FM / operator

/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/

/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
activation_ops_test.py	9691	0644	edit dl rm
adadelta_test.py	7932	0644	edit dl rm
adagrad_test.py	7586	0644	edit dl rm
adagrad_test_helper.py	5181	0644	edit dl rm
adam_test.py	21559	0644	edit dl rm
affine_channel_op_test.py	3784	0644	edit dl rm
alias_with_name_test.py	938	0644	edit dl rm
apmeter_test.py	2738	0644	edit dl rm
arg_ops_test.py	1917	0644	edit dl rm
assert_test.py	797	0644	edit dl rm
async_net_barrier_test.py	946	0644	edit dl rm
atomic_ops_test.py	4104	0644	edit dl rm
basic_rnn_test.py	4720	0644	edit dl rm
batch_box_cox_test.py	5080	0644	edit dl rm
batch_bucketize_op_test.py	3730	0644	edit dl rm
batch_moments_op_test.py	2795	0644	edit dl rm
batch_sparse_to_dense_op_test.py	4199	0644	edit dl rm
bbox_transform_test.py	12258	0644	edit dl rm
bisect_percentile_op_test.py	6227	0644	edit dl rm
blobs_queue_db_test.py	3240	0644	edit dl rm
boolean_mask_test.py	16389	0644	edit dl rm
boolean_unmask_test.py	1711	0644	edit dl rm
box_with_nms_limit_op_test.py	8750	0644	edit dl rm
bucketize_op_test.py	930	0644	edit dl rm
cast_op_test.py	1600	0644	edit dl rm
ceil_op_test.py	888	0644	edit dl rm
channel_backprop_stats_op_test.py	2131	0644	edit dl rm
channel_shuffle_test.py	1794	0644	edit dl rm
channel_stats_op_test.py	2639	0644	edit dl rm
checkpoint_test.py	1500	0644	edit dl rm
clip_op_test.py	1984	0644	edit dl rm
clip_tensor_op_test.py	2076	0644	edit dl rm
collect_and_distribute_fpn_rpn_proposals_op_test.py	11269	0644	edit dl rm
concat_op_cost_test.py	2858	0644	edit dl rm
concat_split_op_test.py	7266	0644	edit dl rm
conditional_test.py	995	0644	edit dl rm
conftest.py	1446	0644	edit dl rm
conv_test.py	32473	0644	edit dl rm
conv_transpose_test.py	15945	0644	edit dl rm
copy_ops_test.py	7374	0644	edit dl rm
copy_rows_to_tensor_op_test.py	2526	0644	edit dl rm
cosine_embedding_criterion_op_test.py	1953	0644	edit dl rm
counter_ops_test.py	3348	0644	edit dl rm
crf_test.py	5315	0644	edit dl rm
cross_entropy_ops_test.py	10085	0644	edit dl rm
ctc_beam_search_decoder_op_test.py	5197	0644	edit dl rm
ctc_greedy_decoder_op_test.py	4743	0644	edit dl rm
cudnn_recurrent_test.py	5817	0644	edit dl rm
dataset_ops_test.py	23847	0644	edit dl rm
data_couple_op_test.py	858	0644	edit dl rm
decay_adagrad_test.py	2694	0644	edit dl rm
deform_conv_test.py	19276	0644	edit dl rm
dense_vector_to_id_list_op_test.py	2044	0644	edit dl rm
depthwise_3x3_conv_test.py	1863	0644	edit dl rm
detectron_keypoints.py	7973	0644	edit dl rm
distance_op_test.py	4351	0644	edit dl rm
dropout_op_test.py	2971	0644	edit dl rm
duplicate_operands_test.py	734	0644	edit dl rm
elementwise_linear_op_test.py	1382	0644	edit dl rm
elementwise_logical_ops_test.py	4617	0644	edit dl rm
elementwise_ops_test.py	33354	0644	edit dl rm
elementwise_op_broadcast_test.py	17466	0644	edit dl rm
emptysample_ops_test.py	1977	0644	edit dl rm
enforce_finite_op_test.py	1286	0644	edit dl rm
ensure_clipped_test.py	1505	0644	edit dl rm
ensure_cpu_output_op_test.py	1244	0644	edit dl rm
erf_op_test.py	749	0644	edit dl rm
expand_op_test.py	2109	0644	edit dl rm
fc_operator_test.py	3720	0644	edit dl rm
feature_maps_ops_test.py	21492	0644	edit dl rm
filler_ops_test.py	8476	0644	edit dl rm
find_op_test.py	1316	0644	edit dl rm
flatten_op_test.py	922	0644	edit dl rm
flexible_top_k_test.py	2609	0644	edit dl rm
floor_op_test.py	894	0644	edit dl rm
fused_nbit_rowwise_conversion_ops_test.py	14077	0644	edit dl rm
fused_nbit_rowwise_test_helper.py	2693	0644	edit dl rm
gather_ops_test.py	9216	0644	edit dl rm
gather_ranges_op_test.py	9125	0644	edit dl rm
given_tensor_byte_string_to_uint8_fill_op_test.py	1392	0644	edit dl rm
given_tensor_fill_op_test.py	1503	0644	edit dl rm
glu_op_test.py	1212	0644	edit dl rm
group_conv_test.py	2870	0644	edit dl rm
group_norm_op_test.py	5252	0644	edit dl rm
gru_test.py	12932	0644	edit dl rm
heatmap_max_keypoint_op_test.py	4770	0644	edit dl rm
histogram_test.py	3097	0644	edit dl rm
hsm_test.py	9456	0644	edit dl rm
hyperbolic_ops_test.py	1472	0644	edit dl rm
im2col_col2im_test.py	4311	0644	edit dl rm
image_input_op_test.py	17345	0644	edit dl rm
index_hash_ops_test.py	2885	0644	edit dl rm
index_ops_test.py	4597	0644	edit dl rm
instance_norm_test.py	9917	0644	edit dl rm
integral_image_ops_test.py	3419	0644	edit dl rm
jsd_ops_test.py	1044	0644	edit dl rm
key_split_ops_test.py	1289	0644	edit dl rm
lars_test.py	1354	0644	edit dl rm
layer_norm_op_test.py	14983	0644	edit dl rm
leaky_relu_test.py	5639	0644	edit dl rm
learning_rate_adaption_op_test.py	2837	0644	edit dl rm
learning_rate_op_test.py	8652	0644	edit dl rm
lengths_pad_op_test.py	1625	0644	edit dl rm
lengths_reducer_fused_nbit_rowwise_ops_test.py	15495	0644	edit dl rm
lengths_tile_op_test.py	1332	0644	edit dl rm
lengths_top_k_ops_test.py	2371	0644	edit dl rm
length_split_op_test.py	4868	0644	edit dl rm
listwise_l2r_operator_test.py	8740	0644	edit dl rm
load_save_test.py	33241	0644	edit dl rm
locally_connected_op_test.py	7761	0644	edit dl rm
loss_ops_test.py	902	0644	edit dl rm
lpnorm_op_test.py	2725	0644	edit dl rm
map_ops_test.py	2249	0644	edit dl rm
margin_ranking_criterion_op_test.py	1816	0644	edit dl rm
math_ops_test.py	1603	0644	edit dl rm
matmul_op_test.py	10096	0644	edit dl rm
mean_op_test.py	1469	0644	edit dl rm
merge_id_lists_op_test.py	2989	0644	edit dl rm
mkl_conv_op_test.py	1547	0644	edit dl rm
mkl_packed_fc_op_test.py	2647	0644	edit dl rm
mod_op_test.py	1459	0644	edit dl rm
moments_op_test.py	1722	0644	edit dl rm
momentum_sgd_test.py	6480	0644	edit dl rm
mpi_test.py	8154	0644	edit dl rm
mul_gradient_benchmark.py	1509	0644	edit dl rm
negate_gradient_op_test.py	1518	0644	edit dl rm
ngram_ops_test.py	2327	0644	edit dl rm
normalize_op_test.py	1679	0644	edit dl rm
numpy_tile_op_test.py	1924	0644	edit dl rm
one_hot_ops_test.py	7478	0644	edit dl rm
onnx_while_test.py	3070	0644	edit dl rm
order_switch_test.py	1306	0644	edit dl rm
pack_ops_test.py	12634	0644	edit dl rm
pack_rnn_sequence_op_test.py	2891	0644	edit dl rm
pad_test.py	1377	0644	edit dl rm
partition_ops_test.py	6838	0644	edit dl rm
percentile_op_test.py	4427	0644	edit dl rm
piecewise_linear_transform_test.py	6187	0644	edit dl rm
pooling_test.py	16508	0644	edit dl rm
prepend_dim_test.py	1505	0644	edit dl rm
python_op_test.py	1312	0644	edit dl rm
quantile_test.py	3276	0644	edit dl rm
rand_quantization_op_speed_test.py	3128	0644	edit dl rm
rank_loss_operator_test.py	5752	0644	edit dl rm
rebatching_queue_test.py	9047	0644	edit dl rm
record_queue_test.py	3125	0644	edit dl rm
recurrent_network_test.py	14048	0644	edit dl rm
recurrent_net_executor_test.py	10922	0644	edit dl rm
reduce_ops_test.py	17341	0644	edit dl rm
reduction_ops_test.py	4664	0644	edit dl rm
reshape_ops_test.py	8211	0644	edit dl rm
resize_op_test.py	9417	0644	edit dl rm
rmac_regions_op_test.py	3178	0644	edit dl rm
rms_norm_op_test.py	1325	0644	edit dl rm
rnn_cell_test.py	59707	0644	edit dl rm
roi_align_rotated_op_test.py	7567	0644	edit dl rm
rowwise_counter_test.py	2205	0644	edit dl rm
scale_op_test.py	2177	0644	edit dl rm
segment_ops_test.py	25745	0644	edit dl rm
self_binning_histogram_test.py	12915	0644	edit dl rm
selu_op_test.py	3232	0644	edit dl rm
sequence_ops_test.py	16000	0644	edit dl rm
shape_inference_test.py	25708	0644	edit dl rm
sinusoid_position_encoding_op_test.py	2308	0644	edit dl rm
softmax_ops_test.py	23685	0644	edit dl rm
softplus_op_test.py	516	0644	edit dl rm
sparse_dropout_with_replacement_op_test.py	2885	0644	edit dl rm
sparse_gradient_checker_test.py	1294	0644	edit dl rm
sparse_itemwise_dropout_with_replacement_op_test.py	2913	0644	edit dl rm
sparse_lengths_sum_benchmark.py	4159	0644	edit dl rm
sparse_lp_regularizer_test.py	2553	0644	edit dl rm
sparse_normalize_test.py	3136	0644	edit dl rm
sparse_ops_test.py	3469	0644	edit dl rm
sparse_to_dense_mask_op_test.py	3693	0644	edit dl rm
spatial_bn_op_test.py	20182	0644	edit dl rm
specialized_segment_ops_test.py	11775	0644	edit dl rm
split_op_cost_test.py	8645	0644	edit dl rm
square_root_divide_op_test.py	2179	0644	edit dl rm
stats_ops_test.py	1789	0644	edit dl rm
stats_put_ops_test.py	6596	0644	edit dl rm
storm_test.py	6507	0644	edit dl rm
string_ops_test.py	4154	0644	edit dl rm
text_file_reader_test.py	2517	0644	edit dl rm
thresholded_relu_op_test.py	2323	0644	edit dl rm
tile_op_test.py	3887	0644	edit dl rm
top_k_test.py	9113	0644	edit dl rm
torch_integration_test.py	39941	0644	edit dl rm
transpose_op_test.py	2722	0644	edit dl rm
trigonometric_op_test.py	1715	0644	edit dl rm
unique_ops_test.py	2255	0644	edit dl rm
unique_uniform_fill_op_test.py	1335	0644	edit dl rm
unsafe_coalesce_test.py	2940	0644	edit dl rm
upsample_op_test.py	7308	0644	edit dl rm
utility_ops_test.py	15054	0644	edit dl rm
video_input_op_test.py	10503	0644	edit dl rm
weighted_multi_sample_test.py	1997	0644	edit dl rm
weighted_sample_test.py	2739	0644	edit dl rm
weighted_sum_test.py	3052	0644	edit dl rm
weight_scale_test.py	2057	0644	edit dl rm
wngrad_test.py	8279	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/elementwise_ops_test.py (33354B)

from caffe2.python import core, workspace
from hypothesis import given, assume, settings
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np

import unittest

class TestElementwiseOps(hu.HypothesisTestCase):

@given(X=hu.tensor(dtype=np.float32), **hu.gcs)
    @settings(deadline=10000)
    def test_abs(self, X, gc, dc):
        op = core.CreateOperator(
            "Abs",
            ["X"],
            ["Y"],
        )

def abs_ref(X):
            return [np.absolute(X)]

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=abs_ref,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, [X], [0])
        self.assertGradientChecks(gc, op, [X], 0, [0], ensure_outputs_are_inferred=True)

@given(X=hu.tensor(dtype=np.float32), inplace=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_exp(self, X, inplace, gc, dc):
        op = core.CreateOperator(
            "Exp",
            ["X"],
            ["X"] if inplace else ["Y"],
        )

def exp_ref(X):
            return [np.exp(X)]

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=exp_ref,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, [X], [0])
        self.assertGradientChecks(gc, op, [X], 0, [0], ensure_outputs_are_inferred=True)

@given(n=st.integers(0, 6), m=st.integers(4, 6),
           seed=st.integers(0, 1000), **hu.gcs)
    @settings(deadline=10000)
    def test_log(self, n, m, gc, dc, seed):
        np.random.seed(seed)
        X = np.random.rand(n, m).astype(np.float32) + 1.0

def log_op(X):
            return [np.log(X)]

op = core.CreateOperator(
            "Log",
            ["X"],
            ["Z"]
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=log_op,
            ensure_outputs_are_inferred=True,
        )

self.assertGradientChecks(
            gc, op, [X], 0, [0], stepsize=1e-4, threshold=1e-2,
            ensure_outputs_are_inferred=True)

@given(n=st.integers(0, 10), m=st.integers(4, 6),
           d=st.integers(2, 3), seed=st.integers(0, 1000), **hu.gcs)
    @settings(deadline=10000)
    def test_powt(self, n, m, d, gc, dc, seed):
        np.random.seed(seed)
        X = np.random.rand(n, m, d).astype(np.float32) + 1.0
        Y = np.random.rand(n, m, d).astype(np.float32) + 2.0

def powt_op(X, Y):
            return [np.power(X, Y)]

#two gradients Y*X^(Y-1) and X^Y * ln(X)
        def powt_grad(g_out, outputs, fwd_inputs):
            [X, Y] = fwd_inputs
            Z = outputs[0]
            return ([Y * np.power(X, Y - 1), Z * np.log(X)] * g_out)

op = core.CreateOperator(
            "Pow",
            ["X", "Y"],
            ["Z"]
        )

self.assertReferenceChecks(device_option=gc,
                                   op=op,
                                   inputs=[X, Y],
                                   reference=powt_op,
                                   output_to_grad="Z",
                                   grad_reference=powt_grad,
                                   ensure_outputs_are_inferred=True)

@given(n=st.integers(0, 6), m=st.integers(4, 6),
           seed=st.integers(0, 1000), **hu.gcs)
    @settings(deadline=10000)
    def test_sqr(self, n, m, gc, dc, seed):
        np.random.seed(seed)
        X = np.random.rand(n, m).astype(np.float32)

def sqr_op(X):
            return [np.square(X)]

op = core.CreateOperator(
            "Sqr",
            ["X"],
            ["Z"]
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=sqr_op,
            ensure_outputs_are_inferred=True,
        )

self.assertGradientChecks(
            gc, op, [X], 0, [0], stepsize=1e-4, threshold=1e-2,
            ensure_outputs_are_inferred=True)

@given(
        X=hu.tensor(
            elements=hu.floats(min_value=0.1, max_value=10),
            # allow empty tensor
            min_value=0),
        inplace=st.booleans(),
        **hu.gcs
    )
    @settings(deadline=10000)
    def test_sqrt(self, X, inplace, gc, dc):
        def sqrt_op(X):
            return [np.sqrt(X)]

op = core.CreateOperator(
            "Sqrt",
            ["X"],
            ["X"] if inplace else ["Y"]
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=sqrt_op,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, [X], [0])
        # stepsize need to be smaller than the possible minimum X, so the
        # sqrt is well defined
        self.assertGradientChecks(
            gc, op, [X], 0, [0], stepsize=1e-2, ensure_outputs_are_inferred=True)

@given(X=hu.tensor(dtype=np.float32), inplace=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_softsign(self, X, inplace, gc, dc):
        op = core.CreateOperator(
            "Softsign",
            ["X"],
            ["X"] if inplace else ["Y"],
        )

def softsign_ref(X):
            return [X / (1.0 + np.absolute(X))]

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=softsign_ref,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, [X], [0])
        if not inplace:
            self.assertGradientChecks(
                gc, op, [X], 0, [0],
                ensure_outputs_are_inferred=True,
            )

@given(X=hu.tensor(elements=hu.floats(min_value=0.1, max_value=10.0), dtype=np.float32),
           inplace=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_rsqrt(self, X, inplace, gc, dc):
        op = core.CreateOperator(
            "Rsqrt",
            ["X"],
            ["X"] if inplace else ["Y"],
        )

def rsqrt_ref(X):
            return [1.0 / np.sqrt(X)]

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=rsqrt_ref,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, [X], [0])
        self.assertGradientChecks(
            gc, op, [X], 0, [0], stepsize=5e-3,
            ensure_outputs_are_inferred=True,
        )

@given(X=hu.tensor(dtype=np.float32), **hu.gcs)
    @settings(deadline=10000)
    def test_cube(self, X, gc, dc):
        op = core.CreateOperator(
            "Cube",
            ["X"],
            ["Y"],
        )

def cube_ref(X):
            return [np.power(X, 3)]

def cube_grad_ref(g_out, outputs, fwd_inputs):
            dY = g_out
            [X] = fwd_inputs
            return [dY * np.square(X) * 3]

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=cube_ref,
            output_to_grad="Y",
            grad_reference=cube_grad_ref,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, [X], [0])

@given(X=hu.tensor(dtype=np.float32), in_place=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_cbrt(self, X, in_place, gc, dc):
        op = core.CreateOperator(
            "Cbrt",
            ["X"],
            ["X"] if in_place else ["Y"],
        )

def cbrt_ref(X):
            return [np.cbrt(X)]

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=cbrt_ref,
            ensure_outputs_are_inferred=True,
        )

@given(X=hu.tensor(elements=hu.floats(min_value=1.0, max_value=10.0), dtype=np.float32),
           in_place=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_cbrt_grad(self, X, in_place, gc, dc):
        op = core.CreateOperator(
            "Cbrt",
            ["X"],
            ["X"] if in_place else ["Y"],
        )

self.assertGradientChecks(
            gc, op, [X], 0, [0],
            ensure_outputs_are_inferred=True,
        )
        self.assertGradientChecks(
            gc, op, [-X], 0, [0],
            ensure_outputs_are_inferred=True,
        )

@given(n=st.integers(0, 6), m=st.integers(4, 6),
           seed=st.integers(0, 1000), **hu.gcs)
    @settings(deadline=10000)
    def test_swish(self, n, m, gc, dc, seed):
        np.random.seed(seed)
        X = np.random.rand(n, m).astype(np.float32)

def swish(X):
            return [np.divide(X, (1. + np.exp(-X)))]

op = core.CreateOperator(
            "Swish",
            ["X"],
            ["Z"]
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=swish,
            ensure_outputs_are_inferred=True,
        )

self.assertGradientChecks(
            gc, op, [X], 0, [0], stepsize=1e-4, threshold=1e-2,
            ensure_outputs_are_inferred=True)

@given(n=st.integers(0, 6), m=st.integers(4, 6),
           seed=st.integers(0, 1000), **hu.gcs)
    @settings(deadline=10000)
    def test_swish_gradient_inplace(self, n, m, gc, dc, seed):
        np.random.seed(seed)

def swish(X):
            return [np.divide(X, (1. + np.exp(-X)))]

def swish_gradient(X, Y, dY):
            return [dY * (Y + np.divide(1. - Y, 1. + np.exp(-X)))]

X = np.random.rand(n, m).astype(np.float32)
        Y = swish(X)[0]
        dY = np.random.rand(n, m).astype(np.float32)
        op = core.CreateOperator(
            "SwishGradient",
            ["X", "Y", "grad"],
            "grad"
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X, Y, dY],
            reference=swish_gradient,
        )

@given(n=st.integers(1, 6),
           m=st.integers(4, 6),
           inplace=st.booleans(),
           allow_broadcast_fastpath=st.booleans(),
           seed=st.integers(0, 1000), **hu.gcs)
    @settings(deadline=10000)
    def test_mul_gradient_inplace_or_broadcast(
        self,
        n: int,
        m: int,
        inplace: bool,
        allow_broadcast_fastpath: bool,
        gc,
        dc,
        seed: int,
    ):
        broadcast = not inplace
        np.random.seed(seed)

def mul_gradient(dC, A, B):
            dA = B * dC
            dB = A * dC
            if broadcast:
                dB = np.sum(dB, axis=0)
            return [dA, dB]

A = np.random.rand(n, m).astype(np.float32)
        if broadcast:
            B = np.random.rand(m).astype(np.float32)
        else:
            B = np.random.rand(n, m).astype(np.float32)
        dC = np.random.rand(n, m).astype(np.float32)
        op_dA_inplace = core.CreateOperator(
            "MulGradient",
            ["dC", "A", "B"],
            ["dC" if inplace else "dA", "dB"],
            allow_broadcast_fastpath=allow_broadcast_fastpath,
        )
        op_dB_inplace = core.CreateOperator(
            "MulGradient",
            ["dC", "A", "B"],
            ["dA", "dC" if inplace else "dB"],
            allow_broadcast_fastpath=allow_broadcast_fastpath,
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op_dA_inplace,
            inputs=[dC, A, B],
            reference=mul_gradient,
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op_dB_inplace,
            inputs=[dC, A, B],
            reference=mul_gradient,
        )

@given(n=st.integers(1, 6),
           m=st.integers(4, 6),
           inplace=st.booleans(),
           allow_broadcast_fastpath=st.booleans(),
           seed=st.integers(0, 1000), **hu.gcs)
    @settings(deadline=10000)
    def test_div_gradient_inplace_or_broadcast(
        self,
        n: int,
        m: int,
        inplace: bool,
        allow_broadcast_fastpath: bool,
        gc,
        dc,
        seed: int,
    ):
        broadcast = not inplace
        np.random.seed(seed)

def div_gradient(dC, _A, B, C):
            dA = dC / B
            dB = -dC * C / B
            if broadcast:
                dB = np.sum(dB, axis=0)
            return [dA, dB]

A = np.random.rand(n, m).astype(np.float32)
        if broadcast:
            B = np.random.rand(m).astype(np.float32) + 1.0
        else:
            B = np.random.rand(n, m).astype(np.float32) + 1.0
        C = A / B
        dC = np.random.rand(n, m).astype(np.float32)
        op = core.CreateOperator(
            "DivGradient",
            ["dC", "A", "B", "C"],
            ["dC" if inplace else "dA", "dB"],
            allow_broadcast_fastpath=allow_broadcast_fastpath,
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[dC, A, B, C],
            reference=div_gradient,
        )

@given(n=st.integers(1, 6),
           m=st.integers(4, 6),
           inplace=st.booleans(),
           allow_broadcast_fastpath=st.booleans(),
           seed=st.integers(0, 1000), **hu.gcs)
    @settings(deadline=10000)
    def test_add_gradient_inplace_or_broadcast(
        self,
        n: int,
        m: int,
        inplace: bool,
        allow_broadcast_fastpath: bool,
        gc,
        dc,
        seed: int,
    ):
        broadcast = not inplace
        np.random.seed(seed)

def add_gradient(dC, _A, _B):
            dA, dB = dC, dC
            if broadcast:
                dB = np.sum(dB, axis=0)
            return [dA, dB]

A = np.random.rand(n, m).astype(np.float32)
        if broadcast:
            B = np.random.rand(m).astype(np.float32)
        else:
            B = np.random.rand(n, m).astype(np.float32)
        dC = np.random.rand(n, m).astype(np.float32)
        op_dA_inplace = core.CreateOperator(
            "AddGradient",
            ["dC", "A", "B"],
            ["dC" if inplace else "dA", "dB"],
            allow_broadcast_fastpath=allow_broadcast_fastpath,
        )
        op_dB_inplace = core.CreateOperator(
            "AddGradient",
            ["dC", "A", "B"],
            ["dA", "dC" if inplace else "dB"],
            allow_broadcast_fastpath=allow_broadcast_fastpath,
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op_dA_inplace,
            inputs=[dC, A, B],
            reference=add_gradient,
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op_dB_inplace,
            inputs=[dC, A, B],
            reference=add_gradient,
        )

@given(n=st.integers(1, 6),
           m=st.integers(4, 6),
           inplace=st.booleans(),
           allow_broadcast_fastpath=st.booleans(),
           seed=st.integers(0, 1000), **hu.gcs)
    @settings(deadline=10000)
    def test_sub_gradient_inplace_or_broadcast(
        self,
        n: int,
        m: int,
        inplace: bool,
        allow_broadcast_fastpath: bool,
        gc,
        dc,
        seed: int,
    ):
        broadcast = not inplace
        np.random.seed(seed)

def sub_gradient(dC, _A, _B):
            dA, dB = dC, -dC
            if broadcast:
                dB = np.sum(dB, axis=0)
            return [dA, dB]

A = np.random.rand(n, m).astype(np.float32)
        if broadcast:
            B = np.random.rand(m).astype(np.float32)
        else:
            B = np.random.rand(n, m).astype(np.float32)
        dC = np.random.rand(n, m).astype(np.float32)
        op_dA_inplace = core.CreateOperator(
            "SubGradient",
            ["dC", "A", "B"],
            ["dC" if inplace else "dA", "dB"],
            allow_broadcast_fastpath=allow_broadcast_fastpath,
        )
        op_dB_inplace = core.CreateOperator(
            "SubGradient",
            ["dC", "A", "B"],
            ["dA", "dC" if inplace else "dB"],
            allow_broadcast_fastpath=allow_broadcast_fastpath,
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op_dA_inplace,
            inputs=[dC, A, B],
            reference=sub_gradient,
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op_dB_inplace,
            inputs=[dC, A, B],
            reference=sub_gradient,
        )

