Delete network/unused_code directory
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David Rotermund
GitHub
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commit
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1 changed files with 0 additions and 186 deletions
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import torch
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import math
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from network.CPP.PyMultiApp import MultiApp
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class LinearApproximation(torch.nn.Module):
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in_features: int | None = None
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out_features: int | None = None
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use_bias: bool = False
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approximation_enable: bool = False
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number_of_trunc_bits: int = -1
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number_of_frac: int = -1
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number_of_processes: int = 1
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weights: torch.nn.parameter.Parameter
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bias: torch.nn.parameter.Parameter | None
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device: torch.device
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dtype: torch.dtype
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def __init__(
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self,
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in_features: int,
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out_features: int,
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bias: bool = True,
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approximation_enable: bool = False,
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number_of_trunc_bits: int = -1,
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number_of_frac: int = -1,
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number_of_processes: int = 1,
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device: torch.device | None = None,
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dtype: torch.dtype | None = None,
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) -> None:
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super().__init__()
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assert device is not None
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self.device = device
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assert dtype is not None
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self.dtype = dtype
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self.in_features = in_features
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self.out_channels = out_features
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self.use_bias = bias
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self.approximation_enable = approximation_enable
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self.number_of_trunc_bits = number_of_trunc_bits
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self.number_of_frac = number_of_frac
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self.number_of_processes = number_of_processes
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if self.use_bias is True:
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self.bias: torch.nn.parameter.Parameter | None = (
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torch.nn.parameter.Parameter(
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torch.empty(
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(out_features),
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dtype=self.dtype,
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device=self.device,
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)
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)
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)
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else:
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self.bias = None
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self.weights: torch.nn.parameter.Parameter = torch.nn.parameter.Parameter(
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torch.empty(
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(out_features, in_features),
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dtype=self.dtype,
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device=self.device,
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)
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)
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self.functional_multi = FunctionalMultiLinear.apply
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self.reset_parameters()
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def reset_parameters(self) -> None:
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# Stolen from original torch conv2 code
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torch.nn.init.kaiming_uniform_(self.weights, a=math.sqrt(5))
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if self.bias is not None:
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fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.weights)
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if fan_in != 0:
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bound = 1 / math.sqrt(fan_in)
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torch.nn.init.uniform_(self.bias, -bound, bound)
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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assert input.dim() == 2
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parameter_list = torch.tensor(
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[
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int(self.approximation_enable), # 0
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int(self.number_of_trunc_bits), # 1
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int(self.number_of_frac), # 2
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int(self.number_of_processes), # 3
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],
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dtype=torch.int64,
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)
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output = self.functional_multi(
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input.unsqueeze(-1).unsqueeze(-1), self.weights, parameter_list
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)
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output = output.squeeze(-1).squeeze(-1)
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if self.bias is not None:
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output += self.bias.unsqueeze(0)
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return output
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class FunctionalMultiLinear(torch.autograd.Function):
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@staticmethod
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def forward( # type: ignore
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ctx,
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input: torch.Tensor,
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weights: torch.Tensor,
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parameter_list: torch.Tensor,
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) -> torch.Tensor:
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assert input.ndim == 4
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assert input.dtype is torch.float32
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assert input.is_contiguous() is True
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assert weights.ndim == 2
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assert weights.dtype is torch.float32
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assert weights.is_contiguous() is True
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assert input.shape[1] == weights.shape[1]
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approximation_enable = bool(parameter_list[0])
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number_of_trunc_bits = int(parameter_list[1])
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number_of_frac = int(parameter_list[2])
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number_of_processes = int(parameter_list[3])
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assert input.device == weights.device
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output = torch.zeros(
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(input.shape[0], weights.shape[0], input.shape[2], input.shape[3]),
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dtype=weights.dtype,
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device=weights.device,
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requires_grad=True,
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)
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assert output.is_contiguous() is True
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multiplier: MultiApp = MultiApp()
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multiplier.update_with_init_vector_multi_pattern(
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input.data_ptr(),
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weights.data_ptr(),
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output.data_ptr(),
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int(output.shape[0]), # pattern
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int(output.shape[1]), # feature channel
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int(output.shape[2]), # x
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int(output.shape[3]), # y
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int(input.shape[1]), # input channel
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int(number_of_processes),
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bool(approximation_enable),
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int(number_of_trunc_bits),
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int(number_of_frac),
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)
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ctx.save_for_backward(
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input.detach(),
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weights.detach(),
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)
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return output
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@staticmethod
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def backward(ctx, grad_output):
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(input, weights) = ctx.saved_tensors
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grad_input = (
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grad_output.unsqueeze(2) * weights.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
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).sum(1)
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grad_weights = (
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(grad_output.unsqueeze(2) * input.unsqueeze(1)).sum(0).sum(-1).sum(-1)
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)
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grad_parameter_list = None
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return (grad_input, grad_weights, grad_parameter_list)
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