samskalicky commented on a change in pull request #15969: [WIP] Partitioning
Gluon HybridBlocks
URL: https://github.com/apache/incubator-mxnet/pull/15969#discussion_r373743013
##########
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
+ data = mx.sym.var('data', shape=(2, 3, 10, 10))
+ ret = mx.sym.exp(data)
+ ret = mx.sym.Group([ret, ret, ret])
+ return (ret, ['data'], [(2, 3, 10, 10)])
+
+def network_structure_5():
+ # the subgraph has two duplicate input entries
+ data = mx.sym.var('data', shape=(2, 3, 10, 10))
+ ret = data + data
+ return (ret, ['data'], [(2, 3, 10, 10)])
+
+def network_structure_6():
+ data1 = mx.sym.Variable('data1', shape=(3, 3, 10, 10), dtype=np.float32)
+ data2 = mx.sym.Variable('data2', shape=(1, 0, 2, 2))
+ data3 = mx.sym.sin(data2)
+ conv = mx.sym.Convolution(data=data1, weight=data3, kernel=(2, 2),
num_filter=1)
+ return (conv, ['data1'], [(3, 3, 10, 10)])
+
+def network_structure_7():
+ # in this graph, the subgraph node and the other two external nodes form a
cycle
+ data = mx.sym.Variable('data', shape=(1,))
+ ret1 = mx.sym.sin(data)
+ ret2 = mx.sym.cos(ret1)
+ for _ in range(5):
+ ret2 = mx.sym.cos(ret2)
+ ret = ret1 + ret2
+ return (ret, ['data'], [(1,)])
+
+def get_graphs():
+ return [
+ (network_structure_1(), ['Convolution']),
+ (network_structure_2(), ['exp', 'sin', '_Plus', 'elemwise_add',
'_plus']),
+ (network_structure_2(), ['exp', 'cos', '_Plus', 'elemwise_add',
'_plus']),
+ (network_structure_3(), ['exp', 'sin', '_Plus', 'elemwise_add',
'_plus']),
+ (network_structure_3(), ['exp', 'cos', '_Plus', 'elemwise_add',
'_plus']),
+ (network_structure_3(), ['exp', 'sin', '_Plus', 'elemwise_add',
'_plus', 'BatchNorm']),
+ (network_structure_3(), ['exp', 'cos', '_Plus', 'elemwise_add',
'_plus', 'BatchNorm']),
+ (network_structure_3(), ['exp', 'BatchNorm']),
+ (network_structure_3(), ['BatchNorm']),
+ (network_structure_4(), ['exp']),
+ (network_structure_5(), ['_plus', '_Plus', 'elemwise_add']),
+ (network_structure_6(), []),
+ (network_structure_6(), [mx.sym.sin.__name__]),
+ (network_structure_6(), [mx.sym.Convolution.__name__]),
+ (network_structure_6(), [mx.sym.sin.__name__,
mx.sym.Convolution.__name__]),
+ (network_structure_7(), ['sin', 'elemwise_add', '_plus', '_Plus'])
+ ]
+
+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()
+ exe = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
+ partitioned_exe = partitioned_sym.simple_bind(ctx=mx.current_context(),
grad_req='null')
+ input_names = sym.list_inputs()
+ for name in input_names:
+ if name in exe.arg_dict:
+ exe.arg_dict[name][:] =
mx.nd.random.uniform(shape=exe.arg_dict[name].shape)
+ partitioned_exe.arg_dict[name][:] = exe.arg_dict[name]
+ else:
+ assert name in exe.aux_dict
+ exe.aux_dict[name][:] =
mx.nd.random.uniform(shape=exe.aux_dict[name].shape)
+ partitioned_exe.aux_dict[name][:] = exe.aux_dict[name]
+ exe.forward()
+ partitioned_exe.forward()
+ assert len(exe.outputs) == len(partitioned_exe.outputs)
+ for i in range(len(exe.outputs)):
+ assert_almost_equal((exe.outputs[i] -
partitioned_exe.outputs[i]).abs().sum().asnumpy(),
+ np.zeros(shape=(1,)))
+
+def check_subgraph_exe2(sym, subgraph_backend, op_names):
+ """Use env var MXNET_SUBGRAPH_BACKEND=default to trigger graph
partitioning in simple_bind
+ and compare results of the partitioned sym and the original sym."""
+ def get_executor(sym, subgraph_backend=None, op_names=None,
original_exec=None):
+ if subgraph_backend is not None:
+ os.environ['MXNET_SUBGRAPH_BACKEND'] = subgraph_backend
+
check_call(_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend),
mx_uint(len(op_names)),
+
c_str_array(op_names)))
exe = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
- partitioned_exe =
partitioned_sym.simple_bind(ctx=mx.current_context(), grad_req='null')
input_names = sym.list_inputs()
for name in input_names:
if name in exe.arg_dict:
- exe.arg_dict[name][:] =
mx.nd.random.uniform(shape=exe.arg_dict[name].shape)
- partitioned_exe.arg_dict[name][:] = exe.arg_dict[name]
+ exe.arg_dict[name][:] =
mx.nd.random.uniform(shape=exe.arg_dict[name].shape)\
+ if original_exec is None else original_exec.arg_dict[name]
else:
assert name in exe.aux_dict
- exe.aux_dict[name][:] =
mx.nd.random.uniform(shape=exe.aux_dict[name].shape)
- partitioned_exe.aux_dict[name][:] = exe.aux_dict[name]
+ exe.aux_dict[name][:] =
mx.nd.random.uniform(shape=exe.aux_dict[name].shape)\
+ if original_exec is None else original_exec.aux_dict[name]
exe.forward()
- partitioned_exe.forward()
- assert len(exe.outputs) == len(partitioned_exe.outputs)
- for i in range(len(exe.outputs)):
- assert_almost_equal((exe.outputs[i] -
partitioned_exe.outputs[i]).abs().sum().asnumpy(),
- np.zeros(shape=(1,)))
-
- def _check_subgraph_exe2(sym, subgraph_backend, op_names):
- """Use env var MXNET_SUBGRAPH_BACKEND=default to trigger graph
partitioning in simple_bind
- and compare results of the partitioned sym and the original sym."""
- def get_executor(sym, subgraph_backend=None, op_names=None,
original_exec=None):
- if subgraph_backend is not None:
- os.environ['MXNET_SUBGRAPH_BACKEND'] = subgraph_backend
-
check_call(_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend),
mx_uint(len(op_names)),
-
c_str_array(op_names)))
- exe = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
- input_names = sym.list_inputs()
- for name in input_names:
- if name in exe.arg_dict:
- exe.arg_dict[name][:] =
mx.nd.random.uniform(shape=exe.arg_dict[name].shape)\
- if original_exec is None else
original_exec.arg_dict[name]
- else:
- assert name in exe.aux_dict
- exe.aux_dict[name][:] =
mx.nd.random.uniform(shape=exe.aux_dict[name].shape)\
- if original_exec is None else
original_exec.aux_dict[name]
- exe.forward()
- if subgraph_backend is not None:
-
check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
- del os.environ['MXNET_SUBGRAPH_BACKEND']
- return exe
-
- original_exec = get_executor(sym)
- partitioned_exec = get_executor(sym, subgraph_backend, op_names,
original_exec)
- outputs1 = original_exec.outputs
- outputs2 = partitioned_exec.outputs
- assert len(outputs1) == len(outputs2)
- for i in range(len(outputs1)):
- assert_almost_equal((outputs1[i] -
outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
-
- def _check_subgraph_exe3(sym, subgraph_backend, op_names):
- """Use the partitioned sym to 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)
- input_names = sym.list_inputs()
- arg_names = sym.list_arguments()
- aux_names = sym.list_auxiliary_states()
- assert partitioned_sym.list_inputs() == input_names
- assert partitioned_sym.list_arguments() == arg_names
- assert partitioned_sym.list_auxiliary_states() == aux_names
+ if subgraph_backend is not None:
+
check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
+ del os.environ['MXNET_SUBGRAPH_BACKEND']
+ return exe
+
+ original_exec = get_executor(sym)
+ partitioned_exec = get_executor(sym, subgraph_backend, op_names,
original_exec)
+ outputs1 = original_exec.outputs
+ outputs2 = partitioned_exec.outputs
+ assert len(outputs1) == len(outputs2)
+ for i in range(len(outputs1)):
+ assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
np.zeros(shape=(1,)))
+
+def check_subgraph_exe3(sym, subgraph_backend, op_names):
+ """Use the partitioned sym to 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)
+ input_names = sym.list_inputs()
+ arg_names = sym.list_arguments()
+ aux_names = sym.list_auxiliary_states()
+ assert partitioned_sym.list_inputs() == input_names
+ assert partitioned_sym.list_arguments() == arg_names
+ assert partitioned_sym.list_auxiliary_states() == aux_names
+ arg_shapes, _, aux_shapes = sym.infer_shape()
+ arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
+ aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
+ exe = sym.bind(ctx=mx.current_context(), args=arg_array,
aux_states=aux_array, grad_req='null')
+ partitioned_exe = partitioned_sym.bind(ctx=mx.current_context(),
args=arg_array,
+ aux_states=aux_array,
grad_req='null')
+ exe.forward()
+ partitioned_exe.forward()
+ assert len(exe.outputs) == len(partitioned_exe.outputs)
+ for i in range(len(exe.outputs)):
+ assert_almost_equal((exe.outputs[i] -
partitioned_exe.outputs[i]).abs().sum().asnumpy(),
+ np.zeros(shape=(1,)))
+
+def check_subgraph_exe4(sym, subgraph_backend, op_names):
+ """Use env var MXNET_SUBGRAPH_BACKEND=default to trigger graph
partitioning in bind
+ and compare results of the partitioned sym and the original sym."""
+ def get_executor(sym, subgraph_backend=None, op_names=None,
original_exec=None):
+ if subgraph_backend is not None:
+ os.environ['MXNET_SUBGRAPH_BACKEND'] = subgraph_backend
+
check_call(_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend),
mx_uint(len(op_names)),
+
c_str_array(op_names)))
arg_shapes, _, aux_shapes = sym.infer_shape()
- arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
- aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
- exe = sym.bind(ctx=mx.current_context(), args=arg_array,
aux_states=aux_array, grad_req='null')
- partitioned_exe = partitioned_sym.bind(ctx=mx.current_context(),
args=arg_array,
- aux_states=aux_array,
grad_req='null')
+ if subgraph_backend is None:
+ arg_array = [mx.nd.random.uniform(shape=shape) for shape in
arg_shapes]
+ aux_array = [mx.nd.random.uniform(shape=shape) for shape in
aux_shapes]
+ else:
+ arg_array = None
+ aux_array = None
+ exe = sym.bind(ctx=mx.current_context(),
+ args=arg_array if subgraph_backend is None else
original_exec.arg_arrays,
+ aux_states=aux_array if subgraph_backend is None else
original_exec.aux_arrays,
+ grad_req='null')
exe.forward()
- partitioned_exe.forward()
- assert len(exe.outputs) == len(partitioned_exe.outputs)
- for i in range(len(exe.outputs)):
- assert_almost_equal((exe.outputs[i] -
partitioned_exe.outputs[i]).abs().sum().asnumpy(),
- np.zeros(shape=(1,)))
-
- def _check_subgraph_exe4(sym, subgraph_backend, op_names):
- """Use env var MXNET_SUBGRAPH_BACKEND=default to trigger graph
partitioning in bind
- and compare results of the partitioned sym and the original sym."""
- def get_executor(sym, subgraph_backend=None, op_names=None,
original_exec=None):
- if subgraph_backend is not None:
- os.environ['MXNET_SUBGRAPH_BACKEND'] = subgraph_backend
-
check_call(_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend),
mx_uint(len(op_names)),
-
c_str_array(op_names)))
- arg_shapes, _, aux_shapes = sym.infer_shape()
- if subgraph_backend is None:
- arg_array = [mx.nd.random.uniform(shape=shape) for shape in
arg_shapes]
- aux_array = [mx.nd.random.uniform(shape=shape) for shape in
aux_shapes]
- else:
- arg_array = None
- aux_array = None
- exe = sym.bind(ctx=mx.current_context(),
- args=arg_array if subgraph_backend is None else
original_exec.arg_arrays,
- aux_states=aux_array if subgraph_backend is None
else original_exec.aux_arrays,
- grad_req='null')
- exe.forward()
- if subgraph_backend is not None:
-
check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
- del os.environ['MXNET_SUBGRAPH_BACKEND']
- return exe
-
- original_exec = get_executor(sym)
- partitioned_exec = get_executor(sym, subgraph_backend, op_names,
original_exec)
- outputs1 = original_exec.outputs
- outputs2 = partitioned_exec.outputs
- assert len(outputs1) == len(outputs2)
- for i in range(len(outputs1)):
- assert_almost_equal((outputs1[i] -
outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
-
- def set_random_inputs(exe1, input_names):
- """Sets random values to exe1's args and auxs"""
- for name in input_names:
- if name in exe1.arg_dict:
- exe1.arg_dict[name][:] =
mx.nd.random.uniform(shape=exe1.arg_dict[name].shape)
- else:
- assert name in exe1.aux_dict
- exe1.aux_dict[name][:] =
mx.nd.random.uniform(shape=exe1.aux_dict[name].shape)
-
- def copy_inputs_between_executors(exe1, exe2, input_names):
- """Copies values of args and auxs from exe1 to exe2"""
- for name in input_names:
- if name in exe2.arg_dict:
- exe2.arg_dict[name][:] = exe1.arg_dict[name]
- else:
- assert name in exe2.aux_dict
- exe2.aux_dict[name][:] = exe1.aux_dict[name]
-
- def _check_subgraph_exe5(sym, subgraph_backend, op_names):
- """Call optimize_for to trigger graph partitioning without infer
shapes/types before,
- then simple_bind and compare results of the partitioned sym and the
original sym."""
- # simple_bind
- exe1 = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
- input_names = sym.list_inputs()
- set_random_inputs(exe1, input_names)
- exe1.forward()
-
- # partition before simple_bind
-
check_call(_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
- c_str_array(op_names)))
- part_sym = sym.optimize_for(subgraph_backend)
-
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
-
- exe2 = part_sym.simple_bind(ctx=mx.current_context(), grad_req='null')
- copy_inputs_between_executors(exe1, exe2, input_names)
- exe2.forward()
-
- # compare outputs
- outputs1 = exe1.outputs
- outputs2 = exe2.outputs
- assert len(outputs1) == len(outputs2)
- for i in range(len(outputs1)):
- assert_almost_equal((outputs1[i] -
outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
-
- def _check_subgraph_exe6(sym, subgraph_backend, op_names):
- """Call optimize_for to trigger graph partitioning without infer
shapes/types before,
- then simple_bind and compare results of the partitioned sym and the
original sym."""
- # simple_bind
- exe1 = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
- input_names = sym.list_inputs()
- set_random_inputs(exe1, input_names)
- exe1.forward()
-
- # infer shape/type before partition before simple_bind
-
check_call(_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
- c_str_array(op_names)))
- part_sym = sym.optimize_for(subgraph_backend, exe1.arg_dict)
-
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
-
- exe2 = part_sym.simple_bind(ctx=mx.current_context(), grad_req='null')
- copy_inputs_between_executors(exe1, exe2, input_names)
- exe2.forward()
-
- # compare outputs
- outputs1 = exe1.outputs
- outputs2 = exe2.outputs
- assert len(outputs1) == len(outputs2)
- for i in range(len(outputs1)):
- assert_almost_equal((outputs1[i] -
outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
-
- def _check_subgraph_exe7(sym, subgraph_backend, op_names):
- """Call optimize_for to trigger graph partitioning without infer
shapes/types before,
- then bind and compare results of the partitioned sym and the original
sym."""
- # bind
- arg_shapes, _, aux_shapes = sym.infer_shape()
- arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
- aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
- exe1 = sym.bind(ctx=mx.current_context(), args=arg_array,
aux_states=aux_array, grad_req='null')
- exe1.forward()
-
- # partition before bind
-
check_call(_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
- c_str_array(op_names)))
- part_sym = sym.optimize_for(subgraph_backend)
-
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
-
- exe2 = part_sym.bind(ctx=mx.current_context(), args=arg_array,
aux_states=aux_array, grad_req='null')
- exe2.forward()
-
- # compare outputs
- outputs1 = exe1.outputs
- outputs2 = exe2.outputs
- assert len(outputs1) == len(outputs2)
- for i in range(len(outputs1)):
- assert_almost_equal((outputs1[i] -
outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
-
- def _check_subgraph_exe8(sym, subgraph_backend, op_names):
- """Call optimize_for to infer shapes, types and dtypes followed by
graph partitioning,
- then bind and compare results of the partitioned sym and the original
sym."""
- # bind
- arg_shapes, _, aux_shapes = sym.infer_shape()
- arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
- aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
- exe1 = sym.bind(ctx=mx.current_context(), args=arg_array,
aux_states=aux_array, grad_req='null')
- exe1.forward()
-
- # infer shape/type before partition before bind
-
check_call(_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
- c_str_array(op_names)))
- part_sym = sym.optimize_for(subgraph_backend, arg_array)
-
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
-
- exe2 = part_sym.bind(ctx=mx.current_context(), args=arg_array,
aux_states=aux_array, grad_req='null')
- exe2.forward()
-
- # compare outputs
- outputs1 = exe1.outputs
- outputs2 = exe2.outputs
- assert len(outputs1) == len(outputs2)
- for i in range(len(outputs1)):
- assert_almost_equal((outputs1[i] -
outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
-
- def check_subgraph_exe(sym, subgraph_backend, op_names):
- _check_subgraph_exe1(sym, subgraph_backend, op_names)
- _check_subgraph_exe2(sym, subgraph_backend, op_names)
- _check_subgraph_exe3(sym, subgraph_backend, op_names)
- _check_subgraph_exe4(sym, subgraph_backend, op_names)
- _check_subgraph_exe5(sym, subgraph_backend, op_names)
- _check_subgraph_exe6(sym, subgraph_backend, op_names)
- _check_subgraph_exe7(sym, subgraph_backend, op_names)
- _check_subgraph_exe8(sym, subgraph_backend, op_names)
-
- def test_network_structure_1(subgraph_backend):
- 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])
- check_subgraph_exe(out, subgraph_backend, ['Convolution'])
-
- def test_network_structure_2(subgraph_backend):
- # 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
- check_subgraph_exe(ret, subgraph_backend, ['exp', 'sin', '_Plus',
'elemwise_add', '_plus'])
- check_subgraph_exe(ret, subgraph_backend, ['exp', 'cos', '_Plus',
'elemwise_add', '_plus'])
-
- def test_network_structure_3(subgraph_backend):
- # 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)
- check_subgraph_exe(ret, subgraph_backend, ['exp', 'sin', '_Plus',
'elemwise_add', '_plus'])
- check_subgraph_exe(ret, subgraph_backend, ['exp', 'cos', '_Plus',
'elemwise_add', '_plus'])
- check_subgraph_exe(ret, subgraph_backend, ['exp', 'sin', '_Plus',
'elemwise_add', '_plus', 'BatchNorm'])
- check_subgraph_exe(ret, subgraph_backend, ['exp', 'cos', '_Plus',
'elemwise_add', '_plus', 'BatchNorm'])
- check_subgraph_exe(ret, subgraph_backend, ['exp', 'BatchNorm'])
- check_subgraph_exe(ret, subgraph_backend, ['BatchNorm'])
-
- def test_network_structure_4(subgraph_backend):
- # the last op has multiple duplicate outputs
- data = mx.sym.var('data', shape=(2, 3, 10, 10))
- ret = mx.sym.exp(data)
- ret = mx.sym.Group([ret, ret, ret])
- check_subgraph_exe(ret, subgraph_backend, ['exp'])
-
- def test_network_structure_5(subgraph_backend):
- # the subgraph has two duplicate input entries
- data = mx.sym.var('data', shape=(2, 3, 10, 10))
- ret = data + data
- check_subgraph_exe(ret, subgraph_backend, ['_plus', '_Plus',
'elemwise_add'])
-
- def test_network_structure_6(subgraph_backend):
- def get_graph():
- data1 = mx.sym.Variable('data1', shape=(3, 3, 10, 10),
dtype=np.float32)
- data2 = mx.sym.Variable('data2', shape=(1, 0, 2, 2))
- data3 = mx.sym.sin(data2)
- conv = mx.sym.Convolution(data=data1, weight=data3, kernel=(2, 2),
num_filter=1)
- rets = [(conv, []),
- (conv, [mx.sym.sin.__name__]),
- (conv, [mx.sym.Convolution.__name__]),
- (conv, [mx.sym.sin.__name__, mx.sym.Convolution.__name__])]
- return rets
-
- for sym, op_names in get_graph():
- check_subgraph_exe(sym, subgraph_backend, op_names)
-
- def test_network_structure_7(subgraph_backend):
- # in this graph, the subgraph node and the other two external nodes
form a cycle
- data = mx.sym.Variable('data', shape=(1,))
- ret1 = mx.sym.sin(data)
- ret2 = mx.sym.cos(ret1)
- for _ in range(5):
- ret2 = mx.sym.cos(ret2)
- ret = ret1 + ret2
- check_subgraph_exe(ret, subgraph_backend, ['sin', 'elemwise_add',
'_plus', '_Plus'])
-
- test_network_structure_1(subgraph_backend)
- test_network_structure_2(subgraph_backend)
- test_network_structure_3(subgraph_backend)
- test_network_structure_4(subgraph_backend)
- test_network_structure_5(subgraph_backend)
- test_network_structure_6(subgraph_backend)
- test_network_structure_7(subgraph_backend)
-
-def test_subgraph_exe():
- _test_subgraph_exe('default')
-
-def test_subgraph_v2_exe():
- _test_subgraph_exe('default_v2')
+ if subgraph_backend is not None:
+
check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
+ del os.environ['MXNET_SUBGRAPH_BACKEND']
+ return exe
+
+ original_exec = get_executor(sym)
+ partitioned_exec = get_executor(sym, subgraph_backend, op_names,
original_exec)
+ outputs1 = original_exec.outputs
+ outputs2 = partitioned_exec.outputs
+ assert len(outputs1) == len(outputs2)
+ for i in range(len(outputs1)):
+ assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
np.zeros(shape=(1,)))
+
+def set_random_inputs(exe1, input_names):
+ """Sets random values to exe1's args and auxs"""
+ for name in input_names:
+ if name in exe1.arg_dict:
+ exe1.arg_dict[name][:] =
mx.nd.random.uniform(shape=exe1.arg_dict[name].shape)
+ else:
+ assert name in exe1.aux_dict
+ exe1.aux_dict[name][:] =
mx.nd.random.uniform(shape=exe1.aux_dict[name].shape)
+
+def copy_inputs_between_executors(exe1, exe2, input_names):
+ """Copies values of args and auxs from exe1 to exe2"""
+ for name in input_names:
+ if name in exe2.arg_dict:
+ exe2.arg_dict[name][:] = exe1.arg_dict[name]
+ else:
+ assert name in exe2.aux_dict
+ exe2.aux_dict[name][:] = exe1.aux_dict[name]
+
+def check_subgraph_exe5(sym, subgraph_backend, op_names):
+ """Call optimize_for to trigger graph partitioning without infer
shapes/types before,
+ then simple_bind and compare results of the partitioned sym and the
original sym."""
+ # simple_bind
+ exe1 = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
+ input_names = sym.list_inputs()
+ set_random_inputs(exe1, input_names)
+ exe1.forward()
+
+ # partition before simple_bind
+ check_call(_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
+ c_str_array(op_names)))
+ part_sym = sym.optimize_for(subgraph_backend)
+ check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
+
+ exe2 = part_sym.simple_bind(ctx=mx.current_context(), grad_req='null')
+ copy_inputs_between_executors(exe1, exe2, input_names)
+ exe2.forward()
+
+ # compare outputs
+ outputs1 = exe1.outputs
+ outputs2 = exe2.outputs
+ assert len(outputs1) == len(outputs2)
+ for i in range(len(outputs1)):
+ assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
np.zeros(shape=(1,)))
+
+def check_subgraph_exe6(sym, subgraph_backend, op_names):
+ """Call optimize_for to trigger graph partitioning without infer
shapes/types before,
+ then simple_bind and compare results of the partitioned sym and the
original sym."""
+ # simple_bind
+ exe1 = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
+ input_names = sym.list_inputs()
+ set_random_inputs(exe1, input_names)
+ exe1.forward()
+
+ # infer shape/type before partition before simple_bind
+ check_call(_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
+ c_str_array(op_names)))
+ part_sym = sym.optimize_for(subgraph_backend, exe1.arg_dict)
+ check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
+
+ exe2 = part_sym.simple_bind(ctx=mx.current_context(), grad_req='null')
+ copy_inputs_between_executors(exe1, exe2, input_names)
+ exe2.forward()
+
+ # compare outputs
+ outputs1 = exe1.outputs
+ outputs2 = exe2.outputs
+ assert len(outputs1) == len(outputs2)
+ for i in range(len(outputs1)):
+ assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
np.zeros(shape=(1,)))
+
+def check_subgraph_exe7(sym, subgraph_backend, op_names):
+ """Call optimize_for to trigger graph partitioning without infer
shapes/types before,
+ then bind and compare results of the partitioned sym and the original
sym."""
