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Added more tests in .github folder #351

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94 changes: 76 additions & 18 deletions .github/workflows/causal_lm_cpp.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -38,20 +38,20 @@ jobs:
source ./ov/setupvars.sh
convert_tokenizer ./Mistral-7B-v0.1/pytorch/dldt/FP16/ --output ./Mistral-7B-v0.1/pytorch/dldt/FP16/ --with-detokenizer --trust-remote-code
timeout 50s ./build/beam_search_causal_lm ./Mistral-7B-v0.1/pytorch/dldt/FP16/ 69 > ./pred.txt
python -c "
import transformers
with open('pred.txt', 'r') as file:
predictions = file.read()
tokenizer = transformers.LlamaTokenizer.from_pretrained('mistralai/Mistral-7B-v0.1')
tokenized = tokenizer('69', return_tensors='pt')
for beam in transformers.LlamaForCausalLM.from_pretrained('mistralai/Mistral-7B-v0.1').generate(**tokenized, num_beam_groups=3, num_beams=15, num_return_sequences=15, diversity_penalty=1.0, max_new_tokens=20, early_stopping=False, length_penalty=1.0, no_repeat_ngram_size=9**9, do_sample=False):
ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True) + '\n'
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
predictions = predictions[:idx] + predictions[idx + len(ref):]
"
echo "69" passed
python -c "
from transformers import AutoModelForCausalLM, AutoTokenizer
with open('pred.txt', 'r') as file:
predictions = file.read()
tokenizer = AutoTokenizer.from_pretrained('mistralai/Mistral-7B-v0.1')
tokenized = tokenizer('69', return_tensors='pt')
for beam in AutoModelForCausalLM.from_pretrained('mistralai/Mistral-7B-v0.1').generate(**tokenized, num_beam_groups=3, num_beams=15, num_return_sequences=15, diversity_penalty=1.0, max_new_tokens=20, early_stopping=False, length_penalty=1.0, no_repeat_ngram_size=9**9, do_sample=False):
ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True) + '\n'
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
predictions = predictions[:idx] + predictions[idx + len(ref):]
"
echo "69" passed

cpp-greedy_causal_lm-ubuntu:
runs-on: ubuntu-20.04-8-cores
Expand Down Expand Up @@ -237,6 +237,20 @@ jobs:
source ./ov/setupvars.sh
convert_tokenizer ./Qwen-7B-Chat/pytorch/dldt/FP16/ --output ./Qwen-7B-Chat/pytorch/dldt/FP16/ --with-detokenizer --trust-remote-code
timeout 50s ./build/beam_search_causal_lm ./Qwen-7B-Chat/pytorch/dldt/FP16/ 69 > ./pred.txt
python -c "
from transformers import AutoModelForCausalLM, AutoTokenizer
with open('pred.txt', 'r') as file:
predictions = file.read()
tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen-7B-Chat', trust_remote_code=True)
tokenized = tokenizer('69', return_tensors='pt')
for beam in AutoModelForCausalLM.from_pretrained('Qwen/Qwen-7B-Chat', trust_remote_code=True).generate(**tokenized, num_beam_groups=3, num_beams=15, num_return_sequences=15, diversity_penalty=1.0, max_new_tokens=20, early_stopping=False, length_penalty=1.0, no_repeat_ngram_size=9**9, do_sample=False):
ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True) + '\n'
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
predictions = predictions[:idx] + predictions[idx + len(ref):]
"
echo "69" passed

cpp-beam_search_causal_lm-Qwen1_5-7B-Chat:
runs-on: ubuntu-20.04-16-cores
Expand Down Expand Up @@ -264,6 +278,22 @@ jobs:
source ./ov/setupvars.sh
convert_tokenizer ./Qwen1.5-7B-Chat/pytorch/dldt/FP16/ --output ./Qwen1.5-7B-Chat/pytorch/dldt/FP16/ --with-detokenizer --trust-remote-code
timeout 50s ./build/beam_search_causal_lm ./Qwen1.5-7B-Chat/pytorch/dldt/FP16/ "你好！" > ./pred_qwen15.txt
python -c "
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
with open('pred_qwen15.txt', 'r') as file:
predictions = file.read()
tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen1.5-7B-Chat', trust_remote_code=True)
tokenized = tokenizer('你好！', return_tensors='pt')
model = AutoModelForCausalLM.from_pretrained('Qwen/Qwen1.5-7B-Chat', trust_remote_code=True)
model.generation_config = GenerationConfig(num_beam_groups=3, num_beams=15, num_return_sequences=15, diversity_penalty=1.0, max_new_tokens=20, early_stopping=False, length_penalty=1.0, no_repeat_ngram_size=9**9, do_sample=False)
for beam in model.generate(**tokenized):
ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True) + '\n'
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
predictions = predictions[:idx] + predictions[idx + len(ref):]
"
echo "你好！" passed

cpp-beam_search_causal_lm-Phi-2:
runs-on: ubuntu-20.04-16-cores
Expand Down Expand Up @@ -291,6 +321,20 @@ jobs:
source ./ov/setupvars.sh
convert_tokenizer ./Phi-2/pytorch/dldt/FP16/ --output ./Phi-2/pytorch/dldt/FP16/ --with-detokenizer --trust-remote-code
timeout 50s ./build/beam_search_causal_lm ./Phi-2/pytorch/dldt/FP16/ 69 > ./pred.txt
python -c "
from transformers import AutoModelForCausalLM, AutoTokenizer
with open('pred.txt', 'r') as file:
predictions = file.read()
tokenizer = AutoTokenizer.from_pretrained('microsoft/phi-2')
tokenized = tokenizer('69', return_tensors='pt')
for beam in AutoModelForCausalLM.from_pretrained('microsoft/phi-2').generate(**tokenized, num_beam_groups=3, num_beams=15, num_return_sequences=15, diversity_penalty=1.0, max_new_tokens=20, early_stopping=False, length_penalty=1.0, no_repeat_ngram_size=9**9, do_sample=False):
ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True) + '\n'
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
predictions = predictions[:idx] + predictions[idx + len(ref):]
"
echo "69" passed

cpp-beam_search_causal_lm-notus-7b-v1:
runs-on: ubuntu-20.04-16-cores
Expand Down Expand Up @@ -318,6 +362,20 @@ jobs:
source ./ov/setupvars.sh
convert_tokenizer ./notus-7b-v1/pytorch/dldt/FP16/ --output ./notus-7b-v1/pytorch/dldt/FP16/ --with-detokenizer --trust-remote-code
timeout 50s ./build/beam_search_causal_lm ./notus-7b-v1/pytorch/dldt/FP16/ 69 > ./pred.txt
python -c "
from transformers import AutoModelForCausalLM, AutoTokenizer
with open('pred.txt', 'r') as file:
predictions = file.read()
tokenizer = AutoTokenizer.from_pretrained('argilla/notus-7b-v1')
tokenized = tokenizer('69', return_tensors='pt')
for beam in AutoModelForCausalLM.from_pretrained('argilla/notus-7b-v1').generate(**tokenized, num_beam_groups=3, num_beams=15, num_return_sequences=15, diversity_penalty=1.0, max_new_tokens=20, early_stopping=False, length_penalty=1.0, no_repeat_ngram_size=9**9, do_sample=False):
ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True) + '\n'
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
predictions = predictions[:idx] + predictions[idx + len(ref):]
"
echo "69" passed

cpp-speculative_decoding_lm-ubuntu:
runs-on: ubuntu-20.04-16-cores
Expand All @@ -331,7 +389,7 @@ jobs:
- name: Install OpenVINO
run: |
mkdir ./ov/
curl https://storage.openvinotoolkit.org/repositories/openvino/packages/2023.3/linux/l_openvino_toolkit_ubuntu20_2023.3.0.13775.ceeafaf64f3_x86_64.tgz | tar --directory ./ov/ --strip-components 1 -xz
curl https://storage.openvinotoolkit.org/repositories/openvino/packages/nightly/2024.1.0-14645-e6dc0865128/l_openvino_toolkit_ubuntu20_2024.1.0.dev20240304_x86_64.tgz | tar --directory ./ov/ --strip-components 1 -xz
sudo ./ov/install_dependencies/install_openvino_dependencies.sh
- name: Download, convert and build
run: |
Expand Down Expand Up @@ -388,12 +446,12 @@ jobs:
- name: Compare
run: |
python -c "
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
with open('pred_greedy.txt', 'r') as file:
predictions = file.read()
tokenizer = transformers.AutoTokenizer.from_pretrained('microsoft/phi-1_5')
tokenizer = AutoTokenizer.from_pretrained('microsoft/phi-1_5')
tokenized = tokenizer('Alan Turing was a', return_tensors='pt')
for output in transformers.AutoModelForCausalLM.from_pretrained('microsoft/phi-1_5').generate(**tokenized, max_length=100, do_sample=False):
for output in AutoModelForCausalLM.from_pretrained('microsoft/phi-1_5').generate(**tokenized, max_length=100, do_sample=False):
ref = tokenizer.decode(output[tokenized['input_ids'].numel():], skip_special_tokens=True) + '\n'
idx = predictions.find(ref)
if -1 == idx:
Expand Down
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