[FEAT][ROCm] Integrate Fused MoE Kernels from AITER (#14967)

Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>
Signed-off-by: tjtanaa <tunjian.tan@embeddedllm.com>
Co-authored-by: tjtanaa <tunjian.tan@embeddedllm.com>

tests/kernels/test_moe.py

@@ -3,7 +3,6 @@
 
 Run `pytest tests/kernels/test_moe.py`.
 """
-
 import pytest
 import torch
 from torch.nn import Parameter
@@ -216,11 +215,17 @@ def test_fused_moe_wn16(m: int, n: int, k: int, e: int, topk: int,
 @pytest.mark.parametrize("dtype",
                          [torch.float32, torch.float16, torch.bfloat16])
 @pytest.mark.parametrize("padding", [True, False])
+@pytest.mark.parametrize(
+    "use_rocm_aiter", [True, False] if current_platform.is_rocm() else [False])
 @torch.inference_mode()
-def test_mixtral_moe(dtype: torch.dtype, padding: bool):
+def test_mixtral_moe(dtype: torch.dtype, padding: bool, use_rocm_aiter: bool,
+                     monkeypatch):
     """Make sure our Mixtral MoE implementation agrees with the one from
     huggingface."""
 
+    if use_rocm_aiter:
+        monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
+
     # Instantiate our and huggingface's MoE blocks
     config = MixtralConfig()
     hf_moe = MixtralSparseMoeBlock(config).to(dtype).to("cuda")
@@ -268,10 +273,18 @@ def test_mixtral_moe(dtype: torch.dtype, padding: bool):
         torch.bfloat16: 1e-2,
     }
 
-    torch.testing.assert_close(hf_states.flatten(0, 1),
-                               vllm_states,
-                               rtol=mixtral_moe_tol[dtype],
-                               atol=mixtral_moe_tol[dtype])
+    if use_rocm_aiter:
+        # The values of rtol and atol are set based on the tests in ROCM AITER package. # noqa: E501
+        # https://github.com/ROCm/aiter/blob/dfed377f4be7da96ca2d75ac0761f569676f7240/op_tests/test_moe.py#L174  # noqa: E501
+        torch.testing.assert_close(hf_states.flatten(0, 1),
+                                   vllm_states,
+                                   rtol=0.01,
+                                   atol=100)
+    else:
+        torch.testing.assert_close(hf_states.flatten(0, 1),
+                                   vllm_states,
+                                   rtol=mixtral_moe_tol[dtype],
+                                   atol=mixtral_moe_tol[dtype])
 
 
 @pytest.mark.parametrize("m", [1, 33, 64, 222])

tests/model_executor/test_enabled_custom_ops.py

@@ -7,6 +7,10 @@ from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.layers.activation import (GeluAndMul,
                                                    ReLUSquaredActivation,
                                                    SiluAndMul)
+from vllm.model_executor.layers.fused_moe.fused_moe import (
+    dispatch_fused_experts_func, dispatch_topk_func,
+    torch_vllm_inplace_fused_experts, torch_vllm_outplace_fused_experts,
+    vllm_topk_softmax)
 from vllm.model_executor.layers.layernorm import (
     RMSNorm, dispatch_cuda_rmsnorm_func, fused_add_rms_norm, rms_norm,
     rocm_aiter_fused_add_rms_norm, rocm_aiter_rms_norm)
@@ -92,6 +96,38 @@ def test_enabled_ops_invalid(env: str):
             RMSNorm(1024).enabled()
 
 
+@pytest.mark.parametrize("use_rocm_aiter", ["0", "1"])
+def test_topk_dispatch(use_rocm_aiter: str, monkeypatch):
+    monkeypatch.setenv("VLLM_ROCM_USE_AITER", use_rocm_aiter)
+    topk_func = dispatch_topk_func()
+
+    if current_platform.is_rocm() and int(use_rocm_aiter):
+        from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+            rocm_aiter_topk_softmax)
+
+        assert topk_func == rocm_aiter_topk_softmax
+    else:
+        assert topk_func == vllm_topk_softmax
+
+
+@pytest.mark.parametrize("use_rocm_aiter", ["0", "1"])
+@pytest.mark.parametrize("inplace", [True, False])
+def test_fused_experts_dispatch(use_rocm_aiter: str, inplace: bool,
+                                monkeypatch):
+
+    monkeypatch.setenv("VLLM_ROCM_USE_AITER", use_rocm_aiter)
+    fused_experts_func = dispatch_fused_experts_func(inplace)
+    if current_platform.is_rocm() and int(use_rocm_aiter):
+        from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+            rocm_aiter_fused_experts)
+
+        assert fused_experts_func == rocm_aiter_fused_experts
+    elif inplace:
+        assert fused_experts_func == torch_vllm_inplace_fused_experts
+    else:
+        assert fused_experts_func == torch_vllm_outplace_fused_experts
+
+
 @pytest.mark.parametrize("add_residual", [True, False])
 @pytest.mark.parametrize("use_rocm_aiter", ["0", "1"])
 @pytest.mark.parametrize("use_rocm_aiter_norm", ["0", "1"])

tests/models/decoder_only/language/test_mistral.py

@@ -174,15 +174,8 @@ SAMPLE_JSON_SCHEMA = {
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("max_tokens", [64])
 @pytest.mark.parametrize("num_logprobs", [5])
-def test_models(
-    hf_runner,
-    vllm_runner,
-    example_prompts,
-    model: str,
-    dtype: str,
-    max_tokens: int,
-    num_logprobs: int,
-) -> None:
+def test_models(hf_runner, vllm_runner, example_prompts, model: str,
+                dtype: str, max_tokens: int, num_logprobs: int) -> None:
     # TODO(sang): Sliding window should be tested separately.
     with hf_runner(model, dtype=dtype) as hf_model:
         hf_outputs = hf_model.generate_greedy_logprobs_limit(
@@ -206,14 +199,8 @@ def test_models(
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("max_tokens", [64])
 @pytest.mark.parametrize("num_logprobs", [5])
-def test_mistral_format(
-    vllm_runner,
-    example_prompts,
-    model: str,
-    dtype: str,
-    max_tokens: int,
-    num_logprobs: int,
-) -> None:
+def test_mistral_format(vllm_runner, example_prompts, model: str, dtype: str,
+                        max_tokens: int, num_logprobs: int) -> None:
     with vllm_runner(
             model,
             dtype=dtype,
@@ -244,11 +231,8 @@ def test_mistral_format(
 
 @pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
 @pytest.mark.parametrize("dtype", ["bfloat16"])
-def test_mistral_symbolic_languages(
-    vllm_runner,
-    model: str,
-    dtype: str,
-) -> None:
+def test_mistral_symbolic_languages(vllm_runner, model: str,
+                                    dtype: str) -> None:
     with vllm_runner(model,
                      dtype=dtype,
                      max_model_len=8192,
@@ -266,11 +250,7 @@ def test_mistral_symbolic_languages(
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("model",
                          MISTRAL_FORMAT_MODELS)  # v1 can't do func calling
-def test_mistral_function_calling(
-    vllm_runner,
-    model: str,
-    dtype: str,
-) -> None:
+def test_mistral_function_calling(vllm_runner, model: str, dtype: str) -> None:
     with vllm_runner(model,
                      dtype=dtype,
                      tokenizer_mode="mistral",
@@ -301,11 +281,8 @@ def test_mistral_function_calling(
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("guided_backend",
                          ["outlines", "lm-format-enforcer", "xgrammar"])
-def test_mistral_guided_decoding(
-    vllm_runner,
-    model: str,
-    guided_backend: str,
-) -> None:
+def test_mistral_guided_decoding(vllm_runner, model: str,
+                                 guided_backend: str) -> None:
     with vllm_runner(model, dtype='bfloat16',
                      tokenizer_mode="mistral") as vllm_model:
 

tests/quantization/test_fp8.py

@@ -23,8 +23,14 @@ MODELS = [
                     reason="FP8 is not supported on this GPU type.")
 @pytest.mark.parametrize("model_id", MODELS)
 @pytest.mark.parametrize("force_marlin", [False, True])
+@pytest.mark.parametrize(
+    "use_rocm_aiter", [True, False] if current_platform.is_rocm() else [False])
 def test_model_load_and_run(vllm_runner, model_id: str, force_marlin: bool,
-                            monkeypatch) -> None:
+                            use_rocm_aiter: bool, monkeypatch) -> None:
+
+    if use_rocm_aiter:
+        monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
+
     if force_marlin:
         monkeypatch.setenv("VLLM_TEST_FORCE_FP8_MARLIN", "1")
 
@@ -47,7 +53,13 @@ KV_CACHE_MODELS = [
 @pytest.mark.skipif(not is_quant_method_supported("fp8"),
                     reason="FP8 is not supported on this GPU type.")
 @pytest.mark.parametrize("model_id", KV_CACHE_MODELS)
-def test_kv_cache_model_load_and_run(vllm_runner, model_id: str, monkeypatch):
+@pytest.mark.parametrize(
+    "use_rocm_aiter", [True, False] if current_platform.is_rocm() else [False])
+def test_kv_cache_model_load_and_run(vllm_runner, model_id: str,
+                                     use_rocm_aiter: bool, monkeypatch):
+    if use_rocm_aiter:
+        monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
+
     # vllm_runner.apply_model() relies on V0 internals.
     monkeypatch.setenv("VLLM_USE_V1", "0")
     with vllm_runner(model_id, kv_cache_dtype="fp8") as llm:
@@ -86,8 +98,13 @@ def test_kv_cache_model_load_and_run(vllm_runner, model_id: str, monkeypatch):
                     reason="FP8 is not supported on this GPU type.")
 @pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8"])
 @pytest.mark.parametrize("force_marlin", [False, True])
+@pytest.mark.parametrize(
+    "use_rocm_aiter", [True, False] if current_platform.is_rocm() else [False])
 def test_load_fp16_model(vllm_runner, kv_cache_dtype: str, force_marlin: bool,
-                         monkeypatch) -> None:
+                         use_rocm_aiter: bool, monkeypatch) -> None:
+    if use_rocm_aiter:
+        monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
+
     # vllm_runner.apply_model() relies on V0 internals.
     monkeypatch.setenv("VLLM_USE_V1", "0")
 

vllm/envs.py

@@ -73,6 +73,8 @@ if TYPE_CHECKING:
     VLLM_DISABLED_KERNELS: list[str] = []
     VLLM_USE_V1: bool = True
     VLLM_ROCM_USE_AITER: bool = False
+    VLLM_ROCM_USE_AITER_MOE: bool = True
+    VLLM_ROCM_USE_AITER_FP8_BLOCK_SCALED_MOE: bool = False
     VLLM_ROCM_USE_AITER_RMSNORM: bool = True
     VLLM_ROCM_FP8_PADDING: bool = True
     VLLM_ROCM_MOE_PADDING: bool = True
@@ -513,6 +515,19 @@ environment_variables: dict[str, Callable[[], Any]] = {
     lambda: (os.getenv("VLLM_ROCM_USE_AITER", "False").lower() in
              ("true", "1")),
 
+    # Whether to use aiter moe ops.
+    # By default is enabled.
+    "VLLM_ROCM_USE_AITER_MOE":
+    lambda: (os.getenv("VLLM_ROCM_USE_AITER_MOE", "True").lower() in
+             ("true", "1")),
+
+    # Whether to use aiter block scaled moe kernel.
+    # By default this is disabled.
+    "VLLM_ROCM_USE_AITER_FP8_BLOCK_SCALED_MOE":
+    lambda:
+    (os.getenv("VLLM_ROCM_USE_AITER_FP8_BLOCK_SCALED_MOE", "false").lower() in
+     ("true", "1")),
+
     # use aiter rms norm op if aiter ops are enabled.
     "VLLM_ROCM_USE_AITER_RMSNORM":
     lambda: (os.getenv("VLLM_ROCM_USE_AITER_RMSNORM", "True").lower() in

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -17,6 +17,10 @@ from vllm.model_executor.layers.quantization.utils.fp8_utils import (
 from vllm.platforms import current_platform
 from vllm.utils import direct_register_custom_op
 
+from .rocm_aiter_fused_moe import (is_rocm_aiter_moe_enabled,
+                                   rocm_aiter_fused_experts,
+                                   rocm_aiter_topk_softmax)
+
 logger = init_logger(__name__)
 
 
@@ -1035,6 +1039,28 @@ def try_get_optimal_moe_config(
     return config
 
 
+def vllm_topk_softmax(topk_weights: torch.Tensor, topk_indices: torch.Tensor,
+                      token_expert_indices: torch.Tensor,
+                      gating_output: torch.Tensor,
+                      renormalize: bool) -> tuple[torch.Tensor, ...]:
+    ops.topk_softmax(
+        topk_weights,
+        topk_indices,
+        token_expert_indices,
+        gating_output,
+    )
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+    return topk_weights, topk_indices
+
+
+def dispatch_topk_func() -> Callable[..., tuple[torch.Tensor, ...]]:
+    if is_rocm_aiter_moe_enabled():
+        return rocm_aiter_topk_softmax
+    return vllm_topk_softmax
+
+
 def fused_topk(
     hidden_states: torch.Tensor,
     gating_output: torch.Tensor,
@@ -1059,17 +1085,14 @@ def fused_topk(
                                         dtype=torch.int32,
                                         device=hidden_states.device)
 
-    ops.topk_softmax(
-        topk_weights,
-        topk_ids,
-        token_expert_indicies,
-        gating_output.float(),  # TODO(woosuk): Optimize this.
-    )
-    del token_expert_indicies  # Not used. Will be used in the future.
+    gating_output_float = gating_output.float()  # TODO(woosuk): Optimize this.
 
-    if renormalize:
-        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+    topk_func = dispatch_topk_func()
+    topk_weights, topk_ids = topk_func(topk_weights, topk_ids,
+                                       token_expert_indicies,
+                                       gating_output_float, renormalize)
 
+    del token_expert_indicies  # Not used. Will be used in the future.
     return topk_weights, topk_ids
 
 
@@ -1259,6 +1282,24 @@ direct_register_custom_op(
 )
 
 
+def torch_vllm_inplace_fused_experts(**kwargs) -> torch.Tensor:
+    torch.ops.vllm.inplace_fused_experts(**kwargs)
+    hidden_states = kwargs['hidden_states']
+    return hidden_states
+
+
+def torch_vllm_outplace_fused_experts(**kwargs) -> torch.Tensor:
+    return torch.ops.vllm.outplace_fused_experts(**kwargs)
+
+
+def dispatch_fused_experts_func(inplace: bool) -> Callable[..., torch.Tensor]:
+    if is_rocm_aiter_moe_enabled():
+        return rocm_aiter_fused_experts
+    if inplace:
+        return torch_vllm_inplace_fused_experts
+    return torch_vllm_outplace_fused_experts
+
+
 def fused_experts(hidden_states: torch.Tensor,
                   w1: torch.Tensor,
                   w2: torch.Tensor,
@@ -1278,20 +1319,25 @@ def fused_experts(hidden_states: torch.Tensor,
                   a1_scale: Optional[torch.Tensor] = None,
                   a2_scale: Optional[torch.Tensor] = None,
                   block_shape: Optional[List[int]] = None) -> torch.Tensor:
-
-    if inplace:
-        torch.ops.vllm.inplace_fused_experts(
-            hidden_states, w1, w2, topk_weights, topk_ids, activation,
-            use_fp8_w8a8, use_int8_w8a16, use_int4_w4a16, global_num_experts,
-            expert_map, w1_scale, w2_scale, w1_zp, w2_zp, a1_scale, a2_scale,
-            block_shape)
-        return hidden_states
-    else:
-        return torch.ops.vllm.outplace_fused_experts(
-            hidden_states, w1, w2, topk_weights, topk_ids, activation,
-            use_fp8_w8a8, use_int8_w8a16, use_int4_w4a16, global_num_experts,
-            expert_map, w1_scale, w2_scale, w1_zp, w2_zp, a1_scale, a2_scale,
-            block_shape)
+    return dispatch_fused_experts_func(inplace)(
+        hidden_states=hidden_states,
+        w1=w1,
+        w2=w2,
+        topk_weights=topk_weights,
+        topk_ids=topk_ids,
+        activation=activation,
+        use_fp8_w8a8=use_fp8_w8a8,
+        use_int8_w8a16=use_int8_w8a16,
+        use_int4_w4a16=use_int4_w4a16,
+        global_num_experts=global_num_experts,
+        expert_map=expert_map,
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
+        w1_zp=w1_zp,
+        w2_zp=w2_zp,
+        a1_scale=a1_scale,
+        a2_scale=a2_scale,
+        block_shape=block_shape)
 
 
 def fused_experts_impl(hidden_states: torch.Tensor,

vllm/model_executor/layers/fused_moe/layer.py

@@ -8,7 +8,7 @@ import torch
 import torch.nn.functional as F
 from torch.nn.parameter import UninitializedParameter
 
-from vllm import envs
+import vllm.envs as envs
 from vllm.config import get_current_vllm_config
 from vllm.distributed import (get_dp_group, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size,
@@ -16,6 +16,8 @@ from vllm.distributed import (get_dp_group, get_tensor_model_parallel_rank,
 from vllm.forward_context import ForwardContext, get_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp
+from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+    is_rocm_aiter_moe_enabled, shuffle_weights)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.utils import set_weight_attrs
@@ -118,6 +120,16 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
             layer.w2_weight.data),
                                              requires_grad=False)
 
+        if is_rocm_aiter_moe_enabled():
+            # reshaping weights is required for aiter moe kernel.
+            shuffled_w13, shuffled_w2 = shuffle_weights(
+                layer.w13_weight.data, layer.w2_weight.data)
+
+            layer.w13_weight = torch.nn.Parameter(shuffled_w13,
+                                                  requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(shuffled_w2,
+                                                 requires_grad=False)
+
         if current_platform.is_cpu():
             if current_platform.get_cpu_architecture() == CpuArchEnum.X86:
                 import intel_extension_for_pytorch as ipex

vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py

+# SPDX-License-Identifier: Apache-2.0
+from typing import List, Optional
+
+import torch
+
+import vllm.envs as envs
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    per_token_group_quant_fp8)
+from vllm.platforms import current_platform
+
+
+def is_rocm_aiter_moe_enabled() -> bool:
+    return current_platform.is_rocm() \
+        and envs.VLLM_ROCM_USE_AITER_MOE \
+        and envs.VLLM_ROCM_USE_AITER \
+
+
+def is_rocm_aiter_block_scaled_moe_enabled() -> bool:
+    return is_rocm_aiter_moe_enabled() and \
+        envs.VLLM_ROCM_USE_AITER_FP8_BLOCK_SCALED_MOE
+
+
+def rocm_aiter_fused_experts(
+        *,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        use_fp8_w8a8: bool = False,
+        w1_scale: Optional[torch.Tensor] = None,
+        w2_scale: Optional[torch.Tensor] = None,
+        block_shape: Optional[List[int]] = None,
+        expert_mask: Optional[torch.Tensor] = None,
+        **kwagrs  # Ignore additional keyword arguments
+) -> torch.Tensor:
+
+    import aiter as rocm_aiter
+    import aiter.fused_moe_bf16_asm as rocm_aiter_asm_fmoe
+
+    if envs.VLLM_ROCM_USE_AITER_FP8_BLOCK_SCALED_MOE and use_fp8_w8a8:
+        assert w1_scale is not None
+        assert w2_scale is not None
+
+        local_E = E = w1.shape[0]
+        if expert_mask is not None:
+            E = expert_mask.numel()
+
+        topk = topk_ids.shape[1]
+        model_dim = w1.shape[-1]
+        dtype = hidden_states.dtype
+        # The default block sizes are 128 in AITER.
+        if block_shape is None:
+            block_shape = [128, 128]
+
+        scale_blk_k = block_shape[1]
+
+        (
+            sorted_token_ids,
+            sorted_weight_buf,
+            sorted_expert_ids,
+            num_valid_ids,
+            out_asm,
+        ) = rocm_aiter_asm_fmoe.moe_sorting_ck(topk_ids,
+                                               topk_weights,
+                                               E,
+                                               model_dim,
+                                               dtype,
+                                               expert_mask=expert_mask)
+
+        a1, a1_scale = per_token_group_quant_fp8(hidden_states, scale_blk_k)
+        rocm_aiter.fmoe_fp8_blockscale_g1u1(
+            out_asm,
+            a1,
+            w1,
+            w2,
+            sorted_token_ids,
+            sorted_weight_buf,
+            sorted_expert_ids,
+            num_valid_ids,
+            topk,
+            w1_scale.view(local_E, -1),
+            w2_scale.view(local_E, -1),
+            a1_scale.t().contiguous(),
+            block_shape[0],
+            block_shape[1],
+            None,
+        )
+        return out_asm
+
+    elif use_fp8_w8a8:
+        return rocm_aiter_asm_fmoe.asm_moe(hidden_states=hidden_states,
+                                           w1=w1,
+                                           w2=w2,
+                                           topk_weight=topk_weights,
+                                           topk_ids=topk_ids,
+                                           fc1_scale=w1_scale,
+                                           fc2_scale=w2_scale,
+                                           fc1_smooth_scale=None,
+                                           fc2_smooth_scale=None,
+                                           a16=False)
+
+    return rocm_aiter.ck_moe(hidden_states=hidden_states,
+                             w1=w1,
+                             w2=w2,
+                             topk_weights=topk_weights,
+                             topk_ids=topk_ids)
+
+
+def rocm_aiter_topk_softmax(topk_weights: torch.Tensor,
+                            topk_indices: torch.Tensor,
+                            token_expert_indices: torch.Tensor,
+                            gating_output: torch.Tensor,
+                            renormalize: bool) -> tuple[torch.Tensor, ...]:
+    import aiter as rocm_aiter
+    rocm_aiter.topk_softmax(topk_weights, topk_indices, token_expert_indices,
+                            gating_output, renormalize)
+
+    return topk_weights, topk_indices
+
+
+def shuffle_weights(*tensors: torch.Tensor) -> tuple[torch.Tensor, ...]:
+    """
+    Applies shuffle_weight function from AITER to each 
+    input tensor and returns them.
+
+    Args:
+    *tensors: Variable number of torch.Tensor objects.
+
+    Returns:
+    A tuple of shuffled tensors.
+    """
+    from aiter.ops.shuffle import shuffle_weight
+
+    return tuple(shuffle_weight(tensor) for tensor in tensors)
+
+
+def expand_weights(*tensors: torch.Tensor,
+                   expansion_dims: list[int]) -> tuple[torch.Tensor, ...]:
+    """
+    Expands the dimensions of input tensors.
+
+    Args:
+        *tensors: A variable number of torch.Tensor objects.
+        expansion_dims: A list of expansion dimensions 
+        corresponding to each tensor.
+
+    Returns:
+        A tuple of tensors with expanded dimensions.
+    """
+
+    assert len(tensors) == len(expansion_dims), \
+    "Number of tensors must match the number of expansion dimensions."
+
+    return tuple(
+        tensor.unsqueeze(-1).unsqueeze(-1).expand((-1, dim, -1))
+        for tensor, dim in zip(tensors, expansion_dims))

vllm/model_executor/layers/quantization/fp8.py

@@ -13,6 +13,9 @@ from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
                                                   FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+    expand_weights, is_rocm_aiter_block_scaled_moe_enabled,
+    is_rocm_aiter_moe_enabled, shuffle_weights)
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
 from vllm.model_executor.layers.quantization.base_config import (
@@ -554,6 +557,15 @@ class Fp8MoEMethod(FusedMoEMethodBase):
             layer.w2_weight = Parameter(w2_weight, requires_grad=False)
             layer.w2_weight_scale_inv = Parameter(w2_weight_scale_inv,
                                                   requires_grad=False)
+            if is_rocm_aiter_block_scaled_moe_enabled():
+                # reshaping weights is required for aiter moe kernel.
+                shuffled_w13, shuffled_w2 = shuffle_weights(
+                    layer.w13_weight.data, layer.w2_weight.data)
+
+                layer.w13_weight = torch.nn.Parameter(shuffled_w13,
+                                                      requires_grad=False)
+                layer.w2_weight = torch.nn.Parameter(shuffled_w2,
+                                                     requires_grad=False)
             return
 
         # If checkpoint is fp16, quantize in place.
@@ -581,6 +593,26 @@ class Fp8MoEMethod(FusedMoEMethodBase):
                                                   requires_grad=False)
             layer.w2_weight = torch.nn.Parameter(w2_weight,
                                                  requires_grad=False)
+            if is_rocm_aiter_moe_enabled():
+                # reshaping weights is required for aiter moe kernel.
+                w13_scales, w2_scales = expand_weights(
+                    layer.w13_weight_scale.data,
+                    layer.w2_weight_scale.data,
+                    expansion_dims=[
+                        layer.w13_weight.shape[1], layer.w2_weight.shape[1]
+                    ])
+                layer.w13_weight_scale = torch.nn.Parameter(
+                    w13_scales.contiguous(), requires_grad=False)
+                layer.w2_weight_scale = torch.nn.Parameter(
+                    w2_scales.contiguous(), requires_grad=False)
+
+                shuffled_w13, shuffled_w2 = shuffle_weights(
+                    layer.w13_weight, layer.w2_weight)
+
+                layer.w13_weight = torch.nn.Parameter(shuffled_w13,
+                                                      requires_grad=False)
+                layer.w2_weight = torch.nn.Parameter(shuffled_w2,
+                                                     requires_grad=False)
             return
 
         # If checkpoint is fp8, we need to handle that the
@@ -648,6 +680,26 @@ class Fp8MoEMethod(FusedMoEMethodBase):
                             dq_weight, max_w13_scales[expert_id])
                     start += shard_size
 
+            if is_rocm_aiter_moe_enabled():
+                # reshaping weights is required for aiter moe kernel.
+                expansion_dims = [
+                    layer.w13_weight.shape[1], layer.w2_weight.shape[1]
+                ]
+                max_w13_scales, w2_scales = expand_weights(
+                    max_w13_scales,
+                    layer.w2_weight_scale.data,
+                    expansion_dims=expansion_dims)
+                layer.w2_weight_scale = torch.nn.Parameter(
+                    w2_scales.contiguous(), requires_grad=False)
+
+                shuffled_w13, shuffled_w2 = shuffle_weights(
+                    layer.w13_weight, layer.w2_weight)
+
+                layer.w13_weight = torch.nn.Parameter(shuffled_w13,
+                                                      requires_grad=False)
+                layer.w2_weight = torch.nn.Parameter(shuffled_w2,
+                                                     requires_grad=False)
+
             layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales,
                                                         requires_grad=False)
             return