vk_pipeline pipeline_snake_f32;
vk_pipeline pipeline_snake_f16;
vk_pipeline pipeline_snake_bf16;
+ vk_pipeline pipeline_pool1d_f32;
vk_pipeline pipeline_pool2d_f32;
vk_pipeline pipeline_rwkv_wkv6_f32;
vk_pipeline pipeline_rwkv_wkv7_f32;
uint32_t ne1;
};
+struct vk_op_pool1d_push_constants {
+ uint32_t IL;
+ uint32_t OL;
+ uint32_t OC;
+ uint32_t pelements;
+ uint32_t op;
+ int32_t k0;
+ int32_t s0;
+ int32_t p0;
+};
+
struct vk_op_pool2d_push_constants {
uint32_t IW; uint32_t IH;
uint32_t OW; uint32_t OH;
ggml_vk_create_pipeline(device, device->pipeline_snake_f16, "snake_f16", snake_f16_len, snake_f16_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_snake_bf16, "snake_bf16", snake_bf16_len, snake_bf16_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1);
+ ggml_vk_create_pipeline(device, device->pipeline_pool1d_f32, "pool1d_f32", pool1d_f32_len, pool1d_f32_data, "main", 2, sizeof(vk_op_pool1d_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_pool2d_f32, "pool2d_f32", pool2d_f32_len, pool2d_f32_data, "main", 2, sizeof(vk_op_pool2d_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv6_f32, "rwkv_wkv6_f32", rwkv_wkv6_f32_len, rwkv_wkv6_f32_data, "main", 7, sizeof(vk_op_rwkv_wkv6_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
case GGML_TYPE_BF16: return ctx->device->pipeline_col2im_1d_bf16;
default: return nullptr;
}
+ case GGML_OP_POOL_1D:
+ if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+ return ctx->device->pipeline_pool1d_f32;
+ }
+ return nullptr;
case GGML_OP_POOL_2D:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_pool2d_f32;
{
elements = { uint32_t(dst->ne[0]), uint32_t(dst->ne[1]), 1 };
} break;
+ case GGML_OP_POOL_1D:
+ {
+ const uint32_t N = dst->ne[3] * dst->ne[2];
+ const uint32_t OC = dst->ne[1];
+ const uint32_t OL = dst->ne[0];
+ elements = { N * OC * OL, 1, 1};
+ } break;
case GGML_OP_POOL_2D:
{
const uint32_t N = dst->ne[3];
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { x_buf, a_buf, inv_b_buf, dst_buf }, pc, elements);
}
+static void ggml_vk_pool_1d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+ uint32_t op = static_cast<uint32_t>(dst->op_params[0]);
+ const int32_t k0 = dst->op_params[1];
+ const int32_t s0 = dst->op_params[2];
+ const int32_t p0 = dst->op_params[3];
+
+ const uint32_t IL = src0->ne[0];
+
+ const uint32_t N = dst->ne[3] * dst->ne[2];
+
+ const uint32_t OC = dst->ne[1];
+ const uint32_t OL = dst->ne[0];
+
+ const uint32_t parallel_elements = N * OC * OL;
+
+ ggml_vk_op_f32<vk_op_pool1d_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_POOL_1D, {
+ IL, OL, OC,
+ parallel_elements,
+ op,
+ k0, s0, p0,
+ });
+}
+
static void ggml_vk_pool_2d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
uint32_t op = static_cast<uint32_t>(dst->op_params[0]);
const int32_t k1 = dst->op_params[1];
case GGML_OP_CONV_TRANSPOSE_1D:
ggml_vk_conv_transpose_1d(ctx, compute_ctx, src0, src1, node);
+ break;
+ case GGML_OP_POOL_1D:
+ ggml_vk_pool_1d(ctx, compute_ctx, src0, node);
+
break;
case GGML_OP_POOL_2D:
ggml_vk_pool_2d(ctx, compute_ctx, src0, node);
case GGML_OP_CONV_2D_DW:
return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16)
&& op->src[1]->type == GGML_TYPE_F32;
+ case GGML_OP_POOL_1D:
+ return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_POOL_2D:
return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_RWKV_WKV6:
const int32_t oc = tensor->op_params[1];
const int32_t p0 = tensor->op_params[2];
tensor_clone = ggml_col2im_1d(ggml_ctx, src_clone[0], stride, oc, p0);
+ } else if (tensor->op == GGML_OP_POOL_1D) {
+ enum ggml_op_pool op = static_cast<ggml_op_pool>(tensor->op_params[0]);
+ const int32_t k0 = tensor->op_params[1];
+ const int32_t s0 = tensor->op_params[2];
+ const int32_t p0 = tensor->op_params[3];
+
+ tensor_clone = ggml_pool_1d(ggml_ctx, src_clone[0], op, k0, s0, p0);
} else if (tensor->op == GGML_OP_POOL_2D) {
enum ggml_op_pool op = static_cast<ggml_op_pool>(tensor->op_params[0]);
const int32_t k0 = tensor->op_params[1];
--- /dev/null
+#version 450
+
+#include "types.glsl"
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout(push_constant) uniform parameter {
+ uint IL;
+ uint OL;
+ uint OC;
+ uint pelements;
+ uint op;
+ int k0;
+ int s0;
+ int p0;
+} p;
+
+#define BLOCK_SIZE 512
+#define FLT_MAX 3.402823466e+38F
+#define OP_POOL_MAX 0u
+#define OP_POOL_AVG 1u
+
+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[];};
+
+void main() {
+ const uint idx = gl_GlobalInvocationID.x;
+ if (idx >= p.pelements) {
+ return;
+ }
+
+ const uint nc = idx / p.OL;
+ const uint cur_ol = idx % p.OL;
+
+ const int start = int(cur_ol) * p.s0 - p.p0;
+ const int bl = max(start, 0);
+ const int el = min(max(start + p.k0, 0), int(p.IL));
+
+ const int window_size = el - bl;
+ const float scale = window_size > 0 ? 1.0 / float(window_size) : 0.0;
+ float res;
+
+ if (p.op == OP_POOL_AVG) {
+ res = 0.0;
+ } else if (p.op == OP_POOL_MAX) {
+ res = -FLT_MAX;
+ } else {
+ return;
+ }
+
+ #pragma unroll
+ for (uint i = bl; i < el; i++) {
+ const float cur = D_TYPE(data_a[nc * p.IL + i]);
+
+ if (p.op == OP_POOL_AVG) {
+ res += cur * scale;
+ } else if (p.op == OP_POOL_MAX) {
+ res = max(res, cur);
+ }
+ }
+
+ data_d[nc * p.OL + cur_ol] = res;
+}
for (ggml_type type_input : {GGML_TYPE_F32}) {
for (ggml_op_pool pool_type : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) {
- for (int k0 : {1, 3}) {
- for (int s0 : {1, 2}) {
- for (int p0 : {0, 1}) {
+ for (int k0 : {1, 2, 3}) {
+ for (int s0 : {1, 2, 3}) {
+ for (int p0 : {0, 1, 2, 3}) {
test_cases.emplace_back(new test_pool1d(pool_type, type_input, { 10, 3, 2, 1 }, k0, s0, p0));
test_cases.emplace_back(new test_pool1d(pool_type, type_input, { 11, 1, 3, 2 }, k0, s0, p0));
test_cases.emplace_back(new test_pool1d(pool_type, type_input, { 128, 2, 1, 3 }, k0, s0, p0));