# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.

from __future__ import annotations

from collections.abc import Iterable
import io
import math
from typing import Optional

import pytest
import torch
from torch.utils.cpp_extension import IS_HIP_EXTENSION

import transformer_engine
import transformer_engine.common.recipe
import transformer_engine.pytorch as te
import transformer_engine.pytorch.ops as te_ops
from transformer_engine.pytorch.ops.fused import (
    BackwardActivationBias,
    BackwardAddRMSNorm,
    BackwardLinearAdd,
    BackwardLinearScale,
    ForwardLinearBiasActivation,
    ForwardLinearBiasAdd,
    ForwardLinearScaleAdd,
)
from transformer_engine.pytorch import (
    QuantizedTensor,
    Float8CurrentScalingQuantizer,
    Float8Quantizer,
    MXFP8Quantizer,
    NVFP4Quantizer,
    is_bf16_available,
)
import transformer_engine_torch as tex

# Import utility functions
from utils import dtype_tols, make_recipe, quantization_tols, reset_rng_states

if IS_HIP_EXTENSION:
    import os
    from functools import cache
    @cache
    def use_hipblaslt() -> bool:
        return (os.getenv("NVTE_USE_HIPBLASLT") is not None
                or os.getenv("NVTE_USE_ROCBLAS") is None )

# Check for supported quantization schemes
fp8_available, reason_for_no_fp8 = te.is_fp8_available(return_reason=True)
mxfp8_available, reason_for_no_mxfp8 = te.is_mxfp8_available(return_reason=True)
nvfp4_available, reason_for_no_nvfp4 = te.is_nvfp4_available(return_reason=True)

# Supported data types
_dtypes: list[torch.dtype] = [torch.float32, torch.float16]
if is_bf16_available():  # bf16 requires sm_80 or higher
    _dtypes.append(torch.bfloat16)

# Supported devices
_devices: list[torch.device] = [torch.device("cpu"), torch.device("cuda")]

# Supported quantization recipes
_quantization_list: list[Optional[str]] = [None]
if fp8_available:
    _quantization_list.extend(("fp8_delayed_scaling", "fp8_current_scaling"))
if mxfp8_available:
    _quantization_list.append("mxfp8")
if nvfp4_available:
    _quantization_list.append("nvfp4")


def maybe_skip_quantization(
    quantization: Optional[str],
    *,
    dims: Optional[Iterable[int] | int] = None,
    device: Optional[torch.device | str] = None,
    dtype: Optional[torch.dtype] = None,
) -> None:
    """Skip test case if a quantization scheme is not supported"""

    # Don't skip if there is no quantization
    if quantization is None:
        return

    # Check if quantization scheme is supported on device
    if device is not None and torch.device(device).type != "cuda":
        pytest.skip("Quantization is only supported on CUDA devices")
    if quantization in ("fp8", "fp8_delayed_scaling", "fp8_current_scaling") and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if quantization == "mxfp8" and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    if quantization == "nvfp4" and not nvfp4_available:
        pytest.skip(reason_for_no_nvfp4)

    # Check dims
    if dims is not None:
        if not isinstance(dims, Iterable):
            dims = (dims,)
        if quantization in ("fp8", "fp8_delayed_scaling", "fp8_current_scaling"):
            if math.prod(dims[:-1]) % 16 != 0 or dims[-1] % 16 != 0:
                pytest.skip("FP8 GEMMs require dims that are divisible by 16")
        elif quantization == "mxfp8":
            if math.prod(dims[:-1]) % 32 != 0 or dims[-1] % 32 != 0:
                pytest.skip("MXFP8 GEMMs require dims that are divisible by 32")
        elif quantization == "nvfp4":
            if math.prod(dims[:-1]) % 16 != 0 or dims[-1] % 16 != 0:
                pytest.skip("NVFP4 GEMMs require dims that are divisible by 16")

    # Check dtype
    if dtype is not None:
        if quantization == "nvfp4" and dtype != torch.bfloat16:
            pytest.skip("NVFP4 quantization is only supported with BF16 data")


@torch.no_grad()
def make_reference_and_test_tensors(
    shape: int | Iterable[int],
    quantization: Optional[str] = None,
    ref_dtype: torch.dtype = torch.float64,
    ref_device: torch.device = "cpu",
    test_dtype: torch.dtype = torch.float32,
    test_device: torch.device = "cuda",
    test_is_quantized: bool = False,
    requires_grad: bool = True,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Construct tensors with the same values

    The reference tensor is intended for use in plain PyTorch
    operations in high precision. The test tensor is intended for use
    in Transformer Engine operations.

    If a quantization scheme is provided, the tensor values are
    quantized so that they are representable.

    """

    # Random reference tensor
    ref = torch.rand(shape, dtype=ref_dtype, device=ref_device)

    # Construct test tensor from reference tensor
    test = ref.to(device=test_device, dtype=test_dtype)
    if quantization is None:
        if test_is_quantized:
            raise ValueError("Quantization scheme not provided")
        if test.data_ptr() == ref.data_ptr():
            test = test.clone()
    elif quantization in ("fp8", "fp8_delayed_scaling"):
        quantizer = Float8Quantizer(
            scale=torch.ones(1, dtype=torch.float32, device=test_device).squeeze(),
            amax=torch.zeros(1, dtype=torch.float32, device=test_device),
            fp8_dtype=tex.DType.kFloat8E4M3,
        )
        test = quantizer(test)
    elif quantization == "fp8_current_scaling":
        quantizer = Float8CurrentScalingQuantizer(
            fp8_dtype=tex.DType.kFloat8E4M3,
            device=test_device,
        )
        test = quantizer(test)
    elif quantization == "mxfp8":
        test = MXFP8Quantizer(fp8_dtype=tex.DType.kFloat8E4M3)(test)
    elif quantization == "nvfp4":
        test = NVFP4Quantizer(
            with_rht=False,
            with_post_rht_amax=False,
            with_2d_quantization=False,
            stochastic_rounding=False,
            with_random_sign_mask=False,
        )(test)
    else:
        raise ValueError(f"Unsupported quantization scheme ({quantization})")
    if isinstance(test, QuantizedTensor) and not test_is_quantized:
        test = test.dequantize()

    # Make sure reference and test tensors match each other
    ref.copy_(test)

    ref.requires_grad_(requires_grad)
    test.requires_grad_(requires_grad)
    return ref, test


class TestSequentialContainer:
    """Tests for sequential container"""

    def test_modules(self) -> None:
        """Check that list of modules can be manipulated as expected"""

        # Construct sequential container
        modules = [
            te_ops.Identity(),
            te_ops.Identity(),
            torch.nn.Identity(),
            te_ops.Identity(),
        ]
        model = te_ops.Sequential(*modules)

        # Length
        assert len(model) == len(modules)

        # Iterator
        for module1, module2 in zip(model, modules):
            assert module1 is module2

        # Index by int
        for i, module in enumerate(modules):
            assert model[i] is module
            assert model[i - len(modules)] is module

        # Index by slice
        model_subset = model[1:-1]
        modules_subset = modules[1:-1]
        assert isinstance(model_subset, te_ops.Sequential)
        for module1, module2 in zip(model_subset, modules_subset):
            assert module1 is module2

        # Set element
        new_module = torch.nn.Identity()
        idx = 1
        modules[idx] = new_module
        model[idx] = new_module
        for module1, module2 in zip(model, modules):
            assert module1 is module2

        # Delete element
        idx = 1
        del modules[idx]
        del model[idx]
        for module1, module2 in zip(model, modules):
            assert module1 is module2

        # Append
        new_module = torch.nn.Identity()
        modules.append(new_module)
        model.append(new_module)
        for module1, module2 in zip(model, modules):
            assert module1 is module2

        # Extend
        new_modules = [te_ops.Identity(), te_ops.Identity()]
        modules.extend(new_modules)
        model.extend(new_modules)
        for module1, module2 in zip(model, modules):
            assert module1 is module2

        # Insert
        new_module = te_ops.Identity()
        idx = 2
        modules.insert(idx, new_module)
        model.insert(idx, new_module)
        for module1, module2 in zip(model, modules):
            assert module1 is module2

        # Pop
        idx = 2
        assert model.pop(idx) is modules.pop(idx)
        for module1, module2 in zip(model, modules):
            assert module1 is module2

        # Out-of-place add
        new_modules = [torch.nn.Identity(), te_ops.Identity()]
        added_modules = modules + new_modules
        added_model = model + te_ops.Sequential(*new_modules)
        for module1, module2 in zip(model, modules):
            assert module1 is module2
        for module1, module2 in zip(added_model, added_modules):
            assert module1 is module2

        # In-place add
        new_modules = [te_ops.Identity(), torch.nn.Identity()]
        modules += new_modules
        model += te_ops.Sequential(*new_modules)
        for module1, module2 in zip(model, modules):
            assert module1 is module2

    def test_module_groups(self) -> None:
        """Check that modules are grouped together correctly"""
        model = te_ops.Sequential(
            te_ops.Identity(),
            te_ops.Identity(),
            torch.nn.Identity(),
            torch.nn.Identity(),
            te_ops.Identity(),
            torch.nn.Identity(),
            te_ops.Identity(),
            te_ops.Identity(),
            te_ops.Identity(),
        )
        model(torch.zeros(1))
        assert len(model._module_groups) == 6

    def test_extra_tensors(self, size: int = 16) -> None:
        """Check that extra inputs are distributed properly between module groups
        and that extra outputs are properly collected"""

        # Construct sequential container
        bias = te_ops.Bias(size=size, device="cpu")
        with torch.no_grad():
            bias.bias.copy_(torch.rand((size,)))
        model = te_ops.Sequential(  #                 | Inputs  | Outputs
            torch.nn.Identity(),  #                   | x1      | x1
            te_ops.MakeExtraOutput(in_place=True),  # | x1      | x1 [x1]
            bias,  #                                  | x1      | h1 (= x1 + b)
            te_ops.MakeExtraOutput(in_place=True),  # | h1      | h1 [h1]
            te_ops.AddExtraInput(in_place=True),  #   | h1 [x2] | x2 (= x2 + h1)
            te_ops.MakeExtraOutput(in_place=True),  # | x2      | x2 [x2]
            torch.nn.Identity(),  #                   | x2      | x2
            bias,  #                                  | x2      | h2 (= x2 + b)
            te_ops.AddExtraInput(in_place=True),  #   | h2 [x3] | x3 (= x3 + h2)
            te_ops.MakeExtraOutput(in_place=True),  # | x3      | x3 [x3]
            te_ops.AddExtraInput(in_place=True),  #   | x3 [x4] | x4 (= x4 + x3)
            torch.nn.Identity(),  #                   | x4      | x4
            te_ops.Identity(),  #                     | x4      | x4
            te_ops.MakeExtraOutput(in_place=True),  # | x4      | x4 [x4]
            te_ops.Identity(),  #                     | x4      | x4
        )

        # Create input tensors
        x1 = torch.rand((size,))
        x2 = torch.rand((size,))
        x3 = torch.rand((size,))
        x4 = torch.rand((size,))

        # Save original input tensor values
        x1_orig = x1.clone()
        x2_orig = x2.clone()
        x3_orig = x3.clone()
        x4_orig = x4.clone()

        # Run forward
        ys = model(x1, x2, x3, x4)

        # Check whether outputs match (x4, x1, h1, x2, x3, x4)
        assert len(ys) == 6
        assert ys[0].data_ptr() == x4.data_ptr()
        assert ys[1].data_ptr() == x1.data_ptr()
        assert ys[2].data_ptr() not in [x.data_ptr() for x in (x1, x2, x3, x4)]
        assert ys[3].data_ptr() == x2.data_ptr()
        assert ys[4].data_ptr() == x3.data_ptr()
        assert ys[5].data_ptr() == x4.data_ptr()

        # Check whether tensors have correct values
        b = bias.bias
        h1 = ys[2]
        torch.testing.assert_close(x1, x1_orig)
        torch.testing.assert_close(h1, x1_orig + b)
        torch.testing.assert_close(x2, x2_orig + h1)
        torch.testing.assert_close(x3, x3_orig + x2 + b)
        torch.testing.assert_close(x4, x4_orig + x3)


class TestFuser:
    """Tests for operation fusion infrastructure"""

    @staticmethod
    def setup_class(cls) -> None:
        reset_rng_states()

    @pytest.mark.skipif(not fp8_available, reason=reason_for_no_fp8)
    def test_fp8_scale_update(
        self,
        size: int = 16,
        dtype: torch.dtype = torch.float32,
        device: torch.device = "cuda",
    ):
        """Test FP8 scaling factors with delayed scaling recipe"""

        # FP8 recipe
        margin = 2
        fp8_format = transformer_engine.common.recipe.Format.HYBRID
        recipe = transformer_engine.common.recipe.DelayedScaling(
            margin=margin,
            fp8_format=fp8_format,
            amax_history_len=8,
            amax_compute_algo="max",
        )

        # Construct model
        with te.quantized_model_init(recipe=recipe):
            model = te_ops.basic.BasicLinear(
                size,
                size,
                device=device,
                dtype=dtype,
            )

        # Training steps
        w_vals = [2, 5, 3, 11]
        x_vals = [7, 3, 5]
        dy_vals = [1, 2, 1]
        with torch.no_grad():
            model.weight.fill_(w_vals[0])
        for step in range(3):

            # Data tensors
            x = torch.full(
                (size, size),
                x_vals[step],
                dtype=dtype,
                device=device,
                requires_grad=True,
            )
            dy = torch.full(
                (size, size),
                dy_vals[step],
                dtype=dtype,
                device=device,
            )

            # Training step
            with te.autocast(recipe=recipe):
                y = model(x)
            y.backward(dy)
            with torch.no_grad():
                model.weight.fill_(w_vals[step + 1])

            # Check that output tensors match expected
            tols = dict(rtol=0, atol=0)
            y_val_ref = w_vals[step] * x_vals[step] * size
            dx_val_ref = w_vals[step] * dy_vals[step] * size
            torch.testing.assert_close(
                y,
                torch.full_like(y, y_val_ref),
                **quantization_tols("fp8_delayed_scaling"),
            )
            torch.testing.assert_close(
                x.grad,
                torch.full_like(x.grad, dx_val_ref),
                **quantization_tols("fp8_delayed_scaling"),
            )

            # Check that scaling factors match expected
            w_amax_ref = max(w_vals[: step + 1])
            x_amax_ref = max(x_vals[: step + 1])
            dy_amax_ref = max(dy_vals[: step + 1])
            w_scale_ref = (fp8_format.value.max_fwd / w_amax_ref) / (2**margin)
            x_scale_ref = (fp8_format.value.max_fwd / x_amax_ref) / (2**margin)
            dy_scale_ref = (fp8_format.value.max_bwd / dy_amax_ref) / (2**margin)
            w_scale = model.get_quantizer("forward", 1).scale
            x_scale = model.get_quantizer("forward", 0).scale
            dy_scale = model.get_quantizer("backward", 0).scale
            torch.testing.assert_close(w_scale, torch.full_like(w_scale, w_scale_ref))
            torch.testing.assert_close(x_scale, torch.full_like(x_scale, x_scale_ref))
            torch.testing.assert_close(dy_scale, torch.full_like(dy_scale, dy_scale_ref))

    @pytest.mark.parametrize("init_dtype", _dtypes)
    @pytest.mark.parametrize("final_dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_dtype_cast(
        self,
        *,
        size: int = 32,
        init_dtype: torch.dtype,
        final_dtype: torch.dtype,
        device: torch.device = "cuda",
        quantization: Optional[str],
    ) -> None:
        """Check dtype cast functions"""

        # Skip invalid configurations
        in_shape = (size, size)
        with_quantization = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=init_dtype)
        maybe_skip_quantization(quantization, dtype=final_dtype)

        # Random data
        dtype = torch.float32
        if torch.float16 in (init_dtype, final_dtype):
            dtype = torch.float16
        if torch.bfloat16 in (init_dtype, final_dtype):
            dtype = torch.bfloat16
        w_ref, w_test = make_reference_and_test_tensors(
            (size, size),
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )

        # Construct operation
        with te.quantized_model_init(enabled=with_quantization, recipe=make_recipe(quantization)):
            op = te_ops.Linear(size, size, bias=False, device=device, dtype=init_dtype)
        with torch.no_grad():
            op.weight.copy_(w_test)
            del w_test

        # Cast operation dtype
        if final_dtype == torch.float32:
            op.float()
        elif final_dtype == torch.float16:
            op.half()
        elif final_dtype == torch.bfloat16:
            op.bfloat16()

        # Check weights
        assert isinstance(op.weight, QuantizedTensor) == with_quantization
        assert op.weight.dtype == final_dtype
        w_test = op.weight.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(w_test, w_ref, **dtype_tols(dtype))

        # Check forward and backward pass
        x = torch.zeros(
            in_shape,
            dtype=init_dtype,
            device=device,
            requires_grad=True,
        )
        y = op(x)
        y.backward(torch.zeros_like(y))
        assert y.dtype == final_dtype
        assert x.grad.dtype == init_dtype
        assert op.weight.grad.dtype == final_dtype

    @pytest.mark.parametrize("model_dtype", _dtypes)
    @pytest.mark.parametrize("autocast_dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_pyt_autocast(
        self,
        *,
        size: int = 32,
        model_dtype: torch.dtype,
        autocast_dtype: torch.dtype,
        device: torch.device = "cuda",
        quantization: Optional[str],
        quantized_weights: bool = False,
    ) -> None:
        """Test with PyTorch autocast"""
        device = torch.device(device)

        # Skip invalid configurations
        in_shape = (size, size)
        quantized_compute = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=model_dtype)
        maybe_skip_quantization(quantization, dtype=autocast_dtype)

        # Construct operation
        recipe = make_recipe(quantization)
        with te.quantized_model_init(enabled=quantized_weights, recipe=recipe):
            op = te_ops.Linear(size, size, bias=False, device=device, dtype=model_dtype)

        # Check forward and backward pass
        x = torch.zeros(
            in_shape,
            dtype=model_dtype,
            device=device,
            requires_grad=True,
        )
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            with torch.autocast(device_type=device.type, dtype=autocast_dtype):
                y = op(x)
        y.backward(torch.zeros_like(y))
        assert y.dtype == autocast_dtype
        assert x.grad.dtype == model_dtype
        assert op.weight.grad.dtype == model_dtype

        # Check forward and backward pass (swapped context order)
        if quantized_compute:
            x.grad = None
            op.weight.grad = None
            with torch.autocast(device_type=device.type, dtype=autocast_dtype):
                with te.autocast(enabled=quantized_compute, recipe=recipe):
                    y = op(x)
            y.backward(torch.zeros_like(y))
            assert y.dtype == autocast_dtype
            assert x.grad.dtype == model_dtype
            assert op.weight.grad.dtype == model_dtype


class TestBasicOps:
    """Tests for individual operations"""

    @staticmethod
    def setup_class(cls) -> None:
        reset_rng_states()

    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("device", ("cuda", "cpu"))
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_identity(
        self,
        *,
        in_shape: Iterable[int] = (32, 32),
        dtype: torch.dtype,
        device: torch.device,
        quantization: Optional[str],
    ) -> None:

        # Skip invalid configurations
        with_quantization = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=with_quantization,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=with_quantization,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = x_ref
        dx_ref = dy_ref

        # Implementation with fusible operation
        op = te_ops.Identity()
        y_test = op(x_test)
        y_test.backward(dy_test)

        # Check results
        tols = dict(rtol=0, atol=0)  # Identity is exact
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, dx_ref, **tols)

        # Make sure we are not trivially passing the test
        with pytest.raises(AssertionError):
            torch.testing.assert_close(y_test, -y_ref, **tols)
        with pytest.raises(AssertionError):
            torch.testing.assert_close(dx_test, -dx_ref, **tols)

