clean up movement_op in cpu and torch
MODIFY
tinygrad/llops/ops_cpu.py
tinygrad/llops/ops_cpu.py
@@ -11,7 +11,8 @@ class CPUBuffer(np.ndarray, GenericExecAST):
BinaryOps.ADD: operator.add, BinaryOps.SUB: operator.sub, BinaryOps.MUL: operator.mul,
BinaryOps.DIV: operator.truediv, BinaryOps.POW: operator.pow, BinaryOps.CMPEQ: lambda x,y: (x==y).float(),
ReduceOps.SUM: lambda x, new_shape: x.sum(shape_to_axis(x.shape, new_shape), keepdims=True) if tuple(x.shape) != tuple(new_shape) else x[:],
- ReduceOps.MAX: lambda x, new_shape: x.amax(shape_to_axis(x.shape, new_shape), keepdims=True) if tuple(x.shape) != tuple(new_shape) else x[:]
+ ReduceOps.MAX: lambda x, new_shape: x.amax(shape_to_axis(x.shape, new_shape), keepdims=True) if tuple(x.shape) != tuple(new_shape) else x[:],
+ MovementOps.SHRINK: lambda x, arg: x[tuple(slice(p[0], p[1], None) for p in arg)]
}
def relu(x): return np.maximum(x, 0)
@@ -24,8 +25,7 @@ class CPUBuffer(np.ndarray, GenericExecAST):
def permute(x, order): return x.transpose(order)
def pad(x, padding): return np.pad(x, padding).view(CPUBuffer)
def expand(x, new_shape): return np.broadcast_to(x, new_shape).view(CPUBuffer)
- def as_strided(x, size, stride): return np.lib.stride_tricks.as_strided(x, shape=size, strides=[y*x.dtype.itemsize for y in stride]).view(CPUBuffer)
- def contiguous(x): return x.ravel().reshape(x.shape)
+ def strided(x, arg): return np.lib.stride_tricks.as_strided(x.ravel().reshape(x.shape), shape=[y[0] for y in arg], strides=[y[1]*x.dtype.itemsize for y in arg]).view(CPUBuffer)
@staticmethod
def fromCPU(x): return x.view(CPUBuffer)
@@ -34,21 +34,13 @@ class CPUBuffer(np.ndarray, GenericExecAST):
def unary_op(x, op): return CPUBuffer.fxn_for_op[op](x)
def binary_op(x, op, y): return CPUBuffer.fxn_for_op[op](x, y)
def reduce_op(x, op, new_shape): return CPUBuffer.fxn_for_op[op](x, new_shape)
+ def movement_op(x, op, arg=None): return CPUBuffer.fxn_for_op[op](x, arg) if op in CPUBuffer.fxn_for_op else getattr(x, op.name.lower())(arg)
- def movement_op(x, op, arg=None):
- if op == MovementOps.SHRINK:
- return x[tuple(slice(p[0], p[1], None) for p in arg)]
- elif op == MovementOps.STRIDED:
- return x.contiguous().as_strided([x[0] for x in arg], [x[1] for x in arg])
- else:
- return getattr(x, op.name.lower())(arg)
-
- PREPAD = True
def processing_op(x,op,w,C):
assert op == ProcessingOps.CONV, f"{op} isn't supported"
tx = x.movement_op(MovementOps.STRIDED, (
(C.bs, C.groups*C.cin*x.shape[2]*x.shape[3]), (C.groups, C.cin*x.shape[2]*x.shape[3]),
(C.oy, C.sy*x.shape[3]), (C.ox, C.sx), (C.cin, x.shape[2]*x.shape[3]), (C.H, C.dy*x.shape[3]), (C.W, C.dx)))
tw = w.reshape(C.groups, C.rcout, C.cin, C.H, C.W)
- out = np.einsum("nGhwCHW, GkCHW -> nGkhw", tx.contiguous(), tw.contiguous())
+ out = np.einsum("nGhwCHW, GkCHW -> nGkhw", tx.ravel().reshape(tx.shape), tw.ravel().reshape(tw.shape))
return out.reshape(C.bs, C.groups*C.rcout, C.oy, C.ox).view(CPUBuffer)
\ No newline at end of file

MODIFY
tinygrad/llops/ops_torch.py
tinygrad/llops/ops_torch.py
@@ -5,6 +5,7 @@ from tinygrad.ops import ProcessingOps, GenericExecAST
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class TorchBuffer(torch.Tensor, GenericExecAST):
def pad(x, padding): return torch.nn.functional.pad(x, [item for sublist in padding[::-1] for item in sublist])
+ def strided(x, arg): return x.contiguous().as_strided([y[0] for y in arg], [y[1] for y in arg])
@staticmethod
def fromCPU(data): return TorchBuffer(torch.from_numpy(data).requires_grad_(False)).to(device)

clean up movement_op in cpu and torch