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#
# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
# Apoorv Vyas <avyas@idiap.ch>
#
import torch
try:
from causal_attention_cuda import causal_dot_product as causal_dot_product_cuda
from causal_attention_cuda import causal_dot_backward as causal_dot_backward_cuda
except ImportError as e:
print(e)
causal_dot_product_cuda = causal_dot_backward_cuda = None
class CausalDotProduct(torch.autograd.Function):
"""Compute the weighted sum of values but attending only to previous
values."""
dot = {
# "cpu": causal_dot_product_cpu,
"cuda": causal_dot_product_cuda
}
dot_backward = {
# "cpu": causal_dot_backward_cpu,
"cuda": causal_dot_backward_cuda
}
@staticmethod
def forward(ctx, Q, K, V):
# Save the inputs for the gradient computation
ctx.save_for_backward(Q, K, V)
# Create the output tensor
device = Q.device
N, H, L, _ = Q.shape
_, _, _, M = V.shape
product = torch.zeros((N, H, L, M), dtype=Q.dtype, device=device)
# Actually perform the dot product
CausalDotProduct.dot[device.type](
Q.data,
K.data,
V.data,
product
)
# breakpoint()
# CausalDotProduct.dot[device.type](Q.data, K.data, V.data, product)
return product
@staticmethod
def backward(ctx, grad_out):
# Extract the saved tensors
Q, K, V = ctx.saved_tensors
# Allocate memory for the gradients
grad_Q = torch.zeros_like(Q)
grad_K = torch.zeros_like(K)
grad_V = torch.zeros_like(V)
# Actually compute the gradients
CausalDotProduct.dot_backward[Q.device.type](
Q.data,
K.data,
V.data,
grad_out,
grad_Q,
grad_K,
grad_V
)
return grad_Q, grad_K, grad_V
# Alias the autograd functions to python style snake case naming
causal_dot_product = CausalDotProduct.apply |