I HATE U hg rebase Y U GO DELETE EVERYTHING

theano/sandbox/multinomial.py

@@ -10,6 +10,289 @@ if cuda_available:
     from theano.sandbox.cuda.basic_ops import host_from_gpu, gpu_from_host
 
 class Multinomial(Op):
+    def __init__(self, odtype):
+        self.odtype=odtype
+    def __eq__(self, other):
+        return type(self) == type(other) and self.odtype==other.odtype
+    def __hash__(self):
+        return hash((type(self), self.odtype))
+    def __str__(self):
+        return '%s{%s}'%(self.__class__.__name__, self.odtype)
+    def __setstate__(self, dct):
+        self.__dict__.update(dct)
+        try:
+            self.odtype
+        except:
+            self.odtype='auto'
+    def make_node(self, pvals, unis):
+        pvals = T.as_tensor_variable(pvals)
+        unis = T.as_tensor_variable(unis)
+        if pvals.ndim != 2:
+            raise NotImplementedError('pvals ndim', pvals.ndim)
+        if unis.ndim != 1:
+            raise NotImplementedError('unis ndim', unis.ndim)
+        if self.odtype=='auto':
+            odtype = pvals.dtype
+        else:
+            odtype = self.odtype
+        return Apply(self, [pvals, unis], [T.matrix(dtype=odtype)])
+
+    def grad(self, ins, outs):
+        pvals, unis = ins
+        (gz,) = outs
+        return [None, None]
+
+    def c_code_cache_version(self):
+        return (5,)
+
+    def c_code(self, node, name, ins, outs, sub):
+        (pvals, unis) = ins
+        (z,) = outs
+
+        fail = sub['fail']
+        return """
+        if (%(pvals)s->nd != 2)
+        {
+            PyErr_Format(PyExc_TypeError, "pvals wrong rank");
+            %(fail)s;
+        }
+        if (%(unis)s->nd != 1)
+        {
+            PyErr_Format(PyExc_TypeError, "unis wrong rank");
+            %(fail)s;
+        }
+
+        if (%(unis)s->dimensions[0] != %(pvals)s->dimensions[0])
+        {
+            PyErr_Format(PyExc_ValueError, "unis.shape[0] != pvals.shape[0]");
+            %(fail)s;
+        }
+
+        if ((NULL == %(z)s)
+            || ((%(z)s->dimensions)[0] != (%(pvals)s->dimensions)[0])
+            || ((%(z)s->dimensions)[1] != (%(pvals)s->dimensions)[1])
+        )
+        {
+            Py_XDECREF(%(z)s);
+            %(z)s = (PyArrayObject*) PyArray_ZEROS(2,
+                %(pvals)s->dimensions,
+                type_num_%(z)s,
+                0);
+            if (!%(z)s)
+            {
+                PyErr_SetString(PyExc_MemoryError, "failed to alloc z output");
+                %(fail)s;
+            }
+        }
+
+        { // NESTED SCOPE
+
+        const int nb_multi = %(pvals)s->dimensions[0];
+        const int nb_outcomes = %(pvals)s->dimensions[1];
+
+        //
+        // For each multinomials, loop over each possible outcome
+        //
+        for (int n = 0; n < nb_multi; ++n)
+        {
+            int waiting = 1;
+            dtype_%(pvals)s cummul = 0.;
+            const dtype_%(unis)s* unis_n = (dtype_%(unis)s*)PyArray_GETPTR1(%(unis)s, n);
+            for (int m = 0; m < nb_outcomes; ++m)
+            {
+                dtype_%(z)s* z_nm = (dtype_%(z)s*)PyArray_GETPTR2(%(z)s, n,m);
+                const dtype_%(pvals)s* pvals_nm = (dtype_%(pvals)s*)PyArray_GETPTR2(%(pvals)s, n,m);
+                cummul += *pvals_nm;
+                if (waiting && (cummul > *unis_n))
+                {
+                    *z_nm = 1.;
+                    waiting = 0;
+                }
+                else
+                {
+                    // if we re-used old z pointer, we have to clear it out.
+                    *z_nm = 0.;
+                }
+            }
+        }
+        } // END NESTED SCOPE
+        """ % locals()
