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Add Sparse Attention 

adjut the file structure and name

Deleted extra information

Do some formatting work

Add test case and fix some document

fix imports
This commit is contained in:
zhihenghu 2021-08-18 17:23:05 +08:00 committed by huangxinjing
parent ba1b86ac07
commit ce12c02343
45 changed files with 475 additions and 90 deletions
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26
mindspore/ops/_grad/grad_inner_ops.py
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@ -97,3 +97,29 @@ def get_bprop_matrix_set_diag(self):
return dx, dy, dz
return bprop
@bprop_getters.register(inner.DSDMatmul)
def get_dsd_matmul_bprop(self):
def bprop(w1_gm, w2_gm, v_gm, out, dout):
d_w1_gm, d_w2_gm, d_v_gm = inner.DSDGrad()(w1_gm, w2_gm, v_gm, out, dout)
return d_w1_gm, d_w2_gm, d_v_gm
return bprop
@bprop_getters.register(inner.MatmulDDS)
def get_bprop(self):
"""brop of the matmulDDS operator"""
def bprop(q, k, local_mask, global_mask, out, d_out):
lc, gc = out
d_lc, d_gc = d_out
dq, dk = inner.MatmulDDSGrad()(q, k, lc, gc, d_lc, d_gc)
dk = P.Transpose()(dk, (1, 0, 3, 2))
# local_mask = 0
# d_local_mask = local_mask
# global_mask = 0
# d_global_mask = global_mask
all_d = (dq, dk, None, None)
return all_d
return bprop

4
mindspore/ops/_op_impl/_custom_op/__init__.py
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@ -33,3 +33,7 @@ from .fake_quant_perlayer import _fake_quant_per_layer_tbe
from .fake_quant_perlayer_grad import _fake_quant_per_layer_grad_tbe
from .minmax_update_perchannel import _minmax_update_perchannel_tbe
from .minmax_update_perlayer import _minmax_update_perlayer_tbe
from .matmul_dds_impl import MatmulDDSImpl
from .matmul_dds_grad_impl import matmul_dds_grad
from .dsd_impl import DSDMatmulimpl
from .dsd_back_impl import dsdbpropimpl

2
mindspore/ops/_op_impl/_custom_op/matmul_dds_grad_impl.py
View File

@ -17,7 +17,7 @@ from te import tik
from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
matmul_dds_grad_op_info = TBERegOp("CusMatmulDDSGrad") \
matmul_dds_grad_op_info = TBERegOp("MatmulDDSGrad") \
.fusion_type("OPAQUE") \
.async_flag(False) \
.binfile_name("matmul_dds_grad.so") \

22
mindspore/ops/_op_impl/_custom_op/matmul_dds_impl.py
View File

@ -16,12 +16,12 @@
from te import tik
from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
matmul_dds_op_info = TBERegOp("CusMatmulDDS") \
matmul_dds_op_info = TBERegOp("MatmulDDS") \
.fusion_type("OPAQUE") \
.async_flag(False) \
.binfile_name("matmul_dds.so") \
.compute_cost(10) \
.kernel_name("CusMatmulDDSImpl") \
.kernel_name("MatmulDDSImpl") \
.partial_flag(True) \
.attr("bs", "required", "int", "all") \
.attr("heads", "required", "int", "all") \
@ -38,15 +38,15 @@ matmul_dds_op_info = TBERegOp("CusMatmulDDS") \
@op_info_register(matmul_dds_op_info)
def CusMatmulDDSImpl(q,
k,
local_mask,
global_mask,
local_prob,
global_prob,
bs,
heads,
kernel_name="CusMatmulDDSImpl"):
def MatmulDDSImpl(q,
k,
local_mask,
global_mask,
local_prob,
global_prob,
bs,
heads,
kernel_name="MatmulDDSImpl"):
"""
:param q: the dict of input q (bs*seq_len, embedding_size) zN
:param k: the dict of input k (bs*seq_len, embedding_size) nZ

2
mindspore/ops/bprop_mindir/ApproximateEqual_bprop.mindir
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@ -15,4 +15,4 @@ bprop.32:x*
bprop.32:y*
bprop.32:out*
bprop.32:dout2
bprop.32:[CNode]35:€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
bprop.32:[CNode]35:€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

2
mindspore/ops/bprop_mindir/Argmax_bprop.mindir
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@ -9,4 +9,4 @@ m
bprop.13:x*
bprop.13:out*
bprop.13:dout2
bprop.13:[CNode]15:€027af68f320ba40d9fbd0893da424c07f9c3a4ec82e98f9543bff9b5a15547a2087787fe3abde92d74a97b5b9f48f23d8ccdd6de450a931c64f578b83dcb5c2f2366f7bd59ea5ec135e982de03b4f7cab6b61d833d046a6e13f78bdaf2fb2b224c332efad4a51b4773cb78093dd53a4ca850b2dc6cdd5f2ae47106b3fda77bb3565f906930f68ca2413e9ad958d105e129e717cd183b95d11d65a8b0b030fc0d65c0e00bc893ef15ec6199798d6c8c46997153587d375b3240c1195ff2c7278c7e635a08323207b4cb3f73fd8437b4d7ee28a7676a68f005a7749bd19e5ed4eca6c407ad6a3b57190d3702d6a45031d13b97bb6952735edf94fb36f73dbff6cdab258748286fc6d783abacce203dfc79d2fc31e23a427ce1f86e08777a687f71be985a048b98205beb531a0e96f3c9c3c36cb9a5fef472e532f1e8041d85d279c414b8c313aac4f85c6217fbbb7009dd079b2d5548f8b695a470a11cb8cc83e6f5e78f5b3c67f2e7bf339b250c3638aee952e1a073002e2834011401f3827260
bprop.13:[CNode]15:€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

2
mindspore/ops/bprop_mindir/Argmin_bprop.mindir
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@ -9,4 +9,4 @@ m
bprop.16:x*
bprop.16:out*
bprop.16:dout2
bprop.16:[CNode]18:€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
bprop.16:[CNode]18:€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

2
mindspore/ops/bprop_mindir/AssignAdd_bprop.mindir
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@ -15,4 +15,4 @@ bprop.21:x*
bprop.21:y*
bprop.21:out*
bprop.21:dout2
bprop.21:[CNode]24:€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
bprop.21:[CNode]24:€027af68f320ba40d9fbd0893da424c07f9c3a4ec82e98f9543bff9b5a15547a2087787fe3abde92d74a97b5b9f48f23d8ccdd6de450a931c64f578b83dcb5c2f2366f7bd59ea5ec135e982de03b4f7cab6b61d833d046a6e13f78bdaf2fb2b224c332efad4a51b4773cb78093dd53a4ca850b2dc6cdd5f2ae47106b3fda77bb3565f906930f68ca2413e9ad958d105e129e717cd183b95d11d65a8b0b030fc0d7e635a08323207b4cb3f73fd8437b4d7ee28a7676a68f005a7749bd19e5ed4eca6c407ad6a3b57190d3702d6a45031d13b97bb6952735edf94fb36f73dbff6cdab258748286fc6d783abacce203dfc79d2fc31e23a427ce1f86e08777a687f71be985a048b98205beb531a0e96f3c9c3c36cb9a5fef472e532f1e8041d85d279c414b8c313aac4f85c6217fbbb7009dd079b2d5548f8b695a470a11cb8cc83e6f5e78f5b3c67f2e7bf339b250c3638aee952e1a073002e2834011401f3827260ffb378d62977d2a1338d9b64be24b7349347c19c65faf6ba72837f4df97ff84c

2
mindspore/ops/bprop_mindir/AssignSub_bprop.mindir
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@ -15,4 +15,4 @@ bprop.25:x*
bprop.25:y*
bprop.25:out*
bprop.25:dout2
bprop.25:[CNode]28:€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
bprop.25:[CNode]28:€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

2
mindspore/ops/bprop_mindir/Assign_bprop.mindir
View File

@ -12,4 +12,4 @@ bprop.60:x*
bprop.60:y*
bprop.60:out*
bprop.60:dout2
bprop.60:[CNode]62:€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
bprop.60:[CNode]62:€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

2
mindspore/ops/bprop_mindir/BNTrainingReduce_bprop.mindir
View File

@ -9,4 +9,4 @@ m
bprop.67:x*
bprop.67:out*
bprop.67:dout2
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2
mindspore/ops/bprop_mindir/Broadcast_bprop.mindir
View File

@ -6,4 +6,4 @@ h
bprop.19:x*
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2
mindspore/ops/bprop_mindir/DropoutDoMask_bprop.mindir
View File

@ -17,4 +17,4 @@
bprop.110:keep_prob*
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2
mindspore/ops/bprop_mindir/DropoutGenMask_bprop.mindir
View File

@ -12,4 +12,4 @@
bprop.50:keep_prob*
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2
mindspore/ops/bprop_mindir/Equal_bprop.mindir
View File

