vbatts/llama.cpp
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scratch buffer bug fixes in forward_batch_wo_cache_flash_attn_train

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data of permute and reshape is the same as their input.
if we want to preserve the output of permute/reshape, we also need to preserve their inputs.

replace reshape(src0, src1) with reshape_nd calls so that we don't need src1.

replace (temporary) t03 with ggml_repeat(ctx0, layer.attention_norm, t02).
in the future we could also use the new broadcasting ggml_mul to avoid these repeat calls.
for this we need backward pass of broadcasting ggml_mul.
This commit is contained in:
xaedes 2023-06-11 23:10:41 +02:00
parent fdeb99784a
commit efd7314d27
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1 changed files with 66 additions and 49 deletions
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115
examples/train-text-from-scratch/train-text-from-scratch.cpp
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@ -1405,21 +1405,26 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
last_buf = buf;
};
auto clr_buf = [&buf_offs] (int buf) {
bool track_max_mem = false;
size_t buf_maxs[3] = { 0, 0, 0 };
auto clr_buf = [ctx0, &last_buf, &buf_offs, &buf_size, &buf_data, &buf_maxs, track_max_mem] (int buf) {
if (buf < 0) return;
// size_t last_offs = 0;
// last_offs = ggml_set_scratch(ctx, { 0, 0, nullptr, });
// if (last_buf >= 0) {
// buf_offs[last_buf] = last_offs;
// }
// buf_max_size[buf] = std::max(buf_max_size[buf], buf_offs[buf]);
if (track_max_mem) {
size_t last_offs = 0;
last_offs = ggml_set_scratch(ctx0, { 0, 0, nullptr, });
if (last_buf >= 0) {
buf_offs[last_buf] = last_offs;
buf_maxs[last_buf] = std::max(buf_maxs[last_buf], buf_offs[last_buf]);
}
}
buf_offs[buf] = 0;
// if (last_buf >= 0) {
// size_t offs = buf_offs[last_buf];
// size_t size = buf_size[last_buf];
// void * data = buf_data[last_buf];
// ggml_set_scratch(ctx0, { offset, size, data, });
// }
if (track_max_mem && last_buf >= 0) {
size_t offs = buf_offs[last_buf];
size_t size = buf_size[last_buf];
void * data = buf_data[last_buf];
ggml_set_scratch(ctx0, { offs, size, data, });
}
};
auto view__q = [ctx0, n_embd, n_head, N, n_batch] (struct ggml_tensor * t) -> struct ggml_tensor * {
@ -1471,6 +1476,13 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
use_buf(-1);
// need to create grads for model parameters, so that expand(..) correctly populates cgraph->leafs & cgraph->grads
// this wastes memory, because unnecessary grad for each op is automatically created:
// the automatically generated grad is unnecessary because we later manually set the grad (e.g. t35->grad = expand(gb, ...) ).
// this discards the automatically generated grad resulting in wasted memory.
// TODO: improve this, possibly by changing expand(..) to not use ggml_build_forward_expand.
// expand should correctly set cgraph->nodes.
// cgraph->leafs & cgraph->grads could be set in another pass after the last expand call.
model->tok_embeddings->grad = ggml_dup_tensor(ctx0, model->tok_embeddings->grad);
model->norm->grad = ggml_dup_tensor(ctx0, model->norm->grad);
model->output->grad = ggml_dup_tensor(ctx0, model->output->grad);
@ -1491,11 +1503,13 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
clr_buf(1);
clr_buf(2);
use_buf(0);
use_buf(-1);
struct ggml_tensor * t00 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch); assert_shape_1d(t00, N*n_batch);
memcpy(t00->data, tokens_input->data, ggml_element_size(t00)*N*n_batch);
use_buf(0);
struct ggml_tensor * t01 = expand(gf, ggml_get_rows(ctx0, model->tok_embeddings, t00)); assert_shape_2d(t01, n_embd, N*n_batch);
// need to remember these for the backward pass
@ -1536,35 +1550,35 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
struct my_llama_layer & layer = model->layers[il];
// tensors with values necessary for backward pass are in persistent buf(0)
// other tensors with buf(1) are only temporary needed, and their memory reused after layer is completed.
