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Unverified Commit 527cc978 authored by Jeffrey Morgan's avatar Jeffrey Morgan Committed by GitHub
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llama: update vendored code to commit 40c6d79f (#7875)

parent a37f4a86
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Showing with 1372 additions and 605 deletions
llama/patches/0002-pretokenizer.patch
View file @ 527cc978
......@@ -8,10 +8,10 @@ Subject: [PATCH] pretokenizer
1 file changed, 3 insertions(+), 11 deletions(-)
diff --git a/src/llama.cpp b/src/llama.cpp
index 4c0a1bb6..800dfb95 100644
index 6a6f4c2a..fa09f3b3 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -6287,16 +6287,7 @@ static void llm_load_vocab(
@@ -6362,16 +6362,7 @@ static void llm_load_vocab(
if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
vocab.tokenizer_add_space_prefix = false;
vocab.tokenizer_clean_spaces = true;
......@@ -29,7 +29,7 @@ index 4c0a1bb6..800dfb95 100644
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
} else if (
tokenizer_pre == "llama3" ||
@@ -6398,7 +6389,8 @@ static void llm_load_vocab(
@@ -6473,7 +6464,8 @@ static void llm_load_vocab(
vocab.tokenizer_add_bos = true;
vocab.tokenizer_clean_spaces = false;
} else {
......
llama/patches/0005-embeddings.patch → llama/patches/0003-embeddings.patch
View file @ 527cc978
......@@ -4,14 +4,14 @@ Date: Mon, 16 Sep 2024 15:53:14 -0700
Subject: [PATCH] embeddings
---
src/llama.cpp | 15 +++++++++------
1 file changed, 9 insertions(+), 6 deletions(-)
src/llama.cpp | 9 ++++++---
1 file changed, 6 insertions(+), 3 deletions(-)
diff --git a/src/llama.cpp b/src/llama.cpp
index 800dfb95..a639522d 100644
index fa09f3b3..d1791af0 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -16920,7 +16920,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
@@ -17398,7 +17398,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
const auto n_embd = hparams.n_embd;
// TODO: use a per-batch flag for logits presence instead
......@@ -20,20 +20,15 @@ index 800dfb95..a639522d 100644
const bool has_embd = cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
@@ -17192,20 +17192,23 @@ static int llama_decode_internal(
// no output
@@ -17693,7 +17693,6 @@ static int llama_decode_internal(
res = nullptr;
embd = nullptr;
- } else if (cparams.embeddings) {
} else if (cparams.embeddings) {
- res = nullptr; // do not extract logits for embedding case
- embd = nullptr;
+ }
+
+ if (cparams.embeddings) {
embd = nullptr;
for (int i = ggml_graph_n_nodes(gf) - 1; i >= 0; --i) {
+ embd = ggml_graph_node(gf, i);
if (strcmp(ggml_graph_node(gf, i)->name, "result_embd_pooled") == 0) {
- embd = ggml_graph_node(gf, i);
@@ -17701,11 +17700,15 @@ static int llama_decode_internal(
break;
}
}
......@@ -46,6 +41,7 @@ index 800dfb95..a639522d 100644
+ if (!cparams.causal_attn) {
+ res = nullptr; // do not extract logits when not needed
+ }
+
// LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
ggml_backend_sched_alloc_graph(lctx.sched, gf);
ggml_backend_sched_alloc_graph(lctx.sched.get(), gf);
llama/patches/0003-metal.patch deleted 100644 → 0
View file @ a37f4a86
From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
From: Michael Yang <mxyng@pm.me>
Date: Mon, 16 Sep 2024 15:53:12 -0700
Subject: [PATCH] metal
---
ggml/src/ggml-metal.m | 30 +++++++++++++-----------------
1 file changed, 13 insertions(+), 17 deletions(-)
diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m
index 9da08fe2..3a433703 100644
--- a/ggml/src/ggml-metal.m
+++ b/ggml/src/ggml-metal.m
@@ -1720,27 +1720,23 @@ static void ggml_metal_encode_node(
// to the matrix-vector kernel
int ne11_mm_min = 1;
-#if 0
// the numbers below are measured on M2 Ultra for 7B and 13B models
// these numbers do not translate to other devices or model sizes
// TODO: need to find a better approach
- if ([ctx->device.name isEqualToString:@"Apple M2 Ultra"]) {
- switch (src0t) {
- case GGML_TYPE_F16: ne11_mm_min = 2; break;
- case GGML_TYPE_Q8_0: ne11_mm_min = 7; break;
- case GGML_TYPE_Q2_K: ne11_mm_min = 15; break;
- case GGML_TYPE_Q3_K: ne11_mm_min = 7; break;
- case GGML_TYPE_Q4_0:
- case GGML_TYPE_Q4_1: ne11_mm_min = 15; break;
- case GGML_TYPE_Q4_K: ne11_mm_min = 11; break;
- case GGML_TYPE_Q5_0: // not tested yet
- case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet
- case GGML_TYPE_Q5_K: ne11_mm_min = 7; break;
- case GGML_TYPE_Q6_K: ne11_mm_min = 7; break;
- default: ne11_mm_min = 1; break;
- }
+ switch (src0t) {
+ case GGML_TYPE_F16: ne11_mm_min = 2; break;
+ case GGML_TYPE_Q8_0: ne11_mm_min = 7; break;
+ case GGML_TYPE_Q2_K: ne11_mm_min = 15; break;
+ case GGML_TYPE_Q3_K: ne11_mm_min = 7; break;
+ case GGML_TYPE_Q4_0:
+ case GGML_TYPE_Q4_1: ne11_mm_min = 15; break;
+ case GGML_TYPE_Q4_K: ne11_mm_min = 11; break;
+ case GGML_TYPE_Q5_0: // not tested yet
+ case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet
+ case GGML_TYPE_Q5_K: ne11_mm_min = 7; break;
+ case GGML_TYPE_Q6_K: ne11_mm_min = 7; break;
+ default: ne11_mm_min = 1; break;
}
-#endif
// for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
// AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
llama/patches/0006-clip-unicode.patch → llama/patches/0004-clip-unicode.patch
View file @ 527cc978
......@@ -8,12 +8,12 @@ Subject: [PATCH] clip-unicode
1 file changed, 39 insertions(+), 1 deletion(-)
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 14e02c8d..6e849d8e 100644
index d7c94352..427d5e02 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -44,6 +44,19 @@
#define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0)
#define LOG_DBG(...) do { fprintf(stderr, __VA_ARGS__); } while (0)
@@ -56,6 +56,19 @@
# define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0)
#endif // defined(LLAVA_LOG_OFF)
+#if defined(_WIN32)
+#define WIN32_LEAN_AND_MEAN
......@@ -31,7 +31,7 @@ index 14e02c8d..6e849d8e 100644
//#define CLIP_DEBUG_FUNCTIONS
// RGB uint8 image
@@ -1225,8 +1238,29 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
@@ -1242,8 +1255,29 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
gguf_free(ctx);
return nullptr;
}
......@@ -62,7 +62,7 @@ index 14e02c8d..6e849d8e 100644
if (!fin) {
LOG_ERR("cannot open model file for loading tensors\n");
clip_free(new_clip);
@@ -1266,7 +1300,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
@@ -1283,7 +1317,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes);
}
}
......
llama/patches/0004-ggml-metal.patch deleted 100644 → 0
View file @ a37f4a86
From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
From: jmorganca <jmorganca@gmail.com>
Date: Wed, 12 Jun 2024 12:18:40 -0700
Subject: [PATCH] ggml-metal
---
ggml/src/ggml-metal.m | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m
index 3a433703..829c5e39 100644
--- a/ggml/src/ggml-metal.m
+++ b/ggml/src/ggml-metal.m
@@ -392,8 +392,8 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
#if GGML_METAL_EMBED_LIBRARY
GGML_METAL_LOG_INFO("%s: using embedded metal library\n", __func__);
- extern const char ggml_metallib_start[];
- extern const char ggml_metallib_end[];
+ extern const char *ggml_metallib_start;
+ extern const char *ggml_metallib_end;
NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
#else
llama/patches/0007-solar-pro.patch → llama/patches/0005-solar-pro.patch
View file @ 527cc978
......@@ -11,14 +11,14 @@ tensor to store the scalar. the scalar is implemented a 1-dimensional
tensor with 2 elements dervied from the model's bskcn_tv configuration.
in general, the values are (bskcn_tv, 1 - bskcn_tv)
---
src/llama.cpp | 269 +++++++++++++++++++++++++++++++++++++++++++++++---
1 file changed, 255 insertions(+), 14 deletions(-)
src/llama.cpp | 267 +++++++++++++++++++++++++++++++++++++++++++++++---
1 file changed, 253 insertions(+), 14 deletions(-)
diff --git a/src/llama.cpp b/src/llama.cpp
index a639522d..83b80b59 100644
index d1791af0..b01770d0 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -217,6 +217,7 @@ enum llm_arch {
@@ -195,6 +195,7 @@ enum llm_arch {
LLM_ARCH_GRANITE,
LLM_ARCH_GRANITE_MOE,
LLM_ARCH_CHAMELEON,
......@@ -26,7 +26,7 @@ index a639522d..83b80b59 100644
LLM_ARCH_UNKNOWN,
};
@@ -270,6 +271,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
@@ -249,6 +250,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_GRANITE, "granite" },
{ LLM_ARCH_GRANITE_MOE, "granitemoe" },
{ LLM_ARCH_CHAMELEON, "chameleon" },
......@@ -34,7 +34,7 @@ index a639522d..83b80b59 100644
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@@ -327,6 +329,7 @@ enum llm_kv {
@@ -306,6 +308,7 @@ enum llm_kv {
LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
LLM_KV_ATTENTION_SLIDING_WINDOW,
LLM_KV_ATTENTION_SCALE,
......@@ -42,7 +42,7 @@ index a639522d..83b80b59 100644
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_FREQ_BASE,
@@ -421,20 +424,21 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
@@ -408,20 +411,21 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_RESIDUAL_SCALE, "%s.residual_scale" },
{ LLM_KV_EMBEDDING_SCALE, "%s.embedding_scale" },
......@@ -78,15 +78,15 @@ index a639522d..83b80b59 100644
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
@@ -608,6 +612,7 @@ enum llm_tensor {
@@ -603,6 +607,7 @@ enum llm_tensor {
LLM_TENSOR_ENC_OUTPUT_NORM,
LLM_TENSOR_CLS,
LLM_TENSOR_CLS_OUT,
+ LLM_TENSOR_BSKCN_TV,
};
static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@@ -1527,6 +1532,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_NAMES = {
@@ -1541,6 +1546,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
},
},
......@@ -111,15 +111,15 @@ index a639522d..83b80b59 100644
{
LLM_ARCH_UNKNOWN,
{
@@ -2360,6 +2383,7 @@ enum e_model {
@@ -2401,6 +2424,7 @@ enum e_model {
MODEL_15B,
MODEL_16B,
MODEL_20B,
+ MODEL_22B,
MODEL_30B,
MODEL_32B,
MODEL_34B,
MODEL_35B,
@@ -2409,6 +2433,8 @@ struct llama_hparams {
@@ -2451,6 +2475,8 @@ struct llama_hparams {
std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
......@@ -128,7 +128,7 @@ index a639522d..83b80b59 100644
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
uint32_t n_lora_kv = 0;
@@ -2479,6 +2505,7 @@ struct llama_hparams {
@@ -2521,6 +2547,7 @@ struct llama_hparams {
if (this->n_head_arr != other.n_head_arr) return true;
if (this->n_head_kv_arr != other.n_head_kv_arr) return true;
if (this->n_ff_arr != other.n_ff_arr) return true;
......@@ -136,7 +136,7 @@ index a639522d..83b80b59 100644
if (this->n_rel_attn_bkts != other.n_rel_attn_bkts) return true;
if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true;
@@ -2588,6 +2615,14 @@ struct llama_hparams {
@@ -2630,6 +2657,14 @@ struct llama_hparams {
return ssm_d_state * ssm_d_inner;
}
}
......@@ -151,7 +151,7 @@ index a639522d..83b80b59 100644
};
static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
@@ -2769,6 +2804,8 @@ struct llama_layer {
@@ -2816,6 +2851,8 @@ struct llama_layer {
struct ggml_tensor * ffn_gate_scale;
struct ggml_tensor * ffn_up_scale;
struct ggml_tensor * ffn_down_scale;
......@@ -160,7 +160,7 @@ index a639522d..83b80b59 100644
};
// very similar to llama_batch,
@@ -6134,6 +6171,21 @@ static void llm_load_hparams(
@@ -6209,6 +6246,21 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
......@@ -182,46 +182,51 @@ index a639522d..83b80b59 100644
default: (void)0;
}
@@ -8831,6 +8883,38 @@ static bool llm_load_tensors(
@@ -7198,6 +7250,7 @@ static const std::map<llm_tensor, llm_tensor_info> llm_tensor_info_mapping = {
{LLM_TENSOR_FFN_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
// this tensor is loaded for T5, but never used
{LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
+ {LLM_TENSOR_BSKCN_TV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}
};
// checks if the weight tensor can be used with the specified buffer type and device
@@ -9205,6 +9258,35 @@ static bool llm_load_tensors(
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+ }
+ } break;
+ case LLM_ARCH_SOLAR:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+ model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+ model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ ggml_context * ctx_layer = ctx_for_layer(i);
+ ggml_context * ctx_split = ctx_for_layer_split(i);
+
+ auto & layer = model.layers[i];
+
+ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
+ layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+ layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
+ layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+ layer.bskcn_tv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_BSKCN_TV, "weight"), {2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+ layer.bskcn_tv = create_tensor(tn(LLM_TENSOR_BSKCN_TV, "weight", i), {2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
@@ -16179,6 +16263,158 @@ struct llm_build_context {
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
@@ -16652,6 +16734,158 @@ struct llm_build_context {
return gf;
}
......@@ -239,7 +244,7 @@ index a639522d..83b80b59 100644
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
......@@ -380,7 +385,7 @@ index a639522d..83b80b59 100644
};
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
@@ -16443,6 +16679,10 @@ static struct ggml_cgraph * llama_build_graph(
@@ -16921,6 +17155,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_chameleon();
} break;
......@@ -391,7 +396,7 @@ index a639522d..83b80b59 100644
default:
GGML_ABORT("fatal error");
}
@@ -19589,6 +19829,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
@@ -20132,6 +20370,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_CHAMELEON:
......
