import pytest import torch import torch.nn.functional as F from fla.ops.gated_delta_product import chunk_gated_delta_product from fla.ops.gated_delta_product.chunk_ref import chunk_gated_delta_product_ref from fla.ops.gated_delta_product.naive import naive_recurrent_gated_delta_product from fla.utils import assert_close, device @pytest.mark.parametrize( ('B', 'T', 'H', 'D', 'scale', 'num_householder', 'use_qk_l2norm_in_kernel', 'dtype'), [ pytest.param( *test, id="B{}-T{}-H{}-D{}-scale{}-num_householder{}-l2norm{}-{}".format(*test), ) for test in [ (1, 63, 1, 64, 0.1, 1, False, torch.float16), (2, 200, 3, 60, 0.1, 1, False, torch.float16), (2, 1000, 4, 64, 0.1, 2, False, torch.float16), (2, 1024, 4, 64, 1, 2, True, torch.float16), (2, 1024, 6, 100, 1, 2, False, torch.float16), (4, 1500, 8, 128, 0.1, 3, False, torch.float16), (2, 2048, 8, 128, 1, 3, False, torch.float16), (2, 2048, 8, 128, 1, 3, True, torch.float16), ] ], ) def test_chunk( B: int, T: int, H: int, D: int, scale: float, num_householder: int, use_qk_l2norm_in_kernel: bool, dtype: torch.dtype, ): torch.manual_seed(42) q = torch.randn(B, T, H, D, dtype=dtype) k = torch.randn(B, T * num_householder, H, D, dtype=dtype) v = torch.randn(B, T * num_householder, H, D, dtype=dtype) beta = torch.rand(B, T * num_householder, H, dtype=dtype).sigmoid() h0 = torch.zeros(B, H, D, D, dtype=torch.float32) q, k, v, beta, h0 = map(lambda x: x.to(device).requires_grad_(True), (q, k, v, beta, h0)) tri, tri_ht = chunk_gated_delta_product( 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=None, beta=beta.clone(), num_householder=num_householder, scale=scale, output_final_state=True, initial_state=h0.clone(), use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel, ) do = torch.randn_like(q) dht = torch.randn_like(h0) ((tri * do).sum() + (tri_ht * dht).sum()).backward(retain_graph=True) tri_dq, tri_dk, tri_dv, tri_dbeta, tri_dh0 = q.grad, k.grad, v.grad, beta.grad, h0.grad q.grad = k.grad = v.grad = beta.grad = h0.grad = None ref, ref_ht = chunk_gated_delta_product_ref( q=F.normalize(q.clone(), p=2, dim=-1), k=F.normalize(k.clone(), p=2, dim=-1), v=v.clone(), g=None, beta=beta.clone(), num_householder=num_householder, 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_dbeta, ref_dh0 = q.grad, k.grad, v.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, 0.008) assert_close('dk', ref_dk, tri_dk, 0.008) assert_close('dv', ref_dv, tri_dv, 0.008) assert_close('db', ref_dbeta, tri_dbeta, 0.02) assert_close('dh0', ref_dh0, tri_dh0, 0.008) @pytest.mark.parametrize( ('H', 'D', 'num_householder', 'cu_seqlens', 'dtype'), [ (2, 64, 3, [0, 63 ], torch.float16), (2, 100, 2, [0, 63, 100, 500, 1000], torch.float16), (2, 128, 2, [0, 100, 300, 800, 1500, 2000], torch.float16), (2, 256, 3, [0, 100, 123, 300, 500, 800, 1000, 1500, 2048], torch.float16), ], ) def test_chunk_varlen( H: int, D: int, num_householder: int, cu_seqlens: list[int], dtype: torch.dtype, ): torch.manual_seed(42) T = cu_seqlens[-1] N = len(cu_seqlens) - 1 cu_seqlens = torch.LongTensor(cu_seqlens).to(device) scale = 1.0 q = torch.nn.functional.normalize(torch.randn((1, T, H, D), dtype=dtype), dim=-1, p=2) k = torch.nn.functional.normalize(torch.randn(1, T*num_householder, H, D, dtype=dtype), dim=-1, p=2) v = torch.randn((1, T*num_householder, H, D), dtype=dtype) beta = torch.rand(1, T*num_householder, H, dtype=dtype).sigmoid() h0 = torch.randn((N, H, D, D), dtype=dtype) q, k, v, beta, h0 = map(lambda x: x.to(device).requires_grad_(), (q, k, v, beta, h0)) do = torch.randn_like(q) dht = torch.rand_like(h0) tri, tri_ht = chunk_gated_delta_product( q=q.clone(), k=k.clone(), v=v.clone(), beta=beta.clone(), g=None, scale=scale, output_final_state=True, num_householder=num_householder, initial_state=h0.clone(), cu_seqlens=cu_seqlens, ) ((tri * do).sum() + (tri_ht * dht).sum()).backward(retain_graph=True) tri_dq, tri_dk, tri_dv, tri_dbeta, tri_dh0 = q.grad, k.grad, v.grad, beta.grad, h0.grad q.grad = k.grad = v.grad = beta.grad = h0.grad = None ref, ref_ht = chunk_gated_delta_product_ref( q=q.clone(), k=k.clone(), v=v.clone(), beta=beta.clone(), g=None, scale=scale, output_final_state=True, num_householder=num_householder, initial_state=h0.clone(), cu_seqlens=cu_seqlens, ) ((ref * do).sum() + (ref_ht * dht).sum()).backward(retain_graph=True) ref_dq, ref_dk, ref_dv, ref_dbeta, ref_dh0 = q.grad, k.grad, v.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('db', ref_dbeta, tri_dbeta, 0.015) assert_close('dh0', ref_dh0, tri_dh0, 0.007) q.grad = k.grad = v.grad = beta.grad = h0.grad = None torch_ref = torch.zeros_like(ref) torch_ref_ht = torch.zeros_like(ref_ht) for i in range(len(cu_seqlens) - 1): start, end = cu_seqlens[i], cu_seqlens[i+1] q_i = q[:, start:end, :, :] k_i = k[:, start*num_householder:end*num_householder, :, :] v_i = v[:, start*num_householder:end*num_householder, :, :] beta_i = beta[:, start*num_householder:end*num_householder, :] o3_i, h3_i = naive_recurrent_gated_delta_product( q_i, k_i, v_i, None, beta_i, scale=scale, cu_seqlens=None, output_final_state=True, num_householder=num_householder, ) torch_ref[:, start:end, :, :] = o3_i torch_ref_ht[i, :, :, :] = h3_i.squeeze(0) ((torch_ref * do).sum() + (torch_ref_ht * dht).sum()).backward(retain_graph=True) 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('db', ref_dbeta, tri_dbeta, 0.015) assert_close('dh0', ref_dh0, tri_dh0, 0.007)