[Iluvatar GPU] fix ci error caused by rebuild_padding param and cuda graph (#4504)

Author: wuyujiji

fastdeploy/model_executor/graph_optimization/utils.py

@@ -20,6 +20,8 @@
 import paddle
 import pynvml
 
+from fastdeploy.platforms import current_platform
+
 
 @dataclass
 class PaddleMemoryInfo:
@@ -46,8 +48,11 @@ def __init__(
         self.device_id = device_id
         self.print_debug_info = print_debug_info
 
-        pynvml.nvmlInit()
-        self.gpu_memory_handle = pynvml.nvmlDeviceGetHandleByIndex(self.device_id)
+        if current_platform.is_iluvatar():
+            self.gpu_memory_handle = None
+        else:
+            pynvml.nvmlInit()
+            self.gpu_memory_handle = pynvml.nvmlDeviceGetHandleByIndex(self.device_id)
 
     def __del__(self):
         """ """

fastdeploy/model_executor/pre_and_post_process.py

@@ -732,7 +732,9 @@ def rebuild_padding(
             seq_lens_decoder,
             seq_lens_encoder,
             output_padding_offset,
+            first_token_out,
             max_input_length,
+            enable_logprob,
         )
     elif current_platform.is_gcu():
         from fastdeploy.model_executor.ops.gcu import rebuild_padding

fastdeploy/worker/iluvatar_model_runner.py

@@ -31,6 +31,8 @@ def __init__(
         rank: int,
         local_rank: int,
     ):
+        # Iluvatar does not support cudagraph
+        fd_config.graph_opt_config.use_cudagraph = False
         super(IluvatarModelRunner, self).__init__(
             fd_config=fd_config, device=device, device_id=device_id, rank=rank, local_rank=local_rank
         )
@@ -40,6 +42,7 @@ def __init__(
         assert not self.cache_config.enable_prefix_caching, "Iluvatar does not support prefix caching"
         self.mla_cache = envs.FD_ATTENTION_BACKEND == "MLA_ATTN"
         assert not self.mla_cache, "Iluvatar does not support MLA"
+        assert not self.use_cudagraph, "Iluvatar does not support cudagraph"
         if self.enable_mm:
             assert (
                 not self.cache_config.enable_chunked_prefill

tests/ci_use/iluvatar_UT/run_ernie300B_4layer.py

@@ -1,42 +1,91 @@
+import functools
+import sys
+import threading
+
 from fastdeploy import LLM, SamplingParams
 from fastdeploy.utils import set_random_seed
 
-set_random_seed(123)
-
-prompts = [
-    "Hello, my name is",
-]
-
-# 采样参数
-sampling_params = SamplingParams(temperature=0.8, top_p=0.00001, max_tokens=16)
-
-# 加载模型
-llm = LLM(
-    model="/data1/fastdeploy/ERNIE_300B_4L",
-    tensor_parallel_size=8,
-    max_model_len=8192,
-    quantization="wint8",
-    block_size=16,
-)
-
-# 批量进行推理（llm内部基于资源情况进行请求排队、动态插入处理）
-outputs = llm.generate(prompts, sampling_params)
-
-assert outputs[0].outputs.token_ids == [
-    23768,
-    97000,
-    47814,
-    59335,
-    68170,
-    183,
-    49080,
-    94717,
-    82966,
-    99140,
-    31615,
-    51497,
-    94851,
-    60764,
-    10889,
-    2,
-], f"{outputs[0].outputs.token_ids}"
+
+def timeout(seconds):
+    def decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            result = [None]
+            exception = [None]
+
+            def target():
+                try:
+                    result[0] = func(*args, **kwargs)
+                except Exception as e:
+                    exception[0] = e
+
+            thread = threading.Thread(target=target)
+            thread.daemon = True
+            thread.start()
+            thread.join(seconds)
+
+            if thread.is_alive():
+                raise TimeoutError(f"Function timed out after {seconds} seconds")
+
+            if exception[0]:
+                raise exception[0]
+
+            return result[0]
+
+        return wrapper
+
+    return decorator
+
+
+@timeout(60)
+def offline_infer_check():
+    set_random_seed(123)
+
+    prompts = [
+        "Hello, my name is",
+    ]
+
+    # 采样参数
+    sampling_params = SamplingParams(temperature=0.8, top_p=0.00001, max_tokens=16)
+
+    # 加载模型
+    llm = LLM(
+        model="/data1/fastdeploy/ERNIE_300B_4L",
+        tensor_parallel_size=8,
+        max_model_len=8192,
+        quantization="wint8",
+        block_size=16,
+    )
+
+    # 批量进行推理（llm内部基于资源情况进行请求排队、动态插入处理）
+    outputs = llm.generate(prompts, sampling_params)
+
+    assert outputs[0].outputs.token_ids == [
+        23768,
+        97000,
+        47814,
+        59335,
+        68170,
+        183,
+        49080,
+        94717,
+        82966,
+        99140,
+        31615,
+        51497,
+        94851,
+        60764,
+        10889,
+        2,
+    ], f"{outputs[0].outputs.token_ids}"
+    print("PASSED")
+
+
+if __name__ == "__main__":
+    try:
+        result = offline_infer_check()
+        sys.exit(0)
+    except TimeoutError:
+        sys.exit(124)
+    except Exception:
+        sys.exit(1)