enable cat for cuda bits types

aten/src/ATen/native/cuda/Shape.cu

@@ -239,7 +239,7 @@ void parallel_cat(const Tensor &out, const MaterializedITensorListRef& inputs, i
                   int nDims, c10::MemoryFormat memory_format) {
   // First, let's set up our kernel parameters. We start with a raw pointer to
   // the storage for the output Tensor.
-  scalar_t *data = out.mutable_data_ptr<scalar_t>();
+  scalar_t *data = (scalar_t *) out.mutable_data_ptr();
   CatArrInputTensorMetadata<scalar_t, unsigned int, batch_size, stride_size> catMetaData;
   TensorSizeStride<unsigned int, CAT_ARRAY_MAX_INPUT_DIMS> outputParam;
 
@@ -289,7 +289,7 @@ void parallel_cat(const Tensor &out, const MaterializedITensorListRef& inputs, i
         dimSize = inputs[i+batchCounter].get().size(dimension);
       }
 
-      catMetaData.input[batchCounter] = inputs[i+batchCounter].get().const_data_ptr<scalar_t>();
+      catMetaData.input[batchCounter] = (scalar_t*)(inputs[i+batchCounter].get().const_data_ptr());
       catMetaData.offset[batchCounter] = offset;
       catMetaData.dimSize[batchCounter] = dimSize;
       catMetaData.nElements[batchCounter] = inputs[i+batchCounter].get().numel();
@@ -375,6 +375,10 @@ void parallel_cat(const Tensor &out, const MaterializedITensorListRef& inputs, i
 #undef HANDLE_CASE
   }
 }
+// The kernels are templated on an opaque, self-aligned type of the correct
+// size to avoid redundant kernels for different types of the same size.
+template <int N> struct alignas(N) OpaqueType { char data[N]; };
+
 } // namespace
 
 TORCH_IMPL_FUNC(cat_out_cuda)
@@ -412,29 +416,48 @@ TORCH_IMPL_FUNC(cat_out_cuda)
   // memory. Therefore, we could pass more inputs to cuda threads.
   // For non-contiguous, we reduce the number of inputs passed to cuda kernel due to the limitation
   // of constant memory.
+
+
+
   if (materialized.size() > 1 &&
       result.dim() <= CAT_ARRAY_MAX_INPUT_DIMS &&
       at::cuda::detail::canUse32BitIndexMath(result) &&
       all_contiguous &&
       all32BitIndexable &&
       all_same_dtype) {
-      AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND4(
-          kComplexHalf, kHalf, kBool, kBFloat16,
-          result.scalar_type(), "cat_cuda", [&]() {
-        parallel_cat<scalar_t, CAT_ARRAY_BATCH_SIZE, 1>(result, materialized, dim, nDims, memory_format);
-      });
+      if (isBitsType(result.scalar_type())) {
+        AT_DISPATCH_BIT_TYPES(result.scalar_type(), "cat_cuda", [&]() {
+          using dtype = OpaqueType<sizeof(scalar_t)>;
+          parallel_cat<dtype, CAT_ARRAY_BATCH_SIZE, 1>(result, materialized, dim, nDims, memory_format);
+        });
+      } else {
+        AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND4(
+            kComplexHalf, kHalf, kBool, kBFloat16,
+            result.scalar_type(), "cat_cuda", [&]() {
+          using dtype = OpaqueType<sizeof(scalar_t)>;
+          parallel_cat<dtype, CAT_ARRAY_BATCH_SIZE, 1>(result, materialized, dim, nDims, memory_format);
+        });
+      }
   } else if (materialized.size() > 1 &&
       result.dim() <= CAT_ARRAY_MAX_INPUT_DIMS &&
       at::cuda::detail::canUse32BitIndexMath(result) &&
       nDims <= CAT_ARRAY_MAX_INPUT_DIMS &&
       all32BitIndexable &&
       all_same_dtype &&
       memory_format == c10::MemoryFormat::Contiguous) {
-      AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND4(
-          kComplexHalf, kHalf, kBool, kBFloat16,
-          result.scalar_type(), "cat_cuda", [&]() {
-        parallel_cat<scalar_t, CAT_ARRAY_BATCH_SIZE/2, CAT_ARRAY_BATCH_SIZE/2>(result, materialized, dim, nDims, memory_format);
-      });
+      if (isBitsType(result.scalar_type())) {
+        AT_DISPATCH_BIT_TYPES(result.scalar_type(), "cat_cuda", [&]() {
+          using dtype = OpaqueType<sizeof(scalar_t)>;
+          parallel_cat<dtype, CAT_ARRAY_BATCH_SIZE/2, CAT_ARRAY_BATCH_SIZE/2>(result, materialized, dim, nDims, memory_format);
+        });
+      } else {
+        AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND4(
+            kComplexHalf, kHalf, kBool, kBFloat16,
+            result.scalar_type(), "cat_cuda", [&]() {
+            using dtype = OpaqueType<sizeof(scalar_t)>;
+            parallel_cat<dtype, CAT_ARRAY_BATCH_SIZE/2, CAT_ARRAY_BATCH_SIZE/2>(result, materialized, dim, nDims, memory_format);
+        });
+      }
   } else {
     int64_t offset = 0;
     for (const Tensor& t : materialized) {

test/quantization/core/experimental/test_bits.py

@@ -1,10 +1,14 @@
 # Owner(s): ["oncall: quantization"]
 
 import torch
+from torch.testing._internal.common_device_type import instantiate_device_type_tests
+
 from torch.testing._internal.common_utils import run_tests, TestCase
 from torch.utils._mode_utils import no_dispatch
 from torch.utils._pytree import tree_map
 
+import itertools
+
 class Int16Tensor(torch.Tensor):
     def __new__(cls, elem):
         assert elem.dtype == torch.bits16
@@ -41,24 +45,42 @@ def __repr__(self) -> str:
 
 
 class TestBits(TestCase):
-    def test_types(self):
+    def test_types(self, device):
         bits_types = [torch.bits1x8, torch.bits2x4, torch.bits4x2, torch.bits8, torch.bits16]
         for bits_type in bits_types:
-            _ = torch.zeros(20, dtype=torch.int32).view(bits_type)
-            _ = torch.empty(20, dtype=bits_type)
-            x = torch.randint(100, (20, 20), dtype=torch.int8).view(bits_type)
+            _ = torch.zeros(20, dtype=torch.int32, device=device).view(bits_type)
+            _ = torch.empty(20, dtype=bits_type, device=device)
+            x = torch.randint(100, (20, 20), dtype=torch.int8, device=device).view(bits_type)
             y = x.t().contiguous()
             view_type = torch.int8 if x.element_size() == 1 else torch.int16
             self.assertEqual(x.t().view(view_type), y.view(view_type))
             y = x.t().clone()
             self.assertEqual(x.t().view(view_type), y.view(view_type))
 
+    def test_cat(self, device):
+        bits_types = [torch.bits1x8, torch.bits2x4, torch.bits4x2, torch.bits8, torch.bits16]
+        for bits_type in bits_types:
+            view_type = torch.int8 if bits_type.itemsize == 1 else torch.int16
+            x_int = torch.randint(100, (512, 512), dtype=view_type, device=device)
+            x = x_int.view(bits_type)
+            y_int = torch.randint(100, (512, 512), dtype=view_type, device=device)
+            y = y_int.view(bits_type)
+            for dim, transpose in itertools.product(range(x_int.ndim), (True, False)):
+                y_ref = y_int.t() if transpose else y_int
+                y_b = y.t() if transpose else y
+                z_ref = torch.cat([x_int, y_ref], dim=dim)
+                z = torch.cat([x, y_b], dim=dim)
+                self.assertEqual(z_ref, z.view(view_type))
+
+
     def test_subclass(self):
         t = torch.zeros(20, dtype=torch.int16).view(torch.bits16)
         s = Int16Tensor(t)
         s = s + 1 - 1
         self.assertTrue(torch.allclose(s, torch.zeros(20, dtype=torch.bits16)))
 
+instantiate_device_type_tests(TestBits, globals())
+
 
 if __name__ == '__main__':
     run_tests()

test/test_quantization.py

@@ -153,7 +153,14 @@
     logging.warning(e)
 
 # Experimental functionality
-from quantization.core.experimental.test_bits import TestBits  # noqa: F401
+try:
+    from quantization.core.experimental.test_bits import TestBitsCPU  # noqa: F401
+except ImportError as e:
+    logging.warning(e)
+try:
+    from quantization.core.experimental.test_bits import TestBitsCUDA  # noqa: F401
+except ImportError as e:
+    logging.warning(e)
 try:
     from quantization.core.experimental.test_float8 import TestFloat8DtypeCPU  # noqa: F401
 except ImportError as e: