7945404458
* initial map to load deepseek 4 arch * wip * wip: match graph build and attn logic for dpv4 * wip: Enhance DeepSeek-V4 architecture with new tensor types and sqrtsoftplus gating function * Update DeepSeek-V4 to support raw key indexing with read/write indices * fix mismatch in attn_raw * Enable FA with CSA/HCA * Fix logit mismatch with FA path * Clean traces and logs for debug * Refactor DSV4 tensor handling for MTP execution and improve raw context management * Refactor DeepSeek4 tensor operations: replace manual weighted sum and post-processing with new helper functions * Share mHC pre-projection and fix packed DSV4 writes * DSV4: add shared top-k selection and improve mask handling * Fix DSV4 c2048 view stride and duplicate loader instantiation * Reuse shared RMS normalization in DSV4 graph * Replace DSV4 indexer rotation with shared Hadamard * Share CSA visibility mask with DSV4 LID * dsv4: document dependency ordering and reset state * Remove DSV4 zero-dependency graph shim * Fix DSV4 packed stream execution * Remove DSV4 l_out backend override * Enable DSV4 quantized K-only cache * Revert "Enable DSV4 quantized K-only cache" This reverts commit 04f9b425321f62ba60e16d1bea2f8de714cfe855. * Fix DSV4 quantized cache accounting * Fail closed on unsupported DSV4 cache lifecycle operations * Various optimizations * llama: fix GGML_METAL=ON build - missing ggml-metal.h include in llama-dflash.cpp (#2134) llama-dflash.cpp calls ggml_backend_is_metal() and ggml_backend_metal_set_n_cb() inside an #ifdef GGML_USE_METAL block but never includes ggml-metal.h, so any Metal-enabled build fails to compile. Add the same guarded include llama.cpp already uses. * New op: ggml_sum_rows_ext (#2132) * Add ggml_sum_rows_ext * openPangu: use ggml_sum_rows_ext also in mhc_post * openPangu: use ggml_sum_rows_ext also in mhc_tail * Minor * Reuse shared inverse RoPE operation for DSV4 * Reuse maintainer CUDA concat implementation * WIP * hc_pre * hc_post * Remove unnecessary mask manipulations * WIP * Take into account swiglu limits * Turn on fused indexer by default * Give names to mat mul results * More named ops * dsv4: do not uselessly copy the KV cache +20% TG at 32k tokens * mask_to_index and make CPU FA work with that * Much better CPU-only, CUDA still not functional * Better CPU TG I'm now at 9.7 t/s for zero context and 6.5 t/s for context of 32k. PP is 120 t/s for short context and 101 t/s at 32k. * Even better CPU TG I'm now at 8.1 t/s for context of 32k tokens. * Turn off DSA on CUDA for now * Fix CUDA DSA * Remove again the unnecessary softmax result buffer * Experiments * Various * More named ops * Forgot to uncomment --------- Co-authored-by: samuel <samueloliveira32df@gmail.com> Co-authored-by: hchengit <95317477+hchengit@users.noreply.github.com> |
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| .. | ||
| __init__.py | ||
| constants.py | ||
| gguf.py | ||
| gguf_reader.py | ||
| gguf_writer.py | ||
| lazy.py | ||
| metadata.py | ||
| py.typed | ||
| quants.py | ||
| tensor_mapping.py | ||
| utility.py | ||
| vocab.py | ||