rebase to the latest

convert-refact-hf-to-gguf.py

@@ -6,10 +6,8 @@ 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
@@ -235,6 +233,27 @@ for part_name in part_names:
     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]
 

gguf-py/gguf/gguf.py

@@ -286,21 +286,18 @@ class TensorNameMap:
         # Attention query
         MODEL_TENSOR.ATTN_Q: (
             "model.layers.{bid}.self_attn.q_proj", # llama-hf
-            "transformer.h.{bid}.attn.q",          # refact
             "layers.{bid}.attention.wq",           # llama-pth
         ),
 
         # Attention key
         MODEL_TENSOR.ATTN_K: (
             "model.layers.{bid}.self_attn.k_proj", # llama-hf
-            "transformer.h.{bid}.attn.k",           # refact
             "layers.{bid}.attention.wk",           # llama-pth
         ),
 
         # Attention value
         MODEL_TENSOR.ATTN_V: (
             "model.layers.{bid}.self_attn.v_proj", # llama-hf
-            "transformer.h.{bid}.attn.v",          # refact
             "layers.{bid}.attention.wv",           # llama-pth
         ),
 
@@ -335,15 +332,13 @@ class TensorNameMap:
             "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
-            "transformer.h.{bid}.mlp.linear_3",        # refact
         ),
 
         # Feed-forward gate
         MODEL_TENSOR.FFN_GATE: (
-            "model.layers.{bid}.mlp.gate_proj", # llama-hf
+            "model.layers.{bid}.mlp.gate_proj", # llama-hf refact
-            "transformer.h.{bid}.mlp.linear_1", # refact
             "layers.{bid}.feed_forward.w1",     # llama-pth
         ),
 

llama.cpp

@@ -3369,17 +3369,10 @@ static struct ggml_cgraph * llm_build_baichaun(
 
 static struct ggml_cgraph * llm_build_refact(
          llama_context & lctx,
-     const llama_token * tokens,
+     const llama_batch & batch) {
-           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 & cparams = lctx.cparams;
 
     const auto & kv_self = lctx.kv_self;
 
@@ -3387,7 +3380,7 @@ static struct ggml_cgraph * llm_build_refact(
 
     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_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();
@@ -3397,6 +3390,12 @@ static struct ggml_cgraph * llm_build_refact(
 
     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 = {
@@ -3414,12 +3413,12 @@ static struct ggml_cgraph * llm_build_refact(
     struct ggml_tensor * cur;
     struct ggml_tensor * inpL;
 
-    if (tokens) {
+    if (batch.token) {
-        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+        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, tokens, N*ggml_element_size(inp_tokens));
+            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
         }
         ggml_set_name(inp_tokens, "inp_tokens");
 
@@ -3429,11 +3428,11 @@ static struct ggml_cgraph * llm_build_refact(
         GGML_ASSERT(false && "not implemented");
 #endif
 
-        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
+        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, embd, N * n_embd * ggml_element_size(inpL));
+            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
         }
     }
 
@@ -3442,9 +3441,6 @@ static struct ggml_cgraph * llm_build_refact(
 
     // offload functions set the tensor output backend to GPU
     // tensors are GPU-accelerated if any input or the output has been offloaded
-    //
-    // with the low VRAM option VRAM scratch is disabled in llama_load_model_internal
-    // in that case ggml_cuda_assign_buffers has no effect
     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;
@@ -3461,12 +3457,36 @@ static struct ggml_cgraph * llm_build_refact(
     }
 #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));
+        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;
+                    }
+                }
+            }
+        }
     }
-    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
 
     for (int il = 0; il < n_layer; ++il) {
         ggml_format_name(inpL, "layer_inp_%d", il);
@@ -3504,36 +3524,33 @@ static struct ggml_cgraph * llm_build_refact(
             offload_func_kq(tmpq);
             ggml_set_name(tmpq, "tmpq");
 
-            struct ggml_tensor * Kcur;
+            struct ggml_tensor * Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens);
-            struct ggml_tensor * Qcur;
-            Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, N);
-            Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, N);
-
             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, n_embd] V matrix
+                // 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));
+                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*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + n_past));
+                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, n_embd_gqa,
+                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 + n_past*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");
 
@@ -3547,7 +3564,7 @@ static struct ggml_cgraph * llm_build_refact(
 
             struct ggml_tensor * K =
                 ggml_view_3d(ctx0, kv_self.k,
-                        n_embd_head, n_past + N, n_head_kv,
+                        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);
@@ -3560,25 +3577,28 @@ static struct ggml_cgraph * llm_build_refact(
             ggml_set_name(KQ, "KQ");
 
             // KQ_scaled = KQ / sqrt(n_embd_head)
-            // KQ_scaled shape [n_past + N, N, n_head, 1]
+            // KQ_scaled shape [n_kv, n_tokens, n_head, 1]
-            struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
+            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
             offload_func_kq(KQ_scaled);
             ggml_set_name(KQ_scaled, "KQ_scaled");
 
-            struct ggml_tensor * KQ_masked;
+            // KQ_masked = mask_past(KQ_scaled)
-            struct ggml_tensor * KQ_scaled_alibi;
+            struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8);
-
-            KQ_scaled_alibi =ggml_alibi(ctx0, KQ_scaled, n_past, n_head, 8);
             ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
-            KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, n_past);
+
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
+            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_past + N, n_embd_head, n_head_kv,
+                        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);
@@ -3593,7 +3613,7 @@ static struct ggml_cgraph * llm_build_refact(
             // 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_past + N, n_embd_head, n_head));
+            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
 
@@ -3602,10 +3622,8 @@ static struct ggml_cgraph * llm_build_refact(
             offload_func_v(KQV_merged);
             ggml_set_name(KQV_merged, "KQV_merged");
 
-            // cur = KQV_merged.contiguous().view(n_embd, N)
+            // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
-            cur = ggml_cpy(ctx0,
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
-                    KQV_merged,
-                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
             offload_func_v(cur);
             ggml_set_name(cur, "KQV_merged_contiguous");
 
@@ -4338,7 +4356,7 @@ static struct ggml_cgraph * llama_build_graph(
             } break;
         case LLM_ARCH_REFACT:
             {
-                result = llm_build_refact(lctx, tokens, embd, n_tokens, n_past);
+                result = llm_build_refact(lctx, batch);
             } break;
         default:
             GGML_ASSERT(false);