Merge nv_main(2.10) to main

Signed-off-by: wenjh <wenjh@sugon.com>

qa/L0_pytorch_unittest/test.sh

@@ -45,7 +45,8 @@ python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_multi_tensor.xml
 python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_fusible_ops.xml $TE_PATH/tests/pytorch/test_fusible_ops.py || test_fail "test_fusible_ops.py"
 python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_permutation.xml $TE_PATH/tests/pytorch/test_permutation.py || test_fail "test_permutation.py"
 python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_parallel_cross_entropy.xml $TE_PATH/tests/pytorch/test_parallel_cross_entropy.py || test_fail "test_parallel_cross_entropy.py"
-NVTE_FLASH_ATTN=0 python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_cpu_offloading.xml $TE_PATH/tests/pytorch/test_cpu_offloading.py || test_fail "test_cpu_offloading.py"
+python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_cpu_offloading.xml $TE_PATH/tests/pytorch/test_cpu_offloading.py || test_fail "test_cpu_offloading.py"
+NVTE_FLASH_ATTN=0 NVTE_CPU_OFFLOAD_V1=1 python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_cpu_offloading_v1.xml $TE_PATH/tests/pytorch/test_cpu_offloading_v1.py || test_fail "test_cpu_offloading_v1.py"
 python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_attention.xml $TE_PATH/tests/pytorch/attention/test_attention.py || test_fail "test_attention.py"
 python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_kv_cache.xml $TE_PATH/tests/pytorch/attention/test_kv_cache.py || test_fail "test_kv_cache.py"
 python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_hf_integration.xml $TE_PATH/tests/pytorch/test_hf_integration.py || test_fail "test_hf_integration.py"

qa/L1_jax_distributed_unittest/test.sh

@@ -2,11 +2,44 @@
 #
 # See LICENSE for license information.
 
-set -xe
+function test_fail() {
+    RET=1
+    FAILED_CASES="$FAILED_CASES $1"
+    echo "Error: sub-test failed: $1"
+}
+
+RET=0
+FAILED_CASES=""
 
 : ${TE_PATH:=/opt/transformerengine}
 : ${XML_LOG_DIR:=/logs}
 mkdir -p "$XML_LOG_DIR"
 
-NVTE_JAX_UNITTEST_LEVEL="L1" python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest.xml $TE_PATH/tests/jax/test_distributed_*
-SCRIPT_NAME=$TE_PATH/tests/jax/test_multi_process_distributed_grouped_gemm.py bash $TE_PATH/tests/jax/multi_process_launch.sh
+export NVTE_JAX_UNITTEST_LEVEL="L1"
+
+# Use --xla_gpu_enable_triton_gemm=false to ensure the reference JAX implementation we are using is accurate.
+export XLA_FLAGS="$XLA_FLAGS --xla_gpu_enable_triton_gemm=false"
+
+python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest_dist_dense.xml $TE_PATH/tests/jax/test_distributed_dense.py || test_fail "test_distributed_dense.py"
+
+python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest_helper.xml $TE_PATH/tests/jax/test_distributed_helper.py || test_fail "test_distributed_helper.py"
+
+python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest_dist_layernorm.xml $TE_PATH/tests/jax/test_distributed_layernorm.py || test_fail "test_distributed_layernorm.py"
+
+python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest_dist_mlp.xml $TE_PATH/tests/jax/test_distributed_layernorm_mlp.py || test_fail "test_distributed_layernorm_mlp.py"
+
+# XLA_FLAGS to WAR for test_distributed_softmax issue with NCCL
+# TODO(Kshitij): remove when NCCL issue is fixed
+XLA_FLAGS="$XLA_FLAGS --xla_gpu_enable_nccl_comm_splitting=false" python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest_dist_softmax.xml $TE_PATH/tests/jax/test_distributed_softmax.py || test_fail "test_distributed_softmax.py"
+
+python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest_dist_fused_attn.xml $TE_PATH/tests/jax/test_distributed_fused_attn.py || test_fail "test_distributed_fused_attn.py"
+
+# TODO(Phuong): add this test back after it is verified
+# SCRIPT_NAME=$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"
+
+if [ $RET -ne 0 ]; then
+    echo "Error: some sub-tests failed: $FAILED_CASES"
+    exit 1
+fi
+echo "All tests passed"
+exit 0

qa/L2_jax_distributed_unittest/test.sh

@@ -8,4 +8,5 @@ set -xe
 : ${XML_LOG_DIR:=/logs}
 mkdir -p "$XML_LOG_DIR"
 
-NVTE_JAX_UNITTEST_LEVEL="L2" python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest.xml $TE_PATH/tests/jax/test_distributed_*
+# Use --xla_gpu_enable_triton_gemm=false to ensure the reference JAX implementation we are using is accurate.
+XLA_FLAGS="$XLA_FLAGS --xla_gpu_enable_triton_gemm=false" NVTE_JAX_UNITTEST_LEVEL="L2" python3 -m pytest -c $TE_PATH/tests/jax/pytest.ini -v --junitxml=$XML_LOG_DIR/pytest.xml $TE_PATH/tests/jax/test_distributed_*

qa/L3_pytorch_FA_versions_test/test.sh

@@ -18,10 +18,10 @@ sm_arch=`python3 -c "import torch; sm = torch.cuda.get_device_capability(0); pri
 export FLASH_ATTN_CUDA_ARCHS=$sm_arch
 if [ $sm_arch -gt 90 ]
 then
-  FA_versions=(2.8.1)
+  FA_versions=(2.8.3)
 elif [ $sm_arch -eq 90 ]
 then
-  FA_versions=(2.7.3 2.8.1 3.0.0b1)
+  FA_versions=(2.7.3 2.8.3 3.0.0b1)
 fi
 
 for fa_version in "${FA_versions[@]}"

setup.py

@@ -10,6 +10,8 @@
 
 from importlib import metadata
 import os
+import shutil
+import subprocess
 import time
 from pathlib import Path
 from typing import List, Tuple
@@ -149,9 +151,64 @@ def setup_requirements() -> Tuple[List[str], List[str]]:
     return [remove_dups(reqs) for reqs in [install_reqs, test_reqs]]
 
 
+def git_check_submodules() -> None:
+    """
+    Attempt to checkout git submodules automatically during setup.
+
+    This runs successfully only if the submodules are
+    either in the correct or uninitialized state.
+
+    Note to devs: With this, any updates to the submodules itself, e.g. moving to a newer
+    commit, must be commited before build. This also ensures that stale submodules aren't
+    being silently used by developers.
+    """
+
+    # Provide an option to skip these checks for development.
+    if bool(int(os.getenv("NVTE_SKIP_SUBMODULE_CHECKS_DURING_BUILD", "0"))):
+        return
+
+    # Require git executable.
+    if shutil.which("git") is None:
+        return
+
+    # Require a .gitmodules file.
+    if not (current_file_path / ".gitmodules").exists():
+        return
+
+    try:
+        submodules = subprocess.check_output(
+            ["git", "submodule", "status", "--recursive"],
+            cwd=str(current_file_path),
+            text=True,
+        ).splitlines()
+
+        for submodule in submodules:
+            # '-' start is for an uninitialized submodule.
+            # ' ' start is for a submodule on the correct commit.
+            assert submodule[0] in (
+                " ",
+                "-",
+            ), (
+                "Submodules are initialized incorrectly. If this is intended, set the "
+                "environment variable `NVTE_SKIP_SUBMODULE_CHECKS_DURING_BUILD` to a "
+                "non-zero value to skip these checks during development. Otherwise, "
+                "run `git submodule update --init --recursive` to checkout the correct"
+                " submodule commits."
+            )
+
+        subprocess.check_call(
+            ["git", "submodule", "update", "--init", "--recursive"],
+            cwd=str(current_file_path),
+        )
+    except subprocess.CalledProcessError:
+        return
+
+
 if __name__ == "__main__":
     __version__ = te_version()
 
+    git_check_submodules()
+
     with open("README.rst", encoding="utf-8") as f:
         long_description = f.read()
 
@@ -163,8 +220,11 @@ if __name__ == "__main__":
         ext_modules = []
         package_data = {}
         include_package_data = False
-        install_requires = ([f"transformer_engine_cu12=={__version__}"],)
+        install_requires = []
         extras_require = {
+            "core": [f"transformer_engine_cu12=={__version__}"],
+            "core_cu12": [f"transformer_engine_cu12=={__version__}"],
+            "core_cu13": [f"transformer_engine_cu13=={__version__}"],
             "pytorch": [f"transformer_engine_torch=={__version__}"],
             "jax": [f"transformer_engine_jax=={__version__}"],
         }

tests/cpp/operator/CMakeLists.txt

@@ -66,12 +66,6 @@ else()
   add_executable(test_operator ${test_hip_sources})
 endif()
 
-# Add profiling and debug flags for CUDA compilation
-set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo")                   # Generate line info for device code  
-set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -g")                          # Add debug symbols for host code
-set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --ptxas-options=-v")          # Add info about registers usage
-# Note: Using -lineinfo instead of -G to avoid conflicts and get line mapping
-
 # Find required packages
 find_package(OpenMP REQUIRED)
 if(USE_CUDA)

tests/cpp/operator/test_cast_nvfp4_transpose.cu

@@ -661,14 +661,9 @@ std::vector<std::vector<size_t>> tensor_dims = {
     {4096, 13312},
 };
 
-// Only GeLU activation tests are supported
+// Only the Identity activation is currently supported.
 std::vector<ActivationType> Activation_types = {
-    ActivationType::Identity,
-    ActivationType::GeLU,
-    ActivationType::SiLU,
-    ActivationType::ReLU,
-    ActivationType::QGeLU,
-    ActivationType::SReLU,
+    ActivationType::Identity
 };
 
 }  // namespace

tests/cpp/operator/test_normalization.h

@@ -128,8 +128,18 @@ void compute_ref_output(NormType norm_type,
         tmp = current * rsigma[i] * g;
       }
 
+      // Write output (scaled only for fp8 paths)
       output[i * H + j] = static_cast<OutputType>(tmp * scale);
-      current_max = fmaxf(current_max, fabsf(tmp));
+
+      // amax semantics:
+      // - fp8_out (scale != 1): amax on pre-scale compute value 'tmp'
+      // - non-fp8_out (scale == 1): amax on value converted to OutputType (e.g., bf16)
+      if (scale != 1.f) {
+        current_max = fmaxf(current_max, fabsf(tmp));
+      } else {
+        OutputType out_t_val = static_cast<OutputType>(tmp);
+        current_max = fmaxf(current_max, fabsf(static_cast<compute_t>(out_t_val)));
+      }
     }
   }
 

tests/jax/distributed_test_base.py

@@ -8,7 +8,7 @@ from itertools import product
 import pytest
 
 import jax
-from jax.experimental.pjit import pjit, _UNSPECIFIED
+from jax._src.sharding_impls import UNSPECIFIED as _UNSPECIFIED
 
 from transformer_engine.jax.sharding import MeshResource
 
@@ -154,13 +154,15 @@ def compare_ops(
         grad_args = tuple(range(len(inputs)))
 
     target_grad_func = jax.value_and_grad(target_func, argnums=grad_args)
-    target_pjitter = pjit(target_grad_func, in_shardings=in_shardings, out_shardings=out_shardings)
-    target_fwd, target_grads = target_pjitter(*inputs, **kwargs)
-    target_hlo = target_pjitter.lower(*inputs, **kwargs).compile().as_text()
+    target_jitter = jax.jit(
+        target_grad_func, in_shardings=in_shardings, out_shardings=out_shardings
+    )
+    target_fwd, target_grads = target_jitter(*inputs, **kwargs)
+    target_hlo = target_jitter.lower(*inputs, **kwargs).compile().as_text()
 
     ref_grad_func = jax.value_and_grad(ref_func, argnums=grad_args)
-    ref_pjitter = pjit(ref_grad_func, in_shardings=in_shardings, out_shardings=out_shardings)
-    ref_fwd, ref_grads = ref_pjitter(*inputs, **kwargs)
+    ref_jitter = jax.jit(ref_grad_func, in_shardings=in_shardings, out_shardings=out_shardings)
+    ref_fwd, ref_grads = ref_jitter(*inputs, **kwargs)
 
     assert_allclose(target_fwd, ref_fwd, dtype=metric_fwd_dtype)
 

tests/jax/multi_process_launch.sh

@@ -18,6 +18,14 @@ do
     CUDA_VISIBLE_DEVICES=$i python $SCRIPT_NAME 127.0.0.1:12345 $i $NUM_RUNS > /dev/null 2>&1 &
 done
 
-CUDA_VISIBLE_DEVICES=0 python $SCRIPT_NAME 127.0.0.1:12345 0 $NUM_RUNS
+CUDA_VISIBLE_DEVICES=0 python $SCRIPT_NAME 127.0.0.1:12345 0 $NUM_RUNS | tee stdout_multi_process.txt
 
 wait
+
+RET=0
+if grep -q "FAILED" stdout_multi_process.txt; then
+  RET=1
+fi
+
+rm -f stdout_multi_process.txt
+exit "$RET"

tests/jax/test_custom_call_compute.py

@@ -40,13 +40,13 @@ from transformer_engine.jax.quantize import (
     QuantizerFactory,
     QuantizeLayout,
     noop_quantizer_set,
-    should_use_rht,
+    QuantizeMetaSet,
+    QuantizeMeta,
 )
 from transformer_engine.jax.quantize import helper
 from transformer_engine.jax.activation import activation
 from transformer_engine.jax.dense import dense, grouped_dense
 from transformer_engine.jax.layernorm_dense import layernorm_dense
-from transformer_engine.common import recipe
 
 GEMM_CASES = [
     (256, 256, 512),
@@ -606,7 +606,12 @@ class TestNorm:
         )
 
     @pytest.mark.skipif(not is_mxfp8_supported, reason=mxfp8_unsupported_reason)
-    @pytest.mark.parametrize("out_dtype", [jnp.float8_e4m3fn, jnp.float8_e5m2])
+    @pytest.mark.parametrize(
+        "out_dtype",
+        [
+            jnp.float8_e4m3fn,
+        ],
+    )
     def test_norm_forward_with_block_scaling_fp8(
         self, n, hidden, norm_type, zero_centered_gamma, epsilon, inp_dtype, out_dtype
     ):
@@ -685,21 +690,14 @@ class TestQuantize:
     Purely quantization related tests that will always test on a wider set of types and shapes
     """
 
-    def _skip_for_fp4(self, input_shape, q_dtype, scaling_mode, q_layout, flatten_axis):
-        """Temporary hack to skip unsupported FP4 cases until we implement them"""
+    def _skip_unsupported_dtypes(self, q_dtype, scaling_mode):
+        """Skip unsupported dtypes for given scaling mode. For example, NVFP4 only supports the float4_e2m1 dtype not float8 dtypes."""
         if q_dtype not in scaling_mode.get_compatible_q_dtypes():
             pytest.skip(f"Quantize dtype {q_dtype} is not supported by {scaling_mode}")
             return
 
-        # HACK: FIXME TODO(jberchtold)
-        row = reduce(operator.mul, input_shape[flatten_axis:], 1)
-        col = reduce(operator.mul, input_shape[:flatten_axis], 1)
-        will_use_rht = should_use_rht(scaling_mode, q_layout=q_layout)
-        if will_use_rht and (row % 64 != 0 or col % 128 != 0):
-            pytest.skip("Unfused RHT is not supported currently, skipping")
-
     def test_qdq(self, in_dtype, input_shape, q_dtype, scaling_mode, q_layout, flatten_axis):
-        self._skip_for_fp4(input_shape, q_dtype, scaling_mode, q_layout, flatten_axis)
+        self._skip_unsupported_dtypes(q_dtype, scaling_mode)
 
         key = jax.random.PRNGKey(0)
 
@@ -780,22 +778,8 @@ class TestQuantize:
             assert_dequantized_scaled_tensor(scaled_tensor, x)
 
     def _should_use_precise_comparison(
-        self, in_dtype, scaling_mode, q_layout, input_shape, flatten_axis
+        self, in_dtype, scaling_mode, quantizer, input_shape, flatten_axis
     ):
-        # TODO(jberchtold): Remove this hack once we have a better solution to ensure bitwise identical results between TE and JAX RHT+quant implementations. Currently for certain shapes the quantized fp4 data differs by a small amount on <0.5% of the values.
-        RHT_SLIGHT_MISMATCH_SHAPES = [
-            ((32, 256, 128), -1),
-            ((64, 32, 32, 256), -1),
-            ((8192, 2, 4096), -2),
-        ]
-
-        if (
-            should_use_rht(scaling_mode, q_layout=q_layout)
-            and (input_shape, flatten_axis) in RHT_SLIGHT_MISMATCH_SHAPES
-        ):
-            # TE fused RHT+quant and JAX RHT+quant have slight implementation differences which can lead to small numerical differences on certain shapes
-            return False
-
         if scaling_mode.is_nvfp4_scaling and in_dtype != jnp.bfloat16:
             # With NVFP4 scaling, TE kernels internally use bfloat16 so using a different input dtype can lead to small numerical differences compared to the JAX implementation
             return False
@@ -805,7 +789,7 @@ class TestQuantize:
     def test_quantize_bitwise(
         self, in_dtype, input_shape, q_dtype, scaling_mode, q_layout, flatten_axis
     ):
-        self._skip_for_fp4(input_shape, q_dtype, scaling_mode, q_layout, flatten_axis)
+        self._skip_unsupported_dtypes(q_dtype, scaling_mode)
 
         key = jax.random.PRNGKey(0)
         input = jax.random.uniform(key, input_shape, in_dtype)
@@ -816,28 +800,20 @@ class TestQuantize:
 
         jax_output = _jax_quantize(input, quantizer=jax_quantizer, flatten_axis=flatten_axis)
 
-        try:
-            te_output = tex.quantize(input, quantizer=te_quantizer, flatten_axis=flatten_axis)
-        except AssertionError as e:
-            if should_use_rht(scaling_mode, q_layout=q_layout) and in_dtype != jnp.bfloat16:
-                error_message = e.args[0]
-                if "RHT requires input to be bfloat16" in error_message:
-                    # Successfully caught the expected error, early return from the test
-                    return
-            raise e
+        te_output = tex.quantize(input, quantizer=te_quantizer, flatten_axis=flatten_axis)
 
         assert_bitwise_scaled_tensors(
             te_output,
             jax_output,
             precise_comparison=self._should_use_precise_comparison(
-                in_dtype, scaling_mode, q_layout, input_shape, flatten_axis
+                in_dtype, scaling_mode, te_quantizer, input_shape, flatten_axis
             ),
         )
 
     def test_quantize_bitwise_jitted(
         self, in_dtype, input_shape, q_dtype, scaling_mode, q_layout, flatten_axis
     ):
-        self._skip_for_fp4(input_shape, q_dtype, scaling_mode, q_layout, flatten_axis)
+        self._skip_unsupported_dtypes(q_dtype, scaling_mode)
 
         key = jax.random.PRNGKey(0)
         input = jax.random.uniform(key, input_shape, in_dtype)
@@ -851,21 +827,13 @@ class TestQuantize:
 
         jax_output = jax_impl_func_jit(input, quantizer=jax_quantizer, flatten_axis=flatten_axis)
 
-        try:
-            te_output = te_impl_func_jit(input, quantizer=te_quantizer, flatten_axis=flatten_axis)
-        except AssertionError as e:
-            if should_use_rht(scaling_mode, q_layout=q_layout) and in_dtype != jnp.bfloat16:
-                error_message = e.args[0]
-                if "RHT requires input to be bfloat16" in error_message:
-                    # Successfully caught the expected error, early return from the test
-                    return
-            raise e
+        te_output = te_impl_func_jit(input, quantizer=te_quantizer, flatten_axis=flatten_axis)
 
         assert_bitwise_scaled_tensors(
             te_output,
             jax_output,
             precise_comparison=self._should_use_precise_comparison(
-                in_dtype, scaling_mode, q_layout, input_shape, flatten_axis
+                in_dtype, scaling_mode, te_quantizer, input_shape, flatten_axis
             ),
         )
 
@@ -914,7 +882,7 @@ class TestStochasticRounding:
         for i in range(num_samples):
             iter_key = jax.random.fold_in(key, i)
             sr_rng_state = jax.random.randint(
-                iter_key, (4,), minval=0, maxval=2**30 - 1, dtype=jnp.uint32
+                iter_key, (1, 4), minval=0, maxval=2**30 - 1, dtype=jnp.uint32
             )
             quantizer = QuantizerFactory.create(
                 q_dtype=q_dtype,
@@ -985,12 +953,6 @@ class TestStochasticRounding:
 
     def test_sr_nvfp4(self, in_dtype, input_shape, q_dtype, scaling_mode, q_layout, flatten_axis):
         """Tests that the mean absolute error of stochastic rounding is smaller than round nearest quantization over multiple samples for both TE and JAX implementations. Asserts that the MAE of both implementations is close to each other."""
-        # HACK: FIXME TODO(jberchtold)
-        row = reduce(operator.mul, input_shape[flatten_axis:], 1)
-        col = reduce(operator.mul, input_shape[:flatten_axis], 1)
-        will_use_rht = should_use_rht(scaling_mode, q_layout=q_layout)
-        if will_use_rht and (row % 64 != 0 or col % 128 != 0):
-            pytest.skip("Unfused RHT is not supported currently, skipping")
 
         key = jax.random.PRNGKey(0)
         inputs = jax.random.uniform(key, input_shape, in_dtype)
@@ -1007,6 +969,97 @@ class TestStochasticRounding:
         assert_allclose(te_mean_error, jax_mean_error, rtol=0.2, atol=1e-4)
 
 
+@pytest_parametrize_wrapper("in_dtype", [jnp.bfloat16])
+@pytest_parametrize_wrapper("q_dtype", [jnp.float4_e2m1fn])
+@pytest_parametrize_wrapper(
+    "scaling_mode", [s for s in supported_scaling_modes if s == ScalingMode.NVFP4_1D_SCALING]
+)
+class TestRandomizedHadamardTransform:
+
+    @pytest_parametrize_wrapper(
+        "q_layout", [QuantizeLayout.ROWWISE_COLWISE, QuantizeLayout.COLWISE]
+    )
+    @pytest_parametrize_wrapper("input_shape,flatten_axis", [((64, 128), -1)])
+    def test_rht_quantize_bitwise_jitted(
+        self, in_dtype, q_dtype, scaling_mode, q_layout, input_shape, flatten_axis
+    ):
+        key = jax.random.PRNGKey(0)
+        inputs = jax.random.uniform(key, input_shape, in_dtype)
+
+        te_quantizer, jax_quantizer = QuantizerFactory.create(
+            n_quantizers=2,
+            q_dtype=q_dtype,
+            scaling_mode=scaling_mode,
+            q_layout=q_layout,
+            use_rht=True,
+        )
+
+        jax_impl_func_jit = jax.jit(_jax_quantize, static_argnums=(2, 3))
+        te_impl_func_jit = jax.jit(tex.quantize, static_argnums=(2,))
+
+        jax_output = jax_impl_func_jit(inputs, quantizer=jax_quantizer, flatten_axis=flatten_axis)
+
+        te_output = te_impl_func_jit(inputs, quantizer=te_quantizer, flatten_axis=flatten_axis)
+
+        assert_bitwise_scaled_tensors(te_output, jax_output)
+
+    def _ref_gemm_with_jnp_dot(self, a, b, data_layout):
+        if data_layout[0] == "T":
+            a = jnp.swapaxes(a, -1, -2)
+        if data_layout[1] == "T":
+            b = jnp.swapaxes(b, -1, -2)
+        return jnp.dot(a, b)
+
+    def _generate_gemm_input(self, m, n, k, data_layout):
+        key = jax.random.PRNGKey(0)
+        subkeys = jax.random.split(key, 2)
+        x = jax.random.uniform(
+            subkeys[0],
+            (m if data_layout[0] == "N" else k, k if data_layout[0] == "N" else m),
+            dtype=jnp.bfloat16,
+        ) / jnp.sqrt(k)
+        w = jax.random.uniform(
+            subkeys[1],
+            (k if data_layout[1] == "N" else n, n if data_layout[1] == "N" else k),
+            dtype=jnp.bfloat16,
+        ) / jnp.sqrt(n)
+        lhs_contracting_dim = (1,) if data_layout[0] == "N" else (0,)
+        rhs_contracting_dim = (0,) if data_layout[1] == "N" else (1,)
+        contracting_dims = (lhs_contracting_dim, rhs_contracting_dim)
+
+        return (x, w, contracting_dims)
+
+    @pytest_parametrize_wrapper("m,n,k", [(64, 32, 64)])
+    # We do not test NN and TT layouts here as they do not have both inputs using RHT due to RHT only supporting the colwise layout currently
+    @pytest_parametrize_wrapper("data_layout", ["TN", "NT"])
+    @pytest_parametrize_wrapper("with_jax_gemm", [True, False])
+    def test_rht_gemm(self, in_dtype, q_dtype, scaling_mode, m, n, k, data_layout, with_jax_gemm):
+        key = jax.random.PRNGKey(0)
+
+        lhs_scaling_mode, rhs_scaling_mode = scaling_mode, scaling_mode
+        x, w, contracting_dims = self._generate_gemm_input(m, n, k, data_layout)
+        lhs_quantizer = QuantizerFactory.create(
+            scaling_mode=lhs_scaling_mode,
+            q_dtype=jnp.float4_e2m1fn,
+            use_rht=True,
+        )
+        rhs_quantizer = QuantizerFactory.create(
+            scaling_mode=rhs_scaling_mode,
+            q_dtype=jnp.float4_e2m1fn,
+            use_rht=True,
+        )
+        with use_jax_gemm(enabled=with_jax_gemm):
+            primitive_out = tex.gemm(
+                x,
+                w,
+                contracting_dims=contracting_dims,
+                lhs_quantizer=lhs_quantizer,
+                rhs_quantizer=rhs_quantizer,
+            )
+        ref_out = self._ref_gemm_with_jnp_dot(x, w, data_layout)
+        assert_allclose(primitive_out, ref_out, dtype=jnp.float4_e2m1fn)
+
+
 @pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
 @pytest_parametrize_wrapper("in_dtype", QUANTIZATION_INPUT_DTYPE)
 @pytest_parametrize_wrapper("input_shape", [(8, 16, 32)])
@@ -1406,7 +1459,12 @@ class TestDense:
         value_n_grad_primitive_func = value_and_grad(primitive_func, (0, 1, 2))
         value_n_grad_ref_func = value_and_grad(ref_func, (0, 1, 2))
 
-        quantizer_set = QuantizerFactory.create_set(fp8_recipe=recipe)
+        quantizer_set = QuantizerFactory.create_set(
+            fp8_recipe=recipe,
+            quantize_meta_set=QuantizeMetaSet(
+                x=QuantizeMeta(), kernel=QuantizeMeta(), grad=QuantizeMeta()
+            ),
+        )
 
         n_iterations = 3 if recipe.delayed() else 1
         with use_jax_gemm(enabled=with_jax_gemm):
@@ -1465,7 +1523,12 @@ class TestFusedDense:
 
         gamma = jax.random.normal(subkeys[2], (k,)).astype(jnp.bfloat16)
 
-        quantizer_set = QuantizerFactory.create_set(fp8_recipe=recipe)
+        quantizer_set = QuantizerFactory.create_set(
+            fp8_recipe=recipe,
+            quantize_meta_set=QuantizeMetaSet(
+                x=QuantizeMeta(), kernel=QuantizeMeta(), grad=QuantizeMeta()
+            ),
+        )
 
         if norm_type == "layernorm":
             beta = jax.random.normal(subkeys[3], (k,)).astype(jnp.bfloat16)
@@ -1554,6 +1617,9 @@ class TestFusedDense:
         quantizer_sets = QuantizerFactory.create_set(
             n_quantizer_sets=2,
             fp8_recipe=recipe,
+            quantize_meta_set=QuantizeMetaSet(
+                x=QuantizeMeta(), kernel=QuantizeMeta(), grad=QuantizeMeta()
+            ),
         )
 
         if norm_type == "layernorm":

tests/jax/test_distributed_layernorm.py

@@ -134,9 +134,12 @@ class TestDistributedLayernorm:
         devices = np.asarray(jax.devices()[:device_count]).reshape(*mesh_shape)
         mesh = Mesh(devices, mesh_axes)
         with mesh, autocast(enabled=True, recipe=fp8_recipe, mesh_resource=mesh_resource):
-            x_ = jax.device_put(x, NamedSharding(mesh, x_pspec))
-            gamma_ = jax.device_put(gamma, NamedSharding(mesh, g_pspec))
-            beta_ = jax.device_put(beta, NamedSharding(mesh, b_pspec))
+            x_named_sharding = NamedSharding(mesh, x_pspec)
+            g_named_sharding = NamedSharding(mesh, g_pspec)
+            b_named_sharding = NamedSharding(mesh, b_pspec)
+            x_ = jax.device_put(x, x_named_sharding)
+            gamma_ = jax.device_put(gamma, g_named_sharding)
+            beta_ = jax.device_put(beta, b_named_sharding)
 
             with warnings.catch_warnings(record=True) as warns:
                 try:
@@ -148,8 +151,11 @@ class TestDistributedLayernorm:
                         grad_args=(0, 1, 2),
                         metric_fwd_dtype=q_dtype,
                         metric_bwd_dtype=q_dtype,
-                        in_shardings=(x_pspec, g_pspec, b_pspec),
-                        out_shardings=(None, (x_pspec, g_pspec, b_pspec)),
+                        in_shardings=(x_named_sharding, g_named_sharding, b_named_sharding),
+                        out_shardings=(
+                            None,
+                            (x_named_sharding, g_named_sharding, b_named_sharding),
+                        ),
                     )
                 except AssertionError as err:
                     # Layernorm should still produce the correct numerical result with
@@ -210,8 +216,10 @@ class TestDistributedLayernorm:
         devices = np.asarray(jax.devices()[:device_count]).reshape(*mesh_shape)
         mesh = Mesh(devices, mesh_axes)
         with mesh, autocast(enabled=True, recipe=fp8_recipe, mesh_resource=mesh_resource):
-            x_ = jax.device_put(x, NamedSharding(mesh, x_pspec))
-            gamma_ = jax.device_put(gamma, NamedSharding(mesh, g_pspec))
+            x_named_sharding = NamedSharding(mesh, x_pspec)
+            g_named_sharding = NamedSharding(mesh, g_pspec)
+            x_ = jax.device_put(x, x_named_sharding)
+            gamma_ = jax.device_put(gamma, g_named_sharding)
 
             with warnings.catch_warnings(record=True) as warns:
                 try:
@@ -223,8 +231,8 @@ class TestDistributedLayernorm:
                         grad_args=(0, 1),
                         metric_fwd_dtype=q_dtype,
                         metric_bwd_dtype=q_dtype,
-                        in_shardings=(x_pspec, g_pspec),
-                        out_shardings=(None, (x_pspec, g_pspec)),
+                        in_shardings=(x_named_sharding, g_named_sharding),
+                        out_shardings=(None, (x_named_sharding, g_named_sharding)),
                     )
                 except AssertionError as err:
                     # RmsNorm should still produce the correct numerical result with

tests/jax/test_distributed_layernorm_mlp.py

@@ -389,6 +389,7 @@ class TestDistributedLayernormMLP:
                     intermediate_dim=INTERMEDIATE,
                     activations=activation_type,
                     use_bias=use_bias,
+                    return_layernorm_output=True,
                 )
                 params_single = ln_mlp_single.init(init_rngs, x, deterministic=True)
                 mlp_out_single, ln_out_single = ln_mlp_single.apply(
@@ -417,6 +418,7 @@ class TestDistributedLayernormMLP:
                     dot_1_input_axes=DOT_1_INPUT_AXES,
                     dot_2_input_axes=DOT_2_INPUT_AXES,
                     name="mlp",
+                    return_layernorm_output=True,
                 )
                 params_sharded = ln_mlp_sharded.init(init_rngs, x, deterministic=True)
                 mlp_out_sharded, ln_out_sharded = ln_mlp_sharded.apply(

tests/jax/test_distributed_softmax.py

@@ -103,8 +103,10 @@ class TestDistributedSoftmax:
         devices = np.asarray(jax.devices()[:device_count]).reshape(*mesh_shape)
         mesh = Mesh(devices, mesh_axes)
         with mesh, autocast(mesh_resource=mesh_resource):
-            x_ = jax.device_put(x, NamedSharding(mesh, x_pspec))
-            mask_ = jax.device_put(mask, NamedSharding(mesh, mask_pspec))
+            x_named_sharding = NamedSharding(mesh, x_pspec)
+            mask_named_sharding = NamedSharding(mesh, mask_pspec)
+            x_ = jax.device_put(x, x_named_sharding)
+            mask_ = jax.device_put(mask, mask_named_sharding)
 
             with warnings.catch_warnings(record=True) as warns:
                 try:
@@ -116,8 +118,8 @@ class TestDistributedSoftmax:
                         grad_args=(0,),
                         metric_fwd_dtype=dtype,
                         metric_bwd_dtype=dtype,
-                        in_shardings=(x_pspec, mask_pspec),
-                        out_shardings=(None, (x_pspec,)),
+                        in_shardings=(x_named_sharding, mask_named_sharding),
+                        out_shardings=(None, x_named_sharding),
                     )
                 except AssertionError as err:
                     # Softmax should still produce the correct numerical result with

tests/jax/test_fused_attn.py

@@ -378,14 +378,14 @@ class FusedAttnRunner:
             pytest.skip(
                 "seqlen_q > seqlen_kv is not supported with sliding window attention in cuDNN"
             )
-
+        # TODO(KshitijLakhani): Set the upper limit for skipping this test when cuDNN adds support
         if (
-            get_device_compute_capability(0) == 100
+            get_device_compute_capability(0) >= 100
             and self.dropout_prob == 0.1
             and self.attn_bias_type is not AttnBiasType.NO_BIAS
         ):
             pytest.skip(
-                "For sm100, bprop kernel support for dropout + determinism (bias) is not supported"
+                "For sm100+, bprop kernel support for dropout + determinism (bias) is not supported"
             )
         # Test the MLA case where head dims for qk differ from head dims for v, only if the tensors
         # are provided in BSHD_BSHD_BSHD or THD_THD_THD formats

tests/jax/test_helper.py

-# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# See LICENSE for license information.
-
-import unittest
-
-import flax
-import jax
-import jax.numpy as jnp
-import numpy as np
-
-from utils import assert_allclose
-from transformer_engine.common.recipe import (
-    DelayedScaling,
-    MXFP8BlockScaling,
-    Float8CurrentScaling,
-    NVFP4BlockScaling,
-)
-from transformer_engine.common.recipe import Format as FP8Format
-from transformer_engine.jax import autocast
-from transformer_engine.jax.quantize import (
-    get_quantize_config,
-    is_scaling_mode_supported,
-    ScalingMode,
-    update_collections,
-    TensorSource,
-)
-from transformer_engine.jax.quantize.helper import _format2dtypes
-from transformer_engine.jax.sharding import MeshResource, global_mesh_resource
-
-is_fp8_supported, reason = is_scaling_mode_supported(ScalingMode.DELAYED_TENSOR_SCALING)
-is_mxfp8_supported, mxfp8_reason = is_scaling_mode_supported(ScalingMode.MXFP8_1D_SCALING)
-is_nvfp4_supported, nvfp4_reason = is_scaling_mode_supported(ScalingMode.NVFP4_1D_SCALING)
-
-
-class TestHelper(unittest.TestCase):
-
-    @unittest.skipIf(not is_fp8_supported, reason=reason)
-    def test_update_collections(self):
-        original_val = 0.0
-        updated_val = 10.0
-
-        original_state = {
-            "test1": original_val,
-            "test2": original_val,
-        }
-        updated_state = update_collections({"test1": updated_val}, original_state)
-        self.assertEqual(updated_state["test1"], updated_val)
-        self.assertEqual(updated_state["test2"], original_val)
-
-        original_state = flax.core.frozen_dict.FrozenDict(original_state)
-        updated_state = update_collections({"test1": updated_val}, original_state)
-        self.assertEqual(updated_state["test1"], updated_val)
-        self.assertEqual(updated_state["test2"], original_val)
-
-
-class TestFP8Functions(unittest.TestCase):
-
-    def _check_default_state(self):
-        self.assertFalse(get_quantize_config().is_fp8_enabled())
-
-    def _compare_delay_scaling(self, test):
-        self.assertEqual(get_quantize_config().MARGIN, test.margin)
-        self.assertEqual(get_quantize_config().FWD_DTYPE, _format2dtypes(test.fp8_format)[0])
-        self.assertEqual(get_quantize_config().BWD_DTYPE, _format2dtypes(test.fp8_format)[1])
-        self.assertEqual(get_quantize_config().AMAX_HISTORY_LEN, test.amax_history_len)
-        self.assertEqual(get_quantize_config().AMAX_COMPUTE_ALGO.value, test.amax_compute_algo)
-
-    def _compare_current_scaling(self, test):
-        self.assertEqual(get_quantize_config().FWD_DTYPE, _format2dtypes(test.fp8_format)[0])
-        self.assertEqual(get_quantize_config().BWD_DTYPE, _format2dtypes(test.fp8_format)[1])
-        for tensor_source in TensorSource:
-            self.assertEqual(
-                get_quantize_config().get_scaling_mode(tensor_source),
-                ScalingMode.CURRENT_TENSOR_SCALING,
-            )
-
-    def _compare_mxfp8_scaling(self, test):
-        self.assertEqual(get_quantize_config().FWD_DTYPE, _format2dtypes(test.fp8_format)[0])
-        self.assertEqual(get_quantize_config().BWD_DTYPE, _format2dtypes(test.fp8_format)[1])
-        for tensor_source in TensorSource:
-            self.assertEqual(
-                get_quantize_config().get_scaling_mode(tensor_source), ScalingMode.MXFP8_1D_SCALING
-            )
-
-    def _compare_nvfp4_scaling(self, test):
-        self.assertEqual(get_quantize_config().FWD_DTYPE, _format2dtypes(test.fp4_format)[0])
-        self.assertEqual(get_quantize_config().BWD_DTYPE, _format2dtypes(test.fp4_format)[1])
-        for tensor_source in TensorSource:
-            target_scaling_mode = (
-                ScalingMode.NVFP4_2D_SCALING
-                if tensor_source == TensorSource.KERNEL
-                else ScalingMode.NVFP4_1D_SCALING
-            )
-            self.assertEqual(
-                get_quantize_config().get_scaling_mode(tensor_source), target_scaling_mode
-            )
-
-    @unittest.skipIf(not is_fp8_supported, reason=reason)
-    def test_autocast_delayed_scaling(self):
-        self._check_default_state()
-
-        with autocast(enabled=False, recipe=DelayedScaling(), mesh_resource=MeshResource()):
-            self._check_default_state()
-
-        self._check_default_state()
-
-        ds = DelayedScaling(margin=5.0, fp8_format=FP8Format.E4M3, amax_history_len=1)
-        with autocast(enabled=True, recipe=ds, mesh_resource=MeshResource()):
-            self.assertTrue(get_quantize_config().is_fp8_enabled())
-            self._compare_delay_scaling(ds)
-
-        self._check_default_state()
-
-        ds = DelayedScaling(margin=3.0, fp8_format=FP8Format.HYBRID, amax_history_len=1)
-        with autocast(enabled=True, recipe=ds, mesh_resource=MeshResource()):
-            self.assertTrue(get_quantize_config().is_fp8_enabled())
-            self._compare_delay_scaling(ds)
-
-        self._check_default_state()
-
-    @unittest.skipIf(not is_fp8_supported, reason=reason)
-    def test_autocast_current_scaling(self):
-        self._check_default_state()
-
-        with autocast(enabled=False, recipe=Float8CurrentScaling(), mesh_resource=MeshResource()):
-            self._check_default_state()
-
-        self._check_default_state()
-
-        cs = Float8CurrentScaling(fp8_format=FP8Format.E4M3)
-        with autocast(enabled=True, recipe=cs, mesh_resource=MeshResource()):
-            self.assertTrue(get_quantize_config().is_fp8_enabled())
-            self._compare_current_scaling(cs)
-
-        self._check_default_state()
-
-        cs = Float8CurrentScaling(fp8_format=FP8Format.HYBRID)
-        with autocast(enabled=True, recipe=cs, mesh_resource=MeshResource()):
-            self.assertTrue(get_quantize_config().is_fp8_enabled())
-            self._compare_current_scaling(cs)
-
-        self._check_default_state()
-
-    @unittest.skipIf(not is_mxfp8_supported, reason=mxfp8_reason)
-    def test_autocast_mxfp8_block_scaling(self):
-        self._check_default_state()
-
-        with autocast(enabled=False, recipe=MXFP8BlockScaling(), mesh_resource=MeshResource()):
-            self._check_default_state()
-
-        self._check_default_state()
-
-        bs = MXFP8BlockScaling()
-        with autocast(enabled=True, recipe=bs, mesh_resource=MeshResource()):
-            self.assertTrue(get_quantize_config().is_fp8_enabled())
-            self._compare_mxfp8_scaling(bs)
-
-        self._check_default_state()
-
-    @unittest.skipIf(not is_nvfp4_supported, reason=nvfp4_reason)
-    def test_autocast_nvfp4_block_scaling(self):
-        self._check_default_state()
-
-        with autocast(enabled=False, recipe=NVFP4BlockScaling(), mesh_resource=MeshResource()):
-            self._check_default_state()
-
-        self._check_default_state()
-
-        bs = NVFP4BlockScaling()
-        with autocast(enabled=True, recipe=bs, mesh_resource=MeshResource()):
-            self.assertTrue(get_quantize_config().is_fp8_enabled())
-            self._compare_nvfp4_scaling(bs)
-
-        self._check_default_state()

tests/jax/test_layer.py

@@ -23,7 +23,8 @@ from utils import EncoderLayer as RefEncoderLayer
 from transformer_engine.common import recipe
 from transformer_engine.jax.flax import TransformerLayer, TransformerLayerType
 from transformer_engine.jax.quantize import (
-    get_quantize_config,
+    get_global_quantize_recipe,
+    get_quantize_config_with_recipe,
     ScalingMode,
     is_fp8_available,
     update_collections,
@@ -358,7 +359,7 @@ class BaseRunner:
 
         ref_params, test_params = self._sync_params(ref_params, test_params)
 
-        if get_quantize_config().is_fp8_enabled():
+        if get_quantize_config_with_recipe(get_global_quantize_recipe()).is_fp8_enabled():
             for _ in range(4):
                 _, updated_state = jax.value_and_grad(self._loss_fn, argnums=(3,), has_aux=False)(
                     inputs,
@@ -368,14 +369,24 @@ class BaseRunner:
                     test_layer,
                 )
                 if (
-                    get_quantize_config().get_scaling_mode(TensorSource.X)
+                    get_quantize_config_with_recipe(get_global_quantize_recipe()).get_scaling_mode(
+                        TensorSource.X
+                    )
                     == ScalingMode.DELAYED_TENSOR_SCALING
                 ):
                     _, updated_quantize_meta = flax.core.pop(
-                        updated_state[0], get_quantize_config().COLLECTION_NAME
+                        updated_state[0],
+                        get_quantize_config_with_recipe(
+                            get_global_quantize_recipe()
+                        ).COLLECTION_NAME,
                     )
                     test_others = update_collections(
-                        {get_quantize_config().COLLECTION_NAME: updated_quantize_meta}, test_others
+                        {
+                            get_quantize_config_with_recipe(
+                                get_global_quantize_recipe()
+                            ).COLLECTION_NAME: updated_quantize_meta
+                        },
+                        test_others,
                     )
                     del updated_quantize_meta
                 del updated_state

tests/jax/test_recipe_characteristics.py

+# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# See LICENSE for license information.
+
+import unittest
+from functools import partial
+from abc import ABC, abstractmethod
+
+import flax
+import jax
+import jax.numpy as jnp
+import numpy as np
+from flax import linen as nn
+
+from utils import assert_allclose, pytest_parametrize_wrapper
+from transformer_engine.common.recipe import (
+    Recipe,
+    DelayedScaling,
+    MXFP8BlockScaling,
+    Float8CurrentScaling,
+    NVFP4BlockScaling,
+)
+from transformer_engine.common.recipe import Format as FP8Format
+from transformer_engine.jax import autocast
+from transformer_engine.jax.quantize import (
+    get_global_quantize_recipe,
+    get_quantize_config_with_recipe,
+    get_supported_quantization_recipes,
+    is_scaling_mode_supported,
+    ScalingMode,
+    update_collections,
+    TensorSource,
+    QuantizeLayout,
+)
+from transformer_engine.jax.quantize.helper import _format2dtypes
+from transformer_engine.jax.sharding import MeshResource, global_mesh_resource
+from transformer_engine.jax.flax.module import TransformerEngineBase
+from transformer_engine.jax import flax as te_flax
+import transformer_engine.jax as te
+
+is_fp8_supported, reason = is_scaling_mode_supported(ScalingMode.DELAYED_TENSOR_SCALING)
+is_mxfp8_supported, mxfp8_reason = is_scaling_mode_supported(ScalingMode.MXFP8_1D_SCALING)
+is_nvfp4_supported, nvfp4_reason = is_scaling_mode_supported(ScalingMode.NVFP4_1D_SCALING)
+
+SUPPORTED_RECIPES = get_supported_quantization_recipes()
+
+
+def quantizer_check_vjp(outer_quantizer_set, assertion_func, x):
+    """Check that the quantizers in the quantizer set are as expected and reconstructed correctly from flattened pytree representations across VJP boundaries."""
+
+    # Define a function with a custom VJP (vector-Jacobian product)
+    @partial(jax.custom_vjp, nondiff_argnums=(1,))
+    def quantizer_check(inner_quantizer_set, assertion_func, x):
+        return quantizer_check_fwd(inner_quantizer_set, assertion_func, x)[0]
+
+    def quantizer_check_fwd(inner_quantizer_set, assertion_func, x):
+        assertion_func(inner_quantizer_set.x, TensorSource.X)
+        assertion_func(inner_quantizer_set.kernel, TensorSource.KERNEL)
+        assertion_func(inner_quantizer_set.dgrad, TensorSource.DGRAD)
+        return x, (inner_quantizer_set,)
+
+    def quantizer_check_bwd(assertion_func, ctx, g):
+        (inner_quantizer_set,) = ctx
+        return (inner_quantizer_set, g)
+
+    quantizer_check.defvjp(quantizer_check_fwd, quantizer_check_bwd)
+    return quantizer_check(outer_quantizer_set, assertion_func, x)
+
+
+class TestModule(TransformerEngineBase):
+    """A simple module to test quantizer creation and reconstruction across VJP boundaries."""
+
+    # Signature: (quantizer: Quantizer, tensor_source: TensorSource) -> None
+    assertion_func: callable
+    direct_recipe: Recipe
+
+    @nn.compact
+    def __call__(self, x):
+        quantizer_set = self.generate_quantizer_set(fp8_recipe=self.direct_recipe)
+        return quantizer_check_vjp(quantizer_set, self.assertion_func, x)
+
+
+class TestHelper(unittest.TestCase):
+
+    @unittest.skipIf(not is_fp8_supported, reason=reason)
+    def test_update_collections(self):
+        original_val = 0.0
+        updated_val = 10.0
+
+        original_state = {
+            "test1": original_val,
+            "test2": original_val,
+        }
+        updated_state = update_collections({"test1": updated_val}, original_state)
+        self.assertEqual(updated_state["test1"], updated_val)
+        self.assertEqual(updated_state["test2"], original_val)
+
+        original_state = flax.core.frozen_dict.FrozenDict(original_state)
+        updated_state = update_collections({"test1": updated_val}, original_state)
+        self.assertEqual(updated_state["test1"], updated_val)
+        self.assertEqual(updated_state["test2"], original_val)
+
+
+def assert_fp8_format(quantizer, tensor_source, fp8_format):
+    if fp8_format == FP8Format.HYBRID:
+        if tensor_source == TensorSource.DGRAD:
+            assert quantizer.q_dtype == jnp.float8_e5m2
+        else:
+            assert quantizer.q_dtype == jnp.float8_e4m3fn
+    elif fp8_format == FP8Format.E4M3:
+        assert quantizer.q_dtype == jnp.float8_e4m3fn
+    else:
+        raise ValueError(f"Unsupported FP8 format: {fp8_format}")
+
+
+class RecipeAssertionBase(ABC):
+    """Base class for defining recipe assertions."""
+
+    @abstractmethod
+    def assert_context(self, ref_recipe, quantize_config):
+        """Asserts that the quantize_config matches the expected properties from the reference recipe when the recipe is used with an autocast context.
+
+        Args:
+            ref_recipe: The reference quantization recipe.
+            quantize_config: The quantization configuration to be checked.
+        """
+        pass
+
+    @abstractmethod
+    def assert_quantizers(self, ref_recipe, quantizer, tensor_source):
+        """Asserts that the quantizer matches the expected properties from the reference recipe. The quantizers are created in a small test Flax module TestModule and passed through a VJP boundary to ensure correct reconstruction.
+
+        Args:
+            ref_recipe: The reference quantization recipe.
+            quantizer: The quantizer to be checked.
+            tensor_source: The source of the tensor (e.g., KERNEL, X, DGRAD).
+        """
+        pass
+
+
+class DelayedScalingRecipeAssertion(RecipeAssertionBase):
+
+    def assert_context(self, ref_recipe, quantize_config):
+        assert quantize_config.MARGIN == ref_recipe.margin
+        assert quantize_config.FWD_DTYPE == _format2dtypes(ref_recipe.fp8_format)[0]
+        assert quantize_config.BWD_DTYPE == _format2dtypes(ref_recipe.fp8_format)[1]
+        assert quantize_config.AMAX_HISTORY_LEN == ref_recipe.amax_history_len
+        assert quantize_config.AMAX_COMPUTE_ALGO.value == ref_recipe.amax_compute_algo
+        for tensor_source in TensorSource:
+            assert (
+                quantize_config.get_scaling_mode(tensor_source)
+                == ScalingMode.DELAYED_TENSOR_SCALING
+            )
+
+    def assert_quantizers(self, ref_recipe: DelayedScaling, quantizer, tensor_source):
+        assert quantizer.scaling_mode == ScalingMode.DELAYED_TENSOR_SCALING
+        assert quantizer.margin == ref_recipe.margin
+        assert quantizer.amax_compute_algo.value == ref_recipe.amax_compute_algo
+        assert quantizer.amax_history.shape == (ref_recipe.amax_history_len,)
+        assert_fp8_format(quantizer, tensor_source, ref_recipe.fp8_format)
+
+
+class CurrentScalingRecipeAssertion(RecipeAssertionBase):
+
+    def assert_context(self, ref_recipe, quantize_config):
+        assert quantize_config.FWD_DTYPE == _format2dtypes(ref_recipe.fp8_format)[0]
+        assert quantize_config.BWD_DTYPE == _format2dtypes(ref_recipe.fp8_format)[1]
+        for tensor_source in TensorSource:
+            assert (
+                quantize_config.get_scaling_mode(tensor_source)
+                == ScalingMode.CURRENT_TENSOR_SCALING
+            )
+
+    def assert_quantizers(self, ref_recipe: Float8CurrentScaling, quantizer, tensor_source):
+        assert quantizer.scaling_mode == ScalingMode.CURRENT_TENSOR_SCALING
+        assert_fp8_format(quantizer, tensor_source, ref_recipe.fp8_format)
+
+
+class MXFP8RecipeAssertion(RecipeAssertionBase):
+
+    def assert_context(self, ref_recipe, quantize_config):
+        assert quantize_config.FWD_DTYPE == _format2dtypes(ref_recipe.fp8_format)[0]
+        assert quantize_config.BWD_DTYPE == _format2dtypes(ref_recipe.fp8_format)[1]
+        for tensor_source in TensorSource:
+            assert quantize_config.get_scaling_mode(tensor_source) == ScalingMode.MXFP8_1D_SCALING
+
+    def assert_quantizers(self, ref_recipe: MXFP8BlockScaling, quantizer, tensor_source):
+        assert quantizer.scaling_mode == ScalingMode.MXFP8_1D_SCALING
+        assert_fp8_format(quantizer, tensor_source, ref_recipe.fp8_format)
+
+
+class NVFP4RecipeAssertion(RecipeAssertionBase):
+
+    def assert_context(self, ref_recipe, quantize_config):
+        assert quantize_config.FWD_DTYPE == _format2dtypes(ref_recipe.fp4_format)[0]
+        assert quantize_config.BWD_DTYPE == _format2dtypes(ref_recipe.fp4_format)[1]
+        for tensor_source in TensorSource:
+            target_scaling_mode = (
+                ScalingMode.NVFP4_2D_SCALING
+                if (not ref_recipe.disable_2d_quantization) and tensor_source == TensorSource.KERNEL
+                else ScalingMode.NVFP4_1D_SCALING
+            )
+            assert quantize_config.get_scaling_mode(tensor_source) == target_scaling_mode
+        assert quantize_config.DISABLE_STOCHASTIC_ROUNDING == ref_recipe.disable_stochastic_rounding
+        assert quantize_config.DISABLE_RHT == ref_recipe.disable_rht
+        assert quantize_config.DISABLE_2D_QUANTIZATION == ref_recipe.disable_2d_quantization
+
+    def assert_quantizers(self, ref_recipe: NVFP4BlockScaling, quantizer, tensor_source):
+        if tensor_source == TensorSource.KERNEL and not ref_recipe.disable_2d_quantization:
+            assert quantizer.scaling_mode == ScalingMode.NVFP4_2D_SCALING
+        else:
+            assert quantizer.scaling_mode == ScalingMode.NVFP4_1D_SCALING
+
+        if ref_recipe.disable_stochastic_rounding or tensor_source != TensorSource.DGRAD:
+            assert quantizer.stochastic_rounding_rng_state is None
+        else:
+            assert quantizer.stochastic_rounding_rng_state is not None
+
+        expected_rht = (
+            quantizer.scaling_mode == ScalingMode.NVFP4_1D_SCALING
+            and quantizer.q_layout in {QuantizeLayout.ROWWISE_COLWISE, QuantizeLayout.COLWISE}
+            and not ref_recipe.disable_rht
+        )
+        assert quantizer.use_rht == expected_rht
+
+
+class TestFP8Functions(unittest.TestCase):
+
+    def _check_default_state(self):
+        self.assertEqual(get_global_quantize_recipe(), None)
+
+    def _test_recipe(self, quantization_recipe: Recipe, cls: RecipeAssertionBase):
+        """Tests a quantization recipe by verifying its behavior in both autocast and direct application contexts."""
+        assert_context_func = cls().assert_context
+        assert_quantizer_func = partial(cls().assert_quantizers, quantization_recipe)
+        self._test_recipe_autocast(quantization_recipe, assert_context_func, assert_quantizer_func)
+        self._test_recipe_direct(quantization_recipe, assert_quantizer_func)
+
+    def _test_recipe_autocast(
+        self, quantization_recipe, assert_context_func, assert_quantizer_func
+    ):
+        """Tests a quantization recipe within an autocast context by verifying the quantize config and quantizers in a test module."""
+        self._check_default_state()
+        with autocast(enabled=False, recipe=quantization_recipe, mesh_resource=MeshResource()):
+            self._check_default_state()
+        with autocast(enabled=True, recipe=quantization_recipe, mesh_resource=MeshResource()):
+            quantize_config = self._get_global_quantize_config()
+            assert_context_func(quantization_recipe, quantize_config)
+            self._test_quantizer_in_model(assert_quantizer_func)
+        self._check_default_state()
+
+    def _test_recipe_direct(self, quantization_recipe, assert_quantizer_func):
+        """Tests a quantization recipe by directly passing it to a test module and verifying the quantizers."""
+        self._check_default_state()
+        self._test_quantizer_in_model(assert_quantizer_func, direct_recipe=quantization_recipe)
+        self._check_default_state()
+
+    def _test_quantizer_in_model(self, assert_quantizer_func, direct_recipe=None):
+        """Tests that the quantizers created in a test module match the expected properties by passing them through a VJP boundary.
+
+        Args:
+            assert_quantizer_func: A function that asserts the properties of the quantizers. The function signature is (quantizer: Quantizer, tensor_source: TensorSource) -> None.
+            direct_recipe: An optional quantization recipe to be passed directly to the test module. This is an alternative API to using autocast contexts.
+        """
+        x = jnp.ones((), dtype=jnp.float32)
+        test_module = TestModule(assertion_func=assert_quantizer_func, direct_recipe=direct_recipe)
+        param_key, sr_key = jax.random.split(jax.random.PRNGKey(0))
+        rngs = {"params": param_key, "sr_rng": sr_key}
+        variables = test_module.init(rngs, x)
+
+        jax.jit(jax.value_and_grad(test_module.apply), static_argnums=(2,))(variables, x, rngs=rngs)
+
+    def _get_global_quantize_config(self):
+        quantization_recipe = get_global_quantize_recipe()
+        assert quantization_recipe is not None, "No global quantization recipe set"
+        quantize_config = get_quantize_config_with_recipe(quantization_recipe)
+        assert (
+            quantize_config.is_fp8_enabled()
+        ), "Quantization not enabled in global quantize config"
+        return quantize_config
+
+    @unittest.skipIf(not is_fp8_supported, reason=reason)
+    def test_autocast_delayed_scaling(self):
+        self._test_recipe(
+            quantization_recipe=DelayedScaling(),
+            cls=DelayedScalingRecipeAssertion,
+        )
+        self._test_recipe(
+            quantization_recipe=DelayedScaling(
+                margin=5.0, fp8_format=FP8Format.E4M3, amax_history_len=1
+            ),
+            cls=DelayedScalingRecipeAssertion,
+        )
+        self._test_recipe(
+            quantization_recipe=DelayedScaling(
+                margin=3.0, fp8_format=FP8Format.HYBRID, amax_history_len=1
+            ),
+            cls=DelayedScalingRecipeAssertion,
+        )
+
+    @unittest.skipIf(not is_fp8_supported, reason=reason)
+    def test_autocast_current_scaling(self):
+        self._test_recipe(
+            quantization_recipe=Float8CurrentScaling(),
+            cls=CurrentScalingRecipeAssertion,
+        )
+        self._test_recipe(
+            quantization_recipe=Float8CurrentScaling(margin=5.0, fp8_format=FP8Format.E4M3),
+            cls=CurrentScalingRecipeAssertion,
+        )
+        self._test_recipe(
+            quantization_recipe=Float8CurrentScaling(margin=3.0, fp8_format=FP8Format.HYBRID),
+            cls=CurrentScalingRecipeAssertion,
+        )
+
+    @unittest.skipIf(not is_mxfp8_supported, reason=mxfp8_reason)
+    def test_autocast_mxfp8_block_scaling(self):
+        self._test_recipe(
+            quantization_recipe=MXFP8BlockScaling(),
+            cls=MXFP8RecipeAssertion,
+        )
+
+    @unittest.skipIf(not is_nvfp4_supported, reason=nvfp4_reason)
+    def test_autocast_nvfp4_block_scaling(self):
+        self._test_recipe(
+            quantization_recipe=NVFP4BlockScaling(),
+            cls=NVFP4RecipeAssertion,
+        )
+        self._test_recipe(
+            quantization_recipe=NVFP4BlockScaling(
+                disable_stochastic_rounding=True,
+                disable_rht=True,
+                disable_2d_quantization=True,
+            ),
+            cls=NVFP4RecipeAssertion,
+        )
+
+
+class TestJaxprAndHlo:
+    """Tests to verify Jaxpr and/or HLO of compiled modules apply expected recipe functionality and optimizations."""
+
+    def _generate_jaxpr_for_layernorm_mlp_fwd_bwd(self, quantization_recipe, ln_mlp_kwargs=None):
+        """Generates the jaxpr for a forward and backward pass of LayerNormMLP under the given quantization recipe."""
+        ln_mlp_kwargs = ln_mlp_kwargs or {}
+        with te.autocast(enabled=True, recipe=quantization_recipe, mesh_resource=te.MeshResource()):
+            model = te_flax.LayerNormMLP(
+                layernorm_type="rmsnorm",
+                return_layernorm_output=False,
+                intermediate_dropout_rate=0.0,
+                dtype=jnp.bfloat16,
+                **ln_mlp_kwargs,
+            )
+
+            var_collect = model.init(
+                jax.random.PRNGKey(0),
+                jnp.ones((128, 128), dtype=jnp.bfloat16),
+            )
+
+            def loss_fn(x, rngs):
+                return jnp.mean(model.apply(var_collect, x, rngs=rngs)[0])
+
+            x = jax.random.normal(jax.random.PRNGKey(0), (128, 128), dtype=jnp.bfloat16)
+            rngs = {"sr_rng": jax.random.PRNGKey(1), "dropout": jax.random.PRNGKey(2)}
+            return jax.make_jaxpr(jax.value_and_grad(loss_fn))(x, rngs=rngs)
+
+    @pytest_parametrize_wrapper(
+        "quantization_recipe",
+        [
+            quantization_recipe
+            for quantization_recipe in SUPPORTED_RECIPES
+            if isinstance(quantization_recipe, NVFP4BlockScaling)
+        ],
+    )
+    def test_layernorm_mlp_reuses_amax_nvfp4(self, quantization_recipe):
+        """Tests that layernorm_mlp reuses the amax computed in layernorm and the activation and does not recompute it during quantizaton."""
+
+        jaxpr = self._generate_jaxpr_for_layernorm_mlp_fwd_bwd(quantization_recipe)
+
+        rht_amax_eqns = [
+            eqn for eqn in jaxpr.jaxpr.eqns if eqn.primitive.name == "te_rht_amax_ffi_wrapper"
+        ]
+
+        assert len(rht_amax_eqns) == 4, f"Expected 4 rht_amax_eqns, got {len(rht_amax_eqns)}"
+
+        def assert_param(index, tensor_name, expected_value: bool):
+            if expected_value:
+                assert rht_amax_eqns[index].params["produce_regular_amax"] == True, (
+                    f"Expected produce_regular_amax for {tensor_name} to be True, indicating no"
+                    " reuse of amax as this tensor does not have a previous operation to fuse"
+                    " with"
+                )
+            else:
+                assert rht_amax_eqns[index].params["produce_regular_amax"] == False, (
+                    f"Expected produce_regular_amax for {tensor_name} to be False, indicating"
+                    " reuse of amax"
+                )
+
+        assert_param(0, "fwd ln+q", False)
+        assert_param(1, "fwd act+q", False)
+        # No previous op before incoming dgrad in the backward so amax is not reused
+        assert_param(2, "bwd dgrad", True)
+        assert_param(3, "bwd dact+q", False)
+
+    @pytest_parametrize_wrapper("quantization_recipe", SUPPORTED_RECIPES)
+    @pytest_parametrize_wrapper(
+        "quantization_checkpoint_name",
+        [None, "quantization", "some_arbitrary_user_checkpoint_name"],
+    )
+    def test_recipe_supports_quantization_checkpointing(
+        self, quantization_recipe, quantization_checkpoint_name
+    ):
+        """Tests that all supported quantization recipes correctly use checkpoint_name."""
+
+        kwargs = {
+            "quantization_checkpoint_name": quantization_checkpoint_name,
+        }
+        jaxpr = self._generate_jaxpr_for_layernorm_mlp_fwd_bwd(quantization_recipe, kwargs)
+
+        checkpoint_name_eqns = [
+            eqn
+            for eqn in jaxpr.jaxpr.eqns
+            if eqn.primitive.name == "name" and eqn.params["name"] == quantization_checkpoint_name
+        ]
+
+        if quantization_checkpoint_name is None:
+            assert len(checkpoint_name_eqns) == 0, (
+                "Expected 0 checkpoint_name eqns when quantization_checkpoint_name is None, got"
+                f" {len(checkpoint_name_eqns)}"
+            )
+            return
+
+        # 12 checkpointed values:
+        # - Fwd pass:
+        #   - Input RMSNorm+Q -> 3 possible output tensors that will be used in the backward
+        #   - Kernel Q -> 3 possible output tensors that will be used in the backward
+        #   - Input Activation+Q -> 3 possible output tensors that will be used in the backward
+        #   - Kernel Q -> 3 possible output tensors that will be used in the backward
+        expected_checkpoint_eqn_count = 12
+
+        assert len(checkpoint_name_eqns) == expected_checkpoint_eqn_count, (
+            f"Expected {expected_checkpoint_eqn_count} checkpoint_name eqns when"
+            f" quantization_checkpoint_name is set, got {len(checkpoint_name_eqns)}"
+        )

tests/jax/utils.py

@@ -364,9 +364,9 @@ class MlpBlock(nn.Module):
 
     transpose_batch_sequence: bool
     intermediate_dim: int = 2048
-    activations: Sequence[Union[str, Callable]] = ("relu",)
+    activations: Sequence[Union[str, Callable]] = ("gelu",)
     kernel_init: Initializer = None
-    intermediate_dropout_rate: float = 0.1
+    intermediate_dropout_rate: float = 0.0
     intermediate_dropout_dims: Sequence[int] = ()
     use_bias: bool = False
     dtype: Any = jnp.float32
@@ -1035,14 +1035,14 @@ class EncoderLayer(nn.Module):
     hidden_dropout: float = 0.1
     hidden_dropout_dims: Sequence[int] = ()
     attention_dropout: float = 0.1
-    intermediate_dropout: float = 0.1
+    intermediate_dropout: float = 0.0
     intermediate_dropout_dims: Sequence[int] = ()
     transpose_batch_sequence: bool = True
     float32_attention_logits: bool = False
     scale_attn_logits: bool = False
     scaled_query_init: bool = True
     mlp_dim: int = 2048
-    mlp_activations: Sequence[str] = ("relu",)
+    mlp_activations: Sequence[str] = ("gelu",)
     use_bias: bool = False
     dtype: Any = jnp.float32
     apply_residual_connection_post_layernorm: bool = False
@@ -1199,14 +1199,14 @@ class DecoderLayer(nn.Module):
     hidden_dropout: float = 0.1
     hidden_dropout_dims: Sequence[int] = ()
     attention_dropout: float = 0.1
-    intermediate_dropout: float = 0.1
+    intermediate_dropout: float = 0.0
     intermediate_dropout_dims: Sequence[int] = ()
     transpose_batch_sequence: bool = True
     float32_attention_logits: bool = False
     scale_attn_logits: bool = False
     scaled_query_init: bool = True
     mlp_dim: int = 2048
-    mlp_activations: Sequence[str] = ("relu",)
+    mlp_activations: Sequence[str] = ("gelu",)
     use_bias: bool = False
     dtype: Any = jnp.float32
     apply_residual_connection_post_layernorm: bool = False

tests/pytorch/attention/run_attention_with_cp.py

@@ -248,6 +248,7 @@ def run_dpa_with_cp(
         attn_mask_type=config.attn_mask_type,
         window_size=config.window_size,
         softmax_type=config.softmax_type,
+        return_max_logit=config.return_max_logit,
     ).cuda()
     if config.softmax_type != "vanilla":
         core_attn.softmax_offset.requires_grad = True
@@ -308,6 +309,7 @@ def run_dpa_with_cp(
         fp8_context = autocast(enabled=True, recipe=fp8_recipe, amax_reduction_group=cp_comm_group)
     else:
         fp8_context = nullcontext()
+    max_logit = None
     with fp8_context:
         # q, k, v, out in FP8; dout in F16
         out = core_attn(
@@ -322,6 +324,8 @@ def run_dpa_with_cp(
             cu_seqlens_kv_padded=cu_seqlens_kv_padded,
             fp8_output=fp8_mha,
         )
+        if config.return_max_logit:
+            out, max_logit = out
         if fp8_bwd and fp8_mha:
             dout_fp8 = dout_quantizer(dout)
             out.backward(dout_fp8)
@@ -400,6 +404,7 @@ def run_dpa_with_cp(
         fp8_context = nullcontext()
 
     # run attention
+    max_logit_ = None
     with fp8_context:
         # q, k, v, out in FP8; dout in F16
         out_ = core_attn(
@@ -414,6 +419,8 @@ def run_dpa_with_cp(
             cu_seqlens_kv_padded=cu_seqlens_kv_padded,
             fp8_output=fp8_mha,
         )
+        if config.return_max_logit:
+            out_, max_logit_ = out_
         if fp8_bwd and fp8_mha:
             dout_fp8_ = dout_quantizer(dout_)
             out_.backward(dout_fp8_)
@@ -495,15 +502,15 @@ def run_dpa_with_cp(
                 )
 
     atol, rtol, rmse_tol = get_tols(config, dtype)
-    tensors_cp = [out_, dq_, dk_, dv_, d_softmax_offset_]
-    tensors_no_cp = [out, dq, dk, dv, d_softmax_offset]
-    names = ["out", "dq", "dk", "dv", "d_softmax_offset"]
+    tensors_cp = [out_, dq_, dk_, dv_, d_softmax_offset_, max_logit_]
+    tensors_no_cp = [out, dq, dk, dv, d_softmax_offset, max_logit]
+    names = ["out", "dq", "dk", "dv", "d_softmax_offset", "max_logit"]
     names_cp = [x + "_cp" for x in names]
     names_no_cp = [x + "_no_cp" for x in names]
     is_fp8 = dtype == "fp8"
     for i, t in enumerate(tensors_no_cp):
         if t is not None:
-            if "softmax_offset" not in names[i]:
+            if "softmax_offset" not in names[i] and "max_logit" not in names[i]:
                 if qkv_format == "bshd":
                     compare_and_assert(
                         t[:, 0],