476 lines
18 KiB
Python
476 lines
18 KiB
Python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch Xcodec model."""
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import inspect
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import json
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import math
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import os
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import tempfile
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import unittest
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from pathlib import Path
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import numpy as np
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from datasets import Audio, load_dataset
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from parameterized import parameterized
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from tests.test_configuration_common import ConfigTester
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from tests.test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
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from transformers import AutoFeatureExtractor, XcodecConfig
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from transformers.testing_utils import (
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is_torch_available,
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require_torch,
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slow,
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torch_device,
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)
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if is_torch_available():
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import torch
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from transformers import DacConfig, HubertConfig, XcodecModel
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@require_torch
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class XcodecModelTester:
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def __init__(
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self,
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parent,
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batch_size=4,
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num_channels=1,
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sample_rate=16000,
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codebook_size=1024,
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num_samples=256,
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is_training=False,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.sample_rate = sample_rate
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self.codebook_size = codebook_size
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self.is_training = is_training
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self.num_samples = num_samples
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self.acoustic_model_config = DacConfig(
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decoder_hidden_size=8, encoder_hidden_size=8, codebook_size=16, downsampling_ratios=[16, 16]
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)
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self.semantic_model_config = HubertConfig(
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=2,
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intermediate_size=12,
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conv_dim=(4, 4, 4, 4, 4, 4, 4),
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)
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def prepare_config_and_inputs(self):
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config = self.get_config()
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inputs_dict = {
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"input_values": floats_tensor([self.batch_size, self.num_channels, self.num_samples], scale=1.0)
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}
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return config, inputs_dict
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def prepare_config_and_inputs_for_common(self):
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config, inputs_dict = self.prepare_config_and_inputs()
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return config, inputs_dict
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def prepare_config_and_inputs_for_model_class(self, model_class):
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config, inputs_dict = self.prepare_config_and_inputs()
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codes_length = math.ceil(self.num_samples / config.hop_length)
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inputs_dict["audio_codes"] = ids_tensor(
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[self.batch_size, config.num_quantizers, codes_length], config.codebook_size
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)
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return config, inputs_dict
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def get_config(self):
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return XcodecConfig(
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sample_rate=self.sample_rate,
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audio_channels=self.num_channels,
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codebook_size=self.codebook_size,
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acoustic_model_config=self.acoustic_model_config,
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semantic_model_config=self.semantic_model_config,
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)
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def create_and_check_model_forward(self, config, inputs_dict):
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model = XcodecModel(config=config).to(torch_device).eval()
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result = model(input_values=inputs_dict["input_values"])
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self.parent.assertEqual(result.audio_values.shape, (self.batch_size, self.num_channels, self.num_samples))
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@require_torch
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class XcodecModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (XcodecModel,) if is_torch_available() else ()
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is_encoder_decoder = True
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test_pruning = False
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test_headmasking = False
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test_resize_embeddings = False
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test_torchscript = False
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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# model does not support returning hidden states
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if "output_attentions" in inputs_dict:
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inputs_dict.pop("output_attentions")
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if "output_hidden_states" in inputs_dict:
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inputs_dict.pop("output_hidden_states")
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return inputs_dict
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def setUp(self):
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self.model_tester = XcodecModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=XcodecConfig, common_properties=[], has_text_modality=False
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)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model_forward(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model_forward(*config_and_inputs)
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["input_values", "audio_codes", "bandwidth", "return_dict"]
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self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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def test_gradient_checkpointing_backward_compatibility(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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if not model_class.supports_gradient_checkpointing:
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continue
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config.text_encoder.gradient_checkpointing = True
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config.audio_encoder.gradient_checkpointing = True
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config.decoder.gradient_checkpointing = True
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model = model_class(config)
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self.assertTrue(model.is_gradient_checkpointing)
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@unittest.skip(reason="The XcodecModel does not have `inputs_embeds` logics")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="The XcodecModel does not have `inputs_embeds` logics")
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def test_model_get_set_embeddings(self):
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pass
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@unittest.skip(reason="The XcodecModel does not have the usual `attention` logic")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip(reason="The XcodecModel does not have the usual `attention` logic")
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def test_torchscript_output_attentions(self):
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pass
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@unittest.skip(reason="The XcodecModel does not have the usual `hidden_states` logic")
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def test_torchscript_output_hidden_state(self):
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pass
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# Copied from transformers.tests.encodec.test_modeling_encodec.XcodecModelTest._create_and_check_torchscript
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def _create_and_check_torchscript(self, config, inputs_dict):
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if not self.test_torchscript:
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self.skipTest(reason="test_torchscript is set to False")
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configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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configs_no_init.torchscript = True
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configs_no_init.return_dict = False
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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model.to(torch_device)
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model.eval()
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inputs = self._prepare_for_class(inputs_dict, model_class)
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main_input_name = model_class.main_input_name
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try:
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main_input = inputs[main_input_name]
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model(main_input)
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traced_model = torch.jit.trace(model, main_input)
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except RuntimeError:
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self.fail("Couldn't trace module.")
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
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try:
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torch.jit.save(traced_model, pt_file_name)
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except Exception:
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self.fail("Couldn't save module.")
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try:
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loaded_model = torch.jit.load(pt_file_name)
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except Exception:
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self.fail("Couldn't load module.")
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model.to(torch_device)
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model.eval()
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loaded_model.to(torch_device)
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loaded_model.eval()
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model_state_dict = model.state_dict()
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loaded_model_state_dict = loaded_model.state_dict()
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non_persistent_buffers = {}
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for key in loaded_model_state_dict.keys():
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if key not in model_state_dict.keys():
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non_persistent_buffers[key] = loaded_model_state_dict[key]
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loaded_model_state_dict = {
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key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
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}
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self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
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model_buffers = list(model.buffers())
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for non_persistent_buffer in non_persistent_buffers.values():
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found_buffer = False
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for i, model_buffer in enumerate(model_buffers):
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if torch.equal(non_persistent_buffer, model_buffer):
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found_buffer = True
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break
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self.assertTrue(found_buffer)
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model_buffers.pop(i)
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model_buffers = list(model.buffers())
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for non_persistent_buffer in non_persistent_buffers.values():
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found_buffer = False
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for i, model_buffer in enumerate(model_buffers):
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if torch.equal(non_persistent_buffer, model_buffer):
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found_buffer = True
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break
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self.assertTrue(found_buffer)
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model_buffers.pop(i)
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models_equal = True
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for layer_name, p1 in model_state_dict.items():
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if layer_name in loaded_model_state_dict:
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p2 = loaded_model_state_dict[layer_name]
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if p1.data.ne(p2.data).sum() > 0:
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models_equal = False
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self.assertTrue(models_equal)
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# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
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# (Even with this call, there are still memory leak by ~0.04MB)
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self.clear_torch_jit_class_registry()
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@unittest.skip(reason="The XcodecModel does not have the usual `attention` logic")
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def test_attention_outputs(self):
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pass
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@unittest.skip(reason="The XcodecModel does not have the usual `hidden_states` logic")
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def test_hidden_states_output(self):
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pass
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# Copied from transformers.tests.encodec.test_modeling_encodecEncodecModelTest.test_determinism
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def test_determinism(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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def check_determinism(first, second):
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# outputs are not tensors but list (since each sequence don't have the same frame_length)
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out_1 = first.cpu().numpy()
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out_2 = second.cpu().numpy()
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out_1 = out_1[~np.isnan(out_1)]
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out_2 = out_2[~np.isnan(out_2)]
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
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second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
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if isinstance(first, tuple) and isinstance(second, tuple):
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for tensor1, tensor2 in zip(first, second):
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check_determinism(tensor1, tensor2)
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else:
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check_determinism(first, second)
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# Copied from transformers.tests.encodec.test_modeling_encodecEncodecModelTest.test_model_outputs_equivalence
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def test_model_outputs_equivalence(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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def set_nan_tensor_to_zero(t):
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t[t != t] = 0
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return t
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def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
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with torch.no_grad():
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tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
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dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs)
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self.assertTrue(isinstance(tuple_output, tuple))
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self.assertTrue(isinstance(dict_output, dict))
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for tuple_value, dict_value in zip(tuple_output, dict_output.values()):
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self.assertTrue(
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torch.allclose(
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set_nan_tensor_to_zero(tuple_value), set_nan_tensor_to_zero(dict_value), atol=1e-5
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),
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msg=(
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"Tuple and dict output are not equal. Difference:"
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f" {torch.max(torch.abs(tuple_value - dict_value))}. Tuple has `nan`:"
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f" {torch.isnan(tuple_value).any()} and `inf`: {torch.isinf(tuple_value)}. Dict has"
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f" `nan`: {torch.isnan(dict_value).any()} and `inf`: {torch.isinf(dict_value)}."
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),
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)
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class)
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check_equivalence(model, tuple_inputs, dict_inputs)
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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# skipping the parametrizations original0 tensor
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if name == "semantic_model.encoder.pos_conv_embed.conv.parametrizations.weight.original0":
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continue
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uniform_init_parms = ["conv"]
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if param.requires_grad:
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if any(x in name for x in uniform_init_parms):
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self.assertTrue(
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-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
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msg=f"Parameter {name} of {model_class.__name__} seems not properly initialized",
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)
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@unittest.skip(reason="The XcodecModel does not have support dynamic compile yet")
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def test_sdpa_can_compile_dynamic(self):
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pass
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# Copied from transformers.tests.encodec.test_modeling_encodec.normalize
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def normalize(arr):
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norm = np.linalg.norm(arr)
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normalized_arr = arr / norm
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return normalized_arr
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# Copied from transformers.tests.encodec.test_modeling_encodec.compute_rmse
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def compute_rmse(arr1, arr2):
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arr1_np = arr1.cpu().numpy().squeeze()
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arr2_np = arr2.cpu().numpy().squeeze()
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max_length = min(arr1.shape[-1], arr2.shape[-1])
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arr1_np = arr1_np[..., :max_length]
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arr2_np = arr2_np[..., :max_length]
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arr1_normalized = normalize(arr1_np)
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arr2_normalized = normalize(arr2_np)
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return np.sqrt(((arr1_normalized - arr2_normalized) ** 2).mean())
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"""
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Integration tests for XCodec
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Code for reproducing expected outputs can be found here:
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https://gist.github.com/ebezzam/cdaf8c223e59e7677b2ea6bc2dc8230b
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One reason for higher tolerances is because of different implementation of `Snake1d` within Transformer version DAC
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See here: https://github.com/huggingface/transformers/pull/39793#issue-3277407384
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"""
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RESULTS_PATH = Path(__file__).parent.parent.parent / "fixtures/xcodec/integration_tests.json"
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with open(RESULTS_PATH, "r") as f:
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raw_data = json.load(f)
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# convert dicts into tuples ordered to match test args
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EXPECTED_OUTPUTS_JSON = [
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(
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f"{d['repo_id']}_{d['bandwidth']}",
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d["repo_id"],
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d["bandwidth"],
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d["codes"],
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d["decoded"],
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d["codec_error"],
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d["codec_tol"],
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d["dec_tol"],
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)
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for d in raw_data
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]
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@slow
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@require_torch
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class XcodecIntegrationTest(unittest.TestCase):
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@parameterized.expand(EXPECTED_OUTPUTS_JSON)
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def test_integration(
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self, test_name, repo_id, bandwidth, exp_codes, exp_decoded, exp_codec_err, codec_tol, dec_tol
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):
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# load model
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model = XcodecModel.from_pretrained(repo_id).to(torch_device).eval()
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feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
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# load audio example
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librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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librispeech_dummy = librispeech_dummy.cast_column(
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"audio", Audio(sampling_rate=feature_extractor.sampling_rate)
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)
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audio_array = librispeech_dummy[0]["audio"]["array"]
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inputs = feature_extractor(
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raw_audio=audio_array, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt"
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).to(torch_device)
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x = inputs["input_values"]
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with torch.no_grad():
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ENC_TOL = 0
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audio_codes = model.encode(x, bandwidth=bandwidth, return_dict=False)
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if exp_codes is not None:
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exp_codes = torch.tensor(exp_codes).to(torch_device)
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torch.testing.assert_close(
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audio_codes[..., : exp_codes.shape[-1]],
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exp_codes,
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rtol=ENC_TOL,
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atol=ENC_TOL,
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)
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# dec_tol = 1e-5 # increased to 1e-4 for passing on 4 kbps
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input_values_dec = model.decode(audio_codes).audio_values
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if exp_decoded is not None:
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exp_decoded = torch.tensor(exp_decoded).to(torch_device)
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torch.testing.assert_close(
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input_values_dec[..., : exp_decoded.shape[-1]],
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exp_decoded,
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rtol=dec_tol,
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atol=dec_tol,
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)
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# compute codec error
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codec_err = compute_rmse(input_values_dec, x)
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torch.testing.assert_close(codec_err, exp_codec_err, rtol=codec_tol, atol=codec_tol)
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# make sure forward and decode gives same result
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audio_values_enc_dec = model(x, bandwidth=bandwidth).audio_values
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torch.testing.assert_close(input_values_dec, audio_values_enc_dec, rtol=1e-6, atol=1e-6)
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