code stringlengths 17 6.64M |
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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 create_pipeline_configuration(DEBUG=False, batch_size=32):
config = {'batch_dim': 0, 'depth': 10000, 'basic_blocks': (T5LayerNorm, Linear, StatelessEmbedding, CrossEntropyLoss, T5Block, Dropout), 'model_inputs': {'attention_mask': {'shape': torch.Size([32, 1, 1, 64]), 'dtype': torch.float32, 'is_batched': Tru... |
class Partition0(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/StatelessEmbedding[embed_tokens]', 'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[0]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[1]', 'T5ForCon... |
class Partition1(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[4]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[5]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[6]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[7]']
TENSORS = []
def __init__(se... |
class Partition2(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[8]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[9]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[10]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[11]']
TENSORS = []
def __init__(... |
class Partition3(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[12]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[13]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[14]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[15]']
TENSORS = []
def __init_... |
class Partition4(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[16]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[17]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[18]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[19]']
TENSORS = []
def __init_... |
class Partition5(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[20]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[21]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[22]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[23]', 'T5ForConditionalGeneration/T5Sta... |
class Partition6(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/T5Block[1]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[2]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[3]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[4]', 'T5ForConditionalGeneration/T5Stack[d... |
class Partition7(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/T5Block[13]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[14]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[15]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[16]', 'T5ForConditionalGeneration/T5Sta... |
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 create_pipeline_configuration(DEBUG=False, batch_size=32):
config = {'batch_dim': 0, 'depth': 10000, 'basic_blocks': (CrossEntropyLoss, Linear, Dropout, T5LayerNorm, T5Block, StatelessEmbedding), 'model_inputs': {'attention_mask': {'shape': torch.Size([32, 1, 1, 64]), 'dtype': torch.float32, 'is_batched': Tru... |
class Partition0(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/StatelessEmbedding[embed_tokens]', 'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]']
TENSORS = ['T5ForConditionalGeneration/Parameter[shared_embed_weight]']
def __init__(self, layers, tensors, device='... |
class Partition1(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[0]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[1]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[2]']
TENSORS = []
def __init__(self, layers, tensors, device='cuda:1'):
super().__i... |
class Partition2(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[3]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[4]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[5]']
TENSORS = []
def __init__(self, layers, tensors, device='cuda:2'):
super().__i... |
class Partition3(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[6]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[7]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[8]']
TENSORS = []
def __init__(self, layers, tensors, device='cuda:3'):
super().__i... |
class Partition4(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[9]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[10]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[11]']
TENSORS = []
def __init__(self, layers, tensors, device='cuda:4'):
super()._... |
class Partition5(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[12]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[13]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[14]']
TENSORS = []
def __init__(self, layers, tensors, device='cuda:5'):
super().... |
class Partition6(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[15]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[16]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[17]']
TENSORS = []
def __init__(self, layers, tensors, device='cuda:6'):
super().... |
class Partition7(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[18]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[19]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[20]']
TENSORS = []
def __init__(self, layers, tensors, device='cuda:7'):
super().... |
class Partition8(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[21]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[22]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[23]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5LayerNorm[final_layer_norm]', 'T5ForCondition... |
class Partition9(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/StatelessEmbedding[embed_tokens]', 'T5ForConditionalGeneration/T5Stack[decoder]/Dropout[dropout]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[0]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[1]']
TENSO... |
class Partition10(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/T5Block[2]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[3]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[4]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[5]']
TENSORS = []
def __init__(s... |
class Partition11(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/T5Block[6]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[7]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[8]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[9]']
TENSORS = []
def __init__(s... |
class Partition12(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/T5Block[10]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[11]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[12]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[13]']
TENSORS = []
def __init... |
class Partition13(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/T5Block[14]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[15]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[16]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[17]']
TENSORS = []
def __init... |
class Partition14(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/T5Block[18]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[19]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[20]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[21]']
TENSORS = []
def __init... |
class Partition15(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/T5Block[22]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[23]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5LayerNorm[final_layer_norm]', 'T5ForConditionalGeneration/T5Stack[decoder]/Dropout[dropout]', 'T5ForCon... |
def traverse_model(module: nn.Module, depth: int, prefix: Optional[str]=None, basic_blocks: Tuple[nn.Module]=(), full: bool=False) -> Iterator[Tuple[(nn.Module, str, nn.Module)]]:
'\n iterate over model layers yielding the layer,layer_scope,encasing_module\n Parameters:\n -----------\n model:\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):
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[k]] = state[k].to(... |
def named_buffers(partition, recurse=True):
params = nn.Module.named_buffers(partition, recurse=recurse)
lookup = partition.lookup
for (k, v) in params:
if (k in lookup):
(yield (lookup[k], v))
else:
assert ('.' in k)
split_idx = k.find('.')
... |
def named_parameters(partition, recurse=True):
params = nn.Module.named_parameters(partition, recurse=recurse)
lookup = partition.lookup
for (k, v) in params:
if (k in lookup):
(yield (lookup[k], v))
else:
assert ('.' in k)
split_idx = k.find('.')
... |
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 create_pipeline_configuration(DEBUG=False, batch_size=32):
config = {'batch_dim': 0, 'depth': 10000, 'basic_blocks': (CrossEntropyLoss, T5Block, T5LayerNorm, StatelessEmbedding, Linear, Dropout), 'model_inputs': {'attention_mask': {'shape': torch.Size([32, 1, 1, 384]), 'dtype': torch.float32, 'is_batched': Tr... |
class Partition0(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/StatelessEmbedding[embed_tokens]', 'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[0]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[1]', 'T5ForCon... |
class Partition1(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[2]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[3]']
TENSORS = []
def __init__(self, layers, tensors, device='cuda:1'):
super().__init__()
for (idx, layer_scope) in enumerate(self.L... |
class Partition2(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[4]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[5]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5LayerNorm[final_layer_norm]', 'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]']
TENSORS ... |
class Partition3(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/Dropout[dropout]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[0]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[1]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[2]', 'T5ForConditionalGeneration/T5S... |
def traverse_model(module: nn.Module, depth: int, prefix: Optional[str]=None, basic_blocks: Tuple[nn.Module]=(), full: bool=False) -> Iterator[Tuple[(nn.Module, str, nn.Module)]]:
'\n iterate over model layers yielding the layer,layer_scope,encasing_module\n Parameters:\n -----------\n model:\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, device=None):
state = nn.Module.state_dict(partition)
lookup = partition.lookup
result = dict()
for (k, v) in state.items():
if (k in lookup):
result[lookup[k]] = (v if (device is None) else v.to(device))
else:
assert ('.' in k)
... |
def load_state_dict(partition, state):
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[k]] = state[k].to(... |
def named_buffers(partition, recurse=True):
params = nn.Module.named_buffers(partition, recurse=recurse)
lookup = partition.lookup
for (k, v) in params:
if (k in lookup):
(yield (lookup[k], v))
else:
assert ('.' in k)
split_idx = k.find('.')
... |
def named_parameters(partition, recurse=True):
params = nn.Module.named_parameters(partition, recurse=recurse)
lookup = partition.lookup
for (k, v) in params:
if (k in lookup):
(yield (lookup[k], v))
else:
assert ('.' in k)
split_idx = k.find('.')
... |
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 create_pipeline_configuration(DEBUG=False, batch_size=32):
config = {'batch_dim': 0, 'depth': 10000, 'basic_blocks': (CrossEntropyLoss, Linear, StatelessEmbedding, T5Block, Dropout, T5LayerNorm), 'model_inputs': {'attention_mask': {'shape': torch.Size([32, 1, 1, 512]), 'dtype': torch.float32, 'is_batched': Tr... |
class Partition0(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/StatelessEmbedding[embed_tokens]', 'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[0]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[1]', 'T5ForCon... |
class Partition1(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[2]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[3]']
TENSORS = []
def __init__(self, layers, tensors, device='cuda:1'):
super().__init__()
for (idx, layer_scope) in enumerate(self.L... |
class Partition2(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5Block[4]', 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[5]']
TENSORS = []
def __init__(self, layers, tensors, device='cuda:2'):
super().__init__()
for (idx, layer_scope) in enumerate(self.L... |
class Partition3(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[encoder]/T5LayerNorm[final_layer_norm]', 'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]']
TENSORS = []
def __init__(self, layers, tensors, device='cuda:3'):
super().__init__()
for (idx, layer_s... |
class Partition4(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/Dropout[dropout]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[0]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[1]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[2]']
TENSORS = []
def __ini... |
class Partition5(nn.Module):
LAYER_SCOPES = ['T5ForConditionalGeneration/T5Stack[decoder]/T5Block[3]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[4]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[5]', 'T5ForConditionalGeneration/T5Stack[decoder]/T5LayerNorm[final_layer_norm]', 'T5ForConditionalG... |
def traverse_model(module: nn.Module, depth: int, prefix: Optional[str]=None, basic_blocks: Tuple[nn.Module]=(), full: bool=False) -> Iterator[Tuple[(nn.Module, str, nn.Module)]]:
'\n iterate over model layers yielding the layer,layer_scope,encasing_module\n Parameters:\n -----------\n model:\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):
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[k]] = state[k].to(... |
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