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Additional test settings for non-default hyperparameters #108

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merged 9 commits into from
Sep 20, 2020
Merged

Additional test settings for non-default hyperparameters #108

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54629c3
ADD: Additional test settings for non-default hyperparameters
sbharadwajj Sep 16, 2020
c8c26b9
ADD: Settings for groups != 1
sbharadwajj Sep 16, 2020
c0acef2
FIX: black
sbharadwajj Sep 16, 2020
567f017
change: renamed setting
sbharadwajj Sep 16, 2020
54c4f78
FIX: changes according to PR
sbharadwajj Sep 17, 2020
aa4590c
FIX: PR comments
sbharadwajj Sep 17, 2020
f3dba72
EDIT: bias=False setting
sbharadwajj Sep 17, 2020
3268777
EDIT: small change in bias=False
sbharadwajj Sep 18, 2020
ddce0e7
fix black
sbharadwajj Sep 18, 2020
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6 changes: 6 additions & 0 deletions backpack/core/derivatives/conv_transposend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,6 +52,9 @@ def _bias_jac_mat_prod(self, module, g_inp, g_out, mat):
return jac_mat.expand(*expand_shape)

def _weight_jac_mat_prod(self, module, g_inp, g_out, mat):
if module.groups != 1:
raise NotImplementedError("Groups greater than 1 are not supported yet")

V = mat.shape[0]
G = module.groups
C_in = module.input0.shape[1]
Expand All @@ -71,6 +74,9 @@ def _weight_jac_mat_prod(self, module, g_inp, g_out, mat):
return self.reshape_like_output(jac_mat, module)

def _weight_jac_t_mat_prod(self, module, g_inp, g_out, mat, sum_batch=True):
if module.groups != 1:
raise NotImplementedError("Groups greater than 1 are not supported yet")

V = mat.shape[0]
G = module.groups
C_in = module.input0.shape[1]
Expand Down
6 changes: 6 additions & 0 deletions backpack/core/derivatives/convnd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -100,6 +100,9 @@ def _bias_jac_t_mat_prod(self, module, g_inp, g_out, mat, sum_batch=True):
return mat.sum(axes)

def _weight_jac_mat_prod(self, module, g_inp, g_out, mat):
if module.groups != 1:
raise NotImplementedError("Groups greater than 1 are not supported yet")

dims = self.dim_text
dims_joined = dims.replace(",", "")

Expand All @@ -109,6 +112,9 @@ def _weight_jac_mat_prod(self, module, g_inp, g_out, mat):
return self.reshape_like_output(jac_mat, module)

def _weight_jac_t_mat_prod(self, module, g_inp, g_out, mat, sum_batch=True):
if module.groups != 1:
raise NotImplementedError("Groups greater than 1 are not supported yet")

V = mat.shape[0]
N, C_out = module.output_shape[0], module.output_shape[1]
C_in = module.input0_shape[1]
Expand Down
149 changes: 149 additions & 0 deletions test/core/derivatives/convolution_settings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -276,3 +276,152 @@
"id_prefix": "non-default-conv",
},
]

