tensor_clone = ggml_arange(ggml_ctx, start, stop, step);
} else if (tensor->op == GGML_OP_FILL) {
const float value = ggml_get_op_params_f32(tensor, 0);
- tensor_clone = ggml_fill(ggml_ctx, tensor_clone, value);
+ tensor_clone = ggml_fill(ggml_ctx, src_clone[0], value);
} else if (tensor->op == GGML_OP_SQR) {
tensor_clone = ggml_sqr(ggml_ctx, src_clone[0]);
} else if (tensor->op == GGML_OP_SQRT) {
barrier();
}
- const FLOAT_TYPE scale = inversesqrt(max(sum[0], FLOAT_TYPE(p.param1)));
+ const FLOAT_TYPE scale = 1.0f / max(sqrt(sum[0]), FLOAT_TYPE(p.param1));
[[unroll]] for (uint i0 = tid; i0 < p.ne00; i0 += BLOCK_SIZE) {
data_d[i3*p.nb13 + i2*p.nb12 + i1*p.nb11 + i0] = D_TYPE(scale * FLOAT_TYPE(data_a[i3*p.nb03 + i2*p.nb02 + i1*p.nb01 + i0]));