826 lines
35 KiB
Python
826 lines
35 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 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 transformers import BloomConfig, is_torch_available
|
|
from transformers.testing_utils import require_torch, require_torch_accelerator, slow, torch_device
|
|
|
|
from ...generation.test_utils import GenerationTesterMixin
|
|
from ...test_configuration_common import ConfigTester
|
|
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
|
|
from ...test_pipeline_mixin import PipelineTesterMixin
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
from transformers import (
|
|
BloomForCausalLM,
|
|
BloomForQuestionAnswering,
|
|
BloomForSequenceClassification,
|
|
BloomForTokenClassification,
|
|
BloomModel,
|
|
BloomTokenizerFast,
|
|
)
|
|
|
|
|
|
@require_torch
|
|
class BloomModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=14,
|
|
seq_length=7,
|
|
is_training=True,
|
|
use_token_type_ids=False,
|
|
use_input_mask=True,
|
|
use_labels=True,
|
|
use_mc_token_ids=True,
|
|
vocab_size=99,
|
|
hidden_size=32,
|
|
num_hidden_layers=2,
|
|
num_attention_heads=4,
|
|
intermediate_size=37,
|
|
hidden_act="gelu",
|
|
hidden_dropout_prob=0.1,
|
|
attention_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,
|
|
scope=None,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.seq_length = seq_length
|
|
self.is_training = is_training
|
|
self.use_token_type_ids = use_token_type_ids
|
|
self.use_input_mask = use_input_mask
|
|
self.use_labels = use_labels
|
|
self.use_mc_token_ids = use_mc_token_ids
|
|
self.vocab_size = vocab_size
|
|
self.hidden_size = hidden_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.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_dropout_prob = attention_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.scope = None
|
|
self.bos_token_id = vocab_size - 1
|
|
self.eos_token_id = vocab_size - 1
|
|
self.pad_token_id = vocab_size - 1
|
|
|
|
def get_large_model_config(self):
|
|
return BloomConfig.from_pretrained("bigscience/bloom")
|
|
|
|
def prepare_config_and_inputs(self, gradient_checkpointing=False):
|
|
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
|
|
input_mask = None
|
|
if self.use_input_mask:
|
|
input_mask = random_attention_mask([self.batch_size, self.seq_length])
|
|
|
|
sequence_labels = None
|
|
if self.use_labels:
|
|
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
|
|
|
|
config = self.get_config(gradient_checkpointing=gradient_checkpointing)
|
|
|
|
return (config, input_ids, input_mask, sequence_labels)
|
|
|
|
def get_config(self, gradient_checkpointing=False, slow_but_exact=True):
|
|
return BloomConfig(
|
|
vocab_size=self.vocab_size,
|
|
seq_length=self.seq_length,
|
|
hidden_size=self.hidden_size,
|
|
n_layer=self.num_hidden_layers,
|
|
n_head=self.num_attention_heads,
|
|
hidden_dropout=self.hidden_dropout_prob,
|
|
attention_dropout=self.attention_dropout_prob,
|
|
n_positions=self.max_position_embeddings,
|
|
type_vocab_size=self.type_vocab_size,
|
|
initializer_range=self.initializer_range,
|
|
use_cache=True,
|
|
bos_token_id=self.bos_token_id,
|
|
eos_token_id=self.eos_token_id,
|
|
pad_token_id=self.pad_token_id,
|
|
num_labels=self.num_labels,
|
|
gradient_checkpointing=gradient_checkpointing,
|
|
slow_but_exact=slow_but_exact,
|
|
dtype="float32",
|
|
)
|
|
|
|
def create_and_check_bloom_model(self, config, input_ids, input_mask, *args):
|
|
model = BloomModel(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.past_key_values), config.n_layer)
|
|
|
|
def create_and_check_bloom_model_past(self, config, input_ids, input_mask, *args):
|
|
model = BloomModel(config=config)
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
# first forward pass
|
|
outputs = model(input_ids, attention_mask=torch.ones_like(input_ids), use_cache=True)
|
|
outputs_use_cache_conf = model(input_ids, attention_mask=torch.ones_like(input_ids))
|
|
outputs_no_past = model(input_ids, use_cache=False, attention_mask=torch.ones_like(input_ids))
|
|
|
|
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
|
|
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
|
|
|
|
past = outputs["past_key_values"]
|
|
|
|
# create hypothetical next token and extent to next_input_ids
|
|
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
|
|
|
# append to next input_ids and token_type_ids
|
|
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
|
|
|
output_from_no_past = model(next_input_ids)["last_hidden_state"]
|
|
output_from_past = model(next_tokens, past_key_values=past)["last_hidden_state"]
|
|
|
|
# select random slice
|
|
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
|
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
|
|
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
|
|
|
|
# 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 create_and_check_bloom_model_attention_mask_past(self, config, input_ids, input_mask, *args):
|
|
model = BloomModel(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
# create attention mask
|
|
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
|
|
half_seq_length = self.seq_length // 2
|
|
attn_mask[:, half_seq_length:] = 0
|
|
|
|
# first forward pass
|
|
output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
|
|
|
|
# create hypothetical next token and extent to next_input_ids
|
|
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
|
|
|
# change a random masked slice from input_ids
|
|
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
|
|
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
|
|
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
|
|
|
|
# append to next input_ids and attn_mask
|
|
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
|
attn_mask = torch.cat(
|
|
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
|
|
dim=1,
|
|
)
|
|
|
|
# get two different outputs
|
|
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
|
|
output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
|
|
|
|
# select random slice
|
|
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
|
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
|
|
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
|
|
|
|
# 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 create_and_check_bloom_model_past_large_inputs(self, config, input_ids, input_mask, *args):
|
|
model = BloomModel(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
# first forward pass
|
|
outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
|
|
|
|
output, past = outputs.to_tuple()
|
|
|
|
# create hypothetical 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 token_type_ids
|
|
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)["last_hidden_state"]
|
|
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past)[
|
|
"last_hidden_state"
|
|
]
|
|
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
|
|
|
|
# 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()
|
|
|
|
# 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 create_and_check_lm_head_model(self, config, input_ids, input_mask, *args):
|
|
model = BloomForCausalLM(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_sequence_classification_model(self, config, input_ids, input_mask, *args):
|
|
config.num_labels = self.num_labels
|
|
model = BloomForSequenceClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
result = model(input_ids, attention_mask=input_mask)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
|
|
|
|
def create_and_check_token_classification_model(self, config, input_ids, input_mask, *args):
|
|
model = BloomForTokenClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
result = model(input_ids, attention_mask=input_mask)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
|
|
|
|
def create_and_check_question_answering_model(self, config, input_ids, input_mask, *args):
|
|
model = BloomForQuestionAnswering(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
result = model(input_ids, attention_mask=input_mask)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
|
|
|
|
def create_and_check_forward_and_backwards(
|
|
self, config, input_ids, input_mask, *args, gradient_checkpointing=False
|
|
):
|
|
model = BloomForCausalLM(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 create_and_check_bloom_weight_initialization(self, config, *args):
|
|
model = BloomModel(config)
|
|
model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layer)
|
|
for key in model.state_dict().keys():
|
|
if "c_proj" in key and "weight" in key:
|
|
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001)
|
|
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01)
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
|
|
config, input_ids, input_mask, sequence_labels = config_and_inputs
|
|
|
|
inputs_dict = {"input_ids": input_ids}
|
|
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class BloomModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (
|
|
(
|
|
BloomModel,
|
|
BloomForCausalLM,
|
|
BloomForSequenceClassification,
|
|
BloomForTokenClassification,
|
|
BloomForQuestionAnswering,
|
|
)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
|
|
all_generative_model_classes = (BloomForCausalLM,) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{
|
|
"feature-extraction": BloomModel,
|
|
"question-answering": BloomForQuestionAnswering,
|
|
"text-classification": BloomForSequenceClassification,
|
|
"text-generation": BloomForCausalLM,
|
|
"token-classification": BloomForTokenClassification,
|
|
"zero-shot": BloomForSequenceClassification,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
fx_compatible = True
|
|
test_missing_keys = False
|
|
test_pruning = False
|
|
test_torchscript = True # torch.autograd functions seems to be not supported
|
|
|
|
def setUp(self):
|
|
self.model_tester = BloomModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=BloomConfig, n_embd=37)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_bloom_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_bloom_model(*config_and_inputs)
|
|
|
|
def test_bloom_model_past(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_bloom_model_past(*config_and_inputs)
|
|
|
|
def test_bloom_model_att_mask_past(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_bloom_model_attention_mask_past(*config_and_inputs)
|
|
|
|
def test_bloom_model_past_large_inputs(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_bloom_model_past_large_inputs(*config_and_inputs)
|
|
|
|
def test_bloom_lm_head_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
|
|
|
|
def test_bloom_sequence_classification_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_sequence_classification_model(*config_and_inputs)
|
|
|
|
def test_bloom_token_classification_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_token_classification_model(*config_and_inputs)
|
|
|
|
def test_bloom_gradient_checkpointing(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
|
|
|
|
def test_bloom_weight_initialization(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_bloom_weight_initialization(*config_and_inputs)
|
|
|
|
@unittest.skip("Bloom has a non-standard KV cache format.")
|
|
def test_past_key_values_format(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "bigscience/bigscience-small-testing"
|
|
model = BloomModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
@slow
|
|
@require_torch_accelerator
|
|
def test_simple_generation(self):
|
|
# This test is a bit flaky. For some GPU architectures, pytorch sets by default allow_fp16_reduced_precision_reduction = True and some operations
|
|
# do not give the same results under this configuration, especially torch.baddmm and torch.bmm. https://pytorch.org/docs/stable/notes/numerical_accuracy.html#fp16-on-mi200
|
|
# As we leave the default value (True) for allow_fp16_reduced_precision_reduction , the tests failed when running in half-precision with smaller models (560m)
|
|
# Please see: https://pytorch.org/docs/stable/notes/cuda.html#reduced-precision-reduction-in-fp16-gemms
|
|
# This discrepancy is observed only when using small models and seems to be stable for larger models.
|
|
# Our conclusion is that these operations are flaky for small inputs but seems to be stable for larger inputs (for the functions `baddmm` and `bmm`), and therefore for larger models.
|
|
|
|
# Here is a summary of an ablation study of our observations
|
|
# EXPECTED_OUTPUT = "I enjoy walking with my cute dog, and I love to watch the kids play. I am a very active person, and I am a very good listener. I am a very good person, and I am a very good person. I am a"
|
|
# 560m + allow_fp16_reduced_precision_reduction = False + torch.bmm ==> PASS
|
|
# 560m + allow_fp16_reduced_precision_reduction = False + torch.baddm ==> PASS
|
|
# 560m + allow_fp16_reduced_precision_reduction = True + torch.baddm ==> PASS
|
|
# 560m + allow_fp16_reduced_precision_reduction = True + torch.bmm ==> FAIL
|
|
|
|
# EXPECTED_OUTPUT = "I enjoy walking with my cute dog, but I also enjoy hiking, biking, and swimming. I love to cook and bake. I love to cook and bake. I love to cook and bake. I love to cook and bake. I love"
|
|
# >=1b1 + allow_fp16_reduced_precision_reduction = True + torch.baddm ==> PASS (for use_cache=True and use_cache=False)
|
|
# >=1b1 + allow_fp16_reduced_precision_reduction = True + torch.bmm ==> PASS
|
|
# >=1b1 + allow_fp16_reduced_precision_reduction = False + torch.bmm ==> PASS
|
