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David Rotermund
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commit
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5 changed files with 206 additions and 87 deletions
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@ -21,6 +21,8 @@ class HDynamicLayer(torch.nn.Module):
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device: torch.device
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default_dtype: torch.dtype
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_force_forward_h_dynamic_on_cpu: bool
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def __init__(
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self,
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output_size: list[int],
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@ -32,6 +34,7 @@ class HDynamicLayer(torch.nn.Module):
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device: torch.device | None = None,
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default_dtype: torch.dtype | None = None,
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gpu_tuning_factor: int = 5,
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force_forward_h_dynamic_on_cpu: bool = False,
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) -> None:
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super().__init__()
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@ -46,11 +49,14 @@ class HDynamicLayer(torch.nn.Module):
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self._output_size = output_size
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self._output_layer = bool(output_layer)
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self._local_learning = bool(local_learning)
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self._force_forward_h_dynamic_on_cpu = force_forward_h_dynamic_on_cpu
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global_sbs_gpu_setting.append(torch.tensor([0]))
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global_sbs_size.append(torch.tensor([0, 0, 0, 0]))
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if device == torch.device("cpu"):
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if (device == torch.device("cpu")) or (
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self._force_forward_h_dynamic_on_cpu is True
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):
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global_sbs_hdynamic_cpp.append(HDynamicCNNCPU())
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else:
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global_sbs_hdynamic_cpp.append(HDynamicCNNGPU())
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@ -146,11 +152,6 @@ class FunctionalSbS(torch.autograd.Function):
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number_of_spikes: int = int(spikes.shape[1])
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if input.device == torch.device("cpu"):
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hdyn_number_of_cpu_processes: int = int(parameter_list[0])
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else:
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hdyn_number_of_cpu_processes = -1
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output_size_0: int = int(parameter_list[1])
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output_size_1: int = int(parameter_list[2])
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gpu_tuning_factor: int = int(parameter_list[3])
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@ -158,6 +159,30 @@ class FunctionalSbS(torch.autograd.Function):
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sbs_gpu_setting_position = int(parameter_list[4])
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sbs_hdynamic_cpp_position = int(parameter_list[5])
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if (
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isinstance(
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global_sbs_hdynamic_cpp[sbs_hdynamic_cpp_position], HDynamicCNNCPU
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)
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is True
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):
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are_we_on_a_cpu: bool = True
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work_device: torch.device = torch.device("cpu")
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else:
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are_we_on_a_cpu = False
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work_device = input.device
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target_device: torch.device = input.device
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if target_device == work_device:
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data_is_on_the_same_device: bool = True
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else:
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data_is_on_the_same_device = False
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if are_we_on_a_cpu is True:
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hdyn_number_of_cpu_processes: int = int(parameter_list[0])
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else:
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hdyn_number_of_cpu_processes = -1
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# ###########################################################
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# H dynamic
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# ###########################################################
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@ -169,7 +194,7 @@ class FunctionalSbS(torch.autograd.Function):
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# Make space for the results
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# ############################################
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output = torch.empty(
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output_work: torch.Tensor = torch.empty(
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(
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int(input.shape[0]),
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int(weights.shape[1]),
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@ -177,17 +202,43 @@ class FunctionalSbS(torch.autograd.Function):
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output_size_1,
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),
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dtype=input.dtype,
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device=input.device,
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device=work_device,
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)
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assert output.is_contiguous() is True
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assert output_work.is_contiguous() is True
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if epsilon_xy is not None:
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assert epsilon_xy.is_contiguous() is True
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assert epsilon_xy.ndim == 3
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if data_is_on_the_same_device is False:
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epsilon_xy_work = epsilon_xy.to(work_device)
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else:
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epsilon_xy_work = epsilon_xy
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else:
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epsilon_xy_work = None
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assert epsilon_t_0.is_contiguous() is True
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if data_is_on_the_same_device is False:
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epsilon_t_0_work = epsilon_t_0.to(work_device)
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else:
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epsilon_t_0_work = epsilon_t_0
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assert weights.is_contiguous() is True
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if data_is_on_the_same_device is False:
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weights_work = weights.to(work_device)
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else:
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weights_work = weights
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assert spikes.is_contiguous() is True
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if data_is_on_the_same_device is False:
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spikes_work = spikes.to(work_device)
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else:
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spikes_work = spikes
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assert h_initial.is_contiguous() is True
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if data_is_on_the_same_device is False:
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h_initial_work = h_initial.to(work_device)
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else:
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h_initial_work = h_initial
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assert weights.ndim == 2
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assert h_initial.ndim == 1
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@ -196,32 +247,32 @@ class FunctionalSbS(torch.autograd.Function):
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sbs_size = global_sbs_size[sbs_gpu_setting_position].clone()
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if input.device != torch.device("cpu"):
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if are_we_on_a_cpu is False:
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if (
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(sbs_profile.numel() == 1)
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or (sbs_size[0] != int(output.shape[0]))
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or (sbs_size[1] != int(output.shape[1]))
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or (sbs_size[2] != int(output.shape[2]))
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or (sbs_size[3] != int(output.shape[3]))
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or (sbs_size[0] != int(output_work.shape[0]))
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or (sbs_size[1] != int(output_work.shape[1]))
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or (sbs_size[2] != int(output_work.shape[2]))
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or (sbs_size[3] != int(output_work.shape[3]))
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):
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sbs_profile = torch.zeros(
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(14, 7), dtype=torch.int64, device=torch.device("cpu")
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)
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global_sbs_hdynamic_cpp[sbs_hdynamic_cpp_position].gpu_occupancy_export(
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int(output.shape[2]),
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int(output.shape[3]),
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int(output.shape[0]),
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int(output.shape[1]),
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int(output_work.shape[2]),
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int(output_work.shape[3]),
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int(output_work.shape[0]),
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int(output_work.shape[1]),
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sbs_profile.data_ptr(),
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int(sbs_profile.shape[0]),
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int(sbs_profile.shape[1]),
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)
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global_sbs_gpu_setting[sbs_gpu_setting_position] = sbs_profile.clone()
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sbs_size[0] = int(output.shape[0])
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sbs_size[1] = int(output.shape[1])
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sbs_size[2] = int(output.shape[2])
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sbs_size[3] = int(output.shape[3])
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sbs_size[0] = int(output_work.shape[0])
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sbs_size[1] = int(output_work.shape[1])
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sbs_size[2] = int(output_work.shape[2])
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sbs_size[3] = int(output_work.shape[3])
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global_sbs_size[sbs_gpu_setting_position] = sbs_size.clone()
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else:
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@ -232,32 +283,41 @@ class FunctionalSbS(torch.autograd.Function):
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)
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global_sbs_hdynamic_cpp[sbs_hdynamic_cpp_position].update(
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output.data_ptr(),
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int(output.shape[0]),
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int(output.shape[1]),
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int(output.shape[2]),
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int(output.shape[3]),
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epsilon_xy.data_ptr() if epsilon_xy is not None else int(0),
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int(epsilon_xy.shape[0]) if epsilon_xy is not None else int(0),
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int(epsilon_xy.shape[1]) if epsilon_xy is not None else int(0),
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int(epsilon_xy.shape[2]) if epsilon_xy is not None else int(0),
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epsilon_t_0.data_ptr(),
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int(epsilon_t_0.shape[0]),
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weights.data_ptr(),
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int(weights.shape[0]),
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int(weights.shape[1]),
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spikes.data_ptr(),
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int(spikes.shape[0]),
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int(spikes.shape[1]),
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int(spikes.shape[2]),
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int(spikes.shape[3]),
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h_initial.data_ptr(),
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int(h_initial.shape[0]),
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output_work.data_ptr(),
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int(output_work.shape[0]),
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int(output_work.shape[1]),
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int(output_work.shape[2]),
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int(output_work.shape[3]),
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epsilon_xy_work.data_ptr() if epsilon_xy_work is not None else int(0),
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int(epsilon_xy_work.shape[0]) if epsilon_xy_work is not None else int(0),
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int(epsilon_xy_work.shape[1]) if epsilon_xy_work is not None else int(0),
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int(epsilon_xy_work.shape[2]) if epsilon_xy_work is not None else int(0),
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epsilon_t_0_work.data_ptr(),
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int(epsilon_t_0_work.shape[0]),
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weights_work.data_ptr(),
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int(weights_work.shape[0]),
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int(weights_work.shape[1]),
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spikes_work.data_ptr(),
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int(spikes_work.shape[0]),
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int(spikes_work.shape[1]),
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int(spikes_work.shape[2]),
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int(spikes_work.shape[3]),
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h_initial_work.data_ptr(),
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int(h_initial_work.shape[0]),
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hdyn_number_of_cpu_processes,
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float(forgetting_offset.cpu().item()),
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int(gpu_tuning_factor),
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)
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if data_is_on_the_same_device is False:
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output = output_work.to(target_device)
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else:
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output = output_work
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# print(output)
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# print(output.sum(dim=1))
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# print(output.sum(dim=1).shape)
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# exit()
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# ###########################################################
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# Save the necessary data for the backward pass
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# ###########################################################
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@ -142,6 +142,9 @@ class Config:
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epsilon_0: float = field(default=1.0)
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forgetting_offset: float = field(default=-1.0)
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force_forward_h_dynamic_on_cpu: bool = field(default=True)
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spike_full_layer_input_distribution: list[bool] = field(default_factory=list)
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def __post_init__(self) -> None:
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"""Post init determines the number of cores.
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Creates the required directory and gives us an optimized
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@ -52,7 +52,9 @@ class SbSLayer(torch.nn.Module):
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_reduction_cooldown: float = 1.0
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_layer_id: int = -1
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spike_full_layer_input_distribution: bool = False
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_spike_full_layer_input_distribution: bool
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_force_forward_h_dynamic_on_cpu: bool
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def __init__(
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self,
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@ -81,6 +83,10 @@ class SbSLayer(torch.nn.Module):
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layer_id: int = -1,
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cooldown_after_number_of_spikes: int = -1,
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reduction_cooldown: float = 1.0,
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force_forward_h_dynamic_on_cpu: bool = True,
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spike_full_layer_input_distribution: bool = False,
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force_forward_spike_on_cpu: bool = False,
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force_forward_spike_output_on_cpu: bool = False,
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) -> None:
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super().__init__()
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@ -109,6 +115,8 @@ class SbSLayer(torch.nn.Module):
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self.reduction_cooldown = float(reduction_cooldown)
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self._layer_id = layer_id
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self._epsilon_xy_use = epsilon_xy_use
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self._force_forward_h_dynamic_on_cpu = force_forward_h_dynamic_on_cpu
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self._spike_full_layer_input_distribution = spike_full_layer_input_distribution
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assert len(input_size) == 2
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self._input_size = input_size
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@ -140,6 +148,8 @@ class SbSLayer(torch.nn.Module):
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number_of_spikes=self._number_of_spikes,
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number_of_cpu_processes=self._number_of_cpu_processes,
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device=self.device,
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force_forward_spike_on_cpu=force_forward_spike_on_cpu,
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force_forward_spike_output_on_cpu=force_forward_spike_output_on_cpu,
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)
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self.h_dynamic = HDynamicLayer(
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@ -152,6 +162,7 @@ class SbSLayer(torch.nn.Module):
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device=device,
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default_dtype=self.default_dtype,
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gpu_tuning_factor=gpu_tuning_factor,
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force_forward_h_dynamic_on_cpu=self._force_forward_h_dynamic_on_cpu,
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)
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assert len(input_size) >= 2
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@ -169,10 +180,6 @@ class SbSLayer(torch.nn.Module):
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number_of_cpu_processes=number_of_cpu_processes,
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)
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# TODO: TEST
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if layer_id == 0:
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self.spike_full_layer_input_distribution = True
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# ###############################################################
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# Initialize the weights
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# ###############################################################
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@ -438,7 +445,7 @@ class SbSLayer(torch.nn.Module):
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else:
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assert self._epsilon_xy is None
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if self.spike_full_layer_input_distribution is False:
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if self._spike_full_layer_input_distribution is False:
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spike = self.spike_generator(input_convolved, int(self._number_of_spikes))
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else:
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input_shape = input.shape
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@ -457,7 +464,9 @@ class SbSLayer(torch.nn.Module):
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(input_shape[0], input_shape[1], input_shape[2], input_shape[3])
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)
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)
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spike = self.spikes_sorter(spike_unsorted).to(device=input_convolved.device)
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spike = self.spikes_sorter(spike_unsorted)
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if self._force_forward_h_dynamic_on_cpu is False:
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spike = spike.to(device=input_convolved.device)
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output = self.h_dynamic(
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input=input_convolved,
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@ -15,12 +15,16 @@ class SpikeLayer(torch.nn.Module):
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_number_of_cpu_processes: int
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_number_of_spikes: int
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device: torch.device
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_force_forward_spike_on_cpu: bool
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_force_forward_spike_output_on_cpu: bool
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def __init__(
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self,
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number_of_spikes: int = -1,
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number_of_cpu_processes: int = 1,
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device: torch.device | None = None,
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force_forward_spike_on_cpu: bool = False,
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force_forward_spike_output_on_cpu: bool = False,
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) -> None:
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super().__init__()
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@ -29,11 +33,15 @@ class SpikeLayer(torch.nn.Module):
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self._number_of_cpu_processes = number_of_cpu_processes
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self._number_of_spikes = number_of_spikes
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self._force_forward_spike_on_cpu = force_forward_spike_on_cpu
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self._force_forward_spike_output_on_cpu = force_forward_spike_output_on_cpu
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global_spike_generation_gpu_setting.append(torch.tensor([0]))
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global_spike_size.append(torch.tensor([0, 0, 0, 0]))
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if device == torch.device("cpu"):
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if (device == torch.device("cpu")) or (
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self._force_forward_spike_on_cpu is True
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):
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global_spike_generation_cpp.append(SpikeGenerationCPU())
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else:
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global_spike_generation_cpp.append(SpikeGenerationGPU())
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@ -66,6 +74,7 @@ class SpikeLayer(torch.nn.Module):
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int(self._spike_generation_cpp_position), # 1
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int(self._spike_generation_gpu_setting_position), # 2
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int(number_of_spikes), # 3
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int(self._force_forward_spike_output_on_cpu), # 4
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],
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dtype=torch.int64,
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)
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@ -83,14 +92,35 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
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assert input.dim() == 4
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if input.device == torch.device("cpu"):
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spike_number_of_cpu_processes: int = int(parameter_list[0])
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else:
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spike_number_of_cpu_processes = -1
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spike_generation_cpp_position = int(parameter_list[1])
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spike_generation_gpu_setting_position = int(parameter_list[2])
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number_of_spikes: int = int(parameter_list[3])
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force_forward_spike_output_on_cpu: bool = bool(parameter_list[4])
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if (
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isinstance(
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global_spike_generation_cpp[spike_generation_cpp_position],
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SpikeGenerationCPU,
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)
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is True
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):
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are_we_on_a_cpu: bool = True
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work_device: torch.device = torch.device("cpu")
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else:
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are_we_on_a_cpu = False
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work_device = input.device
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target_device: torch.device = input.device
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if target_device == work_device:
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data_is_on_the_same_device: bool = True
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else:
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data_is_on_the_same_device = False
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if are_we_on_a_cpu is True:
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spike_number_of_cpu_processes: int = int(parameter_list[0])
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else:
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spike_number_of_cpu_processes = -1
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# ###########################################################
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# Spike generation
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@ -100,7 +130,12 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
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# Normalized cumsum
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# (beware of the pytorch bug! Thus .clone()!)
|
||||
# ############################################
|
||||
input_cumsum: torch.Tensor = torch.cumsum(input, dim=1, dtype=input.dtype)
|
||||
if data_is_on_the_same_device is False:
|
||||
input_work = input.to(work_device)
|
||||
else:
|
||||
input_work = input
|
||||
# input_work = input
|
||||
input_cumsum: torch.Tensor = torch.cumsum(input_work, dim=1, dtype=input.dtype)
|
||||
input_cumsum_last: torch.Tensor = input_cumsum[:, -1, :, :].unsqueeze(1).clone()
|
||||
input_cumsum /= input_cumsum_last
|
||||
|
||||
|
|
@ -115,17 +150,19 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
|
|||
input_cumsum.shape[3],
|
||||
],
|
||||
dtype=input.dtype,
|
||||
device=input.device,
|
||||
device=work_device,
|
||||
)
|
||||
|
||||
# ############################################
|
||||
# Make space for the results
|
||||
# ############################################
|
||||
spikes = torch.empty_like(random_values, dtype=torch.int64, device=input.device)
|
||||
spikes_work = torch.empty_like(
|
||||
random_values, dtype=torch.int64, device=work_device
|
||||
)
|
||||
|
||||
assert input_cumsum.is_contiguous() is True
|
||||
assert random_values.is_contiguous() is True
|
||||
assert spikes.is_contiguous() is True
|
||||
assert spikes_work.is_contiguous() is True
|
||||
|
||||
# time_start: float = time.perf_counter()
|
||||
spike_generation_profile = global_spike_generation_gpu_setting[
|
||||
|
|
@ -136,19 +173,13 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
|
|||
spike_generation_gpu_setting_position
|
||||
].clone()
|
||||
|
||||
if (
|
||||
isinstance(
|
||||
global_spike_generation_cpp[spike_generation_cpp_position],
|
||||
SpikeGenerationGPU,
|
||||
)
|
||||
is True
|
||||
):
|
||||
if are_we_on_a_cpu is False:
|
||||
if (
|
||||
(spike_generation_profile.numel() == 1)
|
||||
or (spike_generation_size[0] != int(spikes.shape[0]))
|
||||
or (spike_generation_size[1] != int(spikes.shape[1]))
|
||||
or (spike_generation_size[2] != int(spikes.shape[2]))
|
||||
or (spike_generation_size[3] != int(spikes.shape[3]))
|
||||
or (spike_generation_size[0] != int(spikes_work.shape[0]))
|
||||
or (spike_generation_size[1] != int(spikes_work.shape[1]))
|
||||
or (spike_generation_size[2] != int(spikes_work.shape[2]))
|
||||
or (spike_generation_size[3] != int(spikes_work.shape[3]))
|
||||
):
|
||||
|
||||
spike_generation_profile = torch.zeros(
|
||||
|
|
@ -157,10 +188,10 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
|
|||
global_spike_generation_cpp[
|
||||
spike_generation_cpp_position
|
||||
].gpu_occupancy_export(
|
||||
int(spikes.shape[2]),
|
||||
int(spikes.shape[3]),
|
||||
int(spikes.shape[0]),
|
||||
int(spikes.shape[1]),
|
||||
int(spikes_work.shape[2]),
|
||||
int(spikes_work.shape[3]),
|
||||
int(spikes_work.shape[0]),
|
||||
int(spikes_work.shape[1]),
|
||||
spike_generation_profile.data_ptr(),
|
||||
int(spike_generation_profile.shape[0]),
|
||||
int(spike_generation_profile.shape[1]),
|
||||
|
|
@ -169,10 +200,10 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
|
|||
spike_generation_gpu_setting_position
|
||||
] = spike_generation_profile.clone()
|
||||
|
||||
spike_generation_size[0] = int(spikes.shape[0])
|
||||
spike_generation_size[1] = int(spikes.shape[1])
|
||||
spike_generation_size[2] = int(spikes.shape[2])
|
||||
spike_generation_size[3] = int(spikes.shape[3])
|
||||
spike_generation_size[0] = int(spikes_work.shape[0])
|
||||
spike_generation_size[1] = int(spikes_work.shape[1])
|
||||
spike_generation_size[2] = int(spikes_work.shape[2])
|
||||
spike_generation_size[3] = int(spikes_work.shape[3])
|
||||
global_spike_size[
|
||||
spike_generation_gpu_setting_position
|
||||
] = spike_generation_size.clone()
|
||||
|
|
@ -197,15 +228,20 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
|
|||
int(random_values.shape[1]),
|
||||
int(random_values.shape[2]),
|
||||
int(random_values.shape[3]),
|
||||
spikes.data_ptr(),
|
||||
int(spikes.shape[0]),
|
||||
int(spikes.shape[1]),
|
||||
int(spikes.shape[2]),
|
||||
int(spikes.shape[3]),
|
||||
spikes_work.data_ptr(),
|
||||
int(spikes_work.shape[0]),
|
||||
int(spikes_work.shape[1]),
|
||||
int(spikes_work.shape[2]),
|
||||
int(spikes_work.shape[3]),
|
||||
int(spike_number_of_cpu_processes),
|
||||
)
|
||||
del random_values
|
||||
del input_cumsum
|
||||
|
||||
if (force_forward_spike_output_on_cpu is True) and (are_we_on_a_cpu is True):
|
||||
spikes = spikes_work
|
||||
elif data_is_on_the_same_device is False:
|
||||
spikes = spikes_work.to(target_device)
|
||||
else:
|
||||
spikes = spikes_work
|
||||
|
||||
return spikes
|
||||
|
||||
|
|
|
|||
|
|
@ -120,6 +120,12 @@ def build_network(
|
|||
cfg.learning_parameters.sbs_skip_gradient_calculation[0]
|
||||
)
|
||||
|
||||
spike_full_layer_input_distribution: bool = False
|
||||
if len(cfg.spike_full_layer_input_distribution) > layer_id:
|
||||
spike_full_layer_input_distribution = (
|
||||
cfg.spike_full_layer_input_distribution[layer_id]
|
||||
)
|
||||
|
||||
# #############################################################
|
||||
# SbS layer:
|
||||
# #############################################################
|
||||
|
|
@ -138,7 +144,10 @@ def build_network(
|
|||
assert number_of_spikes > 0
|
||||
|
||||
logging.info(
|
||||
(
|
||||
f"Layer: {layer_id} -> SbS Layer with {number_of_spikes} spikes "
|
||||
f"-- draw spike from full layer: {spike_full_layer_input_distribution}"
|
||||
)
|
||||
)
|
||||
is_pooling_layer: bool = False
|
||||
if cfg.network_structure.layer_type[layer_id].upper().find("POOLING") != -1:
|
||||
|
|
@ -169,6 +178,8 @@ def build_network(
|
|||
layer_id=layer_id,
|
||||
cooldown_after_number_of_spikes=cfg.cooldown_after_number_of_spikes,
|
||||
reduction_cooldown=cfg.reduction_cooldown,
|
||||
force_forward_h_dynamic_on_cpu=cfg.force_forward_h_dynamic_on_cpu,
|
||||
spike_full_layer_input_distribution=spike_full_layer_input_distribution,
|
||||
)
|
||||
)
|
||||
# Adding the x,y output dimensions
|
||||
|
|
|
|||
Loading…
Reference in a new issue