lambda left, right: left <= right,
"gt":
lambda left, right: left > right,
"ge":
lambda left, right: left >= right,
"pow":
lambda left, right: np.power(left, right),
"minimum":
lambda left, right: np.minimum(left, right),
"maximum":
lambda left, right: np.maximum(left, right),
"dot":
lambda left, right: np.dot(left, right),
# reduction ops
"sum":
lambda op_dict, left: math_cpu.sum(
left, axis=op_dict['axis'], keepdims=True),
"max":
lambda op_dict, left: np.max(left, axis=op_dict['axis'], keepdims=True),
"min":
lambda op_dict, left: np.min(left, axis=op_dict['axis'], keepdims=True),
"argmax":
lambda op_dict, left: CustomNumpy.argmax(
left, axis=op_dict['axis'], keepdims=True),
"argmin":
lambda op_dict, left: CustomNumpy.argmin(
left, axis=op_dict['axis'], keepdims=True),
}
class NervanaMKL(NervanaCPU):
"""
"add": lambda left, right: math_cpu.add(left, right),
"sub": lambda left, right: math_cpu.sub(left, right),
"mul": lambda left, right: math_cpu.mul(left, right),
"div": lambda left, right: math_cpu.div(left, right),
"eq": lambda left, right: left == right,
"ne": lambda left, right: left != right,
"lt": lambda left, right: left < right,
"le": lambda left, right: left <= right,
"gt": lambda left, right: left > right,
"ge": lambda left, right: left >= right,
"pow": lambda left, right: np.power(left, right),
"minimum": lambda left, right: np.minimum(left, right),
"maximum": lambda left, right: np.maximum(left, right),
"dot": lambda left, right: np.dot(left, right),
# reduction ops
"sum": lambda op_dict, left: math_cpu.sum(left, axis=op_dict['axis'], keepdims=True),
"max": lambda op_dict, left: np.max(left, axis=op_dict['axis'], keepdims=True),
"min": lambda op_dict, left: np.min(left, axis=op_dict['axis'], keepdims=True),
"argmax": lambda op_dict, left: CustomNumpy.argmax(left, axis=op_dict['axis'], keepdims=True),
"argmin": lambda op_dict, left: CustomNumpy.argmin(left, axis=op_dict['axis'], keepdims=True),
}
class NervanaMKL(NervanaCPU):
"""
MKL Backend
"""
backend_name = 'mkl'
def __init__(self,
rng_seed=None,
