ref: 4f3761b0199df7024b6e6b2004fc5eb7a6dbb28b
dir: /dnn/torch/weight-exchange/wexchange/c_export/common.py/
'''Copyright (c) 2017-2018 Mozilla
Copyright (c) 2022 Amazon
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
'''
import numpy as np
from .c_writer import CWriter
def print_vector(writer, vector, name, dtype='float', dotp=False, static=True):
f = writer.source
binary_blob = writer.enable_binary_blob
if binary_blob:
f.write(
f'''
#ifndef USE_WEIGHTS_FILE
#define WEIGHTS_{name}_DEFINED
#define WEIGHTS_{name}_TYPE WEIGHT_TYPE_{"qweight" if dotp else "float"}
'''
)
writer.weight_arrays.add(name)
if dotp:
vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8))
vector = vector.transpose((2, 0, 3, 1))
v = np.reshape(vector, (-1))
if static:
f.write('static ')
f.write(f'const {dtype} {name}[{len(v)}] = {{\n ')
for i in range(0, len(v)):
f.write(f'{v[i]}')
if (i!=len(v)-1):
f.write(',')
else:
break
if (i%8==7):
f.write("\n ")
else:
f.write(" ")
f.write('\n};\n\n')
if binary_blob:
f.write(
f'''
#endif /* USE_WEIGHTS_FILE */
'''
)
return vector
def print_sparse_vector(writer, A, name, have_diag=True):
f = writer.source
N = A.shape[0]
M = A.shape[1]
W = np.zeros((0,), dtype='int')
W0 = np.zeros((0,))
if have_diag:
diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])])
A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N]))
A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))
A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))
print_vector(writer, diag, name + '_diag')
AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int')
idx = np.zeros((0,), dtype='int')
for i in range(M//8):
pos = idx.shape[0]
idx = np.append(idx, -1)
nb_nonzero = 0
for j in range(N//4):
block = A[j*4:(j+1)*4, i*8:(i+1)*8]
qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8]
if np.sum(np.abs(block)) > 1e-10:
nb_nonzero = nb_nonzero + 1
idx = np.append(idx, j*4)
vblock = qblock.transpose((1,0)).reshape((-1,))
W0 = np.concatenate([W0, block.reshape((-1,))])
W = np.concatenate([W, vblock])
idx[pos] = nb_nonzero
f.write('#ifdef DOT_PROD\n')
print_vector(writer, W, name, dtype='qweight')
f.write('#else /*DOT_PROD*/\n')
print_vector(writer, W0, name, dtype='qweight')
f.write('#endif /*DOT_PROD*/\n')
print_vector(writer, idx, name + '_idx', dtype='int')
return AQ
def _check_activation(activation):
if not activation in {"TANH", "SIGMOID", "LINEAR", "SWISH", "RELU", "SOFTMAX"}:
raise ValueError(f"error: unknown activation {activation}")
def print_dense_layer(writer : CWriter,
name : str,
weight : np.ndarray,
bias : np.ndarray,
activation: str,
format : str = 'torch'):
_check_activation(activation)
if format == 'torch':
weight = weight.transpose()
print_vector(writer, weight, name + "_weights")
print_vector(writer, bias, name + "_bias")
writer.header.write(f"\n#define {name.upper()}_OUT_SIZE {weight.shape[1]}\n")
if writer.enable_binary_blob:
init_call = f'dense_init(&model->{name}, arrays, "{name}_bias", "{name}_weights", {weight.shape[0]}, {weight.shape[1]}, ACTIVATION_{activation})'
writer.layer_dict[name] = ('DenseLayer', init_call)
else:
writer.source.write(
f"""
const DenseLayer {name} = {{
{name}_bias,
{name}_weights,
{weight.shape[0]},
{weight.shape[1]},
ACTIVATION_{activation}
}};
"""
)
writer.header.write(f"\nextern const DenseLayer {name};\n\n")
def print_conv1d_layer(writer : CWriter,
name : str,
weight : np.ndarray,
bias : np.ndarray,
activation: str,
format : str = 'torch'):
_check_activation(activation)
if format == "torch":
# convert to channels last
weight = np.transpose(weight, (2, 1, 0))
print_vector(writer, weight, name + "_weights")
print_vector(writer, bias, name + "_bias")
writer.header.write(f"\n#define {name.upper()}_OUT_SIZE {weight.shape[2]}\n")
writer.header.write(f"\n#define {name.upper()}_STATE_SIZE ({weight.shape[1]} * ({weight.shape[0] - 1}))\n")
writer.header.write(f"\n#define {name.upper()}_DELAY {(weight.shape[0] - 1) // 2}\n") # CAVE: delay is not a property of the conv layer
if writer.enable_binary_blob:
init_call = f'conv1d_init(&model->{name}, arrays, "{name}_bias", "{name}_weights", {weight.shape[1]}, {weight.shape[0]}, {weight.shape[2]}, ACTIVATION_{activation})'
writer.layer_dict[name] = ('Conv1DLayer', init_call)
else:
writer.source.write(
f"""
const Conv1DLayer {name} = {{
{name}_bias,
{name}_weights,
{weight.shape[1]},
{weight.shape[0]},
{weight.shape[2]},
ACTIVATION_{activation}
}};
"""
)
writer.header.write(f"\nextern const Conv1DLayer {name};\n\n")
return weight.shape[0] * weight.shape[1]
def print_gru_layer(writer : CWriter,
name : str,
weight : np.ndarray,
recurrent_weight : np.ndarray,
bias : np.ndarray,
recurrent_bias : np.ndarray,
activation: str,
format : str = 'torch',
dotp : bool = False,
input_sparse : bool = False,
reset_after : int = 0
):
_check_activation(activation)
if format == "torch":
# transpose weight matrices and change gate order from rzn to zrn
N = weight.shape[0] // 3
for x in [weight, recurrent_weight, bias, recurrent_bias]:
tmp = x[0:N].copy()
x[0:N] = x[N:2*N]
x[N:2*N] = tmp
weight = weight.transpose()
recurrent_weight = recurrent_weight.transpose()
# input weights
if input_sparse:
qweight = print_sparse_vector(writer, weight, name + '_weights', have_diag=False)
else:
qweight = np.clip(np.round(128. * weight).astype('int'), -128, 127)
if dotp:
writer.source.write("#ifdef DOT_PROD\n")
print_vector(writer, qweight, name + '_weights', dtype='qweight', dotp=True)
writer.source.write("#else /*DOT_PROD*/\n")
print_vector(writer, weight, name + '_weights')
if dotp:
writer.source.write("#endif /*DOT_PROD*/\n")
# recurrent weights
recurrent_qweight = np.clip(np.round(128. * recurrent_weight).astype('int'), -128, 127)
if dotp:
writer.source.write("#ifdef DOT_PROD\n")
print_vector(writer, recurrent_qweight, name + '_recurrent_weights', dtype='qweight', dotp=True)
writer.source.write("#else /*DOT_PROD*/\n")
print_vector(writer, recurrent_weight, name + '_recurrent_weights')
if dotp:
writer.source.write("#endif /*DOT_PROD*/\n")
# corrected bias for unsigned int matrix multiplication
subias = bias - np.sum(qweight / 128., axis=0)
recurrent_subias = recurrent_bias - np.sum(recurrent_qweight / 128., axis=0)
print_vector(writer, np.concatenate((bias, recurrent_bias)), name + "_bias")
print_vector(writer, np.concatenate((subias, recurrent_subias)), name + "_subias")
# wrapping it up
writer.header.write(f"\n#define {name.upper()}_OUT_SIZE {N}\n")
writer.header.write(f"\n#define {name.upper()}_STATE_SIZE {N}\n")
if writer.enable_binary_blob:
if input_sparse:
init_call = f'gru_init(&model->{name}, arrays, "{name}_bias", "{name}_subias", "{name}_weights", "{name + "_weights_idx"}", "{name}_recurrent_weights", {weight.shape[0]}, {weight.shape[1] // 3}, ACTIVATION_{activation}, {reset_after})'
else:
init_call = f'gru_init(&model->{name}, arrays, "{name}_bias", "{name}_subias", "{name}_weights", NULL, "{name}_recurrent_weights", {weight.shape[0]}, {weight.shape[1] // 3}, ACTIVATION_{activation}, {reset_after})'
writer.layer_dict[name] = ('GRULayer', init_call)
else:
writer.source.write(
f"""
const GRULayer {name} = {{
{name}_bias,
{name}_subias,
{name}_weights,
{name + "_weights_idx" if input_sparse else "NULL"},
{name}_recurrent_weights,
{weight.shape[0]},
{weight.shape[1] // 3},
ACTIVATION_{activation},
{reset_after}
}};
"""
)
writer.header.write(f"\nextern const GRULayer {name};\n")
return N