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Unit test on final logits and the logits of the last attention layer (#10093)

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* Add unit test on final logits and attention

* Add unit test on final logits and attention

* Modify unit test on final logits and attention
This commit is contained in:
Keyan (Kyrie) Zhang 2024-02-07 14:25:36 +08:00 committed by GitHub
parent 3832eb0ce0
commit 2e80701f58
4 changed files with 181 additions and 168 deletions
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157
python/llm/test/inference_gpu/test_transformers_api.py
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@ -15,7 +15,7 @@
#
import os, time
import os
import pytest
import tempfile
@ -102,160 +102,5 @@ def test_transformers_auto_model_for_speech_seq2seq_int4():
assert 'Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.' in transcription[0]
prompt = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
# @pytest.mark.parametrize('Model, Tokenizer, model_path',[
# (AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
# (AutoModelForCausalLM, AutoTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH'))
# ])
# def test_optimize_model(Model, Tokenizer, model_path):
# with torch.inference_mode():
# tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
# input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
# model = Model.from_pretrained(model_path,
# load_in_4bit=True,
# optimize_model=False,
# trust_remote_code=True)
# model = model.to(device)
# logits_base_model = (model(input_ids)).logits
# model.to('cpu') # deallocate gpu memory
# model = Model.from_pretrained(model_path,
# load_in_4bit=True,
# optimize_model=True,
# trust_remote_code=True)
# model = model.to(device)
# logits_optimized_model = (model(input_ids)).logits
# model.to('cpu')
# tol = 1e-02
# num_false = torch.isclose(logits_optimized_model, logits_base_model, rtol=tol, atol=tol)\
# .flatten().tolist().count(False)
# percent_false = num_false / logits_optimized_model.numel()
# assert percent_false < 1e-02
class Test_Optimize_Gpu_Model:
def setup(self):
self.layer_outputs = []
self.pre_layer_outputs = []
def run_optimize_gpu_model(self, Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound):
with torch.inference_mode():
def forward_hook(module, input, output, layer_name):
self.layer_outputs.append(output)
def pre_forward_hook(module, input, output, layer_name):
self.pre_layer_outputs.append(output)
tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
model = Model.from_pretrained(model_path,
load_in_4bit=True,
optimize_model=False,
trust_remote_code=True)
model = model.to(device)
for layer_name, layer_module in model.named_modules():
if layer_name == layer_norm:
layer_module.register_forward_hook(
lambda module, input, output, layer_name=layer_name: pre_forward_hook(module, input,
output, layer_name))
if layer_name == self_attn:
layer_module.register_forward_hook(
lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
output, layer_name))
logits_base_model = (model(input_ids)).logits
# the list `layer_output` has only one element.
layer_tensor = self.layer_outputs.pop()
model.to('cpu')
opt_model = Model.from_pretrained(model_path,
load_in_4bit=True,
optimize_model=True,
trust_remote_code=True)
opt_model = opt_model.to(device)
def replace_forward_hook(module, input, output, layer_name):
output = self.pre_layer_outputs[0]
return output
for layer_name, layer_module in opt_model.named_modules():
if layer_name == layer_norm:
layer_module.register_forward_hook(
lambda module, input, output, layer_name=layer_name: replace_forward_hook(module, input,
output, layer_name))
if layer_name == self_attn:
layer_module.register_forward_hook(
lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
output, layer_name))
logits_optimized_model = (opt_model(input_ids)).logits
# the list `layer_output` has only one element.
opt_layer_tensor = self.layer_outputs[0]
opt_model.to('cpu')
attn_output_diff = []
for i, (t1, t2) in enumerate(zip(layer_tensor, opt_layer_tensor)):
if t1 is not None and t2 is not None:
if isinstance(t1, torch.Tensor) and isinstance(t2, torch.Tensor):
# 'attn_output' is of type torch.Tensor.
attn_output_diff.append(t1 - t2)
else:
# 'past_key_value'is of type tuple as default.
for i, (t3, t4) in enumerate(zip(t1, t2)):
if model.config.architectures[0] == "ChatGLMModel" and \
hasattr(model.config, 'padded_vocab_size') and \
model.config.padded_vocab_size == 65024:
# chatglm2's past_key_value is expanded 16x for some speedup.
# We need to narrow it here.
t4 = t4[:, :, 15:17, :]
attn_output_diff.append(t3 - t4)
max_diff_tensor = [torch.max(item).item() for item in attn_output_diff]
print(max_diff_tensor)
assert all(max_diff <= lower_bound for max_diff in max_diff_tensor)
# def test_falcon_gpu_model(self):
# Model = AutoModelForCausalLM
# Tokenizer = AutoTokenizer
# model_path = os.environ.get('FALCON_7B_ORIGIN_PATH')
# # currently only compare the output of the last self-attention layer.
# layer_norm = "transformer.h.31.input_layernorm"
# self_attn = "transformer.h.31.self_attention"
# lower_bound = 0
# self.run_optimize_gpu_model(Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
# def test_llama_gpu_model(self):
# Model = AutoModelForCausalLM
# Tokenizer = AutoTokenizer
# model_path = os.environ.get('LLAMA2_7B_ORIGIN_PATH')
# # currently only compare the output of the last self-attention layer.
# layer_norm = "model.layers.31.input_layernorm"
# self_attn = "model.layers.31.self_attn"
# lower_bound = 5e-2
# self.run_optimize_gpu_model(Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
# def test_chatglm2_gpu_model(self):
# Model = AutoModel
# Tokenizer = AutoTokenizer
# model_path = os.environ.get('CHATGLM2_6B_ORIGIN_PATH')
# # currently only need to compare the output of one self-attention layer.
# layer_norm = "transformer.encoder.layers.27.input_layernorm"
# self_attn = "transformer.encoder.layers.27.self_attention"
# lower_bound = 1e-3
# self.run_optimize_gpu_model(Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
if __name__ == '__main__':
pytest.main([__file__])

