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AMD][MI35X] 0706 DSV4 sglang mtp #2108

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749d9c4
Update config and change log
1am9trash Jul 7, 2026
2251cd3
Update config and change log
1am9trash Jul 7, 2026
ff3f169
fix: source benchmark_lib.sh from repo-level benchmarks/ dir
functionstackx Jul 7, 2026
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144 changes: 20 additions & 124 deletions benchmarks/single_node/fixed_seq_len/dsv4_fp4_mi355x_sglang_mtp.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -6,27 +6,9 @@
# 2026-05-27, commit deaba74), which fixes the ROCm HIP-radix backend's
# per-step draft out_cache_loc slicing under CUDA graph (the bug behind the
# false-EOS / truncated-generation symptom in sgl issue #20404) and validates
# GSM8K 0.950 with MTP on. The EAGLE chain follows that PR's accuracy config
# for the DP-attention path (steps=2, topk=1, draft=3); the TP-only
# low-concurrency path uses the (3,1,4) chain shared with dsr1_fp4_mi355x_mtp.sh.
#
# Image: #26383 is on sglang `main`, so this runs on the mainline ROCm nightly
# (lmsysorg/sglang-rocm:v0.5.12.post1-rocm720-mi35x-*), NOT a rocm/sgl-dev:*-DSv4
# build. The -DSv4 images are cut from the amd/deepseek_v4 branch, which has not
# merged #26383 (latest da28108 = f96ac98 + build fixes + an unrelated MLA-decode
# refactor; it still crashes at MTP graph capture, run 26723126211). Mainline
# carries #26383 but omits deep_gemm, which DSv4-Pro's default fp8 wo_a path
# imports. AMD doesn't need deep_gemm (it uses aiter/tilelang/torch), and every
# deep_gemm use on the DSv4 path is behind an env-flag fallback, so the block
# below detects deep_gemm's absence and routes around it: SGLANG_OPT_FP8_WO_A_GEMM=0
# (dequant fp8 wo_a -> bf16 + torch.einsum; also skips the weight-load
# transform_sf_into_required_layout that crashed run 26727984372) and
# SGLANG_TOPK_TRANSFORM_512_TORCH=1 (torch topk). The indexer already routes to
# tilelang + torch paged-MQA-logits and MHC to aiter via flags set below. On a
# -DSv4 image that carries #26383, bump amd-master.yaml and the detect restores
# the deep_gemm perf path. RUN_EVAL on the high-conc points gates accuracy.

source "$(dirname "$0")/../benchmark_lib.sh"
# GSM8K 0.950 with MTP on.

source "$(dirname "$0")/../../benchmark_lib.sh"

check_env_vars \
MODEL \
Expand All @@ -50,97 +32,12 @@ if [[ "$MODEL" != /* ]]; then hf download "$MODEL"; fi
# from the amd/deepseek_v4 branch in sgl-project/sglang). To bump sglang,
# bump the image tag in configs/amd-master.yaml.

# Transformers in the container doesn't recognize the `deepseek_v4` model_type.
# PR #23608's fallback in hf_transformers_utils.get_config tries to handle this
# by writing a patched config to /tmp, but in practice isn't catching the error
# in this image. Patch the cached config.json directly instead: set model_type
# to `deepseek_v3` so AutoConfig.from_pretrained succeeds, and keep
# architectures=['DeepseekV4ForCausalLM'] so SGLang dispatches to its native
# DSv4 model class (python/sglang/srt/models/deepseek_v4.py).
python3 << PYEOF
import json
from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id="$MODEL", filename="config.json")
with open(path) as f:
config = json.load(f)
if config.get("model_type") == "deepseek_v4":
config["model_type"] = "deepseek_v3"
with open(path, "w") as f:
json.dump(config, f, indent=2)
print(f"Patched {path}: model_type deepseek_v4 -> deepseek_v3")
else:
print(f"No patch needed: model_type is {config.get('model_type')!r}")
PYEOF

# DSv4 FP4-experts path. Tracks the env block in python/run_dsv4.sh on the
# amd/deepseek_v4 branch (HEAD's active block is FP8; we override the two
# FP4-specific flags below):
# SGLANG_DSV4_FP4_EXPERTS=True -> route experts through the FP4 kernels
# SGLANG_FORCE_TRITON_MOE_FP8=0 -> dispatch MoE through aiter and apply
# the swiglu_limit clamp in the triton
# MoE fallback path.
export SGLANG_REASONING_EFFORT=max
export SGLANG_OPT_USE_FUSED_COMPRESS=true
export SGLANG_OPT_USE_OLD_COMPRESSOR=false
export SGLANG_OPT_USE_TILELANG_SWA_PREPARE=false
export SGLANG_OPT_USE_JIT_KERNEL_FUSED_TOPK=false
export SGLANG_OPT_USE_FUSED_HASH_TOPK=true
export SGLANG_OPT_DEEPGEMM_HC_PRENORM=false
export SGLANG_OPT_USE_TILELANG_MHC_PRE=false
export SGLANG_OPT_USE_TILELANG_MHC_POST=false
export SGLANG_OPT_USE_AITER_MHC_PRE=true
export SGLANG_OPT_USE_AITER_MHC_POST=true
export SGLANG_ENABLE_THINKING=1
export SGLANG_USE_AITER=1
export SGLANG_USE_ROCM700A=1
export SGLANG_TOPK_TRANSFORM_512_TORCH=0
export SGLANG_FP8_PAGED_MQA_LOGITS_TORCH=1
export SGLANG_DSV4_FP4_EXPERTS=True
export SGLANG_OPT_DPSK_V4_RADIX=1
export SGLANG_OPT_USE_OVERLAP_STORE_CACHE=false
export SGLANG_OPT_USE_FUSED_STORE_CACHE=true
export SGLANG_FORCE_TRITON_MOE_FP8=0
export SGLANG_HACK_FLASHMLA_BACKEND=triton
export SGLANG_OPT_USE_TILELANG_INDEXER=true
export SGLANG_OPT_USE_TRITON_SWA_PREPARE=true
export SGLANG_DEFAULT_THINKING=1
export SGLANG_DSV4_REASONING_EFFORT=max
export SGLANG_USE_ROCM700A=0
export SGLANG_HACK_FLASHMLA_BACKEND=unified_kv_triton
export AITER_BF16_FP8_MOE_BOUND=0
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export SGLANG_OPT_FUSE_WQA_WKV=true
export SGLANG_OPT_USE_FUSED_PAGED_COMPRESS=true
export SGLANG_OPT_USE_MULTI_STREAM_OVERLAP=0

# MTP-specific knobs landed alongside the graph fix in sgl#26383:
# SGLANG_OPT_USE_TRITON_FUSED_MHC -> fused Triton mhc_post_pre for low conc
# (defaults True in post-#26383 images;
# set explicitly so the recipe is auditable)
# SGLANG_OPT_C4_SPARSE_TOPK -> sparse-attention top-k used in the PR's
# DSv4 MTP accuracy run
export SGLANG_OPT_USE_TRITON_FUSED_MHC=1
export SGLANG_OPT_C4_SPARSE_TOPK=512

# Mainline ROCm nightlies carry #26383 but omit deep_gemm (only rocm/sgl-dev:*-DSv4
# builds bundle it). DSv4-Pro's default fp8 wo_a path imports deep_gemm at weight
# load; detect its absence and route the deep_gemm-touching paths to their torch
# fallbacks. No-op on a deep_gemm-bearing image, so this recipe works on both.
# SGLANG_OPT_FP8_WO_A_GEMM=0 -> wo_a fp8 weights dequantized to bf16 at load
# (_dequant_fp8_wo_a) + o-proj via torch.einsum;
# also skips the post-load deep_gemm
# transform_sf_into_required_layout that crashed.
# SGLANG_TOPK_TRANSFORM_512_TORCH=1 -> torch topk-transform instead of the kernel.
# SGLANG_OPT_USE_TOPK_V2=0 -> skip plan_topk_v2 in the indexer metadata;
# its jit kernel is CUDA-only (topk/ptx.cuh
# #includes <cuda/ptx>) and won't build for
# gfx950. topk_metadata is unused on the torch
# topk path, so empty is fine.
# SGLANG_ENABLE_JIT_DEEPGEMM=0 -> global off; nothing to JIT without the module.
if python3 -c "import deep_gemm" >/dev/null 2>&1; then
echo "deep_gemm present -> using fp8 wo_a / deep_gemm perf path"
else
echo "deep_gemm absent -> routing DSv4 fp8 wo_a / topk around it (mainline nightly)"
export SGLANG_OPT_FP8_WO_A_GEMM=0
export SGLANG_TOPK_TRANSFORM_512_TORCH=1
export SGLANG_OPT_USE_TOPK_V2=0
export SGLANG_ENABLE_JIT_DEEPGEMM=0
fi


SERVER_LOG=/workspace/server.log
PORT=${PORT:-8888}
Expand All @@ -156,28 +53,25 @@ start_gpu_monitor
PARALLEL_ARGS=(
--tensor-parallel-size "$TP"
)
# EAGLE chain is selected by DP_ATTENTION. The DP-attention path mirrors the
# sgl#26383 DSv4 ROCm accuracy config (steps=2, topk=1, draft=3); the TP-only
# low-concurrency fallback uses the longer (3,1,4) chain that low batch sizes
# benefit from, matching dsr1_fp4_mi355x_mtp.sh.
SPEC_FLAGS=(
--speculative-algorithm EAGLE
--speculative-num-steps 3
--speculative-eagle-topk 1
--speculative-num-draft-tokens 4
)
CHUNKED_PREFILL_SIZE=$ISL
if [ "${DP_ATTENTION}" = "true" ]; then
export SGLANG_SHARED_EXPERT_TP1=1
export SGLANG_DP_SHARED_EXPERT_LOCAL=1
export SGLANG_DP_USE_GATHERV=1
export SGLANG_DP_USE_REDUCE_SCATTER=1

CHUNKED_PREFILL_SIZE=$((ISL * TP))
PARALLEL_ARGS+=(
--dp "$TP"
--enable-dp-attention
--enable-prefill-delayer
)
SPEC_FLAGS=(
--speculative-algorithm EAGLE
--speculative-num-steps 2
--speculative-eagle-topk 1
--speculative-num-draft-tokens 3
)
fi
if [ "${EP_SIZE:-1}" -gt 1 ]; then
PARALLEL_ARGS+=(--ep-size "$EP_SIZE")
Expand All @@ -192,17 +86,19 @@ python3 -m sglang.launch_server \
"${SPEC_FLAGS[@]}" \
--trust-remote-code \
--disable-radix-cache \
--attention-backend compressed \
--attention-backend dsv4 \
--cuda-graph-max-bs ${CONC} \
--max-running-requests ${CONC} \
--mem-fraction-static 0.90 \
--swa-full-tokens-ratio 0.15 \
--page-size 256 \
--kv-cache-dtype fp8_e4m3 \
--context-length $MAX_MODEL_LEN \
--chunked-prefill-size 8192 \
--chunked-prefill-size $CHUNKED_PREFILL_SIZE \
--disable-shared-experts-fusion \
--tool-call-parser deepseekv4 \
--reasoning-parser deepseek-v4 \
--chat-template "$(dirname "$0")/chat_templates/deepseek_v4_thinking.jinja" \
--chat-template "$(dirname "$0")/../chat_templates/deepseek_v4_thinking.jinja" \
--watchdog-timeout 1800 $EVAL_CONTEXT_ARGS > $SERVER_LOG 2>&1 &

SERVER_PID=$!
Expand Down
10 changes: 1 addition & 9 deletions configs/amd-master.yaml
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Expand Up @@ -1764,16 +1764,8 @@ dsv4-fp4-mi355x-sglang:
# graph + sparse triton attn optimizations, merged to main 2026-05-27). That PR
# fixes the ROCm HIP-radix MTP CUDA-graph bug (the false-EOS symptom in sgl
# #20404) and validates GSM8K 0.950 with MTP on.
#
# #26383 is on sglang `main`, NOT the amd/deepseek_v4 branch the rocm/sgl-dev:*-DSv4
# builds are cut from (latest da28108 = f96ac98 + build fixes + an unrelated
# MLA-decode refactor, still pre-#26383 -> kv_score crash, run 26723126211). So we
# pin the mainline ROCm nightly, which carries #26383. Mainline omits deep_gemm,
# but the recipe detects that and routes the DSv4 fp8 wo_a / topk paths to their
# torch fallbacks (see dsv4_fp4_mi355x_sglang_mtp.sh). When a -DSv4 image carrying
# #26383 ships, bump to it; the recipe auto-restores the deep_gemm perf path.
dsv4-fp4-mi355x-sglang-mtp:
image: lmsysorg/sglang-rocm:v0.5.12.post1-rocm720-mi35x-20260601
image: lmsysorg/sglang-rocm:v0.5.14-rocm720-mi35x-20260706
model: deepseek-ai/DeepSeek-V4-Pro
model-prefix: dsv4
runner: mi355x
Expand Down
7 changes: 7 additions & 0 deletions perf-changelog.yaml
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Expand Up @@ -4574,3 +4574,10 @@
description:
- "Add high concurrency configs"
pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/1994

- config-keys:
- dsv4-fp4-mi355x-sglang-mtp
description:
- "Bump image to lmsysorg/sglang-rocm:v0.5.14-rocm720-mi35x-20260706"
- "Clean the export envs"
pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2108
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