diff --git a/benchmarks/benchmark_flash_attention_csv.py b/benchmarks/benchmark_flash_attention_csv.py
new file mode 100644
index 000000000..ea69c4938
--- /dev/null
+++ b/benchmarks/benchmark_flash_attention_csv.py
@@ -0,0 +1,128 @@
+# Install the newest triton version with
+# pip install "git+https://github.com/openai/triton.git#egg=triton&subdirectory=python"
+import argparse
+import math
+import torch
+import csv
+
+import os
+from datetime import date
+import subprocess
+
+from flash_attn.utils.benchmark import benchmark_forward, benchmark_backward
+from flash_attn import flash_attn_qkvpacked_func, flash_attn_varlen_qkvpacked_func
+
+
+def benchmark_row(row):
+    dtype = row["dtype"]
+    if dtype in ["torch.float16"]:
+        dtype = torch.float16
+    elif dtype in ["torch.bfloat16"]:
+        dtype = torch.bfloat16
+    else:
+        raise ValueError("Wrong data type")
+    batch_size = row["batch size"]
+    nheads = int(row["nheads"])
+    d = int(row["embedding dim"])
+    seqlen = int(row["seqlen"])
+    causal = row["causal"] == 'TRUE'
+    dropout_p = float(row["dropout"])
+
+    torch.manual_seed(0)
+    
+    if not batch_size.isdigit():
+        print(dtype, batch_size, seqlen, nheads, d, causal, dropout_p)
+        cu_seqlens = [int(b) for b in batch_size.split(',')]
+        max_seqlen = 0
+        for cu_seq1, cu_seq2 in zip(cu_seqlens[1:], cu_seqlens[:-1]):
+            max_seqlen = max(max_seqlen, cu_seq1 - cu_seq2)
+        qkv = torch.randn(seqlen, 3, nheads, d, device=device, dtype=dtype, requires_grad=True)
+        fn = lambda qkv: flash_attn_varlen_qkvpacked_func(
+            qkv, torch.tensor(cu_seqlens, dtype=torch.int32).cuda(), max_seqlen, dropout_p, causal=causal, softmax_scale=1/math.sqrt(d)
+        )
+    else:
+        print(dtype, batch_size, seqlen, nheads, d, causal, dropout_p)
+        batch_size = int(batch_size)
+        qkv = torch.randn(batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype, requires_grad=True)
+        fn = lambda qkv: flash_attn_qkvpacked_func(
+            qkv, dropout_p, causal=causal, softmax_scale=1/math.sqrt(d)
+        )
+
+    _, m1 = benchmark_forward(fn, qkv, amp_dtype=dtype, repeats=repeats, verbose=False, desc='FlashAttention')
+    _, m2 = benchmark_backward(fn, qkv, amp_dtype=dtype, repeats=repeats, verbose=False, desc='FlashAttention')
+    
+    fwd_time = m1.mean
+    bwd_time = m2.mean
+    if isinstance(batch_size, str):
+        batch_size = 1
+    fwd_tflops = efficiency(flops(batch_size, seqlen, d, nheads, causal, mode="fwd"), fwd_time)
+    bwd_tflops = efficiency(flops(batch_size, seqlen, d, nheads, causal, mode="bwd"), bwd_time)
+    fwd_bwd_tflops = efficiency(flops(batch_size, seqlen, d, nheads, causal, mode="fwd_bwd"), fwd_time+bwd_time)
+    return [dtype, batch_size, nheads, d, seqlen, causal, dropout_p, format(fwd_time*1000, ".2f"), format(bwd_time*1000, ".2f"), format(fwd_tflops, ".2f"), format(bwd_tflops, ".2f"), format(fwd_bwd_tflops, ".2f")]
+
+
+def flops(batch, seqlen, headdim, nheads, causal, mode="fwd"):
+    assert mode in ["fwd", "bwd", "fwd_bwd"]
+    f = 4 * batch * seqlen**2 * nheads * headdim // (2 if causal else 1)
+    return f if mode == "fwd" else (2.5 * f if mode == "bwd" else 3.5 * f)
+
+
+def efficiency(flop, time):
+    return (flop / time / 10**12) if not math.isnan(time) else 0.0
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Benchmark flash attention.")
+
+    parser.add_argument("--repeats",
+                        type=int,
+                        default=30)
+    parser.add_argument("--output_format",
+                        type=str,
+                        default='csv',
+                        choices=['csv', 'xls'],
+                        help="Export file format")
+    parser.add_argument("--input_csv",
+                    type=str,
+                    required=True,
+                    help="Input csv path")
+    parser.add_argument("--output_csv",
+                    type=str,
+                    required=True,
+                    help="Output csv path")
+    
+    args = parser.parse_args()
+
+    fa_commit = subprocess.run("git rev-parse HEAD", shell=True, capture_output=True).stdout.strip().decode('UTF-8')
+    submodule = subprocess.run("git submodule foreach", shell=True, capture_output=True).stdout.strip().decode('UTF-8')
+    ck_path = submodule.split(' ')[1][1:-1]
+    ck_commit = subprocess.run(f"cd {ck_path} && git rev-parse HEAD", shell=True, capture_output=True).stdout.strip().decode('UTF-8')
+
+    datetime = date.today()
+    labels = ["dtype", "batch size", "seqlen", "nheads", "embedding dim", "causal", "dropout", "fwd(ms)", "bwd(ms)", "fwd(tflops)", "bwd(tflops)", "fwd+bwd(tflops)"]
+    device = 'cuda'
+    
+    repeats = args.repeats
+    with open(args.input_csv, newline='') as input_csv:
+        csvreader = csv.DictReader(input_csv)
+        if args.output_format == 'xls':
+            import xlwt
+            workbook = xlwt.Workbook(encoding = 'utf-8')
+            worksheet = workbook.add_sheet('flash attention')
+            
+            for i, label in enumerate(labels):
+                worksheet.write(0, i, label)
+            
+            i = 1
+            for row in csvreader:
+                output_row = benchmark_row(row)
+                for j, value in enumerate(output_row):
+                    worksheet.write(i, j, str(value))
+                i += 1
+
+            workbook.save(args.output_csv)
+        else:
+            with open(args.output_csv, 'w', newline='') as output_csv:
+                output_csv = csv.writer(output_csv, delimiter=',')
+                output_csv.writerow(labels)
+                output_csv.writerows([benchmark_row(row) for row in csvreader])
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