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impl AprV2Model {
/// Encode text to token IDs using embedded BPE tokenizer (PMAT-172: Fail-Fast)
///
/// APR files MUST have embedded tokenizer. NO FALLBACK to external files.
/// This prevents Silent Failure Recovery where wrong tokenizer produces garbage.
///
/// # Design Principle
///
/// APR format is designed to be ONE self-contained file. If the embedded
/// tokenizer is missing, the APR file is BROKEN and should be re-converted.
///
/// # Returns
///
/// - `Some(tokens)` if APR has embedded tokenizer
/// - `None` if file doesn't exist or isn't APR format
///
/// # Panics
///
/// Prints error and returns None if APR is missing embedded tokenizer.
/// This is intentional - we want users to see the error, not garbage output.
pub fn encode_text(model_path: &Path, text: &str) -> Option<Vec<u32>> {
// Validate model path exists
if !model_path.exists() {
eprintln!(
"[PMAT-172] Error: Model file not found: {}",
model_path.display()
);
return None;
}
// PMAT-172: APR files MUST use embedded tokenizer - NO FALLBACK
if model_path.extension().is_some_and(|e| e == "apr") {
match Self::load(model_path) {
Ok(model) => {
match model.load_embedded_bpe_tokenizer() {
Some(tokenizer) => {
return Some(tokenizer.encode(text));
},
None => {
// PMAT-172: FAIL FAST - Do not fall back to external tokenizers
eprintln!("\n[PMAT-172] ERROR: APR file missing embedded tokenizer.");
eprintln!(" APR format requires self-contained tokenizer.");
eprintln!(
" Re-convert with: apr convert <source>.gguf -o {}",
model_path.display()
);
eprintln!(" Or use the original GGUF file directly.\n");
return None;
},
}
},
Err(e) => {
eprintln!("[PMAT-172] Error loading APR file: {}", e);
return None;
},
}
}
// For non-APR files (SafeTensors), use sibling tokenizer.json ONLY
// NO fallback to HuggingFace cache (PMAT-172: removed Silent Failure Recovery)
// GAP-UX-002: Try hash-prefixed first, then plain filename
let tokenizer_path = match find_sibling_file(model_path, "tokenizer.json") {
Some(path) => path,
None => {
eprintln!(
"\n[PMAT-172] ERROR: No tokenizer found for {}.",
model_path.display()
);
let stem = model_path
.file_stem()
.and_then(|s| s.to_str())
.unwrap_or("model");
eprintln!(
" Expected sibling file: {}.tokenizer.json or tokenizer.json",
stem
);
eprintln!(
" For SafeTensors models, tokenizer.json must be in same directory.\n"
);
return None;
},
};
let content = match fs::read_to_string(&tokenizer_path) {
Ok(c) => c,
Err(e) => {
eprintln!("[PMAT-172] Error reading tokenizer.json: {}", e);
return None;
},
};
let json: serde_json::Value = match serde_json::from_str(&content) {
Ok(j) => j,
Err(e) => {
eprintln!("[PMAT-172] Error parsing tokenizer.json: {}", e);
return None;
},
};
// Extract vocabulary (token -> id)
let vocab_obj = json.get("model")?.get("vocab")?;
let vocab_map = vocab_obj.as_object()?;
let token_to_id: HashMap<String, u32> = vocab_map
.iter()
.filter_map(|(token, id)| Some((token.clone(), id.as_u64()? as u32)))
.collect();
// F-REGR-231: Extract added_tokens (special tokens like <|im_start|>, <|im_end|>)
let special_tokens: HashMap<String, u32> = json
.get("added_tokens")
.and_then(|arr| arr.as_array())
.map(|tokens| {
tokens
.iter()
.filter_map(|t| {
let content = t.get("content")?.as_str()?;
let id = t.get("id")?.as_u64()? as u32;
Some((content.to_string(), id))
})
.collect()
})
.unwrap_or_default();
// Extract merges
let merges = json.get("model")?.get("merges")?.as_array()?;
let merge_rules: Vec<(String, String)> = merges
.iter()
.filter_map(|m| {
let s = m.as_str()?;
let parts: Vec<&str> = s.splitn(2, ' ').collect();
if parts.len() == 2 {
Some((parts[0].to_string(), parts[1].to_string()))
} else {
None
}
})
.collect();
let tokens = bpe_encode(text, &token_to_id, &merge_rules, &special_tokens);
Some(tokens)
}
// PMAT-172: Removed find_tokenizer_json_in_cache() — loading a stale
// HuggingFace cache tokenizer produced garbage output. Now requires
// explicit tokenizer path or embedded APR vocabulary.
/// Load tokenizer from embedded APR metadata (GH-156)
///
/// APR files can contain embedded tokenizer data - this is the preferred
/// way to decode tokens since it doesn't require sibling files.
///
/// Returns a simple decode-only tokenizer (no BPE encoding support).
pub fn load_embedded_tokenizer(&self) -> Option<SimpleTokenizer> {
let vocab = self.metadata.get_embedded_vocabulary()?;
let bos_id = self.metadata.get_embedded_bos_token_id();
let eos_id = self.metadata.get_embedded_eos_token_id();
Some(SimpleTokenizer {
id_to_token: vocab,
bos_token_id: bos_id,
eos_token_id: eos_id,
})
}
/// Load a full BPE tokenizer from embedded APR metadata (PMAT-171)
///
/// APR files converted from GGUF can contain both vocabulary AND BPE merge
/// rules embedded in metadata. This enables standalone encoding without
/// needing sibling tokenizer.json files.
///
/// Returns `Some(BpeTokenizer)` if both vocab and merges are embedded.
/// Returns `None` if either is missing (fall back to sibling file).
pub fn load_embedded_bpe_tokenizer(&self) -> Option<BpeTokenizer> {
let vocab_list = self.metadata.get_embedded_vocabulary()?;
let merges = self.metadata.get_embedded_merges()?;
// Build token_to_id and id_to_token maps
let mut token_to_id: HashMap<String, u32> = HashMap::new();
let mut id_to_token: Vec<String> = Vec::with_capacity(vocab_list.len());
for (id, token) in vocab_list.iter().enumerate() {
token_to_id.insert(token.clone(), id as u32);
id_to_token.push(token.clone());
}
let bos_id = self.metadata.get_embedded_bos_token_id();
let eos_id = self.metadata.get_embedded_eos_token_id();
// GH-189: Extract special tokens from vocabulary for atomic tokenization
// Special tokens like <|im_start|>, <|im_end|> must not be split by BPE
let special_tokens = extract_special_tokens_from_vocab(&token_to_id);
eprintln!(
"[PMAT-171] Loaded embedded BPE tokenizer: {} vocab, {} merges, {} special tokens",
id_to_token.len(),
merges.len(),
special_tokens.len()
);
Some(BpeTokenizer {
token_to_id,
id_to_token,
merge_rules: merges,
bos_id,
eos_id,
special_tokens,
})
}
/// Load a full tokenizer struct from sibling tokenizer.json
///
/// GAP-UX-002: Tries hash-prefixed companion first (`{stem}.tokenizer.json`),
/// then falls back to non-prefixed (`tokenizer.json`) for backwards compatibility.
///
/// Returns a BpeTokenizer that can be reused for multiple encode/decode calls.
/// For decode-only operations, prefer `load_embedded_tokenizer()` first.
pub fn load_tokenizer(model_path: &Path) -> Option<BpeTokenizer> {
let tokenizer_path = find_sibling_file(model_path, "tokenizer.json")?;
Self::load_tokenizer_from_path(&tokenizer_path)
}
/// Load a BPE tokenizer from an explicit tokenizer.json path
///
/// This is used for loading tokenizers from HuggingFace cache or other locations.
/// (PMAT-SHOWCASE-TOKENIZER-001)
pub fn load_tokenizer_from_path(tokenizer_path: &Path) -> Option<BpeTokenizer> {
if !tokenizer_path.exists() {
return None;
}
let content = fs::read_to_string(tokenizer_path).ok()?;
let json: serde_json::Value = serde_json::from_str(&content).ok()?;
// Extract vocabulary
let vocab_obj = json.get("model")?.get("vocab")?;
let vocab_map = vocab_obj.as_object()?;
let mut token_to_id: HashMap<String, u32> = HashMap::new();
let mut id_to_token: Vec<String> = Vec::new();
let mut vocab_vec: Vec<(String, u32)> = vocab_map
.iter()
.filter_map(|(token, id)| Some((token.clone(), id.as_u64()? as u32)))
.collect();
vocab_vec.sort_by_key(|(_, id)| *id);
for (token, id) in vocab_vec {
token_to_id.insert(token.clone(), id);
// Pad id_to_token if needed
while id_to_token.len() <= id as usize {
id_to_token.push(String::new());
}
id_to_token[id as usize] = token;
}
// Extract merges
let merges = json.get("model")?.get("merges")?.as_array()?;
let merge_rules: Vec<(String, String)> = merges
.iter()
.filter_map(|m| {
let s = m.as_str()?;
let parts: Vec<&str> = s.splitn(2, ' ').collect();
if parts.len() == 2 {
Some((parts[0].to_string(), parts[1].to_string()))
} else {
None
}
})
.collect();
// GH-189: Extract ALL added_tokens as special tokens for atomic tokenization
let mut bos_id = None;
let mut eos_id = None;
let mut special_tokens: HashMap<String, u32> = HashMap::new();
if let Some(added_tokens) = json.get("added_tokens").and_then(|v| v.as_array()) {
for token in added_tokens {
let content = token.get("content").and_then(|v| v.as_str());
let id = token
.get("id")
.and_then(serde_json::Value::as_u64)
.map(|v| v as u32);
if let (Some(content), Some(id)) = (content, id) {
// Add ALL added_tokens to special_tokens map for atomic tokenization
special_tokens.insert(content.to_string(), id);
// Also track bos/eos specifically
if content == "<|endoftext|>" || content == "</s>" || content == "<eos>" {
eos_id = Some(id);
}
if content == "<s>" || content == "<bos>" {
bos_id = Some(id);
}
}
}
}
eprintln!(
"[GH-189] Loaded tokenizer from {}: {} special tokens",
tokenizer_path.display(),
special_tokens.len()
);
Some(BpeTokenizer {
token_to_id,
id_to_token,
merge_rules,
bos_id,
eos_id,
special_tokens,
})
}
}
include!("loading_mmap.rs");
include!("forward.rs");