diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 21eb4d97b3e..65fa24f4468 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -456,7 +456,8 @@ jobs: run: | cd build # This is using llvmpipe and runs slower than other backends - ctest -L main --verbose --timeout 900 + # test-backend-ops is too slow on llvmpipe, skip it + ctest -L main -E test-backend-ops --verbose --timeout 900 ubuntu-24-webgpu-wasm: runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} diff --git a/.github/workflows/python-type-check.yml b/.github/workflows/python-type-check.yml index 2d3fa163d23..cbeeb39d05b 100644 --- a/.github/workflows/python-type-check.yml +++ b/.github/workflows/python-type-check.yml @@ -31,7 +31,7 @@ jobs: uses: actions/setup-python@v6 with: python-version: "3.11" - pip-install: -r requirements/requirements-all.txt ty==0.0.33 + pip-install: -r requirements/requirements-all.txt ty==0.0.35 # - name: Type-check with Pyright # uses: jakebailey/pyright-action@v2 # with: diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index bf76fa40614..d79372ceac7 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -9760,6 +9760,73 @@ def prepare_tensors(self): raise ValueError(f"Unprocessed experts: {experts}") +@ModelBase.register("MiMoV2ForCausalLM") +class MiMoV2VisionModel(MmprojModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + assert self.hparams_vision is not None + hp = self.hparams_vision + + hp["image_size"] = hp.get("image_size", 560) + hp["num_attention_heads"] = hp.get("num_heads", 32) + hp["num_hidden_layers"] = hp.get("depth", 28) + + self.n_q_heads = int(hp["num_heads"]) + self.num_kv_heads = int(hp.get("num_key_value_heads", 8)) + self.head_dim = int(hp.get("qk_channels", 64)) + self.spatial_merge_size = int(hp["spatial_merge_size"]) + # MiMoV2 vision RMSNorm: HF uses getattr(config, "rms_norm_eps", 1e-6) and the + # field is absent from MiMo-V2.5's vision_config + self.rms_norm_eps = float(hp.get("rms_norm_eps", 1e-6)) + + # fullatt_block_indexes are also reflected in vit_window_attn_types as -1 + self.fullatt_block_indexes = list(hp.get("fullatt_block_indexes") or []) + self.vit_window_attn_types = list(hp.get("vit_window_attn_types") or []) + self.visual_token_window_size = int(hp.get("visual_token_window_size", -1)) + self.use_sink = bool(hp.get("use_sink", False)) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MIMOVL) + self.gguf_writer.add_vision_use_silu(True) + self.gguf_writer.add_vision_head_count_kv(self.num_kv_heads) + self.gguf_writer.add_vision_spatial_merge_size(self.spatial_merge_size) + self.gguf_writer.add_uint32(gguf.Keys.ClipVision.WINDOW_SIZE, self.visual_token_window_size) + self.gguf_writer.add_vision_wa_pattern_mode(self.vit_window_attn_types) + self.gguf_writer.add_vision_attention_layernorm_eps(self.rms_norm_eps) + self.gguf_writer.add_vision_min_pixels(int(self.preprocessor_config["min_pixels"])) + self.gguf_writer.add_vision_max_pixels(int(self.preprocessor_config["max_pixels"])) + + def tensor_force_quant(self, name, new_name, bid, n_dims): + # Sinks must be F32: any sink-style softmax/mask add in ggml requires + # F32, and we fold sinks into a host-built F32 mask at encode time. + if new_name.endswith(".attn_sinks"): + return gguf.GGMLQuantizationType.F32 + return super().tensor_force_quant(name, new_name, bid, n_dims) + + @classmethod + def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: + name, _ = item + if not name.startswith("visual."): + return None + return super().filter_tensors(item) + + def modify_tensors(self, data_torch, name, bid): + # Conv3D patch embed: split along the temporal axis (kt=2) into two Conv2D + # weights that the existing qwen2vl-style two-Conv2D path consumes. + if name == "visual.patch_embed.proj.weight": + _, _, kt, _, _ = data_torch.shape + if kt != 2: + raise ValueError(f"unexpected temporal_patch_size: {kt}") + embd_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + yield (embd_name + ".weight", data_torch[:, :, 0, ...]) + yield (embd_name + ".weight.1", data_torch[:, :, 1, ...]) + return + + yield from super().modify_tensors(data_torch, name, bid) + + @ModelBase.register("Step3p5ForCausalLM") class Step35Model(TextModel): model_arch = gguf.MODEL_ARCH.STEP35 diff --git a/docs/ops.md b/docs/ops.md index 0b2062dceac..dace2d3d537 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -18,7 +18,7 @@ Legend: | ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | | ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | +| ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | @@ -71,7 +71,7 @@ Legend: | MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ | | NEG | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | -| NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ | +| NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 | @@ -118,5 +118,5 @@ Legend: | TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | TRUNC | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | -| UPSCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | +| UPSCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | | XIELU | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | diff --git a/docs/ops/WebGPU.csv b/docs/ops/WebGPU.csv index 681434bc74c..95042e72d9c 100644 --- a/docs/ops/WebGPU.csv +++ b/docs/ops/WebGPU.csv @@ -334,14 +334,14 @@ "WebGPU: WebGPU","GET_ROWS","type=q8_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=q8_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=q8_0,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","WebGPU" -"WebGPU: 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WebGPU","SSM_SCAN","type=f32,d_state=128,head_dim=128,n_head=4,n_group=4,n_seq_tokens=16,n_seqs=2,xbc_overlap=1","support","1","yes","WebGPU" "WebGPU: WebGPU","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=1,n_seqs=1","support","0","no","WebGPU" "WebGPU: WebGPU","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","0","no","WebGPU" "WebGPU: WebGPU","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","0","no","WebGPU" @@ -6155,24 +6156,24 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" -"WebGPU: 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"WebGPU: WebGPU","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: 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WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=32","support","1","yes","WebGPU" +"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=129","support","1","yes","WebGPU" +"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=32","support","1","yes","WebGPU" +"WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=129","support","1","yes","WebGPU" "WebGPU: WebGPU","SQR","type=f16,ne=[10,5,4,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","SQRT","type=f16,ne=[10,3,3,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","LOG","type=f16,ne=[10,5,4,3]","support","1","yes","WebGPU" @@ -10842,28 +10843,28 @@ "WebGPU: WebGPU","TOP_K","type=f32,ne=[2047,2,1,3],k=15,ties=0","support","1","yes","WebGPU" "WebGPU: WebGPU","TOP_K","type=f32,ne=[2048,2,1,3],k=15,ties=0","support","1","yes","WebGPU" "WebGPU: WebGPU","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","0","no","WebGPU" -"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest","support","0","no","WebGPU" -"WebGPU: WebGPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=nearest","support","0","no","WebGPU" -"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear,transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear,transpose=1","support","0","no","WebGPU" -"WebGPU: 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WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear|align_corners","support","1","yes","WebGPU" +"WebGPU: WebGPU","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear|align_corners","support","1","yes","WebGPU" +"WebGPU: WebGPU","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear|align_corners","support","1","yes","WebGPU" +"WebGPU: WebGPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic|align_corners","support","1","yes","WebGPU" +"WebGPU: WebGPU","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bicubic|align_corners","support","1","yes","WebGPU" +"WebGPU: WebGPU","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bicubic|align_corners","support","1","yes","WebGPU" "WebGPU: WebGPU","SUM","type=f32,ne=[10,5,4,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","SUM","type=f32,ne=[11,5,6,3],permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: 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WebGPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q8_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f32,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=q1_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=128,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=q4_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=128,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q1_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=128,hsv=64,nh=4,nr23=[1,1],kv=64,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=f16,permute=[0,1,2,3]","support","0","no","WebGPU" "WebGPU: WebGPU","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" "WebGPU: WebGPU","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","WebGPU" "WebGPU: WebGPU","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" diff --git a/examples/llama-eval/README.md b/examples/llama-eval/README.md new file mode 100644 index 00000000000..3c5c35f78f6 --- /dev/null +++ b/examples/llama-eval/README.md @@ -0,0 +1,26 @@ +# llama-eval + +Simple evaluation tool for llama.cpp with support for multiple datasets. + +For a full description, usage examples, and sample results, see: + +- [PR 21152](https://github.com/ggml-org/llama.cpp/pull/21152) + +## Quick start + +```bash +# Single server +python3 llama-eval.py \ + --server http://localhost:8033 \ + --model my-model \ + --dataset gsm8k --n_cases 100 \ + --grader-type regex --threads 32 + +# Multiple servers (comma-separated URLs and thread counts) +python3 llama-eval.py \ + --server http://server1:8033,http://server2:8033 \ + --server-name server1,server2 \ + --threads 16,16 \ + --dataset aime2025 --n_cases 240 \ + --grader-type regex +``` diff --git a/examples/llama-eval/llama-eval.py b/examples/llama-eval/llama-eval.py new file mode 100755 index 00000000000..b33a3615be3 --- /dev/null +++ b/examples/llama-eval/llama-eval.py @@ -0,0 +1,1416 @@ +#!/usr/bin/env python3 +# type: ignore + +import argparse +import json +import os +import re +import subprocess +import sys +import threading +import time +from abc import ABC, abstractmethod +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import dataclass, asdict, field +from pathlib import Path +from queue import Queue +from typing import Dict, List, Optional, Any, Tuple +import requests +from tqdm import tqdm +import random +from math import sqrt + + +@dataclass +class ServerConfig: + url: str + threads: int + name: str = "" + +def wilson_interval(correct: int, total: int, z: float = 1.96) -> Tuple[float, float]: + """Wilson score confidence interval for a proportion.""" + if total == 0: + return (0.0, 1.0) + p = correct / total + z2 = z * z / total + center = (p + z2 / 2) / (1 + z2) + margin = z * sqrt((p * (1 - p) + z2 / 4) / total) / (1 + z2) + return (center - margin, center + margin) + +cache_dir = Path.home() / ".cache" / "huggingface" / "datasets" +cache_dir.mkdir(parents=True, exist_ok=True) +os.environ["HF_DATASETS_CACHE"] = str(cache_dir) +os.environ["HF_HUB_DISABLE_TELEMETRY"] = "1" + +GRADER_PATTERNS = { + "aime": r'\boxed{(\d+)}|\b(\d+)\b', + "aime2025": r'\boxed{(\d+)}|\b(\d+)\b', + "gsm8k": r'\b(\d+)\b', +} + +SAMPLE_ANSWERS = { + "aime": [ + "42", + "-123", + "999" + ], + "aime2025": [ + "42", + "-123", + "999" + ], + "gsm8k": [ + "42", + "-123", + "999" + ], + "gpqa": [ + "A", + "D", + "C" + ], +} + +TEMPLATE_REGISTRY = { + "aime": """Solve the following math problem step by step. Put your answer inside \\boxed{{}}. + +{question} + +Remember to put your answer inside \\boxed{{}}. +""", + "aime2025": """Solve the following math problem step by step. Put your answer inside \\boxed{{}}. + +{question} + +Remember to put your answer inside \\boxed{{}}. +""", + "gsm8k": """{question} +Please reason step by step, and put your final numeric answer within \\boxed{{}} without any extra characters. +""", + "gpqa": """Answer the following multiple choice question. The last line of your response should be in the following format: 'Answer: A/B/C/D' (e.g. 'Answer: A'). + +{Question} + +A) {A} +B) {B} +C) {C} +D) {D} +""", +} + + +class BaseDataset(ABC): + @abstractmethod + def get_question(self, index: int) -> Dict: + pass + + @abstractmethod + def get_question_text(self, question: Dict) -> str: + pass + + @abstractmethod + def get_answer(self, question: Dict) -> str: + pass + + @abstractmethod + def get_prompt(self, question: Dict) -> str: + pass + + def __len__(self) -> int: + return len(self.questions) + + +@dataclass +class TaskState: + task_id: str + prompt: str + expected: str + question_text: str = "" + response: Optional[str] = None + answer: Optional[str] = None + grader_log: Dict[str, Any] = field(default_factory=dict) + correct: bool = False + status: str = "pending" + tokens: Optional[int] = None + tps_gen: Optional[float] = None + t_gen_ms: Optional[float] = None + reasoning_content: Optional[str] = None + server_name: Optional[str] = None + + +class EvalState: + def __init__( + self, + dataset_type: str, + sampling_config: Dict[str, Any], + output_file: Path = Path("llama-eval-state.json"), + model_name: Optional[str] = None + ): + self.dataset_type = dataset_type + self.sampling_config = sampling_config + self.output_file = output_file + self.model_name = model_name + self.dataset: Optional[BaseDataset] = None + self.tasks: List[Tuple[int, str]] = [] + self.all_tasks: List[Tuple[int, str]] = [] + self.task_states: Dict[str, Any] = {} + self.total = 0 + self.correct = 0 + self.processed = 0 + self.total_time: float = 0.0 + self._lock = threading.Lock() + + def load_dataset(self, seed: int = 1234): + if self.dataset_type == "aime": + self.dataset = AimeDataset() + elif self.dataset_type == "aime2025": + self.dataset = Aime2025Dataset() + elif self.dataset_type == "gsm8k": + self.dataset = Gsm8kDataset() + elif self.dataset_type == "gpqa": + self.dataset = GpqaDataset(variant="diamond", seed=seed) + else: + raise ValueError(f"Unknown dataset type: {self.dataset_type}") + + def setup_tasks(self, n_cases: Optional[int] = None, seed: int = 1234): + if self.dataset is None: + raise ValueError("Dataset not loaded. Call load_dataset() first.") + + if n_cases is None: + n_cases = len(self.dataset) + + dataset_size = len(self.dataset) + rng = random.Random(seed) + + self.tasks = [] + for chunk_idx in range((n_cases + dataset_size - 1) // dataset_size): + chunk_size = min(dataset_size, n_cases - chunk_idx * dataset_size) + indices = list(range(dataset_size)) + rng.shuffle(indices) + chunk_indices = indices[:chunk_size] + + for i in chunk_indices: + task_id = f"{self.dataset_type}_{chunk_idx:03d}_{i:03d}" + self.tasks.append((i, task_id)) + + self.all_tasks = list(self.tasks) + + def get_case(self, index: int) -> Tuple[str, str, str]: + if self.dataset is None: + raise ValueError("Dataset not loaded.") + question = self.dataset.get_question(index) + question_text = self.dataset.get_question_text(question) + prompt = self.dataset.get_prompt(question) + expected = self.dataset.get_answer(question) + return question_text, prompt, expected + + def add_result( + self, + task_id: str, + prompt: str, + expected: str, + response: Optional[str], + answer: Optional[str], + grader_log: Dict[str, Any], + correct: bool, + status: str, + tokens: Optional[int] = None, + tps_gen: Optional[float] = None, + t_gen_ms: Optional[float] = None, + reasoning_content: Optional[str] = None, + server_name: Optional[str] = None + ): + with self._lock: + if "cases" not in self.task_states: + self.task_states["cases"] = {} + + self.task_states["cases"][task_id] = { + "task_id": task_id, + "prompt": prompt, + "expected": expected, + "response": response, + "answer": answer, + "grader_log": grader_log, + "correct": correct, + "status": status, + "tokens": tokens, + "tps_gen": tps_gen, + "t_gen_ms": t_gen_ms, + "reasoning_content": reasoning_content, + "server_name": server_name + } + + self.correct = sum(1 for c in self.task_states.get("cases", {}).values() if c.get("correct", False)) + + def print_progress(self, task_state: TaskState, total_tasks: int, n_correct: int = 0): + display_answer = task_state.answer if task_state.answer else "N/A" + display_tokens = str(task_state.tokens) if task_state.tokens is not None else "N/A" + display_tps = f"{task_state.tps_gen:.1f}" if task_state.tps_gen is not None else "N/A" + display_t_gen = f"{task_state.t_gen_ms/1000:.1f}" if task_state.t_gen_ms is not None else "N/A" + display_server = task_state.server_name if task_state.server_name else "N/A" + success_ratio = n_correct / self.processed if self.processed > 0 else 0.0 + first_line = task_state.question_text.split('\n')[0] + truncated_question = first_line[:43] + if len(first_line) > 43: + truncated_question += "..." + else: + truncated_question = truncated_question.ljust(43) + "..." + print(f"{self.processed:3}/{total_tasks:3} {task_state.task_id:<20} {self.dataset_type.upper()} {truncated_question:<40} {task_state.expected:<10} {display_answer:<10} {display_tokens:<6} {display_tps:<6} {display_t_gen:<8} {'✓' if task_state.correct else '✗'} [{n_correct:3}/{self.processed:3}, {success_ratio:.3f}] {display_server}") + + def print_summary(self): + if self.total == 0: + print(f"\n{'='*60}") + print(f"Results: 0/0 correct (0.0%)") + print(f"{'='*60}") + else: + ci_lower, ci_upper = self.accuracy_ci() + print(f"\n{'='*60}") + print(f"Results: {self.correct}/{self.total} correct ({self.correct/self.total*100:.1f}%) [{ci_lower*100:.1f}%, {ci_upper*100:.1f}%]") + print(f"{'='*60}") + + def dump(self): + with self._lock: + tasks_to_save = self.all_tasks if self.all_tasks else self.tasks + all_cases = {} + for i, task_id in tasks_to_save: + question_text, prompt, expected = self.get_case(i) + if task_id in self.task_states.get("cases", {}): + all_cases[task_id] = self.task_states["cases"][task_id] + else: + all_cases[task_id] = { + "task_id": task_id, + "prompt": prompt, + "expected": expected, + "question_text": question_text, + "response": None, + "answer": None, + "grader_log": {}, + "correct": False, + "status": "pending", + "tokens": None, + "tps_gen": None, + "t_gen_ms": None, + "reasoning_content": None, + "server_name": None + } + + ci_lower, ci_upper = self.accuracy_ci() + data = { + "id": self.dataset_type, + "model_name": self.model_name, + "tasks": [tid for _, tid in tasks_to_save], + "task_states": { + "total": self.total, + "correct": self.correct, + "total_time": self.total_time, + "ci_lower": ci_lower, + "ci_upper": ci_upper, + "cases": all_cases, + }, + "sampling_config": self.sampling_config + } + with open(self.output_file, "w") as f: + json.dump(data, f, indent=2) + + self.dump_html(tasks_to_save, all_cases) + + def dump_html(self, tasks_to_save: List[Tuple[int, str]], all_cases: Dict[str, Any]): + html_file = Path(str(self.output_file) + ".html") + + cases = all_cases + completed = {tid: c for tid, c in cases.items() if c.get("status") == "ok"} + n_correct = sum(1 for c in completed.values() if c.get("correct", False)) + n_incorrect = len(completed) - n_correct + n_pending = len(tasks_to_save) - len(completed) + accuracy = n_correct / len(completed) * 100 if completed else 0.0 + ci_lower, ci_upper = wilson_interval(n_correct, len(completed)) if completed else (0.0, 1.0) + + sampling_parts = [] + for k, v in self.sampling_config.items(): + if v is not None: + sampling_parts.append(f"{k}={v}") + sampling_str = ", ".join(sampling_parts) if sampling_parts else "default" + + rows = [] + for i, task_id in tasks_to_save: + case = cases.get(task_id, {}) + status = case.get("status", "pending") + expected = case.get("expected", "") + answer = case.get("answer", "") if status == "ok" else "" + is_correct = case.get("correct", False) if status == "ok" else False + response = case.get("response", "") or "" + prompt = case.get("prompt", "") or "" + grader_log = case.get("grader_log", {}) + + if status == "ok": + status_class = "correct" if is_correct else "incorrect" + status_text = "✓" if is_correct else "✗" + elif status == "pending": + status_class = "pending" + status_text = "–" + else: + status_class = "error" + status_text = "!" + + tokens = case.get("tokens") + tokens_str = str(tokens) if tokens is not None else "" + tps_gen = case.get("tps_gen") + tps_str = f"{tps_gen:.1f}" if tps_gen is not None else "" + t_gen_ms = case.get("t_gen_ms") + t_gen_str = f"{t_gen_ms/1000:.1f}" if t_gen_ms is not None else "" + reasoning_content = case.get("reasoning_content", "") or "" + server_name = case.get("server_name", "") or "" + + escaped_response = self._escape_html(response) + escaped_prompt = self._escape_html(prompt) + escaped_reasoning = self._escape_html(reasoning_content) + grader_log_str = self._escape_html(json.dumps(grader_log, indent=2)) + escaped_server = self._escape_html(server_name) + + rows.append(f""" + {task_id} + {status_text} + {self._escape_html(expected)} + {self._escape_html(answer)} + {tokens_str} + {tps_str} + {t_gen_str} + {escaped_server} + + + +
+ Prompt
{escaped_prompt}
+ Response
{escaped_response}
+ {f'Reasoning
{escaped_reasoning}
' if escaped_reasoning else ''} + Grader
{grader_log_str}
+
+ + """) + + rows_html = "\n".join(rows) + + html_content = f""" + + + +{self.dataset_type.upper()} Eval + + + +
+ {self.dataset_type.upper()} + Model: {self.model_name or 'N/A'} + Accuracy: {accuracy:.1f}% [{ci_lower*100:.1f}%, {ci_upper*100:.1f}%] + Correct: {n_correct} / {len(completed)} + Pending: {n_pending} + Time: {self.total_time:.1f}s + Sampling: {sampling_str} +
+ + + + + + + + + + + + + + + {rows_html} + +
IDGoldAnswerTokensT/sGen sServer
+ + +""" + + with open(html_file, "w") as f: + f.write(html_content) + + def _escape_html(self, s: str) -> str: + return (s.replace("&", "&") + .replace("<", "<") + .replace(">", ">") + .replace('"', """) + .replace("'", "'")) + + @classmethod + def load(cls, path: Path) -> "EvalState": + with open(path, "r") as f: + data = json.load(f) + + eval_state = cls( + dataset_type=data["id"], + sampling_config=data["sampling_config"], + output_file=path, + model_name=data.get("model_name") + ) + eval_state.load_dataset() + + eval_state.tasks = [] + eval_state.all_tasks = [] + for task_id in data.get("tasks", []): + parts = task_id.rsplit("_", 2) + if len(parts) >= 3: + idx = int(parts[-1]) + else: + idx = 0 + eval_state.tasks.append((idx, task_id)) + eval_state.all_tasks.append((idx, task_id)) + + eval_state.task_states = data.get("task_states", {}) + + cases = eval_state.task_states.get("cases", {}) + eval_state.total = eval_state.task_states.get("total", 0) + eval_state.correct = eval_state.task_states.get("correct", 0) + eval_state.total_time = eval_state.task_states.get("total_time", 0.0) + + if eval_state.total == 0: + eval_state.total = len(cases) + eval_state.correct = sum(1 for c in cases.values() if c.get("correct", False)) + + return eval_state + + def is_complete(self) -> bool: + if not self.all_tasks: + return False + cases = self.task_states.get("cases", {}) + completed = {tid for tid in self.task_states.get("cases", {}).keys() if cases.get(tid, {}).get("status") == "ok"} + return len(completed) == len(self.all_tasks) + + def get_pending_tasks(self) -> List[Tuple[int, str]]: + cases = self.task_states.get("cases", {}) + pending = [] + for i, task_id in self.all_tasks: + status = cases.get(task_id, {}).get("status", "pending") + if status != "ok": + pending.append((i, task_id)) + return pending + + def print_all_tasks(self): + cases = self.task_states.get("cases", {}) + tasks_to_show = self.all_tasks if self.all_tasks else self.tasks + print() + print("Tasks:") + print(" Task ID Dataset Prompt (first 40 chars) Expected Answer Tokens T/s Gen s Status") + for i, task_id in tasks_to_show: + question, prompt, expected = self.get_case(i) + case = cases.get(task_id, {}) + status = case.get("status", "pending") + answer = case.get("answer", "N/A") if status == "ok" else "N/A" + tokens = case.get("tokens") + tokens_str = str(tokens) if tokens is not None else "N/A" + tps_gen = case.get("tps_gen") + tps_str = f"{tps_gen:.1f}" if tps_gen is not None else "N/A" + t_gen_ms = case.get("t_gen_ms") + t_gen_str = f"{t_gen_ms/1000:.1f}" if t_gen_ms is not None else "N/A" + server_name = case.get("server_name", "") or "" + is_correct = case.get("correct", False) if status == "ok" else False + symbol = "✓ " if is_correct else ("✗ " if status == "ok" else "") + first_line = question.split('\n')[0] + question_trunc = first_line[:43] + if len(first_line) > 43: + question_trunc += "..." + else: + question_trunc = question_trunc.ljust(43) + "..." + print(f" {task_id:<20} {self.dataset_type.upper()} {question_trunc:<40} {expected:<10} {answer:<10} {tokens_str:<6} {tps_str:<6} {t_gen_str:<8} {symbol}{status} {server_name}") + print() + + def print_existing_summary(self): + cases = self.task_states.get("cases", {}) + completed_cases = {tid: c for tid, c in cases.items() if c.get("status") == "ok"} + correct = sum(1 for c in completed_cases.values() if c.get("correct", False)) + total = len(completed_cases) + if total == 0: + print(f"{'='*60}") + print(f"Results: 0/0 correct (0.0%)") + print(f"{'='*60}") + else: + ci_lower, ci_upper = self.accuracy_ci() + print(f"{'='*60}") + print(f"Results: {correct}/{total} correct ({correct/total*100:.1f}%) [{ci_lower*100:.1f}%, {ci_upper*100:.1f}%]") + print(f"{'='*60}") + + def accuracy_ci(self) -> Tuple[float, float]: + """Compute Wilson score confidence interval from completed cases.""" + cases = self.task_states.get("cases", {}) + completed = {tid: c for tid, c in cases.items() if c.get("status") == "ok"} + correct = sum(1 for c in completed.values() if c.get("correct", False)) + total = len(completed) + return wilson_interval(correct, total) + +def normalize_number(s: str) -> Optional[int]: + match = re.match(r"\d+", s) # match digits from the start + if not match: + return None + return int(match.group(0)) + +class AimeDataset(BaseDataset): + def __init__(self, split: str = "train"): + self.split = split + self.questions: List[Dict] = [] + self._load_dataset() + + def _load_dataset(self): + print(f"Loading AIME dataset (split: {self.split})...") + from datasets import load_dataset + + cache_path = cache_dir / "AI-MO___aimo-validation-aime" / "default" / "0.0.0" + if cache_path.exists(): + print(f"Using cached dataset from {cache_path}") + ds = load_dataset("AI-MO/aimo-validation-aime", split=self.split, cache_dir=str(cache_path)) + else: + ds = load_dataset("AI-MO/aimo-validation-aime", split=self.split) + + self.questions = [] + for row in ds: + question = dict(row) + question["dataset_type"] = "aime" + self.questions.append(question) + + print(f"AIME dataset loaded: {len(self.questions)} questions") + + def get_question(self, index: int) -> Dict: + """Get question by index""" + return self.questions[index] + + def get_question_text(self, question: Dict) -> str: + """Get question string""" + return question["problem"] if "problem" in question else question["question"] + + def get_answer(self, question: Dict) -> str: + answer = question["answer"] + if isinstance(answer, str): + normalized = normalize_number(answer) + return str(normalized) if normalized is not None else answer + return str(answer) + + def get_prompt(self, question: Dict) -> str: + """Get formatted prompt for the question""" + return TEMPLATE_REGISTRY[question["dataset_type"]].format( + question=self.get_question_text(question), + ) + +class Aime2025Dataset(BaseDataset): + def __init__(self): + self.questions: List[Dict] = [] + self._load_dataset() + + def _load_dataset(self): + print(f"Loading AIME2025 dataset...") + from datasets import load_dataset + + config_name = "AIME2025-I" + cache_path = cache_dir / "opencompass___AIME2025" / "default" / "0.0.0" + if cache_path.exists(): + print(f"Using cached dataset from {cache_path}") + ds = load_dataset("opencompass/AIME2025", config_name, split="test", cache_dir=str(cache_path)) + else: + ds = load_dataset("opencompass/AIME2025", config_name, split="test") + + self.questions = [] + for row in ds: + question = dict(row) + question["dataset_type"] = "aime2025" + self.questions.append(question) + + print(f"AIME2025 dataset loaded: {len(self.questions)} questions") + + print(f"Loading AIME2025 dataset (part 2)...") + config_name_2 = "AIME2025-II" + cache_path_2 = cache_dir / "opencompass___AIME2025" / "default" / "0.0.0" + if cache_path_2.exists(): + print(f"Using cached dataset from {cache_path_2}") + ds_2 = load_dataset("opencompass/AIME2025", config_name_2, split="test", cache_dir=str(cache_path_2)) + else: + ds_2 = load_dataset("opencompass/AIME2025", config_name_2, split="test") + + for row in ds_2: + question = dict(row) + question["dataset_type"] = "aime2025" + self.questions.append(question) + + print(f"AIME2025 dataset loaded: {len(self.questions)} questions (total)") + + def get_question(self, index: int) -> Dict: + """Get question by index""" + return self.questions[index] + + def get_question_text(self, question: Dict) -> str: + """Get question string""" + return question["question"] + + def get_answer(self, question: Dict) -> str: + answer = question["answer"] + if isinstance(answer, str): + normalized = normalize_number(answer) + return str(normalized) if normalized is not None else answer + return str(answer) + + def get_prompt(self, question: Dict) -> str: + """Get formatted prompt for the question""" + return TEMPLATE_REGISTRY["aime2025"].format( + question=self.get_question_text(question), + ) + +class Gsm8kDataset(BaseDataset): + def __init__(self, split: str = "test"): + self.split = split + self.questions: List[Dict] = [] + self._load_dataset() + + def _load_dataset(self): + print(f"Loading GSM8K dataset (split: {self.split})...") + from datasets import load_dataset + + cache_path = cache_dir / "openai___gsm8k" / "default" / "0.0.0" + if cache_path.exists(): + print(f"Using cached dataset from {cache_path}") + ds = load_dataset("openai/gsm8k", "main", split=self.split, cache_dir=str(cache_path)) + else: + ds = load_dataset("openai/gsm8k", "main", split=self.split) + + self.questions = [] + for row in ds: + question = dict(row) + question["dataset_type"] = "gsm8k" + + # Extract numeric answer from the answer field (already has #### prefix) + gold = question["answer"] + # Split by #### and take the last part + parts = gold.split("####") + if len(parts) > 1: + gold = parts[-1].strip() + # Extract the first number from the remaining text + normalized = normalize_number(gold) + question["gold"] = str(normalized) if normalized is not None else gold + + self.questions.append(question) + + print(f"GSM8K dataset loaded: {len(self.questions)} questions") + + def get_question(self, index: int) -> Dict: + """Get question by index""" + return self.questions[index] + + def get_question_text(self, question: Dict) -> str: + """Get question string""" + return question["problem"] if "problem" in question else question["question"] + + def get_answer(self, question: Dict) -> str: + # GSM8K has pre-extracted gold field, AIME uses answer field + if "gold" in question: + return question["gold"] + answer = question["answer"] + if isinstance(answer, str): + normalized = normalize_number(answer) + return str(normalized) if normalized is not None else answer + return str(answer) + + def get_prompt(self, question: Dict) -> str: + """Get formatted prompt for the question""" + return TEMPLATE_REGISTRY[question["dataset_type"]].format( + question=self.get_question_text(question), + ) + +class GpqaDataset(BaseDataset): + def __init__(self, variant: str = "diamond", seed: int = 1234): + self.variant = variant + self.seed = seed + self.questions: List[Dict] = [] + self._load_dataset() + + def _load_dataset(self): + print(f"Loading GPQA dataset (variant: {self.variant})...") + import pandas as pd + + url = f"https://openaipublic.blob.core.windows.net/simple-evals/gpqa_{self.variant}.csv" + df = pd.read_csv(url) + + rng = random.Random(self.seed) + + self.questions = [] + for _, row in df.iterrows(): + question = row.to_dict() + question["dataset_type"] = "gpqa" + + # Shuffle the answer options + correct_answer = question["Correct Answer"] + incorrect_answers = [ + question["Incorrect Answer 1"], + question["Incorrect Answer 2"], + question["Incorrect Answer 3"] + ] + + # Create list of (answer, is_correct) tuples + options = [(ans, ans == correct_answer) for ans in incorrect_answers] + options.append((correct_answer, True)) + + # Shuffle the options + rng.shuffle(options) + + # Extract shuffled answers and determine correct letter + shuffled_answers = [ans for ans, _ in options] + correct_letter = chr(ord('A') + options.index((correct_answer, True))) + + # Store shuffled answers and correct letter + question["shuffled_answers"] = shuffled_answers + question["correct_letter"] = correct_letter + + self.questions.append(question) + + print(f"GPQA dataset loaded: {len(self.questions)} questions") + + def get_question(self, index: int) -> Dict: + """Get question by index""" + return self.questions[index] + + def get_question_text(self, question: Dict) -> str: + """Get question string""" + return question["Question"] + + def get_answer(self, question: Dict) -> str: + # GPQA returns the correct letter (A, B, C, or D) + return question["correct_letter"] + + def get_prompt(self, question: Dict) -> str: + """Get formatted prompt for the question""" + return TEMPLATE_REGISTRY["gpqa"].format( + Question=self.get_question_text(question), + A=question["shuffled_answers"][0], + B=question["shuffled_answers"][1], + C=question["shuffled_answers"][2], + D=question["shuffled_answers"][3] + ) + +class Grader: + def __init__( + self, + grader_type: str = "llm", + grader_script: Optional[str] = None, + grader_model_name: Optional[str] = None, + grader_server_url: str = "", + dataset_type: str = "aime" + ): + self.grader_type = grader_type + self.grader_script = grader_script + self.grader_model_name = grader_model_name + self.grader_server_url = grader_server_url + self.dataset_type = dataset_type + self.pattern = self._get_pattern() + + def _get_pattern(self) -> Optional[str]: + if self.grader_type == "regex": + return GRADER_PATTERNS.get(self.dataset_type) # Use dataset_type as key + return None + + def _extract_answer_regex(self, pred: str) -> Optional[str]: + """Extract answer using regex pattern""" + if not self.pattern: + return None + + # For AIME datasets, prioritize boxed answers + if self.dataset_type in ["aime", "aime2025"]: + boxed_pattern = r'\\boxed{([^}]+)}' + boxed_matches = re.findall(boxed_pattern, pred, re.IGNORECASE) + if boxed_matches: + # Return the last boxed answer found (most likely the final answer) + return boxed_matches[-1].strip() + + # For other datasets, search for numbers from the end of the text + # This prioritizes numbers that appear later in the response + matches = re.findall(self.pattern, pred, re.IGNORECASE) + if not matches: + return None + + # Process matches from end to start + for match in reversed(matches): + if isinstance(match, tuple): + match = match[0] if match[0] else match[1] + answer = match.strip() + if answer: + return answer + return None + + def _grade_regex(self, gold: str, pred: str) -> Tuple[bool, Optional[str]]: + """Grade using regex pattern matching""" + answer = self._extract_answer_regex(pred) + if answer is None: + return False, None + is_correct = answer.strip() == gold.strip() + return is_correct, answer + + def _grade_cli(self, gold: str, pred: str) -> Tuple[bool, Optional[str]]: + """Grade using external CLI script""" + if not self.grader_script: + raise ValueError("CLI grader requires --grader-script") + + script_path = Path(self.grader_script) + if not script_path.exists(): + raise FileNotFoundError(f"Grader script not found: {self.grader_script}") + + try: + result = subprocess.run( + [str(script_path), "--answer", pred, "--expected", gold], + capture_output=True, + text=True, + timeout=30 + ) + is_correct = result.returncode == 0 + answer = pred if is_correct else None + return is_correct, answer + except subprocess.TimeoutExpired: + return False, None + except Exception as e: + return False, None + + def _grade_llm(self, gold: str, pred: str, problem: str) -> Tuple[bool, Optional[str]]: + """Grade using LLM-based extraction with few-shot examples""" + sample_answers = SAMPLE_ANSWERS.get(self.dataset_type, []) + sample_examples = "\n".join([ + f"Example {i+1}: {ans}" for i, ans in enumerate(sample_answers) + ]) + + system_prompt = f"""You are an answer extraction system. Your task is to extract the answer from the model's response. + +Here are some examples of extracted answers to demonstrate what you are supposed to output: + +{sample_examples} + +When extracting the answer, provide only the extracted answer itself, nothing else. If there is no clear answer that can be extracted from the response, reply with 'no answer'.""" + + user_prompt = f"""Extract the answer from the following response: + +"{pred}" + +Please provide only the extracted answer, nothing else. If there is no clear answer that can be extracted from the response, reply with 'no answer'.""" + + url = f"{self.grader_server_url}/v1/chat/completions" + headers = {"Content-Type": "application/json"} + data = { + "model": self.grader_model_name, + "messages": [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt} + ], + "temperature": 0, + } + #print(json.dumps(data, indent=2)) + + try: + response = requests.post(url, headers=headers, json=data) + response.raise_for_status() + answer = response.json()["choices"][0]["message"]["content"].strip() + is_correct = answer.strip().lower() == gold.strip().lower() + return is_correct, answer + except Exception as e: + return False, None + + def _truncate_response(self, response: str, max_lines: int = 6) -> str: + """Keep only last N lines of response""" + lines = response.split('\n') + return '\n'.join(lines[-max_lines:]) if len(lines) > max_lines else response + + def grade(self, gold: str, pred: str, problem: str = "") -> Tuple[bool, Optional[str]]: + """Grade the response""" + if self.grader_type == "regex": + return self._grade_regex(gold, pred) + elif self.grader_type == "cli": + return self._grade_cli(gold, pred) + elif self.grader_type == "llm": + return self._grade_llm(gold, pred, problem) + else: + raise ValueError(f"Unknown grader type: {self.grader_type}") + +class Processor: + def __init__( + self, + server_configs: List[ServerConfig], + grader: Grader, + model_name: Optional[str] = None, + n_predict: int = -1 + ): + self.server_configs = server_configs + self.grader = grader + self.model_name = model_name + self.n_predict = n_predict + + @staticmethod + def _check_server(server_config: ServerConfig) -> List[str]: + url = f"{server_config.url}/v1/models" + try: + response = requests.get(url) + response.raise_for_status() + models = [m["id"] for m in response.json().get("data", [])] + return models + except Exception as e: + print(f"Error: Cannot reach server {server_config.name} ({server_config.url}): {e}", file=sys.stderr) + sys.exit(1) + + def _make_request( + self, server_config: ServerConfig, eval_state: EvalState, prompt: str + ) -> Tuple[Dict[str, Any], int, Optional[float], Optional[float], str]: + url = f"{server_config.url}/v1/chat/completions" + headers = {"Content-Type": "application/json"} + data = { + "model": self.model_name if self.model_name else "llama", + "messages": [{"role": "user", "content": prompt}], + "n_predict": self.n_predict + } + if eval_state.sampling_config.get("temperature") is not None: + data["temperature"] = eval_state.sampling_config["temperature"] + if eval_state.sampling_config.get("top_k") is not None: + data["top_k"] = eval_state.sampling_config["top_k"] + if eval_state.sampling_config.get("top_p") is not None: + data["top_p"] = eval_state.sampling_config["top_p"] + if eval_state.sampling_config.get("min_p") is not None: + data["min_p"] = eval_state.sampling_config["min_p"] + + response = requests.post(url, headers=headers, json=data) + response.raise_for_status() + result = response.json() + tokens = result.get("usage", {}).get("completion_tokens", 0) + timings = result.get("timings", {}) + tps_gen = timings.get("predicted_per_second") if timings else None + t_gen_ms = timings.get("predicted_ms") if timings else None + finish_reason = result.get("choices", [{}])[0].get("finish_reason", "stop") + return result, tokens, tps_gen, t_gen_ms, finish_reason + + def _process_single_case( + self, server_config: ServerConfig, eval_state: EvalState, i: int, task_id: str + ) -> TaskState: + question_text, prompt, expected = eval_state.get_case(i) + + task_state = TaskState( + task_id=task_id, + prompt=prompt, + expected=expected, + question_text=question_text, + server_name=server_config.name + ) + + try: + response, tokens, tps_gen, t_gen_ms, finish_reason = self._make_request(server_config, eval_state, prompt) + result = response["choices"][0]["message"]["content"] + reasoning_content = response["choices"][0].get("message", {}).get("reasoning_content") + task_state.response = result + task_state.tokens = tokens + task_state.tps_gen = tps_gen + task_state.t_gen_ms = t_gen_ms + task_state.reasoning_content = reasoning_content + + if finish_reason != "stop": + task_state.status = f"error: finish_reason={finish_reason}" + eval_state.add_result( + task_id, prompt, expected, result, None, + {"finish_reason": finish_reason}, False, task_state.status, + tokens, tps_gen, t_gen_ms, reasoning_content, server_config.name + ) + eval_state.dump() + return task_state + + result_truncated = self.grader._truncate_response(result, max_lines=10) + is_correct, answer = self.grader.grade(expected, result_truncated, prompt) + + grader_log = { + "pred": result_truncated, + "grader_type": self.grader.grader_type + } + if self.grader.grader_type == "regex" and self.grader.pattern: + grader_log["pattern"] = self.grader.pattern + + task_state.correct = is_correct + task_state.answer = answer + task_state.grader_log = grader_log + task_state.status = "ok" + + eval_state.add_result( + task_id, prompt, expected, result, answer, + grader_log, is_correct, "ok", + tokens, tps_gen, t_gen_ms, reasoning_content, server_config.name + ) + + eval_state.dump() + + except Exception as e: + task_state.status = f"error: {str(e)}" + + return task_state + + @staticmethod + def _worker( + server_config: ServerConfig, + processor: "Processor", + eval_state: EvalState, + task_queue: Queue, + results_queue: Queue, + ): + """Worker that pulls tasks from a shared queue and sends them to its server.""" + while True: + task = task_queue.get() + if task is None: # sentinel + task_queue.task_done() + break + try: + i, task_id = task + result = processor._process_single_case(server_config, eval_state, i, task_id) + results_queue.put(result) + finally: + task_queue.task_done() + + def evaluate(self, eval_state: EvalState, verbose: bool = False, resume: bool = False): + total_tasks = len(eval_state.tasks) + eval_state.total = len(eval_state.all_tasks) if eval_state.all_tasks else total_tasks + eval_state.processed = 0 + start_time = time.time() + + # Check servers and list models + server_models = [self._check_server(sc) for sc in self.server_configs] + + # Print server info + print(f"\nProcessing {len(eval_state.tasks)} {eval_state.dataset_type.upper()} tasks ...") + print(f"Servers ({len(self.server_configs)}):") + for i, sc in enumerate(self.server_configs): + models_str = ", ".join(server_models[i]) if server_models[i] else "(none)" + print(f" {i+1}. {sc.name} — {sc.url} ({sc.threads} threads) [{models_str}]") + print(f"Model: {self.model_name}") + print(f"Grader: {self.grader.grader_type}") + print(f"Sampling: temp={eval_state.sampling_config.get('temperature', 'skip')}, top-k={eval_state.sampling_config.get('top_k', 'skip')}, top-p={eval_state.sampling_config.get('top_p', 'skip')}, min-p={eval_state.sampling_config.get('min_p', 'skip')}") + print() + + # Shared task queue: all workers compete for tasks + task_queue: Queue = Queue() + for i, task_id in eval_state.tasks: + task_queue.put((i, task_id)) + + # Results queue: workers push completed TaskStates here + results_queue: Queue = Queue() + + # Total worker threads across all servers + total_threads = sum(sc.threads for sc in self.server_configs) + + # Add one sentinel per worker so every worker exits cleanly + for _ in range(total_threads): + task_queue.put(None) + + # Launch workers: one ThreadPoolExecutor per server + executors: List[ThreadPoolExecutor] = [] + worker_futures: List[Any] = [] + for server_config in self.server_configs: + executor = ThreadPoolExecutor(max_workers=server_config.threads) + executors.append(executor) + for _ in range(server_config.threads): + future = executor.submit( + self._worker, server_config, self, eval_state, + task_queue, results_queue + ) + worker_futures.append(future) + + # Drain results as they complete + n_correct = 0 + session_time = 0.0 + completed_count = 0 + + while completed_count < total_tasks: + task_state = results_queue.get() + eval_state.processed += 1 + completed_count += 1 + if task_state.correct: + n_correct += 1 + elapsed = time.time() - start_time + eval_state.total_time += elapsed + session_time += elapsed + start_time = time.time() + eval_state.print_progress(task_state, total_tasks, n_correct) + + if verbose: + print(f"\nCase {eval_state.processed}: {task_state.correct}") + print(f" Expected: {task_state.expected}") + if task_state.response: + print(f" Response: {task_state.response}") + if task_state.answer: + print(f" Answer: {task_state.answer}") + print(f" Status: {task_state.status}") + + # Wait for all workers to finish and shut down executors + for future in worker_futures: + future.result() + for executor in executors: + executor.shutdown(wait=True) + + print(f"\nSession time: {session_time:.1f}s | Total accumulated time: {eval_state.total_time:.1f}s") + eval_state.print_summary() + eval_state.dump() + +def main(): + parser = argparse.ArgumentParser( + description="Simplified evaluation tool for llama.cpp" + ) + parser.add_argument( + "--server", + type=str, + default="http://localhost:8033", + help="Comma-separated llama-server URLs (default: http://localhost:8033)" + ) + parser.add_argument( + "--server-name", + type=str, + default="", + help="Comma-separated display names for servers (default: use URLs)" + ) + parser.add_argument( + "--dataset", + type=str, + default="aime", + choices=["aime", "aime2025", "gsm8k", "gpqa"], + help="Dataset type (default: aime)" + ) + parser.add_argument( + "--n_cases", + type=int, + default=None, + help="Number of cases to evaluate (default: all)" + ) + parser.add_argument( + "--seed", + type=int, + default=1234, + help="Random seed for shuffling (default: 1234)" + ) + parser.add_argument( + "--n_predict", + type=int, + default=-1, + help="Max tokens to predict per prompt (default: -1, infinite)" + ) + parser.add_argument( + "--temperature", + type=float, + default=None, + help="Sampling temperature (default: not passed)" + ) + parser.add_argument( + "--top-k", + type=int, + default=None, + help="Top K sampling (default: not passed)" + ) + parser.add_argument( + "--top-p", + type=float, + default=None, + help="Top P sampling (default: not passed)" + ) + parser.add_argument( + "--min-p", + type=float, + default=None, + help="Min P sampling (default: not passed)" + ) + parser.add_argument( + "--threads", + type=str, + default="32", + help="Comma-separated thread counts per server (default: 32)" + ) + parser.add_argument( + "--model", + type=str, + default=None, + help="Model name to append as query parameter (e.g., gpt-oss-20b-hf)" + ) + parser.add_argument( + "--verbose", + action="store_true", + help="Show detailed output for each case" + ) + parser.add_argument( + "--output", + type=Path, + default=Path("llama-eval-state.json"), + help="Output file for eval state (default: llama-eval-state.json)" + ) + parser.add_argument( + "--grader-type", + type=str, + default="llm", + choices=["regex", "cli", "llm"], + help="Grader type: regex, cli, or llm (default: llm)" + ) + parser.add_argument( + "--grader-script", + type=str, + default=None, + help="CLI grader script path (required for --grader-type cli)" + ) + parser.add_argument( + "--grader-server", + type=str, + default="", + help="Server URL for LLM grader (default: same as main server)" + ) + parser.add_argument( + "--grader-model", + type=str, + default="", + help="Model name for LLM grader (default: same as main model)" + ) + parser.add_argument( + "--resume", + action="store_true", + help="Resume from existing eval state" + ) + + args = parser.parse_args() + + # Parse server URLs and thread counts + server_urls = [u.strip() for u in args.server.split(",") if u.strip()] + thread_counts = [int(t.strip()) for t in args.threads.split(",") if t.strip()] + + if len(server_urls) != len(thread_counts): + print(f"Error: --server ({len(server_urls)} URLs) and --threads ({len(thread_counts)} values) must have the same count") + sys.exit(1) + + # Parse server names (optional, defaults to URLs) + if args.server_name: + server_names = [n.strip() for n in args.server_name.split(",") if n.strip()] + if len(server_names) != len(server_urls): + print(f"Error: --server-name ({len(server_names)} names) and --server ({len(server_urls)} URLs) must have the same count") + sys.exit(1) + else: + server_names = server_urls # fallback to URLs + + server_configs = [ + ServerConfig(url=url, threads=threads, name=name) + for url, threads, name in zip(server_urls, thread_counts, server_names) + ] + + if args.dataset == "gpqa" and args.grader_type != "llm": + print("Error: GPQA dataset requires --grader-type llm") + parser.print_help() + sys.exit(1) + + if args.output.exists(): + print(f"Loading existing eval state from {args.output}") + eval_state = EvalState.load(args.output) + + # Verify model matches + if eval_state.model_name is not None and args.model != eval_state.model_name: + print(f"Error: Model mismatch. State has '{eval_state.model_name}', but --model is '{args.model}'") + sys.exit(1) + + eval_state.print_all_tasks() + eval_state.print_existing_summary() + + if eval_state.is_complete(): + return + + print() + + if not args.resume: + print(f"Evaluation incomplete. Run with --resume to continue.") + return + + pending_tasks = eval_state.get_pending_tasks() + print(f"Resuming from {len(pending_tasks)} pending tasks") + + existing_cases = eval_state.task_states.get("cases", {}) + + eval_state.tasks = pending_tasks + eval_state.task_states["cases"] = existing_cases + + grader_server_url = args.grader_server if args.grader_server else server_configs[0].url + grader_model_name = args.grader_model if args.grader_model else args.model + if args.grader_type == "llm" and not grader_model_name: + print("Error: --grader-type llm requires --grader-model or --model") + sys.exit(1) + grader = Grader( + grader_type=args.grader_type, + grader_script=args.grader_script, + grader_model_name=grader_model_name, + grader_server_url=grader_server_url, + dataset_type=eval_state.dataset_type + ) + resume = True + else: + if args.resume: + print("Error: No existing eval state found to resume") + sys.exit(1) + + grader_server_url = args.grader_server if args.grader_server else server_configs[0].url + grader_model_name = args.grader_model if args.grader_model else args.model + if args.grader_type == "llm" and not grader_model_name: + print("Error: --grader-type llm requires --grader-model or --model") + sys.exit(1) + + grader = Grader( + grader_type=args.grader_type, + grader_script=args.grader_script, + grader_model_name=grader_model_name, + grader_server_url=grader_server_url, + dataset_type=args.dataset + ) + + if args.grader_type == "llm" and not args.grader_server: + print("Warning: Using same server for LLM grader (no --grader-server specified)") + + sampling_config = {} + if args.temperature is not None: + sampling_config["temperature"] = args.temperature + if args.top_k is not None: + sampling_config["top_k"] = args.top_k + if args.top_p is not None: + sampling_config["top_p"] = args.top_p + if args.min_p is not None: + sampling_config["min_p"] = args.min_p + + eval_state = EvalState( + dataset_type=args.dataset, + sampling_config=sampling_config, + output_file=args.output, + model_name=args.model + ) + eval_state.load_dataset(seed=args.seed) + eval_state.setup_tasks(n_cases=args.n_cases, seed=args.seed) + eval_state.dump() + resume = False + + eval_state.print_all_tasks() + + processor = Processor( + server_configs=server_configs, + grader=grader, + model_name=args.model, + n_predict=args.n_predict + ) + + processor.evaluate(eval_state, verbose=args.verbose, resume=resume) + print(f"\nEval state dumped to {args.output}") + +if __name__ == "__main__": + main() diff --git a/examples/llama-eval/llama-server-simulator.py b/examples/llama-eval/llama-server-simulator.py new file mode 100755 index 00000000000..2f9cdc5450d --- /dev/null +++ b/examples/llama-eval/llama-server-simulator.py @@ -0,0 +1,317 @@ +#!/usr/bin/env python3 + +import argparse +import json +import random +import re +import time +import sys +import os +import threading +from http.server import HTTPServer, BaseHTTPRequestHandler +from typing import Dict, List, Optional +from dataclasses import dataclass +from pathlib import Path + +import datasets + +# Set cache directory for HuggingFace datasets +cache_dir = Path.home() / ".cache" / "huggingface" / "datasets" +cache_dir.mkdir(parents=True, exist_ok=True) +os.environ["HF_DATASETS_CACHE"] = str(cache_dir) + +def dice(s1: str, s2: str) -> float: + """Calculate Dice coefficient between two strings based on bigram overlap.""" + if not s1 and not s2: + return 1.0 + + def _bigrams(s: str): + return [s[i : i + 2] for i in range(len(s) - 1)] + + bigrams1 = _bigrams(s1) + bigrams2 = _bigrams(s2) + + if not bigrams1 and not bigrams2: + return 1.0 + + from collections import Counter + + freq1 = Counter(bigrams1) + freq2 = Counter(bigrams2) + + intersection = sum(min(freq1[bg], freq2[bg]) for bg in freq1) + dice_coeff = 2 * intersection / (len(bigrams1) + len(bigrams2)) + return dice_coeff + +def debug_log(message: str): + """Log debug messages to both stdout and a file""" + print(message, file=sys.stderr) + with open("/tmp/simulator-debug.log", "a") as f: + f.write(message + "\n") + +simulator: Optional["Simulator"] = None + +@dataclass +class EvalState: + id: str + tasks: List[str] + task_states: Dict[str, Dict] + sampling_config: Dict + +def normalize_number(s: str) -> Optional[int]: + match = re.match(r"\d+", s) # match digits from the start + if not match: + return None + return int(match.group(0)) + +class AimeDataset: + def __init__(self, split: str = "train"): + self.split = split + self.questions: List[Dict] = [] + self._load_dataset() + + def _load_dataset(self): + print(f"Loading AIME dataset (split: {self.split})...") + + cache_path = Path.home() / ".cache" / "huggingface" / "datasets" / "AI-MO___aimo-validation-aime" / "default" / "0.0.0" + if cache_path.exists(): + print(f"Using cached dataset from {cache_path}") + ds = datasets.load_dataset("AI-MO/aimo-validation-aime", split=self.split, cache_dir=str(cache_path)) + else: + ds = datasets.load_dataset("AI-MO/aimo-validation-aime", split=self.split) + + self.questions = list(ds) + print(f"AIME dataset loaded: {len(self.questions)} questions") + + def find_question(self, request_text: str) -> Optional[Dict]: + best_match = None + best_distance = -1 + best_index = -1 + + for i, question in enumerate(self.questions): + question_text = question["problem"] + request_lower = request_text.lower() + question_lower = question_text.lower() + + # Exact match + if question_lower == request_lower: + debug_log(f"DEBUG: Found exact match at index {i}") + return question + + # Remove LaTeX formatting for more flexible matching + question_no_latex = re.sub(r'\$[^$]+\$', '', question_text) + if question_no_latex.lower() == request_lower: + debug_log(f"DEBUG: Found match (no LaTeX) at index {i}") + return question + + # Calculate Dice coefficient for partial matches + # Only consider if request is at least 50% of question length + if len(request_lower) >= len(question_lower) * 0.5: + distance = dice(question_lower, request_lower) + + if distance > best_distance: + best_distance = distance + best_match = question + best_index = i + + if best_match and best_distance > 0.3: # Threshold for partial match + debug_log(f"DEBUG: Found best partial match at index {best_index} with distance {best_distance:.3f}") + return best_match + + debug_log(f"DEBUG: No matching question found for: {request_text[:100]}...") + return None + + def get_answer(self, question: Dict) -> str: + answer = question["answer"] + if isinstance(answer, str): + normalized = normalize_number(answer) + return str(normalized) if normalized is not None else answer + return str(answer) + +class Simulator: + def __init__( + self, + port: int = 8033, + host: str = "localhost", + success_rate: float = 0.8, + dataset_split: str = "train" + ): + self.port = port + self.host = host + self.success_rate = success_rate + self.dataset = AimeDataset(dataset_split) + self.eval_state = EvalState( + id="aime-2025", + tasks=["aime"], + task_states={}, + sampling_config={"temperature": 0, "max_tokens": 2048} + ) + + def _generate_response( + self, + question: Dict, + should_be_correct: bool + ) -> Dict: + expected_answer = self.dataset.get_answer(question) + + if should_be_correct: + response_text = expected_answer + else: + response_text = self._generate_wrong_answer(question) + + return { + "id": f"chatcmpl-{int(time.time())}", + "object": "chat.completion", + "created": int(time.time()), + "model": "llama", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": response_text + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 100, + "completion_tokens": 50, + "total_tokens": 150 + } + } + + def _generate_wrong_answer(self, question: Dict) -> str: + expected_answer = self.dataset.get_answer(question) + + if expected_answer.isdigit(): + wrong_answer = str(int(expected_answer) + 1) + else: + wrong_answer = expected_answer + " (wrong)" + + return wrong_answer + + def _process_request(self, request_data: Dict) -> Dict: + messages = request_data.get("messages", []) + if not messages: + return {"error": "No messages in request"} + + request_text = messages[0].get("content", "") + debug_log(f"DEBUG: Received request with content: {request_text[:150]}...") + + question = self.dataset.find_question(request_text) + if not question: + debug_log(f"DEBUG: find_question returned None") + return {"error": "No matching question found"} + + should_be_correct = random.random() < self.success_rate + + response = self._generate_response(question, should_be_correct) + + task_id = "aime" + self.eval_state.task_states[task_id] = { + "correct": should_be_correct, + "expected": self.dataset.get_answer(question), + "predicted": response["choices"][0]["message"]["content"] + } + + return response + +class RequestHandler(BaseHTTPRequestHandler): + def do_POST(self): + if self.path != "/v1/chat/completions": + self._send_json({"error": "Not found"}, 404) + return + + try: + content_length = int(self.headers.get("Content-Length", 0)) + body = self.rfile.read(content_length) + request_data = json.loads(body) if body else None + + if not request_data: + self._send_json({"error": "Invalid JSON"}, 400) + return + + if simulator is None: + self._send_json({"error": "Simulator not initialized"}, 500) + return + + response = simulator._process_request(request_data) + self._send_json(response, 200) + + except json.JSONDecodeError: + self._send_json({"error": "Invalid JSON"}, 400) + except Exception as e: + print(f"Error processing request: {e}") + self._send_json({"error": str(e)}, 500) + + def _send_json(self, data: dict, status: int = 200): + body = json.dumps(data).encode("utf-8") + self.send_response(status) + self.send_header("Content-Type", "application/json") + self.send_header("Content-Length", str(len(body))) + self.end_headers() + self.wfile.write(body) + + def log_message(self, format, *args): + # Suppress default request logging + pass + + +def main(): + parser = argparse.ArgumentParser( + description="llama-server simulator for testing eval scripts" + ) + parser.add_argument( + "--port", + type=int, + default=8033, + help="Server port (default: 8033)" + ) + parser.add_argument( + "--host", + type=str, + default="localhost", + help="Server host (default: localhost)" + ) + parser.add_argument( + "--success-rate", + type=float, + default=0.8, + help="Success rate 0-1 (default: 0.8)" + ) + parser.add_argument( + "--dataset-split", + type=str, + default="train", + help="AIME dataset split to use (default: train)" + ) + + args = parser.parse_args() + + global simulator + simulator = Simulator( + port=args.port, + host=args.host, + success_rate=args.success_rate, + dataset_split=args.dataset_split + ) + + server = HTTPServer((args.host, args.port), RequestHandler) + server_thread = threading.Thread(target=server.serve_forever, daemon=True) + server_thread.start() + + print("\n=== llama-server-simulator ===") + print(f"Server running on http://{args.host}:{args.port}") + print(f"Success rate: {args.success_rate}") + print(f"AIME dataset loaded: {len(simulator.dataset.questions)} questions") + print("\nPress Ctrl+C to stop\n") + + try: + server_thread.join() + except KeyboardInterrupt: + print("\nShutting down...") + server.shutdown() + +if __name__ == "__main__": + main() diff --git a/examples/llama-eval/test-simulator.sh b/examples/llama-eval/test-simulator.sh new file mode 100755 index 00000000000..f3ddf3e95d6 --- /dev/null +++ b/examples/llama-eval/test-simulator.sh @@ -0,0 +1,86 @@ +#!/bin/bash + +set -e + +# Get the directory where this script is located +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +echo "=== llama-server-simulator Test Script ===" +echo "" + +PORT=8033 +SUCCESS_RATE=0.8 +TEST_PORT=8034 + +echo "Starting simulator on port $PORT with success rate $SUCCESS_RATE..." +source "$SCRIPT_DIR/venv/bin/activate" +python3 "$SCRIPT_DIR/llama-server-simulator.py" --port $PORT --success-rate $SUCCESS_RATE > /tmp/simulator-test.log 2>&1 & +SIMULATOR_PID=$! + +echo "Waiting for simulator to start..." +sleep 5 + +# Helper function to make a request and extract the answer +make_request() { + local question="$1" + curl -s -X POST http://localhost:$PORT/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d "{ + \"model\": \"llama\", + \"messages\": [ + {\"role\": \"user\", \"content\": \"$question\"} + ], + \"temperature\": 0, + \"max_tokens\": 2048 + }" | python3 -c "import sys, json; data = json.load(sys.stdin); print(data.get('choices', [{}])[0].get('message', {}).get('content', data.get('error', 'No response')))" +} + +# Test question (repeated in multiple tests) +TEST_QUESTION="Quadratic polynomials P(x) and Q(x) have leading coefficients 2 and -2, respectively. The graphs of both polynomials pass through the two points (16,54) and (20,53). Find P(0) + Q(0)." + +echo "" +echo "=== Test 1: Correct Answer ===" +echo "Sending request with known question..." +answer=$(make_request "$TEST_QUESTION") +echo "Answer: $answer" +echo "Expected: 116" +echo "Correct: $([ "$answer" == "116" ] && echo "Yes" || echo "No")" + +echo "" +echo "=== Test 2: Wrong Answer ===" +echo "Sending request with known question (success rate 0.0)..." +answer=$(make_request "$TEST_QUESTION") +echo "Answer: $answer" +echo "Expected: 116" +echo "Correct: $([ "$answer" == "116" ] && echo "Yes" || echo "No")" + +echo "" +echo "=== Test 3: No Matching Question ===" +echo "Sending request with non-matching text..." +response=$(make_request "What is the capital of France?") +echo "Response: $response" +echo "Expected: No matching question found" +echo "Correct: $([ "$response" == "No matching question found" ] && echo "Yes" || echo "No")" + +echo "" +echo "=== Test 4: Success Rate Verification ===" +echo "Sending 10 requests to test success rate..." +correct_count=0 +for i in {1..10}; do + answer=$(make_request "$TEST_QUESTION") + if [ "$answer" == "116" ]; then + correct_count=$((correct_count + 1)) + fi + echo " Request $i: Answer = $answer" +done +echo "Correct answers: $correct_count/10" +echo "Expected: ~8/10 (80% success rate)" +echo "Success rate: $(echo "scale=1; $correct_count * 10" | bc)%" + +echo "" +echo "=== Test Complete ===" +echo "Stopping simulator..." +kill $SIMULATOR_PID 2>/dev/null +wait $SIMULATOR_PID 2>/dev/null || true + +echo "Simulator stopped." diff --git a/examples/model-conversion/Makefile b/examples/model-conversion/Makefile index 9356aaf8546..0130b0493bb 100644 --- a/examples/model-conversion/Makefile +++ b/examples/model-conversion/Makefile @@ -52,6 +52,10 @@ causal-convert-mm-model: METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \ ./scripts/causal/convert-model.sh + $(MAKE) causal-convert-mmproj MM_OUTTYPE="$(MM_OUTTYPE)" + +causal-convert-mmproj: + $(call validate_model_path,causal-convert-mmproj) @MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(MM_OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \ METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \ ./scripts/causal/convert-model.sh --mmproj diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 7e450a559dd..a0a556206d5 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -681,6 +681,15 @@ struct vk_device_struct { bool mul_mat_id_m[GGML_TYPE_COUNT]; bool mul_mat_id_s[GGML_TYPE_COUNT]; + // Separate flags for the q8_1 (integer dot) mmq path, whose shader uses + // a different shared-memory layout than the float matmul shaders. + bool mul_mat_l_int[GGML_TYPE_COUNT]; + bool mul_mat_m_int[GGML_TYPE_COUNT]; + bool mul_mat_s_int[GGML_TYPE_COUNT]; + bool mul_mat_id_l_int[GGML_TYPE_COUNT]; + bool mul_mat_id_m_int[GGML_TYPE_COUNT]; + bool mul_mat_id_s_int[GGML_TYPE_COUNT]; + vk::DescriptorSetLayout dsl; vk_matmul_pipeline pipeline_matmul_f32 {}; @@ -3207,6 +3216,70 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec return supported; } +// Shmem usage for the q8_1 mmq shader (mul_mmq.comp), which uses +// block_a_cache / block_b_cache layouts (see mul_mmq_shmem_types.glsl) rather +// than the float load buffers checked by ggml_vk_matmul_shmem_support. +// Sizes follow std430 rules. Returns false for types without a q8_1 pipeline. +static bool ggml_vk_matmul_int_shmem_support(const vk_device& device, const std::vector& warptile, bool mul_mat_id, ggml_type src0_type) { + + // FLOAT_TYPE in the shader is float16_t with fp16 support, otherwise float. + const uint32_t fp_size = device->fp16 ? 2u : 4u; + const uint32_t fp_align = fp_size; + const uint32_t fp2_size = 2u * fp_size; + const uint32_t fp2_align = device->fp16 ? 4u : 8u; + + struct member { uint32_t size, align; }; + auto std430_size = [](std::initializer_list members) { + uint32_t off = 0, struct_align = 1; + for (const auto &m : members) { + off = (off + m.align - 1) & ~(m.align - 1); + off += m.size; + struct_align = std::max(struct_align, m.align); + } + return (off + struct_align - 1) & ~(struct_align - 1); + }; + + uint32_t block_a_size = 0; + switch (src0_type) { + case GGML_TYPE_Q4_0: block_a_size = std430_size({{16, 4}, {fp_size, fp_align}}); break; // qs[16/4] + dm + case GGML_TYPE_Q4_1: block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}}); break; // qs[16/4] + dm(vec2) + case GGML_TYPE_Q5_0: block_a_size = std430_size({{16, 4}, {4, 4}, {fp_size, fp_align}}); break; // qs[16/4] + qh + dm + case GGML_TYPE_Q5_1: block_a_size = std430_size({{16, 4}, {4, 4}, {fp2_size, fp2_align}}); break; // qs[16/4] + qh + dm(vec2) + case GGML_TYPE_Q8_0: block_a_size = std430_size({{32, 4}, {fp_size, fp_align}}); break; // qs[8] + dm + case GGML_TYPE_MXFP4: block_a_size = std430_size({{32, 4}, {fp_size, fp_align}}); break; // qs[8] + d + case GGML_TYPE_Q2_K: block_a_size = std430_size({{ 8, 4}, {2, 2}, {fp2_size, fp2_align}}); break; // qs[2] + scales(u8vec2) + dm(vec2) + case GGML_TYPE_Q3_K: block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}}); break; // qs[4] + d_scales(vec2) + case GGML_TYPE_Q4_K: block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}}); break; // qs[4] + dm(vec2) + case GGML_TYPE_Q5_K: block_a_size = std430_size({{32, 4}, {fp2_size, fp2_align}}); break; // qs[8] + dm(vec2) + case GGML_TYPE_Q6_K: block_a_size = std430_size({{32, 4}, {fp2_size, fp2_align}}); break; // qs[8] + d_scales(vec2) + default: + return false; + } + + // block_b_cache: { int32_t qs[8]; FLOAT_TYPEV2 ds; } + const uint32_t block_b_size = std430_size({{32, 4}, {fp2_size, fp2_align}}); + + const uint32_t BM = warptile[1]; + const uint32_t BN = warptile[2]; + // mul_mmq.comp: BK_STEP=1 for MUL_MAT_ID, 4 otherwise. + const uint32_t BK_STEP = mul_mat_id ? 1u : 4u; + + const uint32_t buf_a_size = BM * BK_STEP * block_a_size; + const uint32_t buf_b_size = BN * BK_STEP * block_b_size; + const uint32_t mmid_row_ids = mul_mat_id ? (BN * 2u * (uint32_t)sizeof(uint16_t)) : 0u; + + const uint32_t warps = warptile[0] / warptile[10]; + const uint32_t ballots_sh = mul_mat_id ? (warps * 4u * (uint32_t)sizeof(uint32_t)) : 0u; + + const uint32_t total_size = buf_a_size + buf_b_size + mmid_row_ids + ballots_sh; + const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize; + + VK_LOG_DEBUG("ggml_vk_matmul_int_shmem_support(warptile=(" << warptile[0] << "," << warptile[1] << "," << warptile[2] << "), " + "mul_mat_id=" << mul_mat_id << ", src0_type=" << ggml_type_name(src0_type) << ", total=" << total_size << ", supported=" << supported); + + return supported; +} + struct GpuPipelineConfig { // GPU architecture identifier. // Example: vk_device_architecture::AMD_GCN @@ -3453,6 +3526,40 @@ static void ggml_vk_load_shaders(vk_device& device) { } else if (!ggml_vk_matmul_shmem_support(device, l_warptile_mmqid, true, t)) { device->mul_mat_id_l[i] = false; } + + // The q8_1 mmq path has its own (larger) shmem layout, check it separately. + // K-quants use the _int_k warptiles, others use _int. + const bool is_k_quant = (t == GGML_TYPE_Q2_K || t == GGML_TYPE_Q3_K || + t == GGML_TYPE_Q4_K || t == GGML_TYPE_Q5_K || + t == GGML_TYPE_Q6_K); + const auto & s_int = is_k_quant ? s_warptile_mmq_int_k : s_warptile_mmq_int; + const auto & m_int = is_k_quant ? m_warptile_mmq_int_k : m_warptile_mmq_int; + const auto & l_int = is_k_quant ? l_warptile_mmq_int_k : l_warptile_mmq_int; + const auto & s_intid = is_k_quant ? s_warptile_mmqid_int_k : s_warptile_mmqid_int; + const auto & m_intid = is_k_quant ? m_warptile_mmqid_int_k : m_warptile_mmqid_int; + const auto & l_intid = is_k_quant ? l_warptile_mmqid_int_k : l_warptile_mmqid_int; + + if (!ggml_vk_matmul_int_shmem_support(device, s_int, false, t)) { + device->mul_mat_s_int[i] = false; + device->mul_mat_m_int[i] = false; + device->mul_mat_l_int[i] = false; + } else if (!ggml_vk_matmul_int_shmem_support(device, m_int, false, t)) { + device->mul_mat_m_int[i] = false; + device->mul_mat_l_int[i] = false; + } else if (!ggml_vk_matmul_int_shmem_support(device, l_int, false, t)) { + device->mul_mat_l_int[i] = false; + } + + if (!ggml_vk_matmul_int_shmem_support(device, s_intid, true, t)) { + device->mul_mat_id_s_int[i] = false; + device->mul_mat_id_m_int[i] = false; + device->mul_mat_id_l_int[i] = false; + } else if (!ggml_vk_matmul_int_shmem_support(device, m_intid, true, t)) { + device->mul_mat_id_m_int[i] = false; + device->mul_mat_id_l_int[i] = false; + } else if (!ggml_vk_matmul_int_shmem_support(device, l_intid, true, t)) { + device->mul_mat_id_l_int[i] = false; + } } } @@ -4153,11 +4260,6 @@ static void ggml_vk_load_shaders(vk_device& device) { m_wg_denoms = { 64, 64, 1 }; s_wg_denoms = { 32, 32, 1 }; - if (device->vendor_id == VK_VENDOR_ID_INTEL && device->architecture == INTEL_XE2) { - // Xe2/Xe3 - bf16 warptile performance tuning - l_warptile = { 512, 128, 128, 16, subgroup_size_8, 32, 2, 4, 4, 1, subgroup_size_8 }; - } - CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); } @@ -5582,19 +5684,19 @@ static vk_device ggml_vk_get_device(size_t idx) { device->mul_mat_id_m[i] = true; device->mul_mat_id_s[i] = true; break; - case VK_VENDOR_ID_INTEL: - if (!device->coopmat_support || device->architecture != INTEL_XE2) { - device->mul_mat_l[i] = false; - device->mul_mat_id_l[i] = false; - } else { - device->mul_mat_l[i] = true; // if coopmat & XE2+, allow large matmul warptile config for Intel - device->mul_mat_id_l[i] = true; - } + case VK_VENDOR_ID_INTEL: { + // Current Windows driver does not expose BF16 support. + // We only want to use l_warptile if coopmat is available and is Xe2+ + const bool xe2_with_coopmat = device->coopmat_support && device->architecture == INTEL_XE2; + const bool use_l_warptile = (i == GGML_TYPE_BF16) ? (device->coopmat_bf16_support && xe2_with_coopmat) : xe2_with_coopmat; + device->mul_mat_l[i] = use_l_warptile; + device->mul_mat_id_l[i] = use_l_warptile; device->mul_mat_m[i] = true; device->mul_mat_s[i] = true; device->mul_mat_id_m[i] = true; device->mul_mat_id_s[i] = true; break; + } case VK_VENDOR_ID_APPLE: device->mul_mat_l[i] = false; device->mul_mat_m[i] = true; @@ -5613,6 +5715,13 @@ static vk_device ggml_vk_get_device(size_t idx) { device->mul_mat_id_s[i] = true; break; } + + device->mul_mat_l_int[i] = true; + device->mul_mat_m_int[i] = true; + device->mul_mat_s_int[i] = true; + device->mul_mat_id_l_int[i] = true; + device->mul_mat_id_m_int[i] = true; + device->mul_mat_id_s_int[i] = true; } @@ -7220,6 +7329,13 @@ static uint32_t ggml_vk_guess_split_k(ggml_backend_vk_context * ctx, uint32_t m, static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, uint32_t m, uint32_t n, bool aligned, ggml_type src0_type, ggml_type src1_type) { VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")"); + // The q8_1 (integer dot) mmq path uses a different shader with its own + // shared-memory layout, so use the int-specific availability flags. + const bool is_q8_1 = (src1_type == GGML_TYPE_Q8_1); + const bool mm_l = is_q8_1 ? ctx->device->mul_mat_l_int[src0_type] : ctx->device->mul_mat_l[src0_type]; + const bool mm_m = is_q8_1 ? ctx->device->mul_mat_m_int[src0_type] : ctx->device->mul_mat_m[src0_type]; + const bool mm_s = is_q8_1 ? ctx->device->mul_mat_s_int[src0_type] : ctx->device->mul_mat_s[src0_type]; + if (ctx->device->coopmat2) { const uint32_t shader_core_count = ctx->device->shader_core_count; const uint32_t tiles_l = CEIL_DIV(m, mmp->a_l->wg_denoms[0]) * CEIL_DIV(n, mmp->a_l->wg_denoms[1]); @@ -7236,26 +7352,24 @@ static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, // split_k==3 with large tiles likely better than medium tiles with no split_k. (tiles_l <= shader_core_count / 3 && tiles_m > shader_core_count / 2); - if ((ctx->device->mul_mat_l[src0_type] && (n > crossover_large && prefer_large)) || (!ctx->device->mul_mat_m[src0_type] && !ctx->device->mul_mat_s[src0_type])) { + if ((mm_l && (n > crossover_large && prefer_large)) || (!mm_m && !mm_s)) { return aligned ? mmp->a_l : mmp->l; } // Use medium shader when the N dimension is greater than the small shader's tile size uint32_t crossover_medium = mmp->s->wg_denoms[1]; - if ((ctx->device->mul_mat_m[src0_type] && (n > crossover_medium)) || !ctx->device->mul_mat_s[src0_type]) { + if ((mm_m && (n > crossover_medium)) || !mm_s) { return aligned ? mmp->a_m : mmp->m; } return aligned ? mmp->a_s : mmp->s; } - if ((ctx->device->mul_mat_s[src0_type] && (m <= 32 || n <= 32)) || (!ctx->device->mul_mat_m[src0_type] && !ctx->device->mul_mat_l[src0_type])) { + if ((mm_s && (m <= 32 || n <= 32)) || (!mm_m && !mm_l)) { return aligned ? mmp->a_s : mmp->s; } - if ((ctx->device->mul_mat_m[src0_type] && (m <= 64 || n <= 64)) || !ctx->device->mul_mat_l[src0_type]) { + if ((mm_m && (m <= 64 || n <= 64)) || !mm_l) { return aligned ? mmp->a_m : mmp->m; } return aligned ? mmp->a_l : mmp->l; - - GGML_UNUSED(src1_type); } static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type, ggml_type src1_type) { @@ -7312,35 +7426,42 @@ static void ggml_vk_matmul( ctx->prealloc_split_k_need_sync = true; } -static vk_pipeline ggml_vk_guess_matmul_id_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, uint32_t m, uint32_t n, bool aligned, ggml_type src0_type) { - VK_LOG_DEBUG("ggml_vk_guess_matmul_id_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ")"); +static vk_pipeline ggml_vk_guess_matmul_id_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, uint32_t m, uint32_t n, bool aligned, ggml_type src0_type, ggml_type src1_type) { + VK_LOG_DEBUG("ggml_vk_guess_matmul_id_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")"); + + // The q8_1 (integer dot) mmq path uses a different shader with its own + // shared-memory layout, so use the int-specific availability flags. + const bool is_q8_1 = (src1_type == GGML_TYPE_Q8_1); + const bool mm_l = is_q8_1 ? ctx->device->mul_mat_id_l_int[src0_type] : ctx->device->mul_mat_id_l[src0_type]; + const bool mm_m = is_q8_1 ? ctx->device->mul_mat_id_m_int[src0_type] : ctx->device->mul_mat_id_m[src0_type]; + const bool mm_s = is_q8_1 ? ctx->device->mul_mat_id_s_int[src0_type] : ctx->device->mul_mat_id_s[src0_type]; if (ctx->device->coopmat2) { // Use large shader when the N dimension is greater than the medium shader's tile size uint32_t crossover_large = mmp->m->wg_denoms[1]; - if ((ctx->device->mul_mat_id_l[src0_type] && (n > crossover_large)) || (!ctx->device->mul_mat_id_m[src0_type] && !ctx->device->mul_mat_id_s[src0_type])) { + if ((mm_l && (n > crossover_large)) || (!mm_m && !mm_s)) { return aligned ? mmp->a_l : mmp->l; } // Use medium shader when the N dimension is greater than the small shader's tile size uint32_t crossover_medium = mmp->s->wg_denoms[1]; - if ((ctx->device->mul_mat_id_m[src0_type] && (n > crossover_medium)) || !ctx->device->mul_mat_id_s[src0_type]) { + if ((mm_m && (n > crossover_medium)) || !mm_s) { return aligned ? mmp->a_m : mmp->m; } return aligned ? mmp->a_s : mmp->s; } - if ((ctx->device->mul_mat_id_s[src0_type] && (m <= 32 || n <= 32)) || (!ctx->device->mul_mat_id_m[src0_type] && !ctx->device->mul_mat_id_l[src0_type])) { + if ((mm_s && (m <= 32 || n <= 32)) || (!mm_m && !mm_l)) { return aligned ? mmp->a_s : mmp->s; } - if ((ctx->device->mul_mat_id_m[src0_type] && (m <= 64 || n <= 64)) || !ctx->device->mul_mat_id_l[src0_type]) { + if ((mm_m && (m <= 64 || n <= 64)) || !mm_l) { return aligned ? mmp->a_m : mmp->m; } return aligned ? mmp->a_l : mmp->l; } -static uint32_t ggml_vk_guess_matmul_id_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type) { - VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ", " << ggml_type_name(src0_type) << ")"); - return ggml_vk_guess_matmul_id_pipeline(ctx, mmp, m, n, true, src0_type)->align; +static uint32_t ggml_vk_guess_matmul_id_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type, ggml_type src1_type) { + VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")"); + return ggml_vk_guess_matmul_id_pipeline(ctx, mmp, m, n, true, src0_type, src1_type)->align; } static void ggml_vk_matmul_id( @@ -7636,10 +7757,12 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub // Not implemented GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT - const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11, qx_needs_dequant ? f16_type : src0->type, quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type))); + const ggml_type effective_src1_type = quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type); + + const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11, qx_needs_dequant ? f16_type : src0->type, effective_src1_type)); const bool aligned = !quantize_y && ne10 == kpad && ne01 > 8 && ne11 > 8; - vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned, qx_needs_dequant ? f16_type : src0->type, quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type)); + vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned, qx_needs_dequant ? f16_type : src0->type, effective_src1_type); if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) { pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline); @@ -8471,10 +8594,12 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& // Not implemented GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT - const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_id_pipeline_align(ctx, mmp, ne01, nei1, qx_needs_dequant ? f16_type : src0->type)); + const ggml_type effective_src1_type = quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type); + + const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_id_pipeline_align(ctx, mmp, ne01, nei1, qx_needs_dequant ? f16_type : src0->type, effective_src1_type)); const bool aligned = !quantize_y && ne10 == kpad && ne01 > 8 && nei1 > 8; - vk_pipeline pipeline = ggml_vk_guess_matmul_id_pipeline(ctx, mmp, ne01, nei1, aligned, qx_needs_dequant ? f16_type : src0->type); + vk_pipeline pipeline = ggml_vk_guess_matmul_id_pipeline(ctx, mmp, ne01, nei1, aligned, qx_needs_dequant ? f16_type : src0->type, effective_src1_type); if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) { pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline); diff --git a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp index c6dc2c21147..11701e79433 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp @@ -91,6 +91,7 @@ struct ggml_webgpu_shader_lib_context { uint32_t sg_mat_m = 0; uint32_t sg_mat_n = 0; uint32_t sg_mat_k = 0; + uint32_t min_subgroup_size = 0; uint32_t max_subgroup_size = 0; }; @@ -494,6 +495,22 @@ struct ggml_webgpu_binary_pipeline_key_hash { } }; +/* Add_Id */ + +struct ggml_webgpu_add_id_pipeline_key { + bool inplace; + + bool operator==(const ggml_webgpu_add_id_pipeline_key & other) const { return inplace == other.inplace; } +}; + +struct ggml_webgpu_add_id_pipeline_key_hash { + size_t operator()(const ggml_webgpu_add_id_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.inplace); + return seed; + } +}; + /** Unary **/ struct ggml_webgpu_unary_pipeline_key { @@ -531,7 +548,9 @@ enum ggml_webgpu_flash_attn_path : uint32_t { }; struct ggml_webgpu_flash_attn_pipeline_key { + ggml_type q_type; ggml_type kv_type; + ggml_type dst_type; uint32_t head_dim_qk; uint32_t head_dim_v; bool kv_direct; @@ -542,16 +561,19 @@ struct ggml_webgpu_flash_attn_pipeline_key { uint32_t path; bool operator==(const ggml_webgpu_flash_attn_pipeline_key & other) const { - return kv_type == other.kv_type && head_dim_qk == other.head_dim_qk && head_dim_v == other.head_dim_v && - kv_direct == other.kv_direct && kv_overlap == other.kv_overlap && has_mask == other.has_mask && - has_sinks == other.has_sinks && uses_logit_softcap == other.uses_logit_softcap && path == other.path; + return q_type == other.q_type && kv_type == other.kv_type && dst_type == other.dst_type && + head_dim_qk == other.head_dim_qk && head_dim_v == other.head_dim_v && kv_direct == other.kv_direct && + kv_overlap == other.kv_overlap && has_mask == other.has_mask && has_sinks == other.has_sinks && + uses_logit_softcap == other.uses_logit_softcap && path == other.path; } }; struct ggml_webgpu_flash_attn_pipeline_key_hash { size_t operator()(const ggml_webgpu_flash_attn_pipeline_key & key) const { size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.q_type); ggml_webgpu_hash_combine(seed, key.kv_type); + ggml_webgpu_hash_combine(seed, key.dst_type); ggml_webgpu_hash_combine(seed, key.head_dim_qk); ggml_webgpu_hash_combine(seed, key.head_dim_v); ggml_webgpu_hash_combine(seed, key.kv_direct); @@ -595,14 +617,14 @@ inline uint32_t ggml_webgpu_flash_attn_pick_vec_ne(const ggml_webgpu_flash_attn_ } inline ggml_webgpu_flash_attn_pipeline_key ggml_webgpu_flash_attn_make_pipeline_key( - const ggml_webgpu_shader_lib_context & context, - uint32_t path) { + const ggml_webgpu_shader_lib_context & context, + const ggml_webgpu_flash_attn_decisions & decisions) { const bool has_mask = context.src3 != nullptr; const bool has_sinks = context.src4 != nullptr; bool kv_direct = false; - if (path != GGML_WEBGPU_FLASH_ATTN_PATH_TILE) { + if (decisions.path != GGML_WEBGPU_FLASH_ATTN_PATH_TILE) { uint32_t kv_direct_align = GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH; - if (path == GGML_WEBGPU_FLASH_ATTN_PATH_SUBGROUP_MATRIX) { + if (decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_SUBGROUP_MATRIX) { kv_direct_align = context.sg_mat_k; } kv_direct = (context.src1->type == GGML_TYPE_F16) && @@ -611,7 +633,9 @@ inline ggml_webgpu_flash_attn_pipeline_key ggml_webgpu_flash_attn_make_pipeline_ } ggml_webgpu_flash_attn_pipeline_key key = {}; + key.q_type = context.src0->type; key.kv_type = context.src1->type; + key.dst_type = context.dst->type; key.head_dim_qk = (uint32_t) context.src0->ne[0]; key.head_dim_v = (uint32_t) context.src2->ne[0]; key.kv_direct = kv_direct; @@ -619,13 +643,14 @@ inline ggml_webgpu_flash_attn_pipeline_key ggml_webgpu_flash_attn_make_pipeline_ key.has_mask = has_mask; key.has_sinks = has_sinks; key.uses_logit_softcap = ggml_get_op_params_f32(context.dst, 2) != 0.0f; - key.path = path; + key.path = decisions.path; return key; } struct ggml_webgpu_flash_attn_vec_reduce_pipeline_key { - uint32_t head_dim_v; - uint32_t wg_size; + uint32_t head_dim_v; + uint32_t wg_size; + ggml_type dst_type; }; struct ggml_webgpu_flash_attn_vec_reduce_pipeline_key_hash { @@ -633,13 +658,14 @@ struct ggml_webgpu_flash_attn_vec_reduce_pipeline_key_hash { size_t seed = 0; ggml_webgpu_hash_combine(seed, key.head_dim_v); ggml_webgpu_hash_combine(seed, key.wg_size); + ggml_webgpu_hash_combine(seed, key.dst_type); return seed; } }; inline bool operator==(const ggml_webgpu_flash_attn_vec_reduce_pipeline_key & lhs, const ggml_webgpu_flash_attn_vec_reduce_pipeline_key & rhs) { - return lhs.head_dim_v == rhs.head_dim_v && lhs.wg_size == rhs.wg_size; + return lhs.head_dim_v == rhs.head_dim_v && lhs.wg_size == rhs.wg_size && lhs.dst_type == rhs.dst_type; } struct ggml_webgpu_flash_attn_blk_pipeline_key { @@ -662,19 +688,32 @@ inline size_t ggml_webgpu_flash_attn_wg_mem_bytes(uint32_t q_tile, uint32_t head_dim_qk, uint32_t head_dim_v, bool has_mask, - bool kv_direct) { + bool kv_direct, + uint32_t path = GGML_WEBGPU_FLASH_ATTN_PATH_SUBGROUP_MATRIX) { const uint32_t max_head_dim = std::max(head_dim_qk, head_dim_v); size_t f16_elems = 0; size_t f32_elems = 0; - f16_elems += q_tile * head_dim_qk; // q_shmem + if (path == GGML_WEBGPU_FLASH_ATTN_PATH_VEC) { + f32_elems += head_dim_qk; // q_shmem + if (!kv_direct) { + f32_elems += kv_tile * max_head_dim; // kv_shmem + } + f32_elems += head_dim_v; // o_shmem + if (has_mask) { + f32_elems += kv_tile; // mask_shmem + } + f32_elems += kv_tile; // inter_shmem + return f32_elems * GGML_WEBGPU_F32_SIZE_BYTES; + } + f32_elems += q_tile * head_dim_qk; // q_shmem if (!kv_direct) { - f16_elems += kv_tile * max_head_dim; // kv_shmem + f32_elems += kv_tile * max_head_dim; // kv_shmem } - f16_elems += q_tile * head_dim_v; // o_shmem + f32_elems += q_tile * head_dim_v; // o_shmem if (has_mask) { - f16_elems += q_tile * kv_tile; // mask_shmem + f32_elems += q_tile * kv_tile; // mask_shmem } - f16_elems += q_tile * kv_tile; // inter_shmem + f32_elems += q_tile * kv_tile; // inter_shmem f32_elems += q_tile; // row_max_shmem f32_elems += q_tile; // exp_sum_shmem return f16_elems * GGML_WEBGPU_F16_SIZE_BYTES + f32_elems * GGML_WEBGPU_F32_SIZE_BYTES; @@ -684,27 +723,27 @@ inline uint32_t ggml_webgpu_flash_attn_max_kv_tile(const ggml_webgpu_shader_lib_ const ggml_webgpu_flash_attn_pipeline_key & key) { const size_t limit_bytes = context.wg_mem_limit_bytes; uint32_t q_tile = context.sg_mat_m; - uint32_t kv_granularity = context.sg_mat_n; + uint32_t kv_granularity = std::max(1u, context.sg_mat_n); if (key.