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<h1>Source code for torch._tensor_str</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">contextlib</span>
<span class="kn">import</span> <span class="nn">dataclasses</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">textwrap</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Optional</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">inf</span>
<span class="nd">@dataclasses</span><span class="o">.</span><span class="n">dataclass</span>
<span class="k">class</span> <span class="nc">__PrinterOptions</span><span class="p">:</span>
<span class="n">precision</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="n">edgeitems</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">linewidth</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">80</span>
<span class="n">sci_mode</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">PRINT_OPTS</span> <span class="o">=</span> <span class="n">__PrinterOptions</span><span class="p">()</span>
<span class="c1"># We could use **kwargs, but this will give better docs</span>
<div class="viewcode-block" id="set_printoptions"><a class="viewcode-back" href="../../generated/torch.set_printoptions.html#torch.set_printoptions">[docs]</a><span class="k">def</span> <span class="nf">set_printoptions</span><span class="p">(</span>
<span class="n">precision</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">threshold</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">edgeitems</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">linewidth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">profile</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">sci_mode</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Set options for printing. Items shamelessly taken from NumPy</span>
<span class="sd"> Args:</span>
<span class="sd"> precision: Number of digits of precision for floating point output</span>
<span class="sd"> (default = 4).</span>
<span class="sd"> threshold: Total number of array elements which trigger summarization</span>
<span class="sd"> rather than full `repr` (default = 1000).</span>
<span class="sd"> edgeitems: Number of array items in summary at beginning and end of</span>
<span class="sd"> each dimension (default = 3).</span>
<span class="sd"> linewidth: The number of characters per line for the purpose of</span>
<span class="sd"> inserting line breaks (default = 80). Thresholded matrices will</span>
<span class="sd"> ignore this parameter.</span>
<span class="sd"> profile: Sane defaults for pretty printing. Can override with any of</span>
<span class="sd"> the above options. (any one of `default`, `short`, `full`)</span>
<span class="sd"> sci_mode: Enable (True) or disable (False) scientific notation. If</span>
<span class="sd"> None (default) is specified, the value is defined by</span>
<span class="sd"> `torch._tensor_str._Formatter`. This value is automatically chosen</span>
<span class="sd"> by the framework.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # Limit the precision of elements</span>
<span class="sd"> >>> torch.set_printoptions(precision=2)</span>
<span class="sd"> >>> torch.tensor([1.12345])</span>
<span class="sd"> tensor([1.12])</span>
<span class="sd"> >>> # Limit the number of elements shown</span>
<span class="sd"> >>> torch.set_printoptions(threshold=5)</span>
<span class="sd"> >>> torch.arange(10)</span>
<span class="sd"> tensor([0, 1, 2, ..., 7, 8, 9])</span>
<span class="sd"> >>> # Restore defaults</span>
<span class="sd"> >>> torch.set_printoptions(profile='default')</span>
<span class="sd"> >>> torch.tensor([1.12345])</span>
<span class="sd"> tensor([1.1235])</span>
<span class="sd"> >>> torch.arange(10)</span>
<span class="sd"> tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">profile</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="n">profile</span> <span class="o">==</span> <span class="s2">"default"</span><span class="p">:</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">precision</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">linewidth</span> <span class="o">=</span> <span class="mi">80</span>
<span class="k">elif</span> <span class="n">profile</span> <span class="o">==</span> <span class="s2">"short"</span><span class="p">:</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">precision</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">linewidth</span> <span class="o">=</span> <span class="mi">80</span>
<span class="k">elif</span> <span class="n">profile</span> <span class="o">==</span> <span class="s2">"full"</span><span class="p">:</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">precision</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">inf</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">linewidth</span> <span class="o">=</span> <span class="mi">80</span>
<span class="k">if</span> <span class="n">precision</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">precision</span> <span class="o">=</span> <span class="n">precision</span>
<span class="k">if</span> <span class="n">threshold</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">threshold</span>
<span class="k">if</span> <span class="n">edgeitems</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span> <span class="o">=</span> <span class="n">edgeitems</span>
<span class="k">if</span> <span class="n">linewidth</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">linewidth</span> <span class="o">=</span> <span class="n">linewidth</span>
<span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">sci_mode</span> <span class="o">=</span> <span class="n">sci_mode</span></div>
<span class="k">def</span> <span class="nf">get_printoptions</span><span class="p">()</span> <span class="o">-></span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Gets the current options for printing, as a dictionary that</span>
<span class="sd"> can be passed as ``**kwargs`` to set_printoptions().</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">dataclasses</span><span class="o">.</span><span class="n">asdict</span><span class="p">(</span><span class="n">PRINT_OPTS</span><span class="p">)</span>
<span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
<span class="k">def</span> <span class="nf">printoptions</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Context manager that temporarily changes the print options. Accepted</span>
<span class="sd"> arguments are same as :func:`set_printoptions`."""</span>
<span class="n">old_kwargs</span> <span class="o">=</span> <span class="n">get_printoptions</span><span class="p">()</span>
<span class="n">set_printoptions</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">yield</span>
<span class="k">finally</span><span class="p">:</span>
<span class="n">set_printoptions</span><span class="p">(</span><span class="o">**</span><span class="n">old_kwargs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">tensor_totype</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
<span class="n">dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">float</span> <span class="k">if</span> <span class="n">t</span><span class="o">.</span><span class="n">is_mps</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">double</span>
<span class="k">return</span> <span class="n">t</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">_Formatter</span><span class="p">:</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tensor</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">floating_dtype</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">is_floating_point</span>
<span class="bp">self</span><span class="o">.</span><span class="n">int_mode</span> <span class="o">=</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sci_mode</span> <span class="o">=</span> <span class="kc">False</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_width</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="n">tensor_view</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">floating_dtype</span><span class="p">:</span>
<span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">tensor_view</span><span class="p">:</span>
<span class="n">value_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">"</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_width</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_width</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">value_str</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">nonzero_finite_vals</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">masked_select</span><span class="p">(</span>
<span class="n">tensor_view</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">tensor_view</span><span class="p">)</span> <span class="o">&</span> <span class="n">tensor_view</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">nonzero_finite_vals</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="c1"># no valid number, do nothing</span>
<span class="k">return</span>
<span class="c1"># Convert to double for easy calculation. HalfTensor overflows with 1e8, and there's no div() on CPU.</span>
<span class="n">nonzero_finite_abs</span> <span class="o">=</span> <span class="n">tensor_totype</span><span class="p">(</span><span class="n">nonzero_finite_vals</span><span class="o">.</span><span class="n">abs</span><span class="p">())</span>
<span class="n">nonzero_finite_min</span> <span class="o">=</span> <span class="n">tensor_totype</span><span class="p">(</span><span class="n">nonzero_finite_abs</span><span class="o">.</span><span class="n">min</span><span class="p">())</span>
<span class="n">nonzero_finite_max</span> <span class="o">=</span> <span class="n">tensor_totype</span><span class="p">(</span><span class="n">nonzero_finite_abs</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">nonzero_finite_vals</span><span class="p">:</span>
<span class="k">if</span> <span class="n">value</span> <span class="o">!=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">value</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">int_mode</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">break</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">int_mode</span><span class="p">:</span>
<span class="c1"># in int_mode for floats, all numbers are integers, and we append a decimal to nonfinites</span>
<span class="c1"># to indicate that the tensor is of floating type. add 1 to the len to account for this.</span>
<span class="k">if</span> <span class="p">(</span>
<span class="n">nonzero_finite_max</span> <span class="o">/</span> <span class="n">nonzero_finite_min</span> <span class="o">></span> <span class="mf">1000.0</span>
<span class="ow">or</span> <span class="n">nonzero_finite_max</span> <span class="o">></span> <span class="mf">1.0e8</span>
<span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sci_mode</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">nonzero_finite_vals</span><span class="p">:</span>
<span class="n">value_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="se">{{</span><span class="s2">:.</span><span class="si">{</span><span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">precision</span><span class="si">}</span><span class="s2">e</span><span class="se">}}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_width</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_width</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">value_str</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">nonzero_finite_vals</span><span class="p">:</span>
<span class="n">value_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">value</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2">"</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_width</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_width</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">value_str</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># Check if scientific representation should be used.</span>
<span class="k">if</span> <span class="p">(</span>
<span class="n">nonzero_finite_max</span> <span class="o">/</span> <span class="n">nonzero_finite_min</span> <span class="o">></span> <span class="mf">1000.0</span>
<span class="ow">or</span> <span class="n">nonzero_finite_max</span> <span class="o">></span> <span class="mf">1.0e8</span>
<span class="ow">or</span> <span class="n">nonzero_finite_min</span> <span class="o"><</span> <span class="mf">1.0e-4</span>
<span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sci_mode</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">nonzero_finite_vals</span><span class="p">:</span>
