[v1.6] Fix the monitor_callback invalid issue during calibration with variable input shapes

.codecov.yml

@@ -4,6 +4,9 @@ codecov:
     require_ci_to_pass: yes
 
 coverage:
+  status:
+    project: off
+    patch: off
   precision: 2
   round: down
   range: "70...100"

python/mxnet/executor.py

@@ -79,6 +79,7 @@ def __init__(self, handle, symbol, ctx, grad_req, group2ctx):
         self._aux_dict = None
         self._output_dict = None
         self._monitor_callback = None
+        self._monitor_all = None
         self._ctx = copy.deepcopy(ctx)
         self._grad_req = copy.deepcopy(grad_req)
         self._group2ctx = copy.deepcopy(group2ctx)
@@ -253,6 +254,7 @@ def set_monitor_callback(self, callback, monitor_all=False):
         """
         cb_type = ctypes.CFUNCTYPE(None, ctypes.c_char_p, NDArrayHandle, ctypes.c_void_p)
         self._monitor_callback = cb_type(_monitor_callback_wrapper(callback))
+        self._monitor_all = monitor_all
         check_call(_LIB.MXExecutorSetMonitorCallbackEX(
             self.handle,
             self._monitor_callback,
@@ -469,6 +471,13 @@ def reshape(self, partial_shaping=False, allow_up_sizing=False, **kwargs):
         executor.arg_arrays = arg_arrays
         executor.grad_arrays = grad_arrays
         executor.aux_arrays = aux_arrays
+        if (self._monitor_callback is not None) and (self._monitor_all is not None):
+            # rebind callback to the new executor if the callback is valid
+            check_call(_LIB.MXExecutorSetMonitorCallbackEX(
+                handle,
+                self._monitor_callback,
+                None,
+                ctypes.c_int(self._monitor_all)))
         return executor
 
     def debug_str(self):

tests/python/unittest/test_operator.py

@@ -8269,6 +8269,59 @@ def get_output_names_callback(name, arr):
         check_name(us_sym, ['data', 'pooling_data', 'pooling_output'])
         del os.environ['MXNET_SUBGRAPH_BACKEND']
 
+@with_seed()
+def test_monitor_with_variable_input_shape():
+    output = {}
+
+    def get_output_min_callback(name, arr):
+        name = py_str(name)
+        handle = ctypes.cast(arr, NDArrayHandle)
+        arr = NDArray(handle, writable=False)
+        min_val = mx.ndarray.min(arr).asscalar()
+        if name in output:
+            output[name] = min(output[name], min_val)
+        else:
+            output[name] = min_val
+
+    def check_result(output, names):
+        assert len(output) > 0
+        for k, v in output.items():
+            assert k in names
+            assert v is not None
+
+    is_windows = sys.platform.startswith('win')
+    if (is_windows):
+        # Windows doesn't support set environment variable on the fly, so disable it for now
+        pass
+    else:
+        # Disable subgraph in case subgraph will replace symbol
+        os.environ['MXNET_SUBGRAPH_BACKEND'] = "NONE"
+
+        batch_size = 1
+        op_name = 'conv'
+        dshape = (batch_size, 3, 10, 10)
+        data = mx.sym.Variable('data', shape=dshape)
+        sym = mx.sym.Convolution(data, kernel=(1, 1), num_filter=1, name=op_name)
+
+        mod = mx.module.Module(symbol=sym, label_names=None)
+        mod.bind(for_training=False, data_shapes=[('data', dshape)])
+        mod.init_params()
+        mod._exec_group.execs[0].set_monitor_callback(get_output_min_callback, monitor_all=True)
+
+        new_dshape = dshape[:-1] + (dshape[-1] + 4,)
+        new_data = mx.nd.random.uniform(shape=new_dshape)
+        new_data = mx.io.NDArrayIter(data=new_data, batch_size=batch_size)
+        new_data = DummyIter(new_data)
+
+        for batch in new_data:
+            mod.forward(data_batch=batch, is_train=False)
+            mx.nd.waitall()
+            break
+
+        name_list = ['data', 'conv_data', 'conv_weight', 'conv_bias', 'conv_output']
+        check_result(output, name_list)
+        del os.environ['MXNET_SUBGRAPH_BACKEND']
+
 @with_seed()
 @unittest.skip("test fails intermittently. temporarily disabled till it gets fixed. tracked at https://github.com/apache/incubator-mxnet/issues/13915")
 def test_activation():
@@ -9558,7 +9611,7 @@ def convert_bias(F, k_bias, v_bias, num_heads):
     q_proj = mx.sym.FullyConnected(q, weight=q_weight, bias=q_bias, flatten=False,
                                    num_hidden=qkv_units, no_bias=False)
     att_score = mx.sym.contrib.interleaved_matmul_encdec_qk(
-            q_proj, kv_proj, heads=num_heads) 
+            q_proj, kv_proj, heads=num_heads)
     att_score = att_score + sonde
     weighted_value = mx.sym.contrib.interleaved_matmul_encdec_valatt(
             kv_proj, att_score, heads=num_heads)