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add vInference evaluation script and improve sharding logic in tests #…
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| import os | ||
| import sys | ||
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| sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..")) | ||
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| import jax | ||
| import torch | ||
| import transformers | ||
| from jax import numpy as jnp | ||
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| import easydel as ed | ||
| from tqdm import tqdm | ||
| from datasets import load_dataset | ||
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| def calc_accuracy(actuals, preds): | ||
| total_correct = 0 | ||
| total_examples = len(actuals) | ||
| for actual, pred in zip(actuals, preds): | ||
| pred_letter = "A" | ||
| if "A" in pred: | ||
| pred_letter = "A" | ||
| if "B" in pred: | ||
| pred_letter = "B" | ||
| if "C" in pred: | ||
| pred_letter = "C" | ||
| if "D" in pred: | ||
| pred_letter = "D" | ||
| if actual == pred_letter: | ||
| total_correct += 1 | ||
| acc_score = total_correct / total_examples | ||
| return acc_score | ||
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| FORCE_SP = jax.device_count() > 4 # False | ||
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| def main(): | ||
| if jax.device_count() > 4 and not FORCE_SP: | ||
| sharding_axis_dims = (1, 1, 2, -1) | ||
| else: | ||
| sharding_axis_dims = (1, 1, 1, -1) | ||
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| max_length = 4096 | ||
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| # pretrained_model_name_or_path = "meta-llama/Llama-3.2-1B-Instruct" | ||
| pretrained_model_name_or_path = "Qwen/Qwen2.5-7B-Instruct" | ||
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| partition_axis = ed.PartitionAxis() | ||
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| dtype = jnp.bfloat16 | ||
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| print("LOADING MODEL ... ") | ||
| model = ed.AutoEasyDeLModelForCausalLM.from_pretrained( | ||
| pretrained_model_name_or_path, | ||
| auto_shard_model=True, | ||
| sharding_axis_dims=sharding_axis_dims, | ||
| config_kwargs=ed.EasyDeLBaseConfigDict( | ||
| freq_max_position_embeddings=max_length, | ||
| mask_max_position_embeddings=max_length, | ||
| attn_dtype=dtype, | ||
| gradient_checkpointing=ed.EasyDeLGradientCheckPointers.NONE, | ||
| kv_cache_quantization_method=ed.EasyDeLQuantizationMethods.NONE, | ||
| attn_mechanism=ed.AttentionMechanisms.VANILLA, | ||
| ), | ||
| quantization_method=ed.EasyDeLQuantizationMethods.NONE, | ||
| platform=ed.EasyDeLPlatforms.JAX, | ||
| param_dtype=dtype, | ||
| dtype=dtype, | ||
| torch_dtype=torch.float16, | ||
| partition_axis=partition_axis, | ||
| precision=jax.lax.Precision("fastest"), | ||
| ) | ||
| print("MODEL LOADED") | ||
| tokenizer = transformers.AutoTokenizer.from_pretrained(pretrained_model_name_or_path) | ||
| tokenizer.padding_side = "left" | ||
| tokenizer.pad_token_id = tokenizer.eos_token_id | ||
| print("TOKENIZER LOADED") | ||
| model.eval() | ||
| print("CREATING vInference") | ||
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| inference = ed.vInference( | ||
| model=model, | ||
| processor_class=tokenizer, | ||
| generation_config=ed.vInferenceConfig( | ||
| max_new_tokens=1024, | ||
| temperature=0.0, | ||
| do_sample=False, | ||
| top_p=0.95, | ||
| top_k=10, | ||
| eos_token_id=model.generation_config.eos_token_id, | ||
| streaming_chunks=32, | ||
| ), | ||
| ) | ||
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| print(model.model_task) | ||
| print(model.model_type) | ||
| print("Compiling") | ||
| inference.precompile(1, inference.model_prefill_length) | ||
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| print("Done Compiling") | ||
| print("Evaluating on MMLU Lite") | ||
| prompts = [] | ||
| pred_list = [] | ||
| actual_list = [] | ||
| data = load_dataset("CohereForAI/Global-MMLU-Lite", "en", split="test") | ||
| for item in tqdm(data, total=len(data)): | ||
| question = item["question"] | ||
| option_a = item["option_a"] | ||
| option_b = item["option_b"] | ||
| option_c = item["option_c"] | ||
| option_d = item["option_d"] | ||
| actual_list.append(item["answer"]) | ||
| prompt = f"Answer the following question by writing the right answer letter which can be A,B,C or D. Write only the correct answer letter in your response. \nQuestion : {question}\nA. {option_a}. \nB. {option_b}. \nC. {option_c}. \nD. {option_d}" | ||
| prompts.append(prompt) | ||
| messages = [ | ||
| {"role": "system", "content": "You are a helpful AI assistant."}, | ||
| {"role": "user", "content": prompt}, | ||
| ] | ||
| ids = tokenizer.apply_chat_template( | ||
| messages, | ||
| return_tensors="jax", | ||
| return_dict=True, | ||
| max_length=inference.model_prefill_length, | ||
| padding="max_length", | ||
| add_generation_prompt=True, | ||
| ) | ||
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| pad_seq = inference.model_prefill_length | ||
| for response in inference.generate(**ids): | ||
| next_slice = slice( | ||
| pad_seq, | ||
| pad_seq + inference.generation_config.streaming_chunks, | ||
| ) | ||
| pad_seq += inference.generation_config.streaming_chunks | ||
| output = tokenizer.decode( | ||
| response.sequences[0][next_slice], | ||
| skip_special_tokens=True, | ||
| ) | ||
| pred_list.append(output) | ||
| for prompt, pred in zip(prompts, pred_list): | ||
| print("--------------------------------------") | ||
| print(f"Prompt: {prompt}\nPrediction : {pred}") | ||
| print("---------- Evaluation Score -----------------") | ||
| acc_score = calc_accuracy(actual_list, pred_list) | ||
| print(f"accuracy score : {acc_score}") | ||
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| if __name__ == "__main__": | ||
| main() |
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