Re-introduce the `llama` package (#5034)
* Re-introduce the llama package
This PR brings back the llama package, making it possible to call llama.cpp and
ggml APIs from Go directly via CGo. This has a few advantages:
- C APIs can be called directly from Go without needing to use the previous
"server" REST API
- On macOS and for CPU builds on Linux and Windows, Ollama can be built without
a go generate ./... step, making it easy to get up and running to hack on
parts of Ollama that don't require fast inference
- Faster build times for AVX,AVX2,CUDA and ROCM (a full build of all runners
takes <5 min on a fast CPU)
- No git submodule making it easier to clone and build from source
This is a big PR, but much of it is vendor code except for:
- llama.go CGo bindings
- example/: a simple example of running inference
- runner/: a subprocess server designed to replace the llm/ext_server package
- Makefile an as minimal as possible Makefile to build the runner package for
different targets (cpu, avx, avx2, cuda, rocm)
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
* cache: Clear old KV cache entries when evicting a slot
When forking a cache entry, if no empty slots are available we
evict the least recently used one and copy over the KV entries
from the closest match. However, this copy does not overwrite
existing values but only adds new ones. Therefore, we need to
clear the old slot first.
This change fixes two issues:
- The KV cache fills up and runs out of space even though we think
we are managing it correctly
- Performance gets worse over time as we use new cache entries that
are not hot in the processor caches
* doc: explain golang objc linker warning (#6830)
* llama: gather transitive dependencies for rocm for dist packaging (#6848)
* Refine go server makefiles to be more DRY (#6924)
This breaks up the monolithic Makefile for the Go based runners into a
set of utility files as well as recursive Makefiles for the runners.
Files starting with the name "Makefile" are buildable, while files that
end with ".make" are utilities to include in other Makefiles. This
reduces the amount of nearly identical targets and helps set a pattern
for future community contributions for new GPU runner architectures.
When we are ready to switch over to the Go runners, these files should
move to the top of the repo, and we should add targets for the main CLI,
as well as a helper "install" (put all the built binaries on the local
system in a runnable state) and "dist" target (generate the various
tar/zip files for distribution) for local developer use.
* llama: don't create extraneous directories (#6988)
* llama: Exercise the new build in CI (#6989)
Wire up some basic sanity testing in CI for the Go runner. GPU runners are not covered yet.
* llama: Refine developer docs for Go server (#6842)
This enhances the documentation for development focusing on the new Go
server. After we complete the transition further doc refinements
can remove the "transition" discussion.
* runner.go: Allocate batches for all sequences during init
We should tell the model that we could have full batches for all
sequences. We already do this when we allocate the batches but it was
missed during initialization.
* llama.go: Don't return nil from Tokenize on zero length input
Potentially receiving nil in a non-error condition is surprising to
most callers - it's better to return an empty slice.
* runner.go: Remove stop tokens from cache
If the last token is EOG then we don't return this and it isn't
present in the cache (because it was never submitted to Decode).
This works well for extending the cache entry with a new sequence.
However, for multi-token stop sequences, we won't return any of the
tokens but all but the last one will be in the cache. This means
when the conversation continues the cache will contain tokens that
don't overlap with the new prompt.
This works (we will pick up the portion where there is overlap) but
it causes unnecessary cache thrashing because we will fork the original
cache entry as it is not a perfect match.
By trimming the cache to the tokens that we actually return this
issue can be avoided.
* runner.go: Simplify flushing of pending tokens
* runner.go: Update TODOs
* runner.go: Don't panic when processing sequences
If there is an error processing a sequence, we should return a
clean HTTP error back to Ollama rather than panicing. This will
make us more resilient to transient failures.
Panics can still occur during startup as there is no way to serve
requests if that fails.
Co-authored-by: jmorganca <jmorganca@gmail.com>
* runner.go: More accurately capture timings
Currently prompt processing time doesn't capture the that it takes
to tokenize the input, only decoding time. We should capture the
full process to more accurately reflect reality. This is especially
true once we start processing images where the initial processing
can take significant time. This is also more consistent with the
existing C++ runner.
* runner.go: Support for vision models
In addition to bringing feature parity with the C++ runner, this also
incorporates several improvements:
- Cache prompting works with images, avoiding the need to re-decode
embeddings for every message in a conversation
- Parallelism is supported, avoiding the need to restrict to one
sequence at a time. (Though for now Ollama will not schedule
them while we might need to fall back to the old runner.)
Co-authored-by: jmorganca <jmorganca@gmail.com>
* runner.go: Move Unicode checking code and add tests
* runner.go: Export external cache members
Runner and cache are in the same package so the change doesn't
affect anything but it is more internally consistent.
* runner.go: Image embedding cache
Generating embeddings from images can take significant time (on
my machine between 100ms and 8s depending on the model). Although
we already cache the result of decoding these images, the embeddings
need to be regenerated every time. This is not necessary if we get
the same image over and over again, for example, during a conversation.
This currently uses a very small cache with a very simple algorithm
but it is easy to improve as is warranted.
* llama: catch up on patches
Carry forward solar-pro and cli-unicode patches
* runner.go: Don't re-allocate memory for every batch
We can reuse memory allocated from batch to batch since batch
size is fixed. This both saves the cost of reallocation as well
keeps the cache lines hot.
This results in a roughly 1% performance improvement for token
generation with Nvidia GPUs on Linux.
* runner.go: Default to classic input cache policy
The input cache as part of the go runner implemented a cache
policy that aims to maximize hit rate in both single and multi-
user scenarios. When there is a cache hit, the response is
very fast.
However, performance is actually slower when there is an input
cache miss due to worse GPU VRAM locality. This means that
performance is generally better overall for multi-user scenarios
(better input cache hit rate, locality was relatively poor already).
But worse for single users (input cache hit rate is about the same,
locality is now worse).
This defaults the policy back to the old one to avoid a regression
but keeps the new one available through an environment variable
OLLAMA_MULTIUSER_CACHE. This is left undocumented as the goal is
to improve this in the future to get the best of both worlds
without user configuration.
For inputs that result in cache misses, on Nvidia/Linux this
change improves performance by 31% for prompt processing and
13% for token generation.
* runner.go: Increase size of response channel
Generally the CPU can easily keep up with handling reponses that
are generated but there's no reason not to let generation continue
and handle things in larger batches if needed.
* llama: Add CI to verify all vendored changes have patches (#7066)
Make sure we don't accidentally merge changes in the vendored code
that aren't also reflected in the patches.
* llama: adjust clip patch for mingw utf-16 (#7065)
* llama: adjust clip patch for mingw utf-16
* llama: ensure static linking of runtime libs
Avoid runtime dependencies on non-standard libraries
* runner.go: Enable llamafile (all platforms) and BLAS (Mac OS)
These are two features that are shown on llama.cpp's system info
that are currently different between the two runners. On my test
systems the performance difference is very small to negligible
but it is probably still good to equalize the features.
* llm: Don't add BOS/EOS for tokenize requests
This is consistent with what server.cpp currently does. It affects
things like token processing counts for embedding requests.
* runner.go: Don't cache prompts for embeddings
Our integration with server.cpp implicitly disables prompt caching
because it is not part of the JSON object being parsed, this makes
the Go runner behavior similarly.
Prompt caching has been seen to affect the results of text completions
on certain hardware. The results are not wrong either way but they
are non-deterministic. However, embeddings seem to be affected even
on hardware that does not show this behavior for completions. For
now, it is best to maintain consistency with the existing behavior.
* runner.go: Adjust debug log levels
Add system info printed at startup and quiet down noisier logging.
* llama: fix compiler flag differences (#7082)
Adjust the flags for the new Go server to more closely match the
generate flow
* llama: refine developer docs (#7121)
* llama: doc and example clean up (#7122)
* llama: doc and example clean up
* llama: Move new dockerfile into llama dir
Temporary home until we fully transition to the Go server
* llama: runner doc cleanup
* llama.go: Add description for Tokenize error case
---------
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
2024-10-08 15:53:54 +00:00
|
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|
/**
|
2024-10-17 18:59:52 +00:00
|
|
|
* llama.cpp - commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555 - do not edit this file
|
Re-introduce the `llama` package (#5034)
* Re-introduce the llama package
This PR brings back the llama package, making it possible to call llama.cpp and
ggml APIs from Go directly via CGo. This has a few advantages:
- C APIs can be called directly from Go without needing to use the previous
"server" REST API
- On macOS and for CPU builds on Linux and Windows, Ollama can be built without
a go generate ./... step, making it easy to get up and running to hack on
parts of Ollama that don't require fast inference
- Faster build times for AVX,AVX2,CUDA and ROCM (a full build of all runners
takes <5 min on a fast CPU)
- No git submodule making it easier to clone and build from source
This is a big PR, but much of it is vendor code except for:
- llama.go CGo bindings
- example/: a simple example of running inference
- runner/: a subprocess server designed to replace the llm/ext_server package
- Makefile an as minimal as possible Makefile to build the runner package for
different targets (cpu, avx, avx2, cuda, rocm)
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
* cache: Clear old KV cache entries when evicting a slot
When forking a cache entry, if no empty slots are available we
evict the least recently used one and copy over the KV entries
from the closest match. However, this copy does not overwrite
existing values but only adds new ones. Therefore, we need to
clear the old slot first.
This change fixes two issues:
- The KV cache fills up and runs out of space even though we think
we are managing it correctly
- Performance gets worse over time as we use new cache entries that
are not hot in the processor caches
* doc: explain golang objc linker warning (#6830)
* llama: gather transitive dependencies for rocm for dist packaging (#6848)
* Refine go server makefiles to be more DRY (#6924)
This breaks up the monolithic Makefile for the Go based runners into a
set of utility files as well as recursive Makefiles for the runners.
Files starting with the name "Makefile" are buildable, while files that
end with ".make" are utilities to include in other Makefiles. This
reduces the amount of nearly identical targets and helps set a pattern
for future community contributions for new GPU runner architectures.
When we are ready to switch over to the Go runners, these files should
move to the top of the repo, and we should add targets for the main CLI,
as well as a helper "install" (put all the built binaries on the local
system in a runnable state) and "dist" target (generate the various
tar/zip files for distribution) for local developer use.
* llama: don't create extraneous directories (#6988)
* llama: Exercise the new build in CI (#6989)
Wire up some basic sanity testing in CI for the Go runner. GPU runners are not covered yet.
* llama: Refine developer docs for Go server (#6842)
This enhances the documentation for development focusing on the new Go
server. After we complete the transition further doc refinements
can remove the "transition" discussion.
* runner.go: Allocate batches for all sequences during init
We should tell the model that we could have full batches for all
sequences. We already do this when we allocate the batches but it was
missed during initialization.
* llama.go: Don't return nil from Tokenize on zero length input
Potentially receiving nil in a non-error condition is surprising to
most callers - it's better to return an empty slice.
* runner.go: Remove stop tokens from cache
If the last token is EOG then we don't return this and it isn't
present in the cache (because it was never submitted to Decode).
This works well for extending the cache entry with a new sequence.
However, for multi-token stop sequences, we won't return any of the
tokens but all but the last one will be in the cache. This means
when the conversation continues the cache will contain tokens that
don't overlap with the new prompt.
This works (we will pick up the portion where there is overlap) but
it causes unnecessary cache thrashing because we will fork the original
cache entry as it is not a perfect match.
By trimming the cache to the tokens that we actually return this
issue can be avoided.
* runner.go: Simplify flushing of pending tokens
* runner.go: Update TODOs
* runner.go: Don't panic when processing sequences
If there is an error processing a sequence, we should return a
clean HTTP error back to Ollama rather than panicing. This will
make us more resilient to transient failures.
Panics can still occur during startup as there is no way to serve
requests if that fails.
Co-authored-by: jmorganca <jmorganca@gmail.com>
* runner.go: More accurately capture timings
Currently prompt processing time doesn't capture the that it takes
to tokenize the input, only decoding time. We should capture the
full process to more accurately reflect reality. This is especially
true once we start processing images where the initial processing
can take significant time. This is also more consistent with the
existing C++ runner.
* runner.go: Support for vision models
In addition to bringing feature parity with the C++ runner, this also
incorporates several improvements:
- Cache prompting works with images, avoiding the need to re-decode
embeddings for every message in a conversation
- Parallelism is supported, avoiding the need to restrict to one
sequence at a time. (Though for now Ollama will not schedule
them while we might need to fall back to the old runner.)
Co-authored-by: jmorganca <jmorganca@gmail.com>
* runner.go: Move Unicode checking code and add tests
* runner.go: Export external cache members
Runner and cache are in the same package so the change doesn't
affect anything but it is more internally consistent.
* runner.go: Image embedding cache
Generating embeddings from images can take significant time (on
my machine between 100ms and 8s depending on the model). Although
we already cache the result of decoding these images, the embeddings
need to be regenerated every time. This is not necessary if we get
the same image over and over again, for example, during a conversation.
This currently uses a very small cache with a very simple algorithm
but it is easy to improve as is warranted.
* llama: catch up on patches
Carry forward solar-pro and cli-unicode patches
* runner.go: Don't re-allocate memory for every batch
We can reuse memory allocated from batch to batch since batch
size is fixed. This both saves the cost of reallocation as well
keeps the cache lines hot.
This results in a roughly 1% performance improvement for token
generation with Nvidia GPUs on Linux.
* runner.go: Default to classic input cache policy
The input cache as part of the go runner implemented a cache
policy that aims to maximize hit rate in both single and multi-
user scenarios. When there is a cache hit, the response is
very fast.
However, performance is actually slower when there is an input
cache miss due to worse GPU VRAM locality. This means that
performance is generally better overall for multi-user scenarios
(better input cache hit rate, locality was relatively poor already).
But worse for single users (input cache hit rate is about the same,
locality is now worse).
This defaults the policy back to the old one to avoid a regression
but keeps the new one available through an environment variable
OLLAMA_MULTIUSER_CACHE. This is left undocumented as the goal is
to improve this in the future to get the best of both worlds
without user configuration.
For inputs that result in cache misses, on Nvidia/Linux this
change improves performance by 31% for prompt processing and
13% for token generation.
* runner.go: Increase size of response channel
Generally the CPU can easily keep up with handling reponses that
are generated but there's no reason not to let generation continue
and handle things in larger batches if needed.
* llama: Add CI to verify all vendored changes have patches (#7066)
Make sure we don't accidentally merge changes in the vendored code
that aren't also reflected in the patches.
* llama: adjust clip patch for mingw utf-16 (#7065)
* llama: adjust clip patch for mingw utf-16
* llama: ensure static linking of runtime libs
Avoid runtime dependencies on non-standard libraries
* runner.go: Enable llamafile (all platforms) and BLAS (Mac OS)
These are two features that are shown on llama.cpp's system info
that are currently different between the two runners. On my test
systems the performance difference is very small to negligible
but it is probably still good to equalize the features.
* llm: Don't add BOS/EOS for tokenize requests
This is consistent with what server.cpp currently does. It affects
things like token processing counts for embedding requests.
* runner.go: Don't cache prompts for embeddings
Our integration with server.cpp implicitly disables prompt caching
because it is not part of the JSON object being parsed, this makes
the Go runner behavior similarly.
Prompt caching has been seen to affect the results of text completions
on certain hardware. The results are not wrong either way but they
are non-deterministic. However, embeddings seem to be affected even
on hardware that does not show this behavior for completions. For
now, it is best to maintain consistency with the existing behavior.
* runner.go: Adjust debug log levels
Add system info printed at startup and quiet down noisier logging.
* llama: fix compiler flag differences (#7082)
Adjust the flags for the new Go server to more closely match the
generate flow
* llama: refine developer docs (#7121)
* llama: doc and example clean up (#7122)
* llama: doc and example clean up
* llama: Move new dockerfile into llama dir
Temporary home until we fully transition to the Go server
* llama: runner doc cleanup
* llama.go: Add description for Tokenize error case
---------
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
2024-10-08 15:53:54 +00:00
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*
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* MIT License
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*
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* Copyright (c) 2023-2024 The ggml authors
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "ggml-alloc.h"
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#include "ggml-backend-impl.h"
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#include "ggml.h"
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#include "ggml-impl.h"
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#include <assert.h>
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#include <limits.h>
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#include <stdarg.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#define MAX(a, b) ((a) > (b) ? (a) : (b))
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#define MAX_FREE_BLOCKS 256
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//#define GGML_ALLOCATOR_DEBUG
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//#define AT_PRINTF(...) fprintf(stderr, __VA_ARGS__)
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#define AT_PRINTF(...)
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static bool ggml_is_view(const struct ggml_tensor * t) {
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return t->view_src != NULL;
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}
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static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
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if (a->type != b->type) {
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return false;
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}
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for (int i = 0; i < GGML_MAX_DIMS; i++) {
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if (a->ne[i] != b->ne[i]) {
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return false;
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}
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if (a->nb[i] != b->nb[i]) {
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return false;
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}
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}
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return true;
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}
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static bool ggml_op_can_inplace(enum ggml_op op) {
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switch (op) {
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case GGML_OP_SCALE:
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case GGML_OP_DIAG_MASK_ZERO:
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case GGML_OP_DIAG_MASK_INF:
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case GGML_OP_ADD:
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case GGML_OP_ADD1:
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case GGML_OP_SUB:
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case GGML_OP_MUL:
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case GGML_OP_DIV:
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case GGML_OP_SQR:
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case GGML_OP_SQRT:
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case GGML_OP_LOG:
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case GGML_OP_UNARY:
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case GGML_OP_ROPE:
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case GGML_OP_RMS_NORM:
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case GGML_OP_SOFT_MAX:
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return true;
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default:
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return false;
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}
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}
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static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
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assert(alignment && !(alignment & (alignment - 1))); // power of 2
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size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
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return offset + align;
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}
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// tallocr
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struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer) {
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void * base = ggml_backend_buffer_get_base(buffer);
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size_t align = ggml_backend_buffer_get_alignment(buffer);
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assert(align && !(align & (align - 1))); // power of 2
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struct ggml_tallocr talloc = (struct ggml_tallocr) {
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/*.buffer = */ buffer,
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/*.base = */ base,
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/*.alignment = */ align,
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/*.offset = */ aligned_offset(base, 0, align),
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};
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return talloc;
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}
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void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor) {
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size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor);
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size = GGML_PAD(size, talloc->alignment);
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if (talloc->offset + size > ggml_backend_buffer_get_size(talloc->buffer)) {
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fprintf(stderr, "%s: not enough space in the buffer to allocate %s (needed %zu, available %zu)\n",
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__func__, tensor->name, size, ggml_backend_buffer_get_size(talloc->buffer) - talloc->offset);
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GGML_ABORT("not enough space in the buffer");
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}
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void * addr = (char *)ggml_backend_buffer_get_base(talloc->buffer) + talloc->offset;
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talloc->offset += size;
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assert(((uintptr_t)addr % talloc->alignment) == 0);
|
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|
|
|
|
|
|
ggml_backend_tensor_alloc(talloc->buffer, tensor, addr);
|
|
|
|
}
|
|
|
|
|
|
|
|
// dynamic tensor allocator
|
|
|
|
|
|
|
|
struct free_block {
|
|
|
|
size_t offset;
|
|
|
|
size_t size;
|
|
|
|
};
|
|
|
|
|
|
|
|
struct ggml_dyn_tallocr {
|
|
|
|
size_t alignment;
|
|
|
|
int n_free_blocks;
|
|
|
|
struct free_block free_blocks[MAX_FREE_BLOCKS];
|
|
|
|
size_t max_size;
|
|
|
|
|
|
|
|
#ifdef GGML_ALLOCATOR_DEBUG
|
|
|
|
struct {
|
|
|
|
const struct ggml_tensor * tensor;
|
|
|
|
size_t offset;
|
|
|
|
} allocated_tensors[1024];
|
|
|
|
#endif
|
|
|
|
};
|
|
|
|
|
|
|
|
#ifdef GGML_ALLOCATOR_DEBUG
|
|
|
|
static void add_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, const struct ggml_tensor * tensor) {
|
|
|
|
for (int i = 0; i < 1024; i++) {
|
|
|
|
if (alloc->allocated_tensors[i].tensor == NULL) {
|
|
|
|
alloc->allocated_tensors[i].tensor = tensor;
|
|
|
|
alloc->allocated_tensors[i].offset = offset;
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
GGML_ABORT("out of allocated_tensors");
|
|
|
|
}
|
|
|
|
static void remove_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, const struct ggml_tensor * tensor) {
|
|
|
|
for (int i = 0; i < 1024; i++) {
|
|
|
|
if (alloc->allocated_tensors[i].offset == offset) {
|
|
|
|
alloc->allocated_tensors[i].tensor = NULL;
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
GGML_ABORT("tried to free tensor %s not found\n", tensor->name);
|
|
|
|
}
|
|
|
|
#endif
|
|
|
|
|
|
|
|
static size_t ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t size, const struct ggml_tensor * tensor) {
|
|
|
|
size = aligned_offset(NULL, size, alloc->alignment);
|
|
|
|
|
|
|
|
AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
|
|
|
|
|
|
|
|
size_t max_avail = 0;
|
|
|
|
|
|
|
|
// find the best fitting free block besides the last block
|
|
|
|
int best_fit_block = -1;
|
|
|
|
size_t best_fit_size = SIZE_MAX;
|
|
|
|
for (int i = 0; i < alloc->n_free_blocks - 1; i++) {
|
|
|
|
struct free_block * block = &alloc->free_blocks[i];
|
|
|
|
max_avail = MAX(max_avail, block->size);
|
|
|
|
if (block->size >= size && block->size <= best_fit_size) {
|
|
|
|
best_fit_block = i;
|
|
|
|
best_fit_size = block->size;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
if (best_fit_block == -1) {
|
|
|
|
// the last block is our last resort
|
|
|
|
struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1];
|
|
|
|
max_avail = MAX(max_avail, block->size);
|
|
|
|
if (block->size >= size) {
|
|
|
|
best_fit_block = alloc->n_free_blocks - 1;
|
|
|
|
} else {
|
|
|
|
// this should never happen
|
|
|
|
fprintf(stderr, "%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n",
|
|
|
|
__func__, size, max_avail);
|
|
|
|
GGML_ABORT("not enough space in the buffer");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
struct free_block * block = &alloc->free_blocks[best_fit_block];
|
|
|
|
size_t offset = block->offset;
|
|
|
|
block->offset = offset + size;
|
|
|
|
block->size -= size;
|
|
|
|
if (block->size == 0) {
|
|
|
|
// remove block if empty
|
|
|
|
alloc->n_free_blocks--;
|
|
|
|
for (int j = best_fit_block; j < alloc->n_free_blocks; j++) {
|
|
|
|
alloc->free_blocks[j] = alloc->free_blocks[j+1];
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
AT_PRINTF("block %d, offset %zu\n", best_fit_block, offset);
|
|
|
|
|
|
|
|
#ifdef GGML_ALLOCATOR_DEBUG
|
|
|
|
add_allocated_tensor(alloc, offset, tensor);
|
|
|
|
size_t cur_max = offset + size;
|
|
|
|
if (cur_max > alloc->max_size) {
|
|
|
|
// sort allocated_tensors by offset
|
|
|
|
for (int i = 0; i < 1024; i++) {
|
|
|
|
for (int j = i + 1; j < 1024; j++) {
|
|
|
|
if (alloc->allocated_tensors[i].offset > alloc->allocated_tensors[j].offset) {
|
|
|
|
const struct ggml_tensor * tmp_tensor = alloc->allocated_tensors[i].tensor;
|
|
|
|
size_t tmp_offset = alloc->allocated_tensors[i].offset;
|
|
|
|
alloc->allocated_tensors[i].tensor = alloc->allocated_tensors[j].tensor;
|
|
|
|
alloc->allocated_tensors[i].offset = alloc->allocated_tensors[j].offset;
|
|
|
|
alloc->allocated_tensors[j].tensor = tmp_tensor;
|
|
|
|
alloc->allocated_tensors[j].offset = tmp_offset;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
fprintf(stderr, "max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
|
|
|
|
for (int i = 0; i < 1024; i++) {
|
|
|
|
if (alloc->allocated_tensors[i].tensor) {
|
|
|
|
fprintf(stderr, "%s [%zx-%zx] (%.2f MB) ", alloc->allocated_tensors[i].tensor->name,
|
|
|
|
alloc->allocated_tensors[i].offset,
|
|
|
|
alloc->allocated_tensors[i].offset + ggml_nbytes(alloc->allocated_tensors[i].tensor),
|
|
|
|
ggml_nbytes(alloc->allocated_tensors[i].tensor) / 1024.0 / 1024.0);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
fprintf(stderr, "\n");
|
|
|
|
}
|
|
|
|
#endif
|
|
|
|
|
|
|
|
alloc->max_size = MAX(alloc->max_size, offset + size);
|
|
|
|
|
|
|
|
return offset;
|
|
|
|
|
|
|
|
GGML_UNUSED(tensor);
|
|
|
|
}
|
|
|
|
|
|
|
|
// this is a very naive implementation, but for our case the number of free blocks should be very small
|
|
|
|
static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, size_t size, const struct ggml_tensor * tensor) {
|
|
|
|
size = aligned_offset(NULL, size, alloc->alignment);
|
|
|
|
|
|
|
|
AT_PRINTF("%s: freeing %s at %zu (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, offset, size, alloc->n_free_blocks);
|
|
|
|
|
|
|
|
#ifdef GGML_ALLOCATOR_DEBUG
|
|
|
|
remove_allocated_tensor(alloc, offset, tensor);
|
|
|
|
#endif
|
|
|
|
|
|
|
|
// see if we can merge with an existing block
|
|
|
|
for (int i = 0; i < alloc->n_free_blocks; i++) {
|
|
|
|
struct free_block * block = &alloc->free_blocks[i];
|
|
|
|
// check if ptr is at the end of the block
|
|
|
|
if (block->offset + block->size == offset) {
|
|
|
|
block->size += size;
|
|
|
|
// check if we can merge with the next block
|
|
|
|
if (i < alloc->n_free_blocks - 1 && block->offset + block->size == alloc->free_blocks[i+1].offset) {
|
|
|
|
block->size += alloc->free_blocks[i+1].size;
|
|
|
|
alloc->n_free_blocks--;
|
|
|
|
for (int j = i+1; j < alloc->n_free_blocks; j++) {
|
|
|
|
alloc->free_blocks[j] = alloc->free_blocks[j+1];
|
|
|
|
}
|
|
|
|
}
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
// check if ptr is at the beginning of the block
|
|
|
|
if (offset + size == block->offset) {
|
|
|
|
block->offset = offset;
|
|
|
|
block->size += size;
|
|
|
|
// check if we can merge with the previous block
|
|
|
|
if (i > 0 && alloc->free_blocks[i-1].offset + alloc->free_blocks[i-1].size == block->offset) {
|
|
|
|
alloc->free_blocks[i-1].size += block->size;
|
|
|
|
alloc->n_free_blocks--;
|
|
|
|
for (int j = i; j < alloc->n_free_blocks; j++) {
|
|
|
|
alloc->free_blocks[j] = alloc->free_blocks[j+1];
|
|
|
|
}
|
|
|
|
}
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
// otherwise, add a new block
|
|
|
|
GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
|
|
|
|
// insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
|
|
|
|
int insert_pos = 0;
|
|
|
|
while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].offset < offset) {
|
|
|
|
insert_pos++;
|
|
|
|
}
|
|
|
|
// shift all blocks from insert_pos onward to make room for the new block
|
|
|
|
for (int i = alloc->n_free_blocks; i > insert_pos; i--) {
|
|
|
|
alloc->free_blocks[i] = alloc->free_blocks[i-1];
|
|
|
|
}
|
|
|
|
// insert the new block
|
|
|
|
alloc->free_blocks[insert_pos].offset = offset;
|
|
|
|
alloc->free_blocks[insert_pos].size = size;
|
|
|
|
alloc->n_free_blocks++;
|
|
|
|
|
|
|
|
GGML_UNUSED(tensor);
|
|
|
|
}
|
|
|
|
|
|
|
|
static void ggml_dyn_tallocr_reset(struct ggml_dyn_tallocr * alloc) {
|
|
|
|
alloc->n_free_blocks = 1;
|
|
|
|
alloc->free_blocks[0].offset = 0;
|
|
|
|
alloc->free_blocks[0].size = SIZE_MAX/2; // restrict maximum size of a measure allocator to half size_t max to avoid overflows
|
|
|
|
alloc->max_size = 0;
|
2024-10-17 18:59:52 +00:00
|
|
|
|
|
|
|
#ifdef GGML_ALLOCATOR_DEBUG
|
|
|
|
for (int i = 0; i < 1024; i++) {
|
|
|
|
alloc->allocated_tensors[i].tensor = NULL;
|
|
|
|
}
|
|
|
|
#endif
|
Re-introduce the `llama` package (#5034)
* Re-introduce the llama package
This PR brings back the llama package, making it possible to call llama.cpp and
ggml APIs from Go directly via CGo. This has a few advantages:
- C APIs can be called directly from Go without needing to use the previous
"server" REST API
- On macOS and for CPU builds on Linux and Windows, Ollama can be built without
a go generate ./... step, making it easy to get up and running to hack on
parts of Ollama that don't require fast inference
- Faster build times for AVX,AVX2,CUDA and ROCM (a full build of all runners
takes <5 min on a fast CPU)
- No git submodule making it easier to clone and build from source
This is a big PR, but much of it is vendor code except for:
- llama.go CGo bindings
- example/: a simple example of running inference
- runner/: a subprocess server designed to replace the llm/ext_server package
- Makefile an as minimal as possible Makefile to build the runner package for
different targets (cpu, avx, avx2, cuda, rocm)
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
* cache: Clear old KV cache entries when evicting a slot
When forking a cache entry, if no empty slots are available we
evict the least recently used one and copy over the KV entries
from the closest match. However, this copy does not overwrite
existing values but only adds new ones. Therefore, we need to
clear the old slot first.
This change fixes two issues:
- The KV cache fills up and runs out of space even though we think
we are managing it correctly
- Performance gets worse over time as we use new cache entries that
are not hot in the processor caches
* doc: explain golang objc linker warning (#6830)
* llama: gather transitive dependencies for rocm for dist packaging (#6848)
* Refine go server makefiles to be more DRY (#6924)
This breaks up the monolithic Makefile for the Go based runners into a
set of utility files as well as recursive Makefiles for the runners.
Files starting with the name "Makefile" are buildable, while files that
end with ".make" are utilities to include in other Makefiles. This
reduces the amount of nearly identical targets and helps set a pattern
for future community contributions for new GPU runner architectures.
When we are ready to switch over to the Go runners, these files should
move to the top of the repo, and we should add targets for the main CLI,
as well as a helper "install" (put all the built binaries on the local
system in a runnable state) and "dist" target (generate the various
tar/zip files for distribution) for local developer use.
* llama: don't create extraneous directories (#6988)
* llama: Exercise the new build in CI (#6989)
Wire up some basic sanity testing in CI for the Go runner. GPU runners are not covered yet.
* llama: Refine developer docs for Go server (#6842)
This enhances the documentation for development focusing on the new Go
server. After we complete the transition further doc refinements
can remove the "transition" discussion.
* runner.go: Allocate batches for all sequences during init
We should tell the model that we could have full batches for all
sequences. We already do this when we allocate the batches but it was
missed during initialization.
* llama.go: Don't return nil from Tokenize on zero length input
Potentially receiving nil in a non-error condition is surprising to
most callers - it's better to return an empty slice.
* runner.go: Remove stop tokens from cache
If the last token is EOG then we don't return this and it isn't
present in the cache (because it was never submitted to Decode).
This works well for extending the cache entry with a new sequence.
However, for multi-token stop sequences, we won't return any of the
tokens but all but the last one will be in the cache. This means
when the conversation continues the cache will contain tokens that
don't overlap with the new prompt.
This works (we will pick up the portion where there is overlap) but
it causes unnecessary cache thrashing because we will fork the original
cache entry as it is not a perfect match.
By trimming the cache to the tokens that we actually return this
issue can be avoided.
* runner.go: Simplify flushing of pending tokens
* runner.go: Update TODOs
* runner.go: Don't panic when processing sequences
If there is an error processing a sequence, we should return a
clean HTTP error back to Ollama rather than panicing. This will
make us more resilient to transient failures.
Panics can still occur during startup as there is no way to serve
requests if that fails.
Co-authored-by: jmorganca <jmorganca@gmail.com>
* runner.go: More accurately capture timings
Currently prompt processing time doesn't capture the that it takes
to tokenize the input, only decoding time. We should capture the
full process to more accurately reflect reality. This is especially
true once we start processing images where the initial processing
can take significant time. This is also more consistent with the
existing C++ runner.
* runner.go: Support for vision models
In addition to bringing feature parity with the C++ runner, this also
incorporates several improvements:
- Cache prompting works with images, avoiding the need to re-decode
embeddings for every message in a conversation
- Parallelism is supported, avoiding the need to restrict to one
sequence at a time. (Though for now Ollama will not schedule
them while we might need to fall back to the old runner.)
Co-authored-by: jmorganca <jmorganca@gmail.com>
* runner.go: Move Unicode checking code and add tests
* runner.go: Export external cache members
Runner and cache are in the same package so the change doesn't
affect anything but it is more internally consistent.
* runner.go: Image embedding cache
Generating embeddings from images can take significant time (on
my machine between 100ms and 8s depending on the model). Although
we already cache the result of decoding these images, the embeddings
need to be regenerated every time. This is not necessary if we get
the same image over and over again, for example, during a conversation.
This currently uses a very small cache with a very simple algorithm
but it is easy to improve as is warranted.
* llama: catch up on patches
Carry forward solar-pro and cli-unicode patches
* runner.go: Don't re-allocate memory for every batch
We can reuse memory allocated from batch to batch since batch
size is fixed. This both saves the cost of reallocation as well
keeps the cache lines hot.
This results in a roughly 1% performance improvement for token
generation with Nvidia GPUs on Linux.
* runner.go: Default to classic input cache policy
The input cache as part of the go runner implemented a cache
policy that aims to maximize hit rate in both single and multi-
user scenarios. When there is a cache hit, the response is
very fast.
However, performance is actually slower when there is an input
cache miss due to worse GPU VRAM locality. This means that
performance is generally better overall for multi-user scenarios
(better input cache hit rate, locality was relatively poor already).
But worse for single users (input cache hit rate is about the same,
locality is now worse).
This defaults the policy back to the old one to avoid a regression
but keeps the new one available through an environment variable
OLLAMA_MULTIUSER_CACHE. This is left undocumented as the goal is
to improve this in the future to get the best of both worlds
without user configuration.
For inputs that result in cache misses, on Nvidia/Linux this
change improves performance by 31% for prompt processing and
13% for token generation.
* runner.go: Increase size of response channel
Generally the CPU can easily keep up with handling reponses that
are generated but there's no reason not to let generation continue
and handle things in larger batches if needed.
