wehub-resource-sync
110 lines
5.1 KiB
Python
Executable File
110 lines
5.1 KiB
Python
Executable File
# code borrowed from https://github.com/jiaweizzhao/GaLore
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import torch
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class GaLoreProjector:
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def __init__(self, rank, verbose=False, update_proj_gap=200, scale=1.0, proj_type='std'):
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self.rank = rank
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self.verbose = verbose
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self.update_proj_gap = update_proj_gap
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self.scale = scale
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self.ortho_matrix = None
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self.proj_type = proj_type
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def project(self, full_rank_grad, iter):
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if self.proj_type == 'std':
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if full_rank_grad.shape[0] >= full_rank_grad.shape[1]:
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if self.ortho_matrix is None or iter % self.update_proj_gap == 0:
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self.ortho_matrix = self.get_orthogonal_matrix(full_rank_grad, self.rank, type='right')
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low_rank_grad = torch.matmul(full_rank_grad, self.ortho_matrix.t())
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else:
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if self.ortho_matrix is None or iter % self.update_proj_gap == 0:
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self.ortho_matrix = self.get_orthogonal_matrix(full_rank_grad, self.rank, type='left')
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low_rank_grad = torch.matmul(self.ortho_matrix.t(), full_rank_grad)
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elif self.proj_type == 'reverse_std':
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if full_rank_grad.shape[0] >= full_rank_grad.shape[1]:
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if self.ortho_matrix is None or iter % self.update_proj_gap == 0:
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self.ortho_matrix = self.get_orthogonal_matrix(full_rank_grad, self.rank, type='left')
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low_rank_grad = torch.matmul(self.ortho_matrix.t(), full_rank_grad)
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else:
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if self.ortho_matrix is None or iter % self.update_proj_gap == 0:
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self.ortho_matrix = self.get_orthogonal_matrix(full_rank_grad, self.rank, type='right')
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low_rank_grad = torch.matmul(full_rank_grad, self.ortho_matrix.t())
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elif self.proj_type == 'right':
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if self.ortho_matrix is None or iter % self.update_proj_gap == 0:
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self.ortho_matrix = self.get_orthogonal_matrix(full_rank_grad, self.rank, type='right')
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low_rank_grad = torch.matmul(full_rank_grad, self.ortho_matrix.t())
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elif self.proj_type == 'left':
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if self.ortho_matrix is None or iter % self.update_proj_gap == 0:
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self.ortho_matrix = self.get_orthogonal_matrix(full_rank_grad, self.rank, type='left')
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low_rank_grad = torch.matmul(self.ortho_matrix.t(), full_rank_grad)
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elif self.proj_type == 'full':
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if self.ortho_matrix is None or iter % self.update_proj_gap == 0:
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self.ortho_matrix = self.get_orthogonal_matrix(full_rank_grad, self.rank, type='full')
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low_rank_grad = torch.matmul(self.ortho_matrix[0].t(), full_rank_grad) @ self.ortho_matrix[1].t()
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return low_rank_grad
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def project_back(self, low_rank_grad):
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if self.proj_type == 'std':
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if low_rank_grad.shape[0] >= low_rank_grad.shape[1]:
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full_rank_grad = torch.matmul(low_rank_grad, self.ortho_matrix)
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else:
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full_rank_grad = torch.matmul(self.ortho_matrix, low_rank_grad)
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elif self.proj_type == 'reverse_std':
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if low_rank_grad.shape[0] <= low_rank_grad.shape[1]: # note this is different from std
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full_rank_grad = torch.matmul(self.ortho_matrix, low_rank_grad)
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else:
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full_rank_grad = torch.matmul(low_rank_grad, self.ortho_matrix)
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elif self.proj_type == 'right':
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full_rank_grad = torch.matmul(low_rank_grad, self.ortho_matrix)
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elif self.proj_type == 'left':
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full_rank_grad = torch.matmul(self.ortho_matrix, low_rank_grad)
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elif self.proj_type == 'full':
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full_rank_grad = torch.matmul(self.ortho_matrix[0], low_rank_grad) @ self.ortho_matrix[1]
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return full_rank_grad * self.scale
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# svd decomposition
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def get_orthogonal_matrix(self, weights, rank, type):
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module_params = weights
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if module_params.data.dtype != torch.float:
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float_data = False
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original_type = module_params.data.dtype
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original_device = module_params.data.device
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matrix = module_params.data.float()
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else:
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float_data = True
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matrix = module_params.data
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U, s, Vh = torch.linalg.svd(matrix, full_matrices=False)
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# make the smaller matrix always to be orthogonal matrix
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if type == 'right':
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A = U[:, :rank] @ torch.diag(s[:rank])
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B = Vh[:rank, :]
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if not float_data:
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B = B.to(original_device).type(original_type)
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return B
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elif type == 'left':
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A = U[:, :rank]
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B = torch.diag(s[:rank]) @ Vh[:rank, :]
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if not float_data:
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A = A.to(original_device).type(original_type)
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return A
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elif type == 'full':
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A = U[:, :rank]
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B = Vh[:rank, :]
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if not float_data:
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A = A.to(original_device).type(original_type)
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B = B.to(original_device).type(original_type)
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return [A, B]
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else:
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raise ValueError('type should be left, right or full')
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