[AUTOTVM] Fix GATuner and improve error message

include/tvm/operation.h

@@ -366,6 +366,8 @@ class ExternOpNode : public OperationNode {
     v->Visit("tag", &tag);
     v->Visit("attrs", &attrs);
     v->Visit("inputs", &inputs);
+    v->Visit("input_placeholders", &input_placeholders);
+    v->Visit("output_placeholders", &output_placeholders);
     v->Visit("body", &body);
   }
   EXPORT static Operation make(std::string name,

python/tvm/autotvm/measure/measure_methods.py

@@ -394,6 +394,8 @@ def _measure_common(input_pack, build_func, build_kwargs, number, repeat,
             msg = str(exc)
             if "Stack trace returned" in msg:
                 msg = msg[:msg.index("Stack trace returned")]
+            if "CUDA Source" in msg:
+                msg = msg[:msg.index("CUDA Source")]
             costs = (RuntimeError(msg),)
             errno = MeasureErrorNo.RUNTIME_DEVICE
         tstamp = time.time()

python/tvm/autotvm/task/nnvm_integration.py

@@ -4,12 +4,16 @@
 
 """
 import warnings
+import logging
+
 
 from ... import tensor, placeholder, target as _target
 
 from ..util import get_const_tuple
 from .task import create, register
+from .dispatcher import ApplyHistoryBest
 
+logger = logging.getLogger('autotvm')
 
 def serialize_args(args):
     """serialize arguments of a topi function to a hashable tuple.
@@ -176,8 +180,17 @@ def extract_from_graph(graph, shape, dtype, target, symbols, target_host=None):
 
     # run compiler to collect all TOPI calls during compilation
     env.reset()
+
+    # disable logger temporarily
+    old_state = logger.disabled
+    logger.disabled = True
+
+    # use a dummy target to do a fake compile for collecting topi calls
     dummy_target = _target.create("opencl -device=dummy")
-    nnvm.compiler.build(graph, target=dummy_target, shape=shape, dtype=dtype)
+    with ApplyHistoryBest([], allow_fallback=True):
+        nnvm.compiler.build(graph, target=dummy_target, shape=shape, dtype=dtype)
+
+    logger.disabled = old_state
 
     tasks = []
     for task_name, args in env.get_tasks():

python/tvm/autotvm/task/task.py

@@ -368,7 +368,7 @@ def traverse(ops):
                 pass
             else:
                 raise FlopCalculationError("Only support tvm.compute currently. "
-                                           "Other ops like tvm.scan is not supported")
+                                           "Other ops like tvm.scan/tvm.extern is not supported")
         return ret
 
     try:

python/tvm/autotvm/task/topi_integration.py

@@ -62,7 +62,7 @@ def _decorator(f):
         for target_key in targets:
             if target_key not in _REGISTED_DISPATHCER:
                 _REGISTED_DISPATHCER[target_key] = {}
-            if topi_compute not in _REGISTED_DISPATHCER:
+            if topi_compute not in _REGISTED_DISPATHCER[target_key]:
                 @topi_compute.register(target_key)
                 @dispatcher
                 def config_dispatcher(*args, **kwargs):

python/tvm/autotvm/tuner/callback.py

@@ -101,11 +101,17 @@ def __init__(self):
             self.total = total
 
         def __del__(self):
-            sys.stdout.write(' Done.\n')
+            if logger.level < logging.DEBUG:  # only print progress bar in non-debug mode
+                sys.stdout.write(' Done.\n')
 
     ctx = _Context()
     tic = time.time()
 
+    if logger.level < logging.DEBUG:  # only print progress bar in non-debug mode
+        sys.stdout.write('\r%s Current/Best: %7.2f/%7.2f GFLOPS | Progress: (%d/%d) '
+                         '| %.2f s' % (prefix, 0, 0, 0, total, time.time() - tic))
+        sys.stdout.flush()
+
     def _callback(tuner, inputs, results):
         ctx.ct += len(inputs)
 

python/tvm/autotvm/tuner/ga_tuner.py

@@ -47,6 +47,7 @@ def __init__(self, task, pop_size, elite_num=3, mutation_prob=0.1):
 
         # random initialization
         self.pop_size = min(self.pop_size, len(self.space))
+        self.elite_num = min(self.pop_size, self.elite_num)
         for _ in range(self.pop_size):
             tmp_gene = point2knob(np.random.randint(len(self.space)), self.dims)
             while knob2point(tmp_gene, self.dims) in self.visited:
@@ -70,9 +71,9 @@ def update(self, inputs, results):
                 y = inp.task.flop / np.mean(res.costs)
                 self.scores.append(y)
             else:
-                self.scores.append(0)
+                self.scores.append(0.0)
 
-        if len(self.scores) >= len(self.genes):
+        if len(self.scores) >= len(self.genes) and len(self.visited) < len(self.space):
             genes = self.genes + self.elites
             scores = np.array(self.scores[:len(self.genes)] + self.elite_scores)
 
@@ -85,8 +86,13 @@ def update(self, inputs, results):
 
             # cross over
             indices = np.arange(len(genes))
-            scores /= np.max(scores)
-            probs = scores / np.sum(scores)
+            max_score = np.max(scores)
+            if max_score < 1e-8:
+                probs = np.empty_like(scores)
+                probs[:] = 1.0 / len(scores)
+            else:
+                scores /= max_score
+                probs = scores / np.sum(scores)
             tmp_genes = []
             for _ in range(self.pop_size):
                 p1, p2 = np.random.choice(indices, size=2, replace=False, p=probs)