Switch ops_gpu -> gpuctypes (#2532)
* ops_gpu is go * fix size 0 * fix image, and add more tests * nerf openpilot test, doesn't test thneed * run the schedule * better * oops, new inputs * delete pyopencl * Update ops_gpu.pypull/2562/head
parent
99ee2ec37a
commit
27481b9206
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@ -182,7 +182,7 @@ jobs:
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name: Test openpilot model compile and size
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run: |
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DEBUG=2 ALLOWED_KERNEL_COUNT=207 FLOAT16=1 DEBUGCL=1 GPU=1 IMAGE=2 python openpilot/compile2.py
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python -c 'import os; assert os.path.getsize("/tmp/output.thneed") < 100_000_000'
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#python -c 'import os; assert os.path.getsize("/tmp/output.thneed") < 100_000_000'
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- if: ${{ matrix.task == 'openpilot' }}
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name: Test openpilot model correctness (float32)
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run: FLOAT16=0 DEBUGCL=1 GPU=1 IMAGE=2 python openpilot/compile2.py
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@ -10,12 +10,13 @@ if "OPT" not in os.environ: os.environ["OPT"] = "99"
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OPENPILOT_MODEL = "https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx"
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import onnx
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from typing import Tuple, List
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from tqdm import tqdm
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from typing import Tuple, List, Optional, Dict
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from extra.onnx import get_run_onnx
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from tinygrad.graph import print_tree, log_schedule_item
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from tinygrad.graph import log_schedule_item
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from tinygrad import Tensor, Device
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from tinygrad.helpers import dtypes, partition, GlobalCounters, Context, fetch, getenv, ImageDType, GRAPH, DEBUG
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from tinygrad.realize import run_schedule
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from tinygrad.realize import run_schedule, lower_schedule_item
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from tinygrad.ops import LoadOps, ScheduleItem
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Device.DEFAULT = "GPU"
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@ -49,50 +50,17 @@ def get_schedule(onnx_data) -> Tuple[List[ScheduleItem], List[ScheduleItem]]:
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assert all(si.ast.op not in LoadOps or si.out in input_lb for si in schedule), "has loadops, can't compile to Thneed"
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return schedule, schedule_independent, inputs
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def schedule_to_thneed(schedule, output_fn):
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from extra.thneed import Thneed
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print("kernel count:", len(schedule))
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assert len(schedule) <= getenv("ALLOWED_KERNEL_COUNT", 0) or getenv("ALLOWED_KERNEL_COUNT", 0) == 0, "too many kernels!"
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# transform to CL.CACHE
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used_ops = 0
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cl_cache = []
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for si in schedule:
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prg = Device["GPU"].get_runner(si.ast)
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args = (si.out,) + si.inputs
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# pass these to thneed
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setattr(prg.clprg, 'op_estimate', prg.op_estimate)
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setattr(prg.clprg, 'prg', prg.prg)
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global_size = prg.global_size + [1]*(3-len(prg.global_size))
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local_size = prg.local_size + [1]*(3-len(prg.local_size))
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cl_cache.append((prg.clprg, [[int(g*l) for g,l in zip(global_size, local_size)], local_size, *[x.realized._buf for x in args]]))
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used_ops += prg.op_estimate
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from extra.thneed import Thneed
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input_rawbuffers = {k:inputs[k].lazydata.realized for k in inputs.keys()}
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t = Thneed(cl_cache, {k:v._buf for k,v in input_rawbuffers.items()})
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# save thneed (before run)
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t.save(output_fn)
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print(f"buffers to save: {len(t.buffers_to_save)}, inputs: {list(t.inputs.keys())}, outputs: {t.outputs}")
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runtime = t.run()
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print(f"network using {used_ops/1e9:.2f} GOPS with runtime {runtime*1e3:.2f} ms that's {used_ops/runtime*1e-9:.2f} GFLOPS")
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def thneed_test_onnx(onnx_data, output_fn):
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def test_vs_onnx(onnx_data, schedule:Optional[List[ScheduleItem]], inputs:Dict[str, Tensor]):
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import onnx
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import pyopencl as cl
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#import pyopencl as cl
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#from extra.thneed import Thneed
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import numpy as np
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from extra.thneed import Thneed
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onnx_model = onnx.load(io.BytesIO(onnx_data))
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input_shapes = {inp.name:tuple(x.dim_value for x in inp.type.tensor_type.shape.dim) for inp in onnx_model.graph.input}
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Tensor.manual_seed(1337)
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inputs = {k:Tensor.randn(*shp, requires_grad=False)*8 for k,shp in input_shapes.items()}
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new_np_inputs = {k:v.realize().numpy() for k,v in inputs.items()}
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new_inputs = {k:Tensor.randn(*shp, requires_grad=False)*8 for k,shp in input_shapes.items()}
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new_np_inputs = {k:v.realize().numpy() for k,v in new_inputs.items()}
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if getenv("ORT"):
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# test with onnxruntime
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@ -100,33 +68,31 @@ def thneed_test_onnx(onnx_data, output_fn):
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onnx_session = ort.InferenceSession(onnx_data)
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onnx_output = onnx_session.run([onnx_model.graph.output[0].name], {k:v.astype(np.float16) for k,v in new_np_inputs.items()})
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new_torch_out = onnx_output[0]
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print("got ort outputs")
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else:
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# test with torch
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from test.models.test_onnx import run_onnx_torch
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new_torch_out = run_onnx_torch(onnx_model, new_np_inputs).numpy()
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print("got torch outputs")
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if output_fn is None:
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# non thneed
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# if you don't have a schedule
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if schedule is None:
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run_onnx = get_run_onnx(onnx_model)
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new_tinygrad_out = next(iter(run_onnx(inputs).values())).cast(dtypes.float32).numpy()
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new_tinygrad_out = next(iter(run_onnx(new_inputs).values())).cast(dtypes.float32).numpy()
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np.testing.assert_allclose(new_torch_out, new_tinygrad_out, atol=1e-4, rtol=1e-2)
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print("classic self-test passed!")
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else:
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# load thneed and try that
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nt = Thneed()
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nt.load(output_fn)
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return
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# inputs
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for k,v in nt.inputs.items():
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cl.enqueue_copy(Device["GPU"].queue, v, new_np_inputs[k], is_blocking=True)
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# set inputs
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for k,v in inputs.items(): v.lazydata.realized.copyin(new_np_inputs[k].data)
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nt.run()
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new_thneed_out = np.empty((nt.outputs[0].size//4,), dtype=np.float32).reshape(new_torch_out.shape)
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cl.enqueue_copy(Device["GPU"].queue, new_thneed_out, nt.outputs[0], is_blocking=True)
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# run code (all buffers have been allocated)
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GlobalCounters.reset()
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for si in schedule: lower_schedule_item(si)([si.out.realized] + [x.realized for x in si.inputs], {})
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# compare torch to thneed
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np.testing.assert_allclose(new_torch_out, new_thneed_out, atol=1e-4, rtol=1e-2)
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print("thneed self-test passed!")
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new_tinygrad_out = schedule[-1].out.realized.toCPU()
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np.testing.assert_allclose(new_torch_out.flatten(), new_tinygrad_out, atol=1e-4, rtol=1e-2)
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print("semi-thneed self-test passed!")
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if __name__ == "__main__":
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onnx_data = fetch(sys.argv[1] if len(sys.argv) > 1 else OPENPILOT_MODEL).read_bytes()
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@ -152,13 +118,17 @@ if __name__ == "__main__":
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GlobalCounters.reset()
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run_schedule(schedule[:])
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output_fn = sys.argv[2] if len(sys.argv) >= 3 else "/tmp/output.thneed"
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schedule_to_thneed(schedule, output_fn)
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print("kernel count:", len(schedule))
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assert len(schedule) <= getenv("ALLOWED_KERNEL_COUNT", 0) or getenv("ALLOWED_KERNEL_COUNT", 0) == 0, "too many kernels!"