157
python/llm/test/inference_gpu/test_transformers_api_attention.py Normal file
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@ -0,0 +1,157 @@
#
# Copyright 2016 The BigDL Authors.
#
# 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 os
import pytest
import torch
from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel
from transformers import LlamaTokenizer, AutoTokenizer
device = os.environ['DEVICE']
print(f'Running on {device}')
PROMPT = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
TEST_MODEL_LIST = [
("MPT-7B", AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
("Llama2-7B", AutoModelForCausalLM, LlamaTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH')),
("Falcon-7B", AutoModelForCausalLM, AutoTokenizer, os.environ.get('FALCON_7B_ORIGIN_PATH')),
("ChatGLM2-6B", AutoModel, AutoTokenizer, os.environ.get('CHATGLM2_6B_ORIGIN_PATH')),
]
class Test_Optimize_Gpu_Model:
def setup_method(self):
self.layer_outputs = []
self.pre_layer_outputs = []
def run_optimize_gpu_model(self, Name, Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound):
with torch.inference_mode():
def forward_hook(module, input, output, layer_name):
self.layer_outputs.append(output)
def pre_forward_hook(module, input, output, layer_name):
self.pre_layer_outputs.append(output)
tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
input_ids = tokenizer.encode(PROMPT, return_tensors="pt").to(device)
model = Model.from_pretrained(model_path,
load_in_4bit=True,
optimize_model=False,
trust_remote_code=True)
model = model.to(device)
for layer_name, layer_module in model.named_modules():
if layer_name == layer_norm:
layer_module.register_forward_hook(
lambda module, input, output, layer_name=layer_name: pre_forward_hook(module, input,
output, layer_name))
if layer_name == self_attn:
layer_module.register_forward_hook(
lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
output, layer_name))
logits_base_model = (model(input_ids)).logits
# the list `layer_output` has only one element.
layer_tensor = self.layer_outputs.pop()
model.to('cpu')
opt_model = Model.from_pretrained(model_path,
load_in_4bit=True,
optimize_model=True,
trust_remote_code=True)
opt_model = opt_model.to(device)
def replace_forward_hook(module, input, output, layer_name):
output = self.pre_layer_outputs[0]
return output
for layer_name, layer_module in opt_model.named_modules():
if layer_name == layer_norm:
layer_module.register_forward_hook(
lambda module, input, output, layer_name=layer_name: replace_forward_hook(module, input,
output, layer_name))
if layer_name == self_attn:
layer_module.register_forward_hook(
lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
output, layer_name))
logits_optimized_model = (opt_model(input_ids)).logits
# the list `layer_output` has only one element.
opt_layer_tensor = self.layer_outputs[0]
opt_model.to('cpu')
attn_output_diff = []
for i, (t1, t2) in enumerate(zip(layer_tensor, opt_layer_tensor)):
if t1 is not None and t2 is not None:
if isinstance(t1, torch.Tensor) and isinstance(t2, torch.Tensor):
# 'attn_output' is of type torch.Tensor.
attn_output_diff.append(t1 - t2)
else:
# 'past_key_value'is of type tuple as default.
for i, (t3, t4) in enumerate(zip(t1, t2)):
if model.config.architectures[0] == "ChatGLMModel" and \
hasattr(model.config, 'padded_vocab_size') and \
model.config.padded_vocab_size == 65024:
# chatglm2's past_key_value is expanded 16x for some speedup.
# We need to narrow it here.
t4 = t4[:, :, 15:17, :]
attn_output_diff.append(t3 - t4)
max_diff_tensor = [torch.max(item).item() for item in attn_output_diff]
print(max_diff_tensor)
assert all(max_diff <= lower_bound for max_diff in max_diff_tensor)
@pytest.mark.parametrize('Name, Model, Tokenizer, model_path',TEST_MODEL_LIST)
def test_dynamic_functions(self, Name, Model, Tokenizer, model_path):
if Name == "MPT-7B":
self.MPT_7B_gpu_model(Name, Model, Tokenizer, model_path)
elif Name == "Llama2-7B":
self.Llama2_7B_gpu_model(Name, Model, Tokenizer, model_path)
elif Name == "Falcon-7B":
self.Falcon_7B_gpu_model(Name, Model, Tokenizer, model_path)
elif Name == "ChatGLM2-6B":
self.Chatglm2_gpu_model(Name, Model, Tokenizer, model_path)
def MPT_7B_gpu_model(self, Name, Model, Tokenizer, model_path):
# currently only need to compare the output of one self-attention layer.
layer_norm = "transformer.blocks.31.norm_1"
self_attn = "transformer.blocks.31.attn"
lower_bound = 0
self.run_optimize_gpu_model(Name, Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
def Llama2_7B_gpu_model(self, Name, Model, Tokenizer, model_path):
# currently only compare the output of the last self-attention layer.
layer_norm = "model.layers.31.input_layernorm"
self_attn = "model.layers.31.self_attn"
lower_bound = 5e-2
self.run_optimize_gpu_model(Name, Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
def Falcon_7B_gpu_model(self, Name, Model, Tokenizer, model_path):
# currently only compare the output of the last self-attention layer.
layer_norm = "transformer.h.31.input_layernorm"
self_attn = "transformer.h.31.self_attention"
lower_bound = 0
self.run_optimize_gpu_model(Name, Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
def Chatglm2_gpu_model(self, Name, Model, Tokenizer, model_path):
# currently only need to compare the output of one self-attention layer.
layer_norm = "transformer.encoder.layers.27.input_layernorm"
self_attn = "transformer.encoder.layers.27.self_attention"
lower_bound = 5e-3
self.run_optimize_gpu_model(Name, Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)