    @pytest.mark.parametrize(
        "shapes",
        (
            ((1, 2, 3, 4), (2, 12)),
            ((5, 4, 3, 2), (-1, 6)),
            ((30,), (2, 3, -1)),
            ((6, 7), (3, -1, 7)),
        ),
    )
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("quantization", (None, "fp8_current_scaling"))
    def test_reshape(
        self,
        *,
        shapes: tuple[Iterable[int], Iterable[int]],
        dtype: torch.dtype,
        device: torch.device = "cuda",
        memory_format: torch.memory_format = torch.contiguous_format,
        quantization: Optional[str],
    ) -> None:
        in_shape, out_shape = shapes

        # Skip invalid configurations
        if memory_format == torch.channels_last and len(in_shape) != 4:
            pytest.skip("torch.channels_last only supports 4D tensors")
        maybe_skip_quantization(quantization, device=device, dtype=dtype)
        with_quantization = quantization is not None

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=with_quantization,
        )
        x_test = x_test.contiguous(memory_format=memory_format)
        x_test = x_test.detach().requires_grad_()
        dy_ref, dy_test = make_reference_and_test_tensors(
            x_ref.reshape(out_shape).size(),
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=with_quantization,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = x_ref.reshape(out_shape)
        y_ref.backward(dy_ref)

        # Implementation with fusible operation
        op = te_ops.Reshape(out_shape)
        y_test = op(x_test)
        y_test.backward(dy_test)

        # Check results
        tols = dict(rtol=0, atol=0)  # Reshape is exact
        y_test = y_test.to(
            dtype=torch.float64,
            device="cpu",
            memory_format=torch.contiguous_format,
        )
        dx_test = x_test.grad.to(
            dtype=torch.float64,
            device="cpu",
            memory_format=torch.contiguous_format,
        )
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)

    @pytest.mark.parametrize("size", (1, 7, 32))
    @pytest.mark.parametrize("in_shape", ((-1,), (1, 3, -1), (4, 3, 8, -1)))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("device", _devices)
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_bias(
        self,
        *,
        size: int,
        in_shape: Iterable[int],
        dtype: torch.dtype,
        device: torch.device,
        quantization: Optional[str],
    ) -> None:

        # Make input and bias shapes consistent
        in_shape = list(in_shape)[:-1] + [size]

        # Skip invalid configurations
        with_quantization = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=with_quantization,
        )
        b_ref, b_test = make_reference_and_test_tensors(
            size,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=with_quantization,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = x_ref + b_ref.reshape([1] * (len(in_shape) - 1) + [size])
        y_ref.backward(dy_ref)

        # Implementation with fusible operation
        op = te_ops.Bias(size, device=device, dtype=dtype)
        with torch.no_grad():
            op.bias.copy_(b_test)
            del b_test
        y_test = op(x_test)
        y_test.backward(dy_test)

        # Check results
        tols = dtype_tols(dtype)
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        db_test = op.bias.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)
        torch.testing.assert_close(db_test, b_ref.grad, **tols)

    @pytest.mark.parametrize("quantization", _quantization_list)
    @pytest.mark.parametrize("cast_forward", (False, True))
    @pytest.mark.parametrize("cast_backward", (False, True))
    def test_quantize(
        self,
        *,
        in_shape: Iterable[int] = (32, 32),
        dtype: torch.dtype = torch.bfloat16,
        device: torch.device = "cuda",
        quantization: str,
        cast_forward: bool,
        cast_backward: bool,
    ) -> None:
        """Quantize"""

        # Skip invalid configurations
        with_quantization = quantization is not None
        maybe_skip_quantization(quantization, device=device, dtype=dtype)
        if quantization == "mxfp8":
            maybe_skip_quantization(quantization, dims=in_shape)

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            requires_grad=True,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = x_ref
        dx_ref = dy_ref

        # Implementation with fusible operation
        op = te_ops.Quantize(forward=cast_forward, backward=cast_backward)
        recipe = make_recipe(quantization)
        with te.autocast(enabled=with_quantization, recipe=recipe):
            y_test = op(x_test)
        y_test.backward(dy_test)

        # Check tensor types
        if with_quantization:
            assert isinstance(y_test, QuantizedTensor) == cast_forward
            assert isinstance(x_test.grad, QuantizedTensor) == cast_backward

        # Check values
        tols = dict(rtol=0, atol=0)
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, dx_ref, **tols)

    def _test_basic_linear(
        self,
        *,
        weight_shape: tuple[int, int] = (32, 32),
        in_shape: Iterable[int] = (32, -1),
        dtype: torch.dtype = torch.float32,
        device: torch.device = "cuda",
        quantization: Optional[str] = None,
        quantized_compute: bool = False,
        quantized_input: bool = False,
        quantized_weight: bool = False,
        quantized_output: bool = False,
        quantized_grad_output: bool = False,
        quantized_grad_input: bool = False,
        accumulate_into_main_grad: bool = False,
    ) -> None:
        """Helper function for tests with GEMM"""

        # Make input and weight shapes consistent
        out_features, in_features = weight_shape
        in_shape = list(in_shape)[:-1] + [in_features]
        out_shape = in_shape[:-1] + [out_features]

        # Skip invalid configurations
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)
        maybe_skip_quantization(quantization, dims=out_shape)
        quantization_needed = any(
            (
                quantized_compute,
                quantized_input,
                quantized_weight,
                quantized_output,
                quantized_grad_output,
                quantized_grad_input,
            )
        )
        if quantization is None and quantization_needed:
            pytest.skip("Quantization scheme is not specified")
        if quantization is not None and not quantization_needed:
            pytest.skip("Quantization scheme is not used")
        if quantization in ("fp8", "fp8_delayed_scaling", "fp8_current_scaling"):
            if quantized_output and not quantized_compute:
                pytest.skip("FP8 output is only supported with FP8 GEMMs")
            if quantized_grad_input and not quantized_compute:
                pytest.skip("FP8 grad input is only supported with FP8 GEMMs")
        if quantization not in (None, "fp8"):
            if quantized_output or quantized_grad_input:
                pytest.skip("Recipe does not support quantized GEMM output")
        if ( IS_HIP_EXTENSION and not use_hipblaslt() and
            accumulate_into_main_grad and dtype != torch.float32 and not quantized_compute):
            pytest.skip("Parameters combination is not supported by ROCBLAS")

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=quantized_input,
        )
        w_ref, w_test = make_reference_and_test_tensors(
            (out_features, in_features),
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            out_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=quantized_grad_output,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = torch.nn.functional.linear(x_ref, w_ref)
        y_ref.backward(dy_ref)

        # Implementation with fusible operation
        recipe = make_recipe(quantization)
        with te.quantized_model_init(enabled=quantized_weight, recipe=recipe):
            op = te_ops.BasicLinear(
                in_features,
                out_features,
                device=device,
                dtype=dtype,
                accumulate_into_main_grad=accumulate_into_main_grad,
            )
        with torch.no_grad():
            op.weight.copy_(w_test)
            del w_test
            op.weight.main_grad = torch.full_like(op.weight, 0.5, dtype=torch.float32)
        forward = te_ops.Sequential(
            te_ops.Quantize(forward=quantized_input, backward=quantized_grad_input),
            op,
            te_ops.Quantize(forward=quantized_output, backward=quantized_grad_output),
        )
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y_test = forward(x_test)
        y_test.backward(dy_test)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if dtype == torch.float32:
            tols = dtype_tols(torch.float16)  # TF32 GEMM
        if quantized_compute or quantized_output or quantized_grad_input:
            tols = quantization_tols(quantization)

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)
        if accumulate_into_main_grad:
            if op.weight.grad is not None:
                torch.testing.assert_close(
                    op.weight.grad,
                    torch.zeros_like(op.weight.grad),
                    rtol=0,
                    atol=0,
                )
            dw_test = op.weight.main_grad.to(dtype=torch.float64, device="cpu") - 0.5
        else:
            dw_test = op.weight.grad.to(dtype=torch.float64, device="cpu")
            torch.testing.assert_close(
                op.weight.main_grad,
                torch.full_like(op.weight.main_grad, 0.5),
                rtol=0,
                atol=0,
            )
        torch.testing.assert_close(dw_test, w_ref.grad, **tols)

    @pytest.mark.parametrize("weight_shape", ((64, 32), (3, 5)))
    @pytest.mark.parametrize("in_shape", ((-1,), (5, 1, -1), (4, 2, 4, -1)))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    @pytest.mark.parametrize("accumulate_into_main_grad", (False, True))
    def test_basic_linear(
        self,
        *,
        weight_shape: tuple[int, int],
        in_shape: Iterable[int],
        dtype: torch.dtype,
        quantization: Optional[str],
        accumulate_into_main_grad: bool,
    ) -> None:
        """GEMM"""
        self._test_basic_linear(
            weight_shape=weight_shape,
            in_shape=in_shape,
            dtype=dtype,
            quantization=quantization,
            quantized_compute=quantization is not None,
            accumulate_into_main_grad=accumulate_into_main_grad,
        )

    @pytest.mark.skipif(not fp8_available, reason=reason_for_no_fp8)
    @pytest.mark.parametrize("quantization", _quantization_list)
    @pytest.mark.parametrize("quantized_compute", (False, True))
    @pytest.mark.parametrize("quantized_input", (False, True))
    @pytest.mark.parametrize("quantized_weight", (False, True))
    @pytest.mark.parametrize("quantized_output", (False, True))
    @pytest.mark.parametrize("quantized_grad_output", (False, True))
    @pytest.mark.parametrize("quantized_grad_input", (False, True))
    def test_basic_linear_quantized(
        self,
        *,
        quantization: str,
        quantized_compute: bool,
        quantized_input: bool,
        quantized_weight: bool,
        quantized_output: bool,
        quantized_grad_output: bool,
        quantized_grad_input: bool,
    ) -> None:
        """GEMM with FP8 inputs and outputs"""
        if quantization is None:
            pytest.skip("Skipping case without quantization")
        self._test_basic_linear(
            dtype=torch.bfloat16,
            quantization=quantization,
            quantized_compute=quantized_compute,
            quantized_input=quantized_input,
            quantized_weight=quantized_weight,
            quantized_output=quantized_output,
            quantized_grad_output=quantized_grad_output,
            quantized_grad_input=quantized_grad_input,
        )

    @pytest.mark.parametrize("bias", (False, True))
    @pytest.mark.parametrize("quantization", _quantization_list)
    @pytest.mark.parametrize("quantized_compute", (False, True))
    @pytest.mark.parametrize("quantized_weight", (False, True))
    @pytest.mark.parametrize("input_requires_grad", (False, True))
    @pytest.mark.parametrize("weight_requires_grad", (False, True))
    def test_linear(
        self,
        *,
        bias: bool,
        weight_shape: tuple[int, int] = (32, 32),
        in_shape: Iterable[int] = (32, -1),
        dtype: torch.dtype = torch.float32,
        device: torch.device = "cuda",
        quantization: Optional[str],
        quantized_compute: bool,
        quantized_weight: bool,
        input_requires_grad: bool,
        weight_requires_grad: bool,
    ) -> None:
        """GEMM + bias"""

        # Make input and weight shapes consistent
        out_features, in_features = weight_shape
        in_shape = list(in_shape)[:-1] + [in_features]
        out_shape = in_shape[:-1] + [out_features]

        # Skip invalid configurations
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)
        maybe_skip_quantization(quantization, dims=out_shape)
        if quantization is None and (quantized_compute or quantized_weight):
            pytest.skip("Quantization scheme is not specified")
        if quantization is not None and not (quantized_compute or quantized_weight):
            pytest.skip("Quantization scheme is not used")

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        w_ref, w_test = make_reference_and_test_tensors(
            (out_features, in_features),
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        b_ref, b_test = None, None
        if bias:
            b_ref, b_test = make_reference_and_test_tensors(
                out_features,
                test_dtype=dtype,
                test_device=device,
            )
        dy_ref, dy_test = make_reference_and_test_tensors(
            out_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = torch.nn.functional.linear(x_ref, w_ref, bias=b_ref)
        y_ref.backward(dy_ref)

        # Implementation with fusible operation
        recipe = make_recipe(quantization)
        with te.quantized_model_init(enabled=quantized_weight, recipe=recipe):
            op = te_ops.Linear(
                in_features,
                out_features,
                bias=bias,
                device=device,
                dtype=dtype,
            )
        with torch.no_grad():
            op.weight.copy_(w_test)
            if bias:
                op.bias.copy_(b_test)
            del w_test
            del b_test
            for param in op.parameters():
                param.requires_grad_(requires_grad=weight_requires_grad)
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y_test = op(x_test)
        if input_requires_grad or weight_requires_grad:
            y_test.backward(dy_test)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if dtype == torch.float32:
            tols = dtype_tols(torch.float16)  # TF32 GEMM
        if quantized_compute:
            tols = quantization_tols(quantization)

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        if input_requires_grad:
            dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
            torch.testing.assert_close(dx_test, x_ref.grad, **tols)
        if weight_requires_grad:
            dw_test = op.weight.grad.to(dtype=torch.float64, device="cpu")
            torch.testing.assert_close(dw_test, w_ref.grad, **tols)
            if bias:
                db_test = op.bias.grad.to(dtype=torch.float64, device="cpu")
                torch.testing.assert_close(db_test, b_ref.grad, **tols)

    @pytest.mark.parametrize("weight_shape", ((7, 2), (32,)))
    @pytest.mark.parametrize("in_shape", ((-1,), (6, 16, -1)))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("zero_centered_gamma", (False, True))
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_layer_norm(
        self,
        *,
        weight_shape: Iterable[int],
        in_shape: Iterable[int],
        dtype: torch.dtype,
        device: torch.device = "cuda",
        eps: float = 0.3,
        zero_centered_gamma: bool,
        quantization: Optional[str],
    ) -> None:
        """Layer norm"""

        # Make input and weight shapes consistent
        in_shape = list(in_shape)[:-1] + list(weight_shape)

        # Skip invalid configurations
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
        )
        w_ref, w_test = make_reference_and_test_tensors(
            weight_shape,
            test_dtype=dtype,
            test_device=device,
        )
        b_ref, b_test = make_reference_and_test_tensors(
            weight_shape,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = torch.nn.functional.layer_norm(
            x_ref,
            weight_shape,
            weight=(w_ref + 1 if zero_centered_gamma else w_ref),
            bias=b_ref,
            eps=eps,
        )
        y_ref.backward(dy_ref)

        # Implementation with fusible operation
        op = te_ops.LayerNorm(
            weight_shape,
            eps=eps,
            device=device,
            dtype=dtype,
            zero_centered_gamma=zero_centered_gamma,
        )
        with torch.no_grad():
            op.weight.copy_(w_test)
            op.bias.copy_(b_test)
            del w_test
            del b_test
        quantized_compute = quantization is not None
        recipe = make_recipe(quantization)
        forward = te_ops.Sequential(
            op,
            te_ops.Quantize(forward=quantized_compute, backward=False),
        )
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y_test = forward(x_test)
        y_test.backward(dy_test)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if quantized_compute:
            tols = quantization_tols(quantization)

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        dw_test = op.weight.grad.to(dtype=torch.float64, device="cpu")
        db_test = op.bias.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)
        torch.testing.assert_close(dw_test, w_ref.grad, **tols)
        torch.testing.assert_close(db_test, b_ref.grad, **tols)

    def test_layer_norm_autocast(
        self,
        *,
        weight_shape: Iterable[int] = (32,),
        in_shape: Iterable[int] = (32,),
        dtype: torch.dtype = torch.float16,
        autocast_dtype: torch.dtype = torch.float32,
        device: torch.device = "cuda",
        eps: float = 0.3,
    ) -> None:
        """Layer norm with PyTorch autocast"""

        # Make input and weight shapes consistent
        in_shape = list(in_shape)[:-1] + list(weight_shape)

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=autocast_dtype,
            test_device=device,
        )
        w_ref, w_test = make_reference_and_test_tensors(
            weight_shape,
            test_dtype=dtype,
            test_device=device,
        )
        b_ref, b_test = make_reference_and_test_tensors(
            weight_shape,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=autocast_dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = torch.nn.functional.layer_norm(
            x_ref,
            weight_shape,
            weight=w_ref,
            bias=b_ref,
            eps=eps,
        )
        y_ref.backward(dy_ref)

        # Implementation with fusible operation
        op = te_ops.LayerNorm(
            weight_shape,
            eps=eps,
            device=device,
            dtype=dtype,
        )
        with torch.no_grad():
            op.weight.copy_(w_test)
            op.bias.copy_(b_test)
            del w_test
            del b_test
        with torch.autocast(device, dtype=autocast_dtype):
            y_test = op(x_test)
        y_test.backward(dy_test)

        # Check results
        assert y_test.dtype == autocast_dtype
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        dw_test = op.weight.grad.to(dtype=torch.float64, device="cpu")
        db_test = op.bias.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **dtype_tols(autocast_dtype))
        torch.testing.assert_close(dx_test, x_ref.grad, **dtype_tols(autocast_dtype))
        torch.testing.assert_close(dw_test, w_ref.grad, **dtype_tols(dtype))
        torch.testing.assert_close(db_test, b_ref.grad, **dtype_tols(dtype))

    @pytest.mark.parametrize("weight_shape", ((19,), (64,)))
    @pytest.mark.parametrize("in_shape", ((-1,), (6, 16, -1)))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("zero_centered_gamma", (False, True))
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_rmsnorm(
        self,
        *,
        weight_shape: Iterable[int],
        in_shape: Iterable[int],
        dtype: torch.dtype,
        device: torch.device = "cuda",
        eps: float = 0.3,
        zero_centered_gamma: bool,
        quantization: Optional[str],
    ) -> None:
        """Layer norm"""

        # Make input and weight shapes consistent
        in_shape = list(in_shape)[:-1] + list(weight_shape)

        # Skip invalid configurations
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
        )
        w_ref, w_test = make_reference_and_test_tensors(
            weight_shape,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        inner_dims = tuple(range(len(in_shape) - len(weight_shape), len(in_shape)))
        var_ref = x_ref.square().sum(dim=inner_dims, keepdim=True) / math.prod(weight_shape)
        if zero_centered_gamma:
            y_ref = x_ref / torch.sqrt(eps + var_ref) * (1 + w_ref)
        else:
            y_ref = x_ref / torch.sqrt(eps + var_ref) * w_ref
        y_ref.backward(dy_ref)

        # Implementation with fusible operation
        op = te_ops.RMSNorm(
            weight_shape,
            eps=eps,
            device=device,
            dtype=dtype,
            zero_centered_gamma=zero_centered_gamma,
        )
        with torch.no_grad():
            op.weight.copy_(w_test)
            del w_test
        quantized_compute = quantization is not None
        recipe = make_recipe(quantization)
        forward = te_ops.Sequential(
            op,
            te_ops.Quantize(forward=quantized_compute, backward=False),
        )
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y_test = forward(x_test)
        y_test.backward(dy_test)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if quantized_compute:
            tols = quantization_tols(quantization)

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        dw_test = op.weight.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)
        torch.testing.assert_close(dw_test, w_ref.grad, **tols)

    @pytest.mark.parametrize("in_shape", ((32,), (6, 16, 64), (32, 64)))
    @pytest.mark.parametrize("dtype", _dtypes)
    def test_l2normalization(
        self,
        *,
        in_shape: Iterable[int],
        dtype: torch.dtype,
        device: torch.device = "cuda",
        eps: float = 1e-6,
    ) -> None:
        """L2 Normalization"""

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        # L2 norm: x / ||x||_2 = x / sqrt(sum(x^2) + eps)
        l2_norm_squared = x_ref.pow(2).sum(dim=-1, keepdim=True)
        rsqrt_norm = torch.rsqrt(l2_norm_squared + eps)
        y_ref = x_ref * rsqrt_norm
        y_ref.backward(dy_ref)

        # Implementation with fusible operation
        op = te_ops.L2Normalization(
            eps=eps,
        )
        y_test = op(x_test)
        y_test.backward(dy_test)

        # Expected numerical error
        tols = dtype_tols(dtype)

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")

        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)

    @pytest.mark.parametrize("in_place", (True, False))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("device", ("cuda", "cpu"))
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_add_extra_input(
        self,
        *,
        in_shape: Iterable[int] = (32, 32),
        in_place: bool,
        dtype: torch.dtype,
        device: torch.device,
        quantization: Optional[str],
    ) -> None:
        """Add two tensors

        Join in compute graph.

