moe(wip): gpt-oss-20b groundwork — config fields, arch doc, MXFP4 tools
Phase 19 start. config.rs: explicit head_dim (gpt-oss=64) + MoE fields (num_local_experts, num_experts_per_tok, swiglu_limit, sliding_window, layer_types) with accessors; Qwen3/GPT-2 paths unchanged (fall back to hidden/num_heads when head_dim absent). docs/19-moe-gpt-oss.md: architecture + exact HF reference math (router softmax-after-topk, interleaved clamped (up+1)*glu experts, attention sinks, alternating sliding window, rotate_half RoPE theta=150000, head_dim 64), verified tensor layout, MXFP4 dequant plan. docs/MOE_PROGRESS.md: resume/handoff snapshot. tools/mxfp4_probe.py: inspect safetensors + validate MXFP4 decode (done). tools/gptoss_dequant.py: MXFP4 experts -> plain BF16 safetensors dir so the existing loader reads it (no MXFP4 in Rust for the first pass). Verified: llama.cpp (dash5, LLM_ARCH_OPENAI_MOE) runs the gpt-oss-20b MXFP4 GGUF correctly (17*24 -> 408) = the correctness oracle. MXFP4 decode validated in numpy. Model + GGUF staged on dash5. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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@@ -46,6 +46,28 @@ pub struct ModelConfig {
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pub rope_theta: Option<f64>,
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#[serde(default)]
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pub tie_word_embeddings: Option<bool>,
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// Explicit head_dim (gpt-oss: 64, which is NOT hidden/num_heads). When
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// absent, head_dim() falls back to hidden/num_heads (Qwen3, GPT-2).
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#[serde(default)]
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pub head_dim: Option<usize>,
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// MoE (gpt-oss). Absent for dense models.
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#[serde(default)]
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pub num_local_experts: Option<usize>,
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#[serde(default)]
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pub num_experts_per_tok: Option<usize>,
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// gpt-oss clamped-SwiGLU limit (config: swiglu_limit, default 7.0).
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#[serde(default)]
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pub swiglu_limit: Option<f64>,
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// Sliding-window attention (gpt-oss: 128 on alternating layers). The
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// pattern is given by `layer_types` (e.g. "sliding_attention" /
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// "full_attention" per layer); absent for dense models.
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#[serde(default)]
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pub sliding_window: Option<usize>,
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#[serde(default)]
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pub layer_types: Option<Vec<String>>,
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}
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impl ModelConfig {
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@@ -81,7 +103,48 @@ impl ModelConfig {
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}
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pub fn head_dim(&self) -> usize {
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self.hidden() / self.num_heads()
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// gpt-oss sets head_dim explicitly (64 != 2880/64). Dense models omit it.
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self.head_dim.unwrap_or_else(|| self.hidden() / self.num_heads())
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}
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// ----- MoE (gpt-oss) -----
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/// True for MoE models (have an expert count in config).
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pub fn is_moe(&self) -> bool {
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self.num_local_experts.is_some()
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}
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pub fn num_experts(&self) -> usize {
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self.num_local_experts.unwrap_or(0)
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}
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pub fn experts_per_tok(&self) -> usize {
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self.num_experts_per_tok.unwrap_or(0)
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}
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/// Clamp bound for gpt-oss SwiGLU (config `swiglu_limit`, default 7.0).
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pub fn swiglu_limit(&self) -> f32 {
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self.swiglu_limit.unwrap_or(7.0) as f32
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}
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/// Whether layer `i` uses sliding-window attention. gpt-oss alternates per
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/// `layer_types`; if that's absent but `sliding_window` is set, fall back to
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/// the common "every other layer" pattern (even = sliding). Dense → false.
