mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-04-29 03:00:14 -04:00
4885fce56e
* shapetracker from newgpu * touchup ops * test * testst * thneed deletes unused inputs * test * bugfix
197 lines
6.7 KiB
Python
197 lines
6.7 KiB
Python
import os
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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 functools import partial
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from tinygrad.ops import 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
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try:
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from tinygrad.llops.ops_gpu import CL
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except ImportError:
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CL = None
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IN_CHANS = [int(x) for x in os.getenv("IN_CHANS", "4,16,64").split(",")]
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torch_device = torch.device('mps' if int(os.getenv("MPS", "0")) else 'cpu')
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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 > 1.5:
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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 = 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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for _ in range(CNT):
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del ret
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GlobalCounters.global_ops = 0
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GlobalCounters.global_mem = 0
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st = time.monotonic()
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ret = f1(*args)
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if CL is not None and ret.device in ["GPU", "OPENCL"]:
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CL.cl_queue.finish()
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if "mps" in str(ret.device):
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# TODO: better way to sync?
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torch.zeros(1, device='mps').cpu()
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et = (time.monotonic() - st) * 1000
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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.cpu().numpy(), np.min(ets)
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def helper_test_generic_square(name, N, f1, f2):
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torch.manual_seed(0)
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torch_a = (torch.rand(N, N) - 0.5).to(torch_device)
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torch_b = (torch.rand(N, N) - 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:4d}x{N:4d}", partial(f1, torch_a, torch_b), partial(f2, tiny_a, tiny_b))
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prefix = None
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def helper_test_generic(name, f1, f2):
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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)
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val_tinygrad, et_tinygrad = helper_test_speed(lambda: f2().realize())
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flops = save_ops*1e-6
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mem = save_mem*4*1e-6
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print(f"{prefix}{name:40s} {et_torch:7.2f} ms ({flops/et_torch:7.2f} GFLOPS {mem/et_torch:7.2f} GB/s) in torch, {et_tinygrad:7.2f} ms ({flops/et_tinygrad:7.2f} GFLOPS {mem/et_tinygrad:7.2f} GB/s) in tinygrad, {colorize_float(et_tinygrad/et_torch)} slower {flops:7.2f} MOPS {mem:7.2f} MB")
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prefix = " "
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np.testing.assert_allclose(val_tinygrad, val_torch, atol=1e-4, rtol=1e-3)
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class TestSpeed(unittest.TestCase):
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def setUp(self):
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global prefix
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prefix = " " if prefix is None else ""
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return super().setUp()
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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', 4096, f, f)
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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)
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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)
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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) - 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}", partial(f, torch_a), partial(f, 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)
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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)
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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)
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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)
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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 [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_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', 512, 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_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).to(torch_device)
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torch_conv = torch.nn.Conv2d(in_chans, out_chans, 3, bias=None, padding=1).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(): return torch_conv(torch_dat.permute(0,3,1,2))
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def f2(): 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}", f1, f2)
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def test_conv2d(self):
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torch.manual_seed(0)
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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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img_size = 34
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torch_dat = torch.rand(bs, in_chans, img_size, img_size).to(torch_device)
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torch_conv = torch.nn.Conv2d(in_chans, out_chans, 3, bias=None).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)
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tiny_conv.weight = Tensor(torch_conv.weight.detach().cpu().numpy())
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def f1(): return torch_conv(torch_dat)
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def f2(): 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}", f1, f2)
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if __name__ == '__main__':
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unittest.main()
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