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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 RecurrentGemma model."""
import unittest
from transformers import AutoModelForCausalLM, AutoTokenizer, RecurrentGemmaConfig, is_torch_available, set_seed
from transformers.testing_utils import (
require_bitsandbytes,
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 RecurrentGemmaForCausalLM, RecurrentGemmaModel
class RecurrentGemmaModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=12,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
num_hidden_layers=3,
vocab_size=99,
hidden_size=32,
intermediate_size=3 * 32,
num_attention_heads=2,
lru_width=2 * 32,
embeddings_scale_by_sqrt_dim=True,
attention_window_size=16,
conv1d_width=4,
logits_soft_cap=30.0,
rms_norm_eps=1e-6,
use_cache=True,
rope_theta=10000.0,
type_vocab_size=16,
type_sequence_label_size=2,
num_labels=3,
num_choices=4,
pad_token_id=0,
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_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.num_hidden_layers = num_hidden_layers
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_attention_heads = num_attention_heads
self.lru_width = lru_width if lru_width is not None else hidden_size
self.embeddings_scale_by_sqrt_dim = embeddings_scale_by_sqrt_dim
self.attention_window_size = attention_window_size
self.conv1d_width = conv1d_width
self.logits_soft_cap = logits_soft_cap
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
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.pad_token_id = pad_token_id
self.scope = scope
# Copied from tests.models.mistral.test_modeling_mistral.MistralModelTester.prepare_config_and_inputs
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 = torch.tril(torch.ones(self.batch_size, self.seq_length)).to(torch_device)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return RecurrentGemmaConfig(
num_hidden_layers=self.num_hidden_layers,
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
intermediate_size=self.intermediate_size,
num_attention_heads=self.num_attention_heads,
lru_width=self.lru_width,
embeddings_scale_by_sqrt_dim=self.embeddings_scale_by_sqrt_dim,
attention_window_size=self.attention_window_size,
conv1d_width=self.conv1d_width,
logits_soft_cap=self.logits_soft_cap,
rms_norm_eps=self.rms_norm_eps,
use_cache=self.use_cache,
rope_theta=self.rope_theta,
pad_token_id=self.pad_token_id,
output_attentions=False,
)
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->RecurrentGemma
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RecurrentGemmaModel(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))
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model_as_decoder with Llama->RecurrentGemma
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = RecurrentGemmaModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_for_causal_lm with Llama->RecurrentGemma
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = RecurrentGemmaForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_decoder_model_past_large_inputs with Llama->RecurrentGemma
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = RecurrentGemmaForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
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,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["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))
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common with Llama->RecurrentGemma
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_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 RecurrentGemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (RecurrentGemmaForCausalLM,) if is_torch_available() else ()
# all_generative_model_classes = (RecurrentGemmaForCausalLM,) if is_torch_available() else () #TODO @gante not fully supported
pipeline_model_mapping = (
{
"feature-extraction": RecurrentGemmaModel,
"text-generation": RecurrentGemmaForCausalLM,
}
if is_torch_available()
else {}
)
fx_compatible = False # FIXME let's try to support this @ArthurZucker
test_torchscript = False # FIXME let's try to support this @ArthurZucker
test_missing_keys = False
test_model_parallel = False
test_pruning = False
test_head_masking = False # RecurrentGemma does not have attention heads
test_model_parallel = False
# Need to remove 0.9 in `test_cpu_offload`
# This is because we are hitting edge cases with the causal_mask buffer
model_split_percents = [0.5, 0.6]
# TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
return True
def setUp(self):
# We don't output attentions
self.has_attentions = False
self.model_tester = RecurrentGemmaModelTester(self)
self.config_tester = ConfigTester(self, config_class=RecurrentGemmaConfig, 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_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip("RecurrentGemma does not return pkv")
def test_past_key_values_format(self):
pass
@unittest.skip("RecurrentGemma only supports sdpa")
def test_eager_matches_sdpa_generate(self):
pass
@unittest.skip("RecurrentGemma only supports sdpa")
def test_eager_matches_sdpa_inference(self):
pass
@unittest.skip("RecurrentGemma does not return the cache")
def test_contrastive_generate_low_memory(self):
pass
@unittest.skip("RecurrentGemma does not return the cache")
def test_contrastive_generate_dict_outputs_use_cache(self):
pass
@unittest.skip("RecurrentGemma does not return the cache")
def test_contrastive_generate(self):
pass
@unittest.skip("SQRBound is known to have issues with gc")
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
def _check_attentions_for_generate(self, *args, **kwargs):
return True # Model does not return attention
@unittest.skip("Past key values are not returned")
def test_prompt_lookup_decoding_matches_greedy_search(self):
pass
@unittest.skip("Past key values are not returned")
def test_model_parallelism(self):
pass
@unittest.skip("Past key values are not returned")
def test_model_parallel_beam_search(self):
pass
def _check_past_key_values_for_generate(self, *args, **kwargs):
return True
@unittest.skip("Rely on `past_key_values` to crop the assistant pkv. Not supported")
def test_assisted_decoding_matches_greedy_search(self):
pass
@unittest.skip("RecurrentGemma's output different if you pad left or right. This is expected")
def test_left_padding_compatibility(self):
pass
@unittest.skip("Relies on `past_key_values` returned by the model. Not supported with recurrent gemma")
def test_assisted_decoding_sample(self):
pass
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
seq_len = min_length + idx if not use_cache else 1
expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
[expected_shape] * len(iter_hidden_states),
)
@unittest.skip("TODO @arthurzucker not super important and failing.")
