model: Muse Glimmer Support (#26841)
* Get started with Onyx * Add architecture * Skip keys handled in super() * Loading tensors * Shorten * Graph * Apply suggestion from @pcuenca * Remove norm now embedding in transformers weights * Add eot * Explicit output_multiplier * Handle post_norm_eps * No super call; unhardcode eot. The pattern `self._set_vocab_gpt2()` seems preferred throughout the codebase, and it allows `set_vocab()` to be called from a different part of the Python class hierarchy: the drafter model converter that we may need eventually. * Register for drafting * DFlash: inherit rope type from the linked target. Another option would be to store it in the gguf file itself. * mmproj conversion Note: some fields to be renamed after the implementation works. We are keeping compatibility with the reference Meta gguf for testing purposes. * "clip" header declarations * Load mmproj * Pre-processing * Graph * Go back to using delimiters. Otherwise our generations are worse. Transformers does not use them. We need to trace inputs to verify whether they are equivalent. * downsample_factor -> merge_size * Add vision graph lol, forgot from a previous commit * Additional renames, align with llama.cpp / transformers * Prefer _size instead of independent _h and _w * Fix token layout Co-authored-by: Young Han <younghan@fb.com> * onyx: bring the chat parser onto the onyx branch common/chat.cpp on this branch has no Onyx handling, so a converted model serves malformed chat: the assistant preamble leaks into content ("to=self<|message|>...") and tool calls fail with HTTP 500 "The model produced output that does not match the expected peg-native format" common_chat_params_init_onyx exists on onyx-fair-patch, added there by 8bb73dd3d. It was never on this branch, so this is not a regression -- the two lines developed independently. The code here is taken verbatim from that commit. It is the clean side of `git merge origin/onyx-fair-patch`: chat.cpp is one of the files that merges without conflict. The full merge is not viable -- it produces 13 conflicts, including add/add on conversion/onyx.py and src/models/onyx.cpp where the q_norm-folding and metadata-scale approaches contradict each other, and #4/#7 are stacked on this branch's side of that. Verified on this branch: builds with 0 errors, converts an Onyx checkpoint, and serving it gives "4" for "What is 2+2?" plus a correct get_weather {"city":"Paris"} tool call, where the unported branch gives the two failures above. No converter or runtime changes are included, so this should not interact with the q_norm work. Co-authored-by: Beto de Paola <betodepaola@meta.com> * Less params, bilinear pos-emb interpolation as a graph op instead of CPU * Map to symbolic V_MMPROJ instead of strings * Make a couple params explicit * Patchify via build_inp() * No param for rope_theta * Small cleanup * Restore blank line * Unpermute, to adapt to the latest transformers checkpoint * Apply norm after token embeddings This follows the latest transformers approach. * Remove duplicated function * build_vit * onyx: use the model rope theta on sliding-window layers * DFlash: conversion from transformers drafter * Revert rope_type derivation from target NOTE: this breaks compatibility with Meta's distributed DFlash GGUFs, as the Q/K are stored in "NEOX" (rotated half) format, like in transformers. * Apply suggestion from @pcuenca * Set model type * Remove comment that will become obsolete * Hardcode post_norm_rms_eps instead of new param * Derive SWA+RoPE pattern from gguf array or scalar * Fix model type <-> number of layers * Reorder * Rename * Fix typo * DFlash: seed the draft KV cache from multimodal embedding batches `common_speculative_impl_draft_dflash::process()` returned early on any batch carrying embeddings, so an image prefill never had its target-layer features fused through the DFlash encoder and injected into the draft's KV cache. That left a hole spanning the image's positions, and the next injection at a post-image position failed to initialize its batch: ``` decoding image batch 1/1, n_tokens_batch = 256 decode: failed to initialize batch llama_decode: failed to decode, ret = -1 process: llama_decode(ctx_dft) failed rc=-1 (n_tokens=17, offset=0) srv decode: failed to process speculative batch ``` Every image request with `--spec-type draft-dflash` failed with HTTP 500. Text-only was unaffected, since those batches carry token ids and were let through. Restore the earlier condition, which admits a batch that is either tokens or embeddings and skips only the degenerate neither/both cases. The rest of `process()` is already layout-agnostic -- it gathers features via `llama_get_embeddings_layer_inp()` and indexes `batch_in.pos[]` / `batch_in.seq_id[]`, none