[ROCm][Quantization][5/N] Refactor quark_moe w8a8-int8 w/ oracle (#46765)

Signed-off-by: amd-sourjya <amd-sourjya@users.noreply.github.com>
Co-authored-by: amd-sourjya <amd-sourjya@users.noreply.github.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Andreas Karatzas <akaratza@amd.com>

tests/quantization/test_int8_moe_oracle.py

@@ -0,0 +1,105 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Tests for INT8 (W8A8) fused-MoE oracle backend selection.
+
+These exercise ``select_int8_moe_backend`` only (no kernels are launched), so
+they run on any platform where the Triton INT8 MoE kernel is available — CUDA
+(SM >= 7.5) or ROCm — not just gfx950.
+"""
+
+import pytest
+import torch
+
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEParallelConfig,
+    RoutingMethodType,
+)
+from vllm.model_executor.layers.fused_moe.oracle.int8 import (
+    Int8MoeBackend,
+    select_int8_moe_backend,
+)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    kInt8DynamicTensorSym,
+    kInt8DynamicTokenSym,
+    kInt8StaticChannelSym,
+    kInt8StaticTensorSym,
+)
+from vllm.platforms import current_platform
+
+# The Triton int8_w8a8 fused-MoE kernel is available on CUDA (Turing+) and on
+# ROCm CDNA GPUs. Gate on that rather than on a specific arch.
+INT8_MOE_SUPPORTED = (
+    current_platform.is_cuda() and current_platform.has_device_capability((7, 5))
+) or current_platform.is_rocm()
+
+requires_int8_moe = pytest.mark.skipif(
+    not INT8_MOE_SUPPORTED,
+    reason="Requires a GPU with Triton INT8 MoE support (CUDA SM>=7.5 or ROCm)",
+)
+
+
+def _make_int8_moe_config(moe_backend: str = "auto") -> FusedMoEConfig:
+    from vllm.model_executor.layers.fused_moe.activation import MoEActivation
+
+    return FusedMoEConfig(
+        num_experts=8,
+        experts_per_token=2,
+        hidden_dim=256,
+        intermediate_size=256,
+        num_local_experts=8,
+        num_logical_experts=8,
+        moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
+        activation=MoEActivation.SILU,
+        in_dtype=torch.bfloat16,
+        device="cuda",
+        routing_method=RoutingMethodType.Renormalize,
+        moe_backend=moe_backend,
+    )
+
+
+@requires_int8_moe
+@pytest.mark.parametrize(
+    "weight_key,activation_key",
+    [
+        # per-channel weight + dynamic per-token activation
+        (kInt8StaticChannelSym, kInt8DynamicTokenSym),
+        # per-tensor weight + dynamic per-tensor activation
+        (kInt8StaticTensorSym, kInt8DynamicTensorSym),
+    ],
+)
+def test_int8_dynamic_schemes_dispatch_to_triton(weight_key, activation_key):
+    """Both dynamic-activation INT8 MoE schemes (per-channel + per-tensor
+    weights) select the Triton backend."""
+    config = _make_int8_moe_config()
+    backend, experts_cls = select_int8_moe_backend(
+        config, weight_key=weight_key, activation_key=activation_key
+    )
+    assert backend == Int8MoeBackend.TRITON
+    assert experts_cls is not None
+
+
+@requires_int8_moe
+def test_int8_explicit_moe_backend_triton():
+    """An explicit --moe-backend triton selects the Triton INT8 backend."""
+    config = _make_int8_moe_config(moe_backend="triton")
+    backend, experts_cls = select_int8_moe_backend(
+        config,
+        weight_key=kInt8StaticChannelSym,
+        activation_key=kInt8DynamicTokenSym,
+    )
+    assert backend == Int8MoeBackend.TRITON
+    assert experts_cls is not None
+
+
+@requires_int8_moe
+def test_int8_unsupported_moe_backend_raises():
+    """An unsupported --moe-backend for INT8 MoE raises a clear error."""
+    config = _make_int8_moe_config(moe_backend="cutlass")
+    with pytest.raises(ValueError, match="not supported for Int8 MoE"):
+        select_int8_moe_backend(
+            config,
+            weight_key=kInt8StaticChannelSym,
+            activation_key=kInt8DynamicTokenSym,
+        )

tests/quantization/test_quark.py

@@ -150,7 +150,7 @@ def test_quark_int8_w_per_tensor_a_per_tensor(vllm_runner, tp):
 @pytest.mark.parametrize("tp", [1])
 def test_quark_int8_w8a8_moe(vllm_runner, tp):
     """Test W8A8 INT8 MoE quantization with a tiny Qwen3 MoE model."""
-    model_path = "nameistoken/tiny-qwen3-moe-w8a8-int8-quark"
+    model_path = "amd/tiny-qwen3-moe-w8a8-int8"
     with vllm_runner(
         model_path,
         enforce_eager=True,