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[ONNX] Make freeze_param = True and run DynamicToStatic by default #10750

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Merged
masahi merged 4 commits into from
Mar 24, 2022
Merged

[ONNX] Make freeze_param = True and run DynamicToStatic by default #10750

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24c96b8
[ONNX] make freeze_params=True and run DynamicToStatic by default
masahi Mar 23, 2022
910ffb2
remove convert_to_static in onnx test
masahi Mar 23, 2022
eef16de
fixed qlinearconv conversion for freeze_params=True
masahi Mar 23, 2022
de2d601
fixed assert msg placement
masahi Mar 24, 2022
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10 changes: 8 additions & 2 deletions python/tvm/relay/frontend/onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3720,12 +3720,14 @@ def _impl_v10(cls, inputs, attr, params):
if attr["auto_pad"] in ("SAME_UPPER", "SAME_LOWER"):
# Warning: Convolution does not yet support dynamic shapes,
# one will need to run dynamic_to_static on this model after import
zp = fold_constant(x_zero_point)
assert isinstance(zp, relay.Constant), "Zero point expected to be a constant"
data = autopad(
data,
attr.get("strides", [1] * (ndim - 2)),
attr["kernel_shape"],
attr.get("dilations", [1] * (ndim - 2)),
pad_value=x_zero_point.data,
pad_value=zp.data,
mode=attr["auto_pad"],
)
elif attr["auto_pad"] == "VALID":
Expand Down Expand Up @@ -5133,7 +5135,7 @@ def _fix_outputs(self, op_name, outputs):


def from_onnx(
model, shape=None, dtype="float32", opset=None, freeze_params=False, convert_config=None
model, shape=None, dtype="float32", opset=None, freeze_params=True, convert_config=None
):
"""Convert a ONNX model into an equivalent Relay Function.

Expand Down Expand Up @@ -5223,4 +5225,8 @@ def from_onnx(
# Use the graph proto as a scope so that ops can access other nodes if needed.
with g:
mod, params = g.from_onnx(graph, opset)

if freeze_params:
mod = relay.transform.DynamicToStatic()(mod)

return mod, params
31 changes: 4 additions & 27 deletions tests/python/frontend/onnx/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,7 +54,6 @@ def get_tvm_output_with_vm(
dev,
opset=None,
freeze_params=False,
convert_to_static=False,
convert_config=None,
):
"""Generic function to execute and get tvm output with vm executor"""
Expand All @@ -69,9 +68,6 @@ def get_tvm_output_with_vm(
convert_config=convert_config,
)

if convert_to_static:
mod = relay.transform.DynamicToStatic()(mod)

result = relay.create_executor("vm", mod=mod, device=dev, target=target).evaluate()(
*input_data, **params
)
Expand Down Expand Up @@ -154,7 +150,6 @@ def verify_with_ort_with_inputs(
use_vm=False,
opset=None,
freeze_params=False,
convert_to_static=False,
dtype="float32",
rtol=1e-5,
atol=1e-5,
Expand All @@ -174,7 +169,6 @@ def verify_with_ort_with_inputs(
dev,
opset=opset,
freeze_params=freeze_params,
convert_to_static=convert_to_static,
convert_config=convert_config,
)
else:
Expand Down Expand Up @@ -211,7 +205,6 @@ def verify_with_ort(
use_vm=False,
opset=None,
freeze_params=False,
convert_to_static=False,
dtype="float32",
rtol=1e-5,
atol=1e-5,
Expand All @@ -226,7 +219,6 @@ def verify_with_ort(
use_vm=use_vm,
opset=opset,
freeze_params=freeze_params,
convert_to_static=convert_to_static,
dtype=dtype,
rtol=rtol,
atol=atol,
Expand Down Expand Up @@ -2221,7 +2213,6 @@ def verify_prelu(x_shape, a_shape):
[x_shape, a_shape],
out_shape=[list(x_shape)],
use_vm=True,
convert_to_static=True,
target=target,
dev=dev,
)
Expand Down Expand Up @@ -2705,7 +2696,6 @@ def verify_conv(
[x_shape, w_shape],
[y_shape],
use_vm=True,
convert_to_static=True,
target=target,
dev=dev,
)
Expand Down Expand Up @@ -2859,9 +2849,7 @@ def verify_convtranspose_with_padding(

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

verify_with_ort(
model, [x_shape, w_shape], use_vm=True, convert_to_static=True, target=target, dev=dev
)
verify_with_ort(model, [x_shape, w_shape], use_vm=True, target=target, dev=dev)

def verify_convtranspose(x_shape, w_shape, y_shape, p, group=1):
node = onnx.helper.make_node(
Expand Down Expand Up @@ -3042,7 +3030,6 @@ def verify_pooling(x_shape, kernel_shape, strides, pads, out_shape, mode, auto_p
[x_shape],
[out_shape],
use_vm=False,
convert_to_static=True,
target=target,
dev=dev,
)
Expand Down Expand Up @@ -3156,7 +3143,6 @@ def verify_global_pooling(x_shape, mode):
[x_shape],
[out_shape],
use_vm=False,
convert_to_static=True,
target=target,
dev=dev,
)
Expand Down Expand Up @@ -3488,7 +3474,6 @@ def verify_lppool(x_shape, kernel_shape, p, strides, pads, out_shape, auto_pad="
[x_shape],
[out_shape],
use_vm=True,
convert_to_static=True,
target=target,
dev=dev,
)
Expand Down Expand Up @@ -3592,9 +3577,7 @@ def verify_global_lppool(x_shape, p, out_shape, target, dev):
)

model = helper.make_model(graph, producer_name="global_lppool_test")
verify_with_ort(
model, [x_shape], out_shape, use_vm=True, convert_to_static=True, target=target, dev=dev
)
verify_with_ort(model, [x_shape], out_shape, use_vm=True, target=target, dev=dev)


@tvm.testing.parametrize_targets
Expand Down Expand Up @@ -4676,7 +4659,6 @@ def verify_tensor_loop(shapeless_output=False):
input_vals,
use_vm=True,
freeze_params=True,
convert_to_static=True,
opset=11,
target=target,
dev=dev,
Expand Down Expand Up @@ -5272,7 +5254,6 @@ def verify_embedding_bag(num_embedding, embedding_dim, data_shape, num_bags=None
onnx_model,
tvm_inputs,
freeze_params=True,
convert_to_static=True,
target=target,
dev=dev,
)
Expand Down Expand Up @@ -5316,9 +5297,7 @@ def verify_index_put(data_shape, indices, accumulate):
onnx_model = _convert_to_onnx(model, dummy_data)
torch_out = model(dummy_data)

tvm_out = get_tvm_output_with_vm(
onnx_model, tvm_inputs, target, dev, freeze_params=True, convert_to_static=True
)
tvm_out = get_tvm_output_with_vm(onnx_model, tvm_inputs, target, dev, freeze_params=True)
tvm.testing.assert_allclose(torch_out.numpy(), tvm_out)

shape = (3, 5)
Expand Down Expand Up @@ -5347,9 +5326,7 @@ def verify_index_put_slice(data_shape, value_shape, accumulate):
onnx_model = _convert_to_onnx(model, dummy_data)
torch_out = model(dummy_data)

tvm_out = get_tvm_output_with_vm(
onnx_model, tvm_inputs, target, dev, freeze_params=True, convert_to_static=True
)
tvm_out = get_tvm_output_with_vm(onnx_model, tvm_inputs, target, dev, freeze_params=True)
tvm.testing.assert_allclose(torch_out.numpy(), tvm_out)

verify_index_put_slice((3, 3), (2, 2), False)
Expand Down