--- /dev/null
+#!/usr/bin/env python3
+# HF refact--> gguf conversion
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+from pathlib import Path
+
+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 Refact 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,
+ choices=[0, 1],
+ default=1,
+ nargs="?",
+ help="output format - use 0 for float32, 1 for float16",
+ )
+ 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] != "GPTRefactForCausalLM":
+ 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.REFACT
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+# Get refact feed forward dimension
+hidden_dim = hparams["n_embd"]
+inner_dim = 4 * hidden_dim
+hidden_dim = int(2 * inner_dim / 3)
+multiple_of = 256
+ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+block_count = hparams["n_layer"]
+
+gguf_writer.add_name("Refact")
+# refact uses Alibi. So this is from config.json which might be used by training.
+gguf_writer.add_context_length(hparams["n_positions"])
+gguf_writer.add_embedding_length(hparams["n_embd"])
+
+gguf_writer.add_feed_forward_length(ff_dim)
+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_rms_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"])
+)
+
+tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+
+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:
+ text = reverse_vocab[i]
+ 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 = 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 i in range(block_count):
+ if f"transformer.h.{i}.attn.kv.weight" in model_part:
+ data = model_part[f"transformer.h.{i}.attn.kv.weight"]
+ model_part[f"model.layers.{i}.self_attn.k_proj.weight"] = data[
+ : n_head_kv * head_dim
+ ]
+ model_part[f"model.layers.{i}.self_attn.v_proj.weight"] = data[
+ n_head_kv * head_dim :
+ ]
+ del model_part[f"transformer.h.{i}.attn.kv.weight"]
+ if f"transformer.h.{i}.attn.q.weight" in model_part:
+ model_part[f"model.layers.{i}.self_attn.q_proj.weight"] = model_part[
+ f"transformer.h.{i}.attn.q.weight"
+ ]
+ del model_part[f"transformer.h.{i}.attn.q.weight"]
+ if f"transformer.h.{i}.mlp.gate_up_proj.weight" in model_part:
+ data = model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
+ model_part[f"model.layers.{i}.mlp.gate_proj.weight"] = data[:ff_dim]
+ model_part[f"model.layers.{i}.mlp.up_proj.weight"] = data[ff_dim:]
+ del model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
+
+ 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",))
+ 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(
+ new_name
+ + ", n_dims = "
+ + str(n_dims)
+ + ", "
+ + 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("")
GPTNEOX : int = auto()
MPT : int = auto()
STARCODER : int = auto()
+ REFACT : int = auto()
BERT : int = auto()
MODEL_ARCH.GPTNEOX: "gptneox",
MODEL_ARCH.MPT: "mpt",
MODEL_ARCH.STARCODER: "starcoder",
+ MODEL_ARCH.REFACT: "refact",
MODEL_ARCH.BERT: "bert",
}
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
+ MODEL_ARCH.REFACT: [
+ MODEL_TENSOR.TOKEN_EMBD,
+ MODEL_TENSOR.OUTPUT_NORM,
+ MODEL_TENSOR.OUTPUT,
+ MODEL_TENSOR.ATTN_NORM,
+ MODEL_TENSOR.ATTN_Q,
+ MODEL_TENSOR.ATTN_K,
+ MODEL_TENSOR.ATTN_V,
+ MODEL_TENSOR.ATTN_OUT,
+ MODEL_TENSOR.FFN_NORM,
+ MODEL_TENSOR.FFN_GATE,
+ MODEL_TENSOR.FFN_DOWN,
+ MODEL_TENSOR.FFN_UP,
+ ],
MODEL_ARCH.GPT2: [
# TODO
],
# Token embeddings
MODEL_TENSOR.TOKEN_EMBD: (
"gpt_neox.embed_in", # gptneox
- "transformer.wte", # gpt2 gpt-j mpt
+ "transformer.wte", # gpt2 gpt-j mpt refact
"transformer.word_embeddings", # falcon
"model.embed_tokens", # llama-hf
"tok_embeddings", # llama-pth
"norm", # llama-pth
"embeddings.LayerNorm", # bert
"transformer.norm_f", # mpt
+ "ln_f", # refact
),
# Rope frequencies
# Attention norm
MODEL_TENSOR.ATTN_NORM: (
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
- "transformer.h.{bid}.ln_1", # gpt2 gpt-j
+ "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact
"transformer.blocks.{bid}.norm_1", # mpt
"transformer.h.{bid}.input_layernorm", # falcon7b
"transformer.h.{bid}.ln_mlp", # falcon40b
# Attention output
MODEL_TENSOR.ATTN_OUT: (
"gpt_neox.layers.{bid}.attention.dense", # gptneox
- "transformer.h.{bid}.attn.c_proj", # gpt2
+ "transformer.h.{bid}.attn.c_proj", # gpt2 refact
"transformer.blocks.{bid}.attn.out_proj", # mpt
"transformer.h.{bid}.self_attention.dense", # falcon
"model.layers.{bid}.self_attn.o_proj", # llama-hf
# Feed-forward norm
MODEL_TENSOR.FFN_NORM: (
"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
- "transformer.h.{bid}.ln_2", # gpt2
+ "transformer.h.{bid}.ln_2", # gpt2 refact
"transformer.blocks.{bid}.norm_2", # mpt
"model.layers.{bid}.post_attention_layernorm", # llama-hf
"layers.{bid}.ffn_norm", # llama-pth
"transformer.h.{bid}.mlp.c_fc", # gpt2
"transformer.blocks.{bid}.ffn.up_proj", # mpt
"transformer.h.{bid}.mlp.dense_h_to_4h", # falcon
- "model.layers.{bid}.mlp.up_proj", # llama-hf
+ "model.layers.{bid}.mlp.up_proj", # llama-hf refact
"layers.{bid}.feed_forward.w3", # llama-pth
"encoder.layer.{bid}.intermediate.dense", # bert
"transformer.h.{bid}.mlp.fc_in", # gpt-j
# Feed-forward gate
MODEL_TENSOR.FFN_GATE: (
- "model.layers.{bid}.mlp.gate_proj", # llama-hf
+ "model.layers.{bid}.mlp.gate_proj", # llama-hf refact
"layers.{bid}.feed_forward.w1", # llama-pth
),
# Feed-forward down
MODEL_TENSOR.FFN_DOWN: (
"gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
- "transformer.h.{bid}.mlp.c_proj", # gpt2
+ "transformer.h.{bid}.mlp.c_proj", # gpt2 refact
"transformer.blocks.{bid}.ffn.down_proj", # mpt
"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
"model.layers.{bid}.mlp.down_proj", # llama-hf
LLM_ARCH_GPTNEOX,
LLM_ARCH_MPT,
LLM_ARCH_STARCODER,
+ LLM_ARCH_REFACT,
LLM_ARCH_UNKNOWN,
};
{ LLM_ARCH_MPT, "mpt" },
{ LLM_ARCH_BAICHUAN, "baichuan" },
{ LLM_ARCH_STARCODER, "starcoder" },
+ { LLM_ARCH_REFACT, "refact" },
};
enum llm_kv {
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
},
},
+ {
+ LLM_ARCH_REFACT,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
{
LLM_ARCH_UNKNOWN,
{
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
+ case LLM_ARCH_REFACT:
+ {
+ GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
+ switch (hparams.n_layer) {
+ case 32: model.type = e_model::MODEL_1B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
default: (void)0;
}
const auto tn = LLM_TN(model.arch);
switch (model.arch) {
case LLM_ARCH_LLAMA:
+ case LLM_ARCH_REFACT:
{
model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
return gf;
}
+static struct ggml_cgraph * llm_build_refact(
+ llama_context & lctx,
+ const llama_batch & batch) {
+ const auto & model = lctx.model;
+ const auto & hparams = model.hparams;
+ const auto & cparams = lctx.cparams;
+
+ 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 = cparams.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();
+
+ const float norm_rms_eps = hparams.f_norm_rms_eps;
+
+ const int n_gpu_layers = model.n_gpu_layers;
+
+ const int32_t n_tokens = batch.n_tokens;
+ const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
+ const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+
+ // printf("n_kv = %d\n", n_kv);
+
+ 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 * inpL;
+
+ if (batch.token) {
+ struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+
+ ggml_allocr_alloc(lctx.alloc, inp_tokens);
+ if (!ggml_allocr_is_measure(lctx.alloc)) {
+ memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
+ }
+ ggml_set_name(inp_tokens, "inp_tokens");
+
+ inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
+ } else {
+#ifdef GGML_USE_MPI
+ GGML_ASSERT(false && "not implemented");
+#endif
+
+ inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+
+ ggml_allocr_alloc(lctx.alloc, inpL);
+ if (!ggml_allocr_is_measure(lctx.alloc)) {
+ memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
+ }
+ }
+
+ const int i_gpu_start = n_layer - n_gpu_layers;
+ (void) i_gpu_start;
+
+ // offload functions set the tensor output backend to GPU
+ // tensors are GPU-accelerated if any input or the output has been offloaded
+ offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
+ offload_func_t offload_func_kq = llama_nop;
+ offload_func_t offload_func_v = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+ if (n_gpu_layers > n_layer) {
+ offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
+ }
+ if (n_gpu_layers > n_layer + 1) {
+ offload_func_v = ggml_cuda_assign_buffers_no_alloc;
+ }
+ if (n_gpu_layers > n_layer + 2) {
+ offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
+ }
+#endif // GGML_USE_CUBLAS
+
+ // KQ_scale
+ struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+ ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
+ 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_head)));
+ }
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+ offload_func_kq(KQ_mask);
+ ggml_set_name(KQ_mask, "KQ_mask");
+ ggml_allocr_alloc(lctx.alloc, KQ_mask);
+ if (!ggml_allocr_is_measure(lctx.alloc)) {
+ float * data = (float *) KQ_mask->data;
+ memset(data, 0, ggml_nbytes(KQ_mask));
+
+ for (int h = 0; h < 1; ++h) {
+ for (int j = 0; j < n_tokens; ++j) {
+ const llama_pos pos = batch.pos[j];
+ const llama_seq_id seq_id = batch.seq_id[j];
+
+ for (int i = 0; i < n_kv; ++i) {
+ if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
+ data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
+ }
+ }
+ }
+ }
+ }
+
+ for (int il = 0; il < n_layer; ++il) {
+ ggml_format_name(inpL, "layer_inp_%d", il);
+
+ offload_func_t offload_func = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+ if (il >= i_gpu_start) {
+ offload_func = ggml_cuda_assign_buffers_no_alloc;
+ }
+#endif // GGML_USE_CUBLAS
+
+ struct ggml_tensor * inpSA = inpL;
+
+ // norm
+ {
+ cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
+ offload_func(cur);
+ ggml_set_name(cur, "rms_norm_0");
+
+ // cur = cur*attn_norm(broadcasted)
+ cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
+ offload_func(cur);
+ ggml_set_name(cur, "attention_norm_0");
+ }
+
+ // self-attention
+ {
+ // compute Q and K
+ struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+ offload_func_kq(tmpk);
+ ggml_set_name(tmpk, "tmpk");
+
+ struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+ offload_func_kq(tmpq);
+ ggml_set_name(tmpq, "tmpq");
+
+ struct ggml_tensor * Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens);
+ offload_func_kq(Kcur);
+ ggml_set_name(Kcur, "Kcur");
+
+ struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens);
+ offload_func_kq(Qcur);
+ ggml_set_name(Qcur, "Qcur");
+
+ // store key and value to memory
+ {
+ // compute the transposed [n_tokens, n_embd] V matrix
+
+ struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+ offload_func_v(tmpv);
+ ggml_set_name(tmpv, "tmpv");
+
+ struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
+ offload_func_v(Vcur);
+ ggml_set_name(Vcur, "Vcur");
+
+ struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
+ offload_func_kq(k);
+ ggml_set_name(k, "k");
+
+ struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
+ ( n_ctx)*ggml_element_size(kv_self.v),
+ (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+ offload_func_v(v);
+ ggml_set_name(v, "v");
+
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
+ }
+
+ struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+ offload_func_kq(Q);
+ ggml_set_name(Q, "Q");
+
+ struct ggml_tensor * K =
+ ggml_view_3d(ctx0, kv_self.k,
+ n_embd_head, n_kv, 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);
+ offload_func_kq(K);
+ ggml_set_name(K, "K");
+
+ // K * Q
+ struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+ offload_func_kq(KQ);
+ ggml_set_name(KQ, "KQ");
+
+ // KQ_scaled = KQ / sqrt(n_embd_head)
+ // KQ_scaled shape [n_kv, n_tokens, n_head, 1]
+ struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
+ offload_func_kq(KQ_scaled);
+ ggml_set_name(KQ_scaled, "KQ_scaled");
+
+ // KQ_masked = mask_past(KQ_scaled)
+ struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8);
+ ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
+
+ struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
+ offload_func_kq(KQ_masked);
+ ggml_set_name(KQ_masked, "KQ_masked");
+
+ // KQ = soft_max(KQ_masked)
+ struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
+ offload_func_v(KQ_soft_max);
+ 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_kv, 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);
+ offload_func_v(V);
+ ggml_set_name(V, "V");
+
+#if 1
+ struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+ offload_func_v(KQV);
+ ggml_set_name(KQV, "KQV");
+#else
+ // make V contiguous in memory to speed up the matmul, however we waste time on the copy
+ // on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
+ // is there a better way?
+ struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_ctx, n_embd_head, n_head));
+ struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
+#endif
+
+ // KQV_merged = KQV.permute(0, 2, 1, 3)
+ struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+ offload_func_v(KQV_merged);
+ ggml_set_name(KQV_merged, "KQV_merged");
+
+ // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
+ cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
+ offload_func_v(cur);
+ ggml_set_name(cur, "KQV_merged_contiguous");
+
+ // projection (no bias)
+ cur = ggml_mul_mat(ctx0,
+ model.layers[il].wo,
+ cur);
+ offload_func(cur);
+ ggml_set_name(cur, "result_wo");
+ }
+
+ struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
+ offload_func(inpFF);
+ ggml_set_name(inpFF, "inpFF");
+
+ // feed-forward network
+ {
+ // norm
+ {
+ cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
+ offload_func(cur);
+ ggml_set_name(cur, "rms_norm_1");
+
+ // cur = cur*ffn_norm(broadcasted)
+ cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
+ offload_func(cur);
+ ggml_set_name(cur, "ffn_norm");
+ }
+
+ struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
+ model.layers[il].w3,
+ cur);
+ offload_func(tmp);
+ ggml_set_name(tmp, "result_w3");
+
+ cur = ggml_mul_mat(ctx0,
+ model.layers[il].w1,
+ cur);
+ offload_func(cur);
+ ggml_set_name(cur, "result_w1");
+
+ // SILU activation
+ cur = ggml_silu(ctx0, cur);
+ offload_func(cur);
+ ggml_set_name(cur, "silu");
+
+ cur = ggml_mul(ctx0, cur, tmp);
+ offload_func(cur);
+ ggml_set_name(cur, "silu_x_result_w3");
+
+ cur = ggml_mul_mat(ctx0,
+ model.layers[il].w2,
+ cur);
+ offload_func(cur);
+ ggml_set_name(cur, "result_w2");
+ }
+
+ cur = ggml_add(ctx0, cur, inpFF);
+ offload_func(cur);
+ ggml_set_name(cur, "inpFF_+_result_w2");
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ // norm
+ {
+ cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
+ offload_func_nr(cur);
+ ggml_set_name(cur, "rms_norm_2");
+
+ // cur = cur*norm(broadcasted)
+ cur = ggml_mul(ctx0, cur, model.output_norm);
+ // offload_func_nr(cur); // TODO CPU + GPU mirrored backend
+ ggml_set_name(cur, "result_norm");
+ }
+
+ // lm_head
+ 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 * llm_build_falcon(
llama_context & lctx,
const llama_batch & batch) {
{
result = llm_build_starcoder(lctx, batch);
} break;
+ case LLM_ARCH_REFACT:
+ {
+ result = llm_build_refact(lctx, batch);
+ } break;
default:
GGML_ASSERT(false);
}
// If all tensors can be run on the GPU then using more than 1 thread is detrimental.
const bool full_offload_supported = model.arch == LLM_ARCH_LLAMA ||
model.arch == LLM_ARCH_BAICHUAN ||
- model.arch == LLM_ARCH_FALCON;
+ model.arch == LLM_ARCH_FALCON ||
+ model.arch == LLM_ARCH_REFACT;
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
n_threads = 1;
overview / pkg / ggml / sources / llama.cpp / commitdiff