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<li class="toctree-l2"><a class="reference internal" href="generated/torch.clip.html">torch.clip</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.conj_physical.html">torch.conj_physical</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.copysign.html">torch.copysign</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cos.html">torch.cos</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cosh.html">torch.cosh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.deg2rad.html">torch.deg2rad</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.div.html">torch.div</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.divide.html">torch.divide</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.digamma.html">torch.digamma</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.erf.html">torch.erf</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.erfc.html">torch.erfc</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.erfinv.html">torch.erfinv</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.exp.html">torch.exp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.exp2.html">torch.exp2</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.expm1.html">torch.expm1</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fake_quantize_per_channel_affine.html">torch.fake_quantize_per_channel_affine</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fake_quantize_per_tensor_affine.html">torch.fake_quantize_per_tensor_affine</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fix.html">torch.fix</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.float_power.html">torch.float_power</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.floor.html">torch.floor</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.floor_divide.html">torch.floor_divide</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fmod.html">torch.fmod</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.frac.html">torch.frac</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.frexp.html">torch.frexp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.gradient.html">torch.gradient</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.imag.html">torch.imag</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.ldexp.html">torch.ldexp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.lerp.html">torch.lerp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.lgamma.html">torch.lgamma</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.log.html">torch.log</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.log10.html">torch.log10</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.log1p.html">torch.log1p</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.log2.html">torch.log2</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logaddexp.html">torch.logaddexp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logaddexp2.html">torch.logaddexp2</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logical_and.html">torch.logical_and</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logical_not.html">torch.logical_not</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logical_or.html">torch.logical_or</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logical_xor.html">torch.logical_xor</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logit.html">torch.logit</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.hypot.html">torch.hypot</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.i0.html">torch.i0</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.igamma.html">torch.igamma</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.igammac.html">torch.igammac</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mul.html">torch.mul</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.multiply.html">torch.multiply</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mvlgamma.html">torch.mvlgamma</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nan_to_num.html">torch.nan_to_num</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.neg.html">torch.neg</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.negative.html">torch.negative</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nextafter.html">torch.nextafter</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.polygamma.html">torch.polygamma</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.positive.html">torch.positive</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.pow.html">torch.pow</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.quantized_batch_norm.html">torch.quantized_batch_norm</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.quantized_max_pool1d.html">torch.quantized_max_pool1d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.quantized_max_pool2d.html">torch.quantized_max_pool2d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.rad2deg.html">torch.rad2deg</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.real.html">torch.real</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.reciprocal.html">torch.reciprocal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.remainder.html">torch.remainder</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.round.html">torch.round</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.rsqrt.html">torch.rsqrt</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sigmoid.html">torch.sigmoid</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sign.html">torch.sign</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sgn.html">torch.sgn</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.signbit.html">torch.signbit</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sin.html">torch.sin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sinc.html">torch.sinc</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sinh.html">torch.sinh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.softmax.html">torch.softmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sqrt.html">torch.sqrt</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.square.html">torch.square</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sub.html">torch.sub</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.subtract.html">torch.subtract</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.tan.html">torch.tan</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.tanh.html">torch.tanh</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.true_divide.html">torch.true_divide</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.trunc.html">torch.trunc</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xlogy.html">torch.xlogy</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.argmax.html">torch.argmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.argmin.html">torch.argmin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.amax.html">torch.amax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.amin.html">torch.amin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.aminmax.html">torch.aminmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.all.html">torch.all</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.any.html">torch.any</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.max.html">torch.max</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.min.html">torch.min</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.dist.html">torch.dist</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logsumexp.html">torch.logsumexp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mean.html">torch.mean</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nanmean.html">torch.nanmean</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.median.html">torch.median</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nanmedian.html">torch.nanmedian</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mode.html">torch.mode</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.norm.html">torch.norm</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nansum.html">torch.nansum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.prod.html">torch.prod</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.quantile.html">torch.quantile</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nanquantile.html">torch.nanquantile</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.std.html">torch.std</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.std_mean.html">torch.std_mean</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sum.html">torch.sum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.unique.html">torch.unique</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.unique_consecutive.html">torch.unique_consecutive</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.var.html">torch.var</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.var_mean.html">torch.var_mean</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.count_nonzero.html">torch.count_nonzero</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.allclose.html">torch.allclose</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.argsort.html">torch.argsort</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.eq.html">torch.eq</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.equal.html">torch.equal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.ge.html">torch.ge</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.greater_equal.html">torch.greater_equal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.gt.html">torch.gt</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.greater.html">torch.greater</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isclose.html">torch.isclose</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isfinite.html">torch.isfinite</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isin.html">torch.isin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isinf.html">torch.isinf</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isposinf.html">torch.isposinf</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isneginf.html">torch.isneginf</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isnan.html">torch.isnan</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.isreal.html">torch.isreal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.kthvalue.html">torch.kthvalue</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.le.html">torch.le</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.less_equal.html">torch.less_equal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.lt.html">torch.lt</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.less.html">torch.less</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.maximum.html">torch.maximum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.minimum.html">torch.minimum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fmax.html">torch.fmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fmin.html">torch.fmin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.ne.html">torch.ne</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.not_equal.html">torch.not_equal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.sort.html">torch.sort</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.topk.html">torch.topk</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.msort.html">torch.msort</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.stft.html">torch.stft</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.istft.html">torch.istft</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.bartlett_window.html">torch.bartlett_window</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.blackman_window.html">torch.blackman_window</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.hamming_window.html">torch.hamming_window</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.hann_window.html">torch.hann_window</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.kaiser_window.html">torch.kaiser_window</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.atleast_1d.html">torch.atleast_1d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.atleast_2d.html">torch.atleast_2d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.atleast_3d.html">torch.atleast_3d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.bincount.html">torch.bincount</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.block_diag.html">torch.block_diag</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.broadcast_tensors.html">torch.broadcast_tensors</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.broadcast_to.html">torch.broadcast_to</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.broadcast_shapes.html">torch.broadcast_shapes</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.bucketize.html">torch.bucketize</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cartesian_prod.html">torch.cartesian_prod</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cdist.html">torch.cdist</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.clone.html">torch.clone</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.combinations.html">torch.combinations</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.corrcoef.html">torch.corrcoef</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cov.html">torch.cov</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cross.html">torch.cross</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cummax.html">torch.cummax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cummin.html">torch.cummin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cumprod.html">torch.cumprod</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.cumsum.html">torch.cumsum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.diag.html">torch.diag</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.diag_embed.html">torch.diag_embed</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.diagflat.html">torch.diagflat</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.diagonal.html">torch.diagonal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.diff.html">torch.diff</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.einsum.html">torch.einsum</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.flatten.html">torch.flatten</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.flip.html">torch.flip</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.fliplr.html">torch.fliplr</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.flipud.html">torch.flipud</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.kron.html">torch.kron</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.rot90.html">torch.rot90</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.gcd.html">torch.gcd</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.histc.html">torch.histc</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.histogram.html">torch.histogram</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.histogramdd.html">torch.histogramdd</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.meshgrid.html">torch.meshgrid</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.lcm.html">torch.lcm</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.logcumsumexp.html">torch.logcumsumexp</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.ravel.html">torch.ravel</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.renorm.html">torch.renorm</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.repeat_interleave.html">torch.repeat_interleave</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.roll.html">torch.roll</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.searchsorted.html">torch.searchsorted</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.tensordot.html">torch.tensordot</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.trace.html">torch.trace</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.tril.html">torch.tril</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.tril_indices.html">torch.tril_indices</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.triu.html">torch.triu</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.triu_indices.html">torch.triu_indices</a></li>
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</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="nn.html">torch.nn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.LeakyReLU.html">LeakyReLU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.ReLU6.html">ReLU6</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.RReLU.html">RReLU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.CELU.html">CELU</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.GELU.html">GELU</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Sigmoid.html">Sigmoid</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.SiLU.html">SiLU</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Mish.html">Mish</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Softplus.html">Softplus</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Softshrink.html">Softshrink</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Softsign.html">Softsign</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Tanh.html">Tanh</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.GLU.html">GLU</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Softmin.html">Softmin</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Softmax.html">Softmax</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Softmax2d.html">Softmax2d</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.LogSoftmax.html">LogSoftmax</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.RNN.html">RNN</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.GRU.html">GRU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.Bilinear.html">Bilinear</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.utils.parametrizations.spectral_norm.html">torch.nn.utils.parametrizations.spectral_norm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.nn.utils.rnn.pad_packed_sequence.html">torch.nn.utils.rnn.pad_packed_sequence</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="nn.functional.html">torch.nn.functional</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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</ul>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.seed.html">torch.mps.seed</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.empty_cache.html">torch.mps.empty_cache</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.set_per_process_memory_fraction.html">torch.mps.set_per_process_memory_fraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.current_allocated_memory.html">torch.mps.current_allocated_memory</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.driver_allocated_memory.html">torch.mps.driver_allocated_memory</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.recommended_max_memory.html">torch.mps.recommended_max_memory</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.compile_shader.html">torch.mps.compile_shader</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.profiler.start.html">torch.mps.profiler.start</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.profiler.stop.html">torch.mps.profiler.stop</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.profiler.profile.html">torch.mps.profiler.profile</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.profiler.is_capturing_metal.html">torch.mps.profiler.is_capturing_metal</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.profiler.is_metal_capture_enabled.html">torch.mps.profiler.is_metal_capture_enabled</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.profiler.metal_capture.html">torch.mps.profiler.metal_capture</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mps.event.Event.html">Event</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="xpu.html">torch.xpu</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.StreamContext.html">StreamContext</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.current_device.html">torch.xpu.current_device</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.current_stream.html">torch.xpu.current_stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.device.html">device</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.device_count.html">torch.xpu.device_count</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.device_of.html">device_of</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_arch_list.html">torch.xpu.get_arch_list</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_device_capability.html">torch.xpu.get_device_capability</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_device_name.html">torch.xpu.get_device_name</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_device_properties.html">torch.xpu.get_device_properties</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_gencode_flags.html">torch.xpu.get_gencode_flags</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_stream_from_external.html">torch.xpu.get_stream_from_external</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.init.html">torch.xpu.init</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.is_available.html">torch.xpu.is_available</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.is_initialized.html">torch.xpu.is_initialized</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.set_stream.html">torch.xpu.set_stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.stream.html">torch.xpu.stream</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_rng_state.html">torch.xpu.get_rng_state</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.get_rng_state_all.html">torch.xpu.get_rng_state_all</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.initial_seed.html">torch.xpu.initial_seed</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.manual_seed.html">torch.xpu.manual_seed</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.manual_seed_all.html">torch.xpu.manual_seed_all</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.seed.html">torch.xpu.seed</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.seed_all.html">torch.xpu.seed_all</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.set_rng_state.html">torch.xpu.set_rng_state</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.set_rng_state_all.html">torch.xpu.set_rng_state_all</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.Event.html">Event</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.Stream.html">Stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.empty_cache.html">torch.xpu.empty_cache</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.max_memory_allocated.html">torch.xpu.max_memory_allocated</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.max_memory_reserved.html">torch.xpu.max_memory_reserved</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.mem_get_info.html">torch.xpu.mem_get_info</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.memory_allocated.html">torch.xpu.memory_allocated</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.memory_reserved.html">torch.xpu.memory_reserved</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.memory_stats.html">torch.xpu.memory_stats</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.memory_stats_as_nested_dict.html">torch.xpu.memory_stats_as_nested_dict</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.reset_accumulated_memory_stats.html">torch.xpu.reset_accumulated_memory_stats</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.xpu.reset_peak_memory_stats.html">torch.xpu.reset_peak_memory_stats</a></li>
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</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="mtia.html">torch.mtia</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.current_stream.html">torch.mtia.current_stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.default_stream.html">torch.mtia.default_stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.device_count.html">torch.mtia.device_count</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.init.html">torch.mtia.init</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.is_available.html">torch.mtia.is_available</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.is_initialized.html">torch.mtia.is_initialized</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.memory_stats.html">torch.mtia.memory_stats</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.get_device_capability.html">torch.mtia.get_device_capability</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.empty_cache.html">torch.mtia.empty_cache</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.record_memory_history.html">torch.mtia.record_memory_history</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.snapshot.html">torch.mtia.snapshot</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.attach_out_of_memory_observer.html">torch.mtia.attach_out_of_memory_observer</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.set_device.html">torch.mtia.set_device</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.set_stream.html">torch.mtia.set_stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.stream.html">torch.mtia.stream</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.synchronize.html">torch.mtia.synchronize</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.device.html">device</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.set_rng_state.html">torch.mtia.set_rng_state</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.get_rng_state.html">torch.mtia.get_rng_state</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.DeferredMtiaCallError.html">torch.mtia.DeferredMtiaCallError</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.Event.html">Event</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.Stream.html">Stream</a></li>
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</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="mtia.memory.html">torch.mtia.memory</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.mtia.memory.memory_stats.html">torch.mtia.memory.memory_stats</a></li>
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</details></li>
<li class="toctree-l1"><a class="reference internal" href="meta.html">Meta device</a></li>
<li class="toctree-l1"><a class="reference internal" href="backends.html">torch.backends</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="export.html">torch.export</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="export.programming_model.html">torch.export Programming Model</a></li>
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<li class="toctree-l3"><a class="reference internal" href="generated/exportdb/torch.cond.html">torch.cond</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/exportdb/torch.dynamic-shape.html">torch.dynamic-shape</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/exportdb/python.closure.html">python.closure</a></li>
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<li class="toctree-l2"><a class="reference internal" href="cond.html">Control Flow - Cond</a></li>
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<li class="toctree-l1"><a class="reference internal" href="distributed.html">torch.distributed</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.tensor.html">torch.distributed.tensor</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="distributed.elastic.html">torch.distributed.elastic</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="elastic/quickstart.html">Quickstart</a></li>
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<li class="toctree-l1"><a class="reference internal" href="fsdp.html">torch.distributed.fsdp</a></li>
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<li class="toctree-l2"><a class="reference internal" href="torch.compiler_get_started.html">Getting Started</a></li>
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<li class="toctree-l2"><a class="reference internal" href="torch.compiler.config.html">torch.compiler.config</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_fine_grain_apis.html">TorchDynamo APIs for fine-grained tracing</a></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="torch.compiler_aot_inductor.html">AOTInductor: Ahead-Of-Time Compilation for Torch.Export-ed Models</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l3"><a class="reference internal" href="torch.compiler_aot_inductor_minifier.html">AOTInductor Minifier</a></li>
</ul>
</details></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_inductor_profiling.html">TorchInductor GPU Profiling</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_profiling_torch_compile.html">Profiling to understand torch.compile performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_troubleshooting.html">torch.compile Troubleshooting</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_performance_dashboard.html">PyTorch 2.0 Performance Dashboard</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_dynamo_overview.html">Dynamo Overview</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_dynamo_deepdive.html">Dynamo Deep-Dive</a></li>
<li class="toctree-l2"><a class="reference internal" href="torch.compiler_dynamic_shapes.html">Dynamic shapes</a></li>
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<li class="toctree-l3"><a class="reference internal" href="generated/torch.Tensor.q_zero_point.html">torch.Tensor.q_zero_point</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.Tensor.q_per_channel_scales.html">torch.Tensor.q_per_channel_scales</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.Tensor.q_per_channel_zero_points.html">torch.Tensor.q_per_channel_zero_points</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.Tensor.q_per_channel_axis.html">torch.Tensor.q_per_channel_axis</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.Tensor.resize_.html">torch.Tensor.resize_</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.Tensor.sort.html">torch.Tensor.sort</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.Tensor.topk.html">torch.Tensor.topk</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.ObserverBase.html">ObserverBase</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.MinMaxObserver.html">MinMaxObserver</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.MovingAverageMinMaxObserver.html">MovingAverageMinMaxObserver</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.PerChannelMinMaxObserver.html">PerChannelMinMaxObserver</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver.html">MovingAveragePerChannelMinMaxObserver</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.HistogramObserver.html">HistogramObserver</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.PlaceholderObserver.html">PlaceholderObserver</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.RecordingObserver.html">RecordingObserver</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.NoopObserver.html">NoopObserver</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.get_observer_state_dict.html">get_observer_state_dict</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.load_observer_state_dict.html">load_observer_state_dict</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.default_observer.html">default_observer</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.default_placeholder_observer.html">default_placeholder_observer</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.default_debug_observer.html">default_debug_observer</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.default_weight_observer.html">default_weight_observer</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.default_histogram_observer.html">default_histogram_observer</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.default_per_channel_weight_observer.html">default_per_channel_weight_observer</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.default_dynamic_quant_observer.html">default_dynamic_quant_observer</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.default_float_qparams_observer.html">default_float_qparams_observer</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.AffineQuantizedObserverBase.html">AffineQuantizedObserverBase</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.Granularity.html">Granularity</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.MappingType.html">MappingType</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.PerAxis.html">PerAxis</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.PerBlock.html">PerBlock</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.PerGroup.html">PerGroup</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.PerRow.html">PerRow</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.PerTensor.html">PerTensor</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.PerToken.html">PerToken</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.TorchAODType.html">TorchAODType</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.ZeroPointDomain.html">ZeroPointDomain</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.observer.get_block_size.html">get_block_size</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.FakeQuantizeBase.html">FakeQuantizeBase</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.FakeQuantize.html">FakeQuantize</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.html">FixedQParamsFakeQuantize</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize.html">FusedMovingAvgObsFakeQuantize</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_fake_quant.html">default_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_weight_fake_quant.html">default_weight_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_per_channel_weight_fake_quant.html">default_per_channel_weight_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_histogram_fake_quant.html">default_histogram_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_fused_act_fake_quant.html">default_fused_act_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_fused_wt_fake_quant.html">default_fused_wt_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant.html">default_fused_per_channel_wt_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.disable_fake_quant.html">disable_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.enable_fake_quant.html">enable_fake_quant</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.disable_observer.html">disable_observer</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.fake_quantize.enable_observer.html">enable_observer</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.QConfig.html">QConfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_qconfig.html">default_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_debug_qconfig.html">default_debug_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_per_channel_qconfig.html">default_per_channel_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_dynamic_qconfig.html">default_dynamic_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.float16_dynamic_qconfig.html">float16_dynamic_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.float16_static_qconfig.html">float16_static_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.per_channel_dynamic_qconfig.html">per_channel_dynamic_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.float_qparams_weight_only_qconfig.html">float_qparams_weight_only_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_qat_qconfig.html">default_qat_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_weight_only_qconfig.html">default_weight_only_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_activation_only_qconfig.html">default_activation_only_qconfig</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.quantization.qconfig.default_qat_qconfig_v2.html">default_qat_qconfig_v2</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvReLU1d.html">ConvReLU1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvReLU2d.html">ConvReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvReLU3d.html">ConvReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.LinearReLU.html">LinearReLU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBn1d.html">ConvBn1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBn2d.html">ConvBn2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBn3d.html">ConvBn3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBnReLU1d.html">ConvBnReLU1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBnReLU2d.html">ConvBnReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.ConvBnReLU3d.html">ConvBnReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.BNReLU2d.html">BNReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.BNReLU3d.html">BNReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.LinearReLU.html">LinearReLU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvBn1d.html">ConvBn1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvBnReLU1d.html">ConvBnReLU1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvBn2d.html">ConvBn2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvBnReLU2d.html">ConvBnReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvReLU2d.html">ConvReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvBn3d.html">ConvBn3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvBnReLU3d.html">ConvBnReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.ConvReLU3d.html">ConvReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.update_bn_stats.html">update_bn_stats</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.qat.freeze_bn_stats.html">freeze_bn_stats</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.BNReLU2d.html">BNReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.BNReLU3d.html">BNReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.ConvReLU1d.html">ConvReLU1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.ConvReLU2d.html">ConvReLU2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.ConvReLU3d.html">ConvReLU3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.LinearReLU.html">LinearReLU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU.html">LinearReLU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.qat.Conv2d.html">Conv2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.qat.Conv3d.html">Conv3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.qat.Linear.html">Linear</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.qat.dynamic.Linear.html">Linear</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.ReLU6.html">ReLU6</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Hardswish.html">Hardswish</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.ELU.html">ELU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.LeakyReLU.html">LeakyReLU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Sigmoid.html">Sigmoid</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.BatchNorm2d.html">BatchNorm2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.BatchNorm3d.html">BatchNorm3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Conv1d.html">Conv1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Conv2d.html">Conv2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Conv3d.html">Conv3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.ConvTranspose1d.html">ConvTranspose1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.ConvTranspose2d.html">ConvTranspose2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.ConvTranspose3d.html">ConvTranspose3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Embedding.html">Embedding</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.EmbeddingBag.html">EmbeddingBag</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.FloatFunctional.html">FloatFunctional</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.FXFloatFunctional.html">FXFloatFunctional</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.QFunctional.html">QFunctional</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.Linear.html">Linear</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.LayerNorm.html">LayerNorm</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.GroupNorm.html">GroupNorm</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.InstanceNorm1d.html">InstanceNorm1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.InstanceNorm2d.html">InstanceNorm2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.InstanceNorm3d.html">InstanceNorm3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.avg_pool2d.html">avg_pool2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.avg_pool3d.html">avg_pool3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.adaptive_avg_pool2d.html">adaptive_avg_pool2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.adaptive_avg_pool3d.html">adaptive_avg_pool3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.conv1d.html">conv1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.conv2d.html">conv2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.conv3d.html">conv3d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.interpolate.html">interpolate</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.linear.html">linear</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.max_pool1d.html">max_pool1d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.max_pool2d.html">max_pool2d</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.celu.html">celu</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.leaky_relu.html">leaky_relu</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.hardtanh.html">hardtanh</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.hardswish.html">hardswish</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.threshold.html">threshold</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.elu.html">elu</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.hardsigmoid.html">hardsigmoid</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.clamp.html">clamp</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.upsample.html">upsample</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.upsample_bilinear.html">upsample_bilinear</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.functional.upsample_nearest.html">upsample_nearest</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantizable.LSTM.html">LSTM</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantizable.MultiheadAttention.html">MultiheadAttention</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.dynamic.Linear.html">Linear</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.dynamic.LSTM.html">LSTM</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.dynamic.GRU.html">GRU</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.dynamic.RNNCell.html">RNNCell</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.dynamic.LSTMCell.html">LSTMCell</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/torch.ao.nn.quantized.dynamic.GRUCell.html">GRUCell</a></li>
</ul>
</details></li>
<li class="toctree-l2"><a class="reference internal" href="quantization-backend-configuration.html">Quantization Backend Configuration</a></li>
<li class="toctree-l2"><a class="reference internal" href="quantization-accuracy-debugging.html">Quantization Accuracy Debugging</a></li>

</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="rpc.html">Distributed RPC Framework</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="rpc/rref.html">Remote Reference Protocol</a></li>
<li class="toctree-l2"><a class="reference internal" href="rpc/distributed_autograd.html">Distributed Autograd Design</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="random.html">torch.random</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="masked.html">torch.masked</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.absolute.html">torch.absolute</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.log2.html">torch.log2</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.Tensor.expand.html">torch.Tensor.expand</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.Tensor.reshape.html">torch.Tensor.reshape</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.Tensor.unfold.html">torch.Tensor.unfold</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.Tensor.view.html">torch.Tensor.view</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="nested.html">torch.nested</a></li>
<li class="toctree-l1"><a class="reference internal" href="size.html">torch.Size</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="sparse.html">torch.sparse</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="generated/torch.Tensor.is_sparse.html">torch.Tensor.is_sparse</a></li>
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<li class="toctree-l2"><a class="reference internal" href="generated/torch.Tensor.sparse_resize_and_clear_.html">torch.Tensor.sparse_resize_and_clear_</a></li>
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  <div class="section" id="torchdynamo-based-onnx-exporter">
<h1>TorchDynamo-based ONNX Exporter<a class="headerlink" href="#torchdynamo-based-onnx-exporter" title="Permalink to this heading">#</a></h1>
<p class="date-info-last-verified" style="color: #6c6c6d; font-size: small;">Created On: Sep 08, 2023 | Last Updated: Apr 27, 2025</p>
<div class="contents local topic" id="contents">
<ul class="simple">
<li><p><a class="reference internal" href="#overview" id="id1">Overview</a></p></li>
<li><p><a class="reference internal" href="#dependencies" id="id2">Dependencies</a></p></li>
<li><p><a class="reference internal" href="#a-simple-example" id="id3">A simple example</a></p></li>
<li><p><a class="reference internal" href="#inspecting-the-onnx-model-using-gui" id="id4">Inspecting the ONNX model using GUI</a></p></li>
<li><p><a class="reference internal" href="#when-the-conversion-fails" id="id5">When the conversion fails</a></p></li>
<li><p><a class="reference internal" href="#api-reference" id="id6">API Reference</a></p></li>
<li><p><a class="reference internal" href="#deprecated" id="id7">Deprecated</a></p></li>
</ul>
</div>
<div class="section" id="overview">
<h2><a class="toc-backref" href="#id1">Overview</a><a class="headerlink" href="#overview" title="Permalink to this heading">#</a></h2>
<p>The ONNX exporter leverages TorchDynamo engine to hook into Python’s frame evaluation API
and dynamically rewrite its bytecode into an FX Graph.
The resulting FX Graph is then polished before it is finally translated into an ONNX graph.</p>
<p>The main advantage of this approach is that the <a class="reference external" href="https://pytorch.org/docs/stable/fx.html">FX graph</a> is captured using
bytecode analysis that preserves the dynamic nature of the model instead of using traditional static tracing techniques.</p>
<p>In addition, during the export process, memory usage is significantly reduced compared to the TorchScript-enabled exporter.
See the <a class="reference internal" href="onnx_dynamo_memory_usage.html"><span class="doc">memory usage documentation</span></a> for more information.</p>
</div>
<div class="section" id="dependencies">
<h2><a class="toc-backref" href="#id2">Dependencies</a><a class="headerlink" href="#dependencies" title="Permalink to this heading">#</a></h2>
<p>The ONNX exporter depends on extra Python packages:</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference external" href="https://onnx.ai">ONNX</a></p></li>
<li><p><a class="reference external" href="https://microsoft.github.io/onnxscript">ONNX Script</a></p></li>
</ul>
</div></blockquote>
<p>They can be installed through <a class="reference external" href="https://pypi.org/project/pip/">pip</a>:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>--upgrade<span class="w"> </span>onnx<span class="w"> </span>onnxscript
</pre></div>
</div>
<p><a class="reference external" href="https://onnxruntime.ai">onnxruntime</a> can then be used to execute the model
on a large variety of processors.</p>
</div>
<div class="section" id="a-simple-example">
<h2><a class="toc-backref" href="#id3">A simple example</a><a class="headerlink" href="#a-simple-example" title="Permalink to this heading">#</a></h2>
<p>See below a demonstration of exporter API in action with a simple Multilayer Perceptron (MLP) as example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>

<span class="k">class</span> <span class="nc">MLPModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</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="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
      <span class="bp">self</span><span class="o">.</span><span class="n">fc0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
      <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
      <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
      <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

  <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tensor_x</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">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc0</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
      <span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
      <span class="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
      <span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
      <span class="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
      <span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
      <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
      <span class="k">return</span> <span class="n">output</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">MLPModel</span><span class="p">()</span>
<span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">97</span><span class="p">,</span> <span class="mi">8</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">onnx_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="p">(</span><span class="n">tensor_x</span><span class="p">,),</span> <span class="n">dynamo</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>As the code above shows, all you need is to provide <a class="reference internal" href="#torch.onnx.export" title="torch.onnx.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.onnx.export()</span></code></a> with an instance of the model and its input.
The exporter will then return an instance of <a class="reference internal" href="#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.onnx.ONNXProgram</span></code></a> that contains the exported ONNX graph along with extra information.</p>
<p><code class="docutils literal notranslate"><span class="pre">onnx_program.optimize()</span></code> can be called to optimize the ONNX graph with constant folding and elimination of redundant operators. The optimization is done in-place.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">onnx_program</span><span class="o">.</span><span class="n">optimize</span><span class="p">()</span>
</pre></div>
</div>
<p>The in-memory model available through <code class="docutils literal notranslate"><span class="pre">onnx_program.model_proto</span></code> is an <code class="docutils literal notranslate"><span class="pre">onnx.ModelProto</span></code> object in compliance with the <a class="reference external" href="https://github.com/onnx/onnx/blob/main/docs/IR.md">ONNX IR spec</a>.
The ONNX model may then be serialized into a <a class="reference external" href="https://protobuf.dev/">Protobuf file</a> using the <a class="reference internal" href="#torch.onnx.ONNXProgram.save" title="torch.onnx.ONNXProgram.save"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.onnx.ONNXProgram.save()</span></code></a> API.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;mlp.onnx&quot;</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="inspecting-the-onnx-model-using-gui">
<h2><a class="toc-backref" href="#id4">Inspecting the ONNX model using GUI</a><a class="headerlink" href="#inspecting-the-onnx-model-using-gui" title="Permalink to this heading">#</a></h2>
<p>You can view the exported model using <a class="reference external" href="https://netron.app/">Netron</a>.</p>
<a class="reference internal image-reference" href="_images/onnx_dynamo_mlp_model.png"><img alt="MLP model as viewed using Netron" src="_images/onnx_dynamo_mlp_model.png" style="width: 40%;" /></a>
</div>
<div class="section" id="when-the-conversion-fails">
<h2><a class="toc-backref" href="#id5">When the conversion fails</a><a class="headerlink" href="#when-the-conversion-fails" title="Permalink to this heading">#</a></h2>
<p>Function <a class="reference internal" href="#torch.onnx.export" title="torch.onnx.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.onnx.export()</span></code></a> should called a second time with
parameter <code class="docutils literal notranslate"><span class="pre">report=True</span></code>. A markdown report is generated to help the user
to resolve the issue.</p>
<div class="toctree-wrapper compound">
</div>
</div>
<div class="section" id="api-reference">
<h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" href="#api-reference" title="Permalink to this heading">#</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.onnx.export">
<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args=()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_params=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_axes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamo=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">external_data=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_translation_table=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">report=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verify=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dump_exported_program=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">artifacts_dir='.'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fallback=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training=&lt;TrainingMode.EVAL:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator_export_type=&lt;OperatorExportTypes.ONNX:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_constant_folding=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_opsets=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_modules_as_functions=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autograd_inlining=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/onnx/__init__.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.export" title="Permalink to this definition">#</a></dt>
<dd><p>Exports a model into ONNX format.</p>
<p>Setting <code class="docutils literal notranslate"><span class="pre">dynamo=True</span></code> enables the new ONNX export logic
which is based on <a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.export.ExportedProgram</span></code></a> and a more modern
set of translation logic. This is the recommended way to export models
to ONNX.</p>
<p>When <code class="docutils literal notranslate"><span class="pre">dynamo=True</span></code>:</p>
<p>The exporter tries the following strategies to get an ExportedProgram for conversion to ONNX.</p>
<ol class="arabic simple">
<li><p>If the model is already an ExportedProgram, it will be used as-is.</p></li>
<li><p>Use <a class="reference internal" href="export.html#torch.export.export" title="torch.export.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.export.export()</span></code></a> and set <code class="docutils literal notranslate"><span class="pre">strict=False</span></code>.</p></li>
<li><p>Use <a class="reference internal" href="export.html#torch.export.export" title="torch.export.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.export.export()</span></code></a> and set <code class="docutils literal notranslate"><span class="pre">strict=True</span></code>.</p></li>
<li><p>Use <code class="docutils literal notranslate"><span class="pre">draft_export</span></code> which removes some soundness guarantees in data-dependent
operations to allow export to proceed. You will get a warning if the exporter
encounters any unsound data-dependent operation.</p></li>
<li><p>Use <a class="reference internal" href="generated/torch.jit.trace.html#torch.jit.trace" title="torch.jit.trace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.trace()</span></code></a> to trace the model then convert to ExportedProgram.
This is the most unsound strategy but may be useful for converting TorchScript
models to ONNX.</p></li>
</ol>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> (<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a><em> | </em><a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><em>torch.export.ExportedProgram</em></a><em> | </em><a class="reference internal" href="generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><em>torch.jit.ScriptModule</em></a><em> | </em><a class="reference internal" href="generated/torch.jit.ScriptFunction.html#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><em>torch.jit.ScriptFunction</em></a>) – The model to be exported.</p></li>
<li><p><strong>args</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><em>tuple</em></a><em>[</em><em>Any</em><em>, </em><em>...</em><em>]</em>) – Example positional inputs. Any non-Tensor arguments will be hard-coded into the
exported model; any Tensor arguments will become inputs of the exported model,
in the order they occur in the tuple.</p></li>
<li><p><strong>f</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/os.html#os.PathLike" title="(in Python v3.13)"><em>os.PathLike</em></a><em> | </em><em>None</em>) – Path to the output ONNX model file. E.g. “model.onnx”.</p></li>
<li><p><strong>kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><em>Any</em><em>] </em><em>| </em><em>None</em>) – Optional example keyword inputs.</p></li>
<li><p><strong>export_params</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – If false, parameters (weights) will not be exported.</p></li>
<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em> | </em><em>None</em>) – Whether to enable verbose logging.</p></li>
<li><p><strong>input_names</strong> (<em>Sequence</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – names to assign to the input nodes of the graph, in order.</p></li>
<li><p><strong>output_names</strong> (<em>Sequence</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>] </em><em>| </em><em>None</em>) – names to assign to the output nodes of the graph, in order.</p></li>
<li><p><strong>opset_version</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><em>None</em>) – The version of the
<a class="reference external" href="https://github.com/onnx/onnx/blob/master/docs/Operators.md">default (ai.onnx) opset</a>
to target. Must be &gt;= 7.</p></li>
<li><p><strong>dynamic_axes</strong> (<em>Mapping</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><em>Mapping</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>]</em><em>] </em><em>| </em><em>Mapping</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><em>Sequence</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – <p>By default the exported model will have the shapes of all input and output tensors
set to exactly match those given in <code class="docutils literal notranslate"><span class="pre">args</span></code>. To specify axes of tensors as
dynamic (i.e. known only at run-time), set <code class="docutils literal notranslate"><span class="pre">dynamic_axes</span></code> to a dict with schema:</p>
<ul>
<li><dl class="simple">
<dt>KEY (str): an input or output name. Each name must also be provided in <code class="docutils literal notranslate"><span class="pre">input_names</span></code> or</dt><dd><p><code class="docutils literal notranslate"><span class="pre">output_names</span></code>.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>VALUE (dict or list): If a dict, keys are axis indices and values are axis names. If a</dt><dd><p>list, each element is an axis index.</p>
</dd>
</dl>
</li>
</ul>
<p>For example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">SumModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</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">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
    <span class="n">SumModule</span><span class="p">(),</span>
    <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),),</span>
    <span class="s2">&quot;onnx.pb&quot;</span><span class="p">,</span>
    <span class="n">input_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;x&quot;</span><span class="p">],</span>
    <span class="n">output_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;sum&quot;</span><span class="p">],</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Produces:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="p">{</span>
  <span class="n">name</span><span class="p">:</span> <span class="s2">&quot;x&quot;</span>
  <span class="o">...</span>
      <span class="n">shape</span> <span class="p">{</span>
        <span class="n">dim</span> <span class="p">{</span>
          <span class="n">dim_value</span><span class="p">:</span> <span class="mi">2</span>  <span class="c1"># axis 0</span>
        <span class="p">}</span>
        <span class="n">dim</span> <span class="p">{</span>
          <span class="n">dim_value</span><span class="p">:</span> <span class="mi">2</span>  <span class="c1"># axis 1</span>
<span class="o">...</span>
<span class="n">output</span> <span class="p">{</span>
  <span class="n">name</span><span class="p">:</span> <span class="s2">&quot;sum&quot;</span>
  <span class="o">...</span>
      <span class="n">shape</span> <span class="p">{</span>
        <span class="n">dim</span> <span class="p">{</span>
          <span class="n">dim_value</span><span class="p">:</span> <span class="mi">2</span>  <span class="c1"># axis 0</span>
<span class="o">...</span>
</pre></div>
</div>
<p>While:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
    <span class="n">SumModule</span><span class="p">(),</span>
    <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),),</span>
    <span class="s2">&quot;onnx.pb&quot;</span><span class="p">,</span>
    <span class="n">input_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;x&quot;</span><span class="p">],</span>
    <span class="n">output_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;sum&quot;</span><span class="p">],</span>
    <span class="n">dynamic_axes</span><span class="o">=</span><span class="p">{</span>
        <span class="c1"># dict value: manually named axes</span>
        <span class="s2">&quot;x&quot;</span><span class="p">:</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="s2">&quot;my_custom_axis_name&quot;</span><span class="p">},</span>
        <span class="c1"># list value: automatic names</span>
        <span class="s2">&quot;sum&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span>
    <span class="p">},</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Produces:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="p">{</span>
  <span class="n">name</span><span class="p">:</span> <span class="s2">&quot;x&quot;</span>
  <span class="o">...</span>
      <span class="n">shape</span> <span class="p">{</span>
        <span class="n">dim</span> <span class="p">{</span>
          <span class="n">dim_param</span><span class="p">:</span> <span class="s2">&quot;my_custom_axis_name&quot;</span>  <span class="c1"># axis 0</span>
        <span class="p">}</span>
        <span class="n">dim</span> <span class="p">{</span>
          <span class="n">dim_value</span><span class="p">:</span> <span class="mi">2</span>  <span class="c1"># axis 1</span>
<span class="o">...</span>
<span class="n">output</span> <span class="p">{</span>
  <span class="n">name</span><span class="p">:</span> <span class="s2">&quot;sum&quot;</span>
  <span class="o">...</span>
      <span class="n">shape</span> <span class="p">{</span>
        <span class="n">dim</span> <span class="p">{</span>
          <span class="n">dim_param</span><span class="p">:</span> <span class="s2">&quot;sum_dynamic_axes_1&quot;</span>  <span class="c1"># axis 0</span>
<span class="o">...</span>
</pre></div>
</div>
</p></li>
<li><p><strong>keep_initializers_as_inputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – <p>If True, all the
initializers (typically corresponding to model weights) in the
exported graph will also be added as inputs to the graph. If False,
then initializers are not added as inputs to the graph, and only
the user inputs are added as inputs.</p>
<p>Set this to True if you intend to supply model weights at runtime.
Set it to False if the weights are static to allow for better optimizations
(e.g. constant folding) by backends/runtimes.</p>
</p></li>
<li><p><strong>dynamo</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to export the model with <code class="docutils literal notranslate"><span class="pre">torch.export</span></code> ExportedProgram instead of TorchScript.</p></li>
<li><p><strong>external_data</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to save the model weights as an external data file.
This is required for models with large weights that exceed the ONNX file size limit (2GB).
When False, the weights are saved in the ONNX file with the model architecture.</p></li>
<li><p><strong>dynamic_shapes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><em>Any</em><em>] </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><em>tuple</em></a><em>[</em><em>Any</em><em>, </em><em>...</em><em>] </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>[</em><em>Any</em><em>] </em><em>| </em><em>None</em>) – A dictionary or a tuple of dynamic shapes for the model inputs. Refer to
<a class="reference internal" href="export.html#torch.export.export" title="torch.export.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.export.export()</span></code></a> for more details. This is only used (and preferred) when dynamo is True.
Note that dynamic_shapes is designed to be used when the model is exported with dynamo=True, while
dynamic_axes is used when dynamo=False.</p></li>
<li><p><strong>custom_translation_table</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><em>dict</em></a><em>[</em><em>Callable</em><em>, </em><em>Callable</em><em> | </em><em>Sequence</em><em>[</em><em>Callable</em><em>]</em><em>] </em><em>| </em><em>None</em>) – A dictionary of custom decompositions for operators in the model.
The dictionary should have the callable target in the fx Node as the key (e.g. <code class="docutils literal notranslate"><span class="pre">torch.ops.aten.stft.default</span></code>),
and the value should be a function that builds that graph using ONNX Script. This option
is only valid when dynamo is True.</p></li>
<li><p><strong>report</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to generate a markdown report for the export process. This option
is only valid when dynamo is True.</p></li>
<li><p><strong>optimize</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to optimize the exported model. This option
is only valid when dynamo is True. Default is True.</p></li>
<li><p><strong>verify</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to verify the exported model using ONNX Runtime. This option
is only valid when dynamo is True.</p></li>
<li><p><strong>profile</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to profile the export process. This option
is only valid when dynamo is True.</p></li>
<li><p><strong>dump_exported_program</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to dump the <a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.export.ExportedProgram</span></code></a> to a file.
This is useful for debugging the exporter. This option is only valid when dynamo is True.</p></li>
<li><p><strong>artifacts_dir</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/os.html#os.PathLike" title="(in Python v3.13)"><em>os.PathLike</em></a>) – The directory to save the debugging artifacts like the report and the serialized
exported program. This option is only valid when dynamo is True.</p></li>
<li><p><strong>fallback</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to fallback to the TorchScript exporter if the dynamo exporter fails.
This option is only valid when dynamo is True. When fallback is enabled, It is
recommended to set dynamic_axes even when dynamic_shapes is provided.</p></li>
<li><p><strong>training</strong> (<em>_C_onnx.TrainingMode</em>) – Deprecated option. Instead, set the training mode of the model before exporting.</p></li>
<li><p><strong>operator_export_type</strong> (<em>_C_onnx.OperatorExportTypes</em>) – Deprecated option. Only ONNX is supported.</p></li>
<li><p><strong>do_constant_folding</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Deprecated option.</p></li>
<li><p><strong>custom_opsets</strong> (<em>Mapping</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>] </em><em>| </em><em>None</em>) – <p>Deprecated.
A dictionary:</p>
<ul>
<li><p>KEY (str): opset domain name</p></li>
<li><p>VALUE (int): opset version</p></li>
</ul>
<p>If a custom opset is referenced by <code class="docutils literal notranslate"><span class="pre">model</span></code> but not mentioned in this dictionary,
the opset version is set to 1. Only custom opset domain name and version should be
indicated through this argument.</p>
</p></li>
<li><p><strong>export_modules_as_functions</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em> | </em><em>Collection</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><em>type</em></a><em>[</em><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a><em>]</em><em>]</em>) – <p>Deprecated option.</p>
<p>Flag to enable
exporting all <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> forward calls as local functions in ONNX. Or a set to indicate the
particular types of modules to export as local functions in ONNX.
This feature requires <code class="docutils literal notranslate"><span class="pre">opset_version</span></code> &gt;= 15, otherwise the export will fail. This is because
<code class="docutils literal notranslate"><span class="pre">opset_version</span></code> &lt; 15 implies IR version &lt; 8, which means no local function support.
Module variables will be exported as function attributes. There are two categories of function
attributes.</p>
<p>1. Annotated attributes: class variables that have type annotations via
<a class="reference external" href="https://www.python.org/dev/peps/pep-0526/#class-and-instance-variable-annotations">PEP 526-style</a>
will be exported as attributes.
Annotated attributes are not used inside the subgraph of ONNX local function because
they are not created by PyTorch JIT tracing, but they may be used by consumers
to determine whether or not to replace the function with a particular fused kernel.</p>
<p>2. Inferred attributes: variables that are used by operators inside the module. Attribute names
will have prefix “inferred::”. This is to differentiate from predefined attributes retrieved from
python module annotations. Inferred attributes are used inside the subgraph of ONNX local function.</p>
<ul>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code> (default): export <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> forward calls as fine grained nodes.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: export all <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> forward calls as local function nodes.</p></li>
<li><dl class="simple">
<dt>Set of type of nn.Module: export <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> forward calls as local function nodes,</dt><dd><p>only if the type of the <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> is found in the set.</p>
</dd>
</dl>
</li>
</ul>
</p></li>
<li><p><strong>autograd_inlining</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Deprecated.
Flag used to control whether to inline autograd functions.
Refer to <a class="github reference external" href="https://github.com/pytorch/pytorch/pull/74765">pytorch/pytorch#74765</a> for more details.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><a class="reference internal" href="#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.onnx.ONNXProgram</span></code></a> if dynamo is True, otherwise None.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram">ONNXProgram</a> | None</p>
</dd>
</dl>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 2.6: </span><em>training</em> is now deprecated. Instead, set the training mode of the model before exporting.
<em>operator_export_type</em> is now deprecated. Only ONNX is supported.
<em>do_constant_folding</em> is now deprecated. It is always enabled.
<em>export_modules_as_functions</em> is now deprecated.
<em>autograd_inlining</em> is now deprecated.</p>
</div>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 2.7: </span><em>optimize</em> is now True by default.</p>
</div>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">ONNXProgram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exported_program</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#torch.onnx.ONNXProgram" title="Permalink to this definition">#</a></dt>
<dd><p>A class to represent an ONNX program that is callable with torch tensors.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> – The ONNX model as an ONNX IR model object.</p></li>
<li><p><strong>exported_program</strong> – The exported program that produced the ONNX model.</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.apply_weights">
<span class="sig-name descname"><span class="pre">apply_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/onnx/_internal/exporter/_onnx_program.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.apply_weights" title="Permalink to this definition">#</a></dt>
<dd><p>Apply the weights from the specified state dict to the ONNX model.</p>
<p>Use this method to replace FakeTensors or other weights.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>state_dict</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><em>dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>]</em>) – The state dict containing the weights to apply to the ONNX model.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.compute_values">
<span class="sig-name descname"><span class="pre">compute_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/onnx/_internal/exporter/_onnx_program.py#L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.compute_values" title="Permalink to this definition">#</a></dt>
<dd><p>Compute the values of the specified names in the ONNX model.</p>
<p>This method is used to compute the values of the specified names in the ONNX model.
The values are returned as a dictionary mapping names to tensors.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>value_names</strong> (<em>Sequence</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>]</em>) – The names of the values to compute.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A dictionary mapping names to tensors.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>Sequence[<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor">torch.Tensor</a>]</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.initialize_inference_session">
<span class="sig-name descname"><span class="pre">initialize_inference_session</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initializer=&lt;function</span> <span class="pre">_ort_session_initializer&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/onnx/_internal/exporter/_onnx_program.py#L359"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.initialize_inference_session" title="Permalink to this definition">#</a></dt>
<dd><p>Initialize the ONNX Runtime inference session.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>initializer</strong> (<em>Callable</em><em>[</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bytes" title="(in Python v3.13)"><em>bytes</em></a><em>]</em><em>, </em><em>ort.InferenceSession</em><em>]</em>) – The function to initialize the ONNX Runtime inference
session with the specified model. By default, it uses the
<code class="xref py py-func docutils literal notranslate"><span class="pre">_ort_session_initializer()</span></code> function.</p>
</dd>
</dl>
</dd></dl>

<dl class="py property">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.model_proto">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">model_proto</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ModelProto</span></em><a class="headerlink" href="#torch.onnx.ONNXProgram.model_proto" title="Permalink to this definition">#</a></dt>
<dd><p>Return the ONNX <code class="docutils literal notranslate"><span class="pre">ModelProto</span></code> object.</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.optimize">
<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/onnx/_internal/exporter/_onnx_program.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.optimize" title="Permalink to this definition">#</a></dt>
<dd><p>Optimize the ONNX model.</p>
<p>This method optimizes the ONNX model by performing constant folding and
eliminating redundancies in the graph. The optimization is done in-place.</p>
<dl class="field-list simple">
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.release">
<span class="sig-name descname"><span class="pre">release</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/onnx/_internal/exporter/_onnx_program.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.release" title="Permalink to this definition">#</a></dt>
<dd><p>Release the inference session.</p>
<p>You may call this method to release the resources used by the inference session.</p>
<dl class="field-list simple">
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.save">
<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">destination</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_initializers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">external_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/onnx/_internal/exporter/_onnx_program.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.save" title="Permalink to this definition">#</a></dt>
<dd><p>Save the ONNX model to the specified destination.</p>
<p>When <code class="docutils literal notranslate"><span class="pre">external_data</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code> or the model is larger than 2GB,
the weights are saved as external data in a separate file.</p>
<p>Initializer (model weights) serialization behaviors:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">include_initializers=True</span></code>, <code class="docutils literal notranslate"><span class="pre">keep_initializers_as_inputs=False</span></code> (default):
The initializers are included in the saved model.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">include_initializers=True</span></code>, <code class="docutils literal notranslate"><span class="pre">keep_initializers_as_inputs=True</span></code>:
The initializers are included in the saved model and kept as model inputs.
Choose this option if you want the ability to override the model weights
during inference.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">include_initializers=False</span></code>, <code class="docutils literal notranslate"><span class="pre">keep_initializers_as_inputs=False</span></code>:
The initializers are not included in the saved model and are not listed
as model inputs. Choose this option if you want to attach the initializers
to the ONNX model in a separate, post-processing, step.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">include_initializers=False</span></code>, <code class="docutils literal notranslate"><span class="pre">keep_initializers_as_inputs=True</span></code>:
The initializers are not included in the saved model but are listed as model
inputs. Choose this option if you want to supply the initializers during
inference and want to minimize the size of the saved model.</p></li>
</ul>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>destination</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/os.html#os.PathLike" title="(in Python v3.13)"><em>os.PathLike</em></a>) – The path to save the ONNX model to.</p></li>
<li><p><strong>include_initializers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to include the initializers in the saved model.</p></li>
<li><p><strong>keep_initializers_as_inputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to keep the initializers as inputs in the saved model.
If <cite>True</cite>, the initializers are added as inputs to the model which means they can be overwritten.
by providing the initializers as model inputs.</p></li>
<li><p><strong>external_data</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em> | </em><em>None</em>) – Whether to save the weights as external data in a separate file.</p></li>
</ul>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#TypeError" title="(in Python v3.13)"><strong>TypeError</strong></a> – If <code class="docutils literal notranslate"><span class="pre">external_data</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code> and <code class="docutils literal notranslate"><span class="pre">destination</span></code> is not a file path.</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.onnx.is_in_onnx_export">
<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">is_in_onnx_export</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/onnx/__init__.py#L523"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.is_in_onnx_export" title="Permalink to this definition">#</a></dt>
<dd><p>Returns whether it is in the middle of ONNX export.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
</dd>
</dl>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="torch.onnx.OnnxExporterError">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">OnnxExporterError</span></span><a class="headerlink" href="#torch.onnx.OnnxExporterError" title="Permalink to this definition">#</a></dt>
<dd><p>Errors raised by the ONNX exporter. This is the base class for all exporter errors.</p>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.onnx.enable_fake_mode">
<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">enable_fake_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/onnx/_internal/_exporter_legacy.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.enable_fake_mode" title="Permalink to this definition">#</a></dt>
<dd><p>Enable fake mode for the duration of the context.</p>
<p>Internally it instantiates a <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensorMode</span></code> context manager
that converts user input and model parameters into <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensor</span></code>.</p>
<p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensor</span></code>
is a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> with the ability to run PyTorch code without having to
actually do computation through tensors allocated on a <code class="docutils literal notranslate"><span class="pre">meta</span></code> device. Because
there is no actual data being allocated on the device, this API allows for
initializing and exporting large models without the actual memory footprint needed for executing it.</p>
<p>It is highly recommended to initialize the model in fake mode when exporting models that
are too large to fit into memory.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This function does not support torch.onnx.export(…, dynamo=True, optimize=True).
Please call ONNXProgram.optimize() outside of the function after the model is exported.</p>
</div>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)</span>
<span class="o">&gt;&gt;&gt;</span> <span class="kn">import</span> <span class="nn">torch</span>
<span class="o">&gt;&gt;&gt;</span> <span class="k">class</span> <span class="nc">MyModel</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>  <span class="c1"># Model with a parameter</span>
<span class="o">...</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="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="o">...</span>         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="o">...</span>         <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</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="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">42.0</span><span class="p">))</span>
<span class="o">...</span>     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="o">...</span>         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">+</span> <span class="n">x</span>
<span class="o">&gt;&gt;&gt;</span> <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">enable_fake_mode</span><span class="p">():</span>
<span class="o">...</span>     <span class="c1"># When initialized in fake mode, the model&#39;s parameters are fake tensors</span>
<span class="o">...</span>     <span class="c1"># They do not take up memory so we can initialize large models</span>
<span class="o">...</span>     <span class="n">my_nn_module</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span>
<span class="o">...</span>     <span class="n">arg1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">onnx_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="n">my_nn_module</span><span class="p">,</span> <span class="p">(</span><span class="n">arg1</span><span class="p">,),</span> <span class="n">dynamo</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">optimize</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="o">&gt;&gt;&gt;</span> <span class="c1"># Saving model WITHOUT initializers (only the architecture)</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span>
<span class="o">...</span>     <span class="s2">&quot;my_model_without_initializers.onnx&quot;</span><span class="p">,</span>
<span class="o">...</span>     <span class="n">include_initializers</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="o">...</span>     <span class="n">keep_initializers_as_inputs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="o">...</span> <span class="p">)</span>
<span class="o">&gt;&gt;&gt;</span> <span class="c1"># Saving model WITH initializers after applying concrete weights</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">apply_weights</span><span class="p">({</span><span class="s2">&quot;weight&quot;</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="mf">42.0</span><span class="p">)})</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;my_model_with_initializers.onnx&quot;</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
</div>
</dd></dl>

</div>
<div class="section" id="deprecated">
<h2><a class="toc-backref" href="#id7">Deprecated</a><a class="headerlink" href="#deprecated" title="Permalink to this heading">#</a></h2>
<p>The following classes and functions are deprecated and will be removed.</p>
<dl class="py function">
<dt class="sig sig-object py" id="torch.onnx.dynamo_export">
<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">dynamo_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">model_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/onnx/__init__.py#L441"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.dynamo_export" title="Permalink to this definition">#</a></dt>
<dd><p>Export a torch.nn.Module to an ONNX graph.</p>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 2.7: </span>Please use <code class="docutils literal notranslate"><span class="pre">torch.onnx.export(...,</span> <span class="pre">dynamo=True)</span></code> instead.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> (<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a><em> | </em><em>Callable</em><em> | </em><a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><em>torch.export.ExportedProgram</em></a>) – The PyTorch model to be exported to ONNX.</p></li>
<li><p><strong>model_args</strong> – Positional inputs to <code class="docutils literal notranslate"><span class="pre">model</span></code>.</p></li>
<li><p><strong>model_kwargs</strong> – Keyword inputs to <code class="docutils literal notranslate"><span class="pre">model</span></code>.</p></li>
<li><p><strong>export_options</strong> (<a class="reference internal" href="#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><em>ExportOptions</em></a><em> | </em><em>None</em>) – Options to influence the export to ONNX.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>An in-memory representation of the exported ONNX model.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram">ONNXProgram</a></p>
</dd>
</dl>
</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="torch.onnx.ExportOptions">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">ExportOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/8faa22569519b8916dfa0334287cbb849704965f/torch/onnx/__init__.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ExportOptions" title="Permalink to this definition">#</a></dt>
<dd><p>Options for dynamo_export.</p>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 2.7: </span>Please use <code class="docutils literal notranslate"><span class="pre">torch.onnx.export(...,</span> <span class="pre">dynamo=True)</span></code> instead.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><p><strong>dynamic_shapes</strong> – Shape information hint for input/output tensors.
When <code class="docutils literal notranslate"><span class="pre">None</span></code>, the exporter determines the most compatible setting.
When <code class="docutils literal notranslate"><span class="pre">True</span></code>, all input shapes are considered dynamic.
When <code class="docutils literal notranslate"><span class="pre">False</span></code>, all input shapes are considered static.</p>
</dd>
</dl>
</dd></dl>

</div>
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