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE) { q_tile = GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE; - kv_granularity = std::max(1u, context.max_subgroup_size); + kv_granularity = 1u; } else if (key.path == GGML_WEBGPU_FLASH_ATTN_PATH_VEC) { q_tile = 1u; kv_granularity = 8u; } - const size_t base_q_bytes = (key.head_dim_qk + key.head_dim_v) * q_tile * GGML_WEBGPU_F16_SIZE_BYTES + - 2 * q_tile * GGML_WEBGPU_F32_SIZE_BYTES; - size_t bytes_per_kv = 0; - if (!key.kv_direct) { - bytes_per_kv += std::max(key.head_dim_qk, key.head_dim_v); + const size_t base_q_bytes = ggml_webgpu_flash_attn_wg_mem_bytes(q_tile, 0, key.head_dim_qk, key.head_dim_v, + key.has_mask, key.kv_direct, key.path); + if (limit_bytes <= base_q_bytes) { + return 0; } - if (key.has_mask) { - bytes_per_kv += q_tile; + const size_t one_kv_bytes = ggml_webgpu_flash_attn_wg_mem_bytes(q_tile, 1, key.head_dim_qk, key.head_dim_v, + key.has_mask, key.kv_direct, key.path); + const size_t bytes_per_kv = one_kv_bytes - base_q_bytes; + if (bytes_per_kv == 0) { + return 0; } - bytes_per_kv += q_tile; - bytes_per_kv *= GGML_WEBGPU_F16_SIZE_BYTES; - const uint32_t max_kv_tile = (limit_bytes - base_q_bytes) / bytes_per_kv; - return (max_kv_tile / kv_granularity) * kv_granularity; + const size_t max_kv_tile = (limit_bytes - base_q_bytes) / bytes_per_kv; + return (uint32_t) ((max_kv_tile / kv_granularity) * kv_granularity); } inline ggml_webgpu_flash_attn_decisions ggml_webgpu_flash_attn_get_decisions( @@ -731,14 +770,18 @@ inline ggml_webgpu_flash_attn_decisions ggml_webgpu_flash_attn_get_decisions( (v_offset_elems % GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH == 0u); const bool kv_vec_type_supported = K->type == GGML_TYPE_F16 || K->type == GGML_TYPE_Q4_0 || K->type == GGML_TYPE_Q8_0; - const bool use_vec = context.supports_subgroups && (context.src0->ne[1] < 20) && (context.src0->ne[0] % 32 == 0) && - (context.src2->ne[0] % GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH == 0) && - kv_vec_type_supported && (K->type != GGML_TYPE_F16 || f16_vec4_aligned) && - (context.src2->type == K->type); + const bool use_vec = context.supports_subgroups && (context.src0->ne[1] < 20) && (context.src0->ne[0] % 32 == 0) && + (context.src2->ne[0] % GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH == 0) && + kv_vec_type_supported && (K->type != GGML_TYPE_F16 || f16_vec4_aligned) && + (context.src2->type == K->type); + const bool tile_can_dispatch_all_q_rows = + context.max_subgroup_size > 0 && + context.max_wg_size >= GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE * context.max_subgroup_size; const bool use_tile = context.supports_subgroups && !context.supports_subgroup_matrix && K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16 && f16_vec4_aligned && (context.src0->ne[0] % GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH == 0) && - (context.src2->ne[0] % GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH == 0) && !use_vec; + (context.src2->ne[0] % GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH == 0) && + tile_can_dispatch_all_q_rows && !use_vec; decisions.path = use_vec ? GGML_WEBGPU_FLASH_ATTN_PATH_VEC : use_tile ? GGML_WEBGPU_FLASH_ATTN_PATH_TILE : @@ -749,7 +792,7 @@ inline ggml_webgpu_flash_attn_decisions ggml_webgpu_flash_attn_get_decisions( return decisions; } - const ggml_webgpu_flash_attn_pipeline_key key = ggml_webgpu_flash_attn_make_pipeline_key(context, decisions.path); + const ggml_webgpu_flash_attn_pipeline_key key = ggml_webgpu_flash_attn_make_pipeline_key(context, decisions); decisions.kv_direct = key.kv_direct; const uint32_t max_kv_tile = ggml_webgpu_flash_attn_max_kv_tile(context, key); // invalidate if even the smallest kv_tile doesn't fit in shared memory @@ -778,21 +821,20 @@ inline ggml_webgpu_flash_attn_decisions ggml_webgpu_flash_attn_get_decisions( std::min(64u, max_kv_tile) : std::min(max_kv_tile, context.sg_mat_n * GGML_WEBGPU_FLASH_ATTN_PREFERRED_KV_SG_TILES); decisions.wg_size = decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE ? - GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE : + std::min(std::max(1u, context.max_wg_size), + std::max(GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE, + GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE * context.max_subgroup_size)) : std::max(context.max_subgroup_size, GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE); - if (decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE) { - const uint32_t tile_kv_granularity = std::max(1u, context.max_subgroup_size); - decisions.kv_tile = - std::max(tile_kv_granularity, (decisions.kv_tile / tile_kv_granularity) * tile_kv_granularity); + if (decisions.kv_tile == 0) { + return decisions; } if (decisions.kv_direct) { GGML_ASSERT(decisions.kv_tile <= GGML_WEBGPU_KV_SEQ_PAD); while (GGML_WEBGPU_KV_SEQ_PAD % decisions.kv_tile != 0) { - decisions.kv_tile -= decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE ? - std::max(1u, context.max_subgroup_size) : - context.sg_mat_n; + decisions.kv_tile -= + decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE ? context.min_subgroup_size : context.sg_mat_n; } } return decisions; @@ -1032,7 +1074,9 @@ class ggml_webgpu_shader_lib { std::unordered_map pad_pipelines; // circular/non-circular std::unordered_map - binary_pipelines; // type/op/inplace/overlap + binary_pipelines; // type/op/inplace/overlap/src_overlap + std::unordered_map + add_id_pipelines; // inplace std::unordered_map concat_pipelines; // type std::unordered_map @@ -1407,6 +1451,7 @@ class ggml_webgpu_shader_lib { case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_MXFP4: { // Quantized types using u32 buffers for portability. defines.push_back("SRC_TYPE=u32"); @@ -1425,6 +1470,7 @@ class ggml_webgpu_shader_lib { defines.push_back(type_upper + "_SCALE_MIN"); defines.push_back(type_upper + "_TABLES"); defines.push_back(type_upper + "_GRID"); + defines.push_back(type_upper + "_LUT"); variant += "_"; variant += type_str; @@ -1434,7 +1480,7 @@ class ggml_webgpu_shader_lib { if (key.src_type == GGML_TYPE_Q1_0) { defines.push_back("BLOCK_SIZE=128u"); } else if ((key.src_type >= GGML_TYPE_Q4_0 && key.src_type <= GGML_TYPE_Q8_1) || - key.src_type == GGML_TYPE_IQ4_NL) { + key.src_type == GGML_TYPE_IQ4_NL || key.src_type == GGML_TYPE_MXFP4) { defines.push_back("BLOCK_SIZE=32u"); } else if (key.src_type >= GGML_TYPE_Q2_K) { defines.push_back("BLOCK_SIZE=256u"); @@ -1577,7 +1623,7 @@ class ggml_webgpu_shader_lib { key.type = context.dst->type; key.d_state = (int) context.src0->ne[0]; key.xbc_overlap = ggml_webgpu_tensor_overlap(context.src1, context.src4) && - ggml_webgpu_tensor_overlap(context.src1, context.src5); + ggml_webgpu_tensor_overlap(context.src1, context.src5); auto it = ssm_scan_pipelines.find(key); if (it != ssm_scan_pipelines.end()) { @@ -1694,10 +1740,10 @@ class ggml_webgpu_shader_lib { ggml_webgpu_mul_mat_vec_pipeline_key key = {}; key.src0_type = context.src0->type; key.src1_type = context.src1->type; - key.vectorized = (context.src0->ne[0] % 4 == 0 && + key.vectorized = (context.src0->ne[0] % 4 == 0 && (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ? - 1 : - 0; + 1 : + 0; auto it = mul_mat_vec_pipelines.find(key); if (it != mul_mat_vec_pipelines.end()) { @@ -1748,6 +1794,9 @@ class ggml_webgpu_shader_lib { defines.push_back(type_upper + "_GRID"); defines.push_back(type_upper + "_TABLES"); break; + case GGML_TYPE_MXFP4: + defines.push_back(type_upper + "_LUT"); + break; default: break; } @@ -1805,13 +1854,13 @@ class ggml_webgpu_shader_lib { webgpu_pipeline get_mul_mat_fast_pipeline(const ggml_webgpu_shader_lib_context & context) { ggml_webgpu_mul_mat_pipeline_key key = {}; - key.src0_type = context.src0->type; - key.src1_type = context.src1->type; - key.vectorized = (context.src0->ne[0] % 4 == 0 && context.dst->ne[0] % 4 == 0 && - (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ? - 1 : - 0; - key.use_subgroup_matrix = context.supports_subgroup_matrix; + key.src0_type = context.src0->type; + key.src1_type = context.src1->type; + key.vectorized = (context.src0->ne[0] % 4 == 0 && context.dst->ne[0] % 4 == 0 && + (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ? + 1 : + 0; + key.use_subgroup_matrix = context.supports_subgroup_matrix; auto it = mul_mat_fast_pipelines.find(key); if (it != mul_mat_fast_pipelines.end()) { @@ -1882,6 +1931,9 @@ class ggml_webgpu_shader_lib { defines.push_back(type_upper + "_GRID"); defines.push_back(type_upper + "_TABLES"); break; + case GGML_TYPE_MXFP4: + defines.push_back(type_upper + "_LUT"); + break; default: break; } @@ -2016,6 +2068,7 @@ class ggml_webgpu_shader_lib { case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_MXFP4: { // Quantized types using u32 buffers for portability. defines.push_back("SRC0_TYPE=u32"); @@ -2074,10 +2127,10 @@ class ggml_webgpu_shader_lib { key.src0_type = context.src0->type; key.src1_type = context.src1->type; key.n_experts = context.src0->ne[2]; - key.vectorized = (context.src0->ne[0] % 4 == 0 && context.src0->ne[1] % 4 == 0 && + key.vectorized = (context.src0->ne[0] % 4 == 0 && context.src0->ne[1] % 4 == 0 && (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ? - 1 : - 0; + 1 : + 0; auto it = mul_mat_id_pipelines.find(key); if (it != mul_mat_id_pipelines.end()) { @@ -2143,6 +2196,9 @@ class ggml_webgpu_shader_lib { defines.push_back(type_upper + "_GRID"); defines.push_back(type_upper + "_TABLES"); break; + case GGML_TYPE_MXFP4: + defines.push_back(type_upper + "_LUT"); + break; default: break; } @@ -2194,10 +2250,10 @@ class ggml_webgpu_shader_lib { key.src0_type = context.src0->type; key.src1_type = context.src1->type; key.n_experts = context.src0->ne[2]; - key.vectorized = (context.src0->ne[0] % 4 == 0 && + key.vectorized = (context.src0->ne[0] % 4 == 0 && (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ? - 1 : - 0; + 1 : + 0; auto it = mul_mat_id_vec_pipelines.find(key); if (it != mul_mat_id_vec_pipelines.end()) { @@ -2260,6 +2316,9 @@ class ggml_webgpu_shader_lib { defines.push_back(type_upper + "_GRID"); defines.push_back(type_upper + "_TABLES"); break; + case GGML_TYPE_MXFP4: + defines.push_back(type_upper + "_LUT"); + break; default: break; } @@ -2477,6 +2536,37 @@ class ggml_webgpu_shader_lib { return binary_pipelines[key]; } + webgpu_pipeline get_add_id_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_add_id_pipeline_key key = {}; + key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst); + + auto it = add_id_pipelines.find(key); + if (it != add_id_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "add_id"; + const char * shader_src = wgsl_add_id; + + if (key.inplace) { + defines.push_back("INPLACE"); + variant += "_inplace"; + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(shader_src, defines); + auto pipeline_decisions = std::make_shared(); + pipeline_decisions->wg_size = context.max_wg_size; + pipeline_decisions->inplace = key.inplace; + + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = pipeline_decisions; + add_id_pipelines[key] = pipeline; + return pipeline; + } + webgpu_pipeline get_concat_pipeline(const ggml_webgpu_shader_lib_context & context) { ggml_webgpu_concat_pipeline_key key = {}; key.type = context.dst->type; @@ -2558,7 +2648,7 @@ class ggml_webgpu_shader_lib { const ggml_webgpu_flash_attn_decisions decisions = ggml_webgpu_flash_attn_get_decisions(context, storage_offset_alignment); GGML_ASSERT(decisions.path != GGML_WEBGPU_FLASH_ATTN_PATH_NONE); - ggml_webgpu_flash_attn_pipeline_key key = ggml_webgpu_flash_attn_make_pipeline_key(context, decisions.path); + ggml_webgpu_flash_attn_pipeline_key key = ggml_webgpu_flash_attn_make_pipeline_key(context, decisions); auto it = flash_attn_pipelines.find(key); if (it != flash_attn_pipelines.end()) { return it->second; @@ -2586,6 +2676,30 @@ class ggml_webgpu_shader_lib { } variant += std::string("_") + ggml_type_name(key.kv_type); + switch (key.q_type) { + case GGML_TYPE_F32: + defines.push_back("Q_F32"); + break; + case GGML_TYPE_F16: + defines.push_back("Q_F16"); + break; + default: + GGML_ABORT("Unsupported Q type for flash attention shader"); + } + variant += std::string("_q") + ggml_type_name(key.q_type); + + switch (key.dst_type) { + case GGML_TYPE_F32: + defines.push_back("DST_F32"); + break; + case GGML_TYPE_F16: + defines.push_back("DST_F16"); + break; + default: + GGML_ABORT("Unsupported dst type for flash attention shader"); + } + variant += std::string("_dst") + ggml_type_name(key.dst_type); + if (key.has_mask) { defines.push_back("MASK"); if (key.path == GGML_WEBGPU_FLASH_ATTN_PATH_VEC) { @@ -2625,9 +2739,11 @@ class ggml_webgpu_shader_lib { shader_src = wgsl_flash_attn_vec_split; } else if (key.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE) { shader_src = wgsl_flash_attn_tile; - defines.push_back("MAX_SUBGROUP_SIZE=" + std::to_string(context.max_subgroup_size)); + defines.push_back("MIN_SUBGROUP_SIZE=" + std::to_string(context.min_subgroup_size) + "u"); + defines.push_back("MAX_SUBGROUP_SIZE=" + std::to_string(context.max_subgroup_size) + "u"); defines.push_back("KV_STAGE_STRIDE=" + std::to_string(std::max(key.head_dim_qk, key.head_dim_v))); - variant += "_tile"; + variant += "_tile_sg" + std::to_string(context.min_subgroup_size) + "_" + + std::to_string(context.max_subgroup_size); } else { defines.push_back(std::string("SG_MAT_M=") + std::to_string(context.sg_mat_m)); defines.push_back(std::string("SG_MAT_N=") + std::to_string(context.sg_mat_n)); @@ -2677,6 +2793,7 @@ class ggml_webgpu_shader_lib { webgpu_pipeline get_flash_attn_vec_reduce_pipeline(const ggml_webgpu_shader_lib_context & context) { ggml_webgpu_flash_attn_vec_reduce_pipeline_key key = {}; key.head_dim_v = (uint32_t) context.src2->ne[0]; + key.dst_type = context.dst->type; key.wg_size = context.max_wg_size; auto it = flash_attn_vec_reduce_pipelines.find(key); if (it != flash_attn_vec_reduce_pipelines.end()) { @@ -2686,6 +2803,18 @@ class ggml_webgpu_shader_lib { std::vector defines; std::string variant = "flash_attn_vec_reduce"; + switch (key.dst_type) { + case GGML_TYPE_F32: + defines.push_back("DST_F32"); + break; + case GGML_TYPE_F16: + defines.push_back("DST_F16"); + break; + default: + GGML_ABORT("Unsupported dst type for flash attention vec reduce shader"); + } + variant += std::string("_dst") + ggml_type_name(key.dst_type); + defines.push_back(std::string("HEAD_DIM_V=") + std::to_string(key.head_dim_v)); variant += std::string("_hsv") + std::to_string(key.head_dim_v); diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 12f60a9900e..b24101c78b0 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -187,6 +187,7 @@ struct webgpu_capabilities { uint32_t sg_mat_k = 0; uint32_t subgroup_size = 0; + uint32_t min_subgroup_size = 0; uint32_t max_subgroup_size = 0; size_t memset_bytes_per_thread; }; @@ -1410,8 +1411,6 @@ static webgpu_encoded_op ggml_webgpu_mul_mat(webgpu_context & ctx, case GGML_TYPE_Q3_K: case GGML_TYPE_Q2_K: case GGML_TYPE_Q1_0: - use_fast = true; - break; case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ2_XXS: @@ -1421,6 +1420,7 @@ static webgpu_encoded_op ggml_webgpu_mul_mat(webgpu_context & ctx, case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_XS: + case GGML_TYPE_MXFP4: use_fast = true; break; default: @@ -1442,6 +1442,7 @@ static webgpu_encoded_op ggml_webgpu_mul_mat(webgpu_context & ctx, shader_lib_ctx.sg_mat_m = ctx->global_ctx->capabilities.sg_mat_m; shader_lib_ctx.sg_mat_n = ctx->global_ctx->capabilities.sg_mat_n; shader_lib_ctx.sg_mat_k = ctx->global_ctx->capabilities.sg_mat_k; + shader_lib_ctx.min_subgroup_size = ctx->global_ctx->capabilities.min_subgroup_size; shader_lib_ctx.max_subgroup_size = ctx->global_ctx->capabilities.max_subgroup_size; // Get or create pipeline @@ -1750,6 +1751,7 @@ static webgpu_encoded_op ggml_webgpu_flash_attn(webgpu_context & ctx, shader_lib_ctx.sg_mat_m = ctx->global_ctx->capabilities.sg_mat_m; shader_lib_ctx.sg_mat_n = ctx->global_ctx->capabilities.sg_mat_n; shader_lib_ctx.sg_mat_k = ctx->global_ctx->capabilities.sg_mat_k; + shader_lib_ctx.min_subgroup_size = ctx->global_ctx->capabilities.min_subgroup_size; shader_lib_ctx.max_subgroup_size = ctx->global_ctx->capabilities.max_subgroup_size; webgpu_pipeline pipeline = ctx->shader_lib->get_flash_attn_pipeline( shader_lib_ctx, ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); @@ -2142,6 +2144,56 @@ static webgpu_encoded_op ggml_webgpu_binary_op(webgpu_context & ctx, return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); } +static webgpu_encoded_op ggml_webgpu_add_id(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * src2, + ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.src2 = src2; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_add_id_pipeline(shader_lib_ctx); + + auto * decisions = static_cast(pipeline.context.get()); + + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src2->nb[0] / ggml_type_size(src2->type)), + (uint32_t) (src2->nb[1] / ggml_type_size(src2->type)), + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], + }; + + std::vector entries; + + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src2)); + + if (!decisions->inplace) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, dst)); + } + + uint32_t wg_x = 1; + uint32_t wg_y = 1; + uint32_t total_wg = ggml_nrows(dst); + const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y); + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + static webgpu_encoded_op ggml_webgpu_concat(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, @@ -2915,6 +2967,8 @@ static std::optional ggml_webgpu_encode(webgpu_context ctx, case GGML_OP_MUL: case GGML_OP_DIV: return ggml_webgpu_binary_op(ctx, src0, src1, node); + case GGML_OP_ADD_ID: + return ggml_webgpu_add_id(ctx, src0, src1, src2, node); case GGML_OP_CONCAT: return ggml_webgpu_concat(ctx, src0, src1, node); case GGML_OP_REPEAT: @@ -3469,6 +3523,7 @@ static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer shader_lib_ctx.sg_mat_m = ctx->webgpu_global_ctx->capabilities.sg_mat_m; shader_lib_ctx.sg_mat_n = ctx->webgpu_global_ctx->capabilities.sg_mat_n; shader_lib_ctx.sg_mat_k = ctx->webgpu_global_ctx->capabilities.sg_mat_k; + shader_lib_ctx.min_subgroup_size = ctx->webgpu_global_ctx->capabilities.min_subgroup_size; shader_lib_ctx.max_subgroup_size = ctx->webgpu_global_ctx->capabilities.max_subgroup_size; const ggml_webgpu_flash_attn_decisions decisions = ggml_webgpu_flash_attn_get_decisions( @@ -3667,8 +3722,9 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { #endif ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix = valid_subgroup_matrix_config; - // For subgroup matrix code to be the most efficient, we would like the subgroup size to be consistent and accurate. - // Unfortunately, that is not possible, so we use the maximum subgroup size reported by the adapter. + // Runtime subgroup size can be any supported size in this range. Shaders + // that allocate per-lane register arrays must size them for the minimum. + ctx->webgpu_global_ctx->capabilities.min_subgroup_size = info.subgroupMinSize; ctx->webgpu_global_ctx->capabilities.max_subgroup_size = info.subgroupMaxSize; // Initialize device std::vector required_features = { wgpu::FeatureName::ShaderF16 }; @@ -3862,6 +3918,7 @@ static bool ggml_webgpu_supported_qtype(ggml_type type) { case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_XS: + case GGML_TYPE_MXFP4: return true; default: return false; @@ -3900,6 +3957,9 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type) && (src1->type == op->type); break; + case GGML_OP_ADD_ID: + supports_op = src0->type == GGML_TYPE_F32; + break; case GGML_OP_CONCAT: supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32); break; @@ -3957,6 +4017,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_XS: + case GGML_TYPE_MXFP4: supports_op = true; break; default: @@ -3996,6 +4057,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_XS: + case GGML_TYPE_MXFP4: supports_op = true; break; default: @@ -4024,11 +4086,14 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const shader_lib_ctx.dst = const_cast(op); shader_lib_ctx.supports_subgroups = ctx->webgpu_global_ctx->capabilities.supports_subgroups; shader_lib_ctx.supports_subgroup_matrix = ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix; + shader_lib_ctx.max_wg_size = + ctx->webgpu_global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; shader_lib_ctx.wg_mem_limit_bytes = ctx->webgpu_global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize; shader_lib_ctx.sg_mat_m = ctx->webgpu_global_ctx->capabilities.sg_mat_m; shader_lib_ctx.sg_mat_n = ctx->webgpu_global_ctx->capabilities.sg_mat_n; shader_lib_ctx.sg_mat_k = ctx->webgpu_global_ctx->capabilities.sg_mat_k; + shader_lib_ctx.min_subgroup_size = ctx->webgpu_global_ctx->capabilities.min_subgroup_size; shader_lib_ctx.max_subgroup_size = ctx->webgpu_global_ctx->capabilities.max_subgroup_size; const ggml_webgpu_flash_attn_decisions decisions = ggml_webgpu_flash_attn_get_decisions( @@ -4040,9 +4105,9 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const break; } if (decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_VEC) { - const size_t min_bytes = - ggml_webgpu_flash_attn_wg_mem_bytes(decisions.q_tile, decisions.kv_tile, (uint32_t) src0->ne[0], - (uint32_t) src2->ne[0], has_mask, decisions.kv_direct); + const size_t min_bytes = ggml_webgpu_flash_attn_wg_mem_bytes( + decisions.q_tile, decisions.kv_tile, (uint32_t) src0->ne[0], (uint32_t) src2->ne[0], has_mask, + decisions.kv_direct, decisions.path); if (min_bytes > limit_bytes) { supports_op = false; } @@ -4050,9 +4115,9 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const } if (decisions.path == GGML_WEBGPU_FLASH_ATTN_PATH_TILE) { - const size_t min_bytes = - ggml_webgpu_flash_attn_wg_mem_bytes(decisions.q_tile, decisions.kv_tile, (uint32_t) src0->ne[0], - (uint32_t) src2->ne[0], has_mask, decisions.kv_direct); + const size_t min_bytes = ggml_webgpu_flash_attn_wg_mem_bytes( + decisions.q_tile, decisions.kv_tile, (uint32_t) src0->ne[0], (uint32_t) src2->ne[0], has_mask, + decisions.kv_direct, decisions.path); if (min_bytes > limit_bytes) { supports_op = false; } @@ -4063,9 +4128,9 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const supports_op = false; break; } - const size_t min_bytes = - ggml_webgpu_flash_attn_wg_mem_bytes(decisions.q_tile, decisions.kv_tile, (uint32_t) src0->ne[0], - (uint32_t) src2->ne[0], has_mask, decisions.kv_direct); + const size_t min_bytes = ggml_webgpu_flash_attn_wg_mem_bytes( + decisions.q_tile, decisions.kv_tile, (uint32_t) src0->ne[0], (uint32_t) src2->ne[0], has_mask, + decisions.kv_direct, decisions.path); if (min_bytes > limit_bytes) { supports_op = false; } diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/add_id.