mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-13 14:29:52 +00:00
557410b8f0
* llama : greatly reduce logits memory usage * llama : more compact state saving and reloading * llama : fix lctx.n_outputs not being set before building graph * perplexity : adapt to the logits API changes * perplexity : fix Winogrande, use correct logits for second choice start The first logits used to evaluate the second choice were not from the end of the common prefix; instead, they were the logits from the end of the first choice. This has been corrected. The previous implementation sometimes had outliers in the scores of choices for some tasks, and the logic to skip choices words in the log-likelihood evaluation probably was an attempt to reduce those, but it was complex and didn't quite seem to be the right thing. This is simpler now, and the outlier scores aren't there anymore. * perplexity : normalize spaces and punctuation in Winogrande sentences * llama : fix embedding conditions * llama : fix llama_get_embeddings_ith when the resulting id is 0 * llama : fix wrong n_outputs in llama_set_inputs A mismatch happened when using a smaller n_ubatch than n_batch and then using llama_batch_get_one(). The decision of what n_outputs should be now almost fully depends on how lctx.n_outputs is set in llama_decode_internal. The conditions are simpler this way. * llama : when saving the state, recalculate n_outputs This ensures the correct number of outputs for the entire previous batch is stored in the session file, even when n_ubatch is smaller than n_batch. * llama : fix not-skipping outputs of non-causal models * llama : fix running a batch with n_outputs == 0 It previously worked because lctx.inp_out_ids was not initialized, so it pointed to some garbage address which was somehow still valid when I ran my tests. * llama : keep same graph topology even when n_outputs == 0 * ggml : saner ggml_can_repeat with empty tensors * ggml : future-proof ggml_is_empty by using GGML_MAX_DIMS - 1 * ggml : do not multi-thread ops returning empty tensors * ggml : make ggml_is_empty public and work with views * llama : use a vector for ctx->output_ids * llama : rework reallocation logic for llama_output_reserve Now comparing the actual size with the new total size of the output buffer to allow more efficient enabling and disabling of the embeddings and/or logits output in the future. * ggml : skip empty tensors in all backends * llama : fix llama_output_reserve nullptr deref when new_size is 0 * perplexity : make Winogrande work as it does on master The problems with the Winogrande implementation will need to be fixed in a separate PR to ease review. * llama : clearer error messages for invalid logits or embeddings ids * llama : assert all models that can have inp_out_ids Since the graph topology is now constant, this presence check can be done even when there are no outputs. * llama : assert logits and embd buffers exist before writing to them * llama : handle errors from llama_output_reserve at call sites * perplexity : make hellaswag and multiple-choice outputs identical to master Due to how the KV cache is updated, the logprobs for tokens in a batch are very slightly affected by the other tokens present in the batch, so to make hellaswag and multiple-choice return exactly the same results as on master, the last token of each sequence needs to be evaluated even though its output is not used at all. This will probably be changed back in the future to make these benchmarks a tiny bit faster. * perplexity : fix division by zero when using less than 100 multiple-choice tasks * llama : allow loading state saved with a different ctx size When loading a session file, the context size is now only required to be at least enough to load the KV cells contained in that session file, instead of requiring to use exactly the same context size as when saving. Doing this enables the use-case of extending or shrinking the context size of a saved session. This breaks existing session files because the meaning of kv_buf_size is slightly changed (previously it was the size of the whole KV cache, now it's only the size of the saved part of it). This allows for finer-grained sanity checks when loading in an effort to keep kv_buf_size useful even when the kv_size is changed. * llama : minor ggml-ci * readme : update recent API changes, and warn about Vulkan --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
6422 lines
295 KiB
C++
6422 lines
295 KiB
C++
#include "ggml-vulkan.h"
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#ifdef GGML_VULKAN_RUN_TESTS
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#include <chrono>
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#endif
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#include <vulkan/vulkan.hpp>
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#include <algorithm>
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#include <cmath>
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#include <iostream>
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#include <iomanip>
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#include <limits>
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#include <tuple>
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#include <vector>
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#include <sstream>
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#include <utility>
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#include <memory>
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#include "ggml.h"
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#include "ggml-backend-impl.h"
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#include "ggml-vulkan-shaders.hpp"
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#define VK_API_VERSION VK_API_VERSION_1_2
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#define CEIL_DIV(M, N) (((M) + (N)-1) / (N))
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#define VK_VENDOR_ID_AMD 0x1002
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#define VK_VENDOR_ID_APPLE 0x106b
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#define VK_VENDOR_ID_INTEL 0x8086
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#define VK_VENDOR_ID_NVIDIA 0x10de
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#define VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN 0
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#define VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI 1
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#define VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE 2
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#define VK_NUM_TYPES 16
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#define GGML_VK_MAX_NODES 8192
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#define MAX_VK_BUFFERS 256
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#ifndef K_QUANTS_PER_ITERATION
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#define K_QUANTS_PER_ITERATION 1
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#else
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static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
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#endif
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#define VK_CHECK(err, msg) \
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do { \
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vk::Result err_ = (err); \
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if (err_ != vk::Result::eSuccess) { \
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fprintf(stderr, "ggml_vulkan: %s error %s at %s:%d\n", \
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#err, to_string(err_).c_str(), __FILE__, __LINE__); \
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exit(1); \
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} \
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} while (0)
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struct ggml_backend_vk_context;
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struct vk_queue {
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uint32_t queue_family_index;
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vk::Queue queue;
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vk::CommandPool pool;
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uint32_t cmd_buffer_idx;
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std::vector<vk::CommandBuffer> cmd_buffers;
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vk::PipelineStageFlags stage_flags;
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};
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struct vk_pipeline_struct {
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std::string name;
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vk::ShaderModule shader_module;
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vk::DescriptorSetLayout dsl;
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std::vector<vk::DescriptorPool> descriptor_pools;
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std::vector<vk::DescriptorSet> descriptor_sets;
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uint32_t descriptor_set_idx;
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vk::PipelineLayout layout;
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vk::Pipeline pipeline;
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uint32_t push_constant_size;
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uint32_t parameter_count;
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std::array<uint32_t, 3> wg_denoms;
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uint32_t align;
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};
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typedef std::shared_ptr<vk_pipeline_struct> vk_pipeline;
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typedef std::weak_ptr<vk_pipeline_struct> vk_pipeline_ref;
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static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline);
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struct vk_matmul_pipeline_struct {
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vk_pipeline l, m, s;
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vk_pipeline a_l, a_m, a_s;
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};
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typedef std::shared_ptr<vk_matmul_pipeline_struct> vk_matmul_pipeline;
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struct vk_device {
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vk::PhysicalDevice physical_device;
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vk::PhysicalDeviceProperties properties;
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std::string name;
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uint64_t max_memory_allocation_size;
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bool fp16;
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vk::Device device;
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uint32_t vendor_id;
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vk_queue compute_queue;
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vk_queue transfer_queue;
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bool single_queue;
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uint32_t descriptor_set_mode;
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uint32_t subgroup_size;
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bool uma;
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bool initialized;
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size_t idx;
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vk_matmul_pipeline pipeline_matmul_f32;
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vk_matmul_pipeline pipeline_matmul_f16;
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vk_matmul_pipeline pipeline_matmul_f16_f32;
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vk_pipeline pipeline_matmul_split_k_reduce;
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vk_matmul_pipeline pipeline_dequant_mul_mat_mat[VK_NUM_TYPES];
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vk_pipeline pipeline_dequant[VK_NUM_TYPES];
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vk_pipeline pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES];
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vk_pipeline pipeline_mul_mat_vec_p021_f16_f32;
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vk_pipeline pipeline_mul_mat_vec_nc_f16_f32;
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vk_pipeline pipeline_get_rows[VK_NUM_TYPES];
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vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES];
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vk_pipeline pipeline_mul_f32;
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vk_pipeline pipeline_add_f32;
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vk_pipeline pipeline_scale_f32;
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vk_pipeline pipeline_sqr_f32;
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vk_pipeline pipeline_clamp_f32;
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vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16;
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vk_pipeline pipeline_norm_f32;
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vk_pipeline pipeline_rms_norm_f32;
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vk_pipeline pipeline_gelu_f32;
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vk_pipeline pipeline_silu_f32;
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vk_pipeline pipeline_relu_f32;
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vk_pipeline pipeline_diag_mask_inf_f32;
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vk_pipeline pipeline_soft_max_f32;
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vk_pipeline pipeline_rope_f32, pipeline_rope_f16;
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vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
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vk_pipeline pipeline_argsort_f32;
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std::vector<vk_pipeline_ref> pipelines;
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~vk_device() {
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#ifdef GGML_VULKAN_DEBUG
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std::cerr << "destroy device " << name << std::endl;
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#endif
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device.destroyCommandPool(compute_queue.pool);
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if (!single_queue) {
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device.destroyCommandPool(transfer_queue.pool);
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}
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for (auto& pipeline : pipelines) {
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if (pipeline.expired()) {
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continue;
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}
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vk_pipeline pl = pipeline.lock();
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ggml_vk_destroy_pipeline(device, pl);
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}
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pipelines.clear();
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device.destroy();
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}
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};
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struct vk_buffer_struct {
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vk::Buffer buffer;
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vk::DeviceMemory device_memory;
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vk::MemoryPropertyFlags memory_property_flags;
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void * ptr;
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size_t size = 0;
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ggml_backend_vk_context * ctx;
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std::shared_ptr<vk_device> device;
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~vk_buffer_struct() {
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if (size == 0) {
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return;
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}
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#ifdef GGML_VULKAN_DEBUG
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std::cerr << "~vk_buffer_struct(" << buffer << ", " << size << ")" << std::endl;
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#endif
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device->device.freeMemory(device_memory);
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device->device.destroyBuffer(buffer);
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}
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};
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typedef std::shared_ptr<vk_buffer_struct> vk_buffer;
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typedef std::weak_ptr<vk_buffer_struct> vk_buffer_ref;
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struct vk_subbuffer {
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vk_buffer buffer;
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uint64_t offset;
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uint64_t size;
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};
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struct vk_semaphore {
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vk::Semaphore s;
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uint64_t value;
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};
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struct vk_submission {
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vk::CommandBuffer buffer;
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std::vector<vk_semaphore> wait_semaphores;
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std::vector<vk_semaphore> signal_semaphores;
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};
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typedef std::vector<vk_submission> vk_sequence;
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struct vk_op_push_constants {
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uint32_t KX;
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uint32_t KY;
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float param1;
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float param2;
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};
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struct vk_op_unary_push_constants {
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uint32_t ne;
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uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
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uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
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uint32_t d_offset;
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float param1; float param2;
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};
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struct vk_op_binary_push_constants {
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uint32_t ne;
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uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
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uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
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uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23;
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uint32_t d_offset;
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float param1; float param2;
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};
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struct vk_op_diag_mask_push_constants {
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uint32_t ncols;
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uint32_t rows_per_channel;
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int32_t n_past;
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};
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struct vk_op_rope_push_constants {
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uint32_t ncols;
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float freq_scale;
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uint32_t p_delta_rows;
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float freq_base;
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float ext_factor;
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float attn_factor;
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float corr_dims[4];
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};
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struct vk_op_rope_neox_push_constants {
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uint32_t ncols;
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uint32_t ndims;
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float freq_scale;
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uint32_t p_delta_rows;
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float freq_base;
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float ext_factor;
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float attn_factor;
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float corr_dims[4];
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float theta_scale;
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float inv_ndims;
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};
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struct vk_op_soft_max_push_constants {
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uint32_t KX;
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uint32_t KY;
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uint32_t KZ;
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float scale;
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float max_bias;
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float m0;
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float m1;
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uint32_t n_head_log2;
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};
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struct vk_op_argsort_push_constants {
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uint32_t ncols;
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bool ascending;
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};
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// Allow pre-recording command buffers
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struct vk_staging_memcpy {
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vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}
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void * dst;
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const void * src;
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size_t n;
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};
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struct vk_context {
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size_t idx;
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vk_submission * s;
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std::vector<vk_sequence> seqs;
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ggml_tensor * exit_tensor;
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std::vector<vk_staging_memcpy> in_memcpys;
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std::vector<vk_staging_memcpy> out_memcpys;
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vk_queue * q;
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};
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struct ggml_tensor_extra_gpu {
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bool ready;
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size_t ctx_idx;
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vk_buffer_ref buffer_gpu;
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uint64_t offset;
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void reset() {
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ready = false;
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ctx_idx = 0;
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buffer_gpu.reset();
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offset = 0;
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}
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};
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struct ggml_vk_garbage_collector {
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std::vector<vk_semaphore> tl_semaphores;
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std::vector<vk_semaphore> semaphores;
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std::vector<vk::Event> events;
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std::vector<vk_buffer> temp_buffers;
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std::vector<vk_context> contexts;
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};
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struct ggml_backend_vk_context {
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std::string name;
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std::shared_ptr<vk_device> device;
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size_t semaphore_idx, event_idx;
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ggml_vk_garbage_collector gc;
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std::vector<std::tuple<void*, size_t, vk_buffer>> pinned_memory;
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size_t prealloc_size_qx, prealloc_size_qy, prealloc_size_x, prealloc_size_y, prealloc_size_split_k;
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vk_buffer prealloc_qx, prealloc_qy, prealloc_x, prealloc_y, prealloc_split_k;
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vk::Fence fence;
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vk_buffer staging;
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size_t staging_size;
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size_t staging_offset;
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vk_buffer sync_staging;
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vk_buffer buffer_pool[MAX_VK_BUFFERS];
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vk_context * compute_ctx;
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vk_context * transfer_ctx;
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bool disable;
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bool initialized;
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size_t idx;
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};
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struct vk_instance {
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vk::Instance instance;
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std::vector<size_t> device_indices;
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ggml_backend_t backends[GGML_VK_MAX_DEVICES];
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ggml_backend_vk_context contexts[GGML_VK_MAX_DEVICES];
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ggml_backend_buffer_type buffer_types[GGML_VK_MAX_DEVICES];
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bool initialized[GGML_VK_MAX_DEVICES];
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};
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static std::shared_ptr<vk_device> ggml_vk_get_device(size_t idx) {
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#ifdef GGML_VULKAN_DEBUG
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std::cerr << "ggml_vk_get_device(" << idx << ")" << std::endl;
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#endif
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static std::weak_ptr<vk_device> devices[GGML_VK_MAX_DEVICES];
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if (devices[idx].expired()) {
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#ifdef GGML_VULKAN_DEBUG
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std::cerr << "Initializing new vk_device" << std::endl;
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#endif
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std::shared_ptr<vk_device> device = std::make_shared<vk_device>();
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device->initialized = false;
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devices[idx] = device;
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return device;
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}
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return devices[idx].lock();
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}
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#ifdef GGML_VULKAN_CHECK_RESULTS
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static size_t vk_skip_checks;
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static size_t vk_output_tensor;
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static void ggml_vk_print_tensor(ggml_backend * ctx, const ggml_tensor * tensor, const char * name);
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static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor);
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static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor);
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#endif
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typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
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static bool vk_instance_initialized = false;
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static vk_instance vk_instance;
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GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend);
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static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t>&& specialization_constants, uint32_t align) {
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#ifdef GGML_VULKAN_DEBUG
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std::cerr << "ggml_vk_create_pipeline(" << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")" << std::endl;
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#endif
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GGML_ASSERT(parameter_count > 0);
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GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT
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pipeline = std::make_shared<vk_pipeline_struct>();
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pipeline->name = name;
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pipeline->parameter_count = parameter_count;
|
|
pipeline->push_constant_size = push_constant_size;
|
|
pipeline->wg_denoms = wg_denoms;
|
|
pipeline->align = align;
|
|
|
|
vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast<const uint32_t *>(spv_data));
|
|
pipeline->shader_module = ctx->device->device.createShaderModule(shader_module_create_info);
|
|
|
|
std::vector<vk::DescriptorSetLayoutBinding> dsl_binding;
|
|
std::vector<vk::DescriptorBindingFlags> dsl_binding_flags;
|
|
for (uint32_t i = 0; i < parameter_count; i++) {
|
|
dsl_binding.push_back({i, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute});
|
|
dsl_binding_flags.push_back({});
|
|
}
|
|
|
|
vk::DescriptorSetLayoutBindingFlagsCreateInfo dslbfci = { dsl_binding_flags };
|
|
|
|
vk::PushConstantRange pcr(
|
|
vk::ShaderStageFlagBits::eCompute,
|
|
0,
|
|
pipeline->push_constant_size
|
|
);
|
|
|
|
vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info(
|
|
{},
|
|
dsl_binding);
|
|
descriptor_set_layout_create_info.setPNext(&dslbfci);
|
|
pipeline->dsl = ctx->device->device.createDescriptorSetLayout(descriptor_set_layout_create_info);
|
|
|
|
// Check if device supports multiple descriptors per pool
|
|
if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) {
|
|
const uint32_t alloc_count = 2;
|
|
|
|
// Try allocating multiple sets from one pool
|
|
// This fails on AMD for some reason, so add a fall back to allocating one pool per set
|
|
vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count);
|
|
vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, alloc_count, descriptor_pool_size);
|
|
vk::DescriptorPool pool = ctx->device->device.createDescriptorPool(descriptor_pool_create_info);
|
|
|
|
std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
|
|
for (uint32_t i = 0; i < alloc_count; i++) {
|
|
layouts[i] = pipeline->dsl;
|
|
}
|
|
try {
|
|
vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pool, alloc_count, layouts.data());
|
|
std::vector<vk::DescriptorSet> sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info);
|
|
} catch(vk::OutOfPoolMemoryError const&) {
|
|
ctx->device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE;
|
|
}
|
|
|
|
ctx->device->device.destroyDescriptorPool(pool);
|
|
}
|
|
|
|
if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) {
|
|
vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count);
|
|
vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 128, descriptor_pool_size);
|
|
pipeline->descriptor_pools.push_back(ctx->device->device.createDescriptorPool(descriptor_pool_create_info));
|
|
}
|
|
|
|
pipeline->descriptor_set_idx = 0;
|
|
|
|
vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline->dsl, pcr);
|
|
pipeline->layout = ctx->device->device.createPipelineLayout(pipeline_layout_create_info);
|
|
|
|
std::vector<vk::SpecializationMapEntry> specialization_entries(specialization_constants.size());
|
|
|
|
for (size_t i = 0; i < specialization_constants.size(); i++) {
|
|
specialization_entries[i].constantID = i;
|
|
specialization_entries[i].offset = i * sizeof(uint32_t);
|
|
specialization_entries[i].size = sizeof(uint32_t);
|
|
}
|
|
|
|
vk::SpecializationInfo specialization_info(
|
|
specialization_entries.size(),
|
|
specialization_entries.data(),
|
|
specialization_constants.size() * sizeof(uint32_t),
|
|
specialization_constants.data()
|
|
);
|
|
|
|
vk::PipelineShaderStageCreateInfo pipeline_shader_create_info(
|
|
vk::PipelineShaderStageCreateFlags(),
|
|
vk::ShaderStageFlagBits::eCompute,
|
|
pipeline->shader_module,
|
|
entrypoint.c_str(),
|
|
&specialization_info);
|
|
vk::ComputePipelineCreateInfo compute_pipeline_create_info(
|
|
vk::PipelineCreateFlags(),
|
|
pipeline_shader_create_info,
|
|
pipeline->layout);
|
|
pipeline->pipeline = ctx->device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
|
|
|
|
ctx->device->pipelines.push_back(pipeline);
|
|
}
|
|
|
|
static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_pipeline_destroy_pipeline(" << pipeline->name << ")" << std::endl;
|
|
#endif
|
|
for (auto& pool : pipeline->descriptor_pools) {
|
|
device.destroyDescriptorPool(pool);
|
|
}
|
|
pipeline->descriptor_pools.clear();
|
|
pipeline->descriptor_sets.clear();
|
|
pipeline->descriptor_set_idx = 0;
|
|
|
|
device.destroyDescriptorSetLayout(pipeline->dsl);
|
|
|
|
device.destroyPipelineLayout(pipeline->layout);
|
|
|
|
device.destroyShaderModule(pipeline->shader_module);
|
|
|
|
device.destroyPipeline(pipeline->pipeline);
|
|
}
|
|
|
|
static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, uint32_t n) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")" << std::endl;
|
|
#endif
|
|
if (pipeline->descriptor_sets.size() >= pipeline->descriptor_set_idx + n) {
|
|
// Enough descriptors are available
|
|
return;
|
|
}
|
|
|
|
if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) {
|
|
const uint32_t alloc_count = pipeline->descriptor_set_idx + n - pipeline->descriptor_sets.size();
|
|
|
|
std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
|
|
for (uint32_t i = 0; i < alloc_count; i++) {
|
|
layouts[i] = pipeline->dsl;
|
|
}
|
|
vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[0], alloc_count, layouts.data());
|
|
std::vector<vk::DescriptorSet> sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info);
|
|
pipeline->descriptor_sets.insert(pipeline->descriptor_sets.end(), sets.begin(), sets.end());
|
|
} else {
|
|
for (uint32_t i = pipeline->descriptor_sets.size(); i < pipeline->descriptor_set_idx + n; i++) {
|
|
vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count);
|
|
vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 1, descriptor_pool_size);
|
|
pipeline->descriptor_pools.push_back(ctx->device->device.createDescriptorPool(descriptor_pool_create_info));
|
|
|
|
vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[i], 1, &pipeline->dsl);
|
|
std::vector<vk::DescriptorSet> sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info);
|
|
pipeline->descriptor_sets.push_back(sets[0]);
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_pipeline_cleanup(vk_pipeline& pipeline) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_pipeline_cleanup(" << pipeline->name << ")" << std::endl;
|
|
#endif
|
|
pipeline->descriptor_set_idx = 0;
|
|
}
|
|
|
|
static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx, vk_queue& q) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_create_cmd_buffer()" << std::endl;
|
|
#endif
|
|
if (q.cmd_buffers.size() > q.cmd_buffer_idx) {
|
|
// Reuse command buffer
|
|
return q.cmd_buffers[q.cmd_buffer_idx++];
|
|
}
|
|
|
|
vk::CommandBufferAllocateInfo command_buffer_alloc_info(
|
|
q.pool,
|
|
vk::CommandBufferLevel::ePrimary,
|
|
1);
|
|
const std::vector<vk::CommandBuffer> cmd_buffers = ctx->device->device.allocateCommandBuffers(command_buffer_alloc_info);
|
|
auto buf = cmd_buffers.front();
|
|
|
|
q.cmd_buffers.push_back(buf);
|
|
q.cmd_buffer_idx++;
|
|
|
|
return buf;
|
|
}
|
|
|
|
static vk_submission ggml_vk_create_submission(ggml_backend_vk_context * ctx, vk_queue& q, std::vector<vk_semaphore> wait_semaphores, std::vector<vk_semaphore> signal_semaphores) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_create_submission()" << std::endl;
|
|
#endif
|
|
vk_submission s;
|
|
s.buffer = ggml_vk_create_cmd_buffer(ctx, q);
|
|
s.wait_semaphores = std::move(wait_semaphores);
|
|
s.signal_semaphores = std::move(signal_semaphores);
|
|
return s;
|
|
}
|
|
|
|
static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_submit(" << ctx->seqs.size() << ", " << fence << ")" << std::endl;
|
|
#endif
|
|
if (ctx->seqs.empty()) {
|
|
return;
|
|
}
|
|
|
|
std::vector<std::vector<uint64_t>> tl_wait_vals;
|
|
std::vector<std::vector<uint64_t>> tl_signal_vals;
|
|
std::vector<std::vector<vk::Semaphore>> tl_wait_semaphores;
|
|
std::vector<std::vector<vk::Semaphore>> tl_signal_semaphores;
|
|
std::vector<vk::TimelineSemaphoreSubmitInfo> tl_submit_infos;
|
|
std::vector<vk::SubmitInfo> submit_infos;
|
|
int idx = -1;
|
|
std::vector<std::vector<vk::PipelineStageFlags>> stage_flags;
|
|
|
|
size_t reserve = 0;
|
|
|
|
for (const auto& sequence : ctx->seqs) {
|
|
reserve += sequence.size();
|
|
}
|
|
|
|
// Pre-reserve vectors to prevent reallocation, which invalidates pointers
|
|
tl_wait_semaphores.reserve(reserve);
|
|
tl_wait_vals.reserve(reserve);
|
|
tl_signal_semaphores.reserve(reserve);
|
|
tl_signal_vals.reserve(reserve);
|
|
tl_submit_infos.reserve(reserve);
|
|
submit_infos.reserve(reserve);
|
|
stage_flags.reserve(reserve);
|
|
|
|
for (const auto& sequence : ctx->seqs) {
|
|
for (const auto& submission : sequence) {
|
|
stage_flags.push_back({});
|
|
idx++;
|
|
tl_wait_vals.push_back({});
|
|
tl_wait_semaphores.push_back({});
|
|
tl_signal_vals.push_back({});
|
|
tl_signal_semaphores.push_back({});
|
|
for (size_t i = 0; i < submission.wait_semaphores.size(); i++) {
|
|
stage_flags[idx].push_back(ctx->q->stage_flags);
|
|
tl_wait_vals[idx].push_back(submission.wait_semaphores[i].value);
|
|
tl_wait_semaphores[idx].push_back(submission.wait_semaphores[i].s);
|
|
}
|
|
for (size_t i = 0; i < submission.signal_semaphores.size(); i++) {
|
|
tl_signal_vals[idx].push_back(submission.signal_semaphores[i].value);
|
|
tl_signal_semaphores[idx].push_back(submission.signal_semaphores[i].s);
|
|
}
|
|
tl_submit_infos.push_back({
|
|
(uint32_t) submission.wait_semaphores.size(),
|
|
tl_wait_vals[idx].data(),
|
|
(uint32_t) submission.signal_semaphores.size(),
|
|
tl_signal_vals[idx].data(),
|
|
});
|
|
tl_submit_infos[idx].sType = vk::StructureType::eTimelineSemaphoreSubmitInfo;
|
|
tl_submit_infos[idx].pNext = nullptr;
|
|
vk::SubmitInfo si{
|
|
(uint32_t) submission.wait_semaphores.size(),
|
|
tl_wait_semaphores[idx].data(),
|
|
stage_flags[idx].data(),
|
|
1,
|
|
&submission.buffer,
|
|
(uint32_t) submission.signal_semaphores.size(),
|
|
tl_signal_semaphores[idx].data(),
|
|
};
|
|
si.setPNext(&tl_submit_infos[idx]);
|
|
submit_infos.push_back(si);
|
|
}
|
|
}
|
|
|
|
ctx->q->queue.submit(submit_infos, fence);
|
|
|
|
ctx->seqs.clear();
|
|
}
|
|
|
|
static uint32_t ggml_vk_find_queue_family_index(std::vector<vk::QueueFamilyProperties>& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_find_queue_family_index()" << std::endl;
|
|
#endif
|
|
const uint32_t qfsize = queue_family_props.size();
|
|
|
|
// Try with avoid preferences first
|
|
for (uint32_t i = 0; i < qfsize; i++) {
|
|
if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required && !(queue_family_props[i].queueFlags & avoid)) {
|
|
return i;
|
|
}
|
|
}
|
|
|
|
// Fall back to only required
|
|
for (size_t i = 0; i < qfsize; i++) {
|
|
if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required) {
|
|
return i;
|
|
}
|
|
}
|
|
|
|
// Fall back to reusing compute queue
|
|
for (size_t i = 0; i < qfsize; i++) {
|
|
if (queue_family_props[i].queueCount >= min_num_queues && queue_family_props[i].queueFlags & required) {
|
|
return i;
|
|
}
|
|
}
|
|
|
|
// Fall back to ignoring min_num_queries
|
|
for (size_t i = 0; i < qfsize; i++) {
|
|
if (queue_family_props[i].queueFlags & required) {
|
|
return i;
|
|
}
|
|
}
|
|
|
|
// All commands that are allowed on a queue that supports transfer operations are also allowed on a queue that supports either graphics or compute operations.
|
|
// Thus, if the capabilities of a queue family include VK_QUEUE_GRAPHICS_BIT or VK_QUEUE_COMPUTE_BIT, then reporting the VK_QUEUE_TRANSFER_BIT capability separately for that queue family is optional.
|
|
if (compute_index >= 0) {
|
|
return compute_index;
|
|
}
|
|
|
|
std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl;
|
|
|
|
for(auto &q_family : queue_family_props) {
|
|
std::cerr << "Queue number: " + std::to_string(q_family.queueCount) << " flags: " + to_string(q_family.queueFlags) << std::endl;
|
|
}
|
|
abort();
|
|
}
|
|
|
|
static void ggml_vk_create_queue(ggml_backend_vk_context * ctx, vk_queue& q, uint32_t queue_family_index, uint32_t queue_index, vk::PipelineStageFlags&& stage_flags) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_create_queue()" << std::endl;
|
|
#endif
|
|
q.queue_family_index = queue_family_index;
|
|
|
|
vk::CommandPoolCreateInfo command_pool_create_info_compute(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), queue_family_index);
|
|
q.pool = ctx->device->device.createCommandPool(command_pool_create_info_compute);
|
|
|
|
q.cmd_buffer_idx = 0;
|
|
|
|
q.queue = ctx->device->device.getQueue(queue_family_index, queue_index);
|
|
|
|
q.stage_flags = stage_flags;
|
|
}
|
|
|
|
static vk_context * ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_queue& q) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_create_context()" << std::endl;
|
|
#endif
|
|
ctx->gc.contexts.emplace_back();
|
|
vk_context * result = &ctx->gc.contexts[ctx->gc.contexts.size() - 1];
|
|
memset((void *) result, 0, sizeof(vk_context));
|
|
result->idx = ctx->gc.contexts.size() - 1;
|
|
result->q = &q;
|
|
return result;
|
|
}
|
|
|
|
static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * ctx) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl;
|
|
#endif
|
|
vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 };
|
|
vk::SemaphoreCreateInfo ci{};
|
|
ci.setPNext(&tci);
|
|
vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
|
|
ctx->gc.semaphores.push_back({ semaphore, 0 });
|
|
return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1];
|
|
}
|
|
|
|
static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context * ctx) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl;
|
|
#endif
|
|
if (ctx->semaphore_idx >= ctx->gc.tl_semaphores.size()) {
|
|
vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 };
|
|
vk::SemaphoreCreateInfo ci{};
|
|
ci.setPNext(&tci);
|
|
vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
|
|
ctx->gc.tl_semaphores.push_back({ semaphore, 0 });
|
|
}
|
|
return &ctx->gc.tl_semaphores[ctx->semaphore_idx++];
|
|
}
|
|
|
|
static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) {
|
|
if (ctx->event_idx >= ctx->gc.events.size()) {
|
|
ctx->gc.events.push_back(ctx->device->device.createEvent({}));
|
|
}
|
|
return ctx->gc.events[ctx->event_idx++];
|
|
}
|
|
|
|
static void ggml_vk_queue_cleanup(ggml_backend_vk_context * ctx, vk_queue& q) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_queue_cleanup()" << std::endl;
|
|
#endif
|
|
// Requires command buffers to be done
|
|
|
|
ctx->device->device.resetCommandPool(q.pool);
|
|
q.cmd_buffer_idx = 0;
|
|
}
|
|
|
|
static uint32_t find_properties(const vk::PhysicalDeviceMemoryProperties* mem_props, vk::MemoryRequirements* mem_req, vk::MemoryPropertyFlags flags) {
|
|
for (uint32_t i = 0; i < mem_props->memoryTypeCount; ++i) {
|
|
vk::MemoryType memory_type = mem_props->memoryTypes[i];
|
|
if ((mem_req->memoryTypeBits & ((uint64_t)1 << i)) &&
|
|
(flags & memory_type.propertyFlags) == flags &&
|
|
mem_props->memoryHeaps[memory_type.heapIndex].size >= mem_req->size) {
|
|
return static_cast<int32_t>(i);
|
|
}
|
|
}
|
|
return UINT32_MAX;
|
|
}
|
|
|
|
static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ", " << to_string(fallback_flags) << ")" << std::endl;
|
|
#endif
|
|
vk_buffer buf = std::make_shared<vk_buffer_struct>();
|
|
|
|
if (size == 0) {
|
|
buf->size = 0;
|
|
return buf;
|
|
}
|
|
|
|
buf->size = size;
|
|
vk::BufferCreateInfo buffer_create_info{
|
|
vk::BufferCreateFlags(),
|
|
size,
|
|
vk::BufferUsageFlagBits::eStorageBuffer | vk::BufferUsageFlagBits::eTransferSrc | vk::BufferUsageFlagBits::eTransferDst,
|
|
vk::SharingMode::eExclusive,
|
|
0,
|
|
nullptr,
|
|
};
|
|
|
|
buf->buffer = ctx->device->device.createBuffer(buffer_create_info);
|
|
|
|
vk::MemoryRequirements mem_req = ctx->device->device.getBufferMemoryRequirements(buf->buffer);
|
|
|
|
vk::PhysicalDeviceMemoryProperties mem_props = ctx->device->physical_device.getMemoryProperties();
|
|
|
|
uint32_t memory_type_index = UINT32_MAX;
|
|
|
|
memory_type_index = find_properties(&mem_props, &mem_req, req_flags);
|
|
buf->memory_property_flags = req_flags;
|
|
|
|
if (memory_type_index == UINT32_MAX && fallback_flags) {
|
|
memory_type_index = find_properties(&mem_props, &mem_req, fallback_flags);
|
|
buf->memory_property_flags = fallback_flags;
|
|
}
|
|
|
|
if (memory_type_index == UINT32_MAX) {
|
|
ctx->device->device.destroyBuffer(buf->buffer);
|
|
buf->size = 0;
|
|
throw vk::OutOfDeviceMemoryError("No suitable memory type found");
|
|
}
|
|
|
|
try {
|
|
buf->device_memory = ctx->device->device.allocateMemory({ mem_req.size, memory_type_index });
|
|
} catch (const vk::SystemError& e) {
|
|
// Out of Host/Device memory, clean up buffer
|
|
ctx->device->device.destroyBuffer(buf->buffer);
|
|
buf->size = 0;
|
|
throw e;
|
|
}
|
|
buf->ptr = nullptr;
|
|
|
|
if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
|
|
buf->ptr = ctx->device->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE);
|
|
}
|
|
|
|
ctx->device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0);
|
|
|
|
buf->ctx = ctx;
|
|
|
|
buf->device = ctx->device;
|
|
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "Created buffer " << buf->buffer << std::endl;
|
|
#endif
|
|
|
|
return buf;
|
|
}
|
|
|
|
static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
|
|
try {
|
|
return ggml_vk_create_buffer(ctx, size, req_flags, fallback_flags);
|
|
} catch (const vk::SystemError& e) {
|
|
std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl;
|
|
std::cerr << "ggml_vulkan: " << e.what() << std::endl;
|
|
throw e;
|
|
}
|
|
}
|
|
|
|
static vk_buffer ggml_vk_create_buffer_device(ggml_backend_vk_context * ctx, size_t size) {
|
|
vk_buffer buf;
|
|
try {
|
|
if (ctx->device->uma) {
|
|
// Fall back to host memory type
|
|
buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
|
|
} else {
|
|
buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
}
|
|
} catch (const vk::SystemError& e) {
|
|
std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl;
|
|
std::cerr << "ggml_vulkan: " << e.what() << std::endl;
|
|
throw e;
|
|
}
|
|
|
|
return buf;
|
|
}
|
|
|
|
static void ggml_vk_destroy_buffer(vk_buffer& buf) {
|
|
buf.reset();
|
|
}
|
|
|
|
static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) {
|
|
return { buf, 0, VK_WHOLE_SIZE };
|
|
}
|
|
|
|
static void ggml_vk_sync_buffers(vk_context * ctx) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_sync_buffers()" << std::endl;
|
|
#endif
|
|
const std::vector<vk::MemoryBarrier> mem_barriers{ { { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite }, { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite } } };
|
|
|
|
ctx->s->buffer.pipelineBarrier(
|
|
ctx->q->stage_flags,
|
|
ctx->q->stage_flags,
|
|
{},
|
|
mem_barriers,
|
|
{},
|
|
{}
|
|
);
|
|
}
|
|
|
|
static void ggml_vk_wait_events(vk_context * ctx, std::vector<vk::Event>&& events) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_wait_events()" << std::endl;
|
|
#endif
|
|
if (events.empty()) {
|
|
return;
|
|
}
|
|
|
|
ctx->s->buffer.waitEvents(
|
|
events,
|
|
ctx->q->stage_flags,
|
|
ctx->q->stage_flags,
|
|
{},
|
|
{},
|
|
{}
|
|
);
|
|
}
|
|
|
|
static bool ggml_vk_build_shader(ggml_type type) {
|
|
switch(type) {
|
|
case GGML_TYPE_F16:
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_Q5_0:
|
|
case GGML_TYPE_Q5_1:
|
|
case GGML_TYPE_Q8_0:
|
|
case GGML_TYPE_Q2_K:
|
|
case GGML_TYPE_Q3_K:
|
|
case GGML_TYPE_Q4_K:
|
|
case GGML_TYPE_Q5_K:
|
|
case GGML_TYPE_Q6_K:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_load_shaders(" << ctx->name << ")" << std::endl;
|
|
#endif
|
|
|
|
const std::shared_ptr<vk_device> device = ctx->device;
|
|
|
|
// mulmat
|
|
std::initializer_list<uint32_t> warptile_l = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
|
|
std::initializer_list<uint32_t> warptile_m = { 128, 64, 64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
|
|
std::initializer_list<uint32_t> warptile_s = { device->subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size };
|
|
|
|
std::initializer_list<uint32_t> warptile_mmq_l = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
|
|
std::initializer_list<uint32_t> warptile_mmq_m = { 128, 64, 64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
|
|
std::initializer_list<uint32_t> warptile_mmq_s = { device->subgroup_size, 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size };
|
|
|
|
std::array<uint32_t, 3> l_wg_denoms = {128, 128, 1 };
|
|
std::array<uint32_t, 3> m_wg_denoms = { 64, 64, 1 };
|
|
std::array<uint32_t, 3> s_wg_denoms = { 32, 32, 1 };
|
|
|
|
uint32_t l_align = 128;
|
|
uint32_t m_align = 64;
|
|
uint32_t s_align = 32;
|
|
|
|
ctx->device->pipeline_matmul_f32 = std::make_shared<vk_matmul_pipeline_struct>();
|
|
ctx->device->pipeline_matmul_f16_f32 = std::make_shared<vk_matmul_pipeline_struct>();
|
|
ctx->device->pipeline_matmul_f16 = std::make_shared<vk_matmul_pipeline_struct>();
|
|
ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared<vk_matmul_pipeline_struct>();
|
|
ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1] = std::make_shared<vk_matmul_pipeline_struct>();
|
|
ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0] = std::make_shared<vk_matmul_pipeline_struct>();
|
|
ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1] = std::make_shared<vk_matmul_pipeline_struct>();
|
|
ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0] = std::make_shared<vk_matmul_pipeline_struct>();
|
|
|
|
if (device->fp16) {
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_0_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_0_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_0_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
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|
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
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} else {
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
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|
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
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ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
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|
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
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|
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
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|
}
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
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|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
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|
|
|
// dequant shaders
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
|
|
|
|
// get_rows
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
|
|
|
|
ggml_vk_create_pipeline(ctx, ctx->device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1);
|
|
}
|
|
|
|
static void ggml_vk_print_gpu_info(size_t idx) {
|
|
GGML_ASSERT(idx < vk_instance.device_indices.size());
|
|
size_t dev_num = vk_instance.device_indices[idx];
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_print_gpu_info(" << dev_num << ")" << std::endl;
|
|
#endif
|
|
GGML_ASSERT(vk_instance.initialized);
|
|
|
|
std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
|
|
|
|
if (dev_num >= devices.size()) {
|
|
std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl;
|
|
throw std::runtime_error("Device not found");
|
|
}
|
|
|
|
vk::PhysicalDevice physical_device = devices[dev_num];
|
|
std::vector<vk::ExtensionProperties> ext_props = physical_device.enumerateDeviceExtensionProperties();
|
|
|
|
vk::PhysicalDeviceProperties2 props2;
|
|
vk::PhysicalDeviceMaintenance3Properties props3;
|
|
vk::PhysicalDeviceSubgroupProperties subgroup_props;
|
|
props2.pNext = &props3;
|
|
props3.pNext = &subgroup_props;
|
|
physical_device.getProperties2(&props2);
|
|
|
|
const size_t subgroup_size = subgroup_props.subgroupSize;
|
|
const bool uma = props2.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
|
|
|
|
bool fp16_storage = false;
|
|
bool fp16_compute = false;
|
|
|
|
for (auto properties : ext_props) {
|
|
if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
|
|
fp16_storage = true;
|
|
} else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
|
|
fp16_compute = true;
|
|
}
|
|
}
|
|
|
|
const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16");
|
|
bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr;
|
|
|
|
bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
|
|
|
|
vk::PhysicalDeviceFeatures device_features = physical_device.getFeatures();
|
|
|
|
VkPhysicalDeviceFeatures2 device_features2;
|
|
device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
|
|
device_features2.pNext = nullptr;
|
|
device_features2.features = (VkPhysicalDeviceFeatures)device_features;
|
|
|
|
VkPhysicalDeviceVulkan11Features vk11_features;
|
|
vk11_features.pNext = nullptr;
|
|
vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
|
|
device_features2.pNext = &vk11_features;
|
|
|
|
VkPhysicalDeviceVulkan12Features vk12_features;
|
|
vk12_features.pNext = nullptr;
|
|
vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
|
|
vk11_features.pNext = &vk12_features;
|
|
|
|
vkGetPhysicalDeviceFeatures2(physical_device, &device_features2);
|
|
|
|
fp16 = fp16 && vk12_features.shaderFloat16;
|
|
|
|
std::string device_name = props2.properties.deviceName.data();
|
|
std::cerr << GGML_VK_NAME << idx << ": " << device_name << " | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl;
|
|
|
|
if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) {
|
|
std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl;
|
|
}
|
|
}
|
|
|
|
static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
|
|
static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
|
|
|
|
void ggml_vk_instance_init() {
|
|
if (vk_instance_initialized) {
|
|
return;
|
|
}
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_instance_init()" << std::endl;
|
|
#endif
|
|
|
|
vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION };
|
|
|
|
const std::vector<vk::ExtensionProperties> instance_extensions = vk::enumerateInstanceExtensionProperties();
|
|
const bool validation_ext = ggml_vk_instance_validation_ext_available(instance_extensions);
|
|
#ifdef __APPLE__
|
|
const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions);
|
|
#endif
|
|
|
|
std::vector<const char*> layers;
|
|
|
|
if (validation_ext) {
|
|
layers.push_back("VK_LAYER_KHRONOS_validation");
|
|
}
|
|
std::vector<const char*> extensions;
|
|
if (validation_ext) {
|
|
extensions.push_back("VK_EXT_validation_features");
|
|
}
|
|
#ifdef __APPLE__
|
|
if (portability_enumeration_ext) {
|
|
extensions.push_back("VK_KHR_portability_enumeration");
|
|
}
|
|
#endif
|
|
vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions);
|
|
#ifdef __APPLE__
|
|
if (portability_enumeration_ext) {
|
|
instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR;
|
|
}
|
|
#endif
|
|
|
|
std::vector<vk::ValidationFeatureEnableEXT> features_enable;
|
|
vk::ValidationFeaturesEXT validation_features;
|
|
|
|
if (validation_ext) {
|
|
features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices };
|
|
validation_features = {
|
|
features_enable,
|
|
{},
|
|
};
|
|
validation_features.setPNext(nullptr);
|
|
instance_create_info.setPNext(&validation_features);
|
|
|
|
std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl;
|
|
}
|
|
vk_instance.instance = vk::createInstance(instance_create_info);
|
|
|
|
memset(vk_instance.initialized, 0, sizeof(bool) * GGML_VK_MAX_DEVICES);
|
|
|
|
size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size();
|
|
|
|
// Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan
|
|
char * devices_env = getenv("GGML_VK_VISIBLE_DEVICES");
|
|
if (devices_env != nullptr) {
|
|
std::string devices(devices_env);
|
|
std::replace(devices.begin(), devices.end(), ',', ' ');
|
|
|
|
std::stringstream ss(devices);
|
|
size_t tmp;
|
|
while (ss >> tmp) {
|
|
if(tmp >= num_available_devices) {
|
|
std::cerr << "ggml_vulkan: Invalid device index " << tmp << " in GGML_VK_VISIBLE_DEVICES." << std::endl;
|
|
throw std::runtime_error("Invalid Vulkan device index");
|
|
}
|
|
vk_instance.device_indices.push_back(tmp);
|
|
}
|
|
} else {
|
|
vk_instance.device_indices.push_back(0);
|
|
}
|
|
|
|
vk_instance_initialized = true;
|
|
}
|
|
|
|
static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) {
|
|
GGML_ASSERT(idx < vk_instance.device_indices.size());
|
|
size_t dev_num = vk_instance.device_indices[idx];
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_init(" << ctx->name << ", " << dev_num << ")" << std::endl;
|
|
#endif
|
|
ggml_vk_instance_init();
|
|
|
|
std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
|
|
|
|
if (dev_num >= devices.size()) {
|
|
std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl;
|
|
throw std::runtime_error("Device not found");
|
|
}
|
|
|
|
ctx->device = ggml_vk_get_device(idx);
|
|
if (!ctx->device->initialized) {
|
|
ctx->device->physical_device = devices[dev_num];
|
|
const std::vector<vk::ExtensionProperties> ext_props = ctx->device->physical_device.enumerateDeviceExtensionProperties();
|
|
|
|
bool maintenance4_support = false;
|
|
|
|
// Check if maintenance4 is supported
|
|
for (const auto& properties : ext_props) {
|
|
if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) {
|
|
maintenance4_support = true;
|
|
}
|
|
}
|
|
|
|
vk::PhysicalDeviceProperties2 props2;
|
|
vk::PhysicalDeviceMaintenance3Properties props3;
|
|
vk::PhysicalDeviceMaintenance4Properties props4;
|
|
vk::PhysicalDeviceSubgroupProperties subgroup_props;
|
|
props2.pNext = &props3;
|
|
props3.pNext = &subgroup_props;
|
|
if (maintenance4_support) {
|
|
subgroup_props.pNext = &props4;
|
|
}
|
|
ctx->device->physical_device.getProperties2(&props2);
|
|
ctx->device->properties = props2.properties;
|
|
|
|
const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE");
|
|
|
|
if (GGML_VK_FORCE_MAX_ALLOCATION_SIZE != nullptr) {
|
|
ctx->device->max_memory_allocation_size = std::stoi(GGML_VK_FORCE_MAX_ALLOCATION_SIZE);
|
|
} else if (maintenance4_support) {
|
|
ctx->device->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize);
|
|
} else {
|
|
ctx->device->max_memory_allocation_size = props3.maxMemoryAllocationSize;
|
|
}
|
|
|
|
ctx->device->vendor_id = ctx->device->properties.vendorID;
|
|
ctx->device->subgroup_size = subgroup_props.subgroupSize;
|
|
ctx->device->uma = ctx->device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
|
|
|
|
bool fp16_storage = false;
|
|
bool fp16_compute = false;
|
|
|
|
for (const auto& properties : ext_props) {
|
|
if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
|
|
fp16_storage = true;
|
|
} else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
|
|
fp16_compute = true;
|
|
}
|
|
}
|
|
|
|
const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16");
|
|
const bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr;
|
|
|
|
ctx->device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
|
|
|
|
std::vector<vk::QueueFamilyProperties> queue_family_props = ctx->device->physical_device.getQueueFamilyProperties();
|
|
|
|
// Try to find a non-graphics compute queue and transfer-focused queues
|
|
const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1);
|
|
const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1);
|
|
|
|
const float priorities[] = { 1.0f, 1.0f };
|
|
ctx->device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1;
|
|
|
|
std::vector<vk::DeviceQueueCreateInfo> device_queue_create_infos;
|
|
if (compute_queue_family_index != transfer_queue_family_index) {
|
|
device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
|
|
device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1});
|
|
} else if(!ctx->device->single_queue) {
|
|
device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities});
|
|
} else {
|
|
device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
|
|
}
|
|
vk::DeviceCreateInfo device_create_info;
|
|
std::vector<const char *> device_extensions;
|
|
vk::PhysicalDeviceFeatures device_features = ctx->device->physical_device.getFeatures();
|
|
|
|
VkPhysicalDeviceFeatures2 device_features2;
|
|
device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
|
|
device_features2.pNext = nullptr;
|
|
device_features2.features = (VkPhysicalDeviceFeatures)device_features;
|
|
|
|
VkPhysicalDeviceVulkan11Features vk11_features;
|
|
vk11_features.pNext = nullptr;
|
|
vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
|
|
device_features2.pNext = &vk11_features;
|
|
|
|
VkPhysicalDeviceVulkan12Features vk12_features;
|
|
vk12_features.pNext = nullptr;
|
|
vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
|
|
vk11_features.pNext = &vk12_features;
|
|
|
|
vkGetPhysicalDeviceFeatures2(ctx->device->physical_device, &device_features2);
|
|
|
|
ctx->device->fp16 = ctx->device->fp16 && vk12_features.shaderFloat16;
|
|
|
|
if (!vk11_features.storageBuffer16BitAccess) {
|
|
std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl;
|
|
throw std::runtime_error("Unsupported device");
|
|
}
|
|
|
|
device_extensions.push_back("VK_KHR_16bit_storage");
|
|
|
|
#ifdef GGML_VULKAN_VALIDATE
|
|
device_extensions.push_back("VK_KHR_shader_non_semantic_info");
|
|
#endif
|
|
|
|
if (ctx->device->fp16) {
|
|
device_extensions.push_back("VK_KHR_shader_float16_int8");
|
|
}
|
|
ctx->device->name = ctx->device->properties.deviceName.data();
|
|
|
|
device_create_info = {
|
|
vk::DeviceCreateFlags(),
|
|
device_queue_create_infos,
|
|
{},
|
|
device_extensions
|
|
};
|
|
device_create_info.setPNext(&device_features2);
|
|
ctx->device->device = ctx->device->physical_device.createDevice(device_create_info);
|
|
|
|
ctx->device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN;
|
|
|
|
// Queues
|
|
ggml_vk_create_queue(ctx, ctx->device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer });
|
|
|
|
// Shaders
|
|
ggml_vk_load_shaders(ctx);
|
|
|
|
if (!ctx->device->single_queue) {
|
|
const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0;
|
|
ggml_vk_create_queue(ctx, ctx->device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer });
|
|
} else {
|
|
// TODO: Use pointer or reference to avoid copy
|
|
ctx->device->transfer_queue = ctx->device->compute_queue;
|
|
}
|
|
|
|
ctx->device->idx = dev_num;
|
|
ctx->device->initialized = true;
|
|
} else if (ctx->device->idx != dev_num) {
|
|
std::cerr << "ggml_vulkan: Device " << ctx->device->name << " already initialized with index " << ctx->device->idx << ", but trying to reinitialize with index " << dev_num << std::endl;
|
|
throw std::runtime_error("Device already initialized");
|
|
}
|
|
|
|
ctx->fence = ctx->device->device.createFence({});
|
|
|
|
ctx->compute_ctx = nullptr;
|
|
ctx->transfer_ctx = nullptr;
|
|
|
|
ctx->disable = false;
|
|
ctx->initialized = true;
|
|
|
|
ctx->idx = idx;
|
|
|
|
#ifdef GGML_VULKAN_CHECK_RESULTS
|
|
const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS");
|
|
vk_skip_checks = (skip_checks == NULL ? 0 : atoi(skip_checks));
|
|
const char* output_tensor = getenv("GGML_VULKAN_OUTPUT_TENSOR");
|
|
vk_output_tensor = (output_tensor == NULL ? 0 : atoi(output_tensor));
|
|
#endif
|
|
}
|
|
|
|
static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_get_to_fp16()" << std::endl;
|
|
#endif
|
|
switch (type) {
|
|
case GGML_TYPE_F32:
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_Q5_0:
|
|
case GGML_TYPE_Q5_1:
|
|
case GGML_TYPE_Q8_0:
|
|
case GGML_TYPE_Q2_K:
|
|
case GGML_TYPE_Q3_K:
|
|
case GGML_TYPE_Q4_K:
|
|
case GGML_TYPE_Q5_K:
|
|
case GGML_TYPE_Q6_K:
|
|
break;
|
|
default:
|
|
return nullptr;
|
|
}
|
|
|
|
return ctx->device->pipeline_dequant[type];
|
|
}
|
|
|
|
static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_get_mul_mat_mat_pipeline()" << std::endl;
|
|
#endif
|
|
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_matmul_f32;
|
|
}
|
|
if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_matmul_f16_f32;
|
|
}
|
|
if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
|
|
return ctx->device->pipeline_matmul_f16;
|
|
}
|
|
|
|
GGML_ASSERT(src1_type == GGML_TYPE_F32);
|
|
|
|
switch (src0_type) {
|
|
case GGML_TYPE_Q4_0:
|
|
break;
|
|
default:
|
|
return nullptr;
|
|
}
|
|
|
|
return ctx->device->pipeline_dequant_mul_mat_mat[src0_type];
|
|
}
|
|
|
|
static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type type) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl;
|
|
#endif
|
|
switch (type) {
|
|
case GGML_TYPE_F16:
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_Q5_0:
|
|
case GGML_TYPE_Q5_1:
|
|
case GGML_TYPE_Q8_0:
|
|
case GGML_TYPE_Q2_K:
|
|
case GGML_TYPE_Q3_K:
|
|
case GGML_TYPE_Q4_K:
|
|
case GGML_TYPE_Q5_K:
|
|
case GGML_TYPE_Q6_K:
|
|
break;
|
|
default:
|
|
return nullptr;
|
|
}
|
|
|
|
return ctx->device->pipeline_dequant_mul_mat_vec_f32[type];
|
|
}
|
|
|
|
static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_pool_malloc(" << size << ")" << std::endl;
|
|
#endif
|
|
int best_i = -1;
|
|
size_t best_size = std::numeric_limits<size_t>::max(); //smallest unused buffer that fits our needs
|
|
int worst_i = -1;
|
|
size_t worst_size = 0; //largest unused buffer seen so far
|
|
for (int i = 0; i < MAX_VK_BUFFERS; ++i) {
|
|
vk_buffer &b = ctx->buffer_pool[i];
|
|
if (b != nullptr && b->size >= size && b->size < best_size) {
|
|
best_i = i;
|
|
best_size = b->size;
|
|
}
|
|
if (b != nullptr && b->size > worst_size) {
|
|
worst_i = i;
|
|
worst_size = b->size;
|
|
}
|
|
}
|
|
if(best_i != -1) {
|
|
//found the smallest buffer that fits our needs
|
|
vk_buffer b = ctx->buffer_pool[best_i];
|
|
ctx->buffer_pool[best_i].reset();
|
|
return b;
|
|
}
|
|
if(worst_i != -1) {
|
|
//no buffer that fits our needs, resize largest one to save memory
|
|
vk_buffer& b = ctx->buffer_pool[worst_i];
|
|
ggml_vk_destroy_buffer(b);
|
|
}
|
|
|
|
return ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
}
|
|
|
|
static void ggml_vk_pool_free(ggml_backend_vk_context * ctx, vk_buffer& buffer) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_pool_free(" << buffer->size << ")" << std::endl;
|
|
#endif
|
|
for (int i = 0; i < MAX_VK_BUFFERS; ++i) {
|
|
vk_buffer& b = ctx->buffer_pool[i];
|
|
if (b == nullptr) {
|
|
b = buffer;
|
|
return;
|
|
}
|
|
}
|
|
std::cerr << "ggml_vulkan: WARNING: vk buffer pool full, increase MAX_VK_BUFFERS" << std::endl;
|
|
ggml_vk_destroy_buffer(buffer);
|
|
}
|
|
|
|
// Returns an available temporary buffer that may only be used temporarily, it will be reused
|
|
static vk_buffer ggml_vk_create_buffer_temp(ggml_backend_vk_context * ctx, size_t size) {
|
|
// Try to find existing temp buffer with enough capacity
|
|
for (auto& buffer : ctx->gc.temp_buffers) {
|
|
if (buffer->size >= size) {
|
|
return buffer;
|
|
}
|
|
}
|
|
|
|
// Otherwise create new buffer
|
|
vk_buffer buf = ggml_vk_pool_malloc(ctx, size);
|
|
ctx->gc.temp_buffers.push_back(buf);
|
|
|
|
return buf;
|
|
}
|
|
|
|
static void * ggml_vk_host_malloc(ggml_backend_vk_context * ctx, size_t size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl;
|
|
#endif
|
|
vk_buffer buf = ggml_vk_create_buffer(ctx, size,
|
|
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
|
|
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
|
|
|
|
if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) {
|
|
fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n",
|
|
size/1024.0/1024.0);
|
|
ctx->device->device.freeMemory(buf->device_memory);
|
|
ctx->device->device.destroyBuffer(buf->buffer);
|
|
return nullptr;
|
|
}
|
|
|
|
ctx->pinned_memory.push_back(std::make_tuple(buf->ptr, size, buf));
|
|
|
|
return buf->ptr;
|
|
}
|
|
|
|
static void ggml_vk_host_free(ggml_backend_vk_context * ctx, void* ptr) {
|
|
if (ptr == nullptr) {
|
|
return;
|
|
}
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_host_free(" << ptr << ")" << std::endl;
|
|
#endif
|
|
vk_buffer buf;
|
|
size_t index;
|
|
for (size_t i = 0; i < ctx->pinned_memory.size(); i++) {
|
|
const uint8_t* addr = (const uint8_t*) std::get<0>(ctx->pinned_memory[i]);
|
|
const uint8_t* endr = addr + std::get<1>(ctx->pinned_memory[i]);
|
|
if (ptr >= addr && ptr < endr) {
|
|
buf = std::get<2>(ctx->pinned_memory[i]);
|
|
index = i;
|
|
break;
|
|
}
|
|
}
|
|
if (buf == nullptr) {
|
|
fprintf(stderr, "WARNING: failed to free pinned memory: memory not in map\n");
|
|
return;
|
|
}
|
|
|
|
ggml_vk_destroy_buffer(buf);
|
|
|
|
ctx->pinned_memory.erase(ctx->pinned_memory.begin() + index);
|
|
}
|
|
|
|
static void ggml_vk_host_get(ggml_backend_vk_context * ctx, const void * ptr, vk_buffer& buf, size_t& buf_offset) {
|
|
buf = nullptr;
|
|
buf_offset = 0;
|
|
for (size_t i = 0; i < ctx->pinned_memory.size(); i++) {
|
|
const uint8_t* addr = (const uint8_t*) std::get<0>(ctx->pinned_memory[i]);
|
|
const uint8_t* endr = addr + std::get<1>(ctx->pinned_memory[i]);
|
|
if (ptr >= addr && ptr < endr) {
|
|
buf = std::get<2>(ctx->pinned_memory[i]);
|
|
buf_offset = ((const uint8_t *)ptr) - addr;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
static vk_submission ggml_vk_begin_submission(ggml_backend_vk_context * ctx, vk_queue& q, bool one_time = true) {
|
|
vk_submission s;
|
|
s.buffer = ggml_vk_create_cmd_buffer(ctx, q);
|
|
if (one_time) {
|
|
s.buffer.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit });
|
|
} else {
|
|
s.buffer.begin({ vk::CommandBufferUsageFlags{} });
|
|
}
|
|
|
|
return s;
|
|
}
|
|
|
|
static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, std::vector<vk_subbuffer>&& buffers, size_t push_constant_size, const void* push_constants, std::array<uint32_t, 3> elements) {
|
|
const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]);
|
|
const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]);
|
|
const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]);
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline->name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl;
|
|
#endif
|
|
std::vector<vk::DescriptorBufferInfo> descriptor_buffer_infos;
|
|
std::vector<vk::WriteDescriptorSet> write_descriptor_sets;
|
|
GGML_ASSERT(pipeline->descriptor_set_idx < pipeline->descriptor_sets.size());
|
|
GGML_ASSERT(buffers.size() == pipeline->parameter_count);
|
|
vk::DescriptorSet& descriptor_set = pipeline->descriptor_sets[pipeline->descriptor_set_idx++];
|
|
for (uint32_t i = 0; i < pipeline->parameter_count; i++) {
|
|
descriptor_buffer_infos.push_back({buffers[i].buffer->buffer, buffers[i].offset, buffers[i].size});
|
|
}
|
|
for (uint32_t i = 0; i < pipeline->parameter_count; i++) {
|
|
write_descriptor_sets.push_back({descriptor_set, i, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &descriptor_buffer_infos[i]});
|
|
}
|
|
|
|
ctx->device->device.updateDescriptorSets(write_descriptor_sets, {});
|
|
|
|
subctx->s->buffer.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants);
|
|
subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline);
|
|
subctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute,
|
|
pipeline->layout,
|
|
0,
|
|
{ descriptor_set },
|
|
{});
|
|
subctx->s->buffer.dispatch(wg0, wg1, wg2);
|
|
}
|
|
|
|
static void ggml_vk_end_submission(vk_submission& s, std::vector<vk_semaphore> wait_semaphores, std::vector<vk_semaphore> signal_semaphores) {
|
|
s.buffer.end();
|
|
|
|
s.wait_semaphores = std::move(wait_semaphores);
|
|
s.signal_semaphores = std::move(signal_semaphores);
|
|
}
|
|
|
|
static void ggml_vk_ctx_end(vk_context * ctx) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")" << std::endl;
|
|
#endif
|
|
if (ctx->s == nullptr) {
|
|
return;
|
|
}
|
|
|
|
ctx->s->buffer.end();
|
|
ctx->s = nullptr;
|
|
}
|
|
|
|
static void ggml_vk_ctx_begin(ggml_backend_vk_context * ctx, vk_context * subctx) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_ctx_begin(" << ctx << ")" << std::endl;
|
|
#endif
|
|
if (subctx->s != nullptr) {
|
|
ggml_vk_ctx_end(subctx);
|
|
}
|
|
|
|
subctx->seqs.push_back({ ggml_vk_begin_submission(ctx, *subctx->q) });
|
|
subctx->s = subctx->seqs[subctx->seqs.size() - 1].data();
|
|
}
|
|
|
|
static size_t ggml_vk_align_size(size_t width, size_t align) {
|
|
return CEIL_DIV(width, align) * align;
|
|
}
|
|
|
|
static void deferred_memcpy(void * dst, const void * src, size_t size, std::vector<vk_staging_memcpy>* memcpys = nullptr) {
|
|
if (memcpys == nullptr) {
|
|
memcpy(dst, src, size);
|
|
} else {
|
|
memcpys->emplace_back(dst, src, size);
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) {
|
|
if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) {
|
|
ggml_vk_destroy_buffer(ctx->sync_staging);
|
|
ctx->sync_staging = ggml_vk_create_buffer_check(ctx, size,
|
|
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
|
|
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_buffer_write_nc_async(" << tensor << ")" << std::endl;
|
|
#endif
|
|
GGML_ASSERT(!ggml_is_contiguous(tensor));
|
|
// Buffer is already mapped
|
|
if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
|
|
std::cerr << "ggml_vulkan: buffer_write_nc_async dst buffer is host_visible. Use synchronous write." << std::endl;
|
|
GGML_ASSERT(false);
|
|
}
|
|
// Check if src is pinned memory
|
|
vk_buffer buf;
|
|
size_t buf_offset;
|
|
ggml_vk_host_get(ctx, tensor->data, buf, buf_offset);
|
|
|
|
const uint64_t ne0 = tensor->ne[0];
|
|
const uint64_t ne1 = tensor->ne[1];
|
|
const uint64_t ne2 = tensor->ne[2];
|
|
const uint64_t ne3 = tensor->ne[3];
|
|
const uint64_t nb0 = tensor->nb[0];
|
|
const uint64_t nb1 = tensor->nb[1];
|
|
const uint64_t nb2 = tensor->nb[2];
|
|
const uint64_t nb3 = tensor->nb[3];
|
|
const ggml_type type = tensor->type;
|
|
const uint64_t ts = ggml_type_size(type);
|
|
const uint64_t bs = ggml_blck_size(type);
|
|
|
|
const uint64_t dstnb0 = ts;
|
|
const uint64_t dstnb1 = dstnb0*(ne0/bs);
|
|
const uint64_t dstnb2 = dstnb1*ne1;
|
|
const uint64_t dstnb3 = dstnb2*ne2;
|
|
|
|
const uint64_t ne = ggml_nelements(tensor);
|
|
|
|
if (buf != nullptr) {
|
|
// Memory is pinned, use as staging buffer
|
|
std::vector<vk::BufferCopy> slices;
|
|
|
|
for (uint64_t i3 = 0; i3 < ne3; i3++) {
|
|
for (uint64_t i2 = 0; i2 < ne2; i2++) {
|
|
// Find longest contiguous slice
|
|
if (ne1*nb1 == dstnb2) {
|
|
slices.push_back({ buf_offset + i3*nb3 + i2*nb2, offset + i3*dstnb3 + i2*dstnb2, dstnb2 });
|
|
} else {
|
|
for (uint64_t i1 = 0; i1 < ne1; i1++) {
|
|
if (ne0*nb0/bs == dstnb1) {
|
|
slices.push_back({ buf_offset + i3*nb3 + i2*nb2 + i1*nb1, offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, dstnb1 });
|
|
} else {
|
|
const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1;
|
|
const uint64_t d_off = offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1;
|
|
for (uint64_t i0 = 0; i0 < ne0; i0++) {
|
|
slices.push_back({ s_off + i1*nb0, d_off + i0*dstnb0, dstnb0 });
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
ggml_vk_sync_buffers(subctx);
|
|
subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices);
|
|
return;
|
|
}
|
|
|
|
// Staging buffer required
|
|
vk_buffer staging = ctx->staging;
|
|
size_t staging_offset = ctx->staging_offset;
|
|
const size_t copy_size = ts*ne/bs;
|
|
if (ctx->staging->size < ctx->staging_offset + copy_size) {
|
|
if (sync_staging) {
|
|
// Create temporary larger buffer
|
|
ggml_vk_ensure_sync_staging_buffer(ctx, copy_size);
|
|
|
|
staging = ctx->sync_staging;
|
|
staging_offset = 0;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
VkBufferCopy buf_copy{ staging_offset, offset, copy_size };
|
|
|
|
ggml_vk_sync_buffers(subctx);
|
|
vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy);
|
|
|
|
for (uint64_t i3 = 0; i3 < ne3; i3++) {
|
|
for (uint64_t i2 = 0; i2 < ne2; i2++) {
|
|
// Find longest contiguous slice
|
|
if (ne1*nb1 == dstnb2) {
|
|
deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys);
|
|
} else {
|
|
for (uint64_t i1 = 0; i1 < ne1; i1++) {
|
|
if (ne0*nb0/bs == dstnb1) {
|
|
deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys);
|
|
} else {
|
|
const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1;
|
|
const uint64_t d_off = staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1;
|
|
for (uint64_t i0 = 0; i0 < ne0; i0++) {
|
|
deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &subctx->in_memcpys);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_buffer_write_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")" << std::endl;
|
|
#endif
|
|
// Make sure ctx owns the buffer
|
|
GGML_ASSERT(dst->ctx == ctx);
|
|
|
|
// Buffer is already mapped
|
|
if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
|
|
std::cerr << "ggml_vulkan: buffer_write_async dst buffer is host_visible. Use synchronous write." << std::endl;
|
|
GGML_ASSERT(false);
|
|
}
|
|
// Check if src is pinned memory
|
|
vk_buffer buf = nullptr;
|
|
size_t buf_offset;
|
|
ggml_vk_host_get(ctx, src, buf, buf_offset);
|
|
|
|
if (buf != nullptr) {
|
|
// Memory is pinned, use as staging buffer
|
|
std::vector<vk::BufferCopy> slices(1);
|
|
if (width == spitch) {
|
|
// Only do single write if stride is equal
|
|
slices[0].srcOffset = buf_offset;
|
|
slices[0].dstOffset = offset;
|
|
slices[0].size = width * height;
|
|
} else {
|
|
slices.resize(height);
|
|
for (size_t i = 0; i < height; i++) {
|
|
slices[i].srcOffset = buf_offset + i * spitch;
|
|
slices[i].dstOffset = offset + i * width;
|
|
slices[i].size = width;
|
|
}
|
|
}
|
|
|
|
ggml_vk_sync_buffers(subctx);
|
|
subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices);
|
|
return;
|
|
}
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "STAGING" << std::endl;
|
|
#endif
|
|
|
|
// Staging buffer required
|
|
vk_buffer staging = ctx->staging;
|
|
size_t staging_offset = ctx->staging_offset;
|
|
const size_t copy_size = width*height;
|
|
if (ctx->staging == nullptr || ctx->staging->size < ctx->staging_offset + copy_size) {
|
|
if (sync_staging) {
|
|
ggml_vk_ensure_sync_staging_buffer(ctx, copy_size);
|
|
|
|
staging = ctx->sync_staging;
|
|
staging_offset = 0;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
VkBufferCopy buf_copy = {
|
|
staging_offset,
|
|
offset,
|
|
copy_size};
|
|
|
|
ggml_vk_sync_buffers(subctx);
|
|
vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy);
|
|
|
|
if (width == spitch) {
|
|
deferred_memcpy((uint8_t *)staging->ptr + staging_offset, src, width * height, &subctx->in_memcpys);
|
|
} else {
|
|
for (size_t i = 0; i < height; i++) {
|
|
deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys);
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_buffer_write_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_buffer_write_async(" << size << ")" << std::endl;
|
|
#endif
|
|
return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, size, size, 1, sync_staging);
|
|
}
|
|
|
|
static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_buffer_write_2d(" << width << ", " << height << ")" << std::endl;
|
|
#endif
|
|
// Buffer is already mapped
|
|
if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
|
|
GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
|
|
|
|
for (size_t i = 0; i < height; i++) {
|
|
memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width);
|
|
}
|
|
} else {
|
|
vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
|
|
ggml_vk_ctx_begin(ctx, subctx);
|
|
ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, spitch, width, height, true);
|
|
ggml_vk_ctx_end(subctx);
|
|
|
|
for (auto& cpy : subctx->in_memcpys) {
|
|
memcpy(cpy.dst, cpy.src, cpy.n);
|
|
}
|
|
|
|
ggml_vk_submit(subctx, ctx->fence);
|
|
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences");
|
|
ctx->device->device.resetFences({ ctx->fence });
|
|
ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue);
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_buffer_write(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_buffer_write(" << size << ")" << std::endl;
|
|
#endif
|
|
ggml_vk_buffer_write_2d(ctx, dst, offset, src, 0, size, 1);
|
|
}
|
|
|
|
static void ggml_vk_buffer_read_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")" << std::endl;
|
|
#endif
|
|
GGML_ASSERT(width > 0);
|
|
GGML_ASSERT(height > 0);
|
|
GGML_ASSERT(src != nullptr);
|
|
// Make sure ctx owns the buffer
|
|
GGML_ASSERT(src->ctx == ctx);
|
|
|
|
// Check if dst is pinned memory
|
|
vk_buffer buf = nullptr;
|
|
size_t buf_offset;
|
|
ggml_vk_host_get(ctx, dst, buf, buf_offset);
|
|
|
|
std::vector<vk::BufferCopy> slices(1);
|
|
if (width == spitch && width == dpitch) {
|
|
// Only do single write if stride is equal
|
|
slices[0].srcOffset = offset;
|
|
slices[0].dstOffset = buf_offset;
|
|
slices[0].size = width * height;
|
|
} else {
|
|
slices.resize(height);
|
|
for (size_t i = 0; i < height; i++) {
|
|
slices[i].srcOffset = offset + i * spitch;
|
|
slices[i].dstOffset = buf_offset + i * dpitch;
|
|
slices[i].size = width;
|
|
}
|
|
}
|
|
|
|
if (buf != nullptr) {
|
|
// Memory is pinned, use as staging buffer
|
|
ggml_vk_sync_buffers(subctx);
|
|
subctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices);
|
|
|
|
return;
|
|
}
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "STAGING" << std::endl;
|
|
#endif
|
|
|
|
// Fall back to staging buffer
|
|
vk_buffer staging = ctx->staging;
|
|
const size_t copy_size = dpitch * height;
|
|
if (ctx->staging == nullptr || ctx->staging->size < ctx->staging_offset + copy_size) {
|
|
if (sync_staging) {
|
|
// Create temporary larger buffer
|
|
ggml_vk_ensure_sync_staging_buffer(ctx, copy_size);
|
|
|
|
staging = ctx->sync_staging;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
ggml_vk_sync_buffers(subctx);
|
|
subctx->s->buffer.copyBuffer(src->buffer, staging->buffer, slices);
|
|
|
|
deferred_memcpy(dst, staging->ptr, copy_size, &subctx->out_memcpys);
|
|
}
|
|
|
|
static void ggml_vk_buffer_read_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) {
|
|
return ggml_vk_buffer_read_2d_async(ctx, subctx, src, offset, dst, size, size, size, 1, sync_staging);
|
|
}
|
|
|
|
static void ggml_vk_buffer_read(ggml_backend_vk_context * ctx, vk_buffer& src, size_t offset, void * dst, size_t size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_buffer_read(" << offset << ", " << size << ")" << std::endl;
|
|
#endif
|
|
if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
|
|
GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
|
|
|
|
memcpy(dst, (uint8_t *) src->ptr + offset, size);
|
|
} else {
|
|
vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
|
|
ggml_vk_ctx_begin(ctx, subctx);
|
|
ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst, size, true);
|
|
ggml_vk_ctx_end(subctx);
|
|
|
|
ggml_vk_submit(subctx, ctx->fence);
|
|
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences");
|
|
ctx->device->device.resetFences({ ctx->fence });
|
|
|
|
for (auto& cpy : subctx->out_memcpys) {
|
|
memcpy(cpy.dst, cpy.src, cpy.n);
|
|
}
|
|
ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue);
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_buffer_copy_async(" << size << ")" << std::endl;
|
|
#endif
|
|
// Make sure both buffers are on same ctx
|
|
GGML_ASSERT(src->ctx == dst->ctx);
|
|
|
|
VkBufferCopy bc{ src_offset, dst_offset, size };
|
|
|
|
vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc);
|
|
}
|
|
|
|
static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
|
|
if (src->ctx == dst->ctx) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")" << std::endl;
|
|
#endif
|
|
// Copy within the device
|
|
ggml_backend_vk_context * ctx = src->ctx;
|
|
|
|
vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
|
|
ggml_vk_ctx_begin(ctx, subctx);
|
|
ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size);
|
|
ggml_vk_ctx_end(subctx);
|
|
ggml_vk_submit(subctx, ctx->fence);
|
|
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences");
|
|
ctx->device->device.resetFences({ ctx->fence });
|
|
} else {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")" << std::endl;
|
|
#endif
|
|
// Copy device to device
|
|
ggml_backend_vk_context * src_ctx = src->ctx;
|
|
ggml_backend_vk_context * dst_ctx = dst->ctx;
|
|
|
|
ggml_vk_ensure_sync_staging_buffer(src_ctx, size);
|
|
ggml_vk_ensure_sync_staging_buffer(dst_ctx, size);
|
|
|
|
// Copy to src staging buffer
|
|
ggml_vk_buffer_copy(src_ctx->sync_staging, 0, src, src_offset, size);
|
|
// memcpy to dst staging buffer
|
|
memcpy(dst_ctx->sync_staging->ptr, src_ctx->sync_staging->ptr, size);
|
|
// Copy to dst buffer
|
|
ggml_vk_buffer_copy(dst, dst_offset, dst_ctx->sync_staging, 0, size);
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_buffer_memset(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, uint32_t c, size_t size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")" << std::endl;
|
|
#endif
|
|
// Make sure ctx owns the buffer
|
|
GGML_ASSERT(dst->ctx == ctx);
|
|
|
|
vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
|
|
ggml_vk_ctx_begin(ctx, subctx);
|
|
subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c);
|
|
ggml_vk_ctx_end(subctx);
|
|
|
|
ggml_vk_submit(subctx, ctx->fence);
|
|
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_memset waitForFences");
|
|
ctx->device->device.resetFences({ ctx->fence });
|
|
}
|
|
|
|
static void ggml_vk_h2d_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_h2d_tensor_2d(dst=" << dst << ", offset=" << offset << ", src=" << src << ", i3=" << i3 << ", i2=" << i2 << ", i1=" << i1 << ")" << std::endl;
|
|
#endif
|
|
const uint64_t ne0 = src->ne[0];
|
|
const uint64_t ne1 = src->ne[1];
|
|
const uint64_t nb0 = src->nb[0];
|
|
const uint64_t nb1 = src->nb[1];
|
|
const uint64_t nb2 = src->nb[2];
|
|
const uint64_t nb3 = src->nb[3];
|
|
const enum ggml_type type = src->type;
|
|
const size_t ts = ggml_type_size(type);
|
|
const size_t bs = ggml_blck_size(type);
|
|
const size_t row_length = ts*ne0/bs;
|
|
|
|
const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3);
|
|
if (nb0 == ts && nb1 == row_length) {
|
|
return ggml_vk_buffer_write_async(ctx, subctx, dst, offset, x, i1*nb1);
|
|
}
|
|
if (nb0 == ts && (i1 == ne1 || !ggml_is_permuted(src))) {
|
|
return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, x, nb1, row_length, i1);
|
|
}
|
|
|
|
GGML_ASSERT(i3 == 0);
|
|
GGML_ASSERT(i2 == 0);
|
|
GGML_ASSERT(i1 == (uint64_t) ggml_nrows(src));
|
|
|
|
return ggml_vk_buffer_write_nc_async(ctx, subctx, dst, offset, src);
|
|
}
|
|
|
|
static void ggml_vk_d2h_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, const ggml_tensor * dst) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_d2h_tensor_2d()" << std::endl;
|
|
#endif
|
|
const uint64_t ne0 = dst->ne[0];
|
|
const uint64_t ne1 = dst->ne[1];
|
|
const uint64_t ne2 = dst->ne[2];
|
|
const uint64_t ne3 = dst->ne[3];
|
|
const uint64_t nb0 = dst->nb[0];
|
|
const uint64_t nb1 = dst->nb[1];
|
|
// const uint64_t nb2 = dst->nb[2];
|
|
// const uint64_t nb3 = dst->nb[3];
|
|
const enum ggml_type type = dst->type;
|
|
const size_t ts = ggml_type_size(type);
|
|
const size_t bs = ggml_blck_size(type);
|
|
const size_t row_length = ts*ne0/bs;
|
|
|
|
if (ggml_is_contiguous(dst)) {
|
|
return ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst->data, ne1*nb1*ne2*ne3);
|
|
}
|
|
if (nb0 == ts) {
|
|
return ggml_vk_buffer_read_2d_async(ctx, subctx, src, offset, dst->data, nb1, nb1, row_length, ne1*ne2*ne3);
|
|
}
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
static uint32_t ggml_vk_guess_split_k(int m, int n, int k) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")" << std::endl;
|
|
#endif
|
|
if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) {
|
|
return 4;
|
|
}
|
|
|
|
return 1;
|
|
}
|
|
|
|
static vk_pipeline ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) {
|
|
if (m <= 32 || n <= 32) {
|
|
return aligned ? mmp->a_s : mmp->s;
|
|
}
|
|
return aligned ? mmp->a_m : mmp->m;
|
|
|
|
GGML_UNUSED(ctx);
|
|
}
|
|
|
|
static vk_pipeline ggml_vk_guess_matmul_pipeline_apple(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) {
|
|
return aligned ? mmp->a_m : mmp->m;
|
|
|
|
GGML_UNUSED(ctx);
|
|
}
|
|
|
|
static vk_pipeline ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) {
|
|
return aligned ? mmp->a_s : mmp->s;
|
|
|
|
GGML_UNUSED(ctx);
|
|
}
|
|
|
|
static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ")" << std::endl;
|
|
#endif
|
|
switch (ctx->device->vendor_id) {
|
|
case VK_VENDOR_ID_AMD:
|
|
return ggml_vk_guess_matmul_pipeline_amd(ctx, mmp, m, n, aligned);
|
|
case VK_VENDOR_ID_APPLE:
|
|
return ggml_vk_guess_matmul_pipeline_apple(ctx, mmp, aligned);
|
|
case VK_VENDOR_ID_INTEL:
|
|
return ggml_vk_guess_matmul_pipeline_intel(ctx, mmp, aligned);
|
|
}
|
|
|
|
if (m <= 32 || n <= 32) {
|
|
return aligned ? mmp->a_s : mmp->s;
|
|
}
|
|
if (m <= 64 || n <= 64) {
|
|
return aligned ? mmp->a_m : mmp->m;
|
|
}
|
|
return aligned ? mmp->a_l : mmp->l;
|
|
}
|
|
|
|
static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl;
|
|
#endif
|
|
return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, false)->align;
|
|
}
|
|
|
|
static void ggml_vk_matmul(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl;
|
|
#endif
|
|
ggml_vk_sync_buffers(subctx);
|
|
if (split_k == 1) {
|
|
const std::array<uint32_t, 14> pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d };
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch });
|
|
return;
|
|
}
|
|
|
|
GGML_ASSERT(batch_stride_d == m * n);
|
|
|
|
const std::array<uint32_t, 14> pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d };
|
|
// Make sure enough workgroups get assigned for split k to work
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch });
|
|
ggml_vk_sync_buffers(subctx);
|
|
const std::array<uint32_t, 2> pc2 = { (uint32_t)(m * n * batch), split_k };
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 });
|
|
}
|
|
|
|
static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) {
|
|
return
|
|
tensor->nb[0] == ggml_type_size(tensor->type) &&
|
|
tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
|
|
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
|
|
}
|
|
|
|
static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) {
|
|
if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_cpy_f32_f32;
|
|
}
|
|
if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) {
|
|
return ctx->device->pipeline_cpy_f32_f16;
|
|
}
|
|
if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) {
|
|
return ctx->device->pipeline_cpy_f16_f16;
|
|
}
|
|
|
|
std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl;
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), ";
|
|
std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")" << std::endl;
|
|
#endif
|
|
const int tensor_type_size = ggml_type_size(tensor->type);
|
|
|
|
const uint32_t ne = ggml_nelements(tensor);
|
|
|
|
const vk_op_unary_push_constants pc = {
|
|
(uint32_t)ne,
|
|
(uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size,
|
|
(uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], 1 , (uint32_t)tensor->ne[0] , (uint32_t)(tensor->ne[0] * tensor->ne[1]) , (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]),
|
|
0,
|
|
0.0f, 0.0f,
|
|
};
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, { ne, 1, 1 });
|
|
}
|
|
|
|
static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
|
|
std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
|
|
std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
|
|
#endif
|
|
GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT
|
|
GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT
|
|
|
|
const uint64_t ne00 = src0->ne[0];
|
|
const uint64_t ne01 = src0->ne[1];
|
|
const uint64_t ne02 = src0->ne[2];
|
|
const uint64_t ne03 = src0->ne[3];
|
|
|
|
const uint64_t ne10 = src1->ne[0];
|
|
const uint64_t ne11 = src1->ne[1];
|
|
const uint64_t ne12 = src1->ne[2];
|
|
const uint64_t ne13 = src1->ne[3];
|
|
|
|
const uint64_t ne20 = dst->ne[0];
|
|
const uint64_t ne21 = dst->ne[1];
|
|
|
|
const uint64_t r2 = ne12 / ne02;
|
|
const uint64_t r3 = ne13 / ne03;
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
|
|
ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
|
|
|
|
vk_buffer d_Qx;
|
|
size_t qx_buf_offset = 0;
|
|
vk_buffer d_Qy;
|
|
size_t qy_buf_offset = 0;
|
|
|
|
bool src0_uma = false;
|
|
bool src1_uma = false;
|
|
|
|
if (ctx->device->uma) {
|
|
ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset);
|
|
ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
|
|
src0_uma = d_Qx != nullptr;
|
|
src1_uma = d_Qy != nullptr;
|
|
}
|
|
|
|
const bool load_x = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma;
|
|
const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
|
|
|
|
const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0);
|
|
const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1);
|
|
|
|
const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;
|
|
|
|
vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type);
|
|
|
|
const bool qx_needs_dequant = mmp == nullptr || x_non_contig;
|
|
const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig;
|
|
|
|
if (mmp == nullptr) {
|
|
// Fall back to dequant + f16 mulmat
|
|
mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16);
|
|
}
|
|
|
|
// Not implemented
|
|
GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT
|
|
|
|
const int x_ne = ne01 * ne00;
|
|
const int y_ne = ne11 * ne10;
|
|
const int d_ne = ne11 * ne01;
|
|
|
|
const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11));
|
|
const bool aligned = ne10 == kpad;
|
|
|
|
const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10);
|
|
|
|
vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned);
|
|
|
|
const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type);
|
|
const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
|
|
const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne;
|
|
const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
|
|
const uint64_t d_sz = sizeof(float) * d_ne;
|
|
|
|
vk_buffer d_D = extra->buffer_gpu.lock();
|
|
const uint64_t d_buf_offset = extra->offset;
|
|
GGML_ASSERT(d_D != nullptr);
|
|
GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03);
|
|
vk_buffer d_X;
|
|
uint64_t x_buf_offset = 0;
|
|
vk_buffer d_Y;
|
|
uint64_t y_buf_offset = 0;
|
|
if (load_x) {
|
|
d_Qx = ctx->prealloc_qx;
|
|
} else if (!src0_uma) {
|
|
d_Qx = extra_src0->buffer_gpu.lock();
|
|
qx_buf_offset = extra_src0->offset;
|
|
GGML_ASSERT(d_Qx != nullptr);
|
|
}
|
|
if (load_y) {
|
|
d_Qy = ctx->prealloc_qy;
|
|
} else if (!src1_uma) {
|
|
d_Qy = extra_src1->buffer_gpu.lock();
|
|
qy_buf_offset = extra_src1->offset;
|
|
GGML_ASSERT(d_Qy != nullptr);
|
|
}
|
|
if (qx_needs_dequant) {
|
|
d_X = ctx->prealloc_x;
|
|
GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03);
|
|
} else {
|
|
d_X = d_Qx;
|
|
x_buf_offset = qx_buf_offset;
|
|
GGML_ASSERT(qx_sz == x_sz);
|
|
}
|
|
if (qy_needs_dequant) {
|
|
d_Y = ctx->prealloc_y;
|
|
GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03);
|
|
} else {
|
|
d_Y = d_Qy;
|
|
y_buf_offset = qy_buf_offset;
|
|
GGML_ASSERT(qy_sz == y_sz);
|
|
}
|
|
|
|
vk_pipeline to_fp16_vk_0 = nullptr;
|
|
vk_pipeline to_fp16_vk_1 = nullptr;
|
|
|
|
if (x_non_contig) {
|
|
to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16);
|
|
} else {
|
|
to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type);
|
|
}
|
|
if (y_non_contig) {
|
|
to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16);
|
|
} else {
|
|
to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
|
|
}
|
|
GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT
|
|
GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT
|
|
|
|
// Allocate descriptor sets
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1);
|
|
if (qx_needs_dequant) {
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1);
|
|
}
|
|
if (qy_needs_dequant) {
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, 1);
|
|
}
|
|
if (split_k > 1) {
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1);
|
|
}
|
|
|
|
if (x_non_contig) {
|
|
ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
|
|
} else if (load_x || qx_needs_dequant) {
|
|
if (load_x) {
|
|
// copy data to device
|
|
ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0));
|
|
ctx->staging_offset = qx_sz * ne02 * ne03;
|
|
}
|
|
|
|
if (qx_needs_dequant) {
|
|
const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) };
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
|
|
}
|
|
}
|
|
if (y_non_contig) {
|
|
ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
|
|
} else if (load_y) {
|
|
ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1));
|
|
}
|
|
|
|
uint32_t stride_batch_x = ne00*ne01;
|
|
uint32_t stride_batch_y = ne10*ne11;
|
|
|
|
if (!ggml_vk_dim01_contiguous(src0) && !load_x && !qx_needs_dequant) {
|
|
stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
|
|
}
|
|
|
|
if (!ggml_vk_dim01_contiguous(src1) && !load_y && !qy_needs_dequant) {
|
|
stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
|
|
}
|
|
|
|
// compute
|
|
ggml_vk_matmul(ctx, subctx, pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT
|
|
|
|
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
|
|
// copy dst to host
|
|
float * d = (float *) ((char *) dst->data);
|
|
ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13);
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
|
|
std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
|
|
std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
|
|
#endif
|
|
GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT
|
|
GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT
|
|
|
|
const uint64_t ne00 = src0->ne[0];
|
|
const uint64_t ne01 = src0->ne[1];
|
|
const uint64_t ne02 = src0->ne[2];
|
|
const uint64_t ne03 = src0->ne[3];
|
|
|
|
const uint64_t ne10 = src1->ne[0];
|
|
const uint64_t ne11 = src1->ne[1];
|
|
const uint64_t ne12 = src1->ne[2];
|
|
const uint64_t ne13 = src1->ne[3];
|
|
|
|
GGML_ASSERT(ne11 == 1);
|
|
|
|
const uint64_t nb2 = dst->nb[2];
|
|
const uint64_t nb3 = dst->nb[3];
|
|
|
|
const uint64_t r2 = ne12 / ne02;
|
|
const uint64_t r3 = ne13 / ne03;
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
|
|
ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
|
|
|
|
vk_buffer d_Qx;
|
|
size_t qx_buf_offset = 0;
|
|
vk_buffer d_Qy;
|
|
size_t qy_buf_offset = 0;
|
|
|
|
bool src0_uma = false;
|
|
bool src1_uma = false;
|
|
|
|
if (ctx->device->uma) {
|
|
ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset);
|
|
ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
|
|
src0_uma = d_Qx != nullptr;
|
|
src1_uma = d_Qy != nullptr;
|
|
}
|
|
|
|
const bool load_x = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma;
|
|
const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
|
|
|
|
const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0);
|
|
const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1);
|
|
|
|
const bool f16_f32_kernel = src1->type == GGML_TYPE_F32;
|
|
|
|
const bool qx_needs_dequant = x_non_contig;
|
|
const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig;
|
|
|
|
const uint64_t x_ne = ne01 * ne00;
|
|
const uint64_t y_ne = ne11 * ne10;
|
|
const uint64_t d_ne = ne11 * ne01;
|
|
|
|
const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
|
|
const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
|
|
const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
|
|
const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
|
|
const uint64_t d_sz = sizeof(float) * d_ne;
|
|
|
|
vk_buffer d_D = extra->buffer_gpu.lock();
|
|
const uint64_t d_buf_offset = extra->offset;
|
|
GGML_ASSERT(d_D != nullptr);
|
|
vk_buffer d_X;
|
|
uint64_t x_buf_offset = 0;
|
|
vk_buffer d_Y;
|
|
uint64_t y_buf_offset = 0;
|
|
if (load_x) {
|
|
d_Qx = ctx->prealloc_qx;
|
|
} else if(!src1_uma) {
|
|
d_Qx = extra_src0->buffer_gpu.lock();
|
|
qx_buf_offset = extra_src0->offset;
|
|
GGML_ASSERT(d_Qx != nullptr);
|
|
}
|
|
if (load_y) {
|
|
d_Qy = ctx->prealloc_qy;
|
|
} else if(!src1_uma) {
|
|
d_Qy = extra_src1->buffer_gpu.lock();
|
|
qy_buf_offset = extra_src1->offset;
|
|
GGML_ASSERT(d_Qy != nullptr);
|
|
}
|
|
if (qx_needs_dequant) {
|
|
d_X = ctx->prealloc_x;
|
|
} else {
|
|
d_X = d_Qx;
|
|
x_buf_offset = qx_buf_offset;
|
|
GGML_ASSERT(qx_sz == x_sz);
|
|
}
|
|
if (qy_needs_dequant) {
|
|
d_Y = ctx->prealloc_y;
|
|
} else {
|
|
d_Y = d_Qy;
|
|
y_buf_offset = qy_buf_offset;
|
|
GGML_ASSERT(qy_sz == y_sz);
|
|
}
|
|
|
|
vk_pipeline to_fp16_vk_0 = nullptr;
|
|
vk_pipeline to_fp16_vk_1 = nullptr;
|
|
if (x_non_contig) {
|
|
to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type);
|
|
}
|
|
if (y_non_contig) {
|
|
to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type);
|
|
} else {
|
|
to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
|
|
}
|
|
vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type);
|
|
GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT
|
|
GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT
|
|
GGML_ASSERT(dmmv != nullptr);
|
|
|
|
// Allocate descriptor sets
|
|
if (qx_needs_dequant) {
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1);
|
|
}
|
|
if (qy_needs_dequant) {
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13);
|
|
}
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, dmmv, ne12 * ne13);
|
|
|
|
if (x_non_contig) {
|
|
GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
|
|
ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
|
|
} else if (load_x) {
|
|
// copy data to device
|
|
ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0));
|
|
}
|
|
if (y_non_contig) {
|
|
GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
|
|
ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
|
|
} else if (load_y) {
|
|
ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1));
|
|
}
|
|
|
|
for (uint64_t i13 = 0; i13 < ne13; i13++) {
|
|
const uint64_t i03 = i13 / r3;
|
|
for (uint64_t i12 = 0; i12 < ne12; i12++) {
|
|
const uint64_t i02 = i12 / r2;
|
|
|
|
const uint64_t it_idx0 = (i03 * ne02 + i02);
|
|
const uint64_t it_idx1 = (i13 * ne12 + i12);
|
|
const uint64_t x_offset = x_buf_offset + x_sz * it_idx0;
|
|
const uint64_t qy_offset = qy_buf_offset + qy_sz * it_idx1;
|
|
const uint64_t y_offset = y_buf_offset + y_sz * it_idx1;
|
|
const uint64_t d_offset = d_buf_offset + d_sz * it_idx1;
|
|
|
|
const uint64_t y_buffer_offset = (y_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
|
|
const uint64_t y_shader_offset = y_offset - y_buffer_offset;
|
|
|
|
const uint64_t d_buffer_offset = (d_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
|
|
const uint64_t d_shader_offset = d_offset - d_buffer_offset;
|
|
|
|
if (!y_non_contig && qy_needs_dequant) {
|
|
const std::vector<uint32_t> pc = { (uint32_t)ne11, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(y_ne / 32) };
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_1, { { d_Qy, qy_offset, qy_sz }, { d_Y, y_offset, y_sz } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)y_ne, 1, 1});
|
|
}
|
|
|
|
// compute
|
|
const std::array<uint32_t, 3> pc = { (uint32_t)ne00, (uint32_t)(y_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type))};
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1});
|
|
|
|
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
|
|
// copy dst to host
|
|
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_buffer_read_async(ctx, subctx, d_D, d_offset, d, sizeof(float) * d_ne);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
|
|
std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
|
|
std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
|
|
#endif
|
|
GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
|
|
GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
|
|
GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // NOLINT
|
|
GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // NOLINT
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F16);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
|
|
const uint64_t ne00 = src0->ne[0];
|
|
const uint64_t ne01 = src0->ne[1];
|
|
const uint64_t ne02 = src0->ne[2];
|
|
// const uint64_t ne03 = src0->ne[3];
|
|
|
|
const uint64_t ne10 = src1->ne[0];
|
|
const uint64_t ne11 = src1->ne[1];
|
|
const uint64_t ne12 = src1->ne[2];
|
|
// const uint64_t ne13 = src1->ne[3];
|
|
|
|
GGML_ASSERT(ne11 == 1);
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
|
|
ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
|
|
|
|
vk_buffer d_Qy;
|
|
size_t qy_buf_offset = 0;
|
|
|
|
bool src1_uma = false;
|
|
|
|
if (ctx->device->uma) {
|
|
ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
|
|
src1_uma = d_Qy != nullptr;
|
|
}
|
|
|
|
const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
|
|
|
|
const uint64_t x_ne = ne00 * ne01 * ne02;
|
|
const uint64_t y_ne = ne10 * ne11 * ne12;
|
|
const uint64_t d_ne = ne01 * ne11 * ne12;
|
|
|
|
const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
|
|
const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
|
|
const uint64_t d_sz = sizeof(float) * d_ne;
|
|
|
|
vk_buffer d_D = extra->buffer_gpu.lock();
|
|
const uint64_t d_buf_offset = extra->offset;
|
|
GGML_ASSERT(d_D != nullptr);
|
|
vk_buffer d_Qx = extra_src0->buffer_gpu.lock();
|
|
const uint64_t qx_buf_offset = extra_src0->offset;
|
|
GGML_ASSERT(d_Qx != nullptr);
|
|
if (load_y) {
|
|
d_Qy = ctx->prealloc_qy;
|
|
} else if (!src1_uma) {
|
|
d_Qy = extra_src1->buffer_gpu.lock();
|
|
qy_buf_offset = extra_src1->offset;
|
|
GGML_ASSERT(d_Qx != nullptr);
|
|
}
|
|
|
|
// Allocate descriptor sets
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, 1);
|
|
|
|
const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
|
|
const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset;
|
|
|
|
const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
|
|
const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;
|
|
|
|
if (load_y) {
|
|
ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1));
|
|
}
|
|
|
|
// compute
|
|
const std::array<uint32_t, 6> pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
|
|
|
|
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
|
|
// copy dst to host
|
|
float * d = (float *) dst->data;
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne);
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
|
|
std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
|
|
std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
|
|
#endif
|
|
GGML_ASSERT(!ggml_is_transposed(src0));
|
|
GGML_ASSERT(!ggml_is_transposed(src1));
|
|
GGML_ASSERT(!ggml_is_permuted(src0));
|
|
GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F16);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
|
|
const uint64_t ne00 = src0->ne[0];
|
|
const uint64_t ne01 = src0->ne[1];
|
|
const uint64_t ne02 = src0->ne[2];
|
|
// const uint64_t ne03 = src0->ne[3];
|
|
|
|
const uint64_t nb01 = src0->nb[1];
|
|
const uint64_t nb02 = src0->nb[2];
|
|
|
|
// const uint64_t ne10 = src1->ne[0];
|
|
const uint64_t ne11 = src1->ne[1];
|
|
const uint64_t ne12 = src1->ne[2];
|
|
// const uint64_t ne13 = src1->ne[3];
|
|
|
|
GGML_ASSERT(ne11 == 1);
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
|
|
ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
|
|
|
|
vk_buffer d_Qy = nullptr;
|
|
size_t qy_buf_offset = 0;
|
|
|
|
bool src1_uma = false;
|
|
|
|
if (ctx->device->uma) {
|
|
ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
|
|
src1_uma = d_Qy != nullptr;
|
|
}
|
|
|
|
const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
|
|
|
|
const uint64_t d_ne = ne01 * ne11 * ne12;
|
|
|
|
const uint32_t row_stride_x = nb01 / sizeof(ggml_fp16_t);
|
|
const uint32_t channel_stride_x = nb02 / sizeof(ggml_fp16_t);
|
|
|
|
const uint64_t qx_sz = ggml_nbytes(src0);
|
|
const uint64_t qy_sz = ggml_nbytes(src1);
|
|
const uint64_t d_sz = sizeof(float) * d_ne;
|
|
|
|
vk_buffer d_D = extra->buffer_gpu.lock();
|
|
const uint64_t d_buf_offset = extra->offset;
|
|
GGML_ASSERT(d_D != nullptr);
|
|
vk_buffer d_Qx = extra_src0->buffer_gpu.lock();
|
|
const uint64_t qx_buf_offset = extra_src0->offset;
|
|
GGML_ASSERT(d_Qx != nullptr);
|
|
if (load_y) {
|
|
d_Qy = ctx->prealloc_qy;
|
|
} else {
|
|
d_Qy = extra_src1->buffer_gpu.lock();
|
|
qy_buf_offset = extra_src1->offset;
|
|
GGML_ASSERT(d_Qx != nullptr);
|
|
}
|
|
|
|
// Allocate descriptor sets
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1);
|
|
|
|
const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
|
|
const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset;
|
|
|
|
const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
|
|
const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;
|
|
|
|
if (load_y) {
|
|
ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1));
|
|
}
|
|
|
|
// compute
|
|
const std::array<uint32_t, 7> pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
|
|
|
|
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
|
|
// copy dst to host
|
|
float * d = (float *) dst->data;
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne);
|
|
}
|
|
}
|
|
|
|
static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * dst) {
|
|
const uint64_t ne10 = src1->ne[0];
|
|
|
|
const uint64_t ne0 = dst->ne[0];
|
|
const uint64_t ne1 = dst->ne[1];
|
|
|
|
// TODO: find the optimal values for these
|
|
return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
|
|
(src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 || ggml_is_quantized(src1->type)) &&
|
|
dst->type == GGML_TYPE_F32 &&
|
|
((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU);
|
|
}
|
|
|
|
static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl;
|
|
#endif
|
|
if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
|
|
ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst);
|
|
} else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
|
|
ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst);
|
|
} else if (src1->ne[1] == 1 && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) {
|
|
ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst);
|
|
} else {
|
|
ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst);
|
|
}
|
|
}
|
|
|
|
// static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
|
|
//
|
|
// }
|
|
|
|
static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
// guaranteed to be an integer due to the check in ggml_can_repeat
|
|
const uint64_t ne0 = dst->ne[0];
|
|
const uint64_t ne1 = dst->ne[1];
|
|
const uint64_t ne2 = dst->ne[2];
|
|
const uint64_t ne3 = dst->ne[3];
|
|
|
|
const uint64_t ne00 = src0->ne[0];
|
|
const uint64_t ne01 = src0->ne[1];
|
|
const uint64_t ne02 = src0->ne[2];
|
|
const uint64_t ne03 = src0->ne[3];
|
|
|
|
const uint64_t nb0 = dst->nb[0];
|
|
const uint64_t nb1 = dst->nb[1];
|
|
const uint64_t nb2 = dst->nb[2];
|
|
const uint64_t nb3 = dst->nb[3];
|
|
|
|
const uint64_t nb00 = src0->nb[0];
|
|
const uint64_t nb01 = src0->nb[1];
|
|
const uint64_t nb02 = src0->nb[2];
|
|
const uint64_t nb03 = src0->nb[3];
|
|
|
|
const uint64_t nr0 = ne0/ne00;
|
|
const uint64_t nr1 = ne1/ne01;
|
|
const uint64_t nr2 = ne2/ne02;
|
|
const uint64_t nr3 = ne3/ne03;
|
|
|
|
// TODO: support for transposed / permuted tensors
|
|
GGML_ASSERT(nb0 == sizeof(float));
|
|
GGML_ASSERT(nb00 == sizeof(float));
|
|
GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
|
|
GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU);
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
|
|
|
|
const vk_buffer src_buf = extra_src0->buffer_gpu.lock();
|
|
const uint64_t src_offset = extra_src0->offset;
|
|
vk_buffer dst_buf = extra->buffer_gpu.lock();
|
|
const uint64_t dst_offset = extra->offset;
|
|
|
|
std::vector<vk::BufferCopy> copies;
|
|
|
|
for (uint64_t i3 = 0; i3 < nr3; i3++) {
|
|
for (uint64_t k3 = 0; k3 < ne03; k3++) {
|
|
for (uint64_t i2 = 0; i2 < nr2; i2++) {
|
|
for (uint64_t k2 = 0; k2 < ne02; k2++) {
|
|
for (uint64_t i1 = 0; i1 < nr1; i1++) {
|
|
for (uint64_t k1 = 0; k1 < ne01; k1++) {
|
|
for (uint64_t i0 = 0; i0 < nr0; i0++) {
|
|
copies.push_back({
|
|
src_offset + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0,
|
|
dst_offset + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01,
|
|
ne00*nb0,
|
|
});
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
ggml_vk_sync_buffers(subctx);
|
|
subctx->s->buffer.copyBuffer(src_buf->buffer, dst_buf->buffer, copies);
|
|
|
|
GGML_UNUSED(ctx);
|
|
GGML_UNUSED(src1);
|
|
}
|
|
|
|
|
|
static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) {
|
|
switch (op) {
|
|
case GGML_OP_ADD:
|
|
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_add_f32;
|
|
}
|
|
return nullptr;
|
|
case GGML_OP_GET_ROWS:
|
|
GGML_ASSERT(src1->type == GGML_TYPE_I32);
|
|
if (dst->type == GGML_TYPE_F16) {
|
|
return ctx->device->pipeline_get_rows[src0->type];
|
|
}
|
|
if (dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_get_rows_f32[src0->type];
|
|
}
|
|
return nullptr;
|
|
case GGML_OP_MUL:
|
|
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_mul_f32;
|
|
}
|
|
return nullptr;
|
|
case GGML_OP_SCALE:
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_scale_f32;
|
|
}
|
|
return nullptr;
|
|
case GGML_OP_SQR:
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_sqr_f32;
|
|
}
|
|
return nullptr;
|
|
case GGML_OP_CLAMP:
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_clamp_f32;
|
|
}
|
|
return nullptr;
|
|
case GGML_OP_CPY:
|
|
case GGML_OP_CONT:
|
|
case GGML_OP_DUP:
|
|
return ggml_vk_get_cpy_pipeline(ctx, src0->type, dst->type);
|
|
case GGML_OP_NORM:
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_norm_f32;
|
|
}
|
|
return nullptr;
|
|
case GGML_OP_RMS_NORM:
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_rms_norm_f32;
|
|
}
|
|
return nullptr;
|
|
case GGML_OP_UNARY:
|
|
switch (ggml_get_unary_op(dst)) {
|
|
case GGML_UNARY_OP_SILU:
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_silu_f32;
|
|
}
|
|
break;
|
|
case GGML_UNARY_OP_GELU:
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_gelu_f32;
|
|
}
|
|
break;
|
|
case GGML_UNARY_OP_RELU:
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_relu_f32;
|
|
}
|
|
break;
|
|
default:
|
|
break;
|
|
}
|
|
return nullptr;
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_diag_mask_inf_f32;
|
|
}
|
|
return nullptr;
|
|
case GGML_OP_SOFT_MAX:
|
|
if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && (src2 == nullptr || src2->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_soft_max_f32;
|
|
}
|
|
return nullptr;
|
|
case GGML_OP_ROPE:
|
|
{
|
|
const int mode = ((const int32_t *) dst->op_params)[2];
|
|
const bool is_neox = mode & 2;
|
|
const bool is_glm = mode & 4;
|
|
|
|
if (is_glm) {
|
|
return nullptr;
|
|
}
|
|
|
|
if (is_neox) {
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_rope_neox_f32;
|
|
}
|
|
if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
|
|
return ctx->device->pipeline_rope_neox_f16;
|
|
}
|
|
} else {
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
return ctx->device->pipeline_rope_f32;
|
|
}
|
|
if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
|
|
return ctx->device->pipeline_rope_f16;
|
|
}
|
|
}
|
|
return nullptr;
|
|
}
|
|
case GGML_OP_ARGSORT:
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) {
|
|
return ctx->device->pipeline_argsort_f32;
|
|
}
|
|
return nullptr;
|
|
default:
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) {
|
|
switch(op) {
|
|
case GGML_OP_REPEAT:
|
|
return ggml_vk_op_repeat;
|
|
default:
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
template<typename PC>
|
|
static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
|
|
if (src1 != nullptr) {
|
|
std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
|
|
}
|
|
if (src2 != nullptr) {
|
|
std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", backend=" << src2->backend << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3];
|
|
}
|
|
std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")" << std::endl;
|
|
#endif
|
|
GGML_ASSERT(!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))); // NOLINT
|
|
GGML_ASSERT(op == GGML_OP_CPY || ggml_vk_dim01_contiguous(src0)); // NOLINT
|
|
GGML_ASSERT(dst->extra != nullptr);
|
|
const uint64_t ne00 = src0->ne[0];
|
|
const uint64_t ne01 = src0->ne[1];
|
|
const uint64_t ne02 = src0->ne[2];
|
|
const uint64_t ne03 = src0->ne[3];
|
|
const uint64_t ne0 = ne00 * ne01;
|
|
const bool use_src1 = src1 != nullptr;
|
|
const uint64_t ne10 = use_src1 ? src1->ne[0] : 0;
|
|
const uint64_t ne11 = use_src1 ? src1->ne[1] : 0;
|
|
const uint64_t ne12 = use_src1 ? src1->ne[2] : 0;
|
|
const uint64_t ne13 = use_src1 ? src1->ne[3] : 0;
|
|
const uint64_t ne1 = ne10 * ne11;
|
|
// const uint64_t nb10 = use_src1 ? src1->nb[0] : 0;
|
|
const uint64_t nb2 = dst->nb[2];
|
|
const uint64_t nb3 = dst->nb[3];
|
|
|
|
const bool use_src2 = src2 != nullptr;
|
|
const uint64_t ne2 = use_src2 ? src2->ne[0] * src2->ne[1] : 0;
|
|
|
|
vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op);
|
|
ggml_vk_func_t op_func;
|
|
|
|
if (pipeline == nullptr) {
|
|
op_func = ggml_vk_op_get_func(op);
|
|
if (op_func == nullptr) {
|
|
std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(op) << " for " << ggml_type_name(src0->type);
|
|
if (src1 != nullptr) {
|
|
std::cerr << " and " << ggml_type_name(src1->type);
|
|
}
|
|
std::cerr << " to " << ggml_type_name(dst->type) << std::endl;
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
op_func(ctx, subctx, src0, src1, dst);
|
|
return;
|
|
}
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
|
|
ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
|
|
ggml_tensor_extra_gpu * extra_src2 = use_src2 ? (ggml_tensor_extra_gpu *) src2->extra : nullptr;
|
|
|
|
vk_buffer d_X = nullptr;
|
|
size_t x_buf_offset = 0;
|
|
vk_buffer d_Y = nullptr;
|
|
size_t y_buf_offset = 0;
|
|
vk_buffer d_Z = nullptr;
|
|
size_t z_buf_offset = 0;
|
|
|
|
bool src0_uma = false;
|
|
bool src1_uma = false;
|
|
bool src2_uma = false;
|
|
|
|
if (ctx->device->uma) {
|
|
ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset);
|
|
src0_uma = d_X != nullptr;
|
|
if (use_src1) {
|
|
ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset);
|
|
src1_uma = d_Y != nullptr;
|
|
}
|
|
if (use_src2) {
|
|
ggml_vk_host_get(ctx, src1->data, d_Z, z_buf_offset);
|
|
src2_uma = d_Z != nullptr;
|
|
}
|
|
}
|
|
|
|
const bool transfer_src0 = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma;
|
|
const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
|
|
const bool transfer_src2 = use_src2 && src2->backend != GGML_BACKEND_TYPE_GPU && !src2_uma;
|
|
|
|
uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, ctx->device->properties.limits.minStorageBufferOffsetAlignment);
|
|
uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0;
|
|
uint64_t z_sz = use_src2 ? ggml_vk_align_size(ggml_type_size(src2->type) * ne2, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0;
|
|
uint64_t d_sz = ggml_type_size(dst->type) * ne0;
|
|
|
|
vk_buffer d_D = extra->buffer_gpu.lock();
|
|
|
|
// Workaround for tiny tensor inputs on ROPE
|
|
if (use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU && y_sz > d_D->size) {
|
|
y_sz = VK_WHOLE_SIZE;
|
|
}
|
|
|
|
GGML_ASSERT(d_D != nullptr);
|
|
uint64_t d_buf_offset = (extra->offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
|
|
GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY); // NOLINT
|
|
if (transfer_src0) {
|
|
d_X = ctx->prealloc_qx;
|
|
} else if(!src0_uma) {
|
|
d_X = extra_src0->buffer_gpu.lock();
|
|
x_buf_offset = extra_src0->offset;
|
|
GGML_ASSERT(d_X != nullptr);
|
|
}
|
|
if (transfer_src1) {
|
|
d_Y = ctx->prealloc_qy;
|
|
} else if (use_src1 && !src1_uma) {
|
|
d_Y = extra_src1->buffer_gpu.lock();
|
|
y_buf_offset = extra_src1->offset;
|
|
GGML_ASSERT(d_Y != nullptr);
|
|
}
|
|
|
|
GGML_ASSERT(!transfer_src2);
|
|
if (use_src2 && !src2_uma) {
|
|
d_Z = extra_src2->buffer_gpu.lock();
|
|
z_buf_offset = extra_src2->offset;
|
|
GGML_ASSERT(d_Z != nullptr);
|
|
}
|
|
|
|
if (op == GGML_OP_CPY) {
|
|
GGML_ASSERT(!transfer_src0);
|
|
GGML_ASSERT(!transfer_src1);
|
|
x_sz = ggml_nbytes(src0);
|
|
d_sz = ggml_nbytes(dst);
|
|
|
|
if (extra_src0->offset + x_sz >= d_X->size) {
|
|
x_sz = VK_WHOLE_SIZE;
|
|
}
|
|
if (extra->offset + d_sz >= d_D->size) {
|
|
d_sz = VK_WHOLE_SIZE;
|
|
}
|
|
}
|
|
|
|
std::array<uint32_t, 3> elements;
|
|
|
|
// copy src0 to device
|
|
if (transfer_src0) {
|
|
ggml_vk_h2d_tensor_2d(ctx, subctx, d_X, 0, src0, 0, 0, ggml_nrows(src0));
|
|
ctx->staging_offset = x_sz * ne02 * ne03;
|
|
}
|
|
if (transfer_src1) {
|
|
ggml_vk_h2d_tensor_2d(ctx, subctx, d_Y, 0, src1, 0, 0, ggml_nrows(src1));
|
|
}
|
|
|
|
// Single call if dimension 2 is contiguous
|
|
if (op == GGML_OP_CPY || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) {
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1);
|
|
|
|
switch (dst->op) {
|
|
case GGML_OP_NORM:
|
|
case GGML_OP_RMS_NORM:
|
|
case GGML_OP_SOFT_MAX:
|
|
elements = { (uint32_t)ggml_nrows(src0), 1, 1 };
|
|
break;
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
case GGML_OP_ROPE:
|
|
elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 };
|
|
break;
|
|
default:
|
|
elements = { (uint32_t)ggml_nelements(src0), 1, 1 };
|
|
break;
|
|
}
|
|
|
|
if (op != GGML_OP_CPY) {
|
|
if (x_sz != VK_WHOLE_SIZE) {
|
|
x_sz *= ne02 * ne03;
|
|
}
|
|
if (y_sz != VK_WHOLE_SIZE) {
|
|
y_sz *= ne12 * ne13;
|
|
}
|
|
if (d_sz != VK_WHOLE_SIZE) {
|
|
d_sz *= ne02 * ne03;
|
|
}
|
|
}
|
|
|
|
if (op == GGML_OP_SOFT_MAX) {
|
|
// Empty src1 and src2 are possible on soft_max, but the shader needs buffers
|
|
vk_subbuffer subbuf_y;
|
|
if (use_src1) {
|
|
subbuf_y = { d_Y, y_buf_offset, y_sz };
|
|
} else {
|
|
subbuf_y = { ctx->prealloc_y, 0, ctx->prealloc_y->size };
|
|
}
|
|
|
|
vk_subbuffer subbuf_z;
|
|
if (use_src2) {
|
|
subbuf_z = { d_Z, z_buf_offset, z_sz };
|
|
} else {
|
|
subbuf_z = { ctx->prealloc_y, 0, ctx->prealloc_y->size };
|
|
}
|
|
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
|
|
} else if (use_src1) {
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
|
|
} else {
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
|
|
}
|
|
if (dst->backend == GGML_BACKEND_TYPE_CPU && op == GGML_OP_CPY) {
|
|
ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst);
|
|
} else if(dst->backend == GGML_BACKEND_TYPE_CPU) {
|
|
// copy dst to host
|
|
float * d = (float *) dst->data;
|
|
ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz);
|
|
}
|
|
} else {
|
|
GGML_ASSERT(op != GGML_OP_SOFT_MAX);
|
|
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, ne02 * ne03);
|
|
|
|
switch (dst->op) {
|
|
case GGML_OP_NORM:
|
|
case GGML_OP_RMS_NORM:
|
|
case GGML_OP_SOFT_MAX:
|
|
elements = { (uint32_t)ne01, 1, 1 };
|
|
break;
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
case GGML_OP_ROPE:
|
|
elements = { (uint32_t)ne01, (uint32_t)ne00, 1 };
|
|
break;
|
|
default:
|
|
elements = { (uint32_t)ne0, 1, 1 };
|
|
break;
|
|
}
|
|
|
|
for (uint64_t i03 = 0; i03 < ne03; i03++) {
|
|
for (uint64_t i02 = 0; i02 < ne02; i02++) {
|
|
const uint32_t it_idx0 = (i03 * ne02 + i02);
|
|
const uint32_t it_idx1 = use_src1 ? ((i03 % ne13) * ne12 + (i02 % ne12)) : 0;
|
|
const uint32_t x_offset = x_sz * it_idx0;
|
|
const uint32_t y_offset = y_sz * it_idx1;
|
|
const uint32_t d_offset = d_sz * it_idx0;
|
|
|
|
if (use_src1) {
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements);
|
|
} else {
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements);
|
|
}
|
|
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
|
|
// copy dst to host
|
|
ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f });
|
|
}
|
|
|
|
static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f });
|
|
}
|
|
|
|
static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
const uint32_t src0_type_size = ggml_type_size(src0->type);
|
|
const uint32_t src1_type_size = ggml_type_size(src1->type);
|
|
const uint32_t dst_type_size = ggml_type_size(dst->type);
|
|
|
|
ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ADD, {
|
|
(uint32_t)ggml_nelements(src0),
|
|
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
|
|
(uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
|
|
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
|
|
0,
|
|
0.0f, 0.0f,
|
|
});
|
|
}
|
|
|
|
static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
const uint32_t src0_type_size = ggml_type_size(src0->type);
|
|
const uint32_t src1_type_size = ggml_type_size(src1->type);
|
|
const uint32_t dst_type_size = ggml_type_size(dst->type);
|
|
|
|
ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_MUL, {
|
|
(uint32_t)ggml_nelements(src0),
|
|
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
|
|
(uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
|
|
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
|
|
0,
|
|
0.0f, 0.0f,
|
|
});
|
|
}
|
|
|
|
static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
|
float * op_params = (float *)dst->op_params;
|
|
const uint32_t src0_type_size = ggml_type_size(src0->type);
|
|
const uint32_t dst_type_size = ggml_type_size(dst->type);
|
|
|
|
ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SCALE, {
|
|
(uint32_t)ggml_nelements(src0),
|
|
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
|
|
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
|
|
0,
|
|
op_params[0], 0.0f
|
|
});
|
|
}
|
|
|
|
static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
|
const uint32_t src0_type_size = ggml_type_size(src0->type);
|
|
const uint32_t dst_type_size = ggml_type_size(dst->type);
|
|
|
|
ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, {
|
|
(uint32_t)ggml_nelements(src0),
|
|
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
|
|
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
|
|
0,
|
|
0.0f, 0.0f,
|
|
});
|
|
}
|
|
|
|
static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
|
float * op_params = (float *)dst->op_params;
|
|
const uint32_t src0_type_size = ggml_type_size(src0->type);
|
|
const uint32_t dst_type_size = ggml_type_size(dst->type);
|
|
|
|
ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CLAMP, {
|
|
(uint32_t)ggml_nelements(src0),
|
|
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
|
|
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
|
|
0,
|
|
op_params[0], op_params[1],
|
|
});
|
|
}
|
|
|
|
static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
const uint32_t src0_type_size = ggml_type_size(src0->type);
|
|
const uint32_t dst_type_size = ggml_type_size(dst->type);
|
|
const uint32_t d_offset = (extra->offset % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size;
|
|
|
|
ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, {
|
|
(uint32_t)ggml_nelements(src0),
|
|
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
|
|
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
|
|
d_offset,
|
|
0.0f, 0.0f,
|
|
});
|
|
}
|
|
|
|
static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
|
ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f });
|
|
}
|
|
|
|
static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
|
float * op_params = (float *)dst->op_params;
|
|
ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f });
|
|
}
|
|
|
|
static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
|
ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f });
|
|
}
|
|
|
|
static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
|
int32_t * op_params = (int32_t *)dst->op_params;
|
|
ggml_vk_op_f32<vk_op_diag_mask_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] });
|
|
}
|
|
|
|
static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
|
|
float * op_params = (float *)dst->op_params;
|
|
|
|
float scale = op_params[0];
|
|
float max_bias = op_params[1];
|
|
|
|
const uint32_t ncols = (uint32_t)src0->ne[0];
|
|
const uint32_t nrows_x = (uint32_t)ggml_nrows(src0);
|
|
const uint32_t nrows_y = (uint32_t)src0->ne[1];
|
|
|
|
const uint32_t n_head_kv = nrows_x/nrows_y;
|
|
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
|
|
|
|
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
|
|
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
|
|
|
|
ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_SOFT_MAX, {
|
|
ncols,
|
|
nrows_y,
|
|
src2 != nullptr ? (uint32_t)1 : (uint32_t)0,
|
|
scale, max_bias,
|
|
m0, m1,
|
|
n_head_log2,
|
|
});
|
|
}
|
|
|
|
static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
const int n_dims = ((int32_t *) dst->op_params)[1];
|
|
const int mode = ((int32_t *) dst->op_params)[2];
|
|
// const int n_ctx = ((int32_t *) dst->op_params)[3];
|
|
const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
|
|
const float freq_base = ((float *) dst->op_params)[5];
|
|
const float freq_scale = ((float *) dst->op_params)[6];
|
|
const float ext_factor = ((float *) dst->op_params)[7];
|
|
const float attn_factor = ((float *) dst->op_params)[8];
|
|
const float beta_fast = ((float *) dst->op_params)[9];
|
|
const float beta_slow = ((float *) dst->op_params)[10];
|
|
|
|
const bool is_neox = mode & 2;
|
|
const bool is_glm = mode & 4;
|
|
|
|
GGML_ASSERT(!is_glm);
|
|
|
|
float corr_dims[2];
|
|
ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
|
|
|
|
if (is_neox) {
|
|
const float theta_scale = powf(freq_base, -2.0f/n_dims);
|
|
const float inv_ndims = -1.0f / n_dims;
|
|
ggml_vk_op_f32<vk_op_rope_neox_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims });
|
|
} else {
|
|
ggml_vk_op_f32<vk_op_rope_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f });
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
|
int32_t * op_params = (int32_t *)dst->op_params;
|
|
ggml_vk_op_f32<vk_op_argsort_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { (uint32_t)src0->ne[0], ((ggml_sort_order) op_params[0]) == GGML_SORT_ORDER_ASC });
|
|
}
|
|
|
|
static void ggml_vk_nop(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
|
// If backend is CPU, data from src0 has to be copied off the device
|
|
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
|
|
ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
|
|
vk_buffer d_D = extra_src0->buffer_gpu.lock();
|
|
ggml_vk_sync_buffers(subctx);
|
|
ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, dst->data, d_D->size);
|
|
}
|
|
}
|
|
|
|
#ifdef GGML_VULKAN_RUN_TESTS
|
|
static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) {
|
|
if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) {
|
|
return;
|
|
}
|
|
i0 = std::max(i0, 5);
|
|
i1 = std::max(i1, 5);
|
|
i2 = std::max(i2, 0);
|
|
fprintf(stderr, " ");
|
|
for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
|
|
fprintf(stderr, "%7d ", idx1);
|
|
}
|
|
fprintf(stderr, "\n");
|
|
for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
|
|
fprintf(stderr, "%7d: ", idx0);
|
|
for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
|
|
if (idx0 >= 0 && idx0 < ne0 && idx1 >= 0 && idx1 < ne1) {
|
|
float val;
|
|
if (type == GGML_TYPE_F32) {
|
|
val = *((const float *) data + i2*ne1*ne0 + idx1*ne0 + idx0);
|
|
} else if (type == GGML_TYPE_F16) {
|
|
val = ggml_fp16_to_fp32(*((const ggml_fp16_t *) data + i2*ne1*ne0 + idx1*ne0 + idx0));
|
|
}
|
|
fprintf(stderr, "% 7.2f ", val);
|
|
} else {
|
|
fprintf(stderr, " ");
|
|
}
|
|
}
|
|
fprintf(stderr, "\n");
|
|
}
|
|
}
|
|
|
|
template <typename X_TYPE, typename Y_TYPE>
|
|
static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")" << std::endl;
|
|
#endif
|
|
const size_t x_ne = m * k * batch;
|
|
const size_t y_ne = k * n * batch;
|
|
const size_t d_ne = m * n * batch;
|
|
|
|
vk_pipeline p;
|
|
std::string shname;
|
|
if (shader_size == 0) {
|
|
if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f32->a_s;
|
|
shname = "F32_ALIGNED_S";
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f16_f32->a_s;
|
|
shname = "F16_F32_ALIGNED_S";
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f16->a_s;
|
|
shname = "F16_ALIGNED_S";
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
} else if (shader_size == 1) {
|
|
if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f32->a_m;
|
|
shname = "F32_ALIGNED_M";
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f16_f32->a_m;
|
|
shname = "F16_F32_ALIGNED_M";
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f16->a_m;
|
|
shname = "F16_ALIGNED_M";
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
} else if (shader_size == 2) {
|
|
if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f32->a_l;
|
|
shname = "F32_ALIGNED_L";
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f16_f32->a_l;
|
|
shname = "F16_F32_ALIGNED_L";
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f16->a_l;
|
|
shname = "F16_ALIGNED_L";
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
} else {
|
|
GGML_ASSERT(0);
|
|
}
|
|
|
|
const size_t kpad = ggml_vk_align_size(k, p->align);
|
|
|
|
if (k != kpad) {
|
|
if (shader_size == 0) {
|
|
if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f32->s;
|
|
shname = "F32_S";
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f16_f32->s;
|
|
shname = "F16_F32_S";
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f16->s;
|
|
shname = "F16_S";
|
|
}
|
|
} else if (shader_size == 1) {
|
|
if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f32->m;
|
|
shname = "F32_M";
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f16_f32->m;
|
|
shname = "F16_F32_M";
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f16->m;
|
|
shname = "F16_M";
|
|
}
|
|
} else if (shader_size == 2) {
|
|
if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f32->l;
|
|
shname = "F32_L";
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f16_f32->l;
|
|
shname = "F16_F32_L";
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
|
|
p = ctx->device->pipeline_matmul_f16->l;
|
|
shname = "F16_L";
|
|
}
|
|
}
|
|
}
|
|
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it);
|
|
if (split_k > 1) {
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);
|
|
|
|
if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
|
|
// Resize buffer
|
|
if (ctx->prealloc_split_k != nullptr) {
|
|
ggml_vk_destroy_buffer(ctx->prealloc_split_k);
|
|
}
|
|
ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
}
|
|
}
|
|
|
|
vk_buffer d_X = ggml_vk_create_buffer_check(ctx, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
vk_buffer d_Y = ggml_vk_create_buffer_check(ctx, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
vk_buffer d_D = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
|
|
X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne);
|
|
Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne);
|
|
float* d = (float *) malloc(sizeof(float) * d_ne);
|
|
|
|
for (size_t i = 0; i < x_ne; i++) {
|
|
if (std::is_same<float, X_TYPE>()) {
|
|
x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>()) {
|
|
x[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f);
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
for (size_t i = 0; i < y_ne; i++) {
|
|
if (std::is_same<float, Y_TYPE>()) {
|
|
// y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
|
|
y[i] = (i % k == i / k) ? 1.0f : 0.0f;
|
|
} else if (std::is_same<ggml_fp16_t, Y_TYPE>()) {
|
|
// y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f);
|
|
y[i] = ggml_fp32_to_fp16((i % k == i / k) ? 1.0f : 0.0f);
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
ggml_vk_buffer_write(ctx, d_X, 0, x, sizeof(X_TYPE) * k * m * batch);
|
|
ggml_vk_buffer_write(ctx, d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch);
|
|
|
|
vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
|
|
for (size_t i = 0; i < num_it; i++) {
|
|
ggml_vk_ctx_begin(ctx, subctx);
|
|
ggml_vk_matmul(ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n);
|
|
ggml_vk_ctx_end(subctx);
|
|
}
|
|
|
|
auto begin = std::chrono::high_resolution_clock::now();
|
|
ggml_vk_submit(subctx, ctx->fence);
|
|
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences");
|
|
ctx->device->device.resetFences({ ctx->fence });
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
double time = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
|
|
|
|
// copy dst to host
|
|
ggml_vk_buffer_read(ctx, d_D, 0, d, sizeof(float) * d_ne);
|
|
|
|
float * d_chk = (float *) malloc(sizeof(float) * d_ne);
|
|
|
|
ggml_init_params iparams = {
|
|
/*.mem_size =*/ 1024*1024*1024,
|
|
/*.mem_buffer =*/ NULL,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
ggml_context * ggml_ctx = ggml_init(iparams);
|
|
|
|
ggml_type src0_type;
|
|
ggml_type src1_type;
|
|
|
|
if (std::is_same<float, X_TYPE>()) {
|
|
src0_type = GGML_TYPE_F32;
|
|
} else if (std::is_same<ggml_fp16_t, X_TYPE>()) {
|
|
src0_type = GGML_TYPE_F16;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
if (std::is_same<float, Y_TYPE>()) {
|
|
src1_type = GGML_TYPE_F32;
|
|
} else if (std::is_same<ggml_fp16_t, Y_TYPE>()) {
|
|
src1_type = GGML_TYPE_F16;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, src0_type, k, m, batch);
|
|
ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, src1_type, k, n, batch);
|
|
ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml);
|
|
|
|
src0_ggml->data = x;
|
|
src1_ggml->data = y;
|
|
tensor_ggml->data = d_chk;
|
|
|
|
ctx->disable = true;
|
|
|
|
ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
|
|
ggml_build_forward_expand(cgraph, tensor_ggml);
|
|
|
|
ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1);
|
|
|
|
ctx->disable = false;
|
|
|
|
ggml_free(ggml_ctx);
|
|
|
|
double avg_err = 0.0;
|
|
int first_err_n = -1;
|
|
int first_err_m = -1;
|
|
int first_err_b = -1;
|
|
|
|
for (size_t i = 0; i < m*n*batch; i++) {
|
|
double err = std::fabs(d[i] - d_chk[i]);
|
|
avg_err += err;
|
|
|
|
if (err > 0.05f && first_err_n == -1) {
|
|
first_err_b = i / (m * n);
|
|
first_err_n = (i % (m * n)) / m;
|
|
first_err_m = (i % (m * n)) % m;
|
|
}
|
|
}
|
|
|
|
avg_err /= m * n;
|
|
|
|
std::cerr << "TEST " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time / num_it << "ms avg_err=" << avg_err << std::endl;
|
|
|
|
if (avg_err > 0.1) {
|
|
std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
|
|
std::cerr << "Actual result: " << std::endl << std::endl;
|
|
ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
|
|
std::cerr << std::endl;
|
|
ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n + 15, first_err_b);
|
|
std::cerr << "Expected result: " << std::endl << std::endl;
|
|
ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
|
|
|
|
if (split_k > 1) {
|
|
float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
|
|
ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
|
|
|
|
std::cerr << "d_buf0: " << std::endl << std::endl;
|
|
ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
|
|
|
|
std::cerr << "d_buf1: " << std::endl << std::endl;
|
|
ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
|
|
|
|
std::cerr << "d_buf2: " << std::endl << std::endl;
|
|
ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
|
|
|
|
std::cerr << "d_buf3: " << std::endl << std::endl;
|
|
ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
|
|
|
|
free(split_k_buf);
|
|
}
|
|
}
|
|
|
|
free(d_chk);
|
|
|
|
ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue);
|
|
ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue);
|
|
|
|
ggml_vk_destroy_buffer(d_X);
|
|
ggml_vk_destroy_buffer(d_Y);
|
|
ggml_vk_destroy_buffer(d_D);
|
|
|
|
ggml_pipeline_cleanup(p);
|
|
ggml_pipeline_cleanup(ctx->device->pipeline_matmul_split_k_reduce);
|
|
|
|
free(x);
|
|
free(y);
|
|
free(d);
|
|
}
|
|
|
|
static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
|
|
if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
|
|
return;
|
|
}
|
|
i0 = std::max(i0, 5);
|
|
i1 = std::max(i1, 5);
|
|
i2 = std::max(i2, 0);
|
|
i3 = std::max(i3, 0);
|
|
fprintf(stderr, " ");
|
|
for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
|
|
fprintf(stderr, "%7d ", idx1);
|
|
}
|
|
fprintf(stderr, "\n");
|
|
for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
|
|
fprintf(stderr, "%7d: ", idx0);
|
|
for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
|
|
if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) {
|
|
float val;
|
|
if (tensor->type == GGML_TYPE_F32) {
|
|
val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
|
|
} else if (tensor->type == GGML_TYPE_F16) {
|
|
val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]));
|
|
}
|
|
fprintf(stderr, "% 7.2f ", val);
|
|
} else {
|
|
fprintf(stderr, " ");
|
|
}
|
|
}
|
|
fprintf(stderr, "\n");
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_t ne1, size_t ne2, size_t ne3) {
|
|
const size_t ne = ne0 * ne1 * ne2 * ne3;
|
|
|
|
ggml_init_params iparams = {
|
|
/*.mem_size =*/ 1024*1024*1024,
|
|
/*.mem_buffer =*/ NULL,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
ggml_context * ggml_ctx = ggml_init(iparams);
|
|
|
|
ggml_tensor * tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne2, ne1, ne3); // NOLINT
|
|
ggml_tensor * result_tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne1, ne2, ne3);
|
|
|
|
float * data = (float *) ggml_vk_host_malloc(ctx, ggml_nbytes(tensor));
|
|
tensor->data = data;
|
|
|
|
float * result_data = (float *) malloc(ggml_nbytes(tensor));
|
|
result_tensor->data = result_data;
|
|
|
|
// Permute
|
|
{
|
|
size_t tmp = tensor->nb[2];
|
|
tensor->nb[2] = tensor->nb[1];
|
|
tensor->nb[1] = tmp;
|
|
|
|
tensor->ne[2] = ne2;
|
|
tensor->ne[1] = ne1;
|
|
}
|
|
|
|
for (size_t i = 0; i < ne; i++) {
|
|
data[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
|
|
}
|
|
|
|
vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
|
|
ggml_vk_ctx_begin(ctx, subctx);
|
|
|
|
vk_buffer buffer = ggml_vk_create_buffer_check(ctx, ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
|
|
ggml_vk_h2d_tensor_2d(ctx, subctx, buffer, 0, tensor, 0, 0, ggml_nrows(tensor));
|
|
|
|
ggml_vk_ctx_end(subctx);
|
|
ggml_vk_submit(subctx, ctx->fence);
|
|
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_h2d_nc waitForFences");
|
|
ctx->device->device.resetFences({ ctx->fence });
|
|
|
|
ggml_vk_buffer_read(ctx, buffer, 0, result_data, ggml_nbytes(tensor));
|
|
|
|
double avg_err = 0.0;
|
|
int first_err_i0 = -1;
|
|
int first_err_i1 = -1;
|
|
int first_err_i2 = -1;
|
|
int first_err_i3 = -1;
|
|
|
|
for (size_t i3 = 0; i3 < ne3; i3++) {
|
|
for (size_t i2 = 0; i2 < ne2; i2++) {
|
|
for (size_t i1 = 0; i1 < ne1; i1++) {
|
|
for (size_t i0 = 0; i0 < ne0; i0++) {
|
|
float correct = *(float *) ((char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
|
|
float result = *(float *) ((char *) result_data + i3*ne2*ne1*ne0*sizeof(float) + i2*ne1*ne0*sizeof(float) + i1*ne0*sizeof(float) + i0*sizeof(float));
|
|
double err = std::fabs(result - correct);
|
|
|
|
avg_err += err;
|
|
|
|
if (err > 0.05f && first_err_i0 == -1) {
|
|
first_err_i0 = i0;
|
|
first_err_i1 = i1;
|
|
first_err_i2 = i2;
|
|
first_err_i3 = i3;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
avg_err /= ne;
|
|
|
|
std::cerr << "TEST nc copy ne0=" << ne0 << " ne1=" << ne1 << " ne2=" << ne2 << " ne3=" << ne3 << " avg_err=" << avg_err << std::endl;
|
|
|
|
if (avg_err > 0.1) {
|
|
std::cerr << "i0 = " << first_err_i0 << " i1 = " << first_err_i1 << " i2 = " << first_err_i2 << " i3 = " << first_err_i3 << std::endl;
|
|
std::cerr << "Actual result: " << std::endl << std::endl;
|
|
ggml_vk_print_tensor_area(result_tensor, first_err_i0, first_err_i1, first_err_i2, first_err_i3);
|
|
std::cerr << "Expected result: " << std::endl << std::endl;
|
|
ggml_vk_print_tensor_area(tensor, first_err_i0, first_err_i1, first_err_i2, first_err_i3);
|
|
}
|
|
|
|
ggml_free(ggml_ctx);
|
|
|
|
ggml_vk_destroy_buffer(buffer);
|
|
|
|
ggml_vk_host_free(ctx, data);
|
|
free(result_data);
|
|
}
|
|
|
|
static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool pinned) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_test_transfer(" << ne << ")" << std::endl;
|
|
#endif
|
|
// Check transfers are correct
|
|
vk_buffer buffer = ggml_vk_create_buffer_check(ctx, sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
|
|
float * x;
|
|
float * y;
|
|
if (pinned) {
|
|
x = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne);
|
|
y = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne);
|
|
} else {
|
|
x = (float *) malloc(sizeof(float) * ne);
|
|
y = (float *) malloc(sizeof(float) * ne);
|
|
}
|
|
|
|
for (size_t i = 0; i < ne; i++) {
|
|
x[i] = rand() / (float)RAND_MAX;
|
|
}
|
|
|
|
vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
|
|
ggml_vk_ctx_begin(ctx, subctx);
|
|
|
|
auto begin = std::chrono::high_resolution_clock::now();
|
|
|
|
ggml_vk_buffer_write_async(ctx, subctx, buffer, 0, x, sizeof(float) * ne);
|
|
|
|
for (auto& cpy : subctx->in_memcpys) {
|
|
memcpy(cpy.dst, cpy.src, cpy.n);
|
|
}
|
|
subctx->in_memcpys.clear();
|
|
|
|
ggml_vk_ctx_end(subctx);
|
|
ggml_vk_submit(subctx, ctx->fence);
|
|
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences");
|
|
ctx->device->device.resetFences({ ctx->fence });
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
|
|
double ms_to_gpu = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
|
|
|
|
ggml_vk_ctx_begin(ctx, subctx);
|
|
|
|
begin = std::chrono::high_resolution_clock::now();
|
|
|
|
ggml_vk_buffer_read_async(ctx, subctx, buffer, 0, y, sizeof(float) * ne);
|
|
|
|
ggml_vk_ctx_end(subctx);
|
|
ggml_vk_submit(subctx, ctx->fence);
|
|
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences");
|
|
ctx->device->device.resetFences({ ctx->fence });
|
|
|
|
for (auto& cpy : subctx->out_memcpys) {
|
|
memcpy(cpy.dst, cpy.src, cpy.n);
|
|
}
|
|
subctx->out_memcpys.clear();
|
|
|
|
end = std::chrono::high_resolution_clock::now();
|
|
|
|
double ms_from_gpu = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
|
|
|
|
double avg_err = 0.0;
|
|
for (size_t i = 0; i < ne; i++) {
|
|
avg_err += std::fabs(x[i] - y[i]);
|
|
}
|
|
|
|
double kb = ne * sizeof(float) / 1024.0;
|
|
|
|
std::cerr << "TEST TRANSFER " << kb << " KB to_gpu " << ms_to_gpu << "ms (" << kb / ms_to_gpu * 1000.0 / 1024.0 << " MB/s) from_gpu " << ms_from_gpu << "ms (" << kb / ms_from_gpu * 1000.0 / 1024.0 << " MB/s) avg_err=" << avg_err / ne << std::endl;
|
|
|
|
ggml_vk_destroy_buffer(buffer);
|
|
|
|
if (pinned) {
|
|
ggml_vk_host_free(ctx, x);
|
|
ggml_vk_host_free(ctx, y);
|
|
} else {
|
|
free(x);
|
|
free(y);
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) {
|
|
ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr);
|
|
}
|
|
|
|
static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl;
|
|
#endif
|
|
const size_t x_sz = sizeof(float) * ne;
|
|
const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne;
|
|
const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant);
|
|
float * x = (float *) malloc(x_sz);
|
|
void * qx = malloc(qx_sz);
|
|
vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
vk_buffer x_buf = ggml_vk_create_buffer_check(ctx, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16);
|
|
|
|
for (size_t i = 0; i < ne; i++) {
|
|
x[i] = rand() / (float)RAND_MAX;
|
|
}
|
|
|
|
vk_pipeline p = ctx->device->pipeline_dequant[quant];
|
|
|
|
ggml_vk_quantize_data(x, qx, ne, quant);
|
|
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, p, 1);
|
|
|
|
ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz);
|
|
|
|
vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
|
|
ggml_vk_ctx_begin(ctx, subctx);
|
|
const std::vector<uint32_t> pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne };
|
|
ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1});
|
|
ggml_vk_ctx_end(subctx);
|
|
|
|
auto begin = std::chrono::high_resolution_clock::now();
|
|
|
|
ggml_vk_submit(subctx, ctx->fence);
|
|
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
|
|
ctx->device->device.resetFences({ ctx->fence });
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
|
|
double ms_dequant = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
|
|
ggml_vk_buffer_read(ctx, x_buf, 0, x_chk, x_sz_f16);
|
|
|
|
int first_err = -1;
|
|
|
|
double avg_err = 0.0;
|
|
for (size_t i = 0; i < ne; i++) {
|
|
double error = std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i]));
|
|
avg_err += error;
|
|
|
|
if (first_err < 0 && error > 0.05) {
|
|
first_err = i;
|
|
}
|
|
}
|
|
|
|
avg_err /= ne;
|
|
|
|
std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err << std::endl;
|
|
|
|
if (avg_err > 0.1) {
|
|
std::cerr << "first_error = " << first_err << std::endl;
|
|
std::cerr << "Actual result: " << std::endl << std::endl;
|
|
for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
|
|
std::cerr << ggml_fp16_to_fp32(x_chk[i]) << ", ";
|
|
}
|
|
std::cerr << std::endl << "Expected result: " << std::endl << std::endl;
|
|
for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
|
|
std::cerr << x[i] << ", ";
|
|
}
|
|
std::cerr << std::endl;
|
|
}
|
|
|
|
ggml_vk_destroy_buffer(x_buf);
|
|
ggml_vk_destroy_buffer(qx_buf);
|
|
|
|
free(x);
|
|
free(qx);
|
|
free(x_chk);
|
|
}
|
|
|
|
static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, size_t split_k, size_t shader_size, ggml_type quant) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_test_dequant_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << ggml_type_name(quant) << ")" << std::endl;
|
|
#endif
|
|
const size_t x_ne = m * k * batch;
|
|
const size_t y_ne = k * n * batch;
|
|
const size_t d_ne = m * n * batch;
|
|
|
|
vk_pipeline p;
|
|
std::string shname;
|
|
if (shader_size == 0) {
|
|
p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_s;
|
|
shname = std::string(ggml_type_name(quant)) + "_ALIGNED_S";
|
|
} else if (shader_size == 1) {
|
|
p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_m;
|
|
shname = std::string(ggml_type_name(quant)) + "_ALIGNED_M";
|
|
} else if (shader_size == 2) {
|
|
p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_l;
|
|
shname = std::string(ggml_type_name(quant)) + "_ALIGNED_L";
|
|
} else {
|
|
GGML_ASSERT(0);
|
|
}
|
|
|
|
const size_t kpad = ggml_vk_align_size(k, p->align);
|
|
|
|
if (k != kpad) {
|
|
if (shader_size == 0) {
|
|
p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->s;
|
|
shname = std::string(ggml_type_name(quant)) + "_S";
|
|
} else if (shader_size == 1) {
|
|
p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->m;
|
|
shname = std::string(ggml_type_name(quant)) + "_M";
|
|
} else if (shader_size == 2) {
|
|
p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->l;
|
|
shname = std::string(ggml_type_name(quant)) + "_L";
|
|
} else {
|
|
GGML_ASSERT(0);
|
|
}
|
|
}
|
|
|
|
const size_t x_sz = sizeof(float) * x_ne;
|
|
const size_t y_sz = sizeof(float) * y_ne;
|
|
const size_t qx_sz = x_ne * ggml_type_size(quant)/ggml_blck_size(quant);
|
|
const size_t d_sz = sizeof(float) * d_ne;
|
|
float * x = (float *) malloc(x_sz);
|
|
float * y = (float *) malloc(y_sz);
|
|
void * qx = malloc(qx_sz);
|
|
vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
vk_buffer y_buf = ggml_vk_create_buffer_check(ctx, y_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
vk_buffer d_buf = ggml_vk_create_buffer_check(ctx, d_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
float * d = (float *) malloc(d_sz);
|
|
float * d_chk = (float *) malloc(d_sz);
|
|
|
|
for (size_t i = 0; i < x_ne; i++) {
|
|
x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
|
|
}
|
|
|
|
ggml_vk_quantize_data(x, qx, x_ne, quant);
|
|
|
|
for (size_t i = 0; i < y_ne; i++) {
|
|
// y[i] = rand() / (float)RAND_MAX;
|
|
y[i] = (i % k == i / k) ? 1.0f : 0.0f;
|
|
}
|
|
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it);
|
|
if (split_k > 1) {
|
|
ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);
|
|
|
|
if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
|
|
// Resize buffer
|
|
if (ctx->prealloc_split_k != nullptr) {
|
|
ggml_vk_destroy_buffer(ctx->prealloc_split_k);
|
|
}
|
|
ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
|
|
}
|
|
}
|
|
|
|
ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz);
|
|
ggml_vk_buffer_write(ctx, y_buf, 0, y, y_sz);
|
|
|
|
vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
|
|
for (size_t i = 0; i < num_it; i++) {
|
|
ggml_vk_ctx_begin(ctx, subctx);
|
|
ggml_vk_matmul(ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n);
|
|
ggml_vk_ctx_end(subctx);
|
|
}
|
|
|
|
auto begin = std::chrono::high_resolution_clock::now();
|
|
|
|
ggml_vk_submit(subctx, ctx->fence);
|
|
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
|
|
ctx->device->device.resetFences({ ctx->fence });
|
|
|
|
auto end = std::chrono::high_resolution_clock::now();
|
|
|
|
double time_ms = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
|
|
ggml_vk_buffer_read(ctx, d_buf, 0, d, d_sz);
|
|
|
|
ggml_init_params iparams = {
|
|
/*.mem_size =*/ 1024*1024*1024,
|
|
/*.mem_buffer =*/ NULL,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
ggml_context * ggml_ctx = ggml_init(iparams);
|
|
|
|
ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, quant, k, m, batch);
|
|
ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, GGML_TYPE_F32, k, n, batch);
|
|
ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml);
|
|
|
|
src0_ggml->data = qx;
|
|
src1_ggml->data = y;
|
|
tensor_ggml->data = d_chk;
|
|
|
|
ctx->disable = true;
|
|
|
|
ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
|
|
ggml_build_forward_expand(cgraph, tensor_ggml);
|
|
|
|
ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1);
|
|
|
|
ctx->disable = false;
|
|
|
|
ggml_free(ggml_ctx);
|
|
|
|
double avg_err = 0.0;
|
|
int first_err_n = -1;
|
|
int first_err_m = -1;
|
|
int first_err_b = -1;
|
|
|
|
for (size_t i = 0; i < m*n*batch; i++) {
|
|
double err = std::fabs(d[i] - d_chk[i]);
|
|
avg_err += err;
|
|
|
|
if ((err > 0.05f || std::isnan(err)) && first_err_n == -1) {
|
|
first_err_b = i / (m * n);
|
|
first_err_n = (i % (m * n)) / m;
|
|
first_err_m = (i % (m * n)) % m;
|
|
}
|
|
}
|
|
|
|
avg_err /= m * n;
|
|
|
|
std::cerr << "TEST MMQ " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms avg_err=" << avg_err << std::endl;
|
|
|
|
if (avg_err > 0.1 || std::isnan(avg_err)) {
|
|
std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
|
|
std::cerr << "Actual result: " << std::endl << std::endl;
|
|
ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
|
|
std::cerr << std::endl;
|
|
std::cerr << "Expected result: " << std::endl << std::endl;
|
|
ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
|
|
|
|
if (split_k > 1) {
|
|
float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
|
|
ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
|
|
|
|
std::cerr << "d_buf0: " << std::endl << std::endl;
|
|
ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
|
|
|
|
std::cerr << "d_buf1: " << std::endl << std::endl;
|
|
ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
|
|
|
|
std::cerr << "d_buf2: " << std::endl << std::endl;
|
|
ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
|
|
|
|
std::cerr << "d_buf3: " << std::endl << std::endl;
|
|
ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
|
|
|
|
free(split_k_buf);
|
|
}
|
|
}
|
|
|
|
ggml_vk_destroy_buffer(qx_buf);
|
|
ggml_vk_destroy_buffer(y_buf);
|
|
ggml_vk_destroy_buffer(d_buf);
|
|
|
|
free(x);
|
|
free(qx);
|
|
free(y);
|
|
free(d);
|
|
free(d_chk);
|
|
}
|
|
#endif
|
|
|
|
static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_create_extra(" << tensor << " (" << tensor->name << ", " << ggml_op_name(tensor->op) << "))" << std::endl;
|
|
#endif
|
|
ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu;
|
|
extra->reset();
|
|
tensor->extra = extra;
|
|
return extra;
|
|
}
|
|
|
|
static bool ggml_vk_cpu_assist_op(const ggml_tensor * node) {
|
|
return node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID;
|
|
}
|
|
|
|
static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl;
|
|
#endif
|
|
const bool any_on_device = node->backend == GGML_BACKEND_TYPE_GPU
|
|
|| (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_TYPE_GPU || node->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
|
|
|| (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_TYPE_GPU));
|
|
|
|
if (ctx->disable || (!any_on_device && !ggml_vk_cpu_assist_op(node))) {
|
|
return;
|
|
}
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra;
|
|
if (extra == nullptr) {
|
|
// Workaround for CPU backend BLAS matmul calls
|
|
extra = ggml_vk_tensor_create_extra(node);
|
|
}
|
|
|
|
ggml_tensor * src0 = node->src[0];
|
|
ggml_tensor * src1 = node->src[1];
|
|
|
|
const bool use_src0 = src0 != nullptr;
|
|
const int64_t ne00 = use_src0 ? src0->ne[0] : 0;
|
|
const int64_t ne01 = use_src0 ? src0->ne[1] : 0;
|
|
const int64_t ne02 = use_src0 ? src0->ne[2] : 0;
|
|
const int64_t ne03 = use_src0 ? src0->ne[3] : 0;
|
|
const bool use_src1 = src1 != nullptr && node->op != GGML_OP_CPY && node->op != GGML_OP_CONT && node->op != GGML_OP_DUP;
|
|
const int64_t ne10 = use_src1 ? src1->ne[0] : 0;
|
|
const int64_t ne11 = use_src1 ? src1->ne[1] : 0;
|
|
const int64_t ne12 = use_src1 ? src1->ne[2] : 0;
|
|
const int64_t ne13 = use_src1 ? src1->ne[3] : 0;
|
|
const int64_t ne20 = node->ne[0];
|
|
const int64_t ne21 = node->ne[1];
|
|
const int64_t ne22 = node->ne[2];
|
|
const int64_t ne23 = node->ne[3];
|
|
|
|
const bool f16_f32_kernel = use_src1 && src1->type == GGML_TYPE_F32;
|
|
|
|
int split_k;
|
|
if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) {
|
|
split_k = ggml_vk_guess_split_k(ne01, ne11, ne10);
|
|
} else {
|
|
split_k = 1;
|
|
}
|
|
const uint32_t x_ne = ne00 * ne01;
|
|
const uint32_t y_ne = ne10 * ne11;
|
|
const uint32_t d_ne = ne20 * ne21;
|
|
|
|
const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0;
|
|
const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0;
|
|
const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0;
|
|
const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0;
|
|
uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23;
|
|
const uint64_t split_k_size = split_k > 1 ? d_sz * 4 : 0;
|
|
|
|
if (extra->buffer_gpu.expired()) {
|
|
// Workaround for CPU backend BLAS matmul calls
|
|
extra->buffer_gpu = ggml_vk_create_buffer_temp(ctx, d_sz);
|
|
}
|
|
|
|
switch (node->op) {
|
|
case GGML_OP_REPEAT:
|
|
case GGML_OP_GET_ROWS:
|
|
case GGML_OP_RESHAPE:
|
|
case GGML_OP_VIEW:
|
|
case GGML_OP_PERMUTE:
|
|
case GGML_OP_TRANSPOSE:
|
|
case GGML_OP_ADD:
|
|
case GGML_OP_SCALE:
|
|
case GGML_OP_SQR:
|
|
case GGML_OP_CLAMP:
|
|
case GGML_OP_CPY:
|
|
case GGML_OP_CONT:
|
|
case GGML_OP_DUP:
|
|
case GGML_OP_MUL:
|
|
case GGML_OP_NORM:
|
|
case GGML_OP_RMS_NORM:
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
case GGML_OP_SOFT_MAX:
|
|
case GGML_OP_ROPE:
|
|
case GGML_OP_ARGSORT:
|
|
break;
|
|
case GGML_OP_UNARY:
|
|
switch (ggml_get_unary_op(node)) {
|
|
case GGML_UNARY_OP_SILU:
|
|
case GGML_UNARY_OP_GELU:
|
|
case GGML_UNARY_OP_RELU:
|
|
break;
|
|
default:
|
|
return;
|
|
}
|
|
break;
|
|
case GGML_OP_MUL_MAT:
|
|
case GGML_OP_MUL_MAT_ID:
|
|
if (ctx->prealloc_size_qx < qx_sz) {
|
|
ctx->prealloc_size_qx = qx_sz;
|
|
}
|
|
if (ctx->prealloc_size_qy < qy_sz) {
|
|
ctx->prealloc_size_qy = qy_sz;
|
|
}
|
|
if (ctx->prealloc_size_x < x_sz) {
|
|
ctx->prealloc_size_x = x_sz;
|
|
}
|
|
if (ctx->prealloc_size_y < y_sz) {
|
|
ctx->prealloc_size_y = y_sz;
|
|
}
|
|
if (ctx->prealloc_size_split_k < split_k_size) {
|
|
ctx->prealloc_size_split_k = split_k_size;
|
|
}
|
|
if (ctx->staging_size < x_sz + y_sz) {
|
|
ctx->staging_size = x_sz + y_sz;
|
|
}
|
|
break;
|
|
default:
|
|
return;
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
|
|
if (ctx->disable) {
|
|
return;
|
|
}
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_preallocate_buffers(qx_size: " << ctx->prealloc_size_qx << " qy_size: " << ctx->prealloc_size_qy << " x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl;
|
|
#endif
|
|
#if defined(GGML_VULKAN_RUN_TESTS)
|
|
ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul,
|
|
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
|
|
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
|
|
ggml_vk_test_transfer(ctx, 8192 * 1000, false);
|
|
ggml_vk_test_transfer(ctx, 8192 * 1000, true);
|
|
|
|
ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_F32);
|
|
ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_0);
|
|
ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_1);
|
|
ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_0);
|
|
ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_1);
|
|
ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q8_0);
|
|
ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q2_K);
|
|
ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q3_K);
|
|
ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_K);
|
|
ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_K);
|
|
ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q6_K);
|
|
|
|
ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 0);
|
|
ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 1);
|
|
ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 2);
|
|
ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 0);
|
|
ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 1);
|
|
ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 2);
|
|
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_0);
|
|
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_1);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_1);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_1);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_1);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_1);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_1);
|
|
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_0);
|
|
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_1);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_1);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_1);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_1);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_1);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_1);
|
|
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q8_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q8_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q8_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q8_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q8_0);
|
|
ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q8_0);
|
|
|
|
std::cerr << std::endl;
|
|
|
|
const std::vector<size_t> vals {
|
|
8, 8, 8,
|
|
100, 46, 576,
|
|
623, 111, 128,
|
|
100, 46, 558,
|
|
512, 1, 256,
|
|
128, 110, 622,
|
|
511, 511, 127,
|
|
511, 511, 7,
|
|
511, 511, 17,
|
|
49, 49, 128,
|
|
128, 49, 49,
|
|
4096, 49, 4096,
|
|
11008, 49, 4096,
|
|
4096, 49, 11008,
|
|
32000, 49, 4096,
|
|
512, 512, 128,
|
|
128, 512, 512,
|
|
4096, 512, 4096,
|
|
11008, 512, 4096,
|
|
4096, 512, 11008,
|
|
32000, 512, 4096,
|
|
};
|
|
const size_t num_it = 1;
|
|
for (size_t i = 0; i < vals.size(); i += 3) {
|
|
ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0);
|
|
ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1);
|
|
ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2);
|
|
ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0);
|
|
ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1);
|
|
ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2);
|
|
std::cerr << std::endl;
|
|
}
|
|
|
|
GGML_ASSERT(false);
|
|
#endif
|
|
|
|
if (ctx->prealloc_qx == nullptr || (ctx->prealloc_size_qx > 0 && ctx->prealloc_qx->size < ctx->prealloc_size_qx)) {
|
|
// Resize buffer
|
|
if (ctx->prealloc_qx != nullptr) {
|
|
ggml_vk_destroy_buffer(ctx->prealloc_qx);
|
|
}
|
|
ctx->prealloc_qx = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_qx);
|
|
}
|
|
if (ctx->prealloc_qy == nullptr || (ctx->prealloc_size_qy > 0 && ctx->prealloc_qy->size < ctx->prealloc_size_qy)) {
|
|
// Resize buffer
|
|
if (ctx->prealloc_qy != nullptr) {
|
|
ggml_vk_destroy_buffer(ctx->prealloc_qy);
|
|
}
|
|
ctx->prealloc_qy = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_qy);
|
|
}
|
|
if (ctx->prealloc_x == nullptr || (ctx->prealloc_size_x > 0 && ctx->prealloc_x->size < ctx->prealloc_size_x)) {
|
|
// Resize buffer
|
|
if (ctx->prealloc_x != nullptr) {
|
|
ggml_vk_destroy_buffer(ctx->prealloc_x);
|
|
}
|
|
ctx->prealloc_x = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_x);
|
|
}
|
|
if (ctx->prealloc_y == nullptr || (ctx->prealloc_size_y > 0 && ctx->prealloc_y->size < ctx->prealloc_size_y)) {
|
|
// Resize buffer
|
|
if (ctx->prealloc_y != nullptr) {
|
|
ggml_vk_destroy_buffer(ctx->prealloc_y);
|
|
}
|
|
ctx->prealloc_y = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_y);
|
|
}
|
|
if (ctx->prealloc_split_k == nullptr || (ctx->prealloc_size_split_k > 0 && ctx->prealloc_split_k->size < ctx->prealloc_size_split_k)) {
|
|
// Resize buffer
|
|
if (ctx->prealloc_split_k != nullptr) {
|
|
ggml_vk_destroy_buffer(ctx->prealloc_split_k);
|
|
}
|
|
ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_split_k);
|
|
}
|
|
if (ctx->staging == nullptr || (ctx->staging_size > 0 && ctx->staging->size < ctx->staging_size)) {
|
|
// Resize buffer
|
|
if (ctx->staging != nullptr) {
|
|
ggml_vk_destroy_buffer(ctx->staging);
|
|
}
|
|
ctx->staging = ggml_vk_create_buffer_check(ctx, ctx->staging_size,
|
|
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
|
|
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){
|
|
const bool any_on_device = node->backend == GGML_BACKEND_TYPE_GPU
|
|
|| (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_TYPE_GPU || node->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
|
|
|| (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_TYPE_GPU);
|
|
|
|
if (ctx->disable || (!any_on_device && !ggml_vk_cpu_assist_op(node)) || (ggml_vk_cpu_assist_op(node) && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) {
|
|
return;
|
|
}
|
|
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")" << std::endl;
|
|
#endif
|
|
ctx->semaphore_idx = 0;
|
|
ctx->staging_offset = 0;
|
|
|
|
const ggml_tensor * src0 = node->src[0];
|
|
const ggml_tensor * src1 = node->src[1];
|
|
const ggml_tensor * src2 = node->src[2];
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra;
|
|
|
|
switch (node->op) {
|
|
case GGML_OP_UNARY:
|
|
switch (ggml_get_unary_op(node)) {
|
|
case GGML_UNARY_OP_SILU:
|
|
case GGML_UNARY_OP_GELU:
|
|
case GGML_UNARY_OP_RELU:
|
|
break;
|
|
default:
|
|
return;
|
|
}
|
|
break;
|
|
case GGML_OP_REPEAT:
|
|
// case GGML_OP_GET_ROWS:
|
|
case GGML_OP_ADD:
|
|
case GGML_OP_MUL:
|
|
case GGML_OP_SCALE:
|
|
case GGML_OP_SQR:
|
|
case GGML_OP_CLAMP:
|
|
case GGML_OP_CPY:
|
|
case GGML_OP_CONT:
|
|
case GGML_OP_DUP:
|
|
case GGML_OP_RESHAPE:
|
|
case GGML_OP_VIEW:
|
|
case GGML_OP_PERMUTE:
|
|
case GGML_OP_TRANSPOSE:
|
|
case GGML_OP_NORM:
|
|
case GGML_OP_RMS_NORM:
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
case GGML_OP_SOFT_MAX:
|
|
case GGML_OP_ROPE:
|
|
case GGML_OP_MUL_MAT:
|
|
case GGML_OP_MUL_MAT_ID:
|
|
case GGML_OP_NONE:
|
|
case GGML_OP_ARGSORT:
|
|
break;
|
|
default:
|
|
if (any_on_device) {
|
|
std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl;
|
|
GGML_ASSERT(false);
|
|
}
|
|
return;
|
|
}
|
|
|
|
if (ctx->compute_ctx == nullptr) {
|
|
ctx->compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
|
|
ggml_vk_ctx_begin(ctx, ctx->compute_ctx);
|
|
}
|
|
|
|
switch (node->op) {
|
|
case GGML_OP_REPEAT:
|
|
ggml_vk_repeat(ctx, ctx->compute_ctx, src0, src1, node);
|
|
|
|
break;
|
|
case GGML_OP_GET_ROWS:
|
|
ggml_vk_get_rows(ctx, ctx->compute_ctx, src0, src1, node);
|
|
|
|
break;
|
|
case GGML_OP_ADD:
|
|
ggml_vk_add(ctx, ctx->compute_ctx, src0, src1, node);
|
|
|
|
break;
|
|
case GGML_OP_MUL:
|
|
ggml_vk_mul(ctx, ctx->compute_ctx, src0, src1, node);
|
|
|
|
break;
|
|
case GGML_OP_SCALE:
|
|
ggml_vk_scale(ctx, ctx->compute_ctx, src0, node);
|
|
|
|
break;
|
|
case GGML_OP_SQR:
|
|
ggml_vk_sqr(ctx, ctx->compute_ctx, src0, node);
|
|
|
|
break;
|
|
case GGML_OP_CLAMP:
|
|
ggml_vk_clamp(ctx, ctx->compute_ctx, src0, node);
|
|
|
|
break;
|
|
case GGML_OP_CPY:
|
|
case GGML_OP_CONT:
|
|
case GGML_OP_DUP:
|
|
ggml_vk_cpy(ctx, ctx->compute_ctx, src0, node);
|
|
|
|
break;
|
|
case GGML_OP_RESHAPE:
|
|
case GGML_OP_VIEW:
|
|
case GGML_OP_PERMUTE:
|
|
case GGML_OP_TRANSPOSE:
|
|
case GGML_OP_NONE:
|
|
ggml_vk_nop(ctx, ctx->compute_ctx, src0, node);
|
|
|
|
break;
|
|
case GGML_OP_NORM:
|
|
ggml_vk_norm(ctx, ctx->compute_ctx, src0, node);
|
|
|
|
break;
|
|
case GGML_OP_RMS_NORM:
|
|
ggml_vk_rms_norm(ctx, ctx->compute_ctx, src0, node);
|
|
|
|
break;
|
|
case GGML_OP_UNARY:
|
|
switch (ggml_get_unary_op(node)) {
|
|
case GGML_UNARY_OP_SILU:
|
|
case GGML_UNARY_OP_GELU:
|
|
case GGML_UNARY_OP_RELU:
|
|
ggml_vk_unary(ctx, ctx->compute_ctx, src0, node);
|
|
break;
|
|
default:
|
|
return;
|
|
}
|
|
break;
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
ggml_vk_diag_mask_inf(ctx, ctx->compute_ctx, src0, node);
|
|
|
|
break;
|
|
case GGML_OP_SOFT_MAX:
|
|
ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, src2, node);
|
|
|
|
break;
|
|
case GGML_OP_ROPE:
|
|
ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, node);
|
|
|
|
break;
|
|
case GGML_OP_ARGSORT:
|
|
ggml_vk_argsort(ctx, ctx->compute_ctx, src0, node);
|
|
break;
|
|
case GGML_OP_MUL_MAT:
|
|
ggml_vk_mul_mat(ctx, ctx->compute_ctx, src0, src1, node);
|
|
|
|
break;
|
|
case GGML_OP_MUL_MAT_ID:
|
|
//ggml_vk_mul_mat_id(ctx, ctx->compute_ctx, src0, src1, node);
|
|
std::cerr << "ggml_vulkan: GGML_OP_MUL_MAT_ID not implemented yet." << std::endl;
|
|
GGML_ASSERT(false);
|
|
|
|
break;
|
|
default:
|
|
return;
|
|
}
|
|
|
|
extra->ready = true;
|
|
extra->ctx_idx = ctx->compute_ctx->idx;
|
|
|
|
#ifdef GGML_VULKAN_CHECK_RESULTS
|
|
// Force context reset on each node so that each tensor ends up in its own context
|
|
// and can be run and compared to its CPU equivalent separately
|
|
last_node = true;
|
|
#endif
|
|
|
|
if (node->backend == GGML_BACKEND_TYPE_CPU || last_node) {
|
|
ggml_vk_ctx_end(ctx->compute_ctx);
|
|
ctx->compute_ctx->exit_tensor = node;
|
|
ctx->compute_ctx = nullptr;
|
|
}
|
|
}
|
|
|
|
static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){
|
|
const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU
|
|
|| (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
|
|
|| (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU);
|
|
|
|
if (ctx->disable || (!any_on_device && !ggml_vk_cpu_assist_op(tensor))) {
|
|
return false;
|
|
}
|
|
|
|
ggml_tensor_extra_gpu * extra = nullptr;
|
|
|
|
switch (tensor->op) {
|
|
case GGML_OP_ADD:
|
|
case GGML_OP_GET_ROWS:
|
|
case GGML_OP_MUL:
|
|
case GGML_OP_SCALE:
|
|
case GGML_OP_SQR:
|
|
case GGML_OP_CLAMP:
|
|
case GGML_OP_CPY:
|
|
case GGML_OP_CONT:
|
|
case GGML_OP_DUP:
|
|
case GGML_OP_NORM:
|
|
case GGML_OP_RMS_NORM:
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
case GGML_OP_SOFT_MAX:
|
|
case GGML_OP_ROPE:
|
|
case GGML_OP_RESHAPE:
|
|
case GGML_OP_VIEW:
|
|
case GGML_OP_PERMUTE:
|
|
case GGML_OP_TRANSPOSE:
|
|
case GGML_OP_NONE:
|
|
case GGML_OP_ARGSORT:
|
|
extra = (ggml_tensor_extra_gpu *) tensor->extra;
|
|
|
|
break;
|
|
case GGML_OP_UNARY:
|
|
switch (ggml_get_unary_op(tensor)) {
|
|
case GGML_UNARY_OP_SILU:
|
|
case GGML_UNARY_OP_GELU:
|
|
case GGML_UNARY_OP_RELU:
|
|
extra = (ggml_tensor_extra_gpu *) tensor->extra;
|
|
break;
|
|
default:
|
|
return false;
|
|
}
|
|
break;
|
|
case GGML_OP_MUL_MAT:
|
|
case GGML_OP_MUL_MAT_ID:
|
|
if (!any_on_device && !ggml_vk_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) {
|
|
return false;
|
|
}
|
|
|
|
extra = (ggml_tensor_extra_gpu *) tensor->extra;
|
|
|
|
break;
|
|
default:
|
|
return false;
|
|
}
|
|
|
|
if (extra == nullptr) {
|
|
return false;
|
|
}
|
|
|
|
if (params->ith != 0) {
|
|
return true;
|
|
}
|
|
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
|
|
return true;
|
|
}
|
|
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")" << std::endl;
|
|
#endif
|
|
|
|
#ifdef GGML_VULKAN_CHECK_RESULTS
|
|
ggml_vk_check_results_0(ctx, params, tensor);
|
|
#endif
|
|
|
|
GGML_ASSERT(extra->ready);
|
|
|
|
vk_context& subctx = ctx->gc.contexts[extra->ctx_idx];
|
|
|
|
// Only run if ctx hasn't been submitted yet
|
|
if (!subctx.seqs.empty()) {
|
|
// Do staging buffer copies
|
|
for (auto& cpy : subctx.in_memcpys) {
|
|
memcpy(cpy.dst, cpy.src, cpy.n);
|
|
}
|
|
|
|
ggml_vk_submit(&subctx, ctx->fence);
|
|
}
|
|
|
|
if (tensor == subctx.exit_tensor) {
|
|
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences");
|
|
ctx->device->device.resetFences({ ctx->fence });
|
|
|
|
// Do staging buffer copies
|
|
for (auto& cpy : subctx.out_memcpys) {
|
|
memcpy(cpy.dst, cpy.src, cpy.n);
|
|
}
|
|
subctx.in_memcpys.clear();
|
|
subctx.out_memcpys.clear();
|
|
}
|
|
|
|
extra->ready = false;
|
|
|
|
return true;
|
|
}
|
|
|
|
// Clean up after graph processing is done
|
|
static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) {
|
|
if (ctx->disable) {
|
|
return;
|
|
}
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_graph_cleanup()" << std::endl;
|
|
#endif
|
|
for (auto& buffer : ctx->gc.temp_buffers) {
|
|
ggml_vk_pool_free(ctx, buffer);
|
|
}
|
|
ctx->gc.temp_buffers.clear();
|
|
|
|
for (auto& pipeline : ctx->device->pipelines) {
|
|
if (pipeline.expired()) {
|
|
continue;
|
|
}
|
|
|
|
vk_pipeline pl = pipeline.lock();
|
|
ggml_pipeline_cleanup(pl);
|
|
}
|
|
|
|
ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue);
|
|
ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue);
|
|
|
|
for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) {
|
|
ctx->device->device.destroySemaphore({ ctx->gc.semaphores[i].s });
|
|
}
|
|
ctx->gc.semaphores.clear();
|
|
|
|
for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) {
|
|
ctx->device->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s });
|
|
}
|
|
ctx->gc.tl_semaphores.clear();
|
|
ctx->semaphore_idx = 0;
|
|
|
|
ctx->event_idx = 0;
|
|
|
|
for (auto& event : ctx->gc.events) {
|
|
ctx->device->device.resetEvent(event);
|
|
}
|
|
|
|
ctx->staging_offset = 0;
|
|
|
|
ctx->compute_ctx = nullptr;
|
|
ctx->transfer_ctx = nullptr;
|
|
ctx->gc.contexts.clear();
|
|
}
|
|
|
|
// Clean up on backend free
|
|
static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_vk_cleanup(" << ctx->idx << ")" << std::endl;
|
|
#endif
|
|
ggml_vk_graph_cleanup(ctx);
|
|
|
|
ggml_vk_destroy_buffer(ctx->prealloc_qx);
|
|
ggml_vk_destroy_buffer(ctx->prealloc_qy);
|
|
ggml_vk_destroy_buffer(ctx->prealloc_x);
|
|
ggml_vk_destroy_buffer(ctx->prealloc_y);
|
|
ggml_vk_destroy_buffer(ctx->prealloc_split_k);
|
|
ggml_vk_destroy_buffer(ctx->staging);
|
|
ggml_vk_destroy_buffer(ctx->sync_staging);
|
|
|
|
for (auto& buffer : ctx->buffer_pool) {
|
|
ggml_vk_destroy_buffer(buffer);
|
|
}
|
|
|
|
ctx->prealloc_size_qx = 0;
|
|
ctx->prealloc_size_qy = 0;
|
|
ctx->prealloc_size_x = 0;
|
|
ctx->prealloc_size_y = 0;
|
|
ctx->prealloc_size_split_k = 0;
|
|
ctx->staging_size = 0;
|
|
|
|
for (auto& event : ctx->gc.events) {
|
|
ctx->device->device.destroyEvent(event);
|
|
}
|
|
ctx->gc.events.clear();
|
|
|
|
ctx->device->device.destroyFence(ctx->fence);
|
|
}
|
|
|
|
GGML_CALL static int ggml_vk_get_device_count() {
|
|
ggml_vk_instance_init();
|
|
|
|
return vk_instance.device_indices.size();
|
|
}
|
|
|
|
GGML_CALL static void ggml_vk_get_device_description(int device, char * description, size_t description_size) {
|
|
ggml_vk_instance_init();
|
|
|
|
std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
|
|
|
|
vk::PhysicalDeviceProperties props;
|
|
devices[device].getProperties(&props);
|
|
|
|
snprintf(description, description_size, "%s", props.deviceName.data());
|
|
}
|
|
|
|
// CPU assist interface
|
|
|
|
void ggml_vk_init_cpu_assist() {
|
|
ggml_vk_instance_init();
|
|
|
|
std::cerr << "ggml_vulkan: Found " << ggml_vk_get_device_count() << " Vulkan devices:" << std::endl;
|
|
|
|
for (int i = 0; i < ggml_vk_get_device_count(); i++) {
|
|
ggml_vk_print_gpu_info(i);
|
|
}
|
|
// Initialize the first backend to make sure CPU matrix multiplications can be offloaded.
