repo_id stringclasses 409
values | prefix large_stringlengths 34 36.3k | target large_stringlengths 1 498 | assertion_type stringclasses 31
values | difficulty stringclasses 8
values | test_file stringlengths 10 121 | test_function stringlengths 1 104 | test_class stringlengths 0 51 | lineno int32 2 11.3k | commit_idx int32 |
|---|---|---|---|---|---|---|---|---|---|
fla-org/flash-linear-attention | 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) | assert_* | numeric_literal | tests/ops/test_titans.py | test_naive_chunk | 121 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.gated_oja_rule import chunk_gated_oja_rule, fused_recurrent_gated_oja_rule
from fla.utils import assert_close, device, is_intel_alchemist
def recurrent_oja_ref(
q: torch.Tensor,
k: torch.Tensor,
... | 0.002) | assert_* | numeric_literal | tests/ops/test_oja.py | test_naive_chunk_oja | 186 | null | |
fla-org/flash-linear-attention | import pytest
import torch
from fla.ops.utils.index import (
prepare_chunk_indices,
prepare_chunk_offsets,
prepare_position_ids,
prepare_sequence_ids,
prepare_split_cu_seqlens,
prepare_token_indices,
)
from fla.utils import device
def ref_prepare_sequence_ids(cu_seqlens):
seqlens = (cu_seq... | opt.long()) | assert_* | func_call | tests/ops/test_index.py | test_edge_cases | 171 | null | |
fla-org/flash-linear-attention | 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'),
[
pytest.param... | 0.008) | assert_* | numeric_literal | tests/ops/test_delta.py | test_chunk | 77 | null | |
fla-org/flash-linear-attention | 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, 4100, 8192]
test_h_list = [2560, 4096]
test_cu_seqlens_list = [
None,
[0, 4, 7, 40, 128],
[0, 10, 20, 64],
[0, 32],
[0, 1, 3,... | split_at | assert | variable | tests/modules/test_token_shift.py | _split_for_passing | 58 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
from fla.models import Mamba2Config, Mamba2ForCausalLM
from fla.utils import device
@pytest.mark.parametrize(
['L', 'B', 'T', 'H', 'D', 'use_l2warp', 'dtype', 'conv_backend'],
[
pytest.param(*test, id="L{}-B{}-T{}-H{}-D{}-use_l2warp{}-{}-conv-{}".format(*test))
... | (B, T, config.vocab_size) | assert | collection | tests/models/test_modeling_mamba2.py | test_modeling | 67 | null | |
fla-org/flash-linear-attention | 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) | assert_* | variable | tests/modules/test_cross_entropy.py | test_fused_cross_entropy | 39 | null | |
fla-org/flash-linear-attention | 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) | assert_* | variable | tests/modules/test_rotary.py | test_rotary | 30 | null | |
fla-org/flash-linear-attention | 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, 4100, 8192]
test_h_list = [2560, 4096]
test_cu_seqlens_list = [
None,
[0, 4, 7, 40, 128],
[0, 10, 20, 64],
[0, 32],
[0, 1, 3,... | 1e-3) | assert_* | numeric_literal | tests/modules/test_token_shift.py | test_token_shift | 48 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.gated_oja_rule import chunk_gated_oja_rule, fused_recurrent_gated_oja_rule
from fla.utils import assert_close, device, is_intel_alchemist
def recurrent_oja_ref(
q: torch.Tensor,
k: torch.Tensor,
... | 0.02) | assert_* | numeric_literal | tests/ops/test_oja.py | test_chunk | 377 | null | |
fla-org/flash-linear-attention | import os
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_ref(
q: torch.Tensor,
... | 0.002) | assert_* | numeric_literal | tests/ops/test_dplr_delta.py | test_fused_recurrent | 250 | null | |
fla-org/flash-linear-attention | 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) | assert_* | numeric_literal | tests/ops/test_rwkv7.py | test_fused_mul_recurrent_fwd | 131 | null | |
fla-org/flash-linear-attention | import pytest
import torch
from fla.ops.utils.index import (
prepare_chunk_indices,
prepare_chunk_offsets,
prepare_position_ids,
prepare_sequence_ids,
prepare_split_cu_seqlens,
prepare_token_indices,
)
from fla.utils import device
def ref_prepare_sequence_ids(cu_seqlens):
seqlens = (cu_seq... | opt_offsets.long()) | assert_* | func_call | tests/ops/test_index.py | test_chunk_utils_correctness | 116 | null | |
fla-org/flash-linear-attention | import os
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', 'cu_seqlens', 'dtype'),... | 0.004) | assert_* | numeric_literal | tests/ops/test_nsa.py | test_parallel_varlen | 145 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.comba import chunk_comba, fused_recurrent_comba
from fla.ops.comba.naive import naive_chunk_comba
from fla.ops.comba.utils import chunk_comba_cumsum_scalar_fwd
from fla.utils import IS_INTEL_ALCHEMIST, assert_close, device
def cumsum_c... | 0.02) | assert_* | numeric_literal | tests/ops/test_comba.py | test_chunk | 197 | null | |
fla-org/flash-linear-attention | 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_close, device
def chunk_iplr_... | 0.003) | assert_* | numeric_literal | tests/ops/test_iplr_delta.py | test_fused_recurrent | 173 | null | |
fla-org/flash-linear-attention | import os
import numpy as np
import pytest
import torch
from fla.ops.log_linear_attn import chunk_log_linear_attn
from fla.ops.log_linear_attn.naive import naive_log_linear_attn
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize(
("B", "T", "H", "D", "dtype"),
[
pyte... | 0.015) | assert_* | numeric_literal | tests/ops/test_log_linear_attn.py | test_chunk_bwd | 94 | null | |
fla-org/flash-linear-attention | import os
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'),
[
pytest.param(*test, id="H{... | 0.004) | assert_* | numeric_literal | tests/ops/test_retention.py | test_chunk_varlen | 129 | null | |
fla-org/flash-linear-attention | import os
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... | 0.008) | assert_* | numeric_literal | tests/ops/test_gated_delta_product.py | test_chunk | 93 | null | |
fla-org/flash-linear-attention | import logging
import os
import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from fla.modules.convolution import causal_conv1d
from fla.ops.cp import build_cp_context
from fla.utils import assert_close
logging.basicConfig(level=logging.INFO, format='%(message)s')
def init_... | y_cp_global) | assert_* | variable | tests/context_parallel/test_cp_conv.py | run_cp_conv_test_worker | 201 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.gated_oja_rule import chunk_gated_oja_rule, fused_recurrent_gated_oja_rule
from fla.utils import assert_close, device, is_intel_alchemist
def recurrent_oja_ref(
q: torch.Tensor,
k: torch.Tensor,
... | 0.005) | assert_* | numeric_literal | tests/ops/test_oja.py | test_chunk_forward | 297 | null | |
fla-org/flash-linear-attention | import os
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{}-D{}-cu_seqlens{}".forma... | 0.004) | assert_* | numeric_literal | tests/ops/test_attn.py | test_parallel_varlen | 122 | null | |
fla-org/flash-linear-attention | import pytest
import torch
import fla.ops.common.intracard_cp as intracard_cp_mod
from fla.ops.common.intracard_cp import _intracard_cache
from fla.ops.kda import chunk_kda
from fla.utils import device
def clear_intracard_cache():
_intracard_cache.clear()
yield
_intracard_cache.clear()
@pytest.mark.skipi... | cu_seqlens | assert | variable | tests/ops/test_intracard_cache.py | test_chunk_kda_intracard_cache_hit_same_cu_seqlens_object | 89 | null | |
fla-org/flash-linear-attention | import os
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=-1):
dtype = x.dtyp... | tri_dx.to(ref_dx.dtype)) | assert_* | func_call | tests/ops/test_utils.py | test_mean_pooling_varlen | 326 | null | |
fla-org/flash-linear-attention | import pytest
import torch
from fla.ops.utils.index import (
prepare_chunk_indices,
prepare_chunk_offsets,
prepare_position_ids,
prepare_sequence_ids,
prepare_split_cu_seqlens,
prepare_token_indices,
)
from fla.utils import device
def ref_prepare_sequence_ids(cu_seqlens):
seqlens = (cu_seq... | opt_pos) | assert_* | variable | tests/ops/test_index.py | test_prepare_ids_correctness | 93 | null | |
fla-org/flash-linear-attention | 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, 4100, 8192]
test_h_list = [2560, 4096]
test_cu_seqlens_list = [
None,
[0, 4, 7, 40, 128],
[0, 10, 20, 64],
[0, 32],
[0, 1, 3,... | 1 | assert | numeric_literal | tests/modules/test_token_shift.py | _split_for_passing | 57 | null | |
fla-org/flash-linear-attention | import pytest
import torch
from fla.ops.utils.index import (
prepare_chunk_indices,
prepare_chunk_offsets,
prepare_position_ids,
prepare_sequence_ids,
prepare_split_cu_seqlens,
prepare_token_indices,
)
from fla.utils import device
def ref_prepare_sequence_ids(cu_seqlens):
seqlens = (cu_seq... | opt_stack) | assert_* | variable | tests/ops/test_index.py | test_prepare_ids_correctness | 98 | null | |
fla-org/flash-linear-attention | import pytest
import torch
from fla.models.utils import FLACache
from fla.utils import device
@pytest.mark.parametrize(
['num_layers', 'batch_size', 'chunk_size', 'num_chunks', 'hidden_size', 'num_heads'],
[
pytest.param(*test, id=f"L{test[0]}-B{test[1]}-chunk{test[2]}-n{test[3]}-D{test[4]}-H{test[5]}... | expected_seq_len | assert | variable | tests/models/test_cache.py | test_cache_incremental_update | 112 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.comba import chunk_comba, fused_recurrent_comba
from fla.ops.comba.naive import naive_chunk_comba
from fla.ops.comba.utils import chunk_comba_cumsum_scalar_fwd
from fla.utils import IS_INTEL_ALCHEMIST, assert_close, device
def cumsum_c... | 0.002) | assert_* | numeric_literal | tests/ops/test_comba.py | test_fused_recurrent | 110 | null | |
fla-org/flash-linear-attention | 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_close, device
def chunk_iplr_... | 0.008) | assert_* | numeric_literal | tests/ops/test_iplr_delta.py | test_chunk | 235 | null | |
fla-org/flash-linear-attention | import pytest
import torch
import torch.nn.functional as F
from fla.modules.activations import logsigmoid, sigmoid, swiglu, swiglu_linear, swish
from fla.utils import assert_close, device
@pytest.mark.parametrize(
('B', 'T', 'D', 'compile'),
[
(1, 1, 64, False),
(2, 500, 128, False),
(... | 1e-3) | assert_* | numeric_literal | tests/modules/test_activation.py | test_sigmoid | 29 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
from transformers.configuration_utils import PretrainedConfig
from fla.utils import IS_INTEL_ALCHEMIST, IS_NVIDIA_HOPPER, assert_close, device
from .test_modeling_utils import (
GENERATION_UNSUPPORTED,
HOPPER_EXCLUSIVE,
MODELING_UNSUPPORTED_VARLEN,
NOT_READY_FOR_T... | (B, T, config.hidden_size) | assert | collection | tests/models/test_modeling_base.py | run_test_model_forward_backward | 50 | null | |
fla-org/flash-linear-attention | import os
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', 'D', 'scale', 'dtype'),
... | 0.03) | assert_* | numeric_literal | tests/ops/test_ttt.py | test_fused_chunk | 183 | null | |
fla-org/flash-linear-attention | import logging
import os
import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn.functional as F
from fla.ops.cp import build_cp_context
from fla.ops.gated_delta_rule import chunk_gated_delta_rule
from fla.utils import assert_close
logging.basicConfig(level=logg... | 0 | assert | numeric_literal | tests/context_parallel/test_cp_gdn.py | run_cp_gdn_test_worker | 111 | null | |
fla-org/flash-linear-attention | import os
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... | 0.007) | assert_* | numeric_literal | tests/ops/test_gated_delta_product.py | test_chunk_varlen | 179 | null | |
fla-org/flash-linear-attention | import pytest
import torch
import torch.nn.functional as F
from fla.ops.kda import chunk_kda, fused_recurrent_kda
from fla.ops.kda.fused_recurrent import fused_recurrent_kda_fwd
from fla.ops.kda.gate import fused_kda_gate, naive_kda_gate, naive_kda_lowerbound_gate
from fla.ops.kda.naive import naive_chunk_kda, naive_r... | 1 | assert | numeric_literal | tests/ops/test_kda.py | test_chunk_return_intermediate_states | 746 | null | |
fla-org/flash-linear-attention | import os
import numpy as np
import pytest
import torch
from fla.ops.log_linear_attn import chunk_log_linear_attn
from fla.ops.log_linear_attn.naive import naive_log_linear_attn
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize(
("B", "T", "H", "D", "dtype"),
[
pyte... | 0.004) | assert_* | numeric_literal | tests/ops/test_log_linear_attn.py | test_chunk | 46 | null | |
fla-org/flash-linear-attention | import logging
import os
import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn.functional as F
from fla.ops.cp import build_cp_context
from fla.ops.kda import chunk_kda
from fla.ops.kda.gate import naive_kda_lowerbound_gate
from fla.ops.kda.naive import naive_r... | 0 | assert | numeric_literal | tests/context_parallel/test_cp_kda.py | run_cp_kda_test_worker | 169 | null | |
fla-org/flash-linear-attention | import argparse
import os
import random
import sys
from functools import partial
import torch
import torch.distributed as dist
import torch.nn.functional as F
from einops import rearrange, repeat
from fla.models.utils import Cache
from fla.modules.convolution import causal_conv1d
from fla.ops.gated_delta_rule import ... | 'chunk' | assert | string_literal | benchmarks/cp/test_gdn_with_cp.py | gdn_forward | 367 | null | |
fla-org/flash-linear-attention | import pytest
import torch
from fla.ops.deltaformer import deltaformer_attn
from fla.ops.deltaformer.naive import naive_deltaformer_attn
from fla.utils import IS_INTEL_ALCHEMIST, assert_close, device
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'dtype'),
[
pytest.param(*test, id="B{}-T{}-H{}-D{}-{}"... | 0.008) | assert_* | numeric_literal | tests/ops/test_deltaformer.py | test_deltaformer_attn | 61 | null | |
fla-org/flash-linear-attention | import pytest
import torch
from fla.modules.grpo import fused_grpo_loss, grpo_loss_torch
from fla.utils import IS_NVIDIA_HOPPER, assert_close, device, device_torch_lib
@pytest.mark.parametrize("B", [2])
@pytest.mark.parametrize("T", [16, 1024, 4096])
@pytest.mark.parametrize("V", [32000, 65536])
@pytest.mark.parametr... | 2e-3 | assert | numeric_literal | tests/modules/test_grpo.py | test_fused_grpos | 52 | null | |
fla-org/flash-linear-attention | import os
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 import naive_parallel_sim... | 0.004) | assert_* | numeric_literal | tests/ops/test_simple_gla.py | test_chunk | 244 | null | |
fla-org/flash-linear-attention | 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) | assert_* | numeric_literal | tests/ops/test_titans.py | test_naive_chunk | 122 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
from transformers.configuration_utils import PretrainedConfig
from fla.utils import IS_INTEL_ALCHEMIST, IS_NVIDIA_HOPPER, assert_close, device
from .test_modeling_utils import (
GENERATION_UNSUPPORTED,
HOPPER_EXCLUSIVE,
MODELING_UNSUPPORTED_VARLEN,
NOT_READY_FOR_T... | (1, B * T, config.hidden_size) | assert | collection | tests/models/test_modeling_base.py | run_test_model_forward_backward | 59 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.path_attn import naive_path_attn
from fla.ops.path_attn.parallel import parallel_path_attention
from fla.utils import IS_INTEL_ALCHEMIST, assert_close, device
@pytest.mark.parametrize(
('B', 'T', 'H', 'HQ', 'D', 'use_forget_gate', ... | 0.008) | assert_* | numeric_literal | tests/ops/test_path_attn.py | test_parallel | 77 | null | |
fla-org/flash-linear-attention | 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 IS_INTEL_ALCHEMIST, assert_close, device
@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloa... | ratio) | assert_* | variable | tests/modules/test_l2warp.py | test_fused_linear_cross_entropy_l2_warp | 68 | null | |
fla-org/flash-linear-attention | import logging
import os
import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from fla.modules.convolution import causal_conv1d
from fla.ops.cp import build_cp_context
from fla.utils import assert_close
logging.basicConfig(level=logging.INFO, format='%(message)s')
def init_... | 0 | assert | numeric_literal | tests/context_parallel/test_cp_conv.py | run_cp_conv_test_worker | 99 | null | |
fla-org/flash-linear-attention | import os
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', 'D', 'scale', 'dtype'),
... | 0.010) | assert_* | numeric_literal | tests/ops/test_ttt.py | test_chunk | 91 | null | |
fla-org/flash-linear-attention | 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_close, device
def chunk_iplr_... | 0.007) | assert_* | numeric_literal | tests/ops/test_iplr_delta.py | test_chunk | 234 | null | |
fla-org/flash-linear-attention | import argparse
import os
import random
import sys
from functools import partial
import torch
import torch.distributed as dist
import torch.nn.functional as F
from einops import rearrange, repeat
from fla.models.utils import Cache
from fla.modules.convolution import causal_conv1d
from fla.ops.gated_delta_rule import ... | 2 | assert | numeric_literal | benchmarks/cp/test_gdn_with_cp.py | gdn_forward | 357 | null | |
fla-org/flash-linear-attention | import pytest
import torch
from fla.models.utils import FLACache
from fla.utils import device
def test_cache_get_seq_length_nonexistent_layer():
"""
Test that get_seq_length returns 0 for non-existent layers
and handles None layer_idx correctly for populated caches.
"""
cache = FLACache()
# S... | 0 | assert | numeric_literal | tests/models/test_cache.py | test_cache_get_seq_length_nonexistent_layer | 124 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.comba import chunk_comba, fused_recurrent_comba
from fla.ops.comba.naive import naive_chunk_comba
from fla.ops.comba.utils import chunk_comba_cumsum_scalar_fwd
from fla.utils import IS_INTEL_ALCHEMIST, assert_close, device
def cumsum_c... | 0.005) | assert_* | numeric_literal | tests/ops/test_comba.py | test_chunk | 191 | null | |
fla-org/flash-linear-attention | 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) | assert_* | variable | tests/modules/test_cross_entropy.py | test_fused_linear_cross_entropy | 80 | null | |
fla-org/flash-linear-attention | import os
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',
reason="Intel Triton Failure"... | 0.004) | assert_* | numeric_literal | tests/ops/test_rwkv6.py | test_chunk | 98 | null | |
fla-org/flash-linear-attention | 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) | assert_* | variable | tests/modules/test_rotary.py | test_rotary | 32 | null | |
fla-org/flash-linear-attention | import os
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.param(*test, id="B{}-T{... | 0.005) | assert_* | numeric_literal | tests/ops/test_retention.py | test_chunk | 56 | null | |
fla-org/flash-linear-attention | import os
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', 'H', 'D', 'M', 'gate_log... | 0.005) | assert_* | numeric_literal | tests/ops/test_gsa.py | test_fused_recurrent | 84 | null | |
fla-org/flash-linear-attention | 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) | assert_* | variable | tests/modules/test_cross_entropy.py | test_fused_linear_cross_entropy | 81 | null | |
fla-org/flash-linear-attention | 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_clo... | 0.02) | assert_* | numeric_literal | tests/ops/test_delta_product.py | test_chunk | 87 | null | |
fla-org/flash-linear-attention | 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) | assert_* | complex_expr | tests/ops/test_rwkv7.py | test_fused_k_update | 258 | null | |
fla-org/flash-linear-attention | parser.add_argument("--bench", action="store_true")
parser.add_argument("--profile", action="store_true")
parser.add_argument("--profile-path", type=str, default="")
parser.add_argument("--seqlen", type=int, default=1024*32)
parser.add_argument("--mean", type=int, default=1024*32)
parser.add_arg... | name2 | assert | variable | benchmarks/cp/test_gdn_with_cp.py | test_layer | 867 | null | |
fla-org/flash-linear-attention | 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) | assert_* | variable | tests/ops/test_rwkv7.py | test_fused_rwkv7_addcmul | 176 | null | |
fla-org/flash-linear-attention | 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, id="H{}-D{}-cu_seqlens{}... | 0.005) | assert_* | numeric_literal | tests/ops/test_delta.py | test_chunk_varlen | 146 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.gated_oja_rule import chunk_gated_oja_rule, fused_recurrent_gated_oja_rule
from fla.utils import assert_close, device, is_intel_alchemist
def recurrent_oja_ref(
q: torch.Tensor,
k: torch.Tensor,
... | 0.007) | assert_* | numeric_literal | tests/ops/test_oja.py | test_chunk_varlen | 462 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
import torch.nn.functional as F
from einops import repeat
from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
from fla.ops.gated_delta_rule.naive import naive_recurrent_gated_delta_rule
from fla.utils import IS_INTEL_ALCHEMIST, assert_clos... | 0.005) | assert_* | numeric_literal | tests/ops/test_gated_delta.py | test_chunk | 149 | null | |
fla-org/flash-linear-attention | import pytest
import torch
from fla.ops.utils.index import (
prepare_chunk_indices,
prepare_chunk_offsets,
prepare_position_ids,
prepare_sequence_ids,
prepare_split_cu_seqlens,
prepare_token_indices,
)
from fla.utils import device
def ref_prepare_sequence_ids(cu_seqlens):
seqlens = (cu_seq... | opt_indices.long()) | assert_* | func_call | tests/ops/test_index.py | test_chunk_utils_correctness | 126 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
import torch.nn.functional as F
from einops import repeat
from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
from fla.ops.gated_delta_rule.naive import naive_recurrent_gated_delta_rule
from fla.utils import IS_INTEL_ALCHEMIST, assert_clos... | 0.008) | assert_* | numeric_literal | tests/ops/test_gated_delta.py | test_chunk | 151 | null | |
fla-org/flash-linear-attention | 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_clo... | 0.007) | assert_* | numeric_literal | tests/ops/test_delta_product.py | test_chunk_varlen | 157 | null | |
fla-org/flash-linear-attention | 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) | assert_* | complex_expr | tests/ops/test_rwkv7.py | test_channel_mixing_gradients | 68 | null | |
fla-org/flash-linear-attention | import pytest
import torch
from fla.models.utils import FLACache
from fla.utils import device
def test_cache_window_size_does_not_undercount():
"""
Test that window_size truncation doesn't undercount sequence length.
When window_size is applied and input exceeds it, the full input size
should still be... | seq_len | assert | variable | tests/models/test_cache.py | test_cache_window_size_does_not_undercount | 159 | null | |
fla-org/flash-linear-attention | import os
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', 'block_size', 'scale', 'dty... | 0.005) | assert_* | numeric_literal | tests/ops/test_nsa.py | test_parallel | 67 | null | |
fla-org/flash-linear-attention | import os
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_ref(
q: torch.Tensor,
... | 0.007) | assert_* | numeric_literal | tests/ops/test_dplr_delta.py | test_chunk | 350 | null | |
fla-org/flash-linear-attention | 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) | assert_* | numeric_literal | tests/modules/test_l2norm.py | test_l2norm | 36 | null | |
fla-org/flash-linear-attention | import os
import numpy as np
import pytest
import torch
from fla.ops.log_linear_attn import chunk_log_linear_attn
from fla.ops.log_linear_attn.naive import naive_log_linear_attn
from fla.utils import assert_close, device, device_platform
@pytest.mark.parametrize(
("B", "T", "H", "D", "dtype"),
[
pyte... | 0.007) | assert_* | numeric_literal | tests/ops/test_log_linear_attn.py | test_chunk_bwd | 91 | null | |
fla-org/flash-linear-attention | import pytest
import torch
from fla.ops.forgetting_attn import naive_forgetting_attn
from fla.ops.forgetting_attn.parallel import parallel_forgetting_attn
from fla.utils import IS_INTEL_ALCHEMIST, assert_close, check_shared_mem, device
@pytest.mark.parametrize(
('B', 'T', 'H', 'HQ', 'D', 'scale'),
[
p... | 0.005) | assert_* | numeric_literal | tests/ops/test_forgetting_attn.py | test_parallel | 59 | null | |
fla-org/flash-linear-attention | import os
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', 'chunk_size'),
[
... | 0.0001) | assert_* | numeric_literal | tests/ops/test_solve_tril.py | test_solve_tril | 44 | null | |
fla-org/flash-linear-attention | 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) | assert_* | variable | tests/ops/test_rwkv7.py | test_fused_rwkv7_addcmul | 178 | null | |
fla-org/flash-linear-attention | import os
import pytest
import torch
import torch.nn.functional as F
from einops import repeat
from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
from fla.ops.gated_delta_rule.naive import naive_recurrent_gated_delta_rule
from fla.utils import IS_INTEL_ALCHEMIST, assert_clos... | 0.02) | assert_* | numeric_literal | tests/ops/test_gated_delta.py | test_chunk | 154 | null | |
fla-org/flash-linear-attention | import os
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=-1):
dtype = x.dtyp... | tri.to(ref.dtype)) | assert_* | func_call | tests/ops/test_utils.py | test_mean_pooling_varlen | 325 | null | |
argoverse/av2-api | from contextlib import AbstractContextManager
from contextlib import nullcontext as does_not_raise
from typing import Final
import numpy as np
import pytest
import av2.datasets.motion_forecasting.eval.metrics as metrics
from av2.utils.typing import NDArrayFloat
test_N: Final[int] = 10
_STATIONARY_GT_TRAJ = np.zeros(... | forecasted_trajectories.shape[:1] | assert | complex_expr | tests/unit/datasets/motion_forecasting/eval/test_metrics.py | test_compute_is_missed_prediction | 141 | null | |
argoverse/av2-api | from pathlib import Path
import numpy as np
import pytest
from av2.map.drivable_area import DrivableArea
from av2.map.lane_segment import LaneSegment
from av2.map.map_api import ArgoverseStaticMap
from av2.map.map_primitives import Point, Polyline
from av2.map.pedestrian_crossing import PedestrianCrossing
from av2.ut... | 1 | assert | numeric_literal | tests/unit/map/test_map_api.py | test_from_array | TestPolyline | 88 | null |
argoverse/av2-api | from pathlib import Path
import numpy as np
import av2.utils.io as io_utils
from av2.utils.io import read_ego_SE3_sensor, read_feather
def test_read_feather(test_data_root_dir: Path) -> None:
"""Read an Apache Feather file."""
feather_path = (
test_data_root_dir
/ "sensor_dataset_logs"
... | 0 | assert | numeric_literal | tests/unit/utils/test_io.py | test_read_feather | 25 | null | |
argoverse/av2-api | from pathlib import Path
from typing import Any, Callable, Tuple
import numpy as np
import pytest
from scipy.spatial.transform import Rotation
import av2.geometry.geometry as geometry_utils
from av2.datasets.sensor.constants import AnnotationCategories
from av2.geometry.geometry import mat_to_xyz, xyz_to_mat
from av2... | rotation_matrix_expected) | assert_* | variable | tests/unit/geometry/test_geometry.py | test_xyz_to_mat_matrix | 479 | null | |
argoverse/av2-api | from pathlib import Path
from typing import Final, List
import numpy as np
import pandas as pd
import torch
from kornia.geometry.conversions import euler_from_quaternion
from torch.testing._comparison import assert_close
from av2.torch import XYZLWH_QWXYZ_COLUMNS
from av2.torch.structures.cuboids import CuboidMode, C... | cuboids_xyzlwh_qwxyz) | assert_* | variable | tests/unit/torch/structures/test_cuboids.py | test_build_cuboids | 38 | null | |
argoverse/av2-api | import numpy as np
from av2.rendering.color import GRAY_BGR
from av2.structures.ndgrid import BEVGrid, NDGrid
from av2.utils.typing import NDArrayFloat, NDArrayInt
def test_ndgrid_3d() -> None:
"""Unit tests for the NDGrid class."""
min_range_m = (-5.0, -5.0, -5.0)
max_range_m = (+5.0, +5.0, +5.0)
res... | dims_expected | assert | variable | tests/unit/structures/test_ndgrid.py | test_ndgrid_3d | 21 | null | |
argoverse/av2-api | from typing import Any, Callable
import cv2
import numpy as np
from cv2.typing import MatLike
from av2.rendering.ops.draw import (
alpha_blend_kernel,
draw_points_kernel,
gaussian_kernel,
)
from av2.utils.typing import NDArrayByte, NDArrayInt
def _draw_points_cv2(
img: MatLike,
points_xy: NDArray... | out | assert | variable | tests/unit/rendering/ops/test_draw.py | test_gaussian_kernel | 73 | null | |
argoverse/av2-api | import tempfile
from pathlib import Path
import numpy as np
import av2.evaluation.scene_flow.constants as constants
import av2.evaluation.scene_flow.eval as eval
from av2.evaluation.scene_flow.make_annotation_files import write_annotation
from av2.evaluation.scene_flow.utils import write_output_file
gts = np.array(
... | 0 | assert | numeric_literal | tests/unit/evaluation/scene_flow/test_sf_eval.py | test_average_metrics | 332 | null | |
argoverse/av2-api | import logging
import sys
from pathlib import Path
from typing import Final, Tuple
import click
from rich.progress import track
import av2.utils.io as io_utils
from av2.datasets.sensor.av2_sensor_dataloader import AV2SensorDataLoader
from av2.datasets.sensor.constants import RingCameras
from av2.datasets.tbv.splits i... | EXPECTED_LANE_SEGMENT_ATTRIB_KEYS | assert | variable | tests/integration/verify_tbv_download.py | verify_log_map | 189 | null | |
argoverse/av2-api | import math
from pathlib import Path
from tempfile import NamedTemporaryFile
import numpy as np
import pytest
import av2.utils.io as io_utils
from av2.geometry.sim2 import Sim2
from av2.utils.typing import NDArrayFloat
_TEST_DATA_ROOT = Path(__file__).resolve().parent.parent / "test_data"
def test_inverse() -> None... | imgSw.inverse() | assert | func_call | tests/unit/utils/test_sim2.py | test_inverse | 139 | null | |
argoverse/av2-api | from __future__ import annotations
import importlib.util
from pathlib import Path
from types import ModuleType
import pytest
def _load_module() -> ModuleType:
root = Path(__file__).resolve().parents[2]
module_path = root / "scripts" / "validate_tag_version.py"
spec = importlib.util.spec_from_file_locatio... | message | assert | variable | tests/unit/test_validate_tag_version.py | test_validate_tag_alignment_mismatch | 49 | null | |
argoverse/av2-api | from __future__ import annotations
import importlib.util
from pathlib import Path
from types import ModuleType
import pytest
def _load_module() -> ModuleType:
root = Path(__file__).resolve().parents[2]
module_path = root / "scripts" / "validate_tag_version.py"
spec = importlib.util.spec_from_file_locatio... | message.lower() | assert | func_call | tests/unit/test_validate_tag_version.py | test_validate_tag_alignment_skips_non_tag | 56 | null | |
argoverse/av2-api | import numpy as np
import av2.geometry.polyline_utils as polyline_utils
from av2.utils.typing import NDArrayFloat
def test_straight_centerline_to_polygon() -> None:
"""Try converting a simple straight polyline into a polygon.
