Linear¶
- class nvtripy.Linear(in_features: int, out_features: int, bias: bool = True, dtype: dtype = float32, quant_dtype: dtype | None = None, weight_quant_dim: int | None = None)[source]¶
Bases:
Module
Applies a linear transformation to the input:
\(Linear(x) = xW^T + b\)
- Parameters:
in_features (int) – Size of input features.
out_features (int) – Size of output features.
bias (Tensor | None) – Whether to include the bias term.
dtype (dtype) – The data type to use for the weight and bias parameters.
quant_dtype (dtype | None) – The data type for quantization.
weight_quant_dim (int | None) – The dimension along which to apply the weight quantization scale.
Example
1linear = tp.Linear(3, 4) 2 3linear.weight = tp.iota(linear.weight.shape) 4linear.bias = tp.iota(linear.bias.shape) 5 6input = tp.iota((2, 3)) 7output = linear(input)
Local Variables¶>>> linear Linear( weight: Parameter = (shape=(4, 3), dtype=float32), bias: Parameter = (shape=(4,), dtype=float32), ) >>> linear.state_dict() { weight: tensor( [[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3]], dtype=float32, loc=gpu:0, shape=(4, 3)), bias: tensor([0, 1, 2, 3], dtype=float32, loc=gpu:0, shape=(4,)), } >>> input tensor( [[0, 0, 0], [1, 1, 1]], dtype=float32, loc=gpu:0, shape=(2, 3)) >>> output tensor( [[0, 1, 2, 3], [0, 4, 8, 12]], dtype=float32, loc=gpu:0, shape=(2, 4))
- __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 0x79774b89ec10>, linear1.bias: <nvtripy.frontend.module.parameter.DefaultParameter object at 0x79774b806310>, linear2.weight: <nvtripy.frontend.module.parameter.DefaultParameter object at 0x79774b86c400>, linear2.bias: <nvtripy.frontend.module.parameter.DefaultParameter object at 0x79774b86c4f0>, }
- weight_quant_dim: int | None¶
The dimension along which to apply the weight quantization scale.