/home/liu/actions-runner/_work/ccv/ccv/lib/nnc/_ccv_cnnp_model.h

Source
/**********************************************************
 * C-based/Cached/Core Computer Vision Library
 * Liu Liu, 2010-02-01
 **********************************************************/

/**********************************************************
 * CCV - Neural Network Collection
 **********************************************************/

#ifndef GUARD_ccv_cnnp_model_internal_h
#define GUARD_ccv_cnnp_model_internal_h

#include "ccv_nnc.h"
#include "_ccv_nnc_stream.h"
#include "_ccv_nnc_xpu_alloc.h"
#include "3rdparty/khash/khash.h"

typedef void(*ccv_cnnp_cmd_updater_f)(void* const context, const ccv_nnc_graph_exec_symbol_t symbol, const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint);
typedef void(*ccv_cnnp_add_to_array_f)(void* const context, const ccv_nnc_tensor_symbol_t symbol, const int is_trainable);
/**
 * This is the virtual table of the model.
 */
typedef struct {
  void (*deinit)(ccv_cnnp_model_t* const self); /**< It can be nil. */
  void (*build)(ccv_cnnp_model_t* const self, ccv_nnc_symbolic_graph_t* const graph, const ccv_nnc_tensor_symbol_t* const inputs, const int input_size, ccv_nnc_tensor_symbol_t* const outputs, const int output_size); /**< Call this graph to build computation. No need to specify input size or output size, as it is defined along in the model already. */
  void (*init_states)(ccv_cnnp_model_t* const self, ccv_nnc_symbolic_graph_t* const graph, const ccv_cnnp_state_initializer_f initializer, void* const context); /**< This is called to init ccv_nnc_tensor_symbol_t with a exec. */
  void (*add_to_parameter)(ccv_cnnp_model_t* const self, const ccv_cnnp_add_to_array_f add_to_array, void* const parameters, const int is_trainable); /**< This is called to add ccv_nnc_tensor_symbol_t to as list of parameters. */
  void (*add_to_output)(ccv_cnnp_model_t* const self, const ccv_cnnp_add_to_array_f add_to_array, void* const outputs); /**< This is called to add ccv_nnc_tensor_symbol_t to as list of outputs for retention. The final outputs are already added. This method is optional for any additional values we want to retain. */
  ccv_cnnp_model_t* (*copy)(const ccv_cnnp_model_t* const self, void* const context); /**< This is called to make a deep copy of itself. */
  void (*set_is_test)(ccv_cnnp_model_t* const self, const int is_test, const ccv_cnnp_cmd_updater_f updater, void* const context); /**< This is called when it is switched between test or training. */
  void (*add_to_parameter_indices)(ccv_cnnp_model_t* const self, const int index, ccv_array_t* const parameter_indices); /**< This is called when we try to get parameter indices out of a given model */
  void (*notify)(const ccv_cnnp_model_t* const self, const int tag, void* const payload); /**< This is called when we want to notify something to this model. */
} ccv_cnnp_model_vtab_t;

struct ccv_cnnp_model_io_s {
  int param_ref; // Reference to parameter in the model, starts with 1. 0 means no such thing.
  int param_sel; // Selector to parameter in the model, starts with 1. 0 means no selector.
  int visit; // Temporary bits stored in the ccv_cnnp_model_io_t object, whoever uses it should clean it up.
  ccv_cnnp_model_t* model; // Reference back to the model who holds it. This is required because the model is the one whole holds the io.
  ccv_array_t* incomings; // Array of ccv_cnnp_model_io_t. The order is important because it impacts the order of symbols.
  ccv_array_t* dependencies; // Array of ccv_cnnp_model_io_t.
  int dependents; // Number of dependents.
  ccv_array_t* outgoings; // Array of ccv_cnnp_model_io_t.
  ccv_nnc_tensor_symbol_t* outputs; // This is different from the outputs from a model. A model could be reused, causing the outputs on that model to be the most recent one. This keeps the outputs of each.
};

enum {
  CCV_CNNP_MODEL_GRAPH_FIT_MODE, // This mode computes loss, backprop, and then apply gradients.
  CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE_NO_GRAD, // This mode allows you to only use ccv_cnnp_model_evaluate (others require gradient).
  CCV_CNNP_MODEL_GRAPH_MULTISTAGE_MODE, // This mode allows you to use ccv_cnnp_model_evaluate, ccv_cnnp_model_backward, ccv_cnnp_model_apply_gradients separately.
};

enum {
  CCV_CNNP_COMPILED_DATA_GRADIENT_NONE,
  CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES,
  CCV_CNNP_COMPILED_DATA_GRADIENT_TRAINABLES_AND_INPUTS,
};

enum {
  CCV_CNNP_REWIND_GRAPH_EXEC,
  CCV_CNNP_REWIND_TENSOR,
};

typedef struct {
  int type;
  union {
    ccv_nnc_tensor_symbol_t tensor;
    ccv_nnc_graph_exec_symbol_t graph_exec;
  };
} ccv_cnnp_rewind_symbol_t;

#define CCV_NNC_TENSOR(tv) ((ccv_nnc_tensor_t*)((uintptr_t)(tv) & ~(uintptr_t)1))
#define CCV_NNC_INIT_V(v) ((uint32_t*)((uintptr_t)(v) & ~(uintptr_t)1))

// This contains relevant information after model compilation.
typedef struct {
  int graph_mode;
  int gradient_mode; // Have init gradient graph.
  int is_test;
  int stream_type;
  int outgrad_size;
  uint64_t disable_outgrad;
  ccv_nnc_symbolic_graph_compile_param_t compile_params;
  ccv_nnc_xpu_alloc_t xpu_alloc;
  ccv_nnc_graph_t* graph;
  ccv_nnc_tensor_arena_t* tensor_arena;
  ccv_nnc_graph_exec_arena_t* graph_exec_arena;
  khash_t(stream_map)* stream_map; // Keeps track of streams on both GPU / CPU and devices so it can be used properly during execution.
  ccv_array_t* parameters;
  uint64_t* parameter_flags;
  ccv_array_t* internals; // Additional symbols need to retain.
  ccv_nnc_tensor_symbol_t* gradients;
  ccv_nnc_tensor_symbol_t* outgrads;
  ccv_nnc_tensor_symbol_t* updated_parameters;
  ccv_nnc_graph_exec_symbol_t* update_nodes;
  ccv_nnc_tensor_symbol_map_t* saved_aux;
  ccv_array_t* rewindables;
  ccv_array_t* gradient_checkpoints;
  struct {
    int size;
    uint32_t* v; // If the last is 1, we know it is incomplete (thus, the tensors_init_1 hasn't been called yet. This is to save RAM usage.
  } tensors_init;
  struct {
    ccv_nnc_tensor_t** internals; // Additional need to retained tensors.
    ccv_nnc_tensor_t** parameters;
    ccv_nnc_tensor_t** gradients;
    ccv_nnc_tensor_t** accum_gradients;
  } tensors;
  struct {
    ccv_array_t* parameters;
    ccv_array_t* internals;
  } ids;
  struct {
    int to_op_size;
    int to_size;
    ccv_nnc_graph_exec_t* to_ops;
    ccv_nnc_graph_exec_symbol_t* tos;
    ccv_nnc_graph_static_schedule_t* schedule; // The partial schedule for running evaluate step.
  } evaluate; // Data related to ccv_cnnp_model_evaluate
  struct {
    int count; // Called backward how many times. Starting with 0.
    int from_op_size;
    ccv_nnc_graph_exec_t* from_ops; // These are the ops in the main graph.
    int to_size;
    ccv_nnc_graph_exec_symbol_t* tos;
    ccv_nnc_graph_t* accum; // The graph to accumulate gradients.
    ccv_nnc_tensor_arena_t* tensor_arena;
    ccv_nnc_graph_exec_arena_t* graph_exec_arena;
    ccv_nnc_tensor_symbol_t* gradients; // The new gradients.
    ccv_nnc_tensor_symbol_t* accum_gradients; // The old accumulate gradients.
    ccv_nnc_tensor_symbol_t* updated_accum_gradients; // The new accumulate gradients.
    ccv_nnc_graph_static_schedule_t* schedule; // The partial schedule for running backward step.
  } backward;
  struct {
    ccv_nnc_graph_t* graph;
    ccv_nnc_tensor_arena_t* tensor_arena;
    ccv_nnc_graph_exec_arena_t* graph_exec_arena;
  } apply_gradients;
  struct {
    ccv_nnc_cmd_t minimizer;
    ccv_array_t* parameters;
    int max_saved_aux_size;
  } minimize;
  ccv_nnc_cmd_t loss;
  ccv_nnc_tensor_symbol_t* f;
  ccv_nnc_tensor_symbol_t fits[1];
} ccv_cnnp_compiled_data_t;

struct ccv_cnnp_model_s {
  const ccv_cnnp_model_vtab_t* isa;
  int input_size; // This is the best effort number, mostly just for subclass to use.
  int output_size;
  int max_stream_count;
  ccv_array_t* io; // The opaque io that can be nil.
  ccv_array_t* parameter_indices; // The indexes for parameters in the final model.
  ccv_nnc_symbolic_graph_t* graph;
  ccv_nnc_tensor_symbol_t* inputs; // Unlike outputs, which is not dynamically allocated, inputs is dynamically allocated, and may be 0.
  ccv_nnc_tensor_symbol_t* outputs;
  char* name;
  struct {
    ccv_cnnp_model_notify_f func;
    void* context;
  } notify_hook;
  ccv_cnnp_compiled_data_t* compiled_data;
  int parallel_count; // How many parallel devices.
  int memory_compression; // Whether to enable memory compression for training phase.
  int gradient_checkpointing; // Whether to enable gradient checkpointing for training phase.
  int is_trainable; // Whether this model can be trained or not.
  int memory_reduction; // Whether to enable memory reduction techniques for training phase.
  size_t workspace_size; // Set the default workspace size.
  struct {
    ccv_cnnp_model_io_reader_f reader;
    ccv_cnnp_model_io_writer_f writer;
  } rw;
  void* data; // Temporary storage for some internal functions.
};

KHASH_MAP_INIT_STR(ccv_cnnp_model_name_bank, int)

typedef struct {
  int sequence;
  khash_t(ccv_cnnp_model_name_bank)* bank;
  const char* name;
} ccv_cnnp_model_name_t;

typedef struct {
  int it;
  ccv_cnnp_model_t* model;
  khash_t(ccv_cnnp_model_name_bank)* bank;
  ccv_array_t* sequences;
} ccv_cnnp_model_sequence_t;

static inline void ccv_cnnp_model_push(ccv_cnnp_model_t* const self, ccv_cnnp_model_sequence_t* const model_sequence)
{
  // Reset to 0.
  if (!model_sequence->sequences)
    model_sequence->sequences = ccv_array_new(sizeof(ccv_cnnp_model_name_t), 1, 0);
  khash_t(ccv_cnnp_model_name_bank)* bank = model_sequence->sequences->rnum > 0 ? ((ccv_cnnp_model_name_t*)180ccv_array_get180(model_sequence->sequences, model_sequence->sequences->rnum - 1))->bank : model_sequence->bank2.53k;
  int ret;
  khiter_t k = kh_put(ccv_cnnp_model_name_bank, bank, self->name ? self->name : "", &ret);
  int sequence;
  if (ret != 0)
    sequence = kh_val(bank, k) = 0;
  else
    sequence = ++kh_val(bank, k);
  ccv_cnnp_model_name_t name = {
    .bank = kh_init(ccv_cnnp_model_name_bank),
    .name = self->name,
    .sequence = sequence,
  };
  ccv_array_push(model_sequence->sequences, &name);
  model_sequence->model = self;
}

static inline void ccv_cnnp_model_pop(const ccv_cnnp_model_t* const self, ccv_cnnp_model_sequence_t* const model_sequence)
{
  khash_t(ccv_cnnp_model_name_bank)* const bank = ((ccv_cnnp_model_name_t*)ccv_array_get(model_sequence->sequences, model_sequence->sequences->rnum - 1))->bank;
  kh_destroy(ccv_cnnp_model_name_bank, bank);
  --model_sequence->sequences->rnum;
  assert(model_sequence->sequences->rnum >= 0);
  model_sequence->model = 0;
}

static inline ccv_cnnp_model_t* _ccv_cnnp_model_copy(const ccv_cnnp_model_t* const model, void* const context)
{
  assert(model->isa->copy);
  ccv_cnnp_model_t* const copy = model->isa->copy(model, context);
  copy->parallel_count = model->parallel_count;
  copy->memory_compression = model->memory_compression;
  copy->memory_reduction = model->memory_reduction;
  copy->max_stream_count = model->max_stream_count;
  copy->gradient_checkpointing = model->gradient_checkpointing;
  return copy;
}

static inline void ccv_cnnp_model_copy_name(ccv_cnnp_model_t* const self, const char* const name)
{
  if (name)
  {
    const size_t len = strnlen(name, 63);
    const size_t n = len + 1;
    self->name = (char*)ccmalloc(n);
    // Don't use strndup because this way I can have custom allocator (for ccmalloc).
    memcpy(self->name, name, n);
    self->name[len] = 0;
  }
}

static inline void ccv_cnnp_model_add_to_parameter(ccv_cnnp_model_t* const self, const ccv_cnnp_add_to_array_f add_to_array, void* const parameters, const int is_trainable)
{
  if (self->isa->add_to_parameter)
    self->isa->add_to_parameter(self, add_to_array, parameters, is_trainable);
}

static inline void ccv_cnnp_model_add_to_output(ccv_cnnp_model_t* const self, const ccv_cnnp_add_to_array_f add_to_array, void* const outputs)
{
  if (self->isa->add_to_output)
    self->isa->add_to_output(self, add_to_array, outputs);
}

typedef struct {
  int is_trainable;
  int is_gradient_checkpointing;
  ccv_cnnp_model_sequence_t* model_sequence;
  ccv_cnnp_add_to_array_f add_to_array;
  ccv_array_t* parameters;
  struct {
    void* add_to_parameter;
    void* add_to_output;
  } context;
  ccv_array_t* gradient_checkpoints;
} ccv_cnnp_model_build_data_t; // Host temporary data for building models.

typedef struct {
  int input_size;
  int output_size;
  int is_trainable;
  ccv_cnnp_model_t* model;
  void (*build)(ccv_cnnp_model_t* const self, ccv_nnc_symbolic_graph_t* const graph, const ccv_nnc_tensor_symbol_t* const inputs, const int input_size, ccv_nnc_tensor_symbol_t* const outputs, const int output_size);
  ccv_array_t* tensor_symbols;
  ccv_nnc_tensor_symbol_t* inputs;
  ccv_nnc_tensor_symbol_t* outputs;
} ccv_cnnp_model_gradient_checkpoint_t;

static inline ccv_nnc_tensor_symbol_t ccv_cnnp_parameter_from_indice(ccv_cnnp_model_t* const self, const int indice)
{
  assert(self->data);
  ccv_cnnp_model_build_data_t* const build_data = (ccv_cnnp_model_build_data_t*)self->data;
  assert(indice < build_data->parameters->rnum);
  return *(ccv_nnc_tensor_symbol_t*)ccv_array_get(build_data->parameters, indice);
}

typedef struct {
  int record;
  ccv_array_t* tensor_symbols;
  void* old_tensor_symbol_new_hook_context;
  ccv_nnc_tensor_symbol_new_hook_f old_tensor_symbol_new_hook;
  void* old_tensor_symbol_alias_new_hook_context;
  ccv_nnc_tensor_symbol_alias_new_hook_f old_tensor_symbol_alias_new_hook;
} ccv_cnnp_model_gradient_checkpoint_build_context_t;

static void _ccv_cnnp_model_gradient_checkpoint_tensor_symbol_new_hook(void* context, const ccv_nnc_tensor_symbol_t symbol, const ccv_nnc_tensor_param_t info, const char* const name)
{
  ccv_cnnp_model_gradient_checkpoint_build_context_t* const build_context = (ccv_cnnp_model_gradient_checkpoint_build_context_t*)context;
  if (build_context->record)
    ccv_array_push(build_context->tensor_symbols, &symbol);
  if (build_context->old_tensor_symbol_new_hook)
    build_context->old_tensor_symbol_new_hook(build_context->old_tensor_symbol_new_hook_context, symbol, info, name);
}

static void _ccv_cnnp_model_gradient_checkpoint_tensor_symbol_alias_new_hook(void* context, const ccv_nnc_tensor_symbol_t symbol, const ccv_nnc_tensor_symbol_t from_symbol, const int ofs[CCV_NNC_MAX_DIM_ALLOC], const int inc[CCV_NNC_MAX_DIM_ALLOC], const ccv_nnc_tensor_param_t info, const char* const name)
{
  ccv_cnnp_model_gradient_checkpoint_build_context_t* const build_context = (ccv_cnnp_model_gradient_checkpoint_build_context_t*)context;
  if (build_context->record)
    ccv_array_push(build_context->tensor_symbols, &symbol);
  if (build_context->old_tensor_symbol_alias_new_hook)
    build_context->old_tensor_symbol_alias_new_hook(build_context->old_tensor_symbol_alias_new_hook_context, symbol, from_symbol, ofs, inc, info, name);
}

static inline void ccv_cnnp_model_build(ccv_cnnp_model_t* const self, ccv_nnc_symbolic_graph_t* const graph, const ccv_nnc_tensor_symbol_t* const inputs, const int input_size, ccv_nnc_tensor_symbol_t* const outputs, const int output_size)
{
  assert(self->data);
  ccv_cnnp_model_build_data_t* const build_data = (ccv_cnnp_model_build_data_t*)self->data;
  const int old_is_trainable = build_data->is_trainable;
  if (self->is_trainable >= 0)
    build_data->is_trainable = self->is_trainable;
  if (self->name && self->name[0] != '\0'230)
    ccv_cnnp_model_push(self, build_data->model_sequence);
  if (self->gradient_checkpointing == 1 && !build_data->is_gradient_checkpointing2)
  {
    build_data->is_gradient_checkpointing = 1;
    // Prepare to record gradient checkpoint. We will log the build function, inputs, what are the tensors / graph execs we created.
    if (!build_data->gradient_checkpoints)
      build_data->gradient_checkpoints = ccv_array_new(sizeof(ccv_cnnp_model_gradient_checkpoint_t), 0, 0);
    ccv_cnnp_model_gradient_checkpoint_build_context_t build_context = {
      .record = 1,
      .tensor_symbols = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 0, 0),
    };
    build_context.old_tensor_symbol_new_hook_context = ccv_nnc_tensor_symbol_new_hook(graph, _ccv_cnnp_model_gradient_checkpoint_tensor_symbol_new_hook, &build_context, &build_context.old_tensor_symbol_new_hook);
    build_context.old_tensor_symbol_alias_new_hook_context = ccv_nnc_tensor_symbol_alias_new_hook(graph, _ccv_cnnp_model_gradient_checkpoint_tensor_symbol_alias_new_hook, &build_context, &build_context.old_tensor_symbol_alias_new_hook);
    if (outputs && output_size0)
    {
      assert(output_size == self->output_size);
      self->isa->build(self, graph, inputs, input_size, outputs, output_size);
      memcpy(self->outputs, outputs, sizeof(ccv_nnc_tensor_symbol_t) * output_size);
    } else
      self->isa->build(self, graph, inputs, input_size, self->outputs, self->output_size);
    ccv_nnc_tensor_symbol_new_hook(graph, build_context.old_tensor_symbol_new_hook, build_context.old_tensor_symbol_new_hook_context, 0);
    ccv_nnc_tensor_symbol_alias_new_hook(graph, build_context.old_tensor_symbol_alias_new_hook, build_context.old_tensor_symbol_alias_new_hook_context, 0);
    ccv_cnnp_model_gradient_checkpoint_t checkpoint = {
      .input_size = input_size,
      .output_size = (outputs && output_size > 00) ? output_size0 : self->output_size,
      .is_trainable = build_data->is_trainable,
      .model = self,
      .build = self->isa->build,
      .tensor_symbols = build_context.tensor_symbols,
      .inputs = ccmalloc(sizeof(ccv_nnc_tensor_symbol_t) * (input_size + ((outputs && output_size > 00) ? output_size0 : self->output_size))),
    };
    checkpoint.outputs = checkpoint.inputs + input_size;
    if (input_size > 0)
      memcpy(checkpoint.inputs, inputs, sizeof(ccv_nnc_tensor_symbol_t) * input_size);
    if (outputs && output_size > 00)
      memcpy(checkpoint.outputs, outputs, sizeof(ccv_nnc_tensor_symbol_t) * output_size);
    else if (self->outputs && self->output_size > 0)
      memcpy(checkpoint.outputs, self->outputs, sizeof(ccv_nnc_tensor_symbol_t) * self->output_size);
    ccv_array_push(build_data->gradient_checkpoints, &checkpoint);
    build_data->is_gradient_checkpointing = 0;
  } else {
    // If we want to disable gradient checkpointing for this model, we simply not log any new tensors created here so there is no mapping for these.
    int old_record;
    ccv_cnnp_model_gradient_checkpoint_build_context_t* build_context = 0;
    if (build_data->is_gradient_checkpointing)
    {
      if (self->gradient_checkpointing == -1)
      {
        ccv_nnc_tensor_symbol_new_hook_f old_tensor_symbol_new_hook;
        build_context = ccv_nnc_tensor_symbol_new_hook(graph, 0, 0, &old_tensor_symbol_new_hook);
        // Set back the build_context.
        ccv_nnc_tensor_symbol_new_hook(graph, old_tensor_symbol_new_hook, build_context, 0);
        old_record = build_context->record;
        build_context->record = 0;
      } else if (self->gradient_checkpointing == 1) { // Force to turn on gradient checkpointing if it is inside a gradient checkpointing = -1.
        ccv_nnc_tensor_symbol_new_hook_f old_tensor_symbol_new_hook;
        build_context = ccv_nnc_tensor_symbol_new_hook(graph, 0, 0, &old_tensor_symbol_new_hook);
        // Set back the build_context.
        ccv_nnc_tensor_symbol_new_hook(graph, old_tensor_symbol_new_hook, build_context, 0);
        old_record = build_context->record;
        build_context->record = 1;
      }
    }
    // No push checkpoint, easy.
    if (outputs && output_size2.91k)
    {
      assert(output_size == self->output_size);
      self->isa->build(self, graph, inputs, input_size, outputs, output_size);
      memcpy(self->outputs, outputs, sizeof(ccv_nnc_tensor_symbol_t) * output_size);
    } else
      self->isa->build(self, graph, inputs, input_size, self->outputs, self->output_size);
    if (build_context) // Restore previous state even if our gradient checkpointing controlled whether to turn on recording or not.
      build_context->record = old_record;
  }
  // Skip if there is none. This helps to load parameters to a different model when only changes non-parameterized settings (add reshapes, permutations etc).
  // If it is named, we have to push too.
  if (self->isa->add_to_parameter || self->isa->add_to_output2.59k)
  {
    // If we already pushed, no need to push again.
    if (!(self->name && self->name[0] != '\0'124))
      ccv_cnnp_model_push(self, build_data->model_sequence);
    build_data->model_sequence->it = 0;
    ccv_cnnp_model_add_to_parameter(self, build_data->add_to_array, build_data->context.add_to_parameter, build_data->is_trainable);
    build_data->model_sequence->it = 0;
    ccv_cnnp_model_add_to_output(self, build_data->add_to_array, build_data->context.add_to_output);
    ccv_cnnp_model_pop(self, build_data->model_sequence);
  } else if (2.59kself->name2.59k && self->name[0] != '\0'106)
    ccv_cnnp_model_pop(self, build_data->model_sequence);
  build_data->is_trainable = old_is_trainable;
}

static inline void ccv_cnnp_model_init_states(ccv_cnnp_model_t* const self, ccv_nnc_symbolic_graph_t* const graph, const ccv_cnnp_state_initializer_f initializer, void* const context)
{
  if (self->isa->init_states)
    self->isa->init_states(self, graph, initializer, context);
}

static inline void ccv_cnnp_model_set_is_test(ccv_cnnp_model_t* const self, const int is_test, const ccv_cnnp_cmd_updater_f updater, void* const context)
{
  if (self->isa->set_is_test)
    self->isa->set_is_test(self, is_test, updater, context);
}

static inline void ccv_cnnp_model_add_to_parameter_indices(ccv_cnnp_model_t* const self, const int index, ccv_array_t* const parameter_indices)
{
  if (self->isa->add_to_parameter_indices)
    self->isa->add_to_parameter_indices(self, index, parameter_indices);
  else {
    int i;
    if (!self->parameter_indices)
      return;
    if (index == -1)
      for (i = 0; 89i < self->parameter_indices->rnum; i++89)
        ccv_array_push(parameter_indices, ccv_array_get(self->parameter_indices, i));
    else if (index < self->parameter_indices->rnum)
      ccv_array_push(parameter_indices, ccv_array_get(self->parameter_indices, index));
  }
}

typedef struct {
  uint8_t add_parameter_indices;
  char prefix;
  ccv_cnnp_model_sequence_t* sequence;
  ccv_array_t* symbols;
  ccv_array_t* ids;
  ccv_array_t* trainables;
} ccv_cnnp_model_add_to_array_context_t;

void ccv_cnnp_model_tensors_init_0(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data);
void ccv_cnnp_model_tensors_init_1(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data);
int ccv_cnnp_model_tensors_any_to_alloc(const ccv_cnnp_model_t* const model, ccv_cnnp_compiled_data_t* const compiled_data);
ccv_nnc_stream_context_t* ccv_cnnp_compiled_data_get_stream(ccv_cnnp_compiled_data_t* const compiled_data, const int type);
void ccv_cnnp_model_gradient_checkpoints_cleanup_after_build(ccv_cnnp_compiled_data_t* const compiled_data, ccv_nnc_symbolic_graph_t* const graph);
void ccv_cnnp_model_apply_gradient_checkpoints(ccv_cnnp_compiled_data_t* const compiled_data, ccv_nnc_symbolic_graph_t* const graph);
void ccv_cnnp_model_add_to_array(void* const context, const ccv_nnc_tensor_symbol_t symbol, const int is_trainable);

#endif

Coverage Report

Created: 2024-12-10 23:11