* cuda : CUDA GGML_OP_LIGHTNING_INDEXER implementation (generic vector kernel + wmma kernel)
* chore : remove indentation of #pragma unroll
* cuda : remove unnecessary kernel template declarations
* cuda : add WARPS_PER_BLOCK and K_VECS_PER_BLOCK template parameters in lightning indexer kernels to avoid duplication of constants.
* cuda : relax MMA architecture requirements to Turing in lightning indexer implementation
* chore : renamed variables
* chore : rename ggml_cuda_op_lightning_indexer() to ggml_cuda_lightning_indexer()
* chore : TODO for AMD rocWMMA
* chore : whitespace formatting
* chore : another variable rename to fix problems caused by shadowing
* chore : yet another rename, this time uppercased all constants
* cuda : added alignment checks for Q and K tensors in lightning indexer implementation
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* llama : make all KQ masks (except the lightning indexer one) f16 if FA is used and remove zero attention bias in DeepSeek V4
* llama : remove dead code that repeats unified raw_k cache for each stream in DeepSeek V4 - no longer needed as raw_k is always non-unified.
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* ggml : process data in smaller chunks in CUDA ggml_top_k() implementation to reduce temporary buffers memory usage
* ggml : allocate tmp_dst only only once before the loop
* chore : whitespaces
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ggml : use chunked processing in both CUDA CUB top-k and argsort implementations
* chore : separate argsort_f32_i32_cuda_bitonic() call from return statement
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* chore : replace ternary operators with min/max
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* ggml : add support for CPU f16->f16 GGML_OP_SET_ROWS
* ggml : add missing type checks in f16 GGML_OP_SET_ROWS
* ggml : merge ggml_compute_forward_set_rows_f32() and ggml_compute_forward_set_rows_f16() into ggml_compute_forward_set_rows_impl()
* chore : replace assert() with GGML_ASSERT()
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* vulkan : check src0 type in GGML_OP_SET_ROWS to avoid failures due to unimplemented f16 support
* chore : get rid of else
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* cuda : concat implementation for quantized types
* chore : apply am17an clever suggestion to shorten the code
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* llama : introduce llama_set_warmup() API call that controls warmup mode; use all MoE experts during warmup
* common : use new API to enable warmup mode during model warmup
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* convert : extend DEEPSEEK2 model architecture to support DeepseekV3ForCausalLM by adding EXPERT_WEIGHTS_NORM and EXPERT_GATING_FUNC model parameters and FFN_EXP_PROBS_B tensor type
* vocab : add DeepSeek V3 pre-tokenizer regexes
* unicode : handle ACCENT_MARK and SYMBOL categories in regex
* llama : add DeepSeek V3 chat template, handle new model parameters and tensor types
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* convert : use GPT2 vocab for Phi-4 model
* convert : use null value of sliding_window to distinguish Phi-4 from other PHI3-based models
* llama : do not use sliding window attention mask for Phi-4 model
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* gguf-py : add T5ENCODER model architecture
* common : call llama_decode() during warmup only if the model has decoder
* convert-hf : add T5EncoderModel
* llama : add llama_model_has_decoder() API function
* llama : split build_t5() into build_t5_encoder() and build_t5_decoder()
* llama : add support for LLM_ARCH_T5ENCODER
* llama-embedding : add support for LLAMA_POOLING_TYPE_NONE
* llama-embedding : add support for encoder-only models
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* gguf-py, llama : add constants and methods related to Llama-3.1 <|eom_id|> token
* llama : find Llama-3.1 <|eom_id|> token id during vocab loading
* llama-vocab : add Llama-3.1 <|eom_id|> token to the set of tokens stopping the generation
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* llama : add inference support and model types for T5 and FLAN-T5 model families
* llama : add new API functions to support encoder-decoder models: llama_encode(), llama_model_has_encoder(), llama_model_decoder_start_token()
* common, llama-cli, llama-batched : add support for encoder-decoder models
* convert-hf : handle shared token embeddings tensors in T5Model
* convert-hf : add support for SentencePiece BPE tokenizer in T5Model (for Pile-T5 models)
* convert-hf : add MT5ForConditionalGeneration and UMT5ForConditionalGeneration to architectures supported by T5Model
* convert : add t5 tokenizer tests, use "slow" HF tokenizer for t5
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : add T5 model architecture, tensors and model header parameters
* llama : add implementation of Unigram tokenizer with SentencePiece-like text normalization using precompiled charsmap
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* gguf-py : add T5 model architecture
* gguf-py : add separate tensors for encoder and decoder
* gguf-py : add new model header parameters: decoder_start_token_id, attention.relative_buckets_count, tokenizer.ggml.remove_extra_whitespaces, tokenizer.ggml.precompiled_charsmap
* convert-hf : add model conversion support for T5ForConditionalGeneration and T5WithLMHeadModel
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* common : increase max number of experts to 160
* common : add tensors ATTN_Q_A, ATTN_Q_A_NORM, ATTN_Q_B, ATTN_KV_A_MQA, ATTN_KV_A_NORM, ATTN_KV_B needed by DeepSeek-V2 MLA (multi-head latent attention) architecture
* common : add model header parameters: leading_dense_block_count, expert_feed_forward_length, expert_shared_count, expert_weights_scale, attention.q_lora_rank, attention.kv_lora_rank, rope.scaling.yarn_log_multiplier
* convert-hf : add model conversion support for DeepseekV2ForCausalLM
* llama : add model types for DeepSeek-V2 and DeepSeek-V2-Lite models
* llama : add two new llm_build_moe_ffn() arguments: scale_w (whether to scale weights of selected MoE experts) and w_scale (numerical value of the scaling factor)
* llama : add inference support for LLM_ARCH_DEEPSEEK2
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* common : increase max number of experts to 128
* common : add tensor LLM_TENSOR_FFN_NORM_EXPS for normalization before MoE that runs in parallel to attention + ffn
* gguf-py : add architecture-specific block mappings that override selected general block mappings
* convert-hf : add model conversion support for ArcticForCausalLM
* convert-hf : use added_tokens_decoder from tokenizer_config.json to redefine tokens from SentencePiece model (only for ArcticForCausalLM)
* llama : add inference support for LLM_ARCH_ARCTIC
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* convert-hf : add conversion of bloom-style qkv tensor to gpt-style qkv (code borrowed from BloomModel)
* llama : add inference support for LLM_ARCH_GPTNEOX
* llama : add model types for every Pythia variant and GPT-NeoX
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* llama : use n_embd_head_v instead of n_embd_head_k when reshaping kqv
* llama : use n_embd_v_gqa and n_embd_head_v instead of n_embd_k_gqa and n_embd_head_k when making a view of cached value vectors.
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