+#multinomial = Multinomial()
+
+
+class GpuMultinomial(Multinomial):
+
+    def make_node(self, pvals, unis):
+        assert pvals.dtype == 'float32'
+        assert unis.dtype == 'float32'
+        if not isinstance(pvals.type, CudaNdarrayType):
+            raise TypeError('pvals must be cudandarray', pvals)
+        if not isinstance(unis.type, CudaNdarrayType):
+            raise TypeError('unis must be cudandarray', unis)
+        if self.odtype=='auto':
+            odtype = pvals.dtype
+        else:
+            odtype = self.odtype
+        if odtype != pvals.dtype:
+            raise NotImplementedError()
+        return Apply(self, [pvals, unis], [pvals.type()])
+
+    def c_code_cache_version(self):
+        return (6,)
+
+    def c_support_code_apply(self, node, nodename):
+        return """
+        static __global__ void k_multi_warp_%(nodename)s(
+            const int nb_multi,
+            const int nb_outcomes,
+            const int pvals_row_strides,
+            const int pvals_col_strides,
+            const int unis_stride,
+            float * global_pvals,
+            float * global_unis,
+            float * global_outs
+        )
+        {
+            // each thread takes care of one multinomial draw
+            int n = blockDim.x*blockIdx.x + threadIdx.x;
+            if (n < nb_multi)
+            {
+            float cummul = 0.;
+            bool done = false;
+            const float unis_n = global_unis[n*unis_stride];
+            for (int m = 0; m < nb_outcomes; ++m)
+            {
+                float current_out = 0.;
+                if (!done)
+                {
+                    cummul += global_pvals[m * pvals_col_strides + n * pvals_row_strides];
+                    if (unis_n < cummul)
+                    {
+                        current_out = 1.;
+                        done = true;
+                    }
+                }
+                //write out transposed for speed.
+                global_outs[n + m * nb_multi] = current_out;
+            }
+            }
+        }
+
+        """ % locals()
+
+
+    def c_code(self, node, name, ins, outs, sub):
+        (pvals, unis) = ins
+        (z,) = outs
+
+        fail = sub['fail']
+        return """
+        if (%(pvals)s->nd != 2)
+        {
+            PyErr_Format(PyExc_TypeError, "pvals wrong rank");
+            %(fail)s;
+        }
+        if (%(unis)s->nd != 1)
+        {
+            PyErr_Format(PyExc_TypeError, "unis wrong rank");
+            %(fail)s;
+        }
+        if (CudaNdarray_HOST_DIMS(%(unis)s)[0] != CudaNdarray_HOST_DIMS(%(pvals)s)[0])
+        {
+            PyErr_Format(PyExc_ValueError, "unis.shape[0] != pvals.shape[0]");
+            %(fail)s;
+        }
+
+        //N.B. that the output is TRANSPOSED compared with pvals
+        if ((NULL == %(z)s)
+            || (CudaNdarray_HOST_DIMS(%(z)s)[0] != CudaNdarray_HOST_DIMS(%(pvals)s)[1])
+            || (CudaNdarray_HOST_DIMS(%(z)s)[1] != CudaNdarray_HOST_DIMS(%(pvals)s)[0]))
+        {
+            Py_XDECREF(%(z)s);
+            npy_intp dims[2];
+            dims[0] = (CudaNdarray_HOST_DIMS(%(pvals)s)[1]);
+            dims[1] = (CudaNdarray_HOST_DIMS(%(pvals)s)[0]);
+            %(z)s = (CudaNdarray*)CudaNdarray_NewDims(2, dims);
+            if (!%(z)s)
+            {
+                PyErr_SetString(PyExc_MemoryError, "failed to alloc z output");
+                %(fail)s;
+            }
+        }
+
+        { // NESTED SCOPE
+            int nb_multi = CudaNdarray_HOST_DIMS(%(pvals)s)[0];
+            int nb_outcomes = CudaNdarray_HOST_DIMS(%(pvals)s)[1];
+            //TODO : change this for a beautiful constant
+            int max_nb_blocks = 2<<15 - 1;
+            int nb_blocks = max_nb_blocks + 1;
+            int nb_threads=16; // so it really starts at 32, because of the *2
+            do
+            {
+                nb_threads*=2;
+                if (nb_multi %% nb_threads == 0)
+                    nb_blocks = nb_multi/nb_threads;
+                else
+                    nb_blocks = (int)((float)nb_multi/(float)nb_threads + 1.);
+            } while (nb_blocks > max_nb_blocks);
+
+            //printf("\\nN=%%i b=%%i t=%%i t*b=%%i", nb_multi, nb_blocks, nb_threads, nb_blocks*nb_threads);
+
+            // TODO : next line is a bit hardcoded...
+            if (nb_threads > 512)
+            {
+                PyErr_Format(PyExc_ValueError, "Mutinomial is not implemented for so many rows in the matrix (%%i)", nb_multi);
+                %(fail)s;
+            }
+            dim3 n_blocks(nb_blocks,1,1);
+            dim3 n_threads(nb_threads,1,1);
+            int n_shared = 0;
+
+            assert(nb_blocks*nb_threads >= nb_multi);
+
+            k_multi_warp_%(name)s<<<n_blocks, n_threads, n_shared>>>(
+                CudaNdarray_HOST_DIMS(%(z)s)[1],
+                CudaNdarray_HOST_DIMS(%(z)s)[0],
+                CudaNdarray_HOST_STRIDES(%(pvals)s)[0],
+                CudaNdarray_HOST_STRIDES(%(pvals)s)[1],
+                CudaNdarray_HOST_STRIDES(%(unis)s)[0],
+                CudaNdarray_DEV_DATA(%(pvals)s),
+                CudaNdarray_DEV_DATA(%(unis)s),
+                CudaNdarray_DEV_DATA(%(z)s)
+            );
+            CNDA_THREAD_SYNC;
+            cudaError_t sts = cudaGetLastError();
+            if (cudaSuccess != sts)
+            {
+                PyErr_Format(PyExc_RuntimeError, "Cuda error: %%s: %%s. (grid: %%i x %%i; block: %%i x %%i x %%i; shared: %%i)\\n",
+                    "k_multi_warp_%(name)s",
+                    cudaGetErrorString(sts),
+                    n_blocks.x,
+                    n_blocks.y,
+                    n_threads.x,
+                    n_threads.y,
+                    n_threads.z,
+                    n_shared);
+                %(fail)s;
+            }
+
+        } // END NESTED SCOPE
+        """ % locals()
+
+@local_optimizer()
+def use_gpu_multinomial(node):
+    if node.op == multinomial:
+        p, u = node.inputs
+        m, = node.outputs
+        if p.dtype == u.dtype == m.dtype == 'float32':
+            gpu_op = GpuMultinomial(op.odtype)
+            return [host_from_gpu(gpu_op(*[gpu_from_host(i) for i in node.inputs]))]
+if cuda_enabled:#theano.config.device.startswith('gpu'):
+    register_specialize(use_gpu_multinomial)
+
+
+if 0: # I hate you hg rebase, I hate you so very, very much.
+    class Multinomial(Op):
         def __eq__(self, other):
             return type(self) == type(other)
         def __hash__(self):
@@ -103,10 +386,10 @@ class Multinomial(Op):
 
             } // END NESTED SCOPE
             """ % locals()
-multinomial = Multinomial()
+    multinomial = Multinomial()
 
 
-class GpuMultinomial(Multinomial):
+    class GpuMultinomial(Multinomial):
 
         def make_node(self, pvals, unis):
             assert pvals.dtype == 'float32'
@@ -262,12 +545,4 @@ class GpuMultinomial(Multinomial):
 
             } // END NESTED SCOPE
             """ % locals()
-gpu_multinomial = GpuMultinomial()
-
-@local_optimizer()
-def use_gpu_multinomial(node):
-    if node.op == multinomial:
-        return [host_from_gpu(gpu_multinomial(*[gpu_from_host(i) for i in node.inputs]))]
-if cuda_enabled:#theano.config.device.startswith('gpu'):
-    register_specialize(use_gpu_multinomial)
-
+    gpu_multinomial = GpuMultinomial()