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[microNPU] Fix typo in depthwise to allow int8 weights #9299

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[microNPU] Fix typo in depthwise to allow int8 weights #9299

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a7218c6
[microNPU] Fix typo in depthwise to allow int8 weights
lhutton1 Oct 15, 2021
ebc2319
Add testcase
lhutton1 Oct 18, 2021
6ca9535
Add test for incorrect weight data type
lhutton1 Oct 28, 2021
7938edc
Flatten error check, also move test to type inference tests which is …
lhutton1 Oct 28, 2021
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2 changes: 1 addition & 1 deletion src/relay/op/contrib/ethosu/depthwise.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -126,7 +126,7 @@ bool EthosuDepthwiseConv2DRel(const Array<Type>& types, int num_inputs, const At
ICHECK(ifm->dtype == DataType::UInt(8) || ifm->dtype == DataType::Int(8))
<< "Expected ethosu_depthwise_conv2d type(uint8) or type(int8) for ifm but was "
<< ifm->dtype;
ICHECK(weight->dtype == DataType::UInt(8) || ifm->dtype == DataType::Int(8))
ICHECK(weight->dtype == DataType::UInt(8) || weight->dtype == DataType::Int(8))
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@tkonolige tkonolige Oct 28, 2021

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Please use the diagnostic context to report these errors. Here is an example: https://github.com/apache/tvm/blob/main/src/relay/op/nn/nn.cc#L65-L68.

<< "Expected ethosu_depthwise_conv2d type(uint8) or type(int8) for weight but was "
<< weight->dtype;
ICHECK(scale_bias->dtype == DataType::UInt(8))
Expand Down
94 changes: 81 additions & 13 deletions tests/python/contrib/test_ethosu/test_replace_depthwise_conv2d.py
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Expand Up @@ -23,22 +23,83 @@
from tvm import relay
from tvm.relay.testing import run_opt_pass
from tvm.relay.backend.contrib.ethosu.tir.compiler import lower_to_tir