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pub fn layer_uses_sliding_window(&self, layer: usize) -> bool {
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if self.sliding_window.is_none() {
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return false;
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}
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match &self.layer_types {
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Some(types) => types
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.get(layer)
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.map(|t| t.contains("sliding"))
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.unwrap_or(false),
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None => layer % 2 == 0,
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}
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}
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pub fn sliding_window(&self) -> Option<usize> {
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self.sliding_window
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}
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pub fn ln_eps(&self) -> f32 {
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@@ -59,6 +59,41 @@ o = (scores @ v) -> merge heads -> @Wo + bo
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与 Qwen3 的新增点：MoE FFN、MXFP4 反量化、attention sinks（softmax 多一列再丢）、
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交替 sliding window、q/k/v/o bias、head_dim=64、clamped `(up+1)*glu`、rope_theta=150000。
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### 实测张量布局（layer 0，已用 `tools/mxfp4_probe.py` 核对）
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```
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self_attn.q_proj.weight [4096,2880] +bias[4096] # 64 heads*64
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self_attn.k_proj.weight [512,2880] +bias[512] # 8 kv*64
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self_attn.v_proj.weight [512,2880] +bias[512]
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self_attn.o_proj.weight [2880,4096] +bias[2880]
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self_attn.sinks [64] # 每 q-head 一个标量（BF16）
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input_layernorm.weight [2880]; post_attention_layernorm.weight [2880]
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mlp.router.weight [32,2880] +bias[32]
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mlp.experts.gate_up_proj_blocks [32,5760,90,16] U8 + _scales [32,5760,90] U8 + _bias[32,5760] BF16
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mlp.experts.down_proj_blocks [32,2880,90,16] U8 + _scales [32,2880,90] U8 + _bias[32,2880] BF16
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# 全局: model.embed_tokens.weight, model.norm.weight, lm_head.weight (BF16)
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```
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MXFP4 打包：`[..., nblk=90, 16]` U8，每 16 字节 = 32 个 FP4 码（低 nibble=偶 idx，高 nibble=奇 idx），
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每 block 一个 E8M0 scale；`90*32 = 2880 = 输入(hidden)维`。即 gate_up 每 expert 权重逻辑 shape
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`[5760 out, 2880 in]`（**已转置存储**：行=out，列=in，与 HF `nn.Linear` 一致 `y=x·Wᵀ`）。
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### RoPE（**rotate_half，非 interleave**）
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```
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dim = head_dim = 64; base = rope_theta = 150000
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inv_freq = 1 / base^(arange(0,64,2)/64) # 32 项
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freqs = pos ⊗ inv_freq # [S, 32]；cos/sin = cos(freqs)/sin(freqs) (不 doubling)
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# 应用: x=[.., 64], first=x[:32], second=x[32:]
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# out_first = first*cos - second*sin
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# out_second = second*cos + first*sin
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```
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> ⚠️ 与 Qwen3 的 RoPE kernel（interleave）不同 —— gptoss 走 rotate_half。需单独处理。
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### Decoder layer（pre-norm 残差，结构同 Qwen3）
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```
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h = x + attn(input_norm(x)) # attn 含 sinks/bias/滑窗
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out = h + moe(post_norm(h)) # moe = router + top4 experts 加权和
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```
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最终：`logits = lm_head(norm(h_last))`。无 q_norm/k_norm（与 Qwen3 不同，gptoss 没有）。
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## 3. MXFP4 反量化（expert 权重）
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expert 张量名：`model.layers.{i}.mlp.experts.gate_up_proj_blocks/_scales`、
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79
tools/gptoss_dequant.py
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79
tools/gptoss_dequant.py
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@@ -0,0 +1,79 @@
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#!/usr/bin/env python3
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"""Dequantize gpt-oss-20b MXFP4 expert weights -> a plain BF16 safetensors dir.
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Only the expert MLPs are MXFP4 (`*_blocks` uint8 packed 4-bit + `*_scales` uint8
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E8M0, block=32); everything else is already BF16. We decode experts to BF16 and
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re-emit a standard HF-format dir so xserv's normal safetensors loader reads it
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(keeps the first MoE pass free of any MXFP4 code in Rust).
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Fused expert outputs (per layer i), matching HF `GptOssExperts` param shapes:
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model.layers.{i}.mlp.experts.gate_up_proj [E, hidden, 2*inter] bf16
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model.layers.{i}.mlp.experts.down_proj [E, inter, hidden] bf16
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(*_bias tensors pass through unchanged)
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NOTE on transpose: the MXFP4 `_blocks` decode to [E, OUT, IN] (out-major, the
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contraction dim = nblk*32 last). HF's nn.Parameter for these is [E, IN, OUT]
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(it does `x @ gate_up_proj`). We emit [E, IN, OUT] (transpose last two dims) so
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the names/shapes match HF exactly and xserv can treat them uniformly.
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Run on the GPU host (torch + the model + disk):
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python3 tools/gptoss_dequant.py /opt/wjh/models/gpt-oss-20b /opt/wjh/models/gpt-oss-20b-bf16
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"""
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import sys, os, json, glob
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import torch
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from safetensors import safe_open
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from safetensors.torch import save_file
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# FP4 E2M1 code -> value (OCP MX). 16 entries.
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FP4 = torch.tensor(
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[0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0,
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-0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0], dtype=torch.float32)
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def dequant(blocks: torch.Tensor, scales: torch.Tensor) -> torch.Tensor:
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"""blocks uint8 [..., nblk, 16], scales uint8 [..., nblk] -> bf16 [..., nblk*32]."""
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blocks = blocks.to(torch.int64)
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lo = blocks & 0xF
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hi = (blocks >> 4) & 0xF
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codes = torch.stack([lo, hi], dim=-1).reshape(*blocks.shape[:-1], blocks.shape[-1] * 2)
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vals = FP4[codes] # [..., nblk, 32] f32
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scale = torch.exp2(scales.to(torch.float32) - 127.0) # [..., nblk]
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out = vals * scale[..., None]
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out = out.reshape(*out.shape[:-2], out.shape[-2] * out.shape[-1])
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return out.to(torch.bfloat16)
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def main():
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src, dst = sys.argv[1], sys.argv[2]
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os.makedirs(dst, exist_ok=True)
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wm = json.load(open(os.path.join(src, "model.safetensors.index.json")))["weight_map"]
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shards = {}
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for name, shard in wm.items():
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shards.setdefault(shard, []).append(name)
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out = {}
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for shard in sorted(shards):
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h = safe_open(os.path.join(src, shard), framework="pt")
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keys = set(h.keys())
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for name in shards[shard]:
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if name.endswith("_scales"):
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continue
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if name.endswith("_blocks"):
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base = name[:-len("_blocks")] # ...gate_up_proj / ...down_proj
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sc = base + "_scales"
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sc_h = h if sc in keys else safe_open(os.path.join(src, wm[sc]), framework="pt")
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deq = dequant(h.get_tensor(name), sc_h.get_tensor(sc)) # [E, OUT, IN]
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out[base] = deq.transpose(1, 2).contiguous() # [E, IN, OUT] (HF param layout)
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print("dequant", base, tuple(out[base].shape), flush=True)
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else:
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out[name] = h.get_tensor(name) # already bf16/other; pass through
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save_file(out, os.path.join(dst, "model.safetensors"), metadata={"format": "pt"})
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for f in glob.glob(os.path.join(src, "*.json")) + glob.glob(os.path.join(src, "*.jinja")):
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b = os.path.basename(f)
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if b == "model.safetensors.index.json":
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continue
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open(os.path.join(dst, b), "wb").write(open(f, "rb").read())
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print("DEQUANT_DONE ->", dst, flush=True)
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if __name__ == "__main__":
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main()
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82
tools/mxfp4_probe.py
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82
tools/mxfp4_probe.py
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@@ -0,0 +1,82 @@
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#!/usr/bin/env python3
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"""P19.1 — inspect gpt-oss-20b safetensors + validate MXFP4 dequant (CPU only).
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Run: python3 tools/mxfp4_probe.py /path/to/gpt-oss-20b
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Does three things:
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1. List the layer-0 tensor names, shapes, dtypes (esp. expert _blocks/_scales).
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2. Dequantize one expert's gate_up_proj from MXFP4 -> fp32 with our own LUT/decode.
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3. Print stats so we can eyeball sanity (range, mean, a few values).
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MXFP4 (OCP microscaling) as used by gpt-oss:
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- elements are FP4 E2M1 (4-bit), packed 2-per-byte.