@ -15,4 +15,4 @@ bprop.70:x*
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2
mindspore/ops/bprop_mindir/GreaterEqual_bprop.mindir
View File

@ -15,4 +15,4 @@ bprop.82:x*
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2
mindspore/ops/bprop_mindir/Greater_bprop.mindir
View File

@ -15,4 +15,4 @@ bprop.78:x*
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2
mindspore/ops/bprop_mindir/IOU_bprop.mindir
View File

@ -15,4 +15,4 @@ bprop.63:x*
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2
mindspore/ops/bprop_mindir/Identity_bprop.mindir
View File

@ -5,4 +5,4 @@ b
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2
mindspore/ops/bprop_mindir/IsFinite_bprop.mindir
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@ -9,4 +9,4 @@ m
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2
mindspore/ops/bprop_mindir/LessEqual_bprop.mindir
View File

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2
mindspore/ops/bprop_mindir/Less_bprop.mindir
View File

@ -15,4 +15,4 @@ bprop.86:x*
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2
mindspore/ops/bprop_mindir/LinSpace_bprop.mindir
View File

@ -15,4 +15,4 @@
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2
mindspore/ops/bprop_mindir/LogicalAnd_bprop.mindir
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2
mindspore/ops/bprop_mindir/LogicalNot_bprop.mindir
View File

@ -9,4 +9,4 @@ m
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2
mindspore/ops/bprop_mindir/LogicalOr_bprop.mindir
View File

@ -15,4 +15,4 @@ bprop.98:x*
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2
mindspore/ops/bprop_mindir/NotEqual_bprop.mindir
View File

@ -15,4 +15,4 @@ bprop.74:x*
bprop.74:y*
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2
mindspore/ops/bprop_mindir/OneHot_bprop.mindir
View File

@ -19,4 +19,4 @@
bprop.54:off_value*
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2
mindspore/ops/bprop_mindir/OnesLike_bprop.mindir
View File

@ -7,4 +7,4 @@ f
bprop.7:x*
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2
mindspore/ops/bprop_mindir/Range_bprop.mindir
View File

@ -7,4 +7,4 @@ f
bprop.4:x*
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2
mindspore/ops/bprop_mindir/ReLU_bprop.mindir
View File

@ -11,4 +11,4 @@ bprop.0:dxbprop.0:[CNode]1bprop.0:[CNode]1"S-Prim-MakeTuple:Default/S-Prim
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2
mindspore/ops/bprop_mindir/ReduceAll_bprop.mindir
View File

@ -12,4 +12,4 @@
bprop.102:axis*
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bprop.102:dout2
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2
mindspore/ops/bprop_mindir/ReduceAny_bprop.mindir
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@ -12,4 +12,4 @@
bprop.106:axis*
bprop.106:out*
bprop.106:dout2
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2
mindspore/ops/bprop_mindir/Round_bprop.mindir
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@ -9,4 +9,4 @@ m
bprop.42:x*
bprop.42:out*
bprop.42:dout2
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2
mindspore/ops/bprop_mindir/Sign_bprop.mindir
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@ -9,4 +9,4 @@ m
bprop.36:x*
bprop.36:out*
bprop.36:dout2
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2
mindspore/ops/bprop_mindir/ZerosLike_bprop.mindir
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@ -9,4 +9,4 @@ l
bprop.10:x*
bprop.10:out*
bprop.10:dout2
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1
mindspore/ops/operations/__init__.py
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@ -120,6 +120,7 @@ from .sponge_update_ops import (v0coordinaterefresh, v1coordinaterefresh, v2coor
MDIterationLeapFrogWithMaxVel, MDIterationGradientDescent,
BondForceWithAtomEnergyAndVirial, ConstrainForceCycle)
from .rl_ops import (BufferAppend, BufferGetItem, BufferSample)
from ._inner_ops import (MatmulDDS, DSDMatmul)
__all__ = [
'Unique',

82
mindspore/ops/operations/_inner_ops.py
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@ -1293,3 +1293,85 @@ class Roll(Primitive):
elif isinstance(shift, int) and isinstance(axis, int):
validator.check_equal_int(axis, 0, "axis", self.name)
self.init_prim_io_names(inputs=['input_x'], outputs=['output'])
class DSDMatmul(PrimitiveWithInfer):
"""
The definition of the CusSquare primitive.
"""
@prim_attr_register
def __init__(self):
self.init_prim_io_names(inputs=['input_w1', 'input_w2', 'input_v'], outputs=['output_y'])
def infer_shape(self, input_w1_shape, input_w2_shape, input_v_shape):
batch_size = input_w1_shape[0]
head = input_w1_shape[1]
v_embedding = input_v_shape[1] * 16 // head
seq_len = input_v_shape[0] * 16 // batch_size
return (batch_size, head, v_embedding // 16, seq_len // 16, 16, 16)
def infer_dtype(self, data_dtype1, data_dtype2, data_dtype3):
return data_dtype1
class MatmulDDS(PrimitiveWithInfer):
"""MatmulDDS definition"""
@prim_attr_register
def __init__(self, bs, heads):
"""init MatmulDDS"""
self.init_prim_io_names(inputs=['q', 'k', 'local_mask', 'global_mask'],
outputs=['local_prob', 'global_prob'])
self.heads = heads
def infer_shape(self, q, k, local_mask, global_mask):
seq_len = local_mask[0] * local_mask[-1]
bs = q[1] * q[2] // seq_len
global_size = seq_len // 4
size_per_head = q[0] * q[-1] // self.heads
heads = q[0] * q[-1] // size_per_head
# size_per_head = k[0] * k[-1] // heads
block_size = local_mask[1] * local_mask[2] // bs
block_num = seq_len // block_size
l_size = (bs, heads, block_num, block_size // 16, block_size // 16, 16, 16)
g_size = (bs, heads, block_num, global_size // 16, block_size // 16, 16, 16)
return l_size, g_size
def infer_dtype(self, q, k, local_mask, global_mask):
return q, q
class DSDGrad(PrimitiveWithInfer):
"""
The definition of the CusSquare primitive.
"""
@prim_attr_register
def __init__(self):
self.init_prim_io_names(inputs=['w1_gm', 'w2_gm', 'v_gm', 'a_gm', 'd_a_gm'],
outputs=['d_w1_gm', 'd_w2_gm', 'd_v_gm'])
def infer_shape(self, input_w1_shape, input_w2_shape, input_v_shape, input_a_shape, input_da_shape):
return input_w1_shape, input_w2_shape, input_v_shape
def infer_dtype(self, data_dtype1, data_dtype2, data_dtype3, data_dtype4, data_dtype5):
return data_dtype1, data_dtype1, data_dtype1
class MatmulDDSGrad(PrimitiveWithInfer):
"""MatmulDDS definition"""
@prim_attr_register
def __init__(self):
"""init MatmulDDS"""
self.init_prim_io_names(inputs=['q', 'k', 'local_prob', 'global_prob', 'local_prob_grad', 'global_prob_grad'],
outputs=['dq', 'dk'])
def infer_shape(self, q, k, local_prob, global_prob, local_prob_grad, global_prob_grad):
k_size = (q[1], q[0], q[3], q[2])
return q, k_size
def infer_dtype(self, q, k, local_prob, global_prob, local_prob_grad, global_prob_grad):
return q, k

1
mindspore/parallel/nn/__init__.py
View File

@ -26,3 +26,4 @@ __all__ = []
__all__.extend(transformer.__all__)
__all__.extend(loss.__all__)
__all__.extend(op_parallel_config.__all__)
__all__.extend(layers.__all__)

255
mindspore/parallel/nn/layers.py
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@ -17,6 +17,8 @@ The basic layer of the Transformer Networks. This is an experimental interface t
change and/or deletion.
"""
import math
import numpy as np
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer, Tensor
import mindspore.common.dtype as mstype
@ -24,6 +26,56 @@ from mindspore.ops import operations as P
from mindspore._extends import cell_attr_register
from mindspore.nn.cell import Cell
from mindspore.nn.layer import Dense
import mindspore.nn as nn
from mindspore.ops import functional as F
from mindspore._checkparam import Validator
from mindspore.ops.primitive import constexpr
from .op_parallel_config import default_dpmp_config, OpParallelConfig
__all__ = [
"FixedSparseAttention"
]
@constexpr
def _check_input_shape(input_shape, param_name, func_name, target_len):
if len(input_shape) != target_len:
raise ValueError(f"{func_name} {param_name} should be 2d, but got shape {input_shape}")
return True
@constexpr
def _check_past_none_input_none(use_past, param_name, func_name, input_tensor, default_value=None):
""" If the past is True, check whether the inputs is None"""
if not use_past and input_tensor is not default_value:
raise ValueError(f"{func_name} {param_name} should be {default_value}, if use_past is False.")
if use_past and input_tensor is default_value:
raise ValueError(f"{func_name} {param_name} should not be {default_value}, if use_past is True.")
return True
@constexpr
def _check_shape_equal(input_shape, param_name, func_name, target_shape):
if len(input_shape) != len(target_shape):
raise ValueError(f"{func_name} {param_name} shape should be {target_shape},"
f"but got {input_shape}")
for i in range(len(input_shape)):
if input_shape[i] != target_shape[i]:
raise ValueError(f"{func_name} {param_name} shape should be {target_shape},"
f"but got {input_shape}")
return True
@constexpr
def _check_input_dtype(input_dtype, param_name, allow_dtypes, cls_name):
Validator.check_type_name(param_name, input_dtype, allow_dtypes, cls_name)
@constexpr
def _check_input_shape_value(input_shape, dim, param_name, cls_name, target_value):
if input_shape[dim] != target_value:
raise ValueError(f"{cls_name} {param_name} at {dim} shape should be {target_value},"
f"but got {input_shape[dim]}")
class _LayerNorm(Cell):
@ -232,3 +284,206 @@ class _Linear(Dense):
getattr(self.activation, self.act_name).shard(strategy_activation)
return self
class FixedSparseAttention(nn.Cell):
"""
Fixed Sparse Attention Layer
This function contains the sparse attention primitives used in Sparse Transformers (see paper).
https://arxiv.org/abs/1904.10509
Specifically, it includes the following:
1. A faster implementation of normal attention (the upper triangle is not computed, and many operations are fused).
2. An implementation of "strided" and "fixed" attention, as in the Sparse Transformers paper.
Args:
batch_size (int): Number of input batch size.
num_heads (int): Number of attention heads.
block_size (int): An integer determining the block size. Current implementation of sparse self-attention
is based on blocked sparse matrices. In which this parameter defines size of such blocks,
Block X Block. only supports 64 for now
seq_length (int): length of input sequence, only supports 1024 for now
num_different_global_patterns (int):An integer determining number of different global attentions layouts.
While global attention can be fixed by which block/s are representative of
any local window, since there are multi-heads, each head can use a
different global representative, only supports 4 for now
size_per_head (int): An integer determining embedding size of each attention head,
only supports 64, 80, 96, 112, 128 for now
Inputs:
- **q** - Tensor uery (:class:`mstype.fp16` [batch_size, seq_length, hidden_size]): Sequence of
queries to query the context.
- **k** - Tensor key (:class:`mstype.fp16` [batch_size, seq_length, hidden_size]): Sequence of
queries to query the context.
- **v** - Tensor value (:class:`mstype.fp16` [batch size, sequence length, Embedding Size]): Sequence of
queries to query the context.
- **input_mask** - Tensor the mask of (:class:`mstype.fp32` [batch_size, seq_length]):
Sequence of 0 and 1 to pass masked information.
Outputs:
A Tensor. The output of the attention with shape [batch_size, seq_length, hidden_size]
Supported Platforms:
``Ascend``
Examples:
>>> model = FixedSparseAttention(batch_size=2,
... num_heads=8,
... size_per_head=64,
... block_size=64)
>>> q = Tensor(np.ones((2, 1024, 8*64)), dtype.float16)
>>> k = Tensor(np.ones((2, 1024, 8*64)), dtype.float16)
>>> v = Tensor(np.ones((2, 1024, 8*64)), dtype.float16)
>>> input_mask = Tensor(np.ones((2, 1024)), dtype.float16)
>>> output = model(q, k, v, input_mask)
>>> print(output.shape)
(2, 1024, 512)
"""
def __init__(self,
batch_size,
num_heads,
size_per_head,
block_size,
seq_length=1024,
num_different_global_patterns=4,
parallel_config=default_dpmp_config):
super(FixedSparseAttention, self).__init__()
if not isinstance(parallel_config, OpParallelConfig):
raise ValueError(
f"The parallel_config should be a OpParallelConfig type, but found {type(parallel_config)}")
dp, mp = parallel_config.data_parallel, parallel_config.model_parallel
self.seq_length = seq_length
self.batch_size = batch_size
self.hidden_size = size_per_head * num_heads
self.num_heads = num_heads
self.block_size = block_size
self.block_num = seq_length // block_size
self.size_per_head = size_per_head
self.global_size = seq_length // 4
self.reshape = P.Reshape()
self.transpose = P.Transpose().shard(((dp, 1, mp, 1),))
self.batch_matmul = P.BatchMatMul().shard(((dp, 1, 1, 1), (dp, 1, 1, 1)))
self.multiply = P.Mul().shard(((dp, 1, 1, 1), (1, 1, 1)))
self.multiply_data = Tensor([-10000.0,], dtype=mstype.float32)
self.parallel_config = parallel_config
size_per_head_list = [64, 128]
if self.seq_length != 1024:
raise ValueError("seq_length only supports 1024 for now.")
if self.block_size != 64:
raise ValueError("block_size only supports 64 for now.")
if num_different_global_patterns != 4:
raise ValueError("num_different_global_patterns only supports 4 for now.")
if self.size_per_head not in size_per_head_list:
raise ValueError(f"size_per_head only supports {size_per_head_list} for now, "
f"but found {self.size_per_head}")
local_ones = np.ones((self.block_size, self.block_size),
dtype=np.float16)
global_mask_original = np.ones((self.seq_length, self.global_size), dtype=np.float16)
for i in range(self.seq_length):
for j in range(self.global_size):
if i // 16 >= (j // 16 + 1) * 4:
global_mask_original[i, j] = 0.0
global_mask_original = -10000 * global_mask_original
global_mask_fx = global_mask_original.reshape((self.seq_length // 16, 16, self.global_size // 16, 16))
global_mask = np.transpose(global_mask_fx, (2, 0, 1, 3))
global_mask = np.repeat(global_mask[np.newaxis, :, :, :, :,], self.batch_size, axis=0)
global_mask = global_mask.reshape((self.batch_size * self.global_size // 16, self.seq_length // 16, 16, 16))
self.global_mask = Tensor(global_mask, mstype.float32)
self.local_mask_triangle = Tensor(np.tril(local_ones), mstype.float32)
self.scale_factor = Tensor((math.sqrt(self.size_per_head)))
self.matmul_dds = P.MatmulDDS(self.batch_size, self.num_heads).shard(((mp, dp, 1, 1),
(mp, dp, 1, 1),
(1, dp, 1, 1),
(dp, 1, 1, 1)))
self.matmul_dsd = P.DSDMatmul().shard(((dp, mp, 1, 1, 1, 1, 1), (dp, mp, 1, 1, 1, 1, 1), (dp, mp, 1, 1)))
self.sub1 = P.Sub().shard(((1,), (dp, 1, 1, 1)))
self.mul1 = P.Mul().shard(((dp, 1, 1, 1), (1,)))
self.transpose1 = P.Transpose().shard(((dp, 1, 1, 1),))
self.transpose2 = P.Transpose().shard(((dp, 1, 1, 1),))
self.transpose3 = P.Transpose().shard(((dp, mp, 1, 1, 1, 1),))
self.transpose4 = P.Transpose().shard(((dp, mp, 1, 1),))
def _transpose_inputs(self, q, k, v):
"""
do reshape and transpose to inputs
"""
q = self.transpose(
self.reshape(
q,
(-1, 16, self.num_heads * self.size_per_head // 16, 16)),
(2, 0, 1, 3))
k = self.transpose(
self.reshape(
k, (-1, 16, self.num_heads * self.size_per_head // 16, 16)),
(2, 0, 1, 3))
v = self.transpose(
self.reshape(
v,
(-1, 16, self.num_heads * self.size_per_head // 16, 16)),
(0, 2, 3, 1))
return q, k, v
def _generate_attention_mask(self, input_mask):
"""
generate attention mask from input mask
"""
input_shape = P.Shape()(input_mask) # bs, seq_length
# bs, block_num, 1, block_size
local_shape_right = (input_shape[0], self.block_num, 1, self.block_size)
# bs, block_num, block_size, 1
local_shape_left = (input_shape[0], self.block_num, self.block_size, 1)
local_mask_left = self.reshape(input_mask, local_shape_left)
local_mask_right = self.reshape(input_mask, local_shape_right)
# bs, block_num, block_size, block_size
local_attention_mask = self.batch_matmul(local_mask_left, local_mask_right)
lower_triangle = P.ExpandDims()(self.local_mask_triangle, 0)
local_attention_mask = self.multiply(local_attention_mask, lower_triangle)
local_multiplied_out = self.sub1(P.Cast()(F.tuple_to_array((1.0,)), mstype.float32),
P.Cast()(local_attention_mask, mstype.float32))
local_adder = self.mul1(local_multiplied_out, self.multiply_data)
local_mask_original = self.transpose1(local_adder, (0, 2, 1, 3))
local_mask_original = self.reshape(
local_mask_original,
(self.batch_size * self.block_size, self.block_num * self.block_size))
local_mask_fx = self.reshape(
local_mask_original,
(self.batch_size * self.block_size // 16, 16,
self.block_num * self.block_size // 16, 16))
local_mask = self.transpose2(local_mask_fx, (2, 0, 1, 3))
global_mask = self.global_mask
return local_mask, global_mask
def construct(self, q, k, v, input_mask):
_check_shape_equal(F.shape(q), "q", self.cls_name,
[self.batch_size, self.seq_length, self.hidden_size])
_check_input_dtype(F.dtype(q), "q", [mstype.float16], self.cls_name)
_check_shape_equal(F.shape(k), "k", self.cls_name,
[self.batch_size, self.seq_length, self.hidden_size])
_check_input_dtype(F.dtype(k), "k", [mstype.float16], self.cls_name)
_check_shape_equal(F.shape(v), "v", self.cls_name,
[self.batch_size, self.seq_length, self.hidden_size])
_check_input_dtype(F.dtype(v), "v", [mstype.float16], self.cls_name)
_check_shape_equal(F.shape(input_mask), "input_mask", self.cls_name,
[self.batch_size, self.seq_length])
_check_input_dtype(F.dtype(input_mask), "input_mask", [mstype.float32], self.cls_name)
q, k, v = self._transpose_inputs(q, k, v)
local_mask, global_mask = self._generate_attention_mask(input_mask)
q = q / F.cast(self.scale_factor, F.dtype(q))
k = k / F.cast(self.scale_factor, F.dtype(k))
local_prob, global_prob = self.matmul_dds(q, k, local_mask, global_mask)
attention = self.matmul_dsd(local_prob, global_prob, v)
attention_merge = self.transpose3(attention, (0, 1, 3, 4, 2, 5))
attention_merge = F.reshape(
attention_merge,
(-1, self.num_heads, self.seq_length, self.size_per_head))
attention_merge = self.transpose4(attention_merge, (0, 2, 1, 3))
attention_merge = F.reshape(
attention_merge,
(-1, self.seq_length, self.size_per_head * self.num_heads))
return attention_merge

