relax rtol/atol checks on some onnx tests (#2403)

relax the error constraints on these tests due to likely
FP compuation accuracy issues.

nnvm/tests/python/frontend/onnx/test_forward.py

@@ -272,7 +272,7 @@ def test_slice():
     _test_slice_iteration(x, x[:, 1:1000], (1), (1000), (1))
     _test_slice_iteration(x, x[:, 0:-1], (0), (-1), (1))
 
-def _test_onnx_op_elementwise(inshape, outfunc, npargs, dtype, opname, kwargs):
+def _test_onnx_op_elementwise(inshape, outfunc, npargs, dtype, opname, kwargs, rtol=1e-7, atol=1e-7):
     indata = np.random.uniform(-1, 1, size=inshape).astype(dtype)
     outdata = outfunc(indata, **npargs)
 
@@ -290,7 +290,7 @@ def _test_onnx_op_elementwise(inshape, outfunc, npargs, dtype, opname, kwargs):
     for target, ctx in ctx_list():
         tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, dtype)
 
-    tvm.testing.assert_allclose(outdata, tvm_out)
+    tvm.testing.assert_allclose(outdata, tvm_out, rtol=rtol, atol=atol)
 
 def test_floor():
     _test_onnx_op_elementwise((2, 4, 5, 6), np.floor, {}, 'float32', 'Floor', {})
@@ -863,7 +863,7 @@ def test_binary_ops():
     dtype = "float32"
     out_shape = in_shape
 
-    def verify_binary_ops(op, x, y, out_np, broadcast=None):
+    def verify_binary_ops(op, x, y, out_np, broadcast=None, rtol=1e-7, atol=1e-7):
         if broadcast is None:
             z = helper.make_node(op, ['in1', 'in2'], ['out'])
         else:
@@ -879,7 +879,7 @@ def test_binary_ops():
         model = helper.make_model(graph, producer_name='_test')
         for target, ctx in ctx_list():
             tvm_out = get_tvm_output(model, [x, y], target, ctx)
-            tvm.testing.assert_allclose(out_np, tvm_out)
+            tvm.testing.assert_allclose(out_np, tvm_out, rtol=rtol, atol=atol)
 
     x = np.random.uniform(size=in_shape).astype(dtype)
     y = np.random.uniform(size=in_shape).astype(dtype)
@@ -890,8 +890,8 @@ def test_binary_ops():
     verify_binary_ops("Sub", x, z, x - z, broadcast=True)
     verify_binary_ops("Mul",x, y, x * y, broadcast=None)
     verify_binary_ops("Mul", x, z,  x * z, broadcast=True)
-    verify_binary_ops("Div", x, y, x / y, broadcast=None)
-    verify_binary_ops("Div", x, z, x / z, broadcast=True)
+    verify_binary_ops("Div", x, y, x / y, broadcast=None, rtol=1e-5, atol=1e-5)
+    verify_binary_ops("Div", x, z, x / z, broadcast=True, rtol=1e-5, atol=1e-5)
     verify_binary_ops("Sum", x, y, x + y, broadcast=None)
 
 def test_single_ops():
@@ -899,7 +899,7 @@ def test_single_ops():
     dtype = "float32"
     out_shape = in_shape
 
-    def verify_single_ops(op, x, out_np):
+    def verify_single_ops(op, x, out_np, rtol=1e-7, atol=1e-7):
         z = helper.make_node(op, ['in1'], ['out'])
         graph = helper.make_graph([z],
                                    '_test',
@@ -915,8 +915,8 @@ def test_single_ops():
     x = np.random.uniform(size=in_shape).astype(dtype)
     verify_single_ops("Neg",x, -x)
     verify_single_ops("Abs",x, np.abs(x))
-    verify_single_ops("Reciprocal",x, 1/x)
-    verify_single_ops("Sqrt",x, np.sqrt(x))
+    verify_single_ops("Reciprocal",x, 1/x, rtol=1e-5, atol=1e-5)
+    verify_single_ops("Sqrt",x, np.sqrt(x), rtol=1e-5, atol=1e-5)
     verify_single_ops("Relu",x, np.maximum(x, 0))
     verify_single_ops("Exp",x, np.exp(x))
     verify_single_ops("Log",x, np.log(x))
@@ -1004,7 +1004,9 @@ def test_LogSoftmax():
                               {},
                               'float32',
                               'LogSoftmax',
-                              {'axis': 1})
+                              {'axis': 1},
+                              rtol=1e-5,
+                              atol=1e-5)
 
 if __name__ == '__main__':
     # verify_super_resolution_example()