code stringlengths 17 6.64M |
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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': (LayerNorm, Conv2d, GELU, Linear, Dropout, Identity), 'model_inputs': {'input0': {'shape': torch.Size([32, 3, 384, 384]), 'dtype': torch.float32, 'is_batched': True, 'used_by': [0]}}, 'model_ou... |
class Partition0(nn.Module):
LAYER_SCOPES = ['VisionTransformer/PatchEmbed[patch_embed]/Conv2d[proj]', 'VisionTransformer/Dropout[pos_drop]', 'VisionTransformer/ModuleList[blocks]/Block[0]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[0]/Attention[attn]/Linear[qkv]', 'VisionTransformer/ModuleList... |
class Partition1(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[1]/Attention[attn]/Linear[proj]', 'VisionTransformer/ModuleList[blocks]/Block[1]/Attention[attn]/Dropout[proj_drop]', 'VisionTransformer/ModuleList[blocks]/Block[1]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/... |
class Partition2(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[2]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[2]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/Block[3]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[3]/Attention[attn]... |
class Partition3(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[4]/Attention[attn]/Linear[proj]', 'VisionTransformer/ModuleList[blocks]/Block[4]/Attention[attn]/Dropout[proj_drop]', 'VisionTransformer/ModuleList[blocks]/Block[4]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/... |
class Partition4(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[5]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[5]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/Block[6]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[6]/Attention[attn]... |
class Partition5(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[7]/Attention[attn]/Linear[proj]', 'VisionTransformer/ModuleList[blocks]/Block[7]/Attention[attn]/Dropout[proj_drop]', 'VisionTransformer/ModuleList[blocks]/Block[7]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/... |
class Partition6(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[9]/Attention[attn]/Linear[qkv]', 'VisionTransformer/ModuleList[blocks]/Block[9]/Attention[attn]/Dropout[attn_drop]', 'VisionTransformer/ModuleList[blocks]/Block[9]/Attention[attn]/Linear[proj]', 'VisionTransformer/ModuleList[... |
class Partition7(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[10]/Attention[attn]/Dropout[proj_drop]', 'VisionTransformer/ModuleList[blocks]/Block[10]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/Block[10]/LayerNorm[norm2]', 'VisionTransformer/ModuleList[blocks]/Block[10]... |
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):
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=128):
config = {'batch_dim': 0, 'depth': 10000, 'basic_blocks': (Linear, Conv2d, LayerNorm, Dropout, Identity, GELU), 'model_inputs': {'input0': {'shape': torch.Size([128, 3, 384, 384]), 'dtype': torch.float32, 'is_batched': True, 'used_by': [0]}}, 'model_... |
class Partition0(nn.Module):
LAYER_SCOPES = ['VisionTransformer/PatchEmbed[patch_embed]/Conv2d[proj]', 'VisionTransformer/Dropout[pos_drop]', 'VisionTransformer/ModuleList[blocks]/Block[0]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[0]/Attention[attn]/Linear[qkv]', 'VisionTransformer/ModuleList... |
class Partition1(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[2]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[2]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/Block[3]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[3]/Attention[attn]... |
class Partition2(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[5]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[5]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/Block[6]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[6]/Attention[attn]... |
class Partition3(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[8]/Mlp[mlp]/Linear[fc2]', 'VisionTransformer/ModuleList[blocks]/Block[8]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[8]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/Block[9]/LayerNorm[n... |
class Partition4(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[11]/Mlp[mlp]/GELU[act]', 'VisionTransformer/ModuleList[blocks]/Block[11]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[11]/Mlp[mlp]/Linear[fc2]', 'VisionTransformer/ModuleList[blocks]/Block[11]/Mlp[mlp]... |
class Partition5(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[14]/Mlp[mlp]/Linear[fc1]', 'VisionTransformer/ModuleList[blocks]/Block[14]/Mlp[mlp]/GELU[act]', 'VisionTransformer/ModuleList[blocks]/Block[14]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[14]/Mlp[mlp]... |
class Partition6(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[17]/Attention[attn]/Linear[proj]', 'VisionTransformer/ModuleList[blocks]/Block[17]/Attention[attn]/Dropout[proj_drop]', 'VisionTransformer/ModuleList[blocks]/Block[17]/Identity[drop_path]', 'VisionTransformer/ModuleList[block... |
class Partition7(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[20]/Attention[attn]/Dropout[attn_drop]', 'VisionTransformer/ModuleList[blocks]/Block[20]/Attention[attn]/Linear[proj]', 'VisionTransformer/ModuleList[blocks]/Block[20]/Attention[attn]/Dropout[proj_drop]', 'VisionTransformer/M... |
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=128):
config = {'batch_dim': 0, 'depth': 10000, 'basic_blocks': (Linear, Conv2d, LayerNorm, Dropout, Identity, GELU), 'model_inputs': {'input0': {'shape': torch.Size([128, 3, 384, 384]), 'dtype': torch.float32, 'is_batched': True, 'used_by': [0]}}, 'model_... |
class Partition0(nn.Module):
LAYER_SCOPES = ['VisionTransformer/PatchEmbed[patch_embed]/Conv2d[proj]', 'VisionTransformer/Dropout[pos_drop]', 'VisionTransformer/ModuleList[blocks]/Block[0]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[0]/Attention[attn]/Linear[qkv]', 'VisionTransformer/ModuleList... |
class Partition1(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[2]/Mlp[mlp]/Linear[fc2]', 'VisionTransformer/ModuleList[blocks]/Block[2]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[2]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/Block[3]/LayerNorm[n... |
class Partition2(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[5]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[5]/Mlp[mlp]/Linear[fc2]', 'VisionTransformer/ModuleList[blocks]/Block[5]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[5]/Identity... |
class Partition3(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[8]/Mlp[mlp]/GELU[act]', 'VisionTransformer/ModuleList[blocks]/Block[8]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[8]/Mlp[mlp]/Linear[fc2]', 'VisionTransformer/ModuleList[blocks]/Block[8]/Mlp[mlp]/Dro... |
class Partition4(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[11]/Mlp[mlp]/Linear[fc1]', 'VisionTransformer/ModuleList[blocks]/Block[11]/Mlp[mlp]/GELU[act]', 'VisionTransformer/ModuleList[blocks]/Block[11]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[11]/Mlp[mlp]... |
class Partition5(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[14]/Attention[attn]/Dropout[proj_drop]', 'VisionTransformer/ModuleList[blocks]/Block[14]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/Block[14]/LayerNorm[norm2]', 'VisionTransformer/ModuleList[blocks]/Block[14]... |
class Partition6(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[17]/Attention[attn]/Linear[proj]', 'VisionTransformer/ModuleList[blocks]/Block[17]/Attention[attn]/Dropout[proj_drop]', 'VisionTransformer/ModuleList[blocks]/Block[17]/Identity[drop_path]', 'VisionTransformer/ModuleList[block... |
class Partition7(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[20]/Attention[attn]/Dropout[attn_drop]', 'VisionTransformer/ModuleList[blocks]/Block[20]/Attention[attn]/Linear[proj]', 'VisionTransformer/ModuleList[blocks]/Block[20]/Attention[attn]/Dropout[proj_drop]', 'VisionTransformer/M... |
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)]]:
'\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': (Identity, LayerNorm, Conv2d, GELU, Linear, Dropout), 'model_inputs': {'input0': {'shape': torch.Size([32, 3, 384, 384]), 'dtype': torch.float32, 'is_batched': True, 'used_by': [0]}}, 'model_ou... |
class Partition0(nn.Module):
LAYER_SCOPES = ['VisionTransformer/PatchEmbed[patch_embed]/Conv2d[proj]', 'VisionTransformer/Dropout[pos_drop]', 'VisionTransformer/ModuleList[blocks]/Block[0]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[0]/Attention[attn]/Linear[qkv]', 'VisionTransformer/ModuleList... |
class Partition1(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[2]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[2]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/Block[3]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[3]/Attention[attn]... |
class Partition2(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[6]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[6]/Attention[attn]/Linear[qkv]', 'VisionTransformer/ModuleList[blocks]/Block[6]/Attention[attn]/Dropout[attn_drop]', 'VisionTransformer/ModuleList[blocks]/Bloc... |
class Partition3(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[8]/Mlp[mlp]/Dropout[drop]', 'VisionTransformer/ModuleList[blocks]/Block[8]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/Block[9]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[9]/Attention[attn]... |
class Partition4(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[11]/Identity[drop_path]', 'VisionTransformer/ModuleList[blocks]/Block[12]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[12]/Attention[attn]/Linear[qkv]', 'VisionTransformer/ModuleList[blocks]/Block[12]/Attent... |
class Partition5(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[15]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[15]/Attention[attn]/Linear[qkv]', 'VisionTransformer/ModuleList[blocks]/Block[15]/Attention[attn]/Dropout[attn_drop]', 'VisionTransformer/ModuleList[blocks]/B... |
class Partition6(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[18]/LayerNorm[norm1]', 'VisionTransformer/ModuleList[blocks]/Block[18]/Attention[attn]/Linear[qkv]', 'VisionTransformer/ModuleList[blocks]/Block[18]/Attention[attn]/Dropout[attn_drop]', 'VisionTransformer/ModuleList[blocks]/B... |
class Partition7(nn.Module):
LAYER_SCOPES = ['VisionTransformer/ModuleList[blocks]/Block[21]/Attention[attn]/Linear[qkv]', 'VisionTransformer/ModuleList[blocks]/Block[21]/Attention[attn]/Dropout[attn_drop]', 'VisionTransformer/ModuleList[blocks]/Block[21]/Attention[attn]/Linear[proj]', 'VisionTransformer/ModuleLi... |
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):
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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