From 8699786587f1d1328e11f6cc9047869790b6bf9d Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Thu, 30 Apr 2026 02:19:05 +0000 Subject: [PATCH 01/21] docs(pymllm): document qwen3 projection alignment --- docs/pymllm_runtime/models_and_quantization.rst | 17 +++++++++++++++++ docs/pymllm_runtime/setup_and_usage.rst | 3 ++- 2 files changed, 19 insertions(+), 1 deletion(-) diff --git a/docs/pymllm_runtime/models_and_quantization.rst b/docs/pymllm_runtime/models_and_quantization.rst index e7d92dd1e..6bbb63706 100644 --- a/docs/pymllm_runtime/models_and_quantization.rst +++ b/docs/pymllm_runtime/models_and_quantization.rst @@ -75,6 +75,23 @@ Qwen3-VL 图文模型 当前 W8A8 量化主要覆盖语言 decoder 的线性层；视觉 encoder、embedding、LayerNorm 和 ``lm_head`` 保持全精度。 +Fused projection 与 shard-aware loading +---------------------------------------- + +Qwen3 / Qwen3-VL 的 text decoder 使用 fused QKV projection 和 fused gate/up +projection。对未量化模型，这减少了 projection 层的 module 边界；对 W8A8 和 W4A16 +路径，它还避免把同一层拆成多次 activation quant、GEMM 或 Marlin 调用。 + +checkpoint 中的权重仍可能以 HuggingFace 常见的分离形式保存，例如 ``q_proj``、 +``k_proj``、``v_proj``、``gate_proj`` 和 ``up_proj``。``MergedLinear`` 通过 +shard-aware ``weight_loader`` 将这些分离 tensor 写入 fused 参数，运行时布局保持为 +``[Q, K, V]`` 或 ``[gate, up]``。权重加载完成后，``process_weights_after_loading`` +再负责 W8A8 layout 转换或 W4A16 Marlin repack。 + +Qwen3 / Qwen3-VL decoder 还使用 residual-carry 形式组织 RMSNorm 的 fused add 路径。 +在 Qwen3-VL 中，如果 deepstack embedding 需要注入，运行时会先物化当前 residual sum， +再执行注入并重置 carry，避免改变图文 prefill 语义。 + 量化配置解析 ---------------------------------------- diff --git a/docs/pymllm_runtime/setup_and_usage.rst b/docs/pymllm_runtime/setup_and_usage.rst index 3097bbbbf..2cb83ca51 100644 --- a/docs/pymllm_runtime/setup_and_usage.rst +++ b/docs/pymllm_runtime/setup_and_usage.rst @@ -260,7 +260,8 @@ OpenAI-compatible 请求 ``pymllm.executor.model_runner.ModelRunner``，绕过 HTTP server、tokenizer 进程、 scheduler 进程和 detokenizer 进程，用 synthetic text-only token ids 测一次静态 prefill，再测逐 token decode。该工具适合分析模型 forward、KV cache、attention、 -CUDA Graph 与量化 kernel 的模型级开销，不代表在线服务的 TTFT / ITL / E2E 指标。 +CUDA Graph 与量化 kernel 的模型级开销，也适合验证 fused projection、residual carry +等模型图优化。不代表在线服务的 TTFT / ITL / E2E 指标。 典型用法： From ec95dddcab6a904266ec024d279cc6b059e1ce64 Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Sat, 23 May 2026 08:18:34 +0000 Subject: [PATCH 02/21] perf(qwen3-vl): fuse M-RoPE and reuse RoPE cache --- pymllm/layers/__init__.py | 2 + pymllm/layers/rope.py | 207 ++++++++++++++++++++++++++++++ pymllm/models/qwen3_vl.py | 22 +++- pymllm/tests/test_mrope_triton.py | 70 ++++++++++ 4 files changed, 298 insertions(+), 3 deletions(-) create mode 100644 pymllm/tests/test_mrope_triton.py diff --git a/pymllm/layers/__init__.py b/pymllm/layers/__init__.py index d328ca7ef..18e5a1dcf 100644 --- a/pymllm/layers/__init__.py +++ b/pymllm/layers/__init__.py @@ -17,6 +17,7 @@ apply_llama31_rope, apply_llama31_rope_pos_ids, apply_mrope, + apply_mrope_fused_, apply_rope, apply_rope_pos_ids, apply_rope_with_cos_sin_cache, @@ -51,6 +52,7 @@ "RMSNorm", "GemmaRMSNorm", "apply_mrope", + "apply_mrope_fused_", "apply_rope", "apply_llama31_rope", "apply_rope_pos_ids", diff --git a/pymllm/layers/rope.py b/pymllm/layers/rope.py index 94f89b20d..17fdb39ee 100644 --- a/pymllm/layers/rope.py +++ b/pymllm/layers/rope.py @@ -5,6 +5,13 @@ import torch import flashinfer +try: + import triton + import triton.language as tl +except Exception: # pragma: no cover - Triton is optional outside CUDA builds. + triton = None + tl = None + def apply_rope( q: torch.Tensor, @@ -399,3 +406,203 @@ def apply_mrope( else k_rot ) return q_out, k_out + + +if triton is not None: + + @triton.jit + def _triton_mrope_forward_fused( + q_ptr, + k_ptr, + cos_sin_cache_ptr, + positions_ptr, + q_stride, + k_stride, + positions_stride, + n_qh: tl.constexpr, + n_kh: tl.constexpr, + hd: tl.constexpr, + rd: tl.constexpr, + pad_n_qh: tl.constexpr, + pad_n_kh: tl.constexpr, + pad_hd: tl.constexpr, + mrope_section_t: tl.constexpr, + mrope_section_h: tl.constexpr, + mrope_section_w: tl.constexpr, + is_interleaved: tl.constexpr, + is_neox_style: tl.constexpr, + ): + pid = tl.program_id(0) + q_ptr = q_ptr + pid * q_stride + k_ptr = k_ptr + pid * k_stride + half_rd = rd // 2 + + t = tl.load(positions_ptr + pid) + h = tl.load(positions_ptr + positions_stride + pid) + w = tl.load(positions_ptr + 2 * positions_stride + pid) + + t_cos = cos_sin_cache_ptr + t * rd + h_cos = cos_sin_cache_ptr + h * rd + w_cos = cos_sin_cache_ptr + w * rd + t_sin = t_cos + half_rd + h_sin = h_cos + half_rd + w_sin = w_cos + half_rd + + cos_offsets = tl.arange(0, pad_hd // 2) + if is_interleaved: + h_mask = ((cos_offsets % 3) == 1) & ( + cos_offsets <= 3 * mrope_section_h + ) + w_mask = ((cos_offsets % 3) == 2) & ( + cos_offsets <= 3 * mrope_section_w + ) + t_mask = ~(h_mask | w_mask) + else: + t_end = mrope_section_t + h_end = t_end + mrope_section_h + t_mask = cos_offsets < mrope_section_t + h_mask = (t_end <= cos_offsets) & (cos_offsets < h_end) + w_mask = (h_end <= cos_offsets) & (cos_offsets < half_rd) + + t_cos_row = tl.load(t_cos + cos_offsets, mask=t_mask, other=0.0) + t_sin_row = tl.load(t_sin + cos_offsets, mask=t_mask, other=0.0) + h_cos_row = tl.load(h_cos + cos_offsets, mask=h_mask, other=0.0) + h_sin_row = tl.load(h_sin + cos_offsets, mask=h_mask, other=0.0) + w_cos_row = tl.load(w_cos + cos_offsets, mask=w_mask, other=0.0) + w_sin_row = tl.load(w_sin + cos_offsets, mask=w_mask, other=0.0) + cos_row = t_cos_row + h_cos_row + w_cos_row + sin_row = t_sin_row + h_sin_row + w_sin_row + + if is_neox_style: + qh = tl.arange(0, pad_n_qh)[:, None] + kh = tl.arange(0, pad_n_kh)[:, None] + dim = tl.arange(0, pad_hd // 2)[None, :] + q_mask = (qh < n_qh) & (dim < half_rd) + k_mask = (kh < n_kh) & (dim < half_rd) + q_first = qh * hd + dim + k_first = kh * hd + dim + q_second = q_first + half_rd + k_second = k_first + half_rd + + q1 = tl.load(q_ptr + q_first, mask=q_mask, other=0.0).to(cos_row.dtype) + q2 = tl.load(q_ptr + q_second, mask=q_mask, other=0.0).to(cos_row.dtype) + k1 = tl.load(k_ptr + k_first, mask=k_mask, other=0.0).to(cos_row.dtype) + k2 = tl.load(k_ptr + k_second, mask=k_mask, other=0.0).to(cos_row.dtype) + + tl.store(q_ptr + q_first, q1 * cos_row - q2 * sin_row, mask=q_mask) + tl.store(q_ptr + q_second, q2 * cos_row + q1 * sin_row, mask=q_mask) + tl.store(k_ptr + k_first, k1 * cos_row - k2 * sin_row, mask=k_mask) + tl.store(k_ptr + k_second, k2 * cos_row + k1 * sin_row, mask=k_mask) + else: + qh = tl.arange(0, pad_n_qh)[:, None] + kh = tl.arange(0, pad_n_kh)[:, None] + pair = tl.arange(0, pad_hd // 2)[None, :] + q_mask = (qh < n_qh) & (pair < half_rd) + k_mask = (kh < n_kh) & (pair < half_rd) + even = 2 * pair + odd = even + 1 + + q_even = tl.load(q_ptr + qh * hd + even, mask=q_mask, other=0.0).to( + cos_row.dtype + ) + q_odd = tl.load(q_ptr + qh * hd + odd, mask=q_mask, other=0.0).to( + cos_row.dtype + ) + k_even = tl.load(k_ptr + kh * hd + even, mask=k_mask, other=0.0).to( + cos_row.dtype + ) + k_odd = tl.load(k_ptr + kh * hd + odd, mask=k_mask, other=0.0).to( + cos_row.dtype + ) + + tl.store(q_ptr + qh * hd + even, q_even * cos_row - q_odd * sin_row, mask=q_mask) + tl.store(q_ptr + qh * hd + odd, q_odd * cos_row + q_even * sin_row, mask=q_mask) + tl.store(k_ptr + kh * hd + even, k_even * cos_row - k_odd * sin_row, mask=k_mask) + tl.store(k_ptr + kh * hd + odd, k_odd * cos_row + k_even * sin_row, mask=k_mask) + +else: + _triton_mrope_forward_fused = None + + +def _can_use_mrope_fused( + q: torch.Tensor, + k: torch.Tensor, + positions: torch.Tensor, + cos_sin_cache: torch.Tensor, +) -> bool: + return ( + triton is not None + and q.is_cuda + and k.is_cuda + and positions.is_cuda + and cos_sin_cache.is_cuda + and q.dim() == 3 + and k.dim() == 3 + and positions.dim() == 2 + and q.is_contiguous() + and k.is_contiguous() + and q.shape[-1] == k.shape[-1] + and cos_sin_cache.shape[-1] <= q.shape[-1] + ) + + +def apply_mrope_fused_( + q: torch.Tensor, + k: torch.Tensor, + positions: torch.Tensor, + cos_sin_cache: torch.Tensor, + mrope_section: List[int], + mrope_interleaved: bool = True, +) -> Tuple[torch.Tensor, torch.Tensor]: + """Apply M-RoPE in place with a Triton fused kernel when available. + + The fallback returns the reference PyTorch outputs, preserving functional + correctness on CPU or non-contiguous inputs. + """ + if positions.ndim == 1: + positions = positions.unsqueeze(0).expand(3, -1) + if positions.stride(-1) != 1: + positions = positions.contiguous() + + if not _can_use_mrope_fused(q, k, positions, cos_sin_cache): + return apply_mrope( + q, + k, + positions, + cos_sin_cache, + mrope_section, + mrope_interleaved, + ) + + num_tokens, num_q_heads, head_size = q.shape + num_kv_heads = k.shape[1] + rotary_dim = cos_sin_cache.shape[-1] + q_2d = q.reshape(num_tokens, num_q_heads * head_size) + k_2d = k.reshape(num_tokens, num_kv_heads * head_size) + + pad_n_qh = triton.next_power_of_2(num_q_heads) + pad_n_kh = triton.next_power_of_2(num_kv_heads) + pad_hd = triton.next_power_of_2(head_size) + + _triton_mrope_forward_fused[(num_tokens,)]( + q_2d, + k_2d, + cos_sin_cache, + positions, + q_2d.stride(0), + k_2d.stride(0), + positions.stride(0), + num_q_heads, + num_kv_heads, + head_size, + rotary_dim, + pad_n_qh, + pad_n_kh, + pad_hd, + int(mrope_section[0]), + int(mrope_section[1]), + int(mrope_section[2]), + bool(mrope_interleaved), + True, + ) + return q, k diff --git a/pymllm/models/qwen3_vl.py b/pymllm/models/qwen3_vl.py index 2b945fc18..2ce6c6ce3 100644 --- a/pymllm/models/qwen3_vl.py +++ b/pymllm/models/qwen3_vl.py @@ -35,7 +35,7 @@ import torch.nn as nn import torch.nn.functional as F -from pymllm.layers import RMSNorm, apply_mrope +from pymllm.layers import RMSNorm, apply_mrope_fused_ from pymllm.layers.attention.radix_attention import RadixAttention from pymllm.layers.linear import Linear, MergedLinear from pymllm.layers.mlp import MLP @@ -776,6 +776,21 @@ def _get_qm(suffix): layer_id=layer_id, ) + def _match_cos_sin_cache_dtype(self, query: torch.Tensor) -> None: + """Keep the RoPE cache on the same device/dtype as the query. + + The cache is built in FP32 for stability, but repeated ``to()`` + conversions inside the hot path are expensive. Match SGLang by + materialising the converted cache once and reusing it afterwards. + """ + if ( + self.cos_sin_cache.device != query.device + or self.cos_sin_cache.dtype != query.dtype + ): + self.cos_sin_cache = self.cos_sin_cache.to( + device=query.device, dtype=query.dtype + ) + def forward( self, positions: torch.Tensor, @@ -798,11 +813,12 @@ def forward( # as [T] for purely text-only batches; expand to [3, T] in that case. if positions.ndim == 1: positions = positions.unsqueeze(0).expand(3, -1) - q, k = apply_mrope( + self._match_cos_sin_cache_dtype(q) + q, k = apply_mrope_fused_( q, k, positions, - self.cos_sin_cache.to(q.dtype), + self.cos_sin_cache, self.mrope_section, self.mrope_interleaved, ) diff --git a/pymllm/tests/test_mrope_triton.py b/pymllm/tests/test_mrope_triton.py new file mode 100644 index 000000000..93f5e25c4 --- /dev/null +++ b/pymllm/tests/test_mrope_triton.py @@ -0,0 +1,70 @@ +from __future__ import annotations + +import pytest +import torch + +from pymllm.layers.rope import apply_mrope, apply_mrope_fused_ + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required") +def test_apply_mrope_fused_matches_reference_and_keeps_inputs_in_place(): + torch.manual_seed(0) + num_tokens = 17 + num_q_heads = 4 + num_kv_heads = 2 + head_dim = 8 + mrope_section = [2, 1, 1] + + q = torch.randn( + (num_tokens, num_q_heads, head_dim), + device="cuda", + dtype=torch.float16, + ) + k = torch.randn( + (num_tokens, num_kv_heads, head_dim), + device="cuda", + dtype=torch.float16, + ) + positions = torch.randint( + 0, + 32, + (3, num_tokens), + device="cuda", + dtype=torch.long, + ) + cos_sin_cache = torch.randn( + (32, head_dim), + device="cuda", + dtype=torch.float16, + ) + + expected_q, expected_k = apply_mrope( + q, + k, + positions, + cos_sin_cache, + mrope_section, + mrope_interleaved=True, + ) + + q_actual = q.clone() + k_actual = k.clone() + q_ptr = q_actual.data_ptr() + k_ptr = k_actual.data_ptr() + + out_q, out_k = apply_mrope_fused_( + q_actual, + k_actual, + positions, + cos_sin_cache, + mrope_section, + mrope_interleaved=True, + ) + torch.cuda.synchronize() + + assert out_q.data_ptr() == q_ptr + assert out_k.data_ptr() == k_ptr + assert q_actual.data_ptr() == q_ptr + assert k_actual.data_ptr() == k_ptr + torch.testing.assert_close(q_actual, expected_q, atol=1e-2, rtol=1e-2) + torch.testing.assert_close(k_actual, expected_k, atol=1e-2, rtol=1e-2) From d83725206352b9372d63c83853e4313eb6c8e217 Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Sat, 23 May 2026 08:23:46 +0000 Subject: [PATCH 03/21] feat(bench): add multimodal VIT timing benchmark mode --- bench_assets/two_cats.jpg | Bin 0 -> 8580 bytes bench_assets/two_cats_480p.jpg | Bin 0 -> 77615 bytes pymllm/bench_one_batch.py | 291 ++++++++++++++++++++++-- pymllm/models/qwen3_vl.py | 32 ++- pymllm/tests/test_bench_one_batch.py | 124 ++++++++++ pymllm/tests/test_qwen3_vl_deepstack.py | 110 +++++++++ 6 files changed, 539 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When set, " + "--input-len is replaced by the processed prompt length." + ), + ) + group.add_argument( + "--prompt", + default=BenchArgs.prompt, + help="Prompt text used with --image in multimodal benchmark mode.", + ) return parser @@ -180,6 +220,8 @@ def _bench_args_from_namespace(namespace: argparse.Namespace) -> BenchArgs: profile_start_step=namespace.profile_start_step, profile_steps=namespace.profile_steps, skip_warmup=namespace.skip_warmup, + image_path=namespace.image_path, + prompt=namespace.prompt, ) @@ -193,10 +235,11 @@ def parse_args( def generate_settings(args: BenchArgs) -> list[BenchSetting]: + input_lens = [0] if args.image_path is not None else args.input_len return [ BenchSetting(batch_size=batch_size, input_len=input_len, output_len=output_len) for batch_size in args.batch_size - for input_len in args.input_len + for input_len in input_lens for output_len in args.output_len ] @@ -267,6 +310,79 @@ def summarize_latencies( return result +def make_vit_prefill_metrics( + *, + vit_prefill_ms: float, + vit_prefill_tokens: int, +) -> dict[str, Any]: + latency = float(vit_prefill_ms) / 1000.0 + throughput = _safe_div(float(vit_prefill_tokens), latency) + return { + "vit_prefill_latency": latency, + "vit_prefill_ms": float(vit_prefill_ms), + "vit_prefill_tokens": int(vit_prefill_tokens), + "vit_prefill_throughput": throughput, + "vit_prefill_tps": throughput, + } + + +def _get_processor_value(processor_output: Any, key: str) -> Any: + if hasattr(processor_output, "get"): + return processor_output.get(key) + return getattr(processor_output, key, None) + + +def make_multimodal_bench_input_from_processor_output( + processor_output: Any, + *, + batch_size: int, + image_token_id: int, + device: str | torch.device, +) -> MultimodalBenchInput: + input_ids = _get_processor_value(processor_output, "input_ids") + pixel_values = _get_processor_value(processor_output, "pixel_values") + image_grid_thw = _get_processor_value(processor_output, "image_grid_thw") + + if input_ids is None: + raise ValueError("Multimodal processor output does not contain input_ids") + if pixel_values is None: + raise ValueError("Multimodal processor output does not contain pixel_values") + if image_grid_thw is None: + raise ValueError( + "Multimodal processor output does not contain image_grid_thw" + ) + + input_ids_t = torch.as_tensor(input_ids) + if input_ids_t.dim() == 1: + input_ids_t = input_ids_t.unsqueeze(0) + if input_ids_t.shape[0] != 1: + raise ValueError( + "bench_one_batch multimodal mode expects one processed prompt before " + f"batch repetition, got batch dimension {input_ids_t.shape[0]}" + ) + + input_ids_t = input_ids_t.repeat(batch_size, 1).to( + device=device, dtype=torch.int32 + ) + pixel_values_t = torch.as_tensor(pixel_values) + pixel_values_t = pixel_values_t.repeat( + (batch_size,) + (1,) * (pixel_values_t.dim() - 1) + ) + image_grid_thw_t = torch.as_tensor(image_grid_thw) + if image_grid_thw_t.dim() == 1: + image_grid_thw_t = image_grid_thw_t.unsqueeze(0) + image_grid_thw_t = image_grid_thw_t.repeat(batch_size, 1).to( + device=device, dtype=torch.int64 + ) + + return MultimodalBenchInput( + input_ids=input_ids_t, + pixel_values=pixel_values_t.to(device=device), + image_grid_thw=image_grid_thw_t, + vit_prefill_tokens=int((input_ids_t == image_token_id).sum().item()), + ) + + def make_profile_trace_path( *, output_dir: Path, @@ -330,6 +446,74 @@ def _load_hf_config(cfg: GlobalConfig) -> None: logger.info("Loaded model config: %s", cfg.model.hf_config.__class__.__name__) +def _extract_image_token_id(hf_config: Any) -> int: + image_token_id = getattr(hf_config, "image_token_id", None) + if image_token_id is None: + raise ValueError("Model config does not define image_token_id") + return int(image_token_id) + + +def _render_multimodal_prompt( + processor: Any, + *, + prompt: str, + image_path: Path, +) -> str: + if not hasattr(processor, "apply_chat_template"): + return prompt + + messages = [ + { + "role": "user", + "content": [ + {"type": "image", "image": str(image_path)}, + {"type": "text", "text": prompt}, + ], + } + ] + try: + rendered = processor.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + except Exception as exc: + logger.warning("Processor chat template failed, using raw prompt: %s", exc) + return prompt + if isinstance(rendered, list): + if not rendered: + return prompt + return str(rendered[0]) + return str(rendered) + + +def _make_multimodal_processor_output( + *, + cfg: GlobalConfig, + prompt: str, + image_path: Path, +) -> Any: + if cfg.server.tokenizer_path is None: + raise ValueError("--server.tokenizer_path or --server.model_path is required") + if not image_path.exists(): + raise FileNotFoundError(f"Image path does not exist: {image_path}") + + from PIL import Image + from transformers import AutoProcessor + + processor = AutoProcessor.from_pretrained( + str(cfg.server.tokenizer_path), + trust_remote_code=cfg.server.trust_remote_code, + ) + image = Image.open(image_path).convert("RGB") + text = _render_multimodal_prompt( + processor, + prompt=prompt, + image_path=image_path, + ) + return processor(images=[image], text=[text], return_tensors="pt") + + def _append_jsonl(path: Path, row: dict[str, Any]) -> None: path.parent.mkdir(parents=True, exist_ok=True) with path.open("a", encoding="utf-8") as fp: @@ -420,7 +604,14 @@ def clear(self) -> None: self.runner.req_to_token_pool.clear() self.runner.token_to_kv_pool_allocator.clear() - def extend(self, input_ids: torch.Tensor) -> tuple[torch.Tensor, DecodeState]: + def extend( + self, + input_ids: torch.Tensor, + *, + pixel_values: Optional[torch.Tensor] = None, + image_grid_thw: Optional[torch.Tensor] = None, + benchmark_vision_timing: bool = False, + ) -> ExtendResult: if input_ids.dim() != 2: raise ValueError("input_ids must have shape [batch_size, input_len]") @@ -468,6 +659,12 @@ def extend(self, input_ids: torch.Tensor) -> tuple[torch.Tensor, DecodeState]: extend_prefix_lens=extend_prefix_lens, out_cache_loc=out_cache_loc.to(torch.int64), ) + if pixel_values is not None: + forward_batch.pixel_values = pixel_values.to(device=self.device) + if image_grid_thw is not None: + forward_batch.image_grid_thw = image_grid_thw.to(device=self.device) + forward_batch.benchmark_vision_timing = benchmark_vision_timing + logits_output = self.runner.forward(forward_batch) next_token_ids = self._sample_greedy(logits_output, forward_batch) state = DecodeState( @@ -477,7 +674,22 @@ def extend(self, input_ids: torch.Tensor) -> tuple[torch.Tensor, DecodeState]: forward_batch, "mrope_position_deltas", None ), ) - return next_token_ids, state + vit_prefill_ms = getattr(forward_batch, "vit_prefill_ms", None) + vit_prefill_tokens = getattr(forward_batch, "vit_prefill_tokens", None) + vit_prefill_tps = getattr(forward_batch, "vit_prefill_tps", None) + return ExtendResult( + next_token_ids=next_token_ids, + state=state, + vit_prefill_ms=( + float(vit_prefill_ms) if vit_prefill_ms is not None else None + ), + vit_prefill_tokens=( + int(vit_prefill_tokens) if vit_prefill_tokens is not None else None + ), + vit_prefill_tps=( + float(vit_prefill_tps) if vit_prefill_tps is not None else None + ), + ) def decode( self, @@ -561,21 +773,47 @@ def run_single_setting( setting: BenchSetting, seed: int, record_result: bool, + multimodal_processor_output: Any = None, ) -> Optional[dict[str, Any]]: bench_runner.clear() vocab_size = getattr(bench_runner.runner, "vocab_size", 10000) - input_ids = make_synthetic_input_ids( - batch_size=setting.batch_size, - input_len=setting.input_len, - vocab_size=vocab_size, - seed=seed, - device=bench_runner.device, - ) + mm_input = None + effective_setting = setting + if multimodal_processor_output is None: + input_ids = make_synthetic_input_ids( + batch_size=setting.batch_size, + input_len=setting.input_len, + vocab_size=vocab_size, + seed=seed, + device=bench_runner.device, + ) + else: + hf_config = bench_runner.runner.model_config.hf_config + image_token_id = _extract_image_token_id(hf_config) + mm_input = make_multimodal_bench_input_from_processor_output( + multimodal_processor_output, + batch_size=setting.batch_size, + image_token_id=image_token_id, + device=bench_runner.device, + ) + input_ids = mm_input.input_ids + effective_setting = BenchSetting( + batch_size=setting.batch_size, + input_len=int(input_ids.shape[1]), + output_len=setting.output_len, + ) - with _maybe_profile(args=args, setting=setting, stage="prefill"): + with _maybe_profile(args=args, setting=effective_setting, stage="prefill"): prefill_latency, extend_result = _timed_call( bench_runner.device, - lambda: bench_runner.extend(input_ids), + lambda: bench_runner.extend( + input_ids, + pixel_values=mm_input.pixel_values if mm_input is not None else None, + image_grid_thw=( + mm_input.image_grid_thw if mm_input is not None else None + ), + benchmark_vision_timing=mm_input is not None, + ), ) next_token_ids, state = extend_result @@ -592,7 +830,12 @@ def run_single_setting( and profile_start_step <= step < profile_stop_step ) profile_context = ( - _maybe_profile(args=args, setting=setting, stage="decode", step=step) + _maybe_profile( + args=args, + setting=effective_setting, + stage="decode", + step=step, + ) if should_profile_decode else nullcontext() ) @@ -616,13 +859,24 @@ def run_single_setting( return None return summarize_latencies( - setting=setting, + setting=effective_setting, prefill_latency=prefill_latency, decode_latencies=decode_latencies, run_name=args.run_name, device=bench_runner.device, dtype=str(bench_runner.runner.dtype), cuda_graph=bench_runner.runner.graph_runner is not None, + extra=( + make_vit_prefill_metrics( + vit_prefill_ms=extend_result.vit_prefill_ms, + vit_prefill_tokens=extend_result.vit_prefill_tokens, + ) + if ( + extend_result.vit_prefill_ms is not None + and extend_result.vit_prefill_tokens is not None + ) + else None + ), ) @@ -636,6 +890,13 @@ def run_benchmark(cfg: GlobalConfig, args: BenchArgs) -> list[dict[str, Any]]: bench_runner = PymllmBenchRunner.create(cfg) try: settings = generate_settings(args) + multimodal_processor_output = None + if args.image_path is not None: + multimodal_processor_output = _make_multimodal_processor_output( + cfg=cfg, + prompt=args.prompt, + image_path=args.image_path, + ) if not args.skip_warmup and settings: first = settings[0] warmup_setting = BenchSetting( @@ -655,6 +916,7 @@ def run_benchmark(cfg: GlobalConfig, args: BenchArgs) -> list[dict[str, Any]]: setting=warmup_setting, seed=args.seed, record_result=False, + multimodal_processor_output=multimodal_processor_output, ) results: list[dict[str, Any]] = [] @@ -671,6 +933,7 @@ def run_benchmark(cfg: GlobalConfig, args: BenchArgs) -> list[dict[str, Any]]: setting=setting, seed=args.seed + index, record_result=True, + multimodal_processor_output=multimodal_processor_output, ) assert result is not None _append_jsonl(args.result_filename, result) diff --git a/pymllm/models/qwen3_vl.py b/pymllm/models/qwen3_vl.py index 2ce6c6ce3..273a291a4 100644 --- a/pymllm/models/qwen3_vl.py +++ b/pymllm/models/qwen3_vl.py @@ -577,6 +577,21 @@ def _build_cos_sin_cache( return torch.cat([torch.cos(freqs), torch.sin(freqs)], dim=-1).to(dtype) +def _run_with_synchronized_wall_timing( + fn, + *, + device: torch.device, + enabled: bool, +) -> Tuple[torch.Tensor, float]: + if enabled and device.type == "cuda": + torch.cuda.synchronize(device) + tic = time.perf_counter() + result = fn() + if enabled and device.type == "cuda": + torch.cuda.synchronize(device) + return result, (time.perf_counter() - tic) * 1000.0 + + def get_rope_index( input_ids: torch.Tensor, image_grid_thw: Optional[torch.Tensor], @@ -1199,6 +1214,7 @@ def forward( input_deepstack_embeds = None vit_prefill_ms = None vit_prefill_tokens = None + vit_prefill_tps = None llm_prefill_ms = None llm_decode_ms = None @@ -1209,11 +1225,14 @@ def forward( and not forward_batch.forward_mode.is_decode() ): # Run vision encoder - _vit_t0 = time.perf_counter() - vision_features = ( - self.visual(pixel_values, grid_thw=image_grid_thw) + benchmark_vision_timing = bool( + getattr(forward_batch, "benchmark_vision_timing", False) + ) + vision_features, vit_prefill_ms = _run_with_synchronized_wall_timing( + lambda: self.visual(pixel_values, grid_thw=image_grid_thw), + device=pixel_values.device, + enabled=benchmark_vision_timing, ) - vit_prefill_ms = (time.perf_counter() - _vit_t0) * 1000.0 # Separate main embeddings and deepstack embeddings if self.num_deepstack_embeddings > 0: @@ -1227,6 +1246,10 @@ def forward( input_embeds = self.model.embed_tokens(input_ids) image_mask = input_ids == self.image_token_id vit_prefill_tokens = int(image_mask.sum().item()) + if vit_prefill_ms > 0: + vit_prefill_tps = vit_prefill_tokens / (vit_prefill_ms / 1000.0) + else: + vit_prefill_tps = 0.0 if vit_prefill_tokens != int(vision_embeds.shape[0]): raise ValueError( "Image features and image tokens do not match, " @@ -1265,6 +1288,7 @@ def forward( llm_prefill_ms = _llm_ms forward_batch.vit_prefill_ms = vit_prefill_ms forward_batch.vit_prefill_tokens = vit_prefill_tokens + forward_batch.vit_prefill_tps = vit_prefill_tps forward_batch.llm_prefill_ms = llm_prefill_ms forward_batch.llm_decode_ms = None else: diff --git a/pymllm/tests/test_bench_one_batch.py b/pymllm/tests/test_bench_one_batch.py index cc2a87ae7..13a87fb72 100644 --- a/pymllm/tests/test_bench_one_batch.py +++ b/pymllm/tests/test_bench_one_batch.py @@ -2,14 +2,18 @@ import pytest import torch +from types import SimpleNamespace from pymllm.configs.global_config import GlobalConfig from pymllm.bench_one_batch import ( BenchArgs, BenchSetting, + PymllmBenchRunner, generate_settings, + make_multimodal_bench_input_from_processor_output, make_profile_trace_path, make_synthetic_input_ids, + make_vit_prefill_metrics, parse_args, summarize_latencies, ) @@ -53,6 +57,10 @@ def test_parse_args_accepts_server_config_and_list_bench_args(tmp_path): "--profile-activities", "CPU", "GPU", + "--image", + str(tmp_path / "image.jpg"), + "--prompt", + "What is in this image?", ] ) @@ -67,6 +75,8 @@ def test_parse_args_accepts_server_config_and_list_bench_args(tmp_path): assert bench_args.result_filename == result_file assert bench_args.profile_stage == "decode" assert bench_args.profile_activities == ["CPU", "GPU"] + assert bench_args.image_path == tmp_path / "image.jpg" + assert bench_args.prompt == "What is in this image?" def test_generate_settings_has_stable_batch_input_output_order(tmp_path): @@ -85,6 +95,21 @@ def test_generate_settings_has_stable_batch_input_output_order(tmp_path): ] +def test_generate_settings_uses_processed_prompt_length_for_image_mode(tmp_path): + args = BenchArgs( + batch_size=[1, 2], + input_len=[256, 512], + output_len=[8], + result_filename=tmp_path / "out.jsonl", + image_path=tmp_path / "image.jpg", + ) + + assert generate_settings(args) == [ + BenchSetting(batch_size=1, input_len=0, output_len=8), + BenchSetting(batch_size=2, input_len=0, output_len=8), + ] + + def test_make_synthetic_input_ids_is_seeded_int32_and_vocab_capped(): first = make_synthetic_input_ids( batch_size=2, @@ -136,6 +161,105 @@ def test_summarize_latencies_matches_sglang_style_metrics(): assert result["cuda_graph"] is True +def test_make_vit_prefill_metrics_reports_seconds_and_tps(): + result = make_vit_prefill_metrics(vit_prefill_ms=12.5, vit_prefill_tokens=25) + + assert result == { + "vit_prefill_latency": pytest.approx(0.0125), + "vit_prefill_ms": pytest.approx(12.5), + "vit_prefill_tokens": 25, + "vit_prefill_throughput": pytest.approx(2000.0), + "vit_prefill_tps": pytest.approx(2000.0), + } + + +def test_make_multimodal_bench_input_repeats_processor_output_per_batch(): + bench_input = make_multimodal_bench_input_from_processor_output( + { + "input_ids": torch.tensor([[1, 5, 5, 2]], dtype=torch.int64), + "pixel_values": torch.arange(6, dtype=torch.float32).reshape(2, 3), + "image_grid_thw": torch.tensor([[1, 2, 2]], dtype=torch.int64), + }, + batch_size=3, + image_token_id=5, + device="cpu", + ) + + assert bench_input.input_ids.shape == (3, 4) + assert bench_input.input_ids.dtype == torch.int32 + assert bench_input.vit_prefill_tokens == 6 + torch.testing.assert_close( + bench_input.pixel_values, + torch.arange(6, dtype=torch.float32).reshape(2, 3).repeat(3, 1), + ) + torch.testing.assert_close( + bench_input.image_grid_thw, + torch.tensor([[1, 2, 2], [1, 2, 2], [1, 2, 2]], dtype=torch.int64), + ) + + +def test_extend_attaches_multimodal_inputs_and_returns_vit_metrics(): + class _ReqPool: + def alloc(self, batch_size): + return list(range(batch_size)) + + def write(self, index, value): + del index, value + + def clear(self): + pass + + class _KvPool: + def alloc(self, count): + return torch.arange(count, dtype=torch.int64) + + def clear(self): + pass + + class _Runner: + def __init__(self): + self.device = "cpu" + self.dtype = torch.float32 + self.req_to_token_pool = _ReqPool() + self.token_to_kv_pool_allocator = _KvPool() + self.gdn_pool = None + self.last_forward_batch = None + + def prepare_forward_batch_extend(self, **kwargs): + return SimpleNamespace(batch_size=kwargs["req_pool_indices"].shape[0]) + + def forward(self, forward_batch): + self.last_forward_batch = forward_batch + forward_batch.vit_prefill_ms = 4.0 + forward_batch.vit_prefill_tokens = 8 + return object() + + def sample(self, logits_output, forward_batch, **kwargs): + del logits_output, forward_batch, kwargs + return torch.tensor([7], dtype=torch.int32) + + fake_runner = _Runner() + bench_runner = PymllmBenchRunner(fake_runner) + pixel_values = torch.ones((2, 3), dtype=torch.float32) + image_grid_thw = torch.tensor([[1, 2, 2]], dtype=torch.int64) + + result = bench_runner.extend( + torch.tensor([[1, 5, 5, 2]], dtype=torch.int32), + pixel_values=pixel_values, + image_grid_thw=image_grid_thw, + benchmark_vision_timing=True, + ) + + assert result.vit_prefill_ms == pytest.approx(4.0) + assert result.vit_prefill_tokens == 8 + torch.testing.assert_close(fake_runner.last_forward_batch.pixel_values, pixel_values) + torch.testing.assert_close( + fake_runner.last_forward_batch.image_grid_thw, + image_grid_thw, + ) + assert fake_runner.last_forward_batch.benchmark_vision_timing is True + + def test_make_profile_trace_path_is_deterministic_and_sanitized(tmp_path): path = make_profile_trace_path( output_dir=tmp_path, diff --git a/pymllm/tests/test_qwen3_vl_deepstack.py b/pymllm/tests/test_qwen3_vl_deepstack.py index c36bacf39..694bd37c8 100644 --- a/pymllm/tests/test_qwen3_vl_deepstack.py +++ b/pymllm/tests/test_qwen3_vl_deepstack.py @@ -1,6 +1,7 @@ from __future__ import annotations from types import SimpleNamespace +import time import numpy as np import pytest @@ -12,6 +13,7 @@ Qwen3VLTextModel, Qwen3VLVisionModel, _compute_cu_seqlens_from_grid, + _run_with_synchronized_wall_timing, ) @@ -63,6 +65,25 @@ def forward(self, pixel_values, grid_thw): return torch.ones((1, 2), dtype=torch.float32) +class _FakeTextModel(nn.Module): + def __init__(self): + super().__init__() + self.embed_tokens = nn.Embedding(8, 2) + + def forward( + self, + input_ids, + positions, + forward_batch, + input_embeds=None, + input_deepstack_embeds=None, + ): + del positions, forward_batch, input_deepstack_embeds + if input_embeds is not None: + return input_embeds + return self.embed_tokens(input_ids) + + def _make_vl_config() -> SimpleNamespace: text_config = SimpleNamespace( hidden_size=2, @@ -229,6 +250,95 @@ def test_forward_rejects_mismatched_image_token_and_feature_counts(): ) +def test_forward_records_vit_prefill_tps_when_benchmark_timing_enabled(): + model = Qwen3VLForConditionalGeneration(_make_vl_config()) + model.visual = _FakeVisual() + model.model = _FakeTextModel() + + forward_batch = SimpleNamespace( + forward_mode=_Mode(), + batch_size=1, + extend_start_loc=torch.tensor([0], dtype=torch.int64), + extend_seq_lens=torch.tensor([4], dtype=torch.int64), + pixel_values=torch.zeros((1, 3), dtype=torch.float32), + image_grid_thw=torch.tensor([[1, 1, 1]], dtype=torch.int64), + benchmark_vision_timing=True, + ) + + model( + input_ids=torch.tensor([1, 4, 5, 2], dtype=torch.int64), + positions=torch.arange(4, dtype=torch.int64), + forward_batch=forward_batch, + ) + + assert forward_batch.vit_prefill_ms is not None + assert forward_batch.vit_prefill_ms >= 0.0 + assert forward_batch.vit_prefill_tokens == 1 + assert forward_batch.vit_prefill_tps >= 0.0 + + +def test_vision_timing_includes_host_side_work_when_benchmark_enabled(): + def host_heavy_fn(): + time.sleep(0.02) + return torch.tensor([1.0]) + + result, elapsed_ms = _run_with_synchronized_wall_timing( + host_heavy_fn, + device=torch.device("cpu"), + enabled=True, + ) + + torch.testing.assert_close(result, torch.tensor([1.0])) + assert elapsed_ms >= 15.0 + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is not available") +def test_cuda_vision_timing_includes_host_side_work_when_benchmark_enabled(): + device = torch.device("cuda") + + def host_heavy_cuda_fn(): + time.sleep(0.02) + return torch.ones((1,), device=device) + + result, elapsed_ms = _run_with_synchronized_wall_timing( + host_heavy_cuda_fn, + device=device, + enabled=True, + ) + + assert result.device.type == "cuda" + assert elapsed_ms >= 15.0 + + +def test_cuda_vision_timing_uses_wall_clock_not_event_elapsed(monkeypatch): + class _FakeCudaEvent: + def __init__(self, *args, **kwargs): + del args, kwargs + + def record(self): + pass + + def elapsed_time(self, other): + del other + return 0.0 + + monkeypatch.setattr(torch.cuda, "Event", _FakeCudaEvent) + monkeypatch.setattr(torch.cuda, "synchronize", lambda *args, **kwargs: None) + + def host_heavy_fn(): + time.sleep(0.02) + return torch.tensor([1.0]) + + result, elapsed_ms = _run_with_synchronized_wall_timing( + host_heavy_fn, + device=torch.device("cuda"), + enabled=True, + ) + + torch.testing.assert_close(result, torch.tensor([1.0])) + assert elapsed_ms >= 15.0 + + def test_vision_interpolation_indices_match_sglang_hf(): model = Qwen3VLVisionModel( depth=0, From f9be384d5c80450067d6db3c0422e33964e4be9b Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Sat, 23 May 2026 08:24:11 +0000 Subject: [PATCH 04/21] perf(rmsnorm): patch Jetson FlashInfer device properties --- pymllm/layers/rms_norm.py | 55 +++++++++++++++++++++++++++++++++++ pymllm/tests/test_rms_norm.py | 20 +++++++++++++ 2 files changed, 75 insertions(+) diff --git a/pymllm/layers/rms_norm.py b/pymllm/layers/rms_norm.py index e9a4c6ed0..21163b4c2 100644 --- a/pymllm/layers/rms_norm.py +++ b/pymllm/layers/rms_norm.py @@ -10,6 +10,57 @@ from pymllm.layers.utils import set_weight_attrs +_PATCHED_CUDA_DEVICE_PROPERTIES = False + + +class _CudaDevicePropertiesProxy: + def __init__(self, props): + self._props = props + + def __getattr__(self, name: str): + if name == "shared_memory_per_block_optin": + return _infer_shared_memory_per_block_optin(self._props) + return getattr(self._props, name) + + +def _infer_shared_memory_per_block_optin(props) -> int: + """Infer opt-in shared memory for older PyTorch device properties.""" + if hasattr(props, "shared_memory_per_block_optin"): + return int(props.shared_memory_per_block_optin) + if hasattr(props, "shared_memory_per_multiprocessor"): + return int(props.shared_memory_per_multiprocessor) + return int(getattr(props, "shared_memory_per_block", 0)) + + +def _patch_cuda_device_properties_for_flashinfer_norm() -> None: + """Provide a missing PyTorch device property required by FlashInfer norm. + + Some Jetson PyTorch builds expose neither ``shared_memory_per_block_optin`` + nor ``shared_memory_per_multiprocessor`` on ``torch.cuda.DeviceProperties``. + FlashInfer norm kernels query that attribute while choosing their CUTE + kernel config. Wrap the properties object so FlashInfer can still choose + a valid shared-memory limit instead of falling back to slow PyTorch RMSNorm. + """ + global _PATCHED_CUDA_DEVICE_PROPERTIES + if _PATCHED_CUDA_DEVICE_PROPERTIES or not torch.cuda.is_available(): + return + + original_get_device_properties = torch.cuda.get_device_properties + props = original_get_device_properties(0) + if hasattr(props, "shared_memory_per_block_optin"): + _PATCHED_CUDA_DEVICE_PROPERTIES = True + return + + def patched_get_device_properties(*args, **kwargs): + props = original_get_device_properties(*args, **kwargs) + if hasattr(props, "shared_memory_per_block_optin"): + return props + return _CudaDevicePropertiesProxy(props) + + torch.cuda.get_device_properties = patched_get_device_properties + _PATCHED_CUDA_DEVICE_PROPERTIES = True + + def _torch_rmsnorm( x: torch.Tensor, weight: torch.Tensor, @@ -45,6 +96,7 @@ def forward( if residual is not None: try: + _patch_cuda_device_properties_for_flashinfer_norm() flashinfer.norm.fused_add_rmsnorm( x, residual, self.weight.data, self.eps ) @@ -54,6 +106,7 @@ def forward( return _torch_rmsnorm(residual, self.weight, self.eps), residual try: + _patch_cuda_device_properties_for_flashinfer_norm() # FlashInfer rmsnorm accepts 2D/3D input; flatten higher-rank tensors to 2D. if x.dim() in (2, 3): return flashinfer.norm.rmsnorm(x, self.weight, self.eps) @@ -83,6 +136,7 @@ def forward( residual: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: if residual is not None: + _patch_cuda_device_properties_for_flashinfer_norm() flashinfer.norm.gemma_fused_add_rmsnorm( x, residual, self.weight.data, self.eps ) @@ -95,6 +149,7 @@ def forward( ) # gemma_rmsnorm is defined on 2D input; flatten other ranks to 2D. + _patch_cuda_device_properties_for_flashinfer_norm() if x.dim() == 2: return flashinfer.norm.gemma_rmsnorm(x, self.weight, self.eps) diff --git a/pymllm/tests/test_rms_norm.py b/pymllm/tests/test_rms_norm.py index 9663f5444..16756a8aa 100644 --- a/pymllm/tests/test_rms_norm.py +++ b/pymllm/tests/test_rms_norm.py @@ -25,3 +25,23 @@ def fail_fused_add_rmsnorm(*args, **kwargs): _, residual_out = norm(x, residual) torch.testing.assert_close(residual_out, x + residual) + + +def test_flashinfer_norm_device_properties_patch_adds_optin(monkeypatch): + class FakeProps: + shared_memory_per_block = 49152 + shared_memory_per_multiprocessor = 167936 + + monkeypatch.setattr(rms_norm_module, "_PATCHED_CUDA_DEVICE_PROPERTIES", False) + monkeypatch.setattr(rms_norm_module.torch.cuda, "is_available", lambda: True) + monkeypatch.setattr( + rms_norm_module.torch.cuda, + "get_device_properties", + lambda device=0: FakeProps(), + ) + + rms_norm_module._patch_cuda_device_properties_for_flashinfer_norm() + + props = rms_norm_module.torch.cuda.get_device_properties(0) + assert props.shared_memory_per_block_optin == 167936 + assert props.shared_memory_per_block == 49152 From 0e93e6d50dd7122dcd66d740cfd5c918457d2e68 Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Sat, 23 May 2026 08:24:22 +0000 Subject: [PATCH 05/21] perf(sampling): avoid GPU sync for greedy decode --- pymllm/bench_one_batch.py | 1 + pymllm/executor/model_runner.py | 17 +++++++---- pymllm/orchestrator/model_runner_process.py | 2 ++ pymllm/tests/test_model_runner_sampling.py | 31 +++++++++++++++++++++ 4 files changed, 46 insertions(+), 5 deletions(-) create mode 100644 pymllm/tests/test_model_runner_sampling.py diff --git a/pymllm/bench_one_batch.py b/pymllm/bench_one_batch.py index d97864086..55603bc8b 100644 --- a/pymllm/bench_one_batch.py +++ b/pymllm/bench_one_batch.py @@ -744,6 +744,7 @@ def _sample_greedy(self, logits_output: Any, forward_batch: Any) -> torch.Tensor logits_output, forward_batch, temperatures=temperatures, + is_all_greedy=True, ).to(torch.int32) def _require_initialized(self) -> None: diff --git a/pymllm/executor/model_runner.py b/pymllm/executor/model_runner.py index a50baa13e..4851c8a73 100644 --- a/pymllm/executor/model_runner.py +++ b/pymllm/executor/model_runner.py @@ -1287,6 +1287,7 @@ def sample( top_ps: Optional[torch.Tensor] = None, top_ks: Optional[torch.Tensor] = None, penalty_params: Optional[Dict[str, Any]] = None, + is_all_greedy: Optional[bool] = None, ) -> torch.Tensor: """Sample next-token IDs from logits. @@ -1310,6 +1311,10 @@ def sample( ``frequency_penalties``, ``presence_penalties`` (tensors of shape ``[batch_size]``), and ``token_histories`` (list of list of int). + is_all_greedy + CPU-side metadata indicating that every request should use greedy + sampling. Supplying this avoids a CUDA tensor reduction and + synchronization in the decode hot path. Returns ------- @@ -1338,12 +1343,14 @@ def sample( ) # Greedy path: temperature=0 (or all zeros) → argmax, no sampling. - if temperatures is not None: - all_greedy = bool((temperatures < 1e-6).all()) - else: - all_greedy = False + if is_all_greedy is None: + is_all_greedy = ( + bool((temperatures < 1e-6).all()) + if temperatures is not None + else False + ) - if all_greedy: + if is_all_greedy: return logits.argmax(dim=-1).to(torch.int32) # Stochastic path: apply temperature then sample. diff --git a/pymllm/orchestrator/model_runner_process.py b/pymllm/orchestrator/model_runner_process.py index 383fa2da5..9a0e5cf92 100644 --- a/pymllm/orchestrator/model_runner_process.py +++ b/pymllm/orchestrator/model_runner_process.py @@ -272,6 +272,7 @@ def _forward_batch(self, batch: Dict[str, Any]) -> Dict[str, Any]: temps_tensor = torch.tensor(temperatures, dtype=torch.float32, device=device) top_ps_tensor = torch.tensor(top_ps, dtype=torch.float32, device=device) top_ks_tensor = torch.tensor(top_ks, dtype=torch.int32, device=device) + is_all_greedy = all(t < 1e-6 for t in temperatures) # Collect token histories for penalty computation. # Each entry is (input_ids + output_ids_so_far) for the request. @@ -408,6 +409,7 @@ def _forward_batch(self, batch: Dict[str, Any]) -> Dict[str, Any]: top_ps=top_ps_tensor, top_ks=top_ks_tensor, penalty_params=penalty_params, + is_all_greedy=is_all_greedy, ) # ============================================================== diff --git a/pymllm/tests/test_model_runner_sampling.py b/pymllm/tests/test_model_runner_sampling.py new file mode 100644 index 000000000..09d74cbe0 --- /dev/null +++ b/pymllm/tests/test_model_runner_sampling.py @@ -0,0 +1,31 @@ +from __future__ import annotations + +from types import SimpleNamespace + +import torch + +from pymllm.executor.model_runner import LogitsProcessorOutput, ModelRunner + + +def test_sample_uses_cpu_greedy_flag_without_tensor_reduction(monkeypatch): + runner = SimpleNamespace(device="cpu") + logits_output = LogitsProcessorOutput( + next_token_logits=torch.tensor([[1.0, 3.0], [5.0, 2.0]]) + ) + forward_batch = SimpleNamespace(batch_size=2) + + def fail_count_nonzero(*args, **kwargs): + raise AssertionError("greedy sampling should not reduce temperature tensors") + + monkeypatch.setattr(torch, "count_nonzero", fail_count_nonzero) + + next_token_ids = ModelRunner.sample( + runner, + logits_output, + forward_batch, + temperatures=torch.zeros((2,), dtype=torch.float32), + is_all_greedy=True, + ) + + assert next_token_ids.tolist() == [1, 0] + assert next_token_ids.dtype == torch.int32 From 7494bc4210471f92dadaeb531a7ee0fc8420b59e Mon Sep 17 00:00:00 2001 From: Dev Date: Thu, 4 Jun 2026 04:53:36 +0000 Subject: [PATCH 06/21] perf(bench): align CUDA graph capture batch size with sweep (#1) Lift cfg.server.max_running_requests to max(batch_size) before creating the ModelRunner, mirroring SGLang main() which sets server_args.cuda_graph_max_bs = max(bench_args.batch_size). In pymllm the CUDA graph capture set and req_to_token_pool size both derive from ModelRunner.max_running_requests. Without this, sweeping a batch size larger than the default capture set makes decode silently fall off the graph path and run eager, biasing decode latency vs SGLang. No effect on batch_size=1 runs. Co-Authored-By: Claude Opus 4.8 (1M context) --- pymllm/bench_one_batch.py | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/pymllm/bench_one_batch.py b/pymllm/bench_one_batch.py index 55603bc8b..e8ee05c6e 100644 --- a/pymllm/bench_one_batch.py +++ b/pymllm/bench_one_batch.py @@ -881,6 +881,32 @@ def run_single_setting( ) +def _align_runner_capacity_with_batch_sizes( + cfg: GlobalConfig, batch_sizes: Sequence[int] +) -> None: + """Ensure the runner can hold and CUDA-graph-capture the largest batch. + + Mirrors SGLang ``main()`` which sets + ``server_args.cuda_graph_max_bs = max(bench_args.batch_size)``. In pymllm + the CUDA graph capture batch sizes are derived from + ``ModelRunner.max_running_requests`` (see ``CudaGraphRunner``), which also + sizes ``req_to_token_pool``. Without this, sweeping a batch size larger + than the configured capture set makes decode silently fall off the graph + path and run eager, biasing decode latency versus SGLang. + """ + if not batch_sizes: + return + requested = max(batch_sizes) + configured = cfg.server.max_running_requests + if configured is None or configured < requested: + cfg.server.max_running_requests = requested + logger.info( + "Raised max_running_requests to %d to cover bench batch sizes " + "(SGLang cuda_graph_max_bs alignment).", + requested, + ) + + def run_benchmark(cfg: GlobalConfig, args: BenchArgs) -> list[dict[str, Any]]: _load_hf_config(cfg) logger.info( @@ -888,6 +914,7 @@ def run_benchmark(cfg: GlobalConfig, args: BenchArgs) -> list[dict[str, Any]]: "do not chunk this benchmark." ) + _align_runner_capacity_with_batch_sizes(cfg, args.batch_size) bench_runner = PymllmBenchRunner.create(cfg) try: settings = generate_settings(args) From fdec95c5f1be348de7f6431539a1471b87fb31d4 Mon Sep 17 00:00:00 2001 From: Dev Date: Thu, 4 Jun 2026 04:54:12 +0000 Subject: [PATCH 07/21] perf(bench): tensorize decode KV-mapping write, drop per-request .item() The decode loop read req_pool_indices[i].item() and seq_lens[i].item() per request, each a CUDA->CPU sync inside the timed decode region. SGLang and pymllm's own production decode path (orchestrator/model_runner_process) write the KV mapping without any per-request sync. Replace the loop with a single vectorized scatter req_to_token[req_pool_indices, seq_lens - 1] = out_cache_loc, removing 2*batch_size syncs per decode step. Negligible at batch_size=1; removes a linearly growing, SGLang-absent bias for batch_size > 1. Co-Authored-By: Claude Opus 4.8 (1M context) --- pymllm/bench_one_batch.