[Misc] Upgrade vllm hash to 12_14 (#5000)
### What this PR does / why we need it?
### Does this PR introduce _any_ user-facing change?
1. fix https://github.com/vllm-project/vllm/pull/27938
2. fix https://github.com/vllm-project/vllm/pull/27145
pooling models now supports chunked prefill and prefix caching,
3. fix https://github.com/vllm-project/vllm/pull/30181
define the CPU fields in the field config where they really belong.
4. fix https://github.com/vllm-project/vllm/pull/28168
define the CPU fields in the field config where they really belong.
5. fix https://github.com/vllm-project/vllm/pull/30201
some moudle rename
6. fix https://github.com/vllm-project/vllm/pull/29067
fusedmoe moudle refactor
7. fix https://github.com/vllm-project/vllm/pull/29066
fusedmoe moudle refactor
8. fix https://github.com/vllm-project/vllm/pull/29624
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
This commit is contained in:
Li Wang
2
.github/workflows/pr_test_full.yaml
vendored
2
.github/workflows/pr_test_full.yaml
vendored
@@ -74,7 +74,7 @@ jobs:
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name: e2e-full
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strategy:
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matrix:
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vllm_version: [ad32e3e19ccf0526cb6744a5fed09a138a5fb2f9, v0.12.0]
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vllm_version: [97f2f160fda2805f9149b0e44da76b5d3b1f7c7e, v0.12.0]
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needs: [changes]
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if: ${{ needs.changes.outputs.e2e_tracker == 'true' }}
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uses: ./.github/workflows/_e2e_test.yaml
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6
.github/workflows/pr_test_light.yaml
vendored
6
.github/workflows/pr_test_light.yaml
vendored
@@ -42,7 +42,7 @@ jobs:
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lint:
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uses: ./.github/workflows/_pre_commit.yml
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with:
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vllm: ad32e3e19ccf0526cb6744a5fed09a138a5fb2f9
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vllm: 97f2f160fda2805f9149b0e44da76b5d3b1f7c7e
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changes:
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runs-on: linux-aarch64-a2-0
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outputs:
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@@ -90,7 +90,7 @@ jobs:
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SOC_VERSION: ascend910b1
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strategy:
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matrix:
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vllm_version: [ad32e3e19ccf0526cb6744a5fed09a138a5fb2f9, v0.12.0]
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vllm_version: [97f2f160fda2805f9149b0e44da76b5d3b1f7c7e, v0.12.0]
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steps:
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- name: Free up disk space
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@@ -154,7 +154,7 @@ jobs:
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name: e2e-light
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strategy:
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matrix:
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vllm_version: [ad32e3e19ccf0526cb6744a5fed09a138a5fb2f9, v0.12.0]
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vllm_version: [97f2f160fda2805f9149b0e44da76b5d3b1f7c7e, v0.12.0]
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# Note (yikun): If CI resource are limited we can split job into two chain jobs
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needs: [lint, changes]
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# only trigger e2e test after lint passed and the change is e2e related with pull request.
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@@ -45,7 +45,7 @@ The table below is the release compatibility matrix for vLLM Ascend release.
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For main branch of vLLM Ascend, we usually make it compatible with the latest vLLM release and a newer commit hash of vLLM. Please note that this table is usually updated. Please check it regularly.
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| vLLM Ascend | vLLM | Python | Stable CANN | PyTorch/torch_npu |
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|-------------|--------------|------------------|-------------|--------------------|
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| main | ad32e3e19ccf0526cb6744a5fed09a138a5fb2f9, v0.12.0 tag | >= 3.10, < 3.12 | 8.3.RC2 | 2.8.0 / 2.8.0 |
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| main | 97f2f160fda2805f9149b0e44da76b5d3b1f7c7e, v0.12.0 tag | >= 3.10, < 3.12 | 8.3.RC2 | 2.8.0 / 2.8.0 |
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## Release cadence
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@@ -803,7 +803,9 @@ class TestPCPDCPGraphParams(TestBase):
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(q_nope, q_pe, k_nope, k_pe, block_table, seq_lens, num_heads,
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scale, num_kv_heads, out, lse))
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update_mla_attn_dcp_pcp_params(self.update_stream, forward_context, 4)
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with patch("torch_npu._C._npu_setStream", return_value=None):
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update_mla_attn_dcp_pcp_params(self.update_stream, forward_context,
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4)
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_mock_graph_task_end.assert_called_once()
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@@ -842,6 +844,7 @@ class TestPCPDCPGraphParams(TestBase):
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block_table, 128, actual_seq_lengths_kv, actual_seq_lengths_q,
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out, lse, 2, 0, 0))
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update_attn_dcp_pcp_params(self.update_stream, forward_context, 4)
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with patch("torch_npu._C._npu_setStream", return_value=None):
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update_attn_dcp_pcp_params(self.update_stream, forward_context, 4)
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_mock_graph_task_end.assert_called_once()
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@@ -95,6 +95,8 @@ class TestEagleProposerLoadModel(TestBase):
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mock_model = MagicMock()
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mock_model.model.embed_tokens = MagicMock()
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mock_model.lm_head = MagicMock()
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mock_model.multimodal_cpu_fields = None
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mock_model.merge_by_field_config = None
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mock_get_model.return_value = MagicMock()
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self.proposer.name = SpecDcodeType.EAGLE
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@@ -117,6 +119,8 @@ class TestEagleProposerLoadModel(TestBase):
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mock_model = MagicMock()
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original_embed = MagicMock()
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mock_model.multimodal_cpu_fields = None
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mock_model.merge_by_field_config = None
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mock_get_model.return_value = MagicMock(model=MagicMock(
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embed_tokens=original_embed))
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|
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@@ -1,375 +0,0 @@
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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||||
# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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import inspect
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from collections.abc import Sequence
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from typing import Optional
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import numpy as np
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import pytest
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import torch
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from vllm.sampling_params import SamplingParams
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from vllm.utils.torch_utils import make_tensor_with_pad
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from vllm.v1.pool.metadata import PoolingMetadata
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from vllm.v1.sample.logits_processor import LogitsProcessors
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.utils import CpuGpuBuffer
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from vllm_ascend.worker.block_table import BlockTable, MultiGroupBlockTable
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from vllm_ascend.worker.npu_input_batch import CachedRequestState, InputBatch
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VOCAB_SIZE = 1024
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NUM_OUTPUT_TOKENS = 20
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MAX_PROMPT_SIZE = 100
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MAX_NUM_PROMPT_TOKENS = 64
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def _compare_objs(obj1,
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obj2,
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skip: Sequence = ("logitsprocs", "batch_update_builder")):
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attrs = inspect.getmembers(obj1, lambda a: not (inspect.isroutine(a)))
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attr_names = set([
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a[0] for a in attrs
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if not (a[0].startswith('__') and a[0].endswith('__'))
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])
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for attr_name in attr_names:
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if attr_name in skip:
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continue
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a = getattr(obj1, attr_name)
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b = getattr(obj2, attr_name)
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is_same = False
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if isinstance(a, torch.Tensor):
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if (a.numel() == 0 or b.numel() == 0):
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is_same = (a.numel() == 0 and b.numel() == 0)
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elif torch.allclose(a, b):
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is_same = True
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elif isinstance(a, np.ndarray):
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if np.allclose(a, b):
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is_same = True
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elif isinstance(a, MultiGroupBlockTable):
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for a_i, b_i in zip(a.block_tables, b.block_tables):
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_compare_objs(a_i, b_i)
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is_same = True
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elif isinstance(a, (BlockTable, SamplingMetadata, PoolingMetadata)):
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_compare_objs(a, b)
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is_same = True # if we make it here must be same
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elif a == b:
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is_same = True
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elif isinstance(a, CpuGpuBuffer):
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is_same = np.allclose(a.np, b.np) and torch.allclose(a.gpu, b.gpu)
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assert is_same, f"Attribute {attr_name} is different"\
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f" in {obj1} and {obj2}: {a} != {b}"
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def _remove_requests(input_batch: InputBatch, batch_size: int,
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reqs: list[CachedRequestState]) -> set[str]:
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"""
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Remove some requests randomly from the batch and returns
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set of request removed
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"""
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num_reqs_to_remove = np.random.randint(0, batch_size)
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req_indices_to_remove: set[int] = set()
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for _ in range(num_reqs_to_remove):
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req_index_to_remove = np.random.randint(0, batch_size)
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req_indices_to_remove.add(req_index_to_remove)
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req_ids_to_remove: set[str] = set()
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for index in req_indices_to_remove:
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input_batch.remove_request(reqs[index].req_id)
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req_ids_to_remove.add(reqs[index].req_id)
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return req_ids_to_remove
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def _construct_expected_sampling_metadata(
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reqs: list[CachedRequestState],
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req_ids_retained: set[int],
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req_id_index_in_input_batch: dict[str, int],
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device: torch.device,
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) -> SamplingMetadata:
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"""
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Constructs and returns the expected SamplingMetadata for this
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batch.
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"""
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num_reqs = len(req_ids_retained)
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output_token_ids: list[list[int]] = [list() for _ in range(num_reqs)]
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prompt_token_ids: list[list[int]] = [list() for _ in range(num_reqs)]
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presence_penalties = [0.0 for _ in range(num_reqs)]
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frequency_penalties = [0.0 for _ in range(num_reqs)]
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repetition_penalties = [1.0 for _ in range(num_reqs)]
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top_k = [0 for _ in range(num_reqs)]
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top_p = [0.0 for _ in range(num_reqs)]
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temperature = [0.0 for _ in range(num_reqs)]
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min_tokens = {}
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logit_bias = [None] * num_reqs
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allowed_token_ids_mask = torch.zeros(num_reqs,
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VOCAB_SIZE,
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dtype=torch.bool,
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device=device)
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bad_words_token_ids = {}
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for req in reqs:
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if req.req_id not in req_ids_retained:
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continue
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index_in_input_batch = req_id_index_in_input_batch[req.req_id]
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output_token_ids[index_in_input_batch] = req.output_token_ids
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prompt_token_ids[index_in_input_batch] = req.prompt_token_ids
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presence_penalties[
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index_in_input_batch] = req.sampling_params.presence_penalty
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frequency_penalties[index_in_input_batch] = (
|
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req.sampling_params.frequency_penalty)
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repetition_penalties[index_in_input_batch] = (
|
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req.sampling_params.repetition_penalty)
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top_k[index_in_input_batch] = req.sampling_params.top_k
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top_p[index_in_input_batch] = req.sampling_params.top_p
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temperature[index_in_input_batch] = req.sampling_params.temperature
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min_tokens[index_in_input_batch] = (
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req.sampling_params.min_tokens,
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req.sampling_params.all_stop_token_ids)
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logit_bias[index_in_input_batch] = req.sampling_params.logit_bias
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if req.sampling_params.allowed_token_ids:
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allowed_token_ids_mask[index_in_input_batch][
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req.sampling_params.allowed_token_ids] = True
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if req.sampling_params.bad_words_token_ids:
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bad_words_token_ids[
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index_in_input_batch] = req.sampling_params.bad_words_token_ids
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return SamplingMetadata(
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temperature=torch.tensor(temperature, dtype=torch.float,
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device=device),
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all_greedy=False,
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all_random=True,
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top_p=None if all(x == 1.0 for x in top_p) else torch.tensor(
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top_p, dtype=torch.float, device=device),
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top_k=None if all(x == 0 for x in top_k) else torch.tensor(
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top_k, dtype=torch.int, device=device),
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generators={},
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max_num_logprobs=0,
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prompt_token_ids=make_tensor_with_pad(
|
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prompt_token_ids,
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pad=VOCAB_SIZE,
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device=torch.device(device),
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dtype=torch.int64,
|
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),
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frequency_penalties=torch.tensor(frequency_penalties,
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dtype=torch.float,
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device=device),
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presence_penalties=torch.tensor(presence_penalties,
|
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dtype=torch.float,
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device=device),
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repetition_penalties=torch.tensor(repetition_penalties,
|
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dtype=torch.float,
|
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device=device),
|
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output_token_ids=output_token_ids,
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no_penalties=(all(x == 0 for x in presence_penalties)
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and all(x == 0 for x in frequency_penalties)
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and all(x == 1 for x in repetition_penalties)),
|
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allowed_token_ids_mask=allowed_token_ids_mask,
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bad_words_token_ids=bad_words_token_ids,
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logitsprocs=LogitsProcessors(),
|
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)
|
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|
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|
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def _create_sampling_params():
|
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return SamplingParams(
|
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top_k=np.random.randint(1, 10),
|
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top_p=np.random.uniform(0.0, 1.0),
|
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presence_penalty=np.random.uniform(-2.0, 2.0),
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repetition_penalty=np.random.uniform(0.0, 2.0),
|
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frequency_penalty=np.random.uniform(-2.0, 2.0),
|
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min_tokens=np.random.randint(1, 10),
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stop_token_ids=[
|
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np.random.randint(0, VOCAB_SIZE)
|
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for _ in range(np.random.randint(10))
|
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],
|
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logit_bias={0: np.random.uniform(-3.0, 3.0)},
|
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)
|
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|
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|
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def _construct_cached_request_state(req_id_suffix: int):
|
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prompt_token_ids = [
|
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np.random.randint(0, VOCAB_SIZE)
|
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for _ in range(np.random.randint(0, MAX_PROMPT_SIZE))
|
||||
]
|
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output_token_ids = [
|
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np.random.randint(0, VOCAB_SIZE)
|
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for _ in range(np.random.randint(0, NUM_OUTPUT_TOKENS))
|
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]
|
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return CachedRequestState(
|
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req_id=f"req_id_{req_id_suffix}",
|
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prompt_token_ids=prompt_token_ids,
|
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sampling_params=_create_sampling_params(),
|
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pooling_params=None,
|
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mm_kwargs=[],
|
||||
mm_positions=[],
|
||||
block_ids=([], ),
|
||||
generator=None,
|
||||
num_computed_tokens=len(output_token_ids),
|
||||
output_token_ids=output_token_ids,
|
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mm_hashes=None,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("device", ["cpu"])
|
||||
@pytest.mark.parametrize("batch_size", [1, 2, 32, 64])
|
||||
def test_sampling_metadata_in_input_batch(device: str, batch_size: int):
|
||||
"""
|
||||
Tests the logic for managing sampling metadata in the InputBatch.
|
||||
|
||||
This test involves adding a set of requests to the InputBatch,
|
||||
followed by removing a subset of them. Afterward, the batch is compacted,
|
||||
and the `make_sampling_metadata` method is invoked on the batch. The
|
||||
output of `make_sampling_metadata` is then compared against the expected
|
||||
results to ensure correctness.
|
||||
|
||||
Note: Ignore logits processor logic, which is tested separately
|
||||
"""
|
||||
input_batch: InputBatch = InputBatch(
|
||||
max_num_reqs=batch_size,
|
||||
max_model_len=1024,
|
||||
max_num_batched_tokens=1024,
|
||||
device=torch.device(device),
|
||||
pin_memory=False,
|
||||
vocab_size=1024,
|
||||
block_sizes=[1],
|
||||
)
|
||||
reqs: list[CachedRequestState] = []
|
||||
req_id_reqs = {}
|
||||
req_id_output_token_ids = {}
|
||||
|
||||
# Add requests
|
||||
for req_index in range(batch_size):
|
||||
req: CachedRequestState = _construct_cached_request_state(req_index)
|
||||
assigned_req_index = input_batch.add_request(req)
|
||||
assert req_index == assigned_req_index
|
||||
reqs.append(req)
|
||||
req_id_reqs[req.req_id] = req
|
||||
req_id_output_token_ids[req.req_id] = req.output_token_ids
|
||||
|
||||
# Remove some requests
|
||||
req_ids_to_remove = _remove_requests(input_batch, batch_size, reqs)
|
||||
req_ids_retained = set(req_id_reqs.keys()) - req_ids_to_remove
|
||||
|
||||
# Compact the input batch
|
||||
input_batch.condense()
|
||||
|
||||
# Generate the sampling metadata
|
||||
sampling_metadata = input_batch._make_sampling_metadata()
|
||||
|
||||
# Create expected output.
|
||||
expected_sampling_metadata = _construct_expected_sampling_metadata(
|
||||
reqs,
|
||||
req_ids_retained,
|
||||
input_batch.req_id_to_index,
|
||||
device=torch.device(device))
|
||||
|
||||
def same(t1: Optional[torch.Tensor], t2: Optional[torch.Tensor]) -> bool:
|
||||
return (t1 is None
|
||||
and t2 is None) or (t1 is not None and t2 is not None
|
||||
and torch.allclose(t1, t2))
|
||||
|
||||
# Assert the actual and expected output.
|
||||
assert torch.allclose(expected_sampling_metadata.temperature,
|
||||
sampling_metadata.temperature)
|
||||
assert same(expected_sampling_metadata.top_p, sampling_metadata.top_p)
|
||||
assert same(expected_sampling_metadata.top_k, sampling_metadata.top_k)
|
||||
assert torch.allclose(
|
||||
expected_sampling_metadata.frequency_penalties,
|
||||
sampling_metadata.frequency_penalties,
|
||||
)
|
||||
assert torch.allclose(
|
||||
expected_sampling_metadata.presence_penalties,
|
||||
sampling_metadata.presence_penalties,
|
||||
)
|
||||
assert torch.allclose(
|
||||
expected_sampling_metadata.repetition_penalties,
|
||||
sampling_metadata.repetition_penalties,
|
||||
)
|
||||
assert torch.allclose(expected_sampling_metadata.prompt_token_ids,
|
||||
sampling_metadata.prompt_token_ids)
|
||||
assert (expected_sampling_metadata.output_token_ids ==
|
||||
sampling_metadata.output_token_ids)
|
||||
assert expected_sampling_metadata.no_penalties == \
|
||||
sampling_metadata.no_penalties
|
||||
if sampling_metadata.allowed_token_ids_mask:
|
||||
assert torch.allclose(
|
||||
expected_sampling_metadata.allowed_token_ids_mask,
|
||||
sampling_metadata.allowed_token_ids_mask)
|
||||
assert expected_sampling_metadata.bad_words_token_ids == \
|
||||
sampling_metadata.bad_words_token_ids
|
||||
|
||||
|
||||
@pytest.mark.parametrize("device", ["cpu"])
|
||||
@pytest.mark.parametrize("batch_size", [32])
|
||||
@pytest.mark.parametrize("swap_list", [((0, 1), )])
|
||||
def test_swap_states_in_input_batch(device: str, batch_size: int,
|
||||
swap_list: list):
|
||||
"""
|
||||
Tests the logic for managing sampling metadata in the InputBatch.
|
||||
|
||||
This test involves adding a set of requests to the InputBatch,
|
||||
followed by removing a subset of them. Afterward, the batch is compacted,
|
||||
and the `make_sampling_metadata` method is invoked on the batch. The
|
||||
output of `make_sampling_metadata` is then compared against the expected
|
||||
results to ensure correctness.
|
||||
|
||||
Note: Ignore logits processor logic, which is tested separately
|
||||
"""
|
||||
input_batch: InputBatch = InputBatch(
|
||||
max_num_reqs=batch_size,
|
||||
max_model_len=1024,
|
||||
max_num_batched_tokens=1024,
|
||||
device=torch.device(device),
|
||||
pin_memory=False,
|
||||
vocab_size=1024,
|
||||
block_sizes=[1],
|
||||
)
|
||||
ref_input_batch: InputBatch = InputBatch(
|
||||
max_num_reqs=batch_size,
|
||||
max_model_len=1024,
|
||||
max_num_batched_tokens=1024,
|
||||
device=torch.device(device),
|
||||
pin_memory=False,
|
||||
vocab_size=1024,
|
||||
block_sizes=[1],
|
||||
)
|
||||
|
||||
reqs: list[CachedRequestState] = []
|
||||
req_id_reqs = {}
|
||||
req_id_output_token_ids = {}
|
||||
# Add requests
|
||||
for req_index in range(batch_size):
|
||||
req: CachedRequestState = _construct_cached_request_state(req_index)
|
||||
assigned_req_index = input_batch.add_request(req)
|
||||
assert assigned_req_index == req_index
|
||||
reqs.append(req)
|
||||
req_id_reqs[req.req_id] = req
|
||||
req_id_output_token_ids[req.req_id] = req.output_token_ids
|
||||
|
||||
reordered_reqs = reqs.copy()
|
||||
for swap_pair in swap_list:
|
||||
reordered_reqs[swap_pair[0]], reordered_reqs[swap_pair[1]] = \
|
||||
reordered_reqs[swap_pair[1]], reordered_reqs[swap_pair[0]]
|
||||
input_batch.swap_states(swap_pair[0], swap_pair[1])
|
||||
|
||||
for req_index in range(batch_size):
|
||||
req = reordered_reqs[req_index]
|
||||
assigned_req_index = ref_input_batch.add_request(req)
|
||||
assert assigned_req_index == req_index
|
||||
|
||||
input_batch.refresh_metadata()
|
||||
ref_input_batch.refresh_metadata()
|
||||
|
||||
_compare_objs(input_batch, ref_input_batch)
|
||||
@@ -41,7 +41,7 @@ from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
|
||||
from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
|
||||
flashcomm2_o_shared_enabled, is_enable_nz,
|
||||
weak_ref_tensors)
|
||||
from vllm_ascend.worker.npu_input_batch import InputBatch
|
||||
from vllm_ascend.worker.npu_input_batch import NPUInputBatch
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from vllm.v1.core.sched.output import SchedulerOutput
|
||||
@@ -280,7 +280,7 @@ class AscendMLAMetadataBuilder:
|
||||
dtype=torch.uint8,
|
||||
device=device)
|
||||
|
||||
def reorder_batch(self, input_batch: "InputBatch",
|
||||
def reorder_batch(self, input_batch: "NPUInputBatch",
|
||||
scheduler_output: "SchedulerOutput") -> bool:
|
||||
# We now want to reorder the batch so that the "decode" requests are at
|
||||
# the front and the "prefill" requests are at the using the least amount
|
||||
|
||||
@@ -32,7 +32,7 @@ from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
|
||||
from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
|
||||
_round_up, dispose_layer, enable_sp,
|
||||
is_enable_nz, replace_layer)
|
||||
from vllm_ascend.worker.npu_input_batch import InputBatch
|
||||
from vllm_ascend.worker.npu_input_batch import NPUInputBatch
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from vllm.v1.core.sched.output import SchedulerOutput
|
||||
@@ -149,7 +149,7 @@ class AscendSFAMetadataBuilder:
|
||||
self.enable_sfa_cp = enable_sp() and \
|
||||
hasattr(self.model_config.hf_config, "index_topk")
|
||||
|
||||
def reorder_batch(self, input_batch: "InputBatch",
|
||||
def reorder_batch(self, input_batch: "NPUInputBatch",
|
||||
scheduler_output: "SchedulerOutput") -> bool:
|
||||
# No need to reorder for Ascend SFA
|
||||
return False
|
||||
|
||||
@@ -24,7 +24,7 @@ _MOE_LOAD_ASYNC_STREAM = None
|
||||
|
||||
|
||||
def get_expert_map(self, layer_id):
|
||||
return self.model.layers[layer_id].mlp.experts.get_map()
|
||||
return self.model.layers[layer_id].mlp.experts.expert_map
|
||||
|
||||
|
||||
def get_log2phy_map(self, layer_id):
|
||||
|
||||
@@ -153,7 +153,7 @@ class AscendFusedMoE(FusedMoE):
|
||||
AscendFusedMoE.moe_counter += 1
|
||||
self.moe_instance_id = AscendFusedMoE.moe_counter
|
||||
|
||||
self.expert_map = None
|
||||
self._expert_map = None
|
||||
self.log2phy = None
|
||||
|
||||
if self.quant_config is None:
|
||||
@@ -184,7 +184,7 @@ class AscendFusedMoE(FusedMoE):
|
||||
dtype=vllm_config.model_config.dtype)
|
||||
|
||||
# init moe.
|
||||
self.local_num_experts, self.expert_map, _ = determine_expert_map(
|
||||
self.local_num_experts, self._expert_map, _ = determine_expert_map(
|
||||
self.ep_size, self.ep_rank, self.global_num_experts)
|
||||
# TODO: Temporary flag to indicate if static EPLB is enabled. This is a
|
||||
# workaround to bypass a quantization check that fails with float weights.
|
||||
@@ -200,7 +200,7 @@ class AscendFusedMoE(FusedMoE):
|
||||
self.expert_load_balancer.get_global_redundant_expert_num())
|
||||
self.global_num_experts = num_experts + self.global_redundant_expert_num
|
||||
try:
|
||||
self.local_num_experts, self.expert_map = (
|
||||
self.local_num_experts, self._expert_map = (
|
||||
self.expert_load_balancer.get_rank_placement_map(
|
||||
self.moe_instance_id, self.ep_rank))
|
||||
self.log2phy = self.expert_load_balancer.get_rank_log2phy_map(
|
||||
@@ -216,16 +216,16 @@ class AscendFusedMoE(FusedMoE):
|
||||
if self.dynamic_eplb:
|
||||
self.log2phy = determine_default_log2phy_map(
|
||||
self.global_num_experts, self.ep_size, self.ep_rank).npu()
|
||||
if self.expert_map is not None and isinstance(self.expert_map,
|
||||
torch.Tensor):
|
||||
if self._expert_map is not None and isinstance(self._expert_map,
|
||||
torch.Tensor):
|
||||
logger.info_once(
|
||||
"[EP Rank %s/%s] Expert parallelism is enabled. Local/global"
|
||||
" number of experts: %s/%s. Experts local to global index map:"
|
||||
" %s.", self.ep_rank, self.ep_size, self.local_num_experts,
|
||||
self.global_num_experts,
|
||||
get_compressed_expert_map(self.expert_map))
|
||||
get_compressed_expert_map(self._expert_map))
|
||||
local_num_experts = (torch.sum(
|
||||
self.expert_map != -1) if self.expert_map is not None else
|
||||
self._expert_map != -1) if self._expert_map is not None else
|
||||
self.global_num_experts)
|
||||
if self.dynamic_eplb:
|
||||
self.moe_load = torch.zeros(local_num_experts,
|
||||
@@ -276,10 +276,16 @@ class AscendFusedMoE(FusedMoE):
|
||||
return QuantType.NONE
|
||||
|
||||
def update_expert_map(self, new_expert_map):
|
||||
self.expert_map = new_expert_map
|
||||
self._expert_map = new_expert_map
|
||||
|
||||
def get_map(self):
|
||||
return self.expert_map
|
||||
@property
|
||||
def expert_map(self) -> torch.Tensor | None:
|
||||
return self._expert_map
|
||||
|
||||
@expert_map.setter
|
||||
def expert_map(self, new_expert_map):
|
||||
# TODO(Potabk): Remove this once we drop vllm v0.12.0(This makes backward compatibility with vllm v0.12.0)
|
||||
self._expert_map = new_expert_map
|
||||
|
||||
def get_log2phy_map(self):
|
||||
return self.log2phy
|
||||
|
||||
@@ -17,10 +17,15 @@
|
||||
import os
|
||||
|
||||
import vllm_ascend.patch.platform.patch_distributed # noqa
|
||||
import vllm_ascend.patch.platform.patch_ec_connector # noqa
|
||||
import vllm_ascend.patch.platform.patch_mamba_config # noqa
|
||||
import vllm_ascend.patch.platform.patch_sched_yield # noqa
|
||||
from vllm_ascend.utils import vllm_version_is
|
||||
|
||||
if os.getenv("DYNAMIC_EPLB", "false").lower() in ("true", "1") or os.getenv(
|
||||
"EXPERT_MAP_RECORD", "false") == "true":
|
||||
import vllm_ascend.patch.platform.patch_multiproc_executor # noqa
|
||||
|
||||
if vllm_version_is("0.12.0"):
|
||||
import vllm_ascend.patch.platform.patch_ec_connector012 # noqa
|
||||
else:
|
||||
import vllm_ascend.patch.platform.patch_ec_connector # noqa
|
||||
|
||||
@@ -1,16 +1,15 @@
|
||||
import vllm.distributed.ec_transfer.ec_connector.shared_storage_connector
|
||||
import vllm.distributed.ec_transfer.ec_connector.example_connector
|
||||
from safetensors.torch import load_file
|
||||
from vllm.distributed.ec_transfer.ec_connector.base import ECConnectorMetadata
|
||||
from vllm.distributed.ec_transfer.ec_connector.shared_storage_connector import (
|
||||
ECSharedStorageConnector, ECSharedStorageConnectorMetadata)
|
||||
from vllm.distributed.ec_transfer.ec_connector.example_connector import (
|
||||
ECConnectorMetadata, ECExampleConnector)
|
||||
from vllm.logger import logger
|
||||
|
||||
|
||||
class AscendECSharedStorageConnector(ECSharedStorageConnector):
|
||||
class AscendECExampleConnector(ECExampleConnector):
|
||||
|
||||
def start_load_caches(self, encoder_cache, **kwargs) -> None:
|
||||
metadata: ECConnectorMetadata = self._get_connector_metadata()
|
||||
assert isinstance(metadata, ECSharedStorageConnectorMetadata)
|
||||
assert isinstance(metadata, ECConnectorMetadata)
|
||||
assert encoder_cache is not None
|
||||
if metadata is None:
|
||||
logger.warning((
|
||||
@@ -29,4 +28,4 @@ class AscendECSharedStorageConnector(ECSharedStorageConnector):
|
||||
mm_data.mm_hash)
|
||||
|
||||
|
||||
vllm.distributed.ec_transfer.ec_connector.shared_storage_connector.ECSharedStorageConnector = AscendECSharedStorageConnector
|
||||
vllm.distributed.ec_transfer.ec_connector.example_connector.ECExampleConnector = AscendECExampleConnector
|
||||
|
||||
33
vllm_ascend/patch/platform/patch_ec_connector012.py
Normal file
33
vllm_ascend/patch/platform/patch_ec_connector012.py
Normal file
@@ -0,0 +1,33 @@
|
||||
import vllm.distributed.ec_transfer.ec_connector.shared_storage_connector # type: ignore[import-not-found] # noqa
|
||||
from safetensors.torch import load_file
|
||||
from vllm.distributed.ec_transfer.ec_connector.base import \
|
||||
ECConnectorMetadata # type: ignore[import-not-found] # noqa
|
||||
from vllm.distributed.ec_transfer.ec_connector.shared_storage_connector import ( # type: ignore[import-not-found] # noqa
|
||||
ECSharedStorageConnector, ECSharedStorageConnectorMetadata)
|
||||
from vllm.logger import logger
|
||||
|
||||
|
||||
class AscendECSharedStorageConnector(ECSharedStorageConnector):
|
||||
|
||||
def start_load_caches(self, encoder_cache, **kwargs) -> None:
|
||||
metadata: ECConnectorMetadata = self._get_connector_metadata()
|
||||
assert isinstance(metadata, ECSharedStorageConnectorMetadata)
|
||||
assert encoder_cache is not None
|
||||
if metadata is None:
|
||||
logger.warning((
|
||||
"In connector.start_load_caches, ",
|
||||
"but the connector metadata is None",
|
||||
))
|
||||
return
|
||||
# Load the EC for each mm data
|
||||
for mm_data in metadata.mm_datas:
|
||||
if mm_data.mm_hash in encoder_cache:
|
||||
continue
|
||||
filename = self._generate_filename_debug(mm_data.mm_hash)
|
||||
ec_cache = load_file(filename)["ec_cache"].npu()
|
||||
encoder_cache[mm_data.mm_hash] = ec_cache
|
||||
logger.debug("Success load encoder cache for hash %s",
|
||||
mm_data.mm_hash)
|
||||
|
||||
|
||||
vllm.distributed.ec_transfer.ec_connector.shared_storage_connector.ECSharedStorageConnector = AscendECSharedStorageConnector
|
||||
@@ -365,6 +365,10 @@ class NPUPlatform(Platform):
|
||||
use_mla,
|
||||
has_sink=False,
|
||||
use_sparse=False,
|
||||
# NOTE: Please pay special attention to the order of these parameters.
|
||||
# Although we are only using some of them so far
|
||||
# vllm passes them in sequence when using this interface.
|
||||
use_mm_prefix: bool = False,
|
||||
attn_type: str | None = None,
|
||||
):
|
||||
# choose attention backend based on use_mla
|
||||
|
||||
@@ -476,9 +476,10 @@ def update_aclgraph_sizes(vllm_config: VllmConfig) -> None:
|
||||
|
||||
# Calculate maximum supported batch sizes considering model architecture
|
||||
resources_per_graph = num_hidden_layers + 1
|
||||
if vllm_config.speculative_config is not None:
|
||||
draft_model_hf_config = vllm_config.speculative_config.draft_model_config.hf_config
|
||||
resources_per_graph += draft_model_hf_config.num_hidden_layers + 1
|
||||
# For suffix decoding, use the suffix path when no draft_model_config is provided.
|
||||
if (spec := vllm_config.speculative_config) and \
|
||||
(draft := spec.draft_model_config):
|
||||
resources_per_graph += draft.hf_config.num_hidden_layers + 1
|
||||
|
||||
# TODO: Find out whether we need to take into account the pp_size
|
||||
num_comm_groups = sum(size > 1 for size in [
|
||||
|
||||
@@ -121,8 +121,8 @@ from vllm_ascend.spec_decode.mtp_proposer import MtpProposer
|
||||
from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
|
||||
AscendDeviceType, ProfileExecuteDuration,
|
||||
enable_sp, get_ascend_device_type, is_enable_nz,
|
||||
is_moe_model, lmhead_tp_enable)
|
||||
from vllm_ascend.worker.npu_input_batch import InputBatch
|
||||
is_moe_model, lmhead_tp_enable, vllm_version_is)
|
||||
from vllm_ascend.worker.npu_input_batch import NPUInputBatch
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import xgrammar as xgr # type: ignore[import-untyped]
|
||||
@@ -249,13 +249,24 @@ class NPUModelRunner(GPUModelRunner):
|
||||
# Set up Attention
|
||||
self.use_sparse = hasattr(self.vllm_config.model_config.hf_config,
|
||||
"index_topk")
|
||||
self.attn_backend = get_attn_backend(0,
|
||||
self.dtype,
|
||||
None,
|
||||
self.block_size,
|
||||
use_mla=self.model_config.use_mla,
|
||||
use_sparse=self.use_sparse)
|
||||
|
||||
if vllm_version_is('0.12.0'):
|
||||
self.attn_backend = get_attn_backend(
|
||||
0,
|
||||
self.dtype,
|
||||
None,
|
||||
self.block_size,
|
||||
use_mla=self.model_config.use_mla,
|
||||
use_sparse=self.use_sparse)
|
||||
else:
|
||||
self.attn_backend = get_attn_backend(
|
||||
0,
|
||||
self.dtype,
|
||||
None,
|
||||
self.block_size,
|
||||
use_mla=self.model_config.use_mla,
|
||||
use_sparse=self.use_sparse,
|
||||
use_mm_prefix=self.model_config is not None
|
||||
and self.model_config.is_mm_prefix_lm)
|
||||
self.attn_mask_builder = AttentionMaskBuilder(self.device)
|
||||
|
||||
self._set_up_drafter()
|
||||
@@ -353,7 +364,7 @@ class NPUModelRunner(GPUModelRunner):
|
||||
# solution, we initialize the input batch here, and re-initialize it
|
||||
# in `initialize_kv_cache` if the block_sizes here is different from
|
||||
# the block_sizes in the kv cache config.
|
||||
self.input_batch = InputBatch(
|
||||
self.input_batch = NPUInputBatch(
|
||||
max_num_reqs=self.max_num_reqs,
|
||||
max_model_len=self.model_config.max_model_len,
|
||||
max_num_batched_tokens=self.max_num_tokens,
|
||||
@@ -2000,19 +2011,36 @@ class NPUModelRunner(GPUModelRunner):
|
||||
self.speculative_config.method == "mtp":
|
||||
attn_state = AscendAttentionState.SpecDecoding
|
||||
|
||||
common_metadata = CommonAttentionMetadata(
|
||||
query_start_loc=self.query_start_loc.gpu[:num_reqs + 1],
|
||||
query_start_loc_cpu=self.query_start_loc.cpu[:num_reqs +
|
||||
if vllm_version_is("0.12.0"):
|
||||
common_metadata = CommonAttentionMetadata(
|
||||
query_start_loc=self.query_start_loc.gpu[:num_reqs +
|
||||
1],
|
||||
seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
|
||||
seq_lens=self.seq_lens.cpu[:num_reqs],
|
||||
num_reqs=num_reqs,
|
||||
num_actual_tokens=num_tokens,
|
||||
block_table_tensor=block_table_tensor[:num_reqs],
|
||||
slot_mapping=slot_mapping.gpu,
|
||||
num_computed_tokens_cpu=num_computed_tokens_cpu,
|
||||
max_query_len=max_query_len,
|
||||
max_seq_len=seq_lens)
|
||||
query_start_loc_cpu=self.query_start_loc.
|
||||
cpu[:num_reqs + 1],
|
||||
seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
|
||||
seq_lens=self.seq_lens.cpu[:num_reqs],
|
||||
num_reqs=num_reqs,
|
||||
num_actual_tokens=num_tokens,
|
||||
block_table_tensor=block_table_tensor[:num_reqs],
|
||||
slot_mapping=slot_mapping.gpu,
|
||||
num_computed_tokens_cpu=num_computed_tokens_cpu,
|
||||
max_query_len=max_query_len,
|
||||
max_seq_len=seq_lens)
|
||||
else:
|
||||
common_metadata = CommonAttentionMetadata(
|
||||
query_start_loc=self.query_start_loc.gpu[:num_reqs +
|
||||
1],
|
||||
query_start_loc_cpu=self.query_start_loc.
|
||||
cpu[:num_reqs + 1],
|
||||
_seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
|
||||
seq_lens=self.seq_lens.cpu[:num_reqs],
|
||||
num_reqs=num_reqs,
|
||||
num_actual_tokens=num_tokens,
|
||||
block_table_tensor=block_table_tensor[:num_reqs],
|
||||
slot_mapping=slot_mapping.gpu,
|
||||
_num_computed_tokens_cpu=num_computed_tokens_cpu,
|
||||
max_query_len=max_query_len,
|
||||
max_seq_len=seq_lens)
|
||||
|
||||
for attn_group in self.attn_groups[kv_cache_group_id]:
|
||||
builder = attn_group.get_metadata_builder()
|
||||
@@ -2778,7 +2806,7 @@ class NPUModelRunner(GPUModelRunner):
|
||||
"Cannot re-initialize the input batch when CPU weight "
|
||||
"offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 " # noqa: E501
|
||||
"for more details.")
|
||||
self.input_batch = InputBatch(
|
||||
self.input_batch = NPUInputBatch(
|
||||
max_num_reqs=self.max_num_reqs,
|
||||
max_model_len=self.model_config.max_model_len,
|
||||
max_num_batched_tokens=self.max_num_tokens,
|
||||
|
||||
@@ -17,92 +17,29 @@
|
||||
# Adapted from vllm-project/vllm/vllm/worker/gpu_input_batch.py
|
||||
#
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional, cast
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from typing_extensions import deprecated
|
||||
from vllm.lora.request import LoRARequest
|
||||
from vllm.multimodal.inputs import (MultiModalFeatureSpec,
|
||||
MultiModalKwargsItem,
|
||||
MultiModalKwargsItems, PlaceholderRange)
|
||||
from vllm.pooling_params import PoolingParams
|
||||
from vllm.sampling_params import SamplingParams, SamplingType
|
||||
from vllm.utils import length_from_prompt_token_ids_or_embeds
|
||||
from vllm.utils.collection_utils import swap_dict_values
|
||||
from vllm.v1.outputs import LogprobsTensors
|
||||
from vllm.v1.pool.metadata import PoolingMetadata
|
||||
from vllm.v1.sample.logits_processor import (BatchUpdateBuilder,
|
||||
LogitsProcessors,
|
||||
MoveDirectionality)
|
||||
from vllm.v1.sample.metadata import SamplingMetadata
|
||||
from vllm.v1.spec_decode.utils import is_spec_decode_unsupported
|
||||
from vllm.v1.utils import copy_slice
|
||||
LogitsProcessors)
|
||||
from vllm.v1.worker.gpu_input_batch import InputBatch
|
||||
|
||||
from vllm_ascend.worker.block_table import MultiGroupBlockTable
|
||||
|
||||
|
||||
@dataclass
|
||||
class CachedRequestState:
|
||||
class PoolingStates:
|
||||
# NOTE: This should be removed after we drop support of vLLM v0.12.0
|
||||
def __init__(self):
|
||||
# for chunked prefill with ALL pooling
|
||||
self.hidden_states_cache: list[torch.Tensor] = []
|
||||
|
||||
req_id: str
|
||||
prompt_token_ids: Optional[list[int]]
|
||||
sampling_params: Optional[SamplingParams]
|
||||
pooling_params: Optional[PoolingParams]
|
||||
generator: Optional[torch.Generator]
|
||||
|
||||
block_ids: tuple[list[int], ...]
|
||||
num_computed_tokens: int
|
||||
output_token_ids: list[int]
|
||||
|
||||
mrope_positions: Optional[torch.Tensor] = None
|
||||
mrope_position_delta: Optional[int] = None
|
||||
|
||||
mm_features: Optional[list[MultiModalFeatureSpec]] = None
|
||||
# for back-compatibility, will be removed in next major release
|
||||
mm_kwargs: Optional[list[MultiModalKwargsItem]] = None
|
||||
mm_positions: Optional[list[PlaceholderRange]] = None
|
||||
mm_hashes: Optional[list[PlaceholderRange]] = None
|
||||
|
||||
lora_request: Optional[LoRARequest] = None
|
||||
prompt_embeds: Optional[torch.Tensor] = None
|
||||
|
||||
prev_num_draft_len: int = 0 # previous number of draft tokens
|
||||
|
||||
def __post_init__(self):
|
||||
self.num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
|
||||
self.prompt_token_ids, self.prompt_embeds)
|
||||
|
||||
@property
|
||||
def num_tokens(self) -> int:
|
||||
return self.num_prompt_tokens + len(self.output_token_ids)
|
||||
|
||||
# Temporary back-compatibility for plugins that define model runner
|
||||
@property
|
||||
@deprecated("`mm_inputs` is superseded by `mm_kwargs` and will be "
|
||||
"removed in v0.13. Please use `mm_kwargs` instead.")
|
||||
def mm_inputs(self) -> list[MultiModalKwargsItems]:
|
||||
assert self.mm_features is not None
|
||||
return [
|
||||
MultiModalKwargsItems.from_seq([f.data]) for f in self.mm_features
|
||||
if f.data is not None
|
||||
]
|
||||
|
||||
def get_token_id(self, idx: int) -> int:
|
||||
if idx < self.num_prompt_tokens:
|
||||
if self.prompt_token_ids is None:
|
||||
raise ValueError(
|
||||
f"Tried to access token index {idx}, but that token was "
|
||||
"provided via prompt_embeds, and its ID is unknown.")
|
||||
return self.prompt_token_ids[idx]
|
||||
elif idx - self.num_prompt_tokens < len(self.output_token_ids):
|
||||
return self.output_token_ids[idx - self.num_prompt_tokens]
|
||||
else:
|
||||
return -1
|
||||
def clean(self):
|
||||
self.hidden_states_cache.clear()
|
||||
|
||||
|
||||
class InputBatch:
|
||||
class NPUInputBatch(InputBatch):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
@@ -113,12 +50,12 @@ class InputBatch:
|
||||
pin_memory: bool,
|
||||
vocab_size: int,
|
||||
block_sizes: list[int], # The block_size of each kv cache group
|
||||
logitsprocs: Optional[LogitsProcessors] = None,
|
||||
kernel_block_sizes: list[list[int]],
|
||||
logitsprocs: LogitsProcessors | None = None,
|
||||
logitsprocs_need_output_token_ids: bool = False,
|
||||
is_spec_decode: bool = False,
|
||||
is_pooling_model: bool = False,
|
||||
num_speculative_tokens: int = 0,
|
||||
kernel_block_sizes: Optional[list[list[int]]] = None,
|
||||
cp_kv_cache_interleave_size: int = 1,
|
||||
):
|
||||
self.is_pooling_model = is_pooling_model
|
||||
@@ -130,12 +67,12 @@ class InputBatch:
|
||||
self.pin_memory = pin_memory
|
||||
self.vocab_size = vocab_size
|
||||
|
||||
self._req_ids: list[Optional[str]] = []
|
||||
self._req_ids: list[str | None] = []
|
||||
self.req_id_to_index: dict[str, int] = {}
|
||||
|
||||
# TODO(woosuk): This buffer could be too large if max_model_len is big.
|
||||
# Find a way to reduce the CPU memory usage.
|
||||
# This buffer is not directly transferred to the NPU, so it does not
|
||||
# This buffer is not directly transferred to the GPU, so it does not
|
||||
# need to be pinned.
|
||||
self.token_ids_cpu_tensor = torch.zeros(
|
||||
(max_num_reqs, max_model_len),
|
||||
@@ -162,8 +99,8 @@ class InputBatch:
|
||||
dtype=torch.int32,
|
||||
pin_memory=pin_memory,
|
||||
)
|
||||
self.num_computed_tokens_cpu = \
|
||||
self.num_computed_tokens_cpu_tensor.numpy()
|
||||
self.num_computed_tokens_cpu = self.num_computed_tokens_cpu_tensor.numpy(
|
||||
)
|
||||
|
||||
# Block table.
|
||||
self.block_table = MultiGroupBlockTable(
|
||||
@@ -222,8 +159,8 @@ class InputBatch:
|
||||
dtype=torch.float,
|
||||
device="cpu",
|
||||
pin_memory=pin_memory)
|
||||
self.frequency_penalties_cpu = \
|
||||
self.frequency_penalties_cpu_tensor.numpy()
|
||||
self.frequency_penalties_cpu = self.frequency_penalties_cpu_tensor.numpy(
|
||||
)
|
||||
self.frequency_penalties_reqs: set[str] = set()
|
||||
|
||||
# Presence penalty related data structures
|
||||
@@ -247,8 +184,8 @@ class InputBatch:
|
||||
dtype=torch.float,
|
||||
device="cpu",
|
||||
pin_memory=pin_memory)
|
||||
self.repetition_penalties_cpu = \
|
||||
self.repetition_penalties_cpu_tensor.numpy()
|
||||
self.repetition_penalties_cpu = self.repetition_penalties_cpu_tensor.numpy(
|
||||
)
|
||||
self.repetition_penalties_reqs: set[str] = set()
|
||||
|
||||
# Speculative decoding
|
||||
@@ -256,12 +193,12 @@ class InputBatch:
|
||||
dtype=torch.int64,
|
||||
device="cpu",
|
||||
pin_memory=pin_memory)
|
||||
self.num_accepted_tokens_cpu = \
|
||||
self.num_accepted_tokens_cpu_tensor.numpy()
|
||||
self.num_accepted_tokens_cpu = self.num_accepted_tokens_cpu_tensor.numpy(
|
||||
)
|
||||
|
||||
# lora related
|
||||
self.request_lora_mapping = np.zeros((self.max_num_reqs, ),
|
||||
dtype=np.int32)
|
||||
dtype=np.int64)
|
||||
self.lora_id_to_request_ids: dict[int, set[str]] = {}
|
||||
self.lora_id_to_lora_request: dict[int, LoRARequest] = {}
|
||||
|
||||
@@ -271,9 +208,6 @@ class InputBatch:
|
||||
self.generators: dict[int, torch.Generator] = {}
|
||||
|
||||
self.num_logprobs: dict[str, int] = {}
|
||||
# NOTE(rob): num_prompt_logprobs only includes reqs
|
||||
# that are currently in the prefill phase.
|
||||
self.num_prompt_logprobs: dict[str, int] = {}
|
||||
|
||||
# To accumulate prompt logprobs tensor chunks across prefill steps.
|
||||
self.in_progress_prompt_logprobs_cpu: dict[str, LogprobsTensors] = {}
|
||||
@@ -287,8 +221,8 @@ class InputBatch:
|
||||
self.has_allowed_token_ids: set[str] = set()
|
||||
# NOTE(lufang): In the mask tensor, if the corresponding token allowed,
|
||||
# the value is False. Since we use masked_fill_ to set -inf.
|
||||
self.allowed_token_ids_mask: Optional[torch.Tensor] = None
|
||||
self.allowed_token_ids_mask_cpu_tensor: Optional[torch.Tensor] = None
|
||||
self.allowed_token_ids_mask: torch.Tensor | None = None
|
||||
self.allowed_token_ids_mask_cpu_tensor: torch.Tensor | None = None
|
||||
|
||||
# req_index -> bad_words_token_ids
|
||||
self.bad_words_token_ids: dict[int, list[list[int]]] = {}
|
||||
@@ -296,7 +230,7 @@ class InputBatch:
|
||||
self.logits_processing_needs_token_ids = np.zeros(max_num_reqs,
|
||||
dtype=bool)
|
||||
|
||||
self.req_output_token_ids: list[Optional[list[int]]] = []
|
||||
self.req_output_token_ids: list[list[int] | None] = []
|
||||
|
||||
# Store provided logitsprocs. If none are provided, initialize empty
|
||||
# data structure
|
||||
@@ -310,673 +244,15 @@ class InputBatch:
|
||||
# This is updated each time the batch constituents change.
|
||||
self.sampling_metadata = self._make_sampling_metadata()
|
||||
|
||||
# for pooling models
|
||||
self.pooling_params: dict[str, PoolingParams] = {}
|
||||
self.pooling_states: dict[str, PoolingStates] = {}
|
||||
|
||||
# Cached reference to the GPU tensor of previously sampled tokens
|
||||
self.prev_sampled_token_ids: torch.Tensor | None = None
|
||||
self.prev_sampled_token_ids_invalid_indices: Optional[set[int]] = None
|
||||
self.prev_req_id_to_index: dict[str, int] | None = None
|
||||
# These are used to update output_token_ids with real sampled
|
||||
# ids from prior step, if required by current sampling params
|
||||
# (e.g. penalties).
|
||||
self.sampled_token_ids_cpu: torch.Tensor | None = None
|
||||
self.async_copy_ready_event: torch.Event | None = None
|
||||
|
||||
@property
|
||||
def req_ids(self) -> list[str]:
|
||||
# None elements should only be present transiently
|
||||
# while performing state updates to the batch.
|
||||
return cast(list[str], self._req_ids)
|
||||
|
||||
def _register_add_request(self, request: "CachedRequestState") -> int:
|
||||
"""Track add-request operations for logits processors.
|
||||
Not applicable to pooling models.
|
||||
"""
|
||||
|
||||
# Detailed added request metadata is only required for non-pooling
|
||||
# models, to support logitsprocs
|
||||
assert request.sampling_params
|
||||
|
||||
# Fill the next empty index if there is one.
|
||||
if (new_req_index := self.batch_update_builder.pop_removed()) is None:
|
||||
# Append to end otherwise.
|
||||
new_req_index = self.num_reqs
|
||||
|
||||
assert new_req_index < self.max_num_reqs
|
||||
self.batch_update_builder.added.append(
|
||||
(new_req_index, request.sampling_params, request.prompt_token_ids,
|
||||
request.output_token_ids))
|
||||
return new_req_index
|
||||
|
||||
def add_request(
|
||||
self,
|
||||
request: "CachedRequestState",
|
||||
) -> int:
|
||||
if not self.is_pooling_model:
|
||||
# New request index bookkeeping for autoregressive models.
|
||||
req_index = self._register_add_request(request)
|
||||
else:
|
||||
req_index = self.num_reqs
|
||||
|
||||
req_id = request.req_id
|
||||
if req_index == len(self._req_ids):
|
||||
self._req_ids.append(req_id)
|
||||
self.req_output_token_ids.append(request.output_token_ids)
|
||||
self.spec_token_ids.append([])
|
||||
else:
|
||||
self._req_ids[req_index] = req_id
|
||||
self.req_output_token_ids[req_index] = request.output_token_ids
|
||||
self.spec_token_ids[req_index].clear()