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/add_id.wgsl new file mode 100644 index 00000000000..2573926cb89 --- /dev/null +++ b/ggml/src/ggml-webgpu/wgsl-shaders/add_id.wgsl @@ -0,0 +1,64 @@ +struct Params { + offset_src0: u32, + offset_src1: u32, + offset_ids: u32, + offset_dst: u32, + + nb01: u32, + nb02: u32, + nb11: u32, + nb20: u32, + nb21: u32, + + ne0: u32, + ne1: u32, + ne2: u32, +}; + +@group(0) @binding(0) var src0: array; // [n_embd, n_experts_used, n_token] +@group(0) @binding(1) var src1: array; // [n_embd, n_experts] +@group(0) @binding(2) var ids: array; // [n_experts_used, n_token] + +#ifdef INPLACE + +@group(0) @binding(3) +var params: Params; + +#else + +@group(0) @binding(3) +var dst: array; + +@group(0) @binding(4) +var params: Params; + +#endif + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wg_id: vec3, + @builtin(num_workgroups) num_wg: vec3, + @builtin(local_invocation_id) local_id: vec3) { + + let wg_linear = wg_id.x + wg_id.y * num_wg.x; + + if (wg_linear < params.ne1 * params.ne2) { + let thread_id = local_id.x; + let i2 = wg_linear / params.ne1; + let i1 = wg_linear % params.ne1; + + let i11 = u32(ids[params.offset_ids + i1 * params.nb20 + i2 * params.nb21]); + + let src0_row = params.offset_src0 + i1 * params.nb01 + i2 * params.nb02; + let src1_row = params.offset_src1 + i11 * params.nb11; + let dst_row = params.offset_dst + i1 * params.ne0 + i2 * (params.ne0 * params.ne1); + + for (var i = thread_id;i < params.ne0; i += WG_SIZE) { +#ifdef INPLACE + src0[src0_row + i] = src0[src0_row + i] + src1[src1_row + i]; +#else + dst[dst_row + i] = src0[src0_row + i] + src1[src1_row + i]; +#endif + } + } + +} diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl index 14c045b0ba6..372ea79bf9d 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl @@ -896,3 +896,10 @@ const kvalues_iq4nl = array( ); #endif + +#ifdef MXFP4_LUT +const kvalues_mxfp4 = array( + 0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12 +); +#endif + diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl index 37ea23b80c8..ae8036b9ac5 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl @@ -1,12 +1,33 @@ enable f16; enable subgroups; +#ifdef Q_F16 +#define Q_TYPE f16 +#else +#define Q_TYPE f32 +#endif + +#ifdef KV_F32 +#define KV_TYPE f32 +#else +#define KV_TYPE f16 +#endif + +#ifdef DST_F16 +#define DST_TYPE f16 +#else +#define DST_TYPE f32 +#endif + #define HEAD_DIM_QK 64 #define HEAD_DIM_V 64 #define KV_STAGE_STRIDE 64 #define Q_TILE 4 #define KV_TILE 64 #define WG_SIZE 128 +#ifndef MIN_SUBGROUP_SIZE +#define MIN_SUBGROUP_SIZE MAX_SUBGROUP_SIZE +#endif struct Params { offset_q: u32, @@ -41,13 +62,13 @@ struct Params { m1: f32, }; -@group(0) @binding(0) var Q: array; +@group(0) @binding(0) var Q: array; #ifdef KV_OVERLAP -@group(0) @binding(1) var K: array>; +@group(0) @binding(1) var K: array>; #define V K #else -@group(0) @binding(1) var K: array>; -@group(0) @binding(2) var V: array>; +@group(0) @binding(1) var K: array>; +@group(0) @binding(2) var V: array>; #endif #if defined(MASK) && defined(SINKS) @@ -92,17 +113,17 @@ struct Params { #endif #endif -@group(0) @binding(DST_BINDING) var dst: array>; +@group(0) @binding(DST_BINDING) var dst: array>; @group(0) @binding(PARAMS_BINDING) var params: Params; const FLOAT_MIN: f32 = -1.0e9; const Q_CHUNKS: u32 = HEAD_DIM_QK / 4u; const V_CHUNKS: u32 = HEAD_DIM_V / 4u; -const SCORE_REGS_PER_LANE: u32 = (KV_TILE + MAX_SUBGROUP_SIZE - 1u) / MAX_SUBGROUP_SIZE; -const OUT_REGS_PER_LANE: u32 = (V_CHUNKS + MAX_SUBGROUP_SIZE - 1u) / MAX_SUBGROUP_SIZE; +const SCORE_REGS_PER_LANE: u32 = (KV_TILE + MIN_SUBGROUP_SIZE - 1u) / MIN_SUBGROUP_SIZE; +const OUT_REGS_PER_LANE: u32 = (V_CHUNKS + MIN_SUBGROUP_SIZE - 1u) / MIN_SUBGROUP_SIZE; -var q_shmem: array; -var kv_shmem: array; +var q_shmem: array; +var kv_shmem: array; var p_shmem: array; @compute @workgroup_size(WG_SIZE) @@ -158,10 +179,10 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let q_col = elem_idx % HEAD_DIM_QK; let head_q_row = q_row_start + q_tile_row; let global_q_row_offset = q_head_offset + head_q_row * params.stride_q1; - q_shmem[elem_idx] = f16(select( + q_shmem[elem_idx] = select( 0.0, - Q[global_q_row_offset + q_col] * params.scale, - head_q_row < params.seq_len_q)); + f32(Q[global_q_row_offset + q_col]) * params.scale, + head_q_row < params.seq_len_q); } workgroupBarrier(); @@ -192,10 +213,10 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let k_vec_index = (k_head_offset + global_k_row * params.stride_k1 + chunk * 4u) >> 2u; let k4 = K[k_vec_index]; let kv_off = kv_local * KV_STAGE_STRIDE + chunk * 4u; - kv_shmem[kv_off + 0u] = k4.x; - kv_shmem[kv_off + 1u] = k4.y; - kv_shmem[kv_off + 2u] = k4.z; - kv_shmem[kv_off + 3u] = k4.w; + kv_shmem[kv_off + 0u] = f32(k4.x); + kv_shmem[kv_off + 1u] = f32(k4.y); + kv_shmem[kv_off + 2u] = f32(k4.z); + kv_shmem[kv_off + 3u] = f32(k4.w); } workgroupBarrier(); @@ -213,16 +234,16 @@ fn main(@builtin(workgroup_id) wg_id: vec3, for (var chunk = 0u; chunk < Q_CHUNKS; chunk += 1u) { let q_off = q_base + chunk * 4u; let qv = vec4( - f32(q_shmem[q_off + 0u]), - f32(q_shmem[q_off + 1u]), - f32(q_shmem[q_off + 2u]), - f32(q_shmem[q_off + 3u])); + q_shmem[q_off + 0u], + q_shmem[q_off + 1u], + q_shmem[q_off + 2u], + q_shmem[q_off + 3u]); let kv_off = kv_local * KV_STAGE_STRIDE + chunk * 4u; let kv = vec4( - f32(kv_shmem[kv_off + 0u]), - f32(kv_shmem[kv_off + 1u]), - f32(kv_shmem[kv_off + 2u]), - f32(kv_shmem[kv_off + 3u])); + kv_shmem[kv_off + 0u], + kv_shmem[kv_off + 1u], + kv_shmem[kv_off + 2u], + kv_shmem[kv_off + 3u]); dot_val += dot(qv, kv); } #ifdef LOGIT_SOFTCAP @@ -264,10 +285,10 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let v_vec_index = (v_head_offset + global_v_row * params.stride_v1 + chunk * 4u) >> 2u; let v4 = V[v_vec_index]; let kv_off = kv_local * KV_STAGE_STRIDE + chunk * 4u; - kv_shmem[kv_off + 0u] = v4.x; - kv_shmem[kv_off + 1u] = v4.y; - kv_shmem[kv_off + 2u] = v4.z; - kv_shmem[kv_off + 3u] = v4.w; + kv_shmem[kv_off + 0u] = f32(v4.x); + kv_shmem[kv_off + 1u] = f32(v4.y); + kv_shmem[kv_off + 2u] = f32(v4.z); + kv_shmem[kv_off + 3u] = f32(v4.w); } workgroupBarrier(); @@ -288,10 +309,10 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let p = p_shmem[subgroup_p_offset + kv_local]; let kv_off = kv_local * KV_STAGE_STRIDE + chunk * 4u; let v4 = vec4( - f32(kv_shmem[kv_off + 0u]), - f32(kv_shmem[kv_off + 1u]), - f32(kv_shmem[kv_off + 2u]), - f32(kv_shmem[kv_off + 3u])); + kv_shmem[kv_off + 0u], + kv_shmem[kv_off + 1u], + kv_shmem[kv_off + 2u], + kv_shmem[kv_off + 3u]); acc += p * v4; } out_regs[reg_idx] = acc; @@ -324,7 +345,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, continue; } let dst_vec_index = (row_base + chunk * 4u) >> 2u; - dst[dst_vec_index] = out_regs[reg_idx] * inv_exp_sum; + dst[dst_vec_index] = vec4(out_regs[reg_idx] * inv_exp_sum); } } } diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_reduce.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_reduce.wgsl index 9a0de82a56a..1091d744073 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_reduce.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_reduce.wgsl @@ -2,6 +2,12 @@ diagnostic(off, subgroup_uniformity); enable f16; enable subgroups; +#ifdef DST_F16 +#define DST_TYPE f16 +#else +#define DST_TYPE f32 +#endif + // Default values #define HEAD_DIM_V 64 #define WG_SIZE 128 @@ -17,7 +23,7 @@ struct Params { }; @group(0) @binding(0) var tmp: array; -@group(0) @binding(1) var dst: array>; +@group(0) @binding(1) var dst: array>; @group(0) @binding(2) var params: Params; const FLOAT_MIN: f32 = -1.0e9; @@ -72,7 +78,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, if (thread == 0u) { let dst_vec_index = (row_base + elem_base) >> 2u; - dst[dst_vec_index] = vec4(sum_x, sum_y, sum_z, sum_w) * inv_s; + dst[dst_vec_index] = vec4(vec4(sum_x, sum_y, sum_z, sum_w) * inv_s); } } } diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl index b1e234784a8..30ebbebe772 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl @@ -8,6 +8,18 @@ enable subgroups; #define KV_TYPE f16 #endif +#ifdef Q_F16 +#define Q_TYPE f16 +#else +#define Q_TYPE f32 +#endif + +#ifdef DST_F16 +#define DST_TYPE f16 +#else +#define DST_TYPE f32 +#endif + #define HEAD_DIM_QK 64 #define HEAD_DIM_V 64 @@ -89,7 +101,7 @@ struct Params { nwg: u32, }; -@group(0) @binding(0) var Q: array; +@group(0) @binding(0) var Q: array; #ifdef KV_OVERLAP #if defined(KV_Q4_0) || defined(KV_Q8_0) @group(0) @binding(1) var K: array; @@ -191,41 +203,41 @@ struct Params { @group(0) @binding(BLK_BINDING) var blk: array; #endif @group(0) @binding(TMP_BINDING) var tmp: array; -@group(0) @binding(DST_BINDING) var dst: array>; +@group(0) @binding(DST_BINDING) var dst: array>; @group(0) @binding(PARAMS_BINDING) var params: Params; // Just a very small float value. const FLOAT_MIN: f32 = -1.0e9; -var q_shmem: array; +var q_shmem: array; #ifndef KV_DIRECT const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V); // we can reuse the same shmem for K and V since we only need one at a time -var kv_shmem: array; +var kv_shmem: array; #endif -var o_shmem: array; +var o_shmem: array; #ifdef MASK // storage for mask values -var mask_shmem: array; +var mask_shmem: array; #endif // note that we reuse the same storage for both since we only need one at a time -var inter_shmem: array; +var inter_shmem: array; // Storage for row max and exp sum during online softmax fn calc_softmax_term(kv_idx: u32, slope: f32, has_bias: bool, apply_mask: bool) -> f32 { var v = select(FLOAT_MIN, - f32(inter_shmem[kv_idx]) * params.scale, + inter_shmem[kv_idx] * params.scale, kv_idx < KV_TILE); #ifdef LOGIT_SOFTCAP v = params.logit_softcap * tanh(v); #endif #ifdef MASK if (apply_mask) { - var mask_val = select(0.0, f32(mask_shmem[kv_idx]), kv_idx < KV_TILE); + var mask_val = select(0.0, mask_shmem[kv_idx], kv_idx < KV_TILE); v += select(mask_val, slope * mask_val, has_bias); } #endif @@ -289,10 +301,10 @@ fn main(@builtin(workgroup_id) wg_id: vec3, // load the single Q row into shared memory for (var elem_idx = local_id.x; elem_idx < HEAD_DIM_QK; elem_idx += WG_SIZE) { let global_q_row_offset = q_head_offset + q_row_start * params.stride_q1; - q_shmem[elem_idx] = f16(select( + q_shmem[elem_idx] = select( 0.0, - Q[global_q_row_offset + elem_idx], - q_row_start < params.seq_len_q)); + f32(Q[global_q_row_offset + elem_idx]), + q_row_start < params.seq_len_q); } for (var kv_tile = iwg * KV_TILE; kv_tile < params.seq_len_kv; kv_tile += KV_TILE * params.nwg) { @@ -308,7 +320,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let blk_state = blk_state_local; let skip_tile = blk_state == 0u; for (var elem_idx = local_id.x; elem_idx < KV_TILE; elem_idx += WG_SIZE) { - inter_shmem[elem_idx] = f16(0.0); + inter_shmem[elem_idx] = 0.0; } // load k tile into shared memory @@ -331,8 +343,8 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let q_packed = bitcast(vec2(q_0, q_1)); for (var k = 0u; k < 4u; k++) { let q_byte = get_byte(q_packed, k); - let q_hi = (f16((q_byte >> 4) & 0xF) - 8.0) * d; - let q_lo = (f16(q_byte & 0xF) - 8.0) * d; + let q_hi = (f32((q_byte >> 4) & 0xF) - 8.0) * f32(d); + let q_lo = (f32(q_byte & 0xF) - 8.0) * f32(d); let idx = block_k * BLOCK_SIZE + block_offset * 2u + j * 2u + k; kv_shmem[row_offset + idx] = q_lo; kv_shmem[row_offset + idx + 16u] = q_hi; @@ -359,7 +371,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let q_packed = bitcast(vec2(q_0, q_1)); for (var k = 0u; k < 4u; k++) { let q_byte = get_byte_i32(q_packed, k); - let q_val = f16(q_byte) * d; + let q_val = f32(q_byte) * f32(d); let idx = block_k * BLOCK_SIZE + block_offset * 2u + j * 2u + k; kv_shmem[row_offset + idx] = q_val; } @@ -377,10 +389,10 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let in_bounds = global_k_row < params.seq_len_kv && (k_col + 3u) < HEAD_DIM_QK; let vec_idx = (global_k_row_offset + k_col) >> 2u; let k4 = select(vec4(0.0), K[vec_idx], in_bounds); - kv_shmem[elem_idx + 0u] = f16(k4.x); - kv_shmem[elem_idx + 1u] = f16(k4.y); - kv_shmem[elem_idx + 2u] = f16(k4.z); - kv_shmem[elem_idx + 3u] = f16(k4.w); + kv_shmem[elem_idx + 0u] = f32(k4.x); + kv_shmem[elem_idx + 1u] = f32(k4.y); + kv_shmem[elem_idx + 2u] = f32(k4.z); + kv_shmem[elem_idx + 3u] = f32(k4.w); } #endif @@ -401,20 +413,20 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let q_off = i * 4u; let qv = vec4( - f32(q_shmem[q_off + 0u]), - f32(q_shmem[q_off + 1u]), - f32(q_shmem[q_off + 2u]), - f32(q_shmem[q_off + 3u])); + q_shmem[q_off + 0u], + q_shmem[q_off + 1u], + q_shmem[q_off + 2u], + q_shmem[q_off + 3u]); #ifdef KV_DIRECT let idx = k_head_offset + (kv_tile + kv_idx) * params.stride_k1 + (i * 4u); let kv = vec4(K[idx >> 2u]); #else let idx = kv_idx * HEAD_DIM_QK + (i * 4u); let kv = vec4( - f32(kv_shmem[idx + 0u]), - f32(kv_shmem[idx + 1u]), - f32(kv_shmem[idx + 2u]), - f32(kv_shmem[idx + 3u])); + kv_shmem[idx + 0u], + kv_shmem[idx + 1u], + kv_shmem[idx + 2u], + kv_shmem[idx + 3u]); #endif partial_sum += dot(qv, kv); } @@ -435,7 +447,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let sum_bcast = subgroupShuffle(sum, num_of_threads * ty); if (tx == 0u && kv_valid) { - inter_shmem[kv_idx] = f16(sum_bcast); + inter_shmem[kv_idx] = sum_bcast; } } } @@ -450,7 +462,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let global_k_col = kv_tile + elem_idx; let mask_in_bounds = q_row_start < params.seq_len_q && global_k_col < params.seq_len_kv; let mask_idx = mask_global_offset + global_k_col; - mask_shmem[elem_idx] = select(0.0, mask[mask_idx], mask_in_bounds); + mask_shmem[elem_idx] = select(0.0f, f32(mask[mask_idx]), mask_in_bounds); } } #else @@ -483,7 +495,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, kv_tile + kv_idx < params.seq_len_kv && kv_idx < KV_TILE); total_exp_term += subgroupAdd(cur_p); if (kv_idx < KV_TILE) { - inter_shmem[kv_idx] = f16(cur_p); + inter_shmem[kv_idx] = cur_p; } } @@ -493,7 +505,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, exp_sum = exp_sum * cur_exp + total_exp_term; for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) { - o_shmem[elem_idx] = f16(f32(o_shmem[elem_idx]) * cur_exp); + o_shmem[elem_idx] = o_shmem[elem_idx] * cur_exp; } } @@ -517,8 +529,8 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let q_packed = bitcast(vec2(q_0, q_1)); for (var k = 0u; k < 4u; k++) { let q_byte = get_byte(q_packed, k); - let q_hi = (f16((q_byte >> 4) & 0xF) - 8.0) * d; - let q_lo = (f16(q_byte & 0xF) - 8.0) * d; + let q_hi = (f32((q_byte >> 4) & 0xF) - 8.0) * f32(d); + let q_lo = (f32(q_byte & 0xF) - 8.0) * f32(d); let idx = block_k * BLOCK_SIZE + block_offset * 2u + j * 2u + k; kv_shmem[row_offset + idx] = q_lo; kv_shmem[row_offset + idx + 16u] = q_hi; @@ -545,7 +557,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let q_packed = bitcast(vec2(q_0, q_1)); for (var k = 0u; k < 4u; k++) { let q_byte = get_byte_i32(q_packed, k); - let q_val = f16(q_byte) * d; + let q_val = f32(q_byte) * f32(d); let idx = block_k * BLOCK_SIZE + block_offset * 2u + j * 2u + k; kv_shmem[row_offset + idx] = q_val; } @@ -563,10 +575,10 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let in_bounds = global_v_row < params.seq_len_kv && (v_col + 3u) < HEAD_DIM_V; let vec_idx = (global_v_row_offset + v_col) >> 2u; let v4 = select(vec4(0.0), V[vec_idx], in_bounds); - kv_shmem[elem_idx + 0u] = f16(v4.x); - kv_shmem[elem_idx + 1u] = f16(v4.y); - kv_shmem[elem_idx + 2u] = f16(v4.z); - kv_shmem[elem_idx + 3u] = f16(v4.w); + kv_shmem[elem_idx + 0u] = f32(v4.x); + kv_shmem[elem_idx + 1u] = f32(v4.y); + kv_shmem[elem_idx + 2u] = f32(v4.z); + kv_shmem[elem_idx + 3u] = f32(v4.w); } #endif @@ -589,17 +601,17 @@ fn main(@builtin(workgroup_id) wg_id: vec3, continue; } - let p = f32(inter_shmem[kv_idx]); + let p = inter_shmem[kv_idx]; #ifdef KV_DIRECT let v_idx = v_head_offset + v_row * params.stride_v1 + vec_col * 4u; let v4 = vec4(V[v_idx >> 2u]); #else let v_idx = kv_idx * HEAD_DIM_V + vec_col * 4u; let v4 = vec4( - f32(kv_shmem[v_idx + 0u]), - f32(kv_shmem[v_idx + 1u]), - f32(kv_shmem[v_idx + 2u]), - f32(kv_shmem[v_idx + 3u])); + kv_shmem[v_idx + 0u], + kv_shmem[v_idx + 1u], + kv_shmem[v_idx + 2u], + kv_shmem[v_idx + 3u]); #endif lo += p * v4; } @@ -630,10 +642,10 @@ fn main(@builtin(workgroup_id) wg_id: vec3, if (ty_pv == 0u) { let elem_base = vec_col * 4u; - o_shmem[elem_base + 0u] = f16(f32(o_shmem[elem_base + 0u]) + lo_x); - o_shmem[elem_base + 1u] = f16(f32(o_shmem[elem_base + 1u]) + lo_y); - o_shmem[elem_base + 2u] = f16(f32(o_shmem[elem_base + 2u]) + lo_z); - o_shmem[elem_base + 3u] = f16(f32(o_shmem[elem_base + 3u]) + lo_w); + o_shmem[elem_base + 0u] = o_shmem[elem_base + 0u] + lo_x; + o_shmem[elem_base + 1u] = o_shmem[elem_base + 1u] + lo_y; + o_shmem[elem_base + 2u] = o_shmem[elem_base + 2u] + lo_z; + o_shmem[elem_base + 3u] = o_shmem[elem_base + 3u] + lo_w; } } } @@ -660,7 +672,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, exp_sum = exp_sum * max_exp + sink_exp_sum; for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) { - o_shmem[elem_idx] = f16(f32(o_shmem[elem_idx]) * max_exp); + o_shmem[elem_idx] = o_shmem[elem_idx] * max_exp; } } workgroupBarrier(); @@ -681,7 +693,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, ); let dst_vec_index: u32 = (row_base + elem_base) >> 2u; - dst[dst_vec_index] = v; + dst[dst_vec_index] = vec4(v); } } else { let rid = batch_idx * rows_per_batch + head_idx * params.seq_len_q + q_row_start; diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl index 5710cd35469..78d61a93d28 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl @@ -652,6 +652,27 @@ fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { } #endif +#ifdef MXFP4 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 17; + let eu8 = get_byte(load_u32_at_src(block_byte_base), 0); + let d = ldexp(1.0, i32(eu8) - 128); + for (var j: u32 = 0u; j < 4; j++) { + let q_byte_offset = block_byte_base + 1 + j * 4; + let q_packed = load_u32_at_src(q_byte_offset); + for (var k: u32 = 0; k < 4; k++) { + let q_byte = get_byte(q_packed, k); + let q_hi = f32(kvalues_mxfp4[(q_byte >> 4) & 0xF]) * d; + let q_lo = f32(kvalues_mxfp4[q_byte & 0xFu]) * d; + let dst_offset = dst_base + offset * 32 + j * 4 + k; + dst[dst_offset] = q_lo; + dst[dst_offset + 16u] = q_hi; + } + } +} +#endif + + @group(0) @binding(0) var src: array; diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl index 51cf08f196f..eb2a8368f43 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl @@ -100,34 +100,37 @@ const BLOCK_SIZE_BYTES = 18u; // the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. override BLOCKS_K = TILE_K/BLOCK_SIZE; const NQ = 16u; -const WEIGHTS_PER_F16 = 4u; // 4 weights per f16 -const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; +const BYTES_PER_THREAD = 8u; // NQ(16) weights use 8 bytes of q +const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed) fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { let blck_idx = i / BLOCK_SIZE; - let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; - let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + let block_offset = (i % BLOCK_SIZE) / NQ; + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD; let tile_m = blck_idx / BLOCKS_K; let global_m = offset_m + tile_m; let block_k = blck_idx % BLOCKS_K; - let global_k = k_outer / BLOCK_SIZE + block_k; + let global_block_k = k_outer / BLOCK_SIZE + block_k; - if (global_m < params.m && global_k < params.k / BLOCK_SIZE) { - let src0_idx = batch_offset + global_m * params.stride_01 + global_k; + if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k; let block_byte_base = src0_idx * BLOCK_SIZE_BYTES; let d = load_f16_at_src0(block_byte_base); - for (var j = 0u; j < F16_PER_THREAD; j += 2) { - let q_byte_offset = block_byte_base + 2u + 2u * (block_offset + j); + // store NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + + let q_byte_offset = block_byte_base + 2u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; let q_packed = load_u32_at_src0(q_byte_offset); - for (var k = 0u; k < 4u; k++) { + + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { let q_byte = get_byte(q_packed, k); let q_hi = (f16((q_byte >> 4) & 0xF) - 8.0) * d; let q_lo = (f16(q_byte & 0xF) - 8.0) * d; - shmem[shmem_idx + j * 2 + k] = q_lo; - shmem[shmem_idx + j * 2 + k + 16u] = q_hi; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_lo; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = q_hi; } } } @@ -141,35 +144,38 @@ const BLOCK_SIZE_BYTES = 20u; // the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. override BLOCKS_K = TILE_K/BLOCK_SIZE; const NQ = 16u; -const WEIGHTS_PER_F16 = 4u; // 4 weights per f16 -const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; +const BYTES_PER_THREAD = 8u; // NQ(16) weights use 8 bytes of q +const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed) fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { let blck_idx = i / BLOCK_SIZE; - let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; - let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + let block_offset = (i % BLOCK_SIZE) / NQ; + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD; let tile_m = blck_idx / BLOCKS_K; let global_m = offset_m + tile_m; let block_k = blck_idx % BLOCKS_K; - let global_k = k_outer / BLOCK_SIZE + block_k; + let global_block_k = k_outer / BLOCK_SIZE + block_k; - if (global_m < params.m && global_k < params.k / BLOCK_SIZE) { - let src0_idx = batch_offset + global_m * params.stride_01 + global_k; + if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k; let block_byte_base = src0_idx * BLOCK_SIZE_BYTES; let d = load_f16_at_src0(block_byte_base); let m = load_f16_at_src0(block_byte_base + 2u); - for (var j = 0u; j < F16_PER_THREAD; j += 2) { - let q_byte_offset = block_byte_base + 4u + 2u * (block_offset + j); + // store NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + + let q_byte_offset = block_byte_base + 4u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; let q_packed = load_u32_at_src0(q_byte_offset); - for (var k = 0u; k < 4u; k++) { + + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { let q_byte = get_byte(q_packed, k); let q_lo = f16(q_byte & 0xF) * d + m; let q_hi = f16((q_byte >> 4) & 0xF) * d + m; - shmem[shmem_idx + j * 2 + k] = q_lo; - shmem[shmem_idx + j * 2 + k + 16u] = q_hi; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_lo; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = q_hi; } } } @@ -178,52 +184,49 @@ fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u3 #endif // INIT_SRC0_SHMEM_Q4_1 #ifdef INIT_SRC0_SHMEM_Q5_0 -// 32 weights per block, each at 4 bits each = 32 * 4 = 128 bits / 16 = 8 f16s per block const BLOCK_SIZE = 32u; const BLOCK_SIZE_BYTES = 22u; // the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. // tile_k is defined as 32u, so blocks_k ends up being 1 always override BLOCKS_K = TILE_K / BLOCK_SIZE; const NQ = 16u; -const WEIGHTS_PER_F16 = 4u; // 4 weights per f16 -const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; // 16 / 4 = 4 f16s per thread, each thread should handle 4 f16s * 4 weights per = 16 weights +const BYTES_PER_THREAD = 8u; // NQ(16) weights use 8 bytes of q +const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed) fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { let blck_idx = i / BLOCK_SIZE; - let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; - let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + let block_offset = (i % BLOCK_SIZE) / NQ; + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD; let tile_m = blck_idx / BLOCKS_K; let global_m = offset_m + tile_m; let block_k = blck_idx % BLOCKS_K; - let global_k = k_outer / BLOCK_SIZE + block_k; + let global_block_k = k_outer / BLOCK_SIZE + block_k; - if (global_m < params.m && global_k < params.k / BLOCK_SIZE) { - let src0_idx = batch_offset + global_m * params.stride_01 + global_k; + if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k; let block_byte_base = src0_idx * BLOCK_SIZE_BYTES; let d = load_f16_at_src0(block_byte_base); let qh_packed = load_u32_at_src0(block_byte_base + 2u); - for (var j = 0u; j < 2; j++) { - let q_byte_offset = block_byte_base + 6u + 2u * (block_offset + j * 2u); + // store NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + let q_byte_offset = block_byte_base + 6u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; let q_packed = load_u32_at_src0(q_byte_offset); - let j_adjusted = j + (block_offset / 2u); - - - for (var k = 0u; k < 4u; k++) { + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { let q_byte = get_byte(q_packed, k); - let qh_hi = (qh_packed >> (j_adjusted * 4 + k + 12)) & 0x10; + let byte_idx = block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP + k; + let qh_hi = (qh_packed >> (byte_idx + 12u)) & 0x10; let q_hi = (f16(((q_byte >> 4) & 0xF) | qh_hi) - 16.0) * d; - let qh_lo = ((qh_packed >> (j_adjusted * 4 + k)) << 4) & 0x10; + let qh_lo = ((qh_packed >> byte_idx) << 4) & 0x10; let q_lo = (f16((q_byte & 0xF) | qh_lo) - 16.0) * d; - - shmem[shmem_idx + j * 4u + k] = q_lo; // store first weight - shmem[shmem_idx + j * 4u + k + 16u] = q_hi; // store second weight + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_lo; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = q_hi; } } } @@ -232,54 +235,49 @@ fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u3 #endif // INIT_SRC0_SHMEM_Q5_0 #ifdef INIT_SRC0_SHMEM_Q5_1 -// 32 weights per block, each at 4 bits each = 32 * 4 = 128 bits / 16 = 8 f16s per block const BLOCK_SIZE = 32u; const BLOCK_SIZE_BYTES = 24u; // the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. -// tile_k is defined as 32u, so blocks_k ends up being 1 always override BLOCKS_K = TILE_K / BLOCK_SIZE; const NQ = 16u; -const WEIGHTS_PER_F16 = 4u; // 4 weights per f16 -const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; // 16 / 4 = 4 f16s per thread, each thread should handle 4 f16s * 4 weights per = 16 weights +const BYTES_PER_THREAD = 8u; // NQ(16) weights use 8 bytes of q +const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed) fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { let blck_idx = i / BLOCK_SIZE; - let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; - let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + let block_offset = (i % BLOCK_SIZE) / NQ; + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD; let tile_m = blck_idx / BLOCKS_K; let global_m = offset_m + tile_m; let block_k = blck_idx % BLOCKS_K; - let global_k = k_outer / BLOCK_SIZE + block_k; + let global_block_k = k_outer / BLOCK_SIZE + block_k; - if (global_m < params.m && global_k < params.k / BLOCK_SIZE) { - let src0_idx = batch_offset + global_m * params.stride_01 + global_k; + if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k; let block_byte_base = src0_idx * BLOCK_SIZE_BYTES; let d = load_f16_at_src0(block_byte_base); let m = load_f16_at_src0(block_byte_base + 2u); let qh_packed = load_u32_at_src0(block_byte_base + 4u); - for (var j = 0u; j < 2; j++) { - - let q_byte_offset = block_byte_base + 8u + 2u * (block_offset + j * 2u); + // store NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + let q_byte_offset = block_byte_base + 8u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; let q_packed = load_u32_at_src0(q_byte_offset); - let j_adjusted = j + (block_offset / 2u); - - - for (var k = 0u; k < 4u; k++) { + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { let q_byte = get_byte(q_packed, k); - let qh_hi = (qh_packed >> (j_adjusted * 4 + k + 12)) & 0x10; - let q_hi = (f16(((q_byte >> 4) & 0xF) | qh_hi)) * d + m; - let qh_lo = ((qh_packed >> (j_adjusted * 4 + k)) << 4) & 0x10; - let q_lo = (f16((q_byte & 0xF) | qh_lo)) * d + m; - - shmem[shmem_idx + j * 4u + k] = q_lo; // store first weight - shmem[shmem_idx + j * 4u + k + 16u] = q_hi; // store second weight + let byte_idx = block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP + k; + let qh_hi = (qh_packed >> (byte_idx + 12u)) & 0x10; + let q_hi = f16(((q_byte >> 4) & 0xF) | qh_hi) * d + m; + let qh_lo = ((qh_packed >> byte_idx) << 4) & 0x10; + let q_lo = f16((q_byte & 0xF) | qh_lo) * d + m; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_lo; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = q_hi; } } } @@ -293,33 +291,34 @@ const BLOCK_SIZE_BYTES = 34u; // the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. override BLOCKS_K = TILE_K/BLOCK_SIZE; const NQ = 16u; -const WEIGHTS_PER_F16 = 2u; // 2 8-bit weights per f16 -const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; // 8 f16s per thread +const BYTES_PER_THREAD = 16u; // NQ(16) weights use 16 bytes of q +const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed) fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { let blck_idx = i / BLOCK_SIZE; - let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; - let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + let block_offset = (i % BLOCK_SIZE) / NQ; + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD; let tile_m = blck_idx / BLOCKS_K; let global_m = offset_m + tile_m; let block_k = blck_idx % BLOCKS_K; - let global_k = k_outer / BLOCK_SIZE + block_k; + let global_block_k = k_outer / BLOCK_SIZE + block_k; - if (global_m < params.m && global_k < params.k / BLOCK_SIZE) { - let src0_idx = batch_offset + global_m * params.stride_01 + global_k; + if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k; let block_byte_base = src0_idx * BLOCK_SIZE_BYTES; let d = load_f16_at_src0(block_byte_base); - for (var j = 0u; j < F16_PER_THREAD; j+=2) { - let q_byte_offset = block_byte_base + 2u + 2u * (block_offset + j); + // store NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + let q_byte_offset = block_byte_base + 2u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; let q_packed = load_u32_at_src0(q_byte_offset); - for (var k = 0u; k < 4u; k++) { + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { let q_byte = get_byte_i32(q_packed, k); let q_val = f16(q_byte) * d; - shmem[shmem_idx + j * 2 + k] = q_val; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_val; } } } @@ -333,34 +332,35 @@ const BLOCK_SIZE_BYTES = 36u; // the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. override BLOCKS_K = TILE_K/BLOCK_SIZE; const NQ = 16u; -const WEIGHTS_PER_F16 = 2u; // 2 8-bit weights per f16 -const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; // 8 f16s per thread, 2 threads per block +const BYTES_PER_THREAD = 16u; // NQ(16) weights use 16 bytes of q +const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed) fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { let blck_idx = i / BLOCK_SIZE; - let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; - let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + let block_offset = (i % BLOCK_SIZE) / NQ; + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD; let tile_m = blck_idx / BLOCKS_K; let global_m = offset_m + tile_m; let block_k = blck_idx % BLOCKS_K; - let global_k = k_outer / BLOCK_SIZE + block_k; + let global_block_k = k_outer / BLOCK_SIZE + block_k; - if (global_m < params.m && global_k < params.k / BLOCK_SIZE) { - let src0_idx = batch_offset + global_m * params.stride_01 + global_k; + if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k; let block_byte_base = src0_idx * BLOCK_SIZE_BYTES; let d = load_f16_at_src0(block_byte_base); let m = load_f16_at_src0(block_byte_base + 2u); - for (var j = 0u; j < F16_PER_THREAD; j+=2) { - let q_byte_offset = block_byte_base + 4u + 2u * (block_offset + j); + // store NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + let q_byte_offset = block_byte_base + 4u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; let q_packed = load_u32_at_src0(q_byte_offset); - for (var k = 0u; k < 4u; k++) { + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { let q_byte = get_byte_i32(q_packed, k); let q_val = f16(q_byte) * d + m; - shmem[shmem_idx + j * 2 + k] = q_val; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_val; } } } @@ -1163,3 +1163,48 @@ fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u3 } } #endif // INIT_SRC0_SHMEM_IQ3_S + +#ifdef INIT_SRC0_SHMEM_MXFP4 +const BLOCK_SIZE = 32u; +const BLOCK_SIZE_BYTES = 17u; +// the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. +override BLOCKS_K = TILE_K/BLOCK_SIZE; +const NQ = 16u; +const BYTES_PER_THREAD = 8u; // NQ(16) weights uses 8 bytes of q +const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed) + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { + let blck_idx = i / BLOCK_SIZE; + let block_offset = (i % BLOCK_SIZE) / NQ; + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD; + + let tile_m = blck_idx / BLOCKS_K; + let global_m = offset_m + tile_m; + let block_k = blck_idx % BLOCKS_K; + let global_block_k = k_outer / BLOCK_SIZE + block_k; + + if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k; + let block_byte_base = src0_idx * BLOCK_SIZE_BYTES; + let eu8 = get_byte(load_u32_at_src0(block_byte_base), 0); + let e = ldexp(1.0, i32(eu8) - 128); + + // store NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + + let q_byte_offset = block_byte_base + 1u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; + let q_packed = load_u32_at_src0(q_byte_offset); + + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { + let q_byte = get_byte(q_packed, k); + let q_hi = f32(kvalues_mxfp4[(q_byte >> 4) & 0xF]) * e; + let q_lo = f32(kvalues_mxfp4[q_byte & 0xF]) * e; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = f16(q_lo); + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = f16(q_hi); + } + } + } + } +} +#endif // INIT_SRC0_SHMEM_MXFP4 diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl index 1f59bd14863..711c7e829d8 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl @@ -1389,3 +1389,45 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src return acc; } #endif + +#ifdef MUL_ACC_MXFP4 +#define BLOCK_SIZE 32 +#define BLOCK_SIZE_BYTES 17 +#define THREADS_PER_BLOCK 4 +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array { + var acc: array; + + let num_blocks = params.k / BLOCK_SIZE; + let thread_within_block = thread_id % 4; + for (var block = thread_id/THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE/THREADS_PER_BLOCK) { + let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4; + var x_block: array; + for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) { + x_block[i] = f32(src1[x_base + i]); + x_block[i + 4] = f32(src1[x_base + i + 16]); + } + + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let eu8 = get_byte(load_u32_at_src0(block_byte_base), 0); + let e = ldexp(1.0, i32(eu8) - 128); + var row_sum = 0.0; + let q_packed = load_u32_at_src0(block_byte_base + 1u + 4u * thread_within_block); + for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) { + let q_byte = get_byte(q_packed, byte_idx); + let q_lo = f32(kvalues_mxfp4[q_byte & 0xFu]) * e; + let q_hi = f32(kvalues_mxfp4[(q_byte >> 4u) & 0xFu]) * e; + row_sum += q_lo * x_block[byte_idx]; + row_sum += q_hi * x_block[byte_idx + 4u]; + } + acc[row] += row_sum; + } + } + } + + return acc; +} +#endif diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl index b8f1bca1284..8e34e1c9ca0 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl @@ -50,10 +50,25 @@ struct Params { @group(0) @binding(PARAMS_BINDING) var params: Params; +fn erf_approx(x: TYPE) -> TYPE { + let x_f32 = f32(x); + let s = select(-1.0, 1.0, x_f32 >= 0.0); + let ax = abs(x_f32); + + let t = 1.0 / (1.0 + 0.3275911 * ax); + + let y = 1.0 - + (((((1.061405429 * t - 1.453152027) * t + 1.421413741) * t + - 0.284496736) * t + 0.254829592) * t) * + exp(-ax * ax); + + return TYPE(s * y); +} + @compute @workgroup_size(WG_SIZE) fn main(@builtin(global_invocation_id) gid: vec3) { if (gid.x >= params.ne) { - return; + return; } var i = gid.x; let ne2 = params.ne2; @@ -71,15 +86,13 @@ fn main(@builtin(global_invocation_id) gid: vec3) { let i1 = i / ne0; let i0 = i % ne0; - let src_idx = i0 * params.stride_src0 + i1 * params.stride_src1 + - i2 * params.stride_src2 + i3 * params.stride_src3; + let src_idx = i0 * params.stride_src0 + i1 * params.stride_src1 + i2 * params.stride_src2 + i3 * params.stride_src3; #ifdef ABS let res = abs(src[params.offset_src + src_idx]); #endif #ifdef SGN - let res = select(TYPE(select(0.0, -1.0, src[params.offset_src + src_idx] < 0.0)), TYPE(1.0), - src[params.offset_src + src_idx] > 0.0); + let res = select(TYPE(select(0.0, -1.0, src[params.offset_src + src_idx] < 0.0)), TYPE(1.0), src[params.offset_src + src_idx] > 0.0); #endif #ifdef NEG let res = -src[params.offset_src + src_idx]; @@ -94,8 +107,7 @@ fn main(@builtin(global_invocation_id) gid: vec3) { let res = select(0.0, src[params.offset_src + src_idx], src[params.offset_src + src_idx] > 0.0); #endif #ifdef ELU - let res = select(exp(src[params.offset_src + src_idx]) - 1.0, src[params.offset_src + src_idx], - src[params.offset_src + src_idx] > 0.0); + let res = select(exp(src[params.offset_src + src_idx]) - 1.0, src[params.offset_src + src_idx], src[params.offset_src + src_idx] > 0.0); #endif #ifdef HARDSIGMOID let res = min(1.0, max(0.0, (src[params.offset_src + src_idx] + 3.0) / 6.0)); @@ -120,31 +132,16 @@ fn main(@builtin(global_invocation_id) gid: vec3) { let res = TYPE(params.fill_val); #endif #ifdef HARDSWISH - let res = src[params.offset_src + src_idx] * - min(1.0, max(0.0, (src[params.offset_src + src_idx] + 3.0) / 6.0)); + let res = src[params.offset_src + src_idx] * min(1.0, max(0.0, (src[params.offset_src + src_idx] + 3.0) / 6.0)); #endif #ifdef GELU - let res = 0.5 * src[params.offset_src + src_idx] * - (1.0 + tanh(clamp(sqrt(2.0 / 3.14159265) * - (src[params.offset_src + src_idx] + - 0.044715 * pow(src[params.offset_src + src_idx], 3.0)), - -9.010913, 9.010913))); + let res = 0.5 * src[params.offset_src + src_idx] * (1.0 + tanh(clamp(0.7978845608028654 * (src[params.offset_src + src_idx] + 0.044715 * src[params.offset_src + src_idx] * src[params.offset_src + src_idx] * src[params.offset_src + src_idx]), -9.010913, 9.010913))); #endif #ifdef GELU_QUICK - let res = src[params.offset_src + src_idx] * 0.5 * - (1.0 + tanh(clamp(0.79788456 * - (src[params.offset_src + src_idx] + - 0.044715 * src[params.offset_src + src_idx] * - src[params.offset_src + src_idx] * src[params.offset_src + src_idx]), - -9.010913, 9.010913))); + let res = src[params.offset_src + src_idx] * (1.0 / (1.0 + exp(clamp(-1.702 * src[params.offset_src + src_idx], -80.0, 80.0)))); #endif #ifdef GELU_ERF - let res = 0.5 * src[params.offset_src + src_idx] * - (1.0 + tanh(clamp(0.79788456 * - (src[params.offset_src + src_idx] + - 0.044715 * src[params.offset_src + src_idx] * - src[params.offset_src + src_idx] * src[params.offset_src + src_idx]), - -9.010913, 9.010913))); + let res = 0.5 * src[params.offset_src + src_idx] * (1.0 + erf_approx(src[params.offset_src + src_idx] * 0.7071067811865476)); #endif #ifdef XIELU let val = f32(src[params.offset_src + src_idx]); diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 617cbc49d19..4055ec2873a 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -299,30 +299,32 @@ class Clip: HAS_LLAVA_PROJECTOR = "clip.has_llava_projector" class ClipVision: - PROJECTOR_TYPE = "clip.vision.projector_type" # for mixed modality models - IMAGE_SIZE = "clip.vision.image_size" - IMAGE_MIN_PIXELS = "clip.vision.image_min_pixels" - IMAGE_MAX_PIXELS = "clip.vision.image_max_pixels" - PREPROC_MIN_TILES = "clip.vision.preproc_min_tiles" - PREPROC_MAX_TILES = "clip.vision.preproc_max_tiles" - PREPROC_IMAGE_SIZE = "clip.vision.preproc_image_size" - PATCH_SIZE = "clip.vision.patch_size" - EMBEDDING_LENGTH = "clip.vision.embedding_length" - FEED_FORWARD_LENGTH = "clip.vision.feed_forward_length" - PROJECTION_DIM = "clip.vision.projection_dim" - BLOCK_COUNT = "clip.vision.block_count" - IMAGE_MEAN = "clip.vision.image_mean" - IMAGE_STD = "clip.vision.image_std" - SPATIAL_MERGE_SIZE = "clip.vision.spatial_merge_size" - USE_GELU = "clip.use_gelu" - USE_SILU = "clip.use_silu" - N_WA_PATTERN = "clip.vision.n_wa_pattern" # used by qwen2.5vl - WA_LAYER_INDEXES = "clip.vision.wa_layer_indexes" # used by youtuvl - IS_DEEPSTACK_LAYERS = "clip.vision.is_deepstack_layers" - WINDOW_SIZE = "clip.vision.window_size" + PROJECTOR_TYPE = "clip.vision.projector_type" # for mixed modality models + IMAGE_SIZE = "clip.vision.image_size" + IMAGE_MIN_PIXELS = "clip.vision.image_min_pixels" + IMAGE_MAX_PIXELS = "clip.vision.image_max_pixels" + PREPROC_MIN_TILES = "clip.vision.preproc_min_tiles" + PREPROC_MAX_TILES = "clip.vision.preproc_max_tiles" + PREPROC_IMAGE_SIZE = "clip.vision.preproc_image_size" + PATCH_SIZE = "clip.vision.patch_size" + EMBEDDING_LENGTH = "clip.vision.embedding_length" + FEED_FORWARD_LENGTH = "clip.vision.feed_forward_length" + PROJECTION_DIM = "clip.vision.projection_dim" + BLOCK_COUNT = "clip.vision.block_count" + IMAGE_MEAN = "clip.vision.image_mean" + IMAGE_STD = "clip.vision.image_std" + SPATIAL_MERGE_SIZE = "clip.vision.spatial_merge_size" + USE_GELU = "clip.use_gelu" + USE_SILU = "clip.use_silu" + N_WA_PATTERN = "clip.vision.n_wa_pattern" # used by qwen2.5vl + WA_LAYER_INDEXES = "clip.vision.wa_layer_indexes" # used by youtuvl + WA_PATTERN_MODE = "clip.vision.wa_pattern_mode" # used by mimovl, per-layer -1/0/1 + IS_DEEPSTACK_LAYERS = "clip.vision.is_deepstack_layers" + WINDOW_SIZE = "clip.vision.window_size" class Attention: HEAD_COUNT = "clip.vision.attention.head_count" + HEAD_COUNT_KV = "clip.vision.attention.head_count_kv" # used by mimovl (GQA) LAYERNORM_EPS = "clip.vision.attention.layer_norm_epsilon" class Projector: @@ -733,6 +735,7 @@ class MODEL_TENSOR(IntEnum): V_ENC_ATTN_V = auto() V_ENC_ATTN_O = auto() V_ENC_ATTN_O_NORM = auto() + V_ENC_ATTN_SINKS = auto() # mimovl V_ENC_POST_ATTN_NORM = auto() V_ENC_FFN_UP = auto() V_ENC_FFN_GATE = auto() @@ -1246,6 +1249,7 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.V_ENC_INPUT_NORM: "v.blk.{bid}.ln1", MODEL_TENSOR.V_ENC_ATTN_O: "v.blk.{bid}.attn_out", MODEL_TENSOR.V_ENC_ATTN_O_NORM: "v.blk.{bid}.attn_out_norm", + MODEL_TENSOR.V_ENC_ATTN_SINKS: "v.blk.{bid}.attn_sinks", MODEL_TENSOR.V_ENC_POST_ATTN_NORM: "v.blk.{bid}.ln2", MODEL_TENSOR.V_ENC_FFN_UP: "v.blk.{bid}.ffn_up", MODEL_TENSOR.V_ENC_FFN_GATE: "v.blk.{bid}.ffn_gate", @@ -1426,6 +1430,7 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.V_ENC_ATTN_V, MODEL_TENSOR.V_ENC_ATTN_O, MODEL_TENSOR.V_ENC_ATTN_O_NORM, + MODEL_TENSOR.V_ENC_ATTN_SINKS, MODEL_TENSOR.V_ENC_POST_ATTN_NORM, MODEL_TENSOR.V_ENC_FFN_UP, MODEL_TENSOR.V_ENC_FFN_GATE, @@ -4258,6 +4263,7 @@ class VisionProjectorType: HUNYUANVL = "hunyuanvl" MINICPMV4_6 = "minicpmv4_6" GRANITE_SPEECH = "granite_speech" # audio + MIMOVL = "mimovl" # Items here are (block size, type size) diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 35fb01470c4..a101382719d 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -1151,6 +1151,9 @@ def add_vision_block_count(self, value: int) -> None: def add_vision_head_count(self, value: int) -> None: self.add_uint32(Keys.ClipVision.Attention.HEAD_COUNT, value) + def add_vision_head_count_kv(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.Attention.HEAD_COUNT_KV, value) + def add_vision_attention_layernorm_eps(self, value: float) -> None: self.add_float32(Keys.ClipVision.Attention.LAYERNORM_EPS, value) @@ -1222,6 +1225,9 @@ def add_vision_wa_layer_indexes(self, layers: Sequence[int]) -> None: def add_vision_is_deepstack_layers(self, layers: Sequence[bool]) -> None: self.add_array(Keys.ClipVision.IS_DEEPSTACK_LAYERS, layers) + def add_vision_wa_pattern_mode(self, modes: Sequence[int]) -> None: + self.add_array(Keys.ClipVision.WA_PATTERN_MODE, modes) + def add_vision_window_size(self, value: int) -> None: self.add_uint32(Keys.ClipVision.WINDOW_SIZE, value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index f27f0e4c997..f40cb828201 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -1569,6 +1569,10 @@ class TensorNameMap: "vision_model.transformer.resblocks.{bid}.attn.out_proj", # Step3-VL ), + MODEL_TENSOR.V_ENC_ATTN_SINKS: ( + "visual.blocks.{bid}.attn.sinks", # mimovl + ), + MODEL_TENSOR.V_ENC_POST_ATTN_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.layer_norm2", "model.vision_tower.encoder.layers.{bid}.layer_norm2", # minicpmv4_6 diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt index 21d17dbaa41..a76adc9b80b 100644 --- a/tools/mtmd/CMakeLists.txt +++ b/tools/mtmd/CMakeLists.txt @@ -34,6 +34,7 @@ add_library(mtmd models/pixtral.cpp models/qwen2vl.cpp models/qwen3vl.cpp + models/mimovl.cpp models/qwen3a.cpp models/step3vl.cpp models/siglip.cpp diff --git a/tools/mtmd/clip-graph.h b/tools/mtmd/clip-graph.h index d3e7b1ed044..39f069501bc 100644 --- a/tools/mtmd/clip-graph.h +++ b/tools/mtmd/clip-graph.h @@ -98,7 +98,8 @@ struct clip_graph { ggml_tensor * v_cur, ggml_tensor * kq_mask, float kq_scale, - int il) const; + int il, + ggml_tensor * sinks = nullptr) const; // implementation of the 2D RoPE without adding a new op in ggml // this is not efficient (use double the memory), but works on all backends diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index 817bf26b217..8e09f26e988 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -31,6 +31,7 @@ #define KEY_N_BLOCK "clip.%s.block_count" #define KEY_PROJ_DIM "clip.%s.projection_dim" #define KEY_N_HEAD "clip.%s.attention.head_count" +#define KEY_N_HEAD_KV "clip.%s.attention.head_count_kv" #define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon" // vision-specific @@ -53,6 +54,7 @@ #define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints" #define KEY_WIN_ATTN_PATTERN "clip.vision.n_wa_pattern" #define KEY_WIN_ATTN_LAYER_INDEXES "clip.vision.wa_layer_indexes" +#define KEY_WA_PATTERN_MODE "clip.vision.wa_pattern_mode" #define KEY_ATTN_WINDOW_SIZE "clip.vision.window_size" #define KEY_MINICPMV_VERSION "clip.minicpmv_version" #define KEY_MINICPMV_QUERY_NUM "clip.minicpmv_query_num" @@ -86,6 +88,7 @@ #define TN_ATTN_Q "%s.blk.%d.attn_q.%s" #define TN_ATTN_V "%s.blk.%d.attn_v.%s" #define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s" +#define TN_ATTN_SINKS "%s.blk.%d.attn_sinks" #define TN_ATTN_K_NORM "%s.blk.%d.attn_k_norm.%s" #define TN_ATTN_Q_NORM "%s.blk.%d.attn_q_norm.%s" #define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s" @@ -344,6 +347,7 @@ enum projector_type { PROJECTOR_TYPE_HUNYUANVL, PROJECTOR_TYPE_MINICPMV4_6, PROJECTOR_TYPE_GRANITE_SPEECH, + PROJECTOR_TYPE_MIMOVL, PROJECTOR_TYPE_UNKNOWN, }; @@ -393,6 +397,7 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_HUNYUANVL, "hunyuanvl"}, { PROJECTOR_TYPE_MINICPMV4_6, "minicpmv4_6"}, { PROJECTOR_TYPE_GRANITE_SPEECH, "granite_speech"}, + { PROJECTOR_TYPE_MIMOVL, "mimovl"}, }; static projector_type clip_projector_type_from_string(const std::string & str) { diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h index 48f8b1a193f..ce15dbcd11e 100644 --- a/tools/mtmd/clip-model.h +++ b/tools/mtmd/clip-model.h @@ -42,6 +42,7 @@ struct clip_hparams { int32_t n_ff = 0; int32_t projection_dim = 0; int32_t n_head = 0; + int32_t n_head_kv = 0; int32_t n_layer = 0; // idefics3 int32_t n_merge = 0; // number of patch merges **per-side** @@ -83,6 +84,7 @@ struct clip_hparams { int32_t attn_window_size = 0; int32_t n_wa_pattern = 0; std::unordered_set wa_layer_indexes; // explicit layer indexes that use full attention (for irregular patterns like YoutuVL) + std::vector wa_pattern_mode; // mimovl: per-layer window-attention mode // deepseek-ocr (sam) int32_t sam_n_layer = 0; @@ -166,6 +168,8 @@ struct clip_layer { ggml_tensor * o_w = nullptr; ggml_tensor * o_b = nullptr; + ggml_tensor * attn_sinks = nullptr; + ggml_tensor * k_norm = nullptr; ggml_tensor * q_norm = nullptr; diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 513b94f2a65..f0c63d375a3 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -642,7 +642,8 @@ ggml_tensor * clip_graph::build_attn( ggml_tensor * v_cur, ggml_tensor * kq_mask, float kq_scale, - int il) const { + int il, + ggml_tensor * sinks) const { // these nodes are added to the graph together so that they are not reordered // by doing so, the number of splits in the graph is reduced ggml_build_forward_expand(gf, q_cur); @@ -665,6 +666,9 @@ ggml_tensor * clip_graph::build_attn( cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, 0.0f, 0.0f); ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); + if (sinks != nullptr) { + ggml_flash_attn_ext_add_sinks(cur, sinks); + } cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]); @@ -677,6 +681,9 @@ ggml_tensor * clip_graph::build_attn( // ggml_mul_mat_set_prec(kq, GGML_PREC_F32); kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, 0.0f); + if (sinks != nullptr) { + ggml_soft_max_add_sinks(kq, sinks); + } ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq); cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3); @@ -866,6 +873,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 { builder = std::make_unique(ctx, img); } break; + case PROJECTOR_TYPE_MIMOVL: + { + builder = std::make_unique(ctx, img); + } break; case PROJECTOR_TYPE_STEP3VL: { builder = std::make_unique(ctx, img); @@ -1389,6 +1400,22 @@ struct clip_model_loader { LOG_WRN("%s: more info: https://github.com/ggml-org/llama.cpp/issues/16842\n\n", __func__); } } break; + case PROJECTOR_TYPE_MIMOVL: + { + hparams.n_merge = 2; // spatial_merge_size + hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW; + get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false); + get_u32(string_format(KEY_N_HEAD_KV, "vision"), hparams.n_head_kv); + // 1D banded sliding-window radius (visual_token_window_size); required + get_u32(KEY_ATTN_WINDOW_SIZE, hparams.attn_window_size); + std::vector pat; + get_arr_int(KEY_WA_PATTERN_MODE, pat, true); + GGML_ASSERT((int) pat.size() == hparams.n_layer && "mimovl wa_pattern_mode length must equal n_layer"); + hparams.wa_pattern_mode.assign(pat.begin(), pat.end()); + get_u32(KEY_IMAGE_MIN_PIXELS, hparams.image_min_pixels); + get_u32(KEY_IMAGE_MAX_PIXELS, hparams.image_max_pixels); + hparams.set_warmup_n_tokens(46*46); // avoid OOM on warmup + } break; case PROJECTOR_TYPE_STEP3VL: { hparams.n_merge = 4; // two stride-2 downsamplers after patching @@ -1729,6 +1756,8 @@ struct clip_model_loader { layer.ff_down_w = get_tensor(string_format(TN_FFN_DOWN, prefix, il, "weight")); layer.ff_down_b = get_tensor(string_format(TN_FFN_DOWN, prefix, il, "bias"), false); + // mimovl per-head attention sink bias + layer.attn_sinks = get_tensor(string_format(TN_ATTN_SINKS, prefix, il), false); // qwen3vl deepstack layer layer.deepstack_norm_w = get_tensor(string_format(TN_DEEPSTACK_NORM, il, "weight"), false); @@ -1913,6 +1942,13 @@ struct clip_model_loader { model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias")); } break; + case PROJECTOR_TYPE_MIMOVL: + { + model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight")); + model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"), false); + model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); + model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false); + } break; case PROJECTOR_TYPE_STEP3VL: { model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight")); @@ -3011,6 +3047,7 @@ int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: case PROJECTOR_TYPE_QWEN3VL: + case PROJECTOR_TYPE_MIMOVL: case PROJECTOR_TYPE_GLM4V: case PROJECTOR_TYPE_PADDLEOCR: case PROJECTOR_TYPE_HUNYUANOCR: @@ -3032,6 +3069,7 @@ int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: case PROJECTOR_TYPE_QWEN3VL: + case PROJECTOR_TYPE_MIMOVL: case PROJECTOR_TYPE_GLM4V: case PROJECTOR_TYPE_PADDLEOCR: case PROJECTOR_TYPE_HUNYUANVL: @@ -3110,6 +3148,7 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: case PROJECTOR_TYPE_QWEN3VL: + case PROJECTOR_TYPE_MIMOVL: case PROJECTOR_TYPE_GLM4V: case PROJECTOR_TYPE_YOUTUVL: { @@ -3681,6 +3720,89 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima set_input_i32("positions", positions); } break; + case PROJECTOR_TYPE_MIMOVL: + { + const int merge = hparams.n_merge; // 2 + const int merge_unit = merge * merge; // 4 + const int patch = hparams.patch_size; // 16 + const int H = image_size_height / patch; + const int W = image_size_width / patch; + const int n_pos_full = H * W; + const int llm_h = H / merge; + const int llm_w = W / merge; + const int n_units = llm_h * llm_w; // n_pos / merge_unit + + // Row-major merge-tile-ordered (h, w) positions + std::vector pos_h_row(n_pos_full); + std::vector pos_w_row(n_pos_full); + { + int idx = 0; + for (int ty = 0; ty < llm_h; ty++) { + for (int tx = 0; tx < llm_w; tx++) { + for (int dy = 0; dy < merge; dy++) { + for (int dx = 0; dx < merge; dx++) { + pos_h_row[idx] = ty * merge + dy; + pos_w_row[idx] = tx * merge + dx; + idx++; + } + } + } + } + } + + // Col-major merge-unit permutation + std::vector idx_col(n_units); + for (int r = 0; r < llm_h; r++) { + for (int c = 0; c < llm_w; c++) { + int u_row = r * llm_w + c; + int u_col = c * llm_h + r; + idx_col[u_col] = (float) u_row; + } + } + + // Col-mode positions: permute pos_*_row by idx_col + std::vector pos_h_col(n_pos_full); + std::vector pos_w_col(n_pos_full); + for (int u = 0; u < n_units; u++) { + int src = (int) idx_col[u]; + for (int k = 0; k < merge_unit; k++) { + pos_h_col[u * merge_unit + k] = pos_h_row[src * merge_unit + k]; + pos_w_col[u * merge_unit + k] = pos_w_row[src * merge_unit + k]; + } + } + + // Pack into ggml_rope_multi VISION-mode layout. The non-CPU kernels + // only read slots 0 and 1, so pack h in slot 0, w in slot 1: + // positions[0..n_pos) = h + // positions[n_pos..2*n_pos) = w + // positions[2*n_pos..3*n_pos) = 0 + // positions[3*n_pos..4*n_pos) = 0 + std::vector positions_row(static_cast(n_pos_full) * 4, 0); + std::vector positions_col(static_cast(n_pos_full) * 4, 0); + for (int i = 0; i < n_pos_full; i++) { + positions_row[0 * n_pos_full + i] = pos_h_row[i]; + positions_row[1 * n_pos_full + i] = pos_w_row[i]; + positions_col[0 * n_pos_full + i] = pos_h_col[i]; + positions_col[1 * n_pos_full + i] = pos_w_col[i]; + } + + // Banded 1D sliding-window mask + const int window = hparams.attn_window_size; + GGML_ASSERT(window > 0); + std::vector mask(static_cast(n_pos_full) * n_pos_full, std::numeric_limits::lowest()); + for (int q = 0; q < n_pos_full; q++) { + int lo = std::max(0, q - window); + int hi = std::min(n_pos_full - 1, q + window); + for (int k = lo; k <= hi; k++) { + mask[static_cast(q) * n_pos_full + k] = 0.0f; + } + } + + set_input_i32("mimovl_positions_row", positions_row); + set_input_i32("mimovl_positions_col", positions_col); + set_input_f32("mimovl_idx_col", idx_col); + set_input_f32("mimovl_window_mask", mask); + } break; case PROJECTOR_TYPE_PIXTRAL: case PROJECTOR_TYPE_KIMIVL: case PROJECTOR_TYPE_KIMIK25: @@ -4081,6 +4203,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { case PROJECTOR_TYPE_QWEN3VL: // main path + deepstack paths return ctx->model.mm_1_b->ne[0] * (1 + ctx->model.n_deepstack_layers); + case PROJECTOR_TYPE_MIMOVL: + return ctx->model.mm_1_w->ne[1]; case PROJECTOR_TYPE_STEP3VL: return ctx->model.mm_model_proj->ne[1]; case PROJECTOR_TYPE_GEMMA3: diff --git a/tools/mtmd/models/mimovl.cpp b/tools/mtmd/models/mimovl.cpp new file mode 100644 index 00000000000..19db88f132a --- /dev/null +++ b/tools/mtmd/models/mimovl.cpp @@ -0,0 +1,209 @@ +#include "models.h" + +ggml_tensor * clip_graph_mimovl::build_mm(ggml_tensor * w, ggml_tensor * x) const { + ggml_tensor * cur = ggml_mul_mat(ctx0, w, x); + ggml_mul_mat_set_prec(cur, GGML_PREC_F32); + return cur; +} + +// MiMoVL vision tower for MiMo-V2.5 (non-Pro). Qwen2.5-VL-shaped ViT, except: +// 1. GQA in attention (32 Q / 8 KV heads, head_dim 64). +// 2. Per-head attention sinks on every windowed layer. The sinks adjust +// the softmax denominator (equivalently, a virtual extra K column with V=0), +// so they decay attention weight without contributing to the output. +// 3. Per-layer window-attention mode in hparams.wa_pattern_mode: +// -1 -> full, 0 -> row-window+sinks, 1 -> col-window+sinks. +// Col mode transposes the merge-unit grid on entry and restores +// it on exit. Both patch and rotary orderings are pre-computed +// host-side. +// 4. 1D banded sliding window (|q-k| > window_size -> -inf) as a +// single 2D mask broadcast across heads. +// 5. Per-block MLP biases. +ggml_cgraph * clip_graph_mimovl::build() { + GGML_ASSERT(model.patch_embeddings_0 != nullptr); + GGML_ASSERT(model.patch_embeddings_1 != nullptr); + GGML_ASSERT(model.class_embedding == nullptr); + GGML_ASSERT(hparams.n_head_kv > 0); + GGML_ASSERT(n_head % hparams.n_head_kv == 0); + GGML_ASSERT((int) hparams.wa_pattern_mode.size() == n_layer); + + const int batch_size = 1; + const int n_pos = n_patches; + const int n_head_kv = hparams.n_head_kv; + const int merge = hparams.n_merge > 0 ? hparams.n_merge : 2; + const int merge_unit = merge * merge; + const int n_units = n_pos / merge_unit; + GGML_ASSERT(n_units * merge_unit == n_pos); + + // MiMoVL has head_dim=64 with n_embd=1280, so n_embd is NOT n_head*head_dim + // (the base class's d_head = n_embd/n_head = 40 is wrong here). Derive + // head_dim from the fused QKV projection: rows = (n_head + 2*n_head_kv)*head_dim. + GGML_ASSERT(model.layers[0].qkv_w != nullptr); + const int qkv_rows = model.layers[0].qkv_w->ne[1]; + const int head_dim = qkv_rows / (n_head + 2 * n_head_kv); + GGML_ASSERT(head_dim * (n_head + 2 * n_head_kv) == qkv_rows); + const float attn_scale = 1.0f / std::sqrt((float) head_dim); + const int rope_n_dims = head_dim / 2; + int mrope_sections[4] = {rope_n_dims/2, rope_n_dims/2, 0, 0}; + + // Patch embed: Conv3D(kt=2) split into two Conv2D, then interleave-merge + // along the height axis to match the merge-tile token order. + ggml_tensor * inp_raw = build_inp_raw(); + ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, + patch_size, patch_size, 0, 0, 1, 1); + { + ggml_tensor * inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, + patch_size, patch_size, 0, 0, 1, 1); + inp = ggml_add(ctx0, inp, inp_1); + + GGML_ASSERT(img.nx % (patch_size * 2) == 0); + GGML_ASSERT(img.ny % (patch_size * 2) == 0); + + inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w,h,c,b] -> [c,w,h,b] + inp = ggml_cont_4d(ctx0, inp, n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); + inp = ggml_reshape_4d(ctx0, inp, n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); + inp = ggml_permute(ctx0, inp, 0, 2, 1, 3); + inp = ggml_cont_3d(ctx0, inp, n_embd, n_patches_x * n_patches_y, batch_size); + } + cb(inp, "patch_embed", -1); + + ggml_tensor * positions_row = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos * 4); + ggml_set_name(positions_row, "mimovl_positions_row"); + ggml_set_input(positions_row); + + ggml_tensor * positions_col = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos * 4); + ggml_set_name(positions_col, "mimovl_positions_col"); + ggml_set_input(positions_col); + + // idx_col is the col-major merge-unit permutation. Take it as F32 so we can + // derive the inverse permutation in-graph via ggml_argsort; + // ggml_get_rows requires its index tensor to be I32, so cast back as well. + ggml_tensor * idx_col_f = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_units); + ggml_set_name(idx_col_f, "mimovl_idx_col"); + ggml_set_input(idx_col_f); + ggml_tensor * idx_col = ggml_cast(ctx0, idx_col_f, GGML_TYPE_I32); + ggml_tensor * idx_col_inv = ggml_argsort(ctx0, idx_col_f, GGML_SORT_ORDER_ASC); + + ggml_tensor * window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos); + ggml_set_name(window_mask, "mimovl_window_mask"); + ggml_set_input(window_mask); + + ggml_tensor * window_mask_attn = (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) + ? ggml_cast(ctx0, window_mask, GGML_TYPE_F16) + : window_mask; + + // Reorder helper: permute patches at merge-unit granularity. The patch + // sequence is laid out as n_units groups of merge_unit (=4) consecutive + // patches; the row<->col transpose only permutes whole groups. We keep + // the per-group (h,w) ordering intact by reshaping to + // [n_embd*merge_unit, n_units] before ggml_get_rows. + auto reorder = [&](ggml_tensor * x, ggml_tensor * idx) { + ggml_tensor * y = ggml_reshape_2d(ctx0, x, n_embd * merge_unit, n_units); + y = ggml_get_rows(ctx0, y, idx); + return ggml_reshape_3d(ctx0, y, n_embd, n_pos, batch_size); + }; + + ggml_tensor * inpL = inp; + int prev_mode = -1; + + for (int il = 0; il < n_layer; il++) { + const auto & layer = model.layers[il]; + const int mode = hparams.wa_pattern_mode[il]; + const bool is_full = (mode == -1); + const bool is_col = (mode == 1); + + // Reorder transitions on entry/exit of a col-mode run. + if (is_col && prev_mode != 1) { + inpL = reorder(inpL, idx_col); + cb(inpL, "reorder_to_col", il); + } else if (!is_col && prev_mode == 1) { + inpL = reorder(inpL, idx_col_inv); + cb(inpL, "reorder_to_row", il); + } + + ggml_tensor * cur = inpL; + + // Pre-attention RMSNorm. + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_RMS, eps, il); + cb(cur, "ln1", il); + + // Fused QKV with GQA. + ggml_tensor * qkv = build_mm(layer.qkv_w, cur); + qkv = ggml_add(ctx0, qkv, layer.qkv_b); + + const size_t row = ggml_row_size(qkv->type, head_dim); + const size_t off_k = ggml_row_size(qkv->type, n_head * head_dim); + const size_t off_v = ggml_row_size(qkv->type, (n_head + n_head_kv) * head_dim); + + ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, head_dim, n_head, n_pos, row, qkv->nb[1], 0); + ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, head_dim, n_head_kv, n_pos, row, qkv->nb[1], off_k); + ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, head_dim, n_head_kv, n_pos, row, qkv->nb[1], off_v); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + // 2D RoPE + ggml_tensor * pos = is_col ? positions_col : positions_row; + Qcur = ggml_rope_multi(ctx0, Qcur, pos, nullptr, rope_n_dims, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000.0f, 1.0f, 0.0f, 1.0f, 32.0f, 1.0f); + Kcur = ggml_rope_multi(ctx0, Kcur, pos, nullptr, rope_n_dims, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000.0f, 1.0f, 0.0f, 1.0f, 32.0f, 1.0f); + cb(Qcur, "Qcur_rope", il); + cb(Kcur, "Kcur_rope", il); + + // Full layers: plain attention. Windowed layers: banded mask and per-head sinks. + ggml_tensor * mask = is_full ? nullptr : window_mask_attn; + ggml_tensor * sinks = is_full ? nullptr : layer.attn_sinks; + if (!is_full) { + GGML_ASSERT(layer.attn_sinks != nullptr); + } + ggml_tensor * attn_out = build_attn(layer.o_w, layer.o_b, Qcur, Kcur, Vcur, mask, attn_scale, il, sinks); + cb(attn_out, "attn_out", il); + + // Residual 1. + cur = ggml_add(ctx0, attn_out, inpL); + inpL = cur; + cb(cur, "ffn_inp", il); + + // Pre-FFN RMSNorm. + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_RMS, eps, il); + cb(cur, "ffn_inp_normed", il); + + // SwiGLU MLP with biases + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + hparams.ffn_op, il); + cb(cur, "ffn_out", il); + + // Residual 2. + cur = ggml_add(ctx0, inpL, cur); + cb(cur, "layer_out", il); + + inpL = cur; + prev_mode = mode; + } + + // If the last block was col-mode, undo the transpose so the merger sees patches in row order. + if (prev_mode == 1) { + inpL = reorder(inpL, idx_col_inv); + cb(inpL, "reorder_to_row_final", -1); + } + + // Merger: post-LayerNorm + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, 1e-6f, n_layer); + cb(inpL, "post_ln", -1); + + // Spatial merge: pack each merge_unit (=4) of patches into a single + // (n_embd*merge_unit)-wide row, then run the 2-layer MLP. + ggml_tensor * embeddings = ggml_reshape_3d(ctx0, inpL, n_embd * merge_unit, n_units, batch_size); + embeddings = build_ffn(embeddings, + model.mm_0_w, nullptr, + nullptr, nullptr, + model.mm_1_w, nullptr, + FFN_GELU, -1); + cb(embeddings, "vit_out", -1); + + ggml_build_forward_expand(gf, embeddings); + return gf; +} diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h index dbba233b16f..955daa6d6d3 100644 --- a/tools/mtmd/models/models.h +++ b/tools/mtmd/models/models.h @@ -33,6 +33,15 @@ struct clip_graph_qwen3vl : clip_graph { ggml_cgraph * build() override; }; +struct clip_graph_mimovl : clip_graph { + clip_graph_mimovl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; + // Force F32 mat-mul accumulation to avoid F16 overflow in the FFN down-proj + // when the mmproj is stored in F16 (the source weights are BF16; downcasting + // to F16 reduces dynamic range below the SwiGLU output magnitude on the last few layers). + ggml_tensor * build_mm(ggml_tensor * w, ggml_tensor * x) const override; +}; + struct clip_graph_step3vl : clip_graph { clip_graph_step3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} ggml_cgraph * build() override; diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp index 87da6876f7c..22092f6a606 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -325,6 +325,7 @@ struct mtmd_context { case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: case PROJECTOR_TYPE_QWEN3VL: + case PROJECTOR_TYPE_MIMOVL: { // <|vision_start|> ... (image embeddings) ... <|vision_end|> img_beg = "<|vision_start|>";