CS348Project/llama.cpp
5
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2025-10-30 08:42:00 +00:00
Code Issues Packages Projects Releases Wiki Activity

starcoder : add GPU offloading (#3827)

Browse Source 
* starcoder : do not GPU split 1D bias tensors

* starcoder : offload layers to GPU

ggml-ci
This commit is contained in:
Georgi Gerganov
2023-10-28 12:06:08 +03:00
committed by GitHub
parent 41aee4df82
commit fdee152e4e
1 changed files with 85 additions and 21 deletions
Download Patch File Download Diff File Expand all files Collapse all files

106
llama.cpp
View File

@@ -2695,8 +2695,8 @@ static void llm_load_tensors(
} break; } break;
case LLM_ARCH_STARCODER: case LLM_ARCH_STARCODER:
{ {
model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
model.pos_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU); model.pos_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
// output // output
{ {
@@ -2747,19 +2747,19 @@ static void llm_load_tensors(
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend_split); layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend_split); layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend);
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split); layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend);
layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split); layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend);
if (backend == GGML_BACKEND_GPU) { if (backend == GGML_BACKEND_GPU) {
vram_weights += vram_weights +=
@@ -4616,6 +4616,8 @@ static struct ggml_cgraph * llm_build_starcoder(
const float norm_eps = hparams.f_norm_eps; const float norm_eps = hparams.f_norm_eps;
const int n_gpu_layers = model.n_gpu_layers;
const int32_t n_tokens = batch.n_tokens; const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n; const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head; const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
@@ -4660,6 +4662,27 @@ static struct ggml_cgraph * llm_build_starcoder(
} }
} }
const int i_gpu_start = n_layer - n_gpu_layers;
(void) i_gpu_start;
// offload functions set the tensor output backend to GPU
// tensors are GPU-accelerated if any input or the output has been offloaded
offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
offload_func_t offload_func_kq = llama_nop;
offload_func_t offload_func_v = llama_nop;
#ifdef GGML_USE_CUBLAS
if (n_gpu_layers > n_layer) {
offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
}
if (n_gpu_layers > n_layer + 1) {
offload_func_v = ggml_cuda_assign_buffers_no_alloc;
}
if (n_gpu_layers > n_layer + 2) {
offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
}
#endif // GGML_USE_CUBLAS
{ {
// Compute position embeddings. // Compute position embeddings.
struct ggml_tensor * inp_positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); struct ggml_tensor * inp_positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
@@ -4685,6 +4708,7 @@ static struct ggml_cgraph * llm_build_starcoder(
// KQ_mask (mask for 1 head, it will be broadcasted to all heads) // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
ggml_set_name(KQ_mask, "KQ_mask"); ggml_set_name(KQ_mask, "KQ_mask");
offload_func_kq(KQ_mask);
ggml_allocr_alloc(lctx.alloc, KQ_mask); ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) { if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data; float * data = (float *) KQ_mask->data;
@@ -4708,44 +4732,67 @@ static struct ggml_cgraph * llm_build_starcoder(
ggml_set_name(inpL, "inpL"); ggml_set_name(inpL, "inpL");
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
offload_func_t offload_func = llama_nop;
#ifdef GGML_USE_CUBLAS
if (il >= i_gpu_start) {
offload_func = ggml_cuda_assign_buffers_no_alloc;
}
#endif // GGML_USE_CUBLAS
{ {
// Norm // Norm
cur = ggml_norm(ctx0, inpL, norm_eps); cur = ggml_norm(ctx0, inpL, norm_eps);
offload_func(cur);
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].attn_norm), model.layers[il].attn_norm_b); cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].attn_norm), model.layers[il].attn_norm_b);
offload_func(cur);
} }
{ {
// Self Attention // Self Attention
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv); cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
offload_func_kq(cur);
struct ggml_tensor * tmpq = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*n_embd); cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*n_embd); offload_func_kq(cur);
struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*(n_embd + n_embd_gqa));
struct ggml_tensor * Qcur = tmpq; struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
struct ggml_tensor * tmpv = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
ggml_set_name(tmpq, "tmpq");
ggml_set_name(tmpk, "tmpk");
ggml_set_name(tmpv, "tmpv");
offload_func_kq(tmpq);
offload_func_kq(tmpk);
offload_func_v (tmpv);
struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens);
struct ggml_tensor * Kcur = tmpk; struct ggml_tensor * Kcur = tmpk;
{ {
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens)); struct ggml_tensor * Vcur = ggml_transpose(ctx0, tmpv);
offload_func_v(Vcur);
ggml_set_name(Vcur, "Vcur"); ggml_set_name(Vcur, "Vcur");
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)); struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
offload_func_kq(k);
ggml_set_name(k, "k"); ggml_set_name(k, "k");
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa, struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
( n_ctx)*ggml_element_size(kv_self.v), ( n_ctx)*ggml_element_size(kv_self.v),
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v)); (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
offload_func_v(v);
ggml_set_name(v, "v");
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
} }
struct ggml_tensor * Q = struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
ggml_permute(ctx0, offload_func_kq(Q);
ggml_cpy(ctx0,
Qcur,
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd_head, n_head, n_tokens)),
0, 2, 1, 3);
ggml_set_name(Q, "Q"); ggml_set_name(Q, "Q");
struct ggml_tensor * K = struct ggml_tensor * K =
@@ -4754,23 +4801,28 @@ static struct ggml_cgraph * llm_build_starcoder(
ggml_element_size(kv_self.k)*n_embd_gqa, ggml_element_size(kv_self.k)*n_embd_gqa,
ggml_element_size(kv_self.k)*n_embd_head, ggml_element_size(kv_self.k)*n_embd_head,
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il); ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
offload_func_kq(K);
ggml_set_name(K, "K"); ggml_set_name(K, "K");
// K * Q // K * Q
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
offload_func_kq(KQ);
ggml_set_name(KQ, "KQ"); ggml_set_name(KQ, "KQ");
// KQ_scaled = KQ / sqrt(n_embd_head) // KQ_scaled = KQ / sqrt(n_embd_head)
// KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1] // KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1]
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale); struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
offload_func_kq(KQ_scaled);
ggml_set_name(KQ_scaled, "KQ_scaled"); ggml_set_name(KQ_scaled, "KQ_scaled");
// KQ_masked = mask_past(KQ_scaled) // KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask); struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
offload_func_kq(KQ_masked);
ggml_set_name(KQ_masked, "KQ_masked"); ggml_set_name(KQ_masked, "KQ_masked");
// KQ = soft_max(KQ_masked) // KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked); struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
offload_func_v(KQ_soft_max);
ggml_set_name(KQ_soft_max, "KQ_soft_max"); ggml_set_name(KQ_soft_max, "KQ_soft_max");
// split cached V into n_head heads // split cached V into n_head heads
@@ -4783,22 +4835,25 @@ static struct ggml_cgraph * llm_build_starcoder(
ggml_set_name(V, "V"); ggml_set_name(V, "V");
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
offload_func_v(KQV);
ggml_set_name(KQV, "KQV"); ggml_set_name(KQV, "KQV");
// KQV_merged = KQV.permute(0, 2, 1, 3)
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
offload_func_v(KQV_merged);
ggml_set_name(KQV_merged, "KQV_merged"); ggml_set_name(KQV_merged, "KQV_merged");
// cur = KQV_merged.contiguous().view(n_embd, n_tokens)
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens); cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
offload_func_v(cur);
ggml_set_name(cur, "KQV_merged_contiguous"); ggml_set_name(cur, "KQV_merged_contiguous");
} }
// Projection // Projection
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wo, cur), model.layers[il].bo); cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wo, cur), model.layers[il].bo);
offload_func(cur);
// Add the input // Add the input
cur = ggml_add(ctx0, cur, inpL); cur = ggml_add(ctx0, cur, inpL);
offload_func(cur);
struct ggml_tensor * inpFF = cur; struct ggml_tensor * inpFF = cur;
@@ -4807,27 +4862,36 @@ static struct ggml_cgraph * llm_build_starcoder(
// Norm // Norm
{ {
cur = ggml_norm(ctx0, inpFF, norm_eps); cur = ggml_norm(ctx0, inpFF, norm_eps);
offload_func_nr(cur);
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ffn_norm), model.layers[il].ffn_norm_b); cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ffn_norm), model.layers[il].ffn_norm_b);
offload_func_nr(cur);
} }
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3); cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3);
offload_func(cur);
// GELU activation // GELU activation
cur = ggml_gelu(ctx0, cur); cur = ggml_gelu(ctx0, cur);
offload_func(cur);
// Projection // Projection
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w2, cur), model.layers[il].b2); cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w2, cur), model.layers[il].b2);
offload_func(cur);
} }
inpL = ggml_add(ctx0, cur, inpFF); inpL = ggml_add(ctx0, cur, inpFF);
} }
// Output Norm // Output Norm
{ {
cur = ggml_norm(ctx0, inpL, norm_eps); cur = ggml_norm(ctx0, inpL, norm_eps);
offload_func_nr(cur);
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.output_norm), model.output_norm_b); cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.output_norm), model.output_norm_b);
ggml_set_name(cur, "result_norm");
} }
ggml_set_name(cur, "result_norm");
cur = ggml_mul_mat(ctx0, model.output, cur); cur = ggml_mul_mat(ctx0, model.output, cur);
ggml_set_name(cur, "result_output"); ggml_set_name(cur, "result_output");