32
python/llm/test/inference_gpu/test_transformers_api_disable_xmx.py → python/llm/test/inference_gpu/test_transformers_api_final_logits.py
View file

@ -14,33 +14,38 @@
# limitations under the License.
#
import os
import pytest
import torch
from bigdl.llm.transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel
from transformers import LlamaTokenizer, AutoTokenizer
device = os.environ['DEVICE']
print(f'Running on {device}')
prompt = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
PROMPT = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
TEST_MODEL_LIST = [
("MPT-7B", AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
("Falcon-7B", AutoModelForCausalLM, AutoTokenizer, os.environ.get('FALCON_7B_ORIGIN_PATH')),
]
@pytest.mark.parametrize('Model, Tokenizer, model_path',[
(AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
# (AutoModelForCausalLM, AutoTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH')),
(AutoModelForCausalLM, AutoTokenizer, os.environ.get('FALCON_7B_ORIGIN_PATH')),
])
def test_optimize_model(Model, Tokenizer, model_path):
@pytest.mark.parametrize('Name, Model, Tokenizer, model_path',TEST_MODEL_LIST)
def test_optimize_model(Name, Model, Tokenizer, model_path):
with torch.inference_mode():
tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
input_ids = tokenizer.encode(PROMPT, return_tensors="pt").to(device)
model = Model.from_pretrained(model_path,
load_in_4bit=True,
optimize_model=False,
trust_remote_code=True)
model = model.to(device)
logits_base_model = (model(input_ids)).logits
model.to('cpu') # deallocate gpu memory
model = Model.from_pretrained(model_path,
@ -54,6 +59,11 @@ def test_optimize_model(Model, Tokenizer, model_path):
tol = 1e-03
num_false = torch.isclose(logits_optimized_model, logits_base_model, rtol=tol, atol=tol)\
.flatten().tolist().count(False)
percent_false = num_false / logits_optimized_model.numel()
assert percent_false < 1e-02
if __name__ == '__main__':
pytest.main([__file__])

3
python/llm/test/run-llm-inference-tests-gpu.sh
View file

@ -19,7 +19,8 @@ fi
export OMP_NUM_THREADS=$THREAD_NUM
pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api.py -v -s
export BIGDL_LLM_XMX_DISABLED=1
pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_disable_xmx.py -v -s
pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_final_logits.py -v -s
pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_attention.py -v -s
unset BIGDL_LLM_XMX_DISABLED
now=$(date "+%s")