Merge pull request #1151 from NVIDIA/spatial_fast_bottleneck

Spatially Distributed Fast Bottleneck block

apex/contrib/bottleneck/__init__.py

-from .bottleneck import Bottleneck
+from .bottleneck import Bottleneck, SpatialBottleneck

apex/contrib/bottleneck/bottleneck.py

 import torch
+import torch.distributed as dist
 from torch import nn
 import fast_bottleneck
 
@@ -212,3 +213,235 @@ class Bottleneck(torch.nn.Module):
         out = self.relu(out)
 
         return out
+
+
+class SpatialBottleneckFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, spatial_group_size, local_rank, comm, stream1, nhwc, stride_1x1, scale, bias, x, *conv):
+        # TODO: clean up order of tensors
+        args = [x, *conv[0:3], *scale[0:3], *bias[0:3]]
+        ctx.downsample = len(conv) > 3
+        if ctx.downsample:
+            args.append(conv[3])
+            args.append(scale[3])
+            args.append(bias[3])
+
+        # weight buffers are always in nhwc while shape can be nhwc or channels_last
+        # here we pass in flag and let c++ handle it
+        # alternatively, we can put all sizes into a fixed format and pass it in
+        outputs = fast_bottleneck.forward_init(nhwc, stride_1x1, args)
+        fast_bottleneck.forward_out1(nhwc, stride_1x1, args, outputs)
+
+        fast_bottleneck.forward_out2(nhwc, stride_1x1, args, outputs)
+        # do halo exchange for outputs[0] (out1)
+        if spatial_group_size > 1:
+            out1 = outputs[0]
+            N,Hs,W,C = list(out1.shape)
+            padded_out1 = torch.empty((N,Hs+2,W,C),dtype=out1.dtype,device=out1.device)
+            padded_out1[:,1:Hs+1,:,:].copy_(out1)
+            stream1.wait_stream(torch.cuda.current_stream())
+            with torch.cuda.stream(stream1):
+                # copy halos to send buffer
+                send_halos = torch.empty((N,2,W,C),dtype=out1.dtype,device=out1.device)
+                send_halos[:,:1,:,:].copy_(out1[:,:1,:,:])
+                send_halos[:,1:,:,:].copy_(out1[:,Hs-1:,:,:])
+                all_halos = torch.empty((N,2*spatial_group_size,W,C),dtype=out1.dtype,device=out1.device)
+                all_halos = [all_halos[:,i*2:(i+1)*2,:,:] for i in range(spatial_group_size)]
+                dist.all_gather(all_halos,send_halos)
+                padded_out1_top_halo = padded_out1[:,:1,:,:]
+                if local_rank > 0:
+                    top_halo = all_halos[local_rank-1][:,1:,:,:]
+                    padded_out1_top_halo.copy_(top_halo)
+                    fat_top_halo = padded_out1[:,:3,:,:]
+                    top_out2 = fast_bottleneck.forward_out2_halo(nhwc, fat_top_halo, args)
+                else:
+                    padded_out1_top_halo.zero_()
+                padded_out1_btm_halo = padded_out1[:,Hs+1:,:,:]
+                if local_rank < spatial_group_size-1:
+                    btm_halo = all_halos[local_rank+1][:,:1,:,:]
+                    padded_out1_btm_halo.copy_(btm_halo)
+                    fat_btm_halo = padded_out1[:,Hs-1:,:,:]
+                    btm_out2 = fast_bottleneck.forward_out2_halo(nhwc, fat_btm_halo, args)
+                else:
+                    padded_out1_btm_halo.zero_()
+            torch.cuda.current_stream().wait_stream(stream1)
+            out2 = outputs[1]
+            if local_rank > 0:
+                out2[:,:1,:,:].copy_(top_out2)
+            if local_rank < spatial_group_size-1:
+                out2[:,Hs-1:,:,:].copy_(btm_out2)
+            
+        fast_bottleneck.forward_rest(nhwc, stride_1x1, args, outputs)
+        if spatial_group_size > 1:
+            ctx.save_for_backward(*(args+outputs+[padded_out1]))
+        else:
+            ctx.save_for_backward(*(args+outputs))
+        # save relu outputs for drelu
+        ctx.nhwc = nhwc
+        ctx.stride_1x1 = stride_1x1
+        ctx.spatial_group_size = spatial_group_size
+        ctx.local_rank = local_rank
+        ctx.comm = comm
+        ctx.stream1 = stream1
+        return outputs[2]
+
+    # backward relu is not exposed, MUL with mask used now
+    # only support dgrad
+    @staticmethod
+    def backward(ctx, grad_o):
+        if ctx.spatial_group_size > 1:
+            outputs = ctx.saved_tensors[-4:-1]
+        else:
+           outputs = ctx.saved_tensors[-3:]
+
+        if ctx.downsample:
+            grad_conv3, grad_conv4 = drelu_dscale2(grad_o, outputs[2], ctx.saved_tensors[6], ctx.saved_tensors[11])
+        else:
+            grad_conv3, grad_conv4 = drelu_dscale1(grad_o, outputs[2], ctx.saved_tensors[6])
+
+        # create input vector for backward
+        t_list = [*ctx.saved_tensors[0:10]]
+        t_list.append(grad_conv3)
+        t_list.append(grad_conv4)
+
+        # outputs used for wgrad and generating drelu mask
+        t_list.append(outputs[0])
+        t_list.append(outputs[1])
+
+        # in case there is downsample
+        if ctx.downsample:
+            t_list.append(ctx.saved_tensors[10])
+
+        grads = fast_bottleneck.backward_init(ctx.nhwc, ctx.stride_1x1, t_list)
+        grad_out2 = fast_bottleneck.backward_grad_out2(ctx.nhwc, ctx.stride_1x1, t_list, grads)
+        # do halo exchange of grad_out2 here
+        fast_bottleneck.backward_rest(ctx.nhwc, ctx.stride_1x1, t_list, grads, grad_out2)
+
+        return (None, None, None, None, None, None, None, None, *grads)
+
+spatial_bottleneck_function = SpatialBottleneckFunction.apply
+
+class SpatialBottleneck(torch.nn.Module):
+    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
+    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
+    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
+    # This variant is also known as ResNet V1.5 and improves accuracy according to
+    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
+    # here we put it at 1x1
+
+    def __init__(self, in_channels, bottleneck_channels, out_channels, stride=1, groups=1,
+                 dilation=1, norm_func=None, use_cudnn=False, explicit_nhwc=False, 
+                 spatial_group_size=1):
+        super(SpatialBottleneck, self).__init__()
+        if groups != 1:
+            raise RuntimeError('Only support groups == 1')
+        if dilation != 1:
+            raise RuntimeError('Only support dilation == 1')
+        if norm_func == None:
+            norm_func = FrozenBatchNorm2d
+        else:
+            raise RuntimeError('Only support frozen BN now.')
