restore that naming
MODIFY
test/test_llops.py
test/test_llops.py
@@ -6,7 +6,7 @@ import numpy as np
from tinygrad.tensor import Device
from tinygrad.helpers import UnaryOps, BinaryOps, ReduceOps
if Device.DEFAULT == Device.GPU:
- from tinygrad.llops.opencl import GPUBuffer, sync, unary_op, binary_op, reduce_op
+ from tinygrad.llops.ops_gpu import GPUBuffer, sync, unary_op, binary_op, reduce_op
def timeit(fxn, its=1000, done=lambda:None):
fxn()

MODIFY
tinygrad/llops/ops_cpu.py
tinygrad/llops/ops_cpu.py
import numpy as np
from tinygrad.helpers import UnaryOps, BinaryOps, ReduceOps
-class Buffer(np.ndarray):
+class CPUBuffer(np.ndarray):
def toCPU(x): return x
def log(x): return np.log(x)
def exp(x): return np.exp(x)

MODIFY
tinygrad/llops/ops_gpu.py
tinygrad/llops/ops_gpu.py
@@ -17,12 +17,12 @@ i32 = np.int32
def roundup(x, n=4): return (x+(n-1))//n * n
def sync(): cl_queue.finish()
-class Buffer:
+class GPUBuffer:
def __init__(self, shape, hostbuf=None):
require_init_gpu()
self.shape, self.dtype = tuple(shape), np.float32
- self.cl = hostbuf.cl if isinstance(hostbuf, Buffer) else cl.Buffer(cl_ctx, cl.mem_flags.READ_WRITE, 4*roundup(np.prod(shape))) # padding
- if hostbuf is not None and not isinstance(hostbuf, Buffer):
+ self.cl = hostbuf.cl if isinstance(hostbuf, GPUBuffer) else cl.Buffer(cl_ctx, cl.mem_flags.READ_WRITE, 4*roundup(np.prod(shape))) # padding
+ if hostbuf is not None and not isinstance(hostbuf, GPUBuffer):
cl.enqueue_copy(cl_queue, self.cl, hostbuf.astype(np.float32).ravel())
def __repr__(self):
@@ -30,7 +30,7 @@ class Buffer:
@staticmethod
def fromCPU(x):
- return Buffer(x.shape, x.view(np.ndarray))
+ return GPUBuffer(x.shape, x.view(np.ndarray))
def toCPU(self):
data = np.empty(self.shape, dtype=np.float32)
@@ -145,7 +145,7 @@ def reduce_op(op, inp, ret):
def reshape(x, shape):
assert np.prod(x.shape) == np.prod(shape)
- return Buffer(shape, hostbuf=x)
+ return GPUBuffer(shape, hostbuf=x)
def perm_axis(inp, order, ret):
perm = clbuild("perm", """

MODIFY
tinygrad/llops/ops_torch.py
tinygrad/llops/ops_torch.py
@@ -2,16 +2,16 @@ import torch
import numpy as np
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-class Buffer(torch.Tensor):
+class TorchBuffer(torch.Tensor):
def __new__(cls, shape):
if isinstance(shape, torch.Tensor):
return super().__new__(cls, shape)
else:
- return Buffer(torch.zeros(shape))
+ return TorchBuffer(torch.zeros(shape))
custompad = lambda x,padding: torch.nn.functional.pad(x, [item for sublist in padding[::-1] for item in sublist])
@staticmethod
def fromCPU(data):
- return Buffer(torch.from_numpy(data).requires_grad_(False)).to(device)
+ return TorchBuffer(torch.from_numpy(data).requires_grad_(False)).to(device)
def toCPU(x):
return x.cpu().numpy()
def getdtype(self):

MODIFY
tinygrad/mlops.py
tinygrad/mlops.py
@@ -14,18 +14,18 @@ def select_llops(ops):
class _UnaryOp(Function):
def forward(ctx, input):
ctx.save_for_backward(input)
- return ctx.op.unary_op(ctx.fop, input, ctx.op.Buffer(input.shape))
