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[unrevert] Add batch invariant kernel override for FlashInfer backend [2/n] (#26373)

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Signed-off-by: Bram Wasti <bwasti@meta.com>
Signed-off-by: Bram Wasti <bwasti@fb.com>
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Bram Wasti 2025-10-13 07:24:53 -07:00 committed by GitHub
parent 8e67b2557a
commit 3263799056
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4 changed files with 81 additions and 35 deletions
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4
csrc/moe/topk_softmax_kernels.cu
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@ -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);

65
tests/v1/generation/test_batch_invariance.py
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@ -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:
@ -199,8 +204,13 @@ 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"))
@ -208,16 +218,14 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bs2():
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.
@ -238,11 +246,11 @@ 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:
@ -250,17 +258,17 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bs2():
"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,
)

15
vllm/model_executor/layers/batch_invariant.py
View File

@ -8,8 +8,12 @@ from typing import Any
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]
@ -562,5 +566,14 @@ 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()

32
vllm/v1/attention/backends/flashinfer.py
View File

@ -25,6 +25,9 @@ from vllm.attention.backends.abstract import (
)
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,
@ -50,6 +53,7 @@ from vllm.v1.attention.backends.utils import (
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
@ -288,6 +292,15 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
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
@ -391,8 +404,11 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
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
@ -669,6 +685,8 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
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(
@ -730,6 +748,8 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
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
@ -1121,6 +1141,8 @@ 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
@ -1157,6 +1179,10 @@ 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
@ -1222,8 +1248,8 @@ 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