code stringlengths 17 6.64M |
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class Partition5(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[19]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[20]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[21]']
TENSORS = []
def __init__(sel... |
class Partition6(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[22]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[23]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5LayerNorm[final_layer_norm]', 'T5ForConditionalGeneration/T5Stack[en... |
class Partition7(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[7]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[8]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[9]', 'T5ForConditionalGeneration/T5Stack[decod... |
def traverse_model(module: nn.Module, depth: int, prefix: Optional[str]=None, basic_blocks: Tuple[Type[nn.Module]]=(), full: bool=False) -> Iterator[Tuple[(nn.Module, str, nn.Module, Optional[bool])]]:
'\n iterate over model layers yielding the layer,layer_scope,encasing_module\n Parameters:\n ----------... |
def layerDict(model: nn.Module, depth=1000, basic_blocks=()) -> Dict[(str, nn.Module)]:
return {s: l for (l, s, _) in traverse_model(model, depth, basic_blocks=basic_blocks)}
|
def traverse_params_buffs(module: nn.Module, prefix: Optional[str]=None) -> Iterator[Tuple[(torch.tensor, str)]]:
"\n iterate over model's buffers and parameters yielding obj,obj_scope\n\n Parameters:\n -----------\n model:\n the model to iterate over\n "
if (prefix is None):
pre... |
def tensorDict(model: nn.Module) -> OrderedDict[(str, Tensor)]:
return collections.OrderedDict(((s, t) for (t, s) in traverse_params_buffs(model)))
|
def move_tensors(ts, device):
def move(t):
if isinstance(t, (nn.Module, Tensor)):
return t.to(device)
return t
return nested_map(move, ts)
|
def nested_map(func, ts, full=False):
if isinstance(ts, torch.Size):
return func(ts)
elif isinstance(ts, (list, tuple, set)):
return type(ts)((nested_map(func, t, full=full) for t in ts))
elif isinstance(ts, dict):
return {k: nested_map(func, v, full=full) for (k, v) in ts.items()}... |
def flatten(ts):
if isinstance(ts, torch.Size):
(yield ts)
elif isinstance(ts, (list, tuple, set)):
(yield from chain(*[flatten(t) for t in ts]))
elif isinstance(ts, dict):
(yield from chain(*[flatten(t) for (k, t) in sorted(ts.items(), key=(lambda t: t[0]))]))
else:
(y... |
def unflatten(xs, structure):
return _unflatten(xs, structure)[0]
|
def _unflatten(xs, structure):
if isinstance(structure, torch.Size):
return (xs[0], 1)
if (not isinstance(structure, (list, tuple, set, dict))):
return (xs[0], 1)
if isinstance(structure, (list, tuple, set)):
offset = 0
elements = []
for s in structure:
... |
def state_dict(partition, *args, **kwargs):
state = nn.Module.state_dict(partition, *args, **kwargs)
lookup = partition.lookup
result = dict()
for (k, v) in state.items():
if (k in lookup):
result[lookup[k]] = v
else:
assert ('.' in k)
split_idx = k.... |
def load_state_dict(partition, state_dict, strict=True):
reverse_lookup = {v: k for (k, v) in partition.lookup.items()}
device = partition.device
keys = list(partition.state_dict(None).keys())
new_state = dict()
for k in keys:
if (k in reverse_lookup):
new_state[reverse_lookup[... |
def named_buffers(partition, prefix='', recurse=True):
params = nn.Module.named_buffers(partition, prefix=prefix, recurse=recurse)
lookup = partition.lookup
for (k, v) in params:
if (k in lookup):
(yield (lookup[k], v))
else:
assert ('.' in k)
split_idx ... |
def named_parameters(partition, prefix='', recurse=True):
params = nn.Module.named_parameters(partition, prefix=prefix, recurse=recurse)
lookup = partition.lookup
for (k, v) in params:
if (k in lookup):
(yield (lookup[k], v))
else:
assert ('.' in k)
spli... |
def cpu(partition):
partition.device = torch.device('cpu')
return nn.Module.cpu(partition)
|
def cuda(partition, device=None):
if (device is None):
device = torch.cuda.current_device()
partition.device = torch.device(device)
return nn.Module.cuda(partition, partition.device)
|
def to(partition, *args, **kwargs):
device = None
if ('device' in kwargs):
device = kwargs['device']
elif ('tensor' in kwargs):
device = kwargs['tensor'].device
if args:
if isinstance(args[0], (torch.device, int, str)):
device = args[0]
if torch.is_tensor(ar... |
def layer_graph_t5_3b_tied_lmheads_320_8_8p_bw12_squad1_pipedream():
return dict(model_type='t5_stateless', model_name_or_path='t5-3b', do_lower_case=False, output_past=False, stateless_tied=True, explicitly_set_dict={'return_dict': False, 'use_cache': False, 'output_only': True, 'output_attentions': False, 'prec... |
def create_pipeline_configuration(DEBUG=False, batch_size=4):
config = {'batch_dim': 0, 'depth': 10000, 'basic_blocks': (StatelessEmbedding, T5LayerNorm, Dropout, Linear, T5Block), 'model_inputs': {'attention_mask': {'shape': torch.Size([4, 512]), 'dtype': torch.int64, 'is_batched': True, 'used_by': [0, 7]}, 'dec... |
class Partition0(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/StatelessEmbedding[embed_tokens]', 'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[0]', 'T5ForConditionalGeneration/T5Stack[encoder]/Modu... |
class Partition1(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[3]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[4]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[5]']
TENSORS = []
def __init__(self, ... |
class Partition2(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[6]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[7]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[8]']
TENSORS = []
def __init__(self, ... |
class Partition3(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[9]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[10]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[11]']
TENSORS = []
def __init__(self... |
class Partition4(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[12]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[13]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[14]']
TENSORS = []
def __init__(sel... |
class Partition5(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[15]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[16]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[17]']
TENSORS = []
def __init__(sel... |
class Partition6(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[18]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[19]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[20]']
TENSORS = []
def __init__(sel... |
class Partition7(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[21]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[22]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[23]', 'T5ForConditionalGeneration/T5Stack[en... |
class Partition8(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[3]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[4]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[5]']
TENSORS = []
def __init__(self, ... |
class Partition9(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[6]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[7]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[8]']
TENSORS = []
def __init__(self, ... |
class Partition10(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[9]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[10]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[11]']
TENSORS = []
def __init__(sel... |
class Partition11(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[12]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[13]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[14]']
TENSORS = []
def __init__(se... |
class Partition12(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[15]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[16]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[17]']
TENSORS = []
def __init__(se... |
class Partition13(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[18]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[19]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[20]']
TENSORS = []
def __init__(se... |
class Partition14(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[21]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[22]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[23]', 'T5ForConditionalGeneration/T5Stack[d... |
def traverse_model(module: nn.Module, depth: int, prefix: Optional[str]=None, basic_blocks: Tuple[Type[nn.Module]]=(), full: bool=False) -> Iterator[Tuple[(nn.Module, str, nn.Module, Optional[bool])]]:
'\n iterate over model layers yielding the layer,layer_scope,encasing_module\n Parameters:\n ----------... |
def layerDict(model: nn.Module, depth=1000, basic_blocks=()) -> Dict[(str, nn.Module)]:
return {s: l for (l, s, _) in traverse_model(model, depth, basic_blocks=basic_blocks)}
|
def traverse_params_buffs(module: nn.Module, prefix: Optional[str]=None) -> Iterator[Tuple[(torch.tensor, str)]]:
"\n iterate over model's buffers and parameters yielding obj,obj_scope\n\n Parameters:\n -----------\n model:\n the model to iterate over\n "
if (prefix is None):
pre... |
def tensorDict(model: nn.Module) -> OrderedDict[(str, Tensor)]:
return collections.OrderedDict(((s, t) for (t, s) in traverse_params_buffs(model)))
|
def move_tensors(ts, device):
def move(t):
if isinstance(t, (nn.Module, Tensor)):
return t.to(device)
return t
return nested_map(move, ts)
|
def nested_map(func, ts, full=False):
if isinstance(ts, torch.Size):
return func(ts)
elif isinstance(ts, (list, tuple, set)):
return type(ts)((nested_map(func, t, full=full) for t in ts))
elif isinstance(ts, dict):
return {k: nested_map(func, v, full=full) for (k, v) in ts.items()}... |
def flatten(ts):
if isinstance(ts, torch.Size):
(yield ts)
elif isinstance(ts, (list, tuple, set)):
(yield from chain(*[flatten(t) for t in ts]))
elif isinstance(ts, dict):
(yield from chain(*[flatten(t) for (k, t) in sorted(ts.items(), key=(lambda t: t[0]))]))
else:
(y... |
def unflatten(xs, structure):
return _unflatten(xs, structure)[0]
|
def _unflatten(xs, structure):
if isinstance(structure, torch.Size):
return (xs[0], 1)
if (not isinstance(structure, (list, tuple, set, dict))):
return (xs[0], 1)
if isinstance(structure, (list, tuple, set)):
offset = 0
elements = []
for s in structure:
... |
def state_dict(partition, *args, **kwargs):
state = nn.Module.state_dict(partition, *args, **kwargs)
lookup = partition.lookup
result = dict()
for (k, v) in state.items():
if (k in lookup):
result[lookup[k]] = v
else:
assert ('.' in k)
split_idx = k.... |
def load_state_dict(partition, state_dict, strict=True):
reverse_lookup = {v: k for (k, v) in partition.lookup.items()}
device = partition.device
keys = list(partition.state_dict(None).keys())
new_state = dict()
for k in keys:
if (k in reverse_lookup):
new_state[reverse_lookup[... |
def named_buffers(partition, prefix='', recurse=True):
params = nn.Module.named_buffers(partition, prefix=prefix, recurse=recurse)
lookup = partition.lookup
for (k, v) in params:
if (k in lookup):
(yield (lookup[k], v))
else:
assert ('.' in k)
split_idx ... |
def named_parameters(partition, prefix='', recurse=True):
params = nn.Module.named_parameters(partition, prefix=prefix, recurse=recurse)
lookup = partition.lookup
for (k, v) in params:
if (k in lookup):
(yield (lookup[k], v))
else:
assert ('.' in k)
spli... |
def cpu(partition):
partition.device = torch.device('cpu')
return nn.Module.cpu(partition)
|
def cuda(partition, device=None):
if (device is None):
device = torch.cuda.current_device()
partition.device = torch.device(device)
return nn.Module.cuda(partition, partition.device)
|
def to(partition, *args, **kwargs):
device = None
if ('device' in kwargs):
device = kwargs['device']
elif ('tensor' in kwargs):
device = kwargs['tensor'].device
if args:
if isinstance(args[0], (torch.device, int, str)):
device = args[0]
if torch.is_tensor(ar... |
def layer_graph_t5_3b_tied_lmheads_512_4_8p_bw12_async_squad1_mpipe():
return dict(model_type='t5_stateless', model_name_or_path='t5-3b', do_lower_case=False, output_past=False, stateless_tied=True, explicitly_set_dict={'return_dict': False, 'use_cache': False, 'output_only': True, 'output_attentions': False, 'pr... |
def create_pipeline_configuration(DEBUG=False, batch_size=4):
config = {'batch_dim': 0, 'depth': 10000, 'basic_blocks': (Linear, StatelessEmbedding, Dropout, T5LayerNorm, T5Block), 'model_inputs': {'attention_mask': {'shape': torch.Size([4, 512]), 'dtype': torch.int64, 'is_batched': True, 'used_by': [0, 6]}, 'dec... |
class Partition0(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/StatelessEmbedding[embed_tokens]', 'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[0]', 'T5ForConditionalGeneration/T5Stack[encoder]/Modu... |
class Partition1(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[3]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[4]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[5]', 'T5ForConditionalGeneration/T5Stack[encod... |
class Partition2(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[7]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[8]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[9]', 'T5ForConditionalGeneration/T5Stack[encod... |
class Partition3(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[11]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[12]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[13]', 'T5ForConditionalGeneration/T5Stack[en... |
class Partition4(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[15]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[16]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[17]', 'T5ForConditionalGeneration/T5Stack[en... |
class Partition5(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[19]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[20]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[21]']
TENSORS = []
def __init__(sel... |
class Partition6(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[22]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[23]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5LayerNorm[final_layer_norm]', 'T5ForConditionalGeneration/T5Stack[en... |
class Partition7(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[6]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[7]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[8]', 'T5ForConditionalGeneration/T5Stack[decod... |
def traverse_model(module: nn.Module, depth: int, prefix: Optional[str]=None, basic_blocks: Tuple[Type[nn.Module]]=(), full: bool=False) -> Iterator[Tuple[(nn.Module, str, nn.Module, Optional[bool])]]:
'\n iterate over model layers yielding the layer,layer_scope,encasing_module\n Parameters:\n ----------... |
def layerDict(model: nn.Module, depth=1000, basic_blocks=()) -> Dict[(str, nn.Module)]:
return {s: l for (l, s, _) in traverse_model(model, depth, basic_blocks=basic_blocks)}
|
def traverse_params_buffs(module: nn.Module, prefix: Optional[str]=None) -> Iterator[Tuple[(torch.tensor, str)]]:
"\n iterate over model's buffers and parameters yielding obj,obj_scope\n\n Parameters:\n -----------\n model:\n the model to iterate over\n "
if (prefix is None):
pre... |
def tensorDict(model: nn.Module) -> OrderedDict[(str, Tensor)]:
return collections.OrderedDict(((s, t) for (t, s) in traverse_params_buffs(model)))
|
def move_tensors(ts, device):
def move(t):
if isinstance(t, (nn.Module, Tensor)):
return t.to(device)
return t
return nested_map(move, ts)
|
def nested_map(func, ts, full=False):
if isinstance(ts, torch.Size):
return func(ts)
elif isinstance(ts, (list, tuple, set)):
return type(ts)((nested_map(func, t, full=full) for t in ts))
elif isinstance(ts, dict):
return {k: nested_map(func, v, full=full) for (k, v) in ts.items()}... |
def flatten(ts):
if isinstance(ts, torch.Size):
(yield ts)
elif isinstance(ts, (list, tuple, set)):
(yield from chain(*[flatten(t) for t in ts]))
elif isinstance(ts, dict):
(yield from chain(*[flatten(t) for (k, t) in sorted(ts.items(), key=(lambda t: t[0]))]))
else:
(y... |
def unflatten(xs, structure):
return _unflatten(xs, structure)[0]
|
def _unflatten(xs, structure):
if isinstance(structure, torch.Size):
return (xs[0], 1)
if (not isinstance(structure, (list, tuple, set, dict))):
return (xs[0], 1)
if isinstance(structure, (list, tuple, set)):
offset = 0
elements = []
for s in structure:
... |
def state_dict(partition, *args, **kwargs):
state = nn.Module.state_dict(partition, *args, **kwargs)
lookup = partition.lookup
result = dict()
for (k, v) in state.items():
if (k in lookup):
result[lookup[k]] = v
else:
assert ('.' in k)
split_idx = k.... |
def load_state_dict(partition, state_dict, strict=True):
reverse_lookup = {v: k for (k, v) in partition.lookup.items()}
device = partition.device
keys = list(partition.state_dict(None).keys())
new_state = dict()
for k in keys:
if (k in reverse_lookup):
new_state[reverse_lookup[... |
def named_buffers(partition, prefix='', recurse=True):
params = nn.Module.named_buffers(partition, prefix=prefix, recurse=recurse)
lookup = partition.lookup
for (k, v) in params:
if (k in lookup):
(yield (lookup[k], v))
else:
assert ('.' in k)
split_idx ... |
def named_parameters(partition, prefix='', recurse=True):
params = nn.Module.named_parameters(partition, prefix=prefix, recurse=recurse)
lookup = partition.lookup
for (k, v) in params:
if (k in lookup):
(yield (lookup[k], v))
else:
assert ('.' in k)
spli... |
def cpu(partition):
partition.device = torch.device('cpu')
return nn.Module.cpu(partition)
|
def cuda(partition, device=None):
if (device is None):
device = torch.cuda.current_device()
partition.device = torch.device(device)
return nn.Module.cuda(partition, partition.device)
|
def to(partition, *args, **kwargs):
device = None
if ('device' in kwargs):
device = kwargs['device']
elif ('tensor' in kwargs):
device = kwargs['tensor'].device
if args:
if isinstance(args[0], (torch.device, int, str)):
device = args[0]
if torch.is_tensor(ar... |
def layer_graph_t5_3b_tied_lmheads_512_4_8p_bw12_squad1_pipedream():
return dict(model_type='t5_stateless', model_name_or_path='t5-3b', do_lower_case=False, output_past=False, stateless_tied=True, explicitly_set_dict={'return_dict': False, 'use_cache': False, 'output_only': True, 'output_attentions': False, 'prec... |
def create_pipeline_configuration(DEBUG=False, batch_size=64):
config = {'batch_dim': 0, 'depth': 10000, 'basic_blocks': (StatelessEmbedding, T5Block, Dropout, Linear, T5LayerNorm), 'model_inputs': {'attention_mask': {'shape': torch.Size([64, 64]), 'dtype': torch.int64, 'is_batched': True, 'used_by': [0, 8]}, 'de... |
class Partition0(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/StatelessEmbedding[embed_tokens]', 'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[0]', 'T5ForConditionalGeneration/T5Stack[encoder]/Modu... |
class Partition1(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[3]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[4]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[5]']
TENSORS = []
def __init__(self, ... |
class Partition2(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[6]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[7]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[8]']
TENSORS = []
def __init__(self, ... |
class Partition3(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[9]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[10]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[11]']
TENSORS = []
def __init__(self... |
class Partition4(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[12]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[13]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[14]']
TENSORS = []
def __init__(sel... |
class Partition5(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[15]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[16]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[17]']
TENSORS = []
def __init__(sel... |
class Partition6(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[18]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[19]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[20]']
TENSORS = []
def __init__(sel... |
class Partition7(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[21]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[22]', 'T5ForConditionalGeneration/T5Stack[encoder]/ModuleList[block]/T5Block[23]']
TENSORS = []
def __init__(sel... |
class Partition8(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5LayerNorm[final_layer_norm]', 'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]', 'T5ForConditionalGeneration/T5Stack[decoder]/Dropout[dropout]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T... |
class Partition9(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[3]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[4]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[5]']
TENSORS = []
def __init__(self, ... |
class Partition10(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[6]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[7]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[8]']
TENSORS = []
def __init__(self,... |
class Partition11(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[9]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[10]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[11]']
TENSORS = []
def __init__(sel... |
class Partition12(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[12]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[13]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[14]']
TENSORS = []
def __init__(se... |
class Partition13(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[15]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[16]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[17]']
TENSORS = []
def __init__(se... |
class Partition14(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[18]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[19]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[20]']
TENSORS = []
def __init__(se... |
class Partition15(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[21]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[22]', 'T5ForConditionalGeneration/T5Stack[decoder]/ModuleList[block]/T5Block[23]', 'T5ForConditionalGeneration/T5Stack[d... |
def traverse_model(module: nn.Module, depth: int, prefix: Optional[str]=None, basic_blocks: Tuple[Type[nn.Module]]=(), full: bool=False) -> Iterator[Tuple[(nn.Module, str, nn.Module, Optional[bool])]]:
'\n iterate over model layers yielding the layer,layer_scope,encasing_module\n Parameters:\n ----------... |
def layerDict(model: nn.Module, depth=1000, basic_blocks=()) -> Dict[(str, nn.Module)]:
return {s: l for (l, s, _) in traverse_model(model, depth, basic_blocks=basic_blocks)}
|
def traverse_params_buffs(module: nn.Module, prefix: Optional[str]=None) -> Iterator[Tuple[(torch.tensor, str)]]:
"\n iterate over model's buffers and parameters yielding obj,obj_scope\n\n Parameters:\n -----------\n model:\n the model to iterate over\n "
if (prefix is None):
pre... |
def tensorDict(model: nn.Module) -> OrderedDict[(str, Tensor)]:
return collections.OrderedDict(((s, t) for (t, s) in traverse_params_buffs(model)))
|
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