mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 13:20:12 +06:00
a0779b9e19
* 4d mask fixes * Update custom 4D mask logic * test moved to mixin * extra tests 4d mask * upd 4d mask and StaticCache handling * added Mask4DTestHard to mistral tests * post-rebase fixes * test fixes for StaticCache * make fix-copies * upd 1 after #30476 * fix common tests * rm elif attention_mask.dim() == 4: * tests combined, fixed, mixtral supported * bigbird style chg reverted * rm if attention_mask.dim() == 2 * modeling_llama formatting chg --------- Co-authored-by: Joao Gante <joao@huggingface.co>
1070 lines
48 KiB
Python
1070 lines
48 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 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 LLaMA model."""
|
|
|
|
import gc
|
|
import tempfile
|
|
import unittest
|
|
|
|
import pytest
|
|
from packaging import version
|
|
from parameterized import parameterized
|
|
|
|
from transformers import LlamaConfig, StaticCache, is_torch_available, set_seed
|
|
from transformers.testing_utils import (
|
|
require_bitsandbytes,
|
|
require_flash_attn,
|
|
require_read_token,
|
|
require_torch,
|
|
require_torch_accelerator,
|
|
require_torch_gpu,
|
|
require_torch_sdpa,
|
|
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 (
|
|
CodeLlamaTokenizer,
|
|
LlamaForCausalLM,
|
|
LlamaForQuestionAnswering,
|
|
LlamaForSequenceClassification,
|
|
LlamaModel,
|
|
LlamaTokenizer,
|
|
)
|
|
from transformers.models.llama.modeling_llama import (
|
|
LlamaDynamicNTKScalingRotaryEmbedding,
|
|
LlamaLinearScalingRotaryEmbedding,
|
|
LlamaRotaryEmbedding,
|
|
)
|
|
|
|
|
|
class LlamaModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=13,
|
|
seq_length=7,
|
|
is_training=True,
|
|
use_input_mask=True,
|
|
use_token_type_ids=False,
|
|
use_labels=True,
|
|
vocab_size=99,
|
|
hidden_size=32,
|
|
num_hidden_layers=2,
|
|
num_attention_heads=4,
|
|
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,
|
|
initializer_range=0.02,
|
|
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.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.type_vocab_size = type_vocab_size
|
|
self.type_sequence_label_size = type_sequence_label_size
|
|
self.initializer_range = initializer_range
|
|
self.num_labels = num_labels
|
|
self.num_choices = num_choices
|
|
self.pad_token_id = pad_token_id
|
|
self.scope = scope
|
|
|
|
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 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,
|
|
type_vocab_size=self.type_vocab_size,
|
|
is_decoder=False,
|
|
initializer_range=self.initializer_range,
|
|
pad_token_id=self.pad_token_id,
|
|
)
|
|
|
|
def create_and_check_model(
|
|
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
|
):
|
|
model = LlamaModel(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))
|
|
|
|
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 = LlamaModel(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))
|
|
|
|
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 = LlamaForCausalLM(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))
|
|
|
|
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 = LlamaForCausalLM(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))
|
|
|
|
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 LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (
|
|
(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification, LlamaForQuestionAnswering)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
all_generative_model_classes = (LlamaForCausalLM,) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{
|
|
"feature-extraction": LlamaModel,
|
|
"text-classification": LlamaForSequenceClassification,
|
|
"text-generation": LlamaForCausalLM,
|
|
"zero-shot": LlamaForSequenceClassification,
|
|
"question-answering": LlamaForQuestionAnswering,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
test_headmasking = False
|
|
test_pruning = False
|
|
fx_compatible = True
|
|
|
|
# Need to use `0.8` instead of `0.9` for `test_cpu_offload`
|
|
# This is because we are hitting edge cases with the causal_mask buffer
|
|
model_split_percents = [0.5, 0.7, 0.8]
|
|
|
|
def setUp(self):
|
|
self.model_tester = LlamaModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=LlamaConfig, 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_llama_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 = input_ids.ne(1).to(torch_device)
|
|
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
|
|
model = LlamaForSequenceClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
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_llama_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 = input_ids.ne(1).to(torch_device)
|
|
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
|
|
model = LlamaForSequenceClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
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_llama_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 = input_ids.ne(1).to(torch_device)
|
|
sequence_labels = ids_tensor(
|
|
[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
|
|
).to(torch.float)
|
|
model = LlamaForSequenceClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
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("Llama buffers include complex numbers, which breaks this test")
|
|
def test_save_load_fast_init_from_base(self):
|
|
pass
|
|
|
|
@parameterized.expand([("linear",), ("dynamic",)])
|
|
def test_model_rope_scaling_from_config(self, scaling_type):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
short_input = ids_tensor([1, 10], config.vocab_size)
|
|
long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
|
|
|
|
set_seed(42) # Fixed seed at init time so the two models get the same random weights
|
|
original_model = LlamaModel(config)
|
|
original_model.to(torch_device)
|
|
original_model.eval()
|
|
original_short_output = original_model(short_input).last_hidden_state
|
|
original_long_output = original_model(long_input).last_hidden_state
|
|
|
|
set_seed(42) # Fixed seed at init time so the two models get the same random weights
|
|
config.rope_scaling = {"type": scaling_type, "factor": 10.0}
|
|
scaled_model = LlamaModel(config)
|
|
scaled_model.to(torch_device)
|
|
scaled_model.eval()
|
|
scaled_short_output = scaled_model(short_input).last_hidden_state
|
|
scaled_long_output = scaled_model(long_input).last_hidden_state
|
|
|
|
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
|
|
# maximum sequence length, so the outputs for the short input should match.
|
|
if scaling_type == "dynamic":
|
|
self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
|
|
else:
|
|
self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
|
|
|
|
# The output should be different for long inputs
|
|
self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
|
|
|
|
def test_model_rope_scaling(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
hidden_size = config.hidden_size
|
|
num_heads = config.num_attention_heads
|
|
head_dim = hidden_size // num_heads
|
|
scaling_factor = 10
|
|
short_input_length = 10
|
|
long_input_length = int(config.max_position_embeddings * 1.5)
|
|
|
|
# Inputs
|
|
x = torch.randn(1, dtype=torch.float32, device=torch_device) # used exlusively to get the dtype and the device
|
|
position_ids_short = torch.arange(short_input_length, dtype=torch.long, device=torch_device)
|
|
position_ids_short = position_ids_short.unsqueeze(0)
|
|
position_ids_long = torch.arange(long_input_length, dtype=torch.long, device=torch_device)
|
|
position_ids_long = position_ids_long.unsqueeze(0)
|
|
|
|
# Sanity check original RoPE
|
|
original_rope = LlamaRotaryEmbedding(
|
|
head_dim,
|
|
max_position_embeddings=config.max_position_embeddings,
|
|
base=config.rope_theta,
|
|
).to(torch_device)
|
|
original_cos_short, original_sin_short = original_rope(x, position_ids_short)
|
|
original_cos_long, original_sin_long = original_rope(x, position_ids_long)
|
|
torch.testing.assert_close(original_cos_short, original_cos_long[:, :short_input_length, :])
|
|
torch.testing.assert_close(original_sin_short, original_sin_long[:, :short_input_length, :])
|
|
|
|
# Sanity check linear RoPE scaling
|
|
# New position "x" should match original position with index "x/scaling_factor"
|
|
linear_scaling_rope = LlamaLinearScalingRotaryEmbedding(
|
|
head_dim,
|
|
max_position_embeddings=config.max_position_embeddings,
|
|
base=config.rope_theta,
|
|
scaling_factor=scaling_factor,
|
|
).to(torch_device)
|
|
linear_cos_short, linear_sin_short = linear_scaling_rope(x, position_ids_short)
|
|
linear_cos_long, linear_sin_long = linear_scaling_rope(x, position_ids_long)
|
|
torch.testing.assert_close(linear_cos_short, linear_cos_long[:, :short_input_length, :])
|
|
torch.testing.assert_close(linear_sin_short, linear_sin_long[:, :short_input_length, :])
|
|
for new_position in range(0, long_input_length, scaling_factor):
|
|
original_position = int(new_position // scaling_factor)
|
|
torch.testing.assert_close(linear_cos_long[:, new_position, :], original_cos_long[:, original_position, :])
|
|
torch.testing.assert_close(linear_sin_long[:, new_position, :], original_sin_long[:, original_position, :])
|
|
|
|
# Sanity check Dynamic NTK RoPE scaling
|
|
# Scaling should only be observed after a long input is fed. We can observe that the frequencies increase
|
|
# with scaling_factor (or that `inv_freq` decreases)
|
|
ntk_scaling_rope = LlamaDynamicNTKScalingRotaryEmbedding(
|
|
head_dim,
|
|
max_position_embeddings=config.max_position_embeddings,
|
|
base=config.rope_theta,
|
|
scaling_factor=scaling_factor,
|
|
).to(torch_device)
|
|
ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, position_ids_short)
|
|
ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, position_ids_long)
|
|
torch.testing.assert_close(ntk_cos_short, original_cos_short)
|
|
torch.testing.assert_close(ntk_sin_short, original_sin_short)
|
|
with self.assertRaises(AssertionError):
|
|
torch.testing.assert_close(ntk_cos_long, original_cos_long)
|
|
with self.assertRaises(AssertionError):
|
|
torch.testing.assert_close(ntk_sin_long, original_sin_long)
|
|
self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all())
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@require_bitsandbytes
|
|
@pytest.mark.flash_attn_test
|
|
@require_read_token
|
|
@slow
|
|
def test_flash_attn_2_generate_padding_right(self):
|
|
"""
|
|
Overwritting the common test as the test is flaky on tiny models
|
|
"""
|
|
model = LlamaForCausalLM.from_pretrained(
|
|
"meta-llama/Llama-2-7b-hf",
|
|
load_in_4bit=True,
|
|
device_map={"": 0},
|
|
)
|
|
|
|
tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
|
|
|
|
texts = ["hi", "Hello this is a very long sentence"]
|
|
|
|
tokenizer.padding_side = "right"
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
|
|
inputs = tokenizer(texts, return_tensors="pt", padding=True).to(0)
|
|
|
|
output_native = model.generate(**inputs, max_new_tokens=20, do_sample=False)
|
|
output_native = tokenizer.batch_decode(output_native)
|
|
|
|
model = LlamaForCausalLM.from_pretrained(
|
|
"meta-llama/Llama-2-7b-hf", load_in_4bit=True, device_map={"": 0}, attn_implementation="flash_attention_2"
|
|
)
|
|
|
|
output_fa_2 = model.generate(**inputs, max_new_tokens=20, do_sample=False)
|
|
output_fa_2 = tokenizer.batch_decode(output_fa_2)
|
|
|
|
self.assertListEqual(output_native, output_fa_2)
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@slow
|
|
def test_use_flash_attention_2_true(self):
|
|
"""
|
|
NOTE: this is the only test testing that the legacy `use_flash_attention=2` argument still works as intended.
|
|
"""
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
for model_class in self.all_model_classes:
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model = model_class(config)
|
|
model.save_pretrained(tmp_dir)
|
|
|
|
new_model = LlamaForCausalLM.from_pretrained(
|
|
tmp_dir, use_flash_attention_2=True, torch_dtype=torch.float16
|
|
).to("cuda")
|
|
|
|
self.assertTrue(new_model.config._attn_implementation == "flash_attention_2")
|
|
|
|
has_flash = False
|
|
for name, submodule in new_model.named_modules():
|
|
if "FlashAttention" in submodule.__class__.__name__:
|
|
has_flash = True
|
|
break
|
|
if not has_flash:
|
|
raise ValueError("The flash model should have flash attention layers")
|
|
|
|
@require_torch_sdpa
|
|
@slow
|
|
def test_eager_matches_sdpa_generate(self):
|
|
"""
|
|
Overwritting the common test as the test is flaky on tiny models
|
|
"""
|
|
max_new_tokens = 30
|
|
|
|
tokenizer = LlamaTokenizer.from_pretrained("saibo/llama-1B")
|
|
|
|
model_sdpa = LlamaForCausalLM.from_pretrained(
|
|
"saibo/llama-1B",
|
|
torch_dtype=torch.float16,
|
|
low_cpu_mem_usage=True,
|
|
).to(torch_device)
|
|
|
|
self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
|
|
|
|
model_eager = LlamaForCausalLM.from_pretrained(
|
|
"saibo/llama-1B",
|
|
torch_dtype=torch.float16,
|
|
low_cpu_mem_usage=True,
|
|
attn_implementation="eager",
|
|
).to(torch_device)
|
|
|
|
self.assertTrue(model_eager.config._attn_implementation == "eager")
|
|
|
|
for name, submodule in model_eager.named_modules():
|
|
if "SdpaAttention" in submodule.__class__.__name__:
|
|
raise ValueError("The eager model should not have SDPA attention layers")
|
|
|
|
has_sdpa = False
|
|
for name, submodule in model_sdpa.named_modules():
|
|
if "SdpaAttention" in submodule.__class__.__name__:
|
|
has_sdpa = True
|
|
break
|
|
if not has_sdpa:
|
|
raise ValueError("The SDPA model should have SDPA attention layers")
|
|
|
|
texts = [
|
|
"hi here's a longer context, getting longer and",
|
|
"Hello this is a very long sentence my friend, very long for real",
|
|
"Today I am in Paris and",
|
|
]
|
|
|
|
for padding_side in ["left", "right"]:
|
|
tokenizer.padding_side = padding_side
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
|
|
inputs = tokenizer(texts, return_tensors="pt", padding=True).to(torch_device)
|
|
|
|
res_eager = model_eager.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
|
|
res_sdpa = model_sdpa.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
|
|
|
|
with self.subTest(f"{padding_side}"):
|
|
torch.testing.assert_close(
|
|
res_eager,
|
|
res_sdpa,
|
|
msg=f"\n{tokenizer.batch_decode(res_eager)} \nvs\n{tokenizer.batch_decode(res_sdpa)}",
|
|
)
|
|
|
|
|
|
@require_torch_gpu
|
|
class LlamaIntegrationTest(unittest.TestCase):
|
|
# This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
|
|
# Depending on the hardware we get different logits / generations
|
|
cuda_compute_capability_major_version = None
|
|
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
if is_torch_available() and torch.cuda.is_available():
|
|
# 8 is for A100 / A10 and 7 for T4
|
|
cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
|
|
|
|
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
|
|
@slow
|
|
def test_model_7b_logits(self):
|
|
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
|
|
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", device_map="auto")
|
|
out = model(torch.tensor([input_ids]))
|
|
# Expected mean on dim = -1
|
|
EXPECTED_MEAN = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]])
|
|
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
|
|
# slicing logits[0, 0, 0:30]
|
|
EXPECTED_SLICE = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]) # fmt: skip
|
|
torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
|
|
|
|
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
|
|
@slow
|
|
def test_model_13b_logits(self):
|
|
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
|
|
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf", device_map="auto")
|
|
out = model(torch.tensor(input_ids))
|
|
# Expected mean on dim = -1
|
|
EXPECTED_MEAN = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]])
|
|
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
|
|
# slicing logits[0, 0, 0:30]
|
|
EXPECTED_SLICE = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]) # fmt: skip
|
|
torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
|
|
|
|
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
|
|
@slow
|
|
def test_model_13bf_logits(self):
|
|
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
|
|
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf", device_map="auto")
|
|
out = model(torch.tensor(input_ids))
|
|
# Expected mean on dim = -1
|
|
EXPECTED_MEAN = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]])
|
|
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
|
|
# slicing logits[0, 0, 0:30]
|
|
EXPECTED_SLICE = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]) # fmt: skip
|
|
torch.testing.assert_close(out.mean(-1), EXPECTED_SLICE, atol=1e-2, rtol=1e-2)
|
|
|
|
@unittest.skip(
|
|
"Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"
|
|
)
|
|
@slow
|
|
def test_model_70b_logits(self):
|
|
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
|
|
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf", device_map="auto")
|
|
out = model(torch.tensor(input_ids))
|
|
|
|
EXPECTED_MEAN = torch.tensor(
|
|
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]], dtype=torch.float32
|
|
)
|
|
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
|
|
EXPECTED_SLICE = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]) # fmt: skip
|
|
torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
|
|
|
|
@unittest.skip("Model is curently gated")
|
|
@slow
|
|
def test_model_13b_greedy_generation(self):
|
|
EXPECTED_TEXT_COMPLETION = """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi"""
|
|
prompt = "Simply put, the theory of relativity states that "
|
|
tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf")
|
|
input_ids = tokenizer.encode(prompt, return_tensors="pt")
|
|
model = LlamaForCausalLM.from_pretrained(
|
|
"meta-llama/Llama-2-13b-chat-hf", device_map="sequential", use_safetensors=False
|
|
)
|
|
|
|
# greedy generation outputs
|
|
generated_ids = model.generate(input_ids, max_new_tokens=64, top_p=None, temperature=1, do_sample=False)
|
|
text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
|
|
self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
|
|
|
|
@slow
|
|
@require_torch_gpu
|
|
@require_read_token
|
|
def test_compile_static_cache(self):
|
|
# `torch==2.2` will throw an error on this test (as in other compilation tests), but torch==2.1.2 and torch>2.2
|
|
# work as intended. See https://github.com/pytorch/pytorch/issues/121943
|
|
if version.parse(torch.__version__) < version.parse("2.3.0"):
|
|
self.skipTest("This test requires torch >= 2.3 to run.")
|
|
|
|
NUM_TOKENS_TO_GENERATE = 40
|
|
# Note on `EXPECTED_TEXT_COMPLETION`'s diff: the current value matches the original test if the original test
|
|
# was changed to have a cache of 53 tokens (as opposed to 4096), on Ampere GPUs.
|
|
EXPECTED_TEXT_COMPLETION = {
|
|
8: [
|
|
"Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial "
|
|
"reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe "
|
|
"theory of relativ",
|
|
"My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, "
|
|
"my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p",
|
|
],
|
|
7: [
|
|
"Simply put, the theory of relativity states that 1. surely nothing is faster than light.\nThe theory "
|
|
"goes that nothing travels faster than light, but the faster you go, the slower everything else will "
|
|
"be.\nThe theory of relativity",
|
|
"My favorite all time favorite condiment is ketchup. I love it on hamburgers, hot dogs, fries, eggs, "
|
|
"and even on a good old fashioned cheeseburger. I love it on everything. I love it so",
|
|
],
|
|
}
|
|
|
|
prompts = [
|
|
"Simply put, the theory of relativity states that ",
|
|
"My favorite all time favorite condiment is ketchup.",
|
|
]
|
|
tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf", pad_token="</s>", padding_side="right")
|
|
model = LlamaForCausalLM.from_pretrained(
|
|
"meta-llama/Llama-2-7b-hf", device_map="sequential", torch_dtype=torch.float16
|
|
)
|
|
inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
|
|
|
|
# Dynamic Cache
|
|
generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False)
|
|
dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
|
self.assertEqual(EXPECTED_TEXT_COMPLETION[8], dynamic_text) # Both GPU architectures have the same output
|
|
|
|
# Static Cache
|
|
generated_ids = model.generate(
|
|
**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
|
|
)
|
|
static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
|
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_text)
|
|
|
|
# Static Cache + compile
|
|
model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
|
|
generated_ids = model.generate(
|
|
**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
|
|
)
|
|
static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
|
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_compiled_text)
|
|
|
|
|
|
@require_torch
|
|
class CodeLlamaIntegrationTest(unittest.TestCase):
|
|
PROMPTS = [
|
|
'''def remove_non_ascii(s: str) -> str:
|
|
""" <FILL_ME>
|
|
return result
|
|
''',
|
|
"""# Installation instructions:
|
|
```bash
|
|
<FILL_ME>
|
|
```
|
|
This downloads the LLaMA inference code and installs the repository as a local pip package.
|
|
""",
|
|
"""class InterfaceManagerFactory(AbstractManagerFactory):
|
|
def __init__(<FILL_ME>
|
|
def main():
|
|
factory = InterfaceManagerFactory(start=datetime.now())
|
|
managers = []
|
|
for i in range(10):
|
|
managers.append(factory.build(id=i))
|
|
""",
|
|
"""/-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/
|
|
theorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :
|
|
π₁ P = 0 ↔ <FILL_ME> = 0 :=
|
|
begin
|
|
split,
|
|
{ intros h f,
|
|
rw pi_1_etalisation at h,
|
|
simp [h],
|
|
refl
|
|
},
|
|
{ intro h,
|
|
have := @quasi_adjoint C D P,
|
|
simp [←pi_1_etalisation, this, h],
|
|
refl
|
|
}
|
|
end
|
|
""",
|
|
]
|
|
|
|
@require_torch_accelerator
|
|
@slow
|
|
@unittest.skip("Model is too large")
|
|
def test_model_7b_logits(self):
|
|
model = LlamaForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf").to(torch_device)
|
|
tokenizer = CodeLlamaTokenizer.from_pretrained("codellama/CodeLlama-7b-hf")
|
|
# Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.
|
|
# meaning by default this supports passing splitted list of inputs
|
|
processed_text = tokenizer.batch_decode(tokenizer(self.PROMPTS)["input_ids"], add_special_tokens=False)
|
|
# fmt: off
|
|
EXPECTED_TEXT = [
|
|
'<s> <PRE> def remove_non_ascii(s: str) -> str:\n """ <SUF>\n return result\n <MID>',
|
|
'<s> <PRE> # Installation instructions:\n ```bash\n <SUF>\n ```\nThis downloads the LLaMA inference code and installs the repository as a local pip package.\n <MID>',
|
|
'<s> <PRE> class InterfaceManagerFactory(AbstractManagerFactory):\n def __init__( <SUF>\ndef main():\n factory = InterfaceManagerFactory(start=datetime.now())\n managers = []\n for i in range(10):\n managers.append(factory.build(id=i))\n <MID>',
|
|
'<s> <PRE> /-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/\ntheorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :\nπ₁ P = 0 ↔ <SUF> = 0 :=\nbegin\nsplit,\n{ intros h f,\n rw pi_1_etalisation at h,\n simp [h],\n refl\n},\n{ intro h,\n have := @quasi_adjoint C D P,\n simp [←pi_1_etalisation, this, h],\n refl\n}\nend\n <MID>'
|
|
]
|
|
# fmt: on
|
|
self.assertEqual(processed_text, EXPECTED_TEXT)
|
|
processed_text_suffix_first = tokenizer.batch_decode(
|
|
tokenizer(self.PROMPTS, suffix_first=True, add_special_tokens=False)["input_ids"]
|
|
)
|
|
|
|
# fmt: off
|
|
EXPECTED_TEXT = [
|
|
'<PRE> <SUF>\n return result\n <MID> def remove_non_ascii(s: str) -> str:\n """ ',
|
|
'<PRE> <SUF>\n ```\nThis downloads the LLaMA inference code and installs the repository as a local pip package.\n <MID> # Installation instructions:\n ```bash\n',
|
|
'<PRE> <SUF>\ndef main():\n factory = InterfaceManagerFactory(start=datetime.now())\n managers = []\n for i in range(10):\n managers.append(factory.build(id=i))\n <MID> class InterfaceManagerFactory(AbstractManagerFactory):\n def __init__(',
|
|
'<PRE> <SUF> = 0 :=\nbegin\nsplit,\n{ intros h f,\n rw pi_1_etalisation at h,\n simp [h],\n refl\n},\n{ intro h,\n have := @quasi_adjoint C D P,\n simp [←pi_1_etalisation, this, h],\n refl\n}\nend\n <MID> /-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/\ntheorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :\nπ₁ P = 0 ↔ '
|
|
]
|
|
EXPECTED_IDS = torch.tensor([[1, 32007, 822, 3349, 29918, 5464, 29918, 294, 18869, 29898, 29879, 29901, 851, 29897, 1599, 851, 29901, 13, 1678, 9995, 29871, 32008, 13, 1678, 736, 1121, 13, 32009, 15941, 1661, 29899, 28599, 2687, 4890, 515, 263, 1347, 29889, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 269, 29901, 450, 1347, 304, 3349, 1661, 29899, 28599, 2687, 4890, 515, 29889, 13, 13, 1678, 16969, 29901, 13, 4706, 450, 1347, 411, 1661, 29899, 28599, 2687, 4890, 6206, 29889, 13, 1678, 9995, 13, 1678, 1121, 353, 5124, 13, 1678, 363, 274, 297, 269, 29901, 13, 4706, 565, 4356, 29898, 29883, 29897, 529, 29871, 29896, 29906, 29947, 29901, 13, 9651, 1121, 4619, 274, 32010, 2]])
|
|
# fmt: on
|
|
self.assertEqual(processed_text_suffix_first, EXPECTED_TEXT)
|
|
input_ids = tokenizer(self.PROMPTS[0], return_tensors="pt")["input_ids"]
|
|
generated_ids = model.generate(input_ids.to(torch_device), max_new_tokens=128)
|
|
torch.testing.assert_close(generated_ids, EXPECTED_IDS)
|
|
|
|
EXPECTED_INFILLING = [
|
|
'<s> <PRE> def remove_non_ascii(s: str) -> str:\n """ <SUF>\n return result\n <MID>Remove non-ASCII characters from a string.\n\n Args:\n s: The string to remove non-ASCII characters from.\n\n Returns:\n The string with non-ASCII characters removed.\n """\n result = ""\n for c in s:\n if ord(c) < 128:\n result += c <EOT></s>'
|
|
]
|
|
infilling = tokenizer.batch_decode(generated_ids)
|
|
self.assertEqual(infilling, EXPECTED_INFILLING)
|
|
|
|
|
|
@slow
|
|
@require_torch_gpu
|
|
class Mask4DTestHard(unittest.TestCase):
|
|
def tearDown(self):
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def setUp(self):
|
|
model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
|
|
self.model_dtype = torch.float32
|
|
self.tokenizer = LlamaTokenizer.from_pretrained(model_name)
|
|
self.model = LlamaForCausalLM.from_pretrained(model_name, torch_dtype=self.model_dtype).to(torch_device)
|
|
|
|
def get_test_data(self):
|
|
template = "my favorite {}"
|
|
items = ("pet is a", "artist plays a", "name is L") # same number of tokens in each item
|
|
|
|
batch_separate = [template.format(x) for x in items] # 3 separate lines
|
|
batch_shared_prefix = template.format(" ".join(items)) # 1 line with options concatenated
|
|
|
|
input_ids = self.tokenizer(batch_separate, return_tensors="pt").input_ids.to(torch_device)
|
|
input_ids_shared_prefix = self.tokenizer(batch_shared_prefix, return_tensors="pt").input_ids.to(torch_device)
|
|
|
|
mask_shared_prefix = torch.tensor(
|
|
[
|
|
[
|
|
[
|
|
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
|
|
[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
|
|
[1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],
|
|
[1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0],
|
|
[1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0],
|
|
[1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],
|
|
[1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0],
|
|
[1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1],
|
|
]
|
|
]
|
|
],
|
|
device=torch_device,
|
|
)
|
|
|
|
position_ids = torch.arange(input_ids.shape[1]).tile(input_ids.shape[0], 1).to(torch_device)
|
|
|
|
# building custom positions ids based on custom mask
|
|
position_ids_shared_prefix = (mask_shared_prefix.sum(dim=-1) - 1).reshape(1, -1)
|
|
# effectively: position_ids_shared_prefix = torch.tensor([[0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5]]).to(device)
|
|
|
|
# inverting the mask
|
|
min_dtype = torch.finfo(self.model_dtype).min
|
|
mask_shared_prefix = (mask_shared_prefix.eq(0.0)).to(dtype=self.model_dtype) * min_dtype
|
|
|
|
return input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix
|
|
|
|
def test_stacked_causal_mask(self):
|
|
(
|
|
input_ids,
|
|
position_ids,
|
|
input_ids_shared_prefix,
|
|
mask_shared_prefix,
|
|
position_ids_shared_prefix,
|
|
) = self.get_test_data()
|
|
|
|
# regular batch
|
|
logits = self.model.forward(input_ids, position_ids=position_ids).logits
|
|
logits_last = logits[:, -1, :] # last tokens in each batch line
|
|
decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)]
|
|
|
|
# single forward run with 4D custom mask
|
|
logits_shared_prefix = self.model.forward(
|
|
input_ids_shared_prefix, attention_mask=mask_shared_prefix, position_ids=position_ids_shared_prefix
|
|
).logits
|
|
logits_shared_prefix_last = logits_shared_prefix[
|
|
0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1], :
|
|
] # last three tokens
|
|
decoded_shared_prefix = [self.tokenizer.decode(t) for t in logits_shared_prefix_last.argmax(dim=-1)]
|
|
|
|
self.assertEqual(decoded, decoded_shared_prefix)
|
|
|
|
def test_partial_stacked_causal_mask(self):
|
|
# Same as the test above, but the input is passed in two groups. It tests that we can pass partial 4D attention masks
|
|
|
|
(
|
|
input_ids,
|
|
position_ids,
|
|
input_ids_shared_prefix,
|
|
mask_shared_prefix,
|
|
position_ids_shared_prefix,
|
|
) = self.get_test_data()
|
|
|
|
# regular batch
|
|
logits = self.model.forward(input_ids, position_ids=position_ids).logits
|
|
logits_last = logits[:, -1, :] # last tokens in each batch line
|
|
decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)]
|
|
|
|
# 2 forward runs with custom 4D masks
|
|
part_a = 3 # split point
|
|
|
|
input_1a = input_ids_shared_prefix[:, :part_a]
|
|
position_ids_1a = position_ids_shared_prefix[:, :part_a]
|
|
mask_1a = mask_shared_prefix[:, :, :part_a, :part_a]
|
|
|
|
outs_1a = self.model.forward(input_1a, attention_mask=mask_1a, position_ids=position_ids_1a)
|
|
past_key_values_a = outs_1a["past_key_values"]
|
|
|
|
# Case 1: we pass a 4D attention mask regarding the current sequence length (i.e. [..., seq_len, full_len])
|
|
input_1b = input_ids_shared_prefix[:, part_a:]
|
|
position_ids_1b = position_ids_shared_prefix[:, part_a:]
|
|
mask_1b = mask_shared_prefix[:, :, part_a:, :]
|
|
outs_1b = self.model.forward(
|
|
input_1b,
|
|
attention_mask=mask_1b,
|
|
position_ids=position_ids_1b,
|
|
past_key_values=past_key_values_a,
|
|
)
|
|
decoded_1b = [
|
|
self.tokenizer.decode(t)
|
|
for t in outs_1b.logits.argmax(-1)[
|
|
0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1] - part_a
|
|
]
|
|
]
|
|
self.assertEqual(decoded, decoded_1b)
|
|
|
|
def test_stacked_causal_mask_static_cache(self):
|
|
"""same as above but with StaticCache"""
|
|
(
|
|
input_ids,
|
|
position_ids,
|
|
input_ids_shared_prefix,
|
|
mask_shared_prefix,
|
|
position_ids_shared_prefix,
|
|
) = self.get_test_data()
|
|
|
|
# regular batch
|
|
logits = self.model.forward(input_ids, position_ids=position_ids).logits
|
|
logits_last = logits[:, -1, :] # last tokens in each batch line
|
|
decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)]
|
|
|
|
# upgrade the model with StaticCache
|
|
max_cache_len = 16 # note that max_cache_len is greater than the attention_mask.shape[-1]
|
|
past_key_values = StaticCache(
|
|
config=self.model.config,
|
|
max_batch_size=1,
|
|
max_cache_len=max_cache_len,
|
|
device=torch_device,
|
|
dtype=self.model.dtype,
|
|
)
|
|
|
|
padded_attention_mask = torch.nn.functional.pad(
|
|
input=mask_shared_prefix,
|
|
pad=(0, max_cache_len - mask_shared_prefix.shape[-1]),
|
|
mode="constant",
|
|
value=torch.finfo(self.model_dtype).min,
|
|
)
|
|
|
|
# single forward run with 4D custom mask
|
|
logits_shared_prefix = self.model.forward(
|
|
input_ids_shared_prefix,
|
|
attention_mask=padded_attention_mask,
|
|
position_ids=position_ids_shared_prefix,
|
|
cache_position=torch.arange(input_ids_shared_prefix.shape[-1], device=torch_device),
|
|
past_key_values=past_key_values,
|
|
).logits
|
|
logits_shared_prefix_last = logits_shared_prefix[
|
|
0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1], :
|
|
] # last three tokens
|
|
decoded_shared_prefix = [self.tokenizer.decode(t) for t in logits_shared_prefix_last.argmax(dim=-1)]
|
|
|
|
self.assertEqual(decoded, decoded_shared_prefix)
|
|
|
|
def test_partial_stacked_causal_mask_static_cache(self):
|
|
# Same as the test above, but the input is passed in two groups. It tests that we can pass partial 4D attention masks
|
|
# we pass a 4D attention mask shaped [..., seq_len, full_static_cache_len])
|
|
(
|
|
input_ids,
|
|
position_ids,
|
|
input_ids_shared_prefix,
|
|
mask_shared_prefix,
|
|
position_ids_shared_prefix,
|
|
) = self.get_test_data()
|
|
|
|
# regular batch
|
|
logits = self.model.forward(input_ids, position_ids=position_ids).logits
|
|
logits_last = logits[:, -1, :] # last tokens in each batch line
|
|
decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)]
|
|
|
|
# upgrade the model with StaticCache
|
|
max_cache_len = 16 # note that max_cache_len is greater than the attention_mask.shape[-1]
|
|
past_key_values = StaticCache(
|
|
config=self.model.config,
|
|
max_batch_size=1,
|
|
max_cache_len=max_cache_len,
|
|
device=torch_device,
|
|
dtype=self.model.dtype,
|
|
)
|
|
|
|
# forward run for the first part of input
|
|
part_a = 3 # split point
|
|
|
|
input_1a = input_ids_shared_prefix[:, :part_a]
|
|
position_ids_1a = position_ids_shared_prefix[:, :part_a]
|
|
mask_1a = mask_shared_prefix[:, :, :part_a, :part_a]
|
|
|
|
padded_mask_1a = torch.nn.functional.pad(
|
|
input=mask_1a,
|
|
pad=(0, max_cache_len - mask_1a.shape[-1]),
|
|
mode="constant",
|
|
value=torch.finfo(self.model_dtype).min,
|
|
)
|
|
|
|
_ = self.model.forward(
|
|
input_1a,
|
|
attention_mask=padded_mask_1a,
|
|
position_ids=position_ids_1a,
|
|
cache_position=torch.arange(part_a, device=torch_device),
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
# forward run for the second part of input
|
|
input_1b = input_ids_shared_prefix[:, part_a:]
|
|
position_ids_1b = position_ids_shared_prefix[:, part_a:]
|
|
mask_1b = mask_shared_prefix[:, :, part_a:, :]
|
|
|
|
padded_mask_1b = torch.nn.functional.pad(
|
|
input=mask_1b, pad=(0, max_cache_len - mask_1b.shape[-1]), mode="constant", value=0
|
|
)
|
|
|
|
outs_1b = self.model.forward(
|
|
input_1b,
|
|
attention_mask=padded_mask_1b,
|
|
position_ids=position_ids_1b,
|
|
cache_position=torch.arange(
|
|
part_a,
|
|
input_ids_shared_prefix.shape[-1],
|
|
device=torch_device,
|
|
),
|
|
past_key_values=past_key_values,
|
|
)
|
|
decoded_1b = [
|
|
self.tokenizer.decode(t)
|
|
for t in outs_1b.logits.argmax(-1)[
|
|
0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1] - part_a
|
|
]
|
|
]
|
|
self.assertEqual(decoded, decoded_1b)
|