mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-14 06:49:54 +00:00
e32089b2c2
* add python wrapper https://gist.github.com/abetlen/2b90e5f153f6efd00931d098de5c73ce * fix decoding error. adds errors=ignore parameter * add python bindings for functions to get and set the whole llama state (rng, logits, embedding and kv_cache) * update python bindings * add text generating baby-llama from scratch example * fix race condition bug in ggml_compute_forward_diag_mask_f32 * implement ggml_soft_max_back for more performant backward pass of soft_max avoids creating big intermediate matrices of size n_embd x n_embd for llama layers and n_vocab x n_vocab for cross entropy loss * improve softmax backward pass go from quadratic runtime to linear runtime by simplifying the formulas * fix race condition bug in non-inplace ggml_compute_forward_diag_mask_f32 memcpy needs to be synchronized across threads to avoid race conditions. => do it in INIT phase * fix bug in ggml_compute_forward_soft_max_back_f32 on DEBUG build * improve performance of mul_mat backward pass avoid transpose by using mul_mat with swapped arguments * avoid printing too much newlines in baby-llama-text * activate threading in baby-llama-text * add ggml_out_prod and use it for mul_mat backward pass for improved performance performance stats report improvement from 37 seconds to 16 seconds runtime during my training tests * better weight initialization improves training convergence at start * better weight initialization improves training convergence at start * improve ggml_out_prod performance - change iteration order (>15s -> 10s runtime) - parallelize over one more dimension: over dst matrix rows (10s -> <5s runtime) * add llama sampler, shuffle samples and constrain sampling to tokens occurring in train data * fix get_samples call, add model tensor names, increase model size, start training samples after newline * save train trained model to checkpoint and load model to be trained from checkpoint * use inplace functions where possible * initialize rng with srand * use different arguments for input and output checkpoint * ggml fixes to support backward pass on inplace operations * remove duplicate include * fix cross entropy loss - add target probabilities for each sample which is then used in cross entropy loss * print used memory before and after optimization * sample with non-greedy sampling parameters at the end of training * add cmake target for baby-llama-text * add ggml_add1_inplace to header * enable gradient propagation for inplace add1 and scale operations those functions backward passes don't need the original src0, so they also work when forward is inplace * implement AdamW in ggml_opt_adam by adding weight decay parameter (default 0.001f) also add a schedule parameter (default 1.0f) that can be used to scale alpha and decay according to learning schedule. setting the decay parameter to zero disables AdamW resulting in normal Adam optimizer. since the difference between Adam and AdamW is minimal it is not implemented as another optimizer, but integrated into the existing Adam optimizer. * use inplace operations in cross_entropy_loss * fix random weight initialization scale * add missing default parameters for adam optimizer * add ggml_opt_context, so that we can properly resume training otherwise the optimizer states, tracking statistics about the error function and its derivates, will reset to zero each time ggml_opt is called, hindering convergence on resumed training. now the optimizer context and all its memory is stored in a separate struct. * fix bug in llama_sample_token_mirostat_v2 when all candidates are filtered out through mu threshold, the following soft_max operation will fail. so keep at least one. * add forward function without using cache, for more performant training during training on whole samples no cache is required. removing the cache and simplifying the remaining code results in performance and memory usage improvement. * print suppressed newline tokens as string "\n" printing too much actual newlines is suppressed to avoid flooding the console. * store optimizer state in training checkpoint and add learning schedule persistent optimizer state allows to resume training without resetting the optimizer learning schedule consists of linear warmup ramp followed by cosine decay with restarts * remove unused functions * fix bug in get_samples which corrupted training targets * save checkpoint only when it was trained * simplify code * remove trailing whitespace * simplify backward pass for SQRT * replace inefficient repeat backward pass with dedicated repeat_back operation * add ggml_cross_entropy_loss with backward pass for faster training cross entropy loss can also be implemented using softmax and log, but as dedicated operation it is faster and especially avoids unnecessary memory overhead. * add tests for cross_entropy_loss backward pass finite differences regularly results in estimated gradient of zero, despite the backward pass giving non zero gradient. _probably_ the finite differences fails due to numerical issues * use ggml_cross_entropy_loss in text training example * remove trailing whitespace * slightly improve how cross entropy loss is compute btw: directly implemented cross entropy loss seems to have way lower magnitudes than when implemented with softmax and log. probably the input to log gets closer to zero due to float numerics. maybe the multiplication by (1.0-eps)/sum is more accurate.. * add llama_get_vocab to get the vocabulary as output parameters * set default model.type for unknown models with few layers * add export of training checkpoint to llama compatible model file * get vocabulary for exporting training checkpoint to llama compatible model file * implement backward pass of flash attention * bugfixes for backward pass of flash attention * test flash attention backward pass need to set loose error