|
||||
|
||||
self.req_id_to_index[req_id] = req_index
|
||||
|
||||
# Copy the prompt token ids and output token ids.
|
||||
num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
|
||||
request.prompt_token_ids, request.prompt_embeds)
|
||||
self.num_prompt_tokens[req_index] = num_prompt_tokens
|
||||
start_idx = num_prompt_tokens
|
||||
end_idx = start_idx + len(request.output_token_ids)
|
||||
if request.prompt_token_ids is not None:
|
||||
self.token_ids_cpu[
|
||||
req_index, :num_prompt_tokens] = request.prompt_token_ids
|
||||
self.is_token_ids[req_index, :num_prompt_tokens] = True
|
||||
else:
|
||||
self.is_token_ids[req_index, :num_prompt_tokens] = False
|
||||
if request.prompt_embeds is not None:
|
||||
self.req_prompt_embeds[req_index] = request.prompt_embeds
|
||||
self.token_ids_cpu[req_index,
|
||||
start_idx:end_idx] = request.output_token_ids
|
||||
self.is_token_ids[req_index, start_idx:end_idx] = True
|
||||
# Number of token ids in prompt (token_ids_cpu or prompt_embeds).
|
||||
# NOTE(woosuk): This may include spec decode tokens.
|
||||
self.num_tokens[req_index] = request.num_tokens
|
||||
# Number of tokens without spec decode tokens.
|
||||
self.num_tokens_no_spec[req_index] = request.num_tokens
|
||||
|
||||
self.num_computed_tokens_cpu[req_index] = request.num_computed_tokens
|
||||
self.block_table.add_row(request.block_ids, req_index)
|
||||
|
||||
if sampling_params := request.sampling_params:
|
||||
if (self.is_spec_decode
|
||||
and is_spec_decode_unsupported(sampling_params)):
|
||||
self.spec_decode_unsupported_reqs.add(req_id)
|
||||
if sampling_params.sampling_type == SamplingType.GREEDY:
|
||||
# Avoid later division by zero.
|
||||
self.temperature_cpu[req_index] = -1.0
|
||||
self.greedy_reqs.add(req_id)
|
||||
else:
|
||||
self.temperature_cpu[req_index] = sampling_params.temperature
|
||||
self.random_reqs.add(req_id)
|
||||
|
||||
self.top_p_cpu[req_index] = sampling_params.top_p
|
||||
if sampling_params.top_p < 1:
|
||||
self.top_p_reqs.add(req_id)
|
||||
top_k = sampling_params.top_k
|
||||
if 0 < top_k < self.vocab_size:
|
||||
self.top_k_reqs.add(req_id)
|
||||
else:
|
||||
top_k = self.vocab_size
|
||||
self.top_k_cpu[req_index] = top_k
|
||||
self.frequency_penalties_cpu[
|
||||
req_index] = sampling_params.frequency_penalty
|
||||
if sampling_params.frequency_penalty != 0.0:
|
||||
self.frequency_penalties_reqs.add(req_id)
|
||||
self.presence_penalties_cpu[
|
||||
req_index] = sampling_params.presence_penalty
|
||||
if sampling_params.presence_penalty != 0.0:
|
||||
self.presence_penalties_reqs.add(req_id)
|
||||
self.repetition_penalties_cpu[
|
||||
req_index] = sampling_params.repetition_penalty
|
||||
if sampling_params.repetition_penalty != 1.0:
|
||||
self.repetition_penalties_reqs.add(req_id)
|
||||
|
||||
# NOTE(woosuk): self.generators should not include the requests that
|
||||
# do not have their own generator.
|
||||
if request.generator is not None:
|
||||
self.generators[req_index] = request.generator
|
||||
|
||||
if sampling_params.logprobs is not None:
|
||||
self.num_logprobs[req_id] = (self.vocab_size
|
||||
if sampling_params.logprobs == -1
|
||||
else sampling_params.logprobs)
|
||||
if sampling_params.prompt_logprobs is not None:
|
||||
self.num_prompt_logprobs[
|
||||
req_id] = sampling_params.prompt_logprobs
|
||||
|
||||
if sampling_params.allowed_token_ids:
|
||||
self.has_allowed_token_ids.add(req_id)
|
||||
if self.allowed_token_ids_mask_cpu_tensor is None:
|
||||
# Lazy allocation for this tensor, which can be large.
|
||||
# False means we don't fill with -inf.
|
||||
self.allowed_token_ids_mask = torch.zeros(
|
||||
self.max_num_reqs,
|
||||
self.vocab_size,
|
||||
dtype=torch.bool,
|
||||
device=self.device)
|
||||
self.allowed_token_ids_mask_cpu_tensor = torch.zeros(
|
||||
self.max_num_reqs,
|
||||
self.vocab_size,
|
||||
dtype=torch.bool,
|
||||
device="cpu")
|
||||
self.allowed_token_ids_mask_cpu_tensor[req_index] = True
|
||||
# False means we don't fill with -inf.
|
||||
self.allowed_token_ids_mask_cpu_tensor[req_index][
|
||||
sampling_params.allowed_token_ids] = False
|
||||
|
||||
if sampling_params.bad_words_token_ids:
|
||||
self.bad_words_token_ids[
|
||||
req_index] = sampling_params.bad_words_token_ids
|
||||
elif pooling_params := request.pooling_params:
|
||||
self.pooling_params[req_id] = pooling_params
|
||||
self.logits_processing_needs_token_ids[req_index] = (
|
||||
pooling_params.requires_token_ids)
|
||||
else:
|
||||
raise NotImplementedError(request)
|
||||
|
||||
# Speculative decoding: by default 1 token is generated.
|
||||
self.num_accepted_tokens_cpu[req_index] = 1
|
||||
|
||||
# Add request lora ID
|
||||
if request.lora_request:
|
||||
lora_id = request.lora_request.lora_int_id
|
||||
if lora_id not in self.lora_id_to_request_ids:
|
||||
self.lora_id_to_request_ids[lora_id] = set()
|
||||
|
||||
self.request_lora_mapping[req_index] = lora_id
|
||||
self.lora_id_to_request_ids[lora_id].add(request.req_id)
|
||||
self.lora_id_to_lora_request[lora_id] = request.lora_request
|
||||
else:
|
||||
# No LoRA
|
||||
self.request_lora_mapping[req_index] = 0
|
||||
|
||||
return req_index
|
||||
|
||||
def remove_request(self, req_id: str) -> Optional[int]:
|
||||
"""This method must always be followed by a call to condense().
|
||||
|
||||
Args:
|
||||
req_id: request to remove
|
||||
|
||||
Returns:
|
||||
Removed request index, or `None` if `req_id` not recognized
|
||||
"""
|
||||
|
||||
req_index = self.req_id_to_index.pop(req_id, None)
|
||||
if req_index is None:
|
||||
return None
|
||||
if not self.is_pooling_model:
|
||||
# Autoregressive models require bookkeeping of removed requests to
|
||||
# support logitsprocs.
|
||||
self.batch_update_builder.removed_append(req_index)
|
||||
self._req_ids[req_index] = None
|
||||
self.req_output_token_ids[req_index] = None
|
||||
self.spec_token_ids[req_index].clear()
|
||||
|
||||
# LoRA
|
||||
lora_id = self.request_lora_mapping[req_index]
|
||||
if lora_id != 0:
|
||||
lora_req_ids = self.lora_id_to_request_ids[lora_id]
|
||||
lora_req_ids.discard(req_id)
|
||||
if not lora_req_ids:
|
||||
del self.lora_id_to_request_ids[lora_id]
|
||||
del self.lora_id_to_lora_request[lora_id]
|
||||
self.request_lora_mapping[req_index] = 0
|
||||
|
||||
if self.is_pooling_model:
|
||||
self.pooling_params.pop(req_id, None)
|
||||
return req_index
|
||||
|
||||
self.greedy_reqs.discard(req_id)
|
||||
self.random_reqs.discard(req_id)
|
||||
self.top_p_reqs.discard(req_id)
|
||||
self.top_k_reqs.discard(req_id)
|
||||
self.spec_decode_unsupported_reqs.discard(req_id)
|
||||
self.frequency_penalties_reqs.discard(req_id)
|
||||
self.presence_penalties_reqs.discard(req_id)
|
||||
self.repetition_penalties_reqs.discard(req_id)
|
||||
self.generators.pop(req_index, None)
|
||||
self.num_logprobs.pop(req_id, None)
|
||||
self.num_prompt_logprobs.pop(req_id, None)
|
||||
self.in_progress_prompt_logprobs_cpu.pop(req_id, None)
|
||||
|
||||
if self.prev_req_id_to_index is not None:
|
||||
self.prev_req_id_to_index.pop(req_id, None)
|
||||
# LoRA
|
||||
lora_id = self.request_lora_mapping[req_index]
|
||||
if lora_id != 0:
|
||||
self.lora_id_to_request_ids[lora_id].discard(req_id)
|
||||
if len(self.lora_id_to_request_ids[lora_id]) == 0:
|
||||
self.lora_id_to_request_ids.pop(lora_id)
|
||||
self.lora_id_to_lora_request.pop(lora_id)
|
||||
self.request_lora_mapping[req_index] = 0
|
||||
|
||||
self.has_allowed_token_ids.discard(req_id)
|
||||
if self.allowed_token_ids_mask_cpu_tensor is not None:
