GitLab

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/261

X-link: https://github.com/facebookresearch/mobile-vision/pull/71

`is_oss` and `fb_overwritable` are also needed in `mobile_cv`, move them from d2go.

Reviewed By: zhanghang1989

Differential Revision: D36655821

fbshipit-source-id: 421c4d22d4c4620678908fe13d6e47ab39604ae7

Summary:
X-link: https://github.com/facebookresearch/detectron2/pull/4132

X-link: https://github.com/fairinternal/detectron2/pull/568

Pull Request resolved: https://github.com/facebookresearch/d2go/pull/203

For full discussion: https://fb.workplace.com/groups/1405155842844877/posts/5744470455580039

Tracing the `.to(device)` will cause problem when moving the traced torchscript to another device (eg. from cpu to gpu, or even, from `cuda:0` to `cuda:1`). The reason is that `device` is not a `torch.Tensor`, so the tracer just hardcode the value during tracing. The solution is scripting the casting operation.

Here's the code snippet illustrating this:
```
# define the MyModel similar to GeneralizedRCNN, which casts the input to the model's device
class MyModel(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv1 = nn.Conv2d(3, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        # cast the input to the same device as this model, this makes it possible to
        # take a cpu tensor as input when the model is on GPU.
        x = x.to(self.conv1.weight.device)

        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

# export the model by tracing
model = MyModel()
x = torch.zeros([1, 3, 32, 32])
ts = torch.jit.trace(model, x)
print(ts.graph)

# =====================================================
graph(%self.1 : __torch__.MyModel,
      %x : Float(1, 3, 32, 32, strides=[3072, 1024, 32, 1], requires_grad=0, device=cpu)):
  %conv2 : __torch__.torch.nn.modules.conv.___torch_mangle_0.Conv2d = prim::GetAttr[name="conv2"](%self.1)
  %conv1 : __torch__.torch.nn.modules.conv.Conv2d = prim::GetAttr[name="conv1"](%self.1)
  %14 : int = prim::Constant[value=6]() # <ipython-input-2-5abde0efc36f>:11:0
  %15 : int = prim::Constant[value=0]() # <ipython-input-2-5abde0efc36f>:11:0
  %16 : Device = prim::Constant[value="cpu"]() # <ipython-input-2-5abde0efc36f>:11:0
  %17 : NoneType = prim::Constant()
  %18 : bool = prim::Constant[value=0]() # <ipython-input-2-5abde0efc36f>:11:0
  %19 : bool = prim::Constant[value=0]() # <ipython-input-2-5abde0efc36f>:11:0
  %20 : NoneType = prim::Constant()
  %input.1 : Float(1, 3, 32, 32, strides=[3072, 1024, 32, 1], requires_grad=0, device=cpu) = aten::to(%x, %14, %15, %16, %17, %18, %19, %20) # <ipython-input-2-5abde0efc36f>:11:0
  %72 : Tensor = prim::CallMethod[name="forward"](%conv1, %input.1)
  %input.5 : Float(1, 20, 28, 28, strides=[15680, 784, 28, 1], requires_grad=1, device=cpu) = aten::relu(%72) # /mnt/xarfuse/uid-20293/a90d1698-seed-nspid4026533681_cgpid21128615-ns-4026533618/torch/nn/functional.py:1406:0
  %73 : Tensor = prim::CallMethod[name="forward"](%conv2, %input.5)
  %61 : Float(1, 20, 24, 24, strides=[11520, 576, 24, 1], requires_grad=1, device=cpu) = aten::relu(%73) # /mnt/xarfuse/uid-20293/a90d1698-seed-nspid4026533681_cgpid21128615-ns-4026533618/torch/nn/functional.py:1406:0
  return (%61)
# =====================================================

# PyTorch cuda works
model = copy.deepcopy(model)
model.to("cuda")
y = model(x)
# torchscript cpu works
y = ts(x)
# torchscript cuda doesn't work
ts = ts.to("cuda")
y = ts(x)

# =====================================================
RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-4-2aece3ad6c9a> in <module>
      7 # torchscript cuda doesn't work
      8 ts = ts.to("cuda")
----> 9 y = ts(x)
/mnt/xarfuse/uid-20293/a90d1698-seed-nspid4026533681_cgpid21128615-ns-4026533618/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1108         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110             return forward_call(*input, **kwargs)
   1111         # Do not call functions when jit is used
   1112         full_backward_hooks, non_full_backward_hooks = [], []
RuntimeError: The following operation failed in the TorchScript interpreter.
# =====================================================

# One solution is scripting the casting instead of tracing it, the folloing code demonstrate how to do it. We need to use mixed scripting/tracing
torch.jit.script_if_tracing
def cast_device_like(src: torch.Tensor, dst: torch.Tensor) -> torch.Tensor:
    return src.to(dst.device)

class MyModel2(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv1 = nn.Conv2d(3, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        # cast the input to the same device as this model, this makes it possible to
        # take a cpu tensor as input when the model is on GPU.
        x = cast_device_like(x, self.conv1.weight)

        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

# export the model by tracing
model = MyModel2()
x = torch.zeros([1, 3, 32, 32])
ts = torch.jit.trace(model, x)
print(ts.graph)

# =====================================================
graph(%self.1 : __torch__.MyModel2,
      %x : Float(1, 3, 32, 32, strides=[3072, 1024, 32, 1], requires_grad=0, device=cpu)):
  %conv2 : __torch__.torch.nn.modules.conv.___torch_mangle_5.Conv2d = prim::GetAttr[name="conv2"](%self.1)
  %conv1 : __torch__.torch.nn.modules.conv.___torch_mangle_4.Conv2d = prim::GetAttr[name="conv1"](%self.1)
  %conv1.1 : __torch__.torch.nn.modules.conv.___torch_mangle_4.Conv2d = prim::GetAttr[name="conv1"](%self.1)
  %weight.5 : Tensor = prim::GetAttr[name="weight"](%conv1.1)
  %14 : Function = prim::Constant[name="cast_device_like"]()
  %input.1 : Tensor = prim::CallFunction(%14, %x, %weight.5)
  %68 : Tensor = prim::CallMethod[name="forward"](%conv1, %input.1)
  %input.5 : Float(1, 20, 28, 28, strides=[15680, 784, 28, 1], requires_grad=1, device=cpu) = aten::relu(%68) # /mnt/xarfuse/uid-20293/a90d1698-seed-nspid4026533681_cgpid21128615-ns-4026533618/torch/nn/functional.py:1406:0
  %69 : Tensor = prim::CallMethod[name="forward"](%conv2, %input.5)
  %55 : Float(1, 20, 24, 24, strides=[11520, 576, 24, 1], requires_grad=1, device=cpu) = aten::relu(%69) # /mnt/xarfuse/uid-20293/a90d1698-seed-nspid4026533681_cgpid21128615-ns-4026533618/torch/nn/functional.py:1406:0
  return (%55)
# =====================================================

# PyTorch cuda works
model = copy.deepcopy(model)
model.to("cuda")
y = model(x)
# torchscript cpu works
y = ts(x)
# Note that now torchscript cuda works
ts = ts.to("cuda")
y = ts(x)
print(y.device)

# =====================================================
cuda:0
# =====================================================
```

