Fix #17164 symbolblock with BatchNorm inside during cast to fp16 (#17212

)

* fix symbolblock with bn+fp16

* add unittest

* fix

* remove unused

* fix lint

python/mxnet/gluon/block.py

@@ -16,7 +16,7 @@
 # under the License.
 
 # coding: utf-8
-# pylint: disable= arguments-differ, too-many-lines
+# pylint: disable= arguments-differ, too-many-lines, reimported
 """Base container class for all neural network models."""
 __all__ = ['Block', 'HybridBlock', 'SymbolBlock']
 
@@ -25,6 +25,7 @@
 import warnings
 import re
 from collections import OrderedDict, defaultdict
+import numpy as np
 
 from ..base import mx_real_t, MXNetError
 from .. import symbol, ndarray, initializer, np_symbol
@@ -1353,6 +1354,28 @@ def _clear_cached_op(self):
     def cast(self, dtype):
         self._clear_cached_op()
         super(SymbolBlock, self).cast(dtype)
+        if np.dtype(dtype).name == 'float16':
+            # correct BatchNorm types back to float32 due to its special requirement
+            out = self._cached_graph[1]
+            params_list = out.get_internals().list_inputs()
+            for node in params_list:
+                if node.endswith('running_var'):
+                    prefix = node[:-11]
+                    sibs = [prefix + t for t in ('running_mean', 'gamma', 'beta')]
+                    is_bn = all(p in params_list for p in sibs)
+                    if is_bn:
+                        self.params.get(node).cast('float32')
+                        for sib in sibs:
+                            self.params.get(sib).cast('float32')
+                if node.endswith('moving_var'):
+                    # another convention used
+                    prefix = node[:-10]
+                    sibs = [prefix + t for t in ('moving_mean', 'gamma', 'beta')]
+                    is_bn = all(p in params_list for p in sibs)
+                    if is_bn:
+                        self.params.get(node).cast('float32')
+                        for sib in sibs:
+                            self.params.get(sib).cast('float32')
 
     def hybrid_forward(self, F, x, *args, **kwargs):
         raise NotImplementedError

tests/python/gpu/test_gluon_gpu.py

@@ -594,6 +594,24 @@ def hybrid_forward(self, F, a, b):
     assert_raises(ValueError, lambda: foo_hybrid(mx.nd.ones((10,), ctx=mx.gpu()),
                                                  mx.nd.ones((10,), ctx=mx.cpu())))
 
+@with_seed()
+def test_symbol_block_symbolic_bn_fp16_cast():
+    with mx.gpu(0):
+        net = mx.gluon.nn.HybridSequential()
+        sym = mx.sym.var('data')
+        conv = mx.sym.Convolution(sym, kernel=(3, 3), num_filter=16)
+        bn = mx.sym.BatchNorm(conv, name='bn_test')
+        internals = bn.get_internals()
+        net.add(mx.gluon.nn.SymbolBlock([internals['bn_test_output']], [mx.sym.var('data')]))
+        net.add(mx.gluon.nn.Conv2D(10, kernel_size=1))
+        net.initialize()
+        x = mx.nd.zeros((1, 3, 32, 32), dtype='float32')
+        y = net(x)
+        assert np.dtype(y.dtype).name == 'float32'
+        net.cast('float16')
+        x = x.astype('float16')
+        y1 = net(x)
+        assert np.dtype(y1.dtype).name == 'float16'
 
 if __name__ == '__main__':
     import nose