96efd9052f
* Re-introduce the llama package This PR brings back the llama package, making it possible to call llama.cpp and ggml APIs from Go directly via CGo. This has a few advantages: - C APIs can be called directly from Go without needing to use the previous "server" REST API - On macOS and for CPU builds on Linux and Windows, Ollama can be built without a go generate ./... step, making it easy to get up and running to hack on parts of Ollama that don't require fast inference - Faster build times for AVX,AVX2,CUDA and ROCM (a full build of all runners takes <5 min on a fast CPU) - No git submodule making it easier to clone and build from source This is a big PR, but much of it is vendor code except for: - llama.go CGo bindings - example/: a simple example of running inference - runner/: a subprocess server designed to replace the llm/ext_server package - Makefile an as minimal as possible Makefile to build the runner package for different targets (cpu, avx, avx2, cuda, rocm) Co-authored-by: Jesse Gross <jesse@ollama.com> Co-authored-by: Daniel Hiltgen <daniel@ollama.com> * cache: Clear old KV cache entries when evicting a slot When forking a cache entry, if no empty slots are available we evict the least recently used one and copy over the KV entries from the closest match. However, this copy does not overwrite existing values but only adds new ones. Therefore, we need to clear the old slot first. This change fixes two issues: - The KV cache fills up and runs out of space even though we think we are managing it correctly - Performance gets worse over time as we use new cache entries that are not hot in the processor caches * doc: explain golang objc linker warning (#6830) * llama: gather transitive dependencies for rocm for dist packaging (#6848) * Refine go server makefiles to be more DRY (#6924) This breaks up the monolithic Makefile for the Go based runners into a set of utility files as well as recursive Makefiles for the runners. Files starting with the name "Makefile" are buildable, while files that end with ".make" are utilities to include in other Makefiles. This reduces the amount of nearly identical targets and helps set a pattern for future community contributions for new GPU runner architectures. When we are ready to switch over to the Go runners, these files should move to the top of the repo, and we should add targets for the main CLI, as well as a helper "install" (put all the built binaries on the local system in a runnable state) and "dist" target (generate the various tar/zip files for distribution) for local developer use. * llama: don't create extraneous directories (#6988) * llama: Exercise the new build in CI (#6989) Wire up some basic sanity testing in CI for the Go runner. GPU runners are not covered yet. * llama: Refine developer docs for Go server (#6842) This enhances the documentation for development focusing on the new Go server. After we complete the transition further doc refinements can remove the "transition" discussion. * runner.go: Allocate batches for all sequences during init We should tell the model that we could have full batches for all sequences. We already do this when we allocate the batches but it was missed during initialization. * llama.go: Don't return nil from Tokenize on zero length input Potentially receiving nil in a non-error condition is surprising to most callers - it's better to return an empty slice. * runner.go: Remove stop tokens from cache If the last token is EOG then we don't return this and it isn't present in the cache (because it was never submitted to Decode). This works well for extending the cache entry with a new sequence. However, for multi-token stop sequences, we won't return any of the tokens but all but the last one will be in the cache. This means when the conversation continues the cache will contain tokens that don't overlap with the new prompt. This works (we will pick up the portion where there is overlap) but it causes unnecessary cache thrashing because we will fork the original cache entry as it is not a perfect match. By trimming the cache to the tokens that we actually return this issue can be avoided. * runner.go: Simplify flushing of pending tokens * runner.go: Update TODOs * runner.go: Don't panic when processing sequences If there is an error processing a sequence, we should return a clean HTTP error back to Ollama rather than panicing. This will make us more resilient to transient failures. Panics can still occur during startup as there is no way to serve requests if that fails. Co-authored-by: jmorganca <jmorganca@gmail.com> * runner.go: More accurately capture timings Currently prompt processing time doesn't capture the that it takes to tokenize the input, only decoding time. We should capture the full process to more accurately reflect reality. This is especially true once we start processing images where the initial processing can take significant time. This is also more consistent with the existing C++ runner. * runner.go: Support for vision models In addition to bringing feature parity with the C++ runner, this also incorporates several improvements: - Cache prompting works with images, avoiding the need to re-decode embeddings for every message in a conversation - Parallelism is supported, avoiding the need to restrict to one sequence at a time. (Though for now Ollama will not schedule them while we might need to fall back to the old runner.) Co-authored-by: jmorganca <jmorganca@gmail.com> * runner.go: Move Unicode checking code and add tests * runner.go: Export external cache members Runner and cache are in the same package so the change doesn't affect anything but it is more internally consistent. * runner.go: Image embedding cache Generating embeddings from images can take significant time (on my machine between 100ms and 8s depending on the model). Although we already cache the result of decoding these images, the embeddings need to be regenerated every time. This is not necessary if we get the same image over and over again, for example, during a conversation. This currently uses a very small cache with a very simple algorithm but it is easy to improve as is warranted. * llama: catch up on patches Carry forward solar-pro and cli-unicode patches * runner.go: Don't re-allocate memory for every batch We can reuse memory allocated from batch to batch since batch size is fixed. This both saves the cost of reallocation as well keeps the cache lines hot. This results in a roughly 1% performance improvement for token generation with Nvidia GPUs on Linux. * runner.go: Default to classic input cache policy The input cache