test_numerics.py 94.8 KB
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# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
# See LICENSE for license information.

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import math
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import os
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from typing import Dict, List, Tuple, Optional
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import pytest
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import random
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import torch
import torch.nn as nn
from torch.nn import Parameter

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from transformer_engine.pytorch.quantization import (
    FP8GlobalStateManager,
    get_align_size_for_quantization,
)
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from transformer_engine.pytorch.utils import (
    init_method_normal,
    scaled_init_method_normal,
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    attention_mask_func,
)
from transformer_engine.pytorch import (
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    autocast,
    quantized_model_init,
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    DotProductAttention,
    LayerNormLinear,
    LayerNormMLP,
    Linear,
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    GroupedLinear,
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    MultiheadAttention,
    RMSNorm,
    TransformerLayer,
    LayerNorm,
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    Fp8Padding,
    Fp8Unpadding,
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    Float8Quantizer,
    Float8CurrentScalingQuantizer,
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    MXFP8Quantizer,
    get_device_compute_capability,
    is_fp8_available,
    is_mxfp8_available,
    is_fp8_block_scaling_available,
    is_bf16_available,
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    is_nvfp4_available,
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)
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from transformer_engine.pytorch import checkpoint as te_checkpoint
from transformer_engine.pytorch.cpp_extensions import general_gemm, general_grouped_gemm
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from transformer_engine.common import recipe
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import transformer_engine_torch as tex
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from utils import ModelConfig, reset_rng_states
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# Only run FP8 tests on supported devices.
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fp8_available, reason_for_no_fp8 = is_fp8_available(return_reason=True)
mxfp8_available, reason_for_no_mxfp8 = is_mxfp8_available(return_reason=True)
fp8_block_scaling_available = is_fp8_block_scaling_available()
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nvfp4_available = is_nvfp4_available()
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sm_80plus = get_device_compute_capability() >= (8, 0)
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seed = 1234
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# Reset RNG states.
reset_rng_states()
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torch._dynamo.config.recompile_limit = 16

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model_configs = {
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    "small": ModelConfig(1, 128, 8, 16, num_layers=4),
    "126m": ModelConfig(1, 2048, 12, 64, num_layers=12),
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}
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model_configs_inference = {
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    "126m": ModelConfig(1, 256, 12, 64, num_layers=12),
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}
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backends_inference = ["FlashAttention", "UnfusedAttention", "FusedAttention"]
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module_inference = ["TransformerLayer", "MultiheadAttention"]
input_formats_inference = ["sbhd", "bshd"]

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param_types = [torch.float32, torch.float16]
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if is_bf16_available():  # bf16 requires sm_80 or higher
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    param_types.append(torch.bfloat16)

batch_sizes = [1, 2]

all_boolean = [True, False]

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all_activations = [
    "gelu",
    "geglu",
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    "glu",
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    "qgelu",
    "qgeglu",
    "relu",
    "reglu",
    "srelu",
    "sreglu",
    "silu",
    "swiglu",
]
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all_normalizations = ["LayerNorm", "RMSNorm"]

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mask_types = ["causal", "no_mask"]

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NVTE_TEST_NVINSPECT_ENABLED = int(os.environ.get("NVTE_TEST_NVINSPECT_ENABLED", "0"))
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if NVTE_TEST_NVINSPECT_ENABLED:
    # The numerics of all the layers should work the same,
    # when debug=True. I fed them with dummy feature
    # to prevent switching off debug, which can happen if
    # no feature is active.
    import nvdlfw_inspect.api as debug_api

    debug_api.initialize(
        os.environ["NVTE_TEST_NVINSPECT_CONFIG_FILE"],
        feature_dirs=os.environ["NVTE_TEST_NVINSPECT_FEATURE_DIRS"],
    )

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def nvfp4_rht_and_2d_quantization():
    nvfp4_recipe = recipe.NVFP4BlockScaling()
    nvfp4_recipe.fp4_quant_fwd_inp = recipe.QParams(
        random_hadamard_transform=True, fp4_2d_quantization=False
    )
    nvfp4_recipe.fp4_quant_fwd_weight = recipe.QParams(
        random_hadamard_transform=False, fp4_2d_quantization=True
    )
    nvfp4_recipe.fp4_quant_bwd_grad = recipe.QParams(
        random_hadamard_transform=True, fp4_2d_quantization=False
    )
    return nvfp4_recipe


def check_rht_usage(recipe: recipe.Recipe) -> bool:
    # if using RHT, we can only support bf16
    # check fp4_quant_fwd_inp, fp4_quant_fwd_weight, fp4_quant_bwd_grad
    if recipe.nvfp4():
        if (
            recipe.fp4_quant_fwd_inp.random_hadamard_transform
            or recipe.fp4_quant_fwd_weight.random_hadamard_transform
            or recipe.fp4_quant_bwd_grad.random_hadamard_transform
        ):
            return True
    return False


def get_nvfp4_inp_supported_dtypes(recipe: recipe.Recipe, dtype: torch.dtype) -> bool:
    supported_input_dtypes = []
    if recipe.nvfp4():
        supported_input_dtypes.append(torch.bfloat16)
        # if not using RHT, we can add fp32 as well
    if not check_rht_usage(recipe):
        supported_input_dtypes.append(torch.float32)
    return supported_input_dtypes


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fp8_recipes = []
if mxfp8_available:
    fp8_recipes.append(recipe.MXFP8BlockScaling())
if fp8_block_scaling_available:
    fp8_recipes.append(recipe.Float8BlockScaling())
if fp8_available:
    fp8_recipes.append(recipe.Float8CurrentScaling())
    fp8_recipes.append(recipe.DelayedScaling())
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if nvfp4_available:
    fp8_recipes.append(nvfp4_rht_and_2d_quantization())
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use_cutlass_grouped_gemm = [False]
# Only enable cutlass grouped gemm on Hopper
if torch.cuda.get_device_capability() == (9, 0):
    use_cutlass_grouped_gemm.append(True)

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def get_causal_attn_mask(sq: int) -> torch.Tensor:
    return torch.triu(torch.ones(sq, sq, device="cuda"), diagonal=1).bool()


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def dtype_tols(dtype: torch.dtype) -> Dict[str, float]:
    """Estimated numerical error for a datatype
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    Based on tolerances for torch.testing.assert_close.
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    """
    if dtype == torch.float32:
        return dict(rtol=1.3e-6, atol=1e-5)
    if dtype == torch.float16:
        return dict(rtol=1e-3, atol=1e-5)
    if dtype == torch.bfloat16:
        return dict(rtol=1.6e-2, atol=1e-5)
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    raise ValueError(f"Unsupported dtype ({dtype})")
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def assert_allclose(
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    l1: List[torch.Tensor], l2: List[torch.Tensor], atol: float = None, rtol: float = None
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) -> bool:
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    """Ensures two lists are equal."""
    assert len(l1) == len(l2), "Unequal number of outputs."
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    for i, (t1, t2) in enumerate(zip(l1, l2)):
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        tols = dtype_tols(t2.dtype)
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        if rtol is not None:
            tols["rtol"] = rtol
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        if atol is not None:
            tols["atol"] = atol
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        result = torch.allclose(t1, t2, **tols)
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        if not result:
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            diff = torch.abs(t1 - t2)
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            tol = tols["atol"] + (tols["rtol"] * torch.abs(t2))
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            exceed_mask = diff > tol
            if exceed_mask.any():
                indices = torch.nonzero(exceed_mask, as_tuple=True)
                max_diff = diff[exceed_mask].max()
                max_idx = (diff[exceed_mask] == max_diff).nonzero(as_tuple=True)[0][0]
                max_location = [idx[max_idx].item() for idx in indices]
                msg = (
                    f"Outputs not close enough in tensor at idx={i}. "
                    f"Maximum difference at location {max_location} "
                    f"with {t1[exceed_mask][max_idx].item()} vs {t2[exceed_mask][max_idx].item()} "
                    f"(diff {max_diff.item()})."
                )
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            raise AssertionError(msg)
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@pytest.fixture(autouse=True)
def reset_global_fp8_state():
    yield
    FP8GlobalStateManager.reset()
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class TorchScaledMaskedSoftmax(nn.Module):
    def __init__(self) -> None:
        super().__init__()

    def forward(
        self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
    ) -> torch.Tensor:
        dtype = inp.dtype
        inp = inp.float()

        if scale is not None:
            inp = inp * scale
        mask_output = attention_mask_func(inp, mask) if mask is not None else inp

        probs = torch.nn.Softmax(dim=-1)(mask_output)
        probs = probs.to(dtype)
        return probs


class TorchDotProductAttention(torch.nn.Module):
    def __init__(
        self,
        kv_channels: int,
        attention_dropout: float = 0.0,
    ) -> None:
        super().__init__()

        self.norm_factor = math.sqrt(kv_channels)
        self.scale_mask_softmax = TorchScaledMaskedSoftmax()
        self.attention_dropout = torch.nn.Dropout(attention_dropout)

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        batch_size, seqlen = query_layer.shape[1], query_layer.shape[0]

        # [b, np, sq, sk]
        output_size = (
            query_layer.size(1),
            query_layer.size(2),
            query_layer.size(0),
            key_layer.size(0),
        )

        # [sq, b, np, hn] -> [sq, b * np, hn]
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        query_layer = query_layer.reshape(output_size[2], output_size[0] * output_size[1], -1)
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        # [sk, b, np, hn] -> [sk, b * np, hn]
        key_layer = key_layer.reshape(output_size[3], output_size[0] * output_size[1], -1)

        # preallocting result tensor: [b * np, sq, sk]
        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
            dtype=query_layer.dtype,
            device=torch.cuda.current_device(),
        )

        # Raw attention scores. [b * np, sq, sk]
        matmul_result = torch.baddbmm(
            matmul_result,
            query_layer.transpose(0, 1),  # [b * np, sq, hn]
            key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
            beta=0.0,
            alpha=(1.0 / self.norm_factor),
        )

        # change view to [b, np, sq, sk]
        attention_scores = matmul_result.view(*output_size)

        # attention scores and attention mask [b, np, sq, sk]
        attention_probs = self.scale_mask_softmax(attention_scores, attention_mask)
        attention_probs = self.attention_dropout(attention_probs)

        # value_layer -> context layer.
        # [sk, b, np, hn] --> [b, np, sq, hn]
        output_size = (
            value_layer.size(1),
            value_layer.size(2),
            query_layer.size(0),
            value_layer.size(3),
        )

        # change view [sk, b * np, hn]
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        value_layer = value_layer.reshape(value_layer.size(0), output_size[0] * output_size[1], -1)
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        # change view [b * np, sq, sk]
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        attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
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        # matmul: [b * np, sq, hn]
        context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))

        # change view [b, np, sq, hn]
        context_layer = context_layer.view(*output_size)

        # [b, np, sq, hn] --> [sq, b, np, hn]
        context_layer = context_layer.permute(2, 0, 1, 3).contiguous()

        # [sq, b, np, hn] --> [sq, b, hp]
        context_layer = context_layer.view(seqlen, batch_size, -1)

        return context_layer

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class TorchLayerNorm(nn.Module):
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    def __init__(self, in_features: int, eps: float, zero_centered_gamma: bool):
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        super().__init__()
        self.eps = eps
        self.in_features = in_features
        self.zero_centered_gamma = zero_centered_gamma

        initial_value = torch.ones(in_features) if zero_centered_gamma else torch.zeros(in_features)
        self.weight = nn.Parameter(initial_value)
        self.bias = nn.Parameter(torch.zeros(in_features))
        self.register_parameter("weight", self.weight)
        self.register_parameter("bias", self.bias)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        w = self.weight if not self.zero_centered_gamma else 1 + self.weight
        w = w.to(torch.float32)
        b = self.bias.to(torch.float32)
        inp = x.to(torch.float32)
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        out = torch.nn.functional.layer_norm(
            inp, (self.in_features,), weight=w, bias=b, eps=self.eps
        )
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        return out.to(x.dtype)

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# Adapted from https://github.com/bzhangGo/rmsnorm/blob/c6691f20ec0af4128c8159c903071f7575404295/rmsnorm_torch.py
class TorchRMSNorm(nn.Module):
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    def __init__(self, in_features, zero_centered_gamma, eps=1e-5):
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        super().__init__()

        self.eps = eps
        self.in_features = in_features
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        self.zero_centered_gamma = zero_centered_gamma
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        initial_value = torch.ones(in_features) if zero_centered_gamma else torch.zeros(in_features)
        self.weight = nn.Parameter(initial_value)
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        self.register_parameter("weight", self.weight)

    def forward(self, x):
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        norm_x2 = torch.sum(x.float() ** 2, dim=-1, keepdim=True)
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        d_x = self.in_features

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        rms_x2 = norm_x2 / d_x + self.eps
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        r_rms_x = rms_x2 ** (-1.0 / 2)
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        x_normed = x * r_rms_x
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        w = self.weight.float()
        if self.zero_centered_gamma:
            w = 1 + w
        return (w * x_normed).to(x.dtype)
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class TorchLayerNormLinear(nn.Module):
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    def __init__(
        self,
        in_features: int,
        out_features: int,
        eps: float,
        normalization: str = "LayerNorm",
        zero_centered_gamma: bool = False,
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        bias: bool = True,
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    ):
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        super().__init__()
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        if normalization == "LayerNorm":
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            self.layernorm = TorchLayerNorm(
                in_features, eps=eps, zero_centered_gamma=zero_centered_gamma
            )
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        elif normalization == "RMSNorm":
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            self.layernorm = TorchRMSNorm(
                in_features, eps=eps, zero_centered_gamma=zero_centered_gamma
            )
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        else:
            raise RuntimeError("Unsupported normalization")

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        self.linear = nn.Linear(in_features, out_features, bias=bias)
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    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.linear(self.layernorm(x))


class TorchMHA(nn.Module):
    def __init__(self, hidden_size: int, num_attention_heads: int):
        super().__init__()
        self.mhsa = nn.MultiheadAttention(
            embed_dim=hidden_size,
            num_heads=num_attention_heads,
            dropout=0.1,
            bias=True,
            batch_first=False,
        )

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    def forward(self, x, attention_mask=None):
        output = self.mhsa(x, x, x, attn_mask=attention_mask, need_weights=False)
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        if isinstance(output, tuple):
            output = output[0]
        return output

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class TorchQuickGELU(nn.Module):
    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return input * torch.sigmoid(1.702 * input)
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class TorchSquaredRELU(nn.Module):
    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return (input > 0) * input * input

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class TorchGroupedLinearWithPadding(nn.Module):

    def __init__(
        self, num_gemms, in_features, out_features, bias, params_dtype, parallel_mode, fp8
    ) -> None:
        super().__init__()

        self.padding = Fp8Padding(num_gemms)
        self.linear_fn = GroupedLinear(
            num_gemms,
            in_features,
            out_features,
            bias=bias,
            params_dtype=params_dtype,
            parallel_mode=parallel_mode,
            device="cuda",
        )
        self.unpadding = Fp8Unpadding(num_gemms)

        self.fp8 = fp8

    def forward(self, inp: torch.Tensor, m_splits: List[int]) -> torch.Tensor:
        if self.fp8:
            orig_m_splits = m_splits
            inp, m_splits = self.padding(inp, m_splits)

        out = self.linear_fn(inp, m_splits)

        if self.fp8:
            out = self.unpadding(out, orig_m_splits)

        return out


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_supported_act = {
    "gelu": nn.GELU(approximate="tanh"),
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    "geglu": nn.GELU(approximate="tanh"),
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    "glu": nn.Sigmoid(),
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    "qgelu": TorchQuickGELU(),
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    "qgeglu": TorchQuickGELU(),
    "relu": nn.ReLU(),
    "reglu": nn.ReLU(),
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    "srelu": TorchSquaredRELU(),
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    "sreglu": TorchSquaredRELU(),
    "silu": nn.SiLU(),
    "swiglu": nn.SiLU(),
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}
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class TorchGLU(nn.Module):
    def __init__(self, activation: str):
        super().__init__()
        self.act = _supported_act[activation]

    def forward(self, x):
        shape = x.size(-1)
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        a = x[..., : shape // 2]
        b = x[..., (shape // 2) :]
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        a = self.act(a)
        return a * b
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class TorchLayerNormMLP(nn.Module):
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    def __init__(
        self,
        hidden_size: int,
        ffn_hidden_size: int,
        eps: float = 1e-5,
        activation="gelu",
        normalization: str = "LayerNorm",
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        bias: bool = True,
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    ):
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        super().__init__()
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        if normalization == "LayerNorm":
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            self.ln = TorchLayerNorm(hidden_size, eps=eps, zero_centered_gamma=False)
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        elif normalization == "RMSNorm":
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            self.ln = TorchRMSNorm(hidden_size, eps=eps, zero_centered_gamma=False)
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        else:
            raise RuntimeError("Unsupported normalization")
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        if "glu" in activation:
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            fc1_output_features = 2 * ffn_hidden_size
            self.gelu = TorchGLU(activation)
        else:
            fc1_output_features = ffn_hidden_size
            self.gelu = _supported_act[activation]

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        self.fc1 = nn.Linear(hidden_size, fc1_output_features, bias=bias)
        self.fc2 = nn.Linear(ffn_hidden_size, hidden_size, bias=bias)
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    def forward(self, x):
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        t = self.gelu(self.fc1(self.ln(x)))
        return self.fc2(t)
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class TorchGPT(nn.Module):
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    def __init__(
        self, hidden_size: int, eps: float, num_attention_heads: int, parallel_attention_mlp: bool
    ):
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        super().__init__()
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        self.ln = nn.LayerNorm(hidden_size, eps=eps)
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        self.causal_attn = TorchMHA(hidden_size, num_attention_heads)
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        self.ln_mlp = TorchLayerNormMLP(hidden_size, 4 * hidden_size, eps)
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        self.parallel_attention_mlp = parallel_attention_mlp
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    def forward(
        self,
        x: torch.Tensor,
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        attention_mask: Optional[torch.Tensor] = None,
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    ) -> torch.Tensor:
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        a = self.ln(x)
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        b = self.causal_attn(a, attention_mask)
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        if self.parallel_attention_mlp:
            n = self.ln_mlp(x)
            x = x + nn.functional.dropout(b + n, p=0.1, training=self.training)
        else:
            x = x + nn.functional.dropout(b, p=0.1, training=self.training)
            n = self.ln_mlp(x)
            x = x + nn.functional.dropout(n, p=0.1, training=self.training)
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        return x


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569
def _test_e2e_selective_recompute(
    bs, dtype, config, fp8, recipe, fp8_model_params=False, recompute=False
):
570
    reset_rng_states()
571
    FP8GlobalStateManager.reset()
572
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574
575
576

    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

577
    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
578
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        block = TransformerLayer(
            config.hidden_size,
            4 * config.hidden_size,
581
            config.num_heads,
582
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586
            layernorm_epsilon=config.eps,
            init_method=init_method,
            output_layer_init_method=output_layer_init_method,
            hidden_dropout=0.1,
            attention_dropout=0.1,
587
            kv_channels=config.kv_channels,
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592
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            params_dtype=dtype,
            fuse_qkv_params=True,
            device="cuda",
593
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595
        )

    te_inp_hidden_states = torch.randn(
596
        (config.max_seqlen_q, bs, config.hidden_size),
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600
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
601
    te_inp_hidden_states.retain_grad()
602
    te_inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q)
603

604
    with autocast(enabled=fp8, recipe=recipe):
605
606
        te_out = block(
            te_inp_hidden_states,
607
            attention_mask=te_inp_attn_mask,
608
            checkpoint_core_attention=recompute,
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619
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622
        )
    loss = te_out.sum()
    loss.backward()
    torch.cuda.synchronize()

    outputs = [te_out, te_inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
623
@pytest.mark.parametrize("model", ["126m"])
624
@pytest.mark.parametrize("fp8", all_boolean)
625
@pytest.mark.parametrize("recipe", fp8_recipes)
626
@pytest.mark.parametrize("fp8_model_params", all_boolean)
627
def test_gpt_selective_activation_recompute(dtype, bs, model, fp8, recipe, fp8_model_params):
628
629
    if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
630
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632
633
634
    if fp8 and recipe.nvfp4():
        if dtype not in get_nvfp4_inp_supported_dtypes(recipe, dtype):
            pytest.skip(
                f"Input dtype {dtype} not supported for NVFP4 Recipe {recipe.__class__.__name__}"
            )
635

636
637
    config = model_configs[model]

638
    outputs = _test_e2e_selective_recompute(
639
        bs, dtype, config, fp8, recipe, fp8_model_params, recompute=False
640
641
    )
    outputs_recompute = _test_e2e_selective_recompute(
642
        bs, dtype, config, fp8, recipe, fp8_model_params, recompute=True
643
    )
644
645
646
647
648
649
650

    # Check that results match
    tols = dtype_tols(dtype)
    if dtype in (torch.float16, torch.bfloat16):
        tols["atol"] = 1e-4
    if fp8 or fp8_model_params:
        tols.update(dict(rtol=0.125, atol=0.0675))
651

652
653
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658
    for i, (ref, test) in enumerate(zip(outputs, outputs_recompute)):
        torch.testing.assert_close(
            test,
            ref,
            msg=f"Mismatch in tensor {i}",
            **tols,
        )
659
660


661
def _test_e2e_full_recompute(
662
    bs, dtype, config, fp8, recipe, fp8_model_params=False, recompute=False, use_reentrant=True
663
):
664
665
666
    reset_rng_states()
    FP8GlobalStateManager.reset()

667
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669
670
    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

671
    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
672
        block = TransformerLayer(
673
674
            config.hidden_size,
            4 * config.hidden_size,
675
            config.num_heads,
676
677
678
679
680
            layernorm_epsilon=config.eps,
            init_method=init_method,
            output_layer_init_method=output_layer_init_method,
            hidden_dropout=0.1,
            attention_dropout=0.1,
681
            kv_channels=config.kv_channels,
682
683
684
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            params_dtype=dtype,
685
            fuse_qkv_params=True,
686
            device="cuda",
687
        )
688

689
    te_inp_hidden_states = torch.randn(
690
        (config.max_seqlen_q, bs, config.hidden_size),
691
692
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694
        dtype=dtype,
        device="cuda",
        requires_grad=use_reentrant,
    )
695
696
    if use_reentrant:
        te_inp_hidden_states.retain_grad()
697
    te_inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q)
698

699
    with autocast(enabled=fp8, recipe=recipe):
700
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705
        if recompute:
            te_out = te_checkpoint(
                block,
                te_inp_hidden_states,
                attention_mask=te_inp_attn_mask,
                checkpoint_core_attention=False,
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708
                distribute_saved_activations=False,
                tp_group=None,
                use_reentrant=use_reentrant,
709
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719
            )
        else:
            te_out = block(
                te_inp_hidden_states,
                attention_mask=te_inp_attn_mask,
                checkpoint_core_attention=False,
            )
    loss = te_out.sum()
    loss.backward()
    torch.cuda.synchronize()

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    outputs = [te_out]
    names = ["output"]
    if use_reentrant:
        outputs.append(te_inp_hidden_states.grad)
        names.append("input")
    for name, p in block.named_parameters():
726
727
        if p.requires_grad:
            outputs.append(p.grad)
728
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            names.append(name)

    return outputs, names
731
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733
734


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
735
@pytest.mark.parametrize("model", ["126m"])
736
@pytest.mark.parametrize("fp8", all_boolean)
737
@pytest.mark.parametrize("recipe", fp8_recipes)
738
@pytest.mark.parametrize("fp8_model_params", all_boolean)
739
@pytest.mark.parametrize("use_reentrant", all_boolean)
740
741
742
def test_gpt_full_activation_recompute(
    dtype, bs, model, fp8, recipe, fp8_model_params, use_reentrant
):
743
744
    if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
745
746
747
748
749
    if fp8 and recipe.nvfp4():
        if dtype not in get_nvfp4_inp_supported_dtypes(recipe, dtype):
            pytest.skip(
                f"Input dtype {dtype} not supported for NVFP4 Recipe {recipe.__class__.__name__}"
            )
750
751
752

    config = model_configs[model]

753
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755
756
    if not use_reentrant:
        # Non-reentrant checkpoint becomes non-deterministic with bias+GELU fusion
        os.environ["NVTE_BIAS_GELU_NVFUSION"] = "0"

757
    outputs, names = _test_e2e_full_recompute(
758
759
760
761
762
763
764
765
        bs,
        dtype,
        config,
        fp8,
        recipe,
        fp8_model_params,
        recompute=False,
        use_reentrant=use_reentrant,
766
767
    )
    outputs_recompute, _ = _test_e2e_full_recompute(
768
769
770
771
772
773
774
775
        bs,
        dtype,
        config,
        fp8,
        recipe,
        fp8_model_params,
        recompute=True,
        use_reentrant=use_reentrant,
776
    )
777
778
779
780
781

    if not use_reentrant:
        # Reset bias+GELU fusion flag to avoid contaminating other tests
        del os.environ["NVTE_BIAS_GELU_NVFUSION"]

782
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784
785
786
787
788
789
790
791
792
793
794
    # Check that results match
    tols = dtype_tols(dtype)
    if dtype in (torch.float16, torch.bfloat16):
        tols["atol"] = 1e-3
    if fp8 or fp8_model_params:
        tols.update(dict(rtol=0.125, atol=0.0675))
    for i, (ref, test) in enumerate(zip(outputs, outputs_recompute)):
        torch.testing.assert_close(
            test,
            ref,
            msg=f"Mismatch in tensor {i}",
            **tols,
        )
795
796
797
798
799
800


def _test_e2e_checkpointing_get_model(config, dtype):
    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)
801

802
803
804
    return TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
805
        config.num_heads,
806
807
808
809
810
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
811
        kv_channels=config.kv_channels,
812
813
814
815
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        params_dtype=dtype,
        device="cuda",
816
817
818
819
820
821
822
    )


def _test_e2e_checkpointing(bs, dtype, config, checkpoint=False, steps=10, path="checkpoint.pt"):
    reset_rng_states()

    te_inp_hidden_states = torch.randn(
823
        (config.max_seqlen_q, bs, config.hidden_size),
824
825
826
827
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
828
829
830
831
832
833
834
    te_inp_hidden_states.retain_grad()

    block = _test_e2e_checkpointing_get_model(config, dtype)

    for _ in range(steps // 2):
        te_out = block(
            te_inp_hidden_states,
835
            None,
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
        )
        loss = te_out.sum()
        loss.backward()

    if checkpoint:
        # This process is necessary so that we can start afresh with
        # a new model while erasing all internal state to ensure that
        # loading from a checkpoint gives bitwise identical results.
        # Since gradients are being accumulated, it is important to
        # restore them post loading the checkpoint.
        torch.save(block.state_dict(), path)

        param_grads = []
        for p in block.parameters():
            if p.requires_grad:
                param_grads.append(p.grad.clone())

853
854
855
        _cpu_rng_state = torch.get_rng_state()
        _cuda_rng_state = torch.cuda.get_rng_state()

856
857
        del block
        block = _test_e2e_checkpointing_get_model(config, dtype)
858
        block.load_state_dict(torch.load(path, weights_only=False))
859
860
        torch.set_rng_state(_cpu_rng_state)
        torch.cuda.set_rng_state(_cuda_rng_state)
861
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865
866
867
868
869
870

        for p in block.parameters():
            if p.requires_grad:
                p.grad = param_grads.pop(0)

        assert not param_grads, "Oops!"

    for _ in range(steps // 2):
        te_out = block(
            te_inp_hidden_states,
871
            None,
872
873
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875
876
877
878
879
880
881
882
883
884
885
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888
889
        )
        loss = te_out.sum()
        loss.backward()

    torch.cuda.synchronize()

    if os.path.exists(path):
        os.remove(path)

    outputs = [te_out, te_inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
890
@pytest.mark.parametrize("model", ["126m"])
891
892
893
def test_gpt_checkpointing(dtype, bs, model):
    config = model_configs[model]
    outputs = _test_e2e_checkpointing(bs, dtype, config, checkpoint=False)
894
    outputs_checkpoint = _test_e2e_checkpointing(bs, dtype, config, checkpoint=True)
895
896
897
898
899
900
901
902
903
904
905
906

    # Check that results match
    tols = dtype_tols(dtype)
    if dtype in (torch.float16, torch.bfloat16):
        tols.update(dict(rtol=2e-2, atol=2e-3))
    for i, (ref, test) in enumerate(zip(outputs, outputs_checkpoint)):
        torch.testing.assert_close(
            test,
            ref,
            msg=f"Mismatch in tensor {i}",
            **tols,
        )
907
908
909
910
911
912


def _test_e2e_gpt_accuracy(block, bs, dtype, config):
    reset_rng_states()

    inp_hidden_states = torch.randn(
913
        (config.max_seqlen_q, bs, config.hidden_size),
914
915
916
917
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
918
    inp_hidden_states.retain_grad()
919
    inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q)
920

921
    out = block(inp_hidden_states, attention_mask=inp_attn_mask)
922
923
924
925
926
927
928
929
930
931
932
933
934
    loss = out.sum()
    loss.backward()

    torch.cuda.synchronize()
    outputs = [out, inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
935
@pytest.mark.parametrize("model", ["small"])
936
937
@pytest.mark.parametrize("parallel_attention_mlp", all_boolean)
def test_gpt_accuracy(dtype, bs, model, parallel_attention_mlp):
938
939
    config = model_configs[model]

940
941
942
    te_gpt = TransformerLayer(
        hidden_size=config.hidden_size,
        ffn_hidden_size=4 * config.hidden_size,
943
        num_attention_heads=config.num_heads,
944
945
946
947
948
949
950
951
952
        layernorm_epsilon=config.eps,
        attention_dropout=0.1,
        hidden_dropout=0.1,
        params_dtype=dtype,
        fuse_qkv_params=True,
        qkv_weight_interleaved=False,
        parallel_attention_mlp=parallel_attention_mlp,
        device="cuda",
    ).eval()
953
954
955
956
957

    torch_gpt = (
        TorchGPT(
            config.hidden_size,
            config.eps,
958
            config.num_heads,
959
            parallel_attention_mlp=parallel_attention_mlp,
960
961
962
963
964
965
966
967
        )
        .to(dtype=dtype)
        .cuda()
        .eval()
    )

    # Share params
    with torch.no_grad():
968
        torch_gpt.ln.weight = Parameter(
969
970
            te_gpt.self_attention.layernorm_qkv.layer_norm_weight.clone()
        )
971
        torch_gpt.ln.bias = Parameter(te_gpt.self_attention.layernorm_qkv.layer_norm_bias.clone())
972
973
974
975
976
977
978
979
980
981
982
983
        torch_gpt.causal_attn.mhsa.in_proj_weight = Parameter(
            te_gpt.self_attention.layernorm_qkv.weight.clone()
        )
        torch_gpt.causal_attn.mhsa.in_proj_bias = Parameter(
            te_gpt.self_attention.layernorm_qkv.bias.clone()
        )
        torch_gpt.causal_attn.mhsa.out_proj.weight = Parameter(
            te_gpt.self_attention.proj.weight.clone()
        )
        torch_gpt.causal_attn.mhsa.out_proj.bias = Parameter(
            te_gpt.self_attention.proj.bias.clone()
        )
984
985
986
987
988
989
        torch_gpt.ln_mlp.ln.weight = Parameter(te_gpt.layernorm_mlp.layer_norm_weight.clone())
        torch_gpt.ln_mlp.ln.bias = Parameter(te_gpt.layernorm_mlp.layer_norm_bias.clone())
        torch_gpt.ln_mlp.fc1.weight = Parameter(te_gpt.layernorm_mlp.fc1_weight.clone())
        torch_gpt.ln_mlp.fc1.bias = Parameter(te_gpt.layernorm_mlp.fc1_bias.clone())
        torch_gpt.ln_mlp.fc2.weight = Parameter(te_gpt.layernorm_mlp.fc2_weight.clone())
        torch_gpt.ln_mlp.fc2.bias = Parameter(te_gpt.layernorm_mlp.fc2_bias.clone())
990
991
992
993

    te_outputs = _test_e2e_gpt_accuracy(te_gpt, bs, dtype, config)
    torch_outputs = _test_e2e_gpt_accuracy(torch_gpt, bs, dtype, config)

994
995
996
997
998
999
    atol = {
        torch.float32: 5e-3,
        torch.half: 5e-2,
        torch.bfloat16: 1e-1,
    }

1000
    # Check output.
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype])

    # Check gradients, only for small model
    if model == "small":
        atol[torch.float32] = 5e-2
        rtol = {
            torch.float32: 1e-2,
            torch.half: 1e-2,
            torch.bfloat16: 1e-2,
        }
        for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
            assert_allclose(te_output, torch_output, atol[dtype], rtol[dtype])
1013
1014


1015
def _test_mha_accuracy(block, bs, dtype, config, mask_type, te=True):
1016
1017
1018
    reset_rng_states()

    inp_hidden_states = torch.randn(
1019
        (config.max_seqlen_q, bs, config.hidden_size),
1020
1021
1022
1023
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
1024
    inp_hidden_states.retain_grad()
1025
    inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q) if mask_type == "causal" else None
1026

1027
1028
1029
1030
1031
1032
    forward_kwargs = {}
    if te:
        forward_kwargs["attn_mask_type"] = mask_type
    forward_kwargs["attention_mask"] = inp_attn_mask

    out = block(inp_hidden_states, **forward_kwargs)
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
    loss = out.sum()
    loss.backward()

    torch.cuda.synchronize()
    outputs = [out, inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
1046
@pytest.mark.parametrize("model", ["small"])
1047
1048
1049
1050
@pytest.mark.parametrize("mask_type", mask_types)
def test_mha_accuracy(dtype, bs, model, mask_type):
    config = model_configs[model]

1051
1052
    te_mha = MultiheadAttention(
        config.hidden_size,
1053
        config.num_heads,
1054
1055
1056
1057
1058
1059
        fuse_qkv_params=True,
        params_dtype=dtype,
        qkv_weight_interleaved=False,
        input_layernorm=False,
        device="cuda",
    ).eval()
1060
1061
1062
1063

    torch_mha = (
        TorchMHA(
            config.hidden_size,
1064
            config.num_heads,
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
        )
        .to(dtype=dtype)
        .cuda()
        .eval()
    )

    # Share params
    with torch.no_grad():
        torch_mha.mhsa.in_proj_weight = Parameter(te_mha.qkv.weight.clone())
        torch_mha.mhsa.in_proj_bias = Parameter(te_mha.qkv.bias.clone())
        torch_mha.mhsa.out_proj.weight = Parameter(te_mha.proj.weight.clone())
        torch_mha.mhsa.out_proj.bias = Parameter(te_mha.proj.bias.clone())

1078
1079
    te_outputs = _test_mha_accuracy(te_mha, bs, dtype, config, mask_type, te=True)
    torch_outputs = _test_mha_accuracy(torch_mha, bs, dtype, config, mask_type, te=False)
1080
1081
1082
1083
1084
1085
1086

    # Check output.
    if dtype == torch.float32:
        assert_allclose(te_outputs[0], torch_outputs[0], 5e-3)
    else:
        assert_allclose(te_outputs[0], torch_outputs[0], 5e-2)

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    # Check gradients, only for small model
    if model == "small":
        atol = {
            torch.float32: 5e-2,
            torch.half: 5e-2,
            torch.bfloat16: 5e-2,
        }
        rtol = {
            torch.float32: 1e-2,
            torch.half: 1e-2,
            torch.bfloat16: 1e-2,
        }
        for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
            assert_allclose(te_output, torch_output, atol[dtype], rtol[dtype])

1102

1103
def _test_granular_accuracy(block, bs, dtype, config, delay_wgrad_compute=False, recipe=None):
1104
    reset_rng_states()
1105
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    fp8 = recipe is not None
    if fp8:
        FP8GlobalStateManager.reset()
1108
1109

    inp_hidden_states = torch.randn(
1110
        (config.max_seqlen_q, bs, config.hidden_size),
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        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
1115
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    inp_hidden_states.retain_grad()

1117
    with autocast(enabled=fp8, recipe=recipe):
1118
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1120
        out = block(inp_hidden_states)
        if isinstance(out, (List, Tuple)):
            out = out[0]
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    loss = out.sum()
    loss.backward()
1123
1124
    if delay_wgrad_compute:
        block.backward_dw()
1125
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1129

    torch.cuda.synchronize()
    outputs = [out, inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
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            if getattr(p, "main_grad", None) is not None:
                outputs.append(p.main_grad)
                assert p.grad is None  # grad should be None if fuse_wgrad_accumulation is True
            else:
                outputs.append(p.grad)
1135
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    return outputs


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def _test_dpa_accuracy(block, bs, dtype, config):
    reset_rng_states()

1141
    mask = torch.triu(
1142
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        torch.ones(config.max_seqlen_q, config.max_seqlen_kv, dtype=torch.bool, device="cuda"),
        diagonal=1,
1144
    )
1145
    query, key, value = [
1146
        torch.randn(
1147
            (config.max_seqlen_q, bs, config.num_heads, config.kv_channels),
1148
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            dtype=dtype,
            device="cuda",
            requires_grad=True,
        )
        for _ in range(3)
    ]
1154
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1158

    query.retain_grad()
    key.retain_grad()
    value.retain_grad()

1159
    out = block(query, key, value, attention_mask=mask)
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    loss = out.sum()
    loss.backward()

    torch.cuda.synchronize()

    return [out, query.grad, key.grad, value.grad]


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
1170
@pytest.mark.parametrize("model", ["126m"])
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def test_dpa_accuracy(dtype, bs, model):
    config = model_configs[model]

    te_dpa = (
        DotProductAttention(
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            config.num_heads,
            config.kv_channels,
1178
            attention_dropout=0.0,  # disable dropout, FU uses rng differently
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        )
        .to(dtype=dtype)
        .cuda()
    )

    torch_dpa = (
        TorchDotProductAttention(
1186
            config.kv_channels,
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            0.0,  # dropout
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        )
        .to(dtype=dtype)
        .cuda()
    )

    te_outputs = _test_dpa_accuracy(te_dpa, bs, dtype, config)
    torch_outputs = _test_dpa_accuracy(torch_dpa, bs, dtype, config)

    # Check output.
    if dtype == torch.float32:
        assert_allclose(te_outputs[0], torch_outputs[0], 5e-3)
    else:
        assert_allclose(te_outputs[0], torch_outputs[0], 5e-2)

1202
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    for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
        assert_allclose(te_output, torch_output, atol=5e-2, rtol=1e-2)

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class TestReturnBiasModule(nn.Module):
    def __init__(self, mod, **kwargs):
        super().__init__()
        self.te_module = mod(**kwargs)
        self.return_bias = kwargs["return_bias"]
        self.bias = kwargs["bias"]

    def forward(self, x):
        if self.return_bias:
            out, bias = self.te_module(x)
            if self.bias:
                out = out + bias
            return out
        return self.te_module(x)


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@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
1224
@pytest.mark.parametrize("model", ["small"])
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@pytest.mark.parametrize("return_bias", all_boolean)
@pytest.mark.parametrize("bias", all_boolean)
def test_linear_accuracy(dtype, bs, model, return_bias, bias):
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    config = model_configs[model]

1230
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    te_linear = TestReturnBiasModule(
        Linear,
        in_features=config.hidden_size,
        out_features=4 * config.hidden_size,
1234
        params_dtype=dtype,
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        return_bias=return_bias,
        bias=bias,
1237
        device="cuda",
1238
    )
1239

1240
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    torch_linear = torch.nn.Linear(
        config.hidden_size,
        4 * config.hidden_size,
1243
        bias=bias,
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        device="cuda",
        dtype=dtype,
1246
    )
1247
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1249

    # Share params
    with torch.no_grad():
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        torch_linear.weight = Parameter(te_linear.te_module.weight.clone())
        if bias:
            torch_linear.bias = Parameter(te_linear.te_module.bias.clone())
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    te_outputs = _test_granular_accuracy(te_linear, bs, dtype, config)
    torch_outputs = _test_granular_accuracy(torch_linear, bs, dtype, config)

    # Check output.
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    if model == "small":
        tolerance = 5e-3 if dtype == torch.float32 else 5e-2
        rtol = {
            torch.float32: 1.3e-6,
            torch.half: 1e-2,
            torch.bfloat16: 2e-2,
        }
        for te_output, torch_output in zip(te_outputs, torch_outputs):
            assert_allclose(te_output, torch_output, tolerance, rtol[dtype])
1267

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@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("bias", all_boolean)
@pytest.mark.parametrize("fuse_wgrad_accumulation", all_boolean)
def test_linear_accuracy_delay_wgrad_compute(dtype, bs, model, bias, fuse_wgrad_accumulation):
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    if NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("Delayed wgrad compute is not supported in debug mode.")

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    config = model_configs[model]

    te_linear_ref = Linear(
        config.hidden_size,
        4 * config.hidden_size,
        bias=bias,
        params_dtype=dtype,
        device="cuda",
        delay_wgrad_compute=False,
        fuse_wgrad_accumulation=fuse_wgrad_accumulation,
    ).eval()

    te_linear = Linear(
        config.hidden_size,
        4 * config.hidden_size,
        bias=bias,
        params_dtype=dtype,
        device="cuda",
        delay_wgrad_compute=True,
        fuse_wgrad_accumulation=fuse_wgrad_accumulation,
    ).eval()

    # Share params
    with torch.no_grad():
        te_linear_ref.weight = Parameter(te_linear.weight.clone())
        if bias:
            te_linear_ref.bias = Parameter(te_linear.bias.clone())
        if fuse_wgrad_accumulation:
            weight = getattr(te_linear, f"weight")
            weight.main_grad = torch.rand_like(weight, dtype=torch.float32)
            te_linear_ref.weight.main_grad = weight.main_grad.clone()

    te_outputs = _test_granular_accuracy(te_linear, bs, dtype, config, delay_wgrad_compute=True)
    te_outputs_ref = _test_granular_accuracy(
        te_linear_ref, bs, dtype, config, delay_wgrad_compute=False
    )

1315
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    # Should be bit-wise match
    for _, (o, o_ref) in enumerate(zip(te_outputs, te_outputs_ref)):
1317
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1319
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


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@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("recipe", fp8_recipes + [None])
def test_linear_accuracy_save_original_input(dtype, model, recipe):
    bs = 1
    fuse_wgrad_accumulation = True
    fp8_model_params = False
    fp8 = recipe is not None
1328

1329
1330
1331
1332
    if fp8 and recipe.delayed():
        pytest.skip("DelayedScaling recipe is not supported with save_original_input")

    config = model_configs[model]
1333
    if config.max_seqlen_q % 16 != 0 and fp8:
1334
1335
        pytest.skip("FP8 requires sequence length to be divisible by 16.")

1336
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1341
    if recipe is not None and recipe.nvfp4():
        if dtype not in get_nvfp4_inp_supported_dtypes(recipe, dtype):
            pytest.skip(
                f"Input dtype {dtype} not supported for NVFP4 Recipe {recipe.__class__.__name__}"
            )

1342
    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
1343
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        te_linear_ref = Linear(
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            device="cuda",
            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
            save_original_input=False,
        ).eval()

        te_linear = Linear(
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            device="cuda",
            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
            save_original_input=True,
        ).eval()

    # Share params
    with torch.no_grad():
        te_linear_ref.weight = Parameter(te_linear.weight.clone())
        if fuse_wgrad_accumulation:
            weight = getattr(te_linear, f"weight")
            weight.main_grad = torch.rand_like(weight, dtype=torch.float32)
            te_linear_ref.weight.main_grad = weight.main_grad.clone()

    te_outputs = _test_granular_accuracy(te_linear, bs, dtype, config, recipe=recipe)
    te_outputs_ref = _test_granular_accuracy(te_linear_ref, bs, dtype, config, recipe=recipe)

1374
    # Should be bit-wise match
1375
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1378
    for i, (o, o_ref) in enumerate(zip(te_outputs, te_outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


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@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
1381
@pytest.mark.parametrize("model", ["126m"])
1382
@pytest.mark.parametrize("eps", [1e-1, 1e-3, 1e-5, 1e-7])
1383
1384
@pytest.mark.parametrize("zero_centered_gamma", all_boolean)
def test_rmsnorm_accuracy(dtype, bs, model, eps, zero_centered_gamma):
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1386
    config = model_configs[model]

1387
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    te_rmsnorm = RMSNorm(
        config.hidden_size,
        eps=eps,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
        device="cuda",
    ).eval()
1394
1395

    torch_rmsnorm = (
1396
        TorchRMSNorm(config.hidden_size, eps=eps, zero_centered_gamma=zero_centered_gamma)
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        .to(dtype=dtype)
        .cuda()
        .eval()
    )

    # Share params
    with torch.no_grad():
        torch_rmsnorm.weight = Parameter(te_rmsnorm.weight.clone())

    te_outputs = _test_granular_accuracy(te_rmsnorm, bs, dtype, config)
    torch_outputs = _test_granular_accuracy(torch_rmsnorm, bs, dtype, config)

1409
1410
1411
1412
    atol = {
        torch.float32: 1e-7,
        torch.half: 2e-3,
        torch.bfloat16: 2e-2,
1413
    }
1414
1415

    # Check output.
1416
1417
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype])

1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
    atol[torch.float32] = 2e-3
    rtol = {
        torch.float32: 1.3e-6,
        torch.half: 1e-3,
        torch.bfloat16: 1.6e-2,
    }
    # Check gradients
    for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
        assert_allclose(te_output, torch_output, atol[dtype], rtol[dtype])

1428

1429
1430
@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
1431
@pytest.mark.parametrize("model", ["126m"])
1432
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1434
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1436
@pytest.mark.parametrize("eps", [1e-1, 1e-3, 1e-5, 1e-7])
@pytest.mark.parametrize("zero_centered_gamma", all_boolean)
def test_layernorm_accuracy(dtype, bs, model, eps, zero_centered_gamma):
    config = model_configs[model]

1437
1438
1439
1440
1441
1442
1443
    te_layernorm = LayerNorm(
        config.hidden_size,
        eps=eps,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
        device="cuda",
    ).eval()
1444
1445

    torch_layernorm = (
1446
        TorchLayerNorm(config.hidden_size, eps=eps, zero_centered_gamma=zero_centered_gamma)
1447
1448
1449
1450
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1452
1453
1454
1455
1456
1457
1458
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        .to(dtype=dtype)
        .cuda()
        .eval()
    )

    # Share params
    with torch.no_grad():
        torch_layernorm.weight = Parameter(te_layernorm.weight.clone())
        torch_layernorm.bias = Parameter(te_layernorm.bias.clone())

    te_outputs = _test_granular_accuracy(te_layernorm, bs, dtype, config)
    torch_outputs = _test_granular_accuracy(torch_layernorm, bs, dtype, config)

1460
1461
1462
1463
    atol = {
        torch.float32: 1e-7,
        torch.half: 2e-3,
        torch.bfloat16: 2e-2,
1464
    }
1465
1466

    # Check output.
1467
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype])
1468

1469
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1471
1472
1473
1474
1475
1476
1477
1478
    rtol = {
        torch.float32: 1.3e-6,
        torch.half: 1e-3,
        torch.bfloat16: 1.6e-2,
    }
    atol[torch.float32] = 1e-4
    # Check gradients
    for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
        assert_allclose(te_output, torch_output, atol[dtype], rtol[dtype])

1479

1480
1481
@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
1482
@pytest.mark.parametrize("model", ["small"])
1483
@pytest.mark.parametrize("normalization", all_normalizations)
1484
@pytest.mark.parametrize("zero_centered_gamma", all_boolean)
1485
1486
1487
1488
1489
@pytest.mark.parametrize("return_bias", all_boolean)
@pytest.mark.parametrize("bias", all_boolean)
def test_layernorm_linear_accuracy(
    dtype, bs, model, normalization, zero_centered_gamma, return_bias, bias
):
1490
1491
    config = model_configs[model]

1492
1493
1494
1495
1496
    te_ln_linear = TestReturnBiasModule(
        LayerNormLinear,
        in_features=config.hidden_size,
        out_features=4 * config.hidden_size,
        eps=config.eps,
1497
1498
1499
        normalization=normalization,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
1500
1501
        return_bias=return_bias,
        bias=bias,
1502
        device="cuda",
1503
    )
1504
1505
1506
1507
1508
1509

    torch_ln_linear = (
        TorchLayerNormLinear(
            config.hidden_size,
            4 * config.hidden_size,
            config.eps,
1510
            normalization=normalization,
1511
            zero_centered_gamma=zero_centered_gamma,
1512
            bias=bias,
1513
1514
1515
1516
1517
1518
1519
        )
        .to(dtype=dtype)
        .cuda()
    )

    # Share params
    with torch.no_grad():
1520
1521
1522
        torch_ln_linear.layernorm.weight = Parameter(
            te_ln_linear.te_module.layer_norm_weight.clone()
        )
1523
        if normalization != "RMSNorm":
1524
1525
1526
1527
1528
1529
            torch_ln_linear.layernorm.bias = Parameter(
                te_ln_linear.te_module.layer_norm_bias.clone()
            )
        torch_ln_linear.linear.weight = Parameter(te_ln_linear.te_module.weight.clone())
        if bias:
            torch_ln_linear.linear.bias = Parameter(te_ln_linear.te_module.bias.clone())
1530
1531
1532
1533

    te_outputs = _test_granular_accuracy(te_ln_linear, bs, dtype, config)
    torch_outputs = _test_granular_accuracy(torch_ln_linear, bs, dtype, config)

1534
1535
1536
1537
    atol = {
        torch.float32: 2.5e-4,
        torch.half: 2e-3,
        torch.bfloat16: 2e-2,
1538
    }
1539
1540
1541
1542
1543
    rtol = {
        torch.float32: 1e-3,
        torch.half: 4e-2,
        torch.bfloat16: 4e-2,
    }
1544
1545

    # Check output.
1546
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype], rtol[dtype])
1547

1548
1549
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1560
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    if model == "small":
        atol = {
            torch.float32: 1e-3,
            torch.half: 5e-2,
            torch.bfloat16: 5e-2,
        }
        rtol = {
            torch.float32: 1e-3,
            torch.half: 4e-2,
            torch.bfloat16: 4e-2,
        }
        # Check gradients
        for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
            assert_allclose(te_output, torch_output, atol[dtype], rtol[dtype])

1563

1564
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@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("normalization", all_normalizations)
@pytest.mark.parametrize("zero_centered_gamma", all_boolean)
@pytest.mark.parametrize("bias", all_boolean)
@pytest.mark.parametrize("fuse_wgrad_accumulation", all_boolean)
def test_layernorm_linear_accuracy_delay_wgrad_compute(
    dtype, bs, model, normalization, zero_centered_gamma, bias, fuse_wgrad_accumulation
):
1574
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1576
    if NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("Delayed wgrad compute is not supported in debug mode.")

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    config = model_configs[model]

    ln_linear_ref = LayerNormLinear(
        config.hidden_size,
        4 * config.hidden_size,
        config.eps,
        bias=bias,
        normalization=normalization,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
        device="cuda",
        delay_wgrad_compute=False,
        fuse_wgrad_accumulation=fuse_wgrad_accumulation,
    ).eval()

    ln_linear = LayerNormLinear(
        config.hidden_size,
        4 * config.hidden_size,
        config.eps,
        bias=bias,
        normalization=normalization,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
        device="cuda",
        delay_wgrad_compute=True,
        fuse_wgrad_accumulation=fuse_wgrad_accumulation,
    ).eval()

    # Share params
    with torch.no_grad():
        ln_linear_ref.layer_norm_weight = Parameter(ln_linear.layer_norm_weight.clone())
        if normalization != "RMSNorm":
            ln_linear_ref.layer_norm_bias = Parameter(ln_linear.layer_norm_bias.clone())
        ln_linear_ref.weight = Parameter(ln_linear.weight.clone())
        if bias:
            ln_linear_ref.bias = Parameter(ln_linear.bias.clone())
        if fuse_wgrad_accumulation:
            weight = getattr(ln_linear, f"weight")
            weight.main_grad = torch.rand_like(weight, dtype=torch.float32)
            ln_linear_ref.weight.main_grad = weight.main_grad.clone()

    te_outputs = _test_granular_accuracy(ln_linear, bs, dtype, config, delay_wgrad_compute=True)
    te_outputs_ref = _test_granular_accuracy(
        ln_linear_ref, bs, dtype, config, delay_wgrad_compute=False
    )

    # Shoule be bit-wise match
    for i, (o, o_ref) in enumerate(zip(te_outputs, te_outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


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@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
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@pytest.mark.parametrize("model", ["small"])
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@pytest.mark.parametrize("activation", all_activations)
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@pytest.mark.parametrize("normalization", all_normalizations)
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@pytest.mark.parametrize("return_bias", all_boolean)
@pytest.mark.parametrize("bias", all_boolean)
def test_layernorm_mlp_accuracy(dtype, bs, model, activation, normalization, return_bias, bias):
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    config = model_configs[model]

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    te_ln_mlp = TestReturnBiasModule(
        LayerNormMLP,
        hidden_size=config.hidden_size,
        ffn_hidden_size=4 * config.hidden_size,
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        activation=activation,
        normalization=normalization,
        params_dtype=dtype,
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        return_bias=return_bias,
        bias=bias,
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        device="cuda",
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    )
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    torch_ln_mlp = (
        TorchLayerNormMLP(
            config.hidden_size,
            4 * config.hidden_size,
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            activation=activation,
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            normalization=normalization,
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            bias=bias,
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        )
        .to(dtype=dtype)
        .cuda()
    )

    # Share params
    with torch.no_grad():
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        torch_ln_mlp.ln.weight = Parameter(te_ln_mlp.te_module.layer_norm_weight.clone())
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        if normalization != "RMSNorm":
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            torch_ln_mlp.ln.bias = Parameter(te_ln_mlp.te_module.layer_norm_bias.clone())
        torch_ln_mlp.fc1.weight = Parameter(te_ln_mlp.te_module.fc1_weight.clone())
        torch_ln_mlp.fc2.weight = Parameter(te_ln_mlp.te_module.fc2_weight.clone())
        if bias:
            torch_ln_mlp.fc1.bias = Parameter(te_ln_mlp.te_module.fc1_bias.clone())
            torch_ln_mlp.fc2.bias = Parameter(te_ln_mlp.te_module.fc2_bias.clone())
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    te_outputs = _test_granular_accuracy(te_ln_mlp, bs, dtype, config)
    torch_outputs = _test_granular_accuracy(torch_ln_mlp, bs, dtype, config)

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    atol = {
        torch.float32: 2e-2,
        torch.half: 5e-2,
        torch.bfloat16: 5e-2,
    }

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    rtol = {
        torch.float32: 1e-3,
        torch.half: 4e-2,
        torch.bfloat16: 4e-2,
    }

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    # Check output.
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    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype], rtol[dtype])
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    # Check gradients, only for small model
    rtol = {
        torch.float32: 1e-3,
        torch.half: 1e-2,
        torch.bfloat16: 4e-2,
    }
    atol[torch.half] = 2e-1
    atol[torch.bfloat16] = 2e-1
    if model == "small":
        for te_output, torch_output in zip(te_outputs[1:], torch_outputs[1:]):
            assert_allclose(te_output, torch_output, atol[dtype], rtol[dtype])
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@pytest.mark.parametrize("dtype", param_types)
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@pytest.mark.parametrize("bs", [2])
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@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("bias", all_boolean)
@pytest.mark.parametrize("fuse_wgrad_accumulation", all_boolean)
def test_layernorm_mlp_accuracy_delay_wgrad_compute(
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    dtype,
    bs,
    model,
    bias,
    fuse_wgrad_accumulation,
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):
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    if NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("Delayed wgrad compute is not supported in debug mode.")

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    config = model_configs[model]

    ln_mlp = LayerNormMLP(
        hidden_size=config.hidden_size,
        ffn_hidden_size=4 * config.hidden_size,
        eps=config.eps,
        bias=bias,
        params_dtype=dtype,
        device="cuda",
        delay_wgrad_compute=True,
        fuse_wgrad_accumulation=fuse_wgrad_accumulation,
    ).eval()

    ln_mlp_ref = LayerNormMLP(
        hidden_size=config.hidden_size,
        ffn_hidden_size=4 * config.hidden_size,
        eps=config.eps,
        bias=bias,
        params_dtype=dtype,
        device="cuda",
        delay_wgrad_compute=False,
        fuse_wgrad_accumulation=fuse_wgrad_accumulation,
    ).eval()

    # Share params
    with torch.no_grad():
        ln_mlp_ref.layer_norm_weight = Parameter(ln_mlp.layer_norm_weight.clone())
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        ln_mlp_ref.layer_norm_bias = Parameter(ln_mlp.layer_norm_bias.clone())
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        ln_mlp_ref.fc1_weight = Parameter(ln_mlp.fc1_weight.clone())
        ln_mlp_ref.fc2_weight = Parameter(ln_mlp.fc2_weight.clone())
        if bias:
            ln_mlp_ref.fc1_bias = Parameter(ln_mlp.fc1_bias.clone())
            ln_mlp_ref.fc2_bias = Parameter(ln_mlp.fc2_bias.clone())
        if fuse_wgrad_accumulation:
            ln_mlp.fc1_weight.main_grad = torch.rand_like(ln_mlp.fc1_weight, dtype=torch.float32)
            ln_mlp_ref.fc1_weight.main_grad = ln_mlp.fc1_weight.main_grad.clone()
            ln_mlp.fc2_weight.main_grad = torch.rand_like(ln_mlp.fc2_weight, dtype=torch.float32)
            ln_mlp_ref.fc2_weight.main_grad = ln_mlp.fc2_weight.main_grad.clone()

    te_outputs = _test_granular_accuracy(ln_mlp, bs, dtype, config, delay_wgrad_compute=True)
    te_outputs_ref = _test_granular_accuracy(
        ln_mlp_ref, bs, dtype, config, delay_wgrad_compute=False
    )

    # Shoule be bit-wise match
    for i, (o, o_ref) in enumerate(zip(te_outputs, te_outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)
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@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", [2])
@pytest.mark.parametrize("model", ["small"])
@pytest.mark.parametrize("bias", all_boolean)
def test_layernorm_mlp_accuracy_checkpoint(
    dtype,
    bs,
    model,
    bias,
):
    config = model_configs[model]

    ln_mlp = LayerNormMLP(
        hidden_size=config.hidden_size,
        ffn_hidden_size=4 * config.hidden_size,
        eps=config.eps,
        bias=bias,
        params_dtype=dtype,
        device="cuda",
        checkpoint=True,
    ).eval()

    ln_mlp_ref = LayerNormMLP(
        hidden_size=config.hidden_size,
        ffn_hidden_size=4 * config.hidden_size,
        eps=config.eps,
        bias=bias,
        params_dtype=dtype,
        device="cuda",
        checkpoint=False,
    ).eval()

    # Share params
    with torch.no_grad():
        ln_mlp_ref.layer_norm_weight = Parameter(ln_mlp.layer_norm_weight.clone())
        ln_mlp_ref.layer_norm_bias = Parameter(ln_mlp.layer_norm_bias.clone())
        ln_mlp_ref.fc1_weight = Parameter(ln_mlp.fc1_weight.clone())
        ln_mlp_ref.fc2_weight = Parameter(ln_mlp.fc2_weight.clone())
        if bias:
            ln_mlp_ref.fc1_bias = Parameter(ln_mlp.fc1_bias.clone())
            ln_mlp_ref.fc2_bias = Parameter(ln_mlp.fc2_bias.clone())

    te_outputs = _test_granular_accuracy(ln_mlp, bs, dtype, config, delay_wgrad_compute=False)
    te_outputs_ref = _test_granular_accuracy(
        ln_mlp_ref, bs, dtype, config, delay_wgrad_compute=False
    )

    # Shoule be bit-wise match
    for i, (o, o_ref) in enumerate(zip(te_outputs, te_outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)
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def _test_grouped_linear_accuracy(
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    block,
    num_gemms,
    bs,
    dtype,
    config,
    recipe,
    fp8,
    fuse_wgrad_accumulation,
    delay_wgrad_compute=False,
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):
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    reset_rng_states()
    if fp8:
        FP8GlobalStateManager.reset()

    inp_hidden_states = torch.randn(
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        (config.max_seqlen_q, bs, config.hidden_size),
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        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    inp_hidden_states.retain_grad()

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    if num_gemms > 1:
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        split_size = 1
        if fp8:
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            split_size = get_align_size_for_quantization(recipe)
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        m = config.max_seqlen_q // split_size
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        dist = torch.sort(torch.randint(0, m, (num_gemms - 2,))).values.tolist()
        dist.append(dist[-1])  # Manually add a zero
        m_splits = torch.tensor(dist + [m]) - torch.tensor([0] + dist)
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        m_splits = m_splits * split_size
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        assert m_splits.sum() == config.max_seqlen_q and len(m_splits) == num_gemms
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    else:
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        m_splits = torch.tensor([config.max_seqlen_q])
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    with autocast(enabled=fp8, recipe=recipe):
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        if isinstance(block, GroupedLinear):
            m_splits = m_splits * bs
            out = block(inp_hidden_states, m_splits.tolist())
        else:
            out = torch.cat(
                [
                    block[i](inp)
                    for i, inp in enumerate(torch.split(inp_hidden_states, m_splits.tolist()))
                ]
            )
    loss = out.sum()
    loss.backward()
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    if delay_wgrad_compute:
        if isinstance(block, GroupedLinear):
            block.backward_dw()
        else:
            for i in range(num_gemms):
                block[i].backward_dw()
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    torch.cuda.synchronize()
    outputs = [out, inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
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            if getattr(p, "main_grad", None) is not None:
                outputs.append(p.main_grad)
                assert p.grad is None  # grad should be None if fuse_wgrad_accumulation is True
            else:
                outputs.append(p.grad)
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    return outputs


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@pytest.mark.parametrize("dtype", param_types, ids=str)
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@pytest.mark.parametrize("num_gemms", [3, 6])
@pytest.mark.parametrize("bs", batch_sizes)
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@pytest.mark.parametrize("model", ["126m"])
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@pytest.mark.parametrize("recipe", fp8_recipes + [None])
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@pytest.mark.parametrize("fp8_model_params", all_boolean)
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@pytest.mark.parametrize("fuse_wgrad_accumulation", all_boolean)
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@pytest.mark.parametrize("bias", all_boolean)
@pytest.mark.parametrize("delay_wgrad_compute", all_boolean)
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def test_grouped_linear_accuracy(
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    dtype,
    num_gemms,
    bs,
    model,
    recipe,
    fp8_model_params,
    fuse_wgrad_accumulation,
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    bias,
    delay_wgrad_compute,
1907
    parallel_mode=None,
1908
    use_cutlass=False,
1909
):
1910
    fp8 = recipe is not None
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    if fp8 and fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
1912
        pytest.skip("FP8 parameters are not supported in debug mode.")
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    if NVTE_TEST_NVINSPECT_ENABLED and delay_wgrad_compute:
        pytest.skip("Delayed wgrad compute is not supported in debug mode.")
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    config = model_configs[model]
1917
    if config.max_seqlen_q % 16 != 0 and fp8:
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        pytest.skip("FP8 requires sequence length to be divisible by 16.")

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    if recipe is not None and recipe.nvfp4():
        if dtype not in get_nvfp4_inp_supported_dtypes(recipe, dtype):
            pytest.skip(
                f"Input dtype {dtype} not supported for NVFP4 Recipe {recipe.__class__.__name__}"
            )

1926
    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
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        grouped_linear = GroupedLinear(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
1931
            bias=bias,
1932
            params_dtype=dtype,
1933
            parallel_mode=parallel_mode,
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            device="cuda",
1935
            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
1936
            delay_wgrad_compute=delay_wgrad_compute,
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            save_original_input=False,
        ).eval()
        sequential_linear = torch.nn.ModuleList(
            [
                Linear(
                    config.hidden_size,
                    4 * config.hidden_size,
                    bias=bias,
                    params_dtype=dtype,
                    parallel_mode=parallel_mode,
                    device="cuda",
                    fuse_wgrad_accumulation=fuse_wgrad_accumulation,
                ).eval()
                for _ in range(num_gemms)
            ]
        )

    # Share params
    with torch.no_grad():
        for i in range(num_gemms):
            sequential_linear[i].weight = Parameter(getattr(grouped_linear, f"weight{i}").clone())
            if bias:
                sequential_linear[i].bias = Parameter(getattr(grouped_linear, f"bias{i}").clone())
            if fuse_wgrad_accumulation:
                weight_i = getattr(grouped_linear, f"weight{i}")
                weight_i.main_grad = torch.rand_like(weight_i, dtype=torch.float32)
                sequential_linear[i].weight.main_grad = weight_i.main_grad.clone()

    outputs_ref = _test_grouped_linear_accuracy(
        sequential_linear,
        num_gemms,
        bs,
        dtype,
        config,
        recipe,
        fp8,
        fuse_wgrad_accumulation,
        delay_wgrad_compute,
    )
    outputs = _test_grouped_linear_accuracy(
        grouped_linear,
        num_gemms,
        bs,
        dtype,
        config,
        recipe,
        fp8,
        fuse_wgrad_accumulation,
        delay_wgrad_compute,
    )

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    for o, o_ref in zip(outputs, outputs_ref):
        if use_cutlass:
            torch.testing.assert_close(o, o_ref, rtol=1e-3, atol=1e-3)
        else:
            # cuBLAS implementation should be bit-wise match
            torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


@pytest.mark.skipif(
    torch.cuda.get_device_capability() != (9, 0),
    reason="Only enable CUTLASS grouped gemm on Hopper",
)
@pytest.mark.parametrize("dtype", param_types, ids=str)
@pytest.mark.parametrize("num_gemms", [3, 6])
@pytest.mark.parametrize("bs", batch_sizes)
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("fuse_wgrad_accumulation", all_boolean)
@pytest.mark.parametrize("delay_wgrad_compute", all_boolean)
def test_grouped_linear_accuracy_cutlass(
    dtype,
    num_gemms,
    bs,
    model,
    fuse_wgrad_accumulation,
    delay_wgrad_compute,
):
    os.environ["NVTE_USE_CUTLASS_GROUPED_GEMM"] = "1"
    test_grouped_linear_accuracy(
        dtype,
        num_gemms,
        bs,
        model,
        None,
        False,
        fuse_wgrad_accumulation,
        False,
        delay_wgrad_compute,
        None,
        use_cutlass=True,
    )
    os.environ.pop("NVTE_USE_CUTLASS_GROUPED_GEMM", None)
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@pytest.mark.parametrize("dtype", param_types, ids=str)
@pytest.mark.parametrize("num_gemms", [3])
@pytest.mark.parametrize("bs", [1])
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("recipe", fp8_recipes + [None])
@pytest.mark.parametrize("fp8_model_params", [False])
@pytest.mark.parametrize("fuse_wgrad_accumulation", [True])
@pytest.mark.parametrize("bias", [False])
@pytest.mark.parametrize("delay_wgrad_compute", [True])
def test_grouped_linear_accuracy_save_original_input(
    dtype,
    num_gemms,
    bs,
    model,
    recipe,
    fp8_model_params,
    fuse_wgrad_accumulation,
    bias,
    delay_wgrad_compute,
    parallel_mode=None,
):
    fp8 = recipe is not None
2053
    if fp8 and fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
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        pytest.skip("FP8 parameters are not supported in debug mode.")
    if fp8 and recipe.delayed():
        pytest.skip("DelayedScaling recipe is not supported with save_original_input")
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    if NVTE_TEST_NVINSPECT_ENABLED and delay_wgrad_compute:
        pytest.skip("Delayed wgrad compute is not supported in debug mode.")
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    config = model_configs[model]
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    if config.max_seqlen_q % 16 != 0 and fp8:
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        pytest.skip("FP8 requires sequence length to be divisible by 16.")

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    if recipe is not None and recipe.nvfp4():
        if dtype not in get_nvfp4_inp_supported_dtypes(recipe, dtype):
            pytest.skip(
                f"Input dtype {dtype} not supported for NVFP4 Recipe {recipe.__class__.__name__}"
            )

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    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
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        grouped_linear = GroupedLinear(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=bias,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            device="cuda",
            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
            delay_wgrad_compute=delay_wgrad_compute,
            save_original_input=True,
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        ).eval()
        sequential_linear = torch.nn.ModuleList(
            [
                Linear(
                    config.hidden_size,
                    4 * config.hidden_size,
2088
                    bias=bias,
2089
                    params_dtype=dtype,
2090
                    parallel_mode=parallel_mode,
2091
                    device="cuda",
2092
                    fuse_wgrad_accumulation=fuse_wgrad_accumulation,
2093
2094
2095
2096
2097
2098
2099
2100
2101
                ).eval()
                for _ in range(num_gemms)
            ]
        )

    # Share params
    with torch.no_grad():
        for i in range(num_gemms):
            sequential_linear[i].weight = Parameter(getattr(grouped_linear, f"weight{i}").clone())
2102
2103
            if bias:
                sequential_linear[i].bias = Parameter(getattr(grouped_linear, f"bias{i}").clone())
2104
2105
2106
2107
            if fuse_wgrad_accumulation:
                weight_i = getattr(grouped_linear, f"weight{i}")
                weight_i.main_grad = torch.rand_like(weight_i, dtype=torch.float32)
                sequential_linear[i].weight.main_grad = weight_i.main_grad.clone()
2108
2109

    outputs_ref = _test_grouped_linear_accuracy(
2110
2111
2112
2113
2114
2115
2116
2117
2118
        sequential_linear,
        num_gemms,
        bs,
        dtype,
        config,
        recipe,
        fp8,
        fuse_wgrad_accumulation,
        delay_wgrad_compute,
2119
2120
    )
    outputs = _test_grouped_linear_accuracy(
2121
2122
2123
2124
2125
2126
2127
2128
2129
        grouped_linear,
        num_gemms,
        bs,
        dtype,
        config,
        recipe,
        fp8,
        fuse_wgrad_accumulation,
        delay_wgrad_compute,
2130
2131
2132
2133
2134
2135
2136
    )

    # Shoule be bit-wise match
    for i, (o, o_ref) in enumerate(zip(outputs, outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


2137
@pytest.mark.parametrize("recipe", fp8_recipes + [None])
2138
def test_grouped_linear_accuracy_single_gemm(recipe):
2139
2140
2141
2142
2143
    """Split the tests to save CI time"""
    test_grouped_linear_accuracy(
        dtype=torch.float32,
        num_gemms=1,
        bs=2,
2144
        model="126m",
2145
        recipe=recipe,
2146
        fp8_model_params=True,
2147
        fuse_wgrad_accumulation=True,
2148
2149
        bias=True,
        delay_wgrad_compute=False,
2150
2151
2152
    )


2153
def _test_padding_grouped_linear_accuracy(block, num_gemms, bs, dtype, config, recipe, fp8=False):
2154
2155

    def _pad_tensor_for_fp8(hidden_states, tokens_per_expert):
2156
        align_size = get_align_size_for_quantization(recipe)
2157
        padded_tokens_per_expert = [
2158
2159
            (num_tokens + align_size - 1) // align_size * align_size
            for num_tokens in tokens_per_expert
2160
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2209
2210
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2212
        ]
        hidden_states = torch.split(hidden_states, tokens_per_expert)
        padded_hidden_states = []
        for hidden_state, actual_num_tokens, padded_num_tokens in zip(
            hidden_states, tokens_per_expert, padded_tokens_per_expert
        ):
            padded_hidden_states.append(hidden_state)
            if padded_num_tokens > actual_num_tokens:
                pad_tensor = torch.zeros(
                    padded_num_tokens - actual_num_tokens,
                    hidden_state.shape[1],
                    dtype=hidden_state.dtype,
                    device=hidden_state.device,
                )
                padded_hidden_states.append(pad_tensor)
        padded_hidden_states = torch.cat(padded_hidden_states, dim=0)
        return padded_hidden_states, padded_tokens_per_expert

    def _unpad_tensor_for_fp8(padded_hidden_states, actual_tokens_per_expert, tokens_per_expert):
        inputmats = torch.split(
            padded_hidden_states.view(-1, padded_hidden_states.shape[-1]), tokens_per_expert
        )
        hidden_states = torch.cat(
            [
                grad_output_mat[: actual_tokens_per_expert[i]]
                for i, grad_output_mat in enumerate(inputmats)
            ],
            dim=0,
        )

        return hidden_states

    def _generate_random_numbers(n, total_sum):
        if n <= 0:
            return []

        # reset seed
        random.seed(seed)

        breaks = sorted(random.sample(range(1, total_sum), n - 1))
        random_numbers = (
            [breaks[0]]
            + [breaks[i] - breaks[i - 1] for i in range(1, n - 1)]
            + [total_sum - breaks[-1]]
        )

        return random_numbers

    reset_rng_states()
    if fp8:
        FP8GlobalStateManager.reset()

    inp_hidden_states = torch.randn(
2213
        (config.max_seqlen_q * bs, config.hidden_size),
2214
2215
2216
2217
2218
2219
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    inp_hidden_states.retain_grad()

2220
    m_splits = _generate_random_numbers(num_gemms, config.max_seqlen_q * bs)
2221

2222
    with autocast(enabled=fp8, recipe=recipe):
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
        if isinstance(block, TorchGroupedLinearWithPadding):
            out = block(inp_hidden_states, m_splits)
        else:
            if fp8:
                padded_inp_hidden_states, padding_m_splits = _pad_tensor_for_fp8(
                    inp_hidden_states, m_splits
                )
                padded_inp_hidden_states = block(padded_inp_hidden_states, padding_m_splits)
                out = _unpad_tensor_for_fp8(padded_inp_hidden_states, m_splits, padding_m_splits)
            else:
                out = block(inp_hidden_states, m_splits)

    loss = out.sum()
    loss.backward()

    torch.cuda.synchronize()
    outputs = [out, inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("num_gemms", [3, 6])
@pytest.mark.parametrize("bs", batch_sizes)
2249
@pytest.mark.parametrize("model", ["126m"])
2250
@pytest.mark.parametrize("fp8", [True])
2251
@pytest.mark.parametrize("recipe", fp8_recipes)
2252
2253
@pytest.mark.parametrize("fp8_model_params", all_boolean)
def test_padding_grouped_linear_accuracy(
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
    dtype,
    num_gemms,
    bs,
    model,
    fp8,
    recipe,
    fp8_model_params,
    parallel_mode=None,
):
    if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")

    config = model_configs[model]
2267
    if config.max_seqlen_q % 16 != 0 and fp8:
2268
2269
        pytest.skip("FP8 requires sequence length to be divisible by 16.")

