File size: 11,706 Bytes
2facf1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 | import math
from dataclasses import dataclass
import torch
from torch import nn
import torch.nn.functional as F
from task import input_t, output_t
from utils import make_match_reference
class RoPE(nn.Module):
def __init__(self, d_model: int):
super().__init__()
self.d_model = d_model
theta = 10000 ** (-torch.arange(0, d_model//2,dtype=torch.bfloat16) / (d_model//2))
# theta = 10000 ** (-torch.arange(0, d_model//2,dtype=torch.bfloat16) / (d_model//2)).to('cuda') # fast: init on cuda
self.register_buffer("theta", theta)
def rotate_half(self, x: torch.Tensor) -> torch.Tensor:
x1, x2 = x.chunk(2, dim=-1)
return torch.cat((-x2, x1), dim=-1)
def forward(self, x: torch.Tensor, start_pos: int = 0) -> torch.Tensor:
seq_len = x.size(-2)
d_model = x.size(-1)
assert d_model == self.d_model
seq_idx = torch.arange(start_pos, start_pos + seq_len, device=x.device)
idx_theta = torch.einsum('s,d->sd', seq_idx, self.theta)
idx_theta2 = torch.cat([idx_theta, idx_theta], dim=-1)
cos = idx_theta2.cos().to(torch.bfloat16)
sin = idx_theta2.sin().to(torch.bfloat16)
return x * cos + self.rotate_half(x) * sin
class KVCache(nn.Module):
def __init__(self, kv_cache_shape: tuple, **kwargs) -> None:
super().__init__(**kwargs)
self.register_buffer('data', torch.zeros(kv_cache_shape, dtype=torch.bfloat16))
# self.register_buffer('data', torch.zeros(kv_cache_shape, dtype=torch.bfloat16, device='cuda')) # fast: init on cuda
self.seq_len = 0
self.zero()
def zero(self) -> None:
self.data.zero_()
def get_data(self) -> torch.Tensor:
return self.data
def forward(self, c_kv: torch.Tensor) -> torch.Tensor:
assert self.seq_len + c_kv.size(1) <= self.data.size(1), "KV Cache Exceeded"
self.data = self.data.to(c_kv.dtype)
self.data[
:, self.seq_len : self.seq_len + c_kv.size(1), :
] = c_kv
self.seq_len += c_kv.size(1)
return self.data[:, :self.seq_len], self.seq_len
@dataclass
class Config:
batch_size: int
dim: int
n_heads: int
q_lora_rank: int
kv_lora_rank: int
qk_nope_head_dim: int
qk_rope_head_dim: int
v_head_dim: int
seq_len: int
max_seq_len: int
kv_cache_shape: tuple
Q_proj_down_weight: torch.Tensor
Q_proj_up_weight: torch.Tensor
KV_proj_down_weight: torch.Tensor
KV_proj_up_weight: torch.Tensor
wo_weight: torch.Tensor
class MLA(nn.Module):
def __init__(self, config: Config):
super().__init__()
self.dim = config.dim
self.n_heads = config.n_heads
self.q_lora_rank = config.q_lora_rank
self.kv_lora_rank = config.kv_lora_rank
self.nope_head_dim = config.qk_nope_head_dim
self.rope_head_dim = config.qk_rope_head_dim
self.v_head_dim = config.v_head_dim
# Down-projection matrices
self.Q_proj_down = nn.Linear(self.dim, self.q_lora_rank, dtype=torch.bfloat16, bias=False)
self.KV_proj_down = nn.Linear(self.dim, self.kv_lora_rank + self.rope_head_dim, dtype=torch.bfloat16, bias=False)
# Up-projection and rope projection matrices
self.Q_proj_up = nn.Linear(self.q_lora_rank, (self.nope_head_dim + self.rope_head_dim) * self.n_heads, dtype=torch.bfloat16, bias=False)
self.KV_proj_up = nn.Linear(self.kv_lora_rank, (self.nope_head_dim + self.v_head_dim) * self.n_heads, dtype=torch.bfloat16, bias=False)
# RoPE on half embeddings
self.q_rope = RoPE(self.rope_head_dim)
self.k_rope = RoPE(self.rope_head_dim)
# Output projection
self.wo = nn.Linear(self.v_head_dim * self.n_heads, self.dim, dtype=torch.bfloat16, bias=False)
