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import torch
import torch._dynamo as dynamo
from transformers import LlamaConfig
from transformers.cache_utils import DynamicCache
from specforge.modeling.draft.flex_attention import (
compile_friendly_create_block_mask,
compile_friendly_flex_attention,
generate_eagle3_mask,
)
from specforge.modeling.draft.llama3_eagle import (
LlamaAttention,
LlamaFlexAttention,
prepare_decoder_attention_mask,
)
from specforge.utils import padding
from .utils import norm_tensor
dynamo.config.recompile_limit = 64
TTT_LENGTH = 7
torch.manual_seed(0)
class TestFlexAttention(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": "float32",
}
self.config = LlamaConfig(**self.config_dict)
self.seq_lengths = [128, 200, 256, 300, 512, 800, 1024, 2048]
self.dtype = torch.float32
def test_forward_pass_comparison(self):
"""Test forward pass comparison between LlamaAttention and LlamaFlexAttention."""
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)
flex_attention = LlamaFlexAttention(self.config).to("cuda").to(self.dtype)
# Ensure same weights
with torch.no_grad():
flex_attention.q_proj.weight.copy_(attention.q_proj.weight)
flex_attention.k_proj.weight.copy_(attention.k_proj.weight)
flex_attention.v_proj.weight.copy_(attention.v_proj.weight)
flex_attention.o_proj.weight.copy_(attention.o_proj.weight)
attention.eval()
flex_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]
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 = []
flex_hidden_states_list = []
for idx in range(TTT_LENGTH):
hidden_states = norm_tensor(
(batch_size, seq_len, hidden_size), device="cuda", dtype=self.dtype
)
flex_hidden_states = hidden_states.clone().detach()
hidden_states_list.append(hidden_states)
flex_hidden_states_list.append(flex_hidden_states)
############### Flex Attention Inputs ##############
flex_position_ids = position_ids.clone()
past_key_values = DynamicCache()
for idx in range(TTT_LENGTH):
is_last = idx == TTT_LENGTH - 1
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_flex = flex_attention(
hidden_states=flex_hidden_states_list[idx],
attention_mask=attention_mask,
position_ids=flex_position_ids,
past_key_values=past_key_values,
)
torch.testing.assert_close(
output[0][: -1 - idx], output_flex[0][: -1 - idx], atol=1e-2, rtol=1e-2
)
torch.testing.assert_close(
output[1][: padding_start_index - idx],
output_flex[1][: padding_start_index - idx],
atol=1e-2,
rtol=1e-2,
)
# Check output shape
expected_output_shape = (batch_size, seq_len, self.config.hidden_size)
self.assertEqual(output_flex.shape, expected_output_shape)
# Check output is not NaN or Inf
self.assertFalse(torch.isnan(output_flex).any())
self.assertFalse(torch.isinf(output_flex).any())
def test_backward_pass_gradient_comparison(self):
"""Test backward pass comparing gradients between LlamaAttention and LlamaFlexAttention."""
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)
flex_attention = LlamaFlexAttention(self.config).to("cuda").to(self.dtype)
# Ensure same weights
with torch.no_grad():
flex_attention.q_proj.weight.copy_(attention.q_proj.weight)
flex_attention.k_proj.weight.copy_(attention.k_proj.weight)
flex_attention.v_proj.weight.copy_(attention.v_proj.weight)
flex_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]
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,
)
############### Flex Attention Inputs ##############
flex_position_ids = position_ids.clone()
ttt_length = TTT_LENGTH
past_key_values = DynamicCache()
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_flex_list = []
hidden_states_list = []
flex_hidden_states_list = []
for idx in range(TTT_LENGTH):
hidden_states = norm_tensor(
(batch_size, seq_len, hidden_size), device="cuda", dtype=self.dtype
)
flex_hidden_states = hidden_states.clone().detach()
hidden_states_list.append(hidden_states)
flex_hidden_states_list.append(flex_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_flex = flex_attention(
hidden_states=flex_hidden_states_list[idx],
attention_mask=attention_mask,
position_ids=flex_position_ids,
past_key_values=past_key_values,
)
# Apply loss mask on calculation over batch
loss = (output * loss_mask[..., None]).sum().mean()
loss_flex = (output_flex * loss_mask[..., None]).sum().mean()
torch.testing.assert_close(loss, loss_flex, atol=1e-2, rtol=1e-2)
loss_list.append(loss)
loss_flex_list.append(loss_flex)
# 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_flex = sum(loss_flex_list) / len(loss_flex_list)
mean_loss.backward()
mean_loss_flex.backward()
projections = ["q_proj", "k_proj", "v_proj", "o_proj"]
for proj_name in projections:
torch.testing.assert_close(
getattr(attention, proj_name).weight.grad,
getattr(flex_attention, proj_name).weight.grad,
atol=1e-2,
rtol=1e-2,
)
class TestEagle3FlexMask(unittest.TestCase):
def test_eagle3_flex_mask(self):
B = 1
H = 1
S = 128 * 8
D = 128
Q_LEN = S
KV_LEN = S * 3
lck = 128 * 2
data_type = torch.bfloat16
query = norm_tensor((B, H, S, D), device="cuda", dtype=data_type)
key_cache = norm_tensor((B, H, KV_LEN, D), device="cuda", dtype=data_type)
value_cache = norm_tensor((B, H, KV_LEN, D), device="cuda", dtype=data_type)
seq_lengths = torch.tensor([S], device="cuda", dtype=torch.int32)
seq_lengths -= lck
block_mask = compile_friendly_create_block_mask(
mask_mod=generate_eagle3_mask(
seq_lengths=seq_lengths, Q_LEN=Q_LEN, KV_LEN=KV_LEN, lck=lck
),
B=1,
H=1,
Q_LEN=Q_LEN,
KV_LEN=KV_LEN,
device=query.device,
)
# fmt: off
expected_mask = torch.tensor([[[
[1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]]], dtype=torch.int32).to(query.device)
# fmt: on
dense_mask = block_mask.to_dense()
assert torch.allclose(dense_mask, expected_mask)
output = compile_friendly_flex_attention(
query, key_cache, value_cache, block_mask=block_mask
)
if __name__ == "__main__":
unittest.main(verbosity=2)
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