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@ -1182,3 +1182,205 @@ class BatchNormFold2GradReduce(PrimitiveWithInfer):
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def infer_dtype(self, dout_type, x_type):
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validator.check("dout type", dout_type, "x type", x_type)
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return dout_type, dout_type
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class ActsULQ(PrimitiveWithInfer):
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"""
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The ActsULQ(Activation universal learnable quantization).
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Args:
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fixed_min (bool): whether fix clamp min to zero.
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num_bits (int): The bits num used for quantize.
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Inputs:
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- **x** (Tensor) - A Tensor of feature map. With float16 or float32 data type.
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- **clamp_min** (Tensor) - A Tensor of clamp min with the same type as x.
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- **clamp_max** (Tensor) - A Tensor of clamp max with the same type as x.
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Outputs:
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- **y** (Tensor) - A tensor of fake quant of feature map with the same type as `w`.
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- **clamp_min** (Tensor) - A tensor of boolean masks if data in feature map >= clamp_min.
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- **clamp_max** (Tensor) - A tensor of boolean masks if data in feature map <= clamp_max.
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- **x_clamped_loss** (Tensor) - A tensor of clamped loss.
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Examples:
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>>> data_type = np.float32
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>>> x= np.random.uniform(-10, 10, (32, 120)).astype(data_type)
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>>> clamp_max = 0.7 * np.max(x)
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>>> clamp_min = 0.7 * np.min(x)
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>>> clamp_max = np.array([clamp_max], dtype=data_type)
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>>> clamp_min = np.array([clamp_min], dtype=data_type)
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>>> acts_ulq = Q.ActsULQ(fixed_mini=True, num_bits=8)
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>>> quant_x, clamp_min_mask, clamp_max_mask, x_clamped_loss = acts_ulq(Tensor(x), Tensor( clamp_min),
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Tensor(clamp_max))
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"""
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@prim_attr_register
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def __init__(self, fixed_min=False, num_bits=8):
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validator.check_value_type("fixed_min", fixed_min, [bool], self.name)
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validator.check_value_type("num_bits", num_bits, [int], self.name)
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validator.check_int(num_bits, 8, Rel.EQ, "value of num_bits", self.name)
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def infer_shape(self, x_shape, clamp_min_shape, clamp_max_shape):
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"""infer shape of primitive"""
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validator.check_int(len(clamp_min_shape), len(x_shape), Rel.EQ, "dims of clamp_min", self.name)
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validator.check_int(len(clamp_max_shape), len(x_shape), Rel.EQ, "dims of clamp_max", self.name)
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x_shape_len = len(x_shape)
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for i in range(x_shape_len):
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validator.check_int(clamp_min_shape[i], 1, Rel.EQ, "dims of clamp_min", self.name)
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validator.check_int(clamp_max_shape[i], 1, Rel.EQ, "dims of clamp_max", self.name)
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return x_shape, x_shape, x_shape, x_shape
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def infer_dtype(self, x_dtype, clamp_min_dtype, clamp_max_dtype):
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"""infer dtype of primitive"""
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valid_types = [mstype.float32, mstype.float16]
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validator.check_tensor_type_same({"x": x_dtype}, valid_types, self.name)
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validator.check_tensor_type_same({"clamp_min": clamp_min_dtype}, valid_types, self.name)
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validator.check_tensor_type_same({"clamp_max": clamp_max_dtype}, valid_types, self.name)
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return x_dtype, mstype.bool_, mstype.bool_, x_dtype
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class ActsULQInputGrad(PrimitiveWithInfer):
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"""
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The ActsULQInputGrad(grad of ActsULQ).
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Inputs:
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- **y_grad** (Tensor) - A Tensor of grad. With float16 or float32 data type.
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Outputs:
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- **x_grad** (Tensor) - A tensor of data grad with the same type as `y_grad`.
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"""
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@prim_attr_register
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def __init__(self):
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pass
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def infer_shape(self, y_grad_shape, clamp_min_mask_shape, clamp_max_mask_shape):
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return y_grad_shape
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def infer_dtype(self, y_grad_type, clamp_min_mask_type, clamp_max_mask_type):
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valid_types = [mstype.float32, mstype.float16]
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validator.check_tensor_type_same({"y_grad": y_grad_type}, valid_types, self.name)
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return y_grad_type
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class ActULQClampMinGrad(PrimitiveWithInfer):
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"""
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The ActULQClampMinGrad(Activation Universal Linear Quantization on Clamp Minimum Gradient)
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Inputs:
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- **y_grad** (Tensor) - A tensor of gradient, with float16 or float32 type.
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- **clamp_min_mask** - A tensor of mask, only support int8 type.
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- **x_clamped_loss** - A tensor of loss, with the same type as "y_grad".
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Outputs:
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- **clamp_min_grad** - A tensor of clamp minimum gradient, with the same type as "y_grad".
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The length of tensor is 1.
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Examples:
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>>> data_type = np.float32
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>>> y_grad = np.random.uniform(-10, 10, (32, 120)).astype(data_type)
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>>> clamp_min_mask = np.where(np.random.rand(32, 120) >= 0.5, 1, 0)
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>>> x_clamped_loss = np.random.uniform(-10, 10, (32, 120)).astype(data_type)
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>>> act_ulq_clamp_min_grad = Q.ActULQClampMinGrad()
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>>> clamp_min_grad = act_ulq_clamp_min_grad(Tensor(y_grad), Tensor(clamp_min_mask, mindspore.bool_),
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Tensor(x_clamped_loss))
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"""
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@prim_attr_register
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def __init__(self):
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pass
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def infer_shape(self, input_x, input_y, input_z):
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input_x_len = len(input_x)
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output_shape = []
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for _ in range(input_x_len):
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output_shape.append(1)
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return tuple(output_shape)
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def infer_dtype(self, input_x, input_y, input_z):
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return input_x
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class ActULQClampMaxGrad(PrimitiveWithInfer):
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"""
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The ActULQClampMaxGrad(Activation Universal Linear Quantization on Clamp Maximum Gradient)
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Inputs:
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- **y_grad** (Tensor) - A tensor of gradient, with float16 or float32 type.
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- **clamp_max_mask** - A tensor of mask, only support int8 type.
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- **x_clamped_loss** - A tensor of loss, with the same type as "y_grad".
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Outputs:
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- **clamp_max_grad** - A tensor of clamp maximum gradient, with the same type as "y_grad".
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The length of tensor is 1.
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Examples:
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>>> data_type = np.float32
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>>> y_grad = np.random.uniform(-10, 10, (32, 120)).astype(data_type)
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>>> clamp_max_mask = np.where(np.random.rand(32, 120) >= 0.5, 1, 0)
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>>> x_clamped_loss = np.random.uniform(-10, 10, (32, 120)).astype(data_type)
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>>> act_ulq_clamp_max_grad = Q.ActULQClampMaxGrad()
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>>> clamp_max_grad = act_ulq_clamp_max_grad(Tensor(y_grad), Tensor(clamp_max_mask, mindspore.bool_),
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Tensor(x_clamped_loss))
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"""
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@prim_attr_register
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def __init__(self):
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pass
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def infer_shape(self, input_x, input_y, input_z):
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input_x_len = len(input_x)
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output_shape = []
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for _ in range(input_x_len):
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output_shape.append(1)
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return tuple(output_shape)
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def infer_dtype(self, input_x, input_y, input_z):
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return input_x
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class WtsARQ(PrimitiveWithInfer):
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"""
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The WtsARQ(Weights Adaptive Range Quantization).
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Args:
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axes (list): Specify channels for ARQ algorithm.
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num_bits (int): The bits num used for quantize.
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offset_flag (bool): Whether use offset for quantize.
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Inputs:
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- **w** (Tensor) - A Tensor of weights. With float16 or float32 data type.
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Outputs:
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- **scale** (Tensor) - A tensor of optimal scale, has the same type as `w`.
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- **offset** (Tensor) - A tensor of optimal offset, has the same type as `w`.
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- If axis is [],
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the shape of scale and offset is :math:`(1, )`.
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- If axis is [0],
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the shape of scale and offset is :math:`(w_1, )`.
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- If axis is [1],
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the shape of scale and offset is :math:`(w_2, )`.
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- **y** (Tensor) - A tensor of fakequant weights, has the same type and shape as `w`.
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Examples:
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>>> data = Tensor(np.random.rand(1, 3, 6, 4).astype(np.float32))
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>>> wts_arq = Q.WtsARQ(axes=[0], num_bits=8, offset_flag=False)
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>>> scale, offset, y = wts_arq(data)
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"""
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@prim_attr_register
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def __init__(self, num_bits, offset_flag):
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validator.check_value_type("num_bits", num_bits, [int], self.name)
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validator.check_int(num_bits, 8, Rel.EQ, "value of num_bits", self.name)
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validator.check_value_type("offset_flag", offset_flag, [bool], self.name)
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def infer_shape(self, w_shape, w_min_shape, w_max_shape):
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validator.check_int(len(w_min_shape), len(w_shape), Rel.EQ, "dims of w_min", self.name)
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validator.check_int(len(w_max_shape), len(w_shape), Rel.EQ, "dims of w_max", self.name)
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return w_shape
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def infer_dtype(self, w_dtype, w_min_dtype, w_max_dtype):
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valid_types = [mstype.float32, mstype.float16]
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validator.check_tensor_type_same({"w": w_dtype}, valid_types, self.name)
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validator.check_tensor_type_same({"w_min": w_min_dtype}, valid_types, self.name)
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validator.check_tensor_type_same({"w_max": w_max_dtype}, valid_types, self.name)
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return w_dtype
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mindspore-ci-bot
5 years ago
committed by
Gitee