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import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.kda import chunk_kda, fused_recurrent_kda
from fla.ops.kda.gate import fused_kda_gate, kda_gate_ref
from fla.ops.kda.naive import naive_chunk_kda, naive_recurrent_kda
from fla.utils import assert_close, device, is_intel_alchemist
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'scale', 'gate_logit_normalizer', 'dtype'),
[
pytest.param(
*test,
id="B{}-T{}-H{}-D{}-scale{}-gate_logit_normalizer{}-{}".format(*test),
)
for test in [
(1, 64, 1, 64, 1, 1, torch.float),
(2, 512, 3, 60, 1, 1, torch.float),
(4, 1024, 4, 128, 0.1, 1, torch.float),
(4, 1024, 4, 128, 1, 10, torch.float),
]
],
)
def test_naive_chunk(
B: int,
T: int,
H: int,
D: int,
scale: float,
gate_logit_normalizer: float,
dtype: torch.dtype,
):
torch.manual_seed(42)
if is_intel_alchemist and D > 128:
pytest.skip(reason='chunk_gated_delta_rule is not supported on alchemist for D>128')
q = torch.rand(B, T, H, D, dtype=dtype)
k = torch.rand(B, T, H, D, dtype=dtype)
v = torch.rand(B, T, H, D, dtype=dtype)
g = F.logsigmoid(torch.randn(B, T, H, D, dtype=torch.float)) / gate_logit_normalizer
beta = torch.randn(B, T, H, dtype=dtype).sigmoid()
h0 = torch.randn(B, H, D, D, dtype=torch.float32)
q, k, v, g, beta, h0 = map(lambda x: x.to(device).requires_grad_(True), (q, k, v, g, beta, h0))
ref, ref_ht = naive_recurrent_kda(
q=F.normalize(q.clone(), p=2, dim=-1),
k=F.normalize(k.clone(), p=2, dim=-1),
v=v.clone(),
g=g.clone(),
beta=beta.clone(),
scale=scale,
initial_state=h0.clone(),
output_final_state=True,
)
tri, tri_ht = naive_chunk_kda(
q=F.normalize(q.clone(), p=2, dim=-1),
k=F.normalize(k.clone(), p=2, dim=-1),
v=v.clone(),
g=g.clone(),
beta=beta.clone(),
scale=scale,
initial_state=h0.clone(),
output_final_state=True,
)
assert_close('o', ref, tri, 0.005)
assert_close('ht', ref_ht, tri_ht, 0.005)
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'scale', 'gate_logit_normalizer', 'use_qk_l2norm_in_kernel', 'dtype'),
[
pytest.param(
*test,
id="B{}-T{}-H{}-D{}-scale{}-gate_logit_normalizer{}-use_qk_l2norm_in_kernel{}-{}".format(*test),
)
for test in [
(1, 64, 1, 64, 1, 1, False, torch.float),
(2, 512, 3, 60, 1, 1, False, torch.float),
(3, 1000, 4, 100, 0.1, 1, True, torch.float),
(4, 1024, 4, 128, 0.1, 1, False, torch.float),
]
],
)
def test_fused_recurrent(
B: int,
T: int,
H: int,
D: int,
scale: float,
gate_logit_normalizer: float,
use_qk_l2norm_in_kernel: bool,
dtype: torch.dtype,
):
torch.manual_seed(42)
if is_intel_alchemist and D > 128:
pytest.skip(reason='chunk_gated_delta_rule is not supported on alchemist for D>128')
q = torch.rand(B, T, H, D, dtype=dtype)
k = torch.rand(B, T, H, D, dtype=dtype)
v = torch.rand(B, T, H, D, dtype=dtype)
g = F.logsigmoid(torch.randn(B, T, H, D, dtype=torch.float)) / gate_logit_normalizer
beta = torch.randn(B, T, H, dtype=dtype).sigmoid()
h0 = torch.randn(B, H, D, D, dtype=torch.float32)
q, k, v, g, beta, h0 = map(lambda x: x.to(device).requires_grad_(True), (q, k, v, g, beta, h0))
ref, ref_ht = naive_recurrent_kda(
q=F.normalize(q.clone(), p=2, dim=-1),
k=F.normalize(k.clone(), p=2, dim=-1),
v=v.clone(),
g=g.clone(),
beta=beta.clone(),
scale=scale,
initial_state=h0.clone(),
output_final_state=True,
)
tri, tri_ht = fused_recurrent_kda(
q=F.normalize(q.clone(), p=2, dim=-1) if not use_qk_l2norm_in_kernel else q.clone(),
k=F.normalize(k.clone(), p=2, dim=-1) if not use_qk_l2norm_in_kernel else k.clone(),
v=v.clone(),
g=g.clone(),
beta=beta.clone(),
scale=scale,
initial_state=h0.clone(),
output_final_state=True,
use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
)
assert_close('o', ref, tri, 0.005)
assert_close('ht', ref_ht, tri_ht, 0.005)
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'scale', 'gate_logit_normalizer', 'mask_p', 'use_qk_l2norm_in_kernel', 'dtype', 'tma'),
[
pytest.param(
*test,
id="B{}-T{}-H{}-D{}-scale{}-gate_logit_normalizer{}-mask_p{}-use_qk_l2norm_in_kernel{}-{}-tma{}".format(*test),
)
for test in [
(1, 63, 1, 64, 1, 1, 0, False, torch.float16, True),
(2, 500, 3, 60, 1, 1, 0, False, torch.float16, True),
(2, 1000, 3, 64, 0.1, 1, 0.5, False, torch.float16, False),
(3, 1024, 4, 100, 1, 0.1, 0, False, torch.float16, False),
(4, 1024, 4, 128, 0.1, 1, 0, False, torch.float16, True),
(4, 1024, 4, 128, 0.1, 1, 0, True, torch.float16, True),
(2, 1500, 4, 128, 0.1, 10, 0, False, torch.float16, False),
(4, 2048, 8, 64, 0.1, 1, 0, False, torch.float16, True),
]
],
)
def test_chunk(
B: int,
T: int,
H: int,
D: int,
scale: float,
gate_logit_normalizer: float,
mask_p: float,
use_qk_l2norm_in_kernel: bool,
dtype: torch.dtype,
tma: bool,
):
torch.manual_seed(42)
if not tma:
os.environ['FLA_USE_TMA'] = '0'
else:
os.environ['FLA_USE_TMA'] = '1'
q = torch.rand(B, T, H, D, dtype=dtype)
k = torch.rand(B, T, H, D, dtype=dtype)
v = torch.rand(B, T, H, D, dtype=dtype)
g = F.logsigmoid(torch.randn(B, T, H, D, dtype=torch.float)) / gate_logit_normalizer
g = g * (torch.rand_like(g) > mask_p)
beta = torch.randn(B, T, H, dtype=dtype).sigmoid()
h0 = torch.randn(B, H, D, D, dtype=torch.float32)
q, k, v, g, beta, h0 = map(lambda x: x.to(device).requires_grad_(True), (q, k, v, g, beta, h0))
do = torch.randn_like(v)
dht = torch.randn_like(h0)
ref, ref_ht = naive_recurrent_kda(
q=F.normalize(q.clone(), p=2, dim=-1),
k=F.normalize(k.clone(), p=2, dim=-1),
v=v.clone(),
g=g.clone(),
beta=beta.clone(),
scale=scale,
initial_state=h0.clone(),
output_final_state=True,
)
((ref * do).sum() + (ref_ht * dht).sum()).backward(retain_graph=True)
ref_dq, ref_dk, ref_dv, ref_dg, ref_db, ref_dh0 = q.grad, k.grad, v.grad, g.grad, beta.grad, h0.grad
q.grad = k.grad = v.grad = g.grad = beta.grad = h0.grad = None
tri, tri_ht = chunk_kda(
q=F.normalize(q.clone(), p=2, dim=-1) if not use_qk_l2norm_in_kernel else q.clone(),
k=F.normalize(k.clone(), p=2, dim=-1) if not use_qk_l2norm_in_kernel else k.clone(),
v=v.clone(),
g=g.clone(),
beta=beta.clone(),
scale=scale,
initial_state=h0.clone(),
output_final_state=True,
use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
)
((tri * do).sum() + (tri_ht * dht).sum()).backward(retain_graph=True)
tri_dq, tri_dk, tri_dv, tri_dg, tri_db, tri_dh0 = q.grad, k.grad, v.grad, g.grad, beta.grad, h0.grad
q.grad = k.grad = v.grad = g.grad = beta.grad = h0.grad = None
assert_close('o', ref, tri, 0.005)
assert_close('ht', ref_ht, tri_ht, 0.005)
assert_close('dq', ref_dq, tri_dq, 0.008)
assert_close('dk', ref_dk, tri_dk, 0.008)
assert_close('dv', ref_dv, tri_dv, 0.008)
assert_close('dg', ref_dg, tri_dg, 0.02)
assert_close('db', ref_db, tri_db, 0.02)
assert_close('dh0', ref_dh0, tri_dh0, 0.008)
@pytest.mark.parametrize(
('H', 'D', 'mask_p', 'cu_seqlens', 'dtype'),
[
pytest.param(*test, id="H{}-D{}-mask_p{}-cu_seqlens{}-{}".format(*test))
for test in [
(4, 60, 0, [0, 15], torch.float16),
(4, 64, 0, [0, 256, 500, 1000], torch.float16),
(4, 128, 0.5, [0, 256, 500, 1000], torch.float16),
(4, 100, 0, [0, 15, 100, 300, 1200, 2000], torch.float16),
(4, 256, 0, [0, 15, 100, 300, 1200, 4096], torch.float16),
]
],
)
@pytest.mark.skipif(
os.getenv('SKIP_TEST_CHUNK_VARLEN') == '1',
reason='Skipping test_chunk_varlen because SKIP_TEST_CHUNK_VARLEN is set',
)
def test_chunk_varlen(
H: int,
D: int,
mask_p: float,
cu_seqlens: list[int],
dtype: torch.dtype,
):
torch.manual_seed(42)
os.environ['TRITON_F32_DEFAULT'] = 'ieee'
# randomly split the sequence into N segments
cu_seqlens = torch.LongTensor(cu_seqlens).to(device)
T = cu_seqlens[-1]
N = len(cu_seqlens) - 1
# seq-first required for inputs with variable lengths
q = torch.randn((1, T, H, D), dtype=dtype)
k = F.normalize(torch.randn(1, T, H, D, dtype=torch.float32), p=2, dim=-1).to(dtype)
v = torch.randn((1, T, H, D), dtype=dtype)
g = F.logsigmoid(torch.randn(1, T, H, D, dtype=torch.float))
g = g * (torch.rand_like(g) > mask_p)
beta = torch.rand(1, T, H, dtype=dtype).sigmoid()
h0 = torch.randn((N, H, D, D), dtype=dtype)
q, k, v, g, beta, h0 = map(lambda x: x.to(device).requires_grad_(), (q, k, v, g, beta, h0))
do = torch.randn_like(v)
dht = torch.rand_like(h0)
tri, tri_ht = chunk_kda(
q=q.clone(),
k=k.clone(),
v=v.clone(),
g=g.clone(),
beta=beta.clone(),
initial_state=h0.clone(),
output_final_state=True,
cu_seqlens=cu_seqlens,
)
((tri * do).sum() + (tri_ht * dht).sum()).backward(retain_graph=True)
tri_dq, tri_dk, tri_dv, tri_dg, tri_db, tri_dh0 = q.grad, k.grad, v.grad, g.grad, beta.grad, h0.grad
q.grad = k.grad = v.grad = g.grad = beta.grad = h0.grad = None
ref = []
ref_ht = []
for i in range(N):
ref_i, ref_ht_i = naive_recurrent_kda(
q=q[:, cu_seqlens[i]:cu_seqlens[i+1]],
k=k[:, cu_seqlens[i]:cu_seqlens[i+1]],
v=v[:, cu_seqlens[i]:cu_seqlens[i+1]],
beta=beta[:, cu_seqlens[i]:cu_seqlens[i+1]],
g=g[:, cu_seqlens[i]:cu_seqlens[i+1]],
initial_state=h0[i],
output_final_state=True,
)
ref.append(ref_i)
ref_ht.append(ref_ht_i)
ref = torch.cat(ref, 1)
ref_ht = torch.cat(ref_ht, 0)
((ref * do).sum() + (ref_ht * dht).sum()).backward(retain_graph=True)
ref_dq, ref_dk, ref_dv, ref_dg, ref_db, ref_dh0 = q.grad, k.grad, v.grad, g.grad, beta.grad, h0.grad
assert_close('o', ref, tri, 0.005)
assert_close('ht', ref_ht, tri_ht, 0.005)
assert_close('dq', ref_dq, tri_dq, 0.007)
assert_close('dk', ref_dk, tri_dk, 0.008)
assert_close('dv', ref_dv, tri_dv, 0.007)
assert_close('dg', ref_dg, tri_dg, 0.015)
assert_close('db', ref_db, tri_db, 0.015)
assert_close('dh0', ref_dh0, tri_dh0, 0.007)
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'use_bias'),
[
pytest.param(*test, id="B{}-T{}-H{}-D{}-bias{}".format(*test))
for test in [
(1, 2, 2, 12, False),
(1, 32, 2, 16, False),
(2, 64, 4, 32, False),
(4, 128, 8, 64, False),
(4, 128, 8, 128, False),
# Add bias tests
(1, 2, 2, 12, True),
(1, 32, 2, 16, True),
(2, 64, 4, 32, True),
(4, 128, 8, 64, True),
(4, 128, 8, 128, True),
]
],
)
def test_kda_gate(
B: int,
T: int,
H: int,
D: int,
use_bias: bool,
):
"""Test kda gate forward and backward pass - reference vs Triton implementation"""
torch.manual_seed(42)
g = torch.randn(B, T, H * D, dtype=torch.float32)
# Ensure some values are > 20 to test the threshold logic in softplus
g = g * 30 # Scale up to get values > 20
A = torch.log(torch.randn(1, 1, H, 1, dtype=torch.float32).uniform_(1, 16))
g_bias = torch.randn(H * D, dtype=torch.float32) if use_bias else None
# Move to device and set requires_grad
g, A = map(lambda x: x.to(device).requires_grad_(True), (g, A))
if g_bias is not None:
g_bias = g_bias.to(device).requires_grad_(True)
# Create gradient output
do = torch.randn_like(g).view(B, T, H, D)
# Reference implementation
ref = kda_gate_ref(g.clone(), A.clone(), D, g_bias.clone() if g_bias is not None else None)
# Triton implementation
tri = fused_kda_gate(g.clone(), A.clone(), D, g_bias.clone() if g_bias is not None else None)
# Backward pass
((ref * do).sum()).backward(retain_graph=True)
ref_dg, ref_dA = g.grad, A.grad
ref_dgbias = g_bias.grad if g_bias is not None else None
g.grad = A.grad = None
if g_bias is not None:
g_bias.grad = None
((tri * do).sum()).backward(retain_graph=True)
tri_dg, tri_dA = g.grad, A.grad
tri_dgbias = g_bias.grad if g_bias is not None else None
g.grad = A.grad = None
if g_bias is not None:
g_bias.grad = None
assert_close('o', ref, tri, 1e-4)
assert_close('dg', ref_dg, tri_dg, 1e-4)
assert_close('dA', ref_dA, tri_dA, 1e-4)
if use_bias:
assert_close('dgbias', ref_dgbias, tri_dgbias, 1e-4)
|