--- /dev/null
+#!/usr/bin/env python3
+# HF starcoder --> gguf conversion
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import struct
+import sys
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer # type: ignore[import]
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+ sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
+
+
+def bytes_to_unicode():
+ # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
+ """
+ Returns list of utf-8 byte and a corresponding list of unicode strings.
+ The reversible bpe codes work on unicode strings.
+ This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+ When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+ This is a significant percentage of your normal, say, 32K bpe vocab.
+ To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+ And avoids mapping to whitespace/control characters the bpe code barfs on.
+ """
+ bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
+ cs = bs[:]
+ n = 0
+ for b in range(2**8):
+ if b not in bs:
+ bs.append(b)
+ cs.append(2**8+n)
+ n += 1
+ return dict(zip(bs, (chr(n) for n in cs)))
+
+
+def count_model_parts(dir_model: Path) -> int:
+ num_parts = 0
+ for filename in os.listdir(dir_model):
+ if filename.startswith("pytorch_model-"):
+ num_parts += 1
+
+ if num_parts > 0:
+ print("gguf: found " + str(num_parts) + " model parts")
+ return num_parts
+
+
+def parse_args() -> argparse.Namespace:
+ parser = argparse.ArgumentParser(description="Convert a StarCoder model to a GGML compatible file")
+ parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
+ parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
+ parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
+ parser.add_argument("ftype", type=int, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1)
+ return parser.parse_args()
+
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+ print(f'Error: {args.model} is not a directory', file = sys.stderr)
+ sys.exit(1)
+
+# possible tensor data types
+# ftype == 0 -> float32
+# ftype == 1 -> float16
+
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+if args.outfile is not None:
+ fname_out = args.outfile
+else:
+ # output in the same directory as the model by default
+ fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
+
+print("gguf: loading model "+dir_model.name)
+
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+ hparams = json.load(f)
+
+if hparams["architectures"][0] != "GPTBigCodeForCausalLM":
+ print("Model architecture not supported: " + hparams["architectures"][0])
+
+ sys.exit(1)
+
+# get number of model parts
+num_parts = count_model_parts(dir_model)
+
+ARCH=gguf.MODEL_ARCH.STARCODER
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+block_count = hparams["n_layer"]
+
+gguf_writer.add_name("StarCoder")
+gguf_writer.add_context_length(hparams["n_positions"])
+gguf_writer.add_embedding_length(hparams["n_embd"])
+gguf_writer.add_feed_forward_length(4 * hparams["n_embd"])
+gguf_writer.add_block_count(block_count)
+gguf_writer.add_head_count(hparams["n_head"])
+gguf_writer.add_head_count_kv(1)
+gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
+gguf_writer.add_file_type(ftype)
+
+# TOKENIZATION
+
+print("gguf: get tokenizer metadata")
+
+tokens: list[bytearray] = []
+scores: list[float] = []
+toktypes: list[int] = []
+
+tokenizer_json_file = dir_model / 'tokenizer.json'
+if not tokenizer_json_file.is_file():
+ print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
+ sys.exit(1)
+
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
+
+with open(tokenizer_json_file, "r", encoding="utf-8") as f:
+ tokenizer_json = json.load(f)
+
+print("gguf: get gpt2 tokenizer vocab")
+
+# The number of tokens in tokenizer.json can differ from the expected vocab size.
+# This causes downstream issues with mismatched tensor sizes when running the inference
+vocab_size = hparams["vocab_size"] if "vocab_size" in hparams else len(tokenizer_json["model"]["vocab"])
+
+# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
+
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+byte_encoder = bytes_to_unicode()
+byte_decoder = {v: k for k, v in byte_encoder.items()}
+
+for i in range(vocab_size):
+ if i in reverse_vocab:
+ try:
+ text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
+ except KeyError:
+ text = bytearray()
+ for c in reverse_vocab[i]:
+ if ord(c) < 256: # single byte character
+ text.append(byte_decoder[ord(c)])
+ else: # multibyte special token character
+ text.extend(c.encode('utf-8'))
+ else:
+ print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
+ pad_token = f"[PAD{i}]".encode("utf8")
+ text = bytearray(pad_token)
+
+ tokens.append(text)
+ scores.append(0.0) # dymmy
+ toktypes.append(gguf.TokenType.NORMAL) # dummy
+
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_scores(scores)
+gguf_writer.add_token_types(toktypes)
+
+special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
+special_vocab.add_to_gguf(gguf_writer)
+
+# TENSORS
+
+tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
+
+# params for qkv transform
+n_head = hparams["n_head"]
+n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1
+
+head_dim = hparams["n_embd"] // n_head
+
+# tensor info
+print("gguf: get tensor metadata")
+
+if num_parts == 0:
+ part_names = iter(("pytorch_model.bin",))
+else:
+ part_names = (
+ f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
+ )
+
+for part_name in part_names:
+ if args.vocab_only:
+ break
+ print("gguf: loading model part '" + part_name + "'")
+ model_part = torch.load(dir_model / part_name, map_location="cpu")
+
+ for name in model_part.keys():
+ data = model_part[name]
+
+ old_dtype = data.dtype
+
+ # convert any unsupported data types to float32
+ if data.dtype != torch.float16 and data.dtype != torch.float32:
+ data = data.to(torch.float32)
+
+ data = data.squeeze().numpy()
+
+ # map tensor names
+ new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
+ if new_name is None:
+ print("Can not map tensor '" + name + "'")
+ sys.exit()
+
+ n_dims = len(data.shape)
+ data_dtype = data.dtype
+
+ # if f32 desired, convert any float16 to float32
+ if ftype == 0 and data_dtype == np.float16:
+ data = data.astype(np.float32)
+
+ # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+ if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+ data = data.astype(np.float32)
+
+ # if f16 desired, convert any float32 2-dim weight tensors to float16
+ if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+ data = data.astype(np.float16)
+
+ print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+
+ gguf_writer.add_tensor(new_name, data)
+
+
+print("gguf: write header")
+gguf_writer.write_header_to_file()
+print("gguf: write metadata")
+gguf_writer.write_kv_data_to_file()
+if not args.vocab_only:
+ print("gguf: write tensors")
+ gguf_writer.write_tensors_to_file()
+
+gguf_writer.close()
+
+print(f"gguf: model successfully exported to '{fname_out}'")
+print("")
LLM_ARCH_GPTJ,
LLM_ARCH_GPTNEOX,
LLM_ARCH_MPT,
+ LLM_ARCH_STARCODER,
LLM_ARCH_UNKNOWN,
};
static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
- { LLM_ARCH_LLAMA, "llama" },
- { LLM_ARCH_FALCON, "falcon" },
- { LLM_ARCH_GPT2, "gpt2" },
- { LLM_ARCH_GPTJ, "gptj" },
- { LLM_ARCH_GPTNEOX, "gptneox" },
- { LLM_ARCH_MPT, "mpt" },
- { LLM_ARCH_BAICHUAN,"baichuan" },
+ { LLM_ARCH_LLAMA, "llama" },
+ { LLM_ARCH_FALCON, "falcon" },
+ { LLM_ARCH_GPT2, "gpt2" },
+ { LLM_ARCH_GPTJ, "gptj" },
+ { LLM_ARCH_GPTNEOX, "gptneox" },
+ { LLM_ARCH_MPT, "mpt" },
+ { LLM_ARCH_BAICHUAN, "baichuan" },
+ { LLM_ARCH_STARCODER, "starcoder" },
};
enum llm_kv {
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
+ {
+ LLM_ARCH_STARCODER,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_POS_EMBD, "position_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ },
+ },
{
LLM_ARCH_UNKNOWN,
{
// available llama models
enum e_model {
MODEL_UNKNOWN,
+ MODEL_1B,
MODEL_3B,
MODEL_7B,
MODEL_13B,
+ MODEL_15B,
MODEL_30B,
MODEL_34B,
MODEL_40B,
struct ggml_tensor * wo;
struct ggml_tensor * wqkv;
+ // attention bias
+ struct ggml_tensor * bo;
+ struct ggml_tensor * bqkv;
+
// normalization
struct ggml_tensor * ffn_norm;
+ struct ggml_tensor * ffn_norm_b;
// ff
struct ggml_tensor * w1; // ffn_gate
struct ggml_tensor * w2; // ffn_down
struct ggml_tensor * w3; // ffn_up
+
+ // ff bias
+ struct ggml_tensor * b2; // ffn_down
+ struct ggml_tensor * b3; // ffn_up
};
struct llama_kv_cache {
llama_vocab vocab;
struct ggml_tensor * tok_embeddings;
+ struct ggml_tensor * pos_embeddings;
struct ggml_tensor * output_norm;
struct ggml_tensor * output_norm_b;
static const char * llama_model_type_name(e_model type) {
switch (type) {
+ case MODEL_1B: return "1B";
case MODEL_3B: return "3B";
case MODEL_7B: return "7B";
case MODEL_13B: return "13B";
+ case MODEL_15B: return "15B";
case MODEL_30B: return "30B";
case MODEL_34B: return "34B";
case MODEL_40B: return "40B";
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
+ case LLM_ARCH_STARCODER:
+ {
+ GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
+ switch (hparams.n_layer) {
+ case 24: model.type = e_model::MODEL_1B; break;
+ case 36: model.type = e_model::MODEL_3B; break;
+ case 42: model.type = e_model::MODEL_7B; break;
+ case 40: model.type = e_model::MODEL_15B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
default: (void)0;
};
}
}
} break;
+ case LLM_ARCH_STARCODER:
+ {
+ model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+ model.pos_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
+
+ // output
+ {
+ ggml_backend backend_norm;
+ ggml_backend backend_output;
+
+ if (n_gpu_layers > int(n_layer)) {
+ // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
+ // on Windows however this is detrimental unless everything is on the GPU
+#ifndef _WIN32
+ backend_norm = low_vram ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
+#else
+ backend_norm = low_vram || n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
+#endif // _WIN32
+
+ backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
+ } else {
+ backend_norm = GGML_BACKEND_CPU;
+ backend_output = GGML_BACKEND_CPU;
+ }
+
+ model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
+ model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
+ model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
+
+ if (backend_norm == GGML_BACKEND_GPU) {
+ vram_weights += ggml_nbytes(model.output_norm);
+ vram_weights += ggml_nbytes(model.output_norm_b);
+ }
+ if (backend_output == GGML_BACKEND_GPU_SPLIT) {
+ vram_weights += ggml_nbytes(model.output);
+ }
+ }
+
+ const uint32_t n_ff = hparams.n_ff;
+
+ const int i_gpu_start = n_layer - n_gpu_layers;
+
+ model.layers.resize(n_layer);
+
+ for (uint32_t i = 0; i < n_layer; ++i) {
+ const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
+ const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
+
+ auto & layer = model.layers[i];
+
+ layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
+ layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
+
+ layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
+ layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend_split);
+
+ layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
+ layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend_split);
+
+ layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
+ layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
+
+ layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
+ layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split);
+
+ layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
+ layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split);
+
+ if (backend == GGML_BACKEND_GPU) {
+ vram_weights +=
+ ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
+ ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) +
+ ggml_nbytes(layer.wo) + ggml_nbytes(layer.bo) +
+ ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_norm_b) +
+ ggml_nbytes(layer.w2) + ggml_nbytes(layer.b2) +
+ ggml_nbytes(layer.w3) + ggml_nbytes(layer.b3);
+ }
+ }
+ } break;
default:
throw std::runtime_error("unknown architecture");
};
return gf;
}
+static struct ggml_cgraph * llm_build_starcoder(
+ llama_context & lctx,
+ const llama_token * tokens,
+ const float * embd,
+ int n_tokens,
+ int n_past) {
+
+ GGML_ASSERT((!tokens && embd) || (tokens && !embd)); // NOLINT
+
+ const int N = n_tokens;
+
+ const auto & model = lctx.model;
+ const auto & hparams = model.hparams;
+
+ const auto & kv_self = lctx.kv_self;
+
+ GGML_ASSERT(!!kv_self.ctx);
+
+ const int64_t n_embd = hparams.n_embd;
+ const int64_t n_layer = hparams.n_layer;
+ const int64_t n_ctx = hparams.n_ctx;
+ const int64_t n_head = hparams.n_head;
+ const int64_t n_head_kv = hparams.n_head_kv;
+ const int64_t n_embd_head = hparams.n_embd_head();
+ const int64_t n_embd_gqa = hparams.n_embd_gqa();
+
+ GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+ const float norm_eps = hparams.f_norm_eps;
+
+ auto & buf_compute = lctx.buf_compute;
+
+ struct ggml_init_params params = {
+ /*.mem_size =*/ buf_compute.size,
+ /*.mem_buffer =*/ buf_compute.data,
+ /*.no_alloc =*/ false,
+ };
+
+ params.no_alloc = true;
+
+ struct ggml_context * ctx0 = ggml_init(params);
+
+ ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * token;
+ struct ggml_tensor * position;
+ struct ggml_tensor * inpL;
+
+ if (tokens) {
+ struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+
+ ggml_allocr_alloc(lctx.alloc, inp_tokens);
+ if (!ggml_allocr_is_measure(lctx.alloc)) {
+ memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
+ }
+ ggml_set_name(inp_tokens, "inp_tokens");
+
+ token = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
+ } else {
+#ifdef GGML_USE_MPI
+ GGML_ASSERT(false && "not implemented");
+#endif
+
+ token = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
+
+ ggml_allocr_alloc(lctx.alloc, token);
+ if (!ggml_allocr_is_measure(lctx.alloc)) {
+ memcpy(token->data, embd, N * n_embd * ggml_element_size(inpL));
+ }
+ }
+
+ {
+ // Compute position embeddings.
+ struct ggml_tensor * inp_positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+ ggml_allocr_alloc(lctx.alloc, inp_positions);
+ if (!ggml_allocr_is_measure(lctx.alloc)) {
+ for (int i = 0; i < N; ++i) {
+ ((int32_t *) inp_positions->data)[i] = n_past + i;
+ }
+ }
+ ggml_set_name(inp_positions, "inp_positions");
+
+ position = ggml_get_rows(ctx0, model.pos_embeddings, inp_positions);
+ }
+
+ struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+ ggml_allocr_alloc(lctx.alloc, KQ_scale);
+ if (!ggml_allocr_is_measure(lctx.alloc)) {
+ ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
+ }
+ ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
+
+ inpL = ggml_add(ctx0, token, position);
+ ggml_set_name(inpL, "inpL");
+
+ for (int il = 0; il < n_layer; ++il) {
+ {
+ // Norm
+ cur = ggml_norm(ctx0, inpL, norm_eps);
+ cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].attn_norm), model.layers[il].attn_norm_b);
+ }
+
+ {
+ // Self Attention
+ cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv);
+
+ struct ggml_tensor * tmpq = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
+ struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd_gqa, N, cur->nb[1], sizeof(float)*n_embd);
+ struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd_gqa, N, cur->nb[1], sizeof(float)*(n_embd + n_embd_gqa));
+
+ struct ggml_tensor * Qcur = tmpq;
+ struct ggml_tensor * Kcur = tmpk;
+
+ {
+ struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, N));
+ ggml_set_name(Vcur, "Vcur");
+
+ struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + n_past));
+ ggml_set_name(k, "k");
+
+ struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd_gqa,
+ ( n_ctx)*ggml_element_size(kv_self.v),
+ (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + n_past*ggml_element_size(kv_self.v));
+
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
+ }
+
+ struct ggml_tensor * Q =
+ ggml_permute(ctx0,
+ ggml_cpy(ctx0,
+ Qcur,
+ ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd_head, n_head, N)),
+ 0, 2, 1, 3);
+ ggml_set_name(Q, "Q");
+
+ struct ggml_tensor * K =
+ ggml_view_3d(ctx0, kv_self.k,
+ n_embd_head, n_past + N, n_head_kv,
+ ggml_element_size(kv_self.k)*n_embd_gqa,
+ ggml_element_size(kv_self.k)*n_embd_head,
+ ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
+ ggml_set_name(K, "K");
+
+ // K * Q
+ struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+ ggml_set_name(KQ, "KQ");
+
+ // KQ_scaled = KQ / sqrt(n_embd_head)
+ // KQ_scaled shape [n_past + N, N, n_head, 1]
+ struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
+ ggml_set_name(KQ_scaled, "KQ_scaled");
+
+ // KQ_masked = mask_past(KQ_scaled)
+ struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
+ ggml_set_name(KQ_masked, "KQ_masked");
+
+ // KQ = soft_max(KQ_masked)
+ struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
+ ggml_set_name(KQ_soft_max, "KQ_soft_max");
+
+ // split cached V into n_head heads
+ struct ggml_tensor * V =
+ ggml_view_3d(ctx0, kv_self.v,
+ n_past + N, n_embd_head, n_head_kv,
+ ggml_element_size(kv_self.v)*n_ctx,
+ ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
+ ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
+ ggml_set_name(V, "V");
+
+ struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+ ggml_set_name(KQV, "KQV");
+
+ // KQV_merged = KQV.permute(0, 2, 1, 3)
+ struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+ ggml_set_name(KQV_merged, "KQV_merged");
+
+ // cur = KQV_merged.contiguous().view(n_embd, N)
+ cur = ggml_cpy(ctx0,
+ KQV_merged,
+ ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
+ ggml_set_name(cur, "KQV_merged_contiguous");
+ }
+
+ // Projection
+ cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wo, cur), model.layers[il].bo);
+
+ // Add the input
+ cur = ggml_add(ctx0, cur, inpL);
+
+ struct ggml_tensor * inpFF = cur;
+
+ // FF
+ {
+ // Norm
+ {
+ cur = ggml_norm(ctx0, inpFF, norm_eps);
+ cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ffn_norm), model.layers[il].ffn_norm_b);
+ }
+
+ cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3);
+
+ // GELU activation
+ cur = ggml_gelu(ctx0, cur);
+
+ // Projection
+ cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w2, cur), model.layers[il].b2);
+ }
+
+ inpL = ggml_add(ctx0, cur, inpFF);
+ }
+
+ // Output Norm
+ {
+ cur = ggml_norm(ctx0, inpL, norm_eps);
+ cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.output_norm), model.output_norm_b);
+ }
+ ggml_set_name(cur, "result_norm");
+
+ cur = ggml_mul_mat(ctx0, model.output, cur);
+ ggml_set_name(cur, "result_output");
+
+ ggml_build_forward_expand(gf, cur);
+ ggml_free(ctx0);
+
+ return gf;
+}
+
static struct ggml_cgraph * llama_build_graph(
llama_context & lctx,
const llama_token * tokens,
{
result = llm_build_falcon(lctx, tokens, embd, n_tokens, n_past);
} break;
+ case LLM_ARCH_STARCODER:
+ {
+ result = llm_build_starcoder(lctx, tokens, embd, n_tokens, n_past);
+ } break;
default:
GGML_ASSERT(false);
};
overview / pkg / ggml / sources / llama.cpp / commitdiff