use_buf(0); struct ggml_tensor * t02 = expand(gf, ggml_rms_norm (ctx0, cur)); assert_shape_2d(t02, n_embd, N*n_batch); // n_embd, N*n_batch
use_buf(0); struct ggml_tensor * t02 = expand(gf, ggml_rms_norm (ctx0, cur)); assert_shape_2d(t02, n_embd, N*n_batch);
use_buf(1); struct ggml_tensor * t03 = expand(gf, ggml_repeat (ctx0, layer.attention_norm, t02)); assert_shape_2d(t03, n_embd, N*n_batch);
use_buf(0); struct ggml_tensor * t04 = expand(gf, ggml_mul (ctx0, t02, t03)); assert_shape_2d(t04, n_embd, N*n_batch); // n_embd, N*n_batch
use_buf(1); struct ggml_tensor * t05 = expand(gf, ggml_mul_mat (ctx0, layer.wq, t04)); assert_shape_2d(t05, n_embd, N*n_batch);
use_buf(1); struct ggml_tensor * t06 = expand(gf, ggml_reshape_4d (ctx0, t05, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch);
use_buf(1); struct ggml_tensor * t07 = expand(gf, ggml_rope_inplace (ctx0, t06, n_past, n_rot, rope_mode)); assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch);
use_buf(1); struct ggml_tensor * t08 = expand(gf, ggml_mul_mat (ctx0, layer.wk, t04)); assert_shape_2d(t08, n_embd, N*n_batch);
use_buf(1); struct ggml_tensor * t09 = expand(gf, ggml_reshape_4d (ctx0, t08, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch);
use_buf(1); struct ggml_tensor * t10 = expand(gf, ggml_rope_inplace (ctx0, t09, n_past, n_rot, rope_mode)); assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch);
use_buf(1); struct ggml_tensor * t11 = expand(gf, ggml_mul_mat (ctx0, t04, layer.wv)); assert_shape_2d(t11, N*n_batch, n_embd);
use_buf(1); struct ggml_tensor * t12 = expand(gf, ggml_reshape_4d (ctx0, t11, N, n_batch, n_embd/n_head, n_head)); assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head);
use_buf(0); struct ggml_tensor * t13 = expand(gf, ggml_permute (ctx0, t07, 0, 2, 1, 3)); assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch); // n_embd/n_head, N, n_head, n_batch
use_buf(0); struct ggml_tensor * t14 = expand(gf, ggml_permute (ctx0, t10, 0, 2, 1, 3)); assert_shape_4d(t14, n_embd/n_head, N, n_head, n_batch); // n_embd/n_head, N, n_head, n_batch
use_buf(0); struct ggml_tensor * t15 = expand(gf, ggml_permute (ctx0, t12, 0, 3, 1, 2)); assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch); // N, n_embd/n_head, n_head, n_batch
use_buf(1); struct ggml_tensor * t16 = expand(gf, ggml_flash_attn (ctx0, t13, t14, t15, true)); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
use_buf(0); struct ggml_tensor * t04 = expand(gf, ggml_mul (ctx0, t02, t03)); assert_shape_2d(t04, n_embd, N*n_batch);
use_buf(0); struct ggml_tensor * t05 = expand(gf, ggml_mul_mat (ctx0, layer.wq, t04)); assert_shape_2d(t05, n_embd, N*n_batch);
use_buf(0); struct ggml_tensor * t06 = expand(gf, ggml_reshape_4d (ctx0, t05, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch);
use_buf(0); struct ggml_tensor * t07 = expand(gf, ggml_rope_inplace (ctx0, t06, n_past, n_rot, rope_mode)); assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch);
use_buf(0); struct ggml_tensor * t08 = expand(gf, ggml_mul_mat (ctx0, layer.wk, t04)); assert_shape_2d(t08, n_embd, N*n_batch);
use_buf(0); struct ggml_tensor * t09 = expand(gf, ggml_reshape_4d (ctx0, t08, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch);
use_buf(0); struct ggml_tensor * t10 = expand(gf, ggml_rope_inplace (ctx0, t09, n_past, n_rot, rope_mode)); assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch);
use_buf(0); struct ggml_tensor * t11 = expand(gf, ggml_mul_mat (ctx0, t04, layer.wv)); assert_shape_2d(t11, N*n_batch, n_embd);
use_buf(0); struct ggml_tensor * t12 = expand(gf, ggml_reshape_4d (ctx0, t11, N, n_batch, n_embd/n_head, n_head)); assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head);
use_buf(0); struct ggml_tensor * t13 = expand(gf, ggml_permute (ctx0, t07, 0, 2, 1, 3)); assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch);
use_buf(0); struct ggml_tensor * t14 = expand(gf, ggml_permute (ctx0, t10, 0, 2, 1, 3)); assert_shape_4d(t14, n_embd/n_head, N, n_head, n_batch);
use_buf(0); struct ggml_tensor * t15 = expand(gf, ggml_permute (ctx0, t12, 0, 3, 1, 2)); assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch);
use_buf(0); struct ggml_tensor * t16 = expand(gf, ggml_flash_attn (ctx0, t13, t14, t15, true)); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
use_buf(1); struct ggml_tensor * t17 = expand(gf, ggml_permute (ctx0, t16, 0, 2, 1, 3)); assert_shape_4d(t17, n_embd/n_head, n_head, N, n_batch);
use_buf(1); struct ggml_tensor * t18 = expand(gf, ggml_cont (ctx0, t17)); assert_shape_4d(t18, n_embd/n_head, n_head, N, n_batch);
use_buf(0); struct ggml_tensor * t19 = expand(gf, ggml_reshape_2d (ctx0, t18, n_embd, N*n_batch)); assert_shape_2d(t19, n_embd, N*n_batch); // n_embd, N*n_batch
use_buf(0); struct ggml_tensor * t18 = expand(gf, ggml_cont (ctx0, t17)); assert_shape_4d(t18, n_embd/n_head, n_head, N, n_batch);
use_buf(0); struct ggml_tensor * t19 = expand(gf, ggml_reshape_2d (ctx0, t18, n_embd, N*n_batch)); assert_shape_2d(t19, n_embd, N*n_batch);
use_buf(1); struct ggml_tensor * t20 = expand(gf, ggml_mul_mat (ctx0, layer.wo, t19)); assert_shape_2d(t20, n_embd, N*n_batch);
use_buf(0); struct ggml_tensor * t21 = expand(gf, ggml_add (ctx0, t20, cur)); assert_shape_2d(t21, n_embd, N*n_batch); // n_embd, N*n_batch
use_buf(0); struct ggml_tensor * t22 = expand(gf, ggml_rms_norm (ctx0, t21)); assert_shape_2d(t22, n_embd, N*n_batch); // n_embd, N*n_batch
use_buf(0); struct ggml_tensor * t21 = expand(gf, ggml_add (ctx0, t20, cur)); assert_shape_2d(t21, n_embd, N*n_batch);
use_buf(0); struct ggml_tensor * t22 = expand(gf, ggml_rms_norm (ctx0, t21)); assert_shape_2d(t22, n_embd, N*n_batch);
use_buf(1); struct ggml_tensor * t23 = expand(gf, ggml_repeat (ctx0, layer.ffn_norm, t22)); assert_shape_2d(t23, n_embd, N*n_batch);
use_buf(0); struct ggml_tensor * t24 = expand(gf, ggml_mul (ctx0, t23, t22)); assert_shape_2d(t24, n_embd, N*n_batch); // n_embd, N*n_batch
use_buf(0); struct ggml_tensor * t25 = expand(gf, ggml_mul_mat (ctx0, layer.w3, t24)); assert_shape_2d(t25, n_ff, N*n_batch); // n_ff, N*n_batch
use_buf(0); struct ggml_tensor * t26 = expand(gf, ggml_mul_mat (ctx0, layer.w1, t24)); assert_shape_2d(t26, n_ff, N*n_batch); // n_ff, N*n_batch
use_buf(0); struct ggml_tensor * t27 = expand(gf, ggml_silu (ctx0, t26)); assert_shape_2d(t27, n_ff, N*n_batch); // n_ff, N*n_batch
use_buf(0); struct ggml_tensor * t28 = expand(gf, ggml_mul (ctx0, t27, t25)); assert_shape_2d(t28, n_ff, N*n_batch); // n_ff, N*n_batch
use_buf(0); struct ggml_tensor * t24 = expand(gf, ggml_mul (ctx0, t23, t22)); assert_shape_2d(t24, n_embd, N*n_batch);
use_buf(0); struct ggml_tensor * t25 = expand(gf, ggml_mul_mat (ctx0, layer.w3, t24)); assert_shape_2d(t25, n_ff, N*n_batch);
use_buf(0); struct ggml_tensor * t26 = expand(gf, ggml_mul_mat (ctx0, layer.w1, t24)); assert_shape_2d(t26, n_ff, N*n_batch);
use_buf(0); struct ggml_tensor * t27 = expand(gf, ggml_silu (ctx0, t26)); assert_shape_2d(t27, n_ff, N*n_batch);
use_buf(0); struct ggml_tensor * t28 = expand(gf, ggml_mul (ctx0, t27, t25)); assert_shape_2d(t28, n_ff, N*n_batch);
use_buf(1); struct ggml_tensor * t29 = expand(gf, ggml_mul_mat (ctx0, layer.w2, t28)); assert_shape_2d(t29, n_embd, N*n_batch);
use_buf(0); struct ggml_tensor * t30 = expand(gf, ggml_add (ctx0, t21, t29)); assert_shape_2d(t30, n_embd, N*n_batch); // n_embd, N*n_batch
use_buf(0); struct ggml_tensor * t30 = expand(gf, ggml_add (ctx0, t21, t29)); assert_shape_2d(t30, n_embd, N*n_batch);
t02L[il] = t02;
t03L[il] = t03;
t04L[il] = t04;
@ -1602,6 +1616,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
struct ggml_tensor * t31 = expand(gf, ggml_rms_norm (ctx0, cur)); assert_shape_2d(t31, n_embd, N*n_batch);
struct ggml_tensor * t32 = expand(gf, ggml_repeat (ctx0, model->norm, t31)); assert_shape_2d(t32, n_embd, N*n_batch);
struct ggml_tensor * t33 = expand(gf, ggml_mul (ctx0, t32, t31)); assert_shape_2d(t33, n_embd, N*n_batch);
use_buf(-1);
struct ggml_tensor * t34 = expand(gf, ggml_mul_mat (ctx0, model->output, t33)); assert_shape_2d(t34, n_vocab, N*n_batch);
struct ggml_tensor * t35 = expand(gf, ggml_reshape_3d(ctx0, t34, n_vocab, N, n_batch)); assert_shape_3d(t35, n_vocab, N, n_batch);
struct ggml_tensor * t36 = expand(gf, ggml_cross_entropy_loss(ctx0, t35, targets)); assert_shape_1d(t36, 1);
@ -1705,10 +1720,10 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
*gb = *gf;
use_buf(-1);
// t36->grad gets set to one by optimizer, so we need to create the tensor.
// initialize it with 1.0f to make sure.
GGML_ASSERT(t36->grad != NULL);
// use_buf(-1);
// t36->grad = expand(gb, ggml_new_f32(ctx0, 1.0f));
use_buf(1);
@ -1770,7 +1785,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
t30->grad = expand(gb, ggml_add(ctx0, t30->grad, grad_layer_inp->grad)); assert_shape_2d(t30->grad, n_embd, N*n_batch);
}
clr_buf(2);
t29->grad = t30->grad; assert_shape_2d(t29->grad, n_embd, N*n_batch);
t29->grad = t30->grad; assert_shape_2d(t29->grad, n_embd, N*n_batch);
t28->grad = expand(gb, ggml_out_prod(ctx0, layer.w2, ggml_transpose(ctx0, t29->grad))); assert_shape_2d(t28->grad, n_ff, N*n_batch);
t27->grad = expand(gb, ggml_mul(ctx0, t28->grad, t25)); assert_shape_2d(t27->grad, n_ff, N*n_batch);
t26->grad = expand(gb, ggml_silu_back(ctx0, t26, t27->grad)); assert_shape_2d(t26->grad, n_ff, N*n_batch);
@ -1786,7 +1801,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
use_buf(1);
t20->grad = t21->grad; assert_shape_2d(t20->grad, n_embd, N*n_batch);
t19->grad = expand(gb, ggml_out_prod(ctx0, layer.wo, ggml_transpose(ctx0, t20->grad))); assert_shape_2d(t19->grad, n_embd, N*n_batch);
t18->grad = expand(gb, ggml_reshape(ctx0, t19->grad, t18)); assert_shape_4d(t18->grad, n_embd/n_head, n_head, N, n_batch);
t18->grad = expand(gb, ggml_reshape_4d(ctx0, t19->grad, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t18->grad, n_embd/n_head, n_head, N, n_batch);
t17->grad = t18->grad; assert_shape_4d(t17->grad, n_embd/n_head, n_head, N, n_batch);
t16->grad = expand(gb, ggml_permute(ctx0, t17->grad, 0, 2, 1, 3)); assert_shape_4d(t16->grad, n_embd/n_head, N, n_head, n_batch);
struct ggml_tensor * flash_attn = expand(gb, ggml_flash_attn_back(ctx0, t13, t14, t15, t16->grad, true)); assert_shape_4d(flash_attn, n_embd/n_head, N*3, n_head, n_batch);
@ -1794,13 +1809,13 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
t14->grad = expand(gb, view__k(flash_attn)); assert_shape_4d(t14->grad, n_embd/n_head, N, n_head, n_batch);
t13->grad = expand(gb, view__q(flash_attn)); assert_shape_4d(t13->grad, n_embd/n_head, N, n_head, n_batch);
t12->grad = expand(gb, ggml_permute(ctx0, t15->grad, 0, 2, 3, 1)); assert_shape_4d(t12->grad, N, n_batch, n_embd/n_head, n_head);
t11->grad = expand(gb, ggml_reshape(ctx0, ggml_cont(ctx0, t12->grad), t11)); assert_shape_2d(t11->grad, N*n_batch, n_embd);
t11->grad = expand(gb, ggml_reshape_2d(ctx0, ggml_cont(ctx0, t12->grad), N*n_batch, n_embd)); assert_shape_2d(t11->grad, N*n_batch, n_embd);
t10->grad = expand(gb, ggml_permute(ctx0, t14->grad, 0, 2, 1, 3)); assert_shape_4d(t10->grad, n_embd/n_head, n_head, N, n_batch);
t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode)); assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch);
t08->grad = expand(gb, ggml_reshape(ctx0, t09->grad, t08)); assert_shape_2d(t08->grad, n_embd, N*n_batch);
t08->grad = expand(gb, ggml_reshape_2d(ctx0, t09->grad, n_embd, N*n_batch)); assert_shape_2d(t08->grad, n_embd, N*n_batch);
t07->grad = expand(gb, ggml_permute(ctx0, t13->grad, 0, 2, 1, 3)); assert_shape_4d(t07->grad, n_embd/n_head, n_head, N, n_batch);
t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode)); assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch);
t05->grad = expand(gb, ggml_reshape(ctx0, t06->grad, t05)); assert_shape_2d(t05->grad, n_embd, N*n_batch);
t05->grad = expand(gb, ggml_reshape_2d(ctx0, t06->grad, n_embd, N*n_batch)); assert_shape_2d(t05->grad, n_embd, N*n_batch);
t04->grad = expand(gb, ggml_add_inplace(ctx0,
ggml_add_inplace(ctx0,
ggml_out_prod(ctx0, layer.wv, t11->grad),
@ -1808,9 +1823,8 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
ggml_out_prod(ctx0, layer.wq, ggml_transpose(ctx0, t05->grad)))); assert_shape_2d(t04->grad, n_embd, N*n_batch);
t03->grad = expand(gb, ggml_mul(ctx0, t04->grad, t02)); assert_shape_2d(t04->grad, n_embd, N*n_batch);
use_buf(2);
t02->grad = expand(gb, ggml_mul(ctx0, t04->grad, t03)); assert_shape_2d(t02->grad, n_embd, N*n_batch);
t02->grad = expand(gb, ggml_mul(ctx0, t04->grad, ggml_repeat(ctx0, layer.attention_norm, t02))); assert_shape_2d(t02->grad, n_embd, N*n_batch);
back_layer_inp = t02;
use_buf(1);
use_buf(-1);
layer.attention_norm->grad = expand(gb, add_or_set(layer.attention_norm->grad, ggml_repeat_back(ctx0, t03->grad, layer.attention_norm))); assert_shape_1d(layer.attention_norm->grad, n_embd);
@ -1822,18 +1836,21 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
layer.w1->grad = expand(gb, add_or_set(layer.w1->grad, ggml_out_prod(ctx0, t24, t26->grad))); assert_shape_2d(layer.w1->grad, n_embd, n_ff);
layer.w2->grad = expand(gb, add_or_set(layer.w2->grad, ggml_out_prod(ctx0, t28, t29->grad))); assert_shape_2d(layer.w2->grad, n_ff, n_embd);
layer.w3->grad = expand(gb, add_or_set(layer.w3->grad, ggml_out_prod(ctx0, t24, t25->grad))); assert_shape_2d(layer.w3->grad, n_embd, n_ff);
use_buf(1);
}
clr_buf(1);
use_buf(1);
t01->grad = expand(gb, ggml_add_inplace(ctx0, grad_layer_inp->grad, ggml_rms_norm_back(ctx0, t01, back_layer_inp->grad))); assert_shape_2d(t01->grad, n_embd, N*n_batch);
use_buf(-1);
model->tok_embeddings->grad = expand(gb, ggml_get_rows_back(ctx0, t01->grad, t00, model->tok_embeddings)); assert_shape_2d(model->tok_embeddings->grad, n_embd, n_vocab);
clr_buf(2);
clr_buf(1);
*logits = t35;
if (track_max_mem) {
printf("%s: max size compute buf0: %zu\n", __func__, buf_maxs[0]);
printf("%s: max size compute buf1: %zu\n", __func__, buf_maxs[1]);
printf("%s: max size compute buf2: %zu\n", __func__, buf_maxs[2]);
}
return t36;
}