<span class="n">value_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="se">{{</span><span class="s2">:.</span><span class="si">{</span><span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">precision</span><span class="si">}</span><span class="s2">e</span><span class="se">}}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_width</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_width</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">value_str</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">nonzero_finite_vals</span><span class="p">:</span>
<span class="n">value_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="se">{{</span><span class="s2">:.</span><span class="si">{</span><span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">precision</span><span class="si">}</span><span class="s2">f</span><span class="se">}}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_width</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_width</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">value_str</span><span class="p">))</span>
<span class="k">if</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">sci_mode</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sci_mode</span> <span class="o">=</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">sci_mode</span>
<span class="k">def</span> <span class="nf">width</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_width</span>
<span class="k">def</span> <span class="nf">format</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">floating_dtype</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sci_mode</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="se">{{</span><span class="s2">:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">max_width</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">precision</span><span class="si">}</span><span class="s2">e</span><span class="se">}}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">int_mode</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">value</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2">"</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="ow">or</span> <span class="n">math</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">value</span><span class="p">)):</span>
<span class="n">ret</span> <span class="o">+=</span> <span class="s2">"."</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="se">{{</span><span class="s2">:.</span><span class="si">{</span><span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">precision</span><span class="si">}</span><span class="s2">f</span><span class="se">}}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">"</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_width</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">ret</span><span class="p">))</span> <span class="o">*</span> <span class="s2">" "</span> <span class="o">+</span> <span class="n">ret</span>
<span class="k">def</span> <span class="nf">_scalar_str</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">formatter1</span><span class="p">,</span> <span class="n">formatter2</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="n">formatter2</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">real_str</span> <span class="o">=</span> <span class="n">_scalar_str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">formatter1</span><span class="p">)</span>
<span class="n">imag_str</span> <span class="o">=</span> <span class="p">(</span><span class="n">_scalar_str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span> <span class="n">formatter2</span><span class="p">)</span> <span class="o">+</span> <span class="s2">"j"</span><span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">()</span>
<span class="c1"># handles negative numbers, +0.0, -0.0</span>
<span class="k">if</span> <span class="n">imag_str</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"+"</span> <span class="ow">or</span> <span class="n">imag_str</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"-"</span><span class="p">:</span>
<span class="k">return</span> <span class="n">real_str</span> <span class="o">+</span> <span class="n">imag_str</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">real_str</span> <span class="o">+</span> <span class="s2">"+"</span> <span class="o">+</span> <span class="n">imag_str</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">formatter1</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
<span class="k">def</span> <span class="nf">_vector_str</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indent</span><span class="p">,</span> <span class="n">summarize</span><span class="p">,</span> <span class="n">formatter1</span><span class="p">,</span> <span class="n">formatter2</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="c1"># length includes spaces and comma between elements</span>
<span class="n">element_length</span> <span class="o">=</span> <span class="n">formatter1</span><span class="o">.</span><span class="n">width</span><span class="p">()</span> <span class="o">+</span> <span class="mi">2</span>
<span class="k">if</span> <span class="n">formatter2</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="c1"># width for imag_formatter + an extra j for complex</span>
<span class="n">element_length</span> <span class="o">+=</span> <span class="n">formatter2</span><span class="o">.</span><span class="n">width</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">elements_per_line</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span>
<span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">((</span><span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">linewidth</span> <span class="o">-</span> <span class="n">indent</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">element_length</span><span class="p">)))</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">_val_formatter</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">formatter1</span><span class="o">=</span><span class="n">formatter1</span><span class="p">,</span> <span class="n">formatter2</span><span class="o">=</span><span class="n">formatter2</span><span class="p">):</span>
<span class="k">if</span> <span class="n">formatter2</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">real_str</span> <span class="o">=</span> <span class="n">formatter1</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">real</span><span class="p">)</span>
<span class="n">imag_str</span> <span class="o">=</span> <span class="p">(</span><span class="n">formatter2</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">imag</span><span class="p">)</span> <span class="o">+</span> <span class="s2">"j"</span><span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">()</span>
<span class="c1"># handles negative numbers, +0.0, -0.0</span>
<span class="k">if</span> <span class="n">imag_str</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"+"</span> <span class="ow">or</span> <span class="n">imag_str</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"-"</span><span class="p">:</span>
<span class="k">return</span> <span class="n">real_str</span> <span class="o">+</span> <span class="n">imag_str</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">real_str</span> <span class="o">+</span> <span class="s2">"+"</span> <span class="o">+</span> <span class="n">imag_str</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">formatter1</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
<span class="k">if</span> <span class="n">summarize</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span><span class="p">:</span>
<span class="c1"># Deal with edge case that negative zero is zero</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"..."</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">summarize</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">></span> <span class="mi">2</span> <span class="o">*</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">(</span>
<span class="p">[</span><span class="n">_val_formatter</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">[:</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()]</span>
<span class="o">+</span> <span class="p">[</span><span class="s2">" ..."</span><span class="p">]</span>
<span class="o">+</span> <span class="p">[</span><span class="n">_val_formatter</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">[</span><span class="o">-</span><span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span> <span class="p">:]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()]</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">_val_formatter</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">tolist</span><span class="p">()]</span>
<span class="n">data_lines</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">elements_per_line</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">elements_per_line</span><span class="p">)</span>
<span class="p">]</span>
<span class="n">lines</span> <span class="o">=</span> <span class="p">[</span><span class="s2">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">line</span><span class="p">)</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">data_lines</span><span class="p">]</span>
<span class="k">return</span> <span class="s2">"["</span> <span class="o">+</span> <span class="p">(</span><span class="s2">","</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span> <span class="o">+</span> <span class="s2">" "</span> <span class="o">*</span> <span class="p">(</span><span class="n">indent</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">lines</span><span class="p">)</span> <span class="o">+</span> <span class="s2">"]"</span>
<span class="c1"># formatter2 is only used for printing complex tensors.</span>
<span class="c1"># For complex tensors, formatter1 and formatter2 are the formatters for tensor.real</span>
<span class="c1"># and tensor.imag respesectively</span>
<span class="k">def</span> <span class="nf">_tensor_str_with_formatter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indent</span><span class="p">,</span> <span class="n">summarize</span><span class="p">,</span> <span class="n">formatter1</span><span class="p">,</span> <span class="n">formatter2</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span>
<span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="n">_scalar_str</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">formatter1</span><span class="p">,</span> <span class="n">formatter2</span><span class="p">)</span>
<span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="n">_vector_str</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indent</span><span class="p">,</span> <span class="n">summarize</span><span class="p">,</span> <span class="n">formatter1</span><span class="p">,</span> <span class="n">formatter2</span><span class="p">)</span>
<span class="k">if</span> <span class="n">summarize</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">></span> <span class="mi">2</span> <span class="o">*</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span><span class="p">:</span>
<span class="n">slices</span> <span class="o">=</span> <span class="p">(</span>
<span class="p">[</span>
<span class="n">_tensor_str_with_formatter</span><span class="p">(</span>
<span class="bp">self</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">indent</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">summarize</span><span class="p">,</span> <span class="n">formatter1</span><span class="p">,</span> <span class="n">formatter2</span>
<span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span><span class="p">)</span>
<span class="p">]</span>
<span class="o">+</span> <span class="p">[</span><span class="s2">"..."</span><span class="p">]</span>
<span class="o">+</span> <span class="p">[</span>
<span class="n">_tensor_str_with_formatter</span><span class="p">(</span>
<span class="bp">self</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">indent</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">summarize</span><span class="p">,</span> <span class="n">formatter1</span><span class="p">,</span> <span class="n">formatter2</span>
<span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span>
<span class="p">]</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">slices</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">_tensor_str_with_formatter</span><span class="p">(</span>
<span class="bp">self</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">indent</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">summarize</span><span class="p">,</span> <span class="n">formatter1</span><span class="p">,</span> <span class="n">formatter2</span>
