test_numerics.py 93.9 KB
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# Copyright (c) 2022-2025, 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 torch.utils.cpp_extension import IS_HIP_EXTENSION
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from transformer_engine.pytorch.fp8 import (
    FP8GlobalStateManager,
    fp8_autocast,
    fp8_model_init,
)
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from transformer_engine.pytorch.utils import (
    init_method_normal,
    scaled_init_method_normal,
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    attention_mask_func,
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    is_bf16_compatible,
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)
from transformer_engine.pytorch import (
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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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)
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from transformer_engine.pytorch import torch_version
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from transformer_engine.pytorch.distributed import checkpoint as te_checkpoint
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from transformer_engine.pytorch.cpp_extensions import general_gemm, general_grouped_gemm
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from transformer_engine.pytorch.cpp_extensions.fused_attn import FusedAttnBackend
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from transformer_engine.pytorch.tensor.float8_tensor import (
    Float8Quantizer,
    Float8CurrentScalingQuantizer,
)
from transformer_engine.pytorch.tensor.mxfp8_tensor import MXFP8Quantizer
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from transformer_engine.pytorch.module.base import get_multi_stream_cublas_workspace, get_workspace
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from transformer_engine.pytorch.utils import get_device_compute_capability
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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, get_available_attention_backends
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# Only run FP8 tests on supported devices.
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fp8_available, reason_for_no_fp8 = FP8GlobalStateManager.is_fp8_available()
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mxfp8_available, reason_for_no_mxfp8 = FP8GlobalStateManager.is_mxfp8_available()
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fp8_block_scaling_available, reason_for_no_fp8_block_scaling = FP8GlobalStateManager.is_fp8_block_scaling_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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if torch_version() >= (2, 7, 0):
    torch._dynamo.config.recompile_limit = 16
else:
    torch._dynamo.config.cache_size_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_compatible():  # 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",
    "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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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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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 is_fused_attn_available(
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    config: ModelConfig,
    dtype: torch.dtype,
    qkv_layout="bshd_bshd_bshd",
    is_training=True,
    deterministic=False,
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):
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    _, _, fused_attn_backends = get_available_attention_backends(
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        config,
        qkv_dtype=dtype,
        qkv_layout=qkv_layout,
        is_training=is_training,
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        deterministic=deterministic,
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    )
    return FusedAttnBackend["F16_arbitrary_seqlen"] in fused_attn_backends
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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)
    raise ValueError(f"Unsuppored dtype ({dtype})")


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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    "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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def _test_e2e_selective_recompute(
    bs, dtype, config, fp8, recipe, fp8_model_params=False, recompute=False
):
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    reset_rng_states()
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    FP8GlobalStateManager.reset()
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    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

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    with fp8_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
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        block = TransformerLayer(
            config.hidden_size,
            4 * config.hidden_size,
560
            config.num_heads,
561
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563
564
565
            layernorm_epsilon=config.eps,
            init_method=init_method,
            output_layer_init_method=output_layer_init_method,
            hidden_dropout=0.1,
            attention_dropout=0.1,
566
            kv_channels=config.kv_channels,
567
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569
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571
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            params_dtype=dtype,
            fuse_qkv_params=True,
            device="cuda",
572
573
574
        )

    te_inp_hidden_states = torch.randn(
575
        (config.max_seqlen_q, bs, config.hidden_size),
576
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578
579
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
580
    te_inp_hidden_states.retain_grad()
581
    te_inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q)
582

583
    with fp8_autocast(enabled=fp8, fp8_recipe=recipe):
584
585
        te_out = block(
            te_inp_hidden_states,
586
            attention_mask=te_inp_attn_mask,
587
            checkpoint_core_attention=recompute,
588
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590
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592
593
594
595
596
597
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599
600
601
        )
    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)
602
@pytest.mark.parametrize("model", ["126m"])
603
@pytest.mark.parametrize("fp8", all_boolean)
604
@pytest.mark.parametrize("recipe", fp8_recipes)
605
@pytest.mark.parametrize("fp8_model_params", all_boolean)
606
def test_gpt_selective_activation_recompute(dtype, bs, model, fp8, recipe, fp8_model_params):
607
608
    if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
609
610
611
612
613
614
615
    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)

616

617
618
    config = model_configs[model]

619
    outputs = _test_e2e_selective_recompute(
620
        bs, dtype, config, fp8, recipe, fp8_model_params, recompute=False
621
622
    )
    outputs_recompute = _test_e2e_selective_recompute(
623
        bs, dtype, config, fp8, recipe, fp8_model_params, recompute=True
624
    )
625
626
627
628
629
630
631

    # 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))
632

633
634
635
636
637
638
639
    for i, (ref, test) in enumerate(zip(outputs, outputs_recompute)):
        torch.testing.assert_close(
            test,
            ref,
            msg=f"Mismatch in tensor {i}",
            **tols,
        )
640
641


