sync (#658)

backend/headless/fcbh/ops.py

@@ -0,0 +1,46 @@
+import torch
+from contextlib import contextmanager
+
+class Linear(torch.nn.Module):
+    def __init__(self, in_features: int, out_features: int, bias: bool = True,
+                 device=None, dtype=None) -> None:
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weight = torch.nn.Parameter(torch.empty((out_features, in_features), **factory_kwargs))
+        if bias:
+            self.bias = torch.nn.Parameter(torch.empty(out_features, **factory_kwargs))
+        else:
+            self.register_parameter('bias', None)
+
+    def forward(self, input):
+        return torch.nn.functional.linear(input, self.weight, self.bias)
+
+class Conv2d(torch.nn.Conv2d):
+    def reset_parameters(self):
+        return None
+
+def conv_nd(dims, *args, **kwargs):
+    if dims == 2:
+        return Conv2d(*args, **kwargs)
+    else:
+        raise ValueError(f"unsupported dimensions: {dims}")
+
+@contextmanager
+def use_fcbh_ops(device=None, dtype=None): # Kind of an ugly hack but I can't think of a better way
+    old_torch_nn_linear = torch.nn.Linear
+    force_device = device
+    force_dtype = dtype
+    def linear_with_dtype(in_features: int, out_features: int, bias: bool = True, device=None, dtype=None):
+        if force_device is not None:
+            device = force_device
+        if force_dtype is not None:
+            dtype = force_dtype
+        return Linear(in_features, out_features, bias=bias, device=device, dtype=dtype)
+
+    torch.nn.Linear = linear_with_dtype
+    try:
+        yield
+    finally:
+        torch.nn.Linear = old_torch_nn_linear