Mxnet allclose

python/mxnet/test_utils.py

@@ -30,6 +30,7 @@
 import bz2
 import zipfile
 from contextlib import contextmanager
+from collections import OrderedDict
 import numpy as np
 import numpy.testing as npt
 import numpy.random as rnd
@@ -98,6 +99,16 @@ def random_sample(population, k):
     return population_copy[0:k]
 
 
+def _sorted_items(d):
+    """Return (key, value) pairs of dict 'd' in a deterministic order (sorted by key)."""
+    return sorted(d.items(), key=lambda t: t[0])
+
+
+def _sorted_dict(d):
+    """Return ordered dictionary containing items ordered by their keys."""
+    return OrderedDict(_sorted_items(d))
+
+
 def _validate_csr_generation_inputs(num_rows, num_cols, density,
                                     distribution="uniform"):
     """Validates inputs for csr generation helper functions
@@ -443,18 +454,6 @@ def np_reduce(dat, axis, keepdims, numpy_reduce_func):
     return ret
 
 
-def find_max_violation(a, b, rtol=None, atol=None):
-    """Finds and returns the location of maximum violation."""
-    rtol = get_rtol(rtol)
-    atol = get_atol(atol)
-    diff = np.abs(a-b)
-    tol = atol + rtol*np.abs(b)
-    violation = diff/(tol+1e-20)
-    loc = np.argmax(violation)
-    idx = np.unravel_index(loc, violation.shape)
-    return idx, np.max(violation)
-
-
 def same(a, b):
     """Test if two NumPy arrays are the same.
 
@@ -465,35 +464,115 @@ def same(a, b):
     """
     return np.array_equal(a, b)
 
+
 def almost_equal(a, b, rtol=None, atol=None, equal_nan=False):
     """Test if two numpy arrays are almost equal."""
     # pylint: disable=unexpected-keyword-arg
     return np.allclose(a, b, rtol=get_rtol(rtol), atol=get_atol(atol), equal_nan=equal_nan)
     # pylint: enable=unexpected-keyword-arg
 
-def assert_almost_equal(a, b, rtol=None, atol=None, names=('a', 'b'), equal_nan=False):
+
+def assert_almost_equal(a, b, rtol=None, atol=None, names=('a', 'b'), equal_nan=False, mismatches=(10, 10)):
     """Test that two numpy arrays are almost equal. Raise exception message if not.
 
     Parameters
     ----------
-    a : np.ndarray
-    b : np.ndarray
-    threshold : None or float
-        The checking threshold. Default threshold will be used if set to ``None``.
+    a : np.ndarray or mx.nd.array
+    b : np.ndarray or mx.nd.array
+    rtol : None or float
+        The relative threshold. Default threshold will be used if set to ``None``.
+    atol : None or float
+        The absolute threshold. Default threshold will be used if set to ``None``.
+    names : tuple of names, optional
+        The names used in error message when an exception occurs
+    equal_nan : boolean, optional
+        The flag determining how to treat NAN values in comparison
+    mismatches : tuple of mismatches
+        Maximum number of mismatches to be printed (mismatches[0]) and determine (mismatches[1])
     """
     rtol = get_rtol(rtol)
     atol = get_atol(atol)
-    if almost_equal(a, b, rtol, atol, equal_nan=equal_nan):
-        return
+    use_np_allclose = isinstance(a, np.ndarray) and isinstance(b, np.ndarray)
+    if not use_np_allclose:
+        if not (hasattr(a, 'context') and hasattr(b, 'context') and a.context == b.context and a.dtype == b.dtype):
+            use_np_allclose = True
+            if isinstance(a, mx.nd.NDArray):
+                a = a.asnumpy()
+            if isinstance(b, mx.nd.NDArray):
+                b = b.asnumpy()
+
+    if use_np_allclose:
+        if almost_equal(a, b, rtol, atol, equal_nan=equal_nan):
+            return
+    else:
+        output = mx.nd.contrib.allclose(a, b, rtol, atol, equal_nan)
+        if output.asnumpy() == 1:
+            return
+
+        a = a.asnumpy()
+        b = b.asnumpy()
+
+    def find_max_violation(a, b, rtol=None, atol=None):
+        """Finds and returns the location of maximum violation."""
+        rtol = get_rtol(rtol)
+        atol = get_atol(atol)
+        # 'smart' absdiff that considers inf's as equals (to match np.allclose)
+        absdiff = np.where(np.equal(a, b), 0, np.abs(a-b))
+        tol = atol + rtol*np.abs(b)
+        violation = absdiff/(tol+1e-20)
+        loc = np.argmax(violation)
+        idx = np.unravel_index(loc, violation.shape)
+        return idx, np.max(violation)
+
+    def locationError(a, b, index, names, maxError=False):
+        """Create element mismatch comment
+
+        Parameters
+        ----------
+        a, b : compared np.ndarray's
+        index : tuple of coordinate arrays
+            Location of violation
+        names : tuple of names
+            The names of compared arrays.
+        maxError: boolean, optional
+            Flag indicating that maximum error is reporting.
+        """
+        maximum = "maximum " if maxError else ""
+        return "Location of %serror: %s, %s=%.8f, %s=%.8f" \
+               % (maximum, str(index), names[0], a[index], names[1], b[index])
+
     index, rel = find_max_violation(a, b, rtol, atol)
+    indexErr = index
+    relErr = rel
+    actual = a.copy()
+
+    print('\n*** Maximum errors for vector of size {}:  rtol={}, atol={}\n'.format(a.size, rtol, atol))
+    i = 1
+    while i < a.size:
+        if i <= mismatches[0]:
+            print("%3d: Error %f  %s" %(i, rel, locationError(a, b, index, names)))
+
+        a[index] = b[index]
+        if almost_equal(a, b, rtol, atol, equal_nan=equal_nan):
+            break
+
+        i += 1
+        if i <= mismatches[1] or mismatches[1] <= 0:
+            index, rel = find_max_violation(a, b, rtol, atol)
+        else:
+            break
+
+    mismatchDegree = "at least " if mismatches[1] > 0 and i > mismatches[1] else ""
+    errMsg = "Error %f exceeds tolerance rtol=%e, atol=%e (mismatch %s%f%%).\n%s" % \
+             (relErr, rtol, atol, mismatchDegree, 100*i/a.size, \
+             locationError(actual, b, indexErr, names, maxError=True))
     np.set_printoptions(threshold=4, suppress=True)
-    msg = npt.build_err_msg([a, b],
-                            err_msg="Error %f exceeds tolerance rtol=%f, atol=%f. "
-                                    " Location of maximum error:%s, a=%f, b=%f"
-                            % (rel, rtol, atol, str(index), a[index], b[index]),
-                            names=names)
+    msg = npt.build_err_msg([actual, b], err_msg=errMsg)
+
     raise AssertionError(msg)
 
+def assert_allclose(a, b, rtol=1e-07, atol=0, equal_nan=True):
+    assert_almost_equal(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan)
 
 def almost_equal_ignore_nan(a, b, rtol=None, atol=None):
     """Test that two NumPy arrays are almost equal (ignoring NaN in either array).
@@ -600,7 +679,7 @@ def simple_forward(sym, ctx=None, is_train=False, **inputs):
 
 
 def _parse_location(sym, location, ctx, dtype=default_dtype()):
-    """Parses the given location to a dictionary.
+    """Parses the given location to a ordered dictionary.
 
     Arguments of the provided op `sym` are used as dictionary keys
     and elements of `location` are used as values.
@@ -656,7 +735,7 @@ def _parse_location(sym, location, ctx, dtype=default_dtype()):
         location = {k: v for k, v in zip(sym.list_arguments(), location)}
     location = {k: mx.nd.array(v, ctx=ctx, dtype=v.dtype if dtype == "asnumpy" else dtype) \
                if isinstance(v, np.ndarray) else v for k, v in location.items()}
-    return location
+    return _sorted_dict(location)
 
 
 def _parse_aux_states(sym, aux_states, ctx, dtype=default_dtype()):
@@ -1085,7 +1164,8 @@ def check_symbolic_backward(sym, location, out_grads, expected, rtol=1e-5, atol=
     if isinstance(expected, (list, tuple)):
         expected = {k:v for k, v in zip(sym.list_arguments(), expected)}
 
-    args_grad_npy = {k:np.random.normal(size=v.shape) for k, v in expected.items()}
+    # Dirty the output buffer deterministically, for reproducibility.
+    args_grad_npy = {k:np.random.normal(size=v.shape) for k, v in _sorted_items(expected)}
     args_grad_data = {}
     for k, v in args_grad_npy.items():
         nd = mx.nd.array(v, ctx=ctx, dtype=dtype)
@@ -1221,6 +1301,15 @@ def check_speed(sym, location=None, ctx=None, N=20, grad_req=None, typ="whole",
     else:
         raise ValueError('typ can only be "whole" or "forward".')
 