llama/patches/0008-conditional-fattn.patch → llama/patches/0006-conditional-fattn.patch
View file @ 527cc978
......@@ -4,14 +4,14 @@ Date: Wed, 9 Oct 2024 17:26:23 -0700
Subject: [PATCH] conditional-fattn
---
ggml/src/ggml-cuda.cu | 2 ++
ggml/src/ggml-cuda/ggml-cuda.cu | 2 ++
1 file changed, 2 insertions(+)
diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu
index 809d6ab1..fe77b81c 100644
--- a/ggml/src/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda.cu
@@ -2347,9 +2347,11 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 52aec229..cbf4fddf 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2162,9 +2162,11 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_ARGSORT:
ggml_cuda_op_argsort(ctx, dst);
break;
......
llama/patches/0009-blas.patch → llama/patches/0007-blas.patch
View file @ 527cc978
......@@ -4,22 +4,23 @@ Date: Mon, 30 Sep 2024 16:31:04 -0700
Subject: [PATCH] blas
---
ggml/src/ggml-blas.cpp | 4 ++++
ggml/src/ggml-blas/ggml-blas.cpp | 4 ++++
1 file changed, 4 insertions(+)
diff --git a/ggml/src/ggml-blas.cpp b/ggml/src/ggml-blas.cpp
index 6d99c6be..8e1ab99d 100644
--- a/ggml/src/ggml-blas.cpp
+++ b/ggml/src/ggml-blas.cpp
diff --git a/ggml/src/ggml-blas/ggml-blas.cpp b/ggml/src/ggml-blas/ggml-blas.cpp
index ec158dfa..b3ac1fa4 100644
--- a/ggml/src/ggml-blas/ggml-blas.cpp
+++ b/ggml/src/ggml-blas/ggml-blas.cpp
@@ -1,3 +1,5 @@
+#ifdef GGML_USE_BLAS
+
#include "ggml-impl.h"
#include "ggml-blas.h"
#include "ggml-backend-impl.h"
@@ -366,3 +368,5 @@ void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads)
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend_blas->context;
ctx->n_threads = n_threads;
@@ -515,3 +517,5 @@ ggml_backend_reg_t ggml_backend_blas_reg(void) {
}
GGML_BACKEND_DL_IMPL(ggml_backend_blas_reg)
+
+#endif
+#endif // GGML_USE_BLAS
\ No newline at end of file
llama/patches/0010-add-mllama-support.patch → llama/patches/0008-add-mllama-support.patch
View file @ 527cc978
......@@ -12,29 +12,46 @@ kv cache once per run
remaining is to implement the cross attention mask
---
examples/llava/llava.cpp | 2 +-
examples/llava/llava.cpp | 5 +-
include/llama.h | 5 +
src/llama.cpp | 447 +++++++++++++++++++++++++++++++++++++--
3 files changed, 436 insertions(+), 18 deletions(-)
src/llama.cpp | 477 +++++++++++++++++++++++++++++++++++++--
3 files changed, 467 insertions(+), 20 deletions(-)
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 8558c6bd..37b2f2e2 100644
index 4ca53a0b..d56644a8 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -409,7 +409,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
if (n_eval > n_batch) {
@@ -412,7 +412,7 @@ struct llava_embd_batch {
std::vector<llama_seq_id *> seq_ids;
std::vector<int8_t> logits;
llama_batch batch;
- llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
+ llava_embd_batch(float * embd, int32_t n_embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
pos .resize(n_tokens);
n_seq_id.resize(n_tokens);
seq_ids .resize(n_tokens + 1);
@@ -424,6 +424,7 @@ struct llava_embd_batch {
/*n_tokens =*/ n_tokens,
/*tokens =*/ nullptr,
/*embd =*/ embd,
+ /*n_embd =*/ n_embd,
/*pos =*/ pos.data(),
/*n_seq_id =*/ n_seq_id.data(),
/*seq_id =*/ seq_ids.data(),
@@ -447,7 +448,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
n_eval = n_batch;
}
- llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
+ llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), n_embd, nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
if (llama_decode(ctx_llama, batch)) {
float * embd = image_embed->embed+i*n_embd;
- llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0);
+ llava_embd_batch llava_batch = llava_embd_batch(embd, n_embd, n_eval, *n_past, 0);
if (llama_decode(ctx_llama, llava_batch.batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
return false;
diff --git a/include/llama.h b/include/llama.h
index 7cae1bbe..aca09310 100644
index e85f459f..aba85f86 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -240,6 +240,7 @@ extern "C" {
@@ -245,6 +245,7 @@ extern "C" {
llama_token * token;
float * embd;
......@@ -42,7 +59,7 @@ index 7cae1bbe..aca09310 100644
llama_pos * pos;
int32_t * n_seq_id;
llama_seq_id ** seq_id;
@@ -423,6 +424,10 @@ extern "C" {
@@ -419,6 +420,10 @@ extern "C" {
struct llama_model * model,
struct llama_context_params params);
......@@ -54,10 +71,10 @@ index 7cae1bbe..aca09310 100644
LLAMA_API void llama_free(struct llama_context * ctx);
diff --git a/src/llama.cpp b/src/llama.cpp
index 83b80b59..35748488 100644
index b01770d0..46881642 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -169,6 +169,7 @@ static std::string format(const char * fmt, ...) {
@@ -146,6 +146,7 @@ static std::string format(const char * fmt, ...) {
enum llm_arch {
LLM_ARCH_LLAMA,
......@@ -65,7 +82,7 @@ index 83b80b59..35748488 100644
LLM_ARCH_FALCON,
LLM_ARCH_BAICHUAN,
LLM_ARCH_GROK,
@@ -223,6 +224,7 @@ enum llm_arch {
@@ -201,6 +202,7 @@ enum llm_arch {
static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_LLAMA, "llama" },
......@@ -73,7 +90,7 @@ index 83b80b59..35748488 100644
{ LLM_ARCH_FALCON, "falcon" },
{ LLM_ARCH_GROK, "grok" },
{ LLM_ARCH_GPT2, "gpt2" },
@@ -330,6 +332,7 @@ enum llm_kv {
@@ -309,6 +311,7 @@ enum llm_kv {
LLM_KV_ATTENTION_SLIDING_WINDOW,
LLM_KV_ATTENTION_SCALE,
LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,
......@@ -81,7 +98,7 @@ index 83b80b59..35748488 100644
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_FREQ_BASE,
@@ -439,6 +442,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
@@ -426,6 +429,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },
{ LLM_KV_ATTENTION_SCALE, "%s.attention.scale" },
{ LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION, "%s.attention.block_skip_connection.%d" },
......@@ -89,7 +106,7 @@ index 83b80b59..35748488 100644
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
@@ -613,6 +617,14 @@ enum llm_tensor {
@@ -608,6 +612,14 @@ enum llm_tensor {
LLM_TENSOR_CLS,
LLM_TENSOR_CLS_OUT,
LLM_TENSOR_BSKCN_TV,
......@@ -103,8 +120,8 @@ index 83b80b59..35748488 100644
+ LLM_TENSOR_CROSS_ATTN_MLP_GATE,
};
static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@@ -642,6 +654,40 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_NAMES = {
@@ -637,6 +649,40 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
......@@ -145,7 +162,7 @@ index 83b80b59..35748488 100644
{
LLM_ARCH_BAICHUAN,
{
@@ -2390,6 +2436,7 @@ enum e_model {
@@ -2432,6 +2478,7 @@ enum e_model {
MODEL_40B,
MODEL_65B,
MODEL_70B,
......@@ -153,7 +170,7 @@ index 83b80b59..35748488 100644
MODEL_236B,
MODEL_314B,
MODEL_SMALL,
@@ -2434,6 +2481,7 @@ struct llama_hparams {
@@ -2476,6 +2523,7 @@ struct llama_hparams {
std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
std::array<std::array<uint32_t, LLAMA_MAX_LAYERS>, 4> n_bskcn_arr;
......@@ -161,7 +178,7 @@ index 83b80b59..35748488 100644
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
@@ -2502,10 +2550,11 @@ struct llama_hparams {
@@ -2544,10 +2592,11 @@ struct llama_hparams {
if (this->n_expert != other.n_expert) return true;
if (this->n_expert_used != other.n_expert_used) return true;
......@@ -177,7 +194,7 @@ index 83b80b59..35748488 100644
if (this->n_rel_attn_bkts != other.n_rel_attn_bkts) return true;
if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true;
@@ -2623,6 +2672,10 @@ struct llama_hparams {
@@ -2665,6 +2714,10 @@ struct llama_hparams {
GGML_ABORT("fatal error");
}
......@@ -188,7 +205,7 @@ index 83b80b59..35748488 100644
};
static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
@@ -2652,6 +2705,9 @@ struct llama_cparams {
@@ -2694,6 +2747,9 @@ struct llama_cparams {
bool offload_kqv;
bool flash_attn;
bool no_perf;
......@@ -198,7 +215,7 @@ index 83b80b59..35748488 100644
enum llama_pooling_type pooling_type;
@@ -2806,6 +2862,16 @@ struct llama_layer {
@@ -2853,6 +2909,16 @@ struct llama_layer {
struct ggml_tensor * ffn_down_scale;
struct ggml_tensor * bskcn_tv;
......@@ -215,7 +232,7 @@ index 83b80b59..35748488 100644
};
// very similar to llama_batch,
@@ -3452,6 +3518,8 @@ struct llama_context {
@@ -3439,6 +3505,8 @@ struct llama_context {
struct ggml_tensor * inp_pos_bucket; // I32 [n_batch|n_kv, n_batch]
struct ggml_tensor * inp_embd_enc; // F32 [n_embd, n_outputs_enc]
struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
......@@ -224,13 +241,34 @@ index 83b80b59..35748488 100644
};
struct llama_lora_weight {
@@ -3686,6 +3754,18 @@ static bool llama_kv_cache_init(
@@ -3577,6 +3645,39 @@ static bool llama_kv_cache_init(
cache.v_l.reserve(n_layer);
for (int i = 0; i < (int) n_layer; i++) {
+ // for cross attention layers
+ if (model.arch == LLM_ARCH_MLLAMA && hparams.cross_attention_layers(i)) {
+ struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
+ const llama_model::buft_list_t * buft_list;
+ if (offload) {
+ buft_list = model.dev_layer.at(i).buft_list;
+ } else {
+ buft_list = &model.cpu_buft_list;
+ }
+ ggml_backend_buffer_type_t buft = select_buft(*buft_list,
+ [&](ggml_context * ctx) {
+ ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
+ if (hparams.rope_type == LLAMA_ROPE_TYPE_NONE) {
+ return k;
+ }
+ ggml_tensor * p = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1);
+ return ggml_rope(ctx, k, p, hparams.n_rot, hparams.rope_type);
+ });
+ ggml_context * ctx = ctx_for_buft(buft);
+
+ if (!ctx) {
+ LLAMA_LOG_ERROR("%s: failed to create ggml context for kv cache\n", __func__);
+ return false;
+ }
+ ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_k, 6404, hparams.n_head_kv(i));
+ ggml_tensor * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_v, 6404, hparams.n_head_kv(i));
+ ggml_format_name(k, "cache_k_l%d", i);
......@@ -243,7 +281,7 @@ index 83b80b59..35748488 100644
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
@@ -5460,12 +5540,14 @@ static void llm_load_hparams(
@@ -5520,12 +5621,14 @@ static void llm_load_hparams(
}
// zero-out the per-layer hparams
......@@ -263,7 +301,7 @@ index 83b80b59..35748488 100644
// n_head_kv is optional, default to n_head
hparams.n_head_kv_arr = hparams.n_head_arr;
@@ -5514,7 +5596,7 @@ static void llm_load_hparams(
@@ -5574,7 +5677,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
......@@ -272,7 +310,7 @@ index 83b80b59..35748488 100644
if (hparams.n_rot != hparams.n_embd_head_k) {
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
}
@@ -5554,6 +5636,16 @@ static void llm_load_hparams(
@@ -5614,6 +5717,16 @@ static void llm_load_hparams(
}
}
} break;
......@@ -289,63 +327,78 @@ index 83b80b59..35748488 100644
case LLM_ARCH_MINICPM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -7249,6 +7341,55 @@ static bool llm_load_tensors(
layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
@@ -7250,7 +7363,15 @@ static const std::map<llm_tensor, llm_tensor_info> llm_tensor_info_mapping = {
{LLM_TENSOR_FFN_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
// this tensor is loaded for T5, but never used
{LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
- {LLM_TENSOR_BSKCN_TV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}
+ {LLM_TENSOR_BSKCN_TV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+ {LLM_TENSOR_CROSS_ATTN_K_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+ {LLM_TENSOR_CROSS_ATTN_K_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_CROSS_ATTN_O_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_CROSS_ATTN_Q_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+ {LLM_TENSOR_CROSS_ATTN_Q_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_CROSS_ATTN_V_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_CROSS_ATTN_ATTN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+ {LLM_TENSOR_CROSS_ATTN_MLP_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
};
// checks if the weight tensor can be used with the specified buffer type and device
@@ -7754,6 +7875,53 @@ static bool llm_load_tensors(
}
}
} break;
+ case LLM_ARCH_MLLAMA:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab+8});
+ model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab+8}, 0);
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+ model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ // if output is NULL, init from the input tok embed
+ if (model.output == NULL) {
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ ggml_context * ctx_layer = ctx_for_layer(i);
+ ggml_context * ctx_split = ctx_for_layer_split(i);
+
+ auto & layer = model.layers[i];
+
+ if (hparams.cross_attention_layers(i)) {
+ layer.cross_attn_k_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_K_NORM, "weight", i), {128});
+ layer.cross_attn_k_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_K_PROJ, "weight", i), {n_embd, 1024});
+ layer.cross_attn_o_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_O_PROJ, "weight", i), {n_embd, n_embd});
+ layer.cross_attn_q_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_Q_NORM, "weight", i), {128});
+ layer.cross_attn_q_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_Q_PROJ, "weight", i), {n_embd, n_embd});
+ layer.cross_attn_v_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_V_PROJ, "weight", i), {n_embd, 1024});
+ layer.cross_attn_attn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_ATTN_GATE, i), {1});
+ layer.cross_attn_mlp_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_MLP_GATE, i), {1});
+ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+ layer.cross_attn_k_norm = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_K_NORM, "weight", i), {128}, 0);
+ layer.cross_attn_k_proj = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_K_PROJ, "weight", i), {n_embd, 1024}, 0);
+ layer.cross_attn_o_proj = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_O_PROJ, "weight", i), {n_embd, n_embd}, 0);
+ layer.cross_attn_q_norm = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_Q_NORM, "weight", i), {128}, 0);
+ layer.cross_attn_q_proj = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_Q_PROJ, "weight", i), {n_embd, n_embd}, 0);
+ layer.cross_attn_v_proj = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_V_PROJ, "weight", i), {n_embd, 1024}, 0);
+ layer.cross_attn_attn_gate = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_ATTN_GATE, i), {1}, 0);
+ layer.cross_attn_mlp_gate = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_MLP_GATE, i), {1}, 0);
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+ } else {
+ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+ layer.rope_freqs = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+ layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+ layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
+ layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+ layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+ }
+ }
+ } break;
case LLM_ARCH_GROK:
case LLM_ARCH_MINICPM3:
{
if (n_expert == 0) {
@@ -9093,7 +9234,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
const int64_t n_embd_head_qk_rope = hparams.n_rot;
@@ -9463,7 +9631,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
model.hparams.n_vocab != model.vocab.id_to_token.size()) {
......@@ -354,7 +407,7 @@ index 83b80b59..35748488 100644
}
if (params.vocab_only) {
@@ -9193,6 +9334,21 @@ static struct ggml_tensor * llm_build_inp_embd(
@@ -9546,6 +9714,21 @@ static struct ggml_tensor * llm_build_inp_embd(
return inpL;
}
......@@ -376,7 +429,7 @@ index 83b80b59..35748488 100644
static void llm_build_kv_store(
struct ggml_context * ctx,
const llama_hparams & hparams,
@@ -10167,6 +10323,7 @@ struct llm_build_context {
@@ -10513,6 +10696,7 @@ struct llm_build_context {
lctx.inp_pos_bucket = nullptr;
lctx.inp_embd_enc = nullptr;
lctx.inp_KQ_mask_cross = nullptr;
......@@ -384,18 +437,10 @@ index 83b80b59..35748488 100644
}
void free() {
@@ -10754,6 +10911,239 @@ struct llm_build_context {
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
@@ -10992,6 +11176,240 @@ struct llm_build_context {
return gf;
}
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_mllama() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
......@@ -410,7 +455,7 @@ index 83b80b59..35748488 100644
+ struct ggml_tensor * inpL;
+ struct ggml_tensor * inpCAS;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
+ inpCAS = llm_build_inp_cross_attn_state(ctx0, lctx, hparams, cb);
+
+ // inp_pos - contains the positions
......@@ -429,7 +474,7 @@ index 83b80b59..35748488 100644
+ cb(cur, "attn_norm", il);
+
+ if (hparams.cross_attention_layers(il)) {
+ if (!batch.embd && !cparams.cross_attn) {
+ if (!ubatch.embd && !cparams.cross_attn) {
+ continue;
+ }
+
......@@ -447,7 +492,7 @@ index 83b80b59..35748488 100644
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur, * Vcur;
+ if (batch.embd) {
+ if (ubatch.embd) {
+ Kcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_k_proj, inpCAS);
+ cb(Kcur, "Kcur", il);
+
......@@ -621,10 +666,19 @@ index 83b80b59..35748488 100644
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
// lm_head
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
cb(cur, "result_output", -1);
@@ -16501,6 +16891,10 @@ static struct ggml_cgraph * llama_build_graph(
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
struct ggml_cgraph * build_baichuan() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
@@ -16973,6 +17391,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_llama();
} break;
......@@ -635,14 +689,14 @@ index 83b80b59..35748488 100644
case LLM_ARCH_BAICHUAN:
{
result = llm.build_baichuan();
@@ -16761,10 +17155,19 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
@@ -17237,10 +17659,19 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch)
}
if (batch.embd) {
if (ubatch.embd) {
- const int64_t n_embd = hparams.n_embd;
- const int64_t n_tokens = batch.n_tokens;
- const int64_t n_tokens = ubatch.n_tokens;
+ if (lctx.inp_cross_attn_state && lctx.inp_cross_attn_state->buffer) {
+ ggml_backend_tensor_set(lctx.inp_cross_attn_state, batch.embd, 0, ggml_nbytes(lctx.inp_cross_attn_state));
+ ggml_backend_tensor_set(lctx.inp_cross_attn_state, ubatch.embd, 0, ggml_nbytes(lctx.inp_cross_attn_state));
+ // zero out inp_embd since it's not used
+ float * inp_embd_data = (float *)lctx.inp_embd->data;
+ for (int i = 0; i < ggml_nelements(lctx.inp_embd); ++i) {
......@@ -650,24 +704,24 @@ index 83b80b59..35748488 100644
+ }
+ } else {
+ const int64_t n_embd = hparams.n_embd;
+ const int64_t n_tokens = batch.n_tokens;
+ const int64_t n_tokens = ubatch.n_tokens;
- ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
+ ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
- ggml_backend_tensor_set(lctx.inp_embd, ubatch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
+ ggml_backend_tensor_set(lctx.inp_embd, ubatch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
+ }
}
if (batch.pos && lctx.inp_pos) {
@@ -17345,7 +17748,7 @@ static int llama_decode_internal(
if (ubatch.pos && lctx.inp_pos) {
@@ -17841,7 +18272,7 @@ static int llama_decode_internal(
n_outputs = 1;
}
- lctx.sbatch.from_batch(batch_all, n_embd,
+ lctx.sbatch.from_batch(batch_all, batch_all.n_embd,
- lctx.sbatch.from_batch(batch, n_embd,
+ lctx.sbatch.from_batch(batch, batch.n_embd,
/* simple_split */ !kv_self.recurrent,
/* logits_all */ n_outputs == n_tokens_all);
@@ -17638,7 +18041,7 @@ static int llama_encode_internal(
@@ -18151,7 +18582,7 @@ static int llama_encode_internal(
const int64_t n_embd = hparams.n_embd;
......@@ -676,7 +730,7 @@ index 83b80b59..35748488 100644
const llama_ubatch ubatch = lctx.sbatch.split_simple(n_tokens);
@@ -18648,7 +19051,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -19189,7 +19620,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (llama_model_has_encoder(&model)) {
n_attn_layer *= 3;
}
......@@ -687,7 +741,7 @@ index 83b80b59..35748488 100644
}
size_t total_size_org = 0;
@@ -19814,6 +20219,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
@@ -20355,6 +20788,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
// use what we call a normal RoPE, operating on pairs of consecutive head values
case LLM_ARCH_LLAMA:
......@@ -695,7 +749,7 @@ index 83b80b59..35748488 100644
case LLM_ARCH_BAICHUAN:
case LLM_ARCH_STARCODER:
case LLM_ARCH_PLAMO:
@@ -21230,6 +21636,10 @@ void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
@@ -21782,6 +22216,10 @@ void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
ctx->cparams.causal_attn = causal_attn;
}
......@@ -705,8 +759,8 @@ index 83b80b59..35748488 100644
+
struct llama_batch llama_batch_get_one(
llama_token * tokens,
int32_t n_tokens,
@@ -21239,6 +21649,7 @@ struct llama_batch llama_batch_get_one(
int32_t n_tokens) {
@@ -21789,6 +22227,7 @@ struct llama_batch llama_batch_get_one(
/*n_tokens =*/ n_tokens,
/*tokens =*/ tokens,
/*embd =*/ nullptr,
......@@ -714,7 +768,7 @@ index 83b80b59..35748488 100644
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
@@ -21254,6 +21665,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
@@ -21801,6 +22240,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
/*n_tokens =*/ 0,
/*tokens =*/ nullptr,
/*embd =*/ nullptr,
......@@ -722,7 +776,7 @@ index 83b80b59..35748488 100644
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
@@ -21265,6 +21677,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
@@ -21809,6 +22249,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
if (embd) {
batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
......
llama/patches/0011-add-unpad-operator.patch → llama/patches/0009-add-unpad-operator.patch
View file @ 527cc978
......@@ -4,20 +4,21 @@ Date: Thu, 17 Oct 2024 17:19:25 -0700
Subject: [PATCH] add unpad operator
---
ggml/include/ggml.h | 10 ++++
ggml/src/ggml-cuda.cu | 4 ++
ggml/src/ggml-cuda/pad.cu | 46 +++++++++++++++++++
ggml/include/ggml.h | 10 +++++
ggml/src/ggml-cpu/ggml-cpu.c | 57 ++++++++++++++++++++++++++++
ggml/src/ggml-cuda/ggml-cuda.cu | 4 ++
ggml/src/ggml-cuda/pad.cu | 46 ++++++++++++++++++++++
ggml/src/ggml-cuda/pad.cuh | 1 +
ggml/src/ggml-metal.m | 33 ++++++++++++++
ggml/src/ggml-metal.metal | 45 ++++++++++++++++++
ggml/src/ggml.c | 93 +++++++++++++++++++++++++++++++++++++-
7 files changed, 230 insertions(+), 2 deletions(-)
ggml/src/ggml-metal/ggml-metal.m | 33 ++++++++++++++++
ggml/src/ggml-metal/ggml-metal.metal | 45 ++++++++++++++++++++++
ggml/src/ggml.c | 25 +++++++++++-
8 files changed, 219 insertions(+), 2 deletions(-)
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index ce3d92cb..962cb5f7 100644
index 65cb92c4..acbcccc6 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -506,6 +506,7 @@ extern "C" {
@@ -499,6 +499,7 @@ extern "C" {
GGML_OP_POOL_2D_BACK,
GGML_OP_UPSCALE, // nearest interpolate
GGML_OP_PAD,
......@@ -25,7 +26,7 @@ index ce3d92cb..962cb5f7 100644
GGML_OP_ARANGE,
GGML_OP_TIMESTEP_EMBEDDING,
GGML_OP_ARGSORT,
@@ -1764,6 +1765,15 @@ extern "C" {
@@ -1695,6 +1696,15 @@ extern "C" {
int p2,
int p3);
......@@ -41,11 +42,93 @@ index ce3d92cb..962cb5f7 100644
// Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
// timesteps: [N,]
// return: [N, dim]
diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu
index fe77b81c..6e84af56 100644
--- a/ggml/src/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda.cu
@@ -2270,6 +2270,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 23ae2e10..111ff3b0 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -10439,6 +10439,58 @@ static void ggml_compute_forward_pad(
}
}
+static void ggml_compute_forward_unpad_f32(
+ const struct ggml_compute_params *params,
+ struct ggml_tensor *dst) {
+
+ const struct ggml_tensor * src0 = dst->src[0];
+
+ GGML_ASSERT(src0->nb[0] == sizeof(float));
+ GGML_ASSERT( dst->nb[0] == sizeof(float));
+
+ const int ith = params->ith;
+ const int nth = params->nth;
+
+ GGML_TENSOR_UNARY_OP_LOCALS
+
+ float * dst_ptr = (float *) dst->data;
+
+ // TODO: optimize
+
+ for (int64_t i2 = 0; i2 < ne2; ++i2) {
+ for (int64_t i1 = ith; i1 < ne1; i1 += nth) {
+ for (int64_t i0 = 0; i0 < ne0; ++i0) {
+ for (int64_t i3 = 0; i3 < ne3; ++i3) {
+ const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0;
+
+ const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+
+ if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+ dst_ptr[dst_idx] = *src_ptr;
+ }
+ }
+ }
+ }
+ }
+}
+
+static void ggml_compute_forward_unpad(
+ const struct ggml_compute_params * params,
+ struct ggml_tensor * dst) {
+
+ const struct ggml_tensor * src0 = dst->src[0];
+
+ switch (src0->type) {
+ case GGML_TYPE_F32:
+ {
+ ggml_compute_forward_unpad_f32(params, dst);
+ } break;
+ default:
+ {
+ GGML_ABORT("fatal error");
+ }
+ }
+}
// ggml_compute_forward_arange
@@ -12535,6 +12587,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_pad(params, tensor);
} break;
+ case GGML_OP_UNPAD:
+ {
+ ggml_compute_forward_unpad(params, tensor);
+ } break;
case GGML_OP_ARANGE:
{
ggml_compute_forward_arange(params, tensor);
@@ -12877,6 +12933,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
} break;
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
+ case GGML_OP_UNPAD:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_ARGSORT:
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index cbf4fddf..9ca6cb77 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2085,6 +2085,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_PAD:
ggml_cuda_op_pad(ctx, dst);
break;
......@@ -55,7 +138,7 @@ index fe77b81c..6e84af56 100644
case GGML_OP_ARANGE:
ggml_cuda_op_arange(ctx, dst);
break;
@@ -2992,6 +2995,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
@@ -3012,6 +3015,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_OP_GROUP_NORM:
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
......@@ -126,35 +209,35 @@ index 8fd386b0..e2ededc3 100644
void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+void ggml_cuda_op_unpad(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m
index 829c5e39..25702d85 100644
--- a/ggml/src/ggml-metal.m
+++ b/ggml/src/ggml-metal.m
@@ -193,6 +193,7 @@
GGML_METAL_KERNEL_TYPE_IM2COL_F32,
diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 093ae900..cb9a1307 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -310,6 +310,7 @@ static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_conte
GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F16_F32,
GGML_METAL_KERNEL_TYPE_UPSCALE_F32,
GGML_METAL_KERNEL_TYPE_PAD_F32,
+ GGML_METAL_KERNEL_TYPE_UNPAD_F32,
GGML_METAL_KERNEL_TYPE_ARANGE_F32,
GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,
GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
@@ -689,6 +690,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true);
@@ -877,6 +878,7 @@ @implementation GGMLMetalClass
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F16_F32, conv_transpose_1d_f16_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true);
+ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UNPAD_F32, unpad_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true);
@@ -846,6 +848,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_context * ctx
return false;
@@ -1099,6 +1101,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
case GGML_OP_POOL_2D:
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
+ case GGML_OP_UNPAD:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_ARGSORT:
@@ -2655,6 +2658,36 @@ static void ggml_metal_encode_node(
@@ -3258,6 +3261,36 @@ static void ggml_metal_encode_node(
const int nth = MIN(1024, ne0);
......@@ -191,11 +274,11 @@ index 829c5e39..25702d85 100644
[encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ARANGE:
diff --git a/ggml/src/ggml-metal.metal b/ggml/src/ggml-metal.metal
index 2b200032..09887511 100644
--- a/ggml/src/ggml-metal.metal
+++ b/ggml/src/ggml-metal.metal
@@ -2029,6 +2029,51 @@ kernel void kernel_pad_f32(
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 5caa0846..47038c31 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -2897,6 +2897,51 @@ kernel void kernel_pad_f32(
}
}
......@@ -248,10 +331,10 @@ index 2b200032..09887511 100644
device char * dst,
constant int64_t & ne0,
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index bcbc32d9..f4864ac8 100644
index 1a9a7efa..ea2b259b 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -2997,6 +2997,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
@@ -950,6 +950,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"POOL_2D_BACK",
"UPSCALE",
"PAD",
......@@ -259,16 +342,16 @@ index bcbc32d9..f4864ac8 100644
"ARANGE",
"TIMESTEP_EMBEDDING",
"ARGSORT",
@@ -3030,7 +3031,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
@@ -983,7 +984,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"OPT_STEP_ADAMW",
};
-static_assert(GGML_OP_COUNT == 80, "GGML_OP_COUNT != 80");
+static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
-static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
+static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@@ -3091,6 +3092,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
@@ -1045,6 +1046,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"pool_2d_back(x)",
"upscale(x)",
"pad(x)",
......@@ -276,16 +359,16 @@ index bcbc32d9..f4864ac8 100644
"arange(start, stop, step)",
"timestep_embedding(timesteps, dim, max_period)",
"argsort(x)",
@@ -3124,7 +3126,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
@@ -1078,7 +1080,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"adamw(x)",
};
-static_assert(GGML_OP_COUNT == 80, "GGML_OP_COUNT != 80");
+static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
-static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
+static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@@ -6955,6 +6957,32 @@ struct ggml_tensor * ggml_pad(
@@ -4097,6 +4099,25 @@ struct ggml_tensor * ggml_pad(
return result;
}
......@@ -295,12 +378,6 @@ index bcbc32d9..f4864ac8 100644
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ int p0, int p1, int p2, int p3) {
+ bool is_node = false;
+
+ if (a->grad) {
+ GGML_ABORT("fatal error"); // TODO: implement backward
+ is_node = true;
+ }
+
+ struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type,
+ a->ne[0] - p0,
......@@ -309,7 +386,6 @@ index bcbc32d9..f4864ac8 100644
+ a->ne[3] - p3);
+
+ result->op = GGML_OP_UNPAD;
+ result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->src[0] = a;
+
+ return result;
......@@ -318,92 +394,3 @@ index bcbc32d9..f4864ac8 100644
// ggml_arange
struct ggml_tensor * ggml_arange(
@@ -15312,6 +15340,58 @@ static void ggml_compute_forward_pad(
}
}
+static void ggml_compute_forward_unpad_f32(
+ const struct ggml_compute_params *params,
+ struct ggml_tensor *dst) {
+
+ const struct ggml_tensor * src0 = dst->src[0];
+
+ GGML_ASSERT(src0->nb[0] == sizeof(float));
+ GGML_ASSERT( dst->nb[0] == sizeof(float));
+
+ const int ith = params->ith;
+ const int nth = params->nth;
+
+ GGML_TENSOR_UNARY_OP_LOCALS
+
+ float * dst_ptr = (float *) dst->data;
+
+ // TODO: optimize
+
+ for (int64_t i2 = 0; i2 < ne2; ++i2) {
+ for (int64_t i1 = ith; i1 < ne1; i1 += nth) {
+ for (int64_t i0 = 0; i0 < ne0; ++i0) {
+ for (int64_t i3 = 0; i3 < ne3; ++i3) {
+ const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0;
+
+ const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+
+ if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+ dst_ptr[dst_idx] = *src_ptr;
+ }
+ }
+ }
+ }
+ }
+}
+
+static void ggml_compute_forward_unpad(
+ const struct ggml_compute_params * params,
+ struct ggml_tensor * dst) {
+
+ const struct ggml_tensor * src0 = dst->src[0];
+
+ switch (src0->type) {
+ case GGML_TYPE_F32:
+ {
+ ggml_compute_forward_unpad_f32(params, dst);
+ } break;
+ default:
+ {
+ GGML_ABORT("fatal error");
+ }
+ }
+}
// ggml_compute_forward_arange
@@ -17294,6 +17374,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_pad(params, tensor);
} break;
+ case GGML_OP_UNPAD:
+ {
+ ggml_compute_forward_unpad(params, tensor);
+ } break;
case GGML_OP_ARANGE:
{
ggml_compute_forward_arange(params, tensor);
@@ -18369,6 +18453,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ABORT("fatal error"); // TODO: not implemented
}
+ case GGML_OP_UNPAD:
+ {
+ GGML_ABORT("fatal error"); // TODO: not implemented
+ }
case GGML_OP_ARANGE:
{
GGML_ABORT("fatal error"); // TODO: not implemented
@@ -19165,6 +19253,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
} break;
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
+ case GGML_OP_UNPAD:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_ARGSORT:
llama/patches/0012-fix-deepseek-deseret-regex.patch → llama/patches/0010-fix-deepseek-deseret-regex.patch
View file @ 527cc978
......@@ -7,11 +7,11 @@ On windows compiled with gcc the c++ regex library failed to handle
the characters
---
src/llama-vocab.cpp | 2 +-
src/unicode.cpp | 21 +++++++++++++++++++++
2 files changed, 22 insertions(+), 1 deletion(-)
src/unicode.cpp | 22 ++++++++++++++++++++++
2 files changed, 23 insertions(+), 1 deletion(-)
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index d2f34ddd..3ef6af19 100644
index d1dc9627..05ef0e71 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -389,7 +389,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
......@@ -24,7 +24,7 @@ index d2f34ddd..3ef6af19 100644
"\\s+$",
"[一-龥ࠀ-一가-퟿]+",
diff --git a/src/unicode.cpp b/src/unicode.cpp
index f4e941cd..9d78ff16 100644
index 3d459263..51dd81fb 100644
--- a/src/unicode.cpp
+++ b/src/unicode.cpp
@@ -2,6 +2,11 @@
......@@ -39,7 +39,7 @@ index f4e941cd..9d78ff16 100644
#include "unicode.h"
#include "unicode-data.h"
@@ -201,8 +206,24 @@ static std::unordered_map<std::string, uint8_t> unicode_utf8_to_byte_map() {
@@ -201,6 +206,22 @@ static std::unordered_map<std::string, uint8_t> unicode_utf8_to_byte_map() {
}
static inline std::wstring unicode_wstring_from_utf8(const std::string & s) {
......@@ -58,7 +58,13 @@ index f4e941cd..9d78ff16 100644
+ free(wbuf);
+ return ret;
+#else
std::wstring_convert<std::codecvt_utf8<wchar_t>> conv;
+
#if defined(__clang__)
// disable C++17 deprecation warning for std::codecvt_utf8
# pragma clang diagnostic push
@@ -214,6 +235,7 @@ static inline std::wstring unicode_wstring_from_utf8(const std::string & s) {
#endif
return conv.from_bytes(s);
+#endif
}
......
llama/patches/0011-relative-include-paths.patch 0 → 100644
View file @ 527cc978
From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
From: jmorganca <jmorganca@gmail.com>
Date: Tue, 3 Dec 2024 21:30:51 -0800
Subject: [PATCH] relative include paths
---
ggml/src/ggml-cpu/ggml-cpu-aarch64.c | 2 +-
ggml/src/ggml-cpu/ggml-cpu.c | 2 +-
ggml/src/ggml-cpu/ggml-cpu.cpp | 2 +-
ggml/src/ggml-quants.c | 2 +-
4 files changed, 4 insertions(+), 4 deletions(-)
diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.c b/ggml/src/ggml-cpu/ggml-cpu-aarch64.c
index 11152385..bbf8934e 100644
--- a/ggml/src/ggml-cpu/ggml-cpu-aarch64.c
+++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.c
@@ -4,7 +4,7 @@
#include "ggml-quants.h"
#include "ggml-impl.h"
#include "ggml-cpu.h"
-#include "ggml-cpu/ggml-cpu-impl.h"
+#include "ggml-cpu-impl.h"
#include <math.h>
#include <string.h>
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 111ff3b0..df0bd3c6 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -10,7 +10,7 @@
#include "ggml-quants.h"
#include "ggml-cpu-quants.h"
#include "ggml-threading.h"
-#include "amx/amx.h"
+#include "amx.h"
#include "ggml.h"
#if defined(_MSC_VER) || defined(__MINGW32__)
diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp
index 77e5d87a..91476ad0 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu.cpp
@@ -3,7 +3,7 @@
#include "ggml-cpu.h"
#include "ggml-cpu-aarch64.h"
#include "ggml-impl.h"
-#include "amx/amx.h"
+#include "amx.h"
#include <cctype>
#include <string>
#include <vector>
diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index 7301a9c6..49ab3daf 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -3,7 +3,7 @@
#include "ggml-quants.h"
#include "ggml-impl.h"
-#include "ggml-cpu/ggml-cpu-impl.h"
+#include "ggml-cpu-impl.h"
#include "ggml-cpu.h"
#include <math.h>
llama/runner/runner.go
View file @ 527cc978
......@@ -559,7 +559,6 @@ type Options struct {
TopK int `json:"top_k"`
TopP float32 `json:"top_p"`
MinP float32 `json:"min_p"`
TFSZ float32 `json:"tfs_z"`
TypicalP float32 `json:"typical_p"`
RepeatLastN int `json:"repeat_last_n"`
Temperature float32 `json:"temperature"`
......@@ -632,7 +631,6 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
samplingParams.TopK = req.TopK
samplingParams.TopP = req.TopP
samplingParams.MinP = req.MinP
samplingParams.TfsZ = req.TFSZ
samplingParams.TypicalP = req.TypicalP
samplingParams.Temp = req.Temperature
samplingParams.RepeatLastN = req.RepeatLastN
......@@ -930,6 +928,7 @@ func Execute(args []string) error {
level := slog.LevelInfo
if *verbose {
level = slog.LevelDebug
llama.EnableDebug()
}
handler := slog.NewTextHandler(os.Stderr, &slog.HandlerOptions{
Level: level,
......
