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50
.github/ISSUE_TEMPLATE/01-bug-low.yml vendored Normal file
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@ -0,0 +1,50 @@
name: Low Severity Bugs
description: Used to report low severity bugs in llama.cpp (e.g. cosmetic issues, non critical UI glitches)
title: "Bug: "
labels: ["bug-unconfirmed", "low severity"]
body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this bug report!
Please include information about your system, the steps to reproduce the bug,
and the version of llama.cpp that you are using.
If possible, please provide a minimal code example that reproduces the bug.
- type: textarea
id: what-happened
attributes:
label: What happened?
description: Also tell us, what did you expect to happen?
placeholder: Tell us what you see!
validations:
required: true
- type: textarea
id: version
attributes:
label: Name and Version
description: Which executable and which version of our software are you running? (use `--version` to get a version string)
placeholder: |
$./main --version
version: 2999 (42b4109e)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
validations:
required: true
- type: dropdown
id: operating-system
attributes:
label: What operating system are you seeing the problem on?
multiple: true
options:
- Linux
- Mac
- Windows
- BSD
- Other? (Please let us know in description)
validations:
required: false
- type: textarea
id: logs
attributes:
label: Relevant log output
description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
render: shell

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.github/ISSUE_TEMPLATE/02-bug-medium.yml vendored Normal file
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@ -0,0 +1,50 @@
name: Medium Severity Bug
description: Used to report medium severity bugs in llama.cpp (e.g. Malfunctioning Features but generally still useable)
title: "Bug: "
labels: ["bug-unconfirmed", "medium severity"]
body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this bug report!
Please include information about your system, the steps to reproduce the bug,
and the version of llama.cpp that you are using.
If possible, please provide a minimal code example that reproduces the bug.
- type: textarea
id: what-happened
attributes:
label: What happened?
description: Also tell us, what did you expect to happen?
placeholder: Tell us what you see!
validations:
required: true
- type: textarea
id: version
attributes:
label: Name and Version
description: Which executable and which version of our software are you running? (use `--version` to get a version string)
placeholder: |
$./main --version
version: 2999 (42b4109e)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
validations:
required: true
- type: dropdown
id: operating-system
attributes:
label: What operating system are you seeing the problem on?
multiple: true
options:
- Linux
- Mac
- Windows
- BSD
- Other? (Please let us know in description)
validations:
required: false
- type: textarea
id: logs
attributes:
label: Relevant log output
description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
render: shell

50
.github/ISSUE_TEMPLATE/03-bug-high.yml vendored Normal file
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@ -0,0 +1,50 @@
name: High Severity Bug
description: Used to report high severity bugs in llama.cpp (e.g. Malfunctioning features hindering important common workflow)
title: "Bug: "
labels: ["bug-unconfirmed", "high severity"]
body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this bug report!
Please include information about your system, the steps to reproduce the bug,
and the version of llama.cpp that you are using.
If possible, please provide a minimal code example that reproduces the bug.
- type: textarea
id: what-happened
attributes:
label: What happened?
description: Also tell us, what did you expect to happen?
placeholder: Tell us what you see!
validations:
required: true
- type: textarea
id: version
attributes:
label: Name and Version
description: Which executable and which version of our software are you running? (use `--version` to get a version string)
placeholder: |
$./main --version
version: 2999 (42b4109e)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
validations:
required: true
- type: dropdown
id: operating-system
attributes:
label: What operating system are you seeing the problem on?
multiple: true
options:
- Linux
- Mac
- Windows
- BSD
- Other? (Please let us know in description)
validations:
required: false
- type: textarea
id: logs
attributes:
label: Relevant log output
description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
render: shell

50
.github/ISSUE_TEMPLATE/04-bug-critical.yml vendored Normal file
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@ -0,0 +1,50 @@
name: Critical Severity Bug
description: Used to report critical severity bugs in llama.cpp (e.g. Crashing, Corrupted, Dataloss)
title: "Bug: "
labels: ["bug-unconfirmed", "critical severity"]
body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this bug report!
Please include information about your system, the steps to reproduce the bug,
and the version of llama.cpp that you are using.
If possible, please provide a minimal code example that reproduces the bug.
- type: textarea
id: what-happened
attributes:
label: What happened?
description: Also tell us, what did you expect to happen?
placeholder: Tell us what you see!
validations:
required: true
- type: textarea
id: version
attributes:
label: Name and Version
description: Which executable and which version of our software are you running? (use `--version` to get a version string)
placeholder: |
$./main --version
version: 2999 (42b4109e)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
validations:
required: true
- type: dropdown
id: operating-system
attributes:
label: What operating system are you seeing the problem on?
multiple: true
options:
- Linux
- Mac
- Windows
- BSD
- Other? (Please let us know in description)
validations:
required: false
- type: textarea
id: logs
attributes:
label: Relevant log output
description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
render: shell

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.github/ISSUE_TEMPLATE/05-enhancement.yml vendored Normal file
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@ -0,0 +1,51 @@
name: Enhancement
description: Used to request enhancements for llama.cpp
title: "Feature Request: "
labels: ["enhancement"]
body:
- type: markdown
attributes:
value: |
[Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggerganov/llama.cpp/discussions/categories/ideas)
- type: checkboxes
id: prerequisites
attributes:
label: Prerequisites
description: Please confirm the following before submitting your enhancement request.
options:
- label: I am running the latest code. Mention the version if possible as well.
required: true
- label: I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
required: true
- label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
required: true
- label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new and useful enhancement to share.
required: true
- type: textarea
id: feature-description
attributes:
label: Feature Description
description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement.
placeholder: Detailed description of the enhancement
validations:
required: true
- type: textarea
id: motivation
attributes:
label: Motivation
description: Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users.
placeholder: Explanation of why this feature is needed and its benefits
validations:
required: true
- type: textarea
id: possible-implementation
attributes:
label: Possible Implementation
description: If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better.
placeholder: Detailed description of potential implementation
validations:
required: false

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.github/ISSUE_TEMPLATE/06-question.yml vendored Normal file
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@ -0,0 +1,38 @@
name: Question
description: Used to ask questions about llama.cpp
title: "Question: "
labels: ["question"]
body:
- type: markdown
attributes:
value: |
[Please search your question first in Discussion if you got a common general question.](https://github.com/ggerganov/llama.cpp/discussions/categories/q-a)
- type: checkboxes
id: prerequisites
attributes:
label: Prerequisites
description: Please confirm the following before submitting your question.
options:
- label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
required: true
- label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new useful question to share that cannot be answered within Discussions.
required: true
- type: textarea
id: background-description
attributes:
label: Background Description
description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an question.
placeholder: Detailed description of your question
validations:
required: true
- type: textarea
id: possible-answer
attributes:
label: Possible Answer
description: If you have some idea of possible answers you want to confirm, that would also be appreciated.
placeholder: Your idea of possible answers
validations:
required: false

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.github/ISSUE_TEMPLATE/07-refactor.yml vendored Normal file
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@ -0,0 +1,28 @@
name: Refactor (Maintainers)
description: Used to track refactoring opportunities
title: "Refactor: "
labels: ["refactor"]
body:
- type: markdown
attributes:
value: |
Don't forget to [check for existing refactor issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered.
Also you may want to check [Pull request refactor label as well](https://github.com/ggerganov/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too.
- type: textarea
id: background-description
attributes:
label: Background Description
description: Please provide a detailed written description of the pain points you are trying to solve.
placeholder: Detailed description behind your motivation to request refactor
validations:
required: true
- type: textarea
id: possible-approaches
attributes:
label: Possible Refactor Approaches
description: If you have some idea of possible approaches to solve this problem. You may want to make it a todo list.
placeholder: Your idea of possible refactoring opportunity/approaches
validations:
required: false

11
.github/ISSUE_TEMPLATE/bug.md vendored
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@ -1,11 +0,0 @@
---
name: Bug template
about: Used to report bugs in llama.cpp
labels: ["bug-unconfirmed"]
assignees: ''
---
Please include information about your system, the steps to reproduce the bug, and the version of llama.cpp that you are using. If possible, please provide a minimal code example that reproduces the bug.
If the bug concerns the server, please try to reproduce it first using the [server test scenario framework](https://github.com/ggerganov/llama.cpp/tree/master/examples/server/tests).

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.github/ISSUE_TEMPLATE/enhancement.md vendored
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@ -1,28 +0,0 @@
---
name: Enhancement template
about: Used to request enhancements for llama.cpp
labels: ["enhancement"]
assignees: ''
---
# Prerequisites
Please answer the following questions for yourself before submitting an issue.
- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share.
# Feature Description
Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement.
# Motivation
Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users.
# Possible Implementation
If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better.

4
CMakeLists.txt
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@ -628,6 +628,10 @@ if (LLAMA_SYCL)
add_compile_definitions(GGML_SYCL_F16) add_compile_definitions(GGML_SYCL_F16)
endif() endif()
if (LLAMA_CUDA_FORCE_MMQ)
add_compile_definitions(GGML_SYCL_FORCE_MMQ)
endif()
add_compile_options(-I./) #include DPCT add_compile_options(-I./) #include DPCT
add_compile_options(-I/${SYCL_INCLUDE_DIR}) add_compile_options(-I/${SYCL_INCLUDE_DIR})

8
CMakePresets.json
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@ -1,4 +1,4 @@
{ {
"version": 4, "version": 4,
"configurePresets": [ "configurePresets": [
{ {
@ -40,6 +40,10 @@
{ "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] }, { "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] },
{ "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "release" ] }, { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "release" ] },
{ "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] } { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] },
{ "name": "x64-windows-msvc-debug" , "inherits": [ "base", "debug" ] },
{ "name": "x64-windows-msvc-release", "inherits": [ "base", "release" ] },
{ "name": "x64-windows-msvc+static-release", "inherits": [ "base", "release", "static" ] }
] ]
} }

3
Makefile
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@ -441,6 +441,9 @@ endif # JETSON_EOL_MODULE_DETECT
ifdef LLAMA_DEBUG ifdef LLAMA_DEBUG
MK_NVCCFLAGS += -lineinfo MK_NVCCFLAGS += -lineinfo
endif # LLAMA_DEBUG endif # LLAMA_DEBUG
ifdef LLAMA_CUDA_DEBUG
MK_NVCCFLAGS += --device-debug
endif # LLAMA_CUDA_DEBUG
ifdef LLAMA_CUDA_NVCC ifdef LLAMA_CUDA_NVCC
NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC) NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC)
else else

1
README.md
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@ -478,6 +478,7 @@ Building the program with BLAS support may lead to some performance improvements
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. |
| LLAMA_CUDA_FORCE_MMQ | Boolean | false | Force the use of dequantization + matrix multiplication kernels instead of leveraging Math libraries. | |
| LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | | LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
| LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. |

94
convert-hf-to-gguf.py
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@ -1265,6 +1265,17 @@ class LlamaModel(Model):
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
if tokenizer_config_file.is_file():
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
tokenizer_config_json = json.load(f)
if "add_prefix_space" in tokenizer_config_json:
self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
# Apply to granite small models only
if self.hparams.get("vocab_size", 32000) == 49152:
self.gguf_writer.add_add_bos_token(False)
@staticmethod @staticmethod
def permute(weights: Tensor, n_head: int, n_head_kv: int | None): def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
if n_head_kv is not None and n_head != n_head_kv: if n_head_kv is not None and n_head != n_head_kv:
@ -1279,9 +1290,9 @@ class LlamaModel(Model):
n_head = self.hparams["num_attention_heads"] n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads") n_kv_head = self.hparams.get("num_key_value_heads")
if name.endswith("q_proj.weight"): if name.endswith(("q_proj.weight", "q_proj.bias")):
data_torch = LlamaModel.permute(data_torch, n_head, n_head) data_torch = LlamaModel.permute(data_torch, n_head, n_head)
if name.endswith("k_proj.weight"): if name.endswith(("k_proj.weight", "k_proj.bias")):
data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
# process the experts separately # process the experts separately
@ -2568,6 +2579,85 @@ class ArcticModel(Model):
raise ValueError(f"Unprocessed experts: {experts}") raise ValueError(f"Unprocessed experts: {experts}")
@Model.register("DeepseekV2ForCausalLM")
class DeepseekV2Model(Model):
model_arch = gguf.MODEL_ARCH.DEEPSEEK2
def set_vocab(self):
self._set_vocab_gpt2()
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
self.gguf_writer.add_value_length(hparams["v_head_dim"])
self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("type") == "yarn":
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"])
self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * hparams["rope_scaling"]["mscale_all_dim"])
_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# process the experts separately
if name.find("mlp.experts") != -1:
n_experts = self.hparams["n_routed_experts"]
assert bid is not None
if self._experts is None:
self._experts = [{} for _ in range(self.block_count)]
self._experts[bid][name] = data_torch
if len(self._experts[bid]) >= n_experts * 3:
tensors: list[tuple[str, Tensor]] = []
# merge the experts into a single 3d tensor
for w_name in ["down_proj", "gate_proj", "up_proj"]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
datas.append(self._experts[bid][ename])
del self._experts[bid][ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
new_name = self.map_tensor_name(merged_name)
tensors.append((new_name, data_torch))
return tensors
else:
return []
return [(self.map_tensor_name(name), data_torch)]
def write_tensors(self):
super().write_tensors()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
experts = [k for d in self._experts for k in d.keys()]
if len(experts) > 0:
raise ValueError(f"Unprocessed experts: {experts}")
###### CONVERSION LOGIC ###### ###### CONVERSION LOGIC ######

2
examples/llava/clip.h
View file

@ -68,7 +68,7 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8
/** interpret bytes as an image file with length bytes_length, and use the result to populate img */ /** interpret bytes as an image file with length bytes_length, and use the result to populate img */
CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img); CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
/** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */ /** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */
CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs ); CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx); CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);

19
examples/server/public/index.html
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@ -594,7 +594,7 @@
message = html`<${Probabilities} data=${data} />` message = html`<${Probabilities} data=${data} />`
} else { } else {
const text = isArrayMessage ? const text = isArrayMessage ?
data.map(msg => msg.content).join('').replace(/^\s+/, '') : data.map(msg => msg.content).join('') :
data; data;
message = isCompletionMode ? message = isCompletionMode ?
text : text :
@ -877,7 +877,11 @@
// poor mans markdown replacement // poor mans markdown replacement
const Markdownish = (params) => { const Markdownish = (params) => {
const md = params.text const chunks = params.text.split('```');
for (let i = 0; i < chunks.length; i++) {
if (i % 2 === 0) { // outside code block
chunks[i] = chunks[i]
.replace(/&/g, '&amp;') .replace(/&/g, '&amp;')
.replace(/</g, '&lt;') .replace(/</g, '&lt;')
.replace(/>/g, '&gt;') .replace(/>/g, '&gt;')
@ -889,7 +893,14 @@
.replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>') .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
.replace(/`(.*?)`/g, '<code>$1</code>') .replace(/`(.*?)`/g, '<code>$1</code>')
.replace(/\n/gim, '<br />'); .replace(/\n/gim, '<br />');
return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`; } else { // inside code block
chunks[i] = `<pre><code>${chunks[i]}</code></pre>`;
}
}
const restoredText = chunks.join('');
return html`<span dangerouslySetInnerHTML=${{ __html: restoredText }} />`;
}; };
const ModelGenerationInfo = (params) => { const ModelGenerationInfo = (params) => {
@ -903,6 +914,7 @@
` `
} }
// simple popover impl // simple popover impl
const Popover = (props) => { const Popover = (props) => {
const isOpen = useSignal(false); const isOpen = useSignal(false);
@ -1054,4 +1066,3 @@
</body> </body>
</html> </html>

33
ggml-cuda.cu
View file

@ -119,6 +119,20 @@ int ggml_cuda_get_device() {
return id; return id;
} }
static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) {
ggml_cuda_set_device(device);
#if defined(GGML_USE_HIPBLAS) && defined(GGML_HIP_UMA)
auto res = hipMallocManaged(ptr, size);
if (res == hipSuccess) {
// if error we "need" to know why...
CUDA_CHECK(hipMemAdvise(*ptr, size, hipMemAdviseSetCoarseGrain, device));
}
return res;
#else
return cudaMalloc(ptr, size);
#endif
}
static ggml_cuda_device_info ggml_cuda_init() { static ggml_cuda_device_info ggml_cuda_init() {
#ifdef __HIP_PLATFORM_AMD__ #ifdef __HIP_PLATFORM_AMD__
// Workaround for a rocBLAS bug when using multiple graphics cards: // Workaround for a rocBLAS bug when using multiple graphics cards:
@ -271,7 +285,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
size_t look_ahead_size = (size_t) (1.05 * size); size_t look_ahead_size = (size_t) (1.05 * size);
look_ahead_size = 256 * ((look_ahead_size + 255)/256); look_ahead_size = 256 * ((look_ahead_size + 255)/256);
ggml_cuda_set_device(device); ggml_cuda_set_device(device);
CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size)); CUDA_CHECK(ggml_cuda_device_malloc(&ptr, look_ahead_size, device));
*actual_size = look_ahead_size; *actual_size = look_ahead_size;
pool_size += look_ahead_size; pool_size += look_ahead_size;
#ifdef DEBUG_CUDA_MALLOC #ifdef DEBUG_CUDA_MALLOC
@ -537,7 +551,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffe
size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0 size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0
void * dev_ptr; void * dev_ptr;
cudaError_t err = cudaMalloc(&dev_ptr, size); cudaError_t err = ggml_cuda_device_malloc(&dev_ptr, size, buft_ctx->device);
if (err != cudaSuccess) { if (err != cudaSuccess) {
// clear the error // clear the error
cudaGetLastError(); cudaGetLastError();
@ -798,7 +812,7 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_bu
// currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first
ggml_cuda_set_device(id); ggml_cuda_set_device(id);
char * buf; char * buf;
CUDA_CHECK(cudaMalloc(&buf, size)); CUDA_CHECK(ggml_cuda_device_malloc((void**)&buf, size, id));
// set padding to 0 to avoid possible NaN values // set padding to 0 to avoid possible NaN values
if (size > original_size) { if (size > original_size) {
@ -2510,9 +2524,9 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
bool use_cuda_graph = true; bool use_cuda_graph = true;
bool cuda_graph_update_required = false; bool cuda_graph_update_required = false;
// pointer to CUDA cpy kernel, which is required to identify // vector of pointers to CUDA cpy kernels, which are required to identify
// kernel parameters which need updated in the graph for each token // kernel parameters which need updated in the graph for each token
void * ggml_cuda_cpy_fn_ptr = nullptr; std::vector<void *> ggml_cuda_cpy_fn_ptrs;
if (cuda_ctx->cuda_graph->graph == nullptr) { if (cuda_ctx->cuda_graph->graph == nullptr) {
if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) { if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
@ -2588,9 +2602,10 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
if (node->op == GGML_OP_CPY) { if (node->op == GGML_OP_CPY) {
// store the copy op parameter which changes with each token. // store the copy op parameter which changes with each token.
cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data)); cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data));
if (ggml_cuda_cpy_fn_ptr == nullptr) { // store a pointer to each copy op CUDA kernel to identify it later
// store a pointer to the copy op CUDA kernel to identify it later void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
ggml_cuda_cpy_fn_ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]); if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) {
ggml_cuda_cpy_fn_ptrs.push_back(ptr);
} }
} }
@ -2720,7 +2735,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured
int k = 0; int k = 0;
for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) { for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
if (cuda_ctx->cuda_graph->params[i].func == ggml_cuda_cpy_fn_ptr) { if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) {
char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++); char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr; cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i])); CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));

5
ggml-cuda/common.cuh
View file

@ -79,13 +79,8 @@
#define cudaHostRegisterReadOnly hipHostRegisterReadOnly #define cudaHostRegisterReadOnly hipHostRegisterReadOnly
#define cudaHostUnregister hipHostUnregister #define cudaHostUnregister hipHostUnregister
#define cudaLaunchHostFunc hipLaunchHostFunc #define cudaLaunchHostFunc hipLaunchHostFunc
#ifdef GGML_HIP_UMA
#define cudaMalloc hipMallocManaged
#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size)
#else
#define cudaMalloc hipMalloc #define cudaMalloc hipMalloc
#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault) #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
#endif
#define cudaMemcpy hipMemcpy #define cudaMemcpy hipMemcpy
#define cudaMemcpyAsync hipMemcpyAsync #define cudaMemcpyAsync hipMemcpyAsync
#define cudaMemcpyPeerAsync hipMemcpyPeerAsync #define cudaMemcpyPeerAsync hipMemcpyPeerAsync

91
ggml-cuda/concat.cu
View file

@ -1,15 +1,68 @@
#include "concat.cuh" #include "concat.cuh"
static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) { static __global__ void concat_f32_dim0(const float * x, const float * y, float * dst, const int ne0, const int ne00) {
int nidx = threadIdx.x + blockIdx.x * blockDim.x; int nidx = threadIdx.x + blockIdx.x * blockDim.x;
if (nidx >= ne0) { if (nidx >= ne0) {
return; return;
} }
// operation
int offset_dst = int offset_dst =
nidx + nidx +
blockIdx.y * ne0 + blockIdx.y * ne0 +
blockIdx.z * ne0 * gridDim.y; blockIdx.z * ne0 * gridDim.y;
if (nidx < ne00) { // src0
int offset_src =
nidx +
blockIdx.y * ne00 +
blockIdx.z * ne00 * gridDim.y;
dst[offset_dst] = x[offset_src];
} else {
int offset_src =
(nidx - ne00) +
blockIdx.y * (ne0 - ne00) +
blockIdx.z * (ne0 - ne00) * gridDim.y;
dst[offset_dst] = y[offset_src];
}
}
static __global__ void concat_f32_dim1(const float * x, const float * y, float * dst, const int ne0, const int ne01) {
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
if (nidx >= ne0) {
return;
}
int offset_dst =
nidx +
blockIdx.y * ne0 +
blockIdx.z * ne0 * gridDim.y;
if (blockIdx.y < ne01) { // src0
int offset_src =
nidx +
blockIdx.y * ne0 +
blockIdx.z * ne0 * ne01;
dst[offset_dst] = x[offset_src];
} else {
int offset_src =
nidx +
(blockIdx.y - ne01) * ne0 +
blockIdx.z * ne0 * (gridDim.y - ne01);
dst[offset_dst] = y[offset_src];
}
}
static __global__ void concat_f32_dim2(const float * x, const float * y, float * dst, const int ne0, const int ne02) {
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
if (nidx >= ne0) {
return;
}
int offset_dst =
nidx +
blockIdx.y * ne0 +
blockIdx.z * ne0 * gridDim.y;
if (blockIdx.z < ne02) { // src0 if (blockIdx.z < ne02) { // src0
int offset_src = int offset_src =
nidx + nidx +
@ -25,25 +78,53 @@ static __global__ void concat_f32(const float * x,const float * y, float * dst,
} }
} }
static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) { static void concat_f32_cuda(const float * x, const float * y, float * dst, int ne00, int ne01, int ne02, int ne0, int ne1, int ne2, int dim, cudaStream_t stream) {
int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE; int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
dim3 gridDim(num_blocks, ne1, ne2); dim3 gridDim(num_blocks, ne1, ne2);
concat_f32<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02); if (dim == 0) {
concat_f32_dim0<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne00);
return;
}
if (dim == 1) {
concat_f32_dim1<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne01);
return;
}
concat_f32_dim2<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
} }
void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1]; const ggml_tensor * src1 = dst->src[1];
const float * src0_d = (const float *)src0->data; const float * src0_d = (const float *)src0->data;
const float * src1_d = (const float *)src1->data; const float * src1_d = (const float *)src1->data;
float * dst_d = (float *)dst->data; float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream(); cudaStream_t stream = ctx.stream();
const int32_t dim = ((int32_t *) dst->op_params)[0];
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32);
if (dim != 3) {
for (int i3 = 0; i3 < dst->ne[3]; i3++) { for (int i3 = 0; i3 < dst->ne[3]; i3++) {
concat_f32_cuda(src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4), dst_d + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], stream); concat_f32_cuda(
src0_d + i3 * (src0->nb[3] / 4),
src1_d + i3 * (src1->nb[3] / 4),
dst_d + i3 * ( dst->nb[3] / 4),
src0->ne[0], src0->ne[1], src0->ne[2],
dst->ne[0], dst->ne[1], dst->ne[2], dim, stream);
}
} else {
const size_t size0 = ggml_nbytes(src0);
const size_t size1 = ggml_nbytes(src1);
CUDA_CHECK(cudaMemcpyAsync(dst_d, src0_d, size0, cudaMemcpyDeviceToDevice, stream));
CUDA_CHECK(cudaMemcpyAsync(dst_d + size0/4, src1_d, size1, cudaMemcpyDeviceToDevice, stream));
} }
} }

71
ggml-metal.m
View file

@ -35,6 +35,10 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_ROW, GGML_METAL_KERNEL_TYPE_MUL_ROW,
GGML_METAL_KERNEL_TYPE_DIV, GGML_METAL_KERNEL_TYPE_DIV,
GGML_METAL_KERNEL_TYPE_DIV_ROW, GGML_METAL_KERNEL_TYPE_DIV_ROW,
GGML_METAL_KERNEL_TYPE_REPEAT_F32,
GGML_METAL_KERNEL_TYPE_REPEAT_F16,
GGML_METAL_KERNEL_TYPE_REPEAT_I32,
GGML_METAL_KERNEL_TYPE_REPEAT_I16,
GGML_METAL_KERNEL_TYPE_SCALE, GGML_METAL_KERNEL_TYPE_SCALE,
GGML_METAL_KERNEL_TYPE_SCALE_4, GGML_METAL_KERNEL_TYPE_SCALE_4,
GGML_METAL_KERNEL_TYPE_CLAMP, GGML_METAL_KERNEL_TYPE_CLAMP,
@ -184,9 +188,9 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261
GGML_METAL_KERNEL_TYPE_CPY_F32_F16, GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
GGML_METAL_KERNEL_TYPE_CPY_F32_F32, GGML_METAL_KERNEL_TYPE_CPY_F32_F32,
GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,
@ -485,6 +489,10 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F32, repeat_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F16, repeat_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I32, repeat_i32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I16, repeat_i16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true);
@ -634,9 +642,9 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, ctx->support_simdgroup_mm); //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, ctx->support_simdgroup_reduction); //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true);
@ -746,6 +754,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
case GGML_OP_ACC: case GGML_OP_ACC:
case GGML_OP_MUL: case GGML_OP_MUL:
case GGML_OP_DIV: case GGML_OP_DIV:
case GGML_OP_REPEAT:
case GGML_OP_SCALE: case GGML_OP_SCALE:
case GGML_OP_CLAMP: case GGML_OP_CLAMP:
case GGML_OP_SQR: case GGML_OP_SQR:
@ -770,6 +779,9 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
case GGML_OP_LEAKY_RELU: case GGML_OP_LEAKY_RELU:
return true; return true;
case GGML_OP_FLASH_ATTN_EXT: case GGML_OP_FLASH_ATTN_EXT:
if (op->src[0]->ne[0] == 256) {
return false;
}
return ctx->support_simdgroup_mm; // TODO: over-restricted for vec-kernels return ctx->support_simdgroup_mm; // TODO: over-restricted for vec-kernels
case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID: case GGML_OP_MUL_MAT_ID:
@ -976,10 +988,10 @@ static enum ggml_status ggml_metal_graph_compute(
switch (dst->op) { switch (dst->op) {
case GGML_OP_CONCAT: case GGML_OP_CONCAT:
{ {
const int64_t nb = ne00;
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline;
const int32_t dim = ((int32_t *) dst->op_params)[0];
[encoder setComputePipelineState:pipeline]; [encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
@ -1008,7 +1020,7 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24];
[encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25];
[encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26];
[encoder setBytes:&nb length:sizeof(nb) atIndex:27]; [encoder setBytes:&dim length:sizeof(dim) atIndex:27];
const int nth = MIN(1024, ne0); const int nth = MIN(1024, ne0);
@ -1018,11 +1030,14 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_OP_MUL: case GGML_OP_MUL:
case GGML_OP_DIV: case GGML_OP_DIV:
{ {
GGML_ASSERT(src0t == GGML_TYPE_F32);
GGML_ASSERT(src1t == GGML_TYPE_F32);
const size_t offs = 0; const size_t offs = 0;
bool bcast_row = false; bool bcast_row = false;
int64_t nb = ne00; int64_t nb = ne00; // used by the "row" kernels
id<MTLComputePipelineState> pipeline = nil; id<MTLComputePipelineState> pipeline = nil;
@ -1091,6 +1106,42 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} }
} break; } break;
case GGML_OP_REPEAT:
{
id<MTLComputePipelineState> pipeline;
switch (src0t) {
case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F32].pipeline; break;
case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F16].pipeline; break;
case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I32].pipeline; break;
case GGML_TYPE_I16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I16].pipeline; break;
default: GGML_ASSERT(false);
}
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
[encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
[encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11];
[encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12];
[encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17];
const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0);
[encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ACC: case GGML_OP_ACC:
{ {
GGML_ASSERT(src0t == GGML_TYPE_F32); GGML_ASSERT(src0t == GGML_TYPE_F32);
@ -2573,7 +2624,7 @@ static enum ggml_status ggml_metal_graph_compute(
case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break; case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break;
case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break; case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break;
case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break; case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break;
case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break;
default: default:
{ {
GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00); GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
@ -2586,7 +2637,7 @@ static enum ggml_status ggml_metal_graph_compute(
switch (ne00) { switch (ne00) {
case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break; case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break;
case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break;
default: default:
{ {
GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00); GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);

80
ggml-metal.metal
View file

@ -168,6 +168,53 @@ kernel void kernel_div(
} }
} }
template<typename T>
kernel void kernel_repeat(
device const char * src0,
device char * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i3 = tgpig.z;
const int64_t i2 = tgpig.y;
const int64_t i1 = tgpig.x;
const int64_t i03 = i3 % ne03;
const int64_t i02 = i2 % ne02;
const int64_t i01 = i1 % ne01;
device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01;
device char * dst_ptr = dst + i3*nb3 + i2*nb2 + i1*nb1 ;
for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
const int i00 = i0 % ne00;
*((device T *)(dst_ptr + i0*nb0)) = *((device T *)(src0_ptr + i00*nb00));
}
}
typedef decltype(kernel_repeat<float>) kernel_repeat_t;
template [[host_name("kernel_repeat_f32")]] kernel kernel_repeat_t kernel_repeat<float>;
template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat<half>;
template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat<int>;
template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat<short>;
// assumption: src1 is a row // assumption: src1 is a row
// broadcast src1 into src0 // broadcast src1 into src0
kernel void kernel_add_row( kernel void kernel_add_row(
@ -2418,7 +2465,7 @@ template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_f
template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<96>; template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<96>;
template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<112>; template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<112>;
template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<128>; template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<128>;
template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>; //template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>;
template<int64_t D, int64_t Q = 1, int64_t C = 32> // head size, queries per threadgroup, cache items per threadgroup template<int64_t D, int64_t Q = 1, int64_t C = 32> // head size, queries per threadgroup, cache items per threadgroup
kernel void kernel_flash_attn_ext_vec_f16( kernel void kernel_flash_attn_ext_vec_f16(
@ -2696,7 +2743,7 @@ kernel void kernel_flash_attn_ext_vec_f16(
} }
template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>; template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>;
template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>; //template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>;
kernel void kernel_cpy_f16_f16( kernel void kernel_cpy_f16_f16(
device const half * src0, device const half * src0,
@ -3319,31 +3366,30 @@ kernel void kernel_concat(
constant uint64_t & nb1, constant uint64_t & nb1,
constant uint64_t & nb2, constant uint64_t & nb2,
constant uint64_t & nb3, constant uint64_t & nb3,
constant int32_t & dim,
uint3 tgpig[[threadgroup_position_in_grid]], uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]], uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) { uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i03 = tgpig.z; const int64_t i3 = tgpig.z;
const int64_t i02 = tgpig.y; const int64_t i2 = tgpig.y;
const int64_t i01 = tgpig.x; const int64_t i1 = tgpig.x;
const int64_t i13 = i03 % ne13; int64_t o[4] = {0, 0, 0, 0};
const int64_t i12 = i02 % ne12; o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03));
const int64_t i11 = i01 % ne11;
device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + tpitg.x*nb00; device const float * x;
device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11 + tpitg.x*nb10;
device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + tpitg.x*nb0;
for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
if (i02 < ne02) { if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
((device float *)dst_ptr)[0] = ((device float *)src0_ptr)[0]; x = (device const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00);
src0_ptr += ntg.x*nb00;
} else { } else {
((device float *)dst_ptr)[0] = ((device float *)src1_ptr)[0]; x = (device const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10);
src1_ptr += ntg.x*nb10;
} }
dst_ptr += ntg.x*nb0;
device float * y = (device float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
*y = *x;
} }
} }

127
ggml-rpc.cpp
View file

@ -6,6 +6,7 @@
#include <string> #include <string>
#include <vector> #include <vector>
#include <memory> #include <memory>
#include <mutex>
#include <unordered_map> #include <unordered_map>
#include <unordered_set> #include <unordered_set>
#ifdef _WIN32 #ifdef _WIN32
@ -47,6 +48,7 @@ struct socket_t {
sockfd_t fd; sockfd_t fd;
socket_t(sockfd_t fd) : fd(fd) {} socket_t(sockfd_t fd) : fd(fd) {}
~socket_t() { ~socket_t() {
GGML_PRINT_DEBUG("[%s] closing socket %d\n", __func__, this->fd);
#ifdef _WIN32 #ifdef _WIN32
closesocket(this->fd); closesocket(this->fd);
#else #else
@ -97,7 +99,7 @@ static ggml_guid_t ggml_backend_rpc_guid() {
} }
struct ggml_backend_rpc_buffer_type_context { struct ggml_backend_rpc_buffer_type_context {
std::shared_ptr<socket_t> sock; std::string endpoint;
std::string name; std::string name;
size_t alignment; size_t alignment;
size_t max_size; size_t max_size;
@ -106,8 +108,6 @@ struct ggml_backend_rpc_buffer_type_context {
struct ggml_backend_rpc_context { struct ggml_backend_rpc_context {
std::string endpoint; std::string endpoint;
std::string name; std::string name;
std::shared_ptr<socket_t> sock;
ggml_backend_buffer_type_t buft;
}; };
struct ggml_backend_rpc_buffer_context { struct ggml_backend_rpc_buffer_context {
@ -231,14 +231,13 @@ static bool recv_data(sockfd_t sockfd, void * data, size_t size) {
return true; return true;
} }
static bool parse_endpoint(const char * endpoint, std::string & host, int & port) { static bool parse_endpoint(const std::string & endpoint, std::string & host, int & port) {
std::string str(endpoint); size_t pos = endpoint.find(':');
size_t pos = str.find(':');
if (pos == std::string::npos) { if (pos == std::string::npos) {
return false; return false;
} }
host = str.substr(0, pos); host = endpoint.substr(0, pos);
port = std::stoi(str.substr(pos + 1)); port = std::stoi(endpoint.substr(pos + 1));
return true; return true;
} }
@ -273,6 +272,44 @@ static bool send_rpc_cmd(const std::shared_ptr<socket_t> & sock, enum rpc_cmd cm
// RPC client-side implementation // RPC client-side implementation
static std::shared_ptr<socket_t> get_socket(const std::string & endpoint) {
static std::mutex mutex;
std::lock_guard<std::mutex> lock(mutex);
static std::unordered_map<std::string, std::weak_ptr<socket_t>> sockets;
static bool initialized = false;
auto it = sockets.find(endpoint);
if (it != sockets.end()) {
if (auto sock = it->second.lock()) {
return sock;
}
}
std::string host;
int port;
if (!parse_endpoint(endpoint, host, port)) {
return nullptr;
}
#ifdef _WIN32
if (!initialized) {
WSADATA wsaData;
int res = WSAStartup(MAKEWORD(2, 2), &wsaData);
if (res != 0) {
return nullptr;
}
initialized = true;
}
#else
UNUSED(initialized);
#endif
auto sock = socket_connect(host.c_str(), port);
if (sock == nullptr) {
return nullptr;
}
GGML_PRINT_DEBUG("[%s] connected to %s, sockfd=%d\n", __func__, endpoint.c_str(), sock->fd);
sockets[endpoint] = sock;
return sock;
}
GGML_CALL static const char * ggml_backend_rpc_buffer_get_name(ggml_backend_buffer_t buffer) { GGML_CALL static const char * ggml_backend_rpc_buffer_get_name(ggml_backend_buffer_t buffer) {
ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
return ctx->name.c_str(); return ctx->name.c_str();
@ -442,7 +479,8 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer
std::vector<uint8_t> input(input_size, 0); std::vector<uint8_t> input(input_size, 0);
memcpy(input.data(), &size, sizeof(size)); memcpy(input.data(), &size, sizeof(size));
std::vector<uint8_t> output; std::vector<uint8_t> output;
bool status = send_rpc_cmd(buft_ctx->sock, ALLOC_BUFFER, input, output); auto sock = get_socket(buft_ctx->endpoint);
bool status = send_rpc_cmd(sock, ALLOC_BUFFER, input, output);
GGML_ASSERT(status); GGML_ASSERT(status);
GGML_ASSERT(output.size() == 2*sizeof(uint64_t)); GGML_ASSERT(output.size() == 2*sizeof(uint64_t));
// output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) |
@ -453,7 +491,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer
if (remote_ptr != 0) { if (remote_ptr != 0) {
ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft, ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft,
ggml_backend_rpc_buffer_interface, ggml_backend_rpc_buffer_interface,
new ggml_backend_rpc_buffer_context{buft_ctx->sock, {}, remote_ptr, "RPC"}, new ggml_backend_rpc_buffer_context{sock, {}, remote_ptr, "RPC"},
remote_size); remote_size);
return buffer; return buffer;
} else { } else {
@ -508,7 +546,7 @@ GGML_CALL static bool ggml_backend_rpc_buffer_type_supports_backend(ggml_backend
} }
ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context;
ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context;
return buft_ctx->sock == rpc_ctx->sock; return buft_ctx->endpoint == rpc_ctx->endpoint;
} }
static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = { static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = {
@ -521,7 +559,6 @@ static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = {
/* .is_host = */ NULL, /* .is_host = */ NULL,
}; };
GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) { GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) {
ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context;
@ -530,16 +567,13 @@ GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) {
GGML_CALL static void ggml_backend_rpc_free(ggml_backend_t backend) { GGML_CALL static void ggml_backend_rpc_free(ggml_backend_t backend) {
ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context;
ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)rpc_ctx->buft->context;
delete buft_ctx;
delete rpc_ctx->buft;
delete rpc_ctx; delete rpc_ctx;
delete backend; delete backend;
} }
GGML_CALL static ggml_backend_buffer_type_t ggml_backend_rpc_get_default_buffer_type(ggml_backend_t backend) { GGML_CALL static ggml_backend_buffer_type_t ggml_backend_rpc_get_default_buffer_type(ggml_backend_t backend) {
ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context;
return ctx->buft; return ggml_backend_rpc_buffer_type(ctx->endpoint.c_str());
} }
GGML_CALL static void ggml_backend_rpc_synchronize(ggml_backend_t backend) { GGML_CALL static void ggml_backend_rpc_synchronize(ggml_backend_t backend) {
@ -590,7 +624,8 @@ GGML_CALL static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t
std::vector<uint8_t> input; std::vector<uint8_t> input;
serialize_graph(cgraph, input); serialize_graph(cgraph, input);
std::vector<uint8_t> output; std::vector<uint8_t> output;
bool status = send_rpc_cmd(rpc_ctx->sock, GRAPH_COMPUTE, input, output); auto sock = get_socket(rpc_ctx->endpoint);
bool status = send_rpc_cmd(sock, GRAPH_COMPUTE, input, output);
GGML_ASSERT(status); GGML_ASSERT(status);
GGML_ASSERT(output.size() == 1); GGML_ASSERT(output.size() == 1);
return (enum ggml_status)output[0]; return (enum ggml_status)output[0];
@ -624,42 +659,24 @@ static ggml_backend_i ggml_backend_rpc_interface = {
/* .event_synchronize = */ NULL, /* .event_synchronize = */ NULL,
}; };
static std::unordered_map<std::string, ggml_backend_t> instances;
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) { GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) {
ggml_backend_t backend = ggml_backend_rpc_init(endpoint); static std::mutex mutex;
return backend != nullptr ? ggml_backend_rpc_get_default_buffer_type(backend) : nullptr; std::lock_guard<std::mutex> lock(mutex);
} // NOTE: buffer types are allocated and never freed; this is by design
static std::unordered_map<std::string, ggml_backend_buffer_type_t> buft_map;
GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { auto it = buft_map.find(endpoint);
std::string endpoint_str(endpoint); if (it != buft_map.end()) {
if (instances.find(endpoint_str) != instances.end()) { return it->second;
return instances[endpoint_str];
} }
#ifdef _WIN32 auto sock = get_socket(endpoint);
{
WSADATA wsaData;
int res = WSAStartup(MAKEWORD(2, 2), &wsaData);
if (res != 0) {
return nullptr;
}
}
#endif
fprintf(stderr, "Connecting to %s\n", endpoint);
std::string host;
int port;
if (!parse_endpoint(endpoint, host, port)) {
return nullptr;
}
auto sock = socket_connect(host.c_str(), port);
if (sock == nullptr) { if (sock == nullptr) {
return nullptr; return nullptr;
} }
size_t alignment = get_alignment(sock); size_t alignment = get_alignment(sock);
size_t max_size = get_max_size(sock); size_t max_size = get_max_size(sock);
ggml_backend_rpc_buffer_type_context * buft_ctx = new ggml_backend_rpc_buffer_type_context { ggml_backend_rpc_buffer_type_context * buft_ctx = new ggml_backend_rpc_buffer_type_context {
/* .sock = */ sock, /* .endpoint = */ endpoint,
/* .name = */ "RPC" + std::to_string(sock->fd), /* .name = */ "RPC[" + std::string(endpoint) + "]",
/* .alignment = */ alignment, /* .alignment = */ alignment,
/* .max_size = */ max_size /* .max_size = */ max_size
}; };
@ -668,21 +685,22 @@ GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) {
/* .iface = */ ggml_backend_rpc_buffer_type_interface, /* .iface = */ ggml_backend_rpc_buffer_type_interface,
/* .context = */ buft_ctx /* .context = */ buft_ctx
}; };
buft_map[endpoint] = buft;
return buft;
}
GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) {
ggml_backend_rpc_context * ctx = new ggml_backend_rpc_context { ggml_backend_rpc_context * ctx = new ggml_backend_rpc_context {
/* .endpoint = */ endpoint, /* .endpoint = */ endpoint,
/* .name = */ "RPC" + std::to_string(sock->fd), /* .name = */ "RPC",
/* .sock = */ sock,
/* .buft = */ buft
}; };
instances[endpoint] = new ggml_backend { ggml_backend_t backend = new ggml_backend {
/* .guid = */ ggml_backend_rpc_guid(), /* .guid = */ ggml_backend_rpc_guid(),
/* .interface = */ ggml_backend_rpc_interface, /* .interface = */ ggml_backend_rpc_interface,
/* .context = */ ctx /* .context = */ ctx
}; };
return backend;
return instances[endpoint];
} }
GGML_API GGML_CALL bool ggml_backend_is_rpc(ggml_backend_t backend) { GGML_API GGML_CALL bool ggml_backend_is_rpc(ggml_backend_t backend) {
@ -706,14 +724,13 @@ static void get_device_memory(const std::shared_ptr<socket_t> & sock, size_t * f
} }
GGML_API GGML_CALL void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) { GGML_API GGML_CALL void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) {
ggml_backend_t backend = ggml_backend_rpc_init(endpoint); auto sock = get_socket(endpoint);
if (backend == nullptr) { if (sock == nullptr) {
*free = 0; *free = 0;
*total = 0; *total = 0;
return; return;
} }
ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; get_device_memory(sock, free, total);
get_device_memory(ctx->sock, free, total);
} }
// RPC server-side implementation // RPC server-side implementation

464
ggml-sycl.cpp
View file

@ -2944,6 +2944,57 @@ namespace dpct
using shared_memory = detail::device_memory<T, shared, Dimension>; using shared_memory = detail::device_memory<T, shared, Dimension>;
template <typename T,
sycl::access::address_space addressSpace =
sycl::access::address_space::global_space,
sycl::memory_order memoryOrder = sycl::memory_order::relaxed,
sycl::memory_scope memoryScope = sycl::memory_scope::device>
inline T atomic_fetch_add(T *addr, T operand) {
auto atm =
sycl::atomic_ref<T, memoryOrder, memoryScope, addressSpace>(addr[0]);
return atm.fetch_add(operand);
}
template <sycl::access::address_space addressSpace =
sycl::access::address_space::global_space,
sycl::memory_order memoryOrder = sycl::memory_order::relaxed,
sycl::memory_scope memoryScope = sycl::memory_scope::device,
typename T1, typename T2>
inline T1 atomic_fetch_add(T1 *addr, T2 operand) {
auto atm =
sycl::atomic_ref<T1, memoryOrder, memoryScope, addressSpace>(addr[0]);
return atm.fetch_add(operand);
}
template <typename T, sycl::access::address_space addressSpace =
sycl::access::address_space::global_space>
inline T atomic_fetch_add(T *addr, T operand,
sycl::memory_order memoryOrder) {
switch (memoryOrder) {
case sycl::memory_order::relaxed:
return atomic_fetch_add<T, addressSpace, sycl::memory_order::relaxed,
sycl::memory_scope::device>(addr, operand);
case sycl::memory_order::acq_rel:
return atomic_fetch_add<T, addressSpace, sycl::memory_order::acq_rel,
sycl::memory_scope::device>(addr, operand);
case sycl::memory_order::seq_cst:
return atomic_fetch_add<T, addressSpace, sycl::memory_order::seq_cst,
sycl::memory_scope::device>(addr, operand);
default:
assert(false && "Invalid memory_order for atomics. Valid memory_order for "
"atomics are: sycl::memory_order::relaxed, "
"sycl::memory_order::acq_rel, sycl::memory_order::seq_cst!");
}
}
template <sycl::access::address_space addressSpace =
sycl::access::address_space::global_space,
typename T1, typename T2>
inline T1 atomic_fetch_add(T1 *addr, T2 operand,
sycl::memory_order memoryOrder) {
atomic_fetch_add<T1, addressSpace>(addr, operand, memoryOrder);
}
} // COPY from DPCT head files } // COPY from DPCT head files
#define GGML_COMMON_DECL_SYCL #define GGML_COMMON_DECL_SYCL
@ -2971,20 +3022,19 @@ static int g_work_group_size = 0;
// typedef sycl::half ggml_fp16_t; // typedef sycl::half ggml_fp16_t;
#define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP #define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP
#define VER_4VEC 610 //todo for hardward optimize. #define VER_4VEC 130 //todo for hardward optimize.
#define VER_GEN9 700 //todo for hardward optimize. #define VER_GEN9 700 //todo for hardward optimize.
#define VER_GEN12 1000000 //todo for hardward optimize. #define VER_GEN12 1000000 //todo for hardward optimize.
#define VER_GEN13 (VER_GEN12 + 1030) //todo for hardward optimize. #define VER_GEN13 (VER_GEN12 + 1030) //todo for hardward optimize.
#define GGML_SYCL_MAX_NODES 8192 //TODO: adapt to hardwares #define GGML_SYCL_MAX_NODES 8192 //TODO: adapt to hardwares
#if !defined(GGML_SYCL_FORCE_MMQ)
//define for XMX in Intel GPU #define SYCL_USE_XMX
//TODO: currently, it's not used for XMX really. #endif
#define SYCL_USE_XMX
// max batch size to use MMQ kernels when tensor cores are available // max batch size to use MMQ kernels when tensor cores are available
#define XMX_MAX_BATCH_SIZE 32 #define MMQ_MAX_BATCH_SIZE 32
#if defined(_MSC_VER) #if defined(_MSC_VER)
@ -3060,6 +3110,7 @@ void ggml_sycl_get_device_description(int device, char * description, size_t d
bool ggml_backend_is_sycl(ggml_backend_t backend); bool ggml_backend_is_sycl(ggml_backend_t backend);
int ggml_backend_sycl_get_device(ggml_backend_t backend); int ggml_backend_sycl_get_device(ggml_backend_t backend);
int get_main_device(); int get_main_device();
static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer);
void print_ggml_tensor(const char*name, struct ggml_tensor *src); void print_ggml_tensor(const char*name, struct ggml_tensor *src);
void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt); void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt);
@ -8830,12 +8881,11 @@ static void rope(
dst[i + 1] = x0*sin_theta + x1*cos_theta; dst[i + 1] = x0*sin_theta + x1*cos_theta;
} }
template<typename T, bool has_pos> template<typename T, bool has_pos, bool has_freq_facs>
static void rope_neox( static void rope_neox(
const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims,
, const float * freq_factors, const sycl::nd_item<3> &item_ct1) {
const sycl::nd_item<3> &item_ct1) {
const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
item_ct1.get_local_id(1)); item_ct1.get_local_id(1));
@ -8863,8 +8913,10 @@ static void rope_neox(
float cur_rot = inv_ndims * ic - ib; float cur_rot = inv_ndims * ic - ib;
const int p = has_pos ? pos[i2] : 0; const int p = has_pos ? pos[i2] : 0;
const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f;
const float theta_base = const float theta_base =
p * freq_scale * dpct::pow(theta_scale, col / 2.0f); p * freq_scale * dpct::pow(theta_scale, col / 2.0f)/freq_factor;
float cos_theta, sin_theta; float cos_theta, sin_theta;
rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
@ -12413,7 +12465,7 @@ static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows,
const int32_t *pos, float freq_scale, const int32_t *pos, float freq_scale,
int p_delta_rows, float freq_base, float ext_factor, int p_delta_rows, float freq_base, float ext_factor,
float attn_factor, rope_corr_dims corr_dims, float attn_factor, rope_corr_dims corr_dims,
dpct::queue_ptr stream) { const float * freq_factors, dpct::queue_ptr stream) {
GGML_ASSERT(ncols % 2 == 0); GGML_ASSERT(ncols % 2 == 0);
const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE); const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
@ -12423,39 +12475,49 @@ static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows,
const float inv_ndims = -1.0f / n_dims; const float inv_ndims = -1.0f / n_dims;
if (pos == nullptr) { if (pos == nullptr) {
/*
DPCT1049:42: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
dpct::has_capability_or_fail(stream->get_device(), dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16}); {sycl::aspect::fp16});
if (freq_factors == nullptr) {
stream->parallel_for( stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims), sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) { [=](sycl::nd_item<3> item_ct1) {
rope_neox<T, false>(x, dst, ncols, n_dims, pos, freq_scale, rope_neox<T, false, false>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor, p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, corr_dims, theta_scale, inv_ndims, freq_factors,
item_ct1); item_ct1);
}); });
} else { } else {
/*
DPCT1049:43: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for( stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims), sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) { [=](sycl::nd_item<3> item_ct1) {
rope_neox<T, true>(x, dst, ncols, n_dims, pos, freq_scale, rope_neox<T, false, true>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor, p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, item_ct1); corr_dims, theta_scale, inv_ndims, freq_factors,
item_ct1);
}); });
} }
} else {
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
if (freq_factors == nullptr) {
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, true, false>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1);
});
} else {
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, true, true>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1);
});
}
}
} }
static void rope_glm_f32_sycl(const float *x, float *dst, int ncols, int nrows, static void rope_glm_f32_sycl(const float *x, float *dst, int ncols, int nrows,
@ -13501,6 +13563,10 @@ inline void ggml_sycl_op_concat(const ggml_tensor *src0,
const float *src0_dd, const float *src1_dd, const float *src0_dd, const float *src1_dd,
float *dst_dd, float *dst_dd,
const dpct::queue_ptr &main_stream) { const dpct::queue_ptr &main_stream) {
#pragma message("TODO: generalize concat kernel for dim != 2")
#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7563")
int dim = dst->op_params[0];
GGML_ASSERT(dim == 2);
GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32);
@ -13986,9 +14052,7 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst, const float *src0_dd, ggml_tensor *dst, const float *src0_dd,
const float *src1_dd, float *dst_dd, const float *src1_dd, float *dst_dd,
const dpct::queue_ptr &main_stream) { const dpct::queue_ptr &main_stream) {
#pragma message("TODO: implement phi3 frequency factors support") const ggml_tensor * src2 = dst->src[2];
#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225")
GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet");
GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
@ -14014,6 +14078,7 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
const float * freq_factors = nullptr;
const int32_t * pos = nullptr; const int32_t * pos = nullptr;
if ((mode & 1) == 0) { if ((mode & 1) == 0) {
GGML_ASSERT(src1->type == GGML_TYPE_I32); GGML_ASSERT(src1->type == GGML_TYPE_I32);
@ -14024,6 +14089,16 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
const bool is_neox = mode & 2; const bool is_neox = mode & 2;
const bool is_glm = mode & 4; const bool is_glm = mode & 4;
if (is_neox) {
pos = (const int32_t *) src1_dd;
if (src2 != nullptr) {
freq_factors = (const float *) src2->data;
}
} else {
GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox");
}
rope_corr_dims corr_dims; rope_corr_dims corr_dims;
ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v);
@ -14035,13 +14110,13 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
if (src0->type == GGML_TYPE_F32) { if (src0->type == GGML_TYPE_F32) {
rope_neox_sycl( rope_neox_sycl(
(const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, main_stream attn_factor, corr_dims, freq_factors, main_stream
); );
} else if (src0->type == GGML_TYPE_F16) { } else if (src0->type == GGML_TYPE_F16) {
rope_neox_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, rope_neox_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd,
ne00, n_dims, nrows, pos, freq_scale, ne01, ne00, n_dims, nrows, pos, freq_scale, ne01,
freq_base, ext_factor, attn_factor, corr_dims, freq_base, ext_factor, attn_factor, corr_dims,
main_stream); freq_factors, main_stream);
} else { } else {
GGML_ASSERT(false); GGML_ASSERT(false);
} }
@ -15173,6 +15248,29 @@ catch (sycl::exception const &exc) {
std::exit(1); std::exit(1);
} }
inline bool ggml_sycl_supports_mmq(enum ggml_type type) {
// TODO: accuracy issues in MMQ
return false;
}
bool ggml_sycl_supports_dmmv(enum ggml_type type) {
switch (type) {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
case GGML_TYPE_F16:
return true;
default:
return false;
}
}
static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const bool all_on_device = const bool all_on_device =
@ -15189,76 +15287,43 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
} }
} }
// check data types and tensor shapes for custom matrix multiplication kernels:
bool use_dequantize_mul_mat_vec = ggml_sycl_supports_dmmv(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
&& src0->ne[0] % GGML_SYCL_DMMV_X == 0 && src1->ne[1] == 1;
bool use_mul_mat_vec_q = ggml_is_quantized(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
&& src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
bool use_mul_mat_q = ggml_sycl_supports_mmq(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
// mmvq and mmq need the __dp4a instruction which is available for gen12+
// Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e
use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS);
#ifdef SYCL_USE_XMX #ifdef SYCL_USE_XMX
const bool use_xmx = true; use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE);
#else #endif // SYCL_USE_XMX
const bool use_xmx = false;
#endif
// debug helpers if (!split && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
//printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
//printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
//printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]);
//printf(" %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]);
//printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
//printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
// KQ single-batch // KQ single-batch
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_p021\n");
ggml_sycl_mul_mat_vec_p021(src0, src1, dst); ggml_sycl_mul_mat_vec_p021(src0, src1, dst);
} else if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
// KQV single-batch // KQV single-batch
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n");
ggml_sycl_mul_mat_vec_nc(src0, src1, dst); ggml_sycl_mul_mat_vec_nc(src0, src1, dst);
} else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
// KQ + KQV multi-batch // KQ + KQV multi-batch
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n");
ggml_sycl_mul_mat_batched_sycl(src0, src1, dst); ggml_sycl_mul_mat_batched_sycl(src0, src1, dst);
} else if (src0->type == GGML_TYPE_F32) { } else if (use_dequantize_mul_mat_vec) {
// GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n");
ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
} else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
// GGML_SYCL_DEBUG("ggml_is_quantized or GGML_TYPE_F16\n");
if (src1->ne[1] == 1 && src0->ne[0] % GGML_SYCL_DMMV_X == 0) {
#ifdef GGML_SYCL_FORCE_DMMV
const bool use_mul_mat_vec_q = false;
#else
bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type);
use_mul_mat_vec_q = use_mul_mat_vec_q ||
(src0->type == GGML_TYPE_IQ2_XXS) || (src0->type == GGML_TYPE_IQ2_XS) || (src0->type == GGML_TYPE_IQ2_S) ||
(src0->type == GGML_TYPE_IQ3_XXS) || (src0->type == GGML_TYPE_IQ3_S) ||
(src0->type == GGML_TYPE_IQ4_NL) || (src0->type == GGML_TYPE_IQ4_XS) ||
(src0->type == GGML_TYPE_IQ1_S) || (src0->type == GGML_TYPE_IQ1_M);
#endif // GGML_SYCL_FORCE_DMMV
if (use_mul_mat_vec_q) {
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_vec_q path\n");
ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true);
} else {
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_dequantize_mul_mat_vec path\n");
ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false); ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false);
} } else if (use_mul_mat_vec_q) {
} else { ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true);
bool use_mul_mat_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type); } else if (use_mul_mat_q) {
if (use_xmx && min_compute_capability >= VER_GEN9 && src1->ne[1] > XMX_MAX_BATCH_SIZE) {
use_mul_mat_q = false;
}
if (use_mul_mat_q) {
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_q path\n");
ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true); ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true);
} else { } else {
// GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_sycl path\n");
ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
} }
}
} else {
GGML_ASSERT(false);
}
} }
#if 0 #if 0
@ -15434,22 +15499,86 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) {
} }
#endif #endif
struct mmid_row_mapping {
int32_t i1;
int32_t i2;
};
__dpct_inline__ static void k_copy_src1_to_contiguous(
const char *__restrict__ src1_original, char *__restrict__ src1_contiguous,
int *__restrict__ cur_src1_row, mmid_row_mapping *__restrict__ row_mapping,
const char *__restrict ids, int64_t i02, size_t ids_nb1, size_t ids_nb0,
int64_t ne11, int64_t ne10, size_t nb11, size_t nb12,
const sycl::nd_item<3> &item_ct1, int &src1_row) {
int32_t iid1 = item_ct1.get_group(2);
int32_t id = item_ct1.get_group(1);
const int32_t row_id_i = *(const int32_t *) (ids + iid1*ids_nb1 + id*ids_nb0);
if (row_id_i != i02) {
return;
}
const int64_t i11 = id % ne11;
const int64_t i12 = iid1;
if (item_ct1.get_local_id(2) == 0) {
src1_row =
dpct::atomic_fetch_add<sycl::access::address_space::generic_space>(
cur_src1_row, 1);
row_mapping[src1_row] = {id, iid1};
}
/*
DPCT1065:194: Consider replacing sycl::nd_item::barrier() with
sycl::nd_item::barrier(sycl::access::fence_space::local_space) for better
performance if there is no access to global memory.
*/
item_ct1.barrier();
const float * src1_row_original = (const float *)(src1_original + i11*nb11 + i12*nb12);
float * src1_row_contiguous = (float *)(src1_contiguous + src1_row*nb11);
#pragma unroll
for (int i = item_ct1.get_local_id(2); i < ne10;
i += item_ct1.get_local_range(2)) {
src1_row_contiguous[i] = src1_row_original[i];
}
}
__dpct_inline__ static void k_copy_dst_from_contiguous(
char *__restrict__ dst_original, const char *__restrict__ dst_contiguous,
const mmid_row_mapping *__restrict__ row_mapping, int64_t ne0, size_t nb1,
size_t nb2, const sycl::nd_item<3> &item_ct1) {
int32_t i = item_ct1.get_group(2);
const int32_t i1 = row_mapping[i].i1;
const int32_t i2 = row_mapping[i].i2;
const float * dst_row_contiguous = (const float *)(dst_contiguous + i*nb1);
float * dst_row_original = (float *)(dst_original + i1*nb1 + i2*nb2);
#pragma unroll
for (int j = item_ct1.get_local_id(2); j < ne0;
j += item_ct1.get_local_range(2)) {
dst_row_original[j] = dst_row_contiguous[j];
}
}
static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
const ggml_tensor *src1, const ggml_tensor *src1,
ggml_tensor *dst) try { ggml_tensor *dst) try {
GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT && GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer) && "mul_mat_id does not support split buffers");
"mul_mat_id does not support split buffers");
const ggml_tensor *ids = dst->src[2]; const ggml_tensor *ids = dst->src[2];
GGML_TENSOR_BINARY_OP_LOCALS
const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
const size_t nb11 = src1->nb[1]; const int64_t n_as = ne02;
const size_t nb1 = dst->nb[1]; const int64_t n_ids = ids->ne[0];
const int32_t id = ((int32_t *)dst->op_params)[0];
const int32_t n_as = src0->ne[2];
std::vector<char> ids_host(ggml_nbytes(ids)); std::vector<char> ids_host(ggml_nbytes(ids));
const char *ids_dev = (const char *)ids->data; const char * ids_dev = (const char *) ids->data;
SYCL_CHECK(CHECK_TRY_ERROR( SYCL_CHECK(CHECK_TRY_ERROR(
stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids))));
@ -15489,25 +15618,41 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
src0_row.ne[2] = 1; src0_row.ne[2] = 1;
src0_row.ne[3] = 1; src0_row.ne[3] = 1;
src0_row.nb[3] = src0->nb[2]; src0_row.nb[3] = nb02;
if (src1->ne[1] == 1) { src1_row.ne[1] = 1;
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { src1_row.ne[2] = 1;
const int32_t row_id = src1_row.ne[3] = 1;
*(const int32_t *)(ids_host.data() + i01 * ids->nb[1] + src1_row.nb[2] = nb11;
id * ids->nb[0]); src1_row.nb[3] = nb11;
GGML_ASSERT(row_id >= 0 && row_id < n_as); dst_row.ne[1] = 1;
dst_row.ne[2] = 1;
dst_row.ne[3] = 1;
dst_row.nb[2] = nb1;
dst_row.nb[3] = nb1;
if (ne12 == 1) {
for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
for (int64_t id = 0; id < n_ids; id++) {
const int32_t i02 = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
GGML_ASSERT(i02 >= 0 && i02 < n_as);
const int64_t i11 = id % ne11;
const int64_t i12 = iid1;
const int64_t i1 = id;
const int64_t i2 = i12;
src0_row_extra.data_device[g_main_device] = src0_row_extra.data_device[g_main_device] =
src0_original + row_id * src0->nb[2]; src0_original + i02*nb02;
src1_row_extra.data_device[g_main_device] = src1_row_extra.data_device[g_main_device] =
src1_original + i01 * src1->nb[1]; src1_original + + i11*nb11 + i12*nb12;
dst_row_extra.data_device[g_main_device] = dst_row_extra.data_device[g_main_device] =
dst_original + i01 * dst->nb[1]; dst_original + i1*nb1 + i2*nb2;
ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row); ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row);
} }
}
} else { } else {
sycl_pool_alloc<char> src1_contiguous(sizeof(float)*ggml_nelements(src1)); sycl_pool_alloc<char> src1_contiguous(sizeof(float)*ggml_nelements(src1));
sycl_pool_alloc<char> dst_contiguous(sizeof(float)*ggml_nelements(dst)); sycl_pool_alloc<char> dst_contiguous(sizeof(float)*ggml_nelements(dst));
@ -15515,64 +15660,98 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
src1_row_extra.data_device[g_main_device] = src1_contiguous.get(); src1_row_extra.data_device[g_main_device] = src1_contiguous.get();
dst_row_extra.data_device[g_main_device] = dst_contiguous.get(); dst_row_extra.data_device[g_main_device] = dst_contiguous.get();
for (int32_t row_id = 0; row_id < n_as; ++row_id) { for (int64_t i02 = 0; i02 < n_as; i02++) {
int64_t num_src1_rows = 0; int64_t num_src1_rows = 0;
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); for (int64_t id = 0; id < n_ids; id++) {
const int32_t row_id_i = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
if (row_id_i != row_id) { GGML_ASSERT(row_id_i >= 0 && row_id_i < n_as);
if (row_id_i != i02) {
continue; continue;
} }
GGML_ASSERT(row_id >= 0 && row_id < n_as);
SYCL_CHECK(CHECK_TRY_ERROR(
stream->memcpy(src1_contiguous.get() + num_src1_rows * nb11,
src1_original + i01 * nb11, nb11)));
num_src1_rows++; num_src1_rows++;
} }
}
if (num_src1_rows == 0) { if (num_src1_rows == 0) {
continue; continue;
} }
src0_row_extra.data_device[g_main_device] =
src0_original + row_id * src0->nb[2];
sycl_pool_alloc<int> dev_cur_src1_row(1);
sycl_pool_alloc<mmid_row_mapping> dev_row_mapping(num_src1_rows);
SYCL_CHECK(CHECK_TRY_ERROR(
stream->memset(dev_cur_src1_row.get(), 0, sizeof(int))));
{
sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne10, 768u));
sycl::range<3> grid_dims(1, n_ids, ids->ne[1]);
stream->submit([&](sycl::handler &cgh) {
sycl::local_accessor<int, 0> src1_row_acc(cgh);
char *__restrict src1_contiguous_get =
src1_contiguous.get();
int *__restrict dev_cur_src1_row_get =
dev_cur_src1_row.get();
mmid_row_mapping *__restrict dev_row_mapping_get =
dev_row_mapping.get();
size_t ids_nb_ct6 = ids->nb[1];
size_t ids_nb_ct7 = ids->nb[0];
cgh.parallel_for(
sycl::nd_range<3>(grid_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
k_copy_src1_to_contiguous(
src1_original, src1_contiguous_get,
dev_cur_src1_row_get,
dev_row_mapping_get, ids_dev, i02,
ids_nb_ct6, ids_nb_ct7, ne11, ne10, nb11, nb12,
item_ct1, src1_row_acc);
});
});
}
src0_row_extra.data_device[g_main_device] = src0_original + i02*nb02;
GGML_ASSERT(nb11 == sizeof(float)*ne10);
GGML_ASSERT(nb1 == sizeof(float)*ne0);
src1_row.ne[1] = num_src1_rows; src1_row.ne[1] = num_src1_rows;
dst_row.ne[1] = num_src1_rows;
src1_row.nb[1] = nb11; src1_row.nb[1] = nb11;
src1_row.nb[2] = num_src1_rows*nb11; src1_row.nb[2] = num_src1_rows*nb11;
src1_row.nb[3] = num_src1_rows*nb11; src1_row.nb[3] = num_src1_rows*nb11;
dst_row.ne[1] = num_src1_rows;
dst_row.nb[1] = nb1; dst_row.nb[1] = nb1;
dst_row.nb[2] = num_src1_rows*nb1; dst_row.nb[2] = num_src1_rows*nb1;
dst_row.nb[3] = num_src1_rows*nb1; dst_row.nb[3] = num_src1_rows*nb1;
ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row); ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row);
num_src1_rows = 0; {
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne0, 768u));
const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); sycl::range<3> grid_dims(1, 1, num_src1_rows);
stream->submit([&](sycl::handler &cgh) {
const char *__restrict dst_contiguous_get =
dst_contiguous.get();
const mmid_row_mapping *__restrict dev_row_mapping_get =
dev_row_mapping.get();
if (row_id_i != row_id) { cgh.parallel_for(
continue; sycl::nd_range<3>(grid_dims * block_dims, block_dims),
} [=](sycl::nd_item<3> item_ct1) {
k_copy_dst_from_contiguous(dst_original,
GGML_ASSERT(row_id >= 0 && row_id < n_as); dst_contiguous_get,
dev_row_mapping_get,
SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( ne0, nb1, nb2, item_ct1);
dst_original + i01 * nb1, });
dst_contiguous.get() + num_src1_rows * nb1, nb1))); });
num_src1_rows++;
} }
} }
} }
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
}
} }
catch (sycl::exception const &exc) { catch (sycl::exception const &exc) {
std::cerr << exc.what() << "Exception caught at file:" << __FILE__ std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@ -16555,10 +16734,9 @@ GGML_CALL static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backe
UNUSED(buffer); UNUSED(buffer);
} }
// unused at the moment static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) {
//static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name;
// return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; }
//}
GGML_CALL static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { GGML_CALL static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;

2
ggml-vulkan.cpp
View file

@ -6012,6 +6012,8 @@ static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = {
}; };
GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num) { GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num) {
ggml_vk_instance_init();
#ifdef GGML_VULKAN_DEBUG #ifdef GGML_VULKAN_DEBUG
std::cerr << "ggml_backend_vk_buffer_type(" << dev_num << ")" << std::endl; std::cerr << "ggml_backend_vk_buffer_type(" << dev_num << ")" << std::endl;
#endif #endif

55
ggml.c
View file

@ -4882,10 +4882,21 @@ struct ggml_tensor * ggml_repeat_back(
// ggml_concat // ggml_concat
struct ggml_tensor * ggml_concat( struct ggml_tensor * ggml_concat(
struct ggml_context* ctx, struct ggml_context * ctx,
struct ggml_tensor* a, struct ggml_tensor * a,
struct ggml_tensor* b) { struct ggml_tensor * b,
GGML_ASSERT(a->ne[0] == b->ne[0] && a->ne[1] == b->ne[1] && a->ne[3] == b->ne[3]); int dim) {
GGML_ASSERT(dim >= 0 && dim < GGML_MAX_DIMS);
int64_t ne[GGML_MAX_DIMS];
for (int d = 0; d < GGML_MAX_DIMS; ++d) {
if (d == dim) {
ne[d] = a->ne[d] + b->ne[d];
continue;
}
GGML_ASSERT(a->ne[d] == b->ne[d]);
ne[d] = a->ne[d];
}
bool is_node = false; bool is_node = false;
@ -4893,7 +4904,9 @@ struct ggml_tensor * ggml_concat(
is_node = true; is_node = true;
} }
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, a->ne[0], a->ne[1], a->ne[2] + b->ne[2], a->ne[3]); struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, ne);
ggml_set_op_params_i32(result, 0, dim);
result->op = GGML_OP_CONCAT; result->op = GGML_OP_CONCAT;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -5013,6 +5026,7 @@ struct ggml_tensor * ggml_leaky_relu(
} }
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, &negative_slope, sizeof(negative_slope)); ggml_set_op_params(result, &negative_slope, sizeof(negative_slope));
result->op = GGML_OP_LEAKY_RELU; result->op = GGML_OP_LEAKY_RELU;
@ -10967,34 +10981,37 @@ static void ggml_compute_forward_concat_f32(
GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float));
GGML_ASSERT(nb10 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float));
const int32_t dim = ggml_get_op_params_i32(dst, 0);
GGML_ASSERT(dim >= 0 && dim < 4);
int64_t o[4] = {0, 0, 0, 0};
o[dim] = src0->ne[dim];
const float * x;
// TODO: smarter multi-theading
for (int i3 = 0; i3 < ne3; i3++) { for (int i3 = 0; i3 < ne3; i3++) {
for (int i2 = ith; i2 < ne2; i2 += nth) { for (int i2 = ith; i2 < ne2; i2 += nth) {
if (i2 < ne02) { // src0
for (int i1 = 0; i1 < ne1; i1++) { for (int i1 = 0; i1 < ne1; i1++) {
for (int i0 = 0; i0 < ne0; i0++) { for (int i0 = 0; i0 < ne0; i0++) {
const float * x = (float *)((char *) src0->data + i0 * nb00 + i1 * nb01 + i2 * nb02 + i3 * nb03); if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
x = (const float *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03);
} else {
x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13);
}
float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3); float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
*y = *x;
}
}
} // src1
else {
for (int i1 = 0; i1 < ne1; i1++) {
for (int i0 = 0; i0 < ne0; i0++) {
const float * x = (float *)((char *) src1->data + i0 * nb10 + i1 * nb11 + (i2 - ne02) * nb12 + i3 * nb13);
float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3);
*y = *x; *y = *x;
} }
} }
} }
} }
}
} }
static void ggml_compute_forward_concat( static void ggml_compute_forward_concat(
const struct ggml_compute_params* params, const struct ggml_compute_params * params,
struct ggml_tensor* dst) { struct ggml_tensor* dst) {
const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src0 = dst->src[0];

5
ggml.h
View file

@ -1007,12 +1007,13 @@ extern "C" {
struct ggml_tensor * a, struct ggml_tensor * a,
struct ggml_tensor * b); struct ggml_tensor * b);
// concat a and b on dim 2 // concat a and b along dim
// used in stable-diffusion // used in stable-diffusion
GGML_API struct ggml_tensor * ggml_concat( GGML_API struct ggml_tensor * ggml_concat(
struct ggml_context * ctx, struct ggml_context * ctx,
struct ggml_tensor * a, struct ggml_tensor * a,
struct ggml_tensor * b); struct ggml_tensor * b,
int dim);
GGML_API struct ggml_tensor * ggml_abs( GGML_API struct ggml_tensor * ggml_abs(
struct ggml_context * ctx, struct ggml_context * ctx,

52
gguf-py/gguf/constants.py
View file

@ -35,11 +35,15 @@ class Keys:
CONTEXT_LENGTH = "{arch}.context_length" CONTEXT_LENGTH = "{arch}.context_length"
EMBEDDING_LENGTH = "{arch}.embedding_length" EMBEDDING_LENGTH = "{arch}.embedding_length"
BLOCK_COUNT = "{arch}.block_count" BLOCK_COUNT = "{arch}.block_count"
LEADING_DENSE_BLOCK_COUNT = "{arch}.leading_dense_block_count"
FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
EXPERT_FEED_FORWARD_LENGTH = "{arch}.expert_feed_forward_length"
USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
EXPERT_COUNT = "{arch}.expert_count" EXPERT_COUNT = "{arch}.expert_count"
EXPERT_USED_COUNT = "{arch}.expert_used_count" EXPERT_USED_COUNT = "{arch}.expert_used_count"
EXPERT_SHARED_COUNT = "{arch}.expert_shared_count"
EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale"
POOLING_TYPE = "{arch}.pooling_type" POOLING_TYPE = "{arch}.pooling_type"
LOGIT_SCALE = "{arch}.logit_scale" LOGIT_SCALE = "{arch}.logit_scale"
@ -53,6 +57,8 @@ class Keys:
LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
CAUSAL = "{arch}.attention.causal" CAUSAL = "{arch}.attention.causal"
Q_LORA_RANK = "{arch}.attention.q_lora_rank"
KV_LORA_RANK = "{arch}.attention.kv_lora_rank"
class Rope: class Rope:
DIMENSION_COUNT = "{arch}.rope.dimension_count" DIMENSION_COUNT = "{arch}.rope.dimension_count"
@ -62,6 +68,7 @@ class Keys:
SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor" SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor"
SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" SCALING_FINETUNED = "{arch}.rope.scaling.finetuned"
SCALING_YARN_LOG_MUL = "{arch}.rope.scaling.yarn_log_multiplier"
class SSM: class SSM:
CONV_KERNEL = "{arch}.ssm.conv_kernel" CONV_KERNEL = "{arch}.ssm.conv_kernel"
@ -141,6 +148,7 @@ class MODEL_ARCH(IntEnum):
DBRX = auto() DBRX = auto()
OLMO = auto() OLMO = auto()
ARCTIC = auto() ARCTIC = auto()
DEEPSEEK2 = auto()
class MODEL_TENSOR(IntEnum): class MODEL_TENSOR(IntEnum):
@ -186,6 +194,12 @@ class MODEL_TENSOR(IntEnum):
SSM_A = auto() SSM_A = auto()
SSM_D = auto() SSM_D = auto()
SSM_OUT = auto() SSM_OUT = auto()
ATTN_Q_A = auto()
ATTN_Q_B = auto()
ATTN_KV_A_MQA = auto()
ATTN_KV_B = auto()
ATTN_Q_A_NORM = auto()
ATTN_KV_A_NORM = auto()
MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@ -222,6 +236,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.DBRX: "dbrx", MODEL_ARCH.DBRX: "dbrx",
MODEL_ARCH.OLMO: "olmo", MODEL_ARCH.OLMO: "olmo",
MODEL_ARCH.ARCTIC: "arctic", MODEL_ARCH.ARCTIC: "arctic",
MODEL_ARCH.DEEPSEEK2: "deepseek2",
} }
TENSOR_NAMES: dict[MODEL_TENSOR, str] = { TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -267,6 +282,12 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a", MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a",
MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d", MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d",
MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out", MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out",
MODEL_TENSOR.ATTN_Q_A: "blk.{bid}.attn_q_a",
MODEL_TENSOR.ATTN_Q_B: "blk.{bid}.attn_q_b",
MODEL_TENSOR.ATTN_KV_A_MQA: "blk.{bid}.attn_kv_a_mqa",
MODEL_TENSOR.ATTN_KV_B: "blk.{bid}.attn_kv_b",
MODEL_TENSOR.ATTN_Q_A_NORM: "blk.{bid}.attn_q_a_norm",
MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm",
} }
MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@ -758,6 +779,33 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP, MODEL_TENSOR.FFN_UP_EXP,
], ],
MODEL_ARCH.DEEPSEEK2: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_A,
MODEL_TENSOR.ATTN_Q_B,
MODEL_TENSOR.ATTN_KV_A_MQA,
MODEL_TENSOR.ATTN_KV_B,
MODEL_TENSOR.ATTN_Q_A_NORM,
MODEL_TENSOR.ATTN_KV_A_NORM,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_GATE_INP,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_GATE_EXP,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
MODEL_TENSOR.FFN_GATE_SHEXP,
MODEL_TENSOR.FFN_DOWN_SHEXP,
MODEL_TENSOR.FFN_UP_SHEXP,
],
# TODO # TODO
} }
@ -791,6 +839,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD, MODEL_TENSOR.ATTN_ROT_EMBD,
], ],
MODEL_ARCH.DEEPSEEK2: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
} }

21
gguf-py/gguf/gguf_writer.py
View file

@ -374,9 +374,15 @@ class GGUFWriter:
def add_block_count(self, length: int) -> None: def add_block_count(self, length: int) -> None:
self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length) self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length)
def add_leading_dense_block_count(self, length: int) -> None:
self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length)
def add_feed_forward_length(self, length: int) -> None: def add_feed_forward_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_expert_feed_forward_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_parallel_residual(self, use: bool) -> None: def add_parallel_residual(self, use: bool) -> None:
self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
@ -407,6 +413,12 @@ class GGUFWriter:
def add_expert_used_count(self, count: int) -> None: def add_expert_used_count(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count) self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count)
def add_expert_shared_count(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_SHARED_COUNT.format(arch=self.arch), count)
def add_expert_weights_scale(self, value: float) -> None:
self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value)
def add_layer_norm_eps(self, value: float) -> None: def add_layer_norm_eps(self, value: float) -> None:
self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value) self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)
@ -416,6 +428,12 @@ class GGUFWriter:
def add_causal_attention(self, value: bool) -> None: def add_causal_attention(self, value: bool) -> None:
self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)
def add_q_lora_rank(self, length: int) -> None:
self.add_uint32(Keys.Attention.Q_LORA_RANK.format(arch=self.arch), length)
def add_kv_lora_rank(self, length: int) -> None:
self.add_uint32(Keys.Attention.KV_LORA_RANK.format(arch=self.arch), length)
def add_pooling_type(self, value: PoolingType) -> None: def add_pooling_type(self, value: PoolingType) -> None:
self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value) self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
@ -440,6 +458,9 @@ class GGUFWriter:
def add_rope_scaling_finetuned(self, value: bool) -> None: def add_rope_scaling_finetuned(self, value: bool) -> None:
self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value) self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value)
def add_rope_scaling_yarn_log_mul(self, value: float) -> None:
self.add_float32(Keys.Rope.SCALING_YARN_LOG_MUL.format(arch=self.arch), value)
def add_ssm_conv_kernel(self, value: int) -> None: def add_ssm_conv_kernel(self, value: int) -> None:
self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value) self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value)

29
gguf-py/gguf/tensor_mapping.py
View file

@ -256,6 +256,7 @@ class TensorNameMap:
MODEL_TENSOR.FFN_UP_SHEXP: ( MODEL_TENSOR.FFN_UP_SHEXP: (
"model.layers.{bid}.mlp.shared_expert.up_proj", # qwen2moe "model.layers.{bid}.mlp.shared_expert.up_proj", # qwen2moe
"model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek2
), ),
# AWQ-activation gate # AWQ-activation gate
@ -285,6 +286,7 @@ class TensorNameMap:
MODEL_TENSOR.FFN_GATE_SHEXP: ( MODEL_TENSOR.FFN_GATE_SHEXP: (
"model.layers.{bid}.mlp.shared_expert.gate_proj", # qwen2moe "model.layers.{bid}.mlp.shared_expert.gate_proj", # qwen2moe
"model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek2
), ),
# Feed-forward down # Feed-forward down
@ -320,6 +322,7 @@ class TensorNameMap:
MODEL_TENSOR.FFN_DOWN_SHEXP: ( MODEL_TENSOR.FFN_DOWN_SHEXP: (
"model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe "model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe
"model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek2
), ),
MODEL_TENSOR.ATTN_Q_NORM: ( MODEL_TENSOR.ATTN_Q_NORM: (
@ -383,6 +386,30 @@ class TensorNameMap:
"model.layers.{bid}.out_proj", "model.layers.{bid}.out_proj",
"backbone.layers.{bid}.mixer.out_proj", "backbone.layers.{bid}.mixer.out_proj",
), ),
MODEL_TENSOR.ATTN_Q_A: (
"model.layers.{bid}.self_attn.q_a_proj", # deepseek2
),
MODEL_TENSOR.ATTN_Q_B: (
"model.layers.{bid}.self_attn.q_b_proj", # deepseek2
),
MODEL_TENSOR.ATTN_KV_A_MQA: (
"model.layers.{bid}.self_attn.kv_a_proj_with_mqa", # deepseek2
),
MODEL_TENSOR.ATTN_KV_B: (
"model.layers.{bid}.self_attn.kv_b_proj", # deepseek2
),
MODEL_TENSOR.ATTN_Q_A_NORM: (
"model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2
),
MODEL_TENSOR.ATTN_KV_A_NORM: (
"model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2
),
} }
# architecture-specific block mappings # architecture-specific block mappings
@ -415,7 +442,7 @@ class TensorNameMap:
if tensor not in MODEL_TENSORS[arch]: if tensor not in MODEL_TENSORS[arch]:
continue continue
# TODO: make this configurable # TODO: make this configurable
n_experts = 128 n_experts = 160
for xid in range(n_experts): for xid in range(n_experts):
tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid) tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid)
self.mapping[tensor_name] = (tensor, tensor_name) self.mapping[tensor_name] = (tensor, tensor_name)

656
llama.cpp
View file

File diff suppressed because it is too large Load diff

8
llama.h
View file

@ -265,6 +265,8 @@ extern "C" {
bool check_tensors; // validate model tensor data bool check_tensors; // validate model tensor data
}; };
// NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
// https://github.com/ggerganov/llama.cpp/pull/7544
struct llama_context_params { struct llama_context_params {
uint32_t seed; // RNG seed, -1 for random uint32_t seed; // RNG seed, -1 for random
uint32_t n_ctx; // text context, 0 = from model uint32_t n_ctx; // text context, 0 = from model
@ -291,14 +293,14 @@ extern "C" {
ggml_backend_sched_eval_callback cb_eval; ggml_backend_sched_eval_callback cb_eval;
void * cb_eval_user_data; void * cb_eval_user_data;
enum ggml_type type_k; // data type for K cache enum ggml_type type_k; // data type for K cache [EXPERIMENTAL]
enum ggml_type type_v; // data type for V cache enum ggml_type type_v; // data type for V cache [EXPERIMENTAL]
// Keep the booleans together to avoid misalignment during copy-by-value. // Keep the booleans together to avoid misalignment during copy-by-value.
bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
bool embeddings; // if true, extract embeddings (together with logits) bool embeddings; // if true, extract embeddings (together with logits)
bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
bool flash_attn; // whether to use flash attention bool flash_attn; // whether to use flash attention [EXPERIMENTAL]
// Abort callback // Abort callback
// if it returns true, execution of llama_decode() will be aborted // if it returns true, execution of llama_decode() will be aborted

28
tests/test-backend-ops.cpp
View file

@ -1259,22 +1259,26 @@ struct test_im2col : public test_case {
// GGML_OP_CONCAT // GGML_OP_CONCAT
struct test_concat : public test_case { struct test_concat : public test_case {
const ggml_type type; const ggml_type type;
const std::array<int64_t, 4> ne; const std::array<int64_t, 4> ne_a;
const int64_t b_ne2; const int64_t ne_b_d;
const int dim;
std::string vars() override { std::string vars() override {
return VARS_TO_STR3(type, ne, b_ne2); return VARS_TO_STR4(type, ne_a, ne_b_d, dim);
} }
test_concat(ggml_type type = GGML_TYPE_F32, test_concat(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 10}, std::array<int64_t, 4> ne_a = {10, 10, 10, 10},
int64_t b_ne2 = 10) int64_t ne_b_d = 10,
: type(type), ne(ne), b_ne2(b_ne2) {} int dim = 2)
: type(type), ne_a(ne_a), ne_b_d(ne_b_d), dim(dim) {}
ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); auto ne_b = ne_a;
ggml_tensor * b = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], b_ne2, ne[3]); ne_b[dim] = ne_b_d;
ggml_tensor * out = ggml_concat(ctx, a, b); ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data());
ggml_tensor * out = ggml_concat(ctx, a, b, dim);
return out; return out;
} }
}; };
@ -2211,8 +2215,10 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
} }
} }
test_cases.emplace_back(new test_concat(GGML_TYPE_F32)); for (int dim : { 0, 1, 2, 3, }) {
test_cases.emplace_back(new test_concat(GGML_TYPE_I32)); test_cases.emplace_back(new test_concat(GGML_TYPE_F32, {11, 12, 13, 14}, 7, dim));
test_cases.emplace_back(new test_concat(GGML_TYPE_I32, {11, 12, 13, 14}, 7, dim));
}
for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) { for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) {
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order)); test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));

2
tests/test-tokenizer-0.sh
View file

@ -28,6 +28,8 @@ printf "Tokenizing using (cpp) llama.cpp ...\n"
cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in" cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in"
cat /tmp/test-tokenizer-0-$name-cpp.log | grep "tokenized in" cat /tmp/test-tokenizer-0-$name-cpp.log | grep "tokenized in"
set +e
diff $input.tok $input.tokcpp > /dev/null 2>&1 diff $input.tok $input.tokcpp > /dev/null 2>&1
if [ $? -eq 0 ]; then if [ $? -eq 0 ]; then

20
tests/test-tokenizer-random.py
View file

@ -167,8 +167,10 @@ def generator_random_special_tokens(tokenizer, iterations=100) -> Iterator[str]:
for m in range(iterations): for m in range(iterations):
rand.seed(m) rand.seed(m)
words = rand.choices(special_tokens, k=500) words = rand.choices(special_tokens, k=500)
if tokenizer.add_bos_token: # skip spam warning of double BOS if words[0] == tokenizer.bos_token: # skip spam warning of double BOS
while words and words[0] == tokenizer.bos_token: while len(words) > 1 and words[1] == tokenizer.bos_token: # leave one starting BOS
words.pop(0)
if tokenizer.add_bos_token: # drop all starting BOS
words.pop(0) words.pop(0)
yield "".join(words) yield "".join(words)
@ -293,15 +295,17 @@ def main(argv: list[str] = None):
model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096)) model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096))
tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer) tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer)
tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", True)
tokenizer.add_eos_token = getattr(tokenizer, "add_eos_token", False)
def func_tokenize1(text: str): def func_tokenize1(text: str):
return model.tokenize(text, add_special=True, parse_special=True) return model.tokenize(text, add_special=True, parse_special=True)
def func_tokenize2(text: str): def func_tokenize2(text: str):
return tokenizer.encode(text, add_special_tokens=True) return tokenizer.encode(text, add_special_tokens=True)
ids = func_tokenize2("a")
assert 1 <= len(ids) <= 3
add_bos_token = len(ids) > 1 and tokenizer.bos_token_id == ids[0]
tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", add_bos_token)
vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True))) vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True)))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text()) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text())
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases()) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases())
@ -324,8 +328,10 @@ if __name__ == "__main__":
# import os # import os
# tokenizers = os.listdir(path_tokenizers) # tokenizers = os.listdir(path_tokenizers)
tokenizers = [ tokenizers = [
"llama-spm", # SPM # "llama-spm", # SPM
"phi-3", # SPM # "phi-3", # SPM
"jina-v2-en", # WPM
"bert-bge", # WPM
] ]
for tokenizer in tokenizers: for tokenizer in tokenizers: