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support deepseek eager_mode #237

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bfbc864
support deepseek eager_mode
yao-fengchen Jul 1, 2025
487766d
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yao-fengchen Jul 8, 2025
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41 changes: 41 additions & 0 deletions dlinfer/vendor/ascend/pytorch_patch.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,15 @@
# Copyright (c) 2024, DeepLink. All rights reserved.
import os
import pathlib
from functools import lru_cache
from packaging import version

import torch
import torch_npu

origin_torch_compile = torch.compile
from torch_npu.contrib import transfer_to_npu
from torch_npu.utils._path_manager import PathManager

torch.compile = origin_torch_compile

Expand All @@ -20,3 +24,40 @@ def has_triton():
return False

setattr(target_module, func_str, has_triton)


# def set_atb_ops():
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done

# from torch_npu.op_plugin import atb
# for api_name in atb._atb_ops.API_LIST:
# func = getattr(torch_npu, api_name)
# setattr(torch.ops.npu, api_name, func)
# _patch_atb_and_loadso()


@lru_cache(None)
def register_atb_extensions():
npu_path = pathlib.Path(torch_npu.__file__).parents[0]
atb_so_path = os.path.join(npu_path, "lib", "libop_plugin_atb.so")
try:
PathManager.check_directory_path_readable(atb_so_path)
torch.ops.load_library(atb_so_path)
except OSError as e:
nnal_ex = None
nnal_strerror = ""
if "libatb.so" in str(e):
nnal_strerror = (
"Please check that the nnal package is installed. "
"Please run 'source set_env.sh' in the NNAL installation path."
)
if "undefined symbol" in str(e):
nnal_strerror = (
"Please check the version of the NNAL package. "
"An undefined symbol was found, "
"which may be caused by a version mismatch between NNAL and torch_npu."
)
nnal_ex = OSError(e.errno, nnal_strerror)
nnal_ex.__traceback__ = e.__traceback__
raise nnal_ex from e


register_atb_extensions()
162 changes: 100 additions & 62 deletions dlinfer/vendor/ascend/torch_npu_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -105,7 +105,7 @@ def prefill_attention(
if (attn_mask is None or len(attn_mask) == 0)
else attn_mask[0].to(torch.bool)
)
attn_output.view(query.shape)[:] = torch.ops.npu.npu_fusion_attention(
attn_output[:] = torch.ops.npu.npu_fusion_attention(
query,
key,
value,
Expand Down Expand Up @@ -168,10 +168,15 @@ def quant_int8(x, x_scale, x_offset):
key = quant_int8(key, k_scales_zeros[0], k_scales_zeros[1])
value = quant_int8(value, v_scales_zeros[0], v_scales_zeros[1])

key_cache_reshaped = key_cache.view(block_total, head, dim)
value_cache_reshaped = value_cache.view(block_total, head, dim)
torch.ops.npu.npu_scatter_nd_update_(key_cache_reshaped, kv_indices, key)
torch.ops.npu.npu_scatter_nd_update_(value_cache_reshaped, kv_indices, value)
is_mla = key.shape[-1] != value.shape[-1]
if is_mla:
key_cache_reshaped = key_cache.view(block_total, head, dim)
torch.ops.npu.npu_scatter_nd_update_(key_cache_reshaped, kv_indices, key)
else:
key_cache_reshaped = key_cache.view(block_total, head, dim)
value_cache_reshaped = value_cache.view(block_total, head, dim)
torch.ops.npu.npu_scatter_nd_update_(key_cache_reshaped, kv_indices, key)
torch.ops.npu.npu_scatter_nd_update_(value_cache_reshaped, kv_indices, value)
return key_cache, value_cache


Expand Down Expand Up @@ -214,38 +219,53 @@ def paged_decode_attention(
if isinstance(block_table, torch.Tensor) and block_table.dtype != torch.int32:
block_table = block_table.to(torch.int32)

bs, _, dim = query.shape
block_num = key_cache.size(0)
is_mla = key_cache.shape[-1] != value_cache.shape[-1]
query = query.contiguous()
attn_output = attn_output.contiguous()
query = query.view(bs, 1, num_q_heads * dim)
key_cache = key_cache.view(block_num, block_size, -1)
value_cache = value_cache.view(block_num, block_size, -1)
scale_value = softmax_scale if softmax_scale else 1.0 / math.sqrt(dim)

torch.ops.npu_ext.npu_incre_flash_attention_v4_out(
query,
key_cache,
value_cache,
attn_output.view_as(query),
padding_mask=None,
atten_mask=None,
actual_seq_lengths=kv_seq_len.tolist(),
antiquant_scale=kv_scales,
antiquant_offset=kv_zeros,
block_table=block_table,
dequant_scale1=None,
quant_scale1=None,
dequant_scale2=None,
quant_scale2=None,
quant_offset2=None,
num_heads=num_q_heads,
scale_value=scale_value,
input_layout="BSH",
num_key_value_heads=num_kv_heads,
block_size=block_size,
inner_precise=1,
)
if is_mla:
v_head_size = value_cache.shape[-1]
torch.ops.atb._npu_paged_attention_mla(
query=query,
key_cache=key_cache,
num_kv_heads=num_kv_heads,
num_heads=num_q_heads,
scale_value=softmax_scale,
block_table=block_table,
context_lens=kv_seq_len,
mla_vheadsize=v_head_size,
out=attn_output,
)
else:
bs, _, dim = query.shape
block_num = key_cache.size(0)
query = query.view(bs, 1, num_q_heads * dim)
key_cache = key_cache.view(block_num, block_size, -1)
value_cache = value_cache.view(block_num, block_size, -1)
torch.ops.npu_ext.npu_incre_flash_attention_v4_out(
query,
key_cache,
value_cache,
attn_output.view_as(query),
padding_mask=None,
atten_mask=None,
actual_seq_lengths=kv_seq_len.tolist(),
antiquant_scale=kv_scales,
antiquant_offset=kv_zeros,
block_table=block_table,
dequant_scale1=None,
quant_scale1=None,
dequant_scale2=None,
quant_scale2=None,
quant_offset2=None,
num_heads=num_q_heads,
scale_value=scale_value,
input_layout="BSH",
num_key_value_heads=num_kv_heads,
block_size=block_size,
inner_precise=1,
)
return attn_output


Expand Down Expand Up @@ -282,35 +302,53 @@ def paged_prefill_attention(
if block_table.dtype != torch.int32:
block_table = block_table.to(torch.int32)

kv_seq_len_list = kv_seq_len.tolist()
scale_value = softmax_scale if softmax_scale else 1.0 / math.sqrt(query.shape[-1])
query = query.contiguous().view(query.shape[0], 1, -1)
block_num = key_cache.size(0)
key_cache = key_cache.view(block_num, block_size, -1)
value_cache = value_cache.view(block_num, block_size, -1)
torch.ops.npu_ext.npu_incre_flash_attention_v4_out(
query,
key_cache,
value_cache,
attn_output,
padding_mask=None,
atten_mask=attn_mask[0],
actual_seq_lengths=kv_seq_len_list,
antiquant_scale=kv_scales,
antiquant_offset=kv_zeros,
block_table=block_table,
dequant_scale1=None,
quant_scale1=None,
dequant_scale2=None,
quant_scale2=None,
quant_offset2=None,
num_heads=num_q_heads,
scale_value=scale_value,
input_layout="BSH",
num_key_value_heads=num_kv_heads,
block_size=block_size,
inner_precise=1,
)
is_mla = key_cache.shape[-1] != value_cache.shape[-1]
query = query.contiguous()
attn_output = attn_output.contiguous()
scale_value = softmax_scale if softmax_scale else 1.0 / math.sqrt(dim)

if is_mla:
v_head_size = value_cache.shape[-1]
torch.ops.atb._npu_paged_attention_mla(
query=query,
key_cache=key_cache,
num_kv_heads=num_kv_heads,
num_heads=num_q_heads,
scale_value=softmax_scale,
block_table=block_table,
context_lens=kv_seq_len,
mla_vheadsize=v_head_size,
out=attn_output,
)
else:
bs, _, dim = query.shape
block_num = key_cache.size(0)
query = query.view(bs, 1, num_q_heads * dim)
key_cache = key_cache.view(block_num, block_size, -1)
value_cache = value_cache.view(block_num, block_size, -1)
torch.ops.npu_ext.npu_incre_flash_attention_v4_out(
query,
key_cache,
value_cache,
attn_output,
padding_mask=None,
atten_mask=attn_mask[0],
actual_seq_lengths=kv_seq_len.tolist(),
antiquant_scale=kv_scales,
antiquant_offset=kv_zeros,
block_table=block_table,
dequant_scale1=None,
quant_scale1=None,
dequant_scale2=None,
quant_scale2=None,
quant_offset2=None,
num_heads=num_q_heads,
scale_value=scale_value,
input_layout="BSH",
num_key_value_heads=num_kv_heads,
block_size=block_size,
inner_precise=1,
)
return attn_output


Expand Down
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