self.eps = 1e-6
def forward(self, x: torch.Tensor, kv_cache: KVCache) -> torch.Tensor:
# seq_len = 1 always here
batch_size, seq_len, model_dim = x.size()
################################################################################
# Step 1: Handle down-projection + KV cache #
################################################################################
q_lora = self.Q_proj_down(x)
kv_lora = self.KV_proj_down(x)
kv_lora, kv_len = kv_cache(kv_lora)
query_pos = kv_len - 1
################################################################################
# Step 2: Up-project and prepare NoPE + RoPE #
################################################################################
# Handle queries Q first
q_nope_and_rope = self.Q_proj_up(q_lora).view(
batch_size, seq_len, self.n_heads, self.nope_head_dim + self.rope_head_dim)
q_nope, q_rope = torch.split(q_nope_and_rope, [self.nope_head_dim, self.rope_head_dim], dim=-1)
# Handle keys and values K/V. V does not need RoPE
kv_nope, k_rope = torch.split(kv_lora, [self.kv_lora_rank, self.rope_head_dim], dim=-1)
kv_nope = self.KV_proj_up(kv_nope).view(
batch_size, kv_len, self.n_heads, self.nope_head_dim + self.v_head_dim)
k_nope, v = torch.split(kv_nope, [self.nope_head_dim, self.v_head_dim], dim=-1)
################################################################################
# Step 3: Handle RoPE Stream #
################################################################################
# Compute RoPE for queries and combine with no-RoPE part
q_rope = q_rope.permute(0, 2, 1, 3) # bs x n_heads x seq_len x rope_head_dim
q_rope = self.q_rope(q_rope, start_pos=query_pos)
q_nope = q_nope.permute(0, 2, 1, 3) # bs x n_heads x seq_len x rope_head_dim
q = torch.concat([q_nope, q_rope], dim=-1)
# Compute RoPE for keys and combine with no-RoPE part
k_rope = k_rope[:, None, :, :]
k_rope = self.k_rope(k_rope).expand(-1,self.n_heads,-1,-1)
k_nope = k_nope.permute(0, 2, 1, 3) # bs x kv_len x n_heads x rope_head_dim
k = torch.concat([k_nope, k_rope], dim=-1)
################################################################################
# Compute Multi-head Attention #
################################################################################
v = v.permute(0, 2, 1, 3) # bs x n_heads x kv_len x v_head_dim
scores = torch.matmul(q, k.transpose(-1, -2)) / math.sqrt(self.rope_head_dim + self.nope_head_dim)
attn = F.softmax(scores, dim=-1).to(torch.bfloat16)
y = torch.matmul(attn, v).view(batch_size, 1, -1)
y = self.wo(y)
return y, kv_cache.get_data()
def generate_input(batchsize, dim, dq, prefill, seed):
# Sizes derived from: https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py
gen = torch.Generator(device='cuda')
gen.manual_seed(seed)
# Generate weights for linear layers
Q_proj_down_weight = torch.randn((dq, dim), dtype=torch.bfloat16, generator=gen, device='cuda') / math.sqrt(dim)
KV_proj_down_weight = torch.randn((512 + 64, dim), dtype=torch.bfloat16, generator=gen, device='cuda') / math.sqrt(dim)
Q_proj_up_weight = torch.randn(((128 + 64) * 128, dq), dtype=torch.bfloat16, generator=gen, device='cuda') / math.sqrt(dq)
KV_proj_up_weight = torch.randn(((128 + 128) * 128, 512), dtype=torch.bfloat16, generator=gen, device='cuda') / math.sqrt(512)
wo_weight = torch.randn((dim, 128 * 128), dtype=torch.bfloat16, generator=gen, device='cuda') / math.sqrt(128 * 128)
