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llama.cpp
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Refactor variable names

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Ed Addario 2025-08-19 22:29:27 +01:00
parent 1187f6aa9e
commit 5aceb9e3ae
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1 changed files with 12 additions and 17 deletions
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src/llama-quant.cpp
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@ -575,13 +575,13 @@ static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float *
return new_size; return new_size;
} }
// Returns per-tensor overrides of quantization types to meet target BPW with best expected quality. // Returns per-tensor overrides of quantization types to meet target BPW with the lowest ppl
// imatrix_data: map from tensor name -> length (ne[0] * ne[2]) containing per-column E[a^2] by expert // sample_rows_per_expert: Larger values will result in more accurate error estimates, but will take longer to compute
// activations_data: optional map from tensor name -> length (ne[0] * ne[2]) containing per-column E[a] by expert // bias_lambda: Affects the weight of the bias term in the MSE error function. 0.0 means no bias, 1.0 means equal weight
// bias_lambda: relative weight on bias term (|sum e_j * E[a_j]|) vs MSE term (sum e_j^2 * E[a_j^2]) // for bias and error, 2.0 means twice as much weight for bias
static std::unordered_map<std::string, ggml_type> target_bpw_type( static std::unordered_map<std::string, ggml_type> target_bpw_type(
llama_model_loader & ml, llama_model_loader & ml,
std::vector<no_init<uint8_t>> & read_data, std::vector<no_init<uint8_t>> & buffer,
const llama_model & model, const llama_model & model,
const std::vector<const llama_model_loader::llama_tensor_weight *> & tensors, const std::vector<const llama_model_loader::llama_tensor_weight *> & tensors,
const std::map<int, std::string> & mapped, const std::map<int, std::string> & mapped,
@ -735,24 +735,21 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
float total_err = 0.0; float total_err = 0.0;
for (int64_t i03 = 0; i03 < ne2; ++i03) { for (int64_t slice = 0; slice < ne2; ++slice) {
const float * value = values_all ? (values_all + i03 * n_per_row) : nullptr; const float * value = values_all ? (values_all + slice * n_per_row) : nullptr;
const float * activation = activations_all ? (activations_all + i03 * n_per_row) : nullptr; const float * activation = activations_all ? (activations_all + slice * n_per_row) : nullptr;
// Assemble sampled rows into contiguous f32_sample
int64_t rs = 0; int64_t rs = 0;
for (int64_t r = 0; r < rows_per_expert && rs < sample_rows; r += stride) { for (int64_t r = 0; r < rows_per_expert && rs < sample_rows; r += stride) {
const float * src = f32_data + i03 * (n_per_row * rows_per_expert) + r * n_per_row; const float * src = f32_data + slice * (n_per_row * rows_per_expert) + r * n_per_row;
std::memcpy(f32_sample.data() + rs * n_per_row, src, sizeof(float) * n_per_row); std::memcpy(f32_sample.data() + rs * n_per_row, src, sizeof(float) * n_per_row);
++rs; ++rs;
} }
if (rs == 0) { continue; } if (rs == 0) { continue; }
// Quantize sampled rows in one chunk; pass the imatrix for this expert slice
const size_t got = ggml_quantize_chunk(typ, f32_sample.data(), qbuf.data(), 0, rs, n_per_row, value); const size_t got = ggml_quantize_chunk(typ, f32_sample.data(), qbuf.data(), 0, rs, n_per_row, value);
(void)got; // not strictly needed here (void)got;
// Dequantize
traits->to_float(qbuf.data(), deq.data(), rs * n_per_row); traits->to_float(qbuf.data(), deq.data(), rs * n_per_row);
// Compute error proxy per sampled row // Compute error proxy per sampled row
@ -821,10 +818,8 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
LLAMA_LOG_INFO("\t%s: - processing tensor %45s \t(%12d elements)\n", __func__, name.c_str(), (int)ggml_nelements(t)); LLAMA_LOG_INFO("\t%s: - processing tensor %45s \t(%12d elements)\n", __func__, name.c_str(), (int)ggml_nelements(t));
if (!ml.use_mmap) { if (!ml.use_mmap) {
if (read_data.size() < ggml_nbytes(t)) { if (buffer.size() < ggml_nbytes(t)) { buffer.resize(ggml_nbytes(t)); }
read_data.resize(ggml_nbytes(t)); t->data = buffer.data();
}
t->data = read_data.data();
} }
ml.load_data_for(t); ml.load_data_for(t);