Add xlstm model (#39665)
* Add xLSTM cleanly with optimizations. * Fix style. * Fix modeling test. * Make xLSTM package optional. * Fix: Update torch version check. * Fix: Bad variable naming in test. * Fix: Import structure cleaning with Ruff. * Fix: Update docstrings. * Fix: Mitigate unused config attr tests by explicit usage. * Fix: Skip tests, if xlstm library is not installed. * Feat: Enable longer context window for inference by chunking. * Fix: Make training test pass by lowering target accuracy. * Chore: Increase test verbosity for failing generation test. * Update docs/source/en/model_doc/xlstm.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Fix: Make xlstm available even without CUDA. * Chore: Remove unnecessary import. * Fix: Remove BOS insertion. * Chore: Improve xLSTMCache documentation. * Integrate basic xLSTM fallback code. * Chore: Remove unnecessary import. * Chore: Remove duplicate LayerNorm. * chore: update copyright, minor reformatting * fix: refactor mLSTMStateType due to missing torch import * fix: add missing import * Chore: Replace einops. * fix: apply ruff formatting * fix: run `make fix-copies` to re-generate dummy_pt_objects.py * fix: make type hints Python 3.9 compatible * fix: remove obsolete import * fix: remove obsolete method from docs * chore: remove obsolete `force_bos_token_insert` from config * Chore: Remove duplicated xLSTMCache class. * Fix: Formatting of modeling_xlstm.py * Chore: Remove xlstm package requirement from test. Re-add update_rnn_state. * Fix: Update xLSTMCache docstring. * Feat: Add proper initialization of xLSTM. * Chore: Re-format files. * Chore: Adapt format. * Fix: xLSTMCache import restructuring. * Fix: Add __all__ lists to modeling and configuration files. * Chore: Reformat. * Fix: Remove unnecessary update_rnn_state function. * Fix: Undo test accuracy quickfix. * Fix: Update copyright year, remvoe config copy. * Chore: Flatten all internal configs to xLSTMConfig. * Fix: Unused config variables check. * Chore: Remove unnecessary imports. * Fix: Unify xlstm cache argument from batch_size to max_batch_size. * Chore: Remove bad default arg value for xLSTMCache. * Chore: Rename core configuration arguments to HF default in xLSTM. * Chore: Fix formatting. * Fix: xLSTM Cache config access. * Fix: Update xlstm tests for config update. * Feat: Re-add embbeding_dim, num_blocks config options for compat with xLSTM-7B. * Fix: Configuration xLSTM python3.9 syntax. * Fix: Difference to main in test_utils.py assertion. * Fix: Bad syntax in xlstm config for python3.9. * Fix: xLSTMConfig docstring. * Fix: xLSTMConfig docstring. * Fix typing issues in xLSTM and BeiT, Paligemma. * Fix: Exclude xLSTM from test cache utils. * Chore: Fix style. * Chore: Fix format. * Chore: Remove unnecessary LayerNorm, NormLayer layer abstractions. * Chore: Remove asserts and replace with ValueErrors. * Chore: Update __init__.py structure of xLSTM. * Chore: Clean xLSTM initialization of weights. * Fix index names in modeling_xlstm.py * Update xlstm model test typing annotations. * Fix: Remove all asserts. * Revert changes to the main __init__.py * Fix: Move xLSTMCache to modeling_xlstm.py * Fix: Remove xLSTMForCausalLM mapping from modeling_auto.py * Remove xLSTMCache from dummy_pt_objects.py * Fix: Remove extended torchdynamo compilation check integrating cuda graph captures. * Revert test_cache_utils.py xLSTM change. * Fix: Move xLSTM init functions before init call. * Remove xLSTMCache from generation utils. * Fix: Clean xLSTM init functionality for recursive calls. * Fix: Move xLSTMCache before its first call. * Fix formatting. * Add partial docstring for xLSTMModel forward. * Fix xLSTMCache docstring in xLSTMModel. * Remove xLSTMCache from public documentation. Update auto_docstring. * Remove all agressive shape comments * style * Fix names * simplify * remove output_hidden_states * Update modeling_xlstm.py * Update modeling_xlstm.py * Update test_modeling_xlstm.py * Update modeling_xlstm.py * Update modeling_xlstm.py * fix * fix * style * style --------- Co-authored-by: Korbinian Poeppel <korbinian.poeppel@nx-ai.com> Co-authored-by: Korbinian Pöppel <37810656+kpoeppel@users.noreply.github.com> Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> Co-authored-by: Sebastian Böck <sebastian.boeck@nx-ai.com> Co-authored-by: Korbinian Poeppel <poeppel@ml.jku.at>
This commit is contained in:
Cyril Vallez
0
tests/models/xlstm/__init__.py
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tests/models/xlstm/__init__.py
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371
tests/models/xlstm/test_modeling_xlstm.py
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tests/models/xlstm/test_modeling_xlstm.py
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# Copyright 2025 NXAI GmbH. 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, is_torch_available, xLSTMConfig
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from transformers.testing_utils import require_read_token, require_torch, require_torch_gpu, slow, torch_device
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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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xLSTMForCausalLM,
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xLSTMModel,
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)
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from transformers.models.xlstm.modeling_xlstm import xLSTMBlock, xLSTMCache
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from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_2
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else:
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is_torch_greater_or_equal_than_2_2 = False
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class xLSTMModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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num_heads=2,
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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=128,
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qk_dim_factor=0.5,
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v_dim_factor=1.0,
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num_hidden_layers=2,
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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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chunkwise_kernel="chunkwise--native_autograd",
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sequence_kernel="native_sequence__native",
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step_kernel="native",
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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.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.num_hidden_layers = num_hidden_layers
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self.hidden_size = hidden_size
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self.qk_dim_factor = qk_dim_factor
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self.v_dim_factor = v_dim_factor
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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.chunkwise_kernel = chunkwise_kernel
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self.sequence_kernel = sequence_kernel
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self.step_kernel = step_kernel
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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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cfg = xLSTMConfig.from_pretrained("NX-AI/xLSTM-7b")
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return cfg
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def prepare_config_and_inputs(self, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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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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return (
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config,
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input_ids,
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None,
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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):
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cfg = xLSTMConfig(
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num_heads=self.num_heads,
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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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qk_dim_factor=self.qk_dim_factor,
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v_dim_factor=self.v_dim_factor,
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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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chunkwise_kernel=self.chunkwise_kernel,
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sequence_kernel=self.sequence_kernel,
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step_kernel=self.step_kernel,
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tie_word_embeddings=self.tie_word_embeddings,
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)
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# this is needed for compatibility with generic tests
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# cfg.hidden_size = cfg.embedding_dim
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# cfg.num_hidden_layers = cfg.num_blocks
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return cfg
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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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@require_torch
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class xLSTMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (xLSTMModel, xLSTMForCausalLM) if is_torch_available() else ()
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all_generative_model_classes = (xLSTMForCausalLM,) if is_torch_available() else ()
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has_attentions = False # xLSTM does not support attentions
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fx_compatible = False
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test_torchscript = False
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test_model_parallel = False
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test_pruning = False
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test_head_masking = False # xLSTM does not have attention heads
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pipeline_model_mapping = (
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{"feature-extraction": xLSTMModel, "text-generation": xLSTMForCausalLM} if is_torch_available() else {}
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)
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def setUp(self):
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self.model_tester = xLSTMModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=xLSTMConfig, n_embd=37, common_properties=["hidden_size", "num_hidden_layers"]
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)
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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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self.assertTrue(torch.allclose(param.data, torch.ones_like(param.data), atol=1e-5, rtol=1e-5))
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@unittest.skip(reason="xLSTM has no tied weights")
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def test_tied_weights_keys(self):
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pass
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@unittest.skip(reason="xLSTM cache slicing test case is an edge case")
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def test_generate_without_input_ids(self):
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pass
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@unittest.skip(reason="xLSTM cache slicing test case is an edge case")
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@parameterized.expand([("greedy", 1), ("beam search", 2)])
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def test_generate_from_inputs_embeds(self, _, num_beams):
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pass
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@unittest.skip(reason="xLSTM cache slicing test case is an edge case")
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def test_greedy_generate_dict_outputs_use_cache(self):
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pass
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@unittest.skip(reason="xLSTM cache slicing is interacting with beam search")
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def test_beam_search_generate_dict_outputs_use_cache(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, xLSTMCache):
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recursive_check(tuple_object.rnn_state, dict_object.rnn_state)
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elif isinstance(tuple_object, (list, tuple)):
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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 xLSTMIntegrationTest(unittest.TestCase):
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def setUp(self):
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self.model_id = "NX-AI/xLSTM-7b"
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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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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 = xLSTMForCausalLM.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_sentence = """<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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self.assertEqual(output_sentence, ground_truth_sentence)
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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 the xLSTM 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 = xLSTMForCausalLM.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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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 the xLSTM 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 = xLSTMForCausalLM.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)
|
||||
individual_output = tokenizer.batch_decode(individual_gen, skip_special_tokens=True)[0]
|
||||
self.assertEqual(individual_output[:100], batched_output[index_gen][:100])
|
||||
|
||||
@require_torch_gpu
|
||||
def test_xlstm_block_train_vs_eval_equivalence(self):
|
||||
# Based on https://github.com/sustcsonglin/flash-linear-attention/issues/63
|
||||
# Credit to zhixuan-lin
|
||||
|
||||
B, T, D = 4, 512, 768
|
||||
dtype = torch.bfloat16
|
||||
config = xLSTMConfig(num_heads=24, head_dim=64, hidden_size=768, expand=2, n_groups=1)
|
||||
|
||||
torch.manual_seed(42)
|
||||
with torch.amp.autocast(device_type="cuda", dtype=dtype):
|
||||
with torch.no_grad():
|
||||
block = xLSTMBlock(config.to_xlstm_block_config(), layer_idx=0).to("cuda")
|
||||
hidden_states = torch.rand(size=(B, T, D), dtype=dtype, device="cuda")
|
||||
|
||||
block.train()
|
||||
out_train = block(hidden_states)
|
||||
|
||||
block.eval()
|
||||
out_eval = block(hidden_states)
|
||||
|
||||
self.assertTrue(torch.allclose(out_train, out_eval, atol=1e-3))
|
||||
Reference in New Issue
Block a user