mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
cdead5ace1
Summary: Changes in this PR: 1. Intermediate Docker image is shared from build stage to test stage through ECR, in order to fix the Caffe2 flaky CUDA tests. 2. There are ~7 Caffe2 operator tests that are only flaky in `caffe2_py2_gcc4_8_ubuntu14_04_test` on CPU. Disabling those tests on that config only, which is okay to do because we are still running those tests in other test jobs. After this PR is merged, CircleCI will be running on master automatically, and will be running on PRs if the author rebased their PR onto the newest master (which we will ask all the authors to do when we switch off Jenkins for Linux). Pull Request resolved: https://github.com/pytorch/pytorch/pull/12389 Differential Revision: D10224267 Pulled By: yf225 fbshipit-source-id: dd1a90a425c3d13b870d3d328cb301eee2e6e2cd
730 lines
28 KiB
Python
730 lines
28 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import collections
|
|
import functools
|
|
|
|
import numpy as np
|
|
from hypothesis import assume, given
|
|
import hypothesis.strategies as st
|
|
|
|
from caffe2.proto import caffe2_pb2
|
|
from caffe2.python import brew, core, workspace
|
|
import caffe2.python.hypothesis_test_util as hu
|
|
from caffe2.python.model_helper import ModelHelper
|
|
import caffe2.python.serialized_test.serialized_test_util as serial
|
|
import caffe2.python._import_c_extension as C
|
|
|
|
import unittest
|
|
import os
|
|
|
|
|
|
def _cudnn_supports(
|
|
dilation=False,
|
|
nhwc=False,
|
|
backward=False,
|
|
):
|
|
"""Return True if cuDNN supports this configuration."""
|
|
v = workspace.GetCuDNNVersion()
|
|
if backward:
|
|
if nhwc:
|
|
# nhwc isn't supported in backward ops.
|
|
return False
|
|
else:
|
|
# Forward mode.
|
|
if dilation and v < 6000:
|
|
# Dilation not supported until v6
|
|
return False
|
|
if dilation and nhwc:
|
|
# Dilation and NHWC not supported together
|
|
return False
|
|
return True
|
|
|
|
|
|
def _cudnn_convolution_algo_count(direction):
|
|
try:
|
|
if direction == "fwd":
|
|
return st.integers(0, C.cudnn_convolution_fwd_algo_count - 1)
|
|
elif direction == "dgrad":
|
|
return st.integers(0, C.cudnn_convolution_bwd_data_algo_count - 1)
|
|
elif direction == "wgrad":
|
|
return st.integers(0, C.cudnn_convolution_bwd_filter_algo_count - 1)
|
|
else:
|
|
assert False
|
|
except Exception:
|
|
return st.sampled_from([-1])
|
|
|
|
|
|
class TestConvolution(serial.SerializedTestCase):
|
|
# CUDNN does NOT support different padding values and we skip it
|
|
@given(op_type=st.sampled_from(["Conv", "Conv2D"]),
|
|
stride_h=st.integers(1, 3),
|
|
stride_w=st.integers(1, 3),
|
|
pad_t=st.integers(0, 3),
|
|
pad_l=st.integers(0, 3),
|
|
pad_b=st.integers(0, 3),
|
|
pad_r=st.integers(0, 3),
|
|
kernel=st.integers(3, 5),
|
|
size=st.integers(1, 8),
|
|
input_channels=st.integers(1, 3),
|
|
output_channels=st.integers(1, 3),
|
|
batch_size=st.integers(1, 3),
|
|
group=st.integers(1, 2),
|
|
order=st.sampled_from(["NCHW", "NHWC"]),
|
|
engine=st.sampled_from(["", "EIGEN"]),
|
|
shared_buffer=st.booleans(),
|
|
use_bias=st.booleans(),
|
|
**hu.gcs)
|
|
def test_convolution_separate_stride_pad_gradients(
|
|
self, op_type, stride_h, stride_w, pad_t, pad_l, pad_b, pad_r,
|
|
kernel, size, input_channels, output_channels, batch_size, group,
|
|
order, engine, shared_buffer, use_bias, gc, dc):
|
|
if order == "NHWC":
|
|
group = 1
|
|
|
|
input_channels *= group
|
|
output_channels *= group
|
|
|
|
op = core.CreateOperator(
|
|
op_type,
|
|
["X", "w", "b"] if use_bias else ["X", "w"],
|
|
["Y"],
|
|
stride_h=stride_h,
|
|
stride_w=stride_w,
|
|
pad_t=pad_t,
|
|
pad_l=pad_l,
|
|
pad_b=pad_b,
|
|
pad_r=pad_r,
|
|
kernel=kernel,
|
|
group=group,
|
|
order=order,
|
|
engine=engine,
|
|
shared_buffer=int(shared_buffer),
|
|
)
|
|
X = np.random.rand(
|
|
batch_size, size, size, input_channels).astype(np.float32) - 0.5
|
|
w = np.random.rand(
|
|
output_channels, kernel, kernel, int(input_channels / group)
|
|
).astype(np.float32) - 0.5
|
|
b = np.random.rand(output_channels).astype(np.float32) - 0.5
|
|
if order == "NCHW":
|
|
X = X.transpose((0, 3, 1, 2))
|
|
w = w.transpose((0, 3, 1, 2))
|
|
|
|
inputs = [X, w, b] if use_bias else [X, w]
|
|
|
|
# Error handling path.
|
|
if size + pad_r + pad_l < kernel or size + pad_t + pad_b < kernel:
|
|
with self.assertRaises(RuntimeError):
|
|
self.assertDeviceChecks(dc, op, inputs, [0])
|
|
return
|
|
|
|
self.assertDeviceChecks(dc, op, inputs, [0])
|
|
for i in range(len(inputs)):
|
|
self.assertGradientChecks(gc, op, inputs, i, [0])
|
|
|
|
# CUDNN does NOT support different padding values and we skip it
|
|
@given(op_type=st.sampled_from(["Conv", "Conv2D"]),
|
|
stride_h=st.integers(1, 3),
|
|
stride_w=st.integers(1, 3),
|
|
pad_t=st.integers(0, 3),
|
|
pad_l=st.integers(0, 3),
|
|
pad_b=st.integers(0, 3),
|
|
pad_r=st.integers(0, 3),
|
|
kernel=st.integers(1, 5),
|
|
size=st.integers(7, 10),
|
|
input_channels=st.integers(1, 8),
|
|
output_channels=st.integers(1, 8),
|
|
batch_size=st.integers(1, 3),
|
|
engine=st.sampled_from(["", "EIGEN"]),
|
|
use_bias=st.booleans(),
|
|
**hu.gcs)
|
|
def test_convolution_separate_stride_pad_layout(
|
|
self, op_type, stride_h, stride_w, pad_t, pad_l, pad_b, pad_r,
|
|
kernel, size, input_channels, output_channels, batch_size, engine,
|
|
use_bias, gc, dc):
|
|
X = np.random.rand(
|
|
batch_size, size, size, input_channels).astype(np.float32) - 0.5
|
|
w = np.random.rand(
|
|
output_channels, kernel, kernel, input_channels
|
|
).astype(np.float32) - 0.5
|
|
b = np.random.rand(output_channels).astype(np.float32) - 0.5
