Add graph runner support with torch compile on CPU (#7843)

.github/workflows/pr-test-xeon.yml

@@ -70,7 +70,7 @@ jobs:
 
       - name: Run unit tests
         if: steps.check_amx.outcome == 'success'
-        timeout-minutes: 30
+        timeout-minutes: 36
         run: |
           docker exec -w /sglang-checkout/ ci_sglang_xeon \
             bash -c "cd ./test/srt && python3 run_suite.py --suite per-commit-cpu"

docs/platforms/cpu_server.md

@@ -134,7 +134,12 @@ Notes:
     export SGLANG_CPU_OMP_THREADS_BIND="0-39|43-82|86-125|128-167|171-210|214-253"
     ```
 
-3. A warmup step is automatically triggered when the service is started.
+3. For optimizing decoding with torch.compile, please add the flag `--enable-torch-compile`.
+    To specify the maximum batch size when using torch compile, set the flag `--torch-compile-max-bs`.
+    For example, `--enable-torch-compile --torch-compile-max-bs 4` means using torch compile and setting the
+    maximum batch size to 4.
+
+4. A warmup step is automatically triggered when the service is started.
 The server is ready when you see the log `The server is fired up and ready to roll!`.
 
 ## Benchmarking with Requests

python/sglang/srt/distributed/parallel_state.py

@@ -64,6 +64,9 @@ class GraphCaptureContext:
 
 TensorMetadata = namedtuple("TensorMetadata", ["device", "dtype", "size"])
 
+# use int value instead of ReduceOp.SUM to support torch compile
+REDUCE_OP_SUM = int(torch.distributed.ReduceOp.SUM)
+
 
 def _split_tensor_dict(
     tensor_dict: Dict[str, Union[torch.Tensor, Any]]
@@ -489,9 +492,7 @@ class GroupCoordinator:
 
         if input_.is_cpu:
             if is_shm_available(input_.dtype, self.world_size, self.local_size):
-                torch.ops.sgl_kernel.shm_allreduce(
-                    input_, torch.distributed.ReduceOp.SUM
-                )
+                torch.ops.sgl_kernel.shm_allreduce(input_, REDUCE_OP_SUM)
             else:
                 torch.distributed.all_reduce(input_, group=self.device_group)
             return input_

python/sglang/srt/layers/attention/intel_amx_backend.py

@@ -49,6 +49,9 @@ class IntelAMXAttnBackend(AttentionBackend):
             max_extend_len = torch.max(forward_batch.extend_seq_lens).item()
         self.forward_metadata = (attn_logits, max_extend_len)
 
+    def get_graph_seq_len_fill_value(self):
+        return 1
+
     def forward_extend(
         self,
         q,

python/sglang/srt/layers/quantization/fp8.py

@@ -352,6 +352,9 @@ class Fp8LinearMethod(LinearMethodBase):
                     _is_cpu_amx_available
                 ), "Fp8LinearMethod on CPU requires that CPU has AMX support"
                 _amx_process_weight_after_loading(layer, ["weight"])
+                layer.weight_scale_inv = torch.nn.Parameter(
+                    layer.weight_scale_inv.data, requires_grad=False
+                )
                 return
             else:
                 weight, weight_scale = layer.weight.data, layer.weight_scale_inv.data

python/sglang/srt/layers/quantization/w8a8_int8.py

@@ -343,9 +343,8 @@ class W8A8Int8LinearMethod(LinearMethodBase):
                 _is_cpu_amx_available
             ), "W8A8Int8LinearMethod on CPU requires that CPU has AMX support"
             _amx_process_weight_after_loading(layer, ["weight"])
-            return
-
-        layer.weight = Parameter(layer.weight.t(), requires_grad=False)
+        else:
+            layer.weight = Parameter(layer.weight.t(), requires_grad=False)
         layer.weight_scale = Parameter(layer.weight_scale.data, requires_grad=False)
 
     def create_weights(
@@ -486,10 +485,9 @@ class W8A8Int8MoEMethod(FusedMoEMethodBase):
                 _is_cpu_amx_available
             ), "W8A8Int8MoEMethod on CPU requires that CPU has AMX support"
             _amx_process_weight_after_loading(layer, ["w13_weight", "w2_weight"])
-            return
-
-        layer.w13_weight = Parameter(layer.w13_weight, requires_grad=False)
-        layer.w2_weight = Parameter(layer.w2_weight, requires_grad=False)
+        else:
+            layer.w13_weight = Parameter(layer.w13_weight, requires_grad=False)
+            layer.w2_weight = Parameter(layer.w2_weight, requires_grad=False)
         layer.w13_weight_scale = Parameter(
             layer.w13_weight_scale.data, requires_grad=False
         )

