mirror of
https://github.com/huggingface/transformers.git
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1610 lines
64 KiB
Python
1610 lines
64 KiB
Python
# Copyright 2021, 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 Musicgen model."""
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import copy
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import inspect
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import math
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import tempfile
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import unittest
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import numpy as np
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from pytest import mark
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from transformers import (
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EncodecConfig,
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MusicgenConfig,
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MusicgenDecoderConfig,
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MusicgenProcessor,
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PretrainedConfig,
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T5Config,
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)
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from transformers.testing_utils import (
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get_device_properties,
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is_torch_available,
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require_flash_attn,
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require_torch,
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require_torch_accelerator,
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require_torch_fp16,
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require_torch_gpu,
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require_torch_sdpa,
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slow,
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torch_device,
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)
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from transformers.utils import cached_property
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, sdpa_kernel
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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MusicgenForCausalLM,
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MusicgenForConditionalGeneration,
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MusicgenModel,
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set_seed,
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)
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def _config_zero_init(config):
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configs_no_init = copy.deepcopy(config)
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for key in configs_no_init.__dict__.keys():
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if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
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setattr(configs_no_init, key, 1e-10)
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if isinstance(getattr(configs_no_init, key, None), PretrainedConfig):
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no_init_subconfig = _config_zero_init(getattr(configs_no_init, key))
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setattr(configs_no_init, key, no_init_subconfig)
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return configs_no_init
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def prepare_musicgen_decoder_inputs_dict(
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config,
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input_ids,
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attention_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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):
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if attention_mask is None:
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attention_mask = input_ids.reshape(-1, config.num_codebooks, input_ids.shape[-1])[:, 0, :]
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attention_mask = attention_mask.ne(config.pad_token_id)
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if encoder_attention_mask is None and encoder_hidden_states is not None:
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encoder_attention_mask = torch.ones(encoder_hidden_states.shape[:2], device=torch_device)
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return {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"encoder_hidden_states": encoder_hidden_states,
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"encoder_attention_mask": encoder_attention_mask,
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}
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class MusicgenDecoderTester:
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def __init__(
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self,
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parent,
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batch_size=4, # need batch_size != num_hidden_layers
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seq_length=7,
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is_training=True,
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vocab_size=99,
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hidden_size=16,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=4,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=100,
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pad_token_id=99,
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bos_token_id=99,
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num_codebooks=4,
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audio_channels=1,
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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.seq_length = seq_length
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self.is_training = is_training
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.num_codebooks = num_codebooks
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self.audio_channels = audio_channels
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size * self.num_codebooks, self.seq_length], self.vocab_size)
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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config = self.get_config()
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inputs_dict = prepare_musicgen_decoder_inputs_dict(
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config,
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input_ids,
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encoder_hidden_states=encoder_hidden_states,
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)
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return config, inputs_dict
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def get_config(self):
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config = MusicgenDecoderConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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d_ff=self.intermediate_size,
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pad_token_id=self.pad_token_id,
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decoder_start_token_id=self.bos_token_id,
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bos_token_id=self.bos_token_id,
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num_codebooks=self.num_codebooks,
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tie_word_embeddings=False,
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audio_channels=self.audio_channels,
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)
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return config
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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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@require_torch
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class MusicgenDecoderTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (MusicgenModel, MusicgenForCausalLM) if is_torch_available() else ()
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# Doesn't run generation tests. See `greedy_sample_model_classes` below
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all_generative_model_classes = ()
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greedy_sample_model_classes = (
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(MusicgenForCausalLM,) if is_torch_available() else ()
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) # we don't want to run all the generation tests, only a specific subset
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pipeline_model_mapping = {}
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test_pruning = False
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test_resize_embeddings = False
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def setUp(self):
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self.model_tester = MusicgenDecoderTester(self)
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self.config_tester = ConfigTester(self, config_class=MusicgenDecoderConfig, hidden_size=16)
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def test_config(self):
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self.config_tester.run_common_tests()
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# special case for labels
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if return_labels:
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inputs_dict["labels"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_codebooks),
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dtype=torch.long,
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device=torch_device,
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)
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return inputs_dict
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def check_training_gradient_checkpointing(self, gradient_checkpointing_kwargs=None):
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if not self.model_tester.is_training:
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self.skipTest(reason="model_tester.is_training is set to False")
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.use_cache = False
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config.return_dict = True
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model = MusicgenForCausalLM(config)
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model.to(torch_device)
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model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=gradient_checkpointing_kwargs)
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model.train()
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# Contrarily to the initial method, we don't unfreeze freezed parameters.
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# Indeed, sinusoidal position embeddings have frozen weights that should stay frozen.
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
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inputs = self._prepare_for_class(inputs_dict, MusicgenForCausalLM, return_labels=True)
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loss = model(**inputs).loss
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loss.backward()
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optimizer.step()
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for k, v in model.named_parameters():
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if v.requires_grad:
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self.assertTrue(v.grad is not None, f"{k} in {MusicgenForCausalLM.__name__} has no gradient!")