For D2 (https://github.com/facebookresearch/d2go/commit/87374efb134e539090e0b5c476809dc35bf6aedb), this diff creates a `move_tensor_device_same_as_another(A, B)` function to replace `A.to(B.device)`. This diff updates the `rcnn.py` and all its utils.

For D2 (https://github.com/facebookresearch/d2go/commit/87374efb134e539090e0b5c476809dc35bf6aedb)Go, since the exported model will become device-agnostic, we can remove the "_gpu" from predictor-type.

Update (April 11):
Add test to cover tracing on one device and move traced model to another device for inference. When GPU is available, it'll trace on `cuda:0` and run inference on `cpu`, `cuda:0` (and `cuda:N-1` if available).

Summary of the device related patterns
- The usage of `.to(dtype=another_dype)` won't affect device.
- Explicit device casting like `.to(device)` can be generally replaced by `move_device_like`.
- For creating variable directly on device (eg. `torch.zeros`, `torch.arange`), we can replace then with ScriptModule to avoid first create on CPU and then move to new device.
    - Creating things on tracing device and then moving to new device is dangerous, because tracing device (eg. `cuda:0`) might not be available (eg. running on CPU-only machine).
    - It's hard to write `image_list.py` in this pattern because the size behaves differently during tracing (int vs. scalar tensor), in this diff, still create on CPU first and then move to target device.

Reviewed By: tglik

Differential Revision: D35367772

fbshipit-source-id: 02d07e3d96da85f4cfbeb996e3c14c2a6f619beb

Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/154

Reviewed By: zhanghang1989

Differential Revision: D33352204

fbshipit-source-id: e1a9ac6eb2574dfe6931435275e27c9508f66352

Reviewed By: sstsai-adl

Differential Revision: D32216605

fbshipit-source-id: bebee1edae85e940c7dcc6a64dbe341a2fde36a2

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/117

Fix github ci failure due to lack of coco datset. It was cased by D31134064 (https://github.com/facebookresearch/d2go/commit/f018d4a7ceef437d8fc3ca8b2bba4b7321917e06)

Reviewed By: mattcyu1, wat3rBro

Differential Revision: D31179666

fbshipit-source-id: fe25129d167afcdcb577e5c8d82f3432ba939ca9

Reviewed By: zhanghang1989

Differential Revision: D31134064

fbshipit-source-id: 825ca14477243a53f84b8521f4430a2b080324bd