barry-jin opened a new issue #20354:
URL: https://github.com/apache/incubator-mxnet/issues/20354
## Description
Two tests will break after switching to use Gluon2.0 in #20262 .
-
tests/python/mkl/subgraphs/test_conv_subgraph.py::test_pos_concat_scale_align
- Reason: Scale doesn't align in numpy for numpy operators
### Error Message
```python
def check_qsym_scale_align(qsym):
assert
''.join(qsym.attr_dict().keys()).find('quantized_sg_mkldnn_conv') != -1
init = False
for k, v in qsym.attr_dict().items():
if k.find('quantized_sg_mkldnn_conv') != -1:
assert 'min_calib_range' in v
assert 'max_calib_range' in v
if not init:
min_calib_range = v['min_calib_range']
max_calib_range = v['max_calib_range']
init = True
else:
> assert min_calib_range == v['min_calib_range']
E AssertionError
```
## To Reproduce
```python
import mxnet as mx
from mxnet.gluon import nn
from mxnet.contrib import quantization
class ConcatScaleAlign(nn.HybridBlock):
def __init__(self, **kwargs):
super(ConcatScaleAlign, self).__init__(**kwargs)
self.shared_weight = mx.gluon.Parameter('shared_weight', shape=(64,
4, 3, 3),
init=mx.init.Xavier(magnitude=2.24),
dtype='float32',
allow_deferred_init=True)
def forward(self, x):
conv1 = mx.npx.convolution(x, kernel=(3,3), num_filter=64,
weight=self.shared_weight.data(x.ctx),
no_bias=True)
conv2 = mx.npx.convolution(x, kernel=(3,3), num_filter=64,
weight=self.shared_weight.data(x.ctx)*2,
no_bias=True)
return mx.np.concatenate([conv1, conv2], axis=1)
concat = ConcatScaleAlign()
concat.initialize(init=mx.init.Normal(0.5), force_reinit=True)
data = mx.np.random.uniform(-1, 1.0, size=(64, 4, 10, 10), dtype='float32',
ctx=mx.current_context())
outputs = concat(data)
calib_data = mx.gluon.data.DataLoader(data, batch_size=1)
qnet = quantization.quantize_net(concat,
ctx=mx.current_context(),
exclude_layers=None,
exclude_operators=None,
quantized_dtype='int8',
calib_mode='naive',
calib_data=calib_data,
num_calib_batches=1,
quantize_mode='full',
quantize_granularity='tensor-wise')
qsym, _ = qnet.export(None)
init = False
for k, v in qsym.attr_dict().items():
if k.find('quantized_sg_mkldnn_conv') != -1:
assert 'min_calib_range' in v
assert 'max_calib_range' in v
if not init:
min_calib_range = v['min_calib_range']
max_calib_range = v['max_calib_range']
init = True
else:
assert min_calib_range == v['min_calib_range']
assert max_calib_range == v['max_calib_range']
```
- tests/python/mkl/subgraphs/test_fc_subgraph.py::test_fc_eltwise
- Reason: Operator square, square_root, abs, exp cannot be found in numpy
mode
### Error Message
```python
def check_fusion(net_original, data_shape, attrs_dict,
check_fp32_fusion=True, check_quantization=True,
out_types=['uint8', 'int8', 'auto'],
dedup_subgraph=True):
net_original.initialize()
net_original.hybridize(static_alloc=False, static_shape=False)
data = mx.np.random.uniform(size=data_shape, dtype='float32',
ctx=mx.current_context())
net_original(data)
net_fusion = copy.copy(net_original)
sym, params = net_original.export(None)
if check_fp32_fusion:
data_min = -1.0
data_max = 1.0
if ''.join(sym.get_internals().list_outputs()).find('sqrt') != -1:
check_quantization = False
data_min = 0
sym_sg = sym.optimize_for(SG_PASS_NAME,
dedup_subgraph=dedup_subgraph, skip_infer=True)
for name, attrs in attrs_dict.items():
if name in config:
op_name = config[name][OP_NAME]
else:
op_name = name
assert
''.join(sym_sg.get_internals().list_outputs()).find(op_name) != -1
if len(attrs):
found = False
for k, v in sym_sg.attr_dict().items():
if k.find(op_name) != -1:
found = True
for attr_name, attr_value in attrs.items():
assert v[attr_name].lower() == attr_value.lower()
> assert found
E AssertionError
```
## To Reproduce
```python
import mxnet as mx
from mxnet.gluon import nn
class FCEltwise(nn.HybridBlock):
def __init__(self, use_bias, flatten, **kwargs):
super(FCEltwise, self).__init__(**kwargs)
self.fc = nn.Dense(units=64, use_bias=use_bias, flatten=flatten,
weight_initializer=None)
def forward(self, x):
fc_out = self.fc(x)
out = mx.np.square(fc_out)
return out
attrs = {'fc': {'with_eltwise': 'true'}}
net = FCEltwise(True, True)
net.initialize()
net.hybridize(static_alloc=False, static_shape=False)
data = mx.np.random.uniform(size=(64, 4, 10, 10), dtype='float32',
ctx=mx.current_context())
net(data)
sym, params = net.export(None)
sym_sg = sym.optimize_for('MKLDNN', dedup_subgraph=True, skip_infer=True)
for name, attrs in attrs.items():
if len(attrs):
found = False
for k, v in sym_sg.attr_dict().items():
if k.find('sg_mkldnn_fully_connected') != -1:
found = True
for attr_name, attr_value in attrs.items():
assert v[attr_name].lower() == attr_value.lower()
assert found
```
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