Tecorigin sdaa accelerator

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', 'mlu']
+SUPPORTED_ACCELERATOR_LIST = ['cuda', 'cpu', 'xpu', 'xpu.external', 'npu', 'mps', 'hpu', 'mlu', 'sdaa']
 
 ds_accelerator = None
 
@@ -80,6 +80,12 @@ def get_accelerator():
             except ImportError as e:
                 raise ValueError(f"NPU_Accelerator requires torch_npu, which is not installed on this system.")
             pass
+        elif accelerator_name == "sdaa":
+            try:
+                import torch_sdaa  # noqa: F401 # type: ignore
+            except ImportError as e:
+                raise ValueError(f"SDAA_Accelerator requires torch_sdaa, which is not installed on this system.")
+            pass
         elif accelerator_name == "mps":
             try:
                 import torch.mps
@@ -137,6 +143,13 @@ def get_accelerator():
                 accelerator_name = "npu"
             except ImportError as e:
                 pass
+        if accelerator_name is None:
+            try:
+                import torch_sdaa  # noqa: F401,F811 # type: ignore
+
+                accelerator_name = "sdaa"
+            except ImportError as e:
+                pass
         if accelerator_name is None:
             try:
                 import torch.mps
@@ -205,6 +218,10 @@ def get_accelerator():
         from .npu_accelerator import NPU_Accelerator
 
         ds_accelerator = NPU_Accelerator()
+    elif accelerator_name == "sdaa":
+        from .sdaa_accelerator import SDAA_Accelerator
+
+        ds_accelerator = SDAA_Accelerator()
     elif accelerator_name == "mps":
         from .mps_accelerator import MPS_Accelerator
 

