From 9bae70b22549b68f5cdeece8b6c3b3de00c22714 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 17 Apr 2018 02:51:11 +0300 Subject: [PATCH] Accelerated by another 5% using FP16/32 Batch-norm for Tensor Cores. --- Makefile | 1 + src/batchnorm_layer.c | 28 +++++------ src/convolutional_kernels.cu | 91 ++++++++++++++++++++++++++++++++++-- src/convolutional_layer.c | 3 ++ src/layer.h | 2 +- 5 files changed, 105 insertions(+), 20 deletions(-) diff --git a/Makefile b/Makefile index 3a6584c1..b6119ae5 100644 --- a/Makefile +++ b/Makefile @@ -91,6 +91,7 @@ endif ifeq ($(CUDNN_HALF), 1) COMMON+= -DCUDNN_HALF CFLAGS+= -DCUDNN_HALF +ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70] endif OBJ=http_stream.o gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index 883ab344..d35d9d2e 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -190,18 +190,18 @@ void forward_batchnorm_layer_gpu(layer l, network_state state) &one, &zero, l.normDstTensorDesc, - l.x_gpu, + l.x_gpu, // input l.normDstTensorDesc, - l.output_gpu, + l.output_gpu, // output l.normTensorDesc, l.scales_gpu, l.biases_gpu, .01, - l.rolling_mean_gpu, - l.rolling_variance_gpu, + l.rolling_mean_gpu, // output (should be FP32) + l.rolling_variance_gpu, // output (should be FP32) .00001, - l.mean_gpu, - l.variance_gpu); + l.mean_gpu, // output (should be FP32) + l.variance_gpu); // output (should be FP32) #else fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu); @@ -243,18 +243,18 @@ void backward_batchnorm_layer_gpu(layer l, network_state state) &one, &one, l.normDstTensorDesc, - l.x_gpu, + l.x_gpu, // input l.normDstTensorDesc, - l.delta_gpu, + l.delta_gpu, // input l.normDstTensorDesc, - l.x_norm_gpu, + l.x_norm_gpu, // output l.normTensorDesc, - l.scales_gpu, - l.scale_updates_gpu, - l.bias_updates_gpu, + l.scales_gpu, // output (should be FP32) + l.scale_updates_gpu, // output (should be FP32) + l.bias_updates_gpu, // output (should be FP32) .00001, - l.mean_gpu, - l.variance_gpu); + l.mean_gpu, // input (should be FP32) + l.variance_gpu); // input (should be FP32) copy_ongpu(l.outputs*l.batch, l.x_norm_gpu, 1, l.delta_gpu, 1); #else backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 603d5318..324fc508 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -169,7 +169,51 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) l.dstTensorDesc, output16); - cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); + + if (l.batch_normalize) + { + if (state.train) // Training + { + copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1); + //cudaMemcpyAsync(l.x_gpu, output16, l.outputs*l.batch*sizeof(half), cudaMemcpyDefault, get_cuda_stream()); + float one = 1; + float zero = 0; + // Batch-normalization can still take FP16 inputs and outputs, saving half the bandwidth + // compared to FP32, it’s just that the statistics and value adjustment should be done in FP32. + cudnnBatchNormalizationForwardTraining(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + l.normDstTensorDescF16, + l.x_gpu, // input + l.normDstTensorDescF16, + output16, // output + l.normTensorDesc, + l.scales_gpu, + l.biases_gpu, + .01, + l.rolling_mean_gpu, // output (should be FP32) + l.rolling_variance_gpu, // output (should be FP32) + .00001, + l.mean_gpu, // output (should be FP32) + l.variance_gpu); // output (should be FP32) + + cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); + //forward_batchnorm_layer_gpu(l, state); + } + else // Detection + { + cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); + normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); + } + } + else // BIAS only + { + cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); + } #else @@ -186,7 +230,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) &one, l.dstTensorDesc, l.output_gpu); -#endif +#endif // CUDNN_HALF #else @@ -203,12 +247,14 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) } #endif +#ifndef CUDNN_HALF if (l.batch_normalize) { forward_batchnorm_layer_gpu(l, state); } else { add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } +#endif // no CUDNN_HALF activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); @@ -222,12 +268,13 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); +#ifndef CUDNN_HALF if(l.batch_normalize){ backward_batchnorm_layer_gpu(l, state); - //axpy_ongpu(l.outputs*l.batch, -state.net.decay, l.x_gpu, 1, l.delta_gpu, 1); } else { - //axpy_ongpu(l.outputs*l.batch, -state.net.decay, l.output_gpu, 1, l.delta_gpu, 1); + //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); } +#endif // no CUDNN_HALF float *original_input = state.input; if(l.xnor) state.input = l.binary_input_gpu; @@ -256,7 +303,41 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state cuda_convert_f32_to_f16(state.input, input16_size, input16); cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16); - + + if (l.batch_normalize) { + //if (!state.train) { + // l.mean_gpu = l.rolling_mean_gpu; + // l.variance_gpu = l.rolling_variance_gpu; + //} + float one = 1; + float zero = 0; + cudnnBatchNormalizationBackward(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + &one, + &one, + l.normDstTensorDescF16, + l.x_gpu, // input + l.normDstTensorDescF16, + delta16, // input + l.normDstTensorDescF16, + l.x_norm_gpu, // output + l.normTensorDesc, + l.scales_gpu, // output (should be FP32) + l.scale_updates_gpu, // output (should be FP32) + l.bias_updates_gpu, // output (should be FP32) + .00001, + l.mean_gpu, // input (should be FP32) + l.variance_gpu); // input (should be FP32) + copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1); + //cudaMemcpyAsync(delta16, l.x_norm_gpu, l.outputs*l.batch * sizeof(half), cudaMemcpyDefault, get_cuda_stream()); + } + else + { + //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); + } + // convert input: state.input (x), l.delta_gpu (y) from fp32 to fp16 // get output: l.weight_updates_gpu (dw) and convert it to fp32 (ONLY if it is fp16) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 91c5b3c0..9a76bdf8 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -178,6 +178,8 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) // batch norm cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); + + cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w); #if(CUDNN_MAJOR >= 6) cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT); // cudnn >= 6.0 #else @@ -379,6 +381,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int } #ifdef CUDNN cudnnCreateTensorDescriptor(&l.normDstTensorDesc); + cudnnCreateTensorDescriptor(&l.normDstTensorDescF16); cudnnCreateTensorDescriptor(&l.normTensorDesc); cudnnCreateTensorDescriptor(&l.srcTensorDesc); cudnnCreateTensorDescriptor(&l.dstTensorDesc); diff --git a/src/layer.h b/src/layer.h index 75c0358a..81e27adf 100644 --- a/src/layer.h +++ b/src/layer.h @@ -281,7 +281,7 @@ struct layer{ #ifdef CUDNN cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc; cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc; - cudnnTensorDescriptor_t normTensorDesc, normDstTensorDesc; + cudnnTensorDescriptor_t normTensorDesc, normDstTensorDesc, normDstTensorDescF16; cudnnFilterDescriptor_t weightDesc; cudnnFilterDescriptor_t dweightDesc; cudnnConvolutionDescriptor_t convDesc;