def test_patch_module_tied_attention(self):
config = BertConfig.from_pretrained("bert-base-uncased")
model = BertForQuestionAnswering(config)
parameters = LinearPruningParameters(
method="topK",
submethod="default",
ampere_method="annealing",
block_rows=32,
block_cols=32,
)
context = PatcherContext()
p_attention = JointPruningModulePatcher(context, parameters, "attention")
p_dense = LinearPruningModulePatcher(context, parameters)
module_patchers = dict(
query=p_attention,
key=p_attention,
value=p_attention,
att_dense=p_dense,
interm_dense=p_dense,
output_dense=p_dense,
)
patcher = BertLinearModelPatcher(module_patchers)
patcher.patch(model)
self.assertEqual(patcher.stats["patched"], 72)
key_sizes = {k: len(v) for k, v in context.context_modules.items()}
self.assertEqual(key_sizes, {"ampere_mask": 72, "mask": 48})
def patch_model(self, model, trial=None):
layers_count = model.config.num_hidden_layers
sparse_args = self.sparse_args
attention_pruning_method_parts = self.parse_pruning_method(
sparse_args.attention_pruning_method)
if hasattr(sparse_args, "bias_mask"):
bias_mask = sparse_args.bias_mask
else:
bias_mask = False
if hasattr(sparse_args, "linear_min_parameters"):
linear_min_parameters = sparse_args.linear_min_parameters
else:
linear_min_parameters = 0.005
patcher_context = self.patcher_context
if attention_pruning_method_parts[
0] != "disabled" or sparse_args.ampere_pruning_method != "disabled":
args_attention = LinearPruningArgs(
method=attention_pruning_method_parts[0],
submethod=attention_pruning_method_parts[1],
ampere_method=sparse_args.ampere_pruning_method,
block_rows=sparse_args.attention_block_rows,
block_cols=sparse_args.attention_block_cols,
bias_mask=bias_mask,
min_elements=linear_min_parameters,
)
args_attention_t = LinearPruningArgs(
method=attention_pruning_method_parts[0],
submethod=attention_pruning_method_parts[1],
ampere_method=sparse_args.ampere_pruning_method,
block_rows=sparse_args.attention_block_cols,
block_cols=sparse_args.attention_block_rows,
bias_mask=bias_mask,
min_elements=linear_min_parameters,
)
if args_attention.submethod == "joint":
p_attention = JointPruningModulePatcher(patcher_context,
args_attention,
suffix=".attention")
p_attention_t = JointPruningModulePatcher(patcher_context,
args_attention_t,
suffix=".attention")
else:
p_attention = LinearPruningModulePatcher(
patcher_context, args_attention)
p_attention_t = LinearPruningModulePatcher(
patcher_context, args_attention_t)
else:
p_attention = None
p_attention_t = None
dense_pruning_method_parts = self.parse_pruning_method(
sparse_args.dense_pruning_method)
if dense_pruning_method_parts[
0] != "disabled" or sparse_args.ampere_pruning_method != "disabled":
args_dense = LinearPruningArgs(
method=dense_pruning_method_parts[0],
submethod=dense_pruning_method_parts[1],
ampere_method=sparse_args.ampere_pruning_method,
block_rows=sparse_args.dense_block_rows,
block_cols=sparse_args.dense_block_cols,
bias_mask=bias_mask,
min_elements=linear_min_parameters,
)
if args_dense.submethod.startswith("1d"):
p_dense = ChannelPruningModulePatcher(patcher_context,
args_dense,
self.MODEL_STRUCTURE,
suffix="dense")
else:
p_dense = LinearPruningModulePatcher(patcher_context,
args_dense)
else:
p_dense = None
if not hasattr(sparse_args, "attention_output_with_dense"
) or sparse_args.attention_output_with_dense:
p_att_dense = p_dense
else:
p_att_dense = p_attention_t
module_patchers = dict(
query=p_attention,
key=p_attention,
value=p_attention,
att_dense=p_att_dense,
interm_dense=p_dense,
output_dense=p_dense,
)
if hasattr(sparse_args, "layer_norm_patch"):
layer_norm_patch = sparse_args.layer_norm_patch
else:
layer_norm_patch = False
if hasattr(sparse_args, "gelu_patch"):
gelu_patch = sparse_args.gelu_patch
else:
gelu_patch = False
patcher = BertLinearModelPatcher(module_patchers)
patcher.patch(model)
patched_count = 0
if attention_pruning_method_parts[
0] != "disabled" or sparse_args.ampere_pruning_method != "disabled":
patched_count += 4 * layers_count
if dense_pruning_method_parts[
0] != "disabled" or sparse_args.ampere_pruning_method != "disabled":
patched_count += 2 * layers_count
assert (patcher.stats["patched"] == patched_count)
if layer_norm_patch:
def schedule_callback():
mix = self.patcher_context.get_context_data(
"layernorm_to_nonorm_mix")
delta = self.patcher_context.get_context_data(
"layernorm_to_nonorm_delta")
return dict(mix=mix, delta=delta)
layer_norm_patcher = Layer2NoNormPatcher(
schedule_callback=schedule_callback)
layer_norm_patcher.patch(model)
layer_norm_patched_count = 2 * layers_count + 1
assert (layer_norm_patcher.stats["patched"] ==
layer_norm_patched_count)
if gelu_patch:
def schedule_callback():
mix = self.patcher_context.get_context_data("gelu_to_relu_mix")
return dict(mix=mix)
gelu_patcher = GeLU2ReLUModelPatcher(
schedule_callback=schedule_callback)
gelu_patcher.patch(model)
gelu_patcher_count = layers_count
assert (gelu_patcher.stats["patched"] == gelu_patcher_count)
return patcher
def test_base(self):
config = BertConfig.from_pretrained("bert-base-uncased")
model = BertForQuestionAnswering(config)
patcher = BertLinearModelPatcher({})
layers = patcher.get_patchable_layers(model)
def patch_model(self, model, trial):
assert trial is None or len(trial.params) == 0
attention_pruning_method_parts = self.parse_pruning_method(
self.sparse_args.attention_pruning_method)
if hasattr(self.sparse_args, "bias_mask"):
bias_mask = self.sparse_args.bias_mask
else:
bias_mask = False
args_attention = LinearPruningArgs(
method=attention_pruning_method_parts[0],
submethod=attention_pruning_method_parts[1],
ampere_method=self.sparse_args.ampere_pruning_method,
block_rows=self.sparse_args.attention_block_rows,
block_cols=self.sparse_args.attention_block_cols,
bias_mask=bias_mask)
args_attention_t = LinearPruningArgs(
method=attention_pruning_method_parts[0],
submethod=attention_pruning_method_parts[1],
ampere_method=self.sparse_args.ampere_pruning_method,
block_rows=self.sparse_args.attention_block_cols,
block_cols=self.sparse_args.attention_block_rows,
bias_mask=bias_mask)
dense_pruning_method_parts = self.parse_pruning_method(
self.sparse_args.dense_pruning_method)
args_dense = LinearPruningArgs(
method=dense_pruning_method_parts[0],
submethod=dense_pruning_method_parts[1],
ampere_method=self.sparse_args.ampere_pruning_method,
block_rows=self.sparse_args.dense_block_rows,
block_cols=self.sparse_args.dense_block_cols,
bias_mask=bias_mask)
patcher_context = self.patcher_context
if args_attention.submethod == "joint":
p_attention = JointPruningModulePatcher(patcher_context,
args_attention,
suffix=".attention")
p_attention_t = JointPruningModulePatcher(patcher_context,
args_attention_t,
suffix=".attention")
else:
p_attention = LinearPruningModulePatcher(patcher_context,
args_attention)
p_attention_t = LinearPruningModulePatcher(patcher_context,
args_attention_t)
if args_dense.submethod.startswith("1d"):
p_dense = ChannelPruningModulePatcher(patcher_context,
args_dense,
self.MODEL_STRUCTURE,
suffix="dense")
else:
p_dense = LinearPruningModulePatcher(patcher_context, args_dense)
if not hasattr(self.sparse_args, "attention_output_with_dense"
) or self.sparse_args.attention_output_with_dense:
p_att_dense = p_dense
else:
p_att_dense = p_attention_t
module_patchers = dict(
query=p_attention,
key=p_attention,
value=p_attention,
att_dense=p_att_dense,
interm_dense=p_dense,
output_dense=p_dense,
)
patcher = BertLinearModelPatcher(module_patchers)
patcher.patch(model)
assert ((patcher.stats["patched"] % 72) == 0)
return patcher