+
+        if stride != 1 or in_channels != out_channels:
+            self.downsample = nn.Sequential(
+                conv1x1(in_channels, out_channels, stride),
+                norm_func(out_channels),
+            )
+        else:
+            self.downsample = None
+
+        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv1x1(in_channels, bottleneck_channels, stride)
+        self.conv2 = conv3x3(bottleneck_channels, bottleneck_channels)
+        self.conv3 = conv1x1(bottleneck_channels, out_channels)
+        self.relu = nn.ReLU(inplace=True)
+        self.stride = stride
+
+        self.bn1 = norm_func(bottleneck_channels)
+        self.bn2 = norm_func(bottleneck_channels)
+        self.bn3 = norm_func(out_channels)
+
+        self.use_cudnn = use_cudnn
+
+        # setup conv weights
+        self.w_conv = [self.conv1.weight, self.conv2.weight, self.conv3.weight]
+        if self.downsample is not None:
+            self.w_conv.append(self.downsample[0].weight)
+
+        # init weight in nchw format before possible transpose
+        for w in self.w_conv:
+            kaiming_uniform_(w, a=1)
+
+        # TODO: prevent unsupported case usage
+        # support cases
+        #                 native      cudnn
+        # normal             yes         no
+        # channel_last       yes        yes
+        # explicit_nhwc       no        yes
+        self.explicit_nhwc = explicit_nhwc
+        if self.explicit_nhwc:
+            for p in self.parameters():
+                with torch.no_grad():
+                    p.data = p.data.permute(0,2,3,1).contiguous()
+
+        # spatial communicator
+        self.spatial_group_size = spatial_group_size
+        if spatial_group_size > 1:
+            world_size = dist.get_world_size()
+            num_groups = world_size // spatial_group_size
+            assert(num_groups*spatial_group_size == world_size), "torch.distributed.get_world_size() must be multiple of group_size"
+            rank = dist.get_rank()
+            self.local_rank = rank % spatial_group_size
+            for group in range(num_groups):
+                ranks = list(range(group*spatial_group_size,(group+1)*spatial_group_size))
+                comm = torch.distributed.new_group(ranks=ranks)
+                if rank in ranks:
+                    self.communicator = comm
+            self.stream1 = torch.cuda.Stream()
+            self.spatial_args = self.spatial_group_size, self.local_rank, self.communicator, self.stream1
+        else:
+            self.spatial_args = 1, 0, None, None
+
+        return
+
+    def forward(self, x):
+        if self.use_cudnn:
+            # calculate scale/bias from registered buffers
+            # TODO: make this better
+            s1, b1 = self.bn1.get_scale_bias(self.explicit_nhwc)
+            s2, b2 = self.bn2.get_scale_bias(self.explicit_nhwc)
+            s3, b3 = self.bn3.get_scale_bias(self.explicit_nhwc)
+            w_scale = [s1, s2, s3]
+            w_bias = [b1, b2, b3]
+            if self.downsample is not None:
+                s4, b4 = self.downsample[1].get_scale_bias(self.explicit_nhwc)
+                w_scale.append(s4)
+                w_bias.append(b4)
+
+            out = spatial_bottleneck_function(*self.spatial_args, self.explicit_nhwc, self.stride, w_scale, w_bias, x, *self.w_conv)
+            return out
+
+        if self.explicit_nhwc:
+            raise RuntimeError('explicit nhwc with native ops is not supported.')
+
+        # fallback to native ops
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out

apex/contrib/bottleneck/bottleneck_module_test.py

+import os
+import torch
+from maskrcnn_benchmark.modeling.backbone.resnet import Bottleneck
+from maskrcnn_benchmark.layers.nhwc import nhwc_to_nchw_transform, nchw_to_nhwc_transform
+from maskrcnn_benchmark.layers.nhwc.batch_norm import FrozenBatchNorm2d_NHWC
+from apex.contrib.bottleneck import Bottleneck as FastBottleneck
+
+
+def single_module_test(ref, rank, world_size, numtype, device, shape, fast, spatial_group_size, in_channels, bottleneck_channels, out_channels, num_groups, stride_in_1x1, stride, dilation, norm_func, nhwc):
+    # inputs + modules
+    with torch.no_grad():
+        input_shape = [1, in_channels] + list(shape)
+        x = torch.randn(input_shape, dtype=numtype, device=device)
+        if nhwc:
+            x = nchw_to_nhwc_transform(x).contiguous()
+        x.requires_grad = True
+        print(x.shape, x.stride())
+
+        #if spatial_group_size > 1:
+        #    fast = False # hack so fast bottleneck can be run against distributed bottleneck
+        #if spatial_group_size == 1:
+        #    fast = False
+
+        if fast:
+            bottleneck = FastBottleneck(
+                in_channels=in_channels,
+                bottleneck_channels=bottleneck_channels,
+                out_channels=out_channels,
+                stride=stride,
+                dilation=dilation,
+                explicit_nhwc=nhwc,
+                use_cudnn=True)
+            if spatial_group_size > 1:
+                print("WARNING! spatial_group_size ignored by FastBottleneck")
+        else:
+            bottleneck = Bottleneck(
+                in_channels,
+                bottleneck_channels,
+                out_channels,
+                num_groups,
+                stride_in_1x1,
+                stride,
+                dilation,
+                norm_func,
+                nhwc,
+                spatial_group_size)
+        bottleneck = bottleneck.to(dtype=numtype,device=device)
+        weights = dict(bottleneck.named_parameters())
+
+        if ref is not None:
+            ref_x, _, ref_weights = ref
+            Hs,H = x.shape[1], ref_x.shape[1]
+            assert(Hs*spatial_group_size == H), "Hs not a multiple of H"
+            ref_x = ref_x[:,rank*Hs:(rank+1)*Hs,:,:]
+            x.copy_(ref_x)
+            assert(len(weights) == len(ref_weights)), "Reference weights and weights don't match"
+            for k in weights.keys():
+                weights[k].copy_(ref_weights[k])
+
+    # forward
+    out = bottleneck(x)
+    
+    # gradient output
+    with torch.no_grad():
+        grad_out = torch.randn_like(out)
+        if ref is not None:
+            _, ref_grad_out, _ = ref
+            Hs,H = grad_out.shape[1], ref_grad_out.shape[1]
+            assert(Hs*spatial_group_size == H), "Hs not a multiple of H"
+            ref_grad_out = ref_grad_out[:,rank*Hs:(rank+1)*Hs,:,:]
+            grad_out.copy_(ref_grad_out)
+
+    # backward
+    out.backward(grad_out)
+
+    with torch.no_grad():
+        dgrad = x.grad.detach()
+        
+        wgrad = {}
+        for n,p in bottleneck.named_parameters():
+            wgrad[n] = p.grad.detach()
+
+    if world_size > 1:
+        if spatial_group_size == 1:
+            # broadcast x, grad_out and weights from rank 0
+            with torch.no_grad():
+                torch.distributed.broadcast(x,0)
+                torch.distributed.broadcast(grad_out,0)
+                for k in weights.keys():
+                    torch.distributed.broadcast(weights[k],0)
+        else:
+            # gather dgrad (x.grad), sum wgrad (weights)
+            N,Hs,W,C = dgrad.shape
+            H = Hs * spatial_group_size
+            dgrad_gathered = torch.empty((N,H,W,C),dtype=dgrad.dtype,device=dgrad.device)
+            dgrad_tensors = [dgrad_gathered[:,i*Hs:(i+1)*Hs,:,:] for i in range(spatial_group_size)]
+            torch.distributed.all_gather(dgrad_tensors, dgrad)
+            dgrad = dgrad_gathered
+            for k in wgrad.keys():
+                torch.distributed.all_reduce(wgrad[k])
+
+    return x, out, grad_out, weights, dgrad, wgrad
+
+
+def module_tests(rank, world_size, numtype, device, fast, spatial_group_sizes, init_args):
+    r = []
+    for ia in init_args:
+        shape = ia[0:4]
+        args = ia[4:]
+        rr = []
+        ref = None
+        for spatial_group_size in spatial_group_sizes:
+            N,H,W,C = shape
+            H = H//spatial_group_size
+            x, out, grad_out, weights, dgrad, wgrad = single_module_test(ref, rank, world_size, numtype, device, [H,W], fast, spatial_group_size, *args)
+            if ref is None:
+                assert(spatial_group_size == 1), "Wrong reference weights"
+                ref = x, grad_out, weights
+            if rank == 0:
+                rr.append( (out, dgrad, wgrad) )
+            torch.distributed.barrier()
+        r.append(rr)
+    return r
+
+
+def main():
+    total_num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
+    distributed = total_num_gpus > 1
+    ngpus = torch.cuda.device_count()
+
+    if distributed:
+        torch.distributed.init_process_group("nccl")
+        rank, world_size = torch.distributed.get_rank(), torch.distributed.get_world_size()
+        is_master = True if rank == 0 else False
+        local_rank = rank % ngpus
+        torch.cuda.set_device(local_rank)
+        spatial_group_size = total_num_gpus
+    else:
+        rank, local_rank, is_master, world_size, spatial_group_size = 0, 0, True, 1, 1
+
+    #torch.use_deterministic_algorithms(True)
+    torch.backends.cudnn.benchmark = True
+    #torch.backends.cudnn.deterministic = True
+    #torch.backends.cuda.matmul.allow_tf32 = False
+    #torch.backends.cudnn.allow_tf32 = False
+
+    norm_func = FrozenBatchNorm2d_NHWC
+
+    init_args = [
+        (1, 200, 336, 64, 64, 64, 256, 1, True, 1, 1, norm_func, True),
+        (1, 200, 336, 256, 256, 64, 256, 1, True, 1, 1, norm_func, True),
+        (1, 200, 336, 256, 256, 128, 512, 1, True, 2, 1, norm_func, True),
+        (1, 100, 168, 512, 512, 128, 512, 1, True, 1, 1, norm_func, True),
+        (1, 100, 168, 512, 512, 256, 1024, 1, True, 2, 1, norm_func, True),
+        (1, 50, 84, 1024, 1024, 256, 1024, 1, True, 1, 1, norm_func, True),
+        (1, 50, 84, 1024, 1024, 512, 2048, 1, True, 2, 1, norm_func, True),
+        (1, 25, 42, 2048, 2048, 512, 2048, 1, True, 1, 1, norm_func, True),
+        (1, 336, 200, 64, 64, 64, 256, 1, True, 1, 1, norm_func, True),
+        (1, 336, 200, 256, 256, 64, 256, 1, True, 1, 1, norm_func, True),
+        (1, 336, 200, 256, 256, 128, 512, 1, True, 2, 1, norm_func, True),
+        (1, 168, 100, 512, 512, 128, 512, 1, True, 1, 1, norm_func, True),
+        (1, 168, 100, 512, 512, 256, 1024, 1, True, 2, 1, norm_func, True),
+        (1, 84, 50, 1024, 1024, 256, 1024, 1, True, 1, 1, norm_func, True),
+        (1, 84, 50, 1024, 1024, 512, 2048, 1, True, 2, 1, norm_func, True),
+        (1, 42, 25, 2048, 2048, 512, 2048, 1, True, 1, 1, norm_func, True),
+        ]
+
+    # pad H to account for spatial distribution 
+    padded_init_args = []
+    for ia in init_args:
+        N,H,W,C = ia[0:4]
+        m = spatial_group_size * H // (25 if H < W else 42)
+        H = ((H + m - 1) // m) * m
+        args = tuple( [N,H,W,C] + list(ia[4:]) )
+        padded_init_args.append(args)
+    init_args = padded_init_args
+    if rank == 0:
+        for ia in init_args:
+            print(ia)
+
+    spatial_group_sizes = [1]
+    if spatial_group_size > 1:
+        spatial_group_sizes.append(spatial_group_size)
+
+    numtype, device, fast = torch.float16, 'cuda', False
+    r = module_tests(rank, world_size, numtype, device, fast, spatial_group_sizes, init_args)
+    torch.distributed.barrier()
+    if rank == 0:
+        for rr in r:
+            print("***")
+            for out, dgrad, wgrad in rr:
+                gr = [("dgrad",dgrad.norm(p=2,dtype=torch.float64).item())] + [(k+".wgrad",wgrad[k].norm(p=2,dtype=torch.float64).item()) for k in wgrad.keys()]
+                print(gr)
+    torch.distributed.barrier()
+
+
+if __name__ == "__main__":
+    main()

apex/contrib/csrc/bottleneck/bottleneck.cpp

@@ -1606,7 +1606,726 @@ std::vector<at::Tensor> bottleneck_backward(bool explicit_nhwc, int stride_1X1,
   return outputs;
 }
 
+namespace {
+
+struct bottleneck_forward_status {
+
+  int64_t dimA[4];
+  int64_t filterdimA1[4];
+  int64_t filterdimA2[4];
+  int64_t filterdimA3[4];
+  int64_t filterdimA4[4];
+
+  int axis[4];
+
+  int64_t outdimA0[4];
+  int64_t outdimA1[4];
+  int64_t outdimA2[4];
+  int64_t outdimA3[4];
+  int64_t outdimA4[4];
+
+  int64_t padA[2];
+  int64_t padA1[2];
+  int64_t padA2[2];  // halo padding
+  int64_t dilationA[2];
+  int64_t convstrideA[2];
+  int64_t convstride1X1[2];
+
+  int64_t outdim0[4]; // halo input shape
+  int64_t outdim1[4];
+  int64_t outdim2[4];
+  int64_t outdim3[4];
+  int64_t outdim4[4]; // halo output shape
+
+  void init(bool explicit_nhwc, int stride_1X1, std::vector<at::Tensor> inputs) {
+    dimA[0] = dimA[1] = dimA[2] = dimA[3] = 0;
+    filterdimA1[0] = filterdimA1[1] = filterdimA1[2] = filterdimA1[3] = 0;
+    filterdimA2[0] = filterdimA2[1] = filterdimA2[2] = filterdimA2[3] = 0;
+    filterdimA3[0] = filterdimA3[1] = filterdimA3[2] = filterdimA3[3] = 0;
+    filterdimA4[0] = filterdimA4[1] = filterdimA4[2] = filterdimA4[3] = 0;
+
+    // All dim calculation after this order of n,c,h,w
+    if (explicit_nhwc) {
+      axis[0] = 0;
+      axis[1] = 3;
+      axis[2] = 1;
+      axis[3] = 2;
+    } else {
+      axis[0] = 0;
+      axis[1] = 1;
+      axis[2] = 2;
+      axis[3] = 3;
+    }
+
+    for (int dim=0;dim<4;dim++) {
+      dimA[dim] = inputs[0].size(axis[dim]);
+      filterdimA1[dim] = inputs[1].size(axis[dim]);
+      filterdimA2[dim] = inputs[2].size(axis[dim]);
+      filterdimA3[dim] = inputs[3].size(axis[dim]);
+    }
+    if (stride_1X1 != 1 || filterdimA3[0] != dimA[1]) {
+      for (int dim=0;dim<4;dim++) {
+	filterdimA4[dim] = inputs[10].size(axis[dim]);
+      }
+    }
+
+    // output dim in n,c,h,w used by backend
+    outdimA0[0] = outdimA0[1] = outdimA0[2] = outdimA0[3] = 0;
+    outdimA1[0] = outdimA1[1] = outdimA1[2] = outdimA1[3] = 0;
+    outdimA2[0] = outdimA2[1] = outdimA2[2] = outdimA2[3] = 0;
+    outdimA3[0] = outdimA3[1] = outdimA3[2] = outdimA3[3] = 0;
+    outdimA4[0] = outdimA4[1] = outdimA4[2] = outdimA4[3] = 0;
+
+    // use these fixed value for test run
+    padA[0] = 0; padA[1] = 0;
+    padA1[0] = 1; padA1[1] = 1;
+    padA2[0] = 0; padA2[1] = 1;
+    dilationA[0] = 1; dilationA[1] = 1;
+    convstrideA[0] = 1; convstrideA[1] = 1;
+    convstride1X1[0] = stride_1X1; convstride1X1[1] = stride_1X1;
+
+    // compute output from pad/stride/dilation
+    outdimA1[0] = dimA[0];
+    outdimA1[1] = filterdimA1[0];
+    for (int dim = 0; dim < 2; dim++) {
+      outdimA1[dim + 2] = getFwdConvOutputDim(dimA[dim + 2], padA[dim], filterdimA1[dim + 2], convstride1X1[dim], dilationA[dim]);
+    }
+
+    outdimA2[0] = outdimA1[0];
+    outdimA2[1] = filterdimA2[0];
+    for (int dim = 0; dim < 2; dim++) {
+      outdimA2[dim + 2] = getFwdConvOutputDim(outdimA1[dim + 2], padA1[dim], filterdimA2[dim + 2], convstrideA[dim], dilationA[dim]);
+    }
+
+    for (int dim = 0; dim < 4; dim++) {
+      if (dim == 2) {
+	outdimA0[dim] = 3;
+	outdimA4[dim] = 1;
+      } else {
+        outdimA0[dim] = outdimA1[dim];
+	outdimA4[dim] = outdimA2[dim];
+      }
+    }
+
+    outdimA3[0] = outdimA2[0];
+    outdimA3[1] = filterdimA3[0];
+    for (int dim = 0; dim < 2; dim++) {
+      outdimA3[dim + 2] = getFwdConvOutputDim(outdimA2[dim + 2], padA[dim], filterdimA3[dim + 2], convstrideA[dim], dilationA[dim]);
+    }
+
+    // Create output tensor in the correct shape in pytorch's view
+    outdim1[0] = outdim1[1] = outdim1[2] = outdim1[3] = 0;
+    outdim2[0] = outdim2[1] = outdim2[2] = outdim2[3] = 0;
+    outdim3[0] = outdim3[1] = outdim3[2] = outdim3[3] = 0;
+    if (explicit_nhwc) {
+      axis[0] = 0;
+      axis[1] = 2;
+      axis[2] = 3;
+      axis[3] = 1;
+    }
+    for (int dim=0;dim<4;dim++) {
+      outdim0[dim] = outdimA0[axis[dim]];
+      outdim1[dim] = outdimA1[axis[dim]];
+      outdim2[dim] = outdimA2[axis[dim]];
+      outdim3[dim] = outdimA3[axis[dim]];
+      outdim4[dim] = outdimA4[axis[dim]];
+    }
+  }
+};
+
+bottleneck_forward_status forward_state;
+
+} // end of anonymous namespace
+
+std::vector<at::Tensor> bottleneck_forward_init(bool explicit_nhwc, int stride_1X1, std::vector<at::Tensor> inputs) {
+  // NB! Bottleneck_forward and bottleneck_backward are NOT thread safe method.