@given(X=hu.tensor(dtype=np.float32), inplace=st.booleans(),
           engine=st.sampled_from(["", "CUDNN"]), **hu.gcs)
    @settings(deadline=10000)
    def test_sigmoid(self, X, inplace, engine, gc, dc):
        op = core.CreateOperator(
            "Sigmoid",
            ["X"],
            ["X"] if inplace else ["Y"],
            engine=engine,
        )

def sigmoid_ref(X):
            return [1.0 / (1.0 + np.exp(-X))]

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=sigmoid_ref,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, [X], [0])
        self.assertGradientChecks(gc, op, [X], 0, [0], ensure_outputs_are_inferred=True)

@given(X=hu.tensor(dtype=np.float32), inplace=st.booleans(),
           engine=st.sampled_from(["", "CUDNN"]), **hu.gcs)
    @settings(deadline=10000)
    def test_tanh(self, X, inplace, engine, gc, dc):
        op = core.CreateOperator(
            "Tanh",
            ["X"],
            ["X"] if inplace else ["Y"],
            engine=engine,
        )

def tanh_ref(X):
            return [np.tanh(X)]

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=tanh_ref,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, [X], [0])
        self.assertGradientChecks(gc, op, [X], 0, [0], ensure_outputs_are_inferred=True)

@given(X=hu.tensor(dtype=np.float32),
           inplace=st.booleans(),
           alpha=hu.floats(min_value=-100.0, max_value=100.0),
           beta=hu.floats(min_value=-100.0, max_value=100.0),
           engine=st.sampled_from([""]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_hard_sigmoid(self, X, inplace, alpha, beta, engine, gc, dc):
        # Prevent alpha and beta from mutually being 0 to avoid a division
        # error when adjusting our inputs
        assume(alpha != 0.0 or beta != 0.0)
        op = core.CreateOperator(
            "HardSigmoid",
            ["X"],
            ["X"] if inplace else ["Y"],
            alpha=alpha,
            beta=beta,
            engine=engine,
        )

def hard_sigmoid_ref(X):
            return [np.minimum(1.0, np.maximum(0.0, X * alpha + beta))]

# Adjust inputs to avoid differentitating at inflection points
        if abs(alpha) > 0.001:
            Y = X * alpha + beta
            Y += 0.04 * np.sign(Y)
            Y[Y == 0.0] += 0.1
            Y[Y == 1.0] -= 0.1
            X = (Y - beta) / alpha

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=hard_sigmoid_ref,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, [X], [0])
        self.assertGradientChecks(
            gc, op, [X], 0, [0], stepsize=1e-4, threshold=1e-2,
            ensure_outputs_are_inferred=True)

@given(n=st.integers(0, 6), m=st.integers(4, 6), **hu.gcs)
    @settings(deadline=10000)
    def test_eq(self, n, m, gc, dc):
        # Set broadcast and no axis, i.e. broadcasting last dimensions.
        X = np.random.randint(2, size=(n, m))
        Y = np.random.randint(2, size=(n, m))
        op = core.CreateOperator("EQ", ["X", "Y"], "out", broadcast=1)

def eq(X, Y):
            return [X == Y]

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X, Y],
            reference=eq,
            ensure_outputs_are_inferred=True,
        )

workspace.FeedBlob('X', X)
        workspace.FeedBlob('Y', Y)

net = core.Net("batch_bucket_one_hot_test")
        result = net.EQ(["X", "Y"], 1)
        (shapes, types) = workspace.InferShapesAndTypes([net])
        workspace.RunNetOnce(net)

self.assertEqual(shapes[result], list(workspace.blobs[result].shape))
        self.assertEqual(shapes[result], list(X.shape))
        self.assertEqual(types[result], core.DataType.BOOL)

@given(n=st.integers(0, 6), m=st.integers(4, 6), **hu.gcs)
    @settings(deadline=10000)
    def test_eq_bcast(self, n, m, gc, dc):
        # Set broadcast and no axis, i.e. broadcasting last dimensions.
        X = np.random.randint(2, size=(n, m))
        Y = np.random.randint(2, size=(m,))
        op = core.CreateOperator("EQ", ["X", "Y"], "out", broadcast=1)

def eq(X, Y):
            return [X == Y]

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X, Y],
            reference=eq,
            ensure_outputs_are_inferred=True,
        )

workspace.FeedBlob('X', X)
        workspace.FeedBlob('Y', Y)

net = core.Net("eq_bast")
        result = net.EQ(["X", "Y"], 1, broadcast=1)
        (shapes, types) = workspace.InferShapesAndTypes([net])
        workspace.RunNetOnce(net)
        self.assertTrue(str(result) in shapes)
        self.assertEqual(shapes[result], list(workspace.blobs[result].shape))
        self.assertEqual(shapes[result], list(X.shape))
        self.assertEqual(types[result], core.DataType.BOOL)

net_2 = core.Net("eq_bast_invalid")
        result_2 = net_2.EQ(["X", "Y"], 1)
        (shapes, types) = workspace.InferShapesAndTypes([net])
        self.assertTrue(str(result_2) not in shapes)

def _run_single_test(
            self, op, ref, A, B, reverse_inputs, test_grad, gc, dc):
        inputs = [A, B]
        self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=inputs,
            reference=ref,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, inputs, [0])
        if test_grad:
            for i in range(len(inputs)):
                self.assertGradientChecks(
                    gc, op, inputs, i, [0],
                    ensure_outputs_are_inferred=True,
                )

if reverse_inputs:
            inputs = [B, A]
            self.assertReferenceChecks(
                device_option=gc,
                op=op,
                inputs=inputs,
                reference=ref,
                ensure_outputs_are_inferred=True,
           )
            self.assertDeviceChecks(dc, op, inputs, [0])
            if test_grad:
                for i in range(len(inputs)):
                    self.assertGradientChecks(
                        gc, op, inputs, i, [0],
                        ensure_outputs_are_inferred=True,
                    )

def _test_binary_op(
            self, op_name, np_ref, n, m, k, t, bias, test_grad, gc, dc):
        op = core.CreateOperator(
            op_name,
            ["A", "B"],
            ["C"],
        )

def ref(A, B):
            return [np_ref(A, B)]

A = np.random.rand(n, m, k, t).astype(np.float32) + bias
        B = np.random.rand(n, m, k, t).astype(np.float32) + bias
        self._run_single_test(op, ref, A, B, True, test_grad, gc, dc)

A = np.random.rand(1).astype(np.float32) + bias
        B = np.random.rand(n, m, k, t).astype(np.float32) + bias
        self._run_single_test(op, ref, A, B, True, test_grad, gc, dc)

A = np.random.rand(k, t).astype(np.float32) + bias
        B = np.random.rand(n, m, k, t).astype(np.float32) + bias
        self._run_single_test(op, ref, A, B, True, test_grad, gc, dc)

A = np.random.rand(n, m, 1, 1).astype(np.float32) + bias
        B = np.random.rand(n, m, k, t).astype(np.float32) + bias
        self._run_single_test(op, ref, A, B, True, test_grad, gc, dc)

A = np.random.rand(1, m, k, 1).astype(np.float32) + bias
        B = np.random.rand(n, m, k, t).astype(np.float32) + bias
        self._run_single_test(op, ref, A, B, True, test_grad, gc, dc)

A = np.random.rand(m, 1, t).astype(np.float32) + bias
        B = np.random.rand(n, m, k, t).astype(np.float32) + bias
        self._run_single_test(op, ref, A, B, True, test_grad, gc, dc)

A = np.random.rand(1, m, 1, t).astype(np.float32) + bias
        B = np.random.rand(n, 1, k, 1).astype(np.float32) + bias
        self._run_single_test(op, ref, A, B, True, test_grad, gc, dc)

def _test_binary_op_in_place(
            self, op_name, np_ref, n, m, bias, test_grad, in_place_2nd, gc, dc):
        def ref(A, B):
            return [np_ref(A, B)]

op = core.CreateOperator(
            op_name,
            ["A", "B"],
            ["A"],
        )
        A = np.random.rand(n, m).astype(np.float32) + bias
        B = np.random.rand(m).astype(np.float32) + bias

self._run_single_test(op, ref, A, B, False, test_grad, gc, dc)
        A = np.random.rand(n, m).astype(np.float32) + bias
        B = np.random.rand(n, 1).astype(np.float32) + bias
        self._run_single_test(op, ref, A, B, False, test_grad, gc, dc)

if in_place_2nd:
            op = core.CreateOperator(
                op_name,
                ["A", "B"],
                ["B"],
            )
            A = np.random.rand(m).astype(np.float32) + bias
            B = np.random.rand(n, m).astype(np.float32) + bias
            self._run_single_test(op, ref, A, B, False, test_grad, gc, dc)
            A = np.random.rand(n, 1).astype(np.float32) + bias
            B = np.random.rand(n, m).astype(np.float32) + bias
            self._run_single_test(op, ref, A, B, False, test_grad, gc, dc)

@given(n=st.integers(0, 5), m=st.integers(0, 5), k=st.integers(0, 5),
           t=st.integers(0, 5), **hu.gcs)
    @settings(deadline=None, max_examples=50)
    def test_add(self, n, m, k, t, gc, dc):
        self._test_binary_op("Add", np.add, n, m, k, t, -0.5, True, gc, dc)
        self._test_binary_op_in_place(
            "Add", np.add, n, m, -0.5, True, True, gc, dc)

@given(n=st.integers(0, 5), m=st.integers(0, 5), k=st.integers(0, 5),
           t=st.integers(0, 5), **hu.gcs)
    @settings(deadline=None, max_examples=50)
    def test_sub(self, n, m, k, t, gc, dc):
        self._test_binary_op("Sub", np.subtract, n, m,
                             k, t, -0.5, True, gc, dc)
        self._test_binary_op_in_place(
            "Sub", np.subtract, n, m, -0.5, True, True, gc, dc)

@given(n=st.integers(0, 5), m=st.integers(0, 5), k=st.integers(0, 5),
           t=st.integers(0, 5), **hu.gcs)
    @settings(deadline=None, max_examples=50)
    def test_mul(self, n, m, k, t, gc, dc):
        self._test_binary_op("Mul", np.multiply, n, m,
                             k, t, -0.5, True, gc, dc)

@given(n=st.integers(0, 5), m=st.integers(0, 5), k=st.integers(0, 5),
           t=st.integers(0, 5), **hu.gcs)
    @settings(deadline=None, max_examples=50)
    def test_div(self, n, m, k, t, gc, dc):
        self._test_binary_op("Div", np.divide, n, m, k, t, 1.0, True, gc, dc)
        self._test_binary_op_in_place(
            "Div", np.divide, n, m, 1.0, True, False, gc, dc)

@given(n=st.integers(1, 5), m=st.integers(1, 5), broadcast=st.booleans(),
           allow_broadcast_fastpath=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_div_legacy_grad(
        self,
        n: int,
        m: int,
        broadcast: bool,
        allow_broadcast_fastpath: bool,
        gc,
        dc
    ):
        op = core.CreateOperator(
            "DivGradient",
            ["B", "C", "dC"],
            ["dA", "dB"],
            allow_broadcast_fastpath=allow_broadcast_fastpath,
        )

def div_grad_ref(B, C, dC):
            dA = dC / B
            dB = -dC * C / B
            if broadcast:
                dB = np.sum(dB, axis=0)
            return [dA, dB]

if broadcast:
            B = np.random.rand(m).astype(np.float32) + 1.0
        else:
            B = np.random.rand(n, m).astype(np.float32) + 1.0
        C = np.random.randn(n, m).astype(np.float32)
        dC = np.random.randn(n, m).astype(np.float32)
        inputs = [B, C, dC]
        self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=inputs,
            reference=div_grad_ref,
        )
        self.assertDeviceChecks(dc, op, inputs, [0, 1])

def _test_bitwise_binary_op(self, op_name, np_ref, n, m, k, t, gc, dc):
        op = core.CreateOperator(
            op_name,
            ["A", "B"],
            ["C"],
        )

def ref(A, B):
            return [np_ref(A, B)]

A = np.random.randint(128, size=(n, m, k, t))
        B = np.random.randint(128, size=(n, m, k, t))
        self._run_single_test(op, ref, A, B, True, False, gc, dc)

A = np.random.randint(128, size=1)
        B = np.random.randint(128, size=(n, m, k, t))
        self._run_single_test(op, ref, A, B, True, False, gc, dc)

A = np.random.randint(128, size=(k, t))
        B = np.random.randint(128, size=(n, m, k, t))
        self._run_single_test(op, ref, A, B, True, False, gc, dc)

A = np.random.randint(128, size=(n, m, 1, 1))
        B = np.random.randint(128, size=(n, m, k, t))
        self._run_single_test(op, ref, A, B, True, False, gc, dc)

A = np.random.randint(128, size=(1, m, k, 1))
        B = np.random.randint(128, size=(n, m, k, t))
        self._run_single_test(op, ref, A, B, True, False, gc, dc)

A = np.random.randint(128, size=(m, 1, t))
        B = np.random.randint(128, size=(n, m, k, t))
        self._run_single_test(op, ref, A, B, True, False, gc, dc)

A = np.random.randint(128, size=(1, m, 1, t))
        B = np.random.randint(128, size=(n, 1, k, 1))
        self._run_single_test(op, ref, A, B, True, False, gc, dc)

@given(n=st.integers(1, 5), m=st.integers(1, 5), k=st.integers(1, 5),
           t=st.integers(1, 5), **hu.gcs)
    @settings(deadline=10000)
    def test_bitwise_and(self, n, m, k, t, gc, dc):
        self._test_bitwise_binary_op(
            "BitwiseAnd", np.bitwise_and, n, m, k, t, gc, dc)

@given(n=st.integers(1, 5), m=st.integers(1, 5), k=st.integers(1, 5),
           t=st.integers(1, 5), **hu.gcs)
    @settings(deadline=10000)
    def test_bitwise_or(self, n, m, k, t, gc, dc):
        self._test_bitwise_binary_op(
            "BitwiseOr", np.bitwise_or, n, m, k, t, gc, dc)

@given(n=st.integers(1, 5), m=st.integers(1, 5), k=st.integers(1, 5),
           t=st.integers(1, 5), **hu.gcs)
    @settings(deadline=10000)
    def test_bitwise_xor(self, n, m, k, t, gc, dc):
        self._test_bitwise_binary_op(
            "BitwiseXor", np.bitwise_xor, n, m, k, t, gc, dc)

@given(X=hu.tensor(elements=hu.floats(min_value=0.5, max_value=2), dtype=np.float32),
           inplace=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_reciprocal(self, X, inplace, gc, dc):
        def reciprocal_op(X):
            return [np.reciprocal(X)]

op = core.CreateOperator(
            "Reciprocal",
            ["X"],
            ["X"] if inplace else ["Y"]
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=reciprocal_op,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, [X], [0])
        self.assertGradientChecks(
            gc, op, [X], 0, [0], stepsize=1e-3, threshold=0.05,
            ensure_outputs_are_inferred=True)

@given(X=hu.tensor(dtype=np.bool), **hu.gcs)
    @settings(deadline=10000)
    def test_not(self, X, gc, dc):
        def not_op(X):
            return [np.logical_not(X)]

op = core.CreateOperator(
            "Not",
            ["X"],
            ["Y"],
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=not_op,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, [X], [0])

@given(X=hu.tensor(dtype=np.float32), **hu.gcs)
    @settings(deadline=10000)
    def test_log1p(self, X, gc, dc):
        op = core.CreateOperator(
            "Log1p",
            ["X"],
            ["Y"]
        )

def ref_log1p(input):
            result = np.log1p(input)
            return (result,)

def ref_log1p_grad(g_out, outputs, fwd_inputs):
            result = g_out / (fwd_inputs[0] + 1)
            return (result,)

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X],
            reference=ref_log1p,
            output_to_grad="Y",
            grad_reference=ref_log1p_grad,
            ensure_outputs_are_inferred=True,
        )
        self.assertDeviceChecks(dc, op, [X], [0])

if __name__ == "__main__":
    unittest.main()

cmd: