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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.py
pull/2562/head
George Hotz 2023-12-01 22:30:21 -08:00 committed by GitHub
parent 99ee2ec37a
commit 27481b9206
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GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 136 additions and 140 deletions

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@ -182,7 +182,7 @@ jobs:
name: Test openpilot model compile and size name: Test openpilot model compile and size
run: | run: |
DEBUG=2 ALLOWED_KERNEL_COUNT=207 FLOAT16=1 DEBUGCL=1 GPU=1 IMAGE=2 python openpilot/compile2.py DEBUG=2 ALLOWED_KERNEL_COUNT=207 FLOAT16=1 DEBUGCL=1 GPU=1 IMAGE=2 python openpilot/compile2.py
python -c 'import os; assert os.path.getsize("/tmp/output.thneed") < 100_000_000' #python -c 'import os; assert os.path.getsize("/tmp/output.thneed") < 100_000_000'
- if: ${{ matrix.task == 'openpilot' }} - if: ${{ matrix.task == 'openpilot' }}
name: Test openpilot model correctness (float32) name: Test openpilot model correctness (float32)
run: FLOAT16=0 DEBUGCL=1 GPU=1 IMAGE=2 python openpilot/compile2.py 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"
OPENPILOT_MODEL = "https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx" OPENPILOT_MODEL = "https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx"
import onnx import onnx
from typing import Tuple, List from tqdm import tqdm
from typing import Tuple, List, Optional, Dict
from extra.onnx import get_run_onnx from extra.onnx import get_run_onnx
from tinygrad.graph import print_tree, log_schedule_item from tinygrad.graph import log_schedule_item
from tinygrad import Tensor, Device from tinygrad import Tensor, Device
from tinygrad.helpers import dtypes, partition, GlobalCounters, Context, fetch, getenv, ImageDType, GRAPH, DEBUG from tinygrad.helpers import dtypes, partition, GlobalCounters, Context, fetch, getenv, ImageDType, GRAPH, DEBUG
from tinygrad.realize import run_schedule from tinygrad.realize import run_schedule, lower_schedule_item
from tinygrad.ops import LoadOps, ScheduleItem from tinygrad.ops import LoadOps, ScheduleItem
Device.DEFAULT = "GPU" Device.DEFAULT = "GPU"
@ -49,50 +50,17 @@ def get_schedule(onnx_data) -> Tuple[List[ScheduleItem], List[ScheduleItem]]:
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" 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"
return schedule, schedule_independent, inputs return schedule, schedule_independent, inputs
def schedule_to_thneed(schedule, output_fn): def test_vs_onnx(onnx_data, schedule:Optional[List[ScheduleItem]], inputs:Dict[str, Tensor]):
from extra.thneed import Thneed
print("kernel count:", len(schedule))
assert len(schedule) <= getenv("ALLOWED_KERNEL_COUNT", 0) or getenv("ALLOWED_KERNEL_COUNT", 0) == 0, "too many kernels!"
# transform to CL.CACHE
used_ops = 0
cl_cache = []
for si in schedule:
prg = Device["GPU"].get_runner(si.ast)
args = (si.out,) + si.inputs
# pass these to thneed
setattr(prg.clprg, 'op_estimate', prg.op_estimate)
setattr(prg.clprg, 'prg', prg.prg)
global_size = prg.global_size + [1]*(3-len(prg.global_size))
local_size = prg.local_size + [1]*(3-len(prg.local_size))
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]]))
used_ops += prg.op_estimate
from extra.thneed import Thneed
input_rawbuffers = {k:inputs[k].lazydata.realized for k in inputs.keys()}
t = Thneed(cl_cache, {k:v._buf for k,v in input_rawbuffers.items()})
# save thneed (before run)
t.save(output_fn)
print(f"buffers to save: {len(t.buffers_to_save)}, inputs: {list(t.inputs.keys())}, outputs: {t.outputs}")
runtime = t.run()
print(f"network using {used_ops/1e9:.2f} GOPS with runtime {runtime*1e3:.2f} ms that's {used_ops/runtime*1e-9:.2f} GFLOPS")
def thneed_test_onnx(onnx_data, output_fn):
import onnx import onnx
import pyopencl as cl #import pyopencl as cl
#from extra.thneed import Thneed
import numpy as np import numpy as np
from extra.thneed import Thneed
onnx_model = onnx.load(io.BytesIO(onnx_data)) onnx_model = onnx.load(io.BytesIO(onnx_data))
input_shapes = {inp.name:tuple(x.dim_value for x in inp.type.tensor_type.shape.dim) for inp in onnx_model.graph.input} input_shapes = {inp.name:tuple(x.dim_value for x in inp.type.tensor_type.shape.dim) for inp in onnx_model.graph.input}
Tensor.manual_seed(1337) Tensor.manual_seed(1337)
inputs = {k:Tensor.randn(*shp, requires_grad=False)*8 for k,shp in input_shapes.items()} new_inputs = {k:Tensor.randn(*shp, requires_grad=False)*8 for k,shp in input_shapes.items()}
new_np_inputs = {k:v.realize().numpy() for k,v in inputs.items()} new_np_inputs = {k:v.realize().numpy() for k,v in new_inputs.items()}
if getenv("ORT"): if getenv("ORT"):
# test with onnxruntime # test with onnxruntime
@ -100,33 +68,31 @@ def thneed_test_onnx(onnx_data, output_fn):
onnx_session = ort.InferenceSession(onnx_data) onnx_session = ort.InferenceSession(onnx_data)
onnx_output = onnx_session.run([onnx_model.graph.output[0].name], {k:v.astype(np.float16) for k,v in new_np_inputs.items()}) onnx_output = onnx_session.run([onnx_model.graph.output[0].name], {k:v.astype(np.float16) for k,v in new_np_inputs.items()})
new_torch_out = onnx_output[0] new_torch_out = onnx_output[0]
print("got ort outputs")
else: else:
# test with torch # test with torch
from test.models.test_onnx import run_onnx_torch from test.models.test_onnx import run_onnx_torch
new_torch_out = run_onnx_torch(onnx_model, new_np_inputs).numpy() new_torch_out = run_onnx_torch(onnx_model, new_np_inputs).numpy()
print("got torch outputs")
if output_fn is None: # if you don't have a schedule
# non thneed if schedule is None:
run_onnx = get_run_onnx(onnx_model) run_onnx = get_run_onnx(onnx_model)
new_tinygrad_out = next(iter(run_onnx(inputs).values())).cast(dtypes.float32).numpy() new_tinygrad_out = next(iter(run_onnx(new_inputs).values())).cast(dtypes.float32).numpy()
np.testing.assert_allclose(new_torch_out, new_tinygrad_out, atol=1e-4, rtol=1e-2) np.testing.assert_allclose(new_torch_out, new_tinygrad_out, atol=1e-4, rtol=1e-2)
print("classic self-test passed!") print("classic self-test passed!")
else: return
# load thneed and try that
nt = Thneed()
nt.load(output_fn)
# inputs # set inputs
for k,v in nt.inputs.items(): for k,v in inputs.items(): v.lazydata.realized.copyin(new_np_inputs[k].data)
cl.enqueue_copy(Device["GPU"].queue, v, new_np_inputs[k], is_blocking=True)
nt.run() # run code (all buffers have been allocated)
new_thneed_out = np.empty((nt.outputs[0].size//4,), dtype=np.float32).reshape(new_torch_out.shape) GlobalCounters.reset()
cl.enqueue_copy(Device["GPU"].queue, new_thneed_out, nt.outputs[0], is_blocking=True) for si in schedule: lower_schedule_item(si)([si.out.realized] + [x.realized for x in si.inputs], {})
# compare torch to thneed new_tinygrad_out = schedule[-1].out.realized.toCPU()
np.testing.assert_allclose(new_torch_out, new_thneed_out, atol=1e-4, rtol=1e-2) np.testing.assert_allclose(new_torch_out.flatten(), new_tinygrad_out, atol=1e-4, rtol=1e-2)
print("thneed self-test passed!") print("semi-thneed self-test passed!")
if __name__ == "__main__": if __name__ == "__main__":
onnx_data = fetch(sys.argv[1] if len(sys.argv) > 1 else OPENPILOT_MODEL).read_bytes() onnx_data = fetch(sys.argv[1] if len(sys.argv) > 1 else OPENPILOT_MODEL).read_bytes()
@ -152,13 +118,17 @@ if __name__ == "__main__":
GlobalCounters.reset() GlobalCounters.reset()
run_schedule(schedule[:]) run_schedule(schedule[:])
output_fn = sys.argv[2] if len(sys.argv) >= 3 else "/tmp/output.thneed" print("kernel count:", len(schedule))
schedule_to_thneed(schedule, output_fn) assert len(schedule) <= getenv("ALLOWED_KERNEL_COUNT", 0) or getenv("ALLOWED_KERNEL_COUNT", 0) == 0, "too many kernels!"
# TODO: thneed is broken
#output_fn = sys.argv[2] if len(sys.argv) >= 3 else "/tmp/output.thneed"
#schedule_to_thneed(schedule, output_fn)
FLOAT16 = getenv("FLOAT16", 0) FLOAT16 = getenv("FLOAT16", 0)
if FLOAT16 == 0: if FLOAT16 == 0:
try: try:
thneed_test_onnx(onnx_data, output_fn) test_vs_onnx(onnx_data, schedule, inputs)
except ModuleNotFoundError as e: except ModuleNotFoundError as e:
print(f"TEST NOT HAPPENING {e}") print(f"TEST NOT HAPPENING {e}")

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@ -19,7 +19,7 @@ setup(name='tinygrad',
"Programming Language :: Python :: 3", "Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License" "License :: OSI Approved :: MIT License"
], ],
install_requires=["numpy", "tqdm", "pyopencl", "gpuctypes", install_requires=["numpy", "tqdm", "gpuctypes",
"pyobjc-framework-Metal; platform_system=='Darwin'", "pyobjc-framework-Metal; platform_system=='Darwin'",
"pyobjc-framework-libdispatch; platform_system=='Darwin'"], "pyobjc-framework-libdispatch; platform_system=='Darwin'"],
python_requires='>=3.8', python_requires='>=3.8',

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@ -5,6 +5,20 @@ from tinygrad.helpers import ImageDType
@unittest.skipIf(Device.DEFAULT != "GPU", "only images on GPU") @unittest.skipIf(Device.DEFAULT != "GPU", "only images on GPU")
class TestImageDType(unittest.TestCase): class TestImageDType(unittest.TestCase):
def test_image_and_back(self):
data = Tensor.randn(9*27*4).realize()
tst = data.numpy()
it = data.cast(dtypes.imagef((9,27,4))).realize()
assert isinstance(it.lazydata.realized.dtype, ImageDType)
np.testing.assert_equal(tst, it.numpy())
def test_image_and_back_wrong_shape(self):
data = Tensor.randn(9*27*4).realize()
tst = data.numpy()
it = data.cast(dtypes.imagef((9,12,4))).realize()
assert not isinstance(it.lazydata.realized.dtype, ImageDType)
np.testing.assert_equal(tst, it.numpy())
def test_shrink_load_float(self): def test_shrink_load_float(self):
it = Tensor.randn(4).cast(dtypes.imagef((1,1,4))).realize() it = Tensor.randn(4).cast(dtypes.imagef((1,1,4))).realize()
imgv = it.numpy() imgv = it.numpy()

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@ -40,7 +40,7 @@ def partition(lst:List[T], fxn:Callable[[T],bool]):
def unwrap(x:Optional[T]) -> T: def unwrap(x:Optional[T]) -> T:
assert x is not None assert x is not None
return x return x
def unwrap2(x): def unwrap2(x:Tuple[T,Any]) -> T:
ret, err = x ret, err = x
assert err is None, str(err) assert err is None, str(err)
return ret return ret
@ -57,7 +57,7 @@ def to_function_name(s:str): return ''.join([c if c in (string.ascii_letters+str
def getenv(key:str, default=0): return type(default)(os.getenv(key, default)) def getenv(key:str, default=0): return type(default)(os.getenv(key, default))
def temp(x:str) -> str: return (pathlib.Path(tempfile.gettempdir()) / x).as_posix() def temp(x:str) -> str: return (pathlib.Path(tempfile.gettempdir()) / x).as_posix()
def from_mv(mv, to_type=ctypes.c_char): return ctypes.cast(ctypes.addressof(to_type.from_buffer(mv)), ctypes.POINTER(to_type)) def from_mv(mv, to_type=ctypes.c_char): return ctypes.cast(ctypes.addressof(to_type.from_buffer(mv)), ctypes.POINTER(to_type))
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]) 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])
@functools.lru_cache(maxsize=None) @functools.lru_cache(maxsize=None)
def init_c_struct_t(fields: Tuple[Tuple[str, ctypes._SimpleCData], ...]): def init_c_struct_t(fields: Tuple[Tuple[str, ctypes._SimpleCData], ...]):
class CStruct(ctypes.Structure): class CStruct(ctypes.Structure):
@ -288,7 +288,7 @@ def pretty_ptx(s):
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: 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:
check(create_prog(ctypes.byref(prog := prog_t()), prg.encode(), "<null>".encode(), 0, None, None)) check(create_prog(ctypes.byref(prog := prog_t()), prg.encode(), "<null>".encode(), 0, None, None))
status = compile_prog(prog, len(compile_options), to_char_p_p([ctypes.create_string_buffer(o.encode()) for o in compile_options])) status = compile_prog(prog, len(compile_options), to_char_p_p([o.encode() for o in compile_options]))
if status != 0: raise RuntimeError(f"compile failed: {get_bytes(prog, get_log_size, get_log, check).decode()}") if status != 0: raise RuntimeError(f"compile failed: {get_bytes(prog, get_log_size, get_log, check).decode()}")
return get_bytes(prog, get_code_size, get_code, check) return get_bytes(prog, get_code_size, get_code, check)

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@ -26,16 +26,12 @@ def run_schedule(schedule:List[ScheduleItem], disable_logging=False):
# get the program # get the program
prg = lower_schedule_item(si) prg = lower_schedule_item(si)
del si.out.op
for v in si.out.views: del v.op
# we don't have an output buffer, we have to create it, and create to max size if it has symbolic shape # we don't have an output buffer, we have to create it, and create to max size if it has symbolic shape
si.out.realized = si.out.output_buffer if si.out.output_buffer is not None else \ si.out.realized = si.out.output_buffer if si.out.output_buffer is not None else \
Buffer(si.out.device, prod((s if isinstance(s, int) else s.max for s in si.out.shape)), si.out.dtype) Buffer(si.out.device, prod((s if isinstance(s, int) else s.max for s in si.out.shape)), si.out.dtype)
del si.out.op
# get all the buffers for v in si.out.views: del v.op
rawbufs = [si.out.realized] + [x.realized for x in si.inputs]
# run the function (put it in JIT) # run the function (put it in JIT)
if prg: prg.exec(rawbufs, si.var_vals) 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 @@
from __future__ import annotations from __future__ import annotations
import os from typing import Tuple, Optional, Union, List, cast
os.environ['PYOPENCL_NO_CACHE'] = '1' import ctypes, functools
import pathlib, functools import gpuctypes.opencl as cl
import numpy as np from tinygrad.helpers import to_char_p_p, from_mv, diskcache, OSX, DType, ImageDType
import pyopencl as cl
from typing import Optional, List, Tuple
from tinygrad.helpers import DEBUG, getenv, prod, ImageDType, OSX, fromimport, diskcache, DType
from tinygrad.device import Compiled, LRUAllocator
from tinygrad.renderer.opencl import OpenCLRenderer
from tinygrad.codegen.kernel import LinearizerOptions from tinygrad.codegen.kernel import LinearizerOptions
from tinygrad.renderer.opencl import OpenCLRenderer
from tinygrad.device import Compiled, LRUAllocator
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 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
# TODO: if you fork and exit the child process after creating anything with cl on AMD, it hangs on e.wait() def check(status):
ROCM_LLVM_PATH = pathlib.Path("/opt/rocm/llvm/bin") if status != 0: raise RuntimeError(f"OpenCL Error {status}")
if DEBUG >= 6: def checked(ret, status):
early_exec = fromimport("extra.helpers", "enable_early_exec")() check(status.value)
return ret
@diskcache @diskcache
def compile_gpu(prg:str) -> bytes: def compile_cl(prg:str) -> bytes:
clprg = cl.Program(GPUDevice.compile_context, prg) assert CLDevice.compiler_context is not None, 'OpenCL requires a "compiler_context" to compile, init a device before you call this'
clprg.build() prg_bytes = prg.encode()
return clprg.get_info(cl.program_info.BINARIES)[0] 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)
status = cl.clBuildProgram(program, 1, ctypes.byref(CLDevice.compiler_context.device_id), None, cl.clBuildProgram.argtypes[4](), None)
if status != 0:
cl.clGetProgramBuildInfo(program, CLDevice.compiler_context.device_id, cl.CL_PROGRAM_BUILD_LOG, 0, None, ctypes.byref(log_size := ctypes.c_size_t()))
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)
raise RuntimeError(f"OpenCL Compile Error\n\n{ctypes.string_at(mstr, size=log_size.value).decode()}")
binary_sizes = (ctypes.c_size_t * 1)()
check(cl.clGetProgramInfo(program, cl.CL_PROGRAM_BINARY_SIZES, ctypes.sizeof(binary_sizes), ctypes.byref(binary_sizes), None))
binary = (ctypes.c_char * binary_sizes[0])()
binary_pointers = (ctypes.c_char_p * 1)(ctypes.cast(ctypes.addressof(binary), ctypes.c_char_p))
check(cl.clGetProgramInfo(program, cl.CL_PROGRAM_BINARIES, ctypes.sizeof(binary_pointers), ctypes.byref(binary_pointers), None))
check(cl.clReleaseProgram(program))
return bytes(binary)
class CLProgram: class CLProgram:
def __init__(self, device:GPUDevice, name:str, prg:bytes, bufs:int=0, vars:int=0): def __init__(self, device:CLDevice, name:str, prg:bytes, bufs:int=0, vars:int=0):
self.device, self.name, self.clprogram = device, name, cl.Program(device.ctx, [device.ctx.devices[0]], [prg]) self.device = device
self.clprogram.build() self.program = checked(cl.clCreateProgramWithBinary(device.context, 1, ctypes.byref(device.device_id), (ctypes.c_size_t * 1)(len(prg)),
self.clprg = self.clprogram.__getattr__(name) to_char_p_p([prg], ctypes.c_ubyte),
if DEBUG >= 5 and not OSX: ctypes.byref(binary_status := ctypes.c_int32()), ctypes.byref(errcode_ret := ctypes.c_int32())), errcode_ret)
device_name = self.device.ctx.devices[0].name check(binary_status.value)
if 'Adreno' in device_name: check(cl.clBuildProgram(self.program, 1, ctypes.byref(device.device_id), None, cl.clBuildProgram.argtypes[4](), None)) # NOTE: OSX requires this
fromimport('disassemblers.adreno', 'disasm')(prg) self.kernel = checked(cl.clCreateKernel(self.program, name.encode(), ctypes.byref(status := ctypes.c_int32())), status)
elif device_name.startswith('gfx'): self.vars = vars
asm = early_exec(([ROCM_LLVM_PATH / "llvm-objdump", '-d', '-'], prg))
print('\n'.join([x for x in asm.decode('utf-8').split("\n") if 's_code_end' not in x]))
elif "NVIDIA" in device_name:
# print the PTX for NVIDIA.
print(prg.decode('utf-8'))
if vars > 0: self.clprg.set_scalar_arg_dtypes([None]*bufs + [np.int32]*vars)
@staticmethod def __del__(self):
def max_work_group_size(): return GPUDevice.compile_context.devices[0].max_work_group_size if GPUDevice.compile_context is not None else 1024 check(cl.clReleaseKernel(self.kernel))
check(cl.clReleaseProgram(self.program))
def __call__(self, *bufs, global_size:Tuple[int,int,int], local_size:Optional[Tuple[int,int,int]]=None, wait=False) -> Optional[float]: def __call__(self, *bufs:Union[cl.cl_mem, int], global_size:Tuple[int,...], local_size:Optional[Tuple[int,...]]=None, wait=False) -> Optional[float]:
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) 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: if wait:
e.wait() start, end = ctypes.c_ulong(), ctypes.c_ulong()
try: check(cl.clWaitForEvents(1, ctypes.byref(event)))
return ((e.profile.end - e.profile.start) * OSX_TIMING_RATIO) * 1e-9 check(cl.clGetEventProfilingInfo(event, cl.CL_PROFILING_COMMAND_START, ctypes.sizeof(start), ctypes.byref(start), None))
except cl.RuntimeError: # no profiling info available check(cl.clGetEventProfilingInfo(event, cl.CL_PROFILING_COMMAND_END, ctypes.sizeof(end), ctypes.byref(end), None))
return None return float(end.value-start.value) * OSX_TIMING_RATIO * 1e-9
return None return None
class CLAllocator(LRUAllocator): class CLAllocator(LRUAllocator):
def __init__(self, device:GPUDevice): def __init__(self, device:CLDevice):
self.events: List[cl.Event] = []
self.device = device self.device = device
super().__init__() super().__init__()
def _alloc(self, size:int, dtype:DType): def _alloc(self, size:int, dtype:DType):
if isinstance(dtype, ImageDType): 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 return checked(cl.clCreateImage2D(self.device.context, cl.CL_MEM_READ_WRITE,
assert size == prod(dtype.shape), f"image size mismatch {size} != {dtype.shape}" cl.cl_image_format(cl.CL_RGBA, {2: cl.CL_HALF_FLOAT, 4: cl.CL_FLOAT}[dtype.itemsize]), dtype.shape[1], dtype.shape[0],
fmt = cl.ImageFormat(cl.channel_order.RGBA, {2: cl.channel_type.HALF_FLOAT, 4: cl.channel_type.FLOAT}[dtype.itemsize]) 0, None, ctypes.byref(status := ctypes.c_int32())), status)
buf = cl.Image(self.device.ctx, cl.mem_flags.READ_WRITE, fmt, shape=(dtype.shape[1], dtype.shape[0]))
else: else:
buf = cl.Buffer(self.device.ctx, cl.mem_flags.READ_WRITE, size * dtype.itemsize) return checked(cl.clCreateBuffer(self.device.context, cl.CL_MEM_READ_WRITE, size*dtype.itemsize, None, ctypes.byref(status := ctypes.c_int32())), status)
return buf def _free(self, buf:cl.cl_mem): check(cl.clReleaseMemObject(buf))
def copyin(self, dest:cl.Buffer, src:memoryview): self.events.append(cl.enqueue_copy(self.device.queue, dest, src, is_blocking=False)) def copyin(self, dest:cl.cl_mem, src:memoryview):
def copyout(self, dest:memoryview, src:cl.Buffer): check(cl.clEnqueueWriteBuffer(self.device.queue, dest, False, 0, len(src)*src.itemsize, from_mv(src), 0, None, None))
self.events.clear() self.device.pending_copyin.append(src) # NOTE: these can't be freed until the GPU actually executes this command
cl.enqueue_copy(self.device.queue, dest, src, is_blocking=True) 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): class CLDevice(Compiled):
devices = None device_ids = None # this is global and only initted once
compile_context = None 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): def __init__(self, device:str=""):
if GPUDevice.devices is None: if CLDevice.device_ids is None:
cl_platforms = cl.get_platforms() check(cl.clGetPlatformIDs(0, None, ctypes.byref(num_platforms := ctypes.c_uint32())))
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] check(cl.clGetPlatformIDs(num_platforms.value, platform_ids := (cl.cl_platform_id * num_platforms.value)(), None))
GPUDevice.devices = [device for device in platform_devices[getenv('CL_PLATFORM', 0)] if device.name not in getenv('CL_EXCLUDE', "").split(",")] check(cl.clGetDeviceIDs(platform_ids[0], cl.CL_DEVICE_TYPE_DEFAULT, 0, None, ctypes.byref(num_devices := ctypes.c_uint32())))
if DEBUG >= 1: print(f"using devices: {[device.hashable_model_and_version_identifier for device in GPUDevice.devices]}") CLDevice.device_ids = (cl.cl_device_id * num_devices.value)()
self.device = int(device.split(":")[1]) if ":" in device else 0 check(cl.clGetDeviceIDs(platform_ids[0], cl.CL_DEVICE_TYPE_DEFAULT, num_devices, CLDevice.device_ids, None))
self.ctx = cl.Context(devices=[GPUDevice.devices[self.device]]) self.device_id = CLDevice.device_ids[0 if ":" not in device else int(device.split(":")[1])]
if GPUDevice.compile_context is None: GPUDevice.compile_context = self.ctx self.context = checked(cl.clCreateContext(None, 1, ctypes.byref(self.device_id), cl.clCreateContext.argtypes[3](), None, ctypes.byref(status := ctypes.c_int32())), status)
self.queue = cl.CommandQueue(self.ctx, device=self.ctx.devices[0], properties=cl.command_queue_properties.PROFILING_ENABLE) if CLDevice.compiler_context is None: CLDevice.compiler_context = self
super().__init__(CLAllocator(self), LinearizerOptions(), OpenCLRenderer, compile_gpu, functools.partial(CLProgram, self)) self.queue = checked(cl.clCreateCommandQueue(self.context, self.device_id, cl.CL_QUEUE_PROFILING_ENABLE, ctypes.byref(status)), status)
def synchronize(self): self.queue.finish() 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