[Startup] Make DeepGEMM warmup scale with max-num-batched-tokens (vllm-project#24693)

Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>
Signed-off-by: xuebwang-amd <xuebwang@amd.com>

Author: LucasWilkinson

vllm/model_executor/warmup/deep_gemm_warmup.py

@@ -10,6 +10,7 @@
 from tqdm import tqdm
 
 import vllm.envs as envs
+from vllm.distributed.parallel_state import get_dp_group
 from vllm.model_executor.layers.fused_moe.deep_gemm_moe import DeepGemmExperts
 from vllm.model_executor.layers.fused_moe.deep_gemm_utils import (
     compute_aligned_M, deep_gemm_block_shape)
@@ -131,11 +132,9 @@ def _deepgemm_fp8_gemm_nt_warmup(w: torch.Tensor, ws: torch.Tensor,
 GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE: set[torch.Size] = set()
 
 
-def _deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(w1: torch.Tensor,
-                                                    w2: torch.Tensor,
-                                                    w1_scale: torch.Tensor,
-                                                    w2_scale: torch.Tensor,
-                                                    num_topk: int):
+def _deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(
+        w1: torch.Tensor, w2: torch.Tensor, w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor, num_topk: int, max_tokens: int):
     if (w1.size() in GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE
             and w2.size() in GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE):
         return
@@ -147,9 +146,13 @@ def _deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(w1: torch.Tensor,
     num_experts = w1.size(0)
     device = w1.device
 
+    # Assumes all ranks have the same max_num_batched_tokens
+    max_tokens_across_dp = get_dp_group().world_size * max_tokens
+    max_tokens = min(max_tokens_across_dp, envs.VLLM_FUSED_MOE_CHUNK_SIZE)
+
     # This is the maximum GroupedGemm M size that we expect to run
     # the grouped_gemm with.
-    MAX_M = compute_aligned_M(envs.VLLM_FUSED_MOE_CHUNK_SIZE,
+    MAX_M = compute_aligned_M(max_tokens,
                               num_topk,
                               num_experts,
                               block_m,
@@ -201,7 +204,8 @@ def deepgemm_fp8_gemm_nt_warmup(model: torch.nn.Module, max_tokens: int):
         _deepgemm_fp8_gemm_nt_warmup(w=w, ws=ws, max_tokens=max_tokens)
 
 
-def deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(model: torch.nn.Module):
+def deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(model: torch.nn.Module,
+                                                   max_tokens: int):
     dg_modules = [
         m for m in model.modules()
         if _fused_moe_grouped_gemm_may_use_deep_gemm(m)
@@ -211,9 +215,9 @@ def deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(model: torch.nn.Module):
         w13, w13_scale, w2, w2_scale, num_topk = (
             _extract_data_from_fused_moe_module(dgm))
         _deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(
-            w13, w2, w13_scale, w2_scale, num_topk)
+            w13, w2, w13_scale, w2_scale, num_topk, max_tokens)
 
 
 def deep_gemm_warmup(model: torch.nn.Module, max_tokens: int):
     deepgemm_fp8_gemm_nt_warmup(model, max_tokens)
-    deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(model)
+    deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(model, max_tokens)