--- /dev/null
+#!/usr/bin/env python3
+# HF mpt--> 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 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 an MPT 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] != "MPTForCausalLM":
+ print("Model architecture not supported: " + hparams["architectures"][0])
+
+ sys.exit()
+
+# get number of model parts
+num_parts = count_model_parts(dir_model)
+
+ARCH=gguf.MODEL_ARCH.MPT
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+block_count = hparams["n_layers"]
+
+gguf_writer.add_name(dir_model.name)
+gguf_writer.add_context_length(hparams["max_seq_len"])
+gguf_writer.add_embedding_length(hparams["d_model"])
+gguf_writer.add_block_count(block_count)
+gguf_writer.add_feed_forward_length(4 * hparams["d_model"])
+gguf_writer.add_head_count(hparams["n_heads"])
+gguf_writer.add_layer_norm_eps(1e-05)
+if hparams["attn_config"]["clip_qkv"] is not None:
+ gguf_writer.add_clamp_kqv(hparams["attn_config"]["clip_qkv"])
+gguf_writer.add_max_alibi_bias(hparams["attn_config"]["alibi_bias_max"])
+
+# TOKENIZATION
+
+print("gguf: get tokenizer metadata")
+
+tokens: list[bytearray] = []
+scores: list[float] = []
+toktypes: list[int] = []
+
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
+
+print("gguf: get gpt2 tokenizer vocab")
+
+# MPT token embedding tensors have dimension 50432 (hparams["vocab_size"]), but
+# there are only 50254 (len(tokenizer.vocab)) tokens in the vocab, presumably to
+# accomodate some "reserved" tokens; this is causing problems down the line in
+# llama.cpp, so we pad the vocab with dummy tokens:
+
+vocab_size = hparams["vocab_size"]
+
+# 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()}
+
+for i in range(vocab_size):
+ tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")
+ scores.append(0.0) # dummy
+ toktypes.append(gguf.TokenType.NORMAL)
+
+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)
+
+# 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(f"{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("Cannot map tensor '" + name + "'")
+ continue # for the sake of compatibility with some old published models, don't quit
+ 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)
+
+ # note: MPT output is tied to (same as) wte in original model;
+ # for easier implementation in llama.cpp it's duplicated in GGUF, though :/
+ if new_name == "token_embd.weight":
+ gguf_writer.add_tensor("output.weight", 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("")
#define CUDA_SILU_BLOCK_SIZE 256
#define CUDA_CPY_BLOCK_SIZE 32
#define CUDA_SCALE_BLOCK_SIZE 256
+#define CUDA_CLAMP_BLOCK_SIZE 256
#define CUDA_ROPE_BLOCK_SIZE 256
#define CUDA_ALIBI_BLOCK_SIZE 32
#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
dst[i] = scale * x[i];
}
+static __global__ void clamp_f32(const float * x, float * dst, const float min, const float max, const int k) {
+ const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+ if (i >= k) {
+ return;
+ }
+
+ dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
+}
template<int qk, int qr, dequantize_kernel_t dq>
static void get_rows_cuda(const void * x, const int32_t * y, float * dst, const int nrows, const int ncols, cudaStream_t stream) {
scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
}
+static void clamp_f32_cuda(const float * x, float * dst, const float min, const float max, const int k, cudaStream_t stream) {
+ const int num_blocks = (k + CUDA_CLAMP_BLOCK_SIZE - 1) / CUDA_CLAMP_BLOCK_SIZE;
+ clamp_f32<<<num_blocks, CUDA_CLAMP_BLOCK_SIZE, 0, stream>>>(x, dst, min, max, k);
+}
+
template<typename T>
static void rope_cuda(const T * x, T * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
const int64_t ne02 = src0->ne[2];
const int64_t nrows = ggml_nrows(src0);
- const int n_past = ((int32_t *) dst->op_params)[0];
+ //const int n_past = ((int32_t *) dst->op_params)[0];
const int n_head = ((int32_t *) dst->op_params)[1];
float max_bias;
memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
- GGML_ASSERT(ne01 + n_past == ne00);
+ //GGML_ASSERT(ne01 + n_past == ne00);
GGML_ASSERT(n_head == ne02);
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
(void) src1_dd;
}
+inline void ggml_cuda_op_clamp(
+ const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+ const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+ GGML_ASSERT(src0->type == GGML_TYPE_F32);
+ GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+ const float min = ((float *) dst->op_params)[0];
+ const float max = ((float *) dst->op_params)[1];
+
+ clamp_f32_cuda(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream);
+ CUDA_CHECK(cudaGetLastError());
+
+ (void) src1;
+ (void) dst;
+ (void) src1_dd;
+}
+
static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const ggml_cuda_op_flatten_t op) {
const int64_t nrows0 = ggml_nrows(src0);
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_scale);
}
+static void ggml_cuda_clamp(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+ ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_clamp);
+}
+
static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const int64_t ne = ggml_nelements(src0);
GGML_ASSERT(ne == ggml_nelements(src1));
case GGML_OP_SCALE:
func = ggml_cuda_scale;
break;
+ case GGML_OP_CLAMP:
+ if (!any_on_device) {
+ return false;
+ }
+ func = ggml_cuda_clamp;
+ break;
case GGML_OP_CPY:
func = ggml_cuda_cpy;
break;
LLM_ARCH_MPT,
{
{ 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_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
float rope_freq_base_train;
float rope_freq_scale_train;
+ float f_clamp_kqv;
+ float f_max_alibi_bias;
+
bool operator!=(const llama_hparams & other) const {
if (this->vocab_only != other.vocab_only) return true;
if (this->n_vocab != other.n_vocab) return true;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
+ case LLM_ARCH_MPT:
+ {
+ hparams.f_clamp_kqv = 0.0f;
+
+ GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
+ GGUF_GET_KEY(ctx, hparams.f_clamp_kqv, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_CLAMP_KQV));
+ GGUF_GET_KEY(ctx, hparams.f_max_alibi_bias, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS));
+
+ switch (hparams.n_layer) {
+ case 32: model.type = e_model::MODEL_7B; break;
+ case 48: model.type = e_model::MODEL_30B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
default: (void)0;
}
LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_gqa());
LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_eps);
LLAMA_LOG_INFO("%s: f_norm_rms_eps = %.1e\n", __func__, hparams.f_norm_rms_eps);
+ LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv);
+ LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias);
LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff);
LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train);
LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train);
layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}, backend);
}
} break;
+ case LLM_ARCH_MPT:
+ {
+ model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+
+ // output
+ {
+ ggml_backend_type backend_norm;
+ ggml_backend_type 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 = LLAMA_BACKEND_OFFLOAD;
+#else
+ backend_norm = 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 = 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);
+ }
+ 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_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
+ const ggml_backend_type 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.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3*n_embd}, backend_split);
+ layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
+
+ layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
+
+ layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
+ layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
+
+ if (backend == GGML_BACKEND_GPU) {
+ vram_weights +=
+ ggml_nbytes(layer.attn_norm) +
+ ggml_nbytes(layer.wqkv) +
+ ggml_nbytes(layer.wo) +
+ ggml_nbytes(layer.ffn_norm) +
+ ggml_nbytes(layer.w2) +
+ ggml_nbytes(layer.w3);
+ }
+ }
+ } break;
default:
throw std::runtime_error("unknown architecture");
}
return gf;
}
-
static struct ggml_cgraph * llm_build_persimmon(
llama_context & lctx,
const llama_batch & batch) {
return gf;
}
+static struct ggml_cgraph * llm_build_mpt(
+ 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; // == n_head for MPT, as there's no MQA/GQA
+ const int64_t n_embd_head = hparams.n_embd_head();
+ const int64_t n_embd_gqa = hparams.n_embd_gqa();
+
+ const float norm_eps = hparams.f_norm_eps;
+ const float clamp_kqv = hparams.f_clamp_kqv;
+ const float max_alibi_bias = hparams.f_max_alibi_bias;
+
+ 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("kv_head = %d, n_kv = %d, n_tokens = %d, n_ctx = %d, is_measure = %d, has_shift = %d\n",
+ // kv_head, n_kv, n_tokens, n_ctx, ggml_allocr_is_measure(lctx.alloc), kv_self.has_shift);
+
+ 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;
+
+ //int warmup = 0;
+ 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));
+ //warmup = ((uint32_t*) inp_tokens->data)[0] == 0;
+ }
+
+ 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)/n_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) {
+ struct ggml_tensor * attn_norm;
+
+ 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
+
+ // self-attention
+ // TODO: refactor into common function (shared with LLaMA)
+ {
+ attn_norm = ggml_norm(ctx0, inpL, norm_eps);
+ offload_func(attn_norm);
+
+ attn_norm = ggml_mul(ctx0, attn_norm, model.layers[il].attn_norm);
+ offload_func(attn_norm);
+
+ if (1) {
+ cur = attn_norm;
+ }
+
+ // compute QKV
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+ offload_func_kq(cur);
+
+ if (clamp_kqv > 0.0f) {
+ cur = ggml_clamp(ctx0, cur, -clamp_kqv, clamp_kqv);
+ offload_func_kq(cur);
+ }
+
+ const size_t wsize = ggml_type_size(cur->type);
+
+ struct ggml_tensor * Qcur = ggml_view_3d(
+ ctx0, cur, n_embd_head, n_head, n_tokens,
+ wsize * n_embd_head,
+ wsize * n_embd_head * (n_head + 2 * n_head_kv),
+ 0);
+ offload_func_kq(Qcur);
+
+ struct ggml_tensor * Kcur = ggml_view_3d(
+ ctx0, cur, n_embd_head, n_head_kv, n_tokens,
+ wsize * n_embd_head,
+ wsize * n_embd_head * (n_head + 2 * n_head_kv),
+ wsize * n_embd_head * n_head);
+ offload_func_kq(Kcur);
+
+ struct ggml_tensor * tmpv = ggml_view_3d(
+ ctx0, cur, n_embd_head, n_head_kv, n_tokens,
+ wsize * n_embd_head,
+ wsize * n_embd_head * (n_head + 2 * n_head_kv),
+ wsize * n_embd_head * (n_head + n_head_kv));
+ offload_func_kq(Kcur);
+
+ ggml_set_name(Qcur, "Qcur");
+ ggml_set_name(Kcur, "Kcur");
+
+ {
+ struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens));
+ offload_func_v(Vcur);
+ offload_func_v(Vcur->src[0]->src[0]);
+ 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_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");
+
+ struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+ offload_func_kq(KQ);
+ ggml_set_name(KQ, "KQ");
+
+ struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
+ offload_func_kq(KQ_scaled);
+ ggml_set_name(KQ_scaled, "KQ_scaled");
+
+ // TODO: replace with ggml_add()
+ struct ggml_tensor * KQ_scaled_alibi =
+ ggml_alibi(ctx0, KQ_scaled, 0, n_head, max_alibi_bias);
+ offload_func_kq(KQ_scaled_alibi);
+ 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");
+
+ 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");
+
+ 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");
+
+ struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+ offload_func_v(KQV);
+ ggml_set_name(KQV, "KQV");
+
+ 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 = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
+ offload_func_v(cur);
+ ggml_set_name(cur, "KQV_merged_contiguous");
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
+ offload_func(cur);
+ ggml_set_name(cur, "result_wo");
+ }
+
+ // Add the input
+ cur = ggml_add(ctx0, cur, inpL);
+ offload_func(cur);
+
+ struct ggml_tensor * attn_out = cur;
+
+ // feed forward
+ {
+ // Norm
+ {
+ cur = ggml_norm(ctx0, attn_out, norm_eps);
+ offload_func(cur);
+
+ cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
+ offload_func(cur);
+ }
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
+ offload_func(cur);
+
+ cur = ggml_gelu(ctx0, cur);
+ offload_func(cur);
+ cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
+ offload_func(cur);
+ }
+
+ cur = ggml_add(ctx0, cur, attn_out);
+ offload_func(cur);
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ // norm
+ {
+ cur = ggml_norm(ctx0, cur, norm_eps);
+ offload_func_nr(cur);
+
+ cur = ggml_mul(ctx0, cur, model.output_norm);
+ 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_batch & batch) {
{
result = llm_build_refact(lctx, batch);
} break;
+ case LLM_ARCH_MPT:
+ {
+ result = llm_build_mpt(lctx, batch);
+ } break;
default:
GGML_ASSERT(false);
}
const bool full_offload_supported = model.arch == LLM_ARCH_LLAMA ||
model.arch == LLM_ARCH_BAICHUAN ||
model.arch == LLM_ARCH_FALCON ||
- model.arch == LLM_ARCH_REFACT;
+ model.arch == LLM_ARCH_REFACT ||
+ model.arch == LLM_ARCH_MPT;
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;
const std::string name = ggml_get_name(meta);
// TODO: avoid hardcoded tensor names - use the TN_* constants
- if (name.find("attn_v.weight") != std::string::npos) {
+ if (name.find("attn_v.weight") != std::string::npos ||
+ name.find("attn_qkv.weight") != std::string::npos) {
++n_attention_wv;
}
else if (name.find("ffn_down.weight") != std::string::npos) {
overview / pkg / ggml / sources / llama.cpp / commitdiff