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[cherry-pick] Update tutorial to match PyTorch main tutorials page (#2097) #2100

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[cherry-pick] Update tutorial to match PyTorch main tutorials page (#2097) #2100

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64 changes: 25 additions & 39 deletions examples/tutorials/t5_demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,7 @@
T5-Base Model for Summarization, Sentiment Classification, and Translation
==========================================================================

**Author**: `Pendo Abbo <[email protected]>`__
**Author**: `Joe Cummings <[email protected]>`__
**Author**: `Pendo Abbo <[email protected]>`__, `Joe Cummings <[email protected]>`__

"""

Expand All @@ -21,14 +20,6 @@
#
#

######################################################################
# Common imports
# --------------
import torch

DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")


#######################################################################
# Data Transformation
# -------------------
Expand All @@ -43,7 +34,7 @@
#
# T5 uses a SentencePiece model for text tokenization. Below, we use a pre-trained SentencePiece model to build
# the text pre-processing pipeline using torchtext's T5Transform. Note that the transform supports both
# batched and non-batched text input (i.e. one can either pass a single sentence or a list of sentences), however
# batched and non-batched text input (for example, one can either pass a single sentence or a list of sentences), however
# the T5 model expects the input to be batched.
#

Expand All @@ -64,7 +55,7 @@
#######################################################################
# Alternatively, we can also use the transform shipped with the pre-trained models that does all of the above out-of-the-box
#
# ::
# .. code-block::
#
# from torchtext.models import T5_BASE_GENERATION
# transform = T5_BASE_GENERATION.transform()
Expand All @@ -77,8 +68,7 @@
#
# torchtext provides SOTA pre-trained models that can be used directly for NLP tasks or fine-tuned on downstream tasks. Below
# we use the pre-trained T5 model with standard base configuration to perform text summarization, sentiment classification, and
# translation. For additional details on available pre-trained models, please refer to documentation at
# https://pytorch.org/text/main/models.html
# translation. For additional details on available pre-trained models, see `the torchtext documentation <https://pytorch.org/text/main/models.html>`__
#
#
from torchtext.models import T5_BASE_GENERATION
Expand All @@ -88,16 +78,15 @@
transform = t5_base.transform()
model = t5_base.get_model()
model.eval()
model.to(DEVICE)


#######################################################################
# GenerationUtils
# ------------------
#
# We can use torchtext's `GenerationUtils` to produce an output sequence based on the input sequence provided. This calls on the
# We can use torchtext's ``GenerationUtils`` to produce an output sequence based on the input sequence provided. This calls on the
# model's encoder and decoder, and iteratively expands the decoded sequences until the end-of-sequence token is generated
# for all sequences in the batch. The `generate` method shown below uses greedy search to generate the sequences. Beam search and
# for all sequences in the batch. The ``generate`` method shown below uses greedy search to generate the sequences. Beam search and
# other decoding strategies are also supported.
#
#
Expand All @@ -114,12 +103,12 @@
# datapipes and hence support standard flow-control and mapping/transformation using user defined
# functions and transforms.
#
# Below, we demonstrate how to pre-process the CNNDM dataset to include the prefix necessary for the
# Below we demonstrate how to pre-process the CNNDM dataset to include the prefix necessary for the
# model to indentify the task it is performing. The CNNDM dataset has a train, validation, and test
# split. Below we demo on the test split.
#
# The T5 model uses the prefix "summarize" for text summarization. For more information on task
# prefixes, please visit Appendix D of the T5 Paper at https://arxiv.org/pdf/1910.10683.pdf
# prefixes, please visit Appendix D of the `T5 Paper <https://arxiv.org/pdf/1910.10683.pdf>`__
#
# .. note::
# Using datapipes is still currently subject to a few caveats. If you wish
Expand All @@ -144,12 +133,12 @@ def apply_prefix(task, x):
cnndm_datapipe = cnndm_datapipe.map(partial(apply_prefix, task))
cnndm_datapipe = cnndm_datapipe.batch(cnndm_batch_size)
cnndm_datapipe = cnndm_datapipe.rows2columnar(["article", "abstract"])
cnndm_dataloader = DataLoader(cnndm_datapipe, batch_size=None)
cnndm_dataloader = DataLoader(cnndm_datapipe, shuffle=True, batch_size=None)

#######################################################################
# Alternately we can also use batched API (i.e apply the prefix on the whole batch)
# Alternately, we can also use batched API, for example, apply the prefix on the whole batch:
#
# ::
# .. code-block::
#
# def batch_prefix(task, x):
# return {
Expand Down Expand Up @@ -179,11 +168,11 @@ def apply_prefix(task, x):
imdb_batch_size = 3
imdb_datapipe = IMDB(split="test")
task = "sst2 sentence"
labels = {"neg": "negative", "pos": "positive"}
labels = {"1": "negative", "2": "positive"}


def process_labels(labels, x):
return x[1], labels[x[0]]
return x[1], labels[str(x[0])]


imdb_datapipe = imdb_datapipe.map(partial(process_labels, labels))
Expand Down Expand Up @@ -224,7 +213,7 @@ def process_labels(labels, x):
beam_size = 1

model_input = transform(input_text)
model_output = sequence_generator.generate(model_input, eos_idx=eos_idx, beam_size=beam_size)
model_output = sequence_generator.generate(model_input, eos_idx=eos_idx, num_beams=beam_size)
output_text = transform.decode(model_output.tolist())

for i in range(cnndm_batch_size):
Expand All @@ -234,21 +223,19 @@ def process_labels(labels, x):


#######################################################################
# Summarization Output
# Summarization Output (Might vary since we shuffle the dataloader)
# --------------------
#
# ::
# .. code-block::
#
# Example 1:
#
# prediction: the Palestinians become the 123rd member of the international criminal
# court . the accession was marked by a ceremony at the Hague, where the court is based .
# the ICC opened a preliminary examination into the situation in the occupied
# Palestinian territory .
# prediction: the 24-year-old has been tattooed for over a decade . he has landed in australia
# to start work on a new campaign . he says he is 'taking it in your stride' to be honest .
#
# target: Membership gives the ICC jurisdiction over alleged crimes committed in
# Palestinian territories since last June . Israel and the United States opposed the
# move, which could open the door to war crimes investigations against Israelis .
# target: London-based model Stephen James Hendry famed for his full body tattoo . The supermodel
# is in Sydney for a new modelling campaign . Australian fans understood to have already located
# him at his hotel . The 24-year-old heartthrob is recently single .
#
#
# Example 2:
Expand Down Expand Up @@ -314,7 +301,7 @@ def process_labels(labels, x):
beam_size = 1

model_input = transform(input_text)
model_output = sequence_generator.generate(model_input, eos_idx=eos_idx, beam_size=beam_size)
model_output = sequence_generator.generate(model_input, eos_idx=eos_idx, num_beams=beam_size)
output_text = transform.decode(model_output.tolist())

for i in range(imdb_batch_size):
Expand Down Expand Up @@ -372,7 +359,7 @@ def process_labels(labels, x):
# really annoying was the constant cuts to VDs daughter during the last fight scene.<br /><br />
# Not bad. Not good. Passable 4.
#
# prediction: negative
# prediction: positive
#
# target: negative
#
Expand All @@ -399,16 +386,15 @@ def process_labels(labels, x):
# ---------------------
#
# Finally, we can also use the model to generate English to German translations on the first batch of examples from the Multi30k
# test set using a beam size of 4.
# test set.
#

batch = next(iter(multi_dataloader))
input_text = batch["english"]
target = batch["german"]
beam_size = 4

model_input = transform(input_text)
model_output = sequence_generator.generate(model_input, eos_idx=eos_idx, beam_size=beam_size)
model_output = sequence_generator.generate(model_input, eos_idx=eos_idx, num_beams=beam_size)
output_text = transform.decode(model_output.tolist())

for i in range(multi_batch_size):
Expand Down