2
mindspore/parallel/nn/loss.py
View File

@ -22,7 +22,7 @@ from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.nn import Cell
from mindspore.nn.loss.loss import _check_is_tensor
from mindspore.parallel.nn.transformer import _check_input_dtype, _check_input_shape
from mindspore.parallel.nn.layers import _check_input_dtype, _check_input_shape
from .op_parallel_config import default_dpmp_config, OpParallelConfig
__all__ = ["CrossEntropyLoss"]

45
mindspore/parallel/nn/transformer.py
View File

@ -28,9 +28,9 @@ from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.nn.cell import Cell
from mindspore._checkparam import Validator
from mindspore.ops.primitive import constexpr
from mindspore import log as logger
from .layers import _LayerNorm, _Linear
from .layers import _LayerNorm, _Linear, _check_input_shape,\
_check_shape_equal, _check_past_none_input_none, _check_input_dtype, _check_input_shape_value
from .op_parallel_config import default_dpmp_config, _PipeLineConfig, OpParallelConfig, _Config, _check_config
__all__ = [
@ -47,47 +47,6 @@ __all__ = [
"EmbeddingOpParallelConfig"]
@constexpr
def _check_input_shape(input_shape, param_name, func_name, target_len):
if len(input_shape) != target_len:
raise ValueError(f"{func_name} {param_name} should be {target_len}d, but got shape {input_shape}")
return True
@constexpr
def _check_past_none_input_none(use_past, param_name, func_name, input_tensor, default_value=None):
""" If the past is True, check whether the inputs is None"""
if not use_past and input_tensor is not default_value:
raise ValueError(f"{func_name} {param_name} should be {default_value}, if use_past is False.")
if use_past and input_tensor is default_value:
raise ValueError(f"{func_name} {param_name} should not be {default_value}, if use_past is True.")
return True
@constexpr
def _check_shape_equal(input_shape, param_name, func_name, target_shape):
if len(input_shape) != len(target_shape):
raise ValueError(f"{func_name} {param_name} shape should be {target_shape},"
f"but got {input_shape}")
for i in range(len(input_shape)):
if input_shape[i] != target_shape[i]:
raise ValueError(f"{func_name} {param_name} shape should be {target_shape},"
f"but got {input_shape}")
return True
@constexpr
def _check_input_dtype(input_dtype, param_name, allow_dtypes, cls_name):
Validator.check_type_name(param_name, input_dtype, allow_dtypes, cls_name)
@constexpr
def _check_input_shape_value(input_shape, dim, param_name, cls_name, target_value):
if input_shape[dim] != target_value:
raise ValueError(f"{cls_name} {param_name} at {dim} shape should be {target_value},"
f"but got {input_shape[dim]}")
class EmbeddingOpParallelConfig(_Config):
r"""
EmbeddingOpParallelConfig for the setting the data parallel or row slice for the embedding table.

27
tests/st/ops/ascend/test_sparse_attention.py Normal file
View File

@ -0,0 +1,27 @@
import numpy as np
from mindspore import Tensor
from mindspore.parallel.nn.layers import FixedSparseAttention
import mindspore.context as context
context.set_context(device_target="Ascend")
def test_net():
np.random.seed(0)
bs = 2 # batch size
heads = 2
seq_len = 1024 # this op is designed for seq_len = 1024
size_per_head = 128 # maximum size per head value is 128
block_size = 64 # block size is designed to be 64
fixed_sparse = FixedSparseAttention(bs, heads, size_per_head, block_size)
q = np.random.rand(bs, seq_len, heads * size_per_head)
q = q.astype(np.float16)
k = np.random.rand(bs, seq_len, heads * size_per_head)
k = k.astype(np.float16)
v = np.random.rand(bs, seq_len, heads * size_per_head)
v = v.astype(np.float16)
input_mask = np.ones((bs, seq_len), dtype=np.float32)
out = fixed_sparse(Tensor(q), Tensor(k), Tensor(v), Tensor(input_mask))
out_np = out.asnumpy()
print("local output: ", out_np[0, 0])

15
tests/ut/python/nn/test_transformer.py
View File

@ -18,7 +18,7 @@ import pytest
from mindspore import Tensor
from mindspore.common import dtype
from mindspore.parallel.nn import MultiHeadAttention, FeedForward, TransformerEncoderLayer, TransformerEncoder, \
TransformerDecoder, TransformerDecoderLayer, Transformer, CrossEntropyLoss, AttentionMask
TransformerDecoder, TransformerDecoderLayer, Transformer, CrossEntropyLoss, AttentionMask, FixedSparseAttention
from mindspore.common.api import _executor
@ -240,3 +240,16 @@ def test_attention_mask():
model = AttentionMask(seq_length=19)
inputs = Tensor(np.ones((2, 19)), dtype.float32)
_executor.compile(model, inputs)
def test_sparse_attention():
model = FixedSparseAttention(batch_size=2,
seq_length=1024,
size_per_head=64,
num_heads=8,
block_size=64)
q = Tensor(np.ones((2, 1024, 512)), dtype.float16)
k = Tensor(np.ones((2, 1024, 512)), dtype.float16)
v = Tensor(np.ones((2, 1024, 512)), dtype.float16)
mask = Tensor(np.ones((2, 1024)), dtype.float32)
_executor.compile(model, q, k, v, mask)

19
tests/ut/python/parallel/test_parallel_transformer.py
View File

@ -22,7 +22,7 @@ from mindspore.ops import composite as C
from mindspore.ops import functional as F
import mindspore.ops as P
from mindspore.parallel.nn import TransformerEncoder, TransformerDecoder, Transformer, TransformerOpParallelConfig, \
VocabEmbedding, CrossEntropyLoss, OpParallelConfig, EmbeddingOpParallelConfig
VocabEmbedding, CrossEntropyLoss, OpParallelConfig, EmbeddingOpParallelConfig, FixedSparseAttention
from mindspore.nn import Dense as Linear
from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
from mindspore.nn.optim import AdamWeightDecay
@ -355,6 +355,23 @@ def test_vocabembedding_dp_false():
model.train(1, dataset, dataset_sink_mode=False)
def _test_sparse_attention_parallel():
sparse_attention_config = OpParallelConfig(model_parallel=8)
net = FixedSparseAttention(batch_size=2,
seq_length=1024,
size_per_head=64,
num_heads=8,
block_size=64,
parallel_config=sparse_attention_config)
q = Tensor(np.ones((2, 1024, 512)), mstype.float16)
k = Tensor(np.ones((2, 1024, 512)), mstype.float16)
v = Tensor(np.ones((2, 1024, 512)), mstype.float16)
mask = Tensor(np.ones((2, 1024)), mstype.float32)
dataset = Dataset(q, k, v, mask)
model = Model(net)
model.train(1, dataset, dataset_sink_mode=False)
def test_parallel_cross_entroy_loss_semi_auto_parallel():
set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)