зеркало из https://github.com/microsoft/EdgeML.git
cleaned up FastGRNN code
This commit is contained in:
Harsha Vardhan Simhadri
Родитель
a35778fe30
Коммит
bd7732aedd
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@ -201,14 +201,16 @@ class FastGRNNCell(nn.Module):
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else:
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else:
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self.U1 = nn.Parameter(0.1 * torch.randn([uRank, hidden_size]))
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self.U1 = nn.Parameter(0.1 * torch.randn([uRank, hidden_size]))
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self.U2 = nn.Parameter(0.1 * torch.randn([hidden_size, uRank]))
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self.U2 = nn.Parameter(0.1 * torch.randn([hidden_size, uRank]))
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self.copy_previous_state()
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self.bias_gate = nn.Parameter(torch.ones([1, hidden_size]))
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self.bias_gate = nn.Parameter(torch.ones([1, hidden_size]))
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self.bias_update = nn.Parameter(torch.ones([1, hidden_size]))
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self.bias_update = nn.Parameter(torch.ones([1, hidden_size]))
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self.zeta = nn.Parameter(self._zetaInit * torch.ones([1, 1]))
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self.zeta = nn.Parameter(self._zetaInit * torch.ones([1, 1]))
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self.nu = nn.Parameter(self._nuInit * torch.ones([1, 1]))
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self.nu = nn.Parameter(self._nuInit * torch.ones([1, 1]))
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self.oldmats = []
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self.copy_previous_UW()
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@property
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@property
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def state_size(self):
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def state_size(self):
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return self._hidden_size
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return self._hidden_size
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@ -263,7 +265,6 @@ class FastGRNNCell(nn.Module):
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torch.matmul(state, torch.transpose(self.U1, 0, 1)), torch.transpose(self.U2, 0, 1))
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torch.matmul(state, torch.transpose(self.U1, 0, 1)), torch.transpose(self.U2, 0, 1))
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pre_comp = wComp + uComp
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pre_comp = wComp + uComp
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z = gen_nonlinearity(pre_comp + self.bias_gate,
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z = gen_nonlinearity(pre_comp + self.bias_gate,
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self._gate_nonlinearity)
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self._gate_nonlinearity)
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c = gen_nonlinearity(pre_comp + self.bias_update,
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c = gen_nonlinearity(pre_comp + self.bias_update,
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@ -293,22 +294,18 @@ class FastGRNNCell(nn.Module):
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'''
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'''
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Function to get aimed model size
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Function to get aimed model size
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'''
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'''
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totalnnz = 2 # For Zeta and Nu
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mats = self.getVars()
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mats = self.getVars()
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endW = self._num_weight_matrices[0]
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endW = self._num_weight_matrices[0]
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endU = endW + self._num_weight_matrices[1]
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totalnnz = 2 # For Zeta and Nu
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for i in range(0, endW):
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for i in range(0, endW):
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totalnnz += utils.countNNZ(mats[i], self._wSparsity)
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totalnnz += utils.countNNZ(mats[i], self._wSparsity)
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endU = endW + self._num_weight_matrices[1]
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for i in range(endW, endU):
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for i in range(endW, endU):
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totalnnz += utils.countNNZ(mats[i], self._uSparsity)
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totalnnz += utils.countNNZ(mats[i], self._uSparsity)
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for i in range(endU, len(mats)):
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for i in range(endU, mats.len()):
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totalnnz += utils.countNNZ(mats[i], False)
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totalnnz += utils.countNNZ(mats[i], False)
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return totalnnz * 4
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return totalnnz
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#totalnnz += utils.countNNZ(self.bias_gate, False)
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#totalnnz += utils.countNNZ(self.bias_gate, False)
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#totalnnz += utils.countNNZ(self.bias_update, False)
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#totalnnz += utils.countNNZ(self.bias_update, False)
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@ -325,56 +322,79 @@ class FastGRNNCell(nn.Module):
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# totalnnz += utils.countNNZ(self.U2, self._uSparsity)
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# totalnnz += utils.countNNZ(self.U2, self._uSparsity)
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def copy_previous_state(self):
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def copy_previous_UW(self):
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if self._wRank is None:
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mats = self.getVars()
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if self._wSparsity < 1.0:
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num_mats = self._num_weight_matrices[0] + self._num_weight_matrices[1]
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self.W_old = torch.FloatTensor(np.copy(self.W.data.cpu().detach().numpy()))
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if len(self.oldmats) != num_mats:
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self.W_old.to(self.W.device)
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for i in range(num_mats):
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else:
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self.oldmats.append(torch.FloatTensor())
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if self._wSparsity < 1.0:
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for i in range(num_mats):
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self.W1_old = torch.FloatTensor(np.copy(self.W1.data.cpu().detach().numpy()))
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self.oldmats[i] = torch.FloatTensor(np.copy(mats[i].data.cpu().detach().numpy()))
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self.W2_old = torch.FloatTensor(np.copy(self.W2.data.cpu().detach().numpy()))
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self.oldmats[i].to(mats[i].device)
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self.W1_old.to(self.W1.device)
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self.W2_old.to(self.W2.device)
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if self._uRank is None:
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#if self._wRank is None:
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if self._uSparsity < 1.0:
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# if self._wSparsity < 1.0:
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self.U_old = torch.FloatTensor(np.copy(self.U.data.cpu().detach().numpy()))
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# self.W_old = torch.FloatTensor(np.copy(self.W.data.cpu().detach().numpy()))
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self.U_old.to(self.U.device)
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# self.W_old.to(self.W.device)
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else:
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#else:
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if self._uSparsity < 1.0:
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# if self._wSparsity < 1.0:
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self.U1_old = torch.FloatTensor(np.copy(self.U1.data.cpu().detach().numpy()))
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# self.W1_old = torch.FloatTensor(np.copy(self.W1.data.cpu().detach().numpy()))
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self.U2_old = torch.FloatTensor(np.copy(self.U2.data.cpu().detach().numpy()))
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# self.W2_old = torch.FloatTensor(np.copy(self.W2.data.cpu().detach().numpy()))
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self.U1_old.to(self.U1.device)
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# self.W1_old.to(self.W1.device)
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self.U2_old.to(self.U2.device)
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# self.W2_old.to(self.W2.device)
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#if self._uRank is None:
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# if self._uSparsity < 1.0:
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# self.U_old = torch.FloatTensor(np.copy(self.U.data.cpu().detach().numpy()))
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# self.U_old.to(self.U.device)
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#else:
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# if self._uSparsity < 1.0:
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# self.U1_old = torch.FloatTensor(np.copy(self.U1.data.cpu().detach().numpy()))
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# self.U2_old = torch.FloatTensor(np.copy(self.U2.data.cpu().detach().numpy()))
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# self.U1_old.to(self.U1.device)
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# self.U2_old.to(self.U2.device)
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def sparsify(self):
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def sparsify(self):
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if self._wRank is None:
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mats = self.getVars()
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self.W.data = utils.hardThreshold(self.W, self._wSparsity)
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endW = self._num_weight_matrices[0]
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else:
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endU = endW + self._num_weight_matrices[1]
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self.W1.data = utils.hardThreshold(self.W1, self._wSparsity)
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for i in range(0, endW):
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self.W2.data = utils.hardThreshold(self.W2, self._wSparsity)
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mats[i] = utils.hardThreshold(mats[i], self._wSparsity)
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for i in range(endW, endU):
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mats[i] = utils.hardThreshold(mats[i], self._uSparsity)
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self.copy_previous_UW()
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if self._uRank is None:
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#if self._wRank is None:
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self.U.data = utils.hardThreshold(self.U, self._uSparsity)
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# self.W.data = utils.hardThreshold(self.W, self._wSparsity)
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else:
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#else:
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self.U1.data = utils.hardThreshold(self.U1, self._uSparsity)
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# self.W1.data = utils.hardThreshold(self.W1, self._wSparsity)
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self.U2.data = utils.hardThreshold(self.U2, self._uSparsity)
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# self.W2.data = utils.hardThreshold(self.W2, self._wSparsity)
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self.copy_previous_state()
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#if self._uRank is None:
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# self.U.data = utils.hardThreshold(self.U, self._uSparsity)
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#else:
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# self.U1.data = utils.hardThreshold(self.U1, self._uSparsity)
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# self.U2.data = utils.hardThreshold(self.U2, self._uSparsity)
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#self.copy_previous_UW()
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def sparsifyWithSupport(self):
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def sparsifyWithSupport(self):
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if self._wRank is None:
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mats = self.getVars()
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self.W.data = utils.supportBasedThreshold(self.W, self.W_old)
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endU = self._num_weight_matrices[0] + self._num_weight_matrices[1]
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else:
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for i in range(0, endU):
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self.W1.data = utils.supportBasedThreshold(self.W1, self.W1_old)
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mats[i] = utils.supportBasedThreshold(mats[i], self.oldmats[i])
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self.W2.data = utils.supportBasedThreshold(self.W2, self.W2_old)
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if self._uRank is None:
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#if self._wRank is None:
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self.U.data = utils.supportBasedThreshold(self.U, self.U_old)
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# self.W.data = utils.supportBasedThreshold(self.W, self.W_old)
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else:
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#else:
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self.U1.data = utils.supportBasedThreshold(self.U1, self.U1_old)
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# self.W1.data = utils.supportBasedThreshold(self.W1, self.W1_old)
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self.U2.data = utils.supportBasedThreshold(self.U2, self.U2_old)
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# self.W2.data = utils.supportBasedThreshold(self.W2, self.W2_old)
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#self.copy_previous_state()
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#if self._uRank is None:
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# self.U.data = utils.supportBasedThreshold(self.U, self.U_old)
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#else:
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# self.U1.data = utils.supportBasedThreshold(self.U1, self.U1_old)
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# self.U2.data = utils.supportBasedThreshold(self.U2, self.U2_old)
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#self.copy_previous_UW()
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class FastRNNCell(nn.Module):
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class FastRNNCell(nn.Module):
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@ -98,7 +98,7 @@ def fastgrnnmodel(inheritance_class=nn.Module):
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total_size += self.fastgrnn2.cell.getModelSize()
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total_size += self.fastgrnn2.cell.getModelSize()
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if self.num_layers > 2:
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if self.num_layers > 2:
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total_size += self.fastgrnn3.cell.getModelSize()
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total_size += self.fastgrnn3.cell.getModelSize()
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total_size += self.hidden_units_list[2] * self.num_classes
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total_size += 4 * self.hidden_units_list[self.num_layers-1] * self.num_classes
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return total_size
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return total_size
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def normalize(self, mean, std):
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def normalize(self, mean, std):
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@ -231,7 +231,7 @@ class KeywordSpotter(nn.Module):
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optimizer.step()
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optimizer.step()
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if sparsify:
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if sparsify:
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if epoch > num_epochs/3:
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if epoch >= num_epochs/3:
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if epoch < (2*num_epochs)/3:
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if epoch < (2*num_epochs)/3:
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if i_batch % trim_level == 0:
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if i_batch % trim_level == 0:
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self.sparsify()
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self.sparsify()
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