larroy commented on a change in pull request #15969: Partitioning Gluon
HybridBlocks
URL: https://github.com/apache/incubator-mxnet/pull/15969#discussion_r373745860
##########
File path: tests/python/unittest/test_subgraph_op.py
##########
@@ -18,351 +18,448 @@
import os
import ctypes
import mxnet as mx
-from mxnet.base import SymbolHandle, check_call, _LIB, mx_uint, c_str_array,
c_str
+from mxnet.base import SymbolHandle, check_call, _LIB, mx_uint, c_str_array,
c_str, mx_real_t
from mxnet.symbol import Symbol
import numpy as np
from mxnet.test_utils import assert_almost_equal
-
-
-def _test_subgraph_exe(subgraph_backend):
- def _check_subgraph_exe1(sym, subgraph_backend, op_names):
- """Use the partitioned sym to simple_bind an executor and compare the
outputs
- with those of the original executor"""
- out = SymbolHandle()
- check_call(_LIB.MXBuildSubgraphByOpNames(sym.handle,
c_str(subgraph_backend), mx_uint(len(op_names)),
- c_str_array(op_names),
ctypes.byref(out)))
-
- partitioned_sym = Symbol(out)
- assert partitioned_sym.list_inputs() == sym.list_inputs()
- assert partitioned_sym.list_arguments() == sym.list_arguments()
- assert partitioned_sym.list_auxiliary_states() ==
sym.list_auxiliary_states()
+from mxnet import gluon
+from mxnet.gluon import nn
+from mxnet import nd
+
+def network_structure_1():
+ data1 = mx.sym.var('data1', shape=(2, 3, 10, 10))
+ data2 = mx.sym.var('data2')
+ conv1 = mx.sym.Convolution(data=data1, weight=data2, no_bias=True,
kernel=(2, 2), num_filter=1)
+ conv2 = mx.sym.Convolution(data=data2, no_bias=True, kernel=(1, 1),
num_filter=1)
+ out = mx.sym.Group([conv1, conv2])
+ return (out, ['data1'], [(2, 3, 10, 10)])
+
+def network_structure_2():
+ # this tests whether the partitioning algorithm can deal with cycles
+ data = mx.sym.var('data', shape=(2, 3, 10, 10))
+ ret = mx.sym.exp(data)
+ ret1 = mx.sym.cos(ret)
+ ret2 = mx.sym.sin(ret)
+ ret = ret1 + ret2
+ return (ret, ['data'], [(2, 3, 10, 10)])
+
+def network_structure_3():
+ # this tests whether the partitioned sym can distinguish in_args and
aux_states
+ data = mx.sym.var('data', shape=(2, 3, 10, 10))
+ ret = mx.sym.exp(data)
+ ret1 = mx.sym.cos(ret)
+ ret2 = mx.sym.sin(ret)
+ ret = ret1 + ret2
+ ret = mx.sym.BatchNorm(ret)
+ ret = mx.sym.BatchNorm(ret)
+ # Return the same and shape of 'data' and auxiliary states
+ return (ret, ['data', *ret.list_auxiliary_states()], [(2, 3, 10, 10),
(3,), (3,), (3,), (3,)])
+
+def network_structure_4():
+ # the last op has multiple duplicate outputs
Review comment:
I like the comments on these tests
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