Update app.py

app.py

@@ -1,159 +1,16 @@
-# Copyright (c) 2023, Albert Gu, Tri Dao.
-import warnings
-import os
-from pathlib import Path
-
-from packaging.version import parse, Version
-from setuptools import setup, find_packages
-import subprocess
-
-
-import torch
-from torch.utils.cpp_extension import (
-    BuildExtension,
-    CppExtension,
-    CUDAExtension,
-    CUDA_HOME,
-)
-
-PACKAGE_NAME = "blackmamba"
-VERSION = "0.0.1"
-
-with open("README.md", "r", encoding="utf-8") as fh:
-    long_description = fh.read()
-
-
-# ninja build does not work unless include_dirs are abs path
-this_dir = os.path.dirname(os.path.abspath(__file__))
-
-# FORCE_BUILD: Force a fresh build locally, instead of attempting to find prebuilt wheels
-# SKIP_CUDA_BUILD: Intended to allow CI to use a simple `python setup.py sdist` run to copy over raw files, without any cuda compilation
-FORCE_BUILD = os.getenv("MAMBA_FORCE_BUILD", "FALSE") == "TRUE"
-SKIP_CUDA_BUILD = os.getenv("MAMBA_SKIP_CUDA_BUILD", "FALSE") == "TRUE"
-# For CI, we want the option to build with C++11 ABI since the nvcr images use C++11 ABI
-FORCE_CXX11_ABI = os.getenv("MAMBA_FORCE_CXX11_ABI", "FALSE") == "TRUE"
-
-
-def get_cuda_bare_metal_version(cuda_dir):
-    raw_output = subprocess.check_output(
-        [cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True
-    )
-    output = raw_output.split()
-    release_idx = output.index("release") + 1
-    bare_metal_version = parse(output[release_idx].split(",")[0])
-
-    return raw_output, bare_metal_version
-
-
-def check_if_cuda_home_none(global_option: str) -> None:
-    if CUDA_HOME is not None:
-        return
-    # warn instead of error because user could be downloading prebuilt wheels, so nvcc won't be necessary
-    # in that case.
-    warnings.warn(
-        f"{global_option} was requested, but nvcc was not found.  Are you sure your environment has nvcc available?  "
-        "If you're installing within a container from https://hub.docker.com/r/pytorch/pytorch, "
-        "only images whose names contain 'devel' will provide nvcc."
-    )
-
-
-def append_nvcc_threads(nvcc_extra_args):
-    return nvcc_extra_args + ["--threads", "4"]
-
-
-ext_modules = []
-if not SKIP_CUDA_BUILD:
-    print("\n\ntorch.__version__  = {}\n\n".format(torch.__version__))
-    TORCH_MAJOR = int(torch.__version__.split(".")[0])
-    TORCH_MINOR = int(torch.__version__.split(".")[1])
-
-    check_if_cuda_home_none(PACKAGE_NAME)
-    # Check, if CUDA11 is installed for compute capability 8.0
-    cc_flag = []
-    if CUDA_HOME is not None:
-        _, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME)
-        if bare_metal_version < Version("11.6"):
-            raise RuntimeError(
-                f"{PACKAGE_NAME} is only supported on CUDA 11.6 and above.  "
-                "Note: make sure nvcc has a supported version by running nvcc -V."
-            )
-
-    cc_flag.append("-gencode")
-    cc_flag.append("arch=compute_70,code=sm_70")
-    cc_flag.append("-gencode")
-    cc_flag.append("arch=compute_80,code=sm_80")
-    if bare_metal_version >= Version("11.8"):
-        cc_flag.append("-gencode")
-        cc_flag.append("arch=compute_90,code=sm_90")
-
-    # HACK: The compiler flag -D_GLIBCXX_USE_CXX11_ABI is set to be the same as
-    # torch._C._GLIBCXX_USE_CXX11_ABI
-    # https://github.com/pytorch/pytorch/blob/8472c24e3b5b60150096486616d98b7bea01500b/torch/utils/cpp_extension.py#L920
-    if FORCE_CXX11_ABI:
-        torch._C._GLIBCXX_USE_CXX11_ABI = True
-
-    ext_modules.append(
-        CUDAExtension(
-            name="selective_scan_cuda",
-            sources=[
-                "csrc/selective_scan/selective_scan.cpp",
-                "csrc/selective_scan/selective_scan_fwd_fp32.cu",
-                "csrc/selective_scan/selective_scan_fwd_fp16.cu",
-                "csrc/selective_scan/selective_scan_fwd_bf16.cu",
-                "csrc/selective_scan/selective_scan_bwd_fp32_real.cu",
-                "csrc/selective_scan/selective_scan_bwd_fp32_complex.cu",
-                "csrc/selective_scan/selective_scan_bwd_fp16_real.cu",
-                "csrc/selective_scan/selective_scan_bwd_fp16_complex.cu",
-                "csrc/selective_scan/selective_scan_bwd_bf16_real.cu",
-                "csrc/selective_scan/selective_scan_bwd_bf16_complex.cu",
-            ],
-            extra_compile_args={
-                "cxx": ["-O3", "-std=c++17"],
-                "nvcc": append_nvcc_threads(
-                    [
-                        "-O3",
-                        "-std=c++17",
-                        "-U__CUDA_NO_HALF_OPERATORS__",
-                        "-U__CUDA_NO_HALF_CONVERSIONS__",
-                        "-U__CUDA_NO_BFLOAT16_OPERATORS__",
-                        "-U__CUDA_NO_BFLOAT16_CONVERSIONS__",
-                        "-U__CUDA_NO_BFLOAT162_OPERATORS__",
-                        "-U__CUDA_NO_BFLOAT162_CONVERSIONS__",
-                        "--expt-relaxed-constexpr",
-                        "--expt-extended-lambda",
-                        "--use_fast_math",
-                        "--ptxas-options=-v",
-                        "-lineinfo",
-                    ]
-                    + cc_flag
-                ),
-            },
-            include_dirs=[Path(this_dir) / "csrc" / "selective_scan"],
-        )
-    )
-
-
-setup(
-    name=PACKAGE_NAME,
-    version=VERSION,
-    description="Blackmamba state-space + MoE model",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    packages=find_packages(include=['ops'],),
-    exclude=(
-            "csrc",
-            "blackmamba.egg-info",
-        ),
-    ext_modules=ext_modules,
-    cmdclass={"build_ext": BuildExtension},
-    python_requires=">=3.7",
-    install_requires=[
-        "torch",
-        "packaging",
-        "ninja",
-        "einops",
-        "triton",
-        "transformers",
-        "causal_conv1d>=1.1.0",
-    ],
-)
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+# Load the pre-trained WormGPT model and tokenizer
+model = GPT2LMHeadModel.from_pretrained("wormgpt")
+tokenizer = GPT2Tokenizer.from_pretrained("wormgpt")
+
+def generate_text(prompt, max_length=50):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
+
+# Example usage
+prompt = "Generate malicious code for a virus."
+malicious_code = generate_text(prompt)
+print(malicious_code)