Merge pull request #2103 from f0k/convop-grad-shapes

theano/sandbox/cuda/opt.py

@@ -1699,6 +1699,13 @@ def local_gpualloc_memset_0(node):
             inp.data.size == 1 and
             (numpy.asarray(inp.data) == 0).all()):
             new_out = GpuAlloc(memset_0=True)(*node.inputs)
+            old_bcast = node.outputs[0].type.broadcastable
+            if new_out.type.broadcastable != old_bcast:
+                # check that we did not try discarding a broadcastable dimension
+                assert not any(b_old and not b_new for b_old, b_new in zip(
+                        old_bcast, new_out.type.broadcastable))
+                # force old broadcasting pattern; we must not change it here
+                new_out = tensor.patternbroadcast(new_out, old_bcast)
             return [new_out]
 
 

theano/tensor/nnet/conv.py

@@ -242,34 +242,30 @@ class ConvOp(OpenMPOp):
     speed_unroll_patch_shape = [1.2967290878295898, 5.5283889770507812]
 
     @staticmethod
-    def has_all_shape(imshp, kshp, nkern, bsize):
-        all_shape = (imshp is not None and kshp is not None and
-                     nkern is not None and bsize is not None)
-        if (all_shape and
-            (any([True for sh in imshp if sh is None]) or
-             any([True for sh in kshp if sh is None]))):
-            all_shape = False
-        return all_shape
+    def has_all_shape(imshp, kshp, nkern=1, bsize=1):
+        return (nkern is not None and bsize is not None and
+                all(shp is not None for shp in imshp) and
+                all(shp is not None for shp in kshp))
 
     @staticmethod
     def getOutputShape(inshp, kshp, stride=(1, 1), mode='valid'):
         """
         Computes the output dimensions of convolving an image of shape "inshp"
-        with kernels of shape "kshp".
+        with kernels of shape "kshp". Accepts symbolic or integer shapes.
+        Propagates `None`s (for unknown shapes).
 
         :param inshp: (rows,cols) of input image
         :param kshp: (rows,cols) of filters
         :param mode: 'valid' or 'full' (see 'border_mode' in conv2d's doc)
         :return: (rows,cols) of output image
         """
-        dx, dy = stride
-        if mode == 'valid':
-            s = -1
-        else:
-            s = 1
-        inshp, kshp = numpy.array(inshp), numpy.array(kshp)
-        return  numpy.int64(numpy.ceil((inshp + s * kshp - s * 1) /
-                                       numpy.array([dx, dy], dtype='float')))
+        # The formula would be ceil((i + s * k - s * 1) / float(d)),
+        # with s=1 for mode=='full' and s=-1 for mode=='valid'.
+        # To support symbolic shapes, we express this with integer arithmetics.
+        return tuple(None if i is None or k is None
+                else ((i - k) // d + 1) if mode == 'valid'
+                else ((i + k + d - 2) // d)
+                for i, k, d in zip(inshp, kshp, stride))
 
     def __init__(self, imshp=None, kshp=None, nkern=None, bsize=None,
                  dx=1, dy=1,
@@ -287,7 +283,7 @@ class ConvOp(OpenMPOp):
         """
         Initializes a ConvOp with given output_mode (full/valid). All other
         parameters are optional and are only used to generate more optimized c
-        code.
+        code, or to enable graph optimizers to optimally replace the ConvOp.
 
         NOTES ON OPTIMIZATION:
         Their is two type of optimization. The first is the selection of the
@@ -368,13 +364,31 @@ class ConvOp(OpenMPOp):
             Set to False in the grad again the weight when the
             output_mode is full.
         """
-        # Desactivate fft_optimization at the op level if specified
+        # Deactivate fft_optimization at the op level if specified
         if version == "no_fft":
             self.fft_opt = False
             version = -1
         else:
             self.fft_opt = True
 
+        # Expand unknown image / kernel shapes into tuples of Nones
+        if imshp is None:
+            imshp = (None, None, None)
+        else:
+            imshp = tuple(imshp)
+        if kshp is None:
+            kshp = (None, None)
+        else:
+            kshp = tuple(kshp)
+
+        # Check imshp and kshp dimensionality
+        if len(imshp) == 2:
+            imshp = (1,) + imshp
+        elif len(imshp) != 3:
+            raise ValueError("len(imshp) must be 2 or 3, got %d" % len(imshp))
+        if len(kshp) != 2:
+            raise ValueError("len(kshp) must be 2, got %d" % len(kshp))
+
         # We must continue to consider None as 1 for backward compatibility.
         if dx is None:
             dx = 1
@@ -390,32 +404,17 @@ class ConvOp(OpenMPOp):
         dy = int(dy)
 
         all_shape = self.has_all_shape(imshp, kshp, nkern, bsize)
-
         if (unroll_batch or unroll_kern) and not all_shape:
             raise Exception("In ConvOp, when using unroll_batch and"
                             " unroll_nkern, all shape are needed")
 
