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c2d28fb874
pytorch/caffe2/python/hypothesis_test.py
Yury Zemlyanskiy c2d28fb874 RNNs API simplification 
Summary:
This is a first step in improving our RNN story. It provides a wrapper around current RecurrentNetworkOp implementation which infers most of the redundant parameters and makes API much simpler.

Also in order to support general step nets I added an extra argument to the RecurrentNetworkOp.

Future work:

1. Inferring step net output and internal blobs (scratches) sizes and type
2. Avoid accessing blobs by names in c++ part
3. Remove requirement for inputs / output 1:1 correspondence in the step net
4. Make python API support networks with operators like Sum being on the boarder of the Cell net (currently there is an issue with such networks where gradient blobs which are on the side are not explicitly created).

Differential Revision: D4268503

fbshipit-source-id: f8a66491c2b55daa730caeed7e9f2b3921541b49
2016-12-21 09:29:43 -08:00

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import copy
from functools import reduce
from hypothesis import assume, given, settings
import hypothesis.strategies as st
from functools import partial
import unittest
from caffe2.python import core, workspace, tt_core, dyndep
import caffe2.python.hypothesis_test_util as hu
from caffe2.proto.caffe2_pb2 import TensorProto
dyndep.InitOpsLibrary('@/caffe2/caffe2/fb/optimizers:sgd_simd_ops')
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-x))
@st.composite
def _tensor_and_prefix(draw, dtype, elements, min_dim=1, max_dim=4, **kwargs):
dims_ = draw(
st.lists(hu.dims(**kwargs), min_size=min_dim, max_size=max_dim))
extra_ = draw(
st.lists(hu.dims(**kwargs), min_size=min_dim, max_size=max_dim))
return (draw(hu.arrays(dims_ + extra_, dtype, elements)),
draw(hu.arrays(extra_, dtype, elements)))
_NUMPY_TYPE_TO_ENUM = {
np.float32: core.DataType.FLOAT,
np.int32: core.DataType.INT32,
np.bool: core.DataType.BOOL,
np.uint8: core.DataType.UINT8,
np.int8: core.DataType.INT8,
np.uint16: core.DataType.UINT16,
np.int16: core.DataType.INT16,
np.int64: core.DataType.INT64,
np.float64: core.DataType.DOUBLE,
}
def _dtypes(dtypes=None):
dtypes = dtypes if dtypes else [np.int32, np.int64, np.float32]
return st.sampled_from(dtypes)
def _test_binary(name, ref, filter_=None, gcs=hu.gcs,
test_gradient=False, allow_inplace=False, dtypes=_dtypes):
@given(
inputs=dtypes().flatmap(
lambda dtype: hu.tensors(
n=2, dtype=dtype,
elements=hu.elements_of_type(dtype, filter_=filter_))),
out=st.sampled_from(('Y', 'X1', 'X2') if allow_inplace else ('Y',)),
**gcs)
@settings(max_examples=3, timeout=100)
def test_binary(self, inputs, out, gc, dc):
op = core.CreateOperator(name, ["X1", "X2"], [out])
X1, X2 = inputs
self.assertDeviceChecks(dc, op, [X1, X2], [0])
# We only do gradient check with float32 types.
if test_gradient and X1.dtype == np.float32:
self.assertGradientChecks(gc, op, [X1, X2], 0, [0])
self.assertReferenceChecks(gc, op, [X1, X2], ref)
return test_binary
def _test_binary_broadcast(name, ref, filter_=None,
gcs=hu.gcs, allow_inplace=False, dtypes=_dtypes):
@given(
inputs=dtypes().flatmap(lambda dtype: _tensor_and_prefix(
dtype=dtype,
elements=hu.elements_of_type(dtype, filter_=filter_))),
in_place=(st.booleans() if allow_inplace else st.just(False)),
**gcs)
@settings(max_examples=3, timeout=100)
def test_binary_broadcast(self, inputs, in_place, gc, dc):
op = core.CreateOperator(
name, ["X1", "X2"], ["X1" if in_place else "Y"], broadcast=1)
X1, X2 = inputs
self.assertDeviceChecks(dc, op, [X1, X2], [0])
def cast_ref(x, y):
return (np.array(ref(x, y)[0], dtype=x.dtype), )
# gradient not implemented yet
# self.assertGradientChecks(gc, op, [X1, X2], 0, [0])
self.assertReferenceChecks(gc, op, [X1, X2], cast_ref)
return test_binary_broadcast
class TestOperators(hu.HypothesisTestCase):
def test_comparison_ops(self):
ops = {"LT": lambda x1, x2: [x1 < x2],
"LE": lambda x1, x2: [x1 <= x2],
"GT": lambda x1, x2: [x1 > x2],
"GE": lambda x1, x2: [x1 >= x2]}
for name, ref in ops.items():
_test_binary(name, ref, gcs=hu.gcs_cpu_only)(self)
_test_binary_broadcast(name, ref, gcs=hu.gcs_cpu_only)(self)
@given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs)
def test_sum(self, inputs, in_place, gc, dc):
op = core.CreateOperator("Sum", ["X1", "X2"],
["Y" if not in_place else "X1"])
X1, X2 = inputs
self.assertDeviceChecks(dc, op, [X1, X2], [0])
self.assertGradientChecks(gc, op, [X1, X2], 0, [0])
@given(inputs=hu.tensors(n=2, min_dim=2, max_dim=2), **hu.gcs_cpu_only)
def test_row_mul(self, inputs, gc, dc):
op = core.CreateOperator("RowMul", ["X1", "X2"], ["Y"])
X1, Xtmp = inputs
X2 = Xtmp[:, 0]
def ref(x, y):
ret = np.zeros(shape=x.shape, dtype=x.dtype)
for i in range(y.size):
ret[i, ] = x[i, ] * y[i]
return [ret]
self.assertDeviceChecks(dc, op, [X1, X2], [0])
for i in range(2):
self.assertGradientChecks(gc, op, [X1, X2], i, [0])
self.assertReferenceChecks(gc, op, [X1, X2], ref)
@given(inputs=hu.tensors(n=2), **hu.gcs_cpu_only)
def test_max(self, inputs, gc, dc):
op = core.CreateOperator("Max", ["X1", "X2"], ["Y"])
X1, X2 = inputs
# Make X1 and X2 far from each other, since X1=X2 is not differentiable
# and the step size of gradient checker is 0.05
X1[np.logical_and(X1 >= X2 - 0.05, X1 <= X2)] -= 0.05
X1[np.logical_and(X1 <= X2 + 0.05, X1 >= X2)] += 0.05
self.assertDeviceChecks(dc, op, [X1, X2], [0])
for i in range(2):
self.assertGradientChecks(gc, op, [X1, X2], i, [0])
def elementwise_max(X, Y):
return [np.maximum(X, Y)]
self.assertReferenceChecks(gc, op, [X1, X2], elementwise_max)
def test_add(self):
def ref(x, y):
return (x + y, )
_test_binary("Add", ref, test_gradient=True)(self)
_test_binary_broadcast("Add", ref)(self)
def test_sub(self):
def ref(x, y):
return (x - y, )
# TODO(jiayq): enable gradient test when implemented.
_test_binary("Sub", ref, test_gradient=True)(self)
_test_binary_broadcast("Sub", ref)(self)
def test_mul(self):
def ref(x, y):
return (x * y, )
_test_binary("Mul", ref, test_gradient=True)(self)
_test_binary_broadcast("Mul", ref)(self)
def test_div(self):
def ref(x, y):
return (x / y, )
def non_zero(x):
return abs(x) > 10e-5
def div_dtypes():
return st.sampled_from([np.float32, np.float64])
_test_binary(
"Div", ref, filter_=non_zero, test_gradient=True,
dtypes=div_dtypes, gcs=hu.gcs_cpu_only
)(self)
_test_binary(
"Div", ref, filter_=non_zero, test_gradient=False,
dtypes=div_dtypes
)(self)
_test_binary_broadcast(
"Div", ref, filter_=non_zero, dtypes=div_dtypes)(self)
@given(X=hu.tensor(), in_place=st.booleans(), **hu.gcs)
def test_negative(self, X, in_place, gc, dc):
op = core.CreateOperator("Negative", ["X"],
["Y" if not in_place else "X"])
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(X=hu.tensor(), **hu.gcs)
def test_tanh(self, X, gc, dc):
op = core.CreateOperator("Tanh", "X", "Y")
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(X=hu.tensor(), **hu.gcs)
def test_averaged_loss(self, X, gc, dc):
op = core.CreateOperator("AveragedLoss", ["X"], ["loss"])
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(X=hu.tensor(), inplace=st.booleans(), **hu.gcs)
def test_softsign(self, X, inplace, gc, dc):
op = core.CreateOperator("Softsign", ["X"], ["X" if inplace else "Y"])
def softsign(X):
return (X / (1 + np.abs(X)),)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertReferenceChecks(gc, op, [X], softsign)
if inplace:
with self.assertRaises(Exception):
self.assertGradientChecks(gc, op, [X], 0, [0])
else:
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(
device_options=st.lists(
min_size=2,
max_size=4,
elements=st.sampled_from(hu.expanded_device_options)),
set_seed=st.booleans())
def test_random_seed_behaviour(self, device_options, set_seed):
# Assume we are always operating on CUDA or CPU, since RNG is
# inconsistent between CPU and GPU.
device_options = copy.deepcopy(device_options)
assume(len({do.device_type for do in device_options}) == 1)
if set_seed:
for do in device_options:
do.random_seed = 1000
def run(do):
op = core.CreateOperator(
"XavierFill", [], ["Y"],
device_option=do,
shape=[2])
self.ws.run(op)
return self.ws.blobs["Y"].fetch()
ys = [run(do) for do in device_options]
for y in ys[1:]:
if set_seed:
np.testing.assert_array_equal(ys[0], y)
else:
with self.assertRaises(AssertionError):
np.testing.assert_array_equal(ys[0], y)
@given(axis=st.integers(min_value=1, max_value=4),
num_output=st.integers(min_value=4, max_value=8),
engine=st.sampled_from(["", "PACKED"]),
**hu.gcs)
def test_fully_connected_axis(self, axis, num_output, engine, gc, dc):
np.random.seed(1)
X = np.random.randn(1, 2, 3, 2, 1).astype(np.float32)
def prod(xs):
p = 1
for x in xs:
p *= x
return p
K = prod(list(X.shape)[axis:])
N = num_output
W = np.random.randn(N, K).astype(np.float32)
b = np.random.randn(N).astype(np.float32)
op = core.CreateOperator(
"FC",
["X", "W", "b"],
["Y"],
engine=engine,
axis=axis)
for name, param in [("X", X), ("W", W), ("b", b)]:
self.ws.create_blob(name).feed(param)
self.ws.run(op)
Y = self.ws.blobs["Y"].fetch()
self.assertEqual(list(Y.shape), list(X.shape)[:axis] + [N])
inputs = [X, W, b]
self.assertDeviceChecks(dc, op, inputs, [0])
for param, _ in enumerate(inputs):
self.assertGradientChecks(gc, op, inputs, param, [0])
@unittest.skipIf(not workspace.has_gpu_support,
"Skipping test due to no gpu present.")
@given(hidden_size=st.integers(min_value=1, max_value=3),
num_layers=st.integers(min_value=1, max_value=3),
bidirectional=st.booleans(),
rnn_mode=st.sampled_from(["gru", "lstm"]),
input_mode=st.sampled_from(["linear"]),
dropout=st.floats(min_value=0.0, max_value=0.0),
T=st.integers(min_value=1, max_value=4),
N=st.integers(min_value=1, max_value=4),
D=st.integers(min_value=1, max_value=4))
def test_recurrent(self, hidden_size, num_layers, bidirectional, rnn_mode,
input_mode, dropout, T, N, D):
init_op = core.CreateOperator(
"RecurrentInit",
["INPUT"],
["WEIGHT", "DROPOUT_STATES"],
hidden_size=hidden_size,
bidirectional=bidirectional,
rnn_mode=rnn_mode,
dropout=dropout,
input_mode=input_mode,
num_layers=num_layers,
device_option=hu.gpu_do,
engine="CUDNN")
op = core.CreateOperator(
"Recurrent",
["INPUT", "HIDDEN_INPUT", "CELL_INPUT", "WEIGHT"],
["OUTPUT", "HIDDEN_OUTPUT", "CELL_OUTPUT",
"RNN_SCRATCH", "DROPOUT_STATES"],
hidden_size=hidden_size,
bidirectional=bidirectional,
rnn_mode=rnn_mode,
dropout=dropout,
input_mode=input_mode,
num_layers=num_layers,
engine="CUDNN")
num_directions = 2 if bidirectional else 1
X = np.random.randn(T, N, D).astype(np.float32)
self.ws.create_blob("INPUT").feed(X, device_option=hu.gpu_do)
self.ws.run(init_op)
W = self.ws.blobs["WEIGHT"].fetch()
H = np.random.randn(
hidden_size, N, num_layers * num_directions).astype(
np.float32)
C = np.random.randn(
hidden_size, N, num_layers * num_directions).astype(
np.float32) if rnn_mode == "lstm" else \
np.empty((1,)).astype(np.float32) # unused in GRU
inputs = [X, H, C, W]
input_idxs = [i for (i, _) in enumerate(inputs)] \
if rnn_mode == "lstm" else [0, 1, 3] # ignore C
for input_idx in input_idxs:
self.assertGradientChecks(
hu.gpu_do, op, inputs, input_idx, [0, 1, 2])
@given(ndim=st.integers(1, 4),
axis=st.integers(0, 3),
num_inputs=st.integers(2, 4), **hu.gcs)
def test_depth_concat(self, ndim, axis, num_inputs, gc, dc):
assume(axis < ndim)
input_names = ['X0', 'X1', 'X2', 'X3'][:num_inputs]
shape = [2, 3, 5, 7][:ndim]
individual_dims = [1, 2, 3, 4, 5][:num_inputs]
inputs = []
for i in range(num_inputs):
# Sets a unique dim and create the input.
shape[axis] = individual_dims[i]
inputs.append(np.random.randn(*shape).astype(np.float32))
op = core.CreateOperator("Concat", input_names, ["Y", "Y_dims"],
axis=axis)
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(num_inputs):
self.assertGradientChecks(gc, op, inputs, i, [0])
@given(num_inputs=st.integers(2, 4),
order=st.sampled_from([("NCHW", 1), ("NHWC", 3)]),
**hu.gcs)
def test_depth_concat_with_order(self, num_inputs, order, gc, dc):
input_names = ['X0', 'X1', 'X2', 'X3'][:num_inputs]
shape = [2, 3, 5, 7]
individual_dims = [1, 2, 3, 4][:num_inputs]
inputs = []
for i in range(num_inputs):
# Sets a unique dim and create the input.
shape[order[1]] = individual_dims[i]
inputs.append(np.random.rand(*shape).astype(np.float32))
op = core.CreateOperator("Concat", input_names, ["Y", "Y_dims"],
order=order[0])
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(num_inputs):
self.assertGradientChecks(gc, op, inputs, i, [0])
@given(X=hu.arrays(dims=[5, 2],
elements=st.floats(min_value=0.0, max_value=10.0)),
**hu.gcs_cpu_only)
def test_last_n_windows(self, X, gc, dc):
workspace.FeedBlob('input', X)
collect_net = core.Net('collect_net')
collect_net.LastNWindowCollector(
['input'],
['output'],
num_to_collect=7,
)
plan = core.Plan('collect_data')
plan.AddStep(core.execution_step('collect_data',
[collect_net], num_iter=2))
workspace.RunPlan(plan)
output = workspace.FetchBlob('output')
inputs = workspace.FetchBlob('input')
new_output = np.zeros([7, inputs.shape[1]])
for i in range(inputs.shape[0] * 2):
new_output[i % 7] = inputs[i % inputs.shape[0]]
import numpy.testing as npt
npt.assert_almost_equal(output, new_output, decimal=5)
@given(batch_size=st.integers(1, 3),
stride=st.integers(1, 3),
pad=st.integers(0, 3),
kernel=st.integers(1, 5),
dilation=st.integers(1, 3),
size=st.integers(7, 10),
channels=st.integers(1, 8),
**hu.gcs)
def test_im2col_layout(self, batch_size, stride, pad, kernel, dilation,
size, channels, gc, dc):
dkernel = (dilation * (kernel - 1) + 1)
assume(size >= dkernel)
NCHW_TO_NHWC = (0, 2, 3, 1)
NHWC_TO_NCHW = (0, 3, 1, 2)
COL_NHWC_TO_NCHW = (4, 2, 3, 0, 1)
N = batch_size
C = channels
H = size
W = size
out_h = int((H + (2 * pad) - dkernel) / stride + 1)
out_w = int((W + (2 * pad) - dkernel) / stride + 1)
im_nchw = np.random.rand(N, C, H, W).astype(np.float32) - 0.5
im_nhwc = im_nchw.transpose(NCHW_TO_NHWC)
op_im2col_nchw = core.CreateOperator(
"Im2Col",
["im_nchw"], ["col_nchw"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order="NCHW",
device_option=gc)
op_im2col_nhwc = core.CreateOperator(
"Im2Col",
["im_nhwc"], ["col_nhwc"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order="NHWC",
device_option=gc)
self.ws.create_blob("im_nchw").feed(im_nchw, device_option=gc)
self.ws.create_blob("im_nhwc").feed(im_nhwc, device_option=gc)
self.ws.run(op_im2col_nchw)
self.ws.run(op_im2col_nhwc)
# there is probably a clever way to spell this in np
col_nchw = self.ws.blobs["col_nchw"].fetch()
col_nhwc = self.ws.blobs["col_nhwc"].fetch()
col_nchw_ = col_nchw.reshape(N, C, kernel, kernel, out_h, out_w)
col_nhwc_ = col_nhwc.reshape(N, out_h, out_w, kernel, kernel, C)
for i in range(0, N):
np.testing.assert_allclose(
col_nchw_[i],
col_nhwc_[i].transpose(COL_NHWC_TO_NCHW),
atol=1e-4,
rtol=1e-4)
op_col2im_nchw = core.CreateOperator(
"Col2Im",
["col_nchw", "im_nchw"],
["out_nchw"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order="NCHW",
device_option=gc)
op_col2im_nhwc = core.CreateOperator(
"Col2Im",
["col_nhwc", "im_nhwc"],
["out_nhwc"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order="NHWC",
device_option=gc)
self.ws.run(op_col2im_nchw)
self.ws.run(op_col2im_nhwc)
out_nchw = self.ws.blobs["out_nchw"].fetch()
out_nhwc = self.ws.blobs["out_nhwc"].fetch()
np.testing.assert_allclose(
out_nchw,
out_nhwc.transpose(NHWC_TO_NCHW),
atol=1e-4,
rtol=1e-4)
@given(dtype=st.sampled_from([np.float32, np.float64, np.int32, np.bool]))
def test_print(self, dtype):
data = np.random.permutation(6).astype(dtype)
self.ws.create_blob("data").feed(data)
op = core.CreateOperator("Print", "data", [])
self.ws.run(op)
@given(inputs=hu.tensors(n=2),
in_place=st.booleans(),
momentum=st.floats(min_value=0.1, max_value=0.9),
nesterov=st.booleans(),
lr=st.floats(min_value=0.1, max_value=0.9),
**hu.gcs)
def test_momentum_sgd(
self, inputs, in_place, momentum, nesterov, lr, gc, dc):
grad, m = inputs
lr = np.asarray([lr], dtype=np.float32)
op = core.CreateOperator(
"MomentumSGD",
["grad", "m", "lr"],
["grad" if in_place else "grad_o",
"m" if in_place else "m_o"],
momentum=momentum,
nesterov=int(nesterov),
device_option=gc)
self.assertDeviceChecks(
dc, op, [grad, m, lr], [0])
# Reference
def momentum_sgd(grad, m, lr):
lr = lr[0]
if not nesterov:
adjusted_gradient = lr * grad + momentum * m
return (adjusted_gradient, adjusted_gradient)
else:
m_new = momentum * m + lr * grad
return ((1 + momentum) * m_new - momentum * m, m_new)
self.assertReferenceChecks(gc, op, [grad, m, lr], momentum_sgd)
@given(inputs=hu.tensors(n=3),
in_place=st.booleans(),
decay=st.floats(min_value=0.1, max_value=0.9),
momentum=st.floats(min_value=0.1, max_value=0.9),
lr=st.floats(min_value=0.1, max_value=0.9),
epsilon=st.floats(min_value=1e-5, max_value=1e-2),
**hu.gcs)
def test_rmsprop_sgd(self, inputs, in_place, decay, momentum, lr, epsilon,
gc, dc):
grad, ms, mom = inputs
ms = np.abs(ms) + 0.01
lr = np.asarray([lr], dtype=np.float32)
op = core.CreateOperator(
"RmsProp",
["grad", "ms", "mom", "lr"],
["grad" if in_place else "grad_o",
"ms" if in_place else "ms_o",
"mom" if in_place else "mom_o"],
momentum=momentum, decay=decay, epsilon=epsilon, device_option=gc)
self.assertDeviceChecks(dc, op, [grad, ms, mom, lr], [0])
def rmsprop(grad, ms, mom, lr):
lr = lr[0]
ms_o = ms + (1. - decay) * (np.square(grad) - ms)
mom_o = momentum * mom + lr * grad / np.sqrt(epsilon + ms_o)
grad_o = mom_o
return (grad_o, ms_o, mom_o)
self.assertReferenceChecks(gc, op, [grad, ms, mom, lr], rmsprop)
# Reference
@staticmethod
def _dense_adagrad(epsilon, w, h, grad, lr):
lr = lr[0]
h_o = h + np.square(grad)
grad_o = lr * grad / (np.sqrt(h_o) + epsilon)
w_o = w + grad_o
return (w_o, h_o)
# Reference
@staticmethod
def _dense_adam(epsilon, beta1, beta2, w, m1, m2, grad, lr, iters):
lr = lr[0]
iters = iters[0]
t = iters + 1
corrected_local_rate = lr * np.sqrt(1. - np.power(beta2, t)) / \
(1. - np.power(beta1, t))
m1_o = (beta1 * m1) + (1. - beta1) * grad
m2_o = (beta2 * m2) + (1. - beta2) * np.square(grad)
grad_o = corrected_local_rate * m1_o / \
(np.sqrt(m2_o) + epsilon)
w_o = w + grad_o
return (w_o, m1_o, m2_o)
@given(inputs=hu.tensors(n=3),
in_place=st.booleans(),
lr=st.floats(min_value=0.1, max_value=0.9),
epsilon=st.floats(min_value=1e-5, max_value=1e-2),
engine=st.sampled_from([None, "SIMD"]),
**hu.gcs_cpu_only)
def test_adagrad_sgd(self, inputs, in_place, lr, epsilon, engine,
gc, dc):
w, grad, h = inputs
h = np.abs(h) + 0.01
lr = np.asarray([lr], dtype=np.float32)
op = core.CreateOperator(
"Adagrad",
["w", "h", "grad", "lr"],
["w" if in_place else "grad_o",
"h" if in_place else "h_o"],
epsilon=epsilon, engine=engine, device_option=gc)
self.assertDeviceChecks(dc, op, [w, h, grad, lr], [0])
self.assertReferenceChecks(gc, op, [w, h, grad, lr],
partial(self._dense_adagrad, epsilon))
@given(inputs=hu.tensors(n=3),
lr=st.floats(min_value=0.1, max_value=0.9),
epsilon=st.floats(min_value=1e-5, max_value=1e-2),
engine=st.sampled_from([None, "SIMD"]),
**hu.gcs_cpu_only)
def test_sparse_adagrad_sgd(self, inputs, lr, epsilon,
engine, gc, dc):
w, grad, h = inputs
indices = np.arange(h.shape[0])
indices = indices[indices % 2 == 0]
grad = grad[indices]
h = np.abs(h)
lr = np.asarray([lr], dtype=np.float32)
op = core.CreateOperator(
"SparseAdagrad",
["param", "h", "indices", "grad", "lr"],
["param", "h"],
epsilon=epsilon,
engine=engine,
device_option=gc)
self.assertDeviceChecks(
dc, op, [w, h, indices, grad, lr], [0])
def adagrad(param, h, i, grad, lr):
sw, sh = self._dense_adagrad(epsilon, param[i], h[i], grad, lr)
h[i] = sh
param[i] = sw
return (param, h)
self.assertReferenceChecks(gc, op, [w, h, indices, grad, lr], adagrad)
@given(inputs=hu.tensors(n=4),
in_place=st.booleans(),
beta1=st.floats(min_value=0.1, max_value=0.9),
beta2=st.floats(min_value=0.1, max_value=0.9),
lr=st.floats(min_value=0.1, max_value=0.9),
iters=st.integers(min_value=1, max_value=10000),
epsilon=st.floats(min_value=1e-5, max_value=1e-2),
**hu.gcs_cpu_only)
def test_adam_sgd(self, inputs, in_place, beta1, beta2, lr, iters, epsilon,
gc, dc):
w, grad, m1, m2 = inputs
m2 += np.abs(m2) + 0.01
lr = np.asarray([lr], dtype=np.float32)
iters = np.asarray([iters], dtype=np.int64)
op = core.CreateOperator(
"Adam",
["w", "m1", "m2", "grad", "lr", "iters"],
["w" if in_place else "w_o",
"m1" if in_place else "m1_o",
"m2" if in_place else "m2_o"],
beta1=beta1, beta2=beta2, epsilon=epsilon,
device_option=gc)
input_device_options = {"iters": hu.cpu_do}
inputs = [w, m1, m2, grad, lr, iters]
self.assertDeviceChecks(
dc, op, inputs, [0], input_device_options=input_device_options)
self.assertReferenceChecks(gc, op, inputs, partial(self._dense_adam,
epsilon, beta1, beta2),
input_device_options=input_device_options)
@given(inputs=hu.tensors(n=4),
beta1=st.floats(min_value=0.1, max_value=0.9),
beta2=st.floats(min_value=0.1, max_value=0.9),
lr=st.floats(min_value=0.1, max_value=0.9),
iters=st.integers(min_value=1, max_value=10000),
epsilon=st.floats(min_value=1e-5, max_value=1e-2),
**hu.gcs_cpu_only)
def test_sparse_adam_sgd(self, inputs, beta1, beta2, lr, iters,
epsilon, gc, dc):
w, grad, m1, m2 = inputs
indices = np.arange(m1.shape[0])
indices = indices[indices % 2 == 0]
grad = grad[indices]
m2 += np.abs(m2) + 0.01
lr = np.asarray([lr], dtype=np.float32)
iters = np.asarray([iters], dtype=np.int64)
op = core.CreateOperator(
"SparseAdam",
["w", "m1", "m2", "indices", "grad", "lr", "iters"],
["w", "m1", "m2"],
beta1=beta1, beta2=beta2, epsilon=epsilon,
device_option=gc)
input_device_options = {"iters": hu.cpu_do}
inputs = [w, m1, m2, indices, grad, lr, iters]
self.assertDeviceChecks(
dc, op, inputs, [0], input_device_options=input_device_options)
def adam(w, m1, m2, i, grad, lr, iters):
nw, nm1, nm2 = self._dense_adam(epsilon, beta1, beta2, w[i],
m1[i], m2[i], grad, lr, iters)
w[i] = nw
m1[i] = nm1
m2[i] = nm2
return (w, m1, m2)
self.assertReferenceChecks(gc, op, inputs, adam)
# Reference
@staticmethod
def _dense_ftrl(alpha, beta, lambda1, lambda2, w, nz, g):
n = np.take(nz, 0, axis=-1)
z = np.take(nz, 1, axis=-1)
# python port of Sigrid's implementation
g2 = g * g
sigma = (np.sqrt(n + g2) - np.sqrt(n)) / alpha
z += g - sigma * w
n += g2
w = (np.sign(z) * lambda1 - z) / (
(beta + np.sqrt(n)) / alpha + lambda2)
w[np.abs(z) <= lambda1] = 0
return (w, np.stack([n, z], axis=-1))
@given(inputs=hu.tensors(n=4),
in_place=st.booleans(),
alpha=st.floats(min_value=0.01, max_value=0.1),
beta=st.floats(min_value=0.1, max_value=0.9),
lambda1=st.floats(min_value=0.001, max_value=0.1),
lambda2=st.floats(min_value=0.001, max_value=0.1),
engine=st.sampled_from([None, "SIMD"]),
**hu.gcs_cpu_only)
def test_ftrl_sgd(self, inputs, in_place, alpha, beta, lambda1, lambda2,
engine, gc, dc):
var, n, z, grad = inputs
n = np.abs(n)
nz = np.stack([n, z], axis=-1)
op = core.CreateOperator(
"Ftrl",
["var", "nz", "grad"],
["var" if in_place else "var_o",
"nz" if in_place else "nz_o"],
alpha=alpha, beta=beta, lambda1=lambda1, lambda2=lambda2,
engine=engine,
device_option=gc)
self.assertDeviceChecks(
dc, op, [var, nz, grad], [0])
self.assertReferenceChecks(
gc, op, [var, nz, grad],
partial(self._dense_ftrl, alpha, beta, lambda1, lambda2))
@given(inputs=hu.tensors(n=4),
alpha=st.floats(min_value=0.01, max_value=0.1),
beta=st.floats(min_value=0.1, max_value=0.9),
lambda1=st.floats(min_value=0.001, max_value=0.1),
lambda2=st.floats(min_value=0.001, max_value=0.1),
engine=st.sampled_from([None, "SIMD"]),
**hu.gcs_cpu_only)
def test_sparse_ftrl_sgd(self, inputs, alpha, beta, lambda1, lambda2,
engine, gc, dc):
var, n, z, grad = inputs
# generate fake subset manually because hypothesis is too complicated :)
indices = np.arange(var.shape[0])
indices = indices[indices % 2 == 0]
grad = grad[indices]
n = np.abs(n)
nz = np.stack([n, z], axis=-1)
op = core.CreateOperator(
"SparseFtrl",
["var", "nz", "indices", "grad"],
["var", "nz"],
alpha=alpha, beta=beta, lambda1=lambda1, lambda2=lambda2,
engine=engine,
device_option=gc)
self.assertDeviceChecks(
dc, op, [var, nz, indices, grad], [0])
# Reference
def ftrl(w, nz, i, g):
sw, snz = self._dense_ftrl(alpha, beta, lambda1, lambda2,
w[i], nz[i], g)
w[i] = sw
nz[i] = snz
return (w, nz)
self.assertReferenceChecks(gc, op, [var, nz, indices, grad], ftrl)
@given(input=hu.tensor(max_value=20,
max_dim=1,
dtype=np.int32,
elements=st.integers(min_value=0, max_value=10)),
with_remapping=st.booleans(),
**hu.gcs_cpu_only)
def test_unique(self, input, with_remapping, gc, dc):
op = core.CreateOperator(
"Unique",
["input"],
["unique"] + (["remapping"] if with_remapping else []),
device_option=gc)
self.assertDeviceChecks(dc, op, [input], [0])
# Validator
def unique_valid(input, unique, remapping=None):
self.assertEqual(unique.size, len(set(input)))
self.assertEqual(sorted(unique), sorted(set(input)))
if with_remapping:
self.assertEqual(remapping.shape, input.shape)
remapped = [unique[remapping[i]] for i in range(len(input))]
np.testing.assert_array_equal(remapped, input)
self.assertValidationChecks(gc, op, [input], unique_valid)
@given(prediction=hu.arrays(dims=[10, 3],
elements=st.floats(allow_nan=False,
allow_infinity=False,
min_value=0,
max_value=1)),
labels=hu.arrays(dims=[10],
dtype=np.int32,
elements=st.integers(min_value=0,
max_value=3 - 1)),
**hu.gcs)
def test_accuracy(self, prediction, labels, gc, dc):
op = core.CreateOperator(
"Accuracy",
["prediction", "labels"],
["accuracy"]
)
def op_ref(prediction, labels):
N = prediction.shape[0]
correct = 0
max_ids = np.argmax(prediction, axis=1)
for i in range(0, N):
if max_ids[i] == labels[i]:
correct += 1
accuracy = correct / N
return (accuracy,)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[prediction, labels],
reference=op_ref)
@given(target_probabilities=hu.arrays(
dims=[10], elements=st.floats(allow_nan=False,
allow_infinity=False,
min_value=0.01,
max_value=1)),
**hu.gcs)
def test_perplexity(self, target_probabilities, gc, dc):
op = core.CreateOperator(
"Perplexity",
["target_probabilities"],
["perplexity"]
)
def op_ref(target_probabilities):
N = target_probabilities.shape[0]
perplexities = np.power(target_probabilities, -1.0 / N)
perplexity = reduce(lambda x, y: x * y, perplexities)
return (perplexity,)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[target_probabilities],
reference=op_ref)
@given(lengths=st.lists(st.integers(min_value=0, max_value=10),
min_size=0,
max_size=10),
**hu.gcs_cpu_only)
def test_lengths_to_segment_ids(self, lengths, gc, dc):
op = core.CreateOperator(
"LengthsToSegmentIds",
["lengths"],
["segment_ids"])
def op_ref(lengths):
sids = []
for i, l in enumerate(lengths):
sids.extend(l * [i])
return (np.array(sids, dtype=np.int32), )
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[np.array(lengths, dtype=np.int32)],
reference=op_ref)
@given(lengths=st.lists(st.integers(min_value=0, max_value=10),
min_size=0,
max_size=10),
**hu.gcs_cpu_only)
def test_lengths_range_fill(self, lengths, gc, dc):
op = core.CreateOperator(
"LengthsRangeFill",
["lengths"],
["increasing_seq"])
def op_ref(lengths):
sids = []
for i, l in enumerate(lengths):
sids.extend(range(l))
return (np.array(sids, dtype=np.int32), )
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[np.array(lengths, dtype=np.int32)],
reference=op_ref)
@given(**hu.gcs_cpu_only)
def test_segment_ids_to_ranges(self, gc, dc):
lengths = [4, 6, 3, 2, 0, 4]
op = core.CreateOperator(
"SegmentIdsToRanges",
["segment_ids"],
["ranges"])
def op_ref(segment_ids):
ranges = [np.array([0, 0], dtype=np.int32)]
prev = 0
for i, sid in enumerate(segment_ids):
while sid != prev:
prev += 1
ranges.append(np.array([i, 0], dtype=np.int32))
ranges[-1][1] += 1
return (np.array(ranges, dtype=np.int32), )
def lengths_to_segment_ids(lengths):
sids = []
for i, l in enumerate(lengths):
sids.extend(l * [i])
return (np.array(sids, dtype=np.int32), )
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=np.array(lengths_to_segment_ids(lengths), dtype=np.int32),
reference=op_ref)
@given(lengths=st.lists(st.integers(min_value=0, max_value=10),
min_size=0,
max_size=10),
**hu.gcs_cpu_only)
def test_lengths_to_ranges(self, lengths, gc, dc):
op = core.CreateOperator(
"LengthsToRanges",
["lengths"],
["ranges"])
def op_ref(x):
if not x.size:
return (x.reshape((0, 2)), )
return (np.column_stack((np.concatenate(([0], np.cumsum(x)[:-1])),
x)), )
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[np.array(lengths, dtype=np.int32)],
reference=op_ref)
@given(prediction=hu.arrays(dims=[10, 3],
elements=st.floats(allow_nan=False,
allow_infinity=False,
min_value=0,
max_value=1)),
labels=hu.arrays(dims=[10],
dtype=np.int32,
elements=st.integers(min_value=0,
max_value=3 - 1)),
**hu.gcs)
def test_multi_class_accuracy(self, prediction, labels, gc, dc):
op = core.CreateOperator(
"MultiClassAccuracy",
["prediction", "labels"],
["accuracies", "amounts"]
)
def op_ref(prediction, labels):
N = prediction.shape[0]
D = prediction.shape[1]
accuracies = np.empty(D, dtype=float)
accuracies.fill(0)
amounts = np.empty(D, dtype=int)
amounts.fill(0)
max_ids = np.argmax(prediction, axis=1)
for i in range(0, N):
max_id = max_ids[i]
label_id = labels[i]
if max_id == label_id:
accuracies[label_id] += 1
amounts[label_id] += 1
for i in range(0, D):
amount = amounts[i]
if amount:
accuracies[i] /= amount
return (accuracies, amounts,)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[prediction, labels],
reference=op_ref)
@given(lengths=st.lists(st.integers(min_value=0, max_value=10),
min_size=0,
max_size=10),
**hu.gcs_cpu_only)
def test_segment_ids_to_lengths(self, lengths, gc, dc):
op = core.CreateOperator(
"SegmentIdsToLengths",
["segment_ids"],
["lengths"])
def lengths_to_ids(lengths):
sids = []
for i, l in enumerate(lengths):
sids.extend(l * [i])
return sids
segment_ids = lengths_to_ids(lengths)
def ids_to_lengths(ids):
ids_length = len(ids)
if ids_length == 0:
return (np.array([], dtype=np.int32),)
lengths = []
# segment id starts with 0
prev_id = -1
tmp_length = 0
for idx in range(ids_length):
cur_id = ids[idx]
if cur_id != prev_id:
if idx != 0:
lengths.append(tmp_length)
while prev_id + 1 != cur_id:
lengths.append(0)
prev_id += 1
prev_id = cur_id
tmp_length = 0
tmp_length += 1
lengths.append(tmp_length)
return (np.array(lengths, dtype=np.int32),)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[np.array(segment_ids, dtype=np.int32)],
reference=ids_to_lengths)
@given(lengths=st.lists(st.integers(min_value=1, max_value=10),
min_size=0,
max_size=10),
power=st.sampled_from([0.5, 1.0, 1.5, 2.0]),
**hu.gcs_cpu_only)
def test_segment_ids_to_lengths_weight(self, lengths, power, gc, dc):
op = core.CreateOperator(
"SegmentIdsToLengthWeights",
["segment_ids"],
["lengths"],
power=power)
def lengths_to_ids(lengths):
sids = []
for i, l in enumerate(lengths):
sids.extend(l * [i])
return sids
segment_ids = lengths_to_ids(lengths)
def ids_to_length_weights(ids):
ids_length = len(ids)
if ids_length == 0:
return (np.array([], dtype=float),)
lengths = []
# segment id starts with 0
prev_id = -1
tmp_length = 0
for idx in range(ids_length):
cur_id = ids[idx]
if cur_id != prev_id:
if idx != 0:
lengths.append(tmp_length)
while prev_id + 1 != cur_id:
lengths.append(0)
prev_id += 1
prev_id = cur_id
tmp_length = 0
tmp_length += 1
lengths.append(tmp_length)
weighted_length = []
for l in lengths:
weighted_length.extend(l * [1 / pow(l, power)])
return (np.array(weighted_length, dtype=float),)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[np.array(segment_ids, dtype=np.int32)],
reference=ids_to_length_weights)
@given(input_tensor=hu.arrays(
dims=[10], elements=st.floats(allow_nan=False,
allow_infinity=False)),
**hu.gcs)
def test_exp(self, input_tensor, gc, dc):
op = core.CreateOperator(
"Exp",
["input"],
["output"]
)
def exp_ref(input_tensor):
return (np.exp(input_tensor),)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[input_tensor],
reference=exp_ref)
@given(num_threads=st.integers(1, 10), # noqa
num_elements=st.integers(1, 100),
capacity=st.integers(1, 5),
num_blobs=st.integers(1, 3),
do=st.sampled_from(hu.device_options))
def test_blobs_queue_threading(self, num_threads, num_elements,
capacity, num_blobs, do):
"""
- Construct matrices of size N x D
- Start K threads
- Push all N rows into the queue of capacity C
- Pull all N rows out of the queue.
- Verify that the output matrices are permutation of the rows of the
original matrices.
"""
import threading
import Queue
op = core.CreateOperator(
"CreateBlobsQueue",
[],
["queue"],
capacity=capacity,
num_blobs=num_blobs,
device_option=do)
self.ws.run(op)
xs = [np.random.randn(num_elements, 5).astype(np.float32)
for _ in range(num_blobs)]
q = Queue.Queue()
for i in range(num_elements):
q.put([x[i] for x in xs])
def enqueue(t):
while True:
feed_blobs = ["x_{}_{}".format(i, t) for i in range(num_blobs)]
op = core.CreateOperator(
"EnqueueBlobs",
["queue"] + feed_blobs,
feed_blobs,
device_option=do)
try:
elems = q.get_nowait()
for elem, feed_blob in zip(elems, feed_blobs):
self.ws.create_blob(feed_blob).feed(
elem, device_option=do)
self.ws.run(op)
except Queue.Empty:
return
# Create all blobs before racing on multiple threads
# (blob creation is not threadsafe)
for t in range(num_threads):
for i in range(num_blobs):
self.ws.create_blob("x_{}_{}".format(i, t))
threads = [threading.Thread(target=enqueue, args=(t,))
for t in range(num_threads)]
for thread in threads:
thread.start()
for n in range(num_elements):
dequeue_blobs = ["y_{}_{}".format(i, n) for i in range(num_blobs)]
op = core.CreateOperator(
"DequeueBlobs",
["queue"],
dequeue_blobs,
device_option=do)
self.ws.run(op)
for thread in threads:
thread.join()
op = core.CreateOperator("CloseBlobsQueue", ["queue"], [])
self.ws.run(op)
ys = [np.vstack([self.ws.blobs["y_{}_{}".format(i, n)].fetch()
for n in range(num_elements)])
for i in range(num_blobs)]
for i in range(num_blobs):
self.assertEqual(ys[i].shape, xs[i].shape)
for j in range(num_elements):
# Verify that the rows of the returned blob are a
# permutation. The order may be different due to
# different threads racing.
self.assertTrue(
any(np.array_equal(xs[i][j], ys[i][k])
for k in range(num_elements)))
@given(num_producers=st.integers(1, 10),
num_consumers=st.integers(1, 10),
capacity=st.integers(1, 5),
num_blobs=st.integers(1, 3),
do=st.sampled_from(hu.device_options))
def test_safe_blobs_queue(self, num_producers, num_consumers,
capacity, num_blobs, do):
init_net = core.Net('init_net')
queue = init_net.CreateBlobsQueue(
[], 1, capacity=capacity, num_blobs=num_blobs)
producer_steps = []
truth = 0
for i in range(num_producers):
name = 'producer_%d' % i
net = core.Net(name)
blobs = [net.ConstantFill([], 1, value=1.0, run_once=False)
for times in range(num_blobs)]
status = net.NextName()
net.SafeEnqueueBlobs([queue] + blobs, blobs + [status])
count = (i + 1) * 10
step = core.execution_step(name, net, num_iter=count)
truth += count
producer_steps.append(step)
producer_exit_net = core.Net('producer_exit_net')
producer_exit_net.CloseBlobsQueue([queue], 0)
producer_step = core.execution_step('producer', [
core.execution_step(
'producers', producer_steps, concurrent_substeps=True),
core.execution_step('producer_exit', producer_exit_net)]
)
consumer_steps = []
counters = []
const_1 = init_net.ConstantFill([], 1, value=1.0)
for i in range(num_consumers):
name = 'consumer_%d' % i
net1 = core.Net(name)
blobs = net1.SafeDequeueBlobs([queue], num_blobs + 1)
status = blobs[-1]
net2 = core.Net(name + '_counter')
counter = init_net.ConstantFill([], 1, value=0.0)
counters.append(counter)
net2.Add([counter, const_1], counter)
consumer_steps.append(core.execution_step(
name, [net1, net2], should_stop_blob=status))
consumer_step = core.execution_step(
'consumer', consumer_steps, concurrent_substeps=True)
init_step = core.execution_step('init', init_net)
worker_step = core.execution_step(
'worker', [consumer_step, producer_step], concurrent_substeps=True)
plan = core.Plan('test')
plan.AddStep(init_step)
plan.AddStep(worker_step)
self.ws.run(plan)
v = 0
for counter in counters:
v += self.ws.blobs[str(counter)].fetch().tolist()
self.assertEqual(v, truth)
@given(
data=hu.tensor(),
**hu.gcs_cpu_only)
def test_squeeze_expand_dims(self, data, gc, dc):
dims = [0, 0]
if len(data.shape) > 2:
dims.append(2)
op = core.CreateOperator(
"ExpandDims",
["data"],
["expanded"],
dims=dims)
def expand_dims_ref(data, *args, **kw):
inc_dims = list(set(dims))
inc_dims.sort()
r = data
for dim in inc_dims:
r = np.expand_dims(r, axis=dim)
return (r, )
def squeeze_ref(data, *args, **kw):
dec_dims = list(set(dims))
dec_dims.sort(reverse=True)
r = data
for dim in dec_dims:
r = np.squeeze(r, axis=dim)
return (r, )
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[data],
reference=expand_dims_ref,
output_to_grad='expanded',
grad_reference=squeeze_ref)
@given(**hu.gcs_cpu_only)
def test_tt_layer(self, gc, dc):
seed = 1234
np.random.seed(seed)
inp_sizes = [2, 2, 2, 2]
out_sizes = [2, 2, 2, 2]
tt_ranks = [1, 3, 3, 3, 1]
op = core.CreateOperator(
"TT",
["X", "b", "cores"],
["Y"],
inp_sizes=inp_sizes,
out_sizes=out_sizes,
tt_ranks=tt_ranks,
)
X = np.expand_dims(
np.random.rand(16).astype(np.float32), axis=0)
b = np.array([0] * 16).astype(np.float32)
cores = tt_core.init_tt_cores(inp_sizes, out_sizes, tt_ranks)
self.ws.create_blob("X").feed(X)
self.ws.create_blob("b").feed(b)
self.ws.create_blob("cores").feed(cores)
self.ws.run(op)
Y = self.ws.blobs[("Y")].fetch()
Y = Y.reshape([16])
golden = np.array([-9.51763490e-07, -1.28442286e-06,
-2.86281141e-07, 2.28865644e-07,
-1.96180017e-06, -1.78920531e-06,
9.31094666e-07, -2.04273989e-07,
1.70017107e-06, 1.64845711e-06,
-1.06099132e-06, -4.69111137e-07,
6.57552358e-08, -1.28942040e-08,
-2.29114004e-07, -1.04262714e-06])
# This golden array is dependent on the specified inp_sizes, out_sizes,
# tt_ranks, and seed. Changing these will cause the test to fail.
self.assertAlmostEqual(np.linalg.norm(golden - Y), 0, delta=1e-12)
@given(num_workers=st.integers(1, 10),
net_type=st.sampled_from(
["simple", "dag"] +
(["async_dag"] if workspace.has_gpu_support else [])),
do=st.sampled_from(hu.device_options))
def test_dag_net_forking(self, net_type, num_workers, do):
from caffe2.python.cnn import CNNModelHelper
m = CNNModelHelper()
n = 10
d = 2
depth = 2
iters = 5
np.random.seed(1701)
# Build a binary tree of FC layers, summing at each node.
for i in reversed(range(depth)):
for j in range(2 ** i):
bottom_1 = "{}_{}".format(i + 1, 2 * j)
bottom_2 = "{}_{}".format(i + 1, 2 * j + 1)
mid_1 = "{}_{}_m".format(i + 1, 2 * j)
mid_2 = "{}_{}_m".format(i + 1, 2 * j + 1)
top = "{}_{}".format(i, j)
m.FC(
bottom_1, mid_1,
dim_in=d, dim_out=d,
weight_init=m.ConstantInit(np.random.randn()),
bias_init=m.ConstantInit(np.random.randn()))
m.FC(
bottom_2, mid_2,
dim_in=d, dim_out=d,
weight_init=m.ConstantInit(np.random.randn()),
bias_init=m.ConstantInit(np.random.randn()))
m.net.Sum([mid_1, mid_2], top)
m.net.SquaredL2Distance(["0_0", "label"], "xent")
m.net.AveragedLoss("xent", "loss")
input_to_grad = m.AddGradientOperators(["loss"])
m.Proto().device_option.CopyFrom(do)
m.param_init_net.Proto().device_option.CopyFrom(do)
m.Proto().type = net_type
m.Proto().num_workers = num_workers
self.ws.run(m.param_init_net)
print(str(m.Proto()))
def run():
import numpy as np
np.random.seed(1701)
input_blobs = ["{}_{}".format(depth, j) for j in range(2 ** depth)]
for input_blob in input_blobs:
self.ws.create_blob(input_blob).feed(
np.random.randn(n, d).astype(np.float32),
device_option=do)
self.ws.create_blob("label").feed(
np.random.randn(n, d).astype(np.float32),
device_option=do)
self.ws.run(m.net)
gradients = [
self.ws.blobs[str(input_to_grad[input_blob])].fetch()
for input_blob in input_blobs]
return gradients
outputs = [run() for _ in range(iters)]
for output in outputs[1:]:
np.testing.assert_array_equal(outputs[0], output)
self.assertAlmostEqual(np.sum(np.square(output)), 91.81752,
delta=1e-2)
@given(input=hu.tensor(min_dim=2, max_dim=6, dtype=np.int32,
elements=st.integers(min_value=0,
max_value=2**32 - 1)),
slice_dim=st.integers(),
a=st.integers(),
b=st.integers(),
is_empty=st.booleans(),
**hu.gcs_cpu_only)
def test_slice(self, input, slice_dim, a, b, is_empty, gc, dc):
slice_dim = slice_dim % len(input.shape)
if (is_empty):
input = np.random.rand(*([0] + list(input.shape))).astype(np.int32)
slice_dim += 1
a = a % input.shape[slice_dim]
b = b % input.shape[slice_dim] + 1
start_vec = np.zeros(len(input.shape), dtype=np.int32)
end_vec = np.ones(len(input.shape), dtype=np.int32) * -1
start_vec[slice_dim] = min(a, b)
end_vec[slice_dim] = max(a, b)
op = core.CreateOperator(
"Slice",
["input", "start", "end"],
["output"])
def slice_ref(x, s, e):
if len(s.shape) == 0:
return x
slc = [slice(si, None if ei == -1 else ei) for si, ei in zip(s, e)]
return (x[slc], )
self.assertReferenceChecks(gc, op, [input, start_vec, end_vec],
slice_ref)
@given(data=hu.tensor(), **hu.gcs_cpu_only)
def test_shape(self, data, gc, dc):
op = core.CreateOperator("Shape", ["data"], ["shape"])
self.assertReferenceChecks(gc, op, [data], lambda x: (x.shape, ))
@given(data=hu.tensor(), **hu.gcs_cpu_only)
def test_has_elements(self, data, gc, dc):
op = core.CreateOperator("HasElements", ["data"], ["has_elements"])
self.assertReferenceChecks(gc, op, [data], lambda x: (len(x) > 0, ))
op = core.CreateOperator("IsEmpty", ["data"], ["is_empty"])
self.assertReferenceChecks(gc, op, [data], lambda x: (len(x) == 0, ))
@given(initial_iters=st.integers(0, 100),
max_iters=st.integers(0, 100))
def test_should_stop_as_criteria_net_execution_step(
self, initial_iters, max_iters):
net = core.Net("net")
net.Iter(["iter"], ["iter"])
self.ws.create_blob("iter").feed(
np.asarray([initial_iters]).astype(np.int64))
self.ws.create_blob("num_iters").feed(
np.asarray([max_iters]).astype(np.int64))
criteria_net = core.Net("criteria")
criteria_net.GE(["iter", "num_iters"], ["stop"])
criteria_net.Proto().external_output.extend(["stop"])
plan = core.Plan('plan')
plan.AddStep(core.execution_step(
'step', [criteria_net, net],
should_stop_blob=core.BlobReference("stop")))
self.ws.run(plan)
iters = self.ws.blobs[("iter")].fetch()
self.assertEqual(iters.dtype, np.int64)
self.assertEqual(iters[0], max(initial_iters, max_iters))
def test_disabled_execution_step(self):
def createNets(i, disabled):
should_stop = 'should_stop_{}'.format(i)
output = 'output_{}'.format(i)
# init content and stop signal
init = core.Net("init_{}".format(i))
init.ConstantFill(
[],
[output],
shape=[1],
value=0.0
)
init.Cast([output], [should_stop], to='bool')
# decide if disabled or not
criterion = core.Net("criterion_{}".format(i))
tmp = criterion.ConstantFill(
[],
shape=[1],
value=1.0 if disabled else 0.0
)
criterion.Cast([tmp], [should_stop], to='bool')
criterion.Proto().external_output.extend([should_stop])
# the body net is just to turn a 0 blob to 1
net = core.Net("net_{}".format(i))
net.ConstantFill(
[],
[output],
shape=[1],
value=1.0
)
# always end the loop
ender = core.Net("ender_{}".format(i))
tmp = ender.ConstantFill(
[],
shape=[1],
value=1.0
)
ender.Cast([tmp], [should_stop], to='bool')
ender.Proto().external_output.extend([should_stop])
return [init, criterion, net, ender]
nets = [createNets(1, False),
createNets(2, True),
createNets(3, False)]
steps = [
core.execution_step(
'step_1', nets[0],
should_stop_blob=core.BlobReference('should_stop_1')),
core.execution_step(
'step_2', nets[1],
should_stop_blob=core.BlobReference('should_stop_2')),
core.execution_step('step_3', nets[2])
]
expected = [1.0, 0.0, 1.0]
plan = core.Plan('plan')
plan.AddStep(core.execution_step('all_steps', steps, num_iter=3))
self.ws.run(plan)
for i, net in enumerate(nets):
self.assertEqual(
self.ws.blobs['output_{}'.format(i + 1)].fetch()[0],
expected[i])
@given(initial_iters=st.integers(0, 100),
num_iters=st.integers(0, 100))
def test_iter_count_with_execution_step(self, initial_iters, num_iters):
net = core.Net("net")
net.Iter(["iter"], ["iter"])
self.ws.create_blob("iter").feed(
np.asarray([initial_iters]).astype(np.int64))
step = core.ExecutionStep("step", [net])
step.SetIter(num_iters)
plan = core.Plan("plan")
plan.AddStep(step)
self.ws.run(plan)
iters = self.ws.blobs[("iter")].fetch()
self.assertEqual(iters.dtype, np.int64)
self.assertEqual(iters[0], initial_iters + num_iters)
@given(initial_iters=st.integers(0, 100),
num_iters=st.integers(0, 100),
num_nets=st.integers(0, 5))
def test_atomic_iter_with_concurrent_steps(self, initial_iters, num_iters,
num_nets):
init_net = core.Net("init_net")
iter_mutex = init_net.CreateMutex([], ["iter_mutex"])
self.ws.create_blob("iter").feed(
np.asarray([initial_iters]).astype(np.int64))
concurrent_steps = core.ExecutionStep("concurrent_steps",
num_iter=num_iters)
for i in range(num_nets):
net = core.Net("net_{}".format(i))
net.AtomicIter([iter_mutex, "iter"], ["iter"])
step = core.ExecutionStep("step", [net])
concurrent_steps.AddSubstep(step)
concurrent_steps.SetConcurrentSubsteps(True)
plan = core.Plan("plan")
plan.AddStep(concurrent_steps)
self.ws.run(init_net)
self.ws.run(plan)
iters = self.ws.blobs[("iter")].fetch()
self.assertEqual(iters.dtype, np.int64)
self.assertEqual(iters[0], initial_iters + num_iters * num_nets)
@given(a=hu.tensor(),
src=st.sampled_from(_NUMPY_TYPE_TO_ENUM.keys()),
dst=st.sampled_from(_NUMPY_TYPE_TO_ENUM.keys()),
use_name=st.booleans(),
**hu.gcs)
def test_cast(self, a, src, dst, use_name, gc, dc):
a = a.astype(src)
# Casting from a float type outside the range of the integral
# type is UB.
ftypes = [np.float32, np.float64]
if src in ftypes and dst not in ftypes and dst is not np.bool:
info = np.iinfo(dst)
a = np.clip(a, info.min, info.max)
def ref(data):
return [data.astype(dst)]
to = _NUMPY_TYPE_TO_ENUM[dst]
if use_name:
to = TensorProto.DataType.Name(to).lower()
op = core.CreateOperator('Cast', ["X"], ["Y"], to=to)
self.assertDeviceChecks(dc, op, [a], [0])
out, = self.assertReferenceChecks(gc, op, [a], ref)
self.assertEqual(dst, out.dtype)
@given(data=_dtypes(dtypes=[np.int32, np.int64, np.float32, np.bool]).
flatmap(lambda dtype: hu.tensor(
min_dim=1, dtype=dtype, elements=hu.elements_of_type(dtype))),
has_input=st.booleans(),
has_extra_shape=st.booleans(),
extra_shape=st.lists(
min_size=1, max_size=5, elements=st.integers(1, 5)),
**hu.gcs)
def test_constant_fill(self, data, has_input, has_extra_shape, extra_shape,
gc, dc):
dtype = data.dtype.type
# in opt mode, np.bool is converted into np.bool_
if data.dtype == np.dtype(np.bool):
dtype = np.bool
value = data.item(0)
gt_shape = data.shape
inputs = [data]
enum_type = _NUMPY_TYPE_TO_ENUM[dtype]
if has_input:
if has_extra_shape:
op = core.CreateOperator('ConstantFill', ["X"], ["Y"],
dtype=enum_type,
extra_shape=extra_shape,
value=value)
gt_shape += tuple(extra_shape)
else:
op = core.CreateOperator('ConstantFill', ["X"], ["Y"],
dtype=enum_type,
value=value)
else:
op = core.CreateOperator('ConstantFill', [], ["Y"],
dtype=enum_type,
value=value,
shape=list(gt_shape))
inputs = []
def ref(inputs=None):
outputs = np.full(shape=gt_shape, fill_value=value, dtype=dtype)
return [outputs]
self.assertDeviceChecks(dc, op, inputs, [0])
out, = self.assertReferenceChecks(gc, op, inputs, ref)
self.assertEqual(dtype, out.dtype)
@given(t=st.integers(1, 5),
n=st.integers(1, 5),
d=st.integers(1, 5))
def test_elman_recurrent_network(self, t, n, d):
from caffe2.python import cnn
np.random.seed(1701)
step_net = cnn.CNNModelHelper(name="Elman")
# TODO: name scope external inputs and outputs
step_net.Proto().external_input.extend(
["input_t", "seq_lengths", "timestep",
"hidden_t_prev", "gates_t_w", "gates_t_b"])
step_net.Proto().type = "simple"
step_net.Proto().external_output.extend(["hidden_t", "gates_t"])
step_net.FC("hidden_t_prev", "gates_t", dim_in=d, dim_out=d, axis=2)
step_net.net.Sum(["gates_t", "input_t"], ["gates_t"])
step_net.net.Sigmoid(["gates_t"], ["hidden_t"])
# Initialize params for step net in the parent net
for op in step_net.param_init_net.Proto().op:
workspace.RunOperatorOnce(op)
backward_ops, backward_mapping = core.GradientRegistry.GetBackwardPass(
step_net.Proto().op, {"hidden_t": "hidden_t_grad"})
backward_mapping = {str(k): str(v) for k, v
in backward_mapping.items()}
backward_step_net = core.Net("ElmanBackward")
del backward_step_net.Proto().op[:]
backward_step_net.Proto().op.extend(backward_ops)
assert backward_mapping["input_t"] == "gates_t_grad"
links = [
("hidden_t_prev", "hidden", 0),
("hidden_t", "hidden", 1),
("gates_t", "gates", 0),
("input_t", "input", 0),
]
link_internal, link_external, link_offset = zip(*links)
backward_links = [
("hidden_t_prev_grad", "hidden_grad", 0),
("hidden_t_grad", "hidden_grad", 1),
("gates_t_grad", "gates_grad", 0),
]
backward_link_internal, backward_link_external, backward_link_offset = \
zip(*backward_links)
backward_step_net.Proto().external_input.extend(["hidden_t_grad"])
backward_step_net.Proto().external_input.extend(
step_net.Proto().external_input)
backward_step_net.Proto().external_input.extend(
step_net.Proto().external_output)
op = core.CreateOperator(
"RecurrentNetwork",
["input", "seq_lengths", "gates_t_w", "gates_t_b", "hidden_input"],
["output", "hidden", "hidden_output"],
alias_src=["hidden", "hidden"],
alias_dst=["output", "hidden_output"],
alias_offset=[1, -1],
recurrent_states=["hidden"],
recurrent_inputs=["hidden_input"],
recurrent_sizes=[d],
link_internal=link_internal,
link_external=link_external,
link_offset=link_offset,
backward_link_internal=backward_link_internal,
backward_link_external=backward_link_external,
backward_link_offset=backward_link_offset,
backward_alias_src=["gates_grad"],
backward_alias_dst=["input_grad"],
backward_alias_offset=[0],
param=[str(p) for p in step_net.params],
param_gradient=[backward_mapping[str(p)] for p in step_net.params],
scratch=["gates"],
backward_scratch=["gates_grad"],
scratch_sizes=[d],
step_net=str(step_net.Proto()),
backward_step_net=str(backward_step_net.Proto()),
dim_out=d)
workspace.FeedBlob(
"input", np.random.randn(t, n, d).astype(np.float32))
workspace.FeedBlob(
"hidden_input", np.random.randn(1, n, d).astype(np.float32))
workspace.FeedBlob(
"seq_lengths", np.random.randint(0, t, size=(n,)).astype(np.int32))
def reference(input, seq_lengths, gates_w, gates_b, hidden_input):
T = input.shape[0]
N = input.shape[1]
D = input.shape[2]
hidden = np.zeros(shape=(T + 1, N, D))
assert hidden.shape[0] == T + 1
assert hidden.shape[1] == N
assert hidden.shape[2] == D
hidden[0, :, :] = hidden_input
for t in range(T):
input_t = input[t].reshape(1, N, D)
hidden_t_prev = hidden[t].reshape(1, N, D)
gates = np.dot(hidden_t_prev, gates_w.T)
gates = gates.reshape(1, N, D) + input_t.reshape(1, N, D)
hidden[t + 1] = sigmoid(gates)
return hidden[1:], hidden, hidden[-1].reshape(1, N, D)
self.assertReferenceChecks(
hu.cpu_do,
op,
[workspace.FetchBlob(name)
for name in ["input", "seq_lengths", "gates_t_w", "gates_t_b",
"hidden_input"]],
reference)
for param in [0, 2, 3]:
self.assertGradientChecks(
hu.cpu_do,
op,
[workspace.FetchBlob(name)
for name in ["input", "seq_lengths", "gates_t_w", "gates_t_b",
"hidden_input"]],
param,
[0])
@given(n=st.integers(1, 5),
c=st.integers(1, 5),
h=st.integers(1, 5),
w=st.integers(1, 5),
pad=st.integers(0, 2),
block_size=st.integers(2, 3),
**hu.gcs)
def test_space_to_batch(self, n, c, h, w, pad, block_size, gc, dc):
assume((h + 2 * pad) % block_size == 0)
assume((w + 2 * pad) % block_size == 0)
X = np.random.randn(n, c, h, w).astype(np.float32)
op = core.CreateOperator("SpaceToBatch", ["X"], ["Y"],
pad=pad, block_size=block_size)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(n=st.integers(1, 5),
c=st.integers(1, 5),
h=st.integers(1, 5),
w=st.integers(1, 5),
pad=st.integers(0, 2),
block_size=st.integers(2, 3),
**hu.gcs)
def test_batch_to_space(self, n, c, h, w, pad, block_size, gc, dc):
assume((h + 2 * pad) % block_size == 0)
assume((w + 2 * pad) % block_size == 0)
X = np.random.randn(
n * block_size * block_size,
c,
(h + 2 * pad) / block_size,
(w + 2 * pad) / block_size).astype(np.float32)
op = core.CreateOperator("BatchToSpace", ["X"], ["Y"],
pad=pad, block_size=block_size)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(X=hu.tensor(),
in_place=st.booleans(),
scale=st.floats(min_value=-2.0, max_value=2.0),
**hu.gcs)
def test_scale(self, X, in_place, scale, gc, dc):
op = core.CreateOperator(
"Scale", ["X"], ["Y" if not in_place else "X"],
scale=scale)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(X=_dtypes().flatmap(lambda dtype: hu.tensor(dtype=dtype)),
seed=st.integers(min_value=0, max_value=65536),
null_axes=st.booleans(),
**hu.gcs)
@settings(max_examples=2, timeout=100)
def test_transpose(self, X, seed, null_axes, gc, dc):
if null_axes:
axes = None
op = core.CreateOperator("Transpose", "input", "output")
else:
np.random.seed(int(seed))
axes = [int(v) for v in list(np.random.permutation(X.ndim))]
op = core.CreateOperator(
"Transpose", "input", "output", axes=axes)
def transpose_ref(x, axes):
return (np.transpose(x, axes),)
self.assertReferenceChecks(gc, op, [X, axes],
transpose_ref)
if X.dtype != np.int32 and X.dtype != np.int64:
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(s=st.text())
def test_string_serde(self, s):
s = s.encode('ascii', 'ignore')
self.ws.create_blob("a").feed(s)
serialized = self.ws.blobs["a"].serialize("a")
self.ws.create_blob("b").deserialize(serialized)
self.assertEqual(s, self.ws.blobs[("a")].fetch())
self.assertEqual(s, self.ws.blobs[("b")].fetch())
@given(n=st.integers(1, 3),
dim=st.integers(4, 16),
**hu.gcs_cpu_only)
def test_distances(self, n, dim, gc, dc):
X = np.random.uniform(-1, 1, (n, dim)).astype(np.float32)
Y = np.random.uniform(-1, 1, (n, dim)).astype(np.float32)
self.ws.create_blob("X").feed(X)
self.ws.create_blob("Y").feed(Y)
def check_grad(op):
self.assertGradientChecks(gc, op, [X, Y], 0, [0],
stepsize=1e-2, threshold=1e-2)
self.assertGradientChecks(gc, op, [X, Y], 1, [0],
stepsize=1e-2, threshold=1e-2)
l2_op = core.CreateOperator("SquaredL2Distance",
["X", "Y"], ["l2_dist"])
self.ws.run(l2_op)
np.testing.assert_allclose(self.ws.blobs[("l2_dist")].fetch(),
np.square(X - Y).sum(axis=1) * 0.5,
rtol=1e-4, atol=1e-4)
check_grad(l2_op)
dot_op = core.CreateOperator("DotProduct", ["X", "Y"], ["dot"])
self.ws.run(dot_op)
np.testing.assert_allclose(self.ws.blobs[("dot")].fetch(),
np.multiply(X, Y).sum(axis=1),
rtol=1e-4, atol=1e-4)
check_grad(dot_op)
kEps = 1e-12
cos_op = core.CreateOperator("CosineSimilarity", ["X", "Y"], ["cos"])
self.ws.run(cos_op)
cos = np.divide(np.multiply(X, Y).sum(axis=1),
np.multiply(np.linalg.norm(X, axis=1) + kEps,
np.linalg.norm(Y, axis=1) + kEps))
np.testing.assert_allclose(self.ws.blobs[("cos")].fetch(), cos,
rtol=1e-4, atol=1e-4)
check_grad(cos_op)
@given(pad_t=st.integers(0, 3),
pad_l=st.integers(0, 3),
pad_b=st.integers(0, 3),
pad_r=st.integers(0, 3),
size=st.integers(1, 10),
input_channels=st.integers(1, 5),
batch_size=st.integers(1, 5),
order=st.sampled_from(["NCHW", "NHWC"]),
mode=st.sampled_from(["constant", "reflect", "edge"]),
**hu.gcs)
def test_pad_image(self, pad_t, pad_l, pad_b, pad_r, size, input_channels,
batch_size, order, mode, gc, dc):
assume(size > max(pad_b, pad_r, pad_t, pad_l))
op = core.CreateOperator(
"PadImage",
["X"],
["Y"],
pad_t=pad_t,
pad_l=pad_l,
pad_b=pad_b,
pad_r=pad_r,
mode=mode,
order=order,
)
if order == "NHWC":
X = np.random.rand(
batch_size, size, size, input_channels).astype(np.float32) - 0.5
def numpy_pad_ref(x):
return (np.pad(
x, ((0, 0), (pad_t, pad_b), (pad_l, pad_r), (0, 0)),
mode),)
else:
X = np.random.rand(
batch_size, input_channels, size, size).astype(np.float32) - 0.5
def numpy_pad_ref(x):
return (np.pad(
x, ((0, 0), (0, 0), (pad_t, pad_b), (pad_l, pad_r)),
mode),)
self.assertReferenceChecks(gc, op, [X], numpy_pad_ref)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(size=st.integers(7, 10),
input_channels=st.integers(1, 10),
batch_size=st.integers(1, 3),
order=st.sampled_from(["NCHW", "NHWC"]),
epsilon=st.floats(min_value=1e-4, max_value=1e-2),
**hu.gcs_cpu_only)
def test_instance_norm(self, size, input_channels, batch_size, order,
epsilon, gc, dc):
op = core.CreateOperator(
"InstanceNorm",
["X", "scale", "bias"],
["Y"],
order=order,
epsilon=epsilon,
)
np.random.seed(1701)
scale = np.random.rand(input_channels).astype(np.float32) + 0.5
bias = np.random.rand(input_channels).astype(np.float32) - 0.5
X = np.random.rand(
batch_size, input_channels, size, size).astype(np.float32) - 0.5
if order == "NHWC":
X = X.swapaxes(1, 2).swapaxes(2, 3)
def ref_nchw(x, scale, bias):
x = x.reshape(batch_size * input_channels, size * size)
y = (x - x.mean(1)[:, np.newaxis])
y /= np.sqrt(x.var(1) + epsilon)[:, np.newaxis]
y = y.reshape(batch_size, input_channels, size, size)
y = y * scale.reshape(1, input_channels, 1, 1)
y = y + bias.reshape(1, input_channels, 1, 1)
return (y, )
def ref_nhwc(x, scale, bias):
x = x.swapaxes(2, 3).swapaxes(1, 2)
y = ref_nchw(x, scale, bias)[0]
return (y.swapaxes(1, 2).swapaxes(2, 3), )
self.assertReferenceChecks(
gc, op, [X, scale, bias],
ref_nchw if order == "NCHW" else ref_nhwc)
# TODO(jiayq): when there are backward and GPU implementations, enable
# these two.
# self.assertDeviceChecks(dc, op, [X, scale, bias], [0])
# self.assertGradientChecks(gc, op, [X, scale, bias], 0, [0])
ws = workspace.C.Workspace()
feeds = [("X", X), ("scale", scale), ("bias", bias)]
for blob, arr in feeds:
ws.create_blob(blob).feed(arr)
for i in range(100):
ws.run(op)
for blob, arr in feeds:
np.testing.assert_array_equal(ws.blobs[blob].fetch(), arr)
@given(X=hu.tensor(min_dim=2,
max_dim=2,
elements=st.floats(min_value=0.5, max_value=1.0)),
**hu.gcs_cpu_only)
def test_normalize(self, X, gc, dc):
op = core.CreateOperator("Normalize", "X", "Y")
def ref_normalize(X):
x_normed = X / (
np.sqrt((X**2).sum(-1))[:, np.newaxis] + np.finfo(X.dtype).tiny)
return (x_normed,)
self.assertReferenceChecks(gc, op, [X], ref_normalize)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc, op, [X], 0, [0])
if __name__ == "__main__":
unittest.main()
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