ data_d[i] = D_TYPE(val);
+ }
}
+ // reduce across the workgroup
+ vals[tid] = sum;
barrier();
- [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+ [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) {
vals[tid] += vals[tid + s];
}
barrier();
}
+ sum = vals[0];
- const D_TYPE divisor = D_TYPE(vals[0]);
+ FLOAT_TYPE rcpdivisor = 1.0/sum;
- [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
+ [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
const uint col = col0 + tid;
if (col >= p.KX) {
- break;
+ continue;
+ }
+
+ if (idx < DATA_CACHE_SIZE) {
+ data_d[rowx*p.KX + col] = D_TYPE(data_cache[idx] * rcpdivisor);
+ } else {
+ data_d[rowx*p.KX + col] *= D_TYPE(rcpdivisor);
}
+ }
+}
- data_d[rowx*p.KX + col] /= divisor;
+void main() {
+ // instantiate the soft_max function for several different
+ // dimensions, to allow loop unrolling
+ uint num_blocks = (p.KX + BLOCK_SIZE - 1) / BLOCK_SIZE;
+ if (num_blocks > 32) {
+ soft_max(num_blocks);
+ } else if (num_blocks > 16) {
+ soft_max(32);
+ } else if (num_blocks > 8) {
+ soft_max(16);
+ } else if (num_blocks > 4) {
+ soft_max(8);
+ } else if (num_blocks == 4) {
+ soft_max(4);
+ } else if (num_blocks == 3) {
+ soft_max(3);
+ } else if (num_blocks == 2) {
+ soft_max(2);
+ } else if (num_blocks == 1) {
+ soft_max(1);
}
}