From f6793491b5af6da75edad34d6f503ef86d31b09f Mon Sep 17 00:00:00 2001 From: "Nam D. Tran" <42194884+namtranase@users.noreply.github.com> Date: Wed, 27 Dec 2023 22:39:45 +0700 Subject: [PATCH 01/35] llama : add AWQ for llama, llama2, mpt, and mistral models (#4593) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * update: awq support llama-7b model * update: change order * update: benchmark results for llama2-7b * update: mistral 7b v1 benchmark * update: support 4 models * fix: Readme * update: ready for PR * update: readme * fix: readme * update: change order import * black * format code * update: work for bot mpt and awqmpt * update: readme * Rename to llm_build_ffn_mpt_awq * Formatted other files * Fixed params count * fix: remove code * update: more detail for mpt * fix: readme * fix: readme * update: change folder architecture * fix: common.cpp * fix: readme * fix: remove ggml_repeat * update: cicd * update: cicd * uppdate: remove use_awq arg * update: readme * llama : adapt plamo to new ffn ggml-ci --------- Co-authored-by: Trần Đức Nam Co-authored-by: Le Hoang Anh Co-authored-by: Georgi Gerganov --- awq-py/README.md | 116 +++++++++++++++ awq-py/awq/apply_awq.py | 254 +++++++++++++++++++++++++++++++++ awq-py/requirements.txt | 2 + convert-hf-to-gguf.py | 27 +++- convert.py | 14 ++ gguf-py/gguf/constants.py | 3 + gguf-py/gguf/tensor_mapping.py | 5 + llama.cpp | 27 +++- 8 files changed, 443 insertions(+), 5 deletions(-) create mode 100644 awq-py/README.md create mode 100644 awq-py/awq/apply_awq.py create mode 100644 awq-py/requirements.txt diff --git a/awq-py/README.md b/awq-py/README.md new file mode 100644 index 0000000000000..59354f4e329a2 --- /dev/null +++ b/awq-py/README.md @@ -0,0 +1,116 @@ +# AWQ: Activation-aware Weight Quantization for LLM - version apply to llamacpp +[[Paper](https://arxiv.org/abs/2306.00978)][[Original Repo](https://github.com/mit-han-lab/llm-awq)][[Easy-to-use Repo](https://github.com/casper-hansen/AutoAWQ)] + +**Supported models:** + +- [X] LLaMA +- [x] LLaMA 2 +- [X] MPT +- [X] Mistral AI v0.1 +- [ ] Bloom +- [ ] Mixtral MoE + +**TODO:** +- [x] Update version work with both MPT and MPT-AWQ model +- [ ] Add OPT model +- [ ] Add Bloom model +- [ ] Add Mixtral MoE +- [ ] Support w3, w2 + + +## Contents + +- [Install](##Install) +- [Convert](##Convert) +- [Quantize](##Quantize) +- [Test](##Test) +- [Benchmark](##Benchmark) +- [Results](##Results) + +## Install +Install requirements +```bash +pip install -r requirements.txt +``` +Get the pre-computed AWQ search results for multiple model families, including LLaMA, LLaMA2, MPT, OPT +```bash +git clone https://huggingface.co/datasets/mit-han-lab/awq-model-zoo awq_cache +``` + +## Convert +Example for llama model +```bash +# For llama7b and llama2 models +python convert.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/llama_7b_fp16.gguf +# For mistral and mpt models +python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf +``` + +## Quantize +```bash +# We only benchmark and confirm the results on q4_0, q4_1, and q2_k types. +./quantize models/llama_7b_fp16.gguf models/llama_7b_q4_0.gguf q4_0 +``` + +## Test +```bash +# For all models. +./build/bin/main -m models/llama_7b_q4_0.gguf -n 128 --prompt "Once upon a time" +``` + +## Benchmark +The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512. +```bash +# For llama and llama2, and mistral models. +./perplexity -m models/llama_7b_q4_0.gguf -f datasets/wikitext-2-raw/wiki.test.raw +``` + +## Results +Results are run on OpenBLAS (CPU) and CuBLAS (GPU) for fair comparison +We use three types of llamacpp quantization methods to work with our version, including q4_0, q4_1, and q2_k + +### Llama 7B (Build with OpenBLAS) + +| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K | +|-----------:|--------------|-------:|-------:|-------:|-------:| +|Llama 7B | perplexity | 5.9066 | 6.1214 | 6.0643 | 6.5808 | +|Llama 7B | file size | 12.9G | 3.5G | 3.9G | 2.7G | +|Llama 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | +|AWQ-LLama 7B| perplexity | 5.9175 | 6.0252 | 5.9987 | 6.3692 | +|AWQ-LLama 7B| file size | 12.9G | 3.5G | 3.9G | 2.7G | +|AWQ-LLama 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | + + +### Llama2 7B (Build with CuBLAS) + +| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K | +|------------:|--------------|-------:|-------:|-------:|-------:| +|Llama2 7B | perplexity | 5.8664 | 6.0260 | 6.0656 | 6.4496 | +|Llama2 7B | file size | 12.9G | 3.5G | 3.9G | 2.7G | +|Llama2 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | +|AWQ-LLama2 7B| perplexity | 5.8801 | 6.0054 | 5.9849 | 6.3650 | +|AWQ-LLama2 7B| file size | 12.9G | 3.5G | 3.9G | 2.7G | +|AWQ-LLama2 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | + + +### Mistral 7B v0.1 (Build with CuBLAS) + +| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K | +|-------------:|--------------|-------:|-------:|-------:|-------:| +|Mistral 7B | perplexity | 5.6931 | 5.8202 | 5.8268 | 6.1645 | +|Mistral 7B | file size | 14.5G | 4.1G | 4.5G | 3.1G | +|Mistral 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | +|AWQ-Mistral 7B| perplexity | 5.6934 | 5.8020 | 5.7691 | 6.0426 | +|AWQ-Mistral 7B| file size | 14.5G | 4.1G | 4.5G | 3.1G | +|AWQ-Mistral 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | + +### MPT 7B (Build with OpenBLAS) + +| Model | Measure | F16 | Q4_0 | Q4_1 | Q2_K | +|---------:|--------------|-------:|-------:|-------:|--------:| +|MPT 7B | perplexity | 8.4369 | 8.7956 | 8.6265 | 11.4913 | +|MPT 7B | file size | 13.7G | 3.9G | 4.3G | 2.8G | +|MPT 7B | bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | +|AWQ-MPT 7B| perplexity | 8.4944 | 8.7053 | 8.6750 | 10.2873| +|AWQ-MPT 7B| file size | 13.7G | 3.9G | 4.3G | 2.8G | +|AWQ-MPT 7B| bits/weight | 16.0 | 4.5 | 5.0 | 2.6 | diff --git a/awq-py/awq/apply_awq.py b/awq-py/awq/apply_awq.py new file mode 100644 index 0000000000000..11132c5d26e0c --- /dev/null +++ b/awq-py/awq/apply_awq.py @@ -0,0 +1,254 @@ +""" +Implements the AWQ for llama.cpp use cases. +Original paper: https://arxiv.org/abs/2306.00978 + +This code is based on versions of the AWQ implementation found in the following repositories: +* https://github.com/mit-han-lab/llm-awq +* https://github.com/casper-hansen/AutoAWQ +""" + +import os +import torch +import torch.nn as nn + +from transformers import AutoModelForCausalLM, AutoConfig +from transformers.models.bloom.modeling_bloom import BloomGelu +from transformers.models.llama.modeling_llama import LlamaRMSNorm +from transformers.activations import GELUActivation + + +class ScaledActivation(nn.Module): + """ + ScaledActivation module wraps an existing activation function and applies a + scale factor to its output. + + Args: + module (nn.Module): The activation function to be scaled. + scales (torch.Tensor): A tensor of size (num_features,) containing the initial + scale factors for each feature. + + Returns: + torch.Tensor: The scaled output of the activation function. + """ + + def __init__(self, module, scales): + super().__init__() + self.act = module + self.scales = nn.Parameter(scales.data) + + def forward(self, x): + return self.act(x) / self.scales.view(1, 1, -1).to(x.device) + + +def set_op_by_name(layer, name, new_module): + """ + Set the new module for given module's name. + + Args: + layer (nn.Module): The layer in which to replace the submodule. + name (str): The path to the submodule to be replaced, using dot notation + to access nested modules. + new_module (nn.Module): The new module to replace the existing one. + """ + levels = name.split(".") + if len(levels) > 1: + mod_ = layer + for l_idx in range(len(levels) - 1): + if levels[l_idx].isdigit(): + mod_ = mod_[int(levels[l_idx])] + else: + mod_ = getattr(mod_, levels[l_idx]) + setattr(mod_, levels[-1], new_module) + else: + setattr(layer, name, new_module) + + +def get_op_by_name(module, op_name): + """ + Retrieves a submodule within a given layer based on its name. + + Args: + module (nn.Module): The layer containing the submodule to find. + op_name (str): The name of the submodule. + + Returns: + nn.Module: The requested submodule found within the given layer. + + Raises: + ValueError: If the specified submodule cannot be found within the layer. + """ + for name, m in module.named_modules(): + if name == op_name: + return m + raise ValueError(f"Cannot find op {op_name} in module {module}") + + +@torch.no_grad() +def scale_ln_fcs(ln, fcs, scales): + """ + Scales the weights of a LayerNorm and a list of fully-connected layers proportionally. + + Args: + ln (nn.LayerNorm): The LayerNorm module to be scaled. + fcs (List[nn.Linear]): A list of fully-connected layers to be scaled. + scales (torch.Tensor): A 1D tensor of size (num_features,). + """ + + if not isinstance(fcs, list): + fcs = [fcs] + + scales = scales.to(ln.weight.device) + + ln.weight.div_(scales) + if hasattr(ln, "bias") and ln.bias is not None: + ln.bias.div_(scales) + + for fc in fcs: + fc.weight.mul_(scales.view(1, -1)) + + for p in ln.parameters(): + assert torch.isnan(p).sum() == 0 + for fc in fcs: + for p in fc.parameters(): + assert torch.isnan(p).sum() == 0 + + +@torch.no_grad() +def scale_fc_fc(fc1, fc2, scales): + """ + Scales the weights of two fully-connected layers in a specific pattern. + + Args: + fc1 (nn.Linear): The first fully-connected layer to be scaled. + fc2 (nn.Linear): The second fully-connected layer to be scaled. + scales (torch.Tensor): A 1D tensor of size (num_features,). + """ + assert isinstance(fc1, nn.Linear) + assert isinstance(fc2, nn.Linear) + + scales = scales.to(fc1.weight.device) + + fc1.weight[-scales.size(0):].div_(scales.view(-1, 1)) + if fc1.bias is not None: + fc1.bias.div_(scales.view(-1)) + + fc2.weight.mul_(scales.view(1, -1)) + + for p in fc1.parameters(): + assert torch.isnan(p).sum() == 0 + for p in fc2.parameters(): + assert torch.isnan(p).sum() == 0 + + +@torch.no_grad() +def scale_gelu_fc(gelu, fc, scales): + """ + Scales the weight of a GELU activation and a fully-connected layer proportionally. + + Args: + gelu (Union[nn.GELU, BloomGelu, GELUActivation]): The GELU activation module to be scaled. + fc (nn.Linear): The fully-connected layer to be scaled. + scales (torch.Tensor): A 1D tensor of size (num_features,). + + Raises: + TypeError: If the `gelu` module is not of type `nn.GELU`, `BloomGelu`, or `GELUActivation`. + TypeError: If the `fc` module is not of type `nn.Linear`. + """ + assert isinstance(gelu, (nn.GELU, BloomGelu, GELUActivation)) + assert isinstance(fc, nn.Linear) + + fc.weight.mul_(scales.view(1, -1).to(fc.weight.device)) + + for p in fc.parameters(): + assert torch.isnan(p).sum() == 0 + + +def apply_scale(module, scales_list, input_feat_dict=None): + """ + Applies different scaling strategies to layers based on their type and hierarchy within a given module. + + Args: + module (nn.Module): The module containing the layers to be scaled. + scales_list (List[Tuple[str, List[str], torch.Tensor]]): A list of tuples containing: + * prev_op_name (str): The name of the preceding operation or module, + relative to which the layers to be scaled are located. + * layer_names (List[str]): A list of names of the layers to be scaled, relative to the preceding operation. + * scales (torch.Tensor): A 1D tensor of size (num_features,) containing the scaling factors for each feature. + input_feat_dict (Optional[Dict[str, torch.Tensor]]): A dictionary mapping layer names to their corresponding + input features (optional). + """ + for prev_op_name, layer_names, scales in scales_list: + prev_op = get_op_by_name(module, prev_op_name) + layers = [get_op_by_name(module, name) for name in layer_names] + + prev_op.cuda() + for layer in layers: + layer.cuda() + scales.cuda() + + if isinstance(prev_op, nn.Linear): + assert len(layers) == 1 + scale_fc_fc(prev_op, layers[0], scales) + elif isinstance(prev_op, (nn.LayerNorm, LlamaRMSNorm)) or "rmsnorm" in str(prev_op.__class__).lower(): + scale_ln_fcs(prev_op, layers, scales) + elif isinstance(prev_op, (nn.GELU, BloomGelu, GELUActivation)): + new_module = ScaledActivation(prev_op, scales) + set_op_by_name(module, prev_op_name, new_module) + scale_gelu_fc(prev_op, layers[0], scales) + else: + raise NotImplementedError(f"prev_op {type(prev_op)} not supported yet!") + + # apply the scaling to input feat if given; prepare it for clipping + if input_feat_dict is not None: + for layer_name in layer_names: + inp = input_feat_dict[layer_name] + inp.div_(scales.view(1, -1).to(inp.device)) + + prev_op.cpu() + for layer in layers: + layer.cpu() + scales.cpu() + + +@torch.no_grad() +def apply_clip(module, clip_list): + """ + Applies element-wise clipping to the weight of a specific layer within a given module. + + Args: + module (nn.Module): The module containing the layer to be clipped. + clip_list (List[Tuple[str, torch.Tensor]]): A list of tuples containing: + * name (str): The name of the layer to be clipped, relative to the root of the module. + * max_val (torch.Tensor): A 1D or 2D tensor defining the upper bound for each element of the layer's weight. + """ + for name, max_val in clip_list: + layer = get_op_by_name(module, name) + layer.cuda() + max_val = max_val.to(layer.weight.device) + org_shape = layer.weight.shape + layer.weight.data = layer.weight.data.reshape(*max_val.shape[:2], -1) + layer.weight.data = torch.clamp(layer.weight.data, -max_val, max_val) + layer.weight.data = layer.weight.data.reshape(org_shape) + layer.cpu() + + +def add_scale_weights(model_path, scale_path, tmp_path): + """ + Adds pre-computed Activation Weight Quantization (AWQ) results to a model, + including scaling factors and clipping bounds. + + Args: + model_path (str): Path to the pre-trained model to be equipped with AWQ. + scale_path (str): Path to the AWQ scale factors (.pt file). + tmp_path (str): Path to the temporary directory where the equipped model will be saved. + """ + config = AutoConfig.from_pretrained(model_path, trust_remote_code=True) + model = AutoModelForCausalLM.from_pretrained( + model_path, config=config, trust_remote_code=True + ) + model.eval() + awq_results = torch.load(str(scale_path), map_location="cpu") + apply_scale(model, awq_results["scale"]) + apply_clip(model, awq_results["clip"]) + model.save_pretrained(str(tmp_path)) + os.system(f"cp {str(model_path)}/tokenizer* {str(tmp_path)}") diff --git a/awq-py/requirements.txt b/awq-py/requirements.txt new file mode 100644 index 0000000000000..5fe604329b354 --- /dev/null +++ b/awq-py/requirements.txt @@ -0,0 +1,2 @@ +torch>=2.0.0 +transformers>=4.32.0 diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 303d08170ecb0..7dbc2814796cc 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -46,7 +46,7 @@ def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: self.part_names = self._get_part_names() self.hparams = Model.load_hparams(self.dir_model) self.model_arch = self._get_model_architecture() - self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess) + self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False) def set_vocab(self): self._set_vocab_gpt2() @@ -59,7 +59,7 @@ def get_tensors(self) -> Iterator[tuple[str, Tensor]]: from safetensors import safe_open ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu")) else: - ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True)) + ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", weights_only=True)) with ctx as model_part: for name in model_part.keys(): @@ -464,7 +464,11 @@ def write_tensors(self): data = data_torch.squeeze().numpy() # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if "scales" in name: + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias", ".scales")) + new_name = new_name.replace("scales", "act.scales") + else: + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: print(f"Can not map tensor {name!r}") sys.exit() @@ -1095,6 +1099,9 @@ def parse_args() -> argparse.Namespace: "--vocab-only", action="store_true", help="extract only the vocab", ) + parser.add_argument( + "--awq-path", type=Path, default=None, + help="Path to scale awq cache file") parser.add_argument( "--outfile", type=Path, help="path to write to; default: based on input", @@ -1115,6 +1122,20 @@ def parse_args() -> argparse.Namespace: args = parse_args() dir_model = args.model + +if args.awq_path: + sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) + from awq.apply_awq import add_scale_weights + tmp_model_path = args.model / "weighted_model" + dir_model = tmp_model_path + if tmp_model_path.is_dir(): + print(f"{tmp_model_path} exists as a weighted model.") + else: + tmp_model_path.mkdir(parents=True, exist_ok=True) + print("Saving new weighted model ...") + add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) + print(f"Saved weighted model at {tmp_model_path}.") + if not dir_model.is_dir(): print(f'Error: {args.model} is not a directory', file=sys.stderr) sys.exit(1) diff --git a/convert.py b/convert.py index 1f0c4f2f41977..c3f3fc0a1fcd3 100755 --- a/convert.py +++ b/convert.py @@ -1187,6 +1187,7 @@ def main(args_in: list[str] | None = None) -> None: # We currently only support Q8_0 output on little endian systems. output_choices.append("q8_0") parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") + parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None) parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") @@ -1200,6 +1201,19 @@ def main(args_in: list[str] | None = None) -> None: parser.add_argument("--padvocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides") args = parser.parse_args(args_in) + if args.awq_path: + sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) + from awq.apply_awq import add_scale_weights + tmp_model_path = args.model / "weighted_model" + if tmp_model_path.is_dir(): + print(f"{tmp_model_path} exists as a weighted model.") + else: + tmp_model_path.mkdir(parents=True, exist_ok=True) + print("Saving new weighted model ...") + add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) + print(f"Saved weighted model at {tmp_model_path}.") + args.model = tmp_model_path + if args.dump_single: model_plus = lazy_load_file(args.model) do_dump_model(model_plus) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 4cd87cdda8b7e..c9be21119824c 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -120,6 +120,7 @@ class MODEL_TENSOR(IntEnum): FFN_GATE = auto() FFN_DOWN = auto() FFN_UP = auto() + FFN_ACT = auto() FFN_GATE_EXP = auto() FFN_DOWN_EXP = auto() FFN_UP_EXP = auto() @@ -169,6 +170,7 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", + MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn", MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate.{xid}", MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down.{xid}", MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up.{xid}", @@ -269,6 +271,7 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_ACT, ], MODEL_ARCH.GPTJ: [ MODEL_TENSOR.TOKEN_EMBD, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 446c6b6883be9..0b8f704174e59 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -188,6 +188,11 @@ class TensorNameMap: "model.layers.{bid}.block_sparse_moe.experts.{xid}.w3", # mixtral ), + # AWQ-activation gate + MODEL_TENSOR.FFN_ACT: ( + "transformer.blocks.{bid}.ffn.act", # mpt + ), + # Feed-forward gate MODEL_TENSOR.FFN_GATE: ( "model.layers.{bid}.mlp.gate_proj", # llama-hf refact diff --git a/llama.cpp b/llama.cpp index 4aa59c4c0bd88..bf1b01a90dcbe 100644 --- a/llama.cpp +++ b/llama.cpp @@ -354,6 +354,7 @@ enum llm_tensor { LLM_TENSOR_FFN_GATE, LLM_TENSOR_FFN_DOWN, LLM_TENSOR_FFN_UP, + LLM_TENSOR_FFN_ACT, LLM_TENSOR_FFN_DOWN_EXP, LLM_TENSOR_FFN_GATE_EXP, LLM_TENSOR_FFN_UP_EXP, @@ -473,6 +474,7 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_ACT, "blk.%d.ffn.act" }, }, }, { @@ -1285,6 +1287,7 @@ struct llama_hparams { float f_clamp_kqv; float f_max_alibi_bias; + bool operator!=(const llama_hparams & other) const { if (this->vocab_only != other.vocab_only) return true; if (this->n_vocab != other.n_vocab) return true; @@ -1388,6 +1391,7 @@ struct llama_layer { // ff bias struct ggml_tensor * ffn_down_b; // b2 struct ggml_tensor * ffn_up_b; // b3 + struct ggml_tensor * ffn_act; }; struct llama_kv_cell { @@ -3471,7 +3475,6 @@ static bool llm_load_tensors( case LLM_ARCH_MPT: { model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); - // output { ggml_backend_type backend_norm; @@ -3509,6 +3512,9 @@ static bool llm_load_tensors( layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + + // AWQ ScaleActivation layer + layer.ffn_act = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, backend, false); } } break; case LLM_ARCH_STABLELM: @@ -4039,6 +4045,7 @@ static struct ggml_tensor * llm_build_ffn( struct ggml_tensor * gate_b, struct ggml_tensor * down, struct ggml_tensor * down_b, + struct ggml_tensor * act_scales, llm_ffn_op_type type_op, llm_ffn_gate_type type_gate, const llm_build_cb & cb, @@ -4083,6 +4090,10 @@ static struct ggml_tensor * llm_build_ffn( { cur = ggml_gelu(ctx, cur); cb(cur, "ffn_gelu", il); + if (act_scales != NULL) { + cur = ggml_div(ctx, cur, act_scales); + cb(cur, "ffn_act", il); + } } break; case LLM_FFN_RELU: { @@ -4401,6 +4412,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); } else { @@ -4580,6 +4592,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); } @@ -4694,6 +4707,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, NULL, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); cb(cur, "ffn_out", il); } @@ -4798,6 +4812,7 @@ struct llm_build_context { model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); cb(cur, "ffn_out", il); } @@ -5002,6 +5017,7 @@ struct llm_build_context { model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il); cb(cur, "ffn_out", il); } @@ -5088,6 +5104,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); } @@ -5183,6 +5200,7 @@ struct llm_build_context { model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); cb(cur, "ffn_out", il); } @@ -5268,11 +5286,11 @@ struct llm_build_context { NULL, LLM_NORM, cb, il); cb(cur, "ffn_norm", il); - cur = llm_build_ffn(ctx0, cur, model.layers[il].ffn_up, NULL, NULL, NULL, model.layers[il].ffn_down, NULL, + model.layers[il].ffn_act, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); cb(cur, "ffn_out", il); } @@ -5381,6 +5399,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); } @@ -5493,6 +5512,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); } @@ -5600,6 +5620,7 @@ struct llm_build_context { model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); cb(ffn_output, "ffn_out", il); } @@ -5703,6 +5724,7 @@ struct llm_build_context { model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_down, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); } @@ -5887,6 +5909,7 @@ static const std::unordered_map k_offload_map { "ffn_gate", OFFLOAD_FUNC }, { "ffn_gate_b", OFFLOAD_FUNC }, { "ffn_gate_par", OFFLOAD_FUNC }, + { "ffn_act", OFFLOAD_FUNC }, { "ffn_down", OFFLOAD_FUNC }, { "ffn_down_b", OFFLOAD_FUNC }, { "ffn_out", OFFLOAD_FUNC }, From ea5497df5d138c83b2b0ca70aefdc4b1175c1001 Mon Sep 17 00:00:00 2001 From: manikbhandari Date: Thu, 28 Dec 2023 09:03:57 -0500 Subject: [PATCH 02/35] gpt2 : Add gpt2 architecture integration (#4555) --- README.md | 1 + convert-hf-to-gguf.py | 66 +++++++++++ gguf-py/gguf/constants.py | 11 +- gguf-py/gguf/tensor_mapping.py | 10 +- llama.cpp | 206 +++++++++++++++++++++++++++++++-- models/ggml-vocab-gpt2.gguf | Bin 0 -> 1766799 bytes tests/CMakeLists.txt | 1 + 7 files changed, 281 insertions(+), 14 deletions(-) create mode 100644 models/ggml-vocab-gpt2.gguf diff --git a/README.md b/README.md index 3b202a336f933..48dcd6464038e 100644 --- a/README.md +++ b/README.md @@ -103,6 +103,7 @@ as the main playground for developing new features for the [ggml](https://github - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) +- [x] [GPT-2](https://huggingface.co/gpt2) **Multimodal models:** diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 7dbc2814796cc..3557a825eb357 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -182,6 +182,8 @@ def from_model_architecture(model_architecture): return QwenModel if model_architecture == "MixtralForCausalLM": return MixtralModel + if model_architecture == "GPT2LMHeadModel": + return GPT2Model if model_architecture == "PhiForCausalLM": return Phi2Model if model_architecture == "PlamoForCausalLM": @@ -225,6 +227,8 @@ def _get_model_architecture(self) -> gguf.MODEL_ARCH: return gguf.MODEL_ARCH.QWEN if arch == "MixtralForCausalLM": return gguf.MODEL_ARCH.LLAMA + if arch == "GPT2LMHeadModel": + return gguf.MODEL_ARCH.GPT2 if arch == "PhiForCausalLM": return gguf.MODEL_ARCH.PHI2 if arch == "PlamoForCausalLM": @@ -993,6 +997,68 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +class GPT2Model(Model): + def set_gguf_parameters(self): + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_context_length(self.hparams["n_ctx"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias")): + continue + + if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")): + data_torch = data_torch.transpose(1, 0) + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + # note: GPT2 output is tied to (same as) wte in original model + if new_name == "token_embd.weight": + print(f"output.weight, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + self.gguf_writer.add_tensor("output.weight", data) + + class Phi2Model(Model): def set_gguf_parameters(self): block_count = self.hparams["n_layer"] diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index c9be21119824c..ae62cc575499b 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -370,7 +370,16 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.GPT2: [ - # TODO + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, ], MODEL_ARCH.PHI2: [ MODEL_TENSOR.TOKEN_EMBD, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 0b8f704174e59..80c1d5449cc74 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -17,6 +17,7 @@ class TensorNameMap: "tok_embeddings", # llama-pth "embeddings.word_embeddings", # bert "language_model.embedding.word_embeddings", # persimmon + "wte", # gpt2 "transformer.embd.wte", # phi2 ), @@ -34,6 +35,7 @@ class TensorNameMap: MODEL_TENSOR.POS_EMBD: ( "transformer.wpe", # gpt2 "embeddings.position_embeddings", # bert + "wpe", # gpt2 ), # Output @@ -53,7 +55,7 @@ class TensorNameMap: "norm", # llama-pth "embeddings.LayerNorm", # bert "transformer.norm_f", # mpt - "ln_f", # refact bloom qwen + "ln_f", # refact bloom qwen gpt2 "language_model.encoder.final_layernorm", # persimmon "lm_head.ln", # phi2 ), @@ -78,6 +80,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.output.LayerNorm", # bert "language_model.encoder.layers.{bid}.input_layernorm", # persimmon "model.layers.{bid}.ln1", # yi + "h.{bid}.ln_1", # gpt2 "transformer.h.{bid}.ln", # phi2 "model.layers.layers.{bid}.norm", # plamo ), @@ -95,6 +98,7 @@ class TensorNameMap: "transformer.h.{bid}.self_attention.query_key_value", # falcon "h.{bid}.self_attention.query_key_value", # bloom "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon + "h.{bid}.attn.c_attn", # gpt2 "transformer.h.{bid}.mixer.Wqkv", # phi2 ), @@ -137,6 +141,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.output.dense", # bert "transformer.h.{bid}.attn.out_proj", # gpt-j "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon + "h.{bid}.attn.c_proj", # gpt2 "transformer.h.{bid}.mixer.out_proj", # phi2 "model.layers.layers.{bid}.self_attn.o_proj", # plamo ), @@ -159,6 +164,7 @@ class TensorNameMap: "encoder.layer.{bid}.output.LayerNorm", # bert "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon "model.layers.{bid}.ln2", # yi + "h.{bid}.ln_2", # gpt2 ), MODEL_TENSOR.FFN_GATE_INP: ( @@ -179,6 +185,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.fc_in", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon "transformer.h.{bid}.mlp.w1", # qwen + "h.{bid}.mlp.c_fc", # gpt2 "transformer.h.{bid}.mlp.fc1", # phi2 "model.layers.layers.{bid}.mlp.up_proj", # plamo ), @@ -218,6 +225,7 @@ class TensorNameMap: "encoder.layer.{bid}.output.dense", # bert "transformer.h.{bid}.mlp.fc_out", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon + "h.{bid}.mlp.c_proj", # gpt2 "transformer.h.{bid}.mlp.fc2", # phi2 "model.layers.layers.{bid}.mlp.down_proj", # plamo ), diff --git a/llama.cpp b/llama.cpp index bf1b01a90dcbe..68c7cced6bb5a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -423,6 +423,15 @@ static std::map> LLM_TENSOR_NAMES = LLM_ARCH_GPT2, { { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_POS_EMBD, "position_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, }, }, { @@ -1256,6 +1265,10 @@ enum e_model { MODEL_40B, MODEL_65B, MODEL_70B, + MODEL_SMALL, + MODEL_MEDIUM, + MODEL_LARGE, + MODEL_XL, }; static const size_t kiB = 1024; @@ -2552,18 +2565,22 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { static const char * llama_model_type_name(e_model type) { switch (type) { - case MODEL_1B: return "1B"; - case MODEL_3B: return "3B"; - case MODEL_7B: return "7B"; - case MODEL_8B: return "8B"; - case MODEL_13B: return "13B"; - case MODEL_15B: return "15B"; - case MODEL_30B: return "30B"; - case MODEL_34B: return "34B"; - case MODEL_40B: return "40B"; - case MODEL_65B: return "65B"; - case MODEL_70B: return "70B"; - default: return "?B"; + case MODEL_1B: return "1B"; + case MODEL_3B: return "3B"; + case MODEL_7B: return "7B"; + case MODEL_8B: return "8B"; + case MODEL_13B: return "13B"; + case MODEL_15B: return "15B"; + case MODEL_30B: return "30B"; + case MODEL_34B: return "34B"; + case MODEL_40B: return "40B"; + case MODEL_65B: return "65B"; + case MODEL_70B: return "70B"; + case MODEL_SMALL: return "0.1B"; + case MODEL_MEDIUM: return "0.4B"; + case MODEL_LARGE: return "0.8B"; + case MODEL_XL: return "1.5B"; + default: return "?B"; } } @@ -2782,6 +2799,17 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_GPT2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + switch (hparams.n_layer) { + case 12: model.type = e_model::MODEL_SMALL; break; + case 24: model.type = e_model::MODEL_MEDIUM; break; + case 36: model.type = e_model::MODEL_LARGE; break; + case 48: model.type = e_model::MODEL_XL; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -3710,6 +3738,60 @@ static bool llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); } } break; + case LLM_ARCH_GPT2: + { + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU); + + // output + { + ggml_backend_type backend_norm; + ggml_backend_type backend_output; + + if (n_gpu_layers > int(n_layer)) { + backend_norm = llama_backend_offload; + backend_output = llama_backend_offload_split; + } else { + backend_norm = GGML_BACKEND_CPU; + backend_output = GGML_BACKEND_CPU; + } + + model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); + model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); + model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + } + + const uint32_t n_ff = hparams.n_ff; + + const int i_gpu_start = n_layer - n_gpu_layers; + + model.layers.resize(n_layer); + + for (uint32_t i = 0; i < n_layer; ++i) { + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + + layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split); + layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend); + + layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); + + layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split); + layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend); + + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -5754,6 +5836,102 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_gpt2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + struct ggml_tensor * cur; + struct ggml_tensor * pos; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); + cb(pos, "pos_embd", -1); + + inpL = ggml_add(ctx0, inpL, pos); + cb(inpL, "inpL", -1); + + for (int il = 0; il < n_layer; ++il) { + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(ctx0, model, hparams, kv_self, + model.layers[il].wo, model.layers[il].bo, + Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + // add the input + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); + + // FF + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + } + + inpL = ggml_add(ctx0, cur, ffn_inp); + cb(inpL, "l_out", il); + } + + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; // @@ -6269,6 +6447,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_plamo(); } break; + case LLM_ARCH_GPT2: + { + result = llm.build_gpt2(); + } break; default: GGML_ASSERT(false); } diff --git a/models/ggml-vocab-gpt2.gguf b/models/ggml-vocab-gpt2.gguf new file mode 100644 index 0000000000000000000000000000000000000000..1fbc72c1e4d9e210e5c5689b31e1debfa33d4b6a GIT binary patch literal 1766799 zcmZs^`I97Ba^FYYwgw*99`uE-njL~9w2aEis*35X zjNClBx`rf70(4UMeM>2Tph$@#MM&;i)TMzEv>ZYeBw{QJ3 zISylfbTS(zZ+<)(HuH6SE57m1`TfZ-Ur*nye>|Jc&({}iI{fEf@JFVXC)3l@etzx` 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being set to zero, which caused the token candidates array to get shrunk down to one element thus preventing any sampling. Note this only applies to OpenAI API compatible HTTP server requests. The solution is to use the default values that OpenAI documents, as well as ensuring we use the llama.cpp defaults for the rest. I've tested this change still ensures deterministic output by default. If a "temperature" greater than 0 is explicitly passed, then output is unique each time. If "seed" is specified in addition to "temperature" then the output becomes deterministic once more. See mozilla-Ocho/llamafile#117 See mozilla-Ocho/llamafile@9e4bf29 --- examples/server/server.cpp | 31 +++++++++++++++++++------------ 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 72dfe452c2d7a..c5035e202ad57 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -441,7 +441,6 @@ struct llama_client_slot } images.clear(); - // llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? } bool has_budget(gpt_params &global_params) { @@ -921,6 +920,7 @@ struct llama_server_context llama_sampling_free(slot->ctx_sampling); } slot->ctx_sampling = llama_sampling_init(slot->sparams); + llama_set_rng_seed(ctx, slot->params.seed); slot->command = LOAD_PROMPT; all_slots_are_idle = false; @@ -1215,7 +1215,7 @@ struct llama_server_context {"n_ctx", slot.n_ctx}, {"model", params.model_alias}, {"seed", slot.params.seed}, - {"temp", slot.sparams.temp}, + {"temperature", slot.sparams.temp}, {"top_k", slot.sparams.top_k}, {"top_p", slot.sparams.top_p}, {"min_p", slot.sparams.min_p}, @@ -2437,26 +2437,33 @@ json oaicompat_completion_params_parse( llama_params["__oaicompat"] = true; // Map OpenAI parameters to llama.cpp parameters + // + // For parameters that are defined by the OpenAI documentation (e.g. + // temperature), we explicitly specify OpenAI's intended default; we + // need to do that because sometimes OpenAI disagrees with llama.cpp + // + // https://platform.openai.com/docs/api-reference/chat/create + llama_sampling_params default_sparams; llama_params["model"] = json_value(body, "model", std::string("uknown")); llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt' llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); - llama_params["temperature"] = json_value(body, "temperature", 0.8); - llama_params["top_k"] = json_value(body, "top_k", 40); - llama_params["top_p"] = json_value(body, "top_p", 0.95); + llama_params["temperature"] = json_value(body, "temperature", 0.0); + llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k); + llama_params["top_p"] = json_value(body, "top_p", 1.0); llama_params["n_predict"] = json_value(body, "max_tokens", -1); llama_params["logit_bias"] = json_value(body, "logit_bias",json::object()); llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0); llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0); - llama_params["seed"] = json_value(body, "seed", 0); + llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED); llama_params["stream"] = json_value(body, "stream", false); - llama_params["mirostat"] = json_value(body, "mirostat", false); - llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", 0.0); - llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", 0.0); - llama_params["penalize_nl"] = json_value(body, "penalize_nl", false); - llama_params["typical_p"] = json_value(body, "typical_p", 0.0); + llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat); + llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); + llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); + llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl); + llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p); llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", 0); llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); - llama_params["tfs_z"] = json_value(body, "tfs_z", 0.0); + llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); if (body.count("grammar") != 0) { llama_params["grammar"] = json_value(body, "grammar", json::object()); From ca38b8d334baa724bd6c9402470931d26427466f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 29 Dec 2023 14:41:36 +0200 Subject: [PATCH 04/35] scripts : do not sync commits from this repo --- scripts/sync-ggml-am.sh | 46 +++++++++++++++++++++++++++-------------- 1 file changed, 30 insertions(+), 16 deletions(-) diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index 83abe3681d500..93aad88a730bc 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -26,22 +26,36 @@ echo "Syncing ggml changes since commit $lc" cd $SRC_GGML -git log --oneline $lc..HEAD - -git format-patch $lc --stdout -- \ - include/ggml/ggml*.h \ - src/ggml*.h \ - src/ggml*.c \ - src/ggml*.cpp \ - src/ggml*.m \ - src/ggml*.metal \ - src/ggml*.cu \ - tests/test-opt.cpp \ - tests/test-grad0.cpp \ - tests/test-quantize-fns.cpp \ - tests/test-quantize-perf.cpp \ - tests/test-backend-ops.cpp \ - > $SRC_LLAMA/ggml-src.patch +git log --oneline $lc..HEAD | grep -v "(llama/[0-9]*)" | cut -d' ' -f1 > $SRC_LLAMA/ggml-commits + +if [ ! -s $SRC_LLAMA/ggml-commits ]; then + rm -v $SRC_LLAMA/ggml-commits + echo "No new commits" + exit 0 +fi + +if [ -f $SRC_LLAMA/ggml-src.patch ]; then + rm -v $SRC_LLAMA/ggml-src.patch +fi + +while read c; do + git format-patch -k $c~1..$c --stdout -- \ + include/ggml/ggml*.h \ + src/ggml*.h \ + src/ggml*.c \ + src/ggml*.cpp \ + src/ggml*.m \ + src/ggml*.metal \ + src/ggml*.cu \ + tests/test-opt.cpp \ + tests/test-grad0.cpp \ + tests/test-quantize-fns.cpp \ + tests/test-quantize-perf.cpp \ + tests/test-backend-ops.cpp \ + >> $SRC_LLAMA/ggml-src.patch +done < $SRC_LLAMA/ggml-commits + +rm -v $SRC_LLAMA/ggml-commits # delete files if empty if [ ! -s $SRC_LLAMA/ggml-src.patch ]; then From afc8c192919f04613a92d40391bff4c8cd99856b Mon Sep 17 00:00:00 2001 From: bssrdf Date: Fri, 29 Dec 2023 03:32:31 -0500 Subject: [PATCH 05/35] ggml : fix some mul mat cases + add tests for src1 F16 (ggml/669) * fixed mul-mat error for old GPUs * style fixes * add mul mat src1 f16 test cases, fix more cases ggml-ci --------- Co-authored-by: bssrdf Co-authored-by: slaren --- ggml-backend.c | 8 +++- ggml-cuda.cu | 89 +++++++++++++++++++------------------- ggml.c | 2 +- tests/test-backend-ops.cpp | 14 +++--- 4 files changed, 60 insertions(+), 53 deletions(-) diff --git a/ggml-backend.c b/ggml-backend.c index 526ce732be5b5..2c3752067515f 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -614,10 +614,14 @@ static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_c } static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { - return true; + switch (op->op) { + case GGML_OP_MUL_MAT: + return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type; + default: + return true; + } GGML_UNUSED(backend); - GGML_UNUSED(op); } static struct ggml_backend_i cpu_backend_i = { diff --git a/ggml-cuda.cu b/ggml-cuda.cu index abad9cc39e2cf..9a9effcf58932 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -7485,6 +7485,8 @@ static void ggml_cuda_op_dequantize_mul_mat_vec( const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; + GGML_ASSERT(src1->type == GGML_TYPE_F32); + // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics #ifdef GGML_CUDA_F16 cuda_pool_alloc src1_dfloat_a; @@ -7577,6 +7579,7 @@ static void ggml_cuda_op_mul_mat_cublas( const int compute_capability = g_device_caps[id].cc; if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { + //printf("this branch\n"); // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 cuda_pool_alloc src0_as_f16; if (src0->type != GGML_TYPE_F16) { @@ -7614,9 +7617,9 @@ static void ggml_cuda_op_mul_mat_cublas( const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream); - } - else { + } else { cuda_pool_alloc src0_ddq_as_f32; + cuda_pool_alloc src1_ddq_as_f32; if (src0->type != GGML_TYPE_F32) { const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type); @@ -7624,7 +7627,15 @@ static void ggml_cuda_op_mul_mat_cublas( src0_ddq_as_f32.alloc(row_diff*ne00); to_fp32_cuda(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream); } + if (src1->type != GGML_TYPE_F32) { + const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src1->type); + GGML_ASSERT(to_fp32_cuda != nullptr); + src1_ddq_as_f32.alloc(src1_ncols*ne10); + to_fp32_cuda(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream); + } + const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get(); + const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get(); const float alpha = 1.0f; const float beta = 0.0f; @@ -7633,9 +7644,9 @@ static void ggml_cuda_op_mul_mat_cublas( CUBLAS_CHECK( cublasSgemm(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N, row_diff, src1_ncols, ne10, - &alpha, src0_ddf_i, ne00, - src1_ddf_i, ne10, - &beta, dst_dd_i, ldc)); + &alpha, src0_ddf_i, ne00, + src1_ddf1_i, ne10, + &beta, dst_dd_i, ldc)); } (void) dst; @@ -8035,6 +8046,7 @@ static void ggml_cuda_op_mul_mat( GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT); GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); @@ -8481,9 +8493,9 @@ static __global__ void k_compute_batched_ptrs( int64_t i03 = i13 / r3; int64_t i02 = i12 / r2; - ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; - ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; - ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; + ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; + ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; } static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -8492,28 +8504,10 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - const int64_t ne00 = src0->ne[0]; GGML_UNUSED(ne00); - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t nb01 = src0->nb[1]; - const int64_t nb02 = src0->nb[2]; GGML_UNUSED(nb02); - const int64_t nb03 = src0->nb[3]; GGML_UNUSED(nb03); - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int64_t nb11 = src1->nb[1]; - const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12); - const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13); + GGML_TENSOR_BINARY_OP_LOCALS - const int64_t ne1 = ggml_nelements(src1); - const int64_t ne = ggml_nelements(dst); + const int64_t ne_dst = ggml_nelements(dst); ggml_cuda_set_device(g_main_device); cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; @@ -8522,7 +8516,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; void * src0_ddq = src0_extra->data_device[g_main_device]; - half * src0_as_f16 = (half *) src0_ddq; + half * src0_f16 = (half *) src0_ddq; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; @@ -8531,11 +8525,15 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; // convert src1 to fp16 - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); - GGML_ASSERT(to_fp16_cuda != nullptr); - - cuda_pool_alloc src1_as_f16(ne1); - to_fp16_cuda(src1_ddf, src1_as_f16.get(), ne1, main_stream); + cuda_pool_alloc src1_f16_alloc; + if (src1->type != GGML_TYPE_F16) { + const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); + const int64_t ne_src1 = ggml_nelements(src1); + src1_f16_alloc.alloc(ne_src1); + GGML_ASSERT(to_fp16_cuda != nullptr); + to_fp16_cuda(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); + } + half * src1_f16 = src1->type == GGML_TYPE_F16 ? (half *) src1_ddf : src1_f16_alloc.get(); cuda_pool_alloc dst_f16; char * dst_t; @@ -8557,7 +8555,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const const void * beta = &beta_f16; if (dst->op_params[0] == GGML_PREC_DEFAULT) { - dst_t = (char *) dst_f16.alloc(ne); + dst_t = (char *) dst_f16.alloc(ne_dst); nbd2 /= sizeof(float) / sizeof(half); nbd3 /= sizeof(float) / sizeof(half); @@ -8604,9 +8602,9 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_CHECK( cublasGemmStridedBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - alpha, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half), src0->nb[2]/sizeof(half), // strideA - (const char *) src1_as_f16.get(), CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB - beta, ( char *) dst_t, cu_data_type, ne01, dst->nb[2]/sizeof(float), // strideC + alpha, (const char *) src0_f16, CUDA_R_16F, nb01/nb00, nb02/nb00, // strideA + (const char *) src1_f16, CUDA_R_16F, nb11/nb10, nb12/nb10, // strideB + beta, ( char *) dst_t, cu_data_type, ne01, nb2/nb0, // strideC ne12*ne13, cu_compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); @@ -8619,12 +8617,13 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const dim3 block_dims(ne13, ne12); k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( - src0_as_f16, src1_as_f16.get(), dst_t, + src0_f16, src1_f16, dst_t, ptrs_src.get(), ptrs_dst.get(), ne12, ne13, ne23, nb02, nb03, - nb12, nb13, + src1->type == GGML_TYPE_F16 ? nb12 : nb12/2, + src1->type == GGML_TYPE_F16 ? nb13 : nb13/2, nbd2, nbd3, r2, r3); CUDA_CHECK(cudaGetLastError()); @@ -8632,8 +8631,8 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const CUBLAS_CHECK( cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, - alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/sizeof(half), - (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/sizeof(float), + alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/nb00, + (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/nb10, beta, ( void **) (ptrs_dst.get() + 0*ne23), cu_data_type, ne01, ne23, cu_compute_type, @@ -8643,7 +8642,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const if (dst->op_params[0] == GGML_PREC_DEFAULT) { const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); - to_fp32_cuda(dst_f16.get(), dst_ddf, ne, main_stream); + to_fp32_cuda(dst_f16.get(), dst_ddf, ne_dst, main_stream); } } @@ -8682,13 +8681,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } else if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_cuda_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { // KQ + KQV multi-batch ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) { - if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) { + if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src1->type == GGML_TYPE_F32) { #ifdef GGML_CUDA_FORCE_DMMV const bool use_mul_mat_vec_q = false; #else diff --git a/ggml.c b/ggml.c index ed56e60a8893e..a9e1ea9b40ec4 100644 --- a/ggml.c +++ b/ggml.c @@ -9687,7 +9687,7 @@ static void ggml_compute_forward_mul_mat( const size_t row_size = ggml_row_size(vec_dot_type, ne10); assert(params->wsize >= ne11*ne12*ne13*row_size); - assert(src1->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); for (int64_t i13 = 0; i13 < ne13; ++i13) { for (int64_t i12 = 0; i12 < ne12; ++i12) { diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index f3df8a8c62a9a..b115299c0ce30 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -350,13 +350,18 @@ struct test_case { fflush(stdout); // check if backends support op + bool supported = true; for (ggml_backend_t backend : {backend1, backend2}) { if (!ggml_backend_supports_op(backend, out)) { - printf("not supported\n"); - ggml_free(ctx); - return true; + printf("not supported [%s] ", ggml_backend_name(backend)); + supported = false; } } + if (!supported) { + printf("\n"); + ggml_free(ctx); + return true; + } // post-graph sentinel add_sentinel(ctx); @@ -1505,8 +1510,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } for (ggml_type type_a : all_types) { - for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) { - // FIXME: CPU crashes on f16xf16 + for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) { test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {1, 1})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {2, 1})); From 38b3de4658292582a8941a2be5c77b40ce6ac0f2 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 29 Dec 2023 14:56:41 +0200 Subject: [PATCH 06/35] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 1ec1441167c42..6ff2d5233a041 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -76e7f47b69e8334384dc718480c496dafbd47999 +168c43edd1f85ebdecd4c79262cacb32b74eda68 From 441f51dca004debf8b275f1bdc08e0f1af7fd8f8 Mon Sep 17 00:00:00 2001 From: Tamotsu Takahashi Date: Fri, 29 Dec 2023 19:23:27 +0900 Subject: [PATCH 07/35] ci : build with CLBlast + ggml-opencl use GGML_API (whisper/1576) * Build with CLBlast * Declare GGML_API After rebasing, examples/talk-llama failed: "D:\a\whisper.cpp\whisper.cpp\build\ALL_BUILD.vcxproj" (build target) (1) -> "D:\a\whisper.cpp\whisper.cpp\build\examples\talk-llama\talk-llama.vcxproj" (default target) (14) -> (Link target) -> llama.obj : error LNK2019: unresolved external symbol ggml_cl_free_data referenced in function "public: __cdecl llama_model::~llama_model(void)" (??1llama_model@@QEAA@XZ) [D:\a\whisper.cpp\whisper.cpp\build\examples\talk-llama\talk-llama.vcxproj] llama.obj : error LNK2019: unresolved external symbol ggml_cl_transform_tensor referenced in function "public: void __cdecl llama_model_loader::load_all_data(struct ggml_context *,void (__cdecl*)(float,void *),void *,struct llama_mlock *)" (?load_all_data@llama_model_loader@@QEAAXPEAUggml_context@@P6AXMPEAX@Z1PEAUllama_mlock@@@Z) [D:\a\whisper.cpp\whisper.cpp\build\examples\talk-llama\talk-llama.vcxproj] D:\a\whisper.cpp\whisper.cpp\build\bin\Release\talk-llama.exe : fatal error LNK1120: 2 unresolved externals [D:\a\whisper.cpp\whisper.cpp\build\examples\talk-llama\talk-llama.vcxproj] --- ggml-opencl.h | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/ggml-opencl.h b/ggml-opencl.h index a92b445c9d766..44d05bd64a3ad 100644 --- a/ggml-opencl.h +++ b/ggml-opencl.h @@ -6,19 +6,19 @@ extern "C" { #endif -void ggml_cl_init(void); +GGML_API void ggml_cl_init(void); -void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize); +GGML_API void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); +GGML_API bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); +GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); +GGML_API void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize); -void * ggml_cl_host_malloc(size_t size); -void ggml_cl_host_free(void * ptr); +GGML_API void * ggml_cl_host_malloc(size_t size); +GGML_API void ggml_cl_host_free(void * ptr); -void ggml_cl_free_data(const struct ggml_tensor* tensor); +GGML_API void ggml_cl_free_data(const struct ggml_tensor* tensor); -void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor); +GGML_API void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor); #ifdef __cplusplus } From c8255f8a6b2a3b3ebc6cb340cc2487f39fc95ffc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 29 Dec 2023 15:12:35 +0200 Subject: [PATCH 08/35] scripts : print list of sync commits --- scripts/sync-ggml-am.sh | 1 + scripts/sync-ggml.last | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index 93aad88a730bc..91478f177f319 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -26,6 +26,7 @@ echo "Syncing ggml changes since commit $lc" cd $SRC_GGML +git log --oneline $lc..HEAD git log --oneline $lc..HEAD | grep -v "(llama/[0-9]*)" | cut -d' ' -f1 > $SRC_LLAMA/ggml-commits if [ ! -s $SRC_LLAMA/ggml-commits ]; then diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 6ff2d5233a041..5b6a440f751dc 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -168c43edd1f85ebdecd4c79262cacb32b74eda68 +df098ea908764cba4a4889a1cbe7b026b2d31a14 From afd997ab6011dfefe9e917425b04ef4d83614841 Mon Sep 17 00:00:00 2001 From: Peter Sugihara Date: Fri, 29 Dec 2023 05:58:56 -0800 Subject: [PATCH 09/35] llama.swiftui : fix infinite loop, ouput timings, buff UI (#4674) * fix infinite loop * slight UI simplification, clearer UX * clearer UI text, add timings to completion log --- .../llama.cpp.swift/LibLlama.swift | 2 ++ .../llama.swiftui/Models/LlamaState.swift | 27 ++++++++++---- .../llama.swiftui/UI/ContentView.swift | 35 +++---------------- .../llama.swiftui/UI/DownloadButton.swift | 2 +- 4 files changed, 29 insertions(+), 37 deletions(-) diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index 464fb3277aa25..66244382f5cbc 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -1,5 +1,7 @@ import Foundation +// To use this in your own project, add llama.cpp as a swift package dependency +// and uncomment this import line. // import llama enum LlamaError: Error { diff --git a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift index 3393eb242f938..17cb5b9dde942 100644 --- a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift +++ b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift @@ -4,6 +4,7 @@ import Foundation class LlamaState: ObservableObject { @Published var messageLog = "" @Published var cacheCleared = false + let NS_PER_S = 1_000_000_000.0 private var llamaContext: LlamaContext? private var defaultModelUrl: URL? { @@ -20,12 +21,12 @@ class LlamaState: ObservableObject { } func loadModel(modelUrl: URL?) throws { - messageLog += "Loading model...\n" if let modelUrl { + messageLog += "Loading model...\n" llamaContext = try LlamaContext.create_context(path: modelUrl.path()) messageLog += "Loaded model \(modelUrl.lastPathComponent)\n" } else { - messageLog += "Could not locate model\n" + messageLog += "Load a model from the list below\n" } } @@ -34,15 +35,29 @@ class LlamaState: ObservableObject { return } + let t_start = DispatchTime.now().uptimeNanoseconds await llamaContext.completion_init(text: text) + let t_heat_end = DispatchTime.now().uptimeNanoseconds + let t_heat = Double(t_heat_end - t_start) / NS_PER_S + messageLog += "\(text)" - while await llamaContext.n_cur <= llamaContext.n_len { + while await llamaContext.n_cur < llamaContext.n_len { let result = await llamaContext.completion_loop() messageLog += "\(result)" } + + let t_end = DispatchTime.now().uptimeNanoseconds + let t_generation = Double(t_end - t_heat_end) / NS_PER_S + let tokens_per_second = Double(await llamaContext.n_len) / t_generation + await llamaContext.clear() - messageLog += "\n\ndone\n" + messageLog += """ + \n + Done + Heat up took \(t_heat)s + Generated \(tokens_per_second) t/s\n + """ } func bench() async { @@ -56,10 +71,10 @@ class LlamaState: ObservableObject { messageLog += await llamaContext.model_info() + "\n" let t_start = DispatchTime.now().uptimeNanoseconds - await llamaContext.bench(pp: 8, tg: 4, pl: 1) // heat up + let _ = await llamaContext.bench(pp: 8, tg: 4, pl: 1) // heat up let t_end = DispatchTime.now().uptimeNanoseconds - let t_heat = Double(t_end - t_start) / 1_000_000_000.0 + let t_heat = Double(t_end - t_start) / NS_PER_S messageLog += "Heat up time: \(t_heat) seconds, please wait...\n" // if more than 5 seconds, then we're probably running on a slow device diff --git a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift index c78f107b39e0e..147e0c63bd8dd 100644 --- a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift +++ b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift @@ -42,46 +42,27 @@ struct ContentView: View { Button("Send") { sendText() } - .padding(8) - .background(Color.blue) - .foregroundColor(.white) - .cornerRadius(8) Button("Bench") { bench() } - .padding(8) - .background(Color.blue) - .foregroundColor(.white) - .cornerRadius(8) Button("Clear") { clear() } - .padding(8) - .background(Color.blue) - .foregroundColor(.white) - .cornerRadius(8) Button("Copy") { UIPasteboard.general.string = llamaState.messageLog } - .padding(8) - .background(Color.blue) - .foregroundColor(.white) - .cornerRadius(8) - } + }.buttonStyle(.bordered) - VStack { + VStack(alignment: .leading) { DownloadButton( llamaState: llamaState, modelName: "TinyLlama-1.1B (Q4_0, 0.6 GiB)", modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true", filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf" ) - .font(.system(size: 12)) - .padding(.top, 4) - .frame(maxWidth: .infinity, alignment: .leading) DownloadButton( llamaState: llamaState, @@ -89,7 +70,6 @@ struct ContentView: View { modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q8_0.gguf?download=true", filename: "tinyllama-1.1b-1t-openorca.Q8_0.gguf" ) - .font(.system(size: 12)) DownloadButton( llamaState: llamaState, @@ -97,8 +77,6 @@ struct ContentView: View { modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true", filename: "tinyllama-1.1b-f16.gguf" ) - .font(.system(size: 12)) - .frame(maxWidth: .infinity, alignment: .leading) DownloadButton( llamaState: llamaState, @@ -106,7 +84,6 @@ struct ContentView: View { modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true", filename: "phi-2-q4_0.gguf" ) - .font(.system(size: 12)) DownloadButton( llamaState: llamaState, @@ -114,8 +91,6 @@ struct ContentView: View { modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q8_0.gguf?download=true", filename: "phi-2-q8_0.gguf" ) - .font(.system(size: 12)) - .frame(maxWidth: .infinity, alignment: .leading) DownloadButton( llamaState: llamaState, @@ -123,15 +98,15 @@ struct ContentView: View { modelUrl: "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/resolve/main/mistral-7b-v0.1.Q4_0.gguf?download=true", filename: "mistral-7b-v0.1.Q4_0.gguf" ) - .font(.system(size: 12)) Button("Clear downloaded models") { ContentView.cleanupModelCaches() llamaState.cacheCleared = true } - .padding(8) - .font(.system(size: 12)) } + .padding(.top, 4) + .font(.system(size: 12)) + .frame(maxWidth: .infinity, alignment: .leading) } .padding() } diff --git a/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift b/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift index 4bd75cb69283c..c9f322ca14e72 100644 --- a/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift +++ b/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift @@ -93,7 +93,7 @@ struct DownloadButton: View { print("Error: \(err.localizedDescription)") } }) { - Text("\(modelName) (Downloaded)") + Text("Load \(modelName)") } } else { Text("Unknown status") From 82d6eab224862a7044069fb9211dc4b29124264b Mon Sep 17 00:00:00 2001 From: andrijdavid Date: Fri, 29 Dec 2023 15:18:20 +0100 Subject: [PATCH 10/35] main-cmake-pkg : fix build issue (#4665) * Fix main-cmake-pkg compilation * Use glob to load common files * cmake : fix trailing whitespace --------- Co-authored-by: Georgi Gerganov --- examples/main-cmake-pkg/CMakeLists.txt | 27 ++++++-------------------- 1 file changed, 6 insertions(+), 21 deletions(-) diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt index cb00edbbbe374..deb77d588ea9f 100644 --- a/examples/main-cmake-pkg/CMakeLists.txt +++ b/examples/main-cmake-pkg/CMakeLists.txt @@ -7,28 +7,13 @@ find_package(Llama 0.0.1 REQUIRED) # Bake common functionality in with target. Because applications # using the relocatable Llama package should be outside of the # source tree, main-cmake-pkg pretends the dependencies are built-in. - set(_common_path "${CMAKE_CURRENT_LIST_DIR}/../../common") -add_library(common OBJECT - ${_common_path}/common.h - ${_common_path}/common.cpp - ${_common_path}/console.h - ${_common_path}/console.cpp - ${_common_path}/grammar-parser.h - ${_common_path}/grammar-parser.cpp - ${_common_path}/sampling.h - ${_common_path}/sampling.cpp - ) - -# WARNING: because build-info.h is auto-generated, it will only -# be available after the user has built the llama.cpp sources. -# -configure_file(${_common_path}/../build-info.h - ${CMAKE_CURRENT_BINARY_DIR}/build-info.h - COPYONLY) - -target_include_directories(common PUBLIC ${LLAMA_INCLUDE_DIR} - ${CMAKE_CURRENT_BINARY_DIR}) +add_library(common OBJECT) +file(GLOB _common_files + "${_common_path}/*.h" + "${_common_path}/*.cpp" +) +target_sources(common PRIVATE ${_common_files}) # If the common project was part of "main-cmake-pkg" the transient # defines would automatically be attached. Because the common func- From b93edd22f55d3e5268263c3edcdae1818505c078 Mon Sep 17 00:00:00 2001 From: Karthik Sethuraman Date: Fri, 29 Dec 2023 06:22:10 -0800 Subject: [PATCH 11/35] server : allow to generate multimodal embeddings (#4681) --- examples/server/README.md | 4 +++- examples/server/server.cpp | 12 +++++++++++- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index f1e586a1c103a..718a7e0649b62 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -166,7 +166,7 @@ node index.js `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0) - `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:` In this case, `[img-12]` will be replaced by the embeddings of the image id 12 in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. + `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. *Result JSON:* @@ -224,6 +224,8 @@ node index.js `content`: Set the text to process. + `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. + - **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream. *Options:* diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c5035e202ad57..31b8cf33deaa1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3077,7 +3077,17 @@ int main(int argc, char **argv) { prompt = ""; } - const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true, -1); + + json image_data; + if (body.count("image_data") != 0) { + image_data = body["image_data"]; + } + else + { + image_data = ""; + } + + const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, false, true, -1); task_result result = llama.next_result(task_id); return res.set_content(result.result_json.dump(), "application/json; charset=utf-8"); }); From 60f55e888c29cbd87c4238dd19e85d0eef87245d Mon Sep 17 00:00:00 2001 From: SakuraUmi Date: Fri, 29 Dec 2023 22:22:44 +0800 Subject: [PATCH 12/35] server : fix OpenAI server sampling w.r.t. penalty. (#4675) --- examples/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 31b8cf33deaa1..035eb24ac6932 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2461,7 +2461,7 @@ json oaicompat_completion_params_parse( llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl); llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p); - llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", 0); + llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); From db49ff8ed7f0bb201176703441cc02911b08ef2a Mon Sep 17 00:00:00 2001 From: Justine Tunney Date: Fri, 29 Dec 2023 06:24:12 -0800 Subject: [PATCH 13/35] server : replace sleep with condition variables (#4673) The server currently schedules tasks using a sleep(5ms) busy loop. This adds unnecessary latency since most sleep implementations do a round up to the system scheduling quantum (usually 10ms). Other libc sleep impls spin for smaller time intervals which results in the server's busy loop consuming all available cpu. Having the explicit notify() / wait() code also helps aid in the readability of the server code. See mozilla-Ocho/llamafile@711344b --- examples/server/server.cpp | 41 ++++++++++++++++++++++++-------------- 1 file changed, 26 insertions(+), 15 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 035eb24ac6932..0aada8e28029c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -25,6 +25,7 @@ #include #include #include +#include #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 @@ -541,7 +542,9 @@ struct llama_server_context std::vector queue_results; std::vector queue_multitasks; std::mutex mutex_tasks; // also guards id_gen, and queue_multitasks + std::condition_variable condition_tasks; std::mutex mutex_results; + std::condition_variable condition_results; ~llama_server_context() { @@ -1169,7 +1172,7 @@ struct llama_server_context void send_error(task_server& task, std::string error) { - std::lock_guard lock(mutex_results); + std::unique_lock lock(mutex_results); task_result res; res.id = task.id; res.multitask_id = task.multitask_id; @@ -1177,6 +1180,7 @@ struct llama_server_context res.error = true; res.result_json = { { "content", error } }; queue_results.push_back(res); + condition_results.notify_all(); } void add_multi_task(int id, std::vector& sub_ids) @@ -1186,6 +1190,7 @@ struct llama_server_context multi.id = id; std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end())); queue_multitasks.push_back(multi); + condition_tasks.notify_one(); } void update_multi_task(int multitask_id, int subtask_id, task_result& result) @@ -1197,6 +1202,7 @@ struct llama_server_context { multitask.subtasks_remaining.erase(subtask_id); multitask.results.push_back(result); + condition_tasks.notify_one(); } } } @@ -1244,7 +1250,7 @@ struct llama_server_context void send_partial_response(llama_client_slot &slot, completion_token_output tkn) { - std::lock_guard lock(mutex_results); + std::unique_lock lock(mutex_results); task_result res; res.id = slot.task_id; res.multitask_id = slot.multitask_id; @@ -1280,11 +1286,12 @@ struct llama_server_context } queue_results.push_back(res); + condition_results.notify_all(); } void send_final_response(llama_client_slot &slot) { - std::lock_guard lock(mutex_results); + std::unique_lock lock(mutex_results); task_result res; res.id = slot.task_id; res.multitask_id = slot.multitask_id; @@ -1340,11 +1347,12 @@ struct llama_server_context } queue_results.push_back(res); + condition_results.notify_all(); } void send_embedding(llama_client_slot &slot) { - std::lock_guard lock(mutex_results); + std::unique_lock lock(mutex_results); task_result res; res.id = slot.task_id; res.multitask_id = slot.multitask_id; @@ -1372,6 +1380,7 @@ struct llama_server_context }; } queue_results.push_back(res); + condition_results.notify_all(); } int request_completion(json data, bool infill, bool embedding, int multitask_id) @@ -1395,6 +1404,7 @@ struct llama_server_context // otherwise, it's a single-prompt task, we actually queue it queue_tasks.push_back(task); + condition_tasks.notify_one(); return task.id; } @@ -1402,13 +1412,10 @@ struct llama_server_context { while (true) { - std::this_thread::sleep_for(std::chrono::microseconds(5)); - std::lock_guard lock(mutex_results); - - if (queue_results.empty()) - { - continue; - } + std::unique_lock lock(mutex_results); + condition_results.wait(lock, [&]{ + return !queue_results.empty(); + }); for (int i = 0; i < (int) queue_results.size(); i++) { @@ -1504,12 +1511,13 @@ struct llama_server_context void request_cancel(int task_id) { - std::lock_guard lock(mutex_tasks); + std::unique_lock lock(mutex_tasks); task_server task; task.id = id_gen++; task.type = CANCEL_TASK; task.target_id = task_id; queue_tasks.push_back(task); + condition_tasks.notify_one(); } int split_multiprompt_task(task_server& multiprompt_task) @@ -1535,7 +1543,7 @@ struct llama_server_context void process_tasks() { - std::lock_guard lock(mutex_tasks); + std::unique_lock lock(mutex_tasks); while (!queue_tasks.empty()) { task_server task = queue_tasks.front(); @@ -1607,6 +1615,7 @@ struct llama_server_context std::lock_guard lock(mutex_results); queue_results.push_back(aggregate_result); + condition_results.notify_all(); queue_iterator = queue_multitasks.erase(queue_iterator); } @@ -1637,8 +1646,10 @@ struct llama_server_context LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n"); kv_cache_clear(); } - // avoid 100% usage of cpu all time - std::this_thread::sleep_for(std::chrono::milliseconds(5)); + std::unique_lock lock(mutex_tasks); + condition_tasks.wait(lock, [&]{ + return !queue_tasks.empty(); + }); } for (llama_client_slot &slot : slots) From 4af4801566bc262a38fb77f51edf278ac323c2bd Mon Sep 17 00:00:00 2001 From: Justine Tunney Date: Fri, 29 Dec 2023 06:38:38 -0800 Subject: [PATCH 14/35] llava-cli : refactor to use sampling library (#4669) This change makes it possible to use flags like `--grammar` when using the `llava-cli` program. The rest is just code cleanup deleting a long standing TODO comment. This change also ensures that logging information is emitted to stderr which helps the `llava-cli` command be more friendly to shell scripts. See Mozilla-Ocho/llamafile@1cd334f --- examples/llava/llava-cli.cpp | 85 ++++++------------------------------ 1 file changed, 13 insertions(+), 72 deletions(-) diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 31f8cd8e0ef7b..502b788b14aba 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -39,73 +39,11 @@ static bool eval_string(struct llama_context * ctx_llama, const char* str, int n return true; } -// TODO: use common/sampling.h -static llama_token sample_id(llama_context * ctx_llama, gpt_params & params) { - auto & sparams = params.sparams; - - // out of user input, sample next token - const float temp = sparams.temp; - const int32_t top_k = sparams.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : sparams.top_k; - const float top_p = sparams.top_p; - const float tfs_z = sparams.tfs_z; - const float typical_p = sparams.typical_p; - // const int32_t repeat_last_n = sparams.repeat_last_n < 0 ? n_ctx : sparams.repeat_last_n; - // const float repeat_penalty = sparams.repeat_penalty; - // const float alpha_presence = sparams.presence_penalty; - // const float alpha_frequency = sparams.frequency_penalty; - const int mirostat = sparams.mirostat; - const float mirostat_tau = sparams.mirostat_tau; - const float mirostat_eta = sparams.mirostat_eta; - // const bool penalize_nl = sparams.penalize_nl; - - llama_token id = 0; - { - auto logits = llama_get_logits(ctx_llama); - auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama)); - - // Apply params.logit_bias map - for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) { - logits[it->first] += it->second; - } - - std::vector candidates; - candidates.reserve(n_vocab); - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); - } - - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - - if (temp <= 0) { - // Greedy sampling - id = llama_sample_token_greedy(ctx_llama, &candidates_p); - } else { - if (mirostat == 1) { - static float mirostat_mu = 2.0f * mirostat_tau; - const int mirostat_m = 100; - llama_sample_temp(ctx_llama, &candidates_p, temp); - id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); - } else if (mirostat == 2) { - static float mirostat_mu = 2.0f * mirostat_tau; - llama_sample_temp(ctx_llama, &candidates_p, temp); - id = llama_sample_token_mirostat_v2(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); - } else { - // Temperature sampling - llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1); - llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1); - llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1); - llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1); - llama_sample_temp(ctx_llama, &candidates_p, temp); - id = llama_sample_token(ctx_llama, &candidates_p); - } - } - } - - return id; -} - -static const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) { - int id = sample_id(ctx_llama, params); +static const char * sample(struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_llama, + int * n_past) { + const llama_token id = llama_sampling_sample(ctx_sampling, ctx_llama, NULL); + llama_sampling_accept(ctx_sampling, ctx_llama, id, true); static std::string ret; if (id == llama_token_eos(llama_get_model(ctx_llama))) { ret = ""; @@ -174,8 +112,8 @@ struct llava_context { }; static void show_additional_info(int /*argc*/, char ** argv) { - printf("\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); - printf(" note: a lower temperature value like 0.1 is recommended for better quality.\n"); + fprintf(stderr, "\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + fprintf(stderr, " note: a lower temperature value like 0.1 is recommended for better quality.\n"); } static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) { @@ -185,7 +123,7 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para auto prompt = params->prompt; if (prompt_contains_image(prompt)) { if (!params->image.empty()) { - printf("using base64 encoded image instead of command line image path\n"); + fprintf(stderr, "using base64 encoded image instead of command line image path\n"); } embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt); if (!embed) { @@ -217,16 +155,19 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ // generate the response - printf("\n"); + fprintf(stderr, "\n"); + + struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams); for (int i = 0; i < max_tgt_len; i++) { - const char * tmp = sample(ctx_llava->ctx_llama, *params, &n_past); + const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past); if (strcmp(tmp, "") == 0) break; printf("%s", tmp); fflush(stdout); } + llama_sampling_free(ctx_sampling); printf("\n"); } From 97bbca6e8522d18041fcde6c3d0907a52ce36446 Mon Sep 17 00:00:00 2001 From: Cuong Trinh Manh Date: Fri, 29 Dec 2023 21:39:15 +0700 Subject: [PATCH 15/35] cmake : fix ld warning duplicate libraries libllama.a (#4671) * fix "ld: warning: ignoring duplicate libraries: '../libllama.a'" * fix warning in example. --- common/CMakeLists.txt | 2 +- examples/llava/CMakeLists.txt | 2 +- examples/server/CMakeLists.txt | 2 +- tests/CMakeLists.txt | 4 ++-- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index b5d5453d2d357..f79acfef1d133 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -65,4 +65,4 @@ endif() target_include_directories(${TARGET} PUBLIC .) target_compile_features(${TARGET} PUBLIC cxx_std_11) -target_link_libraries(${TARGET} PRIVATE llama build_info) +target_link_libraries(${TARGET} PRIVATE build_info PUBLIC llama) diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index 8ea3e5c836c13..48dae1506e81e 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -32,5 +32,5 @@ endif() set(TARGET llava-cli) add_executable(llava-cli llava-cli.cpp) install(TARGETS llava-cli RUNTIME) -target_link_libraries(llava-cli PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(llava-cli PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(llava PRIVATE cxx_std_11) diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 859cd12c6c6b1..81709e4484c9f 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -6,7 +6,7 @@ install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ ) -target_link_libraries(${TARGET} PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 9b5e69d138bfd..7c932240de82d 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -2,7 +2,7 @@ function(llama_build_executable source) get_filename_component(TEST_TARGET ${source} NAME_WE) add_executable(${TEST_TARGET} ${source}) install(TARGETS ${TEST_TARGET} RUNTIME) - target_link_libraries(${TEST_TARGET} PRIVATE llama common) + target_link_libraries(${TEST_TARGET} PRIVATE common) endfunction() function(llama_test_executable name source) @@ -14,7 +14,7 @@ function(llama_build_and_test_executable source) get_filename_component(TEST_TARGET ${source} NAME_WE) add_executable(${TEST_TARGET} ${source}) install(TARGETS ${TEST_TARGET} RUNTIME) - target_link_libraries(${TEST_TARGET} PRIVATE llama common) + target_link_libraries(${TEST_TARGET} PRIVATE common) add_test(NAME ${TEST_TARGET} COMMAND $ ${ARGN}) endfunction() From 68eccbdc5b56f2a2450f9a8463f9934388cafabf Mon Sep 17 00:00:00 2001 From: Philip Taron Date: Fri, 29 Dec 2023 06:42:26 -0800 Subject: [PATCH 16/35] flake.nix : rewrite (#4605) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * flake.lock: update to hotfix CUDA::cuda_driver Required to support https://github.com/ggerganov/llama.cpp/pull/4606 * flake.nix: rewrite 1. Split into separate files per output. 2. Added overlays, so that this flake can be integrated into others. The names in the overlay are `llama-cpp`, `llama-cpp-opencl`, `llama-cpp-cuda`, and `llama-cpp-rocm` so that they fit into the broader set of Nix packages from [nixpkgs](https://github.com/nixos/nixpkgs). 3. Use [callPackage](https://summer.nixos.org/blog/callpackage-a-tool-for-the-lazy/) rather than `with pkgs;` so that there's dependency injection rather than dependency lookup. 4. Add a description and meta information for each package. The description includes a bit about what's trying to accelerate each one. 5. Use specific CUDA packages instead of cudatoolkit on the advice of SomeoneSerge. 6. Format with `serokell/nixfmt` for a consistent style. 7. Update `flake.lock` with the latest goods. * flake.nix: use finalPackage instead of passing it manually * nix: unclutter darwin support * nix: pass most darwin frameworks unconditionally ...for simplicity * *.nix: nixfmt nix shell github:piegamesde/nixfmt/rfc101-style --command \ nixfmt flake.nix .devops/nix/*.nix * flake.nix: add maintainers * nix: move meta down to follow Nixpkgs style more closely * nix: add missing meta attributes nix: clarify the interpretation of meta.maintainers nix: clarify the meaning of "broken" and "badPlatforms" nix: passthru: expose the use* flags for inspection E.g.: ``` ❯ nix eval .#cuda.useCuda true ``` * flake.nix: avoid re-evaluating nixpkgs too many times * flake.nix: use flake-parts * nix: migrate to pname+version * flake.nix: overlay: expose both the namespace and the default attribute * ci: add the (Nix) flakestry workflow * nix: cmakeFlags: explicit OFF bools * nix: cuda: reduce runtime closure * nix: fewer rebuilds * nix: respect config.cudaCapabilities * nix: add the impure driver's location to the DT_RUNPATHs * nix: clean sources more thoroughly ...this way outPaths change less frequently, and so there are fewer rebuilds * nix: explicit mpi support * nix: explicit jetson support * flake.nix: darwin: only expose the default --------- Co-authored-by: Someone Serge --- .devops/nix/apps.nix | 22 +++ .devops/nix/devshells.nix | 13 ++ .devops/nix/jetson-support.nix | 32 ++++ .devops/nix/nixpkgs-instances.nix | 35 ++++ .devops/nix/package.nix | 265 ++++++++++++++++++++++++++++ .devops/nix/scope.nix | 12 ++ .github/workflows/nix-flakestry.yml | 23 +++ flake.lock | 55 +++--- flake.nix | 226 ++++++++++-------------- 9 files changed, 524 insertions(+), 159 deletions(-) create mode 100644 .devops/nix/apps.nix create mode 100644 .devops/nix/devshells.nix create mode 100644 .devops/nix/jetson-support.nix create mode 100644 .devops/nix/nixpkgs-instances.nix create mode 100644 .devops/nix/package.nix create mode 100644 .devops/nix/scope.nix create mode 100644 .github/workflows/nix-flakestry.yml diff --git a/.devops/nix/apps.nix b/.devops/nix/apps.nix new file mode 100644 index 0000000000000..b8a12cc0a0463 --- /dev/null +++ b/.devops/nix/apps.nix @@ -0,0 +1,22 @@ +{ + perSystem = + { config, lib, ... }: + { + apps = + let + inherit (config.packages) default; + binaries = [ + "llama" + "llama-embedding" + "llama-server" + "quantize" + "train-text-from-scratch" + ]; + mkApp = name: { + type = "app"; + program = "${default}/bin/${name}"; + }; + in + lib.genAttrs binaries mkApp; + }; +} diff --git a/.devops/nix/devshells.nix b/.devops/nix/devshells.nix new file mode 100644 index 0000000000000..1862f0f085100 --- /dev/null +++ b/.devops/nix/devshells.nix @@ -0,0 +1,13 @@ +{ + perSystem = + { config, lib, ... }: + { + devShells = + lib.concatMapAttrs + (name: package: { + ${name} = package.passthru.shell; + ${name + "-extra"} = package.passthru.shell-extra; + }) + config.packages; + }; +} diff --git a/.devops/nix/jetson-support.nix b/.devops/nix/jetson-support.nix new file mode 100644 index 0000000000000..08426d2abb7ec --- /dev/null +++ b/.devops/nix/jetson-support.nix @@ -0,0 +1,32 @@ +{ inputs, ... }: +{ + perSystem = + { + config, + system, + lib, + pkgsCuda, + ... + }: + lib.optionalAttrs (system == "aarch64-linux") { + packages = + let + caps.jetson-xavier = "7.2"; + caps.jetson-orin = "8.7"; + caps.jetson-nano = "5.3"; + + pkgsFor = + cap: + import inputs.nixpkgs { + inherit system; + config = { + cudaSupport = true; + cudaCapabilities = [ cap ]; + cudaEnableForwardCompat = false; + inherit (pkgsCuda.config) allowUnfreePredicate; + }; + }; + in + builtins.mapAttrs (name: cap: ((pkgsFor cap).callPackage ./scope.nix { }).llama-cpp) caps; + }; +} diff --git a/.devops/nix/nixpkgs-instances.nix b/.devops/nix/nixpkgs-instances.nix new file mode 100644 index 0000000000000..6e9872b28c8fb --- /dev/null +++ b/.devops/nix/nixpkgs-instances.nix @@ -0,0 +1,35 @@ +{ inputs, ... }: +{ + # The _module.args definitions are passed on to modules as arguments. E.g. + # the module `{ pkgs ... }: { /* config */ }` implicitly uses + # `_module.args.pkgs` (defined in this case by flake-parts). + perSystem = + { system, ... }: + { + _module.args = { + pkgsCuda = import inputs.nixpkgs { + inherit system; + # Ensure dependencies use CUDA consistently (e.g. that openmpi, ucc, + # and ucx are built with CUDA support) + config.cudaSupport = true; + config.allowUnfreePredicate = + p: + builtins.all + ( + license: + license.free + || builtins.elem license.shortName [ + "CUDA EULA" + "cuDNN EULA" + ] + ) + (p.meta.licenses or [ p.meta.license ]); + }; + # Ensure dependencies use ROCm consistently + pkgsRocm = import inputs.nixpkgs { + inherit system; + config.rocmSupport = true; + }; + }; + }; +} diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix new file mode 100644 index 0000000000000..5f2a7c9f4bb3d --- /dev/null +++ b/.devops/nix/package.nix @@ -0,0 +1,265 @@ +{ + lib, + config, + stdenv, + mkShell, + cmake, + ninja, + pkg-config, + git, + python3, + mpi, + openblas, # TODO: Use the generic `blas` so users could switch betwen alternative implementations + cudaPackages, + darwin, + rocmPackages, + clblast, + useBlas ? builtins.all (x: !x) [ + useCuda + useMetalKit + useOpenCL + useRocm + ], + useCuda ? config.cudaSupport, + useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL, + useMpi ? false, # Increases the runtime closure size by ~700M + useOpenCL ? false, + useRocm ? config.rocmSupport, + llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake +}@inputs: + +let + inherit (lib) + cmakeBool + cmakeFeature + optionals + strings + versionOlder + ; + + # It's necessary to consistently use backendStdenv when building with CUDA support, + # otherwise we get libstdc++ errors downstream. + stdenv = throw "Use effectiveStdenv instead"; + effectiveStdenv = if useCuda then cudaPackages.backendStdenv else inputs.stdenv; + + suffices = + lib.optionals useBlas [ "BLAS" ] + ++ lib.optionals useCuda [ "CUDA" ] + ++ lib.optionals useMetalKit [ "MetalKit" ] + ++ lib.optionals useMpi [ "MPI" ] + ++ lib.optionals useOpenCL [ "OpenCL" ] + ++ lib.optionals useRocm [ "ROCm" ]; + + pnameSuffix = + strings.optionalString (suffices != [ ]) + "-${strings.concatMapStringsSep "-" strings.toLower suffices}"; + descriptionSuffix = + strings.optionalString (suffices != [ ]) + ", accelerated with ${strings.concatStringsSep ", " suffices}"; + + # TODO: package the Python in this repository in a Nix-like way. + # It'd be nice to migrate to buildPythonPackage, as well as ensure this repo + # is PEP 517-compatible, and ensure the correct .dist-info is generated. + # https://peps.python.org/pep-0517/ + llama-python = python3.withPackages ( + ps: [ + ps.numpy + ps.sentencepiece + ] + ); + + # TODO(Green-Sky): find a better way to opt-into the heavy ml python runtime + llama-python-extra = python3.withPackages ( + ps: [ + ps.numpy + ps.sentencepiece + ps.torchWithoutCuda + ps.transformers + ] + ); + + # apple_sdk is supposed to choose sane defaults, no need to handle isAarch64 + # separately + darwinBuildInputs = + with darwin.apple_sdk.frameworks; + [ + Accelerate + CoreVideo + CoreGraphics + ] + ++ optionals useMetalKit [ MetalKit ]; + + cudaBuildInputs = with cudaPackages; [ + cuda_cccl.dev # + + # A temporary hack for reducing the closure size, remove once cudaPackages + # have stopped using lndir: https://github.com/NixOS/nixpkgs/issues/271792 + cuda_cudart.dev + cuda_cudart.lib + cuda_cudart.static + libcublas.dev + libcublas.lib + libcublas.static + ]; + + rocmBuildInputs = with rocmPackages; [ + clr + hipblas + rocblas + ]; +in + +effectiveStdenv.mkDerivation ( + finalAttrs: { + pname = "llama-cpp${pnameSuffix}"; + version = llamaVersion; + + src = lib.cleanSourceWith { + filter = + name: type: + !(builtins.any (_: _) [ + (lib.hasSuffix ".nix" name) # Ignore *.nix files when computing outPaths + (name == "README.md") # Ignore *.md changes whe computing outPaths + (lib.hasPrefix "." name) # Skip hidden files and directories + ]); + src = lib.cleanSource ../../.; + }; + + postPatch = '' + substituteInPlace ./ggml-metal.m \ + --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";" + + # TODO: Package up each Python script or service appropriately. + # If we were to migrate to buildPythonPackage and prepare the `pyproject.toml`, + # we could make those *.py into setuptools' entrypoints + substituteInPlace ./*.py --replace "/usr/bin/env python" "${llama-python}/bin/python" + ''; + + nativeBuildInputs = + [ + cmake + ninja + pkg-config + git + ] + ++ optionals useCuda [ + cudaPackages.cuda_nvcc + + # TODO: Replace with autoAddDriverRunpath + # once https://github.com/NixOS/nixpkgs/pull/275241 has been merged + cudaPackages.autoAddOpenGLRunpathHook + ]; + + buildInputs = + optionals effectiveStdenv.isDarwin darwinBuildInputs + ++ optionals useCuda cudaBuildInputs + ++ optionals useMpi [ mpi ] + ++ optionals useOpenCL [ clblast ] + ++ optionals useRocm rocmBuildInputs; + + cmakeFlags = + [ + (cmakeBool "LLAMA_NATIVE" true) + (cmakeBool "LLAMA_BUILD_SERVER" true) + (cmakeBool "BUILD_SHARED_LIBS" true) + (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true) + (cmakeBool "LLAMA_BLAS" useBlas) + (cmakeBool "LLAMA_CLBLAST" useOpenCL) + (cmakeBool "LLAMA_CUBLAS" useCuda) + (cmakeBool "LLAMA_HIPBLAS" useRocm) + (cmakeBool "LLAMA_METAL" useMetalKit) + (cmakeBool "LLAMA_MPI" useMpi) + ] + ++ optionals useCuda [ + ( + with cudaPackages.flags; + cmakeFeature "CMAKE_CUDA_ARCHITECTURES" ( + builtins.concatStringsSep ";" (map dropDot cudaCapabilities) + ) + ) + ] + ++ optionals useRocm [ + (cmakeFeature "CMAKE_C_COMPILER" "hipcc") + (cmakeFeature "CMAKE_CXX_COMPILER" "hipcc") + + # Build all targets supported by rocBLAS. When updating search for TARGET_LIST_ROCM + # in https://github.com/ROCmSoftwarePlatform/rocBLAS/blob/develop/CMakeLists.txt + # and select the line that matches the current nixpkgs version of rocBLAS. + # Should likely use `rocmPackages.clr.gpuTargets`. + "-DAMDGPU_TARGETS=gfx803;gfx900;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102" + ] + ++ optionals useMetalKit [ (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1") ] + ++ optionals useBlas [ (lib.cmakeFeature "LLAMA_BLAS_VENDOR" "OpenBLAS") ]; + + # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level, + # if they haven't been added yet. + postInstall = '' + mv $out/bin/main $out/bin/llama + mv $out/bin/server $out/bin/llama-server + mkdir -p $out/include + cp $src/llama.h $out/include/ + ''; + + # Define the shells here, but don't add in the inputsFrom to avoid recursion. + passthru = { + inherit + useBlas + useCuda + useMetalKit + useMpi + useOpenCL + useRocm + ; + + shell = mkShell { + name = "shell-${finalAttrs.finalPackage.name}"; + description = "contains numpy and sentencepiece"; + buildInputs = [ llama-python ]; + inputsFrom = [ finalAttrs.finalPackage ]; + }; + + shell-extra = mkShell { + name = "shell-extra-${finalAttrs.finalPackage.name}"; + description = "contains numpy, sentencepiece, torchWithoutCuda, and transformers"; + buildInputs = [ llama-python-extra ]; + inputsFrom = [ finalAttrs.finalPackage ]; + }; + }; + + meta = { + # Configurations we don't want even the CI to evaluate. Results in the + # "unsupported platform" messages. This is mostly a no-op, because + # cudaPackages would've refused to evaluate anyway. + badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin; + + # Configurations that are known to result in build failures. Can be + # overridden by importing Nixpkgs with `allowBroken = true`. + broken = (useMetalKit && !effectiveStdenv.isDarwin); + + description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}"; + homepage = "https://github.com/ggerganov/llama.cpp/"; + license = lib.licenses.mit; + + # Accommodates `nix run` and `lib.getExe` + mainProgram = "llama"; + + # These people might respond, on the best effort basis, if you ping them + # in case of Nix-specific regressions or for reviewing Nix-specific PRs. + # Consider adding yourself to this list if you want to ensure this flake + # stays maintained and you're willing to invest your time. Do not add + # other people without their consent. Consider removing people after + # they've been unreachable for long periods of time. + + # Note that lib.maintainers is defined in Nixpkgs, but you may just add + # an attrset following the same format as in + # https://github.com/NixOS/nixpkgs/blob/f36a80e54da29775c78d7eff0e628c2b4e34d1d7/maintainers/maintainer-list.nix + maintainers = with lib.maintainers; [ + philiptaron + SomeoneSerge + ]; + + # Extend `badPlatforms` instead + platforms = lib.platforms.all; + }; + } +) diff --git a/.devops/nix/scope.nix b/.devops/nix/scope.nix new file mode 100644 index 0000000000000..7932ac1e8a910 --- /dev/null +++ b/.devops/nix/scope.nix @@ -0,0 +1,12 @@ +{ + lib, + newScope, + llamaVersion ? "0.0.0", +}: + +lib.makeScope newScope ( + self: { + inherit llamaVersion; + llama-cpp = self.callPackage ./package.nix { }; + } +) diff --git a/.github/workflows/nix-flakestry.yml b/.github/workflows/nix-flakestry.yml new file mode 100644 index 0000000000000..3abfb3509a648 --- /dev/null +++ b/.github/workflows/nix-flakestry.yml @@ -0,0 +1,23 @@ +# Make the flake discoverable on https://flakestry.dev +name: "Publish a flake to flakestry" +on: + push: + tags: + - "v?[0-9]+.[0-9]+.[0-9]+" + - "v?[0-9]+.[0-9]+" + workflow_dispatch: + inputs: + tag: + description: "The existing tag to publish" + type: "string" + required: true +jobs: + publish-flake: + runs-on: ubuntu-latest + permissions: + id-token: "write" + contents: "read" + steps: + - uses: flakestry/flakestry-publish@main + with: + version: "${{ inputs.tag || github.ref_name }}" diff --git a/flake.lock b/flake.lock index 0455f65617a2d..3fcd1f45d5a41 100644 --- a/flake.lock +++ b/flake.lock @@ -1,30 +1,30 @@ { "nodes": { - "flake-utils": { + "flake-parts": { "inputs": { - "systems": "systems" + "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1694529238, - "narHash": "sha256-zsNZZGTGnMOf9YpHKJqMSsa0dXbfmxeoJ7xHlrt+xmY=", - "owner": "numtide", - "repo": "flake-utils", - "rev": "ff7b65b44d01cf9ba6a71320833626af21126384", + "lastModified": 1701473968, + "narHash": "sha256-YcVE5emp1qQ8ieHUnxt1wCZCC3ZfAS+SRRWZ2TMda7E=", + "owner": "hercules-ci", + "repo": "flake-parts", + "rev": "34fed993f1674c8d06d58b37ce1e0fe5eebcb9f5", "type": "github" }, "original": { - "owner": "numtide", - "repo": "flake-utils", + "owner": "hercules-ci", + "repo": "flake-parts", "type": "github" } }, "nixpkgs": { "locked": { - "lastModified": 1698318101, - "narHash": "sha256-gUihHt3yPD7bVqg+k/UVHgngyaJ3DMEBchbymBMvK1E=", + "lastModified": 1703559957, + "narHash": "sha256-x9PUuMEPGUOMB51zNxrDr2QoHbYWlCS2xhFedm9MC5Q=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "63678e9f3d3afecfeafa0acead6239cdb447574c", + "rev": "75dd68c36f458c6593c5bbb48abfd3e59bfed380", "type": "github" }, "original": { @@ -34,26 +34,29 @@ "type": "github" } }, - "root": { - "inputs": { - "flake-utils": "flake-utils", - "nixpkgs": "nixpkgs" - } - }, - "systems": { + "nixpkgs-lib": { "locked": { - "lastModified": 1681028828, - "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=", - "owner": "nix-systems", - "repo": "default", - "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e", + "dir": "lib", + "lastModified": 1701253981, + "narHash": "sha256-ztaDIyZ7HrTAfEEUt9AtTDNoCYxUdSd6NrRHaYOIxtk=", + "owner": "NixOS", + "repo": "nixpkgs", + "rev": "e92039b55bcd58469325ded85d4f58dd5a4eaf58", "type": "github" }, "original": { - "owner": "nix-systems", - "repo": "default", + "dir": "lib", + "owner": "NixOS", + "ref": "nixos-unstable", + "repo": "nixpkgs", "type": "github" } + }, + "root": { + "inputs": { + "flake-parts": "flake-parts", + "nixpkgs": "nixpkgs" + } } }, "root": "root", diff --git a/flake.nix b/flake.nix index 4cf28d5c11c0f..2209070aa83cd 100644 --- a/flake.nix +++ b/flake.nix @@ -1,139 +1,99 @@ { + description = "Port of Facebook's LLaMA model in C/C++"; + inputs = { nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable"; - flake-utils.url = "github:numtide/flake-utils"; + flake-parts.url = "github:hercules-ci/flake-parts"; }; - outputs = { self, nixpkgs, flake-utils }: - flake-utils.lib.eachDefaultSystem (system: - let - name = "llama.cpp"; - src = ./.; - meta.mainProgram = "llama"; - inherit (pkgs.stdenv) isAarch32 isAarch64 isDarwin; - buildInputs = with pkgs; [ openmpi ]; - osSpecific = with pkgs; buildInputs ++ ( - if isAarch64 && isDarwin then - with pkgs.darwin.apple_sdk_11_0.frameworks; [ - Accelerate - MetalKit - ] - else if isAarch32 && isDarwin then - with pkgs.darwin.apple_sdk.frameworks; [ - Accelerate - CoreGraphics - CoreVideo - ] - else if isDarwin then - with pkgs.darwin.apple_sdk.frameworks; [ - Accelerate - CoreGraphics - CoreVideo - ] - else - with pkgs; [ openblas ] - ); - pkgs = import nixpkgs { inherit system; }; - nativeBuildInputs = with pkgs; [ cmake ninja pkg-config ]; - cudatoolkit_joined = with pkgs; symlinkJoin { - # HACK(Green-Sky): nix currently has issues with cmake findcudatoolkit - # see https://github.com/NixOS/nixpkgs/issues/224291 - # copied from jaxlib - name = "${cudaPackages.cudatoolkit.name}-merged"; - paths = [ - cudaPackages.cudatoolkit.lib - cudaPackages.cudatoolkit.out - ] ++ lib.optionals (lib.versionOlder cudaPackages.cudatoolkit.version "11") [ - # for some reason some of the required libs are in the targets/x86_64-linux - # directory; not sure why but this works around it - "${cudaPackages.cudatoolkit}/targets/${system}" - ]; - }; - llama-python = - pkgs.python3.withPackages (ps: with ps; [ numpy sentencepiece ]); - # TODO(Green-Sky): find a better way to opt-into the heavy ml python runtime - llama-python-extra = - pkgs.python3.withPackages (ps: with ps; [ numpy sentencepiece torchWithoutCuda transformers ]); - postPatch = '' - substituteInPlace ./ggml-metal.m \ - --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";" - substituteInPlace ./*.py --replace '/usr/bin/env python' '${llama-python}/bin/python' - ''; - postInstall = '' - mv $out/bin/main $out/bin/llama - mv $out/bin/server $out/bin/llama-server - mkdir -p $out/include - cp ${src}/llama.h $out/include/ - ''; - cmakeFlags = [ "-DLLAMA_NATIVE=OFF" "-DLLAMA_BUILD_SERVER=ON" "-DBUILD_SHARED_LIBS=ON" "-DCMAKE_SKIP_BUILD_RPATH=ON" ]; - in + + # For inspection, use `nix flake show github:ggerganov/llama.cpp` or the nix repl: + # + # ```bash + # ❯ nix repl + # nix-repl> :lf github:ggerganov/llama.cpp + # Added 13 variables. + # nix-repl> outputs.apps.x86_64-linux.quantize + # { program = "/nix/store/00000000000000000000000000000000-llama.cpp/bin/quantize"; type = "app"; } + # ``` + outputs = + { self, flake-parts, ... }@inputs: + let + # We could include the git revisions in the package names but those would + # needlessly trigger rebuilds: + # llamaVersion = self.dirtyShortRev or self.shortRev; + + # Nix already uses cryptographic hashes for versioning, so we'll just fix + # the fake semver for now: + llamaVersion = "0.0.0"; + in + flake-parts.lib.mkFlake { inherit inputs; } + { - packages.default = pkgs.stdenv.mkDerivation { - inherit name src meta postPatch nativeBuildInputs postInstall; - buildInputs = osSpecific; - cmakeFlags = cmakeFlags - ++ (if isAarch64 && isDarwin then [ - "-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1" - "-DLLAMA_METAL=ON" - ] else [ - "-DLLAMA_BLAS=ON" - "-DLLAMA_BLAS_VENDOR=OpenBLAS" - ]); - }; - packages.opencl = pkgs.stdenv.mkDerivation { - inherit name src meta postPatch nativeBuildInputs postInstall; - buildInputs = with pkgs; buildInputs ++ [ clblast ]; - cmakeFlags = cmakeFlags ++ [ - "-DLLAMA_CLBLAST=ON" - ]; - }; - packages.cuda = pkgs.stdenv.mkDerivation { - inherit name src meta postPatch nativeBuildInputs postInstall; - buildInputs = with pkgs; buildInputs ++ [ cudatoolkit_joined ]; - cmakeFlags = cmakeFlags ++ [ - "-DLLAMA_CUBLAS=ON" - ]; - }; - packages.rocm = pkgs.stdenv.mkDerivation { - inherit name src meta postPatch nativeBuildInputs postInstall; - buildInputs = with pkgs.rocmPackages; buildInputs ++ [ clr hipblas rocblas ]; - cmakeFlags = cmakeFlags ++ [ - "-DLLAMA_HIPBLAS=1" - "-DCMAKE_C_COMPILER=hipcc" - "-DCMAKE_CXX_COMPILER=hipcc" - # Build all targets supported by rocBLAS. When updating search for TARGET_LIST_ROCM - # in github.com/ROCmSoftwarePlatform/rocBLAS/blob/develop/CMakeLists.txt - # and select the line that matches the current nixpkgs version of rocBLAS. - "-DAMDGPU_TARGETS=gfx803;gfx900;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102" - ]; - }; - apps.llama-server = { - type = "app"; - program = "${self.packages.${system}.default}/bin/llama-server"; - }; - apps.llama-embedding = { - type = "app"; - program = "${self.packages.${system}.default}/bin/embedding"; - }; - apps.llama = { - type = "app"; - program = "${self.packages.${system}.default}/bin/llama"; - }; - apps.quantize = { - type = "app"; - program = "${self.packages.${system}.default}/bin/quantize"; - }; - apps.train-text-from-scratch = { - type = "app"; - program = "${self.packages.${system}.default}/bin/train-text-from-scratch"; - }; - apps.default = self.apps.${system}.llama; - devShells.default = pkgs.mkShell { - buildInputs = [ llama-python ]; - packages = nativeBuildInputs ++ osSpecific; - }; - devShells.extra = pkgs.mkShell { - buildInputs = [ llama-python-extra ]; - packages = nativeBuildInputs ++ osSpecific; - }; - }); + + imports = [ + .devops/nix/nixpkgs-instances.nix + .devops/nix/apps.nix + .devops/nix/devshells.nix + .devops/nix/jetson-support.nix + ]; + + # An overlay can be used to have a more granular control over llama-cpp's + # dependencies and configuration, than that offered by the `.override` + # mechanism. Cf. https://nixos.org/manual/nixpkgs/stable/#chap-overlays. + # + # E.g. in a flake: + # ``` + # { nixpkgs, llama-cpp, ... }: + # let pkgs = import nixpkgs { + # overlays = [ (llama-cpp.overlays.default) ]; + # system = "aarch64-linux"; + # config.allowUnfree = true; + # config.cudaSupport = true; + # config.cudaCapabilities = [ "7.2" ]; + # config.cudaEnableForwardCompat = false; + # }; in { + # packages.aarch64-linux.llamaJetsonXavier = pkgs.llamaPackages.llama-cpp; + # } + # ``` + # + # Cf. https://nixos.org/manual/nix/unstable/command-ref/new-cli/nix3-flake.html?highlight=flake#flake-format + flake.overlays.default = + (final: prev: { + llamaPackages = final.callPackage .devops/nix/scope.nix { inherit llamaVersion; }; + inherit (final.llamaPackages) llama-cpp; + }); + + systems = [ + "aarch64-darwin" + "aarch64-linux" + "x86_64-darwin" # x86_64-darwin isn't tested (and likely isn't relevant) + "x86_64-linux" + ]; + + perSystem = + { + config, + lib, + pkgs, + pkgsCuda, + pkgsRocm, + ... + }: + { + # We don't use the overlay here so as to avoid making too many instances of nixpkgs, + # cf. https://zimbatm.com/notes/1000-instances-of-nixpkgs + packages = + { + default = (pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp; + } + // lib.optionalAttrs pkgs.stdenv.isLinux { + opencl = config.packages.default.override { useOpenCL = true; }; + cuda = (pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp; + rocm = (pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp; + + mpi-cpu = config.packages.default.override { useMpi = true; }; + mpi-cuda = config.packages.default.override { useMpi = true; }; + }; + }; + }; } From 04ac0607e913ab91234dfb240e12a76509e30982 Mon Sep 17 00:00:00 2001 From: crasm Date: Fri, 29 Dec 2023 09:50:29 -0500 Subject: [PATCH 17/35] python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel --- .devops/full-cuda.Dockerfile | 3 +- .devops/full-rocm.Dockerfile | 3 +- .devops/full.Dockerfile | 3 +- .devops/main-rocm.Dockerfile | 3 +- .../workflows/python-check-requirements.yml | 29 +++ convert-hf-to-gguf.py | 95 ++++----- convert-lora-to-ggml.py | 183 +++++++++--------- convert-persimmon-to-gguf.py | 1 + requirements-hf-to-gguf.txt | 3 - requirements.txt | 17 +- .../requirements-convert-hf-to-gguf.txt | 2 + ...equirements-convert-llama-ggml-to-gguf.txt | 1 + .../requirements-convert-lora-to-ggml.txt | 2 + ...requirements-convert-persimmon-to-gguf.txt | 2 + requirements/requirements-convert.txt | 5 + scripts/check-requirements.sh | 174 +++++++++++++++++ 16 files changed, 378 insertions(+), 148 deletions(-) create mode 100644 .github/workflows/python-check-requirements.yml mode change 100644 => 100755 convert-persimmon-to-gguf.py delete mode 100644 requirements-hf-to-gguf.txt create mode 100644 requirements/requirements-convert-hf-to-gguf.txt create mode 100644 requirements/requirements-convert-llama-ggml-to-gguf.txt create mode 100644 requirements/requirements-convert-lora-to-ggml.txt create mode 100644 requirements/requirements-convert-persimmon-to-gguf.txt create mode 100644 requirements/requirements-convert.txt create mode 100755 scripts/check-requirements.sh diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile index 360602d6567b8..77a9ddc145d0b 100644 --- a/.devops/full-cuda.Dockerfile +++ b/.devops/full-cuda.Dockerfile @@ -14,7 +14,8 @@ ARG CUDA_DOCKER_ARCH=all RUN apt-get update && \ apt-get install -y build-essential python3 python3-pip git -COPY requirements.txt requirements.txt +COPY requirements.txt requirements.txt +COPY requirements requirements RUN pip install --upgrade pip setuptools wheel \ && pip install -r requirements.txt diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile index 6c521e9b4101f..8b9633dc4ebf5 100644 --- a/.devops/full-rocm.Dockerfile +++ b/.devops/full-rocm.Dockerfile @@ -23,7 +23,8 @@ ARG ROCM_DOCKER_ARCH=\ gfx1101 \ gfx1102 -COPY requirements.txt requirements.txt +COPY requirements.txt requirements.txt +COPY requirements requirements RUN pip install --upgrade pip setuptools wheel \ && pip install -r requirements.txt diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile index 687628b35e996..cef1297d3e156 100644 --- a/.devops/full.Dockerfile +++ b/.devops/full.Dockerfile @@ -5,7 +5,8 @@ FROM ubuntu:$UBUNTU_VERSION as build RUN apt-get update && \ apt-get install -y build-essential python3 python3-pip git -COPY requirements.txt requirements.txt +COPY requirements.txt requirements.txt +COPY requirements requirements RUN pip install --upgrade pip setuptools wheel \ && pip install -r requirements.txt diff --git a/.devops/main-rocm.Dockerfile b/.devops/main-rocm.Dockerfile index 789deff6dc8c1..0a706dc73227d 100644 --- a/.devops/main-rocm.Dockerfile +++ b/.devops/main-rocm.Dockerfile @@ -23,7 +23,8 @@ ARG ROCM_DOCKER_ARCH=\ gfx1101 \ gfx1102 -COPY requirements.txt requirements.txt +COPY requirements.txt requirements.txt +COPY requirements requirements RUN pip install --upgrade pip setuptools wheel \ && pip install -r requirements.txt diff --git a/.github/workflows/python-check-requirements.yml b/.github/workflows/python-check-requirements.yml new file mode 100644 index 0000000000000..92e1108b3af88 --- /dev/null +++ b/.github/workflows/python-check-requirements.yml @@ -0,0 +1,29 @@ +name: Python check requirements.txt + +on: + push: + paths: + - 'scripts/check-requirements.sh' + - 'convert*.py' + - 'requirements.txt' + - 'requirements/*.txt' + pull_request: + paths: + - 'scripts/check-requirements.sh' + - 'convert*.py' + - 'requirements.txt' + - 'requirements/*.txt' + +jobs: + python-check-requirements: + runs-on: ubuntu-latest + name: check-requirements + steps: + - name: Check out source repository + uses: actions/checkout@v3 + - name: Set up Python environment + uses: actions/setup-python@v4 + with: + python-version: "3.11" + - name: Run check-requirements.sh script + run: bash scripts/check-requirements.sh nocleanup diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 3557a825eb357..51724c0dfca56 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -242,7 +242,7 @@ def _set_vocab_gpt2(self): tokens: list[bytearray] = [] toktypes: list[int] = [] - from transformers import AutoTokenizer # type: ignore[attr-defined] + from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(dir_model) vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) assert max(tokenizer.vocab.values()) < vocab_size @@ -856,7 +856,7 @@ def set_gguf_parameters(self): hparams = self.hparams block_count = hparams["num_hidden_layers"] - self.gguf_writer.add_name(dir_model.name) + self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) @@ -902,7 +902,7 @@ def set_vocab(self): tokens: list[bytearray] = [] toktypes: list[int] = [] - from transformers import AutoTokenizer # type: ignore[attr-defined] + from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) vocab_size = hparams["vocab_size"] assert max(tokenizer.get_vocab().values()) < vocab_size @@ -1185,57 +1185,62 @@ def parse_args() -> argparse.Namespace: return parser.parse_args() -args = parse_args() +def main() -> None: + args = parse_args() -dir_model = args.model + dir_model = args.model -if args.awq_path: - sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) - from awq.apply_awq import add_scale_weights - tmp_model_path = args.model / "weighted_model" - dir_model = tmp_model_path - if tmp_model_path.is_dir(): - print(f"{tmp_model_path} exists as a weighted model.") + if args.awq_path: + sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) + from awq.apply_awq import add_scale_weights + tmp_model_path = args.model / "weighted_model" + dir_model = tmp_model_path + if tmp_model_path.is_dir(): + print(f"{tmp_model_path} exists as a weighted model.") + else: + tmp_model_path.mkdir(parents=True, exist_ok=True) + print("Saving new weighted model ...") + add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) + print(f"Saved weighted model at {tmp_model_path}.") + + if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file=sys.stderr) + sys.exit(1) + + ftype_map = { + "f32": gguf.GGMLQuantizationType.F32, + "f16": gguf.GGMLQuantizationType.F16, + } + + if args.outfile is not None: + fname_out = args.outfile else: - tmp_model_path.mkdir(parents=True, exist_ok=True) - print("Saving new weighted model ...") - add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) - print(f"Saved weighted model at {tmp_model_path}.") - -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file=sys.stderr) - sys.exit(1) + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{args.outtype}.gguf' -ftype_map = { - "f32": gguf.GGMLQuantizationType.F32, - "f16": gguf.GGMLQuantizationType.F16, -} + print(f"Loading model: {dir_model.name}") -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{args.outtype}.gguf' + hparams = Model.load_hparams(dir_model) -print(f"Loading model: {dir_model.name}") + with torch.inference_mode(): + model_class = Model.from_model_architecture(hparams["architectures"][0]) + model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) -hparams = Model.load_hparams(dir_model) + print("Set model parameters") + model_instance.set_gguf_parameters() -with torch.inference_mode(): - model_class = Model.from_model_architecture(hparams["architectures"][0]) - model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) + print("Set model tokenizer") + model_instance.set_vocab() - print("Set model parameters") - model_instance.set_gguf_parameters() + if args.vocab_only: + print(f"Exporting model vocab to '{fname_out}'") + model_instance.write_vocab() + else: + print(f"Exporting model to '{fname_out}'") + model_instance.write() - print("Set model tokenizer") - model_instance.set_vocab() + print(f"Model successfully exported to '{fname_out}'") - if args.vocab_only: - print(f"Exporting model vocab to '{fname_out}'") - model_instance.write_vocab() - else: - print(f"Exporting model to '{fname_out}'") - model_instance.write() - print(f"Model successfully exported to '{fname_out}'") +if __name__ == '__main__': + main() diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py index 53bb8a3d97a05..35ce152f4248d 100755 --- a/convert-lora-to-ggml.py +++ b/convert-lora-to-ggml.py @@ -47,95 +47,96 @@ def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_ty fout.seek((fout.tell() + 31) & -32) -if len(sys.argv) < 2: - print(f"Usage: python {sys.argv[0]} [arch]") - print( - "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" - ) - print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") - sys.exit(1) - -input_json = os.path.join(sys.argv[1], "adapter_config.json") -input_model = os.path.join(sys.argv[1], "adapter_model.bin") -output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") - -model = torch.load(input_model, map_location="cpu") -arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" - -if arch_name not in gguf.MODEL_ARCH_NAMES.values(): - print(f"Error: unsupported architecture {arch_name}") - sys.exit(1) - -arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] -name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone - -with open(input_json, "r") as f: - params = json.load(f) - -if params["peft_type"] != "LORA": - print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") - sys.exit(1) - -if params["fan_in_fan_out"] is True: - print("Error: param fan_in_fan_out is not supported") - sys.exit(1) - -if params["bias"] is not None and params["bias"] != "none": - print("Error: param bias is not supported") - sys.exit(1) - -# TODO: these seem to be layers that have been trained but without lora. -# doesn't seem widely used but eventually should be supported -if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: - print("Error: param modules_to_save is not supported") - sys.exit(1) - -with open(output_path, "wb") as fout: - fout.truncate() - - write_file_header(fout, params) - for k, v in model.items(): - orig_k = k - if k.endswith(".default.weight"): - k = k.replace(".default.weight", ".weight") - if k in ["llama_proj.weight", "llama_proj.bias"]: - continue - if k.endswith("lora_A.weight"): - if v.dtype != torch.float16 and v.dtype != torch.float32: +if __name__ == '__main__': + if len(sys.argv) < 2: + print(f"Usage: python {sys.argv[0]} [arch]") + print( + "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" + ) + print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") + sys.exit(1) + + input_json = os.path.join(sys.argv[1], "adapter_config.json") + input_model = os.path.join(sys.argv[1], "adapter_model.bin") + output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") + + model = torch.load(input_model, map_location="cpu") + arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" + + if arch_name not in gguf.MODEL_ARCH_NAMES.values(): + print(f"Error: unsupported architecture {arch_name}") + sys.exit(1) + + arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] + name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone + + with open(input_json, "r") as f: + params = json.load(f) + + if params["peft_type"] != "LORA": + print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") + sys.exit(1) + + if params["fan_in_fan_out"] is True: + print("Error: param fan_in_fan_out is not supported") + sys.exit(1) + + if params["bias"] is not None and params["bias"] != "none": + print("Error: param bias is not supported") + sys.exit(1) + + # TODO: these seem to be layers that have been trained but without lora. + # doesn't seem widely used but eventually should be supported + if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: + print("Error: param modules_to_save is not supported") + sys.exit(1) + + with open(output_path, "wb") as fout: + fout.truncate() + + write_file_header(fout, params) + for k, v in model.items(): + orig_k = k + if k.endswith(".default.weight"): + k = k.replace(".default.weight", ".weight") + if k in ["llama_proj.weight", "llama_proj.bias"]: + continue + if k.endswith("lora_A.weight"): + if v.dtype != torch.float16 and v.dtype != torch.float32: + v = v.float() + v = v.T + else: v = v.float() - v = v.T - else: - v = v.float() - - t = v.detach().numpy() - - prefix = "base_model.model." - if k.startswith(prefix): - k = k[len(prefix) :] - - lora_suffixes = (".lora_A.weight", ".lora_B.weight") - if k.endswith(lora_suffixes): - suffix = k[-len(lora_suffixes[0]):] - k = k[: -len(lora_suffixes[0])] - else: - print(f"Error: unrecognized tensor name {orig_k}") - sys.exit(1) - - tname = name_map.get_name(k) - if tname is None: - print(f"Error: could not map tensor name {orig_k}") - print(" Note: the arch parameter must be specified if the model is not llama") - sys.exit(1) - - if suffix == ".lora_A.weight": - tname += ".weight.loraA" - elif suffix == ".lora_B.weight": - tname += ".weight.loraB" - else: - assert False - - print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") - write_tensor_header(fout, tname, t.shape, t.dtype) - t.tofile(fout) - -print(f"Converted {input_json} and {input_model} to {output_path}") + + t = v.detach().numpy() + + prefix = "base_model.model." + if k.startswith(prefix): + k = k[len(prefix) :] + + lora_suffixes = (".lora_A.weight", ".lora_B.weight") + if k.endswith(lora_suffixes): + suffix = k[-len(lora_suffixes[0]):] + k = k[: -len(lora_suffixes[0])] + else: + print(f"Error: unrecognized tensor name {orig_k}") + sys.exit(1) + + tname = name_map.get_name(k) + if tname is None: + print(f"Error: could not map tensor name {orig_k}") + print(" Note: the arch parameter must be specified if the model is not llama") + sys.exit(1) + + if suffix == ".lora_A.weight": + tname += ".weight.loraA" + elif suffix == ".lora_B.weight": + tname += ".weight.loraB" + else: + assert False + + print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") + write_tensor_header(fout, tname, t.shape, t.dtype) + t.tofile(fout) + + print(f"Converted {input_json} and {input_model} to {output_path}") diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py old mode 100644 new mode 100755 index 206b7d5ff9e31..1ba5864dc25ec --- a/convert-persimmon-to-gguf.py +++ b/convert-persimmon-to-gguf.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 import torch import os from pprint import pprint diff --git a/requirements-hf-to-gguf.txt b/requirements-hf-to-gguf.txt deleted file mode 100644 index f4600539e27ac..0000000000000 --- a/requirements-hf-to-gguf.txt +++ /dev/null @@ -1,3 +0,0 @@ --r requirements.txt -torch==2.1.1 -transformers==4.35.2 diff --git a/requirements.txt b/requirements.txt index 1a116256671e5..d36f745201ef0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,12 @@ -numpy==1.24.4 -sentencepiece==0.1.98 -transformers>=4.34.0 -gguf>=0.1.0 -protobuf>=4.21.0 +# These requirements include all dependencies for all top-level python scripts +# for llama.cpp. Avoid adding packages here directly. +# +# Package versions must stay compatible across all top-level python scripts. +# + +-r ./requirements/requirements-convert.txt + +-r ./requirements/requirements-convert-hf-to-gguf.txt +-r ./requirements/requirements-convert-llama-ggml-to-gguf.txt +-r ./requirements/requirements-convert-lora-to-ggml.txt +-r ./requirements/requirements-convert-persimmon-to-gguf.txt diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert-hf-to-gguf.txt new file mode 100644 index 0000000000000..6ac4026107fbe --- /dev/null +++ b/requirements/requirements-convert-hf-to-gguf.txt @@ -0,0 +1,2 @@ +-r ./requirements-convert.txt +torch~=2.1.1 diff --git a/requirements/requirements-convert-llama-ggml-to-gguf.txt b/requirements/requirements-convert-llama-ggml-to-gguf.txt new file mode 100644 index 0000000000000..a0f37cd1c71e4 --- /dev/null +++ b/requirements/requirements-convert-llama-ggml-to-gguf.txt @@ -0,0 +1 @@ +-r ./requirements-convert.txt diff --git a/requirements/requirements-convert-lora-to-ggml.txt b/requirements/requirements-convert-lora-to-ggml.txt new file mode 100644 index 0000000000000..6ac4026107fbe --- /dev/null +++ b/requirements/requirements-convert-lora-to-ggml.txt @@ -0,0 +1,2 @@ +-r ./requirements-convert.txt +torch~=2.1.1 diff --git a/requirements/requirements-convert-persimmon-to-gguf.txt b/requirements/requirements-convert-persimmon-to-gguf.txt new file mode 100644 index 0000000000000..6ac4026107fbe --- /dev/null +++ b/requirements/requirements-convert-persimmon-to-gguf.txt @@ -0,0 +1,2 @@ +-r ./requirements-convert.txt +torch~=2.1.1 diff --git a/requirements/requirements-convert.txt b/requirements/requirements-convert.txt new file mode 100644 index 0000000000000..a3d6ecec0ac04 --- /dev/null +++ b/requirements/requirements-convert.txt @@ -0,0 +1,5 @@ +numpy~=1.24.4 +sentencepiece~=0.1.98 +transformers>=4.35.2,<5.0.0 +gguf>=0.1.0 +protobuf>=4.21.0,<5.0.0 diff --git a/scripts/check-requirements.sh b/scripts/check-requirements.sh new file mode 100755 index 0000000000000..af7bab7533789 --- /dev/null +++ b/scripts/check-requirements.sh @@ -0,0 +1,174 @@ +#!/bin/bash +set -euo pipefail + +# +# check-requirements.sh checks all requirements files for each top-level +# convert*.py script. +# +# WARNING: This is quite IO intensive, because a fresh venv is set up for every +# python script. As of 2023-12-22, this writes ~2.7GB of data. An adequately +# sized tmpfs /tmp or ramdisk is recommended if running this frequently. +# +# usage: check-requirements.sh [] +# check-requirements.sh nocleanup [] +# +# where: +# - is a directory that can be used as the base for +# setting up the venvs. Defaults to `/tmp`. +# - 'nocleanup' as the first argument will disable automatic cleanup +# of the files created by this script. +# +# requires: +# - bash >= 3.2.57 +# - shellcheck +# +# For each script, it creates a fresh venv, `pip install`s the requirements, and +# finally imports the python script to check for `ImportError`. +# + +log() { + local level=$1 msg=$2 + printf >&2 '%s: %s\n' "$level" "$msg" +} + +debug() { + log DEBUG "$@" +} + +info() { + log INFO "$@" +} + +fatal() { + log FATAL "$@" + exit 1 +} + +cleanup() { + if [[ -n ${workdir+x} && -d $workdir && -w $workdir ]]; then + info "Removing $workdir" + local count=0 + rm -rfv -- "$workdir" | while read -r; do + if (( count++ > 750 )); then + printf . + count=0 + fi + done + printf '\n' + info "Removed $workdir" + fi +} + +do_cleanup=1 +if [[ ${1-} == nocleanup ]]; then + do_cleanup=0; shift +fi + +if (( do_cleanup )); then + trap exit INT TERM + trap cleanup EXIT +fi + +this=$(realpath -- "$0"); readonly this +cd "$(dirname "$this")/.." # PWD should stay in llama.cpp project directory + +shellcheck "$this" + +readonly reqs_dir=requirements + +if [[ ${1+x} ]]; then + tmp_dir=$(realpath -- "$1") + if [[ ! ( -d $tmp_dir && -w $tmp_dir ) ]]; then + fatal "$tmp_dir is not a writable directory" + fi +else + tmp_dir=/tmp +fi + +workdir=$(mktemp -d "$tmp_dir/check-requirements.XXXX"); readonly workdir +info "Working directory: $workdir" + +check_requirements() { + local reqs=$1 + + info "$reqs: beginning check" + pip --disable-pip-version-check install -qr "$reqs" + info "$reqs: OK" +} + +check_convert_script() { + local py=$1 # e.g. ./convert-hf-to-gguf.py + local pyname=${py##*/} # e.g. convert-hf-to-gguf.py + pyname=${pyname%.py} # e.g. convert-hf-to-gguf + + info "$py: beginning check" + + local reqs="$reqs_dir/requirements-$pyname.txt" + if [[ ! -r $reqs ]]; then + fatal "$py missing requirements. Expected: $reqs" + fi + + local venv="$workdir/$pyname-venv" + python3 -m venv "$venv" + + ( + # shellcheck source=/dev/null + source "$venv/bin/activate" + + check_requirements "$reqs" + + python - "$py" "$pyname" <<'EOF' +import sys +from importlib.machinery import SourceFileLoader +py, pyname = sys.argv[1:] +SourceFileLoader(pyname, py).load_module() +EOF + ) + + if (( do_cleanup )); then + rm -rf -- "$venv" + fi + + info "$py: imports OK" +} + +readonly ignore_eq_eq='check_requirements: ignore "=="' + +for req in "$reqs_dir"/*; do + # Check that all sub-requirements are added to top-level requirements.txt + if ! grep -qF "$req" requirements.txt; then + fatal "$req needs to be added to requirements.txt" + fi + + # Make sure exact release versions aren't being pinned in the requirements + # Filters out the ignore string + if grep -vF "$ignore_eq_eq" "$req" | grep -q '=='; then + tab=$'\t' + cat >&2 < Date: Sat, 30 Dec 2023 00:31:19 +0800 Subject: [PATCH 18/35] cuda: fix vmm oom issue on NVIDIA AGX Orin (#4687) Signed-off-by: hydai --- ggml-cuda.