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[v1.7.x] ElementWiseSum fix for oneDNN #18777

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Aug 5, 2020
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[v1.7.x] ElementWiseSum fix for oneDNN #18777

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9e0c23c
Fix ElementwiseSum for DNNL
Jul 23, 2020
d418f0e
Fix sanity and replace push_back with emplace_back
Jul 24, 2020
f8355e5
Change order of the data format conditions
Jul 24, 2020
1e83a3d
Add NOLINT to avoid readability error
Jul 27, 2020
902ca99
Add test for oneDNN ElemwiseSum
Jul 30, 2020
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15 changes: 8 additions & 7 deletions src/operator/tensor/elemwise_sum.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -113,7 +113,14 @@ void ElementWiseSumComputeExCPU(const nnvm::NodeAttrs& attrs,
CHECK_EQ(outputs.size(), 1U);
CHECK_EQ(req.size(), 1U);
if (req[0] == kNullOp) return;
if (common::ContainsOnlyStorage(inputs, kRowSparseStorage) ||
#if MXNET_USE_MKLDNN == 1
if (IsMKLDNNData(inputs)) {
MKLDNNRun(MKLDNNSumForward, attrs, ctx, inputs, req, outputs);
} else if (common::ContainsOnlyStorage(inputs, kDefaultStorage)) {
FallBackCompute(ElementWiseSumCompute<cpu>, attrs, ctx, inputs, req, outputs);
}
#endif
else if (common::ContainsOnlyStorage(inputs, kRowSparseStorage) || // NOLINT(*)
(inputs.size() == 3U && inputs[0].storage_type() == kDefaultStorage &&
inputs[1].storage_type() == kCSRStorage && inputs[2].storage_type() == kDefaultStorage) ||
(inputs.size() > 4U && common::ContainsStorageType(inputs, kDefaultStorage) &&
Expand All @@ -123,12 +130,6 @@ void ElementWiseSumComputeExCPU(const nnvm::NodeAttrs& attrs,
ResourceRequest(ResourceRequest::kTempSpace));
NDArray out_nd = outputs[0];
mxnet::ndarray::ElementwiseSum<cpu>(s, rsc, inputs, &out_nd);
#if MXNET_USE_MKLDNN == 1
} else if (IsMKLDNNData(inputs)) {
MKLDNNRun(MKLDNNSumForward, attrs, ctx, inputs, req, outputs);
} else if (common::ContainsOnlyStorage(inputs, kDefaultStorage)) {
FallBackCompute(ElementWiseSumCompute<cpu>, attrs, ctx, inputs, req, outputs);
#endif
} else {
LogUnimplementedOp(attrs, ctx, inputs, req, outputs);
}
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24 changes: 23 additions & 1 deletion tests/python/mkl/test_mkldnn.py
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Expand Up @@ -254,6 +254,28 @@ def test_flatten_slice_after_conv():
print(p[0])


def test_mkldnn_sum_with_mkldnn_layout():

x_shape = (32, 3, 224, 224)
x_npy = np.ones(x_shape)
w_shape = (32, 3, 3, 3)
w_npy = np.ones(w_shape)

x = mx.sym.Variable("x")
w = mx.sym.Variable("w")
z = mx.symbol.Convolution(data=x, weight=w, num_filter=32, kernel=(3, 3))
num_inputs = [2, 3, 4, 5]
for i in num_inputs:
inputs = []
for n in range(i):
inputs.append(z)
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
single_cov = 27.0
assert_almost_equal(out[0].asnumpy()[0, 0, 0], single_cov*i)

def test_mkldnn_sum_inplace_with_cpu_layout():

x_shape = (32, 3, 224, 224)
Expand All @@ -263,7 +285,7 @@ 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)
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