`_: further speedups by communicating between devices inside the GEMM.
+* `← Hub <../te_jax_integration.html>`_
diff --git a/docs/examples/jax/expert_parallelism.rst b/docs/examples/jax/expert_parallelism.rst
new file mode 100644
index 0000000000..5e94e1d298
--- /dev/null
+++ b/docs/examples/jax/expert_parallelism.rst
@@ -0,0 +1,11 @@
+..
+ Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+ See LICENSE for license information.
+
+JAX: Expert Parallelism with TransformerEngine
+==============================================
+
+**TODO — Coming soon.**
+
+`← Back to the JAX integration overview <../te_jax_integration.html>`_
diff --git a/docs/examples/quickstart_jax_utils.py b/docs/examples/jax/quickstart_jax_utils.py
similarity index 64%
rename from docs/examples/quickstart_jax_utils.py
rename to docs/examples/jax/quickstart_jax_utils.py
index 0c5ec5295e..6547a5ff1a 100644
--- a/docs/examples/quickstart_jax_utils.py
+++ b/docs/examples/jax/quickstart_jax_utils.py
@@ -4,6 +4,7 @@
import jax
import jax.numpy as jnp
+import numpy as np
import time
from typing import Callable, Any, Dict, Optional, Tuple
@@ -99,3 +100,54 @@ def _split_step_rngs(
new_rngs[name] = new_key
step_rngs[name] = step_key
return new_rngs, step_rngs
+
+
+def compare_fwd_bwd(
+ ref_apply_fn: Callable,
+ ref_variables: Any,
+ test_apply_fn: Callable,
+ test_variables: Any,
+ *,
+ input: jnp.ndarray,
+ output_grad: jnp.ndarray,
+ rtol: float = 1e-5,
+ atol: float = 1e-8,
+ rtol_dW: Optional[float] = None,
+ atol_dW: Optional[float] = None,
+) -> None:
+ """Compare forward outputs and VJP gradients between two models.
+
+ Runs ``y, vjp_fn = jax.vjp(apply_fn, variables, input)`` for each model,
+ then applies ``vjp_fn(output_grad)`` to get gradients wrt both the
+ parameters (``dW``) and the input (``dx``). Calls
+ ``numpy.testing.assert_allclose`` on each tensor (``y``, ``dx``, and every
+ leaf of ``dW``). ``rtol_dW`` / ``atol_dW`` override ``rtol`` / ``atol``
+ for the params-grad comparison.
+ """
+ rtol_dW = rtol if rtol_dW is None else rtol_dW
+ atol_dW = atol if atol_dW is None else atol_dW
+
+ def _run(apply_fn: Callable) -> Callable:
+ @jax.jit
+ def go(variables, inp, dy):
+ y, vjp_fn = jax.vjp(apply_fn, variables, inp)
+ dvars, dx = vjp_fn(dy.astype(y.dtype))
+ return y, dvars["params"], dx
+
+ return go
+
+ y_ref, dW_ref, dx_ref = _run(ref_apply_fn)(ref_variables, input, output_grad)
+ y_test, dW_test, dx_test = _run(test_apply_fn)(test_variables, input, output_grad)
+
+ np.testing.assert_allclose(
+ y_test, y_ref, rtol=rtol, atol=atol, err_msg="forward output (y) mismatch"
+ )
+ np.testing.assert_allclose(
+ dx_test, dx_ref, rtol=rtol, atol=atol, err_msg="input grad (dx) mismatch"
+ )
+ for ref_leaf, test_leaf in zip(
+ jax.tree_util.tree_leaves(dW_ref), jax.tree_util.tree_leaves(dW_test)
+ ):
+ np.testing.assert_allclose(
+ test_leaf, ref_leaf, rtol=rtol_dW, atol=atol_dW, err_msg="params grad (dW) mismatch"
+ )
diff --git a/docs/examples/jax/test_dense.py b/docs/examples/jax/test_dense.py
new file mode 100644
index 0000000000..049a7c9566
--- /dev/null
+++ b/docs/examples/jax/test_dense.py
@@ -0,0 +1,86 @@
+# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# See LICENSE for license information.
+
+"""Pytest entry points for ``dense.py``.
+
+These run the same code shown in ``dense.py`` and add numeric / smoke
+assertions so CI catches regressions.
+
+Run with:
+
+ pytest -v docs/examples/jax/test_dense.py
+
+The multi-GPU section auto-skips when fewer than 4 GPUs are visible.
+"""
+
+import jax
+import jax.numpy as jnp
+import pytest
+
+import quickstart_jax_utils as utils
+from transformer_engine.jax.quantize import is_scaling_mode_supported, ScalingMode
+
+# Imports from ``dense`` are intentionally deferred into each test body. dense.py
+# runs ``te_vars = te_model.init(k_init, x)`` at module scope, which raises on
+# devices without MXFP8 support (Hopper or older). A top-level import would fire
+# that before pytest can apply the @requires_mxfp8 skip marks.
+
+_mxfp8_supported, _mxfp8_reason = is_scaling_mode_supported(ScalingMode.MXFP8_1D_SCALING)
+requires_mxfp8 = pytest.mark.skipif(
+ not _mxfp8_supported, reason=f"MXFP8 not supported on this device: {_mxfp8_reason}"
+)
+
+
+def test_baseline_runs():
+ from dense import baseline, baseline_vars, batch, dtype, out_features, seq, x
+
+ out = baseline.apply(baseline_vars, x)
+ assert out.shape == (batch, seq, out_features)
+ assert out.dtype == dtype
+
+
+@requires_mxfp8
+def test_te_dense_runs():
+ from dense import batch, out_features, seq, te_model, te_vars, x
+
+ out = te_model.apply(te_vars, x)
+ assert out.shape == (batch, seq, out_features)
+
+
+@requires_mxfp8
+def test_te_matches_baseline():
+ """TE quantized Dense should match the bf16 baseline within MXFP8 tolerance."""
+ from dense import baseline, baseline_vars, batch, dy, seq, te_model, te_vars, x
+
+ fp8_rel_noise = float(jnp.finfo(jnp.float8_e4m3fn).eps)
+ atol_fwd = 10.0 * fp8_rel_noise
+ atol_dw = atol_fwd * jnp.sqrt(batch * seq).item()
+
+ utils.compare_fwd_bwd(
+ baseline.apply,
+ baseline_vars,
+ te_model.apply,
+ te_vars,
+ input=x,
+ output_grad=dy,
+ rtol=fp8_rel_noise,
+ atol=atol_fwd,
+ rtol_dW=fp8_rel_noise,
+ atol_dW=atol_dw,
+ )
+
+
+@requires_mxfp8
+def test_single_gpu_benchmark():
+ from dense import run_single_gpu_bench
+
+ run_single_gpu_bench()
+
+
+@requires_mxfp8
+@pytest.mark.skipif(len(jax.devices()) < 4, reason="needs 4 GPUs for DP=2/TP=2")
+def test_multi_gpu_benchmark():
+ from dense import run_multi_gpu_bench
+
+ run_multi_gpu_bench()
diff --git a/docs/examples/te_jax_integration.ipynb b/docs/examples/te_jax_integration.ipynb
deleted file mode 100644
index 66d16ed52f..0000000000
--- a/docs/examples/te_jax_integration.ipynb
+++ /dev/null
@@ -1,462 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "962d87bb",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "# JAX: Integrating TE into an existing framework\n",
- "\n",
- "This tutorial will cover how to integrate TransformerEngine into an existing JAX model framework, such as [MaxText's TE integration](https://github.com/AI-Hypercomputer/maxtext/blob/ed517cf80d9aa81f76e236c5516dacebfe39e96d/src/MaxText/layers/quantizations.py#L753) or your own model framework. \n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b36876bb",
- "metadata": {},
- "source": [
- "Let's start with a standard JAX+Flax Transformer layer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "d5284a38",
- "metadata": {},
- "outputs": [],
- "source": [
- "import jax\n",
- "import jax.numpy as jnp\n",
- "from flax import linen as nn\n",
- "import quickstart_jax_utils as utils\n",
- "from typing import Optional"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "a4d1cfdc",
- "metadata": {},
- "outputs": [],
- "source": [
- "class FlaxMLP(nn.Module):\n",
- " \"\"\"Feed-forward network in Transformer layer\n",
- " Built with plain Flax modules.\n",
- " \"\"\"\n",
- " hidden_size: int\n",
- " ffn_hidden_size: int\n",
- " dot_general_cls: callable = lambda: None\n",
- "\n",
- " @nn.compact\n",
- " def __call__(self, x: jnp.ndarray) -> jnp.ndarray:\n",
- " x = nn.Dense(features=self.ffn_hidden_size, use_bias=True, dot_general=self.dot_general_cls())(x)\n",
- " x = nn.gelu(x, approximate=True) # equivalent to tanh approximation\n",
- " x = nn.Dense(features=self.hidden_size, use_bias=True, dot_general=self.dot_general_cls())(x)\n",
- " return x\n",
- "\n",
- "class FlaxTransformerLayer(nn.Module):\n",
- " \"\"\"Basic Transformer layer using plain Flax modules\"\"\"\n",
- " hidden_size: int\n",
- " ffn_hidden_size: int\n",
- " num_attention_heads: int\n",
- " layernorm_eps: float = 1e-5\n",
- " attention_dropout: float = 0.1\n",
- " dot_general_cls: callable = lambda: None\n",
- " \n",
- " def setup(self):\n",
- " self.kv_channels = self.hidden_size // self.num_attention_heads\n",
- "\n",
- " @nn.compact\n",
- " def __call__(\n",
- " self, \n",
- " x: jnp.ndarray, \n",
- " attention_mask: Optional[jnp.ndarray] = None,\n",
- " deterministic: bool = False\n",
- " ) -> jnp.ndarray:\n",
- " # Create causal mask if not provided\n",
- " if attention_mask is None:\n",
- " attention_mask = nn.make_causal_mask(x[..., 0], dtype=jnp.bool_)\n",
- " \n",
- " res = x\n",
- " x = nn.LayerNorm(epsilon=self.layernorm_eps)(x)\n",
- " \n",
- " # Fused QKV projection\n",
- " qkv = nn.Dense(features=3 * self.hidden_size, use_bias=True, dot_general=self.dot_general_cls())(x)\n",
- " qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], self.num_attention_heads, 3 * self.kv_channels)\n",
- " q, k, v = jnp.split(qkv, 3, axis=3)\n",
- " \n",
- " # q, k, v now have shape [batch, seq_len, num_heads, kv_channels]\n",
- " # which is the correct format for dot_product_attention\n",
- " \n",
- " # Apply dot product attention\n",
- " # Note: dot_product_attention expects mask to be broadcastable to \n",
- " # [batch, num_heads, q_length, kv_length], but attention_mask from \n",
- " # nn.make_causal_mask has shape [batch, 1, seq_len, seq_len]\n",
- " \n",
- " # Generate dropout RNG key when needed (not deterministic and dropout_rate > 0)\n",
- " dropout_rng = None\n",
- " if not deterministic and self.attention_dropout > 0:\n",
- " dropout_rng = self.make_rng('dropout')\n",
- " \n",
- " # See quickstart_jax.ipynb for details on using TE's faster fused attention\n",
- " x = nn.dot_product_attention(\n",
- " query=q,\n",
- " key=k,\n",
- " value=v,\n",
- " mask=attention_mask,\n",
- " dropout_rng=dropout_rng,\n",
- " dropout_rate=self.attention_dropout,\n",
- " deterministic=deterministic,\n",
- " broadcast_dropout=True,\n",
- " )\n",
- " \n",
- " # Reshape output from [batch, seq_len, num_heads, kv_channels] to [batch, seq_len, hidden_size]\n",
- " x = x.reshape(x.shape[0], x.shape[1], self.hidden_size)\n",
- "\n",
- " # Output projection\n",
- " x = nn.Dense(features=self.hidden_size, use_bias=True, dot_general=self.dot_general_cls())(x)\n",
- " \n",
- " x = res + x\n",
- " \n",
- " # Second residual connection\n",
- " res = x\n",
- " x = nn.LayerNorm(epsilon=self.layernorm_eps)(x)\n",
- " \n",
- " # MLP\n",
- " mlp = FlaxMLP(\n",
- " hidden_size=self.hidden_size,\n",
- " ffn_hidden_size=self.ffn_hidden_size,\n",
- " dot_general_cls=self.dot_general_cls,\n",
- " )\n",
- " x = mlp(x)\n",
- " \n",
- " return x + res\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "db16bf70",
- "metadata": {},
- "source": [
- "We've exposed `dot_general_cls` here so we can test out different GEMM implementations later. By default, Flax's `nn.Dense` will use JAX's GEMM `jax.lax.dot_general` when `dot_general` is `None`."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fbc3510b",
- "metadata": {},
- "source": [
- "## Testing Performance\n",
- "\n",
- "Now let's test the performance of our FlaxTransformerLayer:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "8b44649d",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Layer configuration\n",
- "hidden_size = 4096\n",
- "sequence_length = 2048\n",
- "batch_size = 4\n",
- "ffn_hidden_size = 16384\n",
- "num_attention_heads = 32\n",
- "dtype = jnp.bfloat16\n",
- "\n",
- "# Synthetic data\n",
- "key, dropout_key = jax.random.split(jax.random.PRNGKey(42))\n",
- "x = jax.random.normal(key, (batch_size, sequence_length, hidden_size)).astype(dtype)\n",
- "dy = jax.random.normal(key, (batch_size, sequence_length, hidden_size)).astype(dtype)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "e44ed26d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Pure Flax FlaxTransformerLayer initialized successfully!\n",
- "Parameter shapes: {'params': {'Dense_0': {'bias': (12288,), 'kernel': (4096, 12288)}, 'Dense_1': {'bias': (4096,), 'kernel': (4096, 4096)}, 'FlaxMLP_0': {'Dense_0': {'bias': (16384,), 'kernel': (4096, 16384)}, 'Dense_1': {'bias': (4096,), 'kernel': (16384, 4096)}}, 'LayerNorm_0': {'bias': (4096,), 'scale': (4096,)}, 'LayerNorm_1': {'bias': (4096,), 'scale': (4096,)}}}\n"
- ]
- }
- ],
- "source": [
- "# Initialize the FlaxTransformerLayer\n",
- "flax_transformer = FlaxTransformerLayer(\n",
- " hidden_size=hidden_size,\n",
- " ffn_hidden_size=ffn_hidden_size,\n",
- " num_attention_heads=num_attention_heads,\n",
- ")\n",
- "\n",
- "# Initialize parameters\n",
- "params = flax_transformer.init(key, x, attention_mask=None, deterministic=False)\n",
- "\n",
- "print(\"Pure Flax FlaxTransformerLayer initialized successfully!\")\n",
- "print(f\"Parameter shapes: {jax.tree_util.tree_map(lambda x: x.shape, params)}\")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "de91af7a",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Input shape: (4, 2048, 4096)\n",
- "Output shape: (4, 2048, 4096)\n",
- "Output dtype: float32\n",
- "Forward pass completed successfully!\n"
- ]
- }
- ],
- "source": [
- "# Example usage of forward pass\n",
- "y = flax_transformer.apply(params, x, attention_mask=None, deterministic=True)\n",
- "print(f\"Input shape: {x.shape}\")\n",
- "print(f\"Output shape: {y.shape}\")\n",
- "print(f\"Output dtype: {y.dtype}\")\n",
- "print(\"Forward pass completed successfully!\")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "037bc8d9",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Mean time: 18.83516788482666 ms\n"
- ]
- }
- ],
- "source": [
- "import importlib\n",
- "import quickstart_jax_utils\n",
- "importlib.reload(quickstart_jax_utils)\n",
- "\n",
- "utils.speedometer(\n",
- " model_apply_fn=flax_transformer.apply,\n",
- " variables=params,\n",
- " input=x,\n",
- " output_grad=dy,\n",
- " forward_kwargs={\"attention_mask\": None, \"deterministic\": False},\n",
- " rngs={\"dropout\": dropout_key},\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5e9310c9",
- "metadata": {},
- "source": [
- "## Transformer Engine"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1f8e213e",
- "metadata": {},
- "source": [
- "TransformerEngine/JAX is currently using Flax Linen. However, it is easily compatible with Flax NNX or Haiku.\n",
- "* [Use Flax NNX and Linen together](https://flax.readthedocs.io/en/latest/guides/bridge_guide.html)\n",
- "* [Haiku and Flax interop](https://dm-haiku.readthedocs.io/en/latest/notebooks/flax.html)\n",
- "\n",
- "Additionally, with the tutorial below, no model parameters need to be managed by TransformerEngine. You can keep all your existing model parameters, initialization, and sharding the same. The only change required is to call TE's dot_general_cls instead of the default Dense dot_general implementation. TE's dot_general_cls is a small module that performs a quantized dense VJP and stores some small recipe-specific state."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4477d4e9",
- "metadata": {},
- "source": [
- "Now we'll select a recipe. `DelayedScaling` and `CurrentScaling` use per-tensor scaling and are supported on Hopper and Blackwell. `MXFP8BlockScaling` and `NVFP4BlockScaling` use block scaling or a combination of both per-tensor and block scaling and are supported on Blackwell.\n",
- "\n",
- "If you would like to customize the recipe further, various options can be changed by passing args to the recipe's constructor."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "5ddf41e7",
- "metadata": {},
- "outputs": [],
- "source": [
- "from transformer_engine.common.recipe import DelayedScaling, Float8CurrentScaling, MXFP8BlockScaling, NVFP4BlockScaling\n",
- "from transformer_engine.jax import flax as te_flax \n",
- "\n",
- "# Choose a quantization recipe. This can be modified to any of the recipes imported above.\n",
- "quantization_recipe = DelayedScaling()\n",
- "\n",
- "te_dot_general_cls = te_flax.make_dot_general_cls(quantization_recipe)\n",
- "\n",
- "rngs = {'dropout': dropout_key}\n",
- "if isinstance(quantization_recipe, NVFP4BlockScaling):\n",
- " # The NVFP4 recipe requires a Flax RNG for stochastic rounding\n",
- " rngs['sr_rng'] = jax.random.PRNGKey(0)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c8769655",
- "metadata": {},
- "source": [
- "Now using this quantized dense in our model is as simple as passing in `dot_general_fn=te_dot_general`. Let's try it out!\n",
- "\n",
- "\n",
- "\n",
- "Important: Remat Policy\n",
- "\n",
- "TE's quantization uses specialized TE quantized GEMM primitives. If you are using any built-in JAX checkpoint policies that look for JAX GEMMs (dots), such as `jax.checkpoint_policies.checkpoint_dots`, please replace the policy with `transformer_engine.jax.checkpoint_policies.checkpoint_dots_and_te_gemms` or similar policies to ensure TE's quantized GEMM primitives are checkpointed correctly.\n",
- "\n",
- "If this is not performed, TE GEMMs will be rematerialized introducing an incorrect performance comparison.\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "8407d2ea",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Pure Flax FlaxTransformerLayer initialized successfully!\n",
- "Parameter shapes: {'Dense_0': {'bias': (12288,), 'kernel': (4096, 12288)}, 'Dense_1': {'bias': (4096,), 'kernel': (4096, 4096)}, 'FlaxMLP_0': {'Dense_0': {'bias': (16384,), 'kernel': (4096, 16384)}, 'Dense_1': {'bias': (4096,), 'kernel': (16384, 4096)}}, 'LayerNorm_0': {'bias': (4096,), 'scale': (4096,)}, 'LayerNorm_1': {'bias': (4096,), 'scale': (4096,)}}\n",
- "Additional state: {'_overwrite_with_gradient': {'FlaxMLP_0': {'TEWrapper_dot_general_0': {'grad_amax_history': (1024,), 'grad_scale': (1,), 'kernel_amax_history': (1024,), 'kernel_scale': (1,), 'x_amax_history': (1024,), 'x_scale': (1,)}, 'TEWrapper_dot_general_1': {'grad_amax_history': (1024,), 'grad_scale': (1,), 'kernel_amax_history': (1024,), 'kernel_scale': (1,), 'x_amax_history': (1024,), 'x_scale': (1,)}}, 'TEWrapper_dot_general_0': {'grad_amax_history': (1024,), 'grad_scale': (1,), 'kernel_amax_history': (1024,), 'kernel_scale': (1,), 'x_amax_history': (1024,), 'x_scale': (1,)}, 'TEWrapper_dot_general_1': {'grad_amax_history': (1024,), 'grad_scale': (1,), 'kernel_amax_history': (1024,), 'kernel_scale': (1,), 'x_amax_history': (1024,), 'x_scale': (1,)}}}\n"
- ]
- }
- ],
- "source": [
- "# Initialize the FlaxTransformerLayer\n",
- "flax_transformer = FlaxTransformerLayer(\n",
- " hidden_size=hidden_size,\n",
- " ffn_hidden_size=ffn_hidden_size,\n",
- " num_attention_heads=num_attention_heads,\n",
- " dot_general_cls=te_dot_general_cls,\n",
- ")\n",
- "\n",
- "# Initialize parameters\n",
- "var_collect = flax_transformer.init(key, x, attention_mask=None, deterministic=False)\n",
- "\n",
- "print(\"Pure Flax FlaxTransformerLayer initialized successfully!\")\n",
- "print(f\"Parameter shapes: {jax.tree_util.tree_map(lambda x: x.shape, var_collect['params'])}\")\n",
- "print(f\"Additional state: {jax.tree_util.tree_map(lambda x: x.shape, {k: v for k, v in var_collect.items() if k != 'params'})}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "abe27237",
- "metadata": {},
- "source": [
- "If using a recipe that stores additional state, such as `DelayedScaling`, you'll see this additional state stored as Flax variables. It is important to maintain and pass the whole state of Flax variables `var_collect` across training steps, not just the model params, for proper usage of stateful recipes like `DelayedScaling`.\n",
- "\n",
- "For example, above inside `Additional state: ` you'll see the `amax_history` of each quantization which is used to compute the per-tensor scale in the `DelayedScaling` recipe."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5ab72935",
- "metadata": {},
- "source": [
- "The reason we need `te_dot_general_cls` as a Flax module instead of a module-less function like `jax.lax.dot_general` is for some quantization recipes to track internal state separate from model parameters.\n",
- "\n",
- "Flax modules can manage 3 things:\n",
- "1. Model parameters/weights, e.g. your Dense \"kernel\", \"bias\", etc.\n",
- "2. RNGs for dropout, stochastic rounding, etc.\n",
- "3. Flax variables. These are additional state variables that are used across training steps but are distinct from model params in that you don't take gradients or optimize them. Currently, we only use this for DelayedScaling's amax_history state\n",
- "\n",
- "With the simplest quantization integration shown in this tutorial, we want users to keep their existing model param setup so they don't need to worry about preserving the sharding, init distribution, etc.. So we don't need point 1 since we don't do model param creation in this codepath with dot_general_cls, but we still do need `te_dot_general_cls()` to produce a Flax module since we potentially need to do points 2 or 3 which need to be in a Flax module."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "3b6b344b",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Input shape: (4, 2048, 4096)\n",
- "Output shape: (4, 2048, 4096)\n",
- "Output dtype: float32\n",
- "Forward pass completed successfully!\n"
- ]
- }
- ],
- "source": [
- "# Example usage of forward pass\n",
- "y = flax_transformer.apply(var_collect, x, attention_mask=None, deterministic=True, rngs=rngs)\n",
- "print(f\"Input shape: {x.shape}\")\n",
- "print(f\"Output shape: {y.shape}\")\n",
- "print(f\"Output dtype: {y.dtype}\")\n",
- "print(\"Forward pass completed successfully!\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d178f247",
- "metadata": {},
- "source": [
- "Now let's measure the performance!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "5cc6c2a7",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Mean time: 10.553865432739258 ms\n"
- ]
- }
- ],
- "source": [
- "import importlib\n",
- "import quickstart_jax_utils\n",
- "importlib.reload(quickstart_jax_utils)\n",
- "\n",
- "utils.speedometer(\n",
- " model_apply_fn=flax_transformer.apply,\n",
- " variables=var_collect,\n",
- " input=x,\n",
- " output_grad=dy,\n",
- " forward_kwargs={\"attention_mask\": None, \"deterministic\": False},\n",
- " rngs=rngs,\n",
- ")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/examples/te_jax_integration.rst b/docs/examples/te_jax_integration.rst
new file mode 100644
index 0000000000..2602b3bbf3
--- /dev/null
+++ b/docs/examples/te_jax_integration.rst
@@ -0,0 +1,94 @@
+..
+ Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+ See LICENSE for license information.
+
+JAX: Integrating TransformerEngine into an existing framework
+=============================================================
+
+This is the landing page for a series of focused documents on bringing
+TransformerEngine into a JAX+Flax codebase one optimization at a time. Each
+linked page isolates a single feature so you can see exactly what changes are
+required and what are the performance benefits.
+
+Pick a topic
+------------
+
+.. list-table::
+ :header-rows: 1
+ :widths: 25, 15, 60
+
+ * - Document
+ - Status
+ - Covers
+ * - `Dense GEMMs `_
+ - **Available**
+ - ``nn.Dense`` → quantized GEMM; single-GPU speedup; multi-GPU speedup;
+ * - `Collective GEMMs `_
+ - *Coming soon*
+ -
+ * - `Attention `_
+ - *Coming soon*
+ -
+ * - `Expert Parallelism `_
+ - *Coming soon*
+ -
+
+
+Quantization recipes at a glance
+--------------------------------
+
+TE exposes its quantization choices as **recipes**. Please see
+`Low-precision Training
+`_
+for a more detailed description of each recipe.
+
+.. list-table::
+ :header-rows: 1
+ :widths: 25, 15, 30, 30
+
+ * - Recipe
+ - Hardware
+ - State
+ - When to use
+ * - ``DelayedScaling``
+ - Hopper+
+ - amax history (Flax variables)
+ - Per-tensor FP8 with amax history
+ * - ``Float8CurrentScaling``
+ - Hopper+
+ - none
+ - Per-tensor FP8 without an amax history
+ * - ``MXFP8BlockScaling``
+ - Blackwell+
+ - none
+ - Block-scaled FP8 (32-element blocks)
+ * - ``NVFP4BlockScaling``
+ - Blackwell+
+ - requires a Flax RNG ``sr_rng``
+ - FP4 with 2D block scaling and stochastic rounding
+
+Import them from ``transformer_engine.common.recipe``.
+
+
+Conventions used across these documents
+---------------------------------------
+
+* **Framework.** Flax Linen. (TE/JAX uses Linen; see
+ `Flax NNX/Linen interop
+ `_ and
+ `Haiku/Flax interop
+ `_ if you're on
+ a different stack.)
+* **Baseline dtype.** bf16 for inputs and parameters.
+* **Benchmarking.** ``quickstart_jax_utils.speedometer`` runs a JIT-compiled
+ fwd+bwd loop with warmup
+
+
+.. toctree::
+ :hidden:
+
+ jax/dense
+ jax/collective_gemm
+ jax/attention
+ jax/expert_parallelism
diff --git a/docs/index.rst b/docs/index.rst
index 7389553679..53c4b0e37e 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -57,7 +57,7 @@ Transformer Engine documentation
examples/te_llama/tutorial_accelerate_hf_llama_with_te.ipynb
examples/te_gemma/tutorial_generation_gemma_with_te.ipynb
examples/onnx/onnx_export.ipynb
- examples/te_jax_integration.ipynb
+ examples/te_jax_integration.rst
examples/op_fuser/op_fuser.rst
.. toctree::
diff --git a/qa/L0_jax_unittest/test.sh b/qa/L0_jax_unittest/test.sh
index 3453e35d2c..ccf10b8843 100644
--- a/qa/L0_jax_unittest/test.sh
+++ b/qa/L0_jax_unittest/test.sh
@@ -42,6 +42,11 @@ python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/py
export XLA_FLAGS="${XLA_FLAGS} --xla_gpu_deterministic_ops"
NVTE_JAX_CUSTOM_CALLS="false" python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest_test_single_gpu_encoder_without_custom_call.xml $TE_PATH/examples/jax/encoder/test_single_gpu_encoder.py || test_fail "test_single_gpu_encoder.py without custom calls"
+# Exercise the docs/examples/jax_examples tutorials. The multi-GPU tests are
+# skipped at runtime when fewer than 4 devices are visible, so this is safe on
+# single-GPU runners.
+python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest_docs_examples_jax.xml $TE_PATH/docs/examples/jax/ || test_fail "docs/examples/jax"
+
if [ $RET -ne 0 ]; then
echo "Error: some sub-tests failed: $FAILED_CASES"
exit 1
diff --git a/qa/L1_jax_distributed_unittest/test.sh b/qa/L1_jax_distributed_unittest/test.sh
index 4f92d1c783..b3b5762d98 100644
--- a/qa/L1_jax_distributed_unittest/test.sh
+++ b/qa/L1_jax_distributed_unittest/test.sh
@@ -37,6 +37,10 @@ XLA_FLAGS="$XLA_FLAGS --xla_gpu_enable_nccl_comm_splitting=false" python3 -m pyt
python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest_dist_fused_attn.xml $TE_PATH/tests/jax/test_distributed_fused_attn.py || test_fail "test_distributed_fused_attn.py"
+# Exercise the multi-GPU tutorial in docs/examples/jax_examples (needs >= 4 GPUs;
+# auto-skips otherwise).
+python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest_docs_examples_jax_distributed.xml -k multi_gpu $TE_PATH/docs/examples/jax/ || test_fail "docs/examples/jax (multi-GPU)"
+
# TODO(Phuong): add this test back after it is verified
# SCRIPT_NAME=$TE_PATH/tests/jax/test_multi_process_distributed_grouped_gemm.py bash $TE_PATH/tests/jax/multi_process_launch.sh || test_fail "test_multi_process_distributed_grouped_gemm.py"