+ # bind
+ arg_shapes, _, aux_shapes = sym.infer_shape()
+ arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
+ aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
+ exe1 = sym.bind(ctx=mx.current_context(), args=arg_array,
aux_states=aux_array, grad_req='null')
+ exe1.forward()
+
+ # partition before bind
+ check_call(_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
+ c_str_array(op_names)))
+ part_sym = sym.optimize_for(subgraph_backend)
+ check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
+
+ exe2 = part_sym.bind(ctx=mx.current_context(), args=arg_array,
aux_states=aux_array, grad_req='null')
+ exe2.forward()
+
+ # compare outputs
+ outputs1 = exe1.outputs
+ outputs2 = exe2.outputs
+ assert len(outputs1) == len(outputs2)
+ for i in range(len(outputs1)):
+ assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
np.zeros(shape=(1,)))
+
+def check_subgraph_exe8(sym, subgraph_backend, op_names):
+ """Call optimize_for to infer shapes, types and dtypes followed by graph
partitioning,
+ then bind and compare results of the partitioned sym and the original
sym."""
+ # bind
+ arg_shapes, _, aux_shapes = sym.infer_shape()
+ arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
+ aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
+ exe1 = sym.bind(ctx=mx.current_context(), args=arg_array,
aux_states=aux_array, grad_req='null')
+ exe1.forward()
+
+ # infer shape/type before partition before bind
+ check_call(_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
+ c_str_array(op_names)))
+ part_sym = sym.optimize_for(subgraph_backend, arg_array)
+ check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
+
+ exe2 = part_sym.bind(ctx=mx.current_context(), args=arg_array,
aux_states=aux_array, grad_req='null')
+ exe2.forward()
+
+ # compare outputs
+ outputs1 = exe1.outputs
+ outputs2 = exe2.outputs
+ assert len(outputs1) == len(outputs2)
+ for i in range(len(outputs1)):
+ assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
np.zeros(shape=(1,)))
+
+def check_subgraph_exe9(sym, subgraph_backend, op_names):
+ inputs = [mx.sym.var(i, dtype=mx_real_t) for i in sym[1]]
+ sym_block = nn.SymbolBlock(sym[0], inputs)
+ sym_block.initialize()
+ shapes = sym[2]
+ x = [mx.nd.random.uniform(shape=s) for s in shapes]
+ outputs1 = sym_block(*x)
+
+ check_call(_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
+ c_str_array(op_names)))
+ sym_block.hybridize(backend=subgraph_backend)
+ outputs2 = sym_block(*x)
+ check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
+
+ # compare outputs
+ assert len(outputs1) == len(outputs2)
+ for i in range(len(outputs1)):
+ assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
np.zeros(shape=(1,)))
+
+def check_subgraph(subgraph_backend):
+ for sym, op_names in get_graphs():
+ check_subgraph_exe1(sym[0], subgraph_backend, op_names)
+ check_subgraph_exe2(sym[0], subgraph_backend, op_names)
+ check_subgraph_exe3(sym[0], subgraph_backend, op_names)
+ check_subgraph_exe4(sym[0], subgraph_backend, op_names)
+
+def check_subgraph_backend_sym(subgraph_backend):
+ for sym, op_names in get_graphs():
+ check_subgraph_exe5(sym[0], subgraph_backend, op_names)
+ check_subgraph_exe6(sym[0], subgraph_backend, op_names)
+ check_subgraph_exe7(sym[0], subgraph_backend, op_names)
+ check_subgraph_exe8(sym[0], subgraph_backend, op_names)
+
+def check_subgraph_backend_gluon(subgraph_backend):
+ for sym, op_names in get_graphs():
+ check_subgraph_exe9(sym, subgraph_backend, op_names)
+
+def test_subgraph():
+ check_subgraph('default')
+
+def test_subgraph_v2():
+ check_subgraph('default_v2')
+
+def test_subgraph_backend_sym():
+ check_subgraph_backend_sym('default')
+
+def test_subgraph_backend_sym_v2():
+ check_subgraph_backend_sym('default_v2')
+
+def test_subgraph_backend_gluon():
+ check_subgraph_backend_gluon('default')
+
+def test_subgraph_backend_gluon_v2():
+ check_subgraph_backend_gluon('default_v2')
+
+def test_subgraph_backend_gluon_ext1():
Review comment:
Need description of function/test
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