EngineX-Ascend/enginex-ascend-910-vllm
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init v0.11.0rc0

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This commit is contained in:
luopingyi
2025-10-14 10:38:28 +08:00
parent 67afd0ea78
commit 66dc16f966
278 changed files with 28130 additions and 11708 deletions
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31
csrc/torch_binding.cpp
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@@ -20,7 +20,6 @@
#include <torch_npu/csrc/core/npu/NPUStream.h>
#include <torch_npu/csrc/framework/OpCommand.h>
#include <torch_npu/csrc/npu/Module.h>
#include <pybind11/pybind11.h>
#include "acl/acl.h"
#include "ops.h"
#include "utils.h"
@@ -142,7 +141,7 @@ std::tuple<at::Tensor, at::Tensor> get_masked_input_and_mask(
TP2, rank 1:
|< -----------BASE----------- >|< -BASE PADDING- >|< -----------LORA PADDING----------- >|
corresponding token_id: | 512 | 513 | 514 | ... | 1009 | -1 | ... | -1 | -1 | ... | -1 | -1 | ... | -1 |
index: | 0 | 1 | 2 | ... | 497 | 498 | ... | 511 | 512 | ... | 519 | 520 | ... | 543 |
index: | 0 | 1 | 2 | ... | 497 | 498 | ... | 511 | 512 | ... | 519 | 520 | ... | 543 |
Parameters:
org_vocab_start_index //base embeddings start
org_vocab_end_index //base embeddings end
@@ -165,22 +164,22 @@ std::tuple<at::Tensor, at::Tensor> get_masked_input_and_mask(
// Create output tensors
at::Tensor masked_input = at::empty_like(input);
at::Tensor mask = at::empty_like(input).to(at::kBool);
// Get data pointers
void *input_ptr = input.data_ptr();
void *masked_input_ptr = masked_input.data_ptr();
void *mask_ptr = mask.data_ptr();
// Get current stream
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
// Get scalar type
at::ScalarType scalar_type = input.scalar_type();
// Create and configure OpCommand
at_npu::native::OpCommand cmd;
cmd.Name("get_masked_input_and_mask");
cmd.SetCustomHandler([scalar_type, size, stream,
cmd.SetCustomHandler([scalar_type, size, stream,
input_ptr, masked_input_ptr, mask_ptr,
org_vocab_start_index, org_vocab_end_index,
num_org_vocab_padding, added_vocab_start_index,
@@ -194,7 +193,7 @@ std::tuple<at::Tensor, at::Tensor> get_masked_input_and_mask(
get_masked_input_and_mask_impl(
stream,
input_ptr,
masked_input_ptr,
masked_input_ptr,
mask_ptr,
org_vocab_start_index,
org_vocab_end_index,
@@ -204,7 +203,7 @@ std::tuple<at::Tensor, at::Tensor> get_masked_input_and_mask(
size,
loop_cnt,
aiv_num);
return 0;
});
cmd.Run();
@@ -321,8 +320,8 @@ void sgmv_shrink(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_indices, at
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("sgmv_shrink");
cmd.SetCustomHandler([scalar_type, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size,
seq_len_ptr, seq_len_size, y_ptr,
cmd.SetCustomHandler([scalar_type, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size,
seq_len_ptr, seq_len_size, y_ptr,
batch_size, input_hidden_token, lora_rank, scale_f]() -> int {
auto dtype = get_dtype_from_torch(scalar_type);
int device_id = 0;
@@ -331,7 +330,7 @@ void sgmv_shrink(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_indices, at
int num_tokens_per_core = (batch_size + aiv_num - 1) / aiv_num;
TORCH_CHECK("num_tokens_per_core != 0", "num_tokens_per_core should not be 0");
sgmv_shrink_impl(dtype, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size,
y_ptr, batch_size,
y_ptr, batch_size,
num_tokens_per_core, input_hidden_token, lora_rank, scale_f);
return 0;
});
@@ -368,7 +367,7 @@ at::Tensor sgmv_expand(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_indic
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("sgmv_expand");
cmd.SetCustomHandler([scalar_type, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size, y_ptr, y_out_ptr,
cmd.SetCustomHandler([scalar_type, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size, y_ptr, y_out_ptr,
batch_size, lora_rank, slice_offset, slice_size, output_full_dim]() -> int {
auto dtype = get_dtype_from_torch(scalar_type);
int device_id = 0;
@@ -376,7 +375,7 @@ at::Tensor sgmv_expand(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_indic
TORCH_CHECK(aclGetDeviceCapability(device_id, ACL_DEVICE_INFO_VECTOR_CORE_NUM, &aiv_num) == ACL_SUCCESS);
int num_tokens_per_core = (batch_size + aiv_num - 1) / aiv_num;
TORCH_CHECK("num_tokens_per_core != 0", "num_tokens_per_core should not be 0");
sgmv_expand_impl(dtype, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size, y_ptr, y_out_ptr,
sgmv_expand_impl(dtype, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size, y_ptr, y_out_ptr,
batch_size, num_tokens_per_core, lora_rank, slice_size, slice_offset, output_full_dim);
return 0;
});
@@ -385,7 +384,7 @@ at::Tensor sgmv_expand(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_indic
}
} // namespace vllm_ascend
TORCH_LIBRARY_EXPAND(_C, ops)
TORCH_LIBRARY_EXPAND(CONCAT(_C, _ascend), ops)
{
// vLLM-Ascend custom ops
ops.def("weak_ref_tensor(Tensor input) -> Tensor");
@@ -424,5 +423,3 @@ TORCH_LIBRARY_EXPAND(_C, ops)
" int slice_offset, int slice_size) -> Tensor");
ops.impl("sgmv_expand", torch::kPrivateUse1, &vllm_ascend::sgmv_expand);
}
REGISTER_EXTENSION(_C)