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FastGRNN CUDA: Sparsify Support #135

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Oct 3, 2019
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FastGRNN CUDA: Sparsify Support #135

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53 changes: 51 additions & 2 deletions pytorch/edgeml_pytorch/graph/rnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -319,8 +319,9 @@ class FastGRNNCUDACell(RNNCell):

'''
def __init__(self, input_size, hidden_size, gate_nonlinearity="sigmoid",
update_nonlinearity="tanh", wRank=None, uRank=None, zetaInit=1.0, nuInit=-4.0, name="FastGRNNCUDACell"):
super(FastGRNNCUDACell, self).__init__(input_size, hidden_size, gate_non_linearity, update_nonlinearity, 1, 1, 2, wRank, uRank)
update_nonlinearity="tanh", wRank=None, uRank=None, zetaInit=1.0, nuInit=-4.0, wSparsity=1.0, uSparsity=1.0, name="FastGRNNCUDACell"):
super(FastGRNNCUDACell, self).__init__(input_size, hidden_size, gate_non_linearity, update_nonlinearity,
1, 1, 2, wRank, uRank, wSparsity, uSparsity)
if utils.findCUDA() is None:
raise Exception('FastGRNNCUDA is supported only on GPU devices.')
NON_LINEARITY = {"sigmoid": 0, "relu": 1, "tanh": 2}
Expand Down Expand Up @@ -1166,6 +1167,54 @@ def getVars(self):
Vars.extend([self.bias_gate, self.bias_update, self.zeta, self.nu])
return Vars

def get_model_size(self):
'''
Function to get aimed model size
'''
mats = self.getVars()
endW = self._num_W_matrices
endU = endW + self._num_U_matrices

totalnnz = 2 # For Zeta and Nu
for i in range(0, endW):
device = mats[i].device
totalnnz += utils.countNNZ(mats[i].cpu(), self._wSparsity)
mats[i].to(device)
for i in range(endW, endU):
device = mats[i].device
totalnnz += utils.countNNZ(mats[i].cpu(), self._uSparsity)
mats[i].to(device)
for i in range(endU, len(mats)):
device = mats[i].device
totalnnz += utils.countNNZ(mats[i].cpu(), False)
mats[i].to(device)
return totalnnz * 4

def copy_previous_UW(self):
mats = self.getVars()
num_mats = self._num_W_matrices + self._num_U_matrices
if len(self.oldmats) != num_mats:
for i in range(num_mats):
self.oldmats.append(torch.FloatTensor())
for i in range(num_mats):
self.oldmats[i] = torch.FloatTensor(mats[i].detach().clone().to(mats[i].device))

def sparsify(self):
mats = self.getVars()
endW = self._num_W_matrices
endU = endW + self._num_U_matrices
for i in range(0, endW):
mats[i] = utils.hardThreshold(mats[i], self._wSparsity)
for i in range(endW, endU):
mats[i] = utils.hardThreshold(mats[i], self._uSparsity)
self.copy_previous_UW()

def sparsifyWithSupport(self):
mats = self.getVars()
endU = self._num_W_matrices + self._num_U_matrices
for i in range(0, endU):
mats[i] = utils.supportBasedThreshold(mats[i], self.oldmats[i])

class SRNN2(nn.Module):

def __init__(self, inputDim, outputDim, hiddenDim0, hiddenDim1, cellType,
Expand Down