[MKLDNN]Fix mkldnn deconvolution forward with bias

src/operator/nn/mkldnn/mkldnn_deconvolution.cc

@@ -296,10 +296,11 @@ static void MKLDNNDeconvFwdBiasPostProcess(const DeconvolutionParam& param,
  typedef float DType;
  Stream<cpu> *s = ctx.get_stream<cpu>();
  Tensor<cpu, 1, DType> b = bias.data().get<cpu, 1, DType>(s);
- // If the output data is stored in a special MKLDNN format, data()
- // automatically converts its format to the default format.
+ // The output data is stored in a special MKLDNN format,
+ // converts its format to the default format.
  // Unfortunately, MKLDNN doesn't support broadcast.
- Tensor<cpu, 4, DType> out_cpu = out_data[deconv::kOut].data().get<cpu, 4, DType>(s);
+ auto out_data_def = out_data[deconv::kOut].Reorder2Default();
+ Tensor<cpu, 4, DType> out_cpu = out_data_def.data().get<cpu, 4, DType>(s);
  out_cpu += mshadow::expr::broadcast<1>(b, out_cpu.shape_);
  }
 }

tests/python/unittest/test_operator.py

@@ -1533,6 +1533,28 @@ def test_deconvolution():
  pad = (3,)
  )
 
+@with_seed()
+def test_deconvolution_forward_with_bias():
+ """Check if deconvolution forward can work well with bias=True
+ """
+ def check_deconvolution_forward_with_bias(shape=(1, 16, 5, 5), num_filter=32, num_group=1, kernel=(3, 3), pad=(1, 1)):
+ x = mx.sym.Variable('x')
+ w = mx.sym.Variable('w')
+ input_data = mx.random.uniform(-5, 5, shape, ctx=mx.cpu())
+ y = mx.sym.Deconvolution(data=x, weight=w, num_filter=num_filter, num_group=num_group, kernel=kernel, no_bias=False, pad=pad)
+ exe = y.simple_bind(ctx=mx.cpu(), x=shape, grad_req='null')
+
+ exe.arg_arrays[0][:] = np.random.normal(size=exe.arg_arrays[0].shape)
+ exe.arg_arrays[1][:] = np.random.normal(size=exe.arg_arrays[1].shape)
+
+ exe.forward(is_train=False)
+ o = exe.outputs[0]
+ t = o.asnumpy()
+ check_deconvolution_forward_with_bias((1, 16, 5), 32, 1, (3,), (1,))
+ check_deconvolution_forward_with_bias((32, 16, 5), 32, 1, (3,), (1,))
+ check_deconvolution_forward_with_bias((1, 16, 5, 5), 32, 1, (3, 3), (1, 1))
+ check_deconvolution_forward_with_bias((32, 16, 5, 5), 32, 1, (3, 3), (1, 1))
+
 
 def check_nearest_upsampling_with_shape(shapes, scale, root_scale):
  arr = {'arg_%d'%i: mx.random.uniform(-10.0, 10.0, shape, ctx=mx.cpu()).copyto(default_context()) for i, shape in zip(range(len(shapes)), shapes)}