+
+def get_tolerance(rtol, ctx):
+    if 'atol' in ctx:
+        return ctx['atol']
+    if 'atol_mult' in ctx:
+        return ctx['atol_mult'] * rtol
+    return rtol
+
+
 def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
                       arg_params=None, aux_params=None, tol=None,
                       raise_on_err=True, ground_truth=None, equal_nan=False,
@@ -1339,12 +1428,15 @@ def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
     for i, exe in enumerate(exe_list):
         if i == max_idx:
             continue
+
+        rtol = tol[dtypes[i]]
+        atol = get_tolerance(rtol, ctx_list[i])
         for name, arr in zip(output_names, exe.outputs):
-            gtarr = gt[name].astype(dtypes[i]).asnumpy()
-            arr = arr.asnumpy()
+            # Previously, the cast was to dtypes[i], but symbol may be mixed-precision,
+            # so casting the ground truth to the actual output type seems more correct.
+            gtarr = gt[name].astype(arr.dtype)
             try:
-                assert_almost_equal(arr, gtarr, rtol=tol[dtypes[i]], atol=tol[dtypes[i]],
-                                    equal_nan=equal_nan)
+                assert_almost_equal(arr, gtarr, rtol=rtol, atol=atol, equal_nan=equal_nan)
             except AssertionError as e:
                 print('Predict Err: ctx %d vs ctx %d at %s'%(i, max_idx, name))
                 traceback.print_exc()
@@ -1362,16 +1454,20 @@ def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
         for i, exe in enumerate(exe_list):
             if i == max_idx:
                 continue
+
+            rtol = tol[dtypes[i]]
+            atol = get_tolerance(rtol, ctx_list[i])
             curr = zip(output_names + arg_names, exe.outputs + exe.grad_arrays)
             for name, arr in curr:
                 if gt[name] is None:
                     assert arr is None
                     continue
-                gtarr = gt[name].astype(dtypes[i]).asnumpy()
-                arr = arr.asnumpy()
+
+                # Previous cast was to dtypes[i], but symbol may be mixed-precision,
+                # so casting the ground truth to the actual output type seems more correct.
+                gtarr = gt[name].astype(arr.dtype)
                 try:
-                    assert_almost_equal(arr, gtarr, rtol=tol[dtypes[i]], atol=tol[dtypes[i]],
-                                        equal_nan=equal_nan)
+                    assert_almost_equal(arr, gtarr, rtol=rtol, atol=atol, equal_nan=equal_nan)
                 except AssertionError as e:
                     print('Train Err: ctx %d vs ctx %d at %s'%(i, max_idx, name))
                     traceback.print_exc()
@@ -1536,7 +1632,7 @@ def get_mnist_iterator(batch_size, input_shape, num_parts=1, part_index=0):
     """
 
     get_mnist_ubyte()
-    flat = False if len(input_shape) == 3 else True
+    flat = len(input_shape) != 3
 
     train_dataiter = mx.io.MNISTIter(
         image="data/train-images-idx3-ubyte",
@@ -1976,12 +2072,14 @@ def verify_generator(generator, buckets, probs, nsamples=1000000, nrepeat=5, suc
 
 def compare_ndarray_tuple(t1, t2, rtol=None, atol=None):
     """Compare ndarray tuple."""
-    if t1 is not None and t2 is not None:
-        if isinstance(t1, tuple):
-            for s1, s2 in zip(t1, t2):
-                compare_ndarray_tuple(s1, s2, rtol, atol)
-        else:
-            assert_almost_equal(t1.asnumpy(), t2.asnumpy(), rtol=rtol, atol=atol)
+    if t1 is None or t2 is None:
+        return
+
+    if isinstance(t1, tuple):
+        for s1, s2 in zip(t1, t2):
+            compare_ndarray_tuple(s1, s2, rtol, atol)
+    else:
+        assert_almost_equal(t1, t2, rtol=rtol, atol=atol)
 
 
 def compare_optimizer(opt1, opt2, shape, dtype, w_stype='default', g_stype='default',
@@ -1990,15 +2088,15 @@ def compare_optimizer(opt1, opt2, shape, dtype, w_stype='default', g_stype='defa
     if w_stype == 'default':
         w2 = mx.random.uniform(shape=shape, ctx=default_context(), dtype=dtype)
         w1 = w2.copyto(default_context())
-    elif w_stype == 'row_sparse' or w_stype == 'csr':
+    elif w_stype in ('row_sparse', 'csr'):
         w2 = rand_ndarray(shape, w_stype, density=1, dtype=dtype)
         w1 = w2.copyto(default_context()).tostype('default')
     else:
         raise Exception("type not supported yet")
     if g_stype == 'default':
         g2 = mx.random.uniform(shape=shape, ctx=default_context(), dtype=dtype)
         g1 = g2.copyto(default_context())
-    elif g_stype == 'row_sparse' or g_stype == 'csr':
+    elif g_stype in ('row_sparse', 'csr'):
         g2 = rand_ndarray(shape, g_stype, dtype=dtype)
         g1 = g2.copyto(default_context()).tostype('default')
     else:
@@ -2013,7 +2111,7 @@ def compare_optimizer(opt1, opt2, shape, dtype, w_stype='default', g_stype='defa
     opt2.update_multi_precision(0, w2, g2, state2)
     if compare_states:
         compare_ndarray_tuple(state1, state2, rtol=rtol, atol=atol)
-    assert_almost_equal(w1.asnumpy(), w2.asnumpy(), rtol=rtol, atol=atol)
+    assert_almost_equal(w1, w2, rtol=rtol, atol=atol)
 
 class EnvManager(object):
     """Environment variable setter and unsetter via with idiom"""