+  // NB! We use a global object to store state.
+  forward_state.init(explicit_nhwc, stride_1X1, inputs);
+
+  // create output vector
+  std::vector<at::Tensor> outputs;
+  auto output_format = explicit_nhwc ? at::MemoryFormat::Contiguous : at::MemoryFormat::ChannelsLast;
+
+  printf("outdim1 = (%d,%d,%d,%d)\n",forward_state.outdim1[0],forward_state.outdim1[1],forward_state.outdim1[2],forward_state.outdim1[3]);
+  auto out1 = at::empty(forward_state.outdim1, inputs[0].type(), output_format);
+  auto out2 = at::empty(forward_state.outdim2, inputs[0].type(), output_format);
+  auto out3 = at::empty(forward_state.outdim3, inputs[0].type(), output_format);
+
+  outputs.push_back(out1);
+  outputs.push_back(out2);
+  outputs.push_back(out3);
+
+  return outputs;
+}
+
+// inputs contains x,w,z,b,(i)
+void bottleneck_forward_out1(bool explicit_nhwc, int stride_1X1, std::vector<at::Tensor> inputs, std::vector<at::Tensor> outputs) {
+
+  std::cout << std::fixed;
+
+  // run
+  at::Half* x = inputs[0].data_ptr<at::Half>();
+  at::Half* w = inputs[1].data_ptr<at::Half>();
+  at::Half* z = inputs[4].data_ptr<at::Half>();
+  at::Half* b = inputs[7].data_ptr<at::Half>();
+  auto out1 = outputs[0];
+  at::Half* y1 = out1.data_ptr<at::Half>();
+
+  run_conv_scale_bias_add_activation(forward_state.dimA,
+                                     forward_state.padA,
+                                     forward_state.convstride1X1,
+                                     forward_state.dilationA,
+                                     forward_state.filterdimA1,
+                                     forward_state.outdimA1,
+                                     CUDNN_DATA_HALF,
+                                     x,
+                                     w,
+                                     y1,
+                                     z,
+                                     b,
+                                     nullptr);
+
+  DEBUG_MSG("[DEBUG] new relu1 : " << out1.to(at::kFloat).sum().item<float>());
+}
+
+// computes halo (top or bottom) from fat halo input.
+// fat halo input is 3 pixels wide in H.
+at::Tensor bottleneck_forward_out2_halo(bool explicit_nhwc, at::Tensor fat_halo_y1, std::vector<at::Tensor> inputs) {
+
+  auto output_format = explicit_nhwc ? at::MemoryFormat::Contiguous : at::MemoryFormat::ChannelsLast;
+
+  // run
+  at::Half* w = inputs[2].data_ptr<at::Half>();
+  at::Half* z = inputs[5].data_ptr<at::Half>();
+  at::Half* b = inputs[8].data_ptr<at::Half>();
+  
+  at::Half* y1 = fat_halo_y1.data_ptr<at::Half>();
+
+  auto halo_y2 = at::empty(forward_state.outdim4, inputs[0].type(), output_format);
+  at::Half* y2 = halo_y2.data_ptr<at::Half>();
+
+  run_conv_scale_bias_add_activation(forward_state.outdimA0,
+                                     forward_state.padA2,
+                                     forward_state.convstrideA,
+                                     forward_state.dilationA,
+                                     forward_state.filterdimA2,
+                                     forward_state.outdimA4,
+                                     CUDNN_DATA_HALF,
+                                     y1,
+                                     w,
+                                     y2,
+                                     z,
+                                     b,
+                                     nullptr);
+
+  return halo_y2;
+}
+
+void bottleneck_forward_out2(bool explicit_nhwc, int stride_1X1, std::vector<at::Tensor> inputs, std::vector<at::Tensor> outputs) {
+
+  std::cout << std::fixed;
+
+  // from _out1 method
+  at::Half* x = inputs[0].data_ptr<at::Half>();
+  auto out1 = outputs[0];
+  at::Half* y1 = out1.data_ptr<at::Half>();
+
+  // run
+  at::Half* w = inputs[2].data_ptr<at::Half>();
+  at::Half* z = inputs[5].data_ptr<at::Half>();
+  at::Half* b = inputs[8].data_ptr<at::Half>();
+  auto out2 = outputs[1];
+  at::Half* y2 = out2.data_ptr<at::Half>();
+
+  printf("forward_state.outdimA1 = {%d,%d,%d,%d}\n",forward_state.outdimA1[0],forward_state.outdimA1[1],forward_state.outdimA1[2],forward_state.outdimA1[3]);
+  printf("forward_state.padA1 = {%d,%d}\n",forward_state.padA1[0],forward_state.padA1[1]);
+  printf("forward_state.convstrideA = {%d,%d}\n",forward_state.convstrideA[0],forward_state.convstrideA[1]);
+  printf("forward_state.dilationA = {%d,%d}\n",forward_state.dilationA[0],forward_state.dilationA[1]);
+  printf("forward_state.filterdimA2 = {%d,%d,%d,%d}\n",forward_state.filterdimA2[0],forward_state.filterdimA2[1],forward_state.filterdimA2[2],forward_state.filterdimA2[3]);
+  printf("forward_state.outdimA2 = {%d,%d,%d,%d}\n",forward_state.outdimA2[0],forward_state.outdimA2[1],forward_state.outdimA2[2],forward_state.outdimA2[3]);
+  run_conv_scale_bias_add_activation(forward_state.outdimA1,
+                                     forward_state.padA1,
+                                     forward_state.convstrideA,
+                                     forward_state.dilationA,
+                                     forward_state.filterdimA2,
+                                     forward_state.outdimA2,
+                                     CUDNN_DATA_HALF,
+                                     y1,
+                                     w,
+                                     y2,
+                                     z,
+                                     b,
+                                     nullptr);
+  DEBUG_MSG("[DEBUG] new relu2 : " << out2.to(at::kFloat).sum().item<float>());
+}
+
