[Torch] Fix conv2d transpose with group#10235

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junrushao merged 4 commits into from
Feb 15, 2022
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[Torch] Fix conv2d transpose with group #10235

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[Torch] Fix conv2d transpose with group
masahi Feb 13, 2022
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masahi Feb 13, 2022
671b7bd
wrong issue number
masahi Feb 13, 2022
15ec776
do not run test on cuda
masahi Feb 13, 2022
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14 changes: 8 additions & 6 deletions python/tvm/relay/frontend/pytorch.py
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -972,19 +972,21 @@ def convolution(self, inputs, input_types):
msg = "Data type %s could not be parsed in conv op" % (type(weight))
raise AssertionError(msg)

# Transposed convolutions have IOHW layout.
if use_transpose:
weight_shape[0], weight_shape[1] = weight_shape[1], weight_shape[0]

channels = weight_shape[0]
groups = int(inputs[8])

if use_transpose:
channels = weight_shape[1] * groups
in_channels = weight_shape[0]
else:
channels = weight_shape[0]
in_channels = weight_shape[1]

# Check if this is depth wise convolution
# We need to reshape weight so that Relay could recognize this is depth wise
# weight_shape[1] is always in_channels // groups
# For depthwise, in_channels == groups, so weight_shape[1] == 1
# If groups > 1 but weight_shape[1] != 1, this is group convolution
if groups > 1 and weight_shape[1] == 1:
if groups > 1 and in_channels == 1:
channel_multiplier = channels // groups
new_weight_shape = (groups, channel_multiplier) + tuple(weight_shape[2:])
weight = _op.transform.reshape(weight, new_weight_shape)
Expand Down
28 changes: 27 additions & 1 deletion tests/python/frontend/pytorch/test_forward.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -1067,6 +1067,32 @@ def test_forward_conv_transpose(
verify_model(conv1d_transpose, conv1d_input_data)


def test_forward_conv2d_transpose_group():
# https://github.com/apache/tvm/issues/10223

class ModulatedConvTranspose2D(torch.nn.Module):
def forward(self, x, w, s):
B, C, H, W = x.shape
I, O, KH, KW = w.shape

# weight is different for each input in batch (this is why we want grouped conv transpose)
w = w.unsqueeze(0) * s.reshape(B, 1, 1, 1, 1)
w = w.reshape(B * I, O, KH, KW)
x = x.reshape(1, B * C, H, W)
x = torch.nn.functional.conv_transpose2d(
x, w, stride=(2, 2), padding=(1, 1), output_padding=(1, 1), groups=B
)
return x.reshape(B, O, H * 2, W * 2)

b, c, h, w, k = 4, 512, 8, 16, 3
inputs = torch.rand(b, c, h, w)
weights = torch.rand(c, c // 2, k, k)
styles = torch.rand(b)

# cuda not supported for group > 1 conv2d_transpose
verify_trace_model(ModulatedConvTranspose2D().eval(), [inputs, weights, styles], ["llvm"])


def test_forward_deform_conv():
torch.set_grad_enabled(False)

Expand DownExpand Up@@ -4115,7 +4141,7 @@ def forward(self, x):

x = torch.rand([4, 4, 16, 32]).float()
script_module = torch.jit.trace(List_tuple(), x, strict=False).eval()
mod, params = relay.frontend.from_pytorch(script_module, [("x", x.shape)])
relay.frontend.from_pytorch(script_module, [("x", x.shape)])


if __name__ == "__main__":
Expand Down
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(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
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btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
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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" + '

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[Torch] Fix conv2d transpose with group#10235

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 Merged

junrushao
merged 4 commits into

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Feb 15, 2022
 Merged


[Torch] Fix conv2d transpose with group
#10235
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[Torch] Fix conv2d transpose with group


masahi Feb 13, 2022
a7e08fb

lint


masahi Feb 13, 2022
671b7bd

wrong issue number


masahi Feb 13, 2022
15ec776

do not run test on cuda


masahi Feb 13, 2022

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14 changes: 8 additions & 6 deletions
python/tvm/relay/frontend/pytorch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -972,19 +972,21 @@ def convolution(self, inputs, input_types):
 msg = "Data type %s could not be parsed in conv op" % (type(weight))
 raise AssertionError(msg)

