Source code for torch.ao.nn.quantized.dynamic.modules.rnn
# mypy: allow-untyped-decorators
# mypy: allow-untyped-defs
import numbers
import warnings
from typing_extensions import deprecated
import torch
import torch.nn as nn
from torch import Tensor # noqa: F401
from torch._jit_internal import Dict, List, Optional, Tuple, Union # noqa: F401
from torch.ao.nn.quantized.modules.utils import _quantize_weight
from torch.nn.utils.rnn import PackedSequence
__all__ = [
"pack_weight_bias",
"PackedParameter",
"RNNBase",
"LSTM",
"GRU",
"RNNCellBase",
"RNNCell",
"LSTMCell",
"GRUCell",
"apply_permutation",
]
def _apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
return tensor.index_select(dim, permutation)
@deprecated(
"`apply_permutation` is deprecated, please use `tensor.index_select(dim, permutation)` instead",
category=FutureWarning,
)
def apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
return _apply_permutation(tensor, permutation, dim)
def pack_weight_bias(qweight, bias, dtype):
if dtype == torch.qint8:
# for each layer, for each direction we need to quantize and pack
# weights and pack parameters in this order:
#
# w_ih, w_hh
packed_weight = torch.ops.quantized.linear_prepack(qweight, bias)
return packed_weight
else:
# for each layer, for each direction we need to quantize and pack
# weights and pack parameters in this order:
#
# packed_ih, packed_hh, b_ih, b_hh
packed_weight = torch.ops.quantized.linear_prepack_fp16(qweight, bias)
return packed_weight
class PackedParameter(torch.nn.Module):
def __init__(self, param):
super().__init__()
self.param = param
def _save_to_state_dict(self, destination, prefix, keep_vars):
super()._save_to_state_dict(destination, prefix, keep_vars)
destination[prefix + "param"] = self.param
def _load_from_state_dict(
self,
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
self.param = state_dict[prefix + "param"]
super()._load_from_state_dict(
state_dict,
prefix,
local_metadata,
False,
missing_keys,
unexpected_keys,
error_msgs,
)
class RNNBase(torch.nn.Module):
_FLOAT_MODULE = nn.RNNBase
_version = 2
def __init__(
self,
mode,
input_size,
hidden_size,
num_layers=1,
bias=True,
batch_first=False,
dropout=0.0,
bidirectional=False,
dtype=torch.qint8,
):
super().__init__()
self.mode = mode
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.bias = bias
self.batch_first = batch_first
self.dropout = float(dropout)
self.bidirectional = bidirectional
self.dtype = dtype
self.version = 2
self.training = False
num_directions = 2 if bidirectional else 1
# "type: ignore" is required since ints and Numbers are not fully comparable
# https://github.com/python/mypy/issues/8566
if (
not isinstance(dropout, numbers.Number)
or not 0 <= dropout <= 1 # type: ignore[operator]
or isinstance(dropout, bool)
):
raise ValueError(
"dropout should be a number in range [0, 1] "
"representing the probability of an element being "
"zeroed"
)
if dropout > 0 and num_layers == 1: # type: ignore[operator]
warnings.warn(
"dropout option adds dropout after all but last "
"recurrent layer, so non-zero dropout expects "
f"num_layers greater than 1, but got dropout={dropout} and "
f"num_layers={num_layers}"
)
if mode == "LSTM":
gate_size = 4 * hidden_size
elif mode == "GRU":
gate_size = 3 * hidden_size
else:
raise ValueError("Unrecognized RNN mode: " + mode)
_all_weight_values = []
for layer in range(num_layers):
for _ in range(num_directions):
layer_input_size = (
input_size if layer == 0 else hidden_size * num_directions
)
w_ih = torch.randn(gate_size, layer_input_size).to(torch.float)
w_hh = torch.randn(gate_size, hidden_size).to(torch.float)
b_ih = torch.randn(gate_size).to(torch.float)
b_hh = torch.randn(gate_size).to(torch.float)
if dtype == torch.qint8:
w_ih = torch.quantize_per_tensor(
w_ih, scale=0.1, zero_point=0, dtype=torch.qint8
)
w_hh = torch.quantize_per_tensor(
w_hh, scale=0.1, zero_point=0, dtype=torch.qint8
)
packed_ih = torch.ops.quantized.linear_prepack(w_ih, b_ih)
packed_hh = torch.ops.quantized.linear_prepack(w_hh, b_hh)
if self.version is None or self.version < 2:
cell_params = (
torch.ops.quantized.make_quantized_cell_params_dynamic(
packed_ih, packed_hh, b_ih, b_hh
)
)
else:
cell_params = (
torch.ops.quantized.make_quantized_cell_params_dynamic(
packed_ih, packed_hh, b_ih, b_hh, True
)
)
else:
packed_ih = torch.ops.quantized.linear_prepack_fp16(w_ih, b_ih)
packed_hh = torch.ops.quantized.linear_prepack_fp16(w_hh, b_hh)
cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
packed_ih, packed_hh
)
_all_weight_values.append(PackedParameter(cell_params))
self._all_weight_values = torch.nn.ModuleList(_all_weight_values)
def _get_name(self):
return "DynamicQuantizedRNN"
def extra_repr(self):
s = "{input_size}, {hidden_size}"
if self.num_layers != 1:
s += ", num_layers={num_layers}"
if self.bias is not True:
s += ", bias={bias}"
if self.batch_first is not False:
s += ", batch_first={batch_first}"
if self.dropout != 0:
s += ", dropout={dropout}"
if self.bidirectional is not False:
s += ", bidirectional={bidirectional}"
return s.format(**self.__dict__)
def __repr__(self):
# We don't want to show `ModuleList` children, hence custom
# `__repr__`. This is the same as nn.Module.__repr__, except the check
# for the `PackedParameter` and `nn.ModuleList`.
# You should still override `extra_repr` to add more info.
extra_lines = []
extra_repr = self.extra_repr()
# empty string will be split into list ['']
if extra_repr:
extra_lines = extra_repr.split("\n")
child_lines = []
for key, module in self._modules.items():
if isinstance(module, (PackedParameter, nn.ModuleList)):
continue
mod_str = repr(module)
mod_str = nn.modules.module._addindent(mod_str, 2)
child_lines.append("(" + key + "): " + mod_str)
lines = extra_lines + child_lines
main_str = self._get_name() + "("
if lines:
# simple one-liner info, which most builtin Modules will use
if len(extra_lines) == 1 and not child_lines:
main_str += extra_lines[0]
else:
main_str += "\n " + "\n ".join(lines) + "\n"
main_str += ")"
return main_str
def check_input(self, input: Tensor, batch_sizes: Optional[Tensor]) -> None:
expected_input_dim = 2 if batch_sizes is not None else 3
if input.dim() != expected_input_dim:
raise RuntimeError(
f"input must have {expected_input_dim} dimensions, got {input.dim()}"
)
if self.input_size != input.size(-1):
raise RuntimeError(
f"input.size(-1) must be equal to input_size. Expected {self.input_size}, got {input.size(-1)}"
)
def get_expected_hidden_size(
self, input: Tensor, batch_sizes: Optional[Tensor]
) -> Tuple[int, int, int]:
if batch_sizes is not None:
mini_batch = int(batch_sizes[0])
else:
mini_batch = input.size(0) if self.batch_first else input.size(1)
num_directions = 2 if self.bidirectional else 1
expected_hidden_size = (
self.num_layers * num_directions,
mini_batch,
self.hidden_size,
)
return expected_hidden_size
def check_hidden_size(
self,
hx: Tensor,
expected_hidden_size: Tuple[int, int, int],
msg: str = "Expected hidden size {}, got {}",
) -> None:
if hx.size() != expected_hidden_size:
raise RuntimeError(msg.format(expected_hidden_size, list(hx.size())))
def check_forward_args(
self, input: Tensor, hidden: Tensor, batch_sizes: Optional[Tensor]
) -> None:
self.check_input(input, batch_sizes)
expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
self.check_hidden_size(
hidden, expected_hidden_size, msg="Expected hidden size {}, got {}"
)
def permute_hidden(self, hx: Tensor, permutation: Optional[Tensor]) -> Tensor:
if permutation is None:
return hx
return _apply_permutation(hx, permutation)
def _load_from_state_dict(
self,
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
version = local_metadata.get("version", None)
self.version = version
super()._load_from_state_dict(
state_dict,
prefix,
local_metadata,
False,
missing_keys,
unexpected_keys,
error_msgs,
)
def set_weight_bias(self, weight_bias_dict):
def weight_bias_name(ihhh, layer, suffix):
weight_name = f"weight_{ihhh}_l{layer}{suffix}"
bias_name = f"bias_{ihhh}_l{layer}{suffix}"
return weight_name, bias_name
num_directions = 2 if self.bidirectional else 1
# TODO: dedup with __init__ of RNNBase
_all_weight_values = []
for layer in range(self.num_layers):
for direction in range(num_directions):
suffix = "_reverse" if direction == 1 else ""
w_ih_name, b_ih_name = weight_bias_name("ih", layer, suffix)
w_hh_name, b_hh_name = weight_bias_name("hh", layer, suffix)
w_ih = weight_bias_dict[w_ih_name]
b_ih = weight_bias_dict[b_ih_name]
w_hh = weight_bias_dict[w_hh_name]
b_hh = weight_bias_dict[b_hh_name]
if w_ih.dtype == torch.qint8:
packed_ih = torch.ops.quantized.linear_prepack(w_ih, b_ih)
packed_hh = torch.ops.quantized.linear_prepack(w_hh, b_hh)
if self.version is None or self.version < 2:
cell_params = (
torch.ops.quantized.make_quantized_cell_params_dynamic(
packed_ih, packed_hh, b_ih, b_hh
)
)
else:
cell_params = (
torch.ops.quantized.make_quantized_cell_params_dynamic(
packed_ih, packed_hh, b_ih, b_hh, True
)
)
else:
packed_ih = torch.ops.quantized.linear_prepack_fp16(w_ih, b_ih)
packed_hh = torch.ops.quantized.linear_prepack_fp16(w_hh, b_hh)
cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
packed_ih, packed_hh
)
_all_weight_values.append(PackedParameter(cell_params))
self._all_weight_values = torch.nn.ModuleList(_all_weight_values)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
assert type(mod) in {
torch.nn.LSTM,
torch.nn.GRU,
}, "nn.quantized.dynamic.RNNBase.from_float only works for nn.LSTM and nn.GRU"
assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
if mod.qconfig is not None and mod.qconfig.weight is not None:
weight_observer_method = mod.qconfig.weight
else:
# We have the circular import issues if we import the qconfig in the beginning of this file:
# https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
# import until we need it.
from torch.ao.quantization.qconfig import default_dynamic_qconfig
weight_observer_method = default_dynamic_qconfig.weight
dtype = weight_observer_method().dtype
supported_scalar_types = [torch.qint8, torch.float16]
if dtype not in supported_scalar_types:
raise RuntimeError(
f"Unsupported dtype for dynamic RNN quantization: {dtype}"
)
# RNNBase can be either LSTM or GRU
qRNNBase: Union[LSTM, GRU]
if mod.mode == "LSTM":
qRNNBase = LSTM(
mod.input_size,
mod.hidden_size,
mod.num_layers,
mod.bias,
mod.batch_first,
mod.dropout,
mod.bidirectional,
dtype,
)
elif mod.mode == "GRU":
qRNNBase = GRU(
mod.input_size,
mod.hidden_size,
mod.num_layers,
mod.bias,
mod.batch_first,
mod.dropout,
mod.bidirectional,
dtype,
)
else:
raise NotImplementedError(
"Only LSTM/GRU is supported for QuantizedRNN for now"
)
num_directions = 2 if mod.bidirectional else 1
assert mod.bias
_all_weight_values = []
for layer in range(qRNNBase.num_layers):
for direction in range(num_directions):
suffix = "_reverse" if direction == 1 else ""
def retrieve_weight_bias(ihhh):
weight_name = f"weight_{ihhh}_l{layer}{suffix}"
bias_name = f"bias_{ihhh}_l{layer}{suffix}"
weight = getattr(mod, weight_name)
bias = getattr(mod, bias_name)
return weight, bias
weight_ih, bias_ih = retrieve_weight_bias("ih")
weight_hh, bias_hh = retrieve_weight_bias("hh")
if dtype == torch.qint8:
def quantize_and_pack(w, b):
weight_observer = weight_observer_method()
weight_observer(w)
qweight = _quantize_weight(w.float(), weight_observer)
packed_weight = torch.ops.quantized.linear_prepack(qweight, b)
return packed_weight
packed_ih = quantize_and_pack(weight_ih, bias_ih)
packed_hh = quantize_and_pack(weight_hh, bias_hh)
if qRNNBase.version is None or qRNNBase.version < 2:
cell_params = (
torch.ops.quantized.make_quantized_cell_params_dynamic(
packed_ih, packed_hh, bias_ih, bias_hh
)
)
else:
cell_params = (
torch.ops.quantized.make_quantized_cell_params_dynamic(
packed_ih, packed_hh, bias_ih, bias_hh, True
)
)
elif dtype == torch.float16:
packed_ih = torch.ops.quantized.linear_prepack_fp16(
weight_ih.float(), bias_ih
)
packed_hh = torch.ops.quantized.linear_prepack_fp16(
weight_hh.float(), bias_hh
)
cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
packed_ih, packed_hh
)
else:
raise RuntimeError(
"Unsupported dtype specified for dynamic quantized LSTM!"
)
_all_weight_values.append(PackedParameter(cell_params))
qRNNBase._all_weight_values = torch.nn.ModuleList(_all_weight_values)
return qRNNBase
def _weight_bias(self):
# Returns a dict of weights and biases
weight_bias_dict: Dict[str, Dict] = {"weight": {}, "bias": {}}
count = 0
num_directions = 2 if self.bidirectional else 1
for layer in range(self.num_layers):
for direction in range(num_directions):
suffix = "_reverse" if direction == 1 else ""
key_name1 = f"weight_ih_l{layer}{suffix}"
key_name2 = f"weight_hh_l{layer}{suffix}"
# packed weights are part of torchbind class, CellParamsSerializationType
# Within the packed weight class, the weight and bias are accessible as Tensors
packed_weight_bias = self._all_weight_values[ # type: ignore[index]
count
].param.__getstate__()[0][4]
weight_bias_dict["weight"][key_name1] = packed_weight_bias[
0
].__getstate__()[0][0]
weight_bias_dict["weight"][key_name2] = packed_weight_bias[
1
].__getstate__()[0][0]
key_name1 = f"bias_ih_l{layer}{suffix}"
key_name2 = f"bias_hh_l{layer}{suffix}"
weight_bias_dict["bias"][key_name1] = packed_weight_bias[
0
].__getstate__()[0][1]
weight_bias_dict["bias"][key_name2] = packed_weight_bias[
1
].__getstate__()[0][1]
count = count + 1
return weight_bias_dict
def get_weight(self):
return self._weight_bias()["weight"]
def get_bias(self):
return self._weight_bias()["bias"]
[docs]class LSTM(RNNBase):
r"""
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::
>>> # xdoctest: +SKIP
>>> 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))
"""
_FLOAT_MODULE = nn.LSTM
__overloads__ = {"forward": ["forward_packed", "forward_tensor"]}
def __init__(self, *args, **kwargs):
super().__init__("LSTM", *args, **kwargs)
def _get_name(self):
return "DynamicQuantizedLSTM"
def forward_impl(
self,
input: Tensor,
hx: Optional[Tuple[Tensor, Tensor]],
batch_sizes: Optional[Tensor],
max_batch_size: int,
sorted_indices: Optional[Tensor],
) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
if hx is None:
num_directions = 2 if self.bidirectional else 1
zeros = torch.zeros(
self.num_layers * num_directions,
max_batch_size,
self.hidden_size,
dtype=input.dtype,
device=input.device,
)
hx = (zeros, zeros)
else:
# Each batch of the hidden state should match the input sequence that
# the user believes he/she is passing in.
hx = self.permute_hidden(hx, sorted_indices)
self.check_forward_args(input, hx, batch_sizes)
_all_params = [m.param for m in self._all_weight_values]
if batch_sizes is None:
result = torch.quantized_lstm(
input,
hx,
_all_params,
self.bias,
self.num_layers,
float(self.dropout),
self.training,
self.bidirectional,
self.batch_first,
dtype=self.dtype,
use_dynamic=True,
)
else:
result = torch.quantized_lstm(
input,
batch_sizes,
hx,
_all_params,
self.bias,
self.num_layers,
float(self.dropout),
self.training,
self.bidirectional,
dtype=self.dtype,
use_dynamic=True,
)
output = result[0]
hidden = result[1:]
return output, hidden
@torch.jit.export
def forward_tensor(
self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None
) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
batch_sizes = None
max_batch_size = input.size(0) if self.batch_first else input.size(1)
sorted_indices = None
unsorted_indices = None
output, hidden = self.forward_impl(
input, hx, batch_sizes, max_batch_size, sorted_indices
)
return output, self.permute_hidden(hidden, unsorted_indices)
@torch.jit.export
def forward_packed(
self, input: PackedSequence, hx: Optional[Tuple[Tensor, Tensor]] = None
) -> Tuple[PackedSequence, Tuple[Tensor, Tensor]]:
input_, batch_sizes, sorted_indices, unsorted_indices = input
max_batch_size = int(batch_sizes[0])
output_, hidden = self.forward_impl(
input_, hx, batch_sizes, max_batch_size, sorted_indices
)
output = PackedSequence(output_, batch_sizes, sorted_indices, unsorted_indices)
return output, self.permute_hidden(hidden, unsorted_indices)
# "type: ignore" is required due to issue #43072
def permute_hidden( # type: ignore[override]
self,
hx: Tuple[Tensor, Tensor],
permutation: Optional[Tensor],
) -> Tuple[Tensor, Tensor]:
if permutation is None:
return hx
return _apply_permutation(hx[0], permutation), _apply_permutation(
hx[1], permutation
)
# "type: ignore" is required due to issue #43072
def check_forward_args( # type: ignore[override]
self,
input: Tensor,
hidden: Tuple[Tensor, Tensor],
batch_sizes: Optional[Tensor],
) -> None:
self.check_input(input, batch_sizes)
expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
self.check_hidden_size(
hidden[0], expected_hidden_size, "Expected hidden[0] size {}, got {}"
)
self.check_hidden_size(
hidden[1], expected_hidden_size, "Expected hidden[1] size {}, got {}"
)
@torch.jit.ignore
def forward(self, input, hx=None):
if isinstance(input, PackedSequence):
return self.forward_packed(input, hx)
else:
return self.forward_tensor(input, hx)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
@classmethod
def from_reference(cls, ref_mod):
assert hasattr(ref_mod, "weight_ih_l0_dtype"), "We are assuming weight_ih_l0 "
"exists in LSTM, may need to relax the assumption to support the use case"
qmod = cls(
ref_mod.input_size,
ref_mod.hidden_size,
ref_mod.num_layers,
ref_mod.bias,
ref_mod.batch_first,
ref_mod.dropout,
ref_mod.bidirectional,
# assuming there is layer 0, which should be OK
ref_mod.weight_ih_l0_dtype,
)
qmod.set_weight_bias(ref_mod.get_quantized_weight_bias_dict())
return qmod
[docs]class GRU(RNNBase):
r"""Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
For each element in the input sequence, each layer computes the following
function:
.. math::
\begin{array}{ll}
r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\
z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\
n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn})) \\
h_t = (1 - z_t) \odot n_t + z_t \odot h_{(t-1)}
\end{array}
where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is the input
at time `t`, :math:`h_{(t-1)}` is the hidden state of the layer
at time `t-1` or the initial hidden state at time `0`, and :math:`r_t`,
:math:`z_t`, :math:`n_t` are the reset, update, and new gates, respectively.
:math:`\sigma` is the sigmoid function, and :math:`\odot` is the Hadamard product.
In a multilayer GRU, the input :math:`x^{(l)}_t` of the :math:`l` -th layer
(:math:`l >= 2`) is the hidden state :math:`h^{(l-1)}_t` of the previous layer multiplied by
dropout :math:`\delta^{(l-1)}_t` where each :math:`\delta^{(l-1)}_t` is a Bernoulli random
variable which is :math:`0` with probability :attr:`dropout`.
Args:
input_size: The number of expected features in the input `x`
hidden_size: The number of features in the hidden state `h`
num_layers: Number of recurrent layers. E.g., setting ``num_layers=2``
would mean stacking two GRUs together to form a `stacked GRU`,
with the second GRU taking in outputs of the first GRU and
computing the final results. Default: 1
bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
Default: ``True``
batch_first: If ``True``, then the input and output tensors are provided
as (batch, seq, feature). Default: ``False``
dropout: If non-zero, introduces a `Dropout` layer on the outputs of each
GRU layer except the last layer, with dropout probability equal to
:attr:`dropout`. Default: 0
bidirectional: If ``True``, becomes a bidirectional GRU. Default: ``False``
Inputs: input, h_0
- **input** of shape `(seq_len, batch, input_size)`: tensor containing the features
of the input sequence. The input can also be a packed variable length
sequence. See :func:`torch.nn.utils.rnn.pack_padded_sequence`
for details.
- **h_0** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor
containing the initial hidden state for each element in the batch.
Defaults to zero if not provided. If the RNN is bidirectional,
num_directions should be 2, else it should be 1.
Outputs: output, h_n
- **output** of shape `(seq_len, batch, num_directions * hidden_size)`: tensor
containing the output features h_t from the last layer of the GRU,
for each `t`. If a :class:`torch.nn.utils.rnn.PackedSequence` has been
given as the input, the output will also be a packed sequence.
For the unpacked case, the directions can be separated
using ``output.view(seq_len, batch, num_directions, hidden_size)``,
with forward and backward being direction `0` and `1` respectively.
Similarly, the directions can be separated in the packed case.
- **h_n** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor
containing the hidden state for `t = seq_len`
Like *output*, the layers can be separated using
``h_n.view(num_layers, num_directions, batch, hidden_size)``.
Shape:
- Input1: :math:`(L, N, H_{in})` tensor containing input features where
:math:`H_{in}=\text{input\_size}` and `L` represents a sequence length.
- Input2: :math:`(S, N, H_{out})` tensor
containing the initial hidden state for each element in the batch.
:math:`H_{out}=\text{hidden\_size}`
Defaults to zero if not provided. where :math:`S=\text{num\_layers} * \text{num\_directions}`
If the RNN is bidirectional, num_directions should be 2, else it should be 1.
- Output1: :math:`(L, N, H_{all})` where :math:`H_{all}=\text{num\_directions} * \text{hidden\_size}`
- Output2: :math:`(S, N, H_{out})` tensor containing the next hidden state
for each element in the batch
Attributes:
weight_ih_l[k] : the learnable input-hidden weights of the :math:`\text{k}^{th}` layer
(W_ir|W_iz|W_in), of shape `(3*hidden_size, input_size)` for `k = 0`.
Otherwise, the shape is `(3*hidden_size, num_directions * hidden_size)`
weight_hh_l[k] : the learnable hidden-hidden weights of the :math:`\text{k}^{th}` layer
(W_hr|W_hz|W_hn), of shape `(3*hidden_size, hidden_size)`
bias_ih_l[k] : the learnable input-hidden bias of the :math:`\text{k}^{th}` layer
(b_ir|b_iz|b_in), of shape `(3*hidden_size)`
bias_hh_l[k] : the learnable hidden-hidden bias of the :math:`\text{k}^{th}` layer
(b_hr|b_hz|b_hn), of shape `(3*hidden_size)`
.. note::
All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
where :math:`k = \frac{1}{\text{hidden\_size}}`
.. note::
The calculation of new gate :math:`n_t` subtly differs from the original paper and other frameworks.
In the original implementation, the Hadamard product :math:`(\odot)` between :math:`r_t` and the
previous hidden state :math:`h_{(t-1)}` is done before the multiplication with the weight matrix
`W` and addition of bias:
.. math::
\begin{aligned}
n_t = \tanh(W_{in} x_t + b_{in} + W_{hn} ( r_t \odot h_{(t-1)} ) + b_{hn})
\end{aligned}
This is in contrast to PyTorch implementation, which is done after :math:`W_{hn} h_{(t-1)}`
.. math::
\begin{aligned}
n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn}))
\end{aligned}
This implementation differs on purpose for efficiency.
.. include:: ../cudnn_persistent_rnn.rst
Examples::
>>> # xdoctest: +SKIP
>>> rnn = nn.GRU(10, 20, 2)
>>> input = torch.randn(5, 3, 10)
>>> h0 = torch.randn(2, 3, 20)
>>> output, hn = rnn(input, h0)
"""
_FLOAT_MODULE = nn.GRU
__overloads__ = {"forward": ["forward_packed", "forward_tensor"]}
def __init__(self, *args, **kwargs):
super().__init__("GRU", *args, **kwargs)
def _get_name(self):
return "DynamicQuantizedGRU"
def check_forward_args(
self, input: Tensor, hidden: Tensor, batch_sizes: Optional[Tensor]
) -> None:
self.check_input(input, batch_sizes)
expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
self.check_hidden_size(
hidden, expected_hidden_size, "Expected hidden size {}, got {}"
)
def forward_impl(
self,
input: Tensor,
hx: Optional[Tensor],
batch_sizes: Optional[Tensor],
max_batch_size: int,
sorted_indices: Optional[Tensor],
) -> Tuple[Tensor, Tensor]:
if hx is None:
num_directions = 2 if self.bidirectional else 1
zeros = torch.zeros(
self.num_layers * num_directions,
max_batch_size,
self.hidden_size,
dtype=input.dtype,
device=input.device,
)
hx = zeros
else:
# Each batch of the hidden state should match the input sequence that
# the user believes he/she is passing in.
hx = self.permute_hidden(hx, sorted_indices)
self.check_forward_args(input, hx, batch_sizes)
_all_params = [m.param for m in self._all_weight_values]
if batch_sizes is None:
result = torch.quantized_gru(
input,
hx,
_all_params,
self.bias,
self.num_layers,
self.dropout,
self.training,
self.bidirectional,
self.batch_first,
)
else:
result = torch.quantized_gru(
input,
batch_sizes,
hx,
_all_params,
self.bias,
self.num_layers,
self.dropout,
self.training,
self.bidirectional,
)
output = result[0]
hidden = result[1]
return output, hidden
@torch.jit.export
def forward_tensor(
self, input: Tensor, hx: Optional[Tensor] = None
) -> Tuple[Tensor, Tensor]:
batch_sizes = None
max_batch_size = input.size(0) if self.batch_first else input.size(1)
sorted_indices = None
unsorted_indices = None
output, hidden = self.forward_impl(
input, hx, batch_sizes, max_batch_size, sorted_indices
)
return output, self.permute_hidden(hidden, unsorted_indices)
@torch.jit.export
def forward_packed(
self, input: PackedSequence, hx: Optional[Tensor] = None
) -> Tuple[PackedSequence, Tensor]:
input_, batch_sizes, sorted_indices, unsorted_indices = input
max_batch_size = int(batch_sizes[0])
output_, hidden = self.forward_impl(
input_, hx, batch_sizes, max_batch_size, sorted_indices
)
output = PackedSequence(output_, batch_sizes, sorted_indices, unsorted_indices)
return output, self.permute_hidden(hidden, unsorted_indices)
def permute_hidden(self, hx: Tensor, permutation: Optional[Tensor]) -> Tensor:
if permutation is None:
return hx
return _apply_permutation(hx, permutation)
@torch.jit.ignore
def forward(self, input, hx=None):
if isinstance(input, PackedSequence):
return self.forward_packed(input, hx)
else:
return self.forward_tensor(input, hx)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
@classmethod
def from_reference(cls, ref_mod):
assert hasattr(ref_mod, "weight_ih_l0_dtype"), "We are assuming weight_ih_l0 "
"exists in LSTM, may need to relax the assumption to support the use case"
qmod = cls(
ref_mod.input_size,
ref_mod.hidden_size,
ref_mod.num_layers,
ref_mod.bias,
ref_mod.batch_first,
ref_mod.dropout,
ref_mod.bidirectional,
# assuming there is layer 0, which should be OK
ref_mod.weight_ih_l0_dtype,
)
qmod.set_weight_bias(ref_mod.get_quantized_weight_bias_dict())
return qmod
class RNNCellBase(torch.nn.Module):
# _FLOAT_MODULE = nn.CellRNNBase
__constants__ = ["input_size", "hidden_size", "bias"]
def __init__(
self, input_size, hidden_size, bias=True, num_chunks=4, dtype=torch.qint8
):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = bias
self.weight_dtype = dtype
if bias:
self.bias_ih = torch.randn(num_chunks * hidden_size).to(dtype=torch.float)
self.bias_hh = torch.randn(num_chunks * hidden_size).to(dtype=torch.float)
else:
self.register_parameter("bias_ih", None)
self.register_parameter("bias_hh", None)
weight_ih = torch.randn(num_chunks * hidden_size, input_size).to(torch.float)
weight_hh = torch.randn(num_chunks * hidden_size, hidden_size).to(torch.float)
if dtype == torch.qint8:
weight_ih = torch.quantize_per_tensor(
weight_ih, scale=1, zero_point=0, dtype=torch.qint8
)
weight_hh = torch.quantize_per_tensor(
weight_hh, scale=1, zero_point=0, dtype=torch.qint8
)
if dtype == torch.qint8:
# for each layer, for each direction we need to quantize and pack
# weights and pack parameters in this order:
#
# w_ih, w_hh
packed_weight_ih = torch.ops.quantized.linear_prepack(
weight_ih, self.bias_ih
)
packed_weight_hh = torch.ops.quantized.linear_prepack(
weight_hh, self.bias_hh
)
else:
# for each layer, for each direction we need to quantize and pack
# weights and pack parameters in this order:
#
# packed_ih, packed_hh, b_ih, b_hh
packed_weight_ih = torch.ops.quantized.linear_prepack_fp16(
weight_ih, self.bias_ih
)
packed_weight_hh = torch.ops.quantized.linear_prepack_fp16(
weight_hh, self.bias_hh
)
self._packed_weight_ih = packed_weight_ih
self._packed_weight_hh = packed_weight_hh
def _get_name(self):
return "DynamicQuantizedRNNBase"
def extra_repr(self):
s = "{input_size}, {hidden_size}"
if "bias" in self.__dict__ and self.bias is not True:
s += ", bias={bias}"
if "nonlinearity" in self.__dict__ and self.nonlinearity != "tanh":
s += ", nonlinearity={nonlinearity}"
return s.format(**self.__dict__)
def check_forward_input(self, input):
if input.size(1) != self.input_size:
raise RuntimeError(
f"input has inconsistent input_size: got {input.size(1)}, expected {self.input_size}"
)
def check_forward_hidden(
self, input: Tensor, hx: Tensor, hidden_label: str = ""
) -> None:
if input.size(0) != hx.size(0):
raise RuntimeError(
f"Input batch size {input.size(0)} doesn't match hidden{hidden_label} batch size {hx.size(0)}"
)
if hx.size(1) != self.hidden_size:
raise RuntimeError(
f"hidden{hidden_label} has inconsistent hidden_size: got {hx.size(1)}, expected {self.hidden_size}"
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
assert type(mod) in {
torch.nn.LSTMCell,
torch.nn.GRUCell,
torch.nn.RNNCell,
}, "nn.quantized.dynamic.RNNCellBase.from_float \
only works for nn.LSTMCell, nn.GRUCell and nn.RNNCell"
assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
if mod.qconfig is not None and mod.qconfig.weight is not None:
weight_observer_method = mod.qconfig.weight
else:
# We have the circular import issues if we import the qconfig in the beginning of this file:
# https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
# import until we need it.
from torch.ao.quantization.qconfig import default_dynamic_qconfig
weight_observer_method = default_dynamic_qconfig.weight
dtype = weight_observer_method().dtype
supported_scalar_types = [torch.qint8, torch.float16]
if dtype not in supported_scalar_types:
raise RuntimeError(
f"Unsupported dtype for dynamic RNN quantization: {dtype}"
)
qRNNCellBase: Union[LSTMCell, GRUCell, RNNCell]
if type(mod) == torch.nn.LSTMCell:
qRNNCellBase = LSTMCell(
mod.input_size, mod.hidden_size, bias=mod.bias, dtype=dtype
)
elif type(mod) == torch.nn.GRUCell:
qRNNCellBase = GRUCell(
mod.input_size, mod.hidden_size, bias=mod.bias, dtype=dtype
)
elif type(mod) == torch.nn.RNNCell:
qRNNCellBase = RNNCell(
mod.input_size,
mod.hidden_size,
bias=mod.bias,
nonlinearity=mod.nonlinearity,
dtype=dtype,
)
else:
raise NotImplementedError(
"Only LSTMCell, GRUCell and RNNCell \
are supported for QuantizedRNN for now"
)
assert mod.bias
def _observe_and_quantize_weight(weight):
if dtype == torch.qint8:
weight_observer = weight_observer_method()
weight_observer(weight)
qweight = _quantize_weight(weight.float(), weight_observer)
return qweight
else:
return weight.float()
qRNNCellBase._packed_weight_ih = pack_weight_bias(
_observe_and_quantize_weight(mod.weight_ih), mod.bias_ih, dtype
)
qRNNCellBase._packed_weight_hh = pack_weight_bias(
_observe_and_quantize_weight(mod.weight_hh), mod.bias_hh, dtype
)
return qRNNCellBase
@classmethod
def from_reference(cls, ref_mod):
assert hasattr(ref_mod, "weight_ih_dtype"), "We are assuming weight_ih "
"exists in reference module, may need to relax the assumption to support the use case"
if hasattr(ref_mod, "nonlinearity"):
qmod = cls(
ref_mod.input_size,
ref_mod.hidden_size,
ref_mod.bias,
ref_mod.nonlinearity,
dtype=ref_mod.weight_ih_dtype,
)
else:
qmod = cls(
ref_mod.input_size,
ref_mod.hidden_size,
ref_mod.bias,
dtype=ref_mod.weight_ih_dtype,
)
weight_bias_dict = {
"weight": {
"weight_ih": ref_mod.get_quantized_weight_ih(),
"weight_hh": ref_mod.get_quantized_weight_hh(),
},
"bias": {
"bias_ih": ref_mod.bias_ih,
"bias_hh": ref_mod.bias_hh,
},
}
qmod.set_weight_bias(weight_bias_dict)
return qmod
def _weight_bias(self):
# Returns a dict of weights and biases
weight_bias_dict: Dict[str, Dict] = {"weight": {}, "bias": {}}
w1, b1 = self._packed_weight_ih.__getstate__()[0]
w2, b2 = self._packed_weight_hh.__getstate__()[0]
# TODO: these can be simplified to one level? e.g. using weight_ih as key
# directly
weight_bias_dict["weight"]["weight_ih"] = w1
weight_bias_dict["weight"]["weight_hh"] = w2
weight_bias_dict["bias"]["bias_ih"] = b1
weight_bias_dict["bias"]["bias_hh"] = b2
return weight_bias_dict
def get_weight(self):
return self._weight_bias()["weight"]
def get_bias(self):
return self._weight_bias()["bias"]
def set_weight_bias(self, weight_bias_dict):
# TODO: these can be simplified to one level? e.g. using weight_ih as key
# directly
self._packed_weight_ih = pack_weight_bias(
weight_bias_dict["weight"]["weight_ih"],
weight_bias_dict["bias"]["bias_ih"],
self.weight_dtype,
)
self._packed_weight_hh = pack_weight_bias(
weight_bias_dict["weight"]["weight_hh"],
weight_bias_dict["bias"]["bias_hh"],
self.weight_dtype,
)
def _save_to_state_dict(self, destination, prefix, keep_vars):
super()._save_to_state_dict(destination, prefix, keep_vars)
destination[prefix + "_packed_weight_ih"] = self._packed_weight_ih
destination[prefix + "_packed_weight_hh"] = self._packed_weight_hh
def _load_from_state_dict(
self,
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
self._packed_weight_ih = state_dict.pop(prefix + "_packed_weight_ih")
self._packed_weight_hh = state_dict.pop(prefix + "_packed_weight_hh")
super()._load_from_state_dict(
state_dict,
prefix,
local_metadata,
False,
missing_keys,
unexpected_keys,
error_msgs,
)
[docs]class RNNCell(RNNCellBase):
r"""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::
>>> # xdoctest: +SKIP
>>> 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)
"""
__constants__ = ["input_size", "hidden_size", "bias", "nonlinearity"]
def __init__(
self, input_size, hidden_size, bias=True, nonlinearity="tanh", dtype=torch.qint8
):
super().__init__(input_size, hidden_size, bias, num_chunks=1, dtype=dtype)
self.nonlinearity = nonlinearity
def _get_name(self):
return "DynamicQuantizedRNNCell"
def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
self.check_forward_input(input)
if hx is None:
hx = torch.zeros(
input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
)
self.check_forward_hidden(input, hx, "")
if self.nonlinearity == "tanh":
ret = torch.ops.quantized.quantized_rnn_tanh_cell_dynamic(
input,
hx,
self._packed_weight_ih,
self._packed_weight_hh,
self.bias_ih,
self.bias_hh,
)
elif self.nonlinearity == "relu":
ret = torch.ops.quantized.quantized_rnn_relu_cell_dynamic(
input,
hx,
self._packed_weight_ih,
self._packed_weight_hh,
self.bias_ih,
self.bias_hh,
)
else:
ret = input # TODO: remove when jit supports exception flow
raise RuntimeError(f"Unknown nonlinearity: {self.nonlinearity}")
return ret
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
[docs]class LSTMCell(RNNCellBase):
r"""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::
>>> # xdoctest: +SKIP
>>> 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)
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, num_chunks=4, **kwargs) # type: ignore[misc]
def _get_name(self):
return "DynamicQuantizedLSTMCell"
def forward(
self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None
) -> Tuple[Tensor, Tensor]:
self.check_forward_input(input)
if hx is None:
zeros = torch.zeros(
input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
)
hx = (zeros, zeros)
self.check_forward_hidden(input, hx[0], "[0]")
self.check_forward_hidden(input, hx[1], "[1]")
return torch.ops.quantized.quantized_lstm_cell_dynamic(
input,
hx,
self._packed_weight_ih,
self._packed_weight_hh,
self.bias_ih,
self.bias_hh,
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
[docs]class GRUCell(RNNCellBase):
r"""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::
>>> # xdoctest: +SKIP
>>> 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)
"""
def __init__(self, input_size, hidden_size, bias=True, dtype=torch.qint8):
super().__init__(input_size, hidden_size, bias, num_chunks=3, dtype=dtype)
def _get_name(self):
return "DynamicQuantizedGRUCell"
def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
self.check_forward_input(input)
if hx is None:
hx = torch.zeros(
input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
)
self.check_forward_hidden(input, hx, "")
return torch.ops.quantized.quantized_gru_cell_dynamic(
input,
hx,
self._packed_weight_ih,
self._packed_weight_hh,
self.bias_ih,
self.bias_hh,
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
