[new] Update Deploy Seq2Seq Tutorial with New TorchScript API

beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py

@@ -1,24 +1,24 @@
 # -*- coding: utf-8 -*-
 """
-Deploying a Seq2Seq Model with the Hybrid Frontend
+Deploying a Seq2Seq Model with TorchScript
 ==================================================
 **Author:** `Matthew Inkawhich <https://github.com/MatthewInkawhich>`_
 """
 
 
 ######################################################################
 # This tutorial will walk through the process of transitioning a
-# sequence-to-sequence model to Torch Script using PyTorch’s Hybrid
-# Frontend. The model that we will convert is the chatbot model from the
-# `Chatbot tutorial <https://pytorch.org/tutorials/beginner/chatbot_tutorial.html>`__. 
+# sequence-to-sequence model to TorchScript using the TorchScript
+# API. The model that we will convert is the chatbot model from the
+# `Chatbot tutorial <https://pytorch.org/tutorials/beginner/chatbot_tutorial.html>`__.
 # You can either treat this tutorial as a “Part 2” to the Chatbot tutorial
 # and deploy your own pretrained model, or you can start with this
 # document and use a pretrained model that we host. In the latter case,
 # you can reference the original Chatbot tutorial for details
 # regarding data preprocessing, model theory and definition, and model
 # training.
 #
-# What is the Hybrid Frontend?
+# What is TorchScript?
 # ----------------------------
 #
 # During the research and development phase of a deep learning-based
@@ -34,13 +34,13 @@
 # to target highly optimized hardware architectures. Also, a graph-based
 # representation enables framework-agnostic model exportation. PyTorch
 # provides mechanisms for incrementally converting eager-mode code into
-# Torch Script, a statically analyzable and optimizable subset of Python
+# TorchScript, a statically analyzable and optimizable subset of Python
 # that Torch uses to represent deep learning programs independently from
 # the Python runtime.
 #
-# The API for converting eager-mode PyTorch programs into Torch Script is
+# The API for converting eager-mode PyTorch programs into TorchScript is
 # found in the torch.jit module. This module has two core modalities for
-# converting an eager-mode model to a Torch Script graph representation:
+# converting an eager-mode model to a TorchScript graph representation:
 # **tracing** and **scripting**. The ``torch.jit.trace`` function takes a
 # module or function and a set of example inputs. It then runs the example
 # input through the function or module while tracing the computational
@@ -52,19 +52,19 @@
 # operations called along the execution route taken by the example input
 # will be recorded. In other words, the control flow itself is not
 # captured. To convert modules and functions containing data-dependent
-# control flow, a **scripting** mechanism is provided. Scripting
-# explicitly converts the module or function code to Torch Script,
-# including all possible control flow routes. To use script mode, be sure
-# to inherit from the the ``torch.jit.ScriptModule`` base class (instead
-# of ``torch.nn.Module``) and add a ``torch.jit.script`` decorator to your
-# Python function or a ``torch.jit.script_method`` decorator to your
-# module’s methods. The one caveat with using scripting is that it only
-# supports a restricted subset of Python. For all details relating to the
-# supported features, see the Torch Script `language
-# reference <https://pytorch.org/docs/master/jit.html>`__. To provide the
-# maximum flexibility, the modes of Torch Script can be composed to
-# represent your whole program, and these techniques can be applied
-# incrementally.
+# control flow, a **scripting** mechanism is provided. The
+# ``torch.jit.script`` function/decorator takes a module or function and
+# does not requires example inputs. Scripting then explicitly converts
+# the module or function code to TorchScript, including all control flows.
+# One caveat with using scripting is that it only supports a subset of
+# Python, so you might need to rewrite the code to make it compatible
+# with the TorchScript syntax.
+#
+# For all details relating to the supported features, see the `TorchScript
+# language reference <https://pytorch.org/docs/master/jit.html>`__.
+# To provide the maximum flexibility, you can also mix tracing and scripting
+# modes together to represent your whole program, and these techniques can
+# be applied incrementally.
 #
 # .. figure:: /_static/img/chatbot/pytorch_workflow.png
 #    :align: center
@@ -273,7 +273,7 @@ def indexesFromSentence(voc, sentence):
 # used by the ``torch.nn.utils.rnn.pack_padded_sequence`` function when
 # padding.
 #
-# Hybrid Frontend Notes:
+# TorchScript Notes:
 # ~~~~~~~~~~~~~~~~~~~~~~
 #
 # Since the encoder’s ``forward`` function does not contain any
@@ -296,6 +296,7 @@ def __init__(self, hidden_size, embedding, n_layers=1, dropout=0):
                           dropout=(0 if n_layers == 1 else dropout), bidirectional=True)
 
     def forward(self, input_seq, input_lengths, hidden=None):
+        # type: (Tensor, Tensor, Optional[Tensor]) -> Tuple[Tensor, Tensor]
         # Convert word indexes to embeddings
         embedded = self.embedding(input_seq)
         # Pack padded batch of sequences for RNN module
@@ -325,18 +326,18 @@ def forward(self, input_seq, input_lengths, hidden=None):
 #
 
 # Luong attention layer
-class Attn(torch.nn.Module):
+class Attn(nn.Module):
     def __init__(self, method, hidden_size):
         super(Attn, self).__init__()
         self.method = method
         if self.method not in ['dot', 'general', 'concat']:
             raise ValueError(self.method, "is not an appropriate attention method.")
         self.hidden_size = hidden_size
         if self.method == 'general':
-            self.attn = torch.nn.Linear(self.hidden_size, hidden_size)
+            self.attn = nn.Linear(self.hidden_size, hidden_size)
         elif self.method == 'concat':
-            self.attn = torch.nn.Linear(self.hidden_size * 2, hidden_size)
-            self.v = torch.nn.Parameter(torch.FloatTensor(hidden_size))
+            self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
+            self.v = nn.Parameter(torch.FloatTensor(hidden_size))
 
     def dot_score(self, hidden, encoder_output):
         return torch.sum(hidden * encoder_output, dim=2)
@@ -383,14 +384,14 @@ def forward(self, hidden, encoder_outputs):
 # weighted sum indicating what parts of the encoder’s output to pay
 # attention to. From here, we use a linear layer and softmax normalization
 # to select the next word in the output sequence.
-#
-# Hybrid Frontend Notes:
+
+# TorchScript Notes:
 # ~~~~~~~~~~~~~~~~~~~~~~
 #
 # Similarly to the ``EncoderRNN``, this module does not contain any
 # data-dependent control flow. Therefore, we can once again use
-# **tracing** to convert this model to Torch Script after it is
-# initialized and its parameters are loaded.
+# **tracing** to convert this model to TorchScript after it
+# is initialized and its parameters are loaded.
 #
 
 class LuongAttnDecoderRNN(nn.Module):
@@ -465,18 +466,18 @@ def forward(self, input_step, last_hidden, encoder_outputs):
 # terminates either if the ``decoded_words`` list has reached a length of
 # *MAX_LENGTH* or if the predicted word is the *EOS_token*.
 #
-# Hybrid Frontend Notes:
+# TorchScript Notes:
 # ~~~~~~~~~~~~~~~~~~~~~~
 #
 # The ``forward`` method of this module involves iterating over the range
 # of :math:`[0, max\_length)` when decoding an output sequence one word at
 # a time. Because of this, we should use **scripting** to convert this
-# module to Torch Script. Unlike with our encoder and decoder models,
+# module to TorchScript. Unlike with our encoder and decoder models,
 # which we can trace, we must make some necessary changes to the
 # ``GreedySearchDecoder`` module in order to initialize an object without
 # error. In other words, we must ensure that our module adheres to the
-# rules of the scripting mechanism, and does not utilize any language
-# features outside of the subset of Python that Torch Script includes.
+# rules of the TorchScript mechanism, and does not utilize any language
+# features outside of the subset of Python that TorchScript includes.
 #
 # To get an idea of some manipulations that may be required, we will go
 # over the diffs between the ``GreedySearchDecoder`` implementation from
@@ -491,12 +492,6 @@ def forward(self, input_step, last_hidden, encoder_outputs):
 # Changes:
 # ^^^^^^^^
 #
-# -  ``nn.Module`` -> ``torch.jit.ScriptModule``
-#
-#    -  In order to use PyTorch’s scripting mechanism on a module, that
-#       module must inherit from the ``torch.jit.ScriptModule``.
-#
-#
 # -  Added ``decoder_n_layers`` to the constructor arguments
 #
 #    -  This change stems from the fact that the encoder and decoder
@@ -523,16 +518,9 @@ def forward(self, input_step, last_hidden, encoder_outputs):
 #       ``self._SOS_token``.
 #
 #
-# -  Add the ``torch.jit.script_method`` decorator to the ``forward``
-#    method
-#
-#    -  Adding this decorator lets the JIT compiler know that the function
-#       that it is decorating should be scripted.
-#
-#
 # -  Enforce types of ``forward`` method arguments
 #
-#    -  By default, all parameters to a Torch Script function are assumed
+#    -  By default, all parameters to a TorchScript function are assumed
 #       to be Tensor. If we need to pass an argument of a different type,
 #       we can use function type annotations as introduced in `PEP
 #       3107 <https://www.python.org/dev/peps/pep-3107/>`__. In addition,
@@ -553,7 +541,7 @@ def forward(self, input_step, last_hidden, encoder_outputs):
 #       ``self._SOS_token``.
 #
 
-class GreedySearchDecoder(torch.jit.ScriptModule):
+class GreedySearchDecoder(nn.Module):
     def __init__(self, encoder, decoder, decoder_n_layers):
         super(GreedySearchDecoder, self).__init__()
         self.encoder = encoder
@@ -564,7 +552,6 @@ def __init__(self, encoder, decoder, decoder_n_layers):
 
     __constants__ = ['_device', '_SOS_token', '_decoder_n_layers']
 
-    @torch.jit.script_method
     def forward(self, input_seq : torch.Tensor, input_length : torch.Tensor, max_length : int):
         # Forward input through encoder model
         encoder_outputs, encoder_hidden = self.encoder(input_seq, input_length)
@@ -613,7 +600,7 @@ def forward(self, input_seq : torch.Tensor, input_length : torch.Tensor, max_len
 # an argument, normalizes it, evaluates it, and prints the response.
 #
 
-def evaluate(encoder, decoder, searcher, voc, sentence, max_length=MAX_LENGTH):
+def evaluate(searcher, voc, sentence, max_length=MAX_LENGTH):
     ### Format input sentence as a batch
     # words -> indexes
     indexes_batch = [indexesFromSentence(voc, sentence)]
@@ -632,7 +619,7 @@ def evaluate(encoder, decoder, searcher, voc, sentence, max_length=MAX_LENGTH):
 
 
 # Evaluate inputs from user input (stdin)
-def evaluateInput(encoder, decoder, searcher, voc):
+def evaluateInput(searcher, voc):
     input_sentence = ''
     while(1):
         try:
@@ -643,7 +630,7 @@ def evaluateInput(encoder, decoder, searcher, voc):
             # Normalize sentence
             input_sentence = normalizeString(input_sentence)
             # Evaluate sentence
-            output_words = evaluate(encoder, decoder, searcher, voc, input_sentence)
+            output_words = evaluate(searcher, voc, input_sentence)
             # Format and print response sentence
             output_words[:] = [x for x in output_words if not (x == 'EOS' or x == 'PAD')]
             print('Bot:', ' '.join(output_words))
@@ -652,12 +639,12 @@ def evaluateInput(encoder, decoder, searcher, voc):
             print("Error: Encountered unknown word.")
 
 # Normalize input sentence and call evaluate()
-def evaluateExample(sentence, encoder, decoder, searcher, voc):
+def evaluateExample(sentence, searcher, voc):
     print("> " + sentence)
     # Normalize sentence
     input_sentence = normalizeString(sentence)
     # Evaluate sentence
-    output_words = evaluate(encoder, decoder, searcher, voc, input_sentence)
+    output_words = evaluate(searcher, voc, input_sentence)
     output_words[:] = [x for x in output_words if not (x == 'EOS' or x == 'PAD')]
     print('Bot:', ' '.join(output_words))
 
@@ -700,14 +687,17 @@ def evaluateExample(sentence, encoder, decoder, searcher, voc):
 #    ``checkpoint = torch.load(loadFilename, map_location=torch.device('cpu'))``
 #    line.
 #
-# Hybrid Frontend Notes:
+# TorchScript Notes:
 # ~~~~~~~~~~~~~~~~~~~~~~
 #
 # Notice that we initialize and load parameters into our encoder and
-# decoder models as usual. Also, we must call ``.to(device)`` to set the
-# device options of the models and ``.eval()`` to set the dropout layers
-# to test mode **before** we trace the models. ``TracedModule`` objects do
-# not inherit the ``to`` or ``eval`` methods.
+# decoder models as usual. If you are using tracing mode(`torch.jit.trace`)
+# for some part of your models, you must call .to(device) to set the device
+# options of the models and .eval() to set the dropout layers to test mode
+# **before** tracing the models. `TracedModule` objects do not inherit the
+# ``to`` or ``eval`` methods. Since in this tutorial we are only using
+# scripting instead of tracing, we only need to do this before we do
+# evaluation (which is the same as we normally do in eager mode).
 #
 
 save_dir = os.path.join("data", "save")
@@ -766,16 +756,14 @@ def evaluateExample(sentence, encoder, decoder, searcher, voc):
 
 
 ######################################################################
-# Convert Model to Torch Script
+# Convert Model to TorchScript
 # -----------------------------
 #
 # Encoder
 # ~~~~~~~
 #
-# As previously mentioned, to convert the encoder model to Torch Script,
-# we use **tracing**. Tracing any module requires running an example input
-# through the model’s ``forward`` method and trace the computational graph
-# that the data encounters. The encoder model takes an input sequence and
+# As previously mentioned, to convert the encoder model to TorchScript,
+# we use **scripting**. The encoder model takes an input sequence and
 # a corresponding lengths tensor. Therefore, we create an example input
 # sequence tensor ``test_seq``, which is of appropriate size (MAX_LENGTH,
 # 1), contains numbers in the appropriate range
@@ -803,13 +791,13 @@ def evaluateExample(sentence, encoder, decoder, searcher, voc):
 # ~~~~~~~~~~~~~~~~~~~
 #
 # Recall that we scripted our searcher module due to the presence of
-# data-dependent control flow. In the case of scripting, we do the
-# conversion work up front by adding the decorator and making sure the
-# implementation complies with scripting rules. We initialize the scripted
-# searcher the same way that we would initialize an un-scripted variant.
+# data-dependent control flow. In the case of scripting, we do necessary
+# language changes to make sure the implementation complies with
+# TorchScript. We initialize the scripted searcher the same way that we
+# would initialize an un-scripted variant.
 #
 
-### Convert encoder model
+### Compile the whole greedy search model to TorchScript model
 # Create artificial inputs
 test_seq = torch.LongTensor(MAX_LENGTH, 1).random_(0, voc.num_words).to(device)
 test_seq_length = torch.LongTensor([test_seq.size()[0]]).to(device)
@@ -824,19 +812,21 @@ def evaluateExample(sentence, encoder, decoder, searcher, voc):
 # Trace the model
 traced_decoder = torch.jit.trace(decoder, (test_decoder_input, test_decoder_hidden, test_encoder_outputs))
 
-### Initialize searcher module
-scripted_searcher = GreedySearchDecoder(traced_encoder, traced_decoder, decoder.n_layers)
+### Initialize searcher module by wrapping ``torch.jit.script`` call
+scripted_searcher = torch.jit.script(GreedySearchDecoder(traced_encoder, traced_decoder, decoder.n_layers))
+
+
 
 
 ######################################################################
 # Print Graphs
 # ------------
 #
-# Now that our models are in Torch Script form, we can print the graphs of
+# Now that our models are in TorchScript form, we can print the graphs of
 # each to ensure that we captured the computational graph appropriately.
-# Since our ``scripted_searcher`` contains our ``traced_encoder`` and
-# ``traced_decoder``, these graphs will print inline.
-#
+# Since TorchScript allow us to recursively compile the whole model
+# hierarchy and inline the ``encoder`` and ``decoder`` graph into a single
+# graph, we just need to print the `scripted_searcher` graph
 
 print('scripted_searcher graph:\n', scripted_searcher.graph)
 
@@ -845,19 +835,25 @@ def evaluateExample(sentence, encoder, decoder, searcher, voc):
 # Run Evaluation
 # --------------
 #
-# Finally, we will run evaluation of the chatbot model using the Torch
-# Script models. If converted correctly, the models will behave exactly as
-# they would in their eager-mode representation.
+# Finally, we will run evaluation of the chatbot model using the TorchScript
+# models. If converted correctly, the models will behave exactly as they
+# would in their eager-mode representation.
 #
 # By default, we evaluate a few common query sentences. If you want to
 # chat with the bot yourself, uncomment the ``evaluateInput`` line and
 # give it a spin.
 #
 
+
+# Use appropriate device
+scripted_searcher.to(device)
+# Set dropout layers to eval mode
+scripted_searcher.eval()
+
 # Evaluate examples
 sentences = ["hello", "what's up?", "who are you?", "where am I?", "where are you from?"]
 for s in sentences:
-    evaluateExample(s, traced_encoder, traced_decoder, scripted_searcher, voc)
+    evaluateExample(s, scripted_searcher, voc)
 
 # Evaluate your input
 #evaluateInput(traced_encoder, traced_decoder, scripted_searcher, voc)
@@ -867,7 +863,7 @@ def evaluateExample(sentence, encoder, decoder, searcher, voc):
 # Save Model
 # ----------
 #
-# Now that we have successfully converted our model to Torch Script, we
+# Now that we have successfully converted our model to TorchScript, we
 # will serialize it for use in a non-Python deployment environment. To do
 # this, we can simply save our ``scripted_searcher`` module, as this is
 # the user-facing interface for running inference against the chatbot