vbatts/llama.cpp
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fix finetune to support grouped-query-attention (using flash-attention)

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note: ggml changes to ggml_out_prod are necessary to support grouped-query-attention without flash-attention.
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xaedes 2023-09-09 17:10:23 +02:00
parent 833a56c144
commit 35260f7d74
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GPG key ID: 30030EDD817EA2B1
1 changed files with 90 additions and 71 deletions
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161
examples/finetune/finetune.cpp
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@ -157,13 +157,26 @@ struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struc
} }
struct my_llama_hparams { struct my_llama_hparams {
uint32_t n_vocab = 32000; uint32_t n_vocab = 32000;
uint32_t n_ctx = 512; // this is provided as user input? uint32_t n_ctx = 512;
uint32_t n_embd = 4096; uint32_t n_embd = 4096;
uint32_t n_ff = 11008; uint32_t n_ff = 11008;
uint32_t n_head = 32; uint32_t n_head = 32;
uint32_t n_layer = 32; uint32_t n_head_kv = 32;
uint32_t n_rot = 64; uint32_t n_layer = 32;
uint32_t n_rot = 64;
uint32_t n_gqa() const {
return n_head/n_head_kv;
}
uint32_t n_embd_head() const {
return n_embd/n_head;
}
uint32_t n_embd_gqa() const {
return n_embd/n_gqa();
}
bool operator!=(const my_llama_hparams& other) const { bool operator!=(const my_llama_hparams& other) const {
return memcmp(this, &other, sizeof(other)); return memcmp(this, &other, sizeof(other));
@ -404,13 +417,14 @@ void init_model(struct llama_model * input, struct my_llama_model * model, uint3
return tn_buf.data(); return tn_buf.data();
}; };
hparams.n_vocab = llama_model_n_vocab(input); hparams.n_vocab = llama_model_n_vocab(input);
hparams.n_ctx = n_ctx; hparams.n_ctx = n_ctx;
hparams.n_embd = llama_model_n_embd(input); hparams.n_embd = llama_model_n_embd(input);
hparams.n_ff = llama_model_n_ff(input); hparams.n_ff = llama_model_n_ff(input);
hparams.n_head = llama_model_n_head(input); hparams.n_head = llama_model_n_head(input);
hparams.n_layer = llama_model_n_layer(input); hparams.n_head_kv = llama_model_n_head_kv(input);
hparams.n_rot = llama_model_n_rot(input); hparams.n_layer = llama_model_n_layer(input);
hparams.n_rot = llama_model_n_rot(input);
model->tok_embeddings = llama_get_model_tensor(input, tn(LLM_TENSOR_TOKEN_EMBD)); model->tok_embeddings = llama_get_model_tensor(input, tn(LLM_TENSOR_TOKEN_EMBD));
model->norm = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT_NORM)); model->norm = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT_NORM));
@ -472,10 +486,11 @@ void set_param_lora(struct my_llama_lora * lora) {
void init_lora(const struct my_llama_model * model, struct my_llama_lora * lora) { void init_lora(const struct my_llama_model * model, struct my_llama_lora * lora) {
const auto & lparams = lora->hparams; const auto & lparams = lora->hparams;
const uint32_t n_embd = model->hparams.n_embd; const uint32_t n_embd = model->hparams.n_embd;
const uint32_t n_layer = model->hparams.n_layer; const uint32_t n_embd_gqa = model->hparams.n_embd_gqa();
const uint32_t n_vocab = model->hparams.n_vocab; const uint32_t n_layer = model->hparams.n_layer;
const uint32_t n_ff = model->hparams.n_ff; const uint32_t n_vocab = model->hparams.n_vocab;
const uint32_t n_ff = model->hparams.n_ff;
lora->train_its = 0; lora->train_its = 0;
lora->train_samples = 0; lora->train_samples = 0;
@ -527,9 +542,9 @@ void init_lora(const struct my_llama_model * model, struct my_llama_lora * lora)
layer.wq_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd); layer.wq_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd);
layer.wq_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd); layer.wq_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd);
layer.wk_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd); layer.wk_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd);
layer.wk_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd); layer.wk_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd_gqa);
layer.wv_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd); layer.wv_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd);
layer.wv_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd); layer.wv_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd_gqa);
layer.wo_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd); layer.wo_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd);
layer.wo_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd); layer.wo_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd);
@ -770,19 +785,23 @@ struct ggml_tensor * llama_build_lora_finetune_graphs(
ggml_set_scratch(ctx, { 0, 0, nullptr, }); ggml_set_scratch(ctx, { 0, 0, nullptr, });
const int n_past = 0; const int n_past = 0;
const int N = n_tokens; const int N = n_tokens;
const auto & hparams = model->hparams; const auto & hparams = model->hparams;
const int n_ctx = hparams.n_ctx; const int n_ctx = hparams.n_ctx;
const int n_vocab = hparams.n_vocab; const int n_vocab = hparams.n_vocab;
const int n_embd = hparams.n_embd; const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer; const int n_layer = hparams.n_layer;
const int n_head = hparams.n_head; const int n_head = hparams.n_head;
const int n_rot = hparams.n_rot; const int n_head_kv = hparams.n_head_kv;
const int n_ff = hparams.n_ff; const int n_rot = hparams.n_rot;
const int n_ff = hparams.n_ff;
const int n_embd_head = hparams.n_embd_head();
const int n_embd_gqa = hparams.n_embd_gqa();
const float rms_norm_eps = lora->hparams.f_norm_rms_eps; const float rms_norm_eps = lora->hparams.f_norm_rms_eps;
const float rope_freq_base = lora->hparams.rope_freq_base; const float rope_freq_base = lora->hparams.rope_freq_base;
const float rope_freq_scale = lora->hparams.rope_freq_scale; const float rope_freq_scale = lora->hparams.rope_freq_scale;
GGML_ASSERT((size_t) n_layer == lora->layers.size()); GGML_ASSERT((size_t) n_layer == lora->layers.size());
GGML_ASSERT(n_embd_head == n_rot);
auto set_name = [](struct ggml_tensor * t, const char * n) { auto set_name = [](struct ggml_tensor * t, const char * n) {
ggml_set_name(t, n); ggml_set_name(t, n);
@ -853,64 +872,64 @@ struct ggml_tensor * llama_build_lora_finetune_graphs(
struct ggml_tensor * w2 = add_to_f32(ctx, layer.w2, ggml_mul_mat(ctx, llayer.w2_a, llayer.w2_b)); struct ggml_tensor * w2 = add_to_f32(ctx, layer.w2, ggml_mul_mat(ctx, llayer.w2_a, llayer.w2_b));
struct ggml_tensor * w3 = add_to_f32(ctx, layer.w3, ggml_mul_mat(ctx, llayer.w3_a, llayer.w3_b)); struct ggml_tensor * w3 = add_to_f32(ctx, layer.w3, ggml_mul_mat(ctx, llayer.w3_a, llayer.w3_b));
struct ggml_tensor * t02 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t02, "t02"); assert_shape_2d(t02, n_embd, N*n_batch); struct ggml_tensor * t02 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t02, "t02"); assert_shape_2d(t02, n_embd, N*n_batch);
struct ggml_tensor * t03 = ggml_repeat (ctx, attention_norm, t02); set_name(t03, "t03"); assert_shape_2d(t03, n_embd, N*n_batch); struct ggml_tensor * t03 = ggml_repeat (ctx, attention_norm, t02); set_name(t03, "t03"); assert_shape_2d(t03, n_embd, N*n_batch);
struct ggml_tensor * t04 = ggml_mul (ctx, t03, t02); set_name(t04, "t04"); assert_shape_2d(t04, n_embd, N*n_batch); struct ggml_tensor * t04 = ggml_mul (ctx, t03, t02); set_name(t04, "t04"); assert_shape_2d(t04, n_embd, N*n_batch);
struct ggml_tensor * t05 = ggml_mul_mat (ctx, wq, t04); set_name(t05, "t05"); assert_shape_2d(t05, n_embd, N*n_batch); struct ggml_tensor * t05 = ggml_mul_mat (ctx, wq, t04); set_name(t05, "t05"); assert_shape_2d(t05, n_embd, N*n_batch);
struct ggml_tensor * t06 = ggml_reshape_4d (ctx, t05, n_embd/n_head, n_head, N, n_batch); set_name(t06, "t06"); assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch); struct ggml_tensor * t06 = ggml_reshape_4d (ctx, t05, n_embd_head, n_head, N, n_batch); set_name(t06, "t06"); assert_shape_4d(t06, n_embd_head, n_head, N, n_batch);
struct ggml_tensor * t07 = rope (t06); set_name(t07, "t07"); assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch); struct ggml_tensor * t07 = rope (t06); set_name(t07, "t07"); assert_shape_4d(t07, n_embd_head, n_head, N, n_batch);
struct ggml_tensor * t08 = ggml_mul_mat (ctx, wk, t04); set_name(t08, "t08"); assert_shape_2d(t08, n_embd, N*n_batch); struct ggml_tensor * t08 = ggml_mul_mat (ctx, wk, t04); set_name(t08, "t08"); assert_shape_2d(t08, n_embd_gqa, N*n_batch);
struct ggml_tensor * t09 = ggml_reshape_4d (ctx, t08, n_embd/n_head, n_head, N, n_batch); set_name(t09, "t09"); assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch); struct ggml_tensor * t09 = ggml_reshape_4d (ctx, t08, n_embd_head, n_head_kv, N, n_batch); set_name(t09, "t09"); assert_shape_4d(t09, n_embd_head, n_head_kv, N, n_batch);
struct ggml_tensor * t10 = rope (t09); set_name(t10, "t10"); assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch); struct ggml_tensor * t10 = rope (t09); set_name(t10, "t10"); assert_shape_4d(t10, n_embd_head, n_head_kv, N, n_batch);
struct ggml_tensor * t11; struct ggml_tensor * t11;
if (ggml_is_quantized(wv->type)) { if (ggml_is_quantized(wv->type)) {
struct ggml_tensor * t11_1 = ggml_mul_mat (ctx, wv, t04); set_name(t11_1, "t11_1"); assert_shape_2d(t11_1, n_embd, N*n_batch); struct ggml_tensor * t11_1 = ggml_mul_mat (ctx, wv, t04); set_name(t11_1, "t11_1"); assert_shape_2d(t11_1, n_embd_gqa, N*n_batch);
struct ggml_tensor * t11_2 = ggml_transpose(ctx, t11_1); set_name(t11_2, "t11_2"); assert_shape_2d(t11_2, N*n_batch, n_embd); struct ggml_tensor * t11_2 = ggml_transpose(ctx, t11_1); set_name(t11_2, "t11_2"); assert_shape_2d(t11_2, N*n_batch, n_embd_gqa);
t11 = ggml_cont (ctx, t11_2); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd); t11 = ggml_cont (ctx, t11_2); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd_gqa);
} else { } else {
t11 = ggml_mul_mat (ctx, t04, wv); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd); t11 = ggml_mul_mat (ctx, t04, wv); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd_gqa);
} }
struct ggml_tensor * t12 = ggml_reshape_4d (ctx, t11, N, n_batch, n_embd/n_head, n_head); set_name(t12, "t12"); assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head); struct ggml_tensor * t12 = ggml_reshape_4d (ctx, t11, N, n_batch, n_embd_head, n_head_kv); set_name(t12, "t12"); assert_shape_4d(t12, N, n_batch, n_embd_head, n_head_kv);