<span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="p">]</span>
<span class="n">tensor_str</span> <span class="o">=</span> <span class="p">(</span><span class="s2">","</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span> <span class="o">*</span> <span class="p">(</span><span class="n">dim</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="s2">" "</span> <span class="o">*</span> <span class="p">(</span><span class="n">indent</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">slices</span><span class="p">)</span>
<span class="k">return</span> <span class="s2">"["</span> <span class="o">+</span> <span class="n">tensor_str</span> <span class="o">+</span> <span class="s2">"]"</span>
<span class="k">def</span> <span class="nf">_tensor_str</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indent</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">"[]"</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_names</span><span class="p">():</span>
<span class="c1"># There are two main codepaths (possibly more) that tensor printing goes through:</span>
<span class="c1"># - tensor data can fit comfortably on screen</span>
<span class="c1"># - tensor data needs to be summarized</span>
<span class="c1"># Some of the codepaths don't fully support named tensors, so we send in</span>
<span class="c1"># an unnamed tensor to the formatting code as a workaround.</span>
<span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
<span class="n">summarize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">></span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">threshold</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_zerotensor</span><span class="p">():</span>
<span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
<span class="c1"># handle the negative bit</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_neg</span><span class="p">():</span>
<span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resolve_neg</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">in</span> <span class="p">[</span>
<span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">float8_e5m2</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">float8_e5m2fnuz</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">float8_e4m3fn</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">float8_e4m3fnuz</span><span class="p">,</span>
<span class="p">]:</span>
<span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">is</span> <span class="n">torch</span><span class="o">.</span><span class="n">complex32</span><span class="p">:</span>
<span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cfloat</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">is_complex</span><span class="p">:</span>
<span class="c1"># handle the conjugate bit</span>
<span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resolve_conj</span><span class="p">()</span>
<span class="n">real_formatter</span> <span class="o">=</span> <span class="n">_Formatter</span><span class="p">(</span>
<span class="n">get_summarized_data</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real</span><span class="p">)</span> <span class="k">if</span> <span class="n">summarize</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">real</span>
<span class="p">)</span>
<span class="n">imag_formatter</span> <span class="o">=</span> <span class="n">_Formatter</span><span class="p">(</span>
<span class="n">get_summarized_data</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">imag</span><span class="p">)</span> <span class="k">if</span> <span class="n">summarize</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">imag</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">_tensor_str_with_formatter</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">indent</span><span class="p">,</span> <span class="n">summarize</span><span class="p">,</span> <span class="n">real_formatter</span><span class="p">,</span> <span class="n">imag_formatter</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">formatter</span> <span class="o">=</span> <span class="n">_Formatter</span><span class="p">(</span><span class="n">get_summarized_data</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="k">if</span> <span class="n">summarize</span> <span class="k">else</span> <span class="bp">self</span><span class="p">)</span>
<span class="k">return</span> <span class="n">_tensor_str_with_formatter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indent</span><span class="p">,</span> <span class="n">summarize</span><span class="p">,</span> <span class="n">formatter</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_add_suffixes</span><span class="p">(</span><span class="n">tensor_str</span><span class="p">,</span> <span class="n">suffixes</span><span class="p">,</span> <span class="n">indent</span><span class="p">,</span> <span class="n">force_newline</span><span class="p">):</span>
<span class="n">tensor_strs</span> <span class="o">=</span> <span class="p">[</span><span class="n">tensor_str</span><span class="p">]</span>
<span class="n">last_line_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">tensor_str</span><span class="p">)</span> <span class="o">-</span> <span class="n">tensor_str</span><span class="o">.</span><span class="n">rfind</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
<span class="k">for</span> <span class="n">suffix</span> <span class="ow">in</span> <span class="n">suffixes</span><span class="p">:</span>
<span class="n">suffix_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">suffix</span><span class="p">)</span>
<span class="k">if</span> <span class="n">force_newline</span> <span class="ow">or</span> <span class="n">last_line_len</span> <span class="o">+</span> <span class="n">suffix_len</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">></span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">linewidth</span><span class="p">:</span>
<span class="n">tensor_strs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">",</span><span class="se">\n</span><span class="s2">"</span> <span class="o">+</span> <span class="s2">" "</span> <span class="o">*</span> <span class="n">indent</span> <span class="o">+</span> <span class="n">suffix</span><span class="p">)</span>
<span class="n">last_line_len</span> <span class="o">=</span> <span class="n">indent</span> <span class="o">+</span> <span class="n">suffix_len</span>
<span class="n">force_newline</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">tensor_strs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">", "</span> <span class="o">+</span> <span class="n">suffix</span><span class="p">)</span>
<span class="n">last_line_len</span> <span class="o">+=</span> <span class="n">suffix_len</span> <span class="o">+</span> <span class="mi">2</span>
<span class="n">tensor_strs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">")"</span><span class="p">)</span>
<span class="k">return</span> <span class="s2">""</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tensor_strs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">get_summarized_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span>
<span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">></span> <span class="mi">2</span> <span class="o">*</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span>
<span class="p">(</span><span class="bp">self</span><span class="p">[:</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span><span class="p">],</span> <span class="bp">self</span><span class="p">[</span><span class="o">-</span><span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span> <span class="p">:])</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">new_empty</span><span class="p">([</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">())</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">></span> <span class="mi">2</span> <span class="o">*</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span><span class="p">:</span>
<span class="n">start</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span><span class="p">)]</span>
<span class="n">end</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-</span> <span class="n">PRINT_OPTS</span><span class="o">.</span><span class="n">edgeitems</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">))]</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">get_summarized_data</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">(</span><span class="n">start</span> <span class="o">+</span> <span class="n">end</span><span class="p">)])</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">get_summarized_data</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">_str_intern</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">tensor_contents</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_functorch</span><span class="o">.</span><span class="n">is_functorch_wrapped_tensor</span><span class="p">(</span><span class="n">inp</span><span class="p">):</span>
<span class="k">return</span> <span class="n">_functorch_wrapper_str_intern</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">tensor_contents</span><span class="o">=</span><span class="n">tensor_contents</span><span class="p">)</span>
<span class="n">is_plain_tensor</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">inp</span><span class="p">)</span> <span class="ow">is</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">inp</span><span class="p">)</span> <span class="ow">is</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span>
<span class="k">if</span> <span class="n">inp</span><span class="o">.</span><span class="n">is_nested</span><span class="p">:</span>
<span class="n">prefix</span> <span class="o">=</span> <span class="s2">"nested_tensor("</span>
<span class="k">elif</span> <span class="n">is_plain_tensor</span><span class="p">:</span>
<span class="n">prefix</span> <span class="o">=</span> <span class="s2">"tensor("</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">prefix</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">inp</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">("</span>
<span class="n">indent</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">prefix</span><span class="p">)</span>
<span class="n">suffixes</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">custom_contents_provided</span> <span class="o">=</span> <span class="n">tensor_contents</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">custom_contents_provided</span><span class="p">:</span>
<span class="n">tensor_str</span> <span class="o">=</span> <span class="n">tensor_contents</span>
<span class="c1"># This is used to extract the primal value and thus disable the forward AD</span>
<span class="c1"># within this function.</span>
<span class="c1"># TODO(albanD) This needs to be updated when more than one level is supported</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">tangent</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">forward_ad</span><span class="o">.</span><span class="n">unpack_dual</span><span class="p">(</span><span class="n">inp</span><span class="p">)</span>
<span class="c1"># Note [Print tensor device]:</span>
<span class="c1"># A general logic here is we only print device when it doesn't match</span>
<span class="c1"># the device specified in default tensor type.</span>
<span class="c1"># Currently torch.set_default_tensor_type() only supports CPU/CUDA, thus</span>
<span class="c1"># torch._C._get_default_device() only returns either cpu or cuda.</span>
<span class="c1"># In other cases, we don't have a way to set them as default yet,</span>
<span class="c1"># and we should always print out device for them.</span>
<span class="k">if</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_default_device</span><span class="p">()</span>
<span class="ow">or</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">"cuda"</span>
<span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_device</span><span class="p">()</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">index</span>
<span class="p">)</span>
<span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">"mps"</span><span class="p">)</span>
<span class="p">):</span>
<span class="n">suffixes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"device='"</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="o">+</span> <span class="s2">"'"</span><span class="p">)</span>
<span class="c1"># Tensor printing performs tensor operations like slice, indexing, etc to make it in a</span>
<span class="c1"># representable format. These operations on ipu/xla/lazy/mtia tensor results in compilations. Hence,</span>
<span class="c1"># to avoid compilations, copying the tensor to cpu before printing.</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">"xla"</span><span class="p">,</span> <span class="s2">"lazy"</span><span class="p">,</span> <span class="s2">"ipu"</span><span class="p">,</span> <span class="s2">"mtia"</span><span class="p">]:</span>
<span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">"cpu"</span><span class="p">)</span>
<span class="c1"># TODO: add an API to map real -> complex dtypes</span>
<span class="n">_default_complex_dtype</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cdouble</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">get_default_dtype</span><span class="p">()</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">double</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">cfloat</span>
<span class="p">)</span>
<span class="n">has_default_dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">in</span> <span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">get_default_dtype</span><span class="p">(),</span>
<span class="n">_default_complex_dtype</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">bool</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_sparse</span><span class="p">:</span>
<span class="n">suffixes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"size="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span>
<span class="kn">from</span> <span class="nn">torch._subclasses.fake_tensor</span> <span class="kn">import</span> <span class="n">FakeTensor</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_meta</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">FakeTensor</span><span class="p">):</span>
<span class="n">suffixes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"nnz="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_nnz</span><span class="p">()))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">has_default_dtype</span><span class="p">:</span>
<span class="n">suffixes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"dtype="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">custom_contents_provided</span><span class="p">:</span>
<span class="n">indices_prefix</span> <span class="o">=</span> <span class="s2">"indices=tensor("</span>
<span class="n">indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_indices</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="n">indices_str</span> <span class="o">=</span> <span class="n">_tensor_str</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">indent</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">indices_prefix</span><span class="p">))</span>
<span class="k">if</span> <span class="n">indices</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">indices_str</span> <span class="o">+=</span> <span class="s2">", size="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">indices</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
<span class="n">values_prefix</span> <span class="o">=</span> <span class="s2">"values=tensor("</span>
<span class="n">values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_values</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="n">values_str</span> <span class="o">=</span> <span class="n">_tensor_str</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">indent</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">values_prefix</span><span class="p">))</span>
<span class="k">if</span> <span class="n">values</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">values_str</span> <span class="o">+=</span> <span class="s2">", size="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">values</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
<span class="n">tensor_str</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">indices_prefix</span>
<span class="o">+</span> <span class="n">indices_str</span>
<span class="o">+</span> <span class="s2">"),</span><span class="se">\n</span><span class="s2">"</span>
<span class="o">+</span> <span class="s2">" "</span> <span class="o">*</span> <span class="n">indent</span>
<span class="o">+</span> <span class="n">values_prefix</span>
<span class="o">+</span> <span class="n">values_str</span>
<span class="o">+</span> <span class="s2">")"</span>
<span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">layout</span> <span class="ow">in</span> <span class="p">{</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sparse_csr</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sparse_csc</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sparse_bsr</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sparse_bsc</span><span class="p">,</span>
<span class="p">}:</span>
<span class="n">suffixes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"size="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span>
<span class="n">suffixes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"nnz="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_nnz</span><span class="p">()))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">has_default_dtype</span><span class="p">:</span>
<span class="n">suffixes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"dtype="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">custom_contents_provided</span><span class="p">:</span>
<span class="n">compressed_indices_method</span><span class="p">,</span> <span class="n">plain_indices_method</span> <span class="o">=</span> <span class="p">{</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sparse_csr</span><span class="p">:</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">crow_indices</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">col_indices</span><span class="p">),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sparse_csc</span><span class="p">:</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">ccol_indices</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">row_indices</span><span class="p">),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sparse_bsr</span><span class="p">:</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">crow_indices</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">col_indices</span><span class="p">),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sparse_bsc</span><span class="p">:</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">ccol_indices</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">row_indices</span><span class="p">),</span>
<span class="p">}[</span><span class="bp">self</span><span class="o">.</span><span class="n">layout</span><span class="p">]</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">layout</span> <span class="ow">in</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">sparse_csr</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_bsr</span><span class="p">}:</span>
<span class="n">cdimname</span><span class="p">,</span> <span class="n">pdimname</span> <span class="o">=</span> <span class="s2">"row"</span><span class="p">,</span> <span class="s2">"column"</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">cdimname</span><span class="p">,</span> <span class="n">pdimname</span> <span class="o">=</span> <span class="s2">"column"</span><span class="p">,</span> <span class="s2">"row"</span>
<span class="n">compressed_indices_prefix</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"c</span><span class="si">{</span><span class="n">cdimname</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span><span class="si">}</span><span class="s2">_indices=tensor("</span>
<span class="n">compressed_indices</span> <span class="o">=</span> <span class="n">compressed_indices_method</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="n">compressed_indices_str</span> <span class="o">=</span> <span class="n">_tensor_str</span><span class="p">(</span>
<span class="n">compressed_indices</span><span class="p">,</span> <span class="n">indent</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">compressed_indices_prefix</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">compressed_indices</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">compressed_indices_str</span> <span class="o">+=</span> <span class="s2">", size="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span>
<span class="nb">tuple</span><span class="p">(</span><span class="n">compressed_indices</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">plain_indices_prefix</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">pdimname</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span><span class="si">}</span><span class="s2">_indices=tensor("</span>
<span class="n">plain_indices</span> <span class="o">=</span> <span class="n">plain_indices_method</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="n">plain_indices_str</span> <span class="o">=</span> <span class="n">_tensor_str</span><span class="p">(</span>
<span class="n">plain_indices</span><span class="p">,</span> <span class="n">indent</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">plain_indices_prefix</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">plain_indices</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">plain_indices_str</span> <span class="o">+=</span> <span class="s2">", size="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">plain_indices</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
<span class="n">values_prefix</span> <span class="o">=</span> <span class="s2">"values=tensor("</span>
<span class="n">values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="n">values_str</span> <span class="o">=</span> <span class="n">_tensor_str</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">indent</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">values_prefix</span><span class="p">))</span>
<span class="k">if</span> <span class="n">values</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">values_str</span> <span class="o">+=</span> <span class="s2">", size="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">values</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
<span class="n">tensor_str</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">compressed_indices_prefix</span>
<span class="o">+</span> <span class="n">compressed_indices_str</span>
<span class="o">+</span> <span class="s2">"),</span><span class="se">\n</span><span class="s2">"</span>
<span class="o">+</span> <span class="s2">" "</span> <span class="o">*</span> <span class="n">indent</span>
<span class="o">+</span> <span class="n">plain_indices_prefix</span>
<span class="o">+</span> <span class="n">plain_indices_str</span>
<span class="o">+</span> <span class="s2">"),</span><span class="se">\n</span><span class="s2">"</span>
<span class="o">+</span> <span class="s2">" "</span> <span class="o">*</span> <span class="n">indent</span>
<span class="o">+</span> <span class="n">values_prefix</span>
<span class="o">+</span> <span class="n">values_str</span>
<span class="o">+</span> <span class="s2">")"</span>
<span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_quantized</span><span class="p">:</span>
<span class="n">suffixes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"size="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">has_default_dtype</span><span class="p">:</span>
<span class="n">suffixes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"dtype="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span>
<span class="n">suffixes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"quantization_scheme="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">qscheme</span><span class="p">()))</span>
<span class="k">if</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">qscheme</span><span class="p">()</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">per_tensor_affine</span>
<span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">qscheme</span><span class="p">()</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">per_tensor_symmetric</span>
<span class="p">):</span>
<span class="n">suffixes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"scale="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">q_scale</span><span class="p">()))</span>
<span class="n">suffixes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"zero_point="</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">q_zero_point</span><span class="p">()))</span>
<span class="k">elif</span> <span class="p">(</span>