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Flake8 package checking python coding style: test_mkldnn.py, small tidy up #20406

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akarbown merged 4 commits into from
Aug 3, 2021
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Flake8 package checking python coding style: test_mkldnn.py, small tidy up #20406

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2945898
Python refactring
mozga-intel Jun 30, 2021
8b5531c
Review changes v.1.0
mozga-intel Jul 8, 2021
4dcfcba
Onlye flake8 chnages
mozga-intel Jul 8, 2021
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Onlye flake8 chnages v2.0
mozga-intel Jul 8, 2021
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72 changes: 38 additions & 34 deletions tests/python/mkl/test_mkldnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@
import os
import numpy as np
import mxnet as mx
import unittest

from mxnet.test_utils import rand_ndarray, assert_almost_equal
from mxnet.module import Module
from mxnet import gluon
Expand Down Expand Up @@ -81,9 +81,12 @@ def test_mkldnn_engine_threading():
with net.name_scope():
net.add(gluon.nn.Conv2D(channels=32, kernel_size=3, activation=None))
net.collect_params().initialize(ctx=mx.cpu())

class Dummy(gluon.data.Dataset):

def __len__(self):
return 2

def __getitem__(self, key):
return key, np.ones((3, 224, 224)), np.ones((10, ))

Expand All @@ -104,15 +107,15 @@ def __getitem__(self, key):
@with_seed()
def test_mkldnn_reshape():
def test_reshape_after_conv(dst_shape):
shape = (1,1,4,4)
shape = (1, 1, 4, 4)
data = mx.symbol.Variable('data')
conv = mx.symbol.Convolution(data=data, num_filter=16, kernel=(1, 1), pad=(0, 0), stride=(1, 1))
res = mx.symbol.reshape(data=conv, shape=dst_shape)
exe = res.simple_bind(mx.cpu(), data=shape, grad_req='null')

val1 = np.random.uniform(-1, 1, shape)
val2 = np.random.uniform(-1, 1, (16, 1, 1, 1))
val3 = np.random.uniform(-1 ,1, (1))
val3 = np.random.uniform(-1, 1, (1))

exe.arg_arrays[0][:] = val1
exe.arg_arrays[1][:] = val2
Expand Down Expand Up @@ -240,9 +243,9 @@ def test_flatten_slice_after_conv():
data = mx.symbol.Variable('data')
weight = mx.symbol.Variable('weight')
bias = mx.symbol.Variable('bias')
conv1= mx.symbol.Convolution(data = data, weight=weight, bias=bias, name='conv1', num_filter=64, kernel=(3,3), stride=(1,1))
flatten1 = mx.symbol.flatten(data = conv1)
slice1 = mx.symbol.slice(data = flatten1, begin=0, end=1)
conv1 = mx.symbol.Convolution(data=data, weight=weight, bias=bias, name='conv1', num_filter=64, kernel=(3, 3), stride=(1, 1))
flatten1 = mx.symbol.flatten(data=conv1)
slice1 = mx.symbol.slice(data=flatten1, begin=0, end=1)

shape = (2, 16, 16, 16)
val = np.random.rand(2, 16, 16, 16).astype(np.float32)
Expand Down Expand Up @@ -270,12 +273,12 @@ def test_mkldnn_sum_with_mkldnn_layout():
inputs = []
for n in range(i):
inputs.append(z)
y = mx.sym.add_n(*inputs) # (only MKLDNN data input)
y = mx.sym.add_n(*inputs) # (only MKLDNN data input)
exe = y.simple_bind(ctx=mx.cpu(), x=x_shape, w=w_shape)
out = exe.forward(is_train=False, x=x_npy, w=np.ones(w_shape))[0]
#conv with kernel (3,3) on ones should give result=27
# conv with kernel (3,3) on ones should give result=27
single_cov = 27.0
assert_almost_equal(out[0].asnumpy()[0, 0, 0], single_cov*i)
assert_almost_equal(out[0].asnumpy()[0, 0, 0], single_cov * i)

def test_mkldnn_sum_inplace_with_cpu_layout():

