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whisper : fix excessive memory usage (#2443)

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* whisper : fix KV cache allocation

* whisper : reduce memory overhead from unused input tensors
This commit is contained in:
Georgi Gerganov 2024-10-05 12:36:40 +03:00 committed by GitHub
parent 2944cb72d9
commit f62a546e03
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1 changed files with 39 additions and 32 deletions
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src/whisper.cpp
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@ -163,7 +163,6 @@ static void whisper_log_callback_default(ggml_log_level level, const char * text
} \
} while (0)
//#define WHISPER_USE_FLASH_FF
#define WHISPER_MAX_DECODERS 8
#define WHISPER_MAX_NODES 4096
@ -817,6 +816,9 @@ struct whisper_state {
int32_t n_fail_p = 0; // number of logprob threshold failures
int32_t n_fail_h = 0; // number of entropy threshold failures
// number of decoders for which we have constructed the KV cache
int32_t kv_self_n_dec = 0;
// unified self-attention KV cache for all decoders
whisper_kv_cache kv_self;
@ -2096,9 +2098,7 @@ static struct ggml_cgraph * whisper_build_graph_encoder(
struct ggml_tensor * Q =
ggml_permute(ctx0,
ggml_cpy(ctx0,
Qcur,
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_state_head, n_head, n_ctx)),
ggml_reshape_3d(ctx0, Qcur, n_state_head, n_head, n_ctx),
0, 2, 1, 3);
if (wctx.params.flash_attn) {
@ -2125,9 +2125,9 @@ static struct ggml_cgraph * whisper_build_graph_encoder(
} else {
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_cpy(ctx0,
Kcur,
ggml_new_tensor_3d(ctx0, wctx.itype, n_state_head, n_head, n_ctx)),
ggml_cast(ctx0,
ggml_reshape_3d(ctx0, Kcur, n_state_head, n_head, n_ctx),
wctx.itype),
0, 2, 1, 3);
// K * Q
@ -2136,22 +2136,19 @@ static struct ggml_cgraph * whisper_build_graph_encoder(
struct ggml_tensor * KQ_soft_max = ggml_soft_max_ext(ctx0, KQ, nullptr, KQscale, 0.0f);
struct ggml_tensor * V =
ggml_cpy(ctx0,
ggml_cast(ctx0,
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
Vcur,
n_state_head, n_head, n_ctx),
1, 2, 0, 3),
ggml_new_tensor_3d(ctx0, wctx.itype, n_ctx, n_state_head, n_head)
);
wctx.itype);
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_ctx));
cur = ggml_cont_2d(ctx0, KQV_merged, n_state, n_ctx);
}
}
@ -2181,11 +2178,6 @@ static struct ggml_cgraph * whisper_build_graph_encoder(
layer.mlp_ln_b);
}
#ifdef WHISPER_USE_FLASH_FF
cur = ggml_flash_ff(ctx0,
ggml_cpy(ctx0, cur, ggml_new_tensor_2d(ctx0, wstate.itype, n_state, n_ctx)),
layer.mlp_0_w, layer.mlp_0_b, layer.mlp_1_w, layer.mlp_1_b);
#else
// fully connected
cur = ggml_mul_mat(ctx0,
layer.mlp_0_w,
@ -2202,7 +2194,6 @@ static struct ggml_cgraph * whisper_build_graph_encoder(
cur);
cur = ggml_add(ctx0, cur, layer.mlp_1_b);
#endif
}
inpL = ggml_add(ctx0, cur, inpFF);
@ -2578,9 +2569,7 @@ static struct ggml_cgraph * whisper_build_graph_decoder(
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_tokens));
cur = ggml_cont_2d(ctx0, KQV_merged, n_state, n_tokens);
}
}
@ -2687,9 +2676,7 @@ static struct ggml_cgraph * whisper_build_graph_decoder(
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_tokens));
cur = ggml_cont_2d(ctx0, KQV_merged, n_state, n_tokens);
}
}
@ -3403,14 +3390,13 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
whisper_mel_init(state->mel, state->backends[0], n_len, n_len, n_mel);
}
// at this point, we don't know yet how many decoders will be used, so we overallocate 3x ctx
// in theory, there can be a case where this is not enough, but in practice it should always be enough
const int factor = 3;
// at this point, we don't know yet how many decoders will be used
// later during decoding, if more decoders are used, we will recreate the KV cache respectively
state->kv_self_n_dec = 1;
if (!whisper_kv_cache_init(state->kv_self, state->backends[0], ctx->itype,
ctx->model.hparams.n_text_state,
ctx->model.hparams.n_text_layer,
GGML_PAD(ctx->model.hparams.n_text_ctx, 256)*factor)) {
GGML_PAD(ctx->model.hparams.n_text_ctx, 256))) {
WHISPER_LOG_ERROR("%s: whisper_kv_cache_init() failed for self-attention cache\n", __func__);
whisper_free_state(state);
return nullptr;
@ -5775,13 +5761,34 @@ int whisper_full_with_state(
}
WHISPER_LOG_DEBUG("\n\n");
// recreate the KV cache if the number of decoders has changed
if (state->kv_self_n_dec < n_decoders_cur) {
WHISPER_LOG_DEBUG("%s: recreating KV cache: n_decoders_cur = %d\n", __func__, n_decoders_cur);
whisper_kv_cache_free(state->kv_self);
// overallocate to workaround KV cache fragmentation issues
const int factor = n_decoders_cur > 1 ? n_decoders_cur + 2 : 1;
if (!whisper_kv_cache_init(state->kv_self, state->backends[0], ctx->itype,
ctx->model.hparams.n_text_state,
ctx->model.hparams.n_text_layer,
GGML_PAD(ctx->model.hparams.n_text_ctx, 256)*factor)) {
WHISPER_LOG_ERROR("%s: whisper_kv_cache_init() failed for self-attention cache\n", __func__);
whisper_free_state(state);
return -7;
}
state->kv_self_n_dec = n_decoders_cur;
}
whisper_kv_cache_clear(state->kv_self);
whisper_batch_prep_legacy(state->batch, prompt.data(), prompt.size(), 0, 0);
if (!whisper_decode_internal(*ctx, *state, state->batch, params.n_threads, false, params.abort_callback, params.abort_callback_user_data)) {
WHISPER_LOG_ERROR("%s: failed to decode\n", __func__);
return -7;
return -8;
}
{
@ -6081,7 +6088,7 @@ int whisper_full_with_state(
if (!whisper_decode_internal(*ctx, *state, state->batch, params.n_threads, false, params.abort_callback, params.abort_callback_user_data)) {
WHISPER_LOG_ERROR("%s: failed to decode\n", __func__);
return -8;
return -9;
}
const int64_t t_start_sample_us = ggml_time_us();