181 lines
7.3 KiB
Python
181 lines
7.3 KiB
Python
# Install the newest triton version with
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# pip install "git+https://github.com/openai/triton.git#egg=triton&subdirectory=python"
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import pickle
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from flash_attn.utils.benchmark import benchmark_all, benchmark_forward, benchmark_backward
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from flash_attn.utils.benchmark import benchmark_fwd_bwd, benchmark_combined
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from flash_attn import flash_attn_qkvpacked_func
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try:
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from triton.ops.flash_attention import attention as attention_triton
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except ImportError:
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attention_triton = None
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try:
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import xformers.ops as xops
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except ImportError:
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xops = None
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def flops(batch, seqlen, headdim, nheads, causal, mode="fwd"):
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assert mode in ["fwd", "bwd", "fwd_bwd"]
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f = 4 * batch * seqlen**2 * nheads * headdim // (2 if causal else 1)
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return f if mode == "fwd" else (2.5 * f if mode == "bwd" else 3.5 * f)
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def efficiency(flop, time):
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return (flop / time / 10**12) if not math.isnan(time) else 0.0
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def attention_pytorch(qkv, dropout_p=0.0, causal=True):
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"""
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Arguments:
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qkv: (batch_size, seqlen, 3, nheads, head_dim)
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dropout_p: float
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Output:
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output: (batch_size, seqlen, nheads, head_dim)
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"""
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batch_size, seqlen, _, nheads, d = qkv.shape
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q, k, v = qkv.unbind(dim=2)
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q = rearrange(q, 'b t h d -> (b h) t d')
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k = rearrange(k, 'b s h d -> (b h) d s')
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softmax_scale = 1.0 / math.sqrt(d)
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# Preallocate attn_weights for `baddbmm`
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scores = torch.empty(batch_size * nheads, seqlen, seqlen, dtype=qkv.dtype, device=qkv.device)
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scores = rearrange(torch.baddbmm(scores, q, k, beta=0, alpha=softmax_scale),
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'(b h) t s -> b h t s', h=nheads)
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if causal:
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# "triu_tril_cuda_template" not implemented for 'BFloat16'
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# So we have to construct the mask in float
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causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
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# TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
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scores = scores + causal_mask.to(dtype=scores.dtype)
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attention = torch.softmax(scores, dim=-1)
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attention_drop = F.dropout(attention, dropout_p)
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output = torch.einsum('bhts,bshd->bthd', attention_drop , v)
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return output.to(dtype=qkv.dtype)
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def time_fwd_bwd(func, *args, **kwargs):
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time_f, time_b = benchmark_fwd_bwd(func, *args, **kwargs)
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return time_f[1].mean, time_b[1].mean
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repeats = 30
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device = 'cuda'
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dtype = torch.float16
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bs_seqlen_vals = [(32, 512), (16, 1024), (8, 2048), (4, 4096), (2, 8192), (1, 16384)]
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causal_vals = [False, True]
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headdim_vals = [64, 128]
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dim = 2048
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dropout_p = 0.0
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methods = (["Flash2", "Pytorch"]
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+ (["Triton"] if attention_triton is not None else [])
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+ (["xformers.c"] if xops is not None else [])
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+ (["xformers.f"] if xops is not None else []))
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time_f = {}
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time_b = {}
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time_f_b = {}
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speed_f = {}
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speed_b = {}
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speed_f_b = {}
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for causal in causal_vals:
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for headdim in headdim_vals:
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for batch_size, seqlen in bs_seqlen_vals:
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config = (causal, headdim, batch_size, seqlen)
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nheads = dim // headdim
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qkv = torch.randn(batch_size, seqlen, 3, nheads, headdim, device=device, dtype=dtype,
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requires_grad=True)
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f, b = time_fwd_bwd(
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flash_attn_qkvpacked_func, qkv, dropout_p, causal=causal, repeats=repeats, verbose=False
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)
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time_f[config, "Flash2"] = f
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time_b[config, "Flash2"] = b
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try:
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qkv = qkv.detach().requires_grad_(True)
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f, b = time_fwd_bwd(
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attention_pytorch, qkv, dropout_p, causal=causal, repeats=repeats, verbose=False
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)
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except: # Skip if OOM
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f, b = float('nan'), float('nan')
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time_f[config, "Pytorch"] = f
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time_b[config, "Pytorch"] = b
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if attention_triton is not None:
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q, k, v = [torch.randn(batch_size, nheads, seqlen, headdim, device=device, dtype=dtype,
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requires_grad=True) for _ in range(3)]
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# Try both values of sequence_parallel and pick the faster one
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try:
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f, b = time_fwd_bwd(
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attention_triton, q, k, v, causal, headdim**(-0.5),
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False, repeats=repeats, verbose=False
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)
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except:
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f, b = float('nan'), float('inf')
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try:
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_, b0 = time_fwd_bwd(
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attention_triton, q, k, v, causal, headdim**(-0.5),
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True, repeats=repeats, verbose=False
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)
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except:
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b0 = float('inf')
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time_f[config, "Triton"] = f
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time_b[config, "Triton"] = min(b, b0) if min(b, b0) < float('inf') else float('nan')
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if xops is not None:
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q, k, v = [torch.randn(batch_size, seqlen, nheads, headdim, device=device, dtype=dtype,
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requires_grad=True) for _ in range(3)]
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f, b = time_fwd_bwd(
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xops.memory_efficient_attention, q, k, v,
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attn_bias=xops.LowerTriangularMask() if causal else None,
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op=(xops.fmha.cutlass.FwOp, xops.fmha.cutlass.BwOp)
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)
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time_f[config, "xformers.c"] = f
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time_b[config, "xformers.c"] = b
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if xops is not None:
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q, k, v = [torch.randn(batch_size, seqlen, nheads, headdim, device=device, dtype=dtype,
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requires_grad=True) for _ in range(3)]
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f, b = time_fwd_bwd(
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xops.memory_efficient_attention, q, k, v,
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attn_bias=xops.LowerTriangularMask() if causal else None,
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op=(xops.fmha.flash.FwOp, xops.fmha.flash.BwOp)
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)
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time_f[config, "xformers.f"] = f
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time_b[config, "xformers.f"] = b
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print(f"### causal={causal}, headdim={headdim}, batch_size={batch_size}, seqlen={seqlen} ###")
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for method in methods:
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time_f_b[config, method] = time_f[config, method] + time_b[config, method]
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speed_f[config, method] = efficiency(
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flops(batch_size, seqlen, headdim, nheads, causal, mode="fwd"),
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time_f[config, method]
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)
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speed_b[config, method] = efficiency(
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flops(batch_size, seqlen, headdim, nheads, causal, mode="bwd"),
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time_b[config, method]
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)
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speed_f_b[config, method] = efficiency(
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flops(batch_size, seqlen, headdim, nheads, causal, mode="fwd_bwd"),
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time_f_b[config, method]
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)
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print(
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f"{method} fwd: {speed_f[config, method]:.2f} TFLOPs/s, "
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f"bwd: {speed_b[config, method]:.2f} TFLOPs/s, "
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f"fwd + bwd: {speed_f_b[config, method]:.2f} TFLOPs/s"
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)
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# with open('flash2_attn_time.plk', 'wb') as fp:
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# pickle.dump((speed_f, speed_b, speed_f_b), fp, protocol=pickle.HIGHEST_PROTOCOL)
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