9c7833faf9
* Vegetarian Filet o Fish model * fix.. atc.. * test cluster_speed_limit * fix.. cluster_speed_limit.. 2 * fix.. clusterspeedlimit3 * cruise speed to roadlimit speed * fix.. * fix.. eng * deltaUp/Down for lanechange * fix.. atc desire... * fix.. * ff * ff * fix.. * fix.. eng * fix engsound * Update desire_helper.py * fix.. connect... * fix curve_min speed * Revert "fix curve_min speed" This reverts commit fcc9c2eb14eb3504abef3e420db93e8882e56f37. * Reapply "fix curve_min speed" This reverts commit 2d2bba476c58a7b4e13bac3c3ad0e4694c95515d. * fix.. auto speed up.. roadlimit * fix.. atc auto lanechange... * Update desire_helper.py * Update cruise.py * debug atc... * fix.. waze alert offset.. * fix.. * test atc.. * fix.. * fix.. atc * atc test.. * fix.. atc * fix.. atc2 * fix.. atc3 * KerryGold Model. latsmooth_sec = 0.0 * lat smooth seconds 0.13 * fix comment * fix.. auto cruise, and speed unit * change lanemode switching. * erase mazda lkas button.
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
#!/usr/bin/env python3
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# cd extra/disassemblers/ && git clone --recursive github.com:geohot/cuda_ioctl_sniffer.git
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# LD_PRELOAD=$PWD/extra/disassemblers/cuda_ioctl_sniffer/out/sniff.so GPU=1 python3 test/external/external_multi_gpu.py
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import numpy as np
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from tinygrad.tensor import Tensor
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from tinygrad.helpers import colored, Timing, getenv
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from tinygrad.device import Device
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d0, d1 = f'{Device.DEFAULT}:0', f'{Device.DEFAULT}:1'
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def sync():
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Device[d0].synchronize()
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Device[d1].synchronize()
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if __name__ == "__main__":
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print("GPU devices", d0, d1)
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sz = getenv("N", 1024*1024*256) # 1 GB
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with Timing("GPU initial sync: "): sync()
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with Timing("CPU creation: ", on_exit=lambda x: f", {(sz*4*2)/x:.2f} GB/sec"):
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c0 = (Tensor.ones(sz, device="CPU")/2).realize()
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c1 = (Tensor.ones(sz, device="CPU")/4).realize()
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print(c0.uop.base.realized)
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print(c1.uop.base.realized)
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with Timing("CPU -> 0: ", on_exit=lambda x: f", {(sz*4)/x:.2f} GB/sec"):
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a0 = c0.to(d0).realize()
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sync()
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with Timing("CPU -> 1: ", on_exit=lambda x: f", {(sz*4)/x:.2f} GB/sec"):
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b1 = c1.to(d1).realize()
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sync()
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# cross copy. this is (sometimes) going through the CPU
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with Timing("0 -> 1: ", on_exit=lambda x: f", {(sz*4)/x:.2f} GB/sec"):
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a1 = a0.to(d1).realize()
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sync()
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with Timing("1 -> 0: ", on_exit=lambda x: f", {(sz*4)/x:.2f} GB/sec"):
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b0 = b1.to(d0).realize()
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sync()
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# sum
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with Timing("0+0 -> 0 (sum): ", on_exit=lambda x: f", {(sz*4)/x:.2f} GB/sec"):
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ab0 = (a0 + b0).realize()
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sync()
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with Timing("1+1 -> 1 (sum): ", on_exit=lambda x: f", {(sz*4)/x:.2f} GB/sec"):
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ab1 = (a1 + b1).realize()
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sync()
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# cross device sum (does this work?)
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with Timing(colored("0+1 -> 0 (sum): ", "red"), on_exit=lambda x: f", {(sz*4)/x:.2f} GB/sec"):
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abx0 = (a0 + b1.to(d0)).realize()
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sync()
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with Timing(colored("1+0 -> 1 (sum): ", "red"), on_exit=lambda x: f", {(sz*4)/x:.2f} GB/sec"):
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abx1 = (b1 + a0.to(d1)).realize()
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sync()
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# copy back
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# NOTE: half of this slowness is caused by allocating memory on the CPU
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with Timing("0 -> CPU: ", on_exit=lambda x: f", {(sz*4)/x:.2f} GB/sec"):
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cc0 = ab0.numpy()
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with Timing("1 -> CPU: ", on_exit=lambda x: f", {(sz*4)/x:.2f} GB/sec"):
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cc1 = ab1.numpy()
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# same
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print("testing")
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np.testing.assert_allclose(cc0, cc1)
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# same (cross)
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print("testing (cross)")
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np.testing.assert_allclose(cc0, abx0.numpy())
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np.testing.assert_allclose(cc0, abx1.numpy())
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# devices
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print(ab0)
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print(ab1)
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print(abx0)
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print(abx1)
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