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Fix softmax parsing in pytorch and add test #1086

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merged 5 commits into from
Oct 25, 2024
Merged

Fix softmax parsing in pytorch and add test #1086

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aaab34a
fix softmax parsing in pytorch and add test
JanFSchulte Oct 22, 2024
a9bfc6a
Merge branch 'main' into softmaxfix_torch
JanFSchulte Oct 22, 2024
655aef6
precommit
JanFSchulte Oct 22, 2024
61695b6
precommit v2
JanFSchulte Oct 22, 2024
a306e3f
add small tweak to fix issue 1054
JanFSchulte Oct 22, 2024
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13 changes: 11 additions & 2 deletions hls4ml/converters/pytorch/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,9 +62,13 @@ def parse_activation_layer(operation, layer_name, input_names, input_shapes, nod
layer['activation'] = 'ThresholdedReLU'
if layer['activ_param'] < 0:
raise Exception('negative threshold values not supported')

if hasattr(node, 'dim'):
if hasattr(class_object, 'dim'):
layer['axis'] = class_object.dim
if layer['class_name'] == 'Softmax' and layer['axis'] is None:
layer['axis'] = -1
if 'IOType' in config:
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We have done these types of checks before, but now I think we should parse everything into the IR, and then backbends should check if the feature is supported. We can have optimizers for that. Check out feature_check.py in Vitis backend. We could add similar checks for all backends and move away from the parser (in a future PR)

if layer['class_name'] == 'Softmax' and config['IOType'] == 'io_stream' and layer['axis'] != -1:
raise Exception('dim needs to be -1 for io_stream')
else:
if layer['class_name'] in ['ReLU', 'Sigmoid', 'Tanh']:
layer['class_name'] = 'Activation'
Expand All @@ -80,6 +84,11 @@ def parse_activation_layer(operation, layer_name, input_names, input_shapes, nod
layer['activation'] = 'ThresholdedReLU'
if 'dim' in node.kwargs:
layer['axis'] = node.kwargs['dim']
if layer['class_name'] == 'Softmax' and layer['axis'] is None:
layer['axis'] = -1
if 'IOType' in config:
if layer['class_name'] == 'Softmax' and config['IOType'] == 'io_stream' and layer['axis'] != -1:
raise Exception('dim needs to be -1 for io_stream')

output_shape = input_shapes[0]
return layer, output_shape
Expand Down
6 changes: 5 additions & 1 deletion hls4ml/model/optimizer/passes/convert_to_channels_last.py
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Expand Up @@ -94,7 +94,11 @@ def transform(self, model, node):
node.add_output_variable(shape, dims)

# Have to transpose back before flattening to get correct order of elements in the flattened tensor
if isinstance(node, Reshape) and len(node.attributes['target_shape']) == 1:
if (
isinstance(node, Reshape)
and len(node.attributes['target_shape']) == 1
and not model.config.config['HLSConfig']['Model']['ChannelsLastConversion'] == "internal"
):
previous_node = node.get_input_node(node.inputs[0])
input = previous_node.name
outshape = previous_node.get_output_variable().shape
Expand Down
10 changes: 10 additions & 0 deletions test/pytest/test_pytorch_api.py
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Expand Up @@ -63,6 +63,7 @@ def test_linear(backend, io_type):
@pytest.mark.parametrize(
"activation_function",
[
nn.Softmax(dim=-1),
nn.ReLU(),
nn.Tanh(),
nn.LeakyReLU(negative_slope=1.0),
Expand Down Expand Up @@ -119,6 +120,14 @@ def forward(self, x):
return nn.functional.relu(x)


class SoftmaxModel(nn.Module):
def __init__(self):
super().__init__()

def forward(self, x):
return nn.functional.softmax(x, dim=-1)


class TanHModel(nn.Module):
def __init__(self):
super().__init__()
Expand Down Expand Up @@ -162,6 +171,7 @@ def forward(self, x):
@pytest.mark.parametrize(
"activation_function",
[
SoftmaxModel(),
ReLuModel(),
TanHModel(),
LeakyReLuModel(),
Expand Down
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