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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

Reporting params.NumGPULayers can be misleading because it is the
requested number of layers, not the actual number that is loaded.
While they are often the same, there are cases where they might mismatch,
such as if the GPU backend is missing.

We're not currently using it, even in cases where we could. Disabling
it improves generation performance by 10-30% with multiple GPUs.

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.

The root cause was an unclean upgrade - this code is fine.

This reverts commit 45f216a9.

This adds some extra logs to make the new engine a bit more consistent
with the llama engine.

This is causing segfaults, so disable it. Currently UUIDs are only
used for debugging purposes, although they planned to be used in
additional ways in the future.

Bug #11211

* 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

We don't check the return status after computing the graph, which
can silently lead to bad outputs if we try to keep going and future
computation succeeds. This appears to happens in certain cases on
Apple M2 devices.

Fixes #11070

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.

FromFloatSlice and FromIntSlice return an error if the shape doesn't
match the passed data or if memory can't be allocated. Since these
are inputs, the memory being allocated is system memory rather than VRAM.

In many cases, the caller can't really handle the error and panics.

Empty and Zeros directly panic if they can't allocate memory.

This makes things consistent by panicing for the first two cases,
removing a fair amount of error handling code. This is also consistent
with how Go typically handles these situations.

This provides granular information about the backend memory allocations
required by the runner:
 - Per backend
 - Per layer
 - Weights, cache and graph
 - Allocation status

This can be used for debugging and validating memory estimates.

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.

* feat: qwen3 dense
* feat: qwen3moe
* fix llama4 moe

Currently, when the backend is created, the tensors are loaded at the
same time, which is a slow operation. This separates them to be two
steps:
 - Create backend, including enumerating tensors and memory allocation
 - Loading tensor data

This allows more flexibility in managing model loading.

* panic if trying to pad 4d

* fix pixel values padding

reduce prompt log to trace level

ml.Dump will preserve default values if not specified

* 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.

Successfully completing processing with an errgroup cancels the
associated context. However, we also have a goroutine that is checking
for cancelation of the context. As a result, there is a race where
the goroutine can pick up the cancelation and report an error,
replacing the sucessful error message.

To avoid that, this replaces the goroutine with a cancelation check
when we are reading files. This also has the advantage of stopping
all reads relatively quickly on error and also ensuring that there are
no outstanding I/O operations when we return in this case.

The downside is that if a file read blocks forever (for example, over
the network) then cancelation of the context effectively won't be
honored. However, this is also true for other smaller files we read
and the tensors are read in small chunks (128K), so it's consistent
and better on balance overall.

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.

Users may have other incompatible GGML installs on their systems.
This will prevent us from trying to load them from the path.