[DeepSeekV3.2] Add pure TP+MTP test (#15088)

Co-authored-by: Baizhou Zhang <sobereddiezhang@gmail.com>

docs/basic_usage/deepseek_v32.md

@@ -64,10 +64,16 @@ python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8
 ## Multi-token Prediction
 SGLang implements Multi-Token Prediction (MTP) for DeepSeek V3.2 based on [EAGLE speculative decoding](https://docs.sglang.io/advanced_features/speculative_decoding.html#EAGLE-Decoding). With this optimization, the decoding speed can be improved significantly on small batch sizes. Please look at [this PR](https://github.com/sgl-project/sglang/pull/11652) for more information.
 
-Example usage:
+Example usage with DP Attention:
 ```bash
 python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --dp 8 --enable-dp-attention --speculative-algorithm EAGLE --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4
 ```
+
+Example usage with Pure TP:
+```bash
+python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --speculative-algorithm EAGLE --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4
+```
+
 - The best configuration for `--speculative-num-steps`, `--speculative-eagle-topk` and `--speculative-num-draft-tokens` can be searched with [bench_speculative.py](https://github.com/sgl-project/sglang/blob/main/scripts/playground/bench_speculative.py) script for given batch size. The minimum configuration is `--speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2`, which can achieve speedup for larger batch sizes.
 - The default value of  `--max-running-requests` is set to `48` for MTP. For larger batch sizes, this value should be increased beyond the default value.
 

test/nightly/test_deepseek_v32_tp.py

@@ -2,9 +2,12 @@ import os
 import unittest
 from types import SimpleNamespace
 
+import requests
+
 from sglang.srt.utils import kill_process_tree
 from sglang.test.ci.ci_register import register_cuda_ci
 from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
+from sglang.test.send_one import BenchArgs, send_one_prompt
 from sglang.test.test_utils import (
     DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
     DEFAULT_URL_FOR_TEST,
@@ -14,7 +17,7 @@ from sglang.test.test_utils import (
     write_github_step_summary,
 )
 
-register_cuda_ci(est_time=600, suite="nightly-8-gpu-h200", nightly=True)
+register_cuda_ci(est_time=900, suite="nightly-8-gpu-h200", nightly=True)
 
 DEEPSEEK_V32_MODEL_PATH = "deepseek-ai/DeepSeek-V3.2-Exp"
 
@@ -134,10 +137,99 @@ class TestDeepseekV32_Partial_TP(CustomTestCase):
                     "accuracy": metrics["accuracy"],
                 }
             )
+        self.assertGreater(metrics["accuracy"], 0.935)
+
+
+class TestDeepseekV32_TP_MTP(CustomTestCase):
+    """Test DeepSeek V3.2 with pure TP + MTP (EAGLE speculative decoding)."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = DEEPSEEK_V32_MODEL_PATH
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        other_args = [
+            "--trust-remote-code",
+            "--tp",
+            "8",
+            "--speculative-algorithm",
+            "EAGLE",
+            "--speculative-num-steps",
+            "3",
+            "--speculative-eagle-topk",
+            "1",
+            "--speculative-num-draft-tokens",
+            "4",
+            "--mem-frac",
+            "0.7",
+        ]
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=other_args,
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_a_gsm8k(self):
+        requests.get(self.base_url + "/flush_cache")
+        args = SimpleNamespace(
+            num_shots=20,
+            data_path=None,
+            num_questions=1400,
+            parallel=1400,
+            max_new_tokens=512,
+            host="http://127.0.0.1",
+            port=int(self.base_url.split(":")[-1]),
+        )
+        metrics = run_eval_few_shot_gsm8k(args)
+        print(f"{metrics=}")
+
+        server_info = requests.get(self.base_url + "/get_server_info")
+        avg_spec_accept_length = server_info.json()["internal_states"][0][
+            "avg_spec_accept_length"
+        ]
+        print(f"{avg_spec_accept_length=}")
+
+        if is_in_ci():
+            TEST_RESULTS.append(
+                {
+                    "variant": "tp_mtp",
+                    "prefill_backend": "flashmla_sparse",
+                    "decode_backend": "flashmla_kv",
+                    "kv_cache": "fp16",
+                    "accuracy": metrics["accuracy"],
+                    "avg_spec_accept_length": avg_spec_accept_length,
+                }
+            )
+        self.assertGreater(metrics["accuracy"], 0.935)
+        self.assertGreater(avg_spec_accept_length, 2.5)
+
+    def test_bs_1_speed(self):
+        args = BenchArgs(port=int(self.base_url.split(":")[-1]), max_new_tokens=2048)
+        acc_length, speed = send_one_prompt(args)
+
+        print(f"{acc_length=:.2f} {speed=:.2f}")
+
+        if is_in_ci():
+            # Update last result with speed data
+            if TEST_RESULTS and TEST_RESULTS[-1]["variant"] == "tp_mtp":
+                TEST_RESULTS[-1]["speed"] = speed
 
             # Write the summary table after all tests complete
             _write_summary_table()
-        self.assertGreater(metrics["accuracy"], 0.935)
+
+        self.assertGreater(acc_length, 2.5)
+        self.assertGreater(speed, 110)
+
+
+def _format_optional_metric(value, fmt=".2f", suffix=""):
+    """Format an optional metric value, returning '-' if not available."""
+    if value is None:
+        return "-"
+    return f"{value:{fmt}}{suffix}"
 
 
 def _write_summary_table():
@@ -147,19 +239,21 @@ def _write_summary_table():
 
     gpu_config = os.getenv("GPU_CONFIG", "8-gpu-h200")
 
-    # Build table header
+    # Build table header - keep original columns + add MTP-specific ones
     summary = (
         f"### {DEEPSEEK_V32_MODEL_PATH} GSM8K Accuracy (TP Tests) [{gpu_config}]\n\n"
     )
-    summary += "| Variant | Prefill Backend | Decode Backend | KV Cache | Accuracy |\n"
+    summary += "| Variant | Prefill Backend | Decode Backend | KV Cache | Accuracy | Spec Acc Len | Speed |\n"
-    summary += "|---------|-----------------|----------------|----------|----------|\n"
+    summary += "|---------|-----------------|----------------|----------|----------|--------------|-------|\n"
 
     # Add each result as a row
     for result in TEST_RESULTS:
         summary += (
             f"| {result['variant']} | {result['prefill_backend']} | "
             f"{result['decode_backend']} | {result['kv_cache']} | "
-            f"{result['accuracy']:.3f} |\n"
+            f"{result['accuracy']:.3f} | "
+            f"{_format_optional_metric(result.get('avg_spec_accept_length'))} | "
+            f"{_format_optional_metric(result.get('speed'), '.1f', ' tok/s')} |\n"
         )
 
     write_github_step_summary(summary)