Merge branch 'master' into olruwase/set_zero_opt_grad

Author: tjruwase

.github/workflows/no-torch.yml

@@ -5,6 +5,7 @@ on:
   pull_request:
     paths:
       - '.github/workflows/no-torch.yml'
+      - 'op_builder/**'
   schedule:
     - cron: "0 0 * * *"
 

.github/workflows/xpu-compile.yml

@@ -0,0 +1,59 @@
+name: xpu-compile
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 0 * * *"
+  pull_request:
+    paths:
+      - ".github/workflows/xpu-compile.yml"
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+  issues: write
+
+jobs:
+  compile-tests:
+    runs-on: [self-hosted, intel, xpu]
+    container:
+      image: intel/oneapi-basekit:2024.2.1-0-devel-ubuntu22.04
+      ports:
+        - 80
+      options: --privileged -it --rm --device /dev/dri:/dev/dri -v /dev/dri/by-path:/dev/dri/by-path --ipc=host --cap-add=ALL
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Install prerequisite
+      run: |
+        apt-get update
+        apt-get install clinfo libaio-dev python3-pip -y
+        pip install torch==2.3.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/torch/
+        pip install intel-extension-for-pytorch==2.3.110+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/intel-extension-for-pytorch/
+        pip install oneccl_bind_pt==2.3.100+xpu -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/oneccl-bind-pt/
+        pip install torchvision==0.18.1 -f https://pytorch-extension.intel.com/release-whl/stable/xpu/us/torchvision/
+        pip install https://github.com/intel/intel-xpu-backend-for-triton/releases/download/v3.0.0b2/triton_xpu-3.0.0b2-cp310-cp310-linux_x86_64.whl
+        pip install py-cpuinfo numpy
+        pip install .[dev,autotuning]
+
+    - name: Check container state
+      run: |
+        ldd --version
+        ds_report
+        python3 -c "import torch; print('torch:', torch.__version__, torch)"
+        python3 -c "import torch; import intel_extension_for_pytorch; print('XPU available:', torch.xpu.is_available())"
+        python3 -c "from deepspeed.accelerator import get_accelerator; print('accelerator:', get_accelerator()._name)"
+        pip list
+
+    - name: Compile Status
+      shell: bash
+      run: |
+        export FI_HMEM=system
+        ulimit -n 1048575
+        cd tests/torch_compile
+        export ZE_AFFINITY_MASK=0,1
+        deepspeed test_compile.py --deepspeed_config ds_config.json 2>&1 | tee log.txt
+        cat log.txt |  grep "'graph_breaks'" | sed 's/,/ /g' | awk '{print $2}' >> $GITHUB_STEP_SUMMARY

accelerator/mlu_accelerator.py

@@ -0,0 +1,300 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+import importlib
+import inspect
+import functools
+
+from .abstract_accelerator import DeepSpeedAccelerator
+import torch
+# During setup stage torch may not be installed, pass on no torch will
+# allow op builder related API to be executed.
+
+
+class MLU_Accelerator(DeepSpeedAccelerator):
+
+    def __init__(self):
+        self._name = 'mlu'
+        self._communication_backend_name = 'cncl'
+        self._compile_backend = "inductor"
+        self.class_dict = None
+
+    def is_synchronized_device(self):
+        return False
+
+    def use_host_timers(self):
+        return self.is_synchronized_device()
+
+    def resolves_data_dependency(self):
+        return self.is_synchronized_device()
+
+    def handles_memory_backpressure(self):
+        return self.is_synchronized_device()
+
+    # Device APIs
+    def device_name(self, device_index=None):
+        if device_index == None:
+            return 'mlu'
+        return 'mlu:{}'.format(device_index)
+
+    def device(self, device_index=None):
+        return torch.mlu.device(device_index)
+
+    def set_device(self, device_index):
+        torch.mlu.set_device(device_index)
+
+    def current_device(self):
+        return torch.mlu.current_device()
+
+    def current_device_name(self):
+        return 'mlu:{}'.format(torch.mlu.current_device())
+
+    def device_count(self):
+        return torch.mlu.device_count()
+
+    def synchronize(self, device_index=None):
+        return torch.mlu.synchronize(device_index)
+
+    # RNG APIs
+    def random(self):
+        return torch.random
+
+    def set_rng_state(self, new_state, device_index=None):
+        if device_index is None:
+            return torch.mlu.set_rng_state(new_state)
+
+        return torch.mlu.set_rng_state(new_state, device_index)
+
+    def get_rng_state(self, device_index=None):
+        if device_index is None:
+            return torch.mlu.get_rng_state()
+
+        return torch.mlu.get_rng_state(device_index)
+
+    def manual_seed(self, seed):
+        return torch.mlu.manual_seed(seed)
+
+    def manual_seed_all(self, seed):
+        return torch.mlu.manual_seed_all(seed)
+
+    def initial_seed(self, seed):
+        return torch.mlu.initial_seed(seed)
+
+    def default_generator(self, device_index):
+        return torch.mlu.default_generators[device_index]
+
+    # Streams/Events
+    @property
+    def Stream(self):
+        return torch.mlu.Stream
+
+    def stream(self, stream):
+        return torch.mlu.stream(stream)
+
+    def current_stream(self, device_index=None):
+        return torch.mlu.current_stream(device_index)
+
+    def default_stream(self, device_index=None):
+        return torch.mlu.default_stream(device_index)
+
+    @property
+    def Event(self):
+        return torch.mlu.Event
+
+    # Memory management
+    def empty_cache(self):
+        return torch.mlu.empty_cache()
+
+    def memory_allocated(self, device_index=None):
+        return torch.mlu.memory_allocated(device_index)
+
+    def max_memory_allocated(self, device_index=None):
+        return torch.mlu.max_memory_allocated(device_index)
+
+    def reset_max_memory_allocated(self, device_index=None):
+        return torch.mlu.reset_max_memory_allocated(device_index)
+
+    def memory_cached(self, device_index=None):
+        return torch.mlu.memory_cached(device_index)
+
+    def max_memory_cached(self, device_index=None):
+        return torch.mlu.max_memory_cached(device_index)
+
+    def reset_max_memory_cached(self, device_index=None):
+        return torch.mlu.reset_max_memory_cached(device_index)
+
+    def memory_stats(self, device_index=None):
+        if hasattr(torch.mlu, 'memory_stats'):
+            return torch.mlu.memory_stats(device_index)
+
+    def reset_peak_memory_stats(self, device_index=None):
+        if hasattr(torch.mlu, 'reset_peak_memory_stats'):
+            return torch.mlu.reset_peak_memory_stats(device_index)
+
+    def memory_reserved(self, device_index=None):
+        if hasattr(torch.mlu, 'memory_reserved'):
+            return torch.mlu.memory_reserved(device_index)
+
+    def max_memory_reserved(self, device_index=None):
+        if hasattr(torch.mlu, 'max_memory_reserved'):
+            return torch.mlu.max_memory_reserved(device_index)
+
+    def total_memory(self, device_index=None):
+        return torch.mlu.get_device_properties(device_index).total_memory
+
+    def available_memory(self, device_index=None):
+        return self.total_memory(device_index) - self.memory_allocated(device_index)
+
+    # Data types
+    def is_bf16_supported(self):
+        return torch.mlu.is_bf16_supported()
+
+    def is_fp16_supported(self):
+        return True
+
+    def supported_dtypes(self):
+        supported_dtypes = [torch.float]
+        if self.is_fp16_supported():
+            supported_dtypes.append(torch.half)
+        if self.is_bf16_supported():
+            supported_dtypes.append(torch.bfloat16)
+        return supported_dtypes
+
+    # Misc
+    def amp(self):
+        if hasattr(torch.mlu, 'amp'):
+            return torch.mlu.amp
+        return None
+
+    def is_available(self):
+        return torch.mlu.is_available()
+
+    def range_push(self, msg):
+        if hasattr(torch.mlu.cnpx, 'range_push'):
+            return torch.mlu.cnpx.range_push(msg)
+
+    def range_pop(self):
+        if hasattr(torch.mlu.cnpx, 'range_pop'):
+            return torch.mlu.cnpx.range_pop()
+
+    def lazy_call(self, callback):
+        return torch.mlu._lazy_call(callback)
+
+    def communication_backend_name(self):
+        return self._communication_backend_name
+
+    def is_triton_supported(self):
+        return True
+
+    # Graph operations
+    def create_graph(self):
+        torch.mlu.MLUGraph()
+
+    def capture_to_graph(self, graph, pool=None, stream=None):
+        return torch.mlu.graph(graph, pool, stream)
+
+    def replay_graph(self, graph):
+        graph.replay()
+        return
+
+    # Tensor operations
+
+    @property
+    def BFloat16Tensor(self):
+        return functools.partial(torch.tensor, dtype=torch.bfloat16, device='mlu')
+
+    @property
+    def ByteTensor(self):
+        return functools.partial(torch.tensor, dtype=torch.uint8, device='mlu')
+
+    @property
+    def DoubleTensor(self):
+        return functools.partial(torch.tensor, dtype=torch.double, device='mlu')
+
+    @property
+    def FloatTensor(self):
+        return functools.partial(torch.tensor, dtype=torch.float, device='mlu')
+
+    @property
+    def HalfTensor(self):
+        return functools.partial(torch.tensor, dtype=torch.half, device='mlu')
+
+    @property
+    def IntTensor(self):
+        return functools.partial(torch.tensor, dtype=torch.int, device='mlu')
+
+    @property
+    def LongTensor(self):
+        return functools.partial(torch.tensor, dtype=torch.long, device='mlu')
+
+    def pin_memory(self, tensor):
+        return tensor.pin_memory()
+
+    def is_pinned(self, tensor):
+        return tensor.is_pinned()
+
+    def on_accelerator(self, tensor):
+        device_str = str(tensor.device)
+        if device_str.startswith('mlu:'):
+            return True
+        else:
+            return False
+
+    def op_builder_dir(self):
+        try:
+            # is op_builder from deepspeed or a 3p version? this should only succeed if it's deepspeed
+            # if successful this also means we're doing a local install and not JIT compile path
+            from op_builder import __deepspeed__  # noqa: F401 # type: ignore
+            return "op_builder.mlu"
+        except ImportError:
+            return "deepspeed.ops.op_builder.mlu"
+
+    def _lazy_init_class_dict(self):
+        if self.class_dict:
+            return
+
+        op_builder_module = importlib.import_module(self.op_builder_dir())
+
+        # get op builder class from op_builder/mlu/__init__.py
+        self.class_dict = {}
+        for class_name, class_obj in inspect.getmembers(op_builder_module, inspect.isclass):
+            self.class_dict[class_name] = class_obj
+
+    # create an instance of op builder and return, name specified by class_name
+    def create_op_builder(self, class_name):
+        builder_class = self.get_op_builder(class_name)
+        return builder_class()
+
+    # return an op builder class, name specified by class_name
+    def get_op_builder(self, class_name):
+        self._lazy_init_class_dict()
+        if class_name in self.class_dict:
+            return self.class_dict[class_name]
+        else:
+            return self.class_dict['NotImplementedBuilder']
+
+    def build_extension(self):
+        from torch.utils.cpp_extension import BuildExtension
+        return BuildExtension
+
+    def export_envs(self):
+        return ['NEUWARE_HOME', 'CNCL', 'LD_LIBRARY', 'PATH']
+
+    def visible_devices_envs(self):
+        return ['MLU_VISIBLE_DEVICES']
+
+    def set_visible_devices_envs(self, current_env, local_accelerator_ids):
+        for env in self.visible_devices_envs():
+            current_env[env] = ",".join(map(str, local_accelerator_ids))
+
+    def get_compile_backend(self):
+        return self._compile_backend
+
+    def set_compile_backend(self, backend):
+        supported_backends = torch._dynamo.list_backends(exclude_tags=())
+        if backend in supported_backends:
+            self._compile_backend = backend
+        else:
+            raise ValueError(
+                f"{backend} not supported by {self.device_name()}. Supported Backends are {supported_backends }")