New operators:
- GGML_OP_SET: implement via aclnnInplaceCopy on target region
- GGML_OP_CUMSUM: implement via aclnnCumsum
- GGML_OP_FILL: implement via aclnnInplaceFillScalar
- GGML_OP_DIAG: implement via aclnnInplaceCopy on diagonal strides
- GGML_OP_TRI (lower/lower_diag/upper_diag/upper): implement via
aclnnTril(-1/0) and aclnnTriu(0/1) with appropriate diagonal offsets
- GGML_OP_SOLVE_TRI: implement via aclnnTriangularSolve
- GGML_UNARY_OP_SOFTPLUS: implement via aclnnSoftplus
Optimizations:
- GLU (SwiGLU/GeGLU/GeGLU_ERF/GeGLU_QUICK): fuse with aclnnSwiGlu /
aclnnGeGluV3 when applicable; fallback conditions now checked inside
each function rather than at the call site
- CROSS_ENTROPY_LOSS: replace 5-kernel sequence (LogSoftmax→Mul→
ReduceSum×2→Muls) with single aclnnSoftmaxCrossEntropyWithLogits call
- L2_NORM: fix in-place ClampMin on norm result (was clamping wrong
tensor); add eps clamping before division to avoid divide-by-zero
- PAD_REFLECT_1D: eliminate per-ne[3] loop; assert contiguity and call
ReflectionPad1d once on the full 4-D view; remove redundant nb copies
- GET_ROWS: replace IndexSelect with GatherV2 per batch slice; refactor
helper into gather_batched lambda with batch loop inlined
- SET_ROWS: replace IndexCopy with InplaceIndexCopy per batch slice;
refactor helper into scatter_batched lambda with batch loop inlined
- OUT_PROD: replace O(ne[3]*ne[2]*ne[1]) Ger+InplaceAdd loop with
per-slice Matmul loop (src0 @ src1^T); handles strided-broadcast
batch dims where ne02/ne03 may differ from ne2/ne3
- backend memset_tensor: implement via aclrtMemset (was NULL)
Bug fixes:
- COUNT_EQUAL: use non-inplace EqTensor into a same-type temporary
buffer instead of InplaceEqTensor, avoiding corruption of src0
- ACL graph cache (USE_ACL_GRAPH): restore node_type and src_type[]
fields in ggml_graph_node_properties; has_matching_properties() was
missing type checks, causing F16 and BF16 tensors (same nb[0]=2) to
incorrectly share cached graphs and produce wrong results (ERR≈679)
- graph cache op_params matching: compare full GGML_MAX_OP_PARAMS
bytes so that ops differing only in parameters are not incorrectly
replayed from cache
* CANN: fix multi-thread set_tensor race conditions
When ollama calls ggml_backend_tensor_set from multiple threads (each
writing a different chunk of the same tensor), the CANN backend had
three concurrency issues:
1. Quantized tensors (Q4_0/Q8_0) require a full-tensor format transform
before uploading to device. Per-chunk transforms produced corrupt data.
2. ND-to-NZ weight conversion requires complete tensor data on device.
Per-chunk conversion operated on incomplete data.
3. The global g_nz_workspaces array had unprotected concurrent access.
Fix by introducing a TensorSetTracker that accumulates write progress
per tensor. For quantized tensors, raw data is staged in a host buffer
and the transform + upload is deferred until all chunks arrive. For NZ
weights, chunks are uploaded directly but conversion is deferred. The
tracker and its staging buffer are released immediately after
post-processing completes.
Add per-device mutex to g_nz_workspaces to prevent data races.
* CANN: fix L2_NORM ignoring eps parameter
The L2_NORM implementation was not using the eps parameter from
op_params, causing incorrect results when eps is large (e.g. 10.0).
The CPU reference computes scale = 1/fmaxf(norm, eps), so add a
Clamp step to clamp the norm to at least eps before dividing.
* ggml/cann: compare op_params for POOL_2D in ACL graph cache matching
When ACL graph mode is enabled, the graph LRU cache checks whether a
cached graph matches the current computation graph. Previously,
GGML_OP_POOL_2D was not included in the op_params comparison, so two
POOL_2D nodes with different pooling parameters (kernel size, stride,
padding) but identical tensor shapes and addresses could incorrectly
reuse a cached graph, leading to wrong results or aclnn errors.
Add GGML_OP_POOL_2D to the list of ops that require op_params matching
in ggml_graph_node_properties::has_matching_properties().
* cann: fix ACL graph cache matching by adding tensor type and unconditional op_params comparison
The ACL graph LRU cache was incorrectly reusing cached graphs for
operations with different tensor types or op_params, causing test
failures for CPY (f16 vs bf16), POOL_2D, L2_NORM, NORM_MUL_ADD,
RMS_NORM_MUL_ADD, and ADD_RMS_NORM.
Changes:
- Add node_type and src_type[] fields to ggml_graph_node_properties
so the cache can distinguish tensors with different types but
identical ne/nb (e.g. f16 and bf16 both have 2-byte elements)
- Compare op_params unconditionally for all ops instead of only for
SCALE/UNARY/GLU/ROPE/POOL_2D
* CANN: add BF16 support for core operators
Add BF16 (bfloat16) type support to the CANN backend for the following
operators: MUL_MAT, MUL_MAT_ID, GET_ROWS, SET_ROWS, CPY, CONT, and
OUT_PROD. This enables BF16 models to run on Ascend NPUs.
