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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Module.update.html">mlx.nn.Module.update</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Module.update_modules.html">mlx.nn.Module.update_modules</a></li>
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</ul>
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</details></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../python/nn/layers.html">Layers</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="../python/nn/_autosummary/mlx.nn.ALiBi.html">mlx.nn.ALiBi</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.AvgPool1d.html">mlx.nn.AvgPool1d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.AvgPool2d.html">mlx.nn.AvgPool2d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.AvgPool3d.html">mlx.nn.AvgPool3d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.BatchNorm.html">mlx.nn.BatchNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.CELU.html">mlx.nn.CELU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Conv1d.html">mlx.nn.Conv1d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Conv2d.html">mlx.nn.Conv2d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Conv3d.html">mlx.nn.Conv3d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.ConvTranspose1d.html">mlx.nn.ConvTranspose1d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.ConvTranspose2d.html">mlx.nn.ConvTranspose2d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.ConvTranspose3d.html">mlx.nn.ConvTranspose3d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Dropout.html">mlx.nn.Dropout</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Dropout2d.html">mlx.nn.Dropout2d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Dropout3d.html">mlx.nn.Dropout3d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Embedding.html">mlx.nn.Embedding</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.ELU.html">mlx.nn.ELU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.GELU.html">mlx.nn.GELU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.GLU.html">mlx.nn.GLU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.GroupNorm.html">mlx.nn.GroupNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.GRU.html">mlx.nn.GRU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.HardShrink.html">mlx.nn.HardShrink</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.HardTanh.html">mlx.nn.HardTanh</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Hardswish.html">mlx.nn.Hardswish</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.InstanceNorm.html">mlx.nn.InstanceNorm</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.LayerNorm.html">mlx.nn.LayerNorm</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.LeakyReLU.html">mlx.nn.LeakyReLU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Linear.html">mlx.nn.Linear</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.LogSigmoid.html">mlx.nn.LogSigmoid</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.LogSoftmax.html">mlx.nn.LogSoftmax</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.LSTM.html">mlx.nn.LSTM</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.MaxPool1d.html">mlx.nn.MaxPool1d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.MaxPool2d.html">mlx.nn.MaxPool2d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.MaxPool3d.html">mlx.nn.MaxPool3d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Mish.html">mlx.nn.Mish</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.MultiHeadAttention.html">mlx.nn.MultiHeadAttention</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.PReLU.html">mlx.nn.PReLU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.QuantizedEmbedding.html">mlx.nn.QuantizedEmbedding</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.QuantizedLinear.html">mlx.nn.QuantizedLinear</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.RMSNorm.html">mlx.nn.RMSNorm</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.ReLU.html">mlx.nn.ReLU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.ReLU6.html">mlx.nn.ReLU6</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.RNN.html">mlx.nn.RNN</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.RoPE.html">mlx.nn.RoPE</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.SELU.html">mlx.nn.SELU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Sequential.html">mlx.nn.Sequential</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Sigmoid.html">mlx.nn.Sigmoid</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.SiLU.html">mlx.nn.SiLU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.SinusoidalPositionalEncoding.html">mlx.nn.SinusoidalPositionalEncoding</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Softmin.html">mlx.nn.Softmin</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Softshrink.html">mlx.nn.Softshrink</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Softsign.html">mlx.nn.Softsign</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Softmax.html">mlx.nn.Softmax</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Softplus.html">mlx.nn.Softplus</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Step.html">mlx.nn.Step</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Tanh.html">mlx.nn.Tanh</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Transformer.html">mlx.nn.Transformer</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Upsample.html">mlx.nn.Upsample</a></li>
|
|
</ul>
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</details></li>
|
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<li class="toctree-l2 has-children"><a class="reference internal" href="../python/nn/functions.html">Functions</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="../python/nn/_autosummary_functions/mlx.nn.elu.html">mlx.nn.elu</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.celu.html">mlx.nn.celu</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.gelu.html">mlx.nn.gelu</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.gelu_approx.html">mlx.nn.gelu_approx</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.gelu_fast_approx.html">mlx.nn.gelu_fast_approx</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.glu.html">mlx.nn.glu</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.hard_shrink.html">mlx.nn.hard_shrink</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.hard_tanh.html">mlx.nn.hard_tanh</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.hardswish.html">mlx.nn.hardswish</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.leaky_relu.html">mlx.nn.leaky_relu</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.log_sigmoid.html">mlx.nn.log_sigmoid</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.log_softmax.html">mlx.nn.log_softmax</a></li>
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|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.mish.html">mlx.nn.mish</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.prelu.html">mlx.nn.prelu</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.relu.html">mlx.nn.relu</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.relu6.html">mlx.nn.relu6</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.selu.html">mlx.nn.selu</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.sigmoid.html">mlx.nn.sigmoid</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.silu.html">mlx.nn.silu</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.softmax.html">mlx.nn.softmax</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.softmin.html">mlx.nn.softmin</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.softplus.html">mlx.nn.softplus</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.softshrink.html">mlx.nn.softshrink</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.step.html">mlx.nn.step</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.tanh.html">mlx.nn.tanh</a></li>
|
|
</ul>
|
|
</details></li>
|
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<li class="toctree-l2 has-children"><a class="reference internal" href="../python/nn/losses.html">Loss Functions</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="../python/nn/_autosummary_functions/mlx.nn.losses.binary_cross_entropy.html">mlx.nn.losses.binary_cross_entropy</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.cosine_similarity_loss.html">mlx.nn.losses.cosine_similarity_loss</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.cross_entropy.html">mlx.nn.losses.cross_entropy</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.gaussian_nll_loss.html">mlx.nn.losses.gaussian_nll_loss</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.hinge_loss.html">mlx.nn.losses.hinge_loss</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.huber_loss.html">mlx.nn.losses.huber_loss</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.kl_div_loss.html">mlx.nn.losses.kl_div_loss</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.l1_loss.html">mlx.nn.losses.l1_loss</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.log_cosh_loss.html">mlx.nn.losses.log_cosh_loss</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.margin_ranking_loss.html">mlx.nn.losses.margin_ranking_loss</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.mse_loss.html">mlx.nn.losses.mse_loss</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.nll_loss.html">mlx.nn.losses.nll_loss</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.smooth_l1_loss.html">mlx.nn.losses.smooth_l1_loss</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.triplet_loss.html">mlx.nn.losses.triplet_loss</a></li>
|
|
</ul>
|
|
</details></li>
|
|
<li class="toctree-l2 has-children"><a class="reference internal" href="../python/nn/init.html">Initializers</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="../python/nn/_autosummary/mlx.nn.init.constant.html">mlx.nn.init.constant</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.init.normal.html">mlx.nn.init.normal</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.init.uniform.html">mlx.nn.init.uniform</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.init.identity.html">mlx.nn.init.identity</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.init.glorot_normal.html">mlx.nn.init.glorot_normal</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.init.glorot_uniform.html">mlx.nn.init.glorot_uniform</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.init.he_normal.html">mlx.nn.init.he_normal</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.init.he_uniform.html">mlx.nn.init.he_uniform</a></li>
|
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</ul>
|
|
</details></li>
|
|
</ul>
|
|
</details></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../python/optimizers.html">Optimizers</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 has-children"><a class="reference internal" href="../python/optimizers/optimizer.html">Optimizer</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="../python/optimizers/_autosummary/mlx.optimizers.Optimizer.state.html">mlx.optimizers.Optimizer.state</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/optimizers/_autosummary/mlx.optimizers.Optimizer.apply_gradients.html">mlx.optimizers.Optimizer.apply_gradients</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/optimizers/_autosummary/mlx.optimizers.Optimizer.init.html">mlx.optimizers.Optimizer.init</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/optimizers/_autosummary/mlx.optimizers.Optimizer.update.html">mlx.optimizers.Optimizer.update</a></li>
|
|
</ul>
|
|
</details></li>
|
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<li class="toctree-l2 has-children"><a class="reference internal" href="../python/optimizers/common_optimizers.html">Common Optimizers</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="../python/optimizers/_autosummary/mlx.optimizers.SGD.html">mlx.optimizers.SGD</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/optimizers/_autosummary/mlx.optimizers.RMSprop.html">mlx.optimizers.RMSprop</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/optimizers/_autosummary/mlx.optimizers.Adagrad.html">mlx.optimizers.Adagrad</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/optimizers/_autosummary/mlx.optimizers.Adafactor.html">mlx.optimizers.Adafactor</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/optimizers/_autosummary/mlx.optimizers.AdaDelta.html">mlx.optimizers.AdaDelta</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/optimizers/_autosummary/mlx.optimizers.Adam.html">mlx.optimizers.Adam</a></li>
|
|
<li class="toctree-l3"><a class="reference internal" href="../python/optimizers/_autosummary/mlx.optimizers.AdamW.html">mlx.optimizers.AdamW</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/optimizers/_autosummary/mlx.optimizers.Adamax.html">mlx.optimizers.Adamax</a></li>
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<h1>Lazy Evaluation</h1>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#why-lazy-evaluation">Why Lazy Evaluation</a><ul class="visible nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#transforming-compute-graphs">Transforming Compute Graphs</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#only-compute-what-you-use">Only Compute What You Use</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#when-to-evaluate">When to Evaluate</a></li>
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<section id="lazy-evaluation">
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<span id="lazy-eval"></span><h1>Lazy Evaluation<a class="headerlink" href="#lazy-evaluation" title="Link to this heading">#</a></h1>
|
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<section id="why-lazy-evaluation">
|
|
<h2>Why Lazy Evaluation<a class="headerlink" href="#why-lazy-evaluation" title="Link to this heading">#</a></h2>
|
|
<p>When you perform operations in MLX, no computation actually happens. Instead a
|
|
compute graph is recorded. The actual computation only happens if an
|
|
<a class="reference internal" href="../python/_autosummary/mlx.core.eval.html#mlx.core.eval" title="mlx.core.eval"><code class="xref py py-func docutils literal notranslate"><span class="pre">eval()</span></code></a> is performed.</p>
|
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<p>MLX uses lazy evaluation because it has some nice features, some of which we
|
|
describe below.</p>
|
|
<section id="transforming-compute-graphs">
|
|
<h3>Transforming Compute Graphs<a class="headerlink" href="#transforming-compute-graphs" title="Link to this heading">#</a></h3>
|
|
<p>Lazy evaluation lets us record a compute graph without actually doing any
|
|
computations. This is useful for function transformations like <a class="reference internal" href="../python/_autosummary/mlx.core.grad.html#mlx.core.grad" title="mlx.core.grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">grad()</span></code></a> and
|
|
<a class="reference internal" href="../python/_autosummary/mlx.core.vmap.html#mlx.core.vmap" title="mlx.core.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> and graph optimizations.</p>
|
|
<p>Currently, MLX does not compile and rerun compute graphs. They are all
|
|
generated dynamically. However, lazy evaluation makes it much easier to
|
|
integrate compilation for future performance enhancements.</p>
|
|
</section>
|
|
<section id="only-compute-what-you-use">
|
|
<h3>Only Compute What You Use<a class="headerlink" href="#only-compute-what-you-use" title="Link to this heading">#</a></h3>
|
|
<p>In MLX you do not need to worry as much about computing outputs that are never
|
|
used. For example:</p>
|
|
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">fun</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
|
|
<span class="n">a</span> <span class="o">=</span> <span class="n">fun1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
|
<span class="n">b</span> <span class="o">=</span> <span class="n">expensive_fun</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span>
|
|
|
|
<span class="n">y</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">fun</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
|
</pre></div>
|
|
</div>
|
|
<p>Here, we never actually compute the output of <code class="docutils literal notranslate"><span class="pre">expensive_fun</span></code>. Use this
|
|
pattern with care though, as the graph of <code class="docutils literal notranslate"><span class="pre">expensive_fun</span></code> is still built, and
|
|
that has some cost associated to it.</p>
|
|
<p>Similarly, lazy evaluation can be beneficial for saving memory while keeping
|
|
code simple. Say you have a very large model <code class="docutils literal notranslate"><span class="pre">Model</span></code> derived from
|
|
<a class="reference internal" href="../python/nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mlx.nn.Module</span></code></a>. You can instantiate this model with <code class="docutils literal notranslate"><span class="pre">model</span> <span class="pre">=</span> <span class="pre">Model()</span></code>.
|
|
Typically, this will initialize all of the weights as <code class="docutils literal notranslate"><span class="pre">float32</span></code>, but the
|
|
initialization does not actually compute anything until you perform an
|
|
<a class="reference internal" href="../python/_autosummary/mlx.core.eval.html#mlx.core.eval" title="mlx.core.eval"><code class="xref py py-func docutils literal notranslate"><span class="pre">eval()</span></code></a>. If you update the model with <code class="docutils literal notranslate"><span class="pre">float16</span></code> weights, your maximum
|
|
consumed memory will be half that required if eager computation was used
|
|
instead.</p>
|
|
<p>This pattern is simple to do in MLX thanks to lazy computation:</p>
|
|
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">()</span> <span class="c1"># no memory used yet</span>
|
|
<span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s2">"weights_fp16.safetensors"</span><span class="p">)</span>
|
|
</pre></div>
|
|
</div>
|
|
</section>
|
|
</section>
|
|
<section id="when-to-evaluate">
|
|
<h2>When to Evaluate<a class="headerlink" href="#when-to-evaluate" title="Link to this heading">#</a></h2>
|
|
<p>A common question is when to use <a class="reference internal" href="../python/_autosummary/mlx.core.eval.html#mlx.core.eval" title="mlx.core.eval"><code class="xref py py-func docutils literal notranslate"><span class="pre">eval()</span></code></a>. The trade-off is between
|
|
letting graphs get too large and not batching enough useful work.</p>
|
|
<p>For example:</p>
|
|
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
|
|
<span class="n">a</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
|
|
<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
|
|
<span class="n">b</span> <span class="o">=</span> <span class="n">b</span> <span class="o">*</span> <span class="mi">2</span>
|
|
<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
|
|
</pre></div>
|
|
</div>
|
|
<p>This is a bad idea because there is some fixed overhead with each graph
|
|
evaluation. On the other hand, there is some slight overhead which grows with
|
|
the compute graph size, so extremely large graphs (while computationally
|
|
correct) can be costly.</p>
|
|
<p>Luckily, a wide range of compute graph sizes work pretty well with MLX:
|
|
anything from a few tens of operations to many thousands of operations per
|
|
evaluation should be okay.</p>
|
|
<p>Most numerical computations have an iterative outer loop (e.g. the iteration in
|
|
stochastic gradient descent). A natural and usually efficient place to use
|
|
<a class="reference internal" href="../python/_autosummary/mlx.core.eval.html#mlx.core.eval" title="mlx.core.eval"><code class="xref py py-func docutils literal notranslate"><span class="pre">eval()</span></code></a> is at each iteration of this outer loop.</p>
|
|
<p>Here is a concrete example:</p>
|
|
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">:</span>
|
|
|
|
<span class="c1"># Nothing has been evaluated yet</span>
|
|
<span class="n">loss</span><span class="p">,</span> <span class="n">grad</span> <span class="o">=</span> <span class="n">value_and_grad_fn</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">batch</span><span class="p">)</span>
|
|
|
|
<span class="c1"># Still nothing has been evaluated</span>
|
|
<span class="n">optimizer</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">grad</span><span class="p">)</span>
|
|
|
|
<span class="c1"># Evaluate the loss and the new parameters which will</span>
|
|
<span class="c1"># run the full gradient computation and optimizer update</span>
|
|
<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
|
|
</pre></div>
|
|
</div>
|
|
<p>An important behavior to be aware of is when the graph will be implicitly
|
|
evaluated. Anytime you <code class="docutils literal notranslate"><span class="pre">print</span></code> an array, convert it to an
|
|
<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>, or otherwise access its memory via <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#memoryview" title="(in Python v3.13)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memoryview</span></code></a>,
|
|
the graph will be evaluated. Saving arrays via <a class="reference internal" href="../python/_autosummary/mlx.core.save.html#mlx.core.save" title="mlx.core.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">save()</span></code></a> (or any other MLX
|
|
saving functions) will also evaluate the array.</p>
|
|
<p>Calling <a class="reference internal" href="../python/_autosummary/mlx.core.array.item.html#mlx.core.array.item" title="mlx.core.array.item"><code class="xref py py-func docutils literal notranslate"><span class="pre">array.item()</span></code></a> on a scalar array will also evaluate it. In the
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example above, printing the loss (<code class="docutils literal notranslate"><span class="pre">print(loss)</span></code>) or adding the loss scalar to
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a list (<code class="docutils literal notranslate"><span class="pre">losses.append(loss.item())</span></code>) would cause a graph evaluation. If
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these lines are before <code class="docutils literal notranslate"><span class="pre">mx.eval(loss,</span> <span class="pre">model.parameters())</span></code> then this
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will be a partial evaluation, computing only the forward pass.</p>
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<p>Also, calling <a class="reference internal" href="../python/_autosummary/mlx.core.eval.html#mlx.core.eval" title="mlx.core.eval"><code class="xref py py-func docutils literal notranslate"><span class="pre">eval()</span></code></a> on an array or set of arrays multiple times is
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perfectly fine. This is effectively a no-op.</p>
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<div class="admonition warning">
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<p class="admonition-title">Warning</p>
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<p>Using scalar arrays for control-flow will cause an evaluation.</p>
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</div>
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<p>Here is an example:</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">fun</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
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<span class="n">h</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">first_layer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<span class="k">if</span> <span class="n">y</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># An evaluation is done here!</span>
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<span class="n">z</span> <span class="o">=</span> <span class="n">second_layer_a</span><span class="p">(</span><span class="n">h</span><span class="p">)</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="n">z</span> <span class="o">=</span> <span class="n">second_layer_b</span><span class="p">(</span><span class="n">h</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">z</span>
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</pre></div>
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</div>
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<p>Using arrays for control flow should be done with care. The above example works
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and can even be used with gradient transformations. However, this can be very
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inefficient if evaluations are done too frequently.</p>
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</section>
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