[2/N] Enable intel GPU for unsloth (#2388)
* add DEVICE_TYPE and resolve device specific API * reuse import torch * move env under device typr * resolve comments * add more comments * add more comments
This commit is contained in:
Lei Zhenyuan
GitHub
parent
5bf77aabf5
commit
fe6b83fd7e
1 changed files with 91 additions and 69 deletions
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@ -46,12 +46,6 @@ pass
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# Fixes https://github.com/unslothai/unsloth/issues/1266
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os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
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# Reduce VRAM usage by reducing fragmentation
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# And optimize pinning of memory
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = \
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"expandable_segments:True,"\
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"roundup_power2_divisions:[32:256,64:128,256:64,>:32]"
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# [TODO] Check why some GPUs don't work
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# "pinned_use_cuda_host_register:True,"\
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# "pinned_num_register_threads:8"
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@ -84,6 +78,21 @@ except Exception as exception:
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raise exception
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pass
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def get_device_type():
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if torch.cuda.is_available():
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return "cuda"
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elif torch.xpu.is_available():
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return "xpu"
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DEVICE_TYPE = get_device_type()
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# Reduce VRAM usage by reducing fragmentation
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# And optimize pinning of memory
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if DEVICE_TYPE == "cuda":
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = \
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"expandable_segments:True,"\
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"roundup_power2_divisions:[32:256,64:128,256:64,>:32]"
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# We support Pytorch 2
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# Fixes https://github.com/unslothai/unsloth/issues/38
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torch_version = torch.__version__.split(".")
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@ -97,9 +106,9 @@ elif (major_torch == 2) and (minor_torch < 2):
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del os.environ["PYTORCH_CUDA_ALLOC_CONF"]
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pass
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# First check if CUDA is available ie a NVIDIA GPU is seen
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if not torch.cuda.is_available():
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raise NotImplementedError("Unsloth: No NVIDIA GPU found? Unsloth currently only supports GPUs!")
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# First check if NVIDIA GPU or INTEL GPU is available
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if not torch.cuda.is_available() and not torch.xpu.is_available():
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raise NotImplementedError("Unsloth: No NVIDIA GPU or Intel XPU found? Unsloth currently only supports NVIDIA GPU or Intel XPU!")
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# Fix Xformers performance issues since 0.0.25
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import importlib.util
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@ -132,77 +141,90 @@ except:
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pass
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# Torch 2.4 has including_emulation
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major_version, minor_version = torch.cuda.get_device_capability()
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SUPPORTS_BFLOAT16 = (major_version >= 8)
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if DEVICE_TYPE == "cuda":
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major_version, minor_version = torch.cuda.get_device_capability()
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SUPPORTS_BFLOAT16 = (major_version >= 8)
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old_is_bf16_supported = torch.cuda.is_bf16_supported
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if "including_emulation" in str(inspect.signature(old_is_bf16_supported)):
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def is_bf16_supported(including_emulation = False):
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return old_is_bf16_supported(including_emulation)
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torch.cuda.is_bf16_supported = is_bf16_supported
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else:
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def is_bf16_supported(): return SUPPORTS_BFLOAT16
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torch.cuda.is_bf16_supported = is_bf16_supported
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pass
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elif DEVICE_TYPE == "xpu":
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# torch.xpu.is_bf16_supported() didn't have including_emulation
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# set SUPPORTS_BFLOAT16 as torch.xpu.is_bf16_supported()
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SUPPORTS_BFLOAT16 = torch.xpu.is_bf16_supported()
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old_is_bf16_supported = torch.cuda.is_bf16_supported
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if "including_emulation" in str(inspect.signature(old_is_bf16_supported)):
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def is_bf16_supported(including_emulation = False):
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return old_is_bf16_supported(including_emulation)
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torch.cuda.is_bf16_supported = is_bf16_supported
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else:
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def is_bf16_supported(): return SUPPORTS_BFLOAT16
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torch.cuda.is_bf16_supported = is_bf16_supported
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pass
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# For Gradio HF Spaces?
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# if "SPACE_AUTHOR_NAME" not in os.environ and "SPACE_REPO_NAME" not in os.environ:
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import triton
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libcuda_dirs = lambda: None
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if Version(triton.__version__) >= Version("3.0.0"):
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try: from triton.backends.nvidia.driver import libcuda_dirs
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except: pass
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else: from triton.common.build import libcuda_dirs
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# here we did not change cuda specific code, only add a if check and tab for python grammar
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if DEVICE_TYPE == "cuda":
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libcuda_dirs = lambda: None
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if Version(triton.__version__) >= Version("3.0.0"):
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try: from triton.backends.nvidia.driver import libcuda_dirs
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except: pass
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else: from triton.common.build import libcuda_dirs
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# Try loading bitsandbytes and triton
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import bitsandbytes as bnb
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try:
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cdequantize_blockwise_fp32 = bnb.functional.lib.cdequantize_blockwise_fp32
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libcuda_dirs()
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except:
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warnings.warn(
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"Unsloth: Running `ldconfig /usr/lib64-nvidia` to link CUDA."\
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)
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if os.path.exists("/usr/lib64-nvidia"):
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os.system("ldconfig /usr/lib64-nvidia")
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elif os.path.exists("/usr/local"):
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# Sometimes bitsandbytes cannot be linked properly in Runpod for example
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possible_cudas = subprocess.check_output(["ls", "-al", "/usr/local"]).decode("utf-8").split("\n")
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find_cuda = re.compile(r"[\s](cuda\-[\d\.]{2,})$")
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possible_cudas = [find_cuda.search(x) for x in possible_cudas]
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possible_cudas = [x.group(1) for x in possible_cudas if x is not None]
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# Try linking cuda folder, or everything in local
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if len(possible_cudas) == 0:
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os.system("ldconfig /usr/local/")
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else:
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find_number = re.compile(r"([\d\.]{2,})")
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latest_cuda = np.argsort([float(find_number.search(x).group(1)) for x in possible_cudas])[::-1][0]
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latest_cuda = possible_cudas[latest_cuda]
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os.system(f"ldconfig /usr/local/{latest_cuda}")
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pass
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importlib.reload(bnb)
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importlib.reload(triton)
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# Try loading bitsandbytes and triton
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import bitsandbytes as bnb
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try:
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libcuda_dirs = lambda: None
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if Version(triton.__version__) >= Version("3.0.0"):
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try: from triton.backends.nvidia.driver import libcuda_dirs
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except: pass
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else: from triton.common.build import libcuda_dirs
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cdequantize_blockwise_fp32 = bnb.functional.lib.cdequantize_blockwise_fp32
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libcuda_dirs()
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except:
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warnings.warn(
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"Unsloth: CUDA is not linked properly.\n"\
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"Try running `python -m bitsandbytes` then `python -m xformers.info`\n"\
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"We tried running `ldconfig /usr/lib64-nvidia` ourselves, but it didn't work.\n"\
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"You need to run in your terminal `sudo ldconfig /usr/lib64-nvidia` yourself, then import Unsloth.\n"\
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"Also try `sudo ldconfig /usr/local/cuda-xx.x` - find the latest cuda version.\n"\
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"Unsloth will still run for now, but maybe it might crash - let's hope it works!"
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"Unsloth: Running `ldconfig /usr/lib64-nvidia` to link CUDA."\
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)
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pass
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if os.path.exists("/usr/lib64-nvidia"):
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os.system("ldconfig /usr/lib64-nvidia")
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elif os.path.exists("/usr/local"):
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# Sometimes bitsandbytes cannot be linked properly in Runpod for example
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possible_cudas = subprocess.check_output(["ls", "-al", "/usr/local"]).decode("utf-8").split("\n")
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find_cuda = re.compile(r"[\s](cuda\-[\d\.]{2,})$")
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possible_cudas = [find_cuda.search(x) for x in possible_cudas]
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possible_cudas = [x.group(1) for x in possible_cudas if x is not None]
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# Try linking cuda folder, or everything in local
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if len(possible_cudas) == 0:
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os.system("ldconfig /usr/local/")
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else:
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find_number = re.compile(r"([\d\.]{2,})")
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latest_cuda = np.argsort([float(find_number.search(x).group(1)) for x in possible_cudas])[::-1][0]
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latest_cuda = possible_cudas[latest_cuda]
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os.system(f"ldconfig /usr/local/{latest_cuda}")
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pass
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importlib.reload(bnb)
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importlib.reload(triton)
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try:
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libcuda_dirs = lambda: None
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if Version(triton.__version__) >= Version("3.0.0"):
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try: from triton.backends.nvidia.driver import libcuda_dirs
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except: pass
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else: from triton.common.build import libcuda_dirs
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cdequantize_blockwise_fp32 = bnb.functional.lib.cdequantize_blockwise_fp32
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libcuda_dirs()
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except:
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warnings.warn(
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"Unsloth: CUDA is not linked properly.\n"\
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"Try running `python -m bitsandbytes` then `python -m xformers.info`\n"\
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"We tried running `ldconfig /usr/lib64-nvidia` ourselves, but it didn't work.\n"\
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"You need to run in your terminal `sudo ldconfig /usr/lib64-nvidia` yourself, then import Unsloth.\n"\
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"Also try `sudo ldconfig /usr/local/cuda-xx.x` - find the latest cuda version.\n"\
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"Unsloth will still run for now, but maybe it might crash - let's hope it works!"
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)
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pass
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elif DEVICE_TYPE == "xpu":
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# currently intel xpu will not support bnb, will add support in the future
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# TODO: check triton for intel installed properly.
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pass
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# Check for unsloth_zoo
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try:
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