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model : fix parallel processing for lfm2 #14705

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Jul 17, 2025
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model : fix parallel processing for lfm2 #14705

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56 changes: 39 additions & 17 deletions src/llama-model.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -16430,46 +16430,68 @@ struct llm_build_lfm2 : public llm_graph_context {
ggml_tensor * cur,
llm_graph_input_rs * inp_recr,
int il) {
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
const uint32_t kv_head = mctx_cur->get_head();
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
const int64_t n_seqs = ubatch.n_seqs;
GGML_ASSERT(n_seqs != 0);
GGML_ASSERT(ubatch.equal_seqs);
GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);

GGML_ASSERT(hparams.n_shortconv_l_cache > 1);
const uint32_t d_conv = hparams.n_shortconv_l_cache - 1;

// {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);

auto * bcx = build_lora_mm(model.layers[il].shortconv.in_proj, cur);
cb(bcx, "model.layers.{}.conv.in_proj", il);

constexpr auto n_chunks = 3;
GGML_ASSERT(bcx->ne[0] % n_chunks == 0);
auto const chunk_size = bcx->ne[0] / n_chunks;
auto * b = ggml_view_2d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->nb[1], 0 * chunk_size * ggml_element_size(bcx));
auto * c = ggml_view_2d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->nb[1], 1 * chunk_size * ggml_element_size(bcx));
auto * x = ggml_view_2d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->nb[1], 2 * chunk_size * ggml_element_size(bcx));
auto * b = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2], 0*chunk_size*ggml_element_size(bcx));
auto * c = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2], 1*chunk_size*ggml_element_size(bcx));
auto * x = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2], 2*chunk_size*ggml_element_size(bcx));

auto * bx = ggml_transpose(ctx0, ggml_mul(ctx0, b, x));

// read conv state directly, with build_rs generation is slower
ggml_tensor * conv_state = mctx_cur->get_r_l(il);
const int64_t n_seqs = ubatch.n_seqs;
ggml_tensor * conv = build_rs(inp_recr, gf, conv_state, hparams.n_embd_r(), n_seqs);
conv = ggml_reshape_3d(ctx0, conv_state, hparams.n_shortconv_l_cache - 1, hparams.n_embd, n_seqs);
// read conv state
auto * conv_state = mctx_cur->get_r_l(il);
auto * conv_rs = build_rs(inp_recr, gf, conv_state, hparams.n_embd_r(), n_seqs);
auto * conv = ggml_reshape_3d(ctx0, conv_rs, d_conv, hparams.n_embd, n_seqs);

bx = ggml_concat(ctx0, conv, bx, 0);
GGML_ASSERT(bx->ne[0] > conv->ne[0]);

auto * new_conv = ggml_view_2d(ctx0, bx, conv->ne[0], bx->ne[1], bx->nb[1], (bx->ne[0] - conv->ne[0]) * ggml_element_size(bx));
// last d_conv columns is a new conv state
auto * new_conv = ggml_view_3d(ctx0, bx, conv->ne[0], bx->ne[1], bx->ne[2], bx->nb[1], bx->nb[2], (bx->ne[0] - conv->ne[0])*ggml_element_size(bx));
GGML_ASSERT(ggml_are_same_shape(conv, new_conv));

// write conv state
ggml_build_forward_expand(gf, ggml_cpy(ctx0, new_conv, conv_state));
// write new conv conv state
ggml_build_forward_expand(
gf,
ggml_cpy(
ctx0,
new_conv,
ggml_view_1d(
ctx0,
conv_state,
ggml_nelements(new_conv),
kv_head*d_conv*n_embd*ggml_element_size(new_conv)
)
)
);

auto * conv_kernel = model.layers[il].shortconv.conv;
GGML_ASSERT(hparams.n_shortconv_l_cache > 0);

// construct ssm_conv op
ggml_tensor * conv_out = ggml_ssm_conv(ctx0, bx, conv_kernel);
auto * conv_out = ggml_ssm_conv(ctx0, bx, conv_kernel);
cb(conv_out, "model.layers.{}.conv.conv", il);

auto * y = ggml_mul(ctx0, c, conv_out);

y = build_lora_mm(model.layers[il].shortconv.out_proj, y);
cb(y, "model.layers.{}.conv.out_proj", il);
// {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
y = ggml_reshape_2d(ctx0, y, y->ne[0], n_seq_tokens * n_seqs);

return y;
}
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