+void bottleneck_forward_rest(bool explicit_nhwc, int stride_1X1, std::vector<at::Tensor> inputs, std::vector<at::Tensor> outputs) {
+
+  std::cout << std::fixed;
+
+  // from _out1 method
+  at::Half* x = inputs[0].data_ptr<at::Half>();
+
+  // create output of conv3
+  auto out3 = outputs[2];
+  at::Half* y3 = out3.data_ptr<at::Half>();
+
+  // create output of conv4 that may exist
+  auto identity = at::empty_like(out3);
+  at::Half* yi = identity.data_ptr<at::Half>();
+
+  at::Half *w, *z, *b;
+
+  if (stride_1X1 != 1 || forward_state.filterdimA3[0] != forward_state.dimA[1]){
+
+    w = inputs[10].data_ptr<at::Half>();
+    z = inputs[11].data_ptr<at::Half>();
+    b = inputs[12].data_ptr<at::Half>();
+    run_conv_scale_bias(forward_state.dimA,
+                        forward_state.padA,
+                        forward_state.convstride1X1,
+                        forward_state.dilationA,
+                        forward_state.filterdimA4,
+                        forward_state.outdimA3,
+                        CUDNN_DATA_HALF,
+                        x,
+                        w,
+                        yi,
+                        z,
+                        b);
+    DEBUG_MSG("[DEBUG] new downsample : " << identity.to(at::kFloat).sum().item<float>());
+  }
+  else {
+    yi = x;
+  }
+
+  auto out2 = outputs[1];
+  at::Half* y2 = out2.data_ptr<at::Half>();
+
+  w = inputs[3].data_ptr<at::Half>();
+  z = inputs[6].data_ptr<at::Half>();
+  b = inputs[9].data_ptr<at::Half>();
+
+  run_conv_scale_bias_add_activation(forward_state.outdimA2,
+                                     forward_state.padA,
+                                     forward_state.convstrideA,
+                                     forward_state.dilationA,
+                                     forward_state.filterdimA3,
+                                     forward_state.outdimA3,
+                                     CUDNN_DATA_HALF,
+                                     y2,
+                                     w,
+                                     y3,
+                                     z,
+                                     b,
+                                     yi);
+  DEBUG_MSG("[DEBUG] new relu3 : " << out3.to(at::kFloat).sum().item<float>());
+}
+
+namespace {
+
+struct bottleneck_backward_state {
+
+  int64_t dimA[4];
+  int64_t filterdimA1[4];
+  int64_t filterdimA2[4];
+  int64_t filterdimA3[4];
+  int64_t filterdimA4[4];
+
+  int axis[4];
+
+  int64_t outdimA1[4];
+  int64_t outdimA2[4];
+  int64_t outdimA3[4];
+
+  int64_t padA[2];
+  int64_t padA1[2];
+  int64_t dilationA[2];
+  int64_t convstrideA[2];
+  int64_t convstride1X1[2];
+
+  int64_t outdim1[4];
+  int64_t outdim2[4];
+  int64_t outdim3[4];
+
+  void init(bool explicit_nhwc, int stride_1X1, std::vector<at::Tensor> inputs) {
+    // setup dimensions
+    dimA[0] = dimA[1] = dimA[2] = dimA[3] = 0;
+    filterdimA1[0] = filterdimA1[1] = filterdimA1[2] = filterdimA1[3] = 0;
+    filterdimA2[0] = filterdimA2[1] = filterdimA2[2] = filterdimA2[3] = 0;
+    filterdimA3[0] = filterdimA3[1] = filterdimA3[2] = filterdimA3[3] = 0;
+    filterdimA4[0] = filterdimA4[1] = filterdimA4[2] = filterdimA4[3] = 0;
+
+    // All dim calculation after this order of n,c,h,w
+    if (explicit_nhwc) {
+      axis[0] = 0;
+      axis[1] = 3;
+      axis[2] = 1;
+      axis[3] = 2;
+    } else {
+      axis[0] = 0;
+      axis[1] = 1;
+      axis[2] = 2;
+      axis[3] = 3;
+    }
+
+    for (int dim=0;dim<4;dim++) {
+      dimA[dim] = inputs[0].size(axis[dim]);
+      filterdimA1[dim] = inputs[1].size(axis[dim]);
+      filterdimA2[dim] = inputs[2].size(axis[dim]);
+      filterdimA3[dim] = inputs[3].size(axis[dim]);
+    }
+    if (stride_1X1 != 1 || filterdimA3[0] != dimA[1]) {
+      for (int dim=0;dim<4;dim++) {
+        filterdimA4[dim] = inputs[14].size(axis[dim]);
+      }
+    }
+
+    // output dim in n,c,h,w used by backend
+    outdimA1[0] = outdimA1[1] = outdimA1[2] = outdimA1[3] = 0;
+    outdimA2[0] = outdimA2[1] = outdimA2[2] = outdimA2[3] = 0;
+    outdimA3[0] = outdimA3[1] = outdimA3[2] = outdimA3[3] = 0;
+
+    // use these fixed value for test run
+    padA[0] = 0; padA[1] = 0;
+    padA1[0] = 1; padA1[1] = 1;
+    dilationA[0] = 1; dilationA[1] = 1;
+    convstrideA[0] = 1; convstrideA[1] = 1;
+    convstride1X1[0] = stride_1X1; convstride1X1[1] = stride_1X1;
+
+    // compute output from pad/stride/dilation
+    outdimA1[0] = dimA[0];
+    outdimA1[1] = filterdimA1[0];
+    for (int dim = 0; dim < 2; dim++) {
+      outdimA1[dim + 2] = getFwdConvOutputDim(dimA[dim + 2], padA[dim], filterdimA1[dim + 2], convstride1X1[dim], dilationA[dim]);
+    }
+
+    outdimA2[0] = outdimA1[0];
+    outdimA2[1] = filterdimA2[0];
+    for (int dim = 0; dim < 2; dim++) {
+      outdimA2[dim + 2] = getFwdConvOutputDim(outdimA1[dim + 2], padA1[dim], filterdimA2[dim + 2], convstrideA[dim], dilationA[dim]);
+    }
+
+    outdimA3[0] = outdimA2[0];
+    outdimA3[1] = filterdimA3[0];
+    for (int dim = 0; dim < 2; dim++) {
+      outdimA3[dim + 2] = getFwdConvOutputDim(outdimA2[dim + 2], padA[dim], filterdimA3[dim + 2], convstrideA[dim], dilationA[dim]);
+    }
+
+    // Create output tensor in the correct shape in pytorch's view
+    outdim1[0] = outdim1[1] = outdim1[2] = outdim1[3] = 0;
+    outdim2[0] = outdim2[1] = outdim2[2] = outdim2[3] = 0;
+    outdim3[0] = outdim3[1] = outdim3[2] = outdim3[3] = 0;
+    if (explicit_nhwc) {
+      axis[0] = 0;
+      axis[1] = 2;
+      axis[2] = 3;
+      axis[3] = 1;
+    }
