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transformers/tests/models/xlstm/__init__.py Normal file
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# Copyright 2025 NXAI GmbH. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, is_torch_available, xLSTMConfig
from transformers.testing_utils import require_read_token, require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
xLSTMForCausalLM,
xLSTMModel,
)
from transformers.models.xlstm.modeling_xlstm import xLSTMBlock, xLSTMCache
class xLSTMModelTester:
def __init__(
self,
parent,
batch_size=13,
num_heads=2,
seq_length=7,
is_training=True,
use_labels=True,
vocab_size=99,
hidden_size=128,
qk_dim_factor=0.5,
v_dim_factor=1.0,
num_hidden_layers=2,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
num_labels=3,
num_choices=4,
scope=None,
chunkwise_kernel="chunkwise--native_autograd",
sequence_kernel="native_sequence__native",
step_kernel="native",
tie_word_embeddings=False,
):
self.parent = parent
self.num_heads = num_heads
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.num_hidden_layers = num_hidden_layers
self.hidden_size = hidden_size
self.qk_dim_factor = qk_dim_factor
self.v_dim_factor = v_dim_factor
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.bos_token_id = vocab_size - 1
self.eos_token_id = vocab_size - 1
self.pad_token_id = vocab_size - 1
self.chunkwise_kernel = chunkwise_kernel
self.sequence_kernel = sequence_kernel
self.step_kernel = step_kernel
self.tie_word_embeddings = tie_word_embeddings
def get_large_model_config(self):
cfg = xLSTMConfig.from_pretrained("NX-AI/xLSTM-7b")
return cfg
def prepare_config_and_inputs(self, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return (
config,
input_ids,
None,
sequence_labels,
token_labels,
choice_labels,
)
def get_config(self):
cfg = xLSTMConfig(
num_heads=self.num_heads,
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
qk_dim_factor=self.qk_dim_factor,
v_dim_factor=self.v_dim_factor,
n_positions=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
use_cache=True,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
chunkwise_kernel=self.chunkwise_kernel,
sequence_kernel=self.sequence_kernel,
step_kernel=self.step_kernel,
tie_word_embeddings=self.tie_word_embeddings,
)
# this is needed for compatibility with generic tests
# cfg.hidden_size = cfg.embedding_dim
# cfg.num_hidden_layers = cfg.num_blocks
return cfg
def prepare_config_and_inputs_for_common(self):
(
config,
input_ids,
_,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
inputs_dict = {"input_ids": input_ids}
return config, inputs_dict
@require_torch
class xLSTMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (xLSTMModel, xLSTMForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (xLSTMForCausalLM,) if is_torch_available() else ()
has_attentions = False # xLSTM does not support attentions
fx_compatible = False
test_torchscript = False
test_model_parallel = False
test_pruning = False
test_head_masking = False # xLSTM does not have attention heads
pipeline_model_mapping = (
{"feature-extraction": xLSTMModel, "text-generation": xLSTMForCausalLM} if is_torch_available() else {}
)
def setUp(self):
self.model_tester = xLSTMModelTester(self)
self.config_tester = ConfigTester(
self, config_class=xLSTMConfig, n_embd=37, common_properties=["hidden_size", "num_hidden_layers"]
)
def test_initialization(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config=config)
for name, param in model.named_parameters():
if "D" in name:
if param.requires_grad:
# check if it's a ones like
self.assertTrue(torch.allclose(param.data, torch.ones_like(param.data), atol=1e-5, rtol=1e-5))
@unittest.skip(reason="xLSTM cache slicing test case is an edge case")
def test_generate_without_input_ids(self):
pass
@unittest.skip(reason="xLSTM cache slicing test case is an edge case")
@parameterized.expand([("greedy", 1), ("beam search", 2)])
def test_generate_from_inputs_embeds(self, _, num_beams):
pass
@unittest.skip(reason="xLSTM cache slicing test case is an edge case")
def test_greedy_generate_dict_outputs_use_cache(self):
pass
@unittest.skip(reason="xLSTM cache slicing is interacting with beam search")
def test_beam_search_generate_dict_outputs_use_cache(self):
pass
@unittest.skip(reason="xLSTM cache is not iterable")
def test_multi_gpu_data_parallel_forward(self):
pass
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, xLSTMCache):
recursive_check(tuple_object.rnn_state, dict_object.rnn_state)
elif isinstance(tuple_object, (list, tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif isinstance(tuple_object, dict):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values(), dict_object.values()
):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(tuple_object, dict_object, atol=1e-5),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
@require_torch
@slow
@require_read_token
@unittest.skip("Model is fully broken currently")
class xLSTMIntegrationTest(unittest.TestCase):
def setUp(self):
self.model_id = "NX-AI/xLSTM-7b"
self.tokenizer = AutoTokenizer.from_pretrained(self.model_id, legacy=False)
self.prompt = ("[INST]Write a hello world program in C++.",)
def test_simple_generate(self):
"""
Simple generate test to avoid regressions.
Note: state-spaces (cuda) implementation and pure torch implementation
have irreconciliable differences as of now, which will cause this test to fail
in an environment with state-spaces installed.
"""
tokenizer = self.tokenizer
tokenizer.pad_token_id = tokenizer.eos_token_id
model = xLSTMForCausalLM.from_pretrained(self.model_id, dtype=torch.bfloat16, device_map=torch_device)
input_ids = tokenizer("[INST]Write a hello world program in C++.[/INST]", return_tensors="pt")["input_ids"].to(
torch_device
)
out = model.generate(input_ids, do_sample=False, use_cache=True, max_new_tokens=30)
output_sentence = tokenizer.decode(out[0])
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"""
self.assertEqual(output_sentence, ground_truth_sentence)
def test_batched_equivalence_with_cache(self):
"""
Verifies that batched generation matches individual generation.
Important because of the specific caching mechanism + statefulness of the xLSTM model.
Depending on precision and devices, differences can be observed from generation to generation.
"""
tokenizer = self.tokenizer
prompt = [
"[INST]Write C#.[/INST]",
"[INST]Write a hello world in C++.[/INST]",
"[INST] Write a simple Fibonacci number computation function in Rust that does memoization, with comments, in safe Rust.[/INST]",
]
model = xLSTMForCausalLM.from_pretrained(self.model_id, dtype=torch.bfloat16, device_map=torch_device)
tokenizer.pad_token_id = tokenizer.eos_token_id
# batched generation
tokenized_prompts = tokenizer(prompt, return_tensors="pt", padding="longest").to(torch_device)
batched_gen = model.generate(**tokenized_prompts, max_new_tokens=30, use_cache=True)
batched_output = tokenizer.batch_decode(batched_gen, skip_special_tokens=True)
# individual generation
for index_gen, individual_prompt in enumerate(prompt):
inputs = tokenizer(individual_prompt, return_tensors="pt", padding="longest").to(torch_device)
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])
def test_batched_equivalence_without_cache(self):
"""
Verifies that batched generation matches individual generation without cache.
Important because of the specific caching mechanism + statefulness of the xLSTM model.
Depending on precision and devices, differences can be observed from generation to generation.
"""
tokenizer = self.tokenizer
prompt = [
"[INST]Write C#.[/INST]",
"[INST]Write a hello world in C++.[/INST]",
"[INST] Write a simple Fibonacci number computation function in Rust that does memoization, with comments, in safe Rust.[/INST]",
]
model = xLSTMForCausalLM.from_pretrained(self.model_id, dtype=torch.bfloat16, device_map=torch_device)
tokenizer.pad_token_id = tokenizer.eos_token_id
# batched generation
tokenized_prompts = tokenizer(prompt, return_tensors="pt", padding="longest").to(torch_device)
batched_gen = model.generate(**tokenized_prompts, max_new_tokens=30, use_cache=True)
batched_output = tokenizer.batch_decode(batched_gen, skip_special_tokens=True)
# individual generation
for index_gen, individual_prompt in enumerate(prompt):
inputs = tokenizer(individual_prompt, return_tensors="pt", padding="longest").to(torch_device)
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()).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))