File size: 7,989 Bytes
2d67aa6 | 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 | import os
import unittest
import torch
import torch.multiprocessing as mp
from accelerate.utils import set_seed
from torch import nn
from transformers import PretrainedConfig
from yunchang import EXTRACT_FUNC_DICT
# Project-specific imports
from specforge.distributed import destroy_distributed, init_distributed
from specforge.modeling.draft.llama3_eagle import LlamaDecoderLayer
from specforge.utils import padding
from tests.utils import get_available_port
def get_model_config():
"""Create and return the model configuration."""
config_dict = {
"architectures": ["LlamaForCausalLMEagle3"],
"eagle_config": {
"eagle_aux_hidden_state_layer_ids": [1, 29, 57],
"use_aux_hidden_state": True,
},
"bos_token_id": 128000,
"eos_token_id": 128001,
"hidden_act": "silu",
"hidden_size": 7168,
"initializer_range": 0.02,
"intermediate_size": 29568,
"max_position_embeddings": 32768,
"model_type": "llama",
"num_attention_heads": 32,
"num_key_value_heads": 8,
"num_hidden_layers": 1,
"pad_token_id": 0,
"rms_norm_eps": 1e-05,
"tie_word_embeddings": False,
"torch_dtype": "float16",
"transformers_version": "4.28.1",
"use_cache": True,
"rope_scaling": None,
"vocab_size": 129280,
"draft_vocab_size": 32000,
"pretraining_tp": 1,
}
return PretrainedConfig.from_dict(config_dict)
def setup_env(rank, world_size, port):
"""Set up distributed environment variables."""
os.environ["RANK"] = str(rank)
os.environ["WORLD_SIZE"] = str(world_size)
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = str(port)
torch.cuda.set_device(rank)
def run_iterative_pass(
decoder_layer,
embed_tokens,
input_ids,
hidden_states,
attention_mask,
position_ids,
ttt_length,
):
"""
Core loop: execute the forward pass `ttt_length` times.
Used for both Golden (SDPA) and Distributed (USP) runs to ensure logic consistency.
"""
# Clone to avoid side effects on original tensors
curr_input_ids = input_ids.clone()
curr_hidden_states = hidden_states.clone()
# Init cache
cache_hidden = [[], []]
past_key_values = None
final_output = None
for idx in range(ttt_length):
is_last = idx == ttt_length - 1
# 1. Embed inputs
inputs_embeds = embed_tokens(curr_input_ids).to(curr_hidden_states.dtype)
# 2. Forward pass
output_hidden_states = decoder_layer(
input_emb=inputs_embeds,
hidden_states=curr_hidden_states,
cache_hidden=cache_hidden,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
output_attentions=False,
use_cache=False,
)
# Update states for next iteration
curr_hidden_states = output_hidden_states
final_output = output_hidden_states
# 3. Simulate TTT padding/shift
if not is_last:
curr_input_ids = padding(curr_input_ids, left=False)
return final_output
def run_test_case(rank, world_size, port):
"""Worker function executed in each process."""
setup_env(rank, world_size, port)
device = torch.device(f"cuda:{rank}")
set_seed(42)
# --- Data & Config Preparation ---
config = get_model_config()
seq_len = 1560
batch_size = 1
ttt_length = 3
# Generate dummy data on GPU
data_input_ids = torch.randint(0, 10000, (batch_size, seq_len), device=device)
data_hidden_states = torch.randn(
batch_size, seq_len, config.hidden_size, device=device, dtype=torch.bfloat16
)
attention_mask = torch.tril(torch.ones(seq_len, seq_len, device=device)).view(
1, 1, seq_len, seq_len
)
position_ids = torch.arange(seq_len, device=device).unsqueeze(0)
# Shared embedding layer
embed_tokens = nn.Embedding(
config.vocab_size, config.hidden_size, config.pad_token_id
).to(device)
# --- Phase 1: Golden Run (SDPA) ---
# Init dist briefly for internal checks, even if running single-device logic
init_distributed(tp_size=1, sp_ulysses_size=1, sp_ring_size=1)
sdpa_decoder = (
LlamaDecoderLayer(config, attention_backend="fa").to(device).to(torch.bfloat16)
)
with torch.no_grad():
sdpa_output = run_iterative_pass(
decoder_layer=sdpa_decoder,
embed_tokens=embed_tokens,
input_ids=data_input_ids,
hidden_states=data_hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
ttt_length=ttt_length,
)
# Save weights for alignment and cleanup SDPA model
state_dict = sdpa_decoder.state_dict()
del sdpa_decoder
destroy_distributed()
# --- Phase 2: Distributed Run (USP) ---
def subtest_usp(sp_ulysses_degree, sp_ring_degree):
"""Run USP with specific topology and compare against Golden."""
try:
init_distributed(
tp_size=1,
sp_ulysses_size=sp_ulysses_degree,
sp_ring_size=sp_ring_degree,
)
# Init USP model and load golden weights
usp_decoder = (
LlamaDecoderLayer(config, attention_backend="usp")
.to(device)
.to(torch.bfloat16)
)
usp_decoder.load_state_dict(state_dict)
# Shard data (Split Input)
extract_func = EXTRACT_FUNC_DICT["basic"]
local_input_ids = (
extract_func(
data_input_ids,
rank,
world_size=world_size,
rd=sp_ring_degree,
ud=sp_ulysses_degree,
)
.detach()
.clone()
)
local_hidden_states = (
extract_func(
data_hidden_states,
rank,
world_size=world_size,
rd=sp_ring_degree,
ud=sp_ulysses_degree,
)
.detach()
.clone()
)
# Run USP forward
with torch.no_grad():
usp_output = run_iterative_pass(
decoder_layer=usp_decoder,
embed_tokens=embed_tokens,
input_ids=local_input_ids,
hidden_states=local_hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
ttt_length=ttt_length,
)
# Verify results
# Slice the golden output to match the current rank's chunk
total_degree = sp_ring_degree * sp_ulysses_degree
chunk_size = sdpa_output.shape[1] // total_degree
start_idx = (rank % total_degree) * chunk_size
end_idx = start_idx + chunk_size
golden_chunk = sdpa_output[:, start_idx:end_idx, :]
assert torch.allclose(usp_output, golden_chunk, rtol=2e-2, atol=2e-2), (
f"[Rank {rank}] USP (U{sp_ulysses_degree}R{sp_ring_degree}) mismatch!\n"
f"Max Diff: {(usp_output - golden_chunk).abs().max().item()}"
)
finally:
destroy_distributed()
# Case 1: Hybrid (Ulysses=2, Ring=1)
subtest_usp(sp_ulysses_degree=2, sp_ring_degree=1)
# Case 2: Hybrid (Ulysses=1, Ring=2)
subtest_usp(sp_ulysses_degree=1, sp_ring_degree=2)
class TestTTTDistributed(unittest.TestCase):
def test_llama_usp_decoder(self):
world_size = 2
port = get_available_port()
mp.spawn(run_test_case, nprocs=world_size, args=(world_size, port))
if __name__ == "__main__":
unittest.main()
|