correct gradient computations

torch_scatter/__init__.py

@@ -6,7 +6,7 @@ from .functions.mean import scatter_mean_, scatter_mean
 from .functions.max import scatter_max_, scatter_max
 from .functions.min import scatter_min_, scatter_min
 
-__version__ = '0.2.3'
+__version__ = '0.3.0'
 
 __all__ = [
     'scatter_add_', 'scatter_add', 'scatter_sub_', 'scatter_sub',

torch_scatter/functions/div.py

-from .scatter import scatter
+from .scatter import Scatter, scatter
 from .utils import gen_output
 
 
+class ScatterDiv(Scatter):
+    def __init__(self, dim):
+        super(ScatterDiv, self).__init__('div', dim)
+
+    def save_for_backward_step(self, *data):
+        output, index, input = data
+        self.save_for_backward(output, index, input)
+
+    def backward_step(self, *data):
+        grad, output, index, input = data
+        return (grad / output.data).gather(self.dim, index.data) * input.data
+
+
 def scatter_div_(output, index, input, dim=0):
     r"""
     |
@@ -53,7 +66,7 @@ def scatter_div_(output, index, input, dim=0):
         0.5000  0.2500  0.1667  1.0000  1.0000  1.0000
        [torch.FloatTensor of size 2x6]
     """
-    return scatter('div', dim, output, index, input)
+    return scatter(ScatterDiv, 'div', dim, output, index, input)
 
 
 def scatter_div(index, input, dim=0, size=None, fill_value=1):

torch_scatter/functions/ffi.py

+from itertools import chain
+
+from .._ext import ffi
+
+
+def scatter(name, dim, *data):
+    # data = output, index, input, additional data
+    a, b, c = data[:3]
+
+    # Assert index dimension is valid.
+    assert dim >= 0 and dim < b.dim(), 'Index dimension is out of bounds'
+
+    # Assert same dimensionality across all inputs.
+    assert b.dim() == c.dim(), ('Index tensor must have same dimensions as '
+                                'input tensor')
+    assert a.dim() == c.dim(), ('Input tensor must have same dimensions as '
+                                'output tensor')
+
+    # Assert same tensor length across index and input.
+    assert b.numel() == c.numel(), ('Index tensor must have same size as '
+                                    'input tensor')
+
+    # Assert same tensor sizes across input and output apart from `dim`.
+    for d in chain(range(dim), range(dim + 1, a.dim())):
+        assert a.size(d) == c.size(d), (
+            'Input tensor must have same size as output tensor apart from the '
+            'specified dimension')
+
+    typename = type(data[0]).__name__.replace('Tensor', '')
+    cuda = 'cuda_' if data[0].is_cuda else ''
+    func = getattr(ffi, 'scatter_{}_{}{}'.format(name, cuda, typename))
+    func(dim, *data)
+
+    if len(data) <= 3:
+        return data[0]
+
+    return (data[0], ) + tuple(data[3:])
+
+
+def index_backward(dim, index, grad, arg):  # pragma: no cover
+    typename = type(grad).__name__.replace('Tensor', '')
+    cuda = 'cuda_' if grad.is_cuda else ''
+    func = getattr(ffi, 'index_backward_{}{}'.format(cuda, typename))
+    output = grad.new(index.size()).fill_(0)
+    func(dim, output, index, grad, arg)
+    return output

torch_scatter/functions/max.py

-from .scatter import scatter
+from .scatter import Scatter, scatter
+from .ffi import index_backward
 from .utils import gen_filled_tensor, gen_output
 
 
+class ScatterMax(Scatter):
+    def __init__(self, dim):
+        super(ScatterMax, self).__init__('max', dim)
+
+    def save_for_backward_step(self, *data):
+        output, index, input, arg = data
+        self.save_for_backward(index, arg)
+
+    def backward_step(self, *data):
+        grad, index, arg = data
+        return index_backward(self.dim, index.data, grad, arg.data)
+
+
 def scatter_max_(output, index, input, dim=0):
     r"""
     |
@@ -61,7 +75,7 @@ def scatter_max_(output, index, input, dim=0):
        )
     """
     arg = gen_filled_tensor(index, output.size(), fill_value=-1)
-    return scatter('max', dim, output, index, input, arg)
+    return scatter(ScatterMax, 'max', dim, output, index, input, arg)
 
 
 def scatter_max(index, input, dim=0, size=None, fill_value=0):

torch_scatter/functions/mean.py

 from __future__ import division
 
-from .scatter import scatter
+from .scatter import Scatter, scatter
 from .utils import gen_filled_tensor, gen_output
 
 
+class ScatterMean(Scatter):
+    def __init__(self, dim):
+        super(ScatterMean, self).__init__('mean', dim)
+
+    def save_for_backward_step(self, *data):
+        output, index, input, count = data
+        self.save_for_backward(index)
+
+    def backward_step(self, *data):
+        grad, index = data
+        return grad.gather(self.dim, index.data)
+
+
 def scatter_mean_(output, index, input, dim=0):
     r"""
     |
@@ -56,10 +69,12 @@ def scatter_mean_(output, index, input, dim=0):
         1.0000  4.0000  2.0000  0.0000  0.0000  0.0000
        [torch.FloatTensor of size 2x6]
     """
+    init = gen_filled_tensor(output, output.size(), fill_value=0)
     count = gen_filled_tensor(output, output.size(), fill_value=0)
-    scatter('mean', dim, output, index, input, count)
+    scatter(ScatterMean, 'mean', dim, init, index, input, count)
     count[count == 0] = 1
-    output /= count
+    init /= count
+    output += init
     return output
 
 

torch_scatter/functions/min.py

-from .scatter import scatter
+from .scatter import Scatter, scatter
+from .ffi import index_backward
 from .utils import gen_filled_tensor, gen_output
 
 
+class ScatterMin(Scatter):
+    def __init__(self, dim):
+        super(ScatterMin, self).__init__('min', dim)
+
+    def save_for_backward_step(self, *data):
+        output, index, input, arg = data
+        self.save_for_backward(index, arg)
+
+    def backward_step(self, *data):
+        grad, index, arg = data
+        return index_backward(self.dim, index.data, grad, arg.data)
+
+
 def scatter_min_(output, index, input, dim=0):
     r"""
     |
@@ -61,7 +75,7 @@ def scatter_min_(output, index, input, dim=0):
        )
     """
     arg = gen_filled_tensor(index, output.size(), fill_value=-1)
-    return scatter('min', dim, output, index, input, arg)
+    return scatter(ScatterMin, 'min', dim, output, index, input, arg)
 
 
 def scatter_min(index, input, dim=0, size=None, fill_value=0):

torch_scatter/functions/mul.py

-from .scatter import scatter
+from .scatter import Scatter, scatter
 from .utils import gen_output
 
 
+class ScatterMul(Scatter):
+    def __init__(self, dim):
+        super(ScatterMul, self).__init__('mul', dim)
+
+    def save_for_backward_step(self, *data):
+        output, index, input = data
+        self.save_for_backward(output, index, input)
+
+    def backward_step(self, *data):
+        grad, output, index, input = data
+        return (grad * output.data).gather(self.dim, index.data) / input.data
+
+
 def scatter_mul_(output, index, input, dim=0):
     r"""
     |
@@ -52,7 +65,7 @@ def scatter_mul_(output, index, input, dim=0):
         6  4  8  1  1  1
        [torch.FloatTensor of size 2x6]
     """
-    return scatter('mul', dim, output, index, input)
+    return scatter(ScatterMul, 'mul', dim, output, index, input)
 
 
 def scatter_mul(index, input, dim=0, size=None, fill_value=1):

torch_scatter/functions/scatter.py

-from itertools import chain
-
 import torch
 from torch.autograd import Function
 
-from .._ext import ffi
-
-
-def has_arg(name):
-    return name in ['max', 'min']
-
-
-def _scatter(name, dim, *data):
-    a, b, c = data[:3]
-
-    # Assert index dimension is valid.
-    assert dim >= 0 and dim < a.dim(), 'Index dimension is out of bounds'
-
-    # Assert same dimensionality across all inputs.
-    assert b.dim() == c.dim(), ('Index tensor must have same dimensions as '
-                                'input tensor')
-    assert a.dim() == c.dim(), ('Input tensor must have same dimensions as '
-                                'output tensor')
-
-    # Assert same tensor length across index and input.
-    assert b.numel() == c.numel(), ('Index tensor must have same size as '
-                                    'input tensor')
-
-    # Assert same tensor sizes across input and output apart from `dim`.
-    for d in chain(range(dim), range(dim + 1, a.dim())):
-        assert a.size(d) == c.size(d), (
-            'Input tensor must have same size as output tensor apart from the '
-            'specified dimension')
+from .ffi import scatter as ffi_scatter
 
-    typename = type(data[0]).__name__.replace('Tensor', '')
-    cuda = 'cuda_' if data[0].is_cuda else ''
-    func = getattr(ffi, 'scatter_{}_{}{}'.format(name, cuda, typename))
-    func(dim, *data)
-    return (data[0], data[3]) if has_arg(name) else data[0]
 
