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combining templates
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@drankincms
drankincms committed Jun 21, 2022
1 parent cb76f4e commit 0a5d209
Showing 1 changed file with 23 additions and 133 deletions.
156 changes: 23 additions & 133 deletions hls4ml/backends/vivado/passes/recurrent_templates.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,13 +41,13 @@

# LSTM + GRU templates

lstm_config_template = """struct config{index} : nnet::lstm_config {{
recr_config_template = """struct config{index} : nnet::{recr_type}_config {{
typedef {accum_t.name} accum_t;
typedef {weight_t.name} weight_t; // Matrix
typedef {bias_t.name} bias_t; // Vector
typedef {config_mult_t1} mult_config1;
typedef {config_mult_t2} mult_config2;
typedef {lstm_act_t} ACT_CONFIG_LSTM;
typedef {recr_act_t} ACT_CONFIG_{RECR_TYPE};
template<class x_T, class y_T, class config_T>
using activation_recr = nnet::activation::{recurrent_activation}<x_T, y_T, config_T>;
typedef {act_t} ACT_CONFIG_T;
Expand All @@ -64,39 +64,14 @@
static const bool use_static = {static};
}};\n"""

lstm_function_template = 'nnet::lstm_stack<{input_t}, {output_t}, {config}>({input}, {output}, {w}, {wr}, {b}, {br});'

gru_config_template = """struct config{index} : nnet::gru_config {{
typedef {accum_t.name} accum_t;
typedef {weight_t.name} weight_t; // Matrix
typedef {bias_t.name} bias_t; // Vector
typedef {config_mult_t1} mult_config1;
typedef {config_mult_t2} mult_config2;
typedef {gru_act_t} ACT_CONFIG_GRU;
template<class x_T, class y_T, class config_T>
using activation_recr = nnet::activation::{recurrent_activation}<x_T, y_T, config_T>;
typedef {act_t} ACT_CONFIG_T;
template<class x_T, class y_T, class config_T>
using activation = nnet::activation::{activation}<x_T, y_T, config_T>;
static const unsigned n_in = {n_in};
static const unsigned n_out = {n_out};
static const unsigned n_state = {n_state};
static const unsigned n_sequence = {n_sequence};
static const unsigned n_sequence_out = {n_sequence_out};
static const unsigned io_type = nnet::{strategy};
static const unsigned reuse_factor = {reuse};
static const bool store_weights_in_bram = false;
static const bool use_static = {static};
}};\n"""

gru_function_template = 'nnet::gru_stack<{input_t}, {output_t}, {config}>({input}, {output}, {w}, {wr}, {b}, {br});'
recr_function_template = 'nnet::{recr_type}_stack<{input_t}, {output_t}, {config}>({input}, {output}, {w}, {wr}, {b}, {br});'

recr_include_list = ['nnet_utils/nnet_recurrent.h']

class LSTMConfigTemplate(LayerConfigTemplate):
class RecurrentConfigTemplate(LayerConfigTemplate):
def __init__(self):
super().__init__(LSTM)
self.template = lstm_config_template
super().__init__((LSTM, GRU))
self.template = recr_config_template
self.act_template = activ_config_template
self.recr_act_template = recr_activ_config_template
self.mult1_template = recr_mult_config_template
Expand All @@ -118,103 +93,17 @@ def format(self, node):
params['n_out'] = node.get_output_variable().dim_names[0]
params['config_mult_t1'] = 'config{}_1'.format(node.index)
params['config_mult_t2'] = 'config{}_2'.format(node.index)
params['lstm_act_t'] = '{}_config{}_recr'.format(node.get_attr('recurrent_activation'), node.index)
params['recr_act_t'] = '{}_config{}_recr'.format(node.get_attr('recurrent_activation'), node.index)
params['act_t'] = '{}_config{}'.format(node.get_attr('activation'), node.index)
params['strategy'] = node.get_attr('strategy')
params['static'] = 'true' if node.attributes['static'] else 'false'
params['recr_type'] = node.class_name.lower()
params['RECR_TYPE'] = node.class_name

recr_config = self.template.format(**params)

act_params = self._default_config_params(node)
recr_act_params = self._default_config_params(node)

act_params['type'] = node.get_attr('activation')
recr_act_params['type'] = node.get_attr('recurrent_activation')
if node.get_attr('return_sequences'):
act_params['n_in'] = node.get_output_variable().dim_names[1]
recr_act_params['n_in'] = node.get_output_variable().dim_names[1] + ' * 3'
else:
act_params['n_in'] = node.get_output_variable().dim_names[0]
recr_act_params['n_in'] = node.get_output_variable().dim_names[0] + ' * 3'

act_config = self.act_template.format(**act_params)
recr_act_config = self.recr_act_template.format(**recr_act_params)

mult_params1 = self._default_config_params(node)
mult_params2 = self._default_config_params(node)

mult_params1['n_in'] = node.get_input_variable().dim_names[1]
if node.get_attr('return_sequences'):
mult_params1['n_out'] = node.get_output_variable().dim_names[1] + ' * 4'
else:
mult_params1['n_out'] = node.get_output_variable().dim_names[0] + ' * 4'
mult_params1['product_type'] = get_backend('vivado').product_type(node.get_input_variable().type.precision, node.get_weights('weight').type.precision)
mult_params1['reuse'] = params['reuse']
mult_params1['index'] = str(node.index) + '_1'
mult_params1['nzeros'] = node.get_weights('weight').nzeros
mult_params1['nonzeros'] = node.get_weights('weight').nonzeros
if node.get_attr('return_sequences'):
mult_params2['n_in'] = node.get_output_variable().dim_names[1]
mult_params2['n_out'] = node.get_output_variable().dim_names[1] + ' * 4'
else:
mult_params2['n_in'] = node.get_output_variable().dim_names[0]
mult_params2['n_out'] = node.get_output_variable().dim_names[0] + ' * 4'
mult_params2['product_type'] = get_backend('vivado').product_type(node.get_input_variable().type.precision, node.get_weights('recurrent_weight').type.precision)
mult_params2['reuse'] = node.attributes['recurrent_reuse_factor']
mult_params2['index'] = str(node.index) + '_2'
mult_params2['nzeros'] = node.get_weights('recurrent_weight').nzeros
mult_params2['nonzeros'] = node.get_weights('recurrent_weight').nonzeros

mult_config1 = self.mult1_template.format(**mult_params1)
mult_config2 = self.mult2_template.format(**mult_params2)

return mult_config1 + '\n' + mult_config2 + '\n' + recr_act_config + '\n' + act_config + '\n' + recr_config

class LSTMFunctionTemplate(FunctionCallTemplate):
def __init__(self):
super().__init__(LSTM, include_header=recr_include_list)
self.template = lstm_function_template

def format(self, node):
params = self._default_function_params(node)
params['w'] = node.get_weights('weight').name
params['b'] = node.get_weights('bias').name
params['wr'] = node.get_weights('recurrent_weight').name
params['br'] = node.get_weights('recurrent_bias').name
params['activation'] = node.get_attr('activation')
params['recurrent_activation'] = node.get_attr('recurrent_activation')

return self.template.format(**params)

class GRUConfigTemplate(LayerConfigTemplate):
def __init__(self):
super().__init__(GRU)
self.template = gru_config_template
self.act_template = activ_config_template
self.recr_act_template = recr_activ_config_template
self.mult1_template = recr_mult_config_template
self.mult2_template = recr_mult_config_template

def format(self, node):

params = self._default_config_params(node)

params['n_in'] = node.get_input_variable().dim_names[1]
params['n_sequence'] = node.get_input_variable().dim_names[0]
if node.get_attr('return_sequences'):
params['n_sequence_out'] = node.get_output_variable().dim_names[0]
params['n_state'] = node.get_output_variable().dim_names[1]
params['n_out'] = node.get_output_variable().dim_names[1]
else:
params['n_sequence_out'] = 1
params['n_state'] = node.get_output_variable().dim_names[0]
params['n_out'] = node.get_output_variable().dim_names[0]
params['config_mult_t1'] = 'config{}_1'.format(node.index)
params['config_mult_t2'] = 'config{}_2'.format(node.index)
params['gru_act_t'] = '{}_config{}_recr'.format(node.get_attr('recurrent_activation'), node.index)
params['act_t'] = '{}_config{}'.format(node.get_attr('activation'), node.index)
params['strategy'] = node.get_attr('strategy')
params['static'] = 'true' if node.attributes['static'] else 'false'
if node.class_name=='LSTM':
n_recr_mult = 4
else: #GRU
n_recr_mult = 3

recr_config = self.template.format(**params)

Expand All @@ -225,10 +114,10 @@ def format(self, node):
recr_act_params['type'] = node.get_attr('recurrent_activation')
if node.get_attr('return_sequences'):
act_params['n_in'] = node.get_output_variable().dim_names[1]
recr_act_params['n_in'] = node.get_output_variable().dim_names[1] + ' * 2'
recr_act_params['n_in'] = node.get_output_variable().dim_names[1] + ' * %i'%(n_recr_mult-1)
else:
act_params['n_in'] = node.get_output_variable().dim_names[0]
recr_act_params['n_in'] = node.get_output_variable().dim_names[0] + ' * 2'
recr_act_params['n_in'] = node.get_output_variable().dim_names[0] + ' * %i'%(n_recr_mult-1)

act_config = self.act_template.format(**act_params)
recr_act_config = self.recr_act_template.format(**recr_act_params)
Expand All @@ -238,20 +127,20 @@ def format(self, node):

mult_params1['n_in'] = node.get_input_variable().dim_names[1]
if node.get_attr('return_sequences'):
mult_params1['n_out'] = node.get_output_variable().dim_names[1] + ' * 3'
mult_params1['n_out'] = node.get_output_variable().dim_names[1] + ' * %i'%n_recr_mult
else:
mult_params1['n_out'] = node.get_output_variable().dim_names[0] + ' * 3'
mult_params1['n_out'] = node.get_output_variable().dim_names[0] + ' * %i'%n_recr_mult
mult_params1['product_type'] = get_backend('vivado').product_type(node.get_input_variable().type.precision, node.get_weights('weight').type.precision)
mult_params1['reuse'] = params['reuse']
mult_params1['index'] = str(node.index) + '_1'
mult_params1['nzeros'] = node.get_weights('weight').nzeros
mult_params1['nonzeros'] = node.get_weights('weight').nonzeros
if node.get_attr('return_sequences'):
mult_params2['n_in'] = node.get_output_variable().dim_names[1]
mult_params2['n_out'] = node.get_output_variable().dim_names[1] + ' * 3'
mult_params2['n_out'] = node.get_output_variable().dim_names[1] + ' * %i'%n_recr_mult
else:
mult_params2['n_in'] = node.get_output_variable().dim_names[0]
mult_params2['n_out'] = node.get_output_variable().dim_names[0] + ' * 3'
mult_params2['n_out'] = node.get_output_variable().dim_names[0] + ' * %i'%n_recr_mult
mult_params2['product_type'] = get_backend('vivado').product_type(node.get_input_variable().type.precision, node.get_weights('recurrent_weight').type.precision)
mult_params2['reuse'] = node.attributes['recurrent_reuse_factor']
mult_params2['index'] = str(node.index) + '_2'
Expand All @@ -263,10 +152,10 @@ def format(self, node):

return mult_config1 + '\n' + mult_config2 + '\n' + recr_act_config + '\n' + act_config + '\n' + recr_config

class GRUFunctionTemplate(FunctionCallTemplate):
class RecurrentFunctionTemplate(FunctionCallTemplate):
def __init__(self):
super().__init__(GRU, include_header=recr_include_list)
self.template = gru_function_template
super().__init__((LSTM, GRU), include_header=recr_include_list)
self.template = recr_function_template

def format(self, node):
params = self._default_function_params(node)
Expand All @@ -276,6 +165,7 @@ def format(self, node):
params['br'] = node.get_weights('recurrent_bias').name
params['activation'] = node.get_attr('activation')
params['recurrent_activation'] = node.get_attr('recurrent_activation')
params['recr_type'] = node.class_name.lower()

return self.template.format(**params)

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