[bench] Added vllm vs nano-vllm bench.

CLAUDE.md

@@ -0,0 +1,73 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Overview
+
+Nano-vLLM is a lightweight vLLM implementation (~1,200 lines) for fast offline LLM inference. Currently supports Qwen3 models.
+
+## Commands
+
+```bash
+# Install
+pip install -e .
+
+# Run example
+python example.py
+
+# Run benchmark
+python bench.py
+```
+
+## Architecture
+
+### Core Components
+
+**LLMEngine** (`nanovllm/engine/llm_engine.py`):
+- Main entry point, wraps ModelRunner and Scheduler
+- Handles tokenization and multi-process tensor parallelism coordination
+- `generate()` method runs the prefill-decode loop until all sequences finish
+
+**ModelRunner** (`nanovllm/engine/model_runner.py`):
+- Loads model weights, allocates KV cache, captures CUDA graphs
+- Rank 0 is the main process; ranks 1+ run in separate processes via `loop()` waiting on shared memory events
+- `run()` prepares inputs and executes model forward pass
+
+**Scheduler** (`nanovllm/engine/scheduler.py`):
+- Two-phase scheduling: prefill (waiting queue) then decode (running queue)
+- Handles preemption when memory is constrained by moving sequences back to waiting
+
+**BlockManager** (`nanovllm/engine/block_manager.py`):
+- Paged attention block allocation with prefix caching via xxhash
+- Blocks are 256 tokens by default, tracked with reference counting
+
+**Sequence** (`nanovllm/engine/sequence.py`):
+- Tracks token IDs, block table, and sampling parameters per request
+- Custom `__getstate__`/`__setstate__` for efficient pickling across processes
+
+### Model Implementation
+
+**Qwen3ForCausalLM** (`nanovllm/models/qwen3.py`):
+- Standard transformer: embedding → decoder layers → RMSNorm → LM head
+- Uses `packed_modules_mapping` for weight loading (q/k/v → qkv_proj, gate/up → gate_up_proj)
+
+**Attention** (`nanovllm/layers/attention.py`):
+- Uses FlashAttention (`flash_attn_varlen_func` for prefill, `flash_attn_with_kvcache` for decode)
+- Custom Triton kernel `store_kvcache_kernel` for KV cache writes
+
+**Parallel Layers** (`nanovllm/layers/linear.py`, `embed_head.py`):
+- Tensor parallelism via column/row parallel linear layers with custom weight loaders
+
+### Key Design Patterns
+
+- **Global Context**: `nanovllm/utils/context.py` stores attention metadata (cu_seqlens, slot_mapping, block_tables) accessed via `get_context()`/`set_context()`
+- **CUDA Graph Capture**: Decode phase uses captured graphs for batch sizes 1, 2, 4, 8, 16, 32... up to max_num_seqs (capped at 512)
+- **Shared Memory IPC**: Tensor parallel workers receive commands via pickled data in SharedMemory, synchronized with Events
+
+### Config Defaults
+
+- `max_num_batched_tokens`: 16384
+- `max_num_seqs`: 512
+- `kvcache_block_size`: 256
+- `gpu_memory_utilization`: 0.9
+- `enforce_eager`: False (enables CUDA graphs)

bench.py

@@ -2,30 +2,58 @@ import os
 import time
 from random import randint, seed
 from nanovllm import LLM, SamplingParams
-# from vllm import LLM, SamplingParams
+
+
+def bench_decode(llm, num_seqs, max_input_len, max_output_len):
+    """Benchmark decode performance (original test)"""
+    seed(0)
+    prompt_token_ids = [[randint(0, 10000) for _ in range(randint(100, max_input_len))] for _ in range(num_seqs)]
+    sampling_params = [SamplingParams(temperature=0.6, ignore_eos=True, max_tokens=randint(100, max_output_len)) for _ in range(num_seqs)]
+
+    t = time.time()
+    llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
+    t = time.time() - t
+    total_output_tokens = sum(sp.max_tokens for sp in sampling_params)
+    throughput = total_output_tokens / t
+    print(f"[Decode] Output: {total_output_tokens}tok, Time: {t:.2f}s, Throughput: {throughput:.2f}tok/s")
+
+
+def bench_prefill(llm, num_seqs, input_len):
+    """Benchmark prefill performance"""
+    seed(0)
+    # Fixed length input, minimal output to focus on prefill
+    prompt_token_ids = [[randint(0, 10000) for _ in range(input_len)] for _ in range(num_seqs)]
+    sampling_params = SamplingParams(temperature=0.6, ignore_eos=True, max_tokens=1)
+
+    t = time.time()
+    llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
+    t = time.time() - t
+    total_input_tokens = num_seqs * input_len
+    throughput = total_input_tokens / t
+    print(f"[Prefill] Input: {total_input_tokens}tok ({num_seqs}x{input_len}), Time: {t:.2f}s, Throughput: {throughput:.2f}tok/s")
 
 
 def main():
-    seed(0)
-    num_seqs = 256
-    max_input_len = 1024
-    max_ouput_len = 1024
-
-    path = os.path.expanduser("~/huggingface/Qwen3-0.6B/")
+    path = os.path.expanduser("~/models/Qwen3-4B-Instruct-2507/")
     llm = LLM(path, enforce_eager=False, max_model_len=4096)
 
-    prompt_token_ids = [[randint(0, 10000) for _ in range(randint(100, max_input_len))] for _ in range(num_seqs)]
-    sampling_params = [SamplingParams(temperature=0.6, ignore_eos=True, max_tokens=randint(100, max_ouput_len)) for _ in range(num_seqs)]
-    # uncomment the following line for vllm
-    # prompt_token_ids = [dict(prompt_token_ids=p) for p in prompt_token_ids]
-
+    # Warmup
     llm.generate(["Benchmark: "], SamplingParams())
-    t = time.time()
-    llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
-    t = (time.time() - t)
-    total_tokens = sum(sp.max_tokens for sp in sampling_params)
-    throughput = total_tokens / t
-    print(f"Total: {total_tokens}tok, Time: {t:.2f}s, Throughput: {throughput:.2f}tok/s")
+
+    print("=" * 60)
+    print("Prefill Benchmark")
+    print("=" * 60)
+    bench_prefill(llm, num_seqs=1, input_len=1024)
+    bench_prefill(llm, num_seqs=1, input_len=2048)
+    bench_prefill(llm, num_seqs=1, input_len=4095)
+    bench_prefill(llm, num_seqs=16, input_len=1024)
+    bench_prefill(llm, num_seqs=64, input_len=1024)
+
+    print("=" * 60)
+    print("Decode Benchmark")
+    print("=" * 60)
+    bench_decode(llm, num_seqs=1, max_input_len=1024, max_output_len=1024)
+    bench_decode(llm, num_seqs=256, max_input_len=1024, max_output_len=1024)
 
 
 if __name__ == "__main__":

bench_vllm.py

@@ -0,0 +1,63 @@
+import os
+os.environ["VLLM_USE_V1"] = "1"
+import time
+from random import randint, seed
+from vllm import LLM, SamplingParams
+
+
+def bench_decode(llm, num_seqs, max_input_len, max_output_len):
+    """Benchmark decode performance (original test)"""
+    seed(0)
+    prompt_token_ids = [[randint(0, 10000) for _ in range(randint(100, max_input_len))] for _ in range(num_seqs)]
+    sampling_params = [SamplingParams(temperature=0.6, ignore_eos=True, max_tokens=randint(100, max_output_len)) for _ in range(num_seqs)]
+    prompt_token_ids = [dict(prompt_token_ids=p) for p in prompt_token_ids]
+
+    t = time.time()
+    llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
+    t = time.time() - t
+    total_output_tokens = sum(sp.max_tokens for sp in sampling_params)
+    throughput = total_output_tokens / t
+    print(f"[Decode] Output: {total_output_tokens}tok, Time: {t:.2f}s, Throughput: {throughput:.2f}tok/s")
+
+
+def bench_prefill(llm, num_seqs, input_len):
+    """Benchmark prefill performance"""
+    seed(0)
+    # Fixed length input, minimal output to focus on prefill
+    prompt_token_ids = [[randint(0, 10000) for _ in range(input_len)] for _ in range(num_seqs)]
+    sampling_params = SamplingParams(temperature=0.6, ignore_eos=True, max_tokens=1)
+    prompt_token_ids = [dict(prompt_token_ids=p) for p in prompt_token_ids]
+
+    t = time.time()
+    llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
+    t = time.time() - t
+    total_input_tokens = num_seqs * input_len
+    throughput = total_input_tokens / t
+    print(f"[Prefill] Input: {total_input_tokens}tok ({num_seqs}x{input_len}), Time: {t:.2f}s, Throughput: {throughput:.2f}tok/s")
+
+
+def main():
+    path = os.path.expanduser("~/models/Qwen3-4B-Instruct-2507/")
+    llm = LLM(path, enforce_eager=False, max_model_len=4096, max_num_seqs=128, gpu_memory_utilization=0.9)
+
+    # Warmup
+    llm.generate([dict(prompt_token_ids=[0])], SamplingParams())
+
+    print("=" * 60)
+    print("Prefill Benchmark")
+    print("=" * 60)
+    bench_prefill(llm, num_seqs=1, input_len=1024)
+    bench_prefill(llm, num_seqs=1, input_len=2048)
+    bench_prefill(llm, num_seqs=1, input_len=4095)
+    bench_prefill(llm, num_seqs=16, input_len=1024)
+    bench_prefill(llm, num_seqs=64, input_len=1024)
+
+    print("=" * 60)
+    print("Decode Benchmark")
+    print("=" * 60)
+    bench_decode(llm, num_seqs=1, max_input_len=1024, max_output_len=1024)
+    bench_decode(llm, num_seqs=256, max_input_len=1024, max_output_len=1024)
+
+
+if __name__ == "__main__":
+    main()

nanovllm/layers/attention.py

@@ -3,7 +3,7 @@ from torch import nn
 import triton
 import triton.language as tl
 
-from flash_attn import flash_attn_varlen_func, flash_attn_with_kvcache
+from flash_attn.flash_attn_interface import flash_attn_varlen_func, flash_attn_with_kvcache
 from nanovllm.utils.context import get_context