Embedding support for nvfuser in inference (#1674)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: Priya2698

thunder/executors/nvfuserex_impl.py

@@ -946,8 +946,9 @@ def fusion_pass(self, trace: TraceCtx) -> TraceCtx:
 register_executor(ex)
 
 
-def register_supported(id: Hashable, translator: Callable, checker: Callable):
-    ex.register_supported(id, checker)
+def register_supported(sym_or_id: Hashable, translator: Callable, checker: Callable):
+    ex.register_supported(sym_or_id, checker)
+    id = sym_or_id.id if isinstance(sym_or_id, Symbol) else sym_or_id
     _translation_map[id] = translator
 
 
@@ -2582,3 +2583,47 @@ def scaled_dot_product_flash_attention_grad(
     execution_transform=scaled_dot_product_flash_attention,
     grad_transform=scaled_dot_product_flash_attention_grad,
 )
+
+
+def _embedding_check(
+    input: TensorProxy,
+    weight: TensorProxy,
+    padding_idx: None | int,
+    max_norm: None | float,
+    norm_type: None | float,
+    scale_grad_by_freq: None | bool,
+    sparse: None | bool,
+) -> bool:
+    if nvfuser_version() < LooseVersion("0.2.25"):
+        return False
+    enable_embedding: None | bool = get_compile_option("nv_enable_embedding", "Enable nvFuser embedding.")
+    if not enable_embedding:
+        return False
+    # Verify input and weight are supported tensors.
+    if not are_supported_tensors(input, weight) or (weight.ndim != 2):
+        return False
+    return True
+
+
+def embedding(
+    input: TensorProxy,
+    weight: TensorProxy,
+    padding_idx: None | int = None,
+    max_norm: None | float = None,
+    norm_type: None | float = 2.0,
+    scale_grad_by_freq: None | bool = False,
+    sparse: None | bool = False,
+    *,
+    fd: FusionDefinition,
+    lc_to_nv_map: dict,
+) -> Any:
+    inputs = [input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse]
+    nv_inputs = []
+    for inp in inputs:
+        nv_inp = getnv(inp, fd, lc_to_nv_map) if inp is not None else None
+        nv_inputs.append(nv_inp)
+    return fd.ops.embedding_fwd(*nv_inputs)
+
+
+register_supported(PrimIDs.EMBEDDING, embedding, _embedding_check)
+register_supported(ltorch.embedding, embedding, _embedding_check)

thunder/tests/test_nvfuser.py

@@ -40,6 +40,7 @@
     get_opinfo,
     linear_opinfo,
     matmul_opinfo,
+    embedding_opinfo,
 )
 from looseversion import LooseVersion
 
@@ -1214,3 +1215,34 @@ def fn(x):
 
     # Make sure there is a fusion symbol.
     assert any(bsym.sym.is_fusion for bsym in fwd_trace.bound_symbols)
+
+
+@instantiate(
+    dtypes=(thunder.float16, thunder.bfloat16),
+    devicetypes=(devices.DeviceType.CUDA,),
+    executors=(nvFuserExecutor,),
+    decorators=(
+        pytest.mark.skipif(
+            nvfuser_version() is None or nvfuser_version() < LooseVersion("0.2.25"),
+            reason="Requires nvFuser version 0.2.25 or later",
+        ),
+    ),
+)
+def test_embedding(
+    executor,
+    device: str,
+    dtype: dtypes.dtype,
+):
+
+    def embedding_fn(inputs):
+        return torch.nn.functional.embedding(*inputs)
+
+    for sample in embedding_opinfo.sample_inputs(device, dtype):
+        compiled_func = thunder.jit(embedding_fn, executors_list=executor.executors_list(), nv_enable_embedding=True)
+        out = compiled_func(sample.args)
+        expected_out = torch.nn.functional.embedding(*sample.args)
+        fwd_trace = thunder.last_traces(compiled_func)[-1]
+        fwd_fusion = examine.get_fusions(fwd_trace)
+
+        assert len(fwd_fusion) == 1
+        torch.testing.assert_close(out, expected_out)