mirror of https://github.com/commaai/tinygrad.git
287 lines
11 KiB
Python
287 lines
11 KiB
Python
import os
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os.environ["NVIDIA_TF32_OVERRIDE"] = "0"
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os.environ["MKL_NUM_THREADS"] = "1"
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os.environ["NUMEXPR_NUM_THREADS"] = "1"
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os.environ["OMP_NUM_THREADS"] = "1"
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import unittest
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import torch
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torch.set_num_threads(1)
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import time
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import numpy as np
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np.set_printoptions(linewidth=160)
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from tinygrad import Device, GlobalCounters
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from tinygrad.tensor import Tensor
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from tinygrad.nn import Conv2d
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from tinygrad.helpers import colored, getenv, CI
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from tinygrad.features.jit import TinyJit
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IN_CHANS = [int(x) for x in getenv("IN_CHANS", "4,16,64").split(",")]
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torch_dt = torch.float16 if getenv("HALF", 0) else torch.float32
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torch_device = torch.device('mps' if getenv("MPS", 0) else ('cuda' if getenv("TORCHCUDA", 0) else 'cpu'))
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if str(torch_device) == "mps":
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import torch.mps
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def sync(): torch.mps.synchronize()
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elif str(torch_device) == "cuda":
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import torch.cuda
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def sync(): torch.cuda.synchronize()
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else:
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def sync(): pass
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def colorize_float(x):
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ret = f"{x:7.2f}x"
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if x < 0.75:
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return colored(ret, 'green')
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elif x > 1.15:
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return colored(ret, 'red')
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else:
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return colored(ret, 'yellow')
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save_ops, save_mem = 0, 0
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CNT = getenv("CNT", 8)
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def helper_test_speed(f1, *args):
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global save_ops, save_mem
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ets = []
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ret = None
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cache_defeat = np.zeros((2048,2048))
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for i in range(CNT):
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del ret
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# operation cache defeats
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args = [(x+1).realize() if isinstance(x, Tensor) else (None if x is None else (x+1)) for x in args]
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# force syncing
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[x.numpy() if isinstance(x, Tensor) or str(torch_device) == "cpu" else x.cpu().numpy() for x in args if x is not None]
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# clear 32MB global memory cache (CPU and global memory only)
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cache_defeat += 1
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# manual pre sync
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if isinstance(args[0], Tensor): Device[args[0].device].synchronize()
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else: sync()
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GlobalCounters.global_ops = 0
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GlobalCounters.global_mem = 0
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st = time.perf_counter()
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ret = f1(*args)
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if isinstance(ret, Tensor): Device[ret.device].synchronize()
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else: sync()
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et = (time.perf_counter() - st) * 1000
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if i >= 1: ets.append(et)
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if GlobalCounters.global_ops:
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save_ops, save_mem = GlobalCounters.global_ops, GlobalCounters.global_mem
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return ret.numpy() if isinstance(ret, Tensor) else ret.cpu().numpy(), np.min(ets)
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def helper_test_generic_square(name, N, f1, f2, onearg=False):
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torch.manual_seed(0)
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torch_a = (torch.rand(N, N, dtype=torch_dt) - 0.5).to(torch_device)
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torch_b = (torch.rand(N, N, dtype=torch_dt) - 0.5).to(torch_device) if not onearg else None
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tiny_a = Tensor(torch_a.cpu().numpy())
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tiny_b = Tensor(torch_b.cpu().numpy()) if not onearg else None
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helper_test_generic(f"{name:30s} {N:5d}x{N:5d}", f1, (torch_a, torch_b), TinyJit(lambda a,b:f2(a,b).realize()), (tiny_a, tiny_b))
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def helper_test_matvec(name, N, M):
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torch.manual_seed(0)
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torch_a = (torch.rand(N, dtype=torch_dt) - 0.5).to(torch_device)
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torch_b = (torch.rand(N, M, dtype=torch_dt) - 0.5).to(torch_device)
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tiny_a = Tensor(torch_a.cpu().numpy())
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tiny_b = Tensor(torch_b.cpu().numpy())
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helper_test_generic(f"{name:30s} {N:5d}x{M:5d}", lambda a,b: a@b, (torch_a, torch_b), TinyJit(lambda a,b:(a@b).realize()), (tiny_a, tiny_b))
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prefix = None
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def helper_test_generic(name, f1, f1_args, f2, f2_args):
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global prefix
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with torch.no_grad():
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val_torch, et_torch = helper_test_speed(f1, *f1_args)
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val_tinygrad, et_tinygrad = helper_test_speed(f2, *f2_args)
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desc = "faster" if et_torch > et_tinygrad else "slower"
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flops = save_ops*1e-6
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mem = save_mem*1e-6
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print(("\r" if not CI else "")+f"{name:42s} {et_torch:7.2f} ms ({flops/et_torch:8.2f} GFLOPS {mem/et_torch:8.2f} GB/s) in torch, {et_tinygrad:7.2f} ms ({flops/et_tinygrad:8.2f} GFLOPS {mem/et_tinygrad:8.2f} GB/s) in tinygrad, {colorize_float(et_tinygrad/et_torch)} {desc} {flops:10.2f} MOPS {mem:8.2f} MB") # noqa: E501
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np.testing.assert_allclose(val_tinygrad, val_torch, atol=1e-3, rtol=1e-3)
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def helper_test_conv(bs, in_chans, out_chans, kernel_size, img_size_y, img_size_x):
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torch.manual_seed(0)
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torch_dat = torch.rand(bs, in_chans, img_size_y, img_size_x, dtype=torch_dt).to(torch_device)
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torch_conv = torch.nn.Conv2d(in_chans, out_chans, kernel_size, bias=None, dtype=torch_dt).to(torch_device)
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tiny_dat = Tensor(torch_dat.cpu().numpy())
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tiny_conv = Conv2d(in_chans, out_chans, kernel_size, bias=None)
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tiny_conv.weight = Tensor(torch_conv.weight.detach().cpu().numpy())
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def f1(torch_dat): return torch_conv(torch_dat)
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def f2(tiny_dat): return tiny_conv(tiny_dat).realize()
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helper_test_generic(f"conv bs:{bs:3d} chans:{in_chans:3d} -> {out_chans:3d} k:{kernel_size}", f1, (torch_dat,), TinyJit(f2), (tiny_dat,))
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@unittest.skipIf(getenv("BIG") == 0, "no big tests")
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@unittest.skipIf(getenv("CUDACPU"), "no CUDACPU")
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class TestBigSpeed(unittest.TestCase):
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def test_add(self):
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def f(a, b): return a+b
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helper_test_generic_square('add', 8192, f, f)
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def test_exp(self):
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def f(a, b): return a.exp()
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helper_test_generic_square('exp', 8192, f, f, onearg=True)
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def test_gemm_2048(self):
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def f(a, b): return a @ b
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helper_test_generic_square('gemm', 2048, f, f)
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def test_gemm_4096(self):
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def f(a, b): return a @ b
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helper_test_generic_square('gemm', 4096, f, f)
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def test_large_conv_1x1(self): helper_test_conv(bs=32, in_chans=128, out_chans=128, kernel_size=1, img_size_y=128, img_size_x=128)
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def test_large_conv_3x3(self): helper_test_conv(bs=4, in_chans=128, out_chans=128, kernel_size=3, img_size_y=130, img_size_x=130)
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def test_large_conv_5x5(self): helper_test_conv(bs=4, in_chans=128, out_chans=128, kernel_size=5, img_size_y=132, img_size_x=132)
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def test_matvec_4096_16384(self): helper_test_matvec('matvec_4096_16384', 4096, 16384)
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def test_matvec_16384_4096(self): helper_test_matvec('matvec_16384_4096', 16384, 4096)
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@unittest.skipIf(getenv("BIG") == 1, "only big tests")
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@unittest.skipIf(getenv("CUDACPU"), "no CUDACPU")
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class TestSpeed(unittest.TestCase):
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def test_sub(self):
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def f(a, b): return a-b
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helper_test_generic_square('sub', 4096, f, f)
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@unittest.skipIf(CI and Device.DEFAULT == "WEBGPU", "breaking on webgpu CI")
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def test_pow(self):
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def f(a, b): return a.pow(b)
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helper_test_generic_square('pow', 2048, f, f)
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def test_sum(self):
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def f(a, b): return a.sum()
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helper_test_generic_square('sum', 2048, f, f, onearg=True)
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helper_test_generic_square('sum', 4096, f, f, onearg=True)
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def test_partial_sum(self):
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R = 256
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def f(a, b): return a.reshape(int(4096//R), int(4096*R)).sum(axis=1)
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helper_test_generic_square('partial_sum', 4096, f, f, onearg=True)
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@unittest.skip("not really used in models")
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def test_cumsum(self):
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def f0(a, b): return a.cumsum(axis=0)
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def f1(a, b): return a.cumsum(axis=1)
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helper_test_generic_square('cumsum_0', 256, f0, f0, onearg=True)
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helper_test_generic_square('cumsum_1', 256, f1, f1, onearg=True)
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def test_cat(self):
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helper_test_generic_square('cat_0', 256, lambda x,y: torch.cat((x,y),dim=0), lambda x,y: x.cat(y,dim=0))
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helper_test_generic_square('cat_1', 256, lambda x,y: torch.cat((x,y),dim=1), lambda x,y: x.cat(y,dim=1))
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def test_array_packing(self):
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N = 2048
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def f(a, b): return a.reshape(N, N // 32, 32).permute(1,0,2).contiguous()
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helper_test_generic_square('array_packing', N, f, f, onearg=True)
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def test_permute(self):
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for N in [1024, 4096]:
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# this is a 64MB tensor, M1 L1 cache is 128kB
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# to fit easily in L1, rotations should be 128x128 chunks. 128x128 is also the AMX size
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def f(a, b): return a.permute(1,0).contiguous()
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helper_test_generic_square('permute', N, f, f, onearg=True)
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def test_double_permute(self):
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N = 64
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torch.manual_seed(0)
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torch_a = (torch.rand(N, N, N, N, dtype=torch_dt) - 0.5).to(torch_device)
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tiny_a = Tensor(torch_a.cpu().numpy())
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def f(a): return a.permute(1,0,3,2).contiguous()
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helper_test_generic(f"double_permute {tiny_a.shape}", f, (torch_a,), TinyJit(lambda a: f(a).realize()), (tiny_a,))
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def test_neg(self):
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def f(a, b): return -a
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helper_test_generic_square('neg', 4096, f, f, onearg=True)
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def test_exp(self):
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def f(a, b): return a.exp()
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helper_test_generic_square('exp', 2048, f, f, onearg=True)
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def test_relu(self):
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def f(a, b): return a.relu()
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helper_test_generic_square('relu', 4096, f, f, onearg=True)
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def test_max(self):
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def f(a, b): return a.max()
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helper_test_generic_square('max', 4096, f, f, onearg=True)
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def test_mul_sum(self):
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def f(a, b): return (a*b).sum()
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helper_test_generic_square('mul_sum', 4096, f, f)
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def test_add(self):
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for N in [1, 1024, 4096]:
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def f(a, b): return a + b
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helper_test_generic_square('add', N, f, f)
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def test_add_constant(self):
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def f(a, b): return a+2.0
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helper_test_generic_square('add_constant', 4096, f, f, onearg=True)
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def test_add_sq(self):
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def f(a, b): return a*a + b*b
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helper_test_generic_square('add_sq', 4096, f, f)
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def test_gemm(self):
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def f(a, b): return a @ b
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helper_test_generic_square('gemm', 1024, f, f)
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def test_gemm_small(self):
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def f(a, b): return a @ b
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helper_test_generic_square('gemm', 256, f, f)
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def test_gemm_unrolled(self):
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N = 512
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def f1(a, b): return a@b.T
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def f2(a, b): return (a.reshape(N, 1, N).expand(N, N, N) * b.reshape(1, N, N).expand(N, N, N)).sum(axis=2)
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helper_test_generic_square('gemm_unrolled', N, f1, f2)
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def test_gemm_unrolled_permute_l(self):
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N = 512
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def f1(a, b): return a.T@b.T
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def f2(a, b): return (a.permute(1,0).reshape(N, 1, N).expand(N, N, N) * b.reshape(1, N, N).expand(N, N, N)).sum(axis=2)
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helper_test_generic_square('gemm_unrolled_permute_l', N, f1, f2)
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def test_gemm_unrolled_permute_r(self):
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N = 512
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def f1(a, b): return a@b
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def f2(a, b): return (a.reshape(N, 1, N).expand(N, N, N) * b.permute(1,0).reshape(1, N, N).expand(N, N, N)).sum(axis=2)
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helper_test_generic_square('gemm_unrolled_permute_r', N, f1, f2)
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def test_gemm_unrolled_permute_lr(self):
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N = 512
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def f1(a, b): return a.T@b
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def f2(a, b): return (a.permute(1,0).reshape(N, 1, N).expand(N, N, N) * b.permute(1,0).reshape(1, N, N).expand(N, N, N)).sum(axis=2)
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helper_test_generic_square('gemm_unrolled_permute_lr', N, f1, f2)
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def test_matvec_1024_1024(self): helper_test_matvec('matvec_1024_1024', 1024, 1024)
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def test_matvec_1024_4096(self): helper_test_matvec('matvec_1024_4096', 1024, 4096)
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def test_matvec_4096_1024(self): helper_test_matvec('matvec_4096_1024', 4096, 1024)
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def test_matvec_4096_4096(self): helper_test_matvec('matvec_4096_4096', 4096, 4096)
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def test_openpilot_conv2d(self):
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bs, in_chans, out_chans = 1,12,32
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torch.manual_seed(0)
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torch_dat = torch.rand(bs, 64, 128, 12, dtype=torch_dt).to(torch_device)
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torch_conv = torch.nn.Conv2d(in_chans, out_chans, 3, bias=None, padding=1, dtype=torch_dt).to(torch_device)
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tiny_dat = Tensor(torch_dat.cpu().numpy())
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tiny_conv = Conv2d(in_chans, out_chans, 3, bias=None, padding=1)
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tiny_conv.weight = Tensor(torch_conv.weight.detach().cpu().numpy())
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def f1(torch_dat): return torch_conv(torch_dat.permute(0,3,1,2))
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def f2(tiny_dat): return tiny_conv(tiny_dat.permute(0,3,1,2)).realize()
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helper_test_generic(f"conv bs:{bs:3d} chans:{in_chans:3d} -> {out_chans:3d} k:3", f1, (torch_dat,), TinyJit(f2), (tiny_dat,))
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def test_conv2d(self):
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for bs in [32]:
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for in_chans in IN_CHANS:
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for out_chans in [32]:
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helper_test_conv(bs, in_chans, out_chans, 3, 34, 34)
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if __name__ == '__main__':
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unittest.main()
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