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cudaregress v 1.0.0.002
This commit is contained in:
jlevine18
GitHub
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commit
e38c12f765
@ -1,180 +1,217 @@
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# Titan Robotics Team 2022: CUDA-based Regressions Module
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# Written by Arthur Lu & Jacob Levine
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# Notes:
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# this should be imported as a python module using 'import cudaregress'
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# this should be included in the local directory or environment variable
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# this module is cuda-optimized and vectorized (except for one small part)
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# setup:
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__version__ = "1.0.0.001"
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# changelog should be viewed using print(cudaregress.__changelog__)
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__changelog__ = """
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1.0.0.001:
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-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
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-already vectorized (except for polynomial generation) and CUDA-optimized
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"""
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__author__ = (
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"Jacob Levine <jlevine@imsa.edu>",
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)
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__all__ = [
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'factorial',
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'take_all_pwrs',
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'set_device',
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'LinearRegKernel',
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'SigmoidalRegKernel',
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'LogRegKernel',
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'PolyRegKernel',
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'ExpRegKernel',
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'SGDTrain',
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'CustomTrain'
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]
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# imports (just one for now):
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import torch
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#set device
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device='cuda:0' if torch.cuda.is_available() else 'cpu'
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#todo: document completely
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def factorial(n):
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if n==0:
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return 1
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else:
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return n*factorial(n-1)
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def take_all_pwrs(vec,pwr):
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#todo: vectorize (kinda)
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combins=torch.combinations(vec, r=pwr, with_replacement=True)
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out=torch.ones(combins.size()[0])
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for i in torch.t(combins):
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out *= i
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return out
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def set_device(new_device):
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global device
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device=new_device
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class LinearRegKernel():
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parameters= []
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weights=None
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bias=None
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.bias]
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def forward(self,mtx):
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long_bias=self.bias.repeat([1,mtx.size()[1]])
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return torch.matmul(self.weights,mtx)+long_bias
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class SigmoidalRegKernel():
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parameters= []
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weights=None
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bias=None
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sigmoid=torch.nn.Sigmoid()
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.bias]
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def forward(self,mtx):
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long_bias=self.bias.repeat([1,mtx.size()[1]])
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return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)
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class LogRegKernel():
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parameters= []
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weights=None
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bias=None
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.bias]
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def forward(self,mtx):
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long_bias=self.bias.repeat([1,mtx.size()[1]])
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return torch.log(torch.matmul(self.weights,mtx)+long_bias)
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class ExpRegKernel():
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parameters= []
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weights=None
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bias=None
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.bias]
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def forward(self,mtx):
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long_bias=self.bias.repeat([1,mtx.size()[1]])
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return torch.exp(torch.matmul(self.weights,mtx)+long_bias)
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class PolyRegKernel():
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parameters= []
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weights=None
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bias=None
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power=None
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def __init__(self, num_vars, power):
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self.power=power
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num_terms=int(factorial(num_vars+power-1) / factorial(power) / factorial(num_vars-1))
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self.weights=torch.rand(num_terms, requires_grad=True, device=device)
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self.bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.bias]
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def forward(self,mtx):
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#TODO: Vectorize the last part
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cols=[]
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for i in torch.t(mtx):
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cols.append(take_all_pwrs(i,self.power))
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new_mtx=torch.t(torch.stack(cols))
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long_bias=self.bias.repeat([1,mtx.size()[1]])
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return torch.matmul(self.weights,new_mtx)+long_bias
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def SGDTrain(kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):
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optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
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data_cuda=data.to(device)
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ground_cuda=ground.to(device)
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if (return_losses):
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losses=[]
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for i in range(iterations):
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with torch.set_grad_enabled(True):
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optim.zero_grad()
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pred=kernel.forward(data_cuda)
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ls=loss(pred,ground_cuda)
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losses.append(ls.item())
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ls.backward()
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optim.step()
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return [kernel,losses]
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else:
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for i in range(iterations):
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with torch.set_grad_enabled(True):
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optim.zero_grad()
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pred=kernel.forward(data_cuda)
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ls=loss(pred,ground_cuda)
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ls.backward()
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optim.step()
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return kernel
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def CustomTrain(kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):
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data_cuda=data.to(device)
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ground_cuda=ground.to(device)
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if (return_losses):
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losses=[]
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for i in range(iterations):
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with torch.set_grad_enabled(True):
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optim.zero_grad()
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pred=kernel.forward(data)
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ls=loss(pred,ground)
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losses.append(ls.item())
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ls.backward()
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optim.step()
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return [kernel,losses]
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else:
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for i in range(iterations):
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with torch.set_grad_enabled(True):
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optim.zero_grad()
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pred=kernel.forward(data_cuda)
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ls=loss(pred,ground_cuda)
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ls.backward()
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optim.step()
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return kernel
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# Titan Robotics Team 2022: CUDA-based Regressions Module
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# Written by Arthur Lu & Jacob Levine
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# Notes:
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# this should be imported as a python module using 'import cudaregress'
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# this should be included in the local directory or environment variable
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# this module is cuda-optimized and vectorized (except for one small part)
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# setup:
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__version__ = "1.0.0.002"
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# changelog should be viewed using print(cudaregress.__changelog__)
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__changelog__ = """
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1.0.0.002:
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-Added more parameters to log, exponential, polynomial
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-
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1.0.0.001:
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-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
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-already vectorized (except for polynomial generation) and CUDA-optimized
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"""
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__author__ = (
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"Jacob Levine <jlevine@imsa.edu>",
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)
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__all__ = [
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'factorial',
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'take_all_pwrs',
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'num_poly_terms',
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'set_device',
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'LinearRegKernel',
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'SigmoidalRegKernel',
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'LogRegKernel',
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'PolyRegKernel',
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'ExpRegKernel',
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'SigmoidalRegKernelArthur',
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'SGDTrain',
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'CustomTrain'
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]
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# imports (just one for now):
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import torch
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device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
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#todo: document completely
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def factorial(n):
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if n==0:
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return 1
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else:
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return n*factorial(n-1)
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def num_poly_terms(num_vars, power):
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if power == 0:
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return 0
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return int(factorial(num_vars+power-1) / factorial(power) / factorial(num_vars-1)) + nt(num_vars, power-1)
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def take_all_pwrs(vec,pwr):
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#todo: vectorize (kinda)
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combins=torch.combinations(vec, r=pwr, with_replacement=True)
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out=torch.ones(combins.size()[0])
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for i in torch.t(combins):
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out *= i
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return torch.cat(out,take_all_pwrs(vec, pwr-1))
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def set_device(new_device):
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global device
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device=new_device
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class LinearRegKernel():
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parameters= []
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weights=None
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bias=None
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.bias]
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def forward(self,mtx):
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long_bias=self.bias.repeat([1,mtx.size()[1]])
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return torch.matmul(self.weights,mtx)+long_bias
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class SigmoidalRegKernel():
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parameters= []
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weights=None
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bias=None
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sigmoid=torch.nn.Sigmoid()
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.bias]
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def forward(self,mtx):
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long_bias=self.bias.repeat([1,mtx.size()[1]])
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return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)
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class SigmoidalRegKernelArthur():
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parameters= []
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weights=None
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in_bias=None
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scal_mult=None
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out_bias=None
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sigmoid=torch.nn.Sigmoid()
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.in_bias=torch.rand(1, requires_grad=True, device=device)
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self.scal_mult=torch.rand(1, requires_grad=True, device=device)
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self.out_bias==torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
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def forward(self,mtx):
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long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
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long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
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return (scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
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class LogRegKernel():
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parameters= []
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weights=None
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in_bias=None
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scal_mult=None
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out_bias=None
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.in_bias=torch.rand(1, requires_grad=True, device=device)
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self.scal_mult=torch.rand(1, requires_grad=True, device=device)
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self.out_bias==torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
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def forward(self,mtx):
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long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
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long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
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return (scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
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class ExpRegKernel():
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parameters= []
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weights=None
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in_bias=None
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scal_mult=None
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out_bias=None
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def __init__(self, num_vars):
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self.weights=torch.rand(num_vars, requires_grad=True, device=device)
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self.in_bias=torch.rand(1, requires_grad=True, device=device)
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self.scal_mult=torch.rand(1, requires_grad=True, device=device)
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self.out_bias==torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
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def forward(self,mtx):
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long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
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long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
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return (scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
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class PolyRegKernel():
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parameters= []
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weights=None
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bias=None
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power=None
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def __init__(self, num_vars, power):
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self.power=power
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num_terms=num_poly_terms(num_vars, power)
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self.weights=torch.rand(num_terms, requires_grad=True, device=device)
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self.bias=torch.rand(1, requires_grad=True, device=device)
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self.parameters=[self.weights,self.bias]
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def forward(self,mtx):
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#TODO: Vectorize the last part
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cols=[]
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for i in torch.t(mtx):
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cols.append(take_all_pwrs(i,self.power))
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new_mtx=torch.t(torch.stack(cols))
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long_bias=self.bias.repeat([1,mtx.size()[1]])
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return torch.matmul(self.weights,new_mtx)+long_bias
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def SGDTrain(kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):
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optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
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data_cuda=data.to(device)
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ground_cuda=ground.to(device)
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if (return_losses):
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losses=[]
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for i in range(iterations):
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with torch.set_grad_enabled(True):
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optim.zero_grad()
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pred=kernel.forward(data_cuda)
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ls=loss(pred,ground_cuda)
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losses.append(ls.item())
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ls.backward()
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optim.step()
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return [kernel,losses]
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else:
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for i in range(iterations):
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with torch.set_grad_enabled(True):
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optim.zero_grad()
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pred=kernel.forward(data_cuda)
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ls=loss(pred,ground_cuda)
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ls.backward()
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optim.step()
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return kernel
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def CustomTrain(kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):
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data_cuda=data.to(device)
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ground_cuda=ground.to(device)
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if (return_losses):
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losses=[]
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for i in range(iterations):
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with torch.set_grad_enabled(True):
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optim.zero_grad()
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pred=kernel.forward(data)
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ls=loss(pred,ground)
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losses.append(ls.item())
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ls.backward()
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optim.step()
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return [kernel,losses]
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else:
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for i in range(iterations):
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with torch.set_grad_enabled(True):
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optim.zero_grad()
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pred=kernel.forward(data_cuda)
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ls=loss(pred,ground_cuda)
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ls.backward()
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optim.step()
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return kernel
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