mirror of
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82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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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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"""
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import paddle
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from fastdeploy.config import FDConfig
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from fastdeploy.model_executor.forward_meta import DCUForwardMeta
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from fastdeploy.worker.gpu_model_runner import GPUModelRunner
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class DCUModelRunner(GPUModelRunner):
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def __init__(
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self,
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fd_config: FDConfig,
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device: str, # logic device
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device_id: int, # physical device id
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rank: int,
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local_rank: int,
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):
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super(DCUModelRunner, self).__init__(
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fd_config=fd_config, device=device, device_id=device_id, rank=rank, local_rank=local_rank
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)
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def initialize_forward_meta(self):
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"""
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Initialize forward meta and attention meta data
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"""
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# Initialize forward meta
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self.forward_meta = DCUForwardMeta(
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input_ids=self.share_inputs["input_ids"],
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ids_remove_padding=self.share_inputs["ids_remove_padding"],
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rotary_embs=self.share_inputs["rope_emb"],
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attn_backend=self.attn_backends[0],
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decoder_batch_ids=self.share_inputs["decoder_batch_ids"],
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decoder_tile_ids_per_batch=self.share_inputs["decoder_tile_ids_per_batch"],
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decoder_num_blocks_cpu=self.share_inputs["decoder_num_blocks_cpu"],
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max_len_tensor_cpu=self.share_inputs["max_len_tensor_cpu"],
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seq_lens_encoder=self.share_inputs["seq_lens_encoder"],
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seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
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seq_lens_this_time=self.share_inputs["seq_lens_this_time"],
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batch_id_per_token=self.share_inputs["batch_id_per_token"],
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cum_offsets=self.share_inputs["cum_offsets"],
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cu_seqlens_q=self.share_inputs["cu_seqlens_q"],
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cu_seqlens_k=self.share_inputs["cu_seqlens_k"],
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block_tables=self.share_inputs["block_tables"],
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caches=self.share_inputs["caches"],
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)
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# Update Batch type for cuda graph
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only_decode_batch = True
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prefill_exists = None
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# mix ep in single node
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if self.fd_config.parallel_config.use_ep and self.fd_config.parallel_config.splitwise_role == "mixed":
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only_decode_batch_list = []
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prefill_exists = self.exist_prefill()
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paddle.distributed.all_gather_object(only_decode_batch_list, not prefill_exists)
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only_decode_batch = all(only_decode_batch_list)
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self.fd_config.parallel_config.moe_phase.phase = "decode" if only_decode_batch else "prefill"
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self.forward_meta.step_use_cudagraph = (
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self.use_cudagraph
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and only_decode_batch
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and not (prefill_exists if prefill_exists is not None else self.exist_prefill())
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)
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# Initialzie attention meta data
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for attn_backend in self.attn_backends:
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attn_backend.init_attention_metadata(self.forward_meta)
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