From cd866609a18fb0dffe1e798a1831bcf698095de5 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Thu, 14 Mar 2024 02:01:07 -0700
Subject: [PATCH 01/69] feat: Add save API for torch-trt compiled models
---
.github/scripts/install-torch-tensorrt.sh | 3 +-
py/torch_tensorrt/_compile.py | 67 +++++++++++++++++++++
py/torch_tensorrt/dynamo/_compiler.py | 9 +--
py/torch_tensorrt/dynamo/_defaults.py | 1 -
py/torch_tensorrt/dynamo/_exporter.py | 17 +-----
py/torch_tensorrt/dynamo/_settings.py | 3 -
tests/py/dynamo/models/test_export_serde.py | 58 +++++++++---------
7 files changed, 104 insertions(+), 54 deletions(-)
diff --git a/.github/scripts/install-torch-tensorrt.sh b/.github/scripts/install-torch-tensorrt.sh
index 2930421d5b..9757fadeb4 100644
--- a/.github/scripts/install-torch-tensorrt.sh
+++ b/.github/scripts/install-torch-tensorrt.sh
@@ -2,7 +2,8 @@
set -eou pipefail
# Source conda so it's available to the script environment
source ${BUILD_ENV_FILE}
-${CONDA_RUN} ${PIP_INSTALL_TORCH} torchvision pyyaml
+${CONDA_RUN} ${PIP_INSTALL_TORCH} torchvision --extra-index-url https://pypi.python.org/simple
+${CONDA_RUN} python -m pip install pyyaml mpmath==1.3.0
export TRT_VERSION=$(${CONDA_RUN} python -c "import versions; versions.tensorrt_version()")
${CONDA_RUN} python -m pip install /opt/torch-tensorrt-builds/torch_tensorrt*+${CU_VERSION}*.whl tensorrt~=${TRT_VERSION} tensorrt-bindings~=${TRT_VERSION} --extra-index-url=https://pypi.ngc.nvidia.com
diff --git a/py/torch_tensorrt/_compile.py b/py/torch_tensorrt/_compile.py
index 9dd816e633..aa1bc53a0a 100644
--- a/py/torch_tensorrt/_compile.py
+++ b/py/torch_tensorrt/_compile.py
@@ -6,6 +6,7 @@
import torch
import torch.fx
+import torch_tensorrt.dynamo
import torch_tensorrt.ts
from torch_tensorrt._enums import dtype
from torch_tensorrt._Input import Input
@@ -29,6 +30,7 @@
__all__ = [
"compile",
"convert_method_to_trt_engine",
+ "save",
]
@@ -332,3 +334,68 @@ def convert_method_to_trt_engine(
)
else:
raise RuntimeError("Module is an unknown format or the ir requested is unknown")
+
+
+def save(
+ module: Any,
+ file_path: str = "",
+ *,
+ output_format: str = "exported_program",
+ inputs: Optional[Sequence[torch.Tensor]] = None,
+ retrace: bool = False,
+) -> None:
+ """
+ Save the model to disk in the specified output format.
+ Arguments:
+ module : Compiled Torch-TensorRT module (Options include torch.jit.ScriptModule | torch.export.ExportedProgram | torch.fx.GraphModule)
+ inputs (torch.Tensor): Torch input tensors
+ """
+ module_type = _parse_module_type(module)
+ accepted_formats = {"exported_program", "torchscript"}
+ if inputs and not all(isinstance(input, torch.Tensor) for input in inputs):
+ raise ValueError(
+ "Not all inputs provided are torch.tensors. Please provide torch.tensors as inputs"
+ )
+ if output_format not in accepted_formats:
+ raise ValueError(
+ f"Provided output_format {output_format} is not supported. Supported options are exported_program | torchscript"
+ )
+ if not file_path:
+ raise ValueError("File path cannot be empty. Please provide a valid file path")
+
+ if module_type == _ModuleType.nn:
+ raise ValueError(
+ "Input model is of type nn.Module. Saving nn.Module directly is not supported. Supported model types torch.jit.ScriptModule | torch.fx.GraphModule | torch.export.ExportedProgram."
+ )
+ elif module_type == _ModuleType.ts:
+ if output_format == "exported_program":
+ raise ValueError(
+ "Provided model is a torch.jit.ScriptModule but the output_format specified is exported_program. Please verify the output_format"
+ )
+ else:
+ torch.jit.save(module, file_path)
+ elif module_type == _ModuleType.ep:
+ if output_format == "torchscript":
+ raise ValueError(
+ "Provided model is a torch.export.ExportedProgram but the output_format specified is torchscript. Please verify the output_format"
+ )
+ else:
+ torch.export.save(module, file_path)
+ elif module_type == _ModuleType.fx:
+ if not inputs:
+ raise ValueError(
+ "Provided model is a torch.fx.GraphModule however the inputs are empty. Please provide valid torch.tensors as inputs to trace and save the model"
+ )
+ # The module type is torch.fx.GraphModule
+ if output_format == "torchscript":
+ module_ts = torch.jit.trace(module, inputs)
+ torch.jit.save(module_ts, file_path)
+ else:
+ if not retrace:
+ from torch_tensorrt.dynamo._exporter import export
+
+ exp_program = export(module, inputs)
+ torch.export.save(exp_program, file_path)
+ else:
+ exp_program = torch.export.export(module, tuple(inputs), strict=False)
+ torch.export.save(exp_program, file_path)
diff --git a/py/torch_tensorrt/dynamo/_compiler.py b/py/torch_tensorrt/dynamo/_compiler.py
index 6312532f1c..b321eabcb2 100644
--- a/py/torch_tensorrt/dynamo/_compiler.py
+++ b/py/torch_tensorrt/dynamo/_compiler.py
@@ -30,7 +30,6 @@
MIN_BLOCK_SIZE,
NUM_AVG_TIMING_ITERS,
OPTIMIZATION_LEVEL,
- OUTPUT_FORMAT,
PASS_THROUGH_BUILD_FAILURES,
PRECISION,
REFIT,
@@ -48,7 +47,6 @@
dryrun_stats_display,
parse_non_trt_nodes,
)
-from torch_tensorrt.dynamo._exporter import export
from torch_tensorrt.dynamo.conversion import (
CompilationSettings,
UnsupportedOperatorException,
@@ -102,9 +100,8 @@ def compile(
enable_experimental_decompositions: bool = ENABLE_EXPERIMENTAL_DECOMPOSITIONS,
dryrun: bool = DRYRUN,
hardware_compatible: bool = HARDWARE_COMPATIBLE,
- output_format: str = OUTPUT_FORMAT,
**kwargs: Any,
-) -> Union[ExportedProgram, torch.jit.ScriptModule, torch.fx.GraphModule]:
+) -> torch.fx.GraphModule:
"""Compile a TorchScript module for NVIDIA GPUs using TensorRT
Takes a existing TorchScript module and a set of settings to configure the compiler
@@ -246,14 +243,12 @@ def compile(
"dla_global_dram_size": dla_global_dram_size,
"dryrun": dryrun,
"hardware_compatible": hardware_compatible,
- "output_format": output_format,
}
settings = CompilationSettings(**compilation_options)
logger.info("Compilation Settings: %s\n", settings)
trt_gm = compile_module(gm, inputs, settings)
- trt_result = export(trt_gm, torch_inputs, output_format)
- return trt_result
+ return trt_gm
def compile_module(
diff --git a/py/torch_tensorrt/dynamo/_defaults.py b/py/torch_tensorrt/dynamo/_defaults.py
index ec038c0dba..3d48ab3def 100644
--- a/py/torch_tensorrt/dynamo/_defaults.py
+++ b/py/torch_tensorrt/dynamo/_defaults.py
@@ -26,7 +26,6 @@
REQUIRE_FULL_COMPILATION = False
DRYRUN = False
HARDWARE_COMPATIBLE = False
-OUTPUT_FORMAT = "exported_program"
def default_device() -> Device:
diff --git a/py/torch_tensorrt/dynamo/_exporter.py b/py/torch_tensorrt/dynamo/_exporter.py
index c7e2f37795..bae20ac235 100644
--- a/py/torch_tensorrt/dynamo/_exporter.py
+++ b/py/torch_tensorrt/dynamo/_exporter.py
@@ -18,27 +18,16 @@
def export(
gm: torch.fx.GraphModule,
inputs: Sequence[torch.Tensor],
- output_format: str,
) -> ExportedProgram:
"""Export the result of TensorRT compilation into the desired output format.
Arguments:
gm (torch.fx.GraphModule): Compiled Torch-TensorRT module, generated by ``torch_tensorrt.dynamo.compile``
inputs (torch.Tensor): Torch input tensors
- output_format (str): Output format of the result of TRT compilation. Options include "exported_program" (or) "ep" | "torchscript" (or) "ts" | "graph_module" (or) "fx". Default is "exported_program"
"""
- if output_format == "torchscript" or output_format == "ts":
- return torch.jit.trace(gm, inputs)
- elif output_format == "exported_program" or output_format == "ep":
- patched_module = transform(gm, inputs)
- exp_program = create_trt_exp_program(patched_module)
- return exp_program
- elif output_format == "graph_module" or output_format == "fx":
- return gm
- else:
- raise ValueError(
- f"Invalid output format {output_format} specified. Supported options include exported_program (or) ep | torchscript (or) ts | graph_module (or) fx"
- )
+ patched_module = transform(gm, inputs)
+ exp_program = create_trt_exp_program(patched_module)
+ return exp_program
def transform(
diff --git a/py/torch_tensorrt/dynamo/_settings.py b/py/torch_tensorrt/dynamo/_settings.py
index c00b049f45..2420a227d8 100644
--- a/py/torch_tensorrt/dynamo/_settings.py
+++ b/py/torch_tensorrt/dynamo/_settings.py
@@ -19,7 +19,6 @@
MIN_BLOCK_SIZE,
NUM_AVG_TIMING_ITERS,
OPTIMIZATION_LEVEL,
- OUTPUT_FORMAT,
PASS_THROUGH_BUILD_FAILURES,
PRECISION,
REFIT,
@@ -71,7 +70,6 @@ class CompilationSettings:
TRT Engines. Prints detailed logs of the graph structure and nature of partitioning. Optionally saves the
ouptut to a file if a string path is specified
hardware_compatible (bool): Build the TensorRT engines compatible with GPU architectures other than that of the GPU on which the engine was built (currently works for NVIDIA Ampere and newer)
- output_format (str): Output format of the result of TRT compilation. Options include "exported_program" (or) "ep" | "torchscript" (or) "ts" | "graph_module" (or) "fx". Default is "exported_program"
"""
precision: torch.dtype = PRECISION
@@ -99,4 +97,3 @@ class CompilationSettings:
dla_global_dram_size: int = DLA_GLOBAL_DRAM_SIZE
dryrun: Union[bool, str] = DRYRUN
hardware_compatible: bool = HARDWARE_COMPATIBLE
- output_format: str = OUTPUT_FORMAT
diff --git a/tests/py/dynamo/models/test_export_serde.py b/tests/py/dynamo/models/test_export_serde.py
index efa593890e..0905c5e859 100644
--- a/tests/py/dynamo/models/test_export_serde.py
+++ b/tests/py/dynamo/models/test_export_serde.py
@@ -42,18 +42,18 @@ def forward(self, x):
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
- trt_exp_program = torchtrt.dynamo.compile(exp_program, **compile_spec)
- torch.export.save(trt_exp_program, "/tmp/trt.ep")
+ trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+ torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
-
+ deser_trt_module = deser_trt_exp_program.module()
# Check Pyt and TRT exported program outputs
- cos_sim = cosine_similarity(model(input), trt_exp_program(input)[0])
+ cos_sim = cosine_similarity(model(input), trt_module(input)[0])
assertions.assertTrue(
cos_sim > COSINE_THRESHOLD,
msg=f"test_base_model_full_compile TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
)
# Check Pyt and deserialized TRT exported program outputs
- cos_sim = cosine_similarity(model(input), deser_trt_exp_program(input)[0])
+ cos_sim = cosine_similarity(model(input), deser_trt_module(input)[0])
assertions.assertTrue(
cos_sim > COSINE_THRESHOLD,
msg=f"test_base_model_full_compile TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
@@ -93,12 +93,13 @@ def forward(self, x):
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
- trt_exp_program = torchtrt.dynamo.compile(exp_program, **compile_spec)
- torch.export.save(trt_exp_program, "/tmp/trt.ep")
+ trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+ torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
+ deser_trt_module = deser_trt_exp_program.module()
# Check Pyt and TRT exported program outputs
outputs_pyt = model(input)
- outputs_trt = trt_exp_program(input)
+ outputs_trt = trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt[idx])
assertions.assertTrue(
@@ -107,7 +108,7 @@ def forward(self, x):
)
# Check Pyt and deserialized TRT exported program outputs
- outputs_trt_deser = deser_trt_exp_program(input)
+ outputs_trt_deser = deser_trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt_deser[idx])
assertions.assertTrue(
@@ -149,12 +150,13 @@ def forward(self, x):
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
- trt_exp_program = torchtrt.dynamo.compile(exp_program, **compile_spec)
- torch.export.save(trt_exp_program, "/tmp/trt.ep")
+ trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+ torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
+ deser_trt_module = deser_trt_exp_program.module()
# Check Pyt and TRT exported program outputs
outputs_pyt = model(input)
- outputs_trt = trt_exp_program(input)
+ outputs_trt = trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt[idx])
assertions.assertTrue(
@@ -163,7 +165,7 @@ def forward(self, x):
)
# Check Pyt and deserialized TRT exported program outputs
- outputs_trt_deser = deser_trt_exp_program(input)
+ outputs_trt_deser = deser_trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt_deser[idx])
assertions.assertTrue(
@@ -207,12 +209,12 @@ def forward(self, x):
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
- trt_exp_program = torchtrt.dynamo.compile(exp_program, **compile_spec)
- torch.export.save(trt_exp_program, "/tmp/trt.ep")
+ trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+ torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
-
+ deser_trt_module = deser_trt_exp_program.module()
outputs_pyt = model(input)
- outputs_trt = trt_exp_program(input)
+ outputs_trt = trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt[idx])
assertions.assertTrue(
@@ -220,7 +222,7 @@ def forward(self, x):
msg=f"test_hybrid_relu_fallback TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
)
- outputs_trt_deser = deser_trt_exp_program(input)
+ outputs_trt_deser = deser_trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt_deser[idx])
assertions.assertTrue(
@@ -248,19 +250,19 @@ def test_resnet18(ir):
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
- trt_exp_program = torchtrt.dynamo.compile(exp_program, **compile_spec)
- torch.export.save(trt_exp_program, "/tmp/trt.ep")
+ trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+ torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
-
+ deser_trt_module = deser_trt_exp_program.module()
outputs_pyt = model(input)
- outputs_trt = trt_exp_program(input)
+ outputs_trt = trt_module(input)
cos_sim = cosine_similarity(outputs_pyt, outputs_trt[0])
assertions.assertTrue(
cos_sim > COSINE_THRESHOLD,
msg=f"test_resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
)
- outputs_trt_deser = deser_trt_exp_program(input)
+ outputs_trt_deser = deser_trt_module(input)
cos_sim = cosine_similarity(outputs_pyt, outputs_trt_deser[0])
assertions.assertTrue(
@@ -303,12 +305,12 @@ def forward(self, x):
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
- trt_exp_program = torchtrt.dynamo.compile(exp_program, **compile_spec)
- torch.export.save(trt_exp_program, "/tmp/trt.ep")
+ trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+ torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
-
+ deser_trt_module = deser_trt_exp_program.module()
outputs_pyt = model(input)
- outputs_trt = trt_exp_program(input)
+ outputs_trt = trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt[idx])
@@ -317,7 +319,7 @@ def forward(self, x):
msg=f"test_hybrid_conv_fallback TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
)
- outputs_trt_deser = deser_trt_exp_program(input)
+ outputs_trt_deser = deser_trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt_deser[idx])
assertions.assertTrue(
From eab0dba2955a87550fe12e7b67ae092597b8c453 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Mon, 18 Mar 2024 12:43:45 -0700
Subject: [PATCH 02/69] chore: Fix save failures
---
core/runtime/TRTEngine.cpp | 2 +-
py/torch_tensorrt/_compile.py | 9 +-
py/torch_tensorrt/dynamo/_exporter.py | 135 +++++++++++++-------
tests/py/dynamo/models/test_export_serde.py | 2 +-
4 files changed, 100 insertions(+), 48 deletions(-)
diff --git a/core/runtime/TRTEngine.cpp b/core/runtime/TRTEngine.cpp
index 92e5d7a8ff..7a046f6d94 100644
--- a/core/runtime/TRTEngine.cpp
+++ b/core/runtime/TRTEngine.cpp
@@ -241,7 +241,7 @@ std::string TRTEngine::to_str() const {
exec_ctx->getEngine().getTensorDataType(out_binding_names[o].c_str()))
<< std::endl;
}
- ss << " }" << std::endl;
+ ss << " ]" << std::endl;
ss << " Device: " << device_info << std::endl;
ss << " Hardware Compatibility: " << (hardware_compatible ? "Enabled" : "Disabled") << std::endl;
// clang-format on
diff --git a/py/torch_tensorrt/_compile.py b/py/torch_tensorrt/_compile.py
index aa1bc53a0a..443dec8869 100644
--- a/py/torch_tensorrt/_compile.py
+++ b/py/torch_tensorrt/_compile.py
@@ -397,5 +397,10 @@ def save(
exp_program = export(module, inputs)
torch.export.save(exp_program, file_path)
else:
- exp_program = torch.export.export(module, tuple(inputs), strict=False)
- torch.export.save(exp_program, file_path)
+ from torch._higher_order_ops.torchbind import enable_torchbind_tracing
+
+ with enable_torchbind_tracing():
+ exp_program = torch.export.export(
+ module, tuple(inputs), strict=False
+ )
+ torch.export.save(exp_program, file_path)
diff --git a/py/torch_tensorrt/dynamo/_exporter.py b/py/torch_tensorrt/dynamo/_exporter.py
index bae20ac235..cf06bc4531 100644
--- a/py/torch_tensorrt/dynamo/_exporter.py
+++ b/py/torch_tensorrt/dynamo/_exporter.py
@@ -1,3 +1,4 @@
+import copy
import operator
from typing import Any, Dict, Sequence, Tuple, cast
@@ -6,8 +7,11 @@
from torch._subclasses.fake_tensor import FakeTensor
from torch.export import ExportedProgram, ExportGraphSignature
from torch.export.exported_program import (
+ CustomObjArgument,
InputKind,
InputSpec,
+ ModuleCallEntry,
+ ModuleCallSignature,
OutputKind,
OutputSpec,
TensorArgument,
@@ -44,24 +48,27 @@ def transform(
Returns an inlined torch.fx.GraphModule
"""
+ gm_export = copy.deepcopy(gm)
# Run shape analysis
- _, outputs_map = partitioning.run_shape_analysis(gm, inputs)
+ _, outputs_map = partitioning.run_shape_analysis(gm_export, inputs)
# Inline TensorRT submodules
- inline_trt_modules(gm, outputs_map)
+ inline_trt_modules(gm_export, outputs_map)
# Inline pytorch submodules
- inline_torch_modules(gm)
+ inline_torch_modules(gm_export)
# Clean the graph
- gm.delete_all_unused_submodules()
- gm.graph.eliminate_dead_code()
- gm.graph.lint()
+ gm_export.delete_all_unused_submodules()
+ gm_export.graph.eliminate_dead_code()
+ gm_export.graph.lint()
- return gm
+ return gm_export
-def lift(gm: torch.fx.GraphModule, graph_signature: Any) -> torch.fx.GraphModule:
+def lift(
+ gm: torch.fx.GraphModule, graph_signature: Any
+) -> Tuple[torch.fx.GraphModule, ExportGraphSignature, Dict[str, Any], Dict[str, Any]]:
"""
Given an unlifted fx.GraphModule, lift all parameters, buffers into placeholders.
Arguments:
@@ -75,6 +82,7 @@ def lift(gm: torch.fx.GraphModule, graph_signature: Any) -> torch.fx.GraphModule
# exp_program.state_dict contains parameters and buffers whereas a graph_module's state_dict
# has all parameters registered as torch.tensors.
state_dict = gm.state_dict()
+ constants = {}
fake_mode = detect_fake_mode(
tuple(node.meta["val"] for node in gm.graph.nodes if node.op == "placeholder")
@@ -89,52 +97,68 @@ def lift(gm: torch.fx.GraphModule, graph_signature: Any) -> torch.fx.GraphModule
break
# At first the user_inputs are only present in the graph_signature.input_specs and hence non_user_input_idx=0
- # The input_specs should be of the form [params, buffers, constant_tensors, user_inputs]
+ # The input_specs should be of the form [params, buffers, constant_tensors, custom_obj, user_inputs]
non_user_input_idx = 0
for node in gm.graph.nodes:
if node.op == "get_attr":
- if node.target not in state_dict:
- raise ValueError(
- f"The get_attr node : {node.name} with target: {node.target} value could not be found in state_dict. Please check the input exported_program's graphmodule parameters."
- )
- constant_tensor = state_dict[node.target]
- input_kind = InputKind.CONSTANT_TENSOR
+ lift_val = None
+ input_kind = None
- # state_dict has these parameters/buffers as torch.Tensors. We override them as torch.nn.Parameter/torch.Tensors respectively.
- for name, _ in gm.named_parameters():
- if node.target == name:
- input_kind = InputKind.PARAMETER
- state_dict[name] = torch.nn.Parameter(state_dict[name])
- break
- for name, _ in gm.named_buffers():
- if node.target == name:
- input_kind = InputKind.BUFFER
- break
+ if node.target not in state_dict:
+ constants[node.target] = getattr(gm, node.target)
+ input_kind = InputKind.CUSTOM_OBJ
+ lift_val = constants[node.target]
+ else:
+ lift_val = state_dict[node.target]
+
+ input_kind = InputKind.CONSTANT_TENSOR
+
+ # state_dict has these parameters/buffers as torch.Tensors. We override them as torch.nn.Parameter/torch.Tensors respectively.
+ for name, _ in gm.named_parameters():
+ if node.target == name:
+ input_kind = InputKind.PARAMETER
+ state_dict[name] = torch.nn.Parameter(state_dict[name])
+ break
+ for name, _ in gm.named_buffers():
+ if node.target == name:
+ input_kind = InputKind.BUFFER
+ break
+
+ assert lift_val is not None and input_kind is not None
# Replace get_attr nodes with placeholder nodes and copy metadata.
with gm.graph.inserting_before(first_user_input):
- const_placeholder_node = gm.graph.placeholder(node.target)
+ const_placeholder_node = gm.graph.placeholder(
+ node.target.replace(".", "_")
+ )
# Copy the node meta into this new placeholder node
const_placeholder_node.meta = node.meta
- const_placeholder_node.meta["val"] = cast(
- FakeTensor,
- torch.empty_strided(
- tuple(constant_tensor.shape),
- tuple([1] * len(constant_tensor.shape)),
- ),
- )
+
+ if isinstance(lift_val, torch.Tensor):
+ const_placeholder_node.meta["val"] = cast(
+ FakeTensor,
+ torch.empty_strided(
+ tuple(lift_val.shape),
+ tuple([1] * len(lift_val.shape)),
+ ),
+ )
node.replace_all_uses_with(const_placeholder_node)
gm.graph.erase_node(node)
# Add these parameters/buffers/constants to the existing graph signature
# before user inputs. These specs are looked up in the state_dict during ExportedProgram creation.
+ input_spec_arg = TensorArgument(name=const_placeholder_node.name)
+ if input_kind == InputKind.CUSTOM_OBJ:
+ input_spec_arg = CustomObjArgument(
+ name=const_placeholder_node.name, class_fqn=""
+ )
graph_signature.input_specs.insert(
non_user_input_idx,
InputSpec(
kind=input_kind,
- arg=TensorArgument(name=const_placeholder_node.name),
+ arg=input_spec_arg,
target=node.target,
),
)
@@ -143,7 +167,7 @@ def lift(gm: torch.fx.GraphModule, graph_signature: Any) -> torch.fx.GraphModule
gm.graph.eliminate_dead_code()
gm.graph.lint()
- return gm, graph_signature, state_dict
+ return gm, graph_signature, state_dict, constants
def get_duplicate_nodes(
@@ -281,18 +305,30 @@ def create_trt_exp_program(
input_specs=input_specs, output_specs=output_specs
)
+ module_call_graph = [
+ ModuleCallEntry(
+ "",
+ ModuleCallSignature(
+ inputs=[],
+ outputs=[],
+ in_spec=gm.graph._codegen.pytree_info.in_spec,
+ out_spec=gm.graph._codegen.pytree_info.out_spec,
+ ),
+ )
+ ]
+
# Lift parameters/buffers/constants in the graph
# torch.export serialization expects them to be lifted
- gm, trt_graph_signature, state_dict = lift(gm, trt_graph_signature)
+ gm, trt_graph_signature, state_dict, constants = lift(gm, trt_graph_signature)
trt_exp_program = ExportedProgram(
- gm,
- gm.graph,
- trt_graph_signature,
- state_dict,
- {},
- [],
- [],
+ root=gm,
+ graph=gm.graph,
+ graph_signature=trt_graph_signature,
+ state_dict=state_dict,
+ range_constraints={},
+ module_call_graph=module_call_graph,
+ constants=constants,
)
return trt_exp_program
@@ -319,9 +355,13 @@ def inline_trt_modules(
num_outputs = len(outputs_map[trt_module_node.name])
# Insert a call_function node to perform inference on TRT engine
with gm.graph.inserting_before(trt_module_node):
+ engine_name = f"{name}_engine"
+ setattr(gm, engine_name, trt_module.engine)
+ engine_node = gm.graph.get_attr(engine_name)
+
trt_node = gm.graph.call_function(
torch.ops.tensorrt.execute_engine.default,
- (trt_module_node.args, trt_module.engine),
+ (trt_module_node.args, engine_node),
)
trt_node.meta["val"] = []
assert num_outputs > 0
@@ -337,6 +377,13 @@ def inline_trt_modules(
)
)
+ # meta["val"] should be a lighter version of a tensor. For eg: it should be a FakeTensor (with output shape and dtype properties)
+ # Lighter version of a custom_obj is not defined clearly. meta["val"] does not have any type expectations but
+ # for custom object nodes, it should be CustomObjArgument
+ engine_node.meta["val"] = CustomObjArgument(
+ name=engine_node.name, class_fqn=""
+ )
+
if num_outputs == 1:
# Insert getitem nodes as outputs (for export serialization to work)
with gm.graph.inserting_after(trt_node):
diff --git a/tests/py/dynamo/models/test_export_serde.py b/tests/py/dynamo/models/test_export_serde.py
index 0905c5e859..40fa01c2c9 100644
--- a/tests/py/dynamo/models/test_export_serde.py
+++ b/tests/py/dynamo/models/test_export_serde.py
@@ -146,7 +146,6 @@ def forward(self, x):
)
],
"ir": ir,
- "debug": True,
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
@@ -306,6 +305,7 @@ def forward(self, x):
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+
torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
deser_trt_module = deser_trt_exp_program.module()
From b191d62bafac9740657cb9dc67ccafb213d4914c Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Mon, 18 Mar 2024 16:29:04 -0700
Subject: [PATCH 03/69] chore: update to 2.3 rc build
---
py/requirements.txt | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/py/requirements.txt b/py/requirements.txt
index cd52d32436..419c325653 100644
--- a/py/requirements.txt
+++ b/py/requirements.txt
@@ -1,9 +1,9 @@
numpy
packaging
pybind11==2.6.2
---extra-index-url https://download.pytorch.org/whl/nightly/cu121
-torch>=2.3.0.dev,<2.4.0
-torchvision>=0.18.0.dev,<0.19.0
+--index-url https://download.pytorch.org/whl/test/cu121
+torch>=2.3.0,<2.4.0
+torchvision>=0.18.0,<0.19.0
--extra-index-url https://pypi.ngc.nvidia.com
tensorrt==8.6.1
pyyaml
From 8674a3c437d767c6e2b09db3a574af9a99c318d3 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 19 Mar 2024 16:18:17 -0700
Subject: [PATCH 04/69] chore: minor fixes
---
py/torch_tensorrt/dynamo/_exporter.py | 1 +
.../lowering/test_aten_lowering_passes.py | 12 ++++++++----
tests/py/dynamo/models/test_models_export.py | 19 ++++++++++---------
tests/py/dynamo/testing_utilities.py | 1 +
4 files changed, 20 insertions(+), 13 deletions(-)
diff --git a/py/torch_tensorrt/dynamo/_exporter.py b/py/torch_tensorrt/dynamo/_exporter.py
