add opencl to sample.lua and inspect_checkpoint.lua, add link to clto…

…rch issues page

Readme.md

@@ -24,7 +24,7 @@ $ luarocks install cutorch
 $ luarocks install cunn
 ```
 
-If you'd like to use OpenCL GPU computing, you'll first need to install the `cltorch` and `clnn` packages, and then use the option `-opencl 1` during training:
+If you'd like to use OpenCL GPU computing, you'll first need to install the `cltorch` and `clnn` packages, and then use the option `-opencl 1` during training ([cltorch issues](https://github.com/hughperkins/cltorch/issues)):
 
 ```bash
 $ luarocks install cltorch

inspect_checkpoint.lua

@@ -14,18 +14,26 @@ cmd:text()
 cmd:text('Options')
 cmd:argument('-model','model to load')
 cmd:option('-gpuid',0,'gpu to use')
+cmd:option('-opencl',0,'use OpenCL (instead of CUDA)')
 cmd:text()
 
 -- parse input params
 opt = cmd:parse(arg)
 
-if opt.gpuid >= 0 then
+if opt.gpuid >= 0 and opt.opencl == 0 then
     print('using CUDA on GPU ' .. opt.gpuid .. '...')
     require 'cutorch'
     require 'cunn'
     cutorch.setDevice(opt.gpuid + 1)
 end
 
+if opt.gpuid >= 0 and opt.opencl == 1 then
+    print('using OpenCL on GPU ' .. opt.gpuid .. '...')
+    require 'cltorch'
+    require 'clnn'
+    cltorch.setDevice(opt.gpuid + 1)
+end
+
 local model = torch.load(opt.model)
 
 print('opt:')

sample.lua

@@ -31,6 +31,7 @@ cmd:option('-primetext',"",'used as a prompt to "seed" the state of the LSTM usi
 cmd:option('-length',2000,'number of characters to sample')
 cmd:option('-temperature',1,'temperature of sampling')
 cmd:option('-gpuid',0,'which gpu to use. -1 = use CPU')
+cmd:option('-opencl',0,'use OpenCL (instead of CUDA)')
 cmd:option('-verbose',1,'set to 0 to ONLY print the sampled text, no diagnostics')
 cmd:text()
 
@@ -43,7 +44,7 @@ function gprint(str)
 end
 
 -- check that cunn/cutorch are installed if user wants to use the GPU
-if opt.gpuid >= 0 then
+if opt.gpuid >= 0 and opt.opencl == 0 then
     local ok, cunn = pcall(require, 'cunn')
     local ok2, cutorch = pcall(require, 'cutorch')
     if not ok then gprint('package cunn not found!') end
@@ -57,6 +58,23 @@ if opt.gpuid >= 0 then
         opt.gpuid = -1 -- overwrite user setting
     end
 end
+
+-- check that clnn/cltorch are installed if user wants to use OpenCL
+if opt.gpuid >= 0 and opt.opencl == 1 then
+    local ok, cunn = pcall(require, 'clnn')
+    local ok2, cutorch = pcall(require, 'cltorch')
+    if not ok then print('package clnn not found!') end
+    if not ok2 then print('package cltorch not found!') end
+    if ok and ok2 then
+        print('using OpenCL on GPU ' .. opt.gpuid .. '...')
+        cltorch.setDevice(opt.gpuid + 1) -- note +1 to make it 0 indexed! sigh lua
+        torch.manualSeed(opt.seed)
+    else
+        gprint('Falling back on CPU mode')
+        opt.gpuid = -1 -- overwrite user setting
+    end
+end
+
 torch.manualSeed(opt.seed)
 
 -- load the model checkpoint
@@ -80,7 +98,8 @@ current_state = {}
 for L = 1,checkpoint.opt.num_layers do
     -- c and h for all layers
     local h_init = torch.zeros(1, checkpoint.opt.rnn_size)
-    if opt.gpuid >= 0 then h_init = h_init:cuda() end
+    if opt.gpuid >= 0 and opt.opencl == 0 then h_init = h_init:cuda() end
+    if opt.gpuid >= 0 and opt.opencl == 1 then h_init = h_init:cl() end
     table.insert(current_state, h_init:clone())
     table.insert(current_state, h_init:clone())
 end
@@ -94,7 +113,8 @@ if string.len(seed_text) > 0 then
     for c in seed_text:gmatch'.' do
         prev_char = torch.Tensor{vocab[c]}
         io.write(ivocab[prev_char[1]])
-        if opt.gpuid >= 0 then prev_char = prev_char:cuda() end
+        if opt.gpuid >= 0 and opt.opencl == 0 then prev_char = prev_char:cuda() end
+        if opt.gpuid >= 0 and opt.opencl == 1 then prev_char = prev_char:cl() end
         local lst = protos.rnn:forward{prev_char, unpack(current_state)}
         -- lst is a list of [state1,state2,..stateN,output]. We want everything but last piece
         current_state = {}
@@ -106,7 +126,8 @@ else
     gprint('missing seed text, using uniform probability over first character')
     gprint('--------------------------')
     prediction = torch.Tensor(1, #ivocab):fill(1)/(#ivocab)
-    if opt.gpuid >= 0 then prediction = prediction:cuda() end
+    if opt.gpuid >= 0 and opt.opencl == 0 then prediction = prediction:cuda() end
+    if opt.gpuid >= 0 and opt.opencl == 1 then prediction = prediction:cl() end
 end
 
 -- start sampling/argmaxing