now it kinda learns again and code is kinda clean
This commit is contained in:
36
bridge.pyx
36
bridge.pyx
@@ -75,24 +75,15 @@ cdef public void f_idx_list_to_print(float* f_idxs, size_t num):
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# return retval
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cdef public void cbow_batch(
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float* batch, size_t bs, size_t win
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):
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batch_np = np.asarray(<float[:bs,:2*win+1]>batch)
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# Deal with X
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X_np = np.concatenate([batch_np[:, :win], batch_np[:, win+1:]], axis=1)
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y_np = nn.onehot(batch_np[:, win], nc=len(nn.vocab))
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eprint(batch_np)
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eprint(X_np)
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eprint(np.argmax(y_np, axis=1))
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cdef public void debug_print(object o):
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eprint(o)
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cdef public object create_network(int win, int embed):
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return nn.create_cbow_network(win, embed)
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try:
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return nn.create_cbow_network(win, embed)
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except Exception as e:
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eprint(e)
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cdef public void set_net_weights(object net, WeightList* wl):
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@@ -102,9 +93,7 @@ cdef public void set_net_weights(object net, WeightList* wl):
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cdef public void step_net(
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object net, float* batch, size_t bs
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):
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# X_train, y_train = cbow_batch(net, batch, bs)
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X_train = None
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y_train = None
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X_train, y_train = cbow_batch(net, batch, bs)
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net.train_on_batch(X_train, y_train)
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@@ -113,7 +102,10 @@ cdef public size_t out_size(object net):
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cdef public float eval_net(object net):
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return net.evaluate(X_test, y_test, verbose=False)
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try:
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return net.evaluate(X_test, y_test, verbose=False)
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except Exception as e:
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eprint(e)
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cdef public void init_weightlist_like(WeightList* wl, object net):
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@@ -156,6 +148,16 @@ cdef public void create_test_dataset(size_t win):
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_create_test_dataset(win)
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cdef tuple cbow_batch(
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object net, float* batch, size_t bs
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):
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win = net.input_shape[1] // 2
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batch_np = np.asarray(<float[:bs,:2*win+1]>batch)
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X_np = np.concatenate([batch_np[:, :win], batch_np[:, win+1:]], axis=1)
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y_np = nn.onehot(batch_np[:, win], nc=len(nn.vocab))
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return X_np, y_np
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cdef list wrap_weight_list(WeightList* wl):
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weights = []
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for i in range(wl.n_weights):
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@@ -29,8 +29,6 @@ def create_test_dataset(win):
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ds = np.array([vocab[w] for w in word_tokenize(f.read())
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if w in vocab])
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idx = np.random.choice(np.arange(win, len(ds) - win), S)
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oh_store = np.zeros((S, len(vocab)), dtype=np.float32)
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onehot(oh_store, ds[idx])
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return (
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# X
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np.stack([
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@@ -39,7 +37,7 @@ def create_test_dataset(win):
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], axis=0).astype(np.float32),
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#y
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oh_store
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onehot(ds[idx], nc=len(vocab))
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)
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def create_mnist_network():
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@@ -67,7 +65,7 @@ def create_cbow_network(win, embed):
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def token_generator(filename):
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with open(filename) as f:
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for i, l in enumerate(f.readlines(1000)):
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for i, l in enumerate(f.readlines()):
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if not l.isspace():
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tok = word_tokenize(l)
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if tok:
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50
main.c
50
main.c
@@ -15,9 +15,9 @@
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#define TAG_SWORD 7
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#define TAG_IWORD 8
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#define COMM 1
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#define ITER 1000
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#define BS 10
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#define COMM 10
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#define ITER 100
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#define BS 32
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#define EMB 20
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#define WIN 2
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#define FSPC 1
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@@ -36,7 +36,7 @@ typedef enum{
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TOKENIZER,
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FILTERER,
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BATCHER,
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SLAVE,
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LEARNER,
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MASTER
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} Role;
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@@ -60,7 +60,7 @@ size_t number_of(Role what) {
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return 1;
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case BATCHER:
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return 1;
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case SLAVE:
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case LEARNER:
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return world_size()
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- number_of(TOKENIZER)
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- number_of(FILTERER)
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@@ -189,11 +189,10 @@ void batcher() {
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}
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}
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if (l_wid[0] == -1) break;
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cbow_batch(batch, BS, WIN);
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// MPI_Recv(&s, 1, MPI_INT, MPI_ANY_SOURCE, TAG_READY, MPI_COMM_WORLD,
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// MPI_STATUS_IGNORE);
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// MPI_Send(batch, bufsize, MPI_FLOAT, s, TAG_BATCH, MPI_COMM_WORLD);
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MPI_Recv(&s, 1, MPI_INT, MPI_ANY_SOURCE, TAG_READY, MPI_COMM_WORLD,
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MPI_STATUS_IGNORE);
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MPI_Send(batch, bufsize, MPI_FLOAT, s, TAG_BATCH, MPI_COMM_WORLD);
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}
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free(l_wid);
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free(batch);
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@@ -233,7 +232,7 @@ void recv_weights(WeightList* wl, int src, int tag) {
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}
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}
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void slave_node() {
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void learner() {
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// 0. Announce readiness?
