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wip : try to compress just mlp

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experiment/model-compression
Georgi Gerganov 2 years ago
parent 4a4a754220
commit 4597c9c19b
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GPG Key ID: 449E073F9DC10735
1 changed files with 204 additions and 130 deletions
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whisper.cpp
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@ -1034,8 +1034,8 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
{
auto hparams = model.hparams;
hparams.n_audio_state /= 2;
hparams.n_text_state /= 2;
//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));
@ -1087,151 +1087,225 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
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));
}
fout.write(reinterpret_cast<char *>(const_cast<char *>(name.data())), length);
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));
}
//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 (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]);
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;
tmp[i1*(ne0/2) + i0] = ggml_fp32_to_fp16(0.25*(v00 + v01 + v10 + v11));
}
}
fout.write(reinterpret_cast<const char *>(&ne0), sizeof(int32_t));
fout.write(reinterpret_cast<char *>(const_cast<char *>(name.data())), length);
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));
}
}
}
printf("name = %s, ne0 = %d\n", name.c_str(), ne0);
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));
}
}
}
std::vector<float> tmp(ne0);
fout.write(reinterpret_cast<char *>(tmp.data()), tmp.size()*sizeof(ggml_fp16_t));
}
} else {
assert(false);
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]);
}
} 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 (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<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];
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);
if (name == "encoder.positional_embedding" || name == "decoder.positional_embedding") {
std::vector<float> tmp((ne0/2)*ne1);
printf("name = %s, ne0 = %d, ne1 = %d\n", name.c_str(), ne0, 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]);
}
}
std::vector<ggml_fp16_t> tmp(ne0*ne1);
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 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]);
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));
tmp[i0 + i1*ne0] = ggml_fp32_to_fp16(0.5*(v00 + v01));
}
}
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];
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));
}
tmp[i1*(ne0/2) + i0] = 0.25*(v00 + v01 + v10 + v11);
}
}
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 *>(tmp.data()), tmp.size()*sizeof(float));
}
} else {
assert(false);
}
fout.write(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
}
}
}

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