as part of the go runner implemented a cache policy that aims to maximize hit rate in both single and multi- user scenarios. When there is a cache hit, the response is very fast. However, performance is actually slower when there is an input cache miss due to worse GPU VRAM locality. This means that performance is generally better overall for multi-user scenarios (better input cache hit rate, locality was relatively poor already). But worse for single users (input cache hit rate is about the same, locality is now worse). This defaults the policy back to the old one to avoid a regression but keeps the new one available through an environment variable OLLAMA_MULTIUSER_CACHE. This is left undocumented as the goal is to improve this in the future to get the best of both worlds without user configuration. For inputs that result in cache misses, on Nvidia/Linux this change improves performance by 31% for prompt processing and 13% for token generation. * runner.go: Increase size of response channel Generally the CPU can easily keep up with handling reponses that are generated but there's no reason not to let generation continue and handle things in larger batches if needed. * llama: Add CI to verify all vendored changes have patches (#7066) Make sure we don't accidentally merge changes in the vendored code that aren't also reflected in the patches. * llama: adjust clip patch for mingw utf-16 (#7065) * llama: adjust clip patch for mingw utf-16 * llama: ensure static linking of runtime libs Avoid runtime dependencies on non-standard libraries * runner.go: Enable llamafile (all platforms) and BLAS (Mac OS) These are two features that are shown on llama.cpp's system info that are currently different between the two runners. On my test systems the performance difference is very small to negligible but it is probably still good to equalize the features. * llm: Don't add BOS/EOS for tokenize requests This is consistent with what server.cpp currently does. It affects things like token processing counts for embedding requests. * runner.go: Don't cache prompts for embeddings Our integration with server.cpp implicitly disables prompt caching because it is not part of the JSON object being parsed, this makes the Go runner behavior similarly. Prompt caching has been seen to affect the results of text completions on certain hardware. The results are not wrong either way but they are non-deterministic. However, embeddings seem to be affected even on hardware that does not show this behavior for completions. For now, it is best to maintain consistency with the existing behavior. * runner.go: Adjust debug log levels Add system info printed at startup and quiet down noisier logging. * llama: fix compiler flag differences (#7082) Adjust the flags for the new Go server to more closely match the generate flow * llama: refine developer docs (#7121) * llama: doc and example clean up (#7122) * llama: doc and example clean up * llama: Move new dockerfile into llama dir Temporary home until we fully transition to the Go server * llama: runner doc cleanup * llama.go: Add description for Tokenize error case --------- Co-authored-by: Jesse Gross <jesse@ollama.com> Co-authored-by: Daniel Hiltgen <daniel@ollama.com> Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
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515 lines
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Vendored
/**
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* llama.cpp - commit 8962422b1c6f9b8b15f5aeaea42600bcc2d44177 - do not edit this file
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*
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* MIT License
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*
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* Copyright (c) 2023-2024 The ggml authors
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "cpy.cuh"
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typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
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static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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float * dsti = (float *) cdsti;
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*dsti = *xi;
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}
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static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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half * dsti = (half *) cdsti;
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*dsti = __float2half(*xi);
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}
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static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) {
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const half * xi = (const half *) cxi;
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half * dsti = (half *) cdsti;
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*dsti = *xi;
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}
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static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) {
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const half * xi = (const half *) cxi;
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float * dsti = (float *) cdsti;
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*dsti = *xi;
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}
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template <cpy_kernel_t cpy_1>
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static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
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const int nb12, const int nb13) {
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const int64_t i = blockDim.x*blockIdx.x + threadIdx.x;
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if (i >= ne) {
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return;
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}
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// determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
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// then combine those indices with the corresponding byte offsets to get the total offsets
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const int64_t i03 = i/(ne00 * ne01 * ne02);
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const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
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const int64_t i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
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const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
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const int64_t x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
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const int64_t i13 = i/(ne10 * ne11 * ne12);
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const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
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const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
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const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
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const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
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cpy_1(cx + x_offset, cdst + dst_offset);
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}
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static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_q8_0 * dsti = (block_q8_0 *) cdsti;
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float amax = 0.0f; // absolute max
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for (int j = 0; j < QK8_0; j++) {
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const float v = xi[j];
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amax = fmaxf(amax, fabsf(v));
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}
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const float d = amax / ((1 << 7) - 1);
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const float id = d ? 1.0f/d : 0.0f;
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dsti->d = d;
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for (int j = 0; j < QK8_0; ++j) {
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const float x0 = xi[j]*id;
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dsti->qs[j] = roundf(x0);
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}
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}
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static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_q4_0 * dsti = (block_q4_0 *) cdsti;
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float amax = 0.0f;
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float vmax = 0.0f;
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for (int j = 0; j < QK4_0; ++j) {
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const float v = xi[j];
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if (amax < fabsf(v)) {
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amax = fabsf(v);
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vmax = v;
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}
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}
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const float d = vmax / -8;
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const float id = d ? 1.0f/d : 0.0f;
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dsti->d = d;
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for (int j = 0; j < QK4_0/2; ++j) {
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const float x0 = xi[0 + j]*id;
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const float x1 = xi[QK4_0/2 + j]*id;
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const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f));
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const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f));
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dsti->qs[j] = xi0;
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dsti->qs[j] |= xi1 << 4;
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}
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}
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static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_q4_1 * dsti = (block_q4_1 *) cdsti;
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float vmin = FLT_MAX;
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float vmax = -FLT_MAX;
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for (int j = 0; j < QK4_1; ++j) {
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const float v = xi[j];
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if (v < vmin) vmin = v;
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if (v > vmax) vmax = v;
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}
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const float d = (vmax - vmin) / ((1 << 4) - 1);
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const float id = d ? 1.0f/d : 0.0f;
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dsti->dm.x = d;
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dsti->dm.y = vmin;
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for (int j = 0; j < QK4_1/2; ++j) {
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const float x0 = (xi[0 + j] - vmin)*id;
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const float x1 = (xi[QK4_1/2 + j] - vmin)*id;
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const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f));
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const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f));
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dsti->qs[j] = xi0;
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dsti->qs[j] |= xi1 << 4;
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}
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}
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static __device__ void cpy_blck_f32_q5_0(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_q5_0 * dsti = (block_q5_0 *) cdsti;
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float amax = 0.0f;
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float vmax = 0.0f;
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for (int j = 0; j < QK5_0; ++j) {
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const float v = xi[j];
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if (amax < fabsf(v)) {
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amax = fabsf(v);
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vmax = v;
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}
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}
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const float d = vmax / -16;
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const float id = d ? 1.0f/d : 0.0f;
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dsti->d = d;
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uint32_t qh = 0;
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for (int j = 0; j < QK5_0/2; ++j) {
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const float x0 = xi[0 + j]*id;
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const float x1 = xi[QK5_0/2 + j]*id;
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const uint8_t xi0 = min(31, (int8_t)(x0 + 16.5f));
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const uint8_t xi1 = min(31, (int8_t)(x1 + 16.5f));
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dsti->qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
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qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
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qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
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}
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memcpy(dsti->qh, &qh, sizeof(qh));
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}
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static __device__ void cpy_blck_f32_q5_1(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_q5_1 * dsti = (block_q5_1 *) cdsti;
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float min = xi[0];
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float max = xi[0];
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for (int j = 1; j < QK5_1; ++j) {
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const float v = xi[j];
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min = v < min ? v : min;
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max = v > max ? v : max;
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}
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const float d = (max - min) / 31;
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const float id = d ? 1.0f/d : 0.0f;
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dsti->dm.x = d;
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dsti->dm.y = min;
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uint32_t qh = 0;
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for (int j = 0; j < QK5_1/2; ++j) {
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const float x0 = (xi[0 + j] - min)*id;
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const float x1 = (xi[QK5_1/2 + j] - min)*id;
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const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
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const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
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dsti->qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
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qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
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qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2);
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}
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memcpy(dsti->qh, &qh, sizeof(qh));
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}
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static __device__ __forceinline__ int best_index_int8(int n, const int8_t * val, float x) {
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if (x <= val[0]) return 0;
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if (x >= val[n-1]) return n-1;
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int ml = 0, mu = n-1;
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while (mu-ml > 1) {
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int mav = (ml+mu)/2;
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if (x < val[mav]) mu = mav; else ml = mav;
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}
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return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
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}
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static __device__ void cpy_blck_f32_iq4_nl(const char * cxi, char * cdsti) {
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const float * xi = (const float *) cxi;
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block_iq4_nl * dsti = (block_iq4_nl *) cdsti;
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float amax = 0.0f;
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float vmax = 0.0f;
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for (int j = 0; j < QK4_NL; ++j) {
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const float v = xi[j];
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if (amax < fabsf(v)) {
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amax = fabsf(v);
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vmax = v;
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}
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}
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float d = vmax / kvalues_iq4nl[0];
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const float id = d ? 1.0f/d : 0.0f;
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float sumqx = 0, sumq2 = 0;
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for (int j = 0; j < QK4_NL/2; ++j) {
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const float x0 = xi[0 + j]*id;
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const float x1 = xi[QK4_NL/2 + j]*id;
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const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl, x0);
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const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl, x1);
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dsti->qs[j] = xi0 | (xi1 << 4);
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const float v0 = kvalues_iq4nl[xi0];
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const float v1 = kvalues_iq4nl[xi1];
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const float w0 = xi[0 + j]*xi[0 + j];
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const float w1 = xi[QK4_NL/2 + j]*xi[QK4_NL/2 + j];
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sumqx += w0*v0*xi[j] + w1*v1*xi[QK4_NL/2 + j];
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sumq2 += w0*v0*v0 + w1*v1*v1;
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}
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dsti->d = sumq2 > 0 ? sumqx/sumq2 : d;
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}