Expand All @@ -286,7 +289,7 @@ def test_mkldnn_sum_inplace_with_cpu_layout():
x = mx.sym.Variable("x")
y = mx.sym.Variable("y")
z = mx.symbol.Convolution(data=x, num_filter=32, kernel=(3, 3))
z = mx.sym.add_n(z, y) # (MKLDNN data, cpu data)
z = mx.sym.add_n(z, y) # (MKLDNN data, cpu data)
exe = z.simple_bind(ctx=mx.cpu(), x=x_shape, y=y_shape)
out = exe.forward(is_train=False, x=x_npy, y=y_npy)[0]
assert_almost_equal(out[0].asnumpy()[0, 0, 0], 1.0)
Expand Down Expand Up @@ -484,7 +487,7 @@ def check_Deconvolution_training(stype):
elif np.array(shape).shape[0] == 5 and stype == "default":
# Unable to test fallback to native implementation for non-default storage types
# as 3D deconvolution is not natively supported
test = mx.symbol.Deconvolution(data=data, kernel=(3,3,3), stride=(2,2,2), num_filter=4)
test = mx.symbol.Deconvolution(data=data, kernel=(3, 3, 3), stride=(2, 2, 2), num_filter=4)
weight_tmp = np.random.normal(-0.1, 0.1, size=(3, 4, 3, 3, 3))
else:
return 0
Expand Down Expand Up @@ -542,10 +545,10 @@ def softmax_forward(input_data, true_output):
nparr = ndarr.asnumpy()
assert_almost_equal(nparr, true_output, rtol=1e-5, atol=1e-5)

softmax_forward(mx.nd.array([[[[-1e30,-1e30]]]]), np.array([1.0,1.0]))
softmax_forward(mx.nd.array([[[[1e30,1e30]]]]), np.array([1.0,1.0]))
softmax_forward(mx.nd.array([[[[-3.4e38,-3.4e38]]]]), np.array([1.0,1.0]))
softmax_forward(mx.nd.array([[[[3.4e38,3.4e38]]]]), np.array([1.0,1.0]))
softmax_forward(mx.nd.array([[[[-1e30, -1e30]]]]), np.array([1.0, 1.0]))
softmax_forward(mx.nd.array([[[[1e30, 1e30]]]]), np.array([1.0, 1.0]))
softmax_forward(mx.nd.array([[[[-3.4e38, -3.4e38]]]]), np.array([1.0, 1.0]))
softmax_forward(mx.nd.array([[[[3.4e38, 3.4e38]]]]), np.array([1.0, 1.0]))

@with_seed()
def test_non_mkldnn_fcomputeex():
Expand Down Expand Up @@ -586,14 +589,14 @@ def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
self.assign(in_grad[0], req[0], out_grad)

data = mx.symbol.Variable('data')
conv = mx.sym.Convolution(data=data, kernel=(5, 5), pad=(1, 1), stride=(1,1), num_filter=8, name="conv", no_bias=True)
conv = mx.sym.Convolution(data=data, kernel=(5, 5), pad=(1, 1), stride=(1, 1), num_filter=8, name="conv", no_bias=True)
custom = mx.symbol.Custom(name='custom', data=conv, op_type='custom')
exec1 = custom.bind(mx.cpu(), args={'data': mx.nd.ones([10,3,96,96]), 'conv_weight': mx.nd.ones([8,3,5,5])})
exec1 = custom.bind(mx.cpu(), args={'data': mx.nd.ones([10, 3, 96, 96]), 'conv_weight': mx.nd.ones([8, 3, 5, 5])})
exec1.forward()[0].wait_to_read()

@with_seed()
def test_conv_transpose():
axes = [(0,2,1,3), (0,2,3,1), (1,2,3,0), (3,2,1,0)]
axes = [(0, 2, 1, 3), (0, 2, 3, 1), (1, 2, 3, 0), (3, 2, 1, 0)]
a = np.random.rand(10, 16, 50, 50)
b = np.random.rand(32, 16, 3, 3)
x = mx.nd.array(a)
Expand All @@ -617,7 +620,7 @@ def __init__(self, factor):

def hybrid_forward(self, F, x):
f1, f2 = self._factors
# (N, f1*f2*C, H, W)