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/pymllm/bench_one_batch.py b/pymllm/bench_one_batch.py index e8ee05c6e..022110998 100644 --- a/pymllm/bench_one_batch.py +++ b/pymllm/bench_one_batch.py @@ -708,13 +708,18 @@ def decode( raise RuntimeError(f"Failed to allocate {batch_size} decode KV slots") seq_lens = state.seq_lens + 1 - for i in range(batch_size): - slot = int(state.req_pool_indices[i].item()) - write_pos = int(seq_lens[i].item()) - 1 - self.runner.req_to_token_pool.write( - (slot, slice(write_pos, write_pos + 1)), - out_cache_loc[i : i + 1], - ) + # Tensorized KV-mapping write. The production decode path + # (orchestrator/model_runner_process.py) keeps slot/write_pos as plain + # CPU bookkeeping and never does a per-request CUDA ``.item()`` sync. + # Doing per-request ``.item()`` here would add 2*batch_size CPU-GPU + # syncs inside the timed decode region that SGLang does not have, + # biasing decode latency once batch_size > 1. Write all rows at once: + # req_to_token[req_pool_indices, seq_lens - 1] = out_cache_loc. + write_positions = (seq_lens - 1).to(torch.int64) + self.runner.req_to_token_pool.write( + (state.req_pool_indices, write_positions), + out_cache_loc.to(torch.int32), + ) forward_batch = self.runner.prepare_forward_batch_decode( input_ids=input_ids.to(device=self.device, dtype=torch.int32), From 87cf2a3abc3e58af7cb93c2866f69de6501f4149 Mon Sep 17 00:00:00 2001 From: Dev Date: Thu, 4 Jun 2026 04:55:07 +0000 Subject: [PATCH 08/21] feat(bench): skip settings exceeding KV pool capacity (#3) Add a SGLang-style capacity guard: when batch_size exceeds max_total_num_tokens // (input_len + output_len), log a skip and return without running, instead of hard-failing mid-sweep on KV slot allocation. run_benchmark now treats a None result as a skip and continues. Mirrors ModelRunner.max_batch_size / latency_test_run_once in SGLang's bench_one_batch. Co-Authored-By: Claude Opus 4.8 (1M context) --- pymllm/bench_one_batch.py | 35 ++++++++++++++++++++++++++++++++++- 1 file changed, 34 insertions(+), 1 deletion(-) diff --git a/pymllm/bench_one_batch.py b/pymllm/bench_one_batch.py index 022110998..ea6037134 100644 --- a/pymllm/bench_one_batch.py +++ b/pymllm/bench_one_batch.py @@ -414,6 +414,20 @@ def _safe_div(numerator: float, denominator: float) -> float: return float(numerator / denominator) +def _max_batch_size_for(runner: Any, input_len: int, output_len: int) -> int: + """SGLang-style capacity bound on the static batch. + + Mirrors ``ModelRunner.max_batch_size`` in SGLang's bench_one_batch: + ``max_total_num_tokens // (input_len + output_len)``. Used to skip + settings the KV pool cannot hold instead of failing mid-run on alloc. + """ + total = int(getattr(runner, "max_total_num_tokens", 0) or 0) + denom = int(input_len) + int(output_len) + if denom <= 0: + return 0 + return total // denom + + def _sync_device(device: str | torch.device) -> None: torch_device = torch.device(device) if torch_device.type == "cuda": @@ -809,6 +823,23 @@ def run_single_setting( output_len=setting.output_len, ) + max_bs = _max_batch_size_for( + bench_runner.runner, + effective_setting.input_len, + effective_setting.output_len, + ) + if effective_setting.batch_size > max_bs: + logger.info( + "skipping (batch_size=%d, input_len=%d, output_len=%d): exceeds max " + "batch size %d (max_total_num_tokens=%d). SGLang-style skip.", + effective_setting.batch_size, + effective_setting.input_len, + effective_setting.output_len, + max_bs, + int(getattr(bench_runner.runner, "max_total_num_tokens", 0) or 0), + ) + return None + with _maybe_profile(args=args, setting=effective_setting, stage="prefill"): prefill_latency, extend_result = _timed_call( bench_runner.device, @@ -968,7 +999,9 @@ def run_benchmark(cfg: GlobalConfig, args: BenchArgs) -> list[dict[str, Any]]: record_result=True, multimodal_processor_output=multimodal_processor_output, ) - assert result is not None + if result is None: + # Setting skipped (e.g. exceeds KV pool capacity); do not record. + continue _append_jsonl(args.result_filename, result) logger.info("Result: %s", json.dumps(result, sort_keys=True)) results.append(result) From 75589034b70a011b4a07162046357dc416df53d9 Mon Sep 17 00:00:00 2001 From: Dev Date: Thu, 4 Jun 2026 04:58:03 +0000 Subject: [PATCH 09/21] feat(bench): align profiling methodology with SGLang (#4) Rework profiling to match SGLang's bench_one_batch: - decode uses a single continuous profiler spanning [profile_start_step, profile_stop_step) -> one decode trace, instead of wrapping each step in its own profiler context (one file per step); - default profile start step is output_len // 2 (was (output_len-1)//2); - torch profiler runs with with_stack=True so kernels map to Python source; - traces are written as .trace.json.gz; - print the key_averages table on stop; - support --profile-activities CUDA_PROFILER to drive nsys via cudaProfilerStart/Stop. Replace the _maybe_profile context manager with start/stop helpers mirroring SGLang's start_profile/stop_profile. Co-Authored-By: Claude Opus 4.8 (1M context) --- pymllm/bench_one_batch.py | 183 +++++++++++++++++++++++++------------- 1 file changed, 122 insertions(+), 61 deletions(-) diff --git a/pymllm/bench_one_batch.py b/pymllm/bench_one_batch.py index ea6037134..6237a8a6e 100644 --- a/pymllm/bench_one_batch.py +++ b/pymllm/bench_one_batch.py @@ -14,7 +14,6 @@ import re import statistics import time -from contextlib import contextmanager, nullcontext from dataclasses import dataclass, field from pathlib import Path from typing import Any, Iterator, Optional, Sequence @@ -145,8 +144,12 @@ def add_bench_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser: group.add_argument( "--profile-activities", nargs="+", - choices=["CPU", "GPU"], + choices=["CPU", "GPU", "CUDA_PROFILER"], default=["CPU", "GPU"], + help=( + "CPU/GPU use the torch profiler; CUDA_PROFILER drives nsys via " + "cudaProfilerStart/Stop (use with nsys --capture-range=cudaProfilerApi)." + ), ) group.add_argument( "--profile-stage", @@ -398,7 +401,7 @@ def make_profile_trace_path( filename = ( f"{safe_prefix}_{safe_run_name}_bs{setting.batch_size}" f"_in{setting.input_len}_out{setting.output_len}_{stage}" - f"{step_part}.trace.json" + f"{step_part}.trace.json.gz" ) return output_dir / filename @@ -552,45 +555,79 @@ def _profiler_activities(args: BenchArgs) -> list[Any]: return activities -@contextmanager -def _maybe_profile( - *, - args: BenchArgs, - setting: BenchSetting, - stage: str, - step: Optional[int] = None, -) -> Iterator[None]: - if not _profile_stage_enabled(args, stage): - with nullcontext(): - yield - return +def _resolve_profile_output_dir() -> Path: + output_dir = Path(os.environ.get("PYMLLM_TORCH_PROFILER_DIR", "/tmp")) + output_dir.mkdir(parents=True, exist_ok=True) + return output_dir + + +def _start_profile(args: BenchArgs, trace_path: Path) -> Any: + """Start profiling and return a handle. + + Mirrors SGLang's ``start_profile``: ``CUDA_PROFILER`` drives nsys via + ``cudaProfilerStart``; otherwise a torch profiler with ``with_stack=True`` + is started so kernels can be mapped back to Python source. Returns + ``"cuda"`` for the nsys path, the profiler object for the torch path, or + ``None`` when no activity is available. ``trace_path`` is accepted for + symmetry with ``_stop_profile`` (the torch path saves it on stop). + """ + if "CUDA_PROFILER" in args.profile_activities: + try: + torch.cuda.cudart().cudaProfilerStart() + logger.info("CUDA profiler started (nsys will begin capturing).") + except Exception as exc: # pragma: no cover - depends on nsys runtime + logger.warning("Failed to start CUDA profiler: %s", exc) + return "cuda" activities = _profiler_activities(args) if not activities: - with nullcontext(): - yield - return + return None from torch.profiler import profile - output_dir = Path(os.environ.get("PYMLLM_TORCH_PROFILER_DIR", "/tmp")) - output_dir.mkdir(parents=True, exist_ok=True) - trace_path = make_profile_trace_path( - output_dir=output_dir, - prefix=args.profile_filename_prefix, - run_name=args.run_name, - setting=setting, - stage=stage, - step=step, - ) - with profile( + profiler = profile( activities=activities, + with_stack=True, record_shapes=args.profile_record_shapes, - ) as profiler: - yield - profiler.step() - profiler.export_chrome_trace(str(trace_path)) - logger.info("Wrote torch profiler trace: %s", trace_path) + ) + profiler.start() + return profiler + + +def _stop_profile(handle: Any, args: BenchArgs, trace_path: Path, stage: str) -> None: + """Stop profiling and, for the torch path, save the chrome trace. + + Mirrors SGLang's ``stop_profile``, including printing the key_averages + table. The trace is written as ``.trace.json.gz`` (torch gzips when the + filename ends with ``.gz``). + """ + if handle is None: + return + if handle == "cuda": + try: + torch.cuda.cudart().cudaProfilerStop() + logger.info("CUDA profiler stopped for %s (nsys dumps traces).", stage) + except Exception as exc: # pragma: no cover - depends on nsys runtime + logger.warning("Failed to stop CUDA profiler: %s", exc) + return + + handle.stop() + trace_path.parent.mkdir(parents=True, exist_ok=True) + handle.export_chrome_trace(str(trace_path)) + try: + sort_key = ( + "self_cuda_time_total" + if torch.cuda.is_available() + else "self_cpu_time_total" + ) + print( + handle.key_averages( + group_by_input_shape=args.profile_record_shapes + ).table(sort_by=sort_key) + ) + except Exception as exc: + logger.warning("Failed to print profiler key_averages: %s", exc) + logger.info("Wrote torch profiler trace for %s: %s", stage, trace_path) class PymllmBenchRunner: @@ -840,47 +877,66 @@ def run_single_setting( ) return None - with _maybe_profile(args=args, setting=effective_setting, stage="prefill"): - prefill_latency, extend_result = _timed_call( - bench_runner.device, - lambda: bench_runner.extend( - input_ids, - pixel_values=mm_input.pixel_values if mm_input is not None else None, - image_grid_thw=( - mm_input.image_grid_thw if mm_input is not None else None - ), - benchmark_vision_timing=mm_input is not None, - ), + prefill_profile = _profile_stage_enabled(args, "prefill") + prefill_trace: Optional[Path] = None + prefill_handle: Any = None + if prefill_profile: + prefill_trace = make_profile_trace_path( + output_dir=_resolve_profile_output_dir(), + prefix=args.profile_filename_prefix, + run_name=args.run_name, + setting=effective_setting, + stage="prefill", ) + prefill_handle = _start_profile(args, prefill_trace) + prefill_latency, extend_result = _timed_call( + bench_runner.device, + lambda: bench_runner.extend( + input_ids, + pixel_values=mm_input.pixel_values if mm_input is not None else None, + image_grid_thw=( + mm_input.image_grid_thw if mm_input is not None else None + ), + benchmark_vision_timing=mm_input is not None, + ), + ) + if prefill_profile: + _stop_profile(prefill_handle, args, prefill_trace, "prefill") next_token_ids, state = extend_result decode_latencies: list[float] = [] decode_steps = max(0, setting.output_len - 1) + decode_profile = _profile_stage_enabled(args, "decode") profile_start_step = args.profile_start_step if profile_start_step is None: - profile_start_step = decode_steps // 2 if decode_steps else 0 + # Align SGLang: default to output_len // 2. + profile_start_step = effective_setting.output_len // 2 profile_stop_step = profile_start_step + args.profile_steps + decode_trace: Optional[Path] = None + decode_handle: Any = None + # One continuous profiler spans [profile_start_step, profile_stop_step), + # producing a single decode trace, matching SGLang (not one file per step). for step in range(decode_steps): - should_profile_decode = ( - _profile_stage_enabled(args, "decode") - and profile_start_step <= step < profile_stop_step - ) - profile_context = ( - _maybe_profile( - args=args, + if decode_profile and step == profile_start_step: + decode_trace = make_profile_trace_path( + output_dir=_resolve_profile_output_dir(), + prefix=args.profile_filename_prefix, + run_name=args.run_name, setting=effective_setting, stage="decode", - step=step, ) - if should_profile_decode - else nullcontext() + decode_handle = _start_profile(args, decode_trace) + + decode_latency, decode_result = _timed_call( + bench_runner.device, + lambda: bench_runner.decode(next_token_ids, state), ) - with profile_context: - decode_latency, decode_result = _timed_call( - bench_runner.device, - lambda: bench_runner.decode(next_token_ids, state), - ) + + if decode_handle is not None and step >= profile_stop_step - 1: + _stop_profile(decode_handle, args, decode_trace, "decode") + decode_handle = None + next_token_ids, state = decode_result decode_latencies.append(decode_latency) @@ -892,6 +948,11 @@ def run_single_setting( decode_latency, ) + # Save if the requested profile window ran past the final decode step. + if decode_handle is not None: + _stop_profile(decode_handle, args, decode_trace, "decode") + decode_handle = None + if not record_result: return None From ac95cd6d7f749a5e0c4063b6ce60144acaae021b Mon Sep 17 00:00:00 2001 From: Dev Date: Thu, 4 Jun 2026 05:00:51 +0000 Subject: [PATCH 10/21] fix(bench): do not profile the warmup run (#2) run_single_setting wrapped both stages in profiling unconditionally, so --profile would also profile the non-recorded warmup run, wasting time and emitting extra traces. Add an allow_profile flag (default True) and pass allow_profile=False for the warmup call, matching SGLang which hardcodes profile=False during warmup. Co-Authored-By: Claude Opus 4.8 (1M context) --- pymllm/bench_one_batch.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/pymllm/bench_one_batch.py b/pymllm/bench_one_batch.py index 6237a8a6e..8dd5ea89f 100644 --- a/pymllm/bench_one_batch.py +++ b/pymllm/bench_one_batch.py @@ -831,6 +831,7 @@ def run_single_setting( seed: int, record_result: bool, multimodal_processor_output: Any = None, + allow_profile: bool = True, ) -> Optional[dict[str, Any]]: bench_runner.clear() vocab_size = getattr(bench_runner.runner, "vocab_size", 10000) @@ -877,7 +878,7 @@ def run_single_setting( ) return None - prefill_profile = _profile_stage_enabled(args, "prefill") + prefill_profile = allow_profile and _profile_stage_enabled(args, "prefill") prefill_trace: Optional[Path] = None prefill_handle: Any = None if prefill_profile: @@ -906,7 +907,7 @@ def run_single_setting( decode_latencies: list[float] = [] decode_steps = max(0, setting.output_len - 1) - decode_profile = _profile_stage_enabled(args, "decode") + decode_profile = allow_profile and _profile_stage_enabled(args, "decode") profile_start_step = args.profile_start_step if profile_start_step is None: # Align SGLang: default to output_len // 2. @@ -1042,6 +1043,7 @@ def run_benchmark(cfg: GlobalConfig, args: BenchArgs) -> list[dict[str, Any]]: seed=args.seed, record_result=False, multimodal_processor_output=multimodal_processor_output, + allow_profile=False, ) results: list[dict[str, Any]] = [] From 78c906dfef5ded1131110254194016b5e0ebea5e Mon Sep 17 00:00:00 2001 From: Dev Date: Thu, 4 Jun 2026 05:02:29 +0000 Subject: [PATCH 11/21] feat(bench): add single-stage correctness mode (#5) Add --correct (--correctness-test): load the tokenizer, encode real prompts, run one full prefill at batch_size=1, greedy-decode output_len tokens, and print the decoded text. main() branches to run_correctness when the flag is set. This is the single-stage variant. SGLang's --correct additionally does a cut_len two-stage prefill to exercise prefix-KV reuse; with greedy decoding the printed per-prompt text is identical either way. The two-stage variant can be layered on later (prepare_forward_batch_extend already accepts extend_prefix_lens > 0). Co-Authored-By: Claude Opus 4.8 (1M context) --- pymllm/bench_one_batch.py | 85 ++++++++++++++++++++++++++++++++++++++- 1 file changed, 84 insertions(+), 1 deletion(-) diff --git a/pymllm/bench_one_batch.py b/pymllm/bench_one_batch.py index 8dd5ea89f..8b7026afe 100644 --- a/pymllm/bench_one_batch.py +++ b/pymllm/bench_one_batch.py @@ -52,6 +52,7 @@ class BenchArgs: skip_warmup: bool = False image_path: Optional[Path] = None prompt: str = "Describe this image." + correctness_test: bool = False @dataclass @@ -193,6 +194,16 @@ def add_bench_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser: default=BenchArgs.prompt, help="Prompt text used with --image in multimodal benchmark mode.", ) + group.add_argument( + "--correct", + "--correctness-test", + dest="correctness_test", + action="store_true", + help=( + "Run a single-stage correctness check (encode real prompts, prefill, " + "greedy decode, print decoded text) instead of the latency benchmark." + ), + ) return parser @@ -225,6 +236,7 @@ def _bench_args_from_namespace(namespace: argparse.Namespace) -> BenchArgs: skip_warmup=namespace.skip_warmup, image_path=namespace.image_path, prompt=namespace.prompt, + correctness_test=namespace.correctness_test, ) @@ -1073,10 +1085,81 @@ def run_benchmark(cfg: GlobalConfig, args: BenchArgs) -> list[dict[str, Any]]: bench_runner.shutdown() +DEFAULT_CORRECTNESS_PROMPTS = ( + "The capital of France is", + "The capital of the United Kindom is", + "Today is a sunny day and I like", +) + + +def _load_tokenizer(cfg: GlobalConfig) -> Any: + if cfg.server.tokenizer_path is None: + raise ValueError("--server.tokenizer_path or --server.model_path is required") + + from transformers import AutoTokenizer + + return AutoTokenizer.from_pretrained( + str(cfg.server.tokenizer_path), + trust_remote_code=cfg.server.trust_remote_code, + ) + + +def correctness_test( + bench_runner: PymllmBenchRunner, + cfg: GlobalConfig, + args: BenchArgs, +) -> None: + """Single-stage correctness check. + + Encode a real prompt, run one full prefill at batch_size=1, greedy-decode + ``output_len`` tokens, and print the decoded text. Unlike SGLang's + ``--correct`` (which exercises a cut_len two-stage prefill to test prefix-KV + reuse), this runs each prompt as a single full prefill. Greedy decoding + makes the per-prompt output identical to SGLang's batched path. The cut_len + two-stage variant can be layered on later: ``prepare_forward_batch_extend`` + already accepts ``extend_prefix_lens > 0`` and ``req_to_token_pool.write`` + can pre-populate prefix KV indices. + """ + tokenizer = _load_tokenizer(cfg) + output_len = args.output_len[0] + prompts = list(DEFAULT_CORRECTNESS_PROMPTS) + + for idx, prompt in enumerate(prompts): + token_ids = list(tokenizer.encode(prompt)) + if not token_ids: + logger.warning("Prompt %d encoded to an empty token list, skipping.", idx) + continue + input_ids = torch.tensor( + [token_ids], dtype=torch.int32, device=bench_runner.device + ) + + bench_runner.clear() + next_token_ids, state = bench_runner.extend(input_ids) + output_ids = token_ids + [int(next_token_ids[0].item())] + for _ in range(max(0, output_len - 1)): + next_token_ids, state = bench_runner.decode(next_token_ids, state) + output_ids.append(int(next_token_ids[0].item())) + + print(f"========== Prompt {idx} ==========") + print(tokenizer.decode(output_ids), "\n") + + +def run_correctness(cfg: GlobalConfig, args: BenchArgs) -> None: + _load_hf_config(cfg) + bench_runner = PymllmBenchRunner.create(cfg) + try: + correctness_test(bench_runner, cfg, args) + finally: + bench_runner.shutdown() + + def main(argv: Optional[Sequence[str]] = None) -> None: cfg, args = parse_args(argv) _configure_logging(cfg.server.log_level) - run_benchmark(cfg, args) + if args.correctness_test: + run_correctness(cfg, args) + else: + run_benchmark(cfg, args) if __name__ == "__main__": From e33a5ef11f351a667d6c85d358f2d9c39c93486e Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Thu, 4 Jun 2026 09:47:38 +0000 Subject: [PATCH 12/21] test(bench): expect gzipped profiler traces --- pymllm/tests/test_bench_one_batch.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pymllm/tests/test_bench_one_batch.py b/pymllm/tests/test_bench_one_batch.py index 13a87fb72..e1ff2bc93 100644 --- a/pymllm/tests/test_bench_one_batch.py +++ b/pymllm/tests/test_bench_one_batch.py @@ -270,4 +270,7 @@ def test_make_profile_trace_path_is_deterministic_and_sanitized(tmp_path): ) assert path.parent == tmp_path - assert path.name == "pymllm_profile_qwen3_vl_w8a8_bs1_in256_out8_decode.trace.json" + assert ( + path.name + == "pymllm_profile_qwen3_vl_w8a8_bs1_in256_out8_decode.trace.json.gz" + ) From 8dcb2a1484ed508c36516b25870874198711044a Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Thu, 4 Jun 2026 09:48:14 +0000 Subject: [PATCH 13/21] test(bench): cover batched decode kv mapping --- pymllm/tests/test_bench_one_batch.py | 64 ++++++++++++++++++++++++++++ 1 file changed, 64 insertions(+) diff --git a/pymllm/tests/test_bench_one_batch.py b/pymllm/tests/test_bench_one_batch.py index e1ff2bc93..8c6dd13ef 100644 --- a/pymllm/tests/test_bench_one_batch.py +++ b/pymllm/tests/test_bench_one_batch.py @@ -8,6 +8,7 @@ from pymllm.bench_one_batch import ( BenchArgs, BenchSetting, + DecodeState, PymllmBenchRunner, generate_settings, make_multimodal_bench_input_from_processor_output, @@ -274,3 +275,66 @@ def test_make_profile_trace_path_is_deterministic_and_sanitized(tmp_path): path.name == "pymllm_profile_qwen3_vl_w8a8_bs1_in256_out8_decode.trace.json.gz" ) + + +def test_decode_writes_batch_kv_mapping_with_tensor_indices(): + from pymllm.mem_cache.memory_pool import ReqToTokenPool, TokenToKVPoolAllocator + + class _Runner: + def __init__(self): + self.device = "cpu" + self.dtype = torch.float32 + self.req_to_token_pool = ReqToTokenPool( + max_reqs=6, + max_context_len=8, + device="cpu", + ) + self.token_to_kv_pool_allocator = TokenToKVPoolAllocator( + size=32, + device="cpu", + ) + self.last_decode_kwargs = None + + def prepare_forward_batch_decode(self, **kwargs): + self.last_decode_kwargs = kwargs + return SimpleNamespace(batch_size=kwargs["req_pool_indices"].shape[0]) + + def forward(self, forward_batch): + del forward_batch + return object() + + def sample(self, logits_output, forward_batch, **kwargs): + del logits_output, forward_batch, kwargs + return torch.tensor([11, 12, 13], dtype=torch.int32) + + fake_runner = _Runner() + bench_runner = PymllmBenchRunner(fake_runner) + state = DecodeState( + req_pool_indices=torch.tensor([2, 0, 4], dtype=torch.int64), + seq_lens=torch.tensor([4, 2, 6], dtype=torch.int32), + ) + + next_token_ids, next_state = bench_runner.decode( + torch.tensor([1, 2, 3], dtype=torch.int32), + state, + ) + + torch.testing.assert_close( + fake_runner.req_to_token_pool.req_to_token[ + torch.tensor([2, 0, 4], dtype=torch.int64), + torch.tensor([4, 2, 6], dtype=torch.int64), + ], + torch.tensor([1, 2, 3], dtype=torch.int32), + ) + torch.testing.assert_close( + next_state.seq_lens, + torch.tensor([5, 3, 7], dtype=torch.int32), + ) + torch.testing.assert_close( + fake_runner.last_decode_kwargs["out_cache_loc"], + torch.tensor([1, 2, 3], dtype=torch.int64), + ) + torch.testing.assert_close( + next_token_ids, + torch.tensor([11, 12, 13], dtype=torch.int32), + ) From e7ed9490a9b7370c1139fa82327a0b9b19716a60 Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Thu, 4 Jun 2026 09:48:44 +0000 Subject: [PATCH 14/21] docs(bench): label correct mode as smoke check --- pymllm/bench_one_batch.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/pymllm/bench_one_batch.py b/pymllm/bench_one_batch.py index 8b7026afe..a40cc4c50 100644 --- a/pymllm/bench_one_batch.py +++ b/pymllm/bench_one_batch.py @@ -200,8 +200,9 @@ def add_bench_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser: dest="correctness_test", action="store_true", help=( - "Run a single-stage correctness check (encode real prompts, prefill, " - "greedy decode, print decoded text) instead of the latency benchmark." + "Run a single-stage smoke correctness check (encode real prompts, " + "prefill, greedy decode, print decoded text) instead of the " + "latency benchmark." ), ) return parser @@ -1109,7 +1110,7 @@ def correctness_test( cfg: GlobalConfig, args: BenchArgs, ) -> None: - """Single-stage correctness check. + """Single-stage smoke correctness check. Encode a real prompt, run one full prefill at batch_size=1, greedy-decode ``output_len`` tokens, and print the decoded text. Unlike SGLang's From 75a67e50af483234d67086860f9c1ca3ac731f8b Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Sun, 7 Jun 2026 06:05:31 +0000 Subject: [PATCH 15/21] feat(bench): sweep multimodal prefill input length --- pymllm/bench_one_batch.py | 181 +++++++++++++++++++++++---- pymllm/tests/test_bench_one_batch.py | 133 ++++++++++++++++++++ 2 files changed, 288 insertions(+), 26 deletions(-) diff --git a/pymllm/bench_one_batch.py b/pymllm/bench_one_batch.py index a40cc4c50..6512310a6 100644 --- a/pymllm/bench_one_batch.py +++ b/pymllm/bench_one_batch.py @@ -13,6 +13,7 @@ import os import re import statistics +import sys import time from dataclasses import dataclass, field from pathlib import Path @@ -52,6 +53,7 @@ class BenchArgs: skip_warmup: bool = False image_path: Optional[Path] = None prompt: str = "Describe this image." + input_len_was_provided: bool = False correctness_test: bool = False @@ -84,6 +86,12 @@ class MultimodalBenchInput: vit_prefill_tokens: int +@dataclass +class MultimodalProcessorBundle: + processor_output: Any + pad_token_id: int + + def _positive_int(value: str) -> int: parsed = int(value) if parsed <= 0: @@ -186,7 +194,9 @@ def add_bench_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser: default=None, help=( "Optional image path for multimodal benchmark mode. When set, " - "--input-len is replaced by the processed prompt length." + "omitted --input-len uses the processed prompt length; explicit " + "--input-len sweeps target total multimodal prefill length " + "(image placeholder tokens + text tokens)." ), ) group.add_argument( @@ -218,7 +228,15 @@ def make_parser() -> argparse.ArgumentParser: return parser -def _bench_args_from_namespace(namespace: argparse.Namespace) -> BenchArgs: +def _argv_has_option(argv: Sequence[str], option: str) -> bool: + return any(arg == option or arg.startswith(f"{option}=") for arg in argv) + + +def _bench_args_from_namespace( + namespace: argparse.Namespace, + *, + input_len_was_provided: bool = False, +) -> BenchArgs: return BenchArgs( run_name=namespace.run_name, batch_size=list(namespace.batch_size), @@ -237,6 +255,7 @@ def _bench_args_from_namespace(namespace: argparse.Namespace) -> BenchArgs: skip_warmup=namespace.skip_warmup, image_path=namespace.image_path, prompt=namespace.prompt, + input_len_was_provided=input_len_was_provided, correctness_test=namespace.correctness_test, ) @@ -245,13 +264,21 @@ def parse_args( argv: Optional[Sequence[str]] = None, ) -> tuple[GlobalConfig, BenchArgs]: parser = make_parser() - cfg = read_args(argv=argv, parser=parser) - namespace = parser.parse_args(argv) - return cfg, _bench_args_from_namespace(namespace) + cli_argv = list(sys.argv[1:] if argv is None else argv) + cfg = read_args(argv=cli_argv, parser=parser) + namespace = parser.parse_args(cli_argv) + return cfg, _bench_args_from_namespace( + namespace, + input_len_was_provided=_argv_has_option(cli_argv, "--input-len"), + ) def generate_settings(args: BenchArgs) -> list[BenchSetting]: - input_lens = [0] if args.image_path is not None else args.input_len + input_lens = ( + [0] + if args.image_path is not None and not args.input_len_was_provided + else args.input_len + ) return [ BenchSetting(batch_size=batch_size, input_len=input_len, output_len=output_len) for batch_size in args.batch_size @@ -342,18 +369,90 @@ def make_vit_prefill_metrics( } +def make_multimodal_prefill_metrics( + *, + prefill_latency: float, + batch_size: int, + input_len: int, +) -> dict[str, Any]: + tokens = int(batch_size) * int(input_len) + throughput = _safe_div(float(tokens), float(prefill_latency)) + return { + "multimodal_prefill_latency": float(prefill_latency), + "multimodal_prefill_ms": float(prefill_latency) * 1000.0, + "multimodal_prefill_tokens": tokens, + "multimodal_prefill_throughput": throughput, + "multimodal_prefill_tps": throughput, + } + + def _get_processor_value(processor_output: Any, key: str) -> Any: if hasattr(processor_output, "get"): return processor_output.get(key) return getattr(processor_output, key, None) +def _resize_multimodal_input_ids( + input_ids: torch.Tensor, + *, + target_input_len: int, + image_token_id: int, + pad_token_id: int, +) -> torch.Tensor: + if int(pad_token_id) == int(image_token_id): + pad_token_id = 0 if int(image_token_id) != 0 else 1 + if target_input_len <= 0: + raise ValueError( + f"target_input_len must be positive, got {target_input_len}" + ) + if input_ids.dim() != 2 or input_ids.shape[0] != 1: + raise ValueError( + "bench_one_batch multimodal resize expects input_ids shape [1, seq_len], " + f"got {tuple(input_ids.shape)}" + ) + + seq = input_ids[0] + image_mask = seq == image_token_id + image_token_count = int(image_mask.sum().item()) + if target_input_len < image_token_count: + raise ValueError( + "target_input_len must be at least the number of image tokens " + f"({image_token_count}), got {target_input_len}" + ) + if int(seq.numel()) == target_input_len: + return input_ids + + text_budget = target_input_len - image_token_count + resized_tokens: list[int] = [] + kept_text = 0 + for token in seq.tolist(): + token_id = int(token) + if token_id == image_token_id: + resized_tokens.append(token_id) + elif kept_text < text_budget: + resized_tokens.append(token_id) + kept_text += 1 + if len(resized_tokens) == target_input_len: + break + + if len(resized_tokens) < target_input_len: + resized_tokens.extend([int(pad_token_id)] * (target_input_len - len(resized_tokens))) + + return torch.tensor( + [resized_tokens], + dtype=input_ids.dtype, + device=input_ids.device, + ) + + def make_multimodal_bench_input_from_processor_output( processor_output: Any, *, batch_size: int, image_token_id: int, device: str | torch.device, + target_input_len: Optional[int] = None, + pad_token_id: int = 0, ) -> MultimodalBenchInput: input_ids = _get_processor_value(processor_output, "input_ids") pixel_values = _get_processor_value(processor_output, "pixel_values") @@ -376,6 +475,13 @@ def make_multimodal_bench_input_from_processor_output( "bench_one_batch multimodal mode expects one processed prompt before " f"batch repetition, got batch dimension {input_ids_t.shape[0]}" ) + if target_input_len is not None: + input_ids_t = _resize_multimodal_input_ids( + input_ids_t, + target_input_len=target_input_len, + image_token_id=image_token_id, + pad_token_id=pad_token_id, + ) input_ids_t = input_ids_t.repeat(batch_size, 1).to( device=device, dtype=torch.int32 @@ -522,7 +628,7 @@ def _make_multimodal_processor_output( cfg: GlobalConfig, prompt: str, image_path: Path, -) -> Any: +) -> MultimodalProcessorBundle: if cfg.server.tokenizer_path is None: raise ValueError("--server.tokenizer_path or --server.model_path is required") if not image_path.exists(): @@ -535,13 +641,22 @@ def _make_multimodal_processor_output( str(cfg.server.tokenizer_path), trust_remote_code=cfg.server.trust_remote_code, ) + tokenizer = getattr(processor, "tokenizer", None) + pad_token_id = getattr(tokenizer, "pad_token_id", None) + if pad_token_id is None: + pad_token_id = getattr(tokenizer, "eos_token_id", None) + if pad_token_id is None: + pad_token_id = 0 image = Image.open(image_path).convert("RGB") text = _render_multimodal_prompt( processor, prompt=prompt, image_path=image_path, ) - return processor(images=[image], text=[text], return_tensors="pt") + return MultimodalProcessorBundle( + processor_output=processor(images=[image], text=[text], return_tensors="pt"), + pad_token_id=int(pad_token_id), + ) def _append_jsonl(path: Path, row: dict[str, Any]) -> None: @@ -843,14 +958,14 @@ def run_single_setting( setting: BenchSetting, seed: int, record_result: bool, - multimodal_processor_output: Any = None, + multimodal_processor_bundle: Optional[MultimodalProcessorBundle] = None, allow_profile: bool = True, ) -> Optional[dict[str, Any]]: bench_runner.clear() vocab_size = getattr(bench_runner.runner, "vocab_size", 10000) mm_input = None effective_setting = setting - if multimodal_processor_output is None: + if multimodal_processor_bundle is None: input_ids = make_synthetic_input_ids( batch_size=setting.batch_size, input_len=setting.input_len, @@ -862,10 +977,16 @@ def run_single_setting( hf_config = bench_runner.runner.model_config.hf_config image_token_id = _extract_image_token_id(hf_config) mm_input = make_multimodal_bench_input_from_processor_output( - multimodal_processor_output, + multimodal_processor_bundle.processor_output, batch_size=setting.batch_size, image_token_id=image_token_id, device=bench_runner.device, + target_input_len=( + setting.input_len + if args.input_len_was_provided + else None + ), + pad_token_id=multimodal_processor_bundle.pad_token_id, ) input_ids = mm_input.input_ids effective_setting = BenchSetting( @@ -970,6 +1091,24 @@ def run_single_setting( if not record_result: return None + extra_metrics = None + if mm_input is not None: + extra_metrics = make_multimodal_prefill_metrics( + prefill_latency=prefill_latency, + batch_size=effective_setting.batch_size, + input_len=effective_setting.input_len, + ) + if ( + extend_result.vit_prefill_ms is not None + and extend_result.vit_prefill_tokens is not None + ): + extra_metrics.update( + make_vit_prefill_metrics( + vit_prefill_ms=extend_result.vit_prefill_ms, + vit_prefill_tokens=extend_result.vit_prefill_tokens, + ) + ) + return summarize_latencies( setting=effective_setting, prefill_latency=prefill_latency, @@ -978,17 +1117,7 @@ def run_single_setting( device=bench_runner.device, dtype=str(bench_runner.runner.dtype), cuda_graph=bench_runner.runner.graph_runner is not None, - extra=( - make_vit_prefill_metrics( - vit_prefill_ms=extend_result.vit_prefill_ms, - vit_prefill_tokens=extend_result.vit_prefill_tokens, - ) - if ( - extend_result.vit_prefill_ms is not None - and extend_result.vit_prefill_tokens is not None - ) - else None - ), + extra=extra_metrics, ) @@ -1029,9 +1158,9 @@ def run_benchmark(cfg: GlobalConfig, args: BenchArgs) -> list[dict[str, Any]]: bench_runner = PymllmBenchRunner.create(cfg) try: settings = generate_settings(args) - multimodal_processor_output = None + multimodal_processor_bundle = None if args.image_path is not None: - multimodal_processor_output = _make_multimodal_processor_output( + multimodal_processor_bundle = _make_multimodal_processor_output( cfg=cfg, prompt=args.prompt, image_path=args.image_path, @@ -1055,7 +1184,7 @@ def run_benchmark(cfg: GlobalConfig, args: BenchArgs) -> list[dict[str, Any]]: setting=warmup_setting, seed=args.seed, record_result=False, - multimodal_processor_output=multimodal_processor_output, + multimodal_processor_bundle=multimodal_processor_bundle, allow_profile=False, ) @@ -1073,7 +1202,7 @@ def run_benchmark(cfg: GlobalConfig, args: BenchArgs) -> list[dict[str, Any]]: setting=setting, seed=args.seed + index, record_result=True, - multimodal_processor_output=multimodal_processor_output, + multimodal_processor_bundle=multimodal_processor_bundle, ) if result is None: # Setting skipped (e.g. exceeds KV pool capacity); do not record. diff --git a/pymllm/tests/test_bench_one_batch.py b/pymllm/tests/test_bench_one_batch.py index 8c6dd13ef..36e9afd71 100644 --- a/pymllm/tests/test_bench_one_batch.py +++ b/pymllm/tests/test_bench_one_batch.py @@ -78,6 +78,24 @@ def test_parse_args_accepts_server_config_and_list_bench_args(tmp_path): assert bench_args.profile_activities == ["CPU", "GPU"] assert bench_args.image_path == tmp_path / "image.jpg" assert bench_args.prompt == "What is in this image?" + assert bench_args.input_len_was_provided is True + + +def test_parse_args_detects_default_input_len_when_omitted_in_image_mode(tmp_path): + model_dir = tmp_path / "model" + model_dir.mkdir() + + _, bench_args = parse_args( + [ + "--server.model_path", + str(model_dir), + "--image", + str(tmp_path / "image.jpg"), + ] + ) + + assert bench_args.input_len == [256, 512, 1024] + assert bench_args.input_len_was_provided is False def test_generate_settings_has_stable_batch_input_output_order(tmp_path): @@ -111,6 +129,24 @@ def test_generate_settings_uses_processed_prompt_length_for_image_mode(tmp_path) ] +def test_generate_settings_sweeps_input_len_when_explicit_in_image_mode(tmp_path): + args = BenchArgs( + batch_size=[1, 2], + input_len=[512, 1024], + output_len=[8], + result_filename=tmp_path / "out.jsonl", + image_path=tmp_path / "image.jpg", + input_len_was_provided=True, + ) + + assert generate_settings(args) == [ + BenchSetting(batch_size=1, input_len=512, output_len=8), + BenchSetting(batch_size=1, input_len=1024, output_len=8), + BenchSetting(batch_size=2, input_len=512, output_len=8), + BenchSetting(batch_size=2, input_len=1024, output_len=8), + ] + + def test_make_synthetic_input_ids_is_seeded_int32_and_vocab_capped(): first = make_synthetic_input_ids( batch_size=2, @@ -174,6 +210,24 @@ def test_make_vit_prefill_metrics_reports_seconds_and_tps(): } +def test_make_multimodal_prefill_metrics_aliases_full_prefill_latency(): + from pymllm.bench_one_batch import make_multimodal_prefill_metrics + + result = make_multimodal_prefill_metrics( + prefill_latency=0.25, + batch_size=2, + input_len=128, + ) + + assert result == { + "multimodal_prefill_latency": pytest.approx(0.25), + "multimodal_prefill_ms": pytest.approx(250.0), + "multimodal_prefill_tokens": 256, + "multimodal_prefill_throughput": pytest.approx(1024.0), + "multimodal_prefill_tps": pytest.approx(1024.0), + } + + def test_make_multimodal_bench_input_repeats_processor_output_per_batch(): bench_input = make_multimodal_bench_input_from_processor_output( { @@ -199,6 +253,85 @@ def test_make_multimodal_bench_input_repeats_processor_output_per_batch(): ) +def test_make_multimodal_bench_input_pads_text_tokens_to_target_len(): + bench_input = make_multimodal_bench_input_from_processor_output( + { + "input_ids": torch.tensor([[101, 5, 5, 102]], dtype=torch.int64), + "pixel_values": torch.arange(6, dtype=torch.float32).reshape(2, 3), + "image_grid_thw": torch.tensor([[1, 2, 2]], dtype=torch.int64), + }, + batch_size=1, + image_token_id=5, + device="cpu", + target_input_len=7, + pad_token_id=0, + ) + + torch.testing.assert_close( + bench_input.input_ids, + torch.tensor([[101, 5, 5, 102, 0, 0, 0]], dtype=torch.int32), + ) + assert bench_input.vit_prefill_tokens == 2 + + +def test_make_multimodal_bench_input_does_not_pad_with_image_token_id(): + bench_input = make_multimodal_bench_input_from_processor_output( + { + "input_ids": torch.tensor([[101, 5, 5, 102]], dtype=torch.int64), + "pixel_values": torch.arange(6, dtype=torch.float32).reshape(2, 3), + "image_grid_thw": torch.tensor([[1, 2, 2]], dtype=torch.int64), + }, + batch_size=1, + image_token_id=5, + device="cpu", + target_input_len=6, + pad_token_id=5, + ) + + torch.testing.assert_close( + bench_input.input_ids, + torch.tensor([[101, 5, 5, 102, 0, 0]], dtype=torch.int32), + ) + assert bench_input.vit_prefill_tokens == 2 + + +def test_make_multimodal_bench_input_truncates_text_tokens_not_image_tokens(): + bench_input = make_multimodal_bench_input_from_processor_output( + { + "input_ids": torch.tensor([[101, 7, 5, 5, 102, 103]], dtype=torch.int64), + "pixel_values": torch.arange(6, dtype=torch.float32).reshape(2, 3), + "image_grid_thw": torch.tensor([[1, 2, 2]], dtype=torch.int64), + }, + batch_size=1, + image_token_id=5, + device="cpu", + target_input_len=4, + pad_token_id=0, + ) + + torch.testing.assert_close( + bench_input.input_ids, + torch.tensor([[101, 7, 5, 5]], dtype=torch.int32), + ) + assert bench_input.vit_prefill_tokens == 2 + + +def test_make_multimodal_bench_input_rejects_target_shorter_than_image_tokens(): + with pytest.raises(ValueError, match="target_input_len"): + make_multimodal_bench_input_from_processor_output( + { + "input_ids": torch.tensor([[5, 5, 5, 102]], dtype=torch.int64), + "pixel_values": torch.arange(9, dtype=torch.float32).reshape(3, 3), + "image_grid_thw": torch.tensor([[1, 3, 1]], dtype=torch.int64), + }, + batch_size=1, + image_token_id=5, + device="cpu", + target_input_len=2, + pad_token_id=0, + ) + + def test_extend_attaches_multimodal_inputs_and_returns_vit_metrics(): class _ReqPool: def alloc(self, batch_size): From 2dae7cee4eadfdba3af679f2cb9fb16528938ce4 Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Sun, 7 Jun 2026 06:59:41 +0000 Subject: [PATCH 16/21] perf(memory): refine static KV pool profiling --- pymllm/executor/model_runner.py | 79 +++++++++--- pymllm/tests/test_model_runner_memory_pool.py | 114 ++++++++++++++++++ 2 files changed, 179 insertions(+), 14 deletions(-) create mode 100644 pymllm/tests/test_model_runner_memory_pool.py diff --git a/pymllm/executor/model_runner.py b/pymllm/executor/model_runner.py index 4851c8a73..081788cf8 100644 --- a/pymllm/executor/model_runner.py +++ b/pymllm/executor/model_runner.py @@ -37,6 +37,7 @@ import gc import logging +import os import time from dataclasses import dataclass from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union @@ -79,6 +80,9 @@ def get_available_gpu_memory(device: str = "cuda", gpu_id: int = 0) -> float: if device != "cuda" or not torch.cuda.is_available(): return 0.0 torch.cuda.set_device(gpu_id) + props = torch.cuda.get_device_properties(gpu_id) + if getattr(props, "is_integrated", False): + return _get_system_available_memory_gb() free, _ = torch.cuda.mem_get_info(gpu_id) return free / (1 << 30) @@ -92,6 +96,26 @@ def get_total_gpu_memory(device: str = "cuda", gpu_id: int = 0) -> float: return total / (1 << 30) +def _get_system_available_memory_gb() -> float: + try: + import psutil + + return psutil.virtual_memory().available / (1 << 30) + except Exception: + pass + + if hasattr(os, "sysconf"): + try: + page_size = os.sysconf("SC_PAGE_SIZE") + avail_pages = os.sysconf("SC_AVPHYS_PAGES") + return (page_size * avail_pages) / (1 << 30) + except (OSError, ValueError): + pass + + free, _ = torch.cuda.mem_get_info() + return free / (1 << 30) + + # --------------------------------------------------------------------------- # LogitsProcessorOutput # --------------------------------------------------------------------------- @@ -274,6 +298,11 @@ def __init__( # Forward pass counter (monotonically increasing). self.forward_pass_id: int = 0 + # GPU memory available before model weights are loaded. This is used to + # match SGLang's mem_fraction_static semantics: static memory includes + # both model weights and the KV cache pool. + self._pre_model_load_available_gb: float = 0.0 + # ------------------------------------------------------------------ # Initialisation # ------------------------------------------------------------------ @@ -301,6 +330,11 @@ def initialize(self) -> None: torch.set_default_dtype(self.dtype) # Load the model + if self.device == "cuda": + self._pre_model_load_available_gb = get_available_gpu_memory( + self.device, + self.gpu_id, + ) self.load_model() # Extract model metadata from hf_config @@ -705,7 +739,8 @@ def init_memory_pool(self) -> None: if self.max_total_num_tokens <= 0: raise RuntimeError( "Not enough memory for KV cache. " - "Try reducing context_length or using a smaller model." + "Try increasing mem_fraction_static, reducing context_length, " + "or using a smaller model." ) # Create ReqToTokenPool @@ -774,24 +809,26 @@ def init_memory_pool(self) -> None: def _profile_max_num_tokens(self) -> int: """Profile available memory to determine maximum KV-cache tokens. - If ``server_config.max_total_tokens`` is explicitly set that value - is used directly. Otherwise a memory-fraction-based heuristic - similar to sglang's ``profile_max_num_token`` is applied. + ``mem_fraction_static`` follows SGLang's semantics: it is the fraction + of total static memory budget used by model weights plus KV cache pool. + ``server_config.max_total_tokens`` is an upper bound on the profiled + capacity, not a replacement for profiling. """ - # If user explicitly set max_total_tokens, use that. - if self.server_config.max_total_tokens is not None: - return self.server_config.max_total_tokens - if self.device != "cuda": # For CPU, use a conservative default. + if self.server_config.max_total_tokens is not None: + return self.server_config.max_total_tokens return 4096 available_gb = get_available_gpu_memory(self.device, self.gpu_id) + pre_model_available_gb = getattr(self, "_pre_model_load_available_gb", 0.0) + if pre_model_available_gb <= 0: + pre_model_available_gb = available_gb - # Determine memory fraction for static allocation (KV cache). + # Determine memory fraction for static allocation (model weights + KV cache). mem_fraction = self.server_config.mem_fraction_static if mem_fraction is None: - mem_fraction = 0.85 # default: use 85% of remaining memory + mem_fraction = 0.85 # Calculate per-token KV cache size in bytes. kv_element_size = torch.tensor([], dtype=self.kv_cache_dtype).element_size() @@ -809,7 +846,8 @@ def _profile_max_num_tokens(self) -> int: ) return 4096 - rest_memory_bytes = int(available_gb * mem_fraction * (1 << 30)) + rest_memory_gb = available_gb - pre_model_available_gb * (1 - mem_fraction) + rest_memory_bytes = int(rest_memory_gb * (1 << 30)) # Reserve memory for GDN pool if hybrid model if self.num_gdn_layers > 0: @@ -863,16 +901,29 @@ def _profile_max_num_tokens(self) -> int: max_num_tokens = rest_memory_bytes // cell_size + if self.server_config.max_total_tokens is not None: + if self.server_config.max_total_tokens > max_num_tokens: + logger.warning( + "max_total_tokens=%d is larger than the profiled value %d. " + "Use the profiled value instead.", + self.server_config.max_total_tokens, + max_num_tokens, + ) + max_num_tokens = min(max_num_tokens, self.server_config.max_total_tokens) + logger.info( - "Memory profiling: avail=%.2f GB, fraction=%.2f, " - "cell_size=%d bytes, max_tokens=%d", + "Memory profiling: pre_model_avail=%.2f GB, avail=%.2f GB, " + "fraction=%.2f, static_kv_budget=%.2f GB, cell_size=%d bytes, " + "max_tokens=%d", + pre_model_available_gb, available_gb, mem_fraction, + rest_memory_gb, cell_size, max_num_tokens, ) - return max(max_num_tokens, 1) # at least 1 + return max_num_tokens # ------------------------------------------------------------------ # Attention backend diff --git a/pymllm/tests/test_model_runner_memory_pool.py b/pymllm/tests/test_model_runner_memory_pool.py new file mode 100644 index 000000000..db4a816ef --- /dev/null +++ b/pymllm/tests/test_model_runner_memory_pool.py @@ -0,0 +1,114 @@ +from __future__ import annotations + +from types import SimpleNamespace + +import pytest +import torch + +from pymllm.executor import model_runner +from pymllm.executor.model_runner import ModelRunner + + +def _make_runner( + *, + mem_fraction_static: float, + max_total_tokens: int | None = None, + free_gb: float = 5.0, + total_gb: float = 10.0, +) -> SimpleNamespace: + return SimpleNamespace( + server_config=SimpleNamespace( + mem_fraction_static=mem_fraction_static, + max_total_tokens=max_total_tokens, + ), + device="cuda", + gpu_id=0, + kv_cache_dtype=torch.float16, + num_kv_heads=1, + head_dim=1, + v_head_dim=1, + num_hidden_layers=1, + num_gdn_layers=0, + _pre_model_load_available_gb=total_gb, + _current_available_gb=free_gb, + ) + + +@pytest.fixture +def _cuda_available(monkeypatch): + monkeypatch.setattr(model_runner.torch.cuda, "is_available", lambda: True) + + +def test_profile_max_num_tokens_treats_mem_fraction_static_as_static_pool_fraction( + _cuda_available, + monkeypatch, +): + runner = _make_runner(mem_fraction_static=0.4, free_gb=5.0, total_gb=10.0) + monkeypatch.setattr( + model_runner, + "get_available_gpu_memory", + lambda *args, **kwargs: runner._current_available_gb, + ) + + # SGLang-compatible formula: + # free_after_model - pre_model_free * (1 - mem_fraction_static) + # = 5 GiB - 10 GiB * 0.6 = -1 GiB, so the pool is not allocatable. + assert ModelRunner._profile_max_num_tokens(runner) <= 0 + + +def test_profile_max_num_tokens_caps_user_max_total_tokens_to_profiled_capacity( + _cuda_available, + monkeypatch, +): + runner = _make_runner( + mem_fraction_static=0.8, + max_total_tokens=8_000_000_000, + free_gb=5.0, + total_gb=10.0, + ) + monkeypatch.setattr( + model_runner, + "get_available_gpu_memory", + lambda *args, **kwargs: runner._current_available_gb, + ) + + # Profiled capacity is 3 GiB / 4 bytes per token. A larger user limit should + # not bypass profiling or force an oversized KV pool allocation. + assert ModelRunner._profile_max_num_tokens(runner) == 805_306_368 + + +def test_profile_max_num_tokens_uses_user_limit_when_below_profiled_capacity( + _cuda_available, + monkeypatch, +): + runner = _make_runner( + mem_fraction_static=0.8, + max_total_tokens=4096, + free_gb=5.0, + total_gb=10.0, + ) + monkeypatch.setattr( + model_runner, + "get_available_gpu_memory", + lambda *args, **kwargs: runner._current_available_gb, + ) + + assert ModelRunner._profile_max_num_tokens(runner) == 4096 + + +def test_available_gpu_memory_uses_system_memory_for_integrated_gpu(monkeypatch): + monkeypatch.setattr(model_runner.torch.cuda, "is_available", lambda: True) + monkeypatch.setattr(model_runner.torch.cuda, "set_device", lambda gpu_id: None) + monkeypatch.setattr( + model_runner.torch.cuda, + "get_device_properties", + lambda gpu_id: SimpleNamespace(is_integrated=True), + ) + monkeypatch.setattr(model_runner, "_get_system_available_memory_gb", lambda: 7.5) + + def fail_mem_get_info(*args, **kwargs): + raise AssertionError("integrated GPU memory should use system available memory") + + monkeypatch.setattr(model_runner.torch.cuda, "mem_get_info", fail_mem_get_info) + + assert model_runner.get_available_gpu_memory("cuda", 0) == 7.5 From 7eb9502920ee36c9695d731936fc29e2396d709a Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Sun, 7 Jun 2026 14:37:25 +0000 Subject: [PATCH 17/21] perf(memory): improve KV cache budget diagnostics --- pymllm/executor/model_runner.py | 95 +++++++++++++++++-- pymllm/tests/test_model_runner_memory_pool.py | 53 +++++++++++ 2 files changed, 141 insertions(+), 7 deletions(-) diff --git a/pymllm/executor/model_runner.py b/pymllm/executor/model_runner.py index 081788cf8..7e10bf818 100644 --- a/pymllm/executor/model_runner.py +++ b/pymllm/executor/model_runner.py @@ -139,6 +139,19 @@ class LogitsProcessorOutput: hidden_states: Optional[torch.Tensor] = None +@dataclass +class MemoryProfileResult: + pre_model_available_gb: float + available_gb: float + mem_fraction: float + static_kv_budget_gb: float + cell_size_bytes: int + profiled_max_tokens: int + requested_max_total_tokens: Optional[int] + effective_max_tokens: int + gdn_pool_gb: float = 0.0 + + # --------------------------------------------------------------------------- # Penalty helpers # --------------------------------------------------------------------------- @@ -302,6 +315,7 @@ def __init__( # match SGLang's mem_fraction_static semantics: static memory includes # both model weights and the KV cache pool. self._pre_model_load_available_gb: float = 0.0 + self._last_memory_profile: Optional[MemoryProfileResult] = None # ------------------------------------------------------------------ # Initialisation @@ -737,11 +751,7 @@ def init_memory_pool(self) -> None: self.max_running_requests = max_reqs if self.max_total_num_tokens <= 0: - raise RuntimeError( - "Not enough memory for KV cache. " - "Try increasing mem_fraction_static, reducing context_length, " - "or using a smaller model." - ) + raise RuntimeError(self._format_kv_cache_memory_error()) # Create ReqToTokenPool self.req_to_token_pool = make_req_to_token_pool( @@ -848,6 +858,7 @@ def _profile_max_num_tokens(self) -> int: rest_memory_gb = available_gb - pre_model_available_gb * (1 - mem_fraction) rest_memory_bytes = int(rest_memory_gb * (1 << 30)) + gdn_pool_gb = 0.0 # Reserve memory for GDN pool if hybrid model if self.num_gdn_layers > 0: @@ -894,12 +905,14 @@ def _profile_max_num_tokens(self) -> int: ) gdn_pool_bytes = recurrent_bytes + conv_bytes rest_memory_bytes -= gdn_pool_bytes + gdn_pool_gb = gdn_pool_bytes / (1 << 30) logger.info( "GDN pool memory reservation: %.2f GB", - gdn_pool_bytes / (1 << 30), + gdn_pool_gb, ) - max_num_tokens = rest_memory_bytes // cell_size + profiled_max_tokens = max(rest_memory_bytes // cell_size, 0) + max_num_tokens = profiled_max_tokens if self.server_config.max_total_tokens is not None: if self.server_config.max_total_tokens > max_num_tokens: @@ -911,6 +924,18 @@ def _profile_max_num_tokens(self) -> int: ) max_num_tokens = min(max_num_tokens, self.server_config.max_total_tokens) + self._last_memory_profile = MemoryProfileResult( + pre_model_available_gb=pre_model_available_gb, + available_gb=available_gb, + mem_fraction=mem_fraction, + static_kv_budget_gb=rest_memory_gb, + cell_size_bytes=cell_size, + profiled_max_tokens=profiled_max_tokens, + requested_max_total_tokens=self.server_config.max_total_tokens, + effective_max_tokens=max_num_tokens, + gdn_pool_gb=gdn_pool_gb, + ) + logger.info( "Memory profiling: pre_model_avail=%.2f GB, avail=%.2f GB, " "fraction=%.2f, static_kv_budget=%.2f GB, cell_size=%d bytes, " @@ -925,6 +950,62 @@ def _profile_max_num_tokens(self) -> int: return max_num_tokens + def _format_kv_cache_memory_error(self) -> str: + profile = getattr(self, "_last_memory_profile", None) + if profile is None: + return ( + "Not enough memory for KV cache. Try increasing " + "--server.mem_fraction_static, reducing --server.max_total_tokens, " + "lowering --server.max_running_requests, or using a " + "smaller/quantized model." + ) + + requested = ( + "unset" + if profile.requested_max_total_tokens is None + else str(profile.requested_max_total_tokens) + ) + message = [ + "Not enough memory for KV cache.", + ( + "Memory profile: " + f"pre_model_avail={profile.pre_model_available_gb:.2f} GB, " + f"avail_after_model={profile.available_gb:.2f} GB, " + f"mem_fraction_static={profile.mem_fraction:.2f}, " + f"static_kv_budget={profile.static_kv_budget_gb:.2f} GB, " + f"cell_size={profile.cell_size_bytes} bytes, " + f"profiled max_tokens={profile.profiled_max_tokens}, " + f"requested max_total_tokens={requested}." + ), + ] + if profile.static_kv_budget_gb <= 0: + message.append( + "The static KV budget is non-positive, so model weights and " + "other static allocations already exceed the requested static " + "memory fraction." + ) + if profile.gdn_pool_gb > 0: + message.append(f"GDN pool reservation={profile.gdn_pool_gb:.2f} GB.") + if ( + profile.requested_max_total_tokens is not None + and profile.profiled_max_tokens < profile.requested_max_total_tokens + ): + message.append( + "The requested max_total_tokens is above the profiled KV " + "capacity for this launch." + ) + message.append( + "Try increasing --server.mem_fraction_static, reducing " + "--server.max_total_tokens, lowering --server.max_running_requests, " + "or using a smaller/quantized model." + ) + message.append( + "Note: server mode may need a higher mem_fraction_static than " + "bench_one_batch because tokenizer/scheduler/detokenizer/HTTP " + "processes and IPC buffers consume additional Jetson unified memory." + ) + return " ".join(message) + # ------------------------------------------------------------------ # Attention backend # ------------------------------------------------------------------ diff --git a/pymllm/tests/test_model_runner_memory_pool.py b/pymllm/tests/test_model_runner_memory_pool.py index db4a816ef..72c36c1a1 100644 --- a/pymllm/tests/test_model_runner_memory_pool.py +++ b/pymllm/tests/test_model_runner_memory_pool.py @@ -56,6 +56,59 @@ def test_profile_max_num_tokens_treats_mem_fraction_static_as_static_pool_fracti assert ModelRunner._profile_max_num_tokens(runner) <= 0 +def test_profile_max_num_tokens_clamps_negative_capacity_and_records_diagnostics( + _cuda_available, + monkeypatch, +): + runner = _make_runner( + mem_fraction_static=0.4, + max_total_tokens=4096, + free_gb=5.0, + total_gb=10.0, + ) + monkeypatch.setattr( + model_runner, + "get_available_gpu_memory", + lambda *args, **kwargs: runner._current_available_gb, + ) + + assert ModelRunner._profile_max_num_tokens(runner) == 0 + profile = runner._last_memory_profile + assert profile.static_kv_budget_gb == pytest.approx(-1.0) + assert profile.profiled_max_tokens == 0 + assert profile.requested_max_total_tokens == 4096 + + +def test_kv_cache_memory_error_includes_profile_details_and_server_hint(): + runner = _make_runner( + mem_fraction_static=0.75, + max_total_tokens=4096, + free_gb=1.99, + total_gb=8.42, + ) + runner._last_memory_profile = model_runner.MemoryProfileResult( + pre_model_available_gb=8.42, + available_gb=1.99, + mem_fraction=0.75, + static_kv_budget_gb=-0.11, + cell_size_bytes=114688, + profiled_max_tokens=0, + requested_max_total_tokens=4096, + effective_max_tokens=0, + gdn_pool_gb=0.0, + ) + + message = ModelRunner._format_kv_cache_memory_error(runner) + + assert "profiled max_tokens=0" in message + assert "requested max_total_tokens=4096" in message + assert "static_kv_budget=-0.11 GB" in message + assert "mem_fraction_static=0.75" in message + assert "server mode may need a higher mem_fraction_static than bench_one_batch" in message + assert "--server.mem_fraction_static" in message + assert "--server.max_total_tokens" in message + + def test_profile_max_num_tokens_caps_user_max_total_tokens_to_profiled_capacity( _cuda_available, monkeypatch, From b56d811dc95d6c77d58209bb0ffb1d2dda8dc209 Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Mon, 8 Jun 2026 10:31:14 +0000 Subject: [PATCH 18/21] docs(pymllm_runtime): rewrite and humanize runtime docs Rewrite the pymllm_runtime web docs for accuracy and readability. - setup_and_usage: condense overview, drop docker-only assumption, fix launch args (mem_fraction_static/max_total_tokens/dtype defaults), refresh bench_one_batch (text/vit/multimodal modes) and profile (torch.profiler supported, nsys experimental); align mem_fraction_static semantics with the model-weights+KV-pool static budget - runtime_design / models_and_quantization / kernels_and_acceleration / developer_guide: humanize prose, trim AI-style scaffolding, keep content Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/pymllm_runtime/developer_guide.rst | 143 ++++----- .../kernels_and_acceleration.rst | 141 ++++----- .../models_and_quantization.rst | 134 ++++----- docs/pymllm_runtime/runtime_design.rst | 151 +++++----- docs/pymllm_runtime/setup_and_usage.rst | 284 +++++++----------- 5 files changed, 382 insertions(+), 471 deletions(-) diff --git a/docs/pymllm_runtime/developer_guide.rst b/docs/pymllm_runtime/developer_guide.rst index 47b528659..47dbe0742 100644 --- a/docs/pymllm_runtime/developer_guide.rst +++ b/docs/pymllm_runtime/developer_guide.rst @@ -4,13 +4,13 @@ pymllm Developer Guide 总览 ---------------------------------------- -本文档面向希望为 ``pymllm`` 增加模型、量化格式、kernel 或性能优化的开发者。当前代码处在 -快速演进阶段，推荐遵循“小步验证、边界清晰、先单测后服务级验证”的工作方式。 +这份文档写给想给 ``pymllm`` 加模型、加量化格式、加 kernel 或做性能优化的开发者。代码还在快速 +演进，建议的工作方式是“小步验证、边界清晰、先单测再服务级验证”。 开发环境建议 ---------------------------------------- -推荐使用 editable install，便于修改 Python 代码后直接验证： +推荐用 editable install，改完 Python 代码能直接验证： .. code-block:: bash @@ -28,9 +28,9 @@ pymllm Developer Guide print("ok") PY -``mllm-kernel`` 的 JIT 编译产物会写入 ``~/.cache/mllm_kernel``。正常修改后重新运行 -会触发相应 kernel 的加载或编译；只有在验证首次编译行为、排查失败缓存、或更换 CUTLASS -等外部头文件来源时，才需要清理对应缓存： +``mllm-kernel`` 的 JIT 编译产物写在 ``~/.cache/mllm_kernel``。正常改完代码重新跑，会按需触发 +对应 kernel 的加载或编译；只有在验证首次编译行为、排查失败缓存、或者换了 CUTLASS 之类外部头 +文件来源时，才需要手动清对应缓存： .. code-block:: bash @@ -39,17 +39,17 @@ pymllm Developer Guide 新增模型 ---------------------------------------- -新增模型时，优先复用现有 ``pymllm.layers`` 和 ``pymllm.executor`` 约定，而不是把 -HuggingFace 模型直接包进服务。 +加模型时，优先复用现有的 ``pymllm.layers`` 和 ``pymllm.executor`` 约定，别把 HuggingFace 模型 +整个塞进服务。 推荐步骤： 1. 新增 ``pymllm/models/.py``。 2. 在 ``pymllm/models/__init__.py`` 注册 architecture 字符串。 -3. 实现模型类，保持 ``forward(input_ids, positions, forward_batch)`` 风格。 +3. 实现模型类，保持 ``forward(input_ids, positions, forward_batch)`` 的风格。 4. 所有 linear layer 都接受 ``quant_method``。 -5. 实现 ``load_weights``，处理 checkpoint key、stacked projection 和 tied embedding。 -6. 增加最小单测。 +5. 实现 ``load_weights``，处理好 checkpoint key、stacked projection 和 tied embedding。 +6. 补最小单测。 7. 最后做服务级 smoke test。 最小测试建议： @@ -63,38 +63,38 @@ HuggingFace 模型直接包进服务。 新增量化 scheme ---------------------------------------- -新增量化路径时，不建议在模型文件里写格式判断。推荐保持以下分层： +加量化路径时，别在模型文件里写格式判断。保持这三层： .. code-block:: text QuantizationConfig - parses checkpoint config - decides whether a layer is quantized + 解析 checkpoint config + 决定某个 layer 是否量化 LinearMethod - owns linear layer lifecycle + 承接 linear layer 生命周期 Scheme - owns checkpoint-facing params - owns post-load layout conversion - owns kernel apply path + 管 checkpoint-facing 参数 + 管 post-load layout 转换 + 管 kernel apply 路径 -``create_weights`` 应注册 checkpoint-facing 参数名。``process_weights_after_loading`` 应作为 -checkpoint layout 到 runtime kernel layout 的唯一转换边界。``apply`` 中只做 forward 必需的 -runtime 计算，不应重复做权重 repack。 +``create_weights`` 注册 checkpoint-facing 的参数名。``process_weights_after_loading`` 是 +checkpoint layout 转 runtime kernel layout 的唯一边界。``apply`` 里只做 forward 必需的 runtime +计算，不要重复做权重 repack。 -新增量化路径至少需要覆盖： +新增量化路径至少要覆盖： - config 解析测试。 - ``ignore`` / prefix 匹配测试。 -- 参数注册 shape/dtype 测试。 +- 参数注册的 shape / dtype 测试。 - post-load layout 转换测试。 - forward correctness 或 smoke test。 新增 CUDA JIT kernel ---------------------------------------- -若 kernel 适合走 ``mllm-kernel`` 的 TVM-FFI JIT 路径，推荐结构如下： +如果 kernel 适合走 ``mllm-kernel`` 的 TVM-FFI JIT 路径，推荐这个结构： .. code-block:: text @@ -103,28 +103,29 @@ runtime 计算，不应重复做权重 repack。 mllm-kernel/tests/test_.py mllm-kernel/benchmarks/bench_.py -Python wrapper 应负责： +Python wrapper 负责： -- 校验输入 shape、dtype、device。 +- 校验输入的 shape、dtype、device。 - 分配输出 tensor。 -- 调用 ``@jit`` 包装后的 compiled module。 -- 暴露稳定、简洁的 Python API。 +- 调 ``@jit`` 包好的 compiled module。 +- 对外暴露一个稳定、干净的 Python API。 -CUDA/C++ source 应尽量只表达 kernel 语义，不混入 checkpoint 配置解析或模型层逻辑。 +CUDA / C++ source 尽量只表达 kernel 语义，别混进 checkpoint 配置解析或模型层逻辑。 -如果 kernel 依赖 CUTLASS 等重模板库，可以先做编译 spike。确认 Jetson 目标设备上的编译时间、 -缓存路径、include 来源和内存占用后，再决定使用 TVM-FFI JIT、torch extension JIT 或 AOT 构建。 +如果 kernel 依赖 CUTLASS 这种重模板库，建议先做一次编译 spike：把 Jetson 目标设备上的编译 +时间、缓存路径、include 来源和内存占用摸清楚，再决定用 TVM-FFI JIT、torch extension JIT 还是 +AOT 构建。 服务级验证 ---------------------------------------- -服务级 smoke test 应覆盖： +服务级 smoke test 至少要覆盖： -- ``/v1/models`` 可返回。 -- 文本 ``/v1/chat/completions`` 可完成。 -- 图文模型能处理容器内图片绝对路径。 -- streaming 与 non-streaming 至少各测一次。 -- 中止请求或客户端断连不会泄漏 running request。 +- ``/v1/models`` 能返回。 +- 文本 ``/v1/chat/completions`` 能跑完。 +- 图文模型能处理容器内的图片绝对路径。 +- streaming 和 non-streaming 各测一次。 +- 中止请求或客户端断连时不会泄漏 running request。 示例： @@ -145,7 +146,7 @@ CUDA/C++ source 应尽量只表达 kernel 语义，不混入 checkpoint 配置 性能验证 ---------------------------------------- -性能数据需要固定口径，否则不同记录之间很难比较。建议记录： +性能数据一定要固定口径，否则不同记录之间根本没法比。建议每次都记下： - commit hash。 - JetPack / L4T 版本。 @@ -154,11 +155,11 @@ CUDA/C++ source 应尽量只表达 kernel 语义，不混入 checkpoint 配置 - 模型路径和量化格式。 - 启动命令。 - prompt token 数、max tokens、temperature。 -- 是否启用 radix cache、CUDA Graph、shared queue。 +- 有没有开 radix cache、CUDA Graph、shared queue。 - 是否包含首次 JIT 编译。 -对服务级请求，建议丢弃第一次 warmup 结果，记录第 2/3 次请求的 prefill/decode 统计。 -对 kernel microbench，建议单独记录 warmup、重复次数、输入 shape 和 dtype。 +服务级请求建议丢掉第一次 warmup 的结果，记第 2 / 3 次请求的 prefill / decode 统计。kernel +microbench 则要单独记 warmup、重复次数、输入 shape 和 dtype。 常见问题定位 ---------------------------------------- @@ -166,55 +167,55 @@ CUDA/C++ source 应尽量只表达 kernel 语义，不混入 checkpoint 配置 启动失败 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -优先确认： +先看： -- ``pymllm`` 和 ``mllm_kernel`` 是否来自预期源码目录或安装版本。 -- ``model_path`` 和 ``tokenizer_path`` 是否在容器内可见。 -- ``transformers`` 是否能读取目标 ``config.json``。 -- CUDA 是否可用，``torch.cuda.get_device_capability()`` 是否符合量化 kernel 要求。 +- ``pymllm`` 和 ``mllm_kernel`` 是不是来自预期的源码目录或安装版本。 +- ``model_path`` 和 ``tokenizer_path`` 在容器内能不能看到。 +- ``transformers`` 能不能读目标 ``config.json``。 +- CUDA 可不可用，``torch.cuda.get_device_capability()`` 满不满足量化 kernel 的要求。 W8A8 编译失败 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -优先确认： +先看： -- ``CUTLASS_HOME`` 是否设置正确。 -- ``flashinfer`` 是否包含 bundled CUTLASS。 -- ``~/.cache/mllm_kernel/cutlass_int8_scaled_mm/`` 是否存在旧的失败缓存。 -- 当前 GPU 是否为 SM80-SM89。 +- ``CUTLASS_HOME`` 设没设对。 +- ``flashinfer`` 里有没有 bundled CUTLASS。 +- ``~/.cache/mllm_kernel/cutlass_int8_scaled_mm/`` 是不是有旧的失败缓存。 +- 当前 GPU 是不是 SM80–SM89。 请求卡住或 CPU 占用高 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -优先确认： +先看： -- scheduler 是否启用了 idle sleep。 -- tokenizer / scheduler / detokenizer 子进程是否全部存活。 -- 是否有请求已经断连但未 abort。 -- ``max_total_tokens`` 是否过小导致 KV allocation 反复失败和 eviction。 +- scheduler 有没有启用 idle sleep。 +- tokenizer / scheduler / detokenizer 子进程是不是都还活着。 +- 是不是有请求已经断连但没 abort。 +- ``max_total_tokens`` 是不是太小，导致 KV allocation 反复失败和 eviction。 输出异常 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -优先确认： +先看： -- tokenizer chat template 是否符合目标模型。 -- EOS token 是否从 config、generation_config 或 tokenizer 中正确解析。 -- 量化模型的 ``ignore`` 是否覆盖视觉分支、embedding、norm 和 lm_head 等不应量化模块。 -- ``process_weights_after_loading`` 是否已执行。 +- tokenizer 的 chat template 对不对得上目标模型。 +- EOS token 有没有从 config、generation_config 或 tokenizer 里正确解析出来。 +- 量化模型的 ``ignore`` 有没有覆盖视觉分支、embedding、norm、lm_head 这些不该量化的模块。 +- ``process_weights_after_loading`` 跑没跑。 贡献建议 ---------------------------------------- -开发时尽量保持以下边界： +开发时尽量守住这些边界： -- 服务协议变化放在 ``pymllm/server``。 -- 请求/响应结构放在 ``pymllm/engine/io_struct.py``。 -- 调度策略放在 ``pymllm/orchestrator/scheduler_process.py``。 -- GPU 资源和 forward 逻辑放在 ``pymllm/executor``。 -- 模型结构放在 ``pymllm/models``。 -- 基础层放在 ``pymllm/layers``。 -- 量化格式放在 ``pymllm/quantization``。 -- 自定义 kernel 放在 ``mllm-kernel``。 +- 服务协议变化放 ``pymllm/server``。 +- 请求 / 响应结构放 ``pymllm/engine/io_struct.py``。 +- 调度策略放 ``pymllm/orchestrator/scheduler_process.py``。 +- GPU 资源和 forward 逻辑放 ``pymllm/executor``。 +- 模型结构放 ``pymllm/models``。 +- 基础层放 ``pymllm/layers``。 +- 量化格式放 ``pymllm/quantization``。 +- 自定义 kernel 放 ``mllm-kernel``。 -这样可以避免把一次模型适配写成跨层补丁，也方便后续把同一能力复用到更多模型和设备。 +守住这些边界，一次模型适配就不会写成跨层补丁，后面把同一份能力复用到更多模型和设备也更省事。 diff --git a/docs/pymllm_runtime/kernels_and_acceleration.rst b/docs/pymllm_runtime/kernels_and_acceleration.rst index d5d09c30c..849c5a5e2 100644 --- a/docs/pymllm_runtime/kernels_and_acceleration.rst +++ b/docs/pymllm_runtime/kernels_and_acceleration.rst @@ -4,28 +4,28 @@ pymllm Kernels and Acceleration 总览 ---------------------------------------- -``pymllm`` 的性能路径由多类加速组件共同组成： +``pymllm`` 的性能由几类加速组件分工撑起来，它们解决的不是同一个问题： -- FlashInfer：paged KV cache attention。 -- CUDA Graph：decode 阶段减少 CPU launch overhead。 -- Triton：W8A8 per-token activation quantization。 -- CUTLASS：W8A8 INT8 Tensor Core GEMM。 -- ``mllm-kernel``：基于 TVM-FFI / torch extension 的 JIT kernel 工具包。 +- **FlashInfer**：paged KV cache attention。 +- **CUDA Graph**：减少 decode 阶段的 CPU launch overhead。 +- **Triton**：W8A8 的 per-token activation quantization。 +- **CUTLASS**：W8A8 的 INT8 Tensor Core GEMM。 +- **mllm-kernel**：基于 TVM-FFI / torch extension 的 JIT kernel 工具包。 -这些组件不是彼此替代关系，而是在不同层次承担职责。attention backend 解决 KV cache -attention；CUDA Graph 解决重复 decode step 的 launch overhead；Triton 和 CUTLASS 解决量化 -linear 的核心计算；``mllm-kernel`` 为项目内自定义 CUDA/C++ kernel 提供封装、缓存和工具。 +简单说：attention backend 管 KV cache attention，CUDA Graph 管重复 decode step 的 launch +开销，Triton 和 CUTLASS 管量化 linear 的核心计算，``mllm-kernel`` 则为项目内自定义的 +CUDA / C++ kernel 提供封装、缓存和工具。 mllm-kernel ---------------------------------------- -``mllm-kernel`` 是 mllm 项目中的高性能 kernel 包。当前 Python 侧主要包含： +``mllm-kernel`` 是 mllm 里的高性能 kernel 包，Python 侧目前主要是： - ``mllm_kernel.cuda.jit``：CUDA JIT kernel wrapper。 - ``mllm_kernel.cpu.jit``：CPU JIT kernel wrapper。 - ``mllm_kernel.jit_utils``：JIT 编译、缓存、注册表和工具函数。 -CUDA JIT kernel 的典型结构是： +一个 CUDA JIT kernel 的典型结构： .. code-block:: text @@ -33,54 +33,54 @@ CUDA JIT kernel 的典型结构是： -> @jit(...) -> include CUDA/C++ source -> export TVM-FFI typed function - -> compile on first use - -> reuse cached shared library + -> 首次使用时编译 + -> 之后复用缓存的 shared library -默认 JIT 缓存目录为： +默认 JIT 缓存目录： .. code-block:: text ~/.cache/mllm_kernel/ -``mllm-kernel`` 的 JIT 路径与 SGLang 的 ``jit_kernel`` 设计关系更直接：二者都强调轻量 -JIT、运行时选择模板实例、避免大型 AOT torch extension 带来的长编译周期。与此同时，SGLang -的 ``sgl-kernel`` AOT kernel 仍然是重要参考，尤其适合对照量化 GEMM 的语义和性能。 +``mllm-kernel`` 的 JIT 思路和 SGLang 的 ``jit_kernel`` 关系更近：都强调轻量 JIT、运行时选模板 +实例、避开大型 AOT torch extension 那种动辄几分钟的编译。同时 SGLang 的 ``sgl-kernel`` AOT +kernel 仍是重要参考，对照量化 GEMM 的语义和性能时尤其有用。 TVM-FFI JIT 路径 ---------------------------------------- -``mllm_kernel.jit_utils.jit`` decorator 会将 Python 函数包装成一个按需编译的 kernel 调用。 -它负责： +``mllm_kernel.jit_utils.jit`` 这个 decorator 把一个 Python 函数包成按需编译的 kernel 调用， +负责： -- 根据 tensor device 推断 CPU/CUDA 目标。 -- 将 Python 参数转换为 C++ template 参数。 -- 拼接 C++/CUDA source 和 export wrapper。 -- 调用 TVM-FFI 编译并加载 shared library。 -- 将编译结果缓存到 ``~/.cache/mllm_kernel``。 +- 根据 tensor device 推断 CPU / CUDA 目标。 +- 把 Python 参数转成 C++ template 参数。 +- 拼 C++/CUDA source 和 export wrapper。 +- 调 TVM-FFI 编译并加载 shared library。 +- 把编译结果缓存到 ``~/.cache/mllm_kernel``。 -这种方式适合小而明确的自定义 kernel，例如： +这种方式适合小而明确的自定义 kernel，比如： -- ``create_kv_indices``：构造 FlashInfer KV index metadata。 -- ``store_cache``：将 K/V 写入 KVPool。 -- ``gptq_marlin_repack``：Marlin weight layout 转换。 +- ``create_kv_indices``：构造 FlashInfer 的 KV index metadata。 +- ``store_cache``：把 K/V 写进 KVPool。 +- ``gptq_marlin_repack``：Marlin 权重 layout 转换。 - ``gptq_marlin_gemm``：W4A16 Marlin GEMM。 -W8A8 CUTLASS kernel 当前使用 ``torch.utils.cpp_extension.load`` 编译。这是因为 CUTLASS -模板和 include 体系较重，当前以稳定通过 Jetson SM87 编译为优先。 +W8A8 的 CUTLASS kernel 目前是个例外，用 ``torch.utils.cpp_extension.load`` 编译——CUTLASS 的 +模板和 include 体系太重，现阶段优先保证它能在 Jetson SM87 上稳定编过。 FlashInfer Attention ---------------------------------------- -``pymllm.layers.attention.flashinfer_backend.FlashInferAttnBackend`` 封装 FlashInfer 的 paged -KV cache attention。它负责： +``pymllm.layers.attention.flashinfer_backend.FlashInferAttnBackend`` 封装了 FlashInfer 的 +paged KV cache attention，负责： - 为 prefill 和 decode 准备 ``kv_indptr``、``kv_indices``、``kv_last_page_len`` 等 metadata。 - 管理全局 workspace buffer。 -- 根据是否存在 sliding window 选择 wrapper dispatch。 -- 在 decode 中根据 GQA group size 和 KV dtype 决定是否使用 tensor core 路径。 -- 为 CUDA Graph capture / replay 提供专用 metadata 初始化接口。 +- 根据有没有 sliding window 选 wrapper dispatch。 +- decode 时按 GQA group size 和 KV dtype 决定走不走 tensor core 路径。 +- 给 CUDA Graph capture / replay 提供专用的 metadata 初始化接口。 -prefill 和 decode 使用不同 wrapper： +prefill 和 decode 用不同 wrapper： .. code-block:: text @@ -91,22 +91,22 @@ prefill 和 decode 使用不同 wrapper： decode BatchDecodeWithPagedKVCacheWrapper -attention backend 只负责 attention 计算和 metadata，不负责请求调度和 KV slot 生命周期。KV slot -的分配、释放和 prefix cache 命中由 scheduler / model runner 侧完成。 +attention backend 只管 attention 计算和 metadata，不碰请求调度和 KV slot 生命周期。KV slot +的分配、释放、prefix cache 命中是 scheduler / model runner 那边的事。 CUDA Graph ---------------------------------------- -``pymllm.executor.cuda_graph_runner.CudaGraphRunner`` 用于 decode step 的 CUDA Graph capture -和 replay。它的目标是减少小 batch decode 中 CPU launch overhead。 +``pymllm.executor.cuda_graph_runner.CudaGraphRunner`` 负责 decode step 的 CUDA Graph capture +和 replay，目的就是把小 batch decode 里的 CPU launch overhead 压下去。 -初始化阶段会按一组离散 batch size 捕获 graph： +初始化时按一组离散 batch size 捕获 graph： .. code-block:: text [1, 2, 4, 8, 12, 16, 24, 32, ...] -每个 captured graph 复用预分配输入 buffer： +每个 captured graph 复用预分配好的输入 buffer： - ``input_ids`` - ``req_pool_indices`` @@ -115,36 +115,36 @@ CUDA Graph - ``positions`` - ``mrope_position_deltas`` -replay 时，真实 batch 会被 padding 到最近的 captured batch size。attention backend 会走专用 -``init_forward_metadata_replay_cuda_graph`` 路径，避免使用普通动态 metadata 初始化。 +replay 时真实 batch 会 padding 到最近的 captured batch size，attention backend 走专用的 +``init_forward_metadata_replay_cuda_graph`` 路径，而不是普通的动态 metadata 初始化。 -CUDA Graph 只覆盖 decode 主路径。调试模型、调试 attention metadata 或定位 shape 问题时，可以 -使用 ``--server.disable_cuda_graph`` 暂时关闭。 +CUDA Graph 只覆盖 decode 主路径。调试模型、查 attention metadata 或定位 shape 问题时，可以 +用 ``--server.disable_cuda_graph`` 临时关掉。 W4A16 Marlin ---------------------------------------- -W4A16 路径复用 Marlin kernel。checkpoint 权重先以 ``weight_packed`` 和 ``weight_scale`` -加载，然后在 post-load 阶段转换为 Marlin runtime layout。 +W4A16 复用 Marlin kernel。checkpoint 权重先以 ``weight_packed`` 和 ``weight_scale`` 加载， +再在 post-load 阶段转成 Marlin 的 runtime layout。 关键 kernel： - ``mllm_kernel.cuda.jit.gptq_marlin_repack`` - ``mllm_kernel.cuda.jit.gptq_marlin`` -执行约束包括： +执行约束： - SM80+ -- output partition 可被 64 整除 -- input partition 可被 128 整除 -- group size 当前主路径为 32 +- output partition 能被 64 整除 +- input partition 能被 128 整除 +- group size 主路径目前是 32 -这种路径适合 AWQ / W4A16 类权重量化模型，activation 保持 FP16/BF16。 +这条路径适合 AWQ / W4A16 这类权重量化模型，activation 保持 FP16/BF16。 W8A8 Triton + CUTLASS ---------------------------------------- -W8A8 路径包含两个核心 kernel： +W8A8 有两个核心 kernel： 1. ``pymllm.quantization.kernels.int8_activation_triton.per_token_quant_int8`` 2. ``mllm_kernel.cuda.jit.int8_scaled_mm_cutlass.int8_scaled_mm`` @@ -156,37 +156,32 @@ W8A8 路径包含两个核心 kernel： [M, K] fp16/bf16 activation -> Triton per-token absmax + round + int8 cast -> [M, K] int8 + [M, 1] fp32 scale - -> CUTLASS int8 GEMM with per-row/per-col scales + -> CUTLASS int8 GEMM（per-row / per-col scale） -> [M, N] fp16/bf16 output -CUTLASS kernel 要求 ``mat_b`` 为 ``[K, N]`` column-major，因此 W8A8 scheme 会在 -``process_weights_after_loading`` 中把 checkpoint 的 ``[N, K]`` INT8 weight 转成对应布局。 +CUTLASS kernel 要求 ``mat_b`` 是 ``[K, N]`` column-major，所以 W8A8 scheme 会在 +``process_weights_after_loading`` 里把 checkpoint 的 ``[N, K]`` INT8 weight 转成对应布局。 -当前 CUTLASS include 查找顺序为： - -1. ``CUTLASS_HOME/include`` -2. ``flashinfer`` bundled CUTLASS -3. 系统 include 目录 - -如果找不到 CUTLASS 头文件，W8A8 初始化会失败。生产环境建议在镜像中固定 CUTLASS 来源，避免 -不同节点使用不同版本头文件。 +CUTLASS 头文件默认用 ``flashinfer`` bundled 的那份；要换版本就设 ``CUTLASS_HOME``。如果头文件 +找不到，W8A8 初始化会直接失败。生产环境建议在镜像里把 CUTLASS 来源固定下来，免得不同节点用上 +不同版本的头文件。 GDN decode kernel ---------------------------------------- -Qwen3.5 等 hybrid 模型可能包含 GDN / linear attention 层。``pymllm`` 为这类模型保留了： +Qwen3.5 这类 hybrid 模型可能带 GDN / linear attention 层。``pymllm`` 给它们预留了： - ``pymllm.layers.attention.gdn_backend`` - ``pymllm.layers.attention.hybrid_backend`` - ``mllm_kernel.cuda.jit.gdn_decode`` - ``MambaRadixCache`` / GDN state cache 相关结构 -当前文档重点覆盖 Qwen3 / Qwen3-VL 主路径。GDN 相关路径仍应以具体模型和测试结果为准。 +本文档重点还是 Qwen3 / Qwen3-VL 主路径，GDN 相关路径以具体模型和测试结果为准。 调试与观测 ---------------------------------------- -常用检查命令： +几条常用检查命令： .. code-block:: bash @@ -194,10 +189,6 @@ Qwen3.5 等 hybrid 模型可能包含 GDN / linear attention 层。``pymllm`` python3 -m mllm_kernel show-config python3 -m pymllm show-config -当首次运行时间异常长时，应区分： - -- 模型权重加载时间。 -- FlashInfer / CUDA context 初始化时间。 -- CUTLASS JIT 编译时间。 -- CUDA Graph capture 时间。 -- 实际 prefill/decode 时间。 +首次运行特别慢的时候，要分清楚时间花在哪：模型权重加载、FlashInfer / CUDA context 初始化、 +CUTLASS JIT 编译、CUDA Graph capture，还是真正的 prefill / decode。别把首次 JIT 或 kernel +初始化的开销当成稳态瓶颈。 