Bfloat16 support for fbgemm::pack_segments_forward/backward_cpu (#3154)

Summary: X-link: facebookresearch/FBGEMM#249 Pull Request resolved: #3154 add bfloat16 support for pack_segments Reviewed By: jspark1105, jianyuh, hanzlfs Differential Revision: D62655781 fbshipit-source-id: 78dea9593796c3b9f4464461ca1500dba17beaa9
pytorch · Sep 23, 2024 · af8ecb0 · af8ecb0
1 parent 2889f3a
commit af8ecb0
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Showing 2 changed files with 13 additions and 7 deletions.
diff --git a/fbgemm_gpu/src/sparse_ops/sparse_ops_cpu.cpp b/fbgemm_gpu/src/sparse_ops/sparse_ops_cpu.cpp
@@ -2843,8 +2843,9 @@ Tensor pack_segments_forward_cpu(
   TORCH_CHECK(
       t_in.dtype() == at::ScalarType::Float ||
           t_in.dtype() == at::ScalarType::Double ||
-          t_in.dtype() == at::ScalarType::Half,
-      "t_in must be of type float or double or half");
+          t_in.dtype() == at::ScalarType::Half ||
+          t_in.dtype() == at::ScalarType::BFloat16,
+      "t_in must be of type float, double, half, or bfloat16");
   TORCH_CHECK_GT(max_length, 0);
 
   const auto t_in_cont = t_in.expect_contiguous();
@@ -2911,8 +2912,9 @@ Tensor pack_segments_backward_cpu(
   TORCH_CHECK(
       data.dtype() == at::ScalarType::Float ||
           data.dtype() == at::ScalarType::Double ||
-          data.dtype() == at::ScalarType::Half,
-      "data must be of type float or double or half");
+          data.dtype() == at::ScalarType::Half ||
+          data.dtype() == at::ScalarType::BFloat16,
+      "data must be of type float, double, half, or bfloat16");
   TORCH_CHECK(
       max_length == data.sizes()[1],
       "max_length should be equal to the second dimension of the packed segments");

diff --git a/fbgemm_gpu/test/sparse/pack_segments_test.py b/fbgemm_gpu/test/sparse/pack_segments_test.py
@@ -91,6 +91,7 @@ def _pack_segments_ref(
             [
                 torch.float,
                 torch.half,
+                torch.bfloat16,
             ]
         ),
         torch_compile=st.booleans(),
@@ -192,6 +193,7 @@ def test_pack_segments(
             [
                 torch.float,
                 torch.half,
+                torch.bfloat16,
             ]
         ),
         torch_compile=st.booleans(),
@@ -207,7 +209,8 @@ def test_pack_segments_smaller_max_len(
         dtype: torch.dtype,
         torch_compile: bool,
     ) -> None:
-        input_data = torch.tensor(np.random.rand(batch_size, n, k), dtype=dtype)
+        input_raw = np.random.rand(batch_size, n, k)
+        input_data = torch.tensor(input_raw, dtype=dtype)
         lengths = torch.tensor(
             get_n_rand_num_summing_to_k(divisions, batch_size), dtype=torch.int
         )
@@ -221,10 +224,10 @@ def test_pack_segments_smaller_max_len(
 
         packed_ref = self._pack_segments_ref(
             lengths,
-            input_data,
+            input_raw,
             max_length=max_length,
         )
-        # pyre-fixme[6]: For 2nd param expected `Tensor` but got `ndarray`.
+        packed_ref = torch.Tensor(packed_ref).to(dtype)
         self.assertTrue(torch.equal(packed_tensor, packed_ref))
 
         if gpu_available:
@@ -248,6 +251,7 @@ def test_pack_segments_smaller_max_len(
             [
                 torch.float,
                 torch.half,
+                torch.bfloat16,
             ]
         ),
     )