|
|
|
path_560m = "bigscience/bloom-560m"
|
|
model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device)
|
|
model = model.eval()
|
|
tokenizer = BloomTokenizerFast.from_pretrained(path_560m)
|
|
|
|
input_sentence = "I enjoy walking with my cute dog"
|
|
# This output has been obtained using fp32 model on the huggingface DGX workstation - NVIDIA A100 GPU
|
|
EXPECTED_OUTPUT = (
|
|
"I enjoy walking with my cute dog, and I love to watch the kids play with the kids. I am a very "
|
|
"active person, and I enjoy working out, and I am a very active person. I am a very active person, and I"
|
|
)
|
|
|
|
input_ids = tokenizer.encode(input_sentence, return_tensors="pt")
|
|
greedy_output = model.generate(input_ids.to(torch_device), max_length=50)
|
|
|
|
self.assertEqual(tokenizer.decode(greedy_output[0], skip_special_tokens=True), EXPECTED_OUTPUT)
|
|
|
|
@slow
|
|
@require_torch_accelerator
|
|
def test_batch_generation(self):
|
|
path_560m = "bigscience/bloom-560m"
|
|
model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device)
|
|
model = model.eval()
|
|
tokenizer = BloomTokenizerFast.from_pretrained(path_560m, padding_side="left")
|
|
|
|
input_sentence = ["I enjoy walking with my cute dog", "I enjoy walking with my cute dog"]
|
|
|
|
inputs = tokenizer.batch_encode_plus(input_sentence, return_tensors="pt", padding=True)
|
|
input_ids = inputs["input_ids"].to(torch_device)
|
|
attention_mask = inputs["attention_mask"]
|
|
greedy_output = model.generate(input_ids, attention_mask=attention_mask, max_length=50, do_sample=False)
|
|
|
|
self.assertEqual(
|
|
tokenizer.decode(greedy_output[0], skip_special_tokens=True),
|
|
tokenizer.decode(greedy_output[1], skip_special_tokens=True),
|
|
)
|
|
|
|
@slow
|
|
@require_torch_accelerator
|
|
def test_batch_generation_padd(self):
|
|
path_560m = "bigscience/bloom-560m"
|
|
model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device)
|
|
model = model.eval()
|
|
tokenizer = BloomTokenizerFast.from_pretrained(path_560m, padding_side="left")
|
|
|
|
input_sentence = ["I enjoy walking with my cute dog", "Hello my name is"]
|
|
input_sentence_without_pad = "Hello my name is"
|
|
|
|
input_ids = tokenizer.batch_encode_plus(input_sentence, return_tensors="pt", padding=True)
|
|
input_ids_without_pad = tokenizer.encode(input_sentence_without_pad, return_tensors="pt")
|
|
|
|
input_ids, attention_mask = input_ids["input_ids"].to(torch_device), input_ids["attention_mask"]
|
|
greedy_output = model.generate(input_ids, attention_mask=attention_mask, max_length=50, do_sample=False)
|
|
greedy_output_without_pad = model.generate(
|
|
input_ids_without_pad.to(torch_device), max_length=50, do_sample=False
|
|
)
|
|
|
|
# test token values
|
|
self.assertEqual(greedy_output[-1, 3:].tolist(), greedy_output_without_pad[0, :-3].tolist())
|
|
|
|
# test reconstructions
|
|
self.assertEqual(
|
|
tokenizer.decode(greedy_output[-1, 3:], skip_special_tokens=True),
|
|
tokenizer.decode(greedy_output_without_pad[0, :-3], skip_special_tokens=True),
|
|
)
|
|
|
|
@slow
|
|
@require_torch_accelerator
|
|
def test_batch_generated_text(self):
|
|
path_560m = "bigscience/bloom-560m"
|
|
|
|
model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device)
|
|
model = model.eval()
|
|
tokenizer = BloomTokenizerFast.from_pretrained(path_560m, padding_side="left")
|
|
|
|
input_sentences = [
|
|
"Hello what is",
|
|
"Running a quick test with the",
|
|
]
|
|
inputs = tokenizer(input_sentences, return_tensors="pt", padding=True, truncation=True)
|
|
generated_ids = model.generate(
|
|
inputs["input_ids"].to(torch_device), attention_mask=inputs["attention_mask"], max_length=20
|
|
)
|
|
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
|
|
|
# these generations match those of the PyTorch model
|
|
EXPECTED_GENERATIONS = [
|
|
"Hello what is the best way to get the data from the server? I have tried",
|
|
"Running a quick test with the following command:\nsudo apt-get install python3\nsudo apt-get install python2",
|
|
]
|
|
|
|
self.assertListEqual(generated_text, EXPECTED_GENERATIONS)
|
|
|
|
|
|
@require_torch
|
|
class BloomEmbeddingTest(unittest.TestCase):
|
|
"""
|
|
The goal here is to compare the embeddings generated by the model trained
|
|
using Megatron-LM with the one from the transformers library, with a small GPT2-like model
|
|
to ensure that the conversion from Megatron-LM to transformers has been done successfully.
|
|
The script compares the logits of the embedding layer and the transformer layers.
|
|
|
|
WARNING: It is expected that these logits will not have exactly the same statistics when running
|
|
the code on CPU or GPU. For more info, please visit:
|
|
- https://github.com/pytorch/pytorch/issues/76052#issuecomment-1103193548
|
|
- https://discuss.pytorch.org/t/reproducibility-issue-between-intel-and-amd-cpus/144779/9
|
|
|
|
|
|
You need to install tokenizers following this readme:
|
|
- https://huggingface.co/bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
|
|
|
|
Tokenizer used during training:
|
|
- https://huggingface.co/bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
|
|
|
|
# TODO change the script (or just add skip) when building the env with tokenizers 0.12.0
|
|
"""
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
self.path_bigscience_model = "bigscience/bigscience-small-testing"
|
|
|
|
@require_torch
|
|
def test_embeddings(self):
|
|
# The config in this checkpoint has `bfloat16` as `torch_dtype` -> model in `bfloat16`
|
|
model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, torch_dtype="auto")
|
|
model.eval()
|
|
|
|
EMBEDDINGS_DS_BEFORE_LN_BF_16_MEAN = {
|
|
3478: 0.0002307891845703125,
|
|
368: -0.000568389892578125,
|
|
109586: -0.0003910064697265625,
|
|
35433: -0.000194549560546875,
|
|
2: 0.0004138946533203125,
|
|
77: 0.000659942626953125,
|
|
132619: -0.00031280517578125,
|
|
2175: 0.000457763671875,
|
|
23714: 0.000263214111328125,
|
|
73173: -0.000286102294921875,
|
|
144252: 0.00052642822265625,
|
|
}
|
|
EMBEDDINGS_DS_BEFORE_LN_BF_16_MIN = {
|
|
3478: -0.00921630859375,
|
|
368: -0.010009765625,
|
|
109586: -0.01031494140625,
|
|
35433: -0.01177978515625,
|
|
2: -0.0074462890625,
|
|
77: -0.00848388671875,
|
|
132619: -0.009521484375,
|
|
2175: -0.0074462890625,
|
|
23714: -0.0145263671875,
|
|
73173: -0.007415771484375,
|
|
144252: -0.01007080078125,
|
|
}
|
|
EMBEDDINGS_DS_BEFORE_LN_BF_16_MAX = {
|
|
3478: 0.0128173828125,
|
|
368: 0.01214599609375,
|
|
109586: 0.0111083984375,
|
|
35433: 0.01019287109375,
|
|
2: 0.0157470703125,
|
|
77: 0.0174560546875,
|
|
132619: 0.0078125,
|
|
2175: 0.0113525390625,
|
|
23714: 0.0146484375,
|
|
73173: 0.01116943359375,
|
|
144252: 0.01141357421875,
|
|
}
|
|
EMBEDDINGS_DS_BEFORE_LN_BF_16_SUM = {"value": 0.08203125}
|
|
|
|
EMBEDDINGS_DS_BEFORE_LN_F_16_MEAN = {
|
|
132619: -0.00031256675720214844,
|
|
3478: 0.00023090839385986328,
|
|
368: -0.0005702972412109375,
|
|
109586: -0.00039124488830566406,
|
|
35433: -0.000194549560546875,
|
|
2: 0.0004146099090576172,
|
|
2175: 0.0004572868347167969,
|
|
23714: 0.00026416778564453125,
|
|
73173: -0.0002865791320800781,
|
|
144252: 0.0005254745483398438,
|
|
77: 0.0006618499755859375,
|
|
}
|
|
EMBEDDINGS_DS_BEFORE_LN_F_16_MIN = {
|
|
3478: -0.00921630859375,
|
|
368: -0.010009765625,
|
|
109586: -0.01031494140625,
|
|
35433: -0.01177978515625,
|
|
2: -0.0074462890625,
|
|
77: -0.00848388671875,
|
|
132619: -0.009521484375,
|
|
2175: -0.0074462890625,
|
|
23714: -0.0145263671875,
|
|
73173: -0.007415771484375,
|
|
144252: -0.01007080078125,
|
|
}
|
|
EMBEDDINGS_DS_BEFORE_LN_F_16_MAX = {
|
|
3478: 0.0128173828125,
|
|
368: 0.01214599609375,
|
|
109586: 0.0111083984375,
|
|
35433: 0.01019287109375,
|
|
2: 0.0157470703125,
|
|
77: 0.0174560546875,
|
|
132619: 0.0078125,
|
|
2175: 0.0113525390625,
|
|
23714: 0.0146484375,
|
|
73173: 0.01116943359375,
|
|
144252: 0.01141357421875,
|
|
}
|
|
EMBEDDINGS_DS_BEFORE_LN_F_16_SUM = {"value": 0.0821533203125}
|
|
|
|
EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN = {
|
|
132619: -0.00031267106533050537,
|
|
3478: 0.00023087859153747559,
|
|
368: -0.0005701072514057159,
|
|
109586: -0.0003911703824996948,
|
|
35433: -0.0001944899559020996,
|
|
2: 0.0004146844148635864,
|
|
2175: 0.00045740045607089996,
|
|
23714: 0.0002641640603542328,
|
|
73173: -0.0002864748239517212,
|
|
144252: 0.0005256589502096176,
|
|
77: 0.0006617321632802486,
|
|
}
|
|
EMBEDDINGS_DS_BEFORE_LN_F_32_MIN = {
|
|
3478: -0.00921630859375,
|
|
368: -0.010009765625,
|
|
109586: -0.01031494140625,
|
|
35433: -0.01177978515625,
|
|
2: -0.0074462890625,
|
|
77: -0.00848388671875,
|
|
132619: -0.009521484375,
|
|
2175: -0.0074462890625,
|
|
23714: -0.0145263671875,
|
|
73173: -0.007415771484375,
|
|
144252: -0.01007080078125,
|
|
}
|
|
EMBEDDINGS_DS_BEFORE_LN_F_32_MAX = {
|
|
3478: 0.0128173828125,
|
|
368: 0.01214599609375,
|
|
109586: 0.0111083984375,
|
|
35433: 0.01019287109375,
|
|
2: 0.0157470703125,
|
|
77: 0.0174560546875,
|
|
132619: 0.0078125,
|
|
2175: 0.0113525390625,
|
|
23714: 0.0146484375,
|
|
73173: 0.01116943359375,
|
|
144252: 0.01141357421875,
|
|
}
|
|
EMBEDDINGS_DS_BEFORE_LN_F_32_SUM = {"value": 0.08217757940292358}
|
|
|
|
TEST_EMBEDDINGS = {
|
|
"torch.bfloat16": {
|
|
"mean": EMBEDDINGS_DS_BEFORE_LN_BF_16_MEAN,
|
|
"max": EMBEDDINGS_DS_BEFORE_LN_BF_16_MAX,
|
|
"min": EMBEDDINGS_DS_BEFORE_LN_BF_16_MIN,
|
|
"sum": EMBEDDINGS_DS_BEFORE_LN_BF_16_SUM,
|
|
},
|
|
"torch.float32": {
|
|
"mean": EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN,
|
|
"max": EMBEDDINGS_DS_BEFORE_LN_F_32_MAX,
