mino.mu

The source code for mino.mu can be found here: github_link

---

defmu

PyTorch Module Mu-Expression Definition Macro

Form:

(defmu module-name required-components &rest forward-procedure)

Args:

module-name (HySymbol)

name of the new PyTorch Module class.

required-components (HyList(HySymbol))

list of parameters/submodules required for the forward procedure.

forward-procedure (&rest HyExpression)

operations run during forward propagation.

Returns:

(HyExpression)

Expanded PyTorch Module class definition code.

Macroexpands to a new namespaced PyTorch Module class definition. Macro’s syntax mirrors the definition of namespaced functions (defn), allowing PyTorch Modules to be expressed with concise function syntax.

All inputs to the forward call are treated as required components and raise a python ValueError if a component is not passed during the forward call. When creating a new instance a mu, default values for components can be set through their corresponding keyword arguments. If set, the value is bound to that component argument and will be used during forward propagation unless another value is supplied during the forward call. Components binding can also be done at any time after creation through the bind macro.

The modules’ extra_repr has been overloaded to show components, forward-procedure expression, and sub-modules/parameters.

A hy-repr is registered which when called on a module, returns Hy code of the module as as mu expressions of the full computational graph with substitutions of all nested components into the root module.

Like function definitions, a docstring can be assigned for the module class with the first form of the forward procedure as a string.

Example:

;- Import Macro
(require [mino.mu [defmu]])

;- Import Torch
(import torch [torch.nn :as nn])

;-- Define Module Thru Mu Expression
(defmu MyModel [x hidden-layer output-layer]
  (output-layer (hidden-layer x)))

;- Create New Instance of MyModel
(setv model (MyModel :hidden-layer (nn.Linear 10 10)
                     :output-layer (nn.Linear 10 1)))

;- Display Model
(print model)

; Returns:

"""
MyModel(
  At: 0x12beafb50
  C: [x hidden-layer output-layer]
  λ: (output-layer (hidden-layer x))


  (hidden_layer): Linear(in_features=10, out_features=10, bias=True)
  (output_layer): Linear(in_features=10, out_features=1, bias=True)
)
"""

;- Revert Model
(print (hy-repr model))

; Returns:
"""
(bind (mu [x hidden-layer output-layer] (output-layer (hidden-layer x)))
      :hidden-layer (torch.nn.modules.linear.Linear :in_features 10 :out_features 10 :bias True))
      :output-layer (torch.nn.modules.linear.Linear :in_features 10 :out_features 1 :bias True)))
"""

;- Forward
(print (model (torch.zeros (, 15 10))))

; Returns:
"""
tensor([[-0.2704],
      [-0.2704],
      [-0.2704],
      [-0.2704],
      [-0.2704],
      [-0.2704],
      [-0.2704],
      [-0.2704],
      [-0.2704],
      [-0.2704],
      [-0.2704],
      [-0.2704],
      [-0.2704],
      [-0.2704],
      [-0.2704]], grad_fn=<AddmmBackward>)
"""

---

mu

Anonymous PyTorch Module Mu-Expression Macro

Form:

(mu required-components &rest forward-procedure)

Args:

required-components (HyList(HySymbol))

the list of parameters/submodules required in the forward procedure.

forward-procedure (&rest HyExpression)

the operations run during forward propagation.

Returns:

(HyExpression): Expanded PyTorch Module class instance code.

Macroexpands to an anonymous PyTorch Module class instance. Macro syntax mirrors the definition of anonymous functions (fn), allowing PyTorch Modules to be expressed with concise function syntax.

mu is used to create a single instance of a PyTorch Module. For more information on the mechanics of the mu objects, refer to defmu documentation.

Example:

;- Import Macro
(require [mino.mu [mu]])

;- Import Torch
(import torch [torch.nn :as nn])

;-- Define Module Thru Mu Expression
(setv model (mu [x hidden-layer output-layer] (output-layer (hidden-layer x))))

;- Display Model
(print model)

; Returns:
"""
μ(
  At: 0x12bbd69d0
  C: [x hidden-layer output-layer]
  λ: (output-layer (hidden-layer x))

)
"""

;- Forward
(print (model (torch.zeros (, 15 10)) (nn.Linear 10 10) (nn.Linear 10 1)))

; Returns:
"""
tensor([[0.3004],
        [0.3004],
        [0.3004],
        [0.3004],
        [0.3004],
        [0.3004],
        [0.3004],
        [0.3004],
        [0.3004],
        [0.3004],
        [0.3004],
        [0.3004],
        [0.3004],
        [0.3004],
        [0.3004]], grad_fn=<AddmmBackward>)
"""