[ROCm][CI] Add missing quantization methods and fix online quant test failures

tests/quantization/test_configs.py

@@ -54,13 +54,13 @@ class ModelPair:
     (
         "TheBloke/OpenHermes-2.5-Mistral-7B-AWQ",
         None,
-        "awq_marlin" if current_platform.is_cuda() else "awq",
+        "awq_marlin" if current_platform.is_cuda_alike() else "awq",
     ),
     ("TheBloke/OpenHermes-2.5-Mistral-7B-AWQ", "awq", "awq"),
     (
         "TheBloke/OpenHermes-2.5-Mistral-7B-AWQ",
         "marlin",
-        "awq_marlin" if current_platform.is_cuda() else "ERROR",
+        "awq_marlin" if current_platform.is_cuda_alike() else "ERROR",
     ),
     ("TheBloke/OpenHermes-2.5-Mistral-7B-AWQ", "gptq", "ERROR"),
 ]

tests/quantization/test_mixed_precision.py

@@ -43,7 +43,7 @@ def get_model_args(self) -> str:
     "amd/Qwen3-8B-WMXFP4FP8-AMXFP4FP8-AMP-KVFP8": {"arc_challenge": 0.52, "mmlu": 0.72},
     # Non-mixed-precision (PTQ) model
     # - Reference for pipeline compatibility verification -> No conflicts or breakings
-    "amd/Llama-2-70b-chat-hf-FP8-MLPerf-fp8_attn_quark_format": {
+    "amd/Llama-2-70b-chat-hf_FP8_MLPerf_V2": {
         "arc_challenge": 0.53,
         "mmlu": 0.61,
     },

tests/quantization/test_turboquant.py

@@ -352,7 +352,10 @@ def generate_rotation_matrix(d: int, seed: int, device: str = "cpu") -> torch.Te
     gen = torch.Generator(device="cpu")
     gen.manual_seed(seed)
     G = torch.randn(d, d, generator=gen, device="cpu", dtype=torch.float32)
-    Q, R = torch.linalg.qr(G)
+    # torch.linalg.qr on CPU requires LAPACK, which some torch wheels
+    # (ROCm) ship without. Run QR on accelerator instead
+    qr_device = "cuda" if torch.cuda.is_available() else "cpu"
+    Q, R = torch.linalg.qr(G.to(qr_device))
     diag_sign = torch.sign(torch.diag(R))
     diag_sign[diag_sign == 0] = 1.0
     Q = Q * diag_sign.unsqueeze(0)

tests/quantization/utils.py

@@ -80,14 +80,13 @@ def _test_online_quant_peak_mem_impl(
     print(f"GPU memory used after loading weights: {model_memory_gib} GiB")
     print(f"Peak GPU memory usage while loading weights: {peak_memory_gib} GiB")
 
-    # model specific, allenai/OLMoE-1B-7B-0125-Instruct fp8 online quant
-    # uses 6.65 GiB for weight loading (bf16 checkpoint is ~12.89 GiB)
     expected_model_memory_gib = 6.7
 
     # for allenai/OLMoE-1B-7B-0125-Instruct the number we see today is 9.06
-    # GiB, which is 1.36x above model_memory_gib. A slightly higher number is
-    # expected as when we load and quantize weights in a streaming fashion we
-    # need to have individual weights in bf16 + fp8 alive at the same time.
+    # GiB on CUDA, which is 1.36x above model_memory_gib. A slightly higher
+    # number is expected as when we load and quantize weights in a streaming
+    # fashion we need to have individual weights in bf16 + fp8 alive at the
+    # same time.
     expected_peak_memory_gib = expected_model_memory_gib * 1.4
 
     assert model_memory_gib < expected_model_memory_gib, (

vllm/model_executor/kernels/linear/scaled_mm/pytorch.py

@@ -144,13 +144,21 @@ def apply_scaled_mm(
         #  For CUDA platform please validate if the torch._scaled_mm supports
         #  rowwise scaled GEMM before using it
 
+        # torch._scaled_mm rowwise requires scale_a = (m, 1), scale_b = (1, n).
+        # CompressedTensors stores weight_scale as (n, 1), so `.t()` yields (1, n).
+        # ModelOpt FP8_PER_CHANNEL_PER_TOKEN stores it as 1-D (n,); reshape to
+        # (1, n) so both paths satisfy the rowwise contract.
+        scale_b = Bs.view(1, -1) if Bs.dim() == 1 else Bs.t()
+        if As.dim() == 1:
+            As = As.view(-1, 1)
+
         # Fused GEMM_DQ Rowwise GEMM
         output = torch._scaled_mm(
             A,
             B,
             out_dtype=out_dtype,
             scale_a=As,
-            scale_b=Bs.t(),
+            scale_b=scale_b,
             bias=bias,
         )
 

vllm/model_executor/layers/quantization/quark/quark_moe.py

@@ -1025,9 +1025,15 @@ def __init__(
             get_current_vllm_config().model_config.hf_config, "model_type", None
         )
 
+        # TODO(aiter): extend once rocm_aiter_fused_experts gains dispatch
+        # for the other OCP MX schemes. Today its CK MoE kernel only has an
+        # entry for `w_mxfp4` (w4a16); mixed schemes like `w_mxfp4_a_mxfp6_*`
+        # fall through to QuantMethod.NO and raise "Unsupported kernel config
+        # for moe heuristic dispatch".
+        _AITER_NATIVE_OCP_MX_SCHEMES = ("w_mxfp4",)
         self.emulate = (
             not current_platform.supports_mx()
-            or not self.ocp_mx_scheme.startswith("w_mxfp4")
+            or self.ocp_mx_scheme not in _AITER_NATIVE_OCP_MX_SCHEMES
         ) and (
             self.mxfp4_backend is Mxfp4MoeBackend.NONE or not self.use_rocm_aiter_moe
         )

vllm/model_executor/layers/quantization/quark/schemes/quark_ocp_mx.py

@@ -376,11 +376,15 @@ def apply_weights(
             dq_w = self.dequant_func(layer.weight, layer.weight_scale, x.dtype)
             qdq_x = self.quant_dequant_func(x)
             return F.linear(qdq_x, dq_w, bias)
-        else:
-            return torch.ops.vllm.gemm_with_dynamic_quant(
-                x,
-                layer.weight,
-                layer.weight_scale,
-                self.rocm_use_aiter_fp4_asm_gemm,
-                self.out_dtype,
-            )
+        y = torch.ops.vllm.gemm_with_dynamic_quant(
+            x,
+            layer.weight,
+            layer.weight_scale,
+            self.rocm_use_aiter_fp4_asm_gemm,
+            self.out_dtype,
+        )
+        # gemm_with_dynamic_quant has no bias argument; add it here so the
+        # native path matches F.linear (e.g. qkv_proj with qkv_bias=True).
+        if bias is not None:
+            y = y + bias
+        return y

vllm/model_executor/model_loader/base_loader.py

@@ -65,7 +65,7 @@ def load_model(
 
             # Log peak GPU memory after loading weights. This is needed
             # to have test coverage on peak memory for online quantization.
-            if current_platform.is_cuda():
+            if current_platform.is_cuda_alike():
                 peak_memory = torch.accelerator.max_memory_allocated()
                 logger.debug_once(
                     "Peak GPU memory after loading weights: %s GiB",

vllm/platforms/rocm.py

@@ -414,10 +414,17 @@ class RocmPlatform(Platform):
         "gguf",
         "quark",
         "mxfp4",
-        "gpt_oss_mxfp4",
+        "mxfp8",
         "torchao",
         "bitsandbytes",
+        "modelopt",
         "modelopt_fp4",
+        "modelopt_mxfp8",
+        "modelopt_mixed",
+        "fp8_per_tensor",
+        "fp8_per_block",
+        "online",
+        "gpt_oss_mxfp4",
     ]
 
     @classmethod
@@ -785,9 +792,9 @@ def get_punica_wrapper(cls) -> str:
     def get_current_memory_usage(
         cls, device: torch.types.Device | None = None
     ) -> float:
+        torch.cuda.empty_cache()
         torch.cuda.reset_peak_memory_stats(device)
-        free_mem, total_mem = torch.cuda.mem_get_info(device)
-        return total_mem - free_mem
+        return torch.cuda.max_memory_allocated(device)
 
     @classmethod
     def get_device_communicator_cls(cls) -> str: