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fix different outputs
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@pavel-esir
pavel-esir committed May 15, 2024
1 parent 2e3cd73 commit 5926121
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Showing 2 changed files with 10 additions and 10 deletions.
18 changes: 9 additions & 9 deletions .github/workflows/causal_lm_cpp.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -74,7 +74,7 @@ jobs:
tokenizer = transformers.LlamaTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
tokenized = tokenizer('Why is the Sun yellow?', return_tensors='pt')
for beam in transformers.LlamaForCausalLM.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0').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'
ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True)
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
Expand All @@ -90,7 +90,7 @@ jobs:
tokenizer = transformers.LlamaTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
tokenized = tokenizer('69', return_tensors='pt')
for beam in transformers.LlamaForCausalLM.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0').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'
ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True)
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
Expand All @@ -106,7 +106,7 @@ jobs:
tokenizer = transformers.LlamaTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
tokenized = tokenizer('Hi', return_tensors='pt')
for beam in transformers.LlamaForCausalLM.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0').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'
ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True)
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
Expand All @@ -122,7 +122,7 @@ jobs:
tokenizer = transformers.LlamaTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
tokenized = tokenizer('return 0', return_tensors='pt')
for beam in transformers.LlamaForCausalLM.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0').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'
ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True)
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
Expand All @@ -138,7 +138,7 @@ jobs:
tokenizer = transformers.LlamaTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
tokenized = tokenizer('你好！ 你好嗎？', return_tensors='pt')
for beam in transformers.LlamaForCausalLM.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0').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'
ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True)
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
Expand All @@ -160,7 +160,7 @@ jobs:
for prompt in prompts:
tokenized = tokenizer(prompt, return_tensors='pt')
for beam in transformers.LlamaForCausalLM.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0').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'
ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True)
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
Expand Down Expand Up @@ -201,7 +201,7 @@ jobs:
echo tokenizer = transformers.LlamaTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0') >> ref.py
echo tokenized = tokenizer('69', return_tensors='pt') >> ref.py
echo for beam in transformers.LlamaForCausalLM.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0').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.py
echo ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True) + '\n' >> ref.py
echo ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True) >> ref.py
echo idx = predictions.find(ref) >> ref.py
echo if -1 == idx: >> ref.py
echo raise RuntimeError(f'Missing "{ref=}" from predictions') >> ref.py
Expand Down Expand Up @@ -441,7 +441,7 @@ jobs:
tokenizer = transformers.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):
ref = tokenizer.decode(output[tokenized['input_ids'].numel():], skip_special_tokens=True) + '\n'
ref = tokenizer.decode(output[tokenized['input_ids'].numel():], skip_special_tokens=True)
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref=}" from predictions')
Expand Down Expand Up @@ -486,7 +486,7 @@ jobs:
tokenizer = transformers.AutoTokenizer.from_pretrained('ikala/redpajama-3b-chat')
tokenized = tokenizer('Alan Turing was a', return_tensors='pt')
for output in transformers.AutoModelForCausalLM.from_pretrained('ikala/redpajama-3b-chat').generate(**tokenized, max_length=100, do_sample=False):
ref = tokenizer.decode(output[tokenized['input_ids'].numel():], skip_special_tokens=True) + '\n'
ref = tokenizer.decode(output[tokenized['input_ids'].numel():], skip_special_tokens=True)
idx = predictions.find(ref)
if -1 == idx:
raise RuntimeError(f'Missing "{ref}" from predictions')
Expand Down
2 changes: 1 addition & 1 deletion text_generation/causal_lm/cpp/beam_search_causal_lm.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@ int main(int argc, char* argv[]) try {

ov::LLMPipeline pipe(model_path, device);
ov::GenerationConfig config = pipe.get_generation_config();
config.max_new_tokens = 100;
config.max_new_tokens = 25;
config.num_beam_groups = 3;
config.num_beams = 15;
config.num_return_sequences = config.num_beams * prompts.size();
Expand Down

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