The following program performs a forward + backward pass on the sum of two length-1 vectors (via the elemwise_add operator)
#include <iostream>
#include "mxnet/c_api.h"
#include "nnvm/c_api.h"
#define checkedMXCall(func, ...) \
{ \
if (func(__VA_ARGS__) != 0) { \
printf("MX call %s failed at line %d:\n%s", \
#func, __LINE__, MXGetLastError()); \
exit(1) ; \
} \
}
int main() {
/* Create symbol */
const char json[] = "{\"nodes\": [{\"op\":\"null\",\"name\":\".Inputs.Input1\",\"inputs\":[]},{\"op\":\"null\",\"name\":\".Inputs.Input2\",\"inputs\":[]},{\"op\":\"elemwise_add\",\"name\":\".$0\",\"inputs\":[[0,0,0],[1,0,0]]},{\"op\":\"_copy\",\"name\":\".Outputs.Output\",\"inputs\":[[2,0,0]]}],\"arg_nodes\":[0,1],\"heads\":[[3,0,0]]}";
SymbolHandle sym;
checkedMXCall(MXSymbolCreateFromJSON, json, &sym);
/* Create NDArray for arguments */
int dev_type = 1;
int dev_id = 0;
mx_uint shape[1] = {1};
void *data;
NDArrayHandle in_arr_1, in_arr_2;
checkedMXCall(MXNDArrayCreate, shape, 1, dev_type, dev_id, 0, 0, &in_arr_1);
checkedMXCall(MXNDArrayCreate, shape, 1, dev_type, dev_id, 0, 0, &in_arr_2);
checkedMXCall(MXNDArrayGetData, in_arr_1, &data);
/* Set values for arguments */
*reinterpret_cast<float*>(data) = 0.4;
checkedMXCall(MXNDArrayGetData, in_arr_2, &data);
*reinterpret_cast<float*>(data) = 0.5;
/* Create NDArray for gradients */
NDArrayHandle grad_arr_1, grad_arr_2;
checkedMXCall(MXNDArrayCreate, shape, 1, dev_type, dev_id, 0, 0, &grad_arr_1);
checkedMXCall(MXNDArrayCreate, shape, 1, dev_type, dev_id, 0, 0, &grad_arr_2);
/* Set values for gradients */
checkedMXCall(MXNDArrayGetData, grad_arr_1, &data);
*reinterpret_cast<float*>(data) = 0;
checkedMXCall(MXNDArrayGetData, grad_arr_2, &data);
*reinterpret_cast<float*>(data) = 0;
/* Attach gradients to arguments */
uint32_t grad_req_1[1] = {1};
uint32_t grad_req_2[1] = {1};
checkedMXCall(MXAutogradMarkVariables, 1, &in_arr_1, grad_req_1, &grad_arr_1);
checkedMXCall(MXAutogradMarkVariables, 1, &in_arr_2, grad_req_2, &grad_arr_2);
/* Create cached op */
const char *cachedop_keys[1] = {"static_alloc"};
const char *cachedop_vals[1] = {"true"};
CachedOpHandle cached_op;
checkedMXCall(MXCreateCachedOp, sym, 1, cachedop_keys, cachedop_vals, &cached_op, false);
/* Set autograd to record & set training mode */
int dummy_prev;
checkedMXCall(MXAutogradSetIsRecording, 1, &dummy_prev);
checkedMXCall(MXAutogradSetIsTraining, 1, &dummy_prev);
/* Run forward */
int n_outs;
NDArrayHandle *out_arr_p = nullptr;
const int *dummy_stypes = nullptr;
NDArrayHandle inputs[] = {in_arr_1, in_arr_2};
checkedMXCall(MXInvokeCachedOp, cached_op, 2, inputs, dev_type, dev_id, &n_outs, &out_arr_p, &dummy_stypes);
checkedMXCall(MXNDArrayWaitToRead, *out_arr_p);
