Remove torch.jit.script (#3562)

Summary: X-link: facebookresearch/FBGEMM#648 as titled Differential Revision: D68051880
pytorch · Jan 11, 2025 · e9b502d · e9b502d
1 parent a93eed4
commit e9b502d
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Showing 3 changed files with 21 additions and 19 deletions.
diff --git a/fbgemm_gpu/test/tbe/ssd/ssd_split_tbe_inference_test.py b/fbgemm_gpu/test/tbe/ssd/ssd_split_tbe_inference_test.py
@@ -150,8 +150,6 @@ def test_nbit_ssd_forward(
             ssd_uniform_init_upper=0.1,
             pooling_mode=PoolingMode.SUM,
         ).cuda()
-        # # NOTE: test TorchScript-compatible!
-        # emb = torch.jit.script(emb)
 
         bs = [
             torch.nn.EmbeddingBag(E, D, mode="sum", sparse=True).cuda()
@@ -293,8 +291,6 @@ def test_nbit_ssd_cache(
             ssd_shards=2,
             pooling_mode=PoolingMode.SUM,
         ).cuda()
-        # # NOTE: test TorchScript-compatible!
-        # emb = torch.jit.script(emb)
 
         bs = [
             torch.nn.EmbeddingBag(E, D, mode="sum", sparse=True).cuda()

diff --git a/fbgemm_gpu/test/tbe/training/backward_dense_test.py b/fbgemm_gpu/test/tbe/training/backward_dense_test.py
@@ -229,9 +229,6 @@ def test_backward_dense(  # noqa C901
             weights_precision=weights_precision,
             output_dtype=output_dtype,
         )
-        if do_pooling and not mixed_B:
-            # NOTE: test TorchScript-compatible!
-            cc = torch.jit.script(cc)
 
         for t in range(T):
             cc.split_embedding_weights()[t].data.copy_(bs[t].weight)

diff --git a/fbgemm_gpu/test/tbe/training/forward_test.py b/fbgemm_gpu/test/tbe/training/forward_test.py
@@ -76,6 +76,27 @@
         "test_faketensor__test_forward_gpu_uvm_cache_int8": [
             unittest.skip("Operator not implemented for Meta tensors"),
         ],
+        # TODO: Make it compatible with opcheck tests
+        "test_faketensor__test_forward_gpu_uvm_cache_fp16": [
+            unittest.skip(
+                "Failed for fbgemm::linearize_cache_indices. Operator not implemented for Meta tensors."
+            ),
+        ],
+        "test_faketensor__test_forward_gpu_uvm_cache_fp32": [
+            unittest.skip(
+                "Failed for fbgemm::linearize_cache_indices. Operator not implemented for Meta tensors."
+            ),
+        ],
+        "test_schema__test_forward_gpu_uvm_cache_fp16": [
+            unittest.skip(
+                "Failed with Argument lxu_cache_locations_output is not defined to alias output but was aliasing"
+            ),
+        ],
+        "test_schema__test_forward_gpu_uvm_cache_fp32": [
+            unittest.skip(
+                "Failed with Argument lxu_cache_locations_output is not defined to alias output but was aliasing"
+            ),
+        ],
         # learning rate tensor needs to be on CPU to avoid D->H sync point since it will be used as float in the kernel
         # this fails fake_tensor test as the test expects all tensors to be on the same device
         "test_pt2_compliant_tag_fbgemm_split_embedding_codegen_lookup_rowwise_adagrad_function": [
@@ -273,18 +294,6 @@ def execute_forward_(  # noqa C901
             use_experimental_tbe=use_experimental_tbe,
         )
 
-        if not use_cpu and torch.cuda.is_available():
-            # NOTE: Test TorchScript-compatible!
-            try:
-                # Occasionally, we run into the following error when running
-                # against PyTorch nightly:
-                #
-                # RuntimeError: Can't redefine method:
-                # forward on class: __torch__.fbgemm_gpu.split_table_batched_embeddings_ops_training.___torch_mangle_0.SplitTableBatchedEmbeddingBagsCodegen (of Python compilation unit at: 0x5e74890)
-                cc = torch.jit.script(cc)
-            except Exception as e:
-                print(f"Torch JIT compilation failed: {e}")
-
         for t in range(T):
             cc.split_embedding_weights()[t].data.copy_(
                 bs[t].weight