|
|
"min": EMBEDDINGS_DS_BEFORE_LN_F_32_MIN,
|
|
"sum": EMBEDDINGS_DS_BEFORE_LN_F_32_SUM,
|
|
},
|
|
"torch.float": {
|
|
"mean": EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN,
|
|
"max": EMBEDDINGS_DS_BEFORE_LN_F_32_MAX,
|
|
"min": EMBEDDINGS_DS_BEFORE_LN_F_32_MIN,
|
|
"sum": EMBEDDINGS_DS_BEFORE_LN_F_32_SUM,
|
|
},
|
|
"torch.float16": {
|
|
"mean": EMBEDDINGS_DS_BEFORE_LN_F_16_MEAN,
|
|
"max": EMBEDDINGS_DS_BEFORE_LN_F_16_MAX,
|
|
"min": EMBEDDINGS_DS_BEFORE_LN_F_16_MIN,
|
|
"sum": EMBEDDINGS_DS_BEFORE_LN_F_16_SUM,
|
|
},
|
|
}
|
|
|
|
EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478] # fmt: skip
|
|
|
|
EMBEDDINGS_DS_AFTER_LN_MEAN = {
|
|
3478: -6.580352783203125e-05,
|
|
368: 0.0001316070556640625,
|
|
109586: -0.00030517578125,
|
|
35433: 4.00543212890625e-05,
|
|
2: -7.2479248046875e-05,
|
|
77: -8.96453857421875e-05,
|
|
132619: 0.0001583099365234375,
|
|
2175: 2.1219253540039062e-05,
|
|
23714: -0.000247955322265625,
|
|
73173: -0.00021839141845703125,
|
|
144252: -0.0001430511474609375,
|
|
}
|
|
EMBEDDINGS_DS_AFTER_LN_MIN = {
|
|
3478: -1.6953125,
|
|
368: -1.6875,
|
|
109586: -1.6875,
|
|
35433: -2.125,
|
|
2: -1.390625,
|
|
77: -1.5390625,
|
|
132619: -1.875,
|
|
2175: -1.4609375,
|
|
23714: -2.296875,
|
|
73173: -1.3515625,
|
|
144252: -1.78125,
|
|
}
|
|
EMBEDDINGS_DS_AFTER_LN_MAX = {
|
|
3478: 2.265625,
|
|
368: 2.28125,
|
|
109586: 1.953125,
|
|
35433: 1.90625,
|
|
2: 2.703125,
|
|
77: 2.828125,
|
|
132619: 1.65625,
|
|
2175: 2.015625,
|
|
23714: 2.234375,
|
|
73173: 2.171875,
|
|
144252: 1.828125,
|
|
}
|
|
|
|
EMBEDDINGS_DS_AFTER_LN = {
|
|
"mean": EMBEDDINGS_DS_AFTER_LN_MEAN,
|
|
"min": EMBEDDINGS_DS_AFTER_LN_MIN,
|
|
"max": EMBEDDINGS_DS_AFTER_LN_MAX,
|
|
}
|
|
|
|
tensor_ids = torch.LongTensor([EXAMPLE_IDS])
|
|
with torch.no_grad():
|
|
embeddings = model.transformer.word_embeddings(tensor_ids)
|
|
embeddings_ln = model.transformer.word_embeddings_layernorm(embeddings) #
|
|
# first check the embeddings before LN
|
|
output_dict = {"min": {}, "max": {}, "mean": {}, "sum": {"value": embeddings.sum().item()}}
|
|
for i, idx in enumerate(EXAMPLE_IDS):
|
|
output_dict["min"][idx] = embeddings.min(dim=-1).values[0][i].item()
|
|
output_dict["max"][idx] = embeddings.max(dim=-1).values[0][i].item()
|
|
output_dict["mean"][idx] = embeddings.mean(dim=-1)[0][i].item()
|
|
|
|
for key in TEST_EMBEDDINGS[str(model.dtype)].keys():
|
|
self.assertDictEqual(TEST_EMBEDDINGS[str(model.dtype)][key], output_dict[key])
|
|
|
|
output_dict_norm = {"min": {}, "max": {}, "mean": {}}
|
|