python/sglang/srt/managers/scheduler.py

@@ -414,7 +414,7 @@ class Scheduler(
                 f"max_prefill_tokens={self.max_prefill_tokens}, "
                 f"max_running_requests={self.max_running_requests}, "
                 f"context_len={self.model_config.context_len}, "
-                f"available_gpu_mem={avail_mem:.2f} GB"
+                f"{'available_cpu_mem' if self.device == 'cpu' else 'available_gpu_mem'}={avail_mem:.2f} GB"
             )
 
         # Init memory pool and cache
@@ -2252,10 +2252,9 @@ class Scheduler(
             "token_capacity": int(self.max_total_num_tokens),
         }
 
-        if not _is_cpu:
-            ret["memory_usage"]["cuda_graph"] = round(
-                self.tp_worker.worker.model_runner.cuda_graph_mem_usage, 2
-            )
+        ret["memory_usage"]["graph"] = round(
+            self.tp_worker.worker.model_runner.graph_mem_usage, 2
+        )
 
         if not self.spec_algorithm.is_none() and self.cum_spec_accept_count > 0:
             ret["avg_spec_accept_length"] = (

python/sglang/srt/managers/scheduler_metrics_mixin.py

@@ -214,7 +214,7 @@ class SchedulerMetricsMixin:
             msg += f"#retracted-req: {len(self.disagg_decode_prealloc_queue.retracted_queue)}, "
 
         msg += (
-            f"cuda graph: {can_run_cuda_graph}, "
+            f"{'cpu graph' if self.device == 'cpu' else 'cuda graph'}: {can_run_cuda_graph}, "
             f"gen throughput (token/s): {self.last_gen_throughput:.2f}, "
             f"#queue-req: {len(self.waiting_queue)}, "
         )

python/sglang/srt/model_executor/forward_batch_info.py

@@ -132,6 +132,9 @@ class ForwardMode(IntEnum):
             or self == ForwardMode.IDLE
         )
 
+    def is_cpu_graph(self):
+        return self == ForwardMode.DECODE
+
     def is_dummy_first(self):
         return self == ForwardMode.DUMMY_FIRST
 

python/sglang/srt/model_executor/model_runner.py

@@ -20,6 +20,7 @@ import json
 import logging
 import os
 import time
+from collections import defaultdict
 from dataclasses import dataclass
 from typing import List, Optional, Tuple, Union
 
@@ -89,6 +90,7 @@ from sglang.srt.mem_cache.memory_pool import (
     ReqToTokenPool,
     SWAKVPool,
 )
+from sglang.srt.model_executor.cpu_graph_runner import CPUGraphRunner
 from sglang.srt.model_executor.cuda_graph_runner import CudaGraphRunner
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
 from sglang.srt.model_executor.npu_graph_runner import NPUGraphRunner
@@ -360,12 +362,12 @@ class ModelRunner:
             self.init_cublas()
             self.init_attention_backend()
             self.init_device_graphs()
-        elif self.device == "npu":
+        elif self.device in ["npu", "cpu"]:
             self.init_attention_backend()
             self.init_device_graphs()
         else:
             self.graph_runner = None
-            self.cuda_graph_mem_usage = 0
+            self.graph_mem_usage = 0
             self.init_attention_backend()
 
         # auxiliary hidden capture mode. TODO: expose this to server args?
@@ -608,6 +610,11 @@ class ModelRunner:
                     # Set local size to hint SGLang to use shared memory based AllReduce
                     os.environ["LOCAL_SIZE"] = str(self.tp_size)
                     torch.ops.sgl_kernel.initialize(self.tp_size, self.tp_rank)
+
+                    @torch.library.register_fake("sgl_kernel::shm_allgather")
+                    def _(data, dim):
+                        return torch.cat([data] * self.tp_size, dim=dim)
+
                 else:
                     logger.warning(
                         "init_cpu_threads_env and shared memory based AllReduce is disabled since intel amx backend is not available"
@@ -1619,30 +1626,39 @@ class ModelRunner:
             )
 
     def init_device_graphs(self):
-        """Capture cuda graphs."""
+        """Capture device graphs."""
         self.graph_runner = None
-        self.cuda_graph_mem_usage = 0
+        self.graph_mem_usage = 0
 
         if not self.is_generation:
             # TODO: Currently, cuda graph only captures decode steps, which only exists for generation models
             return
 