index cf06bc4531..d4a9fd3584 100644
--- a/py/torch_tensorrt/dynamo/_exporter.py
+++ b/py/torch_tensorrt/dynamo/_exporter.py
@@ -129,6 +129,7 @@ def lift(
# Replace get_attr nodes with placeholder nodes and copy metadata.
with gm.graph.inserting_before(first_user_input):
+ # Ensure name doesn't contain period as it is used for submodules
const_placeholder_node = gm.graph.placeholder(
node.target.replace(".", "_")
)
diff --git a/tests/py/dynamo/lowering/test_aten_lowering_passes.py b/tests/py/dynamo/lowering/test_aten_lowering_passes.py
index bc75a8aa3d..3afc5e5923 100644
--- a/tests/py/dynamo/lowering/test_aten_lowering_passes.py
+++ b/tests/py/dynamo/lowering/test_aten_lowering_passes.py
@@ -1,9 +1,12 @@
import torch
-from torch.testing._internal.common_utils import TestCase, run_tests
-
import torch_tensorrt
+from torch.testing._internal.common_utils import TestCase, run_tests
-from ..testing_utilities import DECIMALS_OF_AGREEMENT, lower_graph_testing
+from ..testing_utilities import (
+ DECIMALS_OF_AGREEMENT,
+ DECIMALS_OF_AGREEMENT_3,
+ lower_graph_testing,
+)
class TestInputAsOutput(TestCase):
@@ -444,10 +447,11 @@ def forward(self, input, weight, bias):
max_diff = float(
torch.max(torch.abs(optimized_model_results - torch_model_results))
)
+
self.assertAlmostEqual(
max_diff,
0,
- DECIMALS_OF_AGREEMENT,
+ DECIMALS_OF_AGREEMENT_3,
msg=f"Linear TRT outputs don't match with the original model.",
)
torch._dynamo.reset()
diff --git a/tests/py/dynamo/models/test_models_export.py b/tests/py/dynamo/models/test_models_export.py
index fd7b40592a..bc8bf12c95 100644
--- a/tests/py/dynamo/models/test_models_export.py
+++ b/tests/py/dynamo/models/test_models_export.py
@@ -159,11 +159,11 @@ def test_bert_base_uncased(ir):
model = BertModel.from_pretrained("bert-base-uncased").cuda().eval()
input = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
input2 = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
- model = (
- transformers_trace(model, input_names=["input_ids", "attention_mask"])
- .eval()
- .cuda()
- )
+ # model = (
+ # transformers_trace(model, input_names=["input_ids", "attention_mask"])
+ # .eval()
+ # .cuda()
+ # )
compile_spec = {
"inputs": [
@@ -182,8 +182,8 @@ def test_bert_base_uncased(ir):
"enabled_precisions": {torch.float},
"truncate_long_and_double": True,
"ir": ir,
- "min_block_size": 10,
- "torch_executed_ops": {"torch.ops.aten.gelu.default"},
+ "min_block_size": 15,
+ "debug": True,
}
trt_mod = torchtrt.compile(model, **compile_spec)
model_outputs = model(input, input2)
@@ -192,8 +192,9 @@ def test_bert_base_uncased(ir):
len(model_outputs) == len(trt_model_outputs),
msg=f"Number of outputs for BERT model compilation is different with Pytorch {len(model_outputs)} and TensorRT {len(trt_model_outputs)}. Please check the compilation.",
)
- for index, key in enumerate(model_outputs):
- out, trt_out = model_outputs[key], trt_model_outputs[index]
+
+ for key, _ in model_outputs.items():
+ out, trt_out = model_outputs[key], trt_model_outputs[key]
cos_sim = cosine_similarity(out, trt_out)
assertions.assertTrue(
cos_sim > COSINE_THRESHOLD,
diff --git a/tests/py/dynamo/testing_utilities.py b/tests/py/dynamo/testing_utilities.py
index 742b9fc1a3..c815d2fde4 100644
--- a/tests/py/dynamo/testing_utilities.py
+++ b/tests/py/dynamo/testing_utilities.py
@@ -14,6 +14,7 @@
)
DECIMALS_OF_AGREEMENT = 4
+DECIMALS_OF_AGREEMENT_3 = 3
def fx_dynamo_testing_backend(
From f4e8fe9bc3f114dd8da3760dca510f78a8f58a0d Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 19 Mar 2024 17:04:38 -0700
Subject: [PATCH 05/69] chore: remove duplicate bert test case
---
tests/py/dynamo/models/test_models_export.py | 53 --------------------
1 file changed, 53 deletions(-)
diff --git a/tests/py/dynamo/models/test_models_export.py b/tests/py/dynamo/models/test_models_export.py
index bc8bf12c95..4d0f4e2e7f 100644
--- a/tests/py/dynamo/models/test_models_export.py
+++ b/tests/py/dynamo/models/test_models_export.py
@@ -105,55 +105,6 @@ def test_efficientnet_b0(ir):
torch._dynamo.reset()
-@pytest.mark.unit
-def test_bert_base_uncased(ir):
- model = BertModel.from_pretrained("bert-base-uncased").cuda().eval()
- input = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
- input2 = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
- model = (
- transformers_trace(model, input_names=["input_ids", "attention_mask"])
- .eval()
- .cuda()
- )
-
- compile_spec = {
- "inputs": [
- torchtrt.Input(
- input.shape,
- dtype=input.dtype,
- format=torch.contiguous_format,
- ),
- torchtrt.Input(
- input.shape,
- dtype=input.dtype,
- format=torch.contiguous_format,
- ),
- ],
- "device": torchtrt.Device("cuda:0"),
- "enabled_precisions": {torch.float},
- "truncate_long_and_double": True,
- "ir": ir,
- "min_block_size": 10,
- }
- trt_mod = torchtrt.compile(model, **compile_spec)
- model_outputs = model(input, input2)
- trt_model_outputs = trt_mod(input, input2)
- assertions.assertTrue(
- len(model_outputs) == len(trt_model_outputs),
- msg=f"Number of outputs for BERT model compilation is different with Pytorch {len(model_outputs)} and TensorRT {len(trt_model_outputs)}. Please check the compilation.",
- )
- for index, key in enumerate(model_outputs):
- out, trt_out = model_outputs[key], trt_model_outputs[index]
- cos_sim = cosine_similarity(out, trt_out)
- assertions.assertTrue(
- cos_sim > COSINE_THRESHOLD,
- msg=f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
- )
-
- # Clean up model env
- torch._dynamo.reset()
-
-
@pytest.mark.unit
def test_bert_base_uncased(ir):
model = BertModel.from_pretrained("bert-base-uncased").cuda().eval()
@@ -183,7 +134,6 @@ def test_bert_base_uncased(ir):
"truncate_long_and_double": True,
"ir": ir,
"min_block_size": 15,
- "debug": True,
}
trt_mod = torchtrt.compile(model, **compile_spec)
model_outputs = model(input, input2)
@@ -204,9 +154,6 @@ def test_bert_base_uncased(ir):
# Clean up model env
torch._dynamo.reset()
- with torch.no_grad():
- torch.cuda.empty_cache()
-
@pytest.mark.unit
def test_resnet18_half(ir):
From 4ae6ab95a98f5b1f644c3bfd3824ac76df442dc7 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 19 Mar 2024 17:05:51 -0700
Subject: [PATCH 06/69] chore: remove comments
---
tests/py/dynamo/models/test_models_export.py | 5 -----
1 file changed, 5 deletions(-)
diff --git a/tests/py/dynamo/models/test_models_export.py b/tests/py/dynamo/models/test_models_export.py
index 4d0f4e2e7f..84f6bf7a36 100644
--- a/tests/py/dynamo/models/test_models_export.py
+++ b/tests/py/dynamo/models/test_models_export.py
@@ -110,11 +110,6 @@ def test_bert_base_uncased(ir):
model = BertModel.from_pretrained("bert-base-uncased").cuda().eval()
input = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
input2 = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
- # model = (
- # transformers_trace(model, input_names=["input_ids", "attention_mask"])
- # .eval()
- # .cuda()
- # )
compile_spec = {
"inputs": [
From fff1b800f970f8ae56f9d7e48189095d57840a74 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 12 Mar 2024 02:11:29 -0700
Subject: [PATCH 07/69] chore: Upgrade to TRT 10.0
chore: updates to trt api
chore: trt 10 fixes
chore: more fixes
---
.../converters/impl/constant_pad.cpp | 2 +-
.../converters/impl/conv_deconv.cpp | 6 +-
.../converters/impl/interpolate.cpp | 50 +++---
core/conversion/converters/impl/linear.cpp | 30 ++--
core/runtime/TRTEngine.cpp | 47 ++++--
cpp/include/torch_tensorrt/ptq.h | 8 +-
py/torch_tensorrt/csrc/torch_tensorrt_py.cpp | 5 +
py/torch_tensorrt/dynamo/_compiler.py | 2 +-
.../dynamo/conversion/_TRTInterpreter.py | 6 +-
.../dynamo/conversion/_conversion.py | 3 +-
.../dynamo/conversion/impl/conv.py | 2 +-
.../dynamo/conversion/impl/deconv.py | 2 +-
.../conversion/impl/elementwise/base.py | 11 --
.../dynamo/conversion/impl/pad.py | 8 +-
.../dynamo/conversion/impl/permutation.py | 4 +-
.../dynamo/conversion/impl/upsample.py | 6 +-
.../runtime/_PythonTorchTensorRTModule.py | 142 +++++++++---------
.../fx/converters/acc_ops_converters.py | 27 ++--
py/torch_tensorrt/fx/utils.py | 18 ++-
.../test_convert_method_to_trt_engine.py | 8 +-
20 files changed, 205 insertions(+), 182 deletions(-)
diff --git a/core/conversion/converters/impl/constant_pad.cpp b/core/conversion/converters/impl/constant_pad.cpp
index 4191cb1bab..20a091e730 100644
--- a/core/conversion/converters/impl/constant_pad.cpp
+++ b/core/conversion/converters/impl/constant_pad.cpp
@@ -55,7 +55,7 @@ auto constant_pad_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns
util::toDims(c10::IntArrayRef(stride)));
TORCHTRT_CHECK(slice_layer, "Unable to create slice layer from node: " << *n);
slice_layer->setName((util::node_info(n) + "_slice").c_str());
- slice_layer->setMode(nvinfer1::SliceMode::kFILL);
+ slice_layer->setMode(nvinfer1::SampleMode::kFILL);
slice_layer->setInput(4, *value_itensor);
if (ctx->input_is_dynamic) {
diff --git a/core/conversion/converters/impl/conv_deconv.cpp b/core/conversion/converters/impl/conv_deconv.cpp
index 66620197a9..bf0df00c64 100644
--- a/core/conversion/converters/impl/conv_deconv.cpp
+++ b/core/conversion/converters/impl/conv_deconv.cpp
@@ -61,7 +61,7 @@ nvinfer1::ILayer* add_bias_layer(
auto* sliceLayer = ctx->net->addSlice(*input_tensor, dummy, dummy, stride);
sliceLayer->setInput(1, *start);
sliceLayer->setInput(2, *size);
- sliceLayer->setMode(nvinfer1::SliceMode::kFILL);
+ sliceLayer->setMode(nvinfer1::SampleMode::kFILL);
nvinfer1::ITensor* slice_output = sliceLayer->getOutput(0);
nvinfer1::Dims constantDims;
@@ -194,7 +194,7 @@ bool add_conv_deconv(ConversionCtx* ctx, const torch::jit::Node* n, args& args)
nvinfer1::IConvolutionLayer* convLayer =
ctx->net->addConvolutionNd(*in, num_output_maps, filter_dim, kernel_weights, bias.data);
convLayer->setStrideNd(stride);
- convLayer->setPaddingMode(nvinfer1::PaddingMode::kCAFFE_ROUND_DOWN);
+ convLayer->setPaddingMode(nvinfer1::PaddingMode::kEXPLICIT_ROUND_DOWN);
convLayer->setPaddingNd(padding);
convLayer->setPostPadding(out_padding);
convLayer->setDilationNd(dilation);
@@ -293,7 +293,7 @@ bool add_conv_deconv(ConversionCtx* ctx, const torch::jit::Node* n, args& args)
TORCHTRT_CHECK(conv, "Unable to create convolution layer from node: " << *n);
conv->setStrideNd(stride);
- conv->setPaddingMode(nvinfer1::PaddingMode::kCAFFE_ROUND_DOWN);
+ conv->setPaddingMode(nvinfer1::PaddingMode::kEXPLICIT_ROUND_DOWN);
conv->setPaddingNd(padding);
conv->setPostPadding(out_padding);
conv->setDilationNd(dilation);
diff --git a/core/conversion/converters/impl/interpolate.cpp b/core/conversion/converters/impl/interpolate.cpp
index fad2ca5121..61721e3d2c 100644
--- a/core/conversion/converters/impl/interpolate.cpp
+++ b/core/conversion/converters/impl/interpolate.cpp
@@ -72,7 +72,7 @@ void resize_layer_size(
nvinfer1::ITensor* in,
std::vector<int64_t> out_shape,
std::vector<float> scales,
- nvinfer1::ResizeMode mode,
+ nvinfer1::InterpolationMode mode,
bool align_corners = false) {
TORCHTRT_CHECK((out_shape.size() > 0) ^ (scales.size() > 0), "only one of out_shape or scales should be defined");
auto resize_layer = ctx->net->addResize(*in);
@@ -141,7 +141,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
float scale = args[2].IValue()->toDouble();
std::vector<float> padded_scales(in_shape.size(), 1);
padded_scales[padded_scales.size() - 1] = scale;
- resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::ResizeMode::kNEAREST);
+ resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::InterpolationMode::kNEAREST);
} else {
// Case 2: user uses output size
auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
@@ -150,7 +150,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
auto out_shape = in_shape;
std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
- resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::ResizeMode::kNEAREST);
+ resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::InterpolationMode::kNEAREST);
}
return true;
@@ -172,7 +172,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
float scale = scale_factors[0];
std::vector<float> padded_scales(in_shape.size(), 1);
padded_scales[padded_scales.size() - 1] = scale;
- resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::ResizeMode::kNEAREST);
+ resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::InterpolationMode::kNEAREST);
} else {
// Case 2: user uses output size
auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
@@ -181,7 +181,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
auto out_shape = in_shape;
std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
- resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::ResizeMode::kNEAREST);
+ resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::InterpolationMode::kNEAREST);
}
return true;
@@ -203,7 +203,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
std::vector<float> padded_scales(in_shape.size(), 1);
padded_scales[padded_scales.size() - 2] = scale_h;
padded_scales[padded_scales.size() - 1] = scale_w;
- resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::ResizeMode::kNEAREST);
+ resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::InterpolationMode::kNEAREST);
} else {
// Case 2: user uses output size
auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
@@ -212,7 +212,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
auto out_shape = in_shape;
std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
- resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::ResizeMode::kNEAREST);
+ resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::InterpolationMode::kNEAREST);
}
return true;
@@ -236,7 +236,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
std::vector<float> padded_scales(in_shape.size(), 1);
padded_scales[padded_scales.size() - 2] = scale_h;
padded_scales[padded_scales.size() - 1] = scale_w;
- resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::ResizeMode::kNEAREST);
+ resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::InterpolationMode::kNEAREST);
} else {
// Case 2: user uses output size
auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
@@ -245,7 +245,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
auto out_shape = in_shape;
std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
- resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::ResizeMode::kNEAREST);
+ resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::InterpolationMode::kNEAREST);
}
return true;
@@ -270,7 +270,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
padded_scales[padded_scales.size() - 3] = scale_d;
padded_scales[padded_scales.size() - 2] = scale_h;
padded_scales[padded_scales.size() - 1] = scale_w;
- resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::ResizeMode::kNEAREST);
+ resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::InterpolationMode::kNEAREST);
} else {
// Case 2: user uses output size
auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
@@ -279,7 +279,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
auto out_shape = in_shape;
std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
- resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::ResizeMode::kNEAREST);
+ resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::InterpolationMode::kNEAREST);
}
return true;
@@ -306,7 +306,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
padded_scales[padded_scales.size() - 3] = scale_d;
padded_scales[padded_scales.size() - 2] = scale_h;
padded_scales[padded_scales.size() - 1] = scale_w;
- resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::ResizeMode::kNEAREST);
+ resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::InterpolationMode::kNEAREST);
} else {
// Case 2: user uses output size
auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
@@ -315,7 +315,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
auto out_shape = in_shape;
std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
- resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::ResizeMode::kNEAREST);
+ resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::InterpolationMode::kNEAREST);
}
return true;
@@ -336,7 +336,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
float scale = args[3].IValue()->toDouble();
std::vector<float> padded_scales(in_shape.size(), 1);
padded_scales[padded_scales.size() - 1] = scale;
- resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::ResizeMode::kLINEAR, align_corners);
+ resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::InterpolationMode::kLINEAR, align_corners);
} else {
// Case 2: user uses output size
auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
@@ -345,7 +345,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
auto out_shape = in_shape;
std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
- resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::ResizeMode::kLINEAR, align_corners);
+ resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::InterpolationMode::kLINEAR, align_corners);
}
return true;
@@ -368,7 +368,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
float scale = scale_factors[0];
std::vector<float> padded_scales(in_shape.size(), 1);
padded_scales[padded_scales.size() - 1] = scale;
- resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::ResizeMode::kLINEAR, align_corners);
+ resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::InterpolationMode::kLINEAR, align_corners);
} else {
// Case 2: user uses output size
auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
@@ -377,7 +377,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
auto out_shape = in_shape;
std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
- resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::ResizeMode::kLINEAR, align_corners);
+ resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::InterpolationMode::kLINEAR, align_corners);
}
return true;
@@ -400,7 +400,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
std::vector<float> padded_scales(in_shape.size(), 1);
padded_scales[padded_scales.size() - 2] = scale_h;
padded_scales[padded_scales.size() - 1] = scale_w;
- resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::ResizeMode::kLINEAR, align_corners);
+ resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::InterpolationMode::kLINEAR, align_corners);
} else {
// Case 2: user uses output size
auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
@@ -410,7 +410,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
auto out_shape = in_shape;
std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
- resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::ResizeMode::kLINEAR, align_corners);
+ resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::InterpolationMode::kLINEAR, align_corners);
}
return true;
@@ -435,7 +435,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
std::vector<float> padded_scales(in_shape.size(), 1);
padded_scales[padded_scales.size() - 2] = scale_h;
padded_scales[padded_scales.size() - 1] = scale_w;
- resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::ResizeMode::kLINEAR, align_corners);
+ resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::InterpolationMode::kLINEAR, align_corners);
} else {
// Case 2: user uses output size
auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
@@ -445,7 +445,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
auto out_shape = in_shape;
std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
- resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::ResizeMode::kLINEAR, align_corners);
+ resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::InterpolationMode::kLINEAR, align_corners);
}
return true;
@@ -470,7 +470,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
padded_scales[padded_scales.size() - 3] = scale_d;
padded_scales[padded_scales.size() - 2] = scale_h;
padded_scales[padded_scales.size() - 1] = scale_w;
- resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::ResizeMode::kLINEAR, align_corners);
+ resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::InterpolationMode::kLINEAR, align_corners);
} else {
// Case 2: user uses output size
auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
@@ -480,7 +480,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
auto out_shape = in_shape;
std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
- resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::ResizeMode::kLINEAR, align_corners);
+ resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::InterpolationMode::kLINEAR, align_corners);
}
return true;
@@ -507,7 +507,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
padded_scales[padded_scales.size() - 3] = scale_d;
padded_scales[padded_scales.size() - 2] = scale_h;
padded_scales[padded_scales.size() - 1] = scale_w;
- resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::ResizeMode::kLINEAR, align_corners);
+ resize_layer_size(ctx, n, in, {}, padded_scales, nvinfer1::InterpolationMode::kLINEAR, align_corners);
} else {
// Case 2: user uses output size
auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
@@ -517,7 +517,7 @@ auto interpolate_registrations TORCHTRT_UNUSED =
auto out_shape = in_shape;
std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
- resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::ResizeMode::kLINEAR, align_corners);
+ resize_layer_size(ctx, n, in, out_shape, {}, nvinfer1::InterpolationMode::kLINEAR, align_corners);
}
return true;
diff --git a/core/conversion/converters/impl/linear.cpp b/core/conversion/converters/impl/linear.cpp
index 6289334736..39f9cb055d 100644
--- a/core/conversion/converters/impl/linear.cpp
+++ b/core/conversion/converters/impl/linear.cpp
@@ -40,22 +40,26 @@ auto linear_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pat
in = in_shuffle->getOutput(0);
}
- auto w_tensor = args[1].IValue()->toTensor();
- Weights w = Weights(ctx, w_tensor);
+ // Convert w_tensor to ITensor
+ auto weight = args[1].IValue()->toTensor();
+ auto weight_tensor = tensor_to_const(ctx, weight, util::node_info(n) + "_weight");
+ auto mm_layer = ctx->net->addMatrixMultiply(
+ *in, nvinfer1::MatrixOperation::kNONE, *weight_tensor, nvinfer1::MatrixOperation::kNONE);
- nvinfer1::ILayer* new_layer;
- if (!args[2].IValue()->isNone()) {
- Weights b(ctx, args[2].IValue()->toTensor());
- new_layer = ctx->net->addFullyConnected(*in, w.num_output_maps, w.data, b.data);
- } else {
- LOG_DEBUG("There is no bias for the linear layer");
- new_layer = ctx->net->addFullyConnected(*in, w.num_output_maps, w.data, Weights().data);
- }
+ TORCHTRT_CHECK(mm_layer, "Unable to create linear layer from node: " << *n);
+ mm_layer->setName(util::node_info(n).c_str());
- TORCHTRT_CHECK(new_layer, "Unable to create linear layer from node: " << *n);
+ auto mm_output = mm_layer->getOutput(0);
- new_layer->setName(util::node_info(n).c_str());
- auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], new_layer->getOutput(0));
+ if (!args[2].IValue()->isNone()) {
+ // Convert bias to ITensor
+ auto bias = args[2].IValue()->toTensor();
+ auto bias_tensor = tensor_to_const(ctx, bias, util::node_info(n) + "_bias");
+ auto bias_add_layer = add_elementwise(
+ ctx, nvinfer1::ElementWiseOperation::kSUM, mm_output, bias_tensor, util::node_info(n) + "_bias_add");
+ mm_output = bias_add_layer->getOutput(0);
+ }
+ auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], mm_output);
LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
diff --git a/core/runtime/TRTEngine.cpp b/core/runtime/TRTEngine.cpp
index 7a046f6d94..fcbb403e2c 100644
--- a/core/runtime/TRTEngine.cpp
+++ b/core/runtime/TRTEngine.cpp
@@ -120,16 +120,25 @@ TRTEngine::TRTEngine(
} else {
uint64_t inputs_size = _in_binding_names.size();
in_binding_names.resize(inputs_size);
- for (size_t pyt_idx = 0; pyt_idx < inputs_size; pyt_idx++) {
+ for (uint64_t pyt_idx = 0; pyt_idx < inputs_size; pyt_idx++) {
auto binding_name = _in_binding_names[pyt_idx];
- auto trt_idx = cuda_engine->getBindingIndex(binding_name.c_str());
- std::string engine_binded_name = cuda_engine->getIOTensorName(trt_idx);
- TORCHTRT_CHECK(
- (binding_name == engine_binded_name),
- "Could not find a TensorRT engine binding for input named " << binding_name);
+ // Check if the binding name provided is in the list of engine's bindings
+ // by iterating through nbIOTensors and verify it is an input binding
+ bool is_binding = false, is_input = false;
+ int32_t trt_idx;
+ for (int32_t idx = 0; idx < cuda_engine->getNbIOTensors(); idx++) {
+ std::string curr_bind_name = cuda_engine->getIOTensorName(idx);
+ if (curr_bind_name == binding_name) {
+ is_binding = true;
+ trt_idx = idx;
+ if (cuda_engine->getTensorIOMode(binding_name.c_str()) == nvinfer1::TensorIOMode::kINPUT) {
+ is_input = true;
+ }
+ }
+ }
+ TORCHTRT_CHECK(is_binding, "Could not find a TensorRT engine binding for input named " << binding_name);
TORCHTRT_CHECK(
- (cuda_engine->getTensorIOMode(binding_name.c_str()) == nvinfer1::TensorIOMode::kINPUT),
- "Binding " << binding_name << " specified as input but found as output in TensorRT engine");
+ is_input, "Binding " << binding_name << " specified as input but found as output in TensorRT engine");
LOG_DEBUG(
"Input binding name: " << binding_name << " has TensorRT binding index: " << trt_idx
<< ", Torch binding index: " << pyt_idx);
@@ -141,11 +150,25 @@ TRTEngine::TRTEngine(
out_binding_names.resize(outputs);
for (size_t pyt_idx = 0; pyt_idx < outputs; pyt_idx++) {
auto binding_name = _out_binding_names[pyt_idx];
- auto trt_idx = cuda_engine->getBindingIndex(binding_name.c_str());
- TORCHTRT_CHECK((trt_idx != -1), "Could not find a TensorRT engine binding for output named " << binding_name);
+ // Check if the binding name provided is in the list of engine's bindings
+ // by iterating through nbIOTensors and verify it is an output binding
+ bool is_binding = false, is_output = false;
+ int32_t trt_idx;
+ for (int32_t idx = 0; idx < cuda_engine->getNbIOTensors(); idx++) {
+ std::string curr_bind_name = cuda_engine->getIOTensorName(idx);
+ if (curr_bind_name == binding_name) {
+ is_binding = true;
+ trt_idx = idx;
+ if (cuda_engine->getTensorIOMode(binding_name.c_str()) == nvinfer1::TensorIOMode::kOUTPUT) {
+ is_output = true;
+ }
+ }
+ }
+
+ TORCHTRT_CHECK(is_binding, "Could not find a TensorRT engine binding for output named " << binding_name);
TORCHTRT_CHECK(
- !(cuda_engine->getTensorIOMode(binding_name.c_str()) == nvinfer1::TensorIOMode::kINPUT),
- "Binding " << binding_name << " specified as output but found as input in TensorRT engine");
+ is_output, "Binding " << binding_name << " specified as output but found as input in TensorRT engine");
+
LOG_DEBUG(
"Output binding name: " << binding_name << " has TensorRT binding index: " << trt_idx
<< ", Torch binding index: " << inputs_size + pyt_idx);
diff --git a/cpp/include/torch_tensorrt/ptq.h b/cpp/include/torch_tensorrt/ptq.h
index d8570f0e6e..e88da06b0c 100644
--- a/cpp/include/torch_tensorrt/ptq.h
+++ b/cpp/include/torch_tensorrt/ptq.h
@@ -21,10 +21,10 @@
#include "torch_tensorrt/macros.h"
#ifndef DOXYGEN_SHOULD_SKIP_THIS
-namespace nvinfer1 {
-class IInt8Calibrator;
-class IInt8EntropyCalibrator2;
-} // namespace nvinfer1
+// namespace nvinfer1 {
+// class IInt8Calibrator;
+// class IInt8EntropyCalibrator2;
+// } // namespace nvinfer1
namespace torch_tensorrt {
namespace ptq {
diff --git a/py/torch_tensorrt/csrc/torch_tensorrt_py.cpp b/py/torch_tensorrt/csrc/torch_tensorrt_py.cpp
index 33c7e27398..c0655d9907 100644
--- a/py/torch_tensorrt/csrc/torch_tensorrt_py.cpp
+++ b/py/torch_tensorrt/csrc/torch_tensorrt_py.cpp
@@ -2,6 +2,7 @@
#include "pybind11/stl.h"
#include "ATen/core/jit_type.h"
+#include "NvInferRuntimeBase.h"
#include "Python.h"
#include "core/compiler.h"
#include "core/conversion/conversion.h"
@@ -77,6 +78,10 @@ class pyIInt8Calibrator : public pyCalibratorTrampoline<nvinfer1::IInt8Calibrato
using Derived = pyCalibratorTrampoline<nvinfer1::IInt8Calibrator>;
using Derived::Derived;
+ nvinfer1::InterfaceInfo getInterfaceInfo() const noexcept override {
+ return nvinfer1::InterfaceInfo{"PYTHON CALIBRATOR", 1, 0};
+ }
+
nvinfer1::CalibrationAlgoType getAlgorithm() noexcept override {
try {
PYBIND11_OVERLOAD_PURE_NAME(
diff --git a/py/torch_tensorrt/dynamo/_compiler.py b/py/torch_tensorrt/dynamo/_compiler.py
index b321eabcb2..ac430bf883 100644
--- a/py/torch_tensorrt/dynamo/_compiler.py
+++ b/py/torch_tensorrt/dynamo/_compiler.py
@@ -634,7 +634,7 @@ def convert_module_to_trt_engine(
import io
with io.BytesIO() as engine_bytes:
- engine_bytes.write(interpreter_result.engine.serialize())
+ engine_bytes.write(interpreter_result.engine)
engine_bytearray = engine_bytes.getvalue()
return engine_bytearray
diff --git a/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py b/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py
index 06ae596ed0..ffcc9c195e 100644
--- a/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py
+++ b/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py
@@ -172,7 +172,7 @@ def run(
if version.parse(trt.__version__) >= version.parse("8.2"):
builder_config.profiling_verbosity = (
- trt.ProfilingVerbosity.VERBOSE
+ trt.ProfilingVerbosity.DETAILED
if self.compilation_settings.debug
else trt.ProfilingVerbosity.LAYER_NAMES_ONLY
)
@@ -252,7 +252,7 @@ def run(
if tactic_sources is not None:
builder_config.set_tactic_sources(tactic_sources=tactic_sources)
- engine = self.builder.build_engine(self.ctx.net, builder_config)
+ engine = self.builder.build_serialized_network(self.ctx.net, builder_config)
assert engine
serialized_cache = (
@@ -263,7 +263,7 @@ def run(
_LOGGER.info(
f"Build TRT engine elapsed time: {datetime.now() - build_engine_start_time}"
)
- _LOGGER.info(f"TRT Engine uses: {engine.device_memory_size} bytes of Memory")
+ _LOGGER.info(f"TRT Engine uses: {engine.nbytes} bytes of Memory")
return TRTInterpreterResult(
engine, self._input_names, self._output_names, serialized_cache
diff --git a/py/torch_tensorrt/dynamo/conversion/_conversion.py b/py/torch_tensorrt/dynamo/conversion/_conversion.py
index 844cb6789a..f1f4af9236 100644
--- a/py/torch_tensorrt/dynamo/conversion/_conversion.py
+++ b/py/torch_tensorrt/dynamo/conversion/_conversion.py
@@ -87,8 +87,9 @@ def convert_module(
from torch_tensorrt.dynamo.runtime import TorchTensorRTModule
with io.BytesIO() as engine_bytes:
- engine_bytes.write(interpreter_result.engine.serialize())
+ engine_bytes.write(interpreter_result.engine)
engine_str = engine_bytes.getvalue()
+
return TorchTensorRTModule(
serialized_engine=engine_str,
name=name,
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/conv.py b/py/torch_tensorrt/dynamo/conversion/impl/conv.py
index 26e0d59b8f..6c15b4b5fe 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/conv.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/conv.py
@@ -63,7 +63,7 @@ def convNd(
)
# Process weight terms
- if ctx.net.has_explicit_precision or isinstance(weight, TRTTensor):
+ if isinstance(weight, TRTTensor):
weight = get_trt_tensor(ctx, weight, f"{name}_weight")
# Append new dimension (unsqueeze) if the convolution is 1d
if is_conv1d:
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/deconv.py b/py/torch_tensorrt/dynamo/conversion/impl/deconv.py
index f66bff7c82..03a209e2a5 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/deconv.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/deconv.py
@@ -63,7 +63,7 @@ def deconvNd(
)
# Process weight terms
- if ctx.net.has_explicit_precision or isinstance(weight, TRTTensor):
+ if isinstance(weight, TRTTensor):
weight = get_trt_tensor(ctx, weight, f"{name}_weight")
# Append new dimension (unsqueeze) if the deconvolution is 1d
if is_deconv1d:
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
index 8282ee8698..6664a67cfe 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
@@ -147,17 +147,6 @@ def convert_binary_elementwise(
ctx, rhs_val, trt_promoted_type, name, target, source_ir
)
- # Check the limitation in the doc string.
- if ctx.net.has_implicit_batch_dimension:
- if is_lhs_trt_tensor and not is_rhs_trt_tensor:
- assert len(lhs_val.shape) >= len(
- rhs_val.shape
- ), f"{lhs_val.shape} >= {rhs_val.shape}"
- elif not is_lhs_trt_tensor and is_rhs_trt_tensor:
- assert len(rhs_val.shape) >= len(
- lhs_val.shape
- ), f"{rhs_val.shape} >= {lhs_val.shape}"
-
lhs_val, rhs_val = broadcast(
ctx.net, lhs_val, rhs_val, f"{name}_lhs", f"{name}_rhs"
)
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/pad.py b/py/torch_tensorrt/dynamo/conversion/impl/pad.py
index 3764667ffb..9031426c5c 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/pad.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/pad.py
@@ -53,7 +53,7 @@ def constant_padNd(
)
value_const = get_trt_tensor(ctx, value, f"{name}_value", input.dtype)
layer.set_input(4, value_const)
- layer.mode = trt.SliceMode.FILL
+ layer.mode = trt.SampleMode.FILL
set_layer_name(layer, target, name, source_ir)
return layer.get_output(0)
@@ -91,7 +91,7 @@ def reflection_padNd(
shape=tuple(new_shape),
stride=tuple(stride_list),
)
- layer.mode = trt.SliceMode.REFLECT
+ layer.mode = trt.SampleMode.REFLECT
set_layer_name(layer, target, name, source_ir)
return layer.get_output(0)
@@ -129,7 +129,7 @@ def replication_padNd(
shape=tuple(new_shape),
stride=tuple(stride_list),
)
- layer.mode = trt.SliceMode.CLAMP
+ layer.mode = trt.SampleMode.CLAMP
set_layer_name(layer, target, name, source_ir)
return layer.get_output(0)
@@ -167,7 +167,7 @@ def circular_padNd(
shape=tuple(new_shape),
stride=tuple(stride_list),
)
- layer.mode = trt.SliceMode.WRAP
+ layer.mode = trt.SampleMode.WRAP
set_layer_name(layer, target, name, source_ir)
return layer.get_output(0)
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/permutation.py b/py/torch_tensorrt/dynamo/conversion/impl/permutation.py
index 48a91faa40..4fabebd176 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/permutation.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/permutation.py
@@ -66,7 +66,7 @@ def roll(
shape=shape,
stride=stride,
)
- layer.mode = trt.SliceMode.WRAP
+ layer.mode = trt.SampleMode.WRAP
set_layer_name(layer, target, f"{name}_slice_wrap", source_ir)
return layer.get_output(0)
@@ -83,7 +83,7 @@ def roll(
shape=flatten_shape,
stride=stride,
)
- layer.mode = trt.SliceMode.WRAP
+ layer.mode = trt.SampleMode.WRAP
set_layer_name(layer, target, f"{name}_slice_wrap", source_ir)
output = layer.get_output(0)
output = impl.shuffle.reshape(
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/upsample.py b/py/torch_tensorrt/dynamo/conversion/impl/upsample.py
index 3313730ec3..c61aad4290 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/upsample.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/upsample.py
@@ -29,14 +29,14 @@ def upsample(
resize_layer.scales = [1.0, 1.0] + list(scale_factors)
else:
raise RuntimeError(
- f"At least one of out_shape and scale_factors should be specified."
+ "At least one of out_shape and scale_factors should be specified."
)
# interpolate mode
if resize_mode == "nearest" or None:
- resize_layer.resize_mode = trt.ResizeMode.NEAREST
+ resize_layer.resize_mode = trt.InterpolationMode.NEAREST
elif resize_mode == "bilinear":
- resize_layer.resize_mode = trt.ResizeMode.LINEAR
+ resize_layer.resize_mode = trt.InterpolationMode.LINEAR
if align_corners is None or not align_corners:
raise RuntimeError(
f"Interpolation works differently is align_corners is False for {resize_mode} mode in PyTorch and TensorRT."
diff --git a/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py b/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
index 3a66ed3716..82f5817b01 100644
--- a/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
+++ b/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
@@ -2,7 +2,7 @@
import logging
from contextlib import nullcontext
-from typing import Any, Dict, List, Optional, Sequence, Tuple
+from typing import Any, Dict, List, Optional, Tuple
import tensorrt as trt
import torch
@@ -55,73 +55,69 @@ def __init__(
def _initialize(self) -> None:
self.initialized = True
+ logger = trt.Logger()
+ runtime = trt.Runtime(logger)
+ self.engine = runtime.deserialize_cuda_engine(self.engine)
self.context = self.engine.create_execution_context()
# Indices of inputs/outputs in the trt engine bindings, in the order
# as they are in the original PyTorch model.
- self.input_binding_indices_in_order: Sequence[int] = [
- self.engine.get_binding_index(name) for name in self.input_names
- ]
- self.output_binding_indices_in_order: Sequence[int] = [
- self.engine.get_binding_index(name) for name in self.output_names
- ]
- primary_input_outputs = set()
- primary_input_outputs.update(self.input_binding_indices_in_order)
- primary_input_outputs.update(self.output_binding_indices_in_order)
- self.hidden_output_binding_indices_in_order: Sequence[int] = []
- self.hidden_output_names: Sequence[str] = []
- for i in range(
- self.engine.num_bindings // self.engine.num_optimization_profiles
- ):
- if i not in primary_input_outputs:
- self.hidden_output_binding_indices_in_order.append(i)
- self.hidden_output_names.append(self.engine.get_binding_name(i))
- assert (self.engine.num_bindings // self.engine.num_optimization_profiles) == (
+ # TODO: Verify if the following is required especially the hidden outputs
+ # self.input_binding_indices_in_order: Sequence[int] = [
+ # self.engine.get_binding_index(name) for name in self.input_names
+ # ]
+ # self.output_binding_indices_in_order: Sequence[int] = [
+ # self.engine.get_binding_index(name) for name in self.output_names
+ # ]
+ # primary_input_outputs = set()
+ # primary_input_outputs.update(self.input_binding_indices_in_order)
+ # primary_input_outputs.update(self.output_binding_indices_in_order)
+ # self.hidden_output_binding_indices_in_order: Sequence[int] = []
+ # self.hidden_output_names: Sequence[str] = []
+ # for i in range(
+ # self.engine.num_bindings // self.engine.num_optimization_profiles
+ # ):
+ # if i not in primary_input_outputs:
+ # self.hidden_output_binding_indices_in_order.append(i)
+ # self.hidden_output_names.append(self.engine.get_binding_name(i))
+
+ assert (
+ self.engine.num_io_tensors // self.engine.num_optimization_profiles
+ ) == (
len(self.input_names)
+ len(self.output_names)
- + len(self.hidden_output_names)
+ # + len(self.hidden_output_names) #TODO: Verify if this is required
)
self.input_dtypes = [
unified_dtype_converter(
- self.engine.get_binding_dtype(idx), Frameworks.TORCH
+ self.engine.get_tensor_dtype(input_name), Frameworks.TORCH
)
- for idx in self.input_binding_indices_in_order
+ for input_name in self.input_names
]
- self.input_shapes: Sequence[Sequence[int]] = [
- tuple(self.engine.get_binding_shape(idx))
- for idx in self.input_binding_indices_in_order
+ self.input_shapes = [
+ self.engine.get_tensor_shape(input_name) for input_name in self.input_names
]
self.output_dtypes = [
unified_dtype_converter(
- self.engine.get_binding_dtype(idx), Frameworks.TORCH
+ self.engine.get_tensor_dtype(output_name), Frameworks.TORCH
)
- for idx in self.output_binding_indices_in_order
+ for output_name in self.output_names
]
self.output_shapes = [
- (
- tuple(self.engine.get_binding_shape(idx))
- if self.engine.has_implicit_batch_dimension
- else tuple()
- )
- for idx in self.output_binding_indices_in_order
- ]
- self.hidden_output_dtypes = [
- unified_dtype_converter(
- self.engine.get_binding_dtype(idx), Frameworks.TORCH
- )
- for idx in self.hidden_output_binding_indices_in_order
- ]
- self.hidden_output_shapes = [
- (
- tuple(self.engine.get_binding_shape(idx))
- if self.engine.has_implicit_batch_dimension
- else tuple()
- )
- for idx in self.hidden_output_binding_indices_in_order
+ self.engine.get_tensor_shape(output_name)
+ for output_name in self.output_names
]
+ # TODO: Verify what this is for ?
+ # self.hidden_output_dtypes = [
+ # unified_dtype_converter(
+ # self.engine.get_binding_dtype(idx), Frameworks.TORCH
+ # )
+ # for idx in self.hidden_output_binding_indices_in_order
+ # ]
+
def _check_initialized(self) -> None:
if not self.initialized:
raise RuntimeError("PythonTorchTensorRTModule is not initialized.")
@@ -217,12 +213,12 @@ def forward(self, *inputs: torch.Tensor) -> torch.Tensor | Tuple[torch.Tensor, .
), f"Wrong number of inputs, expect {len(self.input_names)} get {len(inputs)}."
contiguous_inputs: List[torch.Tensor] = [i.contiguous() for i in inputs]
- bindings: List[Any] = [None] * (
- len(self.input_names)
- + len(self.output_names)
- + len(self.hidden_output_names)
- )
-
+ bindings = []
+ # [None] * (
+ # len(self.input_names)
+ # + len(self.output_names)
+ # # + len(self.hidden_output_names) # TODO: Verify if this is required
+ # )
for i, input_name in enumerate(self.input_names):
if not contiguous_inputs[i].is_cuda:
logger.warning(
@@ -241,11 +237,9 @@ def forward(self, *inputs: torch.Tensor) -> torch.Tensor | Tuple[torch.Tensor, .
contiguous_inputs[i].dtype == self.input_dtypes[i]
), f"Dtype mismatch for {i}th input({input_name}). Expect {self.input_dtypes[i]}, got {contiguous_inputs[i].dtype}."
- idx = self.input_binding_indices_in_order[i]
- bindings[idx] = contiguous_inputs[i].data_ptr()
-
- self.context.set_binding_shape(
- idx, tuple(contiguous_inputs[i].shape)
+ bindings.append(contiguous_inputs[i].data_ptr())
+ self.context.set_input_shape(
+ input_name, tuple(contiguous_inputs[i].shape)
)
with (
@@ -258,26 +252,32 @@ def forward(self, *inputs: torch.Tensor) -> torch.Tensor | Tuple[torch.Tensor, .
# create output tensors
outputs: List[torch.Tensor] = []
- for i, idx in enumerate(self.output_binding_indices_in_order):
- shape = tuple(self.context.get_binding_shape(idx))
+ for i, output_name in enumerate(self.output_names):
+ shape = tuple(self.context.get_tensor_shape(output_name))
output = torch.empty(
size=shape,
dtype=self.output_dtypes[i],
device=torch.cuda.current_device(),
)
+ bindings.append(output.data_ptr())
outputs.append(output)
- bindings[idx] = output.data_ptr()
- for i, idx in enumerate(self.hidden_output_binding_indices_in_order):
- shape = tuple(self.context.get_binding_shape(idx))
+ # TODO: Check what is this for ?
+ # for i, idx in enumerate(self.hidden_output_binding_indices_in_order):
+ # shape = tuple(self.context.get_binding_shape(idx))
- output = torch.empty(
- size=shape,
- dtype=self.hidden_output_dtypes[i],
- device=torch.cuda.current_device(),
- )
- bindings[idx] = output.data_ptr()
+ # output = torch.empty(
+ # size=shape,
+ # dtype=self.hidden_output_dtypes[i],
+ # device=torch.cuda.current_device(),
+ # )
+
+ # Assign tensor address appropriately
+ for idx in range(self.engine.num_io_tensors):
+ self.context.set_tensor_address(
+ self.engine.get_tensor_name(idx), bindings[idx]
+ )
with (
torch.autograd.profiler.record_function(
@@ -286,9 +286,7 @@ def forward(self, *inputs: torch.Tensor) -> torch.Tensor | Tuple[torch.Tensor, .
if self.profiling_enabled
else nullcontext()
):
- self.context.execute_async_v2(
- bindings, torch.cuda.current_stream().cuda_stream
- )
+ self.context.execute_async_v3(torch.cuda.current_stream().cuda_stream)
if len(outputs) == 1:
return outputs[0]
diff --git a/py/torch_tensorrt/fx/converters/acc_ops_converters.py b/py/torch_tensorrt/fx/converters/acc_ops_converters.py
index 1765077930..f998ddb27a 100644
--- a/py/torch_tensorrt/fx/converters/acc_ops_converters.py
+++ b/py/torch_tensorrt/fx/converters/acc_ops_converters.py
@@ -3,30 +3,27 @@
import math
import operator
import warnings
-from typing import cast, Dict, Optional, Sequence, Tuple, Union
+from typing import Dict, Optional, Sequence, Tuple, Union, cast
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 torch.fx.immutable_collections import immutable_list
from torch.fx.node import Argument, Target
-
-from ..utils import get_dynamic_dims, unified_dtype_converter, Frameworks
-
-from .converter_utils import * # noqa: F403
+from torch_tensorrt.fx.converters.impl import activation, convolution
from torch_tensorrt.fx.passes.lower_basic_pass import (
trt_transposed_linear,
trt_transposed_matmul,
)
from torch_tensorrt.fx.tracer.acc_tracer.acc_ops import contiguous
-from torch_tensorrt.fx.converters.impl import activation, convolution
+
+from ..converter_registry import tensorrt_converter
+from ..tracer.acc_tracer import acc_ops
+from ..types import * # noqa: F403
+from ..utils import Frameworks, get_dynamic_dims, unified_dtype_converter
+from .converter_utils import * # noqa: F403
_LOGGER: logging.Logger = logging.getLogger(__name__)
@@ -323,7 +320,7 @@ def acc_ops_pad_with_slice_layer(
)
layer.set_input(4, value_const)
- layer.mode = trt.SliceMode.FILL
+ layer.mode = trt.SampleMode.FILL
set_layer_name(layer, target, name)
return layer.get_output(0)
@@ -840,7 +837,7 @@ def acc_ops_tile(
shapes = [1] * len(dims)
strides = [1] * len(dims)
layer = network.add_slice(input_val, starts, shapes, strides)
- layer.mode = trt.SliceMode.WRAP
+ layer.mode = trt.SampleMode.WRAP
set_layer_name(layer, target, name)
if has_dynamic_shape(input_val.shape): # type: ignore[union-attr]
@@ -3536,9 +3533,9 @@ def acc_ops_interpolate(
layer.scales = [1, 1] + list(scale_factor)
if mode.lower() in ["linear", "bilinear", "trilinear"]:
- layer.resize_mode = trt.ResizeMode.LINEAR
+ layer.resize_mode = trt.InterpolationMode.LINEAR
else:
- layer.resize_mode = trt.ResizeMode.NEAREST
+ layer.resize_mode = trt.InterpolationMode.NEAREST
if (align_corners is not None) and align_corners:
layer.coordinate_transformation = (
diff --git a/py/torch_tensorrt/fx/utils.py b/py/torch_tensorrt/fx/utils.py
index 4202e1e96b..9acc53ffc8 100644
--- a/py/torch_tensorrt/fx/utils.py
+++ b/py/torch_tensorrt/fx/utils.py
@@ -1,18 +1,21 @@
from enum import Enum
-from typing import Dict, List, Optional, Callable, Union
+from typing import Callable, Dict, List, Optional, Union
+
import numpy as np
-from packaging import version
# @manual=//deeplearning/trt/python:py_tensorrt
import tensorrt as trt
import torch
from functorch import make_fx
from functorch.experimental import functionalize
+from torch_tensorrt._utils import sanitized_torch_version
from torch_tensorrt.fx.passes.lower_basic_pass import (
replace_op_with_indices,
run_const_fold,
)
-from torch_tensorrt._utils import sanitized_torch_version
+
+from packaging import version
+
from .types import Shape, TRTDataType
@@ -45,6 +48,11 @@ class Frameworks(Enum):
Frameworks.TORCH: torch.float32,
Frameworks.TRT: trt.float32,
},
+ trt.bool: {
+ Frameworks.NUMPY: bool,
+ Frameworks.TORCH: torch.bool,
+ Frameworks.TRT: trt.bool,
+ },
}
if trt.__version__ >= "7.0":
@@ -89,10 +97,10 @@ def unified_dtype_converter(
The equivalent data type in the requested framework.
"""
assert to in Frameworks, f"Expected valid Framework for translation, got {to}"
-
+ trt_major_version = int(trt.__version__.split(".")[0])
if dtype in (np.int8, torch.int8, trt.int8):
return DataTypeEquivalence[trt.int8][to]
- elif trt.__version__ >= "7.0" and dtype in (np.bool_, torch.bool, trt.bool):
+ elif trt_major_version >= 7 and dtype in (np.bool_, torch.bool, trt.bool):
return DataTypeEquivalence[trt.bool][to]
elif dtype in (np.int32, torch.int32, trt.int32):
return DataTypeEquivalence[trt.int32][to]
diff --git a/tests/py/dynamo/runtime/test_convert_method_to_trt_engine.py b/tests/py/dynamo/runtime/test_convert_method_to_trt_engine.py
index b10cae23fa..46a9ab392c 100644
--- a/tests/py/dynamo/runtime/test_convert_method_to_trt_engine.py
+++ b/tests/py/dynamo/runtime/test_convert_method_to_trt_engine.py
@@ -25,12 +25,10 @@ def forward(self, a, b):
symbolic_traced_gm, "forward", inputs=[input_data_0, input_data_1]
)
- # Deserialize the TensorRT engine
- with trt.Logger() as logger, trt.Runtime(logger) as runtime:
- engine = runtime.deserialize_cuda_engine(trt_engine_str)
-
# Inference on TRT Engine
- py_trt_module = PythonTorchTensorRTModule(engine, ["a", "b"], ["output0"])
+ py_trt_module = PythonTorchTensorRTModule(
+ trt_engine_str, ["a", "b"], ["output0"]
+ )
trt_output = py_trt_module(input_data_0, input_data_1).cpu()
# Inference on PyTorch model
From 39ca77df3345179ec1560bfb54da5975fc67ca6b Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Thu, 21 Mar 2024 16:53:35 -0700
Subject: [PATCH 08/69] chore: more fixes
---
.../conversion/impl/elementwise/base.py | 1 +
.../dynamo/conversion/impl/elementwise/ops.py | 1 +
.../dynamo/conversion/impl/select.py | 23 ++++++++++++-------
.../dynamo/conversion/impl/shape.py | 14 +++++++----
.../fx/converters/converter_utils.py | 10 +++++---
py/torch_tensorrt/fx/utils.py | 7 ++++++
tests/py/dynamo/conversion/test_erf_aten.py | 6 +----
tests/py/dynamo/conversion/test_neg_aten.py | 6 +----
8 files changed, 42 insertions(+), 26 deletions(-)
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
index 6664a67cfe..615cd0bd62 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
@@ -150,6 +150,7 @@ def convert_binary_elementwise(
lhs_val, rhs_val = broadcast(
ctx.net, lhs_val, rhs_val, f"{name}_lhs", f"{name}_rhs"
)
+
layer = ctx.net.add_elementwise(lhs_val, rhs_val, op_type)
set_layer_name(layer, target, name, source_ir)
output = layer.get_output(0)
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/ops.py b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/ops.py
index d0f6d29482..77ec68629b 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/ops.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/ops.py
@@ -244,6 +244,7 @@ def add(
lhs_val: Union[TRTTensor, int, float],
rhs_val: Union[TRTTensor, int, float],
) -> TRTTensor:
+
return convert_binary_elementwise(
ctx, target, source_ir, name, trt.ElementWiseOperation.SUM, lhs_val, rhs_val
)
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/select.py b/py/torch_tensorrt/dynamo/conversion/impl/select.py
index db586be65f..955ef2bb76 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/select.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/select.py
@@ -1,5 +1,5 @@
import logging
-from typing import Optional, Sequence, Union, cast
+from typing import Any, Optional, cast
import numpy as np
import tensorrt as trt
@@ -9,6 +9,7 @@
from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
from torch_tensorrt.dynamo.conversion.converter_utils import (
broadcastable,
+ cast_trt_tensor,
get_positive_dim,
get_trt_tensor,
to_numpy,
@@ -81,7 +82,7 @@ def index(
source_ir: Optional[SourceIR],
name: str,
input: TRTTensor,
- index: Sequence[Union[TRTTensor, np.ndarray, torch.Tensor]],
+ index: Any,
) -> TRTTensor:
adv_indx_indices = []
tensor_indices = []
@@ -90,7 +91,7 @@ def index(
# is_numpy is a flag to specify if all the indices are numpy or torchTensor.
# If any is not this flag will be set to False
_LOGGER.debug(
- f"Determining whether aten.index constant-index optimization can be invoked"
+ "Determining whether aten.index constant-index optimization can be invoked"
)
is_numpy = all(
isinstance(ind, (torch.Tensor, np.ndarray)) for ind in index if ind is not None
@@ -123,7 +124,7 @@ def index(
return identity_layer.get_output(0)
elif len(tensor_indices) == 1:
indices_tensor = get_trt_tensor(
- ctx, tensor_indices[0], name + f"_parameter_to_fp32_tensor"
+ ctx, tensor_indices[0], name + "_parameter_to_fp32_tensor"
)
index = adv_indx_indices[0]
_LOGGER.debug(f"The advanced index indices is {adv_indx_indices}")
@@ -204,7 +205,7 @@ def index(
cum_adv_index = cum_adv_index + adv_index
multiplier = multiplier * input_shape[adv_indx_indices[i]]
cum_adv_index = get_trt_tensor(
- ctx, cum_adv_index, name + f"_index_sum_intermediate"
+ ctx, cum_adv_index, name + "_index_sum_intermediate"
)
else:
multiplier = get_trt_tensor(
@@ -255,6 +256,12 @@ def index(
cum_adv_index_shape_layer, target, name + "_cum_adv_index_shape", source_ir
)
cum_adv_index_shape_tensor = cum_adv_index_shape_layer.get_output(0)
+ cum_adv_index_shape_tensor = cast_trt_tensor(
+ ctx,
+ cum_adv_index_shape_tensor,
+ trt.int32,
+ name + "_cum_adv_index_shape_casted",
+ )
cum_adv_index_shape = cum_adv_index.shape
_LOGGER.debug(f"The shape for cumulative adv index is {cum_adv_index_shape}")
# check if all advanced indices are consecutive
@@ -263,7 +270,7 @@ def index(
adv_indx_count
== adv_indx_indices[adv_indx_count - 1] - adv_indx_indices[0] + 1
):
- _LOGGER.debug(f"The indices are continuous in this case")
+ _LOGGER.debug("The indices are continuous in this case")
concat_tensor_reshape.append(
get_trt_tensor(ctx, -1, name + "_dynamic_concat")
)
@@ -287,7 +294,7 @@ def index(
source_ir,
)
unfold_tensor = regular_index_shuffle_layer.get_output(0)
- _LOGGER.debug(f"The tensor is unfolded now")
+ _LOGGER.debug("The tensor is unfolded now")
_LOGGER.debug(f"The unfolded tensor shape is {unfold_tensor.shape}")
# Transpose folded advanced indexed axis to its original location.
@@ -342,7 +349,7 @@ def index(
reshape_output = unfold_advanced_shuffle_layer.get_output(0)
else:
- _LOGGER.debug(f"The indices are not continuous in this case")
+ _LOGGER.debug("The indices are not continuous in this case")
concat_final_tensor = []
concat_final_tensor.append(cum_adv_index_shape_tensor)
for i in range(0, rank):
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/shape.py b/py/torch_tensorrt/dynamo/conversion/impl/shape.py
index ef30b186c1..7551d58059 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/shape.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/shape.py
@@ -8,11 +8,14 @@
from torch.fx.node import Target
from torch_tensorrt.dynamo._SourceIR import SourceIR
from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
-from torch_tensorrt.dynamo.conversion.converter_utils import to_numpy
from torch_tensorrt.dynamo.conversion.impl.elementwise.base import (
convert_binary_elementwise,
)
-from torch_tensorrt.fx.converters.converter_utils import set_layer_name
+from torch_tensorrt.fx.converters.converter_utils import (
+ Frameworks,
+ set_layer_name,
+ unified_dtype_converter,
+)
from torch_tensorrt.fx.types import TRTTensor
@@ -48,16 +51,17 @@ def get_shape_with_dynamic_shape(
"""
# Ger real shape info for input_val
input_shape = ctx.net.add_shape(input_val).get_output(0)
-
+ # input_shape.dtype is int64 in TRT 10.0
+ input_np_dtype = unified_dtype_converter(input_shape.dtype, Frameworks.NUMPY)
scale_layer = ctx.net.add_constant(
- input_shape.shape, np.ascontiguousarray(shape, dtype=np.int32)
+ input_shape.shape, np.ascontiguousarray(shape, dtype=input_np_dtype)
)
set_layer_name(scale_layer, target, f"{name}_scale")
scale_res = scale_layer.get_output(0)
length = input_shape.shape[0]
zero_layer = ctx.net.add_constant(
- input_shape.shape, to_numpy(torch.zeros((length), dtype=torch.int32))
+ input_shape.shape, np.zeros(length, dtype=input_np_dtype)
)
set_layer_name(zero_layer, target, f"{name}_zeros")
diff --git a/py/torch_tensorrt/fx/converters/converter_utils.py b/py/torch_tensorrt/fx/converters/converter_utils.py
index 49bf401f58..510d4ef69b 100644
--- a/py/torch_tensorrt/fx/converters/converter_utils.py
+++ b/py/torch_tensorrt/fx/converters/converter_utils.py
@@ -1,8 +1,8 @@
import operator
import warnings
+from enum import Enum, auto
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
-from enum import Enum, auto
import numpy as np
# @manual=//deeplearning/trt/python:py_tensorrt
@@ -20,7 +20,7 @@
TRTPluginFieldCollection,
TRTTensor,
)
-from ..utils import unified_dtype_converter, Frameworks
+from ..utils import Frameworks, unified_dtype_converter
class SourceIR(Enum):
@@ -351,13 +351,17 @@ def prepend_ones(
# compute the final shape.
if has_dynamic_shape(tensor.shape):
tensor_shape_layer = network.add_shape(tensor)
+ tensor_shape = tensor_shape_layer.get_output(0)
+ tensor_shape = type_cast(
+ network, "shape", name + "shape_casted", tensor_shape, trt.int32
+ )
tensor_shape_layer.name = f"{name}_broadcast_orig_shape"
prepend_shape_layer = network.add_constant(
(num_prepend_ones,), np.ones((num_prepend_ones,), dtype=np.int32)
)
prepend_shape_layer.name = f"{name}_broadcast_prepend_ones"
reshape_dim_layer = network.add_concatenation(
- [prepend_shape_layer.get_output(0), tensor_shape_layer.get_output(0)]
+ [prepend_shape_layer.get_output(0), tensor_shape]
)
reshape_dim_layer.axis = 0
reshape_dim_layer.name = f"{name}_broadcast_final_shape"
diff --git a/py/torch_tensorrt/fx/utils.py b/py/torch_tensorrt/fx/utils.py
index 9acc53ffc8..5bef21b6be 100644
--- a/py/torch_tensorrt/fx/utils.py
+++ b/py/torch_tensorrt/fx/utils.py
@@ -38,6 +38,11 @@ class Frameworks(Enum):
Frameworks.TORCH: torch.int32,
Frameworks.TRT: trt.int32,
},
+ trt.int64: {
+ Frameworks.NUMPY: np.int64,
+ Frameworks.TORCH: torch.int64,
+ Frameworks.TRT: trt.int64,
+ },
trt.float16: {
Frameworks.NUMPY: np.float16,
Frameworks.TORCH: torch.float16,
@@ -104,6 +109,8 @@ def unified_dtype_converter(
return DataTypeEquivalence[trt.bool][to]
elif dtype in (np.int32, torch.int32, trt.int32):
return DataTypeEquivalence[trt.int32][to]
+ elif dtype in (np.int64, torch.int64, trt.int64):
+ return DataTypeEquivalence[trt.int64][to]
elif dtype in (np.float16, torch.float16, trt.float16):
return DataTypeEquivalence[trt.float16][to]
elif dtype in (np.float32, torch.float32, trt.float32):
diff --git a/tests/py/dynamo/conversion/test_erf_aten.py b/tests/py/dynamo/conversion/test_erf_aten.py
index 3f52e436b4..d65788bc85 100644
--- a/tests/py/dynamo/conversion/test_erf_aten.py
+++ b/tests/py/dynamo/conversion/test_erf_aten.py
@@ -22,11 +22,7 @@ def forward(self, input):
return torch.ops.aten.erf.default(input)
inputs = [torch.randn(x, dtype=type)]
- self.run_test(
- erf(),
- inputs,
- precision=type,
- )
+ self.run_test(erf(), inputs, precision=type, output_dtypes=[type])
@parameterized.expand(
[
diff --git a/tests/py/dynamo/conversion/test_neg_aten.py b/tests/py/dynamo/conversion/test_neg_aten.py
index c49fc32c23..818d9c5a83 100644
--- a/tests/py/dynamo/conversion/test_neg_aten.py
+++ b/tests/py/dynamo/conversion/test_neg_aten.py
@@ -22,11 +22,7 @@ def forward(self, input):
return torch.ops.aten.neg.default(input)
inputs = [torch.randn(x, dtype=type)]
- self.run_test(
- neg(),
- inputs,
- precision=type,
- )
+ self.run_test(neg(), inputs, precision=type, output_dtypes=[type])
@parameterized.expand(
[
From 5431ee37dfe879a9bd2cdfbe3779227228dc630b Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Mon, 25 Mar 2024 09:54:01 -0700
Subject: [PATCH 09/69] chore: update trt version
---
py/requirements.txt | 2 +-
pyproject.toml | 4 ++--
2 files changed, 3 insertions(+), 3 deletions(-)
diff --git a/py/requirements.txt b/py/requirements.txt
index 334f1e9c76..19c39c8647 100644
--- a/py/requirements.txt
+++ b/py/requirements.txt
@@ -5,5 +5,5 @@ pybind11==2.6.2
torch==2.3.0
torchvision==0.18.0
--extra-index-url https://pypi.ngc.nvidia.com
-tensorrt==8.6.1
+tensorrt>=10.0
pyyaml
diff --git a/pyproject.toml b/pyproject.toml
index 5c42700ef8..6c4940013d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,7 @@ requires = [
"cffi>=1.15.1",
"typing-extensions>=4.7.0",
"future>=0.18.3",
- "tensorrt>=8.6,<8.7",
+ "tensorrt>=10.0",
"torch >=2.3.0.dev,<2.4.0",
"pybind11==2.6.2",
"numpy",
@@ -42,7 +42,7 @@ requires-python = ">=3.8"
keywords = ["pytorch", "torch", "tensorrt", "trt", "ai", "artificial intelligence", "ml", "machine learning", "dl", "deep learning", "compiler", "dynamo", "torchscript", "inference"]
dependencies = [
"torch >=2.3.0.dev,<2.4.0",
- "tensorrt>=8.6,<8.7",
+ "tensorrt>=10.0",
"packaging>=23",
"numpy",
"typing-extensions>=4.7.0",
From 0c03de5ca11d7c77adf3ce8cbe609bd832eced4e Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Mon, 25 Mar 2024 18:51:39 -0700
Subject: [PATCH 10/69] chore: more updates
---
core/conversion/converters/converter_util.cpp | 9 +++++++--
core/conversion/converters/converter_util.h | 3 +++
core/conversion/converters/impl/constant_pad.cpp | 9 +++------
core/conversion/converters/impl/conv_deconv.cpp | 2 +-
core/conversion/converters/impl/cumsum.cpp | 2 +-
core/conversion/converters/impl/expand.cpp | 9 ++++-----
core/conversion/converters/impl/interpolate.cpp | 2 +-
core/conversion/converters/impl/linear.cpp | 7 +++++--
core/conversion/converters/impl/select.cpp | 13 +++++--------
9 files changed, 30 insertions(+), 26 deletions(-)
diff --git a/core/conversion/converters/converter_util.cpp b/core/conversion/converters/converter_util.cpp
index 3dcd2e9d80..39afe9945f 100644
--- a/core/conversion/converters/converter_util.cpp
+++ b/core/conversion/converters/converter_util.cpp
@@ -39,6 +39,12 @@ nvinfer1::ITensor* addPadding(
}
}
+nvinfer1::ITensor* getShapeOutput(ConversionCtx* ctx, nvinfer1::ITensor* input_tensor, const std::string& name) {
+ nvinfer1::ITensor* input_shape = ctx->net->addShape(*input_tensor)->getOutput(0);
+ input_shape = castITensor(ctx, input_shape, nvinfer1::DataType::kINT32, name);
+ return input_shape;
+}
+
nvinfer1::ITensor* addUnpadding(
ConversionCtx* ctx,
const torch::jit::Node* n,
@@ -134,7 +140,7 @@ nvinfer1::ILayer* add_elementwise(
}
auto otherStaticShapeMask = tensor_to_const(ctx, thOtherStaticShapeMask);
auto otherDynamicShapeMask = tensor_to_const(ctx, thOtherDynamicShapeMask);
- auto selfShape = ctx->net->addShape(*self)->getOutput(0);
+ nvinfer1::ITensor* selfShape = getShapeOutput(ctx, self, std::string(name + "_shape_cast").c_str());
// size of dynamic dimension of other need to the same as that of
// corresponding dimension of self
auto otherDynamicShape =
@@ -348,7 +354,6 @@ nvinfer1::ITensor* normalize_indices(
auto neg_itensor = tensor_to_const(ctx, neg);
// find the indices that = -1
auto signs = clamp(ctx, indices, neg_itensor, zero_itensor, "clamp layer for " + name);
-
// get the inputDim value where indices == -1, else 0
auto mul = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kPROD, signs, input_dim, "prod layer for " + name);
TORCHTRT_CHECK(mul, "Unable to create mul layer in normalize_indices");
diff --git a/core/conversion/converters/converter_util.h b/core/conversion/converters/converter_util.h
index 3342302431..ad57c476e1 100644
--- a/core/conversion/converters/converter_util.h
+++ b/core/conversion/converters/converter_util.h
@@ -62,6 +62,9 @@ nvinfer1::ITensor* castITensor(
nvinfer1::DataType dtype,
const std::string& layer_name_prefix = "");
+// Get the shape of the input tensor and cast it to INT32 type
+nvinfer1::ITensor* getShapeOutput(ConversionCtx* ctx, nvinfer1::ITensor* input_tensor, const std::string& name = "");
+
// Freeze an at::Tensor in a IConstant layer
nvinfer1::ITensor* tensor_to_const(ConversionCtx* ctx, at::Tensor t, const std::string& name = std::string());
diff --git a/core/conversion/converters/impl/constant_pad.cpp b/core/conversion/converters/impl/constant_pad.cpp
index 20a091e730..42947e1c03 100644
--- a/core/conversion/converters/impl/constant_pad.cpp
+++ b/core/conversion/converters/impl/constant_pad.cpp
@@ -60,13 +60,10 @@ auto constant_pad_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns
if (ctx->input_is_dynamic) {
// build the size using inetwork layers
- auto shape_layer = ctx->net->addShape(*in);
- TORCHTRT_CHECK(shape_layer, "Unable to create shape layer from node: " << *n);
- shape_layer->setName((util::node_info(n) + "_shape").c_str());
auto total_padding_itensor = tensor_to_const(ctx, torch::tensor(total_padding, torch::kInt32));
-
- auto add_layer = ctx->net->addElementWise(
- *shape_layer->getOutput(0), *total_padding_itensor, nvinfer1::ElementWiseOperation::kSUM);
+ nvinfer1::ITensor* shapeOutput = getShapeOutput(ctx, in, (util::node_info(n) + "_shape").c_str());
+ auto add_layer =
+ ctx->net->addElementWise(*shapeOutput, *total_padding_itensor, nvinfer1::ElementWiseOperation::kSUM);
TORCHTRT_CHECK(add_layer, "Unable to create add layer from node: " << *n);
add_layer->setName((util::node_info(n) + "_add").c_str());
slice_layer->setInput(2, *add_layer->getOutput(0));
diff --git a/core/conversion/converters/impl/conv_deconv.cpp b/core/conversion/converters/impl/conv_deconv.cpp
index bf0df00c64..aa7acc9cc4 100644
--- a/core/conversion/converters/impl/conv_deconv.cpp
+++ b/core/conversion/converters/impl/conv_deconv.cpp
@@ -33,7 +33,7 @@ nvinfer1::ILayer* add_bias_layer(
nvinfer1::Dims& input_dims,
nvinfer1::Dims& output_padding,
Weights& bias) {
- nvinfer1::ITensor* input_shape = ctx->net->addShape(*input_tensor)->getOutput(0);
+ nvinfer1::ITensor* input_shape = getShapeOutput(ctx, input_tensor, std::string("bias_shape_cast").c_str());
// Add padding layer
nvinfer1::ITensor* start;
nvinfer1::ITensor* totalPadding;
diff --git a/core/conversion/converters/impl/cumsum.cpp b/core/conversion/converters/impl/cumsum.cpp
index 5c518fd635..f856ca5d4e 100644
--- a/core/conversion/converters/impl/cumsum.cpp
+++ b/core/conversion/converters/impl/cumsum.cpp
@@ -36,7 +36,7 @@ auto cumsum_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pat
torch::Tensor axis = torch::tensor(input_dims.d[dim], torch::kInt32);
tripLimit = tensor_to_const(ctx, axis);
} else {
- nvinfer1::ITensor* inpShape = ctx->net->addShape(*in)->getOutput(0);
+ nvinfer1::ITensor* inpShape = getShapeOutput(ctx, in);
torch::Tensor dimValue = torch::tensor(dim, torch::kInt32);
nvinfer1::ITensor* axis = tensor_to_const(ctx, dimValue);
tripLimit = ctx->net->addGather(*inpShape, *axis, 0)->getOutput(0);
diff --git a/core/conversion/converters/impl/expand.cpp b/core/conversion/converters/impl/expand.cpp
index 6b22fea8d4..0e68768e15 100644
--- a/core/conversion/converters/impl/expand.cpp
+++ b/core/conversion/converters/impl/expand.cpp
@@ -19,11 +19,11 @@ nvinfer1::ITensor* concat(int max_rank, int old_rank, ConversionCtx* ctx, nvinfe
if (max_rank - old_rank > 0) {
torch::Tensor thOne = torch::tensor(std::vector<int32_t>(max_rank - old_rank, 1), torch::kInt32);
auto one_tensor = tensor_to_const(ctx, thOne);
- auto in_shape_tensor = ctx->net->addShape(*tensor)->getOutput(0);
+ auto in_shape_tensor = getShapeOutput(ctx, tensor);
nvinfer1::ITensor* const args[2] = {one_tensor, in_shape_tensor};
return ctx->net->addConcatenation(args, 2)->getOutput(0);
} else { // max_rank - old_rank == 0
- return ctx->net->addShape(*tensor)->getOutput(0);
+ return getShapeOutput(ctx, tensor);
}
}
@@ -221,8 +221,7 @@ auto expand_registrations TORCHTRT_UNUSED =
auto targetDims = targetTensor->getDimensions();
LOG_DEBUG("(expand_as layer) Expand input from " << input_dims << " to " << targetDims);
if (ctx->input_is_dynamic) {
- return add_expand_dynamic(
- ctx, n, in, ctx->net->addShape(*targetTensor)->getOutput(0), targetDims, false);
+ return add_expand_dynamic(ctx, n, in, getShapeOutput(ctx, targetTensor), targetDims, false);
} else {
return add_expand(ctx, n, in, targetDims);
}
@@ -357,7 +356,7 @@ auto expand_registrations TORCHTRT_UNUSED =
if (ctx->input_is_dynamic) {
auto start_tensor = tensor_to_const(ctx, torch::tensor(start_vec, torch::kInt32));
- auto expand_output_shape = ctx->net->addShape(*expand->getOutput(0))->getOutput(0);
+ auto expand_output_shape = getShapeOutput(ctx, expand->getOutput(0));
std::vector<int64_t> repeat_const_vec(repeat_shape_dims.nbDims, 1);
repeat_const_vec[dim + 1] = repeats;
auto repeat_const = tensor_to_const(ctx, torch::tensor(repeat_const_vec, torch::kInt32));
diff --git a/core/conversion/converters/impl/interpolate.cpp b/core/conversion/converters/impl/interpolate.cpp
index 61721e3d2c..748e3c9e1a 100644
--- a/core/conversion/converters/impl/interpolate.cpp
+++ b/core/conversion/converters/impl/interpolate.cpp
@@ -91,7 +91,7 @@ void resize_layer_size(
auto dynamic_shape_mask = tensor_to_const(ctx, th_dynamic_shape_mask);
auto static_shape_mask = tensor_to_const(ctx, th_static_shape_mask);
- auto input_shape = ctx->net->addShape(*in)->getOutput(0);
+ nvinfer1::ITensor* input_shape = getShapeOutput(ctx, in);
auto dynamic_shape =
ctx->net->addElementWise(*input_shape, *dynamic_shape_mask, nvinfer1::ElementWiseOperation::kPROD)
->getOutput(0);
diff --git a/core/conversion/converters/impl/linear.cpp b/core/conversion/converters/impl/linear.cpp
index 39f9cb055d..0e4452dec0 100644
--- a/core/conversion/converters/impl/linear.cpp
+++ b/core/conversion/converters/impl/linear.cpp
@@ -40,11 +40,14 @@ auto linear_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pat
in = in_shuffle->getOutput(0);
}
- // Convert w_tensor to ITensor
+ // Convert w_tensor to ITensor and broadcast 2d to 4d if needed
auto weight = args[1].IValue()->toTensor();
auto weight_tensor = tensor_to_const(ctx, weight, util::node_info(n) + "_weight");
+ auto weight_shape = util::toVec(weight_tensor->getDimensions());
+ weight_tensor = addPadding(ctx, n, weight_tensor, in->getDimensions().nbDims, false, false);
+
auto mm_layer = ctx->net->addMatrixMultiply(
- *in, nvinfer1::MatrixOperation::kNONE, *weight_tensor, nvinfer1::MatrixOperation::kNONE);
+ *in, nvinfer1::MatrixOperation::kNONE, *weight_tensor, nvinfer1::MatrixOperation::kTRANSPOSE);
TORCHTRT_CHECK(mm_layer, "Unable to create linear layer from node: " << *n);
mm_layer->setName(util::node_info(n).c_str());
diff --git a/core/conversion/converters/impl/select.cpp b/core/conversion/converters/impl/select.cpp
index 8334205879..d6b49aa609 100644
--- a/core/conversion/converters/impl/select.cpp
+++ b/core/conversion/converters/impl/select.cpp
@@ -368,8 +368,7 @@ auto select_registrations TORCHTRT_UNUSED =
int rank = inDims.nbDims;
LOG_WARNING("If indices include negative values, the exported graph will produce incorrect results.");
int adv_idx_count = adv_idx_indices.size();
- auto in_shape_itensor = ctx->net->addShape(*in)->getOutput(0);
-
+ nvinfer1::ITensor* in_shape_itensor = getShapeOutput(ctx, in);
std::vector<nvinfer1::ITensor*> dim_tensor_list;
for (int i = 0; i < rank; i++) {
auto dim_tensor =
@@ -401,7 +400,7 @@ auto select_registrations TORCHTRT_UNUSED =
// t: [x_1, x_2, ..., x_m, y_1, y_2, ..., y_n] -> t: [x_1*x_2* ...*x_m, y_1*y_2* ...*y_n]
nvinfer1::ITensor* flatten_tensor = NULL;
{
- auto shuffle_shape_tensor = ctx->net->addShape(*shuffle_out)->getOutput(0);
+ nvinfer1::ITensor* shuffle_shape_tensor = getShapeOutput(ctx, shuffle_out);
auto d0 = tensor_to_const(ctx, torch::tensor({1}, torch::kInt32));
for (int i = 0; i < adv_idx_count; i++) {
auto dim_tensor =
@@ -479,7 +478,7 @@ auto select_registrations TORCHTRT_UNUSED =
nvinfer1::ITensor* reshape_output = NULL;
{
- auto cum_adv_index_shape_tensor = ctx->net->addShape(*cum_adv_index)->getOutput(0);
+ nvinfer1::ITensor* cum_adv_index_shape_tensor = getShapeOutput(ctx, cum_adv_index);
// check if all advanced indices are consecutive.
if (adv_idx_count == (adv_idx_indices[adv_idx_count - 1] - adv_idx_indices[0] + 1)) {
// unfold regular index axes
@@ -559,8 +558,7 @@ auto select_registrations TORCHTRT_UNUSED =
bool dynamic_shape = ctx->input_is_dynamic;
auto input_dim = in->getDimensions();
// add Shape Tensor
- auto ishape_layer = ctx->net->addShape(*in);
- auto ishape_tensor = ishape_layer->getOutput(0); // input shape
+ nvinfer1::ITensor* ishape_tensor = getShapeOutput(ctx, in);
std::string node_name = n->outputs()[0]->debugName().c_str();
int startIdx = 0;
@@ -605,6 +603,7 @@ auto select_registrations TORCHTRT_UNUSED =
stride_.d[i] = 1;
}
}
+
if (!dynamic_shape) {
auto slice_layer = ctx->net->addSlice(*in, start_, size_, stride_);
LOG_DEBUG("start_:" << start_);
@@ -617,7 +616,6 @@ auto select_registrations TORCHTRT_UNUSED =
LOG_DEBUG("Using dynamic version of slice");
// start tensor
at::Tensor start_tensor = torch::zeros({nbdims}).to(torch::kI32);
- ;
start_tensor[axis] = startIdx;
auto start_itensor = tensor_to_const(ctx, start_tensor);
@@ -647,7 +645,6 @@ auto select_registrations TORCHTRT_UNUSED =
// calculate size
auto size_itensor = get_slice_size(ctx, out_start, out_end, stride_itensor, nbdims, node_name);
-
// update slice layer
auto slice_layer = ctx->net->addSlice(*in, start_, size_, stride_);
slice_layer->setInput(1, *out_start); // start
From 982dbd25b3ffd2f4edbf5bd8c2fc321f2a35ab56 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Mon, 25 Mar 2024 11:57:39 -0700
Subject: [PATCH 11/69] parent f39e89e3964bc3d6ea3a6989b1e4099e1bb3e6dd author
Dheeraj Peri <peri.dheeraj@gmail.com> 1711393059 -0700 committer Dheeraj Peri
<peri.dheeraj@gmail.com> 1711393072 -0700
chore: minor updates
chore: Fix save failures
chore: minor fixes
chore: remove duplicate bert test case
chore: remove comments
chore: add load api
chore: minor updates
chore: minor updates
chore: minor updates
chore: more updates
---
core/runtime/TRTEngine.cpp | 2 +-
docsrc/user_guide/saving_models.rst | 71 +++++----
py/torch_tensorrt/_compile.py | 111 +++++++++++++-
py/torch_tensorrt/dynamo/_compiler.py | 9 +-
py/torch_tensorrt/dynamo/_defaults.py | 1 -
py/torch_tensorrt/dynamo/_exporter.py | 142 +++++++++++-------
py/torch_tensorrt/dynamo/_settings.py | 3 -
.../lowering/test_aten_lowering_passes.py | 4 +-
tests/py/dynamo/models/test_export_serde.py | 104 ++++++++-----
tests/py/dynamo/models/test_models_export.py | 63 +-------
tests/py/dynamo/models/test_output_format.py | 62 --------
11 files changed, 311 insertions(+), 261 deletions(-)
delete mode 100644 tests/py/dynamo/models/test_output_format.py
diff --git a/core/runtime/TRTEngine.cpp b/core/runtime/TRTEngine.cpp
index 92e5d7a8ff..7a046f6d94 100644
--- a/core/runtime/TRTEngine.cpp
+++ b/core/runtime/TRTEngine.cpp
@@ -241,7 +241,7 @@ std::string TRTEngine::to_str() const {
exec_ctx->getEngine().getTensorDataType(out_binding_names[o].c_str()))
<< std::endl;
}
- ss << " }" << std::endl;
+ ss << " ]" << std::endl;
ss << " Device: " << device_info << std::endl;
ss << " Hardware Compatibility: " << (hardware_compatible ? "Enabled" : "Disabled") << std::endl;
// clang-format on
diff --git a/docsrc/user_guide/saving_models.rst b/docsrc/user_guide/saving_models.rst
index 8379b44f0f..73fee6e23c 100644
--- a/docsrc/user_guide/saving_models.rst
+++ b/docsrc/user_guide/saving_models.rst
@@ -9,23 +9,22 @@ Saving models compiled with Torch-TensorRT
:undoc-members:
:show-inheritance:
-Saving models compiled with Torch-TensorRT varies slightly with the `ir` that has been used for compilation.
+Saving models compiled with Torch-TensorRT can be done using `torch_tensorrt.save` API.
Dynamo IR
-------------
-The output type of `ir=dynamo` compilation of Torch-TensorRT is `torch.export.ExportedProgram` object by default.
-In addition, we provide a new parameter `output_format` in the `CompilationSetting` object provided before compilation.
-The `output_format` can take the following options
+The output type of `ir=dynamo` compilation of Torch-TensorRT is `torch.fx.GraphModule` object by default.
+We can save this object in either `TorchScript` (`torch.jit.ScriptModule`) or `ExportedProgram` (`torch.export.ExportedProgram`) formats by
+specifying the `output_format` flag. Here are the options `output_format` will accept
-* `exported_program` (or) `ep` : This is the default. Returns an ExportedProgram
-* `torchscript` (or) `ts` : This returns a TorchScript module
-* `graph_module` (or) `fx` : This returns a torch.fx.GraphModule which can be traced into Torchscript to save to disk.
+* `exported_program` : This is the default. We perform transformations on the graphmodule first and use `torch.export.save` to save the module.
+* `torchscript` : We trace the graphmodule via `torch.jit.trace` and save it via `torch.jit.save`.
-a) Torchscript
+a) ExportedProgram
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-If you set the `output_format="torchscript"`, this will return a `ScriptModule` which can be serialized via torch.jit.save
+Here's an example usage
.. code-block:: python
@@ -34,19 +33,17 @@ If you set the `output_format="torchscript"`, this will return a `ScriptModule`
model = MyModel().eval().cuda()
inputs = [torch.randn((1, 3, 224, 224)).cuda()]
- # trt_ts is a torch.jit.ScriptModule object
- trt_ts = torch_tensorrt.compile(model, ir="dynamo", inputs, output_format="torchscript")
- torch.jit.save(trt_ts, "trt_model.ts")
+ # trt_ep is a torch.fx.GraphModule object
+ trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+ torchtrt.save(trt_gm, "trt.ep", inputs=inputs)
# Later, you can load it and run inference
- model = torch.jit.load("trt_model.ts").cuda()
+ model = torch.export.load("trt.ep").module()
model(*inputs)
-b) ExportedProgram
+b) Torchscript
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-`torch.export.ExportedProgram`, a new format introduced in Pytorch 2.X is the default return type of Torch-TensorRT compilation.
-
.. code-block:: python
import torch
@@ -54,30 +51,14 @@ b) ExportedProgram
model = MyModel().eval().cuda()
inputs = [torch.randn((1, 3, 224, 224)).cuda()]
- # trt_ep is a torch.export.ExportedProgram object
- trt_ep = torch_tensorrt.compile(model, ir="dynamo", inputs)
- torch.export.save(trt_ep, "trt_model.ep")
+ # trt_gm is a torch.fx.GraphModule object
+ trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+ torch_tensorrt.save(trt_gm, "trt.ts", output_format="torchscript", inputs=inputs)
# Later, you can load it and run inference
- model = torch.export.load("trt_model.ep")
+ model = torch.jit.load("trt.ts").cuda()
model(*inputs)
-c) GraphModule
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-We can also return a `torch.fx.GraphModule` object as the output of Torch-TensorRT compilation by setting `output_format="graph_module"`.
-Internally, partitioning, lowering, conversion phases operate using GraphModule objects. These can be either traced into a Torchscript modules or
-exported into `ExportedProgram` objects
-
-.. code-block:: python
-
- import torch
- import torch_tensorrt
-
- model = MyModel().eval().cuda()
- inputs = [torch.randn((1, 3, 224, 224)).cuda()]
- # trt_gm is a torch.fx.GraphModule object
- trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs, output_format="graph_module")
Torchscript IR
-------------
@@ -99,3 +80,21 @@ For `ir=ts`, this behavior stays the same in 2.X versions as well.
model = torch.jit.load("trt_model.ts").cuda()
model(*inputs)
+
+Loading the models
+--------------------
+
+We can load torchscript or exported_program models using `torch.jit.load` and `torch.export.load` APIs from PyTorch directly.
+Alternatively, we provide a light wrapper `torch_tensorrt.load(file_path)` which can load either of the above model types.
+
+Here's an example usage
+
+.. code-block:: python
+
+ import torch
+ import torch_tensorrt
+
+ # file_path can be trt.ep or trt.ts file obtained via saving the model (refer to the above section)
+ inputs = [torch.randn((1, 3, 224, 224)).cuda()]
+ model = torch_tensorrt.load(<file_path>).module()
+ model(*inputs)
\ No newline at end of file
diff --git a/py/torch_tensorrt/_compile.py b/py/torch_tensorrt/_compile.py
index 9dd816e633..7acf83124a 100644
--- a/py/torch_tensorrt/_compile.py
+++ b/py/torch_tensorrt/_compile.py
@@ -6,6 +6,7 @@
import torch
import torch.fx
+import torch_tensorrt.dynamo
import torch_tensorrt.ts
from torch_tensorrt._enums import dtype
from torch_tensorrt._Input import Input
@@ -26,10 +27,7 @@
logger = logging.getLogger(__name__)
-__all__ = [
- "compile",
- "convert_method_to_trt_engine",
-]
+__all__ = ["compile", "convert_method_to_trt_engine", "save", "load"]
def _non_fx_input_interface(
@@ -332,3 +330,108 @@ def convert_method_to_trt_engine(
)
else:
raise RuntimeError("Module is an unknown format or the ir requested is unknown")
+
+
+def load(file_path: str = "") -> Any:
+ """
+ Load either a Torchscript model or ExportedProgram. Autodetect the type using
+ try, except
+ """
+ try:
+ logger.debug("Loading the provided file using torch.jit.load()")
+ ts_module = torch.jit.load(file_path)
+ return ts_module
+ except Exception:
+ logger.debug(
+ "Loading the provided file via torch.jit.load() failed with the following error",
+ exc_info=True,
+ )
+ pass
+
+ try:
+ logger.debug("Loading the provided file using torch.export.load()")
+ exp_program = torch.export.load(file_path)
+ return exp_program
+ except Exception:
+ logger.debug(
+ "Loading the provided file via torch.export.load() failed with the following error",
+ exc_info=True,
+ )
+ raise ValueError(
+ "The file doesn't correspond to a valid Torchscript module or ExportedProgram. Please verify the file path."
+ )
+
+
+def save(
+ module: Any,
+ file_path: str = "",
+ *,
+ output_format: str = "exported_program",
+ inputs: Optional[Sequence[torch.Tensor]] = None,
+ retrace: bool = False,
+) -> None:
+ """
+ Save the model to disk in the specified output format.
+ Arguments:
+ module : Compiled Torch-TensorRT module (Options include torch.jit.ScriptModule | torch.export.ExportedProgram | torch.fx.GraphModule)
+ inputs (torch.Tensor): Torch input tensors
+ output_format: Format to save the model. Options include exported_program | torchscript.
+ retrace: When the module type is a fx.GraphModule, this option re-exports the graph using torch.export.export(strict=False) to save it.
+ This flag is experimental for now.
+ """
+ module_type = _parse_module_type(module)
+ accepted_formats = {"exported_program", "torchscript"}
+ if inputs is not None and not all(
+ isinstance(input, torch.Tensor) for input in inputs
+ ):
+ raise ValueError(
+ "Not all inputs provided are torch.tensors. Please provide torch.tensors as inputs"
+ )
+ if output_format not in accepted_formats:
+ raise ValueError(
+ f"Provided output_format {output_format} is not supported. Supported options are exported_program | torchscript"
+ )
+ if not file_path:
+ raise ValueError("File path cannot be empty. Please provide a valid file path")
+
+ if module_type == _ModuleType.nn:
+ raise ValueError(
+ "Input model is of type nn.Module. Saving nn.Module directly is not supported. Supported model types torch.jit.ScriptModule | torch.fx.GraphModule | torch.export.ExportedProgram."
+ )
+ elif module_type == _ModuleType.ts:
+ if output_format == "exported_program":
+ raise ValueError(
+ "Provided model is a torch.jit.ScriptModule but the output_format specified is exported_program. Please verify the output_format"
+ )
+ else:
+ torch.jit.save(module, file_path)
+ elif module_type == _ModuleType.ep:
+ if output_format == "torchscript":
+ raise ValueError(
+ "Provided model is a torch.export.ExportedProgram but the output_format specified is torchscript. Please verify the output_format"
+ )
+ else:
+ torch.export.save(module, file_path)
+ elif module_type == _ModuleType.fx:
+ if inputs is None:
+ raise ValueError(
+ "Provided model is a torch.fx.GraphModule however the inputs are empty. Please provide valid torch.tensors as inputs to trace and save the model"
+ )
+ # The module type is torch.fx.GraphModule
+ if output_format == "torchscript":
+ module_ts = torch.jit.trace(module, inputs)
+ torch.jit.save(module_ts, file_path)
+ else:
+ if not retrace:
+ from torch_tensorrt.dynamo._exporter import export
+
+ exp_program = export(module, inputs)
+ torch.export.save(exp_program, file_path)
+ else:
+ from torch._higher_order_ops.torchbind import enable_torchbind_tracing
+
+ with enable_torchbind_tracing():
+ exp_program = torch.export.export(
+ module, tuple(inputs), strict=False
+ )
+ torch.export.save(exp_program, file_path)
diff --git a/py/torch_tensorrt/dynamo/_compiler.py b/py/torch_tensorrt/dynamo/_compiler.py
index 6312532f1c..b321eabcb2 100644
--- a/py/torch_tensorrt/dynamo/_compiler.py
+++ b/py/torch_tensorrt/dynamo/_compiler.py
@@ -30,7 +30,6 @@
MIN_BLOCK_SIZE,
NUM_AVG_TIMING_ITERS,
OPTIMIZATION_LEVEL,
- OUTPUT_FORMAT,
PASS_THROUGH_BUILD_FAILURES,
PRECISION,
REFIT,
@@ -48,7 +47,6 @@
dryrun_stats_display,
parse_non_trt_nodes,
)
-from torch_tensorrt.dynamo._exporter import export
from torch_tensorrt.dynamo.conversion import (
CompilationSettings,
UnsupportedOperatorException,
@@ -102,9 +100,8 @@ def compile(
enable_experimental_decompositions: bool = ENABLE_EXPERIMENTAL_DECOMPOSITIONS,
dryrun: bool = DRYRUN,
hardware_compatible: bool = HARDWARE_COMPATIBLE,
- output_format: str = OUTPUT_FORMAT,
**kwargs: Any,
-) -> Union[ExportedProgram, torch.jit.ScriptModule, torch.fx.GraphModule]:
+) -> torch.fx.GraphModule:
"""Compile a TorchScript module for NVIDIA GPUs using TensorRT
Takes a existing TorchScript module and a set of settings to configure the compiler
@@ -246,14 +243,12 @@ def compile(
"dla_global_dram_size": dla_global_dram_size,
"dryrun": dryrun,
"hardware_compatible": hardware_compatible,
- "output_format": output_format,
}
settings = CompilationSettings(**compilation_options)
logger.info("Compilation Settings: %s\n", settings)
trt_gm = compile_module(gm, inputs, settings)
- trt_result = export(trt_gm, torch_inputs, output_format)
- return trt_result
+ return trt_gm
def compile_module(
diff --git a/py/torch_tensorrt/dynamo/_defaults.py b/py/torch_tensorrt/dynamo/_defaults.py
index ec038c0dba..3d48ab3def 100644
--- a/py/torch_tensorrt/dynamo/_defaults.py
+++ b/py/torch_tensorrt/dynamo/_defaults.py
@@ -26,7 +26,6 @@
REQUIRE_FULL_COMPILATION = False
DRYRUN = False
HARDWARE_COMPATIBLE = False
-OUTPUT_FORMAT = "exported_program"
def default_device() -> Device:
diff --git a/py/torch_tensorrt/dynamo/_exporter.py b/py/torch_tensorrt/dynamo/_exporter.py
index c7e2f37795..e9d166a1cc 100644
--- a/py/torch_tensorrt/dynamo/_exporter.py
+++ b/py/torch_tensorrt/dynamo/_exporter.py
@@ -1,3 +1,4 @@
+import copy
import operator
from typing import Any, Dict, Sequence, Tuple, cast
@@ -6,8 +7,11 @@
from torch._subclasses.fake_tensor import FakeTensor
from torch.export import ExportedProgram, ExportGraphSignature
from torch.export.exported_program import (
+ CustomObjArgument,
InputKind,
InputSpec,
+ ModuleCallEntry,
+ ModuleCallSignature,
OutputKind,
OutputSpec,
TensorArgument,
@@ -18,27 +22,16 @@
def export(
gm: torch.fx.GraphModule,
inputs: Sequence[torch.Tensor],
- output_format: str,
) -> ExportedProgram:
"""Export the result of TensorRT compilation into the desired output format.
Arguments:
gm (torch.fx.GraphModule): Compiled Torch-TensorRT module, generated by ``torch_tensorrt.dynamo.compile``
inputs (torch.Tensor): Torch input tensors
- output_format (str): Output format of the result of TRT compilation. Options include "exported_program" (or) "ep" | "torchscript" (or) "ts" | "graph_module" (or) "fx". Default is "exported_program"
"""
- if output_format == "torchscript" or output_format == "ts":
- return torch.jit.trace(gm, inputs)
- elif output_format == "exported_program" or output_format == "ep":
- patched_module = transform(gm, inputs)
- exp_program = create_trt_exp_program(patched_module)
- return exp_program
- elif output_format == "graph_module" or output_format == "fx":
- return gm
- else:
- raise ValueError(
- f"Invalid output format {output_format} specified. Supported options include exported_program (or) ep | torchscript (or) ts | graph_module (or) fx"
- )
+ patched_module = transform(gm, inputs)
+ exp_program = create_trt_exp_program(patched_module)
+ return exp_program
def transform(
@@ -55,6 +48,10 @@ def transform(
Returns an inlined torch.fx.GraphModule
"""
+ # Make a copy the graph since this function transforms the input graph and changes it's attributes.
+ # This transformed graph is meant to be consumed by `create_trt_exp_program`
+ gm = copy.deepcopy(gm)
+
# Run shape analysis
_, outputs_map = partitioning.run_shape_analysis(gm, inputs)
@@ -72,7 +69,9 @@ def transform(
return gm
-def lift(gm: torch.fx.GraphModule, graph_signature: Any) -> torch.fx.GraphModule:
+def lift(
+ gm: torch.fx.GraphModule, graph_signature: Any
+) -> Tuple[torch.fx.GraphModule, ExportGraphSignature, Dict[str, Any], Dict[str, Any]]:
"""
Given an unlifted fx.GraphModule, lift all parameters, buffers into placeholders.
Arguments:
@@ -86,6 +85,7 @@ def lift(gm: torch.fx.GraphModule, graph_signature: Any) -> torch.fx.GraphModule
# exp_program.state_dict contains parameters and buffers whereas a graph_module's state_dict
# has all parameters registered as torch.tensors.
state_dict = gm.state_dict()
+ constants = {}
fake_mode = detect_fake_mode(
tuple(node.meta["val"] for node in gm.graph.nodes if node.op == "placeholder")
@@ -100,52 +100,69 @@ def lift(gm: torch.fx.GraphModule, graph_signature: Any) -> torch.fx.GraphModule
break
# At first the user_inputs are only present in the graph_signature.input_specs and hence non_user_input_idx=0
- # The input_specs should be of the form [params, buffers, constant_tensors, user_inputs]
+ # The input_specs should be of the form [params, buffers, constant_tensors, custom_obj, user_inputs]
non_user_input_idx = 0
for node in gm.graph.nodes:
if node.op == "get_attr":
- if node.target not in state_dict:
- raise ValueError(
- f"The get_attr node : {node.name} with target: {node.target} value could not be found in state_dict. Please check the input exported_program's graphmodule parameters."
- )
- constant_tensor = state_dict[node.target]
- input_kind = InputKind.CONSTANT_TENSOR
+ lift_val = None
+ input_kind = None
- # state_dict has these parameters/buffers as torch.Tensors. We override them as torch.nn.Parameter/torch.Tensors respectively.
- for name, _ in gm.named_parameters():
- if node.target == name:
- input_kind = InputKind.PARAMETER
- state_dict[name] = torch.nn.Parameter(state_dict[name])
- break
- for name, _ in gm.named_buffers():
- if node.target == name:
- input_kind = InputKind.BUFFER
- break
+ if node.target not in state_dict:
+ constants[node.target] = getattr(gm, node.target)
+ input_kind = InputKind.CUSTOM_OBJ
+ lift_val = constants[node.target]
+ else:
+ lift_val = state_dict[node.target]
+
+ input_kind = InputKind.CONSTANT_TENSOR
+
+ # state_dict has these parameters/buffers as torch.Tensors. We override them as torch.nn.Parameter/torch.Tensors respectively.
+ for name, _ in gm.named_parameters():
+ if node.target == name:
+ input_kind = InputKind.PARAMETER
+ state_dict[name] = torch.nn.Parameter(state_dict[name])
+ break
+ for name, _ in gm.named_buffers():
+ if node.target == name:
+ input_kind = InputKind.BUFFER
+ break
+
+ assert lift_val is not None and input_kind is not None
# Replace get_attr nodes with placeholder nodes and copy metadata.
with gm.graph.inserting_before(first_user_input):
- const_placeholder_node = gm.graph.placeholder(node.target)
+ # Ensure name doesn't contain period as it is used for submodules
+ const_placeholder_node = gm.graph.placeholder(
+ node.target.replace(".", "_")
+ )
# Copy the node meta into this new placeholder node
const_placeholder_node.meta = node.meta
- const_placeholder_node.meta["val"] = cast(
- FakeTensor,
- torch.empty_strided(
- tuple(constant_tensor.shape),
- tuple([1] * len(constant_tensor.shape)),
- ),
- )
+
+ if isinstance(lift_val, torch.Tensor):
+ const_placeholder_node.meta["val"] = cast(
+ FakeTensor,
+ torch.empty_strided(
+ tuple(lift_val.shape),
+ tuple([1] * len(lift_val.shape)),
+ ),
+ )
node.replace_all_uses_with(const_placeholder_node)
gm.graph.erase_node(node)
# Add these parameters/buffers/constants to the existing graph signature
# before user inputs. These specs are looked up in the state_dict during ExportedProgram creation.
+ input_spec_arg = TensorArgument(name=const_placeholder_node.name)
+ if input_kind == InputKind.CUSTOM_OBJ:
+ input_spec_arg = CustomObjArgument(
+ name=const_placeholder_node.name, class_fqn=""
+ )
graph_signature.input_specs.insert(
non_user_input_idx,
InputSpec(
kind=input_kind,
- arg=TensorArgument(name=const_placeholder_node.name),
+ arg=input_spec_arg,
target=node.target,
),
)
@@ -154,7 +171,7 @@ def lift(gm: torch.fx.GraphModule, graph_signature: Any) -> torch.fx.GraphModule
gm.graph.eliminate_dead_code()
gm.graph.lint()
- return gm, graph_signature, state_dict
+ return gm, graph_signature, state_dict, constants
def get_duplicate_nodes(
@@ -292,18 +309,30 @@ def create_trt_exp_program(
input_specs=input_specs, output_specs=output_specs
)
+ module_call_graph = [
+ ModuleCallEntry(
+ "",
+ ModuleCallSignature(
+ inputs=[],
+ outputs=[],
+ in_spec=gm.graph._codegen.pytree_info.in_spec,
+ out_spec=gm.graph._codegen.pytree_info.out_spec,
+ ),
+ )
+ ]
+
# Lift parameters/buffers/constants in the graph
# torch.export serialization expects them to be lifted
- gm, trt_graph_signature, state_dict = lift(gm, trt_graph_signature)
+ gm, trt_graph_signature, state_dict, constants = lift(gm, trt_graph_signature)
trt_exp_program = ExportedProgram(
- gm,
- gm.graph,
- trt_graph_signature,
- state_dict,
- {},
- [],
- [],
+ root=gm,
+ graph=gm.graph,
+ graph_signature=trt_graph_signature,
+ state_dict=state_dict,
+ range_constraints={},
+ module_call_graph=module_call_graph,
+ constants=constants,
)
return trt_exp_program
@@ -330,9 +359,13 @@ def inline_trt_modules(
num_outputs = len(outputs_map[trt_module_node.name])
# Insert a call_function node to perform inference on TRT engine
with gm.graph.inserting_before(trt_module_node):
+ engine_name = f"{name}_engine"
+ setattr(gm, engine_name, trt_module.engine)
+ engine_node = gm.graph.get_attr(engine_name)
+
trt_node = gm.graph.call_function(
torch.ops.tensorrt.execute_engine.default,
- (trt_module_node.args, trt_module.engine),
+ (trt_module_node.args, engine_node),
)
trt_node.meta["val"] = []
assert num_outputs > 0
@@ -348,6 +381,13 @@ def inline_trt_modules(
)
)
+ # meta["val"] should be a lighter version of a tensor. For eg: it should be a FakeTensor (with output shape and dtype properties)
+ # Lighter version of a custom_obj is not defined clearly. meta["val"] does not have any type expectations but
+ # for custom object nodes, it should be CustomObjArgument
+ engine_node.meta["val"] = CustomObjArgument(
+ name=engine_node.name, class_fqn=""
+ )
+
if num_outputs == 1:
# Insert getitem nodes as outputs (for export serialization to work)
with gm.graph.inserting_after(trt_node):
diff --git a/py/torch_tensorrt/dynamo/_settings.py b/py/torch_tensorrt/dynamo/_settings.py
index c00b049f45..2420a227d8 100644
--- a/py/torch_tensorrt/dynamo/_settings.py
+++ b/py/torch_tensorrt/dynamo/_settings.py
@@ -19,7 +19,6 @@
MIN_BLOCK_SIZE,
NUM_AVG_TIMING_ITERS,
OPTIMIZATION_LEVEL,
- OUTPUT_FORMAT,
PASS_THROUGH_BUILD_FAILURES,
PRECISION,
REFIT,
@@ -71,7 +70,6 @@ class CompilationSettings:
TRT Engines. Prints detailed logs of the graph structure and nature of partitioning. Optionally saves the
ouptut to a file if a string path is specified
hardware_compatible (bool): Build the TensorRT engines compatible with GPU architectures other than that of the GPU on which the engine was built (currently works for NVIDIA Ampere and newer)
- output_format (str): Output format of the result of TRT compilation. Options include "exported_program" (or) "ep" | "torchscript" (or) "ts" | "graph_module" (or) "fx". Default is "exported_program"
"""
precision: torch.dtype = PRECISION
@@ -99,4 +97,3 @@ class CompilationSettings:
dla_global_dram_size: int = DLA_GLOBAL_DRAM_SIZE
dryrun: Union[bool, str] = DRYRUN
hardware_compatible: bool = HARDWARE_COMPATIBLE
- output_format: str = OUTPUT_FORMAT
diff --git a/tests/py/dynamo/lowering/test_aten_lowering_passes.py b/tests/py/dynamo/lowering/test_aten_lowering_passes.py
index bc75a8aa3d..b7c895ec11 100644
--- a/tests/py/dynamo/lowering/test_aten_lowering_passes.py
+++ b/tests/py/dynamo/lowering/test_aten_lowering_passes.py
@@ -1,7 +1,6 @@
import torch
-from torch.testing._internal.common_utils import TestCase, run_tests
-
import torch_tensorrt
+from torch.testing._internal.common_utils import TestCase, run_tests
from ..testing_utilities import DECIMALS_OF_AGREEMENT, lower_graph_testing
@@ -444,6 +443,7 @@ def forward(self, input, weight, bias):
max_diff = float(
torch.max(torch.abs(optimized_model_results - torch_model_results))
)
+
self.assertAlmostEqual(
max_diff,
0,
diff --git a/tests/py/dynamo/models/test_export_serde.py b/tests/py/dynamo/models/test_export_serde.py
index efa593890e..b6519815a4 100644
--- a/tests/py/dynamo/models/test_export_serde.py
+++ b/tests/py/dynamo/models/test_export_serde.py
@@ -42,18 +42,17 @@ def forward(self, x):
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
- trt_exp_program = torchtrt.dynamo.compile(exp_program, **compile_spec)
- torch.export.save(trt_exp_program, "/tmp/trt.ep")
- deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
-
+ trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+ torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
+ deser_trt_module = torchtrt.load("/tmp/trt.ep").module()
# Check Pyt and TRT exported program outputs
- cos_sim = cosine_similarity(model(input), trt_exp_program(input)[0])
+ cos_sim = cosine_similarity(model(input), trt_module(input)[0])
assertions.assertTrue(
cos_sim > COSINE_THRESHOLD,
msg=f"test_base_model_full_compile TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
)
# Check Pyt and deserialized TRT exported program outputs
- cos_sim = cosine_similarity(model(input), deser_trt_exp_program(input)[0])
+ cos_sim = cosine_similarity(model(input), deser_trt_module(input)[0])
assertions.assertTrue(
cos_sim > COSINE_THRESHOLD,
msg=f"test_base_model_full_compile TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
@@ -93,12 +92,12 @@ def forward(self, x):
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
- trt_exp_program = torchtrt.dynamo.compile(exp_program, **compile_spec)
- torch.export.save(trt_exp_program, "/tmp/trt.ep")
- deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
+ trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+ torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
+ deser_trt_module = torchtrt.load("/tmp/trt.ep").module()
# Check Pyt and TRT exported program outputs
outputs_pyt = model(input)
- outputs_trt = trt_exp_program(input)
+ outputs_trt = trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt[idx])
assertions.assertTrue(
@@ -107,7 +106,7 @@ def forward(self, x):
)
# Check Pyt and deserialized TRT exported program outputs
- outputs_trt_deser = deser_trt_exp_program(input)
+ outputs_trt_deser = deser_trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt_deser[idx])
assertions.assertTrue(
@@ -145,16 +144,15 @@ def forward(self, x):
)
],
"ir": ir,
- "debug": True,
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
- trt_exp_program = torchtrt.dynamo.compile(exp_program, **compile_spec)
- torch.export.save(trt_exp_program, "/tmp/trt.ep")
- deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
+ trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+ torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
+ deser_trt_module = torchtrt.load("/tmp/trt.ep").module()
# Check Pyt and TRT exported program outputs
outputs_pyt = model(input)
- outputs_trt = trt_exp_program(input)
+ outputs_trt = trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt[idx])
assertions.assertTrue(
@@ -163,7 +161,7 @@ def forward(self, x):
)
# Check Pyt and deserialized TRT exported program outputs
- outputs_trt_deser = deser_trt_exp_program(input)
+ outputs_trt_deser = deser_trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt_deser[idx])
assertions.assertTrue(
@@ -207,12 +205,11 @@ def forward(self, x):
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
- trt_exp_program = torchtrt.dynamo.compile(exp_program, **compile_spec)
- torch.export.save(trt_exp_program, "/tmp/trt.ep")
- deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
-
+ trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+ torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
+ deser_trt_module = torchtrt.load("/tmp/trt.ep").module()
outputs_pyt = model(input)
- outputs_trt = trt_exp_program(input)
+ outputs_trt = trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt[idx])
assertions.assertTrue(
@@ -220,7 +217,7 @@ def forward(self, x):
msg=f"test_hybrid_relu_fallback TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
)
- outputs_trt_deser = deser_trt_exp_program(input)
+ outputs_trt_deser = deser_trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt_deser[idx])
assertions.assertTrue(
@@ -248,19 +245,18 @@ def test_resnet18(ir):
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
- trt_exp_program = torchtrt.dynamo.compile(exp_program, **compile_spec)
- torch.export.save(trt_exp_program, "/tmp/trt.ep")
- deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
-
+ trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+ torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
+ deser_trt_module = torchtrt.load("/tmp/trt.ep").module()
outputs_pyt = model(input)
- outputs_trt = trt_exp_program(input)
+ outputs_trt = trt_module(input)
cos_sim = cosine_similarity(outputs_pyt, outputs_trt[0])
assertions.assertTrue(
cos_sim > COSINE_THRESHOLD,
msg=f"test_resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
)
- outputs_trt_deser = deser_trt_exp_program(input)
+ outputs_trt_deser = deser_trt_module(input)
cos_sim = cosine_similarity(outputs_pyt, outputs_trt_deser[0])
assertions.assertTrue(
@@ -303,12 +299,12 @@ def forward(self, x):
}
exp_program = torchtrt.dynamo.trace(model, **compile_spec)
- trt_exp_program = torchtrt.dynamo.compile(exp_program, **compile_spec)
- torch.export.save(trt_exp_program, "/tmp/trt.ep")
- deser_trt_exp_program = torch.export.load("/tmp/trt.ep")
+ trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+ torchtrt.save(trt_module, "/tmp/trt.ep", inputs=[input])
+ deser_trt_module = torchtrt.load("/tmp/trt.ep").module()
outputs_pyt = model(input)
- outputs_trt = trt_exp_program(input)
+ outputs_trt = trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt[idx])
@@ -317,10 +313,50 @@ def forward(self, x):
msg=f"test_hybrid_conv_fallback TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
)
- outputs_trt_deser = deser_trt_exp_program(input)
+ outputs_trt_deser = deser_trt_module(input)
for idx in range(len(outputs_pyt)):
cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt_deser[idx])
assertions.assertTrue(
cos_sim > COSINE_THRESHOLD,
msg=f"test_hybrid_conv_fallback deserialized TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
)
+
+
+@pytest.mark.unit
+def test_save_load_ts(ir):
+ """
+ This tests save/load API on Torchscript format (model still compiled using dynamo workflow)
+ """
+
+ class MyModule(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.conv = torch.nn.Conv2d(3, 16, 3, stride=1, bias=True)
+ self.relu = torch.nn.ReLU()
+
+ def forward(self, x):
+ conv = self.conv(x)
+ relu = self.relu(conv)
+ mul = relu * 0.5
+ return mul
+
+ model = MyModule().eval().cuda()
+ input = torch.randn((1, 3, 224, 224)).to("cuda")
+
+ trt_gm = torchtrt.compile(model, ir=ir, inputs=[input], min_block_size=1)
+ assertions.assertTrue(
+ isinstance(trt_gm, torch.fx.GraphModule),
+ msg=f"test_save_load_ts output type does not match with torch.fx.GraphModule",
+ )
+ outputs_trt = trt_gm(input)
+ # Save it as torchscript representation
+ torchtrt.save(trt_gm, "/tmp/trt.ts", output_format="torchscript", inputs=[input])
+
+ trt_ts_module = torchtrt.load("/tmp/trt.ts")
+ outputs_trt_deser = trt_ts_module(input)
+
+ cos_sim = cosine_similarity(outputs_trt, outputs_trt_deser)
+ assertions.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"test_save_load_ts TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
diff --git a/tests/py/dynamo/models/test_models_export.py b/tests/py/dynamo/models/test_models_export.py
index fd7b40592a..84f6bf7a36 100644
--- a/tests/py/dynamo/models/test_models_export.py
+++ b/tests/py/dynamo/models/test_models_export.py
@@ -110,11 +110,6 @@ def test_bert_base_uncased(ir):
model = BertModel.from_pretrained("bert-base-uncased").cuda().eval()
input = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
input2 = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
- model = (
- transformers_trace(model, input_names=["input_ids", "attention_mask"])
- .eval()
- .cuda()
- )
compile_spec = {
"inputs": [
@@ -133,7 +128,7 @@ def test_bert_base_uncased(ir):
"enabled_precisions": {torch.float},
"truncate_long_and_double": True,
"ir": ir,
- "min_block_size": 10,
+ "min_block_size": 15,
}
trt_mod = torchtrt.compile(model, **compile_spec)
model_outputs = model(input, input2)
@@ -142,58 +137,9 @@ def test_bert_base_uncased(ir):
len(model_outputs) == len(trt_model_outputs),
msg=f"Number of outputs for BERT model compilation is different with Pytorch {len(model_outputs)} and TensorRT {len(trt_model_outputs)}. Please check the compilation.",
)
- for index, key in enumerate(model_outputs):
- out, trt_out = model_outputs[key], trt_model_outputs[index]
- cos_sim = cosine_similarity(out, trt_out)
- assertions.assertTrue(
- cos_sim > COSINE_THRESHOLD,
- msg=f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
- )
-
- # Clean up model env
- torch._dynamo.reset()
-
-
-@pytest.mark.unit
-def test_bert_base_uncased(ir):
- model = BertModel.from_pretrained("bert-base-uncased").cuda().eval()
- input = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
- input2 = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
- model = (
- transformers_trace(model, input_names=["input_ids", "attention_mask"])
- .eval()
- .cuda()
- )
- compile_spec = {
- "inputs": [
- torchtrt.Input(
- input.shape,
- dtype=input.dtype,
- format=torch.contiguous_format,
- ),
- torchtrt.Input(
- input.shape,
- dtype=input.dtype,
- format=torch.contiguous_format,
- ),
- ],
- "device": torchtrt.Device("cuda:0"),
- "enabled_precisions": {torch.float},
- "truncate_long_and_double": True,
- "ir": ir,
- "min_block_size": 10,
- "torch_executed_ops": {"torch.ops.aten.gelu.default"},
- }
- trt_mod = torchtrt.compile(model, **compile_spec)
- model_outputs = model(input, input2)
- trt_model_outputs = trt_mod(input, input2)
- assertions.assertTrue(
- len(model_outputs) == len(trt_model_outputs),
- msg=f"Number of outputs for BERT model compilation is different with Pytorch {len(model_outputs)} and TensorRT {len(trt_model_outputs)}. Please check the compilation.",
- )
- for index, key in enumerate(model_outputs):
- out, trt_out = model_outputs[key], trt_model_outputs[index]
+ for key, _ in model_outputs.items():
+ out, trt_out = model_outputs[key], trt_model_outputs[key]
cos_sim = cosine_similarity(out, trt_out)
assertions.assertTrue(
cos_sim > COSINE_THRESHOLD,
@@ -203,9 +149,6 @@ def test_bert_base_uncased(ir):
# Clean up model env
torch._dynamo.reset()
- with torch.no_grad():
- torch.cuda.empty_cache()
-
@pytest.mark.unit
def test_resnet18_half(ir):
diff --git a/tests/py/dynamo/models/test_output_format.py b/tests/py/dynamo/models/test_output_format.py
deleted file mode 100644
index 3d2e747ceb..0000000000
--- a/tests/py/dynamo/models/test_output_format.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import unittest
-
-import pytest
-import timm
-import torch
-import torch_tensorrt as torchtrt
-import torchvision.models as models
-from torch_tensorrt.dynamo.utils import COSINE_THRESHOLD, cosine_similarity
-
-assertions = unittest.TestCase()
-
-
-@pytest.mark.unit
-def test_output_format(ir):
- """
- This tests output_format type in the compilation setting
- """
-
- class MyModule(torch.nn.Module):
- def __init__(self):
- super().__init__()
- self.conv = torch.nn.Conv2d(3, 16, 3, stride=1, bias=True)
- self.relu = torch.nn.ReLU()
-
- def forward(self, x):
- conv = self.conv(x)
- relu = self.relu(conv)
- mul = relu * 0.5
- return mul
-
- model = MyModule().eval().cuda()
- input = torch.randn((1, 3, 224, 224)).to("cuda")
-
- trt_ep = torchtrt.compile(model, ir="dynamo", inputs=[input], min_block_size=1)
- assertions.assertTrue(
- isinstance(trt_ep, torch.export.ExportedProgram),
- msg=f"test_output_format output type does not match with torch.export.ExportedProgram",
- )
-
- trt_ts = torchtrt.compile(
- model,
- ir="dynamo",
- inputs=[input],
- min_block_size=1,
- output_format="torchscript",
- )
- assertions.assertTrue(
- isinstance(trt_ts, torch.jit.ScriptModule),
- msg=f"test_output_format output type does not match with torch.jit.ScriptModule",
- )
-
- trt_gm = torchtrt.compile(
- model,
- ir="dynamo",
- inputs=[input],
- min_block_size=1,
- output_format="graph_module",
- )
- assertions.assertTrue(
- isinstance(trt_gm, torch.fx.GraphModule),
- msg=f"test_output_format output type does not match with torch.fx.GraphModule",
- )
From 1ae46e9c3cfd1fe3d13beeac28919fc6e24b1cf2 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 27 Mar 2024 09:07:47 -0700
Subject: [PATCH 12/69] chore: more updates
---
core/conversion/converters/impl/conv_deconv.cpp | 9 ++++-----
core/conversion/evaluators/eval_util.cpp | 14 +++++---------
py/requirements.txt | 2 +-
pyproject.toml | 4 ++--
tests/core/partitioning/test_loading_model.cpp | 2 +-
tests/cpp/test_compiled_modules.cpp | 2 +-
tests/cpp/test_modules_as_engines.cpp | 2 +-
7 files changed, 15 insertions(+), 20 deletions(-)
diff --git a/core/conversion/converters/impl/conv_deconv.cpp b/core/conversion/converters/impl/conv_deconv.cpp
index aa7acc9cc4..a779a3c1b9 100644
--- a/core/conversion/converters/impl/conv_deconv.cpp
+++ b/core/conversion/converters/impl/conv_deconv.cpp
@@ -146,9 +146,9 @@ bool add_conv_deconv(ConversionCtx* ctx, const torch::jit::Node* n, args& args)
// TensorRT expects nbSpatialDims = 2 or 3
filter_dim = util::unsqueezeDims(filter_dim, filter_dim.nbDims, 1, false);
// Reshape input dimensions
- in = addPadding(ctx, n, in, 4);
+ in = addPadding(ctx, n, in, 4, true, true, std::string(util::node_info(n) + "_input_shuffle"));
LOG_DEBUG("Reshaping input dimensions to: " << in->getDimensions());
- kernel = addPadding(ctx, n, kernel, 4);
+ kernel = addPadding(ctx, n, kernel, 4, true, true, std::string(util::node_info(n) + "_kernel_shuffle"));
LOG_DEBUG("Reshaping kernel dimensions to: " << kernel->getDimensions());
if (transposed) {
num_output_maps = kernel_dims.d[1];
@@ -291,11 +291,10 @@ bool add_conv_deconv(ConversionCtx* ctx, const torch::jit::Node* n, args& args)
// shape of convolution's weight: [out, in/groups, ...]
auto conv = ctx->net->addConvolutionNd(*in, w.shape.d[0], w.kernel_shape, w.data, bias.data);
TORCHTRT_CHECK(conv, "Unable to create convolution layer from node: " << *n);
-
conv->setStrideNd(stride);
conv->setPaddingMode(nvinfer1::PaddingMode::kEXPLICIT_ROUND_DOWN);
- conv->setPaddingNd(padding);
- conv->setPostPadding(out_padding);
+ conv->setPrePadding(padding);
+ conv->setPostPadding(padding);
conv->setDilationNd(dilation);
conv->setNbGroups(groups);
new_layer = conv;
diff --git a/core/conversion/evaluators/eval_util.cpp b/core/conversion/evaluators/eval_util.cpp
index 71b6de9eb2..9b6139073d 100644
--- a/core/conversion/evaluators/eval_util.cpp
+++ b/core/conversion/evaluators/eval_util.cpp
@@ -34,11 +34,8 @@ c10::IValue dynamic_size_layer(ConversionCtx* ctx, const torch::jit::Node* n, kw
auto in = args.at(n->input(0)).ITensorOrFreeze(ctx);
auto input_dims = in->getDimensions();
LOG_DEBUG("Input dimensions: " << input_dims);
-
- auto shape_layer = ctx->net->addShape(*in);
- TORCHTRT_CHECK(shape_layer, "Unable to create shape layer from node: " << *n);
- auto shape_1d_tensor = shape_layer->getOutput(0);
-
+ nvinfer1::ITensor* shape_1d_tensor = torch_tensorrt::core::conversion::converters::getShapeOutput(
+ ctx, in, std::string(util::node_info(n) + "_dynamic_shape_layer_cast").c_str());
if (n->inputs().size() != 1) {
auto maxDim = static_cast<int64_t>(in->getDimensions().nbDims);
auto dim = args.at(n->input(1)).unwrapToInt();
@@ -423,13 +420,12 @@ c10::optional<torch::jit::IValue> newTensorLikeImplementation(
// broadcast constant to output shape
std::vector<int64_t> start_vec(self->getDimensions().nbDims, 0);
auto start_offset = util::toDims(c10::IntArrayRef(start_vec));
- auto shape_layer = ctx->net->addShape(*self);
- TORCHTRT_CHECK(shape_layer, "Unable to create shape layer from node: " << *n);
- shape_layer->setName((util::node_info(n) + "_shape").c_str());
+ nvinfer1::ITensor* shape_output = torch_tensorrt::core::conversion::converters::getShapeOutput(
+ ctx, self, std::string(util::node_info(n) + "_shape").c_str());
// slice implements expand
auto slice_layer = ctx->net->addSlice(*constant_itensor, start_offset, self->getDimensions(), start_offset);
TORCHTRT_CHECK(slice_layer, "Unable to create slice layer from node: " << *n);
- slice_layer->setInput(2, *shape_layer->getOutput(0));
+ slice_layer->setInput(2, *shape_output);
slice_layer->setName((util::node_info(n) + "_slice").c_str());
auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], slice_layer->getOutput(0));
LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
diff --git a/py/requirements.txt b/py/requirements.txt
index 19c39c8647..565395b59b 100644
--- a/py/requirements.txt
+++ b/py/requirements.txt
@@ -5,5 +5,5 @@ pybind11==2.6.2
torch==2.3.0
torchvision==0.18.0
--extra-index-url https://pypi.ngc.nvidia.com
-tensorrt>=10.0
+tensorrt==10.0.0b6
pyyaml
diff --git a/pyproject.toml b/pyproject.toml
index 6c4940013d..7468484f48 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,7 @@ requires = [
"cffi>=1.15.1",
"typing-extensions>=4.7.0",
"future>=0.18.3",
- "tensorrt>=10.0",
+ "tensorrt==10.0.0b6",
"torch >=2.3.0.dev,<2.4.0",
"pybind11==2.6.2",
"numpy",
@@ -42,7 +42,7 @@ requires-python = ">=3.8"
keywords = ["pytorch", "torch", "tensorrt", "trt", "ai", "artificial intelligence", "ml", "machine learning", "dl", "deep learning", "compiler", "dynamo", "torchscript", "inference"]
dependencies = [
"torch >=2.3.0.dev,<2.4.0",
- "tensorrt>=10.0",
+ "tensorrt==10.0.0b6",
"packaging>=23",
"numpy",
"typing-extensions>=4.7.0",
diff --git a/tests/core/partitioning/test_loading_model.cpp b/tests/core/partitioning/test_loading_model.cpp
index b42368fe3e..67c42caef2 100644
--- a/tests/core/partitioning/test_loading_model.cpp
+++ b/tests/core/partitioning/test_loading_model.cpp
@@ -7,7 +7,7 @@
#ifndef DISABLE_TEST_IN_CI
-TEST(Partitioning, ComputeResNet50FallbackGraphCorrectly) {
+TEST(Partitioning, ComputeConditionalLoadingGraphCorrectly) {
torch::jit::script::Module mod;
try {
mod = torch::jit::load("tests/modules/conditional_scripted.jit.pt");
diff --git a/tests/cpp/test_compiled_modules.cpp b/tests/cpp/test_compiled_modules.cpp
index 62bae5756d..7def168249 100644
--- a/tests/cpp/test_compiled_modules.cpp
+++ b/tests/cpp/test_compiled_modules.cpp
@@ -58,7 +58,7 @@ INSTANTIATE_TEST_SUITE_P(
PathAndInput({"tests/modules/resnet18_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}}),
PathAndInput({"tests/modules/mobilenet_v2_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}}),
PathAndInput({"tests/modules/efficientnet_b0_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}}),
- PathAndInput({"tests/modules/bert_base_uncased_traced.jit.pt", {{1, 14}, {1, 14}}, {at::kInt, at::kInt}}),
+ PathAndInput({"tests/modules/bert_base_uncased_traced.jit.pt", {{1, 14}, {1, 14}}, {at::kInt, at::kInt}})));
// NOTE: ViT tests are disabled until Python 3.11 issue is resolved
// https://github.com/huggingface/pytorch-image-models/issues/1946 PathAndInput({"tests/modules/vit_scripted.jit.pt",
// {{1, 3, 224, 224}}, {at::kFloat}})));
diff --git a/tests/cpp/test_modules_as_engines.cpp b/tests/cpp/test_modules_as_engines.cpp
index 4cb9dd9f8d..cc9fdd24a4 100644
--- a/tests/cpp/test_modules_as_engines.cpp
+++ b/tests/cpp/test_modules_as_engines.cpp
@@ -29,7 +29,7 @@ INSTANTIATE_TEST_SUITE_P(
testing::Values(
PathAndInput({"tests/modules/resnet18_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}}),
PathAndInput({"tests/modules/mobilenet_v2_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}}),
- PathAndInput({"tests/modules/efficientnet_b0_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}}),
+ PathAndInput({"tests/modules/efficientnet_b0_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}})));
// NOTE: ViT tests are disabled until Python 3.11 issue is resolved
// https://github.com/huggingface/pytorch-image-models/issues/1946 PathAndInput({"tests/modules/vit_scripted.jit.pt",
// {{1, 3, 224, 224}}, {at::kFloat}})));
From beb59206d082d8f0842ccca6d30b714dffccc07f Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 27 Mar 2024 09:22:53 -0700
Subject: [PATCH 13/69] chore: Update versions
---
README.md | 4 ++--
WORKSPACE | 6 +++---
2 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/README.md b/README.md
index 875b640304..eecae762cf 100644
--- a/README.md
+++ b/README.md
@@ -116,10 +116,10 @@ torch.jit.save(trt_ts_module, "trt_torchscript_module.ts") # save the TRT embedd
These are the following dependencies used to verify the testcases. Torch-TensorRT can work with other versions, but the tests are not guaranteed to pass.
- Bazel 5.2.0
-- Libtorch 2.3.0.dev (latest nightly) (built with CUDA 12.1)
+- Libtorch 2.3.0 (built with CUDA 12.1)
- CUDA 12.1
- cuDNN 8.9.5
-- TensorRT 8.6.1
+- TensorRT 10.0.0.6
## Prebuilt Binaries and Wheel files
diff --git a/WORKSPACE b/WORKSPACE
index edc5c9a050..76e9a602fa 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -81,10 +81,10 @@ http_archive(
http_archive(
name = "tensorrt",
build_file = "@//third_party/tensorrt/archive:BUILD",
- sha256 = "0f8157a5fc5329943b338b893591373350afa90ca81239cdadd7580cd1eba254",
- strip_prefix = "TensorRT-8.6.1.6",
+ sha256 = "7ad5c8eab8fe8803bef5d8cee54ec1f407797ac8edb8ea3186cb9f24af726486",
+ strip_prefix = "TensorRT-10.0.0.6",
urls = [
- "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/tars/TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-12.0.tar.gz",
+ "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.0.0/tars/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz",
],
)
From f0068c64ba4b5d214a3e1beb52bf15eaaae7cf21 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 27 Mar 2024 10:15:38 -0700
Subject: [PATCH 14/69] chore: update tensorrt version in CI
---
dev_dep_versions.yml | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/dev_dep_versions.yml b/dev_dep_versions.yml
index 442485474c..b07db5533f 100644
--- a/dev_dep_versions.yml
+++ b/dev_dep_versions.yml
@@ -1,4 +1,4 @@
-__version__: "2.3.0.dev0"
+__version__: "2.3.0"
__cuda_version__: "12.1"
__cudnn_version__: "8.9"
-__tensorrt_version__: "8.6"
+__tensorrt_version__: "10.0.0b6"
From 39261b9d2d421496df5bd1d99b237aa459b9862f Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 27 Mar 2024 14:28:17 -0700
Subject: [PATCH 15/69] chore: more updates
---
core/conversion/converters/impl/conv_deconv.cpp | 3 +--
core/conversion/converters/impl/interpolate.cpp | 13 ++++++++-----
.../passes/unpack_scaled_dot_product_attention.cpp | 8 ++++----
.../test_scaled_dot_product_attention.cpp | 9 ++++++---
4 files changed, 19 insertions(+), 14 deletions(-)
diff --git a/core/conversion/converters/impl/conv_deconv.cpp b/core/conversion/converters/impl/conv_deconv.cpp
index a779a3c1b9..c71007ac03 100644
--- a/core/conversion/converters/impl/conv_deconv.cpp
+++ b/core/conversion/converters/impl/conv_deconv.cpp
@@ -293,8 +293,7 @@ bool add_conv_deconv(ConversionCtx* ctx, const torch::jit::Node* n, args& args)
TORCHTRT_CHECK(conv, "Unable to create convolution layer from node: " << *n);
conv->setStrideNd(stride);
conv->setPaddingMode(nvinfer1::PaddingMode::kEXPLICIT_ROUND_DOWN);
- conv->setPrePadding(padding);
- conv->setPostPadding(padding);
+ conv->setPaddingNd(padding);
conv->setDilationNd(dilation);
conv->setNbGroups(groups);
new_layer = conv;
diff --git a/core/conversion/converters/impl/interpolate.cpp b/core/conversion/converters/impl/interpolate.cpp
index 748e3c9e1a..64542d14f6 100644
--- a/core/conversion/converters/impl/interpolate.cpp
+++ b/core/conversion/converters/impl/interpolate.cpp
@@ -77,7 +77,6 @@ void resize_layer_size(
TORCHTRT_CHECK((out_shape.size() > 0) ^ (scales.size() > 0), "only one of out_shape or scales should be defined");
auto resize_layer = ctx->net->addResize(*in);
TORCHTRT_CHECK(resize_layer, "Unable to create interpolation (resizing) layer from node" << *n);
-
if (out_shape.size() > 0) {
auto th_dynamic_shape_mask = torch::zeros(out_shape.size(), torch::kInt32);
auto th_static_shape_mask = torch::zeros(out_shape.size(), torch::kInt32);
@@ -108,13 +107,17 @@ void resize_layer_size(
resize_layer->setResizeMode(mode);
resize_layer->setName(util::node_info(n).c_str());
-#if NV_TENSORRT_MAJOR < 8
- resize_layer->setAlignCorners(align_corners);
-#else
+
if (align_corners) {
resize_layer->setCoordinateTransformation(nvinfer1::ResizeCoordinateTransformation::kALIGN_CORNERS);
+ } else {
+ if (mode == nvinfer1::InterpolationMode::kLINEAR) {
+ resize_layer->setCoordinateTransformation(nvinfer1::ResizeCoordinateTransformation::kHALF_PIXEL);
+ } else {
+ // kASYMMETRIC is the default transformation in TensorRT
+ resize_layer->setCoordinateTransformation(nvinfer1::ResizeCoordinateTransformation::kASYMMETRIC);
+ }
}
-#endif
auto layer_output = ctx->AssociateValueAndTensor(n->outputs()[0], resize_layer->getOutput(0));
LOG_DEBUG("Output tensor shape: " << layer_output->getDimensions());
diff --git a/core/lowering/passes/unpack_scaled_dot_product_attention.cpp b/core/lowering/passes/unpack_scaled_dot_product_attention.cpp
index bfe0004bd6..3c347f65ca 100644
--- a/core/lowering/passes/unpack_scaled_dot_product_attention.cpp
+++ b/core/lowering/passes/unpack_scaled_dot_product_attention.cpp
@@ -12,12 +12,12 @@ namespace passes {
// https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
void UnpackScaledDotProductAttention(std::shared_ptr<torch::jit::Graph>& graph) {
std::string sdpa_pattern = R"IR(
- graph(%query, %key, %value, %attn_mask, %dropout_p, %is_causal):
- %out: Tensor = aten::scaled_dot_product_attention(%query, %key, %value, %attn_mask, %dropout_p, %is_causal)
+ graph(%query, %key, %value, %attn_mask, %dropout_p, %is_causal, %scale):
+ %out: Tensor = aten::scaled_dot_product_attention(%query, %key, %value, %attn_mask, %dropout_p, %is_causal, %scale)
return (%out))IR";
std::string unpacked_sdpa_pattern = R"IR(
- graph(%query, %key, %value, %attn_mask, %dropout_p, %is_causal):
+ graph(%query, %key, %value, %attn_mask, %dropout_p, %is_causal, %scale):
%none : NoneType = prim::Constant()
%1 : int = prim::Constant[value=-1]()
%2 : int = prim::Constant[value=-2]()
@@ -33,7 +33,7 @@ void UnpackScaledDotProductAttention(std::shared_ptr<torch::jit::Graph>& graph)
return(%out))IR";
std::string unpacked_sdpa_attn_biased_pattern = R"IR(
- graph(%query, %key, %value, %attn_mask, %dropout_p, %is_causal):
+ graph(%query, %key, %value, %attn_mask, %dropout_p, %is_causal, %scale):
%none : NoneType = prim::Constant()
%0 : int = prim::Constant[value=1]()
%1 : int = prim::Constant[value=-1]()
diff --git a/tests/core/conversion/converters/test_scaled_dot_product_attention.cpp b/tests/core/conversion/converters/test_scaled_dot_product_attention.cpp
index 785363ccca..7fa859e324 100644
--- a/tests/core/conversion/converters/test_scaled_dot_product_attention.cpp
+++ b/tests/core/conversion/converters/test_scaled_dot_product_attention.cpp
@@ -10,8 +10,9 @@ TEST(Converters, ATenScaledDotProductAttentionConvertsCorrectly) {
graph(%query : Tensor, %key : Tensor, %value : Tensor):
%none : NoneType = prim::Constant()
%0 : float = prim::Constant[value=0.]()
+ %scale : float = prim::Constant[value=1.]()
%false : bool = prim::Constant[value=0]()
- %3 : Tensor = aten::scaled_dot_product_attention(%query, %key, %value, %none, %0, %false)
+ %3 : Tensor = aten::scaled_dot_product_attention(%query, %key, %value, %none, %0, %false, %scale)
return (%3))IR";
auto g = std::make_shared<torch::jit::Graph>();
@@ -36,7 +37,8 @@ TEST(Converters, ATenScaledDotProductAttnMaskFloatConvertsCorrectly) {
graph(%query : Tensor, %key : Tensor, %value : Tensor, %attn_mask : Tensor):
%0 : float = prim::Constant[value=0.]()
%false : bool = prim::Constant[value=0]()
- %3 : Tensor = aten::scaled_dot_product_attention(%query, %key, %value, %attn_mask, %0, %false)
+ %scale : float = prim::Constant[value=1.]()
+ %3 : Tensor = aten::scaled_dot_product_attention(%query, %key, %value, %attn_mask, %0, %false, %scale)
return (%3))IR";
auto g = std::make_shared<torch::jit::Graph>();
@@ -62,7 +64,8 @@ TEST(Converters, ATenScaledDotProductAttnMaskBoolConvertsCorrectly) {
graph(%query : Tensor, %key : Tensor, %value : Tensor, %attn_mask : Tensor):
%0 : float = prim::Constant[value=0.]()
%false : bool = prim::Constant[value=0]()
- %3 : Tensor = aten::scaled_dot_product_attention(%query, %key, %value, %attn_mask, %0, %false)
+ %scale : float = prim::Constant[value=1.]()
+ %3 : Tensor = aten::scaled_dot_product_attention(%query, %key, %value, %attn_mask, %0, %false, %scale)
return (%3))IR";
auto g = std::make_shared<torch::jit::Graph>();
From 3753150fb75e9a6637acffa35f2f3b4b63d15afa Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 2 Apr 2024 05:12:26 -0700
Subject: [PATCH 16/69] chore: more fixes
---
.../test_scaled_dot_product_attention.cpp | 6 ++---
.../test_trt_intercompatibility.py | 24 ++++++++++---------
2 files changed, 16 insertions(+), 14 deletions(-)
diff --git a/tests/core/conversion/converters/test_scaled_dot_product_attention.cpp b/tests/core/conversion/converters/test_scaled_dot_product_attention.cpp
index 7fa859e324..5550d5409b 100644
--- a/tests/core/conversion/converters/test_scaled_dot_product_attention.cpp
+++ b/tests/core/conversion/converters/test_scaled_dot_product_attention.cpp
@@ -10,7 +10,7 @@ TEST(Converters, ATenScaledDotProductAttentionConvertsCorrectly) {
graph(%query : Tensor, %key : Tensor, %value : Tensor):
%none : NoneType = prim::Constant()
%0 : float = prim::Constant[value=0.]()
- %scale : float = prim::Constant[value=1.]()
+ %scale : NoneType = prim::Constant()
%false : bool = prim::Constant[value=0]()
%3 : Tensor = aten::scaled_dot_product_attention(%query, %key, %value, %none, %0, %false, %scale)
return (%3))IR";
@@ -37,7 +37,7 @@ TEST(Converters, ATenScaledDotProductAttnMaskFloatConvertsCorrectly) {
graph(%query : Tensor, %key : Tensor, %value : Tensor, %attn_mask : Tensor):
%0 : float = prim::Constant[value=0.]()
%false : bool = prim::Constant[value=0]()
- %scale : float = prim::Constant[value=1.]()
+ %scale : NoneType = prim::Constant()
%3 : Tensor = aten::scaled_dot_product_attention(%query, %key, %value, %attn_mask, %0, %false, %scale)
return (%3))IR";
@@ -64,7 +64,7 @@ TEST(Converters, ATenScaledDotProductAttnMaskBoolConvertsCorrectly) {
graph(%query : Tensor, %key : Tensor, %value : Tensor, %attn_mask : Tensor):
%0 : float = prim::Constant[value=0.]()
%false : bool = prim::Constant[value=0]()
- %scale : float = prim::Constant[value=1.]()
+ %scale : NoneType = prim::Constant()
%3 : Tensor = aten::scaled_dot_product_attention(%query, %key, %value, %attn_mask, %0, %false, %scale)
return (%3))IR";
diff --git a/tests/py/ts/integrations/test_trt_intercompatibility.py b/tests/py/ts/integrations/test_trt_intercompatibility.py
index b938e4a1ac..4c84ad088f 100644
--- a/tests/py/ts/integrations/test_trt_intercompatibility.py
+++ b/tests/py/ts/integrations/test_trt_intercompatibility.py
@@ -1,9 +1,10 @@
import unittest
-import torch_tensorrt as torchtrt
+
+import tensorrt as trt
import torch
+import torch_tensorrt as torchtrt
import torchvision.models as models
-import tensorrt as trt
-from utils import cosine_similarity, COSINE_THRESHOLD
+from utils import COSINE_THRESHOLD, cosine_similarity
class TestPyTorchToTRTEngine(unittest.TestCase):
@@ -31,18 +32,19 @@ def test_pt_to_trt(self):
with trt.Runtime(TRT_LOGGER) as rt:
engine = rt.deserialize_cuda_engine(trt_engine)
with engine.create_execution_context() as ctx:
- out = torch.empty(size=tuple(engine.get_binding_shape(1))).to("cuda:0")
+ out = torch.empty(
+ size=tuple(engine.get_tensor_shape(engine.get_tensor_name(1)))
+ ).to("cuda:0")
bindings = [
self.input.contiguous().data_ptr(),
out.contiguous().data_ptr(),
]
- ctx.execute_async(
- batch_size=1,
- bindings=bindings,
- stream_handle=torch.cuda.current_stream(
- device="cuda:0"
- ).cuda_stream,
- )
+
+ # Assign tensor address appropriately
+ for idx in range(engine.num_io_tensors):
+ ctx.set_tensor_address(engine.get_tensor_name(idx), bindings[idx])
+ ctx.execute_async_v3(torch.cuda.current_stream().cuda_stream)
+
cos_sim = cosine_similarity(self.model(self.input), out)
self.assertTrue(
cos_sim > COSINE_THRESHOLD,
From c355766f659f15fe02bf373e004e10039047d58d Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 2 Apr 2024 05:54:57 -0700
Subject: [PATCH 17/69] chore: remove NvUtils.h
---
third_party/tensorrt/local/BUILD | 16 ++++------------
1 file changed, 4 insertions(+), 12 deletions(-)
diff --git a/third_party/tensorrt/local/BUILD b/third_party/tensorrt/local/BUILD
index 9cbe98a41e..59d0ef01d8 100644
--- a/third_party/tensorrt/local/BUILD
+++ b/third_party/tensorrt/local/BUILD
@@ -29,9 +29,7 @@ config_setting(
cc_library(
name = "nvinfer_headers",
hdrs = select({
- ":aarch64_linux": [
- "include/aarch64-linux-gnu/NvUtils.h",
- ] + glob(
+ ":aarch64_linux": glob(
[
"include/aarch64-linux-gnu/NvInfer*.h",
],
@@ -40,9 +38,7 @@ cc_library(
"include/aarch64-linux-gnu/NvInferPluginUtils.h",
],
),
- ":ci_rhel_x86_64_linux": [
- "include/NvUtils.h",
- ] + glob(
+ ":ci_rhel_x86_64_linux": glob(
[
"include/NvInfer*.h",
],
@@ -51,9 +47,7 @@ cc_library(
"include/NvInferPluginUtils.h",
],
),
- ":windows": [
- "include/NvUtils.h",
- ] + glob(
+ ":windows": glob(
[
"include/NvInfer*.h",
],
@@ -62,9 +56,7 @@ cc_library(
"include/NvInferPluginUtils.h",
],
),
- "//conditions:default": [
- "include/x86_64-linux-gnu/NvUtils.h",
- ] + glob(
+ "//conditions:default": glob(
[
"include/x86_64-linux-gnu/NvInfer*.h",
],
From 2d237dca528d0743c4b748e60c7969de46660464 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 2 Apr 2024 07:14:43 -0700
Subject: [PATCH 18/69] chore: more updates
---
WORKSPACE | 10 ----------
core/conversion/converters/impl/chunk.cpp | 4 ----
core/ir/ir.cpp | 1 -
core/plugins/impl/interpolate_plugin.h | 1 -
core/plugins/impl/normalize_plugin.h | 1 -
third_party/tensorrt/local/BUILD | 2 --
.../ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl | 6 ------
7 files changed, 25 deletions(-)
diff --git a/WORKSPACE b/WORKSPACE
index 76e9a602fa..95abcea2d3 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -68,16 +68,6 @@ http_archive(
# Either place them in the distdir directory in third_party and use the --distdir flag
# or modify the urls to "file:///<PATH TO TARBALL>/<TARBALL NAME>.tar.gz
-http_archive(
- name = "cudnn",
- build_file = "@//third_party/cudnn/archive:BUILD",
- sha256 = "2a2eb89a2ab51071151c6082f1e816c702167a711a9372f9f73a7b5c4b06e01a",
- strip_prefix = "cudnn-linux-x86_64-8.9.5.30_cuda12-archive",
- urls = [
- "https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.5/local_installers/12.x/cudnn-linux-x86_64-8.9.5.30_cuda12-archive.tar.xz",
- ],
-)
-
http_archive(
name = "tensorrt",
build_file = "@//third_party/tensorrt/archive:BUILD",
diff --git a/core/conversion/converters/impl/chunk.cpp b/core/conversion/converters/impl/chunk.cpp
index a7191133fb..b3d2441706 100644
--- a/core/conversion/converters/impl/chunk.cpp
+++ b/core/conversion/converters/impl/chunk.cpp
@@ -17,7 +17,6 @@ auto cat_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns()
auto chunks = args[1].unwrapToInt();
auto dim = args[2].unwrapToInt();
bool dynamic_shape = ctx->input_is_dynamic;
- int size = in->getDimensions().nbDims;
int maxDim = static_cast<int32_t>(in->getDimensions().d[dim]);
c10::ListTypePtr lt = n->output()->type()->expect<c10::ListType>();
@@ -41,9 +40,6 @@ auto cat_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns()
size_.nbDims = nbdims;
stride_.nbDims = nbdims;
- int startIdx = 0;
- int endIdx = maxDim;
-
for (int i = 0; i < nbdims; i++) {
start_.d[i] = 0;
size_.d[i] = 0;
diff --git a/core/ir/ir.cpp b/core/ir/ir.cpp
index c98d17c5ef..b67e228f1f 100644
--- a/core/ir/ir.cpp
+++ b/core/ir/ir.cpp
@@ -151,7 +151,6 @@ c10::optional<at::ScalarType> get_value_first_calc_dtype_opt(torch::jit::Block*
// If node outputs a Tensor it might be a result of tensor calcuation so check to see
// if any inputs to the calculation can give us hints
- c10::optional<torch::jit::Node*> const_tensor_n = {};
// Backtrace to constants which will immediately give us the Tensor type if possible
for (auto in : ins) {
diff --git a/core/plugins/impl/interpolate_plugin.h b/core/plugins/impl/interpolate_plugin.h
index ced4cbee20..ce009af03e 100644
--- a/core/plugins/impl/interpolate_plugin.h
+++ b/core/plugins/impl/interpolate_plugin.h
@@ -3,7 +3,6 @@
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAEvent.h>
#include <cuda_runtime_api.h>
-#include <cudnn.h>
#include <iostream>
#include <sstream>
#include <string>
diff --git a/core/plugins/impl/normalize_plugin.h b/core/plugins/impl/normalize_plugin.h
index 28c3a5c5da..5d51a68293 100644
--- a/core/plugins/impl/normalize_plugin.h
+++ b/core/plugins/impl/normalize_plugin.h
@@ -3,7 +3,6 @@
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAEvent.h>
#include <cuda_runtime_api.h>
-#include <cudnn.h>
#include <iostream>
#include <sstream>
#include <string>
diff --git a/third_party/tensorrt/local/BUILD b/third_party/tensorrt/local/BUILD
index 59d0ef01d8..c317e16688 100644
--- a/third_party/tensorrt/local/BUILD
+++ b/third_party/tensorrt/local/BUILD
@@ -104,7 +104,6 @@ cc_library(
"nvinfer_headers",
"nvinfer_lib",
"@cuda//:cudart",
- "@cudnn",
],
)
@@ -358,7 +357,6 @@ cc_library(
deps = [
"nvinfer",
"@cuda//:cudart",
- "@cudnn",
],
alwayslink = True,
)
diff --git a/toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl b/toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl
index db6cfb0b5d..8ff4f74aaf 100644
--- a/toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl
+++ b/toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl
@@ -73,12 +73,6 @@ http_archive(
# Locally installed dependencies (use in cases of custom dependencies or aarch64)
####################################################################################
-new_local_repository(
- name = "cudnn",
- path = "/usr/",
- build_file = "@//third_party/cudnn/local:BUILD"
-)
-
new_local_repository(
name = "tensorrt",
path = "/usr/",
From e4b442913b7200d66af92c90ebb54d77321bd536 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 2 Apr 2024 07:30:35 -0700
Subject: [PATCH 19/69] chore: change lib64 to lib in rhel BUILD file
---
third_party/tensorrt/local/BUILD | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/third_party/tensorrt/local/BUILD b/third_party/tensorrt/local/BUILD
index c317e16688..1967308243 100644
--- a/third_party/tensorrt/local/BUILD
+++ b/third_party/tensorrt/local/BUILD
@@ -329,7 +329,7 @@ cc_library(
name = "nvinferplugin",
srcs = select({
":aarch64_linux": ["lib/aarch64-linux-gnu/libnvinfer_plugin.so"],
- ":ci_rhel_x86_64_linux": ["lib64/libnvinfer_plugin.so"],
+ ":ci_rhel_x86_64_linux": ["lib/libnvinfer_plugin.so"],
":windows": ["lib/nvinfer_plugin.dll"],
"//conditions:default": ["lib/x86_64-linux-gnu/libnvinfer_plugin.so"],
}),
From fa4fb9cea4946d702e4cb869e76942fbaf4bcfda Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 2 Apr 2024 07:40:28 -0700
Subject: [PATCH 20/69] chore: more updates
---
third_party/tensorrt/local/BUILD | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/third_party/tensorrt/local/BUILD b/third_party/tensorrt/local/BUILD
index 1967308243..31aa906ba4 100644
--- a/third_party/tensorrt/local/BUILD
+++ b/third_party/tensorrt/local/BUILD
@@ -79,7 +79,7 @@ cc_import(
name = "nvinfer_static_lib",
static_library = select({
":aarch64_linux": "lib/aarch64-linux-gnu/libnvinfer_static.a",
- ":ci_rhel_x86_64_linux": "lib64/libnvinfer_static.a",
+ ":ci_rhel_x86_64_linux": "lib/libnvinfer_static.a",
":windows": "lib/nvinfer.lib",
"//conditions:default": "lib/x86_64-linux-gnu/libnvinfer_static.a",
}),
@@ -90,7 +90,7 @@ cc_import(
name = "nvinfer_lib",
shared_library = select({
":aarch64_linux": "lib/aarch64-linux-gnu/libnvinfer.so",
- ":ci_rhel_x86_64_linux": "lib64/libnvinfer.so",
+ ":ci_rhel_x86_64_linux": "lib/libnvinfer.so",
":windows": "lib/nvinfer.dll",
"//conditions:default": "lib/x86_64-linux-gnu/libnvinfer.so",
}),
@@ -113,7 +113,7 @@ cc_import(
name = "nvparsers_lib",
shared_library = select({
":aarch64_linux": "lib/aarch64-linux-gnu/libnvparsers.so",
- ":ci_rhel_x86_64_linux": "lib64/libnvparsers.so",
+ ":ci_rhel_x86_64_linux": "lib/libnvparsers.so",
":windows": "lib/nvparsers.dll",
"//conditions:default": "lib/x86_64-linux-gnu/libnvparsers.so",
}),
@@ -177,7 +177,7 @@ cc_import(
name = "nvonnxparser_lib",
shared_library = select({
":aarch64_linux": "lib/aarch64-linux-gnu/libnvonnxparser.so",
- ":ci_rhel_x86_64_linux": "lib64/libnvonnxparser.so",
+ ":ci_rhel_x86_64_linux": "lib/libnvonnxparser.so",
":windows": "lib/nvonnxparser.dll",
"//conditions:default": "lib/x86_64-linux-gnu/libnvonnxparser.so",
}),
@@ -233,7 +233,7 @@ cc_import(
name = "nvonnxparser_runtime_lib",
shared_library = select({
":aarch64_linux": "lib/x86_64-linux-gnu/libnvonnxparser_runtime.so",
- ":ci_rhel_x86_64_linux": "lib64/libnvonnxparser_runtime.so",
+ ":ci_rhel_x86_64_linux": "lib/libnvonnxparser_runtime.so",
":windows": "lib/nvonnxparser_runtime.dll",
"//conditions:default": "lib/x86_64-linux-gnu/libnvonnxparser_runtime.so",
}),
@@ -281,7 +281,7 @@ cc_import(
name = "nvcaffeparser_lib",
shared_library = select({
":aarch64_linux": "lib/aarch64-linux-gnu/libnvcaffe_parsers.so",
- ":ci_rhel_x86_64_linux": "lib64/libnvcaffe_parsers.so",
+ ":ci_rhel_x86_64_linux": "lib/libnvcaffe_parsers.so",
":windows": "lib/nvcaffe_parsers.dll",
"//conditions:default": "lib/x86_64-linux-gnu/libnvcaffe_parsers.so",
}),
From e11eb60edd8718c775f23b753d658a43f83edbe5 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 2 Apr 2024 09:36:52 -0700
Subject: [PATCH 21/69] chore: fix TRT version
---
dev_dep_versions.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/dev_dep_versions.yml b/dev_dep_versions.yml
index b07db5533f..e2434d0c45 100644
--- a/dev_dep_versions.yml
+++ b/dev_dep_versions.yml
@@ -1,4 +1,4 @@
__version__: "2.3.0"
__cuda_version__: "12.1"
__cudnn_version__: "8.9"
-__tensorrt_version__: "10.0.0b6"
+__tensorrt_version__: "10.0"
From 092feb225e8844d94c5e7b11d65b5e413cf88c48 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 2 Apr 2024 09:50:03 -0700
Subject: [PATCH 22/69] chore: more updates
---
dev_dep_versions.yml | 2 +-
packaging/pre_build_script.sh | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/dev_dep_versions.yml b/dev_dep_versions.yml
index e2434d0c45..b07db5533f 100644
--- a/dev_dep_versions.yml
+++ b/dev_dep_versions.yml
@@ -1,4 +1,4 @@
__version__: "2.3.0"
__cuda_version__: "12.1"
__cudnn_version__: "8.9"
-__tensorrt_version__: "10.0"
+__tensorrt_version__: "10.0.0b6"
diff --git a/packaging/pre_build_script.sh b/packaging/pre_build_script.sh
index 18cd5d9fe2..9bab9a58c3 100755
--- a/packaging/pre_build_script.sh
+++ b/packaging/pre_build_script.sh
@@ -5,7 +5,7 @@ python3 -m pip install pyyaml
TRT_VERSION=$(python3 -c "import versions; versions.tensorrt_version()")
yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo
yum check-update
-yum install -y ninja-build gettext tensorrt-${TRT_VERSION}.*
+yum install -y ninja-build gettext tensorrt-${TRT_VERSION}
wget https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 \
&& mv bazelisk-linux-amd64 /usr/bin/bazel \
&& chmod +x /usr/bin/bazel
From 09ecf263741fb0e945033f602c424a2e5aabb4ad Mon Sep 17 00:00:00 2001
From: Evan Li <zewenl@nvidia.com>
Date: Wed, 3 Apr 2024 00:32:54 +0000
Subject: [PATCH 23/69] fix shape bug in bitwise ops
---
.../conversion/impl/elementwise/base.py | 42 ++++++++++++++++---
1 file changed, 36 insertions(+), 6 deletions(-)
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
index 615cd0bd62..f93ec52504 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
@@ -7,12 +7,13 @@
import torch
from torch.fx.node import Target
from torch_tensorrt.dynamo._SourceIR import SourceIR
+from torch_tensorrt.dynamo.conversion import impl
from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
from torch_tensorrt.dynamo.conversion.converter_utils import (
cast_trt_tensor,
get_trt_tensor,
)
-from torch_tensorrt.fx.converters.converter_utils import broadcast, set_layer_name
+from torch_tensorrt.fx.converters.converter_utils import set_layer_name
from torch_tensorrt.fx.types import TRTElementWiseOp, TRTTensor
from torch_tensorrt.fx.utils import Frameworks, unified_dtype_converter
@@ -140,16 +141,45 @@ def convert_binary_elementwise(
if trt_promoted_type != lhs_val.dtype:
lhs_val = cast_trt_tensor(
- ctx, lhs_val, trt_promoted_type, name, target, source_ir
+ ctx, lhs_val, trt_promoted_type, f"{name}_cast_lhs_val", target, source_ir
)
if trt_promoted_type != rhs_val.dtype:
rhs_val = cast_trt_tensor(
- ctx, rhs_val, trt_promoted_type, name, target, source_ir
+ ctx, rhs_val, trt_promoted_type, f"{name}_cast_rhs_val", target, source_ir
)
- lhs_val, rhs_val = broadcast(
- ctx.net, lhs_val, rhs_val, f"{name}_lhs", f"{name}_rhs"
- )
+ lhs_val_shape = lhs_val.shape
+ rhs_val_shape = rhs_val.shape
+ rank_diff = len(lhs_val_shape) - len(rhs_val_shape)
+ if rank_diff > 0:
+ rhs_val = impl.slice.expand(
+ ctx, target, source_ir, f"{name}_expand_rhs_val", rhs_val, lhs_val_shape
+ )
+ elif rank_diff < 0:
+ lhs_val = impl.slice.expand(
+ ctx, target, source_ir, f"{name}_expand_lhs_val", lhs_val, rhs_val_shape
+ )
+ else:
+ if tuple(lhs_val_shape) != tuple(rhs_val_shape):
+ sum_diff = sum(lhs_val_shape) - sum(rhs_val_shape)
+ if sum_diff > 0:
+ rhs_val = impl.slice.expand(
+ ctx,
+ target,
+ source_ir,
+ f"{name}_expand_rhs_val",
+ rhs_val,
+ lhs_val_shape,
+ )
+ elif sum_diff < 0:
+ lhs_val = impl.slice.expand(
+ ctx,
+ target,
+ source_ir,
+ f"{name}_expand_lhs_val",
+ lhs_val,
+ rhs_val_shape,
+ )
layer = ctx.net.add_elementwise(lhs_val, rhs_val, op_type)
set_layer_name(layer, target, name, source_ir)
From 85e04c5d5697e60395b79829cf80322da1de41f9 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 2 Apr 2024 19:07:46 -0700
Subject: [PATCH 24/69] chore: update to rhel9
---
packaging/pre_build_script.sh | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/packaging/pre_build_script.sh b/packaging/pre_build_script.sh
index 9bab9a58c3..ae73d2b773 100755
--- a/packaging/pre_build_script.sh
+++ b/packaging/pre_build_script.sh
@@ -3,7 +3,7 @@
# Install dependencies
python3 -m pip install pyyaml
TRT_VERSION=$(python3 -c "import versions; versions.tensorrt_version()")
-yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo
+yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel9/x86_64/cuda-rhel9.repo
yum check-update
yum install -y ninja-build gettext tensorrt-${TRT_VERSION}
wget https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 \
From 41229d63ae3465304f451a0b43703ea9754e0d97 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 2 Apr 2024 19:26:52 -0700
Subject: [PATCH 25/69] chore: change trt version
---
dev_dep_versions.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/dev_dep_versions.yml b/dev_dep_versions.yml
index b07db5533f..4bbfe9d188 100644
--- a/dev_dep_versions.yml
+++ b/dev_dep_versions.yml
@@ -1,4 +1,4 @@
__version__: "2.3.0"
__cuda_version__: "12.1"
__cudnn_version__: "8.9"
-__tensorrt_version__: "10.0.0b6"
+__tensorrt_version__: "10.0.0.6"
From 9d7a65618a039802eb568264eccf1be4172a3750 Mon Sep 17 00:00:00 2001
From: Evan Li <zewenl@nvidia.com>
Date: Wed, 3 Apr 2024 18:23:40 +0000
Subject: [PATCH 26/69] fix test bug and add more tests
---
tests/py/dynamo/conversion/test_arange_aten.py | 8 ++++++--
1 file changed, 6 insertions(+), 2 deletions(-)
diff --git a/tests/py/dynamo/conversion/test_arange_aten.py b/tests/py/dynamo/conversion/test_arange_aten.py
index 035b957865..e06239eb4e 100644
--- a/tests/py/dynamo/conversion/test_arange_aten.py
+++ b/tests/py/dynamo/conversion/test_arange_aten.py
@@ -15,14 +15,18 @@ class TestArangeConverter(DispatchTestCase):
(5, 0, -1),
(5, 1, -2),
(5, 3, -3),
+ (5, -2, -1),
+ (-5, -2, 2),
+ (-5, -3, 1),
+ (-2, -5, -1),
]
)
def test_arange(self, start, end, step):
class Arange(nn.Module):
def forward(self, x):
- return torch.ops.aten.arange.start_step(start, x.shape[0], step)
+ return torch.ops.aten.arange.start_step(start, end, step)
- inputs = [torch.randn(end, 1)]
+ inputs = [torch.randn(1, 1)]
self.run_test(
Arange(),
inputs,
From 5e911a9765c4aad015a6075afd4b30bcca1efa79 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 3 Apr 2024 11:35:30 -0700
Subject: [PATCH 27/69] chore: delete mirror of rules_pkg
---
WORKSPACE | 1 -
third_party/tensorrt/archive/BUILD | 2 --
2 files changed, 3 deletions(-)
diff --git a/WORKSPACE b/WORKSPACE
index 95abcea2d3..13c07a6008 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -17,7 +17,6 @@ http_archive(
name = "rules_pkg",
sha256 = "8f9ee2dc10c1ae514ee599a8b42ed99fa262b757058f65ad3c384289ff70c4b8",
urls = [
- "https://mirror.bazel.build/github.com/bazelbuild/rules_pkg/releases/download/0.9.1/rules_pkg-0.9.1.tar.gz",
"https://github.com/bazelbuild/rules_pkg/releases/download/0.9.1/rules_pkg-0.9.1.tar.gz",
],
)
diff --git a/third_party/tensorrt/archive/BUILD b/third_party/tensorrt/archive/BUILD
index 221f2ce4b3..5c07794a20 100644
--- a/third_party/tensorrt/archive/BUILD
+++ b/third_party/tensorrt/archive/BUILD
@@ -45,7 +45,6 @@ cc_library(
"nvinfer_headers",
"nvinfer_lib",
"@cuda//:cudart",
- "@cudnn",
],
)
@@ -182,6 +181,5 @@ cc_library(
"nvinferplugin_headers",
"nvinferplugin_lib",
"@cuda//:cudart",
- "@cudnn",
],
)
From dae0eb2321418e575c826231ce2d2e5194837f06 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 3 Apr 2024 16:10:12 -0700
Subject: [PATCH 28/69] chore: fix conv test
---
tests/core/conversion/converters/test_conv_deconv.cpp | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/tests/core/conversion/converters/test_conv_deconv.cpp b/tests/core/conversion/converters/test_conv_deconv.cpp
index 27baa1df5e..faaf7f2474 100644
--- a/tests/core/conversion/converters/test_conv_deconv.cpp
+++ b/tests/core/conversion/converters/test_conv_deconv.cpp
@@ -126,13 +126,13 @@ TEST(Converters, ATenConv1dWithWeightTensorsConvertsCorrectly) {
%5 : int = prim::Constant[value=127]()
%quant_input : Tensor = aten::fake_quantize_per_tensor_affine(%0, %3, %4, %2, %5)
%6 : int = prim::Constant[value=6]()
- %7 : int = prim::Constant[value=5]()
+ %7 : int = prim::Constant[value=4]()
%8 : Device = prim::Constant[value="cuda:0"]()
%9 : None = prim::Constant()
%10 : int[] = prim::ListConstruct(%7)
%11 : Tensor = aten::full(%10, %3, %6, %9, %8, %9)
%12 : int[] = prim::ListConstruct(%7)
- %13 : int = prim::Constant[value=1]()
+ %13 : int = prim::Constant[value=0]()
%14 : Tensor = aten::full(%12, %13, %6, %9, %8, %9)
%quant_wts : Tensor = aten::fake_quantize_per_channel_affine(%1, %11, %14, %13, %2, %5)
%15 : None = prim::Constant()
From 4676cd2d289f23870aa0b1e6829ad498e6855146 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 3 Apr 2024 17:06:25 -0700
Subject: [PATCH 29/69] chore: fix trt version range
---
py/requirements.txt | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/py/requirements.txt b/py/requirements.txt
index 565395b59b..19c39c8647 100644
--- a/py/requirements.txt
+++ b/py/requirements.txt
@@ -5,5 +5,5 @@ pybind11==2.6.2
torch==2.3.0
torchvision==0.18.0
--extra-index-url https://pypi.ngc.nvidia.com
-tensorrt==10.0.0b6
+tensorrt>=10.0
pyyaml
From 88efe8e7258c4af254299c7ba37f4af4176cba5b Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 3 Apr 2024 17:10:04 -0700
Subject: [PATCH 30/69] chore: fix trt rangfe
---
pyproject.toml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/pyproject.toml b/pyproject.toml
index 7468484f48..05404f87b7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,7 @@ requires = [
"cffi>=1.15.1",
"typing-extensions>=4.7.0",
"future>=0.18.3",
- "tensorrt==10.0.0b6",
+ "tensorrt>=10.0",
"torch >=2.3.0.dev,<2.4.0",
"pybind11==2.6.2",
"numpy",
From f9b40e672da686591830b7a1bef1280231af6a2b Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 3 Apr 2024 17:18:01 -0700
Subject: [PATCH 31/69] chore: minor fix
---
pyproject.toml | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/pyproject.toml b/pyproject.toml
index 05404f87b7..7a78047be7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,7 +9,7 @@ requires = [
"typing-extensions>=4.7.0",
"future>=0.18.3",
"tensorrt>=10.0",
- "torch >=2.3.0.dev,<2.4.0",
+ "torch>=2.3.0.dev,<2.4.0",
"pybind11==2.6.2",
"numpy",
]
@@ -42,7 +42,7 @@ requires-python = ">=3.8"
keywords = ["pytorch", "torch", "tensorrt", "trt", "ai", "artificial intelligence", "ml", "machine learning", "dl", "deep learning", "compiler", "dynamo", "torchscript", "inference"]
dependencies = [
"torch >=2.3.0.dev,<2.4.0",
- "tensorrt==10.0.0b6",
+ "tensorrt>=10.0",
"packaging>=23",
"numpy",
"typing-extensions>=4.7.0",
From b86aec27a3c8932d94222ca970d0f4178f742841 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Thu, 4 Apr 2024 10:26:46 -0700
Subject: [PATCH 32/69] chore: update rules_pkg
---
WORKSPACE | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/WORKSPACE b/WORKSPACE
index 13c07a6008..4a7a1556d3 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -15,9 +15,9 @@ py_repositories()
http_archive(
name = "rules_pkg",
- sha256 = "8f9ee2dc10c1ae514ee599a8b42ed99fa262b757058f65ad3c384289ff70c4b8",
+ sha256 = "d250924a2ecc5176808fc4c25d5cf5e9e79e6346d79d5ab1c493e289e722d1d0",
urls = [
- "https://github.com/bazelbuild/rules_pkg/releases/download/0.9.1/rules_pkg-0.9.1.tar.gz",
+ "https://github.com/bazelbuild/rules_pkg/releases/download/0.10.1/rules_pkg-0.10.1.tar.gz",
],
)
From 6630281671d52adb765e4d816a8d5315bfbbc6ce Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Thu, 4 Apr 2024 13:16:53 -0700
Subject: [PATCH 33/69] chore: minor fixes
---
WORKSPACE | 4 ++--
py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py | 1 +
2 files changed, 3 insertions(+), 2 deletions(-)
diff --git a/WORKSPACE b/WORKSPACE
index 4a7a1556d3..13c07a6008 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -15,9 +15,9 @@ py_repositories()
http_archive(
name = "rules_pkg",
- sha256 = "d250924a2ecc5176808fc4c25d5cf5e9e79e6346d79d5ab1c493e289e722d1d0",
+ sha256 = "8f9ee2dc10c1ae514ee599a8b42ed99fa262b757058f65ad3c384289ff70c4b8",
urls = [
- "https://github.com/bazelbuild/rules_pkg/releases/download/0.10.1/rules_pkg-0.10.1.tar.gz",
+ "https://github.com/bazelbuild/rules_pkg/releases/download/0.9.1/rules_pkg-0.9.1.tar.gz",
],
)
diff --git a/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py b/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py
index ffcc9c195e..a1f6614c85 100644
--- a/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py
+++ b/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py
@@ -188,6 +188,7 @@ def run(
if self.compilation_settings.version_compatible:
_LOGGER.info("Using version compatible")
builder_config.set_flag(trt.BuilderFlag.VERSION_COMPATIBLE)
+ builder_config.set_flag(trt.BuilderFlag.EXCLUDE_LEAN_RUNTIME)
if self.compilation_settings.hardware_compatible:
_LOGGER.info("Using hardware compatible")
builder_config.hardware_compatibility_level = (
From fca55fe06bcba9d9282f275a2aa713f94a73b500 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Thu, 4 Apr 2024 14:22:06 -0700
Subject: [PATCH 34/69] chore: expt
---
WORKSPACE | 17 +++++++++++++---
third_party/tensorrt/archive/BUILD | 2 ++
third_party/tensorrt/local/BUILD | 32 ++++++++++++++++++++----------
3 files changed, 37 insertions(+), 14 deletions(-)
diff --git a/WORKSPACE b/WORKSPACE
index 13c07a6008..edc5c9a050 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -17,6 +17,7 @@ http_archive(
name = "rules_pkg",
sha256 = "8f9ee2dc10c1ae514ee599a8b42ed99fa262b757058f65ad3c384289ff70c4b8",
urls = [
+ "https://mirror.bazel.build/github.com/bazelbuild/rules_pkg/releases/download/0.9.1/rules_pkg-0.9.1.tar.gz",
"https://github.com/bazelbuild/rules_pkg/releases/download/0.9.1/rules_pkg-0.9.1.tar.gz",
],
)
@@ -67,13 +68,23 @@ http_archive(
# Either place them in the distdir directory in third_party and use the --distdir flag
# or modify the urls to "file:///<PATH TO TARBALL>/<TARBALL NAME>.tar.gz
+http_archive(
+ name = "cudnn",
+ build_file = "@//third_party/cudnn/archive:BUILD",
+ sha256 = "2a2eb89a2ab51071151c6082f1e816c702167a711a9372f9f73a7b5c4b06e01a",
+ strip_prefix = "cudnn-linux-x86_64-8.9.5.30_cuda12-archive",
+ urls = [
+ "https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.5/local_installers/12.x/cudnn-linux-x86_64-8.9.5.30_cuda12-archive.tar.xz",
+ ],
+)
+
http_archive(
name = "tensorrt",
build_file = "@//third_party/tensorrt/archive:BUILD",
- sha256 = "7ad5c8eab8fe8803bef5d8cee54ec1f407797ac8edb8ea3186cb9f24af726486",
- strip_prefix = "TensorRT-10.0.0.6",
+ sha256 = "0f8157a5fc5329943b338b893591373350afa90ca81239cdadd7580cd1eba254",
+ strip_prefix = "TensorRT-8.6.1.6",
urls = [
- "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.0.0/tars/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz",
+ "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/tars/TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-12.0.tar.gz",
],
)
diff --git a/third_party/tensorrt/archive/BUILD b/third_party/tensorrt/archive/BUILD
index 5c07794a20..221f2ce4b3 100644
--- a/third_party/tensorrt/archive/BUILD
+++ b/third_party/tensorrt/archive/BUILD
@@ -45,6 +45,7 @@ cc_library(
"nvinfer_headers",
"nvinfer_lib",
"@cuda//:cudart",
+ "@cudnn",
],
)
@@ -181,5 +182,6 @@ cc_library(
"nvinferplugin_headers",
"nvinferplugin_lib",
"@cuda//:cudart",
+ "@cudnn",
],
)
diff --git a/third_party/tensorrt/local/BUILD b/third_party/tensorrt/local/BUILD
index 31aa906ba4..9cbe98a41e 100644
--- a/third_party/tensorrt/local/BUILD
+++ b/third_party/tensorrt/local/BUILD
@@ -29,7 +29,9 @@ config_setting(
cc_library(
name = "nvinfer_headers",
hdrs = select({
- ":aarch64_linux": glob(
+ ":aarch64_linux": [
+ "include/aarch64-linux-gnu/NvUtils.h",
+ ] + glob(
[
"include/aarch64-linux-gnu/NvInfer*.h",
],
@@ -38,7 +40,9 @@ cc_library(
"include/aarch64-linux-gnu/NvInferPluginUtils.h",
],
),
- ":ci_rhel_x86_64_linux": glob(
+ ":ci_rhel_x86_64_linux": [
+ "include/NvUtils.h",
+ ] + glob(
[
"include/NvInfer*.h",
],
@@ -47,7 +51,9 @@ cc_library(
"include/NvInferPluginUtils.h",
],
),
- ":windows": glob(
+ ":windows": [
+ "include/NvUtils.h",
+ ] + glob(
[
"include/NvInfer*.h",
],
@@ -56,7 +62,9 @@ cc_library(
"include/NvInferPluginUtils.h",
],
),
- "//conditions:default": glob(
+ "//conditions:default": [
+ "include/x86_64-linux-gnu/NvUtils.h",
+ ] + glob(
[
"include/x86_64-linux-gnu/NvInfer*.h",
],
@@ -79,7 +87,7 @@ cc_import(
name = "nvinfer_static_lib",
static_library = select({
":aarch64_linux": "lib/aarch64-linux-gnu/libnvinfer_static.a",
- ":ci_rhel_x86_64_linux": "lib/libnvinfer_static.a",
+ ":ci_rhel_x86_64_linux": "lib64/libnvinfer_static.a",
":windows": "lib/nvinfer.lib",
"//conditions:default": "lib/x86_64-linux-gnu/libnvinfer_static.a",
}),
@@ -90,7 +98,7 @@ cc_import(
name = "nvinfer_lib",
shared_library = select({
":aarch64_linux": "lib/aarch64-linux-gnu/libnvinfer.so",
- ":ci_rhel_x86_64_linux": "lib/libnvinfer.so",
+ ":ci_rhel_x86_64_linux": "lib64/libnvinfer.so",
":windows": "lib/nvinfer.dll",
"//conditions:default": "lib/x86_64-linux-gnu/libnvinfer.so",
}),
@@ -104,6 +112,7 @@ cc_library(
"nvinfer_headers",
"nvinfer_lib",
"@cuda//:cudart",
+ "@cudnn",
],
)
@@ -113,7 +122,7 @@ cc_import(
name = "nvparsers_lib",
shared_library = select({
":aarch64_linux": "lib/aarch64-linux-gnu/libnvparsers.so",
- ":ci_rhel_x86_64_linux": "lib/libnvparsers.so",
+ ":ci_rhel_x86_64_linux": "lib64/libnvparsers.so",
":windows": "lib/nvparsers.dll",
"//conditions:default": "lib/x86_64-linux-gnu/libnvparsers.so",
}),
@@ -177,7 +186,7 @@ cc_import(
name = "nvonnxparser_lib",
shared_library = select({
":aarch64_linux": "lib/aarch64-linux-gnu/libnvonnxparser.so",
- ":ci_rhel_x86_64_linux": "lib/libnvonnxparser.so",
+ ":ci_rhel_x86_64_linux": "lib64/libnvonnxparser.so",
":windows": "lib/nvonnxparser.dll",
"//conditions:default": "lib/x86_64-linux-gnu/libnvonnxparser.so",
}),
@@ -233,7 +242,7 @@ cc_import(
name = "nvonnxparser_runtime_lib",
shared_library = select({
":aarch64_linux": "lib/x86_64-linux-gnu/libnvonnxparser_runtime.so",
- ":ci_rhel_x86_64_linux": "lib/libnvonnxparser_runtime.so",
+ ":ci_rhel_x86_64_linux": "lib64/libnvonnxparser_runtime.so",
":windows": "lib/nvonnxparser_runtime.dll",
"//conditions:default": "lib/x86_64-linux-gnu/libnvonnxparser_runtime.so",
}),
@@ -281,7 +290,7 @@ cc_import(
name = "nvcaffeparser_lib",
shared_library = select({
":aarch64_linux": "lib/aarch64-linux-gnu/libnvcaffe_parsers.so",
- ":ci_rhel_x86_64_linux": "lib/libnvcaffe_parsers.so",
+ ":ci_rhel_x86_64_linux": "lib64/libnvcaffe_parsers.so",
":windows": "lib/nvcaffe_parsers.dll",
"//conditions:default": "lib/x86_64-linux-gnu/libnvcaffe_parsers.so",
}),
@@ -329,7 +338,7 @@ cc_library(
name = "nvinferplugin",
srcs = select({
":aarch64_linux": ["lib/aarch64-linux-gnu/libnvinfer_plugin.so"],
- ":ci_rhel_x86_64_linux": ["lib/libnvinfer_plugin.so"],
+ ":ci_rhel_x86_64_linux": ["lib64/libnvinfer_plugin.so"],
":windows": ["lib/nvinfer_plugin.dll"],
"//conditions:default": ["lib/x86_64-linux-gnu/libnvinfer_plugin.so"],
}),
@@ -357,6 +366,7 @@ cc_library(
deps = [
"nvinfer",
"@cuda//:cudart",
+ "@cudnn",
],
alwayslink = True,
)
From 1ca01e782915a8073f546e53b9f171e9675e4731 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Thu, 4 Apr 2024 18:19:23 -0700
Subject: [PATCH 35/69] chore: update WORKSPACE tmpl
---
toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl | 1 -
1 file changed, 1 deletion(-)
diff --git a/toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl b/toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl
index 8ff4f74aaf..4a37bb6091 100644
--- a/toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl
+++ b/toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl
@@ -17,7 +17,6 @@ http_archive(
name = "rules_pkg",
sha256 = "8f9ee2dc10c1ae514ee599a8b42ed99fa262b757058f65ad3c384289ff70c4b8",
urls = [
- "https://mirror.bazel.build/github.com/bazelbuild/rules_pkg/releases/download/0.9.1/rules_pkg-0.9.1.tar.gz",
"https://github.com/bazelbuild/rules_pkg/releases/download/0.9.1/rules_pkg-0.9.1.tar.gz",
],
)
From 6ffb85ee051604aa5c57d8c8802bc47525fcc22d Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Fri, 5 Apr 2024 16:50:34 -0700
Subject: [PATCH 36/69] chore: fix
---
packaging/pre_build_script.sh | 3 ++-
pyproject.toml | 4 ++--
2 files changed, 4 insertions(+), 3 deletions(-)
diff --git a/packaging/pre_build_script.sh b/packaging/pre_build_script.sh
index ae73d2b773..41fb47ff79 100755
--- a/packaging/pre_build_script.sh
+++ b/packaging/pre_build_script.sh
@@ -2,10 +2,11 @@
# Install dependencies
python3 -m pip install pyyaml
+yum install -y ninja-build gettext
TRT_VERSION=$(python3 -c "import versions; versions.tensorrt_version()")
yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel9/x86_64/cuda-rhel9.repo
yum check-update
-yum install -y ninja-build gettext tensorrt-${TRT_VERSION}
+yum install tensorrt-${TRT_VERSION}
wget https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 \
&& mv bazelisk-linux-amd64 /usr/bin/bazel \
&& chmod +x /usr/bin/bazel
diff --git a/pyproject.toml b/pyproject.toml
index 1e29a43565..dd4cada7f2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,7 @@ requires = [
"cffi>=1.15.1",
"typing-extensions>=4.7.0",
"future>=0.18.3",
- "tensorrt>=8.6,<8.7",
+ "tensorrt==10.0.0b6",
"torch==2.3.0",
"pybind11==2.6.2",
"numpy",
@@ -42,7 +42,7 @@ requires-python = ">=3.8"
keywords = ["pytorch", "torch", "tensorrt", "trt", "ai", "artificial intelligence", "ml", "machine learning", "dl", "deep learning", "compiler", "dynamo", "torchscript", "inference"]
dependencies = [
"torch==2.3.0",
- "tensorrt>=8.6,<8.7",
+ "tensorrt==10.0.0b6",
"packaging>=23",
"numpy",
"typing-extensions>=4.7.0",
From 76af510ebb7129460d70b759677673878d767c01 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Fri, 5 Apr 2024 17:05:18 -0700
Subject: [PATCH 37/69] chore: remove cudnn dep
---
third_party/tensorrt/archive/BUILD | 2 --
third_party/tensorrt/local/BUILD | 2 --
2 files changed, 4 deletions(-)
diff --git a/third_party/tensorrt/archive/BUILD b/third_party/tensorrt/archive/BUILD
index 221f2ce4b3..5c07794a20 100644
--- a/third_party/tensorrt/archive/BUILD
+++ b/third_party/tensorrt/archive/BUILD
@@ -45,7 +45,6 @@ cc_library(
"nvinfer_headers",
"nvinfer_lib",
"@cuda//:cudart",
- "@cudnn",
],
)
@@ -182,6 +181,5 @@ cc_library(
"nvinferplugin_headers",
"nvinferplugin_lib",
"@cuda//:cudart",
- "@cudnn",
],
)
diff --git a/third_party/tensorrt/local/BUILD b/third_party/tensorrt/local/BUILD
index 9cbe98a41e..5136563521 100644
--- a/third_party/tensorrt/local/BUILD
+++ b/third_party/tensorrt/local/BUILD
@@ -112,7 +112,6 @@ cc_library(
"nvinfer_headers",
"nvinfer_lib",
"@cuda//:cudart",
- "@cudnn",
],
)
@@ -366,7 +365,6 @@ cc_library(
deps = [
"nvinfer",
"@cuda//:cudart",
- "@cudnn",
],
alwayslink = True,
)
From f9cf75ae8b7c91d88195b182fda784fb6fcce792 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Fri, 5 Apr 2024 17:20:35 -0700
Subject: [PATCH 38/69] chore: fix
---
third_party/tensorrt/local/BUILD | 16 ++++------------
1 file changed, 4 insertions(+), 12 deletions(-)
diff --git a/third_party/tensorrt/local/BUILD b/third_party/tensorrt/local/BUILD
index 5136563521..c317e16688 100644
--- a/third_party/tensorrt/local/BUILD
+++ b/third_party/tensorrt/local/BUILD
@@ -29,9 +29,7 @@ config_setting(
cc_library(
name = "nvinfer_headers",
hdrs = select({
- ":aarch64_linux": [
- "include/aarch64-linux-gnu/NvUtils.h",
- ] + glob(
+ ":aarch64_linux": glob(
[
"include/aarch64-linux-gnu/NvInfer*.h",
],
@@ -40,9 +38,7 @@ cc_library(
"include/aarch64-linux-gnu/NvInferPluginUtils.h",
],
),
- ":ci_rhel_x86_64_linux": [
- "include/NvUtils.h",
- ] + glob(
+ ":ci_rhel_x86_64_linux": glob(
[
"include/NvInfer*.h",
],
@@ -51,9 +47,7 @@ cc_library(
"include/NvInferPluginUtils.h",
],
),
- ":windows": [
- "include/NvUtils.h",
- ] + glob(
+ ":windows": glob(
[
"include/NvInfer*.h",
],
@@ -62,9 +56,7 @@ cc_library(
"include/NvInferPluginUtils.h",
],
),
- "//conditions:default": [
- "include/x86_64-linux-gnu/NvUtils.h",
- ] + glob(
+ "//conditions:default": glob(
[
"include/x86_64-linux-gnu/NvInfer*.h",
],
From 33ba8b2c619b57f151d6eba74dfc885f8af03d2a Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Mon, 8 Apr 2024 14:27:27 -0700
Subject: [PATCH 39/69] chore: updates
---
packaging/pre_build_script.sh | 6 +++---
.../WORKSPACE.x86_64.release.rhel.tmpl | 13 +++++--------
2 files changed, 8 insertions(+), 11 deletions(-)
diff --git a/packaging/pre_build_script.sh b/packaging/pre_build_script.sh
index 41fb47ff79..5905e44d2e 100755
--- a/packaging/pre_build_script.sh
+++ b/packaging/pre_build_script.sh
@@ -4,9 +4,9 @@
python3 -m pip install pyyaml
yum install -y ninja-build gettext
TRT_VERSION=$(python3 -c "import versions; versions.tensorrt_version()")
-yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel9/x86_64/cuda-rhel9.repo
-yum check-update
-yum install tensorrt-${TRT_VERSION}
+wget -P /opt/torch-tensorrt-builds/ https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.0.0/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz
+tar -xvzf /opt/torch-tensorrt-builds/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz -C /opt/torch-tensorrt-builds/
+export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
wget https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 \
&& mv bazelisk-linux-amd64 /usr/bin/bazel \
&& chmod +x /usr/bin/bazel
diff --git a/toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl b/toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl
index 4a37bb6091..cad54b1707 100644
--- a/toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl
+++ b/toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl
@@ -68,14 +68,11 @@ http_archive(
urls = ["https://download.pytorch.org/libtorch/test/cu121/libtorch-shared-with-deps-2.3.0%2Bcu121.zip"],
)
-####################################################################################
-# Locally installed dependencies (use in cases of custom dependencies or aarch64)
-####################################################################################
-
-new_local_repository(
- name = "tensorrt",
- path = "/usr/",
- build_file = "@//third_party/tensorrt/local:BUILD"
+http_archive(
+ name = "tensorrt",
+ urls = ["file:////opt/torch-tensorrt-builds/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz",],
+ build_file = "@//third_party/tensorrt/archive:BUILD",
+ strip_prefix = "TensorRT-10.0.0.6"
)
# #########################################################################
From 923377c86e5a664ac13c992188c91748fcc44b07 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Mon, 8 Apr 2024 21:44:50 -0700
Subject: [PATCH 40/69] chore: update post-build script
---
.github/workflows/build-test.yml | 8 +++++++-
packaging/post_build_script.sh | 1 +
2 files changed, 8 insertions(+), 1 deletion(-)
diff --git a/.github/workflows/build-test.yml b/.github/workflows/build-test.yml
index 5573bb8d28..febc475c09 100644
--- a/.github/workflows/build-test.yml
+++ b/.github/workflows/build-test.yml
@@ -36,7 +36,7 @@ jobs:
- repository: pytorch/tensorrt
pre-script: packaging/pre_build_script.sh
env-var-script: packaging/env_vars.txt
- post-script: ""
+ post-script: packaging/post_build_script.sh
smoke-test-script: ""
package-name: torch_tensorrt
name: Build torch-tensorrt whl package
@@ -64,6 +64,7 @@ jobs:
- repository: pytorch/tensorrt
package-name: torch_tensorrt
pre-script: packaging/pre_build_script.sh
+ post-script: packaging/post_build_script.sh
uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
with:
job-name: tests-py-torchscript-fe
@@ -99,6 +100,7 @@ jobs:
- repository: pytorch/tensorrt
package-name: torch_tensorrt
pre-script: packaging/pre_build_script.sh
+ post-script: packaging/post_build_script.sh
uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
with:
job-name: tests-py-dynamo-converters
@@ -126,6 +128,7 @@ jobs:
- repository: pytorch/tensorrt
package-name: torch_tensorrt
pre-script: packaging/pre_build_script.sh
+ post-script: packaging/post_build_script.sh
uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
with:
job-name: tests-py-dynamo-fe
@@ -154,6 +157,7 @@ jobs:
- repository: pytorch/tensorrt
package-name: torch_tensorrt
pre-script: packaging/pre_build_script.sh
+ post-script: packaging/post_build_script.sh
uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
with:
job-name: tests-py-dynamo-serde
@@ -181,6 +185,7 @@ jobs:
- repository: pytorch/tensorrt
package-name: torch_tensorrt
pre-script: packaging/pre_build_script.sh
+ post-script: packaging/post_build_script.sh
uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
with:
job-name: tests-py-torch-compile-be
@@ -209,6 +214,7 @@ jobs:
- repository: pytorch/tensorrt
package-name: torch_tensorrt
pre-script: packaging/pre_build_script.sh
+ post-script: packaging/post_build_script.sh
uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
with:
job-name: tests-py-dynamo-core
diff --git a/packaging/post_build_script.sh b/packaging/post_build_script.sh
index e69de29bb2..c42774070a 100644
--- a/packaging/post_build_script.sh
+++ b/packaging/post_build_script.sh
@@ -0,0 +1 @@
+python -m pip install /opt/torch-tensorrt-builds/TensorRT-10.0.0.6/python/tensorrt-10.0.0b6-cp38-none-linux_x86_64.whl
\ No newline at end of file
From 89f04dbbaf50075e7f4c0e5f4fe86f04ada43f2c Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Mon, 8 Apr 2024 22:07:54 -0700
Subject: [PATCH 41/69] chore: remove trt dep
---
pyproject.toml | 2 --
1 file changed, 2 deletions(-)
diff --git a/pyproject.toml b/pyproject.toml
index dd4cada7f2..acad8561c6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,6 @@ requires = [
"cffi>=1.15.1",
"typing-extensions>=4.7.0",
"future>=0.18.3",
- "tensorrt==10.0.0b6",
"torch==2.3.0",
"pybind11==2.6.2",
"numpy",
@@ -42,7 +41,6 @@ requires-python = ">=3.8"
keywords = ["pytorch", "torch", "tensorrt", "trt", "ai", "artificial intelligence", "ml", "machine learning", "dl", "deep learning", "compiler", "dynamo", "torchscript", "inference"]
dependencies = [
"torch==2.3.0",
- "tensorrt==10.0.0b6",
"packaging>=23",
"numpy",
"typing-extensions>=4.7.0",
From 7620acca4d2d73e01d3edf9af9bf51dc29075d5b Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Mon, 8 Apr 2024 22:20:14 -0700
Subject: [PATCH 42/69] chore: updates
---
packaging/pre_build_script.sh | 2 +-
py/requirements.txt | 7 ++++++-
2 files changed, 7 insertions(+), 2 deletions(-)
diff --git a/packaging/pre_build_script.sh b/packaging/pre_build_script.sh
index 5905e44d2e..3243afd69f 100755
--- a/packaging/pre_build_script.sh
+++ b/packaging/pre_build_script.sh
@@ -10,7 +10,7 @@ export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBR
wget https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 \
&& mv bazelisk-linux-amd64 /usr/bin/bazel \
&& chmod +x /usr/bin/bazel
-
+python -m pip install -r py/requirements.txt
export TORCH_BUILD_NUMBER=$(python -c "import torch, urllib.parse as ul; print(ul.quote_plus(torch.__version__))")
cat toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl | envsubst > WORKSPACE
diff --git a/py/requirements.txt b/py/requirements.txt
index 19c39c8647..360fdbb01d 100644
--- a/py/requirements.txt
+++ b/py/requirements.txt
@@ -5,5 +5,10 @@ pybind11==2.6.2
torch==2.3.0
torchvision==0.18.0
--extra-index-url https://pypi.ngc.nvidia.com
-tensorrt>=10.0
pyyaml
+# Extras
+ninja>=1.11.0
+cffi>=1.15.1
+pybind11==2.6.2
+wheel>=0.40.0
+future>=0.18.3
From 62332fb4236442ca8e08f8323cdad10c3bfec9c0 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 9 Apr 2024 00:17:52 -0700
Subject: [PATCH 43/69] chore: set ld_library path in post script
---
packaging/post_build_script.sh | 1 +
1 file changed, 1 insertion(+)
diff --git a/packaging/post_build_script.sh b/packaging/post_build_script.sh
index c42774070a..1ee8f77467 100644
--- a/packaging/post_build_script.sh
+++ b/packaging/post_build_script.sh
@@ -1 +1,2 @@
+export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
python -m pip install /opt/torch-tensorrt-builds/TensorRT-10.0.0.6/python/tensorrt-10.0.0b6-cp38-none-linux_x86_64.whl
\ No newline at end of file
From 96a8bf61e4027634d8bd85f3e6a4411816912c81 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 9 Apr 2024 13:20:29 -0700
Subject: [PATCH 44/69] chore: updates
---
packaging/pre_build_script.sh | 2 +-
py/torch_tensorrt/__init__.py | 5 ++---
2 files changed, 3 insertions(+), 4 deletions(-)
diff --git a/packaging/pre_build_script.sh b/packaging/pre_build_script.sh
index 3243afd69f..c518992ab6 100755
--- a/packaging/pre_build_script.sh
+++ b/packaging/pre_build_script.sh
@@ -3,7 +3,7 @@
# Install dependencies
python3 -m pip install pyyaml
yum install -y ninja-build gettext
-TRT_VERSION=$(python3 -c "import versions; versions.tensorrt_version()")
+TRT_VERSION=10.0.0.6 #$(python3 -c "import versions; versions.tensorrt_version()")
wget -P /opt/torch-tensorrt-builds/ https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.0.0/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz
tar -xvzf /opt/torch-tensorrt-builds/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz -C /opt/torch-tensorrt-builds/
export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
diff --git a/py/torch_tensorrt/__init__.py b/py/torch_tensorrt/__init__.py
index b9d2af39c5..f97de95d52 100644
--- a/py/torch_tensorrt/__init__.py
+++ b/py/torch_tensorrt/__init__.py
@@ -60,7 +60,7 @@ def _find_lib(name: str, paths: List[str]) -> str:
elif sys.platform.startswith("linux"):
LINUX_PATHS = ["/usr/local/cuda-12.1/lib64", "/usr/lib", "/usr/lib64"]
-
+ print("==== BOOL CHECK", "LD_LIBRARY_PATH" in os.environ)
if "LD_LIBRARY_PATH" in os.environ:
LINUX_PATHS += os.environ["LD_LIBRARY_PATH"].split(os.path.pathsep)
@@ -92,9 +92,8 @@ def _find_lib(name: str, paths: List[str]) -> str:
from torch_tensorrt.runtime import * # noqa: F403
if version.parse(sanitized_torch_version()) >= version.parse("2.1.dev"):
- from torch_tensorrt.dynamo import backend # noqa: F401
-
from torch_tensorrt import dynamo # noqa: F401
+ from torch_tensorrt.dynamo import backend # noqa: F401
def _register_with_torch() -> None:
From 041f6a38e834bb94004c0a4910f08794a3024992 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 9 Apr 2024 13:48:58 -0700
Subject: [PATCH 45/69] chore: updates
---
packaging/post_build_script.sh | 1 +
py/torch_tensorrt/__init__.py | 7 ++++++-
2 files changed, 7 insertions(+), 1 deletion(-)
diff --git a/packaging/post_build_script.sh b/packaging/post_build_script.sh
index 1ee8f77467..0d5d045cd0 100644
--- a/packaging/post_build_script.sh
+++ b/packaging/post_build_script.sh
@@ -1,2 +1,3 @@
export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
+echo $LD_LIBRARY_PATH
python -m pip install /opt/torch-tensorrt-builds/TensorRT-10.0.0.6/python/tensorrt-10.0.0b6-cp38-none-linux_x86_64.whl
\ No newline at end of file
diff --git a/py/torch_tensorrt/__init__.py b/py/torch_tensorrt/__init__.py
index f97de95d52..01f310637b 100644
--- a/py/torch_tensorrt/__init__.py
+++ b/py/torch_tensorrt/__init__.py
@@ -60,7 +60,12 @@ def _find_lib(name: str, paths: List[str]) -> str:
elif sys.platform.startswith("linux"):
LINUX_PATHS = ["/usr/local/cuda-12.1/lib64", "/usr/lib", "/usr/lib64"]
- print("==== BOOL CHECK", "LD_LIBRARY_PATH" in os.environ)
+ print(
+ "==== BOOL CHECK",
+ "LD_LIBRARY_PATH" in os.environ,
+ " path: ",
+ os.environ["LD_LIBRARY_PATH"],
+ )
if "LD_LIBRARY_PATH" in os.environ:
LINUX_PATHS += os.environ["LD_LIBRARY_PATH"].split(os.path.pathsep)
From 83e9a0bc4f7e4281da00ea56df03f965883e285a Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 9 Apr 2024 15:31:31 -0700
Subject: [PATCH 46/69] chore: disable smoke test
---
.github/workflows/build-test.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/.github/workflows/build-test.yml b/.github/workflows/build-test.yml
index febc475c09..b05cb1e276 100644
--- a/.github/workflows/build-test.yml
+++ b/.github/workflows/build-test.yml
@@ -37,7 +37,7 @@ jobs:
pre-script: packaging/pre_build_script.sh
env-var-script: packaging/env_vars.txt
post-script: packaging/post_build_script.sh
- smoke-test-script: ""
+ smoke-test-script: packaging/smoke_test_script.sh
package-name: torch_tensorrt
name: Build torch-tensorrt whl package
uses: pytorch/test-infra/.github/workflows/build_wheels_linux.yml@main
From e8529b057e7658654114e31a2e5fecca7ec0faf9 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 9 Apr 2024 15:51:56 -0700
Subject: [PATCH 47/69] chore: updates
---
.github/workflows/build-test.yml | 16 ++++++++--------
1 file changed, 8 insertions(+), 8 deletions(-)
diff --git a/.github/workflows/build-test.yml b/.github/workflows/build-test.yml
index b05cb1e276..330006f76e 100644
--- a/.github/workflows/build-test.yml
+++ b/.github/workflows/build-test.yml
@@ -15,7 +15,7 @@ on:
jobs:
generate-matrix:
- uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
+ uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@release/2.3
with:
package-type: wheel
os: linux
@@ -40,7 +40,7 @@ jobs:
smoke-test-script: packaging/smoke_test_script.sh
package-name: torch_tensorrt
name: Build torch-tensorrt whl package
- uses: pytorch/test-infra/.github/workflows/build_wheels_linux.yml@main
+ uses: pytorch/test-infra/.github/workflows/build_wheels_linux.yml@release/2.3
with:
repository: ${{ matrix.repository }}
ref: ""
@@ -65,7 +65,7 @@ jobs:
package-name: torch_tensorrt
pre-script: packaging/pre_build_script.sh
post-script: packaging/post_build_script.sh
- uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
+ uses: pytorch/tensorrt/.github/workflows/linux-test.yml@release/2.3
with:
job-name: tests-py-torchscript-fe
repository: "pytorch/tensorrt"
@@ -101,7 +101,7 @@ jobs:
package-name: torch_tensorrt
pre-script: packaging/pre_build_script.sh
post-script: packaging/post_build_script.sh
- uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
+ uses: pytorch/tensorrt/.github/workflows/linux-test.yml@release/2.3
with:
job-name: tests-py-dynamo-converters
repository: "pytorch/tensorrt"
@@ -129,7 +129,7 @@ jobs:
package-name: torch_tensorrt
pre-script: packaging/pre_build_script.sh
post-script: packaging/post_build_script.sh
- uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
+ uses: pytorch/tensorrt/.github/workflows/linux-test.yml@release/2.3
with:
job-name: tests-py-dynamo-fe
repository: "pytorch/tensorrt"
@@ -158,7 +158,7 @@ jobs:
package-name: torch_tensorrt
pre-script: packaging/pre_build_script.sh
post-script: packaging/post_build_script.sh
- uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
+ uses: pytorch/tensorrt/.github/workflows/linux-test.yml@release/2.3
with:
job-name: tests-py-dynamo-serde
repository: "pytorch/tensorrt"
@@ -186,7 +186,7 @@ jobs:
package-name: torch_tensorrt
pre-script: packaging/pre_build_script.sh
post-script: packaging/post_build_script.sh
- uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
+ uses: pytorch/tensorrt/.github/workflows/linux-test.yml@release/2.3
with:
job-name: tests-py-torch-compile-be
repository: "pytorch/tensorrt"
@@ -215,7 +215,7 @@ jobs:
package-name: torch_tensorrt
pre-script: packaging/pre_build_script.sh
post-script: packaging/post_build_script.sh
- uses: pytorch/tensorrt/.github/workflows/linux-test.yml@main
+ uses: pytorch/tensorrt/.github/workflows/linux-test.yml@release/2.3
with:
job-name: tests-py-dynamo-core
repository: "pytorch/tensorrt"
From 1357112a12995d25f9c6865544e8a71434d8a945 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Tue, 9 Apr 2024 16:41:16 -0700
Subject: [PATCH 48/69] chore: updates
---
packaging/smoke_test_script.sh | 0
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 packaging/smoke_test_script.sh
diff --git a/packaging/smoke_test_script.sh b/packaging/smoke_test_script.sh
new file mode 100644
index 0000000000..e69de29bb2
From 608a6d274abea87d0939bb0917ae7e42c9efb605 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 10 Apr 2024 08:20:39 -0700
Subject: [PATCH 49/69] chore: updates
---
.github/scripts/install-torch-tensorrt.sh | 12 +++++++++++-
.github/workflows/build-test.yml | 3 ++-
packaging/post_build_script.sh | 3 ---
packaging/smoke_test_script.sh | 6 ++++++
py/requirements.txt | 8 +-------
5 files changed, 20 insertions(+), 12 deletions(-)
diff --git a/.github/scripts/install-torch-tensorrt.sh b/.github/scripts/install-torch-tensorrt.sh
index 48c6c90cbf..10d5023557 100644
--- a/.github/scripts/install-torch-tensorrt.sh
+++ b/.github/scripts/install-torch-tensorrt.sh
@@ -5,6 +5,16 @@ source ${BUILD_ENV_FILE}
${CONDA_RUN} ${PIP_INSTALL_TORCH} torchvision
${CONDA_RUN} python -m pip install pyyaml mpmath==1.3.0
export TRT_VERSION=$(${CONDA_RUN} python -c "import versions; versions.tensorrt_version()")
-${CONDA_RUN} python -m pip install /opt/torch-tensorrt-builds/torch_tensorrt*+${CU_VERSION}*.whl tensorrt~=${TRT_VERSION} tensorrt-bindings~=${TRT_VERSION} --extra-index-url=https://pypi.ngc.nvidia.com
+
+# Print PYTHON_VERSION
+printf "PYTHON_VERSION is equal to %s" ${PYTHON_VERSION//./}
+
+# Install TensorRT manually
+wget -P /opt/torch-tensorrt-builds/ https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.0.0/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz
+tar -xvzf /opt/torch-tensorrt-builds/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz -C /opt/torch-tensorrt-builds/
+python -m pip install /opt/torch-tensorrt-builds/TensorRT-10.0.0.6/python/tensorrt-10.0.0b6-cp${PYTHON_VERSION//./}-none-linux_x86_64.whl
+
+# Install Torch-TensorRT
+${CONDA_RUN} python -m pip install /opt/torch-tensorrt-builds/torch_tensorrt*+${CU_VERSION}*.whl
echo -e "Running test script";
diff --git a/.github/workflows/build-test.yml b/.github/workflows/build-test.yml
index 330006f76e..89b41df3a3 100644
--- a/.github/workflows/build-test.yml
+++ b/.github/workflows/build-test.yml
@@ -79,7 +79,8 @@ jobs:
export LD_LIBRARY_PATH=/usr/lib64:$LD_LIBRARY_PATH
pushd .
cd tests/modules
- ${CONDA_RUN} python -m pip install --pre -r requirements.txt --use-deprecated=legacy-resolver
+ # Don't use requirements.txt here as it contains tensorrt and torch which should have been installed by now.
+ ${CONDA_RUN} python -m pip install numpy packaging pyyaml pybind11==2.6.2
${CONDA_RUN} python hub.py
popd
pushd .
diff --git a/packaging/post_build_script.sh b/packaging/post_build_script.sh
index 0d5d045cd0..e69de29bb2 100644
--- a/packaging/post_build_script.sh
+++ b/packaging/post_build_script.sh
@@ -1,3 +0,0 @@
-export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
-echo $LD_LIBRARY_PATH
-python -m pip install /opt/torch-tensorrt-builds/TensorRT-10.0.0.6/python/tensorrt-10.0.0b6-cp38-none-linux_x86_64.whl
\ No newline at end of file
diff --git a/packaging/smoke_test_script.sh b/packaging/smoke_test_script.sh
index e69de29bb2..d3bed3249e 100644
--- a/packaging/smoke_test_script.sh
+++ b/packaging/smoke_test_script.sh
@@ -0,0 +1,6 @@
+# Smoke test is intentionally disabled.
+# The issue was smoke test installs the built torch_tensorrt wheel file and checks `import torch_tensorrt; print(torch_tensorrt.__version__)`
+# Since tensorrt cannot be pip installable in CI, the smoke test will fail.
+# One way we tried to handle it is manually install tensorrt wheel while by extracting from the tarball.
+# However, the TensorRT-10.0.0.6/lib path doesn't seem to show up in LD_LIBRARY_PATH even if we explicitly set it.
+# TODO: Implement a custom smoke_test script to verify torch_tensorrt installation.
\ No newline at end of file
diff --git a/py/requirements.txt b/py/requirements.txt
index 360fdbb01d..e3356a8c59 100644
--- a/py/requirements.txt
+++ b/py/requirements.txt
@@ -5,10 +5,4 @@ pybind11==2.6.2
torch==2.3.0
torchvision==0.18.0
--extra-index-url https://pypi.ngc.nvidia.com
-pyyaml
-# Extras
-ninja>=1.11.0
-cffi>=1.15.1
-pybind11==2.6.2
-wheel>=0.40.0
-future>=0.18.3
+pyyaml
\ No newline at end of file
From 1b34b3246da47b4a621b8d818b8e57d86a9679d0 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 10 Apr 2024 08:56:19 -0700
Subject: [PATCH 50/69] chore: updates
---
.github/workflows/build-test.yml | 6 ++++++
1 file changed, 6 insertions(+)
diff --git a/.github/workflows/build-test.yml b/.github/workflows/build-test.yml
index 89b41df3a3..278a4aecf3 100644
--- a/.github/workflows/build-test.yml
+++ b/.github/workflows/build-test.yml
@@ -77,6 +77,7 @@ jobs:
script: |
export USE_HOST_DEPS=1
export LD_LIBRARY_PATH=/usr/lib64:$LD_LIBRARY_PATH
+ export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
pushd .
cd tests/modules
# Don't use requirements.txt here as it contains tensorrt and torch which should have been installed by now.
@@ -113,6 +114,7 @@ jobs:
pre-script: ${{ matrix.pre-script }}
script: |
export USE_HOST_DEPS=1
+ export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
pushd .
cd tests/py/dynamo
${CONDA_RUN} python -m pip install --pre pytest-xdist timm transformers parameterized expecttest==0.1.6 --use-deprecated=legacy-resolver
@@ -141,6 +143,7 @@ jobs:
pre-script: ${{ matrix.pre-script }}
script: |
export USE_HOST_DEPS=1
+ export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
pushd .
cd tests/py/dynamo
${CONDA_RUN} python -m pip install --pre pytest timm transformers parameterized expecttest==0.1.6 --use-deprecated=legacy-resolver
@@ -170,6 +173,7 @@ jobs:
pre-script: ${{ matrix.pre-script }}
script: |
export USE_HOST_DEPS=1
+ export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
pushd .
cd tests/py/dynamo
${CONDA_RUN} python -m pip install --pre pytest timm transformers parameterized expecttest==0.1.6 --use-deprecated=legacy-resolver
@@ -198,6 +202,7 @@ jobs:
pre-script: ${{ matrix.pre-script }}
script: |
export USE_HOST_DEPS=1
+ export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
pushd .
cd tests/py/dynamo
${CONDA_RUN} python -m pip install --pre pytest-xdist timm transformers parameterized expecttest==0.1.6 --use-deprecated=legacy-resolver
@@ -227,6 +232,7 @@ jobs:
pre-script: ${{ matrix.pre-script }}
script: |
export USE_HOST_DEPS=1
+ export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
pushd .
cd tests/py/dynamo
${CONDA_RUN} python -m pip install --pre pytest-xdist timm transformers parameterized expecttest==0.1.6 --use-deprecated=legacy-resolver
From 89cb55a6af0aeacf4063ea5510c3049a5763e401 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 10 Apr 2024 11:45:13 -0700
Subject: [PATCH 51/69] chore: updates
---
.github/workflows/build-test.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/.github/workflows/build-test.yml b/.github/workflows/build-test.yml
index 278a4aecf3..c32d42c78e 100644
--- a/.github/workflows/build-test.yml
+++ b/.github/workflows/build-test.yml
@@ -81,7 +81,7 @@ jobs:
pushd .
cd tests/modules
# Don't use requirements.txt here as it contains tensorrt and torch which should have been installed by now.
- ${CONDA_RUN} python -m pip install numpy packaging pyyaml pybind11==2.6.2
+ ${CONDA_RUN} python -m pip install numpy packaging pyyaml transformers pybind11==2.6.2
${CONDA_RUN} python hub.py
popd
pushd .
From 4323e36461b4caeb90c9eca47e7f6887e92bed15 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 10 Apr 2024 12:41:49 -0700
Subject: [PATCH 52/69] chore: updates
---
.github/workflows/build-test.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/.github/workflows/build-test.yml b/.github/workflows/build-test.yml
index c32d42c78e..1b1fa72f2f 100644
--- a/.github/workflows/build-test.yml
+++ b/.github/workflows/build-test.yml
@@ -81,7 +81,7 @@ jobs:
pushd .
cd tests/modules
# Don't use requirements.txt here as it contains tensorrt and torch which should have been installed by now.
- ${CONDA_RUN} python -m pip install numpy packaging pyyaml transformers pybind11==2.6.2
+ ${CONDA_RUN} python -m pip install numpy packaging pyyaml transformers timm pybind11==2.6.2
${CONDA_RUN} python hub.py
popd
pushd .
From 60b3e51bcc4e6fc93bc84909fe8cfb27617d54af Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Wed, 10 Apr 2024 15:07:23 -0700
Subject: [PATCH 53/69] chore: update hw_compat
---
tests/py/dynamo/runtime/gen_hw_compat.py | 33 ++++++++++++++++++++++
tests/py/dynamo/runtime/test_hw_compat.py | 6 ++--
tests/py/ts/models/hw_compat.ts | Bin 386982 -> 110354 bytes
3 files changed, 35 insertions(+), 4 deletions(-)
create mode 100644 tests/py/dynamo/runtime/gen_hw_compat.py
diff --git a/tests/py/dynamo/runtime/gen_hw_compat.py b/tests/py/dynamo/runtime/gen_hw_compat.py
new file mode 100644
index 0000000000..e279015aa2
--- /dev/null
+++ b/tests/py/dynamo/runtime/gen_hw_compat.py
@@ -0,0 +1,33 @@
+# This script is used to generate hw_compat.ts file that's used in test_hw_compat.py
+# Generate the model on a different hardware compared to the one you're testing on to
+# verify HW compatibility feature.
+
+import torch
+import torch_tensorrt
+
+
+class MyModule(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.conv = torch.nn.Conv2d(3, 16, 3, stride=1, bias=True)
+ self.relu = torch.nn.ReLU()
+
+ def forward(self, x):
+ out = self.conv(x)
+ out = self.relu(out)
+ return out
+
+
+model = MyModule().eval().cuda()
+inputs = torch.randn((1, 3, 224, 224)).to("cuda")
+
+trt_gm = torch_tensorrt.compile(
+ model,
+ ir="dynamo",
+ inputs=inputs,
+ min_block_size=1,
+ hardware_compatible=True,
+ version_compatible=True,
+)
+trt_script_model = torch.jit.trace(trt_gm, inputs)
+torch.jit.save(trt_script_model, "hw_compat.ts")
diff --git a/tests/py/dynamo/runtime/test_hw_compat.py b/tests/py/dynamo/runtime/test_hw_compat.py
index 4218cc7de0..aa238c7a4a 100644
--- a/tests/py/dynamo/runtime/test_hw_compat.py
+++ b/tests/py/dynamo/runtime/test_hw_compat.py
@@ -59,16 +59,14 @@ def forward(self, x):
"Detected incorrect ABI version, please update this test case",
)
def test_hw_compat_3080_build(self):
- inputs = [torch.randn(5, 7).cuda()]
+ inputs = [torch.randn(1, 3, 224, 224).cuda()]
cwd = os.getcwd()
os.chdir(os.path.dirname(os.path.realpath(__file__)))
model = torch.jit.load("../../ts/models/hw_compat.ts").cuda()
out = model(*inputs)
self.assertTrue(
- isinstance(out, tuple)
- and len(out) == 1
- and isinstance(out[0], torch.Tensor),
+ len(out) == 1 and isinstance(out, torch.Tensor),
"Invalid output detected",
)
os.chdir(cwd)
diff --git a/tests/py/ts/models/hw_compat.ts b/tests/py/ts/models/hw_compat.ts
index ab43e5e040ef3850c8e0b879a973dc759565830e..27726233f28119dc4c1b52793e03efc7da7fc3da 100644
GIT binary patch
literal 110354
zcmeFaS+DZkw&%A`Ri#qd%0*O)a-EwaRS9Whx&^+R(;YS#Y=Z$KMX$hs0n@Xv0Vz`I
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From 05627cd8aa329739e8655eed5637fecb5130c673 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Fri, 12 Apr 2024 14:45:50 -0700
Subject: [PATCH 54/69] chore: updates
---
.../conversion/impl/normalization/ops.py | 14 +++++-
.../dynamo/conversion/test_layer_norm_aten.py | 49 -------------------
2 files changed, 13 insertions(+), 50 deletions(-)
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py b/py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py
index f45d067349..bbe566d0b7 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py
@@ -452,6 +452,7 @@ def pdist(
p: float = 2,
) -> Union[TRTTensor, Sequence[TRTTensor]]:
shape = input.shape
+ # Extend input from shape [N, D] to [N, 1, D]
extend_input = impl.shuffle.reshape(
ctx,
target,
@@ -460,7 +461,18 @@ def pdist(
input,
shape=shape[0:1] + (1,) + shape[1:],
)
- x = impl.elementwise.sub(ctx, target, source_ir, f"{name}_sub", extend_input, input)
+ # Expand the input from [N, 1, D] to [N, N, D]
+ x = impl.slice.expand(
+ ctx,
+ target,
+ source_ir,
+ f"{name}_sub",
+ extend_input,
+ (shape[0], shape[0]) + shape[1:],
+ )
+ # Subtract the expanded input from original input. Result shape = [N, N, D]
+ # This matrix has the distance of each sample to every other sample and hence the shape is [N, N, D]
+ x = impl.elementwise.sub(ctx, target, source_ir, f"{name}_sub", x, input)
if p == 0:
# norm = torch.sum(x!=0, dim=2)
diff --git a/tests/py/dynamo/conversion/test_layer_norm_aten.py b/tests/py/dynamo/conversion/test_layer_norm_aten.py
index 8013768214..7f43234211 100644
--- a/tests/py/dynamo/conversion/test_layer_norm_aten.py
+++ b/tests/py/dynamo/conversion/test_layer_norm_aten.py
@@ -24,31 +24,6 @@ def forward(self, x):
inputs,
)
- def test_layernorm_with_dynamic_shape(self):
- class LayerNorm(torch.nn.Module):
- def forward(self, x):
- return torch.ops.aten.layer_norm.default(
- x,
- torch.tensor([3, 224, 224]),
- torch.ones((3, 224, 224)),
- torch.zeros((3, 224, 224)),
- 1e-05,
- True,
- )
-
- input_specs = [
- Input(
- shape=(-1, 3, 224, 224),
- dtype=torch.float32,
- shape_ranges=[((1, 3, 224, 224), (1, 3, 224, 224), (2, 3, 224, 224))],
- ),
- ]
-
- self.run_test_with_dynamic_shape(
- LayerNorm(),
- input_specs,
- )
-
class TestNativeLayerNormConverter(DispatchTestCase):
def test_layer_norm(self):
@@ -68,30 +43,6 @@ def forward(self, x):
inputs,
)
- def test_layernorm_with_dynamic_shape(self):
- class LayerNorm(torch.nn.Module):
- def forward(self, x):
- return torch.ops.aten.native_layer_norm.default(
- x,
- torch.tensor([3, 224, 224]),
- torch.ones((3, 224, 224)),
- torch.zeros((3, 224, 224)),
- 1e-05,
- )[0]
-
- input_specs = [
- Input(
- shape=(-1, 3, 224, 224),
- dtype=torch.float32,
- shape_ranges=[((1, 3, 224, 224), (1, 3, 224, 224), (2, 3, 224, 224))],
- ),
- ]
-
- self.run_test_with_dynamic_shape(
- LayerNorm(),
- input_specs,
- )
-
if __name__ == "__main__":
run_tests()
From d16585f0be1c5085739d7c09b29acb81d359e921 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Fri, 12 Apr 2024 16:26:37 -0700
Subject: [PATCH 55/69] chore: update streams
---
core/runtime/execute_engine.cpp | 3 +--
.../dynamo/runtime/_PythonTorchTensorRTModule.py | 4 ++--
2 files changed, 3 insertions(+), 4 deletions(-)
diff --git a/core/runtime/execute_engine.cpp b/core/runtime/execute_engine.cpp
index 5ff163fbfb..165328f16c 100644
--- a/core/runtime/execute_engine.cpp
+++ b/core/runtime/execute_engine.cpp
@@ -178,8 +178,7 @@ std::vector<at::Tensor> execute_engine(std::vector<at::Tensor> inputs, c10::intr
enqueue_profiler_guard =
std::make_unique<torch::autograd::profiler::RecordProfile>(compiled_engine->enqueue_profile_path);
}
- c10::cuda::CUDAStream stream = c10::cuda::getCurrentCUDAStream(inputs[0].device().index());
-
+ c10::cuda::CUDAStream stream = c10::cuda::getStreamFromPool(/*isHighPriority=*/true, inputs[0].device().index());
// nvinfer1::IExecutionContext::enqueue is not thread safe and we need a mutex for it.
std::unique_lock<std::mutex> lock(compiled_engine->mu);
compiled_engine->exec_ctx->enqueueV3(stream);
diff --git a/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py b/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
index 82f5817b01..a296deaba9 100644
--- a/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
+++ b/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
@@ -59,7 +59,7 @@ def _initialize(self) -> None:
runtime = trt.Runtime(logger)
self.engine = runtime.deserialize_cuda_engine(self.engine)
self.context = self.engine.create_execution_context()
-
+ self.stream = torch.cuda.Stream(torch.cuda.current_device())
# Indices of inputs/outputs in the trt engine bindings, in the order
# as they are in the original PyTorch model.
@@ -286,7 +286,7 @@ def forward(self, *inputs: torch.Tensor) -> torch.Tensor | Tuple[torch.Tensor, .
if self.profiling_enabled
else nullcontext()
):
- self.context.execute_async_v3(torch.cuda.current_stream().cuda_stream)
+ self.context.execute_async_v3(self.stream.cuda_stream)
if len(outputs) == 1:
return outputs[0]
From 3d149ef78a4e39c9a249b66790721d0e02a92e7d Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Fri, 12 Apr 2024 21:30:42 -0700
Subject: [PATCH 56/69] chore: updates
---
py/torch_tensorrt/dynamo/conversion/impl/shape.py | 13 +++++++++++++
1 file changed, 13 insertions(+)
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/shape.py b/py/torch_tensorrt/dynamo/conversion/impl/shape.py
index 0727eecef7..e19f44ab3a 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/shape.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/shape.py
@@ -9,6 +9,7 @@
from torch_tensorrt.dynamo._SourceIR import SourceIR
from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
from torch_tensorrt.dynamo.conversion.converter_utils import (
+ cast_trt_tensor,
get_positive_dim,
get_trt_tensor,
)
@@ -38,6 +39,12 @@ def shape(
"""
shape_layer = ctx.net.add_shape(input_val)
input_shape = shape_layer.get_output(0)
+ input_shape = cast_trt_tensor(
+ ctx,
+ input_shape,
+ trt.int32,
+ name + "_shape_casted",
+ )
set_layer_name(shape_layer, target, name + "_shape", source_ir)
n_dims = len(input_val.shape)
@@ -82,6 +89,12 @@ def get_shape_with_dynamic_shape(
"""
# Ger real shape info for input_val
input_shape = ctx.net.add_shape(input_val).get_output(0)
+ input_shape = cast_trt_tensor(
+ ctx,
+ input_shape,
+ trt.int32,
+ name + "_int32_casted",
+ )
# input_shape.dtype is int64 in TRT 10.0
input_np_dtype = unified_dtype_converter(input_shape.dtype, Frameworks.NUMPY)
scale_layer = ctx.net.add_constant(
From 3addcae373f550603c904dd110c4d906dbf4bfb9 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Sat, 13 Apr 2024 01:07:35 -0700
Subject: [PATCH 57/69] chore: updates
---
.github/scripts/install-torch-tensorrt.sh | 7 ++-----
core/runtime/execute_engine.cpp | 2 +-
packaging/pre_build_script.sh | 6 +++---
3 files changed, 6 insertions(+), 9 deletions(-)
diff --git a/.github/scripts/install-torch-tensorrt.sh b/.github/scripts/install-torch-tensorrt.sh
index 10d5023557..b2b19b139d 100644
--- a/.github/scripts/install-torch-tensorrt.sh
+++ b/.github/scripts/install-torch-tensorrt.sh
@@ -6,12 +6,9 @@ ${CONDA_RUN} ${PIP_INSTALL_TORCH} torchvision
${CONDA_RUN} python -m pip install pyyaml mpmath==1.3.0
export TRT_VERSION=$(${CONDA_RUN} python -c "import versions; versions.tensorrt_version()")
-# Print PYTHON_VERSION
-printf "PYTHON_VERSION is equal to %s" ${PYTHON_VERSION//./}
-
# Install TensorRT manually
-wget -P /opt/torch-tensorrt-builds/ https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.0.0/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz
-tar -xvzf /opt/torch-tensorrt-builds/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz -C /opt/torch-tensorrt-builds/
+wget -q -P /opt/torch-tensorrt-builds/ https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.0.0/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz
+tar -xzf /opt/torch-tensorrt-builds/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz -C /opt/torch-tensorrt-builds/
python -m pip install /opt/torch-tensorrt-builds/TensorRT-10.0.0.6/python/tensorrt-10.0.0b6-cp${PYTHON_VERSION//./}-none-linux_x86_64.whl
# Install Torch-TensorRT
diff --git a/core/runtime/execute_engine.cpp b/core/runtime/execute_engine.cpp
index 165328f16c..a1ee30e994 100644
--- a/core/runtime/execute_engine.cpp
+++ b/core/runtime/execute_engine.cpp
@@ -178,7 +178,7 @@ std::vector<at::Tensor> execute_engine(std::vector<at::Tensor> inputs, c10::intr
enqueue_profiler_guard =
std::make_unique<torch::autograd::profiler::RecordProfile>(compiled_engine->enqueue_profile_path);
}
- c10::cuda::CUDAStream stream = c10::cuda::getStreamFromPool(/*isHighPriority=*/true, inputs[0].device().index());
+ c10::cuda::CUDAStream stream = c10::cuda::getCurrentCUDAStream(inputs[0].device().index());
// nvinfer1::IExecutionContext::enqueue is not thread safe and we need a mutex for it.
std::unique_lock<std::mutex> lock(compiled_engine->mu);
compiled_engine->exec_ctx->enqueueV3(stream);
diff --git a/packaging/pre_build_script.sh b/packaging/pre_build_script.sh
index c518992ab6..befb6e38d9 100755
--- a/packaging/pre_build_script.sh
+++ b/packaging/pre_build_script.sh
@@ -3,9 +3,9 @@
# Install dependencies
python3 -m pip install pyyaml
yum install -y ninja-build gettext
-TRT_VERSION=10.0.0.6 #$(python3 -c "import versions; versions.tensorrt_version()")
-wget -P /opt/torch-tensorrt-builds/ https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.0.0/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz
-tar -xvzf /opt/torch-tensorrt-builds/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz -C /opt/torch-tensorrt-builds/
+TRT_VERSION=$(python3 -c "import versions; versions.tensorrt_version()")
+wget -q -P /opt/torch-tensorrt-builds/ https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.0.0/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz
+tar -xzf /opt/torch-tensorrt-builds/TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz -C /opt/torch-tensorrt-builds/
export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
wget https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 \
&& mv bazelisk-linux-amd64 /usr/bin/bazel \
From b0e92d87979bf10d5a7b7ac356868c8f006f7787 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Mon, 15 Apr 2024 12:49:39 -0700
Subject: [PATCH 58/69] chore: update hw_compat.ts
---
tests/py/ts/models/hw_compat.ts | Bin 110354 -> 110418 bytes
1 file changed, 0 insertions(+), 0 deletions(-)
diff --git a/tests/py/ts/models/hw_compat.ts b/tests/py/ts/models/hw_compat.ts
index 27726233f28119dc4c1b52793e03efc7da7fc3da..ed71663c0c77a406307a3e7c23d331c1b44b7243 100644
GIT binary patch
delta 5077
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From d285d27d6384fa8101fcebe74136400d42effca8 Mon Sep 17 00:00:00 2001
From: Evan Li <zewenl@nvidia.com>
Date: Mon, 15 Apr 2024 14:06:20 -0700
Subject: [PATCH 59/69] fix dynamic shape bugs for test_binary_ops_aten
---
.../conversion/impl/elementwise/base.py | 71 +++++++++++--------
1 file changed, 40 insertions(+), 31 deletions(-)
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
index f93ec52504..d835110159 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
@@ -13,7 +13,11 @@
cast_trt_tensor,
get_trt_tensor,
)
-from torch_tensorrt.fx.converters.converter_utils import set_layer_name
+from torch_tensorrt.fx.converters.converter_utils import (
+ broadcast,
+ has_dynamic_shape,
+ set_layer_name,
+)
from torch_tensorrt.fx.types import TRTElementWiseOp, TRTTensor
from torch_tensorrt.fx.utils import Frameworks, unified_dtype_converter
@@ -148,38 +152,43 @@ def convert_binary_elementwise(
ctx, rhs_val, trt_promoted_type, f"{name}_cast_rhs_val", target, source_ir
)
- lhs_val_shape = lhs_val.shape
- rhs_val_shape = rhs_val.shape
- rank_diff = len(lhs_val_shape) - len(rhs_val_shape)
- if rank_diff > 0:
- rhs_val = impl.slice.expand(
- ctx, target, source_ir, f"{name}_expand_rhs_val", rhs_val, lhs_val_shape
- )
- elif rank_diff < 0:
- lhs_val = impl.slice.expand(
- ctx, target, source_ir, f"{name}_expand_lhs_val", lhs_val, rhs_val_shape
+ if has_dynamic_shape(lhs_val.shape) or has_dynamic_shape(rhs_val.shape):
+ lhs_val, rhs_val = broadcast(
+ ctx.net, lhs_val, rhs_val, f"{name}_lhs", f"{name}_rhs"
)
else:
- if tuple(lhs_val_shape) != tuple(rhs_val_shape):
- sum_diff = sum(lhs_val_shape) - sum(rhs_val_shape)
- if sum_diff > 0:
- rhs_val = impl.slice.expand(
- ctx,
- target,
- source_ir,
- f"{name}_expand_rhs_val",
- rhs_val,
- lhs_val_shape,
- )
- elif sum_diff < 0:
- lhs_val = impl.slice.expand(
- ctx,
- target,
- source_ir,
- f"{name}_expand_lhs_val",
- lhs_val,
- rhs_val_shape,
- )
+ lhs_val_shape = lhs_val.shape
+ rhs_val_shape = rhs_val.shape
+ rank_diff = len(lhs_val_shape) - len(rhs_val_shape)
+ if rank_diff > 0:
+ rhs_val = impl.slice.expand(
+ ctx, target, source_ir, f"{name}_expand_rhs_val", rhs_val, lhs_val_shape
+ )
+ elif rank_diff < 0:
+ lhs_val = impl.slice.expand(
+ ctx, target, source_ir, f"{name}_expand_lhs_val", lhs_val, rhs_val_shape
+ )
+ else:
+ if tuple(lhs_val_shape) != tuple(rhs_val_shape):
+ sum_diff = sum(lhs_val_shape) - sum(rhs_val_shape)
+ if sum_diff > 0:
+ rhs_val = impl.slice.expand(
+ ctx,
+ target,
+ source_ir,
+ f"{name}_expand_rhs_val",
+ rhs_val,
+ lhs_val_shape,
+ )
+ elif sum_diff < 0:
+ lhs_val = impl.slice.expand(
+ ctx,
+ target,
+ source_ir,
+ f"{name}_expand_lhs_val",
+ lhs_val,
+ rhs_val_shape,
+ )
layer = ctx.net.add_elementwise(lhs_val, rhs_val, op_type)
set_layer_name(layer, target, name, source_ir)
From d78a8466c961ae11e061ea4d253a0bcb4809cbcd Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Mon, 15 Apr 2024 18:40:05 -0700
Subject: [PATCH 60/69] chore: revert layer_norm test
---
.../dynamo/conversion/test_layer_norm_aten.py | 49 +++++++++++++++++++
1 file changed, 49 insertions(+)
diff --git a/tests/py/dynamo/conversion/test_layer_norm_aten.py b/tests/py/dynamo/conversion/test_layer_norm_aten.py
index 7f43234211..8013768214 100644
--- a/tests/py/dynamo/conversion/test_layer_norm_aten.py
+++ b/tests/py/dynamo/conversion/test_layer_norm_aten.py
@@ -24,6 +24,31 @@ def forward(self, x):
inputs,
)
+ def test_layernorm_with_dynamic_shape(self):
+ class LayerNorm(torch.nn.Module):
+ def forward(self, x):
+ return torch.ops.aten.layer_norm.default(
+ x,
+ torch.tensor([3, 224, 224]),
+ torch.ones((3, 224, 224)),
+ torch.zeros((3, 224, 224)),
+ 1e-05,
+ True,
+ )
+
+ input_specs = [
+ Input(
+ shape=(-1, 3, 224, 224),
+ dtype=torch.float32,
+ shape_ranges=[((1, 3, 224, 224), (1, 3, 224, 224), (2, 3, 224, 224))],
+ ),
+ ]
+
+ self.run_test_with_dynamic_shape(
+ LayerNorm(),
+ input_specs,
+ )
+
class TestNativeLayerNormConverter(DispatchTestCase):
def test_layer_norm(self):
@@ -43,6 +68,30 @@ def forward(self, x):
inputs,
)
+ def test_layernorm_with_dynamic_shape(self):
+ class LayerNorm(torch.nn.Module):
+ def forward(self, x):
+ return torch.ops.aten.native_layer_norm.default(
+ x,
+ torch.tensor([3, 224, 224]),
+ torch.ones((3, 224, 224)),
+ torch.zeros((3, 224, 224)),
+ 1e-05,
+ )[0]
+
+ input_specs = [
+ Input(
+ shape=(-1, 3, 224, 224),
+ dtype=torch.float32,
+ shape_ranges=[((1, 3, 224, 224), (1, 3, 224, 224), (2, 3, 224, 224))],
+ ),
+ ]
+
+ self.run_test_with_dynamic_shape(
+ LayerNorm(),
+ input_specs,
+ )
+
if __name__ == "__main__":
run_tests()
From 38642bb9ef8b4da63b25363cde751dc17a3812b9 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Thu, 18 Apr 2024 16:07:04 -0700
Subject: [PATCH 61/69] chore: updates
---
py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py b/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py
index 05808dd37c..9a75add755 100644
--- a/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py
+++ b/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py
@@ -313,7 +313,7 @@ def run(
)
timing_cache = self._create_timing_cache(builder_config, existing_cache)
- engine = self.builder.build_engine(self.ctx.net, builder_config)
+ engine = self.builder.build_serialized_network(self.ctx.net, builder_config)
assert engine
serialized_cache = (
From d15dd72c38e91ddc20f10b5452f76239e16f819a Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Thu, 18 Apr 2024 17:21:21 -0700
Subject: [PATCH 62/69] chore: updates
---
py/torch_tensorrt/_enums.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/py/torch_tensorrt/_enums.py b/py/torch_tensorrt/_enums.py
index 5c16cd03cd..062abb9a87 100644
--- a/py/torch_tensorrt/_enums.py
+++ b/py/torch_tensorrt/_enums.py
@@ -107,7 +107,7 @@ def _from(
return dtype.f16
elif t == trt.float32:
return dtype.f32
- elif trt.__version__ >= "7.0" and t == trt.bool:
+ elif t == trt.bool:
return dtype.b
else:
raise TypeError(
From dee9aa02d941be5312f7e3cceef5ad4f887aefe1 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Fri, 19 Apr 2024 09:23:09 -0700
Subject: [PATCH 63/69] chore: updates
---
.github/workflows/build-test.yml | 1 +
tests/py/dynamo/conversion/test_erf_aten.py | 2 +-
tests/py/dynamo/conversion/test_neg_aten.py | 2 +-
3 files changed, 3 insertions(+), 2 deletions(-)
diff --git a/.github/workflows/build-test.yml b/.github/workflows/build-test.yml
index 97f4ad2ba0..f4d39bd056 100644
--- a/.github/workflows/build-test.yml
+++ b/.github/workflows/build-test.yml
@@ -264,6 +264,7 @@ jobs:
pre-script: ${{ matrix.pre-script }}
script: |
export USE_HOST_DEPS=1
+ export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBRARY_PATH
pushd .
cd tests/py/core
${CONDA_RUN} python -m pip install --pre pytest-xdist timm transformers parameterized expecttest==0.1.6 --use-deprecated=legacy-resolver
diff --git a/tests/py/dynamo/conversion/test_erf_aten.py b/tests/py/dynamo/conversion/test_erf_aten.py
index d65788bc85..d9d201b0ae 100644
--- a/tests/py/dynamo/conversion/test_erf_aten.py
+++ b/tests/py/dynamo/conversion/test_erf_aten.py
@@ -22,7 +22,7 @@ def forward(self, input):
return torch.ops.aten.erf.default(input)
inputs = [torch.randn(x, dtype=type)]
- self.run_test(erf(), inputs, precision=type, output_dtypes=[type])
+ self.run_test(erf(), inputs, precision=type)
@parameterized.expand(
[
diff --git a/tests/py/dynamo/conversion/test_neg_aten.py b/tests/py/dynamo/conversion/test_neg_aten.py
index 818d9c5a83..795a78354f 100644
--- a/tests/py/dynamo/conversion/test_neg_aten.py
+++ b/tests/py/dynamo/conversion/test_neg_aten.py
@@ -22,7 +22,7 @@ def forward(self, input):
return torch.ops.aten.neg.default(input)
inputs = [torch.randn(x, dtype=type)]
- self.run_test(neg(), inputs, precision=type, output_dtypes=[type])
+ self.run_test(neg(), inputs, precision=type)
@parameterized.expand(
[
From c05d675d5db3a5b12fee6e2505dfdc9a4b51ccaf Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Fri, 19 Apr 2024 16:25:01 -0700
Subject: [PATCH 64/69] chore: update stream in python runtime
---
.../runtime/_PythonTorchTensorRTModule.py | 3 +--
tests/py/ts/models/hw_compat.ts | Bin 110418 -> 0 bytes
2 files changed, 1 insertion(+), 2 deletions(-)
delete mode 100644 tests/py/ts/models/hw_compat.ts
diff --git a/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py b/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
index ef24a57f8e..49ed64a953 100644
--- a/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
+++ b/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
@@ -59,7 +59,6 @@ def _initialize(self) -> None:
runtime = trt.Runtime(logger)
self.engine = runtime.deserialize_cuda_engine(self.engine)
self.context = self.engine.create_execution_context()
- self.stream = torch.cuda.Stream(torch.cuda.current_device())
# Indices of inputs/outputs in the trt engine bindings, in the order
# as they are in the original PyTorch model.
@@ -282,7 +281,7 @@ def forward(self, *inputs: torch.Tensor) -> torch.Tensor | Tuple[torch.Tensor, .
if self.profiling_enabled
else nullcontext()
):
- self.context.execute_async_v3(self.stream.cuda_stream)
+ self.context.execute_async_v3(torch.cuda.current_stream().cuda_stream)
if len(outputs) == 1:
return outputs[0]
diff --git a/tests/py/ts/models/hw_compat.ts b/tests/py/ts/models/hw_compat.ts
deleted file mode 100644
index ed71663c0c77a406307a3e7c23d331c1b44b7243..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001
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From 23296573c4eb02b7687f44cd56da0658af6a5980 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Fri, 19 Apr 2024 16:42:55 -0700
Subject: [PATCH 65/69] chore: update hw_compat.ts
---
tests/py/ts/models/hw_compat.ts | Bin 0 -> 110481 bytes
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 tests/py/ts/models/hw_compat.ts
diff --git a/tests/py/ts/models/hw_compat.ts b/tests/py/ts/models/hw_compat.ts
new file mode 100644
index 0000000000000000000000000000000000000000..3cf583c78824407d066054b56da3095b37c2615d
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zn<~{kQgbxb0I}5o8N!SzU@CRt62xgWU^Wg9raI871C>kaPOJfRE`;*xKr2(zq4nTW
zsgr85-%^u(kU9<pz$GGQ)c~0C5V!mzleSLdxN#SjbDc5zqQU$V%TJYip2z{Xeh--B
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literal 0
HcmV?d00001
From b8a87099015a5180c5faadc66455542c3299fa2e Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Fri, 19 Apr 2024 23:22:46 -0700
Subject: [PATCH 66/69] chore: updates
---
tests/py/dynamo/lowering/test_aten_lowering_passes.py | 2 +-
tests/py/dynamo/testing_utilities.py | 1 -
2 files changed, 1 insertion(+), 2 deletions(-)
diff --git a/tests/py/dynamo/lowering/test_aten_lowering_passes.py b/tests/py/dynamo/lowering/test_aten_lowering_passes.py
index 5c0a1bb774..2d7a4731f5 100644
--- a/tests/py/dynamo/lowering/test_aten_lowering_passes.py
+++ b/tests/py/dynamo/lowering/test_aten_lowering_passes.py
@@ -453,7 +453,7 @@ def forward(self, input, weight, bias):
self.assertAlmostEqual(
max_diff,
0,
- DECIMALS_OF_AGREEMENT_3,
+ DECIMALS_OF_AGREEMENT,
msg=f"Linear TRT outputs don't match with the original model.",
)
torch._dynamo.reset()
diff --git a/tests/py/dynamo/testing_utilities.py b/tests/py/dynamo/testing_utilities.py
index c815d2fde4..742b9fc1a3 100644
--- a/tests/py/dynamo/testing_utilities.py
+++ b/tests/py/dynamo/testing_utilities.py
@@ -14,7 +14,6 @@
)
DECIMALS_OF_AGREEMENT = 4
-DECIMALS_OF_AGREEMENT_3 = 3
def fx_dynamo_testing_backend(
From 44778e11f9feecad1f5123489911339c9a79bf99 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Fri, 19 Apr 2024 23:49:29 -0700
Subject: [PATCH 67/69] chore: updates
---
cpp/include/torch_tensorrt/ptq.h | 8 ++++----
.../dynamo/runtime/_PythonTorchTensorRTModule.py | 5 -----
pyproject.toml | 2 ++
3 files changed, 6 insertions(+), 9 deletions(-)
diff --git a/cpp/include/torch_tensorrt/ptq.h b/cpp/include/torch_tensorrt/ptq.h
index e88da06b0c..d8570f0e6e 100644
--- a/cpp/include/torch_tensorrt/ptq.h
+++ b/cpp/include/torch_tensorrt/ptq.h
@@ -21,10 +21,10 @@
#include "torch_tensorrt/macros.h"
#ifndef DOXYGEN_SHOULD_SKIP_THIS
-// namespace nvinfer1 {
-// class IInt8Calibrator;
-// class IInt8EntropyCalibrator2;
-// } // namespace nvinfer1
+namespace nvinfer1 {
+class IInt8Calibrator;
+class IInt8EntropyCalibrator2;
+} // namespace nvinfer1
namespace torch_tensorrt {
namespace ptq {
diff --git a/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py b/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
index 49ed64a953..7b82d41cb6 100644
--- a/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
+++ b/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
@@ -209,11 +209,6 @@ def forward(self, *inputs: torch.Tensor) -> torch.Tensor | Tuple[torch.Tensor, .
contiguous_inputs: List[torch.Tensor] = [i.contiguous() for i in inputs]
bindings = []
- # [None] * (
- # len(self.input_names)
- # + len(self.output_names)
- # # + len(self.hidden_output_names) # TODO: Verify if this is required
- # )
for i, input_name in enumerate(self.input_names):
if not contiguous_inputs[i].is_cuda:
logger.warning(
diff --git a/pyproject.toml b/pyproject.toml
index 79e5391f52..ec6c0fe19c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,6 +8,7 @@ requires = [
"cffi>=1.15.1",
"typing-extensions>=4.7.0",
"future>=0.18.3",
+ "tensorrt",
"torch==2.3.0",
"pybind11==2.6.2",
"numpy",
@@ -41,6 +42,7 @@ requires-python = ">=3.8"
keywords = ["pytorch", "torch", "tensorrt", "trt", "ai", "artificial intelligence", "ml", "machine learning", "dl", "deep learning", "compiler", "dynamo", "torchscript", "inference"]
dependencies = [
"torch==2.3.0",
+ "tensorrt",
"packaging>=23",
"numpy",
"typing-extensions>=4.7.0",
From 0dbbcd71da88f437e7c87a040138fdfe51d23031 Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Sat, 20 Apr 2024 01:22:00 -0700
Subject: [PATCH 68/69] chore: updates
---
cpp/include/torch_tensorrt/ptq.h | 8 ++++----
packaging/pre_build_script.sh | 2 +-
py/requirements.txt | 3 ++-
3 files changed, 7 insertions(+), 6 deletions(-)
diff --git a/cpp/include/torch_tensorrt/ptq.h b/cpp/include/torch_tensorrt/ptq.h
index d8570f0e6e..e88da06b0c 100644
--- a/cpp/include/torch_tensorrt/ptq.h
+++ b/cpp/include/torch_tensorrt/ptq.h
@@ -21,10 +21,10 @@
#include "torch_tensorrt/macros.h"
#ifndef DOXYGEN_SHOULD_SKIP_THIS
-namespace nvinfer1 {
-class IInt8Calibrator;
-class IInt8EntropyCalibrator2;
-} // namespace nvinfer1
+// namespace nvinfer1 {
+// class IInt8Calibrator;
+// class IInt8EntropyCalibrator2;
+// } // namespace nvinfer1
namespace torch_tensorrt {
namespace ptq {
diff --git a/packaging/pre_build_script.sh b/packaging/pre_build_script.sh
index befb6e38d9..8f5d1d8acc 100755
--- a/packaging/pre_build_script.sh
+++ b/packaging/pre_build_script.sh
@@ -10,7 +10,7 @@ export LD_LIBRARY_PATH=/opt/torch-tensorrt-builds/TensorRT-10.0.0.6/lib:$LD_LIBR
wget https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 \
&& mv bazelisk-linux-amd64 /usr/bin/bazel \
&& chmod +x /usr/bin/bazel
-python -m pip install -r py/requirements.txt
+
export TORCH_BUILD_NUMBER=$(python -c "import torch, urllib.parse as ul; print(ul.quote_plus(torch.__version__))")
cat toolchains/ci_workspaces/WORKSPACE.x86_64.release.rhel.tmpl | envsubst > WORKSPACE
diff --git a/py/requirements.txt b/py/requirements.txt
index e3356a8c59..d402fd501e 100644
--- a/py/requirements.txt
+++ b/py/requirements.txt
@@ -5,4 +5,5 @@ pybind11==2.6.2
torch==2.3.0
torchvision==0.18.0
--extra-index-url https://pypi.ngc.nvidia.com
-pyyaml
\ No newline at end of file
+pyyaml
+tensorrt
\ No newline at end of file
From 89c3d76b7e94a8f37df8937f0552f75633b3445e Mon Sep 17 00:00:00 2001
From: Dheeraj Peri <peri.dheeraj@gmail.com>
Date: Mon, 22 Apr 2024 16:01:37 -0700
Subject: [PATCH 69/69] chore: updates
---
core/runtime/TRTEngine.cpp | 2 +
cpp/include/torch_tensorrt/ptq.h | 5 --
.../runtime/_PythonTorchTensorRTModule.py | 53 ++-----------------
3 files changed, 6 insertions(+), 54 deletions(-)
diff --git a/core/runtime/TRTEngine.cpp b/core/runtime/TRTEngine.cpp
index fcbb403e2c..4a33907bec 100644
--- a/core/runtime/TRTEngine.cpp
+++ b/core/runtime/TRTEngine.cpp
@@ -133,6 +133,7 @@ TRTEngine::TRTEngine(
trt_idx = idx;
if (cuda_engine->getTensorIOMode(binding_name.c_str()) == nvinfer1::TensorIOMode::kINPUT) {
is_input = true;
+ break;
}
}
}
@@ -161,6 +162,7 @@ TRTEngine::TRTEngine(
trt_idx = idx;
if (cuda_engine->getTensorIOMode(binding_name.c_str()) == nvinfer1::TensorIOMode::kOUTPUT) {
is_output = true;
+ break;
}
}
}
diff --git a/cpp/include/torch_tensorrt/ptq.h b/cpp/include/torch_tensorrt/ptq.h
index e88da06b0c..6650f45fe9 100644
--- a/cpp/include/torch_tensorrt/ptq.h
+++ b/cpp/include/torch_tensorrt/ptq.h
@@ -21,11 +21,6 @@
#include "torch_tensorrt/macros.h"
#ifndef DOXYGEN_SHOULD_SKIP_THIS
-// namespace nvinfer1 {
-// class IInt8Calibrator;
-// class IInt8EntropyCalibrator2;
-// } // namespace nvinfer1
-
namespace torch_tensorrt {
namespace ptq {
TORCHTRT_API bool get_batch_impl(void* bindings[], const char* names[], int nbBindings, torch::Tensor& data);
diff --git a/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py b/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
index 7b82d41cb6..0c152e15f1 100644
--- a/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
+++ b/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py
@@ -15,6 +15,7 @@
_select_rt_device,
multi_gpu_device_check,
)
+from torch_tensorrt.logging import TRT_LOGGER
logger = logging.getLogger(__name__)
@@ -55,39 +56,13 @@ def __init__(
def _initialize(self) -> None:
self.initialized = True
- logger = trt.Logger()
- runtime = trt.Runtime(logger)
+ runtime = trt.Runtime(TRT_LOGGER)
self.engine = runtime.deserialize_cuda_engine(self.engine)
self.context = self.engine.create_execution_context()
- # Indices of inputs/outputs in the trt engine bindings, in the order
- # as they are in the original PyTorch model.
-
- # TODO: Verify if the following is required especially the hidden outputs
- # self.input_binding_indices_in_order: Sequence[int] = [
- # self.engine.get_binding_index(name) for name in self.input_names
- # ]
- # self.output_binding_indices_in_order: Sequence[int] = [
- # self.engine.get_binding_index(name) for name in self.output_names
- # ]
- # primary_input_outputs = set()
- # primary_input_outputs.update(self.input_binding_indices_in_order)
- # primary_input_outputs.update(self.output_binding_indices_in_order)
- # self.hidden_output_binding_indices_in_order: Sequence[int] = []
- # self.hidden_output_names: Sequence[str] = []
- # for i in range(
- # self.engine.num_bindings // self.engine.num_optimization_profiles
- # ):
- # if i not in primary_input_outputs:
- # self.hidden_output_binding_indices_in_order.append(i)
- # self.hidden_output_names.append(self.engine.get_binding_name(i))
assert (
self.engine.num_io_tensors // self.engine.num_optimization_profiles
- ) == (
- len(self.input_names)
- + len(self.output_names)
- # + len(self.hidden_output_names) #TODO: Verify if this is required
- )
+ ) == (len(self.input_names) + len(self.output_names))
self.input_dtypes = [
dtype._from(self.engine.get_tensor_dtype(input_name))
@@ -105,14 +80,6 @@ def _initialize(self) -> None:
for output_name in self.output_names
]
- # TODO: Verify what this is for ?
- # self.hidden_output_dtypes = [
- # unified_dtype_converter(
- # self.engine.get_binding_dtype(idx), Frameworks.TORCH
- # )
- # for idx in self.hidden_output_binding_indices_in_order
- # ]
-
def _check_initialized(self) -> None:
if not self.initialized:
raise RuntimeError("PythonTorchTensorRTModule is not initialized.")
@@ -138,8 +105,7 @@ def _load_from_state_dict(
# Run multi-gpu device check to validate engine instantiation
multi_gpu_device_check()
- logger = trt.Logger()
- runtime = trt.Runtime(logger)
+ runtime = trt.Runtime(TRT_LOGGER)
self.engine = runtime.deserialize_cuda_engine(engine_bytes)
self.input_names = state_dict[prefix + "input_names"]
@@ -253,16 +219,6 @@ def forward(self, *inputs: torch.Tensor) -> torch.Tensor | Tuple[torch.Tensor, .
bindings.append(output.data_ptr())
outputs.append(output)
- # TODO: Check what is this for ?
- # for i, idx in enumerate(self.hidden_output_binding_indices_in_order):
- # shape = tuple(self.context.get_binding_shape(idx))
-
- # output = torch.empty(
- # size=shape,
- # dtype=self.hidden_output_dtypes[i],
- # device=torch.cuda.current_device(),
- # )
-
# Assign tensor address appropriately
for idx in range(self.engine.num_io_tensors):
self.context.set_tensor_address(
@@ -300,7 +256,6 @@ def disable_profiling(self) -> None:
Disable TensorRT profiling.
"""
self._check_initialized()
-
torch.cuda.synchronize()
del self.context
self.context = self.engine.create_execution_context()