for i, idx in enumerate(EXAMPLE_IDS):
|
|
output_dict_norm["min"][idx] = embeddings_ln.min(dim=-1).values[0][i].item()
|
|
output_dict_norm["max"][idx] = embeddings_ln.max(dim=-1).values[0][i].item()
|
|
output_dict_norm["mean"][idx] = embeddings_ln.mean(dim=-1)[0][i].item()
|
|
|
|
# This test does not pass when places = 2
|
|
for i, key in enumerate(output_dict_norm.keys()):
|
|
for j, idx in enumerate(output_dict[key].keys()):
|
|
self.assertAlmostEqual(EMBEDDINGS_DS_AFTER_LN[key][idx], output_dict_norm[key][idx], places=1)
|
|
|
|
@require_torch
|
|
def test_hidden_states_transformers(self):
|
|
cuda_available = torch.cuda.is_available()
|
|
model = BloomModel.from_pretrained(self.path_bigscience_model, use_cache=False, torch_dtype="auto").to(
|
|
torch_device
|
|
)
|
|
model.eval()
|
|
|
|
EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478] # fmt: skip
|
|
|
|
MEAN_VALUE_LAST_LM = -4.3392181396484375e-05
|
|
MIN_MAX_DICT = {"min": -2.0625, "max": 2.75}
|
|
tensor_ids = torch.LongTensor([EXAMPLE_IDS])
|
|
|
|
with torch.no_grad():
|
|
logits = model(tensor_ids.to(torch_device))
|
|
output_dict = {
|
|
"min": logits.last_hidden_state.min(dim=-1).values[0][0].item(),
|
|
"max": logits.last_hidden_state.max(dim=-1).values[0][0].item(),
|
|
}
|
|
|
|
if cuda_available:
|
|
self.assertAlmostEqual(MEAN_VALUE_LAST_LM, logits.last_hidden_state.mean().item(), places=4)
|
|
else:
|
|
self.assertAlmostEqual(MEAN_VALUE_LAST_LM, logits.last_hidden_state.mean().item(), places=3)
|
|
|
|
self.assertDictEqual(MIN_MAX_DICT, output_dict)
|
|
|
|
@require_torch
|
|
def test_logits(self):
|
|
cuda_available = torch.cuda.is_available()
|
|
model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, use_cache=False, torch_dtype="auto").to(
|
|
torch_device
|
|
) # load in bf16
|
|
model.eval()
|
|
|
|
EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478] # fmt: skip
|
|
|
|
MEAN_LOGITS_GPU_1 = -1.823902130126953e-05
|
|
MEAN_LOGITS_GPU_2 = 1.9431114196777344e-05
|
|
|
|
tensor_ids = torch.LongTensor([EXAMPLE_IDS]).to(torch_device)
|
|
with torch.no_grad():
|
|
output = model(tensor_ids).logits
|
|
|
|
output_gpu_1, output_gpu_2 = output.split(125440, dim=-1)
|
|
if cuda_available:
|
|
self.assertAlmostEqual(output_gpu_1.mean().item(), MEAN_LOGITS_GPU_1, places=6)
|
|
self.assertAlmostEqual(output_gpu_2.mean().item(), MEAN_LOGITS_GPU_2, places=6)
|
|
else:
|
|
self.assertAlmostEqual(output_gpu_1.mean().item(), MEAN_LOGITS_GPU_1, places=6) # 1e-06 precision!!
|
|
self.assertAlmostEqual(output_gpu_2.mean().item(), MEAN_LOGITS_GPU_2, places=6)
|