} break;
case GGML_OP_OUT_PROD:
{
- if (ggml_is_quantized(node->src[0]->type)) {
+ if (ggml_is_quantized(node->src[0]->type) ||
+ node->src[0]->type == GGML_TYPE_F16) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
}
} break;
return max_bias == 0.0f;
}
case GGML_OP_IM2COL_BACK:
- return src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32;
+ return src0->type == GGML_TYPE_F32 && (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
case GGML_OP_GET_ROWS_BACK:
return src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16;
case GGML_OP_OUT_PROD:
- return (src0->type == GGML_TYPE_F32 || (ggml_is_quantized(src0->type) && src0->ne[2] == src1->ne[2] && src0->ne[3] == src1->ne[3])) &&
+ return (src0->type == GGML_TYPE_F32 ||
+ ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && src0->ne[2] == src1->ne[2] && src0->ne[3] == src1->ne[3])) &&
src1->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
default:
return true;
}
}
+static void ggml_compute_forward_out_prod_f16_f32(
+ const ggml_compute_params * params,
+ ggml_tensor * dst) {
+
+ const ggml_tensor * src0 = dst->src[0];
+ const ggml_tensor * src1 = dst->src[1];
+
+ GGML_TENSOR_BINARY_OP_LOCALS;
+
+ const int ith = params->ith;
+ const int nth = params->nth;
+
+ GGML_ASSERT(src0->type == GGML_TYPE_F16);
+ GGML_ASSERT(src1->type == GGML_TYPE_F32);
+ GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+ GGML_ASSERT(ne02 == ne12);
+ GGML_ASSERT(ne03 == ne13);
+ GGML_ASSERT(ne2 == ne12);
+ GGML_ASSERT(ne3 == ne13);
+
+ GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+ GGML_ASSERT(nb0 == sizeof(float));
+
+ GGML_ASSERT(ne0 == ne00);
+ GGML_ASSERT(ne1 == ne10);
+ GGML_ASSERT(ne2 == ne02);
+ GGML_ASSERT(ne3 == ne03);
+
+ if (ith == 0) {
+ ggml_vec_set_f32(ne0*ne1*ne2*ne3, (float *)dst->data, 0);
+ }
+ ggml_barrier(params->threadpool);
+
+ const int64_t nr = ne1*ne2*ne3;
+ const int64_t dr = (nr + nth - 1)/nth;
+ const int64_t ir0 = dr*ith;
+ const int64_t ir1 = MIN(ir0 + dr, nr);
+
+ float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith;
+
+ for (int64_t ir = ir0; ir < ir1; ++ir) {
+ const int64_t i3 = ir/(ne2*ne1);
+ const int64_t i2 = (ir - i3*ne2*ne1)/ne1;
+ const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+ const int64_t i02 = i2;
+ const int64_t i03 = i3;
+
+ const int64_t i12 = i2;
+ const int64_t i13 = i3;
+
+ float * d = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
+
+ for (int64_t i01 = 0; i01 < ne01; ++i01) {
+ const int64_t i11 = i01;
+ ggml_fp16_t * s0 = (ggml_fp16_t *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
+ float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13));
+ ggml_fp16_to_fp32_row(s0, wdata, ne0);
+ ggml_vec_mad_f32(ne0, d, wdata, *s1);
+ }
+ }
+}
+
void ggml_compute_forward_out_prod(
const ggml_compute_params * params,
ggml_tensor * dst) {
} break;
case GGML_TYPE_F16:
{
- GGML_ABORT("fatal error"); // todo
- // ggml_compute_forward_out_prod_f16_f32(params, dst);
- }
+ ggml_compute_forward_out_prod_f16_f32(params, dst);
+ } break;
case GGML_TYPE_F32:
{
ggml_compute_forward_out_prod_f32(params, dst);
const ggml_tensor * src1 = dst->src[1]; // convolution kernel
GGML_ASSERT(src0->type == GGML_TYPE_F32);
- GGML_ASSERT(src1->type == GGML_TYPE_F32);
+ GGML_ASSERT(src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
GGML_TENSOR_BINARY_OP_LOCALS;
vk_pipeline pipeline_col2im_1d_f32;
vk_pipeline pipeline_col2im_1d_f16;
vk_pipeline pipeline_col2im_1d_bf16;
+ vk_pipeline pipeline_out_prod_f32;
vk_pipeline pipeline_snake_f32;
vk_pipeline pipeline_snake_f16;
vk_pipeline pipeline_snake_bf16;
ggml_vk_create_pipeline(device, device->pipeline_col2im_1d_f16, "col2im_1d_f16", col2im_1d_f16_len, col2im_1d_f16_data, "main", 2, sizeof(vk_op_col2im_1d_push_constants), {256, 1, 1}, {}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_col2im_1d_bf16, "col2im_1d_bf16", col2im_1d_bf16_len, col2im_1d_bf16_data, "main", 2, sizeof(vk_op_col2im_1d_push_constants), {256, 1, 1}, {}, 1, true);
+ ggml_vk_create_pipeline(device, device->pipeline_out_prod_f32, "out_prod_f32", out_prod_f32_len, out_prod_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {256, 1, 1}, {}, 1);
+
ggml_vk_create_pipeline(device, device->pipeline_snake_f32, "snake_f32", snake_f32_len, snake_f32_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1);
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);
return ctx->device->pipeline_add_id_f32;
}
return nullptr;
+ case GGML_OP_OUT_PROD:
+ if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+ return ctx->device->pipeline_out_prod_f32;
+ }
+ return nullptr;
case GGML_OP_CONCAT: {
if (src0->type != src1->type || src0->type != dst->type) {
return nullptr;
case GGML_OP_DIV:
case GGML_OP_MUL:
case GGML_OP_ADD1:
+ case GGML_OP_OUT_PROD:
case GGML_OP_ARANGE:
case GGML_OP_FILL:
case GGML_OP_SCALE:
});
}
+static void ggml_vk_out_prod(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+ const uint32_t src0_type_size = ggml_type_size(src0->type);
+ const uint32_t src1_type_size = ggml_type_size(src1->type);
+ const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+ ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_OUT_PROD, {
+ (uint32_t)ggml_nelements(dst),
+ (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)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3],
+ (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_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,
+ 0.0f, 0.0f, 0,
+ });
+}
+
static void ggml_vk_sub(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const uint32_t src0_type_size = ggml_type_size(src0->type);
const uint32_t src1_type_size = ggml_type_size(src1->type);
ggml_vk_add(ctx, compute_ctx, src0, src1, node);
}
break;
+ case GGML_OP_OUT_PROD:
+ ggml_vk_out_prod(ctx, compute_ctx, src0, src1, node);
+ break;
case GGML_OP_SUB:
ggml_vk_sub(ctx, compute_ctx, src0, src1, node);
case GGML_OP_OPT_STEP_ADAMW:
case GGML_OP_OPT_STEP_SGD:
return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
+ case GGML_OP_OUT_PROD:
+ return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32
+ && ggml_is_contiguous(op->src[1]) && op->src[1]->type == GGML_TYPE_F32
+ && op->type == GGML_TYPE_F32;
case GGML_OP_LOG:
case GGML_OP_TRI:
case GGML_OP_DIAG:
--- /dev/null
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+ uint ne;
+ uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
+ uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
+ uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23;
+ uint misalign_offsets;
+ float param1; float param2; int param3;
+} p;
+
+layout (binding = 0) readonly buffer A {float data_a[];};
+layout (binding = 1) readonly buffer B {float data_b[];};
+layout (binding = 2) writeonly buffer D {float data_d[];};
+
+uint get_idx() {
+ return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+}
+
+uint get_aoffset() { return p.misalign_offsets >> 16; }
+uint get_boffset() { return (p.misalign_offsets >> 8) & 0xFF; }
+uint get_doffset() { return p.misalign_offsets & 0xFF; }
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+void main() {
+ uint idx = get_idx();
+ if (idx >= p.ne) {
+ return;
+ }
+
+ uint tmp = idx;
+ uint i0 = tmp % p.ne20; tmp /= p.ne20;
+ uint i1 = tmp % p.ne21; tmp /= p.ne21;
+ uint i2 = tmp % p.ne22; tmp /= p.ne22;
+ uint i3 = tmp;
+
+ uint a_i0 = i0 % p.ne00;
+ uint a_i2 = i2 / (p.ne22 / p.ne02);
+ uint a_i3 = i3 / (p.ne23 / p.ne03);
+
+ uint b_i0 = i1 % p.ne10;
+ uint b_i2 = i2;
+ uint b_i3 = i3;
+
+ float sum = 0.0f;
+ uint K = p.ne01;
+ for (uint k = 0; k < K; k++) {
+ uint aoff = get_aoffset() + a_i3*p.nb03 + a_i2*p.nb02 + k*p.nb01 + a_i0*p.nb00;
+ uint boff = get_boffset() + b_i3*p.nb13 + b_i2*p.nb12 + k*p.nb11 + b_i0*p.nb10;
+ sum += data_a[aoff] * data_b[boff];
+ }
+
+ uint doff = get_doffset() + i3*p.nb23 + i2*p.nb22 + i1*p.nb21 + i0*p.nb20;
+ data_d[doff] = sum;
+}
}
}
+ string_to_spv("out_prod_f32", "out_prod.comp", {});
+
string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("conv_transpose_1d_f32", "conv_transpose_1d.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});