[P/D][V1] KV Connector API V1

examples/offline_inference/disaggregated-prefill-v1/decode_example.py

@@ -0,0 +1,36 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from vllm import LLM, SamplingParams
+from vllm.config import KVTransferConfig
+
+# Read prompts from output.txt
+prompts = []
+try:
+    with open("output.txt") as f:
+        for line in f:
+            prompts.append(line.strip())
+    print(f"Loaded {len(prompts)} prompts from output.txt")
+except FileNotFoundError:
+    print("Error: output.txt file not found")
+    exit(-1)
+
+sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=10)
+
+llm = LLM(
+    model="meta-llama/Llama-3.2-1B-Instruct",
+    enforce_eager=True,
+    gpu_memory_utilization=0.8,
+    max_num_batched_tokens=64,
+    max_num_seqs=16,
+    kv_transfer_config=KVTransferConfig.from_cli(
+        '{"kv_connector":"SharedStorageConnector","kv_role":"kv_both",'
+        '"kv_connector_extra_config": {"shared_storage_path": "local_storage"}}'
+    ))  #, max_model_len=2048, max_num_batched_tokens=2048)
+
+# 1ST generation (prefill instance)
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

examples/offline_inference/disaggregated-prefill-v1/prefill_example.py

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+# SPDX-License-Identifier: Apache-2.0
+
+from vllm import LLM, SamplingParams
+from vllm.config import KVTransferConfig
+
+context = "Hi " * 1000
+context2 = "Hey " * 500
+prompts = [
+    context + "Hello, my name is",
+    context + "The capital of France is",
+    context2 + "Your name is",
+    context2 + "The capital of China is",
+]
+
+sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=1)
+
+llm = LLM(model="meta-llama/Llama-3.2-1B-Instruct",
+          enforce_eager=True,
+          gpu_memory_utilization=0.8,
+          kv_transfer_config=KVTransferConfig.from_cli(
+              '{"kv_connector":"SharedStorageConnector","kv_role":"kv_both", '
+              '"kv_connector_extra_config": '
+              '{"shared_storage_path": "local_storage"}}')
+          )  #, max_model_len=2048, max_num_batched_tokens=2048)
+
+# 1ST generation (prefill instance)
+outputs = llm.generate(
+    prompts,
+    sampling_params,
+)
+
+new_prompts = []
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    new_prompts.append(prompt + generated_text)
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+# Write new_prompts to output.txt
+with open("output.txt", "w") as f:
+    for prompt in new_prompts:
+        f.write(prompt + "\n")
+print(f"Saved {len(new_prompts)} prompts to output.txt")

examples/offline_inference/disaggregated-prefill-v1/run.sh

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+rm -rf local_storage/
+rm output.txt
+
+VLLM_ENABLE_V1_MULTIPROCESSING=0 CUDA_VISIBLE_DEVICES=0 python3 prefill_example.py
+VLLM_ENABLE_V1_MULTIPROCESSING=0 CUDA_VISIBLE_DEVICES=0 python3 decode_example.py