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Add Zamba2 (#34517)

Browse Source 
* First commit

* Finish model implementation

* First commit

* Finish model implementation

* Register zamba2

* generated modeling and configuration

* generated modeling and configuration

* added hybrid cache

* fix attention_mask in mamba

* dropped unused loras

* fix flash2

* config docstrings

* fix config and fwd pass

* make fixup fixes

* text_modeling_zamba2

* small fixes

* make fixup fixes

* Fix modular model converter

* added inheritances in modular, renamed zamba cache

* modular rebase

* new modular conversion

* fix generated modeling file

* fixed import for Zamba2RMSNormGated

* modular file cleanup

* make fixup and model tests

* dropped inheritance for Zamba2PreTrainedModel

* make fixup and unit tests

* Add inheritance of rope from GemmaRotaryEmbedding

* moved rope to model init

* drop del self.self_attn and del self.feed_forward

* fix tests

* renamed lora -> adapter

* rewrote adapter implementation

* fixed tests

* Fix torch_forward in mamba2 layer

* Fix torch_forward in mamba2 layer

* Fix torch_forward in mamba2 layer

* Dropped adapter in-place sum

* removed rope from attention init

* updated rope

* created get_layers method

* make fixup fix

* make fixup fixes

* make fixup fixes

* update to new attention standard

* update to new attention standard

* make fixup fixes

* minor fixes

* cache_position

* removed cache_position postion_ids use_cache

* remove config from modular

* removed config from modular (2)

* import apply_rotary_pos_emb from llama

* fixed rope_kwargs

* Instantiate cache in Zamba2Model

* fix cache

* fix @slow decorator

* small fix in modular file

* Update docs/source/en/model_doc/zamba2.md

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* several minor fixes

* inherit mamba2decoder fwd and drop position_ids in mamba

* removed docstrings from modular

* reinstate zamba2 attention decoder fwd

* use regex for tied keys

* Revert "use regex for tied keys"

This reverts commit 9007a522b1f831df6d516a281c0d3fdd20a118f5.

* use regex for tied keys

* add cpu to slow forward tests

* dropped config.use_shared_mlp_adapter

* Update docs/source/en/model_doc/zamba2.md

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* re-convert from modular

---------

Co-authored-by: root <root@node-2.us-southcentral1-a.compute.internal>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
This commit is contained in:
pglorio
2025-01-27 01:51:23 -08:00
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parent 14a9bb520e
commit 33cb1f7b61
18 changed files with 4148 additions and 12 deletions
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tests/models/zamba2/__init__.py Normal file
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# coding=utf-8
# 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 Zamba model."""
import math
import tempfile
import unittest
import pytest
from parameterized import parameterized
from transformers import AutoTokenizer, Zamba2Config, is_torch_available
from transformers.testing_utils import (
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 (
Zamba2ForCausalLM,
Zamba2ForSequenceClassification,
Zamba2Model,
)
from transformers.models.zamba2.modeling_zamba2 import (
Zamba2HybridDynamicCache,
)
class Zamba2ModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
hidden_size=16,
mamba_d_state=2,
chunk_size=8,
mamba_dt_rank="auto",
num_hidden_layers=2,
num_attention_heads=2,
n_mamba_heads=8,
mamba_ngroups=8,
intermediate_size=4,
hidden_act="gelu",
hidden_mamba_act="silu",
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,
layers_block_type=["mamba", "hybrid"],
num_mem_blocks=1,
use_mem_rope=True,
):
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.mamba_dt_rank = mamba_dt_rank
self.mamba_d_state = mamba_d_state
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.n_mamba_heads = n_mamba_heads
self.mamba_ngroups = mamba_ngroups
self.chunk_size = chunk_size
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_mamba_act = hidden_mamba_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
self.layers_block_type = layers_block_type
self.num_mem_blocks = num_mem_blocks
self.use_mem_rope = use_mem_rope
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 Zamba2Config(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
mamba_dt_rank=self.mamba_dt_rank,
mamba_d_state=self.mamba_d_state,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
n_mamba_heads=self.n_mamba_heads,
intermediate_size=self.intermediate_size,
chunk_size=self.chunk_size,
hidden_act=self.hidden_act,
mamba_ngroups=self.mamba_ngroups,
hidden_mamba_act=self.hidden_mamba_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,
layers_block_type=self.layers_block_type,
num_mem_blocks=self.num_mem_blocks,
use_mem_rope=self.use_mem_rope,
)
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 = Zamba2Model(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 = Zamba2ForCausalLM(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 = False
model = Zamba2ForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
# Attention: Zamba2 needs the cache to be initialized to return a cache!
past_key_values = Zamba2HybridDynamicCache(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, 1), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 1), 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[:, -1:, 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 = Zamba2ForSequenceClassification(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 Zamba2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
test_torchscript = False
all_model_classes = (
(
Zamba2Model,
Zamba2ForCausalLM,
Zamba2ForSequenceClassification,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (Zamba2ForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": Zamba2Model,
"text-classification": Zamba2ForSequenceClassification,
"text-generation": Zamba2ForCausalLM,
"zero-shot": Zamba2ForSequenceClassification,
}
if is_torch_available()
else {}
)
test_headmasking = False
test_pruning = False
def setUp(self):
self.model_tester = Zamba2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=Zamba2Config, hidden_size=37)
@unittest.skip("position_ids cannot be used to pad due to Mamba2 layers")
def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
pass
@unittest.skip("Zamba2 has a hybrid cache")
def test_past_key_values_format(self):
r"""
Zamba2's cache shape depends on whether a given layer is mamba or attention.
For mamba layers, the KV cache has shape is empty and has shape [batch_size, 0].
The shape checks of this test assume instead that every layer has an attention cache, so we skip it.
"""
pass
@unittest.skip(reason="A large mamba2 would be necessary (and costly) for that")
def test_multi_gpu_data_parallel_forward(self):
pass
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)
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.n_mamba_heads + 1, dtype=torch.float32)[None, :]
self.assertTrue(torch.allclose(param.data, torch.log(A), atol=1e-5, rtol=1e-5))
elif "D" in name:
# check if it's a ones like
self.assertTrue(torch.allclose(param.data, torch.ones_like(param.data), atol=1e-5, rtol=1e-5))
elif "dt_bias" in name:
dt = torch.exp(
torch.tensor([0, 1]) * (math.log(config.time_step_max) - math.log(config.time_step_min))
+ math.log(config.time_step_min)
).clamp(min=config.time_step_floor)
inv_dt = dt + torch.log(-torch.expm1(-dt))
if param.requires_grad:
self.assertTrue(param.data.max().item() <= inv_dt[1])
self.assertTrue(param.data.min().item() >= inv_dt[0])
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",
)
@unittest.skip(reason="Cumbersome and redundant for Zamba2")
def test_mismatched_shapes_have_properly_initialized_weights(self):
r"""
Overriding the test_mismatched_shapes_have_properly_initialized_weights test because A_log and D params of the
Mamba block are initialized differently and we tested that in test_initialization
"""
pass
def test_attention_outputs(self):
r"""
Overriding the test_attention_outputs test as the Zamba2 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)
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(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.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.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.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
def _get_input_ids_and_config(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
return config, input_ids, input_mask
def test_left_padding_compatibility(self):
r"""
Overriding the test_left_padding_compatibility test as the mamba layers accentuate the numerical differences
effect of the left padding discussed in the issue in the note. Using a more permissive tolerance value.
"""
import inspect
# NOTE: left-padding results in small numerical differences. This is expected.
# See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535
# First, filter out models that don't support left padding - generative and decoder-only.
# Zamba2 is a decoder-only architecture
decoder_only_classes = self.all_generative_model_classes
# Then, test left-padding
def _prepare_model_kwargs(input_ids, attention_mask, signature):
model_kwargs = {"input_ids": input_ids, "attention_mask": attention_mask}
if "position_ids" in signature:
position_ids = torch.cumsum(attention_mask, dim=-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
model_kwargs["position_ids"] = position_ids
if "cache_position" in signature:
cache_position = torch.arange(input_ids.shape[-1], device=torch_device)
model_kwargs["cache_position"] = cache_position
return model_kwargs
for model_class in decoder_only_classes:
config, input_ids, attention_mask = self._get_input_ids_and_config()
model = model_class(config).to(torch_device).eval()
signature = inspect.signature(model.forward).parameters.keys()
# Without padding
model_kwargs = _prepare_model_kwargs(input_ids, attention_mask, signature)
next_logits_wo_padding = model(**model_kwargs).logits[:, -1, :]
# With left-padding (length 32)
pad_size = (input_ids.shape[0], 32)
padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * config.pad_token_id
padded_input_ids = torch.cat((padding, input_ids), dim=1)
padded_attention_mask = torch.cat((torch.zeros_like(padding), attention_mask), dim=1)
model_kwargs = _prepare_model_kwargs(padded_input_ids, padded_attention_mask, signature)
next_logits_with_padding = model(**model_kwargs).logits[:, -1, :]
# They should result in very similar logits
self.assertTrue(torch.allclose(next_logits_wo_padding, next_logits_with_padding, atol=3e-3))
@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 Zamba2 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: Zamba2 does not support right padding + use_cache with FA2.
dummy_attention_mask[:, -1] = 1
model = model_class.from_pretrained(
tmpdirname,
torch_dtype=torch.float16,
attn_implementation="flash_attention_2",
low_cpu_mem_usage=True,
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_flash_attn
@require_torch_gpu
@pytest.mark.flash_attn_test
@slow
def test_flash_attn_2_inference_equivalence_right_padding(self):
r"""
Overriding the test_flash_attn_2_inference_padding_right test as the Zamba2 model, like Mixtral, doesn't support
right padding + use cache with FA2
"""
self.skipTest(reason="Zamba2 flash attention does not support right padding")
@unittest.skip(reason="Zamba2 has its own special cache type")
@parameterized.expand([(1, False), (1, True), (4, False)])
def test_new_cache_format(self, num_beams, do_sample):
pass
@require_torch
class Zamba2ModelIntegrationTest(unittest.TestCase):
model = None
tokenizer = None
@classmethod
@slow
def setUpClass(cls):
model_id = "Zyphra/Zamba2-1.2B"
cls.model = Zamba2ForCausalLM.from_pretrained(
model_id, torch_dtype=torch.float32, low_cpu_mem_usage=True, revision="PR"
)
cls.tokenizer = AutoTokenizer.from_pretrained(model_id, revision="PR")
@parameterized.expand([(torch_device,), ("cpu",)])
@slow
def test_simple_generate(self, torch_device):
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,
"<s> Hey how are you doing on this lovely evening?\n\nI'm doing well, thanks for",
)
with torch.no_grad():
logits = self.model(input_ids=input_ids).logits.to(dtype=torch.float32)
EXPECTED_LOGITS_NO_GRAD = torch.tensor(
[
-5.9587, 10.5152, 7.0382, -2.8728, -4.8143, -4.8142, -4.8142, -4.8144,
-4.8143, -4.8143, -4.8142, -4.8142, 6.0185, 18.0037, -4.8142, -4.8144,
-4.8143, -4.8142, -4.8143, -4.8143, -4.8143, -4.8143, -4.8142, -4.8143,
-4.8144, -4.8143, -4.8143, -4.8141, -4.8142, -4.8142, -4.8142, -4.8144,
-4.8143, -4.8143, -4.8143, -4.8142, -4.8144, -4.8144, -4.8142, -4.8142
]
, dtype=torch.float32) # fmt: skip
torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)
@parameterized.expand([(torch_device,), ("cpu",)])
@slow
def test_simple_batched_generate_with_padding(self, torch_device):
self.model.to(torch_device)
inputs = self.tokenizer(
["Hey how are you doing on this lovely evening?", "When did the Roman empire "],
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],
"<s> Hey how are you doing on this lovely evening?\n\nI'm doing well, thanks for",
)
self.assertEqual(
output_sentences[1],
"[PAD][PAD][PAD][PAD]<s> When did the Roman empire 1st fall?\nThe Roman Empire fell in",
)
with torch.no_grad():
logits = self.model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"]).logits.to(
dtype=torch.float32
)
EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
[
-5.9611, 10.5208, 7.0411, -2.8743, -4.8167, -4.8167, -4.8167, -4.8168,
-4.8167, -4.8167, -4.8167, -4.8166, 6.0218, 18.0062, -4.8167, -4.8168,
-4.8167, -4.8167, -4.8167, -4.8168, -4.8168, -4.8168, -4.8167, -4.8167,
-4.8168, -4.8167, -4.8167, -4.8165, -4.8167, -4.8167, -4.8167, -4.8169,
-4.8168, -4.8168, -4.8168, -4.8166, -4.8169, -4.8168, -4.8167, -4.8167
]
, dtype=torch.float32) # fmt: skip
EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor(
[
0.1966, 6.3449, 3.8350, -5.7291, -6.5106, -6.5104, -6.5103, -6.5104,
-6.5103, -6.5104, -6.5106, -6.5105, 7.8700, 13.5434, -6.5104, -6.5096,
-6.5106, -6.5102, -6.5106, -6.5106, -6.5105, -6.5106, -6.5104, -6.5106,
-6.5105, -6.5106, -6.5106, -6.5113, -6.5102, -6.5105, -6.5108, -6.5105,
-6.5104, -6.5106, -6.5106, -6.5104, -6.5106, -6.5107, -6.5103, -6.5105 ]
, 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=6e-3 if torch_device == "cpu" else 1e-3,
)