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<h1>Source code for torch.testing._creation</h1><div class="highlight"><pre>
<span></span><span class="sd">"""</span>
<span class="sd">This module contains tensor creation utilities.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">collections.abc</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">cast</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="n">_INTEGRAL_TYPES</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">int8</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">int16</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">int32</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">_FLOATING_TYPES</span> <span class="o">=</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">float32</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">]</span>
<span class="n">_FLOATING_8BIT_TYPES</span> <span class="o">=</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_e5m2</span><span class="p">]</span>
<span class="n">_COMPLEX_TYPES</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">complex32</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">complex64</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">complex128</span><span class="p">]</span>
<span class="n">_BOOLEAN_OR_INTEGRAL_TYPES</span> <span class="o">=</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="o">*</span><span class="n">_INTEGRAL_TYPES</span><span class="p">]</span>
<span class="n">_FLOATING_OR_COMPLEX_TYPES</span> <span class="o">=</span> <span class="p">[</span><span class="o">*</span><span class="n">_FLOATING_TYPES</span><span class="p">,</span> <span class="o">*</span><span class="n">_COMPLEX_TYPES</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">_uniform_random_</span><span class="p">(</span><span class="n">t</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">low</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">high</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="c1"># uniform_ requires to-from <= std::numeric_limits<scalar_t>::max()</span>
<span class="c1"># Work around this by scaling the range before and after the PRNG</span>
<span class="k">if</span> <span class="n">high</span> <span class="o">-</span> <span class="n">low</span> <span class="o">>=</span> <span class="n">torch</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">:</span>
<span class="k">return</span> <span class="n">t</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="n">low</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">high</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">t</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">)</span>
<div class="viewcode-block" id="make_tensor"><a class="viewcode-back" href="../../../testing.html#torch.testing.make_tensor">[docs]</a><span class="k">def</span> <span class="nf">make_tensor</span><span class="p">(</span>
<span class="o">*</span><span class="n">shape</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]],</span>
<span class="n">dtype</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
<span class="n">device</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">],</span>
<span class="n">low</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">high</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">requires_grad</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">noncontiguous</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">exclude_zero</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">memory_format</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">memory_format</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Creates a tensor with the given :attr:`shape`, :attr:`device`, and :attr:`dtype`, and filled with</span>
<span class="sd"> values uniformly drawn from ``[low, high)``.</span>
<span class="sd"> If :attr:`low` or :attr:`high` are specified and are outside the range of the :attr:`dtype`'s representable</span>
<span class="sd"> finite values then they are clamped to the lowest or highest representable finite value, respectively.</span>
<span class="sd"> If ``None``, then the following table describes the default values for :attr:`low` and :attr:`high`,</span>
<span class="sd"> which depend on :attr:`dtype`.</span>
<span class="sd"> +---------------------------+------------+----------+</span>
<span class="sd"> | ``dtype`` | ``low`` | ``high`` |</span>
<span class="sd"> +===========================+============+==========+</span>
<span class="sd"> | boolean type | ``0`` | ``2`` |</span>
<span class="sd"> +---------------------------+------------+----------+</span>
<span class="sd"> | unsigned integral type | ``0`` | ``10`` |</span>
<span class="sd"> +---------------------------+------------+----------+</span>
<span class="sd"> | signed integral types | ``-9`` | ``10`` |</span>
<span class="sd"> +---------------------------+------------+----------+</span>
<span class="sd"> | floating types | ``-9`` | ``9`` |</span>
<span class="sd"> +---------------------------+------------+----------+</span>
<span class="sd"> | complex types | ``-9`` | ``9`` |</span>
<span class="sd"> +---------------------------+------------+----------+</span>
<span class="sd"> Args:</span>
<span class="sd"> shape (Tuple[int, ...]): Single integer or a sequence of integers defining the shape of the output tensor.</span>
<span class="sd"> dtype (:class:`torch.dtype`): The data type of the returned tensor.</span>
<span class="sd"> device (Union[str, torch.device]): The device of the returned tensor.</span>
<span class="sd"> low (Optional[Number]): Sets the lower limit (inclusive) of the given range. If a number is provided it is</span>
<span class="sd"> clamped to the least representable finite value of the given dtype. When ``None`` (default),</span>
<span class="sd"> this value is determined based on the :attr:`dtype` (see the table above). Default: ``None``.</span>
<span class="sd"> high (Optional[Number]): Sets the upper limit (exclusive) of the given range. If a number is provided it is</span>
<span class="sd"> clamped to the greatest representable finite value of the given dtype. When ``None`` (default) this value</span>
<span class="sd"> is determined based on the :attr:`dtype` (see the table above). Default: ``None``.</span>
<span class="sd"> .. deprecated:: 2.1</span>
<span class="sd"> Passing ``low==high`` to :func:`~torch.testing.make_tensor` for floating or complex types is deprecated</span>
<span class="sd"> since 2.1 and will be removed in 2.3. Use :func:`torch.full` instead.</span>
<span class="sd"> requires_grad (Optional[bool]): If autograd should record operations on the returned tensor. Default: ``False``.</span>
<span class="sd"> noncontiguous (Optional[bool]): If `True`, the returned tensor will be noncontiguous. This argument is</span>
<span class="sd"> ignored if the constructed tensor has fewer than two elements. Mutually exclusive with ``memory_format``.</span>
<span class="sd"> exclude_zero (Optional[bool]): If ``True`` then zeros are replaced with the dtype's small positive value</span>
<span class="sd"> depending on the :attr:`dtype`. For bool and integer types zero is replaced with one. For floating</span>
<span class="sd"> point types it is replaced with the dtype's smallest positive normal number (the "tiny" value of the</span>
<span class="sd"> :attr:`dtype`'s :func:`~torch.finfo` object), and for complex types it is replaced with a complex number</span>
<span class="sd"> whose real and imaginary parts are both the smallest positive normal number representable by the complex</span>
<span class="sd"> type. Default ``False``.</span>
<span class="sd"> memory_format (Optional[torch.memory_format]): The memory format of the returned tensor. Mutually exclusive</span>
<span class="sd"> with ``noncontiguous``.</span>
<span class="sd"> Raises:</span>
<span class="sd"> ValueError: If ``requires_grad=True`` is passed for integral `dtype`</span>
<span class="sd"> ValueError: If ``low >= high``.</span>
<span class="sd"> ValueError: If either :attr:`low` or :attr:`high` is ``nan``.</span>
<span class="sd"> ValueError: If both :attr:`noncontiguous` and :attr:`memory_format` are passed.</span>
<span class="sd"> TypeError: If :attr:`dtype` isn't supported by this function.</span>
<span class="sd"> Examples:</span>
<span class="sd"> >>> # xdoctest: +SKIP</span>
<span class="sd"> >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)</span>
<span class="sd"> >>> from torch.testing import make_tensor</span>
<span class="sd"> >>> # Creates a float tensor with values in [-1, 1)</span>
<span class="sd"> >>> make_tensor((3,), device='cpu', dtype=torch.float32, low=-1, high=1)</span>
<span class="sd"> >>> # xdoctest: +SKIP</span>
<span class="sd"> tensor([ 0.1205, 0.2282, -0.6380])</span>
<span class="sd"> >>> # Creates a bool tensor on CUDA</span>
<span class="sd"> >>> make_tensor((2, 2), device='cuda', dtype=torch.bool)</span>
<span class="sd"> tensor([[False, False],</span>
<span class="sd"> [False, True]], device='cuda:0')</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">modify_low_high</span><span class="p">(</span>
<span class="n">low</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
<span class="n">high</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">lowest_inclusive</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">highest_exclusive</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">default_low</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">default_high</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Modifies (and raises ValueError when appropriate) low and high values given by the user (input_low, input_high)</span>
<span class="sd"> if required.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">clamp</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">l</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">h</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">min</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">l</span><span class="p">),</span> <span class="n">h</span><span class="p">)</span>
<span class="n">low</span> <span class="o">=</span> <span class="n">low</span> <span class="k">if</span> <span class="n">low</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">default_low</span>
<span class="n">high</span> <span class="o">=</span> <span class="n">high</span> <span class="k">if</span> <span class="n">high</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">default_high</span>
<span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span> <span class="ow">and</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="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="p">[</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">]):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"`low` and `high` cannot be NaN, but got </span><span class="si">{</span><span class="n">low</span><span class="si">=}</span><span class="s2"> and </span><span class="si">{</span><span class="n">high</span><span class="si">=}</span><span class="s2">"</span>
<span class="p">)</span>
<span class="k">elif</span> <span class="n">low</span> <span class="o">==</span> <span class="n">high</span> <span class="ow">and</span> <span class="n">dtype</span> <span class="ow">in</span> <span class="n">_FLOATING_OR_COMPLEX_TYPES</span><span class="p">:</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="s2">"Passing `low==high` to `torch.testing.make_tensor` for floating or complex types "</span>
<span class="s2">"is deprecated since 2.1 and will be removed in 2.3. "</span>
<span class="s2">"Use torch.full(...) instead."</span><span class="p">,</span>
<span class="ne">FutureWarning</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">elif</span> <span class="n">low</span> <span class="o">>=</span> <span class="n">high</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"`low` must be less than `high`, but got </span><span class="si">{</span><span class="n">low</span><span class="si">}</span><span class="s2"> >= </span><span class="si">{</span><span class="n">high</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">high</span> <span class="o"><</span> <span class="n">lowest_inclusive</span> <span class="ow">or</span> <span class="n">low</span> <span class="o">>=</span> <span class="n">highest_exclusive</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"The value interval specified by `low` and `high` is [</span><span class="si">{</span><span class="n">low</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">high</span><span class="si">}</span><span class="s2">), "</span>
<span class="sa">f</span><span class="s2">"but </span><span class="si">{</span><span class="n">dtype</span><span class="si">}</span><span class="s2"> only supports [</span><span class="si">{</span><span class="n">lowest_inclusive</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">highest_exclusive</span><span class="si">}</span><span class="s2">)"</span>
<span class="p">)</span>
<span class="n">low</span> <span class="o">=</span> <span class="n">clamp</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">lowest_inclusive</span><span class="p">,</span> <span class="n">highest_exclusive</span><span class="p">)</span>
<span class="n">high</span> <span class="o">=</span> <span class="n">clamp</span><span class="p">(</span><span class="n">high</span><span class="p">,</span> <span class="n">lowest_inclusive</span><span class="p">,</span> <span class="n">highest_exclusive</span><span class="p">)</span>
<span class="k">if</span> <span class="n">dtype</span> <span class="ow">in</span> <span class="n">_BOOLEAN_OR_INTEGRAL_TYPES</span><span class="p">:</span>
<span class="c1"># 1. `low` is ceiled to avoid creating values smaller than `low` and thus outside the specified interval</span>
<span class="c1"># 2. Following the same reasoning as for 1., `high` should be floored. However, the higher bound of</span>
<span class="c1"># `torch.randint` is exclusive, and thus we need to ceil here as well.</span>
<span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">low</span><span class="p">),</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">high</span><span class="p">)</span>
<span class="k">return</span> <span class="n">low</span><span class="p">,</span> <span class="n">high</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">collections</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">Sequence</span><span class="p">):</span>
<span class="n">shape</span> <span class="o">=</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># type: ignore[assignment]</span>
<span class="n">shape</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">))</span>
<span class="k">if</span> <span class="n">noncontiguous</span> <span class="ow">and</span> <span class="n">memory_format</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"The parameters `noncontiguous` and `memory_format` are mutually exclusive, "</span>
<span class="sa">f</span><span class="s2">"but got </span><span class="si">{</span><span class="n">noncontiguous</span><span class="si">=}</span><span class="s2"> and </span><span class="si">{</span><span class="n">memory_format</span><span class="si">=}</span><span class="s2">"</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">requires_grad</span> <span class="ow">and</span> <span class="n">dtype</span> <span class="ow">in</span> <span class="n">_BOOLEAN_OR_INTEGRAL_TYPES</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"`requires_grad=True` is not supported for boolean and integral dtypes, but got </span><span class="si">{</span><span class="n">dtype</span><span class="si">=}</span><span class="s2">"</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="n">torch</span><span class="o">.</span><span class="n">bool</span><span class="p">:</span>
<span class="n">low</span><span class="p">,</span> <span class="n">high</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span>
<span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
<span class="n">modify_low_high</span><span class="p">(</span>
<span class="n">low</span><span class="p">,</span>
<span class="n">high</span><span class="p">,</span>
<span class="n">lowest_inclusive</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">highest_exclusive</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">default_low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">default_high</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</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">elif</span> <span class="n">dtype</span> <span class="ow">in</span> <span class="n">_BOOLEAN_OR_INTEGRAL_TYPES</span><span class="p">:</span>
<span class="n">low</span><span class="p">,</span> <span class="n">high</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span>
<span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
<span class="n">modify_low_high</span><span class="p">(</span>
<span class="n">low</span><span class="p">,</span>
<span class="n">high</span><span class="p">,</span>
<span class="n">lowest_inclusive</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">iinfo</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">min</span><span class="p">,</span>
<span class="n">highest_exclusive</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">iinfo</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">max</span>
<span class="c1"># In theory, `highest_exclusive` should always be the maximum value + 1. However, `torch.randint`</span>
<span class="c1"># internally converts the bounds to an int64 and would overflow. In other words: `torch.randint` cannot</span>
<span class="c1"># sample 2**63 - 1, i.e. the maximum value of `torch.int64` and we need to account for that here.</span>
<span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">int64</span> <span class="k">else</span> <span class="mi">0</span><span class="p">),</span>
<span class="c1"># This is incorrect for `torch.uint8`, but since we clamp to `lowest`, i.e. 0 for `torch.uint8`,</span>
<span class="c1"># _after_ we use the default value, we don't need to special case it here</span>
<span class="n">default_low</span><span class="o">=-</span><span class="mi">9</span><span class="p">,</span>
<span class="n">default_high</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</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">elif</span> <span class="n">dtype</span> <span class="ow">in</span> <span class="n">_FLOATING_OR_COMPLEX_TYPES</span><span class="p">:</span>
<span class="n">low</span><span class="p">,</span> <span class="n">high</span> <span class="o">=</span> <span class="n">modify_low_high</span><span class="p">(</span>
<span class="n">low</span><span class="p">,</span>
<span class="n">high</span><span class="p">,</span>
<span class="n">lowest_inclusive</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">min</span><span class="p">,</span>
<span class="n">highest_exclusive</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">,</span>
<span class="n">default_low</span><span class="o">=-</span><span class="mi">9</span><span class="p">,</span>
<span class="n">default_high</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</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="n">_uniform_random_</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">view_as_real</span><span class="p">(</span><span class="n">result</span><span class="p">)</span> <span class="k">if</span> <span class="n">dtype</span> <span class="ow">in</span> <span class="n">_COMPLEX_TYPES</span> <span class="k">else</span> <span class="n">result</span><span class="p">,</span> <span class="n">low</span><span class="p">,</span> <span class="n">high</span>
<span class="p">)</span>
<span class="k">elif</span> <span class="n">dtype</span> <span class="ow">in</span> <span class="n">_FLOATING_8BIT_TYPES</span><span class="p">:</span>
<span class="n">low</span><span class="p">,</span> <span class="n">high</span> <span class="o">=</span> <span class="n">modify_low_high</span><span class="p">(</span>
<span class="n">low</span><span class="p">,</span>
<span class="n">high</span><span class="p">,</span>
<span class="n">lowest_inclusive</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">min</span><span class="p">,</span>
<span class="n">highest_exclusive</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">,</span>
<span class="n">default_low</span><span class="o">=-</span><span class="mi">9</span><span class="p">,</span>
<span class="n">default_high</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</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">float32</span><span class="p">)</span>
<span class="n">_uniform_random_</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"The requested dtype '</span><span class="si">{</span><span class="n">dtype</span><span class="si">}</span><span class="s2">' is not supported by torch.testing.make_tensor()."</span>
<span class="s2">" To request support, file an issue at: https://github.com/pytorch/pytorch/issues"</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">noncontiguous</span> <span class="ow">and</span> <span class="n">result</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">repeat_interleave</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="mi">2</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="n">result</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">::</span><span class="mi">2</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">memory_format</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">clone</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">memory_format</span><span class="p">)</span>
<span class="k">if</span> <span class="n">exclude_zero</span><span class="p">:</span>
<span class="n">result</span><span class="p">[</span><span class="n">result</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
<span class="mi">1</span> <span class="k">if</span> <span class="n">dtype</span> <span class="ow">in</span> <span class="n">_BOOLEAN_OR_INTEGRAL_TYPES</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">tiny</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">dtype</span> <span class="ow">in</span> <span class="n">_FLOATING_OR_COMPLEX_TYPES</span><span class="p">:</span>
<span class="n">result</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="n">requires_grad</span>
<span class="k">return</span> <span class="n">result</span></div>
</pre></div>
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