I hereby claim:
- I am harpone on github.
- I am harpone (https://keybase.io/harpone) on keybase.
- I have a public key ASDFQqiM9-Yg0wVF3nsnOaVSan8jKCAgtTLccHHts8RhaQo
To claim this, I am signing this object:
# SERIAL: | |
W = parameter(M, N) | |
def forward(x_: float32[N]) -> float32: | |
# matrix-vector multiplication: | |
zs = float32[M] # let's imagine we have a float32 dtype in Vyper | |
for i, W_row in enumerate(W): | |
zs[i] = dot(W_row, x_) # 'dot' is an external smart contract | |
# summation: |
# PARALLEL: | |
from contractpool import ContractPool # imaginary 'contractpool' library, similar to python's `multiprocessing` | |
W = parameter(M, N) | |
def forward(x_: float32[N]) -> float32: | |
# matrix-vector multiplication: | |
zs = float32[M] # let's imagine we have a float32 dtype in Vyper | |
with ContractPool(dot, M) as p: | |
p.map(W, x_, out=zs) # launches M subcontracts asynchronously, each subcontract writes values to zs |
def forward_sequential(h, xs, U, W, nu, theta): | |
"""Forward pass through the network sequentially over input `xs` of any length. | |
NOTE: has no batch dimension. To be batched with `vmap`. | |
Args: | |
h (torch.tensor): shape [D_h, ]; previous state | |
xs (torch.tensor): shape [T, D_x]; input sequence | |
U (torch.tensor): Parameter matrix of shape [D_h, D_x] | |
W (torch.tensor): Parameter matrix of shape [D_h, D_x] | |
xi (torch.tensor): Parameter vector of shape [D_h, ] |
I hereby claim:
To claim this, I am signing this object: