TorchRunner

TorchRunner#

%cd ..
from pathlib import Path

temp_dir = Path(".temp")
temp_dir.mkdir(exist_ok=True)

/media/pc/data/lxw/ai/tvm-book/doc/read/msc

构建前端模型：

import numpy as np
import torch
from torch import fx
from tvm.relax.frontend.torch import from_fx

def _get_torch_model(name, training=False):
    """Get model from torch vision"""

    # pylint: disable=import-outside-toplevel
    try:
        import torchvision

        model = getattr(torchvision.models, name)()
        if training:
            model = model.train()
        else:
            model = model.eval()
        return model
    except:  # pylint: disable=bare-except
        print("please install torchvision package")
        return None

构建并运行 TorchRunner#

import tvm
from tvm.contrib.msc.framework.torch.runtime import TorchRunner
from tvm.contrib.msc.core import utils as msc_utils


def _test_from_torch(runner_cls, device, training=False, atol=1e-1, rtol=1e-1):
    """Test runner from torch model"""

    torch_model = _get_torch_model("resnet50", training)
    if torch_model:
        path = f"{temp_dir}/test_runner_torch_{runner_cls.__name__}_{device}"
        workspace = msc_utils.set_workspace(msc_utils.msc_dir(path, keep_history=False))
        log_path = workspace.relpath("MSC_LOG", keep_history=False)
        msc_utils.set_global_logger("critical", log_path)
        input_info = [([1, 3, 224, 224], "float32")]
        datas = [np.random.rand(*i[0]).astype(i[1]) for i in input_info]
        torch_datas = [torch.from_numpy(d) for d in datas]
        graph_model = fx.symbolic_trace(torch_model)
        if training:
            input_info = [([tvm.tir.Var("bz", "int64"), 3, 224, 224], "float32")]
        with torch.no_grad():
            golden = torch_model(*torch_datas)
            mod = from_fx(graph_model, input_info)
        runner = runner_cls(mod, device=device, training=training)
        runner.build()
        outputs = runner.run(datas, ret_type="list")
        golden = [msc_utils.cast_array(golden)]
        workspace.destory()
        for gol_r, out_r in zip(golden, outputs):
            np.testing.assert_allclose(gol_r, msc_utils.cast_array(out_r), atol=atol, rtol=rtol)

for training in [True, False]:
    _test_from_torch(TorchRunner, "cpu", training=training)

/media/pc/data/lxw/ai/tvm/python/tvm/contrib/msc/framework/torch/codegen/codegen.py:74: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state_dict = torch.load(folder.relpath(graph.name + ".pth"))
/media/pc/data/lxw/ai/tvm/python/tvm/contrib/msc/framework/torch/codegen/codegen.py:74: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state_dict = torch.load(folder.relpath(graph.name + ".pth"))

for training in [True, False]:
    _test_from_torch(TorchRunner, "cuda", training=training, atol=1e-1, rtol=1e-1)

/media/pc/data/lxw/ai/tvm/python/tvm/contrib/msc/framework/torch/codegen/codegen.py:74: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state_dict = torch.load(folder.relpath(graph.name + ".pth"))
/media/pc/data/lxw/ai/tvm/python/tvm/contrib/msc/framework/torch/codegen/codegen.py:74: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state_dict = torch.load(folder.relpath(graph.name + ".pth"))

from tvm.contrib.msc.framework.tensorrt.runtime import TensorRTRunner
_test_from_torch(TensorRTRunner, "cuda", atol=1e-1, rtol=1e-1)
目录