tinygrad/test/test_speed_v_torch.py

import os
import unittest
import torch
torch.set_num_threads(1)
import time
import numpy as np
from tinygrad.tensor import Tensor
from tinygrad.nn import Conv2d
from tinygrad.llops.ops_gpu import CL
try:
  from termcolor import colored
except ImportError:
  colored = None

IN_CHANS = [int(x) for x in os.getenv("IN_CHANS", "4,16,64").split(",")]

def colorize_float(x):
  ret = f"{x:7.2f}x"
  if colored:
    if x < 0.8:
      return colored(ret, 'green')
    elif x > 1.5:
      return colored(ret, 'red')
    else:
      return colored(ret, 'yellow')
  else:
    return ret

CNT = 8
def test_speed(f1, *args):
  ets = []
  ret = None
  for _ in range(CNT):
    del ret
    st = time.monotonic()
    ret = f1(*args)
    if ret.device in ["GPU", "OPENCL"]:
      CL.cl_queue.finish()
    et = (time.monotonic() - st) * 1000
    ets.append(et)
  return ret.numpy(), np.min(ets)

def test_generic_square(name, N, f1, f2):
  torch.manual_seed(0)
  torch_a = torch.rand(N, N) - 0.5
  torch_b = torch.rand(N, N) - 0.5
  tiny_a = Tensor(torch_a.cpu().numpy())
  tiny_b = Tensor(torch_b.cpu().numpy())

  with torch.no_grad():
    val_torch, et_torch = test_speed(f1, torch_a, torch_b)
  val_tinygrad, et_tinygrad = test_speed(lambda *args: f2(*args).realize(), tiny_a, tiny_b)

  print(f"{name:30s} {N:4d}x{N:4d} {et_torch:7.2f} ms in torch, {et_tinygrad:7.2f} ms in tinygrad, {colorize_float(et_tinygrad/et_torch)} slower", val_torch.sum(), val_tinygrad.sum())
  np.testing.assert_allclose(val_tinygrad, val_torch, atol=1e-4, rtol=1e-3)

class TestSpeed(unittest.TestCase):
  def test_sum(self):
    def f(a, b): return a.sum()
    test_generic_square('sum', 4096, f, f)

  def test_permute(self):
    # this is a 64MB tensor, M1 L1 cache is 128kB
    # to fit easily in L1, rotations should be 128x128 chunks. 128x128 is also the AMX size
    def f1(a, b): return a.permute(1,0).contiguous()
    # NOTE: this isn't being constant folded
    def f2(a, b): return a.permute(1,0) + 0
    test_generic_square('permute', 4096, f1, f2)

  def test_neg(self):
    def f(a, b): return -a
    test_generic_square('neg', 4096, f, f)

  def test_exp(self):
    def f(a, b): return a.exp()
    test_generic_square('exp', 2048, f, f)

  def test_relu(self):
    def f(a, b): return a.relu()
    test_generic_square('relu', 4096, f, f)

  def test_max(self):
    def f(a, b): return a.max()
    test_generic_square('max', 4096, f, f)

  def test_mul_sum(self):
    def f(a, b): return (a*b).sum()
    test_generic_square('mul_sum', 4096, f, f)

  def test_add(self):
    for N in [1024, 4096]:
      def f(a, b): return a + b
      test_generic_square('add', N, f, f)

  def test_add_sq(self):
    def f(a, b): return a*a + b*b
    test_generic_square('add_sq', 4096, f, f)

  def test_gemm(self):
    def f(a, b): return a @ b
    test_generic_square('gemm', 512, f, f)

  def test_gemm_unrolled(self):
    N = 512
    def f1(a, b): return a@b.T
    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)
    test_generic_square('gemm_unrolled', N, f1, f2)

  def test_gemm_unrolled_permute_r(self):
    N = 512
    def f1(a, b): return a@b
    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)
    test_generic_square('gemm_unrolled_permute_r', N, f1, f2)

  def test_gemm_unrolled_permute_lr(self):
    N = 512
    def f1(a, b): return a.T@b
    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)
    test_generic_square('gemm_unrolled_permute_lr', N, f1, f2)

  def test_conv2d(self):
    torch.manual_seed(0)
    for bs in [32]:
      for in_chans in IN_CHANS:
        for out_chans in [32]:
          img_size = 34
          torch_dat = torch.rand(bs, in_chans, img_size, img_size)
          torch_conv = torch.nn.Conv2d(in_chans, out_chans, 3, bias=None)

          tiny_dat = Tensor(torch_dat.cpu().numpy())
          tiny_conv = Conv2d(in_chans, out_chans, 3, bias=None)
          tiny_conv.weight = Tensor(torch_conv.weight.detach().cpu().numpy())

          def f1(): return torch_conv(torch_dat)
          def f2(): return tiny_conv(tiny_dat).realize()

          with torch.no_grad():
            val_torch, et_torch = test_speed(f1)
          val_tinygrad, et_tinygrad = test_speed(f2)

          print(f"bs:{bs:3d} chans:{in_chans:3d} -> {out_chans:3d}                   {et_torch:7.2f} ms in torch, {et_tinygrad:7.2f} ms in tinygrad, {colorize_float(et_tinygrad/et_torch)} slower", val_torch.sum(), val_tinygrad.sum())
          np.testing.assert_allclose(val_tinygrad, val_torch, atol=1e-4)

if __name__ == '__main__':
  unittest.main()