[AMD][DI][CI] 8/N Add GLM-5.2 MXFP4 1P1D DI/CI recipes (base + MTP + DP8/EP8) (#32120)

scripts/ci/slurm/nightly-configs.yaml

@@ -532,3 +532,75 @@ kimik26-mxfp4-mi355x-ep16-mtp-sglang:
       search-space:
         - conc-list: [1, 8, 16, 32, 64, 128, 256]
           config_file: scripts/ci/slurm/recipes/mi355x-fp8/kimik26/1k1k/2p1d-ep16-mtp-mxfp4.yaml
+
+# AMD MI355X 2-node 1P1D disaggregation for GLM-5.2 (MXFP4) over MORI. GLM uses
+# GlmMoeDsaForCausalLM (DeepSeek Sparse Attention, auto-selected) with a built-in
+# NextN MTP head. Four variants mirroring the DeepSeek-V4 basic tier:
+#   * (base)      : TP8, no MTP
+#   * -mtp        : TP8 + NextN MTP
+#   * -dp8ep8     : DP-attention 8 + narrow within-node EP8
+#   * -dp8ep8-mtp : DP8 + narrow EP8 + NextN MTP
+# NOTE: checkpoint on disk is amd/GLM-5.1-MXFP4 (only GLM MXFP4 build mirrored).
+glm52-fp4-mi355x-sglang:
+  model: amd/GLM-5.2-MXFP4
+  model-prefix: glm52
+  model_path: /it-share/model_coverage/models--amd--GLM-5.1-MXFP4
+  runner: mi355x
+  precision: fp4
+  framework: sglang
+  multinode: true
+  disagg: true
+  seq-len-configs:
+    - isl: 1024
+      osl: 1024
+      search-space:
+        - conc-list: [1, 8, 16, 32, 64, 128, 256]
+          config_file: scripts/ci/slurm/recipes/mi355x-fp4/glm52/1k1k/1p1d.yaml
+
+glm52-fp4-mi355x-mtp-sglang:
+  model: amd/GLM-5.2-MXFP4
+  model-prefix: glm52
+  model_path: /it-share/model_coverage/models--amd--GLM-5.1-MXFP4
+  runner: mi355x
+  precision: fp4
+  framework: sglang
+  multinode: true
+  disagg: true
+  seq-len-configs:
+    - isl: 1024
+      osl: 1024
+      search-space:
+        - conc-list: [1, 8, 16, 32, 64, 128, 256]
+          config_file: scripts/ci/slurm/recipes/mi355x-fp4/glm52/1k1k/1p1d-mtp.yaml
+
+glm52-fp4-mi355x-dp8ep8-sglang:
+  model: amd/GLM-5.2-MXFP4
+  model-prefix: glm52
+  model_path: /it-share/model_coverage/models--amd--GLM-5.1-MXFP4
+  runner: mi355x
+  precision: fp4
+  framework: sglang
+  multinode: true
+  disagg: true
+  seq-len-configs:
+    - isl: 1024
+      osl: 1024
+      search-space:
+        - conc-list: [1, 8, 16, 32, 64, 128, 256]
+          config_file: scripts/ci/slurm/recipes/mi355x-fp4/glm52/1k1k/1p1d-dp8ep8.yaml
+
+glm52-fp4-mi355x-dp8ep8-mtp-sglang:
+  model: amd/GLM-5.2-MXFP4
+  model-prefix: glm52
+  model_path: /it-share/model_coverage/models--amd--GLM-5.1-MXFP4
+  runner: mi355x
+  precision: fp4
+  framework: sglang
+  multinode: true
+  disagg: true
+  seq-len-configs:
+    - isl: 1024
+      osl: 1024
+      search-space:
+        - conc-list: [1, 8, 16, 32, 64, 128, 256]
+          config_file: scripts/ci/slurm/recipes/mi355x-fp4/glm52/1k1k/1p1d-dp8ep8-mtp.yaml

scripts/ci/slurm/recipes/mi355x-fp4/glm52/1k1k/1p1d-dp8ep8-mtp.yaml

@@ -0,0 +1,80 @@
+# MI355X GLM-5.2 (MXFP4) 2-node 1P1D disaggregation recipe — DP8 + narrow EP8 + MTP.
+#
+# DSA attention is auto-selected (see 1p1d.yaml). DP-attention 8 + within-node
+# EP8 + built-in NextN MTP head (no external draft).
+#
+# Consumed by:
+#   * scripts/ci/slurm/process_result.py reads `resources` and
+#     `backend.sglang_config` (TP/EP/DP + worker counts) for the summary table.
+#   * scripts/ci/slurm/launch_mi355x.sh reads `runtime`, `bench`, and `mtp`.
+
+resources:
+  prefill_workers: 1
+  decode_workers: 1
+
+backend:
+  sglang_config:
+    prefill:
+      tensor-parallel-size: 8
+      expert-parallel-size: 8
+      data-parallel-size: 8
+    decode:
+      tensor-parallel-size: 8
+      expert-parallel-size: 8
+      data-parallel-size: 8
+
+# Model-specific docker env + sglang server args (generic launcher path; keeps
+# GLM off the hardcoded DeepSeek-V4 parser branch in launch_mi355x.sh). DSA
+# attention is auto-selected for GlmMoeDsaForCausalLM, so no --attention-backend
+# is set. GLM has a shared expert (n_shared_experts=1); shared-experts-fusion is
+# disabled to mirror the DeepSeek-V4 path.
+model:
+  env:
+    SGLANG_USE_AITER: 1
+  server_args:
+    - --reasoning-parser
+    - glm45
+    - --tool-call-parser
+    - glm45
+    - --disable-shared-experts-fusion
+
+runtime:
+  image: lmsysorg/sglang-rocm:v0.5.15.post1-rocm720-mi35x-20260722
+  # attention_backend intentionally unset: DSA is auto-selected for
+  # GlmMoeDsaForCausalLM.
+  # RoCE HCAs MORI uses for cross-node KV transfer.
+  ib_devices: rdma0,rdma1,rdma2,rdma3
+  prefill_port: 30025
+  decode_port: 30026
+  prefill_bootstrap_port: 8998
+  decode_bootstrap_port: 9001
+  lb_port: 8000
+  mem_fraction_static: 0.90
+  page_size: 256
+  max_running_requests: 256
+  chunked_prefill_size: 8192
+  swa_full_tokens_ratio: 0.1
+
+# MTP / EAGLE speculative decoding (built-in NextN head from the base model).
+# Applied to both prefill and decode. No draft_model_path: the NextN head lives
+# in the base checkpoint.
+mtp:
+  enabled: true
+  num_steps: 3
+  eagle_topk: 1
+  num_draft_tokens: 4
+
+bench:
+  # bench_serving --max-concurrency sweep; one result JSON per concurrency.
+  concurrencies: [1, 8, 16, 32, 64, 128, 256]
+  num_prompts_factor: 4      # num-prompts = concurrency * factor
+  random_range_ratio: 1.0
+
+  # Correctness gate run through the PD path before the perf sweep (full GSM8K,
+  # 8-shot, accuracy > 0.91). A regression here fails the nightly even when
+  # throughput looks fine ("fast but wrong").
+  accuracy:
+    enabled: true
+    num_shots: 8
+    num_questions: 1319    # full GSM8K test set
+    threshold: 0.91

scripts/ci/slurm/recipes/mi355x-fp4/glm52/1k1k/1p1d-dp8ep8.yaml

@@ -0,0 +1,71 @@
+# MI355X GLM-5.2 (MXFP4) 2-node 1P1D disaggregation recipe — DP8 + narrow EP8.
+#
+# DSA attention is auto-selected (see 1p1d.yaml). DP-attention 8 + within-node
+# EP8, same topology as the DeepSeek-V4 dp8ep8 leg.
+#
+# Consumed by:
+#   * scripts/ci/slurm/process_result.py reads `resources` and
+#     `backend.sglang_config` (TP/EP/DP + worker counts) for the summary table.
+#   * scripts/ci/slurm/launch_mi355x.sh reads `runtime`, `bench`, and `mtp`.
+
+resources:
+  prefill_workers: 1
+  decode_workers: 1
+
+backend:
+  sglang_config:
+    prefill:
+      tensor-parallel-size: 8
+      expert-parallel-size: 8
+      data-parallel-size: 8
+    decode:
+      tensor-parallel-size: 8
+      expert-parallel-size: 8
+      data-parallel-size: 8
+
+# Model-specific docker env + sglang server args (generic launcher path; keeps
+# GLM off the hardcoded DeepSeek-V4 parser branch in launch_mi355x.sh). DSA
+# attention is auto-selected for GlmMoeDsaForCausalLM, so no --attention-backend
+# is set. GLM has a shared expert (n_shared_experts=1); shared-experts-fusion is
+# disabled to mirror the DeepSeek-V4 path.
+model:
+  env:
+    SGLANG_USE_AITER: 1
+  server_args:
+    - --reasoning-parser
+    - glm45
+    - --tool-call-parser
+    - glm45
+    - --disable-shared-experts-fusion
+
+runtime:
+  image: lmsysorg/sglang-rocm:v0.5.15.post1-rocm720-mi35x-20260722
+  # attention_backend intentionally unset: DSA is auto-selected for
+  # GlmMoeDsaForCausalLM.
+  # RoCE HCAs MORI uses for cross-node KV transfer.
+  ib_devices: rdma0,rdma1,rdma2,rdma3
+  prefill_port: 30025
+  decode_port: 30026
+  prefill_bootstrap_port: 8998
+  decode_bootstrap_port: 9001
+  lb_port: 8000
+  mem_fraction_static: 0.90
+  page_size: 256
+  max_running_requests: 256
+  chunked_prefill_size: 8192
+  swa_full_tokens_ratio: 0.1
+
+bench:
+  # bench_serving --max-concurrency sweep; one result JSON per concurrency.
+  concurrencies: [1, 8, 16, 32, 64, 128, 256]
+  num_prompts_factor: 4      # num-prompts = concurrency * factor
+  random_range_ratio: 1.0
+
+  # Correctness gate run through the PD path before the perf sweep (full GSM8K,
+  # 8-shot, accuracy > 0.91). A regression here fails the nightly even when
+  # throughput looks fine ("fast but wrong").
+  accuracy:
+    enabled: true
+    num_shots: 8
+    num_questions: 1319    # full GSM8K test set
+    threshold: 0.91

scripts/ci/slurm/recipes/mi355x-fp4/glm52/1k1k/1p1d-mtp.yaml

@@ -0,0 +1,81 @@
+# MI355X GLM-5.2 (MXFP4) 2-node 1P1D disaggregation recipe — TP8 + MTP.
+#
+# GLM-5.2 ships a built-in NextN MTP head (num_nextn_predict_layers=1), same
+# mechanism as DeepSeek-V4 — no external draft checkpoint. DSA attention is
+# auto-selected (see 1p1d.yaml).
+#
+# Consumed by:
+#   * scripts/ci/slurm/process_result.py reads `resources` and
+#     `backend.sglang_config` (TP/EP/DP + worker counts) for the summary table.
+#   * scripts/ci/slurm/launch_mi355x.sh reads `runtime`, `bench`, and `mtp`.
+
+resources:
+  prefill_workers: 1
+  decode_workers: 1
+
+backend:
+  sglang_config:
+    prefill:
+      tensor-parallel-size: 8
+      expert-parallel-size: 1
+      data-parallel-size: 1
+    decode:
+      tensor-parallel-size: 8
+      expert-parallel-size: 1
+      data-parallel-size: 1
+
+# Model-specific docker env + sglang server args (generic launcher path; keeps
+# GLM off the hardcoded DeepSeek-V4 parser branch in launch_mi355x.sh). DSA
+# attention is auto-selected for GlmMoeDsaForCausalLM, so no --attention-backend
+# is set. GLM has a shared expert (n_shared_experts=1); shared-experts-fusion is
+# disabled to mirror the DeepSeek-V4 path.
+model:
+  env:
+    SGLANG_USE_AITER: 1
+  server_args:
+    - --reasoning-parser
+    - glm45
+    - --tool-call-parser
+    - glm45
+    - --disable-shared-experts-fusion
+
+runtime:
+  image: lmsysorg/sglang-rocm:v0.5.15.post1-rocm720-mi35x-20260722
+  # attention_backend intentionally unset: DSA is auto-selected for
+  # GlmMoeDsaForCausalLM.
+  # RoCE HCAs MORI uses for cross-node KV transfer.
+  ib_devices: rdma0,rdma1,rdma2,rdma3
+  prefill_port: 30025
+  decode_port: 30026
+  prefill_bootstrap_port: 8998
+  decode_bootstrap_port: 9001
+  lb_port: 8000
+  mem_fraction_static: 0.90
+  page_size: 256
+  max_running_requests: 256
+  chunked_prefill_size: 8192
+  swa_full_tokens_ratio: 0.1
+
+# MTP / EAGLE speculative decoding (built-in NextN head from the base model).
+# Applied to both prefill and decode. No draft_model_path: the NextN head lives
+# in the base checkpoint.
+mtp:
+  enabled: true
+  num_steps: 3
+  eagle_topk: 1
+  num_draft_tokens: 4
+
+bench:
+  # bench_serving --max-concurrency sweep; one result JSON per concurrency.
+  concurrencies: [1, 8, 16, 32, 64, 128, 256]
+  num_prompts_factor: 4      # num-prompts = concurrency * factor
+  random_range_ratio: 1.0
+
+  # Correctness gate run through the PD path before the perf sweep (full GSM8K,
+  # 8-shot, accuracy > 0.91). A regression here fails the nightly even when
+  # throughput looks fine ("fast but wrong").
+  accuracy:
+    enabled: true
+    num_shots: 8
+    num_questions: 1319    # full GSM8K test set
+    threshold: 0.91

scripts/ci/slurm/recipes/mi355x-fp4/glm52/1k1k/1p1d.yaml

@@ -0,0 +1,78 @@
+# MI355X GLM-5.2 (MXFP4) 2-node 1P1D disaggregation recipe.
+#
+# GLM-5.2 uses GlmMoeDsaForCausalLM (DeepSeek Sparse Attention). sglang
+# auto-selects the DSA attention backend for this architecture, so this recipe
+# leaves `attention_backend` unset (empty) and lets the server pick DSA. MXFP4
+# enables SGLANG_DSV4_FP4_EXPERTS in launch_mi355x.sh (driven by PRECISION),
+# same as the DeepSeek-V4 FP4 path.
+#
+# NOTE: the checkpoint on disk is amd/GLM-5.1-MXFP4 (only GLM MXFP4 build
+# currently mirrored on /it-share). model_path in nightly-configs.yaml points at
+# it; the recipe naming tracks the model line we are wiring CI for.
+#
+# Consumed by:
+#   * scripts/ci/slurm/process_result.py reads `resources` and
+#     `backend.sglang_config` (TP/EP/DP + worker counts) for the summary table.
+#   * scripts/ci/slurm/launch_mi355x.sh reads `runtime` and `bench`.
+
+resources:
+  prefill_workers: 1
+  decode_workers: 1
+
+backend:
+  sglang_config:
+    prefill:
+      tensor-parallel-size: 8
+      expert-parallel-size: 1
+      data-parallel-size: 1
+    decode:
+      tensor-parallel-size: 8
+      expert-parallel-size: 1
+      data-parallel-size: 1
+
+# Model-specific docker env + sglang server args (generic launcher path; keeps
+# GLM off the hardcoded DeepSeek-V4 parser branch in launch_mi355x.sh). DSA
+# attention is auto-selected for GlmMoeDsaForCausalLM, so no --attention-backend
+# is set. GLM has a shared expert (n_shared_experts=1); shared-experts-fusion is
+# disabled to mirror the DeepSeek-V4 path.
+model:
+  env:
+    SGLANG_USE_AITER: 1
+  server_args:
+    - --reasoning-parser
+    - glm45
+    - --tool-call-parser
+    - glm45
+    - --disable-shared-experts-fusion
+
+runtime:
+  image: lmsysorg/sglang-rocm:v0.5.15.post1-rocm720-mi35x-20260722
+  # attention_backend intentionally unset: DSA is auto-selected for
+  # GlmMoeDsaForCausalLM.
+  # RoCE HCAs MORI uses for cross-node KV transfer.
+  ib_devices: rdma0,rdma1,rdma2,rdma3
+  prefill_port: 30025
+  decode_port: 30026
+  prefill_bootstrap_port: 8998
+  decode_bootstrap_port: 9001
+  lb_port: 8000
+  mem_fraction_static: 0.90
+  page_size: 256
+  max_running_requests: 256
+  chunked_prefill_size: 8192
+  swa_full_tokens_ratio: 0.1
+
+bench:
+  # bench_serving --max-concurrency sweep; one result JSON per concurrency.
+  concurrencies: [1, 8, 16, 32, 64, 128, 256]
+  num_prompts_factor: 4      # num-prompts = concurrency * factor
+  random_range_ratio: 1.0
+
+  # Correctness gate run through the PD path before the perf sweep
+  # (full GSM8K, 8-shot, accuracy > 0.91). A regression here fails the nightly
+  # even when throughput looks fine ("fast but wrong").
+  accuracy:
+    enabled: true
+    num_shots: 8
+    num_questions: 1319    # full GSM8K test set
+    threshold: 0.91