# coding=utf-8 # Copyright 2024 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 Mimi model.""" import inspect import os import tempfile import unittest import numpy as np from datasets import Audio, load_dataset from pytest import mark from transformers import AutoFeatureExtractor, MimiConfig from transformers.testing_utils import ( is_flaky, is_torch_available, require_flash_attn, require_torch, require_torch_gpu, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor if is_torch_available(): import torch from transformers import MimiModel # Copied from transformers.tests.encodec.test_modeling_encodec.prepare_inputs_dict def prepare_inputs_dict( config, input_ids=None, input_values=None, decoder_input_ids=None, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if input_ids is not None: encoder_dict = {"input_ids": input_ids} else: encoder_dict = {"input_values": input_values} decoder_dict = {"decoder_input_ids": decoder_input_ids} if decoder_input_ids is not None else {} return {**encoder_dict, **decoder_dict} @require_torch class MimiModelTester: def __init__( self, parent, batch_size=5, num_channels=1, is_training=False, intermediate_size=40, hidden_size=32, num_filters=8, num_residual_layers=1, upsampling_ratios=[8, 4], codebook_size=64, vector_quantization_hidden_dimension=64, codebook_dim=64, upsample_groups=32, num_hidden_layers=2, num_attention_heads=2, num_key_value_heads=2, sliding_window=4, use_cache=False, ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.is_training = is_training self.intermediate_size = intermediate_size self.hidden_size = hidden_size self.num_filters = num_filters self.num_residual_layers = num_residual_layers self.upsampling_ratios = upsampling_ratios self.codebook_size = codebook_size self.vector_quantization_hidden_dimension = vector_quantization_hidden_dimension self.codebook_dim = codebook_dim self.upsample_groups = upsample_groups self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.sliding_window = sliding_window self.use_cache = use_cache def prepare_config_and_inputs(self): input_values = floats_tensor([self.batch_size, self.num_channels, self.intermediate_size], scale=1.0) config = self.get_config() inputs_dict = {"input_values": input_values} return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def prepare_config_and_inputs_for_model_class(self, model_class): config, inputs_dict = self.prepare_config_and_inputs() inputs_dict["audio_codes"] = ids_tensor([self.batch_size, 1, self.num_channels], self.codebook_size).type( torch.int32 ) return config, inputs_dict def get_config(self): return MimiConfig( audio_channels=self.num_channels, chunk_in_sec=None, hidden_size=self.hidden_size, num_filters=self.num_filters, num_residual_layers=self.num_residual_layers, upsampling_ratios=self.upsampling_ratios, codebook_size=self.codebook_size, vector_quantization_hidden_dimension=self.vector_quantization_hidden_dimension, upsample_groups=self.upsample_groups, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_heads, sliding_window=self.sliding_window, codebook_dim=self.codebook_dim, use_cache=self.use_cache, ) def create_and_check_model_forward(self, config, inputs_dict): model = MimiModel(config=config).to(torch_device).eval() input_values = inputs_dict["input_values"] result = model(input_values) self.parent.assertEqual( result.audio_values.shape, (self.batch_size, self.num_channels, self.intermediate_size) ) @require_torch class MimiModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (MimiModel,) if is_torch_available() else () is_encoder_decoder = True test_pruning = False test_headmasking = False test_resize_embeddings = False test_torchscript = False def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): # model does support returning hidden states inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if "output_attentions" in inputs_dict: inputs_dict.pop("output_attentions") if "output_hidden_states" in inputs_dict: inputs_dict.pop("output_hidden_states") return inputs_dict def setUp(self): self.model_tester = MimiModelTester(self) self.config_tester = ConfigTester( self, config_class=MimiConfig, hidden_size=37, common_properties=[], has_text_modality=False ) def test_config(self): self.config_tester.run_common_tests() def test_model_forward(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_forward(*config_and_inputs) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["input_values", "padding_mask", "num_quantizers"] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @unittest.skip(reason="The MimiModel does not have `inputs_embeds` logics") def test_inputs_embeds(self): pass @unittest.skip(reason="The MimiModel does not have `inputs_embeds` logics") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="The MimiModel does not have the usual `attention` logic") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="The MimiModel does not have the usual `attention` logic") def test_torchscript_output_attentions(self): pass @unittest.skip(reason="The MimiModel does not have the usual `hidden_states` logic") def test_torchscript_output_hidden_state(self): pass # Copied from transformers.tests.encodec.test_modeling_encodec.MimiModelTest._create_and_check_torchscript def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: self.skipTest(reason="test_torchscript is set to False") configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() inputs = self._prepare_for_class(inputs_dict, model_class) main_input_name = model_class.main_input_name try: main_input = inputs[main_input_name] model(main_input) traced_model = torch.jit.trace(model, main_input) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): if layer_name in loaded_model_state_dict: p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) # Avoid memory leak. Without this, each call increase RAM usage by ~20MB. # (Even with this call, there are still memory leak by ~0.04MB) self.clear_torch_jit_class_registry() @unittest.skip(reason="The MimiModel does not have the usual `attention` logic") def test_attention_outputs(self): pass @unittest.skip(reason="The MimiModel does not have the usual `hidden_states` logic") def test_hidden_states_output(self): pass # Copied from transformers.tests.encodec.test_modeling_encodec.MimiModelTest.test_determinism def test_determinism(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_determinism(first, second): # outputs are not tensors but list (since each sequence don't have the same frame_length) out_1 = first.cpu().numpy() out_2 = second.cpu().numpy() out_1 = out_1[~np.isnan(out_1)] out_2 = out_2[~np.isnan(out_2)] max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): first = model(**self._prepare_for_class(inputs_dict, model_class))[0] second = model(**self._prepare_for_class(inputs_dict, model_class))[0] if isinstance(first, tuple) and isinstance(second, tuple): for tensor1, tensor2 in zip(first, second): check_determinism(tensor1, tensor2) else: check_determinism(first, second) # Copied from transformers.tests.encodec.test_modeling_encodec.MimiModelTest.test_model_outputs_equivalence def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def set_nan_tensor_to_zero(t): t[t != t] = 0 return t def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): with torch.no_grad(): tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs) dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs) self.assertTrue(isinstance(tuple_output, tuple)) self.assertTrue(isinstance(dict_output, dict)) for tuple_value, dict_value in zip(tuple_output, dict_output.values()): self.assertTrue( torch.allclose( set_nan_tensor_to_zero(tuple_value), set_nan_tensor_to_zero(dict_value), atol=1e-5 ), msg=( "Tuple and dict output are not equal. Difference:" f" {torch.max(torch.abs(tuple_value - dict_value))}. Tuple has `nan`:" f" {torch.isnan(tuple_value).any()} and `inf`: {torch.isinf(tuple_value)}. Dict has" f" `nan`: {torch.isnan(dict_value).any()} and `inf`: {torch.isinf(dict_value)}." ), ) for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): uniform_init_parms = ["conv", "input_proj", "output_proj"] if param.requires_grad: if any(x in name for x in uniform_init_parms): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) # Copied from transformers.tests.encodec.test_modeling_encodec.MimiModelTest.test_identity_shortcut def test_identity_shortcut(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() config.use_conv_shortcut = False self.model_tester.create_and_check_model_forward(config, inputs_dict) @require_flash_attn @require_torch_gpu @mark.flash_attn_test @slow @is_flaky() def test_flash_attn_2_inference_equivalence(self): for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.bfloat16) model.to(torch_device) dummy_input = inputs_dict[model.main_input_name][:1] if dummy_input.dtype in [torch.float32, torch.float16]: dummy_input = dummy_input.to(torch.bfloat16) outputs = model(dummy_input) outputs_fa = model_fa(dummy_input) logits = outputs[1] logits_fa = outputs_fa[1] assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2) @unittest.skip(reason="The MimiModel does not support right padding") def test_flash_attn_2_inference_equivalence_right_padding(self): pass @unittest.skip(reason="The MimiModel does not have support dynamic compile yet") def test_sdpa_can_compile_dynamic(self): pass # Copied from transformers.tests.encodec.test_modeling_encodec.normalize def normalize(arr): norm = np.linalg.norm(arr) normalized_arr = arr / norm return normalized_arr # Copied from transformers.tests.encodec.test_modeling_encodec.compute_rmse def compute_rmse(arr1, arr2): arr1_normalized = normalize(arr1) arr2_normalized = normalize(arr2) return np.sqrt(((arr1_normalized - arr2_normalized) ** 2).mean()) @slow @require_torch class MimiIntegrationTest(unittest.TestCase): def test_integration_using_cache_decode(self): expected_rmse = { "8": 0.0018785292, "32": 0.0012330565, } librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") model_id = "kyutai/mimi" model = MimiModel.from_pretrained(model_id, use_cache=True).to(torch_device) processor = AutoFeatureExtractor.from_pretrained(model_id) librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate)) audio_sample = librispeech_dummy[-1]["audio"]["array"] inputs = processor( raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt", ).to(torch_device) for num_codebooks, expected_rmse in expected_rmse.items(): with torch.no_grad(): # use max bandwidth for best possible reconstruction encoder_outputs = model.encode(inputs["input_values"], num_quantizers=int(num_codebooks)) audio_codes = encoder_outputs[0] decoder_outputs_first_part = model.decode(audio_codes[:, :, : audio_codes.shape[2] // 2]) decoder_outputs_second_part = model.decode( audio_codes[:, :, audio_codes.shape[2] // 2 :], decoder_past_key_values=decoder_outputs_first_part.decoder_past_key_values, ) audio_output_entire_context = model.decode(audio_codes)[0] audio_output_concat_context = torch.cat( [decoder_outputs_first_part[0], decoder_outputs_second_part[0]], dim=2 ) # make sure audios are more or less equal # the RMSE of two random gaussian noise vectors with ~N(0, 1) is around 1.0 rmse = compute_rmse( audio_output_concat_context.squeeze().cpu().numpy(), audio_output_entire_context.squeeze().cpu().numpy(), ) self.assertTrue(rmse < 1e-3) def test_integration(self): expected_rmses = { "8": 0.0018785292, "32": 0.0012330565, } expected_codesums = { "8": 426176, "32": 1795819, } librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") model_id = "kyutai/mimi" processor = AutoFeatureExtractor.from_pretrained(model_id) librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate)) audio_sample = librispeech_dummy[-1]["audio"]["array"] inputs = processor( raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt", ).to(torch_device) for use_cache in [False, True]: model = MimiModel.from_pretrained(model_id, use_cache=use_cache).to(torch_device) for num_codebooks, expected_rmse in expected_rmses.items(): with torch.no_grad(): # use max bandwidth for best possible reconstruction encoder_outputs = model.encode(inputs["input_values"], num_quantizers=int(num_codebooks)) audio_code_sums = encoder_outputs[0].sum().item() # make sure audio encoded codes are correct # assert relative difference less than a threshold, because `audio_code_sums` varies a bit # depending on torch version self.assertTrue( np.abs(audio_code_sums - expected_codesums[num_codebooks]) <= (3e-3 * audio_code_sums) ) input_values_dec = model.decode(encoder_outputs[0], padding_mask=inputs["padding_mask"])[0] input_values_enc_dec = model( inputs["input_values"], inputs["padding_mask"], num_quantizers=int(num_codebooks) )[1] # make sure forward and decode gives same result torch.testing.assert_close(input_values_dec, input_values_enc_dec) # make sure shape matches self.assertTrue(inputs["input_values"].shape == input_values_enc_dec.shape) arr = inputs["input_values"][0].cpu().numpy() arr_enc_dec = input_values_enc_dec[0].cpu().numpy() # make sure audios are more or less equal # the RMSE of two random gaussian noise vectors with ~N(0, 1) is around 1.0 rmse = compute_rmse(arr, arr_enc_dec) self.assertTrue(np.abs(rmse - expected_rmse) < 1e-5)