|
|
outputs = {}
|
|
for order in ["NCHW", "NHWC"]:
|
|
op = core.CreateOperator(
|
|
op_type,
|
|
["X", "w", "b"] if use_bias else ["X", "w"],
|
|
["Y"],
|
|
stride_h=stride_h,
|
|
stride_w=stride_w,
|
|
kernel=kernel,
|
|
pad_t=pad_t,
|
|
pad_l=pad_l,
|
|
pad_b=pad_b,
|
|
pad_r=pad_r,
|
|
order=order,
|
|
engine=engine,
|
|
device_option=gc,
|
|
)
|
|
if order == "NCHW":
|
|
X_f = X.transpose((0, 3, 1, 2))
|
|
w_f = w.transpose((0, 3, 1, 2))
|
|
else:
|
|
X_f = X
|
|
w_f = w
|
|
self.ws.create_blob("X").feed(X_f, device_option=gc)
|
|
self.ws.create_blob("w").feed(w_f, device_option=gc)
|
|
self.ws.create_blob("b").feed(b, device_option=gc)
|
|
self.ws.run(op)
|
|
outputs[order] = self.ws.blobs["Y"].fetch()
|
|
np.testing.assert_allclose(
|
|
outputs["NCHW"],
|
|
outputs["NHWC"].transpose((0, 3, 1, 2)),
|
|
atol=1e-4,
|
|
rtol=1e-4)
|
|
|
|
@given(op_type=st.sampled_from(["Conv", "Conv2D"]),
|
|
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),
|
|
input_channels=st.integers(1, 8),
|
|
output_channels=st.integers(1, 8),
|
|
batch_size=st.integers(1, 3),
|
|
group=st.integers(1, 2),
|
|
order=st.sampled_from(["NCHW", "NHWC"]),
|
|
engine=st.sampled_from(["", "CUDNN", "MKLDNN"]),
|
|
use_bias=st.booleans(),
|
|
force_algo_fwd=_cudnn_convolution_algo_count("fwd"),
|
|
force_algo_dgrad=_cudnn_convolution_algo_count("dgrad"),
|
|
force_algo_wgrad=_cudnn_convolution_algo_count("wgrad"),
|
|
**hu.gcs)
|
|
def test_convolution_gradients(
|
|
self, op_type, stride, pad, kernel, dilation, size, input_channels,
|
|
output_channels, batch_size, group, order, engine, use_bias,
|
|
force_algo_fwd, force_algo_dgrad, force_algo_wgrad, gc, dc):
|
|
if order == "NHWC" or engine == "MKLDNN":
|
|
group = 1
|
|
|
|
input_channels *= group
|
|
output_channels *= group
|
|
dkernel = dilation * (kernel - 1) + 1
|
|
|
|
if engine == 'CUDNN':
|
|
assume(_cudnn_supports(dilation=(dilation > 1),
|
|
nhwc=(order == 'NHWC'),
|
|
backward=True))
|
|
|
|
assume(engine != "MKLDNN" or use_bias is True)
|
|
|
|
op = core.CreateOperator(
|
|
op_type,
|
|
["X", "w", "b"] if use_bias else ["X", "w"],
|
|
["Y"],
|
|
stride=stride,
|
|
kernel=kernel,
|
|
dilation=dilation,
|
|
pad=pad,
|
|
group=group,
|
|
order=order,
|
|
engine=engine,
|
|
force_algo_fwd=force_algo_fwd,
|
|
force_algo_dgrad=force_algo_dgrad,
|
|
force_algo_wgrad=force_algo_wgrad,
|
|
)
|
|
X = np.random.rand(
|
|
batch_size, size, size, input_channels).astype(np.float32) - 0.5
|
|
w = np.random.rand(
|
|
output_channels, kernel, kernel, int(input_channels / group)
|
|
).astype(np.float32) - 0.5
|
|
b = np.random.rand(output_channels).astype(np.float32) - 0.5
|
|
if order == "NCHW":
|
|
X = X.transpose((0, 3, 1, 2))
|
|
w = w.transpose((0, 3, 1, 2))
|
|
|
|
inputs = [X, w, b] if use_bias else [X, w]
|
|
# Error handling path.
|
|
if size + pad + pad < dkernel or size + pad + pad < dkernel:
|
|
with self.assertRaises(RuntimeError):
|
|
self.assertDeviceChecks(dc, op, inputs, [0])
|
|
return
|
|
|
|
try:
|
|
self.assertDeviceChecks(dc, op, inputs, [0])
|
|
except RuntimeError as e:
|
|
es = str(e)
|
|
# CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM should always have
|
|
# implementation
|
|
if "status == CUDNN_STATUS_SUCCESS" not in es \
|
|
or "CUDNN_STATUS_NOT_SUPPORTED" not in es \
|
|
or force_algo_fwd == 0:
|
|
raise e
|
|
|
|
for i in range(len(inputs)):
|
|
try:
|
|
self.assertGradientChecks(gc, op, inputs, i, [0])
|
|
except RuntimeError as e:
|
|
es = str(e)
|
|
if "status == CUDNN_STATUS_SUCCESS" not in es \
|
|
or "CUDNN_STATUS_NOT_SUPPORTED" not in es:
|
|
raise e
|
|
|
|
def _nd_convolution_nchw(self, n, input_channels, output_channels,
|
|
batch_size, stride, size, kernel, dilation, pad,
|
|
use_bias, force_algo_fwd, force_algo_dgrad,
|
|
force_algo_wgrad, gc, dc):
|
|
dkernel = dilation * (kernel - 1) + 1
|
|
for op_type in ["Conv", "Conv" + str(n) + "D"]:
|
|
op = core.CreateOperator(
|
|
op_type,
|
|
["X", "w", "b"] if use_bias else ["X", "w"],
|
|
["Y"],
|
|
strides=[stride] * n,
|
|
kernels=[kernel] * n,
|
|
dilations=[dilation] * n,
|
|
pads=[pad] * n * 2,
|
|
order="NCHW",
|
|
engine="",
|
|
force_algo_fwd=force_algo_fwd,
|
|
force_algo_dgrad=force_algo_dgrad,
|
|
force_algo_wgrad=force_algo_wgrad,
|
|
)
|
|
|
|
input_dims = [batch_size, input_channels]
|
|
input_dims.extend([size] * n)
|
|
filter_dims = [output_channels, input_channels]
|
|
filter_dims.extend([kernel] * n)
|
|
|
|
X = np.random.rand(*input_dims).astype(np.float32) - 0.5
|
|
w = np.random.rand(*filter_dims).astype(np.float32) - 0.5
|
|
b = np.random.rand(output_channels).astype(np.float32) - 0.5
|
|
|
|
inputs = [X, w, b] if use_bias else [X, w]
|
|
|
|
if size + pad + pad < dkernel or size + pad + pad < dkernel:
|
|
with self.assertRaises(RuntimeError):
|
|
self.assertDeviceChecks(dc, op, inputs, [0])
|
|
return
|
|
|
|
self.assertDeviceChecks(dc, op, inputs, [0])
|
|
for i in range(len(inputs)):
|
|
self.assertGradientChecks(gc, op, inputs, i, [0])
|
|
|
|
@given(input_channels=st.integers(1, 3),
|
|
output_channels=st.integers(1, 2),
|
|
batch_size=st.integers(1, 3),
|
|
stride=st.integers(1, 3),
|
|
size=st.integers(7, 10),
|
|
kernel=st.integers(1, 2),
|
|
dilation=st.integers(1, 3),
|
|
pad=st.integers(0, 3),
|
|
use_bias=st.booleans(),
|
|
force_algo_fwd=_cudnn_convolution_algo_count("fwd"),
|
|
force_algo_dgrad=_cudnn_convolution_algo_count("dgrad"),
|
|
force_algo_wgrad=_cudnn_convolution_algo_count("wgrad"),
|
|
**hu.gcs)
|
|
def test_1d_convolution_nchw(self, input_channels, output_channels,
|
|
batch_size, stride, size, kernel, dilation,
|
|
pad, use_bias,
|
|
force_algo_fwd, force_algo_dgrad,
|
|
force_algo_wgrad,
|
