[Misc] Add unit tests for MoE ModularKernel combinations + Profiling utility (#20449)

Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com> Co-authored-by: Varun Sundar Rabindranath <vsundarr@redhat.com>
2025-07-11 10:51:46 -04:00
parent 6fb162447b
commit 53fa457391
15 changed files with 1727 additions and 22 deletions
--- a/tests/kernels/moe/modular_kernel_tools/mk_objects.py
+++ b/tests/kernels/moe/modular_kernel_tools/mk_objects.py
@@ -0,0 +1,87 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+# Fused experts and PrepareFinalize imports
+from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
+    BatchedDeepGemmExperts)
+from vllm.model_executor.layers.fused_moe.batched_triton_or_deep_gemm_moe import (  # noqa: E501
+    BatchedTritonOrDeepGemmExperts)
+from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
+from vllm.model_executor.layers.fused_moe.deep_gemm_moe import DeepGemmExperts
+from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
+    BatchedTritonExperts, NaiveBatchedExperts)
+from vllm.model_executor.layers.fused_moe.layer import TritonExperts
+from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+    MoEPrepareAndFinalizeNoEP)
+from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
+    TritonOrDeepGemmExperts)
+from vllm.utils import has_deep_ep, has_pplx
+
+if has_deep_ep():
+    from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import (  # noqa: E501
+        DeepEPHTPrepareAndFinalize)
+    from vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize import (  # noqa: E501
+        DeepEPLLPrepareAndFinalize)
+
+if has_pplx():
+    from vllm.model_executor.layers.fused_moe.pplx_prepare_finalize import (
+        PplxPrepareAndFinalize)
+
+MK_MULTI_GPU_PREPARE_FINALIZE_TYPES = []
+if has_pplx():
+    MK_MULTI_GPU_PREPARE_FINALIZE_TYPES += [PplxPrepareAndFinalize]
+if has_deep_ep():
+    MK_MULTI_GPU_PREPARE_FINALIZE_TYPES += [
+        DeepEPHTPrepareAndFinalize, DeepEPLLPrepareAndFinalize
+    ]
+
+MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES = [MoEPrepareAndFinalizeNoEP]
+
+MK_ALL_PREPARE_FINALIZE_TYPES = (MK_MULTI_GPU_PREPARE_FINALIZE_TYPES +
+                                 MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES)
+
+MK_FUSED_EXPERT_TYPES = [
+    BatchedDeepGemmExperts,
+    BatchedTritonExperts,
+    NaiveBatchedExperts,
+    BatchedTritonOrDeepGemmExperts,
+    CutlassExpertsFp8,
+    DeepGemmExperts,
+    TritonOrDeepGemmExperts,
+    TritonExperts,
+]
+
+MK_QUANT_CONFIGS = [
+    None,
+    # per-channel / per-column weights and per-tensor activations
+    FusedMoEQuantConfig(quant_dtype=torch.float8_e4m3fn,
+                        per_out_ch_quant=True,
+                        per_act_token_quant=False,
+                        block_shape=None),
+    # per-channel / per-column weights and per-token activations
+    FusedMoEQuantConfig(quant_dtype=torch.float8_e4m3fn,
+                        per_out_ch_quant=True,
+                        per_act_token_quant=True,
+                        block_shape=None),
+    # per-tensor weights and per-tensor activations
+    FusedMoEQuantConfig(quant_dtype=torch.float8_e4m3fn,
+                        per_out_ch_quant=False,
+                        per_act_token_quant=False,
+                        block_shape=None),
+    # per-tensor weights and per-token activations
+    FusedMoEQuantConfig(quant_dtype=torch.float8_e4m3fn,
+                        per_out_ch_quant=False,
+                        per_act_token_quant=True,
+                        block_shape=None),
+    # block-quantized weights and 128 block per-token activations
+    FusedMoEQuantConfig(quant_dtype=torch.float8_e4m3fn,
+                        per_out_ch_quant=False,
+                        per_act_token_quant=False,
+                        block_shape=[128, 128]),
+    # TODO (varun) : Should we test the following combinations ?
+    # block-quantized weights and per-token activations
+    # block-quantized weights and per-tensor activations
+]