def test_candidate_scorer():
pytest.importorskip("mxnet")
from mxnet import np
import sockeye.beam_search
scorer = sockeye.beam_search.CandidateScorer(length_penalty_alpha=1.0,
length_penalty_beta=0.0,
brevity_penalty_weight=0.1)
scorer.initialize()
scorer.hybridize(static_alloc=True)
# np.array input
raw_scores = np.random.uniform(0, 1, (5, ))
lengths = np.array([1, 2, 3, 4, 5])
reference_lengths = np.array([2, 3, 4, 5, 6])
scores = scorer(raw_scores, lengths, reference_lengths)
unnormalized_scores = scorer.unnormalize(scores, lengths,
reference_lengths)
assert np.allclose(unnormalized_scores, raw_scores)
# int/float input
raw_scores = 5.6
lengths = 3
reference_lengths = 4
scores = scorer(raw_scores, lengths, reference_lengths)
unnormalized_scores = scorer.unnormalize(scores, lengths,
reference_lengths)
assert np.allclose(unnormalized_scores, raw_scores)
def predict_seq2seq(net, src_sentence, src_vocab, tgt_vocab, num_steps,
device, save_attention_weights=False):
"""Predict for sequence to sequence."""
src_tokens = src_vocab[src_sentence.lower().split(' ')] + [
src_vocab['<eos>']]
enc_valid_len = np.array([len(src_tokens)], ctx=device)
src_tokens = d2l.truncate_pad(src_tokens, num_steps, src_vocab['<pad>'])
# Add the batch axis
enc_X = np.expand_dims(np.array(src_tokens, ctx=device), axis=0)
enc_outputs = net.encoder(enc_X, enc_valid_len)
dec_state = net.decoder.init_state(enc_outputs, enc_valid_len)
# Add the batch axis
dec_X = np.expand_dims(np.array([tgt_vocab['<bos>']], ctx=device), axis=0)
output_seq, attention_weight_seq = [], []
for _ in range(num_steps):
Y, dec_state = net.decoder(dec_X, dec_state)
# We use the token with the highest prediction likelihood as the input
# of the decoder at the next time step
dec_X = Y.argmax(axis=2)
pred = dec_X.squeeze(axis=0).astype('int32').item()
# Save attention weights (to be covered later)
if save_attention_weights:
attention_weight_seq.append(net.decoder.attention_weights)
# Once the end-of-sequence token is predicted, the generation of the
# output sequence is complete
if pred == tgt_vocab['<eos>']:
break
output_seq.append(pred)
return ' '.join(tgt_vocab.to_tokens(output_seq)), attention_weight_seq
def _add_workload_roll():
# test_roll1d(self)
OpArgMngr.add_workload('roll', np.array(_np.arange(10)), 2)
# test_roll2d(self)
x2 = np.array(_np.reshape(_np.arange(10), (2, 5)))
OpArgMngr.add_workload('roll', x2, 1)
OpArgMngr.add_workload('roll', x2, 1, axis=0)
OpArgMngr.add_workload('roll', x2, 1, axis=1)
# # Roll multiple axes at once.
OpArgMngr.add_workload('roll', x2, 1, axis=(0, 1))
OpArgMngr.add_workload('roll', x2, (1, 0), axis=(0, 1))
OpArgMngr.add_workload('roll', x2, (-1, 0), axis=(0, 1))
OpArgMngr.add_workload('roll', x2, (0, 1), axis=(0, 1))
OpArgMngr.add_workload('roll', x2, (0, -1), axis=(0, 1))
OpArgMngr.add_workload('roll', x2, (1, 1), axis=(0, 1))
OpArgMngr.add_workload('roll', x2, (-1, -1), axis=(0, 1))
# # Roll the same axis multiple times.
# OpArgMngr.add_workload('roll', x2, 1, axis=(0, 0)) # Check failed: axes[i - 1] < axes[i] (0 vs. 0) : axes have duplicates [0,0]
# OpArgMngr.add_workload('roll', x2, 1, axis=(1, 1)) # Check failed: axes[i - 1] < axes[i] (1 vs. 1) : axes have duplicates [1,1]
# # Roll more than one turn in either direction.
OpArgMngr.add_workload('roll', x2, 6, axis=1)
OpArgMngr.add_workload('roll', x2, -4, axis=1)
# # test_roll_empty
OpArgMngr.add_workload('roll', np.array([]), 1)
def _get_random_bucketed_data(buckets: List[Tuple[int, int]],
min_count: int,
max_count: int,
bucket_counts: Optional[List[Optional[int]]] = None):
"""
Get random bucket data.
:param buckets: The list of buckets.
:param min_count: The minimum number of samples that will be sampled if no exact count is given.
:param max_count: The maximum number of samples that will be sampled if no exact count is given.
:param bucket_counts: For each bucket an optional exact example count can be given. If it is not given it will be
sampled.
:return: The random source, target and label arrays.
"""
pytest.importorskip('mxnet')
from mxnet import np
if bucket_counts is None:
bucket_counts = [None for _ in buckets]
bucket_counts = [random.randint(min_count, max_count) if given_count is None else given_count
for given_count in bucket_counts]
source = [np.array(np.random.randint(0, 10, (count, random.randint(1, bucket[0]), 1))) for count, bucket in
zip(bucket_counts, buckets)]
target = [np.array(np.random.randint(0, 10, (count, random.randint(2, bucket[1]), 1))) for count, bucket in
zip(bucket_counts, buckets)]
return source, target
def evaluate_ranking(net, test_input, seq, candidates, num_users, num_items,
devices):
ranked_list, ranked_items, hit_rate, auc = {}, {}, [], []
all_items = set([i for i in range(num_users)])
for u in range(num_users):
neg_items = list(all_items - set(candidates[int(u)]))
user_ids, item_ids, x, scores = [], [], [], []
[item_ids.append(i) for i in neg_items]
[user_ids.append(u) for _ in neg_items]
x.extend([np.array(user_ids)])
if seq is not None:
x.append(seq[user_ids, :])
x.extend([np.array(item_ids)])
test_data_iter = gluon.data.DataLoader(gluon.data.ArrayDataset(*x),
shuffle=False,
last_batch="keep",
batch_size=1024)
for index, values in enumerate(test_data_iter):
x = [
gluon.utils.split_and_load(v, devices, even_split=False)
for v in values
]
scores.extend([list(net(*t).asnumpy()) for t in zip(*x)])
scores = [item for sublist in scores for item in sublist]
item_scores = list(zip(item_ids, scores))
ranked_list[u] = sorted(item_scores, key=lambda t: t[1], reverse=True)
ranked_items[u] = [r[0] for r in ranked_list[u]]
temp = hit_and_auc(ranked_items[u], test_input[u], 50)
hit_rate.append(temp[0])
auc.append(temp[1])
return np.mean(np.array(hit_rate)), np.mean(np.array(auc))
def test_boolean_indexing_onedim():
# adapted from numpy's test_indexing.py
# Indexing a 2-dimensional array with
# boolean array of length one
a = np.array([[0., 0., 0.]])
b = np.array([True], dtype=bool)
assert same(a[b].asnumpy(), a.asnumpy())
def predict_s2s_ch9(model, src_sentence, src_vocab, tgt_vocab, num_steps,
device):
"""Predict sequences (defined in Chapter 9)."""
#src_tokens = src_vocab[src_sentence.lower().split(' ')] + [src_vocab['<eos>']]
src_tokens = src_vocab[get_word_list(
src_sentence.lower())] + [src_vocab['<eos>']]
enc_valid_len = np.array([len(src_tokens)], ctx=device)
src_tokens = truncate_pad(src_tokens, num_steps, src_vocab['<pad>'])
# Add the batch axis
enc_X = np.expand_dims(np.array(src_tokens, ctx=device), axis=0)
enc_outputs = model.encoder(enc_X, enc_valid_len)
dec_state = model.decoder.init_state(enc_outputs, enc_valid_len)
# Add the batch axis
dec_X = np.expand_dims(np.array([tgt_vocab['<bos>']], ctx=device), axis=0)
output_seq = []
for _ in range(num_steps):
Y, dec_state = model.decoder(dec_X, dec_state)
# We use the token with the highest prediction likelihood as the input
# of the decoder at the next time step
dec_X = Y.argmax(axis=2)
pred = dec_X.squeeze(axis=0).astype('int32').item()
# Once the end-of-sequence token is predicted, the generation of
# the output sequence is complete
if pred == tgt_vocab['<eos>']:
break
output_seq.append(pred)
return ' '.join(tgt_vocab.to_tokens(output_seq))
def test_rotate():
transformer = transforms.Rotate(10.)
assertRaises(TypeError, transformer, mx.np.ones((3, 30, 60),
dtype='uint8'))
single_image = mx.np.ones((3, 30, 60), dtype='float32')
single_output = transformer(single_image)
assert same(single_output.shape, (3, 30, 60))
batch_image = mx.np.ones((3, 3, 30, 60), dtype='float32')
batch_output = transformer(batch_image)
assert same(batch_output.shape, (3, 3, 30, 60))
input_image = np.array([[[0., 0., 0.], [0., 0., 1.], [0., 0., 0.]]])
rotation_angles_expected_outs = [
(90., np.array([[[0., 1., 0.], [0., 0., 0.], [0., 0., 0.]]])),
(180., np.array([[[0., 0., 0.], [1., 0., 0.], [0., 0., 0.]]])),
(270., np.array([[[0., 0., 0.], [0., 0., 0.], [0., 1., 0.]]])),
(360., np.array([[[0., 0., 0.], [0., 0., 1.], [0., 0., 0.]]])),
]
for rot_angle, expected_result in rotation_angles_expected_outs:
transformer = transforms.Rotate(rot_angle)
ans = transformer(input_image)
print(type(ans), ans, type(expected_result), expected_result)
assert_almost_equal(ans.asnumpy(),
expected_result.asnumpy(),
atol=1e-6)
def predict_s2s_ch9(model, src_sentence, src_vocab, tgt_vocab, num_steps, ctx):
# ORG src_tokens = src_vocab[src_sentence.lower().split(' ')] # TODO : if vocab doesn't contain, instead of 0, assign other index
src_tokens = src_vocab[src_sentence.lower().split(',')]
num_steps = len(src_tokens) # Fix
enc_valid_len = np.array([len(src_tokens)], ctx=ctx)
src_tokens = d2l.truncate_pad(src_tokens, num_steps, src_vocab['<pad>'])
enc_X = np.array(src_tokens, ctx=ctx)
# Add the batch_size dimension
enc_outputs = model.encoder(np.expand_dims(enc_X, axis=0), enc_valid_len)
dec_state = model.decoder.init_state(enc_outputs, enc_valid_len)
dec_X = np.expand_dims(np.array([tgt_vocab['<bos>']], ctx=ctx), axis=0)
predict_tokens = []
for _ in range(num_steps):
Y, dec_state = model.decoder(dec_X, dec_state)
# The token with highest score is used as the next timestep input
dec_X = Y.argmax(axis=2)
py = dec_X.squeeze(axis=0).astype('int32').item()
#print("debug : ", py)
if py == tgt_vocab['<eos>']:
print('py : ', py) #ToDo
break
elif py == tgt_vocab['<unk>']:
print('py : ', py) #ToDo
break
predict_tokens.append(py)
return ' '.join(tgt_vocab.to_tokens(predict_tokens))
def test_to_tensor():
# 3D Input
data_in = np.random.uniform(0, 255, (300, 300, 3)).astype(dtype=np.uint8)
out_nd = transforms.ToTensor()(np.array(data_in, dtype='uint8'))
assert_almost_equal(
out_nd.asnumpy(),
np.transpose(data_in.astype(dtype=np.float32) / 255.0, (2, 0, 1)))
# 4D Input
data_in = np.random.uniform(0, 255,
(5, 300, 300, 3)).astype(dtype=np.uint8)
out_nd = transforms.ToTensor()(np.array(data_in, dtype='uint8'))
assert_almost_equal(
out_nd.asnumpy(),
np.transpose(data_in.astype(dtype=np.float32) / 255.0, (0, 3, 1, 2)))
# Invalid Input
invalid_data_in = np.random.uniform(
0, 255, (5, 5, 300, 300, 3)).astype(dtype=np.uint8)
transformer = transforms.ToTensor()
assertRaises(MXNetError, transformer, invalid_data_in)
# Bounds (0->0, 255->1)
data_in = np.zeros((10, 20, 3)).astype(dtype=np.uint8)
out_nd = transforms.ToTensor()(np.array(data_in, dtype='uint8'))
assert same(
out_nd.asnumpy(),
np.transpose(np.zeros(data_in.shape, dtype=np.float32), (2, 0, 1)))
data_in = np.full((10, 20, 3), 255).astype(dtype=np.uint8)
out_nd = transforms.ToTensor()(np.array(data_in, dtype='uint8'))
assert same(
out_nd.asnumpy(),
np.transpose(np.ones(data_in.shape, dtype=np.float32), (2, 0, 1)))
def read_csv():
data = pd.read_csv(data_file)
inputs, outputs = data.iloc[:, 0:2], data.iloc[:, 2]
inputs = inputs.fillna(inputs.mean())
inputs = pd.get_dummies(inputs, dummy_na=True)
X, y = np.array(inputs.values), np.array(outputs.values)
print("examples '{}/{}', labels '{}/{}'".format(X, type(X), y, type(y)))
def _get_vocab_slice_ids(restrict_lexicon: Optional[lexicon.TopKLexicon],
source_words: np.ndarray,
raw_constraint_list: List[Optional[constrained.RawConstraintList]],
eos_id: int,
beam_size: int) -> Tuple[np.ndarray, int, List[Optional[constrained.RawConstraintList]]]:
vocab_slice_ids = np.array(restrict_lexicon.get_trg_ids(source_words.astype("int32", copy=False).asnumpy()), dtype='int32')
ctx = source_words.ctx
if any(raw_constraint_list):
# Add the constraint IDs to the list of permissibled IDs, and then project them into the reduced space
constraint_ids = np.array(word_id for sent in raw_constraint_list for phr in sent for word_id in phr)
vocab_slice_ids = onp.lib.arraysetops.union1d(vocab_slice_ids, constraint_ids) # type: ignore
full_to_reduced = dict((val, i) for i, val in enumerate(vocab_slice_ids))
raw_constraint_list = [[[full_to_reduced[x] for x in phr] for phr in sent] for sent in
raw_constraint_list]
# pad to a multiple of 8.
vocab_slice_ids = np.pad(vocab_slice_ids, (0, 7 - ((len(vocab_slice_ids) - 1) % 8)),
mode='constant', constant_values=eos_id)
vocab_slice_ids_shape = vocab_slice_ids.shape[0]
if vocab_slice_ids_shape < beam_size + 1:
# This fixes an edge case for toy models, where the number of vocab ids from the lexicon is
# smaller than the beam size.
logger.warning("Padding vocab_slice_ids (%d) with EOS to have at least %d+1 elements to expand",
vocab_slice_ids_shape, beam_size)
n = beam_size - vocab_slice_ids_shape + 1
vocab_slice_ids = np.concatenate((vocab_slice_ids, np.full((n,), fill_value=eos_id, ctx=ctx, dtype='int32')),
axis=0)
logger.debug(f'decoder softmax size: {vocab_slice_ids_shape}')
return vocab_slice_ids, vocab_slice_ids_shape, raw_constraint_list
def predict_snli(net, vocab, premise, hypothesis):
premise = np.array(vocab[premise], ctx=d2l.try_gpu())
hypothesis = np.array(vocab[hypothesis], ctx=d2l.try_gpu())
label = np.argmax(net([premise.reshape((1, -1)),
hypothesis.reshape((1, -1))]), axis=1)
return 'entailment' if label == 0 else 'contradiction' if label == 1 \
else 'neutral'
def test_acc(net,
test_feats,
test_thrpts,
train_thrpt_avg,
train_thrpt_std,
print_log=True):
"""Test the model accuracy."""
valid_error = 0
thrpt_errors = []
if print_log:
log.info(
'\nExpected\tValid-Pred0\tValid-Pred1\tThrpt-Pred\tThrpt-Error')
thrpt_pred_range = (float('inf'), 0)
for start in range(0, len(test_feats), 512):
valid_preds, thrpt_preds = net(
test_feats[start:min(start + 512, len(test_feats))])
# Recover prediction results.
thrpt_preds = (thrpt_preds * train_thrpt_std) + train_thrpt_avg
#thrpt_preds = np.exp(thrpt_preds)
for idx, (valid_pred,
thrpt_pred) in enumerate(zip(valid_preds, thrpt_preds)):
real = test_thrpts[start + idx].tolist()
valid_real = 1 if real > INVALID_THD else 0
valid_prob = valid_pred.tolist()
valid_pred = 1 if valid_prob[0] <= valid_prob[1] else 0
valid_error += 1 if valid_real != valid_pred else 0
thrpt_pred = thrpt_pred.tolist()[0]
if valid_real == 1:
if valid_pred == 0: # Predict invalid will output 0 GFLOPs.
error = 1
else:
error = abs(thrpt_pred - real) / real
thrpt_pred_range = (min(thrpt_pred_range[0], thrpt_pred),
max(thrpt_pred_range[1], thrpt_pred))
thrpt_errors.append(error)
if print_log:
log.info('\t%.6f\t%.2f\t%.2f\t%.6f\t%.2f%%', real,
valid_prob[0], valid_prob[1], thrpt_pred,
100 * error)
elif print_log:
log.info('\t0\t%.2f\t%.2f\t%.6f', valid_prob[0], valid_prob[1],
thrpt_pred)
valid_err_rate = 100.0 * valid_error / len(test_feats)
thrpt_err = (np.array(thrpt_errors).mean().tolist(),
np.array(thrpt_errors).std().tolist())
log.info('Thrpt predict range: %.6f, %.6f', thrpt_pred_range[0],
thrpt_pred_range[1])
if print_log:
log.info('Valid error %.2f%%', valid_err_rate)
log.info('Average error: %.2f (std %.2f)', thrpt_err[0], thrpt_err[1])
return (valid_err_rate, thrpt_err)
def _add_workload_power(array_pool):
OpArgMngr.add_workload('power', array_pool['4x1'], array_pool['1x2'])
OpArgMngr.add_workload('power', array_pool['4x1'], 2)
OpArgMngr.add_workload('power', 2, array_pool['4x1'])
OpArgMngr.add_workload('power', array_pool['4x1'], array_pool['1x1x0'])
OpArgMngr.add_workload('power', np.array([1, 2, 3], np.int32), 2.00001)
OpArgMngr.add_workload('power', np.array([15, 15], np.int64), np.array([15, 15], np.int64))
OpArgMngr.add_workload('power', 0, np.arange(1, 10))
def get_bert_encoding(net, tokens_a, tokens_b=None):
tokens, segments = d2l.get_tokens_and_segments(tokens_a, tokens_b)
ctx = d2l.try_gpu()
token_ids = np.expand_dims(np.array(vocab[tokens], ctx=ctx), axis=0)
segments = np.expand_dims(np.array(segments, ctx=ctx), axis=0)
valid_len = np.expand_dims(np.array(len(tokens), ctx=ctx), axis=0)
encoded_X, _, _ = net(token_ids, segments, valid_len)
return encoded_X
def test_single_bool_index():
# adapted from numpy's test_indexing.py
# Single boolean index
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]], dtype=np.int32)
assert same(a[np.array(True, dtype=np.bool_)].asnumpy(), a[None].asnumpy())
assert same(a[np.array(False, dtype=np.bool_)].asnumpy(), a[None][0:0].asnumpy())
def _add_workload_stack(array_pool):
OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2)
OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2, 1)
OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2, -1)
OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2, -2)
OpArgMngr.add_workload('stack', np.random.normal(size=(2, 4, 3)), 2)
OpArgMngr.add_workload('stack', np.random.normal(size=(2, 4, 3)), -3)
OpArgMngr.add_workload('stack', np.array([[], [], []]), 1)
OpArgMngr.add_workload('stack', np.array([[], [], []]))
def test_ndarray_container():
x = np.array([1, 2, 3])
y = np.array([4, 5, 6])
arr = mxnet._ffi.convert_to_node([x, y])
assert _np.array_equal(arr[0].asnumpy(), x.asnumpy())
assert isinstance(arr[0], NDArray)
amap = mxnet._ffi.convert_to_node({'x': x, 'y': y})
assert "x" in amap
assert _np.array_equal(amap["y"].asnumpy(), y.asnumpy())
assert isinstance(amap["y"], NDArray)
def test_boolean_indexing_twodim():
# adapted from numpy's test_indexing.py
# Indexing a 2-dimensional array with
# 2-dimensional boolean array
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32)
b = np.array(
[[True, False, True], [False, True, False], [True, False, True]],
dtype=np.bool_)
assert same(a[b].asnumpy(), _np.array([1, 3, 5, 7, 9], dtype=a.dtype))
assert same(a[b[1]].asnumpy(), _np.array([[4, 5, 6]], dtype=a.dtype))
assert same(a[b[0]].asnumpy(), a[b[2]].asnumpy())
def vectors():
# scalars are also ndarrays
x = np.array(3.0)
y = np.array(2.0)
print("shape of scalar = {}".format(x.shape))
print("size of scalar = {}".format(x.size))
# vector
xv = np.arange(4)
print("shape of vector {} = {}".format(xv, xv.shape))
print("size of vector = {}".format(xv.size))
print(x + y, x * y, x / y, x ** y)
def __getitem__(self, idx):
file = h5py.File(self.data[idx], 'r')
vol = torch.zeros((len(self.fields), 128,128,128))
## assign requested fields to data category
for i, field in enumerate(self.fields):
if 'velocity' not in field:
shp = file[field][:].shape
vol[i] = F.pad(torch.from_numpy(file[field][:]), \
(0, 128-shp[2], 0, 128-shp[1], 0, 128-shp[0]))
vol[i] = (vol[i]-self.scaling[field]['mean']) \
/self.scaling[field]['std']
if "velocity_divergence" in field:
div = torch.zeros_like(vol[0])
for ii,ax in enumerate('xyz'):
fld = 'velocity_%s'%ax
shp = file[fld].shape
vel_field = F.pad(torch.from_numpy(file[fld][:]), \
(0, 128-shp[2], 0, 128-shp[1], 0, 128-shp[0]))
div += numpy.gradient(\
(vel_field-self.scaling[fld]['mean']\
/self.scaling[fld]['std']))[ii]
vol[i] = div
le = file.attrs['cover_grid_left_edge']
dx = file.attrs['dx']
star_inds = []
for c in file['new_stars_position'][:]:
rel_pos = c - le
star_inds.append((rel_pos)//dx+1)
star_inds = numpy.array(star_inds)
### return matrix of pixel labels
label = torch.zeros((128,128,128))
offset = range(-self.star_blur, (self.star_blur)+1)
## set all cells hosting stars (and those within radius star_blur) to 1
## for segmentations
for s in star_inds:
for dx in offset:
for dy in offset:
for dz in offset:
ind = (s + [dx, dy, dz]).astype(int)
if not numpy.any(ind >= 128):
label[ind[0],ind[1],ind[2]] = 1
if self.transforms != None:
vol, label, le = self.transform(vol, label, dx, le)
## if cropping, have to check the new volumee for stars
if self.classifier == 'classification':
label = (label.sum() >= 1).long()
## apply transforms to data
file.close()
return np.array(vol.numpy()), np.array(label.long().numpy())
def test_np_meshgrid():
nx, ny = (4, 5)
x = np.array(_np.linspace(0, 1, nx), dtype=np.float32)
y = np.array(_np.linspace(0, 1, ny), dtype=np.float32)
z = np.ones(())
xv, yv, zv = np.meshgrid(x, y, z)
xv_expected, yv_expected, zv_expected = _np.meshgrid(
x.asnumpy(), y.asnumpy(), z.asnumpy())
assert same(xv.asnumpy(), xv_expected)
assert same(yv.asnumpy(), yv_expected)
assert same(zv.asnumpy(), zv_expected)
def test_divide():
# np.divide and np.true_divide are the same thing
inp = np.ones((INT_OVERFLOW, 2))
inp[-1, -1] = 10
inp.attach_grad()
with mx.autograd.record():
out = np.divide(inp, np.array([2, 3]))
out.backward()
assert out.shape == inp.shape
assert_almost_equal(out[-1, -1], np.array([10 / 3]), rtol=1e-5, atol=1e-5)
assert inp.grad.shape == inp.shape
assert_almost_equal(inp.grad[-1, -1], np.array([1.0 / 3]), rtol=1e-5, atol=1e-5)
def batch_check(funcs, exp):
inp.attach_grad()
for f, e in zip(funcs, exp):
with mx.autograd.record():
out = f(inp)
out.backward()
assert out.shape == inp.shape
assert_almost_equal(out[-1, -1], np.array([e[0]]), \
rtol=1e-5, atol=1e-5)
assert inp.grad.shape == inp.shape
assert_almost_equal(inp.grad[-1, -1], np.array([e[1]]), \
rtol=1e-5, atol=1e-5)
def test_expm1():
inp = np.ones((2, INT_OVERFLOW))
inp[-1, -1] = 2
inp.attach_grad()
with mx.autograd.record():
out = np.expm1(inp)
out.backward()
assert out.shape == inp.shape
assert_almost_equal(out[0, 0], np.array(np.e**1 - 1), rtol=1e-5, atol=1e-5)
assert_almost_equal(out[-1, -1], np.array(np.e**2 - 1), rtol=1e-5, atol=1e-5)
assert inp.grad.shape == inp.shape
assert_almost_equal(inp.grad[-1, -1], np.array(np.e**2), rtol=1e-5, atol=1e-5)
def test_reciprocal():
inp = np.ones((2, INT_OVERFLOW))
inp[-1, -1] = 3
inp.attach_grad()
with mx.autograd.record():
out = np.reciprocal(inp)
out.backward()
assert out.shape == inp.shape
assert_almost_equal(out[-1, -1], np.array([1.0/3]), rtol=1e-5, atol=1e-5)
assert inp.grad.shape == inp.shape
assert_almost_equal(inp.grad[-1, -1], np.array([-1.0/3**2]), \
rtol=1e-5, atol=1e-5)
def test_index_update():
A = np.zeros((2, INT_OVERFLOW))
ind = np.array([[0, 0], [0, 1]], dtype='int32')
val = np.array([100, 200])
A.attach_grad()
with mx.autograd.record():
B = npx.index_update(A, ind, val)
assert B.shape == (2, INT_OVERFLOW)
assert B[0][0] == 100 and B[0][1] == 200
B.backward()
assert A.grad.shape == (2, INT_OVERFLOW)
assert A.grad[0][0] == 0
def test_length_penalty_default():
pytest.importorskip("mxnet")
from mxnet import np
import sockeye.beam_search
lengths = np.array([[1], [2], [3]])
length_penalty = sockeye.beam_search.LengthPenalty(1.0, 0.0)
expected_lp = np.array([[1.0], [2.], [3.]])
assert np.allclose(length_penalty(lengths), expected_lp)
length_penalty.hybridize()
assert np.allclose(length_penalty(lengths), expected_lp)
def test_setitem_autograd(np_array, index):
x = np.array(np_array, dtype=np_array.dtype)
out_shape = x[index].shape
y = np.array(_np.random.uniform(size=out_shape))
y.attach_grad()
try:
with autograd.record():
x[index] = y
assert False # should not reach here
except mx.base.MXNetError as err:
assert str(err).find(
'Inplace operations (+=, -=, x[:]=, etc) are not supported when recording with'
) != -1