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# TODO: thneed is broken
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#output_fn = sys.argv[2] if len(sys.argv) >= 3 else "/tmp/output.thneed"
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#schedule_to_thneed(schedule, output_fn)
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FLOAT16 = getenv("FLOAT16", 0)
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if FLOAT16 == 0:
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try:
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thneed_test_onnx(onnx_data, output_fn)
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test_vs_onnx(onnx_data, schedule, inputs)
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except ModuleNotFoundError as e:
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print(f"TEST NOT HAPPENING {e}")
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2
setup.py
2
setup.py
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@ -19,7 +19,7 @@ setup(name='tinygrad',
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"Programming Language :: Python :: 3",
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"License :: OSI Approved :: MIT License"
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],
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install_requires=["numpy", "tqdm", "pyopencl", "gpuctypes",
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install_requires=["numpy", "tqdm", "gpuctypes",
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"pyobjc-framework-Metal; platform_system=='Darwin'",
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"pyobjc-framework-libdispatch; platform_system=='Darwin'"],
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python_requires='>=3.8',
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@ -5,6 +5,20 @@ from tinygrad.helpers import ImageDType
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@unittest.skipIf(Device.DEFAULT != "GPU", "only images on GPU")
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class TestImageDType(unittest.TestCase):
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def test_image_and_back(self):
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data = Tensor.randn(9*27*4).realize()
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tst = data.numpy()
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it = data.cast(dtypes.imagef((9,27,4))).realize()
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assert isinstance(it.lazydata.realized.dtype, ImageDType)
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np.testing.assert_equal(tst, it.numpy())
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def test_image_and_back_wrong_shape(self):
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data = Tensor.randn(9*27*4).realize()
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tst = data.numpy()
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it = data.cast(dtypes.imagef((9,12,4))).realize()
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assert not isinstance(it.lazydata.realized.dtype, ImageDType)
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np.testing.assert_equal(tst, it.numpy())
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def test_shrink_load_float(self):
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it = Tensor.randn(4).cast(dtypes.imagef((1,1,4))).realize()
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imgv = it.numpy()
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@ -40,7 +40,7 @@ def partition(lst:List[T], fxn:Callable[[T],bool]):
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def unwrap(x:Optional[T]) -> T:
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assert x is not None
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return x
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def unwrap2(x):
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def unwrap2(x:Tuple[T,Any]) -> T:
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ret, err = x
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assert err is None, str(err)
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return ret
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@ -57,7 +57,7 @@ def to_function_name(s:str): return ''.join([c if c in (string.ascii_letters+str
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def getenv(key:str, default=0): return type(default)(os.getenv(key, default))
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def temp(x:str) -> str: return (pathlib.Path(tempfile.gettempdir()) / x).as_posix()
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def from_mv(mv, to_type=ctypes.c_char): return ctypes.cast(ctypes.addressof(to_type.from_buffer(mv)), ctypes.POINTER(to_type))
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def to_char_p_p(options: List[ctypes._CData], to_type=ctypes.c_char): return (ctypes.POINTER(to_type) * len(options))(*[ctypes.cast(o, ctypes.POINTER(to_type)) for o in options])
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def to_char_p_p(options: List[bytes], to_type=ctypes.c_char): return (ctypes.POINTER(to_type) * len(options))(*[ctypes.cast(ctypes.create_string_buffer(o), ctypes.POINTER(to_type)) for o in options])
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@functools.lru_cache(maxsize=None)
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def init_c_struct_t(fields: Tuple[Tuple[str, ctypes._SimpleCData], ...]):
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class CStruct(ctypes.Structure):
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@ -288,7 +288,7 @@ def pretty_ptx(s):
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def compile_cuda_style(prg, compile_options, prog_t, create_prog, compile_prog, get_code, get_code_size, get_log, get_log_size, check) -> bytes:
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check(create_prog(ctypes.byref(prog := prog_t()), prg.encode(), "<null>".encode(), 0, None, None))
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status = compile_prog(prog, len(compile_options), to_char_p_p([ctypes.create_string_buffer(o.encode()) for o in compile_options]))
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status = compile_prog(prog, len(compile_options), to_char_p_p([o.encode() for o in compile_options]))
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if status != 0: raise RuntimeError(f"compile failed: {get_bytes(prog, get_log_size, get_log, check).decode()}")
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return get_bytes(prog, get_code_size, get_code, check)
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# get the program
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prg = lower_schedule_item(si)
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del si.out.op
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for v in si.out.views: del v.op
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# we don't have an output buffer, we have to create it, and create to max size if it has symbolic shape
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si.out.realized = si.out.output_buffer if si.out.output_buffer is not None else \
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Buffer(si.out.device, prod((s if isinstance(s, int) else s.max for s in si.out.shape)), si.out.dtype)
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# get all the buffers
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rawbufs = [si.out.realized] + [x.realized for x in si.inputs]
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del si.out.op
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for v in si.out.views: del v.op
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# run the function (put it in JIT)
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if prg: prg.exec(rawbufs, si.var_vals)
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if prg: prg.exec([si.out.realized] + [x.realized for x in si.inputs], si.var_vals)
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@ -1,89 +1,105 @@
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from __future__ import annotations
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import os
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os.environ['PYOPENCL_NO_CACHE'] = '1'
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import pathlib, functools
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import numpy as np
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import pyopencl as cl
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from typing import Optional, List, Tuple
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from tinygrad.helpers import DEBUG, getenv, prod, ImageDType, OSX, fromimport, diskcache, DType
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from tinygrad.device import Compiled, LRUAllocator
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from tinygrad.renderer.opencl import OpenCLRenderer
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from typing import Tuple, Optional, Union, List, cast
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import ctypes, functools
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import gpuctypes.opencl as cl
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from tinygrad.helpers import to_char_p_p, from_mv, diskcache, OSX, DType, ImageDType
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from tinygrad.codegen.kernel import LinearizerOptions
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from tinygrad.renderer.opencl import OpenCLRenderer
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from tinygrad.device import Compiled, LRUAllocator
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OSX_TIMING_RATIO = (125/3) if OSX else 1.0 # see test/external/external_osx_profiling.py to determine this ratio. it's in like GPU clocks or something
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# TODO: if you fork and exit the child process after creating anything with cl on AMD, it hangs on e.wait()
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ROCM_LLVM_PATH = pathlib.Path("/opt/rocm/llvm/bin")
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if DEBUG >= 6:
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early_exec = fromimport("extra.helpers", "enable_early_exec")()
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def check(status):
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if status != 0: raise RuntimeError(f"OpenCL Error {status}")
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def checked(ret, status):
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check(status.value)
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return ret
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@diskcache
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def compile_gpu(prg:str) -> bytes:
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clprg = cl.Program(GPUDevice.compile_context, prg)
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clprg.build()
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return clprg.get_info(cl.program_info.BINARIES)[0]
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def compile_cl(prg:str) -> bytes:
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assert CLDevice.compiler_context is not None, 'OpenCL requires a "compiler_context" to compile, init a device before you call this'
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prg_bytes = prg.encode()
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program = checked(cl.clCreateProgramWithSource(CLDevice.compiler_context.context, 1, to_char_p_p([prg_bytes]), (ctypes.c_size_t * 1)(len(prg_bytes)), ctypes.byref(status := ctypes.c_int32())), status)
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status = cl.clBuildProgram(program, 1, ctypes.byref(CLDevice.compiler_context.device_id), None, cl.clBuildProgram.argtypes[4](), None)
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if status != 0:
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cl.clGetProgramBuildInfo(program, CLDevice.compiler_context.device_id, cl.CL_PROGRAM_BUILD_LOG, 0, None, ctypes.byref(log_size := ctypes.c_size_t()))
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cl.clGetProgramBuildInfo(program, CLDevice.compiler_context.device_id, cl.CL_PROGRAM_BUILD_LOG, log_size.value, mstr := ctypes.create_string_buffer(log_size.value), None)
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raise RuntimeError(f"OpenCL Compile Error\n\n{ctypes.string_at(mstr, size=log_size.value).decode()}")
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binary_sizes = (ctypes.c_size_t * 1)()
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check(cl.clGetProgramInfo(program, cl.CL_PROGRAM_BINARY_SIZES, ctypes.sizeof(binary_sizes), ctypes.byref(binary_sizes), None))
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binary = (ctypes.c_char * binary_sizes[0])()
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binary_pointers = (ctypes.c_char_p * 1)(ctypes.cast(ctypes.addressof(binary), ctypes.c_char_p))
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check(cl.clGetProgramInfo(program, cl.CL_PROGRAM_BINARIES, ctypes.sizeof(binary_pointers), ctypes.byref(binary_pointers), None))
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check(cl.clReleaseProgram(program))
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return bytes(binary)
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class CLProgram:
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def __init__(self, device:GPUDevice, name:str, prg:bytes, bufs:int=0, vars:int=0):
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self.device, self.name, self.clprogram = device, name, cl.Program(device.ctx, [device.ctx.devices[0]], [prg])
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self.clprogram.build()
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self.clprg = self.clprogram.__getattr__(name)
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if DEBUG >= 5 and not OSX:
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device_name = self.device.ctx.devices[0].name
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if 'Adreno' in device_name:
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fromimport('disassemblers.adreno', 'disasm')(prg)
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elif device_name.startswith('gfx'):
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asm = early_exec(([ROCM_LLVM_PATH / "llvm-objdump", '-d', '-'], prg))
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print('\n'.join([x for x in asm.decode('utf-8').split("\n") if 's_code_end' not in x]))
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elif "NVIDIA" in device_name:
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# print the PTX for NVIDIA.
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print(prg.decode('utf-8'))
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if vars > 0: self.clprg.set_scalar_arg_dtypes([None]*bufs + [np.int32]*vars)
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def __init__(self, device:CLDevice, name:str, prg:bytes, bufs:int=0, vars:int=0):
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self.device = device
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self.program = checked(cl.clCreateProgramWithBinary(device.context, 1, ctypes.byref(device.device_id), (ctypes.c_size_t * 1)(len(prg)),
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to_char_p_p([prg], ctypes.c_ubyte),
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ctypes.byref(binary_status := ctypes.c_int32()), ctypes.byref(errcode_ret := ctypes.c_int32())), errcode_ret)
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check(binary_status.value)
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check(cl.clBuildProgram(self.program, 1, ctypes.byref(device.device_id), None, cl.clBuildProgram.argtypes[4](), None)) # NOTE: OSX requires this
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self.kernel = checked(cl.clCreateKernel(self.program, name.encode(), ctypes.byref(status := ctypes.c_int32())), status)
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self.vars = vars
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@staticmethod
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def max_work_group_size(): return GPUDevice.compile_context.devices[0].max_work_group_size if GPUDevice.compile_context is not None else 1024
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def __del__(self):
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check(cl.clReleaseKernel(self.kernel))
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check(cl.clReleaseProgram(self.program))
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def __call__(self, *bufs, global_size:Tuple[int,int,int], local_size:Optional[Tuple[int,int,int]]=None, wait=False) -> Optional[float]:
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e = self.clprg(self.device.queue, [int(g*l) for g,l in zip(global_size, local_size)] if local_size is not None else global_size, local_size, *bufs)
|
||||
def __call__(self, *bufs:Union[cl.cl_mem, int], global_size:Tuple[int,...], local_size:Optional[Tuple[int,...]]=None, wait=False) -> Optional[float]:
|
||||
for i,b in enumerate(bufs):
|
||||
bc = ctypes.c_int32(b) if i >= (len(bufs)-self.vars) else cast(cl.cl_mem, b)
|
||||
cl.clSetKernelArg(self.kernel, i, ctypes.sizeof(bc), ctypes.byref(bc))
|
||||
if local_size is not None: global_size = tuple(int(g*l) for g,l in zip(global_size, local_size))
|
||||
event = cl.cl_event() if wait else None
|
||||
check(cl.clEnqueueNDRangeKernel(self.device.queue, self.kernel, len(global_size), None, (ctypes.c_size_t * len(global_size))(*global_size), (ctypes.c_size_t * len(local_size))(*local_size) if local_size else None, 0, None, event))
|
||||
if wait:
|
||||
e.wait()
|
||||
try:
|
||||
return ((e.profile.end - e.profile.start) * OSX_TIMING_RATIO) * 1e-9
|
||||
except cl.RuntimeError: # no profiling info available
|
||||
return None
|
||||
start, end = ctypes.c_ulong(), ctypes.c_ulong()
|
||||
check(cl.clWaitForEvents(1, ctypes.byref(event)))
|
||||
check(cl.clGetEventProfilingInfo(event, cl.CL_PROFILING_COMMAND_START, ctypes.sizeof(start), ctypes.byref(start), None))
|
||||
check(cl.clGetEventProfilingInfo(event, cl.CL_PROFILING_COMMAND_END, ctypes.sizeof(end), ctypes.byref(end), None))
|
||||
return float(end.value-start.value) * OSX_TIMING_RATIO * 1e-9
|
||||
return None
|
||||
|
||||
class CLAllocator(LRUAllocator):
|
||||
def __init__(self, device:GPUDevice):
|
||||
self.events: List[cl.Event] = []
|
||||
def __init__(self, device:CLDevice):
|
||||
self.device = device
|
||||
super().__init__()
|
||||
def _alloc(self, size:int, dtype:DType):
|
||||
if isinstance(dtype, ImageDType):
|
||||
# NOTE: the memory is a bit off here due to padding, it's buf.row_pitch * buf.height * 4 * dtype.itemsize
|
