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kv-cache : avoid modifying recurrent cells when setting inputs (#13834)

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* kv-cache : avoid modifying recurrent cells when setting inputs

* kv-cache : remove inp_s_mask

It was replaced with equivalent and simpler functionality
with rs_z (the first zeroed state) and the already-existing inp_s_copy.

* kv-cache : fix non-consecutive token pos warning for recurrent models

The problem was apparently caused by how the tail cells were swapped.

* graph : simplify logic for recurrent state copies

* kv-cache : use cell without src refs for rs_z in recurrent cache

* llama-graph : fix recurrent state copy

The `state_copy` shuffle assumes everything is moved at once,
which is not true when `states_extra` is copied back to the cache
before copying the range of states between `head` and `head + n_seqs`.
This is only a problem if any of the cells in [`head`, `head + n_seqs`)
have an `src` in [`head + n_seqs`, `head + n_kv`),
which does happen when `n_ubatch > 1` in the `llama-parallel` example.

Changing the order of the operations avoids the potential overwrite
before use, although when copies are avoided (like with Mamba2),
this will require further changes.

* llama-graph : rename n_state to state_size in build_recurrent_state

This naming should reduce confusion between the state size
and the number of states.
This commit is contained in:
compilade
2025-06-10 18:20:14 -04:00
committed by GitHub
parent 55f6b9fa65
commit dad5c44398
6 changed files with 117 additions and 180 deletions
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83
src/llama-graph.cpp
View File

@ -250,22 +250,6 @@ void llm_graph_input_s_copy::set_input(const llama_ubatch * ubatch) {
}
}
void llm_graph_input_s_mask::set_input(const llama_ubatch * ubatch) {
GGML_UNUSED(ubatch);
const int64_t n_kv = kv_state->get_n_kv();
if (s_mask) {
GGML_ASSERT(ggml_backend_buffer_is_host(s_mask->buffer));
float * data = (float *) s_mask->data;
// clear unused states
for (int i = 0; i < n_kv; ++i) {
data[i] = kv_state->s_mask(i);
}
}
}
void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
GGML_UNUSED(ubatch);
@ -987,23 +971,6 @@ ggml_tensor * llm_graph_context::build_inp_s_copy() const {
return cur;
}
ggml_tensor * llm_graph_context::build_inp_s_mask() const {
const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
auto inp = std::make_unique<llm_graph_input_s_mask>(kv_state);
const auto n_kv = kv_state->get_n_kv();
auto & cur = inp->s_mask;
cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_kv);
ggml_set_input(cur);
res->add_input(std::move(inp));
return cur;
}
ggml_tensor * llm_graph_context::build_inp_cross_embd() const {
auto inp = std::make_unique<llm_graph_input_cross_embd>(cross);
@ -1456,43 +1423,53 @@ ggml_tensor * llm_graph_context::build_attn(
return cur;
}
ggml_tensor * llm_graph_context::build_copy_mask_state(
ggml_tensor * llm_graph_context::build_recurrent_state(
ggml_cgraph * gf,
ggml_tensor * s,
ggml_tensor * state_copy,
ggml_tensor * state_mask,
int32_t n_state,
int32_t n_seqs) const {
int32_t state_size,
int32_t n_seqs,
bool avoid_copies) const {
const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
const auto n_kv = kv_state->get_n_kv();
const auto kv_head = kv_state->get_head();
const auto rs_zero = kv_state->get_rs_z();
ggml_tensor * states = ggml_reshape_2d(ctx0, s, n_state, kv_state->get_size());
ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, kv_state->get_size());
// copy states
// NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
// this shrinks the tensors's ne[1] to n_kv
states = ggml_get_rows(ctx0, states, state_copy);
// Clear a single state which will then be copied to the other cleared states.
// Note that this is a no-op when the view is zero-sized.
ggml_tensor * state_zero = ggml_view_1d(ctx0, states, state_size*(rs_zero >= 0), rs_zero*states->nb[1]*(rs_zero >= 0));
ggml_build_forward_expand(gf, ggml_scale_inplace(ctx0, state_zero, 0));
// clear states of sequences which are starting at the beginning of this batch
// FIXME: zero-out NANs?
states = ggml_mul(ctx0, states, state_mask);
ggml_tensor * output_states;
// copy states which won't be changed further (between n_seqs and n_kv)
if (!avoid_copies) {
// copy states
// NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
// {state_size, kv_size} -> {state_size, n_seqs}
output_states = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, state_copy, n_seqs, 0));
ggml_build_forward_expand(gf, output_states);
} else {
// FIXME: make the gathering operation happen before the copy below
// (maybe with an optional lambda function passed as a parameter instead of `avoid_copies`?)
output_states = states;
}
// copy extra states which won't be changed further (between n_seqs and n_kv)
ggml_tensor * states_extra = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, state_copy, n_kv - n_seqs, n_seqs*state_copy->nb[0]));
ggml_build_forward_expand(gf,
ggml_cpy(ctx0,
ggml_view_1d(ctx0, states, n_state*(n_kv - n_seqs), (n_seqs )*n_state*ggml_element_size(states)),
ggml_view_1d(ctx0, s, n_state*(n_kv - n_seqs), (kv_head + n_seqs)*n_state*ggml_element_size(s))));
states_extra,
ggml_view_1d(ctx0, s, state_size*(n_kv - n_seqs), (kv_head + n_seqs)*state_size*ggml_element_size(s))));
// the part of the states that will be used and modified
return ggml_view_2d(ctx0, states, n_state, n_seqs, states->nb[1], 0);
return output_states;
}
ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
ggml_cgraph * gf,
ggml_tensor * state_copy,
ggml_tensor * state_mask,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
@ -1503,8 +1480,8 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
ggml_tensor * token_shift_all = kv_state->get_k_l(il);
ggml_tensor * token_shift = build_copy_mask_state(
gf, token_shift_all, state_copy, state_mask,
ggml_tensor * token_shift = build_recurrent_state(
gf, token_shift_all, state_copy,
hparams.n_embd_k_s(), n_seqs);
token_shift = ggml_reshape_3d(ctx0, token_shift, hparams.n_embd, token_shift_count, n_seqs);

22
src/llama-graph.h
View File

@ -200,18 +200,6 @@ public:
const llama_kv_cache_recurrent_state * kv_state;
};
class llm_graph_input_s_mask : public llm_graph_input_i {
public:
llm_graph_input_s_mask(const llama_kv_cache_recurrent_state * kv_state) : kv_state(kv_state) {}
virtual ~llm_graph_input_s_mask() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * s_mask; // F32 [1, n_kv]
const llama_kv_cache_recurrent_state * kv_state;
};
class llm_graph_input_cross_embd : public llm_graph_input_i {
public:
llm_graph_input_cross_embd(
@ -521,7 +509,6 @@ struct llm_graph_context {
ggml_tensor * build_inp_mean() const;
ggml_tensor * build_inp_cls() const;
ggml_tensor * build_inp_s_copy() const;
ggml_tensor * build_inp_s_mask() const;
ggml_tensor * build_inp_cross_embd() const;
ggml_tensor * build_inp_pos_bucket_enc() const;
@ -606,18 +593,17 @@ struct llm_graph_context {
// recurrent
//
ggml_tensor * build_copy_mask_state(
ggml_tensor * build_recurrent_state(
ggml_cgraph * gf,
ggml_tensor * s,
ggml_tensor * state_copy,
ggml_tensor * state_mask,
int32_t n_state,
int32_t n_seqs) const;
int32_t state_size,
int32_t n_seqs,
bool avoid_copies = false) const;
ggml_tensor * build_rwkv_token_shift_load(
ggml_cgraph * gf,
ggml_tensor * state_copy,
ggml_tensor * state_mask,
const llama_ubatch & ubatch,
int il) const;

135
src/llama-kv-cache-recurrent.cpp
View File

@ -406,21 +406,12 @@ bool llama_kv_cache_recurrent::prepare(const std::vector<llama_ubatch> & ubatche
bool success = true;
// TODO: here we have to verify that all ubatches can fit in the cells
// however, the current implementation is broken because it relies on s_copy() and s_mask() to update the cells
// during the compute of each ubatch. to reproduce, uncomment the following loop and run:
//
// $ llama-parallel -m ./mamba-130m/ggml-model-f16.gguf -np 5 -ns 8
//
// recovery from failures when the batch does not fit in the KV cache will not work correctly until this is fixed
//
GGML_UNUSED(ubatches);
//for (const auto & ubatch : ubatches) {
// if (!find_slot(ubatch)) {
// success = false;
// break;
// }
//}
for (const auto & ubatch : ubatches) {
if (!find_slot(ubatch)) {
success = false;
break;
}
}
// restore the original state
cells = std::move(org_cells);
@ -431,14 +422,13 @@ bool llama_kv_cache_recurrent::prepare(const std::vector<llama_ubatch> & ubatche
}
bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
const uint32_t n_tokens = ubatch.n_tokens;
const uint32_t n_seqs = ubatch.n_seqs;
const uint32_t n_seqs = ubatch.n_seqs;
const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
// if we have enough unused cells before the current head ->
// better to start searching from the beginning of the cache, hoping to fill it
if (head > used + 2*n_tokens) {
if (head > used + 2*n_seqs) {
head = 0;
}
@ -534,16 +524,16 @@ bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
empty_cell.src = orig_cell.src;
orig_cell.seq_id.erase(seq_id);
empty_cell.seq_id.insert(seq_id); // will be overwritten
GGML_ASSERT(!orig_cell.is_empty()); // has at least one remaining seq_id
}
seq_meta.tail = next_empty_cell;
// find next empty cell
if (s + 1 < n_seqs) {
next_empty_cell += 1;
for (uint32_t i = 0; i < size; ++i) {
next_empty_cell += 1;
if (next_empty_cell >= size) { next_empty_cell -= size; }
kv_cell & cell = cells[next_empty_cell];
if (cell.is_empty()) { break; }
next_empty_cell += 1;
}
}
}
@ -553,8 +543,8 @@ bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
// gather and re-order
for (uint32_t s = 0; s < n_seqs; ++s) {
int32_t dst_id = s + min;
int32_t src_id = cells[ubatch.seq_id[s][0]].tail;
const int32_t dst_id = s + min;
const int32_t src_id = cells[ubatch.seq_id[s][0]].tail;
if (dst_id != src_id) {
kv_cell & dst_cell = cells[dst_id];
kv_cell & src_cell = cells[src_id];
@ -563,12 +553,14 @@ bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
std::swap(dst_cell.src, src_cell.src);
std::swap(dst_cell.seq_id, src_cell.seq_id);
// swap tails (assuming they NEVER overlap)
for (const llama_seq_id seq_id : src_cell.seq_id) {
cells[seq_id].tail = src_id;
}
for (const llama_seq_id seq_id : dst_cell.seq_id) {
cells[seq_id].tail = dst_id;
// swap tails
for (uint32_t i = 0; i < size; ++i) {
int32_t & tail = cells[i].tail;
if (tail == src_id) {
tail = dst_id;
} else if (tail == dst_id) {
tail = src_id;
}
}
}
}
@ -576,7 +568,7 @@ bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
// update the pos of the used seqs
for (uint32_t s = 0; s < n_seqs; ++s) {
const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1];
int32_t cell_id = s + min;
const int32_t cell_id = s + min;
kv_cell & cell = cells[cell_id];
if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) {
@ -594,6 +586,38 @@ bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
}
}
// Find first cell without src refs, to use as the zero-ed state
{
// TODO: bake-in src refcounts in the cell metadata
std::vector<int32_t> refcounts(size, 0);
for (size_t i = 0; i < size; ++i) {
const int32_t src = cells[i].src;
if (src >= 0) {
refcounts[src] += 1;
}
}
rs_z = -1;
for (int i = min; i <= max; ++i) {
if (refcounts[i] == 0) {
rs_z = i;
break;
}
}
for (int i = min; i <= max; ++i) {
if (cells[i].src < 0) {
GGML_ASSERT(rs_z >= 0);
cells[i].src0 = rs_z;
} else {
// Stage the source ids for all used cells to allow correct seq_* behavior
// and still make these values available when setting the inputs
cells[i].src0 = cells[i].src;
}
cells[i].src = i; // avoid moving or clearing twice
}
}
// allow getting the range of used cells, from head to head + n
head = min;
n = max - min + 1;
@ -605,47 +629,8 @@ bool llama_kv_cache_recurrent::find_slot(const llama_ubatch & ubatch) {
}
bool llama_kv_cache_recurrent::get_can_shift() const {
return false;
}
int32_t llama_kv_cache_recurrent::s_copy(int i) const {
const uint32_t cell_id = i + head;
//////////////////////////////////////////////
// TODO: this should not mutate the KV cache !
kv_cell & cell = const_cast<kv_cell &>(cells[cell_id]);
// prevent out-of-bound sources
if (cell.src < 0 || (uint32_t) cell.src >= size) {
cell.src = cell_id;
}
int32_t res = cell.src;
// TODO: do not mutate the KV cache
// ensure copy only happens once
if (cell.src != (int32_t) cell_id) {
cell.src = cell_id;
}
return res;
}
float llama_kv_cache_recurrent::s_mask(int i) const {
const uint32_t cell_id = i + head;
//////////////////////////////////////////////
// TODO: this should not mutate the KV cache !
kv_cell & cell = const_cast<kv_cell &>(cells[cell_id]);
float res = (float) (cell.src >= 0);
// only clear once
if (cell.src < 0) {
cell.src = cell_id;
}
return res;
// shifting the pos is trivial for recurrent models
return true;
}
size_t llama_kv_cache_recurrent::total_size() const {
@ -1111,6 +1096,10 @@ uint32_t llama_kv_cache_recurrent_state::get_head() const {
return is_full ? 0 : kv->head;
}
int32_t llama_kv_cache_recurrent_state::get_rs_z() const {
return is_full ? 0 : kv->rs_z;
}
uint32_t llama_kv_cache_recurrent_state::get_size() const {
return kv->size;
}
@ -1124,9 +1113,5 @@ ggml_tensor * llama_kv_cache_recurrent_state::get_v_l(int32_t il) const {
}
int32_t llama_kv_cache_recurrent_state::s_copy(int i) const {
return kv->s_copy(i);
}
float llama_kv_cache_recurrent_state::s_mask(int i) const {
return kv->s_mask(i);
return kv->cells[i + kv->head].src0;
}

12
src/llama-kv-cache-recurrent.h
View File

@ -57,10 +57,6 @@ public:
bool get_can_shift() const override;
// TODO: temporary methods - they are not really const as they do const_cast<>, fix this
int32_t s_copy(int i) const;
float s_mask(int i) const;
// state write/load
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
@ -73,10 +69,14 @@ public:
// computed before each graph build
uint32_t n = 0;
// first zero-ed state
int32_t rs_z = -1;
// TODO: optimize for recurrent state needs
struct kv_cell {
llama_pos pos = -1;
int32_t src = -1; // used to copy states
int32_t src = -1; // used to know where states should be copied from
int32_t src0 = -1; // like src, but only used when setting the inputs (allowing to copy once)
int32_t tail = -1;
std::set<llama_seq_id> seq_id;
@ -157,13 +157,13 @@ public:
uint32_t get_n_kv() const;
uint32_t get_head() const;
int32_t get_rs_z() const;
uint32_t get_size() const;
ggml_tensor * get_k_l(int32_t il) const;
ggml_tensor * get_v_l(int32_t il) const;
int32_t s_copy(int i) const;
float s_mask(int i) const;
private:
const llama_memory_status status;

2
src/llama-kv-cache-unified.cpp
View File

@ -512,8 +512,6 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
head_cur = 0;
}
// otherwise, one cell per token.
if (n_tokens > cells.size()) {
LLAMA_LOG_ERROR("%s: n_tokens = %d > size = %u\n", __func__, n_tokens, cells.size());
return -1;

