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/matmul_op_test.py (10096B)

import inspect

import numpy as np

from hypothesis import assume, given, settings
import hypothesis.strategies as st
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial

class TestMatMul(serial.SerializedTestCase):
    @serial.given(
        M=st.integers(min_value=1, max_value=10),
        K=st.integers(min_value=1, max_value=10),
        N=st.integers(min_value=1, max_value=10),
        trans_a=st.booleans(),
        trans_b=st.booleans(),
        **hu.gcs
    )
    def test_matmul(self, M, K, N, trans_a, trans_b, gc, dc):
        X = np.random.rand(M, K).astype(np.float32) - 0.5
        if trans_a:
            X = X.transpose()

Y = np.random.rand(K, N).astype(np.float32) - 0.5
        if trans_b:
            Y = Y.transpose()

op = core.CreateOperator(
            'MatMul', ['X', 'Y'], 'out', trans_a=trans_a, trans_b=trans_b
        )

def matmul_ref(X, Y, trans_a, trans_b):
            XX = X.transpose() if trans_a else X
            YY = Y.transpose() if trans_b else Y
            return (XX.dot(YY), )

# Check against numpy reference
        self.assertReferenceChecks(gc, op, [X, Y, trans_a, trans_b], matmul_ref)
        # Check over multiple devices
        self.assertDeviceChecks(dc, op, [X, Y], [0])
        # Gradient check wrt X
        self.assertGradientChecks(gc, op, [X, Y], 0, [0])
        # Gradient check wrt Y
        self.assertGradientChecks(gc, op, [X, Y], 1, [0])

@given(
        M=st.integers(min_value=1, max_value=10),
        K=st.integers(min_value=1, max_value=10),
        N=st.integers(min_value=1, max_value=10),
        axis_a=st.sampled_from([-3, -2, -1, 1, 2, 3]),
        axis_b=st.sampled_from([-3, -2, -1, 1, 2, 3]),
        trans_a=st.booleans(),
        trans_b=st.booleans(),
        **hu.gcs
    )
    @settings(deadline=10000)
    def test_matmul_axis(
        self, M, K, N, axis_a, axis_b, trans_a, trans_b, gc, dc
    ):
        X = np.random.rand(M, K).astype(np.float32) - 0.5
        if trans_a:
            X = X.transpose()
        shape_x = [X.shape[0], 1, 1, 1]
        shape_x[axis_a] = X.shape[1]
        X = X.reshape(*shape_x)

Y = np.random.rand(K, N).astype(np.float32) - 0.5
        if trans_b:
            Y = Y.transpose()
        shape_y = [Y.shape[0], 1, 1, 1]
        shape_y[axis_b] = Y.shape[1]
        Y = Y.reshape(*shape_y)
        op = core.CreateOperator(
            'MatMul', ['X', 'Y'],
            'out',
            axis_a=axis_a,
            axis_b=axis_b,
            trans_a=trans_a,
            trans_b=trans_b
        )

def size_to_dim(X, axis):
            dim = 1
            for i in range(axis):
                dim *= X.shape[i]
            return dim

def size_from_dim(X, axis):
            dim = 1
            for i in range(axis, X.ndim):
                dim *= X.shape[i]
            return dim

def reshape(X, axis):
            dim_0, dim_1 = size_to_dim(X, axis), size_from_dim(X, axis)
            return X.reshape(dim_0, dim_1)

def canonical_axis(axis, ndim):
            return ndim + axis if axis < 0 else axis

def matmul_ref(X, Y, axis_a, axis_b, trans_a, trans_b):
            can_axis_a = canonical_axis(axis_a, X.ndim)
            can_axis_b = canonical_axis(axis_b, Y.ndim)
            X, Y = reshape(X, can_axis_a), reshape(Y, can_axis_b)
            XX = X.transpose() if trans_a else X
            YY = Y.transpose() if trans_b else Y
            return (XX.dot(YY), )

# Check against numpy reference
        self.assertReferenceChecks(
            gc, op, [X, Y, axis_a, axis_b, trans_a, trans_b], matmul_ref
        )
        # Check over multiple devices
        self.assertDeviceChecks(dc, op, [X, Y], [0])
        # Gradient check wrt X
        self.assertGradientChecks(gc, op, [X, Y], 0, [0])
        # Gradient check wrt Y
        self.assertGradientChecks(gc, op, [X, Y], 1, [0])

class TestBatchMatMul(serial.SerializedTestCase):
    @settings(max_examples=30, deadline=None)
    @given(
        C=st.integers(min_value=0, max_value=3),  # number of batch dims
        M=st.integers(min_value=1, max_value=10),
        K=st.integers(min_value=1, max_value=10),
        N=st.integers(min_value=1, max_value=10),
        trans_a=st.booleans(),
        trans_b=st.booleans(),
        dtype=st.sampled_from([np.float32, np.float16]),
        **hu.gcs
    )
    def test_batch_matmul(self, C, M, K, N, trans_a, trans_b, dtype, gc, dc):
        if dtype == np.float16:
            # fp16 is only supported with CUDA/HIP
            assume(core.IsGPUDeviceType(gc.device_type))
            dc = [d for d in dc if core.IsGPUDeviceType(d.device_type)]

batch_dims = np.random.randint(
            low=1,
            high=3,
            size=C,
            dtype=np.int64).tolist()
        X = np.random.rand(*(batch_dims + [M, K])).astype(dtype) - 0.5
        if trans_a:
            X = X.swapaxes(-1, -2)
        Y = np.random.rand(*(batch_dims + [K, N])).astype(dtype) - 0.5
        if trans_b:
            Y = Y.swapaxes(-1, -2)

op = core.CreateOperator(
            'BatchMatMul', ['X', 'Y'], 'out', trans_a=trans_a, trans_b=trans_b
        )

def matmul_ref(X, Y, trans_a, trans_b, dtype):
            XX = (X.swapaxes(-1, -2) if trans_a else X).astype(np.float32)
            YY = (Y.swapaxes(-1, -2) if trans_b else Y).astype(np.float32)
            return (np.matmul(XX, YY).astype(dtype),)

# relaxing the "threshold" for fp16 to 150x of the default
        def relax_fp16_check(check_func, *args, **kwargs):
            # inspect the default "threshold" value in check_func
            argspec = inspect.getargspec(check_func)
            threshold = argspec.defaults[
                argspec.args.index('threshold') -
                (len(argspec.args) - len(argspec.defaults))]

if dtype == np.float16:
                threshold = 150 * threshold
            check_func(*args, threshold=threshold, **kwargs)

# Check against numpy reference
        relax_fp16_check(self.assertReferenceChecks, gc, op, [X, Y, trans_a, trans_b, dtype], matmul_ref)
        # Check over multiple devices
        relax_fp16_check(self.assertDeviceChecks, dc, op, [X, Y], [0])
        # Gradient check wrt X
        relax_fp16_check(self.assertGradientChecks, gc, op, [X, Y], 0, [0])
        # Gradient check wrt Y
        relax_fp16_check(self.assertGradientChecks, gc, op, [X, Y], 1, [0])

def _test_batch_matmul_with_broadcast_common(
        self,
        X,
        Y,
        dtype,
        gc,
        dc,
        trans_a=None,
        trans_b=None,
    ):
        if trans_a is not None and trans_b is not None:
            op = core.CreateOperator(
                'BatchMatMul', ['X', 'Y'], 'out', trans_a=trans_a, trans_b=trans_b, broadcast=1
            )
        else:
            op = core.CreateOperator(
                'BatchMatMul', ['X', 'Y'], 'out', broadcast=1
            )

# Check against numpy reference
        self.assertReferenceChecks(gc, op, [X, Y, trans_a, trans_b, dtype], matmul_ref)
        # Check over multiple devices
        self.assertDeviceChecks(dc, op, [X, Y], [0])

@given(
        C_1=st.integers(min_value=0, max_value=3),  # number of batch dims
        C_2=st.integers(min_value=0, max_value=3),
        M=st.integers(min_value=1, max_value=10),
        K=st.integers(min_value=1, max_value=10),
        N=st.integers(min_value=1, max_value=10),
        trans_a=st.booleans(),
        trans_b=st.booleans(),
        **hu.gcs
    )
    @settings(deadline=10000)
    def test_numpy_batch_matmul(self, C_1, C_2, M, K, N, trans_a, trans_b, gc, dc):
        dtype = np.float32
        batch_dims = np.random.randint(
            low=0,
            high=3,
            size=max(C_1, C_2),
            dtype=np.int64).tolist()
        lbd = len(batch_dims)
        X = np.random.rand(*(batch_dims[lbd - C_1:] + [M, K])).astype(dtype) - 0.5
        if trans_a:
            X = X.swapaxes(-1, -2)
        Y = np.random.rand(*(batch_dims[lbd - C_2:] + [K, N])).astype(dtype) - 0.5
        if trans_b:
            Y = Y.swapaxes(-1, -2)

self._test_batch_matmul_with_broadcast_common(X, Y, dtype, gc, dc, trans_a, trans_b)

@settings(max_examples=30, deadline=None)
    @given(
        K=st.integers(min_value=1, max_value=10),
        **hu.gcs
    )
    def test_numpy_batch_matmul_1d(self, K, gc, dc):
        dtype = np.float32
        X = np.random.rand(K).astype(dtype) - 0.5
        # TODO: test trans_a and trans_b
        Y = np.random.rand(K).astype(dtype) - 0.5

self._test_batch_matmul_with_broadcast_common(X, Y, dtype, gc, dc)

@settings(max_examples=30, deadline=None)
    @given(
        K=st.integers(min_value=1, max_value=10),
        N=st.integers(min_value=1, max_value=10),
        **hu.gcs
    )
    def test_numpy_batch_matmul_1d_2d(self, K, N, gc, dc):
        dtype = np.float32
        X = np.random.rand(K).astype(dtype) - 0.5
        # TODO: test trans_a and trans_b
        Y = np.random.rand(*[K, N]).astype(dtype) - 0.5

self._test_batch_matmul_with_broadcast_common(X, Y, dtype, gc, dc)

@settings(max_examples=30, deadline=None)
    @given(
        M=st.integers(min_value=1, max_value=10),
        K=st.integers(min_value=1, max_value=10),
        **hu.gcs
    )
    def test_numpy_batch_matmul_2d_1d(self, M, K, gc, dc):
        dtype = np.float32
        X = np.random.rand(*[M, K]).astype(dtype) - 0.5
        # TODO: test trans_a and trans_b
        Y = np.random.rand(K).astype(dtype) - 0.5

self._test_batch_matmul_with_broadcast_common(X, Y, dtype, gc, dc)

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

cmd: