def test_stack():
W = ng.make_axis(length=4)
H = ng.make_axis(length=5)
I = ng.make_axis(length=3)
axes = ng.make_axes([W, H])
rng = RandomTensorGenerator(0, np.float32)
a_v = [rng.uniform(0, 1, axes) for i in range(I.length)]
for pos in range(len(axes) + 1):
a = [ng.placeholder(axes, initial_value=p) for p in a_v]
s = ng.stack(a, I, pos)
with ExecutorFactory() as ex:
num_funs = [
ex.numeric_derivative(s, p, delta,
*(np for np in a if np is not p))
for p in a
]
sym_funs = [
ex.derivative(s, p, *(np for np in a if np is not p))
for p in a
]
for n_fun, s_fun, a_i in zip(num_funs, sym_funs, a_v):
na_is = list(na_i for na_i in a_v if na_i is not a_i)
d_n = n_fun(a_i, *na_is)
d_s = s_fun(a_i, *na_is)
ng.testing.assert_allclose(d_n, d_s, rtol=rtol, atol=atol)
def test_stack(transformer_factory):
if transformer_factory.name == flex_gpu_transformer_name:
pytest.skip("Allowed to fail until PR2")
W = ng.make_axis(length=4)
H = ng.make_axis(length=5)
I = ng.make_axis(length=3)
axes = ng.make_axes([W, H])
rng = RandomTensorGenerator(0, np.float32)
a_v = [rng.uniform(0, 1, axes) for i in range(I.length)]
for pos in range(len(axes) + 1):
a = [ng.placeholder(axes, initial_value=_) for _ in a_v]
s = ng.stack(a, I, pos)
with ExecutorFactory() as ex:
num_funs = [ex.numeric_derivative(s, _, delta) for _ in a]
sym_funs = [ex.derivative(s, _) for _ in a]
for n_fun, s_fun, a_i in zip(num_funs, sym_funs, a_v):
d_n = n_fun(a_i)
d_s = s_fun(a_i)
ng.testing.allclose(d_n, d_s, rtol=rtol, atol=atol)
def execute_convolution(image_height,
image_width,
filter_height,
filter_width,
channel=16,
batch_size=32,
filter_count=8,
image_3rd_dim=1,
filter_3rd_dim=1,
padding=(0, 0, 0),
stride=(1, 1, 1),
dilation=1,
np_comparison=False):
pad_h, pad_w, pad_d = padding
str_h, str_w, str_d = stride
cf = ConvParams(C=channel,
N=batch_size,
K=filter_count,
D=image_3rd_dim,
H=image_height,
W=image_width,
T=filter_3rd_dim,
R=filter_height,
S=filter_width,
pad_d=pad_d,
pad_h=pad_h,
pad_w=pad_w,
str_d=str_d,
str_h=str_h,
str_w=str_w,
dil_d=dilation,
dil_h=dilation,
dil_w=dilation)
inputs = ng.placeholder(cf.ax_i)
filters = ng.placeholder(cf.ax_f)
rng = RandomTensorGenerator(0, np.float32)
input_value = rng.uniform(-4, 4, cf.ax_i, dtype=int)
filter_value = rng.uniform(-4, 4, cf.ax_f, dtype=int)
error_value = rng.uniform(-0.5, 0.5, cf.ax_o)
with executor(
ng.convolution(cf.conv_params, inputs, filters, axes=cf.ax_o),
inputs, filters) as const_executor:
out = const_executor(input_value, filter_value)
if np_comparison:
np_out, gradInp, gradF_np = \
reference_conv(cf.dimI, cf.dimF, cf.dimO, cf.conv_params, input_value, filter_value,
error_value)
return out, np_out
return out
import ngraph as ng
import ngraph.transformers as ngt
from ngraph.testing import RandomTensorGenerator, ExecutorFactory
from ngraph.frontends.neon import ax
from third_party.warp_ctc.ctc import CTC
import pytest
pytestmark = [
pytest.mark.transformer_dependent("module"),
pytest.config.flex_disabled(reason="#1898 no CTCKernel for Flex"),
pytest.config.argon_disabled(
reason="Argon Transformer error - TODO triage)")
]
rng = RandomTensorGenerator(0, np.float32)