accelerator/sdaa_accelerator.py

@@ -0,0 +1,328 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# BSD 3- Clause License Copyright (c) 2023, Tecorigin Co., Ltd. All rights
+# reserved.
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# Redistributions of source code must retain the above copyright notice,
+# this list of conditions and the following disclaimer.
+# Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+# Neither the name of the copyright holder nor the names of its contributors
+# may be used to endorse or promote products derived from this software
+# without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION)
+# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+# STRICT LIABILITY,OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY
+# WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY
+# OF SUCH DAMAGE.
+
+# DeepSpeed Team
+
+import importlib
+import inspect
+import functools
+
+from .abstract_accelerator import DeepSpeedAccelerator
+# During setup stage torch may not be installed, pass on no torch will
+# allow op builder related API to be executed.
+try:
+    import torch.sdaa
+except ImportError:
+    pass
+
+
+class SDAA_Accelerator(DeepSpeedAccelerator):
+
+    def __init__(self):
+        self._name = 'sdaa'
+        self._communication_backend_name = 'tccl'
+        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 is None:
+            return 'sdaa'
+        return 'sdaa:{}'.format(device_index)
+
+    def device(self, device_index=None):
+        return torch.sdaa.device(device_index)
+
+    def set_device(self, device_index):
+        torch.sdaa.set_device(device_index)
+
+    def current_device(self):
+        return torch.sdaa.current_device()
+
+    def current_device_name(self):
+        return 'sdaa:{}'.format(torch.sdaa.current_device())
+
+    def device_count(self):
+        return torch.sdaa.device_count()
+
+    def synchronize(self, device_index=None):
+        return torch.sdaa.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.sdaa.set_rng_state(new_state)
+
+        return torch.sdaa.set_rng_state(new_state, device_index)
+
+    def get_rng_state(self, device_index=None):
+        if device_index is None:
+            return torch.sdaa.get_rng_state()
+
+        return torch.sdaa.get_rng_state(device_index)
+
+    def manual_seed(self, seed):
+        return torch.sdaa.manual_seed(seed)
+
+    def manual_seed_all(self, seed):
+        return torch.sdaa.manual_seed_all(seed)
+
+    def initial_seed(self):
+        return torch.sdaa.initial_seed()
+
+    def default_generator(self, device_index):
+        return torch.sdaa.default_generators[device_index]
+
+    # Streams/Events
+    @property
+    def Stream(self):
+        return torch.sdaa.Stream
+
+    def stream(self, stream):
+        return torch.sdaa.stream(stream)
+
+    def current_stream(self, device_index=None):
+        return torch.sdaa.current_stream(device_index)
+
+    def default_stream(self, device_index=None):
+        return torch.sdaa.default_stream(device_index)
+
+    @property
+    def Event(self):
+        return torch.sdaa.Event
+
+    # Memory management
+    def empty_cache(self):
+        return torch.sdaa.empty_cache()
+
+    def memory_allocated(self, device_index=None):
+        return torch.sdaa.memory_allocated(device_index)
+
+    def max_memory_allocated(self, device_index=None):
+        return torch.sdaa.max_memory_allocated(device_index)
+
+    def reset_max_memory_allocated(self, device_index=None):
+        return torch.sdaa.reset_max_memory_allocated(device_index)
+
+    def memory_cached(self, device_index=None):
+        return torch.sdaa.memory_cached(device_index)
+
+    def max_memory_cached(self, device_index=None):
+        return torch.sdaa.max_memory_cached(device_index)
+
+    def reset_max_memory_cached(self, device_index=None):
+        return torch.sdaa.reset_max_memory_cached(device_index)
+
+    def memory_stats(self, device_index=None):
+        if hasattr(torch.sdaa, 'memory_stats'):
+            return torch.sdaa.memory_stats(device_index)
+
+    def reset_peak_memory_stats(self, device_index=None):
+        if hasattr(torch.sdaa, 'reset_peak_memory_stats'):
+            return torch.sdaa.reset_peak_memory_stats(device_index)
+
+    def memory_reserved(self, device_index=None):
+        if hasattr(torch.sdaa, 'memory_reserved'):
+            return torch.sdaa.memory_reserved(device_index)
+
+    def max_memory_reserved(self, device_index=None):
+        if hasattr(torch.sdaa, 'max_memory_reserved'):
+            return torch.sdaa.max_memory_reserved(device_index)
+
+    def total_memory(self, device_index=None):
+        return torch.sdaa.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.sdaa.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.sdaa, 'amp'):
+            return torch.sdaa.amp
+        return None
+
+    def is_available(self):
+        return torch.sdaa.is_available()
+
+    def range_push(self, msg):
+        return
+
+    def range_pop(self):
+        return
+
+    def lazy_call(self, callback):
+        return torch.sdaa._lazy_call(callback)
+
+    def communication_backend_name(self):
+        return self._communication_backend_name
+
+    def is_triton_supported(self):
+        return False
+
+    # Graph operations
+    def create_graph(self):
+        return None
+
+    def capture_to_graph(self, graph, pool=None, stream=None):
+        from deepspeed.runtime.utils import noop_context
+        return noop_context()
+
+    def replay_graph(self, graph):
+        return
+
+    # Tensor operations
+
+    @property
+    def BFloat16Tensor(self):
+        return functools.partial(torch.tensor, dtype=torch.bfloat16, device='sdaa')
+
+    @property
+    def ByteTensor(self):
+        return functools.partial(torch.tensor, dtype=torch.uint8, device='sdaa')
+
+    @property
+    def DoubleTensor(self):
+        return functools.partial(torch.tensor, dtype=torch.double, device='sdaa')
+
+    @property
+    def FloatTensor(self):
+        return functools.partial(torch.tensor, dtype=torch.float, device='sdaa')
+
+    @property
+    def HalfTensor(self):
+        return functools.partial(torch.tensor, dtype=torch.half, device='sdaa')
+
+    @property
+    def IntTensor(self):
+        return functools.partial(torch.tensor, dtype=torch.int, device='sdaa')
+
+    @property
+    def LongTensor(self):
+        return functools.partial(torch.tensor, dtype=torch.long, device='sdaa')
+
+    def pin_memory(self, tensor, align_bytes=1):
+        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('sdaa:'):
+            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.sdaa"
+        except ImportError:
+            return "deepspeed.ops.op_builder.sdaa"
+
+    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/sdaa/__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 ['NCCL', 'LD_LIBRARY', 'PATH']
+
+    def visible_devices_envs(self):
+        return ['SDAA_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}")