Embedding¶

class nvtripy.Embedding(num_embeddings: int, embedding_dim: int, dtype: dtype = float32)[source]¶

Bases: Module

A lookup table for embedding vectors of a fixed size. Embedding vectors can be retrieved by their indices.

Parameters:

num_embeddings (int) – Number of embedding vectors in the lookup table.
embedding_dim (int) – Size of each embedding vector in the lookup table.
dtype (dtype) – The data type to use for the weight parameter.

Example

embedding = tp.Embedding(num_embeddings=4, embedding_dim=6)

input = tp.Tensor([0, 2], dtype=tp.int32)
output = embedding(input)

Local Variables¶

>>> embedding
Embedding(
    weight: Parameter = (shape=[4, 6], dtype=float32),
)
>>> embedding.state_dict()
{
    weight: tensor(
        [[0.0000, 1.0000, 2.0000, 3.0000, 4.0000, 5.0000],
         [6.0000, 7.0000, 8.0000, 9.0000, 10.0000, 11.0000],
         [12.0000, 13.0000, 14.0000, 15.0000, 16.0000, 17.0000],
         [18.0000, 19.0000, 20.0000, 21.0000, 22.0000, 23.0000]], 
        dtype=float32, loc=gpu:0, shape=(4, 6)),
}

>>> input
tensor([0, 2], dtype=int32, loc=gpu:0, shape=(2,))

>>> output
tensor(
    [[0.0000, 1.0000, 2.0000, 3.0000, 4.0000, 5.0000],
     [12.0000, 13.0000, 14.0000, 15.0000, 16.0000, 17.0000]], 
    dtype=float32, loc=gpu:0, shape=(2, 6))

load_state_dict(state_dict: Dict[str, Tensor], strict: bool = True) → Tuple[Set[str], Set[str]]¶

Loads parameters from the provided state_dict into the current module. This will recurse over any nested child modules.

Parameters:

state_dict (Dict[str, Tensor]) – A dictionary mapping names to parameters.
strict (bool) – If True, keys in state_dict must exactly match those in this module. If not, an error will be raised.

Returns:

missing_keys: keys that are expected by this module but not provided in state_dict.
unexpected_keys: keys that are not expected by this module but provided in state_dict.

Return type:

A tuple of two sets of strings representing

Example

# Using the `module` and `state_dict` from the `state_dict()` example:
print(f"Before: {module.param}")

state_dict["param"] = tp.zeros((2,), dtype=tp.float32)
module.load_state_dict(state_dict)

print(f"After: {module.param}")

Output¶

Before: tensor([1.0000, 1.0000], dtype=float32, loc=gpu:0, shape=(2,))
After: tensor([0.0000, 0.0000], dtype=float32, loc=gpu:0, shape=(2,))

See also

state_dict()

named_children() → Iterator[Tuple[str, Module]]¶

Returns an iterator over immediate children of this module, yielding tuples containing the name of the child module and the child module itself.

Returns:: An iterator over tuples containing the name of the child module and the child module itself.
Return type:: Iterator[Tuple[str, Module]]

Example

class StackedLinear(tp.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = tp.Linear(2, 2)
        self.linear2 = tp.Linear(2, 2)


stacked_linear = StackedLinear()

for name, module in stacked_linear.named_children():
    print(f"{name}: {type(module).__name__}")

Output¶

linear1: Linear
linear2: Linear

named_parameters() → Iterator[Tuple[str, Tensor]]¶

Returns:: An iterator over tuples containing the name of a parameter and the parameter itself.
Return type:: Iterator[Tuple[str, Tensor]]

Example

class MyModule(tp.Module):
    def __init__(self):
        super().__init__()
        self.alpha = tp.Tensor(1)
        self.beta = tp.Tensor(2)


linear = MyModule()

for name, parameter in linear.named_parameters():
    print(f"{name}: {parameter}")

Output¶

alpha: tensor(1, dtype=int32, loc=gpu:0, shape=())
beta: tensor(2, dtype=int32, loc=gpu:0, shape=())

state_dict() → Dict[str, Tensor]¶

Returns a dictionary mapping names to parameters in the module. This will recurse over any nested child modules.

Returns:: A dictionary mapping names to parameters.
Return type:: Dict[str, Tensor]

Example

class MyModule(tp.Module):
    def __init__(self):
        super().__init__()
        self.param = tp.ones((2,), dtype=tp.float32)
        self.linear1 = tp.Linear(2, 2)
        self.linear2 = tp.Linear(2, 2)


module = MyModule()

state_dict = module.state_dict()

Local Variables¶

>>> state_dict
{
    param: tensor([1.0000, 1.0000], dtype=float32, loc=gpu:0, shape=(2,)),
    linear1.weight: tensor(
        [[0.0000, 1.0000],
         [2.0000, 3.0000]], 
        dtype=float32, loc=gpu:0, shape=(2, 2)),
    linear1.bias: tensor([0.0000, 1.0000], dtype=float32, loc=gpu:0, shape=(2,)),
    linear2.weight: tensor(
        [[0.0000, 1.0000],
         [2.0000, 3.0000]], 
        dtype=float32, loc=gpu:0, shape=(2, 2)),
    linear2.bias: tensor([0.0000, 1.0000], dtype=float32, loc=gpu:0, shape=(2,)),
}

dtype: dtype¶: The data type used to perform the operation

weight: Tensor¶: The embedding lookup table of shape \([\text{num_embeddings}, \text{embedding_dim}]\).

__call__(x: Tensor) → Tensor[source]¶

Parameters:: x (Tensor) – A tensor of shape \([N]\) containing the indices of the desired embedding vectors.
Returns:: A tensor of shape \([N, \text{embedding_dim}]\) containing the embedding vectors.
Return type:: Tensor