Create ep_gpt_oss.py

ep_gpt_oss.py

@@ -0,0 +1,59 @@
+import os
+import torch
+from torch import distributed as dist
+from transformers import GptOssForCausalLM, PreTrainedTokenizerFast
+from transformers.distributed import DistributedConfig
+
+def initialize_process():
+    # torchrun exports: RANK, LOCAL_RANK, WORLD_SIZE, MASTER_ADDR, MASTER_PORT
+    local_rank = int(os.environ["LOCAL_RANK"])
+    torch.cuda.set_device(local_rank)
+    dist.init_process_group(backend="nccl", device_id=local_rank)
+
+def run_inference():
+    model_id = "openai/gpt-oss-20b"
+    tok = PreTrainedTokenizerFast.from_pretrained(model_id)
+    
+    model = GptOssForCausalLM.from_pretrained(
+        model_id,
+        distributed_config=DistributedConfig(enable_expert_parallel=True),
+        dtype="auto",
+    ).eval()
+
+    messages = [
+        {"role": "system", "content": "Be concise."},
+        {"role": "user", "content": "Explain KV caching briefly."},
+    ]
+    inputs = tok.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt",
+        return_dict=True,
+        reasoning_effort="low",
+    )
+
+    # Place inputs on *this process's* GPU
+    local_rank = int(os.environ["LOCAL_RANK"])
+    device = torch.device(f"cuda:{local_rank}")
+    inputs = {k: v.to(device, non_blocking=True) for k, v in inputs.items()}
+
+    with torch.inference_mode():
+        out = model.generate(**inputs, max_new_tokens=128)
+        torch.cuda.synchronize(device)
+
+    # keep output from rank 0 only
+    dist.barrier(
+        device_ids=[int(os.environ["LOCAL_RANK"])]
+    )
+    if dist.get_rank() == 0:
+        print(tok.decode(out[0][inputs["input_ids"].shape[-1]:]))
+
+def main():
+    initialize_process()
+    try:
+        run_inference()
+    finally:
+        dist.destroy_process_group()
+
+if __name__ == "__main__":
+    main()