[unrevert] Add batch invariant kernel override for FlashInfer backend [2/n]

csrc/moe/topk_softmax_kernels.cu

@@ -21,7 +21,6 @@
 #include <c10/cuda/CUDAGuard.h>
 #include "../cuda_compat.h"
 #include "../cub_helpers.h"
-#include "../core/batch_invariant.hpp"
 
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
@@ -406,8 +405,7 @@ void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, f
     using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM>;
     static constexpr int VPT = Constants::VPT;
     static constexpr int ROWS_PER_WARP = Constants::ROWS_PER_WARP;
-    const bool batch_invariant_launch = vllm::vllm_kernel_override_batch_invariant();
-    const int num_warps = batch_invariant_launch ? 32 : (num_rows + ROWS_PER_WARP - 1) / ROWS_PER_WARP;
+    const int num_warps = (num_rows + ROWS_PER_WARP - 1) / ROWS_PER_WARP;
     const int num_blocks = (num_warps + WARPS_PER_TB - 1) / WARPS_PER_TB;
 
     dim3 block_dim(WARP_SIZE_PARAM, WARPS_PER_TB);

tests/v1/generation/test_batch_invariance.py

@@ -76,18 +76,21 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle():
       seed.
     - Keep max_tokens and max_model_len bounded for speed and memory use.
     """
-    random.seed(12345)
+    seed = int(os.getenv("VLLM_TEST_SEED", "12345"))
+    random.seed(seed)
 
     # Allow overrides from environment (useful for CI tuning)
     # "facebook/opt-125m" is too small, doesn't reliably test determinism
     model = os.getenv("VLLM_TEST_MODEL", "Qwen/Qwen3-1.7B")
     num_trials = int(os.getenv("VLLM_NEEDLE_TRIALS", "5"))
-    batch_size = int(os.getenv("VLLM_NEEDLE_BATCH_SIZE", "64"))
-    assert batch_size >= 2, "Batch size should be >= 2 to mix needle."
+    max_batch_size = int(os.getenv("VLLM_NEEDLE_BATCH_SIZE", "128"))
+    min_random_prompt = int(os.getenv("VLLM_MIN_PROMPT", "1024"))
+    max_random_prompt = int(os.getenv("VLLM_MAX_PROMPT", "2048"))
+    assert max_batch_size >= 2, "Batch size should be >= 2 to mix needle."
 
     # Keep GPU memory usage low to avoid startup allocation failures.
-    gpu_mem_util = float(os.getenv("VLLM_GPU_MEMORY_UTILIZATION", "0.3"))
-    max_model_len = int(os.getenv("VLLM_MAX_MODEL_LEN", "4096"))
+    gpu_mem_util = float(os.getenv("VLLM_GPU_MEMORY_UTILIZATION", "0.4"))
+    max_model_len = int(os.getenv("VLLM_MAX_MODEL_LEN", "5120"))
     swap_space_gb = int(os.getenv("VLLM_SWAP_SPACE_GB", "4"))
 
     # Sampling parameters: longer outputs with a more random-sounding
@@ -111,7 +114,7 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle():
         # Engine with bs=1 behavior
         llm_bs1 = LLM_with_max_seqs(
             model=model,
-            max_num_seqs=1,
+            max_num_seqs=max_batch_size,
             gpu_memory_utilization=gpu_mem_util,
             max_model_len=max_model_len,
             swap_space=swap_space_gb,
@@ -126,7 +129,7 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle():
         # Engine with larger batch limit (e.g., 64)
         llm_bsN = LLM_with_max_seqs(
             model=model,
-            max_num_seqs=batch_size,
+            max_num_seqs=max_batch_size,
             gpu_memory_utilization=gpu_mem_util,
             max_model_len=max_model_len,
             swap_space=swap_space_gb,
@@ -135,15 +138,16 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle():
         mismatches = 0
 
         for trial in range(num_trials):
-            # Create a batch of size `batch_size` and insert the needle at
+            # Create a batch of size `max_batch_size` and insert the needle at
             # a random index
             prompts: list[str] = []
+            batch_size = random.randint(max_batch_size // 2, max_batch_size)
             needle_pos = random.randint(0, batch_size - 1)
             for i in range(batch_size):
                 if i == needle_pos:
                     prompts.append(needle_prompt)
                 else:
-                    prompts.append(_random_prompt())
+                    prompts.append(_random_prompt(min_random_prompt, max_random_prompt))
 
             # Generate with the larger-batch engine
             outputs = llm_bsN.generate(prompts, sampling)
@@ -154,19 +158,20 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle():
             text = needle_output.outputs[0].text
 
             if text != baseline_text:
+                print(f"{text}\n\n== Not the same as ==\n\n{baseline_text}\n\n")
                 mismatches += 1
 
         passes = num_trials - mismatches
         # Dump how many passed vs failed
         print(
             f"[determinism] total={num_trials}, passed={passes}, "
-            f"failed={mismatches}, batch_size={batch_size}"
+            f"failed={mismatches}, max_batch_size={max_batch_size}"
         )
 
         if mismatches > 0:
             pytest.fail(
                 f"Nondeterministic outputs detected: {mismatches} failed out "
-                f"of {num_trials} trials (batch_size={batch_size})."
+                f"of {num_trials} trials (max_batch_size={max_batch_size})."
             )
 
     finally:
@@ -199,25 +204,28 @@ def _extract_step_logprobs(request_output):
     not torch.cuda.is_available(),
     reason="Requires CUDA to match production inference path.",
 )
-def test_logprobs_bitwise_batch_invariance_bs1_vs_bs2():
-    # model_name = os.getenv("VLLM_TEST_MODEL", "facebook/opt-125m")
+@pytest.mark.parametrize("backend", ["FLEX_ATTENTION", "FLASHINFER"])
+def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(backend):
+    backend = os.getenv("VLLM_ATTENTION_BACKEND", backend)
+    os.environ["VLLM_ATTENTION_BACKEND"] = backend
+
+    seed = int(os.getenv("VLLM_TEST_SEED", "12345"))
+    random.seed(seed)
     model_name = os.getenv("VLLM_TEST_MODEL", "Qwen/Qwen3-1.7B")
     tp_size = int(os.getenv("VLLM_TEST_TP_SIZE", "1"))
 
     # Force float32 to avoid precision-induced differences.
     llm = LLM(
         model=model_name,
         tensor_parallel_size=tp_size,
-        enforce_eager=True,  # helps reduce nondeterminism from some backends
+        enforce_eager=True,
+        enable_prefix_caching=False,
     )
 
-    prompts = [
-        "The capital of France is",
-        "The capital of Germany is",
-    ]
+    prompts = [_random_prompt(10, 1024) for i in range(100)]
 
     sp = SamplingParams(
-        temperature=0.0,
+        temperature=0.6,
         top_p=1.0,
         max_tokens=8,
         # Seed shouldn't matter at temperature=0, but keeping it stable anyway.
@@ -238,29 +246,29 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bs2():
             )
         bs1_logprobs_per_prompt.append(step_logprobs)
 
-    # BS=2: run prompts in a batch and collect logprobs per step for each
+    # BS=N: run prompts in a batch and collect logprobs per step for each
     # prompt.
     outs_batched = llm.generate(prompts, sp, use_tqdm=False)
     assert len(outs_batched) == len(prompts)
-    bs2_logprobs_per_prompt = []
+    bsN_logprobs_per_prompt = []
     for o in outs_batched:
         step_logprobs = _extract_step_logprobs(o)
         if step_logprobs is None:
             pytest.skip(
                 "Logits are not available on RequestOutput; "
                 "enable logprobs return to run this test."
             )
-        bs2_logprobs_per_prompt.append(step_logprobs)
+        bsN_logprobs_per_prompt.append(step_logprobs)
 
-    # Compare step-by-step logprobs for each prompt between BS=1 and BS=2 runs.
-    for i, (logprobs_bs1, logprobs_bs2) in enumerate(
-        zip(bs1_logprobs_per_prompt, bs2_logprobs_per_prompt)
+    # Compare step-by-step logprobs for each prompt between BS=1 and BS=N runs.
+    for i, (logprobs_bs1, logprobs_bsN) in enumerate(
+        zip(bs1_logprobs_per_prompt, bsN_logprobs_per_prompt)
     ):
-        assert len(logprobs_bs1) == len(logprobs_bs2), (
+        assert len(logprobs_bs1) == len(logprobs_bsN), (
             f"Different number of generation steps for prompt index {i}: "
-            f"{len(logprobs_bs1)} (BS=1) vs {len(logprobs_bs2)} (BS=2)"
+            f"{len(logprobs_bs1)} (BS=1) vs {len(logprobs_bsN)} (BS=N)"
         )
-        for t, (a, b) in enumerate(zip(logprobs_bs1, logprobs_bs2)):
+        for t, (a, b) in enumerate(zip(logprobs_bs1, logprobs_bsN)):
             assert a.shape == b.shape, (
                 f"Logits shape mismatch at prompt {i}, step {t}: {a.shape} vs {b.shape}"
             )
@@ -297,6 +305,7 @@ def LLM_with_max_seqs(
         tensor_parallel_size=int(os.getenv("VLLM_TP_SIZE", "1")),
         trust_remote_code=os.getenv("VLLM_TRUST_REMOTE_CODE", "0") == "1",
         enable_prefix_caching=False,
+        enforce_eager=True,
         # Enable for MOE models
         # enable_expert_parallel=True,
     )

vllm/model_executor/layers/batch_invariant.py

@@ -8,8 +8,12 @@
 
 import torch
 
+import vllm.envs as envs
+from vllm.logger import init_logger
 from vllm.triton_utils import tl, triton
 
+logger = init_logger(__name__)
+
 
 def _matmul_launch_metadata(
     grid: Callable[..., Any], kernel: Any, args: dict[str, Any]
@@ -564,5 +568,14 @@ def vllm_kernel_override_batch_invariant():
 def init_batch_invariance():
     # this will hit all the csrc overrides as well
     if vllm_kernel_override_batch_invariant():
-        os.environ["VLLM_ATTENTION_BACKEND"] = "FLEX_ATTENTION"
+        curr_attn_backend = envs.VLLM_ATTENTION_BACKEND
+        supported_backends = ["FLEX_ATTENTION", "FLASHINFER"]
+        if curr_attn_backend not in supported_backends:
+            warning = (
+                "Forcibly updating attention backend to"
+                f" {supported_backends[0]} for batch_invariant. "
+                f" Supported backends: {supported_backends}."
+            )
+            logger.warning_once(warning)
+            os.environ["VLLM_ATTENTION_BACKEND"] = supported_backends[0]
         enable_batch_invariant_mode()

vllm/v1/attention/backends/flashinfer.py

@@ -27,6 +27,9 @@
 )
 from vllm.config import CUDAGraphMode, VllmConfig
 from vllm.logger import init_logger
+from vllm.model_executor.layers.batch_invariant import (
+    vllm_kernel_override_batch_invariant,
+)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
     kFp8StaticTensorSym,
@@ -52,6 +55,7 @@
 from vllm.v1.kv_cache_interface import AttentionSpec
 
 FLASHINFER_WORKSPACE_BUFFER_SIZE = 256 * 1024 * 1024
+FLASHINFER_WORKSPACE_BUFFER_SIZE_BATCH_INVARIANT = 2048 * 1024 * 1024
 
 FP8_DTYPE = current_platform.fp8_dtype()
 FP4_DTYPE = torch.uint8
@@ -290,6 +294,15 @@ def __init__(
         self._prefill_wrapper = None  # Wrapper for prefill/append
         self._decode_wrapper = None  # Wrapper for decode (general shape)
 
+        if vllm_kernel_override_batch_invariant():
+            self.decode_fixed_split_size = 2048
+            self.prefill_fixed_split_size = 4096
+            self.disable_split_kv = True
+        else:
+            self.decode_fixed_split_size = -1
+            self.prefill_fixed_split_size = -1
+            self.disable_split_kv = False
+
         self.compilation_config = vllm_config.compilation_config
         max_num_pages_per_req = cdiv(
             self.model_config.max_model_len, self.kv_cache_spec.block_size
@@ -393,8 +406,11 @@ def __init__(
 
     def _get_workspace_buffer(self):
         if self._workspace_buffer is None:
+            buffer_size = FLASHINFER_WORKSPACE_BUFFER_SIZE
+            if vllm_kernel_override_batch_invariant():
+                buffer_size = FLASHINFER_WORKSPACE_BUFFER_SIZE_BATCH_INVARIANT
             self._workspace_buffer = torch.zeros(
-                FLASHINFER_WORKSPACE_BUFFER_SIZE, dtype=torch.uint8, device=self.device
+                buffer_size, dtype=torch.uint8, device=self.device
             )
         return self._workspace_buffer
 
@@ -671,6 +687,8 @@ def build(
                         logits_soft_cap=self.logits_soft_cap,
                         q_data_type=self.q_data_type,
                         kv_data_type=self.kv_cache_dtype,
+                        fixed_split_size=self.prefill_fixed_split_size,
+                        disable_split_kv=self.disable_split_kv,
                     )
                 else:
                     attn_metadata.qo_indptr_gpu = qo_indptr_cpu.to(
@@ -732,6 +750,8 @@ def build(
                         logits_soft_cap=self.logits_soft_cap,
                         q_data_type=self.q_data_type,
                         kv_data_type=self.kv_cache_dtype,
+                        fixed_split_size=self.decode_fixed_split_size,
+                        disable_split_kv=self.disable_split_kv,
                     )
         return attn_metadata
 
@@ -1123,6 +1143,8 @@ def fast_plan_decode(
     rope_scale: float | None = None,
     rope_theta: float | None = None,
     non_blocking: bool = True,
+    fixed_split_size: int = -1,
+    disable_split_kv: bool = False,
 ) -> None:
     """
     A faster version of BatchDecodeWithPagedKVCacheWrapper::plan used for
@@ -1159,6 +1181,10 @@ def fast_plan_decode(
             rope_scale,
             rope_theta,
             non_blocking,
+            None,  # block_tables
+            None,  # seq_lens
+            fixed_split_size,
+            disable_split_kv,
         )
         self.vllm_first_call = False
         return
@@ -1224,8 +1250,8 @@ def fast_plan_decode(
             head_dim,
             False,  # causal
             window_left,
-            -1,  # fixed_split_size
-            False,  # disable_split_kv
+            fixed_split_size,
+            disable_split_kv,
         )
     except Exception as e:
         raise RuntimeError(f"Error in tensor core plan: {e}") from e