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# override since we have to compute the input embeddings over codebooks
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def test_inputs_embeds(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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model = model_class(config)
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model.to(torch_device)
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model.eval()
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inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
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input_ids = inputs["input_ids"]
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del inputs["input_ids"]
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embed_tokens = model.get_input_embeddings()
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input_ids = input_ids.reshape(-1, config.num_codebooks, input_ids.shape[-1])
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inputs["inputs_embeds"] = sum(
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[embed_tokens[codebook](input_ids[:, codebook]) for codebook in range(config.num_codebooks)]
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)
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with torch.no_grad():
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model(**inputs)[0]
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# override since we have embeddings / LM heads over multiple codebooks
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def test_model_get_set_embeddings(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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model = model_class(config)
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first_embed = model.get_input_embeddings()[0]
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self.assertIsInstance(first_embed, torch.nn.Embedding)
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lm_heads = model.get_output_embeddings()
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self.assertTrue(lm_heads is None or isinstance(lm_heads[0], torch.nn.Linear))
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@unittest.skip(reason="MusicGen does not use inputs_embeds")
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def test_inputs_embeds_matches_input_ids(self):
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pass
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@unittest.skip(reason="MusicGen does not support all arguments tested")
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def test_model_outputs_equivalence(self):
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pass
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@unittest.skip(reason="MusicGen has multiple inputs embeds and lm heads that should not be tied")
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def test_tie_model_weights(self):
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pass
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@unittest.skip(reason="MusicGen has multiple inputs embeds and lm heads that should not be tied")
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def test_tied_weights_keys(self):
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pass
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def _get_logits_processor_kwargs(self, do_sample=False, config=None):
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logits_processor_kwargs = {}
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return logits_processor_kwargs
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def test_greedy_generate_stereo_outputs(self):
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original_audio_channels = self.model_tester.audio_channels
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self.model_tester.audio_channels = 2
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super().test_greedy_generate_dict_outputs()
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self.model_tester.audio_channels = original_audio_channels
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@require_flash_attn
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@require_torch_gpu
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@mark.flash_attn_test
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@slow
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# Copied from tests.test_modeling_common.ModelTesterMixin.test_flash_attn_2_inference_equivalence
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def test_flash_attn_2_inference_equivalence(self):
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for model_class in self.all_model_classes:
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if not model_class._supports_flash_attn_2:
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self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model_fa = model_class.from_pretrained(
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tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2"
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)
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model_fa.to(torch_device)
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model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.bfloat16)
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model.to(torch_device)
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# Ignore copy
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dummy_input = inputs_dict[model.main_input_name]
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if dummy_input.dtype in [torch.float32, torch.float16]:
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dummy_input = dummy_input.to(torch.bfloat16)
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dummy_attention_mask = inputs_dict.get("attention_mask", None)
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if dummy_attention_mask is not None:
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# Ignore copy
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dummy_attention_mask[:, 1:] = 1
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dummy_attention_mask[:, :1] = 0
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# Ignore copy
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outputs = model(dummy_input, output_hidden_states=True)
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# Ignore copy
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outputs_fa = model_fa(dummy_input, output_hidden_states=True)
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logits = (
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outputs.hidden_states[-1]
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if not model.config.is_encoder_decoder
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else outputs.decoder_hidden_states[-1]
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)
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logits_fa = (
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outputs_fa.hidden_states[-1]
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if not model.config.is_encoder_decoder
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else outputs_fa.decoder_hidden_states[-1]
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)
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assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2)
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# Ignore copy
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other_inputs = {
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"output_hidden_states": True,
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}
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if dummy_attention_mask is not None:
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other_inputs["attention_mask"] = dummy_attention_mask
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outputs = model(dummy_input, **other_inputs)
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outputs_fa = model_fa(dummy_input, **other_inputs)
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logits = (
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outputs.hidden_states[-1]
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if not model.config.is_encoder_decoder
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else outputs.decoder_hidden_states[-1]
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)
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logits_fa = (
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outputs_fa.hidden_states[-1]
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if not model.config.is_encoder_decoder
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else outputs_fa.decoder_hidden_states[-1]
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)
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assert torch.allclose(logits_fa[1:], logits[1:], atol=4e-2, rtol=4e-2)
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# check with inference + dropout
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model.train()
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_ = model_fa(dummy_input, **other_inputs)
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@require_flash_attn
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@require_torch_gpu
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@mark.flash_attn_test
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@slow
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# Copied from tests.test_modeling_common.ModelTesterMixin.test_flash_attn_2_inference_equivalence_right_padding
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def test_flash_attn_2_inference_equivalence_right_padding(self):
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for model_class in self.all_model_classes:
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if not model_class._supports_flash_attn_2:
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self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model_fa = model_class.from_pretrained(
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tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2"
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)
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model_fa.to(torch_device)
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model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.bfloat16)
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model.to(torch_device)
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# Ignore copy
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dummy_input = inputs_dict[model.main_input_name]
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if dummy_input.dtype in [torch.float32, torch.float16]:
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dummy_input = dummy_input.to(torch.bfloat16)
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dummy_attention_mask = inputs_dict.get("attention_mask", None)
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if dummy_attention_mask is not None:
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# Ignore copy
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dummy_attention_mask[:, :-1] = 1
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dummy_attention_mask[:, -1:] = 0
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if model.config.is_encoder_decoder:
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decoder_input_ids = inputs_dict.get("decoder_input_ids", dummy_input)
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outputs = model(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True)
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outputs_fa = model_fa(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True)
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else:
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outputs = model(dummy_input, output_hidden_states=True)
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outputs_fa = model_fa(dummy_input, output_hidden_states=True)
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logits = (
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outputs.hidden_states[-1]
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if not model.config.is_encoder_decoder
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else outputs.decoder_hidden_states[-1]
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)
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logits_fa = (
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outputs_fa.hidden_states[-1]
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if not model.config.is_encoder_decoder
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else outputs_fa.decoder_hidden_states[-1]
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)
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assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2)
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# Ignore copy
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other_inputs = {
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"output_hidden_states": True,
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}
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if dummy_attention_mask is not None:
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other_inputs["attention_mask"] = dummy_attention_mask
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outputs = model(dummy_input, **other_inputs)
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outputs_fa = model_fa(dummy_input, **other_inputs)
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logits = (
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outputs.hidden_states[-1]
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if not model.config.is_encoder_decoder
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else outputs.decoder_hidden_states[-1]
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)
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logits_fa = (
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outputs_fa.hidden_states[-1]
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if not model.config.is_encoder_decoder
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else outputs_fa.decoder_hidden_states[-1]
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)
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assert torch.allclose(logits_fa[:-1], logits[:-1], atol=4e-2, rtol=4e-2)
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@unittest.skip(
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reason=(
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"MusicGen has a custom set of generation tests that rely on `GenerationTesterMixin`, controlled by "
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"`greedy_sample_model_classes`"
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)
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)
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def test_generation_tester_mixin_inheritance(self):
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pass
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def prepare_musicgen_inputs_dict(
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config,
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input_ids,
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decoder_input_ids,
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attention_mask=None,
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decoder_attention_mask=None,
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labels=None,
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):
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if decoder_attention_mask is None:
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decoder_attention_mask = decoder_input_ids.reshape(
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-1, config.decoder.num_codebooks, decoder_input_ids.shape[-1]
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)[:, 0, :]
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decoder_attention_mask = decoder_attention_mask.ne(config.decoder.pad_token_id)
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return {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"decoder_input_ids": decoder_input_ids,
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"decoder_attention_mask": decoder_attention_mask,
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"labels": labels,
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}
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class MusicgenTester:
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|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=4, # need batch_size != num_hidden_layers
|
|
seq_length=7,
|
|
is_training=True,
|
|
vocab_size=99,
|
|
hidden_size=16,
|
|
num_hidden_layers=2,
|
|
num_attention_heads=4,
|
|
intermediate_size=4,
|
|
hidden_act="gelu",
|
|
hidden_dropout_prob=0.1,
|
|
attention_probs_dropout_prob=0.1,
|
|
max_position_embeddings=100,
|
|
pad_token_id=99,
|
|
bos_token_id=99,
|
|
num_codebooks=4,
|
|
num_filters=4,
|
|
codebook_size=128,
|
|
audio_channels=1,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.seq_length = seq_length
|
|
self.is_training = is_training
|
|
self.vocab_size = vocab_size
|
|
self.hidden_size = hidden_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.intermediate_size = intermediate_size
|
|
self.hidden_act = hidden_act
|
|
self.hidden_dropout_prob = hidden_dropout_prob
|
|
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.pad_token_id = pad_token_id
|
|
self.bos_token_id = bos_token_id
|
|
self.num_codebooks = num_codebooks
|
|
self.num_filters = num_filters
|
|
self.codebook_size = codebook_size
|
|
self.audio_channels = audio_channels
|
|
|
|
def prepare_config_and_inputs(self):
|
|
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
decoder_input_ids = ids_tensor([self.batch_size * self.num_codebooks, self.seq_length], self.vocab_size)
|
|
|
|
config = self.get_config()
|
|
inputs_dict = prepare_musicgen_inputs_dict(config, input_ids, decoder_input_ids=decoder_input_ids)
|
|
return config, inputs_dict
|
|
|
|
def get_config(self):
|
|
text_encoder_config = T5Config(
|
|
vocab_size=self.vocab_size,
|
|
d_model=self.hidden_size,
|
|
d_ff=self.intermediate_size,
|
|
num_layers=self.num_hidden_layers,
|
|
num_heads=self.num_attention_heads,
|
|
)
|
|
audio_encoder_config = EncodecConfig(
|
|
hidden_size=self.vocab_size,
|
|
compress=1,
|
|
num_filters=self.num_filters,
|
|
codebook_size=self.codebook_size,
|
|
codebook_dim=self.vocab_size,
|
|
)
|
|
decoder_config = MusicgenDecoderConfig(
|
|
vocab_size=self.vocab_size,
|
|
hidden_size=self.hidden_size,
|
|
num_hidden_layers=self.num_hidden_layers,
|
|
num_attention_heads=self.num_attention_heads,
|
|
ffn_dim=self.intermediate_size,
|
|
pad_token_id=self.pad_token_id,
|
|
decoder_start_token_id=self.bos_token_id,
|
|
bos_token_id=self.bos_token_id,
|
|
num_codebooks=self.num_codebooks,
|
|
tie_word_embeddings=False,
|
|
audio_channels=self.audio_channels,
|
|
)
|
|
config = MusicgenConfig.from_sub_models_config(text_encoder_config, audio_encoder_config, decoder_config)
|
|
return config
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config, inputs_dict = self.prepare_config_and_inputs()
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class MusicgenTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (MusicgenForConditionalGeneration,) if is_torch_available() else ()
|
|
# Doesn't run generation tests. See `greedy_sample_model_classes` below
|
|
all_generative_model_classes = ()
|
|
greedy_sample_model_classes = (MusicgenForConditionalGeneration,) if is_torch_available() else ()
|
|
pipeline_model_mapping = {"text-to-audio": MusicgenForConditionalGeneration} if is_torch_available() else {}
|
|
test_pruning = False # training is not supported yet for MusicGen
|
|
test_headmasking = False
|
|
test_resize_embeddings = False
|
|
# not to test torchscript as the model tester doesn't prepare `input_values` and `padding_mask`
|
|
# (and `torchscript` hates `None` values).
|
|
test_torchscript = False
|
|
_is_composite = True
|
|
|
|
def setUp(self):
|
|
self.model_tester = MusicgenTester(self)
|
|
|
|
# special case for labels
|
|
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
|
|
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
|
|
|
|
if return_labels:
|
|
inputs_dict["labels"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_codebooks),
|
|
dtype=torch.long,
|
|
device=torch_device,
|
|
)
|
|
return inputs_dict
|
|
|
|
def check_training_gradient_checkpointing(self, gradient_checkpointing_kwargs=None):
|
|
if not self.model_tester.is_training:
|
|
self.skipTest(reason="model_tester.is_training is set to False")
|
|
|
|
for model_class in self.all_model_classes:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.use_cache = False
|
|
config.return_dict = True
|
|
model = model_class(config)
|
|
|
|
model.to(torch_device)
|
|
model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=gradient_checkpointing_kwargs)
|
|
model.train()
|
|
|
|
# The audio encoder weights are not used during the forward pass (only during the generate pass)
|
|
# So we need to freeze it to be able to train.
|
|
model.freeze_audio_encoder()
|
|
|
|
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
|
|
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
loss = model(**inputs).loss
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
for k, v in model.named_parameters():
|
|
if v.requires_grad:
|
|
self.assertTrue(v.grad is not None, f"{k} in {model_class.__name__} has no gradient!")
|
|
|
|
def _check_output_with_attentions(self, outputs, config, input_ids, decoder_input_ids):
|
|
text_encoder_config = config.text_encoder
|
|
decoder_config = config.decoder
|
|
|
|
encoder_attentions = outputs["encoder_attentions"]
|
|
self.assertEqual(len(encoder_attentions), text_encoder_config.num_hidden_layers)
|
|
|
|
self.assertEqual(
|
|
encoder_attentions[0].shape[-3:],
|
|
(text_encoder_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),
|
|
)
|
|
|
|
decoder_attentions = outputs["decoder_attentions"]
|
|
num_decoder_layers = decoder_config.num_hidden_layers
|
|
self.assertEqual(len(decoder_attentions), num_decoder_layers)
|
|
|
|
self.assertEqual(
|
|
decoder_attentions[0].shape[-3:],
|
|
(decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]),
|
|
)
|
|
|
|
cross_attentions = outputs["cross_attentions"]
|
|
self.assertEqual(len(cross_attentions), num_decoder_layers)
|
|
|
|
cross_attention_input_seq_len = decoder_input_ids.shape[-1]
|
|
self.assertEqual(
|
|
cross_attentions[0].shape[-3:],
|
|
(decoder_config.num_attention_heads, cross_attention_input_seq_len, input_ids.shape[-1]),
|
|
)
|
|
|
|
def check_musicgen_model_output_attentions(
|
|
self,
|
|
model_class,
|
|
config,
|
|
input_ids,
|
|
attention_mask,
|
|
decoder_input_ids,
|
|
decoder_attention_mask,
|
|
**kwargs,
|
|
):
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(
|
|
input_ids=input_ids,
|
|
decoder_input_ids=decoder_input_ids,
|
|
attention_mask=attention_mask,
|
|
decoder_attention_mask=decoder_attention_mask,
|
|
output_attentions=True,
|
|
**kwargs,
|
|
)
|
|
self._check_output_with_attentions(outputs, config, input_ids, decoder_input_ids)
|
|
|
|
def check_musicgen_model_output_attentions_from_config(
|
|
self,
|
|
model_class,
|
|
config,
|
|
input_ids,
|
|
attention_mask,
|
|
decoder_input_ids,
|
|
decoder_attention_mask,
|
|
**kwargs,
|
|
):
|
|
# Similar to `check_musicgen_model_output_attentions`, but with `output_attentions` triggered from the
|
|
# config file. Contrarily to most models, changing the model's config won't work -- the defaults are loaded
|
|
# from the inner models' configurations.
|
|
config.output_attentions = True # model config -> won't work
|
|
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(
|
|
input_ids=input_ids,
|
|
decoder_input_ids=decoder_input_ids,
|
|
attention_mask=attention_mask,
|
|
decoder_attention_mask=decoder_attention_mask,
|
|
**kwargs,
|
|
)
|
|
self.assertTrue(
|
|
all(key not in outputs for key in ["encoder_attentions", "decoder_attentions", "cross_attentions"])
|
|
)
|
|
config.text_encoder.output_attentions = True # inner model config -> will work
|
|
config.audio_encoder.output_attentions = True
|
|
config.decoder.output_attentions = True
|
|
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(
|
|
input_ids=input_ids,
|
|
decoder_input_ids=decoder_input_ids,
|
|
attention_mask=attention_mask,
|
|
decoder_attention_mask=decoder_attention_mask,
|
|
**kwargs,
|
|
)
|
|
self._check_output_with_attentions(outputs, config, input_ids, decoder_input_ids)
|
|
|
|
# override since changing `output_attentions` from the top-level model config won't work
|
|
def test_attention_outputs(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
self.check_musicgen_model_output_attentions(model_class, config, **inputs_dict)
|
|
self.check_musicgen_model_output_attentions_from_config(model_class, config, **inputs_dict)
|
|
|
|
# override since we have a specific forward signature for musicgen
|
|
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_ids",
|
|
"attention_mask",
|
|
"input_values",
|
|
"padding_mask",
|
|
"decoder_input_ids",
|
|
"decoder_attention_mask",
|
|
]
|
|
expected_arg_names.extend(
|
|
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
|
|
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
|
|
else ["encoder_outputs"]
|
|
)
|
|
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
|
|
|
|
# override since changing `gradient_checkpointing` from the top-level model config won't work
|
|
def test_gradient_checkpointing_backward_compatibility(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
if not model_class.supports_gradient_checkpointing:
|
|
continue
|
|
|
|
config.text_encoder.gradient_checkpointing = True
|
|
config.audio_encoder.gradient_checkpointing = True
|
|
config.decoder.gradient_checkpointing = True
|
|
model = model_class(config)
|
|
self.assertTrue(model.is_gradient_checkpointing)
|
|
|
|
@unittest.skip(reason="MusicGen has multiple inputs embeds and lm heads that should not be tied.")
|
|
def test_tie_model_weights(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="MusicGen has multiple inputs embeds and lm heads that should not be tied.")
|
|
def test_tied_model_weights_key_ignore(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="MusicGen has multiple inputs embeds and lm heads that should not be tied.")
|
|
def test_tied_weights_keys(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="No support for low_cpu_mem_usage=True.")
|
|
def test_save_load_low_cpu_mem_usage(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="No support for low_cpu_mem_usage=True.")
|
|
def test_save_load_low_cpu_mem_usage_checkpoints(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="No support for low_cpu_mem_usage=True.")
|
|
def test_save_load_low_cpu_mem_usage_no_safetensors(self):
|
|
pass
|
|
|
|
# override since changing `output_hidden_states` / `output_attentions` from the top-level model config won't work
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.text_encoder.output_hidden_states = True
|
|
config.audio_encoder.output_hidden_states = True
|
|
config.decoder.output_hidden_states = True
|
|
|
|
config.text_encoder.output_attentions = True
|
|
config.decoder.output_attentions = True
|
|
|
|
# no need to test all models as different heads yield the same functionality
|
|
model_class = self.all_model_classes[0]
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
|
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
outputs = model(**inputs)
|
|
|
|
output = outputs[0]
|
|
|
|
encoder_hidden_states = outputs.encoder_hidden_states[0]
|
|
encoder_hidden_states.retain_grad()
|
|
|
|
decoder_hidden_states = outputs.decoder_hidden_states[0]
|
|
decoder_hidden_states.retain_grad()
|
|
|
|
if self.has_attentions:
|
|
encoder_attentions = outputs.encoder_attentions[0]
|
|
encoder_attentions.retain_grad()
|
|
|
|
decoder_attentions = outputs.decoder_attentions[0]
|
|
decoder_attentions.retain_grad()
|
|
|
|
cross_attentions = outputs.cross_attentions[0]
|
|
cross_attentions.retain_grad()
|
|
|
|
output.flatten()[0].backward(retain_graph=True)
|
|
|
|
self.assertIsNotNone(encoder_hidden_states.grad)
|
|
self.assertIsNotNone(decoder_hidden_states.grad)
|
|
|
|
if self.has_attentions:
|
|
self.assertIsNotNone(encoder_attentions.grad)
|
|
self.assertIsNotNone(decoder_attentions.grad)
|
|
self.assertIsNotNone(cross_attentions.grad)
|
|
|
|
# override since changing `output_hidden_states` from the top-level model config won't work
|
|
def test_hidden_states_output(self):
|
|
def check_hidden_states_output(inputs_dict, config, model_class):
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
hidden_states = outputs.encoder_hidden_states
|
|
|
|
expected_num_layers = self.model_tester.num_hidden_layers + 1
|
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
|
|
|
seq_length = self.model_tester.seq_length
|
|
self.assertListEqual(
|
|
list(hidden_states[0].shape[-2:]),
|
|
[seq_length, self.model_tester.hidden_size],
|
|
)
|
|
|
|
hidden_states = outputs.decoder_hidden_states
|
|
self.assertIsInstance(hidden_states, (list, tuple))
|
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
|
|
|
self.assertListEqual(
|
|
list(hidden_states[0].shape[-2:]),
|
|
[seq_length, self.model_tester.hidden_size],
|
|
)
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_hidden_states"] = True
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
# check that output_hidden_states also work using config
|
|
del inputs_dict["output_hidden_states"]
|
|
config.text_encoder.output_hidden_states = True
|
|
config.audio_encoder.output_hidden_states = True
|
|
config.decoder.output_hidden_states = True
|
|
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
# override since the conv layers and lstm's in encodec are exceptions
|
|
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"]
|
|
ignore_init = ["lstm"]
|
|
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",
|
|
)
|
|
elif not any(x in name for x in ignore_init):
|
|
self.assertIn(
|
|
((param.data.mean() * 1e9).round() / 1e9).item(),
|
|
[0.0, 1.0],
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
# override since we have embeddings / LM heads over multiple codebooks
|
|
def test_model_get_set_embeddings(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
self.assertIsInstance(model.get_input_embeddings(), torch.nn.Embedding)
|
|
lm_heads = model.get_output_embeddings()
|
|
self.assertTrue(lm_heads is None or isinstance(lm_heads[0], torch.nn.Linear))
|
|
|
|
def _get_logits_processor_kwargs(self, do_sample=False, config=None):
|
|
logits_processor_kwargs = {}
|
|
return logits_processor_kwargs
|
|
|
|
@require_torch_fp16
|
|
@require_torch_accelerator # not all operations are supported in fp16 on CPU
|
|
def test_generate_fp16(self):
|
|
config, input_dict = self.model_tester.prepare_config_and_inputs()
|
|
|
|
for model_class in self.greedy_sample_model_classes:
|
|
model = model_class(config).eval().to(torch_device)
|
|
model.half()
|
|
# greedy
|
|
model.generate(input_dict["input_ids"], attention_mask=input_dict["attention_mask"], max_new_tokens=10)
|
|
# sampling
|
|
model.generate(
|
|
input_dict["input_ids"], attention_mask=input_dict["attention_mask"], do_sample=True, max_new_tokens=10
|
|
)
|
|
|
|
def test_greedy_generate_stereo_outputs(self):
|
|
original_audio_channels = self.model_tester.audio_channels
|
|
self.model_tester.audio_channels = 2
|
|
super().test_greedy_generate_dict_outputs()
|
|
self.model_tester.audio_channels = original_audio_channels
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@mark.flash_attn_test
|
|
@slow
|
|
# Adapted from tests.test_modeling_common.ModelTesterMixin.test_flash_attn_2_inference_equivalence
|
|
def test_flash_attn_2_inference_equivalence(self):
|
|
for model_class in self.all_model_classes:
|
|
if not model_class._supports_flash_attn_2:
|
|
self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
|
|
|
|
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={"decoder": "flash_attention_2", "audio_encoder": None, "text_encoder": None},
|
|
)
|
|
model_fa.to(torch_device)
|
|
|
|
model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.bfloat16)
|
|
model.to(torch_device)
|
|
|
|
# Ignore copy
|
|
dummy_input = inputs_dict[model.main_input_name]
|
|
if dummy_input.dtype in [torch.float32, torch.float16]:
|
|
dummy_input = dummy_input.to(torch.bfloat16)
|
|
|
|
dummy_attention_mask = inputs_dict.get("attention_mask", None)
|
|
|
|
if dummy_attention_mask is not None:
|
|
# Ignore copy
|
|
dummy_attention_mask[:, 1:] = 1
|
|
dummy_attention_mask[:, :1] = 0
|
|
|
|
# Ignore copy
|
|
decoder_input_ids = inputs_dict.get("decoder_input_ids", dummy_input)
|
|
# Ignore copy
|
|
outputs = model(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True)
|
|
# Ignore copy
|
|
outputs_fa = model_fa(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True)
|
|
|
|
logits = (
|
|
outputs.hidden_states[-1]
|
|
if not model.config.is_encoder_decoder
|
|
else outputs.decoder_hidden_states[-1]
|
|
)
|
|
logits_fa = (
|
|
outputs_fa.hidden_states[-1]
|
|
if not model.config.is_encoder_decoder
|
|
else outputs_fa.decoder_hidden_states[-1]
|
|
)
|
|
|
|
assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2)
|
|
# Ignore copy
|
|
other_inputs = {
|
|
"decoder_input_ids": decoder_input_ids,
|
|
"decoder_attention_mask": dummy_attention_mask,
|
|
"output_hidden_states": True,
|
|
}
|
|
# Ignore copy
|
|
if dummy_attention_mask is not None:
|
|
other_inputs["attention_mask"] = dummy_attention_mask
|
|
# Ignore copy
|
|
outputs = model(dummy_input, **other_inputs)
|
|
# Ignore copy
|
|
outputs_fa = model_fa(dummy_input, **other_inputs)
|
|
|
|
logits = (
|
|
outputs.hidden_states[-1]
|
|
if not model.config.is_encoder_decoder
|
|
else outputs.decoder_hidden_states[-1]
|
|
)
|
|
logits_fa = (
|
|
outputs_fa.hidden_states[-1]
|
|
if not model.config.is_encoder_decoder
|
|
else outputs_fa.decoder_hidden_states[-1]
|
|
)
|
|
|
|
assert torch.allclose(logits_fa[1:], logits[1:], atol=4e-2, rtol=4e-2)
|
|
|
|
# check with inference + dropout
|
|
model.train()
|
|
_ = model_fa(dummy_input, **other_inputs)
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@mark.flash_attn_test
|
|
@slow
|
|
def test_flash_attn_2_conversion(self):
|
|
if not self.has_attentions:
|
|
self.skipTest(reason="Model architecture does not support attentions")
|
|
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
if not model_class._supports_flash_attn_2:
|
|
self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
|
|
|
|
model = model_class(config)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname)
|
|
model = model_class.from_pretrained(
|
|
tmpdirname,
|
|
torch_dtype=torch.float16,
|
|
attn_implementation={"decoder": "flash_attention_2", "audio_encoder": None, "text_encoder": None},
|
|
).to(torch_device)
|
|
|
|
for _, module in model.named_modules():
|
|
if "FlashAttention" in module.__class__.__name__:
|
|
return
|
|
|
|
self.assertTrue(False, "FlashAttention2 modules not found in model")
|
|
|
|
@require_torch_sdpa
|
|
@require_torch_gpu
|
|
@slow
|
|
def test_sdpa_can_dispatch_on_flash(self):
|
|
if not self.has_attentions:
|
|
self.skipTest(reason="Model architecture does not support attentions")
|
|
|
|
(device_type, major) = get_device_properties()
|
|
if device_type == "cuda" and major < 8:
|
|
self.skipTest(reason="This test requires an NVIDIA GPU with compute capability >= 8.0")
|
|
elif device_type == "rocm" and major < 9:
|
|
self.skipTest(reason="This test requires an AMD GPU with compute capability >= 9.0")
|
|
else:
|
|
self.skipTest(reason="This test requires a Nvidia or AMD GPU")
|
|
|
|
torch.compiler.reset()
|
|
|
|
for model_class in self.all_model_classes:
|
|
if not model_class._supports_sdpa:
|
|
self.skipTest(f"{model_class.__name__} does not support SDPA")
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
|
|
if config.model_type in ["llava", "llava_next", "vipllava", "video_llava"]:
|
|
self.skipTest(
|
|
reason="Llava-like models currently (transformers==4.39.1) requires an attention_mask input"
|
|
)
|
|
if config.model_type in ["paligemma"]:
|
|
self.skipTest(
|
|
"PaliGemma-like models currently (transformers==4.41.0) requires an attention_mask input"
|
|
)
|
|
if config.model_type in ["idefics", "idefics2", "idefics3"]:
|
|
self.skipTest(reason="Idefics currently (transformers==4.39.1) requires an image_attention_mask input")
|
|
