repo-sync-2024-03-08T18:25:24+0800

.circleci/release-config.yml

@@ -30,38 +30,6 @@ parameters:
 # Define a job to be invoked later in a workflow.
 # See: https://circleci.com/docs/2.0/configuration-reference/#jobs
 jobs:
-  macOS_x64_publish:
-    macos:
-      xcode: 15.1
-    resource_class: macos.x86.medium.gen2
-    parameters:
-      python_ver:
-        type: string
-    steps:
-      - checkout
-      - run:
-          name: "Install homebrew dependencies"
-          command: |
-            brew install bazelisk cmake ninja nasm libomp wget go
-      - run:
-          name: "Install Miniconda"
-          command: |
-            wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O ~/miniconda.sh
-            bash ~/miniconda.sh -b -p $HOME/miniconda
-            source $HOME/miniconda/bin/activate
-            conda init zsh bash
-      - run:
-          name: "build package and publish"
-          command: |
-            set +e
-            conda create -n build python=<< parameters.python_ver >> -y
-            conda activate build
-
-            sh ./build_wheel_entrypoint.sh
-            python3 -m pip install twine
-            ls dist/*.whl
-
-            python3 -m twine upload -r pypi -u __token__ -p ${PYPI_TWINE_TOKEN} dist/*.whl
   macOS_arm64_publish:
     macos:
       xcode: 15.1
@@ -158,14 +126,6 @@ workflows:
           filters:
             tags:
               only: /.*/
-      - macOS_x64_publish:
-          matrix:
-            parameters:
-              python_ver: ["3.9", "3.10", "3.11"]
-          # This is mandatory to trigger a pipeline when pushing a tag
-          filters:
-            tags:
-              only: /.*/
       - macOS_arm64_publish:
           matrix:
             parameters:

CHANGELOG.md

@@ -10,6 +10,13 @@
 >
 > please add your unreleased change here.
 
+- [Feature] Add minimax approximation for log
+- [Feature] Support jax.lax.top_k
+- [Improvement] Default log approximation to minmax
+- [Improvement] Improve median performance
+
+## 20240306
+
 - [Feature] Support more generic Torch model inference
 - [Improvement] Optimize one-time setup for yacl ot
 - [Improvement] Optimize sort performance

README.md

@@ -34,9 +34,9 @@ This documentation also contains instructions for [build and testing](CONTRIBUTI
 |            | Linux x86_64 | Linux aarch64 | macOS x64      | macOS Apple Silicon | Windows x64    | Windows WSL2    x64 |
 |------------|--------------|---------------|----------------|---------------------|----------------|---------------------|
 | CPU        | yes          | yes           | yes<sup>1</sup>| yes                 | no             | yes                 |
-| NVIDIA GPU | experimental | no            | no             | n/a                 | no             | experimental                  |
+| NVIDIA GPU | experimental | no            | no             | n/a                 | no             | experimental        |
 
-1. Due to CI resource limitation, macOS x64 prebuild binary will no longer available since next release (0.9.x).
+1. Due to CI resource limitation, macOS x64 prebuild binary is no longer available.
 
 ### Instructions
 

bazel/patches/seal.patch

@@ -218,4 +218,18 @@ index dabd3bab..afaa71dc 100644
 +                    else
                      {
                          inverse_ntt_negacyclic_harvey_lazy(get<0, 1>(J), get<2>(J));
-                     }
+                     }
+
+diff --git a/CMakeLists.txt b/CMakeLists.txt
+index 1a7a2bfd..bc4ad9d9 100644
+--- a/CMakeLists.txt
++++ b/CMakeLists.txt
+@@ -223,7 +223,7 @@ if(SEAL_USE_INTEL_HEXL)
+         message(STATUS "Intel HEXL: download ...")
+         seal_fetch_thirdparty_content(ExternalIntelHEXL)
+     else()
+-        find_package(HEXL 1.2.4)
++        find_package(HEXL 1.2.5)
+         if (NOT TARGET HEXL::hexl)
+             message(FATAL_ERROR "Intel HEXL: not found")
+         endif()

bazel/repositories.bzl

@@ -140,8 +140,8 @@ def _com_github_xtensor_xtl():
     )
 
 def _com_github_openxla_xla():
-    OPENXLA_COMMIT = "d1cf2382e57b1efba3bb17d6dd9d8657453405ca"
-    OPENXLA_SHA256 = "a7f439d54a4e35c7977c2ea17b3a2493b306c9629ccc8071b4962c905ac9f692"
+    OPENXLA_COMMIT = "495516d2d0b4453d5831905e152594614c8b4797"
+    OPENXLA_SHA256 = "13f6490065db594c6a7f9914e59213b6785ceb81af1f2cb28d5409f3f18aac8e"
 
     maybe(
         http_archive,

bazel/seal.BUILD

@@ -53,11 +53,17 @@ x64_hexl_config = {
 
 spu_cmake_external(
     name = "seal",
-    cache_entries = default_config,
+    cache_entries = select({
+        ":can_use_hexl": x64_hexl_config,
+        "//conditions:default": default_config,
+    }),
     lib_source = "@com_github_microsoft_seal//:all",
     out_include_dir = "include/SEAL-4.1",
     out_static_libs = ["libseal-4.1.a"],
     deps = [
         "@com_github_facebook_zstd//:zstd",
-    ],
+    ] + select({
+        "@platforms//cpu:x86_64": ["@com_intel_hexl//:hexl"],
+        "//conditions:default": [],
+    }),
 )

examples/python/ml/flax_llama7b_split/flax_llama7b_split.py

@@ -12,37 +12,35 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import argparse
+import json
+
 # Start nodes.
 # > bazel run -c opt //examples/python/utils:nodectl -- --config `pwd`/examples/python/ml/flax_llama_split/3pc.json" up
 # Run this example script.
 # > bazel run -c opt //examples/python/ml/flax_llama7b -- --config `pwd`/examples/python/ml/flax_llama_split/3pc.json
 import time
-import argparse
-import json
+from contextlib import contextmanager
+from typing import Any, Optional, Tuple, Union
+
+import flax.linen as nn
 import jax
-import jax.numpy as jnp
 import jax.nn as jnn
-import flax.linen as nn
-from flax.linen.linear import Array
-from typing import Any, Optional, Tuple, Union
-from transformers import LlamaTokenizer
+import jax.numpy as jnp
 from EasyLM.checkpoint import StreamingCheckpointer
 from EasyLM.models.llama.llama_model import FlaxLLaMAForCausalLM
 from EasyLM.models.llama.llama_model_splited_transformer import (
     FlaxLLaMAForCausalLMClient,
+    FlaxLLaMAForCausalLMMid,
     FlaxLLaMAForCausalLMServer,
     FlaxLLaMAModule,
-    FlaxLLaMAForCausalLMMid,
     LLaMAConfig,
 )
+from flax.linen.linear import Array
+from transformers import LlamaTokenizer
 
-
-import spu.utils.distributed as ppd
-from contextlib import contextmanager
 import spu.spu_pb2 as spu_pb2
-
-from flax.linen.linear import Array
-from typing import Any, Optional, Tuple, Union
+import spu.utils.distributed as ppd
 
 parser = argparse.ArgumentParser(description='distributed driver.')
 parser.add_argument(

examples/python/ml/flax_llama7b_split/llama_model_splited_transformer.py

@@ -16,55 +16,49 @@
 # Original Source Code Form
 # [EasyLM](https://github.com/young-geng/EasyLM/tree/main)
 
-import os
-from shutil import copyfile
-from typing import Any, Dict, List, Optional, Tuple, Union
 import json
+import os
 import tempfile
 from functools import partial
-from jax import jit
-import numpy as np
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import einops
+import flax.linen as nn
 import jax
 import jax.numpy as jnp
-from jax import lax
-from jax.sharding import PartitionSpec as PS
-import flax.linen as nn
+import numpy as np
+import sentencepiece as spm
+from EasyLM.bpt import blockwise_attn, blockwise_ffn
+from EasyLM.jax_utils import (
+    get_gradient_checkpoint_policy,
+    get_jax_mesh,
+    with_sharding_constraint,
+)
 from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
 from flax.linen import combine_masks, make_causal_mask
+from flax.linen import partitioning as nn_partitioning
 from flax.linen.attention import dot_product_attention_weights
 from flax.traverse_util import flatten_dict, unflatten_dict
-from flax.linen import partitioning as nn_partitioning
-import einops
-
-import sentencepiece as spm
+from jax import jit, lax
+from jax.sharding import PartitionSpec as PS
+from ml_collections import ConfigDict
+from ml_collections.config_dict import config_dict
+from mlxu import function_args_to_config, load_pickle, open_file
 from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-from transformers.tokenization_utils import PreTrainedTokenizer
 from transformers.modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput
 from transformers.modeling_flax_utils import (
     ACT2FN,
     FlaxPreTrainedModel,
     append_call_sample_docstring,
 )
+from transformers.tokenization_utils import PreTrainedTokenizer
 from transformers.utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     logging,
 )
 
-
-from ml_collections import ConfigDict
-from ml_collections.config_dict import config_dict
-from mlxu import function_args_to_config, load_pickle, open_file
-
-from EasyLM.bpt import blockwise_ffn, blockwise_attn
-from EasyLM.jax_utils import (
-    with_sharding_constraint,
-    get_jax_mesh,
-    get_gradient_checkpoint_policy,
-)
-
-
 LLAMA_STANDARD_CONFIGS = {
     '7b': {
         'vocab_size': 32000,

examples/python/ml/flax_resnet/flax_resnet_inference.py

@@ -16,8 +16,8 @@
 import json
 
 import jax
-import spu.utils.distributed as ppd
 
+import spu.utils.distributed as ppd
 
 parser = argparse.ArgumentParser(description='distributed driver.')
 parser.add_argument("-c", "--config", default="3pc.json")
@@ -29,14 +29,13 @@
 ppd.init(conf["nodes"], conf["devices"])
 
 
+from datasets import load_dataset
 from transformers import (
-    AutoImageProcessor,
     AutoConfig,
+    AutoImageProcessor,
     FlaxResNetForImageClassification,
 )
 
-from datasets import load_dataset
-
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 

examples/python/ml/flax_whisper/flax_whisper.py

@@ -23,8 +23,8 @@
 import os
 
 import jax.numpy as jnp
-from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
 from datasets import load_dataset
+from transformers import FlaxWhisperForConditionalGeneration, WhisperProcessor
 
 import spu.utils.distributed as ppd
 from spu import spu_pb2

examples/python/ml/ml_test.py

@@ -21,11 +21,11 @@
 import unittest
 from time import perf_counter
 
-import multiprocess
 import numpy.testing as npt
 import pandas as pd
 
 import spu.utils.distributed as ppd
+from spu.utils.polyfill import Process
 
 with open("examples/python/conf/3pc.json", 'r') as file:
     conf = json.load(file)
@@ -70,15 +70,13 @@ class UnitTests(unittest.TestCase):
     def setUpClass(cls):
         cls.workers = []
         for node_id in conf["nodes"].keys():
-            worker = multiprocess.Process(
-                target=ppd.RPC.serve, args=(node_id, conf["nodes"])
-            )
+            worker = Process(target=ppd.RPC.serve, args=(node_id, conf["nodes"]))
             worker.start()
             cls.workers.append(worker)
         import time
 
         # wait for all process serving.
-        time.sleep(0.05)
+        time.sleep(2)
 
         rt_config = conf["devices"]["SPU"]["config"]["runtime_config"]
         rt_config["enable_pphlo_profile"] = False

examples/python/ml/torch_lr_experiment/torch_lr_experiment.py

@@ -22,7 +22,6 @@
 
 import spu.utils.distributed as ppd
 
-
 # Start nodes.
 # > bazel run -c opt //examples/python/utils:nodectl -- up
 #
@@ -114,6 +113,7 @@ def run_inference_on_cpu(model):
 ppd.init(conf["nodes"], conf["devices"], framework=ppd.Framework.EXP_TORCH)
 
 from collections import OrderedDict
+
 from jax.tree_util import tree_map
 
 

examples/python/utils/nodectl.py

@@ -15,9 +15,8 @@
 import argparse
 import json
 
-import multiprocess
-
 import spu.utils.distributed as ppd
+from spu.utils.polyfill import Process
 
 parser = argparse.ArgumentParser(description='SPU node service.')
 parser.add_argument(
@@ -44,9 +43,7 @@
     elif args.command == 'up':
         workers = []
         for node_id in nodes_def.keys():
-            worker = multiprocess.Process(
-                target=ppd.RPC.serve, args=(node_id, nodes_def)
-            )
+            worker = Process(target=ppd.RPC.serve, args=(node_id, nodes_def))
             worker.start()
             workers.append(worker)
 

libspu/compiler/core/core.cc

@@ -55,6 +55,10 @@ void Core::buildPipeline(mlir::PassManager *pm) {
   optPM.addPass(mlir::spu::pphlo::createDecomposeMinMaxPass());
   optPM.addPass(mlir::spu::pphlo::createSortLowering());
 
+  if (!options.disable_partial_sort_optimization()) {
+    optPM.addPass(mlir::spu::pphlo::createPartialSortToTopK());
+  }
+
   if (!options.disable_sqrt_plus_epsilon_rewrite()) {
     optPM.addPass(mlir::spu::pphlo::createOptimizeSqrtPlusEps());
   }