[FEAT][ROCm] Add AITER grouped topk for DeepSeekV2 (#18825)

Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>

tests/kernels/moe/test_rocm_aiter_topk.py

@@ -35,6 +35,15 @@ def test_rocm_aiter_biased_grouped_topk_custom_op_registration():
     assert callable(torch.ops.vllm.rocm_aiter_biased_grouped_topk)
 
 
+def test_rocm_aiter_grouped_topk_custom_op_registration():
+    """Test that the custom op is correctly registered."""
+    # Check if the op exists in torch.ops.vllm
+    assert hasattr(torch.ops.vllm, 'rocm_aiter_grouped_topk')
+
+    # Check if the op is callable
+    assert callable(torch.ops.vllm.rocm_aiter_grouped_topk)
+
+
 def test_rocm_aiter_biased_grouped_topk_torch_compile_compatibility():
     """Test that the op can be used with torch.compile."""
     # Create test tensors
@@ -120,3 +129,87 @@ def test_rocm_aiter_biased_grouped_topk_torch_compile_compatibility():
                           rtol=1e-2,
                           atol=1e-2)
     assert torch.allclose(topk_ids_original, topk_ids_compiled)
+
+
+def test_rocm_aiter_grouped_topk_torch_compile_compatibility():
+    """Test that the op can be used with torch.compile."""
+    # Create test tensors
+    token = 64
+    expert = 256
+    num_expert_group = 8
+    topk = 8
+    topk_group = 4
+    renormalize = True
+    scoring_func = "softmax"
+    scale_factor = 1.0
+
+    gating_output = torch.randn((token, expert),
+                                dtype=torch.bfloat16,
+                                device="cuda")
+
+    device = gating_output.device
+    topk_ids = torch.empty((token, topk), dtype=torch.int32, device=device)
+    topk_weights = torch.empty((token, topk),
+                               dtype=torch.float32,
+                               device=device)
+
+    # Define a function that uses the op
+    def grouped_topk_fn(gating_output, topk_weights, topk_ids, scoring_func):
+        return torch.ops.vllm.rocm_aiter_grouped_topk(
+            gating_output, topk_weights, topk_ids, num_expert_group,
+            topk_group, renormalize, scoring_func, scale_factor)
+
+    # Verify the op's fake implementation
+    torch.library.opcheck(torch.ops.vllm.rocm_aiter_grouped_topk,
+                          (gating_output, topk_weights, topk_ids),
+                          kwargs={
+                              "num_expert_group": num_expert_group,
+                              "topk_group": topk_group,
+                              "need_renorm": renormalize,
+                              "scoring_func": scoring_func,
+                              "routed_scaling_factor": scale_factor
+                          },
+                          test_utils=("test_faketensor"))
+
+    # Compile the function with appropriate settings
+    compiled_fn = torch.compile(grouped_topk_fn,
+                                fullgraph=True,
+                                backend="inductor",
+                                mode="reduce-overhead",
+                                dynamic=False)
+
+    topk_weights_original = torch.empty((token, topk),
+                                        dtype=torch.float32,
+                                        device=device)
+    topk_ids_original = torch.empty((token, topk),
+                                    dtype=torch.int32,
+                                    device=device)
+
+    topk_weights_compiled = torch.empty((token, topk),
+                                        dtype=torch.float32,
+                                        device=device)
+    topk_ids_compiled = torch.empty((token, topk),
+                                    dtype=torch.int32,
+                                    device=device)
+
+    # Run both compiled (V1 graph mode) and uncompiled versions (V1 eager mode)
+    grouped_topk_fn(gating_output, topk_weights_original, topk_ids_original,
+                    scoring_func)
+    compiled_fn(gating_output, topk_weights_compiled, topk_ids_compiled,
+                scoring_func)
+
+    # Sort the results for comparison since the order might not be deterministic
+    topk_ids_original, indices_original = torch.sort(topk_ids_original)
+    topk_weights_original = torch.gather(topk_weights_original, 1,
+                                         indices_original)
+
+    topk_ids_compiled, indices_compiled = torch.sort(topk_ids_compiled)
+    topk_weights_compiled = torch.gather(topk_weights_compiled, 1,
+                                         indices_compiled)
+
+    # Verify results match
+    assert torch.allclose(topk_weights_original,
+                          topk_weights_compiled,
+                          rtol=1e-2,
+                          atol=1e-2)
+    assert torch.allclose(topk_ids_original, topk_ids_compiled)

vllm/model_executor/layers/fused_moe/layer.py

@@ -45,7 +45,7 @@ else:
     FusedMoEPrepareAndFinalize = None  # type: ignore
 if is_rocm_aiter_moe_enabled():
     from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (  # noqa: E501
-        rocm_aiter_biased_group_topk as grouped_topk)
+        rocm_aiter_grouped_topk as grouped_topk)
 else:
     from vllm.model_executor.layers.fused_moe.fused_moe import grouped_topk
 if current_platform.is_tpu():

vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py

@@ -140,6 +140,36 @@ def rocm_aiter_biased_grouped_topk_fake(
     pass
 
 
+def rocm_aiter_grouped_topk_impl(
+        gating_output: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        num_expert_group: int,
+        topk_group: int,
+        need_renorm: bool,
+        scoring_func: str = "softmax",
+        routed_scaling_factor: float = 1.0  # mul to topk_weights
+) -> None:
+
+    from aiter import grouped_topk
+
+    grouped_topk(gating_output, topk_weights, topk_ids, num_expert_group,
+                 topk_group, need_renorm, scoring_func, routed_scaling_factor)
+
+
+def rocm_aiter_grouped_topk_fake(
+        gating_output: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        num_expert_group: int,
+        topk_group: int,
+        need_renorm: bool,
+        scoring_func: str = "softmax",
+        routed_scaling_factor: float = 1.0  # mul to topk_weights
+) -> None:
+    pass
+
+
 def rocm_aiter_fused_moe_impl(
     hidden_states: torch.Tensor,
     w1: torch.Tensor,
@@ -218,27 +248,33 @@ if current_platform.is_rocm():
         dispatch_key=current_platform.dispatch_key,
     )
 
+    direct_register_custom_op(
+        op_name="rocm_aiter_grouped_topk",
+        op_func=rocm_aiter_grouped_topk_impl,
+        mutates_args=["topk_weights", "topk_ids"],
+        fake_impl=rocm_aiter_grouped_topk_fake,
+        dispatch_key=current_platform.dispatch_key,
+    )
 
-def rocm_aiter_biased_group_topk(
+
+def rocm_aiter_grouped_topk(
     hidden_states: torch.Tensor,
     gating_output: torch.Tensor,
     topk: int,
     renormalize: bool,
     num_expert_group: int = 0,
     topk_group: int = 0,
-    scoring_func: str = "sigmoid",
+    scoring_func: str = "softmax",
     e_score_correction_bias: Optional[torch.Tensor] = None
 ) -> tuple[torch.Tensor, torch.Tensor]:
-    assert scoring_func == "sigmoid", (
-        "rocm_aiter_biased_group_topk only supports 'sigmoid' scoring_func.")
-    assert e_score_correction_bias is not None, (
-        "'e_score_correction_bias' must not be None.")
     token = hidden_states.shape[0]
     device = hidden_states.device
     topk_ids = torch.empty((token, topk), dtype=torch.int32, device=device)
     topk_weights = torch.empty((token, topk),
                                dtype=torch.float32,
                                device=device)
+
+    if e_score_correction_bias is not None:
         torch.ops.vllm.rocm_aiter_biased_grouped_topk(
             gating_output,
             e_score_correction_bias,
@@ -248,6 +284,18 @@ def rocm_aiter_biased_group_topk(
             topk_group,
             renormalize,
         )
+    else:
+        assert (scoring_func == "softmax" or scoring_func == "sigmoid")
+        torch.ops.vllm.rocm_aiter_grouped_topk(
+            gating_output,
+            topk_weights,
+            topk_ids,
+            num_expert_group,
+            topk_group,
+            renormalize,
+            scoring_func,
+        )
+
     return topk_weights, topk_ids