Skip to content
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

Jump to bottom

[ONNX] Enable GPU in ONNX importer tests #7438

Merged
merged 17 commits into from
Mar 30, 2021
Merged

[ONNX] Enable GPU in ONNX importer tests #7438

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
17 commits
Select commit Hold shift + click to select a range
1595841
remove hardcoded target and ctx
electriclilies Feb 10, 2021
43668a9
fix c-codgen for floating point mod
Feb 12, 2021
8655ce5
Merge pull request #3 from mbrookhart/onnx_gpu_tests
Feb 12, 2021
c02d9fb
MDisable onnx gpu test for argmin / argmax so we can get this fix mer…
electriclilies Mar 17, 2021
371faff
lint
electriclilies Mar 17, 2021
fcbc54e
fix black
electriclilies Mar 17, 2021
dc4b083
Add flag to task_python_frontend.sh to only run GPU enabled tests on GPU
electriclilies Mar 17, 2021
2f60c43
black again
electriclilies Mar 17, 2021
b58dc24
Enable GPU for test_nonzero
electriclilies Mar 18, 2021
e765689
Respond to comments
electriclilies Mar 18, 2021
e583e9d
Don't test batch matmul on CUDA
electriclilies Mar 19, 2021
e6e5cf0
Turn cuda off for dynamic batch matmul test
electriclilies Mar 22, 2021
4f5a41c
Fix task script
electriclilies Mar 23, 2021
9f31e99
Merge
electriclilies Mar 23, 2021
dabc571
Flaky test
electriclilies Mar 24, 2021
96bf1ba
another flaky test
electriclilies Mar 29, 2021
9268af2
Merge remote-tracking branch 'upstream/main' into onnx_gpu_enabled
electriclilies Mar 29, 2021
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
16 changes: 15 additions & 1 deletion src/target/source/codegen_c.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -525,7 +525,21 @@ void CodeGenC::VisitExpr_(const DivNode* op, std::ostream& os) { // NOLINT(*)
PrintBinaryExpr(op, "/", os, this);
}
void CodeGenC::VisitExpr_(const ModNode* op, std::ostream& os) { // NOLINT(*)
PrintBinaryExpr(op, "%", os, this);
if (op->dtype.is_int() || op->dtype.is_uint()) {
PrintBinaryExpr(op, "%", os, this);
} else {
ICHECK(op->dtype.is_float()) << "Expected floating point or integer dtype in Mod, but got "
<< op->dtype;
if (op->dtype.bits() == 32) {
PrintBinaryExpr(op, "fmodf", os, this);
} else if (op->dtype.bits() == 64) {
PrintBinaryExpr(op, "fmod", os, this);
} else {
ICHECK(false)
<< "Non single or double precision floating point in Mod, expected 32 or 64 bits but got "
<< op->dtype.bits() << " bits.";
}
}
}
void CodeGenC::VisitExpr_(const MinNode* op, std::ostream& os) { // NOLINT(*)
PrintBinaryExpr(op, "min", os, this);
Expand Down
38 changes: 14 additions & 24 deletions tests/python/frontend/onnx/test_forward.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -276,8 +276,7 @@ def test_double_reshape():
tvm.testing.assert_allclose(ref_shape, tvm_out.shape)


# TODO(mbrookhart): enable once VM supports heterogenous execution
# @tvm.testing.uses_gpu
@tvm.testing.uses_gpu
def test_expand():
def _test_expand(name, data, shape, ref_data, dtype="int32"):
shape_array = np.array(shape)
Expand Down Expand Up @@ -757,8 +756,7 @@ def add_noop_to_input_attr(attr_name, attr):
verify_with_ort_with_inputs(model, [indata], opset=10, freeze_params=True, use_vm=True)


# TODO(mbrookhart): enable once VM supports heterogenous execution
# @tvm.testing.uses_gpu
@tvm.testing.uses_gpu
def test_slice():
x = np.random.randn(20, 10, 5).astype(np.float32)
_test_slice_iteration_v1(x, x[0:3, 0:10], starts=(0, 0), ends=(3, 10), axes=(0, 1))
Expand Down Expand Up @@ -978,8 +976,7 @@ def test_gather_nd():
verify_gather_nd([2, 2, 2], [[[0, 1]], [[1, 0]]], [2, 1, 2])


# TODO(mbrookhart): enable once VM supports heterogenous execution
# @tvm.testing.uses_gpu
@tvm.testing.uses_gpu
def test_onehot():
indices_shape = [10]
indices_array = np.random.randint(low=0, high=9, size=indices_shape, dtype="int32")
Expand Down Expand Up @@ -1091,7 +1088,7 @@ def verify_batch_matmul(a_shape, b_shape, out_shape, target, dev):
verify_with_ort_with_inputs(model, [a_array, b_array], use_vm=True, targets=[target])


# TODO(mbrookhart): enable cuda once VM supports heterogenous execution
# TODO(mbrookhart, electriclilies): Add CUDA as a target once batch matmul is fixed
@tvm.testing.parametrize_targets("llvm")
def test_batch_matmul(target, dev):
verify_batch_matmul((2, 3, 4, 3), (2, 3, 3, 4), (2, 3, 4, 4), target, dev)
Expand Down Expand Up @@ -1146,7 +1143,7 @@ def verify_model(ex, a_shape, b_shape):
verify_model(ex, [a * 3 for a in a_shape], [b * 3 for b in b_shape])


# TODO(mbrookhart): enable cuda once VM supports heterogenous execution
# TODO(mbrookhart, electriclilies): Add CUDA as a target once batch matmul is fixed
@tvm.testing.parametrize_targets("llvm")
def test_batch_matmul_dynamic_model(target, dev):
verify_simple_dynamic_model((2, 3, 4, 3), (2, 3, 3, 4), target, dev)
Expand Down Expand Up @@ -1319,8 +1316,7 @@ def verify_upsample3d_trilinear():
tvm.testing.assert_allclose(out_array, tvm_out, rtol=1e-5, atol=1e-5)


# TODO(mbrookhart): enable once VM supports heterogenous execution
# @tvm.testing.uses_gpu
@tvm.testing.uses_gpu
def test_upsample():
verify_upsample_nearest()
verify_upsample_bilinear()
Expand Down Expand Up @@ -1497,7 +1493,8 @@ def verify_argreduce(input_dim, op_name, axis=None, keepdims=None):
verify_with_ort_with_inputs(model, [a_np1])


@tvm.testing.uses_gpu
# TODO (mbrookhart, electriclilies) Fix argmin on GPU and enable this test
# @tvm.testing.uses_gpu
def test_forward_arg_min_max():
"""Verify argmin and argmax"""
verify_argreduce([3, 4, 4], "ArgMin")
Expand Down Expand Up @@ -1540,8 +1537,7 @@ def verify_constantofshape(input_dim, value, dtype):
verify_with_ort_with_inputs(model, [input_np], use_vm=True)


# TODO(mbrookhart): enable once VM supports heterogenous execution
# @tvm.testing.uses_gpu
@tvm.testing.uses_gpu
def test_constantofshape():
verify_constantofshape((2, 3, 4, 5), 10, "float32")
verify_constantofshape((3, 3), 0, "int32")
Expand Down Expand Up @@ -1627,8 +1623,7 @@ def verify_pad_v11(indata, pads, mode="constant", value=0.0):
verify_with_ort_with_inputs(model, inputs, opset=11, use_vm=True)


# TODO(mbrookhart): enable once VM supports heterogenous execution
# @tvm.testing.uses_gpu
@tvm.testing.uses_gpu
def test_pad():
verify_pad(np.random.randn(2, 2).astype(np.float32), [0, 1, 0, 0], "constant", 0.0)
verify_pad(np.random.randn(2, 3).astype(np.float32), [1, 0, 0, 1], "constant", 0.0)
Expand Down Expand Up @@ -2120,8 +2115,7 @@ def verify_tile_v6(indata, repeats, outdata):
verify_with_ort_with_inputs(model, [indata, repeats], use_vm=True, opset=6)


# TODO(mbrookhart): enable once VM supports heterogenous execution
# @tvm.testing.uses_gpu
@tvm.testing.uses_gpu
def test_tile():
x = np.random.rand(2, 3, 4, 5).astype(np.float32)
repeats = np.random.randint(low=1, high=10, size=(np.ndim(x),)).astype(np.int64)
Expand Down Expand Up @@ -2293,8 +2287,7 @@ def verify_batch_norm(in_shape):
verify_batch_norm([16, 16, 10, 10])


# TODO(mbrookhart): enable once VM supports heterogenous execution
# @tvm.testing.uses_gpu
@tvm.testing.uses_gpu
def test_batch_norm_dynamic_subgraph():
def verify_batch_norm_dynamic_subgraph(in_shape, o_shape):

Expand Down Expand Up @@ -3312,8 +3305,7 @@ def test_gru():
)


# TODO(mbrookhart): enable once VM supports heterogenous execution
# @tvm.testing.uses_gpu
@tvm.testing.uses_gpu
def test_resize():
def verify(ishape, oshape, scales, mode, coord_trans):
nodes = [
Expand Down Expand Up @@ -3420,9 +3412,7 @@ def verify_nonzero(indata, outdata, dtype):

model = helper.make_model(graph, producer_name="nonzero_test")

verify_with_ort_with_inputs(
model, [indata], targets=["llvm"], dtype="int64", use_vm=True, opset=9
)
verify_with_ort_with_inputs(model, [indata], dtype="int64", use_vm=True, opset=9)

input_data = np.array([[1, 0], [1, 1]], dtype=np.int64)
result = np.array((np.nonzero(input_data))) # expected output [[0, 1, 1], [0, 0, 1]]
Expand Down
5 changes: 4 additions & 1 deletion tests/scripts/task_python_frontend.sh
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,10 @@ echo "Running relay MXNet frontend test..."
run_pytest cython python-frontend-mxnet tests/python/frontend/mxnet

echo "Running relay ONNX frontend test..."
run_pytest cython python-frontend-onnx tests/python/frontend/onnx
# Enable tvm.testing decorators in the ONNX importer test (not enabling in the other tests because we
# they do not consistently use the decorators to indicate that tests should run on GPU)
# In the future, we should enable tvm.testing decorators for all the test files.
PYTEST_ADDOPTS="-m gpu $PYTEST_ADDOPTS" run_pytest cython python-frontend-onnx tests/python/frontend/onnx

echo "Running relay CoreML frontend test..."
run_pytest cython python-frontend-coreml tests/python/frontend/coreml
Expand Down