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models.py
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# Copyright (C) 2021 Xilinx, Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import reduce
from os.path import realpath
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import init
from brevitas.core.quant import QuantType
from brevitas.core.scaling import ScalingImplType
from brevitas.nn import QuantHardTanh, QuantReLU
from pyverilator import PyVerilator
from logicnets.quant import QuantBrevitasActivation
from logicnets.nn import SparseLinearNeq, ScalarBiasScale, RandomFixedSparsityMask2D
from logicnets.init import random_restrict_fanin
class JetSubstructureNeqModel(nn.Module):
def __init__(self, model_config):
super(JetSubstructureNeqModel, self).__init__()
self.model_config = model_config
self.num_neurons = [model_config["input_length"]] + model_config["hidden_layers"] + [model_config["output_length"]]
layer_list = []
for i in range(1, len(self.num_neurons)):
in_features = self.num_neurons[i-1]
out_features = self.num_neurons[i]
bn = nn.BatchNorm1d(out_features)
if i == 1:
bn_in = nn.BatchNorm1d(in_features)
input_bias = ScalarBiasScale(scale=False, bias_init=-0.25)
input_quant = QuantBrevitasActivation(QuantHardTanh(model_config["input_bitwidth"], max_val=1., narrow_range=False, quant_type=QuantType.INT, scaling_impl_type=ScalingImplType.PARAMETER), pre_transforms=[bn_in, input_bias])
output_quant = QuantBrevitasActivation(QuantReLU(bit_width=model_config["hidden_bitwidth"], max_val=1.61, quant_type=QuantType.INT, scaling_impl_type=ScalingImplType.PARAMETER), pre_transforms=[bn])
mask = RandomFixedSparsityMask2D(in_features, out_features, fan_in=model_config["input_fanin"])
layer = SparseLinearNeq(in_features, out_features, input_quant=input_quant, output_quant=output_quant, sparse_linear_kws={'mask': mask})
layer_list.append(layer)
elif i == len(self.num_neurons)-1:
output_bias_scale = ScalarBiasScale(bias_init=0.33)
output_quant = QuantBrevitasActivation(QuantHardTanh(bit_width=model_config["output_bitwidth"], max_val=1.33, narrow_range=False, quant_type=QuantType.INT, scaling_impl_type=ScalingImplType.PARAMETER), pre_transforms=[bn], post_transforms=[output_bias_scale])
mask = RandomFixedSparsityMask2D(in_features, out_features, fan_in=model_config["output_fanin"])
layer = SparseLinearNeq(in_features, out_features, input_quant=layer_list[-1].output_quant, output_quant=output_quant, sparse_linear_kws={'mask': mask}, apply_input_quant=False)
layer_list.append(layer)
else:
output_quant = QuantBrevitasActivation(QuantReLU(bit_width=model_config["hidden_bitwidth"], max_val=1.61, quant_type=QuantType.INT, scaling_impl_type=ScalingImplType.PARAMETER), pre_transforms=[bn])
mask = RandomFixedSparsityMask2D(in_features, out_features, fan_in=model_config["hidden_fanin"])
layer = SparseLinearNeq(in_features, out_features, input_quant=layer_list[-1].output_quant, output_quant=output_quant, sparse_linear_kws={'mask': mask}, apply_input_quant=False)
layer_list.append(layer)
self.module_list = nn.ModuleList(layer_list)
self.is_verilog_inference = False
self.latency = 1
self.verilog_dir = None
self.top_module_filename = None
self.dut = None
self.logfile = None
def verilog_inference(self, verilog_dir, top_module_filename, logfile: bool = False, add_registers: bool = False):
self.verilog_dir = realpath(verilog_dir)
self.top_module_filename = top_module_filename
self.dut = PyVerilator.build(f"{self.verilog_dir}/{self.top_module_filename}", verilog_path=[self.verilog_dir], build_dir=f"{self.verilog_dir}/verilator")
self.is_verilog_inference = True
self.logfile = logfile
if add_registers:
self.latency = len(self.num_neurons)
def pytorch_inference(self):
self.is_verilog_inference = False
def verilog_forward(self, x):
# Get integer output from the first layer
input_quant = self.module_list[0].input_quant
output_quant = self.module_list[-1].output_quant
_, input_bitwidth = self.module_list[0].input_quant.get_scale_factor_bits()
_, output_bitwidth = self.module_list[-1].output_quant.get_scale_factor_bits()
input_bitwidth, output_bitwidth = int(input_bitwidth), int(output_bitwidth)
total_input_bits = self.module_list[0].in_features*input_bitwidth
total_output_bits = self.module_list[-1].out_features*output_bitwidth
num_layers = len(self.module_list)
input_quant.bin_output()
self.module_list[0].apply_input_quant = False
y = torch.zeros(x.shape[0], self.module_list[-1].out_features)
x = input_quant(x)
self.dut.io.rst = 0
self.dut.io.clk = 0
for i in range(x.shape[0]):
x_i = x[i,:]
y_i = self.pytorch_forward(x[i:i+1,:])[0]
xv_i = list(map(lambda z: input_quant.get_bin_str(z), x_i))
ys_i = list(map(lambda z: output_quant.get_bin_str(z), y_i))
xvc_i = reduce(lambda a,b: a+b, xv_i[::-1])
ysc_i = reduce(lambda a,b: a+b, ys_i[::-1])
self.dut["M0"] = int(xvc_i, 2)
for j in range(self.latency + 1):
#print(self.dut.io.M5)
res = self.dut[f"M{num_layers}"]
result = f"{res:0{int(total_output_bits)}b}"
self.dut.io.clk = 1
self.dut.io.clk = 0
expected = f"{int(ysc_i,2):0{int(total_output_bits)}b}"
result = f"{res:0{int(total_output_bits)}b}"
assert(expected == result)
res_split = [result[i:i+output_bitwidth] for i in range(0, len(result), output_bitwidth)][::-1]
yv_i = torch.Tensor(list(map(lambda z: int(z, 2), res_split)))
y[i,:] = yv_i
# Dump the I/O pairs
if self.logfile is not None:
with open(self.logfile, "a") as f:
f.write(f"{int(xvc_i,2):0{int(total_input_bits)}b}{int(ysc_i,2):0{int(total_output_bits)}b}\n")
return y
def pytorch_forward(self, x):
for l in self.module_list:
x = l(x)
return x
def forward(self, x):
if self.is_verilog_inference:
return self.verilog_forward(x)
else:
return self.pytorch_forward(x)
class JetSubstructureLutModel(JetSubstructureNeqModel):
pass
class JetSubstructureVerilogModel(JetSubstructureNeqModel):
pass