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transformers/tests/models/falcon_mamba/__init__.py Normal file
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# Copyright 2024 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 math
import unittest
from unittest.util import safe_repr
import pytest
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, FalconMambaConfig, is_torch_available
from transformers.testing_utils import (
Expectations,
cleanup,
require_bitsandbytes,
require_torch,
require_torch_accelerator,
require_torch_large_accelerator,
require_torch_multi_accelerator,
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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FalconMambaCache,
FalconMambaForCausalLM,
FalconMambaModel,
)
# Copied from transformers.tests.models.mamba.MambaModelTester with Mamba->FalconMamba,mamba->falcon_mamba
class FalconMambaModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
intermediate_size=32,
hidden_act="silu",
hidden_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,
tie_word_embeddings=True,
):
self.parent = parent
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.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.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.tie_word_embeddings = tie_word_embeddings
# Ignore copy
def get_large_model_config(self):
return FalconMambaConfig.from_pretrained("tiiuae/falcon-mamba-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)
attention_mask = ids_tensor([self.batch_size, self.seq_length], 1)
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,
attention_mask,
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 FalconMambaConfig(
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,
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,
tie_word_embeddings=self.tie_word_embeddings,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 300
return config
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
attention_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
return (
config,
input_ids,
attention_mask,
sequence_labels,
token_labels,
choice_labels,
)
def create_and_check_falcon_mamba_model(self, config, input_ids, *args):
config.output_hidden_states = True
model = FalconMambaModel(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_causal_lm(self, config, input_ids, *args):
model = FalconMambaForCausalLM(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, *args):
model = FalconMambaModel(config=config)
model.to(torch_device)
model.eval()
outputs = model(input_ids)
output_whole = outputs.last_hidden_state
outputs = model(
input_ids[:, :-1],
use_cache=True,
cache_position=torch.arange(0, config.conv_kernel, device=input_ids.device),
)
output_one = outputs.last_hidden_state
# Using the state computed on the first inputs, we will get the same output
outputs = model(
input_ids[:, -1:],
use_cache=True,
cache_params=outputs.cache_params,
cache_position=torch.arange(config.conv_kernel, config.conv_kernel + 1, device=input_ids.device),
)
output_two = outputs.last_hidden_state
self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two], dim=1), output_whole, atol=1e-5))
# TODO the original mamba does not support decoding more than 1 token neither do we
def create_and_check_falcon_mamba_cached_slow_forward_and_backwards(
self, config, input_ids, *args, gradient_checkpointing=False
):
model = FalconMambaModel(config)
model.to(torch_device)
if gradient_checkpointing:
model.gradient_checkpointing_enable()
# create cache
cache = model(input_ids, use_cache=True).cache_params
cache.reset()
# use cache
token_emb = model.embeddings(input_ids)
outputs = model.layers[0].mixer.slow_forward(
token_emb, cache, cache_position=torch.arange(0, config.conv_kernel, device=input_ids.device)
)
loss = torch.log1p(torch.abs(outputs.sum()))
self.parent.assertEqual(loss.shape, ())
self.parent.assertEqual(outputs.shape, (self.batch_size, self.seq_length, self.hidden_size))
loss.backward()
def create_and_check_falcon_mamba_lm_head_forward_and_backwards(
self, config, input_ids, *args, gradient_checkpointing=False
):
model = FalconMambaForCausalLM(config)
model.to(torch_device)
if gradient_checkpointing:
model.gradient_checkpointing_enable()
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))
result.loss.backward()
def prepare_config_and_inputs_for_common(self):
(
config,
input_ids,
attention_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_torch
# Copied from transformers.tests.models.mamba.MambaModelTest with Mamba->Falcon,mamba->falcon_mamba,FalconMambaCache->MambaCache
class FalconMambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (FalconMambaModel, FalconMambaForCausalLM) if is_torch_available() else ()
has_attentions = False # FalconMamba does not support attentions
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 # FalconMamba does not have attention heads
pipeline_model_mapping = (
{"feature-extraction": FalconMambaModel, "text-generation": FalconMambaForCausalLM}
if is_torch_available()
else {}
)
def setUp(self):
self.model_tester = FalconMambaModelTester(self)
self.config_tester = ConfigTester(
self, config_class=FalconMambaConfig, 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_falcon_mamba_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_falcon_mamba_model(*config_and_inputs)
def test_falcon_mamba_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_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_falcon_mamba_cached_slow_forward_and_backwards(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_falcon_mamba_cached_slow_forward_and_backwards(*config_and_inputs)
def test_falcon_mamba_lm_head_forward_and_backwards(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_falcon_mamba_lm_head_forward_and_backwards(*config_and_inputs)
def test_initialization(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
config.rescale_prenorm_residual = True
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 "dt_proj.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])
elif "A_log" in name:
A = torch.arange(1, config.state_size + 1, dtype=torch.float32)[None, :]
A = A.expand(config.intermediate_size, -1).contiguous()
torch.testing.assert_close(param.data, torch.log(A), rtol=1e-5, atol=1e-5)
elif "D" 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)
else:
if param.requires_grad:
if (
"mixer.conv1d.weight" in name
or "mixer.dt_proj.weight" in name
or "mixer.out_proj.weight" in name
):
continue
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",
)
@slow
# Ignore copy
def test_model_from_pretrained(self):
model = FalconMambaModel.from_pretrained("tiiuae/falcon-mamba-7b", dtype=torch.float16)
self.assertIsNotNone(model)
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, FalconMambaCache): # MODIFIED PART START
recursive_check(tuple_object.conv_states, dict_object.conv_states)
recursive_check(tuple_object.ssm_states, dict_object.ssm_states)
elif isinstance(tuple_object, (list, tuple)): # MODIFIED PART END
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})
@unittest.skip("Mamba models do not support DDP.")
def test_multi_gpu_data_parallel_forward(self):
pass
@require_torch
@require_torch_accelerator
@slow
class FalconMambaIntegrationTests(unittest.TestCase):
def setUp(self):
self.model_id = "tiiuae/falcon-mamba-7b"
self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
self.text = "Hello today"
cleanup(torch_device, gc_collect=True)
def tearDown(self):
cleanup(torch_device, gc_collect=True)
# On T4, get `NotImplementedError: Cannot copy out of meta tensor; no data!`
@require_torch_large_accelerator
def test_generation_fp16(self):
model = AutoModelForCausalLM.from_pretrained(self.model_id, dtype=torch.float16, device_map="auto")
inputs = self.tokenizer(self.text, return_tensors="pt").to(torch_device)
out = model.generate(**inputs, max_new_tokens=20, do_sample=False)
EXPECTED_OUTPUTS = Expectations(
{
("cuda", 7): "Hello today I am going to show you how to make a simple and easy to make paper plane.\nStep",
("cuda", 8): 'Hello today Iava,\n\nI am writing to you today to discuss the importance of maintaining a healthy lifestyle',
}
) # fmt: skip
EXPECTED_OUTPUT = EXPECTED_OUTPUTS.get_expectation()
self.assertEqual(
self.tokenizer.batch_decode(out, skip_special_tokens=False)[0],
EXPECTED_OUTPUT,
)
@require_bitsandbytes
def test_generation_4bit(self):
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
model = AutoModelForCausalLM.from_pretrained(self.model_id, quantization_config=quantization_config).to(
torch_device
)
inputs = self.tokenizer(self.text, return_tensors="pt").to(torch_device)
out = model.generate(**inputs, max_new_tokens=20, do_sample=False)
self.assertEqual(
self.tokenizer.batch_decode(out, skip_special_tokens=False)[0],
"Hello today Iava,\n\nI'm sorry to hear that you're having trouble with the ",
)
@pytest.mark.torch_compile_test
def test_generation_torch_compile(self):
model = AutoModelForCausalLM.from_pretrained(self.model_id, dtype=torch.float16).to(torch_device)
model = torch.compile(model)
inputs = self.tokenizer(self.text, return_tensors="pt").to(torch_device)
out = model.generate(**inputs, max_new_tokens=20, do_sample=False)
print(self.tokenizer.batch_decode(out, skip_special_tokens=False)[0])
self.assertEqual(
self.tokenizer.batch_decode(out, skip_special_tokens=False)[0],
"Hello today Iava,\n\nI am writing to you today to discuss the importance of maintaining a healthy lifestyle",
)
def test_batched_generation(self):
model_id = "tiiuae/falcon-mamba-7b"
tok = AutoTokenizer.from_pretrained(model_id)
tok.pad_token_id = tok.eos_token_id
texts = ["Hello today", "Hello my name is Younes and today"]
EXPECTED_OUTPUTS = Expectations(
{
("cuda", 7): [
'Hello today I will be talking about the “Theory of Relativity” by Albert Einstein.\nThe',
'Hello my name is Younes and today I will be talking about the importance of the internet in our lives.\nThe internet is a global',
],
("cuda", 8): [
'Hello today I am going to talk about the “Theory of Relativity” by Albert Einstein.\n',
'Hello my name is Younes and today I will be talking about the importance of the internet in our lives.\nThe internet is a global',
],
}
) # fmt: skip
EXPECTED_OUTPUT = EXPECTED_OUTPUTS.get_expectation()
inputs = tok(texts, return_tensors="pt", padding=True, return_token_type_ids=False).to(torch_device)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map=0, dtype=torch.float16)
out = model.generate(**inputs, max_new_tokens=20, do_sample=False)
out = tok.batch_decode(out, skip_special_tokens=True)
self.assertListEqual(out, EXPECTED_OUTPUT)
# We test the same generations with inputs_embeds
with torch.no_grad():
inputs_embeds = model.get_input_embeddings()(inputs.pop("input_ids"))
inputs["inputs_embeds"] = inputs_embeds
out = model.generate(**inputs, max_new_tokens=20, do_sample=False)
out = tok.batch_decode(out, skip_special_tokens=True)
EXPECTED_OUTPUTS = Expectations(
{
("cuda", 7): [
' I will be talking about the “Theory of Relativity” by Albert Einstein.\nThe',
' I will be talking about the importance of the internet in our lives.\nThe internet is a global',
],
("cuda", 8): [
' I am going to talk about the “Theory of Relativity” by Albert Einstein.\n',
' I will be talking about the importance of the internet in our lives.\nThe internet is a global'
],
}
) # fmt: skip
EXPECTED_OUTPUT = EXPECTED_OUTPUTS.get_expectation()
self.assertListEqual(out, EXPECTED_OUTPUT)
@require_torch_multi_accelerator
def test_training_kernel(self):
model_id = "tiiuae/falcon-mamba-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", dtype=torch.float16)
tokenizer.pad_token_id = tokenizer.eos_token_id
text = "Hello today"
inputs = tokenizer(text, return_tensors="pt").to(torch_device)
with torch.no_grad():
logits = torch.argmax(model(**inputs).logits, dim=-1)
out_no_training = tokenizer.batch_decode(logits)
model.train()
lm_logits = model(**inputs).logits
next_token = torch.argmax(lm_logits, dim=-1)
out_training = tokenizer.batch_decode(next_token)
# Just verify backward works
loss = (1 - lm_logits).mean()
loss.backward()
self.assertEqual(out_training, out_no_training)