+    for (int dim=0;dim<4;dim++) {
+      outdim1[dim] = outdimA1[axis[dim]];
+      outdim2[dim] = outdimA2[axis[dim]];
+      outdim3[dim] = outdimA3[axis[dim]];
+    }
+  }
+};
+
+bottleneck_backward_state backward_state;
+
+}
+
+std::vector<at::Tensor> bottleneck_backward_init(bool explicit_nhwc, int stride_1X1, std::vector<at::Tensor> inputs) {
+
+  std::cout << std::fixed;
+
+  backward_state.init(explicit_nhwc, stride_1X1, inputs);
+
+  // create output vector
+  std::vector<at::Tensor> outputs;
+  auto output_format = explicit_nhwc ? at::MemoryFormat::Contiguous : at::MemoryFormat::ChannelsLast;
+
+  auto grad_x = at::empty_like(inputs[0]);
+  auto wgrad1 = at::empty_like(inputs[1]);
+  auto wgrad2 = at::empty_like(inputs[2]);
+  auto wgrad3 = at::empty_like(inputs[3]);
+
+  outputs.push_back(grad_x);
+  outputs.push_back(wgrad1);
+  outputs.push_back(wgrad2);
+  outputs.push_back(wgrad3);
+  if (stride_1X1 != 1 || backward_state.filterdimA3[0] != backward_state.dimA[1]) {
+    auto wgrad4 = at::empty_like(inputs[14]);
+    outputs.push_back(wgrad4);
+  }
+
+  return outputs;
+}
+
+at::Tensor bottleneck_backward_grad_out2(bool explicit_nhwc, int stride_1X1, std::vector<at::Tensor> inputs, std::vector<at::Tensor> outputs) {
+
+  bool requires_grad = inputs[0].requires_grad();
+
+  std::cout << std::fixed;
+  auto output_format = explicit_nhwc ? at::MemoryFormat::Contiguous : at::MemoryFormat::ChannelsLast;
+
+  // dconv3+drelu2+dscale2
+  at::Half* conv_in = inputs[13].data_ptr<at::Half>();
+  at::Half* dy3 = inputs[10].data_ptr<at::Half>();
+
+  DEBUG_MSG("[DEBUG] new dconv3 : " << inputs[10].to(at::kFloat).sum().item<float>());
+
+  // wgrad
+  auto wgrad3 = outputs[3];
+  at::Half* dw3 = wgrad3.data_ptr<at::Half>();
+  run_dconv(backward_state.outdimA2,
+            backward_state.padA,
+            backward_state.convstrideA,
+            backward_state.dilationA,
+            backward_state.filterdimA3,
+            backward_state.outdimA3,
+            CUDNN_DATA_HALF,
+            conv_in,
+            dw3,
+            dy3,
+            CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR);
+
+  // dgrad
+  auto grad_out2 = at::empty(backward_state.outdim2, inputs[0].type(), output_format);
+  at::Half* dy2 = grad_out2.data_ptr<at::Half>();
+  at::Half* w = inputs[3].data_ptr<at::Half>();
+  at::Half* z = inputs[5].data_ptr<at::Half>();
+
+  at::Half* relu2 = inputs[13].data_ptr<at::Half>();
+
+  run_dconv_drelu_dscale(backward_state.outdimA2,
+                         backward_state.padA,
+                         backward_state.convstrideA,
+                         backward_state.dilationA,
+                         backward_state.filterdimA3,
+                         backward_state.outdimA3,
+                         CUDNN_DATA_HALF,
+                         dy2,
+                         w,
+                         dy3,
+                         z,
+                         relu2);
+
+  // do halo exchange of dy2 here
+
+  DEBUG_MSG("[DEBUG] new dconv2 : " << grad_out2.to(at::kFloat).sum().item<float>());
+
+  return grad_out2;
+}
+
+void bottleneck_backward_rest(bool explicit_nhwc, int stride_1X1, std::vector<at::Tensor> inputs, std::vector<at::Tensor> outputs, at::Tensor grad_out2) {
+
+  bool requires_grad = inputs[0].requires_grad();
+
+  std::cout << std::fixed;
+  auto output_format = explicit_nhwc ? at::MemoryFormat::Contiguous : at::MemoryFormat::ChannelsLast;
+
+  // dgrad
+  at::Half* dy2 = grad_out2.data_ptr<at::Half>();
+
+  // dconv2+drelu1+dscale1
+  at::Half* conv_in = inputs[12].data_ptr<at::Half>();
+
+  // wgrad
+
+  auto wgrad2 = outputs[2];
+  at::Half* dw2 = wgrad2.data_ptr<at::Half>();
+  printf("outdimA1 = (%d,%d,%d,%d)\n",backward_state.outdimA1[0],backward_state.outdimA1[1],backward_state.outdimA1[2],backward_state.outdimA1[3]);
+  run_dconv(backward_state.outdimA1,
+            backward_state.padA1,
+            backward_state.convstrideA,
+            backward_state.dilationA,
+            backward_state.filterdimA2,
+            backward_state.outdimA2,
+            CUDNN_DATA_HALF,
+            conv_in,
+            dw2,
+            dy2,
+            CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR);
+
+  // dgrad
+  auto grad_out1 = at::empty(backward_state.outdim1, inputs[0].type(), output_format);
+  at::Half* dy1 = grad_out1.data_ptr<at::Half>();
+  at::Half* w = inputs[2].data_ptr<at::Half>();
+  at::Half* z = inputs[4].data_ptr<at::Half>();
+
+  at::Half* relu1 = inputs[12].data_ptr<at::Half>();
+  // fused dgrad
+  run_dconv_drelu_dscale(backward_state.outdimA1,
+                         backward_state.padA1,
+                         backward_state.convstrideA,
+                         backward_state.dilationA,
+                         backward_state.filterdimA2,
+                         backward_state.outdimA2,
+                         CUDNN_DATA_HALF,
+                         dy1,
+                         w,
+                         dy2,
+                         z,
+                         relu1);
+
+/*
+  // backward strided conv cannot be fused
+  // if stride == 1 but channel changes, we can fuse here
+  if (stride_1X1 != 1){
+    // dgrad
+    run_dconv(outdimA1,
+              padA1,
+              convstride1X1,
+              dilationA,
+              filterdimA2,
+              outdimA2,
+              CUDNN_DATA_HALF,
+              dy1,
+              w,
+              dy2,
+              CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR);
+
+    // mul fused mask
+    grad_out1.mul_(inputs[15]);
+  }
+  else {
+    at::Half* relu1 = inputs[12].data_ptr<at::Half>();
+    // fused dgrad
+    run_dconv_drelu_dscale(outdimA1,
+                           padA1,
+                           convstride1X1,
+                           dilationA,
+                           filterdimA2,
+                           outdimA2,
+                           CUDNN_DATA_HALF,
+                           dy1,
+                           w,
+                           dy2,
+                           z,
+                           relu1);
+  }
+*/
+  DEBUG_MSG("[DEBUG] new dconv1 : " << grad_out1.to(at::kFloat).sum().item<float>());
+
+  // create grads of conv4 that may exist
+  auto grad_x_conv4 = at::empty_like(inputs[0]);
+  at::Half* dx_conv4 = grad_x_conv4.data_ptr<at::Half>();
+  at::Tensor wgrad4;
+
+  // x used for dconv1 and dconv4 wgrad
+  at::Half* x = inputs[0].data_ptr<at::Half>();
+
+  if (stride_1X1 != 1 || backward_state.filterdimA3[0] != backward_state.dimA[1]){
+    w = inputs[14].data_ptr<at::Half>();
+    at::Half* dy_conv4 = inputs[11].data_ptr<at::Half>();
+    if (requires_grad) {
+      run_dconv(backward_state.dimA,
+                backward_state.padA,
+                backward_state.convstride1X1,
+                backward_state.dilationA,
+                backward_state.filterdimA4,
+                backward_state.outdimA3,
+                CUDNN_DATA_HALF,
+                dx_conv4,
+                w,
+                dy_conv4,
+                CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR);
+      // we don't print here since we can't hook out this grad in pytorch alone to compare, due to addition with dx
+      // DEBUG_MSG("[DEBUG] new dx_identity : " << grad_x_conv4.to(at::kFloat).sum().item<float>());
+    }
+    // wgrad
+    wgrad4 = outputs[4];
+    at::Half* dw4 = wgrad4.data_ptr<at::Half>();
+    run_dconv(backward_state.dimA,
+              backward_state.padA,
+              backward_state.convstride1X1,
+              backward_state.dilationA,
+              backward_state.filterdimA4,
+              backward_state.outdimA3,
+              CUDNN_DATA_HALF,
+              x,
+              dw4,
+              dy_conv4,
+              CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR);
+  }
+  else {
+    // if there is no downsample, dx_conv4 is fork of drelu3
+    dx_conv4 = inputs[11].data_ptr<at::Half>();
+  }
+
+  // dconv1+add
+  // wgrad
+  auto wgrad1 = outputs[1];
+  at::Half* dw1 = wgrad1.data_ptr<at::Half>();
+  run_dconv(backward_state.dimA,
+            backward_state.padA,
+            backward_state.convstride1X1,
+            backward_state.dilationA,
+            backward_state.filterdimA1,
+            backward_state.outdimA1,
+            CUDNN_DATA_HALF,
+            x,
+            dw1,
+            dy1,
+            CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR);
+
+  // dgrad
+  w = inputs[1].data_ptr<at::Half>();
+  auto grad_x = outputs[0];
+  at::Half* dx = grad_x.data_ptr<at::Half>();
+
+  // backward strided conv cannot be fused
+  // if stride == 1 but channel changes, we can fuse here
+  if (requires_grad){
+    if (stride_1X1 != 1){
+      run_dconv(backward_state.dimA,
+                backward_state.padA,
+                backward_state.convstride1X1,
+                backward_state.dilationA,
+                backward_state.filterdimA1,
+                backward_state.outdimA1,
+                CUDNN_DATA_HALF,
+                dx,
+                w,
+                dy1,
+                CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR);
+      // add 2 together
+      grad_x.add_(grad_x_conv4);
+    }
+    else {
+      run_dconv_add(backward_state.dimA,
+                    backward_state.padA,
+                    backward_state.convstride1X1,
+                    backward_state.dilationA,
+                    backward_state.filterdimA1,
+                    backward_state.outdimA1,
+                    CUDNN_DATA_HALF,
+                    dx,
+                    w,
+                    dy1,
+                    dx_conv4);
+    }
+  }
+
+  DEBUG_MSG("[DEBUG] new dx : " << grad_x.to(at::kFloat).sum().item<float>());
+  DEBUG_MSG("[DEBUG] new wgrad1 : " << wgrad1.to(at::kFloat).sum().item<float>());
+  DEBUG_MSG("[DEBUG] new wgrad2 : " << wgrad2.to(at::kFloat).sum().item<float>());
+  DEBUG_MSG("[DEBUG] new wgrad3 : " << wgrad3.to(at::kFloat).sum().item<float>());
+
+  if (stride_1X1 != 1 || backward_state.filterdimA3[0] != backward_state.dimA[1]) {
+    DEBUG_MSG("[DEBUG] new wgrad4 : " << wgrad4.to(at::kFloat).sum().item<float>());
+  }
+}
+
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("forward", &bottleneck_forward, "Bottleneck block forward");
   m.def("backward", &bottleneck_backward, "Bottleneck block backward");
+  m.def("forward_init", &bottleneck_forward_init, "Bottleneck block init");
+  m.def("forward_out1", &bottleneck_forward_out1, "Bottleneck block forward");
+  m.def("forward_out2", &bottleneck_forward_out2, "Bottleneck block forward");
+  m.def("forward_out2_halo", &bottleneck_forward_out2_halo, "Bottleneck block forward");
+  m.def("forward_rest", &bottleneck_forward_rest, "Bottleneck block forward");
+  m.def("backward_init", &bottleneck_backward_init, "Bottleneck block backward init");
+  m.def("backward_grad_out2", &bottleneck_backward_grad_out2, "Bottleneck block backward");
+  m.def("backward_rest", &bottleneck_backward_rest, "Bottleneck block backward");
 }