* CANN: skip NZ weight format for BF16 and add 310P compile guards
NZ weight format conversion does not support BF16 tensors, skip it
in set_tensor, get_alloc_size and mul_mat. Remove BF16 from MUL_MAT_ID
and OUT_PROD as there are no BF16 use cases. Add #ifndef ASCEND_310P
guards for all BF16 operator support since 310P does not support BF16.
Implement ggml_cann_mul_mat_id_quant function to support quantized matrix
multiplication for Mixture of Experts (MoE) architectures on CANN backend.
Key features:
- Support Q4_0 and Q8_0 quantized weight formats
- Use IndexSelect to dynamically route expert-specific weights based on indices
- Leverage WeightQuantBatchMatmulV2 for efficient quantized computation
- Handle automatic F16 type conversion for hardware compatibility
- Support both per-expert and broadcast input modes
Implementation details:
- Extract expert weights and scales using CANN IndexSelect operation
- Process each batch and expert combination independently
- Create proper tensor views with correct stride for matmul operations
- Automatic input/output type casting to/from F16 as needed
Testing: All test cases passed for supported types (F32, F16, Q4_0, Q8_0).
* CANN: support gated linear attn
This change adds support for the GGML_OP_GATED_LINEAR_ATTN operator.
The feature was implemented by YushengZhao. Because the previous
submission was based on an outdated codebase, this PR was rebased to
merge.
Co-authored-by: YushengZhao <yusheng.chao@outlook.com>
Co-authored-by: hipudding <huafengchun@gmail.com>
* CANN: optimize OP gla
Optimize gla for high preformance
* Remove unused comments
---------
Co-authored-by: 赵禹昇 <2501112001@cninfer02.localdomain>
Co-authored-by: YushengZhao <yusheng.chao@outlook.com>
In #18624, get_env in ggml-cann was renamed to get_env_as_lowercase
to accurately reflect the function’s behavior and reduce the chance
of misuse. However, the update missed renaming call sites in other
files. This commit fixes that oversight.
* CANN: ROPE supports both MROPE and IMROPE.
1. Optimize the caching logic of rope_cache_init.
2. Add support for mRoPE and i-mRoPE.
Note that on Ascend 910B devices, it is necessary to disable FA
in CLIP and disable NZ-format conversion. These two issues are
still under investigation.
* Resolve review comments
* CANN: Use smart pointers to manage ACL objects
Previously, ACL objects were managed via manual destruction, which
led to multiple memory-leak issues during runtime. This patch replaces
manual memory management with smart pointers so that ACL objects
are properly released and ownership is clearly defined.
Note that the ownership of an ACL object belongs to the function
that creates it. Other internal functions should operate on these ACL
objects using raw pointers to avoid unintended ownership transfers.
Additionally, since aclTensorList automatically frees its contained
aclTensor objects, any aclTensor added to a tensor list must release
ownership to avoid double free operations.
This PR also removes the asynchronous task submission mechanism.
Due to changes in recent CANN versions, tiling time has significantly
decreased. Even with a dual-thread submission model, the dispatch
overhead still falls on the critical path, making async submission
less beneficial. Moreover, aclGraph support provides a much better
path to reducing operator dispatch latency.
* CANN: resolve review comments
Many Ascend operators internally use FP16 precision for computation.
If input data is in FP32, it must first be cast to FP16 before
computation, and then cast back to FP32 after computation, which
introduces unnecessary cast operations. Moreover, FP16 computation
requires significantly less workload compared to FP32, leading to
noticeable efficiency improvements.
In this change, `get_rows`, `rms_norm`, and `flash_attn_ext` are extended
to support multiple data types. Validation on the Qwen2 0.5b model shows
correct accuracy and about 10% performance gain in concurrent scenarios.
Co-authored-by: noemotiovon <757486878@qq.com>
Since the prefill length is not fixed, graphs constructed for the
prefill stage cannot be reused. For this reason, ACL graph
execution is disabled by default during prefill.
Previously, the slope tensor was set to fp16 to improve efficiency.
While this worked correctly in FA, it caused precision issues in soft_max.
This change applies different data types for different operators
to balance both accuracy and performance.
* CANN: fix RoPE cache issue on multi-device
RoPE cache only needs to be computed once per token.
However, in multi-device scenarios, not every device starts
computation from layer 0, which may lead to unallocated memory
issues and precision errors.
This commit records the first layer of each device to avoid
the above issues.
* CANN: Optimize first-layer detection method
* CANN: Remove trailing whitespace
* CANN: Only cache the data that can be determined as unchanged through the parameters.
* CANN: Update function comment
Submit operators using asynchronous threads to improve performance.
Use the environment variable GGML_CANN_ASYNC_MODE to control whether
asynchronous submission is enabled. It is disabled by default.
Testing shows a 10%–20% performance improvement in scenarios with
small parameter sizes, especially in quantized models.
* [CANN] Add Ascend NPU backend
Ascend is a full-stack AI computing infrastructure for industry
applications and services based on Huawei Ascend processors and
software.
CANN (Compute Architecture of Neural Networks), developped by
Huawei, is a heterogeneous computing architecture for AI.
Co-authored-by: wangshuai09 <391746016@qq.com>
* delete trailing whitespaces
* Modify the code based on review comment
* Rename LLAMA_CANN to GGML_CANN
* Make ggml-common.h private
* add ggml_cann prefix for acl funcs
* Add logging for CANN backend
* Delete Trailing whitespace
---------
Co-authored-by: wangshuai09 <391746016@qq.com>