bounds to pass. the finitie differences are close to numeric limits and often return quite different values than the backward pass. reducing eps further lets the gradients vanish completely. likewise setting eps to big results in wronger values. the softmax in the middle of the function is probably the most responsible for the numeric issues using finite differences. * add option to train with flash attention and move options to the top of the main function training from scratch also works with flash attention training convergence and generation results after fix number of iterations are worse than when not using flash attention. maybe there still lingers a bug in the flash attention backward pass? but training works, just with slower convergence. flash attention is still worth to use, because it requires way less memory and is faster with high n_ctx * add train_params and command line option parser * remove unnecessary comments * add train params to specify memory size * remove python bindings * rename baby-llama-text to train-text-from-scratch * replace auto parameters in lambda function * add #include <climits> * add explicit cast to fix compile error "error: non-constant-expression cannot be narrowed from type 'int64_t' (aka 'long long') to 'uint32_t' (aka 'unsigned int') in initializer list [-Wc++11-narrowing]" * remove trailing whitespace * add ggml_opt_resume_g which accepts forward and backward cgraphs * fix formulas in comments * bug fix for ggml_compute_forward_get_rows_back_f32 the result should be set to zero, not to whatever data is in opt0 * improve training memory usage with scratch buffers instead of relying on the automatic backward pass, we manually create the graph for the backward pass. it turns out that all backward pass operations need only temporary memory which can be reused after each layer. will compute backward pass for ALL model parameters * add option to use scratch buffers in training or not make it configurable because currently training with scratch buffers implies flash attention and optimization over all parameters. * ci : disable temporary * store view offset and permute axes in opt[0] instead of storing it in padding use memcpy to store offset, because offset is of type size_t. when storing it as int32_t offset would have to be smaller than 2^31 which is not necessarily true. * minor : fix compile warnings + minor style changes * fix bug in threaded indices calculation of ggml_compute_forward_flash_attn_back_f32 * store view offset like in master branch * bug fix in forward_batch_wo_cache_flash_attn_train * scratch buffer bug fixes in forward_batch_wo_cache_flash_attn_train data of permute and reshape is the same as their input. if we want to preserve the output of permute/reshape, we also need to preserve their inputs. replace reshape(src0, src1) with reshape_nd calls so that we don't need src1. replace (temporary) t03 with ggml_repeat(ctx0, layer.attention_norm, t02). in the future we could also use the new broadcasting ggml_mul to avoid these repeat calls. for this we need backward pass of broadcasting ggml_mul. * remove unnecessary scratch buffer 0 buf 0 is persistent memory, so we can just disable scratch for this by using buf -1 * avoid creating unnecessary grad tensors previously we need to create grads for model parameters, so that expand(..) correctly populates cgraph->leafs & cgraph->grads this wasted memory, because unnecessary grad for each op were automatically created: the automatically generated grad was unnecessary because we later manually set the grad (e.g. t35->grad = expand(gb, ...) ). this discarded the automatically generated grad resulting in wasted memory. improved this by changing expand(..) to not use ggml_build_forward_expand. expand set cgraph->nodes but not the leafs. cgraph->leafs & cgraph->grads are set in another pass after the last expand call. * print used training seed * zero initialize gfbuf and gbbuf * ci : re-enable workflows + add README for training --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
1190 lines
39 KiB
C
1190 lines
39 KiB
C
#include "ggml.h"
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <assert.h>
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#define MAX_NARGS 3
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#undef MIN
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#undef MAX
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#define MIN(a, b) ((a) < (b) ? (a) : (b))
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#define MAX(a, b) ((a) > (b) ? (a) : (b))
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#define GGML_SILU_FP16
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//
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// logging
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//
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#if (GGML_DEBUG >= 1)
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#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
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#else
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#define GGML_PRINT_DEBUG(...)
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#endif
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#if (GGML_DEBUG >= 5)
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#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
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#else
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#define GGML_PRINT_DEBUG_5(...)
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#endif
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#if (GGML_DEBUG >= 10)
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#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
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#else
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#define GGML_PRINT_DEBUG_10(...)
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#endif
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#define GGML_PRINT(...) printf(__VA_ARGS__)
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float frand(void) {
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return (float)rand()/(float)RAND_MAX;
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}
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int irand(int n) {
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if (n == 0) return 0;
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else return rand()%n;
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}
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void get_random_dims(int64_t * dims, int ndims) {
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dims[0] = dims[1] = dims[2] = dims[3] = 1;
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for (int i = 0; i < ndims; i++) {
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dims[i] = 1 + irand(4);
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}
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}
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struct ggml_tensor * get_random_tensor(
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struct ggml_context * ctx0,
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int ndims,
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int64_t ne[],
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float fmin,
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float fmax) {
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struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F32, ndims, ne);
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switch (ndims) {
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case 1:
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)result->data)[i0] = frand()*(fmax - fmin) + fmin;
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}
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break;
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case 2:
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for (int i1 = 0; i1 < ne[1]; i1++) {
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)result->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
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}
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}
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break;
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case 3:
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for (int i2 = 0; i2 < ne[2]; i2++) {
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for (int i1 = 0; i1 < ne[1]; i1++) {
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
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}
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}
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}
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break;
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case 4:
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for (int i3 = 0; i3 < ne[3]; i3++) {
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for (int i2 = 0; i2 < ne[2]; i2++) {
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for (int i1 = 0; i1 < ne[1]; i1++) {
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
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}
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}
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}
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}
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break;
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default:
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assert(false);
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};
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return result;
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}
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struct ggml_tensor * get_random_tensor_int(
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struct ggml_context * ctx0,
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int ndims,
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int64_t ne[],
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int32_t imin,
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int32_t imax) {
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struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_I32, ndims, ne);
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switch (ndims) {
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case 1:
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((int32_t *)result->data)[i0] = irand(imax - imin) + imin;
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}
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break;
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case 2:
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for (int i1 = 0; i1 < ne[1]; i1++) {
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((int32_t *)result->data)[i1*ne[0] + i0] = irand(imax - imin) + imin;
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}
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}
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break;
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case 3:
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for (int i2 = 0; i2 < ne[2]; i2++) {
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for (int i1 = 0; i1 < ne[1]; i1++) {
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((int32_t *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = irand(imax - imin) + imin;
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}
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}
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}
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break;
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case 4:
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for (int i3 = 0; i3 < ne[3]; i3++) {
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for (int i2 = 0; i2 < ne[2]; i2++) {
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for (int i1 = 0; i1 < ne[1]; i1++) {
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((int32_t *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = irand(imax - imin) + imin;
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}
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}
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}
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}
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break;
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default:
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assert(false);
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};
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return result;
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}
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float get_element(const struct ggml_tensor * t, int idx) {
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if (t->type == GGML_TYPE_F32) {
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return ((float *)t->data)[idx];
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} else if (t->type == GGML_TYPE_I32) {
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return ((int32_t *)t->data)[idx];
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} else {
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assert(false);
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return INFINITY;
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}
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}
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void set_element(struct ggml_tensor * t, int idx, float value) {
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((float *)t->data)[idx] = value;
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}
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void print_elements(const char* label, const struct ggml_tensor * t) {
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if (!t) {
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printf("%s: %s = null\n", __func__, label);
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return;
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}
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const int nelements = ggml_nelements(t);
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printf("%s: %s = [", __func__, label);
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for (int k = 0; k < nelements; ++k) {
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if (k > 0) { printf(", "); }
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printf("%.5f", get_element(t, k));
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}
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printf("] shape: [");
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for (int k = 0; k < t->n_dims; ++k) {
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if (k > 0) { printf(", "); }
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printf("%d", (int)t->ne[k]);
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}
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printf("]\n");
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}
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bool check_gradient(
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const char * op_name,
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struct ggml_context * ctx0,
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struct ggml_tensor * x[],
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struct ggml_tensor * f,
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int ndims,
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int nargs,
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float eps,
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float max_error_abs,
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float max_error_rel) {
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struct ggml_cgraph gf = ggml_build_forward (f);
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struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false);
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ggml_graph_compute(ctx0, &gf);
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ggml_graph_reset (&gf);
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ggml_set_f32 (f->grad, 1.0f);
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ggml_graph_compute(ctx0, &gb);
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// ggml_graph_dump_dot(&gf, NULL, "test-grad0-forward.dot");
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// ggml_graph_dump_dot(&gb, &gf, "test-grad0-backward.dot");
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for (int i = 0; i < nargs; ++i) {
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const int nelements = ggml_nelements(x[i]);
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for (int k = 0; k < nelements; ++k) {
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// compute gradient using finite differences
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const float x0 = get_element(x[i], k);
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const float xm = x0 - eps;
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const float xp = x0 + eps;
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set_element(x[i], k, xp);
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ggml_graph_compute(ctx0, &gf);
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const float f0 = ggml_get_f32_1d(f, 0);
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set_element(x[i], k, xm);
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ggml_graph_compute(ctx0, &gf);
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const float f1 = ggml_get_f32_1d(f, 0);
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const float g0 = (f0 - f1)/(2.0f*eps);
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set_element(x[i], k, x0);
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// compute gradient using backward graph
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ggml_graph_reset (&gf);
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ggml_set_f32 (f->grad, 1.0f);
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ggml_graph_compute(ctx0, &gb);
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const float g1 = get_element(x[i]->grad, k);
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const float error_abs = fabsf(g0 - g1);
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const float error_rel = g0 != 0 ? fabsf(g0 - g1)/fabs(g0) : 0;
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if (error_abs > max_error_abs || error_rel > max_error_rel) {
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printf("%s: ndims=%d, i=%d, k=%d, x0=%f, xm=%f, xp=%f, f0=%f, f1=%f, g0=%f, g1=%f, eps=%f, error_abs=%f, error_rel=%f\n",
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op_name, ndims, i, k, x0, xm, xp, f0, f1, g0, g1, eps, error_abs, error_rel);
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//assert(false);
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return false;
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}
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}
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}
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return true;
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}
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// TODO: clean-up this ..
