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Hongxin Liu
079bf3cb26
|
1 year ago | |
|---|---|---|
| .. | ||
| _subclasses | 1 year ago | |
| fx | 1 year ago | |
| README.md | ||
| __init__.py | 2 years ago | |
| envs.py | ||
README.md
Analyzer
Overview
The Analyzer is a collection of static graph utils including Colossal-AI FX. Features include:
- MetaTensor -- enabling:
- Ahead-of-time Profiling
- Shape Propagation
- Ideal Flop Counter
- symbolic_trace()
- Robust Control-flow Tracing / Recompile
- Robust Activation Checkpoint Tracing / CodeGen
- Easy-to-define Bias-Addition Split
- symbolic_profile()
- Support
MetaTensorMode, where all Tensor operations are executed symbolically. - Shape Inference Across Device and Unified
MetaInfo - Ideal Flop Counter https://dev-discuss.pytorch.org/t/the-ideal-pytorch-flop-counter-with-torch-dispatch/505
- Support
Quickstart
Analyzer.FX
Reference:
https://pytorch.org/docs/stable/fx.html [paper]
torch.FX is a toolkit for developers to use to transform nn.Module instances. FX consists of three main components: a symbolic tracer, an intermediate representation, and Python code generation. FX.Tracer hacks __torch_function__ and use a Proxy object to propagate through any forward function of torch.nn.Module.
ColossalAI FX is modified from torch.FX, with the extra capability of ahead-of-time profiling enabled by the subclass of MetaTensor.
Analyzer.FX.symbolic_trace()
A drawback of the original torch.FX implementation is that it is poor at handling control flow. All control flow is not PyTorch native operands and requires actual instances that specify the branches to execute on. For example,
class MyModule(nn.Module):
def forward(self, x):
if x.dim() == 3:
return x * 2 + 1
else:
return x - 5
The above function has the computation graph of
However, since Proxy does not have concrete data, applying x.dim() will return nothing. In the context of the auto-parallel system, at least the control-flow dependencies for tensor shape should be removed, since any searched strategy could only auto-parallelize a specific computation graph with the same tensor shape. It is native to attach concrete data onto a Proxy, and propagate them through control flow.
With MetaTensor, the computation during shape propagation can be virtualized. This speeds up tracing by avoiding allocating actual memory on devices.
Remarks
There is no free lunch for PyTorch to unify all operands in both its repo and other repos in its eco-system. For example, the einops library currently has no intention to support torch.FX (See https://github.com/arogozhnikov/einops/issues/188). To support different PyTorch-based libraries without modifying source code, good practices can be to allow users to register their implementation to substitute the functions not supported by torch.FX, or to avoid entering incompatible submodules.
Analyzer.FX.symbolic_profile()
symbolic_profile is another important feature of Colossal-AI's auto-parallel system. Profiling DNN can be costly, as you need to allocate memory and execute on real devices. However, since the profiling requirements for auto-parallel is enough if we can detect when and where the intermediate activations (i.e. Tensor) are generated, we can profile the whole procedure without actually executing it. symbolic_profile, as its name infers, profiles the whole network with symbolic information only.
with MetaTensorMode():
model = MyModule().cuda()
sample = torch.rand(100, 3, 224, 224).cuda()
meta_args = dict(
x = sample,
)
gm = symbolic_trace(model, meta_args=meta_args)
gm = symbolic_profile(gm, sample)
symbolic_profile is enabled by ShapeProp and GraphProfile.
ShapeProp
Both Tensor Parallel and Activation Checkpoint solvers need to know the shape information ahead of time. Unlike PyTorch's implementation, this ShapeProp can be executed under MetaTensorMode. With this, all the preparation for auto-parallel solvers can be done in milliseconds.
Meanwhile, it is easy to keep track of the memory usage of each node when doing shape propagation. However, the drawbacks of FX is that not every call_function saves its input for backward, and different tensor that flows within one FX.Graph can actually have the same layout. This raises problems for fine-grained profiling.
To address this problem, I came up with a simulated environment enabled by torch.autograd.graph.saved_tensor_hooks and fake data_ptr (check _subclasses/meta_tensor.py for more details of data_ptr updates).
class sim_env(saved_tensors_hooks):
"""
A simulation of memory allocation and deallocation in the forward pass
using ``saved_tensor_hooks``.
Attributes:
ctx (Dict[int, torch.Tensor]): A dictionary that maps the
data pointer of a tensor to the tensor itself. This is used
to track the memory allocation and deallocation.
param_ctx (Dict[int, torch.Tensor]): A dictionary that maps the
data pointer of all model parameters to the parameter itself.
This avoids overestimating the memory usage of the intermediate activations.
"""
def __init__(self, module: Optional[torch.nn.Module] = None):
super().__init__(self.pack_hook, self.unpack_hook)
self.ctx = {}
self.param_ctx = {param.data_ptr(): param for param in module.parameters()}
self.buffer_ctx = {buffer.data_ptr(): buffer for buffer in module.buffers()} if module else {}
def pack_hook(self, tensor: torch.Tensor):
if tensor.data_ptr() not in self.param_ctx and tensor.data_ptr() not in self.buffer_ctx:
self.ctx[tensor.data_ptr()] = tensor
return tensor
def unpack_hook(self, tensor):
return tensor
The ctx variable will keep track of all saved tensors with a unique identifier. It is likely that nn.Parameter is also counted in the ctx, which is not desired. To avoid this, we can use param_ctx to keep track of all parameters in the model. The buffer_ctx is used to keep track of all buffers in the model. The local_ctx that is attached to each Node marks the memory usage of the stage to which the node belongs. With simple intersect, union and subtract operations, we can get any memory-related information. For non-profileable nodes, you might add your customized profile rules to simulate the memory allocation. If a Graph is modified with some non-PyTorch functions, such as fused operands, you can register the shape propagation rule with the decorator.
@register_shape_impl(fuse_conv_bn)
def fuse_conv_bn_shape_impl(*args, **kwargs):
# infer output shape here
return torch.empty(output_shape, device=output_device)
An important notice is that ShapeProp will attach additional information to the graph, which will be exactly the input of Profiler.
GraphProfiler
GraphProfiler executes at the node level, and profiles both forward and backward within one node. For example, FlopProfiler will profile the forward and backward FLOPs of a node, and CommunicationProfiler will profile the forward and backward communication cost of a node. The GraphProfiler will attach the profiling results to the Node. These procedures are decoupled for better extensibility.
To provide a general insight of the profiled results, you can set verbose=True to print the summary as well.
model = tm.resnet18()
sample = torch.rand(100, 3, 224, 224)
meta_args = dict(x=sample)
gm = symbolic_trace(model, meta_args=meta_args)
gm = symbolic_profile(gm, sample, verbose=True)
============================================================ Results =====================================================================
Op type Op Accumulate size Incremental size Output size Temp size Param size Backward size Fwd FLOPs Bwd FLOPs
------------- ---------------------------------------------- ----------------- ------------------ ------------- ----------- ------------ --------------- ------------- -------------
placeholder x 4.59 Mb 0 b 4.59 Mb 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module conv_proj 4.59 Mb 0 b 0 b 4.59 Mb 2.25 Mb 4.59 Mb 924.84 MFLOPs 924.84 MFLOPs
call_method reshape 4.59 Mb 0 b 0 b 4.59 Mb 0 b 4.59 Mb 0 FLOPs 0 FLOPs
call_method permute 4.59 Mb 0 b 0 b 4.59 Mb 0 b 4.59 Mb 0 FLOPs 0 FLOPs
get_attr class_token 4.59 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_method expand 4.59 Mb 0 b 0 b 24.00 Kb 3.00 Kb 0 b 0 FLOPs 6.14 kFLOPs
call_function cat 4.59 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
get_attr encoder_pos_embedding 4.59 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_function add 9.21 Mb 4.62 Mb 4.62 Mb 0 b 591.00 Kb 4.62 Mb 1.21 MFLOPs 1.21 MFLOPs
call_module encoder_dropout 9.21 Mb 0 b 4.62 Mb 0 b 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_0_ln_1 9.22 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_0_self_attention 46.52 Mb 37.30 Mb 0 b 4.62 Mb 9.01 Mb 13.85 Mb 4.20 GFLOPs 8.40 GFLOPs
call_function getitem 46.52 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
call_function getitem_1 46.52 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_0_dropout 46.52 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_1 51.14 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_0_ln_2 51.15 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_0_mlp_0 74.24 Mb 23.09 Mb 18.47 Mb 0 b 9.01 Mb 4.62 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_0_mlp_1 92.71 Mb 18.47 Mb 18.47 Mb 0 b 0 b 18.47 Mb 4.84 MFLOPs 4.84 MFLOPs
call_module encoder_layers_encoder_layer_0_mlp_2 92.71 Mb 0 b 18.47 Mb 0 b 0 b 18.47 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_0_mlp_3 92.71 Mb 0 b 0 b 4.62 Mb 9.00 Mb 18.47 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_0_mlp_4 92.71 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_2 97.32 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_1_ln_1 101.95 Mb 4.63 Mb 4.62 Mb 0 b 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_1_self_attention 134.63 Mb 32.68 Mb 0 b 4.62 Mb 9.01 Mb 13.85 Mb 4.20 GFLOPs 8.40 GFLOPs
call_function getitem_2 134.63 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
call_function getitem_3 134.63 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_1_dropout 134.63 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_3 139.25 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_1_ln_2 139.26 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_1_mlp_0 162.35 Mb 23.09 Mb 18.47 Mb 0 b 9.01 Mb 4.62 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_1_mlp_1 180.82 Mb 18.47 Mb 18.47 Mb 0 b 0 b 18.47 Mb 4.84 MFLOPs 4.84 MFLOPs
call_module encoder_layers_encoder_layer_1_mlp_2 180.82 Mb 0 b 18.47 Mb 0 b 0 b 18.47 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_1_mlp_3 180.82 Mb 0 b 0 b 4.62 Mb 9.00 Mb 18.47 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_1_mlp_4 180.82 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_4 185.43 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_2_ln_1 190.06 Mb 4.63 Mb 4.62 Mb 0 b 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_2_self_attention 222.74 Mb 32.68 Mb 0 b 4.62 Mb 9.01 Mb 13.85 Mb 4.20 GFLOPs 8.40 GFLOPs
call_function getitem_4 222.74 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
call_function getitem_5 222.74 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_2_dropout 222.74 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_5 227.36 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_2_ln_2 227.37 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_2_mlp_0 250.46 Mb 23.09 Mb 18.47 Mb 0 b 9.01 Mb 4.62 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_2_mlp_1 268.93 Mb 18.47 Mb 18.47 Mb 0 b 0 b 18.47 Mb 4.84 MFLOPs 4.84 MFLOPs
call_module encoder_layers_encoder_layer_2_mlp_2 268.93 Mb 0 b 18.47 Mb 0 b 0 b 18.47 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_2_mlp_3 268.93 Mb 0 b 0 b 4.62 Mb 9.00 Mb 18.47 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_2_mlp_4 268.93 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_6 273.54 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_3_ln_1 278.17 Mb 4.63 Mb 4.62 Mb 0 b 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_3_self_attention 310.86 Mb 32.68 Mb 0 b 4.62 Mb 9.01 Mb 13.85 Mb 4.20 GFLOPs 8.40 GFLOPs
call_function getitem_6 310.86 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
call_function getitem_7 310.86 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_3_dropout 310.86 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_7 315.47 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_3_ln_2 315.48 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_3_mlp_0 338.57 Mb 23.09 Mb 18.47 Mb 0 b 9.01 Mb 4.62 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_3_mlp_1 357.04 Mb 18.47 Mb 18.47 Mb 0 b 0 b 18.47 Mb 4.84 MFLOPs 4.84 MFLOPs
call_module encoder_layers_encoder_layer_3_mlp_2 357.04 Mb 0 b 18.47 Mb 0 b 0 b 18.47 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_3_mlp_3 357.04 Mb 0 b 0 b 4.62 Mb 9.00 Mb 18.47 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_3_mlp_4 357.04 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_8 361.66 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_4_ln_1 366.29 Mb 4.63 Mb 4.62 Mb 0 b 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_4_self_attention 398.97 Mb 32.68 Mb 0 b 4.62 Mb 9.01 Mb 13.85 Mb 4.20 GFLOPs 8.40 GFLOPs
call_function getitem_8 398.97 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
call_function getitem_9 398.97 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_4_dropout 398.97 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_9 403.58 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_4_ln_2 403.60 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_4_mlp_0 426.68 Mb 23.09 Mb 18.47 Mb 0 b 9.01 Mb 4.62 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_4_mlp_1 445.15 Mb 18.47 Mb 18.47 Mb 0 b 0 b 18.47 Mb 4.84 MFLOPs 4.84 MFLOPs
call_module encoder_layers_encoder_layer_4_mlp_2 445.15 Mb 0 b 18.47 Mb 0 b 0 b 18.47 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_4_mlp_3 445.15 Mb 0 b 0 b 4.62 Mb 9.00 Mb 18.47 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_4_mlp_4 445.15 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_10 449.77 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_5_ln_1 454.40 Mb 4.63 Mb 4.62 Mb 0 b 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_5_self_attention 487.08 Mb 32.68 Mb 0 b 4.62 Mb 9.01 Mb 13.85 Mb 4.20 GFLOPs 8.40 GFLOPs
call_function getitem_10 487.08 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
call_function getitem_11 487.08 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_5_dropout 487.08 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_11 491.70 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_5_ln_2 491.71 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_5_mlp_0 514.79 Mb 23.09 Mb 18.47 Mb 0 b 9.01 Mb 4.62 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_5_mlp_1 533.26 Mb 18.47 Mb 18.47 Mb 0 b 0 b 18.47 Mb 4.84 MFLOPs 4.84 MFLOPs
call_module encoder_layers_encoder_layer_5_mlp_2 533.26 Mb 0 b 18.47 Mb 0 b 0 b 18.47 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_5_mlp_3 533.26 Mb 0 b 0 b 4.62 Mb 9.00 Mb 18.47 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_5_mlp_4 533.26 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_12 537.88 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_6_ln_1 542.51 Mb 4.63 Mb 4.62 Mb 0 b 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_6_self_attention 575.19 Mb 32.68 Mb 0 b 4.62 Mb 9.01 Mb 13.85 Mb 4.20 GFLOPs 8.40 GFLOPs
call_function getitem_12 575.19 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
call_function getitem_13 575.19 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_6_dropout 575.19 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_13 579.81 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_6_ln_2 579.82 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_6_mlp_0 602.90 Mb 23.09 Mb 18.47 Mb 0 b 9.01 Mb 4.62 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_6_mlp_1 621.37 Mb 18.47 Mb 18.47 Mb 0 b 0 b 18.47 Mb 4.84 MFLOPs 4.84 MFLOPs
call_module encoder_layers_encoder_layer_6_mlp_2 621.37 Mb 0 b 18.47 Mb 0 b 0 b 18.47 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_6_mlp_3 621.37 Mb 0 b 0 b 4.62 Mb 9.00 Mb 18.47 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_6_mlp_4 621.37 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_14 625.99 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_7_ln_1 630.62 Mb 4.63 Mb 4.62 Mb 0 b 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_7_self_attention 663.30 Mb 32.68 Mb 0 b 4.62 Mb 9.01 Mb 13.85 Mb 4.20 GFLOPs 8.40 GFLOPs
call_function getitem_14 663.30 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
call_function getitem_15 663.30 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_7_dropout 663.30 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_15 667.92 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_7_ln_2 667.93 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_7_mlp_0 691.02 Mb 23.09 Mb 18.47 Mb 0 b 9.01 Mb 4.62 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_7_mlp_1 709.48 Mb 18.47 Mb 18.47 Mb 0 b 0 b 18.47 Mb 4.84 MFLOPs 4.84 MFLOPs
call_module encoder_layers_encoder_layer_7_mlp_2 709.48 Mb 0 b 18.47 Mb 0 b 0 b 18.47 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_7_mlp_3 709.48 Mb 0 b 0 b 4.62 Mb 9.00 Mb 18.47 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_7_mlp_4 709.48 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_16 714.10 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_8_ln_1 718.73 Mb 4.63 Mb 4.62 Mb 0 b 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_8_self_attention 751.41 Mb 32.68 Mb 0 b 4.62 Mb 9.01 Mb 13.85 Mb 4.20 GFLOPs 8.40 GFLOPs
call_function getitem_16 751.41 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
call_function getitem_17 751.41 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_8_dropout 751.41 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_17 756.03 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_8_ln_2 756.04 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_8_mlp_0 779.13 Mb 23.09 Mb 18.47 Mb 0 b 9.01 Mb 4.62 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_8_mlp_1 797.60 Mb 18.47 Mb 18.47 Mb 0 b 0 b 18.47 Mb 4.84 MFLOPs 4.84 MFLOPs
call_module encoder_layers_encoder_layer_8_mlp_2 797.60 Mb 0 b 18.47 Mb 0 b 0 b 18.47 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_8_mlp_3 797.60 Mb 0 b 0 b 4.62 Mb 9.00 Mb 18.47 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_8_mlp_4 797.60 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_18 802.21 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_9_ln_1 806.84 Mb 4.63 Mb 4.62 Mb 0 b 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_9_self_attention 839.52 Mb 32.68 Mb 0 b 4.62 Mb 9.01 Mb 13.85 Mb 4.20 GFLOPs 8.40 GFLOPs
call_function getitem_18 839.52 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
call_function getitem_19 839.52 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_9_dropout 839.52 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_19 844.14 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_9_ln_2 844.15 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_9_mlp_0 867.24 Mb 23.09 Mb 18.47 Mb 0 b 9.01 Mb 4.62 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_9_mlp_1 885.71 Mb 18.47 Mb 18.47 Mb 0 b 0 b 18.47 Mb 4.84 MFLOPs 4.84 MFLOPs
call_module encoder_layers_encoder_layer_9_mlp_2 885.71 Mb 0 b 18.47 Mb 0 b 0 b 18.47 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_9_mlp_3 885.71 Mb 0 b 0 b 4.62 Mb 9.00 Mb 18.47 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_9_mlp_4 885.71 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_20 890.32 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_10_ln_1 894.95 Mb 4.63 Mb 4.62 Mb 0 b 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_10_self_attention 927.63 Mb 32.68 Mb 0 b 4.62 Mb 9.01 Mb 13.85 Mb 4.20 GFLOPs 8.40 GFLOPs
call_function getitem_20 927.63 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
call_function getitem_21 927.63 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_10_dropout 927.63 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_21 932.25 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_10_ln_2 932.26 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_10_mlp_0 955.35 Mb 23.09 Mb 18.47 Mb 0 b 9.01 Mb 4.62 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_10_mlp_1 973.82 Mb 18.47 Mb 18.47 Mb 0 b 0 b 18.47 Mb 4.84 MFLOPs 4.84 MFLOPs
call_module encoder_layers_encoder_layer_10_mlp_2 973.82 Mb 0 b 18.47 Mb 0 b 0 b 18.47 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_10_mlp_3 973.82 Mb 0 b 0 b 4.62 Mb 9.00 Mb 18.47 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_10_mlp_4 973.82 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_22 978.44 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_11_ln_1 983.06 Mb 4.63 Mb 4.62 Mb 0 b 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_11_self_attention 1015.75 Mb 32.68 Mb 0 b 4.62 Mb 9.01 Mb 13.85 Mb 4.20 GFLOPs 8.40 GFLOPs
call_function getitem_22 1015.75 Mb 0 b 0 b 4.62 Mb 0 b 0 b 0 FLOPs 0 FLOPs
call_function getitem_23 1015.75 Mb 0 b 0 b 0 b 0 b 0 b 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_11_dropout 1015.75 Mb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_23 1020.36 Mb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_11_ln_2 1020.38 Mb 12.31 Kb 0 b 4.62 Mb 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_module encoder_layers_encoder_layer_11_mlp_0 1.02 Gb 23.09 Mb 18.47 Mb 0 b 9.01 Mb 4.62 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_11_mlp_1 1.04 Gb 18.47 Mb 18.47 Mb 0 b 0 b 18.47 Mb 4.84 MFLOPs 4.84 MFLOPs
call_module encoder_layers_encoder_layer_11_mlp_2 1.04 Gb 0 b 18.47 Mb 0 b 0 b 18.47 Mb 0 FLOPs 0 FLOPs
call_module encoder_layers_encoder_layer_11_mlp_3 1.04 Gb 0 b 0 b 4.62 Mb 9.00 Mb 18.47 Mb 3.72 GFLOPs 7.44 GFLOPs
call_module encoder_layers_encoder_layer_11_mlp_4 1.04 Gb 0 b 0 b 4.62 Mb 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_function add_24 1.04 Gb 4.62 Mb 4.62 Mb 0 b 0 b 9.23 Mb 1.21 MFLOPs 0 FLOPs
call_module encoder_ln 1.04 Gb 36.31 Kb 24.00 Kb 0 b 6.00 Kb 4.62 Mb 6.05 MFLOPs 6.05 MFLOPs
call_function getitem_24 1.04 Gb 0 b 24.00 Kb 0 b 0 b 4.62 Mb 0 FLOPs 0 FLOPs
call_module heads_head 1.04 Gb 0 b 0 b 31.25 Kb 2.93 Mb 24.00 Kb 6.14 MFLOPs 12.30 MFLOPs
output output 1.04 Gb 0 b 0 b 31.25 Kb 0 b 31.25 Kb 0 FLOPs 0 FLOPs