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import torch
import torch.nn.functional as F
from transformers import LlamaConfig
from specforge.modeling.draft.llama3_eagle import (
LlamaAttention,
LlamaFlashAttention,
prepare_decoder_attention_mask,
)
from specforge.utils import padding
from tests.test_utils.utils import norm_tensor
TTT_LENGTH = 7
torch.manual_seed(0)
def assert_similar(ref, out):
# We are looser with the checks since we are comparing bf16 backends
ref = ref.to(torch.float32)
out = out.to(torch.float32)
similarity = F.cosine_similarity(ref.flatten(), out.flatten(), dim=0)
norm_ratio = torch.linalg.norm(ref) / torch.linalg.norm(out)
assert similarity >= 0.975, f"{similarity=}"
assert abs(1 - norm_ratio) <= 0.025, f"{norm_ratio=}"
class TestFlashAttention(unittest.TestCase):
def setUp(self):
torch.manual_seed(0)
self.config_dict = {
"hidden_size": 128,
"num_attention_heads": 8,
"num_key_value_heads": 2,
"max_position_embeddings": 4096,
"rms_norm_eps": 1e-05,
"vocab_size": 32000,
"intermediate_size": 688,
"hidden_act": "silu",
"num_hidden_layers": 1,
"torch_dtype": "bfloat16",
}
self.config = LlamaConfig(**self.config_dict)
self.seq_lengths = [128, 200, 256, 300, 512, 800, 1024, 2048]
self.dtype = torch.bfloat16
def test_forward_pass_comparison(self):
"""Test forward pass comparison between LlamaAttention and LlamaFlashAttention."""
for seq_len in self.seq_lengths:
with self.subTest(seq_len=seq_len):
self._test_forward_pass_comparison_for_seq_len(seq_len)
def _test_forward_pass_comparison_for_seq_len(self, seq_len):
"""Helper method to test forward pass comparison for a specific sequence length."""
attention = LlamaAttention(self.config).to("cuda").to(self.dtype)
flash_attention = LlamaFlashAttention(self.config).to("cuda").to(self.dtype)
# Ensure same weights
with torch.no_grad():
flash_attention.q_proj.weight.copy_(attention.q_proj.weight)
flash_attention.k_proj.weight.copy_(attention.k_proj.weight)
flash_attention.v_proj.weight.copy_(attention.v_proj.weight)
flash_attention.o_proj.weight.copy_(attention.o_proj.weight)
attention.eval()
flash_attention.eval()
batch_size = 2
hidden_size = self.config.hidden_size * 2
############### Attention Inputs ##############
position_ids = (
torch.arange(seq_len).unsqueeze(0).repeat(batch_size, 1).to("cuda")
)
cache_hidden = [[], []] # [cache_k, cache_v]
flash_cache_hidden = [[], []] # [cache_k, cache_v]
attention_mask = torch.ones(batch_size, seq_len, dtype=self.dtype).to("cuda")
# Simulate one item in the batch is masked and not taking a full block.
padding_start_index = seq_len - min(
200, seq_len // 3
) # Adjust padding based on seq_len
attention_mask[1, padding_start_index:] = False
input_embeds = norm_tensor(
(batch_size, seq_len, self.config.hidden_size),
device="cuda",
dtype=self.dtype,
)
decoder_attention_mask = prepare_decoder_attention_mask(
attention_mask=attention_mask,
input_shape=(batch_size, seq_len),
inputs_embeds=input_embeds,
past_key_values_length=0,
)
hidden_states_list = []
flash_hidden_states_list = []
for idx in range(TTT_LENGTH):
hidden_states = norm_tensor(
(batch_size, seq_len, hidden_size), device="cuda", dtype=self.dtype
)
flash_hidden_states = hidden_states.clone().detach()
hidden_states_list.append(hidden_states)
flash_hidden_states_list.append(flash_hidden_states)
############### Flash Attention Inputs ##############
flash_position_ids = position_ids.clone()
for idx in range(TTT_LENGTH):
with torch.no_grad():
output = attention(
hidden_states=hidden_states_list[idx],
attention_mask=decoder_attention_mask,
position_ids=position_ids,
cache_hidden=cache_hidden,
output_attentions=False,
use_cache=True,
)
with torch.no_grad():
output_flash = flash_attention(
hidden_states=flash_hidden_states_list[idx],
position_ids=flash_position_ids,
cache_hidden=flash_cache_hidden,
)
assert_similar(output[0][: -1 - idx], output_flash[0][: -1 - idx])
assert_similar(
output[1][: padding_start_index - idx],
output_flash[1][: padding_start_index - idx],
)
# Check output shape
expected_output_shape = (batch_size, seq_len, self.config.hidden_size)
self.assertEqual(output_flash.shape, expected_output_shape)
# Check output is not NaN or Inf
self.assertFalse(torch.isnan(output_flash).any())
self.assertFalse(torch.isinf(output_flash).any())
def test_backward_pass_gradient_comparison(self):
"""Test backward pass comparing gradients between LlamaAttention and LlamaFlashAttention."""
