xc-llm-ascend/vllm_ascend/torchair/torchair_model_runner.py

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
# Adapted from vllm-project/vllm/vllm/worker/gpu_model_runner.py
#

from typing import Optional

import torch
import torch_npu
from vllm.config import VllmConfig
from vllm.forward_context import get_forward_context
from vllm.logger import logger

from vllm_ascend.platform import NPUPlatform
from vllm_ascend.torchair.utils import (TorchairCommonAttentionMetadata,
                                        check_torchair_cache_exist,
                                        register_torchair_model,
                                        write_kv_cache_bytes_to_file)
from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
                               maybe_converting_weight_acl_format)
from vllm_ascend.worker.model_runner_v1 import NPUModelRunner


class NPUTorchairModelRunner(NPUModelRunner):

    def __init__(self, vllm_config: VllmConfig, device: torch.device):
        super().__init__(vllm_config, device)
        register_torchair_model()

    def _get_forward_metadata_across_dp_and_pad(
            self, num_tokens: int, with_prefill: bool, enable_dbo: bool
    ) -> tuple[int, Optional[torch.Tensor], bool, bool]:
        """Override from NPUModelRunner to pad num_tokens"""
        if self.dp_size == 1:
            if not with_prefill:
                maybe_padded_num_tokens = self.select_torchair_padded_batch_size(
                    num_tokens)
                return maybe_padded_num_tokens, None, with_prefill, enable_dbo
            return num_tokens, None, with_prefill, enable_dbo

        num_tokens_across_dp, with_prefill, enable_dbo = self._get_forward_metadata_across_dp(
            num_tokens, with_prefill, enable_dbo)

        if not with_prefill:
            max_num_token = num_tokens_across_dp.max().item()
            maybe_padded_num_tokens = self.select_torchair_padded_batch_size(
                max_num_token)
            num_tokens_across_dp = torch.full((self.dp_size, ),
                                              maybe_padded_num_tokens,
                                              dtype=torch.int32,
                                              device="cpu")
        else:
            maybe_padded_num_tokens = num_tokens

        return maybe_padded_num_tokens, num_tokens_across_dp, with_prefill, enable_dbo

    def _build_attention_metadata(self, with_prefill, num_reqs, skip_attn):
        # NOTE: If torchair graph mode and not with_prefill,
        # we can't skip_attn, it will cause graph recompile.
        if not with_prefill:
            common_attn_metadata = TorchairCommonAttentionMetadata(
                num_reqs=num_reqs,
                num_actual_tokens=1,
                actual_seq_lengths_q=self.actual_seq_lengths_q,
                attn_mask=self.attn_mask,
                spec_attn_mask=self.spec_attn_mask,
                decode_token_per_req=self.decode_token_per_req,
            )
            attn_metadata = self.attn_metadata_builder.build_torchair_graph_dummy(
                common_attn_metadata)
        else:
            attn_metadata = super()._build_attention_metadata(
                with_prefill, num_reqs, skip_attn)
        return attn_metadata

    def _generate_dummy_run_hidden_states(self, with_prefill,
                                          is_torchair_compile, input_ids,
                                          positions, attn_metadata, num_tokens,
                                          intermediate_tensors, inputs_embeds):

        if not with_prefill:
            # Only mark static while compiling
            if is_torchair_compile:
                torch._dynamo.mark_static(input_ids)
                torch._dynamo.mark_static(positions)
                torch._dynamo.mark_static(attn_metadata.decode.block_table)
                torch._dynamo.mark_static(attn_metadata.decode.input_positions)
                torch._dynamo.mark_static(get_forward_context().mc2_mask)
                if hasattr(attn_metadata.decode, "sin"):
                    torch._dynamo.mark_static(attn_metadata.decode.sin)
                    torch._dynamo.mark_static(attn_metadata.decode.cos)
                torch._dynamo.mark_static(attn_metadata.slot_mapping)
                if self.speculative_config:
                    torch._dynamo.mark_static(attn_metadata.decode.attn_mask)
                for kv in self.kv_caches:
                    assert isinstance(kv, tuple), "kv_cache must be a tuple"
                    torch._dynamo.mark_static(kv[0])
                    torch._dynamo.mark_static(kv[1])

            maybe_converting_weight_acl_format(self.model,
                                               ACL_FORMAT_FRACTAL_NZ)

            compiled_model = self._get_torchair_lazy_compiled_model(num_tokens)
            model_kwargs = {}
            model_kwargs["kv_caches"] = self.kv_caches
            model_kwargs["attn_metadata"] = attn_metadata
            hidden_states = compiled_model(
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=None,
                **model_kwargs,
            )
        else:
            hidden_states = super()._generate_dummy_run_hidden_states(
                with_prefill, is_torchair_compile, input_ids, positions,
                attn_metadata, num_tokens, intermediate_tensors, inputs_embeds)
        return hidden_states

    def _convert_torch_format(self, kv_cache):
        kv_cache = torch_npu.npu_format_cast(kv_cache, ACL_FORMAT_FRACTAL_ND)
        return kv_cache

    def _compile_torchair_graph(self, torchair_graph_batch_sizes) -> None:
        # Trigger torchair graph capture for specific shapes.
        # Capture the large shapes first so that the smaller shapes
        # can reuse the memory pool allocated for the large shapes.
        for idx, num_tokens in enumerate(reversed(torchair_graph_batch_sizes)):
            for _ in range(self.vllm_config.compilation_config.
                           cudagraph_num_of_warmups):
                self._dummy_run(num_tokens, is_torchair_compile=True)
            self._dummy_run(num_tokens, is_torchair_compile=True)
            logger.info("Batchsize %d is compiled successfully: %d/%d.",
                        num_tokens, idx + 1, len(torchair_graph_batch_sizes))

    def _capture_model(self):
        """Override from NPUModelRunner to use torchair graph capture."""
        # TODO(NeverRaR): Calling graph_capture(device=self.device) in
        # torchair graph capture can cause some issues, so now we just
        # temporarily split the codepath for the two different graph patterns.
        torchair_graph_batch_sizes = self.torchair_graph_batch_sizes
        graph_num = len(torchair_graph_batch_sizes)

        if self.use_cached_npu_graph and not check_torchair_cache_exist():
            # If caching is enabled but does not exist, we will compile the model twice. The first
            # time is used to generate the cache, and the second time is used to load the cache to
            # skip the overhead caused by Dynamo guard mechanism.
            logger.info(
                "Use cached npu graph but cache doesn't exist! Now we compile graph to genetate torchair cache, this usually takes %.1f~%.1f mins.",
                0.5 * graph_num, 1.5 * graph_num)
            self._compile_torchair_graph(torchair_graph_batch_sizes)
            NPUPlatform.synchronize()
            torch._dynamo.reset()
            self.torchair_compiled_models.clear()
        if self.use_cached_npu_graph:
            logger.info(
                "Loading torchair graph cache, this usually takes %.1f~%.1f mins.",
                0.3 * graph_num, 0.5 * graph_num)
            self._compile_torchair_graph(torchair_graph_batch_sizes)
        else:
            logger.info(
                "Capturing torchair graph, this usually takes %.1f~%.1f mins.",
                0.5 * graph_num, 1.5 * graph_num)
            self._compile_torchair_graph(torchair_graph_batch_sizes)

        if self.new_kv_cache_bytes > 0:
            write_kv_cache_bytes_to_file(torch.distributed.get_rank(),
                                         self.new_kv_cache_bytes)