add recipe and fix bugs

15 files changed, 113 insertions, 156 deletions

diff --git a/nerv/Makefile b/nerv/Makefile
index 7921bd9..68465a1 100644
--- a/nerv/Makefile
+++ b/nerv/Makefile
@@ -44,7 +44,7 @@ LUA_LIBS := matrix/init.lua io/init.lua init.lua \
 			layer/elem_mul.lua layer/lstm.lua layer/lstm_gate.lua layer/dropout.lua layer/gru.lua \
 			layer/graph.lua layer/rnn.lua layer/duplicate.lua layer/identity.lua \
 			nn/init.lua nn/layer_repo.lua nn/param_repo.lua nn/network.lua \
-			io/sgd_buffer.lua
+			io/sgd_buffer.lua io/seq_buffer.lua
 
 INCLUDE := -I $(LUA_INCDIR) -DLUA_USE_APICHECK
 CUDA_INCLUDE := -I $(CUDA_BASE)/include/
diff --git a/lua/config.lua b/nerv/examples/network_debug/config.lua
index ff98ae0..e20d5a9 100644
--- a/lua/config.lua
+++ b/nerv/examples/network_debug/config.lua
@@ -29,7 +29,6 @@ function get_layers(global_conf)
         ['nerv.SelectLinearLayer'] = {
             ['select'] = {dim_in = {1}, dim_out = {global_conf.hidden_size}, vocab = global_conf.vocab_size, pr = pr},
         },
-        ['nerv.CombinerLayer'] = {},
         ['nerv.AffineLayer'] = {
             output = {dim_in = {global_conf.hidden_size}, dim_out = {global_conf.vocab_size}, pr = pr}
         },
@@ -38,9 +37,8 @@ function get_layers(global_conf)
         },
     }
     for i = 1, global_conf.layer_num do
-        layers['nerv.LSTMLayer']['lstm' .. i] = {dim_in = {global_conf.hidden_size, global_conf.hidden_size, global_conf.hidden_size}, dim_out = {global_conf.hidden_size, global_conf.hidden_size}, pr = pr}
+        layers['nerv.LSTMLayer']['lstm' .. i] = {dim_in = {global_conf.hidden_size}, dim_out = {global_conf.hidden_size}, pr = pr}
         layers['nerv.DropoutLayer']['dropout' .. i] = {dim_in = {global_conf.hidden_size}, dim_out = {global_conf.hidden_size}}
-        layers['nerv.CombinerLayer']['dup' .. i] = {dim_in = {global_conf.hidden_size}, dim_out = {global_conf.hidden_size, global_conf.hidden_size}, lambda = {1}}
     end
     return layers
 end
@@ -55,11 +53,8 @@ function get_connections(global_conf)
         {'softmax[1]', '<output>[1]', 0},
     }
     for i = 1, global_conf.layer_num do
-        table.insert(connections, {'lstm' .. i .. '[1]', 'dup' .. i .. '[1]', 0})
-        table.insert(connections, {'lstm' .. i .. '[2]', 'lstm' .. i .. '[3]', 1})
-        table.insert(connections, {'dup' .. i .. '[1]', 'lstm' .. i .. '[2]', 1})
-        table.insert(connections, {'dup' .. i .. '[2]', 'dropout' .. i .. '[1]', 0})
-        if i > 1 then
+        table.insert(connections, {'lstm' .. i .. '[1]', 'dropout' .. i .. '[1]', 0})
+        if i < 1 then
             table.insert(connections, {'dropout' .. (i - 1) .. '[1]', 'lstm' .. i .. '[1]', 0})
         end
     end
diff --git a/lua/main.lua b/nerv/examples/network_debug/main.lua
index 39818aa..790c404 100644
--- a/lua/main.lua
+++ b/nerv/examples/network_debug/main.lua
@@ -8,7 +8,7 @@ local timer = global_conf.timer
 
 timer:tic('IO')
 
-local data_path = 'nerv/nerv/examples/lmptb/PTBdata/'
+local data_path = 'examples/lmptb/PTBdata/'
 local train_reader = nerv.Reader(data_path .. 'vocab', data_path .. 'ptb.train.txt.adds')
 local val_reader = nerv.Reader(data_path .. 'vocab', data_path .. 'ptb.valid.txt.adds')
 
diff --git a/lua/network.lua b/nerv/examples/network_debug/network.lua
index d106ba1..5518e27 100644
--- a/lua/network.lua
+++ b/nerv/examples/network_debug/network.lua
@@ -71,6 +71,7 @@ function nn:process(data, do_train)
     local timer = self.gconf.timer
     local total_err = 0
     local total_frame = 0
+    self.network:epoch_init()
     for id = 1, #data do
         data[id].do_train = do_train
         timer:tic('network')
@@ -101,6 +102,9 @@ end
 
 function nn:epoch()
     local train_error = self:process(self.train_data, true)
+    local tmp = self.gconf.dropout_rate
+    self.gconf.dropout_rate = 0
     local val_error = self:process(self.val_data, false)
+    self.gconf.dropout_rate = tmp
     return train_error, val_error
 end
diff --git a/lua/reader.lua b/nerv/examples/network_debug/reader.lua
index d2624d3..d2624d3 100644
--- a/lua/reader.lua
+++ b/nerv/examples/network_debug/reader.lua
diff --git a/lua/select_linear.lua b/nerv/examples/network_debug/select_linear.lua
index a7e20cc..91beedf 100644
--- a/lua/select_linear.lua
+++ b/nerv/examples/network_debug/select_linear.lua
@@ -5,10 +5,7 @@ local SL = nerv.class('nerv.SelectLinearLayer', 'nerv.Layer')
 --layer_conf: table
 --Get Parameters
 function SL:__init(id, global_conf, layer_conf)
-    self.id = id
-    self.dim_in = layer_conf.dim_in
-    self.dim_out = layer_conf.dim_out
-    self.gconf = global_conf
+    nerv.Layer.__init(self, id, global_conf, layer_conf)
 
     self.vocab = layer_conf.vocab
     self.ltp = self:find_param("ltp", layer_conf, global_conf, nerv.LinearTransParam, {self.vocab, self.dim_out[1]}) --layer_conf.ltp
diff --git a/lua/timer.lua b/nerv/examples/network_debug/timer.lua
index 2c54ca8..2c54ca8 100644
--- a/lua/timer.lua
+++ b/nerv/examples/network_debug/timer.lua
diff --git a/lua/tnn.lua b/nerv/examples/network_debug/tnn.lua
index bf9f118..bf9f118 100644
--- a/lua/tnn.lua
+++ b/nerv/examples/network_debug/tnn.lua
diff --git a/nerv/io/init.lua b/nerv/io/init.lua
index eb2e3e5..c36d850 100644
--- a/nerv/io/init.lua
+++ b/nerv/io/init.lua
@@ -52,8 +52,9 @@ function DataBuffer:__init(global_conf, buffer_conf)
     nerv.error_method_not_implemented()
 end
 
-function DataBuffer:get_batch()
+function DataBuffer:get_data()
     nerv.error_method_not_implemented()
 end
 
 nerv.include('sgd_buffer.lua')
+nerv.include('seq_buffer.lua')
diff --git a/nerv/io/seq_buffer.lua b/nerv/io/seq_buffer.lua
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/nerv/io/seq_buffer.lua
diff --git a/nerv/layer/dropout.lua b/nerv/layer/dropout.lua
index 1a379c9..39a8963 100644
--- a/nerv/layer/dropout.lua
+++ b/nerv/layer/dropout.lua
@@ -2,8 +2,7 @@ local DropoutLayer = nerv.class("nerv.DropoutLayer", "nerv.Layer")
 
 function DropoutLayer:__init(id, global_conf, layer_conf)
     nerv.Layer.__init(self, id, global_conf, layer_conf)
-    self.rate = layer_conf.dropout_rate or global_conf.dropout_rate
-    if self.rate == nil then
+    if self.gconf.dropout_rate == nil then
         nerv.warning("[DropoutLayer:propagate] dropout rate is not set")
     end
     self:check_dim_len(1, 1) -- two inputs: nn output and label
@@ -41,12 +40,12 @@ function DropoutLayer:propagate(input, output, t)
     if t == nil then
         t = 1
     end
-    if self.rate then
+    if self.gconf.dropout_rate ~= 0 then
         self.mask[t]:rand_uniform()
         -- since we will lose a portion of the actvations, we multiply the
         -- activations by 1 / (1 - rate) to compensate
-        self.mask[t]:thres_mask(self.mask[t], self.rate,
-                                0, 1 / (1.0 - self.rate))
+        self.mask[t]:thres_mask(self.mask[t], self.gconf.dropout_rate,
+                                0, 1 / (1.0 - self.gconf.dropout_rate))
         output[1]:mul_elem(input[1], self.mask[t])
     else
         output[1]:copy_fromd(input[1])
@@ -61,7 +60,7 @@ function DropoutLayer:back_propagate(bp_err, next_bp_err, input, output, t)
     if t == nil then
         t = 1
     end
-    if self.rate then
+    if self.gconf.dropout_rate then
         next_bp_err[1]:mul_elem(bp_err[1], self.mask[t])
     else
         next_bp_err[1]:copy_fromd(bp_err[1])
diff --git a/nerv/layer/graph.lua b/nerv/layer/graph.lua
index 5f42fca..68d5f51 100644
--- a/nerv/layer/graph.lua
+++ b/nerv/layer/graph.lua
@@ -112,7 +112,7 @@ function GraphLayer:graph_init(layer_repo, connections)
             end
             for i = 1, #ref.dim_out do
                 if ref.outputs[i] == nil then
-                   nerv.error('dangling output port %d os layer %s', i, id)
+                   nerv.error('dangling output port %d of layer %s', i, id)
                 end
             end
         end
diff --git a/nerv/layer/lstm.lua b/nerv/layer/lstm.lua
index 641d5dc..5dbcc20 100644
--- a/nerv/layer/lstm.lua
+++ b/nerv/layer/lstm.lua
@@ -1,144 +1,85 @@
-local LSTMLayer = nerv.class('nerv.LSTMLayer', 'nerv.Layer')
+local LSTMLayer = nerv.class('nerv.LSTMLayer', 'nerv.GraphLayer')
 
 function LSTMLayer:__init(id, global_conf, layer_conf)
-    -- input1:x
-    -- input2:h
-    -- input3:c
     nerv.Layer.__init(self, id, global_conf, layer_conf)
-    -- prepare a DAGLayer to hold the lstm structure
+    self:check_dim_len(1, 1)
+
+    local din = layer_conf.dim_in[1]
+    local dout = layer_conf.dim_out[1]
+
     local pr = layer_conf.pr
     if pr == nil then
         pr = nerv.ParamRepo({}, self.loc_type)
     end
-    
-    local function ap(str)
-        return self.id .. '.' .. str
-    end
-    local din1, din2, din3 = self.dim_in[1], self.dim_in[2], self.dim_in[3]
-    local dout1, dout2, dout3 = self.dim_out[1], self.dim_out[2], self.dim_out[3]
-    local layers = {
-        ["nerv.CombinerLayer"] = {
-            [ap("inputXDup")] = {dim_in = {din1},
-                                      dim_out = {din1, din1, din1, din1},
-                                      lambda = {1}},
 
-            [ap("inputHDup")] = {dim_in = {din2},
-                                      dim_out = {din2, din2, din2, din2},
-                                      lambda = {1}},
-
-            [ap("inputCDup")] = {dim_in = {din3},
-                                      dim_out = {din3, din3, din3},
-                                      lambda = {1}},
-
-            [ap("mainCDup")] = {dim_in = {din3, din3},
-                                     dim_out = {din3, din3, din3},
-                                     lambda = {1, 1}},
+    local layers = {
+        ['nerv.CombinerLayer'] = {
+            mainCombine = {dim_in = {dout, dout}, dim_out = {dout}, lambda = {1, 1}},
         },
-        ["nerv.AffineLayer"] = {
-            [ap("mainAffineL")] = {dim_in = {din1, din2},
-                                        dim_out = {dout1},
-                                        pr = pr},
+        ['nerv.DuplicateLayer'] = {
+            inputDup = {dim_in = {din}, dim_out = {din, din, din, din}},
+            outputDup = {dim_in = {dout}, dim_out = {dout, dout, dout, dout, dout}},
+            cellDup = {dim_in = {dout}, dim_out = {dout, dout, dout, dout, dout}},
         },
-        ["nerv.TanhLayer"] = {
-            [ap("mainTanhL")] = {dim_in = {dout1}, dim_out = {dout1}},
-            [ap("outputTanhL")] = {dim_in = {dout1}, dim_out = {dout1}},
+        ['nerv.AffineLayer'] = {
+            mainAffine = {dim_in = {din, dout}, dim_out = {dout}, pr = pr},
         },
-        ["nerv.LSTMGateLayer"] = {
-            [ap("forgetGateL")] = {dim_in = {din1, din2, din3},
-                                        dim_out = {din3}, pr = pr},
-            [ap("inputGateL")] = {dim_in = {din1, din2, din3},
-                                        dim_out = {din3}, pr = pr},
-            [ap("outputGateL")] = {dim_in = {din1, din2, din3},
-                                        dim_out = {din3}, pr = pr},
-
+        ['nerv.TanhLayer'] = {
+            mainTanh = {dim_in = {dout}, dim_out = {dout}},
+            outputTanh = {dim_in = {dout}, dim_out = {dout}},
         },
-        ["nerv.ElemMulLayer"] = {
-            [ap("inputGMulL")] = {dim_in = {din3, din3},
-                                        dim_out = {din3}},
-            [ap("forgetGMulL")] = {dim_in = {din3, din3},
-                                        dim_out = {din3}},
-            [ap("outputGMulL")] = {dim_in = {din3, din3},
-                                        dim_out = {din3}},
+        ['nerv.LSTMGateLayer'] = {
+            forgetGate = {dim_in = {din, dout, dout}, dim_out = {dout}, pr = pr},
+            inputGate = {dim_in = {din, dout, dout}, dim_out = {dout}, pr = pr},
+            outputGate = {dim_in = {din, dout, dout}, dim_out = {dout}, pr = pr},
+        },
+        ['nerv.ElemMulLayer'] = {
+            inputGateMul = {dim_in = {dout, dout}, dim_out = {dout}},
+            forgetGateMul = {dim_in = {dout, dout}, dim_out = {dout}},
+            outputGateMul = {dim_in = {dout, dout}, dim_out = {dout}},
         },
     }
     
