Refactor the internal codebase from fx2trt_oss to torch_tensorrt

docs/_modules/torch_tensorrt/_compile.html

@@ -518,7 +518,7 @@ <h1>Source code for torch_tensorrt._compile</h1><div class="highlight"><pre>
                     <span class="c1"># profiling_verbosity=trt.ProfilingVerbosity.DETAILED, #For profile</span>
                 <span class="p">)</span>
                 <span class="c1"># For profile</span>
-                <span class="c1"># from fx2trt_oss.fx.tools.trt_profiler_sorted import profile_trt_module</span>
+                <span class="c1"># from torch_tensorrt.fx.tools.trt_profiler_sorted import profile_trt_module</span>
                 <span class="c1"># profile_trt_module(&quot;&quot;, trt_mod, acc_inputs)</span>
                 <span class="n">trt_mod</span> <span class="o">=</span> <span class="n">TRTModule</span><span class="p">(</span><span class="o">*</span><span class="n">r</span><span class="p">)</span>
 

py/torch_tensorrt/_compile.py

@@ -172,7 +172,7 @@ def get_input(self, inputs):
                     # profiling_verbosity=trt.ProfilingVerbosity.DETAILED, #For profile
                 )
                 # For profile
-                # from fx2trt_oss.fx.tools.trt_profiler_sorted import profile_trt_module
+                # from torch_tensorrt.fx.tools.trt_profiler_sorted import profile_trt_module
                 # profile_trt_module("", trt_mod, acc_inputs)
                 trt_mod = TRTModule(*r)
 

py/torch_tensorrt/fx/converters/acc_ops_converters.py

@@ -4,22 +4,21 @@
 import warnings
 from typing import cast, Dict, Optional, Sequence, Tuple, Union
 
-from ..tracer.acc_tracer import acc_ops
 import numpy as np
 
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
+
 from ..converter_registry import tensorrt_converter
+
+from ..tracer.acc_tracer import acc_ops
 from ..types import *  # noqa: F403
-from ..utils import (
-    get_dynamic_dims,
-    torch_dtype_from_trt,
-    torch_dtype_to_trt,
-)
 from torch.fx.immutable_collections import immutable_list
 from torch.fx.node import Argument, Target
 
+from ..utils import get_dynamic_dims, torch_dtype_from_trt, torch_dtype_to_trt
+
 from .converter_utils import *  # noqa: F403
 
 

py/torch_tensorrt/fx/converters/activation.py

@@ -3,6 +3,7 @@
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
+
 from ..converter_registry import tensorrt_converter
 
 from .converter_utils import mark_as_int8_layer

py/torch_tensorrt/fx/converters/adaptive_avgpool.py

@@ -1,6 +1,7 @@
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
+
 from ..converter_registry import tensorrt_converter
 
 from .converter_utils import extend_mod_attr_to_tuple, mark_as_int8_layer

py/torch_tensorrt/fx/converters/add.py

@@ -3,6 +3,7 @@
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
+
 from ..converter_registry import tensorrt_converter
 
 from .converter_utils import get_dyn_range, mark_as_int8_layer

py/torch_tensorrt/fx/converters/batchnorm.py

@@ -3,6 +3,7 @@
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
+
 from ..converter_registry import tensorrt_converter
 
 from .converter_utils import get_dyn_range, mark_as_int8_layer, to_numpy

py/torch_tensorrt/fx/converters/converter_utils.py

@@ -7,6 +7,8 @@
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
+from torch.fx.node import Argument, Target
+
 from ..types import (
     Shape,
     TRTDataType,
@@ -18,7 +20,6 @@
     TRTTensor,
 )
 from ..utils import torch_dtype_from_trt
-from torch.fx.node import Argument, Target
 
 
 def get_trt_plugin(

py/torch_tensorrt/fx/converters/convolution.py

@@ -2,6 +2,7 @@
 import numpy as np
 import tensorrt as trt
 import torch
+
 from ..converter_registry import tensorrt_converter
 
 from .converter_utils import (

py/torch_tensorrt/fx/converters/linear.py

@@ -1,6 +1,7 @@
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
+
 from ..converter_registry import tensorrt_converter
 
 from .converter_utils import get_dyn_range, mark_as_int8_layer, to_numpy

py/torch_tensorrt/fx/converters/maxpool.py

@@ -1,6 +1,7 @@
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
+
 from ..converter_registry import tensorrt_converter
 
 from .converter_utils import extend_mod_attr_to_tuple, mark_as_int8_layer

py/torch_tensorrt/fx/converters/mul.py

@@ -3,6 +3,7 @@
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
+
 from ..converter_registry import tensorrt_converter
 
 from .converter_utils import get_dyn_range, mark_as_int8_layer

py/torch_tensorrt/fx/converters/quantization.py

@@ -1,6 +1,7 @@
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
+
 from ..converter_registry import tensorrt_converter
 
 from .converter_utils import get_dyn_range, get_inputs_from_args_and_kwargs

py/torch_tensorrt/fx/converters/transformation.py

@@ -1,6 +1,7 @@
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
+
 from ..converter_registry import tensorrt_converter
 
 from .converter_utils import mark_as_int8_layer

py/torch_tensorrt/fx/example/fx2trt_example.py

@@ -1,11 +1,11 @@
 # type: ignore[]
 
-import fx2trt_oss.tracer.acc_tracer.acc_tracer as acc_tracer
 import torch
 import torch.fx
 import torch.nn as nn
-from fx2trt_oss.fx import InputTensorSpec, TRTInterpreter, TRTModule
-from fx2trt_oss.fx.tools.trt_splitter import TRTSplitter
+import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
+from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter, TRTModule
+from torch_tensorrt.fx.tools.trt_splitter import TRTSplitter
 
 
 # The purpose of this example is to demonstrate the overall flow of lowering a PyTorch
@@ -83,12 +83,12 @@ def forward(self, x):
     %x : [#users=1] = placeholder[target=x]
     %linear_weight : [#users=1] = get_attr[target=linear.weight]
     %linear_bias : [#users=1] = get_attr[target=linear.bias]
-    %linear_1 : [#users=1] = call_function[target=fx2trt_oss.tracer.acc_tracer.acc_ops.linear](args = (), ...
-    %relu_1 : [#users=1] = call_function[target=fx2trt_oss.tracer.acc_tracer.acc_ops.relu](args = (), ...
+    %linear_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linear](args = (), ...
+    %relu_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.relu](args = (), ...
     return relu_1
 graph():
     %relu_1 : [#users=1] = placeholder[target=relu_1]
-    %linalg_norm_1 : [#users=1] = call_function[target=fx2trt_oss.tracer.acc_tracer.acc_ops.linalg_norm](args = (), ...
+    %linalg_norm_1 : [#users=1] = call_function[target=torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linalg_norm](args = (), ...
     return linalg_norm_1
 """
 

py/torch_tensorrt/fx/example/lower_example.py

@@ -4,8 +4,8 @@
 
 import torch
 import torchvision
-from fx2trt_oss.fx.lower import lower_to_trt
-from fx2trt_oss.fx.utils import LowerPrecision
+from torch_tensorrt.fx.lower import lower_to_trt
+from torch_tensorrt.fx.utils import LowerPrecision
 
 
 """

py/torch_tensorrt/fx/example/quantized_resnet_test.py

@@ -1,16 +1,16 @@
 import copy
 
-import fx2trt_oss.tracer.acc_tracer.acc_tracer as acc_tracer
-
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch.fx
+
+import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
 import torchvision.models as models
-from fx2trt_oss.fx import InputTensorSpec, TRTInterpreter, TRTModule
-from fx2trt_oss.fx.utils import LowerPrecision
 from torch.ao.quantization.quantize_fx import convert_fx, prepare_fx
 from torch.fx.experimental.normalize import NormalizeArgs
 from torch.fx.passes import shape_prop
+from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter, TRTModule
+from torch_tensorrt.fx.utils import LowerPrecision
 
 rn18 = models.resnet18().eval()
 

py/torch_tensorrt/fx/example/test_fx2trt.py

@@ -1,29 +1,35 @@
-import torch_tensorrt
 import torch
+import torch_tensorrt
+
 
 class MyModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
-        self.linear = torch.nn.Linear(5,3)
+        self.linear = torch.nn.Linear(5, 3)
         self.relu = torch.nn.functional.relu
-    def forward(self,x):
+
+    def forward(self, x):
         x = self.linear(x)
         x = self.relu(x)
         return x
 
 
-model = MyModel().eval() # torch module needs to be in eval (not training) mode
+model = MyModel().eval()  # torch module needs to be in eval (not training) mode
 
 # torch tensorrt
-inputs = [torch_tensorrt.Input(
-            (2,5),
-            dtype=torch.half,
-        )]
-enabled_precisions = {torch.float, torch.half} # Run with fp16
-
-trt_ts_module = torch_tensorrt.compile(model, inputs=inputs, enabled_precisions=enabled_precisions)
-
-inputs_ts = [torch.ones(2,5)]
+inputs = [
+    torch_tensorrt.Input(
+        (2, 5),
+        dtype=torch.half,
+    )
+]
+enabled_precisions = {torch.float, torch.half}  # Run with fp16
+
+trt_ts_module = torch_tensorrt.compile(
+    model, inputs=inputs, enabled_precisions=enabled_precisions
+)
+
+inputs_ts = [torch.ones(2, 5)]
 inputs_ts = [i.cuda().half() for i in inputs_ts]
 result = trt_ts_module(*inputs_ts)
 print(result)
@@ -33,12 +39,14 @@ def forward(self,x):
 print(ref)
 
 # fx2trt
-inputs_fx = [torch.ones((2,5))]
+inputs_fx = [torch.ones((2, 5))]
 
 model.cuda().half()
 inputs_fx = [i.cuda().half() for i in inputs_fx]
 
-trt_fx_module = torch_tensorrt.compile(model, ir="fx", inputs=inputs_fx, enabled_precisions={torch.half})
+trt_fx_module = torch_tensorrt.compile(
+    model, ir="fx", inputs=inputs_fx, enabled_precisions={torch.half}
+)
 result = trt_fx_module(*inputs_fx)
 print(result)
 

py/torch_tensorrt/fx/example/torchdynamo_example.py

@@ -5,8 +5,8 @@
 import torch
 import torchdynamo
 import torchvision
-from fx2trt_oss.fx.lower import lower_to_trt
-from fx2trt_oss.fx.utils import LowerPrecision
+from torch_tensorrt.fx.lower import lower_to_trt
+from torch_tensorrt.fx.utils import LowerPrecision
 from torchdynamo.optimizations import backends
 
 """

py/torch_tensorrt/fx/fx2trt.py

@@ -9,12 +9,12 @@
 import tensorrt as trt
 import torch
 import torch.fx
-from .observer import Observer
 from torch.fx.node import _get_qualified_name
 from torch.fx.passes.shape_prop import TensorMetadata
 
 from .converter_registry import CONVERTERS
 from .input_tensor_spec import InputTensorSpec
+from .observer import Observer
 from .utils import get_dynamic_dims, LowerPrecision, torch_dtype_to_trt
 
 

py/torch_tensorrt/fx/lower.py

@@ -2,23 +2,27 @@
 import logging
 from typing import Any, Callable, Sequence
 
-from .tracer.acc_tracer import acc_tracer
-
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
 import torch.fx as fx
 import torch.nn as nn
-from .lower_setting import LowerSetting
-from .passes.pass_utils import decorate_method, validate_inference
-from .passes.splitter_base import SplitResult
+from torch.fx.passes.splitter_base import SplitResult
 
 from .fx2trt import TRTInterpreter, TRTInterpreterResult
 from .input_tensor_spec import InputTensorSpec
+from .lower_setting import LowerSetting
 from .passes.lower_pass_manager_builder import LowerPassManagerBuilder
-from .passes.pass_utils import chain_passes, PassFunc
+from .passes.pass_utils import (
+    chain_passes,
+    decorate_method,
+    PassFunc,
+    validate_inference,
+)
 from .tools.timing_cache_utils import TimingCacheManager
 from .tools.trt_splitter import TRTSplitter, TRTSplitterSetting
+
+from .tracer.acc_tracer import acc_tracer
 from .trt_module import TRTModule
 from .utils import LowerPrecision
 

py/torch_tensorrt/fx/lower_setting.py

@@ -1,15 +1,13 @@
 import dataclasses as dc
 from typing import List, Optional, Sequence, Set, Type
 
-from .input_tensor_spec import InputTensorSpec
-from .passes.lower_basic_pass import (
-    fuse_permute_linear,
-    fuse_permute_matmul,
-)
-from .utils import LowerPrecision
 from torch import nn
 from torch.fx.passes.pass_manager import PassManager
 
+from .input_tensor_spec import InputTensorSpec
+from .passes.lower_basic_pass import fuse_permute_linear, fuse_permute_matmul
+from .utils import LowerPrecision
+
 
 @dc.dataclass
 class LowerSetting:

py/torch_tensorrt/fx/passes/lower_basic_pass.py

@@ -3,13 +3,15 @@
 import warnings
 from typing import Any
 
-from ..tracer.acc_tracer import acc_ops
 import torch
 import torch.fx
+from torch.fx.experimental.const_fold import split_const_subgraphs
+
 from ..observer import observable
-from .pass_utils import log_before_after, validate_inference
+
+from ..tracer.acc_tracer import acc_ops
 from ..tracer.acc_tracer.acc_utils import get_attr
-from torch.fx.experimental.const_fold import split_const_subgraphs
+from .pass_utils import log_before_after, validate_inference
 
 # Create an alias for module input type to avoid littering pyre-ignore for Any
 # throughout the file.
@@ -46,15 +48,15 @@ def fuse_sparse_matmul_add(gm: torch.fx.GraphModule, input: Input):
     def forward(self, x):
         a = self.a
         b = self.b
-        addmm_mm = fx2trt_oss.tracer.acc_tracer.acc_ops.matmul(input = a, other = b);  a = b = None
-        addmm_add = fx2trt_oss.tracer.acc_tracer.acc_ops.add(input = addmm_mm, other = x);  addmm_mm = x = None
+        addmm_mm = torch_tensorrt.fx.tracer.acc_tracer.acc_ops.matmul(input = a, other = b);  a = b = None
+        addmm_add = torch_tensorrt.fx.tracer.acc_tracer.acc_ops.add(input = addmm_mm, other = x);  addmm_mm = x = None
         return addmm_add
 
     After:
     def forward(self, x):
         a = self.a
         b = self.b
-        linear_1 = fx2trt_oss.tracer.acc_tracer.acc_ops.linear(input = a, weight = b, bias = x);  a = b = x = None
+        linear_1 = torch_tensorrt.fx.tracer.acc_tracer.acc_ops.linear(input = a, weight = b, bias = x);  a = b = x = None
         return linear_1
     """
     counter = 0
@@ -198,8 +200,8 @@ def fuse_permute_matmul(gm: torch.fx.GraphModule, input: Input):
 try:
     # @manual=//deeplearning/trt/python:py_tensorrt
     import tensorrt as trt
-    from fx2trt_oss.fx.converter_registry import tensorrt_converter
-    from fx2trt_oss.fx.converters.converter_utils import (
+    from torch_tensorrt.fx.converter_registry import tensorrt_converter
+    from torch_tensorrt.fx.converters.converter_utils import (
         add_binary_elementwise_layer,
         broadcast,
         get_trt_tensor,