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vllm-ascend support Ascend950 with Qwen dense model. #4228

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vllm-ascend support Ascend950 with Qwen dense model. #4228

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1 change: 1 addition & 0 deletions setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,6 +160,7 @@ def gen_build_info():
"ascend310p3vir02": "_310P",
"ascend310p3vir04": "_310P",
"ascend310p3vir08": "_310P",
"ascend910_9579": "_910_95",
}

assert soc_version in soc_to_device, f"Undefined soc_version: {soc_version}. Please file an issue to vllm-ascend."
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61 changes: 44 additions & 17 deletions vllm_ascend/attention/attention_v1.py
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Expand Up @@ -41,7 +41,9 @@
split_decodes_and_prefills)
from vllm_ascend.compilation.acl_graph import (get_graph_params,
update_graph_params_workspaces)
from vllm_ascend.utils import prefill_context_parallel_enable, weak_ref_tensors
from vllm_ascend.utils import (AscendDeviceType, get_ascend_device_type,
prefill_context_parallel_enable,
weak_ref_tensors)

# isort: off
if prefill_context_parallel_enable():
Expand Down Expand Up @@ -1413,12 +1415,20 @@ def forward(
if has_decode:
slot_mapping = attn_metadata.slot_mapping[:num_decode_tokens * self.pcp_size: self.pcp_size] \
if self.pcp_size * self.dcp_size > 1 else attn_metadata.slot_mapping[:num_decode_tokens]
torch_npu._npu_reshape_and_cache(
key=key[:num_decode_tokens],
value=value[:num_decode_tokens],
key_cache=self.key_cache,
value_cache=self.value_cache,
slot_indices=slot_mapping)
if get_ascend_device_type() == AscendDeviceType._910_95:
torch_npu.npu_scatter_pa_kv_cache(
key=key[:num_decode_tokens],
value=value[:num_decode_tokens],
key_cache=self.key_cache,
value_cache=self.value_cache,
slot_indices=slot_mapping)
else:
torch_npu._npu_reshape_and_cache(
key=key[:num_decode_tokens],
value=value[:num_decode_tokens],
key_cache=self.key_cache,
value_cache=self.value_cache,
slot_indices=slot_mapping)

if has_prefill:
if self.pcp_size > 1:
Expand All @@ -1432,18 +1442,35 @@ def forward(
key, value = all_kv.split([self.head_size, self.head_size],
dim=-1)

torch_npu._npu_reshape_and_cache(
key=key[self.pcp_size * num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded],
value=value[self.pcp_size *
if get_ascend_device_type() == AscendDeviceType._910_95:
torch_npu.npu_scatter_pa_kv_cache(
key=key[self.pcp_size *
num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded],
key_cache=self.key_cache,
value_cache=self.value_cache,
slot_indices=attn_metadata.
slot_mapping[self.pcp_size *
num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded])
value=value[self.pcp_size *
num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded].contiguous(),
key_cache=self.key_cache,
value_cache=self.value_cache,
slot_mapping=attn_metadata.
slot_mapping[self.pcp_size *
num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded],
out=(self.key_cache, self.value_cache))
else:
torch_npu._npu_reshape_and_cache(
key=key[self.pcp_size *
num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded],
value=value[self.pcp_size *
num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded],
key_cache=self.key_cache,
value_cache=self.value_cache,
slot_indices=attn_metadata.
slot_mapping[self.pcp_size *
num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded])

forward_context: ForwardContext = get_forward_context()
if not forward_context.capturing:
Expand Down
3 changes: 2 additions & 1 deletion vllm_ascend/ops/rotary_embedding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -414,7 +414,8 @@ def forward_oot(
# TODO: This judgment will be removed once the mrope precision issue is fixed
if self.mrope_section != [
16, 24, 24
] or NPUPlatform.get_cpu_architecture() == CpuArchEnum.X86:
] or NPUPlatform.get_cpu_architecture() == CpuArchEnum.X86 or \
get_ascend_device_type() == AscendDeviceType._910_95:
return super().forward_oot(positions, query, key)

import torch_npu
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6 changes: 4 additions & 2 deletions vllm_ascend/worker/worker_v1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,8 @@
from vllm_ascend.distributed.parallel_state import init_ascend_model_parallel
from vllm_ascend.ops.triton.triton_utils import init_device_properties_triton
from vllm_ascend.platform import NPUPlatform
from vllm_ascend.utils import (check_ascend_device_type, is_enable_nz,
from vllm_ascend.utils import (AscendDeviceType, check_ascend_device_type,
get_ascend_device_type, is_enable_nz,
prefill_context_parallel_enable,
register_ascend_customop, sleep_mode_enabled,
try_register_lib)
Expand Down Expand Up @@ -344,7 +345,8 @@ def compile_or_warm_up_model(self) -> None:
self.model_runner.capture_model()
# Call ATB matmul to warm up; otherwise, the first operation (ReshapeAndCache)
# may cause performance degradation at runtime.
self._warm_up_atb()
if get_ascend_device_type() != AscendDeviceType._910_95:
self._warm_up_atb()
# Reset the seed to ensure that the random state is not affected by
# the model initialization and profiling.
NPUPlatform.seed_everything(self.model_config.seed)
Expand Down
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