[Feat] Return hidden states (experimental) (#3364)

Co-authored-by: Chayenne <zhaochen20@outlook.com>

.github/workflows/pr-test.yml

@@ -103,7 +103,7 @@ jobs:
           bash scripts/ci_install_dependency.sh
 
       - name: Run test
-        timeout-minutes: 25
+        timeout-minutes: 30
         run: |
           RANGE=${{ matrix.range }}
           range_begin=${RANGE%-*}

docs/backend/offline_engine_api.ipynb

@@ -179,6 +179,59 @@
     "asyncio.run(main())"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm.shutdown()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Return Hidden States"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sglang as sgl\n",
+    "\n",
+    "llm = sgl.Engine(\n",
+    "    model_path=\"meta-llama/Meta-Llama-3.1-8B-Instruct\", return_hidden_states=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompts = [\n",
+    "    \"Hello, my name is\",\n",
+    "    \"The president of the United States is\",\n",
+    "    \"The capital of France is\",\n",
+    "    \"The future of AI is\",\n",
+    "]\n",
+    "\n",
+    "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95, \"max_new_tokens\": 10}\n",
+    "\n",
+    "outputs = llm.generate(prompts, sampling_params=sampling_params)\n",
+    "for prompt, output in zip(prompts, outputs):\n",
+    "    print(\"===============================\")\n",
+    "    print(\n",
+    "        f\"Prompt: {prompt}\\nGenerated text: {output['text']}\\nPrompt_Tokens: {output['meta_info']['prompt_tokens']}\\tCompletion_tokens: {output['meta_info']['completion_tokens']}\\nHidden states: {[i.shape for i in output['meta_info']['hidden_states']]}\"\n",
+    "    )\n",
+    "    print()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

python/sglang/srt/managers/detokenizer_manager.py

@@ -210,6 +210,7 @@ class DetokenizerManager:
                     input_top_logprobs_idx=recv_obj.input_top_logprobs_idx,
                     output_top_logprobs_val=recv_obj.output_top_logprobs_val,
                     output_top_logprobs_idx=recv_obj.output_top_logprobs_idx,
+                    output_hidden_states=recv_obj.output_hidden_states,
                 )
             )
 

python/sglang/srt/managers/io_struct.py

@@ -371,6 +371,8 @@ class BatchTokenIDOut:
     output_top_logprobs_val: List[List]
     output_top_logprobs_idx: List[List]
 
+    output_hidden_states: List[List[float]]
+
 
 @dataclass
 class BatchStrOut:
@@ -397,6 +399,8 @@ class BatchStrOut:
     output_top_logprobs_val: List[List]
     output_top_logprobs_idx: List[List]
 
+    output_hidden_states: List[List[float]]
+
 
 @dataclass
 class BatchEmbeddingOut:

python/sglang/srt/managers/schedule_batch.py

@@ -315,6 +315,7 @@ class Req:
             self.output_token_logprobs_val = self.output_token_logprobs_idx = (
                 self.output_top_logprobs_val
             ) = self.output_top_logprobs_idx = None
+        self.hidden_states = []
 
         # Logprobs (internal values)
         # The tokens is prefilled but need to be considered as decode tokens
@@ -604,6 +605,9 @@ class ScheduleBatch:
     # Enable custom logit processor
     enable_custom_logit_processor: bool = False
 
+    # Return hidden states
+    return_hidden_states: bool = False
+
     @classmethod
     def init_new(
         cls,
@@ -615,6 +619,7 @@ class ScheduleBatch:
         enable_overlap: bool,
         spec_algorithm: SpeculativeAlgorithm,
         enable_custom_logit_processor: bool,
+        return_hidden_states: bool = False,
     ):
         return cls(
             reqs=reqs,
@@ -629,6 +634,7 @@ class ScheduleBatch:
             device=req_to_token_pool.device,
             spec_algorithm=spec_algorithm,
             enable_custom_logit_processor=enable_custom_logit_processor,
+            return_hidden_states=return_hidden_states,
         )
 
     def batch_size(self):
@@ -1196,9 +1202,15 @@ class ScheduleBatch:
             spec_algorithm=self.spec_algorithm,
             spec_info=self.spec_info,
             capture_hidden_mode=(
-                getattr(self.spec_info, "capture_hidden_mode", CaptureHiddenMode.NULL)
+                CaptureHiddenMode.FULL
+                if self.return_hidden_states
+                else (
+                    getattr(
+                        self.spec_info, "capture_hidden_mode", CaptureHiddenMode.NULL
+                    )
                     if self.spec_info
                     else CaptureHiddenMode.NULL
+                )
             ),
         )
 

python/sglang/srt/managers/scheduler.py

@@ -997,6 +997,7 @@ class Scheduler:
             self.enable_overlap,
             self.spec_algorithm,
             self.server_args.enable_custom_logit_processor,
+            self.server_args.return_hidden_states,
         )
         new_batch.prepare_for_extend()
 
@@ -1156,6 +1157,8 @@ class Scheduler:
                         logits_output.input_token_logprobs.tolist()
                     )
 
+            hidden_state_offset = 0
+
             # Check finish conditions
             logprob_pt = 0
             for i, (req, next_token_id) in enumerate(zip(batch.reqs, next_token_ids)):
@@ -1182,6 +1185,21 @@ class Scheduler:
                             i, req, logprob_pt, next_token_ids, logits_output
                         )
 
+                    if (
+                        self.server_args.return_hidden_states
+                        and logits_output.hidden_states is not None
+                    ):
+                        req.hidden_states.append(
+                            logits_output.hidden_states[
+                                hidden_state_offset : (
+                                    hidden_state_offset := hidden_state_offset
+                                    + len(req.origin_input_ids)
+                                )
+                            ]
+                            .cpu()
+                            .clone()
+                        )
+
                     if req.grammar is not None:
                         req.grammar.accept_token(next_token_id)
                         req.grammar.finished = req.finished()
@@ -1275,6 +1293,12 @@ class Scheduler:
                         logits_output.next_token_top_logprobs_idx[i]
                     )
 
+            if (
+                self.server_args.return_hidden_states
+                and logits_output.hidden_states is not None
+            ):
+                req.hidden_states.append(logits_output.hidden_states[i].cpu().clone())
+
             if req.grammar is not None:
                 req.grammar.accept_token(next_token_id)
                 req.grammar.finished = req.finished()
@@ -1398,6 +1422,7 @@ class Scheduler:
             completion_tokens = []
             cached_tokens = []
             spec_verify_ct = []
+            hidden_states = []
 
             if return_logprob:
                 input_token_logprobs_val = []
@@ -1464,6 +1489,8 @@ class Scheduler:
                         output_top_logprobs_val.append(req.output_top_logprobs_val)
                         output_top_logprobs_idx.append(req.output_top_logprobs_idx)
 
+                    hidden_states.append(req.hidden_states)
+
             # Send to detokenizer
             if rids:
                 self.send_to_detokenizer.send_pyobj(
@@ -1490,6 +1517,7 @@ class Scheduler:
                         input_top_logprobs_idx,
                         output_top_logprobs_val,
                         output_top_logprobs_idx,
+                        hidden_states,
                     )
                 )
         else:  # embedding or reward model
@@ -1553,6 +1581,7 @@ class Scheduler:
             self.enable_overlap,
             self.spec_algorithm,
             self.server_args.enable_custom_logit_processor,
+            self.server_args.return_hidden_states,
         )
         idle_batch.prepare_for_idle()
         return idle_batch

python/sglang/srt/managers/tokenizer_manager.py

@@ -796,6 +796,12 @@ class TokenizerManager:
                     }
                 )
 
+            if (
+                hasattr(recv_obj, "output_hidden_states")
+                and len(recv_obj.output_hidden_states[i]) > 0
+            ):
+                meta_info["hidden_states"] = recv_obj.output_hidden_states[i]
+
             if isinstance(recv_obj, BatchStrOut):
                 out_dict = {
                     "text": recv_obj.output_strs[i],

python/sglang/srt/managers/tp_worker_overlap_thread.py

@@ -156,6 +156,10 @@ class TpModelWorkerClient:
                     logits_output.input_token_logprobs = (
                         logits_output.input_token_logprobs.to("cpu", non_blocking=True)
                     )
+            if logits_output.hidden_states is not None:
+                logits_output.hidden_states = logits_output.hidden_states.to(
+                    "cpu", non_blocking=True
+                )
             next_token_ids = next_token_ids.to("cpu", non_blocking=True)
             copy_done.record()
 

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -349,7 +349,13 @@ class CudaGraphRunner:
             spec_algorithm=self.model_runner.spec_algorithm,
             spec_info=spec_info,
             capture_hidden_mode=(
-                spec_info.capture_hidden_mode if spec_info else CaptureHiddenMode.NULL
+                CaptureHiddenMode.FULL
+                if self.model_runner.server_args.return_hidden_states
+                else (
+                    spec_info.capture_hidden_mode
+                    if spec_info
+                    else CaptureHiddenMode.NULL
+                )
             ),
         )
 

python/sglang/srt/server_args.py

@@ -160,6 +160,7 @@ class ServerArgs:
     delete_ckpt_after_loading: bool = False
     enable_memory_saver: bool = False
     allow_auto_truncate: bool = False
+    return_hidden_states: bool = False
 
     # Custom logit processor
     enable_custom_logit_processor: bool = False
@@ -896,6 +897,11 @@ class ServerArgs:
             action="store_true",
             help="Enable users to pass custom logit processors to the server (disabled by default for security)",
         )
+        parser.add_argument(
+            "--return-hidden-states",
+            action="store_true",
+            help="Return hidden states in the response.",
+        )
         # Function Calling
         parser.add_argument(
             "--tool-call-parser",

test/srt/run_suite.py

@@ -46,6 +46,7 @@ suites = {
         "test_torchao.py",
         "test_triton_attention_kernels.py",
         "test_triton_attention_backend.py",
+        "test_hidden_states.py",
         "test_update_weights_from_disk.py",
         "test_update_weights_from_tensor.py",
         "test_vision_chunked_prefill.py",

test/srt/test_hidden_states.py

+import unittest
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+import sglang as sgl
+from sglang.test.test_utils import is_in_ci
+
+
+class TestHiddenState(unittest.TestCase):
+    def test_return_hidden_states(self):
+        prompts = ["Today is", "Today is a sunny day and I like"]
+        model_path = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+        tokenizer = AutoTokenizer.from_pretrained(model_path)
+        input_ids = tokenizer(prompts).input_ids
+
+        sampling_params = {"temperature": 0, "max_new_tokens": 8}
+
+        engine = sgl.Engine(
+            model_path=model_path,
+            random_seed=42,
+            return_hidden_states=True,
+            skip_tokenizer_init=True,
+        )
+        outputs = engine.generate(input_ids=input_ids, sampling_params=sampling_params)
+        engine.shutdown()
+
+        for output in outputs:
+            self.assertEqual(len(output["meta_info"]["hidden_states"]), 8)
+            for hidden_state in output["meta_info"]["hidden_states"]:
+                self.assertIsInstance(hidden_state, torch.Tensor)
+        # Checks that splicing of the batch was done correctly
+        self.assertGreater(
+            outputs[1]["meta_info"]["hidden_states"][0].shape[0],
+            outputs[0]["meta_info"]["hidden_states"][0].shape[0],
+        )
+
+        model = AutoModelForCausalLM.from_pretrained(
+            model_path, torch_dtype=torch.bfloat16, device_map="cuda"
+        )
+
+        for input_id, output in zip(input_ids, outputs):
+            with torch.inference_mode():
+                hf_out = model(
+                    torch.tensor(
+                        [input_id + output["token_ids"][:-1]], device=model.device
+                    ),
+                    output_hidden_states=True,
+                )
+            print("=== HF Hiddens ===")
+            print(hf_out["hidden_states"][-1][0])
+            sg_hidden_states = torch.cat(
+                [
+                    i.unsqueeze(0) if len(i.shape) == 1 else i
+                    for i in output["meta_info"]["hidden_states"]
+                ]
+            ).to("cuda")
+            print("=== SRT Hiddens ===")
+            print(sg_hidden_states)
+
+            print(
+                f"Max diff: {torch.max(torch.abs(hf_out['hidden_states'][-1][0] - sg_hidden_states))}"
+            )
+
+            atol = 0.8 if is_in_ci() else 0.4
+            self.assertTrue(
+                torch.allclose(
+                    hf_out["hidden_states"][-1][0],
+                    sg_hidden_states,
+                    atol=atol,
+                    rtol=0,
+                )
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()