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<section id="torch-tensorrt-ts">
<span id="torch-tensorrt-ts-py"></span><h1>torch_tensorrt.ts<a class="headerlink" href="#torch-tensorrt-ts" title="Permalink to this headline">¶</a></h1>
<span class="target" id="module-torch_tensorrt.ts"></span><section id="functions">
<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.ts.compile">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_signature=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=<torch_tensorrt._Device.Device</span> <span class="pre">object></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=<EngineCapability.default:</span> <span class="pre">0></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_full_compilation=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_block_size=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_ops=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_modules=[]</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">torch.jit._script.ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.compile" title="Permalink to this definition">¶</a></dt>
<dd><p>Compile a TorchScript module for NVIDIA GPUs using TensorRT</p>
<p>Takes a existing TorchScript module and a set of settings to configure the compiler
and will convert methods to JIT Graphs which call equivalent TensorRT engines</p>
<p>Converts specifically the forward method of a TorchScript Module</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
to select device type.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
torch_tensorrt.Input(
min_shape=(1, 224, 224, 3),
opt_shape=(1, 512, 512, 3),
max_shape=(1, 1024, 1024, 3),
dtype=torch.int32
format=torch.channel_last
), # Dynamic input shape for input #2
torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
]
</pre></div>
</div>
</p></li>
<li><p><strong>Union</strong> (<em>input_signature</em>) – <p>A formatted collection of input specifications for the module. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum to select device type. <strong>This API should be considered beta-level stable and may change in the future</strong></p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input_signature=([
torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
torch_tensorrt.Input(
min_shape=(1, 224, 224, 3),
opt_shape=(1, 512, 512, 3),
max_shape=(1, 1024, 1024, 3),
dtype=torch.int32
format=torch.channel_last
), # Dynamic input shape for input #2
], torch.randn((1, 3, 224, 244))) # Use an example tensor and let torch_tensorrt infer settings for input #3
</pre></div>
</div>
</p></li>
<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">"dla:1"</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
</pre></div>
</div>
</p></li>
<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>torch_tensorrt.EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
<li><p><strong>calibrator</strong> (<em>Union</em><em>(</em><em>torch_tensorrt._C.IInt8Calibrator</em><em>, </em><em>tensorrt.IInt8Calibrator</em><em>)</em>) – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</p></li>
<li><p><strong>min_block_size</strong> (<em>python:int</em>) – The minimum number of contiguous TensorRT convertable operations in order to run a set of operations in TensorRT</p></li>
<li><p><strong>torch_executed_ops</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of aten operators that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
<li><p><strong>torch_executed_modules</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Compiled TorchScript Module, when run it will execute via TensorRT</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>torch.jit.ScriptModule</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.ts.convert_method_to_trt_engine">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">convert_method_to_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'forward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=<torch_tensorrt._Device.Device</span> <span class="pre">object></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=<EngineCapability.default:</span> <span class="pre">0></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">bytearray</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#convert_method_to_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.convert_method_to_trt_engine" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert a TorchScript module method to a serialized TensorRT engine</p>
<p>Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></li>
<li><p><strong>method_name</strong> (<em>str</em>) – Name of method to convert</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
to select device type.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
torch_tensorrt.Input(
min_shape=(1, 224, 224, 3),
opt_shape=(1, 512, 512, 3),
max_shape=(1, 1024, 1024, 3),
dtype=torch.int32
format=torch.channel_last
), # Dynamic input shape for input #2
torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
]
</pre></div>
</div>
</p></li>
<li><p><strong>Union</strong> (<em>input_signature</em>) – <p>A formatted collection of input specifications for the module. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum to select device type. <strong>This API should be considered beta-level stable and may change in the future</strong></p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input_signature=([
torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
torch_tensorrt.Input(
min_shape=(1, 224, 224, 3),
opt_shape=(1, 512, 512, 3),
max_shape=(1, 1024, 1024, 3),
dtype=torch.int32
format=torch.channel_last
), # Dynamic input shape for input #2
], torch.randn((1, 3, 224, 244))) # Use an example tensor and let torch_tensorrt infer settings for input #3
</pre></div>
</div>
</p></li>
<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">"dla:1"</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
</pre></div>
</div>
</p></li>
<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>torch_tensorrt.EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
<li><p><strong>calibrator</strong> (<em>Union</em><em>(</em><em>torch_tensorrt._C.IInt8Calibrator</em><em>, </em><em>tensorrt.IInt8Calibrator</em><em>)</em>) – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>bytearray</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.ts.check_method_op_support">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">check_method_op_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'forward'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#check_method_op_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.check_method_op_support" title="Permalink to this definition">¶</a></dt>
<dd><p>Checks to see if a method is fully supported by torch_tensorrt</p>
<p>Checks if a method of a TorchScript module can be compiled by torch_tensorrt, if not, a list of operators
that are not supported are printed out and the function returns false, else true.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></li>
<li><p><strong>method_name</strong> (<em>str</em>) – Name of method to check</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>True if supported Method</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>bool</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.ts.embed_engine_in_new_module">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">embed_engine_in_new_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">serialized_engine:</span> <span class="pre">bytes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device:</span> <span class="pre">torch_tensorrt._Device.Device</span> <span class="pre">=</span> <span class="pre"><torch_tensorrt._Device.Device</span> <span class="pre">object></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_binding_names:</span> <span class="pre">typing.List[str]</span> <span class="pre">=</span> <span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_binding_names:</span> <span class="pre">typing.List[str]</span> <span class="pre">=</span> <span class="pre">[]</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">torch.jit._script.ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#embed_engine_in_new_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.embed_engine_in_new_module" title="Permalink to this definition">¶</a></dt>
<dd><p>Takes a pre-built serialized TensorRT engine and embeds it within a TorchScript module</p>
<p>Takes a pre-built serialied TensorRT engine (as bytes) and embeds it within a TorchScript module.
Registers the forward method to execute the TensorRT engine with the function signature:</p>
<blockquote>
<div><p>forward(Tensor[]) -> Tensor[]</p>
</div></blockquote>
<dl>
<dt>TensorRT bindings either be explicitly specified using <code class="docutils literal notranslate"><span class="pre">[in/out]put_binding_names</span></code> or have names with the following format:</dt><dd><ul class="simple">
<li><p>[symbol].[index in input / output array]</p></li>
</ul>
<p>ex.
- [x.0, x.1, x.2] -> [y.0]</p>
</dd>
</dl>
<p>Module can be save with engine embedded with torch.jit.save and moved / loaded according to torch_tensorrt portability rules</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>serialized_engine</strong> (<em>bytes</em>) – Serialized TensorRT engine from either torch_tensorrt or TensorRT APIs</p>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – Target device to run engine on. Must be compatible with engine provided. Default: Current active device</p></li>
<li><p><strong>input_binding_names</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of names of TensorRT bindings in order to be passed to the encompassing PyTorch module</p></li>
<li><p><strong>output_binding_names</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of names of TensorRT bindings in order that should be returned from the encompassing PyTorch module</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>New TorchScript module with engine embedded</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>torch.jit.ScriptModule</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.ts.TensorRTCompileSpec">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">TensorRTCompileSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_signature=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=<torch_tensorrt._Device.Device</span> <span class="pre">object></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=<EngineCapability.default:</span> <span class="pre">0></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre"><torch.ScriptClass</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fe033a7b270></span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compile_spec.html#TensorRTCompileSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.TensorRTCompileSpec" title="Permalink to this definition">¶</a></dt>
<dd><p>Utility to create a formated spec dictionary for using the PyTorch TensorRT backend</p>
<dl class="field-list simple">
<dt class="field-odd">Keyword Arguments</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
to select device type.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
torch_tensorrt.Input(
min_shape=(1, 224, 224, 3),
opt_shape=(1, 512, 512, 3),
max_shape=(1, 1024, 1024, 3),
dtype=torch.int32
format=torch.channel_last
), # Dynamic input shape for input #2
torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
]
</pre></div>
</div>
</p></li>
<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">"dla:1"</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
</pre></div>
</div>
</p></li>
<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>torch_tensorrt.EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
<li><p><strong>calibrator</strong> – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</section>
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