Sync from v0.13

tests/distributed/test_torchrun_example_moe.py

@@ -0,0 +1,76 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# unit test for `examples/offline_inference/torchrun_example.py`
+import os
+import random
+
+import torch.distributed as dist
+
+from vllm import LLM, SamplingParams
+from vllm.distributed.parallel_state import get_tp_group, get_world_group
+
+dist.init_process_group(backend="gloo")
+
+# Create prompts
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+] * 10
+dp_size = int(os.getenv("DP_SIZE", "1"))
+dp_rank = int(os.getenv("DP_RANK", "0"))
+
+if dp_size > 1:
+    # distribute the prompts across the data parallel ranks
+    prompts = [prompt for idx, prompt in enumerate(prompts) if idx % dp_size == dp_rank]
+
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+# set different `gpu_memory_utilization` and `swap_space` for different ranks,
+# to test if all ranks agree on the same kv cache configuration.
+llm = LLM(
+    model="microsoft/Phi-mini-MoE-instruct",
+    tensor_parallel_size=int(os.getenv("TP_SIZE", "1")),
+    pipeline_parallel_size=int(os.getenv("PP_SIZE", "1")),
+    enable_expert_parallel=int(os.getenv("ENABLE_EP", "0")) == 1,
+    distributed_executor_backend="external_launcher",
+    gpu_memory_utilization=random.uniform(0.7, 0.9),
+    swap_space=random.randint(1, 4),
+    seed=0,
+)
+
+outputs = llm.generate(prompts, sampling_params)
+
+group = get_world_group() if dp_size == 1 else get_tp_group()
+cpu_group = group.cpu_group
+group_rank = dist.get_rank(group=cpu_group)
+
+
+def test_consistent_across_ranks(obj):
+    if group_rank == 0:
+        dist.broadcast_object_list([obj], src=group.ranks[0], group=cpu_group)
+    else:
+        container = [None]
+        dist.broadcast_object_list(container, src=group.ranks[0], group=cpu_group)
+        assert container[0] == obj
+
+
+test_consistent_across_ranks(llm.llm_engine.vllm_config.cache_config.num_cpu_blocks)
+test_consistent_across_ranks(llm.llm_engine.vllm_config.cache_config.num_gpu_blocks)
+
+# make sure we can access the model parameters from the calling process
+# of the `LLM` instance.
+params = list(
+    llm.llm_engine.model_executor.driver_worker.worker.model_runner.model.parameters()
+)
+test_consistent_across_ranks(len(params))
+
+# all ranks should have the same outputs
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    test_consistent_across_ranks(prompt)
+    test_consistent_across_ranks(generated_text)
+    print(f"Rank {group_rank}, Prompt: {prompt!r}, Generated text: {generated_text!r}")