CodeGeeX inference support oneflow backend #65
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codegeex support oneflow backend
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| __pycache__/ | ||
| codegeex.egg-info/ |
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| import os | ||
| import copy | ||
| import time | ||
| import oneflow as torch | ||
| import random | ||
| import argparse | ||
| import numpy as np | ||
|
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| from codegeex.oneflow.inference import get_token_stream | ||
| from codegeex.oneflow import CodeGeeXModel | ||
| from codegeex.tokenizer import CodeGeeXTokenizer | ||
| from codegeex.quantization import quantize | ||
| os.environ["ONEFLOW_KERNEL_ENABLE_FUSED_LINEAR"] = "1" | ||
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| def model_provider(args): | ||
| """Build the model.""" | ||
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| model = CodeGeeXModel( | ||
| args.hidden_size, | ||
| args.num_layers, | ||
| args.num_attention_heads, | ||
| args.padded_vocab_size, | ||
| args.max_position_embeddings | ||
| ) | ||
|
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| return model | ||
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| def add_code_generation_args(parser): | ||
| group = parser.add_argument_group(title="code generation") | ||
| group.add_argument( | ||
| "--num-layers", | ||
| type=int, | ||
| default=39, | ||
| ) | ||
| group.add_argument( | ||
| "--hidden-size", | ||
| type=int, | ||
| default=5120, | ||
| ) | ||
| group.add_argument( | ||
| "--num-attention-heads", | ||
| type=int, | ||
| default=40, | ||
| ) | ||
| group.add_argument( | ||
| "--padded-vocab-size", | ||
| type=int, | ||
| default=52224, | ||
| ) | ||
| group.add_argument( | ||
| "--max-position-embeddings", | ||
| type=int, | ||
| default=2048, | ||
| ) | ||
| group.add_argument( | ||
| "--temperature", | ||
| type=float, | ||
| default=1.0, | ||
| help="Sampling temperature.", | ||
| ) | ||
| group.add_argument( | ||
| "--greedy", | ||
| action="store_true", | ||
| default=False, | ||
| help="Use greedy sampling.", | ||
| ) | ||
| group.add_argument( | ||
| "--top-p", | ||
| type=float, | ||
| default=0.0, | ||
| help="Top p sampling.", | ||
| ) | ||
| group.add_argument( | ||
| "--top-k", | ||
| type=int, | ||
| default=0, | ||
| help="Top k sampling.", | ||
| ) | ||
| group.add_argument( | ||
| "--out-seq-length", | ||
| type=int, | ||
| default=2048, | ||
| help="Size of the output generated text.", | ||
| ) | ||
| group.add_argument( | ||
| "--prompt-file", | ||
| type=str, | ||
| default="./test_prompt.txt", | ||
| ) | ||
| group.add_argument( | ||
| "--tokenizer-path", | ||
| type=str, | ||
| default="./tokenizer", | ||
| ) | ||
| group.add_argument( | ||
| "--load", | ||
| type=str, | ||
| ) | ||
| group.add_argument( | ||
| "--state-dict-path", | ||
| type=str, | ||
| ) | ||
| group.add_argument( | ||
| "--micro-batch-size", | ||
| type=int, | ||
| default=1, | ||
| ) | ||
| group.add_argument( | ||
| "--quantize", | ||
| action="store_true", | ||
| ) | ||
|
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| return parser | ||
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| def main(): | ||
| parser = argparse.ArgumentParser() | ||
| parser = add_code_generation_args(parser) | ||
| args, _ = parser.parse_known_args() | ||
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| print("Loading tokenizer ...") | ||
| tokenizer = CodeGeeXTokenizer( | ||
| tokenizer_path=args.tokenizer_path, | ||
| mode="codegeex-13b") | ||
|
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| print("Loading state dict ...") | ||
| state_dict = torch.load(args.load, map_location="cpu") | ||
| state_dict = state_dict["module"] | ||
|
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| print("Building CodeGeeX model ...") | ||
| model = model_provider(args) | ||
| model.load_state_dict(state_dict) | ||
| model.eval() | ||
| model.half() | ||
| if args.quantize: | ||
| model = quantize(model, weight_bit_width=8, backend="torch") | ||
| model.cuda() | ||
| torch.cuda.synchronize() | ||
| with open(args.prompt_file, "r") as f: | ||
| prompt = f.readlines() | ||
| prompt = "".join(prompt) | ||
|
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| times = {} | ||
| out_seq_lengths = [args.out_seq_length] | ||
| micro_batch_size = args.micro_batch_size | ||
| seq_length = args.max_position_embeddings | ||
| for out_seq_length in out_seq_lengths: | ||
| print(f"Generating with out_seq_len {out_seq_length}...") | ||
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| times[out_seq_length] = [] | ||
| for prompt in [prompt]: | ||
| t0 = time.perf_counter() | ||
| tokens = tokenizer.encode_code(prompt) | ||
| print(tokens) | ||
| print("Current prompt:") | ||
| print(prompt) | ||
| n_token_prompt = len(tokens) | ||
| print("N_token_prompt:", n_token_prompt) | ||
| token_stream = get_token_stream( | ||
| model, | ||
| tokenizer, | ||
| seq_length, | ||
| out_seq_length, | ||
| [copy.deepcopy(tokens) for _ in range(micro_batch_size)], | ||
| micro_batch_size=micro_batch_size, | ||
| topk=args.top_k, | ||
| topp=args.top_p, | ||
| temperature=args.temperature, | ||
| greedy=args.greedy, | ||
| ) | ||
| is_finished = [False for _ in range(micro_batch_size)] | ||
| for i, generated in enumerate(token_stream): | ||
| generated_tokens = generated[0] | ||
| for j in range(micro_batch_size): | ||
| if is_finished[j]: | ||
| continue | ||
| generated_token_numpy = generated_tokens[j].numpy() | ||
| if generated_token_numpy[-1] == tokenizer.eos_token_id or len( | ||
| generated_tokens[j]) >= out_seq_length: | ||
| is_finished[j] = True | ||
| generated_tokens_ = generated_token_numpy.tolist() | ||
| generated_code = tokenizer.decode_code(generated_tokens_[n_token_prompt:]) | ||
| generated_code = "".join(generated_code) | ||
| t1 = time.perf_counter() | ||
| print("Total generation time:", t1 - t0, "# Tokens:", len(generated_tokens_) - n_token_prompt) | ||
| print(f"{(t1 - t0) / (len(generated_tokens_) - n_token_prompt)}s/token") | ||
| times[out_seq_length].append(t1 - t0) | ||
| print("================================= Generated code:") | ||
| print(generated_code) | ||
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| if all(is_finished): | ||
| break | ||
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| print(times) | ||
| for out_seq_length in times.keys(): | ||
| print(out_seq_length, np.mean(times[out_seq_length])) | ||
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| print("Generation finished.") | ||
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| if __name__ == "__main__": | ||
| main() | ||
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优化4: matmul支持和bias_add进行融合。