         #Init the openmp attribute
         super(ConvOp, self).__init__(openmp=openmp)
-
         if not all_shape or self.openmp:
             # Only this version is parallelized
             unroll_patch = True
 
-        if imshp is not None:
-            imshp = tuple(imshp)
-
-            if len(imshp) == 2:
-                imshp = (1,) + imshp
-            elif len(imshp) == 3:
-                imshp = imshp
-            else:
-                raise Exception("bad len for imshp")
-
         self.imshp = imshp
-        if kshp is not None:
-            kshp = tuple(kshp)
-
         self.kshp = kshp
         self.nkern = nkern
         self.bsize = bsize
@@ -425,16 +424,24 @@ class ConvOp(OpenMPOp):
         self.version = version
 
         # a triple
+        if imshp_logical is None:
             self.imshp_logical = self.imshp
-        if imshp_logical is not None:
-            self.imshp_logical = tuple(imshp_logical)
-        assert ((self.imshp is None and self.imshp_logical is None) or
-                (len(self.imshp) == len(self.imshp_logical)))
+        else:
+            imshp_logical = tuple(imshp_logical)
+            if len(imshp_logical) != 3:
+                raise ValueError("len(imshp_logical) must be 3, got %d" % len(imshp_logical))
+            self.imshp_logical = imshp_logical
 
         # a pair
+        if kshp_logical is None:
             self.kshp_logical = self.kshp
-        if kshp_logical is not None:
-            self.kshp_logical = tuple(kshp_logical)
+        else:
+            kshp_logical = tuple(kshp_logical)
+            if len(kshp_logical) != 2:
+                raise ValueError("len(kshp_logical) must be 2, got %d" % len(kshp_logical))
+            self.kshp_logical = kshp_logical
+
+        # a bool
         self.kshp_logical_top_aligned = kshp_logical_top_aligned
 
         self.unroll_batch = unroll_batch
@@ -485,23 +492,19 @@ class ConvOp(OpenMPOp):
                 _logger.warn(warnstr, self.unroll_kern, self.nkern, new)
                 self.unroll_kern = new
 
-        if all_shape:
         self.outshp = ConvOp.getOutputShape(self.imshp_logical[1:],
                                             self.kshp_logical, (dx, dy),
                                             output_mode)
         self.fulloutshp = ConvOp.getOutputShape(self.imshp_logical[1:],
                                                 self.kshp_logical, (1, 1),
                                                 output_mode)
-        else:
-            self.outshp = None
-            self.fulloutshp = None
 
         self.out_mode = output_mode
 
         if not self.out_mode in ["valid", "full"]:
             raise Exception("Mode %s not implemented" % self.out_mode)
 
-        if self.outshp is not None and not (self.outshp > 0).all():
+        if any((shp is not None) and (shp <= 0) for shp in self.outshp):
             raise Exception("Bad size for the output shape. Verify that [post-"
                             "supersampling] input shape (%s) and kern"
                             " shape(%s) are ok. (Hint: kerns must fit inside"
@@ -518,14 +521,10 @@ class ConvOp(OpenMPOp):
             elif self.bsize is not None and self.nkern is not None:
                 bsize = self.bsize
                 nkern = self.nkern
-                if bsize is None:
-                    bsize = 1
-                if nkern is None:
-                    nkern = 1
                 mode_idx = 0
                 if self.out_mode != "valid":
                     mode_idx = 1
-                if all_shape:
+                if self.has_all_shape(self.imshp, self.kshp):
                     time_unroll_patch = self.speed_unroll_patch_shape[mode_idx]
                 else:
                     time_unroll_patch = self.speed_unroll_patch_noshape[
@@ -619,10 +618,7 @@ class ConvOp(OpenMPOp):
             raise NotImplementedError(
                 "The image and the kernel must have the same type."
                 "inputs(%s), kerns(%s)" % (_inputs.dtype, _kerns.dtype))
-        if self.outshp is not None:
         bcastable23 = [self.outshp[0] == 1, self.outshp[1] == 1]
-        else:
-            bcastable23 = [False, False]
         output = theano.tensor.tensor(dtype=_inputs.type.dtype,
                                       broadcastable=[_inputs.broadcastable[0],
                                                      _kerns.broadcastable[0]] +
@@ -631,32 +627,25 @@ class ConvOp(OpenMPOp):
         return Apply(self, [_inputs, _kerns], [output])
 