/* Create NDArray for outgrad */
NDArrayHandle outgrad_arr;
checkedMXCall(MXNDArrayCreate, shape, 1, dev_type, dev_id, 0, 0, &outgrad_arr);
/* Set values for outgrad */
checkedMXCall(MXNDArrayGetData, outgrad_arr, &data);
*reinterpret_cast<float*>(data) = 1;
/* Run backward */
checkedMXCall(MXAutogradBackward, 1, out_arr_p, &outgrad_arr, false);
checkedMXCall(MXNDArrayWaitToRead, grad_arr_1);
checkedMXCall(MXNDArrayWaitToRead, grad_arr_2);
/* Check results */
checkedMXCall(MXNDArrayGetData, in_arr_1, &data);
std::cout << "INPUT 1: " << *reinterpret_cast<float*>(data) << "\n";
checkedMXCall(MXNDArrayGetData, in_arr_2, &data);
std::cout << "INPUT 2: " << *reinterpret_cast<float*>(data) << "\n";
checkedMXCall(MXNDArrayGetData, *out_arr_p, &data);
std::cout << "OUTPUT: " << *reinterpret_cast<float*>(data) << "\n";
checkedMXCall(MXNDArrayGetData, outgrad_arr, &data);
std::cout << "OUTGRAD: " << *reinterpret_cast<float*>(data) << "\n";
checkedMXCall(MXNDArrayGetData, grad_arr_1, &data);
std::cout << "GRAD 1: " << *reinterpret_cast<float*>(data) << "\n";
checkedMXCall(MXNDArrayGetData, grad_arr_2, &data);
std::cout << "GRAD 2: " << *reinterpret_cast<float*>(data) << "\n";
return 0;
}
INPUT 1: 0.4
INPUT 2: 0.5
OUTPUT: 0.9
OUTGRAD: 1
GRAD 1: 1
GRAD 2: 1
Switching off the gradient request for the second input, i.e. defining the grad_req arrays as
uint32_t grad_req_1[1] = {1};
uint32_t grad_req_2[1] = {0};
also behaves as expected, producing 0 for GRAD 2 (gradient arrays are initialized with 0):
INPUT 1: 0.4
INPUT 2: 0.5
OUTPUT: 0.9
OUTGRAD: 1
GRAD 1: 1
GRAD 2: 0
uint32_t grad_req_1[1] = {0};
uint32_t grad_req_2[1] = {1};
INPUT 1: 0.4
INPUT 2: 0.5
OUTPUT: 0.9
OUTGRAD: 1
GRAD 1: 0
GRAD 2: 0
I must be doing something wrong here, because the equivalent python example works just fine with gradient requests 0 and 1 ('null' and 'write'):
import mxnet as mx
json = "{\"nodes\": [{\"op\":\"null\",\"name\":\".Inputs.Input1\",\"inputs\":[]},{\"op\":\"null\",\"name\":\".Inputs.Input2\",\"inputs\":[]},{\"op\":\"elemwise_add\",\"name\":\".$0\",\"inputs\":[[0,0,0],[1,0,0]]},{\"op\":\"_copy\",\"name\":\".Outputs.Output\",\"inputs\":[[2,0,0]]}],\"arg_nodes\":[0,1],\"heads\":[[3,0,0]]}"
sym = mx.symbol.fromjson(json)
ex = sym._bind(
mx.cpu(),
{'.Inputs.Input1': mx.nd.array([0.4]), '.Inputs.Input2': mx.nd.array([0.5])},
args_grad={
'.Inputs.Input1': mx.ndarray.zeros((1)),
'.Inputs.Input2': mx.ndarray.zeros((1))
},
grad_req={'.Inputs.Input1': 'null', '.Inputs.Input2': 'write'}
)
ex.forward(is_train=True)
print(ex.outputs)
ex.backward(out_grads=mx.nd.array([1]))
print(ex.grad_arrays)
[
[0.9]
<NDArray 1 @cpu(0)>]
[None,
[1.]
<NDArray 1 @cpu(0)>]
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