# 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 tempfile import unittest import numpy as np import pytest 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_accelerator, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, 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, ): 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_length=None): input_values = floats_tensor( [ self.batch_size, self.num_channels, self.intermediate_size if input_values_length is None else input_values_length, ], 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, input_values_length=None): config, inputs_dict = self.prepare_config_and_inputs(input_values_length=input_values_length) 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_resize_embeddings = 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_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) # 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_accelerator @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, dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained(tmpdirname, 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") @pytest.mark.torch_compile_test 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_encode_with_padding_cache(self): """ We test here the possibility to run Mimi in a streaming manner, i.e. chunk by chunk. 1. we encode a first time the entire audio 2. we encode the audio chunk by chunk, each chunk being the smallest size possible for the model (i.e. the frame size) This test must be run on CPU since GPU floating point operations accumulate rounding errors that cause test failures. """ 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("cpu") 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("cpu") frame_size = model.config.frame_size audio_codes = model.encode(inputs["input_values"]).audio_codes # streaming chunk by chunk encoder_past_key_values = None padding_cache = None encoded_frames_list = [] for start in range(0, inputs["input_values"].shape[-1], frame_size): input_values_chunk = inputs["input_values"][:, :, start : start + frame_size] encoder_outputs = model.encode( input_values_chunk, padding_cache=padding_cache, encoder_past_key_values=encoder_past_key_values, use_streaming=True, ) encoder_past_key_values = encoder_outputs.encoder_past_key_values padding_cache = encoder_outputs.padding_cache encoded_frames_list.append(encoder_outputs.audio_codes) streamed_audio_codes = torch.cat(encoded_frames_list, dim=-1) torch.testing.assert_close(streamed_audio_codes, audio_codes) 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)