llama/sampling.cpp
View file @ 527cc978
/**
* llama.cpp - commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555 - do not edit this file
* llama.cpp - commit 40c6d79fb52f995f47507fedfeaae2ac05d9b35c - do not edit this file
*
* MIT License
*
......@@ -124,8 +124,8 @@ struct ring_buffer {
std::vector<T> data;
};
struct gpt_sampler {
gpt_sampler_params params;
struct common_sampler {
common_params_sampling params;
struct llama_sampler * grmr;
struct llama_sampler * chain;
......@@ -151,26 +151,28 @@ struct gpt_sampler {
}
};
std::string gpt_sampler_params::print() const {
std::string common_params_sampling::print() const {
char result[1024];
snprintf(result, sizeof(result),
"\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
"\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n"
"\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n"
"\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n"
"\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
top_k, tfs_z, top_p, min_p, typ_p, temp,
dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n,
top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp,
mirostat, mirostat_eta, mirostat_tau);
return std::string(result);
}
struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const struct gpt_sampler_params & params) {
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
lparams.no_perf = params.no_perf;
auto * result = new gpt_sampler {
auto * result = new common_sampler {
/* .params = */ params,
/* .grmr = */ llama_sampler_init_grammar(model, params.grammar.c_str(), "root"),
/* .chain = */ llama_sampler_chain_init(lparams),
......@@ -197,33 +199,45 @@ struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const st
params.penalize_nl,
params.ignore_eos));
if (params.temp > 0.0f) {
if (params.mirostat == 0) {
for (const auto & cnstr : params.samplers) {
switch (cnstr) {
case GPT_SAMPLER_TYPE_TOP_K:
case COMMON_SAMPLER_TYPE_DRY:
{
std::vector<const char*> c_breakers;
c_breakers.reserve(params.dry_sequence_breakers.size());
for (const auto& str : params.dry_sequence_breakers) {
c_breakers.push_back(str.c_str());
}
llama_sampler_chain_add(result->chain, llama_sampler_init_dry (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
}
break;
case COMMON_SAMPLER_TYPE_TOP_K:
llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k));
break;
case GPT_SAMPLER_TYPE_TOP_P:
case COMMON_SAMPLER_TYPE_TOP_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep));
break;
case GPT_SAMPLER_TYPE_MIN_P:
case COMMON_SAMPLER_TYPE_MIN_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep));
break;
case GPT_SAMPLER_TYPE_TFS_Z:
llama_sampler_chain_add(result->chain, llama_sampler_init_tail_free(params.tfs_z, params.min_keep));
case COMMON_SAMPLER_TYPE_XTC:
llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
break;
case GPT_SAMPLER_TYPE_TYPICAL_P:
case COMMON_SAMPLER_TYPE_TYPICAL_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep));
break;
case GPT_SAMPLER_TYPE_TEMPERATURE:
case COMMON_SAMPLER_TYPE_TEMPERATURE:
llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
break;
case COMMON_SAMPLER_TYPE_INFILL:
llama_sampler_chain_add(result->chain, llama_sampler_init_infill (model));
break;
default:
GGML_ASSERT(false && "unknown sampler type");
}
}
llama_sampler_chain_add(result->chain, llama_sampler_init_softmax());
llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
} else if (params.mirostat == 1) {
llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
......@@ -234,23 +248,11 @@ struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const st
} else {
GGML_ASSERT(false && "unknown mirostat version");
}
} else {
if (params.n_probs > 0) {
// some use cases require to sample greedily, but still obtain the probabilities of the top tokens
// ref: https://github.com/ggerganov/llama.cpp/pull/9605
//
// the following will not produce exactly the same probs as applyging softmax to the full vocabulary, but
// it is much faster, since we avoid sorting all tokens and should give a good approximation
llama_sampler_chain_add(result->chain, llama_sampler_init_top_k(params.n_probs));
llama_sampler_chain_add(result->chain, llama_sampler_init_softmax());
}
llama_sampler_chain_add(result->chain, llama_sampler_init_greedy());
}
return result;
}
void gpt_sampler_free(struct gpt_sampler * gsmpl) {
void common_sampler_free(struct common_sampler * gsmpl) {
if (gsmpl) {
llama_sampler_free(gsmpl->grmr);
......@@ -260,7 +262,7 @@ void gpt_sampler_free(struct gpt_sampler * gsmpl) {
}
}
void gpt_sampler_accept(struct gpt_sampler * gsmpl, llama_token token, bool accept_grammar) {
void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
if (accept_grammar) {
llama_sampler_accept(gsmpl->grmr, token);
}
......@@ -270,14 +272,14 @@ void gpt_sampler_accept(struct gpt_sampler * gsmpl, llama_token token, bool acce
gsmpl->prev.push_back(token);
}
void gpt_sampler_reset(struct gpt_sampler * gsmpl) {
void common_sampler_reset(struct common_sampler * gsmpl) {
llama_sampler_reset(gsmpl->grmr);
llama_sampler_reset(gsmpl->chain);
}
struct gpt_sampler * gpt_sampler_clone(gpt_sampler * gsmpl) {
return new gpt_sampler {
struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
return new common_sampler {
/* .params = */ gsmpl->params,
/* .grmr = */ llama_sampler_clone(gsmpl->grmr),
/* .chain = */ llama_sampler_clone(gsmpl->chain),
......@@ -287,7 +289,7 @@ struct gpt_sampler * gpt_sampler_clone(gpt_sampler * gsmpl) {
};
}
void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler * gsmpl) {
void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl) {
// TODO: measure grammar performance
if (gsmpl) {
......@@ -298,7 +300,7 @@ void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler *
}
}
llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
gsmpl->set_logits(ctx, idx);
auto & grmr = gsmpl->grmr;
......@@ -344,21 +346,60 @@ llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context
return cur_p.data[cur_p.selected].id;
}
uint32_t gpt_sampler_get_seed(const struct gpt_sampler * gsmpl) {
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first) {
GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1");
std::vector<llama_token> result;
result.reserve(idxs.size());
size_t i = 0;
for (; i < draft.size(); i++) {
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
common_sampler_accept(gsmpl, id, true);
result.push_back(id);
if (draft[i] != id) {
break;
}
}
if (i == draft.size()) {
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
common_sampler_accept(gsmpl, id, true);
result.push_back(id);
}
return result;
}
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) {
std::vector<int> idxs(draft.size() + 1);
for (size_t i = 0; i < idxs.size(); ++i) {
idxs[i] = i;
}
return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first);
}
uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
return llama_sampler_get_seed(gsmpl->chain);
}
// helpers
llama_token_data_array * gpt_sampler_get_candidates(struct gpt_sampler * gsmpl) {
llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl) {
return &gsmpl->cur_p;
}
llama_token gpt_sampler_last(const struct gpt_sampler * gsmpl) {
llama_token common_sampler_last(const struct common_sampler * gsmpl) {
return gsmpl->prev.rat(0);
}
std::string gpt_sampler_print(const struct gpt_sampler * gsmpl) {
std::string common_sampler_print(const struct common_sampler * gsmpl) {
std::string result = "logits ";
for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) {
......@@ -369,7 +410,7 @@ std::string gpt_sampler_print(const struct gpt_sampler * gsmpl) {
return result;
}
std::string gpt_sampler_prev_str(gpt_sampler * gsmpl, llama_context * ctx_main, int n) {
std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx_main, int n) {
n = std::min(n, (int) gsmpl->prev.size());
if (n <= 0) {
......@@ -384,63 +425,67 @@ std::string gpt_sampler_prev_str(gpt_sampler * gsmpl, llama_context * ctx_main,
GGML_ASSERT(id != LLAMA_TOKEN_NULL && "null token in the sampling history - should not happen");
result += llama_token_to_piece(ctx_main, id);
result += common_token_to_piece(ctx_main, id);
}
return result;
}
char gpt_sampler_type_to_chr(enum gpt_sampler_type cnstr) {
char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
switch (cnstr) {
case GPT_SAMPLER_TYPE_TOP_K: return 'k';
case GPT_SAMPLER_TYPE_TFS_Z: return 'f';
case GPT_SAMPLER_TYPE_TYPICAL_P: return 'y';
case GPT_SAMPLER_TYPE_TOP_P: return 'p';
case GPT_SAMPLER_TYPE_MIN_P: return 'm';
case GPT_SAMPLER_TYPE_TEMPERATURE: return 't';
case COMMON_SAMPLER_TYPE_DRY: return 'd';
case COMMON_SAMPLER_TYPE_TOP_K: return 'k';
case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y';
case COMMON_SAMPLER_TYPE_TOP_P: return 'p';
case COMMON_SAMPLER_TYPE_MIN_P: return 'm';
case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
case COMMON_SAMPLER_TYPE_XTC: return 'x';
case COMMON_SAMPLER_TYPE_INFILL: return 'i';
default : return '?';
}
}
std::string gpt_sampler_type_to_str(enum gpt_sampler_type cnstr) {
std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
switch (cnstr) {
case GPT_SAMPLER_TYPE_TOP_K: return "top_k";
case GPT_SAMPLER_TYPE_TFS_Z: return "tfs_z";
case GPT_SAMPLER_TYPE_TYPICAL_P: return "typ_p";
case GPT_SAMPLER_TYPE_TOP_P: return "top_p";
case GPT_SAMPLER_TYPE_MIN_P: return "min_p";
case GPT_SAMPLER_TYPE_TEMPERATURE: return "temperature";
case COMMON_SAMPLER_TYPE_DRY: return "dry";
case COMMON_SAMPLER_TYPE_TOP_K: return "top_k";
case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p";
case COMMON_SAMPLER_TYPE_TOP_P: return "top_p";
case COMMON_SAMPLER_TYPE_MIN_P: return "min_p";
case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
case COMMON_SAMPLER_TYPE_XTC: return "xtc";
case COMMON_SAMPLER_TYPE_INFILL: return "infill";
default : return "";
}
}
std::vector<gpt_sampler_type> gpt_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
std::unordered_map<std::string, gpt_sampler_type> sampler_canonical_name_map {
{ "top_k", GPT_SAMPLER_TYPE_TOP_K },
{ "top_p", GPT_SAMPLER_TYPE_TOP_P },
{ "typ_p", GPT_SAMPLER_TYPE_TYPICAL_P },
{ "min_p", GPT_SAMPLER_TYPE_MIN_P },
{ "tfs_z", GPT_SAMPLER_TYPE_TFS_Z },
{ "temperature", GPT_SAMPLER_TYPE_TEMPERATURE },
std::vector<common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
std::unordered_map<std::string, common_sampler_type> sampler_canonical_name_map {
{ "dry", COMMON_SAMPLER_TYPE_DRY },
{ "top_k", COMMON_SAMPLER_TYPE_TOP_K },
{ "top_p", COMMON_SAMPLER_TYPE_TOP_P },
{ "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "min_p", COMMON_SAMPLER_TYPE_MIN_P },
{ "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
{ "xtc", COMMON_SAMPLER_TYPE_XTC },
{ "infill", COMMON_SAMPLER_TYPE_INFILL },
};
// since samplers names are written multiple ways
// make it ready for both system names and input names
std::unordered_map<std::string, gpt_sampler_type> sampler_alt_name_map {
{ "top-k", GPT_SAMPLER_TYPE_TOP_K },
{ "top-p", GPT_SAMPLER_TYPE_TOP_P },
{ "nucleus", GPT_SAMPLER_TYPE_TOP_P },
{ "typical-p", GPT_SAMPLER_TYPE_TYPICAL_P },
{ "typical", GPT_SAMPLER_TYPE_TYPICAL_P },
{ "typ-p", GPT_SAMPLER_TYPE_TYPICAL_P },
{ "typ", GPT_SAMPLER_TYPE_TYPICAL_P },
{ "min-p", GPT_SAMPLER_TYPE_MIN_P },
{ "tfs-z", GPT_SAMPLER_TYPE_TFS_Z },
{ "tfs", GPT_SAMPLER_TYPE_TFS_Z },
{ "temp", GPT_SAMPLER_TYPE_TEMPERATURE },
std::unordered_map<std::string, common_sampler_type> sampler_alt_name_map {
{ "top-k", COMMON_SAMPLER_TYPE_TOP_K },
{ "top-p", COMMON_SAMPLER_TYPE_TOP_P },
{ "nucleus", COMMON_SAMPLER_TYPE_TOP_P },
{ "typical-p", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "typical", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "typ-p", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "typ", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "min-p", COMMON_SAMPLER_TYPE_MIN_P },
{ "temp", COMMON_SAMPLER_TYPE_TEMPERATURE },
};
std::vector<gpt_sampler_type> samplers;
std::vector<common_sampler_type> samplers;
samplers.reserve(names.size());
for (const auto & name : names) {
......@@ -460,17 +505,19 @@ std::vector<gpt_sampler_type> gpt_sampler_types_from_names(const std::vector<std
return samplers;
}
std::vector<gpt_sampler_type> gpt_sampler_types_from_chars(const std::string & chars) {
std::unordered_map<char, gpt_sampler_type> sampler_name_map = {
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TOP_K), GPT_SAMPLER_TYPE_TOP_K },
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TFS_Z), GPT_SAMPLER_TYPE_TFS_Z },
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TYPICAL_P), GPT_SAMPLER_TYPE_TYPICAL_P },
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TOP_P), GPT_SAMPLER_TYPE_TOP_P },
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_MIN_P), GPT_SAMPLER_TYPE_MIN_P },
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TEMPERATURE), GPT_SAMPLER_TYPE_TEMPERATURE }
std::vector<common_sampler_type> common_sampler_types_from_chars(const std::string & chars) {
std::unordered_map<char, common_sampler_type> sampler_name_map = {
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_DRY), COMMON_SAMPLER_TYPE_DRY },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL), COMMON_SAMPLER_TYPE_INFILL },
};
std::vector<gpt_sampler_type> samplers;
std::vector<common_sampler_type> samplers;
samplers.reserve(chars.size());
for (const auto & c : chars) {
......
llama/sampling.h
View file @ 527cc978
/**
* llama.cpp - commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555 - do not edit this file
* llama.cpp - commit 40c6d79fb52f995f47507fedfeaae2ac05d9b35c - do not edit this file
*
* MIT License
*
......@@ -33,7 +33,7 @@
#include <string>
#include <vector>
// gpt_sampler extends llama_sampler with additional functionality:
// common_sampler extends llama_sampler with additional functionality:
//
// - grammar support
// - custom sampler logic based on the parameters
......@@ -49,30 +49,30 @@
// token in order to verify if it fits the grammar. And only if the token doesn't fit the grammar, the
// grammar constraints are applied to the full vocabulary and the token is resampled.
//
// The gpt_sampler also maintains a container with the last accepted tokens. In the future, this can
// The common_sampler also maintains a container with the last accepted tokens. In the future, this can
// be moved into the core llama library.
//
// For convenience, the gpt_sampler also maintains a container with the current candidate tokens.
// For convenience, the common_sampler also maintains a container with the current candidate tokens.
// This can be used to access the probabilities of the rest of the non-sampled tokens.
//
// TODO: measure grammar performance
//
struct gpt_sampler;
struct common_sampler;
// llama_sampler API overloads
struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const struct gpt_sampler_params & params);
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params);
void gpt_sampler_free(struct gpt_sampler * gsmpl);
void common_sampler_free(struct common_sampler * gsmpl);
// if accept_grammar is true, the token is accepted both by the sampling chain and the grammar
void gpt_sampler_accept(struct gpt_sampler * gsmpl, llama_token token, bool accept_grammar);
void gpt_sampler_reset (struct gpt_sampler * gsmpl);
struct gpt_sampler * gpt_sampler_clone (struct gpt_sampler * gsmpl);
void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar);
void common_sampler_reset (struct common_sampler * gsmpl);
struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
// arguments can be nullptr to skip printing
void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler * gsmpl);
void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);
// extended sampling implementation:
//
......@@ -84,26 +84,47 @@ void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler *
// if grammar_first is true, the grammar is applied before the samplers (slower)
// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar
//
llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
uint32_t gpt_sampler_get_seed(const struct gpt_sampler * gsmpl);
// generalized version of common_sampler_sample
//
// will cross-reference the sampled tokens with a batch of draft tokens and accept those that match
// if the sampler disagrees at some point, we stop and return the accepted tokens up to now
//
// common_sampler_sample_n(gsmpl, ctx, { idx }, {});
//
// is equivalent to
//
// common_sampler_sample(gsmpl, ctx, idx);
// common_sampler_accept(gsmpl, token, true);
//
// requires: idxs.size() == draft.size() + 1
//
// returns at least 1 token, up to idxs.size()
//
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first = false);
// assume idxs == [ 0, 1, 2, ..., draft.size() ]
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false);
uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);
// helpers
// access the internal list of current candidate tokens
llama_token_data_array * gpt_sampler_get_candidates(struct gpt_sampler * gsmpl);
llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl);
// get the last accepted token
llama_token gpt_sampler_last(const struct gpt_sampler * gsmpl);
llama_token common_sampler_last(const struct common_sampler * gsmpl);
// print the sampler chain into a string
std::string gpt_sampler_print(const struct gpt_sampler * gsmpl);
std::string common_sampler_print(const struct common_sampler * gsmpl);
// get a string representation of the last accepted tokens
std::string gpt_sampler_prev_str(gpt_sampler * gsmpl, llama_context * ctx, int n);
std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx, int n);
char gpt_sampler_type_to_chr(enum gpt_sampler_type cnstr);
std::string gpt_sampler_type_to_str(enum gpt_sampler_type cnstr);
char common_sampler_type_to_chr(enum common_sampler_type cnstr);
std::string common_sampler_type_to_str(enum common_sampler_type cnstr);
std::vector<enum gpt_sampler_type> gpt_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
std::vector<enum gpt_sampler_type> gpt_sampler_types_from_chars(const std::string & chars);
std::vector<enum common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std::string & chars);
llama/sampling_ext.cpp
View file @ 527cc978
......@@ -3,16 +3,12 @@
#include "sampling_ext.h"
#include "json-schema-to-grammar.h"
struct gpt_sampler *gpt_sampler_cinit(
const struct llama_model *model, struct gpt_sampler_cparams *params)
{
try
{
gpt_sampler_params sparams;
struct common_sampler *common_sampler_cinit(const struct llama_model *model, struct common_sampler_cparams *params) {
try {
common_params_sampling sparams;
sparams.top_k = params->top_k;
sparams.top_p = params->top_p;
sparams.min_p = params->min_p;
sparams.tfs_z = params->tfs_z;
sparams.typ_p = params->typical_p;
sparams.temp = params->temp;
sparams.penalty_last_n = params->penalty_last_n;
......@@ -25,38 +21,28 @@ struct gpt_sampler *gpt_sampler_cinit(
sparams.penalize_nl = params->penalize_nl;
sparams.seed = params->seed;
sparams.grammar = params->grammar;
return gpt_sampler_init(model, sparams);
}
catch (const std::exception &err)
{
sparams.xtc_probability = 0.0;
sparams.xtc_threshold = 0.5;
return common_sampler_init(model, sparams);
} catch (const std::exception &err) {
return nullptr;
}
}
void gpt_sampler_cfree(struct gpt_sampler *sampler)
{
gpt_sampler_free(sampler);
void common_sampler_cfree(struct common_sampler *sampler) {
common_sampler_free(sampler);
}
void gpt_sampler_creset(struct gpt_sampler *sampler)
{
gpt_sampler_reset(sampler);
void common_sampler_creset(struct common_sampler *sampler) {
common_sampler_reset(sampler);
}
llama_token gpt_sampler_csample(
struct gpt_sampler *sampler,
struct llama_context *ctx_main,
int idx)
{
return gpt_sampler_sample(sampler, ctx_main, idx);
void common_sampler_caccept(struct common_sampler *sampler, llama_token id, bool apply_grammar) {
common_sampler_accept(sampler, id, apply_grammar);
}
void gpt_sampler_caccept(
struct gpt_sampler *sampler,
llama_token id,
bool apply_grammar)
{
gpt_sampler_accept(sampler, id, apply_grammar);
llama_token common_sampler_csample(struct common_sampler *sampler, struct llama_context *ctx, int idx) {
return common_sampler_sample(sampler, ctx, idx);
}
int schema_to_grammar(const char *json_schema, char *grammar, size_t max_len)
......
llama/sampling_ext.h
View file @ 527cc978
// TODO: this is a temporary wrapper to allow calling C++ code from CGo
#ifndef GPT_SAMPLER_EXT_H
#define GPT_SAMPLER_EXT_H
#ifndef SAMPLING_EXT_H
#define SAMPLING_EXT_H
#ifdef __cplusplus
extern "C"
......@@ -9,14 +9,11 @@ extern "C"
// Forward declaration to avoid include of "sampling.h" which has c++
// includes
struct gpt_sampler;
struct gpt_sampler_cparams
{
struct common_sampler;
struct common_sampler_cparams {
int32_t top_k;
float top_p;
float min_p;
float tfs_z;
float typical_p;
float temp;
int32_t penalty_last_n;
......@@ -31,21 +28,11 @@ extern "C"
char *grammar;
};
struct gpt_sampler *gpt_sampler_cinit(
const struct llama_model *model,
struct gpt_sampler_cparams *params);
void gpt_sampler_cfree(struct gpt_sampler *sampler);
void gpt_sampler_creset(struct gpt_sampler *sampler);
llama_token gpt_sampler_csample(
struct gpt_sampler *sampler,
struct llama_context *ctx_main,
int idx);
void gpt_sampler_caccept(
struct gpt_sampler *sampler,
llama_token id,
bool apply_grammar);
struct common_sampler *common_sampler_cinit(const struct llama_model *model, struct common_sampler_cparams *params);
void common_sampler_cfree(struct common_sampler *sampler);
void common_sampler_creset(struct common_sampler *sampler);
void common_sampler_caccept(struct common_sampler *sampler, llama_token id, bool apply_grammar);
llama_token common_sampler_csample(struct common_sampler *sampler, struct llama_context *ctx, int idx);
int schema_to_grammar(const char *json_schema, char *grammar, size_t max_len);
......@@ -53,4 +40,4 @@ extern "C"
}
#endif
#endif // GPT_SAMPLER_EXT_H
#endif // SAMPLING_EXT_H
llama/sgemm.cpp
View file @ 527cc978
......@@ -106,6 +106,10 @@ inline float16x8_t sub(float16x8_t x, float16x8_t y) { return vsubq_f16(x, y); }
inline float16x8_t mul(float16x8_t x, float16x8_t y) { return vmulq_f16(x, y); }
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
#if defined(__MMA__)
typedef vector unsigned char vec_t;
typedef __vector_quad acc_t;
#endif
////////////////////////////////////////////////////////////////////////////////////////////////////
// VECTORIZED FUSED MULTIPLY ADD
......@@ -942,6 +946,36 @@ class tinyBLAS_Q0_AVX {
return _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(x, 4)), _mm_set1_epi8(8));
}
inline __m256i load(const block_q5_0 *b) {
return _mm256_or_si256(denibble(b->qs), bittobyte(b->qh));
}
inline __m128i load0(const block_q5_0* b) {
const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs));
uint32_t x32;
memcpy(&x32, b->qh, sizeof(uint32_t));
__m128i qxl = _mm_and_si128(_mm_set1_epi8(15), x);
__m128i bytesl = _mm_cmpeq_epi8(_mm_set1_epi64x(-1),
_mm_or_si128(_mm_set1_epi64x(0x7fbfdfeff7fbfdfe),
_mm_shuffle_epi8(_mm_set1_epi32(x32),
_mm_set_epi64x(0x0101010101010101, 0x0000000000000000))));
bytesl = _mm_andnot_si128(bytesl, _mm_set1_epi8((char)0xF0));
return _mm_or_si128(qxl, bytesl);
}
inline __m128i load1(const block_q5_0* b) {
const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs));
uint32_t x32;
memcpy(&x32, b->qh, sizeof(uint32_t));
__m128i qxh = _mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(x, 4));
__m128i bytesh = _mm_cmpeq_epi8(_mm_set1_epi64x(-1),
_mm_or_si128(_mm_set1_epi64x(0x7fbfdfeff7fbfdfe),
_mm_shuffle_epi8(_mm_set1_epi32(x32),
_mm_set_epi64x(0x0303030303030303, 0x0202020202020202))));
bytesh = _mm_andnot_si128(bytesh, _mm_set1_epi8((char)0xF0));
return _mm_or_si128(qxh, bytesh);
}
inline __m256i load(const block_iq4_nl *b) {
return MM256_SET_M128I(load1(b), load0(b));
}
......@@ -973,6 +1007,17 @@ class tinyBLAS_Q0_AVX {
_mm_srli_epi16(x, 4), 1));
}
static inline __m256i bittobyte(const uint8_t *p) {
uint32_t x32;
memcpy(&x32, p, sizeof(uint32_t));
__m256i bytes = _mm256_cmpeq_epi8(_mm256_set1_epi64x(-1),
_mm256_or_si256(_mm256_set1_epi64x(0x7fbfdfeff7fbfdfe),
_mm256_shuffle_epi8(_mm256_set1_epi32(x32),
_mm256_set_epi64x(0x0303030303030303, 0x0202020202020202,
0x0101010101010101, 0x0000000000000000))));
return _mm256_andnot_si256(bytes, _mm256_set1_epi8((char)0xF0));
}
const TA *const A;
const TB *const B;
TC *const C;
......@@ -985,6 +1030,600 @@ class tinyBLAS_Q0_AVX {
};
#endif // __AVX__
//PPC Implementation
#if defined(__MMA__)
#define SAVE_ACC(ACC, ii, jj) \
__builtin_mma_disassemble_acc(vec_C, ACC); \
for (int I = 0; I < 4; I++) { \
for (int J = 0; J < 4; J++) { \
*((float*)(C+ii+((jj+J)*ldc)+I)) = *((float*)&vec_C[I]+J); \
} \
} \
template <typename TA, typename TB, typename TC>
class tinyBLAS_PPC {
public:
tinyBLAS_PPC(int64_t k,
const TA *A, int64_t lda,
const TB *B, int64_t ldb,
TC *C, int64_t ldc,
int ith, int nth)
: A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
}
void matmul(int64_t m, int64_t n) {
mnpack(0, m, 0, n);
}
private:
void (tinyBLAS_PPC::*kernel)(int64_t, int64_t);
void READ_BLOCK(const float* a, int64_t lda, int rows, int cols, float* vec) {
int64_t i, j;
float *aoffset = NULL, *boffset = NULL;
float *aoffset1 = NULL, *aoffset2 = NULL, *aoffset3 = NULL, *aoffset4 = NULL;
float *aoffset5 = NULL, *aoffset6 = NULL, *aoffset7 = NULL, *aoffset8 = NULL;
aoffset = const_cast<float*>(a);
boffset = vec;
j = (rows >> 3);
if (j > 0) {
do {
aoffset1 = aoffset;
aoffset2 = aoffset1 + lda;
aoffset3 = aoffset2 + lda;
aoffset4 = aoffset3 + lda;
aoffset5 = aoffset4 + lda;
aoffset6 = aoffset5 + lda;
aoffset7 = aoffset6 + lda;
aoffset8 = aoffset7 + lda;
aoffset += 8 * lda;
i = (cols >> 3);
if (i > 0) {
__vector_pair C1, C2, C3, C4, C5, C6, C7, C8;
vector float c1[2], c2[2], c3[2], c4[2], c5[2], c6[2], c7[2], c8[2];
vector float t1, t2, t3, t4, t5, t6, t7, t8;
do {
C1 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset1);
C2 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset2);
C3 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset3);
C4 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset4);
C5 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset5);
C6 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset6);
C7 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset7);
C8 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset8);
__builtin_vsx_disassemble_pair(c1, &C1);
__builtin_vsx_disassemble_pair(c2, &C2);
__builtin_vsx_disassemble_pair(c3, &C3);
__builtin_vsx_disassemble_pair(c4, &C4);
__builtin_vsx_disassemble_pair(c5, &C5);
__builtin_vsx_disassemble_pair(c6, &C6);
__builtin_vsx_disassemble_pair(c7, &C7);
__builtin_vsx_disassemble_pair(c8, &C8);
t1 = vec_mergeh(c1[0], c2[0]);
t2 = vec_mergeh(c3[0], c4[0]);
t3 = vec_mergeh(c5[0], c6[0]);
t4 = vec_mergeh(c7[0], c8[0]);
t5 = vec_xxpermdi(t1, t2, 0);
t6 = vec_xxpermdi(t3, t4, 0);
t7 = vec_xxpermdi(t1, t2, 3);
t8 = vec_xxpermdi(t3, t4, 3);
vec_xst(t5, 0, boffset);
vec_xst(t6, 0, boffset+4);
vec_xst(t7, 0, boffset+8);
vec_xst(t8, 0, boffset+12);
t1 = vec_mergel(c1[0], c2[0]);
t2 = vec_mergel(c3[0], c4[0]);
t3 = vec_mergel(c5[0], c6[0]);
t4 = vec_mergel(c7[0], c8[0]);
t5 = vec_xxpermdi(t1, t2, 0);
t6 = vec_xxpermdi(t3, t4, 0);
t7 = vec_xxpermdi(t1, t2, 3);
t8 = vec_xxpermdi(t3, t4, 3);
vec_xst(t5, 0, boffset+16);
vec_xst(t6, 0, boffset+20);
vec_xst(t7, 0, boffset+24);
vec_xst(t8, 0, boffset+28);
t1 = vec_mergeh(c1[1], c2[1]);
t2 = vec_mergeh(c3[1], c4[1]);
t3 = vec_mergeh(c5[1], c6[1]);
t4 = vec_mergeh(c7[1], c8[1]);
t5 = vec_xxpermdi(t1, t2, 0);
t6 = vec_xxpermdi(t3, t4, 0);
t7 = vec_xxpermdi(t1, t2, 3);
t8 = vec_xxpermdi(t3, t4, 3);
vec_xst(t5, 0, boffset+32);
vec_xst(t6, 0, boffset+36);
vec_xst(t7, 0, boffset+40);
vec_xst(t8, 0, boffset+44);
t1 = vec_mergel(c1[1], c2[1]);
t2 = vec_mergel(c3[1], c4[1]);
t3 = vec_mergel(c5[1], c6[1]);
t4 = vec_mergel(c7[1], c8[1]);
t5 = vec_xxpermdi(t1, t2, 0);
t6 = vec_xxpermdi(t3, t4, 0);
t7 = vec_xxpermdi(t1, t2, 3);
t8 = vec_xxpermdi(t3, t4, 3);
vec_xst(t5, 0, boffset+48);
vec_xst(t6, 0, boffset+52);
vec_xst(t7, 0, boffset+56);
vec_xst(t8, 0, boffset+60);
aoffset1 += 8*lda;
aoffset2 += 8*lda;
aoffset3 += 8*lda;
aoffset4 += 8*lda;
boffset += 64;
i--;
} while(i > 0);
}
if (cols & 4) {
vector float c1, c2, c3, c4, c5, c6, c7, c8;
vector float t1, t2, t3, t4, t5, t6, t7, t8;
c1 = vec_xl(0, aoffset1);
c2 = vec_xl(0, aoffset2);
c3 = vec_xl(0, aoffset3);
c4 = vec_xl(0, aoffset4);
c5 = vec_xl(0, aoffset5);
c6 = vec_xl(0, aoffset6);
c7 = vec_xl(0, aoffset7);
c8 = vec_xl(0, aoffset8);
t1 = vec_mergeh(c1, c2);
t2 = vec_mergeh(c3, c4);
t3 = vec_mergeh(c5, c6);
t4 = vec_mergeh(c7, c8);
t5 = vec_xxpermdi(t1, t2, 0);
t6 = vec_xxpermdi(t3, t4, 0);
t7 = vec_xxpermdi(t1, t2, 3);
t8 = vec_xxpermdi(t3, t4, 3);
vec_xst(t5, 0, boffset);
vec_xst(t6, 0, boffset+4);
vec_xst(t7, 0, boffset+8);
vec_xst(t8, 0, boffset+12);
t1 = vec_mergel(c1, c2);
t2 = vec_mergel(c3, c4);
t3 = vec_mergel(c5, c6);
t4 = vec_mergel(c7, c8);
t5 = vec_xxpermdi(t1, t2, 0);
t6 = vec_xxpermdi(t3, t4, 0);
t7 = vec_xxpermdi(t1, t2, 3);
t8 = vec_xxpermdi(t3, t4, 3);
vec_xst(t5, 0, boffset+16);
vec_xst(t6, 0, boffset+20);
vec_xst(t7, 0, boffset+24);
vec_xst(t8, 0, boffset+28);
}
j--;
} while(j > 0);
}
if (rows & 4) {
aoffset1 = aoffset;
aoffset2 = aoffset1 + lda;
aoffset3 = aoffset2 + lda;
aoffset4 = aoffset3 + lda;
aoffset += 4 * lda;
i = (cols >> 3);
if (i > 0) {
__vector_pair C1, C2, C3, C4;
vector float c1[2], c2[2], c3[2], c4[2];
vector float t1, t2, t3, t4, t5, t6, t7, t8;
do {
C1 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset1);
C2 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset2);
C3 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset3);
C4 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset4);
__builtin_vsx_disassemble_pair(c1, &C1);
__builtin_vsx_disassemble_pair(c2, &C2);
__builtin_vsx_disassemble_pair(c3, &C3);
__builtin_vsx_disassemble_pair(c4, &C4);
t1 = vec_mergeh(c1[0], c2[0]);
t2 = vec_mergeh(c3[0], c4[0]);
t3 = vec_mergel(c1[0], c2[0]);
t4 = vec_mergel(c3[0], c4[0]);
t5 = vec_xxpermdi(t1, t2, 0);
t6 = vec_xxpermdi(t1, t2, 3);
t7 = vec_xxpermdi(t3, t4, 0);
t8 = vec_xxpermdi(t3, t4, 3);
vec_xst(t5, 0, boffset);
vec_xst(t6, 0, boffset+4);
vec_xst(t7, 0, boffset+8);
vec_xst(t8, 0, boffset+12);
t1 = vec_mergeh(c1[1], c2[1]);
t2 = vec_mergeh(c3[1], c4[1]);
t3 = vec_mergel(c1[1], c2[1]);
t4 = vec_mergel(c3[1], c4[1]);
t5 = vec_xxpermdi(t1, t2, 0);
t6 = vec_xxpermdi(t1, t2, 3);
t7 = vec_xxpermdi(t3, t4, 0);
t8 = vec_xxpermdi(t3, t4, 3);
vec_xst(t5, 0, boffset+16);
vec_xst(t6, 0, boffset+20);
vec_xst(t7, 0, boffset+24);
vec_xst(t8, 0, boffset+28);
aoffset1 += 8*lda;
aoffset2 += 8*lda;
aoffset3 += 8*lda;
aoffset4 += 8*lda;
boffset += 32;
i--;
} while(i > 0);
}
if (cols & 4) {
vector float c1, c2, c3, c4;
vector float t1, t2, t3, t4;
c1 = vec_xl(0, aoffset1);
c2 = vec_xl(0, aoffset2);
c3 = vec_xl(0, aoffset3);
c4 = vec_xl(0, aoffset4);
t1 = vec_mergeh(c1, c2);
t2 = vec_mergeh(c3, c4);
t3 = vec_xxpermdi(t1, t2, 0);
t4 = vec_xxpermdi(t1, t2, 3);
vec_xst(t3, 0, boffset);
vec_xst(t4, 0, boffset+4);
t1 = vec_mergel(c1, c2);
t2 = vec_mergel(c3, c4);
t3 = vec_xxpermdi(t1, t2, 0);
t4 = vec_xxpermdi(t1, t2, 3);
vec_xst(t3, 0, boffset+8);
vec_xst(t4, 0, boffset+12);
}
}
if (rows & 3) {
aoffset1 = aoffset;
aoffset2 = aoffset1 + lda;
aoffset3 = aoffset2 + lda;
if (cols & 4) {
vector float c1, c2, c3, c4 = {0};
vector float t1, t2, t3, t4;
c1 = vec_xl(0, aoffset1);
c2 = vec_xl(0, aoffset2);
c3 = vec_xl(0, aoffset3);
t1 = vec_mergeh(c1, c2);
t2 = vec_mergeh(c3, c4);
t3 = vec_xxpermdi(t1, t2, 0);
t4 = vec_xxpermdi(t1, t2, 3);
vec_xst(t3, 0, boffset);
vec_xst(t4, 0, boffset+4);
t1 = vec_mergel(c1, c2);
t2 = vec_mergel(c3, c4);
t3 = vec_xxpermdi(t1, t2, 0);
t4 = vec_xxpermdi(t1, t2, 3);
vec_xst(t3, 0, boffset+8);
vec_xst(t4, 0, boffset+12);
}
}
}
void KERNEL_4x4(int64_t ii, int64_t jj) {
vec_t vec_A[4], vec_B[4], vec_C[4];
acc_t acc_0;
__builtin_mma_xxsetaccz(&acc_0);
for (int l = 0; l < k; l+=4) {
READ_BLOCK(A+(ii*lda)+l, lda, 4, 4, (float*)vec_A);
READ_BLOCK(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B);
__builtin_mma_xvf32gerpp(&acc_0, vec_A[0], vec_B[0]);
__builtin_mma_xvf32gerpp(&acc_0, vec_A[1], vec_B[1]);
__builtin_mma_xvf32gerpp(&acc_0, vec_A[2], vec_B[2]);
__builtin_mma_xvf32gerpp(&acc_0, vec_A[3], vec_B[3]);
}
SAVE_ACC(&acc_0, ii, jj);
}
void KERNEL_4x8(int64_t ii, int64_t jj) {
vec_t vec_A[4], vec_B[8], vec_C[4];
acc_t acc_0, acc_1;
__builtin_mma_xxsetaccz(&acc_0);
__builtin_mma_xxsetaccz(&acc_1);
for (int64_t l = 0; l < k; l+=4) {
READ_BLOCK(A+(ii*lda)+l, lda, 4, 4, (float*)vec_A);
READ_BLOCK(B+(jj*ldb)+l, ldb, 8, 4, (float*)vec_B);
__builtin_mma_xvf32gerpp(&acc_0, vec_A[0], (vec_t)vec_B[0]);
__builtin_mma_xvf32gerpp(&acc_1, vec_A[0], (vec_t)vec_B[1]);
__builtin_mma_xvf32gerpp(&acc_0, vec_A[1], (vec_t)vec_B[2]);
__builtin_mma_xvf32gerpp(&acc_1, vec_A[1], (vec_t)vec_B[3]);
__builtin_mma_xvf32gerpp(&acc_0, vec_A[2], (vec_t)vec_B[4]);
__builtin_mma_xvf32gerpp(&acc_1, vec_A[2], (vec_t)vec_B[5]);
__builtin_mma_xvf32gerpp(&acc_0, vec_A[3], (vec_t)vec_B[6]);
__builtin_mma_xvf32gerpp(&acc_1, vec_A[3], (vec_t)vec_B[7]);
}
SAVE_ACC(&acc_0, ii, jj);
SAVE_ACC(&acc_1, ii, jj+4);
}
void KERNEL_8x4(int64_t ii, int64_t jj) {
vec_t vec_A[8], vec_B[4], vec_C[4];
acc_t acc_0, acc_1;
__builtin_mma_xxsetaccz(&acc_0);
__builtin_mma_xxsetaccz(&acc_1);
for (int64_t l = 0; l < k; l+=4) {
READ_BLOCK(A+(ii*lda)+l, lda, 8, 4, (float*)vec_A);
READ_BLOCK(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B);
__builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[0], vec_B[0]);
__builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[1], vec_B[0]);
__builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[2], vec_B[1]);
__builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[3], vec_B[1]);
__builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[4], vec_B[2]);
__builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[5], vec_B[2]);
__builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[6], vec_B[3]);
__builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[7], vec_B[3]);
}
SAVE_ACC(&acc_0, ii, jj);
SAVE_ACC(&acc_1, ii+4, jj);
}
void KERNEL_8x8(int64_t ii, int64_t jj) {
vec_t vec_A[16], vec_B[16], vec_C[4];
acc_t acc_0, acc_1, acc_2, acc_3;
__builtin_mma_xxsetaccz(&acc_0);
__builtin_mma_xxsetaccz(&acc_1);
__builtin_mma_xxsetaccz(&acc_2);
__builtin_mma_xxsetaccz(&acc_3);
for (int l = 0; l < k; l+=8) {
READ_BLOCK(A+(ii*lda)+l, lda, 8, 8, (float*)vec_A);
READ_BLOCK(B+(jj*ldb)+l, ldb, 8, 8, (float*)vec_B);
for(int x = 0; x < 16; x+=2) {
__builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[x], vec_B[x]);
__builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[x], vec_B[x+1]);
__builtin_mma_xvf32gerpp(&acc_2, (vec_t)vec_A[x+1], vec_B[x]);
__builtin_mma_xvf32gerpp(&acc_3, (vec_t)vec_A[x+1], vec_B[x+1]);
}
}
SAVE_ACC(&acc_0, ii, jj);
SAVE_ACC(&acc_1, ii, jj+4);
SAVE_ACC(&acc_2, ii+4, jj);
SAVE_ACC(&acc_3, ii+4, jj+4);
}
void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) {
int64_t mc, nc, mp, np;
int m_rem = MIN(m - m0, 16);
int n_rem = MIN(n - n0, 16);
if (m_rem >= 16 && n_rem >= 8) {
mc = 8;
nc = 8;
gemm<8,8>(m0, m, n0, n);
} else if(m_rem >= 8 && n_rem >= 16) {
mc = 8;
nc = 8;
gemm<8,8>(m0, m, n0, n);
} else if (m_rem >= 8 && n_rem >= 8) {
mc = 8;
nc = 8;
gemm<8,8>(m0, m, n0, n);
} else if (m_rem >= 4 && n_rem >= 8) {
mc = 4;
nc = 8;
gemm<4,8>(m0, m, n0, n);
} else if (m_rem >= 8 && n_rem >= 4) {
mc = 8;
nc = 4;
gemm<8,4>(m0, m, n0, n);
} else if (m_rem >= 4 && n_rem >= 4) {
mc = 4;
nc = 4;
gemm<4,4>(m0, m, n0, n);
} else if ((m_rem < 4) && (n_rem > 4)) {
nc = 4;
switch(m_rem) {
case 1:
mc = 1;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 2:
mc = 2;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 3:
mc = 3;
gemm_small(m0, m, n0, n, mc, nc);
break;
default:
return;
}
} else if ((m_rem > 4) && (n_rem < 4)) {
mc = 4;
switch(n_rem) {
case 1:
nc = 1;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 2:
nc = 2;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 3:
nc = 3;
gemm_small(m0, m, n0, n, mc, nc);
break;
default:
return;
}
} else {
switch((m_rem << 4) | n_rem) {
case 0x43:
mc = 4;
nc = 3;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x42:
mc = 4;
nc = 2;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x41:
mc = 4;
nc = 1;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x34:
mc = 3;
nc = 4;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x33:
mc = 3;
nc = 3;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x32:
mc = 3;
nc = 2;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x31:
mc = 3;
nc = 1;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x24:
mc = 2;
nc = 4;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x23:
mc = 2;
nc = 3;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x22:
mc = 2;
nc = 2;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x21:
mc = 2;
nc = 1;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x14:
mc = 1;
nc = 4;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x13:
mc = 1;
nc = 3;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x12:
mc = 1;
nc = 2;
gemm_small(m0, m, n0, n, mc, nc);
break;
case 0x11:
mc = 1;
nc = 1;
gemm_small(m0, m, n0, n, mc, nc);
break;
default:
return;
}
}
mp = m0 + (m - m0) / mc * mc;
np = n0 + (n - n0) / nc * nc;
mnpack(mp, m, n0, np);
mnpack(m0, m, np, n);
}
void gemm_small(int64_t m0, int64_t m, int64_t n0, int64_t n, int RM, int RN) {
int64_t ytiles = (m - m0) / RM;
int64_t xtiles = (n - n0) / RN;
int64_t tiles = xtiles * ytiles;
int64_t duty = (tiles + nth - 1) / nth;
int64_t start = duty * ith;
int64_t end = start + duty;
if (end > tiles)
end = tiles;
for (int64_t job = start; job < end; ++job) {
int64_t ii = m0 + job / xtiles * RM;
int64_t jj = n0 + job % xtiles * RN;
vec_t vec_C[4];
acc_t acc_0;
__builtin_mma_xxsetaccz(&acc_0);
vec_t vec_A[4], vec_B[4];
for (int l=0; l<k; l+=4) {
if (RN >= 4 && RM == 1) {
float* a = const_cast<float*>(A+(ii)*lda+l);
READ_BLOCK(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B);
vec_A[0] = (vec_t)vec_xl(0,a);
vec_A[1] = (vec_t)vec_splats(*((float*)&vec_A+1));
vec_A[2] = (vec_t)vec_splats(*((float*)&vec_A+2));
vec_A[3] = (vec_t)vec_splats(*((float*)&vec_A+3));
} else {
READ_BLOCK(A+(ii*lda)+l, lda, RM, 4, (float*)vec_A);
READ_BLOCK(B+(jj*ldb)+l, ldb, RN, 4, (float*)vec_B);
}
__builtin_mma_xvf32gerpp(&acc_0, vec_A[0], vec_B[0]);
__builtin_mma_xvf32gerpp(&acc_0, vec_A[1], vec_B[1]);
__builtin_mma_xvf32gerpp(&acc_0, vec_A[2], vec_B[2]);
__builtin_mma_xvf32gerpp(&acc_0, vec_A[3], vec_B[3]);
}
__builtin_mma_disassemble_acc(vec_C, &acc_0);
for (int I = 0; I < RM; I++) {
for (int J = 0; J < RN; J++) {
*((float*)(C+ii+((jj+J)*ldc)+I)) = *((float*)&vec_C[I]+J);
}
}
}
}
template <int RM, int RN>
NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) {
int64_t ytiles = (m - m0) / RM;
int64_t xtiles = (n - n0) / RN;
int64_t tiles = xtiles * ytiles;
int64_t duty = (tiles + nth - 1) / nth;
int64_t start = duty * ith;
int64_t end = start + duty;
if (RM == 4 && RN == 4) {
kernel = &tinyBLAS_PPC::KERNEL_4x4;
} else if (RM == 4 && RN == 8) {
kernel = &tinyBLAS_PPC::KERNEL_4x8;
} else if (RM == 8 && RN == 4) {
kernel = &tinyBLAS_PPC::KERNEL_8x4;
} else if (RM == 8 && RN == 8) {
kernel = &tinyBLAS_PPC::KERNEL_8x8;
}
if (end > tiles)
end = tiles;
for (int64_t job = start; job < end; ++job) {
int64_t ii = m0 + job / xtiles * RM;
int64_t jj = n0 + job % xtiles * RN;
(this->*kernel)(ii, jj);
}
}
const TA *const A;
const TB *const B;
TC *C;
TA *At;
TB *Bt;
const int64_t k;
const int64_t lda;
const int64_t ldb;
const int64_t ldc;
const int ith;
const int nth;
};
#endif
} // namespace
/**
......@@ -1073,6 +1712,16 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
ith, nth};
tb.matmul(m, n);
return true;
#elif defined(__MMA__)
if (k % 8)
return false;
tinyBLAS_PPC<float, float, float> tb{
k, (const float *)A, lda,
(const float *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n);
return true;
#else
return false;
#endif
......@@ -1182,6 +1831,22 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
#endif
}
case GGML_TYPE_Q5_0: {
if (Btype != GGML_TYPE_Q8_0)
return false;
#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__)
tinyBLAS_Q0_AVX<block_q5_0, block_q8_0, float> tb{
k, (const block_q5_0 *)A, lda,
(const block_q8_0 *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n);
return true;
#else
return false;
#endif
}
case GGML_TYPE_IQ4_NL: {
if (Btype != GGML_TYPE_Q8_0)
return false;
......
llama/unicode-data.cpp
View file @ 527cc978
/**
* llama.cpp - commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555 - do not edit this file
* llama.cpp - commit 40c6d79fb52f995f47507fedfeaae2ac05d9b35c - do not edit this file
*
* MIT License
*
......@@ -33,7 +33,7 @@
#include <unordered_map>
#include <unordered_set>
const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = { // start, flags // last=next_start-1
const std::initializer_list<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = { // start, flags // last=next_start-1
{0x000000, 0x0080},
{0x000020, 0x0008},
{0x000021, 0x0020},
......@@ -2337,7 +2337,8 @@ const std::unordered_set<uint32_t> unicode_set_whitespace = {
0x003000,
};
const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase = {
// list is always in ascending order, to enable binary search
const std::initializer_list<std::pair<uint32_t, uint32_t>> unicode_map_lowercase = {
{0x000041, 0x000061},
{0x000042, 0x000062},
{0x000043, 0x000063},
......@@ -3773,7 +3774,8 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase = {
{0x01E921, 0x01E943},
};
const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
// list is always in ascending order, to enable binary search
const std::initializer_list<std::pair<uint32_t, uint32_t>> unicode_map_uppercase = {
{0x000061, 0x000041},
{0x000062, 0x000042},
{0x000063, 0x000043},
......@@ -5226,7 +5228,7 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
{0x01E943, 0x01E921},
};
const std::vector<range_nfd> unicode_ranges_nfd = { // start, last, nfd
const std::initializer_list<range_nfd> unicode_ranges_nfd = { // start, last, nfd
{0x000000, 0x000000, 0x000000},
{0x0000C0, 0x0000C5, 0x000041},
{0x0000C7, 0x0000C7, 0x000043},
......
llama/unicode-data.h
View file @ 527cc978
/**
* llama.cpp - commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555 - do not edit this file
* llama.cpp - commit 40c6d79fb52f995f47507fedfeaae2ac05d9b35c - do not edit this file
*
* MIT License
*
......@@ -39,8 +39,8 @@ struct range_nfd {
static const uint32_t MAX_CODEPOINTS = 0x110000;
extern const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags;
extern const std::initializer_list<std::pair<uint32_t, uint16_t>> unicode_ranges_flags;
extern const std::unordered_set<uint32_t> unicode_set_whitespace;
extern const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase;
extern const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase;
extern const std::vector<range_nfd> unicode_ranges_nfd;
extern const std::initializer_list<std::pair<uint32_t, uint32_t>> unicode_map_lowercase;
extern const std::initializer_list<std::pair<uint32_t, uint32_t>> unicode_map_uppercase;
extern const std::initializer_list<range_nfd> unicode_ranges_nfd;
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