Add halo correction using new cudnn masking feature

apex/contrib/bottleneck/bottleneck.py

@@ -220,7 +220,7 @@ class Bottleneck(torch.nn.Module):
 
 class SpatialBottleneckFunction(torch.autograd.Function):
     @staticmethod
-    def forward(ctx, spatial_group_size, spatial_group_rank, spatial_communicator, spatial_halo_exchanger, spatial_method, explicit_nhwc, stride_1x1, scale, bias, x, *conv):
+    def forward(ctx, spatial_group_size, spatial_group_rank, spatial_communicator, spatial_halo_exchanger, spatial_method, explicit_nhwc, stride_1x1, scale, bias, thresholdTop, thresholdBottom, x, *conv):
         if spatial_group_size > 1:
             stream1 = spatial_halo_exchanger.stream1
             stream2 = spatial_halo_exchanger.stream2
@@ -271,56 +271,74 @@ class SpatialBottleneckFunction(torch.autograd.Function):
                 if spatial_group_rank < spatial_group_size-1:
                     stream2.wait_stream(stream1)
                     with torch.cuda.stream(stream2):
-                        btm_fat_halo = torch.empty((N,3,W,C),dtype=out1.dtype,device=out1.device)
                         if explicit_nhwc:
+                            btm_fat_halo = torch.empty((N,3,W,C),dtype=out1.dtype,device=out1.device)
                             btm_fat_halo[:,0:2,:,:].copy_(out1[:,Hs-2:,:,:])
                             btm_fat_halo[:,2:,:,:].copy_(btm_out1_halo)
                         else:
+                            btm_fat_halo = torch.empty((N,C,3,W),dtype=out1.dtype,device=out1.device)
                             btm_fat_halo[:,:,0:2,:].copy_(out1[:,:,Hs-2:,:])
                             btm_fat_halo[:,:,2:,:].copy_(btm_out1_halo)
                         btm_out2 = fast_bottleneck.forward_out2_halo(explicit_nhwc, btm_fat_halo, args)
                 if spatial_group_rank > 0:
                     with torch.cuda.stream(stream1):
-                        top_fat_halo = torch.empty((N,3,W,C),dtype=out1.dtype,device=out1.device)
                         if explicit_nhwc:
+                            top_fat_halo = torch.empty((N,3,W,C),dtype=out1.dtype,device=out1.device)
                             top_fat_halo[:,:1,:,:].copy_(top_out1_halo)
                             top_fat_halo[:,1:3,:,:].copy_(out1[:,:2,:,:])
                         else:
+                            top_fat_halo = torch.empty((N,C,3,W),dtype=out1.dtype,device=out1.device)
                             top_fat_halo[:,:,:1,:].copy_(top_out1_halo)
                             top_fat_halo[:,:,1:3,:].copy_(out1[:,:,:2,:])
                         top_out2 = fast_bottleneck.forward_out2_halo(explicit_nhwc, top_fat_halo, args)
                 inc.add_delay(10)
+            elif spatial_method != 2 and spatial_method != 3:
+                assert(False), "spatial_method must be 1, 2 or 3"
+
+        if spatial_group_size <= 1:
+            fast_bottleneck.forward_out2(explicit_nhwc, stride_1x1, args, outputs)
+        elif spatial_method == 1:
+            fast_bottleneck.forward_out2(explicit_nhwc, stride_1x1, args, outputs)
         elif spatial_method == 2:
             # wait for halo transfer to finish before doing a full convolution of padded x
             torch.cuda.current_stream().wait_stream(stream1)
             torch.cuda.current_stream().wait_stream(stream3)
             fast_bottleneck.forward_out2_pad(explicit_nhwc, stride_1x1, args, outputs, out1_pad)
-            else:
-                assert(False), "spatial_method must be 1 or 2"
-
-        if spatial_group_size <= 1 or spatial_method == 1:
-            fast_bottleneck.forward_out2(explicit_nhwc, stride_1x1, args, outputs)
+        elif spatial_method == 3:
+            fast_bottleneck.forward_out2_mask(explicit_nhwc, stride_1x1, args, outputs, thresholdTop, thresholdBottom)
 
         # compute halo cells for outputs[1] (out2)
-        if spatial_group_size > 1 and spatial_method == 1:
+        if spatial_group_size > 1:
             out2 = outputs[1]
-            if spatial_group_rank > 0:
-                torch.cuda.current_stream().wait_stream(stream1)
             if explicit_nhwc:
-                    out2[:,:1,:,:].copy_(top_out2)
+                top_out2_halo = out2[:,:1,:,:]
+                btm_out2_halo = out2[:,Hs-1:,:,:]
             else:
-                    out2[:,:,:1,:].copy_(top_out2)
+                top_out2_halo = out2[:,:,:1,:]
+                btm_out2_halo = out2[:,:,Hs-1:,:]
+            if spatial_method == 1:
+                if spatial_group_rank > 0:
+                    torch.cuda.current_stream().wait_stream(stream1)
+                    top_out2_halo.copy_(top_out2)
                 if spatial_group_rank < spatial_group_size-1:
                     torch.cuda.current_stream().wait_stream(stream2)
-                if explicit_nhwc:
-                    out2[:,Hs-1:,:,:].copy_(btm_out2)
-                else:
-                    out2[:,:,Hs-1:,:].copy_(btm_out2)
-            torch.cuda.current_stream().wait_stream(stream3)
+                    btm_out2_halo.copy_(btm_out2)
+            elif spatial_method == 3:
+                if spatial_group_rank > 0:
+                    w1by3 = args[2][:,:,2:3,:].contiguous(memory_format=torch.preserve)
+                    top_out1_halo = top_out1_halo.contiguous(memory_format=memory_format)
+                    top_out2 = fast_bottleneck.forward_out2_halo_corr(explicit_nhwc, top_out1_halo, args, w1by3, top_out2_halo.contiguous(memory_format=memory_format))
+                    top_out2_halo.copy_(top_out2)
+                if spatial_group_rank < spatial_group_size-1:
+                    w1by3 = args[2][:,:,:1,:].contiguous(memory_format=torch.preserve)
+                    btm_out1_halo = btm_out1_halo.contiguous(memory_format=memory_format)
+                    btm_out2 = fast_bottleneck.forward_out2_halo_corr(explicit_nhwc, btm_out1_halo, args, w1by3, btm_out2_halo.contiguous(memory_format=memory_format))
+                    btm_out2_halo.copy_(btm_out2)
             
         fast_bottleneck.forward_rest(explicit_nhwc, stride_1x1, args, outputs)
         # save halos for backward pass
         if spatial_group_size > 1:
+            if spatial_method != 2:
                 torch.cuda.current_stream().wait_stream(stream3)
             ctx.save_for_backward(*(args+outputs+[out1_pad,]))
         else:
@@ -460,7 +478,7 @@ class SpatialBottleneckFunction(torch.autograd.Function):
         fast_bottleneck.backward_rest(ctx.explicit_nhwc, ctx.stride_1x1, t_list, grads, grad_out2, grad_out1, wgrad2)
         torch.cuda.current_stream().wait_stream(wgrad2_stream)
 
-        return (None, None, None, None, None, None, None, None, None, *grads)
+        return (None, None, None, None, None, None, None, None, None, None, None, *grads)
 
 spatial_bottleneck_function = SpatialBottleneckFunction.apply
 
@@ -515,6 +533,8 @@ class SpatialBottleneck(torch.nn.Module):
         for w in self.w_conv:
             kaiming_uniform_(w, a=1)
 
+        self.thresholdTop, self.thresholdBottom = None, None
+
         # TODO: prevent unsupported case usage
         # support cases
         #                 native      cudnn
@@ -536,6 +556,14 @@ class SpatialBottleneck(torch.nn.Module):
 
     def forward(self, x):
         if self.use_cudnn:
+            if self.thresholdTop is None:
+                spatial_group_size, spatial_group_rank, _, _, _ = self.spatial_parallel_args
+                if self.explicit_nhwc:
+                    N,H,W,C = list(x.shape)
+                else:
+                    N,C,H,W = list(x.shape)
+                self.thresholdTop = torch.tensor([1 if spatial_group_rank > 0 else 0], dtype=torch.int32, device='cuda')
+                self.thresholdBottom = torch.tensor([H-2 if spatial_group_rank < spatial_group_size - 1 else H-1], dtype=torch.int32, device='cuda')
             # calculate scale/bias from registered buffers
             # TODO: make this better
             s1, b1 = self.bn1.get_scale_bias(self.explicit_nhwc)
@@ -548,7 +576,7 @@ class SpatialBottleneck(torch.nn.Module):
                 w_scale.append(s4)
                 w_bias.append(b4)
 
-            out = spatial_bottleneck_function(*self.spatial_parallel_args, self.explicit_nhwc, self.stride, w_scale, w_bias, x, *self.w_conv)
+            out = spatial_bottleneck_function(*self.spatial_parallel_args, self.explicit_nhwc, self.stride, w_scale, w_bias, self.thresholdTop, self.thresholdBottom, x, *self.w_conv)
             return out
 
         if self.explicit_nhwc: