一、动态形状处理深度指南
1.1 动态形状基础与配置
# dynamic_shape_basics.py
import mindspore as ms
import mindspore.nn as nn
import mindspore.ops as ops
import numpy as np
from typing import Tuple, Optional, Union
class DynamicShapeConfig:
"""动态形状配置管理器"""
def __init__(self,
enable_dynamic_shape: bool = True,
max_dynamic_memory: str = "80%", # 最大动态形状内存
min_dynamic_memory: str = "2GB", # 最小动态形状内存
enable_shape_cache: bool = True,
cache_capacity: int = 50,
enable_auto_padding: bool = True):
self.enable_dynamic_shape = enable_dynamic_shape
self.max_dynamic_memory = max_dynamic_memory
self.min_dynamic_memory = min_dynamic_memory
self.enable_shape_cache = enable_shape_cache
self.cache_capacity = cache_capacity
self.enable_auto_padding = enable_auto_padding
# 动态形状策略
self.strategies = {
"padding": self._padding_strategy,
"reshape": self._reshape_strategy,
"slice": self._slice_strategy,
"batch_aware": self._batch_aware_strategy
}
# 形状历史记录
self.shape_history = {}
self.cache_hits = 0
self.cache_misses = 0
def configure_context(self):
"""配置动态形状相关上下文"""
if not self.enable_dynamic_shape:
return
# 设置昇腾动态形状配置
ascend_config = {
"dynamic_shape_enable": True,
"dynamic_shape_mem_limit": self.max_dynamic_memory,
"dynamic_shape_min_mem": self.min_dynamic_memory,
"dynamic_shape_cache_enable": self.enable_shape_cache,
"dynamic_shape_cache_capacity": self.cache_capacity,
"dynamic_inputs_shape_range": {} # 动态形状范围
}
# 设置MindSpore上下文
ms.set_context(
mode=ms.GRAPH_MODE,
device_target="Ascend",
ascend_config=ascend_config,
enable_dynamic_shape=True,
max_device_memory=self.max_dynamic_memory,
graph_kernel_flags="--enable_dynamic_shape_fusion=True"
)
print(f"动态形状已启用，内存限制: {self.max_dynamic_memory}")
if self.enable_shape_cache:
print(f"形状缓存容量: {self.cache_capacity}")
def set_dynamic_range(self, model, input_shapes_ranges):
"""
设置动态形状范围
Args:
model: 模型实例
input_shapes_ranges: 输入形状范围字典
Example: {
'input1': [(None, 3, 224, 224), # 动态batch
(32, 3, 224, 224), # 最小batch
(256, 3, 224, 224)] # 最大batch
}
"""
if not self.enable_dynamic_shape:
return
# 设置动态输入
dynamic_inputs = []
for name, shape_range in input_shapes_ranges.items():
min_shape, opt_shape, max_shape = shape_range
# 创建动态张量
dynamic_tensor = ms.Tensor(
shape=[s if s is not None else -1 for s in min_shape],
dtype=ms.float32
)
dynamic_inputs.append(dynamic_tensor)
# 记录形状范围
self.shape_history[name] = {
'min': min_shape,
'opt': opt_shape,
'max': max_shape,
'current': None
}
# 编译模型时设置动态输入
model.set_inputs(*dynamic_inputs)
print(f"设置动态形状范围: {input_shapes_ranges}")
def _padding_strategy(self, tensor, target_shape):
"""填充策略 - 处理可变长度"""
current_shape = tensor.shape
pad_widths = []
for curr, target in zip(current_shape, target_shape):
if curr < target:
pad_widths.append((0, target - curr))
else:
pad_widths.append((0, 0))
return ops.Pad(pad_widths)(tensor)
def _reshape_strategy(self, tensor, target_shape):
"""重塑策略 - 重新排列数据"""
# 确保总元素数不变
current_elements = np.prod(tensor.shape)
target_elements = np.prod(target_shape)
if current_elements != target_elements:
raise ValueError(f"元素数不匹配: {current_elements} != {target_elements}")
return ops.Reshape()(tensor, target_shape)
def _slice_strategy(self, tensor, target_shape):
"""切片策略 - 截断多余部分"""
slices = []
for curr, target in zip(tensor.shape, target_shape):
if curr > target:
slices.append(slice(0, target))
else:
slices.append(slice(None))
return tensor[tuple(slices)]
def _batch_aware_strategy(self, tensor, target_shape):
"""批处理感知策略 - 智能处理批维度"""
# 分离批维度和特征维度
batch_dim = 0
batch_size = tensor.shape[batch_dim]
target_batch = target_shape[batch_dim]
if batch_size == target_batch:
return tensor
if batch_size < target_batch:
# 需要填充
return self._batch_padding(tensor, target_shape)
else:
# 需要切片
return self._batch_slicing(tensor, target_shape)
def _batch_padding(self, tensor, target_shape):
"""批维度填充"""
pad_config = [(0, target_shape[0] - tensor.shape[0])]
pad_config.extend([(0, 0)] * (len(tensor.shape) - 1))
return ops.Pad(pad_config)(tensor)
def _batch_slicing(self, tensor, target_shape):
"""批维度切片"""
slices = [slice(0, target_shape[0])]
slices.extend([slice(None)] * (len(tensor.shape) - 1))
return tensor[tuple(slices)]
def adapt_shape(self, tensor, target_shape, strategy="batch_aware"):
"""自适应形状调整"""
if strategy not in self.strategies:
raise ValueError(f"未知策略: {strategy}")
# 检查是否已经匹配
if tensor.shape == target_shape:
return tensor
# 应用策略
adapted = self.strategies[strategy](tensor, target_shape)
# 记录形状变化
self._log_shape_adaptation(tensor.shape, target_shape, strategy)
return adapted
def _log_shape_adaptation(self, src_shape, dst_shape, strategy):
"""记录形状适配日志"""
key = f"{src_shape}->{dst_shape}:{strategy}"
if key in self.shape_history:
self.shape_history[key]['count'] += 1
self.cache_hits += 1
else:
self.shape_history[key] = {
'count': 1,
'strategy': strategy,
'timestamp': time.time()
}
self.cache_misses += 1
# 打印重要形状变化
if src_shape[0] != dst_shape[0]: # 批维度变化
print(f"批维度变化: {src_shape[0]} -> {dst_shape[0]} "
f"(策略: {strategy})")
1.2 动态形状模型设计
# dynamic_shape_models.py
class DynamicConv2D(nn.Cell):
"""动态卷积层 - 支持可变输入尺寸"""
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]] = 3,
stride: Union[int, Tuple[int, int]] = 1,
padding: Union[int, Tuple[int, int]] = 0,
dilation: Union[int, Tuple[int, int]] = 1,
groups: int = 1,
dynamic_kernel: bool = False):
super().__init__()
# 基本卷积参数
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size if isinstance(kernel_size, tuple) else (kernel_size, kernel_size)
self.stride = stride if isinstance(stride, tuple) else (stride, stride)
self.padding = padding if isinstance(padding, tuple) else (padding, padding)
self.dilation = dilation if isinstance(dilation, tuple) else (dilation, dilation)
self.groups = groups
# 动态特性
self.dynamic_kernel = dynamic_kernel
self.shape_adapter = DynamicShapeConfig()
# 卷积核
if dynamic_kernel:
# 动态卷积核 - 可适应不同输入尺寸
self.kernel_generator = self._create_kernel_generator()
self.current_kernel = None
else:
# 固定卷积核
self.conv = nn.Conv2d(
in_channels, out_channels, kernel_size,
stride=stride, pad_mode='pad', padding=padding,
dilation=dilation, group=groups,
has_bias=True,
weight_init='HeUniform'
)
def _create_kernel_generator(self):
"""创建动态卷积核生成器"""
class KernelGenerator(nn.Cell):
def __init__(self, base_channels, base_kernel):
super().__init__()
self.base_weight = ms.Parameter(
ms.Tensor(
np.random.randn(*base_kernel) * 0.01,
dtype=ms.float32
)
)
self.scale_factors = nn.Dense(base_channels, base_channels)
def construct(self, input_shape):
# 根据输入形状调整卷积核
_, _, h, w = input_shape
# 计算缩放因子
scale_h = h / 224 # 假设224是基准高度
scale_w = w / 224 # 假设224是基准宽度
# 调整卷积核
# 这里简化为插值，实际可能需要更复杂的调整
if scale_h != 1.0 or scale_w != 1.0:
# 使用双线性插值调整卷积核大小
weight = ops.ResizeBilinear(
size=(int(self.base_weight.shape[2] * scale_h),
int(self.base_weight.shape[3] * scale_w)),
align_corners=False
)(self.base_weight)
else:
weight = self.base_weight
return weight
base_kernel = (self.out_channels, self.in_channels // self.groups,
self.kernel_size[0], self.kernel_size[1])
return KernelGenerator(self.in_channels, base_kernel)
def construct(self, x):
"""前向传播 - 动态形状处理"""
batch_size, channels, height, width = x.shape
# 动态调整卷积核（如果需要）
if self.dynamic_kernel:
# 生成适应当前形状的卷积核
kernel = self.kernel_generator(x.shape)
# 动态卷积
output = self._dynamic_conv2d(x, kernel)
else:
# 标准卷积
output = self.conv(x)
# 动态调整输出形状（如果需要）
output = self._adapt_output_shape(output, x.shape)
return output
def _dynamic_conv2d(self, x, weight):
"""动态卷积实现"""
# 使用自定义卷积实现支持动态形状
# 这里简化实现，实际可能需要更复杂的处理
# 计算输出形状
out_h = (x.shape[2] + 2 * self.padding[0] -
self.dilation[0] * (weight.shape[2] - 1) - 1) // self.stride[0] + 1
out_w = (x.shape[3] + 2 * self.padding[1] -
self.dilation[1] * (weight.shape[3] - 1) - 1) // self.stride[1] + 1
# 实现卷积（简化版）
# 实际生产环境应使用优化实现
output = ops.Conv2D(
out_channel=self.out_channels,
kernel_size=self.kernel_size,
mode=1,
pad_mode="pad",
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.groups
)(x, weight)
return output
def _adapt_output_shape(self, output, input_shape):
"""调整输出形状"""
# 这里可以添加后处理逻辑
# 例如：动态批归一化、动态激活等
return output
class DynamicSequenceModel(nn.Cell):
"""动态序列模型 - 支持可变序列长度"""
def __init__(self,
input_dim: int,
hidden_dim: int,
num_layers: int = 2,
bidirectional: bool = True,
dynamic_length: bool = True):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.bidirectional = bidirectional
self.dynamic_length = dynamic_length
# RNN层（支持动态序列长度）
self.rnn = nn.LSTM(
input_size=input_dim,
hidden_size=hidden_dim,
num_layers=num_layers,
has_bias=True,
bidirectional=bidirectional,
dropout=0.0,
batch_first=True
)
# 动态形状处理器
self.shape_processor = DynamicSequenceProcessor(
hidden_dim * (2 if bidirectional else 1)
)
def construct(self, x, seq_lengths=None):
"""
前向传播
Args:
x: 输入张量 [batch, seq_len, features]
seq_lengths: 每个序列的实际长度 [batch]
"""
batch_size, seq_len, features = x.shape
# 处理动态序列长度
if self.dynamic_length and seq_lengths is not None:
# 使用pack_padded_sequence处理变长序列
x_packed = self._pack_sequences(x, seq_lengths)
# RNN处理
output_packed, (h_n, c_n) = self.rnn(x_packed)
# 解包
output, output_lengths = self._unpack_sequences(
output_packed, seq_lengths, batch_size, seq_len
)
else:
# 固定长度处理
output, (h_n, c_n) = self.rnn(x)
output_lengths = None
# 动态后处理
output = self.shape_processor(output, seq_lengths)
return output, h_n
def _pack_sequences(self, x, lengths):
"""打包变长序列"""
# 按长度降序排序
sorted_lengths, sorted_indices = ops.Sort(descending=True)(lengths)
sorted_x = x[sorted_indices]
# 打包序列
packed = nn.PackSequence(sorted_x, sorted_lengths)
return packed
def _unpack_sequences(self, packed_output, lengths, batch_size, max_len):
"""解包序列"""
# 恢复原始顺序
_, original_indices = ops.Sort()(lengths)
original_indices = ops.Argsort()(original_indices)
# 解包
output, output_lengths = nn.UnpackSequence(packed_output, batch_size, max_len)
# 恢复原始顺序
output = output[original_indices]
return output, output_lengths
class DynamicSequenceProcessor(nn.Cell):
"""动态序列处理器"""
def __init__(self, hidden_dim):
super().__init__()
# 动态注意力机制
self.attention = DynamicAttention(hidden_dim)
# 动态层归一化
self.layer_norm = DynamicLayerNorm(hidden_dim)
# 动态dropout
self.dropout = nn.Dropout(keep_prob=0.9)
def construct(self, x, lengths=None):
"""处理序列"""
# 动态掩码（如果提供了长度）
if lengths is not None:
mask = self._create_mask(x.shape[:2], lengths)
x = x * mask.unsqueeze(-1)
# 动态注意力
x = self.attention(x, mask if lengths is not None else None)
# 动态层归一化
x = self.layer_norm(x)
# dropout
x = self.dropout(x)
return x
def _create_mask(self, shape, lengths):
"""创建序列掩码"""
batch_size, max_len = shape
# 创建范围张量
range_tensor = ops.arange(max_len).broadcast_to((batch_size, max_len))
# 创建掩码
lengths_expanded = lengths.view(-1, 1)
mask = range_tensor < lengths_expanded
return mask.astype(ms.float32)
1.3 动态形状训练循环
# dynamic_training.py
class DynamicShapeTrainingLoop:
"""动态形状训练循环"""
def __init__(self,
model: nn.Cell,
optimizer: nn.Optimizer,
loss_fn: nn.Cell,
dynamic_config: DynamicShapeConfig,
enable_gradient_accumulation: bool = True,
accumulation_steps: int = 4):
self.model = model
self.optimizer = optimizer
self.loss_fn = loss_fn
self.dynamic_config = dynamic_config
self.enable_gradient_accumulation = enable_gradient_accumulation
self.accumulation_steps = accumulation_steps
# 梯度累积
self.accumulated_gradients = None
self.accumulation_counter = 0
# 形状统计
self.shape_statistics = {
'batch_sizes': [],
'sequence_lengths': [],
'image_sizes': []
}
# 性能监控
self.recompilation_count = 0
self.cache_hit_rate = 0.0
def train_step(self, data, labels, sample_info=None):
"""动态形状训练步骤"""
# 记录输入形状
self._record_input_shapes(data, sample_info)
# 动态调整模型（如果需要）
if self._needs_recompilation(data.shape):
self._recompile_model(data.shape)
# 前向传播
outputs = self.model(data)
# 计算损失
loss = self.loss_fn(outputs, labels)
# 反向传播
grads = self._compute_gradients(loss)
# 梯度累积
if self.enable_gradient_accumulation:
grads = self._accumulate_gradients(grads)
# 优化器步骤
if not self.enable_gradient_accumulation or self.accumulation_counter == self.accumulation_steps:
self.optimizer(grads)
self._reset_accumulation()
return loss, outputs
def _record_input_shapes(self, data, sample_info):
"""记录输入形状统计"""
shape = data.shape
# 记录批大小
self.shape_statistics['batch_sizes'].append(shape[0])
# 记录序列长度或图像尺寸
if len(shape) == 4: # 图像 [B, C, H, W]
self.shape_statistics['image_sizes'].append((shape[2], shape[3]))
elif len(shape) == 3: # 序列 [B, T, F]
self.shape_statistics['sequence_lengths'].append(shape[1])
# 限制历史大小
for key in self.shape_statistics:
if len(self.shape_statistics[key]) > 1000:
self.shape_statistics[key] = self.shape_statistics[key][-500:]
def _needs_recompilation(self, new_shape):
"""检查是否需要重新编译"""
if not hasattr(self.model, 'last_compiled_shape'):
return True
last_shape = self.model.last_compiled_shape
# 检查批维度变化
if new_shape[0] != last_shape[0]:
return True
# 检查序列长度变化（对于序列模型）
if len(new_shape) >= 3 and new_shape[1] != last_shape[1]:
return True
# 检查图像尺寸变化（对于视觉模型）
if len(new_shape) == 4 and (new_shape[2] != last_shape[2] or
new_shape[3] != last_shape[3]):
return True
return False
def _recompile_model(self, new_shape):
"""重新编译模型以适应新形状"""
print(f"重新编译模型以适应形状: {new_shape}")
# 设置动态输入
dynamic_input = ms.Tensor(shape=new_shape, dtype=ms.float32)
self.model.set_inputs(dynamic_input)
# 编译模型
self.model.compile()
# 记录编译形状
self.model.last_compiled_shape = new_shape
# 更新统计
self.recompilation_count += 1
def _compute_gradients(self, loss):
"""计算梯度"""
# 使用MindSpore的自动微分
grads = ms.grad(self._forward_and_loss, None, self.optimizer.parameters)(loss)
return grads
def _forward_and_loss(self, data, labels):
"""前向传播和损失计算"""
outputs = self.model(data)
loss = self.loss_fn(outputs, labels)
return loss
def _accumulate_gradients(self, grads):
"""梯度累积"""
if self.accumulated_gradients is None:
self.accumulated_gradients = [
ms.ops.zeros_like(g) if g is not None else None
for g in grads
]
# 累积梯度
for i, grad in enumerate(grads):
if grad is not None and self.accumulated_gradients[i] is not None:
self.accumulated_gradients[i] += grad / self.accumulation_steps
self.accumulation_counter += 1
return self.accumulated_gradients if self.accumulation_counter == self.accumulation_steps else None
def _reset_accumulation(self):
"""重置梯度累积"""
self.accumulated_gradients = None
self.accumulation_counter = 0
def get_shape_statistics(self):
"""获取形状统计信息"""
stats = {}
for key, values in self.shape_statistics.items():
if values:
stats[f'{key}_mean'] = np.mean(values)
stats[f'{key}_std'] = np.std(values)
stats[f'{key}_min'] = np.min(values)
stats[f'{key}_max'] = np.max(values)
stats[f'{key}_unique'] = len(np.unique(values))
stats['recompilation_count'] = self.recompilation_count
stats['cache_hit_rate'] = self.cache_hit_rate
return stats
class DynamicBatchSampler:
"""动态批采样器 - 根据序列长度动态调整批大小"""
def __init__(self,
dataset_lengths, # 每个样本的长度
max_tokens_per_batch: int = 4096,
max_sequences_per_batch: int = 32,
shuffle: bool = True):
self.dataset_lengths = dataset_lengths
self.max_tokens_per_batch = max_tokens_per_batch
self.max_sequences_per_batch = max_sequences_per_batch
self.shuffle = shuffle
# 索引数组
self.indices = np.arange(len(dataset_lengths))
# 当前批次
self.current_batch = []
self.current_tokens = 0
def __iter__(self):
"""迭代器"""
if self.shuffle:
np.random.shuffle(self.indices)
self.current_batch = []
self.current_tokens = 0
for idx in self.indices:
sample_length = self.dataset_lengths[idx]
# 检查是否可以添加到当前批次
if (len(self.current_batch) < self.max_sequences_per_batch and
self.current_tokens + sample_length <= self.max_tokens_per_batch):
self.current_batch.append(idx)
self.current_tokens += sample_length
else:
# 返回当前批次
if self.current_batch:
yield self.current_batch
# 开始新批次
self.current_batch = [idx]
self.current_tokens = sample_length
# 返回最后一批
if self.current_batch:
yield self.current_batch
def __len__(self):
"""估计批次数"""
# 这里简化为固定估计，实际需要动态计算
return len(self.indices) // self.max_sequences_per_batch
二、稀疏计算高级特性
2.1 稀疏张量基础
# sparse_tensor_basics.py
import mindspore as ms
from mindspore import Tensor, CSRTensor, COOTensor
import numpy as np
from scipy import sparse
class SparseTensorFactory:
"""稀疏张量工厂"""
@staticmethod
def dense_to_csr(dense_tensor: Tensor, threshold: float = 0.0):
"""稠密张量转CSR格式"""
dense_np = dense_tensor.asnumpy()
# 创建稀疏矩阵
sparse_matrix = sparse.csr_matrix(dense_np)
# 应用阈值（可选）
if threshold > 0:
sparse_matrix.data[np.abs(sparse_matrix.data) < threshold] = 0
sparse_matrix.eliminate_zeros()
# 转换为MindSpore CSRTensor
indptr = Tensor(sparse_matrix.indptr, dtype=ms.int32)
indices = Tensor(sparse_matrix.indices, dtype=ms.int32)
values = Tensor(sparse_matrix.data, dtype=dense_tensor.dtype)
shape = dense_tensor.shape
return CSRTensor(indptr, indices, values, shape)
@staticmethod
def dense_to_coo(dense_tensor: Tensor, threshold: float = 0.0):
"""稠密张量转COO格式"""
dense_np = dense_tensor.asnumpy()
# 创建稀疏矩阵
sparse_matrix = sparse.coo_matrix(dense_np)
# 应用阈值（可选）
if threshold > 0:
mask = np.abs(sparse_matrix.data) >= threshold
sparse_matrix = sparse.coo_matrix(
(sparse_matrix.data[mask],
(sparse_matrix.row[mask], sparse_matrix.col[mask])),
shape=sparse_matrix.shape
)
# 转换为MindSpore COOTensor
indices = Tensor(np.stack([sparse_matrix.row, sparse_matrix.col], axis=1),
dtype=ms.int32)
values = Tensor(sparse_matrix.data, dtype=dense_tensor.dtype)
shape = dense_tensor.shape
return COOTensor(indices, values, shape)
@staticmethod
def create_random_sparse(shape, density=0.1, format='csr'):
"""创建随机稀疏张量"""
total_elements = np.prod(shape)
num_nonzero = int(total_elements * density)
# 生成随机位置和值
indices = np.random.choice(total_elements, num_nonzero, replace=False)
values = np.random.randn(num_nonzero).astype(np.float32)
# 转换为坐标
coords = np.unravel_index(indices, shape)
if format == 'coo':
# COO格式
indices_tensor = Tensor(np.stack(coords, axis=1), dtype=ms.int32)
values_tensor = Tensor(values, dtype=ms.float32)
return COOTensor(indices_tensor, values_tensor, shape)
elif format == 'csr':
# CSR格式（假设是2D矩阵）
if len(shape) != 2:
raise ValueError("CSR格式仅支持2D张量")
rows, cols = coords
sparse_matrix = sparse.csr_matrix((values, (rows, cols)), shape=shape)
indptr = Tensor(sparse_matrix.indptr, dtype=ms.int32)
indices = Tensor(sparse_matrix.indices, dtype=ms.int32)
values_tensor = Tensor(sparse_matrix.data, dtype=ms.float32)
return CSRTensor(indptr, indices, values_tensor, shape)
@staticmethod
def analyze_sparsity(tensor):
"""分析稀疏性"""
if isinstance(tensor, (CSRTensor, COOTensor)):
nnz = tensor.values.shape[0]
total = np.prod(tensor.shape)
density = nnz / total
return {
'format': type(tensor).__name__,
'shape': tensor.shape,
'nnz': nnz,
'total': total,
'density': density,
'sparsity': 1 - density,
'storage_saving': (1 - (nnz * 2 + (len(tensor.shape) + 1)) / total) * 100
}
else:
# 稠密张量
total = np.prod(tensor.shape)
nnz = np.count_nonzero(tensor.asnumpy())
density = nnz / total
return {
'format': 'dense',
'shape': tensor.shape,
'nnz': nnz,
'total': total,
'density': density,
'sparsity': 1 - density
}
class SparseOperations:
"""稀疏张量操作"""
@staticmethod
def sparse_matmul(sparse_tensor, dense_tensor):
"""稀疏-稠密矩阵乘法"""
if isinstance(sparse_tensor, CSRTensor):
# CSR格式矩阵乘法
return ms.ops.csr_mm(sparse_tensor, dense_tensor)
elif isinstance(sparse_tensor, COOTensor):
# COO格式矩阵乘法
return ms.ops.coo_mm(sparse_tensor, dense_tensor)
else:
raise TypeError(f"不支持的稀疏格式: {type(sparse_tensor)}")
@staticmethod
def sparse_add(sparse_tensor, dense_tensor):
"""稀疏-稠密加法"""
if isinstance(sparse_tensor, CSRTensor):
return ms.ops.csr_add(sparse_tensor, dense_tensor)
elif isinstance(sparse_tensor, COOTensor):
return ms.ops.coo_add(sparse_tensor, dense_tensor)
@staticmethod
def sparse_conv2d(sparse_tensor, weight, stride=1, padding=0):
"""稀疏卷积"""
# 转换稀疏张量为稠密进行卷积
dense_tensor = sparse_tensor.to_dense()
return ms.ops.conv2d(dense_tensor, weight, stride=stride, padding=padding)
@staticmethod
def sparse_attention(query, key, value, sparse_mask=None):
"""稀疏注意力机制"""
# 计算注意力分数
scores = ms.ops.matmul(query, key.transpose(0, 1, 3, 2))
# 应用稀疏掩码
if sparse_mask is not None:
if isinstance(sparse_mask, (CSRTensor, COOTensor)):
# 转换为稠密掩码
sparse_mask = sparse_mask.to_dense()
scores = scores * sparse_mask
# softmax
attention_weights = ms.ops.softmax(scores, axis=-1)
# 注意力输出
output = ms.ops.matmul(attention_weights, value)
return output
2.2 稀疏神经网络层
# sparse_neural_layers.py
class SparseLinear(nn.Cell):
"""稀疏线性层"""
def __init__(self,
in_features: int,
out_features: int,
sparsity: float = 0.9,
sparse_format: str = 'csr',
bias: bool = True):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.sparsity = sparsity
self.sparse_format = sparse_format
self.has_bias = bias
# 初始化权重（稀疏）
self.weight = self._init_sparse_weight()
# 偏置
if bias:
self.bias = ms.Parameter(
ms.ops.zeros(out_features, ms.float32)
)
else:
self.bias = None
# 稀疏操作器
self.sparse_ops = SparseOperations()
def _init_sparse_weight(self):
"""初始化稀疏权重"""
# 创建随机稀疏权重
shape = (self.out_features, self.in_features)
if self.sparse_format == 'csr':
weight_tensor = SparseTensorFactory.create_random_sparse(
shape, density=1-self.sparsity, format='csr'
)
elif self.sparse_format == 'coo':
weight_tensor = SparseTensorFactory.create_random_sparse(
shape, density=1-self.sparsity, format='coo'
)
else:
raise ValueError(f"不支持的稀疏格式: {self.sparse_format}")
