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values | commit_sha stringclasses 400
values | commit_index int32 0 951 | in_repo_split stringclasses 1
value | cross_repo_split stringclasses 1
value | test_file stringlengths 7 121 | test_function stringlengths 1 108 | assertion_type stringclasses 32
values | difficulty stringclasses 8
values | context_lines int32 3 600 | prefix large_stringlengths 44 113k | target large_stringlengths 1 498 | anchor_sha stringclasses 400
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fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_cross_entropy.py | test_fused_cross_entropy | assert_* | variable | 37 | import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [512, 1024])
@pytest.mark.parametrize... | tri) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_cross_entropy.py | test_fused_cross_entropy | assert_* | variable | 38 | import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [512, 1024])
@pytest.mark.parametrize... | tri_d) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_cross_entropy.py | test_fused_linear_cross_entropy | assert_* | variable | 47 | import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [512, 1024])
@pytest.mark.parametrize... | tri_dx) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_cross_entropy.py | test_fused_linear_cross_entropy | assert_* | variable | 48 | import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [512, 1024])
@pytest.mark.parametrize... | tri_dw) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_cross_entropy.py | test_fused_linear_cross_entropy | assert_* | variable | 49 | import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [512, 1024])
@pytest.mark.parametrize... | tri_db) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_grpo.py | test_fused_grpos | assert | numeric_literal | 54 | import pytest
import torch
from fla.modules.grpo import fused_grpo_loss, grpo_loss_torch
from fla.utils import assert_close, device, device_torch_lib, is_nvidia_hopper
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [16, 1024, 4096])
@pytest.mark.parametrize("V", [32000, 65536, 131072])
@pytest.mark.... | 1e-3 | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_grpo.py | test_fused_grpos | assert_* | numeric_literal | 55 | import pytest
import torch
from fla.modules.grpo import fused_grpo_loss, grpo_loss_torch
from fla.utils import assert_close, device, device_torch_lib, is_nvidia_hopper
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [16, 1024, 4096])
@pytest.mark.parametrize("V", [32000, 65536, 131072])
@pytest.mark.... | 3e-3) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_kl_div.py | test_fused | assert_* | numeric_literal | 41 | import pytest
import torch
import torch.nn.functional as F
from fla.modules import FusedKLDivLoss
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [16, 32])
@pytest.mark.parametrize("D", [1024, 2048])
@pytest.mark.parametrize("V", [32000, 100... | 1e-2) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_l2norm.py | test_l2norm | assert_* | numeric_literal | 35 | import pytest
import torch
import torch.nn.functional as F
from fla.modules.l2norm import l2_norm
from fla.utils import assert_close, device
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'dtype'),
[
pytest.param(*test, id="B{}-T{}-H{}-D{}-{}".format(*test))
for test in [
(1, 63, 1... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_l2warp.py | test_fused_linear_cross_entropy_l2_warp | assert_* | variable | 66 | import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
from fla.modules import FusedLinearCrossEntropyLoss
from fla.modules.l2warp import l2_warp as standalone_l2_warp
from fla.utils import assert_close, device, is_intel_alchemist
@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloa... | ratio) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_layernorm.py | test_layernorm | assert_* | numeric_literal | 41 | import pytest
import torch
import torch.nn as nn
from einops import rearrange
from transformers.models.llama.modeling_llama import LlamaRMSNorm
from fla.modules import GroupNorm, GroupNormLinear, LayerNorm, LayerNormLinear, RMSNorm, RMSNormLinear
from fla.modules.layernorm import GroupNormRef
from fla.utils import ass... | 1e-3) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_layernorm_gated.py | test_layernorm_gated | assert_* | numeric_literal | 44 | import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
from fla.modules import FusedLayerNormGated, FusedRMSNormGated
from fla.utils import assert_close, device
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("H", [2])
@pytest.mark.parametrize("T", [1, 50, 512, 1000, 2048])
@pyte... | 1e-3) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_rotary.py | test_rotary | assert_* | variable | 29 | import pytest
import torch
from fla.modules.rotary import RotaryEmbedding, rotary_embedding_ref
from fla.utils import assert_close, device
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [2048, 4096])
@pytest.mark.parametrize("H", [4])
@pytest.mark.parametrize("G", [1, 4])
@pytest.mark.parametrize("D... | tri_q) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_rotary.py | test_rotary | assert_* | variable | 30 | import pytest
import torch
from fla.modules.rotary import RotaryEmbedding, rotary_embedding_ref
from fla.utils import assert_close, device
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [2048, 4096])
@pytest.mark.parametrize("H", [4])
@pytest.mark.parametrize("G", [1, 4])
@pytest.mark.parametrize("D... | tri_k) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_rotary.py | test_rotary | assert_* | variable | 31 | import pytest
import torch
from fla.modules.rotary import RotaryEmbedding, rotary_embedding_ref
from fla.utils import assert_close, device
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [2048, 4096])
@pytest.mark.parametrize("H", [4])
@pytest.mark.parametrize("G", [1, 4])
@pytest.mark.parametrize("D... | tri_dq) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_rotary.py | test_rotary | assert_* | variable | 32 | import pytest
import torch
from fla.modules.rotary import RotaryEmbedding, rotary_embedding_ref
from fla.utils import assert_close, device
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [2048, 4096])
@pytest.mark.parametrize("H", [4])
@pytest.mark.parametrize("G", [1, 4])
@pytest.mark.parametrize("D... | tri_dk) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/modules/test_token_shift.py | test_token_shift | assert_* | numeric_literal | 47 | import pytest
import torch
from fla.modules.token_shift import token_shift, token_shift_ref
from fla.utils import assert_close, device
test_b_list = [4]
test_t_list = [512, 4096]
test_h_list = [2560, 4096]
test_cu_seqlens_list = [
None,
[0, 4, 7, 40, 128],
[0, 10, 20, 64],
[0, 32],
[0, 1, 3, 4]
]
... | 1e-3) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_attn.py | test_parallel | assert_* | numeric_literal | 56 | import os
from typing import List
import pytest
import torch
from fla.ops.attn.parallel import parallel_attn
from fla.ops.utils import prepare_lens
from fla.utils import assert_close, check_shared_mem, device
@pytest.mark.parametrize(
('B', 'T', 'H', 'HQ', 'D', 'scale'),
[
pytest.param(*test, id="B{}... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_attn.py | test_parallel_varlen | assert_* | numeric_literal | 66 | import os
from typing import List
import pytest
import torch
from fla.ops.attn.parallel import parallel_attn
from fla.ops.utils import prepare_lens
from fla.utils import assert_close, check_shared_mem, device
@pytest.mark.parametrize(
('H', 'HQ', 'D', 'cu_seqlens'),
[
pytest.param(*test, id="H{}-HQ{}... | 0.004) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_comba.py | chunk_comba_ref | assert | numeric_literal | 61 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.comba import chunk_comba, fused_recurrent_comba
from fla.ops.comba.utils import chunk_comba_cumsum_scalar_fwd
from fla.utils import assert_close, device, is_intel_alchemist
def cumsu... | 0 | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_comba.py | test_chunk | assert_* | numeric_literal | 187 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.comba import chunk_comba, fused_recurrent_comba
from fla.ops.comba.utils import chunk_comba_cumsum_scalar_fwd
from fla.utils import assert_close, device, is_intel_alchemist
def cumsu... | 0.02) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_comba.py | test_fused_recurrent | assert_* | numeric_literal | 161 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.comba import chunk_comba, fused_recurrent_comba
from fla.ops.comba.utils import chunk_comba_cumsum_scalar_fwd
from fla.utils import assert_close, device, is_intel_alchemist
def cumsu... | 0.002) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_comba.py | test_chunk | assert_* | numeric_literal | 181 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.comba import chunk_comba, fused_recurrent_comba
from fla.ops.comba.utils import chunk_comba_cumsum_scalar_fwd
from fla.utils import assert_close, device, is_intel_alchemist
def cumsu... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_comba.py | test_chunk | assert_* | numeric_literal | 184 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.comba import chunk_comba, fused_recurrent_comba
from fla.ops.comba.utils import chunk_comba_cumsum_scalar_fwd
from fla.utils import assert_close, device, is_intel_alchemist
def cumsu... | 0.008) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_comba.py | test_chunk_varlen | assert_* | numeric_literal | 187 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.comba import chunk_comba, fused_recurrent_comba
from fla.ops.comba.utils import chunk_comba_cumsum_scalar_fwd
from fla.utils import assert_close, device, is_intel_alchemist
def cumsu... | 0.007) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_comba.py | test_cumsum_local_scalar_fwd | assert_* | complex_expr | 47 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.comba import chunk_comba, fused_recurrent_comba
from fla.ops.comba.utils import chunk_comba_cumsum_scalar_fwd
from fla.utils import assert_close, device, is_intel_alchemist
def cumsu... | 0.001 if dtype == torch.float else 0.003) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_delta.py | test_chunk | assert_* | numeric_literal | 75 | from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.delta_rule import chunk_delta_rule, fused_recurrent_delta_rule
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'scale', 'use_qk_l2norm_in_kernel', 'dtype'),
... | 0.006) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_delta.py | test_chunk | assert_* | numeric_literal | 77 | from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.delta_rule import chunk_delta_rule, fused_recurrent_delta_rule
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'scale', 'use_qk_l2norm_in_kernel', 'dtype'),
... | 0.008) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_delta.py | test_chunk_varlen | assert_* | numeric_literal | 73 | from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.delta_rule import chunk_delta_rule, fused_recurrent_delta_rule
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize(
('H', 'D', 'cu_seqlens', 'dtype'),
[
pytest.param(*test,... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_delta_product.py | test_chunk | assert_* | numeric_literal | 87 | from typing import List
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 fl... | 0.02) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_delta_product.py | test_chunk | assert_* | numeric_literal | 82 | from typing import List
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 fl... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_delta_product.py | test_chunk | assert_* | numeric_literal | 84 | from typing import List
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 fl... | 0.008) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_delta_product.py | test_chunk_varlen | assert_* | numeric_literal | 79 | from typing import List
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 fl... | 0.007) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_dplr_delta.py | test_recurrent_fwd | assert_* | numeric_literal | 187 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.generalized_delta_rule.dplr import chunk_dplr_delta_rule, fused_recurrent_dplr_delta_rule
from fla.utils import assert_close, device, device_platform
def recurrent_dplr_delta_rule_re... | 0.001) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_dplr_delta.py | test_fused_recurrent | assert_* | numeric_literal | 185 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.generalized_delta_rule.dplr import chunk_dplr_delta_rule, fused_recurrent_dplr_delta_rule
from fla.utils import assert_close, device, device_platform
def recurrent_dplr_delta_rule_re... | 0.002) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_dplr_delta.py | test_chunk | assert_* | numeric_literal | 205 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.generalized_delta_rule.dplr import chunk_dplr_delta_rule, fused_recurrent_dplr_delta_rule
from fla.utils import assert_close, device, device_platform
def recurrent_dplr_delta_rule_re... | 0.007) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_dplr_delta.py | test_chunk | assert_* | numeric_literal | 206 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.generalized_delta_rule.dplr import chunk_dplr_delta_rule, fused_recurrent_dplr_delta_rule
from fla.utils import assert_close, device, device_platform
def recurrent_dplr_delta_rule_re... | 0.008) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_forgetting_attn.py | test_parallel | assert_* | numeric_literal | 80 | from typing import List, Optional
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from fla.ops.forgetting_attn.parallel import parallel_forgetting_attn
from fla.utils import assert_close, check_shared_mem, device, is_intel_alchemist
def naive_forgetting_attn(
q: to... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_forgetting_attn.py | test_parallel_varlen | assert_* | numeric_literal | 91 | from typing import List, Optional
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from fla.ops.forgetting_attn.parallel import parallel_forgetting_attn
from fla.utils import assert_close, check_shared_mem, device, is_intel_alchemist
def naive_forgetting_attn(
q: to... | 0.004) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gated_delta.py | chunk_gated_delta_rule_ref | assert | numeric_literal | 81 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
from fla.utils import assert_close, device, is_intel_alchemist
def recurrent_gated_delta_rule... | 0 | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gated_delta.py | test_chunk | assert_* | numeric_literal | 200 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
from fla.utils import assert_close, device, is_intel_alchemist
def recurrent_gated_delta_rule... | 0.02) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gated_delta.py | test_fused_recurrent | assert_* | numeric_literal | 177 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
from fla.utils import assert_close, device, is_intel_alchemist
def recurrent_gated_delta_rule... | 0.002) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gated_delta.py | test_chunk | assert_* | numeric_literal | 195 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
from fla.utils import assert_close, device, is_intel_alchemist
def recurrent_gated_delta_rule... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gated_delta.py | test_chunk | assert_* | numeric_literal | 197 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
from fla.utils import assert_close, device, is_intel_alchemist
def recurrent_gated_delta_rule... | 0.008) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gated_delta.py | test_chunk_varlen | assert_* | numeric_literal | 202 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
from fla.utils import assert_close, device, is_intel_alchemist
def recurrent_gated_delta_rule... | 0.007) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gated_delta_product.py | test_chunk | assert_* | numeric_literal | 95 | import os
from typing import List
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_produ... | 0.02) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gated_delta_product.py | test_chunk | assert_* | numeric_literal | 90 | import os
from typing import List
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_produ... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gated_delta_product.py | test_chunk | assert_* | numeric_literal | 92 | import os
from typing import List
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_produ... | 0.008) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gated_delta_product.py | test_chunk_varlen | assert_* | numeric_literal | 92 | import os
from typing import List
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_produ... | 0.007) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gla.py | test_fused_recurrent | assert_* | numeric_literal | 81 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.gla import chunk_gla, fused_recurrent_gla
from fla.ops.gla.naive import naive_recurrent_gla
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'gate_lo... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gla.py | test_chunk | assert_* | numeric_literal | 80 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.gla import chunk_gla, fused_recurrent_gla
from fla.ops.gla.naive import naive_recurrent_gla
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'gate_lo... | 0.004) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gsa.py | test_fused_recurrent | assert_* | numeric_literal | 84 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.gsa import chunk_gsa, fused_recurrent_gsa
from fla.ops.gsa.naive import naive_recurrent_gsa
from fla.utils import assert_close, check_shared_mem, device, device_platform
@pytest.mark.parametrize(
('B', 'T', ... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_gsa.py | test_chunk_varlen | assert_* | numeric_literal | 99 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.gsa import chunk_gsa, fused_recurrent_gsa
from fla.ops.gsa.naive import naive_recurrent_gsa
from fla.utils import assert_close, check_shared_mem, device, device_platform
@pytest.mark.parametrize(
('H', 'D', ... | 0.004) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_hgrn.py | test_fused_recurrent | assert_* | numeric_literal | 55 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.hgrn import chunk_hgrn, fused_recurrent_hgrn
from fla.ops.hgrn.naive import naive_recurrent_hgrn
from fla.utils import assert_close, device
@pytest.mark.parametrize(
('B', 'T', 'D', 'dtype'),
[
p... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_iplr_delta.py | test_fused_recurrent | assert_* | numeric_literal | 171 | from typing import Optional
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.generalized_delta_rule.iplr.chunk import chunk_iplr_delta_rule
from fla.ops.generalized_delta_rule.iplr.fused_recurrent import fused_recurrent_iplr_delta_rule
from fla.utils import assert_c... | 0.003) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_iplr_delta.py | test_chunk | assert_* | numeric_literal | 166 | from typing import Optional
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.generalized_delta_rule.iplr.chunk import chunk_iplr_delta_rule
from fla.ops.generalized_delta_rule.iplr.fused_recurrent import fused_recurrent_iplr_delta_rule
from fla.utils import assert_c... | 0.007) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_iplr_delta.py | test_chunk | assert_* | numeric_literal | 167 | from typing import Optional
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.generalized_delta_rule.iplr.chunk import chunk_iplr_delta_rule
from fla.ops.generalized_delta_rule.iplr.fused_recurrent import fused_recurrent_iplr_delta_rule
from fla.utils import assert_c... | 0.008) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_linear_attn.py | test_fused_recurrent | assert_* | numeric_literal | 56 | from typing import Optional
import pytest
import torch
from fla.ops.linear_attn import chunk_linear_attn, fused_chunk_linear_attn, fused_recurrent_linear_attn
from fla.ops.linear_attn.naive import naive_recurrent_linear_attn
from fla.utils import assert_close, device
@pytest.mark.parametrize(
('B', 'T', 'H', 'D'... | 0.001) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_mesa.py | test_chunk | assert_* | numeric_literal | 90 | import os
from typing import List, Tuple
import pytest
import torch
import torch.nn.functional as F
from fla.ops.mesa_net import chunk_mesa_net, mesa_net_decoding_one_step, naive_mesa_net_decoding_one_step, naive_mesa_net_exact
from fla.utils import assert_close, device, device_platform, is_intel_alchemist
@pytest.m... | 0.006) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_mesa.py | test_chunk | assert_* | numeric_literal | 91 | import os
from typing import List, Tuple
import pytest
import torch
import torch.nn.functional as F
from fla.ops.mesa_net import chunk_mesa_net, mesa_net_decoding_one_step, naive_mesa_net_decoding_one_step, naive_mesa_net_exact
from fla.utils import assert_close, device, device_platform, is_intel_alchemist
@pytest.m... | 0.008) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_mesa.py | test_decoding_one_step | assert_* | numeric_literal | 75 | import os
from typing import List, Tuple
import pytest
import torch
import torch.nn.functional as F
from fla.ops.mesa_net import chunk_mesa_net, mesa_net_decoding_one_step, naive_mesa_net_decoding_one_step, naive_mesa_net_exact
from fla.utils import assert_close, device, device_platform, is_intel_alchemist
@pytest.m... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_nsa.py | test_parallel | assert_* | numeric_literal | 69 | import os
from typing import List
import pytest
import torch
import triton
from fla.ops.nsa.naive import naive_nsa
from fla.ops.nsa.parallel import parallel_nsa
from fla.ops.utils import prepare_token_indices
from fla.utils import assert_close, device
@pytest.mark.parametrize(
('B', 'T', 'H', 'HQ', 'D', 'S', 'bl... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_nsa.py | test_parallel_varlen | assert_* | numeric_literal | 89 | import os
from typing import List
import pytest
import torch
import triton
from fla.ops.nsa.naive import naive_nsa
from fla.ops.nsa.parallel import parallel_nsa
from fla.ops.utils import prepare_token_indices
from fla.utils import assert_close, device
@pytest.mark.parametrize(
('H', 'HQ', 'D', 'S', 'block_size',... | 0.004) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_path_attn.py | test_parallel | assert_* | numeric_literal | 126 | import os
from typing import List
import pytest
import torch
from einops import rearrange
from fla.ops.path_attn.parallel import parallel_path_attention
from fla.utils import assert_close, check_shared_mem, device, is_intel_alchemist
def naive_path_attn(q, k, v, w, beta, g, scale, BT=64):
original_dtype = q.dtyp... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_retention.py | test_chunk | assert_* | numeric_literal | 56 | import os
from typing import List
import pytest
import torch
from fla.ops.retention import chunk_retention, fused_chunk_retention, fused_recurrent_retention, parallel_retention
from fla.utils import assert_close, device
@pytest.mark.parametrize(
('B', 'T', 'H', 'K', 'expand_ratio', 'dtype'),
[
pytest... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_retention.py | test_chunk_varlen | assert_* | numeric_literal | 77 | import os
from typing import List
import pytest
import torch
from fla.ops.retention import chunk_retention, fused_chunk_retention, fused_recurrent_retention, parallel_retention
from fla.utils import assert_close, device
@pytest.mark.parametrize(
('H', 'K', 'expand_ratio', 'cu_seqlens', 'dtype'),
[
py... | 0.004) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv6.py | test_chunk | assert_* | numeric_literal | 98 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.rwkv6 import chunk_rwkv6
from fla.ops.rwkv6.fused_recurrent import fused_recurrent_rwkv6
from fla.utils import assert_close, device, device_platform
@pytest.mark.skipif(
device_platform == 'intel',
reaso... | 0.004) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv6.py | test_chunk | assert_* | numeric_literal | 99 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.rwkv6 import chunk_rwkv6
from fla.ops.rwkv6.fused_recurrent import fused_recurrent_rwkv6
from fla.utils import assert_close, device, device_platform
@pytest.mark.skipif(
device_platform == 'intel',
reaso... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_fused_rwkv7_addcmul | assert_* | variable | 47 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | xr1) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_fused_rwkv7_addcmul | assert_* | variable | 48 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | xw1) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_fused_rwkv7_addcmul | assert_* | variable | 49 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | xk1) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_fused_rwkv7_addcmul | assert_* | variable | 50 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | xv1) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_fused_rwkv7_addcmul | assert_* | variable | 51 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | xa1) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_fused_k_update | assert_* | variable | 44 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | ref) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_fused_mul_recurrent_fwd | assert_* | numeric_literal | 71 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | 0.002) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_fused_k_update | assert_* | complex_expr | 45 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | k.grad) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_fused_k_update | assert_* | complex_expr | 46 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | a.grad) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_channel_mixing_gradients | assert_* | complex_expr | 65 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | x2.grad) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_fused_k_update | assert_* | complex_expr | 47 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | ka.grad) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_channel_mixing_gradients | assert_* | complex_expr | 68 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | K_2.grad) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_channel_mixing_gradients | assert_* | complex_expr | 69 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | V_2.grad) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_channel_mixing_gradients | assert_* | complex_expr | 67 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | x_k2.grad) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_rwkv7.py | test_channel_mixing_gradients | assert_* | complex_expr | 66 | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.generalized_delta_rule.dplr.fused_recurrent import fused_recurrent_dplr_delta_rule
from fla.ops.rwkv7.channel_mixing import channel_mixing_rwkv7, channel_mixing_rwkv7_torch
from fla.ops.rwkv7.fused_addcmul import fused_addcmul_rwkv7, to... | x_prev2.grad) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_simple_gla.py | test_fused_recurrent | assert_* | numeric_literal | 83 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.simple_gla.chunk import chunk_simple_gla
from fla.ops.simple_gla.fused_chunk import fused_chunk_simple_gla
from fla.ops.simple_gla.fused_recurrent import fused_recurrent_simple_gla
from fla.ops.simple_gla.naive i... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_simple_gla.py | test_chunk | assert_* | numeric_literal | 82 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.simple_gla.chunk import chunk_simple_gla
from fla.ops.simple_gla.fused_chunk import fused_chunk_simple_gla
from fla.ops.simple_gla.fused_recurrent import fused_recurrent_simple_gla
from fla.ops.simple_gla.naive i... | 0.004) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_solve_tril.py | test_solve_tril | assert_* | numeric_literal | 44 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.common.chunk_scaled_dot_kkt import chunk_scaled_dot_kkt_fwd
from fla.ops.utils.solve_tril import solve_tril
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize(
('B', 'T', 'H... | 0.0001) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_titans.py | test_naive_chunk | assert_* | numeric_literal | 119 | import pytest
import torch
import torch.nn.functional as F
from fla.ops.titans.naive import chunk_titans_linear_ref
from fla.utils import assert_close, device
def initialize_chunked_param(B, H, T, BT, dtype=torch.float32):
# Calculate number of complete chunks and remaining elements
num_complete_chunks = T //... | 0.006) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_titans.py | test_naive_chunk | assert_* | numeric_literal | 120 | import pytest
import torch
import torch.nn.functional as F
from fla.ops.titans.naive import chunk_titans_linear_ref
from fla.utils import assert_close, device
def initialize_chunked_param(B, H, T, BT, dtype=torch.float32):
# Calculate number of complete chunks and remaining elements
num_complete_chunks = T //... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_ttt.py | test_fused_chunk | assert_* | numeric_literal | 95 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.ttt import chunk_ttt_linear, fused_chunk_ttt_linear
from fla.ops.ttt.naive import chunk_ttt_linear_ref
from fla.utils import assert_close, check_shared_mem, device
@pytest.mark.parametrize(
('B', 'T', 'H', '... | 0.03) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_ttt.py | test_chunk | assert_* | numeric_literal | 87 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.ttt import chunk_ttt_linear, fused_chunk_ttt_linear
from fla.ops.ttt.naive import chunk_ttt_linear_ref
from fla.utils import assert_close, check_shared_mem, device
@pytest.mark.parametrize(
('B', 'T', 'H', '... | 0.005) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_ttt.py | test_chunk | assert_* | numeric_literal | 91 | import os
from typing import List
import pytest
import torch
import torch.nn.functional as F
from fla.ops.ttt import chunk_ttt_linear, fused_chunk_ttt_linear
from fla.ops.ttt.naive import chunk_ttt_linear_ref
from fla.utils import assert_close, check_shared_mem, device
@pytest.mark.parametrize(
('B', 'T', 'H', '... | 0.010) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_utils.py | test_global_cumsum | assert_* | numeric_literal | 44 | import os
from typing import List
import pytest
import torch
from fla.ops.utils import chunk_global_cumsum, chunk_local_cumsum, mean_pooling
from fla.ops.utils.index import prepare_lens
from fla.ops.utils.pack import pack_sequence, unpack_sequence
from fla.utils import assert_close, device
def reversed_cumsum(x, dim... | 1e-3) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_utils.py | test_mean_pooling_varlen | assert_* | func_call | 61 | import os
from typing import List
import pytest
import torch
from fla.ops.utils import chunk_global_cumsum, chunk_local_cumsum, mean_pooling
from fla.ops.utils.index import prepare_lens
from fla.ops.utils.pack import pack_sequence, unpack_sequence
from fla.utils import assert_close, device
def reversed_cumsum(x, dim... | tri.to(ref.dtype)) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
fla-org/flash-linear-attention | 20277368e8e49579997358ac01664cba389483f3 | 232 | train | train | tests/ops/test_utils.py | test_mean_pooling_varlen | assert_* | func_call | 62 | import os
from typing import List
import pytest
import torch
from fla.ops.utils import chunk_global_cumsum, chunk_local_cumsum, mean_pooling
from fla.ops.utils.index import prepare_lens
from fla.ops.utils.pack import pack_sequence, unpack_sequence
from fla.utils import assert_close, device
def reversed_cumsum(x, dim... | tri_dx.to(ref_dx.dtype)) | 20277368e8e49579997358ac01664cba389483f3 | 232 | v2_extractor_at_anchor |
flashbots/mev-inspect-py | 7b44046926a04ffee3a65c4d1afb95758cc0369b | 35 | train | train | tests/liquidation_test.py | _assert_equal_list_of_liquidations | assert | complex_expr | 15 | from typing import List
from mev_inspect.aave_liquidations import get_aave_liquidations
from mev_inspect.classifiers.trace import TraceClassifier
from mev_inspect.schemas.liquidations import Liquidation
from mev_inspect.schemas.traces import Protocol
from mev_inspect.transfers import ETH_TOKEN_ADDRESS
from tests.utils... | expected_liquidations[i] | 7b44046926a04ffee3a65c4d1afb95758cc0369b | 35 | v2_extractor_at_anchor |
flashbots/mev-inspect-py | 7b44046926a04ffee3a65c4d1afb95758cc0369b | 35 | train | train | tests/test_0x.py | test_fillLimitOrder_swap | assert | numeric_literal | 35 | from mev_inspect.classifiers.trace import TraceClassifier
from mev_inspect.schemas.swaps import Swap
from mev_inspect.schemas.traces import Protocol
from mev_inspect.swaps import get_swaps
from tests.utils import load_test_block
def test_fillLimitOrder_swap(trace_classifier: TraceClassifier):
transaction_hash = (
... | 1 | 7b44046926a04ffee3a65c4d1afb95758cc0369b | 35 | v2_extractor_at_anchor |
flashbots/mev-inspect-py | 7b44046926a04ffee3a65c4d1afb95758cc0369b | 35 | train | train | tests/test_arbitrage_integration.py | test_arbitrage_real_block | assert | numeric_literal | 16 | from mev_inspect.arbitrages import get_arbitrages
from mev_inspect.classifiers.trace import TraceClassifier
from mev_inspect.swaps import get_swaps
from .utils import load_test_block
def test_arbitrage_real_block(trace_classifier: TraceClassifier):
block = load_test_block(12914944)
classified_traces = trace_c... | 2 | 7b44046926a04ffee3a65c4d1afb95758cc0369b | 35 | v2_extractor_at_anchor |
flashbots/mev-inspect-py | 7b44046926a04ffee3a65c4d1afb95758cc0369b | 35 | train | train | tests/test_arbitrage_integration.py | test_arbitrage_real_block | assert | numeric_literal | 31 | from mev_inspect.arbitrages import get_arbitrages
from mev_inspect.classifiers.trace import TraceClassifier
from mev_inspect.swaps import get_swaps
from .utils import load_test_block
def test_arbitrage_real_block(trace_classifier: TraceClassifier):
block = load_test_block(12914944)
classified_traces = trace_c... | 3 | 7b44046926a04ffee3a65c4d1afb95758cc0369b | 35 | v2_extractor_at_anchor |
flashbots/mev-inspect-py | 7b44046926a04ffee3a65c4d1afb95758cc0369b | 35 | train | train | tests/test_arbitrage_integration.py | test_reverting_arbitrage | assert | numeric_literal | 16 | from mev_inspect.arbitrages import get_arbitrages
from mev_inspect.classifiers.trace import TraceClassifier
from mev_inspect.swaps import get_swaps
from .utils import load_test_block
def test_reverting_arbitrage(trace_classifier: TraceClassifier):
block = load_test_block(11473321)
classified_traces = trace_cl... | 4 | 7b44046926a04ffee3a65c4d1afb95758cc0369b | 35 | v2_extractor_at_anchor |
flashbots/mev-inspect-py | 7b44046926a04ffee3a65c4d1afb95758cc0369b | 35 | train | train | tests/test_arbitrage_integration.py | test_reverting_arbitrage | assert | numeric_literal | 13 | from mev_inspect.arbitrages import get_arbitrages
from mev_inspect.classifiers.trace import TraceClassifier
from mev_inspect.swaps import get_swaps
from .utils import load_test_block
def test_reverting_arbitrage(trace_classifier: TraceClassifier):
block = load_test_block(11473321)
classified_traces = trace_cl... | 38 | 7b44046926a04ffee3a65c4d1afb95758cc0369b | 35 | v2_extractor_at_anchor |
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