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merge into: Migrations:master
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Migrations:experiment/model-compression
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pull from: Migrations:experiment/model-compression
Branches Tags
Migrations:timing
Migrations:master
Migrations:musl
Migrations:coreml
Migrations:4-bit
Migrations:guided
Migrations:diarization
Migrations:chess
Migrations:arghh
Migrations:fa-decoder
Migrations:threads
Migrations:nvblas
Migrations:macros-cvt-fp16
Migrations:stream
Migrations:metal
Migrations:avx512
Migrations:word-ts-2
Migrations:experiment/model-compression

2 Commits
master ... experiment

Author SHA1 Message Date
Georgi Gerganov 4597c9c19b
wip : try to compress just mlp
2 years ago
Georgi Gerganov 4a4a754220
wip : experimenting
2 years ago
1 changed files with 295 additions and 2 deletions
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297
whisper.cpp
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@ -1003,7 +1003,7 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
//printf("%24s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
total_size += ggml_nbytes(tensor);
n_loaded++;
}
@ -1020,6 +1020,299 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
fin.close();
// dump minified model
{
std::ofstream fout(fname + ".min", std::ios::binary);
// magic
{
uint32_t magic = 0x67676d6c;
fout.write(reinterpret_cast<char *>(&magic), sizeof(magic));
}
// hparams
{
auto hparams = model.hparams;
//hparams.n_audio_state /= 2;
//hparams.n_text_state /= 2;
fout.write(reinterpret_cast<char *>(&hparams.n_vocab), sizeof(hparams.n_vocab));
fout.write(reinterpret_cast<char *>(&hparams.n_audio_ctx), sizeof(hparams.n_audio_ctx));
fout.write(reinterpret_cast<char *>(&hparams.n_audio_state), sizeof(hparams.n_audio_state));
fout.write(reinterpret_cast<char *>(&hparams.n_audio_head), sizeof(hparams.n_audio_head));
fout.write(reinterpret_cast<char *>(&hparams.n_audio_layer), sizeof(hparams.n_audio_layer));
fout.write(reinterpret_cast<char *>(&hparams.n_text_ctx), sizeof(hparams.n_text_ctx));
fout.write(reinterpret_cast<char *>(&hparams.n_text_state), sizeof(hparams.n_text_state));
fout.write(reinterpret_cast<char *>(&hparams.n_text_head), sizeof(hparams.n_text_head));
fout.write(reinterpret_cast<char *>(&hparams.n_text_layer), sizeof(hparams.n_text_layer));
fout.write(reinterpret_cast<char *>(&hparams.n_mels), sizeof(hparams.n_mels));
fout.write(reinterpret_cast<char *>(&hparams.f16), sizeof(hparams.f16));
}
// mel filters
{
auto & filters = wctx.model.filters;
fout.write(reinterpret_cast<char *>(&filters.n_mel), sizeof(filters.n_mel));
fout.write(reinterpret_cast<char *>(&filters.n_fft), sizeof(filters.n_fft));
fout.write(reinterpret_cast<char *>(filters.data.data()), filters.data.size()*sizeof(float));
}
// vocab
{
fout.write(reinterpret_cast<char *>(&vocab.n_vocab), sizeof(vocab.n_vocab));
for (int i = 0; i < vocab.n_vocab; ++i) {
const auto & token = vocab.id_to_token[i];
const uint32_t len = token.size();
fout.write(reinterpret_cast<char *>(const_cast<uint32_t *>(&len)), sizeof(len));
fout.write(reinterpret_cast<char *>(const_cast<char *>(token.data())), len);
}
}
// weights
{
for (const auto & kv : model.tensors) {
const auto & name = kv.first;
const auto & tensor = kv.second;
const int32_t n_dims = tensor->n_dims;
const int32_t length = name.size();
const int32_t ftype = tensor->type == GGML_TYPE_F32 ? 0 : 1;
fout.write(reinterpret_cast<char *>(const_cast<int32_t *>(&n_dims)), sizeof(n_dims));
fout.write(reinterpret_cast<char *>(const_cast<int32_t *>(&length)), sizeof(length));
fout.write(reinterpret_cast<char *>(const_cast<int32_t *>(&ftype)), sizeof(ftype));
printf("name = %42s, n_dims = %d, ne0 = %d, ne1 = %d, ne2 = %d, ftype = %d\n", name.data(), n_dims, tensor->ne[0], tensor->ne[1], tensor->ne[2], ftype);
//for (int i = 0; i < n_dims; ++i) {
// const int32_t ne = (tensor->ne[i]%model.hparams.n_audio_state == 0) ? tensor->ne[i]/2 : tensor->ne[i];
// fout.write(reinterpret_cast<char *>(const_cast<int32_t *>(&ne)), sizeof(ne));
//}
//if (tensor->type == GGML_TYPE_F16) {
// if (name == "decoder.token_embedding.weight") {
// const int ne0 = tensor->ne[0];
// const int ne1 = tensor->ne[1];
// std::vector<ggml_fp16_t> tmp((ne0/2)*ne1);
// const ggml_fp16_t * src = (const ggml_fp16_t *) tensor->data;
// for (int i1 = 0; i1 < ne1; ++i1) {
// for (int i0 = 0; i0 < ne0/2; ++i0) {
// const float v00 = ggml_fp16_to_fp32(src[i0*2+0 + i1*ne0]);
// const float v01 = ggml_fp16_to_fp32(src[i0*2+1 + i1*ne0]);
// tmp[i1*ne0/2 + i0] = ggml_fp32_to_fp16(0.5f*(v00 + v01));
// }
// }
// fout.write(reinterpret_cast<char *>(tmp.data()), tmp.size()*sizeof(ggml_fp16_t));
// } else if (tensor->n_dims == 2) {
// const int ne0 = tensor->ne[0];
// const int ne1 = tensor->ne[1];
// std::vector<ggml_fp16_t> tmp((ne0/2)*(ne1/2));
// const ggml_fp16_t * src = (const ggml_fp16_t *) tensor->data;
// for (int i1 = 0; i1 < ne1/2; ++i1) {
// for (int i0 = 0; i0 < ne0/2; ++i0) {
// const float v00 = ggml_fp16_to_fp32(src[2*i0 + 2*i1*ne0]);
// const float v01 = ggml_fp16_to_fp32(src[2*i0 + 1 + 2*i1*ne0]);
// const float v10 = ggml_fp16_to_fp32(src[2*i0 + (2*i1+1)*ne0]);
// const float v11 = ggml_fp16_to_fp32(src[2*i0 + 1 + (2*i1+1)*ne0]);
// tmp[i1*(ne0/2) + i0] = ggml_fp32_to_fp16(0.25*(v00 + v01 + v10 + v11));
// }
// }
// fout.write(reinterpret_cast<char *>(tmp.data()), tmp.size()*sizeof(ggml_fp16_t));
// } else if (tensor->n_dims == 3) {
// const int ne0 = tensor->ne[0];
// const int ne1 = tensor->ne[1];
// const int ne2 = tensor->ne[2];
// if (ne1 == 80) {
// std::vector<ggml_fp16_t> tmp(ne0*ne1*(ne2/2));
// const ggml_fp16_t * src = (const ggml_fp16_t *) tensor->data;
// for (int i2 = 0; i2 < ne2/2; ++i2) {
// for (int i1 = 0; i1 < ne1; ++i1) {
// for (int i0 = 0; i0 < ne0; ++i0) {
// const float v0 = ggml_fp16_to_fp32(src[i0 + i1*ne0 + 2*i2*ne0*ne1]);
// const float v1 = ggml_fp16_to_fp32(src[i0 + i1*ne0 + (2*i2+1)*ne0*ne1]);
// tmp[i0 + i1*ne0 + i2*ne0*ne1] = ggml_fp32_to_fp16(0.5*(v0 + v1));
// }
// }
// }
// fout.write(reinterpret_cast<char *>(tmp.data()), tmp.size()*sizeof(ggml_fp16_t));
// } else {
// std::vector<ggml_fp16_t> tmp(ne0*(ne1/2)*(ne2/2));
// const ggml_fp16_t * src = (const ggml_fp16_t *) tensor->data;
// for (int i2 = 0; i2 < ne2/2; ++i2) {
// for (int i1 = 0; i1 < ne1/2; ++i1) {
// for (int i0 = 0; i0 < ne0; ++i0) {
// const float v00 = ggml_fp16_to_fp32(src[i0 + 2*i1*ne0 + 2*i2*ne0*ne1]);
// const float v01 = ggml_fp16_to_fp32(src[i0 + (2*i1+1)*ne0 + 2*i2*ne0*ne1]);
// const float v10 = ggml_fp16_to_fp32(src[i0 + 2*i1*ne0 + (2*i2+1)*ne0*ne1]);
// const float v11 = ggml_fp16_to_fp32(src[i0 + (2*i1+1)*ne0 + (2*i2+1)*ne0*ne1]);
// tmp[i0 + i1*ne0 + i2*ne0*(ne1/2)] = ggml_fp32_to_fp16(0.25*(v00 + v01 + v10 + v11));
// }
// }
// }
// fout.write(reinterpret_cast<char *>(tmp.data()), tmp.size()*sizeof(ggml_fp16_t));
// }
// } else {
// assert(false);
// }
//} else {
// if (tensor->n_dims == 1) {
// const int ne0 = tensor->ne[0];
// std::vector<float> tmp(ne0/2);
// const float * src = (const float *) tensor->data;
// for (int i0 = 0; i0 < ne0/2; ++i0) {
// tmp[i0] = 0.5*(src[2*i0] + src[2*i0+1]);
// }
// fout.write(reinterpret_cast<char *>(tmp.data()), tmp.size()*sizeof(float));
// } else if (tensor->n_dims == 2) {
// const int ne0 = tensor->ne[0];
// const int ne1 = tensor->ne[1];
// if (name == "encoder.positional_embedding" || name == "decoder.positional_embedding") {
// std::vector<float> tmp((ne0/2)*ne1);
// const float * src = (const float *) tensor->data;
// for (int i1 = 0; i1 < ne1; ++i1) {
// for (int i0 = 0; i0 < ne0/2; ++i0) {
// tmp[i0 + i1*(ne0/2)] = 0.5*(src[2*i0 + i1*ne0] + src[2*i0 + 1 + i1*ne0]);
// }
// }
// fout.write(reinterpret_cast<char *>(tmp.data()), tmp.size()*sizeof(float));
// } else if (name == "encoder.conv1.bias" || name == "encoder.conv2.bias") {
// std::vector<float> tmp(ne0*(ne1/2));
// const float * src = (const float *) tensor->data;
// for (int i1 = 0; i1 < ne1/2; ++i1) {
// for (int i0 = 0; i0 < ne0; ++i0) {
// tmp[i0 + i1*ne0] = 0.5*(src[i0 + 2*i1*ne0] + src[i0 + (2*i1+1)*ne0]);
// }
// }
// fout.write(reinterpret_cast<char *>(tmp.data()), tmp.size()*sizeof(float));
// } else {
// std::vector<float> tmp((ne0/2)*(ne1/2));
// const float * src = (const float *) tensor->data;
// for (int i1 = 0; i1 < ne1/2; ++i1) {
// for (int i0 = 0; i0 < ne0/2; ++i0) {
// const float v00 = src[2*i0 + 2*i1*ne0];
// const float v01 = src[2*i0 + 1 + 2*i1*ne0];
// const float v10 = src[2*i0 + (2*i1+1)*ne0];
// const float v11 = src[2*i0 + 1 + (2*i1+1)*ne0];
// tmp[i1*(ne0/2) + i0] = 0.25*(v00 + v01 + v10 + v11);
// }
// }
// fout.write(reinterpret_cast<char *>(tmp.data()), tmp.size()*sizeof(float));
// }
// } else {
// assert(false);
// }
//}
// if name ends with ".mlp.0.weight"
if (name.substr(name.size() - 13) == ".mlp.0.weight") {
const int32_t ne0 = tensor->ne[0];
const int32_t ne1 = tensor->ne[1]/2;
fout.write(reinterpret_cast<const char *>(&ne0), sizeof(int32_t));
fout.write(reinterpret_cast<const char *>(&ne1), sizeof(int32_t));
fout.write(reinterpret_cast<char *>(const_cast<char *>(name.data())), length);
printf("name = %s, ne0 = %d, ne1 = %d\n", name.c_str(), ne0, ne1);
std::vector<ggml_fp16_t> tmp(ne0*ne1);
const ggml_fp16_t * src = (const ggml_fp16_t *) tensor->data;
for (int i1 = 0; i1 < ne1; ++i1) {
for (int i0 = 0; i0 < ne0; ++i0) {
const float v00 = ggml_fp16_to_fp32(src[i0 + 2*i1*ne0]);
const float v01 = ggml_fp16_to_fp32(src[i0 + (2*i1+1)*ne0]);
tmp[i0 + i1*ne0] = ggml_fp32_to_fp16(0.5*(v00 + v01));
}
}
fout.write(reinterpret_cast<char *>(tmp.data()), tmp.size()*sizeof(ggml_fp16_t));
} else if (name.substr(name.size() - 11) == ".mlp.0.bias") {
const int32_t ne0 = tensor->ne[0]/2;
fout.write(reinterpret_cast<const char *>(&ne0), sizeof(int32_t));
fout.write(reinterpret_cast<char *>(const_cast<char *>(name.data())), length);
printf("name = %s, ne0 = %d\n", name.c_str(), ne0);
std::vector<float> tmp(ne0);
const float * src = (const float *) tensor->data;
for (int i0 = 0; i0 < ne0; ++i0) {
tmp[i0] = 0.5*(src[2*i0] + src[2*i0+1]);
}
fout.write(reinterpret_cast<char *>(tmp.data()), tmp.size()*sizeof(float));
} else if (name.substr(name.size() - 13) == ".mlp.2.weight") {
const int32_t ne0 = tensor->ne[0]/2;
const int32_t ne1 = tensor->ne[1];
fout.write(reinterpret_cast<const char *>(&ne0), sizeof(int32_t));
fout.write(reinterpret_cast<const char *>(&ne1), sizeof(int32_t));
fout.write(reinterpret_cast<char *>(const_cast<char *>(name.data())), length);
printf("name = %s, ne0 = %d, ne1 = %d\n", name.c_str(), ne0, ne1);
std::vector<ggml_fp16_t> tmp(ne0*ne1);
const ggml_fp16_t * src = (const ggml_fp16_t *) tensor->data;
for (int i1 = 0; i1 < ne1; ++i1) {
for (int i0 = 0; i0 < ne0; ++i0) {
const float v00 = ggml_fp16_to_fp32(src[2*i0 + i1*ne0]);
const float v01 = ggml_fp16_to_fp32(src[2*i0 + 1 + i1*ne0]);
tmp[i0 + i1*ne0] = ggml_fp32_to_fp16(0.5*(v00 + v01));
}
}
fout.write(reinterpret_cast<char *>(tmp.data()), tmp.size()*sizeof(ggml_fp16_t));
} else {
for (int i = 0; i < n_dims; ++i) {
const int32_t ne = tensor->ne[i];
fout.write(reinterpret_cast<char *>(const_cast<int32_t *>(&ne)), sizeof(ne));
}
fout.write(reinterpret_cast<char *>(const_cast<char *>(name.data())), length);
//printf("name = %s, ne = %d, %d, %d, %d\n", name.c_str(), tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
fout.write(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
}
}
}
fout.close();
}
return true;
}
@ -2429,7 +2722,7 @@ int whisper_full(
prompt.push_back(id);
result_cur.push_back({ seek + 2*(tid - whisper_token_beg(ctx)), id });
//printf("%s: %s\n", __func__, ctx->vocab.id_to_token[id].c_str());
printf("%s: %s\n", __func__, ctx->vocab.id_to_token[id].c_str());
// end of text token
if (id == whisper_token_eot(ctx)) {

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