Add more support for intel Gaudi accelerators (#2357)

examples/runtime/engine/offline_batch_inference.py

@@ -1,7 +1,13 @@
+import argparse
+import dataclasses
+
 import sglang as sgl
+from sglang.srt.server_args import ServerArgs
 
 
-def main():
+def main(
+    server_args: ServerArgs,
+):
     # Sample prompts.
     prompts = [
         "Hello, my name is",
@@ -13,7 +19,7 @@ def main():
     sampling_params = {"temperature": 0.8, "top_p": 0.95}
 
     # Create an LLM.
-    llm = sgl.Engine(model_path="meta-llama/Meta-Llama-3.1-8B-Instruct")
+    llm = sgl.Engine(**dataclasses.asdict(server_args))
 
     outputs = llm.generate(prompts, sampling_params)
     # Print the outputs.
@@ -25,4 +31,8 @@ def main():
 # The __main__ condition is necessary here because we use "spawn" to create subprocesses
 # Spawn starts a fresh program every time, if there is no __main__, it will run into infinite loop to keep spawning processes from sgl.Engine
 if __name__ == "__main__":
-    main()
+    parser = argparse.ArgumentParser()
+    ServerArgs.add_cli_args(parser)
+    args = parser.parse_args()
+    server_args = ServerArgs.from_cli_args(args)
+    main(server_args)

python/sglang/srt/layers/sampler.py

@@ -111,5 +111,7 @@ def top_k_top_p_min_p_sampling_from_probs_torch(
     probs_sort[probs_sort < min_p_thresholds.view(-1, 1)] = 0.0
     probs_sort.div_(probs_sort.max(dim=-1, keepdim=True)[0])
     sampled_index = torch.multinomial(probs_sort, num_samples=1)
+    # int32 range is enough to represent the token ids
+    probs_idx = probs_idx.to(torch.int32)
     batch_next_token_ids = torch.gather(probs_idx, dim=1, index=sampled_index).view(-1)
     return batch_next_token_ids

python/sglang/srt/managers/scheduler.py

@@ -993,7 +993,7 @@ class Scheduler:
             self.process_batch_result_prefill(batch, result)
         elif batch.forward_mode.is_dummy_first():
             batch.next_batch_sampling_info.update_regex_vocab_mask()
-            torch.cuda.current_stream().synchronize()
+            torch.get_device_module(self.device).current_stream().synchronize()
             batch.next_batch_sampling_info.sampling_info_done.set()
 
     def process_batch_result_prefill(self, batch: ScheduleBatch, result):
@@ -1055,7 +1055,7 @@ class Scheduler:
 
             if batch.next_batch_sampling_info:
                 batch.next_batch_sampling_info.update_regex_vocab_mask()
-                torch.cuda.current_stream().synchronize()
+                torch.get_device_module(self.device).current_stream().synchronize()
                 batch.next_batch_sampling_info.sampling_info_done.set()
 
         else:  # embedding or reward model
@@ -1130,7 +1130,7 @@ class Scheduler:
 
         if batch.next_batch_sampling_info:
             batch.next_batch_sampling_info.update_regex_vocab_mask()
-            torch.cuda.current_stream().synchronize()
+            torch.get_device_module(self.device).current_stream().synchronize()
             batch.next_batch_sampling_info.sampling_info_done.set()
 
         self.stream_output(batch.reqs)

python/sglang/srt/managers/tp_worker_overlap_thread.py

@@ -32,12 +32,13 @@ from sglang.srt.managers.io_struct import (
 from sglang.srt.managers.schedule_batch import ModelWorkerBatch
 from sglang.srt.managers.tp_worker import TpModelWorker
 from sglang.srt.server_args import ServerArgs
+from sglang.srt.utils import get_compiler_backend
 from sglang.utils import get_exception_traceback
 
 logger = logging.getLogger(__name__)
 
 
-@torch.compile(dynamic=True)
+@torch.compile(dynamic=True, backend=get_compiler_backend())
 def resolve_future_token_ids(input_ids, future_token_ids_map):
     input_ids[:] = torch.where(
         input_ids < 0,
@@ -73,7 +74,7 @@ class TpModelWorkerClient:
         # Launch threads
         self.input_queue = Queue()
         self.output_queue = Queue()
-        self.forward_stream = torch.cuda.Stream()
+        self.forward_stream = torch.get_device_module(self.device).Stream()
         self.forward_thread = threading.Thread(
             target=self.forward_thread_func,
         )
@@ -97,7 +98,7 @@ class TpModelWorkerClient:
 
     def forward_thread_func(self):
         try:
-            with torch.cuda.stream(self.forward_stream):
+            with torch.get_device_module(self.device).stream(self.forward_stream):
                 self.forward_thread_func_()
         except Exception:
             traceback = get_exception_traceback()
@@ -122,7 +123,7 @@ class TpModelWorkerClient:
 
             # Create event
             self.launch_done = threading.Event()
-            copy_done = torch.cuda.Event()
+            copy_done = torch.get_device_module(self.device).Event()
 
             # Resolve future tokens in the input
             input_ids = model_worker_batch.input_ids
@@ -190,7 +191,7 @@ class TpModelWorkerClient:
         )
 
         # A cuda stream sync here to avoid the cuda illegal memory access error.
-        torch.cuda.current_stream().synchronize()
+        torch.get_device_module(self.device).current_stream().synchronize()
 
         # Push a new batch to the queue
         self.input_queue.put((model_worker_batch, self.future_token_ids_ct))

python/sglang/srt/mem_cache/memory_pool.py

@@ -27,6 +27,7 @@ from typing import List, Tuple, Union
 import torch
 
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.utils import get_compiler_backend
 
 logger = logging.getLogger(__name__)
 
@@ -129,6 +130,9 @@ class BaseTokenToKVPool:
         return select_index.to(self.device, non_blocking=True)
 
     def free(self, free_index: torch.Tensor):
+        if free_index.numel() == 0:
+            return
+
         if self.is_not_in_free_group:
             self.free_slots = torch.concat((self.free_slots, free_index.cpu()))
         else:
@@ -234,7 +238,7 @@ class MHATokenToKVPool(BaseTokenToKVPool):
 
 # This compiled version is slower in the unit test
 # python3 -m unittest test_bench_serving.TestBenchServing.test_offline_throughput_non_stream_small_batch_size
-@torch.compile(dynamic=True)
+@torch.compile(dynamic=True, backend=get_compiler_backend())
 def copy_two_array(loc, dst_1, src_1, dst_2, src_2, dtype, store_dtype):
     dst_1[loc] = src_1.to(dtype).view(store_dtype)
     dst_2[loc] = src_2.to(dtype).view(store_dtype)

python/sglang/srt/models/commandr.py

@@ -62,10 +62,10 @@ from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
-from sglang.srt.utils import set_weight_attrs
+from sglang.srt.utils import get_compiler_backend, set_weight_attrs
 
 
-@torch.compile
+@torch.compile(backend=get_compiler_backend())
 def layer_norm_func(hidden_states, weight, variance_epsilon):
     input_dtype = hidden_states.dtype
     hidden_states = hidden_states.to(torch.float32)

python/sglang/srt/server_args.py

@@ -25,6 +25,7 @@ import torch
 from sglang.srt.hf_transformers_utils import check_gguf_file
 from sglang.srt.utils import (
     get_amdgpu_memory_capacity,
+    get_hpu_memory_capacity,
     get_nvgpu_memory_capacity,
     is_flashinfer_available,
     is_hip,
@@ -158,6 +159,8 @@ class ServerArgs:
             gpu_mem = get_amdgpu_memory_capacity()
         elif torch.cuda.is_available():
             gpu_mem = get_nvgpu_memory_capacity()
+        elif self.device == "hpu":
+            gpu_mem = get_hpu_memory_capacity()
         else:
             # GPU memory is not known yet or no GPU is available.
             gpu_mem = None
@@ -194,6 +197,10 @@ class ServerArgs:
                 self.cuda_graph_max_bs = 160
 
         # Choose kernel backends
+        if self.device == "hpu":
+            self.attention_backend = "torch_native"
+            self.sampling_backend = "pytorch"
+
         if self.attention_backend is None:
             self.attention_backend = (
                 "flashinfer" if is_flashinfer_available() else "triton"

python/sglang/srt/utils.py

@@ -201,6 +201,18 @@ def get_available_gpu_memory(device, gpu_id, distributed=False):
         total_gpu_memory = torch.xpu.get_device_properties(gpu_id).total_memory
         free_gpu_memory = total_gpu_memory - used_memory
 
+    elif device == "hpu":
+        num_gpus = torch.hpu.device_count()
+        assert gpu_id < num_gpus
+
+        if torch.hpu.current_device() != gpu_id:
+            print(
+                f"WARNING: current device is not {gpu_id}, but {torch.hpu.current_device()}, ",
+                "which may cause useless memory allocation for torch HPU context.",
+            )
+
+        free_gpu_memory, total_gpu_memory = torch.hpu.mem_get_info()
+
     if distributed:
         tensor = torch.tensor(free_gpu_memory, dtype=torch.float32).to(
             torch.device(device, gpu_id)
@@ -939,6 +951,37 @@ def get_nvgpu_memory_capacity():
         )
 
 
+def get_hpu_memory_capacity():
+    try:
+        # Run hl-smi and capture the output
+        result = subprocess.run(
+            ["hl-smi --query | grep 'Total'"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            shell=True,
+            text=True,
+        )
+
+        if result.returncode != 0:
+            raise RuntimeError(f"hl-smi error: {result.stderr.strip()}")
+
+        # Parse the output to extract memory values in MiB
+        memory_values = [
+            float(mem.split(" ")[-2]) for mem in result.stdout.strip().split("\n")
+        ]
+
+        if not memory_values:
+            raise ValueError("No GPU memory values found.")
+
+        # Return the minimum memory value
+        return min(memory_values)
+
+    except FileNotFoundError:
+        raise RuntimeError(
+            "hl-smi not found. Ensure Habana drivers are installed and accessible."
+        )
+
+
 # Copy from pytorch and OpenRLHF to allow creating multiple main groups.
 # https://github.com/pytorch/pytorch/blob/main/torch/distributed/distributed_c10d.py
 # https://github.com/OpenRLHF/OpenRLHF/blob/main/openrlhf/utils/distributed_util.py
@@ -1062,6 +1105,13 @@ def get_device_capability(device_id: int = 0) -> Tuple[int, int]:
     return major, minor
 
 
+def get_compiler_backend() -> str:
+    if hasattr(torch, "hpu") and torch.hpu.is_available():
+        return "hpu_backend"
+
+    return "inductor"
+
+
 sglang_lib = Library("sglang", "FRAGMENT")  # noqa