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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 numberDiff line numberDiff 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))

@tkonoligetkonoligeOct 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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Open in desktop
Original file line numberDiff line numberDiff line change
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
View file
Open in desktop
Original file line numberDiff line numberDiff 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 DownExpand 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__])
, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
[microNPU] Fix typo in depthwise to allow int8 weights by lhutton1 · Pull Request #9299 · apache/tvm · GitHub
Skip to content
[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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Open in desktop

Original file line numberDiff line numberDiff 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))
@tkonoligetkonoligeOct 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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Open in desktop

Original file line numberDiff line numberDiff line change
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

View file


Open in desktop

Original file line numberDiff line numberDiff 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 DownExpand 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__])

, 'i');
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// Force GitHub README to respect dark mode
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var style = document.createElement('style');
style.textContent = '
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
[microNPU] Fix typo in depthwise to allow int8 weights by lhutton1 · Pull Request #9299 · apache/tvm · GitHub
Skip to content
[microNPU] Fix typo in depthwise to allow int8 weights#9299

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 Closed

lhutton1
wants to merge
4
commits into

from

 Closed


[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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Open in desktop

Original file line numberDiff line numberDiff 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))
@tkonoligetkonoligeOct 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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Open in desktop

Original file line numberDiff line numberDiff line change
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

View file


Open in desktop

Original file line numberDiff line numberDiff 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 DownExpand 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__])

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
[microNPU] Fix typo in depthwise to allow int8 weights by lhutton1 · Pull Request #9299 · apache/tvm · GitHub
Skip to content
[microNPU] Fix typo in depthwise to allow int8 weights#9299

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lhutton1
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commits into

from

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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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Open in desktop

Original file line numberDiff line numberDiff 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))
@tkonoligetkonoligeOct 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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Open in desktop

Original file line numberDiff line numberDiff line change
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

View file


Open in desktop

Original file line numberDiff line numberDiff 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 DownExpand 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__])

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
[microNPU] Fix typo in depthwise to allow int8 weights by lhutton1 · Pull Request #9299 · apache/tvm · GitHub
Skip to content
[microNPU] Fix typo in depthwise to allow int8 weights#9299

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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

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privacy statement. We’ll occasionally send you account related emails.

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 Closed

lhutton1
wants to merge
4
commits into

from

 Closed


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


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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

View file


Open in desktop

Original file line numberDiff line numberDiff 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))
@tkonoligetkonoligeOct 28, 2021

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Contributor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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

View file


Open in desktop

Original file line numberDiff line numberDiff line change
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

View file


Open in desktop

Original file line numberDiff line numberDiff 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 DownExpand 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__])

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
[microNPU] Fix typo in depthwise to allow int8 weights by lhutton1 · Pull Request #9299 · apache/tvm · GitHub
Skip to content
[microNPU] Fix typo in depthwise to allow int8 weights#9299

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lhutton1
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commits into

from

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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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Open in desktop

Original file line numberDiff line numberDiff 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))
@tkonoligetkonoligeOct 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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Open in desktop

Original file line numberDiff line numberDiff line change
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

View file


Open in desktop

Original file line numberDiff line numberDiff 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 DownExpand 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__])

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
[microNPU] Fix typo in depthwise to allow int8 weights by lhutton1 · Pull Request #9299 · apache/tvm · GitHub
Skip to content
[microNPU] Fix typo in depthwise to allow int8 weights#9299

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.

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Sign in
to your account

 Closed

lhutton1
wants to merge
4
commits into

from

 Closed


[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

View file


Open in desktop

Original file line numberDiff line numberDiff 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))
@tkonoligetkonoligeOct 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

View file


Open in desktop

Original file line numberDiff line numberDiff line change
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 numberDiff line numberDiff 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 DownExpand 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__])

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
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enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
[microNPU] Fix typo in depthwise to allow int8 weights by lhutton1 · Pull Request #9299 · apache/tvm · GitHub
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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 numberDiff line numberDiff 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))
@tkonoligetkonoligeOct 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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Original file line numberDiff line numberDiff line change
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

View file


Open in desktop

Original file line numberDiff line numberDiff 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 DownExpand 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__])