Add benchmarking code for Alibi (from Sanghun Cho)

Author: tridao

benchmarks/benchmark_alibi.py

@@ -0,0 +1,275 @@
+# Copyright (c) 2024, Sanghun Cho, Tri Dao.
+
+import pickle
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from einops import rearrange, repeat
+from flash_attn.layers.rotary import apply_rotary_emb
+
+from flash_attn.utils.benchmark import benchmark_all, benchmark_forward, benchmark_backward
+from flash_attn.utils.benchmark import benchmark_fwd_bwd, benchmark_combined
+
+from flash_attn import flash_attn_qkvpacked_func, flash_attn_func
+
+try:
+    import xformers.ops as xops
+except ImportError:
+    xops = None
+
+
+def generate_cos_sin(seqlen, rotary_dim, device, dtype):
+    assert rotary_dim % 2 == 0
+    angle = torch.rand(seqlen * 2, rotary_dim // 2, device=device) * 2 * math.pi
+    cos = torch.cos(angle).to(dtype=dtype)
+    sin = torch.sin(angle).to(dtype=dtype)
+    return cos, sin
+
+
+def flash_rotary(q, k, v, cos, sin, causal=False):
+    # corrected by @tridao comments
+    q = apply_rotary_emb(
+        q, cos, sin, seqlen_offsets=0, interleaved=False, inplace=True
+    )
+    k = apply_rotary_emb(
+        k, cos, sin, seqlen_offsets=0, interleaved=False, inplace=True
+    )
+
+    return flash_attn_func(q, k, v, causal=causal)
+
+
+def attn_bias_from_alibi_slopes(
+    slopes, seqlen_q, seqlen_k, query_padding_mask=None, key_padding_mask=None, causal=False
+):
+    batch, nheads = slopes.shape
+    device = slopes.device
+    slopes = rearrange(slopes, "b h -> b h 1 1")
+    if causal:
+        return torch.arange(-seqlen_k + 1, 1, device=device, dtype=torch.float32) * slopes
+    else:
+        row_idx = rearrange(torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1")
+        col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long)
+        sk = (
+            seqlen_k
+            if key_padding_mask is None
+            else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1")
+        )
+        sq = (
+            seqlen_q
+            if query_padding_mask is None
+            else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1")
+        )
+        relative_pos = torch.abs(row_idx + sk - sq - col_idx)
+        return -slopes * relative_pos.to(dtype=slopes.dtype)
+
+
+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
+
+
+def attention_pytorch(q, k, v, dropout_p=0.0, causal=True, attn_bias=None):
+    """
+    Arguments:
+        q, k, v: (batch_size, seqlen, nheads, head_dim)
+        dropout_p: float
+        attn_bias: (batch_size, nheads, seqlen, seqlen) or (1, nheads, seqlen, seqlen)
+    Output:
+        output: (batch_size, seqlen, nheads, head_dim)
+    """
+    batch_size, seqlen, nheads, d = q.shape
+    q = rearrange(q, 'b t h d -> (b h) t d')
+    k = rearrange(k, 'b s h d -> (b h) d s')
+    softmax_scale = 1.0 / math.sqrt(d)
+    # Preallocate attn_weights for `baddbmm`
+    if attn_bias is not None:
+        scores = rearrange(attn_bias, 'b h t s -> (b h) t s')
+    else:
+        scores = torch.empty(batch_size * nheads, seqlen, seqlen, dtype=q.dtype, device=q.device)
+    scores = rearrange(torch.baddbmm(scores, q, k, beta=1.0, alpha=softmax_scale),
+                       '(b h) t s -> b h t s', h=nheads)
+    if causal:
+        # "triu_tril_cuda_template" not implemented for 'BFloat16'
+        # So we have to construct the mask in float
+        causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
+        # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
+        scores = scores + causal_mask.to(dtype=scores.dtype)
+    attention = torch.softmax(scores, dim=-1)
+    attention_drop = F.dropout(attention, dropout_p)
+    output = torch.einsum('bhts,bshd->bthd', attention_drop , v)
+    return output.to(dtype=q.dtype)
+
+
+def time_fwd_bwd(func, *args, **kwargs):
+    time_f, time_b = benchmark_fwd_bwd(func, *args, **kwargs)
+    return time_f[1].mean, time_b[1].mean
+
+
+repeats = 30
+device = 'cuda'
+dtype = torch.float16
+
+bs_seqlen_vals = [(32, 512), (16, 1024), (8, 2048), (4, 4096), (2, 8192), (1, 16384)]
+causal_vals = [False, True]
+headdim_vals = [64, 128]
+dim = 2048
+dropout_p = 0.0
+
+methods = (["fa2_alibi", "torch"]
+           + (["xformers"] if xops is not None else [])
+           + ["sdpa"]
+           + ["fa2_baseline"]
+           + ["fa2_rotary"])
+
+time_f = {}
+time_b = {}
+time_f_b = {}
+speed_f = {}
+speed_b = {}
+speed_f_b = {}
+for causal in causal_vals:
+    for headdim in headdim_vals:
+        for batch_size, seqlen in bs_seqlen_vals:
+            config = (causal, headdim, batch_size, seqlen)
+            nheads = dim // headdim
+            q, k, v = [torch.randn(batch_size, seqlen, nheads, headdim, device=device, dtype=dtype,
+                                    requires_grad=True) for _ in range(3)]
+            # alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
+            alibi_slopes = torch.rand(1, nheads, device=device, dtype=torch.float32) * 0.3
+            attn_bias = attn_bias_from_alibi_slopes(alibi_slopes, seqlen, seqlen, causal=causal).to(dtype)
+            attn_bias = repeat(attn_bias, "1 ... -> b ...", b=batch_size)
+            f, b = time_fwd_bwd(
+                flash_attn_func,
+                q, k, v,
+                dropout_p,
+                causal=causal,
+                # alibi_slopes=alibi_slopes,
+                alibi_slopes=None,
+                repeats=repeats,
+                verbose=False
+            )
+            time_f[config, "fa2_baseline"] = f
+            time_b[config, "fa2_baseline"] = b
+
+            q = q.detach().requires_grad_(True)
+            k = k.detach().requires_grad_(True)
+            v = v.detach().requires_grad_(True)
+            f, b = time_fwd_bwd(
+                flash_attn_func,
+                q, k, v,
+                dropout_p,
+                causal=causal,
+                alibi_slopes=rearrange(alibi_slopes, "1 h -> h"),
+                # alibi_slopes=None,
+                repeats=repeats,
+                verbose=False
+            )
+            time_f[config, "fa2_alibi"] = f
+            time_b[config, "fa2_alibi"] = b
+
+            try:
+                q = q.detach().requires_grad_(True)
+                k = k.detach().requires_grad_(True)
+                v = v.detach().requires_grad_(True)
+                f, b = time_fwd_bwd(
+                    attention_pytorch,
+                    q, k, v,
+                    dropout_p,
+                    causal=causal,
+                    attn_bias=attn_bias,
+                    repeats=repeats,
+                    verbose=False
+                )
+            except:  # Skip if OOM
+                f, b = float('nan'), float('nan')
+            time_f[config, "torch"] = f
+            time_b[config, "torch"] = b
+
+            # F.sdpa doesn't currently (torch 2.1) dispatch to flash-attn but just to be safe
+            with torch.backends.cuda.sdp_kernel(enable_flash=False):
+                q_pt = q.detach().requires_grad_(True).transpose(1, 2)
+                k_pt = k.detach().requires_grad_(True).transpose(1, 2)
+                v_pt = v.detach().requires_grad_(True).transpose(1, 2)
+                f, b = time_fwd_bwd(
+                    F.scaled_dot_product_attention,
+                    q_pt, k_pt, v_pt,
+                    attn_mask=attn_bias,
+                    dropout_p=dropout_p,
+                    is_causal=causal,
+                    repeats=repeats,
+                    verbose=False
+                )
+                time_f[config, "sdpa"] = f
+                time_b[config, "sdpa"] = b
+
+            if xops is not None:
+                q = q.detach().requires_grad_(True)
+                k = k.detach().requires_grad_(True)
+                v = v.detach().requires_grad_(True)
+                if causal:
+                    attn_bias_xops = xops.LowerTriangularMask().add_bias(attn_bias.expand(-1, -1, seqlen, -1).to(dtype=q.dtype))
+                    # NotImplementedError: No operator found for `memory_efficient_attention_backward` with inputs:
+                    # `[email protected]` is not supported because:
+                    #     attn_bias type is <class 'xformers.ops.fmha.attn_bias.LowerTriangularMaskWithTensorBias'>
+                    # `cutlassB` is not supported because:
+                    #     attn_bias type is <class 'xformers.ops.fmha.attn_bias.LowerTriangularMaskWithTensorBias'>
+                    attn_bias_xops = attn_bias_xops.materialize((batch_size, nheads, seqlen, seqlen), dtype=q.dtype, device=device)
+                else:
+                    attn_bias_xops = attn_bias.to(dtype=q.dtype)
+                f, b = time_fwd_bwd(
+                    xops.memory_efficient_attention,
+                    q, k, v,
+                    attn_bias_xops,
+                    dropout_p,
+                    repeats=repeats,
+                    verbose=False
+                )
+                time_f[config, "xformers"] = f
+                time_b[config, "xformers"] = b
+
+            q = q.detach().requires_grad_(True)
+            k = k.detach().requires_grad_(True)
+            v = v.detach().requires_grad_(True)
+            cos, sin = generate_cos_sin(seqlen, headdim, device, dtype)
+            f, b = time_fwd_bwd(
+                flash_rotary,
+                q, k, v,
+                cos, sin,
+                causal,
+                repeats=repeats,
+                verbose=False
+            )
+            time_f[config, "fa2_rotary"] = f
+            time_b[config, "fa2_rotary"] = b
+
+            print(f"### causal={causal}, headdim={headdim}, batch_size={batch_size}, seqlen={seqlen} ###")
+            csv_output = ""
+            csv_output += f"{causal},{headdim},{batch_size},{seqlen},"
+            for method in methods:
+                time_f_b[config, method] = time_f[config, method] + time_b[config, method]
+                speed_f[config, method] = efficiency(
+                    flops(batch_size, seqlen, headdim, nheads, causal, mode="fwd"),
+                    time_f[config, method]
+                )
+                speed_b[config, method] = efficiency(
+                    flops(batch_size, seqlen, headdim, nheads, causal, mode="bwd"),
+                    time_b[config, method]
+                )
+                speed_f_b[config, method] = efficiency(
+                    flops(batch_size, seqlen, headdim, nheads, causal, mode="fwd_bwd"),
+                    time_f_b[config, method]
+                )
+                print(
+                    f"{method} fwd: {speed_f[config, method]:.2f} TFLOPs/s, "
+                    f"bwd: {speed_b[config, method]:.2f} TFLOPs/s, "
+                    f"fwd + bwd: {speed_f_b[config, method]:.2f} TFLOPs/s"
+                )
+                csv_output += f"{speed_f[config, method]:.2f},{speed_b[config, method]:.2f},{speed_f_b[config, method]:.2f},"
+            print(csv_output)