Add Llama Flax Implementation (#24587)
* Copies `modeling_flax_gpt_neo.py` to start * MLP Block. WIP Attention and Block * Adds Flax implementation of `LlamaMLP` Validated with in-file test. Some slight numeric differences, but assuming it isn't an issue * Adds `FlaxLlamaRMSNorm` layer `flax.linen` includes `RMSNorm` layer but not necessarily in all versions. Hence, we add in-file. * Adds FlaxLlamaAttention Copied from GPT-J as it has efficient caching implementation as well as rotary embeddings. Notice numerically different, but not by a huge amount. Needs investigating * Adds `FlaxLlamaDecoderLayer` numerically inaccurate, debugging.. * debugging rotary mismatch gptj uses interleaved whilst llama uses contiguous i think they match now but still final result is wrong. maybe drop back to just debugging attention layer? * fixes bug with decoder layer still somewhat numerically inaccurate, but close enough for now * adds markers for what to implement next the structure here diverges a lot from the PT version. not a big fan of it, but just get something working for now * implements `FlaxLlamaBlockCollection`] tolerance must be higher than expected, kinda disconcerting * Adds `FlaxLlamaModule` equivalent PyTorch model is `LlamaModel` yay! a language model🤗 * adds `FlaxLlamaForCausalLMModule` equivalent to `LlamaForCausalLM` still missing returning dict or tuple, will add later * start porting pretrained wrappers realised it probably needs return dict as a prereq * cleanup, quality, style * readds `return_dict` and model output named tuples * (tentatively) pretrained wrappers work 🔥 * fixes numerical mismatch in `FlaxLlamaRMSNorm` seems `jax.lax.rsqrt` does not match `torch.sqrt`. manually computing `1 / jax.numpy.sqrt` results in matching values. * [WIP] debugging numerics * numerical match I think issue was accidental change of backend. forcing CPU fixes test. We expect some mismatch on GPU. * adds in model and integration tests for Flax Llama summary of failing: - mul invalid combination of dimensions - one numerical mismatch - bf16 conversion (maybe my local backend issue) - params are not FrozenDict * adds missing TYPE_CHECKING import and `make fixup` * adds back missing docstrings needs review on quality of docstrings, not sure what is required. Furthermore, need to check if `CHECKPOINT_FOR_DOC` is valid. See TODO * commenting out equivalence test as can just use common * debugging * Fixes bug where mask and pos_ids were swapped in pretrained models This results in all tests passing now 🔥 * cleanup of modeling file * cleanup of test file * Resolving simpler review comments * addresses more minor review comments * fixing introduced pytest errors from review * wip additional slow tests * wip tests need to grab a GPU machine to get real logits for comparison otherwise, slow tests should be okay * `make quality`, `make style` * adds slow integration tests - checking logits - checking hidden states - checking generation outputs * `make fix-copies` * fix mangled function following `make fix-copies` * adds missing type checking imports * fixes missing parameter checkpoint warning * more finegrained 'Copied from' tags avoids issue of overwriting `LLAMA_INPUTS_DOCSTRING` * swaps import guards ??? how did these get swapped initially? * removing `inv_freq` again as pytorch version has now removed * attempting to get CI to pass * adds doc entries for llama flax models * fixes typo in __init__.py imports * adds back special equivalence tests these come from the gpt neo flax tests. there is special behaviour for these models that needs to override the common version * overrides tests with dummy to see if CI passes need to fill in these tests later * adds my contribution to docs * `make style; make quality` * replaces random masking with fixed to work with flax version * `make quality; make style` * Update src/transformers/models/llama/modeling_flax_llama.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * Update src/transformers/models/llama/modeling_flax_llama.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * Update src/transformers/models/llama/modeling_flax_llama.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * Update src/transformers/models/llama/modeling_flax_llama.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * Update src/transformers/models/llama/modeling_flax_llama.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * Update src/transformers/models/llama/modeling_flax_llama.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * updates `x`->`tensor` in `rotate_half` * addresses smaller review comments * Update docs/source/en/model_doc/llama.md Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * adds integration test class * adds `dtype` to rotary embedding to cast outputs * adds type to flax llama rotary layer * `make style` * `make fix-copies` * Apply suggestions from code review Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * applies suggestions from review * Update modeling_flax_llama.py * `make fix-copies` * Update tests/models/llama/test_modeling_llama.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * Update src/transformers/models/llama/modeling_flax_llama.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * fixes shape mismatch in FlaxLlamaMLP * applies some suggestions from reviews * casts attn output logits to f32 regardless of dtype * adds attn bias using `LlamaConfig.attention_bias` * adds Copied From comments to Flax Llama test * mistral and persimmon test change -copy from llama * updates docs index * removes Copied from in tests it was preventing `make fix-copies` from succeeding * quality and style * ignores FlaxLlama input docstring * adds revision to `_CHECKPOINT_FOR_DOC` * repo consistency and quality * removes unused import * removes copied from from Phi test now diverges from llama tests following FlaxLlama changes * adds `_REAL_CHECKPOINT_FOR_DOC` * removes refs from pr tests * reformat to make ruff happy --------- Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
This commit is contained in:
Alex McKinney
261
tests/models/llama/test_modeling_flax_llama.py
Normal file
261
tests/models/llama/test_modeling_flax_llama.py
Normal file
@@ -0,0 +1,261 @@
|
||||
# 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
|
||||
|
||||
import numpy as np
|
||||
|
||||
from transformers import LlamaConfig, is_flax_available, is_tokenizers_available
|
||||
from transformers.testing_utils import require_flax, slow
|
||||
|
||||
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
|
||||
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
|
||||
|
||||
|
||||
if is_flax_available():
|
||||
import jax.numpy as jnp
|
||||
|
||||
from transformers.models.llama.modeling_flax_llama import FlaxLlamaForCausalLM, FlaxLlamaModel
|
||||
|
||||
|
||||
if is_tokenizers_available():
|
||||
from transformers import LlamaTokenizerFast
|
||||
|
||||
|
||||
class FlaxLlamaModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=2,
|
||||
seq_length=7,
|
||||
is_training=True,
|
||||
use_input_mask=True,
|
||||
use_token_type_ids=False,
|
||||
use_labels=True,
|
||||
vocab_size=99,
|
||||
hidden_size=16,
|
||||
num_hidden_layers=2,
|
||||
num_attention_heads=2,
|
||||
intermediate_size=64,
|
||||
hidden_act="gelu",
|
||||
hidden_dropout_prob=0.1,
|
||||
attention_probs_dropout_prob=0.1,
|
||||
max_position_embeddings=512,
|
||||
window_size=7,
|
||||
initializer_range=0.02,
|
||||
):
|
||||
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.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
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.window_size = window_size
|
||||
self.initializer_range = initializer_range
|
||||
self.scope = None
|
||||
self.bos_token_id = vocab_size - 1
|
||||
self.eos_token_id = vocab_size - 1
|
||||
self.pad_token_id = 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 = np.tril(np.ones((self.batch_size, self.seq_length)))
|
||||
|
||||
config = LlamaConfig(
|
||||
vocab_size=self.vocab_size,
|
||||
hidden_size=self.hidden_size,
|
||||
num_hidden_layers=self.num_hidden_layers,
|
||||
num_attention_heads=self.num_attention_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,
|
||||
use_cache=True,
|
||||
is_decoder=False,
|
||||
initializer_range=self.initializer_range,
|
||||
)
|
||||
|
||||
return (config, input_ids, input_mask)
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
config, input_ids, attention_mask = config_and_inputs
|
||||
inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
|
||||
return config, inputs_dict
|
||||
|
||||
def check_use_cache_forward(self, model_class_name, config, input_ids, attention_mask):
|
||||
max_decoder_length = 20
|
||||
model = model_class_name(config)
|
||||
|
||||
past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length)
|
||||
attention_mask = jnp.ones((input_ids.shape[0], max_decoder_length), dtype="i4")
|
||||
|
||||
position_ids = jnp.broadcast_to(
|
||||
jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1)
|
||||
)
|
||||
outputs_cache = model(
|
||||
input_ids[:, :-1],
|
||||
attention_mask=attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
position_ids=position_ids,
|
||||
)
|
||||
|
||||
position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
|
||||
outputs_cache_next = model(
|
||||
input_ids[:, -1:],
|
||||
attention_mask=attention_mask,
|
||||
past_key_values=outputs_cache.past_key_values,
|
||||
position_ids=position_ids,
|
||||
)
|
||||
|
||||
outputs = model(input_ids)
|
||||
|
||||
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
|
||||
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
|
||||
|
||||
def check_use_cache_forward_with_attn_mask(self, model_class_name, config, input_ids, attention_mask):
|
||||
max_decoder_length = 20
|
||||
model = model_class_name(config)
|
||||
|
||||
attention_mask_cache = jnp.concatenate(
|
||||
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))],
|
||||
axis=-1,
|
||||
)
|
||||
|
||||
past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length)
|
||||
position_ids = jnp.broadcast_to(
|
||||
jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1)
|
||||
)
|
||||
|
||||
outputs_cache = model(
|
||||
input_ids[:, :-1],
|
||||
attention_mask=attention_mask_cache,
|
||||
past_key_values=past_key_values,
|
||||
position_ids=position_ids,
|
||||
)
|
||||
position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
|
||||
outputs_cache_next = model(
|
||||
input_ids[:, -1:],
|
||||
past_key_values=outputs_cache.past_key_values,
|
||||
attention_mask=attention_mask_cache,
|
||||
position_ids=position_ids,
|
||||
)
|
||||
|
||||
outputs = model(input_ids, attention_mask=attention_mask)
|
||||
|
||||
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
|
||||
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
|
||||
|
||||
|
||||
@require_flax
|
||||
class FlaxLlamaModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (FlaxLlamaModel, FlaxLlamaForCausalLM) if is_flax_available() else ()
|
||||
all_generative_model_classes = (FlaxLlamaForCausalLM,) if is_flax_available() else ()
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = FlaxLlamaModelTester(self)
|
||||
|
||||
def test_use_cache_forward(self):
|
||||
for model_class_name in self.all_model_classes:
|
||||
config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_use_cache_forward(model_class_name, config, input_ids, attention_mask)
|
||||
|
||||
def test_use_cache_forward_with_attn_mask(self):
|
||||
for model_class_name in self.all_model_classes:
|
||||
config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_use_cache_forward_with_attn_mask(
|
||||
model_class_name, config, input_ids, attention_mask
|
||||
)
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_class_name in self.all_model_classes:
|
||||
model = model_class_name.from_pretrained("openlm-research/open_llama_3b_v2", from_pt=True)
|
||||
outputs = model(np.ones((1, 1)))
|
||||
self.assertIsNotNone(outputs)
|
||||
|
||||
|
||||
@slow
|
||||
@require_flax
|
||||
class FlaxLlamaIntegrationTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.model_id = "openlm-research/open_llama_3b_v2"
|
||||
self.model = FlaxLlamaForCausalLM.from_pretrained(self.model_id, from_pt=True)
|
||||
self.test_batch = jnp.arange(32).reshape(4, 8) + 1911
|
||||
|
||||
def test_model_logits(self):
|
||||
flax_logits = self.model(self.test_batch).logits
|
||||
|
||||
# fmt: off
|
||||
EXPECTED_LOGITS = [-74.4243, -74.0680, -65.2507, -79.1658, -77.7460, -69.2379, -86.4588, -84.8933, -77.8456]
|
||||
EXPECTED_MIN, EXPECTED_MAX, EXPECTED_MEAN = -96.9952
|
||||
EXPECTED_MAX = -18.4571
|
||||
EXPECTED_MEAN = -65.0608
|
||||
# fmt: on
|
||||
|
||||
self.assertTrue(np.allclose(flax_logits[0, :3, :3].flatten(), EXPECTED_LOGITS, atol=1e-4))
|
||||
self.assertAlmostEqual(flax_logits.min(), EXPECTED_MIN, places=3)
|
||||
self.assertAlmostEqual(flax_logits.max(), EXPECTED_MAX, places=3)
|
||||
self.assertAlmostEqual(flax_logits.mean(), EXPECTED_MEAN, places=3)
|
||||
|
||||
def test_model_hidden_states(self):
|
||||
flax_hidden_states = self.model(self.test_batch, output_hidden_states=True).hidden_states
|
||||
flax_hidden_means = [h.mean() for h in flax_hidden_states]
|
||||
|
||||
# fmt: off
|
||||
EXPECTED_HIDDEN_MEANS = [
|
||||
-0.00007,-0.00049,-0.00169,-0.00253,-0.00271,
|
||||
-0.00290,-0.00252,0.00230,0.00230,0.00198,
|
||||
0.00196,0.00174,0.00246,0.00205,0.00242,
|
||||
0.00171,0.00092,0.00054,0.00102,0.00024,
|
||||
0.00029,0.00037,-0.00101,-0.00062,-0.00341,-0.00636,-0.00357
|
||||
]
|
||||
# fmt: on
|
||||
|
||||
self.assertTrue(np.allclose(flax_hidden_means, EXPECTED_HIDDEN_MEANS, atol=1e-4))
|
||||
|
||||
def test_generated_text(self):
|
||||
tokenizer = LlamaTokenizerFast.from_pretrained(self.model_id)
|
||||
tokenizer.pad_token_id = 2
|
||||
test_batch = ["Aloha, World! ", "2 + 2 = ", "Paris is the capital of ", "我很高興認識"]
|
||||
|
||||
inputs = tokenizer(test_batch, return_tensors="np", truncation=True, padding=True)
|
||||
generated_ids = self.model.generate(**inputs, max_length=15).sequences
|
||||
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
||||
|
||||
# fmt: off
|
||||
EXPECTED_GENERATION = [
|
||||
"Aloha, World! 201",
|
||||
"2 + 2 = 4\n2",
|
||||
"Paris is the capital of Île-",
|
||||
"我很高興認識你，我"
|
||||
]
|
||||
# fmt: on
|
||||
|
||||
self.assertListEqual(generated_text, EXPECTED_GENERATION)
|
||||
@@ -14,7 +14,6 @@
|
||||
# limitations under the License.
|
||||
""" Testing suite for the PyTorch LLaMA model. """
|
||||
|
||||
|
||||
import unittest
|
||||
|
||||
import pytest
|
||||
@@ -33,7 +32,7 @@ from transformers.testing_utils import (
|
||||
|
||||
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_modeling_common import ModelTesterMixin, ids_tensor
|
||||
from ...test_pipeline_mixin import PipelineTesterMixin
|
||||
|
||||
|
||||
@@ -105,7 +104,7 @@ class LlamaModelTester:
|
||||
|
||||
input_mask = None
|
||||
if self.use_input_mask:
|
||||
input_mask = random_attention_mask([self.batch_size, self.seq_length])
|
||||
input_mask = torch.tril(torch.ones(self.batch_size, self.seq_length))
|
||||
|
||||
token_type_ids = None
|
||||
if self.use_token_type_ids:
|
||||
|
||||
@@ -34,7 +34,7 @@ from transformers.testing_utils import (
|
||||
|
||||
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_modeling_common import ModelTesterMixin, ids_tensor
|
||||
from ...test_pipeline_mixin import PipelineTesterMixin
|
||||
|
||||
|
||||
@@ -107,7 +107,7 @@ class MistralModelTester:
|
||||
|
||||
input_mask = None
|
||||
if self.use_input_mask:
|
||||
input_mask = random_attention_mask([self.batch_size, self.seq_length])
|
||||
input_mask = torch.tril(torch.ones(self.batch_size, self.seq_length))
|
||||
|
||||
token_type_ids = None
|
||||
if self.use_token_type_ids:
|
||||
|
||||
@@ -32,7 +32,7 @@ from transformers.testing_utils import (
|
||||
|
||||
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_modeling_common import ModelTesterMixin, ids_tensor
|
||||
from ...test_pipeline_mixin import PipelineTesterMixin
|
||||
|
||||
|
||||
@@ -104,7 +104,7 @@ class PersimmonModelTester:
|
||||
|
||||
input_mask = None
|
||||
if self.use_input_mask:
|
||||
input_mask = random_attention_mask([self.batch_size, self.seq_length])
|
||||
input_mask = torch.tril(torch.ones(self.batch_size, self.seq_length))
|
||||
|
||||
token_type_ids = None
|
||||
if self.use_token_type_ids:
|
||||
|
||||
@@ -38,7 +38,6 @@ if is_torch_available():
|
||||
)
|
||||
|
||||
|
||||
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester with Llama->Phi
|
||||
class PhiModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
|
||||
Reference in New Issue
Block a user