        """

        # Skip invalid configurations
        with_quantization = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)

        # Random data
        x1_ref, x1_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=with_quantization,
        )
        x2_ref, x2_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=with_quantization,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=with_quantization,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = x2_ref.detach()
        y_ref += x1_ref
        dx1_ref = dy_ref
        dx2_ref = dy_ref

        # Implementation with fusible operation
        op = te_ops.AddExtraInput(in_place=in_place)
        y_test = op(x1_test, x2_test)
        y_test.backward(dy_test)

        # Check results
        tols = dtype_tols(dtype)
        if in_place:
            if quantization in ("fp8_delayed_scaling", "fp8_current_scaling", "mxfp8"):
                tols = dtype_tols(x1_test._fp8_dtype)
            elif quantization == "nvfp4":
                tols = dtype_tols(x1_test._fp4_dtype)
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx1_test = x1_test.grad.to(dtype=torch.float64, device="cpu")
        dx2_test = x2_test.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx1_test, dx1_ref, rtol=0, atol=0)
        torch.testing.assert_close(dx2_test, dx2_ref, rtol=0, atol=0)

    @pytest.mark.parametrize("in_place", (True, False))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("device", ("cuda", "cpu"))
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_make_extra_output(
        self,
        *,
        in_shape: Iterable[int] = (32, 32),
        in_place: bool,
        dtype: torch.dtype,
        device: torch.device,
        quantization: Optional[str],
    ) -> None:
        """Output tensor twice

        Split in compute graph.

        """

        # Skip invalid configurations
        with_quantization = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=with_quantization,
        )
        dy1_ref, dy1_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=with_quantization,
            requires_grad=False,
        )
        dy2_ref, dy2_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=with_quantization,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y1_ref = x_ref
        y2_ref = x_ref
        (y1_ref * dy1_ref + y2_ref * dy2_ref).sum().backward()

        # Implementation with fusible operation
        op = te_ops.MakeExtraOutput(in_place=in_place)
        y1_test, y2_test = op(x_test)
        (y1_test * dy1_test + y2_test * dy2_test).sum().backward()

        # Check results
        tols = dtype_tols(dtype)
        y1_test = y1_test.to(dtype=torch.float64, device="cpu")
        y2_test = y2_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y1_test, y1_ref, rtol=0, atol=0)
        torch.testing.assert_close(y2_test, y2_ref, rtol=0, atol=0)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)

    @pytest.mark.parametrize(
        "activation",
        ("gelu", "geglu", "qgelu", "qgeglu", "relu", "reglu", "srelu", "sreglu", "silu", "swiglu"),
    )
    @pytest.mark.parametrize("out_shape", ((37,), (2, 13), (32, 1, 32)))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    @pytest.mark.parametrize("cache_quantized_input", (False, True))
    def test_activation(
        self,
        *,
        activation: str,
        out_shape: Iterable[int],
        dtype: torch.dtype,
        device: torch.device = "cuda",
        quantization: Optional[str],
        cache_quantized_input: bool,
    ) -> None:
        """Activation functions"""

        # Tensor dimensions
        in_shape = list(out_shape)
        if activation in ("geglu", "qgeglu", "reglu", "sreglu", "swiglu"):
            in_shape[-1] *= 2

        # Skip invalid configurations
        quantized_compute = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)
        if cache_quantized_input:
            maybe_skip_quantization("fp8_current_scaling", device=device)

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            quantization="fp8_current_scaling" if cache_quantized_input else None,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            out_shape,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref: torch.Tensor
        if activation == "gelu":
            y_ref = torch.nn.functional.gelu(x_ref, approximate="tanh")
        elif activation == "geglu":
            x1, x2 = x_ref.chunk(2, dim=-1)
            y_ref = torch.nn.functional.gelu(x1, approximate="tanh") * x2
        elif activation == "qgelu":
            y_ref = x_ref * torch.sigmoid(1.702 * x_ref)
        elif activation == "qgeglu":
            x1, x2 = x_ref.chunk(2, dim=-1)
            y_ref = x1 * torch.sigmoid(1.702 * x1) * x2
        elif activation == "relu":
            y_ref = torch.nn.functional.relu(x_ref)
        elif activation == "reglu":
            x1, x2 = x_ref.chunk(2, dim=-1)
            y_ref = torch.nn.functional.relu(x1) * x2
        elif activation == "srelu":
            y_ref = torch.nn.functional.relu(x_ref) ** 2
        elif activation == "sreglu":
            x1, x2 = x_ref.chunk(2, dim=-1)
            y_ref = torch.nn.functional.relu(x1) ** 2 * x2
        elif activation == "silu":
            y_ref = torch.nn.functional.silu(x_ref)
        elif activation == "swiglu":
            x1, x2 = x_ref.chunk(2, dim=-1)
            y_ref = torch.nn.functional.silu(x1) * x2
        else:
            raise ValueError(f"Unexpected activation function ({activation})")
        y_ref.backward(dy_ref)

        # Implementation with fusible operation
        recipe = make_recipe(quantization)
        make_op = dict(
            gelu=te_ops.GELU,
            geglu=te_ops.GEGLU,
            qgelu=te_ops.QGELU,
            qgeglu=te_ops.QGEGLU,
            relu=te_ops.ReLU,
            reglu=te_ops.ReGLU,
            srelu=te_ops.SReLU,
            sreglu=te_ops.SReGLU,
            silu=te_ops.SiLU,
            swiglu=te_ops.SwiGLU,
        )[activation]
        forward = te_ops.Sequential(
            te_ops.Quantize(forward=False, backward=quantized_compute),
            make_op(cache_quantized_input=cache_quantized_input),
            te_ops.Quantize(forward=quantized_compute, backward=False),
        )
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y_test = forward(x_test)
        y_test.backward(dy_test)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if quantized_compute:
            tols = quantization_tols(quantization)
        elif cache_quantized_input:
            tols = quantization_tols("fp8_current_scaling")

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)

    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    @pytest.mark.parametrize("quantize_forward", (False, True))
    @pytest.mark.parametrize("quantize_backward", (False, True))
    def test_swiglu(
        self,
        *,
        out_shape: Iterable[int] = (32, 32),
        dtype: torch.dtype,
        device: torch.device = "cuda",
        quantization: Optional[str],
        quantize_forward: bool,
        quantize_backward: bool,
    ):

        # Tensor dimensions
        in_shape = list(out_shape)
        in_shape[-1] *= 2

        # Skip invalid configurations
        quantized_compute = quantization is not None
        if not quantized_compute and (quantize_forward or quantize_backward):
            pytest.skip("Quantization scheme has not been provided")
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            out_shape,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        x1, x2 = x_ref.chunk(2, dim=-1)
        y_ref = torch.nn.functional.silu(x1) * x2
        y_ref.backward(dy_ref)

        # Implementation with fusible operation
        recipe = make_recipe(quantization)
        forward = te_ops.Sequential(
            te_ops.Quantize(forward=False, backward=quantize_backward),
            te_ops.SwiGLU(),
            te_ops.Quantize(forward=quantize_forward, backward=False),
        )
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y_test = forward(x_test)
        y_test.backward(dy_test)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if quantized_compute:
            tols = quantization_tols(quantization)

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)

    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    @pytest.mark.parametrize("quantize_forward", (False, True))
    @pytest.mark.parametrize("quantize_backward", (False, True))
    def test_clamped_swiglu(
        self,
        *,
        out_shape: Iterable[int] = (32, 32),
        dtype: torch.dtype,
        device: torch.device = "cuda",
        quantization: Optional[str],
        quantize_forward: bool,
        quantize_backward: bool,
        limit: float = 0.75,
        alpha: float = 1.702,
    ):
        # Test SwiGLU variant used in GPT OSS.
        # Tensor dimensions
        in_shape = list(out_shape)
        in_shape[-1] *= 2

        # Skip invalid configurations
        quantized_compute = quantization is not None
        if not quantized_compute and (quantize_forward or quantize_backward):
            pytest.skip("Quantization scheme has not been provided")
        maybe_skip_quantization(quantization, dims=in_shape, device=device)

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            out_shape,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        x_glu, x_linear = x_ref.chunk(2, dim=-1)
        x_glu = x_glu.clamp(min=None, max=limit)
        x_linear = x_linear.clamp(min=-limit, max=limit)
        out_glu = x_glu * torch.sigmoid(alpha * x_glu)
        y_ref = out_glu * (x_linear + 1)
        y_ref.backward(dy_ref)

        # Implementation with fusible operation
        recipe = make_recipe(quantization)

        forward = te_ops.Sequential(
            te_ops.Quantize(forward=False, backward=quantize_backward),
            te_ops.ClampedSwiGLU(limit=limit, alpha=alpha),
            te_ops.Quantize(forward=quantize_forward, backward=False),
        )
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y_test = forward(x_test)

        y_test.backward(dy_test)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if quantized_compute and quantization == "nvfp4":
            tols = dtype_tols(tex.DType.kFloat4E2M1)
        elif quantized_compute:
            tols = dtype_tols(tex.DType.kFloat8E4M3)

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)

    @pytest.mark.parametrize("scale", (1, 0, -2.5, 3.5))
    @pytest.mark.parametrize("shape", ((), (1, 13), (4, 4, 2)))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("device", _devices)
    def test_constant_scale(
        self,
        *,
        scale: float,
        shape: Iterable[int],
        dtype: torch.dtype,
        device: torch.device,
    ):

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            shape,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            shape,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = scale * x_ref
        y_ref.backward(dy_ref)

        # Implementation with fusible operation
        op = te_ops.ConstantScale(scale)
        y_test = op(x_test)
        y_test.backward(dy_test)

        # Check results
        tols = dtype_tols(dtype)
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)

    @pytest.mark.parametrize("prob", (0.0625, 0.5, 0.75))
    @pytest.mark.parametrize("is_training", (True, False))
    @pytest.mark.parametrize("quantization", (None, "fp8_current_scaling"))
    @pytest.mark.parametrize("shape", ((101,), (2, 4, 16), (128, 128)))
    @pytest.mark.parametrize("dtype", _dtypes)
    def test_dropout(
        self,
        *,
        prob: float,
        is_training: bool,
        quantization: Optional[str],
        shape: Iterable[int],
        dtype: torch.dtype,
        device: torch.device = "cuda",
    ):

        # Skip invalid configurations
        quantized_input = quantization is not None
        maybe_skip_quantization(quantization, dims=shape, device=device, dtype=dtype)

        # Random data
        # Note: Shift values to make sure inputs are non-zero
        x_ref, x_test = make_reference_and_test_tensors(
            shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            test_is_quantized=quantized_input,
        )
        with torch.no_grad():
            x_test += 1
            x_ref.copy_(x_test)
        dy_ref, dy_test = make_reference_and_test_tensors(
            shape,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Apply dropout
        op = te_ops.Dropout(prob)
        if is_training:
            op.train()
        else:
            op.eval()
        y_test = op(x_test)
        y_test.backward(dy_test)

        # Check values
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        if is_training:
            tols = dtype_tols(dtype)
            mask = ((y_test != 0) / (1 - prob)).to(dtype=dtype)
            torch.testing.assert_close(y_test, x_ref * mask, **tols)
            torch.testing.assert_close(dx_test, dy_ref * mask, **tols)
        else:
            torch.testing.assert_close(y_test, x_ref, rtol=0, atol=0)
            torch.testing.assert_close(dx_test, dy_ref, rtol=0, atol=0)

        # Hypothesis testing for number of zeros
        # Note: A Bernoulli random variable with probability p has
        # mean p and standard deviation sqrt(p*(1-p)). By the central
        # limit theorem, the mean of n iid Bernoulli variables
        # converges to a normal random variable with mean p and
        # standard deviation sqrt(p*(1-p)/n). If the observed mean is
        # below the 0.5th or above the 99.5th percentiles, then the
        # p-value is less than 1% and we assume that the dropout
        # distribution is incorrect.
        if is_training:
            prob_observed = 1 - torch.count_nonzero(y_test).item() / y_test.numel()
            z_score = (prob_observed - prob) / math.sqrt(prob * (1 - prob) / y_test.numel())
            assert (
                abs(z_score) < 2.5758
            ), f"Number of zeros is outside 99% confidence interval ({prob=}, {prob_observed=})"


class TestFusedOps:
    """Tests for fused operations"""

    @staticmethod
    def setup_class(cls) -> None:
        reset_rng_states()

    @pytest.mark.parametrize("weight_shape", ((32, 64), (3, 5)))
    @pytest.mark.parametrize("in_shape", ((-1,), (1, 7, -1), (8, 2, 10, -1)))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    @pytest.mark.parametrize("quantized_weight", (False, True))
    def test_forward_linear_bias_activation(
        self,
        *,
        bias: bool = True,
        weight_shape: tuple[int, int],
        in_shape: Iterable[int],
        dtype: torch.dtype,
        device: torch.device = "cuda",
        quantization: Optional[str],
        quantized_weight: bool,
    ) -> None:
        """Forward GEMM + bias + activation"""

        # Make input and weight shapes consistent
        out_features, in_features = weight_shape
        in_shape = list(in_shape)[:-1] + [in_features]
        out_shape = in_shape[:-1] + [out_features]

        # Skip invalid configurations
        quantized_compute = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)
        maybe_skip_quantization(quantization, dims=out_shape)
        if dtype not in (torch.float16, torch.bfloat16):
            pytest.skip(
                "FP8 fused linear-bias-activation is only supported with FP16 or BF16 output"
            )

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        w_ref, w_test = make_reference_and_test_tensors(
            (out_features, in_features),
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        b_ref, b_test = None, None
        if bias:
            b_ref, b_test = make_reference_and_test_tensors(
                out_features,
                test_dtype=dtype,
                test_device=device,
            )
        dy_ref, dy_test = make_reference_and_test_tensors(
            out_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = torch.nn.functional.linear(x_ref, w_ref, bias=b_ref)
        y_ref.backward(dy_ref)

        # Implementation with fusible operations
        recipe = make_recipe(quantization)
        with te.quantized_model_init(enabled=quantized_compute, recipe=recipe):
            model = te_ops.Sequential(
                te_ops.Linear(
                    in_features,
                    out_features,
                    bias=bias,
                    device=device,
                    dtype=dtype,
                ),
            )
        with torch.no_grad():
            model[0].weight.copy_(w_test)
            if bias:
                model[0].bias.copy_(b_test)
            del w_test
            del b_test
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y_test = model(x_test)
        y_test.backward(dy_test)

        # Check that forward operations have been fused
        forward_ops = model._module_groups[0]._forward_ops
        assert len(forward_ops) == 1
        assert isinstance(forward_ops[0][0], ForwardLinearBiasActivation)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if dtype == torch.float32:
            tols = dtype_tols(torch.float16)  # TF32 GEMM
        if quantized_compute:
            tols = quantization_tols(quantization)

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        dw_test = model[0].weight.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)
        torch.testing.assert_close(dw_test, w_ref.grad, **tols)
        if bias:
            db_test = model[0].bias.grad.to(dtype=torch.float64, device="cpu")
            torch.testing.assert_close(db_test, b_ref.grad, **tols)

    @pytest.mark.parametrize("bias", (False, True))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_forward_linear_bias_add(
        self,
        *,
        bias: bool,
        weight_shape: tuple[int, int] = (32, 32),
        in_shape: Iterable[int] = (32, -1),
        dtype: torch.dtype,
        device: torch.device = "cuda",
        quantization: Optional[str],
        quantized_weight: bool = False,
    ) -> None:
        """Forward GEMM + bias + add"""

        # Make input and weight shapes consistent
        out_features, in_features = weight_shape
        in_shape = list(in_shape)[:-1] + [in_features]
        out_shape = in_shape[:-1] + [out_features]

        # Skip invalid configurations
        quantized_compute = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)
        maybe_skip_quantization(quantization, dims=out_shape)
        if quantized_compute and dtype not in (torch.float16, torch.bfloat16):
            pytest.skip("FP8 GEMM is only supported with FP8, FP16, or BF16 output")

        # Random data
        x1_ref, x1_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        w_ref, w_test = make_reference_and_test_tensors(
            (out_features, in_features),
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        b_ref, b_test = None, None
        if bias:
            b_ref, b_test = make_reference_and_test_tensors(
                out_features,
                test_dtype=dtype,
                test_device=device,
            )
        x2_ref, x2_test = make_reference_and_test_tensors(
            out_shape,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            out_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = torch.nn.functional.linear(x1_ref, w_ref, bias=b_ref) + x2_ref
        y_ref.backward(dy_ref)

        # Implementation with fusible operations
        recipe = make_recipe(quantization)
        with te.quantized_model_init(enabled=quantized_weight, recipe=recipe):
            model = te_ops.Sequential(
                te_ops.Linear(
                    in_features,
                    out_features,
                    bias=bias,
                    device=device,
                    dtype=dtype,
                ),
                te_ops.AddExtraInput(in_place=True),
            )
        with torch.no_grad():
            model[0].weight.copy_(w_test)
            if bias:
                model[0].bias.copy_(b_test)
            del w_test
            del b_test
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y_test = model(x1_test, x2_test)
        y_test.backward(dy_test)

        # Check that forward operations have been fused
        forward_ops = model._module_groups[0]._forward_ops
        assert len(forward_ops) == 1
        assert isinstance(forward_ops[0][0], ForwardLinearBiasAdd)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if dtype == torch.float32:
            tols = dtype_tols(torch.float16)  # TF32 GEMM
        if quantized_compute:
            tols = quantization_tols(quantization)

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx1_test = x1_test.grad.to(dtype=torch.float64, device="cpu")
        dx2_test = x2_test.grad.to(dtype=torch.float64, device="cpu")
        dw_test = model[0].weight.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx1_test, x1_ref.grad, **tols)
        torch.testing.assert_close(dx2_test, x2_ref.grad, **tols)
        torch.testing.assert_close(dw_test, w_ref.grad, **tols)
        if bias:
            db_test = model[0].bias.grad.to(dtype=torch.float64, device="cpu")
            torch.testing.assert_close(db_test, b_ref.grad, **tols)

    @pytest.mark.parametrize("scale", (1, 0, -2.5, 3.5))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_forward_linear_scale_add(
        self,
        *,
        scale: float,
        weight_shape: tuple[int, int] = (32, 32),
        in_shape: Iterable[int] = (32, -1),
        dtype: torch.dtype,
        device: torch.device = "cuda",
        quantization: Optional[str],
        quantized_weight: bool = False,
    ) -> None:
        """Forward GEMM + scale + add"""
        if IS_HIP_EXTENSION and scale != 1:
            pytest.skip("alpha must be 1.0 for hip")
        # Make input and weight shapes consistent
        out_features, in_features = weight_shape
        in_shape = list(in_shape)[:-1] + [in_features]
        out_shape = in_shape[:-1] + [out_features]

        # Skip invalid configurations
        quantized_compute = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)
        maybe_skip_quantization(quantization, dims=out_shape)
        if quantized_compute and dtype not in (torch.float16, torch.bfloat16):
            pytest.skip("FP8 GEMM is only supported with FP8, FP16, or BF16 output")

        # Random data
        x1_ref, x1_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        w_ref, w_test = make_reference_and_test_tensors(
            (out_features, in_features),
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        x2_ref, x2_test = make_reference_and_test_tensors(
            out_shape,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            out_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = torch.nn.functional.linear(x1_ref, w_ref) * scale + x2_ref
        y_ref.backward(dy_ref)

        # Implementation with fusible operations
        recipe = make_recipe(quantization)
        with te.quantized_model_init(enabled=quantized_weight, recipe=recipe):
            model = te_ops.Sequential(
                te_ops.Linear(
                    in_features,
                    out_features,
                    bias=False,
                    device=device,
                    dtype=dtype,
                ),
                te_ops.ConstantScale(scale),
                te_ops.AddExtraInput(in_place=True),
                te_ops.Quantize(),
            )
        with torch.no_grad():
            model[0].weight.copy_(w_test)
            del w_test
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y_test = model(x1_test, x2_test)
        y_test.backward(dy_test)

        # Check that forward operations have been fused
        forward_ops = model._module_groups[0]._forward_ops
        assert len(forward_ops) == 2
        assert isinstance(forward_ops[0][0], ForwardLinearScaleAdd)
        assert isinstance(forward_ops[1][0], te_ops.Quantize)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if dtype == torch.float32:
            tols = dtype_tols(torch.float16)  # TF32 GEMM
        if quantized_compute:
            tols = quantization_tols(quantization)

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx1_test = x1_test.grad.to(dtype=torch.float64, device="cpu")
        dx2_test = x2_test.grad.to(dtype=torch.float64, device="cpu")
        dw_test = model[0].weight.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx1_test, x1_ref.grad, **tols)
        torch.testing.assert_close(dx2_test, x2_ref.grad, **tols)
        torch.testing.assert_close(dw_test, w_ref.grad, **tols)

    @pytest.mark.parametrize("activation", ("relu", "gelu"))
    @pytest.mark.parametrize("out_shape", ((32, 32), (32, 1, 32), (8, 2, 2, 32)))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_backward_activation_bias(
        self,
        *,
        activation: str,
        out_shape: Iterable[int],
        dtype: torch.dtype,
        device: torch.device = "cuda",
        quantization: Optional[str],
    ) -> None:
        """Backward dact + dbias + quantize"""

        # Tensor dimensions
        in_shape = list(out_shape)
        hidden_size = in_shape[-1]

        # Skip invalid configurations
        with_quantization = quantization is not None
        maybe_skip_quantization(quantization, device=device, dtype=dtype)
        if quantization == "mxfp8" and (len(in_shape) < 2 or in_shape[-1] % 32 != 0):
            pytest.skip("Unsupported tensor size for MXFP8")

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
        )
        b_ref, b_test = make_reference_and_test_tensors(
            hidden_size,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = x_ref + b_ref.reshape([1] * (len(in_shape) - 1) + [hidden_size])
        if activation == "gelu":
            y_ref = torch.nn.functional.gelu(y_ref, approximate="tanh")
        elif activation == "relu":
            y_ref = torch.nn.functional.relu(y_ref)
        else:
            raise ValueError(f"Unexpected activation function ({activation})")
        y_ref.backward(dy_ref)

        # Implementation with fusible operations
        recipe = make_recipe(quantization)
        act_type = te_ops.GELU if activation == "gelu" else te_ops.ReLU
        model = te_ops.Sequential(
            te_ops.Quantize(forward=False, backward=True),
            te_ops.Bias(hidden_size, device=device, dtype=dtype),
            act_type(),
        )
        with torch.no_grad():
            model[1].bias.copy_(b_test)
            del b_test
        with te.autocast(enabled=with_quantization, recipe=recipe):
            y_test = model(x_test)
        y_test.backward(dy_test)

        # Check that backward operations have been fused
        backward_ops = model._module_groups[0]._backward_ops
        if with_quantization:
            assert len(backward_ops) == 2
            assert isinstance(backward_ops[0][0], BackwardActivationBias)
            assert isinstance(backward_ops[1][0], te_ops.Quantize)
        else:
            assert len(backward_ops) == 3
            assert isinstance(backward_ops[0][0], act_type)
            assert isinstance(backward_ops[1][0], te_ops.Bias)
            assert isinstance(backward_ops[2][0], te_ops.Quantize)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if with_quantization:
            tols = quantization_tols(quantization)

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        db_test = model[1].bias.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)
        torch.testing.assert_close(db_test, b_ref.grad, **tols)

    @pytest.mark.parametrize("weight_shape", ((19,), (64,)))
    @pytest.mark.parametrize("in_shape", ((-1,), (6, 16, -1)))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("zero_centered_gamma", (False, True))
    def test_backward_add_rmsnorm(
        self,
        *,
        weight_shape: Iterable[int],
        in_shape: Iterable[int],
        dtype: torch.dtype,
        device: torch.device = "cuda",
        eps: float = 0.3,
        zero_centered_gamma: bool,
    ) -> None:
        """Fused backward RMNorm + add"""

        # Make input and weight shapes consistent
        in_shape = list(in_shape)[:-1] + list(weight_shape)

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
        )
        w_ref, w_test = make_reference_and_test_tensors(
            weight_shape,
            test_dtype=dtype,
            test_device=device,
        )
        dy1_ref, dy1_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )
        dy2_ref, dy2_test = make_reference_and_test_tensors(
            in_shape,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        inner_dims = tuple(range(len(in_shape) - len(weight_shape), len(in_shape)))
        var_ref = x_ref.square().sum(dim=inner_dims, keepdim=True) / math.prod(weight_shape)
        if zero_centered_gamma:
            y1_ref = x_ref / torch.sqrt(eps + var_ref) * (1 + w_ref)
        else:
            y1_ref = x_ref / torch.sqrt(eps + var_ref) * w_ref
        y2_ref = x_ref
        (y1_ref * dy1_ref + y2_ref * dy2_ref).sum().backward()

        # Implementation with fusible operations
        model = te_ops.Sequential(
            te_ops.MakeExtraOutput(),
            te_ops.RMSNorm(
                weight_shape,
                eps=eps,
                device=device,
                dtype=dtype,
                zero_centered_gamma=zero_centered_gamma,
            ),
        )
        with torch.no_grad():
            model[1].weight.copy_(w_test)
            del w_test
        y1_test, y2_test = model(x_test)
        (y1_test * dy1_test + y2_test * dy2_test).sum().backward()

        # Check that backward operations have been fused
        backward_ops = model._module_groups[0]._backward_ops
        assert len(backward_ops) == 1
        assert isinstance(backward_ops[0][0], BackwardAddRMSNorm)

        # Expected numerical error
        tols = dtype_tols(dtype)

        # Check results
        y1_test = y1_test.to(dtype=torch.float64, device="cpu")
        y2_test = y2_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        dw_test = model[1].weight.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y1_test, y1_ref, **tols)
        torch.testing.assert_close(y2_test, y2_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)
        torch.testing.assert_close(dw_test, w_ref.grad, **tols)

    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_backward_linear_add(
        self,
        *,
        weight_shape: tuple[int, int] = (32, 32),
        in_shape: Iterable[int] = (32, -1),
        dtype: torch.dtype,
        device: torch.device = "cuda",
        quantization: Optional[str],
        quantized_weight: bool = False,
    ) -> None:
        """Backward dgrad GEMM + add"""

        # Make input and weight shapes consistent
        out_features, in_features = weight_shape
        in_shape = list(in_shape)[:-1] + [in_features]
        out_shape = in_shape[:-1] + [out_features]

        # Skip invalid configurations
        quantized_compute = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)
        maybe_skip_quantization(quantization, dims=out_shape)
        if quantized_compute and dtype not in (torch.float16, torch.bfloat16):
            pytest.skip("FP8 GEMM is only supported with FP8, FP16, or BF16 output")

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        w_ref, w_test = make_reference_and_test_tensors(
            (out_features, in_features),
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        dy1_ref, dy1_test = make_reference_and_test_tensors(
            out_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )
        dy2_ref, dy2_test = make_reference_and_test_tensors(
            out_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y1_ref = torch.nn.functional.linear(x_ref, w_ref)
        y2_ref = x_ref
        (y1_ref * dy1_ref + y2_ref * dy2_ref).sum().backward()

        # Implementation with fusible operations
        recipe = make_recipe(quantization)
        with te.quantized_model_init(enabled=quantized_weight):
            model = te_ops.Sequential(
                te_ops.MakeExtraOutput(in_place=True),
                te_ops.Linear(
                    in_features,
                    out_features,
                    bias=False,
                    device=device,
                    dtype=dtype,
                ),
            )
        with torch.no_grad():
            model[1].weight.copy_(w_test)
            del w_test
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y1_test, y2_test = model(x_test)
        (y1_test * dy1_test + y2_test * dy2_test).sum().backward()

        # Check that backward operations have been fused
        backward_ops = model._module_groups[0]._backward_ops
        assert len(backward_ops) == 1
        assert isinstance(backward_ops[0][0], BackwardLinearAdd)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if dtype == torch.float32:
            tols = dtype_tols(torch.float16)  # TF32 GEMM
        if quantized_compute:
            tols = quantization_tols(quantization)

        # Check results
        y1_test = y1_test.to(dtype=torch.float64, device="cpu")
        y2_test = y2_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        dw_test = model[1].weight.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y1_test, y1_ref, **tols)
        torch.testing.assert_close(y2_test, y2_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)
        torch.testing.assert_close(dw_test, w_ref.grad, **tols)

    @pytest.mark.parametrize("scale", (1, 0, -2.5, 3.5))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_backward_linear_scale(
        self,
        *,
        scale: float,
        weight_shape: tuple[int, int] = (32, 32),
        in_shape: Iterable[int] = (32, -1),
        dtype: torch.dtype,
        device: torch.device = "cuda",
        quantization: Optional[str],
        quantized_weight: bool = False,
    ) -> None:
        """Backward dgrad GEMM + scale"""
        if IS_HIP_EXTENSION and scale != 1:
            pytest.skip("alpha must be 1.0 for hip")
        # Make input and weight shapes consistent
        out_features, in_features = weight_shape
        in_shape = list(in_shape)[:-1] + [in_features]
        out_shape = in_shape[:-1] + [out_features]

        # Skip invalid configurations
        quantized_compute = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)
        maybe_skip_quantization(quantization, dims=out_shape)
        if quantized_compute and dtype not in (torch.float16, torch.bfloat16):
            pytest.skip("FP8 GEMM is only supported with FP8, FP16, or BF16 output")

        # Random data
        x_ref, x_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        w_ref, w_test = make_reference_and_test_tensors(
            (out_features, in_features),
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
        )
        dy_ref, dy_test = make_reference_and_test_tensors(
            out_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Plain PyTorch implementation
        y_ref = torch.nn.functional.linear(x_ref, w_ref) * scale
        y_ref.backward(dy_ref)

        # Implementation with fusible operations
        recipe = make_recipe(quantization)
        with te.quantized_model_init(enabled=quantized_weight):
            model = te_ops.Sequential(
                te_ops.Linear(
                    in_features,
                    out_features,
                    bias=False,
                    device=device,
                    dtype=dtype,
                ),
                te_ops.ConstantScale(scale),
            )
        with torch.no_grad():
            model[0].weight.copy_(w_test)
            del w_test
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y_test = model(x_test)
        (y_test * dy_test).sum().backward()

        # Check that backward operations have been fused
        backward_ops = model._module_groups[0]._backward_ops
        assert len(backward_ops) == 1
        assert isinstance(backward_ops[0][0], BackwardLinearScale)

        # Expected numerical error
        tols = dtype_tols(dtype)
        if dtype == torch.float32:
            tols = dtype_tols(torch.float16)  # TF32 GEMM
        if quantized_compute:
            tols = quantization_tols(quantization)

        # Check results
        y_test = y_test.to(dtype=torch.float64, device="cpu")
        dx_test = x_test.grad.to(dtype=torch.float64, device="cpu")
        dw_test = model[0].weight.grad.to(dtype=torch.float64, device="cpu")
        torch.testing.assert_close(y_test, y_ref, **tols)
        torch.testing.assert_close(dx_test, x_ref.grad, **tols)
        torch.testing.assert_close(dw_test, w_ref.grad, **tols)


class TestCheckpointing:
    """Tests for checkpointing"""

    @staticmethod
    def setup_class(cls) -> None:
        reset_rng_states()

    @pytest.mark.parametrize("quantization", _quantization_list)
    @pytest.mark.parametrize("quantized_weight", (False, True))
    def test_linear(
        self,
        *,
        pre_checkpoint_steps: int = 2,
        post_checkpoint_steps: int = 2,
        weight_shape: tuple[int, int] = (32, 32),
        in_shape: Iterable[int] = (32, -1),
        dtype: torch.dtype = torch.float32,
        device: torch.device = "cuda",
        quantization: Optional[str],
        quantized_weight: bool,
    ) -> None:
        """Check checkpointing with linear op"""

        # Make input and weight shapes consistent
        out_features, in_features = weight_shape
        in_shape = list(in_shape)[:-1] + [in_features]
        out_shape = in_shape[:-1] + [out_features]

        # Skip invalid configurations
        quantized_compute = quantization is not None
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)
        maybe_skip_quantization(quantization, dims=out_shape)

        # Construct model
        recipe = make_recipe(quantization)
        with te.quantized_model_init(enabled=quantized_weight, recipe=recipe):
            model_save = te_ops.Sequential(
                te_ops.Linear(in_features, out_features, device=device, dtype=dtype)
            )
        optim_save = torch.optim.SGD(model_save.parameters(), lr=0.25)

        # Warmup training steps
        for _ in range(pre_checkpoint_steps):
            x = torch.randn(in_shape, dtype=dtype, device=device, requires_grad=True)
            dy = torch.randn(out_shape, dtype=dtype, device=device)
            optim_save.zero_grad()
            with te.autocast(enabled=quantized_compute, recipe=recipe):
                y = model_save(x)
            y.backward(dy)
            optim_save.step()

        # Save checkpoint
        byte_stream = io.BytesIO()
        torch.save(
            {"model": model_save.state_dict(), "optim": optim_save.state_dict()},
            byte_stream,
        )
        checkpoint_bytes = byte_stream.getvalue()
        del byte_stream

        # Synthetic data for evaluation
        xs_save = [
            torch.randn(in_shape, dtype=dtype, device=device, requires_grad=True)
            for _ in range(post_checkpoint_steps)
        ]
        with torch.no_grad():
            xs_load = [x.clone().requires_grad_() for x in xs_save]
        dys = [
            torch.randn(out_shape, dtype=dtype, device=device) for _ in range(post_checkpoint_steps)
        ]

        # Training steps with original model
        ys_save = []
        for i in range(post_checkpoint_steps):
            optim_save.zero_grad()
            with te.autocast(enabled=quantized_compute, recipe=recipe):
                y = model_save(xs_save[i])
            y.backward(dys[i])
            optim_save.step()
            ys_save.append(y)

        # Load checkpoint
        with te.quantized_model_init(enabled=quantized_weight, recipe=recipe):
            model_load = te_ops.Sequential(
                te_ops.Linear(in_features, out_features, device=device, dtype=dtype)
            )
        optim_load = torch.optim.SGD(model_load.parameters(), lr=0.25)
        state_dict = torch.load(io.BytesIO(checkpoint_bytes), weights_only=False)
        model_load.load_state_dict(state_dict["model"])
        optim_load.load_state_dict(state_dict["optim"])

        # Training steps with loaded model
        ys_load = []
        for i in range(post_checkpoint_steps):
            optim_load.zero_grad()
            with te.autocast(enabled=quantized_compute, recipe=recipe):
                y = model_load(xs_load[i])
            y.backward(dys[i])
            optim_load.step()
            ys_load.append(y)

        # Check that original and loaded model match exactly
        tols = {"rtol": 0, "atol": 0}
        for param_load, param_save in zip(model_load.parameters(), model_save.parameters()):
            torch.testing.assert_close(param_load, param_save, **tols)
            torch.testing.assert_close(param_load.grad, param_save.grad, **tols)
        for y_load, y_save in zip(ys_load, ys_save):
            torch.testing.assert_close(y_load, y_save, **tols)
        for x_load, x_save in zip(xs_load, xs_save):
            torch.testing.assert_close(x_load.grad, x_save.grad, **tols)


class TestSequentialModules:
    """Test for larger Sequentials with modules commonly used together"""

    @staticmethod
    def setup_class(cls) -> None:
        reset_rng_states()

    @pytest.mark.parametrize("requires_grad", (False, True))
    @pytest.mark.parametrize("bias", (False, True))
    @pytest.mark.parametrize("normalization", ("LayerNorm", "RMSNorm"))
    @pytest.mark.parametrize("quantized_compute", (False, True))
    @pytest.mark.parametrize("quantized_weight", (False, True))
    @pytest.mark.parametrize("dtype", _dtypes)
    @pytest.mark.parametrize("quantization", _quantization_list)
    def test_layernorm_mlp(
        self,
        *,
        requires_grad: bool,
        bias: bool,
        normalization: str,
        quantized_compute: bool,
        quantized_weight: bool,
        dtype: torch.dtype,
        quantization: Optional[str],
        device: torch.device = "cuda",
        hidden_size: int = 32,
        sequence_length: int = 512,
        batch_size: int = 4,
        ffn_hidden_size: int = 64,
        layernorm_epsilon: float = 1e-5,
    ) -> None:
        """
        LayerNorm/RMSNorm + Linear + GELU + Linear

        Note that this test checks only if the module runs
        as when chaining multiple modules it is hard to validate
        numerical accuracy.
        """

        # Make input shape
        in_shape = (sequence_length, batch_size, hidden_size)
        ffn_shape = in_shape[:-1] + (ffn_hidden_size,)

        # Skip invalid configurations
        maybe_skip_quantization(quantization, dims=in_shape, device=device, dtype=dtype)
        maybe_skip_quantization(quantization, dims=ffn_shape, device=device)
        quantization_needed = quantized_compute or quantized_weight
        if quantization is None and quantization_needed:
            pytest.skip("Quantization scheme is not specified")
        if quantization is not None and not quantization_needed:
            pytest.skip("Quantization scheme is not used")

        # Random data
        _, x_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            requires_grad=requires_grad,
        )
        _, dy_test = make_reference_and_test_tensors(
            in_shape,
            quantization=quantization,
            test_dtype=dtype,
            test_device=device,
            requires_grad=False,
        )

        # Implementation with fusible operations
        recipe = make_recipe(quantization)
        with te.quantized_model_init(enabled=quantized_weight, recipe=recipe):
            if normalization == "LayerNorm":
                norm = te_ops.LayerNorm(
                    hidden_size,
                    eps=layernorm_epsilon,
                    device=device,
                    dtype=dtype,
                )
            else:
                norm = te_ops.RMSNorm(
                    hidden_size,
                    eps=layernorm_epsilon,
                    device=device,
                    dtype=dtype,
                )
            ffn1 = te_ops.Linear(
                hidden_size,
                ffn_hidden_size,
                bias=bias,
                device=device,
                dtype=dtype,
            )
            act = te_ops.GELU()
            ffn2 = te_ops.Linear(
                ffn_hidden_size,
                hidden_size,
                bias=bias,
                device=device,
                dtype=dtype,
            )
        forward = te_ops.Sequential(norm, ffn1, act, ffn2)
        with te.autocast(enabled=quantized_compute, recipe=recipe):
            y_test = forward(x_test)
        y_test.backward(dy_test)
