[kernel] MiniMax-Text-01 prefill lightning_attn with triton (#2911)

benchmark/kernels/minmax-text-01-lighting_attention/benchmark_lighting_attention_prefill.py

+import itertools
+import math
+import os
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import triton
+import triton.language as tl
+from einops import rearrange
+
+
+# Adapted from https://github.com/OpenNLPLab/lightning-attention/blob/main/lightning_attn/ops/triton/lightning_attn2.py
+@triton.jit
+def _fwd_kernel(
+    Q,
+    K,
+    V,
+    Out,
+    S,  # log lambda
+    b: tl.constexpr,
+    h: tl.constexpr,
+    n: tl.constexpr,
+    d: tl.constexpr,
+    e: tl.constexpr,
+    BLOCK: tl.constexpr,
+    NUM_BLOCK: tl.constexpr,
+    BLOCK_MODEL: tl.constexpr,
+):
+    ##### get offset
+    off_bh = tl.program_id(0)
+    off_h = off_bh % h
+    off_e = tl.program_id(1)
+    qk_offset = off_bh * n * d
+    v_offset = off_bh * n * e
+    o_offset = off_bh * n * e
+    # channel offset
+    e_offset = off_e * BLOCK_MODEL
+
+    ##### get block ptr
+    Q_block_ptr = Q + qk_offset + tl.arange(0, d)[None, :]
+    K_trans_block_ptr = K + qk_offset + tl.arange(0, d)[:, None]
+    V_block_ptr = V + v_offset + e_offset + tl.arange(0, BLOCK_MODEL)[None, :]
+    O_block_ptr = Out + o_offset + e_offset + tl.arange(0, BLOCK_MODEL)[None, :]
+    S_block_ptr = S + off_h
+
+    ##### init diag decay(Lambda); q, k decay; kv
+    s = tl.load(S_block_ptr)
+    # q, k decay
+    off_block = tl.arange(
+        0, BLOCK
+    )  # Not bug, this is a bit different from algorithm 1, but is mathematically equivalent
+    q_decay = tl.exp(-s.to(tl.float32) * off_block[:, None])
+    k_trans_decay = tl.exp(-s.to(tl.float32) * (BLOCK - off_block[None, :]))
+    block_decay = tl.exp(-s.to(tl.float32) * BLOCK)
+    # diag decay
+    index = off_block[:, None] - off_block[None, :]
+    s_index = s * index
+    s_index = tl.where(index >= 0, -s_index, float("-inf"))
+    diag_decay = tl.exp(s_index)
+    kv = tl.zeros([d, BLOCK_MODEL], dtype=tl.float32)
+
+    ##### compute
+    for i in range(NUM_BLOCK):
+        # load
+        q = tl.load(
+            Q_block_ptr + off_block[:, None] * d, mask=off_block[:, None] < n, other=0.0
+        ).to(tl.float32)
+        k_trans = tl.load(
+            K_trans_block_ptr + off_block[None, :] * d,
+            mask=off_block[None, :] < n,
+            other=0.0,
+        ).to(tl.float32)
+        v = tl.load(
+            V_block_ptr + off_block[:, None] * e, mask=off_block[:, None] < n, other=0.0
+        ).to(tl.float32)
+
+        # compute
+        qk = tl.dot(q, k_trans) * diag_decay
+        o_intra = tl.dot(qk, v)
+        o_inter = tl.dot(q, kv) * q_decay
+        o = o_intra + o_inter
+
+        # save and update
+        tl.store(
+            O_block_ptr + off_block[:, None] * e,
+            o.to(O_block_ptr.dtype.element_ty),
+            mask=off_block[:, None] < n,
+        )
+        kv = block_decay * kv + tl.dot(k_trans * k_trans_decay, v)
+        off_block += BLOCK
+
+
+def lightning_attn2(q, k, v, s):
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+    s = s.contiguous()
+
+    b, h, n, d = q.shape
+    e = v.shape[-1]
+
+    # Pad d to next power of 2
+    d_padded = next_power_of_2(d)
+    if d_padded != d:
+        q_padded = F.pad(q, (0, d_padded - d))
+        k_padded = F.pad(k, (0, d_padded - d))
+    else:
+        q_padded = q
+        k_padded = k
+
+    # Pad e to next power of 2
+    e_padded = next_power_of_2(e)
+    if e_padded != e:
+        v_padded = F.pad(v, (0, e_padded - e))
+    else:
+        v_padded = v
+
+    o_padded = torch.empty((b, h, n, e_padded), dtype=q.dtype, device=q.device)
+
+    BLOCK = 64
+    NUM_BLOCK = triton.cdiv(q.shape[2], BLOCK)
+    # parallel over channel
+    BLOCK_MODEL = min(triton.next_power_of_2(e_padded), 32)
+    grid = (b * h, triton.cdiv(e_padded, BLOCK_MODEL))
+
+    _fwd_kernel[grid](
+        q_padded,
+        k_padded,
+        v_padded,
+        o_padded,
+        s,
+        b,
+        h,
+        n,
+        d_padded,
+        e_padded,
+        BLOCK=BLOCK,
+        NUM_BLOCK=NUM_BLOCK,
+        BLOCK_MODEL=BLOCK_MODEL,
+    )
+
+    # Remove padding from output
+    if e_padded != e:
+        o = o_padded[..., :e]
+    else:
+        o = o_padded
+
+    return o
+
+
+def is_support(dim):
+    return 16 % dim
+
+
+def next_power_of_2(n):
+    return 2 ** (int(math.ceil(math.log(n, 2))))
+
+
+def lightning_attn_func(q, k, v, s):
+    b, h, n, d = q.shape
+    e = v.shape[-1]
+    assert is_support(d) and is_support(e)
+
+    # pad v's feature dim to power of 2
+    e_pad = next_power_of_2(e)
+    need_pad = e_pad != e
+    if need_pad:
+        v = F.pad(v, (0, e_pad - e))
+
+    if d > 128:
+        # split over head
+        if 64 % d:
+            m = 64
+        elif 32 % d:
+            m = 32
+        elif 16 % d:
+            m = 16
+        arr = [m * i for i in range(d // m + 1)]
+        if arr[-1] != d:
+            arr.append(d)
+        n = len(arr)
+        o = 0
+        for i in range(n - 1):
+            start = arr[i]
+            end = arr[i + 1]
+            q1 = q[..., start:end]
+            k1 = k[..., start:end]
+            o += lightning_attn2(q1, k1, v, s)
+    else:
+        o = lightning_attn2(q, k, v, s)
+
+    if need_pad:
+        o = o[:, :, :, :e]
+
+    return o
+
+
+debug = eval(os.environ.get("debug", default="False"))
+
+BLOCK = 256
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->MiniMaxText01
+class MiniMaxText01RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MiniMaxText01RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/modeling_minimax_text_01.py
+def get_activation_fn(activation):
+    if debug:
+        logger.info(f"activation: {activation}")
+    if activation == "gelu":
+        return F.gelu
+    elif activation == "relu":
+        return F.relu
+    elif activation == "elu":
+        return F.elu
+    elif activation == "sigmoid":
+        return F.sigmoid
+    elif activation == "exp":
+
+        def f(x):
+            with torch.no_grad():
+                x_max = torch.max(x, dim=-1, keepdims=True).values
+            y = torch.exp(x - x_max)
+
+            return y
+
+        return f
+    elif activation == "leak":
+        return F.leaky_relu
+    elif activation == "1+elu":
+
+        def f(x):
+            return 1 + F.elu(x)
+
+        return f
+    elif activation == "2+elu":
+
+        def f(x):
+            return 2 + F.elu(x)
+
+        return f
+    elif activation == "silu" or activation == "swish":
+        return F.silu
+    elif activation == "sine":
+        return torch.sin
+    else:
+        logger.info(f"activation: does not support {activation}, use Identity!!!")
+        return lambda x: x
+
+
+# Copied from https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/modeling_minimax_text_01.py
+class MiniMaxText01LightningAttention(nn.Module):
+    def __init__(self, config=None, layer_idx: Optional[int] = None, **kwargs):
+        super().__init__()
+        if config is None:
+            config = type("Config", (), kwargs)
+
+        bias = False
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
+
+        self.out_proj = nn.Linear(
+            self.head_dim * self.num_heads, self.hidden_size, bias=bias
+        )
+        self.act = get_activation_fn(config.hidden_act)
+        self.norm = MiniMaxText01RMSNorm(self.head_dim * self.num_heads)
+
+        self.qkv_proj = nn.Linear(
+            self.hidden_size, 3 * self.head_dim * self.num_heads, bias=bias
+        )
+        self.output_gate = nn.Linear(
+            self.hidden_size, self.head_dim * self.num_heads, bias=bias
+        )
+
+        # for inference only
+        self.offset = 0
+        self.layer_idx = layer_idx
+
+    def forward(
+        self,
+        hidden_states,
+        attn_mask: Optional[torch.Tensor] = None,  # (b, h, n, m)
+        output_attentions: bool = False,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        use_cache: bool = False,
+        slope_rate: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        if (not self.training) and (not do_eval):
+            return self.inference(
+                hidden_states,
+                attn_mask,
+                output_attentions,
+                past_key_value,
+                use_cache,
+                slope_rate,
+            )
+
+    def inference(
+        self,
+        x,
+        attn_mask: Optional[torch.Tensor] = None,  # (b, n)
+        output_attentions: bool = False,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        use_cache: bool = False,
+        slope_rate: Optional[torch.Tensor] = None,  # (h, 1, 1)
+    ):
+        # x: b n d
+        b, n, d = x.shape
+        # linear map
+        qkv = self.act(self.qkv_proj(x))
+        new_shape = qkv.size()[:-1] + (self.num_heads, -1)
+        qkv = qkv.view(*new_shape)
+        q, k, v = torch.split(qkv, [self.head_dim] * 3, dim=3)
+        q = q.transpose(1, 2)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+
+        if past_key_value is None:
+            self.offset = q.shape[-2]
+        else:
+            self.offset += 1
+
+        # for align with metaseq
+        ratio = torch.exp(-slope_rate)
+
+        # only use for the first time
+        if past_key_value is None:
+            slope_rate = slope_rate.to(torch.float32)
+            if attn_mask is not None:
+                v = v.masked_fill(
+                    (1 - attn_mask).unsqueeze(1).unsqueeze(-1).to(torch.bool), 0
+                )
+            NUM_BLOCK = (n + BLOCK - 1) // BLOCK
+            b, h, n, d = q.shape
+            e = v.shape[-1]
+            # other
+            array = torch.arange(BLOCK).to(q) + 1
+            q_decay = torch.exp(-slope_rate * array.reshape(-1, 1))
+            k_decay = torch.exp(-slope_rate * (BLOCK - array.reshape(-1, 1)))
+            index = array[:, None] - array[None, :]
+            s_index = (
+                slope_rate
+                * index[
+                    None,
+                    None,
+                ]
+            )
+            s_index = torch.where(index >= 0, -s_index, float("-inf"))
+            diag_decay = torch.exp(s_index)
+
+            kv = torch.zeros(b, h, d, e).to(torch.float32).to(q.device)
+            output = torch.empty((b, h, n, e), dtype=q.dtype, device=q.device)
+            for i in range(NUM_BLOCK):
+                si = i * BLOCK
+                ei = min(si + BLOCK, n)
+                m = ei - si
+                qi = q[:, :, si:ei].contiguous()
+                ki = k[:, :, si:ei].contiguous()
+                vi = v[:, :, si:ei].contiguous()
+                qkv_none_diag = torch.matmul(qi * q_decay[:, :m], kv).to(torch.float32)
+
+                # diag
+                qk = (
+                    torch.matmul(qi, ki.transpose(-1, -2)).to(torch.float32)
+                    * diag_decay[:, :, :m, :m]
+                )
+                qkv_diag = torch.matmul(qk, vi.to(torch.float32))
+                block_decay = torch.exp(-slope_rate * m)
+                output[:, :, si:ei] = qkv_none_diag + qkv_diag
+                kv = block_decay * kv + torch.matmul(
+                    (ki * k_decay[:, -m:]).transpose(-1, -2).to(vi.dtype), vi
+                )
+
+        else:
+            kv = past_key_value
+            output = []
+            for i in range(n):
+                kv = ratio * kv + torch.einsum(
+                    "... n d, ... n e -> ... d e",
+                    k[:, :, i : i + 1],
+                    v[:, :, i : i + 1],
+                )
+                qkv = torch.einsum(
+                    "... n e, ... e d -> ... n d", q[:, :, i : i + 1], kv.to(q.dtype)
+                )
+                output.append(qkv)
+            output = torch.concat(output, dim=-2)
+        # reshape
+        output = rearrange(output, "b h n d -> b n (h d)")
+        # normalize
+        output = self.norm(output)
+        # gate
+        output = F.sigmoid(self.output_gate(x)) * output
+        # outproj
+        output = self.out_proj(output)
+
+        attn_weights = None
+
+        return output, attn_weights, kv
+
+
+def _build_slope_tensor(n_attention_heads: int):
+    def get_slopes(n):
+        def get_slopes_power_of_2(n):
+            start = 2 ** (-(2 ** -(math.log2(n) - 3)))
+            ratio = start
+            return [start * ratio**i for i in range(n)]
+
+        if math.log2(n).is_integer():
+            return get_slopes_power_of_2(
+                n
+            )  # In the paper, we only train models that have 2^a heads for some a. This function has
+        else:  # some good properties that only occur when the input is a power of 2. To maintain that even
+            closest_power_of_2 = 2 ** math.floor(
+                math.log2(n)
+            )  # when the number of heads is not a power of 2, we use this workaround.
+            return (
+                get_slopes_power_of_2(closest_power_of_2)
+                + get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
+            )
+
+    # h, 1, 1
+    slopes = torch.tensor(get_slopes(n_attention_heads)).reshape(
+        n_attention_heads, 1, 1
+    )
+
+    return slopes
+
+
+def test_lightning_attention_implementations(model_params):
+    torch.manual_seed(42)
+
+    batch_size = 2
+    seq_len = 1024
+    dtype = torch.bfloat16
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    hidden_states = torch.randn(
+        batch_size, seq_len, model_params["hidden_size"], dtype=dtype, device=device
+    )
+
+    attention_mask = torch.ones(batch_size, seq_len, dtype=dtype, device=device)
+
+    slope_rate = _build_slope_tensor(model_params["num_attention_heads"]).to(device)
+
+    model_attn = MiniMaxText01LightningAttention(**model_params).to(dtype).to(device)
+    model_attn.eval()
+
+    with torch.no_grad():
+        model_output, _, _ = model_attn.inference(
+            hidden_states, attn_mask=attention_mask, slope_rate=slope_rate
+        )
+
+    qkv = model_attn.act(model_attn.qkv_proj(hidden_states))
+    new_shape = qkv.size()[:-1] + (model_attn.num_heads, -1)
+    qkv = qkv.view(*new_shape)
+    q, k, v = torch.split(qkv, [model_attn.head_dim] * 3, dim=-1)
+    q = q.transpose(1, 2)
+    k = k.transpose(1, 2)
+    v = v.transpose(1, 2)
+
+    lib_output = lightning_attn_func(q, k, v, slope_rate)
+    lib_output = lib_output.transpose(1, 2).contiguous()
+    lib_output = lib_output.view(batch_size, seq_len, -1)
+    lib_output = model_attn.norm(lib_output)
+    lib_output = torch.sigmoid(model_attn.output_gate(hidden_states)) * lib_output
+    lib_output = model_attn.out_proj(lib_output)
+
+    torch.testing.assert_close(
+        model_output,
+        lib_output,
+        rtol=1e-3,
+        atol=1e-2,
+        msg="Lightning attention implementations produce different results",
+    )
+
+
+def get_benchmark():
+    batch_size_range = [2**i for i in range(0, 7)]  # max 64
+    seq_length_range = [256, 512, 1024, 2048, 4096]  # max 4096
+    configs = list(itertools.product(batch_size_range, seq_length_range))
+
+    @triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["batch_size", "seq_len"],
+            x_vals=[list(_) for _ in configs],
+            line_arg="provider",
+            line_vals=["MiniMax-Text-01", "OpenNLPLab"],
+            line_names=[
+                "MiniMax-Text-01 Model Implementation",
+                "OpenNLPLab Library Implementation",
+            ],
+            styles=[("blue", "-"), ("green", "-")],
+            ylabel="us",
+            plot_name="lightning-attention-prefill-performance",
+            args={},
+        )
+    )
+    def benchmark(batch_size, seq_len, provider):
+        dtype = torch.bfloat16
+        device = torch.device("cuda")
+
+        params = {
+            "hidden_size": 6144,
+            "num_attention_heads": 64,
+            "head_dim": 96,
+            "hidden_act": "gelu",
+        }
+
+        hidden_states = torch.randn(
+            batch_size, seq_len, params["hidden_size"], dtype=dtype, device=device
+        )
+
+        attention_mask = torch.ones(batch_size, seq_len, dtype=dtype, device=device)
+
+        slope_rate = _build_slope_tensor(params["num_attention_heads"]).to(device)
+        model_attn = MiniMaxText01LightningAttention(**params).to(dtype).to(device)
+        model_attn.eval()
+
+        quantiles = [0.5, 0.2, 0.8]
+        if provider == "MiniMax-Text-01":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: model_attn.inference(
+                    hidden_states, attn_mask=attention_mask, slope_rate=slope_rate
+                ),
+                quantiles=quantiles,
+            )
+        else:
+
+            def run_lib():
+                qkv = model_attn.act(model_attn.qkv_proj(hidden_states))
+                new_shape = qkv.size()[:-1] + (model_attn.num_heads, -1)
+                qkv = qkv.view(*new_shape)
+                q, k, v = torch.split(qkv, [model_attn.head_dim] * 3, dim=-1)
+                q = q.transpose(1, 2)
+                k = k.transpose(1, 2)
+                v = v.transpose(1, 2)
+
+                lib_output = lightning_attn_func(q, k, v, slope_rate)
+                lib_output = lib_output.transpose(1, 2).contiguous()
+                lib_output = lib_output.view(batch_size, seq_len, -1)
+                lib_output = model_attn.norm(lib_output)
+                lib_output = (
+                    torch.sigmoid(model_attn.output_gate(hidden_states)) * lib_output
+                )
+                return model_attn.out_proj(lib_output)
+
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                run_lib,
+                quantiles=quantiles,
+            )
+
+        return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+    return benchmark
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--save_path",
+        type=str,
+        default="./configs/benchmark_ops/lightning_attention_prefill/",
+        help="Path to save lightning attention prefill benchmark results",
+    )
+    args = parser.parse_args()
+
+    # Run correctness test first
+    # Adapted from https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/config.json
+    params = {
+        "hidden_size": 6144,
+        "num_attention_heads": 64,
+        "head_dim": 96,
+        "hidden_act": "silu",
+    }
+    test_lightning_attention_implementations(params)
+
+    # Run performance benchmark
+    benchmark = get_benchmark()
+    benchmark.run(print_data=True, save_path=args.save_path)