'''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