Merge pull request #5323 from aam-at/max_pool_rop

theano/gpuarray/opt.py

@@ -51,7 +51,7 @@ from .blas import (gpu_dot22, GpuGemm, GpuGer, GpuGemmBatch,
                    gpugemv_no_inplace, gpugemv_inplace,
                    GpuCorrMM, GpuCorrMM_gradInputs, GpuCorrMM_gradWeights,
                    GpuCorr3dMM, GpuCorr3dMM_gradInputs, GpuCorr3dMM_gradWeights)
-from .pool import (GpuPool, GpuMaxPoolGrad, GpuAveragePoolGrad,
+from .pool import (GpuPool, GpuMaxPoolGrad, GpuAveragePoolGrad, GpuMaxPoolRop,
                    GpuDownsampleFactorMaxGradGrad)
 from .blocksparse import (GpuSparseBlockGemv, GpuSparseBlockOuter,
                           gpu_sparse_block_outer,
@@ -1747,6 +1747,29 @@ def local_gpu_downsample_factor_max_grad_grad(op, ctx_name, inputs, outputs):
         return unpad_dims(ret_padded, inp, 2, nd)
 
 
+@register_opt()
+@op_lifter([pool.MaxPoolRop])
+@register_opt2([pool.MaxPoolRop])
+def local_gpu_max_pool_rop(op, ctx_name, inputs, outputs):
+    assert op.__props__ == ('ignore_border', 'mode', 'ndim')
+    inp, eval_inp, ws, stride, pad = inputs
+    nd = op.ndim
+    if nd not in (2, 3):
+        return
+    inp = gpu_contiguous(as_gpuarray_variable(inp, ctx_name))
+    eval_inp = gpu_contiguous(as_gpuarray_variable(eval_inp, ctx_name))
+
+    op = GpuMaxPoolRop(op.ignore_border, op.mode, op.ndim)
+    if inp.ndim == nd + 2:
+        return op(inp, eval_inp, ws, stride, pad)
+    else:
+        # reshape to 4D or 5D with 2 non-pooling dimensions
+        inp_padded = pad_dims(inp, 2, nd)
+        eval_inp_padded = pad_dims(eval_inp, 2, nd)
+        ret_padded = op(inp_padded, eval_inp_padded, ws, stride, pad)
+        return unpad_dims(ret_padded, inp, 2, nd)
+
+
 @register_opt("low_memory")
 @local_optimizer([GpuCAReduceCuda])
 def local_gpu_elemwise_careduce(node):

theano/gpuarray/pool.py

@@ -112,6 +112,26 @@ class GpuPool(CGpuKernelBase):
     def connection_pattern(self, node):
         return [[1], [0], [0], [0]]
 
+    def R_op(self, inputs, eval_points):
+        if self.mode != 'max':
+            # Rop for average or sum is simply pooling evaluated at eval point
+            eval_inputs = [eval_points[0]] + inputs[1:]
+            return [self(*eval_inputs)]
+
+        # R_op can receive None as eval_points.
+        # That mean there is no diferientiable path through that input
+        # If this imply that you cannot compute some outputs,
+        # return None for those.
+        if eval_points[0] is None:
+            return [None]
+        z = self(*inputs)
+        x, ws, stride, pad = inputs
+        return [
+            GpuDownsampleFactorMaxGradGrad(self.ignore_border, self.mode,
+                                           self.ndim)(x, z, eval_points[0], ws,
+                                                      stride, pad)
+        ]
+
 
 class GpuMaxPoolGrad(CGpuKernelBase):
     """
@@ -334,3 +354,72 @@ class GpuDownsampleFactorMaxGradGrad(CGpuKernelBase):
 
     def connection_pattern(self, node):
         return [[1], [1], [1], [0], [0], [0]]
+
+
+class GpuMaxPoolRop(CGpuKernelBase):
+    """
+    Implements the R-operator for the downsample operation.
+
+    """
+    __props__ = ('ignore_border', 'mode', 'ndim')
+
+    def __init__(self, ignore_border, mode='max', ndim=2):
+        self.ndim = ndim
+        self.ignore_border = ignore_border
+        self.mode = mode
+        CGpuKernelBase.__init__(self, ['pool_max_rop.c'],
+                                'APPLY_SPECIFIC(max_pool_rop)')
+        assert mode == 'max'
+        assert ndim in [2, 3]
+
+    def c_headers(self):
+        return ['gpuarray_api.h', 'gpuarray_helper.h', 'numpy_compat.h']
+
+    def c_header_dirs(self):
+        return [os.path.dirname(__file__), pygpu.get_include()]
+
+    def make_node(self, inp, eval_point, ws, stride=None, pad=None):
+        ctx_name = infer_context_name(inp)
+        nd = self.ndim
+        inp = as_gpuarray_variable(inp, ctx_name)
+        assert (inp.ndim == nd + 2)
+        eval_point = as_gpuarray_variable(eval_point, ctx_name)
+        assert (eval_point.ndim == nd + 2)
+
+        if stride is None:
+            stride = ws
+        if pad is None:
+            pad = (0,) * nd
+        elif isinstance(pad, (tuple, list)):
+            if max(pad) != 0 and not self.ignore_border:
+                raise ValueError('Padding works only with ignore_border=True')
+            if isinstance(ws, (tuple, list)):
+                if any(pad[i] >= ws[i] for i in range(nd)):
+                    raise ValueError('Padding must be smaller than strides')
+
+        ws = as_tensor_variable(ws)
+        stride = as_tensor_variable(stride)
+        pad = as_tensor_variable(pad)
+        assert ws.ndim == stride.ndim and ws.ndim == pad.ndim
+        assert ws.ndim == 1
+        if not ws.dtype.startswith('int'):
+            raise TypeError('Window shape parameters must be ints.')
+        if not stride.dtype.startswith('int'):
+            raise TypeError('Stride parameters must be ints.')
+        if not pad.dtype.startswith('int'):
+            raise TypeError('Padding parameters must be ints.')
+
+        return Apply(self, [inp, eval_point, ws, stride, pad], [eval_point.type()])
+
+    def get_params(self, node):
+        return node.inputs[0].type.context
+
+    def get_op_params(self):
+        ignore_border = int(self.ignore_border)
+        return [('IGNORE_BORDER', ignore_border)]
+
+    def infer_shape(self, node, in_shapes):
+        ws, stride, pad = [node.inputs[2], node.inputs[3], node.inputs[4]]
+        shp = Pool.out_shape(in_shapes[0], ws, self.ignore_border, stride,
+                             pad, self.ndim)
+        return [shp]

theano/gpuarray/pool_max_rop.c

+#section kernels
+
+#kernel max_pool2d_rop_kernel : size, size, size, size, size, size, size, *, *, size, size, size, size, size, size, * :
+
+// (borrowed from Caffe: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
+KERNEL void max_pool2d_rop_kernel(const ga_size nthreads,
+   const ga_size num, const ga_size channels, const ga_size pooled_height,
+   const ga_size pooled_width, const ga_size height, const ga_size width,
+   GLOBAL_MEM const DTYPE_i0 *x, GLOBAL_MEM const DTYPE_i1 *ex,
+   const ga_size kernel_h, const ga_size kernel_w,
+   const ga_size stride_h, const ga_size stride_w,
+   const ga_size pad_h, const ga_size pad_w,
+   GLOBAL_MEM DTYPE_o0 *z)
+{
+  // grid stride looping
+  for (ga_size index = GID_0 * LDIM_0 + LID_0;
+       index < nthreads;
+       index += LDIM_0 * GDIM_0) {
+    const ga_size pw = index % pooled_width;
+    const ga_size ph = (index / pooled_width) % pooled_height;
+    const ga_size c = (index / pooled_width / pooled_height) % channels;
+    const ga_size n = (index / pooled_width / pooled_height / channels);
+    ga_int hstart = static_cast<ga_int>(ph*stride_h) - static_cast<ga_int>(pad_h);
+    const ga_size hend = min(hstart + kernel_h, height);
+    ga_int wstart = static_cast<ga_int>(pw*stride_w) - static_cast<ga_int>(pad_w);
+    const ga_size wend = min(wstart + kernel_w, width);
+    hstart = max(hstart, 0);
+    wstart = max(wstart, 0);
+
+    const ga_size offset = (n*channels + c) * height * width;
+    const DTYPE_i0* x_slice = x + offset;
+    const DTYPE_i1* ex_slice = ex + offset;
+    DTYPE_o0 maxval = x_slice[hstart*width + wstart];
+    DTYPE_o0 collector = ex_slice[hstart*width + wstart];
+
+    for (ga_size h=hstart; h < hend; ++h) {
+      for (ga_size w=wstart; w < wend; ++w) {
+        // maximum in the region
+        if (x_slice[h*width + w] > maxval) {
+          maxval = x_slice[h*width + w];
+          collector = ex_slice[h*width + w];
+        }
+      }
+    }
+    z[index] = collector;
+  }
+}
+
+#kernel max_pool3d_rop_kernel : size, size, size, size, size, size, size, size, size, *, *, size, size, size, size, size, size, size, size, size, * :
+
+// (adopted from Caffe: https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
+KERNEL void max_pool3d_rop_kernel(const ga_size nthreads,
+   const ga_size num, const ga_size channels, const ga_size pooled_depth,
+   const ga_size pooled_height, const ga_size pooled_width,
+   const ga_size depth, const ga_size height, const ga_size width,
+   GLOBAL_MEM const DTYPE_i0 *x, GLOBAL_MEM const DTYPE_i1 *ex,
+   const ga_size kernel_d, const ga_size kernel_h, const ga_size kernel_w,
+   const ga_size stride_d, const ga_size stride_h, const ga_size stride_w,
+   const ga_size pad_d, const ga_size pad_h, const ga_size pad_w,
+   GLOBAL_MEM DTYPE_o0 *z)
+{
+  // grid stride looping
+  for (ga_size index = GID_0 * LDIM_0 + LID_0;
+       index < nthreads;
+       index += LDIM_0 * GDIM_0) {
+    const ga_size pw = index % pooled_width;
+    const ga_size ph = (index / pooled_width) % pooled_height;
+    const ga_size pd = (index / pooled_width / pooled_height) % pooled_depth;
+    const ga_size c = (index / pooled_width / pooled_height / pooled_depth) % channels;
+    const ga_size n = (index / pooled_width / pooled_height / pooled_depth / channels);
+    ga_int dstart = static_cast<ga_int>(pd*stride_d) - static_cast<ga_int>(pad_d);
+    const ga_size dend = min(dstart + kernel_d, depth);
+    ga_int hstart = static_cast<ga_int>(ph*stride_h) - static_cast<ga_int>(pad_h);
+    const ga_size hend = min(hstart + kernel_h, height);
+    ga_int wstart = static_cast<ga_int>(pw*stride_w) - static_cast<ga_int>(pad_w);
+    const ga_size wend = min(wstart + kernel_w, width);
+    dstart = max(dstart, 0);
+    hstart = max(hstart, 0);
+    wstart = max(wstart, 0);
+
+    const ga_size offset = (n*channels + c) * depth * height * width;
+    const DTYPE_i0* x_slice = x + offset;
+    const DTYPE_i1* ex_slice = ex + offset;
+    DTYPE_o0 maxval = x_slice[(dstart*height + hstart)*width + wstart];
+    DTYPE_o0 collector = ex_slice[(dstart*height + hstart)*width + wstart];
+
+    for (ga_size d=dstart; d < dend; ++d) {
