Merge branch 'master' into tohtana/clean_up_prefetch_param

.github/workflows/nv-accelerate-v100.yml

@@ -55,4 +55,4 @@ jobs:
           # tmp fix: force newer datasets version
           #pip install "datasets>=2.0.0"
           pip list
-          pytest $PYTEST_OPTS --color=yes --durations=0 --verbose tests/deepspeed
+          pytest $PYTEST_OPTS --color=yes --durations=0 --verbose tests/deepspeed -k "not test_prepare_multiple_models_zero3_inference"

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 }")

accelerator/real_accelerator.py

@@ -20,7 +20,7 @@
 except ImportError as e:
     dsa2 = None
 
-SUPPORTED_ACCELERATOR_LIST = ['cuda', 'cpu', 'xpu', 'xpu.external', 'npu', 'mps', 'hpu']
+SUPPORTED_ACCELERATOR_LIST = ['cuda', 'cpu', 'xpu', 'xpu.external', 'npu', 'mps', 'hpu', 'mlu']
 
 ds_accelerator = None
 
@@ -94,6 +94,11 @@ def get_accelerator():
             except ImportError as e:
                 raise ValueError(
                     f"HPU_Accelerator requires habana_frameworks.torch.hpu, which is not installed on this system.")
+        elif accelerator_name == "mlu":
+            try:
+                import torch_mlu  # noqa: F401
+            except ImportError as e:
+                raise ValueError(f"MLU_Accelerator requires torch_mlu, which is not installed on this system.")
         elif accelerator_name not in SUPPORTED_ACCELERATOR_LIST:
             raise ValueError(f'DS_ACCELERATOR must be one of {SUPPORTED_ACCELERATOR_LIST}. '
                              f'Value "{accelerator_name}" is not supported')
@@ -149,6 +154,13 @@ def get_accelerator():
                 accelerator_name = "hpu"
             except ImportError as e:
                 pass
+        if accelerator_name is None:
+            try:
+                import torch_mlu  # noqa: F401,F811
+
+                accelerator_name = "mlu"
+            except ImportError as e:
+                pass
         if accelerator_name is None:
             # borrow this log from PR#5084
             try:
@@ -198,6 +210,10 @@ def get_accelerator():
         from .hpu_accelerator import HPU_Accelerator
 
         ds_accelerator = HPU_Accelerator()
+    elif accelerator_name == 'mlu':
+        from .mlu_accelerator import MLU_Accelerator
+
+        ds_accelerator = MLU_Accelerator()
     _validate_accelerator(ds_accelerator)
     if accel_logger is not None:
         accel_logger.info(f"Setting ds_accelerator to {ds_accelerator._name} ({ds_set_method})")

bin/ds_io

@@ -0,0 +1,6 @@
+#!/usr/bin/env python3
+
+from deepspeed.nvme import ds_io_main
+
+if __name__ == '__main__':
+    ds_io_main()

bin/ds_nvme_tune

@@ -0,0 +1,9 @@
+#!/usr/bin/env python3
+
+from deepspeed.nvme import sweep_main, generate_main, parse_sweep_arguments
+
+if __name__ == '__main__':
+    args = parse_sweep_arguments()
+    print(f"Running DeepNVMe performance tuning on {args.nvme_dir}")
+    sweep_main(args)
+    generate_main(args.log_dir)