+ return ctx.op.unary_op(ctx.fop, input, ctx.buffer(input.shape))
def backward(ctx, grad_output):
input, = ctx.saved_tensors
- return ctx.op.binary_op(ctx.bop, input, grad_output, ctx.op.Buffer(input.shape))
+ return ctx.op.binary_op(ctx.bop, input, grad_output, ctx.buffer(input.shape))
class ReLU(_UnaryOp):
fop = UnaryOps.RELU
def backward(ctx, grad_output):
input, = ctx.saved_tensors
- ret = ctx.op.Buffer(input.shape)
+ ret = ctx.buffer(input.shape)
ctx.op.unary_op(UnaryOps.SIGN, input, ret)
ctx.op.unary_op(UnaryOps.RELU, ret, ret)
return ctx.op.binary_op(BinaryOps.MUL, ret, grad_output, ret)
@@ -36,7 +36,7 @@ class Log(_UnaryOp):
class Exp(_UnaryOp):
def forward(ctx, input):
- ret = ctx.op.unary_op(UnaryOps.EXP, input, ctx.op.Buffer(input.shape))
+ ret = ctx.op.unary_op(UnaryOps.EXP, input, ctx.buffer(input.shape))
ctx.save_for_backward(ret) # we save the output here, not the input
return ret
@@ -50,37 +50,37 @@ def reduce_shape(shape, axis):
class Sum(Function):
def forward(ctx, input, axis=None):
ctx.save_for_backward(input.shape)
- return ctx.op.reduce_op(ReduceOps.SUM, input, ctx.op.Buffer(reduce_shape(input.shape, axis)))
+ return ctx.op.reduce_op(ReduceOps.SUM, input, ctx.buffer(reduce_shape(input.shape, axis)))
def backward(ctx, grad_output):
shape_input, = ctx.saved_tensors
# NOTE: the b Buffer isn't used, since this is just for broadcast
- ret = ctx.op.Buffer(shape_input)
+ ret = ctx.buffer(shape_input)
return ctx.op.binary_op(BinaryOps.A, grad_output, ret, ret)
class Max(Function):
def forward(ctx, input, axis=None):
- ret = ctx.op.reduce_op(ReduceOps.MAX, input, ctx.op.Buffer(reduce_shape(input.shape, axis)))
+ ret = ctx.op.reduce_op(ReduceOps.MAX, input, ctx.buffer(reduce_shape(input.shape, axis)))
ctx.save_for_backward(input, ret)
return ret
def backward(ctx, grad_output):
input, ret = ctx.saved_tensors
- ret2 = ctx.op.binary_op(BinaryOps.CMPEQ, input, ret, ctx.op.Buffer(input.shape))
- div = ctx.op.reduce_op(ReduceOps.SUM, ret2, ctx.op.Buffer(grad_output.shape))
+ ret2 = ctx.op.binary_op(BinaryOps.CMPEQ, input, ret, ctx.buffer(input.shape))
+ div = ctx.op.reduce_op(ReduceOps.SUM, ret2, ctx.buffer(grad_output.shape))
ctx.op.binary_op(BinaryOps.DIV, div, ret2, ret2)
return ctx.op.binary_op(BinaryOps.MUL, ret2, grad_output, ret2)
# ************* binary ops *************
def unbroadcast(ctx, out, in_sh):
- return ctx.op.reduce_op(ReduceOps.SUM, out, ctx.op.Buffer(in_sh))
+ return ctx.op.reduce_op(ReduceOps.SUM, out, ctx.buffer(in_sh))
class Add(Function):
def forward(ctx, x, y):
ctx.save_for_backward(x.shape, y.shape)
- buf = ctx.op.Buffer(binary_broadcast(x.shape, y.shape))
- return ctx.op.binary_op(BinaryOps.ADD, x, y, buf) #ctx.op.Buffer(binary_broadcast(x.shape, y.shape)))
+ buf = ctx.buffer(binary_broadcast(x.shape, y.shape))
+ return ctx.op.binary_op(BinaryOps.ADD, x, y, buf) #ctx.buffer(binary_broadcast(x.shape, y.shape)))
def backward(ctx, grad_output):
shape_x, shape_y = ctx.saved_tensors
@@ -90,39 +90,39 @@ class Add(Function):
class Sub(Function):
def forward(ctx, x, y):
ctx.save_for_backward(x.shape, y.shape)
- return ctx.op.binary_op(BinaryOps.SUB, x, y, ctx.op.Buffer(binary_broadcast(x.shape, y.shape)))
+ return ctx.op.binary_op(BinaryOps.SUB, x, y, ctx.buffer(binary_broadcast(x.shape, y.shape)))
def backward(ctx, grad_output):
shape_x, shape_y = ctx.saved_tensors
- neg_grad_output = ctx.op.unary_op(UnaryOps.NEG, grad_output, ctx.op.Buffer(grad_output.shape))
+ neg_grad_output = ctx.op.unary_op(UnaryOps.NEG, grad_output, ctx.buffer(grad_output.shape))
return unbroadcast(ctx, grad_output, shape_x) if ctx.needs_input_grad[0] else None, \
unbroadcast(ctx, neg_grad_output, shape_y) if ctx.needs_input_grad[1] else None
class Mul(Function):
def forward(ctx, x, y):
ctx.save_for_backward(x, y)
- return ctx.op.binary_op(BinaryOps.MUL, x, y, ctx.op.Buffer(binary_broadcast(x.shape, y.shape)))
+ return ctx.op.binary_op(BinaryOps.MUL, x, y, ctx.buffer(binary_broadcast(x.shape, y.shape)))
def backward(ctx, grad_output):
x,y = ctx.saved_tensors
- tmp = ctx.op.Buffer(grad_output.shape)
+ tmp = ctx.buffer(grad_output.shape)
grad_x = unbroadcast(ctx, ctx.op.binary_op(BinaryOps.MUL, y, grad_output, tmp), x.shape) if ctx.needs_input_grad[0] else None
grad_y = unbroadcast(ctx, ctx.op.binary_op(BinaryOps.MUL, x, grad_output, tmp), y.shape) if ctx.needs_input_grad[1] else None
return grad_x, grad_y
class Pow(Function):
def forward(ctx, x, y):
- ret = ctx.op.Buffer(binary_broadcast(x.shape, y.shape))
+ ret = ctx.buffer(binary_broadcast(x.shape, y.shape))
ctx.save_for_backward(x, y, ret)
return ctx.op.binary_op(BinaryOps.POW, x, y, ret)
def backward(ctx, grad_output):
x,y,powxy = ctx.saved_tensors
- tmp = ctx.op.Buffer(grad_output.shape)
+ tmp = ctx.buffer(grad_output.shape)
ctx.op.binary_op(BinaryOps.DIV, x, powxy, tmp) # pow(x,y)/x
ctx.op.binary_op(BinaryOps.MUL, y, tmp, tmp) # y * pow(x,y)/x
grad_x = unbroadcast(ctx, ctx.op.binary_op(BinaryOps.MUL, grad_output, tmp, tmp), x.shape) if ctx.needs_input_grad[0] else None
- log_x = ctx.op.unary_op(UnaryOps.LOG, x, ctx.op.Buffer(x.shape))
+ log_x = ctx.op.unary_op(UnaryOps.LOG, x, ctx.buffer(x.shape))
ctx.op.binary_op(BinaryOps.MUL, log_x, powxy, tmp) # log(x) * pow(x,y)
grad_y = unbroadcast(ctx, ctx.op.binary_op(BinaryOps.MUL, grad_output, tmp, tmp), y.shape) if ctx.needs_input_grad[1] else None
return grad_x, grad_y
@@ -142,24 +142,24 @@ class Reshape(Function):
class Transpose(Function):
def forward(ctx, x, order=(1,0)):
ctx.save_for_backward(order)
- ret = ctx.op.Buffer([x.shape[i] for i in order])
+ ret = ctx.buffer([x.shape[i] for i in order])
return ctx.op.perm_axis(x, order, ret)
def backward(ctx, grad_output):
norder = np.argsort(ctx.order).tolist()
- ret = ctx.op.Buffer([grad_output.shape[i] for i in norder])
+ ret = ctx.buffer([grad_output.shape[i] for i in norder])
return ctx.op.perm_axis(grad_output, norder, ret)
class Slice(Function):
def forward(ctx, x, arg=None):
ctx.save_for_backward(x.shape)
- ret = ctx.op.Buffer([y[1]-y[0] for y in arg])
+ ret = ctx.buffer([y[1]-y[0] for y in arg])
return ctx.op.inner_slice(x, arg, ret)
def backward(ctx, grad_output):
shape, = ctx.saved_tensors
narg = [(0-p[0], grad_output.shape[i]+(shape[i]-p[1])) for i,p in enumerate(ctx.arg)]
- ret = ctx.op.Buffer([y[1]-y[0] for y in narg])
+ ret = ctx.buffer([y[1]-y[0] for y in narg])
return ctx.op.inner_slice(grad_output, narg, ret)
# ************* processing ops *************
@@ -167,14 +167,14 @@ class Slice(Function):
class Matmul(Function):
def forward(ctx, input, weight):
assert input.shape[-1] == weight.shape[-2]
- ret = ctx.op.Buffer(list(input.shape[0:-1])+[weight.shape[-1]])
+ ret = ctx.buffer(list(input.shape[0:-1])+[weight.shape[-1]])
ctx.save_for_backward(input, weight)
return ctx.op.matmul(input, weight, ret)
def backward(ctx, grad_output):
input, weight = ctx.saved_tensors
- grad_input = ctx.op.matmul(grad_output, weight, ctx.op.Buffer(input.shape), transpose_b=True) if ctx.needs_input_grad[0] else None
- grad_weight = ctx.op.matmul(input, grad_output, ctx.op.Buffer(weight.shape), transpose_a=True) if ctx.needs_input_grad[1] else None
+ grad_input = ctx.op.matmul(grad_output, weight, ctx.buffer(input.shape), transpose_b=True) if ctx.needs_input_grad[0] else None
+ grad_weight = ctx.op.matmul(input, grad_output, ctx.buffer(weight.shape), transpose_a=True) if ctx.needs_input_grad[1] else None
return grad_input, grad_weight
class Conv2D(Function):
@@ -192,7 +192,7 @@ class Conv2D(Function):
# output buffer
conv_args = H, W, ctx.groups, rcout, cin, oy, ox, iy, ix, ys, xs, bs
- return ctx.op.conv(x, w, ctx.op.Buffer((bs, cout, oy, ox)), conv_args)
+ return ctx.op.conv(x, w, ctx.buffer((bs, cout, oy, ox)), conv_args)
def backward(ctx, grad_output):
bs,_,oy,ox = grad_output.shape
@@ -206,6 +206,6 @@ class Conv2D(Function):
rcout = cout//ctx.groups
conv_args = H, W, ctx.groups, rcout, cin, oy, ox, iy, ix, ys, xs, bs
- dx = ctx.op.convdx(w, grad_output, ctx.op.Buffer((bs, cin_, iy, ix)), conv_args) if ctx.needs_input_grad[0] else None
- dw = ctx.op.convdw(x, grad_output, ctx.op.Buffer((cout, cin, H, W)), conv_args) if ctx.needs_input_grad[1] else None
+ dx = ctx.op.convdx(w, grad_output, ctx.buffer((bs, cin_, iy, ix)), conv_args) if ctx.needs_input_grad[0] else None
+ dw = ctx.op.convdw(x, grad_output, ctx.buffer((cout, cin, H, W)), conv_args) if ctx.needs_input_grad[1] else None
return dx, dw
\ No newline at end of file

MODIFY
tinygrad/tensor.py
tinygrad/tensor.py
@@ -75,7 +75,7 @@ class Device:
DEFAULT = i if os.environ.get(name, 0) == "1" else DEFAULT
try:
llops[i] = importlib.import_module('tinygrad.llops.'+op)
- buffers[i] = llops[i].Buffer
+ buffers[i] = [cls for name, cls in inspect.getmembers(llops[i], inspect.isclass) if name.endswith("Buffer")][0]
except ImportError as e:
print(op, "not available", e)
DEFAULT = CPU if DEFAULT is None else DEFAULT

restore that naming