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Merged
merged 3 commits into from
Feb 19, 2025
Merged

put code_gen.h in custom namespace #1104

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da75e68
isolate code_gen in namespace change
calad0i Oct 30, 2024
5d92747
fix silly typo
calad0i Oct 30, 2024
c14ca73
apply changes to template from PR1171
calad0i Feb 12, 2025
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4 changes: 2 additions & 2 deletions hls4ml/backends/fpga/fpga_backend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -755,7 +755,7 @@ def generate_conv1d_line_buffer_fn(self, layer_idx, n_partitions, in_W, in_C, ke

generated_code = (
"template<class data_T, typename CONFIG_T>\n"
"class fill_buffer_{index} : public FillConv1DBuffer<data_T, CONFIG_T> {{\n"
"class fill_buffer_{index} : public nnet::FillConv1DBuffer<data_T, CONFIG_T> {{\n"
" public:\n"
" static void fill_buffer(\n"
" data_T data[CONFIG_T::in_width * CONFIG_T::n_chan],\n"
Expand Down Expand Up @@ -885,7 +885,7 @@ def generate_conv2d_line_buffer_fn(

generated_code = (
"template<class data_T, typename CONFIG_T>\n"
"class fill_buffer_{index} : public FillConv2DBuffer<data_T, CONFIG_T> {{\n"
"class fill_buffer_{index} : public nnet::FillConv2DBuffer<data_T, CONFIG_T> {{\n"
" public:\n"
" static void fill_buffer(\n"
" data_T data[CONFIG_T::in_height * CONFIG_T::in_width * CONFIG_T::n_chan],\n"
Expand Down
4 changes: 3 additions & 1 deletion hls4ml/backends/fpga/passes/hgq_proxy_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -75,10 +75,12 @@ def transform(self, model, node: FixedPointQuantizer):
class ProcessFixedPointQuantizerCall(FunctionCallTemplate):
def __init__(self):
super().__init__(FixedPointQuantizer, include_header=[])
self.template = 'nnet::{name}<{input_t}, {output_t}>({input}, {output});'
self.template = '{namespace}::{name}<{input_t}, {output_t}>({input}, {output});'

def format(self, node):
params = self._default_function_params(node)
namespace = node.model.config.writer_config.get('Namespace', None) or 'nnet'
params['namespace'] = namespace

return self.template.format(**params)

Expand Down
1 change: 1 addition & 0 deletions hls4ml/backends/template.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,6 +62,7 @@ def _default_config_params(self, layer):
params = self._default_params(layer)
params['iotype'] = layer.model.config.get_config_value('IOType')
params['reuse'] = layer.get_attr('reuse_factor')
params['namespace'] = layer.model.config.get_writer_config().get('Namespace', None) or 'nnet'

return params

Expand Down
55 changes: 32 additions & 23 deletions hls4ml/backends/vivado/passes/convolution_templates.py
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Expand Up @@ -23,7 +23,7 @@
typedef {bias_t.name} bias_t;
typedef {weight_t.name} weight_t;
template<class data_T, class res_T, class CONFIG_T>
using kernel = nnet::{dense_function}<data_T, res_T, CONFIG_T>;
using kernel = {dense_function}<data_T, res_T, CONFIG_T>;
template<class x_T, class y_T>
using product = nnet::product::{product_type}<x_T, y_T>;
}};\n"""
Expand Down Expand Up @@ -53,7 +53,7 @@
static const unsigned n_partitions = {n_partitions};
static const unsigned n_pixels = out_width / n_partitions;
template<class data_T, class CONFIG_T>
using fill_buffer = nnet::{fill_fn}<data_T, CONFIG_T>;
using fill_buffer = {fill_fn}<data_T, CONFIG_T>;
typedef {accum_t.name} accum_t;
typedef {bias_t.name} bias_t;
typedef {weight_t.name} weight_t;
Expand Down Expand Up @@ -91,9 +91,10 @@ def format(self, node):
params['scale_index_type'] = 'scale_index_regular'

if node.model.config.get_config_value('IOType') == 'io_parallel':
params['fill_fn'] = f'fill_buffer_{node.index}'
namespace = params['namespace']
params['fill_fn'] = f'{namespace}::fill_buffer_{node.index}'
else:
params['fill_fn'] = 'FillConv1DBuffer'
params['fill_fn'] = 'nnet::FillConv1DBuffer'

is_pointwise_parallel_latency = (
node.get_attr('filt_width') == 1
Expand Down Expand Up @@ -127,16 +128,18 @@ def format(self, node):
node.get_input_variable().type.precision, node.get_weights('weight').type.precision
)

namespace = params['namespace']

if node.get_attr('strategy').lower() == 'latency':
mult_params['dense_function'] = 'DenseLatency'
mult_params['dense_function'] = 'nnet::DenseLatency'
elif node.get_attr('strategy').lower() == 'resource':
if int(mult_params['reuse_factor']) <= int(mult_params['n_in']):
mult_params['dense_function'] = 'DenseResource_rf_leq_nin'
mult_params['dense_function'] = 'nnet::DenseResource_rf_leq_nin'
else:
mult_params['dense_function'] = 'DenseResource_rf_gt_nin_rem0'
mult_params['dense_function'] = 'nnet::DenseResource_rf_gt_nin_rem0'
# The 3rd case is never used
elif node.get_attr('strategy').lower() == 'resource_unrolled':
mult_params['dense_function'] = f'dense_resource_unrolled_{node.index}'
mult_params['dense_function'] = f'{namespace}::dense_resource_unrolled_{node.index}'

mult_config = self.mult_template.format(**mult_params)

Expand Down Expand Up @@ -194,7 +197,7 @@ def __init__(self):
static const unsigned n_partitions = {n_partitions};
static const unsigned n_pixels = out_height * out_width / n_partitions;
template<class data_T, class CONFIG_T>
using fill_buffer = nnet::{fill_fn}<data_T, CONFIG_T>;
using fill_buffer = {fill_fn}<data_T, CONFIG_T>;
typedef {accum_t.name} accum_t;
typedef {bias_t.name} bias_t;
typedef {weight_t.name} weight_t;
Expand Down Expand Up @@ -238,9 +241,10 @@ def format(self, node):
params['scale_index_width_type'] = 'scale_index_regular'

if node.model.config.get_config_value('IOType') == 'io_parallel':
params['fill_fn'] = f'fill_buffer_{node.index}'
namespace = params['namespace']
params['fill_fn'] = f'{namespace}::fill_buffer_{node.index}'
else:
params['fill_fn'] = 'FillConv2DBuffer'
params['fill_fn'] = 'nnet::FillConv2DBuffer'

params['min_height'] = node.get_attr('min_height', node.get_attr('in_height'))
params['min_width'] = node.get_attr('min_width', node.get_attr('in_width'))
Expand All @@ -256,16 +260,17 @@ def format(self, node):
node.get_input_variable().type.precision, node.get_weights('weight').type.precision
)

namespace = params['namespace']
if node.get_attr('strategy').lower() == 'latency':
mult_params['dense_function'] = 'DenseLatency'
mult_params['dense_function'] = 'nnet::DenseLatency'
elif node.get_attr('strategy').lower() == 'resource':
if int(mult_params['reuse_factor']) <= int(mult_params['n_in']):
mult_params['dense_function'] = 'DenseResource_rf_leq_nin'
mult_params['dense_function'] = 'nnet::DenseResource_rf_leq_nin'
else:
mult_params['dense_function'] = 'DenseResource_rf_gt_nin_rem0'
mult_params['dense_function'] = 'nnet::DenseResource_rf_gt_nin_rem0'
# The 3rd case is never used
elif node.get_attr('strategy').lower() == 'resource_unrolled':
mult_params['dense_function'] = f'dense_resource_unrolled_{node.index}'
mult_params['dense_function'] = f'{namespace}::dense_resource_unrolled_{node.index}'

mult_config = self.mult_template.format(**mult_params)

Expand Down Expand Up @@ -341,9 +346,10 @@ def format(self, node):
params['weight_t'] = node.get_weights('depthwise').type
params['bias_t'] = node.get_weights('zero_bias').type
if node.model.config.get_config_value('IOType') == 'io_parallel':
params['fill_fn'] = f'fill_buffer_{node.index}_dw'
namespace = params['namespace']
params['fill_fn'] = f'{namespace}::fill_buffer_{node.index}_dw'
else:
params['fill_fn'] = 'FillConv1DBuffer'
params['fill_fn'] = 'nnet::FillConv1DBuffer'

if node.get_attr('unscaled'):
params['scale_index_type'] = 'scale_index_unscaled'
Expand Down Expand Up @@ -387,9 +393,10 @@ def format(self, node):
params['min_width'] = params['in_width']
params['instructions'] = '0'
if node.model.config.get_config_value('IOType') == 'io_parallel':
params['fill_fn'] = f'fill_buffer_{node.index}_pw'
namespace = params['namespace']
params['fill_fn'] = f'{namespace}::fill_buffer_{node.index}_pw'
else:
params['fill_fn'] = 'FillConv1DBuffer'
params['fill_fn'] = 'nnet::FillConv1DBuffer'

if node.get_attr('unscaled'):
params['scale_index_type'] = 'scale_index_unscaled'
Expand Down Expand Up @@ -474,9 +481,10 @@ def format(self, node):
params['index'] = str(node.index) + '_depthwise'
params['weight_t'] = node.get_weights('depthwise').type
if node.model.config.get_config_value('IOType') == 'io_parallel':
params['fill_fn'] = f'fill_buffer_{node.index}_dw'
namespace = params['namespace']
params['fill_fn'] = f'{namespace}::fill_buffer_{node.index}_dw'
else:
params['fill_fn'] = 'FillConv2DBuffer'
params['fill_fn'] = 'nnet::FillConv2DBuffer'

if node.get_attr('unscaled_h'):
params['scale_index_height_type'] = 'scale_index_unscaled'
Expand Down Expand Up @@ -528,9 +536,10 @@ def format(self, node):
params['min_width'] = params['in_width']
params['instructions'] = '0'
if node.model.config.get_config_value('IOType') == 'io_parallel':
params['fill_fn'] = f'fill_buffer_{node.index}_pw'
namespace = params['namespace']
params['fill_fn'] = f'{namespace}::fill_buffer_{node.index}_pw'
else:
params['fill_fn'] = 'FillConv2DBuffer'
params['fill_fn'] = 'nnet::FillConv2DBuffer'

if node.get_attr('unscaled_h'):
params['scale_index_height_type'] = 'scale_index_unscaled'
Expand Down
12 changes: 7 additions & 5 deletions hls4ml/backends/vivado/passes/core_templates.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
typedef {weight_t.name} weight_t;
typedef {index_t.name} index_t;
template<class data_T, class res_T, class CONFIG_T>
using kernel = nnet::{dense_function}<data_T, res_T, CONFIG_T>;
using kernel = {dense_function}<data_T, res_T, CONFIG_T>;
template<class x_T, class y_T>
using product = nnet::product::{product_type}<x_T, y_T>;
}};\n"""
Expand All @@ -43,16 +43,18 @@ def format(self, node):
node.get_input_variable().type.precision, node.get_weights('weight').type.precision
)

namespace = params['namespace']

if node.get_attr('strategy').lower() == 'latency':
params['dense_function'] = 'DenseLatency'
params['dense_function'] = 'nnet::DenseLatency'
elif node.get_attr('strategy').lower() == 'resource':
if int(params['reuse_factor']) <= int(params['n_in']):
params['dense_function'] = 'DenseResource_rf_leq_nin'
params['dense_function'] = 'nnet::DenseResource_rf_leq_nin'
else:
params['dense_function'] = 'DenseResource_rf_gt_nin_rem0'
params['dense_function'] = 'nnet::DenseResource_rf_gt_nin_rem0'
# The 3rd case is never used
elif node.get_attr('strategy').lower() == 'resource_unrolled':
params['dense_function'] = f'dense_resource_unrolled_{node.index}'
params['dense_function'] = f'{namespace}::dense_resource_unrolled_{node.index}'

return self.template.format(**params)

Expand Down
22 changes: 12 additions & 10 deletions hls4ml/backends/vivado/passes/recurrent_templates.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
typedef {bias_t.name} bias_t;
typedef {weight_t.name} weight_t;
template<class data_T, class res_T, class CONFIG_T>
using kernel = nnet::{dense_function}<data_T, res_T, CONFIG_T>;
using kernel = {dense_function}<data_T, res_T, CONFIG_T>;
template<class x_T, class y_T>
using product = nnet::product::{product_type}<x_T, y_T>;
}};\n"""
Expand All @@ -35,7 +35,7 @@
typedef {recurrent_bias_t.name} bias_t;
typedef {recurrent_weight_t.name} weight_t;
template<class data_T, class res_T, class CONFIG_T>
using kernel = nnet::{dense_function}<data_T, res_T, CONFIG_T>;
using kernel = {dense_function}<data_T, res_T, CONFIG_T>;
template<class x_T, class y_T>
using product = nnet::product::{product_type}<x_T, y_T>;
}};\n"""
Expand Down Expand Up @@ -161,16 +161,18 @@ def format(self, node):
mult_params1['nzeros'] = node.get_weights('weight').nzeros
mult_params1['nonzeros'] = node.get_weights('weight').nonzeros

namespace = params['namespace']

if node.get_attr('strategy').lower() == 'latency':
mult_params1['dense_function'] = 'DenseLatency'
mult_params1['dense_function'] = 'nnet::DenseLatency'
elif node.get_attr('strategy').lower() == 'resource':
if int(mult_params1['reuse_factor']) <= int(mult_params1['n_in']):
mult_params1['dense_function'] = 'DenseResource_rf_leq_nin'
mult_params1['dense_function'] = 'nnet::DenseResource_rf_leq_nin'
else:
mult_params1['dense_function'] = 'DenseResource_rf_gt_nin_rem0'
mult_params1['dense_function'] = 'nnet::DenseResource_rf_gt_nin_rem0'
# The 3rd case is never used
elif node.get_attr('strategy').lower() == 'resource_unrolled':
mult_params1['dense_function'] = f'dense_resource_unrolled_{node.index}_1'
mult_params1['dense_function'] = f'{namespace}::dense_resource_unrolled_{node.index}_1'

if node.get_attr('return_sequences'):
mult_params2['n_in'] = node.get_output_variable().shape[1]
Expand All @@ -187,15 +189,15 @@ def format(self, node):
mult_params2['nonzeros'] = node.get_weights('recurrent_weight').nonzeros

if node.get_attr('strategy').lower() == 'latency':
mult_params2['dense_function'] = 'DenseLatency'
mult_params2['dense_function'] = 'nnet::DenseLatency'
elif node.get_attr('strategy').lower() == 'resource':
if int(mult_params2['reuse_factor']) <= int(mult_params2['n_in']):
mult_params2['dense_function'] = 'DenseResource_rf_leq_nin'
mult_params2['dense_function'] = 'nnet::DenseResource_rf_leq_nin'
else:
mult_params2['dense_function'] = 'DenseResource_rf_gt_nin_rem0'
mult_params2['dense_function'] = 'nnet::DenseResource_rf_gt_nin_rem0'
# The 3rd case is never used
elif node.get_attr('strategy').lower() == 'resource_unrolled':
mult_params2['dense_function'] = f'dense_resource_unrolled_{node.index}_2'
mult_params2['dense_function'] = f'{namespace}::dense_resource_unrolled_{node.index}_2'

mult_config1 = self.mult1_template.format(**mult_params1)
mult_config2 = self.mult2_template.format(**mult_params2)
Expand Down
4 changes: 4 additions & 0 deletions hls4ml/writer/vivado_writer.py
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Expand Up @@ -792,6 +792,7 @@ def write_generated_code(self, model):
contents = f.readlines()
f.close()
f = open(path, 'w')
namespace = model.config.get_writer_config().get('Namespace', None)

for line in contents:
if '// hls4ml insert code' in line:
Expand All @@ -801,6 +802,9 @@ def write_generated_code(self, model):
newline += str(generated_code)
else:
newline = line
if namespace is not None:
if 'namespace nnet' in newline:
newline = newline.replace('namespace nnet', f'namespace {namespace}')
f.write(newline)
f.close()

Expand Down
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