+      for (ga_size h=hstart; h < hend; ++h) {
+        for (ga_size w=wstart; w < wend; ++w) {
+          // maximum in the region
+          if (x_slice[(d*height + h)*width + w] > maxval) {
+            maxval = x_slice[(d*height + h)*width + w];
+            collector = ex_slice[(d*height + h)*width + w];
+          }
+        }
+      }
+    }
+    z[index] = collector;
+  }
+}
+
+
+#section support_code
+
+// output shape for a given input padded shape, window shape and stride
+#define OUTPUT_DIMS(in_dim, ws, st)                       \
+  (IGNORE_BORDER ? (in_dim - ws)/st + 1 :                 \
+   (st > ws ? (in_dim - 1)/st + 1 :                       \
+    std::max<size_t>(0, (in_dim - 1 - ws + st)/st) + 1))
+
+#section support_code_struct
+
+int APPLY_SPECIFIC(max_pool_rop)(PyGpuArrayObject *x,
+                                 PyGpuArrayObject *ex,
+                                 PyArrayObject *ws,
+                                 PyArrayObject *stride,
+                                 PyArrayObject *pad,
+                                 PyGpuArrayObject **z,
+                                 PyGpuContextObject *ctx) {
+  if (!GpuArray_IS_C_CONTIGUOUS(&x->ga) || !GpuArray_IS_C_CONTIGUOUS(&ex->ga))
+    {
+      PyErr_Format(PyExc_ValueError,
+                   "GpuMaxPoolRop: requires data to be C-contiguous");
+      return 1;
+    }
+  size_t ndims = PyArray_DIM(ws, 0);
+  if (PyGpuArray_NDIM(x) != ndims + 2 || PyGpuArray_NDIM(ex) != ndims + 2)
+    {
+      PyErr_SetString(PyExc_ValueError, "GpuMaxPoolRop: rank error");
+      return 1;
+    }
+  // prepare output
+  const size_t* x_dims = PyGpuArray_DIMS(x);
+  size_t z_dims[5]; // avoid warning if use 2 + nd
+  size_t w[3];
+  size_t s[3];
+  size_t p[3]; z_dims[0] = x_dims[0]; z_dims[1] = x_dims[1];
+  int nonzero_padding = 0;
+  for (int i = 0; i < ndims; i++) {
+    w[i] = *((npy_intp*)PyArray_GETPTR1(ws, i));
+    s[i] = *((npy_intp*)PyArray_GETPTR1(stride, i));
+    p[i] = *((npy_intp*)PyArray_GETPTR1(pad, i));
+    z_dims[2 + i] = OUTPUT_DIMS(x_dims[2 + i] + 2*p[i], w[i], s[i]);
+    if (p[i] > 0) {
+      nonzero_padding = 1;
+    }
+  }
+  if (!IGNORE_BORDER && nonzero_padding) {
+    PyErr_SetString(PyExc_ValueError,
+                    "GpuMaxPoolRop: padding works only with ignore_border=True");
+    return 1;
+  }
+
+  if (theano_prep_output(z, PyGpuArray_NDIM(ex), z_dims,
+                         ex->ga.typecode, GA_C_ORDER, ctx) != 0)
+    {
+      PyErr_SetString(PyExc_RuntimeError,
+                      "GpuMaxPoolRop: failed to allocate memory");
+      return 1;
+    }
+  {
+    // scope for running kernel
+    int err;
+
+    if (ndims == 2) {
+      size_t num_kernels = z_dims[0] * z_dims[1] * z_dims[2] * z_dims[3];
+      err = max_pool2d_rop_kernel_scall(1, &num_kernels, 0, num_kernels,
+                                        z_dims[0], z_dims[1], z_dims[2], z_dims[3],
+                                        x_dims[2], x_dims[3],
+                                        x->ga.data, ex->ga.data,
+                                        w[0], w[1], s[0], s[1], p[0], p[1],
+                                        (*z)->ga.data);
+      if (err != GA_NO_ERROR) {
+        PyErr_Format(PyExc_RuntimeError,
+                     "GpuMaxPoolRop: max_pool2d_rop_kernel %s.",
+                     GpuKernel_error(&k_max_pool2d_rop_kernel, err));
+        return 1;
+      }
+    }
+    else if (ndims == 3) {
+      size_t num_kernels = z_dims[0] * z_dims[1] * z_dims[2] * z_dims[3] * z_dims[4];
+      err = max_pool3d_rop_kernel_scall(1, &num_kernels, 0, num_kernels,
+                                        z_dims[0], z_dims[1], z_dims[2], z_dims[3], z_dims[4],
+                                        x_dims[2], x_dims[3], x_dims[4],
+                                        x->ga.data, ex->ga.data,
+                                        w[0], w[1], w[2], s[0], s[1], s[2],
+                                        p[0], p[1], p[2], (*z)->ga.data);
+      if (err != GA_NO_ERROR) {
+        PyErr_Format(PyExc_RuntimeError,
+                     "GpuMaxPoolRop: max_pool3d_rop_kernel %s.",
+                     GpuKernel_error(&k_max_pool2d_rop_kernel, err));
+        return 1;
+      }
+    }
+  }
+  return 0;
+}

theano/gpuarray/tests/test_pool.py

@@ -133,11 +133,26 @@ def test_pool2d():
 
                 assert numpy.allclose(g(), g2()), (shp, ws, st, pad, mode, ignore_border)
 
-                # test grad grad for max pooling
+                # test rop and grad grad for max pooling
                 # for average pooling grad grad is just average pooling grad
                 if mode != 'max':
                     continue
 
+                ea = theano.shared(rand(*shp), 'ea')
+
+                gr = theano.function([], tensor.Rop(a_pooled, a, ea), mode=gpu_mode)
+                gr2 = theano.function([], tensor.Rop(a_pooled, a, ea), mode=ref_mode)
+
+                assert any([
+                    isinstance(node.op, GpuDownsampleFactorMaxGradGrad)
+                    for node in gr.maker.fgraph.toposort()
+                ])
+                assert any([
+                    isinstance(node.op, DownsampleFactorMaxGradGrad)
+                    for node in gr2.maker.fgraph.toposort()
+                ])
+                assert numpy.allclose(gr(), gr2()), (shp, ws, st, pad, mode, ignore_border)
+
                 ggf = gradient.Lop(tensor.grad((a_pooled**2).sum(), a), a, a)
 
                 gg = theano.function([], ggf, mode=gpu_mode)
@@ -228,11 +243,26 @@ def test_pool3d():
 
                 assert numpy.allclose(g(), g2()), (shp, ws, st, pad, mode, ignore_border)
 
-                # test grad grad for max pooling
+                # test rop and grad grad for max pooling
                 # for average pooling grad grad is just average pooling grad
                 if mode != 'max':
                     continue
 
+                ea = theano.shared(rand(*shp), 'ea')
+
+                gr = theano.function([], tensor.Rop(a_pooled, a, ea), mode=gpu_mode)
+                gr2 = theano.function([], tensor.Rop(a_pooled, a, ea), mode=ref_mode)
+
+                assert any([
+                    isinstance(node.op, GpuDownsampleFactorMaxGradGrad)
+                    for node in gr.maker.fgraph.toposort()
+                ])
+                assert any([
+                    isinstance(node.op, DownsampleFactorMaxGradGrad)
+                    for node in gr2.maker.fgraph.toposort()
+                ])
+                assert numpy.allclose(gr(), gr2()), (shp, ws, st, pad, mode, ignore_border)
+
                 ggf = gradient.Lop(tensor.grad((a_pooled**2).sum(), a), a, a)
 
                 gg = theano.function([], ggf, mode=gpu_mode)

theano/tests/test_rop.py

@@ -17,10 +17,12 @@ from theano.tests import unittest_tools as utt
 from theano import function
 import theano
 from theano import tensor
+import itertools
 import numpy
 from theano.gof import Op, Apply
 from theano.gradient import grad_undefined
 from theano.tests.unittest_tools import SkipTest
+from theano.tensor.signal.pool import Pool
 from theano.tensor.nnet import conv, conv2d
 
 '''
@@ -255,6 +257,47 @@ class test_RopLop(RopLop_checker):
             self.x[:4].dimshuffle('x', 0), 0).sum(axis=1),
             (1,))
 
+    def test_downsample(self):
+        rng = numpy.random.RandomState(utt.fetch_seed())
+        # ws, shp
+        examples = (
+            ((2,), (16,)),
+            ((2,), (4, 16,)),
+            ((2,), (4, 2, 16,)),
+            ((1, 1), (4, 2, 16, 16)),
+            ((2, 2), (4, 2, 16, 16)),
+            ((3, 3), (4, 2, 16, 16)),
+            ((3, 2), (4, 2, 16, 16)),
+            ((3, 2, 2), (3, 2, 16, 16, 16)),
+            ((2, 3, 2), (3, 2, 16, 16, 16)),
+            ((2, 2, 3), (3, 2, 16, 16, 16)),
+            ((2, 2, 3, 2), (3, 2, 6, 6, 6, 5)),
+        )
+
+        for example, ignore_border in itertools.product(examples, [True, False]):
+            (ws, shp) = example
+            vx = rng.rand(*shp)
+            vex = rng.rand(*shp)
+
+            x = theano.shared(vx)
+            ex = theano.shared(vex)
+
+            maxpool_op = Pool(ignore_border, ndim=len(ws))
+            a_pooled = maxpool_op(x, ws).flatten()
+            yv = tensor.Rop(a_pooled, x, ex)
+            mode = None
+            if theano.config.mode == "FAST_COMPILE":
+                mode = "FAST_RUN"
+            rop_f = function([], yv, on_unused_input='ignore', mode=mode)
+            sy, _ = theano.scan(lambda i, y, x, v:
+                                (tensor.grad(y[i], x) * v).sum(),
+                                sequences=tensor.arange(a_pooled.shape[0]),
+                                non_sequences=[a_pooled, x, ex])
+            scan_f = function([], sy, on_unused_input='ignore', mode=mode)
+            v1 = rop_f()
+            v2 = scan_f()
+            assert numpy.allclose(v1, v2), ("Rop mismatch: %s %s" % (v1, v2))
+
     def test_conv(self):
         for conv_op in [conv.conv2d, conv2d]:
             for border_mode in ['valid', 'full']: