def bprop(x, indices, axis, out, dout):
x_shp = shape_op(x)
if axis == 0:
indices_size = (size_op(indices), )
x_tail_shp = x_shp[1:]
values_shape = indices_size + x_tail_shp
values = reshape(dout, values_shape)
indices = reshape(indices, indices_size)
return RowTensor(indices, values,
x_shp), zeros_like(indices), zeros_like(axis)
if F.rank(dout) == 0:
dout = P.ExpandDims()(dout, -1)
if F.rank(indices) == 0:
indices = P.ExpandDims()(indices, -1)
out_shp = shape_op(dout)
ind_shp = shape_op(indices)
# Example: out_shape:(3,2,3) axis 1 -> (1,0,2)
perm_1 = _generate_shape_index(out_shp, ind_shp, axis)
values_transpose = transpose(dout, perm_1)
params_grad = unsorted_segment_sum(values_transpose, indices,
shape_op(x)[axis])
# Example: out_shape:(3,2,3) axis 2 -> (1,2,0)
perm_2 = _generate_inverse_index(x_shp, axis)
params_grad = transpose(params_grad, perm_2)
return params_grad, zeros_like(indices), zeros_like(axis)
def construct(self, input_indices, input_values, field_ids):
_check_input_2d(F.shape(input_indices), "input_indices", self.cls_name)
_check_input_2d(F.shape(input_values), "input_values", self.cls_name)
_check_input_2d(F.shape(field_ids), "field_ids", self.cls_name)
_check_input_dtype(F.dtype(input_indices), "input_indices", [mstype.int32, mstype.int64], self.cls_name)
_check_input_dtype(F.dtype(input_values), "input_values", [mstype.float32], self.cls_name)
_check_input_dtype(F.dtype(field_ids), "field_ids", [mstype.int32], self.cls_name)
batch_size = self.shape(input_indices)[0]
num_segments = batch_size * self.field_size
bias = Range(0, num_segments, self.field_size)()
bias = self.reshape(bias, (batch_size, -1))
field_ids = self.bias_add(field_ids, bias)
if self.target == "CPU":
out = self.embeddinglookup(self.embedding_table, input_indices, 0)
else:
if self.forward_unique:
shp = self.shape(input_indices) + (self.embedding_size,)
indices_flatten = self.reshape(input_indices, (-1,))
unique_id, unique_idx = self.unique(indices_flatten)
weight_unique = self.gatherv2(self.embedding_table, unique_id, 0)
weight_flatten = self.gather_revert(weight_unique, unique_idx, 0)
out = self.reshape(weight_flatten, shp)
else:
out = self.gatherv2(self.embedding_table, input_indices, 0)
if self.max_norm is not None:
axis = _make_axis_range(F.rank(input_indices), F.rank(out))
clip_by_norm = ClipByNorm(axis)
out = clip_by_norm(out, self.max_norm)
weights = self.reshape(input_values, (batch_size, self.shape(input_indices)[1], 1))
embedding = self.mul(weights, out)
if self.operator == 'MAX':
# Fill the padding value to -inf, so the padded value will not influence the results
negative_inf_mask = self.cast(self.equal(weights, 0), mstype.float32)
inf_mask = self.inf_mask_mul(negative_inf_mask, self.negative_inf_value)
embedding = self.inf_add(embedding, inf_mask)
embedding = self.reshape(embedding, (-1, self.embedding_size))
field_ids = self.reshape(field_ids, (-1,))
merged_vectors = self.merge_op(embedding, field_ids, num_segments)
if self.operator == 'MAX':
value_count = self.count_op(self.abs(self.reshape(input_values, (-1,))), field_ids, num_segments)
value_zeros = self.cast(self.max_no_equal(value_count, 0.0), mstype.float32)
count = self.expand(value_zeros, -1)
merged_vectors = self.max_mask_mul(merged_vectors, count)
if self.operator == 'MEAN':
value_count = self.count_op(self.abs(input_values), field_ids, num_segments)
value_count = self.expand(value_count, -1)
merged_vectors = self.div_no_nan(merged_vectors, value_count)
merged_vectors = self.reshape(merged_vectors, (batch_size, self.field_size, -1))
return merged_vectors
def construct(self, indices):
if self.target == "CPU":
out = self.embeddinglookup(self.embedding_table, indices, 0)
else:
out = self.gatherv2(self.embedding_table, indices, 0)
if self.max_norm is not None:
axis = _make_axis_range(F.rank(indices), F.rank(out))
clip_by_norm = ClipByNorm(axis)
out = clip_by_norm(out, self.max_norm)
return out
def construct(self, indices):
if self.target == "CPU":
out = self.embeddinglookup(self.embedding_table, indices, 0)
else:
if self.forward_unique:
shp = self.shape(indices) + (self.embedding_size,)
indices_flatten = self.reshape_first(indices, (-1,))
unique_id, unique_idx = self.unique(indices_flatten)
weight_unique = self.gatherv2(self.embedding_table, unique_id, 0)
weight_flatten = self.gather_revert(weight_unique, unique_idx, 0)
out = self.reshape(weight_flatten, shp)
else:
out = self.gatherv2(self.embedding_table, indices, 0)
if self.max_norm is not None:
axis = _make_axis_range(F.rank(indices), F.rank(out))
clip_by_norm = ClipByNorm(axis)
out = clip_by_norm(out, self.max_norm)
return out
def matmul(x1, x2, dtype=None):
"""
Returns the matrix product of two arrays.
Note:
Numpy arguments `out`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
On GPU, the supported dtypes are np.float16 and np.float32.
On CPU, the supported dtypes are np.float16 and np.float32.
Args:
x1 (Tensor): Input tensor, scalar not allowed.
x2 (Tensor): Input tensor, scalar not allowed.
dtype (:class:`mindspore.dtype`, optional): defaults to None. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the matrix product of the inputs. This is a scalar only
when both `x1`, `x2` are 1-d vectors.
Raises:
ValueError: If the last dimension of `x1` is not the same size as the
second-to-last dimension of `x2`, or if a scalar value is passed in.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x1 = Tensor(np.arange(2*3*4).reshape(2, 3, 4), mindspore.float32)
>>> x2 = Tensor(np.arange(4*5).reshape(4, 5), mindspore.float32)
>>> output = ops.matmul(x1, x2)
>>> print(output)
[[[ 70. 76. 82. 88. 94.]
[ 190. 212. 234. 256. 278.]
[ 310. 348. 386. 424. 462.]]
[[ 430. 484. 538. 592. 646.]
[ 550. 620. 690. 760. 830.]
[ 670. 756. 842. 928. 1014.]]]
"""
# performs type promotion
dtype1 = F.dtype(x1)
dtype2 = F.dtype(x2)
if not _check_same_type(dtype1, dtype2):
x1 = x1.astype(mstype.float32)
x2 = x2.astype(mstype.float32)
ndim1_orig, ndim2_orig = F.rank(x1), F.rank(x2)
shape1_orig, shape2_orig = F.shape(x1), F.shape(x2)
transpose_b = ndim2_orig == 1
shape_backbone = _check_matmul_shapes(shape1_orig, shape2_orig)
# infers the shape of the output
shape_out = shape_backbone + _infer_shape_rem(
shape1_orig, shape2_orig, ndim1_orig, ndim2_orig, transpose_b)
x1 = _expand(x1, 2)
x2 = _expand(x2, 2)
if F.rank(x2) == 2:
if F.rank(x1) > 2:
x1 = F.reshape(x1, (-1, shape1_orig[-1]))
res = P.MatMul(False, transpose_b)(x1, x2)
else:
# broadcasts x1.shape[:-2] with x2.shape[:-2]
ndim_aligned = _max(ndim1_orig, ndim2_orig)
x1 = _expand(x1, ndim_aligned)
x2 = _expand(x2, ndim_aligned)
shape1_aligned, shape2_aligned = F.shape(x1), F.shape(x2)
x1 = _broadcast_to(x1, shape1_aligned[:-2], shape_backbone,
ndim_aligned)
x2 = _broadcast_to(x2, shape2_aligned[:-2], shape_backbone,
ndim_aligned)
res = P.BatchMatMul(False, transpose_b)(x1, x2)
if dtype is not None:
res = res.astype(dtype)
return F.reshape(res, shape_out)
def _expand(x, ndim):
"""Expand x to ndim from axis, which can be 0 or -1."""
while F.rank(x) < ndim:
x = F.expand_dims(x, 0)
return x
