[UX] Speedup DeepGEMM warmup with heuristics (#25619)

Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: Michael Goin <mgoin64@gmail.com>
Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Varun Sundar Rabindranath <vsundarr@redhat.com>

vllm/model_executor/warmup/deep_gemm_warmup.py

@@ -26,6 +26,55 @@ from vllm.model_executor.layers.quantization.fp8 import Fp8LinearMethod
 from vllm.utils.deep_gemm import fp8_gemm_nt, m_grouped_fp8_gemm_nt_contiguous
 
 
+def _generate_optimal_warmup_m_values(
+    max_tokens: int, n: int, device: torch.device
+) -> list[int]:
+    """
+    Generate M values that cover all possible DeepGEMM kernel configurations.
+    Reference: https://github.com/deepseek-ai/DeepGEMM/blob/79f48ee15a82dd5fad5cd9beaa393c1f755e6b55/csrc/jit_kernels/heuristics/common.hpp
+
+    Args:
+        max_tokens: Maximum number of tokens to warmup for
+        n: The actual N dimension from the weight tensor
+        device: The torch device to get properties from.
+    """
+
+    def ceil_div(a: int, b: int) -> int:
+        return (a + b - 1) // b
+
+    # DeepGEMM's possible block sizes
+    block_ms = [64, 128, 256]
+    block_ns = list(range(16, min(257, n + 1), 16))
+    num_sms = torch.cuda.get_device_properties(device).multi_processor_count
+
+    m_values = set()
+
+    # Always include small cases
+    m_values.update([1, 2, 4] + [i for i in range(8, 65, 8)])
+
+    # Collect M values where different wave patterns occur
+    for block_m in block_ms:
+        for block_n in block_ns:
+            if block_n > n:
+                continue
+
+            # Add key M boundaries for this block combination
+            for wave in range(1, 11):  # Up to 10 waves
+                # M where this block config transitions to next wave
+                target_blocks = wave * num_sms
+                m = target_blocks * block_m // ceil_div(n, block_n)
+                if 1 <= m <= max_tokens:
+                    m_values.add(m)
+
+            # Add block_m boundaries
+            for multiple in range(1, max_tokens // block_m + 1):
+                m = multiple * block_m
+                if m <= max_tokens:
+                    m_values.add(m)
+
+    return sorted(m_values)
+
+
 def _extract_data_from_linear_base_module(
     m: torch.nn.Module,
 ) -> tuple[torch.Tensor, torch.Tensor, list[int]]:
@@ -136,14 +185,27 @@ def _deepgemm_fp8_gemm_nt_warmup(w: torch.Tensor, ws: torch.Tensor, max_tokens:
     )
     out = torch.empty((max_tokens, n), device=device, dtype=torch.bfloat16)
 
-    pbar = tqdm(total=max_tokens, desc=f"DeepGemm(fp8_gemm_nt) warmup (W={w.size()})")
-    num_tokens = max_tokens
-    while num_tokens > 0:
+    # Use optimal M values only if VLLM_DEEP_GEMM_WARMUP is set to "relax".
+    # Otherwise warmup all token sizes to avoid JIT compilation in hotpath
+    if envs.VLLM_DEEP_GEMM_WARMUP == "relax":
+        m_values = _generate_optimal_warmup_m_values(max_tokens, n, device)
+        desc = f"DeepGemm(fp8_gemm_nt) warmup (W={w.size()}) [relaxed]"
+    else:
+        assert envs.VLLM_DEEP_GEMM_WARMUP == "full", (
+            "Expected "
+            'VLLM_DEEP_GEMM_WARMUP env to be set to "full" but got '
+            f"{envs.VLLM_DEEP_GEMM_WARMUP}"
+        )
+        m_values = list(range(1, max_tokens + 1))
+        desc = f"DeepGemm(fp8_gemm_nt) warmup (W={w.size()}) [all tokens]"
+
+    pbar = tqdm(total=len(m_values), desc=desc)
+
+    for num_tokens in m_values:
         fp8_gemm_nt(
             (a1q[:num_tokens], a1q_scales[:num_tokens]), (w, ws), out[:num_tokens]
         )
         pbar.update(1)
-        num_tokens -= 1
 
     FP8_GEMM_NT_WARMUP_CACHE.add(w.size())
 
@@ -195,12 +257,16 @@ def _deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(
         )
         out = torch.empty((MAX_M, n), device=device, dtype=torch.bfloat16)
 
+        # Generate M values in block_m increments (already optimized for MoE)
+        m_values = list(range(block_m, MAX_M + 1, block_m))
+
         pbar = tqdm(
-            total=MAX_BLOCKS,
-            desc=f"DeepGemm(m_grouped_fp8_gemm_nt_contiguous) warmup (W={w.size()})",
+            total=len(m_values),
+            desc=f"DeepGemm(m_grouped_fp8_gemm_nt_contiguous) warmup (W={w.size()}) "
+            f"[{len(m_values)} values, block_m={block_m}]",
         )
-        num_tokens = MAX_M
-        while num_tokens > 0:
+
+        for num_tokens in m_values:
             m_grouped_fp8_gemm_nt_contiguous(
                 (a1q[:num_tokens], a1q_scales[:num_tokens]),
                 (w, w_scale),
@@ -208,7 +274,6 @@ def _deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(
                 expert_ids[:num_tokens],
             )
             pbar.update(1)
-            num_tokens = num_tokens - block_m
 
     for w, ws in [(w1, w1_scale), (w2, w2_scale)]:
         if w.size() not in GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE:

vllm/model_executor/warmup/kernel_warmup.py

@@ -29,7 +29,7 @@ def kernel_warmup(worker: "Worker"):
     do_deep_gemm_warmup = (
         envs.VLLM_USE_DEEP_GEMM
         and is_deep_gemm_supported()
-        and not envs.VLLM_SKIP_DEEP_GEMM_WARMUP
+        and envs.VLLM_DEEP_GEMM_WARMUP != "skip"
     )
     if do_deep_gemm_warmup:
         model = worker.get_model()