x = F.reshape(x, (0, -4, -1, f1 * f2, 0, 0)) # (N, C, f1*f2, H, W)
x = F.reshape(x, (0, 0, -4, f1, f2, 0, 0)) # (N, C, f1, f2, H, W)
x = F.transpose(x, (0, 1, 4, 2, 5, 3)) # (N, C, H, f1, W, f2)
Expand Down Expand Up @@ -655,7 +658,7 @@ def test_weight_async_reorder():
mod.bind(for_training=False, data_shapes=[('data', (10, 16, 50, 50))])
mod.init_params(initializer=mx.init.Xavier(magnitude=2.))
data = [mx.random.uniform(-1.0, 1.0, shape=(10, 16, 50, 50), ctx=mx.current_context())]
batch=mx.io.DataBatch(data, [])
batch = mx.io.DataBatch(data, [])
for i in range(2):
mod.forward(batch, is_train=False)
for output in mod.get_outputs():
Expand All @@ -664,7 +667,7 @@ def test_weight_async_reorder():
@with_seed()
def test_concat():
def ref_concat(a, b, axis):
return np.concatenate((a, b), axis=axis)
return np.concatenate((a, b), axis=axis)

a_sym = mx.sym.Variable("a")
b_sym = mx.sym.Variable("b")
Expand All @@ -673,14 +676,14 @@ def ref_concat(a, b, axis):
b_shape = tuple(dshape)

for axis in range(0, 4):
z = mx.sym.concat(a_sym, b_sym, dim=axis)
a = np.random.uniform(-1, 1, a_shape)
b = np.random.uniform(-1, 1, b_shape)
exe = z.simple_bind(ctx=mx.cpu(), a=a_shape, b=b_shape)
out = exe.forward(is_train=False, a=a, b=b)
ref_out = ref_concat(a, b, axis=axis)
out = out[0].asnumpy()
assert_almost_equal(out, ref_out)
z = mx.sym.concat(a_sym, b_sym, dim=axis)
a = np.random.uniform(-1, 1, a_shape)
b = np.random.uniform(-1, 1, b_shape)
exe = z.simple_bind(ctx=mx.cpu(), a=a_shape, b=b_shape)
out = exe.forward(is_train=False, a=a, b=b)
ref_out = ref_concat(a, b, axis=axis)
out = out[0].asnumpy()
assert_almost_equal(out, ref_out)

def check_concat_training(stype):
data_shape = rand_shape_nd(4)
Expand Down Expand Up @@ -731,7 +734,7 @@ def calc_output_of_layer(layer):
@with_seed()
def test_elemwise_add():
def ref_add(a, b):
return np.add(a, b)
return np.add(a, b)

a_sym = mx.sym.Variable("a")
b_sym = mx.sym.Variable("b")
Expand Down Expand Up @@ -768,10 +771,11 @@ def test_rnn():
STATE_SIZE = [1, 2]
BATCH_SIZE = [4]
INPUT_SIZE = [4]

def batch_check(seq_length, state_size, batch_size, input_size):
modes_params = [('rnn_relu', mx.np.random.normal(0, 1, ((input_size + state_size + 2)*state_size),)),
('rnn_tanh', mx.np.random.normal(0, 1, ((input_size + state_size + 2)*state_size),)),
('gru', mx.np.random.normal(0, 1, ((input_size + state_size + 2)*state_size*3),))
modes_params = [('rnn_relu', mx.np.random.normal(0, 1, ((input_size + state_size + 2) * state_size),)),
('rnn_tanh', mx.np.random.normal(0, 1, ((input_size + state_size + 2) * state_size),)),
('gru', mx.np.random.normal(0, 1, ((input_size + state_size + 2) * state_size * 3),))
]
for m, p in modes_params:
data = mx.np.random.normal(0, 1, (seq_length, batch_size, input_size))
Expand All @@ -780,7 +784,7 @@ def batch_check(seq_length, state_size, batch_size, input_size):
state.attach_grad()

with mx.autograd.record():
y = mx.npx.rnn(data=data, parameters=p, mode=m, \
y = mx.npx.rnn(data=data, parameters=p, mode=m,
state=state, state_size=state_size, num_layers=1)
assert y.shape == (seq_length, batch_size, state_size)
assert type(y[0]).__name__ == 'ndarray'
Expand Down