Add typing annotations for torch.nn.quantized.dynamic.modules.rnn (#43186)

guilhermeleobas · facebook-github-bot · commit 63a0bb0ab9b5 · 2020-09-01T10:25:10.000-07:00
Summary: Fixes #43185 xref: [gh-43072](#43072) Pull Request resolved: #43186 Reviewed By: ezyang Differential Revision: D23441259 Pulled By: malfet fbshipit-source-id: 80265ae7f3a70f0087e620969dbd4aa8ca17c317
diff --git a/mypy.ini b/mypy.ini
@@ -170,9 +170,6 @@ ignore_errors = True
 [mypy-torch.nn.qat.modules.conv]
 ignore_errors = True
 
-[mypy-torch.nn.quantized.dynamic.modules.rnn]
-ignore_errors = True
-
 [mypy-torch.nn.quantized.dynamic.modules.linear]
 ignore_errors = True
 
diff --git a/torch/nn/quantized/dynamic/modules/rnn.py b/torch/nn/quantized/dynamic/modules/rnn.py
@@ -6,12 +6,11 @@
 import torch
 import torch.nn as nn
 from torch import Tensor  # noqa: F401
-from torch._jit_internal import Tuple, Optional, List  # noqa: F401
+from torch._jit_internal import Tuple, Optional, List, Union, Dict  # noqa: F401
 from torch.nn.utils.rnn import PackedSequence
 from torch.nn.quantized.modules.utils import _quantize_weight
 
-def apply_permutation(tensor, permutation, dim=1):
-    # type: (Tensor, Tensor, int) -> Tensor
+def apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
     return tensor.index_select(dim, permutation)
 
 class PackedParameter(torch.nn.Module):
@@ -53,12 +52,14 @@ def __init__(self, mode, input_size, hidden_size,
         self.training = False
         num_directions = 2 if bidirectional else 1
 
-        if not isinstance(dropout, numbers.Number) or not 0 <= dropout <= 1 or \
-                isinstance(dropout, bool):
+        # "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 or isinstance(dropout, bool):  # type: ignore
             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:
+        if dropout > 0 and num_layers == 1:  # type: ignore
             warnings.warn("dropout option adds dropout after all but last "
                           "recurrent layer, so non-zero dropout expects "
                           "num_layers greater than 1, but got dropout={} and "
@@ -149,8 +150,7 @@ def __repr__(self):
         main_str += ')'
         return main_str
 
-    def check_input(self, input, batch_sizes):
-        # type: (Tensor, Optional[Tensor]) -> None
+    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(
@@ -161,33 +161,31 @@ def check_input(self, input, batch_sizes):
                 'input.size(-1) must be equal to input_size. Expected {}, got {}'.format(
                     self.input_size, input.size(-1)))
 
-    def get_expected_hidden_size(self, input, batch_sizes):
-        # type: (Tensor, Optional[Tensor]) -> Tuple[int, int, int]
+    def get_expected_hidden_size(self, input: Tensor, batch_sizes: Optional[Tensor]) -> Tuple[int, int, int]:
         if batch_sizes is not None:
-            mini_batch = batch_sizes[0]
-            mini_batch = int(mini_batch)
+            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, expected_hidden_size, msg='Expected hidden size {}, got {}'):
-        # type: (Tensor, Tuple[int, int, int], str) -> None
+    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, hidden, batch_sizes):
-        # type: (Tensor, Tensor, Optional[Tensor]) -> None
+    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, permutation):
-        # type: (Tensor, Optional[Tensor]) -> Tensor
+    def permute_hidden(self, hx: Tensor, permutation: Optional[Tensor]) -> Tensor:
         if permutation is None:
             return hx
         return apply_permutation(hx, permutation)
@@ -287,7 +285,7 @@ def quantize_and_pack(w, b):
 
     def _weight_bias(self):
         # Returns a dict of weights and biases
-        weight_bias_dict = {'weight' : {}, 'bias' : {}}
+        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):
@@ -337,8 +335,11 @@ def __init__(self, *args, **kwargs):
     def _get_name(self):
         return 'DynamicQuantizedLSTM'
 
-    def forward_impl(self, input, hx, batch_sizes, max_batch_size, sorted_indices):
-        # type: (Tensor, Optional[Tuple[Tensor, Tensor]], Optional[Tensor], int, Optional[Tensor]) -> Tuple[Tensor, Tuple[Tensor, Tensor]]  # noqa
+    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,
@@ -367,8 +368,9 @@ def forward_impl(self, input, hx, batch_sizes, max_batch_size, sorted_indices):
         return output, hidden
 
     @torch.jit.export
-    def forward_tensor(self, input, hx=None):
-        # type: (Tensor, Optional[Tuple[Tensor, Tensor]]) -> Tuple[Tensor, Tuple[Tensor, Tensor]]
+    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
@@ -380,27 +382,32 @@ def forward_tensor(self, input, hx=None):
         return output, self.permute_hidden(hidden, unsorted_indices)
 
     @torch.jit.export
-    def forward_packed(self, input, hx=None):
-        # type: (PackedSequence, Optional[Tuple[Tensor, Tensor]]) -> Tuple[PackedSequence, Tuple[Tensor, Tensor]]  # noqa
-        input, batch_sizes, sorted_indices, unsorted_indices = input
+    def forward_packed(
+        self, input: PackedSequence, hx: Optional[Tuple[Tensor, Tensor]] = None
+    ) -> Tuple[PackedSequence, Tuple[Tensor, Tensor]]:  # noqa
+        input_, batch_sizes, sorted_indices, unsorted_indices = input
         max_batch_size = batch_sizes[0]
         max_batch_size = int(max_batch_size)
 
-        output, hidden = self.forward_impl(
-            input, hx, batch_sizes, max_batch_size, sorted_indices)
+        output_, hidden = self.forward_impl(
+            input_, hx, batch_sizes, max_batch_size, sorted_indices)
 
-        output = PackedSequence(output, batch_sizes,
+        output = PackedSequence(output_, batch_sizes,
                                 sorted_indices, unsorted_indices)
         return output, self.permute_hidden(hidden, unsorted_indices)
 
-    def permute_hidden(self, hx, permutation):
-        # type: (Tuple[Tensor, Tensor], Optional[Tensor]) -> Tuple[Tensor, Tensor]
+    # "type: ignore" is required due to issue #43072
+    def permute_hidden(  # type: ignore
+        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)
 
-    def check_forward_args(self, input, hidden, batch_sizes):
-        # type: (Tensor, Tuple[Tensor, Tensor], Optional[Tensor])->None
+    # "type: ignore" is required due to issue #43072
+    def check_forward_args(  # type: ignore
+        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)
 
@@ -483,8 +490,7 @@ def check_forward_input(self, input):
                 "input has inconsistent input_size: got {}, expected {}".format(
                     input.size(1), self.input_size))
 
-    def check_forward_hidden(self, input, hx, hidden_label=''):
-        # type: (Tensor, Tensor, str) -> None
+    def check_forward_hidden(self, input: Tensor, hx: Tensor, hidden_label: str = '') -> None:
         if input.size(0) != hx.size(0):
             raise RuntimeError(
                 "Input batch size {} doesn't match hidden{} batch size {}".format(
@@ -518,6 +524,8 @@ def from_float(cls, mod):
         if dtype not in supported_scalar_types:
             raise RuntimeError('Unsupported dtype for dynamic RNN quantization: {}'.format(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:
@@ -561,7 +569,7 @@ def process_weights(weight, bias, dtype):
 
     def _weight_bias(self):
         # Returns a dict of weights and biases
-        weight_bias_dict = {'weight' : {}, 'bias' : {}}
+        weight_bias_dict: Dict[str, Dict] = {'weight' : {}, 'bias' : {}}
         w1, b1 = self._packed_weight_ih.__getstate__()[0]
         w2, b2 = self._packed_weight_hh.__getstate__()[0]
         weight_bias_dict['weight']['weight_ih'] = w1
@@ -614,8 +622,7 @@ def __init__(self, input_size, hidden_size, bias=True, nonlinearity="tanh", dtyp
     def _get_name(self):
         return 'DynamicQuantizedRNNCell'
 
-    def forward(self, input, hx=None):
-        # type: (Tensor, Optional[Tensor]) -> Tensor
+    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)
@@ -661,13 +668,12 @@ class LSTMCell(RNNCellBase):
     """
 
     def __init__(self, *args, **kwargs):
-        super(LSTMCell, self).__init__(*args, num_chunks=4, **kwargs)
+        super(LSTMCell, self).__init__(*args, num_chunks=4, **kwargs)  # type: ignore
 
     def _get_name(self):
         return 'DynamicQuantizedLSTMCell'
 
-    def forward(self, input, hx=None):
-        # type: (Tensor, Optional[Tuple[Tensor, Tensor]]) -> Tuple[Tensor, Tensor]
+    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)
@@ -707,8 +713,7 @@ def __init__(self, input_size, hidden_size, bias=True, dtype=torch.qint8):
     def _get_name(self):
         return 'DynamicQuantizedGRUCell'
 
-    def forward(self, input, hx=None):
-        # type: (Tensor, Optional[Tensor]) -> Tensor
+    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)
