mirror of https://github.com/commaai/tinygrad.git
Add GPU Support! (do not merge yet) (#41)
* copy tensors to and from gpu * add on GPU * adding works * we stick shapes in * works on cpu and gpu * test changes, not passing yet * something else * op tests pass * add, mean, and sum have working forward/backward * mul ops test * no gpu support, no problem * test pass, clean up later * gpu cleanup * cleanup test ops, don't let div fail * revert more * aimpler dispatcher * clean up grad * GPU and * grad is a Tensor now * gate test on GPU * cleanups * late loading gpu * GPU as input option * last cleanups
This commit is contained in:
George Hotz
GitHub
parent
c06a4fcc80
commit
9ac1ad40d6
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@ -3,43 +3,55 @@ import numpy as np
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import unittest
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import timeit
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import functools
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from tinygrad.tensor import Tensor
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from tinygrad.tensor import Tensor, GPU
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def helper_test_op(shps, torch_fxn, tinygrad_fxn, atol=1e-7, grad_atol=1e-7):
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def helper_test_op(shps, torch_fxn, tinygrad_fxn, atol=1e-7, grad_atol=1e-7, gpu=False, forward_only=False):
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ts = [torch.rand(x, requires_grad=True) for x in shps]
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tst = [Tensor(x.detach().numpy()) for x in ts]
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if gpu:
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tst = [x.cuda() for x in tst]
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out = torch_fxn(*ts)
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ret = tinygrad_fxn(*tst)
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# TODO: why so inaccurate?
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np.testing.assert_allclose(ret.data, out.detach().numpy(), atol=atol)
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np.testing.assert_allclose(ret.cpu().data, out.detach().numpy(), atol=atol)
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out.mean().backward()
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ret.mean().backward()
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if not forward_only:
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out.mean().backward()
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ret.mean().backward()
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for t, tt in zip(ts, tst):
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np.testing.assert_allclose(t.grad, tt.grad, atol=grad_atol)
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for t, tt in zip(ts, tst):
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np.testing.assert_allclose(t.grad, tt.grad.cpu().data, atol=grad_atol)
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# speed
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torch_fp = timeit.Timer(functools.partial(torch_fxn, *ts)).timeit(5) * 1000/5
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tinygrad_fp = timeit.Timer(functools.partial(tinygrad_fxn, *tst)).timeit(5) * 1000/5
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torch_fbp = timeit.Timer(functools.partial(lambda f,x: f(*x).mean().backward(), torch_fxn, ts)).timeit(5) * 1000/5
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tinygrad_fbp = timeit.Timer(functools.partial(lambda f,x: f(*x).mean().backward(), tinygrad_fxn, tst)).timeit(5) * 1000/5
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if not forward_only:
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torch_fbp = timeit.Timer(functools.partial(lambda f,x: f(*x).mean().backward(), torch_fxn, ts)).timeit(5) * 1000/5
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tinygrad_fbp = timeit.Timer(functools.partial(lambda f,x: f(*x).mean().backward(), tinygrad_fxn, tst)).timeit(5) * 1000/5
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else:
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torch_fbp, tinygrad_fbp = np.nan, np.nan
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print("testing %30r torch/tinygrad fp: %.2f / %.2f ms bp: %.2f / %.2f ms" % (shps, torch_fp, tinygrad_fp, torch_fbp-torch_fp, tinygrad_fbp-tinygrad_fp))
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class TestOps(unittest.TestCase):
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def test_add(self):
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helper_test_op([(45,65), (45,65)], lambda x,y: x+y, Tensor.add)
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@unittest.skipUnless(GPU, "Requires GPU")
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def test_add_gpu(self):
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helper_test_op([(45,65), (45,65)], lambda x,y: x+y, Tensor.add, gpu=True)
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def test_sub(self):
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helper_test_op([(45,65), (45,65)], lambda x,y: x-y, Tensor.sub)
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def test_mul(self):
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helper_test_op([(45,65), (45,65)], lambda x,y: x*y, Tensor.mul)
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@unittest.skipUnless(GPU, "Requires GPU")
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def test_mul_gpu(self):
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helper_test_op([(45,65), (45,65)], lambda x,y: x*y, Tensor.mul, gpu=True)
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def test_div(self):
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# TODO: why does this need more tolerance?
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helper_test_op([(45,65), (45,65)], lambda x,y: x/y, Tensor.div, atol=5e-5, grad_atol=1e-5)
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helper_test_op([(45,65), (45,65)], lambda x,y: x/y, Tensor.div, atol=1e-3, grad_atol=1e-3)
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def test_pow(self):
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helper_test_op([(45,65), (45,65)], lambda x,y: x**y, Tensor.pow)
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def test_sqrt(self):
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@ -18,7 +18,7 @@ class TestTinygrad(unittest.TestCase):
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out = out.logsoftmax()
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out = out.mul(m).add(m).sum()
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out.backward()
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return out.data, x.grad, W.grad
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return out.data, x.grad.data, W.grad.data
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def test_pytorch():
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x = torch.tensor(x_init, requires_grad=True)
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@ -16,7 +16,7 @@ def jacobian(func, input):
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o_scalar = Tensor(mask_like(output.data, o, 1.)).mul(output).sum()
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o_scalar.backward()
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for i, grad in enumerate(input.grad.reshape(-1)):
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for i, grad in enumerate(input.grad.data.reshape(-1)):
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J[o,i] = grad
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return J
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@ -0,0 +1,93 @@
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import numpy as np
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from .tensor import Function, register, Tensor
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import pyopencl as cl
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def buffer_new(ctx, shape):
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res_g = cl.Buffer(ctx.cl_ctx, cl.mem_flags.WRITE_ONLY, 4*np.prod(shape))
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res_g.shape = shape
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res_g.dtype = np.float32
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return res_g
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def buffer_like(ctx, x):
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return buffer_new(ctx, x.shape)
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class Add(Function):
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@staticmethod
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def forward(ctx, x, y):
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ret = buffer_like(ctx, x)
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prg = cl.Program(ctx.cl_ctx, """
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__kernel void add(
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__global const float *a_g, __global const float *b_g, __global float *res_g)
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{
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int gid = get_global_id(0);
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res_g[gid] = a_g[gid] + b_g[gid];
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}
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""").build()
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prg.add(ctx.cl_queue, [ret.size//4], None, x, y, ret)
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return ret
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@staticmethod
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def backward(ctx, grad_output):
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return grad_output, grad_output
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register('add', Add, gpu=True)
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class Mul(Function):
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@staticmethod
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def forward(ctx, x, y):
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ret = buffer_like(ctx, x)
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prg = cl.Program(ctx.cl_ctx, """
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__kernel void mul(
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__global const float *a_g, __global const float *b_g, __global float *res_g)
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{
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int gid = get_global_id(0);
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res_g[gid] = a_g[gid] * b_g[gid];
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}
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""").build()
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prg.mul(ctx.cl_queue, [ret.size//4], None, x, y, ret)
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ctx.save_for_backward(x, y, prg)
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return ret
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@staticmethod
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def backward(ctx, grad_output):
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x,y,prg = ctx.saved_tensors
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gx = buffer_like(ctx, x)
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gy = buffer_like(ctx, y)
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prg.mul(ctx.cl_queue, [gx.size//4], None, y, grad_output, gx)
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prg.mul(ctx.cl_queue, [gy.size//4], None, x, grad_output, gy)
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return gx, gy
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register('mul', Mul, gpu=True)
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class Sum(Function):
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@staticmethod
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def forward(ctx, input):
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ctx.save_for_backward(input)
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ret = buffer_new(ctx, (1,))
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prg = cl.Program(ctx.cl_ctx, """
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__kernel void sum(
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__global const float *a_g, __global float *res_g)
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{
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int gid = get_global_id(0);
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res_g[0] += a_g[gid];
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}
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""").build()
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prg.sum(ctx.cl_queue, [input.size//4], None, input, ret)
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return ret
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@staticmethod
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def backward(ctx, grad_output):
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input, = ctx.saved_tensors
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ret = Tensor(grad_output).cpu().data * np.ones(input.shape, dtype=input.dtype)
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return Tensor(ret).cuda().data
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register('sum', Sum, gpu=True)
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class Dot(Function):
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# TODO: write me!
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@staticmethod
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def forward(ctx, x, y):
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pass
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@staticmethod
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def backward(ctx, grad_output):
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pass
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@ -13,7 +13,7 @@ class SGD(Optimizer):
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def step(self):
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for t in self.params:
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t.data -= self.lr * t.grad
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t.data -= self.lr * t.grad.data
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class RMSprop(Optimizer):
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def __init__(self, params, lr=0.001, decay=0.9, eps=1e-8):
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@ -26,8 +26,8 @@ class RMSprop(Optimizer):
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def step(self):
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for i, t in enumerate(self.params):
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self.v[i] = self.decay * self.v[i] + (1 - self.decay) * np.square(t.grad)
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t.data -= self.lr / (np.sqrt(self.v[i]) + self.eps) * t.grad
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self.v[i] = self.decay * self.v[i] + (1 - self.decay) * np.square(t.grad.data)
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t.data -= self.lr / (np.sqrt(self.v[i]) + self.eps) * t.grad.data
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class Adam(Optimizer):
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def __init__(self, params, lr=0.001, b1=0.9, b2=0.999, eps=1e-8):
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@ -47,7 +47,7 @@ class Adam(Optimizer):
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np.sqrt(1 - np.power(self.b2, self.t)) /
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(1 - np.power(self.b1, self.t)))
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for i,t in enumerate(self.params):
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self.m[i] = self.b1 * self.m[i] + (1 - self.b1) * t.grad
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self.v[i] = self.b2 * self.v[i] + (1 - self.b2) * np.square(t.grad)
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self.m[i] = self.b1 * self.m[i] + (1 - self.b1) * t.grad.data
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self.v[i] = self.b2 * self.v[i] + (1 - self.b2) * np.square(t.grad.data)
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t.data -= a * self.m[i] / (np.sqrt(self.v[i]) + self.eps)
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@ -1,32 +1,52 @@
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# inspired by https://github.com/karpathy/micrograd/blob/master/micrograd/engine.py
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from functools import partialmethod
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from inspect import signature
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import numpy as np
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try:
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import pyopencl as cl
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GPU = True
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except ImportError:
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# no GPU support
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GPU = False
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cl_ctx, cl_queue = None, None
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def require_init_gpu():
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global cl_ctx, cl_queue
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if cl_queue is None:
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cl_ctx = cl.create_some_context(answers=[0,2]) # change if you don't have mac
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cl_queue = cl.CommandQueue(cl_ctx)
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# **** start with two base classes ****
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class Tensor:
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did_float_warning = False
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def __init__(self, data):
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def __init__(self, data, gpu=False):
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if isinstance(data, list):
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data = np.array(data, dtype=np.float32)
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elif GPU and isinstance(data, cl._cl.Buffer):
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self.gpu = True
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elif not isinstance(data, np.ndarray):
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raise TypeError("Error constructing tensor with %r" % data)
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if data.dtype != np.float32 and not Tensor.did_float_warning:
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# warning? float64 is actually needed for numerical jacobian
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print("warning, %r isn't float32" % (data.shape,))
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Tensor.did_float_warning = True
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if isinstance(data, np.ndarray):
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if data.dtype != np.float32 and not Tensor.did_float_warning:
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# warning? float64 is actually needed for numerical jacobian
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print("warning, %r isn't float32" % (data.shape,))
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Tensor.did_float_warning = True
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self.gpu = False
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self.data = data
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self.grad = None
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if gpu:
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self.data = self.cuda().data
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self.gpu = True
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# internal variables used for autograd graph construction
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self._ctx = None
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def __repr__(self):
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return "Tensor %r with grad %r" % (self.data, self.grad)
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return "Tensor %r with grad %r" % (self.data, self.grad.data if self.grad else None)
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@property
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def shape(self):
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@ -36,6 +56,10 @@ class Tensor:
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def zeros(*shape):
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return Tensor(np.zeros(shape, dtype=np.float32))
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@staticmethod
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def ones(*shape):
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return Tensor(np.ones(shape, dtype=np.float32))
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@staticmethod
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def randn(*shape):
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return Tensor(np.random.randn(*shape).astype(np.float32))
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@ -43,7 +67,7 @@ class Tensor:
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@staticmethod
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def eye(dim):
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return Tensor(np.eye(dim).astype(np.float32))
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def backward(self, allow_fill=True):
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#print("running backward on", self)
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if self._ctx is None:
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@ -52,12 +76,12 @@ class Tensor:
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if self.grad is None and allow_fill:
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# fill in the first grad with one
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# this is "implicit gradient creation"
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assert self.data.size == 1
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self.grad = np.ones_like(self.data)
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assert self.data.shape == (1,)
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self.grad = Tensor(np.ones(self.data.shape, dtype=self.data.dtype), gpu=self.gpu)
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assert(self.grad is not None)
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grads = self._ctx.backward(self._ctx, self.grad)
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grads = self._ctx.backward(self._ctx, self.grad.data)
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if len(self._ctx.parents) == 1:
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grads = [grads]
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for t,g in zip(self._ctx.parents, grads):
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@ -67,21 +91,49 @@ class Tensor:
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print("grad shape must match tensor shape in %r, %r != %r" %
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(self._ctx, g.shape, t.data.shape))
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assert(False)
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t.grad = g
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t.grad = Tensor(g)
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t.backward(False)
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# ***** tinygrad supports CPU and GPU *****
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def cpu(self):
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if self.gpu:
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data = np.empty(self.shape, dtype=np.float32)
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cl.enqueue_copy(cl_queue, data, self.data)
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return Tensor(data)
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else:
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return self
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def cuda(self):
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if not GPU:
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raise Exception("No GPU Support")
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if not self.gpu:
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require_init_gpu()
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assert self.data.dtype == np.float32 # only float32 on GPU
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data = cl.Buffer(cl_ctx, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=self.data)
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data.shape = self.shape
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data.dtype = self.data.dtype
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return Tensor(data)
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else:
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return self
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# ***** put ops in these dicts *****
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ops = {}
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opsgpu = {}
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# ***** non first class ops *****
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def mean(self):
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div = Tensor(np.array([1/self.data.size], dtype=self.data.dtype))
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div = Tensor(np.array([1/np.prod(self.shape)], dtype=self.data.dtype), gpu=self.gpu)
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return self.sum().mul(div)
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def sqrt(self):
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root = Tensor(np.zeros(self.shape, dtype=self.data.dtype)+0.5)
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root = Tensor(np.zeros(self.shape, dtype=self.data.dtype)+0.5, gpu=self.gpu)
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return self.pow(root)
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def div(self, y):
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root = Tensor(np.zeros(self.shape, dtype=self.data.dtype)-1)
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root = Tensor(np.zeros(self.shape, dtype=self.data.dtype)-1, gpu=self.gpu)
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return self.mul(y.pow(root))
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# An instantiation of the Function is the Context
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@ -93,15 +145,8 @@ class Function:
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def save_for_backward(self, *x):
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self.saved_tensors.extend(x)
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# note that due to how partialmethod works, self and arg are switched
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def apply(self, arg, *x, **kwargs):
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# support the args in both orders
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if type(arg) == Tensor:
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op = self
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x = [arg]+list(x)
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else:
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op = arg
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x = [self]+list(x)
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def apply(self, *x, **kwargs):
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op = self
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ctx = op(*x)
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# use default params
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params = signature(op.forward).parameters
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@ -115,9 +160,19 @@ class Function:
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ret._ctx = ctx
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return ret
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def register(name, fxn):
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setattr(Tensor, name, partialmethod(fxn.apply, fxn))
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def register(name, fxn, gpu=False):
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if gpu:
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Tensor.opsgpu[name] = fxn
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else:
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Tensor.ops[name] = fxn
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def dispatch(self, *x, **kwargs):
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f = (Tensor.opsgpu if self.gpu else Tensor.ops)[name]
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f.cl_ctx, f.cl_queue = cl_ctx, cl_queue
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return f.apply(f, self, *x, **kwargs)
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setattr(Tensor, name, dispatch)
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# this registers all the operations
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import tinygrad.ops
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if GPU:
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import tinygrad.opsgpu
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