from .infra import make_ethosu_depthwise_conv2d, get_convolutional_args


@pytest.mark.parametrize(
"trial",
[
[(1, 8, 8, 3), 3, (3, 2), (0, 0), (1, 1), (1, 1), "CLIP", "NHWC", "NHWC"],
[(1, 8, 8, 3), 3, (1, 1), (2, 1), (1, 1), (1, 1), "TANH", "NHWC", "NHWC"],
[(1, 8, 8, 3), 3, (1, 1), (0, 0), (1, 1), (1, 1), "NONE", "NHWC", "NHWC"],
[(1, 1, 1, 1), 1, (1, 1), (0, 0), (1, 1), (1, 1), "CLIP", "NHWC", "NHWC"],
[(1, 7, 9, 4), 4, (3, 2), (1, 2), (2, 1), (1, 2), "SIGMOID", "NHWC", "NHWC"],
[(1, 8, 2, 8, 16), 18, (1, 1), (2, 1), (1, 1), (1, 1), "CLIP", "NHCWB16", "NHWC"],
[(1, 7, 9, 40), 40, (3, 2), (1, 2), (2, 1), (1, 2), "CLIP", "NHWC", "NHCWB16"],
[(1, 4, 12, 9, 16), 182, (2, 3), (6, 3), (2, 2), (1, 1), "CLIP", "NHCWB16", "NHCWB16"],
[(1, 7, 9, 4), 4, (3, 2), (1, 2), (2, 1), (2, 2), "CLIP", "NHWC", "NHWC"],
[(1, 7, 9, 41), 41, (3, 2), (1, 2), (2, 1), (2, 2), "CLIP", "NHWC", "NHCWB16"],
[(1, 8, 8, 3), 3, (3, 2), (0, 0), (1, 1), (1, 1), "CLIP", "NHWC", "NHWC", "int8", "int8"],
[(1, 8, 8, 3), 3, (1, 1), (2, 1), (1, 1), (1, 1), "TANH", "NHWC", "NHWC", "int8", "int8"],
[(1, 8, 8, 3), 3, (1, 1), (0, 0), (1, 1), (1, 1), "NONE", "NHWC", "NHWC", "uint8", "int8"],
[(1, 1, 1, 1), 1, (1, 1), (0, 0), (1, 1), (1, 1), "CLIP", "NHWC", "NHWC", "uint8", "int8"],
[
(1, 7, 9, 4),
4,
(3, 2),
(1, 2),
(2, 1),
(1, 2),
"SIGMOID",
"NHWC",
"NHWC",
"uint8",
"uint8",
],
[
(1, 8, 2, 8, 16),
18,
(1, 1),
(2, 1),
(1, 1),
(1, 1),
"CLIP",
"NHCWB16",
"NHWC",
"int8",
"int8",
],
[
(1, 7, 9, 40),
40,
(3, 2),
(1, 2),
(2, 1),
(1, 2),
"CLIP",
"NHWC",
"NHCWB16",
"int8",
"int8",
],
[
(1, 4, 12, 9, 16),
182,
(2, 3),
(6, 3),
(2, 2),
(1, 1),
"CLIP",
"NHCWB16",
"NHCWB16",
"int8",
"int8",
],
[(1, 7, 9, 4), 4, (3, 2), (1, 2), (2, 1), (2, 2), "CLIP", "NHWC", "NHWC", "int8", "int8"],
[
(1, 7, 9, 41),
41,
(3, 2),
(1, 2),
(2, 1),
(2, 2),
"CLIP",
"NHWC",
"NHCWB16",
"int8",
"int8",
],
[
(1, 13, 12, 19, 16),
182,
Expand All @@ -49,6 +110,8 @@
"CLIP",
"NHCWB16",
"NHCWB16",
"int8",
"int8",
],
],
)
Expand All @@ -63,8 +126,10 @@ def _get_func(
activation,
ifm_layout,
ofm_layout,
dtype,
weight_dtype,
):
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
ifm = relay.var("ifm", shape=ifm_shape, dtype=dtype)
depthwise = make_ethosu_depthwise_conv2d(
ifm,
channels,
Expand All @@ -75,6 +140,7 @@ def _get_func(
activation,
ifm_layout,
ofm_layout,
weight_dtype,
)
func = relay.Function(relay.analysis.free_vars(depthwise), depthwise)
func = run_opt_pass(func, relay.transform.InferType())
Expand All @@ -99,6 +165,8 @@ def _visit(stmt):
activation,
ifm_layout,
ofm_layout,
dtype,
_,
) = trial
dilated_kernel_h = (kernel_shape[0] - 1) * dilation[0] + 1
dilated_kernel_w = (kernel_shape[1] - 1) * dilation[1] + 1
Expand All @@ -125,7 +193,7 @@ def _visit(stmt):
ofm_stride_h = 16 * ofm_width * ((channels - 1) // 16 + 1)

answer = [
"int8",
dtype,
ifm_shape[1],
ifm_shape[2] if ifm_layout == "NHWC" else ifm_shape[3],
channels,
Expand All @@ -142,7 +210,7 @@ def _visit(stmt):
ifm_stride_h,
ifm_stride_w,
ifm_stride_c,
"int8",
dtype,
ofm_height,
ofm_width,
channels,
Expand Down
26 changes: 25 additions & 1 deletion tests/python/contrib/test_ethosu/test_type_inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@

pytest.importorskip("ethosu.vela")

from tvm import relay
from tvm import relay, TVMError
from tvm.relay.testing import run_opt_pass
from .infra import make_ethosu_conv2d
from .infra import make_ethosu_depthwise_conv2d
Expand Down Expand Up @@ -92,5 +92,29 @@ def test_ethosu_depthwise_conv2d_type_inference(
assert tuple(f.body.checked_type.shape) == ofm_shape


def test_incompatible_weight_data_type():
ifm = relay.var("ifm", shape=(1, 8, 8, 3), dtype="int8")
depthwise = make_ethosu_depthwise_conv2d(
ifm=ifm,
channels=3,
kernel_shape=(3, 2),
padding=(0, 0),
strides=(1, 1),
dilation=(1, 1),
activation="NONE",
ifm_layout="NHWC",
ofm_layout="NHWC",
weight_dtype="int16",
)

func = relay.Function(relay.analysis.free_vars(depthwise), depthwise)

message = (
r"Expected ethosu_depthwise_conv2d type\(uint8\) or type\(int8\) for weight but was int16"
)
with pytest.raises(TVMError, match=message):
run_opt_pass(func, relay.transform.InferType())


if __name__ == "__main__":
pytest.main([__file__])