for seq_len in self.seq_lengths:
with self.subTest(seq_len=seq_len):
self._test_backward_pass_gradient_comparison_for_seq_len(seq_len)
def _test_backward_pass_gradient_comparison_for_seq_len(self, seq_len):
"""Helper method to test backward pass gradient comparison for a specific sequence length."""
attention = LlamaAttention(self.config).to("cuda").to(self.dtype)
flash_attention = LlamaFlashAttention(self.config).to("cuda").to(self.dtype)
# Ensure same weights
with torch.no_grad():
flash_attention.q_proj.weight.copy_(attention.q_proj.weight)
flash_attention.k_proj.weight.copy_(attention.k_proj.weight)
flash_attention.v_proj.weight.copy_(attention.v_proj.weight)
flash_attention.o_proj.weight.copy_(attention.o_proj.weight)
batch_size = 2
hidden_size = self.config.hidden_size * 2
############### Attention Inputs ##############
position_ids = (
torch.arange(seq_len).unsqueeze(0).repeat(batch_size, 1).to("cuda")
)
cache_hidden = [[], []] # [cache_k, cache_v]
flash_cache_hidden = [[], []] # [cache_k, cache_v]
attention_mask = torch.ones(batch_size, seq_len, dtype=torch.bool).to("cuda")
# Simulate one item in the batch is masked and not taking a full block.
# padding_start_index = seq_len - 50
# attention_mask[1, padding_start_index:] = False
input_embeds = norm_tensor(
(batch_size, seq_len, self.config.hidden_size),
device="cuda",
dtype=self.dtype,
)
decoder_attention_mask = prepare_decoder_attention_mask(
attention_mask=attention_mask,
input_shape=(batch_size, seq_len),
inputs_embeds=input_embeds,
past_key_values_length=0,
)
############### Flash Attention Inputs ##############
flash_position_ids = position_ids.clone()
loss_mask = torch.ones(
batch_size, seq_len, dtype=self.dtype, requires_grad=False
).to("cuda")
# Create input tensors that require gradients
loss_list = []
loss_flash_list = []
hidden_states_list = []
flash_hidden_states_list = []
for idx in range(TTT_LENGTH):
hidden_states = norm_tensor(
(batch_size, seq_len, hidden_size), device="cuda", dtype=self.dtype
)
flash_hidden_states = hidden_states.clone().detach()
hidden_states_list.append(hidden_states)
flash_hidden_states_list.append(flash_hidden_states)
for idx in range(TTT_LENGTH):
is_last = idx == TTT_LENGTH - 1
output = attention(
hidden_states=hidden_states_list[idx],
attention_mask=decoder_attention_mask,
position_ids=position_ids,
cache_hidden=cache_hidden,
output_attentions=False,
use_cache=True,
)
output_flash = flash_attention(
hidden_states=flash_hidden_states_list[idx],
position_ids=flash_position_ids,
cache_hidden=flash_cache_hidden,
)
# Apply loss mask on calculation over batch
loss = (output * loss_mask[..., None]).sum().mean()
loss_flash = (output_flash * loss_mask[..., None]).sum().mean()
loss_list.append(loss)
loss_flash_list.append(loss_flash)
# Compare gradients
if not is_last:
# Step 5.7: we need to update the loss mask
loss_mask = padding(loss_mask, left=False)
mean_loss = sum(loss_list) / len(loss_list)
mean_loss_flash = sum(loss_flash_list) / len(loss_flash_list)
mean_loss.backward()
mean_loss_flash.backward()
projections = ["q_proj", "k_proj", "v_proj", "o_proj"]
for proj_name in projections:
assert_similar(
getattr(attention, proj_name).weight.grad,
getattr(flash_attention, proj_name).weight.grad,
)
if __name__ == "__main__":
unittest.main(verbosity=2)
|