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whisper : improve printfs

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This commit is contained in:
Georgi Gerganov 2022-11-24 17:54:16 +02:00
parent 454b91de16
commit 128aaadb93
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GPG Key ID: 449E073F9DC10735
1 changed files with 14 additions and 13 deletions
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whisper.cpp
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@ -518,15 +518,6 @@ static bool whisper_model_load(const std::string & fname, whisper_context & wctx
wctx.buf_memory.resize(MEM_REQ_MEMORY.at(model.type));
wctx.buf_compute.resize(std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type)));
wctx.buf_compute_layer.resize(std::max(MEM_REQ_ENCODE_LAYER.at(model.type), MEM_REQ_DECODE_LAYER.at(model.type)));
// this is the total memory required to run the inference
const size_t mem_required =
wctx.buf_model->size() +
wctx.buf_memory.size() +
wctx.buf_compute.size() +
wctx.buf_compute_layer.size();
fprintf(stderr, "%s: mem_required = %.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);
}
// load mel filters
@ -599,11 +590,21 @@ static bool whisper_model_load(const std::string & fname, whisper_context & wctx
}
}
{
// this is the total memory required to run the inference
const size_t mem_required =
wctx.buf_model->size() +
wctx.buf_memory.size() +
wctx.buf_compute.size() +
wctx.buf_compute_layer.size();
fprintf(stderr, "%s: mem_required = %7.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);
}
// for the big tensors, we have the option to store the data in 16-bit floats
// in order to save memory and also to speed up the computation
const ggml_type wtype = model.hparams.f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
size_t ctx_size = 0;
size_t ctx_mem_size = 0;
@ -722,7 +723,7 @@ static bool whisper_model_load(const std::string & fname, whisper_context & wctx
ctx_size += (15 + 15*n_audio_layer + 24*n_text_layer)*256; // object overhead
fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
fprintf(stderr, "%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
}
// create the ggml context
@ -983,7 +984,7 @@ static bool whisper_model_load(const std::string & fname, whisper_context & wctx
ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v) +
ggml_nbytes(model.memory_cross_k) + ggml_nbytes(model.memory_cross_v);
fprintf(stderr, "%s: memory size = %8.2f MB\n", __func__, memory_size/1024.0/1024.0);
fprintf(stderr, "%s: memory size = %7.2f MB\n", __func__, memory_size/1024.0/1024.0);
}
// load weights
@ -1047,7 +1048,7 @@ static bool whisper_model_load(const std::string & fname, whisper_context & wctx
model.n_loaded++;
}
fprintf(stderr, "%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
fprintf(stderr, "%s: model size = %7.2f MB\n", __func__, total_size/1024.0/1024.0);
if (model.n_loaded == 0) {
fprintf(stderr, "%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);