CONVOLUTION_FAIL_SETTINGS = [
# groups - 2
{
"module_fn": lambda: torch.nn.Conv1d(
in_channels=4,
out_channels=6,
kernel_size=2,
padding=0,
dilation=2,
groups=2,
),
"input_fn": lambda: torch.rand(size=(3, 4, 7)),
"id_prefix": "groups-2",
},
{
"module_fn": lambda: torch.nn.Conv2d(
in_channels=4,
out_channels=6,
kernel_size=2,
padding=0,
dilation=2,
groups=2,
),
"input_fn": lambda: torch.rand(size=(3, 4, 7, 7)),
"id_prefix": "groups-2",
},
{
"module_fn": lambda: torch.nn.Conv3d(
in_channels=4,
out_channels=6,
kernel_size=2,
padding=0,
dilation=2,
groups=2,
),
"input_fn": lambda: torch.rand(size=(3, 4, 3, 7, 7)),
"id_prefix": "groups-2",
},
{
"module_fn": lambda: torch.nn.ConvTranspose1d(
in_channels=4,
out_channels=6,
kernel_size=2,
padding=0,
dilation=2,
groups=2,
),
"input_fn": lambda: torch.rand(size=(3, 4, 7)),
"id_prefix": "groups-2",
},
{
"module_fn": lambda: torch.nn.ConvTranspose2d(
in_channels=4,
out_channels=6,
kernel_size=2,
padding=0,
dilation=2,
groups=2,
),
"input_fn": lambda: torch.rand(size=(3, 4, 7, 7)),
"id_prefix": "groups-2",
},
{
"module_fn": lambda: torch.nn.ConvTranspose3d(
in_channels=4,
out_channels=6,
kernel_size=2,
padding=0,
dilation=2,
groups=2,
),
"input_fn": lambda: torch.rand(size=(3, 4, 3, 7, 7)),
"id_prefix": "groups-2",
},
# groups - 3
{
"module_fn": lambda: torch.nn.Conv1d(
in_channels=6,
out_channels=9,
kernel_size=2,
padding=0,
dilation=2,
groups=3,
),
"input_fn": lambda: torch.rand(size=(3, 6, 7)),
"id_prefix": "groups-3",
},
{
"module_fn": lambda: torch.nn.Conv2d(
in_channels=6,
out_channels=9,
kernel_size=2,
padding=0,
dilation=2,
groups=3,
),
"input_fn": lambda: torch.rand(size=(3, 6, 7, 7)),
"id_prefix": "groups-3",
},
{
"module_fn": lambda: torch.nn.Conv3d(
in_channels=6,
out_channels=9,
kernel_size=2,
padding=0,
dilation=2,
groups=3,
),
"input_fn": lambda: torch.rand(size=(3, 6, 3, 7, 7)),
"id_prefix": "groups-3",
},
{
"module_fn": lambda: torch.nn.ConvTranspose1d(
in_channels=6,
out_channels=9,
kernel_size=2,
padding=0,
dilation=2,
groups=3,
),
"input_fn": lambda: torch.rand(size=(3, 6, 7)),
"id_prefix": "groups-3",
},
{
"module_fn": lambda: torch.nn.ConvTranspose2d(
in_channels=6,
out_channels=9,
kernel_size=2,
padding=0,
dilation=2,
groups=3,
),
"input_fn": lambda: torch.rand(size=(3, 6, 7, 7)),
"id_prefix": "groups-3",
},
{
"module_fn": lambda: torch.nn.ConvTranspose3d(
in_channels=6,
out_channels=9,
kernel_size=2,
padding=0,
dilation=2,
groups=3,
),
"input_fn": lambda: torch.rand(size=(3, 6, 3, 7, 7)),
"id_prefix": "groups-3",
},
]
19 changes: 19 additions & 0 deletions test/core/derivatives/derivatives_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
from test.core.derivatives.problem import make_test_problems
from test.core.derivatives.settings import SETTINGS
from test.core.derivatives.loss_settings import LOSS_FAIL_SETTINGS
from test.core.derivatives.convolution_settings import CONVOLUTION_FAIL_SETTINGS

import pytest
import torch
Expand All @@ -30,6 +31,9 @@
LOSS_FAIL_PROBLEMS = make_test_problems(LOSS_FAIL_SETTINGS)
LOSS_FAIL_IDS = [problem.make_id() for problem in LOSS_FAIL_PROBLEMS]

CONVOLUTION_FAIL_PROBLEMS = make_test_problems(CONVOLUTION_FAIL_SETTINGS)
CONVOLUTION_FAIL_IDS = [problem.make_id() for problem in CONVOLUTION_FAIL_PROBLEMS]


@pytest.mark.parametrize("problem", NO_LOSS_PROBLEMS, ids=NO_LOSS_IDS)
def test_jac_mat_prod(problem, V=3):
Expand Down Expand Up @@ -200,6 +204,21 @@ def test_sqrt_hessian_squared_equals_hessian(problem):
problem.tear_down()


@pytest.mark.parametrize("problem", CONVOLUTION_FAIL_PROBLEMS, ids=CONVOLUTION_FAIL_IDS)
def test_weight_jac_mat_prod_should_fail(problem):
with pytest.raises(NotImplementedError):
test_weight_jac_mat_prod(problem)


@pytest.mark.parametrize(
"sum_batch", [True, False], ids=["sum_batch=True", "sum_batch=False"]
)
@pytest.mark.parametrize("problem", CONVOLUTION_FAIL_PROBLEMS, ids=CONVOLUTION_FAIL_IDS)
def test_weight_jac_t_mat_prod_should_fail(problem, sum_batch):
with pytest.raises(NotImplementedError):
test_weight_jac_t_mat_prod(problem, sum_batch)


@pytest.mark.parametrize("problem", LOSS_FAIL_PROBLEMS, ids=LOSS_FAIL_IDS)
def test_sqrt_hessian_should_fail(problem):
with pytest.raises(ValueError):
Expand Down
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