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[Port] TensorFlow implementation of Mistral (#29708)

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* chore: initial commit

* chore: adding imports and inits

* chore: adding the causal and classification code

* chore: adding names to the layers

* chore: using single self attn layer

* chore: built the model and layers

* chore: start with testing

* chore: docstring change, transpose fix

* fix: rotary embedding

* chore: adding cache implementation

* remove unused torch

* chore: fixing the indexing issue

* make fix-copies

* Use modeling_tf_utils.keras

* make fixup

* chore: fixing tests

* chore: adding past key value logic

* chore: adding multi label classfication test

* fix: switching on the built parameters in the layers

* fixing repo consistency

* ruff formats

* style changes

* fix: tf and pt equivalence

* removing returns from docstrings

* fix docstrings

* fix docstrings

* removing todos

* fix copies

* fix docstring

* fix docstring

* chore: using easier rotate_half

* adding integration tests

* chore: addressing review related to rotary embedding layer

* review changes

* [run-slow] mistral

* skip: test save load after resize token embedding

* style

---------

Co-authored-by: Matt <rocketknight1@gmail.com>
This commit is contained in:
Aritra Roy Gosthipaty
2024-05-23 22:18:49 +05:30
committed by GitHub
parent 2a89673fe5
commit 965e98dc54
8 changed files with 1512 additions and 3 deletions
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2
docs/source/en/index.md
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@@ -200,7 +200,7 @@ Flax), PyTorch, and/or TensorFlow.
| [Megatron-BERT](model_doc/megatron-bert) | ✅ | ❌ | ❌ |
| [Megatron-GPT2](model_doc/megatron_gpt2) | ✅ | ✅ | ✅ |
| [MGP-STR](model_doc/mgp-str) | ✅ | ❌ | ❌ |
| [Mistral](model_doc/mistral) | ✅ | ❌ | ✅ |
| [Mistral](model_doc/mistral) | ✅ | ✅ | ✅ |
| [Mixtral](model_doc/mixtral) | ✅ | ❌ | ❌ |
| [mLUKE](model_doc/mluke) | ✅ | ❌ | ❌ |
| [MMS](model_doc/mms) | ✅ | ✅ | ✅ |

17
docs/source/en/model_doc/mistral.md
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@@ -216,4 +216,19 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
## FlaxMistralForCausalLM
[[autodoc]] FlaxMistralForCausalLM
- __call__
- __call__
## TFMistralModel
[[autodoc]] TFMistralModel
- call
## TFMistralForCausalLM
[[autodoc]] TFMistralForCausalLM
- call
## TFMistralForSequenceClassification
[[autodoc]] TFMistralForSequenceClassification
- call

9
src/transformers/__init__.py
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@@ -3974,6 +3974,9 @@ else:
_import_structure["models.mbart"].extend(
["TFMBartForConditionalGeneration", "TFMBartModel", "TFMBartPreTrainedModel"]
)
_import_structure["models.mistral"].extend(
["TFMistralForCausalLM", "TFMistralForSequenceClassification", "TFMistralModel", "TFMistralPreTrainedModel"]
)
_import_structure["models.mobilebert"].extend(
[
"TFMobileBertForMaskedLM",
@@ -8067,6 +8070,12 @@ if TYPE_CHECKING:
TFMBartModel,
TFMBartPreTrainedModel,
)
from .models.mistral import (
TFMistralForCausalLM,
TFMistralForSequenceClassification,
TFMistralModel,
TFMistralPreTrainedModel,
)
from .models.mobilebert import (
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,

3
src/transformers/models/auto/modeling_tf_auto.py
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@@ -65,6 +65,7 @@ TF_MODEL_MAPPING_NAMES = OrderedDict(
("lxmert", "TFLxmertModel"),
("marian", "TFMarianModel"),
("mbart", "TFMBartModel"),
("mistral", "TFMistralModel"),
("mobilebert", "TFMobileBertModel"),
("mobilevit", "TFMobileViTModel"),
("mpnet", "TFMPNetModel"),
@@ -178,6 +179,7 @@ TF_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
("gpt-sw3", "TFGPT2LMHeadModel"),
("gpt2", "TFGPT2LMHeadModel"),
("gptj", "TFGPTJForCausalLM"),
("mistral", "TFMistralForCausalLM"),
("openai-gpt", "TFOpenAIGPTLMHeadModel"),
("opt", "TFOPTForCausalLM"),
("rembert", "TFRemBertForCausalLM"),
@@ -320,6 +322,7 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
("layoutlm", "TFLayoutLMForSequenceClassification"),
("layoutlmv3", "TFLayoutLMv3ForSequenceClassification"),
("longformer", "TFLongformerForSequenceClassification"),
("mistral", "TFMistralForSequenceClassification"),
("mobilebert", "TFMobileBertForSequenceClassification"),
("mpnet", "TFMPNetForSequenceClassification"),
("openai-gpt", "TFOpenAIGPTForSequenceClassification"),

34
src/transformers/models/mistral/__init__.py
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@@ -13,7 +13,13 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_import_structure = {
@@ -47,6 +53,19 @@ else:
"FlaxMistralPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_mistral"] = [
"TFMistralModel",
"TFMistralForCausalLM",
"TFMistralForSequenceClassification",
"TFMistralPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mistral import MistralConfig
@@ -77,6 +96,19 @@ if TYPE_CHECKING:
FlaxMistralPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mistral import (
TFMistralForCausalLM,
TFMistralForSequenceClassification,
TFMistralModel,
TFMistralPreTrainedModel,
)
else:
import sys

1055
src/transformers/models/mistral/modeling_tf_mistral.py Normal file
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28
src/transformers/utils/dummy_tf_objects.py
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@@ -1801,6 +1801,34 @@ class TFMBartPreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["tf"])
class TFMistralForCausalLM(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFMistralForSequenceClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFMistralModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFMistralPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFMobileBertForMaskedLM(metaclass=DummyObject):
_backends = ["tf"]

367
tests/models/mistral/test_modeling_tf_mistral.py Normal file
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@@ -0,0 +1,367 @@
# coding=utf-8
# Copyright 2024 Mistral AI and 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 TF 2.0 Mistral model."""
import unittest
import numpy as np
from transformers import AutoTokenizer, MistralConfig, is_tf_available
from transformers.testing_utils import (
require_tf,
slow,
)
from ...generation.test_tf_utils import TFGenerationIntegrationTests
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.mistral.modeling_tf_mistral import (
TFMistralForCausalLM,
TFMistralForSequenceClassification,
TFMistralModel,
)
class TFMistralModelTester:
def __init__(self, parent):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_input_mask = True
self.use_token_type_ids = False
self.use_labels = True
self.vocab_size = 99
self.hidden_size = 32
self.num_hidden_layers = 2
self.num_attention_heads = 4
self.num_key_value_heads = 2
self.intermediate_size = 37
self.hidden_act = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.pad_token_id = 0
self.scope = None
self.bos_token_id = self.vocab_size - 1
self.eos_token_id = self.vocab_size - 1
self.pad_token_id = self.vocab_size - 1
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], self.vocab_size)
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 = MistralConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
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=False,
initializer_range=self.initializer_range,
pad_token_id=self.pad_token_id,
)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFMistralModel(config=config)
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_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 = TFMistralModel(config)
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))
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 = TFMistralForCausalLM(config=config)
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))
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 = TFMistralForCausalLM(config=config)
# 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 = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([input_mask, next_mask], axis=-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(np.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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_tf
class TFMistralModelTest(TFModelTesterMixin, TFGenerationIntegrationTests, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(TFMistralModel, TFMistralForCausalLM, TFMistralForSequenceClassification) if is_tf_available() else ()
)
all_generative_model_classes = (TFMistralForCausalLM,) if is_tf_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": TFMistralModel,
"text-classification": TFMistralForSequenceClassification,
"text-generation": TFMistralForCausalLM,
"zero-shot": TFMistralForSequenceClassification,
}
if is_tf_available()
else {}
)
test_onnx = False
test_pruning = False
test_missing_keys = False
test_head_masking = False
# 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):
self.model_tester = TFMistralModelTester(self)
self.config_tester = ConfigTester(self, config_class=MistralConfig, 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_Mistral_sequence_classification_model(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
input_ids = input_dict["input_ids"]
attention_mask = tf.not_equal(input_ids, 1)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = TFMistralForSequenceClassification(config)
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_Mistral_sequence_classification_model_for_single_label(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.problem_type = "single_label_classification"
input_ids = input_dict["input_ids"]
attention_mask = tf.not_equal(input_ids, 1)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = TFMistralForSequenceClassification(config)
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_Mistral_sequence_classification_model_for_multi_label(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.problem_type = "multi_label_classification"
input_ids = input_dict["input_ids"]
attention_mask = tf.not_equal(input_ids, 1)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = TFMistralForSequenceClassification(config)
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
@unittest.skip("Mistral buffers include complex numbers, which breaks this test")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip("Mistral uses GQA on all models so the KV cache is a non standard format")
def test_past_key_values_format(self):
pass
@unittest.skip("Vocab resizing is not supported")
def test_save_load_after_resize_token_embeddings(self):
pass
@require_tf
class TFMistralIntegrationTest(unittest.TestCase):
@slow
def test_model_7b_logits(self):
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
model = TFMistralForCausalLM.from_pretrained(
"hf-internal-testing/tiny-random-MistralForCausalLM", from_pt=True
)
input_ids = tf.constant([input_ids])
out = model(input_ids).logits
# Expected mean on dim = -1
EXPECTED_MEAN = tf.constant(
[[-1.281e-04, -2.869e-04, -9.989e-05, -8.995e-05, 2.494e-04, -3.083e-04, -2.672e-04, -1.239e-04]]
)
tf.debugging.assert_near(tf.reduce_mean(out, axis=-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
# slicing logits[0, 0, 0:30]
EXPECTED_SLICE = tf.constant([0.1033, 0.1493, -0.0041, -0.0021, -0.1686, 0.0356, 0.0812, 0.2218, -0.1257, 0.1920, 0.0929, 0.1181, 0.0111, 0.0395, -0.0064, 0.1712, -0.0751, 0.0625, -0.2409, 0.1541, -0.1271, -0.2296, -0.0099, -0.0160, 0.0311, -0.0824, -0.1518, 0.0722, 0.0187, 0.0484]) # fmt: skip
tf.debugging.assert_near(out[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4)
@slow
def test_model_7b_generation(self):
EXPECTED_TEXT_COMPLETION = """My favourite condiment is Werk a EgyadjustPrintfigiousPDFPHPct guns Ein motor conceti barSequ内 infrastructure millretval"""
prompt = "My favourite condiment is "
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM", use_fast=False)
model = TFMistralForCausalLM.from_pretrained(
"hf-internal-testing/tiny-random-MistralForCausalLM", from_pt=True
)
input_ids = tokenizer.encode(prompt, return_tensors="tf")
# greedy generation outputs
generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION, text)