* llama: Add CI to verify all vendored changes have patches (#7066)
Make sure we don't accidentally merge changes in the vendored code
that aren't also reflected in the patches.
* llama: adjust clip patch for mingw utf-16 (#7065)
* llama: adjust clip patch for mingw utf-16
* llama: ensure static linking of runtime libs
Avoid runtime dependencies on non-standard libraries
* runner.go: Enable llamafile (all platforms) and BLAS (Mac OS)
These are two features that are shown on llama.cpp's system info
that are currently different between the two runners. On my test
systems the performance difference is very small to negligible
but it is probably still good to equalize the features.
* llm: Don't add BOS/EOS for tokenize requests
This is consistent with what server.cpp currently does. It affects
things like token processing counts for embedding requests.
* runner.go: Don't cache prompts for embeddings
Our integration with server.cpp implicitly disables prompt caching
because it is not part of the JSON object being parsed, this makes
the Go runner behavior similarly.
Prompt caching has been seen to affect the results of text completions
on certain hardware. The results are not wrong either way but they
are non-deterministic. However, embeddings seem to be affected even
on hardware that does not show this behavior for completions. For
now, it is best to maintain consistency with the existing behavior.
* runner.go: Adjust debug log levels
Add system info printed at startup and quiet down noisier logging.
* llama: fix compiler flag differences (#7082)
Adjust the flags for the new Go server to more closely match the
generate flow
* llama: refine developer docs (#7121)
* llama: doc and example clean up (#7122)
* llama: doc and example clean up
* llama: Move new dockerfile into llama dir
Temporary home until we fully transition to the Go server
* llama: runner doc cleanup
* llama.go: Add description for Tokenize error case
---------
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
2024-10-08 15:53:54 +00:00
|
|
|
}
|
|
|
|
|
|
|
|
static struct ggml_dyn_tallocr * ggml_dyn_tallocr_new(size_t alignment) {
|
|
|
|
struct ggml_dyn_tallocr * alloc = (struct ggml_dyn_tallocr *)malloc(sizeof(struct ggml_dyn_tallocr));
|
|
|
|
|
|
|
|
*alloc = (struct ggml_dyn_tallocr) {
|
|
|
|
/*.alignment = */ alignment,
|
|
|
|
/*.n_free_blocks = */ 0,
|
|
|
|
/*.free_blocks = */ {{0}},
|
|
|
|
/*.max_size = */ 0,
|
|
|
|
#ifdef GGML_ALLOCATOR_DEBUG
|
|
|
|
/*.allocated_tensors = */ {{0}},
|
|
|
|
#endif
|
|
|
|
};
|
|
|
|
|
|
|
|
ggml_dyn_tallocr_reset(alloc);
|
|
|
|
|
|
|
|
return alloc;
|
|
|
|
}
|
|
|
|
|
|
|
|
static void ggml_dyn_tallocr_free(struct ggml_dyn_tallocr * alloc) {
|
|
|
|
free(alloc);
|
|
|
|
}
|
|
|
|
|
|
|
|
static size_t ggml_dyn_tallocr_max_size(struct ggml_dyn_tallocr * alloc) {
|
|
|
|
return alloc->max_size;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
/////////////////////////////////////
|
|
|
|
|
|
|
|
// graph allocator
|
|
|
|
|
|
|
|
struct hash_node {
|
|
|
|
int n_children;
|
|
|
|
int n_views;
|
|
|
|
int buffer_id;
|
|
|
|
size_t offset; // offset within the buffer
|
|
|
|
bool allocated;
|
|
|
|
};
|
|
|
|
|
|
|
|
struct tensor_alloc {
|
|
|
|
int buffer_id;
|
|
|
|
size_t offset;
|
|
|
|
size_t size_max; // 0 = pre-allocated, unused, or view
|
|
|
|
};
|
|
|
|
|
|
|
|
struct leaf_alloc {
|
|
|
|
int buffer_id;
|
|
|
|
struct tensor_alloc leaf;
|
|
|
|
};
|
|
|
|
|
|
|
|
struct node_alloc {
|
|
|
|
struct tensor_alloc dst;
|
|
|
|
struct tensor_alloc src[GGML_MAX_SRC];
|
|
|
|
};
|
|
|
|
|
|
|
|
struct ggml_gallocr {
|
|
|
|
ggml_backend_buffer_type_t * bufts; // [n_buffers]
|
|
|
|
ggml_backend_buffer_t * buffers; // [n_buffers]
|
|
|
|
struct ggml_dyn_tallocr ** buf_tallocs; // [n_buffers]
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|
|
|
int n_buffers;
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|
|
|
|
|
|
|
struct ggml_hash_set hash_set;
|
|
|
|
struct hash_node * hash_values; // [hash_set.size]
|
|
|
|
|
|
|
|
struct node_alloc * node_allocs; // [n_nodes]
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|
|
|
int n_nodes;
|
|
|
|
|
|
|
|
struct leaf_alloc * leaf_allocs; // [n_leafs]
|
|
|
|
int n_leafs;
|
|
|
|
};
|
|
|
|
|
|
|
|
ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs) {
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|
|
|
ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(1, sizeof(struct ggml_gallocr));
|
|
|
|
GGML_ASSERT(galloc != NULL);
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|
|
|
|
|
|
|
galloc->bufts = calloc(n_bufs, sizeof(ggml_backend_buffer_type_t));
|
|
|
|
GGML_ASSERT(galloc->bufts != NULL);
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|
|
|
|
|
|
|
galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t));
|
|
|
|
GGML_ASSERT(galloc->buffers != NULL);
|
|
|
|
|
|
|
|
galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *));
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|
|
|
GGML_ASSERT(galloc->buf_tallocs != NULL);
|
|
|
|
|
|
|
|
for (int i = 0; i < n_bufs; i++) {
|
|
|
|
galloc->bufts[i] = bufts[i];
|
|
|
|
galloc->buffers[i] = NULL;
|
|
|
|
|
|
|
|
// check if the same buffer type is used multiple times and reuse the same allocator
|
|
|
|
for (int j = 0; j < i; j++) {
|
|
|
|
if (bufts[i] == bufts[j]) {
|
|
|
|
galloc->buf_tallocs[i] = galloc->buf_tallocs[j];
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
if (galloc->buf_tallocs[i] == NULL) {
|
|
|
|
size_t alignment = ggml_backend_buft_get_alignment(bufts[i]);
|
|
|
|
galloc->buf_tallocs[i] = ggml_dyn_tallocr_new(alignment);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
galloc->n_buffers = n_bufs;
|
|
|
|
|
|
|
|
return galloc;
|
|
|
|
}
|
|
|
|
|
|
|
|
ggml_gallocr_t ggml_gallocr_new(ggml_backend_buffer_type_t buft) {
|
|
|
|
return ggml_gallocr_new_n(&buft, 1);
|
|
|
|
}
|
|
|
|
|
|
|
|
void ggml_gallocr_free(ggml_gallocr_t galloc) {
|
|
|
|
if (galloc == NULL) {
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
|
|
|
|
for (int i = 0; i < galloc->n_buffers; i++) {
|
|
|
|
if (galloc->buffers != NULL) {
|
|
|
|
// skip if already freed
|
|
|
|
bool freed = false;
|
|
|
|
for (int j = 0; j < i; j++) {
|
|
|
|
if (galloc->buffers[j] == galloc->buffers[i]) {
|
|
|
|
freed = true;
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (!freed) {
|
|
|
|
ggml_backend_buffer_free(galloc->buffers[i]);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (galloc->buf_tallocs != NULL) {
|
|
|
|
// skip if already freed
|
|
|
|
bool freed = false;
|
|
|
|
for (int j = 0; j < i; j++) {
|
|
|
|
if (galloc->buf_tallocs[j] == galloc->buf_tallocs[i]) {
|
|
|
|
freed = true;
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (!freed) {
|
|
|
|
ggml_dyn_tallocr_free(galloc->buf_tallocs[i]);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
ggml_hash_set_free(&galloc->hash_set);
|
|
|
|
free(galloc->hash_values);
|
|
|
|
free(galloc->bufts);
|
|
|
|
free(galloc->buffers);
|
|
|
|
free(galloc->buf_tallocs);
|
|
|
|
free(galloc->node_allocs);
|
|
|
|
free(galloc->leaf_allocs);
|
|
|
|
free(galloc);
|
|
|
|
}
|
|
|
|
|
|
|
|
typedef struct ggml_gallocr * ggml_gallocr_t;
|
|
|
|
|
|
|
|
static struct hash_node * ggml_gallocr_hash_get(ggml_gallocr_t galloc, struct ggml_tensor * t) {
|
|
|
|
size_t i = ggml_hash_find_or_insert(&galloc->hash_set, t);
|
|
|
|
return &galloc->hash_values[i];
|
|
|
|
}
|
|
|
|
|
|
|
|
static bool ggml_gallocr_is_own(ggml_gallocr_t galloc, struct ggml_tensor * t) {
|
|
|
|
return ggml_gallocr_hash_get(galloc, t)->allocated;
|
|
|
|
}
|
|
|
|
|
|
|
|
static void ggml_gallocr_set_node_offset(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, size_t offset) {
|
|
|
|
struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
|
|
|
|
hn->buffer_id = buffer_id;
|
|
|
|
hn->offset = offset;
|
|
|
|
hn->allocated = true;
|
|
|
|
}
|
|
|
|
|
|
|
|
static bool ggml_gallocr_is_allocated(ggml_gallocr_t galloc, struct ggml_tensor * t) {
|
|
|
|
return t->data != NULL || ggml_gallocr_hash_get(galloc, t)->allocated;
|
|
|
|
}
|
|
|
|
|
|
|
|
static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id) {
|
|
|
|
struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
|
|
|
|
|
|
|
|
if (!ggml_gallocr_is_allocated(galloc, node) && !ggml_is_view(node)) {
|
|
|
|
hn->allocated = true;
|
|
|
|
assert(hn->offset == 0);
|
|
|
|
|
|
|
|
// try to reuse a parent's buffer (inplace)
|
|
|
|
if (ggml_op_can_inplace(node->op)) {
|
|
|
|
for (int i = 0; i < GGML_MAX_SRC; i++) {
|
|
|
|
struct ggml_tensor * parent = node->src[i];
|
|
|
|
if (parent == NULL) {
|
|
|
|
continue;
|
|
|
|
}
|
|
|
|
|
|
|
|
// if the node's data is external, then we cannot re-use it
|
|
|
|
if (!ggml_gallocr_is_own(galloc, parent)) {
|
|
|
|
AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data);
|
|
|
|
continue;
|
|
|
|
}
|
|
|
|
|
|
|
|
// outputs cannot be reused
|
|
|
|
if (parent->flags & GGML_TENSOR_FLAG_OUTPUT || (parent->view_src != NULL && parent->view_src->flags & GGML_TENSOR_FLAG_OUTPUT)) {
|
|
|
|
AT_PRINTF("not reusing parent %s for %s as it is an output\n", parent->name, node->name);
|
|
|
|
continue;
|
|
|
|
}
|
|
|
|
|
|
|
|
if (!ggml_are_same_layout(node, parent)) {
|
|
|
|
AT_PRINTF("not reusing parent %s for %s as layouts are different\n", parent->name, node->name);
|
|
|
|
continue;
|
|
|
|
}
|
|
|
|
|
|
|
|
struct hash_node * p_hn = ggml_gallocr_hash_get(galloc, parent);
|
|
|
|
if (p_hn->n_children == 1 && p_hn->n_views == 0) {
|
|
|
|
if (ggml_is_view(parent)) {
|
|
|
|
struct ggml_tensor * view_src = parent->view_src;
|
|
|
|
struct hash_node * view_src_hn = ggml_gallocr_hash_get(galloc, view_src);
|
|
|
|
if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
|
|
|
|
AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
|
|
|
|
assert(view_src_hn->offset == p_hn->offset);
|
|
|
|
hn->buffer_id = p_hn->buffer_id;
|
|
|
|
hn->offset = p_hn->offset;
|
|
|
|
p_hn->allocated = false; // avoid freeing the parent
|
|
|
|
view_src_hn->allocated = false;
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
} else {
|
|
|
|
AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
|
|
|
|
hn->buffer_id = p_hn->buffer_id;
|
|
|
|
hn->offset = p_hn->offset;
|
|
|
|
p_hn->allocated = false; // avoid freeing the parent
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
// allocate tensor from the buffer
|
|
|
|
struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id];
|
|
|
|
ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id];
|
|
|
|
size_t size = ggml_backend_buft_get_alloc_size(buft, node);
|
|
|
|
size_t offset = ggml_dyn_tallocr_alloc(alloc, size, node);
|
|
|
|
hn->buffer_id = buffer_id;
|
|
|
|
hn->offset = offset;
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * node) {
|
|
|
|
// graph outputs are never freed
|
|
|
|
if (node->flags & GGML_TENSOR_FLAG_OUTPUT) {
|
|
|
|
AT_PRINTF("not freeing output %s\n", node->name);
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
|
|
|
|
struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
|
|
|
|
size_t offset = hn->offset;
|
|
|
|
int buffer_id = hn->buffer_id;
|
|
|
|
struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id];
|
|
|
|
ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id];
|
|
|
|
size_t size = ggml_backend_buft_get_alloc_size(buft, node);
|
|
|
|
ggml_dyn_tallocr_free_tensor(alloc, offset, size, node);
|
|
|
|
hn->allocated = false;
|
|
|
|
}
|
|
|
|
|
|
|
|
static int get_node_buffer_id(const int * node_buffer_ids, int i) {
|
|
|
|
return node_buffer_ids ? node_buffer_ids[i] : 0;
|
|
|
|
}
|
|
|
|
|
|
|
|
static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
|
|
|
|
// clear hash tables
|
|
|
|
ggml_hash_set_reset(&galloc->hash_set);
|
|
|
|
memset(galloc->hash_values, 0, sizeof(struct hash_node) * galloc->hash_set.size);
|
|
|
|
|
|
|
|
// allocate leafs
|
|
|
|
// these may be tensors that the application is not using in the graph, but may still want to allocate for other purposes
|
|
|
|
for (int i = 0; i < graph->n_leafs; i++) {
|
|
|
|
struct ggml_tensor * leaf = graph->leafs[i];
|
|
|
|
ggml_gallocr_allocate_node(galloc, leaf, get_node_buffer_id(leaf_buffer_ids, i));
|
|
|
|
}
|
|
|
|
|
|
|
|
// count number of children and views
|
|
|
|
// allocate other graph inputs and leafs first to avoid overwriting them
|
|
|
|
for (int i = 0; i < graph->n_nodes; i++) {
|
|
|
|
struct ggml_tensor * node = graph->nodes[i];
|
|
|
|
|
|
|
|
// TODO: better way to add external dependencies
|
|
|
|
// GGML_OP_NONE does not appear normally in the graph nodes, but is used by ggml-backend to add dependencies to
|
|
|
|
// control when some tensors are allocated and freed. in this case, the dependencies are in `src`, but the node
|
|
|
|
// itself is never used and should not be considered a dependency
|
|
|
|
if (ggml_is_view(node) && node->op != GGML_OP_NONE) {
|
|
|
|
struct ggml_tensor * view_src = node->view_src;
|
|
|
|
ggml_gallocr_hash_get(galloc, view_src)->n_views += 1;
|
|
|
|
}
|
|
|
|
|
|
|
|
if (node->flags & GGML_TENSOR_FLAG_INPUT) {
|
|
|
|
ggml_gallocr_allocate_node(galloc, graph->nodes[i], get_node_buffer_id(node_buffer_ids, i));
|
|
|
|
}
|
|
|
|
|
|
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
|
|
struct ggml_tensor * src = node->src[j];
|
|
|
|
if (src == NULL) {
|
|
|
|
continue;
|
|
|
|
}
|
|
|
|
|
|
|
|
ggml_gallocr_hash_get(galloc, src)->n_children += 1;
|
|
|
|
|
|
|
|
// allocate explicit inputs
|
|
|
|
if (src->flags & GGML_TENSOR_FLAG_INPUT) {
|
|
|
|
ggml_gallocr_allocate_node(galloc, src, get_node_buffer_id(node_buffer_ids, i));
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// allocate tensors
|
|
|
|
for (int i = 0; i < graph->n_nodes; i++) {
|
|
|
|
struct ggml_tensor * node = graph->nodes[i];
|
|
|
|
int buffer_id = get_node_buffer_id(node_buffer_ids, i);
|
|
|
|
|
|
|
|
// allocate parents (only leafs need to be allocated at this point)
|
|
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
|
|
struct ggml_tensor * parent = node->src[j];
|
|
|
|
if (parent == NULL) {
|
|
|
|
continue;
|
|
|
|
}
|
|
|
|
ggml_gallocr_allocate_node(galloc, parent, buffer_id);
|
|
|
|
}
|
|
|
|
|
|
|
|
// allocate node
|
|
|
|
ggml_gallocr_allocate_node(galloc, node, buffer_id);
|
|
|
|
|
|
|
|
AT_PRINTF("exec: %s (%s) <= ", ggml_op_desc(node), node->name);
|
|
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
|
|
struct ggml_tensor * parent = node->src[j];
|
|
|
|
if (parent == NULL) {
|
|
|
|
continue;
|
|
|
|
}
|
|
|
|
AT_PRINTF("%s", parent->name);
|
|
|
|
if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
|
|
|
|
AT_PRINTF(", ");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
AT_PRINTF("\n");
|
|
|
|
|
|
|
|
// update parents
|
|
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
|
|
struct ggml_tensor * parent = node->src[j];
|
|
|
|
if (parent == NULL) {
|
|
|
|
continue;
|
|
|
|
}
|
|
|
|
struct hash_node * p_hn = ggml_gallocr_hash_get(galloc, parent);
|
|
|
|
p_hn->n_children -= 1;
|
|
|
|
|
|
|
|
AT_PRINTF("parent %s: %d children, %d views, allocated: %d\n",
|
|
|
|
parent->name, p_hn->n_children, p_hn->n_views, p_hn->allocated);
|
|
|
|
|
|
|
|
if (p_hn->n_children == 0 && p_hn->n_views == 0) {
|
|
|
|
if (ggml_is_view(parent)) {
|
|
|
|
struct ggml_tensor * view_src = parent->view_src;
|
|
|
|
struct hash_node * view_src_hn = ggml_gallocr_hash_get(galloc, view_src);
|
|
|
|
view_src_hn->n_views -= 1;
|
|
|
|
AT_PRINTF("view_src %s: %d children, %d views\n",
|
|
|
|
view_src->name, view_src_hn->n_children, view_src_hn->n_views);
|
|
|
|
if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src_hn->allocated) {
|
|
|
|
ggml_gallocr_free_node(galloc, view_src);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
else if (p_hn->allocated) {
|
|
|
|
ggml_gallocr_free_node(galloc, parent);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
AT_PRINTF("\n");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
|
|
|
|
size_t min_hash_size = graph->n_nodes + graph->n_leafs;
|
|
|
|
// add 25% margin to avoid hash collisions
|
|
|
|
min_hash_size += min_hash_size / 4;
|
|
|
|
|
|
|
|
// initialize hash table
|
|
|
|
if (galloc->hash_set.size < min_hash_size) {
|
|
|
|
ggml_hash_set_free(&galloc->hash_set);
|
|
|
|
galloc->hash_set = ggml_hash_set_new(min_hash_size);
|
|
|
|
GGML_ASSERT(galloc->hash_set.keys != NULL);
|
|
|
|
|
|
|
|
free(galloc->hash_values);
|
|
|
|
galloc->hash_values = malloc(sizeof(struct hash_node) * galloc->hash_set.size);
|
|
|
|
GGML_ASSERT(galloc->hash_values != NULL);
|
|
|
|
}
|
|
|
|
|
|
|
|
// reset allocators
|
|
|
|
for (int i = 0; i < galloc->n_buffers; i++) {
|
|
|
|
ggml_dyn_tallocr_reset(galloc->buf_tallocs[i]);
|
|
|
|
}
|
|
|
|
|
|
|
|
// allocate in hash table
|
|
|
|
ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids);
|
|
|
|
|
|
|
|
// set the node_allocs from the hash table
|
|
|
|
if (galloc->n_nodes < graph->n_nodes) {
|
|
|
|
free(galloc->node_allocs);
|
|
|
|
galloc->node_allocs = calloc(graph->n_nodes, sizeof(struct node_alloc));
|
|
|
|
GGML_ASSERT(galloc->node_allocs != NULL);
|
|
|
|
}
|
|
|
|
galloc->n_nodes = graph->n_nodes;
|
|
|
|
for (int i = 0; i < graph->n_nodes; i++) {
|
|
|
|
struct ggml_tensor * node = graph->nodes[i];
|
|
|
|
struct node_alloc * node_alloc = &galloc->node_allocs[i];
|
|
|
|
if (node->view_src || node->data) {
|
|
|
|
node_alloc->dst.buffer_id = -1;
|
|
|
|
node_alloc->dst.offset = SIZE_MAX;
|
|
|
|
node_alloc->dst.size_max = 0;
|
|
|
|
} else {
|
|
|
|
struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
|
|
|
|
node_alloc->dst.buffer_id = hn->buffer_id;
|
|
|
|
node_alloc->dst.offset = hn->offset;
|
|
|
|
node_alloc->dst.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], node);
|
|
|
|
}
|
|
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
|
|
struct ggml_tensor * src = node->src[j];
|
|
|
|
if (!src || src->view_src || src->data) {
|
|
|
|
node_alloc->src[j].buffer_id = -1;
|
|
|
|
node_alloc->src[j].offset = SIZE_MAX;
|
|
|
|
node_alloc->src[j].size_max = 0;
|
|
|
|
} else {
|
|
|
|
struct hash_node * hn = ggml_gallocr_hash_get(galloc, src);
|
|
|
|
node_alloc->src[j].buffer_id = hn->buffer_id;
|
|
|
|
node_alloc->src[j].offset = hn->offset;
|
|
|
|
node_alloc->src[j].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], src);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (galloc->n_leafs < graph->n_leafs) {
|
|
|
|
free(galloc->leaf_allocs);
|
|
|
|
galloc->leaf_allocs = calloc(graph->n_leafs, sizeof(galloc->leaf_allocs[0]));
|
|
|
|
GGML_ASSERT(galloc->leaf_allocs != NULL);
|
|
|
|
}
|
|
|
|
galloc->n_leafs = graph->n_leafs;
|
|
|
|
for (int i = 0; i < graph->n_leafs; i++) {
|
|
|
|
struct ggml_tensor * leaf = graph->leafs[i];
|
|
|
|
struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
|
|
|
|
galloc->leaf_allocs[i].buffer_id = hn->buffer_id;
|
|
|
|
if (leaf->view_src || leaf->data) {
|
|
|
|
galloc->leaf_allocs[i].leaf.buffer_id = -1;
|
|
|
|
galloc->leaf_allocs[i].leaf.offset = SIZE_MAX;
|
|
|
|
galloc->leaf_allocs[i].leaf.size_max = 0;
|
|
|
|
} else {
|
|
|
|
galloc->leaf_allocs[i].leaf.buffer_id = hn->buffer_id;
|
|
|
|
galloc->leaf_allocs[i].leaf.offset = hn->offset;
|
|
|
|
galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// reallocate buffers if needed
|
|
|
|
for (int i = 0; i < galloc->n_buffers; i++) {
|
|
|
|
// if the buffer type is used multiple times, we reuse the same buffer
|
|
|
|
for (int j = 0; j < i; j++) {
|
|
|
|
if (galloc->buf_tallocs[j] == galloc->buf_tallocs[i]) {
|
|
|
|
galloc->buffers[i] = galloc->buffers[j];
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0;
|
|
|
|
size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]);
|
|
|
|
|
|
|
|
// even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views
|
|
|
|
if (new_size > cur_size || galloc->buffers[i] == NULL) {
|
|
|
|
#ifndef NDEBUG
|
|
|
|
fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
|
|
|
|
#endif
|
|
|
|
|
|
|
|
ggml_backend_buffer_free(galloc->buffers[i]);
|
|
|
|
galloc->buffers[i] = ggml_backend_buft_alloc_buffer(galloc->bufts[i], new_size);
|
|
|
|
if (galloc->buffers[i] == NULL) {
|
|
|
|
fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
ggml_backend_buffer_set_usage(galloc->buffers[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
|
|
|
|
return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL);
|
|
|
|
}
|
|
|
|
|
|
|
|
static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, struct tensor_alloc * tensor_alloc) {
|
|
|
|
int buffer_id = tensor_alloc->buffer_id;
|
|
|
|
assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
|
|
|
|
|
|
|
|
if (tensor->view_src != NULL) {
|
|
|
|
if (tensor->buffer == NULL) {
|
|
|
|
assert(tensor_alloc->offset == SIZE_MAX);
|
|
|
|
if (tensor->view_src->buffer == NULL) {
|
|
|
|
// this tensor was allocated without ggml-backend
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
ggml_backend_view_init(tensor);
|
|
|
|
}
|
|
|
|
} else {
|
|
|
|
if (tensor->data == NULL) {
|
|
|
|
assert(tensor_alloc->offset != SIZE_MAX);
|
|
|
|
assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
|
|
|
|
void * base = ggml_backend_buffer_get_base(galloc->buffers[buffer_id]);
|
|
|
|
void * addr = (char *)base + tensor_alloc->offset;
|
|
|
|
ggml_backend_tensor_alloc(galloc->buffers[buffer_id], tensor, addr);
|
|
|
|
} else {
|
|
|
|
if (tensor->buffer == NULL) {
|
|
|
|
// this tensor was allocated without ggml-backend
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct tensor_alloc * talloc) {
|
|
|
|
size_t node_size = (node->data || node->view_src) ? 0 : ggml_backend_buft_get_alloc_size(galloc->bufts[talloc->buffer_id], node);
|
|
|
|
return talloc->size_max >= node_size;
|
|
|
|
}
|
|
|
|
|
|
|
|
static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph * graph) {
