llama : fix quantization when tensors are missing (#5423 )

swift : package no longer use ggml dependency (#5465 )
* Revert "swift : update Package.swift to use ggml as dependency (#4691)" This reverts commit ece9a45e8f. * spm : add ggml headers
2026-06-16 10:46:43 +02:00 · 2024-02-12 20:14:39 +02:00 · 2024-02-12 19:54:29 +02:00 · 2024-02-12 19:29:57 +02:00 · 2024-02-12 20:22:05 +05:30 · 2024-02-12 10:38:44 +02:00
9 changed files with 64 additions and 77 deletions
@@ -16,5 +16,5 @@ jobs:
      - name: flake8 Lint
        uses: py-actions/flake8@v2
        with:
-            ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704"
+            ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503"
            exclude: "examples/*,examples/*/**,*/**/__init__.py"
@@ -13,17 +13,31 @@ let package = Package(
    products: [
        .library(name: "llama", targets: ["llama"]),
    ],
-    dependencies: [
-        .package(url: "https://github.com/ggerganov/ggml.git", .branch("release"))
-    ],
    targets: [
        .target(
            name: "llama",
-            dependencies: ["ggml"],
            path: ".",
-            exclude: ["ggml-metal.metal"],
+            exclude: [
+               "cmake",
+               "examples",
+               "scripts",
+               "models",
+               "tests",
+               "CMakeLists.txt",
+               "ggml-cuda.cu",
+               "ggml-cuda.h",
+               "Makefile"
+            ],
            sources: [
+                "ggml.c",
                "llama.cpp",
+                "ggml-alloc.c",
+                "ggml-backend.c",
+                "ggml-quants.c",
+                "ggml-metal.m",
+            ],
+            resources: [
+                .process("ggml-metal.metal")
            ],
            publicHeadersPath: "spm-headers",
            cSettings: [
@@ -88,7 +88,8 @@ def main():
    gguf_writer.add_embedding_length(hidden_size)
    gguf_writer.add_block_count(block_count)
    gguf_writer.add_feed_forward_length(hparams.ffn_hidden_size)
-    gguf_writer.add_rope_dimension_count(hidden_size // head_count)
+    # ref: https://github.com/ggerganov/llama.cpp/pull/4889/commits/eea19039fc52ea2dbd1aab45b59ab4e3e29a3443
+    gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2)
    gguf_writer.add_head_count(head_count)
    gguf_writer.add_head_count_kv(head_count_kv)
    gguf_writer.add_rope_freq_base(hparams.rotary_emb_base)
@@ -71,7 +71,7 @@ def bytes_to_unicode():
    return dict(zip(bs, cs))


-ap = argparse.ArgumentParser(prog="convert_hf_to_gguf.py")
+ap = argparse.ArgumentParser()
 ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
 ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
 ap.add_argument("--text-only", action="store_true", required=False,
@@ -11578,11 +11578,8 @@ static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst,
    }
    char * dst_ptr = (char *) dst;

-    const int64_t ne0 = src->ne[0];
-    const int64_t nb0 = src->nb[0];
-    const int64_t nb1 = src->nb[1];
-    const int64_t nb2 = src->nb[2];
-    const int64_t nb3 = src->nb[3];
+    GGML_TENSOR_LOCALS_1(int64_t, ne, src, ne);
+    GGML_TENSOR_LOCALS(int64_t, nb, src, nb);
    const enum ggml_type type = src->type;
    const int64_t ts = ggml_type_size(type);
    const int64_t bs = ggml_blck_size(type);
@@ -12426,9 +12423,7 @@ inline void ggml_sycl_op_alibi(const ggml_tensor *src0, const ggml_tensor *src1,
    GGML_ASSERT(src0->type == GGML_TYPE_F32);
    GGML_ASSERT( dst->type == GGML_TYPE_F32);

-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
+    GGML_TENSOR_LOCALS_3(int64_t, ne0, src0, ne);
    const int64_t nrows = ggml_nrows(src0);

    //const int n_past = ((int32_t *) dst->op_params)[0];
@@ -12758,15 +12753,9 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
                                 ggml_sycl_op_mul_mat_t op,
                                 const bool convert_src1_to_q8_1) try {

-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);

-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
    const int64_t nrows1 = ggml_nrows(src1);

    GGML_ASSERT(ne03 == ne13);
@@ -13337,23 +13326,13 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0,
    GGML_ASSERT(src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);

-    const int64_t ne00 = src0->ne[0]; GGML_UNUSED(ne00);
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);

-    const int64_t nb01 = src0->nb[1];
-    const int64_t nb02 = src0->nb[2]; GGML_UNUSED(nb02);
-    const int64_t nb03 = src0->nb[3]; GGML_UNUSED(nb03);
+    GGML_TENSOR_LOCALS(int64_t, nb0, src0, nb);

-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);

-    const int64_t nb11 = src1->nb[1];
-    const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12);
-    const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13);
+    GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb);

    const int64_t ne1 = ggml_nelements(src1);
    const int64_t ne  = ggml_nelements(dst);
@@ -13655,23 +13634,15 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) {
    GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);

-    const int64_t ne00 = src00->ne[0]; GGML_UNUSED(ne00);
-    const int64_t ne01 = src00->ne[1];
-    const int64_t ne02 = src00->ne[2];
-    const int64_t ne03 = src00->ne[3];
+    GGML_TENSOR_LOCALS(int64_t, ne0, src00, ne);

    //const int64_t nb01 = src00->nb[1];
-    const int64_t nb02 = src00->nb[2]; GGML_UNUSED(nb02);
-    const int64_t nb03 = src00->nb[3]; GGML_UNUSED(nb03);
+    GGML_TENSOR_LOCALS(int64_t, nb0, src00, nb);

-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);

+    GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb);
    //const int64_t nb11 = src1->nb[1];
-    const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12);
-    const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13);

    const int64_t ne1 = ggml_nelements(src1);
    const int64_t ne  = ggml_nelements(dst);
@@ -13940,25 +13911,7 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1,
    GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
    GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);

-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-
-
-    const int64_t nb00 = src0->nb[0];
-    const int64_t nb01 = src0->nb[1];
-    const int64_t nb02 = src0->nb[2];
-    const int64_t nb03 = src0->nb[3];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-
-
-    const int64_t nb10 = src1->nb[0];
-    const int64_t nb11 = src1->nb[1];
-    const int64_t nb12 = src1->nb[2];
-    const int64_t nb13 = src1->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;

    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
    dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0];
@@ -772,22 +772,37 @@ struct LLM_TN {
    llm_arch arch;

    std::string operator()(llm_tensor tensor) const {
+        if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) {
+            return "__missing__";
+        }
        return LLM_TENSOR_NAMES[arch].at(tensor);
    }

    std::string operator()(llm_tensor tensor, const std::string & suffix) const {
+        if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) {
+            return "__missing__";
+        }
        return LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix;
    }

    std::string operator()(llm_tensor tensor, int bid) const {
+        if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) {
+            return "__missing__";
+        }
        return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid);
    }

    std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
+        if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) {
+            return "__missing__";
+        }
        return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix;
    }

    std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const {
+        if (LLM_TENSOR_NAMES[arch].find(tensor) == LLM_TENSOR_NAMES[arch].end()) {
+            return "__missing__";
+        }
        return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid, xid) + "." + suffix;
    }
 };
@@ -10227,6 +10242,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
        }
        ++qs.i_ffn_up;
    }
+
    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
    //}
    // IK: let's remove this, else Q2_K is almost the same as Q3_K_S
@@ -10286,19 +10302,19 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s

        // K-quants
        case LLAMA_FTYPE_MOSTLY_Q2_K_S:
-        case LLAMA_FTYPE_MOSTLY_Q2_K:   quantized_type = GGML_TYPE_Q2_K; break;
+        case LLAMA_FTYPE_MOSTLY_Q2_K:    quantized_type = GGML_TYPE_Q2_K;    break;
        case LLAMA_FTYPE_MOSTLY_Q3_K_XS:
        case LLAMA_FTYPE_MOSTLY_Q3_K_S:
        case LLAMA_FTYPE_MOSTLY_Q3_K_M:
-        case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
+        case LLAMA_FTYPE_MOSTLY_Q3_K_L:  quantized_type = GGML_TYPE_Q3_K;    break;
        case LLAMA_FTYPE_MOSTLY_Q4_K_S:
-        case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_K_M:  quantized_type = GGML_TYPE_Q4_K;    break;
        case LLAMA_FTYPE_MOSTLY_Q5_K_S:
-        case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
-        case LLAMA_FTYPE_MOSTLY_Q6_K:   quantized_type = GGML_TYPE_Q6_K; break;
-        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:quantized_type = GGML_TYPE_IQ2_XXS; break;
-        case LLAMA_FTYPE_MOSTLY_IQ2_XS :quantized_type = GGML_TYPE_IQ2_XS;  break;
-        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:quantized_type = GGML_TYPE_IQ3_XXS; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_K_M:  quantized_type = GGML_TYPE_Q5_K;    break;
+        case LLAMA_FTYPE_MOSTLY_Q6_K:    quantized_type = GGML_TYPE_Q6_K;    break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_XXS: quantized_type = GGML_TYPE_IQ2_XXS; break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_XS:  quantized_type = GGML_TYPE_IQ2_XS;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_XXS: quantized_type = GGML_TYPE_IQ3_XXS; break;

        default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
    }
@@ -0,0 +1 @@
+../ggml-alloc.h
@@ -0,0 +1 @@
+../ggml-backend.h
@@ -0,0 +1 @@
+../ggml.h
Author	SHA1	Message	Date
Georgi Gerganov	099afc6274	llama : fix quantization when tensors are missing (#5423 )	2024-02-12 20:14:39 +02:00
Georgi Gerganov	df334a1125	swift : package no longer use ggml dependency (#5465 ) * Revert "swift : update Package.swift to use ggml as dependency (#4691)" This reverts commit `ece9a45e8f`. * spm : add ggml headers	2024-02-12 19:54:29 +02:00
Lee	dbd8828eb0	py : fix persimmon `n_rot` conversion (#5460 ) * convert : fix persimmon offical weight conversion to write correct n_rot. * Update convert-persimmon-to-gguf.py --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2024-02-12 19:29:57 +02:00
Abhilash Majumder	43fe07c1a4	ggml-sycl: Replace 3d ops with macro (#5458 ) * use macro * use macro * fix format	2024-02-12 20:22:05 +05:30
Daniel Bevenius	4a46d2b792	llava : remove prog parameter from ArgumentParser (#5457 ) * llava: remove prog parameter from ArgumentParser This commit removes the `prog` parameter from `ArgumentParser` so that it uses the default value which is the name of the script. The motivation for this change is that currently the usage output looks like this: ```console $ python examples/llava/convert-image-encoder-to-gguf.py --help usage: convert_hf_to_gguf.py [-h] ... ``` And with this change it will look like this: ```console $ python examples/llava/convert-image-encoder-to-gguf.py --help usage: convert-image-encoder-to-gguf.py [-h] ... ``` Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com> * ci: add W503 to flake8 ignore list This commit adds W503 to the ignore list for flake8. This is done to avoid the following error: W503 line break before binary operator Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com> --------- Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>	2024-02-12 10:38:44 +02:00