move dnn epsilon assert to c code

theano/gpuarray/dnn.py

@@ -1448,7 +1448,6 @@ class GpuDnnBatchNorm(DnnBase):
         x = as_gpuarray_variable(x, ctx_name)
         scale = as_gpuarray_variable(scale, ctx_name)
         bias = as_gpuarray_variable(bias, ctx_name)
-        assert (epsilon >= 1e-5)
         epsilon = as_scalar(epsilon).astype('float64')
         assert x.ndim == 4
         assert scale.ndim == 4
@@ -1513,7 +1512,6 @@ class GpuDnnBatchNormInference(DnnBase):
         bias = as_gpuarray_variable(bias, ctx_name)
         estimated_mean = as_gpuarray_variable(estimated_mean, ctx_name)
         estimated_variance = as_gpuarray_variable(estimated_variance, ctx_name)
-        assert (epsilon >= 1e-5)
         epsilon = as_scalar(epsilon).astype('float64')
         assert x.ndim == 4
         assert scale.ndim == 4
@@ -1577,7 +1575,6 @@ class GpuDnnBatchNormGrad(DnnBase):
         scale = as_gpuarray_variable(scale, ctx_name)
         x_mean = as_gpuarray_variable(x_mean, ctx_name)
         x_invstd = as_gpuarray_variable(x_invstd, ctx_name)
-        assert (epsilon >= 1e-5)
         epsilon = as_scalar(epsilon).astype('float64')
         assert x.ndim == 4 and dy.ndim == 4 and scale.ndim == 4 and x_mean.ndim == 4 and x_invstd.ndim == 4
         return Apply(self, [x, dy, scale, x_mean, x_invstd, epsilon], [x.type(), scale.type(), scale.type()])

theano/gpuarray/dnn_batchnorm.c

@@ -9,6 +9,9 @@ int dnn_batchnorm_op(PyGpuArrayObject *inp, PyGpuArrayObject *scale,
   if (c_set_tensorNd(scale, bn_params) != 0)
     return 1;
 
+  if (epsilon < 1e-5)
+    return 1;
+
   if (theano_prep_output(outp, inp->ga.nd, inp->ga.dimensions, inp->ga.typecode, GA_C_ORDER, c) != 0)
     return 1;
   if (theano_prep_output(x_mean, scale->ga.nd, scale->ga.dimensions, scale->ga.typecode, GA_C_ORDER, c) != 0)

theano/gpuarray/dnn_batchnorm_grad.c

@@ -32,6 +32,9 @@ int dnn_batchnorm_grad(PyGpuArrayObject *inp, PyGpuArrayObject *doutp,
   if (c_set_tensorNd(scale, bn_params) != 0)
     return 1;
 
+  if (epsilon < 1e-5)
+    return 1;
+
   if (theano_prep_output(dinp, inp->ga.nd, inp->ga.dimensions, inp->ga.typecode, GA_C_ORDER, c) != 0)
     return 1;
   if (theano_prep_output(dscale, scale->ga.nd, scale->ga.dimensions, scale->ga.typecode, GA_C_ORDER, c) != 0)

theano/gpuarray/dnn_batchnorm_inf.c

@@ -9,6 +9,9 @@ int dnn_batchnorm_op(PyGpuArrayObject *inp, PyGpuArrayObject *scale,
   if (c_set_tensorNd(scale, bn_params) != 0)
     return 1;
 
+  if (epsilon < 1e-5)
+    return 1;
+
   if (theano_prep_output(outp, inp->ga.nd, inp->ga.dimensions, inp->ga.typecode, GA_C_ORDER, c) != 0)
     return 1;