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template <cpy_kernel_t cpy_blck, int qk>
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static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne,
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const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
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const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
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const int nb12, const int nb13) {
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const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk;
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if (i >= ne) {
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return;
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}
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const int i03 = i/(ne00 * ne01 * ne02);
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const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
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const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
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const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
|
|
const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
|
|
|
|
const int i13 = i/(ne10 * ne11 * ne12);
|
|
const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
|
|
const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
|
|
const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
|
|
const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
|
|
|
|
cpy_blck(cx + x_offset, cdst + dst_offset);
|
|
}
|
|
|
|
static void ggml_cpy_f16_f32_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
|
|
cpy_f32_f16<cpy_1_f16_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f32_f32_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
|
|
cpy_f32_f16<cpy_1_f32_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f32_f16_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
|
|
cpy_f32_f16<cpy_1_f32_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f32_q8_0_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
GGML_ASSERT(ne % QK8_0 == 0);
|
|
const int num_blocks = ne / QK8_0;
|
|
cpy_f32_q<cpy_blck_f32_q8_0, QK8_0><<<num_blocks, 1, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f32_q4_0_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
GGML_ASSERT(ne % QK4_0 == 0);
|
|
const int num_blocks = ne / QK4_0;
|
|
cpy_f32_q<cpy_blck_f32_q4_0, QK4_0><<<num_blocks, 1, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f32_q4_1_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
GGML_ASSERT(ne % QK4_1 == 0);
|
|
const int num_blocks = ne / QK4_1;
|
|
cpy_f32_q<cpy_blck_f32_q4_1, QK4_1><<<num_blocks, 1, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f32_q5_0_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
GGML_ASSERT(ne % QK5_0 == 0);
|
|
const int num_blocks = ne / QK5_0;
|
|
cpy_f32_q<cpy_blck_f32_q5_0, QK5_0><<<num_blocks, 1, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f32_q5_1_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
GGML_ASSERT(ne % QK5_1 == 0);
|
|
const int num_blocks = ne / QK5_1;
|
|
cpy_f32_q<cpy_blck_f32_q5_1, QK5_1><<<num_blocks, 1, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f32_iq4_nl_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
GGML_ASSERT(ne % QK4_NL == 0);
|
|
const int num_blocks = ne / QK4_NL;
|
|
cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL><<<num_blocks, 1, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
static void ggml_cpy_f16_f16_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
|
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
|
|
|
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
|
|
cpy_f32_f16<cpy_1_f16_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
|
}
|
|
|
|
void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1) {
|
|
const int64_t ne = ggml_nelements(src0);
|
|
GGML_ASSERT(ne == ggml_nelements(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];
|
|
|
|
//GGML_ASSERT(src0->ne[3] == 1);
|
|
|
|
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];
|
|
|
|
//GGML_ASSERT(src1->ne[3] == 1);
|
|
|
|
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];
|
|
|
|
cudaStream_t main_stream = ctx.stream();
|
|
|
|
char * src0_ddc = (char *) src0->data;
|
|
char * src1_ddc = (char *) src1->data;
|
|
|
|
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
|
|
ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
|
|
ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
|
|
ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
|
|
ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
|
|
ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) {
|
|
ggml_cpy_f32_q5_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) {
|
|
ggml_cpy_f32_iq4_nl_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) {
|
|
ggml_cpy_f32_q5_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
|
|
ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
|
|
ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
|
|
} else {
|
|
fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
|
|
ggml_type_name(src0->type), ggml_type_name(src1->type));
|
|
GGML_ABORT("fatal error");
|
|
}
|
|
}
|
|
|
|
void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
|
const ggml_tensor * src0 = dst->src[0];
|
|
ggml_cuda_cpy(ctx, src0, dst);
|
|
}
|
|
|
|
void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
|
|
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
|
|
return (void*) cpy_f32_f16<cpy_1_f32_f32>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
|
|
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
|
|
return (void*) cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
|
|
return (void*) cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
|
|
return (void*) cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) {
|
|
return (void*) cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) {
|
|
return (void*) cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>;
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) {
|
|
return (void*) cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>;
|
|
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
|
|
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
|
|
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
|
|
return (void*) cpy_f32_f16<cpy_1_f16_f32>;
|
|
} else {
|
|
fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
|
|
ggml_type_name(src0->type), ggml_type_name(src1->type));
|
|
GGML_ABORT("fatal error");
|
|
}
|
|
}
|