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// 1. Receive weights from master ([ ] has to know its master)
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// 2. Request batch from reader ([ ] has to choose a reader)
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@@ -246,35 +245,22 @@ void slave_node() {
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create_test_dataset(WIN);
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WeightList wl;
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init_weightlist_like(&wl, net);
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size_t entry_size = (2*WIN + 1);
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size_t bufsize = BS * entry_size;
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size_t vocab = out_size(net);
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size_t n_words = (BS + WIN + WIN);
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size_t X_numel = BS * (WIN + WIN);
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size_t y_numel = BS * vocab;
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float* X = malloc(X_numel * sizeof(float));
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float* y = malloc(y_numel * sizeof(float));
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float* f_widx = malloc(n_words * sizeof(float));
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float* batch = malloc(bufsize * sizeof(float));
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for in_range(i, COMM) {
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// MPI_Send(&me, 1, MPI_INT, mpi_id_from_role_id(MASTER, 0),
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// TAG_READY, MPI_COMM_WORLD);
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// recv_weights(&wl, mpi_id_from_role_id(MASTER, 0), TAG_WEIGH);
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// set_net_weights(net, &wl);
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for in_range(k, ITER) {
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MPI_Send(&me, 1, MPI_INT, mpi_id_from_role_id(BATCHER, 0),
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TAG_READY, MPI_COMM_WORLD);
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MPI_Recv(f_widx, n_words, MPI_FLOAT,
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MPI_Recv(batch, bufsize, MPI_FLOAT,
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mpi_id_from_role_id(BATCHER, 0), TAG_BATCH, MPI_COMM_WORLD,
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MPI_STATUS_IGNORE);
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// cbow_batch(X, y, f_widx, BS, WIN);
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step_net(net, X, BS);
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#warning "fix this"
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INFO_PRINTLN(".");
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step_net(net, batch, BS);
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}
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printf("%d net: %f\n", my_mpi_id(), eval_net(net));
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update_weightlist(&wl, net);
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// send_weights(&wl, mpi_id_from_role_id(MASTER, 0), TAG_WEIGH);
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}
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Py_DECREF(net);
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free_weightlist(&wl);
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@@ -293,7 +279,7 @@ void master_node() {
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init_weightlist_like(&wl, frank);
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update_weightlist(&wl, frank);
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int spr = number_of(SLAVE) * FSPC; // Slaves per round
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int spr = number_of(LEARNER) * FSPC; // Slaves per round
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int s;
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WeightList *wls = malloc(sizeof(WeightList) * spr);
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@@ -350,9 +336,9 @@ int main (int argc, const char **argv) {
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case BATCHER:
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batcher();
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break;
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// case SLAVE:
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// slave_node();
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// break;
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case LEARNER:
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learner();
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break;
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default:
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INFO_PRINTLN("DYING HORRIBLY!");
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// case SLAVE: slave_node(); break;
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