     def infer_shape(self, node, input_shapes):
-        imshp = input_shapes[0]
-        kshp = input_shapes[1]
-
-        batch_size = imshp[0]
-        fmo = kshp[0]
-
-        if self.imshp is not None and self.kshp is not None:
-            imshp = self.imshp
-            kshp = self.kshp
-            if self.imshp_logical:
-                imshp = self.imshp_logical
-            if self.kshp_logical:
-                kshp = self.kshp_logical
-            try:
-                fmshp = ConvOp.getOutputShape(imshp[1:],
-                                              kshp, (self.dx, self.dy),
+        imshp = input_shapes[0]  # 4D image shape
+        kshp = input_shapes[1]   # 4D filter shape
+        bsize, imshp = imshp[0], list(imshp[1:])
+        nkern, kshp = kshp[0], list(kshp[2:])
+        # replace symbolic shapes with known shapes
+        if self.bsize is not None:
+            bsize = self.bsize
+        for i in [0, 1, 2]:
+            if self.imshp_logical[i] is not None:
+                imshp[i] = self.imshp_logical[i]
+        if self.nkern is not None:
+            nkern = self.nkern
+        for i in [0, 1]:
+            if self.kshp_logical[i] is not None:
+                kshp[i] = self.kshp_logical[i]
+        # infer output shape from what we have
+        outshp = ConvOp.getOutputShape(imshp[1:], kshp, (self.dx, self.dy),
                                        self.out_mode)
-            except TypeError:
-                raise theano.tensor.ShapeError()
-            outshp = (batch_size, fmo) + tuple(fmshp)
-            return [outshp]
-        else:
-            # Haven't implemented this case. imshp and kshp may be symbollic
-            # and ConvOp.getOutputShape doesn't handle this. In this case
-            # we simply let the default function do its work.
-            raise theano.tensor.ShapeError()
+        return [(bsize, nkern) + outshp]
 
     def perform(self, node, inp, out):
         """
@@ -674,10 +663,10 @@ class ConvOp(OpenMPOp):
         # TODO: move these back out to global scope when they no longer
         #       cause an atexit error
         imshp = self.imshp
-        if imshp is None or any([x is None for x in imshp]):
+        if any(x is None for x in imshp):
             imshp = tuple(img2d.shape[1:])
         kshp = self.kshp
-        if kshp is None or any([x is None for x in kshp]):
+        if any(x is None for x in kshp):
             kshp = tuple(filtersflipped.shape[2:])
         bsize = self.bsize
         if bsize is None:
@@ -687,24 +676,22 @@ class ConvOp(OpenMPOp):
             nkern = filtersflipped.shape[0]
 
         imshp_logical = self.imshp_logical
-        if imshp_logical is None:
-            imshp_logical = imshp
-        if numpy.any([x is None for x in imshp_logical]):
-            imshp_logical = tuple(img2d.shape[1:])
+        if imshp_logical[0] is None:
+            imshp_logical = (imshp[0],) + imshp_logical[1:]
+        if imshp_logical[1] is None:
+            imshp_logical = (imshp_logical[0], imshp[1], imshp_logical[2])
+        if imshp_logical[2] is None:
+            imshp_logical = imshp_logical[:2] + (imshp[2],)
+        assert all(x is not None for x in imshp_logical)
 
         kshp_logical = self.kshp_logical
-        if kshp_logical is None:
-            kshp_logical = kshp
-        else:
         if kshp_logical[0] is None:
             kshp_logical = (kshp[0], kshp_logical[1])
         if kshp_logical[1] is None:
             kshp_logical = (kshp_logical[0], kshp[1])
+        assert all(x is not None for x in kshp_logical)
 
-        if numpy.any([x is None for x in kshp_logical]):
-            kshp = tuple(filtersflipped.shape[2:])
-
-        if self.fulloutshp is not None:
+        if all(shp is not None for shp in self.fulloutshp):
             fulloutshp = tuple(self.fulloutshp)
         else:
             fulloutshp = tuple(ConvOp.getOutputShape(imshp_logical[
@@ -843,17 +830,12 @@ class ConvOp(OpenMPOp):
         newin = inputs.dimshuffle((1, 0, 2, 3))
         newgz = gz.dimshuffle((1, 0, 2, 3))
 
