[ROCm][Kernel] MoE weights padding (#14454)

Signed-off-by: Gregory Shtrasberg <Gregory.Shtrasberg@amd.com>
Signed-off-by: charlifu <charlifu@amd.com>
Co-authored-by: charlifu <charlifu@amd.com>

tests/kernels/test_moe.py

@@ -3,8 +3,11 @@
 
 Run `pytest tests/kernels/test_moe.py`.
 """
+
 import pytest
 import torch
+from torch.nn import Parameter
+from torch.nn import functional as F
 from transformers import MixtralConfig
 from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
 
@@ -37,6 +40,7 @@ TOP_KS = [2, 6]
 @pytest.mark.parametrize("topk", TOP_KS)
 @pytest.mark.parametrize("ep_size", EP_SIZE)
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("padding", [True, False])
 def test_fused_moe(
     m: int,
     n: int,
@@ -45,6 +49,7 @@ def test_fused_moe(
     topk: int,
     ep_size: int,
     dtype: torch.dtype,
+    padding: bool,
 ):
     a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
     w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
@@ -65,16 +70,7 @@ def test_fused_moe(
     else:
         e_map = None
 
-    triton_output = fused_moe(a,
-                              w1,
-                              w2,
-                              score,
-                              topk,
-                              global_num_experts=e,
-                              expert_map=e_map,
-                              renormalize=False)
     torch_output = torch_moe(a, w1, w2, score, topk, e_map)
-    torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
     iterative_output = iterative_moe(a,
                                      w1,
                                      w2,
@@ -83,6 +79,23 @@ def test_fused_moe(
                                      global_num_experts=e,
                                      expert_map=e_map,
                                      renormalize=False)
+
+    # Pad the weight if moe padding is enabled
+    if padding:
+        w1 = F.pad(w1, (0, 128), "constant", 0)[..., 0:-128]
+        torch.cuda.empty_cache()
+        w2 = F.pad(w2, (0, 128), "constant", 0)[..., 0:-128]
+        torch.cuda.empty_cache()
+
+    triton_output = fused_moe(a,
+                              w1,
+                              w2,
+                              score,
+                              topk,
+                              global_num_experts=e,
+                              expert_map=e_map,
+                              renormalize=False)
+    torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
     torch.testing.assert_close(iterative_output,
                                torch_output,
                                atol=2e-2,
@@ -202,8 +215,9 @@ 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])
 @torch.inference_mode()
-def test_mixtral_moe(dtype: torch.dtype):
+def test_mixtral_moe(dtype: torch.dtype, padding: bool):
     """Make sure our Mixtral MoE implementation agrees with the one from
     huggingface."""
 
@@ -233,6 +247,17 @@ def test_mixtral_moe(dtype: torch.dtype):
     # vLLM uses 1D query [num_tokens, hidden_dim]
     vllm_inputs = hf_inputs.flatten(0, 1)
 
+    # Pad the weight if moe padding is enabled
+    if padding:
+        vllm_moe.experts.w13_weight = Parameter(F.pad(
+            vllm_moe.experts.w13_weight, (0, 128), "constant", 0)[..., 0:-128],
+                                                requires_grad=False)
+        torch.cuda.empty_cache()
+        vllm_moe.experts.w2_weight = Parameter(F.pad(
+            vllm_moe.experts.w2_weight, (0, 128), "constant", 0)[..., 0:-128],
+                                               requires_grad=False)
+        torch.cuda.empty_cache()
+
     # Run forward passes for both MoE blocks
     hf_states, _ = hf_moe.forward(hf_inputs)
     vllm_states = vllm_moe.forward(vllm_inputs)

vllm/envs.py

@@ -75,6 +75,7 @@ if TYPE_CHECKING:
     VLLM_ROCM_USE_AITER: bool = False
     VLLM_ROCM_USE_AITER_RMSNORM: bool = True
     VLLM_ROCM_FP8_PADDING: bool = True
+    VLLM_ROCM_MOE_PADDING: bool = True
     VLLM_ENABLE_V1_MULTIPROCESSING: bool = True
     VLLM_LOG_BATCHSIZE_INTERVAL: float = -1
     VLLM_DISABLE_COMPILE_CACHE: bool = False
@@ -520,6 +521,10 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_ROCM_FP8_PADDING":
     lambda: bool(int(os.getenv("VLLM_ROCM_FP8_PADDING", "1"))),
 
+    # Pad the weights for the moe kernel
+    "VLLM_ROCM_MOE_PADDING":
+    lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "1"))),
+
     # Divisor for dynamic query scale factor calculation for FP8 KV Cache
     "Q_SCALE_CONSTANT":
     lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -800,7 +800,7 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
             expert_ids,
             num_tokens_post_padded,
             B.shape[1],
-            A.shape[1],
+            B.shape[2],
             EM,
             topk_ids.numel(),
             A.stride(0),
@@ -1322,8 +1322,8 @@ def fused_experts_impl(hidden_states: torch.Tensor,
 
     assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
     assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
-    assert w1.is_contiguous(), "Expert weights1 must be contiguous"
+    assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
-    assert w2.is_contiguous(), "Expert weights2 must be contiguous"
+    assert w2.stride(-1) == 1, "Stride of last dimension must be 1"
     assert hidden_states.dtype in [
         torch.float32, torch.float16, torch.bfloat16
     ]

vllm/model_executor/layers/fused_moe/layer.py

@@ -5,6 +5,7 @@ from enum import Enum
 from typing import Callable, List, Optional, Tuple
 
 import torch
+import torch.nn.functional as F
 from torch.nn.parameter import UninitializedParameter
 
 from vllm import envs
@@ -96,9 +97,27 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         layer.register_parameter("w2_weight", w2_weight)
         set_weight_attrs(w2_weight, extra_weight_attrs)
 
+    def _maybe_pad_weight(self, weight: torch.Tensor) -> torch.Tensor:
+        # Pad the weight tensor. This is an optimization on ROCm platform, which
+        # can benefit from tensors located far enough from one another in memory
+        if (envs.VLLM_ROCM_MOE_PADDING and current_platform.is_rocm()
+                and weight.stride(-1) == 1
+                and (weight.stride(-2) * weight.element_size()) % 512 == 0):
+            num_pad = 256 // weight.element_size()
+            weight = F.pad(weight, (0, num_pad), "constant", 0)[..., :-num_pad]
+            torch.cuda.empty_cache()
+        return weight
+
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         super().process_weights_after_loading(layer)
 
+        layer.w13_weight = torch.nn.Parameter(self._maybe_pad_weight(
+            layer.w13_weight.data),
+                                              requires_grad=False)
+        layer.w2_weight = torch.nn.Parameter(self._maybe_pad_weight(
+            layer.w2_weight.data),
+                                             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/quantization/fp8.py

@@ -255,7 +255,7 @@ class Fp8LinearMethod(LinearMethodBase):
             else:
                 layer.register_parameter("input_scale", None)
 
-    def add_padding_to_weight(self, weight: torch.Tensor) -> torch.Tensor:
+    def _maybe_pad_weight(self, weight: torch.Tensor) -> torch.Tensor:
         # Pad the weight tensor. This is an optimization on ROCm platform, which
         # can benefit from tensors located far enough from one another in memory
         if (envs.VLLM_ROCM_FP8_PADDING and current_platform.is_rocm()
@@ -279,7 +279,7 @@ class Fp8LinearMethod(LinearMethodBase):
                 weight = layer.weight.data
                 weight_scale_inv = layer.weight_scale_inv.data
 
-            weight = self.add_padding_to_weight(weight)
+            weight = self._maybe_pad_weight(weight)
 
             # Torch.compile cannot use Parameter subclasses.
             layer.weight = Parameter(weight, requires_grad=False)
@@ -343,7 +343,7 @@ class Fp8LinearMethod(LinearMethodBase):
                     logical_widths=layer.logical_widths,
                 )
 
-            weight = self.add_padding_to_weight(weight)
+            weight = self._maybe_pad_weight(weight)
             # Update layer with new values.
             layer.weight = Parameter(weight.t(), requires_grad=False)
             layer.weight_scale = Parameter(weight_scale, requires_grad=False)