LLM: Add mlp layer unit tests (#10200)
* add mlp layer unit tests * add download baichuan-13b * exclude llama for now * install additional packages * rename bash file * switch to Baichuan2 * delete attention related code * fix name errors in yml file
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4 changed files with 176 additions and 1 deletions
19
.github/workflows/llm_unit_tests.yml
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19
.github/workflows/llm_unit_tests.yml
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@ -235,6 +235,9 @@ jobs:
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echo "MPT_7B_ORIGIN_PATH=${ORIGIN_DIR}/mpt-7b-chat" >> "$GITHUB_ENV"
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echo "WHISPER_TINY_ORIGIN_PATH=${ORIGIN_DIR}/whisper-tiny" >> "$GITHUB_ENV"
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echo "MISTRAL_7B_INSTRUCT_V0_1_ORIGIN_PATH=${ORIGIN_DIR}/Mistral-7B-Instruct-v0.1" >> "$GITHUB_ENV"
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echo "BAICHUAN2_7B_ORIGIN_PATH=${ORIGIN_DIR}/Baichuan2-7B-Chat" >> "$GITHUB_ENV"
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echo "QWEN_7B_ORIGIN_PATH=${ORIGIN_DIR}/Qwen-7B-Chat" >> "$GITHUB_ENV"
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- name: Checkout repo
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uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # actions/checkout@v3
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@ -303,6 +306,18 @@ jobs:
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echo "Directory $SPEECH_DATASET_PATH not found. Downloading from FTP server..."
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wget -r -nH --no-verbose --cut-dirs=2 $LLM_FTP_URL/llm/datasets/librispeech_asr_dummy -P $DATASET_DIR
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fi
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if [ ! -d $MISTRAL_7B_INSTRUCT_V0_1_ORIGIN_PATH ]; then
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echo "Directory $MISTRAL_7B_INSTRUCT_V0_1_ORIGIN_PATH not found. Downloading from FTP server..."
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wget -r -nH --no-verbose --cut-dirs=1 $LLM_FTP_URL/llm/Mistral-7B-Instruct-v0.1 -P $ORIGIN_DIR
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fi
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if [ ! -d $QWEN_7B_ORIGIN_PATH ]; then
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echo "Directory $QWEN_7B_ORIGIN_PATH not found. Downloading from FTP server..."
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wget -r -nH --no-verbose --cut-dirs=1 $LLM_FTP_URL/llm/Qwen-7B-Chat -P $ORIGIN_DIR
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fi
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if [ ! -d $BAICHUAN2_7B_ORIGIN_PATH ]; then
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echo "Directory $BAICHUAN2_7B_ORIGIN_PATH not found. Downloading from FTP server..."
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wget -r -nH --no-verbose --cut-dirs=1 $LLM_FTP_URL/llm/Baichuan2-7B-Chat -P $ORIGIN_DIR
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fi
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- name: Run LLM inference test
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shell: bash
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@ -313,8 +328,10 @@ jobs:
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elif [[ '${{ matrix.pytorch-version }}' == '2.0' ]]; then
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source /opt/intel/oneapi/setvars.sh
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fi
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python -m pip install datasets librosa soundfile einops
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python -m pip install datasets librosa soundfile einops tiktoken transformers_stream_generator
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bash python/llm/test/run-llm-inference-tests-gpu.sh
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python -m pip install transformers==4.34.0
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bash python/llm/test/run-llm-inference-tests-gpu-434.sh
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- name: Run LLM example tests
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shell: bash
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129
python/llm/test/inference_gpu/test_transformers_api_mlp.py
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python/llm/test/inference_gpu/test_transformers_api_mlp.py
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@ -0,0 +1,129 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import pytest
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import torch
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from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel
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from transformers import LlamaTokenizer, AutoTokenizer
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device = os.environ['DEVICE']
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print(f'Running on {device}')
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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"
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TEST_MODEL_LIST = [
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("Qwen-7B-Chat", AutoModelForCausalLM, AutoTokenizer, os.environ.get('QWEN_7B_ORIGIN_PATH')),
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("Mistral-7B-Instruct-v0.1", AutoModelForCausalLM, AutoTokenizer, os.environ.get('MISTRAL_7B_INSTRUCT_V0_1_ORIGIN_PATH'))
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]
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class Test_Optimize_Gpu_Model:
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def setup_method(self):
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self.layer_outputs = []
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self.pre_layer_outputs = []
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def run_optimize_gpu_model(self, Name, Model, Tokenizer, model_path, MLP_layer, layer_before_MLP, lower_bound):
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with torch.inference_mode():
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def pre_forward_hook(module, input, output, layer_name):
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self.pre_layer_outputs.append(output)
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def forward_hook(module, input, output, layer_name):
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self.layer_outputs.append(output)
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tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
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input_ids = tokenizer.encode(PROMPT, return_tensors="pt").to(device)
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model = Model.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=False,
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trust_remote_code=True)
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model = model.to(device)
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for layer_name, layer_module in model.named_modules():
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if layer_name == layer_before_MLP:
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layer_module.register_forward_hook(
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lambda module, input, output, layer_name=layer_name: pre_forward_hook(module, input,
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output, layer_name))
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if layer_name == MLP_layer:
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layer_module.register_forward_hook(
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lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
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output, layer_name))
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logits_base_model = (model(input_ids)).logits
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# the list `layer_output` has only one element.