of which assume token ids -- so this is the whole fix. Validated against `muse-glimmer-30B-bf16.gguf` + `mmproj-muse-glimmer-30B-bf16.gguf` + a DFlash draft head, on an image describe-the-shapes request: - before: HTTP 500, `failed to process speculative batch` - after: HTTP 200, draft acceptance 0.34012 (167 accepted / 491 generated), mean len 3.04 Output equivalence holds, which is the property that matters: at temperature 0 the drafted response is byte-identical to the same request served with no draft attached (1213/1213 chars), so the draft is drafting correctly through the image context rather than merely not crashing. * Conversion: prefer rewrite to mapping * Revert "Conversion: prefer rewrite to mapping" This reverts commit a92d0ac584d315e876741e85b6dad3dbc8b23bf7. * fix lint * sliding_window metadata is not optional * disable state save/load * Apply suggestion from @pcuenca --------- Co-authored-by: Young Han <younghan@fb.com> Co-authored-by: Beto de Paola <betodepaola@meta.com> Co-authored-by: Daniel Han <michaelhan2050@gmail.com> Co-authored-by: ruanrms <ruanslv@gmail.com> Co-authored-by: Xuan Son Nguyen <son@huggingface.co> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
This commit is contained in:
Pedro Cuenca
co-authored by
Young Han
Beto de Paola
Daniel Han
ruanrms
Xuan Son Nguyen
Sigbjørn Skjæret
Young Han
Beto de Paola
Daniel Han
ruanrms
Xuan Son Nguyen
Sigbjørn Skjæret
parent
a52077c4ca
commit
62bf73d25c
+151
@@ -3086,6 +3086,151 @@ static common_chat_params common_chat_params_init_minicpm5(const common_chat_tem
|
||||
return data;
|
||||
}
|
||||
|
||||
// An assistant turn is rendered as one or more messages, each
|
||||
// "<|start|>assistant to=<recipient><|message|>{content}{END}" where END is
|
||||
// <|eom|> (more messages follow) or <|eot|> (end of turn):
|
||||
// - chain-of-thought: to=self, terminated by <|eom|>
|
||||
// - final answer: to=user, terminated by <|eot|>
|
||||
// The generation prompt is just "<|start|>assistant"; the model emits its own
|
||||
// " to=...<|message|>".
|
||||
static common_chat_params common_chat_params_init_muse_glimmer(const common_chat_template & tmpl,
|
||||
const autoparser::generation_params & inputs) {
|
||||
common_chat_params data;
|
||||
|
||||
data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs);
|
||||
data.generation_prompt = "<|start|>assistant";
|
||||
data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
|
||||
data.supports_thinking = true;
|
||||
|
||||
data.preserved_tokens = {
|
||||
"<|start|>", "<|message|>", "<|eom|>", "<|eot|>",
|
||||
// ATEM tool-call markup emitted on " to=<tool>" turns.
|
||||
"<atem:function_calls>", "<atem:invoke", "<atem:parameter", "</atem:parameter>",
|
||||
"</atem:invoke>", "</atem:function_calls>",
|
||||
};
|
||||
|
||||
data.message_delimiters = {
|
||||
{ COMMON_CHAT_ROLE_ASSISTANT, "<|start|>assistant" },
|
||||
{ COMMON_CHAT_ROLE_USER, "<|start|>user" },
|
||||
{ COMMON_CHAT_ROLE_SYSTEM, "<|start|>system" },
|
||||
{ COMMON_CHAT_ROLE_TOOL, "<|start|>tool" },
|
||||
};
|
||||
|
||||
if (inputs.has_continuation()) {
|
||||
const auto & msg = inputs.continue_msg;
|
||||
|
||||
data.generation_prompt = "<|start|>assistant to=self<|message|>" + msg.reasoning_content;
|
||||
if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
|
||||
data.generation_prompt += "<|eom|><|start|>assistant to=user<|message|>" + msg.render_content();
|
||||
}
|
||||
|
||||
data.prompt += data.generation_prompt;
|
||||
}
|
||||
|
||||
auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
|
||||
|
||||
auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
|
||||
// Constrained grammar whenever tools are offered.
|
||||
auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
|
||||
|
||||
auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
|
||||
auto start = p.rule("start", p.literal("<|start|>assistant"));
|
||||
|
||||
if (!extract_reasoning && !include_grammar) {
|
||||
return start + p.content(p.rest());
|
||||
}
|
||||
|
||||
if (extract_reasoning) {
|
||||
p.rule("analysis", p.literal(" to=self<|message|>") + p.reasoning(p.until("<|eom|>")) + p.literal("<|eom|>"));
|
||||
} else {
|
||||
p.rule("analysis", p.literal(" to=self<|message|>") + p.content(p.until("<|eom|>")) + p.literal("<|eom|>"));
|
||||
}
|
||||
auto analysis = p.ref("analysis");
|
||||
|
||||
auto recipient = p.optional(p.literal(" to=user"));
|
||||
auto final_msg = p.rule("final", recipient + p.literal("<|message|>") + p.content(p.until("<|eot|>")));
|
||||
|
||||
if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
|
||||
auto string_value = p.ac(
|
||||
p.tool_arg_string_value(p.until("</atem:parameter>")) + p.tool_arg_close(p.literal("</atem:parameter>")),
|
||||
"</atem:parameter>");
|
||||
|
||||
auto tool_choice = p.choice();
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool.at("function");
|
||||
const std::string name = function.at("name");
|
||||
auto params = function.contains("parameters") ? function.at("parameters") : json::object();
|
||||
|
||||
auto args = p.eps();
|
||||
if (params.contains("properties") && params.at("properties").is_object() && !params.at("properties").empty()) {
|
||||
auto schema_info = common_schema_info();
|
||||
schema_info.resolve_refs(params);
|
||||
|
||||
auto arg_choice = p.choice();
|
||||
for (const auto & [prop_name, prop_schema] : params.at("properties").items()) {
|
||||
auto value_parser = p.eps();
|
||||
if (schema_info.resolves_to_string(prop_schema)) {
|
||||
value_parser = string_value;
|
||||
} else {
|
||||
value_parser = p.tool_arg_json_value(
|
||||
p.schema(p.json(), "tool-" + name + "-arg-" + prop_name + "-schema", prop_schema, false))
|
||||
+ p.tool_arg_close(p.literal("</atem:parameter>"));
|
||||
}
|
||||
|
||||
auto arg_rule = p.tool_arg(
|
||||
p.tool_arg_open(p.literal("<atem:parameter name=\"") + p.tool_arg_name(p.literal(prop_name)) + p.literal("\">")) +
|
||||
value_parser);
|
||||
|
||||
arg_choice |= arg_rule;
|
||||
}
|
||||
args = p.zero_or_more(arg_choice + p.space());
|
||||
}
|
||||
|
||||
auto tool_parser = p.tool(
|
||||
p.tool_open(p.literal(" to=") + p.until("<|message|>") +
|
||||
p.literal("<|message|><atem:function_calls>") + p.space() +
|
||||
p.literal("<atem:invoke name=\"") + p.tool_name(p.literal(name)) + p.literal("\">") + p.space())
|
||||
<< p.tool_args(args)
|
||||
<< p.tool_close(p.literal("</atem:invoke>") + p.space() + p.literal("</atem:function_calls>")));
|
||||
|
||||
tool_choice |= p.rule("tool-" + name, tool_parser);
|
||||
});
|
||||
|
||||
auto tool_calls = inputs.parallel_tool_calls
|
||||
? p.trigger_rule("tool-call", tool_choice + p.zero_or_more(p.literal("<|eom|>") + start + tool_choice))
|
||||
: p.trigger_rule("tool-call", tool_choice);
|
||||
|
||||
|
||||
if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
|
||||
return p.zero_or_more(start + analysis) + start + tool_calls;
|
||||
}
|
||||
return p.zero_or_more(start + analysis) + start + (tool_calls | final_msg);
|
||||
}
|
||||
|
||||
return p.zero_or_more(start + analysis) + start + final_msg;
|
||||
});
|
||||
|
||||
data.parser = parser.save();
|
||||
|
||||
if (include_grammar) {
|
||||
data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
|
||||
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool.at("function");
|
||||
auto schema = function.contains("parameters") ? function.at("parameters") : json::object();
|
||||
builder.resolve_refs(schema);
|
||||
});
|
||||
parser.build_grammar(builder, data.grammar_lazy);
|
||||
});
|
||||
data.grammar_triggers = {
|
||||
{ COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
|
||||
"<\\|start\\|>assistant( to=(?!self<\\|message\\|>)(?!user<\\|message\\|>)[^<]*?<\\|message\\|>)" },
|
||||
};
|
||||
}
|
||||
|
||||
return data;
|
||||
}
|
||||
|
||||
static json common_chat_extra_context() {
|
||||
json ctx = json::object();
|
||||
std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
|
||||
@@ -3114,6 +3259,12 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
|
||||
return common_chat_params_init_gpt_oss(tmpl, params);
|
||||
}
|
||||
|
||||
// Muse Glimmer format using " to=<recipient>" recipients and <|eom|>/<|eot|> message terminators.
|
||||
if (src.find("<atem:function_calls>") != std::string::npos && src.find("<|eom|>") != std::string::npos) {
|
||||
LOG_DBG("Using specialized template: Muse Glimmer\n");
|
||||
return common_chat_params_init_muse_glimmer(tmpl, params);
|
||||
}
|
||||
|
||||
// Functionary v3.2 - uses recipient-based format with >>>recipient\n{content}
|
||||
// Detection: template has ">>>all" for content and ">>>" prefix for tool calls
|
||||
if (src.find(">>>all") != std::string::npos && src.find(">>>${recipient}") != std::string::npos) {
|
||||
|
||||
@@ -1032,7 +1032,14 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
|
||||
return true;
|
||||
}
|
||||
|
||||
if (batch_in.token == nullptr || batch_in.embd != nullptr) {
|
||||
// Target prefill may contain token IDs or multimodal embeddings. Both
|
||||
// produce the target-layer features used to seed the draft KV cache, so
|
||||
// skipping the embedding batches leaves a hole in the draft's cache and
|
||||
// the next injection fails to initialize.
|
||||
// TODO: revisit after https://github.com/ggml-org/llama.cpp/pull/24669 is merged
|
||||
const bool has_tokens = batch_in.token != nullptr;
|
||||
const bool has_embeddings = batch_in.embd != nullptr;
|
||||
if (has_tokens == has_embeddings) {
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user