Add SpeechEncoderDecoder & Speech2Text2 (#13186)

* fix_torch_device_generate_test * remove @ * up * correct some bugs * correct model * finish speech2text extension * up * up * up * up * Update utils/custom_init_isort.py * up * up * update with tokenizer * correct old tok * correct old tok * fix bug * up * up * add more tests * up * fix docs * up * fix some more tests * add better config * correct some more things " * fix tests * improve docs * Apply suggestions from code review * Apply suggestions from code review * final fixes * finalize * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * apply suggestions Lysandre and Sylvain * apply nicos suggestions * upload everything * finish Co-authored-by: Patrick von Platen <patrick@huggingface.co> Co-authored-by: your_github_username <your_github_email> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
2021-09-01 13:33:31 +02:00
parent 9396b40433
commit 0b8c84e110
30 changed files with 3649 additions and 73 deletions
--- a/tests/test_modeling_speech_to_text_2.py
+++ b/tests/test_modeling_speech_to_text_2.py
@@ -0,0 +1,211 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. 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.
+""" Testing suite for the PyTorch Speech2Text model. """
+
+import unittest
+
+from transformers import Speech2Text2Config
+from transformers.testing_utils import is_torch_available, require_torch, torch_device
+
+from .test_configuration_common import ConfigTester
+from .test_generation_utils import GenerationTesterMixin
+from .test_modeling_common import ModelTesterMixin, ids_tensor
+
+
+if is_torch_available():
+    import torch
+
+    from transformers.models.speech_to_text_2.modeling_speech_to_text_2 import (
+        Speech2Text2Decoder,
+        Speech2Text2ForCausalLM,
+    )
+
+
+@require_torch
+class Speech2Text2StandaloneDecoderModelTester:
+    def __init__(
+        self,
+        parent,
+        vocab_size=99,
+        batch_size=13,
+        d_model=16,
+        decoder_seq_length=7,
+        is_training=True,
+        is_decoder=True,
+        use_attention_mask=True,
+        use_cache=False,
+        use_labels=True,
+        decoder_start_token_id=2,
+        decoder_ffn_dim=32,
+        decoder_layers=4,
+        decoder_attention_heads=4,
+        max_position_embeddings=30,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.decoder_seq_length = decoder_seq_length
+        # For common tests
+        self.seq_length = self.decoder_seq_length
+        self.is_training = is_training
+        self.use_attention_mask = use_attention_mask
+        self.use_labels = use_labels
+
+        self.vocab_size = vocab_size
+        self.d_model = d_model
+        self.hidden_size = d_model
+        self.num_hidden_layers = decoder_layers
+        self.decoder_layers = decoder_layers
+        self.decoder_ffn_dim = decoder_ffn_dim
+        self.decoder_attention_heads = decoder_attention_heads
+        self.num_attention_heads = decoder_attention_heads
+        self.eos_token_id = eos_token_id
+        self.bos_token_id = bos_token_id
+        self.pad_token_id = pad_token_id
+        self.decoder_start_token_id = decoder_start_token_id
+        self.use_cache = use_cache
+        self.max_position_embeddings = max_position_embeddings
+
+        self.scope = None
+        self.decoder_key_length = decoder_seq_length
+        self.base_model_out_len = 2
+        self.decoder_attention_idx = 1
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
+
+        attention_mask = None
+        if self.use_attention_mask:
+            attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
+
+        lm_labels = None
+        if self.use_labels:
+            lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
+
+        config = Speech2Text2Config(
+            vocab_size=self.vocab_size,
+            d_model=self.d_model,
+            decoder_layers=self.decoder_layers,
+            decoder_ffn_dim=self.decoder_ffn_dim,
+            decoder_attention_heads=self.decoder_attention_heads,
+            eos_token_id=self.eos_token_id,
+            bos_token_id=self.bos_token_id,
+            use_cache=self.use_cache,
+            pad_token_id=self.pad_token_id,
+            decoder_start_token_id=self.decoder_start_token_id,
+            max_position_embeddings=self.max_position_embeddings,
+        )
+
+        return (
+            config,
+            input_ids,
+            attention_mask,
+            lm_labels,
+        )
+
+    def create_and_check_decoder_model_past(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+        lm_labels,
+    ):
+        config.use_cache = True
+        model = Speech2Text2Decoder(config=config).to(torch_device).eval()
+        input_ids = input_ids[:2]
+
+        input_ids[input_ids == 0] += 1
+        # first forward pass
+        outputs = model(input_ids, use_cache=True)
+        outputs_use_cache_conf = model(input_ids)
+        outputs_no_past = model(input_ids, use_cache=False)
+
+        self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
+        self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
+
+        past_key_values = outputs["past_key_values"]
+
+        # create hypothetical next token and extent to next_input_ids
+        next_tokens = ids_tensor((2, 1), config.vocab_size - 1) + 1
+
+        # append to next input_ids and
+        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+
+        print(next_input_ids)
+
+        output_from_no_past = model(next_input_ids)["last_hidden_state"]
+        output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
+        output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
+        output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
+
+        # test that outputs are equal for slice
+        assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            attention_mask,
+            lm_labels,
+        ) = config_and_inputs
+
+        inputs_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class Speech2Text2StandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+    all_model_classes = (Speech2Text2Decoder, Speech2Text2ForCausalLM) if is_torch_available() else ()
+    all_generative_model_classes = (Speech2Text2ForCausalLM,) if is_torch_available() else ()
+    test_pruning = False
+
+    def setUp(
+        self,
+    ):
+        self.model_tester = Speech2Text2StandaloneDecoderModelTester(self, is_training=False)
+        self.config_tester = ConfigTester(self, config_class=Speech2Text2Config)
+
+    # not implemented currently
+    def test_inputs_embeds(self):
+        pass
+
+    # speech2text2 has no base model
+    def test_save_load_fast_init_from_base(self):
+        pass
+
+    # speech2text2 has no base model
+    def test_save_load_fast_init_to_base(self):
+        pass
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_decoder_model_past(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
+
+    # decoder cannot keep gradients
+    def test_retain_grad_hidden_states_attentions(self):
+        return