Enable bulking test on windows

tests/python/gpu/test_operator_gpu.py

@@ -2102,63 +2102,51 @@ def test_bilinear_sampler_versions():
                     assert_almost_equal(exe.grad_dict['grid'].asnumpy(), exe_list[ref_idx].grad_dict['grid'].asnumpy(), rtol=1e-3, atol=1e-5)
 
 
-@with_seed()
-@unittest.skip("test fails on windows gpu. temporarily disabled till it gets fixed. tracked at https://github.com/apache/incubator-mxnet/issues/14368")
-def test_bulking():
-    # Return the execution time of a model with the specified limits to the bulked op segments
-    def test_bulking_helper(data_shape, num_ops, num_iterations,
-                            max_fwd_segment_size, max_bwd_segment_size, enable_bulking_in_training):
-        orig_environ = os.environ.copy()
-        try:
-            # Explore different ways of setting the env vars.
-            # The framework does not cache the bulked seg size env var lookups during symbolic.
-            os.environ['MXNET_EXEC_BULK_EXEC_TRAIN'] = str(enable_bulking_in_training)
-            if max_fwd_segment_size == max_bwd_segment_size:
-                os.environ['MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN'] = str(max_fwd_segment_size)
-                os.environ.pop('MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD', None)
-                os.environ.pop('MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_BWD', None)
-            else:
-                os.environ.pop('MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN', None)
-                os.environ['MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD'] = str(max_fwd_segment_size)
-                os.environ['MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_BWD'] = str(max_bwd_segment_size)
-
-            ctx = default_context()
-            # build symbol
-            X = mx.sym.Variable('X')
-            sym = mx.sym.flip(X, axis=0)
-            for _ in range(num_ops-1):
-                sym = mx.sym.flip(sym, axis=0)
-            x = mx.ndarray.zeros(data_shape)
-            dx = mx.ndarray.zeros(data_shape)
-            dy = mx.ndarray.ones(data_shape)
-            exe = sym.bind(ctx=ctx, args=[x], args_grad = {'X':dx})
-
-            # time a number of forward() and backward() executions after some warm-up iterations
-            warmups = 1
-            for i in range(num_iterations+warmups):
-                if i == warmups:
-                    start = time.time()
-                exe.forward(is_train=True)
-                exe.backward(dy)
-                dx.wait_to_read()
-            time_per_iteration = (time.time() - start) / num_iterations
-        finally:
-            os.environ.clear()
-            os.environ.update(orig_environ)
-        return time_per_iteration
-
+# isolated execution bulking test function to be invoked with different env var settings
+def _test_bulking_in_process(seed, time_per_iteration):
     data_shape = (10,)
     num_ops = 1000
     num_iterations = 20
 
+    ctx = default_context()
+    # build symbol
+    X = mx.sym.Variable('X')
+    sym = mx.sym.flip(X, axis=0)
+    for _ in range(num_ops-1):
+        sym = mx.sym.flip(sym, axis=0)
+    x = mx.ndarray.zeros(data_shape)
+    dx = mx.ndarray.zeros(data_shape)
+    dy = mx.ndarray.ones(data_shape)
+    exe = sym.bind(ctx=ctx, args=[x], args_grad = {'X':dx})
+
+    # time a number of forward() and backward() executions after some warm-up iterations
+    warmups = 1
+    for i in range(num_iterations+warmups):
+        if i == warmups:
+            start = time.time()
+        exe.forward(is_train=True)
+        exe.backward(dy)
+        dx.wait_to_read()
+    time_per_iteration.value = (time.time() - start) / num_iterations
+
+@with_seed()
+def test_bulking():
     # test case format: (max_fwd_segment_size, max_bwd_segment_size, enable_bulking_in_training)
     test_cases = [(0,0,True), (1,1,True), (15,15,False), (15,0,True), (0,15,True), (15,15,True)]
     times = {}
     times_str = ''
     for seg_sizes in test_cases:
-        times[seg_sizes] = test_bulking_helper(data_shape, num_ops, num_iterations,
-                                               seg_sizes[0], seg_sizes[1], seg_sizes[2])
-        times_str +=\
+        # Create shared variable to return measured time from test process
+        time_per_iteration = mp.Manager().Value('d', 0.0)
+        if not run_in_spawned_process(_test_bulking_in_process,
+                                      {'MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD' : seg_sizes[0],
+                                       'MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_BWD' : seg_sizes[1],
+                                       'MXNET_EXEC_BULK_EXEC_TRAIN' : seg_sizes[2]},
+                                      time_per_iteration):
+            # skip test since the python version can't run it properly.  Warning msg was logged.
+            return
+        times[seg_sizes] = time_per_iteration.value
+        times_str += \
             '\n    runtime of (fwd,bwd,enable) op seg setting ({},{},{}) =\t{:.1f} msec'.format(
             seg_sizes[0], seg_sizes[1], seg_sizes[2], 1000.0 * times[seg_sizes])
 
@@ -2170,12 +2158,12 @@ def test_bulking_helper(data_shape, num_ops, num_iterations,
     print(times_str)
     # Non-bulked times[0,0,True], times[1,1,True] and times[15,15,False] should be about the same,
     # slower than both half-bulked times[0,15,True] and times[15,0,True]
-    assert slowest_half_bulked_time < fastest_non_bulked_time,\
-        'A half-bulked exec time is slower than the non-bulked time by {} secs! {}'\
+    assert slowest_half_bulked_time < fastest_non_bulked_time, \
+        'A half-bulked exec time is slower than the non-bulked time by {} secs! {}' \
             .format(slowest_half_bulked_time - fastest_non_bulked_time, times_str)
     # The fully bulked times[15,15,True] should be faster than both half-bulked runs
-    assert fully_bulked_time < fastest_half_bulked_time,\
-        'The fully-bulked exec time is slower than a half-bulked time by {} secs! {}'\
+    assert fully_bulked_time < fastest_half_bulked_time, \
+        'The fully-bulked exec time is slower than a half-bulked time by {} secs! {}' \
             .format(fully_bulked_time - fastest_half_bulked_time, times_str)