Files
ollama-for-amd/llama/llama.cpp/src/llama-batch.h
Michael Yang 1a19df1f3a update vendored llama.cpp and ggml (#11823)
* TEMPORARY: Update the llama.cpp upstream to my fork's Granite Four branch

This will be redone once my branch is merged upstream in llama.cpp

* feat: Update all patches

There are a number that are no longer needed at all:

- 0003-embeddings: Embeddings entirely overhauled on master
- 0008-ensure-KV-cache-is-fully-defragmented: KV caching entirely
    overhauled on master
- 0019-metal-add-mean-kernel-14267: Merged upstream
- 0020-CUDA-add-mean-operation-14313: Merged upstream

* feat: Sync llama.cpp and ggml

* fix: Update rsync-filter for all moved/new/removed files

* fix: Add files missing from sync

* fix: Update ggml rsync-filter for new ggml-cpu/arch subdirs

* fix: Add ggml files missing from sync

* fix: Narrow llama.cpp rsync-filter to not include mtmd main tool cpp files

* fix: Remove mtmd main cpp files

* fix: Add missing include in sampling_ext.cpp

* fix: Update llama.go to use mtmd instead of clip/llava

* fix: Add patch for mtmd_input_text

* chore: Ignore *.patched in the patch directory

* fix: Fix support for arch-specific ggml-cpu source files with new arrangement

In https://github.com/ggml-org/llama.cpp/pull/13892, all arch-specific
implementations were split out into a nested tree structure under
ggml-cpu/arch. This conflicts with standard CGO layout where all
arch-specific source files are expected to live in the same directory as
the parent go module and use suffixes based on GOOS and GOARCH. As such,
there were really two options for getting this to work:

1. Add a patch on top of the GGML sync to rearrange the files to match the
GO layout convention
2. Use CGO directives to conditionally include the nested source files in
the compilation units

This commit does (2) in order to minimize the set of changes needed on top
of the upstream file layout. To get this to work, there are two key things
needed:

1. In cpu.go, #cgo directives are added to explicitly set __${GOARCH}__ in
the preprocessor directives
2. In arch-impls.c|cpp, use an #ifdef | #elif defined | #endif chain to
explicitly include the .c|.cpp files for the given architecture from the
nested directory

* fix: Use mtmd_helper to correctly load the bitmap for the image

* fix: Apply patch for mtmd_text_input

* fix: Add missing stb to llama.cpp rsync-filter

* fix: Add sync'ed stb vendored header

* fix: Use c++17 and include vendor for go wrapper modules

* fix: Update patch 0015 for upstream implementation of uuid

* feat: Bump to the latest tip of the branch

* fix: Update patches for bump

* feat: Bump back to the cenral repo and point at the latest master

This includes granite 4 and a number of other model architectures!

* fix: Revert changes to ggml export GPU UUID patch

* fix: Add patch for GGML_VERSION and GGML_COMMIT constants

* feat: Sync all patched code

* build: Include cmake/common.cmake in ggml sync

* build: Add top-level include for GNUINstallDirs in CMakeLists.txt

This is used to populate CMAKE_INSTALL_BINDIR

* fix: Add a patch to avoid power throttling API on non-msvc windows builds

* fix: Sync patch changes for ggml-cpu.c

* feat: Bump llama.cpp to 4a4f42

This picks up support for Kimi K2 and PLaMO-2

* feat: Sync llama.cpp

* fix: Handle multi-chunk image encodings from mtmd

* fix: Re-number patches after merge with `main`

* feat: Bump to 41e78c in the makefile

* fix: Fix Solar and argsort/copy patches after bump

* fix: Remove Gemma3n CUDA Graphs patch

It was implemented upstream:
https://github.com/ggml-org/llama.cpp/pull/14741

* feat: Sync llama.cpp / ggml after latest bump

* build: Remove unnecessary CFLAGS definitions in cpu.go

* fix: Remove unnecessary additions in the rsync-filter

* fix: Remove unused vendored code for chat template parsing

* Revert "fix: Remove Gemma3n CUDA Graphs patch"

This reverts commit d724caced3ce21f08924d4b7801f94ce6638f6ea.

* fix: Update 0020 CUDA Graphs for gemma3n to keep both llama.cpp and ollama fixes

https://github.com/ollama/ollama/pull/11195#issuecomment-3137312394

* fix: Sync ggml-cuda.cu after keeping both style cuda graph fixes for gemma3n

* unwind mxfp4 patch

Prepare to bump ggml with their impl for mxfp4

* bump

* fix windows build error

* Convert tensors at load time

Repack the mxfp4 tensors as ggmls kernels expect them to be.

* convert mlp bf16 to f32

* buffer the conversion better

* reshape earlier

* openai swiglu

* add ids

* split qkv, gate_up

* fix nested alt tags

* fast attention

* remove debug messages

* fix lint

* remove redundant test

* remap values only if source/target are different

* add back i32->i32 copy

* refactor cpu quants

* clean up vendor

* update patch instructions

* clean up patches

* remove webgpu

* update mem

* also handle gpt-oss

* revert convert changes

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
2025-08-14 14:42:58 -07:00

161 lines
5.8 KiB
C++
Vendored

#pragma once
#include "llama.h"
#include "llama-cparams.h"
#include <array>
#include <vector>
#include <set>
#include <bitset>
#include <memory>
#include <unordered_map>
// keep this struct lightweight
struct llama_ubatch {
bool equal_seqs() const {
return b_equal_seqs != 0;
}
uint32_t b_equal_seqs; // note: this is a boolean, but we use an int32_t for alignment
// otherwise address sanitizer complains
// TODO: whole_seqs for embeddings?
uint32_t n_tokens; // total tokens (n_seq_tokens * n_seqs)
uint32_t n_seq_tokens; // tokens per sequence set
uint32_t n_seqs; // sequence sets in the ubatch
uint32_t n_seqs_unq; // unique sequence ids in the ubatch
// seq_id_unq: unique sequence ids in the ubatch
// seq_idx: indices of the unique sequence ids in the ubatch in [0, n_seqs_unq)
// used for extracting sequence pooled embeddings
// // size | idx | val
llama_token * token; // [n_tokens] | i | id, token
float * embd; // [n_embd, n_tokens] | i | embd
llama_pos * pos; // [n_tokens] | i | pos
int32_t * n_seq_id; // [n_tokens] | i | -
llama_seq_id ** seq_id; // [n_tokens] | s | s0, s1, seq_id
llama_seq_id * seq_id_unq; // [n_seqs_unq] | s | seq_id
int32_t * seq_idx; // [LLAMA_MAX_SEQ] | - | seq_idx
int8_t * output; // [n_tokens] | i | -
struct data_t {
std::vector<llama_token> token;
std::vector<float> embd;
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id *> seq_id;
std::vector<llama_seq_id> seq_id_unq;
std::vector<int32_t> seq_idx;
std::vector<int8_t> output;
};
// the llama_ubatch pointers above point to this data if set. otherwise - points to non-owning data
std::shared_ptr<data_t> data;
};
// a helper for sanitizing, fulfilling and splitting a batch
class llama_batch_allocr {
public:
llama_batch_allocr(uint32_t n_pos_per_embd);
// sanitize and auto-gen missing data in the input batch
// memory is optional. if provided will be used to check for sequence continuity and to determine the positions
bool init(
const llama_batch & batch_inp,
const llama_vocab & vocab,
const llama_memory_i * memory,
uint32_t n_embd,
uint32_t n_seq_max,
bool output_all);
const llama_batch & get_batch() const;
uint32_t get_n_tokens() const;
uint32_t get_n_outputs() const;
uint32_t get_n_used() const;
// the array of output indices in the order they were encountered during the ubatch splitting
std::vector<int32_t> & get_out_ids();
// min/max positions of each sequence in the current ubatch
llama_pos seq_pos_min(llama_seq_id seq_id) const;
llama_pos seq_pos_max(llama_seq_id seq_id) const;
// call once before splitting the batch to reset the internal state
void split_reset();
// simple split, unknown number of sequence sets of unequal lengths
llama_ubatch split_simple(uint32_t n_ubatch);
// make ubatches of equal-length sequences sets
// if sequential == true, the tokens in the ubatch will have increasing sequential sequence ids
llama_ubatch split_equal(uint32_t n_ubatch, bool sequential);
// sequence-set-wise split - each ubatch contains a single sequence-set
llama_ubatch split_seq(uint32_t n_ubatch);
// a helper method for creating a well-defined ubatch of tokens
// TODO: support embeddings if needed in the future
llama_ubatch ubatch_reserve(uint32_t n_seq_tokens, uint32_t n_seqs);
private:
void clear();
// create the next ubatch based on the provided batch indices (idxs) and the number of sequence sets (n_seqs)
// return llama_ubatch.n_tokens == 0 if the entire batch was consumed
llama_ubatch ubatch_add(const std::vector<int32_t> & idxs, uint32_t n_seqs, bool equal_seqs);
// for debugging, start with LLAMA_BATCH_DEBUG=2
void ubatch_print(const llama_ubatch & ubatch, int debug);
llama_batch batch;
// only for debugging purposes
const llama_vocab * vocab;
// TODO: this is more of a temporary solution until we have a better way to handle multiple positions per token/embd
// ref: https://github.com/ggml-org/llama.cpp/issues/13694#issuecomment-2983871762
const uint32_t n_pos_per_embd;
uint32_t n_embd;
uint32_t n_seq_max;
uint32_t n_outputs;
std::array<llama_seq_id, 1> seq_id_0 = { 0 }; // default sequence id
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id *> seq_id;
std::vector<llama_seq_id> seq_id_unq;
std::vector<int32_t> seq_idx;
std::vector<int8_t> output;
using pos_set_t = std::set<llama_pos>;
using seq_cpl_t = std::vector<bool>;
// helper flag to quickly determine if there are any coupled sequences in the batch
bool has_cpl = false;
std::vector<pos_set_t> seq_pos; // seq_pos[s]: the set of positions in sequence s
std::vector<seq_cpl_t> seq_cpl; // seq_cpl[s0][s1]: if sequence s0 is coupled to sequence s1
using idx_vec_t = std::vector<int32_t>;
using seq_set_t = std::bitset<LLAMA_MAX_SEQ>;
std::vector<seq_set_t> seq_set; // seq_set[i]: the sequence set of token i
std::unordered_map<seq_set_t, idx_vec_t> seq_set_map; // the indices at which the sequence set appears
// batch indices of the output
std::vector<int32_t> out_ids;
uint32_t n_used;
// used[i] indicates if token i has already been used in a previous ubatch
std::vector<bool> used;
int debug;
};