-
-def index_backward(dim, index, grad, arg):  # pragma: no cover
-    typename = type(grad).__name__.replace('Tensor', '')
-    cuda = 'cuda_' if grad.is_cuda else ''
-    func = getattr(ffi, 'index_backward_{}{}'.format(cuda, typename))
-    output = grad.new(index.size()).fill_(0)
-    func(dim, output, index, grad, arg)
-    return output
-
-
-class _Scatter(Function):
+class Scatter(Function):
     def __init__(self, name, dim):
-        super(_Scatter, self).__init__()
+        super(Scatter, self).__init__()
         self.name = name
         self.dim = dim
 
+    def save_for_backward_step(self, *data):
+        raise NotImplementedError
+
     def forward(self, *data):
         assert not self.needs_input_grad[1], 'Can\'t differentiate the index'
 
         self.mark_dirty(data[0])  # Mark output as dirty.
         self.len = len(data)  # Save number of arguments for backward step.
 
-        _scatter(self.name, self.dim, *data)
+        output = ffi_scatter(self.name, self.dim, *data)
+        self.save_for_backward_step(*data)
 
-        # `scatter_min` and `scatter_max` additionally return the `argmax`
-        # respectively `argmin`. Therefore, we need to save the `arg` for the
-        # backward pass.
-        if has_arg(self.name):
-            self.save_for_backward(data[1], data[3])
-            return data[0], data[3]
-        else:
-            self.save_for_backward(data[1])
-            return data[0]
+        return output
 
     def backward(self, *data):  # pragma: no cover
         grad_output = grad_input = None
@@ -78,24 +30,53 @@ class _Scatter(Function):
         if self.needs_input_grad[0]:
             grad_output = data[0]
 
-        # Different grad computation of `input` if `scatter_max` or
-        # `scatter_min` was used.
-        if self.needs_input_grad[2] and not has_arg(self.name):
-            index, = self.saved_variables
-            grad_input = data[0].gather(self.dim, index.data)
+        # Call grad computation of `input` for the specific scatter operation.
+        if self.needs_input_grad[2]:
+            grad_input = self.backward_step(data[0], *self.saved_variables)
 
-        if self.needs_input_grad[2] and has_arg(self.name):
-            index, arg = self.saved_variables
-            data = (index.data, data[0], arg.data)
-            grad_input = index_backward(self.dim, *data)
-
-        # Return and fill with empty grads for non-differentiable passed
-        # arguments in forward pass.
+        # Return and fill with empty grads for non-differentiable arguments.
         return (grad_output, None, grad_input) + (None, ) * (self.len - 3)
 
+    def backward_step(self, *data):
+        raise NotImplementedError
+
 
-def scatter(name, dim, *data):
+def scatter(Clx, name, dim, *data):
     if torch.is_tensor(data[0]):
-        return _scatter(name, dim, *data)
+        return ffi_scatter(name, dim, *data)
     else:
-        return _Scatter(name, dim)(*data)
+        return Clx(dim)(*data)
+
+
+# def index_backward(dim, index, grad, arg):  # pragma: no cover
+#     typename = type(grad).__name__.replace('Tensor', '')
+#     cuda = 'cuda_' if grad.is_cuda else ''
+#     func = getattr(ffi, 'index_backward_{}{}'.format(cuda, typename))
+#     output = grad.new(index.size()).fill_(0)
+#     func(dim, output, index, grad, arg)
+#     return output
+
+# def _scatter_backward(name, dim, saved, *data):
+#     # saved = (index, ), (index, arg) or (index, count)
+
+#     print(name)
+#     print(len(data))
+#     print(len(saved))
+#     print(saved[1].size())
+#     # data = (grad, )
+#     # index, = seved
+#     if has_arg(name):
+#         return index_backward(dim, saved[0].data, data[0], saved[1].data)
+
+#     if has_count(name):
+#         return (data[0] / saved[1]).gather(dim, saved[0].data)
+#     # Different grad computation of `input` if `scatter_max` or
+#     # `scatter_min` was used.
+#     # if self.needs_input_grad[2] and not has_arg(self.name):
+#     #     index, = self.saved_variables
+#     #     grad_input = data[0].gather(self.dim, index.data)
+
+#     # if self.needs_input_grad[2] and has_arg(self.name):
+#     #     index, arg = self.saved_variables
+#     #     data = (index.data, data[0], arg.data)
+#     grad_input = index_backward(self.dim, *data)