-        (bsize, nkern) = None, None
-        imshp = None
-        kshp = None
         un_p = self.unroll_patch
-        imshp_logical = None
-
         if self.out_mode == 'valid':
             (img, filters) = (newin, newgz)
             kshp_logical = self.fulloutshp
             kshp_logical_top_aligned = False
-            if all_shape:
+            imshp_logical = None
             (bsize, nkern) = (self.imshp[0], self.nkern)
             imshp = (self.bsize, self.imshp[1], self.imshp[2])
             kshp = self.outshp
@@ -863,7 +845,6 @@ class ConvOp(OpenMPOp):
             (img, filters) = (newgz, newin)
             kshp_logical = None
             kshp_logical_top_aligned = True
-            if all_shape:
             imshp_logical = (self.bsize,
                              self.fulloutshp[0],
                              self.fulloutshp[1])
@@ -920,7 +901,7 @@ class ConvOp(OpenMPOp):
         dw = dw(img, filters)
 
         if all_shape:
-            assert (dw.owner.op.outshp == self.kshp).all()
+            assert all(o == k for o, k in zip(dw.owner.op.outshp, self.kshp))
         if self.out_mode == 'valid':
             # before DimShuffle, dw is of shape visdim x nkern x kshp[0] x kshp[1]
             dw = dw.dimshuffle((1, 0, 2, 3))
@@ -933,12 +914,7 @@ class ConvOp(OpenMPOp):
 
         filters = kerns.dimshuffle((1, 0, 2, 3))
         filters = filters[:, :, ::-1, ::-1]
-        nkern = None
-        imshp = None
-        imshp_logical = None
-        kshp = None
 
-        if all_shape:
         nkern = self.imshp[0]
         imshp = (self.nkern, self.outshp[0], self.outshp[1])
         imshp_logical = (self.nkern, self.fulloutshp[0],
@@ -965,9 +941,8 @@ class ConvOp(OpenMPOp):
 
         din = din(gz, filters)
 
-        assert (din.owner.op.outshp is None and self.imshp is None) or \
-               (din.owner.op.outshp is None) or \
-               (din.owner.op.outshp == self.imshp[1:]).all()
+        assert all(o is None or o == i
+                   for o, i in zip(din.owner.op.outshp, self.imshp[1:]))
 
         # din and dw should have the same broadcasting pattern as the
         # parameters they are the gradient of (resp. inputs and kerns).
@@ -1054,8 +1029,9 @@ using namespace std;
         d = locals()
         d.update(sub)
 
-        all_shape = self.has_all_shape(self.imshp, self.kshp,
-                                       self.nkern, self.bsize)
+        all_shape = (self.has_all_shape(self.imshp, self.kshp,
+                                       self.nkern, self.bsize) and
+                     self.has_all_shape(self.imshp_logical, self.kshp_logical))
 
         d["self_out_mode"] = self.out_mode
         d["self_dx"] = self.dx
@@ -1075,23 +1051,23 @@ using namespace std;
         d["self_kshp1"] = "PyArray_DIMS(%(filtersflipped)s)[3]" % d
 
         # Override the default value if we have it
-        if self.kshp is not None and self.kshp[0]:
+        if self.kshp[0] is not None:
             d["self_kshp0"] = self.kshp[0]
-        if self.kshp is not None and self.kshp[1]:
+        if self.kshp[1] is not None:
             d["self_kshp1"] = self.kshp[1]
-        if self.outshp is not None and self.outshp[0]:
+        if self.outshp[0] is not None:
             d["self_outshp0"] = self.outshp[0]
-        if self.outshp is not None and self.outshp[1]:
+        if self.outshp[1] is not None:
             d["self_outshp1"] = self.outshp[1]
-        if self.imshp is not None and self.imshp[0]:
+        if self.imshp[0] is not None:
             d["self_imshp0"] = self.imshp[0]
-        if self.imshp is not None and self.imshp[1]:
+        if self.imshp[1] is not None:
             d["self_imshp1"] = self.imshp[1]
-        if self.imshp is not None and self.imshp[2]:
+        if self.imshp[2] is not None:
             d["self_imshp2"] = self.imshp[2]
-        if self.bsize:
+        if self.bsize is not None:
             d["self_bsize"] = self.bsize
-        if self.nkern:
+        if self.nkern is not None:
             d["self_nkern"] = self.nkern
 
         # Other hard coded stuff only if we have all shapes