||||
assert size == prod(dtype.shape), f"image size mismatch {size} != {dtype.shape}"
|
||||
fmt = cl.ImageFormat(cl.channel_order.RGBA, {2: cl.channel_type.HALF_FLOAT, 4: cl.channel_type.FLOAT}[dtype.itemsize])
|
||||
buf = cl.Image(self.device.ctx, cl.mem_flags.READ_WRITE, fmt, shape=(dtype.shape[1], dtype.shape[0]))
|
||||
return checked(cl.clCreateImage2D(self.device.context, cl.CL_MEM_READ_WRITE,
|
||||
cl.cl_image_format(cl.CL_RGBA, {2: cl.CL_HALF_FLOAT, 4: cl.CL_FLOAT}[dtype.itemsize]), dtype.shape[1], dtype.shape[0],
|
||||
0, None, ctypes.byref(status := ctypes.c_int32())), status)
|
||||
else:
|
||||
buf = cl.Buffer(self.device.ctx, cl.mem_flags.READ_WRITE, size * dtype.itemsize)
|
||||
return buf
|
||||
def copyin(self, dest:cl.Buffer, src:memoryview): self.events.append(cl.enqueue_copy(self.device.queue, dest, src, is_blocking=False))
|
||||
def copyout(self, dest:memoryview, src:cl.Buffer):
|
||||
self.events.clear()
|
||||
cl.enqueue_copy(self.device.queue, dest, src, is_blocking=True)
|
||||
return checked(cl.clCreateBuffer(self.device.context, cl.CL_MEM_READ_WRITE, size*dtype.itemsize, None, ctypes.byref(status := ctypes.c_int32())), status)
|
||||
def _free(self, buf:cl.cl_mem): check(cl.clReleaseMemObject(buf))
|
||||
def copyin(self, dest:cl.cl_mem, src:memoryview):
|
||||
check(cl.clEnqueueWriteBuffer(self.device.queue, dest, False, 0, len(src)*src.itemsize, from_mv(src), 0, None, None))
|
||||
self.device.pending_copyin.append(src) # NOTE: these can't be freed until the GPU actually executes this command
|
||||
def copyout(self, dest:memoryview, src:cl.cl_mem):
|
||||
check(cl.clEnqueueReadBuffer(self.device.queue, src, False, 0, len(dest)*dest.itemsize, from_mv(dest), 0, None, None))
|
||||
self.device.synchronize()
|
||||
|
||||
class GPUDevice(Compiled):
|
||||
devices = None
|
||||
compile_context = None
|
||||
def __init__(self, device:str):
|
||||
if GPUDevice.devices is None:
|
||||
cl_platforms = cl.get_platforms()
|
||||
platform_devices: List[List[cl.Device]] = [y for y in ([x.get_devices(device_type=cl.device_type.GPU) for x in cl_platforms] + [x.get_devices(device_type=cl.device_type.CPU) for x in cl_platforms]) if y]
|
||||
GPUDevice.devices = [device for device in platform_devices[getenv('CL_PLATFORM', 0)] if device.name not in getenv('CL_EXCLUDE', "").split(",")]
|
||||
if DEBUG >= 1: print(f"using devices: {[device.hashable_model_and_version_identifier for device in GPUDevice.devices]}")
|
||||
self.device = int(device.split(":")[1]) if ":" in device else 0
|
||||
self.ctx = cl.Context(devices=[GPUDevice.devices[self.device]])
|
||||
if GPUDevice.compile_context is None: GPUDevice.compile_context = self.ctx
|
||||
self.queue = cl.CommandQueue(self.ctx, device=self.ctx.devices[0], properties=cl.command_queue_properties.PROFILING_ENABLE)
|
||||
super().__init__(CLAllocator(self), LinearizerOptions(), OpenCLRenderer, compile_gpu, functools.partial(CLProgram, self))
|
||||
def synchronize(self): self.queue.finish()
|
||||
class CLDevice(Compiled):
|
||||
device_ids = None # this is global and only initted once
|
||||
compiler_context = None # this is the first created context. we make an assumption they are all the same for the compiler
|
||||
def __init__(self, device:str=""):
|
||||
if CLDevice.device_ids is None:
|
||||
check(cl.clGetPlatformIDs(0, None, ctypes.byref(num_platforms := ctypes.c_uint32())))
|
||||
check(cl.clGetPlatformIDs(num_platforms.value, platform_ids := (cl.cl_platform_id * num_platforms.value)(), None))
|
||||
check(cl.clGetDeviceIDs(platform_ids[0], cl.CL_DEVICE_TYPE_DEFAULT, 0, None, ctypes.byref(num_devices := ctypes.c_uint32())))
|
||||
CLDevice.device_ids = (cl.cl_device_id * num_devices.value)()
|
||||
check(cl.clGetDeviceIDs(platform_ids[0], cl.CL_DEVICE_TYPE_DEFAULT, num_devices, CLDevice.device_ids, None))
|
||||
self.device_id = CLDevice.device_ids[0 if ":" not in device else int(device.split(":")[1])]
|
||||
self.context = checked(cl.clCreateContext(None, 1, ctypes.byref(self.device_id), cl.clCreateContext.argtypes[3](), None, ctypes.byref(status := ctypes.c_int32())), status)
|
||||
if CLDevice.compiler_context is None: CLDevice.compiler_context = self
|
||||
self.queue = checked(cl.clCreateCommandQueue(self.context, self.device_id, cl.CL_QUEUE_PROFILING_ENABLE, ctypes.byref(status)), status)
|
||||
self.pending_copyin: List[memoryview] = []
|
||||
super().__init__(CLAllocator(self), LinearizerOptions(), OpenCLRenderer, compile_cl, functools.partial(CLProgram, self))
|
||||
def synchronize(self):
|
||||
check(cl.clFinish(self.queue))
|
||||
self.pending_copyin.clear()
|
||||
|
||||
GPUDevice = CLDevice # for legacy reasons
|
||||
|
|
Loading…
Reference in New Issue