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@sgugger @mkardas
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from ...torch_core import *
from torch.utils.cpp_extension import load
from torch.autograd import Function
__all__ = ['QRNNLayer', 'QRNN']
import fastai
if torch.cuda.is_available():
fastai_path = Path(fastai.__path__[0])/'text'/'models'
files = ['forget_mult_cuda.cpp', 'forget_mult_cuda_kernel.cu']
forget_mult_cuda = load(name='forget_mult_cuda', sources=[fastai_path/f for f in files])
files = ['bwd_forget_mult_cuda.cpp', 'bwd_forget_mult_cuda_kernel.cu']
bwd_forget_mult_cuda = load(name='bwd_forget_mult_cuda', sources=[fastai_path/f for f in files])
def dispatch_cuda(cuda_class, cpu_func, x):
return cuda_class.apply if x.device.type == 'cuda' else cpu_func
class ForgetMultGPU(Function):
@staticmethod
def forward(ctx, x:Tensor, f:Tensor, hidden_init:Optional[Tensor]=None, batch_first:bool=True):
if batch_first:
batch_size, seq_size, hidden_size = f.size()
output = f.new_zeros(batch_size, seq_size + 1, hidden_size)
if hidden_init is not None: output[:, 0] = hidden_init
else: output.zero_()
else:
seq_size, batch_size, hidden_size = f.size()
output = f.new(seq_size + 1, batch_size, hidden_size)
if hidden_init is not None: output[0] = hidden_init
else: output.zero_()
output = forget_mult_cuda.forward(x, f, output, batch_first)
ctx.save_for_backward(x, f, hidden_init, output)
ctx.batch_first = batch_first
return output[:,1:] if batch_first else output[1:]
@staticmethod
def backward(ctx, grad_output):
x, f, hidden_init, output = ctx.saved_tensors
grad_x, grad_f, grad_h = forget_mult_cuda.backward(x, f, output, grad_output, ctx.batch_first)
return (grad_x, grad_f, (None if hidden_init is None else grad_h), None)
class BwdForgetMultGPU(Function):
@staticmethod
def forward(ctx, x:Tensor, f:Tensor, hidden_init:Optional[Tensor]=None, batch_first:bool=True):
if batch_first:
batch_size, seq_size, hidden_size = f.size()
output = f.new(batch_size, seq_size + 1, hidden_size)
if hidden_init is not None: output[:, -1] = hidden_init
else: output.zero_()
else:
seq_size, batch_size, hidden_size = f.size()
output = f.new(seq_size + 1, batch_size, hidden_size)
if hidden_init is not None: output[-1] = hidden_init
else: output.zero_()
output = bwd_forget_mult_cuda.forward(x, f, output, batch_first)
ctx.save_for_backward(x, f, hidden_init, output)
ctx.batch_first = batch_first
return output[:,:-1] if batch_first else output[:-1]
@staticmethod
def backward(ctx, grad_output:Tensor):
x, f, hidden_init, output = ctx.saved_tensors
grad_x, grad_f, grad_h = bwd_forget_mult_cuda.backward(x, f, output, grad_output, ctx.batch_first)
return (grad_x, grad_f, (None if hidden_init is None else grad_h), None)
def forget_mult_CPU(x:Tensor, f:Tensor, hidden_init:Optional[Tensor]=None, batch_first:bool=True, backward:bool=False):
result = []
dim = (1 if batch_first else 0)
forgets = f.split(1, dim=dim)
inputs = x.split(1, dim=dim)
prev_h = None if hidden_init is None else hidden_init.unsqueeze(1 if batch_first else 0)
idx_range = range(len(inputs)-1,-1,-1) if backward else range(len(inputs))
for i in idx_range:
prev_h = inputs[i] * forgets[i] if prev_h is None else inputs[i] * forgets[i] + (1-forgets[i]) * prev_h
if backward: result.insert(0, prev_h)
else: result.append(prev_h)
return torch.cat(result, dim=dim)
class QRNNLayer(nn.Module):
"Apply a single layer Quasi-Recurrent Neural Network (QRNN) to an input sequence."
def __init__(self, input_size:int, hidden_size:int=None, save_prev_x:bool=False, zoneout:float=0, window:int=1,
output_gate:bool=True, batch_first:bool=True, backward:bool=False):
super().__init__()
assert window in [1, 2], "This QRNN implementation currently only handles convolutional window of size 1 or size 2"
self.save_prev_x,self.zoneout,self.window = save_prev_x,zoneout,window
self.output_gate,self.batch_first,self.backward = output_gate,batch_first,backward
hidden_size = ifnone(hidden_size, input_size)
#One large matmul with concat is faster than N small matmuls and no concat
mult = (3 if output_gate else 2)
self.linear = nn.Linear(window * input_size, mult * hidden_size)
self.prevX = None
def reset(self):
# If you are saving the previous value of x, you should call this when starting with a new state
self.prevX = None
def forward(self, inp, hid=None):
y = self.linear(self._get_source(inp))
if self.output_gate: z_gate,f_gate,o_gate = y.chunk(3, dim=2)
else: z_gate,f_gate = y.chunk(2, dim=2)
z_gate.tanh_()
f_gate.sigmoid_()
if self.zoneout and self.training:
mask = dropout_mask(f_gate, f_gate.size(), self.zoneout).requires_grad_(False)
f_gate = f_gate * mask
z_gate,f_gate = z_gate.contiguous(),f_gate.contiguous()
if self.backward: forget_mult = dispatch_cuda(BwdForgetMultGPU, partial(forget_mult_CPU, backward=True), inp)
else: forget_mult = dispatch_cuda(ForgetMultGPU, forget_mult_CPU, inp)
c_gate = forget_mult(z_gate, f_gate, hid, self.batch_first)
output = torch.sigmoid(o_gate) * c_gate if self.output_gate else c_gate
if self.window > 1 and self.save_prev_x:
if self.backward: self.prevX = (inp[:, :1] if self.batch_first else inp[:1]).detach()
else: self.prevX = (inp[:, -1:] if self.batch_first else inp[-1:]).detach()
idx = 0 if self.backward else -1
return output, (c_gate[:, idx] if self.batch_first else c_gate[idx])
def _get_source(self, inp):
if self.window == 1: return inp
dim = (1 if self.batch_first else 0)
inp_shift = [torch.zeros_like(inp[:,:1] if self.batch_first else inp[:1]) if self.prevX is None else self.prevX]
if self.backward: inp_shift.insert(0,inp[:,1:] if self.batch_first else inp[1:])
else: inp_shift.append(inp[:,:-1] if self.batch_first else inp[:-1])
inp_shift = torch.cat(inp_shift, dim)
return torch.cat([inp, inp_shift], 2)
class QRNN(nn.Module):
"Apply a multiple layer Quasi-Recurrent Neural Network (QRNN) to an input sequence."
def __init__(self, input_size:int, hidden_size:int, n_layers:int=1, bias:bool=True, batch_first:bool=True,
dropout:float=0, bidirectional:bool=False, save_prev_x:bool=False, zoneout:float=0, window:int=None,
output_gate:bool=True):
assert not (save_prev_x and bidirectional), "Can't save the previous X with bidirectional."
assert bias == True, 'Removing underlying bias is not yet supported'
super().__init__()
kwargs = dict(batch_first=batch_first, zoneout=zoneout, output_gate=output_gate)
self.layers = nn.ModuleList([QRNNLayer(input_size if l == 0 else hidden_size, hidden_size,
window=((2 if l ==0 else 1) if window is None else window), **kwargs)
for l in range(n_layers)])
if bidirectional:
self.layers_bwd = nn.ModuleList([QRNNLayer(input_size if l == 0 else hidden_size, hidden_size,
backward=True, window=((2 if l ==0 else 1) if window is None else window),
**kwargs) for l in range(n_layers)])
self.n_layers,self.batch_first,self.dropout,self.bidirectional = n_layers,batch_first,dropout,bidirectional
def reset(self):
"If your convolutional window is greater than 1 and you save previous xs, you must reset at the beginning of each new sequence."
for layer in self.layers: layer.reset()
if self.bidirectional:
for layer in self.layers_bwd: layer.reset()
def forward(self, inp, hid=None):
new_hid = []
if self.bidirectional: inp_bwd = inp.clone()
for i, layer in enumerate(self.layers):
inp, h = layer(inp, None if hid is None else hid[2*i if self.bidirectional else i])
new_hid.append(h)
if self.bidirectional:
inp_bwd, h_bwd = self.layers_bwd[i](inp_bwd, None if hid is None else hid[2*i+1])
new_hid.append(h_bwd)
if self.dropout != 0 and i < len(self.layers) - 1:
for o in ([inp, inp_bwd] if self.bidirectional else [inp]):
o = F.dropout(o, p=self.dropout, training=self.training, inplace=False)
if self.bidirectional: inp = torch.cat([inp, inp_bwd], dim=2)
return inp, torch.stack(new_hid, 0)
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