-        if self.server_args.disable_cuda_graph:
+        if self.device != "cpu" and self.server_args.disable_cuda_graph:
+            return
+
+        if self.device == "cpu" and not self.server_args.enable_torch_compile:
             return
 
         tic = time.perf_counter()
         before_mem = get_available_gpu_memory(self.device, self.gpu_id)
         logger.info(
-            f"Capture cuda graph begin. This can take up to several minutes. avail mem={before_mem:.2f} GB"
+            f"Capture {'cpu graph' if self.device == 'cpu' else 'cuda graph'} begin. This can take up to several minutes. avail mem={before_mem:.2f} GB"
         )
-        self.graph_runner = (
-            CudaGraphRunner(self) if not _is_npu else NPUGraphRunner(self)
+        graph_runners = defaultdict(
+            lambda: CudaGraphRunner,
+            {
+                "cpu": CPUGraphRunner,
+                "npu": NPUGraphRunner,
+            },
         )
+        self.graph_runner = graph_runners[self.device](self)
+
         after_mem = get_available_gpu_memory(self.device, self.gpu_id)
-        self.cuda_graph_mem_usage = before_mem - after_mem
+        self.graph_mem_usage = before_mem - after_mem
         logger.info(
-            f"Capture cuda graph end. Time elapsed: {time.perf_counter() - tic:.2f} s. "
-            f"mem usage={self.cuda_graph_mem_usage:.2f} GB. avail mem={after_mem:.2f} GB."
+            f"Capture {'cpu graph' if self.device == 'cpu' else 'cuda graph'} end. Time elapsed: {time.perf_counter() - tic:.2f} s. "
+            f"mem usage={self.graph_mem_usage:.2f} GB. avail mem={after_mem:.2f} GB."
         )
 
     def init_threads_binding(self):
@@ -1787,18 +1803,24 @@ class ModelRunner:
         reinit_attn_backend: bool = False,
         split_forward_count: int = 1,
     ) -> Tuple[Union[LogitsProcessorOutput, PPProxyTensors], bool]:
-        can_run_cuda_graph = bool(
-            forward_batch.forward_mode.is_cuda_graph()
+        mode_check = (
+            forward_batch.forward_mode.is_cpu_graph
+            if self.device == "cpu"
+            else forward_batch.forward_mode.is_cuda_graph
+        )
+        can_run_graph = bool(
+            mode_check()
             and self.graph_runner
             and self.graph_runner.can_run(forward_batch)
         )
-        if can_run_cuda_graph:
+
+        if can_run_graph:
             ret = self.graph_runner.replay(
                 forward_batch,
                 skip_attn_backend_init=skip_attn_backend_init,
                 pp_proxy_tensors=pp_proxy_tensors,
             )
-            return ret, can_run_cuda_graph
+            return ret, can_run_graph
 
         # For MLP sync
         if forward_batch.global_num_tokens_cpu is not None:
@@ -1833,7 +1855,7 @@ class ModelRunner:
         ):
             forward_batch.post_forward_mlp_sync_batch(ret)
 
-        return ret, can_run_cuda_graph
+        return ret, can_run_graph
 
     def _preprocess_logits(
         self, logits_output: LogitsProcessorOutput, sampling_info: SamplingBatchInfo

python/sglang/srt/utils.py

@@ -230,8 +230,16 @@ except:
     is_intel_amx_backend_available = False
 
 
+try:
+    # move torch._C._cpu._is_amx_tile_supported() from cpu_has_amx_support
+    # to support torch compile
+    is_amx_tile_supported = torch._C._cpu._is_amx_tile_supported()
+except:
+    is_amx_tile_supported = False
+
+
 def cpu_has_amx_support():
-    return torch._C._cpu._is_amx_tile_supported() and is_intel_amx_backend_available
+    return is_amx_tile_supported and is_intel_amx_backend_available
 
 
 def use_intel_amx_backend(layer):

sgl-kernel/csrc/cpu/torch_extension_cpu.cpp

@@ -239,7 +239,7 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
   m.impl("rmsnorm_cpu", torch::kCPU, &rmsnorm_cpu);
   m.def("l2norm_cpu(Tensor input, float eps) -> Tensor");
   m.impl("l2norm_cpu", torch::kCPU, &l2norm_cpu);
-  m.def("fused_add_rmsnorm_cpu(Tensor input, Tensor residual, Tensor weight, float eps) -> ()");
+  m.def("fused_add_rmsnorm_cpu(Tensor(a!) input, Tensor residual, Tensor weight, float eps) -> ()");
   m.impl("fused_add_rmsnorm_cpu", torch::kCPU, &fused_add_rmsnorm_cpu);
 
   // topk
@@ -262,14 +262,14 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
 
   // decode
   m.def(
-      "decode_attention_cpu(Tensor query, Tensor k_cache, Tensor v_cahce, Tensor output, Tensor key, Tensor value, "
+      "decode_attention_cpu(Tensor query, Tensor k_cache, Tensor v_cahce, Tensor(a!) output, Tensor key, Tensor value, "
       "Tensor loc, Tensor attn_logits, Tensor req_to_token, Tensor req_pool_indices, Tensor seq_lens, float sm_scale, "
       "float logit_cap) -> ()");
   m.impl("decode_attention_cpu", torch::kCPU, &decode_attention_cpu);
 
   // extend
   m.def(
-      "extend_attention_cpu(Tensor q_extend, Tensor k_extend, Tensor v_extend, Tensor o_extend, Tensor k_buffer, "
+      "extend_attention_cpu(Tensor q_extend, Tensor k_extend, Tensor v_extend, Tensor(a!) o_extend, Tensor k_buffer, "
       "Tensor v_buffer, Tensor req_to_token, Tensor req_pool_indices, Tensor seq_lens, Tensor extend_seq_lens, Tensor "
       "extend_start_loc, int max_len_extend, float sm_scale, float logit_cap) -> ()");
   m.impl("extend_attention_cpu", torch::kCPU, &extend_attention_cpu);
@@ -305,7 +305,7 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
   m.impl("int8_scaled_mm_with_quant", torch::kCPU, &int8_scaled_mm_with_quant);
 
   // bmm
-  m.def("bmm_cpu(Tensor out, Tensor mat1, Tensor mat2, bool is_vnni, Tensor? scale) -> ()");
+  m.def("bmm_cpu(Tensor(a!) out, Tensor mat1, Tensor mat2, bool is_vnni, Tensor? scale) -> ()");
   m.impl("bmm_cpu", torch::kCPU, &bmm_cpu);
 
   // moe
@@ -342,7 +342,7 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
 
   // all reduce
   m.def("initialize(int size, int rank) -> ()");
-  m.def("shm_allreduce(Tensor data, int reduce_op) -> ()");
+  m.def("shm_allreduce(Tensor(a!) data, int reduce_op) -> ()");
   m.impl("shm_allreduce", torch::kCPU, &shm_allreduce);
   m.def("shm_allgather(Tensor data, int dim) -> Tensor");
   m.impl("shm_allgather", torch::kCPU, &shm_allgather);

test/srt/run_suite.py

@@ -276,6 +276,7 @@ suite_xeon = {
         TestFile("cpu/test_shared_expert.py"),
         TestFile("cpu/test_topk.py"),
         TestFile("test_intel_amx_attention_backend.py"),
+        TestFile("test_cpu_graph.py"),
     ],
 }
 

test/srt/test_cpu_graph.py

+"""
+Usage:
+python3 -m unittest test_cpu_graph.TestCPUGraph.test_mmlu_torch_compile_cpu
+"""
+
+import copy
+import os
+import unittest
+from types import SimpleNamespace
+
+from test_intel_amx_attention_backend import intel_amx_benchmark
+
+from sglang.srt.utils import get_cpu_ids_by_node, kill_process_tree
+from sglang.test.run_eval import run_eval
+from sglang.test.test_utils import (
+    DEFAULT_MLA_MODEL_NAME_FOR_TEST,
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    is_in_ci,
+    popen_launch_server,
+)
+
+
+class TestCPUGraph(CustomTestCase):
+
+    @intel_amx_benchmark(
+        extra_args=[
+            "--batch-size",
+            "1",
+            "--mem-fraction-static",
+            "0.05",
+            "--enable-torch-compile",
+            "--torch-compile-max-bs",
+            "1",
+        ],
+        min_throughput=10,
+    )
+    def test_latency_torch_compile_cpu(self):
+        return DEFAULT_MLA_MODEL_NAME_FOR_TEST
+
+    def test_mmlu_torch_compile_cpu(self):
+        model = DEFAULT_MLA_MODEL_NAME_FOR_TEST
+        base_url = DEFAULT_URL_FOR_TEST
+        cpu_ids_by_node = get_cpu_ids_by_node()
+        n_numa_node = len(cpu_ids_by_node)
+        env = copy.deepcopy(os.environ)
+        env["SGLANG_CPU_OMP_THREADS_BIND"] = "all"
+        process = popen_launch_server(
+            model,
+            base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--attention-backend",
+                "intel_amx",
+                "--mem-fraction-static",
+                "0.05",
+                "--disable-radix",
+                "--trust-remote-code",
+                "--disable-overlap-schedule",
+                "--enable-torch-compile",
+                "--torch-compile-max-bs",
+                "1",
+                "--tp",
+                f"{n_numa_node}",
+            ],
+            env=env,
+        )
+
+        try:
+            args = SimpleNamespace(
+                base_url=base_url,
+                model=model,
+                eval_name="mmlu",
+                num_examples=64,
+                num_threads=32,
+            )
+
+            metrics = run_eval(args)
+            if is_in_ci():
+                self.assertGreater(metrics["score"], 0.45)
+        finally:
+            kill_process_tree(process.pid)
+
+
+if __name__ == "__main__":
+    unittest.main()

test/srt/test_intel_amx_attention_backend.py

@@ -3,7 +3,6 @@ Usage:
 python3 -m unittest test_intel_amx_attention_backend.TestIntelAMXAttnBackend.test_mmlu
 """
 
-import os
 import unittest
 from functools import wraps
 from types import SimpleNamespace
@@ -35,8 +34,6 @@ def intel_amx_benchmark(extra_args=None, min_throughput=None):
                 "intel_amx",
                 "--disable-radix",
                 "--trust-remote-code",
-                "--batch-size",
-                "4",
             ]
             full_args = common_args + (extra_args or [])
 
@@ -60,28 +57,33 @@ def intel_amx_benchmark(extra_args=None, min_throughput=None):
 
 class TestIntelAMXAttnBackend(CustomTestCase):
 
-    @intel_amx_benchmark(min_throughput=10)
+    @intel_amx_benchmark(extra_args=["--batch-size", "4"], min_throughput=10)
     def test_latency_mla_model(self):
         return DEFAULT_MLA_MODEL_NAME_FOR_TEST
 
-    @intel_amx_benchmark(min_throughput=40)
+    @intel_amx_benchmark(extra_args=["--batch-size", "4"], min_throughput=40)
     def test_latency_default_model(self):
         return DEFAULT_MODEL_NAME_FOR_TEST
 
-    @intel_amx_benchmark(min_throughput=150)
+    @intel_amx_benchmark(extra_args=["--batch-size", "4"], min_throughput=150)
     def test_latency_fp8_qwen(self):
         return DEFAULT_MODEL_NAME_FOR_TEST_QWEN_FP8
 
-    @intel_amx_benchmark(min_throughput=50)
+    @intel_amx_benchmark(extra_args=["--batch-size", "4"], min_throughput=50)
     def test_latency_fp8_moe_model(self):
         return DEFAULT_MODEL_NAME_FOR_TEST_FP8_WITH_MOE
 
-    @intel_amx_benchmark(extra_args=["--quantization", "w8a8_int8"], min_throughput=100)
+    @intel_amx_benchmark(
+        extra_args=["--batch-size", "4", "--quantization", "w8a8_int8"],
+        min_throughput=100,
+    )
     def test_latency_w8a8_default_model(self):
         return DEFAULT_MODEL_NAME_FOR_TEST_W8A8
 
     @intel_amx_benchmark(
         extra_args=[
+            "--batch-size",
+            "4",
             "--quantization",
             "w8a8_int8",
             "--mem-fraction-static",