|
||||
# False means we don't fill with -inf.
|
||||
self.allowed_token_ids_mask_cpu_tensor[req_index].fill_(False)
|
||||
self.bad_words_token_ids.pop(req_index, None)
|
||||
self.pooling_params.pop(req_id, None)
|
||||
return req_index
|
||||
|
||||
def swap_states(self, i1: int, i2: int) -> None:
|
||||
# For autoregressive models, track detailed request reordering info
|
||||
# to support logitsprocs
|
||||
self.batch_update_builder.moved.append(
|
||||
(i1, i2, MoveDirectionality.SWAP))
|
||||
old_id_i1 = self._req_ids[i1]
|
||||
old_id_i2 = self._req_ids[i2]
|
||||
self._req_ids[i1], self._req_ids[i2] =\
|
||||
self._req_ids[i2], self._req_ids[i1] # noqa
|
||||
self.req_output_token_ids[i1], self.req_output_token_ids[i2] =\
|
||||
self.req_output_token_ids[i2], self.req_output_token_ids[i1]
|
||||
self.spec_token_ids[i1], self.spec_token_ids[i2] = (
|
||||
self.spec_token_ids[i2],
|
||||
self.spec_token_ids[i1],
|
||||
)
|
||||
assert old_id_i1 is not None and old_id_i2 is not None
|
||||
self.req_id_to_index[old_id_i1], self.req_id_to_index[old_id_i2] =\
|
||||
self.req_id_to_index[old_id_i2], self.req_id_to_index[old_id_i1]
|
||||
self.num_tokens[i1], self.num_tokens[i2] =\
|
||||
self.num_tokens[i2], self.num_tokens[i1]
|
||||
self.num_tokens_no_spec[i1], self.num_tokens_no_spec[i2] =\
|
||||
self.num_tokens_no_spec[i2], self.num_tokens_no_spec[i1]
|
||||
self.num_prompt_tokens[i1], self.num_prompt_tokens[i2] =\
|
||||
self.num_prompt_tokens[i2], self.num_prompt_tokens[i1]
|
||||
self.num_computed_tokens_cpu[i1], self.num_computed_tokens_cpu[i2] =\
|
||||
self.num_computed_tokens_cpu[i2], self.num_computed_tokens_cpu[i1]
|
||||
self.temperature_cpu[i1], self.temperature_cpu[i2] =\
|
||||
self.temperature_cpu[i2], self.temperature_cpu[i1]
|
||||
self.top_p_cpu[i1], self.top_p_cpu[i2] =\
|
||||
self.top_p_cpu[i2], self.top_p_cpu[i1]
|
||||
self.top_k_cpu[i1], self.top_k_cpu[i2] =\
|
||||
self.top_k_cpu[i2], self.top_k_cpu[i1]
|
||||
self.frequency_penalties_cpu[i1], self.frequency_penalties_cpu[i2] =\
|
||||
self.frequency_penalties_cpu[i2], self.frequency_penalties_cpu[i1]
|
||||
self.presence_penalties_cpu[i1], self.presence_penalties_cpu[i2] =\
|
||||
self.presence_penalties_cpu[i2], self.presence_penalties_cpu[i1]
|
||||
self.repetition_penalties_cpu[i1], self.repetition_penalties_cpu[i2] =\
|
||||
self.repetition_penalties_cpu[i2], self.repetition_penalties_cpu[i1]
|
||||
self.num_accepted_tokens_cpu[i1], self.num_accepted_tokens_cpu[i2] =\
|
||||
self.num_accepted_tokens_cpu[i2], self.num_accepted_tokens_cpu[i1]
|
||||
|
||||
# NOTE: the following is unsafe
|
||||
# self.token_ids_cpu[i1, ...], self.token_ids_cpu[i2, ...], =\
|
||||
# self.token_ids_cpu[i2, ...], self.token_ids_cpu[i1, ...]
|
||||
# instead, we need to temporiarily copy the data for one of the indices
|
||||
# TODO(lucas): optimize this by only copying valid indices
|
||||
tmp = self.token_ids_cpu[i1, ...].copy()
|
||||
self.token_ids_cpu[i1, ...] = self.token_ids_cpu[i2, ...]
|
||||
self.token_ids_cpu[i2, ...] = tmp
|
||||
|
||||
self.is_token_ids[[i1, i2], ...] = self.is_token_ids[[i2, i1], ...]
|
||||
|
||||
# Swap prompt embeddings if they exist
|
||||
embeds_i1 = self.req_prompt_embeds.get(i1)
|
||||
embeds_i2 = self.req_prompt_embeds.get(i2)
|
||||
if embeds_i1 is not None:
|
||||
self.req_prompt_embeds[i2] = embeds_i1
|
||||
else:
|
||||
self.req_prompt_embeds.pop(i2, None)
|
||||
if embeds_i2 is not None:
|
||||
self.req_prompt_embeds[i1] = embeds_i2
|
||||
else:
|
||||
self.req_prompt_embeds.pop(i1, None)
|
||||
|
||||
swap_dict_values(self.generators, i1, i2)
|
||||
swap_dict_values(self.bad_words_token_ids, i1, i2)
|
||||
|
||||
self.request_lora_mapping[i1], self.request_lora_mapping[i2] =\
|
||||
self.request_lora_mapping[i2], self.request_lora_mapping[i1]
|
||||
|
||||
if self.allowed_token_ids_mask_cpu_tensor is not None:
|
||||
self.allowed_token_ids_mask_cpu_tensor[i1], \
|
||||
self.allowed_token_ids_mask_cpu_tensor[i2] =\
|
||||
self.allowed_token_ids_mask_cpu_tensor[i2], \
|
||||
self.allowed_token_ids_mask_cpu_tensor[i1]
|
||||
self.block_table.swap_row(i1, i2)
|
||||
|
||||
def condense(self) -> None:
|
||||
"""Slide non-empty requests down into lower, empty indices.
|
||||
|
||||
Any consecutive empty indices at the very end of the list are not
|
||||
filled.
|
||||
|
||||
Args:
|
||||
empty_req_indices: empty indices which may be filled.
|
||||
|
||||
Returns:
|
||||
swaps: list of (from,to) swap tuples for moved requests
|
||||
empty_req_indices: indices not filled by condensation
|
||||
"""
|
||||
num_reqs = self.num_reqs
|
||||
|
||||
if self.is_pooling_model:
|
||||
# Will be contiguous in pooling case, just trim the lists.
|
||||
del self._req_ids[num_reqs:]
|
||||
del self.req_output_token_ids[num_reqs:]
|
||||
return
|
||||
|
||||
if not (empty_req_indices := self.batch_update_builder.removed):
|
||||
# All removed requests were replaced by added requests, or else no
|
||||
# requests were removed at all. No condense() needed
|
||||
return
|
||||
if num_reqs == 0:
|
||||
# The batched states are empty.
|
||||
self._req_ids.clear()
|
||||
self.req_output_token_ids.clear()
|
||||
self.spec_token_ids.clear()
|
||||
return
|
||||
|
||||
# NOTE(woosuk): This function assumes that the empty_req_indices
|
||||
# is sorted in descending order.
|
||||
last_req_index = num_reqs + len(empty_req_indices) - 1
|
||||
while empty_req_indices:
|
||||
# Find the largest non-empty index.
|
||||
while last_req_index in empty_req_indices:
|
||||
last_req_index -= 1
|
||||
|
||||
# Find the smallest empty index.
|
||||
empty_index = self.batch_update_builder.peek_removed()
|
||||
assert empty_index is not None
|
||||
if empty_index >= last_req_index:
|
||||
break
|
||||
|
||||
# Move active request down into empty request
|
||||
# index.
|
||||
self.batch_update_builder.pop_removed()
|
||||
# Autoregressive models require detailed tracking of condense
|
||||
# operations to support logitsprocs
|
||||
self.batch_update_builder.moved.append(
|
||||
(last_req_index, empty_index,
|
||||
MoveDirectionality.UNIDIRECTIONAL))
|
||||
req_id = self._req_ids[last_req_index]
|
||||
output_token_ids = self.req_output_token_ids[last_req_index]
|
||||
assert req_id is not None
|
||||
self._req_ids[empty_index] = req_id
|
||||
self._req_ids[last_req_index] = None
|
||||
self.req_output_token_ids[empty_index] = output_token_ids
|
||||
self.req_output_token_ids[last_req_index] = None
|
||||
self.req_id_to_index[req_id] = empty_index
|
||||
|
||||
if last_req_index != empty_index:
|
||||
(
|
||||
self.spec_token_ids[last_req_index],
|
||||
self.spec_token_ids[empty_index],
|
||||
) = (
|
||||
self.spec_token_ids[empty_index],
|
||||
self.spec_token_ids[last_req_index],
|
||||
)
|
||||
self.spec_token_ids[last_req_index].clear()
|
||||
|
||||
num_tokens = self.num_tokens[last_req_index]
|
||||
self.token_ids_cpu[empty_index, :num_tokens] = self.token_ids_cpu[
|
||||
last_req_index, :num_tokens]
|
||||
self.is_token_ids[empty_index, :num_tokens] = self.is_token_ids[
|
||||
last_req_index, :num_tokens]
|
||||
if last_req_index in self.req_prompt_embeds:
|
||||
self.req_prompt_embeds[
|
||||
empty_index] = self.req_prompt_embeds.pop(last_req_index)
|
||||
self.num_tokens[empty_index] = num_tokens
|
||||
self.num_tokens_no_spec[empty_index] = self.num_tokens_no_spec[
|
||||
last_req_index]
|
||||
self.num_prompt_tokens[empty_index] = self.num_prompt_tokens[
|
||||
last_req_index]
|
||||
self.num_computed_tokens_cpu[
|
||||
empty_index] = self.num_computed_tokens_cpu[last_req_index]
|
||||
self.block_table.move_row(last_req_index, empty_index)
|
||||
self.temperature_cpu[empty_index] = self.temperature_cpu[
|
||||
last_req_index]
|
||||
self.top_p_cpu[empty_index] = self.top_p_cpu[last_req_index]
|
||||
self.top_k_cpu[empty_index] = self.top_k_cpu[last_req_index]
|
||||
self.frequency_penalties_cpu[
|
||||
empty_index] = self.frequency_penalties_cpu[last_req_index]
|
||||
self.presence_penalties_cpu[
|
||||
empty_index] = self.presence_penalties_cpu[last_req_index]
|
||||
self.repetition_penalties_cpu[
|
||||
empty_index] = self.repetition_penalties_cpu[last_req_index]
|
||||
self.num_accepted_tokens_cpu[
|
||||
empty_index] = self.num_accepted_tokens_cpu[last_req_index]
|
||||
generator = self.generators.pop(last_req_index, None)
|
||||
if generator is not None:
|
||||
self.generators[empty_index] = generator
|
||||
|
||||
self.request_lora_mapping[empty_index] = self.request_lora_mapping[
|
||||
last_req_index]
|
||||
|
||||
# TODO convert these to LogitsProcessors
|
||||
if self.allowed_token_ids_mask_cpu_tensor is not None:
|
||||
self.allowed_token_ids_mask_cpu_tensor[
|
||||
empty_index] = self.allowed_token_ids_mask_cpu_tensor[
|
||||
last_req_index]
|
||||
|
||||
bad_words_token_ids = self.bad_words_token_ids.pop(
|
||||
last_req_index, None)
|
||||
if bad_words_token_ids is not None:
|
||||
self.bad_words_token_ids[empty_index] = bad_words_token_ids
|
||||
|
||||
# Decrement last_req_index since it is now empty.
|
||||
last_req_index -= 1
|
||||
|
||||
# Trim lists to the batch size.
|
||||
del self._req_ids[num_reqs:]
|
||||
del self.req_output_token_ids[num_reqs:]
|
||||
del self.spec_token_ids[num_reqs:]
|
||||
|
||||
def refresh_metadata(self):
|
||||
"""Apply any batch updates to sampling metadata."""
