vbatts/llama.cpp
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llm : add Falcon support (#2717)

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* llama : refactor GGUF constants into static maps

* llama : check if model architecture is known

* llama : refactor llama_model_load_internal()

* gguf : add KV constant maps

* llm : read arch-specific KVs

* convert : add dummy scores + types

* falcon : load tensor data (CPU only)

* llama : fix loading progress bar

* llama : add arch member to llama_model

* falcon : CPU inference working

* falcon : support non-40B models

* falcon : minor

* llama : minor updates

ggml-ci

* convert-falcon-hf-to-gguf.py : fix special token mapping

* llama.cpp : llama default UNK token = id 0

* llama.cpp : fix bpe tokenizer

* llama.cpp : fix the fix of bpe tokenizer

* ggml : pass eps to ggml_norm

* metal : implement RoPE (mode = 2) + avoid ggml_repeat

* ggml : ggml_repeat always creates new tensor

* falcon : copy-paste self-attention from LLaMA

* metal : print extra compute pipeline info

* falcon : minor changes (still chasing the Metal problem)

* llama.cpp : fix linefeed token

* metal : fix GELU kernel numerical stability by using precise::tanh

* metal : temporary workaround for the concurrency optimization bug

* falcon : add CUDA offloading (#2739)

* llama : better model naming and size reporting

* llama : prep new tokenizer support

* llama : advanced BPE tokenizer based on ggllm.cpp imlpementation

* llama : remove oboslete comment

ggml-ci

* common : remove obsolete BPE API + disable test-tokenizer-1

* llama : revert BPE special-case in llama_byte_to_token()

* cuda : add TODOs for RoPE NeoX implementation

* llama : default special tokens based on vocab type

* perplexity : add log for start of tokenization

---------

Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Georgi Gerganov 2023-08-23 23:08:04 +03:00 • committed by GitHub
parent a192860cfe
commit cf658adc83
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18 changed files with 1596 additions and 668 deletions
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ggml.c
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@ -3554,9 +3554,9 @@ inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) {
inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; }
inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; }
static const float GELU_COEF_A = 0.044715f;
static const float GELU_QUICK_COEF = -1.702f;
static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
static const float GELU_COEF_A = 0.044715f;
static const float GELU_QUICK_COEF = -1.702f;
static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
inline static float ggml_gelu_f32(float x) {
return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
@ -5555,10 +5555,6 @@ struct ggml_tensor * ggml_repeat(
is_node = true;
}
if (ggml_are_same_shape(a, b) && !is_node) {
return a;
}
struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, b->n_dims, b->ne);
result->op = GGML_OP_REPEAT;
@ -5789,6 +5785,7 @@ struct ggml_tensor * ggml_silu_back(
static struct ggml_tensor * ggml_norm_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
float eps,
bool inplace) {
bool is_node = false;
@ -5799,7 +5796,7 @@ static struct ggml_tensor * ggml_norm_impl(
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
// TODO: maybe store epsilon here?
ggml_set_op_params(result, &eps, sizeof(eps));
result->op = GGML_OP_NORM;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -5810,14 +5807,16 @@ static struct ggml_tensor * ggml_norm_impl(
struct ggml_tensor * ggml_norm(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_norm_impl(ctx, a, false);
struct ggml_tensor * a,
float eps) {
return ggml_norm_impl(ctx, a, eps, false);
}
struct ggml_tensor * ggml_norm_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_norm_impl(ctx, a, true);
struct ggml_tensor * a,
float eps) {
return ggml_norm_impl(ctx, a, eps, true);
}
// ggml_rms_norm
@ -10619,7 +10618,8 @@ static void ggml_compute_forward_norm_f32(
GGML_TENSOR_UNARY_OP_LOCALS;
const float eps = 1e-5f; // TODO: make this a parameter
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
// TODO: optimize
for (int64_t i03 = 0; i03 < ne03; i03++) {
@ -12537,7 +12537,7 @@ static void ggml_compute_forward_rope_f32(
dst_data[1] = x0*sin_theta*zeta + x1*cos_theta*zeta;
}
} else {
// TODO: this is probably wrong, but I can't figure it out ..
// TODO: this might be wrong for ne0 != n_dims - need double check
// ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
for (int64_t ic = 0; ic < n_dims; ic += 2) {
@ -12666,7 +12666,7 @@ static void ggml_compute_forward_rope_f16(
dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
}
} else {
// TODO: this is probably wrong, but I can't figure it out ..
// TODO: this might be wrong for ne0 != n_dims - need double check
// ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
for (int64_t ic = 0; ic < n_dims; ic += 2) {