v2.1.11: fix #385, fix volta (V100) wgrad kernel

CHANGELOG.md

 # Changelog
 
+## [2.1.11] - 2021-11-22
+### Fixed
+- Fixed a bug Volta kernels (TITAN V, Tesla V100), backward weight kernels use f16 as accumulator. we should use f32.
+- Fixed a corner case when user use kernel size = 1x1 but stride != 1.
+- Fixed a corner case when input feature is non-contiguous when maxpool.
+
 ## [2.1.10] - 2021-11-19
 ### Fixed
 - Fixed a bug in utils.PointToVoxel, shouldn't get cuda stream in cpu code

spconv/core.py

@@ -449,7 +449,7 @@ IMPLGEMM_VOLTA_PARAMS = [
     *gen_conv_params(ConvBwdWeight, (64, 64, 32), (32, 32, 32),
                      NDIM_DONT_CARE,
                      ConvIterAlgo.Optimized,
-                     2, ["f16,f16,f16,f16,f16"],
+                     2, ["f16,f16,f16,f32,f32"],
                      NHWC,
                      NHWC,
                      NHWC,
@@ -461,7 +461,7 @@ IMPLGEMM_VOLTA_PARAMS = [
     *gen_conv_params(ConvBwdWeight, (64, 64, 32), (32, 32, 32),
                      NDIM_DONT_CARE,
                      ConvIterAlgo.Optimized,
-                     2, ["f16,f16,f16,f16,f16"],
+                     2, ["f16,f16,f16,f32,f32"],
                      NHWC,
                      NHWC,
                      NHWC,
@@ -473,7 +473,7 @@ IMPLGEMM_VOLTA_PARAMS = [
     *gen_conv_params(ConvBwdWeight, (128, 128, 32), (32, 64, 32),
                      NDIM_DONT_CARE,
                      ConvIterAlgo.Optimized,
-                     2, ["f16,f16,f16,f16,f16"],
+                     2, ["f16,f16,f16,f32,f32"],
                      NHWC,
                      NHWC,
                      NHWC,

spconv/pytorch/conv.py

@@ -108,7 +108,11 @@ class SparseConvolution(SparseModule):
         self.out_channels = out_channels
         self.kernel_size = kernel_size
         kv = int(np.prod(kernel_size))
+        kv_stride = int(np.prod(stride))
         self.conv1x1 = kv == 1
+        # TODO we should deprecate support for ksize == 1 but stride != 1.
+        if not subm:
+            self.conv1x1 &= kv_stride == 1
         self.stride = stride
         self.padding = padding
         self.dilation = dilation
@@ -247,6 +251,8 @@ class SparseConvolution(SparseModule):
             if self.bias is not None:
                 features += self.bias
             out_tensor = out_tensor.replace_feature(features)
+            # padding may change spatial shape of conv 1x1.
+            out_tensor.spatial_shape = out_spatial_shape
             return out_tensor
         indice_dict = input.indice_dict.copy()
 

spconv/pytorch/ops.py

@@ -789,6 +789,9 @@ def indice_conv_backward(features: torch.Tensor,
     filters = filters.reshape(-1, *filters.shape[-2:])
     kv = filters.shape[0]
     kv_center = kv // 2
+    # TODO handle this in nn.Module to make sure features in backward is contiguous
+    if not features.is_contiguous():
+        features = features.contiguous()
     if not out_bp.is_contiguous():
         out_bp = out_bp.contiguous()
     assert out_bp.is_contiguous()
@@ -1200,6 +1203,9 @@ def implicit_gemm_backward(features: torch.Tensor,
         raise NotImplementedError("work in progress")
     if not out_bp.is_contiguous():
         out_bp = out_bp.contiguous()
+    if not features.is_contiguous():
+        features = features.contiguous()
+
     assert out_bp.is_contiguous()
     assert filters.is_contiguous()
     assert features.is_contiguous()
@@ -1357,6 +1363,8 @@ def indice_maxpool(features: torch.Tensor, indice_pairs: torch.Tensor,
     # stream = get_current_stream()
     # CONV.stream_synchronize(stream)
     # t = time.time()
+    if not features.is_contiguous():
+        features = features.contiguous()
 
     out_channel = features.shape[-1]
     out_features = torch.zeros((num_activate_out, out_channel),
@@ -1399,6 +1407,9 @@ def indice_maxpool_backward(features, out_features, out_bp, indice_pairs,
     indice_pair_num_cpu = indice_pair_num.cpu().tolist()
     if not out_bp.is_contiguous():
         out_bp = out_bp.contiguous()
+    if not features.is_contiguous():
+        features = features.contiguous()
+
     out_features_tv = torch_tensor_to_tv(out_features)
     features_tv = torch_tensor_to_tv(features)
     out_bp_tv = torch_tensor_to_tv(out_bp)
@@ -1428,6 +1439,8 @@ def indice_maxpool_implicit_gemm(features: torch.Tensor,
     stream = get_current_stream()
     # CONV.stream_synchronize(stream)
     # t = time.time()
+    if not features.is_contiguous():
+        features = features.contiguous()
 
     out_channel = features.shape[-1]
     out_features = torch.empty((num_activate_out, out_channel),
@@ -1456,6 +1469,9 @@ def indice_maxpool_implicit_gemm_backward(features, out_features, out_bp,
     assert features.is_cuda
     if not out_bp.is_contiguous():
         out_bp = out_bp.contiguous()
+    if not features.is_contiguous():
+        features = features.contiguous()
+
     stream = get_current_stream()
     out_features_tv = torch_tensor_to_tv(out_features)
     features_tv = torch_tensor_to_tv(features)

version.txt

-2.1.10
+2.1.11