Add cuda version check to skip building quantization ops for versions…

… less than 8.0 (apache#10710) * Add cuda version check to skip building quantization ops for version less 8 * Clearer error message on cuda and cudnn versions * Trigger CI
zheng-da · Apr 29, 2018 · 23d933b · 23d933b
1 parent 92c373b
commit 23d933b
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Showing 3 changed files with 18 additions and 10 deletions.
diff --git a/src/operator/quantization/quantized_conv.cu b/src/operator/quantization/quantized_conv.cu
@@ -49,7 +49,7 @@ struct QuantizedBiasAddKernel {
   }
 };
 
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6
+#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
 template<typename SrcType, typename DstType, typename CmpType>
 class QuantizedCuDNNConvOp {
  public:
@@ -260,7 +260,7 @@ class QuantizedCuDNNConvOp {
   float alpha_ = 1.0f;
   float beta_ = 0.0f;
 };  // class QuantizedCuDNNConvOp
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6
+#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
 
 void QuantizedConvForwardGPU(const nnvm::NodeAttrs& attrs,
                              const OpContext& ctx,
@@ -270,7 +270,7 @@ void QuantizedConvForwardGPU(const nnvm::NodeAttrs& attrs,
   const ConvolutionParam& param = nnvm::get<ConvolutionParam>(attrs.parsed);
   CHECK_EQ(param.kernel.ndim(), 2U)
     << "QuantizedConvForward<gpu> only supports 2D convolution for now";
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6
+#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
   typedef QuantizedCuDNNConvOp<int8_t, float, int32_t> QuantizedConvOpInt8;
 #if DMLC_CXX11_THREAD_LOCAL
   static thread_local QuantizedConvOpInt8 op;
@@ -280,8 +280,9 @@ void QuantizedConvForwardGPU(const nnvm::NodeAttrs& attrs,
   op.Init(param, ctx, {inputs[0].shape_, inputs[1].shape_}, {outputs[0].shape_});
   op.Forward(ctx, inputs, req, outputs);
 #else
-  LOG(FATAL) << "QuantizedConvForward<gpu> only supports cudnnConvolutionForward for now";
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6
+  LOG(FATAL) << "QuantizedConvForward<gpu> only supports cudnnConvolutionForward "
+                "with CUDNN >= 6.0 and CUDA >= 8.0";
+#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
 }
 
 NNVM_REGISTER_OP(_contrib_quantized_conv)

diff --git a/src/operator/quantization/quantized_fully_connected.cu b/src/operator/quantization/quantized_fully_connected.cu
@@ -30,6 +30,7 @@
 namespace mxnet {
 namespace op {
 
+#if CUDA_VERSION >= 8000
 // value + bias_value * (range1 / limit_range1) * (limit_range2 / range2)
 struct QuantizedBiasAddKernel {
   MSHADOW_XINLINE static void Map(int i, size_t k, int32_t *out,
@@ -49,13 +50,15 @@ struct QuantizedBiasAddKernel {
              float_for_one_out_quant;
   }
 };
+#endif  // CUDA_VERSION >= 8000
 
 template<typename SrcType, typename DstType, typename CmpType>
 void QuantizedFullyConnectedForwardGPU(const nnvm::NodeAttrs& attrs,
                                        const OpContext &ctx,
                                        const std::vector<TBlob> &inputs,
                                        const std::vector<OpReqType> &req,
                                        const std::vector<TBlob> &outputs) {
+#if CUDA_VERSION >= 8000
   const FullyConnectedParam& param = nnvm::get<FullyConnectedParam>(attrs.parsed);
   using namespace mshadow;
   using namespace mxnet_op;
@@ -113,6 +116,9 @@ void QuantizedFullyConnectedForwardGPU(const nnvm::NodeAttrs& attrs,
         outputs[1].dptr<float>(), outputs[2].dptr<float>(),
          inputs[7].dptr<float>(),  inputs[8].dptr<float>());
   }
+#else
+  LOG(FATAL) << "QuantizedFullyConnectedForwardGPU only supports CUDA >= 8.0";
+#endif  // CUDA_VERSION >= 8000
 }
 
 NNVM_REGISTER_OP(_contrib_quantized_fully_connected)

diff --git a/src/operator/quantization/quantized_pooling.cu b/src/operator/quantization/quantized_pooling.cu
@@ -29,7 +29,7 @@
 namespace mxnet {
 namespace op {
 
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6
+#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
 template<typename DType>
 class QuantizedCuDNNPoolingOp {
  public:
@@ -115,7 +115,7 @@ class QuantizedCuDNNPoolingOp {
   cudnnTensorDescriptor_t out_desc_;
   cudnnPoolingDescriptor_t pool_desc_;
 };  // class QuantizedCuDNNPoolingOp
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6
+#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
 
 void QuantizedPoolingForwardGPU(const nnvm::NodeAttrs& attrs,
                                 const OpContext& ctx,
@@ -125,7 +125,7 @@ void QuantizedPoolingForwardGPU(const nnvm::NodeAttrs& attrs,
   const PoolingParam& param = nnvm::get<PoolingParam>(attrs.parsed);
   CHECK_EQ(param.kernel.ndim(), 2U)
     << "QuantizedPoolingForward<gpu> only supports 2D convolution for now";
-#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6
+#if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
 #if DMLC_CXX11_THREAD_LOCAL
   static thread_local QuantizedCuDNNPoolingOp<int8_t> op;
 #else
@@ -134,8 +134,9 @@ void QuantizedPoolingForwardGPU(const nnvm::NodeAttrs& attrs,
   op.Init(param, {inputs[0].shape_}, {outputs[0].shape_});
   op.Forward(ctx.get_stream<gpu>(), inputs, req, outputs);
 #else
-  LOG(FATAL) << "QuantizedPoolingForward<gpu> only supports cudnnPoolingForward for now";
-#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6
+  LOG(FATAL) << "QuantizedPoolingForward<gpu> only supports cudnnPoolingForward "
+                "with CUDNN >= 6.0 and CUDA >= 8.0";
+#endif  // MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 6 && CUDA_VERSION >= 8000
 }
 
 NNVM_REGISTER_OP(_contrib_quantized_pooling)