/third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/ |
D | reverse_sequence_impl.cu | 56 __global__ void ReverseSequence(const size_t size, const T *input, const S *seq_len, const int64_t … in ReverseSequence() argument 68 cur_slice_seq_len = seq_len[cur_slice]; in ReverseSequence() 87 void CalReverseSequence(const size_t size, const T *input, const S *seq_len, const int64_t batch_di… in CalReverseSequence() argument 92 …size, input, seq_len, batch_dim, seq_dim, cur_pos_arr, input_shape_ptr, input_shape_cum_ptr, shape… in CalReverseSequence() 96 …te void CalReverseSequence<int8_t, int>(const size_t size, const int8_t *input, const int *seq_len, 100 …CalReverseSequence<int8_t, int64_t>(const size_t size, const int8_t *input, const int64_t *seq_len, 104 … void CalReverseSequence<int16_t, int>(const size_t size, const int16_t *input, const int *seq_len, 108 …lReverseSequence<int16_t, int64_t>(const size_t size, const int16_t *input, const int64_t *seq_len, 112 template void CalReverseSequence<int, int>(const size_t size, const int *input, const int *seq_len, 116 … void CalReverseSequence<int, int64_t>(const size_t size, const int *input, const int64_t *seq_len, [all …]
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D | reverse_sequence_impl.cuh | 23 void CalReverseSequence(const size_t size, const T *input, const S *seq_len, const int64_t batch_di…
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/third_party/mindspore/tests/st/ops/ascend/ |
D | test_sparse_attention.py | 13 seq_len = 1024 # this op is designed for seq_len = 1024 18 q = np.random.rand(bs, seq_len, heads * size_per_head) 20 k = np.random.rand(bs, seq_len, heads * size_per_head) 22 v = np.random.rand(bs, seq_len, heads * size_per_head) 24 attention_mask = np.ones((bs, seq_len, seq_len), dtype=np.float32)
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/third_party/mindspore/tests/st/ops/gpu/ |
D | test_lstm_op.py | 32 …def __init__(self, seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirection… argument 57 … self.x = Parameter(initializer(Tensor(input_np), [seq_len, batch_size, input_size]), name='x') 112 seq_len = 5 126 …net = LstmNet(seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirectional, d… 164 …def __init__(self, seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirection… argument 189 … self.x = Parameter(initializer(Tensor(input_np), [seq_len, batch_size, input_size]), name='x') 262 seq_len = 5 276 …net = BiLstmNet(seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirectional,… 321 …def __init__(self, seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirection… argument 346 … self.x = Parameter(initializer(Tensor(input_np), [seq_len, batch_size, input_size]), name='x') [all …]
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/third_party/mindspore/mindspore/ops/_op_impl/_custom_op/ |
D | matmul_dds_grad_impl.py | 73 seq_len = shape_q[1] * shape_q[2] // bs 74 block_num = seq_len // block_size 78 mat_q = tik_inst.Tensor("float16", (size_per_head * heads // 16, bs * seq_len // 16, 16, 16), 81 mat_k = tik_inst.Tensor("float16", (size_per_head * heads // 16, bs * seq_len // 16, 16, 16), 96 mat_dq = tik_inst.Tensor("float16", (size_per_head * heads // 16, bs * seq_len // 16, 16, 16), 99 mat_dk = tik_inst.Tensor("float16", (bs * seq_len // 16, size_per_head * heads // 16, 16, 16), 145 head * size_per_head // 16, b * seq_len // 16 + 147 0, size_per_head // 16, 16, bs * seq_len - 16, 0) 308 mat_k[head * size_per_head // 16, b * seq_len // 16 + ( 310 0, size_per_head // 16, 16, bs * seq_len - 16, 0) [all …]
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D | dsd_impl.py | 58 seq_len = input_v_shape[0] * 16 // batch_size 61 cpt_time = seq_len//512 68 v_gm = tik_inst.Tensor('float16', (batch_size*seq_len//16, 71 …output_gm = tik_inst.Tensor('float16', (batch_size, head, v_embedding // 16, seq_len//16, 16, 16),… 99 tik_inst.data_move(v_global_l1[0, 0, 0, 0], v_gm[bs_idx * seq_len // 16 + global_idx, 100 … head_idx * v_embedding // 16, 0, 0], 0, seq_len // (4 * 16), 137 v_gm[bs_idx * seq_len//16 + w_idx * 4, head_idx * 157 (seq_len - block_size)*16*2//block_bite_size)
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D | matmul_dds_impl.py | 70 seq_len = shape_q[1] * shape_q[2] // bs 74 block_num = seq_len // block_size # block number only support 16 for now 75 global_size = seq_len // 4 # global size only support 256 for now 79 mat_q = tik_inst.Tensor("float16", (size_per_head * heads // 16, bs * seq_len // 16, 16, 16), 82 mat_k = tik_inst.Tensor("float16", (size_per_head * heads // 16, bs * seq_len // 16, 16, 16), 88 mat_gm = tik_inst.Tensor("float32", (bs * global_size // 16, seq_len // 16, 16, 16), 137 b * seq_len // 16 + global_idx, 0, 0], 174 mat_k[head * size_per_head // 16, b * seq_len // 16 + ( 176 0, size_per_head // 16, block_size, bs * seq_len - block_size, 0) 191 mat_q[head * size_per_head // 16, b * seq_len // 16 + ( [all …]
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D | dsd_back_impl.py | 65 seq_len = input_v_shape[0] * 16 // batch_size 82 (batch_size*seq_len//16, head*v_embedding//16, 16, 16), 89 16, seq_len // 16, 16, 16), 97 16, seq_len // 16, 16, 16), 119 (batch_size*seq_len//16, head*v_embedding//16, 16, 16), 133 d_a_l1 = tik_inst.Tensor('float16', (seq_len // 16, v_embedding // 16, 16, 16), 138 seq_len//16, 16*16*2//block_bite_size, 229 tik_inst.data_move(d_v_gm[bs_idx*seq_len//16+w_idx * (block_size // 16) + h_idx, 261 v_gm[bs_idx*seq_len//16+w_idx * 291 v_gm[bs_idx*seq_len//16 + (
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/third_party/mindspore/mindspore/lite/test/ut/nnacl/infer/ |
D | lstm_infer_test.cc | 29 int seq_len = 2; in TEST_F() local 35 inputs[0]->shape_[0] = seq_len; in TEST_F() 57 ASSERT_EQ(outputs[0]->shape_[0], seq_len); in TEST_F()
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/third_party/mindspore/mindspore/lite/src/runtime/kernel/arm/fp32/ |
D | gru_fp32.cc | 253 auto seq_len = reinterpret_cast<int *>(in_tensors_.at(5)->data()); in Run() local 254 CHECK_NULL_RETURN(seq_len); in Run() 255 if (!std::equal(seq_len + 1, seq_len + gru_param_->batch_, seq_len)) { in Run() 259 check_seq_len = MSMIN(check_seq_len, MSMAX(0, seq_len[0])); in Run()
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/third_party/mindspore/mindspore/lite/src/runtime/kernel/arm/fp16/ |
D | gru_fp16.cc | 275 int *seq_len = reinterpret_cast<int *>(in_tensors_.at(5)->data()); in Run() local 276 MS_ASSERT(seq_len != nullptr); in Run() 277 if (!std::equal(seq_len + 1, seq_len + gru_param_->batch_, seq_len)) { in Run() 281 check_seq_len = MSMIN(check_seq_len, MSMAX(0, seq_len[0])); in Run()
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/third_party/mindspore/tests/st/ops/cpu/ |
D | test_lstm_op.py | 155 seq_len = 5 191 error_y = np.ones([seq_len, batch_size, hidden_size]) * 1.0e-4 293 …def __init__(self, seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirection… argument 305 … self.x = Parameter(initializer(Tensor(input_np), [seq_len, batch_size, input_size]), name='x') 352 seq_len = 5 360 …net = Grad(Net(seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirectional, …
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/third_party/mindspore/tests/ut/python/parallel/ |
D | test_dsd_matmul.py | 50 self.seq_len = 1024 53 self.block_num = self.seq_len // self.block_size 93 dsd = self.reshape(dsd, (-1, self.seq_len, self.v_embedding * self.num_heads))