|
||||
|
||||
if self.is_pooling_model:
|
||||
# Batch changes every step for pooling models.
|
||||
self.sampling_metadata = self._make_sampling_metadata()
|
||||
return
|
||||
|
||||
# For non-pooling models - generate and apply logitsprocs update;
|
||||
# reset batch update tracking.
|
||||
# Update sampling metadata if batch state is changed.
|
||||
batch_update = self.batch_update_builder.get_and_reset(self.num_reqs)
|
||||
for logit_proc in self.logitsprocs.all:
|
||||
logit_proc.update_state(batch_update)
|
||||
if batch_update:
|
||||
self.sampling_metadata = self._make_sampling_metadata()
|
||||
|
||||
def _make_sampling_metadata(self) -> SamplingMetadata:
|
||||
num_reqs = self.num_reqs
|
||||
if not self.all_greedy:
|
||||
temperature = copy_slice(self.temperature_cpu_tensor,
|
||||
self.temperature, num_reqs)
|
||||
else:
|
||||
temperature = None
|
||||
if not self.no_top_p:
|
||||
copy_slice(self.top_p_cpu_tensor, self.top_p, num_reqs)
|
||||
if not self.no_top_k:
|
||||
copy_slice(self.top_k_cpu_tensor, self.top_k, num_reqs)
|
||||
|
||||
if not self.no_penalties:
|
||||
# Since syncing these tensors is expensive only copy them
|
||||
# if necessary i.e. if there are requests which require
|
||||
# penalties to be applied during sampling.
|
||||
copy_slice(self.frequency_penalties_cpu_tensor,
|
||||
self.frequency_penalties, num_reqs)
|
||||
copy_slice(self.presence_penalties_cpu_tensor,
|
||||
self.presence_penalties, num_reqs)
|
||||
copy_slice(self.repetition_penalties_cpu_tensor,
|
||||
self.repetition_penalties, num_reqs)
|
||||
|
||||
needs_prompt_token_ids = (
|
||||
not self.no_penalties
|
||||
or self.logits_processing_needs_token_ids[:num_reqs].any())
|
||||
if needs_prompt_token_ids:
|
||||
# The prompt tokens are used only for applying penalties or
|
||||
# step pooling during the sampling/pooling process.
|
||||
# Hence copy these tensors only when there are requests which
|
||||
# need penalties/step_pooler to be applied.
|
||||
prompt_token_ids = self._make_prompt_token_ids_tensor()
|
||||
else:
|
||||
prompt_token_ids = None
|
||||
|
||||
allowed_token_ids_mask: Optional[torch.Tensor] = None
|
||||
if not self.no_allowed_token_ids:
|
||||
assert self.allowed_token_ids_mask is not None
|
||||
copy_slice(self.allowed_token_ids_mask_cpu_tensor,
|
||||
self.allowed_token_ids_mask, num_reqs)
|
||||
allowed_token_ids_mask = self.allowed_token_ids_mask[:num_reqs]
|
||||
|
||||
return SamplingMetadata(
|
||||
temperature=temperature,
|
||||
all_greedy=self.all_greedy,
|
||||
all_random=self.all_random,
|
||||
top_p=None if self.no_top_p else self.top_p[:num_reqs],
|
||||
top_k=None if self.no_top_k else self.top_k[:num_reqs],
|
||||
generators=self.generators,
|
||||
max_num_logprobs=self.max_num_logprobs,
|
||||
prompt_token_ids=prompt_token_ids,
|
||||
frequency_penalties=self.frequency_penalties[:num_reqs],
|
||||
presence_penalties=self.presence_penalties[:num_reqs],
|
||||
repetition_penalties=self.repetition_penalties[:num_reqs],
|
||||
output_token_ids=cast(list[list[int]], self.req_output_token_ids),
|
||||
spec_token_ids=cast(list[list[int]], self.spec_token_ids),
|
||||
no_penalties=self.no_penalties,
|
||||
allowed_token_ids_mask=allowed_token_ids_mask,
|
||||
bad_words_token_ids=self.bad_words_token_ids,
|
||||
logitsprocs=self.logitsprocs,
|
||||
)
|
||||
|
||||
def get_pooling_params(self) -> list[PoolingParams]:
|
||||
assert len(self.req_ids) == len(self.pooling_params)
|
||||
return [self.pooling_params[req_id] for req_id in self.req_ids]
|
||||
|
||||
def get_pooling_metadata(self) -> PoolingMetadata:
|
||||
pooling_params = self.get_pooling_params()
|
||||
|
||||
return PoolingMetadata(
|
||||
prompt_lens=torch.from_numpy(
|
||||
self.num_prompt_tokens[:self.num_reqs]),
|
||||
prompt_token_ids=self.sampling_metadata.prompt_token_ids,
|
||||
pooling_params=pooling_params,
|
||||
)
|
||||
|
||||
def _make_prompt_token_ids_tensor(self) -> torch.Tensor:
|
||||
max_prompt_len = self.num_prompt_tokens[:self.num_reqs].max()
|
||||
prompt_token_ids_cpu_tensor = torch.empty(
|
||||
(self.num_reqs, max_prompt_len),
|
||||
device="cpu",
|
||||
dtype=torch.int64,
|
||||
pin_memory=self.pin_memory,
|
||||
)
|
||||
prompt_token_ids = prompt_token_ids_cpu_tensor.numpy()
|
||||
prompt_token_ids[:] = self.token_ids_cpu[:self.
|
||||
num_reqs, :max_prompt_len]
|
||||
# Use the value of vocab_size as a pad since we don't have a
|
||||
# token_id of this value.
|
||||
for i in range(self.num_reqs):
|
||||
prompt_token_ids[i, self.num_prompt_tokens[i]:] = self.vocab_size
|
||||
return prompt_token_ids_cpu_tensor.to(device=self.device,
|
||||
non_blocking=True)
|
||||
|
||||
def make_lora_inputs(
|
||||
self, num_scheduled_tokens: np.ndarray, num_sampled_tokens: np.ndarray
|
||||
) -> tuple[tuple[int, ...], tuple[int, ...], set[LoRARequest]]:
|
||||
"""
|
||||
Given the num_scheduled_tokens for each request in the batch, return
|
||||
datastructures used to activate the current LoRAs.
|
||||
Returns:
|
||||
1. prompt_lora_mapping: A tuple of size self.num_reqs where,
|
||||
prompt_lora_mapping[i] is the LoRA id to use for the ith prompt.
|
||||
2. token_lora_mapping: A tuple of size np.sum(num_scheduled_tokens)
|
||||
where, token_lora_mapping[i] is the LoRA id to use for ith token.
|
||||
3. lora_requests: Set of relevant LoRA requests.
|
||||
"""
|
||||
|
||||
req_lora_mapping = self.request_lora_mapping[:self.num_reqs]
|
||||
prompt_lora_mapping = tuple(req_lora_mapping)
|
||||
token_lora_mapping = tuple(
|
||||
req_lora_mapping.repeat(num_scheduled_tokens))
|
||||
active_lora_requests: set[LoRARequest] = set(
|
||||
self.lora_id_to_lora_request.values())
|
||||
|
||||
return prompt_lora_mapping, token_lora_mapping, active_lora_requests
|
||||
|
||||
def set_async_sampled_token_ids(
|
||||
self,
|
||||
sampled_token_ids_cpu: torch.Tensor,
|
||||
async_copy_ready_event: torch.Event,
|
||||
) -> None:
|
||||
"""
|
||||
In async scheduling case, store ref to sampled_token_ids_cpu
|
||||
tensor and corresponding copy-ready event. Used to repair
|
||||
output_token_ids prior to sampling, if needed by logits processors.
|
||||
"""
|
||||
if self.sampling_metadata.output_token_ids:
|
||||
self.sampled_token_ids_cpu = sampled_token_ids_cpu
|
||||
self.async_copy_ready_event = async_copy_ready_event
|
||||
else:
|
||||
self.sampled_token_ids_cpu = None
|
||||
self.async_copy_ready_event = None
|
||||
|
||||
def update_async_output_token_ids(self) -> None:
|
||||
"""
|
||||
In async scheduling case, update output_token_ids in sampling metadata
|
||||
from prior steps sampled token ids once they've finished copying to CPU.
|
||||
This is called right before they are needed by the logits processors.
|
||||
"""
|
||||
output_token_ids = self.sampling_metadata.output_token_ids
|
||||
if self.sampled_token_ids_cpu is None or not output_token_ids:
|
||||
# Output token ids not needed or not async scheduling.
|
||||
return
|
||||
|
||||
assert self.prev_req_id_to_index is not None
|
||||
sampled_token_ids = None
|
||||
for index, req_id in enumerate(self.req_ids):
|
||||
prev_index = self.prev_req_id_to_index.get(req_id)
|
||||
if prev_index is None:
|
||||
continue
|
||||
req_output_token_ids = output_token_ids[index]
|
||||
if not req_output_token_ids or req_output_token_ids[-1] != -1:
|
||||
# Final output id is not a placeholder, some tokens must have
|
||||
# been discarded after a kv-load failure.
|
||||
continue
|
||||
if sampled_token_ids is None:
|
||||
assert self.async_copy_ready_event is not None
|
||||
self.async_copy_ready_event.synchronize()
|
||||
sampled_token_ids = self.sampled_token_ids_cpu.squeeze(
|
||||
-1).tolist()
|
||||
# Replace placeholder token id with actual sampled id.
|
||||
req_output_token_ids[-1] = sampled_token_ids[prev_index]
|
||||
|
||||
@property
|
||||
def num_reqs(self) -> int:
|
||||
return len(self.req_id_to_index)
|
||||
|
||||
@property
|
||||
def all_greedy(self) -> bool:
|
||||
return len(self.random_reqs) == 0
|
||||
|
||||
@property
|
||||
def all_random(self) -> bool:
|
||||
return len(self.greedy_reqs) == 0
|
||||
|
||||
@property
|
||||
def no_top_p(self) -> bool:
|
||||
return len(self.top_p_reqs) == 0
|
||||
|
||||
@property
|
||||
def no_top_k(self) -> bool:
|
||||
return len(self.top_k_reqs) == 0
|
||||
|
||||
@property
|
||||
def no_penalties(self) -> bool:
|
||||
return (len(self.presence_penalties_reqs) == 0
|
||||
and len(self.frequency_penalties_reqs) == 0
|
||||
and len(self.repetition_penalties_reqs) == 0)
|
||||
|
||||
@property
|
||||
def max_num_logprobs(self) -> Optional[int]:
|
||||
return max(self.num_logprobs.values()) if self.num_logprobs else None
|
||||
|
||||
@property
|
||||
def no_prompt_logprob(self) -> bool:
|
||||
return not self.num_prompt_logprobs
|
||||
|
||||
@property
|
||||
def no_allowed_token_ids(self) -> bool:
|
||||
return len(self.has_allowed_token_ids) == 0
|
||||
|
||||
Reference in New Issue
Block a user