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layer_tensor = self.layer_outputs.pop()
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model.to('cpu')
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opt_model = Model.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=True,
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trust_remote_code=True)
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opt_model = opt_model.to(device)
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def replace_forward_hook(module, input, output, layer_name):
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output = self.pre_layer_outputs[0]
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return output
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for layer_name, layer_module in opt_model.named_modules():
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if layer_name == layer_before_MLP:
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layer_module.register_forward_hook(
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lambda module, input, output, layer_name=layer_name: replace_forward_hook(module, input,
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output, layer_name))
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if layer_name == MLP_layer:
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layer_module.register_forward_hook(
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lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
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output, layer_name))
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logits_optimized_model = (opt_model(input_ids)).logits
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# the list `layer_output` has only one element.
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opt_layer_tensor = self.layer_outputs[0]
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opt_model.to('cpu')
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MLP_output_diff = []
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for i, (t1, t2) in enumerate(zip(layer_tensor, opt_layer_tensor)):
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if t1 is not None and t2 is not None:
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if isinstance(t1, torch.Tensor) and isinstance(t2, torch.Tensor):
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MLP_output_diff.append(t1 - t2)
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else:
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# 'past_key_value'is of type tuple as default.
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for i, (t3, t4) in enumerate(zip(t1, t2)):
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MLP_output_diff.append(t3 - t4)
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max_diff_tensor = [torch.max(item).item() for item in MLP_output_diff]
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print(max_diff_tensor)
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assert all(max_diff <= lower_bound for max_diff in max_diff_tensor)
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@pytest.mark.parametrize('Name, Model, Tokenizer, model_path',TEST_MODEL_LIST)
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def test_dynamic_functions(self, Name, Model, Tokenizer, model_path):
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if Name == "Qwen-7B-Chat":
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self.Qwen_7B_gpu_model(Name, Model, Tokenizer, model_path)
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elif Name == "Mistral-7B-Instruct-v0.1":
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self.Mistral_7B_Instruct_gpu_model(Name, Model, Tokenizer, model_path)
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def Qwen_7B_gpu_model(self, Name, Model, Tokenizer, model_path):
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# currently only compare the output of the last mlp layer.
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layer_before_MLP = "transformer.h.31.ln_2"
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MLP_layer = "transformer.h.31.mlp"
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lower_bound = 0
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self.run_optimize_gpu_model(Name, Model, Tokenizer, model_path, MLP_layer, layer_before_MLP, lower_bound)
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def Mistral_7B_Instruct_gpu_model(self, Name, Model, Tokenizer, model_path):
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# currently only compare the output of the last mlp layer.
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layer_before_MLP = "model.layers.31.post_attention_layernorm"
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MLP_layer = "model.layers.31.mlp"
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lower_bound = 0
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self.run_optimize_gpu_model(Name, Model, Tokenizer, model_path, MLP_layer, layer_before_MLP, lower_bound)
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28
python/llm/test/run-llm-inference-tests-gpu-434.sh
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python/llm/test/run-llm-inference-tests-gpu-434.sh
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#!/bin/bash
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export ANALYTICS_ZOO_ROOT=${ANALYTICS_ZOO_ROOT}
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export LLM_HOME=${ANALYTICS_ZOO_ROOT}/python/llm/src
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export LLM_INFERENCE_TEST_DIR=${ANALYTICS_ZOO_ROOT}/python/llm/test/inference_gpu
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export DEVICE='xpu'
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set -e
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echo "# Start testing inference"
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start=$(date "+%s")
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if [ -z "$THREAD_NUM" ]; then
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THREAD_NUM=2
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fi
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export OMP_NUM_THREADS=$THREAD_NUM
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export BIGDL_LLM_XMX_DISABLED=1
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pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_mlp.py -v -s -k "Mistral"
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unset BIGDL_LLM_XMX_DISABLED
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now=$(date "+%s")
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time=$((now-start))
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echo "Bigdl-llm gpu inference tests for transformers 4.34.0 finished"
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echo "Time used:$time seconds"
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@ -21,6 +21,7 @@ pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api.py -v -s
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export BIGDL_LLM_XMX_DISABLED=1
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pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_final_logits.py -v -s
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pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_attention.py -v -s
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pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_mlp.py -v -s -k "not Mistral"
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unset BIGDL_LLM_XMX_DISABLED
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now=$(date "+%s")
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