harpone

# 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:

harpone

# 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

harpone

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, ]

harpone

Keybase proof

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: