vbatts/llama.cpp
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Merge commit 'f954edda93' into archive_lib

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# Conflicts:
#	ggml.c
This commit is contained in:
Concedo 2023-05-14 18:34:56 +08:00
parent b692e4d2a4
commit c81dd58e76
4 changed files with 18 additions and 18 deletions
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4
ggml.c
View file

@ -7514,8 +7514,8 @@ static void ggml_compute_forward_add1_q_f32(
const size_t nb3 = dst->nb[3];
const enum ggml_type type = src0->type;
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q;
dequantize_row_q_t const dequantize_row_q = get_quantize_fn(type).dequantize_row_q;
quantize_row_q_t const quantize_row_q = get_quantize_fn(type).quantize_row_q;
// we don't support permuted src0
GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);

8
otherarch/gpt2_v2.cpp
View file

@ -481,18 +481,18 @@ bool gpt2_eval(
// KQ_scaled = KQ / sqrt(n_embd/n_head)
// [n_past + N, N, 12]
struct ggml_tensor * KQ_scaled =
ggml_scale(ctx0,
ggml_scale_inplace(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
);
// KQ_masked = mask_past(KQ_scaled)
// [n_past + N, N, 12]
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
// KQ = soft_max(KQ_masked)
// [n_past + N, N, 12]
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
// [n_past + N, 64, 12]
@ -619,7 +619,7 @@ bool gpt2_eval(
inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);
// logits -> probs
//inpL = ggml_soft_max(ctx0, inpL);
//inpL = ggml_soft_max_inplace(ctx0, inpL);
// run the computation
ggml_build_forward_expand(&gf, inpL);

12
otherarch/gptj_v2.cpp
View file

@ -416,8 +416,8 @@ bool gptj_eval(
// self-attention
{
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
// store key and value to memory
{
@ -451,16 +451,16 @@ bool gptj_eval(
// KQ_scaled = KQ / sqrt(n_embd/n_head)
struct ggml_tensor * KQ_scaled =
ggml_scale(ctx0,
ggml_scale_inplace(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
);
// KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
// KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
struct ggml_tensor * V =
@ -544,7 +544,7 @@ bool gptj_eval(
}
// logits -> probs
//inpL = ggml_soft_max(ctx0, inpL);
//inpL = ggml_soft_max_inplace(ctx0, inpL);
// run the computation
ggml_build_forward_expand(&gf, inpL);

12
otherarch/neox.cpp
View file

@ -423,8 +423,8 @@ bool stablelm_eval(
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 2*sizeof(float)*n_embd/n_head));
// using mode = 2 for GPT-NeoX mode
Qcur = ggml_rope(ctx0, Qcur, n_past, n_rot, 2);
Kcur = ggml_rope(ctx0, Kcur, n_past, n_rot, 2);
Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, n_rot, 2);
Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, n_rot, 2);
// store key and value to memory
{
@ -458,16 +458,16 @@ bool stablelm_eval(
// KQ_scaled = KQ / sqrt(n_embd/n_head)
struct ggml_tensor * KQ_scaled =
ggml_scale(ctx0,
ggml_scale_inplace(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
);
// KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
// KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
struct ggml_tensor * V =
@ -578,7 +578,7 @@ bool stablelm_eval(
}
// logits -> probs
//inpL = ggml_soft_max(ctx0, inpL);
//inpL = ggml_soft_max_inplace(ctx0, inpL);
// run the computation
ggml_build_forward_expand(&gf, inpL);