feat: support deterministic FA4 for GLM-4.7-Flash (#33945)

docs/docs/advanced_features/server_arguments.mdx

@@ -1448,9 +1448,9 @@ Please consult the documentation below and [server_args.py](https://github.com/s
     </tr>
         <tr>
       <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`--bf16-gemm-backend`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Choose the backend for unquantized BF16 GEMM operations. Options: 'auto' (default; selects <code>cutedsl</code> on SM100/SM103 (Blackwell), otherwise uses cuBLAS via `torch.nn.functional.linear`), 'cutedsl' (SGLang JIT CuTe DSL TGV BF16 GEMM on SM10X; dispatches between the CuTe DSL kernel and cuBLAS).</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Choose the backend for unquantized BF16 GEMM operations. Options: <code>auto</code> (default; selects <code>cutedsl</code> on SM10x GPUs, except deterministic inference selects <code>torch</code>; otherwise uses cuBLAS via <code>torch.nn.functional.linear</code>), <code>cutedsl</code> (SGLang JIT CuTe DSL TGV BF16 GEMM on SM10x; dispatches between the CuTe DSL kernel and cuBLAS), <code>torch</code> (always uses cuBLAS via <code>torch.nn.functional.linear</code>).</td>
       <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`auto`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>auto</code>, <code>cutedsl</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>auto</code>, <code>cutedsl</code>, <code>torch</code></td>
     </tr>
         <tr>
       <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`--disable-flashinfer-autotune`</td>

python/sglang/srt/arg_groups/overrides.py

@@ -2050,6 +2050,7 @@ def _deterministic_is_deepseek_model(view: Any) -> bool:
             "MistralLarge3ForCausalLM",
             "PixtralForConditionalGeneration",
             "GlmMoeDsaForCausalLM",
+            "Glm4MoeLiteForCausalLM",
         ]
     except Exception:
         return False

python/sglang/srt/layers/quantization/unquant.py

@@ -90,11 +90,18 @@ def initialize_bf16_gemm_config(server_args: ServerArgs) -> None:
 
     backend_str = server_args.bf16_gemm_backend
     if backend_str == "auto" and is_sm100_supported():
-        backend_str = "cutedsl"
+        backend_str = (
+            "torch" if server_args.enable_deterministic_inference else "cutedsl"
+        )
 
     backend = Bf16GemmBackend(backend_str)
 
     if backend.is_cutedsl():
+        if server_args.enable_deterministic_inference:
+            raise ValueError(
+                "--bf16-gemm-backend cutedsl is batch-size dependent and cannot "
+                "be combined with --enable-deterministic-inference"
+            )
         if not is_sm100_supported():
             raise ValueError("--bf16-gemm-backend cutedsl requires an SM10x GPU")
 

python/sglang/srt/server_args.py

@@ -1754,7 +1754,7 @@ class ServerArgs:
     bf16_gemm_backend: A[
         str,
         Arg(
-            help="Choose the backend for unquantized BF16 GEMM operations. Options: 'auto' (default; selects 'cutedsl' on SM10x GPUs, otherwise uses cuBLAS via torch.nn.functional.linear), 'cutedsl' (SGLang JIT CuTe DSL TGV BF16 GEMM on SM10x; dispatches between the CuTe DSL kernel and cuBLAS), 'torch' (always uses cuBLAS via torch.nn.functional.linear).",
+            help="Choose the backend for unquantized BF16 GEMM operations. Options: 'auto' (default; selects 'cutedsl' on SM10x GPUs, except deterministic inference selects 'torch'; otherwise uses cuBLAS via torch.nn.functional.linear), 'cutedsl' (SGLang JIT CuTe DSL TGV BF16 GEMM on SM10x; dispatches between the CuTe DSL kernel and cuBLAS), 'torch' (always uses cuBLAS via torch.nn.functional.linear).",
             cli_name="--bf16-gemm-backend",
             choices=BF16_GEMM_BACKEND_CHOICES,
         ),
@@ -8082,6 +8082,7 @@ class ServerArgs:
                         "MistralLarge3ForCausalLM",
                         "PixtralForConditionalGeneration",
                         "GlmMoeDsaForCausalLM",
+                        "Glm4MoeLiteForCausalLM",
                     ]
                 except Exception:
                     pass
@@ -8096,11 +8097,11 @@ class ServerArgs:
                     not in RADIX_SUPPORTED_DETERMINISTIC_ATTENTION_BACKEND
                 ):
                     raise ValueError(
-                        f"Currently only {RADIX_SUPPORTED_DETERMINISTIC_ATTENTION_BACKEND} attention backends are supported for deterministic inference with DeepSeek models. But you're using {attention_backend}."
+                        f"Currently only {RADIX_SUPPORTED_DETERMINISTIC_ATTENTION_BACKEND} attention backends are supported for deterministic inference with absorbed-MLA models. But you're using {attention_backend}."
                     )
                 if attention_backend == "fa4" and not is_sm100_or_sm110_supported():
                     raise ValueError(
-                        "Deterministic inference with DeepSeek models on the fa4 "
+                        "Deterministic inference with absorbed-MLA models on the fa4 "
                         "attention backend requires SM100/SM110: it runs "
                         "absorbed MLA, whose qv argument flash_attn.cute only "
                         "implements on those archs."

