Upgrade fbcode/deeplearning/fbgemm to Python Scientific Stack 2 (pyto…

…rch#3250)

Summary:
Pull Request resolved: pytorch#3250

X-link: facebookresearch/FBGEMM#350

Differential Revision: D64008037

fbshipit-source-id: a77d4662299ce65827f707ba583b6766ba17b285

fbgemm_gpu/bench/split_table_batched_embeddings_benchmark.py

@@ -365,6 +365,8 @@ def context_factory(on_trace_ready: Callable[[profile], None]):
     if do_pooling:
         grad_output = torch.randn(B, sum(Ds)).to(get_device())
     else:
+        # pyre-fixme[6]: For 2nd argument expected `Union[int, SymInt]` but got
+        #  `Union[floating[typing.Any], int]`.
         grad_output = torch.randn(B * T * L, D).to(get_device())
 
     with context_factory(lambda p: _kineto_trace_handler(p, "fwd_bwd")):
@@ -839,6 +841,8 @@ def cache(  # noqa C901
     )
     logging.info(
         f"Accessed weights per batch: {B * T * L} rows, "
+        # pyre-fixme[58]: `*` is not supported for operand types `int` and
+        #  `Union[np.floating[typing.Any], int]`.
         f"{B * T * L * D * param_size_multiplier / 1.0e9: .2f} GB"
     )
 
@@ -1049,6 +1053,8 @@ def nbit_cpu(  # noqa C901
     )
     logging.info(
         f"Accessed weights per batch: {B * T * L} rows, "
+        # pyre-fixme[58]: `*` is not supported for operand types `int` and
+        #  `Union[np.floating[typing.Any], int]`.
         f"{B * T * L * D * param_size_multiplier / 1.0e9: .2f} GB"
     )
 
@@ -1232,6 +1238,8 @@ def nbit_device(  # noqa C901
     )
     logging.info(
         f"Accessed weights per batch: {B * T * L} rows, "
+        # pyre-fixme[58]: `*` is not supported for operand types `int` and
+        #  `Union[np.floating[typing.Any], int]`.
         f"{B * T * L * D * param_size_multiplier / 1.0e9: .2f} GB"
     )
 
@@ -1337,6 +1345,8 @@ def nbit_device(  # noqa C901
                 B,
                 L,
                 E,
+                # pyre-fixme[6]: For 6th argument expected `int` but got
+                #  `Union[floating[typing.Any], int]`.
                 D,
                 pooling,
                 weighted,
@@ -2037,6 +2047,7 @@ def nbit_uvm_compare_direct_mapped(
             round_up(np.random.randint(low=int(0.5 * D), high=int(1.5 * D)), 4)
             for _ in range(T)
         ]
+        # pyre-fixme[9]: D has type `int`; used as `floating[typing.Any]`.
         D = np.average(Ds)
     else:
         Ds: List[int] = [D] * T
@@ -2323,6 +2334,8 @@ def nbit_cache(  # noqa C901
     )
     logging.info(
         f"Accessed weights per batch: {B * T * L} rows, "
+        # pyre-fixme[58]: `*` is not supported for operand types `int` and
+        #  `Union[np.floating[typing.Any], int]`.
         f"{B * T * L * D * param_size_multiplier / 1.0e9: .2f} GB"
     )
 

fbgemm_gpu/bench/ssd_table_batched_embeddings_benchmark.py

@@ -286,6 +286,7 @@ def ssd_training(  # noqa C901
             round_up(np.random.randint(low=int(0.5 * D), high=int(1.5 * D)), 4)
             for _ in range(T)
         ]
+        # pyre-fixme[9]: D has type `int`; used as `floating[typing.Any]`.
         D = np.average(Ds)
     else:
         Ds: List[int] = [D] * T

fbgemm_gpu/codegen/genscript/optimizer_args.py

@@ -34,6 +34,7 @@
 
 @dataclass
 class OptimizerArgsSetItem:
+    # pyre-fixme[11]: Annotation `ArgType` is not defined as a type.
     ty: ArgType  # type
     name: str
     default: Union[float, ArgType] = 0  # DEFAULT_ARG_VAL

fbgemm_gpu/fbgemm_gpu/tbe/utils/offsets.py

@@ -25,6 +25,8 @@ def get_table_batched_offsets_from_dense(
     if L is None and total_B is None:
         (T, B, L) = merged_indices.size()
         total_B = T * B
+    # pyre-fixme[6]: For 1st argument expected `Union[Sequence[SupportsIndex],
+    #  SupportsIndex]` but got `Optional[int]`.
     lengths = np.ones(total_B) * L
     return (
         to_device(merged_indices.contiguous().view(-1), use_cpu),

fbgemm_gpu/test/batched_unary_embeddings_test.py

@@ -106,6 +106,8 @@ def _generate_unary_features(
         offset = 0
         for _ in range(batch_size):
             n_indices = 1
+            # pyre-fixme[6]: For 1st argument expected `Iterable[typing.Any]` but
+            #  got `float`.
             indices += np.round(
                 np.random.random(n_indices) * (num_embeddings - 1)
             ).tolist()

fbgemm_gpu/test/sparse/misc_ops_test.py

@@ -70,7 +70,7 @@ def test_offsets_range(
         offsets_type: "Union[Type[torch.int32], Type[torch.int64]]",
     ) -> None:
         lengths = np.array([np.random.randint(low=0, high=20) for _ in range(N)])
-        offsets = np.cumsum(np.concatenate(([0], lengths)))[:-1]
+        offsets = np.cumsum(np.concatenate([[0], lengths]))[:-1]
         range_ref = torch.from_numpy(
             np.concatenate([np.arange(size) for size in lengths])
         )