[Fix] Fix Inference Example, Tests, and Requirements (#5688)

* clean requirements

* modify example inference struct

* add test ci scripts

* mark test_infer as submodule

* rm deprecated cls & deps

* import of HAS_FLASH_ATTN

* prune inference tests to be run

* prune triton kernel tests

* increment pytest timeout mins

* revert import path in openmoe

.github/workflows/build_on_pr.yml

@@ -91,7 +91,7 @@ jobs:
     container:
       image: hpcaitech/pytorch-cuda:2.1.0-12.1.0
       options: --gpus all --rm -v /dev/shm -v /data/scratch/llama-tiny:/data/scratch/llama-tiny
-    timeout-minutes: 60
+    timeout-minutes: 75
     defaults:
       run:
         shell: bash

colossalai/inference/README.md

@@ -81,7 +81,7 @@ import colossalai
 from colossalai.inference import InferenceEngine, InferenceConfig
 from pprint import pprint
 
-colossalai.launch_from_torch(config={})
+colossalai.launch_from_torch()
 
 # Step 1: create a model in "transformers" way
 model_path = "lmsys/vicuna-7b-v1.3"

colossalai/inference/spec/README.md

@@ -23,7 +23,7 @@ from colossalai.inference.core.engine import InferenceEngine, GenerationConfig
 from colossalai.inference.modeling.models.glide_llama import GlideLlamaForCausalLM, GlideLlamaConfig
 
 # launch colossalai, setup distributed environment
-colossalai.launch_from_torch(config={})
+colossalai.launch_from_torch()
 
 # main model
 model_path_or_name = "REPLACE_TO_VICUNA_7B_PATH_OR_MODEL_CARD"

colossalai/inference/struct.py

@@ -1,11 +1,7 @@
 import enum
 from dataclasses import dataclass
-from typing import Any, List, Tuple, Union
+from typing import Any, List
 
-import torch
-from ordered_set import OrderedSet
-
-from colossalai.inference.flash_decoding_utils import FDIntermTensors
 from colossalai.logging import get_dist_logger
 
 logger = get_dist_logger(__name__)
@@ -170,242 +166,6 @@ class Sequence:
         )
 
 
-@dataclass
-class BatchInfo:
-    """
-    Information to be passed and used for a batch of sequences.
-    """
-
-    max_batch_size: int
-    kv_max_split_num: int
-    num_heads: int
-    head_dim: int
-    sequences_set: OrderedSet[Sequence] = None
-    is_prompts: bool = True
-    device: torch.device = None
-    dtype: torch.dtype = None
-    fd_inter_tensor: FDIntermTensors = None
-
-    def __post_init__(self):
-        if self.device is None:
-            self.device = torch.cuda.current_device()
-        if self.sequences_set is None:
-            self.sequences_set = OrderedSet()
-        if self.fd_inter_tensor is None:
-            self.fd_inter_tensor = FDIntermTensors()
-
-    def init_fd_tensors(self):
-        if not self.fd_inter_tensor.is_initialized:
-            self.fd_inter_tensor.initialize(
-                max_batch_size=self.max_batch_size,
-                num_attn_heads=self.num_heads,
-                kv_max_split_num=self.kv_max_split_num,
-                head_dim=self.head_dim,
-                dtype=self.dtype,
-                device=self.device,
-            )
-
-    def get_block_table_tensor(self) -> None:
-        tesnor_list = []
-        block_table = None
-
-        assert len(self.sequences_set) > 0, "Batch has not been initialized yet. Please initialize batch first."
-
-        for seq in self.sequences_set:
-            block_table = seq.block_table
-            assert (
-                block_table is not None
-            ), f"The sequence(request_id {seq.request_id}) has not initialized the block_table."
-            tesnor_list.append(seq.block_table)
-
-        block_table = torch.stack(tesnor_list)
-        return block_table
-
-    def clear_batch(self) -> None:
-        """
-        Clear sequence set and block table if we need to abort this batch.
-            Prefill: clear sequence set and move them to running batch(external)
-            Decoding: mark unfinished sequences as aborted.
-        """
-        if self.is_prompts:
-            self.sequences_set.clear()
-        else:
-            for seq in self.sequences_set:
-                seq.mark_aborted()
-                if seq.check_finish():
-                    seq.mark_finished()
-
-            self.sequences_set.clear()
-
-    def fliter_batch(self) -> List["Sequence"]:
-        """
-        Remove completed sentences from a batch.
-
-        Returns:
-            List["Sequence"]: List of finished sequences.
-        """
-        finish_seqs = []
-        for seq in self.sequences_set:
-            if seq.check_finish():
-                finish_seqs.append(seq)
-        for finish_seq in finish_seqs:
-            self.sequences_set.discard(finish_seq)
-        return finish_seqs
-
-    def abort_seq(self, seq: "Sequence") -> "Sequence":
-        """
-        Remove sequence from the batch.
-        """
-        if not seq.check_finish():
-            seq.status = RequestStatus.ABORTED
-        self.sequences_set.discard(seq)
-        return seq
-
-    def add_seqs(self, seqs: Union[Sequence, List[Sequence]]) -> None:
-        """
-        Add new sequence to batch
-
-        Args:
-            seqs (List["Sequence"]): The list of new sequences.
-        """
-        # covnert single sequence to list
-        if isinstance(seqs, Sequence):
-            seqs = [seqs]
-
-        for seq in seqs:
-            if seq in self.sequences_set:
-                logger.warning(f"The sequence(request_id {seq.request_id}) is already in sequences_set.")
-                continue
-            self.sequences_set.add(seq)
-
-    def del_seq(self, seq: Sequence) -> Sequence:
-        """
-        Delete sequence in batch
-        """
-        self.sequences_set.discard(seq)
-
-    @property
-    def is_empty(self) -> None:
-        """
-        Check whether sequences_set is empty.
-        """
-        return not self.sequences_set
-
-    def update_batch_tokens(self, tokens: Union[List[int], List[List[int]], torch.Tensor]) -> None:
-        """
-        Add an output token for each sentence in the batch.
-
-        Args:
-            tokens (List[int]): A batch of tokens
-        """
-
-        if isinstance(tokens, torch.Tensor):
-            tokens = tokens.tolist()
-
-        assert self.get_batch_size() == len(tokens), "The number of tokens does not match batch_size."
-
-        for seq, token in zip(self.sequences_set, tokens):
-            if not isinstance(token, list):
-                if not isinstance(token, int):
-                    raise TypeError(f"The token type must be List[int] or int, but got {type(token)}.")
-                token = [token]
-            seq.output_token_id += token
-            seq.check_finish()
-
-    def get_batch_size(self) -> int:
-        """
-        Get batch_size of this batch
-        """
-        return len(self.sequences_set)
-
-    def get_batch_inputs(self) -> torch.LongTensor:
-        """
-        Get bacth inputs for forward inference computation.
-        """
-
-        input_list = []
-
-        assert len(self.sequences_set) > 0, "Batch has not been initialized yet. Please initialize batch first."
-
-        for seq in self.sequences_set:
-            if self.is_prompts:
-                if seq.output_len > 0:
-                    input_list.append(seq.input_token_id + seq.output_token_id)
-                else:
-                    input_list.append(seq.input_token_id)
-            else:
-                input_list.append([seq.output_token_id[-1]])
-
-        max_seq_len = max(len(sub_list) for sub_list in input_list)
-
-        # We assume that all the padding_id in seq are the same at present.
-        return _make_tensor_with_pad(input_list, max_seq_len, self.sequences_set[0].pad_token_id, dtype=torch.int)
-
-    def get_1D_inputs(self) -> Tuple[torch.LongTensor, torch.Tensor]:
-        """
-        Flattening the input tokens.
-        """
-        input_list = []
-
-        assert len(self.sequences_set) > 0, "Batch has not been initialized yet. Please initialize batch first."
-
-        for seq in self.sequences_set:
-            if self.is_prompts:
-                input_list.extend(seq.input_token_id)
-            else:
-                input_list.append(seq.output_token_id[-1])
-
-        return torch.tensor(input_list, dtype=torch.long, device=self.device)
-
-    def get_sequence_lengths(self):
-        """
-        Get the input_len of each sentence in this batch.
-        """
-        len_list = []
-
-        assert len(self.sequences_set) > 0, "Batch has not been initialized yet. Please initialize batch first."
-
-        for seq in self.sequences_set:
-            len_list.append(seq.sentence_len)
-
-        return torch.tensor(len_list, dtype=torch.int, device=self.device)
-
-    def get_attn_mask(self) -> torch.Tensor:
-        """
-        Generate and return attention mask.
-        """
-        assert len(self.sequences_set) > 0, "Batch has not been initialized yet. Please initialize batch first."
-
-        past_values = []
-        # We assume that all the padding_id in seq are the same at present.
-        padding_id = self.sequences_set[0].pad_token_id
-
-        for seq in self.sequences_set:
-            past_values.append(seq.input_token_id + seq.output_token_id)
-
-        max_seq_len = max(len(sub_list) for sub_list in past_values)
-        attn_mask = _make_tensor_with_pad(
-            past_values, max_seq_len, self.sequences_set[0].pad_token_id, dtype=torch.int, device=self.device
-        )
-
-        return attn_mask.ne(padding_id).long()
-
-    def __repr__(self) -> str:
-        return f"(sequences_set={self.sequences_set}, " f"is_prompts={self.is_prompts})"
-
-
 def _pad_to_max(x: List[int], max_len: int, pad: int) -> List[int]:
     assert len(x) <= max_len
     return [pad] * (max_len - len(x)) + x
-
-
-def _make_tensor_with_pad(
-    x: Union[List[List[int]], List[int]],
-    max_len: int,
-    pad: int,
-    dtype: torch.dtype,
-    device: Union[str, torch.device] = "cuda",
-    pin_memory: bool = False,
-):
-    padded_x = [_pad_to_max(x_i, max_len, pad) for x_i in x]
-    return torch.tensor(padded_x, dtype=dtype, device=device, pin_memory=pin_memory and str(device) == "cpu")

examples/inference/llama/benchmark_llama3.py

@@ -182,7 +182,7 @@ def benchmark_inference(args):
 
 
 def inference(rank, world_size, port, args):
-    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     benchmark_inference(args)
 
 

examples/inference/llama/llama_generation.py

@@ -17,7 +17,7 @@ def infer(args):
     # ==============================
     # Launch colossalai, setup distributed environment
     # ==============================
-    colossalai.launch_from_torch(config={})
+    colossalai.launch_from_torch()
     coordinator = DistCoordinator()
 
     # ==============================
@@ -59,7 +59,7 @@ def infer(args):
     coordinator.print_on_master(out[0])
 
 
-# colossalai run --nproc_per_node 1 llama_gen.py -m MODEL_PATH
+# colossalai run --nproc_per_node 1 llama_generation.py -m MODEL_PATH
 if __name__ == "__main__":
     # ==============================
     # Parse Arguments

examples/inference/llama/test_ci.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+echo "Skip the test (this test is slow)"
+
+# bash ./run_benchmark.sh

examples/language/openmoe/model/modeling_openmoe.py

@@ -35,7 +35,7 @@ from transformers.utils import (
     replace_return_docstrings,
 )
 
-from colossalai.kernel.extensions.pybind.flash_attention import HAS_FLASH_ATTN
+from colossalai.kernel.extensions.flash_attention import HAS_FLASH_ATTN
 from colossalai.kernel.triton.llama_act_combine_kernel import HAS_TRITON
 from colossalai.moe.layers import SparseMLP
 from colossalai.moe.manager import MOE_MANAGER

requirements/requirements-infer.txt

@@ -1,2 +0,0 @@
-ordered_set
-transformers==4.36.2

requirements/requirements-test.txt

@@ -1,6 +1,4 @@
 diffusers
-fbgemm-gpu==0.2.0
-ordered_set
 pytest
 coverage==7.2.3
 git+https://github.com/hpcaitech/pytest-testmon

tests/test_infer/test_config_and_struct.py

@@ -2,7 +2,7 @@ import pytest
 
 import colossalai
 from colossalai.inference.config import InferenceConfig
-from colossalai.inference.struct import BatchInfo, RequestStatus, Sequence
+from colossalai.inference.struct import RequestStatus, Sequence
 from colossalai.testing import rerun_if_address_is_in_use, spawn
 
 
@@ -20,27 +20,6 @@ def check_config_and_inference():
         max_output_len=256,
     )
 
-    sequence2 = Sequence(
-        request_id=2,
-        prompt="bcd",
-        input_token_id=[4, 5, 6],
-        block_size=16,
-        sample_params=None,
-        eos_token_id=2,
-        pad_token_id=2,
-        max_output_len=256,
-    )
-
-    sequence3 = Sequence(
-        request_id=3,
-        prompt="efg",
-        input_token_id=[7, 8, 9],
-        block_size=16,
-        sample_params=None,
-        eos_token_id=2,
-        pad_token_id=2,
-        max_output_len=256,
-    )
     sequence.mark_running()
     assert sequence.status == RequestStatus.RUNNING
     sequence.recycle()
@@ -51,33 +30,6 @@ def check_config_and_inference():
     assert sequence.output_len == 0
     assert sequence.check_finish() == False
 
-    batch = BatchInfo(
-        max_batch_size=8,
-        kv_max_split_num=16,
-        num_heads=2,
-        head_dim=128,
-    )
-    batch.add_seqs([sequence])
-    batch.add_seqs([sequence2, sequence3])
-
-    # add duplicated sequence to test that it will not be counted twice
-    batch.add_seqs([sequence])
-
-    assert batch.is_empty == False
-    assert batch.get_batch_size() == 3
-    batch.update_batch_tokens([1, 2, 3])
-    seq = batch.abort_seq(sequence)
-    seq2 = batch.fliter_batch()[0]
-
-    assert batch.get_batch_size() == 1
-    assert seq.output_len == 1
-    assert seq.output_token_id == [1]
-    assert seq2.output_len == 1
-    assert seq2.output_token_id == [2]
-
-    batch.clear_batch()
-    assert batch.is_empty == True
-
 
 def run_dist(rank, world_size, port):
     colossalai.launch(rank=rank, world_size=world_size, port=port, host="localhost")

tests/test_infer/test_cuda_graph.py

@@ -86,7 +86,7 @@ def run_dist(rank, world_size, port):
     check_output_consistency(128)
 
 
-@pytest.mark.dist
+@pytest.mark.largedist
 @rerun_if_address_is_in_use()
 def test_cuda_graph_infer():
     spawn(run_dist, 1)

tests/test_infer/test_drafter.py

@@ -11,13 +11,16 @@ MAX_LEN = 100
 SPEC_NUM = 5
 
 
+@pytest.fixture(scope="module")
+def tokenizer():
+    return AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
+
+
 @pytest.mark.parametrize("spec_num", [SPEC_NUM])
-def test_drafter(spec_num: int):
+def test_drafter(tokenizer, spec_num: int):
     torch.manual_seed(123)
 
     device = get_current_device()
-
-    tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
     toy_config = LlamaConfig(num_hidden_layers=NUM_LAYERS)
     toy_config.pad_token_id = tokenizer.eos_token_id
     drafter_model = LlamaForCausalLM(toy_config)
@@ -39,10 +42,9 @@ def test_drafter(spec_num: int):
     assert trimmed_past_key_values[0][0].size(2) == past_kv_length - reject_num
 
 
-def test_spec_dec():
+def test_spec_dec(tokenizer):
     spec_num = SPEC_NUM
     device = get_current_device()
-    tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
     tokenizer.pad_token = tokenizer.eos_token
 
     # Dummy config for Glide Model
@@ -67,5 +69,6 @@ def test_spec_dec():
 
 
 if __name__ == "__main__":
-    test_drafter(spec_num=SPEC_NUM)
+    dummy_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
-    test_spec_dec()
+    test_drafter(dummy_tokenizer, spec_num=SPEC_NUM)
+    test_spec_dec(dummy_tokenizer)

tests/test_infer/test_inference_engine.py

@@ -165,8 +165,10 @@ def run_dist(rank, world_size, port, func_to_run, ret=None, **kwargs):
         func_to_run(**kwargs)
 
 
+@pytest.mark.largedist
 @parameterize("prompt_template", [None, "llama"])
 @parameterize("do_sample", [False])
+@rerun_if_address_is_in_use()
 def test_tp_engine(prompt_template, do_sample):
     kwargs1 = {
         "use_engine": True,
@@ -186,18 +188,14 @@ def test_tp_engine(prompt_template, do_sample):
         assert s1 == s2, f"\nColossalAI TP=1 Output: {s1}\nColossalAI TP=2 Output: {s2}"
 
 
+@pytest.mark.largedist
 @parameterize("num_layers", [1])
 @parameterize("max_length", [64])
+@rerun_if_address_is_in_use()
 def test_spec_dec(num_layers, max_length):
     spawn(run_dist, 1, func_to_run=check_spec_dec, num_layers=num_layers, max_length=max_length)
 
 
-@pytest.mark.dist
+if __name__ == "__main__":
-@rerun_if_address_is_in_use()
-def test_inference_engine():
     test_tp_engine()
     test_spec_dec()
-
-
-if __name__ == "__main__":
-    test_inference_engine()

tests/test_infer/test_kernels/triton/test_context_attn_unpad.py

@@ -86,11 +86,11 @@ def torch_attn_unpad(
 
 
 @pytest.mark.skipif(not (HAS_TRITON and TRITON_CUDA_SUPPORT), reason="requires triton")
-@pytest.mark.parametrize("bsz", [4, 7, 32])
+@pytest.mark.parametrize("bsz", [7, 32])
-@pytest.mark.parametrize("block_size", [16, 32, 64])
+@pytest.mark.parametrize("block_size", [16, 32])
-@pytest.mark.parametrize("max_num_blocks_per_seq", [8, 32])
+@pytest.mark.parametrize("max_num_blocks_per_seq", [8, 16])
 @pytest.mark.parametrize("num_attn_heads", [16])
-@pytest.mark.parametrize("kv_group_num", [1, 2, 16])
+@pytest.mark.parametrize("kv_group_num", [1, 4])
 @pytest.mark.parametrize("same_context_len", [True, False])
 @pytest.mark.parametrize("use_alibi_slopes", [True, False])
 @pytest.mark.parametrize("use_new_kcache_layout", [True, False])

tests/test_infer/test_kernels/triton/test_decoding_attn.py

@@ -68,11 +68,11 @@ def prepare_data(
 
 
 @pytest.mark.skipif(not (HAS_TRITON and TRITON_CUDA_SUPPORT), reason="requires triton")
-@pytest.mark.parametrize("bsz", [4, 7, 32])
+@pytest.mark.parametrize("bsz", [7, 16])
-@pytest.mark.parametrize("block_size", [16, 32, 64])
+@pytest.mark.parametrize("block_size", [16, 32])
-@pytest.mark.parametrize("max_num_blocks_per_seq", [8, 32])
+@pytest.mark.parametrize("max_num_blocks_per_seq", [8, 16])
 @pytest.mark.parametrize("num_attn_heads", [16])
-@pytest.mark.parametrize("kv_group_num", [1, 2, 16])
+@pytest.mark.parametrize("kv_group_num", [1, 4])
 @pytest.mark.parametrize("same_context_len", [True, False])
 @pytest.mark.parametrize("q_len", [1, 5])
 @pytest.mark.parametrize("use_alibi_slopes", [True, False])
@@ -187,7 +187,7 @@ def test_flash_decoding(
 
     rtol = 1e-4
     # After the shape becomes larger, some data elements are too small, leading to excessively large relative errors.
-    if bsz == 32 and use_alibi_slopes:
+    if bsz >= 16 and use_alibi_slopes:
         rtol = 100
 
     numpy_allclose(out_torch, out_triton, atol=1e-3, rtol=rtol)

tests/test_infer/test_kernels/triton/test_kvcache_copy.py

@@ -70,9 +70,9 @@ def prepare_data(
 
 
 @pytest.mark.skipif(not (HAS_TRITON and TRITON_CUDA_SUPPORT), reason="requires triton")
-@pytest.mark.parametrize("bsz", [4, 7, 32])
+@pytest.mark.parametrize("bsz", [7, 32])
 @pytest.mark.parametrize("block_size", [16, 32, 64])
-@pytest.mark.parametrize("max_num_blocks_per_seq", [8, 32])
+@pytest.mark.parametrize("max_num_blocks_per_seq", [16])
 @pytest.mark.parametrize("num_kv_heads", [16])
 @pytest.mark.parametrize("same_context_len", [True, False])
 @pytest.mark.parametrize("n_tokens", [1, 5])

tests/test_infer/test_models/test_attention.py

@@ -1,3 +1,4 @@
+import pytest
 import torch
 from transformers.cache_utils import DynamicCache
 from transformers.modeling_attn_mask_utils import AttentionMaskConverter
@@ -7,6 +8,7 @@ from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotar
 from colossalai.inference.modeling.layers.attention import PagedAttention, convert_kvcache, copy_to_cache
 
 
+@pytest.mark.skip(reason="This test is not used in the current version.")
 def test_copy_to_cache():
     key = torch.ones((2, 11, 3, 3))
     key[0, 9, :, :] = 0
@@ -24,6 +26,7 @@ def test_copy_to_cache():
     assert cache[3, 0, 0, 0] == 1
 
 
+@pytest.mark.skip(reason="This test is not used in the current version.")
 def test_convert_kvcache():
     cache = torch.ones(8, 3, 8, 3)
     key = torch.ones(2, 1, 3, 3) + 1
@@ -34,6 +37,7 @@ def test_convert_kvcache():
     assert converted_cache.shape == (2, 10, 3, 3)
 
 
+@pytest.mark.skip(reason="This test is not used in the current version.")
 def test_context_attention():
     """
     test config: head_num = 4, head_size = 4
@@ -86,6 +90,7 @@ def test_context_attention():
     assert torch.allclose(pad_attn_output, attn_output, atol=1e-3, rtol=1e-3)
 
 
+@pytest.mark.skip(reason="This test is not used in the current version.")
 def test_decoding_attention():
     # test the pipeline of decoding attention
     attn = PagedAttention()

tests/test_infer/test_models/test_baichuan.py

@@ -128,7 +128,7 @@ def check_tp_engine(prompt_template, do_sample, use_cuda_kernel):
     not os.path.exists(BAICHUAN_MODEL_NAME_OR_PATH),
     reason="There is no local model address included, please replace this address with a valid one.",
 )
-@pytest.mark.dist
+@pytest.mark.largedist
 @rerun_if_address_is_in_use()
 def test_inference_engine():
     check_tp_engine()