def test_initialization(self):
pass
@require_torch_gpu
@slow
class RecurrentGemmaIntegrationTest(unittest.TestCase):
input_text = ["Hello I am doing", "Hi today"]
model_id = "google/recurrentgemma-2b"
@require_read_token
def test_2b_generate(self):
EXPECTED_TEXTS = ['Hello I am doing a project on the topic of "The impact of the internet on the society" and I am looking for some information on the topic. I am looking for some information on the impact of the internet on the society. I am looking for some information on the impact of the internet on the society. I am looking for some', 'Hi today is a very good day for you. You will be able to do all the work you want to do. You will be able to do all the work you want to do. You will be able to do all the work you want to do. You will be able to do all the work you want to do.'] # fmt: skip
model = AutoModelForCausalLM.from_pretrained(self.model_id, low_cpu_mem_usage=True).to(torch_device)
tokenizer = AutoTokenizer.from_pretrained(self.model_id)
tokenizer.padding_side = "right"
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
output = model.generate(**inputs, max_new_tokens=64, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXTS)
tokenizer.padding_side = "left"
EXPECTED_TEXTS = ['Hello I am doing a project on the topic of "The impact of the internet on the society" and I am looking for some information on the topic. I am looking for some information on the impact of the internet on the society. I am looking for some information on the impact of the internet on the society. I am looking for some', 'Hi today I am going to share with you the best <strong><em>free online video editing software</em></strong>.\n\n<h2><strong>Best Free Online Video Editing Software</strong></h2>\n\n<strong>1.</strong> <strong>Wondershare Filmora</strong>\n\nWondershare Filmora is a free online video editing software that is used to edit videos.'] # fmt: skip
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
output = model.generate(**inputs, max_new_tokens=64, do_sample=False)
del model
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXTS)
model = AutoModelForCausalLM.from_pretrained(
self.model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16
).to(torch_device)
output = model.generate(**inputs, max_new_tokens=64, do_sample=False)
del model
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXTS)
@require_read_token
def test_2b_sample(self):
set_seed(0)
EXPECTED_TEXT = ['Where is Paris ?\n\nAnswer this question "yes" or "no": Could a person pass out in subzero temperatures?\n\nFor the sentence below, underline the pronoun in parentheses that agrees with its antecedent.\n\nExample 1. Mary and Pam will have the opportunity to prove (herself, $\\underline{\\text{themselves}}$) at the concert.\n\nThe waiters and the manager at the restaurant will do <em>(his, their)</em> best to assist you.\n\nA vocabulary word appears in italics in the short passage below. Think about how the word is used. Then write a definition for the vocabulary word.\n\nAfter a one-hour $'] # fmt: skip
model = AutoModelForCausalLM.from_pretrained(self.model_id).to(torch_device)
tokenizer = AutoTokenizer.from_pretrained(self.model_id)
inputs = tokenizer("Where is Paris ?", return_tensors="pt", padding=True).to(torch_device)
output = model.generate(**inputs, max_new_tokens=128, do_sample=True)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXT)
@require_bitsandbytes
@require_read_token
def test_model_2b_8bit(self):
EXPECTED_TEXTS = ['<bos>Hello I am doing a project on the topic of "The impact of the internet on the society" and I am looking', "<bos>Hi today<pad><pad> I'm going to show you how to make a simple and easy to use <strong><em><u>"] # fmt: skip
model = AutoModelForCausalLM.from_pretrained(
"gg-hf/recurrent-gemma-2b-hf", device_map={"": torch_device}, load_in_8bit=True, torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(self.model_id)
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXTS)
@require_read_token
def test_long_context(self):
input_text = [
'<bos><s>Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane. Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation." He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a phone at the wreckage site. The two publications described the supposed video, but did not post it on their websites. The publications said that they watched the video, which was found by a source close to the investigation. "One can hear cries of \'My God\' in several languages," Paris Match reported. "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the cockpit door with a heavy object. Towards the end, after a heavy shake, stronger than the others, the screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt, editor-in-chief of Bild online. An official with France\'s accident investigation agency, the BEA, said the agency is not aware of any such video. Lt. Col.'
]
EXPECTED_GENERATION = [
' Jean-Paul Delannoy told CNN that the BEA is "not aware of any video footage that could have been taken on board the plane." "We are not aware of any video footage that could have been taken on board the plane," Delannoy said. "We are not aware of any video footage that could'
]
model = AutoModelForCausalLM.from_pretrained(
self.model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16
).to(torch_device)
tokenizer = AutoTokenizer.from_pretrained(self.model_id)
inputs = tokenizer(input_text, return_tensors="pt").to(torch_device)
output = model.generate(**inputs, max_new_tokens=64, do_sample=False)
output_text = tokenizer.batch_decode(output[:, inputs.input_ids.shape[1] :], skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_GENERATION)
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