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transformers/tests/models/rwkv/__init__.py Normal file
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# Copyright 2023 The HuggingFace 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.
import unittest
from unittest.util import safe_repr
from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
RwkvForCausalLM,
RwkvModel,
)
class RwkvModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_token_type_ids=False,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=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,
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_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
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.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
def get_large_model_config(self):
return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b")
def prepare_config_and_inputs(
self, gradient_checkpointing=False, 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)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
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)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], 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(
gradient_checkpointing=gradient_checkpointing,
scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
reorder_and_upcast_attn=reorder_and_upcast_attn,
)
return (
config,
input_ids,
input_mask,
None,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def get_config(
self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
):
return RwkvConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
intermediate_size=self.intermediate_size,
activation_function=self.hidden_act,
resid_pdrop=self.hidden_dropout_prob,
attn_pdrop=self.attention_probs_dropout_prob,
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,
gradient_checkpointing=gradient_checkpointing,
scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
reorder_and_upcast_attn=reorder_and_upcast_attn,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 300
return config
def create_and_check_rwkv_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
config.output_hidden_states = True
model = RwkvModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(len(result.hidden_states), config.num_hidden_layers + 1)
def create_and_check_causl_lm(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = RwkvForCausalLM(config)
model.to(torch_device)
model.eval()
result = model(input_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_state_equivalency(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = RwkvModel(config=config)
model.to(torch_device)
model.eval()
outputs = model(input_ids)
output_whole = outputs.last_hidden_state
outputs = model(input_ids[:, :2])
output_one = outputs.last_hidden_state
# Using the state computed on the first inputs, we will get the same output
outputs = model(input_ids[:, 2:], state=outputs.state)
output_two = outputs.last_hidden_state
self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two], dim=1), output_whole, atol=1e-5))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids}
return config, inputs_dict
@require_torch
class RwkvModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": RwkvModel, "text-generation": RwkvForCausalLM} if is_torch_available() else {}
)
fx_compatible = False
test_missing_keys = False
test_model_parallel = False
test_pruning = False
test_head_masking = False # Rwkv does not support head masking
def setUp(self):
self.model_tester = RwkvModelTester(self)
self.config_tester = ConfigTester(
self, config_class=RwkvConfig, n_embd=37, common_properties=["hidden_size", "num_hidden_layers"]
)
def assertInterval(self, member, container, msg=None):
r"""
Simple utility function to check if a member is inside an interval.
"""
if isinstance(member, torch.Tensor):
max_value, min_value = member.max().item(), member.min().item()
elif isinstance(member, (list, tuple)):
max_value, min_value = max(member), min(member)
if not isinstance(container, list):
raise TypeError("container should be a list or tuple")
elif len(container) != 2:
raise ValueError("container should have 2 elements")
expected_min, expected_max = container
is_inside_interval = (min_value >= expected_min) and (max_value <= expected_max)
if not is_inside_interval:
standardMsg = f"{safe_repr(member)} not found in {safe_repr(container)}"
self.fail(self._formatMessage(msg, standardMsg))
def test_config(self):
self.config_tester.run_common_tests()
def test_rwkv_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_rwkv_model(*config_and_inputs)
def test_rwkv_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causl_lm(*config_and_inputs)
def test_state_equivalency(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_state_equivalency(*config_and_inputs)
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 "time_decay" in name:
if param.requires_grad:
self.assertTrue(param.data.max().item() == 3.0)
self.assertTrue(param.data.min().item() == -5.0)
elif "time_first" in name:
if param.requires_grad:
# check if it's a ones like
torch.testing.assert_close(param.data, torch.ones_like(param.data), rtol=1e-5, atol=1e-5)
elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]):
if param.requires_grad:
self.assertInterval(
param.data,
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
elif "time_mix_value" in name:
if param.requires_grad:
self.assertInterval(
param.data,
[0.0, 1.3],
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 attention outputs of Rwkv are different from other models
it has a shape `batch_size, seq_len, hidden_size`.
"""
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)
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()
inputs = self._prepare_for_class(inputs_dict, model_class)
batch_size = inputs["input_ids"].shape[0]
with torch.no_grad():
outputs = model(**inputs)
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# 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()
inputs = self._prepare_for_class(inputs_dict, model_class)
batch_size = inputs["input_ids"].shape[0]
with torch.no_grad():
outputs = model(**inputs)
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[batch_size, seq_len, config.hidden_size],
)
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()
inputs = self._prepare_for_class(inputs_dict, model_class)
batch_size = inputs["input_ids"].shape[0]
with torch.no_grad():
outputs = model(**inputs)
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[batch_size, seq_len, config.hidden_size],
)
@slow
def test_model_from_pretrained(self):
model_name = "RWKV/rwkv-4-169m-pile"
model = RwkvModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_beam_sample_generate_dict_output(self):
# This model has a custom attention output shape AND config flags, let's skip those checks
old_has_attentions = self.has_attentions
self.has_attentions = False
super().test_beam_sample_generate_dict_output()
self.has_attentions = old_has_attentions
def test_beam_search_generate_dict_output(self):
# This model has a custom attention output shape AND config flags, let's skip those checks
old_has_attentions = self.has_attentions
self.has_attentions = False
super().test_beam_search_generate_dict_output()
self.has_attentions = old_has_attentions
def test_greedy_generate_dict_outputs(self):
# This model has a custom attention output shape AND config flags, let's skip those checks
old_has_attentions = self.has_attentions
self.has_attentions = False
super().test_greedy_generate_dict_outputs()
self.has_attentions = old_has_attentions
def test_sample_generate_dict_output(self):
# This model has a custom attention output shape AND config flags, let's skip those checks
old_has_attentions = self.has_attentions
self.has_attentions = False
super().test_sample_generate_dict_output()
self.has_attentions = old_has_attentions
@unittest.skip("This model doesn't support padding")
def test_left_padding_compatibility(self):
pass
@slow
class RWKVIntegrationTests(unittest.TestCase):
def setUp(self):
self.model_id = "RWKV/rwkv-4-169m-pile"
self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
def test_simple_generate(self):
expected_output = "Hello my name is Jasmine and I am a newbie to the"
model = RwkvForCausalLM.from_pretrained(self.model_id).to(torch_device)
input_ids = self.tokenizer("Hello my name is", return_tensors="pt").input_ids.to(torch_device)
output = model.generate(input_ids, max_new_tokens=10)
output_sentence = self.tokenizer.decode(output[0].tolist())
self.assertEqual(output_sentence, expected_output)
def test_simple_generate_bf16(self):
expected_output = "Hello my name is Jasmine and I am a newbie to the"
input_ids = self.tokenizer("Hello my name is", return_tensors="pt").input_ids.to(torch_device)
model = RwkvForCausalLM.from_pretrained(self.model_id, dtype=torch.bfloat16).to(torch_device)
output = model.generate(input_ids, max_new_tokens=10)
output_sentence = self.tokenizer.decode(output[0].tolist())
self.assertEqual(output_sentence, expected_output)