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## @package fc
# Module caffe2.python.helpers.fc
from caffe2.python import core
from caffe2.python.modeling import initializers
from caffe2.python.modeling.parameter_info import ParameterTags
def _FC_or_packed_FC(
model, op_call, blob_in, blob_out, dim_in, dim_out, weight_init=None,
bias_init=None, WeightInitializer=None, BiasInitializer=None,
enable_tensor_core=False, float16_compute=False, **kwargs
):
WeightInitializer = initializers.update_initializer(
WeightInitializer, weight_init, ("XavierFill", {})
)
BiasInitializer = initializers.update_initializer(
BiasInitializer, bias_init, ("ConstantFill", {})
)
if not model.init_params:
WeightInitializer = initializers.ExternalInitializer()
BiasInitializer = initializers.ExternalInitializer()
blob_out = blob_out or model.net.NextName()
bias_tags = [ParameterTags.BIAS]
if 'freeze_bias' in kwargs:
bias_tags.append(ParameterTags.COMPUTED_PARAM)
weight = model.create_param(
param_name=blob_out + '_w',
shape=[dim_out, dim_in],
initializer=WeightInitializer,
tags=ParameterTags.WEIGHT
)
bias = model.create_param(
param_name=blob_out + '_b',
shape=[dim_out, ],
initializer=BiasInitializer,
tags=bias_tags
)
# enable TensorCore by setting appropriate engine
if enable_tensor_core:
kwargs['engine'] = 'TENSORCORE'
# Enable float 16 compute kernel (relevant for CUDA)
if float16_compute:
kwargs['float16_compute'] = True
return op_call([blob_in, weight, bias], blob_out, **kwargs)
def fc(model, *args, **kwargs):
return _FC_or_packed_FC(model, model.net.FC, *args, **kwargs)
def packed_fc(model, *args, **kwargs):
return _FC_or_packed_FC(model, model.net.PackedFC, *args, **kwargs)
def fc_decomp(
model, blob_in, blob_out, dim_in, dim_out,
rank_approx=5, weight_init=None, bias_init=None,
WeightInitializer=None, BiasInitializer=None, **kwargs
):
"""FC_Decomp version
Here we assume that the rank of original input is bigger than 5.
"""
WeightInitializer = initializers.update_initializer(
WeightInitializer, weight_init, ("XavierFill", {})
)
BiasInitializer = initializers.update_initializer(
BiasInitializer, bias_init, ("ConstantFill", {})
)
blob_out = blob_out or model.net.NextName()
u = model.create_param(
param_name=blob_out + '_u',
shape=[dim_out, rank_approx],
initializer=WeightInitializer,
)
v = model.create_param(
param_name=blob_out + '_v',
shape=[dim_in, rank_approx],
initializer=WeightInitializer,
)
bias = model.create_param(
param_name=blob_out + '_b',
shape=[dim_out, ],
initializer=BiasInitializer,
)
return model.net.FC_Decomp([blob_in, u, v, bias], blob_out, **kwargs)
def fc_prune(
model, blob_in, blob_out, dim_in, dim_out,
weight_init=None, bias_init=None, mask_init=None,
threshold=0.00001, need_compress_rate=False,
comp_lb=0.05,
**kwargs
):
"""FC_Prune version
Runnable so far. Great!:)
"""
weight_init = weight_init if weight_init else ('XavierFill', {})
bias_init = bias_init if bias_init else ('ConstantFill', {})
mask_init = mask_init if mask_init else ('ConstantFill', {})
blob_out = blob_out or model.net.NextName()
compress_rate = blob_out + '_compress_rate'
if model.init_params:
compress_lb = model.param_init_net.ConstantFill(
[],
blob_out + '_lb',
shape=[1],
value=comp_lb
)
weight = model.param_init_net.__getattr__(weight_init[0])(
[],
blob_out + '_w',
shape=[dim_out, dim_in],
**weight_init[1]
)
mask = model.param_init_net.ConstantFill(
[],
blob_out + '_m',
shape=[dim_out, dim_in],
value=1.0
)
ag_dw = model.param_init_net.__getattr__(mask_init[0])(
[],
blob_out + '_ag_dw',
shape=[dim_out, dim_in],
**mask_init[1]
)
bias = model.param_init_net.__getattr__(bias_init[0])(
[],
blob_out + '_b',
shape=[dim_out, ],
**bias_init[1]
)
mask_seq = model.param_init_net.__getattr__(mask_init[0])(
[],
blob_out + '_mask_seq',
shape=[dim_out, dim_in],
**mask_init[1]
)
thres = model.param_init_net.ConstantFill(
[],
blob_out + '_thres',
shape=[1],
value=threshold
)
else:
compress_lb = core.ScopedBlobReference(
blob_out + '_lb', model.param_init_net)
weight = core.ScopedBlobReference(
blob_out + '_w', model.param_init_net)
bias = core.ScopedBlobReference(
blob_out + '_b', model.param_init_net)
mask = core.ScopedBlobReference(
blob_out + '_m', model.param_init_net)
ag_dw = core.ScopedBlobReference(
blob_out + '_ag_dw', model.param_init_net)
mask_seq = core.ScopedBlobReference(
blob_out + '_mask_seq', model.param_init_net)
thres = core.ScopedBlobReference(
blob_out + '_thres', model.param_init_net)
model.AddParameter(weight)
model.AddParameter(bias)
if need_compress_rate:
return model.net.FC_Prune([blob_in, weight, mask, bias, ag_dw, mask_seq,
thres, compress_lb],
[blob_out, compress_rate], **kwargs)
else:
return model.net.FC_Prune([blob_in, weight, mask,
bias, ag_dw, mask_seq,
thres, compress_lb],
blob_out, **kwargs)
def fc_sparse(
model, blob_in, blob_out, w_csr, iw, jw, bias,
**kwargs
):
"""FC_Sparse: Only takes in allocated weights"""
if not (w_csr and iw and jw and bias):
print("Warning...")
model.AddParameter(w_csr)
model.AddParameter(iw)
model.AddParameter(jw)
model.AddParameter(bias)
return model.net.FC_Sparse([blob_in, w_csr, iw, jw, bias],
blob_out, **kwargs)
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