|
|
ggml_backend_vk_init(0);
|
|
}
|
|
|
|
void ggml_vk_preallocate_buffers_graph_cpu_assist(ggml_tensor * node) {
|
|
ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
|
|
|
|
if (!ctx->initialized) {
|
|
return;
|
|
}
|
|
|
|
ggml_vk_preallocate_buffers_graph(ctx, node);
|
|
}
|
|
|
|
void ggml_vk_preallocate_buffers_cpu_assist() {
|
|
ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
|
|
|
|
if (!ctx->initialized) {
|
|
return;
|
|
}
|
|
|
|
ggml_vk_preallocate_buffers(ctx);
|
|
}
|
|
|
|
void ggml_vk_build_graph_cpu_assist(ggml_tensor * node, bool last_node) {
|
|
ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
|
|
|
|
if (!ctx->initialized) {
|
|
return;
|
|
}
|
|
|
|
ggml_vk_build_graph(ctx, node, last_node);
|
|
}
|
|
|
|
bool ggml_vk_compute_forward_cpu_assist(ggml_compute_params * params, ggml_tensor * tensor){
|
|
ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
|
|
|
|
if (!ctx->initialized) {
|
|
return false;
|
|
}
|
|
|
|
return ggml_vk_compute_forward(ctx, params, tensor);
|
|
}
|
|
|
|
void ggml_vk_graph_cleanup_cpu_assist() {
|
|
ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
|
|
|
|
if (!ctx->initialized) {
|
|
return;
|
|
}
|
|
|
|
ggml_vk_graph_cleanup(ctx);
|
|
}
|
|
|
|
void ggml_vk_free_cpu_assist() {
|
|
ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
|
|
|
|
if (!ctx->initialized || vk_instance.backends[0] == nullptr) {
|
|
return;
|
|
}
|
|
|
|
ggml_backend_vk_free(vk_instance.backends[0]);
|
|
}
|
|
|
|
// backend interface
|
|
|
|
#define UNUSED GGML_UNUSED
|
|
|
|
// device backend
|
|
|
|
static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000; // NOLINT
|
|
|
|
struct ggml_backend_vk_buffer_context {
|
|
ggml_backend_vk_context * ctx;
|
|
vk_buffer dev_buffer;
|
|
ggml_tensor_extra_gpu * temp_tensor_extras = nullptr;
|
|
size_t temp_tensor_extra_index = 0;
|
|
std::string name;
|
|
|
|
ggml_backend_vk_buffer_context(ggml_backend_vk_context * ctx, vk_buffer&& dev_buffer, std::string& name) :
|
|
ctx(ctx),
|
|
dev_buffer(dev_buffer),
|
|
name(name) {
|
|
}
|
|
|
|
~ggml_backend_vk_buffer_context() {
|
|
ggml_vk_destroy_buffer(dev_buffer);
|
|
delete[] temp_tensor_extras;
|
|
}
|
|
|
|
ggml_tensor_extra_gpu * ggml_vk_alloc_temp_tensor_extra() {
|
|
if (temp_tensor_extras == nullptr) {
|
|
temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_VK_MAX_NODES];
|
|
}
|
|
|
|
size_t alloc_index = temp_tensor_extra_index;
|
|
temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_VK_MAX_NODES;
|
|
ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index];
|
|
extra->reset();
|
|
|
|
return extra;
|
|
}
|
|
};
|
|
|
|
GGML_CALL static const char * ggml_backend_vk_buffer_get_name(ggml_backend_buffer_t buffer) {
|
|
ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
|
|
return ctx->name.c_str();
|
|
}
|
|
|
|
GGML_CALL static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) {
|
|
return buffer->iface.get_name == ggml_backend_vk_buffer_get_name;
|
|
}
|
|
|
|
GGML_CALL static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_buffer_free_buffer()" << std::endl;
|
|
#endif
|
|
ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
|
|
ggml_vk_destroy_buffer(ctx->dev_buffer);
|
|
delete ctx;
|
|
}
|
|
|
|
GGML_CALL static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t buffer) {
|
|
return vk_ptr_base;
|
|
|
|
UNUSED(buffer);
|
|
}
|
|
|
|
GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")" << std::endl;
|
|
#endif
|
|
ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
|
|
|
|
ggml_tensor_extra_gpu * extra = ctx->ggml_vk_alloc_temp_tensor_extra();
|
|
if (tensor->view_src != nullptr && tensor->view_src->extra != nullptr) {
|
|
GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft);
|
|
ggml_tensor_extra_gpu * extra_view = (ggml_tensor_extra_gpu *) tensor->view_src->extra;
|
|
extra->buffer_gpu = extra_view->buffer_gpu;
|
|
extra->offset = extra_view->offset + tensor->view_offs;
|
|
} else {
|
|
extra->buffer_gpu = ctx->dev_buffer;
|
|
extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base;
|
|
}
|
|
|
|
tensor->backend = GGML_BACKEND_TYPE_GPU;
|
|
tensor->extra = extra;
|
|
}
|
|
|
|
GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl;
|
|
#endif
|
|
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
|
|
|
|
ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
|
|
|
|
vk_buffer buf = extra->buffer_gpu.lock();
|
|
|
|
ggml_vk_buffer_write(ctx->ctx, buf, extra->offset + offset, data, size);
|
|
}
|
|
|
|
GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl;
|
|
#endif
|
|
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
|
|
|
|
ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
|
|
|
|
vk_buffer buf = extra->buffer_gpu.lock();
|
|
|
|
ggml_vk_buffer_read(ctx->ctx, buf, extra->offset + offset, data, size);
|
|
}
|
|
|
|
GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
|
|
if (ggml_backend_buffer_is_vk(src->buffer)) {
|
|
ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra;
|
|
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
|
|
vk_buffer src_buf = src_extra->buffer_gpu.lock();
|
|
vk_buffer dst_buf = dst_extra->buffer_gpu.lock();
|
|
|
|
ggml_vk_buffer_copy(dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src));
|
|
|
|
return true;
|
|
}
|
|
return false;
|
|
|
|
UNUSED(buffer);
|
|
}
|
|
|
|
GGML_CALL static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
|
ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
|
|
|
|
ggml_vk_buffer_memset(ctx->ctx, ctx->dev_buffer, 0, value, buffer->size);
|
|
}
|
|
|
|
static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = {
|
|
/* .get_name = */ ggml_backend_vk_buffer_get_name,
|
|
/* .free_buffer = */ ggml_backend_vk_buffer_free_buffer,
|
|
/* .get_base = */ ggml_backend_vk_buffer_get_base,
|
|
/* .init_tensor = */ ggml_backend_vk_buffer_init_tensor,
|
|
/* .set_tensor = */ ggml_backend_vk_buffer_set_tensor,
|
|
/* .get_tensor = */ ggml_backend_vk_buffer_get_tensor,
|
|
/* .cpy_tensor = */ ggml_backend_vk_buffer_cpy_tensor,
|
|
/* .clear = */ ggml_backend_vk_buffer_clear,
|
|
/* .reset = */ NULL,
|
|
};
|
|
|
|
// vk buffer type
|
|
struct ggml_backend_vk_buffer_type_context {
|
|
std::string name;
|
|
ggml_backend_vk_context * ctx;
|
|
};
|
|
|
|
GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
|
ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *)buft->context;
|
|
|
|
return ctx->name.c_str();
|
|
}
|
|
|
|
GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl;
|
|
#endif
|
|
ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
|
|
vk_buffer dev_buffer = ggml_vk_create_buffer_device(ctx->ctx, size);
|
|
|
|
ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->ctx, std::move(dev_buffer), ctx->name);
|
|
|
|
return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, bufctx, size);
|
|
}
|
|
|
|
GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
|
ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
|
|
return ctx->ctx->device->properties.limits.minStorageBufferOffsetAlignment;
|
|
}
|
|
|
|
GGML_CALL static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
|
|
ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
|
|
return ctx->ctx->device->max_memory_allocation_size;
|
|
}
|
|
|
|
GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
|
|
return ggml_nbytes(tensor);
|
|
|
|
UNUSED(buft);
|
|
}
|
|
|
|
GGML_CALL static bool ggml_backend_vk_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
|
|
if (!ggml_backend_is_vk(backend)) {
|
|
return false;
|
|
}
|
|
|
|
ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context;
|
|
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
|
|
|
|
return buft_ctx->ctx->idx == ctx->idx;
|
|
}
|
|
|
|
static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = {
|
|
/* .get_name = */ ggml_backend_vk_buffer_type_name,
|
|
/* .alloc_buffer = */ ggml_backend_vk_buffer_type_alloc_buffer,
|
|
/* .get_alignment = */ ggml_backend_vk_buffer_type_get_alignment,
|
|
/* .get_max_size = */ ggml_backend_vk_buffer_type_get_max_size,
|
|
/* .get_alloc_size = */ ggml_backend_vk_buffer_type_get_alloc_size,
|
|
/* .supports_backend = */ ggml_backend_vk_buffer_type_supports_backend,
|
|
/* .is_host = */ NULL,
|
|
};
|
|
|
|
GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t idx) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_buffer_type(" << idx << ")" << std::endl;
|
|
#endif
|
|
|
|
GGML_ASSERT(idx < vk_instance.device_indices.size());
|
|
|
|
ggml_backend_vk_init(idx);
|
|
|
|
return &vk_instance.buffer_types[idx];
|
|
}
|
|
|
|
// host buffer type
|
|
|
|
GGML_CALL static const char * ggml_backend_vk_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
|
return GGML_VK_NAME "_Host";
|
|
|
|
UNUSED(buft);
|
|
}
|
|
|
|
GGML_CALL static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buffer_t buffer) {
|
|
return GGML_VK_NAME "_Host";
|
|
|
|
UNUSED(buffer);
|
|
}
|
|
|
|
GGML_CALL static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_host_buffer_free_buffer()" << std::endl;
|
|
#endif
|
|
ggml_vk_host_free(&vk_instance.contexts[0], buffer->context);
|
|
}
|
|
|
|
GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")" << std::endl;
|
|
#endif
|
|
void * ptr = nullptr;
|
|
try {
|
|
ptr = ggml_vk_host_malloc(&vk_instance.contexts[0], size);
|
|
} catch (vk::SystemError& e) {
|
|
std::cerr << "ggml_vulkan: Failed to allocate pinned memory." << std::endl;
|
|
std::cerr << "ggml_vulkan: " << e.what() << std::endl;
|
|
// fallback to cpu buffer
|
|
return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
|
|
}
|
|
|
|
ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
|
|
buffer->buft = buft;
|
|
buffer->iface.get_name = ggml_backend_vk_host_buffer_name;
|
|
buffer->iface.free_buffer = ggml_backend_vk_host_buffer_free_buffer;
|
|
|
|
return buffer;
|
|
}
|
|
|
|
GGML_CALL static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
|
return vk_instance.contexts[0].device->properties.limits.minMemoryMapAlignment;
|
|
|
|
UNUSED(buft);
|
|
}
|
|
|
|
GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() {
|
|
static struct ggml_backend_buffer_type ggml_backend_vk_buffer_type_host = {
|
|
/* .iface = */ {
|
|
/* .get_name = */ ggml_backend_vk_host_buffer_type_name,
|
|
/* .alloc_buffer = */ ggml_backend_vk_host_buffer_type_alloc_buffer,
|
|
/* .get_alignment = */ ggml_backend_vk_host_buffer_type_get_alignment,
|
|
/* .get_max_size = */ NULL, // defaults to SIZE_MAX
|
|
/* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
|
|
/* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
|
|
/* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host,
|
|
},
|
|
/* .context = */ nullptr,
|
|
};
|
|
|
|
if (!vk_instance.contexts[0].initialized) {
|
|
// Fall back to CPU
|
|
return ggml_backend_cpu_buffer_type();
|
|
}
|
|
|
|
return &ggml_backend_vk_buffer_type_host;
|
|
}
|
|
|
|
// backend
|
|
|
|
GGML_CALL static const char * ggml_backend_vk_name(ggml_backend_t backend) {
|
|
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
|
|
|
|
return ctx->name.c_str();
|
|
}
|
|
|
|
GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend) {
|
|
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_free(" << ctx->name << ")" << std::endl;
|
|
#endif
|
|
|
|
size_t idx = ctx->idx;
|
|
|
|
ggml_vk_cleanup(ctx);
|
|
|
|
ctx->device.reset();
|
|
ctx->initialized = false;
|
|
|
|
vk_instance.initialized[idx] = false;
|
|
vk_instance.backends[idx] = nullptr;
|
|
memset(&vk_instance.buffer_types[idx], 0, sizeof(ggml_backend_buffer_type));
|
|
delete backend;
|
|
}
|
|
|
|
GGML_CALL static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_type(ggml_backend_t backend) {
|
|
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
|
|
|
|
GGML_ASSERT(ctx->initialized);
|
|
|
|
return ggml_backend_vk_buffer_type(ctx->idx);
|
|
}
|
|
|
|
GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_set_tensor_async(" << size << ")" << std::endl;
|
|
#endif
|
|
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
|
|
GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
|
|
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
|
|
|
|
if (ctx->transfer_ctx == nullptr) {
|
|
// Initialize new transfer context
|
|
ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
|
|
ggml_vk_ctx_begin(ctx, ctx->transfer_ctx);
|
|
}
|
|
|
|
vk_buffer buf = extra->buffer_gpu.lock();
|
|
|
|
ggml_vk_buffer_write_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size);
|
|
}
|
|
|
|
GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_get_tensor_async(" << size << ")" << std::endl;
|
|
#endif
|
|
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
|
|
GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
|
|
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
|
|
|
|
if (ctx->transfer_ctx == nullptr) {
|
|
// Initialize new transfer context
|
|
ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
|
|
ggml_vk_ctx_begin(ctx, ctx->transfer_ctx);
|
|
}
|
|
|
|
vk_buffer buf = extra->buffer_gpu.lock();
|
|
|
|
ggml_vk_buffer_read_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size);
|
|
}
|
|
|
|
GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_cpy_tensor_async()" << std::endl;
|
|
#endif
|
|
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
|
|
if ((dst->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) {
|
|
ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra;
|
|
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
|
|
if (ctx->transfer_ctx == nullptr) {
|
|
// Initialize new transfer context
|
|
ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
|
|
ggml_vk_ctx_begin(ctx, ctx->transfer_ctx);
|
|
}
|
|
|
|
vk_buffer src_buf = src_extra->buffer_gpu.lock();
|
|
vk_buffer dst_buf = dst_extra->buffer_gpu.lock();
|
|
|
|
ggml_vk_buffer_copy_async(ctx->transfer_ctx, src_buf, src_extra->offset, dst_buf, dst_extra->offset, ggml_nbytes(src));
|
|
return true;
|
|
}
|
|
|
|
return false;
|
|
}
|
|
|
|
GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) {
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_synchronize()" << std::endl;
|
|
#endif
|
|
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
|
|
if(ctx->transfer_ctx == nullptr) {
|
|
return;
|
|
}
|
|
|
|
ggml_vk_ctx_end(ctx->transfer_ctx);
|
|
|
|
for (auto& cpy : ctx->transfer_ctx->in_memcpys) {
|
|
memcpy(cpy.dst, cpy.src, cpy.n);
|
|
}
|
|
|
|
ggml_vk_submit(ctx->transfer_ctx, ctx->fence);
|
|
VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences");
|
|
ctx->device->device.resetFences({ ctx->fence });
|
|
|
|
for (auto& cpy : ctx->transfer_ctx->out_memcpys) {
|
|
memcpy(cpy.dst, cpy.src, cpy.n);
|
|
}
|
|
|
|
ctx->transfer_ctx = nullptr;
|
|
}
|
|
|
|
GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
|
|
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
|
|
|
|
for (int i = 0; i < cgraph->n_nodes; i++) {
|
|
ggml_vk_preallocate_buffers_graph(ctx, cgraph->nodes[i]);
|
|
}
|
|
ggml_vk_preallocate_buffers(ctx);
|
|
|
|
int last_node = cgraph->n_nodes - 1;
|
|
|
|
// If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly
|
|
while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_TYPE_GPU) {
|
|
last_node -= 1;
|
|
}
|
|
|
|
for (int i = 0; i < cgraph->n_nodes; i++) {
|
|
ggml_vk_build_graph(ctx,cgraph->nodes[i], i == last_node);
|
|
}
|
|
|
|
ggml_compute_params params = {};
|
|
params.type = GGML_TASK_TYPE_COMPUTE;
|
|
params.ith = 0;
|
|
for (int i = 0; i < cgraph->n_nodes; i++) {
|
|
ggml_tensor * node = cgraph->nodes[i];
|
|
|
|
if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
|
|
continue;
|
|
}
|
|
|
|
bool ok = ggml_vk_compute_forward(ctx, ¶ms, node);
|
|
if (!ok) {
|
|
fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
|
|
}
|
|
#ifdef GGML_VULKAN_CHECK_RESULTS
|
|
else {
|
|
ggml_vk_check_results_1(ctx, ¶ms, node);
|
|
}
|
|
#endif
|
|
GGML_ASSERT(ok);
|
|
}
|
|
|
|
ggml_vk_graph_cleanup(ctx);
|
|
|
|
return GGML_STATUS_SUCCESS;
|
|
|
|
UNUSED(backend);
|
|
}
|
|
|
|
GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
|
|
switch (op->op) {
|
|
case GGML_OP_UNARY:
|
|
switch (ggml_get_unary_op(op)) {
|
|
case GGML_UNARY_OP_GELU:
|
|
case GGML_UNARY_OP_SILU:
|
|
case GGML_UNARY_OP_RELU:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
break;
|
|
case GGML_OP_MUL_MAT:
|
|
case GGML_OP_MUL_MAT_ID:
|
|
{
|
|
struct ggml_tensor * a;
|
|
struct ggml_tensor * b;
|
|
if (op->op == GGML_OP_MUL_MAT) {
|
|
a = op->src[0];
|
|
b = op->src[1];
|
|
} else {
|
|
a = op->src[2];
|
|
b = op->src[1];
|
|
}
|
|
if (a->ne[3] != b->ne[3]) {
|
|
return false;
|
|
}
|
|
return true;
|
|
} break;
|
|
// case GGML_OP_GET_ROWS:
|
|
// {
|
|
// switch (op->src[0]->type) {
|
|
// case GGML_TYPE_F16:
|
|
// case GGML_TYPE_F32:
|
|
// case GGML_TYPE_Q4_0:
|
|
// case GGML_TYPE_Q4_1:
|
|
// case GGML_TYPE_Q5_0:
|
|
// case GGML_TYPE_Q5_1:
|
|
// case GGML_TYPE_Q8_0:
|
|
// return true;
|
|
// default:
|
|
// return false;
|
|
// }
|
|
// } break;
|
|
case GGML_OP_CPY:
|
|
{
|
|
ggml_type src0_type = op->src[0]->type;
|
|
ggml_type src1_type = op->src[1]->type;
|
|
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
|
|
return true;
|
|
}
|
|
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
|
|
return true;
|
|
}
|
|
if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
|
|
return true;
|
|
}
|
|
return false;
|
|
} break;
|
|
case GGML_OP_DUP:
|
|
// case GGML_OP_REPEAT:
|
|
// {
|
|
// ggml_type src0_type = op->src[0]->type;
|
|
// return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
|
|
// } break;
|
|
case GGML_OP_ROPE:
|
|
{
|
|
const int mode = ((const int32_t *) op->op_params)[2];
|
|
const bool is_glm = mode & 4;
|
|
|
|
return !is_glm;
|
|
} break;
|
|
case GGML_OP_NONE:
|
|
case GGML_OP_RESHAPE:
|
|
case GGML_OP_VIEW:
|
|
case GGML_OP_PERMUTE:
|
|
case GGML_OP_TRANSPOSE:
|
|
case GGML_OP_NORM:
|
|
case GGML_OP_ADD:
|
|
case GGML_OP_MUL:
|
|
case GGML_OP_RMS_NORM:
|
|
case GGML_OP_SCALE:
|
|
case GGML_OP_SQR:
|
|
case GGML_OP_CLAMP:
|
|
case GGML_OP_CONT:
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
case GGML_OP_SOFT_MAX:
|
|
case GGML_OP_ARGSORT:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
|
|
UNUSED(backend);
|
|
}
|
|
|
|
// TODO: enable async and synchronize
|
|
static ggml_backend_i ggml_backend_vk_interface = {
|
|
/* .get_name = */ ggml_backend_vk_name,
|
|
/* .free = */ ggml_backend_vk_free,
|
|
/* .get_default_buffer_type = */ ggml_backend_vk_get_default_buffer_type,
|
|
/* .set_tensor_async = */ NULL, // ggml_backend_vk_set_tensor_async,
|
|
/* .get_tensor_async = */ NULL, // ggml_backend_vk_get_tensor_async,
|
|
/* .cpy_tensor_async = */ NULL, // ggml_backend_vk_cpy_tensor_async,
|
|
/* .synchronize = */ NULL, // ggml_backend_vk_synchronize,
|
|
/* .graph_plan_create = */ NULL,
|
|
/* .graph_plan_free = */ NULL,
|
|
/* .graph_plan_compute = */ NULL,
|
|
/* .graph_compute = */ ggml_backend_vk_graph_compute,
|
|
/* .supports_op = */ ggml_backend_vk_supports_op,
|
|
/* .offload_op = */ NULL,
|
|
/* .event_new = */ NULL,
|
|
/* .event_free = */ NULL,
|
|
/* .event_record = */ NULL,
|
|
/* .event_wait = */ NULL,
|
|
/* .event_synchronize = */ NULL,
|
|
};
|
|
|
|
static ggml_guid_t ggml_backend_vk_guid() {
|
|
static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b };
|
|
return &guid;
|
|
}
|
|
|
|
GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t idx) {
|
|
if (vk_instance.initialized[idx]) {
|
|
return vk_instance.backends[idx];
|
|
}
|
|
#ifdef GGML_VULKAN_DEBUG
|
|
std::cerr << "ggml_backend_vk_init(" << idx << ")" << std::endl;
|
|
#endif
|
|
|
|
ggml_backend_vk_context * ctx = &vk_instance.contexts[idx];
|
|
ggml_vk_init(ctx, idx);
|
|
ctx->name = GGML_VK_NAME + std::to_string(idx);
|
|
vk_instance.buffer_types[idx] = {
|
|
/* .iface = */ ggml_backend_vk_buffer_type_interface,
|
|
/* .context = */ new ggml_backend_vk_buffer_type_context{ ctx->name, ctx },
|
|
};
|
|
vk_instance.initialized[idx] = true;
|
|
|
|
ggml_backend_t vk_backend = new ggml_backend {
|
|
/* .guid = */ ggml_backend_vk_guid(),
|
|
/* .interface = */ ggml_backend_vk_interface,
|
|
/* .context = */ &vk_instance.contexts[ctx->idx],
|
|
};
|
|
|
|
vk_instance.backends[idx] = vk_backend;
|
|
|
|
return vk_backend;
|
|
}
|
|
|
|
GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend) {
|
|
return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid());
|
|
}
|
|
|
|
GGML_CALL int ggml_backend_vk_get_device_count() {
|
|
return ggml_vk_get_device_count();
|
|
}
|
|
|
|
GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) {
|
|
ggml_vk_get_device_description(device, description, description_size);
|
|
}
|
|
|
|
GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) {
|
|
GGML_ASSERT(device < (int) vk_instance.device_indices.size());
|
|
|
|
vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]];
|
|
|
|
vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties();
|
|
|
|
for (const vk::MemoryHeap& heap : memprops.memoryHeaps) {
|
|
if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) {
|
|
*total = heap.size;
|
|
*free = heap.size;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
// backend registry
|
|
GGML_CALL static ggml_backend_t ggml_backend_reg_vk_init(const char * params, void * user_data) {
|
|
ggml_backend_t vk_backend = ggml_backend_vk_init((int) (intptr_t) user_data);
|
|
return vk_backend;
|
|
|
|
UNUSED(params);
|
|
}
|
|
|
|
extern "C" GGML_CALL int ggml_backend_vk_reg_devices();
|
|
|
|
GGML_CALL int ggml_backend_vk_reg_devices() {
|
|
for (auto idx : vk_instance.device_indices) {
|
|
char name[128];
|
|
snprintf(name, sizeof(name), "%s%ld", GGML_VK_NAME, idx);
|
|
ggml_backend_register(name, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(idx), (void *) (intptr_t) idx);
|
|
}
|
|
return vk_instance.device_indices.size();
|
|
}
|
|
|
|
// Extension availability
|
|
static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
|
|
#ifdef GGML_VULKAN_VALIDATE
|
|
bool portability_enumeration_ext = false;
|
|
// Check for portability enumeration extension for MoltenVK support
|
|
for (const auto& properties : instance_extensions) {
|
|
if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
|
|
return true;
|
|
}
|
|
}
|
|
if (!portability_enumeration_ext) {
|
|
std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
|
|
}
|
|
#endif
|
|
return false;
|
|
|
|
UNUSED(instance_extensions);
|
|
}
|
|
static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
|
|
#ifdef __APPLE__
|
|
bool portability_enumeration_ext = false;
|
|
// Check for portability enumeration extension for MoltenVK support
|
|
for (const auto& properties : instance_extensions) {
|
|
if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
|
|
return true;
|
|
}
|
|
}
|
|
if (!portability_enumeration_ext) {
|
|
std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
|
|
}
|
|
#endif
|
|
return false;
|
|
|
|
UNUSED(instance_extensions);
|
|
}
|
|
|
|
// checks
|
|
|
|
#ifdef GGML_VULKAN_CHECK_RESULTS
|
|
static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vector<const ggml_tensor *>& done, int level = 0) {
|
|
if (std::find(done.begin(), done.end(), tensor) != done.end() || level > 10) {
|
|
return;
|
|
}
|
|
for (int j = 0; j < level; j++) {
|
|
std::cerr << " ";
|
|
}
|
|
std::cerr << ggml_op_name(tensor->op) << " gpu=" << (tensor->extra != nullptr) << " backend=" << tensor->backend << std::endl;
|
|
|
|
done.push_back(tensor);
|
|
|
|
for (int i = 0; i < GGML_MAX_SRC; i++) {
|
|
if (tensor->src[i] != nullptr) {
|
|
ggml_vk_print_graph_origin(tensor->src[i], done, level + 1);
|
|
}
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * data, int i0, int i1, int i2, int i3) {
|
|
if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
|
|
return;
|
|
}
|
|
i0 = std::max(i0, 5);
|
|
i1 = std::max(i1, 5);
|
|
i2 = std::max(i2, 0);
|
|
i3 = std::max(i3, 0);
|
|
fprintf(stderr, " ");
|
|
for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
|
|
fprintf(stderr, "%7d ", idx1);
|
|
}
|
|
fprintf(stderr, "\n");
|
|
for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
|
|
fprintf(stderr, "%7d: ", idx0);
|
|
for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
|
|
if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) {
|
|
float val;
|
|
if (tensor->type == GGML_TYPE_F32) {
|
|
val = *(const float *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
|
|
} else if (tensor->type == GGML_TYPE_F16) {
|
|
val = ggml_fp16_to_fp32(*(const ggml_fp16_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]));
|
|
}
|
|
fprintf(stderr, "% 7.2f ", val);
|
|
} else {
|
|
fprintf(stderr, " ");
|
|
}
|
|
}
|
|
fprintf(stderr, "\n");
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tensor * tensor, const char * name) {
|
|
void * tensor_data = tensor->data;
|
|
|
|
if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
|
|
const size_t tensor_size = ggml_nbytes(tensor);
|
|
tensor_data = malloc(tensor_size);
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
|
|
|
|
vk_buffer buffer_gpu = extra->buffer_gpu.lock();
|
|
ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size);
|
|
}
|
|
|
|
std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl;
|
|
std::cerr << "tensor=" << tensor << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl;
|
|
if (tensor->src[0] != nullptr) {
|
|
std::cerr << "tensor->src[0]=" << tensor->src[0] << " name=" << tensor->src[0]->name << " op=" << ggml_op_name(tensor->src[0]->op) << " type=" << ggml_type_name(tensor->src[0]->type) << " backend=" << tensor->src[0]->backend << " ne0=" << tensor->src[0]->ne[0] << " nb0=" << tensor->src[0]->nb[0] << " ne1=" << tensor->src[0]->ne[1] << " nb1=" << tensor->src[0]->nb[1] << " ne2=" << tensor->src[0]->ne[2] << " nb2=" << tensor->src[0]->nb[2] << " ne3=" << tensor->src[0]->ne[3] << " nb3=" << tensor->src[0]->nb[3] << std::endl;
|
|
}
|
|
if (tensor->src[1] != nullptr) {
|
|
std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " backend=" << tensor->src[1]->backend << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl;
|
|
}
|
|
std::cerr << std::endl << "Result:" << std::endl;
|
|
ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0);
|
|
std::cerr << std::endl;
|
|
std::cerr << std::endl << "Result:" << std::endl;
|
|
ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 1, 0);
|
|
std::cerr << std::endl;
|
|
std::vector<const ggml_tensor *> done;
|
|
ggml_vk_print_graph_origin(tensor, done);
|
|
|
|
if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
|
|
free(tensor_data);
|
|
}
|
|
}
|
|
|
|
static void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) {
|
|
return;
|
|
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_CPU);
|
|
if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
|
|
return;
|
|
}
|
|
for (int i3 = 0; i3 < tensor->ne[3]; i3++) {
|
|
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
|
|
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
|
|
float val = 0.0f;
|
|
if (tensor->type == GGML_TYPE_F32) {
|
|
val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
|
|
} else if (tensor->type == GGML_TYPE_F16) {
|
|
val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]));
|
|
}
|
|
if (std::isnan(val)) {
|
|
std::cerr << "ERROR: TENSOR CHECK " << name << ": Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " val=" << val << std::endl;
|
|
std::cerr << "tensor=" << tensor << " tensor->type=" << ggml_type_name(tensor->type) << " tensor->backend: " << tensor->backend << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl;
|
|
std::cerr << std::endl;
|
|
ggml_vk_print_tensor_area(tensor, tensor->data, i0, i1, i2, i3);
|
|
std::cerr << std::endl;
|
|
std::vector<const ggml_tensor *> done;
|
|
ggml_vk_print_graph_origin(tensor, done);
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void * comp_result;
|
|
size_t comp_size;
|
|
size_t comp_nb[GGML_MAX_DIMS];
|
|
size_t check_counter = 0;
|
|
static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) {
|
|
if (params->ith != 0) {
|
|
return;
|
|
}
|
|
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
|
|
return;
|
|
}
|
|
|
|
check_counter++;
|
|
if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
|
|
return;
|
|
}
|
|
|
|
ggml_tensor * src0 = tensor->src[0];
|
|
ggml_tensor * src1 = tensor->src[1];
|
|
ggml_tensor * src2 = tensor->src[2];
|
|
|
|
struct ggml_init_params iparams = {
|
|
/*.mem_size =*/ 1024*1024*1024,
|
|
/*.mem_buffer =*/ NULL,
|
|
/*.no_alloc =*/ false,
|
|
};
|
|
|
|
struct ggml_context * ggml_ctx = ggml_init(iparams);
|
|
|
|
struct ggml_tensor * src0_clone = nullptr;
|
|
struct ggml_tensor * src1_clone = nullptr;
|
|
struct ggml_tensor * src2_clone = nullptr;
|
|
struct ggml_tensor * tensor_clone = nullptr;
|
|
|
|
size_t src0_size;
|
|
size_t src1_size;
|
|
size_t src2_size;
|
|
|
|
void * src0_buffer;
|
|
void * src1_buffer;
|
|
void * src2_buffer;
|
|
|
|
if (src0 != nullptr) {
|
|
src0_clone = ggml_dup_tensor(ggml_ctx, src0);
|
|
|
|
src0_size = ggml_nbytes(src0);
|
|
|
|
src0_buffer = malloc(src0_size);
|
|
src0_clone->data = src0_buffer;
|
|
if (src0->backend == GGML_BACKEND_TYPE_CPU) {
|
|
memcpy(src0_clone->data, src0->data, src0_size);
|
|
memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
|
|
} else if (src0->backend == GGML_BACKEND_TYPE_GPU) {
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra;
|
|
vk_buffer buffer_gpu = extra->buffer_gpu.lock();
|
|
uint64_t offset = extra->offset;
|
|
if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) {
|
|
for (int i3 = 0; i3 < src0->ne[3]; i3++) {
|
|
for (int i2 = 0; i2 < src0->ne[2]; i2++) {
|
|
const int idx = i3*src0->ne[2] + i2;
|
|
ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]);
|
|
}
|
|
}
|
|
|
|
src0_clone->nb[0] = src0->nb[0];
|
|
src0_clone->nb[1] = src0->nb[1];
|
|
for (int i = 2; i < GGML_MAX_DIMS; i++) {
|
|
src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1];
|
|
}
|
|
} else {
|
|
if (offset + src0_size >= buffer_gpu->size) {
|
|
src0_size = buffer_gpu->size - offset;
|
|
}
|
|
ggml_vk_buffer_read(ctx, buffer_gpu, offset, src0_clone->data, src0_size);
|
|
memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
|
|
ggml_vk_print_tensor(ctx, src0, "src0");
|
|
}
|
|
|
|
ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src0", src0_clone);
|
|
}
|
|
if (src1 != nullptr) {
|
|
src1_clone = ggml_dup_tensor(ggml_ctx, src1);
|
|
|
|
src1_size = ggml_nbytes(src1);
|
|
|
|
src1_buffer = malloc(src1_size);
|
|
src1_clone->data = src1_buffer;
|
|
if (src1->backend == GGML_BACKEND_TYPE_CPU) {
|
|
memcpy(src1_clone->data, src1->data, src1_size);
|
|
memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
|
|
} else if (src1->backend == GGML_BACKEND_TYPE_GPU) {
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra;
|
|
vk_buffer buffer_gpu = extra->buffer_gpu.lock();
|
|
uint64_t offset = extra->offset;
|
|
if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) {
|
|
for (int i3 = 0; i3 < src1->ne[3]; i3++) {
|
|
for (int i2 = 0; i2 < src1->ne[2]; i2++) {
|
|
const int idx = i3*src1->ne[2] + i2;
|
|
ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]);
|
|
}
|
|
}
|
|
|
|
src1_clone->nb[0] = src1->nb[0];
|
|
src1_clone->nb[1] = src1->nb[1];
|
|
for (int i = 2; i < GGML_MAX_DIMS; i++) {
|
|
src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1];
|
|
}
|
|
} else {
|
|
if (offset + src1_size >= buffer_gpu->size) {
|
|
src1_size = buffer_gpu->size - offset;
|
|
}
|
|
ggml_vk_buffer_read(ctx, buffer_gpu, offset, src1_clone->data, src1_size);
|
|
memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
|
|
ggml_vk_print_tensor(ctx, src1, "src1");
|
|
std::cerr << "TENSOR CHECK: " << ggml_op_name(src1_clone->op) << " (check " << check_counter << ")" << std::endl;
|
|
std::cerr << "src1_clone=" << tensor << " src1_clone->backend: " << src1_clone->backend << " src1_clone->type: " << ggml_type_name(src1_clone->type) << " ne0=" << src1_clone->ne[0] << " nb0=" << src1_clone->nb[0] << " ne1=" << src1_clone->ne[1] << " nb1=" << src1_clone->nb[1] << " ne2=" << src1_clone->ne[2] << " nb2=" << src1_clone->nb[2] << " ne3=" << src1_clone->ne[3] << " nb3=" << src1_clone->nb[3] << std::endl;
|
|
if (src1->src[0] != nullptr) {
|
|
std::cerr << "src1->src[0]=" << src1->src[0] << " op=" << ggml_op_name(src1->src[0]->op) << " type=" << ggml_type_name(src1->src[0]->type) << " backend=" << src1->src[0]->backend << " ne0=" << src1->src[0]->ne[0] << " nb0=" << src1->src[0]->nb[0] << " ne1=" << src1->src[0]->ne[1] << " nb1=" << src1->src[0]->nb[1] << " ne2=" << src1->src[0]->ne[2] << " nb2=" << src1->src[0]->nb[2] << " ne3=" << src1->src[0]->ne[3] << " nb3=" << src1->src[0]->nb[3] << std::endl;
|
|
}
|
|
if (src1->src[1] != nullptr) {
|
|
std::cerr << "src1->src[1]=" << src1->src[1] << " op=" << ggml_op_name(src1->src[1]->op) << " type=" << ggml_type_name(src1->src[1]->type) << " backend=" << src1->src[1]->backend << " ne0=" << src1->src[1]->ne[0] << " nb0=" << src1->src[1]->nb[0] << " ne1=" << src1->src[1]->ne[1] << " nb1=" << src1->src[1]->nb[1] << " ne2=" << src1->src[1]->ne[2] << " nb2=" << src1->src[1]->nb[2] << " ne3=" << src1->src[1]->ne[3] << " nb3=" << src1->src[1]->nb[3] << std::endl;
|
|
}
|
|
std::cerr << std::endl << "Result:" << std::endl;
|
|
ggml_vk_print_tensor_area(src1_clone, src1_clone->data, 5, 5, 0, 0);
|
|
std::cerr << std::endl;
|
|
std::cerr << std::endl << "Result:" << std::endl;
|
|
ggml_vk_print_tensor_area(src1_clone, src1_clone->data, 5, 5, 1, 0);
|
|
std::cerr << std::endl;
|
|
std::vector<const ggml_tensor *> done;
|
|
ggml_vk_print_graph_origin(src1_clone, done);
|
|
}
|
|
|
|
ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src1", src1_clone);
|
|
}
|
|
if (src2 != nullptr) {
|
|
src2_clone = ggml_dup_tensor(ggml_ctx, src2);
|
|
|
|
src2_size = ggml_nbytes(src2);
|
|
|
|
src2_buffer = malloc(src2_size);
|
|
src2_clone->data = src2_buffer;
|
|
if (src2->backend == GGML_BACKEND_TYPE_CPU) {
|
|
memcpy(src2_clone->data, src2->data, src2_size);
|
|
memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS);
|
|
} else if (src2->backend == GGML_BACKEND_TYPE_GPU) {
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra;
|
|
vk_buffer buf = extra->buffer_gpu.lock();
|
|
uint64_t offset = extra->offset;
|
|
if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) {
|
|
for (int i3 = 0; i3 < src2->ne[3]; i3++) {
|
|
for (int i2 = 0; i2 < src2->ne[2]; i2++) {
|
|
const int idx = i3*src2->ne[2] + i2;
|
|
ggml_vk_buffer_read(ctx, buf, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]);
|
|
}
|
|
}
|
|
|
|
src2_clone->nb[0] = src2->nb[0];
|
|
src2_clone->nb[1] = src2->nb[1];
|
|
for (int i = 2; i < GGML_MAX_DIMS; i++) {
|
|
src2_clone->nb[i] = src2_clone->nb[i - 1]*src2_clone->ne[i - 1];
|
|
}
|
|
} else {
|
|
if (offset + src2_size >= buf->size) {
|
|
src2_size = buf->size - offset;
|
|
}
|
|
ggml_vk_buffer_read(ctx, buf, offset, src2_clone->data, src2_size);
|
|
memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS);
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
|
|
ggml_vk_print_tensor(ctx, src2, "src2");
|
|
std::cerr << "TENSOR CHECK: " << ggml_op_name(src2_clone->op) << " (check " << check_counter << ")" << std::endl;
|
|
std::cerr << "src2_clone=" << tensor << " src2_clone->backend: " << src2_clone->backend << " src2_clone->type: " << ggml_type_name(src2_clone->type) << " ne0=" << src2_clone->ne[0] << " nb0=" << src2_clone->nb[0] << " ne1=" << src2_clone->ne[1] << " nb1=" << src2_clone->nb[1] << " ne2=" << src2_clone->ne[2] << " nb2=" << src2_clone->nb[2] << " ne3=" << src2_clone->ne[3] << " nb3=" << src2_clone->nb[3] << std::endl;
|
|
if (src2->src[0] != nullptr) {
|
|
std::cerr << "src2->src[0]=" << src2->src[0] << " op=" << ggml_op_name(src2->src[0]->op) << " type=" << ggml_type_name(src2->src[0]->type) << " backend=" << src2->src[0]->backend << " ne0=" << src2->src[0]->ne[0] << " nb0=" << src2->src[0]->nb[0] << " ne1=" << src2->src[0]->ne[1] << " nb1=" << src2->src[0]->nb[1] << " ne2=" << src2->src[0]->ne[2] << " nb2=" << src2->src[0]->nb[2] << " ne3=" << src2->src[0]->ne[3] << " nb3=" << src2->src[0]->nb[3] << std::endl;
|
|
}
|
|
if (src2->src[1] != nullptr) {
|
|
std::cerr << "src2->src[1]=" << src2->src[1] << " op=" << ggml_op_name(src2->src[1]->op) << " type=" << ggml_type_name(src2->src[1]->type) << " backend=" << src2->src[1]->backend << " ne0=" << src2->src[1]->ne[0] << " nb0=" << src2->src[1]->nb[0] << " ne1=" << src2->src[1]->ne[1] << " nb1=" << src2->src[1]->nb[1] << " ne2=" << src2->src[1]->ne[2] << " nb2=" << src2->src[1]->nb[2] << " ne3=" << src2->src[1]->ne[3] << " nb3=" << src2->src[1]->nb[3] << std::endl;
|
|
}
|
|
std::cerr << std::endl << "Result:" << std::endl;
|
|
ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 0, 0);
|
|
std::cerr << std::endl;
|
|
std::cerr << std::endl << "Result:" << std::endl;
|
|
ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 1, 0);
|
|
std::cerr << std::endl;
|
|
std::vector<const ggml_tensor *> done;
|
|
ggml_vk_print_graph_origin(src2_clone, done);
|
|
}
|
|
|
|
ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src2", src2_clone);
|
|
}
|
|
|
|
if (tensor->op == GGML_OP_MUL_MAT) {
|
|
tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone);
|
|
} else if (tensor->op == GGML_OP_MUL) {
|
|
tensor_clone = ggml_mul(ggml_ctx, src0_clone, src1_clone);
|
|
} else if (tensor->op == GGML_OP_SCALE) {
|
|
tensor_clone = ggml_scale(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0]);
|
|
} else if (tensor->op == GGML_OP_SQR) {
|
|
tensor_clone = ggml_sqr(ggml_ctx, src0_clone);
|
|
} else if (tensor->op == GGML_OP_CLAMP) {
|
|
tensor_clone = ggml_clamp(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]);
|
|
} else if (tensor->op == GGML_OP_ADD) {
|
|
tensor_clone = ggml_add(ggml_ctx, src0_clone, src1_clone);
|
|
} else if (tensor->op == GGML_OP_NORM) {
|
|
tensor_clone = ggml_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params);
|
|
} else if (tensor->op == GGML_OP_RMS_NORM) {
|
|
tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params);
|
|
} else if (tensor->op == GGML_OP_SOFT_MAX) {
|
|
if (src1 != nullptr) {
|
|
tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]);
|
|
} else {
|
|
tensor_clone = ggml_soft_max(ggml_ctx, src0_clone);
|
|
}
|
|
} else if (tensor->op == GGML_OP_DIAG_MASK_INF) {
|
|
tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params);
|
|
} else if (tensor->op == GGML_OP_ROPE) {
|
|
const int n_dims = ((int32_t *) tensor->op_params)[1];
|
|
const int mode = ((int32_t *) tensor->op_params)[2];
|
|
const int n_ggml_ctx = ((int32_t *) tensor->op_params)[3];
|
|
const int n_orig_ggml_ctx = ((int32_t *) tensor->op_params)[4];
|
|
float freq_base = ((float *) tensor->op_params)[5];
|
|
float freq_scale = ((float *) tensor->op_params)[6];
|
|
float ext_factor = ((float *) tensor->op_params)[7];
|
|
float attn_factor = ((float *) tensor->op_params)[8];
|
|
float beta_fast = ((float *) tensor->op_params)[9];
|
|
float beta_slow = ((float *) tensor->op_params)[10];
|
|
tensor_clone = ggml_rope_custom(ggml_ctx, src0_clone, src1_clone, n_dims, mode, n_ggml_ctx, n_orig_ggml_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
|
|
} else if (tensor->op == GGML_OP_UNARY) {
|
|
switch (ggml_get_unary_op(tensor)) {
|
|
case GGML_UNARY_OP_SILU:
|
|
tensor_clone = ggml_silu(ggml_ctx, src0_clone);
|
|
break;
|
|
case GGML_UNARY_OP_GELU:
|
|
tensor_clone = ggml_gelu(ggml_ctx, src0_clone);
|
|
break;
|
|
case GGML_UNARY_OP_RELU:
|
|
tensor_clone = ggml_relu(ggml_ctx, src0_clone);
|
|
break;
|
|
default:
|
|
std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
|
|
GGML_ASSERT(false);
|
|
}
|
|
} else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) {
|
|
if (src1 == nullptr) {
|
|
tensor_clone = ggml_dup(ggml_ctx, src0_clone);
|
|
tensor_clone->type = tensor->type;
|
|
} else {
|
|
tensor_clone = ggml_cpy(ggml_ctx, src0_clone, src1_clone);
|
|
}
|
|
} else if (tensor->op == GGML_OP_CONT) {
|
|
tensor_clone = ggml_cont_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
|
|
} else if (tensor->op == GGML_OP_RESHAPE) {
|
|
tensor_clone = ggml_reshape_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
|
|
} else if (tensor->op == GGML_OP_VIEW) {
|
|
tensor_clone = ggml_view_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]);
|
|
} else if (tensor->op == GGML_OP_PERMUTE) {
|
|
int32_t * params = (int32_t *)tensor->op_params;
|
|
tensor_clone = ggml_permute(ggml_ctx, src0_clone, params[0], params[1], params[2], params[3]);
|
|
} else if (tensor->op == GGML_OP_TRANSPOSE) {
|
|
tensor_clone = ggml_transpose(ggml_ctx, src0_clone);
|
|
} else {
|
|
std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
// Disable vulkan here to avoid the hooks in ggml.c
|
|
ctx->disable = true;
|
|
|
|
ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
|
|
ggml_build_forward_expand(cgraph, tensor_clone);
|
|
|
|
ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8);
|
|
|
|
ctx->disable = false;
|
|
|
|
ggml_vk_check_tensor(ggml_op_name(tensor->op), tensor_clone);
|
|
if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
|
|
ggml_vk_print_tensor(ctx, tensor_clone, "tensor_clone");
|
|
}
|
|
|
|
comp_size = ggml_nbytes(tensor_clone);
|
|
|
|
comp_result = malloc(comp_size);
|
|
memcpy(comp_result, tensor_clone->data, comp_size);
|
|
memcpy(comp_nb, tensor_clone->nb, sizeof(size_t) * GGML_MAX_DIMS);
|
|
|
|
if (src0 != nullptr) {
|
|
free(src0_buffer);
|
|
}
|
|
if (src1 != nullptr) {
|
|
free(src1_buffer);
|
|
}
|
|
if (src2 != nullptr) {
|
|
free(src1_buffer);
|
|
}
|
|
|
|
ggml_free(ggml_ctx);
|
|
}
|
|
|
|
static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) {
|
|
if (params->ith != 0) {
|
|
return;
|
|
}
|
|
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
|
|
return;
|
|
}
|
|
if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
|
|
return;
|
|
}
|
|
|
|
ggml_tensor * src0 = tensor->src[0];
|
|
ggml_tensor * src1 = tensor->src[1];
|
|
|
|
void * tensor_data = tensor->data;
|
|
|
|
if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
|
|
size_t tensor_size = ggml_nbytes(tensor);
|
|
tensor_data = malloc(tensor_size);
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
|
|
|
|
vk_buffer buffer_gpu = extra->buffer_gpu.lock();
|
|
if (extra->offset + tensor_size >= buffer_gpu->size) {
|
|
tensor_size = buffer_gpu->size - (extra->offset);
|
|
}
|
|
|
|
ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size);
|
|
}
|
|
|
|
float first_error_result = -1.0f;
|
|
float first_error_correct = -1.0f;
|
|
std::array<int, 4> first_error = { -1, -1, -1, -1 };
|
|
double avg_err = 0.0;
|
|
size_t counter = 0;
|
|
|
|
for (int i3 = 0; i3 < tensor->ne[3]; i3++) {
|
|
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
|
|
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
|
|
const bool buffer_size_fit = i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0] < comp_size;
|
|
float correct = 0.0f;
|
|
float result = 0.0f;
|
|
|
|
if (buffer_size_fit) {
|
|
if (tensor->type == GGML_TYPE_F32) {
|
|
correct = *(float *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]);
|
|
result = *(float *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
|
|
} else if (tensor->type == GGML_TYPE_F16) {
|
|
correct = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]));
|
|
result = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]));
|
|
} else {
|
|
std::cerr << "comp_size=" << comp_size << " but required is " << (i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]) << std::endl;
|
|
}
|
|
} else {
|
|
std::cerr << "Missing debug code for type " << ggml_type_name(tensor->type) << std::endl;
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
if ((std::isnan(correct) != std::isnan(result)) || (std::isinf(correct) != std::isinf(result)) || !buffer_size_fit) {
|
|
std::cerr << "ERROR: Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " result=" << result << " correct=" << correct << " avg_err=" << (avg_err / counter) << std::endl;
|
|
std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
|
|
if (src0 != nullptr) {
|
|
std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
|
|
}
|
|
if (src1 != nullptr) {
|
|
std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
|
|
}
|
|
std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
|
|
std::cerr << std::endl << "Result:" << std::endl;
|
|
ggml_vk_print_tensor_area(tensor, tensor_data, i0, i1, i2, i3);
|
|
std::cerr << std::endl << "Correct:" << std::endl;
|
|
ggml_vk_print_tensor_area(tensor, comp_result, i0, i1, i2, i3);
|
|
std::cerr << std::endl;
|
|
std::vector<const ggml_tensor *> done;
|
|
ggml_vk_print_graph_origin(tensor, done);
|
|
GGML_ASSERT(false);
|
|
}
|
|
if (first_error[0] == -1 && std::fabs(correct - result) > 0.1f) {
|
|
first_error[0] = i0;
|
|
first_error[1] = i1;
|
|
first_error[2] = i2;
|
|
first_error[3] = i3;
|
|
first_error_result = result;
|
|
first_error_correct = correct;
|
|
}
|
|
|
|
// Special case, value is infinite, avoid NaN result in avg_err
|
|
// NaN also appears in results, if both are nan error is 0
|
|
if (!std::isinf(correct) && !std::isinf(result) && !std::isnan(correct) && !std::isnan(result)) {
|
|
avg_err += std::fabs(correct - result);
|
|
}
|
|
counter++;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
avg_err /= counter;
|
|
|
|
if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
|
|
std::cerr << "TENSOR CHECK: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl;
|
|
std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
|
|
if (src0 != nullptr) {
|
|
std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
|
|
}
|
|
if (src1 != nullptr) {
|
|
std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
|
|
}
|
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std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
|
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std::cerr << std::endl << "Result:" << std::endl;
|
|
ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0);
|
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std::cerr << std::endl << "Correct:" << std::endl;
|
|
ggml_vk_print_tensor_area(tensor, comp_result, 5, 5, 0, 0);
|
|
std::cerr << std::endl;
|
|
std::cerr << std::endl << "Result:" << std::endl;
|
|
ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 1, 0);
|
|
std::cerr << std::endl << "Correct:" << std::endl;
|
|
ggml_vk_print_tensor_area(tensor, comp_result, 5, 5, 1, 0);
|
|
std::cerr << std::endl;
|
|
std::vector<const ggml_tensor *> done;
|
|
ggml_vk_print_graph_origin(tensor, done);
|
|
}
|
|
|
|
if (avg_err > 0.05 || std::isnan(avg_err)) {
|
|
std::cerr << "ERROR: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl;
|
|
std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
|
|
if (src0 != nullptr) {
|
|
std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
|
|
}
|
|
if (src1 != nullptr) {
|
|
std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
|
|
}
|
|
std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
|
|
std::cerr << std::endl << "Result:" << std::endl;
|
|
ggml_vk_print_tensor_area(tensor, tensor_data, first_error[0], first_error[1], first_error[2], first_error[3]);
|
|
std::cerr << std::endl << "Correct:" << std::endl;
|
|
ggml_vk_print_tensor_area(tensor, comp_result, first_error[0], first_error[1], first_error[2], first_error[3]);
|
|
std::cerr << std::endl;
|
|
std::vector<const ggml_tensor *> done;
|
|
ggml_vk_print_graph_origin(tensor, done);
|
|
GGML_ASSERT(false);
|
|
} else {
|
|
std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " backend=" << tensor->backend << " avg_err=" << avg_err << std::endl;
|
|
}
|
|
|
|
free(comp_result);
|
|
comp_result = nullptr;
|
|
comp_size = 0;
|
|
|
|
if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
|
|
free(tensor_data);
|
|
}
|
|
}
|
|
|
|
void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
|
|
ggml_backend_vk_context * ctx = &vk_instance.contexts[0];
|
|
|
|
ggml_vk_check_results_0(ctx, params, tensor);
|
|
}
|
|
#endif
|