config = Config(
batch_size=batchsize,
dim=dim,
q_lora_rank=dq,
n_heads=128,
kv_lora_rank=512,
qk_nope_head_dim=128,
qk_rope_head_dim=64,
v_head_dim=128,
seq_len=1,
max_seq_len=8192,
kv_cache_shape=(batchsize, 8192, 512 + 64),
Q_proj_down_weight=Q_proj_down_weight,
Q_proj_up_weight=Q_proj_up_weight,
KV_proj_down_weight=KV_proj_down_weight,
KV_proj_up_weight=KV_proj_up_weight,
wo_weight=wo_weight,
)
x = torch.randn((config.batch_size, 1, config.dim), dtype=torch.bfloat16, generator=gen, device='cuda')
# Pre-fill KV cache
kv_cache = KVCache((config.batch_size, config.max_seq_len, config.kv_lora_rank + config.qk_rope_head_dim)).to('cuda')
# kv_cache = KVCache((config.batch_size, config.max_seq_len, config.kv_lora_rank + config.qk_rope_head_dim)) # fast: init on cuda
pre_filled_cache = torch.randn((config.batch_size, prefill, config.kv_lora_rank + config.qk_rope_head_dim),
dtype=torch.bfloat16, generator=gen, device='cuda')
kv_cache(pre_filled_cache)
return config, x, kv_cache
def ref_kernel(data: input_t) -> output_t:
config, x, kv_cache = data
# Load in model weights
model = MLA(config).to('cuda')
# model = MLA(config) # fast: init on cuda
model.Q_proj_down.weight = nn.Parameter(config.Q_proj_down_weight)
model.Q_proj_up.weight = nn.Parameter(config.Q_proj_up_weight)
model.KV_proj_down.weight = nn.Parameter(config.KV_proj_down_weight)
model.KV_proj_up.weight = nn.Parameter(config.KV_proj_up_weight)
model.wo.weight = nn.Parameter(config.wo_weight)
output, kv_cache = model(x, kv_cache)
return output, kv_cache
check_implementation = make_match_reference(ref_kernel, rtol=2e-02, atol=8e-03)
def time_mla(model, x, kv_cache, num_warmup=3, num_trials=5):
# Warmup runs
for _ in range(1):
output, _ = model(x, kv_cache)
torch.cuda.synchronize()
# Timed runs
times = []
for _ in range(num_trials):
kv_cache = KVCache((config.batch_size, config.max_seq_len, config.kv_lora_rank + config.qk_rope_head_dim)).to('cuda')
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
output, updated_kv = model(x, kv_cache)
end.record()
torch.cuda.synchronize()
times.append(start.elapsed_time(end))
avg_time = sum(times) / len(times)
return output, updated_kv, avg_time, times
if __name__ == "__main__":
# Generate test input
batchsize = 128
dim = 7168
dq = 1536
prefill = 512
seed = 97
# Create model and inputs
config, x, kv_cache = generate_input(batchsize, dim, dq, prefill, seed)
model = MLA(config).to('cuda')
# Run model with timing
output, updated_kv, avg_time, times = time_mla(model, x, kv_cache)
# Test reference kernel
ref_output, ref_kv = ref_kernel((config, x, kv_cache))
print("\nReference kernel output:")
print(f"Output shape: {ref_output.shape}")
print(f"KV cache shape: {ref_kv.shape}")
print("\nFirst few values of reference output:")
print(ref_output[0, :10])
# Compare outputs
print("\nOutput difference:")
print(f"Max absolute difference: {torch.max(torch.abs(output - ref_output))}")
print(f"Mean absolute difference: {torch.mean(torch.abs(output - ref_output))}")
print(f"Input shape: {x.shape}")
print(f"Output shape: {output.shape}")
print(f"Updated KV cache shape: {updated_kv.shape}")
print("\nFirst few values of output:")
print(output[0, :10])
print(f"\nTiming results over {len(times)} runs (ms):")
print(f"Average: {avg_time:.2f}")
print(f"Individual times: {[f'{t:.2f}' for t in times]}")
|