43
src/llama-model.cpp
View File

@ -8857,7 +8857,6 @@ struct llm_build_mamba : public llm_graph_context {
inpL = build_inp_embd(model.tok_embd);
ggml_tensor * state_copy = build_inp_s_copy();
ggml_tensor * state_mask = build_inp_s_mask();
for (int il = 0; il < n_layer; ++il) {
// norm
@ -8866,8 +8865,7 @@ struct llm_build_mamba : public llm_graph_context {
LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
//cur = build_mamba_layer(gf, cur, state_copy, state_mask, il);
cur = build_mamba_layer(gf, cur, state_copy, state_mask, ubatch, il);
cur = build_mamba_layer(gf, cur, state_copy, ubatch, il);
if (il == n_layer - 1) {
// skip computing output for unused tokens
@ -8908,7 +8906,6 @@ struct llm_build_mamba : public llm_graph_context {
ggml_cgraph * gf,
ggml_tensor * cur,
ggml_tensor * state_copy,
ggml_tensor * state_mask,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
@ -8935,12 +8932,12 @@ struct llm_build_mamba : public llm_graph_context {
ggml_tensor * ssm_states_all = kv_state->get_v_l(il);
// (ab)using the KV cache to store the states
ggml_tensor * conv = build_copy_mask_state(
gf, conv_states_all, state_copy, state_mask,
ggml_tensor * conv = build_recurrent_state(
gf, conv_states_all, state_copy,
hparams.n_embd_k_s(), n_seqs);
conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner, n_seqs);
ggml_tensor * ssm = build_copy_mask_state(
gf, ssm_states_all, state_copy, state_mask,
ggml_tensor * ssm = build_recurrent_state(
gf, ssm_states_all, state_copy,
hparams.n_embd_v_s(), n_seqs);
ssm = ggml_reshape_3d(ctx0, ssm, d_state, d_inner, n_seqs);
@ -11656,7 +11653,6 @@ struct llm_build_rwkv6_base : public llm_graph_context {
ggml_tensor * cur,
ggml_tensor * x_prev,
ggml_tensor * state_copy,
ggml_tensor * state_mask,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
@ -11780,8 +11776,8 @@ struct llm_build_rwkv6_base : public llm_graph_context {
k = ggml_sub(ctx0, k, ggml_mul(ctx0, k, w));
}
ggml_tensor * wkv_state = build_copy_mask_state(
gf, kv_state->get_v_l(il), state_copy, state_mask,
ggml_tensor * wkv_state = build_recurrent_state(
gf, kv_state->get_v_l(il), state_copy,
hparams.n_embd_v_s(), n_seqs);
ggml_tensor * wkv_output;
@ -11837,7 +11833,6 @@ struct llm_build_rwkv6 : public llm_build_rwkv6_base {
inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
ggml_tensor * state_copy = build_inp_s_copy();
ggml_tensor * state_mask = build_inp_s_mask();
const auto n_embd = hparams.n_embd;
const auto n_seq_tokens = ubatch.n_seq_tokens;
@ -11848,7 +11843,7 @@ struct llm_build_rwkv6 : public llm_build_rwkv6_base {
inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
ggml_tensor * token_shift = build_rwkv_token_shift_load(
gf, state_copy, state_mask, ubatch, il
gf, state_copy, ubatch, il
);
ggml_tensor * att_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
@ -11864,7 +11859,7 @@ struct llm_build_rwkv6 : public llm_build_rwkv6_base {
1
);
cur = build_rwkv6_time_mix(gf, att_norm, x_prev, state_copy, state_mask, ubatch, il);
cur = build_rwkv6_time_mix(gf, att_norm, x_prev, state_copy, ubatch, il);
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
cb(ffn_inp, "ffn_inp", il);
@ -11935,7 +11930,6 @@ struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base {
inpL = build_inp_embd(model.tok_embd);
ggml_tensor * state_copy = build_inp_s_copy();
ggml_tensor * state_mask = build_inp_s_mask();
const auto n_embd = hparams.n_embd;
const auto n_seq_tokens = ubatch.n_seq_tokens;
@ -11946,7 +11940,7 @@ struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base {
inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
ggml_tensor * token_shift = build_rwkv_token_shift_load(
gf, state_copy, state_mask, ubatch, il
gf, state_copy, ubatch, il
);
ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);
@ -11959,7 +11953,7 @@ struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base {
1
);
cur = build_rwkv6_time_mix(gf, att_norm, x_prev, state_copy, state_mask, ubatch, il);
cur = build_rwkv6_time_mix(gf, att_norm, x_prev, state_copy, ubatch, il);
token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm));
ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
@ -12051,7 +12045,6 @@ struct llm_build_rwkv7_base : public llm_graph_context {
ggml_tensor * cur,
ggml_tensor * x_prev,
ggml_tensor * state_copy,
ggml_tensor * state_mask,
ggml_tensor *& first_layer_value,
const llama_ubatch & ubatch,
int il) const {
@ -12134,8 +12127,8 @@ struct llm_build_rwkv7_base : public llm_graph_context {
v = ggml_reshape_3d(ctx0, v, head_size, head_count, n_tokens);
a = ggml_reshape_3d(ctx0, a, head_size, head_count, n_tokens);
ggml_tensor * wkv_state = build_copy_mask_state(
gf, kv_state->get_v_l(il), state_copy, state_mask,
ggml_tensor * wkv_state = build_recurrent_state(
gf, kv_state->get_v_l(il), state_copy,
hparams.n_embd_v_s(), n_seqs);
ggml_tensor * wkv_output = ggml_rwkv_wkv7(ctx0, r, w, k, v, ggml_neg(ctx0, kk), ggml_mul(ctx0, kk, a), wkv_state);
@ -12193,7 +12186,6 @@ struct llm_build_rwkv7 : public llm_build_rwkv7_base {
inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
ggml_tensor * state_copy = build_inp_s_copy();
ggml_tensor * state_mask = build_inp_s_mask();
const auto n_embd = hparams.n_embd;
const auto n_seq_tokens = ubatch.n_seq_tokens;
@ -12204,7 +12196,7 @@ struct llm_build_rwkv7 : public llm_build_rwkv7_base {
inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
ggml_tensor * token_shift = build_rwkv_token_shift_load(
gf, state_copy, state_mask, ubatch, il
gf, state_copy, ubatch, il
);
ggml_tensor * att_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
@ -12220,7 +12212,7 @@ struct llm_build_rwkv7 : public llm_build_rwkv7_base {
1
);
cur = build_rwkv7_time_mix(gf, att_norm, x_prev, state_copy, state_mask, v_first, ubatch, il);
cur = build_rwkv7_time_mix(gf, att_norm, x_prev, state_copy, v_first, ubatch, il);
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
cb(ffn_inp, "ffn_inp", il);
@ -12287,7 +12279,6 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base {
inpL = build_inp_embd(model.tok_embd);
ggml_tensor * state_copy = build_inp_s_copy();
ggml_tensor * state_mask = build_inp_s_mask();
const auto n_embd = hparams.n_embd;
const auto n_seq_tokens = ubatch.n_seq_tokens;
@ -12298,7 +12289,7 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base {
inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
ggml_tensor * token_shift = build_rwkv_token_shift_load(
gf, state_copy, state_mask, ubatch, il
gf, state_copy, ubatch, il
);
ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);
@ -12311,7 +12302,7 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base {
1
);
cur = build_rwkv7_time_mix(gf, att_norm, x_prev, state_copy, state_mask, v_first, ubatch, il);
cur = build_rwkv7_time_mix(gf, att_norm, x_prev, state_copy, v_first, ubatch, il);
token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm));
ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));