SandAI-org
diff --git a/‎exps/attn/run_packgqa_swapab_bench.py‎
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+# Copyright (c) 2025-2026 SandAI. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from datetime import datetime
+
+import torch
+from baselines.attn_impl import ffa_func
+from baselines.utils import seed_everything
+from einops import rearrange
+
+from magi_attention.benchmarking import Benchmark, do_bench_flops, perf_report
+from magi_attention.utils.sparse_utils import (
+    flatten_block_mask_to_kv_shape,
+    generate_block_sparse_pattern,
+    generate_ranges_from_block_mask,
+)
+
+impls = ["ffa_packgqa_swapab", "ffa"]
+
+# actual seqlen
+seqlens = [8192 * 8]
+
+# current block sparse attention always has low sparsity
+sparsity_ratio = [0.05]
+# ss = [k * 1024 for k in [4, 96, 128]]
+ds = [128]
+wds = ["fwd"]
+attn_modes = ["GQA"]  # MHA, GQA
+nhqs = [64]
+num_groups = [8, 16]
+# small K block
+# q_block_sizes = [64, 64, 64, 64, 64]
+# k_block_sizes = [64, 32, 16, 8, 1]
+# small Q block
+# q_block_sizes = [64, 32, 16, 8]
+# k_block_sizes = [64, 64, 64, 64]
+q_block_sizes = [1]
+k_block_sizes = [64]
+# large Q block and K block
+# q_block_sizes = [64, 128]
+# k_block_sizes = [64, 128]
+
+assert len(q_block_sizes) == len(k_block_sizes)
+
+b = 1
+
+dtype = torch.bfloat16
+
+bias = None
+softmax_scale = None
+dropout_p = 0.0
+return_attn_probs = False
+
+quantiles = [0.5, 0.2, 0.8]
+
+
+attn_flops_configs = [
+    Benchmark(
+        x_names=["sparsity_ratio"],  # Argument names to use as an x-axis for the plot.
+        x_vals=sparsity_ratio,  # Different possible values for `x_name`.
+        x_log=False,  # x axis is logarithmic.
+        line_arg="attn_impl",  # Argument name whose value corresponds to a different line in the plot.
+        line_vals=impls,  # Possible values for `line_arg`.
+        line_names=impls,  # Label name for the lines.
+        styles=[  # Line styles.
+            ("green", "--"),
+            ("orange", "--"),
+            ("steelblue", "--"),
+            ("red", "-"),
+        ],
+        ylabel={  # Label name for the y-axis.
+            "flops": "Throughout (TFLOPs/s)",
+            "mem": "Peak Memory (GB)",
+        },
+        plot_name=(
+            f"block sparse attn-{wd} attn_mode-{attn_mode} "
+            f"{'n_head-' + str(nhq) if attn_mode == 'MHA' else f'n_head-{nhq}:{nhq // num_group}'}\n"
+            f"block_size-{q_block_size}:{k_block_size} seq_len {seqlen}"
+        ),
+        # Name for the plot. Used also as a file name for saving the plot.
+        args={  # Values for function arguments not in `x_names` and `y_name`.
+            "hd": hd,
+            "wd": wd,
+            "q_block_size": q_block_size,
+            "k_block_size": k_block_size,
+            "seqlen": seqlen,
+            "num_group": num_group,
+            "attn_mode": attn_mode,
+            "nhq": nhq,
+        },
+    )
+    for hd in ds
+    for wd in wds
+    for q_block_size, k_block_size in zip(q_block_sizes, k_block_sizes)
+    for seqlen in seqlens
+    for num_group in num_groups
+    for attn_mode in attn_modes
+    for nhq in nhqs
+]
+
+seed_everything()
+
+
+@perf_report(attn_flops_configs)
+def sparse_attn_benchmark(
+    sparsity_ratio,
+    hd,
+    wd,
+    q_block_size,
+    k_block_size,
+    seqlen,
+    num_group,
+    attn_mode,
+    nhq,
+    attn_impl,
+):
+    assert b == 1, "for now, we only supports b=1 for ffa"
+    is_attn_impl_support_this_mask = True
+    already_known_oom_before_run = False
+
+    # --------- prepare arguments --------- #
+
+    device = torch.cuda.current_device()
+    orig_seq_len_q = orig_seq_len_k = seqlen  # fi square mask where sq == sk
+    block_m = q_block_size
+    block_n = k_block_size
+
+    num_q_blocks_orig = orig_seq_len_q // block_m
+    num_kv_blocks_orig = orig_seq_len_k // block_n
+    orig_head = nhq
+    if attn_mode == "MHA":
+        nhk = nhq
+    elif attn_mode == "GQA":
+        nhk = nhq // num_group
+
+    # prepare q, k ranges and calculate attn_flops
+    # for now, we only do bench for block sparse mask.
+    # block_mask, scores = generate_global_block_sparse_pattern(
+    #    orig_head, num_q_blocks_orig, num_kv_blocks_orig, sparsity_ratio, device="cuda"
+    # )
+
+    block_mask, scores = generate_block_sparse_pattern(
+        num_q_heads=nhq,
+        num_kv_heads=nhk,
+        num_q_blocks=num_q_blocks_orig,
+        num_kv_blocks=num_kv_blocks_orig,
+        sparsity=sparsity_ratio,
+        device="cuda",
+    )
+
+    attn_flops = 4 * orig_seq_len_q * orig_seq_len_k * orig_head * hd * sparsity_ratio
+    # --------- prepare data --------- #
+    # flash style shape: (b,s,h,d)
+    q = torch.randn(
+        b, orig_seq_len_q, nhq, hd, device=device, dtype=dtype, requires_grad=False
+    )
+    k = torch.randn(
+        b, orig_seq_len_k, nhk, hd, device=device, dtype=dtype, requires_grad=False
+    )
+    v = torch.randn(
+        b, orig_seq_len_k, nhk, hd, device=device, dtype=dtype, requires_grad=False
+    )
+
+    # ffa style shape: (t,h,d)
+    if attn_impl in ("ffa_packgqa_swapab", "ffa"):
+        h1 = nhk
+        q = rearrange(q, "b s (h1 h2) d -> (b h1 s) h2 d", h1=h1)
+        k = rearrange(k, "b s h d -> (b h s) 1 d")
+        v = rearrange(v, "b s h d -> (b h s) 1 d")
+
+    if attn_impl in ("sdpa", "vsa", "vsa_triton", "flashinfer", "flex"):
+        q = rearrange(q, "b s h d -> b h s d")
+        k = rearrange(k, "b s h d -> b h s d")
+        v = rearrange(v, "b s h d -> b h s d")
+
+    # --------- prepare grads --------- #
+
+    if wd == "bwd":
+        attn_flops = attn_flops * 2.5
+        do = torch.randn_like(q)
+        # require grads
+        [x.requires_grad_(True) for x in [q, k, v, do]]
+
+    # --------- prepare func --------- #
+    # is_attn_impl_support_this_mask = block_sparse_available(
+    #    attn_impl, nhq, nhk, q_block_size, k_block_size, wd
+    # )
+    is_attn_impl_support_this_mask = True
+    if is_attn_impl_support_this_mask:
+        if attn_impl == "ffa_packgqa_swapab":
+            # flatten headdim for ffa cause
+            flat_block_sparse_mask = flatten_block_mask_to_kv_shape(block_mask)
+            q_ranges, k_ranges = generate_ranges_from_block_mask(
+                flat_block_sparse_mask, block_m, block_n
+            )
+
+            attn_type_map = torch.zeros(len(q_ranges), dtype=torch.int32, device="cuda")
+
+            # TODO: we need to optimize choose_ref_block.
+            # You'd better set ref_blocks manually now
+            # ref_block_size = choose_ref_block((q_block_size, k_block_size))
+            ref_block_size = (64, 64)
+
+            def fn():
+                return ffa_func(
+                    q,
+                    k,
+                    v,
+                    q_ranges=q_ranges,
+                    k_ranges=k_ranges,
+                    attn_type_map=attn_type_map,
+                    auto_range_merge=True,  # we should enable auto_range_merge for block sparse mask.
+                    ref_block_size=ref_block_size,
+                    pack_gqa=True,
+                    swap_ab=True,
+                    disable_fwd_atomic_reduction=True,
+                )
+
+            if wd == "bwd":
+                try:
+                    o, *rest = fn()
+                except Exception as e:
+                    if "CUDA out of memory" not in str(e):
+                        print(
+                            f"Error occured before running {attn_impl} with "
+                            f"{q_block_size=}, {k_block_size=} "
+                            f"when {seqlen=}, {hd=} during {wd}: {e=}"
+                        )
+                        raise e
+                    already_known_oom_before_run = True
+
+                def fn():
+                    o.backward(do, retain_graph=True)
+
+        elif attn_impl == "ffa":
+            # flatten headdim for ffa cause
+            flat_block_sparse_mask = flatten_block_mask_to_kv_shape(block_mask)
+            q_ranges, k_ranges = generate_ranges_from_block_mask(
+                flat_block_sparse_mask, block_m, block_n
+            )
+
+            attn_type_map = torch.zeros(len(q_ranges), dtype=torch.int32, device="cuda")
+
+            # ref_block_size = choose_ref_block((q_block_size, k_block_size))
+            ref_block_size = (64, 64)
+
+            def fn():
+                return ffa_func(
+                    q,
+                    k,
+                    v,
+                    q_ranges=q_ranges,
+                    k_ranges=k_ranges,
+                    attn_type_map=attn_type_map,
+                    auto_range_merge=True,  # we should enable auto_range_merge for block sparse mask.
+                    ref_block_size=ref_block_size,
+                    pack_gqa=False,
+                    disable_fwd_atomic_reduction=True,
+                )
+
+            if wd == "bwd":
+                try:
+                    o, *rest = fn()
+                except Exception as e:
+                    if "CUDA out of memory" not in str(e):
+                        print(
+                            f"Error occured before running {attn_impl} with "
+                            f"{q_block_size=}, {k_block_size=} "
+                            f"when {seqlen=}, {hd=} during {wd}: {e=}"
+                        )
+                        raise e
+                    already_known_oom_before_run = True
+
+                def fn():
+                    o.backward(do, retain_graph=True)
+
+    # --------- try do the bench --------- #
+    if is_attn_impl_support_this_mask:
+        if already_known_oom_before_run:
+            # -1 indicates oom
+            perf_dict = {
+                "flops": [-1, -1, -1],
+                "mem": [-1, -1, -1],
+            }
+        else:
+            try:
+                # disable mem test to only test flops for now
+                perf_dict = do_bench_flops(
+                    fn,
+                    quantiles=quantiles,
+                    mem_record_mode="peak",
+                )
+
+                # --------- process report --------- #
+
+                # post process the perf_dict
+                def ms_to_tflops(ms: float) -> float:
+                    return attn_flops / ms * 1e-9
+
+                perf_dict["flops"] = list(map(ms_to_tflops, perf_dict["flops"]))
+
+                # disable mem test
+                def gb(m):
+                    return m / 1024**3
+
+                # perf_dict["mem"] = list(map(gb, perf_dict["mem"]))
+            except Exception as e:
+                if "CUDA out of memory" not in str(e):
+                    print(
+                        f"Error occured before running {attn_impl} with "
+                        f"{q_block_size=}, {k_block_size=} "
+                        f"when {seqlen=}, {hd=} during {wd}: {e=}"
+                    )
+                    perf_dict = {
+                        "flops": [-2, -2, -2],
+                        "mem": [-2, -2, -2],
+                    }
+                    # raise e
+                # -1 indicates oom
+                perf_dict = {
+                    "flops": [-1, -1, -1],
+                    "mem": [-1, -1, -1],
+                }
+                print(
+                    f"Error occured before running {attn_impl} with {q_block_size=}, {k_block_size=} "
+                    f"when {seqlen=}, {hd=} during {wd}: {e=}"
+                )
+    else:
+        # -2 indicates not support
+        perf_dict = {
+            "flops": [-2, -2, -2],
+            "mem": [-2, -2, -2],
+        }
+
+    return perf_dict
+
+
+if __name__ == "__main__":
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    current_time = datetime.strftime(datetime.now(), "%Y-%m-%d_%H-%M-%S")
+    out_root = os.path.join(
+        script_dir, os.path.join("outs", f"bench_attn_{current_time}")
+    )
+
+    sparse_attn_benchmark.run(
+        print_data=True, print_value_on_bar=False, save_path=out_root
+    )