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https://github.com/ggerganov/whisper.cpp.git
synced 2024-12-24 23:59:02 +01:00
minor : small code cleanups (#302)
* Small code cleanups - fix indentation - remove extra semicolons - remove extra break after returns in case statements - remove unnecessary call to .data() on string - use empty() instead of checking size() - no need to check for nullptr before free - remove unnecessary initialization of string to "" * minor : switch case always break Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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16
ggml.c
16
ggml.c
@ -1134,10 +1134,10 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float v) {
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y2 = _mm256_loadu_ps(y + i + 16);
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y3 = _mm256_loadu_ps(y + i + 24);
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y0 = _mm256_mul_ps(y0, v4);
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y1 = _mm256_mul_ps(y1, v4);
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y2 = _mm256_mul_ps(y2, v4);
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y3 = _mm256_mul_ps(y3, v4);
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y0 = _mm256_mul_ps(y0, v4);
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y1 = _mm256_mul_ps(y1, v4);
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y2 = _mm256_mul_ps(y2, v4);
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y3 = _mm256_mul_ps(y3, v4);
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_mm256_storeu_ps(y + i + 0, y0);
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_mm256_storeu_ps(y + i + 8, y1);
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@ -1475,7 +1475,7 @@ bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
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return
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tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] &&
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tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
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tensor->nb[3] == tensor->nb[2]*tensor->ne[2];;
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tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
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}
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bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
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@ -6624,7 +6624,7 @@ void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tenso
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{
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GGML_ASSERT(false);
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} break;
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};
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}
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}
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////////////////////////////////////////////////////////////////////////////////
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@ -6870,7 +6870,7 @@ void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tenso
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{
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GGML_ASSERT(false);
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} break;
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};
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}
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}
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void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * node) {
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@ -7339,7 +7339,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
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{
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assert(false);
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} break;
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};
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}
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}
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if (cgraph->work != NULL && work_size > cgraph->work_size) {
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14
whisper.cpp
14
whisper.cpp
@ -1024,7 +1024,7 @@ static bool whisper_model_load(const std::string & fname, whisper_context & wctx
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fin.read( &tmp[0], tmp.size() ); // read to buffer
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name.assign(&tmp[0], tmp.size());
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if (model.tensors.find(name.data()) == model.tensors.end()) {
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if (model.tensors.find(name) == model.tensors.end()) {
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fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
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return false;
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}
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@ -2187,7 +2187,7 @@ static std::vector<whisper_vocab::id> tokenize(const whisper_vocab & vocab, cons
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// find the longest tokens that form the words:
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std::vector<whisper_vocab::id> tokens;
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for (const auto & word : words) {
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if (word.size() == 0) continue;
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if (word.empty()) continue;
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int i = 0;
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int n = word.size();
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@ -2868,7 +2868,7 @@ int whisper_full(
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prompt.clear();
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// if we have already generated some text, use it as a prompt to condition the next generation
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if (prompt_past.size() > 0) {
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if (!prompt_past.empty()) {
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int n_take = std::min(std::min(params.n_max_text_ctx, whisper_n_text_ctx(ctx)/2), int(prompt_past.size()));
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prompt = { whisper_token_prev(ctx) };
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@ -2979,7 +2979,7 @@ int whisper_full(
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if (failed) {
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// when we fail to sample timestamp token, retry by clearing the past prompt
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// if it fails again, then we advance the window by 1 second
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if (prompt_past.size() > 0) {
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if (!prompt_past.empty()) {
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prompt_past.clear();
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} else {
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fprintf(stderr, "\n%s: failed to generate timestamp token - skipping one second\n\n", __func__);
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@ -2996,11 +2996,11 @@ int whisper_full(
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}
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// store the text from this iteration
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if (tokens_cur.size() > 0) {
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if (!tokens_cur.empty()) {
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int i0 = 0;
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auto t0 = seek + 2*(tokens_cur.front().tid - whisper_token_beg(ctx));
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std::string text = "";
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std::string text;
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for (int i = 0; i < (int) tokens_cur.size(); i++) {
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//printf("%s: %18s %6.3f %18s %6.3f\n", __func__,
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@ -3207,7 +3207,7 @@ int whisper_full_parallel(
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results_i[j].t1 += 100*((i + 1)*n_samples_per_processor)/WHISPER_SAMPLE_RATE + offset_t;
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// make sure that segments are not overlapping
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if (ctx->result_all.size() > 0) {
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if (!ctx->result_all.empty()) {
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results_i[j].t0 = std::max(results_i[j].t0, ctx->result_all.back().t1);
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}
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