ggml_fp16_t * d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size);
ggml_fp16_t * d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size);
float * d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size);
-#else
- float * const wdata = params->wdata;
#endif
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
wdata[id++] = GGML_FP32_TO_FP16(*(float *) ((char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11 + i00*nb10));
}
}
+
+ assert(id*sizeof(ggml_fp16_t) <= params->wsize);
}
#else
+ float * const wdata = params->wdata;
{
size_t id = 0;
for (int64_t i01 = 0; i01 < ne01; ++i01) {
wdata[id++] = GGML_FP16_TO_FP32(*(ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00));
}
}
+
+ assert(id*sizeof(float) <= params->wsize);
}
#endif
dequantize_row_q((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
id += ne00;
}
+
+ assert(id*sizeof(float) <= params->wsize);
}
+
const float * x = wdata;
#endif
//const int nb3 = src0->nb[3];
assert(nb0 == sizeof(float));
- assert(ne1+n_past == ne0);
+ assert(ne1 + n_past == ne0); (void) n_past;
// add alibi to src0 (KQ_scaled)
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
//const int nb3 = src0->nb[3];
assert(nb0 == sizeof(ggml_fp16_t));
- assert(ne1+n_past == ne0);
+ assert(ne1 + n_past == ne0); (void) n_past;
// add alibi to src0 (KQ_scaled)
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1; // TODO: this actually is doing nothing
// the threads are still spinning
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
+#if defined(GGML_USE_CUBLAS)
+ // with cuBLAS, we need memory for the full 3D / 4D data of src1
+ cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
+#else
// here we need memory just for single 2D matrix from src0
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
-#else
- // with GPU, we need memory for the full 3D / 4D data
- cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*MAX(ggml_nelements(node->src1), ggml_nelements(node->src0));
#endif
} else {
cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
#endif
} else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
cur = 0;
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS)
+#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1;
}
struct ggml_tensor * c1);
// Mapping operations
- GGML_API typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *);
- GGML_API typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
+ typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *);
+ typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
GGML_API struct ggml_tensor * ggml_map_unary_f32(
struct ggml_context * ctx,