[Fix] Remove obsolete files - inference (#5650)

examples/inference/build_smoothquant_weight.py

@@ -1,59 +0,0 @@
-import argparse
-import os
-
-import torch
-from datasets import load_dataset
-from transformers import LlamaTokenizer
-
-from colossalai.inference.quant.smoothquant.models.llama import SmoothLlamaForCausalLM
-
-
-def build_model_and_tokenizer(model_name):
-    tokenizer = LlamaTokenizer.from_pretrained(model_name, model_max_length=512)
-    kwargs = {"torch_dtype": torch.float16, "device_map": "sequential"}
-    model = SmoothLlamaForCausalLM.from_pretrained(model_name, **kwargs)
-    model = model.to(torch.float32)
-    return model, tokenizer
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model-name", type=str, help="model name")
-    parser.add_argument(
-        "--output-path",
-        type=str,
-        help="where to save the checkpoint",
-    )
-    parser.add_argument(
-        "--dataset-path",
-        type=str,
-        help="location of the calibration dataset",
-    )
-    parser.add_argument("--num-samples", type=int, default=10)
-    parser.add_argument("--seq-len", type=int, default=512)
-    args = parser.parse_args()
-    return args
-
-
-@torch.no_grad()
-def main():
-    args = parse_args()
-    model_path = args.model_name
-    dataset_path = args.dataset_path
-    output_path = args.output_path
-    num_samples = args.num_samples
-    seq_len = args.seq_len
-
-    model, tokenizer = build_model_and_tokenizer(model_path)
-    if not os.path.exists(dataset_path):
-        raise FileNotFoundError(f"Cannot find the dataset at {args.dataset_path}")
-    dataset = load_dataset("json", data_files=dataset_path, split="train")
-
-    model.quantized(tokenizer, dataset, num_samples=num_samples, seq_len=seq_len)
-    model = model.cuda()
-
-    model.save_quantized(output_path, model_basename="llama-7b")
-
-
-if __name__ == "__main__":
-    main()

examples/inference/run_llama_inference.py

@@ -1,98 +0,0 @@
-import argparse
-
-import torch
-import torch.distributed as dist
-from transformers import LlamaForCausalLM, LlamaTokenizer
-
-import colossalai
-from colossalai.accelerator import get_accelerator
-from colossalai.inference import InferenceEngine
-from colossalai.testing import spawn
-
-INPUT_TEXTS = [
-    "What is the longest river in the world?",
-    "Explain the difference between process and thread in compouter science.",
-]
-
-
-def run_inference(args):
-    llama_model_path = args.model_path
-    llama_tokenize_path = args.tokenizer_path or args.model_path
-
-    max_input_len = args.max_input_len
-    max_output_len = args.max_output_len
-    max_batch_size = args.batch_size
-    micro_batch_size = args.micro_batch_size
-    tp_size = args.tp_size
-    pp_size = args.pp_size
-    rank = dist.get_rank()
-
-    tokenizer = LlamaTokenizer.from_pretrained(llama_tokenize_path, padding_side="left")
-    tokenizer.pad_token_id = tokenizer.eos_token_id
-
-    if args.quant is None:
-        model = LlamaForCausalLM.from_pretrained(llama_model_path, pad_token_id=tokenizer.pad_token_id)
-    elif args.quant == "gptq":
-        from auto_gptq import AutoGPTQForCausalLM
-
-        model = AutoGPTQForCausalLM.from_quantized(
-            llama_model_path, inject_fused_attention=False, device=torch.cuda.current_device()
-        )
-    elif args.quant == "smoothquant":
-        from colossalai.inference.quant.smoothquant.models.llama import SmoothLlamaForCausalLM
-
-        model = SmoothLlamaForCausalLM.from_quantized(llama_model_path, model_basename=args.smoothquant_base_name)
-        model = model.cuda()
-
-    engine = InferenceEngine(
-        tp_size=tp_size,
-        pp_size=pp_size,
-        model=model,
-        max_input_len=max_input_len,
-        max_output_len=max_output_len,
-        max_batch_size=max_batch_size,
-        micro_batch_size=micro_batch_size,
-        quant=args.quant,
-        dtype=args.dtype,
-    )
-
-    inputs = tokenizer(INPUT_TEXTS, return_tensors="pt", padding="longest", max_length=max_input_len, truncation=True)
-    inputs = {k: v.to(get_accelerator().get_current_device()) for k, v in inputs.items()}
-    outputs = engine.generate(inputs)
-
-    if rank == 0:
-        output_texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)
-        for input_text, output_text in zip(INPUT_TEXTS, output_texts):
-            print(f"Input: {input_text}")
-            print(f"Output: {output_text}")
-
-
-def run_tp_pipeline_inference(rank, world_size, port, args):
-    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
-    run_inference(args)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-p", "--model_path", type=str, help="Model path", required=True)
-    parser.add_argument("-i", "--input", default="What is the longest river in the world?")
-    parser.add_argument("-t", "--tokenizer_path", type=str, help="Tokenizer path", default=None)
-    parser.add_argument(
-        "-q",
-        "--quant",
-        type=str,
-        choices=["gptq", "smoothquant"],
-        default=None,
-        help="quantization type: 'gptq' or 'smoothquant'",
-    )
-    parser.add_argument("--smoothquant_base_name", type=str, default=None, help="soothquant base name")
-    parser.add_argument("--tp_size", type=int, default=1, help="Tensor parallel size")
-    parser.add_argument("--pp_size", type=int, default=1, help="Pipeline parallel size")
-    parser.add_argument("-b", "--batch_size", type=int, default=4, help="Maximum batch size")
-    parser.add_argument("--max_input_len", type=int, default=2048, help="Maximum input length")
-    parser.add_argument("--max_output_len", type=int, default=64, help="Maximum output length")
-    parser.add_argument("--micro_batch_size", type=int, default=1, help="Micro batch size")
-    parser.add_argument("--dtype", default="fp16", type=str)
-
-    args = parser.parse_args()
-    spawn(run_tp_pipeline_inference, nprocs=args.tp_size * args.pp_size, args=args)