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4 Commits
b6027 ... b6031

Author SHA1 Message Date
Georgi Gerganov
00131d6eaf tests : update for LLAMA_SET_ROWS=1 (#14961) 
* test-thread-safety : each context uses a single sequence

* embedding : handle --parallel argument

ggml-ci

* save-load : handle -np 1

ggml-ci

* thread-safety : avoid overriding threads, reduce test case arg

ggml-ci
 2025-07-30 15:12:02 +03:00
Georgi Gerganov
1e15bfd42c graph : fix stack-use-after-return (#14960) 
ggml-ci
 2025-07-30 13:52:11 +03:00
Douglas Hanley
a118d80233 embeddings: fix extraction of CLS pooling results (#14927) 
* embeddings: fix extraction of CLS pooling results

* merge RANK pooling into CLS case for inputs
 2025-07-30 08:25:05 +03:00
Xinpeng Dou
61550f8231 CANN: update ops docs (#14935) 
* CANN:add ops docs

* CANN: update ops docs
 2025-07-30 08:39:24 +08:00
9 changed files with 8271 additions and 129 deletions
Expand all files Collapse all files

176
docs/ops.md
View File

@@ -12,91 +12,91 @@ Legend:
- 🟡 Partially supported by this backend
- ❌ Not supported by this backend
| Operation | BLAS | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan |
|-----------|------|------|------|------|------|------|------|
| ABS | ❌ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| ACC | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| ADD | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
| ADD1 | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
| ARANGE | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| ARGMAX | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| CLAMP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 |
| CONCAT | ❌ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ |
| CONT | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 |
| CONV_2D | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ |
| CONV_2D_DW | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| CONV_TRANSPOSE_2D | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
| COS | ❌ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
| COUNT_EQUAL | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
| CROSS_ENTROPY_LOSS_BACK | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
| DIAG_MASK_INF | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
| DIV | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
| DUP | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 |
| ELU | ❌ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| EXP | ❌ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| FLASH_ATTN_EXT | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 |
| GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
| GEGLU | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
| GEGLU_ERF | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
| GEGLU_QUICK | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
| GELU | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| GELU_ERF | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| GELU_QUICK | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| GET_ROWS | ❌ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 |
| GET_ROWS_BACK | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ |
| GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| HARDSIGMOID | ❌ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| HARDSWISH | ❌ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| IM2COL | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ |
| L2_NORM | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| LOG | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
| MEAN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| MUL | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
| MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| MUL_MAT_ID | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ |
| NEG | ❌ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| NORM | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
| OPT_STEP_ADAMW | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| OUT_PROD | 🟡 | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ |
| PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| PAD_REFLECT_1D | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| POOL_2D | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| REGLU | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
| RELU | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| REPEAT | ❌ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 |
| REPEAT_BACK | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| RMS_NORM | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ |
| RMS_NORM_BACK | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| RMS_NORM_MUL_ADD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| ROLL | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ |
| ROPE | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| ROPE_BACK | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| RWKV_WKV6 | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| RWKV_WKV7 | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| SCALE | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| SET | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| SGN | ❌ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| SIGMOID | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| SILU | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| SILU_BACK | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| SIN | ❌ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
| SOFT_MAX | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ |
| SOFT_MAX_BACK | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ |
| SQR | ❌ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
| SQRT | ❌ | ✅ | ✅ | 🟡 | ❌ | ✅ | ❌ |
| SSM_CONV | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| SSM_SCAN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| STEP | ❌ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| SUB | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
| SUM | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ |
| SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| SWIGLU | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
| TANH | ❌ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 |
| TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| UPSCALE | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ |
| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan |
|-----------|------|------|------|------|------|------|------|------|
| ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
| ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
| ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 |
| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ |
| CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 |
| CONV_2D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ |
| CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
| CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
| DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
| DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
| DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 |
| ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 |
| GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
| GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
| GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
| GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
| GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 |
| GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ |
| GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ |
| L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
| MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
| MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ |
| NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
| OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ |
| PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| PAD_REFLECT_1D | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
| RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 |
| REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ |
| RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| RMS_NORM_MUL_ADD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| ROLL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ |
| ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
| SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| SET | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
| SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ |
| SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ |
| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ❌ |
| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
| SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
| SUM | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ |
| SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
| TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 |
| TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ |

8133
docs/ops/CANN.csv Normal file
View File

File diff suppressed because it is too large Load Diff

8
examples/embedding/embedding.cpp
View File

@@ -81,6 +81,14 @@ int main(int argc, char ** argv) {
params.embedding = true;
// if the number of prompts that would be encoded is known in advance, it's more efficient to specify the
// --parallel argument accordingly. for convenience, if not specified, we fallback to unified KV cache
// in order to support any number of prompts
if (params.n_parallel == 1) {
LOG_INF("%s: n_parallel == 1 -> unified KV cache is enabled\n", __func__);
params.kv_unified = true;
}
// utilize the full context
if (params.n_batch < params.n_ctx) {
LOG_WRN("%s: setting batch size to %d\n", __func__, params.n_ctx);

6
examples/save-load-state/save-load-state.cpp
View File

@@ -15,6 +15,12 @@ int main(int argc, char ** argv) {
return 1;
}
if (params.n_parallel == 1) {
// the example uses 2 sequences, so when n_parallel == 1, we need to enable unified kv cache
printf("%s: n_parallel == 1, enabling unified kv cache\n", __func__);
params.kv_unified = true;
}
common_init();
if (params.n_predict < 0) {

2
src/llama-batch.cpp
View File

@@ -59,7 +59,7 @@ bool llama_batch_allocr::init(
for (int32_t i = 0; i < batch.n_tokens; ++i) {
for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) {
if (batch.seq_id && (batch.seq_id[i][s] < 0 || batch.seq_id[i][s] >= (llama_seq_id) n_seq_max)) {
LLAMA_LOG_ERROR("%s: invalid seq_id[%d][%d] = %d > %d\n", __func__, i, s, batch.seq_id[i][s], (llama_seq_id) n_seq_max);
LLAMA_LOG_ERROR("%s: invalid seq_id[%d][%d] = %d >= %d\n", __func__, i, s, batch.seq_id[i][s], (llama_seq_id) n_seq_max);
return false;
}
}

43
src/llama-graph.cpp
View File

@@ -188,38 +188,23 @@ void llm_graph_input_mean::set_input(const llama_ubatch * ubatch) {
void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) {
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_seq_tokens = ubatch->n_seq_tokens;
const int64_t n_seqs_unq = ubatch->n_seqs_unq;
if (cparams.embeddings && (
cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
cparams.pooling_type == LLAMA_POOLING_TYPE_RANK
)) {
cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
cparams.pooling_type == LLAMA_POOLING_TYPE_RANK ||
cparams.pooling_type == LLAMA_POOLING_TYPE_LAST
)) {
GGML_ASSERT(cls);
GGML_ASSERT(ggml_backend_buffer_is_host(cls->buffer));
uint32_t * data = (uint32_t *) cls->data;
memset(cls->data, 0, n_seqs_unq*ggml_element_size(cls));
for (int i = 0; i < n_tokens; i += n_seq_tokens) {
for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[i][s];
const int32_t seq_idx = ubatch->seq_idx[seq_id];
std::vector<int> target_pos(n_seqs_unq, -1);
std::vector<int> target_row(n_seqs_unq, -1);
data[seq_idx] = i;
}
}
}
if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) {
GGML_ASSERT(cls);
GGML_ASSERT(ggml_backend_buffer_is_host(cls->buffer));
uint32_t * data = (uint32_t *) cls->data;
memset(cls->data, 0, n_seqs_unq*ggml_element_size(cls));
std::vector<int> last_pos(n_seqs_unq, -1);
std::vector<int> last_row(n_seqs_unq, -1);
bool last = cparams.pooling_type == LLAMA_POOLING_TYPE_LAST;
for (int i = 0; i < n_tokens; ++i) {
const llama_pos pos = ubatch->pos[i];
@@ -228,16 +213,20 @@ void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) {
const llama_seq_id seq_id = ubatch->seq_id[i][s];
const int32_t seq_idx = ubatch->seq_idx[seq_id];
if (pos >= last_pos[seq_idx]) {
last_pos[seq_idx] = pos;
last_row[seq_idx] = i;
if (
(target_pos[seq_idx] == -1) ||
( last && pos >= target_pos[seq_idx]) ||
(!last && pos < target_pos[seq_idx])
) {
target_pos[seq_idx] = pos;
target_row[seq_idx] = i;
}
}
}
for (int s = 0; s < n_seqs_unq; ++s) {
if (last_row[s] >= 0) {
data[s] = last_row[s];
if (target_row[s] >= 0) {
data[s] = target_row[s];
}
}
}

27
src/llama-graph.h
View File

@@ -144,7 +144,7 @@ public:
ggml_tensor * pos_bucket = nullptr; // I32 [n_batch, n_batch]
const llama_hparams & hparams;
const llama_hparams hparams;
};
class llm_graph_input_pos_bucket_kv : public llm_graph_input_i {
@@ -158,7 +158,7 @@ public:
ggml_tensor * pos_bucket = nullptr; // I32 [n_kv, n_batch]
const llama_hparams & hparams;
const llama_hparams hparams;
const llama_kv_cache_unified_context * mctx;
};
@@ -177,8 +177,8 @@ public:
ggml_tensor * out_ids; // I32 [n_outputs]
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_hparams hparams;
const llama_cparams cparams;
const uint32_t n_outputs;
};
@@ -192,7 +192,7 @@ public:
ggml_tensor * mean; // F32 [n_batch, n_batch]
const llama_cparams & cparams;
const llama_cparams cparams;
};
class llm_graph_input_cls : public llm_graph_input_i {
@@ -204,7 +204,7 @@ public:
ggml_tensor * cls; // I32 [n_batch]
const llama_cparams & cparams;
const llama_cparams cparams;
};
class llm_graph_input_rs : public llm_graph_input_i {
@@ -247,8 +247,8 @@ public:
ggml_tensor * kq_mask = nullptr; // F32 [n_tokens, n_batch, 1, 1]
ggml_tensor * kq_mask_cnv = nullptr; // [n_tokens, n_batch, 1, 1]
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_hparams hparams;
const llama_cparams cparams;
};
class llm_graph_input_attn_kv_unified : public llm_graph_input_i {
@@ -278,8 +278,11 @@ public:
ggml_tensor * self_kq_mask = nullptr; // F32 [n_kv, n_batch/n_stream, 1, n_stream]
ggml_tensor * self_kq_mask_cnv = nullptr; // [n_kv, n_batch/n_stream, 1, n_stream]
const llama_hparams & hparams;
const llama_cparams & cparams;
// note: these have to be copies because in order to be able to reuse a graph, its inputs
// need to carry these parameters with them. otherwise, they can point to freed
// llm_graph_params from a previous batch, causing stack-use-after-return
const llama_hparams hparams;
const llama_cparams cparams;
const llama_kv_cache_unified_context * mctx;
};
@@ -318,8 +321,8 @@ public:
ggml_tensor * self_kq_mask_swa = nullptr; // F32 [n_kv, n_batch/n_stream, 1, n_stream]
ggml_tensor * self_kq_mask_swa_cnv = nullptr; // [n_kv, n_batch/n_stream, 1, n_stream]
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_hparams hparams;
const llama_cparams cparams;
const llama_kv_cache_unified_iswa_context * mctx;
};

2
tests/CMakeLists.txt
View File

@@ -185,7 +185,7 @@ llama_build_and_test(test-json-partial.cpp)
llama_build_and_test(test-log.cpp)
llama_build_and_test(test-regex-partial.cpp)
llama_build_and_test(test-thread-safety.cpp ARGS -hf ggml-org/models -hff tinyllamas/stories15M-q4_0.gguf -ngl 99 -p "The meaning of life is" -n 128 -c 256 -ub 32 -np 4)
llama_build_and_test(test-thread-safety.cpp ARGS -hf ggml-org/models -hff tinyllamas/stories15M-q4_0.gguf -ngl 99 -p "The meaning of life is" -n 128 -c 256 -ub 32 -np 4 -t 2)
# this fails on windows (github hosted runner) due to curl DLL not found (exit code 0xc0000135)
if (NOT WIN32)

3
tests/test-thread-safety.cpp
View File

@@ -34,6 +34,9 @@ int main(int argc, char ** argv) {
auto cparams = common_context_params_to_llama(params);
// each context has a single sequence
cparams.n_seq_max = 1;
int dev_count = ggml_backend_dev_count();
int gpu_dev_count = 0;
for (int i = 0; i < dev_count; ++i) {