mirror of
https://github.com/firestar5683/StarPilot.git
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firestar5683
22a8575199
Keep official openpilot ancestry and dependency licenses. Store resources directly in Git, remove LFS setup requirements, and leave the downloadable big model outside the source tree.
98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
import ctypes, array
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from hexdump import hexdump
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from tinygrad.runtime.ops_cl import CLDevice
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from tinygrad.helpers import getenv, to_mv, mv_address
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from tinygrad.dtype import dtypes
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from tinygrad import Tensor, TinyJit
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from tinygrad.runtime.autogen import opencl as cl
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if getenv("IOCTL"): import extra.qcom_gpu_driver.opencl_ioctl # noqa: F401 # pylint: disable=unused-import
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# create raw opencl buffer.
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gdev = CLDevice()
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cl_buf = cl.clCreateBuffer(gdev.context, cl.CL_MEM_READ_WRITE, 0x100, None, status := ctypes.c_int32())
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assert status.value == 0
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# fill it with something for fun
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data = memoryview(array.array('I', [i for i in range(64)]))
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cl.clEnqueueWriteBuffer(gdev.queue, cl_buf, False, 0, 0x100, mv_address(data), 0, None, None)
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cl.clFinish(gdev.queue) # wait writes to complete
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# get raw gpu pointer from opencl buffer.
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## get buf desc
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hexdump(to_mv(ctypes.addressof(cl_buf), 0x40))
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cl_buf_desc_ptr = to_mv(ctypes.addressof(cl_buf), 8).cast('Q')[0]
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## get buf device ptr
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hexdump(to_mv(cl_buf_desc_ptr, 0x100))
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rawbuf_ptr = to_mv(cl_buf_desc_ptr, 0x100).cast('Q')[20] # offset 0xA0 is a raw gpu pointer.
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# create QCOM tensor with the externally managed buffer
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x = Tensor.from_blob(rawbuf_ptr, (8, 8), dtype=dtypes.int, device='QCOM')
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y = (x + 1).reshape(-1).tolist()
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print(y[:10])
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assert y == [i + 1 for i in range(64)]
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# all calculations are done, save to free the object
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cl.clReleaseMemObject(cl_buf)
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# all together with jit
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@TinyJit
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def calc(x): return x + 2
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for i in range(4):
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cl_buf = cl.clCreateBuffer(gdev.context, cl.CL_MEM_READ_WRITE, 2*2*4, None, status := ctypes.c_int32())
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assert status.value == 0
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data = memoryview(array.array('I', [x+i for x in range(2*2)]))
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cl.clEnqueueWriteBuffer(gdev.queue, cl_buf, False, 0, 2*2*4, mv_address(data), 0, None, None)
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cl.clFinish(gdev.queue) # wait writes to complete
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cl_buf_desc_ptr = to_mv(ctypes.addressof(cl_buf), 8).cast('Q')[0]
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rawbuf_ptr = to_mv(cl_buf_desc_ptr, 0x100).cast('Q')[20]
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y = calc(x = Tensor.from_blob(rawbuf_ptr, (2, 2), dtype=dtypes.int, device='QCOM')).tolist()
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print(f'jit {i}\n', y)
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# all calculations are done, save to free the object
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cl.clReleaseMemObject(cl_buf)
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# now images!
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h, w = 128, 128
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cl_img = cl.clCreateImage2D(gdev.context, cl.CL_MEM_READ_WRITE, cl.cl_image_format(cl.CL_RGBA, cl.CL_FLOAT), w, h, 0, None, status := ctypes.c_int32())
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assert status.value == 0
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# fill it with something for fun
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data = memoryview(array.array('f', [i for i in range(h*w*4)]))
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cl.clEnqueueWriteImage(gdev.queue, cl_img, False, (ctypes.c_size_t * 3)(0,0,0), (ctypes.c_size_t * 3)(w,h,1), 0, 0, mv_address(data), 0, None, None)
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cl.clFinish(gdev.queue) # wait writes to complete
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# get raw gpu pointer from opencl buffer.
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## get buf desc
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hexdump(to_mv(ctypes.addressof(cl_img), 0x40))
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cl_buf_desc_ptr = to_mv(ctypes.addressof(cl_img), 8).cast('Q')[0]
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## get buf device ptr
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hexdump(to_mv(cl_buf_desc_ptr, 0x100))
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rawbuf_ptr = to_mv(cl_buf_desc_ptr, 0x100).cast('Q')[20] # offset 0xA0 is a raw gpu pointer.
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# create QCOM tensor with the externally managed buffer
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x = Tensor.from_blob(rawbuf_ptr, (h,w,4), dtype=dtypes.float, device='QCOM')
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y = (x + 1).tolist()
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print(y[:10])
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# all calculations are done, save to free the object
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cl.clReleaseMemObject(cl_img)
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# from numpy
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import numpy as np
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YUV_SIZE = 50
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a_np = (32*np.random.randn(YUV_SIZE).astype(np.float32) + 128).clip(0,255).astype(np.uint8)
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a = Tensor.from_blob(a_np.ctypes.data, (YUV_SIZE,), dtype=dtypes.uint8, device='QCOM').realize()
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print(a.numpy()[:10], a_np[:10])
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assert np.all(a.numpy() == a_np)
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assert np.all((a - 1).numpy() == a_np - 1)
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