Update the example of exporting Bart + BeamSearch to ONNX module to r…

…esolve comments. (huggingface#14310)

* Update code to resolve comments left in previous PR.

* Add README.md file for this example.

* Update examples/onnx/pytorch/translation/README.md

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update examples/onnx/pytorch/translation/README.md

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update examples/onnx/pytorch/translation/README.md

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update README.md file to resolve comments.

* Add a section name.

* Update examples/onnx/pytorch/translation/README.md

Co-authored-by: Gary Miguel <garymm@garymm.org>

* Add more comments for _convert_past_list_to_tuple().

* Change the default file name to a consistent one.

* Fix a format issue.

* Update examples/onnx/pytorch/translation/README.md

Co-authored-by: Gary Miguel <garymm@garymm.org>

* Update examples/onnx/pytorch/translation/run_onnx_exporter.py

Co-authored-by: Gary Miguel <garymm@garymm.org>

* Update examples/onnx/pytorch/translation/README.md

Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>

* Change the folder to summarization and address some other coments.

* Update the torch version.

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Gary Miguel <garymm@garymm.org>
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>

examples/onnx/pytorch/summarization/README.md

@@ -0,0 +1,43 @@
+<!---
+Copyright 2021 The HuggingFace Team. All rights reserved.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Bart + Beam Search to ONNX
+
+
+
+This folder contains an example of exporting Bart + Beam Search generation (`BartForConditionalGeneration`) to ONNX.
+
+Beam Search contains a for-loop workflow, so we need to make them TorchScript-compatible for exporting to ONNX. This example shows how to make a Bart model be TorchScript-compatible by wrapping up it into a new model. In addition, some changes were made to the `beam_search()` function to make it TorchScript-compatible.
+
+
+## How to run the example
+
+To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
+
+```bash
+git clone https://github.com/huggingface/transformers
+cd transformers
+pip install .
+```
+Then cd in this example folder and run
+```bash
+pip install -r requirements.txt
+```
+
+Now you can run the example command below to get the example ONNX file:
+
+```bash
+python run_onnx_exporter.py --model_name_or_path facebook/bart-base
+```

examples/onnx/pytorch/translation/bart_onnx/generation_onnx.py → examples/onnx/pytorch/summarization/bart_onnx/generation_onnx.py

@@ -1,4 +1,5 @@
 import copy
+import itertools
 from typing import List, Optional, Tuple
 
 import torch
@@ -8,23 +9,23 @@
 from transformers.generation_utils import GenerationMixin
 
 
-def flatten_list(past):
-    values = []
-    if past is not None:
-        for i, p in enumerate(past):
-            for j, q in enumerate(p):
-                values.append(q)
-
-    return values
-
+def _convert_past_list_to_tuple(past_key_values):
+    """
+    In Bart model, the type of past_key_values is tuple(tuple(torch.FloatTensor)) which is not
+    TorchScript-compatible. To support this, we have to convert it during the export process.
+    This function will convert past values from a list to tuple(tuple(torch.FloatTensor)) for
+    the inner decoder.
 
-def list_to_tuple(past):
+    According to the definition of past_key_values, each inner tuple(torch.FloatTensor) has 4 tensors,
+    so we convert every 4 elements in the list as a tuple(torch.FloatTensor).
+    """
+    count_of_each_inner_tuple = 4
     results = ()
     temp_result = ()
-    count_n = len(past) // 4
+    count_n = len(past_key_values) // count_of_each_inner_tuple
     for idx in range(count_n):
-        real_idx = idx * 4
-        temp_result = tuple(past[real_idx : real_idx + 4])
+        real_idx = idx * count_of_each_inner_tuple
+        temp_result = tuple(past_key_values[real_idx : real_idx + count_of_each_inner_tuple])
         results += ((temp_result),)
 
     return results
@@ -51,7 +52,7 @@ def __init__(self, decoder):
     def forward(self, input_ids, encoder_state, attention_mask, past=None):
         all_results = None
         if past is not None:
-            all_results = list_to_tuple(past)
+            all_results = _convert_past_list_to_tuple(past)
             input_ids = input_ids[:, -1:]
 
         last_hidden_state, past_key_values = self.decoder(
@@ -68,28 +69,33 @@ def forward(self, input_ids, encoder_state, attention_mask, past=None):
         return last_hidden_state, past_values
 
 
-def create_traced_encoder(encoder, input_ids, attention_mask):
+def _create_traced_encoder(encoder, input_ids, attention_mask):
     encoder_c = copy.deepcopy(encoder)
     encoder_for_onnx = EncoderForONNX(encoder_c)
 
-    # return torch.jit.trace(encoder, (input_ids, attention_mask))
     return torch.jit.trace(encoder_for_onnx, (input_ids, attention_mask))
 
 
-def create_traced_decoder(decoder, input_ids, encoder_state, attention_mask, past=None):
+def _create_traced_decoder(decoder, input_ids, encoder_state, attention_mask, past=None):
     decoder_c = copy.deepcopy(decoder)
     decoder_for_onnx = DecoderForONNX(decoder_c)
-    past_values = flatten_list(past)
+    past_values = list(itertools.chain.from_iterable(past or ()))
 
     # Do this twice so we got 2 different decoders for further work.
-    if past_values is None or len(past_values) == 0:
-        return torch.jit.trace(decoder_for_onnx, (input_ids, encoder_state, attention_mask))
-    else:
+    if past_values:
         return torch.jit.trace(decoder_for_onnx, (input_ids, encoder_state, attention_mask, past_values))
+    else:
+        return torch.jit.trace(decoder_for_onnx, (input_ids, encoder_state, attention_mask))
 
 
 class BartConfigTS(BartConfig, torch.nn.Module):
-    def init_module(self):
+    """
+    BartConfigTS is a TorchScript-compatible transformers.models.bart.configuration_bart.BartConfig.
+    TorchScript only supports sub-classes of torch.nn.Module.
+    """
+
+    def __init__(self, config):
+        BartConfig.__init__(self, config)
         torch.nn.Module.__init__(self)
 
 
@@ -127,7 +133,6 @@ class BARTGenerator(torch.nn.Module, GenerationMixin):
     def __init__(self, model):
         super().__init__()
         self.config = BartConfigTS(model.config)
-        self.config.init_module()
         self.config.force_bos_token_to_be_generated = False
         self._trace_modules(model)
         self.logits_processor = MinLengthLogitsProcessorTS(self.config.min_length, self.config.eos_token_id)
@@ -136,7 +141,6 @@ def __init__(self, model):
         self.decoder_layers = model.config.decoder_layers
 
     def _trace_modules(self, model):
-        # Be aware of the last one 2 should be kept.
         input_ids = torch.tensor(
             [
                 [
@@ -200,89 +204,25 @@ def _trace_modules(self, model):
                     57,
                     8629,
                     5,
-                    2,
+                    model.config.eos_token_id,
                 ]
             ],
             device=model.device,
             dtype=torch.long,
         )
         attention_mask = torch.tensor(
-            [
-                [
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                    True,
-                ]
-            ],
+            [[True] * input_ids.shape[-1]],
             device=model.device,
             dtype=torch.bool,
         )
-        self.encoder = create_traced_encoder(model.get_encoder(), input_ids, attention_mask)
+        self.encoder = _create_traced_encoder(model.get_encoder(), input_ids, attention_mask)
         encoder_outputs = model.get_encoder()(input_ids, attention_mask=attention_mask, return_dict=True)
         decoder = model.model.decoder
         decoder_outputs = decoder(input_ids, attention_mask, encoder_outputs["last_hidden_state"], None, None, None)
-        self.decoder_no_past = create_traced_decoder(
+        self.decoder_no_past = _create_traced_decoder(
             model.model.decoder, input_ids, encoder_outputs["last_hidden_state"], attention_mask
         )
-        self.decoder_with_past = create_traced_decoder(
+        self.decoder_with_past = _create_traced_decoder(
             model.model.decoder, input_ids, encoder_outputs["last_hidden_state"], attention_mask, decoder_outputs[1]
         )
 
@@ -414,8 +354,8 @@ def __init__(self):
         self._beam_hyps_count = torch.zeros(self.batch_size, dtype=torch.long)
         self._beam_hyps_worst_scores = torch.zeros(self.batch_size) + 1e9
         self._beam_hyps_max_length: int = self.max_length - 1
-        self._beam_hyps: List[torch.Tensor] = [torch.zeros(2)]  # placeholder for TorchScript compatible
-        self._beam_scores: List[torch.Tensor] = [torch.zeros(2)]  # placeholder for TorchScript compatible
+        self._beam_hyps: List[torch.Tensor] = [torch.zeros(2)]  # placeholder for TorchScript compatibility
+        self._beam_scores: List[torch.Tensor] = [torch.zeros(2)]  # placeholder for TorchScript compatibility
 
     def is_done(self) -> torch.Tensor:
         return self._done.all()
@@ -474,11 +414,11 @@ def hypo_add(self, hyp: torch.Tensor, sum_logprobs: float, hypo_idx: int):
         score = sum_logprobs / (hyp.shape[-1] ** self.length_penalty)
         hyps_count = self.hypo_len(hypo_idx)
         if hyps_count < self.num_beams or score > self._beam_hyps_worst_scores[hypo_idx]:
-            # NOTE: work around difference of torch.sum(empty_tensor) = 0, while error in onnx.
+            # NOTE: work around difference of torch.sum(empty_tensor) == 0, while error in onnx.
+            # Bug: https://msdata.visualstudio.com/Vienna/_workitems/edit/1486599
             beam_idx = (
                 torch.sum(self._beam_hyps_count[:hypo_idx]) if hypo_idx != 0 else torch.tensor(0, dtype=torch.long)
             )
-            # beam_idx = torch.sum(_beam_hyps_count[:hypo_idx])
             self._beam_scores.insert(beam_idx, torch.tensor([score]))
             self._beam_hyps.insert(beam_idx, hyp)
             if hyps_count + 1 > self.num_beams:
@@ -605,7 +545,7 @@ def finalize(
                 self.hypo_add(final_tokens, final_score, batch_idx)
 
         # select the best hypotheses
-        # NOTE: new is not scriptable
+        # NOTE: torch.Tensor.new_zeros() is not scriptable
         sent_lengths = torch.zeros(batch_size * self.num_beam_hyps_to_keep, dtype=torch.long)
         best = []
         best_scores = torch.zeros(
@@ -782,7 +722,6 @@ def forward(self, input_ids, attention_mask, num_beams, max_length, decoder_star
             bos_token_id=bos_token_id,
         )
 
-        # from generation_utils.py
         batch_size = input_ids.shape[0]
 
         length_penalty = self.config.length_penalty

examples/onnx/pytorch/translation/bart_onnx/reduce_onnx_size.py → examples/onnx/pytorch/summarization/bart_onnx/reduce_onnx_size.py

@@ -1,11 +1,15 @@
+"""
+Code to remove duplicate initializers to reduce ONNX model size.
+"""
+
 import os
 
 import numpy
 
 import onnx
 
 
-def is_equal_tensor_proto(a, b):
+def _is_equal_tensor_proto(a, b):
     name_a = a.name
     name_b = b.name
 
@@ -20,25 +24,25 @@ def is_equal_tensor_proto(a, b):
     return res
 
 
-def node_replace_input_with(node_proto, name, new_name):
+def _node_replace_input_with(node_proto, name, new_name):
     for i, input_name in enumerate(node_proto.input):
         if input_name == name:
             node_proto.input.insert(i, new_name)
             node_proto.input.pop(i + 1)
 
     if node_proto.op_type == "If":
-        graph_replace_input_with(node_proto.attribute[0].g, name, new_name)
-        graph_replace_input_with(node_proto.attribute[1].g, name, new_name)
+        _graph_replace_input_with(node_proto.attribute[0].g, name, new_name)
+        _graph_replace_input_with(node_proto.attribute[1].g, name, new_name)
     if node_proto.op_type == "Loop":
-        graph_replace_input_with(node_proto.attribute[0].g, name, new_name)
+        _graph_replace_input_with(node_proto.attribute[0].g, name, new_name)
 
 
-def graph_replace_input_with(graph_proto, name, new_name):
+def _graph_replace_input_with(graph_proto, name, new_name):
     for n in graph_proto.node:
-        node_replace_input_with(n, name, new_name)
+        _node_replace_input_with(n, name, new_name)
 
 
-def remove_dup_initializers_from_model(model, model_without_ext, ind_to_replace):
+def _remove_dup_initializers_from_model(model, model_without_ext, ind_to_replace):
     inits_with_data = [i for i in model.graph.initializer]
     inits = [i for i in model_without_ext.graph.initializer]
     for i, ref_i in ind_to_replace:
@@ -52,10 +56,15 @@ def remove_dup_initializers_from_model(model, model_without_ext, ind_to_replace)
         model_without_ext.graph.initializer.remove(inits[i])
 
         # for n in model.graph.node:
-        graph_replace_input_with(model_without_ext.graph, name_i, name_ref)
+        _graph_replace_input_with(model_without_ext.graph, name_i, name_ref)
 
 
 def remove_dup_initializers(onnx_file_path):
+    """
+    Removes duplicate initializers from the model to reduce its size.
+    Writes a new file in the same directory as onnx_file_path and returns the path to that file.
+    """
+
     model_file_folder = os.path.dirname(onnx_file_path)
     model_file_name = os.path.basename(onnx_file_path)
 
@@ -76,7 +85,7 @@ def remove_dup_initializers(onnx_file_path):
         for j in range(i + 1, len(inits)):
             if j in dup_set:
                 continue
-            if is_equal_tensor_proto(inits[i], inits[j]):
+            if _is_equal_tensor_proto(inits[i], inits[j]):
                 dup_set.add(i)
                 dup_set.add(j)
 
@@ -103,8 +112,8 @@ def remove_dup_initializers(onnx_file_path):
 
     print("total reduced size: ", total_reduced_size / 1024 / 1024 / 1024, "GB")
 
-    ind_to_replace = sorted(ind_to_replace, key=lambda x: x[0])
-    remove_dup_initializers_from_model(model, model, ind_to_replace)
+    ind_to_replace = sorted(ind_to_replace)
+    _remove_dup_initializers_from_model(model, model, ind_to_replace)
 
     optimized_model_file_name = "optimized_" + model_file_name
     new_model = os.path.join(model_file_folder, optimized_model_file_name)

examples/onnx/pytorch/summarization/requirements.txt

@@ -0,0 +1 @@
+torch >= 1.10