forked from mindspore-Ecosystem/mindspore
Move API inner.DynamicGRUV2 to P.DynamicGRUV2.
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@ -892,7 +892,7 @@ def get_bprop_dynamic_rnn(self):
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return bprop
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@bprop_getters.register(inner.DynamicGRUV2)
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@bprop_getters.register(P.DynamicGRUV2)
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def get_bprop_dynamic_gru_v2(self):
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"""Grad definition for `DynamicGRUV2` operation."""
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dynamic_gru_v2_grad = G.DynamicGRUV2Grad(self.direction, self.cell_depth, self.keep_prob, self.cell_clip,
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@ -72,7 +72,7 @@ from .nn_ops import (LSTM, SGD, Adam, FusedSparseAdam, FusedSparseLazyAdam, Adam
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MaxPoolWithArgmax, OneHot, Pad, MirrorPad, PReLU, ReLU, ReLU6, ReLUV2, HSwish, HSigmoid,
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ResizeBilinear, Sigmoid,
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SigmoidCrossEntropyWithLogits,
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SmoothL1Loss, Softmax, Softsign, Softplus, LRN, RNNTLoss, DynamicRNN,
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SmoothL1Loss, Softmax, Softsign, Softplus, LRN, RNNTLoss, DynamicRNN, DynamicGRUV2,
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SoftmaxCrossEntropyWithLogits, ROIAlign,
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SparseSoftmaxCrossEntropyWithLogits, Tanh,
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TopK, BinaryCrossEntropy, KLDivLoss, SparseApplyAdagrad, LARSUpdate, ApplyFtrl, SparseApplyFtrl,
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@ -119,6 +119,7 @@ __all__ = [
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'Rsqrt',
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'Sqrt',
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'Square',
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'DynamicGRUV2',
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'SquaredDifference',
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'Xdivy',
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'Xlogy',
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@ -529,159 +529,6 @@ class MatrixSetDiag(PrimitiveWithInfer):
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return assist_shape
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class DynamicGRUV2(PrimitiveWithInfer):
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r"""
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DynamicGRUV2 Operator.
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Args:
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direction (str): A string identifying the direction in the op. Default: 'UNIDIRECTIONAL'.
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Only 'UNIDIRECTIONAL' is currently supported.
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cell_depth (int): An integer identifying the cell depth in the op. Default: 1.
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keep_prob (float): A float identifying the keep prob in the op. Default: 1.0.
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cell_clip (float): A float identifying the cell clip in the op. Default: -1.0.
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num_proj (int): An integer identifying the num proj in the op. Default: 0.
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time_major (bool): A bool identifying the time major in the op. Default: True.
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activation (str) : A string identifying the type of activation function in the op. Default: 'tanh'.
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Only 'tanh' is currently supported.
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gate_order (str): A string identifying the gate order in weight and bias. Default: 'rzh.
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'zrh' is another option.
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reset_after (bool): A bool identifying whether to apply reset gate after matrix multiplication. Default: True.
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is_training (bool): A bool identifying is training in the op. Default: True.
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Inputs:
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- **x** (Tensor) - Current words.
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Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{input_size})`.
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The data type must be float16.
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- **weight_input** (Tensor) - Input-hidden weight.
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Tensor of shape :math:`(\text{input_size}, 3 \times \text{hidden_size})`.
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The data type must be float16.
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- **weight_hidden** (Tensor) - Hidden-hidden weight.
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Tensor of shape :math:`(\text{hidden_size}, 3 \times \text{hidden_size})`.
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The data type must be float16.
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- **bias_input** (Tensor) - Input-hidden bias. Tensor of shape :math:`(3 \times \text{hidden_size})`, or None.
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The data type must be float16 or float32.
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- **bias_hidden** (Tensor) - Hidden-hidden bias. Tensor of shape :math:`(3 \times \text{hidden_size})`, or None.
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The data type must be float16 or float32.
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- **seq_length** (Tensor) - The length of each batch. Tensor of shape :math:`(\text{batch_size})`.
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Only `None` is currently supported.
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- **init_h** (Tensor) - Hidden state of initial time.
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Tensor of shape :math:`(\text{batch_size}, \text{hidden_size})`.
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The data type must be float16 or float32.
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Outputs:
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- **y** (Tensor) - A Tensor of shape :math:
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if num_proj > 0 `(num_step, batch_size, min(hidden_size, num_proj)`,
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if num_proj == 0 `(num_step, batch_size, hidden_size)`.
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Has the same data type with input `bais_type`.
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- **output_h** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
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Has the same data type with input `bais_type`.
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- **update** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
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Has the same data type with input `bais_type`.
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- **reset** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
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Has the same data type with input `bais_type`.
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- **new** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
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Has the same data type with input `bais_type`.
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- **hidden_new** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
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Has the same data type with input `bais_type`.
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- If `bias_input` and `bias_hidden` both are `None`, `bias_type` is float32.
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- If `bias_input` is not `None`, `bias_type` is the date type of `bias_input`.
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- If `bias_input` is `None` and `bias_hidden` is not `None, `bias_type` is the date type of `bias_hidden`.
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Examples:
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>>> x = Tensor(np.random.rand(2, 8, 64).astype(np.float16))
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>>> weight_i = Tensor(np.random.rand(64, 48).astype(np.float16))
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>>> weight_h = Tensor(np.random.rand(16, 48).astype(np.float16))
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>>> bias_i = Tensor(np.random.rand(48).astype(np.float16))
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>>> bias_h = Tensor(np.random.rand(48).astype(np.float16))
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>>> init_h = Tensor(np.random.rand(8, 16).astype(np.float16))
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>>> dynamic_gru_v2 = ops.DynamicGRUV2()
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>>> output = dynamic_gru_v2(x, weight_i, weight_h, bias_i, bias_h, None, init_h)
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>>> result = output[0].shape
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>>> print(result)
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(2, 8, 16)
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"""
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@prim_attr_register
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def __init__(self,
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direction='UNIDIRECTIONAL',
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cell_depth=1,
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keep_prob=1.0,
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cell_clip=-1.0,
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num_proj=0,
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time_major=True,
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activation="tanh",
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gate_order="rzh",
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reset_after=True,
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is_training=True):
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self.cell_depth = validator.check_value_type("cell_depth", cell_depth, [int], self.name)
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self.keep_prob = validator.check_value_type("keep_prob", keep_prob, [float], self.name)
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self.cell_clip = validator.check_value_type("cell_clip", cell_clip, [float], self.name)
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self.num_proj = validator.check_non_negative_int(num_proj, "num_proj", self.name)
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self.time_major = validator.check_value_type("time_major", time_major, [bool], self.name)
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self.is_training = validator.check_value_type("is_training", is_training, [bool], self.name)
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self.direction = validator.check_string(direction, ['UNIDIRECTIONAL'], "direction", self.name)
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self.activation = validator.check_string(activation, ['tanh'], "activation", self.name)
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self.gate_order = validator.check_string(gate_order, ['zrh', 'rzh'], "gate_order", self.name)
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self.reset_after = validator.check_value_type("reset_after", reset_after, [bool], self.name)
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self.add_prim_attr("io_format", "ND")
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def infer_shape(self, x_shape, winput_shape, whidden_shape, binput_shape, bhidden_shape, seq_shape, h_shape):
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validator.check_int(len(x_shape), 3, Rel.EQ, "x shape", self.name)
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validator.check_int(len(winput_shape), 2, Rel.EQ, "weight input shape rank", self.name)
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validator.check_int(len(whidden_shape), 2, Rel.EQ, "weight hidden shape rank", self.name)
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num_step, batch_size, input_size = x_shape
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hidden_size = winput_shape[-1] // 3
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if winput_shape[-1] % 3 != 0:
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raise ValueError(f"For {self.name}, weight_input_shape[-1] should multiple of 3.")
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self.placeholder_index = [3, 4, 5]
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if binput_shape is not None:
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validator.check_int(len(binput_shape), 1, Rel.EQ, "bias input shape rank", self.name)
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validator.check("bias_input_shape", binput_shape, "3 * hidden_shape", [3 * hidden_size], Rel.EQ, self.name)
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self.placeholder_index.remove(3)
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if bhidden_shape is not None:
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validator.check_int(len(bhidden_shape), 1, Rel.EQ, "bias hidden shape rank", self.name)
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validator.check("bias_hidden_shape", bhidden_shape,
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"3 * hidden_shape", [3 * hidden_size], Rel.EQ, self.name)
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self.placeholder_index.remove(4)
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if seq_shape is not None:
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raise ValueError(f"For {self.name}, seq_shape should be None.")
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validator.check_int(len(h_shape), 2, Rel.EQ, "init_h shape rank", self.name)
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validator.check("init_h_shape[0]", h_shape[0], "batch_size", batch_size, Rel.EQ, self.name)
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validator.check("init_h_shape[1]", h_shape[1], "hidden_size", hidden_size, Rel.EQ, self.name)
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validator.check("weight_input_shape[-1]", winput_shape[-1], "weight_hidden_shape[-1]",
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whidden_shape[-1], Rel.EQ, self.name)
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validator.check("weight_input_shape[0]", winput_shape[0], "input_size", input_size, Rel.EQ, self.name)
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validator.check("weight_hidden_shape[0]", whidden_shape[0], "hidden_size", hidden_size, Rel.EQ, self.name)
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if self.num_proj > 0:
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y_shape = (num_step, batch_size, min(hidden_size, self.num_proj))
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else:
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y_shape = (num_step, batch_size, hidden_size)
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out_shape = (num_step, batch_size, hidden_size)
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self.add_prim_attr("placeholder_index", self.placeholder_index)
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return y_shape, out_shape, out_shape, out_shape, out_shape, out_shape
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def infer_dtype(self, x_dtype, winput_dtype, whidden_dtype, binput_dtype, bhidden_dtype, seq_dtype, h_dtype):
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validator.check_tensor_dtype_valid("x dtype", x_dtype, [mstype.float16], self.name)
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validator.check_tensor_dtype_valid("weight input dtype", winput_dtype, [mstype.float16], self.name)
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validator.check_tensor_dtype_valid("weight hidden dtype", whidden_dtype, [mstype.float16], self.name)
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validator.check_tensor_dtype_valid("init_h dtype", h_dtype, (mstype.float16, mstype.float32), self.name)
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b_dtype = mstype.float32
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if binput_dtype is not None:
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validator.check_tensor_dtype_valid("bias input dtype", binput_dtype,
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(mstype.float16, mstype.float32), self.name)
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b_dtype = binput_dtype
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elif bhidden_dtype is not None:
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validator.check_tensor_dtype_valid("bias hidden dtype", bhidden_dtype,
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(mstype.float16, mstype.float32), self.name)
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b_dtype = bhidden_dtype
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return b_dtype, b_dtype, b_dtype, b_dtype, b_dtype, b_dtype
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class ConfusionMulGrad(PrimitiveWithInfer):
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"""
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`output0` is the dot product result of input0 and input1.
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@ -6403,32 +6403,18 @@ class DynamicRNN(PrimitiveWithInfer):
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- **tanhct** (Tensor) - A Tensor of shape (`num_step`, `batch_size`, `hidden_size`).
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Has the same type with input `b`.
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Supported Platforms:
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``Ascend``
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Examples:
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>>> import mindspore
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>>> import mindspore.nn as nn
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>>> import numpy as np
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>>> from mindspore import Parameter
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>>> from mindspore import Tensor
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>>> from mindspore.ops import operations as ops
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>>> import mindspore.context as context
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>>> context.set_context(mode=context.GRAPH_MODE)
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>>> class DynamicRNNNet(nn.Cell):
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>>> def __init__(self):
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>>> super(DynamicRNNNet, self).__init__()
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>>> self.dynamic_rnn = ops.DynamicRNN()
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>>>
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>>> def construct(self, x, w, b, init_h, init_c):
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>>> out = self.dynamic_rnn(x, w, b, None, init_h, init_c)
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>>> return out
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>>>
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>>> x = Tensor(np.random.rand(2, 16, 64).astype(np.float16))
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>>> w = Tensor(np.random.rand(96, 128).astype(np.float16))
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>>> b = Tensor(np.random.rand(128).astype(np.float16))
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>>> init_h = Tensor(np.random.rand(1, 16, 32).astype(np.float16))
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>>> init_c = Tensor(np.random.rand(1, 16, 32).astype(np.float16))
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>>> net = DynamicRNNNet()
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>>> output = net(x, w, b, init_h, init_c)
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>>> output[0].shape
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>>> dynamic_rnn = ops.DynamicRNNN()
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>>> output = dynamic_rnn(x, w, b, None, init_h, init_c)
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>>> print(output[0].shape)
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(2, 16, 32)
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"""
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@ -6493,6 +6479,161 @@ class DynamicRNN(PrimitiveWithInfer):
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return b_dtype, x_dtype, b_dtype, b_dtype, b_dtype, b_dtype, b_dtype, b_dtype
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class DynamicGRUV2(PrimitiveWithInfer):
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r"""
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DynamicGRUV2 Operator.
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Args:
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direction (str): A string identifying the direction in the op. Default: 'UNIDIRECTIONAL'.
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Only 'UNIDIRECTIONAL' is currently supported.
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cell_depth (int): An integer identifying the cell depth in the op. Default: 1.
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keep_prob (float): A float identifying the keep prob in the op. Default: 1.0.
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cell_clip (float): A float identifying the cell clip in the op. Default: -1.0.
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num_proj (int): An integer identifying the num proj in the op. Default: 0.
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time_major (bool): A bool identifying the time major in the op. Default: True.
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activation (str) : A string identifying the type of activation function in the op. Default: 'tanh'.
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Only 'tanh' is currently supported.
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gate_order (str): A string identifying the gate order in weight and bias. Default: 'rzh.
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'zrh' is another option.
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reset_after (bool): A bool identifying whether to apply reset gate after matrix multiplication. Default: True.
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is_training (bool): A bool identifying is training in the op. Default: True.
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Inputs:
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- **x** (Tensor) - Current words.
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Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{input_size})`.
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The data type must be float16.
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- **weight_input** (Tensor) - Input-hidden weight.
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Tensor of shape :math:`(\text{input_size}, 3 \times \text{hidden_size})`.
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The data type must be float16.
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- **weight_hidden** (Tensor) - Hidden-hidden weight.
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Tensor of shape :math:`(\text{hidden_size}, 3 \times \text{hidden_size})`.
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The data type must be float16.
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- **bias_input** (Tensor) - Input-hidden bias. Tensor of shape :math:`(3 \times \text{hidden_size})`, or None.
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The data type must be float16 or float32.
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- **bias_hidden** (Tensor) - Hidden-hidden bias. Tensor of shape :math:`(3 \times \text{hidden_size})`, or None.
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The data type must be float16 or float32.
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- **seq_length** (Tensor) - The length of each batch. Tensor of shape :math:`(\text{batch_size})`.
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Only `None` is currently supported.
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- **init_h** (Tensor) - Hidden state of initial time.
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Tensor of shape :math:`(\text{batch_size}, \text{hidden_size})`.
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The data type must be float16 or float32.
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Outputs:
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- **y** (Tensor) - A Tensor of shape :math:
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if num_proj > 0 `(num_step, batch_size, min(hidden_size, num_proj)`,
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if num_proj == 0 `(num_step, batch_size, hidden_size)`.
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Has the same data type with input `bais_type`.
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- **output_h** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
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Has the same data type with input `bais_type`.