 # Transposed convolutions have IOHW layout.
 if use_transpose:
 weight_shape[0], weight_shape[1] = weight_shape[1], weight_shape[0]

 channels = weight_shape[0]
 groups = int(inputs[8])

 if use_transpose:
 channels = weight_shape[1] * groups
 in_channels = weight_shape[0]
 else:
 channels = weight_shape[0]
 in_channels = weight_shape[1]

 # Check if this is depth wise convolution
 # We need to reshape weight so that Relay could recognize this is depth wise
 # weight_shape[1] is always in_channels // groups
 # For depthwise, in_channels == groups, so weight_shape[1] == 1
 # If groups > 1 but weight_shape[1] != 1, this is group convolution
 if groups > 1 and weight_shape[1] == 1:
 if groups > 1 and in_channels == 1:
 channel_multiplier = channels // groups
 new_weight_shape = (groups, channel_multiplier) + tuple(weight_shape[2:])
 weight = _op.transform.reshape(weight, new_weight_shape)
Expand Down

28 changes: 27 additions & 1 deletion
tests/python/frontend/pytorch/test_forward.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1067,6 +1067,32 @@ def test_forward_conv_transpose(
 verify_model(conv1d_transpose, conv1d_input_data)


def test_forward_conv2d_transpose_group():
 # https://github.com/apache/tvm/issues/10223

 class ModulatedConvTranspose2D(torch.nn.Module):
 def forward(self, x, w, s):
 B, C, H, W = x.shape
 I, O, KH, KW = w.shape

 # weight is different for each input in batch (this is why we want grouped conv transpose)
 w = w.unsqueeze(0) * s.reshape(B, 1, 1, 1, 1)
 w = w.reshape(B * I, O, KH, KW)
 x = x.reshape(1, B * C, H, W)
 x = torch.nn.functional.conv_transpose2d(
 x, w, stride=(2, 2), padding=(1, 1), output_padding=(1, 1), groups=B
 )
 return x.reshape(B, O, H * 2, W * 2)

 b, c, h, w, k = 4, 512, 8, 16, 3
 inputs = torch.rand(b, c, h, w)
 weights = torch.rand(c, c // 2, k, k)
 styles = torch.rand(b)

 # cuda not supported for group > 1 conv2d_transpose
 verify_trace_model(ModulatedConvTranspose2D().eval(), [inputs, weights, styles], ["llvm"])


def test_forward_deform_conv():
 torch.set_grad_enabled(False)

Expand DownExpand Up@@ -4115,7 +4141,7 @@ def forward(self, x):

 x = torch.rand([4, 4, 16, 32]).float()
 script_module = torch.jit.trace(List_tuple(), x, strict=False).eval()
mod, params = relay.frontend.from_pytorch(script_module, [("x", x.shape)])
 relay.frontend.from_pytorch(script_module, [("x", x.shape)])


if __name__ == "__main__":
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
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.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
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var __re = new RegExp('^' + ".*" + '

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[Torch] Fix conv2d transpose with group#10235

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 Merged

junrushao
merged 4 commits into

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Feb 15, 2022
 Merged


[Torch] Fix conv2d transpose with group
#10235
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[Torch] Fix conv2d transpose with group


masahi Feb 13, 2022
a7e08fb

lint


masahi Feb 13, 2022
671b7bd

wrong issue number


masahi Feb 13, 2022
15ec776

do not run test on cuda


masahi Feb 13, 2022

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14 changes: 8 additions & 6 deletions
python/tvm/relay/frontend/pytorch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -972,19 +972,21 @@ def convolution(self, inputs, input_types):
 msg = "Data type %s could not be parsed in conv op" % (type(weight))
 raise AssertionError(msg)

 # Transposed convolutions have IOHW layout.
 if use_transpose:
 weight_shape[0], weight_shape[1] = weight_shape[1], weight_shape[0]

 channels = weight_shape[0]
 groups = int(inputs[8])

 if use_transpose:
 channels = weight_shape[1] * groups
 in_channels = weight_shape[0]
 else:
 channels = weight_shape[0]
 in_channels = weight_shape[1]

 # Check if this is depth wise convolution
 # We need to reshape weight so that Relay could recognize this is depth wise
 # weight_shape[1] is always in_channels // groups
 # For depthwise, in_channels == groups, so weight_shape[1] == 1
 # If groups > 1 but weight_shape[1] != 1, this is group convolution
 if groups > 1 and weight_shape[1] == 1:
 if groups > 1 and in_channels == 1:
 channel_multiplier = channels // groups
 new_weight_shape = (groups, channel_multiplier) + tuple(weight_shape[2:])
 weight = _op.transform.reshape(weight, new_weight_shape)
Expand Down

28 changes: 27 additions & 1 deletion
tests/python/frontend/pytorch/test_forward.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1067,6 +1067,32 @@ def test_forward_conv_transpose(
 verify_model(conv1d_transpose, conv1d_input_data)


def test_forward_conv2d_transpose_group():
 # https://github.com/apache/tvm/issues/10223

 class ModulatedConvTranspose2D(torch.nn.Module):
 def forward(self, x, w, s):
 B, C, H, W = x.shape
 I, O, KH, KW = w.shape

 # weight is different for each input in batch (this is why we want grouped conv transpose)
 w = w.unsqueeze(0) * s.reshape(B, 1, 1, 1, 1)
 w = w.reshape(B * I, O, KH, KW)
 x = x.reshape(1, B * C, H, W)
 x = torch.nn.functional.conv_transpose2d(
 x, w, stride=(2, 2), padding=(1, 1), output_padding=(1, 1), groups=B
 )
 return x.reshape(B, O, H * 2, W * 2)

 b, c, h, w, k = 4, 512, 8, 16, 3
 inputs = torch.rand(b, c, h, w)
 weights = torch.rand(c, c // 2, k, k)
 styles = torch.rand(b)

 # cuda not supported for group > 1 conv2d_transpose
 verify_trace_model(ModulatedConvTranspose2D().eval(), [inputs, weights, styles], ["llvm"])


def test_forward_deform_conv():
 torch.set_grad_enabled(False)

Expand DownExpand Up@@ -4115,7 +4141,7 @@ def forward(self, x):

 x = torch.rand([4, 4, 16, 32]).float()
 script_module = torch.jit.trace(List_tuple(), x, strict=False).eval()
mod, params = relay.frontend.from_pytorch(script_module, [("x", x.shape)])
 relay.frontend.from_pytorch(script_module, [("x", x.shape)])


if __name__ == "__main__":
Expand Down

, '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')) {
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style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
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var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
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found = true;
var frag = document.createDocumentFragment();
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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);
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node.parentNode.replaceChild(frag, node);
}
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[Torch] Fix conv2d transpose with group#10235

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junrushao
merged 4 commits into

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Feb 15, 2022
 Merged


[Torch] Fix conv2d transpose with group
#10235
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c5ef5dd

[Torch] Fix conv2d transpose with group


masahi Feb 13, 2022
a7e08fb

lint


masahi Feb 13, 2022
671b7bd

wrong issue number


masahi Feb 13, 2022
15ec776

do not run test on cuda


masahi Feb 13, 2022

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14 changes: 8 additions & 6 deletions
python/tvm/relay/frontend/pytorch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -972,19 +972,21 @@ def convolution(self, inputs, input_types):
 msg = "Data type %s could not be parsed in conv op" % (type(weight))
 raise AssertionError(msg)

 # Transposed convolutions have IOHW layout.
 if use_transpose:
 weight_shape[0], weight_shape[1] = weight_shape[1], weight_shape[0]

 channels = weight_shape[0]
 groups = int(inputs[8])

 if use_transpose:
 channels = weight_shape[1] * groups
 in_channels = weight_shape[0]
 else:
 channels = weight_shape[0]
 in_channels = weight_shape[1]

 # Check if this is depth wise convolution
 # We need to reshape weight so that Relay could recognize this is depth wise
 # weight_shape[1] is always in_channels // groups
 # For depthwise, in_channels == groups, so weight_shape[1] == 1
 # If groups > 1 but weight_shape[1] != 1, this is group convolution
 if groups > 1 and weight_shape[1] == 1:
 if groups > 1 and in_channels == 1:
 channel_multiplier = channels // groups
 new_weight_shape = (groups, channel_multiplier) + tuple(weight_shape[2:])
 weight = _op.transform.reshape(weight, new_weight_shape)
Expand Down

28 changes: 27 additions & 1 deletion
tests/python/frontend/pytorch/test_forward.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1067,6 +1067,32 @@ def test_forward_conv_transpose(
 verify_model(conv1d_transpose, conv1d_input_data)


def test_forward_conv2d_transpose_group():
 # https://github.com/apache/tvm/issues/10223

 class ModulatedConvTranspose2D(torch.nn.Module):
 def forward(self, x, w, s):
 B, C, H, W = x.shape
 I, O, KH, KW = w.shape

 # weight is different for each input in batch (this is why we want grouped conv transpose)
 w = w.unsqueeze(0) * s.reshape(B, 1, 1, 1, 1)
 w = w.reshape(B * I, O, KH, KW)
 x = x.reshape(1, B * C, H, W)
 x = torch.nn.functional.conv_transpose2d(
 x, w, stride=(2, 2), padding=(1, 1), output_padding=(1, 1), groups=B
 )
 return x.reshape(B, O, H * 2, W * 2)

 b, c, h, w, k = 4, 512, 8, 16, 3
 inputs = torch.rand(b, c, h, w)
 weights = torch.rand(c, c // 2, k, k)
 styles = torch.rand(b)

 # cuda not supported for group > 1 conv2d_transpose
 verify_trace_model(ModulatedConvTranspose2D().eval(), [inputs, weights, styles], ["llvm"])


def test_forward_deform_conv():
 torch.set_grad_enabled(False)

Expand DownExpand Up@@ -4115,7 +4141,7 @@ def forward(self, x):

 x = torch.rand([4, 4, 16, 32]).float()
 script_module = torch.jit.trace(List_tuple(), x, strict=False).eval()
mod, params = relay.frontend.from_pytorch(script_module, [("x", x.shape)])
 relay.frontend.from_pytorch(script_module, [("x", x.shape)])


if __name__ == "__main__":
Expand Down

, '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;
}
}
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document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
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mutations.forEach(function(m) {
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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); }
})();
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try {
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[Torch] Fix conv2d transpose with group#10235

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 Merged

junrushao
merged 4 commits into

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Feb 15, 2022
 Merged


[Torch] Fix conv2d transpose with group
#10235
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c5ef5dd

[Torch] Fix conv2d transpose with group


masahi Feb 13, 2022
a7e08fb

lint


masahi Feb 13, 2022
671b7bd

wrong issue number


masahi Feb 13, 2022
15ec776

do not run test on cuda


masahi Feb 13, 2022

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14 changes: 8 additions & 6 deletions
python/tvm/relay/frontend/pytorch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -972,19 +972,21 @@ def convolution(self, inputs, input_types):
 msg = "Data type %s could not be parsed in conv op" % (type(weight))
 raise AssertionError(msg)

 # Transposed convolutions have IOHW layout.
 if use_transpose:
 weight_shape[0], weight_shape[1] = weight_shape[1], weight_shape[0]

 channels = weight_shape[0]
 groups = int(inputs[8])

 if use_transpose:
 channels = weight_shape[1] * groups
 in_channels = weight_shape[0]
 else:
 channels = weight_shape[0]
 in_channels = weight_shape[1]

 # Check if this is depth wise convolution
 # We need to reshape weight so that Relay could recognize this is depth wise
 # weight_shape[1] is always in_channels // groups
 # For depthwise, in_channels == groups, so weight_shape[1] == 1
 # If groups > 1 but weight_shape[1] != 1, this is group convolution
 if groups > 1 and weight_shape[1] == 1:
 if groups > 1 and in_channels == 1:
 channel_multiplier = channels // groups
 new_weight_shape = (groups, channel_multiplier) + tuple(weight_shape[2:])
 weight = _op.transform.reshape(weight, new_weight_shape)
Expand Down

28 changes: 27 additions & 1 deletion
tests/python/frontend/pytorch/test_forward.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1067,6 +1067,32 @@ def test_forward_conv_transpose(
 verify_model(conv1d_transpose, conv1d_input_data)


def test_forward_conv2d_transpose_group():
 # https://github.com/apache/tvm/issues/10223

 class ModulatedConvTranspose2D(torch.nn.Module):
 def forward(self, x, w, s):
 B, C, H, W = x.shape
 I, O, KH, KW = w.shape

 # weight is different for each input in batch (this is why we want grouped conv transpose)
 w = w.unsqueeze(0) * s.reshape(B, 1, 1, 1, 1)
 w = w.reshape(B * I, O, KH, KW)
 x = x.reshape(1, B * C, H, W)
 x = torch.nn.functional.conv_transpose2d(
 x, w, stride=(2, 2), padding=(1, 1), output_padding=(1, 1), groups=B
 )
 return x.reshape(B, O, H * 2, W * 2)

 b, c, h, w, k = 4, 512, 8, 16, 3
 inputs = torch.rand(b, c, h, w)
 weights = torch.rand(c, c // 2, k, k)
 styles = torch.rand(b)

 # cuda not supported for group > 1 conv2d_transpose
 verify_trace_model(ModulatedConvTranspose2D().eval(), [inputs, weights, styles], ["llvm"])


def test_forward_deform_conv():
 torch.set_grad_enabled(False)

Expand DownExpand Up@@ -4115,7 +4141,7 @@ def forward(self, x):

 x = torch.rand([4, 4, 16, 32]).float()
 script_module = torch.jit.trace(List_tuple(), x, strict=False).eval()
mod, params = relay.frontend.from_pytorch(script_module, [("x", x.shape)])
 relay.frontend.from_pytorch(script_module, [("x", x.shape)])


if __name__ == "__main__":
Expand Down

, '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('^' + ".*" + '

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junrushao
merged 4 commits into

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Feb 15, 2022
 Merged


[Torch] Fix conv2d transpose with group
#10235
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[Torch] Fix conv2d transpose with group


masahi Feb 13, 2022
a7e08fb

lint


masahi Feb 13, 2022
671b7bd

wrong issue number


masahi Feb 13, 2022
15ec776

do not run test on cuda


masahi Feb 13, 2022

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14 changes: 8 additions & 6 deletions
python/tvm/relay/frontend/pytorch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -972,19 +972,21 @@ def convolution(self, inputs, input_types):
 msg = "Data type %s could not be parsed in conv op" % (type(weight))
 raise AssertionError(msg)

 # Transposed convolutions have IOHW layout.
 if use_transpose:
 weight_shape[0], weight_shape[1] = weight_shape[1], weight_shape[0]

 channels = weight_shape[0]
 groups = int(inputs[8])

 if use_transpose:
 channels = weight_shape[1] * groups
 in_channels = weight_shape[0]
 else:
 channels = weight_shape[0]
 in_channels = weight_shape[1]

 # Check if this is depth wise convolution
 # We need to reshape weight so that Relay could recognize this is depth wise
 # weight_shape[1] is always in_channels // groups
 # For depthwise, in_channels == groups, so weight_shape[1] == 1
 # If groups > 1 but weight_shape[1] != 1, this is group convolution
 if groups > 1 and weight_shape[1] == 1:
 if groups > 1 and in_channels == 1:
 channel_multiplier = channels // groups
 new_weight_shape = (groups, channel_multiplier) + tuple(weight_shape[2:])
 weight = _op.transform.reshape(weight, new_weight_shape)
Expand Down

28 changes: 27 additions & 1 deletion
tests/python/frontend/pytorch/test_forward.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1067,6 +1067,32 @@ def test_forward_conv_transpose(
 verify_model(conv1d_transpose, conv1d_input_data)


def test_forward_conv2d_transpose_group():
 # https://github.com/apache/tvm/issues/10223

 class ModulatedConvTranspose2D(torch.nn.Module):
 def forward(self, x, w, s):
 B, C, H, W = x.shape
 I, O, KH, KW = w.shape

 # weight is different for each input in batch (this is why we want grouped conv transpose)
 w = w.unsqueeze(0) * s.reshape(B, 1, 1, 1, 1)
 w = w.reshape(B * I, O, KH, KW)
 x = x.reshape(1, B * C, H, W)
 x = torch.nn.functional.conv_transpose2d(
 x, w, stride=(2, 2), padding=(1, 1), output_padding=(1, 1), groups=B
 )
 return x.reshape(B, O, H * 2, W * 2)

 b, c, h, w, k = 4, 512, 8, 16, 3
 inputs = torch.rand(b, c, h, w)
 weights = torch.rand(c, c // 2, k, k)
 styles = torch.rand(b)

 # cuda not supported for group > 1 conv2d_transpose
 verify_trace_model(ModulatedConvTranspose2D().eval(), [inputs, weights, styles], ["llvm"])


def test_forward_deform_conv():
 torch.set_grad_enabled(False)

Expand DownExpand Up@@ -4115,7 +4141,7 @@ def forward(self, x):

 x = torch.rand([4, 4, 16, 32]).float()
 script_module = torch.jit.trace(List_tuple(), x, strict=False).eval()
mod, params = relay.frontend.from_pytorch(script_module, [("x", x.shape)])
 relay.frontend.from_pytorch(script_module, [("x", x.shape)])


if __name__ == "__main__":
Expand Down

, '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('^' + ".*" + '

Skip to content
[Torch] Fix conv2d transpose with group#10235

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 Merged

junrushao
merged 4 commits into

from

Feb 15, 2022
 Merged


[Torch] Fix conv2d transpose with group
#10235
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c5ef5dd

[Torch] Fix conv2d transpose with group


masahi Feb 13, 2022
a7e08fb

lint


masahi Feb 13, 2022
671b7bd

wrong issue number


masahi Feb 13, 2022
15ec776

do not run test on cuda


masahi Feb 13, 2022

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14 changes: 8 additions & 6 deletions
python/tvm/relay/frontend/pytorch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -972,19 +972,21 @@ def convolution(self, inputs, input_types):
 msg = "Data type %s could not be parsed in conv op" % (type(weight))
 raise AssertionError(msg)

 # Transposed convolutions have IOHW layout.
 if use_transpose:
 weight_shape[0], weight_shape[1] = weight_shape[1], weight_shape[0]

 channels = weight_shape[0]
 groups = int(inputs[8])

 if use_transpose:
 channels = weight_shape[1] * groups
 in_channels = weight_shape[0]
 else:
 channels = weight_shape[0]
 in_channels = weight_shape[1]

 # Check if this is depth wise convolution
 # We need to reshape weight so that Relay could recognize this is depth wise
 # weight_shape[1] is always in_channels // groups
 # For depthwise, in_channels == groups, so weight_shape[1] == 1
 # If groups > 1 but weight_shape[1] != 1, this is group convolution
 if groups > 1 and weight_shape[1] == 1:
 if groups > 1 and in_channels == 1:
 channel_multiplier = channels // groups
 new_weight_shape = (groups, channel_multiplier) + tuple(weight_shape[2:])
 weight = _op.transform.reshape(weight, new_weight_shape)
Expand Down

28 changes: 27 additions & 1 deletion
tests/python/frontend/pytorch/test_forward.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1067,6 +1067,32 @@ def test_forward_conv_transpose(
 verify_model(conv1d_transpose, conv1d_input_data)


def test_forward_conv2d_transpose_group():
 # https://github.com/apache/tvm/issues/10223

 class ModulatedConvTranspose2D(torch.nn.Module):
 def forward(self, x, w, s):
 B, C, H, W = x.shape
 I, O, KH, KW = w.shape

 # weight is different for each input in batch (this is why we want grouped conv transpose)
 w = w.unsqueeze(0) * s.reshape(B, 1, 1, 1, 1)
 w = w.reshape(B * I, O, KH, KW)
 x = x.reshape(1, B * C, H, W)
 x = torch.nn.functional.conv_transpose2d(
 x, w, stride=(2, 2), padding=(1, 1), output_padding=(1, 1), groups=B
 )
 return x.reshape(B, O, H * 2, W * 2)

 b, c, h, w, k = 4, 512, 8, 16, 3
 inputs = torch.rand(b, c, h, w)
 weights = torch.rand(c, c // 2, k, k)
 styles = torch.rand(b)

 # cuda not supported for group > 1 conv2d_transpose
 verify_trace_model(ModulatedConvTranspose2D().eval(), [inputs, weights, styles], ["llvm"])


def test_forward_deform_conv():
 torch.set_grad_enabled(False)

Expand DownExpand Up@@ -4115,7 +4141,7 @@ def forward(self, x):

 x = torch.rand([4, 4, 16, 32]).float()
 script_module = torch.jit.trace(List_tuple(), x, strict=False).eval()
mod, params = relay.frontend.from_pytorch(script_module, [("x", x.shape)])
 relay.frontend.from_pytorch(script_module, [("x", x.shape)])


if __name__ == "__main__":
Expand Down

, '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
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
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');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
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[Torch] Fix conv2d transpose with group#10235

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 Merged

junrushao
merged 4 commits into

from

Feb 15, 2022
 Merged


[Torch] Fix conv2d transpose with group
#10235
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c5ef5dd

[Torch] Fix conv2d transpose with group


masahi Feb 13, 2022
a7e08fb

lint


masahi Feb 13, 2022
671b7bd

wrong issue number


masahi Feb 13, 2022
15ec776

do not run test on cuda


masahi Feb 13, 2022

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14 changes: 8 additions & 6 deletions
python/tvm/relay/frontend/pytorch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -972,19 +972,21 @@ def convolution(self, inputs, input_types):
 msg = "Data type %s could not be parsed in conv op" % (type(weight))
 raise AssertionError(msg)

 # Transposed convolutions have IOHW layout.
 if use_transpose:
 weight_shape[0], weight_shape[1] = weight_shape[1], weight_shape[0]

 channels = weight_shape[0]
 groups = int(inputs[8])

 if use_transpose:
 channels = weight_shape[1] * groups
 in_channels = weight_shape[0]
 else:
 channels = weight_shape[0]
 in_channels = weight_shape[1]

 # Check if this is depth wise convolution
 # We need to reshape weight so that Relay could recognize this is depth wise
 # weight_shape[1] is always in_channels // groups
 # For depthwise, in_channels == groups, so weight_shape[1] == 1
 # If groups > 1 but weight_shape[1] != 1, this is group convolution
 if groups > 1 and weight_shape[1] == 1:
 if groups > 1 and in_channels == 1:
 channel_multiplier = channels // groups
 new_weight_shape = (groups, channel_multiplier) + tuple(weight_shape[2:])
 weight = _op.transform.reshape(weight, new_weight_shape)
Expand Down

28 changes: 27 additions & 1 deletion
tests/python/frontend/pytorch/test_forward.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1067,6 +1067,32 @@ def test_forward_conv_transpose(
 verify_model(conv1d_transpose, conv1d_input_data)


def test_forward_conv2d_transpose_group():
 # https://github.com/apache/tvm/issues/10223

 class ModulatedConvTranspose2D(torch.nn.Module):
 def forward(self, x, w, s):
 B, C, H, W = x.shape
 I, O, KH, KW = w.shape

 # weight is different for each input in batch (this is why we want grouped conv transpose)
 w = w.unsqueeze(0) * s.reshape(B, 1, 1, 1, 1)
 w = w.reshape(B * I, O, KH, KW)
 x = x.reshape(1, B * C, H, W)
 x = torch.nn.functional.conv_transpose2d(
 x, w, stride=(2, 2), padding=(1, 1), output_padding=(1, 1), groups=B
 )
 return x.reshape(B, O, H * 2, W * 2)

 b, c, h, w, k = 4, 512, 8, 16, 3
 inputs = torch.rand(b, c, h, w)
 weights = torch.rand(c, c // 2, k, k)
 styles = torch.rand(b)

 # cuda not supported for group > 1 conv2d_transpose
 verify_trace_model(ModulatedConvTranspose2D().eval(), [inputs, weights, styles], ["llvm"])


def test_forward_deform_conv():
 torch.set_grad_enabled(False)

Expand DownExpand Up@@ -4115,7 +4141,7 @@ def forward(self, x):

 x = torch.rand([4, 4, 16, 32]).float()
 script_module = torch.jit.trace(List_tuple(), x, strict=False).eval()
mod, params = relay.frontend.from_pytorch(script_module, [("x", x.shape)])
 relay.frontend.from_pytorch(script_module, [("x", x.shape)])


if __name__ == "__main__":
Expand Down