Merge remote-tracking branch 'origin/master' into cli-ui-update

.ecrc

@@ -0,0 +1,5 @@
+{
+  "Disable": {
+    "IndentSize": true
+  }
+}

.editorconfig

@@ -0,0 +1,16 @@
+# https://EditorConfig.org
+
+# Top-most EditorConfig file
+root = true
+
+# Unix-style newlines with a newline ending every file, utf-8 charset
+[*]
+end_of_line = lf
+insert_final_newline = true
+trim_trailing_whitespace = true
+charset = utf-8
+indent_style = space
+indent_size = 4
+
+[Makefile]
+indent_style = tab

.github/workflows/editorconfig.yml

@@ -0,0 +1,17 @@
+name: EditorConfig Checker
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    branches:
+      - master
+
+jobs:
+  editorconfig:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: editorconfig-checker/action-editorconfig-checker@main
+      - run: editorconfig-checker

README.md

@@ -42,6 +42,7 @@ New features will probably be added mostly through community contributions.
 - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
 - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
 - [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894)
+- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
 
 **Bindings:**
 

examples/main/main.cpp

@@ -1,3 +1,8 @@
+// Defines sigaction on msys:
+#ifndef _GNU_SOURCE
+#define _GNU_SOURCE
+#endif
+
 #include "common.h"
 #include "llama.h"
 

examples/quantize/quantize.cpp

@@ -5,15 +5,15 @@
 #include <string>
 
 // usage:
-//  ./llama-quantize models/llama/ggml-model.bin models/llama/ggml-model-quant.bin type
+//  ./quantize models/llama/ggml-model.bin models/llama/ggml-model-quant.bin type
 //
 int main(int argc, char ** argv) {
     ggml_time_init();
 
     if (argc != 4) {
         fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]);
-        fprintf(stderr, "  type = 2 - q4_0\n");
+        fprintf(stderr, "  type = %d - q4_0\n", LLAMA_FTYPE_MOSTLY_Q4_0);
-        fprintf(stderr, "  type = 3 - q4_1\n");
+        fprintf(stderr, "  type = %d - q4_1\n", LLAMA_FTYPE_MOSTLY_Q4_1);
         return 1;
     }
 
@@ -27,7 +27,7 @@ int main(int argc, char ** argv) {
     const std::string fname_inp = argv[1];
     const std::string fname_out = argv[2];
 
-    const int itype = atoi(argv[3]);
+    const enum llama_ftype ftype = (enum llama_ftype)atoi(argv[3]);
 
     const int64_t t_main_start_us = ggml_time_us();
 
@@ -37,7 +37,7 @@ int main(int argc, char ** argv) {
     {
         const int64_t t_start_us = ggml_time_us();
 
-        if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype)) {
+        if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype)) {
             fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
             return 1;
         }

ggml.c

@@ -1,4 +1,4 @@
-// Defines CLOCK_MONOTONIC and asprintf on Linux
+// Defines CLOCK_MONOTONIC on Linux
 #define _GNU_SOURCE
 
 #include "ggml.h"
@@ -26,14 +26,9 @@
 #define static_assert(cond, msg) struct global_scope_noop_trick
 #endif
 
-#if defined _MSC_VER || defined(__MINGW32__)
+#if defined(_WIN32)
 
-#if !defined(__MINGW32__)
-#include <Windows.h>
-#else
-// ref: https://github.com/ggerganov/whisper.cpp/issues/168
 #include <windows.h>
-#endif
 
 typedef volatile LONG atomic_int;
 typedef atomic_int atomic_bool;
@@ -55,6 +50,7 @@ typedef HANDLE pthread_t;
 
 typedef DWORD thread_ret_t;
 static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void*), void* arg) {
+    (void) unused;
     HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL);
     if (handle == NULL)
     {
@@ -66,6 +62,7 @@ static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void
 }
 
 static int pthread_join(pthread_t thread, void* unused) {
+    (void) unused;
     return (int) WaitForSingleObject(thread, INFINITE);
 }
 
@@ -599,10 +596,7 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int
         for (int l = 0; l < 2; l++) amaxv[4*l] = vmaxq_f32(amaxv[4*l], amaxv[4*l+2]);
         for (int l = 0; l < 1; l++) amaxv[8*l] = vmaxq_f32(amaxv[8*l], amaxv[8*l+4]);
 
-        // absolute max
+        const float amax = vmaxvq_f32(amaxv[0]);
-        const float amax = MAX(
-                MAX(vgetq_lane_f32(amaxv[0], 0), vgetq_lane_f32(amaxv[0], 1)),
-                MAX(vgetq_lane_f32(amaxv[0], 2), vgetq_lane_f32(amaxv[0], 3)));
 
         const float d = amax / ((1 << 3) - 1);
         const float id = d ? 1.0f/d : 0.0f;
@@ -924,7 +918,7 @@ static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int
         float32x4_t minv[8];
         float32x4_t maxv[8];
 
-        for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*32 + 4*l);
+        for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*QK + 4*l);
 
         for (int l = 0; l < 4; l++) minv[2*l] = vminq_f32(srcv[2*l], srcv[2*l + 1]);
         for (int l = 0; l < 2; l++) minv[4*l] = vminq_f32(minv[4*l], minv[4*l + 2]);
@@ -947,7 +941,8 @@ static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int
 
         for (int l = 0; l < 8; l++) {
             const float32x4_t v  = vmulq_n_f32(vsubq_f32(srcv[l], minv0), id);
-            const int32x4_t   vi = vcvtq_s32_f32(v);
+            const float32x4_t vf = vaddq_f32(v, vdupq_n_f32(0.5f)); // needed to round to nearest
+            const int32x4_t   vi = vcvtq_s32_f32(vf);
 
             y[i].qs[2*l + 0] = vgetq_lane_s32(vi, 0) | (vgetq_lane_s32(vi, 1) << 4);
             y[i].qs[2*l + 1] = vgetq_lane_s32(vi, 2) | (vgetq_lane_s32(vi, 3) << 4);
@@ -1961,7 +1956,6 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest
 
     // Main loop
     for (int i = 0; i < nb; i+=UNROLL_COUNT) {
-
         // This loop will be unrolled by the compiler
         for (int u=0;u<UNROLL_COUNT;u++)  {
             /* Compute combined scale for the block */
@@ -2014,7 +2008,6 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest
             /* Multiply q with scale and accumulate */
             acc = _mm256_fmadd_ps( scale, q, acc );
         }
-       
     }
 
     // Return horizontal sum of the acc vector
@@ -2076,18 +2069,18 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest
     float sum1 = 0.0f;
 
     for (int i = 0; i < nb; i += 2) {
-        const block_q4_0 * restrict x0 = &px[i + 0];
+        const block_q4_0 * restrict x0 = &x[i + 0];
-        const block_q4_0 * restrict y0 = &py[i + 0];
+        const block_q4_0 * restrict y0 = &y[i + 0];
-        const block_q4_0 * restrict x1 = &px[i + 1];
+        const block_q4_0 * restrict x1 = &x[i + 1];
-        const block_q4_0 * restrict y1 = &py[i + 1];
+        const block_q4_0 * restrict y1 = &y[i + 1];
 
         const v128_t m4b = wasm_u8x16_splat(0xf);
         const v128_t s8b = wasm_i8x16_splat(0x8);
 
-        const v128_t v0_0 = wasm_v128_load(x0.qs);
+        const v128_t v0_0 = wasm_v128_load(x0->qs);
-        const v128_t v0_1 = wasm_v128_load(y0.qs);
+        const v128_t v0_1 = wasm_v128_load(y0->qs);
-        const v128_t v1_0 = wasm_v128_load(x1.qs);
+        const v128_t v1_0 = wasm_v128_load(x1->qs);
-        const v128_t v1_1 = wasm_v128_load(y1.qs);
+        const v128_t v1_1 = wasm_v128_load(y1->qs);
 
         // 4-bit -> 8-bit
         const v128_t v0_0l = wasm_v128_and(v0_0, m4b);
@@ -2567,29 +2560,26 @@ inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x
 //
 
 static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = {
-    QK,
+    [GGML_TYPE_F32]  = 1,
-    QK,
+    [GGML_TYPE_F16]  = 1,
-    1,
+    [GGML_TYPE_Q4_0] = QK,
-    1,
+    [GGML_TYPE_Q4_1] = QK,
-    1,
+    [GGML_TYPE_I8]   = 1,
-    1,
+    [GGML_TYPE_I16]  = 1,
-    1,
+    [GGML_TYPE_I32]  = 1,
 };
-
+static_assert(GGML_TYPE_COUNT == 7, "GGML_BLCK_SIZE is outdated");
-static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_COUNT != 5");
 
 static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
-    sizeof(block_q4_0),
+    [GGML_TYPE_F32]  = sizeof(float),
-    sizeof(block_q4_1),
+    [GGML_TYPE_F16]  = sizeof(ggml_fp16_t),
-    sizeof(int8_t ),
+    [GGML_TYPE_Q4_0] = sizeof(block_q4_0),
-    sizeof(int16_t),
+    [GGML_TYPE_Q4_1] = sizeof(block_q4_1),
-    sizeof(int32_t),
+    [GGML_TYPE_I8]   = sizeof(int8_t),
-    sizeof(ggml_fp16_t),
+    [GGML_TYPE_I16]  = sizeof(int16_t),
-    sizeof(float  ),
+    [GGML_TYPE_I32]  = sizeof(int32_t),
 };
-
+static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_SIZE is outdated");
-// don't forget to update the array above when adding new types
-static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_COUNT != 5");
 
 static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
     "NONE",
@@ -2618,6 +2608,7 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
 
     "SCALE",
     "CPY",
+    "CONT",
     "RESHAPE",
     "VIEW",
     "PERMUTE",
@@ -2633,7 +2624,7 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
     "FLASH_FF",
 };
 
-static_assert(GGML_OP_COUNT == 35, "GGML_OP_COUNT != 35");
+static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -2662,6 +2653,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
 
     "x*v",
     "x-\\>y",
+    "cont(x)",
     "reshape(x)",
     "view(x)",
     "permute(x)",
@@ -2677,7 +2669,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "flash_ff(x)",
 };
 
-static_assert(GGML_OP_COUNT == 35, "GGML_OP_COUNT != 35");
+static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36");
 
 static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
 static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
@@ -4310,6 +4302,41 @@ struct ggml_tensor * ggml_cpy_inplace(
     return ggml_cpy_impl(ctx, a, b, true);
 }
 
+// ggml_cont
+
+struct ggml_tensor * ggml_cont_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        bool inplace) {
+    bool is_node = false;
+
+    if (!inplace && a->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_CONT;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src0 = a;
+    result->src1 = NULL;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_cont(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a) {
+    return ggml_cont_impl(ctx, a, false);
+}
+
+struct ggml_tensor * ggml_cont_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a) {
+    return ggml_cont_impl(ctx, a, true);
+}
+
 // ggml_reshape
 
 struct ggml_tensor * ggml_reshape(
@@ -4852,6 +4879,85 @@ static void ggml_compute_forward_dup_f16(
 
     // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy
 
+    if (ggml_is_contiguous(dst)) {
+        if (src0->nb[0] == sizeof(ggml_fp16_t)) {
+            if (dst->type == GGML_TYPE_F16) {
+                size_t id = 0;
+                const size_t rs = ne00*nb00;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        for (int i01 = 0; i01 < ne01; i01++) {
+                            const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
+                            char * dst_ptr = (char *) dst->data + id*rs;
+
+                            memcpy(dst_ptr, src0_ptr, rs);
+
+                            id++;
+                        }
+                    }
+                }
+            } else if (dst->type == GGML_TYPE_F32) {
+                size_t id = 0;
+                float * dst_ptr = (float *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        for (int i01 = 0; i01 < ne01; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
+                                id++;
+                            }
+                        }
+                    }
+                }
+            } else {
+                GGML_ASSERT(false); // TODO: implement
+            }
+        } else {
+            //printf("%s: this is not optimal - fix me\n", __func__);
+
+            if (dst->type == GGML_TYPE_F32) {
+                size_t id = 0;
+                float * dst_ptr = (float *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        for (int i01 = 0; i01 < ne01; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
+                                id++;
+                            }
+                        }
+                    }
+                }
+            } else if (dst->type == GGML_TYPE_F16) {
+                size_t id = 0;
+                ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        for (int i01 = 0; i01 < ne01; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = *src0_ptr;
+                                id++;
+                            }
+                        }
+                    }
+                }
+            } else {
+                GGML_ASSERT(false); // TODO: implement
+            }
+        }
+        return;
+    }
+
     // dst counters
     int64_t i10 = 0;
     int64_t i11 = 0;
@@ -4946,6 +5052,105 @@ static void ggml_compute_forward_dup_f32(
         return;
     }
 
+    if (src0->type == dst->type &&
+        src0->ne[0] == dst->ne[0] &&
+        src0->nb[0] == GGML_TYPE_SIZE[src0->type] && dst->nb[0] == GGML_TYPE_SIZE[dst->type]) {
+        // copy by rows
+        const size_t rs = ne00*nb00;
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = 0; i01 < ne01; i01++) {
+                    memcpy(
+                        ((char *)  dst->data + i01*nb1  + i02*nb2  + i03*nb3),
+                        ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03),
+                        rs);
+                }
+            }
+        }
+        return;
+    }
+
+    if (ggml_is_contiguous(dst)) {
+        // TODO: simplify
+        if (src0->nb[0] == sizeof(float)) {
+            if (dst->type == GGML_TYPE_F32) {
+                size_t id = 0;
+                const size_t rs = ne00*nb00;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        for (int i01 = 0; i01 < ne01; i01++) {
+                            const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
+                            char * dst_ptr = (char *) dst->data + id*rs;
+
+                            memcpy(dst_ptr, src0_ptr, rs);
+
+                            id++;
+                        }
+                    }
+                }
+            } else if (dst->type == GGML_TYPE_F16) {
+                size_t id = 0;
+                ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        for (int i01 = 0; i01 < ne01; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
+                                id++;
+                            }
+                        }
+                    }
+                }
+            } else {
+                GGML_ASSERT(false); // TODO: implement
+            }
+        } else {
+            //printf("%s: this is not optimal - fix me\n", __func__);
+
+            if (dst->type == GGML_TYPE_F32) {
+                size_t id = 0;
+                float * dst_ptr = (float *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        for (int i01 = 0; i01 < ne01; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = *src0_ptr;
+                                id++;
+                            }
+                        }
+                    }
+                }
+            } else if (dst->type == GGML_TYPE_F16) {
+                size_t id = 0;
+                ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
+
+                for (int i03 = 0; i03 < ne03; i03++) {
+                    for (int i02 = 0; i02 < ne02; i02++) {
+                        for (int i01 = 0; i01 < ne01; i01++) {
+                            for (int i00 = 0; i00 < ne00; i00++) {
+                                const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+
+                                dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
+                                id++;
+                            }
+                        }
+                    }
+                }
+            } else {
+                GGML_ASSERT(false); // TODO: implement
+            }
+        }
+
+        return;
+    }
+
     // dst counters
     int64_t i10 = 0;
     int64_t i11 = 0;
@@ -5066,14 +5271,18 @@ static void ggml_compute_forward_add_f32(
     GGML_ASSERT(nb00 == sizeof(float));
 
     if (nb10 == sizeof(float)) {
-        const int j0 = (n/nth)*ith;
+        for (int j = ith; j < n; j += nth) {
-        const int j1 = ith == nth - 1 ? n : (n/nth)*(ith + 1);
+#ifdef GGML_USE_ACCELERATE
-
+            vDSP_vadd(
-        for (int j = j0; j < j1; j++) {
+                    (float *) ((char *) src0->data + j*nb01), 1,
+                    (float *) ((char *) src1->data + j*nb11), 1,
+                    (float *) ((char *) dst->data  + j*nb1),  1, nc);
+#else
             ggml_vec_add_f32(nc,
                     (float *) ((char *) dst->data  + j*nb1),
                     (float *) ((char *) src0->data + j*nb01),
                     (float *) ((char *) src1->data + j*nb11));
+#endif
         }
     } else {
         // src1 is not contiguous
@@ -6821,6 +7030,15 @@ static void ggml_compute_forward_cpy(
     ggml_compute_forward_dup(params, src0, dst);
 }
 
+// ggml_compute_forward_cont
+
+static void ggml_compute_forward_cont(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    ggml_compute_forward_dup(params, src0, dst);
+}
+
 // ggml_compute_forward_reshape
 
 static void ggml_compute_forward_reshape(
@@ -8651,6 +8869,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_cpy(params, tensor->src0, tensor);
             } break;
+        case GGML_OP_CONT:
+            {
+                ggml_compute_forward_cont(params, tensor->src0, tensor);
+            } break;
         case GGML_OP_RESHAPE:
             {
                 ggml_compute_forward_reshape(params, tensor->src0, tensor);
@@ -8895,8 +9117,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                     src1->grad =
                         ggml_add_impl(ctx,
                                 src1->grad,
-                                // TODO: fix transpose, the node will break the graph connections
+                                ggml_mul_mat(ctx,
-                                ggml_mul_mat(ctx, ggml_transpose(ctx, src0), tensor->grad),
+                                    ggml_cont(ctx, ggml_transpose(ctx, src0)),
+                                    tensor->grad),
                                 inplace);
                 }
             } break;
@@ -8908,6 +9131,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 GGML_ASSERT(false); // TODO: not implemented
             } break;
+        case GGML_OP_CONT:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
         case GGML_OP_RESHAPE:
             {
                 GGML_ASSERT(false); // TODO: not implemented
@@ -9362,6 +9589,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
                         node->n_tasks = n_threads;
                     } break;
                 case GGML_OP_CPY:
+                case GGML_OP_CONT:
                 case GGML_OP_RESHAPE:
                 case GGML_OP_VIEW:
                 case GGML_OP_PERMUTE:

ggml.h

@@ -198,13 +198,14 @@ struct ggml_object;
 struct ggml_context;
 
 enum ggml_type {
-    GGML_TYPE_Q4_0,
+    // explicitly numbered values are used in llama.cpp files
-    GGML_TYPE_Q4_1,
+    GGML_TYPE_F32  = 0,
+    GGML_TYPE_F16  = 1,
+    GGML_TYPE_Q4_0 = 2,
+    GGML_TYPE_Q4_1 = 3,
     GGML_TYPE_I8,
     GGML_TYPE_I16,
     GGML_TYPE_I32,
-    GGML_TYPE_F16,
-    GGML_TYPE_F32,
     GGML_TYPE_COUNT,
 };
 
@@ -236,6 +237,7 @@ enum ggml_op {
 
     GGML_OP_SCALE,
     GGML_OP_CPY,
+    GGML_OP_CONT,
     GGML_OP_RESHAPE,
     GGML_OP_VIEW,
     GGML_OP_PERMUTE,
@@ -525,6 +527,11 @@ struct ggml_tensor * ggml_cpy(
         struct ggml_tensor  * a,
         struct ggml_tensor  * b);
 
+// make contiguous
+struct ggml_tensor * ggml_cont(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a);
+
 // return view(a), b specifies the new shape
 // TODO: when we start computing gradient, make a copy instead of view
 struct ggml_tensor * ggml_reshape(

llama.cpp

@@ -1,3 +1,8 @@
+// Defines fileno on msys:
+#ifndef _GNU_SOURCE
+#define _GNU_SOURCE
+#endif
+
 #include "llama_util.h"
 #include "llama.h"
 #include "llama_internal.h"
@@ -77,7 +82,7 @@ struct llama_hparams {
     uint32_t n_head  = 32;
     uint32_t n_layer = 32;
     uint32_t n_rot   = 64;
-    uint32_t f16     = 1;
+    enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
 
     bool operator!=(const llama_hparams & other) const {
         return memcmp(this, &other, sizeof(llama_hparams));
@@ -427,7 +432,7 @@ struct llama_file_loader {
         hparams.n_head = file.read_u32();
         hparams.n_layer = file.read_u32();
         hparams.n_rot = file.read_u32();
-        hparams.f16 = file.read_u32();
+        hparams.ftype = (enum llama_ftype) file.read_u32();
     }
     void read_vocab() {
         vocab.id_to_token.resize(hparams.n_vocab);
@@ -453,20 +458,21 @@ struct llama_file_loader {
             llama_load_tensor_shard shard;
             uint32_t n_dims = file.read_u32();
             uint32_t name_len = file.read_u32();
-            uint32_t ftype = file.read_u32();
+            shard.type = (enum ggml_type) file.read_u32();
             shard.ne.resize(n_dims);
             file.read_raw(shard.ne.data(), sizeof(shard.ne[0]) * n_dims);
             std::string name = file.read_string(name_len);
             if (n_dims < 1 || n_dims > 2) {
                 throw format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims);
             }
-            switch (ftype) {
+            switch (shard.type) {
-                case 0: shard.type = GGML_TYPE_F32; break;
+                case GGML_TYPE_F32:
-                case 1: shard.type = GGML_TYPE_F16; break;
+                case GGML_TYPE_F16:
-                case 2: shard.type = GGML_TYPE_Q4_0; break;
+                case GGML_TYPE_Q4_0:
-                case 3: shard.type = GGML_TYPE_Q4_1; break;
+                case GGML_TYPE_Q4_1:
+                    break;
                 default: {
-                    throw format("unrecognized ftype %u\n", ftype);
+                    throw format("unrecognized tensor type %u\n", shard.type);
                 }
             }
 
@@ -497,18 +503,18 @@ struct llama_file_loader {
 struct llama_file_saver {
     llama_file file;
     llama_file_loader * any_file_loader;
-    llama_file_saver(const char * fname, llama_file_loader * any_file_loader, uint32_t new_f16)
+    llama_file_saver(const char * fname, llama_file_loader * any_file_loader, enum llama_ftype new_ftype)
         : file(fname, "wb"), any_file_loader(any_file_loader) {
         fprintf(stderr, "llama.cpp: saving model to %s\n", fname);
         write_magic();
-        write_hparams(new_f16);
+        write_hparams(new_ftype);
         write_vocab();
     }
     void write_magic() {
         file.write_u32('ggjt'); // magic
         file.write_u32(1); // version
     }
-    void write_hparams(uint32_t new_f16) {
+    void write_hparams(enum llama_ftype new_ftype) {
         const llama_hparams & hparams = any_file_loader->hparams;
         file.write_u32(hparams.n_vocab);
         file.write_u32(hparams.n_embd);
@@ -516,7 +522,7 @@ struct llama_file_saver {
         file.write_u32(hparams.n_head);
         file.write_u32(hparams.n_layer);
         file.write_u32(hparams.n_rot);
-        file.write_u32(new_f16);
+        file.write_u32(new_ftype);
     }
     void write_vocab() {
         if (any_file_loader->file_version == LLAMA_FILE_VERSION_GGML) {
@@ -531,17 +537,17 @@ struct llama_file_saver {
         }
     }
     void write_tensor(llama_load_tensor & tensor, enum ggml_type new_type, const void * new_data, size_t new_size) {
-        uint32_t ftype;
         switch (new_type) {
-            case GGML_TYPE_F32:  ftype = 0; break;
+            case GGML_TYPE_F32:
-            case GGML_TYPE_F16:  ftype = 1; break;
+            case GGML_TYPE_F16:
-            case GGML_TYPE_Q4_0: ftype = 2; break;
+            case GGML_TYPE_Q4_0:
-            case GGML_TYPE_Q4_1: ftype = 3; break;
+            case GGML_TYPE_Q4_1:
+                break;
             default: LLAMA_ASSERT(false);
         }
         file.write_u32((uint32_t) tensor.ne.size());
         file.write_u32((uint32_t) tensor.name.size());
-        file.write_u32(ftype);
+        file.write_u32(new_type);
         file.write_raw(tensor.ne.data(), sizeof(tensor.ne[0]) * tensor.ne.size());
         file.write_raw(tensor.name.data(), tensor.name.size());
         file.seek(-file.tell() & 31, SEEK_CUR);
@@ -815,6 +821,16 @@ static const char *llama_file_version_name(llama_file_version version) {
     }
 }
 
+static const char *llama_ftype_name(enum llama_ftype ftype) {
+    switch (ftype) {
+        case LLAMA_FTYPE_ALL_F32:     return "all F32";
+        case LLAMA_FTYPE_MOSTLY_F16:  return "mostly F16";
+        case LLAMA_FTYPE_MOSTLY_Q4_0: return "mostly Q4_0";
+        case LLAMA_FTYPE_MOSTLY_Q4_1: return "mostly Q4_1";
+        default: LLAMA_ASSERT(false);
+    }
+}
+
 static const char *llama_model_type_name(e_model type) {
     switch (type) {
         case MODEL_7B: return "7B";
@@ -867,7 +883,7 @@ static void llama_model_load_internal(
         fprintf(stderr, "%s: n_head     = %u\n",  __func__, hparams.n_head);
         fprintf(stderr, "%s: n_layer    = %u\n",  __func__, hparams.n_layer);
         fprintf(stderr, "%s: n_rot      = %u\n",  __func__, hparams.n_rot);
-        fprintf(stderr, "%s: f16        = %u\n",  __func__, hparams.f16);
+        fprintf(stderr, "%s: ftype      = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
         fprintf(stderr, "%s: n_ff       = %u\n",  __func__, n_ff);
         fprintf(stderr, "%s: n_parts    = %zu\n", __func__, ml->file_loaders.size());
         fprintf(stderr, "%s: model size = %s\n",  __func__, llama_model_type_name(model.type));
@@ -1539,17 +1555,17 @@ static llama_vocab::id llama_sample_top_p_top_k(
 // quantization
 //
 
-static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, int itype) {
+static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, enum llama_ftype ftype) {
     ggml_type quantized_type;
-    switch (itype) {
+    switch (ftype) {
-        case 2: quantized_type = GGML_TYPE_Q4_0; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
-        case 3: quantized_type = GGML_TYPE_Q4_1; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
-        default: throw format("invalid quantization type %d\n", itype);
+        default: throw format("invalid output file type %d\n", ftype);
     };
 
     std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp.c_str(), /*use_mmap*/ false,
                                                                             /*vocab_only*/ false));
-    llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), (uint32_t) itype);
+    llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), ftype);
 
     size_t total_size_org = 0;
     size_t total_size_new = 0;
@@ -1740,9 +1756,9 @@ void llama_free(struct llama_context * ctx) {
 int llama_model_quantize(
         const char * fname_inp,
         const char * fname_out,
-               int   itype) {
+  enum llama_ftype   ftype) {
     try {
-        llama_model_quantize_internal(fname_inp, fname_out, itype);
+        llama_model_quantize_internal(fname_inp, fname_out, ftype);
         return 0;
     } catch (const std::string & err) {
         fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.c_str());

llama.h

@@ -65,6 +65,14 @@ extern "C" {
         void * progress_callback_user_data;
     };
 
+    // model file types
+    enum llama_ftype {
+        LLAMA_FTYPE_ALL_F32     = 0,
+        LLAMA_FTYPE_MOSTLY_F16  = 1,  // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_0 = 2,  // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_1 = 3,  // except 1d tensors
+    };
+
     LLAMA_API struct llama_context_params llama_context_default_params();
 
     LLAMA_API bool llama_mmap_supported();
@@ -85,7 +93,7 @@ extern "C" {
     LLAMA_API int llama_model_quantize(
             const char * fname_inp,
             const char * fname_out,
-                   int   itype);
+      enum llama_ftype   ftype);
 
     // Returns the KV cache that will contain the context for the
     // ongoing prediction with the model.

llama_util.h

@@ -26,7 +26,9 @@
 
 #if defined(_WIN32)
     #define WIN32_LEAN_AND_MEAN
+    #ifndef NOMINMAX
         #define NOMINMAX
+    #endif
     #include <windows.h>
     #include <io.h>
     #include <stdio.h> // for _fseeki64
@@ -209,6 +211,7 @@ struct llama_mmap {
             throw format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str());
         }
 
+        #if _WIN32_WINNT >= _WIN32_WINNT_WIN8
         // Advise the kernel to preload the mapped memory
         WIN32_MEMORY_RANGE_ENTRY range;
         range.VirtualAddress = addr;
@@ -217,6 +220,9 @@ struct llama_mmap {
             fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
                     llama_format_win_err(GetLastError()).c_str());
         }
+        #else
+        #pragma message("warning: You are building for pre-Windows 8; prefetch not supported")
+        #endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8
     }
 
     ~llama_mmap() {
@@ -338,8 +344,8 @@ struct llama_mlock {
             // Hopefully a megabyte is enough overhead:
             size_t increment = size + 1048576;
             // The minimum must be <= the maximum, so we need to increase both:
-            min_ws_size += size;
+            min_ws_size += increment;
-            max_ws_size += size;
+            max_ws_size += increment;
             if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
                 fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
                         llama_format_win_err(GetLastError()).c_str());