Represents the conversion from a centerline to a lane segment polygon.
Note th... | (7, 2) | assert | collection | tests/unit/utils/test_polyline_utils.py | test_straight_centerline_to_polygon | 69 | null | |
argoverse/av2-api | import math
from pathlib import Path
from tempfile import NamedTemporaryFile
import numpy as np
import pytest
import av2.utils.io as io_utils
from av2.geometry.sim2 import Sim2
from av2.utils.typing import NDArrayFloat
_TEST_DATA_ROOT = Path(__file__).resolve().parent.parent / "test_data"
def rotmat2d(theta: float)... | 0 | assert | numeric_literal | tests/unit/utils/test_sim2.py | test_sim2_theta_deg_1 | 243 | null | |
argoverse/av2-api | from typing import List
import numpy as np
import av2.geometry.mesh_grid as mesh_grid_utils
from av2.utils.typing import NDArrayFloat
def test_get_mesh_grid_as_point_cloud_3x3square() -> None:
"""Ensure a sampled regular grid returns 9 grid points from 1 meter resolution on 2x2 meter area."""
min_x = -3 # i... | (9, 2) | assert | collection | tests/unit/utils/test_mesh_grid.py | test_get_mesh_grid_as_point_cloud_3x3square | 25 | null | |
argoverse/av2-api | from pathlib import Path
from typing import Final
from av2.torch.data_loaders.detection import DetectionDataLoader
TEST_DATA_DIR: Final = (
Path(__file__).parent.parent.parent.resolve() / "test_data" / "sensor_dataset_logs"
)
def test_build_data_loader() -> None:
"""Test building the PyTorch Detection DataLo... | None | assert | none_literal | tests/unit/torch/data_loaders/test_detection_dataloader.py | test_build_data_loader | 22 | null | |
argoverse/av2-api | import math
from pathlib import Path
from tempfile import NamedTemporaryFile
import numpy as np
import pytest
import av2.utils.io as io_utils
from av2.geometry.sim2 import Sim2
from av2.utils.typing import NDArrayFloat
_TEST_DATA_ROOT = Path(__file__).resolve().parent.parent / "test_data"
def test_scale() -> None:
... | 3.0 | assert | numeric_literal | tests/unit/utils/test_sim2.py | test_scale | 85 | null | |
argoverse/av2-api | from enum import Enum
from typing import Any, Callable, List, Tuple
import numpy as np
import pytest
from av2.datasets.sensor.constants import AnnotationCategories
from av2.geometry.se3 import SE3
from av2.structures.cuboid import Cuboid, CuboidList
from av2.utils.typing import NDArrayBool, NDArrayFloat
def _get_dum... | height_m_expected | assert | variable | tests/unit/structures/test_cuboid.py | test_cuboid_constructor | 163 | null | |
argoverse/av2-api | from pathlib import Path
from typing import Final
import pandas as pd
from av2.torch.structures.cuboids import Cuboids
from av2.torch.structures.lidar import Lidar
from av2.torch.structures.sweep import Sweep
from av2.torch.structures.utils import SE3_from_frame
TEST_DATA_DIR: Final = Path(__file__).parent.parent.pa... | None | assert | none_literal | tests/unit/torch/structures/test_sweep.py | test_build_sweep | 38 | null | |
argoverse/av2-api | import unittest
from av2.map.lane_segment import LaneMarkType, LaneSegment, LaneType
from av2.map.map_primitives import Point, Polyline
class TestLaneSegment(unittest.TestCase):
def test_from_dict(self) -> None:
"""Ensure object is generated correctly from a dictionary."""
json_data = {
... | LaneType("VEHICLE") | assert | func_call | tests/unit/map/test_lane_segment.py | test_from_dict | TestLaneSegment | 42 | null |
argoverse/av2-api | import unittest
from av2.map.drivable_area import DrivableArea
class TestDrivableArea(unittest.TestCase):
def test_from_dict(self) -> None:
"""Ensure object is generated correctly from a dictionary.
Note: 3 arbitrary waypoints taken from the dummy log map file.
"""
json_data = {
... | 4499430 | assert | numeric_literal | tests/unit/map/test_drivable_area.py | test_from_dict | TestDrivableArea | 30 | null |
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