merge from upstream #40
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l3utterfly
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Oct 1, 2024
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l3utterfly
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* update oneapi to 2024.2 * use 2024.1 --------- Co-authored-by: arthw <[email protected]>
* ggml: Added run-time detection of neon, i8mm and sve Adds run-time detection of the Arm instructions set features neon, i8mm and sve for Linux and Apple build targets. * ggml: Extend feature detection to include non aarch64 Arm arch * ggml: Move definition of ggml_arm_arch_features to the global data section
* convert chameleon hf to gguf * add chameleon tokenizer tests * fix lint * implement chameleon graph * add swin norm param * return qk norm weights and biases to original format * implement swin norm * suppress image token output * rem tabs * add comment to conversion * fix ci * check for k norm separately * adapt to new lora implementation * fix layer input for swin norm * move swin_norm in gguf writer * add comment regarding special token regex in chameleon pre-tokenizer * Update src/llama.cpp Co-authored-by: compilade <[email protected]> * fix punctuation regex in chameleon pre-tokenizer (@compilade) Co-authored-by: compilade <[email protected]> * fix lint * trigger ci --------- Co-authored-by: compilade <[email protected]>
* refactor tokenizer * llama : make llm_tokenizer more private ggml-ci * refactor tokenizer * refactor tokenizer * llama : make llm_tokenizer more private ggml-ci * remove unused files * remove unused fileds to avoid unused filed build error * avoid symbol link error * Update src/llama.cpp * Update src/llama.cpp --------- Co-authored-by: Georgi Gerganov <[email protected]>
* test-backend-ops : use flops for some performance tests - parallelize tensor quantization - use a different set of cases for performance and correctness tests - run each test for at least one second
* py : add XLMRobertaForSequenceClassification [no ci] * py : fix scalar-tensor conversion [no ci] * py : fix position embeddings chop [no ci] * llama : read new cls tensors [no ci] * llama : add classigication head (wip) [no ci] * llama : add "rank" pooling type ggml-ci * server : add rerank endpoint ggml-ci * llama : aboud ggml_repeat during classification * rerank : cleanup + comments * server : accept /rerank endpoint in addition to /v1/rerank [no ci] * embedding : parse special tokens * jina : support v1 reranker * vocab : minor style ggml-ci * server : initiate tests for later ggml-ci * server : add docs * llama : add comment [no ci] * llama : fix uninitialized tensors * ci : add rerank tests ggml-ci * add reranking test * change test data * Update examples/server/server.cpp Co-authored-by: Xuan Son Nguyen <[email protected]> * add `--reranking` argument * update server docs * llama : fix comment [no ci] ggml-ci --------- Co-authored-by: Xuan Son Nguyen <[email protected]> Co-authored-by: Xuan Son Nguyen <[email protected]>
…9668) A crash was observed when the number of tokens added to a batch exceeds llama_batch size. An assertion in llama_batch_add was added to protect against llama_batch size overflow.
a return before a barrier (that happens only in some threads in a workgroup) leads to UB. While the old code actually works on some devices, it fails on some others (i.e. "smaller" GPUs). BTW, I think it would be better to set specialization constants when the graph is built, in that way the local workgroup could be sized appropriately. But it would take a lot of work. Signed-off-by: Salvatore Mesoraca <[email protected]>
ggml-ci Co-authored-by: Willy Tarreau <[email protected]>
* utf-8 fix for windows stdin * Update common/console.cpp --------- Co-authored-by: Georgi Gerganov <[email protected]>
Flake lock file updates:
• Updated input 'nixpkgs':
'github:NixOS/nixpkgs/c04d5652cfa9742b1d519688f65d1bbccea9eb7e?narHash=sha256-PmUr/2GQGvFTIJ6/Tvsins7Q43KTMvMFhvG6oaYK%2BWk%3D' (2024-09-19)
→ 'github:NixOS/nixpkgs/1925c603f17fc89f4c8f6bf6f631a802ad85d784?narHash=sha256-J%2BPeFKSDV%2BpHL7ukkfpVzCOO7mBSrrpJ3svwBFABbhI%3D' (2024-09-26)
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
* update transfomers version. * update hfh version.
…erganov#9641) * Fix Docker ROCM builds, use AMDGPU_TARGETS instead of GPU_TARGETS * Set ROCM_DOCKER_ARCH as string due it incorrectly build and cause OOM exit code
* convert : refactor rope_freqs generation This should also fix vocab-only conversion for Phi-3. * convert : adapt MiniCPM3 to separate rope_freqs insertion MiniCPM3's tokenizer is treated as a SentencePiece tokenizer to avoid having to run its custom Python code which mixes tokenization in the same file as tool calls. gguf-py : add long and short RoPE factors to tensor mappings Empty, but the key names are used to populate the mappings.
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