GitLab

Some AMD GPUs do not provide UUIDs and report only "XX". In these
cases, we should use the ordinal ID as an alternate identifier.
This is the same as we always need to do on Windows for AMD.

In addition, this prints out the ID for each GPU when enumerating
them for easier debugging in the future.

Callers can set a backend buffer type to be no-alloc, meaning that
it does not allocate memory for tensors or operations. This can
be used for calculating memory requirements. Tensors and graphs
must be recreated with no-alloc set to false before loading data.

Defaults to false for newly created backend buffer types.

* bf16

* tests

* gpt-oss

* enable gptoss for engine

* rough estimate

* convert to mxfp4

* handle safetensors U8

* clamp glu/linear

* update tokenizer

* MXFP4 support

This implements the Open Compute Microscaling (MX) FP4 format
as a tensor type with backend implementations focusing
on mulmat and mulmatid on CPU, CUDA, and Metal.

* Unit tests for MXFP4 support

This exercises various operations and shapes on both CPU and GPU (if detected
on the system)

* cuda graph

* unit test adjustments

* cuda: optimize memory access

Read 4 bytes at a time (8 elements) when performing mul_mat_vec_mxfp4

* mac: fix crash on old macos versions

cblas_sgemm is only supported on v13.3 and up, however bf16 is
only supported on v14+ so we were falling back to ggml-blas and
crashing on bf16 tensors.  Checking for the function being null
seems to be the simplest way to condittionally avoid registering the
backend.

* server: Minimum context length for gptoss

This model requires a minimum context length of 8192 to function
effectively. Users can set higher values through all normal mechanisms
but lower values will be silently reset.

* ggml: Multiply by numParallel for gptoss sliding window

When computing the graph size estimate, the context size is already
multiplied by numParallel so estimates reflect that. However, since
sliding window models use a smaller, fixed context size, they need
to manually take numParallel into account.

* gpt-oss integration

includes harmony parser and thinking levels, etc.

* fix sync

* fix tests

* fix lint

---------
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Devon Rifkin <drifkin@drifkin.net>

Support for bf16 was added in MacOS v14+ and attempting to enable
on older versions causes runtime failures.

* Enable CUDA Graphs for gemma3n.

Similar to
https://github.com/ggml-org/llama.cpp/pull/14741,
though ollama has a slightly different model graph
than llama.cpp which requires different workaround
checks.

* Remove residual check by reshaping differently in gemma3n model

This should make the heuristics more robust

We don't get valid UUIDs for AMD GPUs on Windows, so the best option
is to use the ordinal IDs. This brings us in line with what we currently
do on the Ollama server - the only exception is AMD GPUs on Linux, which
falls back to using ordinal IDs. The GGML implementation has no fallback
but it doesn't appear to occur for any of the GPUs that we support.

It's also possible that there are collisions between ordinal IDs for
different libraries - however the only places where we use them are
AMD on Windows and Metal on Mac, which can never occur on the same
system.

* update patches

* cherry pick metal mean kernel

* cherry pick cuda mean kernel

* gemma3n

* Re-remove cuda v11

Revert the revert - drop v11 support requiring drivers newer than Feb 23

This reverts commit c6bcdc42.

* Simplify layout

With only one version of the GPU libraries, we can simplify things down somewhat.  (Jetsons still require special handling)

* distinct sbsa variant for linux arm64

This avoids accidentally trying to load the sbsa cuda libraries on
a jetson system which results in crashes.

* temporary prevent rocm+cuda mixed loading

Reverts PR #11115. The original change was mistakingly reverted instead of #10822

This reverts commit aaa78180.

This enables matching up devices and information reported by the backend
with system management libraries such as nvml to get accurate free
memory reporting.

GGML has a function to report the allocated size of a backend buffer.
However, this returns 0 if we tried to allocate a buffer and it failed.
For memory management purposes, it's important to know how much we were
trying to allocate. This extends the API to report attempted sizes for
all buffers and whether it succeeeded.

* get eos_token_id from generation_config.json

* refactor

* include both ids and strings in trace

* comments

* remove special case for gemma3 special vocab (#10743)

* Move quantization logic to GGML via new backend

This moves the model aware logic to Go code and calls GGMLs quantization code for model creation.

* Remove "add model quantizations"

This is no longer needed now that quantization is implemented in Go+GGML code directly.

Some options listed in api/types.go are not supported in
newer models, or have been deprecated in the past. This is
the first of a series of PRs to clean up the API options

Worst case graph preallocation was disabled by a27462b7
"ollamarunner: Temporarily disable worst case graph preallocation"
since it caused crashes with large batches when not using the GPU.

This backports upstream llama.cpp commit f057808
"ggml: Don't assert fail when tensor data changes (#13222)", which
fixes the underlying bug and allows reverting the previous workaround.

When ggml_backend_buffer_free() is called, the device memory
is released but not all backends consistently release the actual
ggml_backend_buffer_t in system RAM, causing a memory leak.

Bug #10040

Mistral is a popular research lab making open source models. This updates
the forward pass of llama architecture models to support both llama models
and mistral models by accounting for additional metadata present in mistral
models, and finding the correct dimensions for the output projection.

Clear KV cache when shift operation is not supported by model.
Added KvCacheCanShift() check to handle models that can't perform cache shifts,
falling back to full cache clear while preserving logical token history to
maintain expected behavior when context window fills up.