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D | test_cus_matmul_dds.py | 46 self.seq_len = 1024 48 self.block_num = self.seq_len // self.block_size 53 …self.global_mask = Tensor(np.ones((batch_size * self.global_size // 16, self.seq_len // 16, 16, 16…
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/third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/ |
D | reverse_sequence_gpu_kernel.h | 54 S *seq_len = GetDeviceAddress<S>(inputs, 1); in Launch() local 63 …CalReverseSequence(input_size_, input, seq_len, batch_dim_, seq_dim_, cur_pos_arr, input_shape_ptr, in Launch()
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/third_party/wpa_supplicant/wpa_supplicant-2.9_standard/src/drivers/ |
D | driver_privsep.c | 218 size_t seq_len = params->seq_len; in wpa_driver_privsep_set_key() local 234 if (seq && seq_len > 0 && seq_len < sizeof(cmd.seq)) { in wpa_driver_privsep_set_key() 235 os_memcpy(cmd.seq, seq, seq_len); in wpa_driver_privsep_set_key() 236 cmd.seq_len = seq_len; in wpa_driver_privsep_set_key()
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D | driver_wext.c | 1719 size_t seq_len, in wpa_driver_wext_set_key_ext() argument 1728 if (seq_len > IW_ENCODE_SEQ_MAX_SIZE) { in wpa_driver_wext_set_key_ext() 1730 __FUNCTION__, (unsigned long) seq_len); in wpa_driver_wext_set_key_ext() 1787 if (seq && seq_len) { in wpa_driver_wext_set_key_ext() 1789 os_memcpy(ext->rx_seq, seq, seq_len); in wpa_driver_wext_set_key_ext() 1831 size_t seq_len = params->seq_len; in wpa_driver_wext_set_key() local 1838 (unsigned long) seq_len, (unsigned long) key_len); in wpa_driver_wext_set_key() 1841 seq, seq_len, key, key_len, key_flag); in wpa_driver_wext_set_key()
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/third_party/wpa_supplicant/wpa_supplicant-2.9/src/drivers/ |
D | driver_privsep.c | 211 const u8 *seq, size_t seq_len, in wpa_driver_privsep_set_key() argument 228 if (seq && seq_len > 0 && seq_len < sizeof(cmd.seq)) { in wpa_driver_privsep_set_key() 229 os_memcpy(cmd.seq, seq, seq_len); in wpa_driver_privsep_set_key() 230 cmd.seq_len = seq_len; in wpa_driver_privsep_set_key()
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D | driver_wext.h | 57 int set_tx, const u8 *seq, size_t seq_len,
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D | driver_openbsd.c | 74 size_t seq_len, const u8 *key, size_t key_len) in wpa_driver_openbsd_set_key() argument
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/third_party/mindspore/mindspore/ops/operations/ |
D | _inner_ops.py | 1282 seq_len = input_v_shape[0] * 16 // batch_size 1283 return (batch_size, head, v_embedding // 16, seq_len // 16, 16, 16) 1301 seq_len = local_mask[0] * local_mask[-1] 1302 bs = q[1] * q[2] // seq_len 1303 global_size = seq_len // 4 1307 block_num = seq_len // block_size
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/third_party/wpa_supplicant/wpa_supplicant-2.9/src/common/ |
D | privsep_commands.h | 82 size_t seq_len; member
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/third_party/wpa_supplicant/wpa_supplicant-2.9_standard/src/common/ |
D | privsep_commands.h | 82 size_t seq_len; member
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/third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/cpu/ |
D | ctcloss_cpu_kernel.cc | 225 void CTCLossCPUKernel::GenLabelWithBlank(const uint32_t *seq_len, const std::vector<std::vector<uin… in GenLabelWithBlank() argument 243 if (!ignore_longer_outputs_than_inputs_ && l.size() > seq_len[b]) { in GenLabelWithBlank() 245 << seq_len[b] << "< " << l.size(); in GenLabelWithBlank()
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D | ctcloss_cpu_kernel.h | 42 …void GenLabelWithBlank(const uint32_t *seq_len, const std::vector<std::vector<uint32_t>> &batch_la…
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