From 92ff0de2432e48403cdef176391f3b2f1a97f50b Mon Sep 17 00:00:00 2001 From: joshcarp Date: Tue, 7 May 2024 14:44:59 -0400 Subject: [PATCH] Cleanup --- convert-hf-to-gguf.py | 8 -- examples/eval-callback/eval-callback.cpp | 158 ----------------------- llama.cpp | 11 -- 3 files changed, 177 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index facc7de12..7c8bad15f 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -2916,12 +2916,8 @@ class OpenELM(Model): head_dim = self.find_hparam(["head_dim"]) n_head = n_embd // head_dim rot_pct = 1.0 - self.gguf_writer.add_context_length(self.find_hparam(["max_context_length"])) - # self.gguf_writer.add_embedding_length(n_embd) self.gguf_writer.add_block_count(self.block_count) - # self.gguf_writer.add_head_count(n_head) - # self.gguf_writer.add_head_count_kv(n_head) self.gguf_writer.add_head_count_kv(n_head*10) self.gguf_writer.add_head_count(n_head*10) self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) @@ -2979,8 +2975,6 @@ class OpenELM(Model): def write_tensors(self): block_count = self.hparams.get("num_transformer_layers", self.hparams.get("num_hidden_layers", self.hparams.get("num_transformer_layers"))) tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - n_head = self.hparams.get("model_dim") // self.hparams.get("head_dim") # TODO: propagate this - foobar = {} for name, data_torch in self.get_tensors(): old_dtype = data_torch.dtype # convert any unsupported data types to float32 @@ -3006,8 +3000,6 @@ class OpenELM(Model): data = data.astype(np.float16) print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) - foobar[name] = (data_torch, new_name, data) - foobar ###### CONVERSION LOGIC ###### diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index 029befbd4..614242de7 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -38,115 +38,6 @@ static std::string ggml_nb_string(const ggml_tensor * t) { return str; } - -static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) { - GGML_ASSERT(n > 0); - float sum = 0; - - for (int64_t i0 = 0; i0 < 3; i0++) { - if (i0 == n && ne[0] > 2*n) { - printf("..., "); - i0 = ne[0] - n; - } - size_t i = i0;//i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0]; - float v; - if (type == GGML_TYPE_F16) { - v = ggml_fp16_to_fp32(*(ggml_fp16_t *) data + i); - } else if (type == GGML_TYPE_F32) { - v = *(float *) data + i; - } else if (type == GGML_TYPE_I32) { - v = (float) *((int32_t *) data + i); - } else if (type == GGML_TYPE_I16) { - v = (float) *(int16_t *) data + i; - } else if (type == GGML_TYPE_I8) { - v = (float) *(int8_t *) data + i; - } else { - GGML_ASSERT(false); - } - printf("%12.4f", v); - sum += v; - } - printf("\n"); - - - - - // for (int64_t i3 = 0; i3 < ne[3]; i3++) { - // printf(" [\n"); - // for (int64_t i2 = 0; i2 < ne[2]; i2++) { - // if (i2 == n && ne[2] > 2*n) { - // printf(" ..., \n"); - // i2 = ne[2] - n; - // } - // printf(" [\n"); - // for (int64_t i1 = 0; i1 < ne[1]; i1++) { - // if (i1 == n && ne[1] > 2*n) { - // printf(" ..., \n"); - // i1 = ne[1] - n; - // } - // printf(" ["); - // for (int64_t i0 = 0; i0 < ne[0]; i0++) { - // if (i0 == n && ne[0] > 2*n) { - // printf("..., "); - // i0 = ne[0] - n; - // } - // size_t i = i0;//i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0]; - // float v; - // if (type == GGML_TYPE_F16) { - // v = ggml_fp16_to_fp32(*(ggml_fp16_t *) data + i); - // } else if (type == GGML_TYPE_F32) { - // v = *(float *) data + i; - // } else if (type == GGML_TYPE_I32) { - // v = (float) *((int32_t *) data + i); - // } else if (type == GGML_TYPE_I16) { - // v = (float) *(int16_t *) data + i; - // } else if (type == GGML_TYPE_I8) { - // v = (float) *(int8_t *) data + i; - // } else { - // GGML_ASSERT(false); - // } - // printf("%12.4f", v); - // sum += v; - // if (i0 < ne[0] - 1) printf(", "); - // } - // printf("],\n"); - // } - // printf(" ],\n"); - // } - // printf(" ]\n"); - // printf(" sum = %f\n", sum); - // } -} - -float Sum(float *arr, int64_t N){ - float s = 0.0; - for (int i = 0; i < N; i++){ - s += arr[i]; - } - return s; -} -float PrintArr(const char * name, float * arr, int64_t N){ - float sum = 0.0; - if (arr != NULL){ - sum = Sum(arr, N); - printf("%s %d %10f \n",name, N, sum); - } else { - printf("%s %d %10f \n",name, 0, 0.0); - } - return sum; -} - -size_t get_nth_element(const int64_t *ne, const size_t *nb, int64_t nth) { - size_t offset = 0; - size_t divisor = 1; - for (int i = 3; i >= 0; --i) { - size_t index = size_t(floor(nth / divisor)) % ne[i]; - offset += index * nb[i]/4; - divisor *= ne[i]; - } - return offset; -} - void print_tensor(const ggml_tensor * src0) { float sum = 0; @@ -164,8 +55,6 @@ void print_tensor(const ggml_tensor * src0) { if (i == n) { buf2 += sprintf(buf2, "..., "); } - // int64_t offset = get_nth_element(src0->ne, src0->nb, i); - // offset *= ggml_element_size(src0); int64_t offset = i; float v; if (type == GGML_TYPE_F16) { @@ -186,17 +75,6 @@ void print_tensor(const ggml_tensor * src0) { } sum += v; } - - int i = 0; - while (i < ggml_nbytes(src0)/4){ - float val = (((float *) src0->data)[i]); - float diff = abs(val - 0.0022226818837225437164306640625); - if (diff < 0.000001 ){ - printf("found %s: %d = %f\n", src0->name, i, val); - } - i += 1; - } - int max_name_length = 15; int max_dim_length = 15; int max_str_length = 15; @@ -206,10 +84,6 @@ void print_tensor(const ggml_tensor * src0) { max_dim_length, ggml_ne_string(src0).c_str(), max_str_length, ggml_nb_string(src0).c_str(), src0->data); - - - - // printf("%s\n", buf); } /** @@ -231,50 +105,19 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) { if (ask) { return true; // Always retrieve data } - char src1_str[128] = {0}; - // if (src1) { - // sprintf(src1_str, "%s{%s}\n", src1->name, ggml_ne_string(src1).c_str()); - // } - if (src0) { print_tensor(src0); - // printf("%s{%s} n=%d %f\n", src0->name, ggml_ne_string(src0).c_str(),src0->ne[0], Sum(static_cast(src0->data), src0->ne[0])); - // printf("%s{%s}", src0->name, ggml_ne_string(src0).c_str()); - // enum ggml_type type = src0->name == "inp_tokens" ? GGML_TYPE_I32:src0->type; - // ggml_print_tensor(static_cast(src0->data), src0->type, src0->ne, src0->nb, 3); - // PrintArr(src0->name, static_cast(src0->data), src0->ne[0]); } if (src1) { print_tensor(src1); - // printf("%s{%s} n=%d %f\n", src1->name, ggml_ne_string(src1).c_str(),src0->ne[0], Sum(static_cast(src1->data), src1->ne[0])); - // enum ggml_type type = src1->name == "inp_tokens" ? GGML_TYPE_I32:src1->type; - // ggml_print_tensor(static_cast(src1->data), type, src1->ne, src1->nb, 3); - // ggml_print_tensor(static_cast(src1->data), src1->type, src1->ne, src1->nb, 3); - // PrintArr(src1->name, static_cast(src1->data), src1->ne[0]); } printf("%s ==\n", ggml_op_desc(t)); if (t) { print_tensor(t); - // printf("%s{%s} n=%d %f\n", t->name, ggml_ne_string(t).c_str(),src0->ne[0], Sum(static_cast(t->data), t->ne[0])); - // printf("%s{%s}", t->name, ggml_ne_string(t).c_str()); - // PrintArr(t->name, static_cast(t->data), t->ne[0]); - // ggml_print_tensor(static_cast(t->data), t->type, t->ne, t->nb, 3); - // printf("\n == \n"); } printf("\n\n"); - - - - // printf("%24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", - // - // - // t->name, ggml_op_desc(t), src0->name, ggml_ne_string(src0).c_str(), - // src1 ? src1_str : "", - // ggml_ne_string(t).c_str()); - - // copy the data from the GPU memory if needed const bool is_host = ggml_backend_buffer_is_host(t->buffer); @@ -286,7 +129,6 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) { if (!ggml_is_quantized(t->type)) { uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data(); - // ggml_print_tensor(data, t->type, t->ne, t->nb, 3); } return true; diff --git a/llama.cpp b/llama.cpp index fb2a33cc4..30af4cdbe 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6955,17 +6955,6 @@ struct llm_build_context { return lctx.inp_KQ_mask; } - struct ggml_tensor * build_inp_KQ_mask2(int64_t n_kv, bool causal = true) { - if (causal) { - lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, n_tokens); - } else { - lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens); - } - cb(lctx.inp_KQ_mask, "KQ_mask", -1); - ggml_set_input(lctx.inp_KQ_mask); - return lctx.inp_KQ_mask; - } - struct ggml_tensor * build_inp_KQ_pos() { lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_kv); cb(lctx.inp_KQ_pos, "KQ_pos", -1);