diff --git a/test_flashmask_use_varlen.py b/test_flashmask_use_varlen.py
new file mode 100644
index 0000000..ddcbadf
--- /dev/null
+++ b/test_flashmask_use_varlen.py
@@ -0,0 +1,217 @@
+"""
+Test: FlashMask (Paddle) vs flash_attn_varlen_func (PyTorch) via convert_to_varlen.
+
+Workflow:
+  1. Generate q, k, v, causal, startend_row_indices (Paddle tensors, padded layout).
+  2. Call convert_to_varlen() to transform startend_row_indices into varlen format:
+       - q_varlen, k_varlen, v_varlen: concatenated Paddle tensors (total_q, nheads, d)
+       - cu_seqlens_q, cu_seqlens_k: cumulative sequence lengths (Paddle, int32)
+       - max_seqlen_q, max_seqlen_k: maximum sequence lengths (int)
+  3. Call Paddle's flashmask_attention with the original padded input.
+  4. Convert varlen tensors from Paddle to PyTorch, then call PyTorch's
+     flash_attn_varlen_func.
+  5. Compare the two outputs via np.allclose.
+"""
+
+import os
+import glob
+import math
+import itertools
+import pytest
+import numpy as np
+from functools import partial
+
+import paddle
+
+# ── Paddle: flashmask_attention ──────────────────────────────────────────────
+# from flash_mask import flashmask_attention
+from paddlefleet.ops.flash_mask import flashmask_attention
+# import flash_mask
+
+# ── Mask generators (Paddle) ────────────────────────────────────────────────
+from generate_startend_row_indices import (
+    startend_row_indices_to_attn_bias,
+    generate_causal_document_mask,
+    generate_document_mask,
+    generate_causal_document_mask_diff_batch,
+    generate_document_mask_diff_batch,
+    generate_document_mask_simu,
+    generate_document_mask_diff_batch_simu,
+)
+
+from test_util import attention_ref
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Test parameters
+# ─────────────────────────────────────────────────────────────────────────────
+
+# (batch_size, seqlen_q, seqlen_k, nheads, nheads_kv)
+shape_cases = [
+    (2840, 32, 32, 16, 4),
+    (1, 300, 300, 16, 16),
+    (1, 256, 256, 4, 4),
+    (2, 512, 512, 8, 2),
+    (1, 1024, 1024, 4, 1),
+    (2, 300, 300, 6, 2),
+    (1, 128, 128, 1, 1),
+    (2, 1000, 1000, 4, 1),
+    (2, 8192, 8192, 4, 1),
+    (2, 8192, 8192, 14, 1),
+    (2, 16384, 16384, 4, 1),
+    (2, 1000, 1000, 4, 1),
+    (2, 2000, 2000, 4, 1),
+    (2, 3000, 3000, 4, 1),
+    (1, 4000, 4000, 1, 1),
+    (2, 7600, 7600, 32, 8),
+]
+
+
+def generate_shapes():
+    for batch_size, seqlen_q, seqlen_k, nheads, nheads_kv in shape_cases:
+        nheads_startend_row_indices = 1
+        yield (batch_size, seqlen_q, seqlen_k, nheads, nheads_kv, nheads_startend_row_indices)
+
+
+# Only test mask types that are compatible with varlen (causal-style masks).
+mask_generators = [
+    partial(generate_document_mask),                # document
+    partial(generate_causal_document_mask),                # causal document
+    partial(generate_document_mask_diff_batch),                # document
+    partial(generate_causal_document_mask_diff_batch),                # causal document
+    partial(generate_document_mask_simu),                # simu causal document
+    partial(generate_document_mask_diff_batch_simu),                # simu causal document diff batch
+]
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# The test
+# ─────────────────────────────────────────────────────────────────────────────
+
+@pytest.mark.parametrize("dtype", [paddle.bfloat16])
+@pytest.mark.parametrize("d, dv", [(192, 128), (256, 256)])
+@pytest.mark.parametrize(
+    "batch_size, seqlen_q, seqlen_k, nheads, nheads_kv, nheads_startend_row_indices",
+    list(generate_shapes()),
+)
+@pytest.mark.parametrize("softmax_scale", [None, 1.0 / math.sqrt(64)])
+@pytest.mark.parametrize("gen_startend_row_indices", mask_generators)
+def test_flashmask_to_varlen(
+    batch_size,
+    seqlen_q,
+    seqlen_k,
+    nheads,
+    nheads_kv,
+    d,
+    dv,
+    softmax_scale,
+    nheads_startend_row_indices,
+    dtype,
+    gen_startend_row_indices,
+):
+    """
+    Compare Paddle flashmask_attention output with PyTorch flash_attn_varlen_func output
+    after converting startend_row_indices to varlen format via convert_to_varlen().
+    """
+    paddle.seed(2024)
+    assert nheads % nheads_kv == 0
+
+    # ── 1. Generate padded Q, K, V (Paddle) ─────────────────────────────────
+    q_ref = paddle.randn(shape=[batch_size, seqlen_q, nheads, d], dtype=dtype)
+    k_ref = paddle.randn(shape=[batch_size, seqlen_k, nheads_kv, d], dtype=dtype)
+    v_ref = paddle.randn(shape=[batch_size, seqlen_k, nheads_kv, dv], dtype=dtype)
+
+    q_ref.stop_gradient = False
+    k_ref.stop_gradient = False
+    v_ref.stop_gradient = False
+
+    q_bf16, k_bf16, v_bf16 = [x.detach().clone() for x in (q_ref, k_ref, v_ref)]
+
+    q_bf16.stop_gradient = False
+    k_bf16.stop_gradient = False
+    v_bf16.stop_gradient = False
+
+    q, k, v = [x.detach().clone() for x in (q_ref, k_ref, v_ref)]
+
+    q.stop_gradient = False
+    k.stop_gradient = False
+    v.stop_gradient = False
+
+    # Generate mask
+    startend_row_indices, causal = gen_startend_row_indices(
+        batch_size, seqlen_q, seqlen_k, nheads_startend_row_indices
+    )
+
+    # ── 3. Call naive ref ─────────────────────────────────
+    attn_bias = startend_row_indices_to_attn_bias(startend_row_indices, seqlen_q, nheads, dtype, causal)
+
+    out_ref, attn_ref = attention_ref(
+        q_ref,
+        k_ref,
+        v_ref,
+        causal=causal,
+        attn_bias=attn_bias,
+        softmax_scale=softmax_scale,
+    )
+
+    out_bf16, attn_bf16 = attention_ref(
+        q_bf16,
+        k_bf16,
+        v_bf16,
+        causal=causal,
+        attn_bias=attn_bias,
+        upcast=False,
+        reorder_ops=True,
+        softmax_scale=softmax_scale,
+    )
+
+    # # Numerical error if we just do any arithmetic on out_ref
+    fwd_atol = 2 * (out_ref + 0.3 - 0.3 - out_ref).abs().max().item()
+    rtol = 2
+
+    print(f"Paddle naive bf16 Output max diff: {(out_bf16 - out_ref).abs().max().item()}")
+    print(f"Paddle naive bf16 Output mean diff: {(out_bf16 - out_ref).abs().mean().item()}")
+
+
+    # ── 4. Call flashmask with use_varlen ─────────────────────────────
+    # Convert Paddle varlen tensors to PyTorch CUDA tensors
+    paddle.set_flags({"FLAGS_flash_attn_version": 4})
+    out = flashmask_attention(
+        q,
+        k,
+        v,
+        startend_row_indices=startend_row_indices,
+        causal=causal,
+        return_softmax_lse=False,
+        use_varlen=True,
+        softmax_scale=softmax_scale,
+    )
+    print(f"flashmask Output max diff: {(out - out_ref).abs().max().item()}")
+    print(f"flashmask Output mean diff: {(out - out_ref).abs().mean().item()}")
+
+    assert (out - out_ref).abs().max().item() <= rtol * (out_bf16 - out_ref).abs().max().item() + fwd_atol
+
+    g = paddle.randn(shape=out.shape, dtype=out.dtype)
+    out.backward(g)
+    out_ref.backward(g)
+    out_bf16.backward(g)
+
+    print(f"flashmask dQ max diff: {(q.grad - q_ref.grad).abs().max().item()}")
+    print(f"flashmask dK max diff: {(k.grad - k_ref.grad).abs().max().item()}")
+    print(f"flashmask dV max diff: {(v.grad - v_ref.grad).abs().max().item()}")
+    print(f"flashmask dQ mean diff: {(q.grad - q_ref.grad).abs().mean().item()}")
+    print(f"flashmask dK mean diff: {(k.grad - k_ref.grad).abs().mean().item()}")
+    print(f"flashmask dV mean diff: {(v.grad - v_ref.grad).abs().mean().item()}")
+
+    print(f"Paddle naive bf16 dQ max diff: {(q_bf16.grad - q_ref.grad).abs().max().item()}")
+    print(f"Paddle naive bf16 dK max diff: {(k_bf16.grad - k_ref.grad).abs().max().item()}")
+    print(f"Paddle naive bf16 dV max diff: {(v_bf16.grad - v_ref.grad).abs().max().item()}")
+    print(f"Paddle naive bf16 dQ mean diff: {(q_bf16.grad - q_ref.grad).abs().mean().item()}")
+    print(f"Paddle naive bf16 dK mean diff: {(k_bf16.grad - k_ref.grad).abs().mean().item()}")
+    print(f"Paddle naive bf16 dV mean diff: {(v_bf16.grad - v_ref.grad).abs().mean().item()}")
+
+    dq_atol = 2 * (q_ref.grad + 0.3 - 0.3 - q_ref.grad).abs().max().item()
+    assert (q.grad - q_ref.grad).abs().max().item() <= rtol * (q_bf16.grad - q_ref.grad).abs().max().item() + dq_atol
+    dk_atol = 2 * (k_ref.grad + 0.3 - 0.3 - k_ref.grad).abs().max().item()
+    assert (k.grad - k_ref.grad).abs().max().item() <= rtol * (k_bf16.grad - k_ref.grad).abs().max().item() + dk_atol
+    dv_atol = 2 * (v_ref.grad + 0.3 - 0.3 - v_ref.grad).abs().max().item()
+    assert (v.grad - v_ref.grad).abs().max().item() <= rtol * (v_bf16.grad - v_ref.grad).abs().max().item() + dv_atol
diff --git a/test_util.py b/test_util.py
index f4120bd..b36a0e2 100644
--- a/test_util.py
+++ b/test_util.py
@@ -59,6 +59,7 @@ def attention_ref(
     upcast=True,
     reorder_ops=False,
     intermediate_dtype=None,
+    softmax_scale=None,
 ):
     """
     Arguments:
@@ -119,7 +120,8 @@ def attention_ref(
 
     d = q.shape[-1]
     dv = v.shape[-1]
-    softmax_scale = 1.0 / math.sqrt(d if qv is None else d + dv)
+    if softmax_scale is None:
+        softmax_scale = 1.0 / math.sqrt(d if qv is None else d + dv)
 
     if not reorder_ops:
         scores = paddle.matmul(q * softmax_scale, k, transpose_y=True)