torch.nn.quantized.dynamic

Linear

class torch.nn.quantized.dynamic.Linear(in_features, out_features, bias_=True, dtype=torch.qint8) [source]

A dynamic quantized linear module with floating point tensor as inputs and outputs. We adopt the same interface as torch.nn.Linear, please see https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.

Similar to torch.nn.Linear, attributes will be randomly initialized at module creation time and will be overwritten later

Variables

• ~Linear.weight (Tensor) – the non-learnable quantized weights of the module which are of shape $(\text{out\_features}, \text{in\_features})$ .
• ~Linear.bias (Tensor) – the non-learnable floating point bias of the module of shape $(\text{out\_features})$ . If bias is True, the values are initialized to zero.

Examples:

>>> m = nn.quantized.dynamic.Linear(20, 30)
>>> input = torch.randn(128, 20)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])

classmethod from_float(mod) [source]

Create a dynamic quantized module from a float module or qparams_dict

Parameters

mod (Module) – a float module, either produced by torch.quantization utilities or provided by the user

LSTM

class torch.nn.quantized.dynamic.LSTM(*args, **kwargs) [source]

A dynamic quantized LSTM module with floating point tensor as inputs and outputs. We adopt the same interface as torch.nn.LSTM, please see https://pytorch.org/docs/stable/nn.html#torch.nn.LSTM for documentation.

Examples:

>>> rnn = nn.LSTM(10, 20, 2)
>>> input = torch.randn(5, 3, 10)
>>> h0 = torch.randn(2, 3, 20)
>>> c0 = torch.randn(2, 3, 20)
>>> output, (hn, cn) = rnn(input, (h0, c0))

LSTMCell

class torch.nn.quantized.dynamic.LSTMCell(*args, **kwargs) [source]

A long short-term memory (LSTM) cell.

A dynamic quantized LSTMCell module with floating point tensor as inputs and outputs. Weights are quantized to 8 bits. We adopt the same interface as torch.nn.LSTMCell, please see https://pytorch.org/docs/stable/nn.html#torch.nn.LSTMCell for documentation.

Examples:

>>> rnn = nn.LSTMCell(10, 20)
>>> input = torch.randn(6, 3, 10)
>>> hx = torch.randn(3, 20)
>>> cx = torch.randn(3, 20)
>>> output = []
>>> for i in range(6):
        hx, cx = rnn(input[i], (hx, cx))
        output.append(hx)

GRUCell

class torch.nn.quantized.dynamic.GRUCell(input_size, hidden_size, bias=True, dtype=torch.qint8) [source]

A gated recurrent unit (GRU) cell

A dynamic quantized GRUCell module with floating point tensor as inputs and outputs. Weights are quantized to 8 bits. We adopt the same interface as torch.nn.GRUCell, please see https://pytorch.org/docs/stable/nn.html#torch.nn.GRUCell for documentation.

Examples:

>>> rnn = nn.GRUCell(10, 20)
>>> input = torch.randn(6, 3, 10)
>>> hx = torch.randn(3, 20)
>>> output = []
>>> for i in range(6):
        hx = rnn(input[i], hx)
        output.append(hx)

RNNCell

class torch.nn.quantized.dynamic.RNNCell(input_size, hidden_size, bias=True, nonlinearity='tanh', dtype=torch.qint8) [source]

An Elman RNN cell with tanh or ReLU non-linearity. A dynamic quantized RNNCell module with floating point tensor as inputs and outputs. Weights are quantized to 8 bits. We adopt the same interface as torch.nn.RNNCell, please see https://pytorch.org/docs/stable/nn.html#torch.nn.RNNCell for documentation.

Examples:

>>> rnn = nn.RNNCell(10, 20)
>>> input = torch.randn(6, 3, 10)
>>> hx = torch.randn(3, 20)
>>> output = []
>>> for i in range(6):
        hx = rnn(input[i], hx)
        output.append(hx)