Skip to content

[CI] Hotfix CI (see #7010) #7025

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
tqchen merged 2 commits into from
Dec 3, 2020
Merged

[CI] Hotfix CI (see #7010) #7025

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
ba70225
hotfix CI (see #7010)
altanh Dec 3, 2020
931ae11
lint
altanh Dec 3, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 10 additions & 2 deletions python/tvm/relay/testing/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,15 @@ def _np_randn_from_type(t, scale=1, mean=0):


def check_grad(
func, inputs=None, test_inputs=None, eps=1e-6, atol=1e-5, rtol=1e-3, scale=None, mean=0
func,
inputs=None,
test_inputs=None,
eps=1e-6,
atol=1e-5,
rtol=1e-3,
scale=None,
mean=0,
mode="higher_order",
):
"""Perform numerical gradient checking given a relay function.

Expand Down Expand Up @@ -112,7 +120,7 @@ def check_grad(
"""

fwd_func = run_infer_type(func)
bwd_func = run_infer_type(gradient(fwd_func))
bwd_func = run_infer_type(gradient(fwd_func, mode=mode))

if scale is None:
scale = 10 * eps
Expand Down
51 changes: 1 addition & 50 deletions tests/python/relay/test_op_grad_level2.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -168,63 +168,14 @@ def test_global_avg_pool2d_grad():


def verify_conv2d_grad(dshape, wshape, strides, padding, dilation, groups=1, mode="higher_order"):
try:
import torch
import torch.nn.functional as F
except ImportError:
print("Skip because pytorch is not installed")
return

dtype = "float32"
data = relay.var("data", shape=dshape, dtype=dtype)
weight = relay.var("weight", shape=wshape, dtype=dtype)
conv = relay.nn.conv2d(
data, weight, strides=strides, padding=padding, dilation=dilation, groups=groups
)
fwd_func = relay.Function([data, weight], conv)
fwd_func = run_infer_type(fwd_func)
bwd_func = run_infer_type(gradient(fwd_func, mode=mode))

data_pt = torch.randn(*dshape, dtype=torch.float32, requires_grad=True)
weight_pt = torch.randn(*wshape, dtype=torch.float32, requires_grad=True)
out_pt = F.conv2d(
data_pt, weight_pt, stride=strides, padding=padding, dilation=dilation, groups=groups
)
grad_output_pt = torch.ones(out_pt.shape)
grad_input_pt = (
F.grad.conv2d_input(
dshape,
weight_pt,
grad_output_pt,
stride=strides,
padding=padding,
dilation=dilation,
groups=groups,
)
.detach()
.numpy()
)
grad_weight_pt = (
F.grad.conv2d_weight(
data_pt,
wshape,
grad_output_pt,
stride=strides,
padding=padding,
dilation=dilation,
groups=groups,
)
.detach()
.numpy()
)

for target, ctx in tvm.testing.enabled_targets():
data = tvm.nd.array(data_pt.detach().numpy(), ctx)
weight = tvm.nd.array(weight_pt.detach().numpy(), ctx)
intrp = relay.create_executor(ctx=ctx, target=target)
op_res, (grad_input, grad_weight) = intrp.evaluate(bwd_func)(data, weight)
np.testing.assert_allclose(grad_input.asnumpy(), grad_input_pt, rtol=1e-4, atol=1e-4)
np.testing.assert_allclose(grad_weight.asnumpy(), grad_weight_pt, rtol=1e-4, atol=1e-4)
check_grad(fwd_func, mode=mode)


@tvm.testing.uses_gpu
Expand Down