model = model_class(config)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname)
|
|
model = model_class.from_pretrained(
|
|
tmpdirname,
|
|
torch_dtype=torch.float16,
|
|
attn_implementation={"decoder": "sdpa", "audio_encoder": None, "text_encoder": None},
|
|
)
|
|
model.to(torch_device)
|
|
|
|
inputs_dict.pop("attention_mask", None)
|
|
inputs_dict.pop("decoder_attention_mask", None)
|
|
|
|
for name, inp in inputs_dict.items():
|
|
if isinstance(inp, torch.Tensor) and inp.dtype in [torch.float32, torch.float16]:
|
|
inputs_dict[name] = inp.to(torch.float16)
|
|
|
|
with sdpa_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
|
|
_ = model(**inputs_dict)
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@mark.flash_attn_test
|
|
@slow
|
|
# Adapted from tests.test_modeling_common.ModelTesterMixin.test_flash_attn_2_inference_equivalence_right_padding
|
|
def test_flash_attn_2_inference_equivalence_right_padding(self):
|
|
for model_class in self.all_model_classes:
|
|
if not model_class._supports_flash_attn_2:
|
|
self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
|
|
|
|
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={"decoder": "flash_attention_2", "audio_encoder": None, "text_encoder": None},
|
|
)
|
|
model_fa.to(torch_device)
|
|
|
|
model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.bfloat16)
|
|
model.to(torch_device)
|
|
|
|
# Ignore copy
|
|
dummy_input = inputs_dict[model.main_input_name]
|
|
if dummy_input.dtype in [torch.float32, torch.float16]:
|
|
dummy_input = dummy_input.to(torch.bfloat16)
|
|
|
|
dummy_attention_mask = inputs_dict.get("attention_mask", None)
|
|
|
|
if dummy_attention_mask is not None:
|
|
# Ignore copy
|
|
dummy_attention_mask[:, :-1] = 1
|
|
dummy_attention_mask[:, -1:] = 0
|
|
|
|
# Ignore copy
|
|
decoder_input_ids = inputs_dict.get("decoder_input_ids", dummy_input)
|
|
# Ignore copy
|
|
outputs = model(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True)
|
|
# Ignore copy
|
|
outputs_fa = model_fa(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True)
|
|
|
|
logits = (
|
|
outputs.hidden_states[-1]
|
|
if not model.config.is_encoder_decoder
|
|
else outputs.decoder_hidden_states[-1]
|
|
)
|
|
logits_fa = (
|
|
outputs_fa.hidden_states[-1]
|
|
if not model.config.is_encoder_decoder
|
|
else outputs_fa.decoder_hidden_states[-1]
|
|
)
|
|
|
|
assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2)
|
|
|
|
# Ignore copy
|
|
other_inputs = {
|
|
"decoder_input_ids": decoder_input_ids,
|
|
"decoder_attention_mask": dummy_attention_mask,
|
|
"output_hidden_states": True,
|
|
}
|
|
# Ignore copy
|
|
if dummy_attention_mask is not None:
|
|
other_inputs["attention_mask"] = dummy_attention_mask
|
|
# Ignore copy
|
|
outputs = model(dummy_input, **other_inputs)
|
|
# Ignore copy
|
|
outputs_fa = model_fa(dummy_input, **other_inputs)
|
|
|
|
logits = (
|
|
outputs.hidden_states[-1]
|
|
if not model.config.is_encoder_decoder
|
|
else outputs.decoder_hidden_states[-1]
|
|
)
|
|
logits_fa = (
|
|
outputs_fa.hidden_states[-1]
|
|
if not model.config.is_encoder_decoder
|
|
else outputs_fa.decoder_hidden_states[-1]
|
|
)
|
|
|
|
assert torch.allclose(logits_fa[:-1], logits[:-1], atol=4e-2, rtol=4e-2)
|
|
|
|
@require_torch_sdpa
|
|
def test_sdpa_can_dispatch_composite_models(self):
|
|
if not self.has_attentions:
|
|
self.skipTest(reason="Model architecture does not support attentions")
|
|
|
|
if not self._is_composite:
|
|
self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
|
|
|
|
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_sdpa = model_class.from_pretrained(tmpdirname)
|
|
model_sdpa = model_sdpa.eval().to(torch_device)
|
|
|
|
audio_encoder_attn = "sdpa" if model.audio_encoder._supports_sdpa else "eager"
|
|
text_encoder_attn = "sdpa" if model.text_encoder._supports_sdpa else "eager"
|
|
decoder_attn = "sdpa" if model.decoder._supports_sdpa else "eager"
|
|
|
|
# `None` as it is the requested one which will be assigned to each sub-config
|
|
# Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present)
|
|
self.assertTrue(model_sdpa.audio_encoder.config._attn_implementation == audio_encoder_attn)
|
|
self.assertTrue(model_sdpa.text_encoder.config._attn_implementation == text_encoder_attn)
|
|
self.assertTrue(model_sdpa.decoder.config._attn_implementation == decoder_attn)
|
|
self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
|
|
model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
|
|
model_eager = model_eager.eval().to(torch_device)
|
|
|
|
self.assertTrue(model_eager.audio_encoder.config._attn_implementation == "eager")
|
|
self.assertTrue(model_eager.text_encoder.config._attn_implementation == "eager")
|
|
self.assertTrue(model_eager.decoder.config._attn_implementation == "eager")
|
|
self.assertTrue(model_eager.config._attn_implementation == "eager")
|
|
|
|
for name, submodule in model_eager.named_modules():
|
|
if "SdpaAttention" in submodule.__class__.__name__:
|
|
raise ValueError("The eager model should not have SDPA attention layers")
|
|
|
|
has_sdpa = False
|
|
for name, submodule in model_sdpa.named_modules():
|
|
if "SdpaAttention" in submodule.__class__.__name__:
|
|
has_sdpa = True
|
|
break
|
|
if not has_sdpa and model_sdpa.config.model_type != "falcon":
|
|
raise ValueError("The SDPA model should have SDPA attention layers")
|
|
|
|
def test_requires_grad_with_frozen_encoders(self):
|
|
config = self.model_tester.get_config()
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
model.freeze_audio_encoder()
|
|
|
|
audio_encoder_grads = [param.requires_grad for param in model.audio_encoder.parameters()]
|
|
text_encoder_grads = [param.requires_grad for param in model.text_encoder.parameters()]
|
|
|
|
self.assertFalse(all(audio_encoder_grads))
|
|
self.assertTrue(all(text_encoder_grads))
|
|
|
|
model = model_class(config)
|
|
model.freeze_text_encoder()
|
|
|
|
audio_encoder_grads = [param.requires_grad for param in model.audio_encoder.parameters()]
|
|
text_encoder_grads = [param.requires_grad for param in model.text_encoder.parameters()]
|
|
|
|
self.assertTrue(all(audio_encoder_grads))
|
|
self.assertFalse(all(text_encoder_grads))
|
|
|
|
@unittest.skip(
|
|
reason=(
|
|
"MusicGen has a custom set of generation tests that rely on `GenerationTesterMixin`, controlled by "
|
|
"`greedy_sample_model_classes`"
|
|
)
|
|
)
|
|
def test_generation_tester_mixin_inheritance(self):
|
|
pass
|
|
|
|
|
|
def get_bip_bip(bip_duration=0.125, duration=0.5, sample_rate=32000):
|
|
"""Produces a series of 'bip bip' sounds at a given frequency."""
|
|
timesteps = np.arange(int(duration * sample_rate)) / sample_rate
|
|
wav = np.cos(2 * math.pi * 440 * timesteps)
|
|
time_period = (timesteps % (2 * bip_duration)) / (2 * bip_duration)
|
|
envelope = time_period >= 0.5
|
|
return wav * envelope
|
|
|
|
|
|
def place_dict_on_device(dict_to_place, device):
|
|
for key in dict_to_place:
|
|
if dict_to_place[key] is not None and isinstance(dict_to_place[key], torch.Tensor):
|
|
dict_to_place[key] = dict_to_place[key].to(device)
|
|
return dict_to_place
|
|
|
|
|
|
@require_torch
|
|
class MusicgenIntegrationTests(unittest.TestCase):
|
|
@cached_property
|
|
def model(self):
|
|
return MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small").to(torch_device)
|
|
|
|
@cached_property
|
|
def processor(self):
|
|
return MusicgenProcessor.from_pretrained("facebook/musicgen-small")
|
|
|
|
@slow
|
|
def test_logits_text_prompt(self):
|
|
model = self.model
|
|
processor = self.processor
|
|
|
|
inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt")
|
|
|
|
# prepare the encoder inputs
|
|
input_ids = inputs.input_ids.to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
|
|
# prepare the decoder inputs
|
|
pad_token_id = model.generation_config.pad_token_id
|
|
decoder_input_ids = (
|
|
torch.ones((input_ids.shape[0] * model.decoder.num_codebooks, 1), dtype=torch.long).to(torch_device)
|
|
* pad_token_id
|
|
)
|
|
|
|
with torch.no_grad():
|
|
logits = model(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
decoder_input_ids=decoder_input_ids,
|
|
).logits
|
|
|
|
# fmt: off
|
|
EXPECTED_LOGITS = torch.tensor(
|
|
[
|
|
-0.9708, -3.0149, -4.6415, -1.4754, -0.2786, -2.3523, -2.6049, -6.7467,
|
|
-1.0206, -3.2984, -3.3968, -1.5108, -1.5786, -3.1493, -1.1503, -0.0545,
|
|
]
|
|
)
|
|
# fmt: on
|
|
|
|
self.assertTrue(logits.shape == (*decoder_input_ids.shape, model.decoder.config.vocab_size))
|
|
torch.testing.assert_close(logits[0, 0, :16].cpu(), EXPECTED_LOGITS, rtol=1e-4, atol=1e-4)
|
|
|
|
@slow
|
|
def test_logits_text_audio_prompt(self):
|
|
model = self.model
|
|
processor = self.processor
|
|
|
|
audio = [get_bip_bip(duration=0.5), get_bip_bip(duration=1.0)]
|
|
text = ["80s music", "Club techno"]
|
|
|
|
inputs = processor(audio=audio, text=text, padding=True, return_tensors="pt")
|
|
|
|
# prepare the text encoder inputs
|
|
input_ids = inputs.input_ids.to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
|
|
# prepare the audio encoder inputs
|
|
input_values = inputs.input_values.to(torch_device)
|
|
padding_mask = inputs.padding_mask.to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
logits = model(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
input_values=input_values,
|
|
padding_mask=padding_mask,
|
|
).logits
|
|
|
|
# fmt: off
|
|
EXPECTED_LOGITS = torch.tensor(
|
|
[
|
|
0.1841, -2.9324, -0.7898, 0.1857, 0.4971, -2.8685, -1.6525, -1.6541,
|
|
2.7757, -2.5942, -3.0959, -1.0120, -1.0147, -0.4605, -0.8885, 0.6820,
|
|
]
|
|
)
|
|
# fmt: on
|
|
|
|
self.assertTrue(logits.shape == (8, 50, 2048))
|
|
torch.testing.assert_close(logits[0, -1, :16].cpu(), EXPECTED_LOGITS, rtol=1e-4, atol=1e-4)
|
|
|
|
@slow
|
|
def test_generate_unconditional_greedy(self):
|
|
model = self.model
|
|
|
|
# only generate 1 sample with greedy - since it's deterministic all elements of the batch will be the same
|
|
unconditional_inputs = model.get_unconditional_inputs(num_samples=1)
|
|
unconditional_inputs = place_dict_on_device(unconditional_inputs, device=torch_device)
|
|
|
|
output_values = model.generate(**unconditional_inputs, do_sample=False, max_new_tokens=5)
|
|
|
|
# fmt: off
|
|
EXPECTED_VALUES = torch.tensor(
|
|
[
|
|
0.0056, 0.0064, 0.0063, 0.0054, 0.0042, 0.0033, 0.0024, 0.0015,
|
|
0.0015, 0.0010, 0.0004, -0.0012, -0.0036, -0.0055, -0.0067, -0.0071,
|
|
]
|
|
)
|
|
# fmt: on
|
|
|
|
self.assertTrue(output_values.shape == (1, 1, 3200))
|
|
torch.testing.assert_close(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, rtol=1e-4, atol=1e-4)
|
|
|
|
@slow
|
|
def test_generate_unconditional_sampling(self):
|
|
model = self.model
|
|
|
|
# for stochastic sampling we can generate multiple outputs
|
|
unconditional_inputs = model.get_unconditional_inputs(num_samples=2)
|
|
unconditional_inputs = place_dict_on_device(unconditional_inputs, device=torch_device)
|
|
|
|
set_seed(0)
|
|
output_values = model.generate(**unconditional_inputs, do_sample=True, max_new_tokens=10)
|
|
|
|
# fmt: off
|
|
EXPECTED_VALUES = torch.tensor(
|
|
[
|
|
-0.0099, -0.0140, 0.0079, 0.0080, -0.0046, 0.0065, -0.0068, -0.0185,
|
|
0.0105, 0.0059, 0.0329, 0.0249, -0.0204, -0.0341, -0.0465, 0.0053,
|
|
]
|
|
)
|
|
# fmt: on
|
|
|
|
self.assertTrue(output_values.shape == (2, 1, 4480))
|
|
torch.testing.assert_close(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, rtol=1e-4, atol=1e-4)
|
|
|
|
@slow
|
|
def test_generate_text_prompt_greedy(self):
|
|
model = self.model
|
|
processor = self.processor
|
|
|
|
inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt")
|
|
|
|
# prepare the encoder inputs
|
|
input_ids = inputs.input_ids.to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
|
|
output_values = model.generate(
|
|
input_ids, attention_mask=attention_mask, do_sample=False, guidance_scale=None, max_new_tokens=10
|
|
)
|
|
|
|
# fmt: off
|
|
EXPECTED_VALUES = torch.tensor(
|
|
[
|
|
-1.1998e-04, -2.2302e-04, 4.6296e-04, 1.0524e-03, 2.4827e-04,
|
|
-4.0288e-05, -1.2468e-04, 4.9846e-05, 7.1485e-04, 4.4197e-04,
|
|
]
|
|
)
|
|
# fmt: on
|
|
|
|
self.assertTrue(output_values.shape == (2, 1, 4480))
|
|
torch.testing.assert_close(output_values[0, 0, :10].cpu(), EXPECTED_VALUES, rtol=1e-4, atol=1e-4)
|
|
|
|
@slow
|
|
def test_generate_text_prompt_greedy_with_classifier_free_guidance(self):
|
|
model = self.model
|
|
processor = self.processor
|
|
|
|
inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt")
|
|
|
|
# prepare the encoder inputs
|
|
input_ids = inputs.input_ids.to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
|
|
output_values = model.generate(
|
|
input_ids, attention_mask=attention_mask, do_sample=False, guidance_scale=3, max_new_tokens=10
|
|
)
|
|
|
|
# fmt: off
|
|
EXPECTED_VALUES = torch.tensor(
|
|
[
|
|
0.0283, 0.0246, 0.0650, 0.0640, 0.0599, 0.0711, 0.0420, 0.0112,
|
|
0.0511, 0.0746, 0.1363, 0.1213, 0.0185, -0.0578, -0.0908, 0.0443,
|
|
]
|
|
)
|
|
# fmt: on
|
|
|
|
self.assertTrue(output_values.shape == (2, 1, 4480))
|
|
torch.testing.assert_close(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, rtol=1e-4, atol=1e-4)
|
|
|
|
@slow
|
|
def test_generate_text_prompt_sampling(self):
|
|
model = self.model
|
|
processor = self.processor
|
|
|
|
inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt")
|
|
|
|
# prepare the encoder inputs
|
|
input_ids = inputs.input_ids.to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
|
|
set_seed(0)
|
|
output_values = model.generate(
|
|
input_ids, attention_mask=attention_mask, do_sample=True, guidance_scale=None, max_new_tokens=10
|
|
)
|
|
|
|
# fmt: off
|
|
EXPECTED_VALUES = torch.tensor(
|
|
[
|
|
-0.0111, -0.0154, 0.0047, 0.0058, -0.0068, 0.0012, -0.0109, -0.0229,
|
|
0.0010, -0.0038, 0.0167, 0.0042, -0.0421, -0.0610, -0.0764, -0.0326,
|
|
]
|
|
)
|
|
# fmt: on
|
|
|
|
self.assertTrue(output_values.shape == (2, 1, 4480))
|
|
torch.testing.assert_close(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, rtol=1e-4, atol=1e-4)
|
|
|
|
@slow
|
|
def test_generate_text_audio_prompt(self):
|
|
model = self.model
|
|
processor = self.processor
|
|
|
|
audio = [get_bip_bip(duration=0.5), get_bip_bip(duration=1.0)]
|
|
text = ["80s music", "Club techno"]
|
|
|
|
inputs = processor(audio=audio, text=text, padding=True, return_tensors="pt")
|
|
inputs = place_dict_on_device(inputs, device=torch_device)
|
|
|
|
output_values = model.generate(**inputs, do_sample=False, guidance_scale=None, max_new_tokens=10)
|
|
|
|
# fmt: off
|
|
EXPECTED_VALUES = torch.tensor(
|
|
[
|
|
-0.0036, -0.0130, -0.0261, -0.0384, -0.0557, -0.0718, -0.0680, -0.0632,
|
|
-0.0529, -0.0403, -0.0289, -0.0198, -0.0136, -0.0101, -0.0095, -0.0040,
|
|
]
|
|
)
|
|
# fmt: on
|
|
|
|
self.assertTrue(
|
|
output_values.shape == (2, 1, 36480)
|
|
) # input values take shape 32000 and we generate from there
|
|
torch.testing.assert_close(output_values[0, 0, -16:].cpu(), EXPECTED_VALUES, rtol=2e-4, atol=2e-4)
|
|
|
|
|
|
@require_torch
|
|
class MusicgenStereoIntegrationTests(unittest.TestCase):
|
|
@cached_property
|
|
def model(self):
|
|
return MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-stereo-small").to(torch_device)
|
|
|
|
@cached_property
|
|
def processor(self):
|
|
return MusicgenProcessor.from_pretrained("facebook/musicgen-stereo-small")
|
|
|
|
@slow
|
|
def test_generate_unconditional_greedy(self):
|
|
model = self.model
|
|
|
|
# only generate 1 sample with greedy - since it's deterministic all elements of the batch will be the same
|
|
unconditional_inputs = model.get_unconditional_inputs(num_samples=1)
|
|
unconditional_inputs = place_dict_on_device(unconditional_inputs, device=torch_device)
|
|
|
|
output_values = model.generate(**unconditional_inputs, do_sample=False, max_new_tokens=12)
|
|
|
|
# fmt: off
|
|
EXPECTED_VALUES_LEFT = torch.tensor(
|
|
[
|
|
0.0017, 0.0004, 0.0004, 0.0005, 0.0002, 0.0002, -0.0002, -0.0013,
|
|
-0.0010, -0.0015, -0.0018, -0.0032, -0.0060, -0.0082, -0.0096, -0.0099,
|
|
]
|
|
)
|
|
EXPECTED_VALUES_RIGHT = torch.tensor(
|
|
[
|
|
0.0038, 0.0028, 0.0031, 0.0032, 0.0031, 0.0032, 0.0030, 0.0019,
|
|
0.0021, 0.0015, 0.0009, -0.0008, -0.0040, -0.0067, -0.0087, -0.0096,
|
|
]
|
|
)
|
|
# fmt: on
|
|
|
|
# (bsz, channels, seq_len)
|
|
self.assertTrue(output_values.shape == (1, 2, 5760))
|
|
torch.testing.assert_close(output_values[0, 0, :16].cpu(), EXPECTED_VALUES_LEFT, rtol=1e-4, atol=1e-4)
|
|
torch.testing.assert_close(output_values[0, 1, :16].cpu(), EXPECTED_VALUES_RIGHT, rtol=1e-4, atol=1e-4)
|
|
|
|
@slow
|
|
def test_generate_text_audio_prompt(self):
|
|
model = self.model
|
|
processor = self.processor
|
|
|
|
# create stereo inputs
|
|
audio = [get_bip_bip(duration=0.5)[None, :].repeat(2, 0), get_bip_bip(duration=1.0)[None, :].repeat(2, 0)]
|
|
text = ["80s music", "Club techno"]
|
|
|
|
inputs = processor(audio=audio, text=text, padding=True, return_tensors="pt")
|
|
inputs = place_dict_on_device(inputs, device=torch_device)
|
|
|
|
output_values = model.generate(**inputs, do_sample=False, guidance_scale=3.0, max_new_tokens=12)
|
|
|
|
# fmt: off
|
|
EXPECTED_VALUES_LEFT = torch.tensor(
|
|
[
|
|
0.2535, 0.2008, 0.1471, 0.0896, 0.0306, -0.0200, -0.0501, -0.0728,
|
|
-0.0832, -0.0856, -0.0867, -0.0884, -0.0864, -0.0866, -0.0744, -0.0430,
|
|
]
|
|
)
|
|
EXPECTED_VALUES_RIGHT = torch.tensor(
|
|
[
|
|
0.1695, 0.1213, 0.0732, 0.0239, -0.0264, -0.0705, -0.0935, -0.1103,
|
|
-0.1163, -0.1139, -0.1104, -0.1082, -0.1027, -0.1004, -0.0900, -0.0614,
|
|
]
|
|
)
|
|
# fmt: on
|
|
|
|
# (bsz, channels, seq_len)
|
|
self.assertTrue(output_values.shape == (2, 2, 37760))
|
|
# input values take shape 32000 and we generate from there - we check the last (generated) values
|
|
torch.testing.assert_close(output_values[0, 0, -16:].cpu(), EXPECTED_VALUES_LEFT, rtol=2e-4, atol=2e-4)
|
|
torch.testing.assert_close(output_values[0, 1, -16:].cpu(), EXPECTED_VALUES_RIGHT, rtol=2e-4, atol=2e-4)
|