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- **update** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
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Has the same data type with input `bais_type`.
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- **reset** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
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Has the same data type with input `bais_type`.
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- **new** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
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Has the same data type with input `bais_type`.
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- **hidden_new** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
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Has the same data type with input `bais_type`.
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- If `bias_input` and `bias_hidden` both are `None`, `bias_type` is float32.
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- If `bias_input` is not `None`, `bias_type` is the date type of `bias_input`.
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- If `bias_input` is `None` and `bias_hidden` is not `None, `bias_type` is the date type of `bias_hidden`.
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Supported Platforms:
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``Ascend``
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Examples:
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>>> x = Tensor(np.random.rand(2, 8, 64).astype(np.float16))
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>>> weight_i = Tensor(np.random.rand(64, 48).astype(np.float16))
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>>> weight_h = Tensor(np.random.rand(16, 48).astype(np.float16))
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>>> bias_i = Tensor(np.random.rand(48).astype(np.float16))
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>>> bias_h = Tensor(np.random.rand(48).astype(np.float16))
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>>> init_h = Tensor(np.random.rand(8, 16).astype(np.float16))
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>>> dynamic_gru_v2 = ops.DynamicGRUV2()
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>>> output = dynamic_gru_v2(x, weight_i, weight_h, bias_i, bias_h, None, init_h)
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>>> print(output[0].shape)
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(2, 8, 16)
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"""
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@prim_attr_register
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def __init__(self,
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direction='UNIDIRECTIONAL',
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cell_depth=1,
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keep_prob=1.0,
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cell_clip=-1.0,
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num_proj=0,
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time_major=True,
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activation="tanh",
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gate_order="rzh",
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reset_after=True,
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is_training=True):
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self.cell_depth = validator.check_value_type("cell_depth", cell_depth, [int], self.name)
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self.keep_prob = validator.check_value_type("keep_prob", keep_prob, [float], self.name)
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self.cell_clip = validator.check_value_type("cell_clip", cell_clip, [float], self.name)
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self.num_proj = validator.check_non_negative_int(num_proj, "num_proj", self.name)
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self.time_major = validator.check_value_type("time_major", time_major, [bool], self.name)
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self.is_training = validator.check_value_type("is_training", is_training, [bool], self.name)
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self.direction = validator.check_string(direction, ['UNIDIRECTIONAL'], "direction", self.name)
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self.activation = validator.check_string(activation, ['tanh'], "activation", self.name)
|
||||
self.gate_order = validator.check_string(gate_order, ['zrh', 'rzh'], "gate_order", self.name)
|
||||
self.reset_after = validator.check_value_type("reset_after", reset_after, [bool], self.name)
|
||||
self.add_prim_attr("io_format", "ND")
|
||||
|
||||
def infer_shape(self, x_shape, winput_shape, whidden_shape, binput_shape, bhidden_shape, seq_shape, h_shape):
|
||||
validator.check_int(len(x_shape), 3, Rel.EQ, "x shape", self.name)
|
||||
validator.check_int(len(winput_shape), 2, Rel.EQ, "weight input shape rank", self.name)
|
||||
validator.check_int(len(whidden_shape), 2, Rel.EQ, "weight hidden shape rank", self.name)
|
||||
|
||||
num_step, batch_size, input_size = x_shape
|
||||
hidden_size = winput_shape[-1] // 3
|
||||
if winput_shape[-1] % 3 != 0:
|
||||
raise ValueError(f"For {self.name}, weight_input_shape[-1] should multiple of 3.")
|
||||
|
||||
self.placeholder_index = [3, 4, 5]
|
||||
if binput_shape is not None:
|
||||
validator.check_int(len(binput_shape), 1, Rel.EQ, "bias input shape rank", self.name)
|
||||
validator.check("bias_input_shape", binput_shape, "3 * hidden_shape", [3 * hidden_size], Rel.EQ, self.name)
|
||||
self.placeholder_index.remove(3)
|
||||
if bhidden_shape is not None:
|
||||
validator.check_int(len(bhidden_shape), 1, Rel.EQ, "bias hidden shape rank", self.name)
|
||||
validator.check("bias_hidden_shape", bhidden_shape,
|
||||
"3 * hidden_shape", [3 * hidden_size], Rel.EQ, self.name)
|
||||
self.placeholder_index.remove(4)
|
||||
if seq_shape is not None:
|
||||
raise ValueError(f"For {self.name}, seq_shape should be None.")
|
||||
|
||||
validator.check_int(len(h_shape), 2, Rel.EQ, "init_h shape rank", self.name)
|
||||
validator.check("init_h_shape[0]", h_shape[0], "batch_size", batch_size, Rel.EQ, self.name)
|
||||
validator.check("init_h_shape[1]", h_shape[1], "hidden_size", hidden_size, Rel.EQ, self.name)
|
||||
validator.check("weight_input_shape[-1]", winput_shape[-1], "weight_hidden_shape[-1]",
|
||||
whidden_shape[-1], Rel.EQ, self.name)
|
||||
validator.check("weight_input_shape[0]", winput_shape[0], "input_size", input_size, Rel.EQ, self.name)
|
||||
validator.check("weight_hidden_shape[0]", whidden_shape[0], "hidden_size", hidden_size, Rel.EQ, self.name)
|
||||
if self.num_proj > 0:
|
||||
y_shape = (num_step, batch_size, min(hidden_size, self.num_proj))
|
||||
else:
|
||||
y_shape = (num_step, batch_size, hidden_size)
|
||||
out_shape = (num_step, batch_size, hidden_size)
|
||||
self.add_prim_attr("placeholder_index", self.placeholder_index)
|
||||
return y_shape, out_shape, out_shape, out_shape, out_shape, out_shape
|
||||
|
||||
def infer_dtype(self, x_dtype, winput_dtype, whidden_dtype, binput_dtype, bhidden_dtype, seq_dtype, h_dtype):
|
||||
validator.check_tensor_dtype_valid("x dtype", x_dtype, [mstype.float16], self.name)
|
||||
validator.check_tensor_dtype_valid("weight input dtype", winput_dtype, [mstype.float16], self.name)
|
||||
validator.check_tensor_dtype_valid("weight hidden dtype", whidden_dtype, [mstype.float16], self.name)
|
||||
validator.check_tensor_dtype_valid("init_h dtype", h_dtype, (mstype.float16, mstype.float32), self.name)
|
||||
b_dtype = mstype.float32
|
||||
if binput_dtype is not None:
|
||||
validator.check_tensor_dtype_valid("bias input dtype", binput_dtype,
|
||||
(mstype.float16, mstype.float32), self.name)
|
||||
b_dtype = binput_dtype
|
||||
elif bhidden_dtype is not None:
|
||||
validator.check_tensor_dtype_valid("bias hidden dtype", bhidden_dtype,
|
||||
(mstype.float16, mstype.float32), self.name)
|
||||
b_dtype = bhidden_dtype
|
||||
|
||||
return b_dtype, b_dtype, b_dtype, b_dtype, b_dtype, b_dtype
|
||||
|
||||
|
||||
class InTopK(PrimitiveWithInfer):
|
||||
r"""
|
||||
Determines whether the targets are in the top `k` predictions.
|
||||
|
|
|
|||
|
|
@ -822,7 +822,7 @@ class DynamicGRUV2Net(nn.Cell):
|
|||
|
||||
def __init__(self):
|
||||
super(DynamicGRUV2Net, self).__init__()
|
||||
self.dynamic_gru = inner.DynamicGRUV2()
|
||||
self.dynamic_gru = P.DynamicGRUV2()
|
||||
|
||||
def construct(self, x, w_i, w_h, b_i, b_h, init_h):
|
||||
return self.dynamic_gru(x, w_i, w_h, b_i, b_h, None, init_h)
|
||||
|
|
|
|||
Loading…
Reference in New Issue