mirror of https://github.com/google/gemma.cpp.git
Extend LayersOutputFunc to take query index and auxillary int
PiperOrigin-RevId: 657574814
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@ -59,10 +59,13 @@ int Run(int argc, char** argv) {
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env.MutableConfig().layers_output =
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prompt_args.layers_output.Empty()
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? LayersOutputFunc()
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: [&json_output](int pos, const std::string& key, const float* values,
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size_t values_len) {
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std::vector<float> v{values, values + values_len};
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json_output[std::to_string(pos)][key] = v;
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: [&json_output](size_t query_idx, size_t pos, const std::string& key,
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int layer, const float* values, size_t values_len) {
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const std::string& debug_key =
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layer < 0 ? key : (key + "." + std::to_string(layer));
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const std::vector<float> v{values, values + values_len};
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json& json_base = json_output[std::to_string(query_idx)];
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json_base[std::to_string(pos)][debug_key] = v;
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};
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const auto [answer, token_count] = env.QueryModel(prompt_args.prompt);
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@ -805,8 +805,10 @@ HWY_NOINLINE void Transformer(const int* tokens, size_t num_tokens,
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const size_t num_interleaved = num_tokens * num_queries;
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if (layers_output) {
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for (size_t token_idx = 0; token_idx < num_interleaved; ++token_idx) {
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float token_f = tokens[token_idx];
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layers_output(pos + token_idx, "Tokens", &token_f, 1);
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const size_t query_idx = token_idx % num_queries;
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const size_t logical_pos = (pos + token_idx) / num_queries;
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const float token_f = tokens[token_idx];
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layers_output(query_idx, logical_pos, "tokens", -1, &token_f, 1);
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}
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}
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constexpr size_t kModelDim = TConfig::kModelDim;
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@ -821,9 +823,9 @@ HWY_NOINLINE void Transformer(const int* tokens, size_t num_tokens,
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layer_weights, activations, kv_caches, pool);
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if (layers_output) {
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const std::string block_name = "blocks." + std::to_string(layer);
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for (size_t token_idx = 0; token_idx < num_interleaved; ++token_idx) {
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layers_output(pos + token_idx, block_name,
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const size_t logical_pos = (pos + token_idx) / num_queries;
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layers_output(token_idx % num_queries, logical_pos, "blocks", layer,
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activations.x.Batch(token_idx), kModelDim);
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}
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}
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@ -833,7 +835,9 @@ HWY_NOINLINE void Transformer(const int* tokens, size_t num_tokens,
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activations.x.All(), kModelDim);
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if (layers_output) {
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for (size_t token_idx = 0; token_idx < num_interleaved; ++token_idx) {
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layers_output(pos + token_idx, "final_norm",
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const size_t query_idx = token_idx % num_queries;
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const size_t logical_pos = (pos + token_idx) / num_queries;
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layers_output(query_idx, logical_pos, "final_norm", -1,
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activations.x.Batch(token_idx), kModelDim);
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}
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}
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@ -49,13 +49,15 @@ using AcceptFunc = std::function<bool(int, float)>;
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// If not empty, SampleFunc is called with the probability distribution for the
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// next token, and its return value is used as the next generated token.
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using SampleFunc = std::function<int(const float*, size_t)>;
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// Will be called for layers output with:
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// If not empty, LayersOutputFunc is called for layer outputs, specified with:
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// - index of query within containing batch (if any); zero otherwise.
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// - position in the tokens sequence
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// - name of the data, p.ex. "tokens", "block.1", "final_norm"
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// - name of the data, e.g. "tokens", "blocks", "final_norm"
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// - layer index (or -1 for global outputs), e.g. "blocks" exposes x per-layer
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// - pointer to the data array
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// - size of the data array
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using LayersOutputFunc =
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std::function<void(int, const std::string&, const float*, size_t)>;
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std::function<void(size_t, size_t, const std::string&, int, const float*, size_t)>;
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struct RuntimeConfig {
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bool StreamToken(size_t query_idx, size_t pos, int token, float prob) const {
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