struct ggml_tensor * t13 = ggml_permute (ctx, t07, 0, 2, 1, 3); set_name(t13, "t13"); assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch); struct ggml_tensor * t13 = ggml_permute (ctx, t07, 0, 2, 1, 3); set_name(t13, "t13"); assert_shape_4d(t13, n_embd_head, N, n_head, n_batch);
struct ggml_tensor * t14 = ggml_permute (ctx, t10, 0, 2, 1, 3); set_name(t14, "t14"); assert_shape_4d(t14, n_embd/n_head, N, n_head, n_batch); struct ggml_tensor * t14 = ggml_permute (ctx, t10, 0, 2, 1, 3); set_name(t14, "t14"); assert_shape_4d(t14, n_embd_head, N, n_head_kv, n_batch);
struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch); struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd_head, n_head_kv, n_batch);
struct ggml_tensor * t16; struct ggml_tensor * t16;
if (enable_flash_attn) { if (enable_flash_attn) {
t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch);
} else { } else {
struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch); struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch);
struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch); struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch);
struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past); set_name(t16_2, "t16_2"); assert_shape_4d(t16_2, N, N, n_head, n_batch); struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past); set_name(t16_2, "t16_2"); assert_shape_4d(t16_2, N, N, n_head, n_batch);
struct ggml_tensor * t16_3 = ggml_soft_max_inplace (ctx, t16_2); set_name(t16_3, "t16_3"); assert_shape_4d(t16_3, N, N, n_head, n_batch); struct ggml_tensor * t16_3 = ggml_soft_max_inplace (ctx, t16_2); set_name(t16_3, "t16_3"); assert_shape_4d(t16_3, N, N, n_head, n_batch);
t16 = ggml_mul_mat(ctx, t15, t16_3); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); t16 = ggml_mul_mat(ctx, t15, t16_3); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch);
} }
struct ggml_tensor * t17 = ggml_permute (ctx, t16, 0, 2, 1, 3); set_name(t17, "t17"); assert_shape_4d(t17, n_embd/n_head, n_head, N, n_batch); struct ggml_tensor * t17 = ggml_permute (ctx, t16, 0, 2, 1, 3); set_name(t17, "t17"); assert_shape_4d(t17, n_embd_head, n_head, N, n_batch);
struct ggml_tensor * t18 = ggml_cont (ctx, t17); set_name(t18, "t18"); assert_shape_4d(t18, n_embd/n_head, n_head, N, n_batch); struct ggml_tensor * t18 = ggml_cont (ctx, t17); set_name(t18, "t18"); assert_shape_4d(t18, n_embd_head, n_head, N, n_batch);
struct ggml_tensor * t19 = ggml_reshape_2d (ctx, t18, n_embd, N*n_batch); set_name(t19, "t19"); assert_shape_2d(t19, n_embd, N*n_batch); struct ggml_tensor * t19 = ggml_reshape_2d (ctx, t18, n_embd, N*n_batch); set_name(t19, "t19"); assert_shape_2d(t19, n_embd, N*n_batch);
struct ggml_tensor * t20 = ggml_mul_mat (ctx, wo, t19); set_name(t20, "t20"); assert_shape_2d(t20, n_embd, N*n_batch); struct ggml_tensor * t20 = ggml_mul_mat (ctx, wo, t19); set_name(t20, "t20"); assert_shape_2d(t20, n_embd, N*n_batch);
struct ggml_tensor * t21 = ggml_add (ctx, t20, cur); set_name(t21, "t21"); assert_shape_2d(t21, n_embd, N*n_batch); struct ggml_tensor * t21 = ggml_add (ctx, t20, cur); set_name(t21, "t21"); assert_shape_2d(t21, n_embd, N*n_batch);
struct ggml_tensor * t22 = ggml_rms_norm (ctx, t21, rms_norm_eps); set_name(t22, "t22"); assert_shape_2d(t22, n_embd, N*n_batch); struct ggml_tensor * t22 = ggml_rms_norm (ctx, t21, rms_norm_eps); set_name(t22, "t22"); assert_shape_2d(t22, n_embd, N*n_batch);
struct ggml_tensor * t23 = ggml_repeat (ctx, ffn_norm, t22); set_name(t23, "t23"); assert_shape_2d(t23, n_embd, N*n_batch); struct ggml_tensor * t23 = ggml_repeat (ctx, ffn_norm, t22); set_name(t23, "t23"); assert_shape_2d(t23, n_embd, N*n_batch);
struct ggml_tensor * t24 = ggml_mul (ctx, t23, t22); set_name(t24, "t24"); assert_shape_2d(t24, n_embd, N*n_batch); struct ggml_tensor * t24 = ggml_mul (ctx, t23, t22); set_name(t24, "t24"); assert_shape_2d(t24, n_embd, N*n_batch);
struct ggml_tensor * t25 = ggml_mul_mat (ctx, w3, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch); struct ggml_tensor * t25 = ggml_mul_mat (ctx, w3, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch);
struct ggml_tensor * t26 = ggml_mul_mat (ctx, w1, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch); struct ggml_tensor * t26 = ggml_mul_mat (ctx, w1, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch);
struct ggml_tensor * t27 = ggml_silu (ctx, t26); set_name(t27, "t27"); assert_shape_2d(t27, n_ff, N*n_batch); struct ggml_tensor * t27 = ggml_silu (ctx, t26); set_name(t27, "t27"); assert_shape_2d(t27, n_ff, N*n_batch);
struct ggml_tensor * t28 = ggml_mul (ctx, t27, t25); set_name(t28, "t28"); assert_shape_2d(t28, n_ff, N*n_batch); struct ggml_tensor * t28 = ggml_mul (ctx, t27, t25); set_name(t28, "t28"); assert_shape_2d(t28, n_ff, N*n_batch);
struct ggml_tensor * t29 = ggml_mul_mat (ctx, w2, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch); struct ggml_tensor * t29 = ggml_mul_mat (ctx, w2, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch);
struct ggml_tensor * t30 = ggml_add (ctx, t29, t21); set_name(t30, "t30"); assert_shape_2d(t30, n_embd, N*n_batch); struct ggml_tensor * t30 = ggml_add (ctx, t29, t21); set_name(t30, "t30"); assert_shape_2d(t30, n_embd, N*n_batch);
cur = t30; cur = t30;
if (enable_checkpointing) { if (enable_checkpointing) {
checkpoints.push_back(cur); checkpoints.push_back(cur);
} }
} }
struct ggml_tensor * t31 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t31, "t31"); assert_shape_2d(t31, n_embd, N*n_batch); struct ggml_tensor * t31 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t31, "t31"); assert_shape_2d(t31, n_embd, N*n_batch);
struct ggml_tensor * t32 = ggml_repeat (ctx, norm, t31); set_name(t32, "t32"); assert_shape_2d(t32, n_embd, N*n_batch); struct ggml_tensor * t32 = ggml_repeat (ctx, norm, t31); set_name(t32, "t32"); assert_shape_2d(t32, n_embd, N*n_batch);
struct ggml_tensor * t33 = ggml_mul (ctx, t32, t31); set_name(t33, "t33"); assert_shape_2d(t33, n_embd, N*n_batch); struct ggml_tensor * t33 = ggml_mul (ctx, t32, t31); set_name(t33, "t33"); assert_shape_2d(t33, n_embd, N*n_batch);
struct ggml_tensor * t34 = ggml_mul_mat (ctx, output, t33); set_name(t34, "t34"); assert_shape_2d(t34, n_vocab, N*n_batch); struct ggml_tensor * t34 = ggml_mul_mat (ctx, output, t33); set_name(t34, "t34"); assert_shape_2d(t34, n_vocab, N*n_batch);
struct ggml_tensor * t35 = ggml_reshape_3d (ctx, t34, n_vocab, N, n_batch); set_name(t35, "t35"); assert_shape_3d(t35, n_vocab, N, n_batch); struct ggml_tensor * t35 = ggml_reshape_3d (ctx, t34, n_vocab, N, n_batch); set_name(t35, "t35"); assert_shape_3d(t35, n_vocab, N, n_batch);
struct ggml_tensor * t36 = ggml_cross_entropy_loss(ctx, t35, targets); set_name(t36, "t36"); assert_shape_1d(t36, 1); struct ggml_tensor * t36 = ggml_cross_entropy_loss(ctx, t35, targets); set_name(t36, "t36"); assert_shape_1d(t36, 1);
if (enable_checkpointing) { if (enable_checkpointing) {
checkpoints.push_back(t31); checkpoints.push_back(t31);