|
gc, dc):
|
|
self._nd_convolution_nchw(
|
|
1, input_channels, output_channels, batch_size, stride, size,
|
|
kernel, dilation, pad, use_bias, force_algo_fwd, force_algo_dgrad,
|
|
force_algo_wgrad, gc, dc
|
|
)
|
|
|
|
@given(input_channels=st.integers(1, 2),
|
|
output_channels=st.integers(1, 2),
|
|
batch_size=st.integers(1, 2),
|
|
stride=st.integers(1, 2),
|
|
size=st.integers(4, 5),
|
|
kernel=st.integers(1, 2),
|
|
dilation=st.integers(1, 2),
|
|
pad=st.integers(0, 2),
|
|
use_bias=st.booleans(),
|
|
force_algo_fwd=_cudnn_convolution_algo_count("fwd"),
|
|
force_algo_dgrad=_cudnn_convolution_algo_count("dgrad"),
|
|
force_algo_wgrad=_cudnn_convolution_algo_count("wgrad"),
|
|
**hu.gcs)
|
|
def test_3d_convolution_nchw(self, input_channels, output_channels,
|
|
batch_size, stride, size, kernel, dilation,
|
|
pad, use_bias,
|
|
force_algo_fwd, force_algo_dgrad,
|
|
force_algo_wgrad,
|
|
gc, dc):
|
|
self._nd_convolution_nchw(
|
|
3, input_channels, output_channels, batch_size, stride, size,
|
|
kernel, dilation, pad, use_bias, force_algo_fwd, force_algo_dgrad,
|
|
force_algo_wgrad, gc, dc
|
|
)
|
|
|
|
@given(op_type=st.sampled_from(["Conv", "Conv3D"]),
|
|
batch_size=st.integers(1, 2),
|
|
stride=st.integers(1, 2),
|
|
size=st.integers(3, 5),
|
|
kernel=st.integers(1, 2),
|
|
dilation=st.integers(1, 2),
|
|
pad=st.integers(0, 2),
|
|
use_bias=st.booleans(),
|
|
force_algo_fwd=_cudnn_convolution_algo_count("fwd"),
|
|
force_algo_dgrad=_cudnn_convolution_algo_count("dgrad"),
|
|
force_algo_wgrad=_cudnn_convolution_algo_count("wgrad"),
|
|
**hu.gcs)
|
|
def test_3d_convolution_cudnn_nchw(self, op_type, batch_size, stride, size,
|
|
kernel, dilation, pad, use_bias,
|
|
force_algo_fwd, force_algo_dgrad,
|
|
force_algo_wgrad, gc, dc):
|
|
input_channels = 1
|
|
output_channels = 1
|
|
n = 3
|
|
dkernel = dilation * (kernel - 1) + 1
|
|
order = "NCHW"
|
|
|
|
op = core.CreateOperator(
|
|
op_type,
|
|
["X", "w", "b"] if use_bias else ["X", "w"],
|
|
["Y"],
|
|
strides=[stride] * n,
|
|
kernels=[kernel] * n,
|
|
dilations=[dilation] * n,
|
|
pads=[pad] * n * 2,
|
|
order=order,
|
|
engine="CUDNN",
|
|
force_algo_fwd=force_algo_fwd,
|
|
force_algo_dgrad=force_algo_dgrad,
|
|
force_algo_wgrad=force_algo_wgrad,
|
|
)
|
|
|
|
input_dims = [batch_size, input_channels]
|
|
input_dims.extend([size] * n)
|
|
filter_dims = [output_channels, input_channels]
|
|
filter_dims.extend([kernel] * n)
|
|
X = np.random.rand(*input_dims).astype(np.float32) - 0.5
|
|
w = np.random.rand(*filter_dims).astype(np.float32) - 0.5
|
|
b = np.random.rand(output_channels).astype(np.float32) - 0.5
|
|
|
|
inputs = [X, w, b] if use_bias else [X, w]
|
|
|
|
if size + pad + pad < dkernel or size + pad + pad < dkernel:
|
|
with self.assertRaises(RuntimeError):
|
|
self.assertDeviceChecks(dc, op, inputs, [0])
|
|
return
|
|
|
|
try:
|
|
self.assertDeviceChecks(dc, op, inputs, [0])
|
|
except RuntimeError as e:
|
|
es = str(e)
|
|
# CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM should always have
|
|
# implementation
|
|
if "status == CUDNN_STATUS_SUCCESS" not in es \
|
|
or "CUDNN_STATUS_NOT_SUPPORTED" not in es \
|
|
or force_algo_fwd == 0:
|
|
raise e
|
|
|
|
for i in range(len(inputs)):
|
|
try:
|
|
self.assertGradientChecks(gc, op, inputs, i, [0])
|
|
except RuntimeError as e:
|
|
es = str(e)
|
|
if "status == CUDNN_STATUS_SUCCESS" not in es \
|
|
or "CUDNN_STATUS_NOT_SUPPORTED" not in es:
|
|
raise e
|
|
|
|
@given(op_type=st.sampled_from(["Conv", "Conv2D"]),
|
|
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),
|
|
input_channels=st.integers(1, 8),
|
|
output_channels=st.integers(1, 8),
|
|
batch_size=st.integers(1, 3),
|
|
use_bias=st.booleans(),
|
|
**hu.gcs)
|
|
def test_convolution_layout(self, op_type, stride, pad, kernel, dilation,
|
|
size, input_channels, output_channels,
|
|
batch_size, use_bias, gc, dc):
|
|
assume(size >= dilation * (kernel - 1) + 1)
|
|
|
|
X = np.random.rand(
|
|
batch_size, size, size, input_channels).astype(np.float32) - 0.5
|
|
w = np.random.rand(
|
|
output_channels, kernel, kernel, input_channels
|
|
).astype(np.float32) - 0.5
|
|
b = np.random.rand(output_channels).astype(np.float32) - 0.5
|
|
Output = collections.namedtuple("Output", ["Y", "engine", "order"])
|
|
outputs = []
|
|
|
|
for order in ["NCHW", "NHWC"]:
|
|
engine_list = ['']
|
|
if _cudnn_supports(dilation=(dilation > 1), nhwc=(order == 'NHWC')):
|
|
engine_list.append('CUDNN')
|
|
|
|
for engine in engine_list:
|
|
op = core.CreateOperator(
|
|
op_type,
|
|
["X", "w", "b"] if use_bias else ["X", "w"],
|
|
["Y"],
|
|
stride=stride,
|
|
kernel=kernel,
|
|
dilation=dilation,
|
|
pad=pad,
|
|
order=order,
|
|
engine=engine,
|
|
device_option=gc,
|
|
exhaustive_search=True,
|
|
)
|
|
if order == "NCHW":
|
|
X_f = X.transpose((0, 3, 1, 2))
|
|
w_f = w.transpose((0, 3, 1, 2))
|
|
else:
|
|
X_f = X
|
|
w_f = w
|
|
self.assertDeviceChecks(
|
|
dc,
|
|
op,
|
|
[X_f, w_f, b] if use_bias else [X_f, w_f],
|
|
[0])
|
|
self.ws.create_blob("X").feed(X_f, device_option=gc)
|
|
self.ws.create_blob("w").feed(w_f, device_option=gc)
|
|
self.ws.create_blob("b").feed(b, device_option=gc)
|
|
self.ws.run(op)
|
|
outputs.append(Output(
|
|
Y=self.ws.blobs["Y"].fetch(), engine=engine, order=order))
|
|
|
|
def canonical(o):
|
|
if o.order == "NHWC":
|
|
return o.Y.transpose((0, 3, 1, 2))
|
|
else:
|
|
return o.Y
|
|
|
|
for o in outputs:
|
|
np.testing.assert_allclose(
|
|
canonical(outputs[0]),
|
|
canonical(o),
|
|
atol=1e-4,
|
|
rtol=1e-4)
|
|
|
|
@given(num_workers=st.integers(1, 4),
|
|
net_type=st.sampled_from(
|
|
["simple", "dag"] +
|
|
(["async_dag"] if workspace.has_gpu_support or
|
|
workspace.has_hip_support else [])),
|
|
engine=st.sampled_from(["CUDNN", ""]),
|
|
**hu.gcs_no_hip)
|
|
def test_convolution_sync(self, net_type, num_workers, engine, gc, dc):
|
|
m = ModelHelper(name="test_model")
|
|
n = 1
|
|
d = 2
|
|
depth = 3
|
|
iters = 5
|
|
h = 5
|
|
w = 5
|
|
workspace.ResetWorkspace()
|
|
|
|
use_cudnn = (engine == 'CUDNN')
|
|
|
|
np.random.seed(1701)
|
|
# Build a binary tree of conv 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)
|
|
w1, b1, w2, b2 = np.random.randn(4).tolist()
|
|
brew.conv(
|
|
m, bottom_1, mid_1,
|
|
dim_in=d, dim_out=d,
|
|
kernel=3,
|
|
weight_init=('ConstantFill', dict(value=w1)),
|
|
bias_init=('ConstantFill', dict(value=b1)),
|
|
cudnn_state=np.random.randint(0, 3),
|
|
stride=1,
|
|
pad=1,
|
|
deterministic=1,
|
|
use_cudnn=use_cudnn,
|
|
engine=engine)
|
|
brew.conv(
|
|
m, bottom_2, mid_2,
|
|
dim_in=d, dim_out=d,
|
|
kernel=3,
|
|
stride=1,
|
|
pad=1,
|
|
weight_init=('ConstantFill', dict(value=w2)),
|
|
bias_init=('ConstantFill', dict(value=b2)),
|
|
deterministic=1,
|
|
cudnn_state=np.random.randint(0, 3),
|
|
use_cudnn=use_cudnn,
|
|
engine=engine)
|
|
m.net.Sum([mid_1, mid_2], top)
|
|
|
|
m.net.Flatten(["0_0"], ["0_0_flat"])
|
|
m.net.SquaredL2Distance(["0_0_flat", "label"], "xent")
|
|
m.net.AveragedLoss("xent", "loss")
|
|
input_to_grad = m.AddGradientOperators(["loss"])
|
|
m.Proto().device_option.CopyFrom(gc)
|
|
m.param_init_net.Proto().device_option.CopyFrom(gc)
|
|
m.Proto().type = net_type
|
|
m.Proto().num_workers = num_workers
|
|
self.ws.run(m.param_init_net)
|
|
|
|
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, h, w).astype(np.float32),
|
|
device_option=gc)
|
|
self.ws.create_blob("label").feed(
|
|
np.random.randn(n, d * h * w).astype(np.float32),
|
|
device_option=gc)
|
|
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)
|
|
np.testing.assert_allclose(
|
|
np.sum(np.square(output)),
|
|
1763719461732352.0,
|
|
rtol=1e-5)
|
|
|
|
def test_use_cudnn_engine_interactions(self):
|
|
"""Make sure the use_cudnn and engine kwargs work as expected."""
|
|
for model_default in [None, True, False]:
|
|
arg_scope = {}
|
|
if model_default is not None:
|
|
arg_scope['use_cudnn'] = model_default
|
|
else:
|
|
model_default = True # the default
|
|
|
|
model = ModelHelper(arg_scope=arg_scope)
|
|
self.assertEqual(model.arg_scope['use_cudnn'], model_default)
|
|
f = functools.partial(brew.conv, model,
|
|
'conv_in', 'conv_out', 10, 10, 5)
|
|
|
|
for op_cudnn in [None, True, False]:
|
|
for op_engine in [None, '', 'CUDNN']:
|
|
kwargs = {}
|
|
if op_cudnn is not None:
|
|
kwargs['use_cudnn'] = op_cudnn
|
|
else:
|
|
op_cudnn = False # the default
|
|
if op_engine is not None:
|
|
kwargs['engine'] = op_engine
|
|
|
|
calculated_cudnn = kwargs.get('use_cudnn', model_default)
|
|
expected_engine = kwargs.get(
|
|
'engine',
|
|
'CUDNN' if calculated_cudnn else '')
|
|
|
|
if ((calculated_cudnn is False and op_engine == 'CUDNN') or
|
|
(calculated_cudnn is True and op_engine == '')):
|
|
with self.assertRaises(ValueError):
|
|
f(**kwargs)
|
|
else:
|
|
f(**kwargs)
|
|
self.assertEqual(model.Proto().op[-1].engine,
|
|
expected_engine)
|
|
|
|
@serial.given(
|
|
op_type=st.sampled_from(["Conv", "Conv2D"]), N=st.integers(1, 4),
|
|
G=st.integers(1, 4), DX=st.integers(1, 4), DY=st.integers(1, 4),
|
|
H=st.integers(1, 4), W=st.integers(1, 4), use_bias=st.booleans(),
|
|
order=st.sampled_from(["NCHW", "NHWC"]),
|
|
force_algo_fwd=_cudnn_convolution_algo_count("fwd"),
|
|
force_algo_dgrad=_cudnn_convolution_algo_count("dgrad"),
|
|
force_algo_wgrad=_cudnn_convolution_algo_count("wgrad"),
|
|
**hu.gcs)
|
|
def test_1x1_conv(self, op_type, N, G, DX, DY, H, W, use_bias, order,
|
|
force_algo_fwd, force_algo_dgrad,
|
|
force_algo_wgrad, gc, dc):
|
|
if order == "NHWC":
|
|
G = 1
|
|
|
|
C = G * DX
|
|
M = G * DY
|
|
|
|
op = core.CreateOperator(
|
|
op_type,
|
|
["X", "filter", "bias"] if use_bias else ["X", "filter"],
|
|
["Y"],
|
|
stride_h=1,
|
|
stride_w=1,
|
|
pad_t=0,
|
|
pad_l=0,
|
|
pad_b=0,
|
|
pad_r=0,
|
|
kernel=1,
|
|
order=order,
|
|
group=G,
|
|
force_algo_fwd=force_algo_fwd,
|
|
force_algo_dgrad=force_algo_dgrad,
|
|
force_algo_wgrad=force_algo_wgrad,
|
|
)
|
|
|
|
if order == "NCHW":
|
|
X = np.random.randn(N, C, H, W).astype(np.float32)
|
|
filter = np.random.randn(M, DX, 1, 1).astype(np.float32)
|
|
else:
|
|
X = np.random.randn(N, H, W, C).astype(np.float32)
|
|
filter = np.random.randn(M, 1, 1, DX).astype(np.float32)
|
|
bias = np.random.randn(M).astype(np.float32)
|
|
inputs = [X, filter, bias] if use_bias else [X, filter]
|
|
|
|
def conv_1x1_nchw_ref(X, filter, bias=None):
|
|
X = X.reshape(N, G, DX, -1)
|
|
filter = filter.reshape(G, DY, DX)
|
|
Y = np.zeros(shape=(N, G, DY, H * W), dtype=np.float32)
|
|
for i in range(N):
|
|
for j in range(G):
|
|
Y[i, j, :, :] = np.dot(filter[j, :, :], X[i, j, :, :])
|
|
Y = Y.reshape(N, M, H, W)
|
|
if bias is not None:
|
|
bias = bias.reshape(1, M, 1, 1)
|
|
Y = np.add(Y, bias)
|
|
return [Y]
|
|
|
|
def conv_1x1_nhwc_ref(X, filter, bias=None):
|
|
X = X.reshape(N, -1, G, DX)
|
|
filter = filter.reshape(G, DY, DX)
|
|
Y = np.zeros(shape=(N, H * W, G, DY), dtype=np.float32)
|
|
for i in range(N):
|
|
for j in range(G):
|
|
Y[i, :, j, :] = np.dot(
|
|
X[i, :, j, :], filter[j, :, :].transpose())
|
|
Y = Y.reshape(N, H, W, M)
|
|
if bias is not None:
|
|
bias = bias.reshape(1, 1, 1, M)
|
|
Y = np.add(Y, bias)
|
|
return [Y]
|
|
|
|
if order == "NCHW":
|
|
conv_1x1_ref = conv_1x1_nchw_ref
|
|
else:
|
|
conv_1x1_ref = conv_1x1_nhwc_ref
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=inputs,
|
|
reference=conv_1x1_ref,
|
|
)
|
|
self.assertDeviceChecks(dc, op, inputs, [0])
|
|
for i in range(len(inputs)):
|
|
self.assertGradientChecks(gc, op, inputs, i, [0])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
unittest.main()
|