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
1embedding = tp.Embedding(num_embeddings=4, embedding_dim=6) 2 3embedding.weight = tp.iota(embedding.weight.shape) 4 5input = tp.Tensor([0, 2], dtype=tp.int32) 6output = embedding(input)
Local Variables¶>>> embedding Embedding( weight: Parameter = (shape=(4, 6), dtype=float32), ) >>> embedding.state_dict() { weight: tensor( [[0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3]], dtype=float32, loc=gpu:0, shape=(4, 6)), } >>> input tensor([0, 2], dtype=int32, loc=cpu:0, shape=(2,)) >>> output tensor( [[0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 2, 2]], dtype=float32, loc=gpu:0, shape=(2, 6))
- __call__(*args: Any, **kwargs: Any) Any ¶
Calls the module with the specified arguments.
- Parameters:
*args (Any) – Positional arguments to the module.
**kwargs (Any) – Keyword arguments to the module.
- Returns:
The outputs computed by the module.
- Return type:
Any
Example
1class Module(tp.Module): 2 def forward(self, x): 3 return tp.relu(x) 4 5 6module = Module() 7 8input = tp.arange(-3, 3) 9out = module(input) # Note that we do not call `forward` directly.
Local Variables¶>>> module Module( ) >>> module.state_dict() {} >>> input tensor([-3, -2, -1, 0, 1, 2], dtype=float32, loc=gpu:0, shape=(6,)) >>> out tensor([0, 0, 0, 0, 1, 2], dtype=float32, loc=gpu:0, shape=(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:
- 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 twoset
s of strings representing
Example
1class MyModule(tp.Module): 2 def __init__(self): 3 super().__init__() 4 self.param = tp.ones((2,), dtype=tp.float32) 5 6 7module = MyModule() 8 9print(f"Before: {module.param}") 10 11module.load_state_dict({"param": tp.zeros((2,), dtype=tp.float32)}) 12 13print(f"After: {module.param}")
Output¶Before: tensor([1, 1], dtype=float32, loc=gpu:0, shape=(2,)) After: tensor([0, 0], dtype=float32, loc=gpu:0, shape=(2,))
See also
- 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
1class StackedLinear(tp.Module): 2 def __init__(self): 3 super().__init__() 4 self.linear1 = tp.Linear(2, 2) 5 self.linear2 = tp.Linear(2, 2) 6 7 8stacked_linear = StackedLinear() 9 10for name, module in stacked_linear.named_children(): 11 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
1class MyModule(tp.Module): 2 def __init__(self): 3 super().__init__() 4 self.alpha = tp.Tensor(1) 5 self.beta = tp.Tensor(2) 6 7 8linear = MyModule() 9 10for name, parameter in linear.named_parameters(): 11 print(f"{name}: {parameter}")
Output¶alpha: tensor(1, dtype=int32, loc=cpu:0, shape=()) beta: tensor(2, dtype=int32, loc=cpu: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
1class MyModule(tp.Module): 2 def __init__(self): 3 super().__init__() 4 self.param = tp.ones((2,), dtype=tp.float32) 5 self.linear1 = tp.Linear(2, 2) 6 self.linear2 = tp.Linear(2, 2) 7 8 9module = MyModule() 10 11state_dict = module.state_dict()
Local Variables¶>>> state_dict { param: tensor([1, 1], dtype=float32, loc=gpu:0, shape=(2,)), linear1.weight: <nvtripy.frontend.module.parameter.DefaultParameter object at 0x79774c352e50>, linear1.bias: <nvtripy.frontend.module.parameter.DefaultParameter object at 0x79774c355cd0>, linear2.weight: <nvtripy.frontend.module.parameter.DefaultParameter object at 0x79774c35d700>, linear2.bias: <nvtripy.frontend.module.parameter.DefaultParameter object at 0x79774c35d820>, }