def pytest_generate_tests(metafunc):
bsz = [1]
if 'data_args' in metafunc.fixturenames:
fargs = []
nout = [5, 7, 9]
bsz = [4, 32]
max_utt_len = [23, 31, 39]
max_lbl_len = [8, 9, 10]
fargs = itt.product(nout, bsz, max_utt_len, max_lbl_len)
metafunc.parametrize('data_args', fargs)
def test_recvop_tensorupdate():
"""
The tensor (RecvOp_#_#) associated with the following conv op has two views:
1) Non-flat view (e.g. RecvOp_#_#_1_1_1_1_4.shape=(1,1,1,1,4))
2) Flat view (e.g. RecvOp_#_#_1_4.shape = (1,4))
This test ensures that inside RecvOp code generation, the generated code
should make sure both views get updated (e.g. by using update_RecvOp_#_# API)
In this test, ng.dot operation tends to use the flat view (i.e. RecvOp_#_#_1_4)
And previously RecvOp with RecvOp_#_#_1_1_1_1_4 = recv_from_send(send_id) failed
to update both two views (i.e. flat and non-flat view of the same buffer/tensor)
"""
class ConvParams(object):
def __init__(self,
C=1,
N=1,
K=1,
D=1,
H=1,
W=1,
T=1,
R=1,
S=1,
pad_d=0,
pad_h=0,
pad_w=0,
str_d=1,
str_h=1,
str_w=1):
from ngraph.frontends.common.utils import conv_output_dim
M = conv_output_dim(D, T, pad_d, str_d)
P = conv_output_dim(H, R, pad_h, str_h)
Q = conv_output_dim(W, S, pad_w, str_w)
self.dimO = (K, M, P, Q, N)
self.dimI = (C, D, H, W, N)
self.dimF = (C, T, R, S, K)
self.conv_params = dict(pad_d=pad_d,
pad_h=pad_h,
pad_w=pad_w,
str_d=str_d,
str_h=str_h,
str_w=str_w,
dil_d=1,
dil_h=1,
dil_w=1)
self.batch_axis = ng.make_axis(name='N', length=N)
self.ax_i = ng.make_axes([
ng.make_axis(name='C', length=C),
ng.make_axis(name='D', length=D),
ng.make_axis(name='H', length=H),
ng.make_axis(name='W', length=W), self.batch_axis
])
self.ax_f = ng.make_axes([
ng.make_axis(name='C', length=C),
ng.make_axis(name='D', length=T),
ng.make_axis(name='H', length=R),
ng.make_axis(name='W', length=S),
ng.make_axis(name='K', length=K),
])
self.ax_o = ng.make_axes([
ng.make_axis(name='C', length=K),
ng.make_axis(name='D', length=M),
ng.make_axis(name='H', length=P),
ng.make_axis(name='W', length=Q), self.batch_axis
])
# Layer 1, using convolutation introduces multi/flatten view of tensors
cf = ConvParams(C=2, N=4, K=1, H=2, W=2, R=2, S=2)
inputs = ng.placeholder(axes=cf.ax_i)
filters = ng.placeholder(axes=cf.ax_f)
# randomly initialize
from ngraph.testing import RandomTensorGenerator
rng = RandomTensorGenerator(0, np.float32)
# put value 1 into inputs/filters for conv
input_value = rng.uniform(1, 1, cf.ax_i)
filter_value = rng.uniform(1, 1, cf.ax_f)
conv = ng.convolution(cf.conv_params, inputs, filters, axes=cf.ax_o)
# Layer 2, using dot to ensure recv_op.axes == send_op.axes
from ngraph.frontends.neon import UniformInit
# put value 1 into weights for dot
init_uni = UniformInit(1, 1)
W_A = ng.make_axis(length=2)
w_axes = ng.make_axes(W_A) + conv.axes.feature_axes()
w = ng.variable(axes=w_axes, initial_value=init_uni)
with ng.metadata(device_id='1'):
dot = ng.dot(w, conv)
with ExecutorFactory() as ex:
dot_comp = ex.executor(dot, filters, inputs)
dot_val = dot_comp(filter_value, input_value)
np.testing.assert_array_equal(dot_val,
[[8., 8., 8., 8.], [8., 8., 8., 8.]])