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- every block of 32 consecutive elements shares one E8M0 scale (8-bit exponent).
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- value = fp4_e2m1_lut[code] * 2**(scale_e8m0 - 127)
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The `_blocks` tensor holds the packed 4-bit codes; `_scales` holds the per-block
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E8M0 exponents (uint8). We confirm shapes line up (last dim of blocks * 2 / ... )
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and that decoded values are in a sane range.
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"""
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import sys, json, os
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import numpy as np
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# FP4 E2M1 code -> value lookup (OCP MX spec). 16 codes: sign(1) exp(2) mant(1).
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# Values: 0, 0.5, 1, 1.5, 2, 3, 4, 6 and their negatives.
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FP4_E2M1 = np.array(
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[0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0,
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-0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0],
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dtype=np.float32,
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)
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def dequant_mxfp4(blocks: np.ndarray, scales: np.ndarray) -> np.ndarray:
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"""gpt-oss layout:
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blocks: uint8 [..., nblk, 16] -> each 16-byte row = 32 FP4 codes (2/byte)
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scales: uint8 [..., nblk] -> one E8M0 exponent per block
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Returns fp32 [..., nblk*32] (the input/contraction dim)."""
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lo = blocks & 0x0F
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hi = (blocks >> 4) & 0x0F
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# within a byte, low nibble is element 2i, high nibble is element 2i+1
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codes = np.empty(blocks.shape[:-1] + (blocks.shape[-1] * 2,), dtype=np.uint8)
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codes[..., 0::2] = lo
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codes[..., 1::2] = hi # [..., nblk, 32]
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vals = FP4_E2M1[codes] # [..., nblk, 32]
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scale_f = np.power(2.0, scales.astype(np.float32) - 127.0) # [..., nblk]
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out = vals * scale_f[..., None] # [..., nblk, 32]
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return out.reshape(out.shape[:-2] + (out.shape[-2] * out.shape[-1],)) # [..., nblk*32]
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def main():
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d = sys.argv[1] if len(sys.argv) > 1 else "/home/gahow/models/gpt-oss-20b"
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from safetensors import safe_open
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idx = json.load(open(os.path.join(d, "model.safetensors.index.json")))
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wm = idx["weight_map"]
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l0 = {k: v for k, v in wm.items() if "layers.0." in k}
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print("=== layer 0 tensors ===")
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# open each shard lazily
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handles = {}
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def get(name):
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shard = wm[name]
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if shard not in handles:
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handles[shard] = safe_open(os.path.join(d, shard), framework="numpy")
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return handles[shard]
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for k in sorted(l0):
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h = get(k)
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t = h.get_slice(k)
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print(f" {k.replace('model.layers.0.','L0.')} shape={t.get_shape()} dtype={t.get_dtype()}")
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# find expert gate_up blocks/scales
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gu_b = next((k for k in l0 if "gate_up_proj_blocks" in k), None)
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gu_s = next((k for k in l0 if "gate_up_proj_scales" in k), None)
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if gu_b and gu_s:
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print(f"\n=== dequant {gu_b.split('.')[-1]} (expert 0) ===")
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blocks = get(gu_b).get_tensor(gu_b)
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scales = get(gu_s).get_tensor(gu_s)
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print(" blocks", blocks.shape, blocks.dtype, " scales", scales.shape, scales.dtype)
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b0 = blocks[0]; s0 = scales[0] # expert 0: blocks [5760,90,16], scales [5760,90]
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deq = dequant_mxfp4(b0.astype(np.uint8), s0.astype(np.uint8)) # -> [5760, 2880]
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print(" expert0 dequant shape", deq.shape, "(expect [5760, 2880] = [2*inter, hidden])",
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"\n min %.4f max %.4f mean %.4f std %.4f" % (deq.min(), deq.max(), deq.mean(), deq.std()))
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print(" row0 first 8 vals:", np.round(deq[0, :8], 4))
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else:
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print("\n(no gate_up_proj_blocks/_scales found — check tensor names above)")
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if __name__ == "__main__":
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main()
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