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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch Jamba model."""
import math
import tempfile
import unittest
import pytest
from transformers import AutoTokenizer, JambaConfig, is_torch_available
from transformers.testing_utils import (
DeviceProperties,
Expectations,
get_device_properties,
is_flaky,
require_bitsandbytes,
require_flash_attn,
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, _config_zero_init, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
JambaForCausalLM,
JambaForSequenceClassification,
JambaModel,
)
from transformers.models.jamba.modeling_jamba import (
HybridMambaAttentionDynamicCache,
)
class JambaConfigTester(ConfigTester):
def _create_attn_config(self, attn_layer_offset: int, attn_layer_period: int):
_input_dict = self.inputs_dict.copy()
_input_dict["attn_layer_offset"] = attn_layer_offset
_input_dict["attn_layer_period"] = attn_layer_period
return self.config_class(**_input_dict)
def _create_expert_config(self, expert_layer_offset: int, expert_layer_period: int):
_input_dict = self.inputs_dict.copy()
_input_dict["expert_layer_offset"] = expert_layer_offset
_input_dict["expert_layer_period"] = expert_layer_period
return self.config_class(**_input_dict)
def test_attn_offsets(self):
self._create_attn_config(attn_layer_offset=0, attn_layer_period=4)
self._create_attn_config(attn_layer_offset=1, attn_layer_period=4)
self._create_attn_config(attn_layer_offset=2, attn_layer_period=4)
self._create_attn_config(attn_layer_offset=3, attn_layer_period=4)
with self.parent.assertRaises(ValueError):
self._create_attn_config(attn_layer_offset=4, attn_layer_period=4)
with self.parent.assertRaises(ValueError):
self._create_attn_config(attn_layer_offset=5, attn_layer_period=4)
def test_expert_offsets(self):
self._create_expert_config(expert_layer_offset=0, expert_layer_period=4)
self._create_expert_config(expert_layer_offset=1, expert_layer_period=4)
self._create_expert_config(expert_layer_offset=2, expert_layer_period=4)
self._create_expert_config(expert_layer_offset=3, expert_layer_period=4)
with self.parent.assertRaises(ValueError):
self._create_expert_config(expert_layer_offset=4, expert_layer_period=4)
with self.parent.assertRaises(ValueError):
self._create_expert_config(expert_layer_offset=5, expert_layer_period=4)
def test_jamba_offset_properties(self):
self.test_attn_offsets()
self.test_expert_offsets()
def run_common_tests(self):
self.test_jamba_offset_properties()
return super().run_common_tests()
class JambaModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
attn_layer_offset=1,
attn_layer_period=8,
num_attention_heads=2,
num_key_value_heads=2,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.attn_layer_offset = attn_layer_offset
self.attn_layer_period = attn_layer_period
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_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.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
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, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return JambaConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
attn_layer_offset=self.attn_layer_offset,
attn_layer_period=self.attn_layer_period,
num_attention_heads=self.num_attention_heads,
num_key_value_heads=self.num_key_value_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=True,
initializer_range=self.initializer_range,
use_mamba_kernels=False,
num_experts=2,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = JambaModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = JambaForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids, labels=token_labels)
result = model(input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.is_decoder = True
config.add_cross_attention = True
model = JambaForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
# Attention: Jamba needs the cache to be initialized to return a cache!
past_key_values = HybridMambaAttentionDynamicCache(
config, input_ids.shape[0], model.dtype, device=model.device
)
outputs = model(
input_ids,
attention_mask=input_mask,
past_key_values=past_key_values,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
cache_position=torch.arange(
input_ids.shape[1], input_ids.shape[1] + next_tokens.shape[1], device=model.device
),
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_sequence_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = JambaForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class JambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
JambaModel,
JambaForCausalLM,
JambaForSequenceClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": JambaModel,
"text-classification": JambaForSequenceClassification,
"text-generation": JambaForCausalLM,
"zero-shot": JambaForSequenceClassification,
}
if is_torch_available()
else {}
)
test_headmasking = False
test_pruning = False
def _check_past_key_values_for_generate(self, batch_size, decoder_past_key_values, cache_length, config):
self.assertIsInstance(decoder_past_key_values, HybridMambaAttentionDynamicCache)
# (batch, head, seq_length, head_features)
expected_shape = (
batch_size,
config.num_key_value_heads if hasattr(config, "num_key_value_heads") else config.num_attention_heads,
cache_length,
config.hidden_size // config.num_attention_heads,
)
self.assertListEqual(
[key_tensor.shape for key_tensor in decoder_past_key_values.key_cache],
[expected_shape] * len(decoder_past_key_values.key_cache),
)
self.assertListEqual(
[value_cache.shape for value_cache in decoder_past_key_values.value_cache],
[expected_shape] * len(decoder_past_key_values.value_cache),
)
def setUp(self):
self.model_tester = JambaModelTester(self)
self.config_tester = JambaConfigTester(self, config_class=JambaConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
# After #40617, we still have 0.01 % of failure rate here.
@is_flaky(max_attempts=2)
def test_load_balancing_loss(self):
r"""
Let's make sure we can actually compute the loss and do a backward on it.
"""
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.num_experts = 3
config.output_router_logits = True
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(config.pad_token_id).to(torch_device)
model = JambaForCausalLM(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask)
bs, seqlen = input_ids.shape
self.assertEqual(result.router_logits[0].shape, (bs * seqlen, config.num_experts))
# After #40617, we still have 0.01 % of failure rate here.
torch.testing.assert_close(result.aux_loss.cpu(), torch.tensor(2, dtype=torch.float32), rtol=1e-2, atol=1e-2)
# First, we make sure that adding padding tokens doesn't change the loss
# loss(input_ids, attention_mask=None) == loss(input_ids + padding, attention_mask=attention_mask_with_padding)
# (This length is selected from experiments)
pad_length = input_ids.shape[1] * 4
# Add padding tokens to input_ids
padding_block = config.pad_token_id * torch.ones(input_ids.shape[0], pad_length, dtype=torch.int32).to(
torch_device
)
padded_input_ids = torch.cat((padding_block, input_ids), dim=1) # this is to simulate padding to the left
padded_attention_mask = padded_input_ids.ne(config.pad_token_id).to(torch_device)
padded_result = model(padded_input_ids, attention_mask=padded_attention_mask)
torch.testing.assert_close(result.aux_loss.cpu(), padded_result.aux_loss.cpu(), rtol=1e-4, atol=1e-4)
# We make sure that the loss of including padding tokens != the loss without padding tokens
# if attention_mask=None --> we don't exclude padding tokens
include_padding_result = model(padded_input_ids, attention_mask=None)
# This is to mimic torch.testing.assert_not_close
# After #40617, we still have 0.003 % of failure rate here.
self.assertNotAlmostEqual(include_padding_result.aux_loss.item(), result.aux_loss.item())
def test_initialization(self):
r"""
Overriding the test_initialization test as the A_log and D params of the Mamba block are initialized differently
"""
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():
if param.requires_grad:
if "A_log" in name:
A = torch.arange(1, config.mamba_d_state + 1, dtype=torch.float32)[None, :]
A = A.expand(config.mamba_expand * config.hidden_size, -1).contiguous()
torch.testing.assert_close(param.data, torch.log(A), rtol=1e-5, atol=1e-5)
elif "D" in name:
# check if it's a ones like
torch.testing.assert_close(param.data, torch.ones_like(param.data), rtol=1e-5, atol=1e-5)
else:
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",
)
def test_attention_outputs(self):
r"""
Overriding the test_attention_outputs test as the Jamba model outputs attention only for its attention layers
"""
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
expected_num_attentions = math.ceil(
(self.model_tester.num_hidden_layers - self.model_tester.attn_layer_offset)
/ self.model_tester.attn_layer_period
)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class._from_config(config, attn_implementation="eager")
config = model.config
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), expected_num_attentions)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
@require_flash_attn
@require_torch_gpu
@require_bitsandbytes
@pytest.mark.flash_attn_test
@slow
def test_flash_attn_2_fp32_ln(self):
r"""
Overriding the test_flash_attn_2_fp32_ln test as the Jamba model, like Mixtral, doesn't support
right padding + use cache with FA2
"""
for model_class in self.all_generative_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)
dummy_input = inputs_dict[model.main_input_name]
dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input))
# NOTE: Jamba does not support right padding + use_cache with FA2.
dummy_attention_mask[:, -1] = 1
model = model_class.from_pretrained(
tmpdirname,
dtype=torch.float16,
attn_implementation="flash_attention_2",
load_in_4bit=True,
)
for _, param in model.named_parameters():
# upcast only layer norms
if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):
param.data = param.data.to(torch.float32)
_ = model(dummy_input)
# with attention mask
_ = model(dummy_input, attention_mask=dummy_attention_mask)
@require_torch
class JambaModelIntegrationTest(unittest.TestCase):
model = None
tokenizer = None
# This variable is used to determine which acclerator are we using for our runners (e.g. A10 or T4)
# Depending on the hardware we get different logits / generations
device_properties: DeviceProperties = (None, None, None)
@classmethod
def setUpClass(cls):
model_id = "ai21labs/Jamba-tiny-dev"
cls.model = JambaForCausalLM.from_pretrained(
model_id,
dtype=torch.bfloat16,
)
cls.tokenizer = AutoTokenizer.from_pretrained(model_id)
cls.device_properties = get_device_properties()
@slow
def test_simple_generate(self):
# ("cuda", 8) for A100/A10, and ("cuda", 7) for T4.
#
# considering differences in hardware processing and potential deviations in generated text.
# fmt: off
EXPECTED_TEXTS = Expectations(
{
("cuda", 7): "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh<|reserved_797|>cw algunas",
("cuda", 8): "<|startoftext|>Hey how are you doing on this lovely evening? I'm so glad you're here.",
("rocm", 9): "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew llam bb",
}
)
# fmt: on
expected_sentence = EXPECTED_TEXTS.get_expectation()
self.model.to(torch_device)
input_ids = self.tokenizer("Hey how are you doing on this lovely evening?", return_tensors="pt")[
"input_ids"
].to(torch_device)
out = self.model.generate(input_ids, do_sample=False, max_new_tokens=10)
output_sentence = self.tokenizer.decode(out[0, :])
self.assertEqual(output_sentence, expected_sentence)
# TODO: there are significant differences in the logits across major cuda versions, which shouldn't exist
if self.device_properties[0] == "cuda" and self.device_properties[1] == 8:
with torch.no_grad():
logits = self.model(input_ids=input_ids).logits
EXPECTED_LOGITS_NO_GRAD = torch.tensor(
[
-7.6875, -7.6562, 8.9375, -7.7812, -7.4062, -7.9688, -8.3125, -7.4062,
-7.8125, -8.1250, -7.8125, -7.3750, -7.8438, -7.5000, -8.0625, -8.0625,
-7.5938, -7.9688, -8.2500, -7.5625, -7.7500, -7.7500, -7.6562, -7.6250,
-8.1250, -8.0625, -8.1250, -7.8750, -8.1875, -8.2500, -7.5938, -8.0000,
-7.5000, -7.7500, -7.9375, -7.4688, -8.0625, -7.3438, -8.0000, -7.5000
]
, dtype=torch.float32) # fmt: skip
torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)
@slow
def test_simple_batched_generate_with_padding(self):
# ("cuda", 8) for A100/A10, and ("cuda", 7) for T4.
#
# considering differences in hardware processing and potential deviations in generated text.
# fmt: off
EXPECTED_TEXTS = Expectations(
{
("cuda", 7): ["<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew cases Cats", "<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",],
("cuda", 8): ["<|startoftext|>Hey how are you doing on this lovely evening? I'm so glad you're here.", "<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a story about a woman who was born in the United States",],
("rocm", 9): ["<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh<|reserved_797|>cw algunas", "<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",],
}
)
# fmt: on
expected_sentences = EXPECTED_TEXTS.get_expectation()
self.model.to(torch_device)
inputs = self.tokenizer(
["Hey how are you doing on this lovely evening?", "Tell me a story"], padding=True, return_tensors="pt"
).to(torch_device)
out = self.model.generate(**inputs, do_sample=False, max_new_tokens=10)
output_sentences = self.tokenizer.batch_decode(out)
self.assertEqual(output_sentences[0], expected_sentences[0])
self.assertEqual(output_sentences[1], expected_sentences[1])
# TODO: there are significant differences in the logits across major cuda versions, which shouldn't exist
if self.device_properties[0] == "cuda" and self.device_properties[1] == 8:
with torch.no_grad():
logits = self.model(input_ids=inputs["input_ids"]).logits
# TODO fix logits
EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
[
-7.7188, -7.6875, 8.8750, -7.8125, -7.4062, -8.0000, -8.3125, -7.4375,
-7.8125, -8.1250, -7.8125, -7.4062, -7.8438, -7.5312, -8.0625, -8.0625,
-7.6250, -8.0000, -8.3125, -7.5938, -7.7500, -7.7500, -7.6562, -7.6562,
-8.1250, -8.0625, -8.1250, -7.8750, -8.1875, -8.2500, -7.5938, -8.0625,
-7.5000, -7.7812, -7.9375, -7.4688, -8.0625, -7.3750, -8.0000, -7.50003
]
, dtype=torch.float32) # fmt: skip
EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor(
[
-3.5469, -4.0625, 8.5000, -3.8125, -3.6406, -3.7969, -3.8125, -3.3594,
-3.7188, -3.7500, -3.7656, -3.5469, -3.7969, -4.0000, -3.5625, -3.6406,
-3.7188, -3.6094, -4.0938, -3.6719, -3.8906, -3.9844, -3.8594, -3.4219,
-3.2031, -3.4375, -3.7500, -3.6562, -3.9688, -4.1250, -3.6406, -3.57811,
-3.0312, -3.4844, -3.6094, -3.5938, -3.7656, -3.8125, -3.7500, -3.8594
]
, dtype=torch.float32) # fmt: skip
torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_0, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(logits[1, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_1, rtol=1e-3, atol=1e-3)