-    self.lrepo = nerv.LayerRepo(layers, pr, global_conf)
-
     local connections = {
-        ["<input>[1]"] = ap("inputXDup[1]"),
-        ["<input>[2]"] = ap("inputHDup[1]"),
-        ["<input>[3]"] = ap("inputCDup[1]"),
-
-        [ap("inputXDup[1]")] = ap("mainAffineL[1]"),
-        [ap("inputHDup[1]")] = ap("mainAffineL[2]"),
-        [ap("mainAffineL[1]")] = ap("mainTanhL[1]"),
-
-        [ap("inputXDup[2]")] = ap("inputGateL[1]"),
-        [ap("inputHDup[2]")] = ap("inputGateL[2]"),
-        [ap("inputCDup[1]")] = ap("inputGateL[3]"),
-        
-        [ap("inputXDup[3]")] = ap("forgetGateL[1]"),
-        [ap("inputHDup[3]")] = ap("forgetGateL[2]"),
-        [ap("inputCDup[2]")] = ap("forgetGateL[3]"),
-
-        [ap("mainTanhL[1]")] = ap("inputGMulL[1]"),
-        [ap("inputGateL[1]")] = ap("inputGMulL[2]"),
-        
-        [ap("inputCDup[3]")] = ap("forgetGMulL[1]"),
-        [ap("forgetGateL[1]")] = ap("forgetGMulL[2]"),
-
-        [ap("inputGMulL[1]")] = ap("mainCDup[1]"),
-        [ap("forgetGMulL[1]")] = ap("mainCDup[2]"),
-
-        [ap("inputXDup[4]")] = ap("outputGateL[1]"),
-        [ap("inputHDup[4]")] = ap("outputGateL[2]"),
-        [ap("mainCDup[3]")] = ap("outputGateL[3]"),
-
-        [ap("mainCDup[2]")] = "<output>[2]",
-        [ap("mainCDup[1]")] = ap("outputTanhL[1]"),
-        
-        [ap("outputTanhL[1]")] = ap("outputGMulL[1]"),
-        [ap("outputGateL[1]")] = ap("outputGMulL[2]"),
-
-        [ap("outputGMulL[1]")] = "<output>[1]",
+        -- lstm input
+        {'<input>[1]', 'inputDup[1]', 0},
+
+        -- input gate
+        {'inputDup[1]', 'inputGate[1]', 0},
+        {'outputDup[1]', 'inputGate[2]', 1},
+        {'cellDup[1]', 'inputGate[3]', 1},
+
+        -- forget gate
+        {'inputDup[2]', 'forgetGate[1]', 0},
+        {'outputDup[2]', 'forgetGate[2]', 1},
+        {'cellDup[2]', 'forgetGate[3]', 1},
+
+        -- lstm cell
+        {'forgetGate[1]', 'forgetGateMul[1]', 0},
+        {'cellDup[3]', 'forgetGateMul[2]', 1},
+        {'inputDup[3]', 'mainAffine[1]', 0},
+        {'outputDup[3]', 'mainAffine[2]', 1},
+        {'mainAffine[1]', 'mainTanh[1]', 0},
+        {'inputGate[1]', 'inputGateMul[1]', 0},
+        {'mainTanh[1]', 'inputGateMul[2]', 0},
+        {'inputGateMul[1]', 'mainCombine[1]', 0},
+        {'forgetGateMul[1]', 'mainCombine[2]', 0},
+        {'mainCombine[1]', 'cellDup[1]', 0},
+
+        -- forget gate
+        {'inputDup[4]', 'outputGate[1]', 0},
+        {'outputDup[4]', 'outputGate[2]', 1},
+        {'cellDup[4]', 'outputGate[3]', 0},
+
+        -- lstm output
+        {'cellDup[5]', 'outputTanh[1]', 0},
+        {'outputGate[1]', 'outputGateMul[1]', 0},
+        {'outputTanh[1]', 'outputGateMul[2]', 0},
+        {'outputGateMul[1]', 'outputDup[1]', 0},
+        {'outputDup[5]', '<output>[1]', 0},
     }
-    self.dag = nerv.DAGLayer(self.id, global_conf,
-                                {dim_in = self.dim_in,
-                                 dim_out = self.dim_out,
-                                 sub_layers = self.lrepo,
-                                 connections = connections})
-    
-    self:check_dim_len(3, 2) -- x, h, c and h, c
-end
-
-function LSTMLayer:bind_params()
-    local pr = layer_conf.pr
-    if pr == nil then
-        pr = nerv.ParamRepo({}, self.loc_type)
-    end
-    self.lrepo:rebind(pr)
-end
-
-function LSTMLayer:init(batch_size, chunk_size)
-    self.dag:init(batch_size, chunk_size)
-end
-
-function LSTMLayer:batch_resize(batch_size, chunk_size)
-    self.dag:batch_resize(batch_size, chunk_size)
-end
-
-function LSTMLayer:update(bp_err, input, output, t)
-    self.dag:update(bp_err, input, output, t)
-end
-
-function LSTMLayer:propagate(input, output, t)
-    self.dag:propagate(input, output, t)
-end
-
-function LSTMLayer:back_propagate(bp_err, next_bp_err, input, output, t)
-    self.dag:back_propagate(bp_err, next_bp_err, input, output, t)
-end
 
