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234 lines
7.7 KiB
234 lines
7.7 KiB
5 months ago
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import torch
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class MkldnnLinear(torch.jit.ScriptModule):
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def __init__(self, dense_module, dtype):
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super().__init__()
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self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype))
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if dense_module.bias is not None:
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# Bias can be fp32 or bf16 for OneDNN bf16 path, but for good accuracy,
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# we use fp32 dtype.
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self.register_buffer('bias', dense_module.bias.to_mkldnn())
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else:
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# TODO: Remove this once ScriptModule supports registering None buffer
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self.register_buffer(
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'bias',
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torch.zeros([dense_module.weight.size(0)], dtype=torch.float).to_mkldnn())
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@torch.jit.script_method
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def __getstate__(self):
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return (self.weight.to_dense(), self.bias.to_dense(), self.training)
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@torch.jit.script_method
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def __setstate__(self, state):
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self.weight = state[0].to_mkldnn()
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self.bias = state[1].to_mkldnn()
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self.training = state[2]
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@torch.jit.script_method
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def forward(self, x):
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x_mkldnn = x if x.is_mkldnn else x.to_mkldnn()
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y_mkldnn = torch._C._nn.mkldnn_linear(x_mkldnn, self.weight, self.bias)
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y = y_mkldnn if x.is_mkldnn else y_mkldnn.to_dense()
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return y
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class _MkldnnConvNd(torch.jit.ScriptModule):
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"""Common base of MkldnnConv1d and MkldnnConv2d."""
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__constants__ = ['stride', 'padding', 'dilation', 'groups']
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def __init__(self, dense_module):
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super().__init__()
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self.stride = dense_module.stride
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self.padding = dense_module.padding
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self.dilation = dense_module.dilation
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self.groups = dense_module.groups
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if dense_module.bias is not None:
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self.register_buffer('bias', dense_module.bias.to_mkldnn())
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else:
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# Bias can be fp32 or bf16 for OneDNN bf16 path, but for good accuracy,
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# we use fp32 dtype.
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# TODO: Remove this once ScriptModule supports registering None buffer
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self.register_buffer(
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'bias',
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torch.zeros([dense_module.weight.size(0)], dtype=torch.float).to_mkldnn())
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@torch.jit.script_method
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def __getstate__(self):
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return (self.weight.to_dense(), self.bias.to_dense(), self.training)
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@torch.jit.script_method
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def forward(self, x):
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return torch.mkldnn_convolution(
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x,
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self.weight,
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self.bias,
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self.padding,
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self.stride,
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self.dilation,
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self.groups)
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class MkldnnConv1d(_MkldnnConvNd):
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def __init__(self, dense_module, dtype):
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super().__init__(dense_module)
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self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype))
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@torch.jit.script_method
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def __setstate__(self, state):
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self.weight = state[0].to_mkldnn()
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self.bias = state[1].to_mkldnn()
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self.training = state[2]
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class MkldnnConv2d(_MkldnnConvNd):
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def __init__(self, dense_module, dtype):
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super().__init__(dense_module)
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self.register_buffer('weight', torch._C._nn.mkldnn_reorder_conv2d_weight(
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dense_module.weight.to_mkldnn(dtype),
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self.padding,
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self.stride,
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self.dilation,
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self.groups))
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@torch.jit.script_method
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def __setstate__(self, state):
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self.weight = torch._C._nn.mkldnn_reorder_conv2d_weight(
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state[0].to_mkldnn(),
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self.padding,
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self.stride,
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self.dilation,
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self.groups)
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self.bias = state[1].to_mkldnn()
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self.training = state[2]
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class MkldnnConv3d(_MkldnnConvNd):
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def __init__(self, dense_module, dtype):
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super().__init__(dense_module)
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self.register_buffer('weight', torch._C._nn.mkldnn_reorder_conv3d_weight(
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dense_module.weight.to_mkldnn(dtype),
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self.padding,
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self.stride,
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self.dilation,
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self.groups))
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@torch.jit.script_method
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def __setstate__(self, state):
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self.weight = torch._C._nn.mkldnn_reorder_conv3d_weight(
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state[0].to_mkldnn(),
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self.padding,
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self.stride,
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self.dilation,
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self.groups)
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self.bias = state[1].to_mkldnn()
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self.training = state[2]
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class MkldnnBatchNorm(torch.jit.ScriptModule):
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__constants__ = ['exponential_average_factor', 'eps']
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def __init__(self, dense_module):
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super().__init__()
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assert not dense_module.training
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assert dense_module.track_running_stats
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assert dense_module.affine
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if dense_module.momentum is None:
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self.exponential_average_factor = 0.0
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else:
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self.exponential_average_factor = dense_module.momentum
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self.eps = dense_module.eps
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self.register_buffer('weight', dense_module.weight.to_mkldnn())
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self.register_buffer('bias', dense_module.bias.to_mkldnn())
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self.register_buffer('running_mean', dense_module.running_mean.to_mkldnn())
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self.register_buffer('running_var', dense_module.running_var.to_mkldnn())
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@torch.jit.script_method
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def __getstate__(self):
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weight = self.weight.to_dense()
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bias = self.bias.to_dense()
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running_mean = self.running_mean.to_dense()
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running_var = self.running_var.to_dense()
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return (weight, bias, running_mean, running_var, self.training)
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@torch.jit.script_method
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def __setstate__(self, state):
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self.weight = state[0].to_mkldnn()
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self.bias = state[1].to_mkldnn()
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self.running_mean = state[2].to_mkldnn()
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self.running_var = state[3].to_mkldnn()
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self.training = state[4]
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@torch.jit.script_method
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def forward(self, x):
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return torch.batch_norm(
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x,
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self.weight,
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self.bias,
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self.running_mean,
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self.running_var,
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False, # training
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self.exponential_average_factor,
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self.eps,
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False, # cuda_enabled
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)
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class MkldnnPrelu(torch.jit.ScriptModule):
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def __init__(self, dense_module, dtype):
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super().__init__()
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self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype))
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@torch.jit.script_method
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def __getstate__(self):
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return (self.weight.to_dense(), self.training)
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@torch.jit.script_method
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def __setstate__(self, state):
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self.weight = state[0].to_mkldnn()
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self.training = state[1]
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@torch.jit.script_method
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def forward(self, x):
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x_mkldnn = x if x.is_mkldnn else x.to_mkldnn()
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y_mkldnn = torch.prelu(x_mkldnn, self.weight)
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y = y_mkldnn if x.is_mkldnn else y_mkldnn.to_dense()
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return y
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def to_mkldnn(module, dtype=torch.float):
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assert dtype in [torch.float, torch.bfloat16, torch.half], \
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"MKLDNN only support float, bfloat16, and half path now"
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def m_fn(m, d):
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if isinstance(m, torch.nn.Linear):
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return MkldnnLinear(m, d)
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elif isinstance(m, torch.nn.Conv1d):
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return MkldnnConv1d(m, d)
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elif isinstance(m, torch.nn.Conv2d):
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return MkldnnConv2d(m, d)
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elif isinstance(m, torch.nn.Conv3d):
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return MkldnnConv3d(m, d)
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elif isinstance(m, (torch.nn.BatchNorm2d, torch.nn.BatchNorm3d)):
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# For batchnorm bf16 path, OneDNN requires weight and bias need fp32 dtype.
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# so it doesn't need dtype argument.
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return MkldnnBatchNorm(m)
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elif isinstance(m, torch.nn.PReLU):
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return MkldnnPrelu(m, d)
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else:
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return m
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def m_fn_rec(m, d):
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new_m = m_fn(m, d)
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for name, sub_m in m.named_children():
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setattr(new_m, name, m_fn_rec(sub_m, d))
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return new_m
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return m_fn_rec(module, dtype)
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