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 9a9effcf58932..09585b07d9051 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6662,7 +6662,7 @@ static void ggml_cuda_pool_free_leg(int device, void * ptr, size_t size) { // pool with virtual memory static CUdeviceptr g_cuda_pool_addr[GGML_CUDA_MAX_DEVICES] = {0}; static size_t g_cuda_pool_used[GGML_CUDA_MAX_DEVICES] = {0}; -static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 36; // 64 GB +static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB static void * ggml_cuda_pool_malloc_vmm(int device, size_t size, size_t * actual_size) { scoped_spin_lock lock(g_cuda_pool_lock); From ce18d727a47f2473ca863a6f78bf3ad480008f72 Mon Sep 17 00:00:00 2001 From: Steward Garcia <57494570+FSSRepo@users.noreply.github.com> Date: Fri, 29 Dec 2023 11:52:15 -0500 Subject: [PATCH 19/35] clip : enable gpu backend (#4205) * clip: enable CUDA backend * add missing kernels * add enough padding for alignment * remove ggml_repeat of clip.cpp * add metal backend * llava : fixes - avoid ggml_repeat - use GGML_USE_ instead of CLIP_USE_ macros - remove unused vars --------- Co-authored-by: Georgi Gerganov --- examples/llava/CMakeLists.txt | 3 +- examples/llava/clip.cpp | 231 +++++++++++++++++++--------------- 2 files changed, 131 insertions(+), 103 deletions(-) diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index 48dae1506e81e..2985caff8379a 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -24,7 +24,8 @@ endif() if (NOT MSVC) target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h - endif() +endif() + if(TARGET BUILD_INFO) add_dependencies(llava BUILD_INFO) endif() diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index f06ec400d9a6e..f9326a5cc3dff 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -16,12 +16,19 @@ #include "clip.h" #include "ggml.h" #include "ggml-alloc.h" +#include "ggml-backend.h" + +#ifdef GGML_USE_CUBLAS +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif #define STB_IMAGE_IMPLEMENTATION #include "stb_image.h" -#define CLIP_DEBUG - static std::string format(const char * fmt, ...) { va_list ap; va_list ap2; @@ -196,20 +203,6 @@ struct clip_vision_model { struct ggml_tensor * mm_2_b; }; -// Replacement for std::vector that doesn't require zero-initialization. -struct clip_buffer { - uint8_t * data = NULL; - size_t size = 0; - - void resize(size_t size) { - delete[] data; - data = new uint8_t[size]; - this->size = size; - } - - ~clip_buffer() { delete[] data; } -}; - struct clip_ctx { bool has_text_encoder = false; bool has_vision_encoder = false; @@ -223,9 +216,10 @@ struct clip_ctx { struct gguf_context * ctx_gguf; // memory buffers to evaluate the model - clip_buffer buf_compute; - clip_buffer buf_alloc; - ggml_allocr * alloc = NULL; + ggml_backend_buffer_t params_buffer = NULL; + ggml_backend_buffer_t compute_buffer = NULL; + ggml_backend_t backend = NULL; + ggml_allocr * compute_alloc = NULL; }; static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_image_f32_batch * imgs) { @@ -252,25 +246,20 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima if(ctx->has_llava_projector) { GGML_ASSERT(batch_size == 1); } - - const auto & buf_compute = ctx->buf_compute; - struct ggml_init_params params = { - /*.mem_size =*/ buf_compute.size, - /*.mem_buffer =*/ buf_compute.data, - /*.no_alloc =*/ false, + /*.mem_size =*/ GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, }; - params.no_alloc = true; - struct ggml_context * ctx0 = ggml_init(params); struct ggml_cgraph * gf = ggml_new_graph(ctx0); struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size, image_size, 3, batch_size); - ggml_allocr_alloc(ctx->alloc, inp_raw); + ggml_allocr_alloc(ctx->compute_alloc, inp_raw); - if (!ggml_allocr_is_measure(ctx->alloc)) { - float * data = (float *)ggml_get_data(inp_raw); + if (!ggml_allocr_is_measure(ctx->compute_alloc)) { + float * data = (float *)malloc(ggml_nbytes(inp_raw)); for (size_t i = 0; i < imgs->size; i++) { const int nx = imgs->data[i].nx; @@ -289,6 +278,8 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima } } } + ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw)); + free(data); } struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1); @@ -298,36 +289,39 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima // concat class_embeddings and patch_embeddings struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); - ggml_allocr_alloc(ctx->alloc, embeddings); - if (!ggml_allocr_is_measure(ctx->alloc)) { - ggml_set_zero(embeddings); + ggml_allocr_alloc(ctx->compute_alloc, embeddings); + if (!ggml_allocr_is_measure(ctx->compute_alloc)) { + void* zero_mem = malloc(ggml_nbytes(embeddings)); + memset(zero_mem, 0, ggml_nbytes(embeddings)); + ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); + free(zero_mem); } - struct ggml_tensor * temp = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, 1, batch_size); - ggml_allocr_alloc(ctx->alloc, temp); + embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); - embeddings = ggml_acc(ctx0, embeddings, ggml_repeat(ctx0, model.class_embedding, temp), embeddings->nb[1], - embeddings->nb[2], embeddings->nb[3], 0); - embeddings = - ggml_acc(ctx0, embeddings, inp, embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); + embeddings = ggml_acc(ctx0, embeddings, inp, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions); - ggml_allocr_alloc(ctx->alloc, positions); - if (!ggml_allocr_is_measure(ctx->alloc)) { + ggml_allocr_alloc(ctx->compute_alloc, positions); + if (!ggml_allocr_is_measure(ctx->compute_alloc)) { + int* positions_data = (int*)malloc(ggml_nbytes(positions)); for (int i = 0; i < num_positions; i++) { - ggml_set_i32_1d(positions, i, i); + positions_data[i] = i; } + ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); + free(positions_data); } embeddings = - ggml_add(ctx0, embeddings, ggml_repeat(ctx0, ggml_get_rows(ctx0, model.position_embeddings, positions), embeddings)); + ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); // pre-layernorm { embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.pre_ln_w, embeddings), embeddings), - ggml_repeat(ctx0, model.pre_ln_b, embeddings)); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b); } // loop over layers @@ -340,15 +334,15 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima { cur = ggml_norm(ctx0, cur, eps); - cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_1_w, cur), cur), - ggml_repeat(ctx0, model.layers[il].ln_1_b, cur)); + cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w), + model.layers[il].ln_1_b); } // self-attention { struct ggml_tensor * Q = - ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].q_b, cur), ggml_mul_mat(ctx0, model.layers[il].q_w, cur)); + ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b); Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head)); Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size); @@ -356,14 +350,14 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size); struct ggml_tensor * K = - ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].k_b, cur), ggml_mul_mat(ctx0, model.layers[il].k_w, cur)); + ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b); K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size); K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size); struct ggml_tensor * V = - ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].v_b, cur), ggml_mul_mat(ctx0, model.layers[il].v_w, cur)); + ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b); V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size); V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); @@ -379,7 +373,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima } // attention output - cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].o_b, cur), ggml_mul_mat(ctx0, model.layers[il].o_w, cur)); + cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b); // re-add the layer input, e.g., residual cur = ggml_add(ctx0, cur, embeddings); @@ -390,12 +384,11 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima { cur = ggml_norm(ctx0, cur, eps); - cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_2_w, cur), cur), - ggml_repeat(ctx0, model.layers[il].ln_2_b, cur)); + cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b); } cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur); - cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].ff_i_b, cur), cur); + cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b); if (ctx->use_gelu) { cur = ggml_gelu_inplace(ctx0, cur); @@ -404,7 +397,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima } cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur); - cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].ff_o_b, cur), cur); + cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b); // residual 2 cur = ggml_add(ctx0, embeddings, cur); @@ -417,23 +410,26 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches); - ggml_allocr_alloc(ctx->alloc, patches); - if (!ggml_allocr_is_measure(ctx->alloc)) { - for (int i = 0; i < num_patches; ++i) { - ggml_set_i32_1d(patches, i, i+1); + ggml_allocr_alloc(ctx->compute_alloc, patches); + if (!ggml_allocr_is_measure(ctx->compute_alloc)) { + int* patches_data = (int*)malloc(ggml_nbytes(patches)); + for (int i = 0; i < num_positions; i++) { + patches_data[i] = i + 1; } + ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches)); + free(patches_data); } embeddings = ggml_get_rows(ctx0, embeddings, patches); // mm projection 0 embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); - embeddings = ggml_add(ctx0, ggml_repeat(ctx0, model.mm_0_b, embeddings), embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); embeddings = ggml_gelu(ctx0, embeddings); embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); - embeddings = ggml_add(ctx0, ggml_repeat(ctx0, model.mm_2_b, embeddings), embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); } // build the graph @@ -446,7 +442,6 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima // read and create ggml_context containing the tensors and their data struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { - struct ggml_context * meta = NULL; struct gguf_init_params params = { @@ -479,7 +474,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { printf("%s: ftype: %s\n", __func__, ftype_str.c_str()); printf("\n"); } - + const int n_tensors = gguf_get_n_tensors(ctx); // kv if (verbosity >= 3) { const int n_kv = gguf_get_n_kv(ctx); @@ -493,27 +488,38 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } // data - size_t ctx_size = 0; + size_t buffer_size = 0; { - const int n_tensors = gguf_get_n_tensors(ctx); - for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name(ctx, i); const size_t offset = gguf_get_tensor_offset(ctx, i); - struct ggml_tensor * cur = ggml_get_tensor(meta, name); - ctx_size += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE; size_t tensor_size = ggml_nbytes(cur); - size_t padded_size = ggml_nbytes_pad(cur); - ctx_size += padded_size; + buffer_size += tensor_size; if (verbosity >= 3) { - printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, padded_size=%zu, offset=%zu\n", __func__, i, - ggml_n_dims(cur), cur->name, tensor_size, padded_size, offset); + printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu\n", __func__, i, + ggml_n_dims(cur), cur->name, tensor_size, offset); } } } + buffer_size += n_tensors * 128 /* CLIP PADDING */; + clip_ctx * new_clip = new clip_ctx; +#ifdef GGML_USE_CUBLAS + new_clip->backend = ggml_backend_cuda_init(0); + printf("%s: CLIP using CUDA backend\n", __func__); +#endif + +#ifdef GGML_USE_METAL + new_clip->backend = ggml_backend_metal_init(); + printf("%s: CLIP using Metal backend\n", __func__); +#endif + + if (!new_clip->backend) { + new_clip->backend = ggml_backend_cpu_init(); + printf("%s: CLIP using CPU backend\n", __func__); + } // model size and capabilities { @@ -539,17 +545,20 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { printf("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder); printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder); printf("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector); - printf("%s: model size: %.2f MB\n", __func__, (ctx_size / 1024.0 / 1024.0)); + printf("%s: model size: %.2f MB\n", __func__, buffer_size / 1024.0 / 1024.0); printf("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0); } } + printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, buffer_size / (1024.0 * 1024.0), n_tensors); + // load tensors { + std::vector read_buf; struct ggml_init_params params = { - /*.mem_size =*/ ctx_size, + /*.mem_size =*/ (n_tensors + 1) * ggml_tensor_overhead(), /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ false, + /*.no_alloc =*/ true, }; new_clip->ctx = ggml_init(params); @@ -566,13 +575,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { return nullptr; } - const int n_tensors = gguf_get_n_tensors(ctx); + // add tensors to context for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name(ctx, i); struct ggml_tensor * t = ggml_get_tensor(meta, name); struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx, t); ggml_set_name(cur, name); + } + // alloc memory and offload data + new_clip->params_buffer = ggml_backend_alloc_buffer(new_clip->backend, buffer_size); + ggml_allocr* alloc = ggml_allocr_new_from_buffer(new_clip->params_buffer); + for (int i = 0; i < n_tensors; ++i) { + const char * name = gguf_get_tensor_name(ctx, i); + struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx, name); + ggml_allocr_alloc(alloc, cur); const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i); fin.seekg(offset, std::ios::beg); if (!fin) { @@ -580,10 +597,22 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { clip_free(new_clip); return nullptr; } - - fin.read(reinterpret_cast(cur->data), ggml_nbytes(t)); + int num_bytes = ggml_nbytes(cur); + if (ggml_backend_is_cpu(new_clip->backend) +#ifdef GGML_USE_METAL + || ggml_backend_is_metal(new_clip->backend) +#endif + ) { + // for the CPU and Metal backend, we can read directly into the tensor + fin.read(reinterpret_cast(cur->data), num_bytes); + } else { + // read into a temporary buffer first, then copy to device memory + read_buf.resize(num_bytes); + fin.read(reinterpret_cast(read_buf.data()), num_bytes); + ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes); + } } - + ggml_allocr_free(alloc); fin.close(); } @@ -657,18 +686,16 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { // measure mem requirement and allocate { - static const size_t tensor_alignment = 32; - new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead()); - new_clip->alloc = ggml_allocr_new_measure(tensor_alignment); + new_clip->compute_alloc = ggml_allocr_new_measure_from_backend(new_clip->backend); clip_image_f32_batch batch; batch.size = 1; ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch); - size_t alloc_size = ggml_allocr_alloc_graph(new_clip->alloc, gf) + tensor_alignment; - ggml_allocr_free(new_clip->alloc); - new_clip->buf_alloc.resize(alloc_size); - new_clip->alloc = ggml_allocr_new(new_clip->buf_alloc.data, new_clip->buf_alloc.size, tensor_alignment); + size_t compute_memory_buffer_size = ggml_allocr_alloc_graph(new_clip->compute_alloc, gf); + ggml_allocr_free(new_clip->compute_alloc); + new_clip->compute_buffer = ggml_backend_alloc_buffer(new_clip->backend, compute_memory_buffer_size); + new_clip->compute_alloc = ggml_allocr_new_from_buffer(new_clip->compute_buffer); - printf("%s: total allocated memory: %.2f MB\n", __func__, (new_clip->buf_compute.size + alloc_size)/1024.0/1024.0); + printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0); } return new_clip; @@ -852,29 +879,29 @@ bool clip_image_batch_encode(const clip_ctx * ctx, const int n_threads, const cl } // reset alloc buffer to clean the memory from previous invocations - ggml_allocr_reset(ctx->alloc); + ggml_allocr_reset(ctx->compute_alloc); // build the inference graph ggml_cgraph * gf = clip_image_build_graph(ctx, imgs); - ggml_allocr_alloc_graph(ctx->alloc, gf); + ggml_allocr_alloc_graph(ctx->compute_alloc, gf); + + if (ggml_backend_is_cpu(ctx->backend)) { + ggml_backend_cpu_set_n_threads(ctx->backend, n_threads); + } - struct ggml_cplan plan = ggml_graph_plan(gf, n_threads); - if (plan.work_size > 0) { - plan.work_data = (uint8_t *)malloc(plan.work_size); +#ifdef GGML_USE_METAL + if (ggml_backend_is_metal(ctx->backend)) { + ggml_backend_metal_set_n_cb(ctx->backend, n_threads); } +#endif - ggml_graph_compute(gf, &plan); + ggml_backend_graph_compute(ctx->backend, gf); // the last node is the embedding tensor -struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 1]; + struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 1]; // copy the embeddings to the location passed by the user - memcpy(vec, ggml_get_data_f32(embeddings), ggml_nbytes(embeddings)); - - if (plan.work_size > 0) { - free(plan.work_data); - } - + ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings)); return true; } @@ -1045,8 +1072,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i gguf_free(ctx_out); { - printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); - printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); + printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); + printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); int64_t sum_all = 0; for (size_t i = 0; i < hist_all.size(); ++i) { From 0235b9b571f3cc7d2b8836409a5404b41ce1379c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 29 Dec 2023 18:53:34 +0200 Subject: [PATCH 20/35] clip : use ggml_backend_buffer_is_host (#4205) --- examples/llava/clip.cpp | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index f9326a5cc3dff..6a731eeecbc4c 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -598,11 +598,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { return nullptr; } int num_bytes = ggml_nbytes(cur); - if (ggml_backend_is_cpu(new_clip->backend) -#ifdef GGML_USE_METAL - || ggml_backend_is_metal(new_clip->backend) -#endif - ) { + if (ggml_backend_buffer_is_host(new_clip->params_buffer)) { // for the CPU and Metal backend, we can read directly into the tensor fin.read(reinterpret_cast(cur->data), num_bytes); } else { From a20f3c7465d6d1b33767757c2760643b799a81bf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 29 Dec 2023 23:12:53 +0100 Subject: [PATCH 21/35] CUDA: fix tensor core logic for Pascal and HIP (#4682) --- ggml-cuda.cu | 72 ++++++++++++++++++++++++++++------------------------ 1 file changed, 39 insertions(+), 33 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 09585b07d9051..71a64ca09ec8b 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -123,24 +123,6 @@ #define GGML_CUDA_MAX_NODES 8192 -// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication -// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant -// for large computational tasks. the drawback is that this requires some extra amount of VRAM: -// - 7B quantum model: +100-200 MB -// - 13B quantum model: +200-400 MB -// -//#define GGML_CUDA_FORCE_MMQ - -// TODO: improve this to be correct for more hardware -// for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores -// probably other such cases, and not sure what happens on AMD hardware -#if !defined(GGML_CUDA_FORCE_MMQ) -#define CUDA_USE_TENSOR_CORES -#endif - -// max batch size to use MMQ kernels when tensor cores are available -#define MMQ_MAX_BATCH_SIZE 32 - #if defined(GGML_USE_HIPBLAS) #define __CUDA_ARCH__ 1300 @@ -207,6 +189,23 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { } #endif // defined(GGML_USE_HIPBLAS) +// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication +// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant +// for large computational tasks. the drawback is that this requires some extra amount of VRAM: +// - 7B quantum model: +100-200 MB +// - 13B quantum model: +200-400 MB +// +//#define GGML_CUDA_FORCE_MMQ + +// TODO: improve this to be correct for more hardware +// for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores +#if !defined(GGML_CUDA_FORCE_MMQ) && (!defined(GGML_USE_HIPBLAS) || defined(RDNA3)) +#define CUDA_USE_TENSOR_CORES +#endif + +// max batch size to use MMQ kernels when tensor cores are available +#define MMQ_MAX_BATCH_SIZE 32 + #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data #endif @@ -8661,11 +8660,26 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } } -#ifdef CUDA_USE_TENSOR_CORES - const bool use_tensor_cores = true; +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + const bool fp16_performance_good = true; + +#ifdef RDNA3 + const bool use_mul_mat_q = false; #else - const bool use_tensor_cores = false; -#endif + const bool use_mul_mat_q = true; +#endif // RDNA3 + +#else + + const bool fp16_performance_good = min_compute_capability >= CC_VOLTA; + bool use_mul_mat_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type); +#ifdef CUDA_USE_TENSOR_CORES + // when tensor cores are available, use them for large batch size + // ref: https://github.com/ggerganov/llama.cpp/pull/3776 + use_mul_mat_q = use_mul_mat_q && !(fp16_performance_good && src1->ne[1] > MMQ_MAX_BATCH_SIZE); +#endif // CUDA_USE_TENSOR_CORES + +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) // debug helpers //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); @@ -8675,13 +8689,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 //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_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + if (!split && all_on_device && !fp16_performance_good && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { // KQ single-batch ggml_cuda_mul_mat_vec_p021(src0, src1, dst); - } else if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + } else if (!split && all_on_device && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_cuda_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && all_on_device && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { // KQ + KQV multi-batch ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { @@ -8701,14 +8715,6 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); } } else { - bool use_mul_mat_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type); - - // when tensor cores are available, use them for large batch size - // ref: https://github.com/ggerganov/llama.cpp/pull/3776 - if (use_tensor_cores && min_compute_capability >= CC_VOLTA && src1->ne[1] > MMQ_MAX_BATCH_SIZE) { - use_mul_mat_q = false; - } - if (use_mul_mat_q) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); } else { From 24a447e20af425fa44cf10feaa632b6bb596c80f Mon Sep 17 00:00:00 2001 From: automaticcat Date: Sat, 30 Dec 2023 15:07:48 +0700 Subject: [PATCH 22/35] ggml : add ggml_cpu_has_avx_vnni() (#4589) * feat: add avx_vnni based on intel documents * ggml: add avx vnni based on intel document * llama: add avx vnni information display * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * Update ggml.c Fix indentation upgate Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- README.md | 30 ++++++++++++++++++++++-------- common/common.cpp | 1 + ggml.c | 8 ++++++++ ggml.h | 1 + llama.cpp | 1 + 5 files changed, 33 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 48dcd6464038e..ca6d14e175b09 100644 --- a/README.md +++ b/README.md @@ -385,16 +385,30 @@ Building the program with BLAS support may lead to some performance improvements Check [BLIS.md](docs/BLIS.md) for more information. -- #### Intel MKL +- #### Intel oneMKL + - Using manual oneAPI installation: + By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: + ```bash + mkdir build + cd build + source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-runtime docker image, only required for manual installation + cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON + cmake --build . --config Release + ``` - By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. You may also specify it by: + - Using oneAPI docker image: + If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-runtime](https://hub.docker.com/r/intel/oneapi-runtime) - ```bash - mkdir build - cd build - cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx - cmake --build . --config Release - ``` + ```bash + mkdir build + cd build + cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON + cmake --build . --config Release + ``` + + Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. + + Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information. - #### cuBLAS diff --git a/common/common.cpp b/common/common.cpp index b3425ab09eaf8..eacaee18e0907 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1394,6 +1394,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER); fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); + fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false"); fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); diff --git a/ggml.c b/ggml.c index a9e1ea9b40ec4..bcec200f65e04 100644 --- a/ggml.c +++ b/ggml.c @@ -19638,6 +19638,14 @@ int ggml_cpu_has_avx(void) { #endif } +int ggml_cpu_has_avx_vnni(void) { +#if defined(__AVXVNNI__) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_avx2(void) { #if defined(__AVX2__) return 1; diff --git a/ggml.h b/ggml.h index 67d6bc4f1ef1b..64f4e45e880fa 100644 --- a/ggml.h +++ b/ggml.h @@ -2198,6 +2198,7 @@ extern "C" { // GGML_API int ggml_cpu_has_avx (void); + GGML_API int ggml_cpu_has_avx_vnni (void); GGML_API int ggml_cpu_has_avx2 (void); GGML_API int ggml_cpu_has_avx512 (void); GGML_API int ggml_cpu_has_avx512_vbmi(void); diff --git a/llama.cpp b/llama.cpp index 68c7cced6bb5a..a833d4c15a9d0 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10780,6 +10780,7 @@ const char * llama_print_system_info(void) { s = ""; s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | "; + s += "AVX_VNNI = " + std::to_string(ggml_cpu_has_avx_vnni()) + " | "; s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | "; s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | "; s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | "; From 39d8bc71edcb8b6f99d46fa4216af7a15232e218 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 30 Dec 2023 13:52:01 +0100 Subject: [PATCH 23/35] CUDA: fixed tensor cores not being used on RDNA3 (#4697) --- ggml-cuda.cu | 47 ++++++++++++++++++++++++----------------------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 71a64ca09ec8b..8c2712308a45d 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -119,10 +119,29 @@ #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products #define CC_VOLTA 700 #define CC_OFFSET_AMD 1000000 +#define CC_RDNA1 (CC_OFFSET_AMD + 1010) #define CC_RDNA2 (CC_OFFSET_AMD + 1030) +#define CC_RDNA3 (CC_OFFSET_AMD + 1100) #define GGML_CUDA_MAX_NODES 8192 +// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication +// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant +// for large computational tasks. the drawback is that this requires some extra amount of VRAM: +// - 7B quantum model: +100-200 MB +// - 13B quantum model: +200-400 MB +// +//#define GGML_CUDA_FORCE_MMQ + +// TODO: improve this to be correct for more hardware +// for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores +#if !defined(GGML_CUDA_FORCE_MMQ) +#define CUDA_USE_TENSOR_CORES +#endif + +// max batch size to use MMQ kernels when tensor cores are available +#define MMQ_MAX_BATCH_SIZE 32 + #if defined(GGML_USE_HIPBLAS) #define __CUDA_ARCH__ 1300 @@ -189,23 +208,6 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { } #endif // defined(GGML_USE_HIPBLAS) -// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication -// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant -// for large computational tasks. the drawback is that this requires some extra amount of VRAM: -// - 7B quantum model: +100-200 MB -// - 13B quantum model: +200-400 MB -// -//#define GGML_CUDA_FORCE_MMQ - -// TODO: improve this to be correct for more hardware -// for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores -#if !defined(GGML_CUDA_FORCE_MMQ) && (!defined(GGML_USE_HIPBLAS) || defined(RDNA3)) -#define CUDA_USE_TENSOR_CORES -#endif - -// max batch size to use MMQ kernels when tensor cores are available -#define MMQ_MAX_BATCH_SIZE 32 - #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data #endif @@ -8661,13 +8663,12 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - const bool fp16_performance_good = true; -#ifdef RDNA3 - const bool use_mul_mat_q = false; -#else - const bool use_mul_mat_q = true; -#endif // RDNA3 + const bool fp16_performance_good = min_compute_capability >= CC_RDNA1; + bool use_mul_mat_q = ggml_is_quantized(src0->type); +#ifdef CUDA_USE_TENSOR_CORES + use_mul_mat_q = use_mul_mat_q && min_compute_capability < CC_RDNA3; +#endif // CUDA_USE_TENSOR_CORES #else From 9fbda719de18a9400a064c28759c39d55d687d3e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 30 Dec 2023 23:24:42 +0200 Subject: [PATCH 24/35] clip : refactor + bug fixes (#4696) * clip : refactor + bug fixes ggml-ci * server : add log message --- examples/llava/clip.cpp | 239 +++++++++++++++++++++---------------- examples/llava/clip.h | 48 +++----- examples/llava/llava.cpp | 4 +- examples/server/server.cpp | 38 +++--- 4 files changed, 168 insertions(+), 161 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 6a731eeecbc4c..cfb79e78940a7 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -146,6 +146,27 @@ static std::string get_ftype(int ftype) { } } +// +// image data +// + +// RGB uint8 image +struct clip_image_u8 { + int nx; + int ny; + + std::vector buf; +}; + +// RGB float32 image (NHWC) +// Memory layout: RGBRGBRGB... +struct clip_image_f32 { + int nx; + int ny; + + std::vector buf; +}; + // // clip layers // @@ -204,16 +225,21 @@ struct clip_vision_model { }; struct clip_ctx { - bool has_text_encoder = false; - bool has_vision_encoder = false; + bool has_text_encoder = false; + bool has_vision_encoder = false; bool has_llava_projector = false; + struct clip_vision_model vision_model; + float image_mean[3]; float image_std[3]; bool use_gelu = false; int32_t ftype = 1; - struct ggml_context * ctx; + struct gguf_context * ctx_gguf; + struct ggml_context * ctx_data; + + std::vector buf_compute_meta; // memory buffers to evaluate the model ggml_backend_buffer_t params_buffer = NULL; @@ -222,7 +248,7 @@ struct clip_ctx { ggml_allocr * compute_alloc = NULL; }; -static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_image_f32_batch * imgs) { +static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) { if (!ctx->has_vision_encoder) { printf("This gguf file seems to have no vision encoder\n"); return nullptr; @@ -243,13 +269,14 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima //const int projection_dim = hparams.projection_dim; const float eps = hparams.eps; int batch_size = imgs->size; - if(ctx->has_llava_projector) { + if (ctx->has_llava_projector) { GGML_ASSERT(batch_size == 1); } + struct ggml_init_params params = { - /*.mem_size =*/ GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead(), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, + /*.mem_size =*/ ctx->buf_compute_meta.size(), + /*.mem_buffer =*/ ctx->buf_compute_meta.data(), + /*.no_alloc =*/ true, }; struct ggml_context * ctx0 = ggml_init(params); @@ -272,7 +299,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima for (int k = 0; k < 3; k++) { for (int y = 0; y < ny; y++) { for (int x = 0; x < nx; x++) { - data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].data[3 * (y * nx + x) + k]; + data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k]; } } } @@ -413,7 +440,7 @@ static ggml_cgraph * clip_image_build_graph(const clip_ctx * ctx, const clip_ima ggml_allocr_alloc(ctx->compute_alloc, patches); if (!ggml_allocr_is_measure(ctx->compute_alloc)) { int* patches_data = (int*)malloc(ggml_nbytes(patches)); - for (int i = 0; i < num_positions; i++) { + for (int i = 0; i < num_patches; i++) { patches_data[i] = i + 1; } ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches)); @@ -561,8 +588,8 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { /*.no_alloc =*/ true, }; - new_clip->ctx = ggml_init(params); - if (!new_clip->ctx) { + new_clip->ctx_data = ggml_init(params); + if (!new_clip->ctx_data) { fprintf(stderr, "%s: ggml_init() failed\n", __func__); clip_free(new_clip); return nullptr; @@ -579,7 +606,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name(ctx, i); struct ggml_tensor * t = ggml_get_tensor(meta, name); - struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx, t); + struct ggml_tensor * cur = ggml_dup_tensor(new_clip->ctx_data, t); ggml_set_name(cur, name); } @@ -588,7 +615,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { ggml_allocr* alloc = ggml_allocr_new_from_buffer(new_clip->params_buffer); for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name(ctx, i); - struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx, name); + struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx_data, name); ggml_allocr_alloc(alloc, cur); const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i); fin.seekg(offset, std::ios::beg); @@ -617,20 +644,20 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { // load vision model auto & vision_model = new_clip->vision_model; auto & hparams = vision_model.hparams; - hparams.hidden_size = get_u32(ctx, format(KEY_N_EMBD, "vision")); - hparams.n_head = get_u32(ctx, format(KEY_N_HEAD, "vision")); + hparams.hidden_size = get_u32(ctx, format(KEY_N_EMBD, "vision")); + hparams.n_head = get_u32(ctx, format(KEY_N_HEAD, "vision")); hparams.n_intermediate = get_u32(ctx, format(KEY_N_FF, "vision")); - hparams.n_layer = get_u32(ctx, format(KEY_N_BLOCK, "vision")); - hparams.image_size = get_u32(ctx, KEY_IMAGE_SIZE); - hparams.patch_size = get_u32(ctx, KEY_PATCH_SIZE); + hparams.n_layer = get_u32(ctx, format(KEY_N_BLOCK, "vision")); + hparams.image_size = get_u32(ctx, KEY_IMAGE_SIZE); + hparams.patch_size = get_u32(ctx, KEY_PATCH_SIZE); hparams.projection_dim = get_u32(ctx, format(KEY_PROJ_DIM, "vision")); - hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision")); + hparams.eps = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision")); int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN); - int idx_std = get_key_idx(ctx, KEY_IMAGE_STD); + int idx_std = get_key_idx(ctx, KEY_IMAGE_STD); for (int i = 0; i < 3; ++i) { new_clip->image_mean[i] = *((const float *)gguf_get_arr_data(ctx, idx_mean)); - new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std)); + new_clip->image_std[i] = *((const float *)gguf_get_arr_data(ctx, idx_std)); } if (verbosity >= 2) { @@ -644,35 +671,35 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { printf("v_n_layer %d\n", hparams.n_layer); } - vision_model.patch_embeddings = get_tensor(new_clip->ctx, TN_PATCH_EMBD); - vision_model.class_embedding = get_tensor(new_clip->ctx, TN_CLASS_EMBD); - vision_model.position_embeddings = get_tensor(new_clip->ctx, format(TN_POS_EMBD, "v")); - vision_model.pre_ln_w = get_tensor(new_clip->ctx, format(TN_LN_PRE, "v", "weight")); - vision_model.pre_ln_b = get_tensor(new_clip->ctx, format(TN_LN_PRE, "v", "bias")); - vision_model.mm_0_w = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 0, "weight")); - vision_model.mm_0_b = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 0, "bias")); - vision_model.mm_2_w = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 2, "weight")); - vision_model.mm_2_b = get_tensor(new_clip->ctx, format(TN_LLAVA_PROJ, 2, "bias")); + vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); + vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); + vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v")); + vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); + vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); + vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight")); + vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias")); + vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight")); + vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias")); vision_model.layers.resize(hparams.n_layer); for (int il = 0; il < hparams.n_layer; ++il) { auto & layer = vision_model.layers[il]; - layer.k_w = get_tensor(new_clip->ctx, format(TN_ATTN_K, "v", il, "weight")); - layer.q_w = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "v", il, "weight")); - layer.v_w = get_tensor(new_clip->ctx, format(TN_ATTN_V, "v", il, "weight")); - layer.o_w = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "v", il, "weight")); - layer.ln_1_w = get_tensor(new_clip->ctx, format(TN_LN_1, "v", il, "weight")); - layer.ln_2_w = get_tensor(new_clip->ctx, format(TN_LN_2, "v", il, "weight")); - layer.ff_i_w = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "v", il, "weight")); - layer.ff_o_w = get_tensor(new_clip->ctx, format(TN_FFN_UP, "v", il, "weight")); - layer.k_b = get_tensor(new_clip->ctx, format(TN_ATTN_K, "v", il, "bias")); - layer.q_b = get_tensor(new_clip->ctx, format(TN_ATTN_Q, "v", il, "bias")); - layer.v_b = get_tensor(new_clip->ctx, format(TN_ATTN_V, "v", il, "bias")); - layer.o_b = get_tensor(new_clip->ctx, format(TN_ATTN_OUTPUT, "v", il, "bias")); - layer.ln_1_b = get_tensor(new_clip->ctx, format(TN_LN_1, "v", il, "bias")); - layer.ln_2_b = get_tensor(new_clip->ctx, format(TN_LN_2, "v", il, "bias")); - layer.ff_i_b = get_tensor(new_clip->ctx, format(TN_FFN_DOWN, "v", il, "bias")); - layer.ff_o_b = get_tensor(new_clip->ctx, format(TN_FFN_UP, "v", il, "bias")); + layer.k_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "weight")); + layer.q_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q, "v", il, "weight")); + layer.v_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_V, "v", il, "weight")); + layer.o_w = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "weight")); + layer.ln_1_w = get_tensor(new_clip->ctx_data, format(TN_LN_1, "v", il, "weight")); + layer.ln_2_w = get_tensor(new_clip->ctx_data, format(TN_LN_2, "v", il, "weight")); + layer.ff_i_w = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN, "v", il, "weight")); + layer.ff_o_w = get_tensor(new_clip->ctx_data, format(TN_FFN_UP, "v", il, "weight")); + layer.k_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_K, "v", il, "bias")); + layer.q_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_Q, "v", il, "bias")); + layer.v_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_V, "v", il, "bias")); + layer.o_b = get_tensor(new_clip->ctx_data, format(TN_ATTN_OUTPUT, "v", il, "bias")); + layer.ln_1_b = get_tensor(new_clip->ctx_data, format(TN_LN_1, "v", il, "bias")); + layer.ln_2_b = get_tensor(new_clip->ctx_data, format(TN_LN_2, "v", il, "bias")); + layer.ff_i_b = get_tensor(new_clip->ctx_data, format(TN_FFN_DOWN, "v", il, "bias")); + layer.ff_o_b = get_tensor(new_clip->ctx_data, format(TN_FFN_UP, "v", il, "bias")); } } @@ -680,8 +707,9 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { new_clip->ctx_gguf = ctx; -// measure mem requirement and allocate + // measure mem requirement and allocate { + new_clip->buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead()); new_clip->compute_alloc = ggml_allocr_new_measure_from_backend(new_clip->backend); clip_image_f32_batch batch; batch.size = 1; @@ -697,26 +725,27 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { return new_clip; } -clip_image_u8 * make_clip_image_u8() { - auto img = new clip_image_u8(); - return img; +struct clip_image_u8 * clip_image_u8_init() { + return new clip_image_u8(); +} + +struct clip_image_f32 * clip_image_f32_init() { + return new clip_image_f32(); } -clip_image_f32 * make_clip_image_f32() { return new clip_image_f32(); } -void clip_image_u8_free(clip_image_u8 * img) { if (img->data) { delete[] img->data; } delete img; } -void clip_image_f32_free(clip_image_f32 * img) { if (img->data) { delete[] img->data; } delete img; } +void clip_image_u8_free (struct clip_image_u8 * img) { delete img; } +void clip_image_f32_free(struct clip_image_f32 * img) { delete img; } static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) { img->nx = nx; img->ny = ny; - img->size = nx * ny * 3; - img->data = new uint8_t[img->size](); - memcpy(img->data, data, img->size); + img->buf.resize(3 * nx * ny); + memcpy(img->buf.data(), data, img->buf.size()); } bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { int nx, ny, nc; - auto data = stbi_load(fname, &nx, &ny, &nc, 3); + auto * data = stbi_load(fname, &nx, &ny, &nc, 3); if (!data) { fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname); return false; @@ -728,7 +757,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img) { int nx, ny, nc; - auto data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3); + auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3); if (!data) { fprintf(stderr, "%s: failed to decode image bytes\n", __func__); return false; @@ -740,7 +769,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length // normalize: x = (x - mean) / std // TODO: implement bicubic interpolation instead of linear. -bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) { +bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) { if (!ctx->has_vision_encoder) { printf("This gguf file seems to have no vision encoder\n"); return false; @@ -749,18 +778,17 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104) // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156 - clip_image_u8 * temp = make_clip_image_u8(); // we will keep the input image data here temporarily + clip_image_u8 * temp = clip_image_u8_init(); // we will keep the input image data here temporarily if (pad2square && img->nx != img->ny) { int longer_side = std::max(img->nx, img->ny); temp->nx = longer_side; temp->ny = longer_side; - temp->size = 3 * longer_side * longer_side; - temp->data = new uint8_t[temp->size](); - uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA + temp->buf.resize(3 * longer_side * longer_side); + const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA // fill with background color - for (size_t i = 0; i < temp->size; i++) { - temp->data[i] = bc[i % 3]; + for (size_t i = 0; i < temp->buf.size(); i++) { + temp->buf[i] = bc[i % 3]; } // copy from the input image @@ -768,17 +796,16 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip for (int x = 0; x < img->nx; x++) { const int i = 3 * (y * img->nx + x); const int j = 3 * (y * temp->nx + x); - temp->data[j] = img->data[i]; - temp->data[j+1] = img->data[i+1]; - temp->data[j+2] = img->data[i+2]; + temp->buf[j] = img->buf[i]; + temp->buf[j+1] = img->buf[i+1]; + temp->buf[j+2] = img->buf[i+2]; } } } else { - temp->nx = img->nx; - temp->ny = img->ny; - temp->size = img->size; - temp->data = new uint8_t[temp->size](); - memcpy(&temp->data[0], &img->data[0], temp->size); // copy + temp->nx = img->nx; + temp->ny = img->ny; + temp->buf.resize(img->buf.size()); + memcpy(temp->buf.data(), img->buf.data(), temp->buf.size()); } const int nx = temp->nx; @@ -789,8 +816,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip res->nx = nx2; res->ny = ny2; - res->size = 3 * nx2 * ny2; - res->data = new float[res->size](); + res->buf.resize(3 * nx2 * ny2); const float scale = std::max(nx, ny) / (float)ctx->vision_model.hparams.image_size; @@ -821,10 +847,10 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip const int j10 = 3 * (y1 * nx + x0) + c; const int j11 = 3 * (y1 * nx + x1) + c; - const float v00 = temp->data[j00]; - const float v01 = temp->data[j01]; - const float v10 = temp->data[j10]; - const float v11 = temp->data[j11]; + const float v00 = temp->buf[j00]; + const float v01 = temp->buf[j01]; + const float v10 = temp->buf[j10]; + const float v11 = temp->buf[j11]; const float v0 = v00 * (1.0f - dx) + v01 * dx; const float v1 = v10 * (1.0f - dx) + v11 * dx; @@ -835,7 +861,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip const int i = 3 * (y * nx3 + x) + c; - res->data[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c]; + res->buf[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c]; } } } @@ -845,12 +871,13 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip } void clip_free(clip_ctx * ctx) { - ggml_free(ctx->ctx); + ggml_free(ctx->ctx_data); gguf_free(ctx->ctx_gguf); + delete ctx; } -bool clip_image_encode(const clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) { +bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) { if (!ctx->has_vision_encoder) { printf("This gguf file seems to have no vision encoder\n"); return false; @@ -862,8 +889,7 @@ bool clip_image_encode(const clip_ctx * ctx, const int n_threads, clip_image_f32 return clip_image_batch_encode(ctx, n_threads, &imgs, vec); } -bool clip_image_batch_encode(const clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) { - +bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) { if (!ctx->has_vision_encoder) { printf("This gguf file seems to have no vision encoder\n"); return false; @@ -906,31 +932,32 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i ggml_type type = GGML_TYPE_Q4_1; switch (itype) { - case 2: - type = GGML_TYPE_Q4_0; - break; - case 3: - type = GGML_TYPE_Q4_1; - break; - case 6: - type = GGML_TYPE_Q5_0; - break; - case 7: - type = GGML_TYPE_Q5_1; - break; - case 8: - type = GGML_TYPE_Q8_0; - break; - default: - fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype); - return false; + case 2: + type = GGML_TYPE_Q4_0; + break; + case 3: + type = GGML_TYPE_Q4_1; + break; + case 6: + type = GGML_TYPE_Q5_0; + break; + case 7: + type = GGML_TYPE_Q5_1; + break; + case 8: + type = GGML_TYPE_Q8_0; + break; + default: + fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype); + return false; }; - auto ctx_clip = clip_model_load(fname_inp, 2); + auto * ctx_clip = clip_model_load(fname_inp, 2); + const auto & ctx_src = ctx_clip->ctx_gguf; - const auto & ctx_data = ctx_clip->ctx; + const auto & ctx_data = ctx_clip->ctx_data; - auto ctx_out = gguf_init_empty(); + auto * ctx_out = gguf_init_empty(); gguf_set_kv(ctx_out, ctx_src); gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION); gguf_set_val_u32(ctx_out, "general.file_type", itype); diff --git a/examples/llava/clip.h b/examples/llava/clip.h index f11df85de9a73..458a256a107fe 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -35,31 +35,14 @@ struct clip_vision_hparams { float eps; }; -/** load mmproj model */ -CLIP_API struct clip_ctx * clip_model_load(const char * fname, const int verbosity); -/** free mmproj model */ +CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity); + CLIP_API void clip_free(struct clip_ctx * ctx); -size_t clip_embd_nbytes(const struct clip_ctx * ctx); -int clip_n_patches(const struct clip_ctx * ctx); -int clip_n_mmproj_embd(const struct clip_ctx * ctx); +CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx); -// RGB uint8 image -struct clip_image_u8 { - int nx; - int ny; - uint8_t * data = NULL; - size_t size; -}; - -// RGB float32 image (NHWC) -// Memory layout: RGBRGBRGB... -struct clip_image_f32 { - int nx; - int ny; - float * data = NULL; - size_t size; -}; +CLIP_API int clip_n_patches (const struct clip_ctx * ctx); +CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx); struct clip_image_u8_batch { struct clip_image_u8 * data; @@ -71,21 +54,22 @@ struct clip_image_f32_batch { size_t size; }; -struct clip_image_u8 * make_clip_image_u8(); -struct clip_image_f32 * make_clip_image_f32(); -CLIP_API void clip_image_u8_free(clip_image_u8 * img); -CLIP_API void clip_image_f32_free(clip_image_f32 * img); +CLIP_API struct clip_image_u8 * clip_image_u8_init (); +CLIP_API struct clip_image_f32 * clip_image_f32_init(); + +CLIP_API void clip_image_u8_free (struct clip_image_u8 * img); +CLIP_API void clip_image_f32_free(struct clip_image_f32 * img); + CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * 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); -bool clip_image_preprocess(const struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, const bool pad2square); -bool clip_image_encode(const struct clip_ctx * ctx, const int n_threads, struct clip_image_f32 * img, float * vec); - -bool clip_image_batch_encode(const struct clip_ctx * ctx, const int n_threads, const struct clip_image_f32_batch * imgs, - float * vec); +CLIP_API bool clip_image_preprocess (struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, bool pad2square); +CLIP_API bool clip_image_encode (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec); +CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec); -bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype); +CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype); #ifdef __cplusplus } diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 0cae8c4b10a3a..d42e7582e8c66 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -10,7 +10,7 @@ #include "base64.hpp" static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) { - clip_image_f32 * img_res = make_clip_image_f32(); + clip_image_f32 * img_res = clip_image_f32_init(); if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) { fprintf(stderr, "%s: unable to preprocess image\n", __func__); clip_image_f32_free(img_res); @@ -86,7 +86,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_ } LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) { - clip_image_u8 * img = make_clip_image_u8(); + clip_image_u8 * img = clip_image_u8_init(); if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) { clip_image_u8_free(img); fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 0aada8e28029c..52d9b97680182 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -82,7 +82,7 @@ static inline bool is_base64(uint8_t c) return (isalnum(c) || (c == '+') || (c == '/')); } -static std::vector base64_decode(std::string const &encoded_string) +static std::vector base64_decode(const std::string & encoded_string) { int i = 0; int j = 0; @@ -209,10 +209,10 @@ struct slot_image int32_t id; bool request_encode_image = false; - float* image_embedding = nullptr; + float * image_embedding = nullptr; int32_t image_tokens = 0; - clip_image_u8 img_data; + clip_image_u8 * img_data; std::string prefix_prompt; // before of this image }; @@ -434,10 +434,12 @@ struct llama_client_slot generated_token_probs.clear(); - for (slot_image &img : images) + for (slot_image & img : images) { free(img.image_embedding); - delete[] img.img_data.data; + if (img.img_data) { + clip_image_u8_free(img.img_data); + } img.prefix_prompt = ""; } @@ -851,24 +853,17 @@ struct llama_server_context { for (const auto &img : *images_data) { - std::string data_b64 = img["data"].get(); + const std::vector image_buffer = base64_decode(img["data"].get()); + slot_image img_sl; img_sl.id = img.count("id") != 0 ? img["id"].get() : slot->images.size(); - int width, height, channels; - std::vector image_buffer = base64_decode(data_b64); - data_b64.clear(); - auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3); - if (!data) { + img_sl.img_data = clip_image_u8_init(); + if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data)) + { LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id); return false; } - LOG_TEE("slot %i - image loaded [id: %i] resolution (%i x %i)\n", slot->id, img_sl.id, width, height); - img_sl.img_data.nx = width; - img_sl.img_data.ny = height; - img_sl.img_data.size = width * height * 3; - img_sl.img_data.data = new uint8_t[width * height * 3](); - memcpy(img_sl.img_data.data, data, width * height * 3); - stbi_image_free(data); + LOG_TEE("slot %i - loaded image\n", slot->id); img_sl.request_encode_image = true; slot->images.push_back(img_sl); } @@ -1143,8 +1138,8 @@ struct llama_server_context { continue; } - clip_image_f32 img_res; - if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true)) + clip_image_f32 * img_res = clip_image_f32_init(); + if (!clip_image_preprocess(clp_ctx, img.img_data, img_res, /*pad2square =*/ true)) { LOG_TEE("Error processing the given image"); clip_free(clp_ctx); @@ -1159,11 +1154,12 @@ struct llama_server_context return false; } LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id); - if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding)) + if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding)) { LOG_TEE("Unable to encode image\n"); return false; } + clip_image_f32_free(img_res); img.request_encode_image = false; } From e39106c0554cbd0e9310e08fb3b2a577ea4b6273 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 31 Dec 2023 11:43:31 +0200 Subject: [PATCH 25/35] ggml : add ggml_vdotq_s32 alias (#4715) ggml-ci --- ggml-quants.c | 118 ++++++++++++++++++++++++++------------------------ 1 file changed, 61 insertions(+), 57 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 05ef8f9b7e50b..55a9496d1b37b 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -410,13 +410,17 @@ inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) { #if !defined(__ARM_FEATURE_DOTPROD) -inline static int32x4_t vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) { +inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) { const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b)); const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b)); return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1))); } +#else + +#define ggml_vdotq_s32(a, b, c) vdotq_s32(a, b, c) + #endif #endif @@ -2481,8 +2485,8 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); // dot product into int32x4_t - const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h); - const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h); + const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h); + const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); @@ -2769,8 +2773,8 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); // dot product into int32x4_t - const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h); - const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h); + const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h); + const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d); @@ -2936,11 +2940,11 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( - vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), - vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( - vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), - vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); @@ -3228,11 +3232,11 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( - vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), - vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d); + ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( - vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), - vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d); + ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d); } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1; @@ -3483,12 +3487,12 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri const int8x16_t y1_1 = vld1q_s8(y1->qs + 16); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( - vdotq_s32(vdupq_n_s32(0), x0_0, y0_0), - vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + ggml_vdotq_s32(vdupq_n_s32(0), x0_0, y0_0), + ggml_vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( - vdotq_s32(vdupq_n_s32(0), x1_0, y1_0), - vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + ggml_vdotq_s32(vdupq_n_s32(0), x1_0, y1_0), + ggml_vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); @@ -3598,8 +3602,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri // We use this macro instead of a function call because for some reason // the code runs 2-3% slower, even if the function is declared inline #define MULTIPLY_ACCUM_WITH_SCALE(index)\ - isum += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\ - isum += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\ + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)]; #define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\ q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;\ @@ -3973,10 +3977,10 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri q2bytes.val[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 4), m3)); q2bytes.val[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 6), m3)); - isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0]; - isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1]; - isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2]; - isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3]; + isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0]; + isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1]; + isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2]; + isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3]; sum += d * (isum1 + isum2); } @@ -4256,10 +4260,10 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2])); q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3])); - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3]; scale += 4; @@ -4273,10 +4277,10 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2])); q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3])); - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3]; scale += 4; @@ -4757,10 +4761,10 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri q3bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 4), m3b), q3h.val[2])); q3bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q3bits, 6), q3h.val[3])); - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1]; - isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3]; sum += d * isum; @@ -5109,14 +5113,14 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); - const int32x4_t p1 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); sumi1 += vaddvq_s32(p1) * scales[2*j+0]; q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); - const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); sumi2 += vaddvq_s32(p2) * scales[2*j+1]; } @@ -5449,13 +5453,13 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); - const int32x4_t p1 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); const int32_t sumi1 = vaddvq_s32(p1) * scales[0]; q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); - const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]); const int32_t sumi2 = vaddvq_s32(p2) * scales[1]; sumf += d * (sumi1 + sumi2); @@ -5722,8 +5726,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri q5bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[0], 4), q5h.val[2])); q5bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[1], 4), q5h.val[3])); - sumi += vaddvq_s32(vdotq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++; - sumi += vaddvq_s32(vdotq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++; + sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++; + sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++; } sumf += d * sumi - dmin * sumi_mins; @@ -6112,10 +6116,10 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri q5bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[0], 4)), vreinterpretq_s8_u8(q5h.val[2])); q5bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[1], 4)), vreinterpretq_s8_u8(q5h.val[3])); - int32_t sumi1 = sc[0] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0])); - int32_t sumi2 = sc[1] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1])); - int32_t sumi3 = sc[2] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2])); - int32_t sumi4 = sc[3] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3])); + int32_t sumi1 = sc[0] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0])); + int32_t sumi2 = sc[1] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1])); + int32_t sumi3 = sc[2] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2])); + int32_t sumi4 = sc[3] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3])); sumf += d * (sumi1 + sumi2 + sumi3 + sumi4); } @@ -6399,10 +6403,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2])); q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3])); - isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; scale += 4; @@ -6426,10 +6430,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2])); q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3])); - isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; scale += 4; } //sum += isum * d_all * y[i].d; @@ -6816,10 +6820,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[2])), m32s); q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[3])), m32s); - isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; sum += isum * d_all * y[i].d; From 1e3900ebacb3a0b385271389686403c97ad76d88 Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Fri, 29 Dec 2023 16:15:37 +0000 Subject: [PATCH 26/35] flake.nix: expose full scope in legacyPackages --- .devops/nix/jetson-support.nix | 19 +++++++++++++------ flake.nix | 20 +++++++++++++++++--- 2 files changed, 30 insertions(+), 9 deletions(-) diff --git a/.devops/nix/jetson-support.nix b/.devops/nix/jetson-support.nix index 08426d2abb7ec..78e2e40e03864 100644 --- a/.devops/nix/jetson-support.nix +++ b/.devops/nix/jetson-support.nix @@ -8,12 +8,13 @@ pkgsCuda, ... }: - lib.optionalAttrs (system == "aarch64-linux") { - packages = + { + legacyPackages = let - caps.jetson-xavier = "7.2"; - caps.jetson-orin = "8.7"; - caps.jetson-nano = "5.3"; + caps.llamaPackagesXavier = "7.2"; + caps.llamaPackagesOrin = "8.7"; + caps.llamaPackagesTX2 = "6.2"; + caps.llamaPackagesNano = "5.3"; pkgsFor = cap: @@ -27,6 +28,12 @@ }; }; in - builtins.mapAttrs (name: cap: ((pkgsFor cap).callPackage ./scope.nix { }).llama-cpp) caps; + builtins.mapAttrs (name: cap: (pkgsFor cap).callPackage ./scope.nix { }) caps; + + packages = lib.optionalAttrs (system == "aarch64-linux") { + jetson-xavier = config.legacyPackages.llamaPackagesXavier.llama-cpp; + jetson-orin = config.legacyPackages.llamaPackagesOrin.llama-cpp; + jetson-nano = config.legacyPackages.llamaPackagesNano.llama-cpp; + }; }; } diff --git a/flake.nix b/flake.nix index 2209070aa83cd..6785b52f442e5 100644 --- a/flake.nix +++ b/flake.nix @@ -80,16 +80,30 @@ ... }: { + # Unlike `.#packages`, legacyPackages may contain values of + # arbitrary types (including nested attrsets) and may even throw + # exceptions. This attribute isn't recursed into by `nix flake + # show` either. + # + # You can add arbitrary scripts to `.devops/nix/scope.nix` and + # access them as `nix build .#llamaPackages.${scriptName}` using + # the same path you would with an overlay. + legacyPackages = { + llamaPackages = pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; }; + llamaPackagesCuda = pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; }; + llamaPackagesRocm = pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; }; + }; + # We don't use the overlay here so as to avoid making too many instances of nixpkgs, # cf. https://zimbatm.com/notes/1000-instances-of-nixpkgs packages = { - default = (pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp; + default = config.legacyPackages.llamaPackages.llama-cpp; } // lib.optionalAttrs pkgs.stdenv.isLinux { opencl = config.packages.default.override { useOpenCL = true; }; - cuda = (pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp; - rocm = (pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; }).llama-cpp; + cuda = config.legacyPackages.llamaPackagesCuda.llama-cpp; + rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp; mpi-cpu = config.packages.default.override { useMpi = true; }; mpi-cuda = config.packages.default.override { useMpi = true; }; From a5c088d8c698299b973d2709153e5d95295606d9 Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Tue, 26 Dec 2023 23:34:40 +0000 Subject: [PATCH 27/35] flake.nix: rocm not yet supported on aarch64, so hide the output --- flake.nix | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/flake.nix b/flake.nix index 6785b52f442e5..920a79906b707 100644 --- a/flake.nix +++ b/flake.nix @@ -74,6 +74,7 @@ { config, lib, + system, pkgs, pkgsCuda, pkgsRocm, @@ -103,10 +104,12 @@ // lib.optionalAttrs pkgs.stdenv.isLinux { opencl = config.packages.default.override { useOpenCL = true; }; cuda = config.legacyPackages.llamaPackagesCuda.llama-cpp; - rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp; mpi-cpu = config.packages.default.override { useMpi = true; }; mpi-cuda = config.packages.default.override { useMpi = true; }; + } + // lib.optionalAttrs (system == "x86_64-linux") { + rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp; }; }; }; From 356ea17e0f92bfbbf28a4f69261bed48eff68d9c Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Fri, 29 Dec 2023 16:21:50 +0000 Subject: [PATCH 28/35] flake.nix: expose checks --- flake.nix | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/flake.nix b/flake.nix index 920a79906b707..8d0f095d71d6d 100644 --- a/flake.nix +++ b/flake.nix @@ -111,6 +111,11 @@ // lib.optionalAttrs (system == "x86_64-linux") { rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp; }; + + # Packages exposed in `.#checks` will be built by the CI and by + # `nix flake check`. Currently we expose all packages, but we could + # make more granular choices + checks = config.packages; }; }; } From 7adedecbe39bd552bc14142f496246d55a43ac4e Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Tue, 26 Dec 2023 19:17:26 +0000 Subject: [PATCH 29/35] workflows: nix-ci: init; build flake outputs --- .github/workflows/build.yml | 1 - .github/workflows/nix-ci.yml | 44 ++++++++++++++++++++++++++++++++++++ 2 files changed, 44 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/nix-ci.yml diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index a5090e398c1cc..0a28a11112251 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -515,7 +515,6 @@ jobs: - name: Build Xcode project run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build - # freeBSD-latest: # runs-on: macos-12 # steps: diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml new file mode 100644 index 0000000000000..f82b2cb3debec --- /dev/null +++ b/.github/workflows/nix-ci.yml @@ -0,0 +1,44 @@ +name: Nix CI + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix'] + pull_request: + types: [opened, synchronize, reopened] + paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix'] + +jobs: + nix-build: + if: ${{ vars.CACHIX_NAME != '' }} + strategy: + fail-fast: false + matrix: + os: [ ubuntu-latest, macos-latest ] + runs-on: ${{ matrix.os }} + steps: + - name: Checkout repository + uses: actions/checkout@v4 + - name: Install Nix + uses: DeterminateSystems/nix-installer-action@v9 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + extra-conf: | + extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org + extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= + - uses: DeterminateSystems/magic-nix-cache-action@v2 + with: + upstream-cache: https://${{ matrix.cachixName }}.cachix.org + - name: Set-up cachix to push the results to + uses: cachix/cachix-action@v13 + with: + authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' + name: ${{ vars.CACHIX_NAME }} + - name: Build + run: > + nix run github:Mic92/nix-fast-build + -- --skip-cached --no-nom + --flake + ".#checks.$(nix eval --raw --impure --expr builtins.currentSystem)" From 1e9ae54cf24d27afe3900d1250634a2a33423db1 Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Sat, 30 Dec 2023 17:19:11 +0000 Subject: [PATCH 30/35] workflows: nix-ci: add a job for eval --- .github/workflows/nix-ci.yml | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml index f82b2cb3debec..845b93bfb8e97 100644 --- a/.github/workflows/nix-ci.yml +++ b/.github/workflows/nix-ci.yml @@ -11,6 +11,33 @@ on: paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix'] jobs: + nix-eval: + strategy: + fail-fast: false + matrix: + os: [ ubuntu-latest, macos-latest ] + runs-on: ${{ matrix.os }} + steps: + - name: Checkout repository + uses: actions/checkout@v4 + - name: Install Nix + uses: DeterminateSystems/nix-installer-action@v9 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + extra-conf: | + extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org + extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= + - uses: DeterminateSystems/magic-nix-cache-action@v2 + with: + upstream-cache: https://${{ matrix.cachixName }}.cachix.org + - name: List all flake outputs + run: nix flake show --all-systems + - name: Show all output paths + run: > + nix run github:nix-community/nix-eval-jobs + -- --gc-roots-dir gcroot + --flake + ".#packages.$(nix eval --raw --impure --expr builtins.currentSystem)" nix-build: if: ${{ vars.CACHIX_NAME != '' }} strategy: From c5239944bab0ff71915df8f2dc7e42fc2c138ff6 Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Sat, 30 Dec 2023 16:38:36 +0000 Subject: [PATCH 31/35] workflows: weekly `nix flake update` --- .github/workflows/nix-flake-update.yml | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 .github/workflows/nix-flake-update.yml diff --git a/.github/workflows/nix-flake-update.yml b/.github/workflows/nix-flake-update.yml new file mode 100644 index 0000000000000..fa936084198fc --- /dev/null +++ b/.github/workflows/nix-flake-update.yml @@ -0,0 +1,22 @@ +name: update-flake-lock +on: + workflow_dispatch: + schedule: + - cron: '0 0 * * 0' # runs weekly on Sunday at 00:00 + +jobs: + lockfile: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + - name: Install Nix + uses: DeterminateSystems/nix-installer-action@main + - name: Update flake.lock + uses: DeterminateSystems/update-flake-lock@main + with: + pr-title: "nix: update flake.lock" + pr-labels: | + nix + pr-reviewers: philiptaron,SomeoneSerge + token: ${{ secrets.GITHUB_TOKEN }} From 06f2a5d1909a1385b1a16dab4ade68377e121bdd Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Sat, 30 Dec 2023 17:36:08 +0000 Subject: [PATCH 32/35] workflows: nix-flakestry: drop tag filters ...and add a job for flakehub.com --- .github/workflows/nix-flakestry.yml | 23 ---------------- .github/workflows/nix-publish-flake.yml | 36 +++++++++++++++++++++++++ 2 files changed, 36 insertions(+), 23 deletions(-) delete mode 100644 .github/workflows/nix-flakestry.yml create mode 100644 .github/workflows/nix-publish-flake.yml diff --git a/.github/workflows/nix-flakestry.yml b/.github/workflows/nix-flakestry.yml deleted file mode 100644 index 3abfb3509a648..0000000000000 --- a/.github/workflows/nix-flakestry.yml +++ /dev/null @@ -1,23 +0,0 @@ -# Make the flake discoverable on https://flakestry.dev -name: "Publish a flake to flakestry" -on: - push: - tags: - - "v?[0-9]+.[0-9]+.[0-9]+" - - "v?[0-9]+.[0-9]+" - workflow_dispatch: - inputs: - tag: - description: "The existing tag to publish" - type: "string" - required: true -jobs: - publish-flake: - runs-on: ubuntu-latest - permissions: - id-token: "write" - contents: "read" - steps: - - uses: flakestry/flakestry-publish@main - with: - version: "${{ inputs.tag || github.ref_name }}" diff --git a/.github/workflows/nix-publish-flake.yml b/.github/workflows/nix-publish-flake.yml new file mode 100644 index 0000000000000..2c3c1ebdaeff1 --- /dev/null +++ b/.github/workflows/nix-publish-flake.yml @@ -0,0 +1,36 @@ +# Make the flake discoverable on https://flakestry.dev and https://flakehub.com/flakes +name: "Publish a flake to flakestry & flakehub" +on: + push: + tags: + - "*" + workflow_dispatch: + inputs: + tag: + description: "The existing tag to publish" + type: "string" + required: true +jobs: + flakestry-publish: + runs-on: ubuntu-latest + permissions: + id-token: "write" + contents: "read" + steps: + - uses: flakestry/flakestry-publish@main + with: + version: "${{ inputs.tag || github.ref_name }}" + flakehub-publish: + runs-on: "ubuntu-latest" + permissions: + id-token: "write" + contents: "read" + steps: + - uses: "actions/checkout@v4" + with: + ref: "${{ (inputs.tag != null) && format('refs/tags/{0}', inputs.tag) || '' }}" + - uses: "DeterminateSystems/nix-installer-action@main" + - uses: "DeterminateSystems/flakehub-push@main" + with: + visibility: "public" + tag: "${{ inputs.tag }}" From d8361747317c5cb2e00e7fb3b59ff4dce5a176a5 Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Sat, 30 Dec 2023 18:01:07 +0000 Subject: [PATCH 33/35] workflows: nix-ci: add a qemu job for jetsons --- .github/workflows/nix-ci.yml | 41 ++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml index 845b93bfb8e97..a38c6ead456b0 100644 --- a/.github/workflows/nix-ci.yml +++ b/.github/workflows/nix-ci.yml @@ -69,3 +69,44 @@ jobs: -- --skip-cached --no-nom --flake ".#checks.$(nix eval --raw --impure --expr builtins.currentSystem)" + nix-build-aarch64: + if: ${{ vars.CACHIX_NAME != '' }} + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + - name: Install QEMU + # Copy-paste from https://github.com/orgs/community/discussions/8305#discussioncomment-5888654 + run: | + sudo apt-get install -y qemu-user-static qemu-system-aarch64 + sudo usermod -a -G kvm $USER + - name: Install Nix + uses: DeterminateSystems/nix-installer-action@v9 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + extra-conf: | + extra-platforms = aarch64-linux + extra-system-features = nixos-test kvm + extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org + extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= + - uses: DeterminateSystems/magic-nix-cache-action@v2 + with: + upstream-cache: https://${{ matrix.cachixName }}.cachix.org + - name: Set-up cachix to push the results to + uses: cachix/cachix-action@v13 + with: + authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' + name: ${{ vars.CACHIX_NAME }} + - name: Show all output paths + run: > + nix run github:nix-community/nix-eval-jobs + -- --gc-roots-dir gcroot + --flake + ".#packages.aarch64-linux" + - name: Build + run: > + nix run github:Mic92/nix-fast-build + -- --skip-cached --no-nom + --systems aarch64-linux + --flake + ".#checks.aarch64-linux" From 198ed7ebfc89b8f2b35a8b1655d57bfb57530c1a Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Sat, 30 Dec 2023 18:25:25 +0000 Subject: [PATCH 34/35] flake.nix: suggest the binary caches --- flake.nix | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/flake.nix b/flake.nix index 8d0f095d71d6d..488ed6c59d963 100644 --- a/flake.nix +++ b/flake.nix @@ -6,6 +6,29 @@ flake-parts.url = "github:hercules-ci/flake-parts"; }; + # Optional binary cache + nixConfig = { + extra-substituters = [ + # Populated by the CI in ggerganov/llama.cpp + "https://llama-cpp.cachix.org" + + # A development cache for nixpkgs imported with `config.cudaSupport = true`. + # Populated by https://hercules-ci.com/github/SomeoneSerge/nixpkgs-cuda-ci. + # This lets one skip building e.g. the CUDA-enabled openmpi. + # TODO: Replace once nix-community obtains an official one. + "https://cuda-maintainers.cachix.org" + ]; + + # Verify these are the same keys as published on + # - https://app.cachix.org/cache/llama-cpp + # - https://app.cachix.org/cache/cuda-maintainers + extra-trusted-public-keys = [ + "llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc=" + "cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=" + ]; + }; + + # For inspection, use `nix flake show github:ggerganov/llama.cpp` or the nix repl: # # ```bash From edd1ab7bc34c10a780ee7f9a4499f7689cdad36d Mon Sep 17 00:00:00 2001 From: Someone Serge Date: Sun, 31 Dec 2023 17:42:22 +0000 Subject: [PATCH 35/35] flake.lock: update to a commit recently cached by nixpkgs-cuda-ci --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index 3fcd1f45d5a41..15a0a1a8e10fa 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1703559957, - "narHash": "sha256-x9PUuMEPGUOMB51zNxrDr2QoHbYWlCS2xhFedm9MC5Q=", + "lastModified": 1703637592, + "narHash": "sha256-8MXjxU0RfFfzl57Zy3OfXCITS0qWDNLzlBAdwxGZwfY=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "75dd68c36f458c6593c5bbb48abfd3e59bfed380", + "rev": "cfc3698c31b1fb9cdcf10f36c9643460264d0ca8", "type": "github" }, "original": {