From a844f703b1711f6ff7be53cc9f6446b2fc821ea7 Mon Sep 17 00:00:00 2001 From: jlevine18 Date: Thu, 26 Sep 2019 13:31:22 -0500 Subject: [PATCH] cudaregress 1.0.0.002 --- data analysis/cudaregress.py | 217 +++++++++++++++++++++++++++++++++++ 1 file changed, 217 insertions(+) create mode 100644 data analysis/cudaregress.py diff --git a/data analysis/cudaregress.py b/data analysis/cudaregress.py new file mode 100644 index 00000000..2c268a34 --- /dev/null +++ b/data analysis/cudaregress.py @@ -0,0 +1,217 @@ +# Titan Robotics Team 2022: CUDA-based Regressions Module +# Written by Arthur Lu & Jacob Levine +# Notes: +# this should be imported as a python module using 'import cudaregress' +# this should be included in the local directory or environment variable +# this module is cuda-optimized and vectorized (except for one small part) +# setup: + +__version__ = "1.0.0.002" + +# changelog should be viewed using print(cudaregress.__changelog__) +__changelog__ = """ +1.0.0.002: + -Added more parameters to log, exponential, polynomial + - + +1.0.0.001: + -initial release, with linear, log, exponential, polynomial, and sigmoid kernels + -already vectorized (except for polynomial generation) and CUDA-optimized + +""" + +__author__ = ( + "Jacob Levine ", +) + +__all__ = [ + 'factorial', + 'take_all_pwrs', + 'num_poly_terms', + 'set_device', + 'LinearRegKernel', + 'SigmoidalRegKernel', + 'LogRegKernel', + 'PolyRegKernel', + 'ExpRegKernel', + 'SigmoidalRegKernelArthur', + 'SGDTrain', + 'CustomTrain' +] + + +# imports (just one for now): + +import torch + +device = "cuda:0" if torch.torch.cuda.is_available() else "cpu" + +#todo: document completely + +def factorial(n): + if n==0: + return 1 + else: + return n*factorial(n-1) +def num_poly_terms(num_vars, power): + if power == 0: + return 0 + return int(factorial(num_vars+power-1) / factorial(power) / factorial(num_vars-1)) + nt(num_vars, power-1) + +def take_all_pwrs(vec,pwr): + #todo: vectorize (kinda) + combins=torch.combinations(vec, r=pwr, with_replacement=True) + out=torch.ones(combins.size()[0]) + for i in torch.t(combins): + out *= i + return torch.cat(out,take_all_pwrs(vec, pwr-1)) + +def set_device(new_device): + global device + device=new_device + +class LinearRegKernel(): + parameters= [] + weights=None + bias=None + def __init__(self, num_vars): + self.weights=torch.rand(num_vars, requires_grad=True, device=device) + self.bias=torch.rand(1, requires_grad=True, device=device) + self.parameters=[self.weights,self.bias] + def forward(self,mtx): + long_bias=self.bias.repeat([1,mtx.size()[1]]) + return torch.matmul(self.weights,mtx)+long_bias + +class SigmoidalRegKernel(): + parameters= [] + weights=None + bias=None + sigmoid=torch.nn.Sigmoid() + def __init__(self, num_vars): + self.weights=torch.rand(num_vars, requires_grad=True, device=device) + self.bias=torch.rand(1, requires_grad=True, device=device) + self.parameters=[self.weights,self.bias] + def forward(self,mtx): + long_bias=self.bias.repeat([1,mtx.size()[1]]) + return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias) + +class SigmoidalRegKernelArthur(): + parameters= [] + weights=None + in_bias=None + scal_mult=None + out_bias=None + sigmoid=torch.nn.Sigmoid() + def __init__(self, num_vars): + self.weights=torch.rand(num_vars, requires_grad=True, device=device) + self.in_bias=torch.rand(1, requires_grad=True, device=device) + self.scal_mult=torch.rand(1, requires_grad=True, device=device) + self.out_bias==torch.rand(1, requires_grad=True, device=device) + self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias] + def forward(self,mtx): + long_in_bias=self.in_bias.repeat([1,mtx.size()[1]]) + long_out_bias=self.out_bias.repeat([1,mtx.size()[1]]) + return (scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias + +class LogRegKernel(): + parameters= [] + weights=None + in_bias=None + scal_mult=None + out_bias=None + def __init__(self, num_vars): + self.weights=torch.rand(num_vars, requires_grad=True, device=device) + self.in_bias=torch.rand(1, requires_grad=True, device=device) + self.scal_mult=torch.rand(1, requires_grad=True, device=device) + self.out_bias==torch.rand(1, requires_grad=True, device=device) + self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias] + def forward(self,mtx): + long_in_bias=self.in_bias.repeat([1,mtx.size()[1]]) + long_out_bias=self.out_bias.repeat([1,mtx.size()[1]]) + return (scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias + +class ExpRegKernel(): + parameters= [] + weights=None + in_bias=None + scal_mult=None + out_bias=None + def __init__(self, num_vars): + self.weights=torch.rand(num_vars, requires_grad=True, device=device) + self.in_bias=torch.rand(1, requires_grad=True, device=device) + self.scal_mult=torch.rand(1, requires_grad=True, device=device) + self.out_bias==torch.rand(1, requires_grad=True, device=device) + self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias] + def forward(self,mtx): + long_in_bias=self.in_bias.repeat([1,mtx.size()[1]]) + long_out_bias=self.out_bias.repeat([1,mtx.size()[1]]) + return (scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias + +class PolyRegKernel(): + parameters= [] + weights=None + bias=None + power=None + def __init__(self, num_vars, power): + self.power=power + num_terms=num_poly_terms(num_vars, power) + self.weights=torch.rand(num_terms, requires_grad=True, device=device) + self.bias=torch.rand(1, requires_grad=True, device=device) + self.parameters=[self.weights,self.bias] + def forward(self,mtx): + #TODO: Vectorize the last part + cols=[] + for i in torch.t(mtx): + cols.append(take_all_pwrs(i,self.power)) + new_mtx=torch.t(torch.stack(cols)) + long_bias=self.bias.repeat([1,mtx.size()[1]]) + return torch.matmul(self.weights,new_mtx)+long_bias + +def SGDTrain(kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False): + optim=torch.optim.SGD(kernel.parameters, lr=learning_rate) + data_cuda=data.to(device) + ground_cuda=ground.to(device) + if (return_losses): + losses=[] + for i in range(iterations): + with torch.set_grad_enabled(True): + optim.zero_grad() + pred=kernel.forward(data_cuda) + ls=loss(pred,ground_cuda) + losses.append(ls.item()) + ls.backward() + optim.step() + return [kernel,losses] + else: + for i in range(iterations): + with torch.set_grad_enabled(True): + optim.zero_grad() + pred=kernel.forward(data_cuda) + ls=loss(pred,ground_cuda) + ls.backward() + optim.step() + return kernel + +def CustomTrain(kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False): + data_cuda=data.to(device) + ground_cuda=ground.to(device) + if (return_losses): + losses=[] + for i in range(iterations): + with torch.set_grad_enabled(True): + optim.zero_grad() + pred=kernel.forward(data) + ls=loss(pred,ground) + losses.append(ls.item()) + ls.backward() + optim.step() + return [kernel,losses] + else: + for i in range(iterations): + with torch.set_grad_enabled(True): + optim.zero_grad() + pred=kernel.forward(data_cuda) + ls=loss(pred,ground_cuda) + ls.backward() + optim.step() + return kernel