2270
2271
2272
2273
2274
2275
    if recipe is not None and recipe.nvfp4():
        if dtype not in get_nvfp4_inp_supported_dtypes(recipe, dtype):
            pytest.skip(
                f"Input dtype {dtype} not supported for NVFP4 Recipe {recipe.__class__.__name__}"
            )

2276
    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
        grouped_linear = TorchGroupedLinearWithPadding(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            fp8=fp8,
        ).eval()

2287
    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
        ref_grouped_linear = GroupedLinear(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            device="cuda",
            save_original_input=False,
        ).eval()

    # Share params
    with torch.no_grad():
        inner_grouped_linear = grouped_linear.linear_fn
        for i in range(num_gemms):
            setattr(
                ref_grouped_linear,
                f"weight{i}",
                Parameter(getattr(inner_grouped_linear, f"weight{i}").clone()),
            )

    outputs = _test_padding_grouped_linear_accuracy(
        grouped_linear, num_gemms, bs, dtype, config, recipe, fp8
    )
    outputs_ref = _test_padding_grouped_linear_accuracy(
        ref_grouped_linear, num_gemms, bs, dtype, config, recipe, fp8
    )

    # Shoule be bit-wise match
    for i, (o, o_ref) in enumerate(zip(outputs, outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("num_gemms", [3])
@pytest.mark.parametrize("bs", [1])
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("fp8", [True])
@pytest.mark.parametrize("recipe", fp8_recipes)
@pytest.mark.parametrize("fp8_model_params", [False])
def test_padding_grouped_linear_accuracy_save_original_input(
    dtype,
    num_gemms,
    bs,
    model,
    fp8,
    recipe,
    fp8_model_params,
    parallel_mode=None,
2337
):
2338
2339
    if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
2340
2341
    if fp8 and recipe.delayed():
        pytest.skip("DelayedScaling recipe is not supported with save_original_input")
2342
2343

    config = model_configs[model]
2344
    if config.max_seqlen_q % 16 != 0 and fp8:
2345
2346
        pytest.skip("FP8 requires sequence length to be divisible by 16.")

2347
2348
2349
2350
2351
2352
    if recipe is not None and recipe.nvfp4():
        if dtype not in get_nvfp4_inp_supported_dtypes(recipe, dtype):
            pytest.skip(
                f"Input dtype {dtype} not supported for NVFP4 Recipe {recipe.__class__.__name__}"
            )

2353
    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
        grouped_linear = TorchGroupedLinearWithPadding(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            fp8=fp8,
        ).eval()

2364
    with quantized_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
2365
2366
2367
2368
2369
2370
2371
2372
        ref_grouped_linear = GroupedLinear(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            device="cuda",
2373
            save_original_input=True,
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
        ).eval()

    # Share params
    with torch.no_grad():
        inner_grouped_linear = grouped_linear.linear_fn
        for i in range(num_gemms):
            setattr(
                ref_grouped_linear,
                f"weight{i}",
                Parameter(getattr(inner_grouped_linear, f"weight{i}").clone()),
            )

    outputs = _test_padding_grouped_linear_accuracy(
2387
        grouped_linear, num_gemms, bs, dtype, config, recipe, fp8
2388
2389
    )
    outputs_ref = _test_padding_grouped_linear_accuracy(
2390
        ref_grouped_linear, num_gemms, bs, dtype, config, recipe, fp8
2391
2392
2393
2394
2395
2396
2397
    )

    # Shoule be bit-wise match
    for i, (o, o_ref) in enumerate(zip(outputs, outputs_ref)):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)


2398
2399
2400
2401
2402
2403
2404
def _test_gpt_e2e_cuda_graph(block, bs, dtype, config, graph):
    reset_rng_states()

    # Initialize loss function and optimizer.
    loss_fn = torch.nn.MSELoss()
    optimizer = torch.optim.SGD(block.parameters(), lr=0.1)

2405
    # Placeholders used for graph capture.
2406
    static_input = torch.randn(
2407
2408
2409
2410
        config.max_seqlen_q, bs, config.hidden_size, device="cuda", dtype=dtype, requires_grad=True
    )
    static_target = torch.randn(
        config.max_seqlen_q, bs, config.hidden_size, device="cuda", dtype=dtype
2411
    )
2412
2413
2414
2415

    real_input = torch.rand_like(static_input)
    real_target = torch.rand_like(static_target)

2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
    # Basic training loop.
    def train_step():
        optimizer.zero_grad(set_to_none=False)
        out = block(static_input)
        loss = loss_fn(out, static_target)
        loss.backward()
        optimizer.step()
        return out

    # Warmup steps in a separate stream.
    s = torch.cuda.Stream()
    s.wait_stream(torch.cuda.current_stream())
    with torch.cuda.stream(s):
        for _ in range(3):
            train_step()
    torch.cuda.current_stream().wait_stream(s)

    # Capture graph.
    g = None
    static_output = None
2436
2437
2438
    if graph:
        g = torch.cuda.CUDAGraph()
        with torch.cuda.graph(g):
2439
2440
2441
2442
2443
2444
2445
            static_output = train_step()

    # Run with new data.
    with torch.no_grad():
        static_input.copy_(real_input)
        static_target.copy_(real_target)
    if graph:
2446
2447
        g.replay()
    else:
2448
        static_output = train_step()
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461

    grads = [static_input.grad]
    for p in block.parameters():
        if p.requires_grad:
            grads.append(p.grad)

    with torch.no_grad():
        output = static_output.clone()
    return output, grads


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
2462
@pytest.mark.parametrize("model", ["126m"])
2463
def test_gpt_cuda_graph(dtype, bs, model):
2464
2465
    if NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("Cuda Graphs are not supported in debug mode.")
2466
2467
2468
2469
2470
2471
    config = model_configs[model]

    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

2472
    block_args = (
2473
2474
        config.hidden_size,
        4 * config.hidden_size,
2475
        config.num_heads,
2476
2477
    )
    block_kwargs = dict(
2478
2479
2480
2481
2482
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
2483
        kv_channels=config.kv_channels,
2484
2485
2486
2487
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        device="cuda",
2488
    )
2489
2490
2491
2492
2493
    block = TransformerLayer(*block_args, **block_kwargs)
    graphed_block = TransformerLayer(*block_args, **block_kwargs)
    with torch.no_grad():
        for param1, param2 in zip(block.parameters(), graphed_block.parameters()):
            param2.copy_(param1)
2494

2495
2496
2497
2498
    out, grads = _test_gpt_e2e_cuda_graph(block, bs, dtype, config, False)
    graphed_out, graphed_grads = _test_gpt_e2e_cuda_graph(graphed_block, bs, dtype, config, True)
    params = list(block.parameters())
    graphed_params = list(graphed_block.parameters())
2499

2500
2501
2502
2503
    # Check that results match
    assert_allclose(out, graphed_out, 1e-3)
    assert_allclose(params, graphed_params, 1e-3)
    assert_allclose(grads, graphed_grads, 1e-3)
2504
2505


2506
def _test_gpt_fp8_parameters(bs, dtype, config, fp8_model_params, recipe):
2507
2508
2509
2510
2511
2512
2513
    reset_rng_states()
    FP8GlobalStateManager.reset()

    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

2514
    with quantized_model_init(enabled=fp8_model_params, recipe=recipe):
2515
2516
2517
        block = TransformerLayer(
            config.hidden_size,
            4 * config.hidden_size,
2518
            config.num_heads,
2519
2520
2521
2522
2523
            layernorm_epsilon=config.eps,
            init_method=init_method,
            output_layer_init_method=output_layer_init_method,
            hidden_dropout=0.1,
            attention_dropout=0.1,
2524
            kv_channels=config.kv_channels,
2525
2526
2527
2528
2529
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            params_dtype=dtype,
            fuse_qkv_params=True,
            device="cuda",
2530
2531
2532
        )

    te_inp_hidden_states = torch.randn(
2533
        (config.max_seqlen_q, bs, config.hidden_size),
2534
2535
2536
2537
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
2538
    te_inp_hidden_states.retain_grad()
2539
    te_inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q)
2540

2541
    with autocast(enabled=True, recipe=recipe):
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
        te_out = block(te_inp_hidden_states, attention_mask=te_inp_attn_mask)
    loss = te_out.sum()
    loss.backward()
    torch.cuda.synchronize()

    outputs = [te_out, te_inp_hidden_states.grad]
    for p in block.parameters():
        if p.requires_grad:
            outputs.append(p.grad)
    return outputs


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
2556
@pytest.mark.parametrize("model", ["126m"])
2557
2558
@pytest.mark.parametrize("recipe", fp8_recipes)
def test_gpt_fp8_parameters(dtype, bs, model, recipe):
2559
2560
    if NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
2561

2562
2563
2564
2565
2566
2567
    if recipe.nvfp4():
        if dtype not in get_nvfp4_inp_supported_dtypes(recipe, dtype):
            pytest.skip(
                f"Input dtype {dtype} not supported for NVFP4 Recipe {recipe.__class__.__name__}"
            )

2568
2569
    config = model_configs[model]

2570
2571
    outputs = _test_gpt_fp8_parameters(bs, dtype, config, False, recipe)
    outputs_fp8_params = _test_gpt_fp8_parameters(bs, dtype, config, True, recipe)
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583

    # Check that results match
    tols = dict(rtol=0.125, atol=0.0675)
    for i, (ref, test) in enumerate(zip(outputs, outputs_fp8_params)):
        torch.testing.assert_close(
            test,
            ref,
            msg=f"Mismatch in tensor {i}",
            rtol=0.125,
            atol=0.0675,
        )

2584
2585
2586

@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
2587
@pytest.mark.parametrize("model", ["126m"])
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
def test_transformer_layer_hidden_states_format(dtype, bs, model):
    config = model_configs[model]

    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

    # Set `torch.manual_seed` to make sure the weights are identical to the
    # other layer. Set `*dropout` values to 0 to make sure the forward pass
    # is identical to the other layer.
    torch.manual_seed(0)
2599
2600
2601
    block_sbhd = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
2602
        config.num_heads,
2603
2604
2605
2606
2607
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0,
        attention_dropout=0,
2608
        kv_channels=config.kv_channels,
2609
2610
2611
2612
2613
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        device="cuda",
        attn_input_format="sbhd",
2614
2615
2616
2617
2618
2619
    )

    # Set `torch.manual_seed` to make sure the weights are identical to the
    # other layer. Set `*dropout` values to 0 to make sure the forward pass
    # is identical to the other layer.
    torch.manual_seed(0)
2620
2621
2622
    block_bshd = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
2623
        config.num_heads,
2624
2625
2626
2627
2628
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0,
        attention_dropout=0,
2629
        kv_channels=config.kv_channels,
2630
2631
2632
2633
2634
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        device="cuda",
        attn_input_format="bshd",
2635
2636
    )

2637
2638
2639
2640
    torch.manual_seed(0)
    block_thd = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
2641
        config.num_heads,
2642
2643
2644
2645
2646
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0,
        attention_dropout=0,
2647
        kv_channels=config.kv_channels,
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        device="cuda",
        attn_input_format="thd",
        self_attn_mask_type="padding_causal",
    )

    for (n1, p1), (n2, p2), (n3, p3) in zip(
        block_bshd.named_parameters(), block_sbhd.named_parameters(), block_thd.named_parameters()
    ):
        assert torch.all(torch.eq(p1, p2) & torch.eq(p1, p3)), f"{n1}, {n2} and {n3} not identical"
2660
2661

    x_sbhd = torch.randn(
2662
        (config.max_seqlen_q, bs, config.hidden_size),
2663
2664
2665
2666
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
2667

2668
    x_bshd = x_sbhd.transpose(0, 1).contiguous()
2669
2670
    x_thd = x_bshd.reshape(bs * config.max_seqlen_q, config.hidden_size).contiguous()
    x_thd_cumsum = torch.arange(bs + 1, device="cuda", dtype=torch.int32) * config.max_seqlen_q
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681

    # To make sure forward is also identical (just in case some module decides
    # to act fancy)
    torch.manual_seed(0)
    y_sbhd = block_sbhd(x_sbhd)

    # To make sure forward is also identical (just in case some module decides
    # to act fancy)
    torch.manual_seed(0)
    y_bshd = block_bshd(x_bshd)

2682
2683
2684
    # Check that results match
    torch.testing.assert_close(
        y_bshd,
2685
        y_sbhd.transpose(0, 1).contiguous(),
2686
    )
2687

2688
2689
2690
2691
2692
2693
2694
2695
2696
    # THD is not supported in float32 and on GPUs older than Ampere, skip the test here
    if dtype != torch.float32 and sm_80plus:
        # To make sure forward is also identical (just in case some module decides
        # to act fancy)
        torch.manual_seed(0)
        y_thd = block_thd(
            x_thd,
            cu_seqlens_q=x_thd_cumsum,
            cu_seqlens_kv=x_thd_cumsum,
2697
2698
            max_seqlen_q=config.max_seqlen_q,
            max_seqlen_kv=config.max_seqlen_kv,
2699
2700
2701
2702
        )

        torch.testing.assert_close(
            y_bshd,
2703
            y_thd.reshape(bs, config.max_seqlen_q, config.hidden_size).contiguous(),
2704
2705
        )

2706
2707
2708
2709
2710
2711
2712
2713
2714
2715

@pytest.mark.parametrize(
    "shape",
    [
        (1, 127, 128, 512),
        (8, 15, 128, 512),
        (8, 1027, 128, 512),
        (16, 10027, 128, 512),
    ],
)
2716
@pytest.mark.parametrize("dtype", param_types, ids=str)
2717
2718
@pytest.mark.parametrize("layout", ["TN", "NN", "NT"])
@pytest.mark.parametrize("accumulate", [False, True])
2719
2720
@pytest.mark.parametrize("use_cutlass", use_cutlass_grouped_gemm)
def test_grouped_gemm(shape, dtype, layout, accumulate, use_cutlass):
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
    torch.manual_seed(0)
    z, m, k, n = shape

    dist = torch.sort(torch.randint(0, m, (z - 1,))).values.tolist()
    m_splits = torch.tensor(dist + [m]) - torch.tensor([0] + dist)
    assert m_splits.sum() == m and len(m_splits) == z
    m_splits = m_splits.tolist()

    if layout == "TN":
        A = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # weight
2731
2732
2733
        B = list(torch.split(torch.randn(m, k, dtype=dtype, device="cuda"), m_splits))  # input
        out = [torch.randn(m, n, dtype=dtype, device="cuda")]  # output
        out_ref = [o.clone() for o in torch.split(out[0], m_splits)]
2734
        grad = False
2735
        single_output = True
2736
2737
    elif layout == "NN":
        A = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # weight
2738
2739
2740
2741
2742
        B = list(
            torch.split(torch.randn(m, n, dtype=dtype, device="cuda"), m_splits)
        )  # grad_output
        out = [torch.randn(m, k, dtype=dtype, device="cuda")]  # dgrad
        out_ref = [o.clone() for o in torch.split(out[0], m_splits)]
2743
        grad = True
2744
        single_output = True
2745
    else:  # layout == "NT"
2746
2747
2748
2749
        A = list(torch.split(torch.randn(m, k, dtype=dtype, device="cuda"), m_splits))  # input
        B = list(
            torch.split(torch.randn(m, n, dtype=dtype, device="cuda"), m_splits)
        )  # grad_output
2750
        out = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # wgrad
2751
        out_ref = [o.clone() for o in out]
2752
        grad = True
2753
        single_output = False
2754

2755
2756
2757
    if use_cutlass:
        os.environ["NVTE_USE_CUTLASS_GROUPED_GEMM"] = "1"

2758
    for i in range(z):
2759
        general_gemm(
2760
2761
            A[i],
            B[i],
2762
            dtype,
2763
2764
2765
2766
2767
            grad=grad,
            accumulate=accumulate,
            layout=layout,
            out=out_ref[i],
        )
2768
2769
    if single_output:
        out_ref = [torch.cat(out_ref)]
2770

2771
    general_grouped_gemm(
2772
        A,
2773
2774
        B,
        out,
2775
        [None] * z,
2776
        dtype,
2777
        m_splits=m_splits,
2778
2779
2780
        grad=grad,
        accumulate=accumulate,
        layout=layout,
2781
        single_output=single_output,
2782
2783
2784
    )

    for o, o_ref in zip(out, out_ref):
2785
2786
2787
2788
2789
2790
2791
2792
        if not use_cutlass:
            # cublas implementation should be bit-wise match
            torch.testing.assert_close(o, o_ref, rtol=0, atol=0)
        else:
            torch.testing.assert_close(o, o_ref, rtol=1.5e-2, atol=1.5e-2)

    if use_cutlass:
        os.environ.pop("NVTE_USE_CUTLASS_GROUPED_GEMM", None)
2793
2794


2795
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2800
2801
2802
2803
2804
2805
2806
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@pytest.mark.parametrize("N", [32])
@pytest.mark.parametrize("datatype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize(
    "input_quantizer",
    [
        Float8CurrentScalingQuantizer(fp8_dtype=tex.DType.kFloat8E4M3, device="cuda"),
        MXFP8Quantizer(fp8_dtype=tex.DType.kFloat8E4M3),
    ],
)
@pytest.mark.parametrize(
    "out_quantizer",
    [
        Float8CurrentScalingQuantizer(fp8_dtype=tex.DType.kFloat8E4M3, device="cuda"),
        MXFP8Quantizer(fp8_dtype=tex.DType.kFloat8E4M3),
        Float8Quantizer(
            torch.ones(1).cuda().squeeze(), torch.ones(1).cuda().squeeze(), tex.DType.kFloat8E4M3
        ),
    ],
)
def test_fp8gemm_with_unfused_quantization(N, datatype, input_quantizer, out_quantizer):
    # For MXFP8 and CurrentScaling, below unfused quantization should happen
    # FP8 input --> cublas GEMM --> BF16 output --> Quantize to FP8 --> fp8 Output
    # Skip invalid configurations
    is_mxfp8_needed = isinstance(input_quantizer, MXFP8Quantizer) or isinstance(
        out_quantizer, MXFP8Quantizer
    )
    if not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if is_mxfp8_needed and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    inp_fp8 = input_quantizer(torch.randn(N, N, device="cuda", dtype=datatype))
    weight_fp8 = input_quantizer(torch.randn(N, N, device="cuda", dtype=datatype))
    outp_type = torch.float32
    quantized_out, *_ = general_gemm(
        weight_fp8,
        inp_fp8,
        outp_type,
        quantization_params=out_quantizer,
        bias=None,
        use_split_accumulator=False,
    )

    out, *_ = general_gemm(
        weight_fp8,
        inp_fp8,
        outp_type,
        quantization_params=None,
        bias=None,
        use_split_accumulator=False,
    )
    expected_quantized_out = out_quantizer(out)

    # Match results again Pytorch GEMM and allow for quantization tolerance
    pytorch_out = torch.matmul(
        inp_fp8.dequantize().to(torch.float64),
        torch.transpose(weight_fp8.dequantize().to(torch.float64), 0, 1),
    )
    fp8_tols = dict(rtol=0.125, atol=0.0675)
    torch.testing.assert_close(
        pytorch_out.to(outp_type), expected_quantized_out.dequantize(), **fp8_tols
    )
    # Match results between quantization happening inside vs outside general_gemm
    torch.testing.assert_close(expected_quantized_out.dequantize(), quantized_out.dequantize())


2860
2861
2862
2863
2864
2865
2866
2867
2868
@pytest.mark.parametrize(
    "shape",
    [
        (1, 128, 128, 512),
        (8, 1024, 128, 512),
        (16, 4096, 128, 512),
    ],
)
@pytest.mark.parametrize("accumulate", [False, True])
2869
def test_fp8_grouped_gemm(shape, accumulate):
2870
2871
2872
2873
    if not fp8_available:
        pytest.skip(reason_for_no_fp8)

    z, m, k, n = shape
2874
    m_splits = [m // z] * z
2875
2876
2877
2878
2879
2880
2881
2882

    dtype = torch.bfloat16
    A = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # weight
    B = torch.split(torch.randn(m, k, dtype=dtype, device="cuda"), m_splits)  # input
    out = torch.split(torch.randn(m, n, dtype=dtype, device="cuda"), m_splits)  # output
    out_ref = [o.clone() for o in out]

    # fp8 should be robust enough to this fake scale
2883
2884
    scale = 1 + torch.rand(1, dtype=torch.float32, device="cuda").squeeze()
    amax = torch.zeros(1, 1, dtype=torch.float32, device="cuda")
2885

2886
2887
2888
2889
    a_quantizers = [
        Float8Quantizer(
            scale.clone(),
            amax.clone(),
2890
2891
            tex.DType.kFloat8E4M3,
        )
2892
        for _ in range(z)
2893
    ]
2894
2895
2896
2897
2898
    b_quantizers = [
        Float8Quantizer(
            scale.clone(),
            amax.clone(),
            tex.DType.kFloat8E4M3,
2899
        )
2900
        for _ in range(z)
2901
2902
    ]

2903
2904
2905
2906
2907
2908
    A_fp8 = []
    B_fp8 = []

    for i in range(z):
        A_fp8.append(a_quantizers[i](A[i]))
        B_fp8.append(b_quantizers[i](B[i]))
2909
2910
2911

    # baseline
    for i in range(z):
2912
        general_gemm(
2913
2914
            A_fp8[i],
            B_fp8[i],
2915
            dtype,
2916
2917
2918
            out=out_ref[i],
            accumulate=accumulate,
        )
2919
2920
2921
2922
    general_grouped_gemm(
        A_fp8,
        B_fp8,
        out,
2923
        [None] * z,
2924
        dtype,
2925
        m_splits=m_splits,
2926
2927
        accumulate=accumulate,
    )
2928
2929
2930
2931

    # should be bit-wise match
    for o, o_ref in zip(out, out_ref):
        torch.testing.assert_close(o, o_ref, rtol=0, atol=0)
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
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2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980


def test_noncontiguous():
    def _create2modules(m, params):
        mod1 = m(*params)
        mod2 = m(*params)
        for p1, p2 in zip(mod1.parameters(), mod2.parameters()):
            p2.data = p1.data.clone()

        return mod1, mod2

    def _run_module(m, inp):
        out = m(inp)
        out.sum().backward()
        ret = [out]
        if inp.grad is not None:
            ret.append(inp.grad)

        for p in m.parameters():
            if p.requires_grad:
                ret.append(p.grad)
        return ret

    a = torch.randn((128, 256), device="cuda", requires_grad=True)
    a = a.T
    assert not a.is_contiguous(), "The test is supposed to test noncontiguous input."

    b = a.contiguous()

    # LayerNorm
    ln1, ln2 = _create2modules(LayerNorm, [128])
    outT = _run_module(ln1, a)
    out = _run_module(ln2, b)

    assert_allclose(out, outT, 1e-7)

    # RMSNorm
    ln1, ln2 = _create2modules(RMSNorm, [128])
    outT = _run_module(ln1, a)
    out = _run_module(ln2, b)

    assert_allclose(out, outT, 1e-7)

    # GEMM
    g1, g2 = _create2modules(Linear, [128, 128])
    outT = _run_module(g1, a)
    out = _run_module(g2, b)

    assert_allclose(out, outT, 1e-7)