642
def _test_e2e_full_recompute(
643
    bs, dtype, config, fp8, recipe, fp8_model_params=False, recompute=False, use_reentrant=True
644
):
645
646
647
    reset_rng_states()
    FP8GlobalStateManager.reset()

648
649
650
651
    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

652
    with fp8_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
653
        block = TransformerLayer(
654
655
            config.hidden_size,
            4 * config.hidden_size,
656
            config.num_heads,
657
658
659
660
661
            layernorm_epsilon=config.eps,
            init_method=init_method,
            output_layer_init_method=output_layer_init_method,
            hidden_dropout=0.1,
            attention_dropout=0.1,
662
            kv_channels=config.kv_channels,
663
664
665
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            params_dtype=dtype,
666
            fuse_qkv_params=True,
667
            device="cuda",
668
        )
669

670
    te_inp_hidden_states = torch.randn(
671
        (config.max_seqlen_q, bs, config.hidden_size),
672
673
674
675
        dtype=dtype,
        device="cuda",
        requires_grad=use_reentrant,
    )
676
677
    if use_reentrant:
        te_inp_hidden_states.retain_grad()
678
    te_inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q)
679

680
    with fp8_autocast(enabled=fp8, fp8_recipe=recipe):
681
682
683
684
685
686
        if recompute:
            te_out = te_checkpoint(
                block,
                te_inp_hidden_states,
                attention_mask=te_inp_attn_mask,
                checkpoint_core_attention=False,
687
688
689
                distribute_saved_activations=False,
                tp_group=None,
                use_reentrant=use_reentrant,
690
691
692
693
694
695
696
697
698
699
700
            )
        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()

701
702
703
704
705
706
    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():
707
708
        if p.requires_grad:
            outputs.append(p.grad)
709
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711
            names.append(name)

    return outputs, names
712
713
714
715


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
716
@pytest.mark.parametrize("model", ["126m"])
717
@pytest.mark.parametrize("fp8", all_boolean)
718
@pytest.mark.parametrize("recipe", fp8_recipes)
719
@pytest.mark.parametrize("fp8_model_params", all_boolean)
720
@pytest.mark.parametrize("use_reentrant", all_boolean)
721
722
723
def test_gpt_full_activation_recompute(
    dtype, bs, model, fp8, recipe, fp8_model_params, use_reentrant
):
724
725
726
727
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
728
729
    if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
730
731
732
    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)

733
734
735

    config = model_configs[model]

736
737
738
739
    if not use_reentrant:
        # Non-reentrant checkpoint becomes non-deterministic with bias+GELU fusion
        os.environ["NVTE_BIAS_GELU_NVFUSION"] = "0"

740
    outputs, names = _test_e2e_full_recompute(
741
742
743
744
745
746
747
748
        bs,
        dtype,
        config,
        fp8,
        recipe,
        fp8_model_params,
        recompute=False,
        use_reentrant=use_reentrant,
749
750
    )
    outputs_recompute, _ = _test_e2e_full_recompute(
751
752
753
754
755
756
757
758
        bs,
        dtype,
        config,
        fp8,
        recipe,
        fp8_model_params,
        recompute=True,
        use_reentrant=use_reentrant,
759
    )
760
761
762
763
764

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

765
766
767
768
769
770
771
772
773
774
775
776
777
    # 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,
        )
778
779
780
781
782
783


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)
784

785
786
787
    return TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
788
        config.num_heads,
789
790
791
792
793
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
794
        kv_channels=config.kv_channels,
795
796
797
798
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        params_dtype=dtype,
        device="cuda",
799
800
801
802
803
804
805
    )


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

    te_inp_hidden_states = torch.randn(
806
        (config.max_seqlen_q, bs, config.hidden_size),
807
808
809
810
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
811
812
813
814
815
816
817
    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,
818
            None,
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
        )
        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())

836
837
838
        _cpu_rng_state = torch.get_rng_state()
        _cuda_rng_state = torch.cuda.get_rng_state()

839
840
        del block
        block = _test_e2e_checkpointing_get_model(config, dtype)
841
        block.load_state_dict(torch.load(path, weights_only=False))
842
843
        torch.set_rng_state(_cpu_rng_state)
        torch.cuda.set_rng_state(_cuda_rng_state)
844
845
846
847
848
849
850
851
852
853

        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,
854
            None,
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
        )
        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)
873
@pytest.mark.parametrize("model", ["126m"])
874
875
def test_gpt_checkpointing(dtype, bs, model):
    config = model_configs[model]
876
    if not is_fused_attn_available(config, dtype, deterministic=True):
877
        pytest.skip("No attention backend available.")
878
    outputs = _test_e2e_checkpointing(bs, dtype, config, checkpoint=False)
879
    outputs_checkpoint = _test_e2e_checkpointing(bs, dtype, config, checkpoint=True)
880
881
882
883
884
885
886
887
888
889
890
891

    # 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,
        )
892
893
894
895
896
897


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

    inp_hidden_states = torch.randn(
898
        (config.max_seqlen_q, bs, config.hidden_size),
899
900
901
902
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
903
    inp_hidden_states.retain_grad()
904
    inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q)
905

906
    out = block(inp_hidden_states, attention_mask=inp_attn_mask)
907
908
909
910
911
912
913
914
915
916
917
918
919
    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)
920
@pytest.mark.parametrize("model", ["small"])
921
922
@pytest.mark.parametrize("parallel_attention_mlp", all_boolean)
def test_gpt_accuracy(dtype, bs, model, parallel_attention_mlp):
923
    config = model_configs[model]
924
925
926
    if not is_fused_attn_available(
        config, dtype, qkv_layout="sb3hd", is_training=True, deterministic=True
    ):
927
        pytest.skip("No attention backend available.")
928

929
930
931
    te_gpt = TransformerLayer(
        hidden_size=config.hidden_size,
        ffn_hidden_size=4 * config.hidden_size,
932
        num_attention_heads=config.num_heads,
933
934
935
936
937
938
939
940
941
        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()
942
943
944
945
946

    torch_gpt = (
        TorchGPT(
            config.hidden_size,
            config.eps,
947
            config.num_heads,
948
            parallel_attention_mlp=parallel_attention_mlp,
949
950
951
952
953
954
955
956
        )
        .to(dtype=dtype)
        .cuda()
        .eval()
    )

    # Share params
    with torch.no_grad():
957
        torch_gpt.ln.weight = Parameter(
958
959
            te_gpt.self_attention.layernorm_qkv.layer_norm_weight.clone()
        )
960
        torch_gpt.ln.bias = Parameter(te_gpt.self_attention.layernorm_qkv.layer_norm_bias.clone())
961
962
963
964
965
966
967
968
969
970
971
972
        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()
        )
973
974
975
976
977
978
        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())
979
980
981
982

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

983
984
985
986
987
988
    atol = {
        torch.float32: 5e-3,
        torch.half: 5e-2,
        torch.bfloat16: 1e-1,
    }

989
    # Check output.
990
991
992
993
994
995
996
997
998
999
1000
1001
    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])
1002
1003


1004
def _test_mha_accuracy(block, bs, dtype, config, mask_type, te=True):
1005
1006
1007
    reset_rng_states()

    inp_hidden_states = torch.randn(
1008
        (config.max_seqlen_q, bs, config.hidden_size),
1009
1010
1011
1012
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
1013
    inp_hidden_states.retain_grad()
1014
    inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q) if mask_type == "causal" else None
1015

1016
1017
1018
1019
1020
1021
    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)
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
    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)
1035
@pytest.mark.parametrize("model", ["small"])
1036
1037
1038
@pytest.mark.parametrize("mask_type", mask_types)
def test_mha_accuracy(dtype, bs, model, mask_type):
    config = model_configs[model]
1039
1040
1041
    if not is_fused_attn_available(
        config, dtype, qkv_layout="sb3hd", is_training=True, deterministic=True
    ):
1042
        pytest.skip("No attention backend available.")
1043

1044
1045
    te_mha = MultiheadAttention(
        config.hidden_size,
1046
        config.num_heads,
1047
1048
1049
1050
1051
1052
        fuse_qkv_params=True,
        params_dtype=dtype,
        qkv_weight_interleaved=False,
        input_layernorm=False,
        device="cuda",
    ).eval()
1053
1054
1055
1056

    torch_mha = (
        TorchMHA(
            config.hidden_size,
1057
            config.num_heads,
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
        )
        .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())

1071
1072
    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)
1073
1074
1075
1076
1077
1078
1079

    # 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)

1080
1081
1082
1083
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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])

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def _test_granular_accuracy(block, bs, dtype, config, delay_wgrad_compute=False, recipe=None):
1097
    reset_rng_states()
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    fp8 = recipe is not None
    if fp8:
        FP8GlobalStateManager.reset()
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    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,
    )
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    inp_hidden_states.retain_grad()

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    with fp8_autocast(enabled=fp8, fp8_recipe=recipe):
        out = block(inp_hidden_states)
        if isinstance(out, (List, Tuple)):
            out = out[0]
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    loss = out.sum()
    loss.backward()
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    if delay_wgrad_compute:
        block.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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def _test_dpa_accuracy(block, bs, dtype, config):
    reset_rng_states()

1134
    mask = torch.triu(
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        torch.ones(config.max_seqlen_q, config.max_seqlen_kv, dtype=torch.bool, device="cuda"),
        diagonal=1,
1137
    )
1138
    query, key, value = [
1139
        torch.randn(
1140
            (config.max_seqlen_q, bs, config.num_heads, config.kv_channels),
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            dtype=dtype,
            device="cuda",
            requires_grad=True,
        )
        for _ in range(3)
    ]
1147
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    query.retain_grad()
    key.retain_grad()
    value.retain_grad()

1152
    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)
1163
@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,
1171
            attention_dropout=0.0,  # disable dropout, FU uses rng differently
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        )
        .to(dtype=dtype)
        .cuda()
    )

    torch_dpa = (
        TorchDotProductAttention(
1179
            config.kv_channels,
1180
            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)

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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)
1217
@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]

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    te_linear = TestReturnBiasModule(
        Linear,
        in_features=config.hidden_size,
        out_features=4 * config.hidden_size,
1227
        params_dtype=dtype,
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        return_bias=return_bias,
        bias=bias,
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        device="cuda",
1231
    )
1232

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    torch_linear = torch.nn.Linear(
        config.hidden_size,
        4 * config.hidden_size,
1236
        bias=bias,
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        device="cuda",
        dtype=dtype,
1239
    )
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    # 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])
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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):
    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
    )

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    # Should be bit-wise match
    for _, (o, o_ref) in enumerate(zip(te_outputs, te_outputs_ref)):
1307
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        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
1318
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    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
1320
1321
    if fp8 and recipe.delayed():
        pytest.skip("DelayedScaling recipe is not supported with save_original_input")
1322
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1325
    if fp8 and recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    if fp8 and recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)
1326
1327

    config = model_configs[model]
1328
    if config.max_seqlen_q % 16 != 0 and fp8:
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        pytest.skip("FP8 requires sequence length to be divisible by 16.")

    with fp8_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        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)

    # 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)
1370
@pytest.mark.parametrize("model", ["126m"])
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@pytest.mark.parametrize("eps", [1e-1, 1e-3, 1e-5, 1e-7])
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@pytest.mark.parametrize("zero_centered_gamma", all_boolean)
def test_rmsnorm_accuracy(dtype, bs, model, eps, zero_centered_gamma):
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    config = model_configs[model]

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    te_rmsnorm = RMSNorm(
        config.hidden_size,
        eps=eps,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
        device="cuda",
    ).eval()
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    torch_rmsnorm = (
1385
        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)

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    atol = {
        torch.float32: 1e-7,
        torch.half: 2e-3,
        torch.bfloat16: 2e-2,
1402
    }
1403
1404

    # Check output.
1405
1406
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype])

1407
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    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])

1417

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@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
1420
@pytest.mark.parametrize("model", ["126m"])
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@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]

1426
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1432
    te_layernorm = LayerNorm(
        config.hidden_size,
        eps=eps,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
        device="cuda",
    ).eval()
1433
1434

    torch_layernorm = (
1435
        TorchLayerNorm(config.hidden_size, eps=eps, zero_centered_gamma=zero_centered_gamma)
1436
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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)

1449
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1451
1452
    atol = {
        torch.float32: 1e-7,
        torch.half: 2e-3,
        torch.bfloat16: 2e-2,
1453
    }
1454
1455

    # Check output.
1456
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype])
1457

1458
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1461
1462
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1464
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    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])

1468

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@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
1471
@pytest.mark.parametrize("model", ["small"])
1472
@pytest.mark.parametrize("normalization", all_normalizations)
1473
@pytest.mark.parametrize("zero_centered_gamma", all_boolean)
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@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
):
1479
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    config = model_configs[model]

1481
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    te_ln_linear = TestReturnBiasModule(
        LayerNormLinear,
        in_features=config.hidden_size,
        out_features=4 * config.hidden_size,
        eps=config.eps,
1486
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1488
        normalization=normalization,
        params_dtype=dtype,
        zero_centered_gamma=zero_centered_gamma,
1489
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        return_bias=return_bias,
        bias=bias,
1491
        device="cuda",
1492
    )
1493
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1498

    torch_ln_linear = (
        TorchLayerNormLinear(
            config.hidden_size,
            4 * config.hidden_size,
            config.eps,
1499
            normalization=normalization,
1500
            zero_centered_gamma=zero_centered_gamma,
1501
            bias=bias,
1502
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1504
1505
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1507
1508
        )
        .to(dtype=dtype)
        .cuda()
    )

    # Share params
    with torch.no_grad():
1509
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1511
        torch_ln_linear.layernorm.weight = Parameter(
            te_ln_linear.te_module.layer_norm_weight.clone()
        )
1512
        if normalization != "RMSNorm":
1513
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            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())
1519
1520
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1522

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

1523
1524
1525
1526
    atol = {
        torch.float32: 2.5e-4,
        torch.half: 2e-3,
        torch.bfloat16: 2e-2,
1527
    }
1528
1529
1530
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1532
    rtol = {
        torch.float32: 1e-3,
        torch.half: 4e-2,
        torch.bfloat16: 4e-2,
    }
1533
1534

    # Check output.
1535
    assert_allclose(te_outputs[0], torch_outputs[0], atol[dtype], rtol[dtype])
1536

1537
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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])

1552

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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
):
    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)
1616
@pytest.mark.parametrize("model", ["small"])
1617
@pytest.mark.parametrize("activation", all_activations)
1618
@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):
1622
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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,
1633
        device="cuda",
1634
    )
1635
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    torch_ln_mlp = (
        TorchLayerNormMLP(
            config.hidden_size,
            4 * config.hidden_size,
1640
            activation=activation,
1641
            normalization=normalization,
1642
            bias=bias,
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        )
        .to(dtype=dtype)
        .cuda()
    )

    # Share params
    with torch.no_grad():
1650
        torch_ln_mlp.ln.weight = Parameter(te_ln_mlp.te_module.layer_norm_weight.clone())
1651
        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.
1675
    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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1690
@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(
1696
    dtype, bs, model, bias, fuse_wgrad_accumulation
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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())
1725
        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)


1747
def _test_grouped_linear_accuracy(
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    block,
    num_gemms,
    bs,
    dtype,
    config,
    recipe,
    fp8,
    fuse_wgrad_accumulation,
    delay_wgrad_compute=False,
1757
):
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    reset_rng_states()
    if fp8:
        FP8GlobalStateManager.reset()

    inp_hidden_states = torch.randn(
1763
        (config.max_seqlen_q, bs, config.hidden_size),
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        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    inp_hidden_states.retain_grad()

1770
    if num_gemms > 1:
1771
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        split_size = 1
        if fp8:
1773
            split_size = 16
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            if recipe.mxfp8():
                split_size = 128
1776
        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
1782
    else:
1783
        m_splits = torch.tensor([config.max_seqlen_q])
1784

1785
    with fp8_autocast(enabled=fp8, 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


1817
@pytest.mark.parametrize("dtype", param_types, ids=str)
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1819
@pytest.mark.parametrize("num_gemms", [3, 6])
@pytest.mark.parametrize("bs", batch_sizes)
1820
@pytest.mark.parametrize("model", ["126m"])
1821
@pytest.mark.parametrize("recipe", fp8_recipes + [None])
1822
@pytest.mark.parametrize("fp8_model_params", all_boolean)
1823
@pytest.mark.parametrize("fuse_wgrad_accumulation", all_boolean)
1824
1825
@pytest.mark.parametrize("bias", all_boolean)
@pytest.mark.parametrize("delay_wgrad_compute", all_boolean)
1826
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,
1836
    parallel_mode=None,
1837
    use_cutlass=False,
1838
):
1839
    fp8 = recipe is not None
1840
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    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if fp8 and recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    if fp8 and recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)
1846
    if fp8 and fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
1847
        pytest.skip("FP8 parameters are not supported in debug mode.")
1848
1849

    config = model_configs[model]
1850
    if config.max_seqlen_q % 16 != 0 and fp8:
1851
1852
        pytest.skip("FP8 requires sequence length to be divisible by 16.")

1853
    with fp8_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
1854
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1856
1857
        grouped_linear = GroupedLinear(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
1858
            bias=bias,
1859
            params_dtype=dtype,
1860
            parallel_mode=parallel_mode,
1861
            device="cuda",
1862
            fuse_wgrad_accumulation=fuse_wgrad_accumulation,
1863
            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()
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    if IS_HIP_EXTENSION:
        os.environ["NVTE_FORCE_ROCM_GEMM"] = "1"
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    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,
    )
1915
1916
    if IS_HIP_EXTENSION:
        os.environ["NVTE_FORCE_ROCM_GEMM"] = "0"
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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
1982
    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 fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if fp8 and recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    if fp8 and recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)
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    config = model_configs[model]
1994
    if config.max_seqlen_q % 16 != 0 and fp8:
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2008
        pytest.skip("FP8 requires sequence length to be divisible by 16.")

    with fp8_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        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,
2009
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2014
        ).eval()
        sequential_linear = torch.nn.ModuleList(
            [
                Linear(
                    config.hidden_size,
                    4 * config.hidden_size,
2015
                    bias=bias,
2016
                    params_dtype=dtype,
2017
                    parallel_mode=parallel_mode,
2018
                    device="cuda",
2019
                    fuse_wgrad_accumulation=fuse_wgrad_accumulation,
2020
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                ).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())
2029
2030
            if bias:
                sequential_linear[i].bias = Parameter(getattr(grouped_linear, f"bias{i}").clone())
2031
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2034
            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()
2035

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2039
    # Force the sequential_linear and grouped_linear to use hipblaslt rather than hipblas
    if IS_HIP_EXTENSION:
        os.environ["NVTE_FORCE_ROCM_GEMM"] = "1"
    
2040
    outputs_ref = _test_grouped_linear_accuracy(
2041
2042
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2046
2047
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2049
        sequential_linear,
        num_gemms,
        bs,
        dtype,
        config,
        recipe,
        fp8,
        fuse_wgrad_accumulation,
        delay_wgrad_compute,
2050
2051
    )
    outputs = _test_grouped_linear_accuracy(
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2053
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2057
2058
2059
2060
        grouped_linear,
        num_gemms,
        bs,
        dtype,
        config,
        recipe,
        fp8,
        fuse_wgrad_accumulation,
        delay_wgrad_compute,
2061
    )
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2063
    if IS_HIP_EXTENSION:
        os.environ["NVTE_FORCE_ROCM_GEMM"] = "0"
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    # 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)


2070
@pytest.mark.parametrize("recipe", fp8_recipes + [None])
2071
def test_grouped_linear_accuracy_single_gemm(recipe):
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2076
    """Split the tests to save CI time"""
    test_grouped_linear_accuracy(
        dtype=torch.float32,
        num_gemms=1,
        bs=2,
2077
        model="126m",
2078
        recipe=recipe,
2079
        fp8_model_params=True,
2080
        fuse_wgrad_accumulation=True,
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        bias=True,
        delay_wgrad_compute=False,
2083
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2085
    )


2086
def _test_padding_grouped_linear_accuracy(block, num_gemms, bs, dtype, config, recipe, fp8=False):
2087
2088

    def _pad_tensor_for_fp8(hidden_states, tokens_per_expert):
2089
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        align_size = 16
        if recipe.mxfp8():
            align_size = 32
2092
        padded_tokens_per_expert = [
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            (num_tokens + align_size - 1) // align_size * align_size
            for num_tokens in tokens_per_expert
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        ]
        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(
2148
        (config.max_seqlen_q * bs, config.hidden_size),
2149
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2154
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    inp_hidden_states.retain_grad()

2155
    m_splits = _generate_random_numbers(num_gemms, config.max_seqlen_q * bs)
2156

2157
    with fp8_autocast(enabled=fp8, fp8_recipe=recipe):
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        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)
2184
@pytest.mark.parametrize("model", ["126m"])
2185
@pytest.mark.parametrize("fp8", [True])
2186
@pytest.mark.parametrize("recipe", fp8_recipes)
2187
2188
@pytest.mark.parametrize("fp8_model_params", all_boolean)
def test_padding_grouped_linear_accuracy(
2189
2190
2191
2192
2193
2194
2195
2196
2197
    dtype,
    num_gemms,
    bs,
    model,
    fp8,
    recipe,
    fp8_model_params,
    parallel_mode=None,
):
2198
2199
2200
2201
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
2202
2203
    if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
2204
2205
    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)
2206
2207

    config = model_configs[model]
2208
    if config.max_seqlen_q % 16 != 0 and fp8:
2209
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2213
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2216
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2220
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2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
        pytest.skip("FP8 requires sequence length to be divisible by 16.")

    with fp8_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        grouped_linear = TorchGroupedLinearWithPadding(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            fp8=fp8,
        ).eval()

    with fp8_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
        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,
2272
):
2273
2274
    if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
2275
2276
    if fp8 and recipe.delayed():
        pytest.skip("DelayedScaling recipe is not supported with save_original_input")
2277
2278
2279
2280
2281
2282
    if fp8 and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)
2283
2284

    config = model_configs[model]
2285
    if config.max_seqlen_q % 16 != 0 and fp8:
2286
2287
        pytest.skip("FP8 requires sequence length to be divisible by 16.")

2288
    with fp8_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
        grouped_linear = TorchGroupedLinearWithPadding(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            fp8=fp8,
        ).eval()

2299
    with fp8_model_init(enabled=fp8 and fp8_model_params, recipe=recipe):
2300
2301
2302
2303
2304
2305
2306
2307
        ref_grouped_linear = GroupedLinear(
            num_gemms,
            config.hidden_size,
            4 * config.hidden_size,
            bias=False,
            params_dtype=dtype,
            parallel_mode=parallel_mode,
            device="cuda",
2308
            save_original_input=True,
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
        ).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(
2322
        grouped_linear, num_gemms, bs, dtype, config, recipe, fp8
2323
2324
    )
    outputs_ref = _test_padding_grouped_linear_accuracy(
2325
        ref_grouped_linear, num_gemms, bs, dtype, config, recipe, fp8
2326
2327
2328
2329
2330
2331
2332
    )

    # 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)


2333
2334
2335
2336
2337
2338
2339
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)

2340
    # Placeholders used for graph capture.
2341
    static_input = torch.randn(
2342
2343
2344
2345
        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
2346
    )
2347
2348
2349
2350

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

2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
    # 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
2371
2372
2373
    if graph:
        g = torch.cuda.CUDAGraph()
        with torch.cuda.graph(g):
2374
2375
2376
2377
2378
2379
2380
            static_output = train_step()

    # Run with new data.
    with torch.no_grad():
        static_input.copy_(real_input)
        static_target.copy_(real_target)
    if graph:
2381
2382
        g.replay()
    else:
2383
        static_output = train_step()
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396

    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)
2397
@pytest.mark.parametrize("model", ["126m"])
2398
def test_gpt_cuda_graph(dtype, bs, model):
2399
2400
    if NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("Cuda Graphs are not supported in debug mode.")
2401
2402
2403
2404
2405
2406
    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)

2407
    block_args = (
2408
2409
        config.hidden_size,
        4 * config.hidden_size,
2410
        config.num_heads,
2411
2412
    )
    block_kwargs = dict(
2413
2414
2415
2416
2417
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
2418
        kv_channels=config.kv_channels,
2419
2420
2421
2422
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        device="cuda",
2423
    )
2424
2425
2426
2427
2428
    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)
2429

2430
2431
2432
2433
    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())
2434

2435
2436
2437
2438
    # 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)
2439
2440


2441
def _test_gpt_fp8_parameters(bs, dtype, config, fp8_model_params, recipe):
2442
2443
2444
2445
2446
2447
2448
    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)

2449
    with fp8_model_init(enabled=fp8_model_params, recipe=recipe):
2450
2451
2452
        block = TransformerLayer(
            config.hidden_size,
            4 * config.hidden_size,
2453
            config.num_heads,
2454
2455
2456
2457
2458
            layernorm_epsilon=config.eps,
            init_method=init_method,
            output_layer_init_method=output_layer_init_method,
            hidden_dropout=0.1,
            attention_dropout=0.1,
2459
            kv_channels=config.kv_channels,
2460
2461
2462
2463
2464
            apply_residual_connection_post_layernorm=False,
            output_layernorm=False,
            params_dtype=dtype,
            fuse_qkv_params=True,
            device="cuda",
2465
2466
2467
        )

    te_inp_hidden_states = torch.randn(
2468
        (config.max_seqlen_q, bs, config.hidden_size),
2469
2470
2471
2472
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
2473
    te_inp_hidden_states.retain_grad()
2474
    te_inp_attn_mask = get_causal_attn_mask(config.max_seqlen_q)
2475

2476
    with fp8_autocast(enabled=True, fp8_recipe=recipe):
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
        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)
2491
@pytest.mark.parametrize("model", ["126m"])
2492
2493
@pytest.mark.parametrize("recipe", fp8_recipes)
def test_gpt_fp8_parameters(dtype, bs, model, recipe):
2494
2495
2496
2497
    if not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if recipe.mxfp8() and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)
2498
2499
    if NVTE_TEST_NVINSPECT_ENABLED:
        pytest.skip("FP8 parameters are not supported in debug mode.")
2500
2501
    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
        pytest.skip(reason_for_no_fp8_block_scaling)
2502
2503
2504

    config = model_configs[model]

2505
2506
    outputs = _test_gpt_fp8_parameters(bs, dtype, config, False, recipe)
    outputs_fp8_params = _test_gpt_fp8_parameters(bs, dtype, config, True, recipe)
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518

    # 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,
        )

2519
2520
2521

@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes)
2522
@pytest.mark.parametrize("model", ["126m"])
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
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)
2534
2535
2536
    block_sbhd = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
2537
        config.num_heads,
2538
2539
2540
2541
2542
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0,
        attention_dropout=0,
2543
        kv_channels=config.kv_channels,
2544
2545
2546
2547
2548
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        device="cuda",
        attn_input_format="sbhd",
2549
2550
2551
2552
2553
2554
    )

    # 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)
2555
2556
2557
    block_bshd = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
2558
        config.num_heads,
2559
2560
2561
2562
2563
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0,
        attention_dropout=0,
2564
        kv_channels=config.kv_channels,
2565
2566
2567
2568
2569
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        device="cuda",
        attn_input_format="bshd",
2570
2571
    )

2572
2573
2574
2575
    torch.manual_seed(0)
    block_thd = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
2576
        config.num_heads,
2577
2578
2579
2580
2581
        layernorm_epsilon=config.eps,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0,
        attention_dropout=0,
2582
        kv_channels=config.kv_channels,
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
        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"
2595
2596

    x_sbhd = torch.randn(
2597
        (config.max_seqlen_q, bs, config.hidden_size),
2598
2599
2600
2601
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
2602

2603
    x_bshd = x_sbhd.transpose(0, 1).contiguous()
2604
2605
    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
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616

    # 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)

2617
2618
2619
    # Check that results match
    torch.testing.assert_close(
        y_bshd,
2620
        y_sbhd.transpose(0, 1).contiguous(),
2621
    )
2622

2623
2624
2625
2626
2627
2628
2629
2630
2631
    # 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,
2632
2633
            max_seqlen_q=config.max_seqlen_q,
            max_seqlen_kv=config.max_seqlen_kv,
2634
2635
2636
2637
        )

        torch.testing.assert_close(
            y_bshd,
2638
            y_thd.reshape(bs, config.max_seqlen_q, config.hidden_size).contiguous(),
2639
        )
2640

2641
2642
2643
2644
2645
2646
2647
2648
2649
2650

@pytest.mark.parametrize(
    "shape",
    [
        (1, 127, 128, 512),
        (8, 15, 128, 512),
        (8, 1027, 128, 512),
        (16, 10027, 128, 512),
    ],
)
2651
@pytest.mark.parametrize("dtype", param_types, ids=str)
2652
2653
@pytest.mark.parametrize("layout", ["TN", "NN", "NT"])
@pytest.mark.parametrize("accumulate", [False, True])
2654
2655
@pytest.mark.parametrize("use_cutlass", use_cutlass_grouped_gemm)
def test_grouped_gemm(shape, dtype, layout, accumulate, use_cutlass):
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
    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
2666
2667
2668
        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)]
2669
        grad = False
2670
        single_output = True
2671
2672
    elif layout == "NN":
        A = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # weight
2673
2674
2675
2676
2677
        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)]
2678
        grad = True
2679
        single_output = True
2680
    else:  # layout == "NT"
2681
2682
2683
2684
        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
2685
        out = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # wgrad
2686
        out_ref = [o.clone() for o in out]
2687
        grad = True
2688
        single_output = False
2689

2690
2691
2692
    if use_cutlass:
        os.environ["NVTE_USE_CUTLASS_GROUPED_GEMM"] = "1"

2693
2694
    # Force the sequential_linear and grouped_linear to use hipblaslt rather than hipblas
    if IS_HIP_EXTENSION:
2695
        ori_force_rocm_gemm = os.environ.get("NVTE_FORCE_ROCM_GEMM", None)
2696
2697
        os.environ["NVTE_FORCE_ROCM_GEMM"] = "1"

2698
    for i in range(z):
2699
        general_gemm(
2700
2701
2702
            A[i],
            B[i],
            get_workspace(),
2703
            dtype,
2704
2705
2706
2707
2708
            grad=grad,
            accumulate=accumulate,
            layout=layout,
            out=out_ref[i],
        )
2709
2710
    if single_output:
        out_ref = [torch.cat(out_ref)]
2711

2712
    general_grouped_gemm(
2713
        A,
2714
2715
        B,
        out,
2716
2717
        dtype,
        get_multi_stream_cublas_workspace(),
2718
        m_splits=m_splits,
2719
2720
2721
        grad=grad,
        accumulate=accumulate,
        layout=layout,
2722
        single_output=single_output,
2723
    )
2724
    if IS_HIP_EXTENSION:
2725
2726
2727
2728
        if ori_force_rocm_gemm is not None:
            os.environ["NVTE_FORCE_ROCM_GEMM"] = ori_force_rocm_gemm
        else:
            del os.environ["NVTE_FORCE_ROCM_GEMM"]
2729
2730

    for o, o_ref in zip(out, out_ref):
2731
2732
2733
2734
2735
2736
2737
2738
        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)
2739
2740


2741
2742
2743
2744
2745
2746
2747
2748
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2750
2751
2752
2753
2754
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2800
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2804
2805
@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,
        get_workspace(),
        outp_type,
        quantization_params=out_quantizer,
        bias=None,
        use_split_accumulator=False,
    )

    out, *_ = general_gemm(
        weight_fp8,
        inp_fp8,
        get_workspace(),
        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())
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816


@pytest.mark.parametrize(
    "shape",
    [
        (1, 128, 128, 512),
        (8, 1024, 128, 512),
        (16, 4096, 128, 512),
    ],
)
@pytest.mark.parametrize("accumulate", [False, True])
2817
def test_fp8_grouped_gemm(shape, accumulate):
2818
2819
2820
2821
    if not fp8_available:
        pytest.skip(reason_for_no_fp8)

    z, m, k, n = shape
2822
    m_splits = [m // z] * z
2823
2824
2825
2826
2827
2828
2829
2830

    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
2831
2832
    scale = 1 + torch.rand(1, dtype=torch.float32, device="cuda").squeeze()
    amax = torch.zeros(1, 1, dtype=torch.float32, device="cuda")
2833

2834
2835
2836
2837
    a_quantizers = [
        Float8Quantizer(
            scale.clone(),
            amax.clone(),
2838
2839
            tex.DType.kFloat8E4M3,
        )
2840
        for _ in range(z)
2841
    ]
2842
2843
2844
2845
2846
    b_quantizers = [
        Float8Quantizer(
            scale.clone(),
            amax.clone(),
            tex.DType.kFloat8E4M3,
2847
        )
2848
        for _ in range(z)
2849
2850
    ]

2851
2852
2853
2854
2855
2856
    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]))
2857
2858
2859

    # baseline
    for i in range(z):
2860
        general_gemm(
2861
2862
2863
            A_fp8[i],
            B_fp8[i],
            get_workspace(),
2864
            dtype,
2865
2866
2867
            out=out_ref[i],
            accumulate=accumulate,
        )
2868
2869
2870
2871
2872
2873
    general_grouped_gemm(
        A_fp8,
        B_fp8,
        out,
        dtype,
        get_multi_stream_cublas_workspace(),
2874
        m_splits=m_splits,
2875
2876
        accumulate=accumulate,
    )
2877
2878
2879
2880

    # 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)
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929


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)