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Remove BinaryOps.SUB. Replace SUB by ADD and NEG in all tests. Regenerate dataset (#4977)

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* feat: remove BinaryOps.SUB

* remove SUB in test_early_end_local

* regenerate dataset. remove SUB in test_linearizer_*

* reenable overflow tests

* simplify tensor.sub function by returning a+(-b)

* remove whitespaces

---------

Co-authored-by: chenyu <chenyu@fastmail.com>
This commit is contained in:
Junjun Dong 2024-06-18 06:06:13 -07:00 • committed by GitHub
commit c8cd6e725c
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15 changed files with 50 additions and 76 deletions
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tinygrad/function.py
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@ -42,7 +42,7 @@ class Sin(Function):
return x.e(UnaryOps.SIN)
def backward(self, grad_output:LazyBuffer) -> LazyBuffer:
return self.x.const(math.pi / 2).e(BinaryOps.SUB, self.x).e(UnaryOps.SIN).e(BinaryOps.MUL, grad_output)
return self.x.const(math.pi / 2).e(BinaryOps.ADD, self.x.e(UnaryOps.NEG)).e(UnaryOps.SIN).e(BinaryOps.MUL, grad_output)
# NOTE: maximum(x, 0) behaves differently where x=0
class Relu(Function):
@ -84,7 +84,7 @@ class Sigmoid(Function):
return self.ret
def backward(self, grad_output:LazyBuffer) -> LazyBuffer:
return self.ret.e(BinaryOps.MUL, self.ret.const(1).e(BinaryOps.SUB, self.ret)).e(BinaryOps.MUL, grad_output)
return self.ret.e(BinaryOps.MUL, self.ret.const(1).e(BinaryOps.ADD, self.ret.e(UnaryOps.NEG))).e(BinaryOps.MUL, grad_output)
class Sign(Function):
def forward(self, x:LazyBuffer) -> LazyBuffer:
@ -113,13 +113,6 @@ class Add(Function):
return grad_output if self.needs_input_grad[0] else None, \
grad_output if self.needs_input_grad[1] else None
class Sub(Function):
def forward(self, x:LazyBuffer, y:LazyBuffer) -> LazyBuffer: return x.e(BinaryOps.SUB, y)
def backward(self, grad_output:LazyBuffer) -> Tuple[Optional[LazyBuffer], Optional[LazyBuffer]]:
return grad_output if self.needs_input_grad[0] else None, \
grad_output.e(UnaryOps.NEG) if self.needs_input_grad[1] else None
class Mul(Function):
def forward(self, x:LazyBuffer, y:LazyBuffer) -> LazyBuffer:
self.x, self.y = x, y
@ -166,7 +159,8 @@ class Max(Function):
def backward(self, grad_output:LazyBuffer) -> LazyBuffer:
# 1s in locations where the max was chosen (can be two locations)
max_is_1s = self.x.const(1.0).cast(dtypes.float).e(BinaryOps.SUB, self.x.e(BinaryOps.CMPNE, self.ret.expand(self.x.shape)).cast(dtypes.float))
max_is_1s = self.x.const(1.0).cast(dtypes.float).e(BinaryOps.ADD, self.x.e(BinaryOps.CMPNE, \
self.ret.expand(self.x.shape)).cast(dtypes.float).e(UnaryOps.NEG))
div = max_is_1s.r(ReduceOps.SUM, self.axis).expand(self.x.shape)
return max_is_1s.e(BinaryOps.MUL, div.e(UnaryOps.RECIP)).cast(grad_output.dtype).e(BinaryOps.MUL, grad_output.expand(self.x.shape))