|
|
|
|
if (galloc->n_nodes != graph->n_nodes) {
|
|
|
|
#ifndef NDEBUG
|
|
|
|
fprintf(stderr, "%s: graph has different number of nodes\n", __func__);
|
|
|
|
#endif
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
if (galloc->n_leafs != graph->n_leafs) {
|
|
|
|
#ifndef NDEBUG
|
|
|
|
fprintf(stderr, "%s: graph has different number of leafs\n", __func__);
|
|
|
|
#endif
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
for (int i = 0; i < graph->n_nodes; i++) {
|
|
|
|
struct ggml_tensor * node = graph->nodes[i];
|
|
|
|
struct node_alloc * node_alloc = &galloc->node_allocs[i];
|
|
|
|
|
|
|
|
if (!ggml_gallocr_node_needs_realloc(galloc, node, &node_alloc->dst)) {
|
|
|
|
#ifndef NDEBUG
|
|
|
|
fprintf(stderr, "%s: node %s is not valid\n", __func__, node->name);
|
|
|
|
#endif
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
|
|
struct ggml_tensor * src = node->src[j];
|
|
|
|
if (src == NULL) {
|
|
|
|
continue;
|
|
|
|
}
|
|
|
|
if (!ggml_gallocr_node_needs_realloc(galloc, src, &node_alloc->src[j])) {
|
|
|
|
#ifndef NDEBUG
|
|
|
|
fprintf(stderr, "%s: src %d (%s) of node %s is not valid\n", __func__, j, src->name, node->name);
|
|
|
|
#endif
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph) {
|
|
|
|
if (ggml_gallocr_needs_realloc(galloc, graph)) {
|
|
|
|
if (galloc->n_buffers == 1) {
|
|
|
|
#ifndef NDEBUG
|
|
|
|
fprintf(stderr, "%s: reallocating buffers automatically\n", __func__);
|
|
|
|
#endif
|
|
|
|
if (!ggml_gallocr_reserve(galloc, graph)) {
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
} else {
|
|
|
|
#ifndef NDEBUG
|
|
|
|
fprintf(stderr, "%s: cannot reallocate multi buffer graph automatically, call reserve\n", __func__);
|
|
|
|
#endif
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// reset buffers
|
|
|
|
for (int i = 0; i < galloc->n_buffers; i++) {
|
|
|
|
if (galloc->buffers[i] != NULL) {
|
|
|
|
ggml_backend_buffer_reset(galloc->buffers[i]);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// allocate the graph tensors from the previous assignments
|
|
|
|
// leafs
|
|
|
|
for (int i = 0; i < graph->n_leafs; i++) {
|
|
|
|
struct ggml_tensor * leaf = graph->leafs[i];
|
|
|
|
struct leaf_alloc * leaf_alloc = &galloc->leaf_allocs[i];
|
|
|
|
ggml_gallocr_init_tensor(galloc, leaf, &leaf_alloc->leaf);
|
|
|
|
}
|
|
|
|
// nodes
|
|
|
|
for (int i = 0; i < graph->n_nodes; i++) {
|
|
|
|
struct ggml_tensor * node = graph->nodes[i];
|
|
|
|
struct node_alloc * node_alloc = &galloc->node_allocs[i];
|
|
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
|
|
struct ggml_tensor * src = node->src[j];
|
|
|
|
if (src == NULL) {
|
|
|
|
continue;
|
|
|
|
}
|
|
|
|
ggml_gallocr_init_tensor(galloc, src, &node_alloc->src[j]);
|
|
|
|
}
|
|
|
|
ggml_gallocr_init_tensor(galloc, node, &node_alloc->dst);
|
|
|
|
}
|
|
|
|
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id) {
|
|
|
|
GGML_ASSERT(buffer_id >= 0 && buffer_id < galloc->n_buffers);
|
|
|
|
|
|
|
|
if (galloc->buffers[buffer_id] == NULL) {
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
|
|
|
|
for (int i = 0; i < buffer_id; i++) {
|
|
|
|
if (galloc->buffers[i] == galloc->buffers[buffer_id]) {
|
|
|
|
// this buffer is the same as a previous one due to the same buffer type being used multiple times
|
|
|
|
// only return the buffer size the first time it appears to avoid double counting
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
return ggml_backend_buffer_get_size(galloc->buffers[buffer_id]);
|
|
|
|
}
|
|
|
|
|
|
|
|
// utils
|
|
|
|
|
|
|
|
static bool alloc_tensor_range(struct ggml_context * ctx,
|
|
|
|
struct ggml_tensor * first, struct ggml_tensor * last,
|
|
|
|
ggml_backend_buffer_type_t buft, size_t size,
|
|
|
|
ggml_backend_buffer_t ** buffers, size_t * n_buffers) {
|
|
|
|
ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size);
|
|
|
|
if (buffer == NULL) {
|
|
|
|
#ifndef NDEBUG
|
|
|
|
fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(buft), size);
|
|
|
|
#endif
|
|
|
|
for (size_t i = 0; i < *n_buffers; i++) {
|
|
|
|
ggml_backend_buffer_free((*buffers)[i]);
|
|
|
|
}
|
|
|
|
free(*buffers);
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
struct ggml_tallocr tallocr = ggml_tallocr_new(buffer);
|
|
|
|
|
|
|
|
for (struct ggml_tensor * t = first; t != last; t = ggml_get_next_tensor(ctx, t)) {
|
|
|
|
if (t->data == NULL) {
|
|
|
|
if (t->view_src == NULL) {
|
|
|
|
ggml_tallocr_alloc(&tallocr, t);
|
|
|
|
} else if (t->buffer == NULL) {
|
|
|
|
ggml_backend_view_init(t);
|
|
|
|
}
|
|
|
|
} else {
|
|
|
|
if (t->view_src != NULL && t->buffer == NULL) {
|
|
|
|
// view of a pre-allocated tensor
|
|
|
|
ggml_backend_view_init(t);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
*buffers = realloc(*buffers, sizeof(ggml_backend_buffer_t) * (*n_buffers + 1));
|
|
|
|
(*buffers)[(*n_buffers)++] = buffer;
|
|
|
|
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
|
|
|
|
GGML_ASSERT(ggml_get_no_alloc(ctx) == true);
|
|
|
|
|
|
|
|
size_t alignment = ggml_backend_buft_get_alignment(buft);
|
|
|
|
size_t max_size = ggml_backend_buft_get_max_size(buft);
|
|
|
|
|
|
|
|
ggml_backend_buffer_t * buffers = NULL;
|
|
|
|
size_t n_buffers = 0;
|
|
|
|
|
|
|
|
size_t cur_buf_size = 0;
|
|
|
|
struct ggml_tensor * first = ggml_get_first_tensor(ctx);
|
|
|
|
for (struct ggml_tensor * t = first; t != NULL; t = ggml_get_next_tensor(ctx, t)) {
|
|
|
|
size_t this_size = 0;
|
|
|
|
if (t->data == NULL && t->view_src == NULL) {
|
|
|
|
this_size = GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment);
|
|
|
|
}
|
|
|
|
|
|
|
|
if (this_size > max_size) {
|
|
|
|
fprintf(stderr, "%s: tensor %s is too large to fit in a %s buffer (tensor size: %zu, max buffer size: %zu)\n",
|
|
|
|
__func__, t->name,
|
|
|
|
ggml_backend_buft_name(buft),
|
|
|
|
this_size, max_size);
|
|
|
|
for (size_t i = 0; i < n_buffers; i++) {
|
|
|
|
ggml_backend_buffer_free(buffers[i]);
|
|
|
|
}
|
|
|
|
free(buffers);
|
|
|
|
return NULL;
|
|
|
|
}
|
|
|
|
|
|
|
|
if ((cur_buf_size + this_size) > max_size) {
|
|
|
|
// allocate tensors in the current buffer
|
|
|
|
if (!alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) {
|
|
|
|
return NULL;
|
|
|
|
}
|
|
|
|
first = t;
|
|
|
|
cur_buf_size = this_size;
|
|
|
|
} else {
|
|
|
|
cur_buf_size += this_size;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// allocate remaining tensors
|
|
|
|
if (cur_buf_size > 0) {
|
|
|
|
if (!alloc_tensor_range(ctx, first, NULL, buft, cur_buf_size, &buffers, &n_buffers)) {
|
|
|
|
return NULL;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
if (n_buffers == 0) {
|
|
|
|
#ifndef NDEBUG
|
|
|
|
fprintf(stderr, "%s: all tensors in the context are already allocated\n", __func__);
|
|
|
|
#endif
|
|
|
|
return NULL;
|
|
|
|
}
|
|
|
|
|
|
|
|
ggml_backend_buffer_t buffer;
|
|
|
|
if (n_buffers == 1) {
|
|
|
|
buffer = buffers[0];
|
|
|
|
} else {
|
|
|
|
buffer = ggml_backend_multi_buffer_alloc_buffer(buffers, n_buffers);
|
|
|
|
}
|
|
|
|
free(buffers);
|
|
|
|
return buffer;
|
|
|
|
}
|
|
|
|
|
|
|
|
ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend) {
|
|
|
|
return ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_get_default_buffer_type(backend));
|
|
|
|
}
|