python/sglang/test/test_deterministic_utils.py

@@ -31,7 +31,9 @@ class TestDeterministicBase(CustomTestCase):
     def setUpClass(cls):
         cls.model = cls.get_model()
         cls.base_url = DEFAULT_URL_FOR_TEST
-        if "--attention-backend" not in cls.get_server_args():
+        # Identity, not a probe for --attention-backend: a subclass that
+        # deliberately leaves the backend unspecified is a real test case.
+        if cls is TestDeterministicBase:
             raise unittest.SkipTest("Skip the base test class")
 
         cls.process = popen_launch_server(

test/registered/attention/test_glm4_moe_lite_deterministic.py

@@ -0,0 +1,68 @@
+"""Deterministic inference for GLM-4.7-Flash (Glm4MoeLiteForCausalLM) on Blackwell.
+
+Two entry paths matter. The fa4 backend is what this model needs for
+deterministic absorbed MLA, and the unspecified-backend path has to reach
+triton through the absorbed-MLA arch probe instead of the flashinfer default
+that every non-absorbed model gets on Blackwell.
+
+Usage:
+python3 -m unittest test_glm4_moe_lite_deterministic.TestGlm4MoeLiteFa4Deterministic
+"""
+
+import unittest
+
+import requests
+
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.test_deterministic_utils import (
+    COMMON_SERVER_ARGS,
+    TestDeterministicBase,
+)
+from sglang.test.test_utils import DEFAULT_URL_FOR_TEST
+
+register_cuda_ci(est_time=900, stage="nightly", runner_config="4-gpu-gb300")
+
+GLM_MODEL = "zai-org/GLM-4.7-Flash"
+
+# COMMON_SERVER_ARGS is shared module state; copy it. Extending it in place
+# would leak the fa4 flag into the auto class below and silently make that
+# test a second fa4 test.
+SERVER_ARGS = COMMON_SERVER_ARGS + [
+    "--chunked-prefill-size",
+    "2048",
+    "--max-prefill-tokens",
+    "2048",
+    "--mem-fraction-static",
+    "0.8",
+]
+
+
+class TestGlm4MoeLiteFa4Deterministic(TestDeterministicBase):
+    @classmethod
+    def get_model(cls):
+        return GLM_MODEL
+
+    @classmethod
+    def get_server_args(cls):
+        return SERVER_ARGS + ["--attention-backend", "fa4"]
+
+
+class TestGlm4MoeLiteAutoBackendDeterministic(TestDeterministicBase):
+    @classmethod
+    def get_model(cls):
+        return GLM_MODEL
+
+    @classmethod
+    def get_server_args(cls):
+        return SERVER_ARGS
+
+    def test_auto_backend_resolves_to_triton(self):
+        # Guards the arch probe itself: if Glm4MoeLiteForCausalLM stopped
+        # counting as absorbed MLA the fill would hand back flashinfer, which
+        # deterministic inference then rejects at launch.
+        info = requests.get(DEFAULT_URL_FOR_TEST + "/server_info").json()
+        self.assertEqual(info["attention_backend"], "triton")
+
+
+if __name__ == "__main__":
+    unittest.main()