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bool check_mat_mul(
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const struct ggml_tensor * y,
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const struct ggml_tensor * x0,
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const struct ggml_tensor * x1) {
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float * dst = (float *) y->data;
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float * src0 = (float *) x0->data;
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float * src1 = (float *) x1->data;
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const int nc = x0->ne[1];
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const int nr = x1->ne[1];
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const int nk = x0->ne[0];
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GGML_PRINT_DEBUG("check_mat_mul: nc=%d, nr=%d, nk=%d\n", nc, nr, nk);
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GGML_PRINT_DEBUG("x0:\n");
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for (int j = 0; j < x0->ne[1]; ++j) {
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for (int i = 0; i < x0->ne[0]; ++i) {
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GGML_PRINT_DEBUG("%6.3f ", src0[j*nk + i]);
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}
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GGML_PRINT_DEBUG("\n");
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}
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GGML_PRINT_DEBUG("\n");
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GGML_PRINT_DEBUG("x1:\n");
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for (int j = 0; j < x1->ne[1]; ++j) {
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for (int i = 0; i < x1->ne[0]; ++i) {
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GGML_PRINT_DEBUG("%6.3f ", src1[j*nk + i]);
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}
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GGML_PRINT_DEBUG("\n");
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}
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GGML_PRINT_DEBUG("\n");
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GGML_PRINT_DEBUG("y: n_dims = %d, (%lld, %lld)\n", y->n_dims, y->ne[0], y->ne[1]);
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for (int j = 0; j < y->ne[1]; ++j) {
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for (int i = 0; i < y->ne[0]; ++i) {
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GGML_PRINT_DEBUG("%6.3f ", dst[j*nr + i]);
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}
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GGML_PRINT_DEBUG("\n");
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}
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for (int i = 0; i < nr; ++i) {
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for (int j = 0; j < nc; ++j) {
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float sum = 0.0f;
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for (int k = 0; k < nk; ++k) {
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sum += src0[j*nk + k]*src1[i*nk + k];
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}
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if (fabsf(dst[i*nc + j] - sum) > 1e-5f) {
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fprintf(stderr, "check_mat_mul: dst[%d] = %f, sum = %f\n", i*nc + j, dst[i*nc + j], sum);
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assert(false);
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return false;
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}
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}
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}
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return true;
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}
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#define NUM_PERMUTATIONS (4*3*2*1)
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int main(int argc, const char ** argv) {
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struct ggml_init_params params = {
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.mem_size = 128*1024*1024,
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.mem_buffer = NULL,
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.no_alloc = false,
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};
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int64_t ne[4];
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int all_permutations[4 * NUM_PERMUTATIONS];
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{
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int count = 0;
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for (int ax0=0; ax0<4; ++ax0) {
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for (int ax1=0; ax1<4; ++ax1) {
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if (ax1 == ax0) continue;
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for (int ax2=0; ax2<4; ++ax2) {
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if (ax2 == ax0) continue;
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if (ax2 == ax1) continue;
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for (int ax3=0; ax3<4; ++ax3) {
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if (ax3 == ax0) continue;
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if (ax3 == ax1) continue;
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if (ax3 == ax2) continue;
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assert(count < NUM_PERMUTATIONS);
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all_permutations[count*4+0] = ax0;
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all_permutations[count*4+1] = ax1;
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all_permutations[count*4+2] = ax2;
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all_permutations[count*4+3] = ax3;
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++count;
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}
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}
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}
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}
|
|
}
|
|
|
|
|
|
// original loop: 1000
|
|
int niter = 4;
|
|
const char *env = getenv("GGML_NLOOP");
|
|
if (env != NULL) {
|
|
niter = atoi(env);
|
|
}
|
|
if (argc > 1) {
|
|
niter = atoi(argv[1]);
|
|
}
|
|
for (int iter = 0; iter < niter; ++iter) {
|
|
printf("test-grad0: iter:%d/%d\n", iter, niter);
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
get_random_dims(ne, 4);
|
|
|
|
struct ggml_tensor * x[MAX_NARGS];
|
|
|
|
// add
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_add(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("add", ctx0, x, f, ndims, nargs, 1e-3f, 2e-3f, 2e-3f);
|
|
}
|
|
}
|
|
|
|
// sub
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sub(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("sub", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
|
|
}
|
|
}
|
|
|
|
// mul
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_mul(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("mul", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// div
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor(ctx0, ndims, ne, 0.5f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_div(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("div", ctx0, x, f, ndims, nargs, 1e-3f, 1e-1f, 1e-1f);
|
|
}
|
|
}
|
|
|
|
// sqr
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, x[0]));
|
|
|
|
check_gradient("sqr", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// sqrt
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqrt(ctx0, x[0]));
|
|
|
|
check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f);
|
|
}
|
|
}
|
|
|
|
// log
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_log(ctx0, x[0]));
|
|
|
|
check_gradient("log", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f);
|
|
}
|
|
}
|
|
|
|
// sum
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, x[0]);
|
|
|
|
check_gradient("sum", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
|
|
}
|
|
}
|
|
|
|
|
|
// sum_rows
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sum_rows(ctx0, x[0])));
|
|
|
|
check_gradient("sum_rows", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// repeat
|
|
{
|
|
int64_t ne2[4];
|
|
get_random_dims(ne2, 4);
|
|
|
|
ne2[0] = ne[0] * ne2[0];
|
|
ne2[1] = ne[1] * ne2[1];
|
|
ne2[2] = 1;
|
|
ne2[3] = 1;
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
x[1] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x[1], ggml_repeat(ctx0, x[0], x[1]))));
|
|
|
|
check_gradient("repeat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY);
|
|
}
|
|
|
|
}
|
|
|
|
// abs (finite differences do not work)
|
|
//{
|
|
// const int nargs = 1;
|
|
|
|
// for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
// for (int i = 0; i < nargs; ++i) {
|
|
// x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
// ggml_set_param(ctx0, x[i]);
|
|
// }
|
|
|
|
// struct ggml_tensor * f = ggml_sum(ctx0, ggml_abs(ctx0, x[0]));
|
|
|
|
// check_gradient("abs", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-3f);
|
|
// }
|
|
//}
|
|
|
|
// mul_mat
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 2; ndims <= 2; ++ndims) {
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
{
|
|
int64_t ne2[4];
|
|
get_random_dims(ne2, 4);
|
|
ne2[0] = ne[0];
|
|
x[1] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
}
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
struct ggml_tensor * m = ggml_mul_mat(ctx0, x[1], x[0]);
|
|
struct ggml_tensor * f = ggml_sum(ctx0, m);
|
|
|
|
GGML_PRINT_DEBUG("testing: mul_mat, [%lld, %lld] (%d) * [%lld, %lld] (%d)\n", x[1]->ne[0], x[1]->ne[1], x[1]->n_dims, x[0]->ne[0], x[0]->ne[1], x[0]->n_dims);
|
|
|
|
check_gradient("mul_mat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
check_mat_mul(m, x[1], x[0]);
|
|
}
|
|
}
|
|
|
|
// silu
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_silu(ctx0, x[0]));
|
|
|
|
#ifdef GGML_SILU_FP16
|
|
// due to GGML_SILU_FP16 the finite difference method will be slightly wrong -> increase error bounds.
|
|
check_gradient("silu", ctx0, x, f, ndims, nargs, 1e-3f, 0.5, INFINITY);
|
|
#else
|
|
check_gradient("silu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
#endif
|
|
}
|
|
}
|
|
|
|
// rms_norm
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rms_norm(ctx0, x[0]));
|
|
|
|
check_gradient("rms_norm", ctx0, x, f, ndims, nargs, 1e-4f, 1.0f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// scale
|
|
{
|
|
const int nargs = 2;
|
|
|
|
int64_t ne2[4];
|
|
ne2[0] = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
x[1] = get_random_tensor(ctx0, 1, ne2, -1.0f, 1.0f);
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_scale(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("scale", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// cpy
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
// x[1] is overwritten by x[0], so the gradients don't propagate to x[1]
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cpy(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("cpy", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// reshape (1d->nd)
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
int64_t ne2[4];
|
|
ne2[0] = 1;
|
|
ne2[1] = 1;
|
|
ne2[2] = 1;
|
|
ne2[3] = 1;
|
|
for (int i = 0; i < ndims; ++i) {
|
|
ne2[0] *= ne[i];
|
|
}
|
|
x[0] = get_random_tensor(ctx0, 1, ne2, -1.0f, 1.0f);
|
|
x[1] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_reshape(ctx0, x[0], x[1]));
|
|
check_gradient("reshape", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// reshape (nd->1d)
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
int64_t ne2[4];
|
|
ne2[0] = 1;
|
|
ne2[1] = 1;
|
|
ne2[2] = 1;
|
|
ne2[3] = 1;
|
|
for (int i = 0; i < ndims; ++i) {
|
|
ne2[0] *= ne[i];
|
|
}
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
x[1] = get_random_tensor(ctx0, 1, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_reshape(ctx0, x[0], x[1]));
|
|
check_gradient("reshape", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// acc 1d
|
|
{
|
|
int64_t ne2[4] = { 1, 1, 1, 1 };
|
|
|
|
const int nargs = 2;
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
get_random_dims(ne2, 1);
|
|
while ((ne2[0] > ne[0]) || (ne2[0] > ggml_nelements(x[0]))) {
|
|
get_random_dims(ne2, 1);
|
|
}
|
|
|
|
x[1] = get_random_tensor(ctx0, 1, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
const int max_offset = MAX(0, ggml_nelements(x[0]) - ggml_nelements(x[1]));
|
|
const int offset = irand(max_offset) * ggml_element_size(x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset));
|
|
|
|
check_gradient("acc 1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// acc 2d
|
|
{
|
|
int64_t ne2[4] = { 1, 1, 1, 1 };
|
|
int64_t max_offsets[4] = { 0, 0, 0, 0 };
|
|
int64_t offsets[4] = { 0, 0, 0, 0 };
|
|
|
|
const int nargs = 2;
|
|
for (int ndims = 2; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
get_random_dims(ne2, 2);
|
|
while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[0]*ne2[1] > ggml_nelements(x[0]))) {
|
|
get_random_dims(ne2, 2);
|
|
}
|
|
|
|
x[1] = get_random_tensor(ctx0, 2, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]);
|
|
max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]);
|
|
offsets[0] = irand(max_offsets[0]) * x[0]->nb[0];
|
|
offsets[1] = irand(max_offsets[1]) * x[0]->nb[1];
|
|
const int offset = offsets[0] + offsets[1];
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset));
|
|
|
|
check_gradient("acc 2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// acc 3d
|
|
{
|
|
int64_t ne2[4] = { 1, 1, 1, 1 };
|
|
int64_t max_offsets[4] = { 0, 0, 0, 0 };
|
|
int64_t offsets[4] = { 0, 0, 0, 0 };
|
|
|
|
const int nargs = 2;
|
|
for (int ndims = 3; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
get_random_dims(ne2, 3);
|
|
while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[2] > ne[2]) || (ne2[0]*ne2[1]*ne2[2] > ggml_nelements(x[0]))) {
|
|
get_random_dims(ne2, 3);
|
|
}
|
|
|
|
x[1] = get_random_tensor(ctx0, 3, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]);
|
|
max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]);
|
|
max_offsets[2] = MAX(0, x[0]->ne[2] - x[1]->ne[2]);
|
|
offsets[0] = irand(max_offsets[0]) * x[0]->nb[0];
|
|
offsets[1] = irand(max_offsets[1]) * x[0]->nb[1];
|
|
offsets[2] = irand(max_offsets[2]) * x[0]->nb[2];
|
|
const int offset = offsets[0] + offsets[1] + offsets[2];
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset));
|
|
|
|
check_gradient("acc 3d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// acc 4d
|
|
{
|
|
int64_t ne2[4] = { 1, 1, 1, 1 };
|
|
int64_t max_offsets[4] = { 0, 0, 0, 0 };
|
|
int64_t offsets[4] = { 0, 0, 0, 0 };
|
|
|
|
const int nargs = 2;
|
|
for (int ndims = 4; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
get_random_dims(ne2, 4);
|
|
while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[2] > ne[2]) || (ne2[3] > ne[3]) || (ne2[0]*ne2[1]*ne2[2]*ne2[3] > ggml_nelements(x[0]))) {
|
|
get_random_dims(ne2, 4);
|
|
}
|
|
|
|
x[1] = get_random_tensor(ctx0, 4, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]);
|
|
max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]);
|
|
max_offsets[2] = MAX(0, x[0]->ne[2] - x[1]->ne[2]);
|
|
max_offsets[3] = MAX(0, x[0]->ne[3] - x[1]->ne[3]);
|
|
offsets[0] = irand(max_offsets[0]) * x[0]->nb[0];
|
|
offsets[1] = irand(max_offsets[1]) * x[0]->nb[1];
|
|
offsets[2] = irand(max_offsets[2]) * x[0]->nb[2];
|
|
offsets[3] = irand(max_offsets[3]) * x[0]->nb[3];
|
|
const int offset = offsets[0] + offsets[1] + offsets[2] + offsets[3];
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset));
|
|
|
|
check_gradient("acc 4d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// set_1d
|
|
{
|
|
int64_t ne2[4];
|
|
|
|
const int nargs = 2;
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
get_random_dims(ne2, 1);
|
|
while ((ne2[0] > ne[0]) || (ne2[0] > ggml_nelements(x[0]))) {
|
|
get_random_dims(ne2, 1);
|
|
}
|
|
|
|
x[1] = get_random_tensor(ctx0, 1, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
const int max_offset = MAX(0, ggml_nelements(x[0]) - ggml_nelements(x[1]));
|
|
const int offset = irand(max_offset) * ggml_element_size(x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_set_1d(ctx0, x[0], x[1], offset));
|
|
|
|
check_gradient("set_1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// set_2d
|
|
{
|
|
int64_t ne2[4];
|
|
int64_t max_offsets[4] = { 0, 0, 0, 0 };
|
|
int64_t offsets[4] = { 0, 0, 0, 0 };
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 2; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
get_random_dims(ne2, 2);
|
|
while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[0]*ne2[1] > ggml_nelements(x[0]))) {
|
|
get_random_dims(ne2, 2);
|
|
}
|
|
|
|
x[1] = get_random_tensor(ctx0, 2, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]);
|
|
max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]);
|
|
offsets[0] = irand(max_offsets[0]) * x[0]->nb[0];
|
|
offsets[1] = irand(max_offsets[1]) * x[0]->nb[1];
|
|
const int offset = offsets[0] + offsets[1];
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_set_2d(ctx0, x[0], x[1], x[1]->nb[1], offset));
|
|
|
|
check_gradient("set_2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// view_1d
|
|
{
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
const int k0 = irand(ggml_nelements(x[0]));
|
|
const int k1 = irand(ggml_nelements(x[0]));
|
|
const int i0 = MIN(k0, k1);
|
|
const int i1 = MAX(k0, k1);
|
|
|
|
const int offset = i0 * sizeof(float);
|
|
const int nelem = i1 - i0;
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_1d(ctx0, x[0], nelem, offset));
|
|
|
|
check_gradient("view_1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// view_2d
|
|
{
|
|
int64_t ne2[4];
|
|
int64_t nb2[4];
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
|
|
get_random_dims(ne2, 2);
|
|
while (ne2[0]*ne2[1] > ggml_nelements(x[0])) {
|
|
get_random_dims(ne2, 2);
|
|
}
|
|
const int count = ne2[0]*ne2[1];
|
|
|
|
nb2[0] = sizeof(float);
|
|
nb2[1] = nb2[0]*ne2[0];
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
const int max_offset = ggml_nelements(x[0]) - count;
|
|
const int offset = irand(max_offset+1) * sizeof(float);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_2d(ctx0, x[0], ne2[0], ne2[1], nb2[1], offset));
|
|
|
|
check_gradient("view_2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// view_3d
|
|
{
|
|
int64_t ne2[4] = {1,1,1,1};
|
|
int64_t nb2[4] = {0,0,0,0};
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
|
|
get_random_dims(ne2, 3);
|
|
while (ne2[0]*ne2[1]*ne2[2] > ggml_nelements(x[0])) {
|
|
get_random_dims(ne2, 3);
|
|
}
|
|
const int count = ne2[0]*ne2[1]*ne2[2];
|
|
|
|
nb2[0] = sizeof(float);
|
|
nb2[1] = nb2[0]*ne2[0];
|
|
nb2[2] = nb2[1]*ne2[1];
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
const int max_offset = ggml_nelements(x[0]) - count;
|
|
const int offset = irand(max_offset+1) * sizeof(float);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_3d(ctx0, x[0], ne2[0], ne2[1], ne2[2], nb2[1], nb2[2], offset));
|
|
|
|
check_gradient("view_3d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// permute
|
|
{
|
|
int64_t ne2[4];
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 4; ++ndims)
|
|
{
|
|
// ggml_permute will set axes of dimensions below n_dims to 1.
|
|
// to make ggml_permute work correctly on all axes,
|
|
// the input tensor needs maximal n_dim of 4.
|
|
for (int i=0; i<ndims; ++i) {
|
|
ne2[i] = ne[i];
|
|
}
|
|
for (int i=ndims; i<4; ++i) {
|
|
ne2[i] = 1;
|
|
}
|
|
x[0] = get_random_tensor(ctx0, 4, ne2, -1.0f, 1.0f);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
const int p = irand(NUM_PERMUTATIONS);
|
|
const int ax0 = all_permutations[p*4+0];
|
|
const int ax1 = all_permutations[p*4+1];
|
|
const int ax2 = all_permutations[p*4+2];
|
|
const int ax3 = all_permutations[p*4+3];
|
|
|
|
// sum requires contiguous tensor rows
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cont(ctx0, ggml_permute(ctx0, x[0], ax0, ax1, ax2, ax3)));
|
|
|
|
check_gradient("permute", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// transpose
|
|
{
|
|
int64_t ne2[4];
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 4; ++ndims)
|
|
{
|
|
// ggml_transpose will set axes of dimensions below n_dims to 1.
|
|
// to make ggml_transpose work correctly on all axes,
|
|
// the input tensor needs maximal n_dim of 4.
|
|
for (int i=0; i<ndims; ++i) {
|
|
ne2[i] = ne[i];
|
|
}
|
|
for (int i=ndims; i<4; ++i) {
|
|
ne2[i] = 1;
|
|
}
|
|
x[0] = get_random_tensor(ctx0, 4, ne2, -1.0f, 1.0f);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
// sum requires contiguous tensor rows
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, x[0])));
|
|
|
|
check_gradient("transpose", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// get_rows
|
|
{
|
|
int64_t ne2[4] = {ne[0], ne[1], 1, 1};
|
|
int64_t ne3[4] = {1+irand(ne[1]), 1, 1, 1};
|
|
const int nargs = 1;
|
|
const int ndims = 2;
|
|
x[0] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
x[1] = get_random_tensor_int(ctx0, 1, ne3, 0, ne2[1]);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_get_rows(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("get_rows", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
|
|
// diag_mask_inf
|
|
{
|
|
const int nargs = 1;
|
|
const int ndims = 2;
|
|
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
int n_past = irand(ne[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_diag_mask_inf(ctx0, x[0], n_past));
|
|
|
|
check_gradient("diag_mask_inf", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
|
|
// diag_mask_zero
|
|
{
|
|
const int nargs = 1;
|
|
const int ndims = 2;
|
|
|
|
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
int n_past = irand(ne[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_diag_mask_zero(ctx0, x[0], n_past));
|
|
|
|
check_gradient("diag_mask_zero", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
|
|
// softmax
|
|
{
|
|
const int nargs = 1;
|
|
|
|
int64_t ne2[4];
|
|
get_random_dims(ne2, 4);
|
|
|
|
for (int ndims = 1; ndims <= 3; ++ndims) {
|
|
x[0] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_soft_max(ctx0, x[0]));
|
|
|
|
check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// cross_entropy_loss
|
|
{
|
|
const int nargs = 1;
|
|
|
|
int64_t ne2[4];
|
|
get_random_dims(ne2, 4);
|
|
|
|
for (int ndims = 1; ndims <= 3; ++ndims) {
|
|
x[0] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
x[1] = get_random_tensor(ctx0, ndims, ne2, 0.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cross_entropy_loss(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-1f, 1e-2f, INFINITY);
|
|
// finite differences regularly fails!
|
|
}
|
|
}
|
|
|
|
// rope
|
|
{
|
|
const int nargs = 1;
|
|
|
|
int64_t ne2[4];
|
|
get_random_dims(ne2, 4);
|
|
ne2[0] += ne2[0] % 2;
|
|
int n_rot = ne2[0];
|
|
|
|
for (int ndims = 3; ndims <= 4; ++ndims) {
|
|
for (int mode = 0; mode < 4; ++mode) {
|
|
for (int n_past = 1; n_past < ne2[2]; ++n_past) {
|
|
x[0] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
const bool skip_past = (mode & 1);
|
|
if (skip_past) {
|
|
// we have no past, so this would have to work on uninitialized memory.
|
|
// we only test the gradients here;
|
|
// skip_past should have no influence on gradient computation.
|
|
// so when other modes work, we assume that this does as well.
|
|
continue;
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode));
|
|
|
|
GGML_PRINT_DEBUG("rope: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
|
|
check_gradient("rope", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// flash_attn
|
|
{
|
|
const int nargs = 3;
|
|
|
|
int64_t ne2[4];
|
|
|
|
get_random_dims(ne2, 4);
|
|
int64_t D = ne2[0];
|
|
int64_t N = ne2[1];
|
|
int64_t M = ne2[2] + N;
|
|
int64_t B = ne2[3];
|
|
|
|
for (int masked = 0; masked <= 1; ++masked) {
|
|
for (int ndims = 2; ndims <= 4; ++ndims) {
|
|
int64_t neq[4] = { D, N, B, ne[3] };
|
|
int64_t nek[4] = { D, M, B, ne[3] };
|
|
int64_t nev[4] = { M, D, B, ne[3] };
|
|
if (ndims == 2) {
|
|
neq[2] = 1; neq[3] = 1;
|
|
nek[2] = 1; nek[3] = 1;
|
|
nev[2] = 1; nev[3] = 1;
|
|
} else if (ndims == 3) {
|
|
neq[3] = 1;
|
|
nek[3] = 1;
|
|
nev[3] = 1;
|
|
}
|
|
x[0] = get_random_tensor(ctx0, ndims, neq, -0.1250f, 0.1250f);
|
|
x[1] = get_random_tensor(ctx0, ndims, nek, -0.1250f, 0.1250f);
|
|
x[2] = get_random_tensor(ctx0, ndims, nev, -0.1250f, 0.1250f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
ggml_set_param(ctx0, x[1]);
|
|
ggml_set_param(ctx0, x[2]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
|
|
|
|
check_gradient("flash_attn", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
|
|
}
|
|
}
|
|
}
|
|
ggml_free(ctx0);
|
|
}
|
|
|
|
return 0;
|
|
}
|