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https://github.com/huggingface/transformers.git
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935bbbc711
* Add config validation and style tweaks * Fix style issues * Fix style issues * style * Small fixes for copy/paste errors --------- Co-authored-by: Cyrile <cyrile.delestre@arkea.com>
484 lines
20 KiB
Python
484 lines
20 KiB
Python
# Copyright 2024 The HuggingFace 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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import unittest
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from parameterized import parameterized
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from transformers import AutoTokenizer, Mamba2Config, is_torch_available
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from transformers.testing_utils import (
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Expectations,
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require_read_token,
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require_torch,
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require_torch_accelerator,
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slow,
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torch_device,
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)
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from transformers.utils.import_utils import is_causal_conv1d_available, is_mamba_2_ssm_available
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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, ids_tensor
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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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Mamba2ForCausalLM,
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Mamba2Model,
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)
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from transformers.models.mamba2.modeling_mamba2 import Mamba2Cache, Mamba2Mixer
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class Mamba2ConfigTester(ConfigTester):
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def _create_config(self, hidden_size: int, num_heads: int, expand: int, head_dim: int):
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_input_dict = self.inputs_dict.copy()
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_input_dict["hidden_size"] = hidden_size
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_input_dict["num_heads"] = num_heads
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_input_dict["expand"] = expand
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_input_dict["head_dim"] = head_dim
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return self.config_class(**_input_dict)
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def test_hidden_size_compatibility(self):
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self._create_config(hidden_size=2, num_heads=2, expand=2, head_dim=2)
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self._create_config(hidden_size=4, num_heads=4, expand=2, head_dim=2)
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self._create_config(hidden_size=2, num_heads=4, expand=4, head_dim=2)
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with self.parent.assertRaises(ValueError):
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self._create_config(hidden_size=2, num_heads=4, expand=2, head_dim=4)
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with self.parent.assertRaises(ValueError):
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self._create_config(hidden_size=4, num_heads=2, expand=4, head_dim=2)
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def run_common_tests(self):
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self.test_hidden_size_compatibility()
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return super().run_common_tests()
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class Mamba2ModelTester:
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def __init__(
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self,
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parent,
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batch_size=14,
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num_heads=8,
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n_groups=8,
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state_size=2,
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head_dim=8,
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conv_kernel=4,
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chunk_size=8,
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seq_length=7,
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is_training=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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hidden_act="silu",
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hidden_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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num_labels=3,
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num_choices=4,
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scope=None,
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tie_word_embeddings=False,
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):
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self.parent = parent
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self.num_heads = num_heads
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self.n_groups = n_groups
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self.head_dim = head_dim
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self.state_size = state_size
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self.conv_kernel = conv_kernel
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self.chunk_size = chunk_size
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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.use_labels = use_labels
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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.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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self.bos_token_id = vocab_size - 1
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self.eos_token_id = vocab_size - 1
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self.pad_token_id = vocab_size - 1
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self.tie_word_embeddings = tie_word_embeddings
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def get_large_model_config(self):
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return Mamba2Config.from_pretrained("mistralai/Mamba-Codestral-7B-v0.1")
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def prepare_config_and_inputs(
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self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
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):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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# Only left padding is valid
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attention_mask = torch.ones(size=(self.batch_size, self.seq_length), device=input_ids.device, dtype=torch.long)
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attention_mask[0, :1] = 0
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config(
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gradient_checkpointing=gradient_checkpointing,
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)
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return (
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config,
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input_ids,
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attention_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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)
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def get_config(self, gradient_checkpointing=False):
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return Mamba2Config(
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head_dim=self.head_dim,
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num_heads=self.num_heads,
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n_groups=self.n_groups,
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state_size=self.state_size,
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conv_kernel=self.conv_kernel,
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chunk_size=self.chunk_size,
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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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activation_function=self.hidden_act,
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n_positions=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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use_cache=True,
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bos_token_id=self.bos_token_id,
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eos_token_id=self.eos_token_id,
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pad_token_id=self.pad_token_id,
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gradient_checkpointing=gradient_checkpointing,
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tie_word_embeddings=self.tie_word_embeddings,
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)
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def prepare_config_and_inputs_for_common(self):
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(
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config,
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input_ids,
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_,
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sequence_labels,
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token_labels,
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choice_labels,
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) = self.prepare_config_and_inputs()
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inputs_dict = {"input_ids": input_ids}
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return config, inputs_dict
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def create_and_check_mamba2_caching(self, config, input_ids, attention_mask, *args):
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model = Mamba2Model(config=config)
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model.to(torch_device)
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model.eval()
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output_whole = model(input_ids, attention_mask=attention_mask).last_hidden_state
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outputs = model(
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input_ids[:, :-1],
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attention_mask=attention_mask[:, :-1],
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use_cache=True,
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cache_position=torch.arange(0, config.conv_kernel, device=input_ids.device),
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)
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output_one = outputs.last_hidden_state
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# Using the state computed on the first inputs, we will get the same output
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outputs = model(
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input_ids[:, -1:],
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attention_mask=attention_mask[:, -1:],
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use_cache=True,
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cache_params=outputs.cache_params,
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cache_position=torch.arange(config.conv_kernel, config.conv_kernel + 1, device=input_ids.device),
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)
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output_two = outputs.last_hidden_state
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self.parent.assertTrue(
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torch.allclose(torch.cat([output_one, output_two], dim=1), output_whole, atol=1e-3, rtol=1e-3)
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)
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def create_and_check_mamba2_slow_vs_fast_forward(self, config, input_ids, *args, gradient_checkpointing=False):
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model = Mamba2Model(config)
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model.eval()
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if not (is_mamba_2_ssm_available() and is_causal_conv1d_available()):
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self.parent.skipTest(
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"This test needs the Mamba2 fast path. Skipping as the necessary packages have not been found."
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)
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if torch_device != "cuda":
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self.parent.skipTest("This test needs the Mamba2 fast path. Skipping as we need a cuda capable device.")
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model.to(torch_device)
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if gradient_checkpointing:
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model.gradient_checkpointing_enable()
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token_emb = model.embeddings(input_ids)
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outputs_fast = model.layers[0].mixer.cuda_kernels_forward(token_emb)
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outputs_slow = model.layers[0].mixer.torch_forward(token_emb)
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self.parent.assertTrue(torch.allclose(outputs_fast, outputs_slow, atol=1e-3, rtol=1e-3))
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@require_torch
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class Mamba2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (Mamba2Model, Mamba2ForCausalLM) if is_torch_available() else ()
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has_attentions = False # Mamba does not support attentions
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fx_compatible = False # FIXME let's try to support this @molbap
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test_torchscript = False # FIXME I think this should be doable @molbap @ArthurZucker
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test_missing_keys = False
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test_model_parallel = False
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test_pruning = False
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test_head_masking = False # Mamba does not have attention heads
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pipeline_model_mapping = (
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{"feature-extraction": Mamba2Model, "text-generation": Mamba2ForCausalLM} if is_torch_available() else {}
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)
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def setUp(self):
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self.model_tester = Mamba2ModelTester(self)
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self.config_tester = Mamba2ConfigTester(
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self, config_class=Mamba2Config, n_embd=37, common_properties=["hidden_size", "num_hidden_layers"]
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)
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def test_mamba2_caching(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_mamba2_caching(*config_and_inputs)
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def test_mamba2_slow_vs_fast_forward(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_mamba2_slow_vs_fast_forward(*config_and_inputs)
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# This test adjusts n_groups to half the original setting and effectively
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# creates a grouped SSD configuration in the mamba2 layers
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# See https://github.com/huggingface/transformers/pull/37533/
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def test_mamba2_slow_vs_fast_forward_grouped(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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config_and_inputs[0].n_groups //= 2
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self.model_tester.create_and_check_mamba2_slow_vs_fast_forward(*config_and_inputs)
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def test_initialization(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config=config)
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for name, param in model.named_parameters():
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if "D" in name:
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if param.requires_grad:
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# check if it's a ones like
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torch.testing.assert_close(param.data, torch.ones_like(param.data), rtol=1e-5, atol=1e-5)
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@unittest.skip(reason="Mamba 2 weights are not tied")
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def test_tied_weights_keys(self):
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pass
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@unittest.skip(reason="A large mamba2 would be necessary (and costly) for that")
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def test_multi_gpu_data_parallel_forward(self):
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pass
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def test_model_outputs_equivalence(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
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with torch.no_grad():
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tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
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dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
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def recursive_check(tuple_object, dict_object):
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if isinstance(tuple_object, Mamba2Cache): # MODIFIED PART START
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recursive_check(tuple_object.conv_states, dict_object.conv_states)
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recursive_check(tuple_object.ssm_states, dict_object.ssm_states)
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elif isinstance(tuple_object, (list, tuple)): # MODIFIED PART END
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for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
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recursive_check(tuple_iterable_value, dict_iterable_value)
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elif isinstance(tuple_object, dict):
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for tuple_iterable_value, dict_iterable_value in zip(
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tuple_object.values(), dict_object.values()
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):
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recursive_check(tuple_iterable_value, dict_iterable_value)
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elif tuple_object is None:
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return
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else:
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self.assertTrue(
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torch.allclose(tuple_object, dict_object, atol=1e-5),
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msg=(
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"Tuple and dict output are not equal. Difference:"
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f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
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f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
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f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
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),
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)
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recursive_check(tuple_output, dict_output)
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class)
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check_equivalence(model, tuple_inputs, dict_inputs)
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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check_equivalence(model, tuple_inputs, dict_inputs)
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class)
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check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
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@require_torch
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@slow
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@require_read_token
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class Mamba2IntegrationTest(unittest.TestCase):
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def setUp(self):
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self.model_id = "mistralai/Mamba-Codestral-7B-v0.1"
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_id, from_slow=True, legacy=False)
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self.prompt = ("[INST]Write a hello world program in C++.",)
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@require_read_token
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@parameterized.expand(
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[
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(torch_device,),
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]
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)
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@slow
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@require_torch
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def test_simple_generate(self, device):
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"""
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Simple generate test to avoid regressions.
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Note: state-spaces (cuda) implementation and pure torch implementation
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have irreconciliable differences as of now, which will cause this test to fail
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in an environment with state-spaces installed.
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"""
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tokenizer = self.tokenizer
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tokenizer.pad_token_id = tokenizer.eos_token_id
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model = Mamba2ForCausalLM.from_pretrained(self.model_id, torch_dtype=torch.bfloat16)
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model.to(device)
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input_ids = tokenizer("[INST]Write a hello world program in C++.[/INST]", return_tensors="pt")["input_ids"].to(
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device
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)
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out = model.generate(input_ids, do_sample=False, use_cache=True, max_new_tokens=30)
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output_sentence = tokenizer.decode(out[0])
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ground_truth_sentences = Expectations(
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{
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("xpu", 3): """<s>[INST]Write a hello world program in C++.[/INST] Sure, here is a simple "Hello, World!" program written in C++:\n\n```cpp\n#include <iostream>\n""",
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("cuda", 7): """<s>[INST]Write a hello world program in C++.[/INST] Sure, here is a simple "Hello, World!" program in C++:\n\n```cpp\n#include <iostream>\n\n""",
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}
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) # fmt: skip
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ground_truth_sentence = ground_truth_sentences.get_expectation()
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self.assertEqual(output_sentence, ground_truth_sentence)
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@require_read_token
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@slow
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@require_torch_accelerator
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def test_batched_equivalence_with_cache(self):
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"""
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Verifies that batched generation matches individual generation.
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Important because of the specific caching mechanism + statefulness of mamba model.
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Depending on precision and devices, differences can be observed from generation to generation.
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"""
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tokenizer = self.tokenizer
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prompt = [
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"[INST]Write C#.[/INST]",
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"[INST]Write a hello world in C++.[/INST]",
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"[INST] Write a simple Fibonacci number computation function in Rust that does memoization, with comments, in safe Rust.[/INST]",
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]
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model = Mamba2ForCausalLM.from_pretrained(self.model_id, torch_dtype=torch.bfloat16).to(torch_device)
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tokenizer.pad_token_id = tokenizer.eos_token_id
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# batched generation
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tokenized_prompts = tokenizer(prompt, return_tensors="pt", padding="longest").to(torch_device)
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batched_gen = model.generate(**tokenized_prompts, max_new_tokens=30, use_cache=True)
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batched_output = tokenizer.batch_decode(batched_gen, skip_special_tokens=True)
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# individual generation
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for index_gen, individual_prompt in enumerate(prompt):
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inputs = tokenizer(individual_prompt, return_tensors="pt", padding="longest").to(torch_device)
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individual_gen = model.generate(**inputs, max_new_tokens=30, use_cache=True)
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individual_output = tokenizer.batch_decode(individual_gen, skip_special_tokens=True)[0]
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self.assertEqual(individual_output[:100], batched_output[index_gen][:100])
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@require_read_token
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@slow
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@require_torch_accelerator
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def test_batched_equivalence_without_cache(self):
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"""
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Verifies that batched generation matches individual generation without cache.
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Important because of the specific caching mechanism + statefulness of mamba model.
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Depending on precision and devices, differences can be observed from generation to generation.
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"""
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tokenizer = self.tokenizer
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prompt = [
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"[INST]Write C#.[/INST]",
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"[INST]Write a hello world in C++.[/INST]",
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"[INST] Write a simple Fibonacci number computation function in Rust that does memoization, with comments, in safe Rust.[/INST]",
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]
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model = Mamba2ForCausalLM.from_pretrained(self.model_id, torch_dtype=torch.bfloat16).to(torch_device)
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tokenizer.pad_token_id = tokenizer.eos_token_id
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# batched generation
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tokenized_prompts = tokenizer(prompt, return_tensors="pt", padding="longest").to(torch_device)
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batched_gen = model.generate(**tokenized_prompts, max_new_tokens=30, use_cache=True)
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batched_output = tokenizer.batch_decode(batched_gen, skip_special_tokens=True)
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# individual generation
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for index_gen, individual_prompt in enumerate(prompt):
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inputs = tokenizer(individual_prompt, return_tensors="pt", padding="longest").to(torch_device)
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individual_gen = model.generate(**inputs, max_new_tokens=30, use_cache=True)
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individual_output = tokenizer.batch_decode(individual_gen, skip_special_tokens=True)[0]
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self.assertEqual(individual_output[:100], batched_output[index_gen][:100])
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@slow
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@require_torch_accelerator
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def test_mamba2_mixer_train_vs_eval_equivalence(self):
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# Based on https://github.com/sustcsonglin/flash-linear-attention/issues/63
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# Credit to zhixuan-lin
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B, T, D = 4, 512, 768
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dtype = torch.bfloat16
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config = Mamba2Config(num_heads=24, head_dim=64, hidden_size=768, expand=2, n_groups=1)
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torch.manual_seed(42)
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with torch.amp.autocast(device_type=torch_device, dtype=dtype):
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with torch.no_grad():
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mixer = Mamba2Mixer(config, layer_idx=0).to(torch_device)
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hidden_states = torch.rand(size=(B, T, D), dtype=dtype, device=torch_device)
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mixer.train()
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out_train = mixer(hidden_states)
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mixer.eval()
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out_eval = mixer(hidden_states)
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torch.testing.assert_close(out_train, out_eval, rtol=1e-3, atol=1e-3)
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