diff --git a/docs/pymllm_runtime/models_and_quantization.rst b/docs/pymllm_runtime/models_and_quantization.rst index 6bbb63706..cb1c885d6 100644 --- a/docs/pymllm_runtime/models_and_quantization.rst +++ b/docs/pymllm_runtime/models_and_quantization.rst @@ -4,15 +4,16 @@ pymllm Models and Quantization 总览 ---------------------------------------- -``pymllm`` 的模型实现遵循 PyTorch ``nn.Module`` 风格，并通过 HuggingFace -``config.architectures`` 字段选择模型类。当前重点支持 Qwen3 family： +``pymllm`` 的模型实现就是标准的 PyTorch ``nn.Module`` 写法，运行时按 HuggingFace config +里的 ``architectures`` 字段挑模型类。当前重点是 Qwen3 family： - ``Qwen3ForCausalLM``：文本模型，例如 Qwen3-0.6B。 - ``Qwen3VLForConditionalGeneration``：图文模型，例如 Qwen3-VL-2B-Instruct。 -- ``Qwen3_5ForCausalLM`` 和 ``Qwen3_5ForConditionalGeneration``：hybrid attention / GDN - 相关模型骨架。 +- ``Qwen3_5ForCausalLM`` / ``Qwen3_5ForConditionalGeneration``：hybrid attention / GDN + 方向的模型骨架。 -量化系统以 linear layer 为核心，使用插件式 ``LinearMethodBase`` 生命周期： +量化系统围绕 linear layer 展开，用一套插件式的 ``LinearMethodBase`` 生命周期把格式细节 +和模型主逻辑隔开： .. code-block:: text @@ -26,8 +27,8 @@ pymllm Models and Quantization 模型注册 ---------------------------------------- -模型注册表位于 ``pymllm/models/__init__.py``。运行时会根据 HuggingFace config 中的 -architecture 字符串懒加载模型类： +模型注册表在 ``pymllm/models/__init__.py``。运行时按 HuggingFace config 里的 architecture +字符串懒加载对应模型类： .. code-block:: text @@ -40,37 +41,28 @@ architecture 字符串懒加载模型类： "Qwen3_5ForCausalLM" -> pymllm.models.qwen3_5.Qwen3_5ForCausalLM -这种注册方式让服务启动阶段只导入目标模型所需的代码，避免在命令行工具或轻量检查中提前加载 -大量 PyTorch/CUDA 依赖。 +懒加载的好处是：服务启动时只导入目标模型用到的代码，命令行工具或轻量检查不会被迫提前拉起 +一大堆 PyTorch / CUDA 依赖。 Qwen3 文本模型 ---------------------------------------- -``Qwen3ForCausalLM`` 使用标准 decoder-only 结构： - -- token embedding -- 多层 decoder block -- Q/K Norm -- 1D RoPE -- MLP -- final norm -- lm head - -它复用 ``RadixAttention``、``RMSNorm``、``MLP``、``ColumnParallelLinear`` 和 -``RowParallelLinear`` 等基础层。与 Qwen3-VL 文本分支相比，Qwen3 文本模型使用 1D RoPE， -不需要多模态 M-RoPE 的三维 position 逻辑。 +``Qwen3ForCausalLM`` 是标准的 decoder-only 结构：token embedding、多层 decoder block、 +Q/K Norm、1D RoPE、MLP、final norm、lm head。它复用 ``RadixAttention``、``RMSNorm``、 +``MLP``、``ColumnParallelLinear``、``RowParallelLinear`` 这些基础层。和 Qwen3-VL 的文本分支 +比，区别在于这里用的是 1D RoPE，不需要多模态 M-RoPE 那套三维 position 逻辑。 Qwen3-VL 图文模型 ---------------------------------------- -``Qwen3VLForConditionalGeneration`` 在文本 decoder 外增加视觉输入处理和 M-RoPE 位置编码。 -在一次图文请求中： +``Qwen3VLForConditionalGeneration`` 在文本 decoder 之外多了视觉输入处理和 M-RoPE 位置编码。 +一次图文请求大致是这样走的： 1. tokenizer / processor 处理 messages 和图片路径。 -2. ``TokenizerProcess`` 生成 token ids 和多模态输入 tensor。 +2. ``TokenizerProcess`` 产出 token ids 和多模态输入 tensor。 3. 多模态 tensor 通过 ZMQ 或 shared queue 送到 scheduler。 -4. 模型 forward 中先处理视觉侧输入，再进入语言模型 prefill/decode。 -5. decode 阶段使用每个请求保存的 ``mrope_position_delta`` 修正位置。 +4. 模型 forward 里先过视觉侧输入，再进语言模型的 prefill / decode。 +5. decode 阶段用每个请求保存的 ``mrope_position_delta`` 修正位置。 当前 W8A8 量化主要覆盖语言 decoder 的线性层；视觉 encoder、embedding、LayerNorm 和 ``lm_head`` 保持全精度。 @@ -78,31 +70,30 @@ Qwen3-VL 图文模型 Fused projection 与 shard-aware loading ---------------------------------------- -Qwen3 / Qwen3-VL 的 text decoder 使用 fused QKV projection 和 fused gate/up -projection。对未量化模型，这减少了 projection 层的 module 边界；对 W8A8 和 W4A16 -路径，它还避免把同一层拆成多次 activation quant、GEMM 或 Marlin 调用。 +Qwen3 / Qwen3-VL 的 text decoder 用了 fused QKV projection 和 fused gate/up projection。 +对非量化模型，这减少了 projection 层的 module 边界；对 W8A8 和 W4A16 路径，它还顺手省掉了 +把同一层拆成多次 activation quant、GEMM 或 Marlin 调用的开销。 -checkpoint 中的权重仍可能以 HuggingFace 常见的分离形式保存，例如 ``q_proj``、 -``k_proj``、``v_proj``、``gate_proj`` 和 ``up_proj``。``MergedLinear`` 通过 -shard-aware ``weight_loader`` 将这些分离 tensor 写入 fused 参数，运行时布局保持为 -``[Q, K, V]`` 或 ``[gate, up]``。权重加载完成后，``process_weights_after_loading`` -再负责 W8A8 layout 转换或 W4A16 Marlin repack。 +checkpoint 里的权重往往还是 HuggingFace 常见的分离形式，比如 ``q_proj``、``k_proj``、 +``v_proj`` 和 ``gate_proj``、``up_proj``。``MergedLinear`` 用 shard-aware 的 ``weight_loader`` +把这些分离 tensor 写进 fused 参数，运行时布局保持 ``[Q, K, V]`` 或 ``[gate, up]``。权重加载 +完之后，``process_weights_after_loading`` 再去做 W8A8 layout 转换或 W4A16 Marlin repack。 -Qwen3 / Qwen3-VL decoder 还使用 residual-carry 形式组织 RMSNorm 的 fused add 路径。 -在 Qwen3-VL 中，如果 deepstack embedding 需要注入，运行时会先物化当前 residual sum， -再执行注入并重置 carry，避免改变图文 prefill 语义。 +Qwen3 / Qwen3-VL decoder 还用 residual-carry 的形式组织 RMSNorm 的 fused add 路径。在 +Qwen3-VL 里，如果需要注入 deepstack embedding，运行时会先把当前 residual sum 物化出来，再 +执行注入并重置 carry，避免破坏图文 prefill 的语义。 量化配置解析 ---------------------------------------- -服务启动时，``ModelRunner`` 会解析量化配置。优先级为： +服务启动时 ``ModelRunner`` 解析量化配置，优先级是： 1. 命令行 ``--quantization.method``。 -2. checkpoint 目录中的量化配置文件。 -3. ``config.json`` 中的 ``quantization_config`` 字段。 +2. checkpoint 目录里的量化配置文件。 +3. ``config.json`` 里的 ``quantization_config`` 字段。 -``compressed-tensors`` 路径使用 ``pymllm.quantization.methods.compressed_tensors``， -当前支持两类签名： +``compressed-tensors`` 路径走 ``pymllm.quantization.methods.compressed_tensors``，目前支持 +两类签名： .. list-table:: :header-rows: 1 @@ -123,13 +114,12 @@ Qwen3 / Qwen3-VL decoder 还使用 residual-carry 形式组织 RMSNorm 的 fused - INT8 dynamic per-token activation - Triton quant + CUTLASS INT8 GEMM -``ignore`` 字段会让匹配前缀的模块跳过量化。例如 Qwen3-VL 的视觉分支通常保留为全精度。 +``ignore`` 字段会让前缀匹配上的模块跳过量化，比如 Qwen3-VL 的视觉分支通常整体保留全精度。 W4A16 / AWQ Marlin 路径 ---------------------------------------- -W4A16 路径面向 ``compressed-tensors`` 的 ``pack-quantized`` checkpoint。当前支持的 -约束是： +W4A16 面向 ``compressed-tensors`` 的 ``pack-quantized`` checkpoint。当前的约束是： - ``format == "pack-quantized"`` - ``weights.num_bits == 4`` @@ -138,7 +128,7 @@ W4A16 路径面向 ``compressed-tensors`` 的 ``pack-quantized`` checkpoint。 - ``actorder == null`` - GPU capability 不低于 SM80 -权重加载和执行分为三个阶段： +权重加载和执行分三步： .. code-block:: text @@ -149,20 +139,20 @@ W4A16 路径面向 ``compressed-tensors`` 的 ``pack-quantized`` checkpoint。 process_weights_after_loading() gptq_marlin_repack() marlin_permute_scales() - create runtime-only zero/g_idx placeholders + 建好 runtime-only 的 zero / g_idx 占位 │ ▼ apply() gptq_marlin_gemm() -``create_weights`` 注册与 checkpoint 对齐的参数名，保证 safetensors 加载逻辑可以按名称写入。 -``process_weights_after_loading`` 是 checkpoint layout 到 runtime kernel layout 的边界，repack -不应放在通用权重加载器或每次 forward 中。 +``create_weights`` 注册和 checkpoint 对齐的参数名，让 safetensors 加载逻辑能按名字写进去。 +``process_weights_after_loading`` 是 checkpoint layout 转 runtime kernel layout 的那条边界， +repack 只该放在这里，不该塞进通用权重加载器，更不该每次 forward 都做。 W8A8 INT8 路径 ---------------------------------------- -W8A8 路径面向 ``compressed-tensors`` 的 ``int-quantized`` checkpoint。当前支持的约束是： +W8A8 面向 ``compressed-tensors`` 的 ``int-quantized`` checkpoint。当前的约束是： - ``format == "int-quantized"`` - ``weights.num_bits == 8`` @@ -175,10 +165,10 @@ W8A8 路径面向 ``compressed-tensors`` 的 ``int-quantized`` checkpoint。当 - ``input_activations.strategy == "token"`` - ``input_activations.dynamic == true`` - ``input_activations.symmetric == true`` -- W8A8 CUTLASS 路径当前支持 Ampere / SM8x GPU（SM80-SM89）。已验证目标为 - Jetson Orin SM87；Hopper / SM90 暂不包含在当前支持范围内。 +- W8A8 CUTLASS 路径当前支持 Ampere / SM8x（SM80–SM89）。已验证目标是 Jetson Orin SM87； + Hopper / SM90 暂不在支持范围内。 -执行链路如下： +执行链路： .. code-block:: text @@ -195,49 +185,47 @@ W8A8 路径面向 ``compressed-tensors`` 的 ``int-quantized`` checkpoint。当 │ └── output(fp16/bf16) -checkpoint 中的 INT8 权重通常是 ``[N, K]`` row-major。``process_weights_after_loading`` -会将其转换为 ``[K, N]`` column-major 视图并整理 ``weight_scale``，以满足 CUTLASS kernel -接口约定。 +checkpoint 里的 INT8 权重通常是 ``[N, K]`` row-major。``process_weights_after_loading`` 会把它 +转成 ``[K, N]`` column-major 视图并整理 ``weight_scale``，以满足 CUTLASS kernel 的接口约定。 LinearMethod 生命周期 ---------------------------------------- -所有 linear layer 都持有一个 ``quant_method``： +每个 linear layer 都持有一个 ``quant_method``： -- 未量化时使用 ``UnquantizedLinearMethod``，注册普通 ``weight`` 并调用 ``F.linear``。 +- 不量化时用 ``UnquantizedLinearMethod``，注册普通 ``weight`` 并调 ``F.linear``。 - 量化时由 ``QuantizationConfig.get_quant_method(layer, prefix)`` 返回具体方法。 典型生命周期： -1. 模型构造时，linear layer 调用 ``quant_method.create_weights`` 注册参数。 -2. ``model.load_weights`` 根据参数名和 ``weight_loader`` 写入 checkpoint tensor。 -3. 所有权重加载完成后，``ModelRunner`` 遍历模块并调用 - ``process_weights_after_loading``。 -4. forward 时，linear layer 委托 ``quant_method.apply`` 执行。 +1. 模型构造时，linear layer 调 ``quant_method.create_weights`` 注册参数。 +2. ``model.load_weights`` 按参数名和 ``weight_loader`` 写进 checkpoint tensor。 +3. 权重全部加载完，``ModelRunner`` 遍历模块调 ``process_weights_after_loading``。 +4. forward 时 linear layer 委托 ``quant_method.apply`` 执行。 -这个边界使新增量化方法时不需要改动模型主逻辑，只需要实现新的 config 和 scheme。 +有了这条边界，新增量化方法时基本不用碰模型主逻辑，只要实现新的 config 和 scheme。 新增模型的建议流程 ---------------------------------------- -新增模型时建议遵循以下顺序： +新增模型时建议按这个顺序来： -1. 在 ``pymllm/models/`` 中新增模型文件。 +1. 在 ``pymllm/models/`` 加模型文件。 2. 在 ``pymllm/models/__init__.py`` 注册 HuggingFace architecture 字符串。 3. 实现最小 forward 接口：``forward(input_ids, positions, forward_batch)``。 4. 复用现有基础层，并确保 linear layer 接受 ``quant_method``。 -5. 实现 ``load_weights``，处理 checkpoint 前缀、stacked projection 和 tied embedding。 -6. 增加 registry、weight loading、forward timing 的单元测试。 +5. 实现 ``load_weights``，处理好 checkpoint 前缀、stacked projection 和 tied embedding。 +6. 补 registry、weight loading、forward timing 的单元测试。 7. 最后再做服务级 smoke test。 新增量化方法的建议流程 ---------------------------------------- -新增量化方法时建议保持三层结构： +新增量化方法时保持三层结构： 1. ``QuantizationConfig``：解析 checkpoint 配置，决定某个 layer 是否量化。 2. ``LinearMethod``：承接 layer 生命周期。 3. ``Scheme``：处理具体格式的参数注册、post-load 转换和 kernel apply。 -不要把 checkpoint 格式判断写入模型类，也不要把 runtime repack 隐藏在通用 -``weight_loader`` 中。这样可以保证模型结构、权重格式和 kernel layout 三者的边界清晰。 +不要把 checkpoint 格式判断写进模型类，也不要把 runtime repack 藏在通用 ``weight_loader`` +里。守住这条，模型结构、权重格式、kernel layout 三者的边界才不会糊在一起。 diff --git a/docs/pymllm_runtime/runtime_design.rst b/docs/pymllm_runtime/runtime_design.rst index 309ea7a21..7f1c31f66 100644 --- a/docs/pymllm_runtime/runtime_design.rst +++ b/docs/pymllm_runtime/runtime_design.rst @@ -4,13 +4,12 @@ pymllm Runtime Design 总览 ---------------------------------------- -``pymllm`` 是 mllm 的 Python serving runtime。它不是传统意义上的 mllm C++ -Backend，而是一套围绕 PyTorch/CUDA 生态构建的在线推理服务运行时。当前实现面向 -Jetson Orin 等边缘 GPU 设备，重点支持 Qwen3、Qwen3-VL 和 Qwen3.5 系列模型。 +``pymllm`` 是 mllm 的 Python serving runtime。它不是 mllm 的 C++ Backend，而是一套 +围绕 PyTorch / CUDA 生态搭起来的在线推理服务运行时，目标设备是 Jetson Orin 这类边缘 +GPU，重点支持 Qwen3、Qwen3-VL、Qwen3.5 系列。 -它的设计参考了 SGLang serving runtime 的核心分层，但进行了明显收缩：当前主路径以 -单机单 GPU 为目标，优先保证在 Jetson 上可运行、可调试、可扩展，而不是覆盖大规模 -分布式 serving 的全部复杂度。 +它的分层借鉴了 SGLang serving runtime，但做了明显收缩：主路径只盯单机单 GPU，优先保证 +在 Jetson 上跑得起来、调得动、改得动，而不是去覆盖大规模分布式 serving 的全部复杂度。 .. figure:: ../_static/img/pymllm-arch.png :width: 100% @@ -22,23 +21,23 @@ Jetson Orin 等边缘 GPU 设备，重点支持 Qwen3、Qwen3-VL 和 Qwen3.5 系 整体分层 ---------------------------------------- -从开发者视角看，``pymllm`` 可以分为五层： +从开发者视角看，``pymllm`` 大致分五层： 1. **服务入口层**：FastAPI HTTP server，提供 OpenAI-compatible API 和原生 ``/generate`` API。 -2. **配置层**：``ServerConfig``、``ModelConfig``、``QuantizationConfig`` 统一解析 - 模型路径、dtype、调度参数、缓存参数、量化参数和加速开关。 -3. **控制面**：``Engine`` 启动 tokenizer、scheduler、detokenizer 子进程，并在主进程中 - 维护 request/response 状态。 -4. **数据面**：scheduler 持有 GPU-owning ``ModelRunnerProcess``，负责 batch 构造、 - KV cache 分配、prefix cache 命中、forward 和 sampling。 +2. **配置层**：``ServerConfig``、``ModelConfig``、``QuantizationConfig`` 统一解析模型 + 路径、dtype、调度参数、缓存参数、量化参数和各类加速开关。 +3. **控制面**：``Engine`` 拉起 tokenizer、scheduler、detokenizer 子进程，主进程里维护 + request/response 状态。 +4. **数据面**：scheduler 持有 GPU 的 ``ModelRunnerProcess``，负责 batch 构造、KV cache + 分配、prefix cache 命中、forward 和 sampling。 5. **加速层**：FlashInfer、CUDA Graph、Triton、CUTLASS 和 ``mllm-kernel`` 提供 attention、 - quantization、GEMM 和缓存写入等高频算子。 + 量化、GEMM、缓存写入这些高频算子。 进程拓扑 ---------------------------------------- -``Engine`` 在启动时创建三个子进程，并在主进程中保留 request/response 管理逻辑： +``Engine`` 启动时创建三个子进程，request/response 的管理逻辑留在主进程： .. code-block:: text @@ -64,34 +63,33 @@ Jetson Orin 等边缘 GPU 设备，重点支持 Qwen3、Qwen3-VL 和 Qwen3.5 系 ▼ RequestResponseProcess -这个拓扑的核心取舍是：GPU 资源由 scheduler 进程内的 ``ModelRunnerProcess`` 直接持有。 -这样 scheduler 可以在同一进程中完成调度、KV cache 资源释放、prefix cache 更新和模型 -forward，避免再引入 model worker 进程之间的 GPU 资源同步。 +这里最关键的一个取舍是：GPU 资源由 scheduler 进程内的 ``ModelRunnerProcess`` 直接持有。 +这样调度、KV cache 释放、prefix cache 更新和模型 forward 都在同一个进程里完成，省掉了 +model worker 进程之间同步 GPU 资源的那套复杂度。 请求生命周期 ---------------------------------------- -一次 chat completion 请求的典型路径如下： - -1. HTTP server 接收请求并转换为 ``GenerateReqInput``。 -2. ``RequestResponseProcess`` 为请求分配 request id，并把请求送入 tokenizer。 -3. ``TokenizerProcess`` 调用 tokenizer / processor，生成 ``TokenizedGenerateReqInput``。 -4. ``SchedulerProcess`` 接收 tokenized request，创建 ``Req``，放入等待队列。 -5. scheduler 根据 token budget、running request 数量和 prefill/decode 状态构造 - ``ScheduleBatch``。 -6. ``ModelRunnerProcess`` 为 batch 分配 request slot 和 KV slot，执行 prefix matching。 -7. ``ModelRunner`` 构造 ``ForwardBatch``，初始化 attention backend metadata，调用模型 - ``forward``，并对 logits 做 sampling。 +一次 chat completion 请求的典型路径： + +1. HTTP server 收到请求，转成 ``GenerateReqInput``。 +2. ``RequestResponseProcess`` 分配 request id，把请求送进 tokenizer。 +3. ``TokenizerProcess`` 调 tokenizer / processor，产出 ``TokenizedGenerateReqInput``。 +4. ``SchedulerProcess`` 接到 tokenized request，创建 ``Req``，放进等待队列。 +5. scheduler 按 token budget、running request 数和 prefill/decode 状态构造 ``ScheduleBatch``。 +6. ``ModelRunnerProcess`` 为 batch 分配 request slot 和 KV slot，做 prefix matching。 +7. ``ModelRunner`` 构造 ``ForwardBatch``，初始化 attention backend metadata，调模型 + ``forward``，再对 logits 做 sampling。 8. scheduler 更新每个 ``Req`` 的输出 token、finished reason 和 timing 字段。 -9. ``DetokenizerProcess`` 将 token id 转回文本。 -10. HTTP server 以普通 JSON 或 SSE streaming 形式返回结果。 +9. ``DetokenizerProcess`` 把 token id 转回文本。 +10. HTTP server 以普通 JSON 或 SSE streaming 返回。 控制面：Engine 与配置 ---------------------------------------- -``pymllm.configs.server_config.ServerConfig`` 是服务运行时的主配置对象。它覆盖： +``pymllm.configs.server_config.ServerConfig`` 是服务运行时的主配置对象，覆盖几类参数： -- 模型和 tokenizer：``model_path``、``tokenizer_path``、``load_format``、``dtype``。 +- 模型与 tokenizer：``model_path``、``tokenizer_path``、``load_format``、``dtype``。 - HTTP server：``host``、``port``、``api_key``、``served_model_name``。 - 调度与内存：``max_running_requests``、``max_total_tokens``、``max_prefill_tokens``、 ``mem_fraction_static``。 @@ -101,80 +99,79 @@ forward，避免再引入 model worker 进程之间的 GPU 资源同步。 ``cuda_ipc_pool_size_mb``。 - 观测与调试：``log_level``、``decode_log_interval``。 -``Engine`` 启动前会加载 HuggingFace config，解析 EOS token、默认输出长度和 dtype，并确保 -model/tokenizer 路径可用。启动后，``Engine`` 会监控子进程健康状态；任一核心子进程异常退出， -服务会被标记为 unhealthy。 +``Engine`` 启动前会先加载 HuggingFace config，解析 EOS token、默认输出长度和 dtype，并 +确认 model / tokenizer 路径可用。启动之后它会盯着子进程的健康状态，任何一个核心子进程异常 +退出，整个服务都会被标记为 unhealthy。 调度器 ---------------------------------------- -``SchedulerProcess`` 是 pymllm 的中心调度组件。它负责： +``SchedulerProcess`` 是 pymllm 的中心调度组件，干这几件事： -- 接收 tokenized requests。 -- 将输入请求转换为内部 ``Req`` 状态。 -- 根据 prefill/decode 状态构造 ``ScheduleBatch``。 -- 控制 ``max_running_requests``、``max_total_tokens``、``max_prefill_tokens`` 等资源约束。 -- 在请求结束或中止时释放 request slot 和 KV slot。 -- 将 decode token 发送给 detokenizer。 +- 接收 tokenized request。 +- 把输入请求转成内部 ``Req`` 状态。 +- 按 prefill / decode 状态构造 ``ScheduleBatch``。 +- 守住 ``max_running_requests``、``max_total_tokens``、``max_prefill_tokens`` 这些资源约束。 +- 请求结束或中止时释放 request slot 和 KV slot。 +- 把 decode token 发给 detokenizer。 -当前调度策略以 FCFS 和单 GPU 资源约束为主。``max_prefill_tokens`` 用于限制一轮调度 -可接纳的 prefill token 数；长 prompt 的运行时 chunked prefill 切分仍待后续接入。 +当前调度策略以 FCFS 加单 GPU 资源约束为主。``max_prefill_tokens`` 用来限制一轮调度能接纳的 +prefill token 数；长 prompt 的运行时 chunked prefill 切分还没接进来，是后续的事。 ModelRunner ---------------------------------------- -``ModelRunner`` 是真正执行模型 forward 的组件。它在初始化阶段完成： +``ModelRunner`` 是真正跑模型 forward 的组件。初始化阶段它会： 1. 设置 CUDA device 和默认 dtype。 2. 加载模型类和 safetensors 权重。 -3. 解析模型 metadata，例如 layer 数、head 数、head dim、context length。 +3. 解析模型 metadata，比如 layer 数、head 数、head dim、context length。 4. 初始化 request-to-token pool、token-to-KV pool 和 KV allocator。 5. 初始化 attention backend。 6. 预热 cuBLAS。 -7. 按配置捕获 decode CUDA Graph。 +7. 按配置 capture decode CUDA Graph。 -forward 阶段分为 extend 和 decode 两类： +forward 分 extend 和 decode 两类： -- **extend / prefill**：处理 prompt token，写入 KV cache，并返回每个请求最后一个 token 的 - logits。 +- **extend / prefill**：处理 prompt token，写 KV cache，返回每个请求最后一个 token 的 logits。 - **decode**：每个请求生成一个新 token，复用已有 KV cache 和 attention metadata。 KV cache 与 prefix cache ---------------------------------------- -``pymllm.mem_cache.memory_pool`` 中的 KV 管理采用三层结构： +``pymllm.mem_cache.memory_pool`` 里的 KV 管理是三层结构： .. code-block:: text ReqToTokenPool - maps (request slot, position) -> kv index + (request slot, position) -> kv index TokenToKVPoolAllocator - manages free integer KV slots + 管理空闲的整数 KV slot KVPool - stores per-layer K/V tensors on GPU + 在 GPU 上存每层的 K/V tensor -``TokenToKVPoolAllocator`` 使用 free-list 管理 KV slot，并通过批量释放接口降低大量请求结束或 -prefix cache eviction 时的开销。``KVPool`` 在条件满足时会调用 ``mllm-kernel`` 的 -``store_cache`` JIT kernel 写入 K/V；否则回退到 PyTorch indexing。 +``TokenToKVPoolAllocator`` 用 free-list 管理 KV slot，并提供批量释放接口，在大量请求结束或 +prefix cache eviction 时降低开销。``KVPool`` 在条件满足时调用 ``mllm-kernel`` 的 +``store_cache`` JIT kernel 写 K/V，否则回退到 PyTorch indexing。 -Prefix cache 当前有三种实现： +prefix cache 目前有三种实现： -- ``RadixCache``：标准 radix-tree prefix cache。 -- ``ChunkCache``：关闭 radix cache 时使用的简单缓存路径。 -- ``MambaRadixCache``：为包含 GDN / Mamba-like 状态的 hybrid 模型预留的状态缓存路径。 +- ``RadixCache``：标准的 radix-tree prefix cache。 +- ``ChunkCache``：关掉 radix cache 时用的简单缓存路径。 +- ``MambaRadixCache``：给带 GDN / Mamba-like 状态的 hybrid 模型预留的状态缓存路径。 -当启用 ``RadixCache`` 时，extend batch 会先执行 prefix matching。命中的 prefix token 不再 -重复计算，但对应 radix tree 节点会被 lock，直到请求结束或资源释放时再 unlock。 +开 ``RadixCache`` 时，extend batch 会先做 prefix matching：命中的 prefix token 不再重复计算， +但对应的 radix tree 节点会被 lock 住，直到请求结束或资源释放才 unlock。 IPC 与多模态数据传输 ---------------------------------------- -普通控制消息通过 ZMQ 传输。多模态请求中的大 tensor 可以走 shared queue fast path， -由 ``enable_shared_queue`` 和 ``tensor_transport_mode`` 控制。 +普通控制消息走 ZMQ。多模态请求里的大 tensor 可以走 shared queue 这条 fast path，由 +``enable_shared_queue`` 和 ``tensor_transport_mode`` 控制。 -``tensor_transport_mode`` 支持三种模式： +``tensor_transport_mode`` 有三种模式： .. list-table:: :header-rows: 1 @@ -183,22 +180,22 @@ IPC 与多模态数据传输 - 行为 - 适用场景 * - ``default`` - - GPU tensor 先拷到 CPU，再放入 POSIX shared memory。 + - GPU tensor 先拷回 CPU，再放进 POSIX shared memory。 - 最稳妥，调试优先。 * - ``cuda_ipc`` - GPU tensor 通过 CUDA IPC handle 跨进程共享。 - - 避免 GPU->CPU 拷贝，但长服务中可能有 PyTorch IPC 生命周期问题。 + - 省掉 GPU→CPU 拷贝，但长时间服务里可能踩到 PyTorch IPC 的生命周期问题。 * - ``cuda_ipc_pool`` - - 使用预分配 GPU workspace，发送方回收 chunk。 - - 面向生产服务的推荐 GPU tensor 传输方式。 + - 用预分配的 GPU workspace，发送方回收 chunk。 + - 面向生产服务推荐的 GPU tensor 传输方式。 与 mllm C++ Backend 的关系 ---------------------------------------- -``pymllm`` 和 ``cpu_backend``、``qnn_backend``、``ascend_backend`` 的层级不同： +``pymllm`` 和 ``cpu_backend``、``qnn_backend``、``ascend_backend`` 不在同一个层级： -- C++ Backend 接入的是 mllm C++ 的 Tensor、Op、Module、Dispatcher 和设备 allocator。 -- ``pymllm`` 接入的是 Python/PyTorch serving pipeline，主要服务于在线推理、模型加载、 - KV cache、调度和 CUDA kernel 集成。 -- ``mllm-kernel`` 是两者可以共享思想的低层 kernel 工具包，但当前 ``pymllm`` 更直接依赖 - 其中的 Python JIT CUDA kernel。 +- C++ Backend 接的是 mllm C++ 那套 Tensor、Op、Module、Dispatcher 和设备 allocator。 +- ``pymllm`` 接的是 Python / PyTorch serving pipeline，服务在线推理、模型加载、KV cache、 + 调度和 CUDA kernel 集成。 +- ``mllm-kernel`` 是两边都可以借鉴的低层 kernel 工具包，不过目前 ``pymllm`` 更直接依赖其中 + 的 Python JIT CUDA kernel。 diff --git a/docs/pymllm_runtime/setup_and_usage.rst b/docs/pymllm_runtime/setup_and_usage.rst index 2cb83ca51..b42a75e11 100644 --- a/docs/pymllm_runtime/setup_and_usage.rst +++ b/docs/pymllm_runtime/setup_and_usage.rst @@ -4,19 +4,16 @@ pymllm Setup and Usage 总览 ---------------------------------------- -``pymllm`` 是 mllm 面向 Python 生态的推理服务运行时，主要面向 NVIDIA Jetson -Orin 系列边缘 GPU 设备，例如 Jetson Orin NX 与 Jetson AGX Orin。它覆盖 -Qwen3 / Qwen3-VL 的 BF16、W4A16 和 W8A8 推理路径，并提供 OpenAI-compatible -HTTP API。 +``pymllm`` 是 mllm 面向 Python / CUDA 生态的推理服务运行时，主要跑在 NVIDIA Jetson +Orin 系列边缘 GPU（Orin NX / AGX Orin）上。它针对 Orin Ampere Tensor Core 的 INT8 +算力做了系统级适配，支持 BF16 原生推理以及 W4A16、W8A8_INT8 两种量化方案，兼顾推理 +速度与模型精度，目前已完成对 Qwen3、Qwen3-VL、Qwen3.5 的支持，并对外提供一套 +OpenAI-compatible 的 HTTP API。 环境要求 ---------------------------------------- -当前推荐基于 `jetson-containers `_ -提供的 Jetson PyTorch/CUDA 基础镜像进行开发。这样可以避免在 Jetson 上手工处理 -PyTorch、CUDA、cuDNN、Python ABI 等基础依赖。 - -已验证环境如下： +下面是当前已经跑通的一组版本： .. list-table:: :header-rows: 1 @@ -47,7 +44,7 @@ PyTorch、CUDA、cuDNN、Python ABI 等基础依赖。 安装依赖 ---------------------------------------- -在 Jetson 容器中克隆仓库后，进入仓库根目录安装 ``pymllm`` 和 ``mllm-kernel``： +克隆仓库后，进入根目录安装 ``pymllm`` 和 ``mllm-kernel``： .. code-block:: bash @@ -55,67 +52,47 @@ PyTorch、CUDA、cuDNN、Python ABI 等基础依赖。 SKBUILD_WHEEL_CMAKE=false python3 -m pip install -e . python3 -m pip install -e /mllm-kernel --no-deps --no-build-isolation -``transformers`` 可按项目需要自行安装。``triton`` 和 ``flashinfer`` 可以从 -Jetson AI Lab 的 wheel 源安装，也可以从官方 PyPI 或对应上游项目安装： +``triton`` 和 ``flashinfer`` 有两个来源，任选其一： .. code-block:: bash - # 方式一：从 Jetson AI Lab 安装 Jetson wheel。 + # 方式一：从 Jetson AI Lab 装 Jetson wheel。 python3 -m pip install --extra-index-url https://pypi.jetson-ai-lab.io/ triton flashinfer - # 方式二：从官方 PyPI 固定 Triton，再单独安装 FlashInfer。 + # 方式二：从官方 PyPI 固定 Triton 版本，FlashInfer 仍从 Jetson AI Lab 装。 python3 -m pip install --index-url https://pypi.org/simple triton==3.6.0 python3 -m pip install --extra-index-url https://pypi.jetson-ai-lab.io/ flashinfer -在 Jetson / aarch64 上，Triton wheel 的可用性会受到 wheel 来源、CUDA 路径和 -``ptxas`` / ``cuda.h`` 查找路径影响。Jetson AI Lab 源提供面向 JetPack 6 / -CUDA 12.6 的 Triton wheel；在已验证环境中，官方 PyPI 的 ``triton==3.6.0`` -manylinux aarch64 wheel 更接近开箱即用。若使用 Jetson AI Lab wheel 遇到 -``ptxas`` 或 CUDA 头文件查找问题，可显式设置 ``TRITON_PTXAS_PATH`` 和 -``CPATH`` 后重试。无论选择哪个来源，都建议用最小 Triton kernel 或 -``per_token_quant_int8`` 做 smoke test。 - -最小导入检查： - -.. code-block:: bash +在 aarch64 上，Triton wheel 能不能开箱即用，主要取决于 wheel 来源以及 +``ptxas`` / ``cuda.h`` 的查找路径。在上面这组已验证环境里，官方 PyPI 的 +``triton==3.6.0`` manylinux aarch64 wheel 更接近开箱即用；如果用 Jetson AI Lab +的 wheel 碰到 ``ptxas`` 或 CUDA 头文件找不到的问题，显式设置 ``TRITON_PTXAS_PATH`` +和 ``CPATH`` 再重试通常能解决。装完后建议用 ``per_token_quant_int8`` 之类的最小 +kernel 跑一次 smoke test，确认 Triton 真的能编译。 - python3 - <<'PY' - import pymllm - import mllm_kernel - - print("pymllm import ok") - print("mllm_kernel import ok") - PY - -CUTLASS 头文件 +W8A8 首次运行的 JIT 编译 ---------------------------------------- -W8A8 的高性能 GEMM 路径依赖 CUTLASS 头文件。当前查找顺序为： - -1. ``CUTLASS_HOME/include`` -2. ``flashinfer`` 内置的 ``data/cutlass/include`` -3. ``/usr/local/include``、``/usr/include``、``/usr/local/cuda/include`` +W8A8 的 INT8 GEMM 走 CUTLASS，依赖 CUTLASS 头文件。默认情况下不需要额外配置—— +``flashinfer`` 自带了一份 bundled CUTLASS，可以直接用；如果想换成自己的版本，设置 +``CUTLASS_HOME`` 即可。 -首次调用 CUTLASS W8A8 kernel 会触发 JIT 编译，编译产物会复用： +第一次调用 W8A8 kernel 会触发一次 JIT 编译，编译产物缓存在： .. code-block:: text ~/.cache/mllm_kernel/cutlass_int8_scaled_mm/ -如果需要重新验证首次编译行为，可以删除该目录后再次运行。 +之后复用缓存，不会再编译。想重新验证首次编译行为时，删掉这个目录再跑一次就行。 启动服务 ---------------------------------------- -``pymllm`` 的服务入口是 ``pymllm.server.launch``。服务启动后会提供 -``/health``、``/v1/models``、``/v1/completions``、``/v1/chat/completions``、 -``/generate`` 等接口。 +服务入口是 ``pymllm.server.launch``，启动后提供 ``/health``、``/v1/models``、 +``/v1/completions``、``/v1/chat/completions``、``/generate`` 等接口。 -W4A16 / W8A8 量化模型 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -``compressed-tensors`` 量化模型使用同一个启动入口。运行时会根据模型 -``config.json`` 中的量化配置识别 W4A16 或 W8A8 路径。 +W4A16 / W8A8 量化模型和 BF16 原生模型共用同一个入口，运行时会读 ``config.json`` +里的量化配置，自动走 W4A16 或 W8A8 路径。一条典型的量化模型启动命令： .. code-block:: bash @@ -123,45 +100,17 @@ W4A16 / W8A8 量化模型 python3 -m pymllm.server.launch \ --server.model_path \ - --server.tokenizer_path \ - --server.load_format safetensors \ --server.dtype float16 \ --quantization.method compressed-tensors \ --server.host 0.0.0.0 \ --server.port 30000 \ - --server.attention_backend auto \ - --server.gdn_decode_backend pytorch \ - --server.mem_fraction_static 0.05 \ + --server.mem_fraction_static 0.8 \ --server.max_running_requests 1 \ - --server.max_total_tokens 256 \ - --server.max_prefill_tokens 128 \ + --server.max_total_tokens 4096 \ --server.disable_radix_cache \ - --server.disable_cuda_graph \ --server.log_level debug -BF16 原生模型 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -BF16 或 FP16 原生模型不需要设置 ``--quantization.method``： - -.. code-block:: bash - - cd - - python3 -m pymllm.server.launch \ - --server.model_path \ - --server.tokenizer_path \ - --server.load_format safetensors \ - --server.dtype bfloat16 \ - --server.host 0.0.0.0 \ - --server.port 30000 \ - --server.attention_backend auto \ - --server.mem_fraction_static 0.05 \ - --server.max_running_requests 1 \ - --server.max_total_tokens 256 \ - --server.max_prefill_tokens 128 \ - --server.disable_radix_cache \ - --server.log_level info +BF16 / FP16 原生模型用同一条命令，去掉 ``--quantization.method`` 即可。 