osobhandClaude Opus 4.7
e61bd70e03
Two real bugs found via code inspection against HF source:
1. candle's .gelu() is the tanh approximation; PyTorch's default 'gelu'
activation (used in WavLM via ACT2FN['gelu']) is the exact erf-based
version. Switched all 3 sites (feature extractor convs, pos_conv,
FFN) from .gelu() to .gelu_erf() to match the reference.
2. gru_rel_pos_const lookup used vb.pp("name").get(shape, "") which
resolves to "<prefix>.name." (trailing dot) and fails to find the
tensor. The .or_else(|_| zeros) silently swallowed the failure,
leaving all 12 layers' gating constants at zero instead of the
trained values. Fixed to attn.get(shape, "gru_rel_pos_const") which
resolves correctly.
examples/wavlm_sv_inspect.rs: utility for sanity-checking specific
tensors inside converted safetensors (e.g. layer_weights).
Same-content same-speaker cosine: 0.9985 -> 0.9963 (≈unchanged).
Cross-content same-speaker cosine: 0.4882 -> 0.4118 (still drifting).
Phase 5d (Python reference comparison) remains the gate for
identifying the residual numerical drift.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
53 lines
1.7 KiB
Rust
53 lines
1.7 KiB
Rust
//! Inspect specific tensors inside a converted WavLM-SV safetensors.
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//! Useful for sanity-checking weight loading and key naming.
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use anyhow::Result;
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use candle_core::Device;
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use clap::Parser;
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use std::path::PathBuf;
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#[derive(Debug, Parser)]
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struct Cli {
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#[arg(long)]
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weights: PathBuf,
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/// Tensor name to dump (e.g. "layer_weights", "projector.weight").
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#[arg(long)]
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key: String,
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/// Apply softmax along dim=0 before printing (for layer_weights).
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#[arg(long)]
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softmax: bool,
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/// Number of leading elements to print (default: all).
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#[arg(long)]
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head: Option<usize>,
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}
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fn main() -> Result<()> {
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let cli = Cli::parse();
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let tensors = candle_core::safetensors::load(&cli.weights, &Device::Cpu)?;
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let t = tensors
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.get(&cli.key)
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.ok_or_else(|| anyhow::anyhow!("key not found: {}", cli.key))?;
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println!("{}: dtype={:?}, shape={:?}", cli.key, t.dtype(), t.dims());
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let to_print = if cli.softmax {
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candle_nn::ops::softmax(t, 0)?
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} else {
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t.clone()
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};
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let v: Vec<f32> = to_print
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.flatten_all()?
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.to_dtype(candle_core::DType::F32)?
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.to_vec1()?;
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let limit = cli.head.unwrap_or(v.len()).min(v.len());
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let head: Vec<&f32> = v.iter().take(limit).collect();
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println!("first {limit} values: {head:?}");
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if v.len() > limit {
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let tail: Vec<&f32> = v.iter().rev().take(8).collect();
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println!("(last 8 values, reversed): {tail:?}");
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}
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let sum: f32 = v.iter().sum();
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let max = v.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
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let min = v.iter().cloned().fold(f32::INFINITY, f32::min);
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println!("sum={sum:.6}, min={min:.6}, max={max:.6}");
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Ok(())
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}
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