[fx] meta registration compatibility

colossalai/fx/_compatibility.py

@@ -2,11 +2,21 @@
 
 import torch
 
-try:
-    from . import _meta_registrations
-    META_COMPATIBILITY = True
-except:
+TORCH_MAJOR = int(torch.__version__.split('.')[0])
+TORCH_MINOR = int(torch.__version__.split('.')[1])
+
+if TORCH_MAJOR == 1 and TORCH_MINOR < 12:
     META_COMPATIBILITY = False
+elif TORCH_MAJOR == 1 and TORCH_MINOR == 12:
+    from . import _meta_regist_12
+    META_COMPATIBILITY = True
+elif TORCH_MAJOR == 1 and TORCH_MINOR == 13:
+    from . import _meta_regist_13
+    META_COMPATIBILITY = True
+elif TORCH_MAJOR == 2:
+    from . import _meta_regist_13
+    META_COMPATIBILITY = True
+    raise UserWarning("Colossalai is not tested with torch2.0 yet!!!")
 
 
 def compatibility(is_backward_compatible: bool = False) -> Callable:

colossalai/fx/_meta_regist_13.py

@@ -0,0 +1,57 @@
+import torch
+from torch._meta_registrations import register_meta
+from torch._prims_common import check
+
+aten = torch.ops.aten
+
+
+# since we fix the torch version to 1.13.1, we have to add unimplemented meta ops
+# all these functions are from here https://github.com/pytorch/pytorch/blob/master/torch/_meta_registrations.py
+@register_meta([aten.convolution_backward.default])
+def meta_convolution_backward(
+    grad_output_,
+    input_,
+    weight_,
+    bias_sizes_opt,
+    stride,
+    padding,
+    dilation,
+    transposed,
+    output_padding,
+    groups,
+    output_mask,
+):
+    # High level logic taken from slow_conv3d_backward_cpu which should
+    # be representative of all convolution_backward impls
+    backend_grad_input = None
+    backend_grad_weight = None
+    backend_grad_bias = None
+
+    if output_mask[0]:
+        backend_grad_input = grad_output_.new_empty(input_.size())
+    if output_mask[1]:
+        backend_grad_weight = grad_output_.new_empty(weight_.size())
+    if output_mask[2]:
+        backend_grad_bias = grad_output_.new_empty(bias_sizes_opt)
+
+    return (backend_grad_input, backend_grad_weight, backend_grad_bias)
+
+
+@register_meta(aten._adaptive_avg_pool2d_backward.default)
+def meta__adaptive_avg_pool2d_backward(grad_out, self):
+    ndim = grad_out.ndim
+    for i in range(1, ndim):
+        check(
+            grad_out.size(i) > 0,
+            lambda: f"adaptive_avg_pool2d_backward(): Expected grad_output to have non-zero \
+                      size for non-batch dimensions, {grad_out.shape} with dimension {i} being empty",
+        )
+    check(
+        ndim == 3 or ndim == 4,
+        lambda: f"adaptive_avg_pool2d_backward(): Expected 3D or 4D tensor, but got {self.shape}",
+    )
+    check(
+        self.dtype == grad_out.dtype,
+        lambda: f"expected dtype {self.dtype} for `grad_output` but got dtype {grad_out.dtype}",
+    )
+    return self.new_empty(self.shape)