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https://github.com/ggerganov/whisper.cpp.git
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whisper : wip sched (not working yet)
This commit is contained in:
Georgi Gerganov
parent
005b8ccbf0
commit
bf4110dbcf
91
whisper.cpp
91
whisper.cpp
@ -715,6 +715,11 @@ struct whisper_state {
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int32_t n_fail_p = 0; // number of logprob threshold failures
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int32_t n_fail_h = 0; // number of entropy threshold failures
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// TODO: free this
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struct ggml_context * ctx;
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ggml_backend_buffer_t buffer;
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// cross-attention KV cache for the decoders
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// shared between all decoders
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whisper_kv_cache kv_cross;
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@ -725,9 +730,6 @@ struct whisper_state {
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// buffer for swapping KV caches between decoders during beam-search
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std::vector<kv_buf> kv_swap_bufs;
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// reusable buffer for `struct ggml_graph_plan.work_data`
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std::vector<uint8_t> work_buffer;
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// backend schedulers
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ggml_backend_sched_t sched_conv;
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ggml_backend_sched_t sched_encode;
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@ -735,8 +737,10 @@ struct whisper_state {
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ggml_backend_sched_t sched_decode;
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// result of the encoder
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struct ggml_tensor * embd_conv = nullptr;
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struct ggml_tensor * embd_enc = nullptr;
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struct ggml_tensor * inp_mel = nullptr;
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struct ggml_tensor * inp_kq_scale = nullptr;
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struct ggml_tensor * out_conv = nullptr;
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struct ggml_tensor * out_enc = nullptr;
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// decode output (2-dimensional array: [n_tokens][n_vocab])
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std::vector<float> logits;
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@ -1546,7 +1550,8 @@ static struct ggml_cgraph * whisper_build_graph_conv(
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ggml_cgraph * gf = ggml_new_graph(ctx0);
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struct ggml_tensor * mel = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 2*n_ctx, n_mels);
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//struct ggml_tensor * mel = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 2*n_ctx, n_mels);
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struct ggml_tensor * mel = wstate.inp_mel;
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//ggml_allocr_alloc(alloc, mel);
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//assert(mel->type == GGML_TYPE_F32);
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@ -1588,9 +1593,8 @@ static struct ggml_cgraph * whisper_build_graph_conv(
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cur);
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cur = ggml_gelu(ctx0, cur);
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ggml_set_name(cur, "out_conv");
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}
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wstate.embd_conv = cur;
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} else {
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#ifdef WHISPER_USE_COREML
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cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_ctx);
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@ -1609,7 +1613,7 @@ static struct ggml_cgraph * whisper_build_graph_conv(
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}
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#endif
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wstate.embd_enc = cur;
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ggml_set_name(cur, "out_enc");
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}
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ggml_build_forward_expand(gf, cur);
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@ -1650,7 +1654,8 @@ static struct ggml_cgraph * whisper_build_graph_encoder(
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// ggml_set_f32(KQscale, 1.0f/sqrt(float(n_state)/n_head));
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//}
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struct ggml_tensor * cur = ggml_view_tensor(ctx0, wstate.embd_conv);
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// TODO: use ggml_backend_tensor_copy to copy the data from the previous graph
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struct ggml_tensor * cur = wstate.out_conv;
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// ===================================================================
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// NOTE: experimenting with partial evaluation of the encoder (ignore)
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@ -1670,7 +1675,6 @@ static struct ggml_cgraph * whisper_build_graph_encoder(
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const size_t e_pe_offset = model.e_pe->ne[0]*ggml_element_size(model.e_pe)*n_ctx*iter;
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struct ggml_tensor * e_pe = ggml_view_2d(ctx0, model.e_pe, model.e_pe->ne[0], n_ctx, e_pe_stride, e_pe_offset);
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cur = ggml_add(ctx0, e_pe, ggml_cont(ctx0, ggml_transpose(ctx0, cur)));
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// ===================================================================
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@ -1851,7 +1855,7 @@ static struct ggml_cgraph * whisper_build_graph_encoder(
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ggml_build_forward_expand(gf, cur);
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wstate.embd_enc = cur;
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//wstate.out_enc = cur;
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//ggml_graph_print(gf);
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@ -1893,7 +1897,8 @@ static struct ggml_cgraph * whisper_build_graph_cross(
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ggml_cgraph * gf = ggml_new_graph(ctx0);
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struct ggml_tensor * cur = ggml_view_tensor(ctx0, wstate.embd_enc);
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// TODO: use ggml_backend_tensor_copy to copy the data from the previous graph
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struct ggml_tensor * cur = wstate.out_enc;
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struct ggml_tensor * Kscale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
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//ggml_allocr_alloc(alloc, Kscale);
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@ -2862,15 +2867,73 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
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state->decoders[0].logits.reserve (ctx->vocab.n_vocab);
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state->decoders[0].logprobs.reserve(ctx->vocab.n_vocab);
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// per-state context for input and output tensors
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{
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const size_t n_tensors = 100; // TODO: fix
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struct ggml_init_params params = {
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/*.mem_size =*/ ggml_tensor_overhead() * n_tensors,
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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state->ctx = ggml_init(params);
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if (!state->ctx) {
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WHISPER_LOG_ERROR("%s: ggml_init() failed\n", __func__);
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delete state;
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return nullptr;
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}
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}
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auto & backends = ctx->backends;
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// allocate input tensors
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{
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const auto & hparams = ctx->model.hparams;
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const int n_ctx = state->exp_n_audio_ctx > 0 ? state->exp_n_audio_ctx : hparams.n_audio_ctx;
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const int n_mels = hparams.n_mels;
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const int n_state = hparams.n_audio_state;
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auto & ctx = state->ctx;
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state->inp_mel = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 2*n_ctx, n_mels);
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state->inp_kq_scale = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
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state->out_conv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ctx, n_state);
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state->out_enc = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_state, n_ctx);
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const size_t size_inp =
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(ggml_tensor_overhead() + ggml_nbytes(state->inp_mel)) +
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(ggml_tensor_overhead() + ggml_nbytes(state->inp_kq_scale)) +
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(ggml_tensor_overhead() + ggml_nbytes(state->out_conv)) +
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(ggml_tensor_overhead() + ggml_nbytes(state->out_enc));
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ggml_backend_t backend_input = backends[0];
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state->buffer = ggml_backend_alloc_buffer(backend_input, size_inp);
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printf("%s: backend_in = %s (%zu bytes)\n", __func__, ggml_backend_name(backend_input), size_inp);
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ggml_allocr * alloc = ggml_allocr_new_from_buffer(state->buffer);
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ggml_allocr_alloc(alloc, state->inp_mel);
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ggml_allocr_alloc(alloc, state->inp_kq_scale);
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ggml_allocr_alloc(alloc, state->out_conv);
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ggml_allocr_alloc(alloc, state->out_enc);
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ggml_allocr_free(alloc);
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// initialize KQ_scale
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//float s = 1.0f/sqrtf(float(hparams.n_embd)/hparams.n_head);
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//ggml_backend_tensor_set(model.KQ_scale, &s, 0, sizeof(s));
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}
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// conv allocator
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{
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auto & sched = state->sched_conv;
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sched = ggml_backend_sched_new(backends.data(), backends.size());
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ggml_backend_sched_init_measure(sched, whisper_build_graph_conv(*ctx, *state, 0));
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struct ggml_cgraph * gf = whisper_build_graph_conv(*ctx, *state, 0);
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ggml_backend_sched_init_measure(sched, gf);
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WHISPER_LOG_INFO("%s: compute buffer (conv) = %7.2f MB\n", __func__, whisper_sched_size(sched, backends) / 1024.0 / 1024.0);
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}
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