常用参数 ---------------------------------------- @@ -174,21 +123,22 @@ BF16 或 FP16 原生模型不需要设置 ``--quantization.method``： * - ``--server.model_path`` - 模型权重目录，通常是 HuggingFace 或 ModelScope 格式。 * - ``--server.tokenizer_path`` - - tokenizer 目录；不设置时默认等于 ``model_path``。 + - tokenizer 目录；不设置时默认等于 ``model_path``，一般不用单独传。 * - ``--server.dtype`` - - 模型运行 dtype，可选 ``auto``、``float16``、``bfloat16``、``float32``。 + - 模型运行 dtype，可选 ``auto``、``float16``、``bfloat16``。 * - ``--quantization.method compressed-tensors`` - - 启用 ``compressed-tensors`` 权重加载与线性层执行路径。 + - 启用 ``compressed-tensors`` 权重加载和量化线性层执行路径。 + * - ``--server.mem_fraction_static`` + - ``模型权重 + KV cache pool`` 占 GPU 总显存的静态预算比例。设太小，KV pool 预算 + 不足会导致启动报错；设太大，留给 activation 和 CUDA Graph 的动态空间不够。 + Jetson 上 Qwen3-VL-2B 量化模型一般在 ``0.5``–``0.8`` 之间起调。 * - ``--server.max_running_requests`` - - 同时运行的请求数。Jetson 小显存环境下通常从 ``1`` 开始调试。 + - 同时运行的请求数。Jetson 小显存环境一般从 ``1`` 开始调。 * - ``--server.max_total_tokens`` - - KV cache token pool 的总容量上限。 - * - ``--server.max_prefill_tokens`` - - 单轮 prefill 可处理的 token 上限。 + - KV cache token pool 的容量上限，是整个 worker 全局共享的池子（不是单请求上限）。 + 实际容量取 ``min(profile 可承载 token 数, max_total_tokens)``，不会绕过显存 profile。 * - ``--server.disable_radix_cache`` - 关闭 Radix Cache，改用 ``ChunkCache``。 - * - ``--server.disable_cuda_graph`` - - 关闭 decode CUDA Graph，便于调试动态路径。 OpenAI-compatible 请求 ---------------------------------------- @@ -213,7 +163,7 @@ OpenAI-compatible 请求 "stream": false }' ; echo -图文请求中，图片路径需要是容器内可访问的绝对路径，不要带 ``file://`` 前缀： +图文请求里的图片路径要用服务进程可访问的绝对路径，不要带 ``file://`` 前缀： .. code-block:: bash @@ -239,122 +189,106 @@ OpenAI-compatible 请求 -H "Content-Type: application/json" \ --data @/tmp/mm_req_path.json ; echo -开发与测试 +Benchmark ---------------------------------------- -常用单元测试： - -.. code-block:: bash - - pytest pymllm/tests/test_compressed_tensors_config.py -q - pytest pymllm/tests/test_compressed_tensors_runtime.py -q - pytest pymllm/tests/test_qwen3_model_registry.py -q - pytest pymllm/tests/test_qwen3_weight_loading.py -q - pytest pymllm/tests/test_qwen3_forward_timing.py -q - pytest mllm-kernel/tests/test_int8_scaled_mm_cutlass.py -q +``bench_one_batch`` 是一个低层离线 benchmark。它直接初始化 +``pymllm.executor.model_runner.ModelRunner``，绕过 HTTP server、tokenizer、scheduler、 +detokenizer 这些进程，只测模型本身一次静态 prefill 加逐 token decode 的开销，因此适合 +分析模型 forward、KV cache、attention、CUDA Graph 和量化 kernel 的模型级表现，也方便 +验证 fused projection、residual-carry 这类模型图优化。它测不到在线服务的 TTFT / ITL / +E2E，这两个口径不要混用。 -模型级 benchmark： -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +目前 ``bench_one_batch`` 支持三种测速口径： -``bench_one_batch`` 是对齐 SGLang 口径的低层离线 benchmark。它直接初始化 -``pymllm.executor.model_runner.ModelRunner``，绕过 HTTP server、tokenizer 进程、 -scheduler 进程和 detokenizer 进程，用 synthetic text-only token ids 测一次静态 -prefill，再测逐 token decode。该工具适合分析模型 forward、KV cache、attention、 -CUDA Graph 与量化 kernel 的模型级开销，也适合验证 fused projection、residual carry -等模型图优化。不代表在线服务的 TTFT / ITL / E2E 指标。 +- **纯文本**：用 synthetic token ids 测纯文本的 prefill / decode； +- **视觉编码（vit_prefill）**：同步墙钟只包住视觉 encoder（``self.visual(...)``）， + 反映纯视觉编码速度； +- **多模态 prefill（multimodal_prefill）**：覆盖“视觉编码 + 图像/文本 token 的 LLM + prefill”，反映完整多模态 prefill 速度。 -典型用法： +纯文本用法： .. code-block:: bash PYTHONPATH="$PWD:$PWD/mllm-kernel" python3 -m pymllm.bench_one_batch \ --server.model_path \ - --server.tokenizer_path \ - --server.load_format safetensors \ --server.dtype float16 \ --quantization.method compressed-tensors \ - --server.mem_fraction_static 0.1 \ + --server.mem_fraction_static 0.8 \ --server.max_running_requests 1 \ --server.max_total_tokens 2048 \ - --server.disable_radix_cache \ --server.log_level info \ --run-name qwen3vl_w8a8_bench_one_batch \ --batch-size 1 \ --input-len 256 512 1024 \ --output-len 128 \ - --result-filename /tmp/pymllm_bench_one_batch.jsonl + --result-filename + +``--batch-size``、``--input-len``、``--output-len`` 都支持多个值，脚本会遍历所有组合 +并把结果追加到 JSONL 文件。``output_len`` 用的是总输出 token 语义：prefill 之后已经 +拿到第一个 next token，后续 decode loop 再跑 ``output_len - 1`` 步。 + +多模态 prefill 用法。给 ``--image`` 传一张真实图片，再显式传 ``--input-len`` 时，长度 +口径是 ``image placeholder tokens + text prompt tokens`` 的目标总长——脚本只在文本 +token 上做补齐或截断，绝不动 image token，因此可以用同一张图 sweep +``314/512/1024/2048`` 等不同总长，测包含视觉编码的完整多模态 prefill 速度： + +.. code-block:: bash + + PYTHONPATH="$PWD:$PWD/mllm-kernel" python3 -m pymllm.bench_one_batch \ + --server.model_path \ + --server.trust_remote_code true \ + --server.dtype float16 \ + --quantization.method compressed-tensors \ + --server.mem_fraction_static 0.8 \ + --server.max_running_requests 1 \ + --server.disable_cuda_graph \ + --batch-size 1 \ + --input-len 314 512 1024 2048 \ + --output-len 1 \ + --image \ + --prompt "Describe this image." \ + --run-name qwen3vl_w8a8_multimodal_prefill \ + --result-filename -其中 ``--batch-size``、``--input-len`` 和 ``--output-len`` 都支持多个值，脚本会遍历 -所有组合并向 JSONL 文件追加结果。``output_len`` 采用 SGLang 的总输出 token 语义： -prefill 后已得到第一个 next token，后续 decode loop 执行 ``output_len - 1`` 步。 +JSONL 里 ``vit_prefill_ms`` 只包住 ``self.visual(...)``，``multimodal_prefill_*`` +则是完整 VIT + LLM prefill 的别名字段，两者口径不同。在 AGX Orin 32GB 上的实测中， +W8A8 在长 prefill 上明显领先 FP16 / W4A16。 -执行结构： +脚本的整体执行流程大致是： .. code-block:: text pymllm.bench_one_batch CLI | - |-- parse GlobalConfig args and BenchArgs - |-- load HuggingFace AutoConfig into cfg.model.hf_config + |-- 解析 GlobalConfig 参数和 BenchArgs + |-- 加载 HuggingFace AutoConfig 到 cfg.model.hf_config | |-- ModelRunner.initialize() - | |-- load model and quantization config - | |-- initialize KV pools and attention backend - | |-- optionally capture decode CUDA Graph + | |-- 加载模型和量化配置 + | |-- 初始化 KV pool 和 attention backend + | |-- 按需 capture decode CUDA Graph | - |-- warmup once + |-- warmup 一次 | - |-- for each (batch_size, input_len, output_len): - | - |-- clear req_to_token_pool and token_to_kv_pool_allocator - |-- build synthetic input_ids - |-- prefill: - | allocate request slots and KV slots - | write prompt KV mapping - | prepare ForwardBatch(EXTEND) - | synchronize, run forward + sampling, synchronize - | - |-- decode loop: - allocate one KV slot per request - write current token mapping - prepare ForwardBatch(DECODE) - synchronize, run forward + sampling, synchronize - update seq_lens and next token ids + |-- 遍历每个 (batch_size, input_len, output_len): + | |-- 清空 req_to_token_pool 和 token_to_kv_pool_allocator + | |-- 构造 synthetic input_ids + | |-- prefill：分配 request/KV slot，写 KV 映射，跑 forward + sampling + | |-- decode loop：逐步分配 KV slot，跑 forward + sampling，更新 seq_lens | - |-- append JSONL result rows + |-- 追加 JSONL 结果行 -Profile 辅助入口： -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -``bench_one_batch`` 保留了基于 ``torch.profiler`` 的 profile 参数，主要用于本地 -kernel timeline 分析。当前公开 benchmark 记录没有使用 profile 结果，因此它不作为标准 -性能数据口径的一部分。使用前建议先用较小的 ``input_len`` / ``output_len`` 做一次 -trace 生成验证，再扩大到正式 case。 - -.. code-block:: bash - - PYMLLM_TORCH_PROFILER_DIR=/tmp \ - PYTHONPATH="$PWD:$PWD/mllm-kernel" python3 -m pymllm.bench_one_batch \ - --server.model_path \ - --server.tokenizer_path \ - --server.load_format safetensors \ - --server.dtype bfloat16 \ - --server.mem_fraction_static 0.1 \ - --server.max_running_requests 1 \ - --server.max_total_tokens 2048 \ - --server.log_level info \ - --batch-size 1 \ - --input-len 256 \ - --output-len 128 \ - --profile \ - --profile-stage decode \ - --profile-steps 1 +Profile +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -已知限制 ----------------------------------------- +``bench_one_batch`` 内置了 profile 入口，方便在本地看 kernel timeline。目前有两条路径： -- W8A8 CUTLASS 当前通过 JIT 编译，首次启动有明显编译开销。 -- W8A8 激活量化使用 Triton kernel；decode 小 batch 下固定量化开销仍是后续优化点。 -- Qwen3-VL 的 ViT、``lm_head``、embedding 和 LayerNorm 不在当前 W8A8 量化范围内。 -- 当前文档中的 Jetson 性能与稳定性结论主要来自 Orin NX / SM87，需要在其他 GPU 上重新验证。 -- OpenAI-compatible API 的服务级指标和 ``bench_one_batch`` 的模型级指标口径不同，不应直接混用。 +- **torch.profiler（已支持）**：``--profile-activities CPU GPU``（默认），输出 + ``.trace.json.gz`` timeline，可以直接在 Perfetto / chrome://tracing 里看。输出目录 + 由 ``PYMLLM_TORCH_PROFILER_DIR`` 指定，默认 ``/tmp``。 +- **Nsight Systems / nsys（实验性）**：``--profile-activities CUDA_PROFILER`` 通过 + ``cudaProfilerStart/Stop`` 驱动 nsys，需要外层用 + ``nsys --capture-range=cudaProfilerApi`` 包住命令。这条路径还在打磨中，部分场景下 + 可能不够顺手，仅作为可选的深入分析手段。 From d442aae220b45988245daf0f8e03b5e00537bb5b Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Mon, 8 Jun 2026 11:26:32 +0000 Subject: [PATCH 19/21] docs(pymllm): rewrite README to mirror runtime setup doc Slim down README.md and README-ZH.md to match the pymllm_runtime setup_and_usage doc: fix launch args (drop stale flags, mem_fraction_static 0.8 / max_total_tokens 4096), align mem_fraction_static semantics, refresh bench_one_batch (text / vit / multimodal modes) and profile guidance, and drop redundant status / forward-path / microbench / known-limitations sections. Co-Authored-By: Claude Opus 4.8 (1M context) --- pymllm/README-ZH.md | 315 +++++++++++++++++++--------------------- pymllm/README.md | 343 +++++++++++++++++++++----------------------- 2 files changed, 306 insertions(+), 352 deletions(-) diff --git a/pymllm/README-ZH.md b/pymllm/README-ZH.md index a32c6580c..fcb77fd2e 100644 --- a/pymllm/README-ZH.md +++ b/pymllm/README-ZH.md @@ -2,179 +2,127 @@ ![pymllm-arch](../assets/pymllm-arch.png) -`pymllm` 是 `mllm` 的 Python 推理服务入口。本目录当前重点覆盖 -Jetson Orin 上的 Qwen3 / Qwen3-VL 推理、OpenAI-compatible server、 -`compressed-tensors` 量化加载，以及 W8A8 INT8 kernel 路径。 +## 总览 -本文档按 2026-04-27 的开发状态整理，适用于当前集成分支： +`pymllm` 是 mllm 面向 Python / CUDA 生态的推理服务运行时，主要跑在 NVIDIA Jetson +Orin 系列边缘 GPU（Orin NX / AGX Orin）上。它针对 Orin Ampere Tensor Core 的 INT8 +算力做了系统级适配，支持 BF16 原生推理以及 W4A16、W8A8_INT8 两种量化方案，兼顾推理 +速度与模型精度，目前已完成对 Qwen3、Qwen3-VL、Qwen3.5 的支持，并对外提供一套 +OpenAI-compatible 的 HTTP API。 -```text -feature/jetson-qwen3-family-bf16-w4a16-w8a8 -``` - -## 当前状态 - -已验证路径： +## 环境要求 -- `Qwen3-VL-2B-Instruct`：BF16 原生模型服务可用。 -- `Qwen3-VL-2B-Instruct-AWQ-4bit`：`compressed-tensors` - W4A16 / AWQ Marlin 路径可用。 -- `Qwen3-VL-2B-Instruct-quantized.w8a8`：`compressed-tensors` - W8A8 `int-quantized` 路径端到端可用。 +下面是当前已经跑通的一组版本： -已实现并纳入单元测试的模型/组件： +| 组件 | 版本或说明 | +| --- | --- | +| JetPack / Jetson Linux | JetPack `6.2.1` / Jetson Linux `36.4.4` (L4T `R36.4.4`) | +| Python | `3.10.12` | +| PyTorch | `2.4.0` | +| torchvision | `0.19.0a0+48b1edf` | +| transformers | `5.3.0` | +| safetensors | `0.7.0` | +| flashinfer | `0.6.7` | +| Triton Language | `triton==3.6.0` aarch64 wheel | +| CUDA | `12.6` | +| GPU | Jetson Orin NX，SM87 | -- `Qwen3VLForConditionalGeneration`：图文模型服务主路径。 -- `Qwen3ForCausalLM`：文本模型骨架、权重加载与 timing 字段测试。 -- `compressed-tensors`： - - `pack-quantized` 4-bit 权重路径，使用 GPTQ Marlin。 - - `int-quantized` W8A8 路径，使用 Triton 激活量化 + CUTLASS - `int8_scaled_mm`。 +## 安装依赖 -W8A8 当前前向链路： +克隆仓库后，进入根目录安装 `pymllm` 和 `mllm-kernel`： -```text -x(fp16/bf16) - -> per_token_quant_int8 [Triton, dynamic per-token activation quant] - -> int8_scaled_mm [CUTLASS, INT8 Tensor Core, fused scales] - -> output(fp16/bf16) +```bash +cd +SKBUILD_WHEEL_CMAKE=false python3 -m pip install -e . +python3 -m pip install -e /mllm-kernel --no-deps --no-build-isolation ``` -## 已验证环境 +`triton` 和 `flashinfer` 有两个来源，任选其一： -以下命令基于 Jetson Orin 环境整理： +```bash +# 方式一：从 Jetson AI Lab 装 Jetson wheel。 +python3 -m pip install --extra-index-url https://pypi.jetson-ai-lab.io/ triton flashinfer -- JetPack / L4T：`R36.4.4`（来自 `/etc/nv_tegra_release`） -- Python：`3.10.12` -- PyTorch：`2.4.0` -- torchvision：`0.19.0a0+48b1edf` -- transformers：`5.3.0` -- safetensors：`0.7.0` -- flashinfer：`0.6.7` -- Triton Language：官方 PyPI `triton==3.6.0` manylinux aarch64 wheel -- CUDA：`12.6` -- GPU：Jetson Orin NX，SM87 +# 方式二：从官方 PyPI 固定 Triton 版本，FlashInfer 仍从 Jetson AI Lab 装。 +python3 -m pip install --index-url https://pypi.org/simple triton==3.6.0 +python3 -m pip install --extra-index-url https://pypi.jetson-ai-lab.io/ flashinfer +``` -这里的 Triton 指 GPU kernel DSL，不是 Triton Inference Server。Jetson-AI-Lab -源也提供 `3.4.0`、`3.5.1`、`3.6.0`，但实测中可能需要额外设置 -`TRITON_PTXAS_PATH` 和 `CPATH`。当前建议优先使用官方 PyPI 的 -`triton==3.6.0`，并用最小 CUDA kernel 或 `per_token_quant_int8` 做 smoke test。 +在 aarch64 上，Triton wheel 能不能开箱即用，主要取决于 wheel 来源以及 +`ptxas` / `cuda.h` 的查找路径。在上面这组已验证环境里，官方 PyPI 的 +`triton==3.6.0` manylinux aarch64 wheel 更接近开箱即用；如果用 Jetson AI Lab +的 wheel 碰到 `ptxas` 或 CUDA 头文件找不到的问题，显式设置 `TRITON_PTXAS_PATH` +和 `CPATH` 再重试通常能解决。装完后建议用 `per_token_quant_int8` 之类的最小 +kernel 跑一次 smoke test，确认 Triton 真的能编译。 -W8A8 CUTLASS JIT 需要能找到 CUTLASS 头文件。当前查找顺序为： +## W8A8 首次运行的 JIT 编译 -1. `CUTLASS_HOME/include` -2. `flashinfer` 内置的 `data/cutlass/include` -3. `/usr/local/include`、`/usr/include`、`/usr/local/cuda/include` +W8A8 的 INT8 GEMM 走 CUTLASS，依赖 CUTLASS 头文件。默认情况下不需要额外配置—— +`flashinfer` 自带了一份 bundled CUTLASS，可以直接用；如果想换成自己的版本，设置 +`CUTLASS_HOME` 即可。 -首次调用 CUTLASS kernel 会触发 JIT 编译，耗时约 100 秒；后续会复用： +第一次调用 W8A8 kernel 会触发一次 JIT 编译，编译产物缓存在： ```text ~/.cache/mllm_kernel/cutlass_int8_scaled_mm/ ``` -## 安装开发环境 - -在仓库根目录执行： - -```bash -cd -SKBUILD_WHEEL_CMAKE=false python3 -m pip install -e . -python3 -m pip install -e /mllm-kernel --no-deps --no-build-isolation -``` - -最小导入检查： - -```bash -python3 - <<'PY' -import pymllm -import mllm_kernel - -print("pymllm import ok") -print("mllm_kernel import ok") -PY -``` +之后复用缓存，不会再编译。想重新验证首次编译行为时，删掉这个目录再跑一次就行。 ## 启动服务 -### 量化模型（W4A16 / W8A8） +服务入口是 `pymllm.server.launch`，启动后提供 `/health`、`/v1/models`、 +`/v1/completions`、`/v1/chat/completions`、`/generate` 等接口。 + +W4A16 / W8A8 量化模型和 BF16 原生模型共用同一个入口，运行时会读 `config.json` +里的量化配置，自动走 W4A16 或 W8A8 路径。一条典型的量化模型启动命令： ```bash cd python3 -m pymllm.server.launch \ --server.model_path \ - --server.tokenizer_path \ - --server.load_format safetensors \ --server.dtype float16 \ --quantization.method compressed-tensors \ --server.host 0.0.0.0 \ --server.port 30000 \ - --server.attention_backend auto \ - --server.gdn_decode_backend pytorch \ - --server.mem_fraction_static 0.05 \ + --server.mem_fraction_static 0.8 \ --server.max_running_requests 1 \ - --server.max_total_tokens 256 \ - --server.max_prefill_tokens 128 \ - --server.chunked_prefill_size 128 \ + --server.max_total_tokens 4096 \ --server.disable_radix_cache \ - --server.disable_cuda_graph \ --server.log_level debug ``` -说明： +BF16 / FP16 原生模型用同一条命令，去掉 `--quantization.method` 即可。 -- `--quantization.method compressed-tensors` 会按模型 `config.json` - 自动识别 W4A16 或 W8A8 签名。 -- W8A8 路径要求 GPU capability 不低于 SM80。 -- `--server.disable_radix_cache` 会使用 `ChunkCache`，当前已修复该模式下的 - KV slot 泄漏问题。 -- 若 `30000` 已被占用，可改成其他空闲端口。 +## 常用参数 -### BF16 原生模型 +| 参数 | 说明 | +| --- | --- | +| `--server.model_path` | 模型权重目录，通常是 HuggingFace 或 ModelScope 格式。 | +| `--server.tokenizer_path` | tokenizer 目录；不设置时默认等于 `model_path`，一般不用单独传。 | +| `--server.dtype` | 模型运行 dtype，可选 `auto`、`float16`、`bfloat16`。 | +| `--quantization.method compressed-tensors` | 启用 `compressed-tensors` 权重加载和量化线性层执行路径。 | +| `--server.mem_fraction_static` | `模型权重 + KV cache pool` 占 GPU 总显存的静态预算比例。设太小，KV pool 预算不足会导致启动报错；设太大，留给 activation 和 CUDA Graph 的动态空间不够。Jetson 上 Qwen3-VL-2B 量化模型一般在 `0.5`–`0.8` 之间起调。 | +| `--server.max_running_requests` | 同时运行的请求数。Jetson 小显存环境一般从 `1` 开始调。 | +| `--server.max_total_tokens` | KV cache token pool 的容量上限，是整个 worker 全局共享的池子（不是单请求上限）。实际容量取 `min(profile 可承载 token 数, max_total_tokens)`，不会绕过显存 profile。 | +| `--server.disable_radix_cache` | 关闭 Radix Cache，改用 `ChunkCache`。 | -```bash -cd +## OpenAI-compatible 请求 -python3 -m pymllm.server.launch \ - --server.model_path \ - --server.tokenizer_path \ - --server.load_format safetensors \ - --server.dtype float16 \ - --server.host 0.0.0.0 \ - --server.port 30000 \ - --server.attention_backend auto \ - --server.gdn_decode_backend pytorch \ - --server.mem_fraction_static 0.05 \ - --server.max_running_requests 1 \ - --server.max_total_tokens 256 \ - --server.max_prefill_tokens 128 \ - --server.chunked_prefill_size 128 \ - --server.disable_radix_cache \ - --server.disable_cuda_graph \ - --server.log_level debug -``` - -## 调用示例 - -### 健康检查 +健康检查： ```bash curl -s --noproxy '*' http://127.0.0.1:30000/v1/models ; echo ``` -期望返回中包含： - -```text -"owned_by":"pymllm" -``` - -### 文本请求 +文本请求： ```bash curl -s --noproxy '*' http://127.0.0.1:30000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ - "model": "None", + "model": "default", "messages": [{"role": "user", "content": "你好，只回复：ok"}], "max_tokens": 8, "temperature": 0.0, @@ -182,73 +130,100 @@ curl -s --noproxy '*' http://127.0.0.1:30000/v1/chat/completions \ }' ; echo ``` -### 图文请求 - -图片路径请使用容器内可访问的绝对路径，不要使用 `file://...` 前缀。 +图文请求里的图片路径要用服务进程可访问的绝对路径，不要带 `file://` 前缀： ```bash -python3 - <<'PY' -import json - -payload = { - "model": "None", - "messages": [ - { - "role": "user", - "content": [ - {"type": "text", "text": "请详细描述这张图片。"}, - {"type": "image_url", "image_url": {"url": "/workspace/xcd_mllm/test.png"}}, - ], - } - ], - "max_tokens": 128, - "temperature": 0.0, - "stream": False, +cat > /tmp/mm_req_path.json <<'JSON' +{ + "model": "default", + "messages": [ + { + "role": "user", + "content": [ + {"type": "text", "text": "请描述这张图片。"}, + {"type": "image_url", "image_url": {"url": "/workspace/test.png"}} + ] + } + ], + "max_tokens": 128, + "temperature": 0.0, + "stream": false } - -with open("/tmp/mm_req_path.json", "w", encoding="utf-8") as f: - json.dump(payload, f, ensure_ascii=False) - -print("saved /tmp/mm_req_path.json") -PY +JSON curl -s --noproxy '*' http://127.0.0.1:30000/v1/chat/completions \ -H "Content-Type: application/json" \ --data @/tmp/mm_req_path.json ; echo ``` -## 开发与测试 +## Benchmark -常用单元测试： +`bench_one_batch` 是一个低层离线 benchmark。它直接初始化 +`pymllm.executor.model_runner.ModelRunner`，绕过 HTTP server、tokenizer、scheduler、 +detokenizer 这些进程，只测模型本身一次静态 prefill 加逐 token decode 的开销，因此适合 +分析模型 forward、KV cache、attention、CUDA Graph 和量化 kernel 的模型级表现，也方便 +验证 fused projection、residual-carry 这类模型图优化。它测不到在线服务的 TTFT / ITL / +E2E，这两个口径不要混用。 -```bash -pytest pymllm/tests/test_compressed_tensors_config.py -q -pytest pymllm/tests/test_compressed_tensors_runtime.py -q -pytest pymllm/tests/test_qwen3_model_registry.py -q -pytest pymllm/tests/test_qwen3_weight_loading.py -q -pytest pymllm/tests/test_qwen3_forward_timing.py -q -pytest mllm-kernel/tests/test_int8_scaled_mm_cutlass.py -q -``` +目前 `bench_one_batch` 支持三种测速口径： + +- **纯文本**：用 synthetic token ids 测纯文本的 prefill / decode； +- **视觉编码（vit_prefill）**：同步墙钟只包住视觉 encoder（`self.visual(...)`），反映纯视觉编码速度； +- **多模态 prefill（multimodal_prefill）**：覆盖“视觉编码 + 图像/文本 token 的 LLM prefill”，反映完整多模态 prefill 速度。 -常用 microbench： +纯文本用法： ```bash -python3 pymllm/tests/bench_w8a8_activation_quant.py -python3 mllm-kernel/benchmarks/bench_int8_scaled_mm.py -python3 mllm-kernel/benchmarks/bench_w4a16_vs_w8a8.py +PYTHONPATH="$PWD:$PWD/mllm-kernel" python3 -m pymllm.bench_one_batch \ + --server.model_path \ + --server.dtype float16 \ + --quantization.method compressed-tensors \ + --server.mem_fraction_static 0.8 \ + --server.max_running_requests 1 \ + --server.max_total_tokens 2048 \ + --batch-size 1 \ + --input-len 256 512 1024 \ + --output-len 128 \ + --result-filename ``` -如果需要重新测 CUTLASS 首次编译，可先清理 JIT 缓存： +`--batch-size`、`--input-len`、`--output-len` 都支持多个值，脚本会遍历所有组合并把结果 +追加到 JSONL 文件。`output_len` 用的是总输出 token 语义：prefill 之后已经拿到第一个 +next token，后续 decode loop 再跑 `output_len - 1` 步。 + +多模态 prefill 用法。给 `--image` 传一张真实图片，再显式传 `--input-len` 时，长度口径是 +`image placeholder tokens + text prompt tokens` 的目标总长——脚本只在文本 token 上做补齐 +或截断，绝不动 image token，因此可以用同一张图 sweep `314/512/1024/2048` 等不同总长，测 +包含视觉编码的完整多模态 prefill 速度： ```bash -rm -rf ~/.cache/mllm_kernel/cutlass_int8_scaled_mm/ +PYTHONPATH="$PWD:$PWD/mllm-kernel" python3 -m pymllm.bench_one_batch \ + --server.model_path \ + --server.trust_remote_code true \ + --server.dtype float16 \ + --quantization.method compressed-tensors \ + --server.mem_fraction_static 0.8 \ + --server.max_running_requests 1 \ + --server.disable_cuda_graph \ + --batch-size 1 \ + --input-len 314 512 1024 2048 \ + --output-len 1 \ + --image \ + --prompt "Describe this image." \ + --result-filename ``` -## 已知限制 +JSONL 里 `vit_prefill_ms` 只包住 `self.visual(...)`，`multimodal_prefill_*` 则是完整 +VIT + LLM prefill 的别名字段，两者口径不同。在 AGX Orin 32GB 上的实测中，W8A8 在长 +prefill 上明显领先 FP16 / W4A16。 + +### Profile + +`bench_one_batch` 内置了 profile 入口，方便在本地看 kernel timeline。目前有两条路径： -- W8A8 CUTLASS 当前通过 JIT 编译，首次启动存在约 100 秒编译开销。 -- W8A8 激活量化使用 Triton kernel；decode 下固定量化开销仍是后续优化点。 -- Qwen3-VL 的 ViT、`lm_head`、embedding 和 LayerNorm 不在当前 W8A8 量化范围内。 -- 其他 GPU 需要重新验证 tile dispatch、JIT 编译和性能。 -- 为对齐 SGLang/OpenAI 兼容响应，OpenAI API 默认不返回 debug timing。 - 仅在本地诊断时使用 `--server.enable_debug_timing`；严格模型级计时应使用专用 benchmark。 +- **torch.profiler（已支持）**：`--profile-activities CPU GPU`（默认），输出 + `.trace.json.gz` timeline，可以直接在 Perfetto / chrome://tracing 里看。输出目录由 + `PYMLLM_TORCH_PROFILER_DIR` 指定，默认 `/tmp`。 +- **Nsight Systems / nsys（实验性）**：`--profile-activities CUDA_PROFILER` 通过 + `cudaProfilerStart/Stop` 驱动 nsys，需要外层用 `nsys --capture-range=cudaProfilerApi` + 包住命令。这条路径还在打磨中，部分场景下可能不够顺手，仅作为可选的深入分析手段。 diff --git a/pymllm/README.md b/pymllm/README.md index 439f74bc7..0aed552eb 100644 --- a/pymllm/README.md +++ b/pymllm/README.md @@ -2,184 +2,131 @@ ![pymllm-arch](../assets/pymllm-arch.png) -`pymllm` is the Python inference and serving entry point for `mllm`. This -directory currently focuses on Qwen3 / Qwen3-VL serving on Jetson Orin, -OpenAI-compatible APIs, `compressed-tensors` quantized loading, and the W8A8 -INT8 kernel path. +## Overview -This README reflects the development state as of 2026-04-27 for the integration -branch: +`pymllm` is mllm's Python / CUDA inference and serving runtime, running mainly +on NVIDIA Jetson Orin edge GPUs (Orin NX / AGX Orin). It is adapted for the INT8 +throughput of the Orin Ampere Tensor Cores, supports BF16 native inference plus +two quantization schemes (W4A16 and W8A8_INT8), and currently covers Qwen3, +Qwen3-VL, and Qwen3.5, exposing an OpenAI-compatible HTTP API. -```text -feature/jetson-qwen3-family-bf16-w4a16-w8a8 -``` - -## Current status - -Validated paths: +## Environment -- `Qwen3-VL-2B-Instruct`: BF16 base-model serving. -- `Qwen3-VL-2B-Instruct-AWQ-4bit`: `compressed-tensors` W4A16 / AWQ Marlin - serving. -- `Qwen3-VL-2B-Instruct-quantized.w8a8`: `compressed-tensors` W8A8 - `int-quantized` end-to-end serving. +A known-good set of versions: -Implemented and unit-tested models/components: +| Component | Version / notes | +| --- | --- | +| JetPack / Jetson Linux | JetPack `6.2.1` / Jetson Linux `36.4.4` (L4T `R36.4.4`) | +| Python | `3.10.12` | +| PyTorch | `2.4.0` | +| torchvision | `0.19.0a0+48b1edf` | +| transformers | `5.3.0` | +| safetensors | `0.7.0` | +| flashinfer | `0.6.7` | +| Triton Language | `triton==3.6.0` aarch64 wheel | +| CUDA | `12.6` | +| GPU | Jetson Orin NX, SM87 | -- `Qwen3VLForConditionalGeneration`: the main multimodal serving path. -- `Qwen3ForCausalLM`: text-only model skeleton, weight loading, and timing - tests. -- `compressed-tensors`: - - `pack-quantized` 4-bit weight path via GPTQ Marlin. - - `int-quantized` W8A8 path via Triton activation quantization and CUTLASS - `int8_scaled_mm`. +## Install -The current W8A8 forward path is: +Clone the repo, then install `pymllm` and `mllm-kernel` from the repo root: -```text -x(fp16/bf16) - -> per_token_quant_int8 [Triton, dynamic per-token activation quant] - -> int8_scaled_mm [CUTLASS, INT8 Tensor Core, fused scales] - -> output(fp16/bf16) +```bash +cd +SKBUILD_WHEEL_CMAKE=false python3 -m pip install -e . +python3 -m pip install -e /mllm-kernel --no-deps --no-build-isolation ``` -## Validated environment +`triton` and `flashinfer` have two sources; pick either one: -The commands below were validated on Jetson Orin with: +```bash +# Option 1: Jetson wheels from Jetson AI Lab. +python3 -m pip install --extra-index-url https://pypi.jetson-ai-lab.io/ triton flashinfer -- JetPack / L4T: `R36.4.4` (`/etc/nv_tegra_release`) -- Python: `3.10.12` -- PyTorch: `2.4.0` -- torchvision: `0.19.0a0+48b1edf` -- transformers: `5.3.0` -- safetensors: `0.7.0` -- flashinfer: `0.6.7` -- Triton Language: official PyPI `triton==3.6.0` manylinux aarch64 wheel -- CUDA: `12.6` -- GPU: Jetson Orin NX, SM87 +# Option 2: pin Triton from official PyPI, still get FlashInfer from Jetson AI Lab. +python3 -m pip install --index-url https://pypi.org/simple triton==3.6.0 +python3 -m pip install --extra-index-url https://pypi.jetson-ai-lab.io/ flashinfer +``` -Triton here means the GPU kernel DSL, not Triton Inference Server. The -Jetson-AI-Lab index also provides `3.4.0`, `3.5.1`, and `3.6.0`, but the tested -environment may require extra `TRITON_PTXAS_PATH` and `CPATH` settings with -those wheels. For this project, prefer the official PyPI `triton==3.6.0` wheel -and verify it with a minimal CUDA kernel or `per_token_quant_int8` smoke test. +On aarch64, whether the Triton wheel works out of the box mostly depends on the +wheel source and the `ptxas` / `cuda.h` lookup paths. In the validated +environment above, the official PyPI `triton==3.6.0` manylinux aarch64 wheel is +closest to working out of the box; if a Jetson AI Lab wheel hits `ptxas` or CUDA +header lookup issues, setting `TRITON_PTXAS_PATH` and `CPATH` explicitly usually +fixes it. After installing, run a smoke test with a minimal kernel such as +`per_token_quant_int8` to confirm Triton actually compiles. -The W8A8 CUTLASS JIT path requires CUTLASS headers. The lookup order is: +## W8A8 first-run JIT compilation -1. `CUTLASS_HOME/include` -2. `flashinfer` bundled `data/cutlass/include` -3. `/usr/local/include`, `/usr/include`, `/usr/local/cuda/include` +The W8A8 INT8 GEMM goes through CUTLASS and needs CUTLASS headers. No extra +setup is required by default — `flashinfer` ships a bundled CUTLASS; set +`CUTLASS_HOME` if you want to point at your own copy. -The first CUTLASS kernel call triggers JIT compilation and may take about -100 seconds. Later runs reuse: +The first W8A8 kernel call triggers a one-time JIT compile, cached at: ```text ~/.cache/mllm_kernel/cutlass_int8_scaled_mm/ ``` -## Install the development environment - -Run from the repository root: - -```bash -cd -SKBUILD_WHEEL_CMAKE=false python3 -m pip install -e . -python3 -m pip install -e /mllm-kernel --no-deps --no-build-isolation -``` - -Run a minimal import check: - -```bash -python3 - <<'PY' -import pymllm -import mllm_kernel - -print("pymllm import ok") -print("mllm_kernel import ok") -PY -``` +Later runs reuse the cache. To re-check the first-compile behavior, delete this +directory and run again. ## Launch the server -### Quantized models (W4A16 / W8A8) +The entry point is `pymllm.server.launch`. Once up, it serves `/health`, +`/v1/models`, `/v1/completions`, `/v1/chat/completions`, and `/generate`. + +W4A16 / W8A8 quantized models and BF16 base models share the same entry point; +the runtime reads the quantization config in `config.json` and picks the W4A16 +or W8A8 path automatically. A typical quantized-model launch: ```bash cd python3 -m pymllm.server.launch \ --server.model_path \ - --server.tokenizer_path \ - --server.load_format safetensors \ --server.dtype float16 \ --quantization.method compressed-tensors \ --server.host 0.0.0.0 \ --server.port 30000 \ - --server.attention_backend auto \ - --server.gdn_decode_backend pytorch \ - --server.mem_fraction_static 0.05 \ + --server.mem_fraction_static 0.8 \ --server.max_running_requests 1 \ - --server.max_total_tokens 256 \ - --server.max_prefill_tokens 128 \ - --server.chunked_prefill_size 128 \ + --server.max_total_tokens 4096 \ --server.disable_radix_cache \ - --server.disable_cuda_graph \ --server.log_level debug ``` -Notes: +For BF16 / FP16 base models, use the same command and drop +`--quantization.method`. -- `--quantization.method compressed-tensors` reads the model `config.json` and - selects the W4A16 or W8A8 signature automatically. -- W8A8 requires SM80 or newer GPUs. -- `--server.disable_radix_cache` uses `ChunkCache`; the KV slot leak in this - mode has been fixed. -- If port `30000` is already in use, switch to another free port. +## Common parameters -### BF16 base models - -```bash -cd - -python3 -m pymllm.server.launch \ - --server.model_path \ - --server.tokenizer_path \ - --server.load_format safetensors \ - --server.dtype float16 \ - --server.host 0.0.0.0 \ - --server.port 30000 \ - --server.attention_backend auto \ - --server.gdn_decode_backend pytorch \ - --server.mem_fraction_static 0.05 \ - --server.max_running_requests 1 \ - --server.max_total_tokens 256 \ - --server.max_prefill_tokens 128 \ - --server.chunked_prefill_size 128 \ - --server.disable_radix_cache \ - --server.disable_cuda_graph \ - --server.log_level debug -``` +| Parameter | Description | +| --- | --- | +| `--server.model_path` | Model weight directory, usually HuggingFace or ModelScope format. | +| `--server.tokenizer_path` | Tokenizer directory; defaults to `model_path` when unset, so you rarely pass it. | +| `--server.dtype` | Runtime dtype: `auto`, `float16`, or `bfloat16`. | +| `--quantization.method compressed-tensors` | Enables `compressed-tensors` weight loading and the quantized linear path. | +| `--server.mem_fraction_static` | Static budget for `model weights + KV cache pool` as a fraction of total GPU memory. Too small fails to allocate the KV pool at startup; too large leaves no room for activations and CUDA Graph. On Jetson, Qwen3-VL-2B usually starts around `0.5`–`0.8`. | +| `--server.max_running_requests` | Concurrent requests. On small-VRAM Jetson, start from `1`. | +| `--server.max_total_tokens` | Upper bound on the KV cache token pool, shared globally across the worker (not a per-request limit). Actual capacity is `min(profiled tokens, max_total_tokens)` and does not bypass the memory profile. | +| `--server.disable_radix_cache` | Disables Radix Cache, uses `ChunkCache` instead. | -## Request examples +## OpenAI-compatible requests -### Health check +Health check: ```bash curl -s --noproxy '*' http://127.0.0.1:30000/v1/models ; echo ``` -Expected response contains: - -```text -"owned_by":"pymllm" -``` - -### Text request +Text request: ```bash curl -s --noproxy '*' http://127.0.0.1:30000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ - "model": "None", + "model": "default", "messages": [{"role": "user", "content": "Reply with: ok"}], "max_tokens": 8, "temperature": 0.0, @@ -187,79 +134,111 @@ curl -s --noproxy '*' http://127.0.0.1:30000/v1/chat/completions \ }' ; echo ``` -### Image request - -Use a container-visible absolute image path. Do not use the `file://...` -prefix. +For image requests, use an absolute path the server process can read; do not use +the `file://` prefix: ```bash -python3 - <<'PY' -import json - -payload = { - "model": "None", - "messages": [ - { - "role": "user", - "content": [ - {"type": "text", "text": "Please describe this image in detail."}, - {"type": "image_url", "image_url": {"url": "/workspace/xcd_mllm/test.png"}}, - ], - } - ], - "max_tokens": 128, - "temperature": 0.0, - "stream": False, +cat > /tmp/mm_req_path.json <<'JSON' +{ + "model": "default", + "messages": [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Please describe this image."}, + {"type": "image_url", "image_url": {"url": "/workspace/test.png"}} + ] + } + ], + "max_tokens": 128, + "temperature": 0.0, + "stream": false } - -with open("/tmp/mm_req_path.json", "w", encoding="utf-8") as f: - json.dump(payload, f, ensure_ascii=False) - -print("saved /tmp/mm_req_path.json") -PY +JSON curl -s --noproxy '*' http://127.0.0.1:30000/v1/chat/completions \ -H "Content-Type: application/json" \ --data @/tmp/mm_req_path.json ; echo ``` -## Development and tests +## Benchmark -Common unit tests: +`bench_one_batch` is a low-level offline benchmark. It initializes +`pymllm.executor.model_runner.ModelRunner` directly, bypassing the HTTP server, +tokenizer, scheduler, and detokenizer, and only measures one static prefill plus +per-token decode of the model itself. It is good for analyzing model forward, KV +cache, attention, CUDA Graph, and quantized kernels at the model level, and for +checking graph optimizations such as fused projection and residual-carry. It +does not measure online-serving TTFT / ITL / E2E — don't mix the two. -```bash -pytest pymllm/tests/test_compressed_tensors_config.py -q -pytest pymllm/tests/test_compressed_tensors_runtime.py -q -pytest pymllm/tests/test_qwen3_model_registry.py -q -pytest pymllm/tests/test_qwen3_weight_loading.py -q -pytest pymllm/tests/test_qwen3_forward_timing.py -q -pytest mllm-kernel/tests/test_int8_scaled_mm_cutlass.py -q -``` +`bench_one_batch` supports three measurement modes: + +- **Text-only**: prefill / decode with synthetic token ids. +- **Vision encoding (`vit_prefill`)**: a synchronized wall clock around the + vision encoder (`self.visual(...)`) only, reflecting pure vision-encode speed. +- **Multimodal prefill (`multimodal_prefill`)**: covers "vision encoding + LLM + prefill over image/text tokens", reflecting full multimodal prefill speed. -Common microbenchmarks: +Text-only: ```bash -python3 pymllm/tests/bench_w8a8_activation_quant.py -python3 mllm-kernel/benchmarks/bench_int8_scaled_mm.py -python3 mllm-kernel/benchmarks/bench_w4a16_vs_w8a8.py +PYTHONPATH="$PWD:$PWD/mllm-kernel" python3 -m pymllm.bench_one_batch \ + --server.model_path \ + --server.dtype float16 \ + --quantization.method compressed-tensors \ + --server.mem_fraction_static 0.8 \ + --server.max_running_requests 1 \ + --server.max_total_tokens 2048 \ + --batch-size 1 \ + --input-len 256 512 1024 \ + --output-len 128 \ + --result-filename ``` -To measure first-use CUTLASS compilation again, clear the JIT cache: +`--batch-size`, `--input-len`, and `--output-len` all accept multiple values; +the script sweeps every combination and appends results to the JSONL file. +`output_len` uses total-output-token semantics: the first next token is already +produced after prefill, so the decode loop runs `output_len - 1` more steps. + +Multimodal prefill: pass a real image to `--image`, and when you also pass +`--input-len` explicitly, the length means the target total of +`image placeholder tokens + text prompt tokens` — the script only pads or +truncates text tokens, never image tokens. So you can sweep different totals such +as `314/512/1024/2048` on the same image to measure full multimodal prefill +including vision encoding: ```bash -rm -rf ~/.cache/mllm_kernel/cutlass_int8_scaled_mm/ +PYTHONPATH="$PWD:$PWD/mllm-kernel" python3 -m pymllm.bench_one_batch \ + --server.model_path \ + --server.trust_remote_code true \ + --server.dtype float16 \ + --quantization.method compressed-tensors \ + --server.mem_fraction_static 0.8 \ + --server.max_running_requests 1 \ + --server.disable_cuda_graph \ + --batch-size 1 \ + --input-len 314 512 1024 2048 \ + --output-len 1 \ + --image \ + --prompt "Describe this image." \ + --result-filename ``` -## Known limitations - -- The W8A8 CUTLASS path is JIT-compiled, so first startup includes about - 100 seconds of compilation overhead. -- W8A8 activation quantization uses a Triton kernel; its fixed decode-time - cost remains a future optimization target. -- Qwen3-VL ViT, `lm_head`, embeddings, and LayerNorm are outside the current - W8A8 quantized scope. -- Other GPUs need separate validation for tile dispatch, JIT compilation, and - performance. -- OpenAI-compatible responses hide debug timing by default for SGLang/OpenAI - compatibility. Use `--server.enable_debug_timing` only for local diagnostics; - strict model-level timing should use dedicated benchmarks. +In the JSONL, `vit_prefill_ms` wraps only `self.visual(...)`, while +`multimodal_prefill_*` are alias fields for the full VIT + LLM prefill — the two +have different scopes. In measurements on AGX Orin 32GB, W8A8 clearly leads FP16 +/ W4A16 on long prefill. + +### Profile + +`bench_one_batch` has a built-in profile entry for inspecting kernel timelines +locally. There are two paths: + +- **torch.profiler (supported)**: `--profile-activities CPU GPU` (default), + emits a `.trace.json.gz` timeline you can open in Perfetto / chrome://tracing. + The output directory is set by `PYMLLM_TORCH_PROFILER_DIR`, defaulting to + `/tmp`. +- **Nsight Systems / nsys (experimental)**: `--profile-activities CUDA_PROFILER` + drives nsys via `cudaProfilerStart/Stop`, and needs an outer wrapper like + `nsys --capture-range=cudaProfilerApi`. This path is still being polished and + may be rough in places; treat it as an optional deep-dive tool. From f346552893d726d68171493f26fc1c3b75f5fab3 Mon Sep 17 00:00:00 2001 From: jialilve <3485723235@qq.com> Date: Mon, 8 Jun 2026 16:16:01 +0000 Subject: [PATCH 20/21] docs(readme): highlight Jetson Orin pymllm performance --- README-ZH.md | 51 ++++++++++++++---- README.md | 51 ++++++++++++++---- .../pymllm-jetson-prefill-throughput-2048.jpg | Bin 0 -> 363270 bytes .../pymllm-jetson-speedup-summary-2048.jpg | Bin 0 -> 355032 bytes 4 files changed, 80 insertions(+), 22 deletions(-) create mode 100644 assets/jetson/pymllm-jetson-prefill-throughput-2048.jpg create mode 100644 assets/jetson/pymllm-jetson-speedup-summary-2048.jpg diff --git a/README-ZH.md b/README-ZH.md index b5592d1e9..9106c4153 100644 --- a/README-ZH.md +++ b/README-ZH.md @@ -17,6 +17,8 @@ mllm ## 最新动态 +- [2026 年 6 月 8 日] `pymllm` 已覆盖 Qwen3、Qwen3-VL 与 Qwen3.5 在 Jetson Orin 上的 W4A16 / W8A8 serving；Qwen3-VL-2B W8A8 在 AGX Orin 32GB 上最高达到 3.12x prefill 加速比，decode 吞吐整体与 llama.cpp 接近。 +- [2026 年 4 月 30 日] `pymllm` 新增面向 Jetson 的 Qwen3 / Qwen3-VL BF16、W4A16 和 W8A8 serving 支持，覆盖 compressed-tensors AWQ 与 W8A8 INT8 路径。 - [2026 年 3 月 18 日] 🔥🔥🔥 `pymllm` 已支持在 Jetson Orin 和 Jetson Thor 设备上使用 CUDA（实验特性，仍在持续开发中）。 - [2026 年 2 月 3 日] 🔥🔥🔥 MLLM Qnn AOT 已支持在 NPU 上全图执行！[快速开始](https://ubiquitouslearning.github.io/mllm/qnn_backend/aot_execute.html), [技术报告](https://chenghuawang.github.io/News/2026-01-29-mllm-qnn-aot-support/) - [2025 年 11 月 27 日] Android Demo 更新：通过一种全新的 In-App Go 服务架构，在 Android 上实现了 Qwen3 和 DeepSeek-OCR 的稳定流式推理。 @@ -29,6 +31,29 @@ mllm - 更加完善、精细的工程实现 - [2025 年 7 月 30 日] 为 QNN 后端模型新增旋转量化（Rotation Quantization）方法，并支持 Qwen-2-VL 2B（ViT 性能分析将在 v2 中集成） +## Jetson Orin CUDA Runtime + +`pymllm` 现已支持 Qwen3、Qwen3-VL 与 Qwen3.5 在 Jetson Orin 上运行，覆盖 BF16 serving 以及 W4A16、W8A8 两种量化 serving 路径。其中，W4A16 使用 AWQ compressed tensors 与 Marlin GEMM，W8A8 使用 Triton per-token activation quantization 与 CUTLASS INT8 GEMM。 + +在 `input_len=2048`、`output_len=128` 的测速口径下，`pymllm` 在 Jetson Orin 上的 prefill 性能相对 llama.cpp 有明显提升。Qwen3-VL-2B W8A8 在 AGX Orin 32GB 上最高达到 **3.12x prefill 加速比**，prefill 吞吐约 **12243 tok/s**。decode 吞吐整体与 llama.cpp 接近，不同模型、设备和量化格式下会有小幅领先或回落。 + +

+ +
+ +
+ +
+ +对于多模态 prefill，`bench_one_batch --image` 测量“视觉编码 + 图像/文本 token prefill”的完整路径。下表使用 `input_len=2048`，TPS 为多次运行的 mean latency 计算结果。 + +| Device | Model | FP16 | W4A16 | W8A8 | +|---|---|---:|---:|---:| +| AGX Orin 32GB | Qwen3-VL-2B | 4875.75 | 4700.28 | 6443.59 | +| AGX Orin 32GB | Qwen3-VL-4B | - | 2499.46 | 3837.07 | +| Orin NX 16GB | Qwen3-VL-2B | 2438.27 | 2494.89 | 3200.40 | +| Orin NX 16GB | Qwen3-VL-4B | - | 1231.21 | 1673.93 | + ## Android Demo & Architecture 我们已对 Android 端实现进行了重构，采用了一种稳健的、完全在设备端运行的 **Client-Server** 架构。 @@ -75,17 +100,21 @@ mllm 框架可以与主流社区框架的模型检查点无缝集成。通过 ml ### mllm v2 -| Model(v2) | CPU | Hexagon NPU
INT8 | -|-----------------------------------------------------------------------------|------|-----------------------| -| [Qwen3-0.6B](https://github.com/QwenLM/Qwen3) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen3-0.6B-w4a32kai) | | -| [Qwen3-1.7B](https://github.com/QwenLM/Qwen3) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen3-1.7B-w4a8-i8mm-kai) | [W4A16-SM8650](https://modelscope.cn/models/mllmTeam/Qwen3-1.7B-Qnn-AOT-SM8650/summary) | -| [Qwen3-4B](https://github.com/QwenLM/Qwen3) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen3-4B-w4a8-i8mm-kai) | | -| [DeepSeek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/DeepSeek-OCR-w4a8-i8mm-kai) | | -| [SmolLM3](https://huggingface.co/blog/smollm3)| [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/SmolLM3-3B-w4a8-i8mm-kai) | | -| [Qwen2-VL-2B-Instruct](https://qwenlm.github.io/zh/blog/qwen2-vl/)|[✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2-VL-2B-Instruct-w4a32kai) || -| [Qwen2-VL-7B-Instruct](https://qwenlm.github.io/zh/blog/qwen2-vl/)|[✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2-VL-7B-Instruct-w4a32kai)|| -| [Qwen2.5-VL-3B-Instruct](https://qwenlm.github.io/blog/qwen2.5-vl/)|[✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2.5-VL-3B-Instruct-w4a32kai)|| -| [Qwen2.5-VL-7B-Instruct](https://qwenlm.github.io/blog/qwen2.5-vl/)|[✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2.5-VL-7B-Instruct-w4a32kai)|| +| Model(v2) | CPU | Jetson Orin CUDA | Hexagon NPU
INT8 | +|-----------------------------------------------------------------------------|------|------------------|-----------------------| +| [Qwen3-0.6B](https://github.com/QwenLM/Qwen3) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen3-0.6B-w4a32kai) | | | +| [Qwen3-1.7B](https://github.com/QwenLM/Qwen3) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen3-1.7B-w4a8-i8mm-kai) | | [W4A16-SM8650](https://modelscope.cn/models/mllmTeam/Qwen3-1.7B-Qnn-AOT-SM8650/summary) | +| [Qwen3-4B](https://github.com/QwenLM/Qwen3) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen3-4B-w4a8-i8mm-kai) | | | +| Qwen3.5-2B | | ✔️ W4A16 / W8A8 | | +| Qwen3.5-4B | | ✔️ W4A16 / W8A8 | | +| Qwen3-VL-2B-Instruct | | ✔️ W4A16 / W8A8 | | +| Qwen3-VL-4B-Instruct | | ✔️ W4A16 / W8A8 | | +| [DeepSeek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/DeepSeek-OCR-w4a8-i8mm-kai) | | | +| [SmolLM3](https://huggingface.co/blog/smollm3)| [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/SmolLM3-3B-w4a8-i8mm-kai) | | | +| [Qwen2-VL-2B-Instruct](https://qwenlm.github.io/zh/blog/qwen2-vl/) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2-VL-2B-Instruct-w4a32kai) | | | +| [Qwen2-VL-7B-Instruct](https://qwenlm.github.io/zh/blog/qwen2-vl/) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2-VL-7B-Instruct-w4a32kai) | | | +| [Qwen2.5-VL-3B-Instruct](https://qwenlm.github.io/blog/qwen2.5-vl/) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2.5-VL-3B-Instruct-w4a32kai) | | | +| [Qwen2.5-VL-7B-Instruct](https://qwenlm.github.io/blog/qwen2.5-vl/) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2.5-VL-7B-Instruct-w4a32kai) | | | ### mllm v1 diff --git a/README.md b/README.md index 8b77ae0ca..3459c2329 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,8 @@ mllm ## Latest News +- [2026 Jun 08] `pymllm` now covers Qwen3, Qwen3-VL, and Qwen3.5 on Jetson Orin with W4A16 / W8A8 serving; Qwen3-VL-2B W8A8 reaches up to 3.12x prefill speedup on AGX Orin 32GB, while decode throughput stays broadly close to llama.cpp. +- [2026 Apr 30] `pymllm` adds Jetson-oriented Qwen3 / Qwen3-VL BF16, W4A16, and W8A8 serving support, including compressed-tensors AWQ and W8A8 INT8 paths. - [2026 Mar 18] 🔥🔥🔥 `pymllm` now supports CUDA on Jetson Orin and Jetson Thor devices (experimental; still under active development). - [2026 Feb 03] 🔥🔥🔥 MLLM Qnn AOT Support for Full Graph Execution on NPU! [Quick Start](https://ubiquitouslearning.github.io/mllm/qnn_backend/aot_execute.html), [Technical Report](https://chenghuawang.github.io/News/2026-01-29-mllm-qnn-aot-support-en/) - [2025 Nov 27] Android Demo Update: Enabled stable Qwen3 and DeepSeek-OCR streaming on Android via a novel In-App Go Server Architecture. @@ -28,6 +30,29 @@ mllm - A more refined engineering implementation - [2025 Jul 30] Add Rotation Quantization method for QNN backend models and support Qwen-2-VL 2B（ViT profiling will integrate in v2） +## Jetson Orin CUDA Runtime + +`pymllm` now supports Qwen3, Qwen3-VL, and Qwen3.5 on Jetson Orin with BF16 serving plus W4A16 and W8A8 quantized serving. The W4A16 path uses AWQ compressed tensors and Marlin GEMM. The W8A8 path uses Triton per-token activation quantization and CUTLASS INT8 GEMM. + +For `input_len=2048` and `output_len=128`, `pymllm` shows strong prefill gains over llama.cpp on Jetson Orin. Qwen3-VL-2B W8A8 reaches up to **3.12x prefill speedup** on AGX Orin 32GB and about **12243 tok/s** prefill throughput. Decode throughput is generally close to llama.cpp, with small wins or losses depending on model, device, and quantization. + +
+ +
+ +
+ +
+ +For multimodal prefill, `bench_one_batch --image` measures the full path of vision encoding plus image/text token prefill. The table below uses `input_len=2048` and reports mean TPS across repeated runs. + +| Device | Model | FP16 | W4A16 | W8A8 | +|---|---|---:|---:|---:| +| AGX Orin 32GB | Qwen3-VL-2B | 4875.75 | 4700.28 | 6443.59 | +| AGX Orin 32GB | Qwen3-VL-4B | - | 2499.46 | 3837.07 | +| Orin NX 16GB | Qwen3-VL-2B | 2438.27 | 2494.89 | 3200.40 | +| Orin NX 16GB | Qwen3-VL-4B | - | 1231.21 | 1673.93 | + ## Android Demo & Architecture We have refactored the Android implementation to use a robust **Client-Server** architecture entirely on-device. @@ -73,17 +98,21 @@ The mllm framework integrates seamlessly with popular community frameworks' chec ### mllm v2 -| Model(v1) | CPU | Hexagon NPU
INT8 | -|-----------------------------------------------------------------------------|------|-----------------------| -| [Qwen3-0.6B](https://github.com/QwenLM/Qwen3) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen3-0.6B-w4a32kai) | | -| [Qwen3-1.7B](https://github.com/QwenLM/Qwen3) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen3-1.7B-w4a8-i8mm-kai) | [W4A16-SM8650](https://modelscope.cn/models/mllmTeam/Qwen3-1.7B-Qnn-AOT-SM8650/) | -| [Qwen3-4B](https://github.com/QwenLM/Qwen3) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen3-4B-w4a8-i8mm-kai) | | -| [DeepSeek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/DeepSeek-OCR-w4a8-i8mm-kai) | | -| [SmolLM3](https://huggingface.co/blog/smollm3)| [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/SmolLM3-3B-w4a8-i8mm-kai) | | -| [Qwen2-VL-2B-Instruct](https://qwenlm.github.io/zh/blog/qwen2-vl/)|[✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2-VL-2B-Instruct-w4a32kai) || -| [Qwen2-VL-7B-Instruct](https://qwenlm.github.io/zh/blog/qwen2-vl/)|[✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2-VL-7B-Instruct-w4a32kai)|| -| [Qwen2.5-VL-3B-Instruct](https://qwenlm.github.io/blog/qwen2.5-vl/)|[✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2.5-VL-3B-Instruct-w4a32kai)|| -| [Qwen2.5-VL-7B-Instruct](https://qwenlm.github.io/blog/qwen2.5-vl/)|[✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2.5-VL-7B-Instruct-w4a32kai)|| +| Model(v2) | CPU | Jetson Orin CUDA | Hexagon NPU
INT8 | +|-----------------------------------------------------------------------------|------|------------------|-----------------------| +| [Qwen3-0.6B](https://github.com/QwenLM/Qwen3) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen3-0.6B-w4a32kai) | | | +| [Qwen3-1.7B](https://github.com/QwenLM/Qwen3) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen3-1.7B-w4a8-i8mm-kai) | | [W4A16-SM8650](https://modelscope.cn/models/mllmTeam/Qwen3-1.7B-Qnn-AOT-SM8650/) | +| [Qwen3-4B](https://github.com/QwenLM/Qwen3) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen3-4B-w4a8-i8mm-kai) | | | +| Qwen3.5-2B | | ✔️ W4A16 / W8A8 | | +| Qwen3.5-4B | | ✔️ W4A16 / W8A8 | | +| Qwen3-VL-2B-Instruct | | ✔️ W4A16 / W8A8 | | +| Qwen3-VL-4B-Instruct | | ✔️ W4A16 / W8A8 | | +| [DeepSeek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/DeepSeek-OCR-w4a8-i8mm-kai) | | | +| [SmolLM3](https://huggingface.co/blog/smollm3)| [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/SmolLM3-3B-w4a8-i8mm-kai) | | | +| [Qwen2-VL-2B-Instruct](https://qwenlm.github.io/zh/blog/qwen2-vl/) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2-VL-2B-Instruct-w4a32kai) | | | +| [Qwen2-VL-7B-Instruct](https://qwenlm.github.io/zh/blog/qwen2-vl/) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2-VL-7B-Instruct-w4a32kai) | | | +| [Qwen2.5-VL-3B-Instruct](https://qwenlm.github.io/blog/qwen2.5-vl/) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2.5-VL-3B-Instruct-w4a32kai) | | | +| [Qwen2.5-VL-7B-Instruct](https://qwenlm.github.io/blog/qwen2.5-vl/) | [✔️ w4a8](https://www.modelscope.cn/models/mllmTeam/Qwen2.5-VL-7B-Instruct-w4a32kai) | | | ### mllm v1 diff --git a/assets/jetson/pymllm-jetson-prefill-throughput-2048.jpg b/assets/jetson/pymllm-jetson-prefill-throughput-2048.jpg new file mode 100644 index 0000000000000000000000000000000000000000..1ea55c57f7fb87324577444c7bd824f67ffb80df GIT binary patch literal 363270 zcmeFZWmp`+(=WOS1ef4Wf-UZ_I0Sch4{nPu9w5POA-IzSw}r4c3n92eu*D&`Lm)`- z%m2LRdERqBob%;=yr;%~(^XSbT|G11UER+-%|HDHyjD?ARsg(s0RX&sUI0%UFXokH zWi7RIG!>N94AB@(Ir};;vwy|J>_8^dF0H?A-KqpN|;N8#TZipaW0@ z$owb&&*#tNUI+jPUjhIqpZ`0}IvW6JjsyV67ymnr@hbp;9{~U~P5gJ-fBVG4%FF71 zs6&0;zqGdp0M5$*09<1Lfan(hfMxnWc+b24jcsqAS=7&Vxj!!s05HG~@D`v9a0A!? 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