qsdy
/
mindspore2022
forked from huawei/mindspore2022
0
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

update pangu reshape and softmax performance. 

Add layer norm judge

Fix layer norm name error

Fix input tyoe check

Fix ut test

Add 3d supports
This commit is contained in:
linqingke 2021-09-17 11:23:02 +08:00 committed by huangxinjing
parent c287709f37
commit 1c47b6a277
6 changed files with 304 additions and 115 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
mindspore/ccsrc/frontend/parallel/step_parallel.cc
View File

@ -788,7 +788,7 @@ void StepReplaceGraph(const ReplaceGraphPtr &replace_graph, const CNodePtr &node
// However, the segment_sum operation needs two inputs, To solve this
// We maintain a dict to count the times of the same operations,
// and bind the inputs according to the times of the op appears.
static std::unordered_map<AnfNodePtr, int> input_map = {};
std::unordered_map<AnfNodePtr, int> input_map = {};
static int appear_count = 0;
for (auto &replace_input : replace_graph->first) {
auto pre_node = node->input(LongToSize(replace_input.second));

93
mindspore/parallel/nn/layers.py
View File

@ -26,7 +26,7 @@ import mindspore.common.dtype as mstype
from mindspore.ops import operations as P
from mindspore._extends import cell_attr_register
from mindspore.nn.cell import Cell
import mindspore.nn as nn
from mindspore import nn
from mindspore.nn.layer.activation import get_activation
from mindspore.ops import functional as F
from mindspore._checkparam import Validator
@ -87,11 +87,65 @@ def _valid_value_checks(types, class_name):
return validator_check_func
@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
class _LayerInputCheck:
"""
A input check class for the inputs of the transformer model.
"""
@staticmethod
def check_shape_length(input_shape, param_name, func_name, target_len):
"""
Check the input shape's length is equal to the expected shape
:param input_shape(list): a list of the tensor shapes.
:param param_name(str): the name of the checked parameter.
:param func_name(str): the name of the function.
:param target_len: the expected length of the shape.
:return:
"""
if not isinstance(target_len, list):
target_len = [target_len]
matched = False
for item in target_len:
if len(input_shape) == item:
matched = True
if not matched:
raise ValueError(f"{func_name} {param_name} shape length should be one of {target_len} dimension, "
f"but got shape {input_shape}")
return True
@staticmethod
def check_shape_equal(input_shape, param_name, func_name, target_shape):
"""
Check the input shape's is equal to the expected shape
:param input_shape(list): a list of the tensor shapes.
:param param_name(str): the name of the checked parameter.
:param func_name(str): the name of the function.
:param target_shape: the expected shape.
:return:
"""
if not isinstance(target_shape[0], list):
target_shape = [target_shape]
if isinstance(input_shape, tuple):
input_shape = list(input_shape)
_LayerInputCheck.check_shape_length(input_shape, param_name, func_name,
[len(item) for item in target_shape])
matched = False
for item in target_shape:
if item == input_shape:
matched = True
break
if not matched:
raise ValueError(f"{func_name} {param_name} shape should be one of {target_shape},"
f"but got {input_shape}")
return True
@staticmethod
def check_shape_value_on_axis(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]}")
return True
@constexpr
@ -109,28 +163,27 @@ def _check_past_none_input_none(use_past, param_name, func_name, default_value,
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(input_shape, param_name, func_name, target_len):
# check the input length
_LayerInputCheck.check_shape_length(input_shape, param_name, func_name, target_len)
@constexpr
def _check_shape_equal(input_shape, param_name, func_name, target_shape):
# check the input length
_LayerInputCheck.check_shape_equal(input_shape, param_name, func_name, target_shape)
@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]}")
_LayerInputCheck.check_shape_value_on_axis(input_shape, dim, param_name, cls_name, target_value)
class _LayerNorm(Cell):

6
mindspore/parallel/nn/moe.py
View File

@ -125,7 +125,7 @@ class MoE(Cell):
def construct(self, input_tensor):
bs = self.shape(input_tensor)[0]
input_shape = F.shape(input_tensor)
input_tensor = self.reshape(input_tensor, (-1, self.hidden_size))
bs_and_dmodel = self.shape(input_tensor)
tokens_per_device = bs_and_dmodel[0] / self.expert_parallel
@ -148,7 +148,7 @@ class MoE(Cell):
expert_capacity))
# expert_input's shape: (self.expert_dim, self.expert_parallel, expert_capacity, self.hidden_size)
expert_input = self.transpose2(expert_input, (2, 0, 3, 1))
expert_input = self.reshape(expert_input, (self.expert_dim, self.expert_parallel * expert_capacity,
expert_input = self.reshape(expert_input, (self.expert_dim * self.expert_parallel * expert_capacity,
self.hidden_size))
# expert_output's shape: (self.expert_dim, self.expert_parallel*expert_capacity, self.hidden_size)
@ -170,7 +170,7 @@ class MoE(Cell):
# combined_output's shape: (self.expert_parallel, tokens_per_device, self.hidden_size)
combined_output = self.transpose5(combined_output, (0, 2, 1))
combined_output = self.reshape(combined_output, (bs_and_dmodel[0], bs_and_dmodel[1]))
combined_output = self.reshape(combined_output, (bs, -1, self.hidden_size))
combined_output = self.reshape(combined_output, input_shape)
aux_loss = self.mul(self.aux_loss_factor, aux_loss)
return combined_output, aux_loss

194
mindspore/parallel/nn/transformer.py
View File

@ -281,10 +281,12 @@ class FeedForward(Cell):
default args.
Inputs:
- **x** (Tensor) - should be `[batch, seq_length, hidden_size]`. Float tensor.
- **x** (Tensor) - should be `[batch, seq_length, hidden_size] or [batch * seq_length, hidden_size]`.
Float tensor.
Outputs:
Tensor, the output of this layer after mapping. The shape is `[batch, seq_length, hidden_size]`.
Tensor, the output of this layer after mapping. The shape is `[batch, seq_length, hidden_size]
or [batch * seq_length, hidden_size]`.
Raises:
ValueError: `hidden_act` is not a string.
@ -344,11 +346,11 @@ class FeedForward(Cell):
if expert_num > 1:
self.mapping.shard(strategy_matmul=((ep, 1, 1), (ep, 1, mp)),
strategy_bias=((ep, 1, mp), (mp,)),
strategy_activation=((ep, 1, mp),))
strategy_activation=((ep, mp),))
else:
self.mapping.shard(strategy_matmul=((dp, 1), (1, mp)),
strategy_bias=((dp, mp), (mp,)),
strategy_activation=((dp, 1, mp),))
strategy_activation=((dp, mp),))
# Project back to hidden_size
self.projection = _Linear(in_channels=output_size,
out_channels=input_size,
@ -363,17 +365,17 @@ class FeedForward(Cell):
strategy_bias=((dp, 1), (1,)))
self.projection.bias.parallel_optimizer = False
self.dropout = nn.Dropout(1 - dropout_rate)
self.dropout.dropout.shard(((dp, 1, 1),))
self.dropout.dropout.shard(((dp, 1),))
self.cast = P.Cast()
def construct(self, x):
_check_input_shape(F.shape(x), "x", self.cls_name, 3)
_check_input_shape(F.shape(x), "x", self.cls_name, [2, 3])
_check_input_dtype(F.dtype(x), "x", [mstype.float32, mstype.float16], self.cls_name)
x = self.cast(x, mstype.float16)
# returned shape is [bs, seq_length, ffn_hidden_size]
# returned shape is [bs, seq_length, ffn_hidden_size] or [bs * seq_length, ffn_hidden_size]
hidden = self.mapping(x)
output = self.projection(hidden)
# returned shape is [bs, seq_length, hidden_size]
# returned shape is [bs, seq_length, ffn_hidden_size] or [bs * seq_length, ffn_hidden_size]
output = self.dropout(output)
return output
@ -547,18 +549,21 @@ class MultiHeadAttention(Cell):
attention_dropout_rate(float): The dropout rate of the attention scores. Default:0.1
compute_dtype(dtype.Number): The computation type of dense. Default dtype.float16.
Should be dtype.float32 or dtype.float16.
param_init_type(dtype.Number). The parameter initialization type of the module. Default dtype.float32.
param_init_type(dtype.Number): The parameter initialization type of the module. Default dtype.float32.
Should be dtype.float32 or dtype.float16.
softmax_compute_type(dtype.Number). The type of softmax computation module. Default dtype.float32.
softmax_compute_type(dtype.Number): The type of softmax computation module. Default dtype.float32.
Should be dtype.float32 or dtype.float16.
use_past(bool): Use the past state to compute, used for incremental prediction. Default False.
parallel_config(OpParallelConfig): The parallel configure. Default `default_dpmp_config`,
an instance of `OpParallelConfig` with default args.
Inputs:
- **query_tensor** (Tensor) - the query vector with shape (batch_size, src_seq_length, hidden_size).
- **key_tensor** (Tensor) - the key vector with shape (batch_size, tgt_seq_length, hidden_size).
- **value_tensor** (Tensor) - the value vector with shape (batch_size, tgt_seq_length, hidden_size).
- **query_tensor** (Tensor) - the query vector with shape (batch_size, src_seq_length, hidden_size) or
(batch_size * src_seq_length, hidden_size).
- **key_tensor** (Tensor) - the key vector with shape (batch_size, tgt_seq_length, hidden_size) or
(batch_size * src_seq_length, hidden_size).
- **value_tensor** (Tensor) - the value vector with shape (batch_size, tgt_seq_length, hidden_size) or
(batch_size * src_seq_length, hidden_size).
- **attention_mask** (Tensor) - the attention mask matrix with shape (batch_size, src_seq_length,
tgt_seq_length).
- **key_past** (Tensor) - Float16 tensor with shape (batch_size, num_heads, size_per_head, tgt_seq_length).
@ -574,7 +579,7 @@ class MultiHeadAttention(Cell):
Tuple, a tuple contains(`output`, `layer_present`)
- **output** (Tensor) - Tensor, the float tensor of the output of the layer with
shape (batch_size, src_seq_length, hidden_size)
shape (batch_size, src_seq_length, hidden_size) or (batch_size * src_seq_length, hidden_size)
- **layer_present** (Tuple) - A tuple of the Tensor of the projected key and value vector with
((batch_size, num_heads, size_per_head, tgt_seq_length),
@ -684,7 +689,7 @@ class MultiHeadAttention(Cell):
self.scale_factor = Tensor(math.sqrt(self.size_per_head))
self.use_past = use_past
self.dropout = nn.Dropout(1 - hidden_dropout_rate)
self.dropout.dropout.shard(((parallel_config.data_parallel, 1, 1),))
self.dropout.dropout.shard(((parallel_config.data_parallel, 1),))
self.prob_dropout = nn.Dropout(1 - attention_dropout_rate)
self.prob_dropout.dropout.shard(
((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1),))
@ -738,14 +743,11 @@ class MultiHeadAttention(Cell):
value_past=None, batch_valid_length=None):
self._check_inputs(query_tensor, key_tensor, value_tensor, attention_mask, key_past,
value_past, batch_valid_length)
query_tensor_original_shape = F.shape(query_tensor)
query_tensor = F.reshape(query_tensor, (-1, query_tensor_original_shape[-1]))
key_tensor_original_shape = F.shape(key_tensor)
key_tensor = F.reshape(key_tensor, (-1, key_tensor_original_shape[-1]))
value_tensor_original_shape = F.shape(value_tensor)
value_tensor = F.reshape(value_tensor, (-1, value_tensor_original_shape[-1]))
batch_size = F.shape(attention_mask)[0]
query_tensor, key_tensor, value_tensor, batch_size, ori_shape = self._convert_to_2d_tensor(query_tensor,
key_tensor,
value_tensor,
attention_mask)
# multi head attention: query, key, value are derived from the same inputs
query = self.dense1(query_tensor)
@ -755,18 +757,18 @@ class MultiHeadAttention(Cell):
query = self.transpose(
F.reshape(
query,
(-1, query_tensor_original_shape[1], self.n_head, self.size_per_head)),
(batch_size, -1, self.n_head, self.size_per_head)),
(0, 2, 1, 3))
# the returned shape is [bs, num_heads, size_per_head, seq_length]
# the returned shape is [bs, size_per_head, seq_length, num_heads]
key = self.transpose(
F.reshape(
key, (-1, key_tensor_original_shape[1], self.n_head, self.size_per_head)),
key, (batch_size, -1, self.n_head, self.size_per_head)),
(0, 2, 3, 1))
# the returned shape is [bs, num_heads, seq_length, size_per_head]
value = self.transpose(
F.reshape(
value,
(-1, value_tensor_original_shape[1], self.n_head, self.size_per_head)),
(batch_size, -1, self.n_head, self.size_per_head)),
(0, 2, 1, 3))
# support input shape is [bs, seq, seq] or [bs, heads, seq, seq]
if len(F.shape(attention_mask)) == 3:
@ -811,11 +813,12 @@ class MultiHeadAttention(Cell):
layer_present = (key_present, value_present)
# multi head attention considering attention mask
# the return shape is [bs, seq_length, hidden_size]
# the return shape is [bs * seq_length, hidden_size]
attention = self._attn(query, key, value, attention_mask)
# Output
output = self.projection(attention)
output = self.dropout(output)
output = F.reshape(output, ori_shape)
return output, layer_present
def _check_inputs(self, query_tensor, key_tensor, value_tensor, attention_mask, key_past=None,
@ -823,11 +826,14 @@ class MultiHeadAttention(Cell):
r"""Check inputs"""
if not self.use_past or (self.use_past and self.is_first_iteration):
_check_shape_equal(F.shape(query_tensor), "query_tensor", self.cls_name,
[self.batch_size, self.src_seq_length, self.hidden_size])
[[self.batch_size, self.src_seq_length, self.hidden_size],
[self.batch_size * self.src_seq_length, self.hidden_size]])
_check_shape_equal(F.shape(key_tensor), "key_tensor", self.cls_name,
[self.batch_size, self.tgt_seq_length, self.hidden_size])
[[self.batch_size, self.tgt_seq_length, self.hidden_size],
[self.batch_size * self.tgt_seq_length, self.hidden_size]])
_check_shape_equal(F.shape(value_tensor), "value_tensor", self.cls_name,
[self.batch_size, self.tgt_seq_length, self.hidden_size])
[[self.batch_size, self.tgt_seq_length, self.hidden_size],
[self.batch_size * self.tgt_seq_length, self.hidden_size]])
_check_shape_equal(F.shape(attention_mask), "attention_mask", self.cls_name,
[self.batch_size, self.src_seq_length, self.tgt_seq_length])
else:
@ -868,20 +874,30 @@ class MultiHeadAttention(Cell):
_check_input_dtype(F.dtype(batch_valid_length), "batch_valid_length", [mstype.int32], self.cls_name)
return True
def _convert_to_2d_tensor(self, query_tensor, key_tensor, value_tensor, attention_mask):
"""convert a nd tensor to a 2d tensor"""
query_shape = F.shape(query_tensor)
query_tensor = F.reshape(query_tensor, (-1, query_shape[-1]))
key_shape = F.shape(key_tensor)
key_tensor = F.reshape(key_tensor, (-1, key_shape[-1]))
value_shape = F.shape(value_tensor)
value_tensor = F.reshape(value_tensor, (-1, value_shape[-1]))
return query_tensor, key_tensor, value_tensor, F.shape(attention_mask)[0], query_shape
def _merge_heads(self, x):
"""
convert a 4d input to a 3d output
convert a 4d input to a 2d output
Inputs:
x: input tensor
Output:
x_merge: the 3d output
x_merge: the 2d output
"""
x = self.merger_head_transpose(
x, (0, 2, 1, 3)) # bs, seq_length, head, size_per_head
x_shape = P.Shape()(x)
new_shape = x_shape[:-2] + (x_shape[-2] * x_shape[-1],)
new_shape = (-1, x_shape[-2] * x_shape[-1])
x_merge = self.reshape(x, new_shape)
return x_merge
@ -976,7 +992,8 @@ class TransformerEncoderLayer(Cell):
an instance of `OpParallelConfig` with default args.
Inputs:
- **x** (Tensor) - Float Tensor, shape should be [batch_size, seq_length, hidden_size].
- **x** (Tensor) - Float Tensor, shape should be [batch_size, seq_length, hidden_size] or
[batch_size * seq_length, hidden_size].
- **input_mask** (Tensor) - Float Tensor, attention mask with shape [batch_size, seq_length, seq_length].
- **init_reset** (Tensor) - A bool tensor with shape [1], used to clear the past key parameter and
past value parameter used in the incremental prediction. Only valid when use_past is True. Default True.
@ -987,7 +1004,7 @@ class TransformerEncoderLayer(Cell):
Tuple, a tuple contains(`output`, `layer_present`).
- **output** (Tensor) - The float tensor of the output of the layer with
shape (batch_size, seq_length, hidden_size).
shape (batch_size, seq_length, hidden_size) or (batch_size * seq_length, hidden_size).
- **layer_present** (Tuple) - A tuple of the Tensor of the projected key and value vector with
((batch_size, num_heads, size_per_head, seq_length),
@ -1063,9 +1080,9 @@ class TransformerEncoderLayer(Cell):
self.hidden_size = hidden_size
self.batch_size = batch_size
self.layernorm1 = _LayerNorm((hidden_size,)).to_float(layernorm_compute_type)
self.layernorm1.shard(((parallel_config.data_parallel, 1, 1),))
self.layernorm1.shard(((parallel_config.data_parallel, 1),))
self.layernorm2 = _LayerNorm((hidden_size,)).to_float(layernorm_compute_type)
self.layernorm2.shard(((parallel_config.data_parallel, 1, 1),))
self.layernorm2.shard(((parallel_config.data_parallel, 1),))
self.attention = MultiHeadAttention(batch_size=batch_size,
src_seq_length=seq_length,
@ -1096,7 +1113,8 @@ class TransformerEncoderLayer(Cell):
hidden_act=hidden_act,
parallel_config=parallel_config)
self.post_layernorm_residual = post_layernorm_residual
self.add = P.Add().shard(((parallel_config.data_parallel, 1, 1), (parallel_config.data_parallel, 1, 1)))
self.add = P.Add().shard(((parallel_config.data_parallel, 1), (parallel_config.data_parallel, 1)))
self.add_3d = P.Add().shard(((parallel_config.data_parallel, 1, 1), (parallel_config.data_parallel, 1, 1)))
self.dtype = mstype.float16
self.key_past = None
self.value_past = None
@ -1118,6 +1136,8 @@ class TransformerEncoderLayer(Cell):
def construct(self, x, input_mask, init_reset=True, batch_valid_length=None):
self._check_input(x, input_mask, init_reset, batch_valid_length)
x_shape = F.shape(x)
x = F.reshape(x, (-1, x_shape[-1]))
input_x = self.layernorm1(x)
input_x = F.cast(input_x, self.dtype)
@ -1166,10 +1186,23 @@ class TransformerEncoderLayer(Cell):
mlp_logit = F.depend(mlp_logit, value_update)
mlp_logit = F.depend(mlp_logit, key_update)
if self.post_layernorm_residual:
output = self.add(output_x, mlp_logit)
# if shape is 3d, we reshape the inputs of the add
if len(x_shape) == 3:
output_x = P.Reshape()(output_x, x_shape)
mlp_logit = P.Reshape()(mlp_logit, x_shape)
x = P.Reshape()(x, x_shape)
if self.post_layernorm_residual:
output = self.add_3d(output_x, mlp_logit)
else:
output = self.add_3d(x, mlp_logit)
else:
output = self.add(x, mlp_logit)
if self.post_layernorm_residual:
output = self.add(output_x, mlp_logit)
else:
output = self.add(x, mlp_logit)
output = F.reshape(output, x_shape)
if self.use_moe is True:
return output, layer_present, aux_loss
return output, layer_present
@ -1177,7 +1210,9 @@ class TransformerEncoderLayer(Cell):
def _check_input(self, x, input_mask, init_reset, batch_valid_length):
r"""Check inputs"""
if not self.use_past or (self.use_past and self.is_first_iteration):
_check_shape_equal(F.shape(x), "x", self.cls_name, [self.batch_size, self.seq_length, self.hidden_size])
_check_shape_equal(F.shape(x), "x", self.cls_name,
[[self.batch_size, self.seq_length, self.hidden_size],
[self.batch_size * self.seq_length, self.hidden_size]])
_check_shape_equal(F.shape(input_mask), "input_mask", self.cls_name,
[self.batch_size, self.seq_length, self.seq_length])
else:
@ -1235,10 +1270,12 @@ class TransformerDecoderLayer(Cell):
an instance of `OpParallelConfig` with default args.
Inputs:
- **hidden_stats** (Tensor) - the input tensor with shape [batch_size, tgt_seq_length, hidden_size].
- **hidden_stats** (Tensor) - the input tensor with shape [batch_size, tgt_seq_length, hidden_size] or
[batch_size * tgt_seq_length, hidden_size].
- **decoder_mask** (Tensor) - the attention mask for decoder with shape [batch_size, src_seq_length,
seq_length].
- **encoder_output** (Tensor) - the output of the encoder with shape [batch_size, seq_length, hidden_size].
- **encoder_output** (Tensor) - the output of the encoder with shape [batch_size, seq_length, hidden_size] or
[batch_size * seq_length, hidden_size].
- **memory_mask** (Tensor) - the memory mask of the cross attention with shape [batch, tgt_seq_length,
src_seq_length], where tgt_seq_length is the length of the decoder.
- **init_reset** (Tensor) - A bool tensor with shape [1], used to clear the past key parameter and
@ -1249,7 +1286,8 @@ class TransformerDecoderLayer(Cell):
Outputs:
Tuple, a tuple contains(`output`, `layer_present`)
- **output** (Tensor) - the output logit of this layer. The shape is [batch, seq_length, hidden_size]
- **output** (Tensor) - the output logit of this layer. The shape is [batch, seq_length, hidden_size] or
[batch * seq_length, hidden_size].
- **layer_present** (Tensor) - A tuple, where each tuple is the tensor of the projected key and value
vector in self attention with shape ((batch_size, num_heads, size_per_head, tgt_seq_length),
(batch_size, num_heads, tgt_seq_length, size_per_head), and of the projected key and value vector
@ -1340,9 +1378,9 @@ class TransformerDecoderLayer(Cell):
self.hidden_size = hidden_size
self.layernorm1 = _LayerNorm((hidden_size,)).to_float(layernorm_compute_type)
self.layernorm1.shard(((parallel_config.data_parallel, 1, 1),))
self.layernorm1.shard(((parallel_config.data_parallel, 1),))
self.layernorm2 = _LayerNorm((hidden_size,)).to_float(layernorm_compute_type)
self.layernorm2.shard(((parallel_config.data_parallel, 1, 1),))
self.layernorm2.shard(((parallel_config.data_parallel, 1),))
self.attention = MultiHeadAttention(hidden_size=hidden_size,
num_heads=num_heads,
@ -1369,7 +1407,7 @@ class TransformerDecoderLayer(Cell):
parallel_config=parallel_config)
self.cross_attention_layernorm = _LayerNorm((hidden_size,)).to_float(
layernorm_compute_type)
self.cross_attention_layernorm.shard(((parallel_config.data_parallel, 1, 1),))
self.cross_attention_layernorm.shard(((parallel_config.data_parallel, 1),))
self.use_moe = (moe_config.expert_num > 1)
if self.use_moe is True:
self.output = MoE(hidden_size=hidden_size,
@ -1388,7 +1426,8 @@ class TransformerDecoderLayer(Cell):
param_init_type=param_init_type,
parallel_config=parallel_config)
self.post_layernorm_residual = post_layernorm_residual
self.add = P.Add().shard(((parallel_config.data_parallel, 1, 1), (parallel_config.data_parallel, 1, 1)))
self.add = P.Add().shard(((parallel_config.data_parallel, 1), (parallel_config.data_parallel, 1)))
self.add_3d = P.Add().shard(((parallel_config.data_parallel, 1, 1), (parallel_config.data_parallel, 1, 1)))
self.dtype = mstype.float16
self.key_past = None
self.value_past = None
@ -1413,7 +1452,9 @@ class TransformerDecoderLayer(Cell):
memory_mask=None,
init_reset=True, batch_valid_length=None):
self._check_input(hidden_stats, decoder_mask, encoder_output, memory_mask, init_reset, batch_valid_length)
# the returned shape is [bs, seq_length, embedding_size]
# the returned shape is [bs, seq_length, embedding_size] or [bs * seq_length, embedding_size]
hidden_shape = F.shape(hidden_stats)
hidden_stats = F.reshape(hidden_stats, (-1, hidden_shape[-1]))
input_x = self.layernorm1(hidden_stats)
input_x = F.cast(input_x, self.dtype)
@ -1475,10 +1516,23 @@ class TransformerDecoderLayer(Cell):
mlp_logit = F.depend(mlp_logit, value_update)
mlp_logit = F.depend(mlp_logit, key_update)
if self.post_layernorm_residual:
output = self.add(output_x, mlp_logit)
# if shape is 3d, we reshape the inputs of the add
if len(hidden_shape) == 3:
output_x = P.Reshape()(output_x, hidden_shape)
mlp_logit = P.Reshape()(mlp_logit, hidden_shape)
x = P.Reshape()(x, hidden_shape)
if self.post_layernorm_residual:
output = self.add_3d(output_x, mlp_logit)
else:
output = self.add_3d(x, mlp_logit)
else:
output = self.add(x, mlp_logit)
if self.post_layernorm_residual:
output = self.add(output_x, mlp_logit)
else:
output = self.add(x, mlp_logit)
output = F.reshape(output, hidden_shape)
if self.use_moe is True:
return output, layer_present, aux_loss
return output, layer_present
@ -1487,7 +1541,8 @@ class TransformerDecoderLayer(Cell):
r"""Check inputs"""
if not self.use_past or (self.use_past and self.is_first_iteration):
_check_shape_equal(F.shape(hidden_states), "hidden_states", self.cls_name,
[self.batch_size, self.tgt_seq_length, self.hidden_size])
[[self.batch_size, self.tgt_seq_length, self.hidden_size],
[self.batch_size * self.tgt_seq_length, self.hidden_size]])
_check_shape_equal(F.shape(attention_mask), "attention_mask", self.cls_name,
[self.batch_size, self.tgt_seq_length, self.tgt_seq_length])
@ -1496,12 +1551,12 @@ class TransformerDecoderLayer(Cell):
[self.batch_size, 1, self.hidden_size])
_check_shape_equal(F.shape(attention_mask), "attention_mask", self.cls_name,
[self.batch_size, 1, self.tgt_seq_length])
_check_input_dtype(F.dtype(hidden_states), "hidden_states", [mstype.float32, mstype.float16], self.cls_name)
_check_input_dtype(F.dtype(attention_mask), "attention_mask", [mstype.float32, mstype.float16], self.cls_name)
if encoder_output is not None:
_check_shape_equal(F.shape(encoder_output), "encoder_output", self.cls_name,
[self.batch_size, self.src_seq_length, self.hidden_size])
[[self.batch_size, self.src_seq_length, self.hidden_size],
[self.batch_size * self.src_seq_length, self.hidden_size]])
_check_input_dtype(F.dtype(encoder_output), "encoder_output",
[mstype.float32, mstype.float16], self.cls_name)
if memory_mask is not None:
@ -1607,7 +1662,8 @@ class TransformerEncoder(Cell):
an instance of `TransformerOpParallelConfig` with default args.
Inputs:
- **hidden_states** (Tensor) - Tensor, shape should be [batch_size, seq_length, hidden_size]
- **hidden_states** (Tensor) - Tensor, shape should be [batch_size, seq_length, hidden_size] or
[batch_size * seq_length, hidden_size]
- **attention_mask** (Tensor) - Tensor, attention mask with shape [batch_size, seq_length, seq_length]
- **init_reset** (Tensor) - A bool tensor with shape [1], used to clear the past key parameter and
past value parameter used in the incremental prediction. Only valid when use_past is True. Default True
@ -1618,7 +1674,7 @@ class TransformerEncoder(Cell):
Tuple, a tuple contains(`output`, `layer_present`)
- **output** (Tensor) - The float tensor of the output of the layer with
shape (batch_size, seq_length, hidden_size)
shape (batch_size, seq_length, hidden_size) or (batch_size * seq_length, hidden_size)
- **layer_present** (Tuple) - A tuple with size of num_layers, where each tuple contains the Tensor the
projected key and value vector with shape ((batch_size, num_heads, size_per_head, seq_length),
and (batch_size, num_heads, seq_length, size_per_head)).
@ -1777,9 +1833,11 @@ class TransformerDecoder(Cell):
an instance of `TransformerOpParallelConfig` with default args.
Inputs:
- **hidden_stats** (Tensor) - the input tensor with shape [batch_size, seq_length, hidden_size]
- **hidden_stats** (Tensor) - the input tensor with shape [batch_size, seq_length, hidden_size] or
[batch_size * seq_length, hidden_size]
- **attention_mask** (Tensor) - the attention mask for decoder with shape [batch_size, seq_length, seq_length]
- **encoder_output** (Tensor) - the output of the encoder with shape [batch_size, seq_length, hidden_size]
- **encoder_output** (Tensor) - the output of the encoder with shape [batch_size, seq_length, hidden_size] or
[batch_size * seq_length, hidden_size]
- **memory_mask** (Tensor) - the memory mask of the cross attention with shape [batch, tgt_seq_length,
src_seq_length] where tgt_seq_length is the length of the decoder. the output of the encoder with shape
[batch_size, seq_length, hidden_size],
@ -1791,7 +1849,8 @@ class TransformerDecoder(Cell):
Outputs:
Tuple, a tuple contains(`output`, `layer_present`)
- **output** (Tensor) - The output logit of this layer. The shape is [batch, tgt_seq_length, hidden_size]
- **output** (Tensor) - The output logit of this layer. The shape is [batch, tgt_seq_length, hidden_size] or
[batch * tgt_seq_length, hidden_size]
- **layer_present** (Tuple) - A tuple with size of num_layers, where each tuple is the tensor of the projected
key and value vector in self attention with shape ((batch_size, num_heads, size_per_head, tgt_seq_length),
(batch_size, num_heads, tgt_seq_length, size_per_head), and of the projected key and value vector
@ -1972,9 +2031,11 @@ class Transformer(Cell):
an instance of `TransformerOpParallelConfig` with default args.
Inputs:
- **encoder_inputs** (Tensor) - the input tensor with shape [batch_size, seq_length, hidden_size].
- **encoder_inputs** (Tensor) - the input tensor with shape [batch_size, seq_length, hidden_size] or
[batch_size * seq_length, hidden_size].
- **encoder_masks** (Tensor) - the attention mask for decoder with shape [batch_size, seq_length, seq_length].
- **decoder_inputs** (Tensor) - the output of the encoder with shape [batch_size, seq_length, hidden_size],
- **decoder_inputs** (Tensor) - the output of the encoder with shape [batch_size, seq_length, hidden_size] or
[batch_size * seq_length, hidden_size],
this should be none if the decoder layer is 0.
- **decoder_masks** (Tensor) - the attention mask for decoder with shape [batch_size, seq_length, seq_length]
- **memory_mask** (Tensor) - the memory mask of the cross attention with shape [batch, tgt_seq_length,
@ -1990,8 +2051,9 @@ class Transformer(Cell):
Tuple, a tuple contains(`output`, `encoder_layer_present`, `encoder_layer_present`)
- **output** (Tensor) - If there is only encoder, the output logit of the encoder layer. The shape is
[batch, src_seq_length, hidden_size], if there are encoder and decoders, the output is from the
decoder layer. The shape is [batch, tgt_seq_length, hidden_size].
[batch, src_seq_length, hidden_size] or [batch * src_seq_length, hidden_size], if there are encoder and
decoders, the output is from the decoder layer. The shape is [batch, tgt_seq_length, hidden_size] or
[batch * tgt_seq_length, hidden_size].
- **encoder_layer_present** (Tuple) - A tuple with size of num_layers, where each tuple is the tensor the
projected key and value vector in self attention with shape ((batch_size, num_heads, size_per_head,
src_seq_length), (batch_size, num_heads, src_seq_length, size_per_head)).

36
tests/ut/python/parallel/test_parallel_moe.py
View File

@ -20,7 +20,7 @@ from mindspore.context import set_auto_parallel_context, ParallelMode
from mindspore.ops import composite as C
from mindspore.parallel.nn import Transformer, TransformerOpParallelConfig, MoEConfig
from mindspore.nn.optim import AdamWeightDecay
from mindspore.nn.wrap.cell_wrapper import TrainOneStepCell
from mindspore.nn.wrap.cell_wrapper import TrainOneStepCell, _VirtualDatasetCell
from mindspore.train import Model
from tests.dataset_mock import MindData
from tests.ut.python.ops.test_math_ops import VirtualLoss
@ -77,7 +77,7 @@ def test_transformer_model():
ffn_hidden_size=64,
moe_config=moe_config,
parallel_config=config)
net = _VirtualDatasetCell(net)
encoder_input_value = Tensor(np.ones((2, 20, 64)), mstype.float32)
encoder_input_mask = Tensor(np.ones((2, 20, 20)), mstype.float16)
decoder_input_value = Tensor(np.ones((2, 10, 64)), mstype.float32)
@ -92,3 +92,35 @@ def test_transformer_model():
model = Model(net_with_grad)
model.train(1, dataset, dataset_sink_mode=False)
def test_transformer_model_2d():
set_auto_parallel_context(device_num=16, global_rank=0,
full_batch=True, enable_alltoall=True,
parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
net = Transformer(encoder_layers=1,
decoder_layers=1,
batch_size=2,
src_seq_length=20,
tgt_seq_length=10,
hidden_size=64,
num_heads=8,
ffn_hidden_size=64,
moe_config=moe_config,
parallel_config=config)
net = _VirtualDatasetCell(net)
encoder_input_value = Tensor(np.ones((40, 64)), mstype.float32)
encoder_input_mask = Tensor(np.ones((2, 20, 20)), mstype.float16)
decoder_input_value = Tensor(np.ones((20, 64)), mstype.float32)
decoder_input_mask = Tensor(np.ones((2, 10, 10)), mstype.float16)
memory_mask = Tensor(np.ones((2, 10, 20)), mstype.float16)
net = NetWithLossFiveInputs(net)
params = net.trainable_params()
optimizer = AdamWeightDecay(params)
dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask,
memory_mask)
net_with_grad = TrainOneStepCell(net, optimizer=optimizer)
model = Model(net_with_grad)
model.train(1, dataset, dataset_sink_mode=False)

88
tests/ut/python/parallel/test_parallel_transformer.py
View File

@ -56,6 +56,28 @@ class Dataset(MindData):
self.index = 0
class TransformerNet(nn.Cell):
def __init__(self, en_layer, de_layer, parallel_config):
super(TransformerNet, self).__init__()
self.embedding = VocabEmbedding(vocab_size=240, embedding_size=20,
parallel_config=config.embedding_dp_mp_config)
self.network = Transformer(encoder_layers=en_layer,
decoder_layers=de_layer,
batch_size=2,
src_seq_length=20,
tgt_seq_length=10,
hidden_size=64,
num_heads=8,
ffn_hidden_size=64,
parallel_config=parallel_config)
self.head = Linear(in_channels=64, out_channels=200)
self.loss = CrossEntropyLoss(parallel_config=config.dp_mp_config)
def construct(self, x1, x2, x3, x4, x5, y, mask):
predict, _, _ = self.network(x1, x2, x3, x4, x5)
predict = P.Reshape()(predict, (-1, F.shape(predict)[-1]))
return self.loss(predict, y, mask)
config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8, vocab_emb_dp=False)
pipeline_config = TransformerOpParallelConfig(data_parallel=1, model_parallel=8, pipeline_stage=4,
micro_batch_num=4, vocab_emb_dp=False)
@ -84,28 +106,6 @@ def run_total_transformer_model_head(e_layer,
full_batch=True,
global_rank=0, parallel_mode=mode)
class Net(nn.Cell):
def __init__(self, en_layer, de_layer, parallel_config):
super(Net, self).__init__()
self.embedding = VocabEmbedding(vocab_size=240, embedding_size=20,
parallel_config=config.embedding_dp_mp_config)
self.network = Transformer(encoder_layers=en_layer,
decoder_layers=de_layer,
batch_size=2,
src_seq_length=20,
tgt_seq_length=10,
hidden_size=64,
num_heads=8,
ffn_hidden_size=64,
parallel_config=parallel_config)
self.head = Linear(in_channels=64, out_channels=200)
self.loss = CrossEntropyLoss(parallel_config=config.dp_mp_config)
def construct(self, x1, x2, x3, x4, x5, y, mask):
predict, _, _ = self.network(x1, x2, x3, x4, x5)
predict = P.Reshape()(predict, (-1, F.shape(predict)[-1]))
return self.loss(predict, y, mask)
encoder_input_value = Tensor(np.ones((2, 20, 64)), mstype.float32)
encoder_input_mask = Tensor(np.ones((2, 20, 20)), mstype.float16)
decoder_input_value = Tensor(np.ones((2, 10, 64)), mstype.float32)
@ -116,7 +116,8 @@ def run_total_transformer_model_head(e_layer,
seq = 10
label = Tensor(np.ones((2 * seq,)), mstype.int32)
input_mask = Tensor(np.ones((2 * seq,)), mstype.float32)
net = Net(en_layer=e_layer, de_layer=d_layer, parallel_config=arg_parallel_config)
net = TransformerNet(en_layer=e_layer, de_layer=d_layer, parallel_config=arg_parallel_config)
net = _VirtualDatasetCell(net)
params = net.trainable_params()
optimizer = AdamWeightDecay(params)
dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask,
@ -147,6 +148,38 @@ def test_transformer_model():
decoder_input_mask = Tensor(np.ones((2, 10, 10)), mstype.float16)
memory_mask = Tensor(np.ones((2, 10, 20)), mstype.float16)
net = NetWithLossFiveInputs(net)
net = _VirtualDatasetCell(net)
params = net.trainable_params()
optimizer = AdamWeightDecay(params)
dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask,
memory_mask)
net_with_grad = TrainOneStepCell(net, optimizer=optimizer)
model = Model(net_with_grad)
model.train(1, dataset, dataset_sink_mode=False)
def test_transformer_model_2d_inputs():
set_auto_parallel_context(device_num=8, global_rank=0,
full_batch=True,
parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL)
net = Transformer(encoder_layers=1,
decoder_layers=2,
batch_size=2,
src_seq_length=20,
tgt_seq_length=10,
hidden_size=64,
num_heads=8,
ffn_hidden_size=64,
parallel_config=config)
encoder_input_value = Tensor(np.ones((40, 64)), mstype.float32)
encoder_input_mask = Tensor(np.ones((2, 20, 20)), mstype.float16)
decoder_input_value = Tensor(np.ones((20, 64)), mstype.float32)
decoder_input_mask = Tensor(np.ones((2, 10, 10)), mstype.float16)
memory_mask = Tensor(np.ones((2, 10, 20)), mstype.float16)
net = NetWithLossFiveInputs(net)
net = _VirtualDatasetCell(net)
params = net.trainable_params()
optimizer = AdamWeightDecay(params)
dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask,
@ -177,6 +210,7 @@ def test_transformer_model_int64_inputs():
decoder_input_mask = Tensor(np.ones((2, 10, 10)), mstype.float16)
memory_mask = Tensor(np.ones((2, 10, 20)), mstype.float16)
net = NetWithLossFiveInputs(net)
net = _VirtualDatasetCell(net)
params = net.trainable_params()
optimizer = AdamWeightDecay(params)
dataset = Dataset(encoder_input_value, encoder_input_mask, decoder_input_value, decoder_input_mask,
@ -330,6 +364,8 @@ def test_encoder():
net = NetWithLoss(net)
net = _VirtualDatasetCell(net)
dataset = Dataset(encoder_input_value, encoder_input_mask)
model = Model(net)
@ -367,6 +403,8 @@ def test_decoder():
net = NetWithLoss(net)
net = _VirtualDatasetCell(net)
dataset = Dataset(decoder_input_value, decoder_input_mask, encoder_input_value, memory_mask)
model = Model(net)
@ -388,6 +426,7 @@ def test_vocabembedding_dp_true():
net = VocabEmbedding(vocab_size=160, embedding_size=16, parallel_config=config.embedding_dp_mp_config)
net = NetWithLoss(net)
net = _VirtualDatasetCell(net)
encoder_input_value = Tensor(np.ones((2, 64)), mstype.int32)
dataset = Dataset(encoder_input_value)
@ -410,6 +449,7 @@ def test_vocabembedding_dp_false():
net = VocabEmbedding(vocab_size=160, embedding_size=16, parallel_config=config.embedding_dp_mp_config)
net = NetWithLoss(net)
net = _VirtualDatasetCell(net)
encoder_input_value = Tensor(np.ones((2, 64)), mstype.int32)
dataset = Dataset(encoder_input_value)
@ -484,6 +524,7 @@ def test_sparse_attention_parallel_dp():
num_heads=8,
block_size=64,
parallel_config=sparse_attention_config)
net = _VirtualDatasetCell(net)
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)
@ -509,6 +550,7 @@ def test_parallel_cross_entroy_loss_semi_auto_parallel():
net = VocabEmbedding(vocab_size=160, embedding_size=16, parallel_config=config.embedding_dp_mp_config)
net = NetWithLoss(net, config.dp_mp_config)
net = _VirtualDatasetCell(net)
embed_ids = Tensor(np.ones((2, 64)), mstype.int32)
labels = Tensor(np.ones((2 * 64,)), mstype.int32)
input_mask = Tensor(np.ones((2 * 64,)), mstype.float32)