torch.cat() 的使用方法非常簡單,具體看下方的程式碼。import torch a = torch.tensor([1, 2, 3]) b = torch.tensor([4, 5, 6]) ab = torch.cat((a, b), 0) ba = torch.cat((b, a), 0) print('ab:', ab) print('ba:', ba) ...
torch.cat()的使用方法非常簡單,具體看下方的程式碼。 importtorch a=torch.tensor([1,2,3])b=torch.tensor([4,5,6])ab=torch.cat((a,b),0)ba=torch.cat((b,a),0)print('ab:',ab)print('ba:',ba) Output: ab: tensor([1, 2, 3, 4, 5, 6]) ba: tensor([4, 5, 6, 1, 2, 3...
out (Tensor, optional): the output tensor. dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. Default: if ``None``, uses a global default (see :func:`torch.set_default_tensor_type`). layout (:class:`torch.layout`, optional): the desired layout of ret...
tensor([[1, 3, 0], [1, 2, 4]]).cuda() out = model(mix, offsets) print(out) # 转成onnx模型 ONNX_FILE_PATH = "./test.onnx" torch.onnx.export(model, (mix, offsets), ONNX_FILE_PATH, opset_version=12, verbose=True, input_names=["input_ids", "offsets"], output_names...
fill_value: tensor matrix whose diagonals we want to fill. wrap: it takes boolean, it enables us to work with a non-square matrix. 例1: 在这个例子中,首先我们将使用 torch.zeros,3)创建一个大小为(3,3)的张量。 Python 3 a = torch.zeros(3, 3) a 是大小为(3,3)的张量,它的所有元素...
30 31 // NOTE(Zihao): doesn't have to be contiguous 31 32 CHECK_LAST_DIM_CONTIGUOUS_INPUT(paged_k_cache); 32 33 CHECK_LAST_DIM_CONTIGUOUS_INPUT(paged_v_cache); @@ -35,20 +36,24 @@ void append_paged_kv_cache(torch::Tensor append_key, torch::Tensor append_value, 35 36 CHE...
tensor([13, 8, 9, 6], dtype=torch.int32, device="cuda:0") batch_indices, positions = flashinfer.get_batch_indices_positions( kv_append_indptr, flashinfer.get_seq_lens(kv_append_indptr, kv_last_page_len, page_size), flashinfer.get_seq_lens(kv_page_indptr, kv_last_page_len, page...
torch.cat() 的使用方法非常簡單,具體看下方的程式碼。import torch a = torch.tensor([1, 2, 3]) b = torch.tensor([4, 5, 6]) ab = torch.cat((a, b), 0) ba = torch.cat((b, a), 0) print('ab:', ab) print('ba:', ba) ...
13 @@ std::vector<torch::Tensor> single_prefill_with_kv_cache_custom_mask( const LogitsPostHook logits_post_hook= logits_soft_cap > 0.f ? LogitsPost::kSoftCap : LogitsHook::kNone; bool success = DISPATCH_PYTORCH_DTYPETO_CTYPE_FP16(q.scalar_type(), c_type, [&] { auto ...
tensor_parallel_size=torch.cuda.device_count() ) ` It works great until I send multiple concurrent request and I get: /lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 594, in _process_sequence_group_outputs parent_child_dict[sample.parent_seq_id].append(sample) KeyError: 11...