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Fix various issues with renamer.
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@liuliu
liuliu committed Jan 24, 2024
1 parent c051814 commit 1f1497a
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Showing 2 changed files with 85 additions and 26 deletions.
53 changes: 30 additions & 23 deletions lib/nnc/ccv_cnnp_model.c
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2206,9 +2206,6 @@ static void _ccv_cnnp_model_to_parameter_indices_and_from_parameter_indices(ccv_
{ assert((*parameter_indices)->rnum == 1); }
else if (*param_ref >= 0)
{ assert(*param_ref < (*parameter_indices)->rnum); }
// Should be exactly the same tensor.
if (*param_ref < 0 && *from_param_ref < 0)
{ assert((*from_parameter_indices)->rnum == (*parameter_indices)->rnum); }
}

void ccv_cnnp_model_set_parameters(ccv_cnnp_model_t* const model, const ccv_cnnp_model_io_t parameters, const ccv_cnnp_model_t* const from_model, const ccv_cnnp_model_io_t from_parameters)
Expand All @@ -2218,6 +2215,9 @@ void ccv_cnnp_model_set_parameters(ccv_cnnp_model_t* const model, const ccv_cnnp
ccv_array_t* from_parameter_indices;
int from_param_ref;
_ccv_cnnp_model_to_parameter_indices_and_from_parameter_indices(model, parameters, from_model, from_parameters, &to_parameter_indices, &to_param_ref, &from_parameter_indices, &from_param_ref, 0);
// Should be exactly the same tensor.
if (to_param_ref < 0 && from_param_ref < 0)
{ assert(from_parameter_indices->rnum == to_parameter_indices->rnum); }
// To models.
ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
assert(to_compiled_data);
Expand Down Expand Up @@ -2267,6 +2267,9 @@ void ccv_cnnp_model_share_parameters(ccv_cnnp_model_t* const model, const ccv_cn
ccv_array_t* from_parameter_indices;
int from_param_ref;
_ccv_cnnp_model_to_parameter_indices_and_from_parameter_indices(model, parameters, from_model, from_parameters, &to_parameter_indices, &to_param_ref, &from_parameter_indices, &from_param_ref, 1);
// Should be exactly the same tensor.
if (renamer == 0 && to_param_ref < 0 && from_param_ref < 0)
{ assert(from_parameter_indices->rnum == to_parameter_indices->rnum); }
// To models.
ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
assert(to_compiled_data);
Expand Down Expand Up @@ -2302,37 +2305,38 @@ void ccv_cnnp_model_share_parameters(ccv_cnnp_model_t* const model, const ccv_cn
const size_t dest_name_len = ccv_min(strnlen(dest_name, 1023), 1023);
memcpy(updated_name, dest_name, dest_name_len);
updated_name[dest_name_len] = 0;
if (renamer(context, src_name, updated_name, 1024) == 0)
if (renamer(context, src_name, updated_name, 1024) != 0)
continue; // Skip this.
if (memcmp(updated_name, dest_name, dest_name_len) == 0 && strnlen(updated_name, 1023) == dest_name_len)
{
if (memcmp(updated_name, dest_name, dest_name_len) == 0 && strnlen(updated_name, 1023) == dest_name_len)
// Nothing changed.
} else {
if (!id_map)
{
// Nothing changed.
} else {
if (!id_map)
{
id_map = kh_init(ccv_cnnp_parameter_id);
for (j = 0; j < to_parameter_size; j++)
{
int ret;
const khiter_t k = kh_put(ccv_cnnp_parameter_id, id_map, *(char**)ccv_array_get(to_compiled_data->ids.parameters, j), &ret);
assert(ret != 0);
kh_val(id_map, k) = j;
}
}
const khiter_t k = kh_get(ccv_cnnp_parameter_id, id_map, updated_name);
if (k != kh_end(id_map))
id_map = kh_init(ccv_cnnp_parameter_id);
for (j = 0; j < to_parameter_size; j++)
{
dest_d = kh_val(id_map, k);
assert(dest_d >= 0);
assert(dest_d < to_parameter_size);
int ret;
const khiter_t k = kh_put(ccv_cnnp_parameter_id, id_map, *(char**)ccv_array_get(to_compiled_data->ids.parameters, j), &ret);
assert(ret != 0);
kh_val(id_map, k) = j;
}
}
const khiter_t k = kh_get(ccv_cnnp_parameter_id, id_map, updated_name);
if (k == kh_end(id_map)) // Cannot find the name, skip.
continue;
dest_d = kh_val(id_map, k);
assert(dest_d >= 0);
assert(dest_d < to_parameter_size);
}
}
for (j = 0; j < parallel_count; j++)
{
ccv_nnc_tensor_t* const src = CCV_NNC_TENSOR(from_compiled_data->tensors.parameters[src_d + j * from_parameter_size]);
assert(src);
ccv_nnc_tensor_t* const dest = to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size];
if (dest && !((uintptr_t)dest & (uintptr_t)1))
ccv_nnc_tensor_free(dest);
to_compiled_data->tensors.parameters[dest_d + j * to_parameter_size] = (ccv_nnc_tensor_t*)((uintptr_t)src | (uintptr_t)1);
}
// Mark this symbol as init'ed.
Expand Down Expand Up @@ -2371,6 +2375,9 @@ void ccv_cnnp_model_parameters_zip_map(ccv_cnnp_model_t* const model, const ccv_
ccv_array_t* from_parameter_indices;
int from_param_ref;
_ccv_cnnp_model_to_parameter_indices_and_from_parameter_indices(model, parameters, from_model, from_parameters, &to_parameter_indices, &to_param_ref, &from_parameter_indices, &from_param_ref, 0);
// Should be exactly the same tensor.
if (to_param_ref < 0 && from_param_ref < 0)
{ assert(from_parameter_indices->rnum == to_parameter_indices->rnum); }
// To models.
ccv_cnnp_compiled_data_t* const to_compiled_data = model->compiled_data;
assert(to_compiled_data);
Expand Down
58 changes: 55 additions & 3 deletions test/unit/nnc/cnnp.core.tests.c
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1682,9 +1682,11 @@ TEST_CASE("LoRA fine-tuning convolution set is_trainable to false")
ccv_cnnp_model_free(final);
}

static int _ccv_nnc_renamer(void* context, const char* src_name, char* updated_name, const size_t provided_size)
static int _ccv_nnc_same_namer(void* context, const char* src_name, char* updated_name, const size_t provided_size)
{
updated_name[0] = '\0';
const size_t src_len = ccv_min(strnlen(src_name, provided_size - 1), provided_size - 1);
memcpy(updated_name, src_name, src_len);
updated_name[src_len] = '\0';
return 0;
}

Expand Down Expand Up @@ -1726,7 +1728,57 @@ TEST_CASE("two models share the same parameters")
.requires_grad = 0,
}, TENSOR_LIST(x), TENSOR_LIST(y0), 0, 0);
ccv_cnnp_model_compile(final1, TENSOR_PARAM_LIST(input_params), CMD_NOOP(), CMD_NOOP());
ccv_cnnp_model_share_parameters(final1, ccv_cnnp_model_parameters(final1, ALL_PARAMETERS, ALL_PARAMETERS), final0, ccv_cnnp_model_parameters(final0, ALL_PARAMETERS, ALL_PARAMETERS), _ccv_nnc_renamer, 0);
ccv_cnnp_model_share_parameters(final1, ccv_cnnp_model_parameters(final1, ALL_PARAMETERS, ALL_PARAMETERS), final0, ccv_cnnp_model_parameters(final0, ALL_PARAMETERS, ALL_PARAMETERS), 0, 0);
ccv_cnnp_model_evaluate(final1, (ccv_cnnp_evaluate_param_t){
.requires_grad = 0,
}, TENSOR_LIST(x), TENSOR_LIST(y1), 0, 0);
REQUIRE_TENSOR_EQ(y0, y1, "two model now shares the weights, should have the same result");
CNNP_MODEL_GEN(final0, CCV_NNC_LONG_DOT_GRAPH);
ccv_nnc_tensor_free(x);
ccv_nnc_tensor_free(y0);
ccv_nnc_tensor_free(y1);
ccv_cnnp_model_free(final0);
ccv_cnnp_model_free(final1);
}

TEST_CASE("two models, one with LoRA, one with not, share the same parameters")
{
const ccv_cnnp_model_io_t input0 = ccv_cnnp_input();
ccv_cnnp_model_t* const linear0 = ccv_cnnp_dense(10, 1, -1, "linear");
ccv_cnnp_model_io_t out0 = ccv_cnnp_model_apply(linear0, MODEL_IO_LIST(input0));
ccv_cnnp_model_t* const final0 = ccv_cnnp_model_new(MODEL_IO_LIST(input0), MODEL_IO_LIST(out0), 0, "tiny");

const ccv_cnnp_model_io_t input1 = ccv_cnnp_input();
ccv_cnnp_model_t* const linear1 = ccv_cnnp_dense(10, 1, -1, "linear");
ccv_cnnp_model_t* const down1 = ccv_cnnp_dense(2, 1, 1, "down");
ccv_cnnp_model_t* const up1 = ccv_cnnp_dense(10, 1, 1, "up");
ccv_cnnp_model_io_t out1 = ccv_cnnp_model_apply(linear1, MODEL_IO_LIST(input1));
ccv_cnnp_model_io_t out1_down = ccv_cnnp_model_apply(down1, MODEL_IO_LIST(input1));
ccv_cnnp_model_io_t out1_up = ccv_cnnp_model_apply(up1, MODEL_IO_LIST(out1_down));
ccv_cnnp_model_t* const add1 = ccv_cnnp_sum("sum");
ccv_cnnp_model_io_t out1_final = ccv_cnnp_model_apply(add1, MODEL_IO_LIST(out1, out1_up));
ccv_cnnp_model_t* const final1 = ccv_cnnp_model_new(MODEL_IO_LIST(input1), MODEL_IO_LIST(out1_final), 0, "tiny");

ccv_nnc_tensor_t* const x = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 10), 0);
dsfmt_t dsfmt;
int i;
dsfmt_init_gen_rand(&dsfmt, 1);
for (i = 0; i < 10; i++)
x->data.f32[i] = dsfmt_genrand_open_close(&dsfmt) * 2 - 1;
ccv_nnc_tensor_t* const y0 = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 10), 0);
ccv_nnc_tensor_t* const y1 = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 10), 0);
ccv_nnc_tensor_param_t input_params = CPU_TENSOR_NHWC(32F, 10);
ccv_cnnp_model_compile(final0, TENSOR_PARAM_LIST(input_params), CMD_NOOP(), CMD_NOOP());
ccv_cnnp_model_evaluate(final0, (ccv_cnnp_evaluate_param_t){
.requires_grad = 0,
}, TENSOR_LIST(x), TENSOR_LIST(y0), 0, 0);
ccv_cnnp_model_compile(final1, TENSOR_PARAM_LIST(input_params), CMD_NOOP(), CMD_NOOP());
ccv_nnc_tensor_t* const up_weights = ccv_nnc_tensor_new(0, CPU_TENSOR_NHWC(32F, 2, 10), 0);
for (i = 0; i < 2 * 10; i++)
up_weights->data.f32[i] = 0;
ccv_cnnp_model_set_parameter(final1, ccv_cnnp_model_parameters(up1, ALL_PARAMETERS, ALL_PARAMETERS), up_weights);
ccv_nnc_tensor_free(up_weights);
ccv_cnnp_model_share_parameters(final1, ccv_cnnp_model_parameters(final1, ALL_PARAMETERS, ALL_PARAMETERS), final0, ccv_cnnp_model_parameters(final0, ALL_PARAMETERS, ALL_PARAMETERS), _ccv_nnc_same_namer, 0);
ccv_cnnp_model_evaluate(final1, (ccv_cnnp_evaluate_param_t){
.requires_grad = 0,
}, TENSOR_LIST(x), TENSOR_LIST(y1), 0, 0);
Expand Down

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