-function LSTMLayer:get_params()
-    return self.dag:get_params()
+    self:add_prefix(layers, connections)
+    local layer_repo = nerv.LayerRepo(layers, pr, global_conf)
+    self:graph_init(layer_repo, connections)
 end
diff --git a/nerv/layer/rnn.lua b/nerv/layer/rnn.lua
index e59cf5b..0b5ccaa 100644
--- a/nerv/layer/rnn.lua
+++ b/nerv/layer/rnn.lua
@@ -4,6 +4,10 @@ function RNNLayer:__init(id, global_conf, layer_conf)
     nerv.Layer.__init(self, id, global_conf, layer_conf)
     self:check_dim_len(1, 1)
 
+    if layer_conf.activation == nil then
+        layer_conf.activation = 'nerv.SigmoidLayer'
+    end
+
     local din = layer_conf.dim_in[1]
     local dout = layer_conf.dim_out[1]
 
@@ -16,20 +20,20 @@ function RNNLayer:__init(id, global_conf, layer_conf)
         ['nerv.AffineLayer'] = {
             main = {dim_in = {din, dout}, dim_out = {dout}, pr = pr},
         },
-        ['nerv.SigmoidLayer'] = {
-            sigmoid = {dim_in = {dout}, dim_out = {dout}},
+        [layers.activation] = {
+            activation = {dim_in = {dout}, dim_out = {dout}},
         },
         ['nerv.DuplicateLayer'] = {
-            dup = {dim_in = {dout}, dim_out = {dout, dout}},
-        }
+            duplicate = {dim_in = {dout}, dim_out = {dout, dout}},
+        },
     }
 
     local connections = {
         {'<input>[1]', 'main[1]', 0},
-        {'main[1]', 'sigmoid[1]', 0},
-        {'sigmoid[1]', 'dup[1]', 0},
-        {'dup[1]', 'main[2]', 1},
-        {'dup[2]', '<output>[1]', 0},
+        {'main[1]', 'activation[1]', 0},
+        {'activation[1]', 'duplicate[1]', 0},
+        {'duplicate[1]', 'main[2]', 1},
+        {'duplicate[2]', '<output>[1]', 0},
     }
 
     self:add_prefix(layers, connections)
diff --git a/nerv/matrix/init.lua b/nerv/matrix/init.lua
index cf85004..722c780 100644
--- a/nerv/matrix/init.lua
+++ b/nerv/matrix/init.lua
@@ -40,7 +40,8 @@ end
 --- Assign each element in a matrix using the value returned by a callback `gen`.
 -- @param gen the callback used to generated the values in the matrix, to which
 -- the indices of row and column will be passed (e.g., `gen(i, j)`)
-function nerv.Matrix:generate(gen)
+
+function nerv.Matrix:_generate(gen)
     if (self:dim() == 2) then
         for i = 0, self:nrow() - 1 do
             local row = self[i]
@@ -55,6 +56,21 @@ function nerv.Matrix:generate(gen)
     end
 end
 
+function nerv.Matrix:generate(gen)
+    local tmp
+    if nerv.is_type(self, 'nerv.CuMatrixFloat') then
+        tmp = nerv.MMatrixFloat(self:nrow(), self:ncol())
+    elseif nerv.is_type(self, 'nerv.CuMatrixDouble') then
+        tmp = nerv.MMatrixDouble(self:nrow(), self:ncol())
+    else
+        tmp = self
+    end
+    tmp:_generate(gen)
+    if nerv.is_type(self, 'nerv.CuMatrix') then
+        self:copy_fromh(tmp)
+    end
+end
+
 --- Create a fresh new matrix of the same matrix type (as `self`).
 -- @param nrow optional, the number of rows in the created matrix if specified,
 -- otherwise `self:nrow()` will be used